Extraction of high-resolution frames from video sequences. The human visual system appears to be capable of temporally integrating information in a video sequence in such a way that the perceived spatial resolution of a sequence appears much higher than the spatial resolution of an individual frame. While the mechanisms in the human visual system that do this are unknown, the effect is not too surprising given that temporally adjacent frames in a video sequence contain slightly different, but unique, information. This paper addresses the use of both the spatial and temporal information present in a short image sequence to create a single high-resolution video frame. A novel observation model based on motion compensated subsampling is proposed for a video sequence. Since the reconstruction problem is ill-posed, Bayesian restoration with a discontinuity-preserving prior image model is used to extract a high-resolution video still given a short low-resolution sequence. Estimates computed from a low-resolution image sequence containing a subpixel camera pan show dramatic visual and quantitative improvements over bilinear, cubic B-spline, and Bayesian single frame interpolations. Visual and quantitative improvements are also shown for an image sequence containing objects moving with independent trajectories. Finally, the video frame extraction algorithm is used for the motion-compensated scan conversion of interlaced video data, with a visual comparison to the resolution enhancement obtained from progressively scanned frames.

Question: How to extract a high-resolution video frame from a low-resolution video sequence?


Fuzzy Cognitive Maps Tool for Scenario Analysis and Pattern Classification. After 30 years of research, challenges and solutions, Fuzzy Cognitive Maps (FCMs) have become a suitable knowledgebased methodology for modeling and simulation. This technique is especially attractive when modeling systems that are characterized by ambiguity, complexity and non-trivial causality. FCMs are well-known due to the transparency achieved during modeling tasks. The literature reports successful studies related to the modeling of complex systems using FCMs. However, the situation is not the same when it comes to software implementations where domain experts can design FCM-based systems, run simulations or perform more advanced experiments. The existing implementations are not proficient in providing many options to adjust essential parameters during the modeling steps. The gap between the theoretical advances and the development of accurate, transparent and sound FCM-based systems advocates for the creation of more complete and flexible software products. Therefore, the goal of this paper is to introduce FCM Expert, a software tool for fuzzy cognitive modeling oriented to scenario analysis and pattern classification. The main features of FCM Expert rely on Machine Learning algorithms to compute the parameters defining the model, optimize the network topology and improve the system convergence without losing information. On the other hand, FCM Expert allows performing WHAT-IF simulations and studying the system behavior through a friendly, intuitive and easy-to-use graphical user interface.

Question: What is fuzzy cognitive maps?


Gait support for complete spinal cord injury patient by synchronized leg-swing with HAL. Biped walking improves the circulation of blood as well as bone density of the lower limbs, thereby enhancing the quality of life (QOL). It is significant not only to healthy people but also to physically challenged persons such as complete spinal cord injury (SCI) patients. The purpose of this paper is to propose an estimation algorithm that infers the intention related to the forward leg-swing in order to support the gait for complete SCI patients wearing an exoskeleton system called a Hybrid Assistive Limb (HAL), and to verify the effectiveness of the proposed algorithm through a clinical trial. The proposed algorithm infers the patient's intention in synchronization with the deviation of the center of the ground reaction force (CoGRF) that is observed immediately before a person starts walking. The patient conveys this intention by inducing the deviation of the CoGRF, using crutches or handrails with both of his/her arms. In the clinical trial, we confirmed that the algorithm inferred the patient's intention to swing the leg forward, and achieved a smooth gait in synchronization with it. As a result, the gait speed and cadence of the SCI patient with HAL during the 10-meter walking test increased to 6.67 [m/min] and 20 [steps/min], respectively after several trials.

Question: How does the HAL help the patient?


Automatic Solution of Jigsaw Puzzles. We present a method for automatically solving apictorial jigsaw puzzles that is based on an extension of the method of differential invariant signatures. Our algorithms are designed to solve challenging puzzles, without having to impose any restrictive assumptions on the shape of the puzzle, the shapes of the individual pieces, or their intrinsic arrangement. As a demonstration, the method was successfully used to solve two commercially available puzzles. Finally we perform some preliminary investigations into scalability of the algorithm for even larger puzzles.

Question: How to solve jigsaw puzzles?


Lemmatization and Morphological Tagging in German and Latin: A Comparison and a Survey of the State-of-the-art. This paper relates to the challenge of morphological tagging and lemmatization in morphologically rich languages by example of German and Latin. We focus on the question what a practitioner can expect when using state-of-the-art solutions out of the box. Moreover, we contrast these with old(er) methods and implementations for POS tagging. We examine to what degree recent efforts in tagger development pay out in improved accuracies — and at what cost, in terms of training and processing time. We also conduct in-domain vs. out-domain evaluation. Out-domain evaluations are particularly insightful because the distribution of the data which is being tagged by a user will typically differ from the distribution on which the tagger has been trained. Furthermore, two lemmatization techniques are evaluated. Finally, we compare pipeline tagging vs. a tagging approach that acknowledges dependencies between inflectional categories.

Question: What is the difference between lemmatization and morphological tagging?


Short text classification based on Wikipedia and Word2vec. Different from long texts, the features of Chinese short texts is much sparse, which is the primary cause of the low accuracy in the classification of short texts by using traditional classification methods. In this paper, a novel method was proposed to tackle the problem by expanding the features of short text based on Wikipedia and Word2vec. Firstly, build the semantic relevant concept sets of Wikipedia. We get the articles that have high relevancy with Wikipedia concepts and use the word2vec tools to measure the semantic relatedness between target concepts and related concepts. And then we use the relevant concept sets to extend the short texts. Compared to traditional similarity measurement between concepts using statistical method, this method can get more accurate semantic relatedness. The experimental results show that by expanding the features of short texts, the classification accuracy can be improved. Specifically, our method appeared to be more effective.

Question: What is the difference between the traditional similarity measurement and the semantic relatedness measurement?


One selector-one resistor (1S1R) crossbar array for high-density flexible memory applications. Lack of a suitable selection device to suppress sneak current has impeded the development of 4F2 crossbar memory array utilizing stable and scalable bipolar resistive-switching. We report a high-performance nonlinear bipolar selector realized by a simple Ni/TiO2/Ni MIM structure with a high current density of 105 A/cm2, and a Ni/TiO2/Ni/HfO2/Pt vertically stacked 1S1R cell capable of gigabit memory implementation. Furthermore, the demonstration of 1S1R array fabricated completely at room temperature on a plastic substrate highlights the promise of future extremely low-cost flexible nonvolatile memory.

Question: What is a selector?


Convolutional Recurrent Deep Learning Model for Sentence Classification. As the amount of unstructured text data that humanity produces overall and on the Internet grows, so does the need to intelligently to process it and extract different types of knowledge from it. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been applied to natural language processing systems with comparative, remarkable results. The CNN is a noble approach to extract higher level features that are invariant to local translation. However, it requires stacking multiple convolutional layers in order to capture long-term dependencies, due to the locality of the convolutional and pooling layers. In this paper, we describe a joint CNN and RNN framework to overcome this problem. Briefly, we use an unsupervised neural language model to train initial word embeddings that are further tuned by our deep learning network, then, the pre-trained parameters of the network are used to initialize the model. At a final stage, the proposed framework combines former information with a set of feature maps learned by a convolutional layer with long-term dependencies learned via long-short-term memory. Empirically, we show that our approach, with slight hyperparameter tuning and static vectors, achieves outstanding results on multiple sentiment analysis benchmarks. Our approach outperforms several existing approaches in term of accuracy; our results are also competitive with the state-of-the-art results on the Stanford Large Movie Review data set with 93.3% accuracy, and the Stanford Sentiment Treebank data set with 48.8% fine-grained and 89.2% binary accuracy, respectively. Our approach has a significant role in reducing the number of parameters and constructing the convolutional layer followed by the recurrent layer as a substitute for the pooling layer. Our results show that we were able to reduce the loss of detailed, local information and capture long-term dependencies with an ef

Question: What is the convolutional recurrent deep learning model for sentence classification?


The induction of apoptosis and autophagy by Wasabia japonica extract in colon cancer.. PURPOSE Wasabia japonica (wasabi) has been shown to exhibit properties of detoxification, anti-inflammation and the induction of apoptosis in cancer cells. This study aimed to investigate the molecular mechanism of the cytotoxicity of wasabi extract (WE) in colon cancer cells to evaluate the potential of wasabi as a functional food for chemoprevention.   METHODS Colo 205 cells were treated with different doses of WE, and the cytotoxicity was analyzed by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide. Apoptosis and autophagy were detected by 4',6-diamidino-2-phenylindole, 5,5',6,6'-tetrachloro-1,1',3,3'-tetraethyl-imidacarbo-yanine iodide and staining for acidic vascular organelles (AVOs), along with Western blotting.   RESULTS The results demonstrated that WE induced the extrinsic pathway and mitochondrial death machinery through the activation of TNF-α, Fas-L, caspases, truncated Bid and cytochrome C. WE also induced autophagy by decreasing the phosphorylation of Akt and mTOR and promoting the expression of microtubule-associated protein 1 light chain 3-II and AVO formation. An in vivo xenograft model verified that tumor growth was delayed by WE treatment.   CONCLUSION Our studies revealed that WE exhibits anti-colon cancer properties through the induction of apoptosis and autophagy. These results provide support for the application of WE as a chemopreventive functional food and as a prospective treatment of colon cancer.

Question: What is the molecular mechanism of the cytotoxicity of wasabi extract in colon cancer cells?


Amorphous Silicon Solar Vivaldi Antenna. An ultrawideband solar Vivaldi antenna is proposed. Cut from amorphous silicon cells, it maintains a peak power at 4.25 V, which overcomes a need for lossy power management components. The wireless communications device can yield solar energy or function as a rectenna for dual-source energy harvesting. The solar Vivaldi performs with 0.5-2.8 dBi gain from 0.95-2.45 GHz , and in rectenna mode, it covers three bands for wireless energy scavenging.

Question: What is an ultrawideband solar Vivaldi antenna?


Missing value imputation for gene expression data: computational techniques to recover missing data from available information. Microarray gene expression data generally suffers from missing value problem due to a variety of experimental reasons. Since the missing data points can adversely affect downstream analysis, many algorithms have been proposed to impute missing values. In this survey, we provide a comprehensive review of existing missing value imputation algorithms, focusing on their underlying algorithmic techniques and how they utilize local or global information from within the data, or their use of domain knowledge during imputation. In addition, we describe how the imputation results can be validated and the different ways to assess the performance of different imputation algorithms, as well as a discussion on some possible future research directions. It is hoped that this review will give the readers a good understanding of the current development in this field and inspire them to come up with the next generation of imputation algorithms.

Question: What is the missing value imputation for gene expression data?


Ontologies: principles, methods and applications. This paper is intended to serve as a comprehensive introduction to the emerging eld concerned with the design and use of ontologies. We observe that disparate backgrounds, languages, tools, and techniques are a major barrier to e ective communication among people, organisations, and/or software systems. We show how the development and implementation of an explicit account of a shared understanding (i.e. an `ontology') in a given subject area, can improve such communication, which in turn, can give rise to greater reuse and sharing, inter-operability, and more reliable software. After motivating their need, we clarify just what ontologies are and what purposes they serve. We outline a methodology for developing and evaluating ontologies, rst discussing informal techniques, concerning such issues as scoping, handling ambiguity, reaching agreement and producing de nitions. We then consider the bene ts of and describe, a more formal approach. We re-visit the scoping phase, and discuss the role of formal languages and techniques in the speci cation, implementation and evaluation of ontologies. Finally, we review the state of the art and practice in this emerging eld, considering various case studies, software tools for ontology development, key research issues and future prospects. AIAI-TR-191 Ontologies Page i

Question: What is an ontology?


A reconfigurable three- and single-phase AC/DC non-isolated bi-directional converter for multiple worldwide voltages. AC/DC converters for worldwide deployment or multiple applications are often required to operate with single-or three-phase AC connections at a variety of voltages. Electric vehicle charging is an excellent example: a typical AC/DC converter optimized for a 400 V three-phase European connection will only produce about 35% of rated power when operating on a 240 V single-phase connection in North America. This paper proposes a reconfigurable AC-DC converter with integral DC-DC stage that allows the single-phase AC power to roughly double compared to a standard configuration, up to 2/3 of the DC/DC stage rating. The system is constructed with two conventional back-to-back three-phase two-level bridges. When operating in single-phase mode, rather than leaving the DC side under-utilized, our proposed design repurposes one of the DC phase legs and the unused AC phase to create two parallel single-phase connections. This roughly doubles the AC output current and hence doubles the single phase AC power.

Question: What is a reconfigurable AC/DC converter?


Threats to Feminist Identity and Reactions to Gender Discrimination. The aim of this research was to examine conditions that modify feminists' support for women as targets of gender discrimination. In an experimental study we tested a hypothesis that threatened feminist identity will lead to greater differentiation between feminists and conservative women as victims of discrimination and, in turn, a decrease in support for non-feminist victims. The study was conducted among 96 young Polish female professionals and graduate students from Gender Studies programs in Warsaw who self-identified as feminists (Mage  = 22.23). Participants were presented with a case of workplace gender discrimination. Threat to feminist identity and worldview of the discrimination victim (feminist vs. conservative) were varied between research conditions. Results indicate that identity threat caused feminists to show conditional reactions to discrimination. Under identity threat, feminists perceived the situation as less discriminatory when the target held conservative views on gender relations than when the target was presented as feminist. This effect was not observed under conditions of no threat. Moreover, feminists showed an increase in compassion for the victim when she was portrayed as a feminist compared to when she was portrayed as conservative. Implications for the feminist movement are discussed.

Question: What is the feminist movement?


Coarse-to-Fine Question Answering for Long Documents. We present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-ofthe-art models. While most successful approaches for reading comprehension rely on recurrent neural networks (RNNs), running them over long documents is prohibitively slow because it is difficult to parallelize over sequences. Inspired by how people first skim the document, identify relevant parts, and carefully read these parts to produce an answer, we combine a coarse, fast model for selecting relevant sentences and a more expensive RNN for producing the answer from those sentences. We treat sentence selection as a latent variable trained jointly from the answer only using reinforcement learning. Experiments demonstrate the state of the art performance on a challenging subset of the WIKIREADING dataset (Hewlett et al., 2016) and on a new dataset, while speeding up the model by 3.5x-6.7x.

Question: What is the state of the art performance on a challenging subset of the WIKIREADING dataset?


Question: What is a dynamic switching network?


A Study on the Effective Permittivity of Carbon/PI Honeycomb Composites for Radar Absorbing Design. Radar absorbing honeycomb composites with different coating thicknesses are prepared by impregnation of aramid paper frame with solutions containing conductive carbon blacks (non-magnetic) and PI (polyimide). Expressions for the effective permittivity of the composites are studied and validated both in theory and experiment. It is found that a theoretical equivalent panel with given permittivity can be obtained to represent the honeycomb structure in the quasistatic approximation, which provides a feasible way to optimize the design of radar absorbing honeycomb structure by connecting the effective electromagnetic parameters with the unit cell dimensions. The effective permittivity is measured by a network analyzer system in the frequency range of 8-12 GHz and compared with the theoretical result.

Question: What is the effective permittivity of a honeycomb structure?


One-Class Kernel Spectral Regression for Outlier Detection. The paper introduces a new efficient nonlinear oneclass classifier formulated as the Rayleigh quotient criterion optimisation. The method, operating in a reproducing kernel Hilbert subspace, minimises the scatter of target distribution along an optimal projection direction while at the same time keeping projections of positive observations distant from the mean of the negative class. We provide a graph embedding view of the problem which can then be solved efficiently using the spectral regression approach. In this sense, unlike previous similar methods which often require costly eigen-computations of dense matrices, the proposed approach casts the problem under consideration into a regression framework which is computationally more efficient. In particular, it is shown that the dominant complexity of the proposed method is the complexity of computing the kernel matrix. Additional appealing characteristics of the proposed one-class classifier are: 1-the ability to be trained in an incremental fashion (allowing for application in streaming data scenarios while also reducing the computational complexity in a non-streaming operation mode); 2-being unsupervised, but providing the option for refining the solution using negative training examples, when available; Last but not least, 3-the use of the kernel trick which facilitates a nonlinear mapping of the data into a high-dimensional feature space to seek better solutions. Extensive experiments conducted on several datasets verify the merits of the proposed approach in comparison with other alternatives.

Question: What is the kernel trick?


Open Graphs and Monoidal Theories. String diagrams are a powerful tool for reasoning about physical processes, logic circuits, tensor networks, and many other compositional structures. The distinguishing feature of these diagrams is that edges need not be connected to vertices at both ends, and these unconnected ends can be interpreted as the inputs and outputs of a diagram. In this paper, we give a concrete construction for string diagrams using a special kind of typed graph called an open-graph. While the category of open-graphs is not itself adhesive, we introduce the notion of a selective adhesive functor, and show that such a functor embeds the category of open-graphs into the ambient adhesive category of typed graphs. Using this functor, the category of open-graphs inherits "enough adhesivity" from the category of typed graphs to perform double-pushout (DPO) graph rewriting. A salient feature of our theory is that it ensures rewrite systems are "type-safe" in the sense that rewriting respects the inputs and outputs. This formalism lets us safely encode the interesting structure of a computational model, such as evaluation dynamics, with succinct, explicit rewrite rules, while the graphical representation absorbs many of the tedious details. Although topological formalisms exist for string diagrams, our construction is discrete, finitary, and enjoys decidable algorithms for composition and rewriting. We also show how open-graphs can be parameterised by graphical signatures, similar to the monoidal signatures of Joyal and Street, which define types for vertices in the diagrammatic language and constraints on how they can be connected. Using typed open-graphs, we can construct free symmetric monoidal categories, PROPs, and more general monoidal theories. Thus open-graphs give us a tool for mechanised reasoning in monoidal categories.

Question: What is a monoidal theory?


Parallel Formulations of Decision-Tree Classification Algorithms. Classification decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud detection, etc. Highly parallel algorithms for constructing classification decision trees are desirable for dealing with large data sets in reasonable amount of time. Algorithms for building classification decision trees have a natural concurrency, but are difficult to parallelize due to the inherent dynamic nature of the computation. In this paper, we present parallel formulations of classification decision tree learning algorithm based on induction. We describe two basic parallel formulations. One is based on Synchronous Tree Construction Approach and the other is based on Partitioned Tree Construction Approach. We discuss the advantages and disadvantages of using these methods and propose a hybrid method that employs the good features of these methods. We also provide the analysis of the cost of computation and communication of the proposed hybrid method. Moreover, experimental results on an IBM SP-2 demonstrate excellent speedups and scalability.

Question: What is the difference between classification decision tree algorithms and classification decision trees?


A 65nm logic technology featuring 35nm gate lengths, enhanced channel strain, 8 Cu interconnect layers, low-k ILD and 0.57 /spl mu/m/sup 2/ SRAM cell. A 65nm generation logic technology with 1.2nm physical gate oxide, 35nm gate length, enhanced channel strain, NiSi, 8 layers of Cu interconnect, and low-k ILD for dense high performance logic is presented. Transistor gate length is scaled down to 35nm while not scaling the gate oxide as a means to improve performance and reduce power. Increased NMOS and PMOS drive currents are achieved by enhanced channel strain and junction engineering. 193nm lithography along with APSM mask technology is used on critical layers to provide aggressive design rules and a 6-T SRAM cell size of 0.57/spl mu/m/sup 2/. Process yield, performance and reliability are demonstrated on a 70 Mbit SRAM test vehicle with >0.5 billion transistors.

Question: What is the process technology?


Dynamic Highway-Node Routing. We introduce a dynamic technique for fast route planning in large road networks. For the first time, it is possible to handle the practically relevant scenarios that arise in present-day navigation systems: When an edge weight changes (e.g., due to a traffic jam), we can update the preprocessed information in 2–40 ms allowing subsequent fast queries in about one millisecond on average. When we want to perform only a single query, we can skip the comparatively expensive update step and directly perform a prudent query that automatically takes the changed situation into account. If the overall cost function changes (e.g., due to a different vehicle type), recomputing the preprocessed information takes typically less than two minutes. The foundation of our dynamic method is a new static approach that generalises and combines several previous speedup techniques. It has outstandingly low memory requirements of only a few bytes per node.

Question: How fast is the dynamic highway-node routing?


Vocabulary acquisition from extensive reading : A case study. A number of studies have shown that second language learners acquire vocabulary through reading, but only relatively small amounts. However, most of these studies used only short texts, measured only the acquisition of meaning, and did not credit partial learning of words. This case study of a learner of French explores whether an extensive reading program can enhance lexical knowledge. The study assessed a relatively large number of words (133), and examined whether one month of extensive reading enhanced knowledge of these target words' spelling, meaning, and grammatical characteristics. The measurement procedure was a one-on-one interview that allowed a very good indication of whether learning occurred. The study also explores how vocabulary acquisition varies according to how often words are encountered in the texts. The results showed that knowledge of 65% of the target words was enhanced in some way, for a pickup rate of about 1 of every 1.5 words tested. Spelling was strongly enhanced, even from a small number of exposures. Meaning and grammatical knowledge were also enhanced, but not to the same extent. Overall, the study indicates that more vocabulary acquisition is possible from extensive reading than previous studies have suggested.

Question: How much vocabulary can be acquired from extensive reading?


A Novel Method for Comparative Analysis of DNA Sequences by Ramanujan-Fourier Transform. Alignment-free sequence analysis approaches provide important alternatives over multiple sequence alignment (MSA) in biological sequence analysis because alignment-free approaches have low computation complexity and are not dependent on high level of sequence identity. However, most of the existing alignment-free methods do not employ true full information content of sequences and thus can not accurately reveal similarities and differences among DNA sequences. We present a novel alignment-free computational method for sequence analysis based on Ramanujan-Fourier transform (RFT), in which complete information of DNA sequences is retained. We represent DNA sequences as four binary indicator sequences and apply RFT on the indicator sequences to convert them into frequency domain. The Euclidean distance of the complete RFT coefficients of DNA sequences are used as similarity measures. To address the different lengths of RFT coefficients in Euclidean space, we pad zeros to short DNA binary sequences so that the binary sequences equal the longest length in the comparison sequence data. Thus, the DNA sequences are compared in the same dimensional frequency space without information loss. We demonstrate the usefulness of the proposed method by presenting experimental results on hierarchical clustering of genes and genomes. The proposed method opens a new channel to biological sequence analysis, classification, and structural module identification.

Question: What is the difference between alignment-free and alignment-based methods?


SAM: Semantic Attribute Modulated Language Modeling. As a fundamental task in the natural language processing field, language modeling aims to estimate the distribution of the word sequences. However, the most existing algorithms have focused on the main texts while often ignoring the vastly-accessible semantic attributes of the documents, e.g., titles, authors, sentiments and tags. To address this issue, we propose Semantic Attribute Modulated (SAM) language modeling, a novel language modeling framework that incorporates the various semantic attributes. Attributes are selected automatically with an attribute attention mechanism. We build three text datasets with a diversity of semantic attributes. On the three text datasets, we empirically examine the language model perplexities of several typical corpora, and then demonstrate the superiority of our model with the different combinations of the attributes. Extensive qualitative results, including word semantic analysis, attention values and an interesting lyric generation, further demonstrate the effectiveness of our SAMmethod.

Question: What is the difference between the main texts and the semantic attributes?


Efficient Phrase-Based Document Similarity for Clustering. In this paper, we propose a phrase-based document similarity to compute the pair-wise similarities of documents based on the suffix tree document (STD) model. By mapping each node in the suffix tree of STD model into a unique feature term in the vector space document (VSD) model, the phrase-based document similarity naturally inherits the term tf-idf weighting scheme in computing the document similarity with phrases. We apply the phrase-based document similarity to the group-average Hierarchical Agglomerative Clustering (HAC) algorithm and develop a new document clustering approach. Our evaluation experiments indicate that, the new clustering approach is very effective on clustering the documents of two standard document benchmark corpora OHSUMED and RCV1. The quality of the clustering results significantly surpass the results of traditional single-word \textit{tf-idf} similarity measure in the same HAC algorithm, especially in large document data sets. Furthermore, by studying the property of STD model, we conclude that the feature vector of phrase terms in the STD model can be considered as an expanded feature vector of the traditional single-word terms in the VSD model. This conclusion sufficiently explains why the phrase-based document similarity works much better than the single-word tf-idf similarity measure.

Question: What is the difference between the suffix tree and the vector space document model?


Efficient reinforcement learning using Gaussian processes. IX

Question: What is reinforcement learning?


Crowdsourcing Annotations for Visual Object Detection. A large number of images with ground truth object bounding boxes are critical for learning object detectors, which is a fundamental task in compute vision. In this paper, we study strategies to crowd-source bounding box annotations. The core challenge of building such a system is to effectively control the data quality with minimal cost. Our key observation is that drawing a bounding box is significantly more difficult and time consuming than giving answers to multiple choice questions. Thus quality control through additional verification tasks is more cost effective than consensus based algorithms. In particular, we present a system that consists of three simple sub-tasks — a drawing task, a quality verification task and a coverage verification task. Experimental results demonstrate that our system is scalable, accurate, and cost-effective.

Question: How to crowdsource annotations for visual object detection?


Low-voltage low-power fast-settling CMOS operational transconductance amplifiers for switched-capacitor applications. This paper presents a new fully differential operational transconductance amplifier (OTA) for low-voltage and fast-settling switched-capacitor circuits in digital CMOS technology. The proposed two-stage OTA is a hybrid class A/AB that combines a folded cascode as the first stage with active current mirrors as the second stage. It employs a hybrid cascode compensation scheme, merged Ahuja and improved Ahuja style compensations, for fast settling.

Question: What is a hybrid class A/AB?


Beyond Bitcoin - Part I: A critical look at blockchain-based systems. After more than six years from the launch of Bitcoin, it has become evident that the decentralized transaction ledger functionality implemented through the blockchain technology can be used not only for cryptocurrencies, but to register, confirm and transfer any kind of contract and property. In this work we analyze the most relevant functionalities and known issues of this technology, with the intent of pointing out the possible behaviours that are not as efficient as they should when thinking with a broader outlook. Our analysis would be the starting point for the introduction of a new approach to blockchain creation and management, which will be the subject of a forthcoming paper.

Question: What is the difference between blockchain and bitcoin?


Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach. Learning from imbalanced data sets, where the number of examples of one (majority) class is much higher than the others, presents an important challenge to the machine learning community. Traditional machine learning algorithms may be biased towards the majority class, thus producing poor predictive accuracy over the minority class. In this paper, we describe a new approach that combines boosting, an ensemble-based learning algorithm, with data generation to improve the predictive power of classifiers against imbalanced data sets consisting of two classes. In the DataBoost-IM method, hard examples from both the majority and minority classes are identified during execution of the boosting algorithm. Subsequently, the hard examples are used to separately generate synthetic examples for the majority and minority classes. The synthetic data are then added to the original training set, and the class distribution and the total weights of the different classes in the new training set are rebalanced. The DataBoost-IM method was evaluated, in terms of the F-measures, G-mean and overall accuracy, against seventeen highly and moderately imbalanced data sets using decision trees as base classifiers. Our results are promising and show that the DataBoost-IM method compares well in comparison with a base classifier, a standard benchmarking boosting algorithm and three advanced boosting-based algorithms for imbalanced data set. Results indicate that our approach does not sacrifice one class in favor of the other, but produces high predictions against both minority and majority classes.

Question: How to deal with imbalanced data sets?


Multi-Scale Convolutional Neural Networks for Time Series Classification. Time series classification (TSC), the problem of predicting class labels of time series, has been around for decades within the community of data mining and machine learning, and found many important applications such as biomedical engineering and clinical prediction. However, it still remains challenging and falls short of classification accuracy and efficiency. Traditional approaches typically involve extracting discriminative features from the original time series using dynamic time warping (DTW) or shapelet transformation, based on which an off-the-shelf classifier can be applied. These methods are ad-hoc and separate the feature extraction part with the classification part, which limits their accuracy performance. Plus, most existing methods fail to take into account the fact that time series often have features at different time scales. To address these problems, we propose a novel end-to-end neural network model, Multi-scale Convolutional Neural Network (MCNN), which incorporates feature extraction and classification in a single framework. Leveraging a novel multi-branch layer and learnable convolutional layers, MCNN automatically extracts features at different scales and frequencies, leading to superior feature representation. MCNN is also computationally efficient, as it naturally leverages GPU computing. We conduct comprehensive empirical evaluation with various existing methods on a large number of benchmark datasets, and show that MCNN advances the state-of-the-art by achieving superior accuracy performance than other leading methods.

Question: What is the state-of-the-art in time series classification?


Network Decoupling: From Regular to Depthwise Separable Convolutions. Depthwise separable convolution has shown great efficiency in network design, but requires time-consuming training procedure with full training-set available. This paper first analyzes the mathematical relationship between regular convolutions and depthwise separable convolutions, and proves that the former one could be approximated with the latter one in closed form. We show depthwise separable convolutions are principal components of regular convolutions. And then we propose network decoupling (ND), a training-free method to accelerate convolutional neural networks (CNNs) by transferring pre-trained CNN models into the MobileNet-like depthwise separable convolution structure, with a promising speedup yet negligible accuracy loss. We further verify through experiments that the proposed method is orthogonal to other training-free methods like channel decomposition, spatial decomposition, etc. Combining the proposed method with them will bring even larger CNN speedup. For instance, ND itself achieves about 2× speedup for the widely used VGG16, and combined with other methods, it reaches 3.7× speedup with graceful accuracy degradation. We demonstrate that ND is widely applicable to classification networks like ResNet, and object detection network like SSD300.

Question: What is the mathematical relationship between regular convolutions and depthwise separable convolutions?


Manufacturing Problem Solving in a Job Shop — Research in Layout Planning. For ensuring efficient operation of a job shop, it is important to minimize waste, which has no value addition to the final product. For a job shop, minimizing movement is considered as the highest priority for waste prevention. For this reason, the layout for a job shop should be designed in such a way to ensure the lowest possible cost for production by reducing nonvalue added activities, such as movement of work-in-process. An effective and efficient way of layout planning for a job shop is a key for solving movement inefficiencies and facilitating communication and interaction between workers and supervisors. This involves relocation of equipment and machinery to streamline materials flow. The primary objective of relocation is to avoid flow conflicts, reduce process time, and increase efficiency of labor usage. Proximity of the most frequently used machines minimizes the movement cost significantly, which eventually minimizes the cost of production. This paper describes the research done in process flow improvements in a job shop manufacturing steel components. The literature focused mainly on mathematical modeling with assumptions that are not applicable for a typical small-scale job shop operation. However, this was overcome by collecting material movement data over three months and analyzing the information using a From-To chart. By analyzing the chart, the actual loads between departments for the operation period were tabulated in available plant space. From this information, the inter-departmental flow was shown by a model. This provides the basic layout pattern, which was improved. A second step was to determine the cost of this layout by multiplying the material handling cost by the number of loads moved between each pair of departments. As a recommendation for solving the problem, two layout models have been developed for ensuring the lowest movement cost. Introduction Transporta

Question: How to solve the problem of material flow in a job shop?


SEGAN: Speech Enhancement Generative Adversarial Network. Current speech enhancement techniques operate on the spectral domain and/or exploit some higher-level feature. The majority of them tackle a limited number of noise conditions and rely on first-order statistics. To circumvent these issues, deep networks are being increasingly used, thanks to their ability to learn complex functions from large example sets. In this work, we propose the use of generative adversarial networks for speech enhancement. In contrast to current techniques, we operate at the waveform level, training the model end-to-end, and incorporate 28 speakers and 40 different noise conditions into the same model, such that model parameters are shared across them. We evaluate the proposed model using an independent, unseen test set with two speakers and 20 alternative noise conditions. The enhanced samples confirm the viability of the proposed model, and both objective and subjective evaluations confirm the effectiveness of it. With that, we open the exploration of generative architectures for speech enhancement, which may progressively incorporate further speech-centric design choices to improve their performance.

Question: What is speech enhancement?


AcuBot: a robot for radiological interventions. We report the development of a robot for radiological percutaneous interventions using uniplanar fluoroscopy, biplanar fluoroscopy, or computed tomography (CT) for needle biopsy, radio frequency ablation, cryotherapy, and other needle procedures. AcuBot is a compact six-degree-of-freedom robot for manipulating a needle or other slender surgical instrument in the confined space of the imager without inducing image artifacts. Its distinctive characteristic is its decoupled motion capability correlated to the positioning, orientation, and instrument insertion steps of the percutaneous intervention. This approach allows each step of the intervention to be performed using a separate mechanism of the robot. One major advantage of this kinematic approach is patient safety. The first feasibility experiment performed with the robot, a cadaver study of perispinal blocks under biplanar fluoroscopy, is presented. The main expected application of this system is to CT-based procedures. AcuBot has received Food and Drug Administration clearance (IDE G010331/S1), and a clinical trial of using the robot for perispinal nerve and facet blocks is presently underway at Georgetown University, Washington, DC.

Question: What is a robot for radiological interventions?


Practical Lattice-Based Cryptography: A Signature Scheme for Embedded Systems. Nearly all of the currently used and well-tested signature schemes (e.g. RSA or DSA) are based either on the factoring assumption or the presumed intractability of the discrete logarithm problem. Further algorithmic advances on these problems may lead to the unpleasant situation that a large number of schemes have to be replaced with alternatives. In this work we present such an alternative – a signature scheme whose security is derived from the hardness of lattice problems. It is based on recent theoretical advances in lattice-based cryptography and is highly optimized for practicability and use in embedded systems. The public and secret keys are roughly 12000 and 2000 bits long, while the signature size is approximately 9000 bits for a security level of around 100 bits. The implementation results on reconfigurable hardware (Spartan/Virtex 6) are very promising and show that the scheme is scalable, has low area consumption, and even outperforms some classical schemes.

Question: What is the security level of the scheme?


Health Effects Related to Wind Turbine Noise Exposure: A Systematic Review. BACKGROUND Wind turbine noise exposure and suspected health-related effects thereof have attracted substantial attention. Various symptoms such as sleep-related problems, headache, tinnitus and vertigo have been described by subjects suspected of having been exposed to wind turbine noise.   OBJECTIVE This review was conducted systematically with the purpose of identifying any reported associations between wind turbine noise exposure and suspected health-related effects.   DATA SOURCES A search of the scientific literature concerning the health-related effects of wind turbine noise was conducted on PubMed, Web of Science, Google Scholar and various other Internet sources.   STUDY ELIGIBILITY CRITERIA All studies investigating suspected health-related outcomes associated with wind turbine noise exposure were included.   RESULTS Wind turbines emit noise, including low-frequency noise, which decreases incrementally with increases in distance from the wind turbines. Likewise, evidence of a dose-response relationship between wind turbine noise linked to noise annoyance, sleep disturbance and possibly even psychological distress was present in the literature. Currently, there is no further existing statistically-significant evidence indicating any association between wind turbine noise exposure and tinnitus, hearing loss, vertigo or headache.   LIMITATIONS Selection bias and information bias of differing magnitudes were found to be present in all current studies investigating wind turbine noise exposure and adverse health effects. Only articles published in English, German or Scandinavian languages were reviewed.   CONCLUSIONS Exposure to wind turbines does seem to increase the risk of annoyance and self-reported sleep disturbance in a dose-response relationship. There appears, though, to be a tolerable level of around LAeq of 35 dB. Of the many other claimed health effects of wind turbine noise ex

Question: What is the canadian military?


Clear underwater vision. Underwater imaging is important for scientific research and technology, as well as for popular activities. We present a computer vision approach which easily removes degradation effects in underwater vision. We analyze the physical effects of visibility degradation. We show that the main degradation effects can be associated with partial polarization of light. We therefore present an algorithm which inverts the image formation process, to recover a good visibility image of the object. The algorithm is based on a couple of images taken through a polarizer at different orientations. As a by product, a distance map of the scene is derived as well. We successfully used our approach when experimenting in the sea using a system we built. We obtained great improvement of scene contrast and color correction, and nearly doubled the underwater visibility range.

Question: How to improve underwater vision?


An integrated charger using segmented windings of interior permanent magnet motor based on 3 phase with 9 windings. Connecting the plug of electric vehicles (EVs) into the grid source directly means to charge the battery. The electric power generated from the grid goes through the electric motor, crosses the bidirectional inverter used as three-boost rectifier and is received by the load (battery). An innovative fast-integrated charger based on segmented windings of permanent magnet synchronous motor (PMSM) is introduced for plug-in electric vehicles (PEV). The three-phase with nine windings of PMSM is used during the traction and charging modes. Three (or single) phases of grid source are directly connected to the neutral points of segmented PMSM windings to provide high power density to the battery. The system configuration and operation of charging mode are explained in detail for proposed integrated on-board charger. The simulation models are designed by using Ansoft Maxwell, Ansys Maxwell circuit editor, and Matlab/Simulink softwares, and simulation results are presented to verify the performance of the proposed system. In addition, the experimental setup of the integrated system is under progress.

Question: What is the name of the proposed charger?


Blepharophimosis mental retardation syndrome Say-Barber/Biesecker/Young-Simpson type - new findings with neuroimaging.. We report on a female patient with blepharophimosis mental retardation syndrome of Say/Barber/Biesecker/Young-Simpson (SBBYS) type. Main findings in her were marked developmental delay, blepharophimosis, ptosis, cleft palate, external auditory canal stenosis, small and malformed teeth, hypothyroidism, hearing impairment, and joint limitations. We performed diffusion tensor magnetic resonance imaging (MRI) and tractography of the brain which showed inappropriate myelination and disturbed white matter integrity. Cytogenetic analysis, subtelomeric fluorescence in situ hybridization and comparative genomic hybridization failed to identify an abnormality. It remains uncertain whether the MRI findings are specific to the present patient or form part of the SBBYS syndrome.

Question: What is the name of the syndrome?


Ground plane segmentation for mobile robot visual navigation. We describe a method of mobile robot monocular visual navigation, which uses multiple visual cues to detect and segment the ground plane in the robot's field of view. Corner points are tracked through an image sequence and grouped into coplanar regions using a method which we call an H-based tracker. The H-based tracker employs planar homographys and is initialised by 5-point planar projective invariants. This allows us to detect ground plane patches and the colour within such patches is subsequently modelled. These patches are grown by colour classification to give a ground plane segmentation, which is then used as an input to a new variant of the artificial potential field algorithm.

Question: How to segment the ground plane in a robot's field of view?


Taking the Human Out of the Loop: A Review of Bayesian Optimization. Big Data applications are typically associated with systems involving large numbers of users, massive complex software systems, and large-scale heterogeneous computing and storage architectures. The construction of such systems involves many distributed design choices. The end products (e.g., recommendation systems, medical analysis tools, real-time game engines, speech recognizers) thus involve many tunable configuration parameters. These parameters are often specified and hard-coded into the software by various developers or teams. If optimized jointly, these parameters can result in significant improvements. Bayesian optimization is a powerful tool for the joint optimization of design choices that is gaining great popularity in recent years. It promises greater automation so as to increase both product quality and human productivity. This review paper introduces Bayesian optimization, highlights some of its methodological aspects, and showcases a wide range of applications.

Question: What is Bayesian optimization?


Guide to Health Informatics. From the very earliest moments in the modern history of the computer, scientists have dreamed of creating an " electronic brain ". Of all the modern technological quests, this search to create artificially intelligent (AI) computer systems has been one of the most ambitious and, not surprisingly, controversial. It also seems that very early on, scientists and doctors alike were captivated by the potential such a technology might have in medicine (e.g. Ledley and Lusted, 1959). With intelligent computers able to store and process vast stores of knowledge, the hope was that they would become perfect 'doctors in a box', assisting or surpassing clinicians with tasks like diagnosis. With such motivations, a small but talented community of computer scientists and healthcare professionals set about shaping a research program for a new discipline called Artificial Intelligence in Medicine (AIM). These researchers had a bold vision of the way AIM would revolutionise medicine, and push forward the frontiers of technology. AI in medicine at that time was a largely US-based research community. Work originated out of a number of campuses, including MIT-Tufts, Pittsburgh, Stanford and Rutgers (e. The field attracted many of the best computer scientists and by any measure their output in the first decade of the field remains a remarkable achievement. In reviewing this new field in 1984, Clancey and Shortliffe provided the following definition: 'Medical artificial intelligence is primarily concerned with the construction of AI programs that perform diagnosis and make therapy recommendations. Unlike medical applications based on other programming methods, such as purely statistical and probabilistic methods, medical AI programs are based on symbolic models of disease entities and their relationship to patient factors and clinical manifestations.' Much has changed since then, and today the importance of diagnosis as a task requiring computer support in ro

Question: What is the definition of medical artificial intelligence?


The flipped classroom: a course redesign to foster learning and engagement in a health professions school.. Recent calls for educational reform highlight ongoing concerns about the ability of current curricula to equip aspiring health care professionals with the skills for success. Whereas a wide range of proposed solutions attempt to address apparent deficiencies in current educational models, a growing body of literature consistently points to the need to rethink the traditional in-class, lecture-based course model. One such proposal is the flipped classroom, in which content is offloaded for students to learn on their own, and class time is dedicated to engaging students in student-centered learning activities, like problem-based learning and inquiry-oriented strategies. In 2012, the authors flipped a required first-year pharmaceutics course at the University of North Carolina Eshelman School of Pharmacy. They offloaded all lectures to self-paced online videos and used class time to engage students in active learning exercises. In this article, the authors describe the philosophy and methodology used to redesign the Basic Pharmaceutics II course and outline the research they conducted to investigate the resulting outcomes. This article is intended to serve as a guide to instructors and educational programs seeking to develop, implement, and evaluate innovative and practical strategies to transform students' learning experience. As class attendance, students' learning, and the perceived value of this model all increased following participation in the flipped classroom, the authors conclude that this approach warrants careful consideration as educators aim to enhance learning, improve outcomes, and fully equip students to address 21st-century health care needs.

Question: What is the flipped classroom?


Passive returning mechanism for twisted string actuators. The twisted string actuator is an actuator that is gaining popularity in various engineering and robotics and applications. However, the fundamental limitation of actuators of this type is the uni-directional action, meaning that the actuator can contract but requires external power to return to its initial state. This paper proposes 2 novel passive extension mechanisms based on buckling effect to solve the uni-directional issue of the twisted string actuator. The proposed mechanisms are mechanically simple and compact and provide a nearly-constant extension force throughout the operation range. The constant force can fully extend the twisted string actuator with minimal loss of force during contraction. The designed mechanisms are evaluated in a series of practical tests, and their performances are compared and discussed.

Question: What is the twisted string actuator?


AFFDEX SDK: A Cross-Platform Real-Time Multi-Face Expression Recognition Toolkit. We present a real-time facial expression recognition toolkit that can automatically code the expressions of multiple people simultaneously. The toolkit is available across major mobile and desktop platforms (Android, iOS, Windows). The system is trained on the world's largest dataset of facial expressions and has been optimized to operate on mobile devices and with very few false detections. The toolkit offers the potential for the design of novel interfaces that respond to users' emotional states based on their facial expressions. We present a demonstration application that provides real-time visualization of the expressions captured by the camera.

Question: What is a real-time multi-face expression recognition toolkit?


The Fox News Effect: Media Bias and Voting∗. Does media bias affect voting? We address this question by looking at the entry of Fox News in cable markets and its impact on voting. Between October 1996 and November 2000, the conservative Fox News Channel was introduced in the cable programming of 20 percent of US towns. Fox News availability in 2000 appears to be largely idiosyncratic. Using a data set of voting data for 9,256 towns, we investigate if Republicans gained vote share in towns where Fox News entered the cable market by the year 2000. We find a significant effect of the introduction of Fox News on the vote share in Presidential elections between 1996 and 2000. Republicans gain 0.4 to 0.6 percentage points in the towns which broadcast Fox News. The results are robust to town-level controls, district and county fixed effects, and alternative specifications. We also find a significant effect of Fox News on Senate vote share and, to a lesser extent, on voter turnout. Our estimates imply that Fox News convinced 3 to 8 percent of its viewers to vote Republican. The evidence is most consistent with voters suffering from persuasion bias. We also discuss a model of rational voter updating that fits most facts. ∗George Akerlof, Stephen Ansolabehere, Larry M. Bartels, Matthew Gentzkow, Alan Gerber, Jay Hamilton, Alan Krueger, Marco Manacorda, Enrico Moretti, Torsten Persson, Sam Popkin, Riccardo Puglisi, Matthew Rabin, Jesse Shapiro, David Stromberg, and audiences at Bonn University, EUI (Florence), Fuqua, Harvard University, IIES (Stockholm), Princeton University, UC Berkeley, University of Chicago GSB, and the NBER 2005 Political Economy and Labor Studies Meetings provided useful comments. We would like to specially thank Jim Collins and Matthew Gentzkow for providing the Scarborough data. Shawn Bananzadeh, Jessica Chan, Marguerite Converse, Neil Dandavati, Tatyana Deryugina, Monica Deza, Dylan Fox, Melissa Galicia, Calvin Ho, Sudhamas Khanchanawong, Richard Kim,

Question: Does media bias affect voting?


Control Strategy to Naturally Balance Hybrid Converter for Variable-Speed Medium-Voltage Drive Applications. In this paper, a novel control strategy to naturally balance a hybrid converter is presented for medium-voltage induction motor drive applications. Fundamental current of the motor is produced by a three-level quasi-square-wave converter. Several single-phase full-bridge converters are connected in series with this quasi-square-wave converter to eliminate harmonic voltages from the net output voltage of this hybrid converter. Various novel modulation strategies for these series cells are proposed. These lead to a naturally regulated dc-bus voltage of series cells. These also optimize the number of series cells required. Variable fundamental voltage and frequency control of induction motor is adopted with a constant dc-bus voltage of the three-level quasi-square-wave converter. Experimental results are also produced here to validate the proposed modulation strategies for induction motor drives using low-voltage protomodel. Based on the aforementioned, a 2.2-kV induction motor drive has been built, and results are also produced in this paper.

Question: How to control a variable speed induction motor?


Low-frequency Fourier analysis of speech rhythm.. A method for studying speech rhythm is presented, using Fourier analysis of the amplitude envelope of bandpass-filtered speech. Rather than quantifying rhythm with time-domain measurements of interval durations, a frequency-domain representation is used--the rhythm spectrum. This paper describes the method in detail, and discusses approaches to characterizing rhythm with low-frequency spectral information.

Question: What is the low-frequency Fourier analysis of speech rhythm?


Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 1 . Background. Battery management systems (BMS) in hybrid-electric-vehicle (HEV) battery packs must estimate values descriptive of the pack's present operating condition. These include: battery state of charge, power fade, capacity fade, and instantaneous available power. The estimation mechanism must adapt to changing cell characteristics as cells age and therefore provide accurate estimates over the lifetime of the pack. In a series of three papers, we propose a method, based on extended Kalman filtering (EKF), that is able to accomplish these goals on a lithium-ion polymer battery pack. We expect that it will also work well on other battery chemistries. These papers cover the required mathematical background, cell modeling and system identification requirements, and the final solution, together with results. This first paper investigates the estimation requirements for HEV BMS in some detail, in parallel to the requirements for other battery-powered applications. The comparison leads us to understand that the HEV environment is very challenging on batteries and the BMS, and that precise estimation of some parameters will improve performance and robustness, and will ultimately lengthen the useful lifetime of the pack. This conclusion motivates the use of more complex algorithms than might be used in other applications. Our premise is that EKF then becomes a very attractive approach. This paper introduces the basic method, gives some intuitive feel to the necessary computational steps, and concludes by presenting an illustrative example as to the type of results that may be obtained using EKF. © 2004 Elsevier B.V. All rights reserved.

Question: What is the difference between a battery management system and a battery?


A Category-Based Model for ABAC. In Attribute-Based Access Control (ABAC) systems, access to resources is controlled by evaluating rules against the attributes of the user and the object involved in the access request, as well as the values of the relevant attributes from the environment. This is a powerful concept: ABAC is able to enforce DAC and RBAC policies, as well as more general, dynamic access control policies, where the decision to grant or deny an access request is based on the system's state. However, in its current definition, ABAC does not lend itself well to some operations, such as review queries, and it is in general more costly to specify and maintain than simpler systems such as RBAC. To address these issues, in this paper we propose a formal model of ABAC based on the notion of a category that underlies the general category-based metamodel of access control (CBAC). Our proposed approach adds structure to ABAC, so that policies are easier to design and understand, review queries become easy to evaluate, and simple systems such as RBAC can be implemented as instances of ABAC without additional costs.

Question: What is a category-based model for ABAC?


SECURE COMPUTER SYSTEMS : MATHEMATICAL FOUNDATIONS.

Question: What is the mathematical foundation of secure computer systems?


RMDL: Random Multimodel Deep Learning for Classification. The continually increasing number of complex datasets each year necessitates ever improving machine learning methods for robust and accurate categorization of these data. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification. Deep learning models have achieved state-of-the-art results across many domains. RMDL solves the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of deep learning architectures. RDML can accept as input a variety data to include text, video, images, and symbolic. This paper describes RMDL and shows test results for image and text data including MNIST, CIFAR-10, WOS, Reuters, IMDB, and 20newsgroup. These test results show that RDML produces consistently better performance than standard methods over a broad range of data types and classification problems.

Question: What is a deep learning model?


Partially overlapping neural networks for real and imagined hand movements.. Neuroimagery findings have shown similar cerebral networks associated with imagination and execution of a movement. On the other hand, neuropsychological studies of parietal-lesioned patients suggest that these networks may be at least partly distinct. In the present study, normal subjects were asked to either imagine or execute auditory-cued hand movements. Compared with rest, imagination and execution showed overlapping networks, including bilateral premotor and parietal areas, basal ganglia and cerebellum. However, direct comparison between the two experimental conditions showed that specific cortico-subcortical areas were more engaged in mental simulation, including bilateral premotor, prefrontal, supplementary motor and left posterior parietal areas, and the caudate nuclei. These results suggest that a specific neuronal substrate is involved in the processing of hand motor representations.

Question: What is the difference between imagination and execution?


Efficient Memory Disaggregation with Infiniswap. Memory-intensive applications suffer large performance loss when their working sets do not fully fit in memory. Yet, they cannot leverage otherwise unused remote memory when paging out to disks even in the presence of large imbalance in memory utilizations across a cluster. Existing proposals for memory disaggregation call for new architectures, new hardware designs, and/or new programming models, making them infeasible. This paper describes the design and implementation of INFINISWAP, a remote memory paging system designed specifically for an RDMA network. INFINISWAP opportunistically harvests and transparently exposes unused memory to unmodified applications by dividing the swap space of each machine into many slabs and distributing them across many machines' remote memory. Because one-sided RDMA operations bypass remote CPUs, INFINISWAP leverages the power of many choices to perform decentralized slab placements and evictions. We have implemented and deployed INFINISWAP on an RDMA cluster without any modifications to user applications or the OS and evaluated its effectiveness using multiple workloads running on unmodified VoltDB, Memcached, PowerGraph, GraphX, and Apache Spark. Using INFINISWAP, throughputs of these applications improve between 4× (0.94×) to 15.4× (7.8×) over disk (Mellanox nbdX), and median and tail latencies between 5.4× (2×) and 61× (2.3×). INFINISWAP achieves these with negligible remote CPU usage, whereas nbdX becomes CPU-bound. INFINISWAP increases the overall memory utilization of a cluster and works well at scale.

Question: How does INFINISWAP work?


Mining Social Science Publications for Survey Variables. Research in Social Science is usually based on survey data where individual research questions relate to observable concepts (variables). However, due to a lack of standards for data citations a reliable identification of the variables used is often difficult. In this paper, we present a work-in-progress study that seeks to provide a solution to the variable detection task based on supervised machine learning algorithms, using a linguistic analysis pipeline to extract a rich feature set, including terminological concepts and similarity metric scores. Further, we present preliminary results on a small dataset that has been specifically designed for this task, yielding a significant increase in performance over the random baseline.

Question: What is a survey variable?


Multi-linear polarization reconfigurable center-fed circular patch antenna with shorting posts. In this paper, a novel multi-linear polarization reconfigurable antenna with shorting posts, which can achieve four linear polarizations (0°, 45°, 90°, 135°), has been proposed. By switching the diodes between two groups of shorting posts, four linear polarizations can be realized. The dimensions of the proposed antenna are about 0.56λ× 0.56λ× 0.07λ at 2.4 GHz. The measured results agree well with the simulated ones.

Question: What is the dimensions of the antenna?


Adversarial Machine Learning at Scale. Adversarial examples are malicious inputs designed to fool machine learning models. They often transfer from one model to another, allowing attackers to mount black box attacks without knowledge of the target model's parameters. Adversarial training is the process of explicitly training a model on adversarial examples, in order to make it more robust to attack or to reduce its test error on clean inputs. So far, adversarial training has primarily been applied to small problems. In this research, we apply adversarial training to ImageNet (Russakovsky et al., 2014). Our contributions include: (1) recommendations for how to succesfully scale adversarial training to large models and datasets, (2) the observation that adversarial training confers robustness to single-step attack methods, (3) the finding that multi-step attack methods are somewhat less transferable than singlestep attack methods, so single-step attacks are the best for mounting black-box attacks, and (4) resolution of a "label leaking" effect that causes adversarially trained models to perform better on adversarial examples than on clean examples, because the adversarial example construction process uses the true label and the model can learn to exploit regularities in the construction process.

Question: How to scale adversarial training to large models and datasets?


Why do they still use paper?: understanding data collection and use in Autism education. Autism education programs for children collect and use large amounts of behavioral data on each student. Staff use paper almost exclusively to collect these data, despite significant problems they face in tracking student data in situ, filling out data sheets and graphs on a daily basis, and using the sheets in collaborative decision making. We conducted fieldwork to understand data collection and use in the domain of autism education to explain why current technology had not met staff needs. We found that data needs are complex and unstandardized, immediate demands of the job interfere with staff ability to collect in situ data, and existing technology for data collection is inadequate. We also identified opportunities for technology to improve sharing and use of data. We found that data sheets are idiosyncratic and not useful without human mediation; improved communication with parents could benefit children's development; and staff are willing, and even eager, to incorporate technology. These factors explain the continued dependence on paper for data collection in this environment, and reveal opportunities for technology to support data collection and improve use of collected data.

Question: Why do they still use paper?


Real-Time Visibility-Based Fusion of Depth Maps. We present a viewpoint-based approach for the quick fusion of multiple stereo depth maps. Our method selects depth estimates for each pixel that minimize violations of visibility constraints and thus remove errors and inconsistencies from the depth maps to produce a consistent surface. We advocate a two-stage process in which the first stage generates potentially noisy, overlapping depth maps from a set of calibrated images and the second stage fuses these depth maps to obtain an integrated surface with higher accuracy, suppressed noise, and reduced redundancy. We show that by dividing the processing into two stages we are able to achieve a very high throughput because we are able to use a computationally cheap stereo algorithm and because this architecture is amenable to hardware-accelerated (GPU) implementations. A rigorous formulation based on the notion of stability of a depth estimate is presented first. It aims to determine the validity of a depth estimate by rendering multiple depth maps into the reference view as well as rendering the reference depth map into the other views in order to detect occlusions and free- space violations. We also present an approximate alternative formulation that selects and validates only one hypothesis based on confidence. Both formulations enable us to perform video-based reconstruction at up to 25 frames per second. We show results on the multi-view stereo evaluation benchmark datasets and several outdoors video sequences. Extensive quantitative analysis is performed using an accurately surveyed model of a real building as ground truth.

Question: How to fuse multiple depth maps?


Second to fourth digit ratio: a predictor of adult penile length.. The second to fourth digit ratio (2D:4D) has been proposed as a putative biomarker for prenatal testosterone and covaries with the sensitivity of the androgen receptor (AR). Both prenatal testosterone and the AR play a central role in penile growth. In this study, we investigated the relationship between digit ratio and penile length. Korean men who were hospitalized for urological surgery at a single tertiary academic centre were examined in this study, and 144 men aged 20 years or older who gave informed consent were prospectively enrolled. Right-hand second- and fourth-digit lengths were measured by a single investigator prior to measurement of penile length. Under anaesthesia, flaccid and stretched penile lengths were measured by another investigator who did not measure nor have any the information regarding the digit lengths. Univariate and multivariate analysis using linear regression models showed that only height was a significant predictive factor for flaccid penile length (univariate analysis: r=0.185, P=0.026; multivariate analysis: r=0.172, P=0.038) and that only digit ratio was a significant predictive factor for stretched penile length (univariate analysis:r=-0.216, P=0.009; multivariate analysis: r=-0.201, P=0.024; stretched penile length=-9.201×digit ratio + 20.577). Based on this evidence, we suggest that the digit ratio can predict adult penile size and that the effects of prenatal testosterone may in part explain the differences in adult penile length.

Question: What is the relationship between digit ratio and adult penile length?


The Ever-Changing Social Perception of Autism Spectrum Disorders in the United States. This paper aims to examine the comprehensive social perception of autism spectrum disorders (ASDs) within the United States today. In order to study the broad public view of those with ASDs, this study investigates the evolution of the syndrome in both sociological and scientific realms. By drawing on the scientific progression of the syndrome and the mixture of this research with concurrent social issues and media representations, this study infers why such a significant amount of stigmatization has become attached to those with ASDs and how these stigmatizations have varied throughout history. After studying this evolving social perception of ASDs in the United States, the writer details suggestions for the betterment of this awareness, including boosted and specified research efforts, increased collaboration within those experts in autism, and positive visibility of those with ASDs and their families. Overall, the writer suggests that public awareness has increased and thus negative stigmatization has decreased in recent years; however, there remains much to be done to increase general social understanding of ASDs. "Autism is about having a pure heart and being very sensitive... It is about finding a way to survive in an overwhelming, confusing world... It is about developing differently, in a different pace and with different leaps." -Trisha Van Berkel The identification of autism, in both sociological and scientific terms, has experienced a drastic evolution since its original definition in the early 20th century. From its original designation by Leo Kanner (1943), public understanding of autism spectrum disorders (ASDs) has been shrouded in mystery and misperception. The basic core features of all ASDs include problems with basic socialization and communication, strange intonation and facial expressions, and intense preoccupations or repetitive behaviors; however, one import

Question: What is the definition of autism?


A Security Risk Assessment Framework for Smart Car. As the automobile industry has recently adopted information technologies, the latter are being used to replace mechanical systems with electronically-controlled systems. Moreover, automobiles are evolving into smart cars or connected cars as they are connected to various IT devices and networks such as VANET (Vehicular Ad hoc NETwork). Although there were no concerns about the hacking of automobiles in the past, various security threats are now emerging as electronic systems are gradually filling up the interiors of many automobiles, which are in turn being connected to external networks. As such, researchers have begun studying smart car security, leading to the disclosure of security threats through the testing or development of various automobile security technologies. However, the security threats facing smart cars do not occur frequently and, practically speaking, it is unrealistic to attempt to cope with every possible security threat when considering such factors as performance, compatibility, and so forth. Moreover, the excessive application of security technology will increase the overall vehicle cost and lower the effectiveness of investment. Therefore, smart car security risks should be assessed and prioritized to establish efficient security measures. To that end, this study constructed a security risk assessment framework in a bid to establish efficient measures for smart car security. The proposed security risk assessment framework configured the assessment procedure based on the conventional security risk analysis model GMITS (ISO13335) and utilized 'attack tree analysis' to assess the threats and vulnerabilities.

Question: What is the security risk assessment framework for smart cars?


Estimation of Detection Thresholds for Redirected Walking Techniques. In immersive virtual environments (IVEs), users can control their virtual viewpoint by moving their tracked head and walking through the real world. Usually, movements in the real world are mapped one-to-one to virtual camera motions. With redirection techniques, the virtual camera is manipulated by applying gains to user motion so that the virtual world moves differently than the real world. Thus, users can walk through large-scale IVEs while physically remaining in a reasonably small workspace. In psychophysical experiments with a two-alternative forced-choice task, we have quantified how much humans can unknowingly be redirected on physical paths that are different from the visually perceived paths. We tested 12 subjects in three different experiments: (E1) discrimination between virtual and physical rotations, (E2) discrimination between virtual and physical straightforward movements, and (E3) discrimination of path curvature. In experiment E1, subjects performed rotations with different gains, and then had to choose whether the visually perceived rotation was smaller or greater than the physical rotation. In experiment E2, subjects chose whether the physical walk was shorter or longer than the visually perceived scaled travel distance. In experiment E3, subjects estimate the path curvature when walking a curved path in the real world while the visual display shows a straight path in the virtual world. Our results show that users can be turned physically about 49 percent more or 20 percent less than the perceived virtual rotation, distances can be downscaled by 14 percent and upscaled by 26 percent, and users can be redirected on a circular arc with a radius greater than 22 m while they believe that they are walking straight.

Question: How much can you be redirected?


Appropriateness of plantar pressure measurement devices: a comparative technical assessment.. Accurate plantar pressure measurements are mandatory in both clinical and research contexts. Differences in accuracy, precision and reliability of the available devices have prevented so far the onset of standardization processes or the definition of reliable reference datasets. In order to comparatively assess the appropriateness of the most used pressure measurement devices (PMD) on-the-market, in 2006 the Institute the author is working for approved a two-year scientific project aimed to design, validate and implement dedicated testing methods for both in-factory and on-the field assessment. A first testing phase was also performed which finished in December 2008. Five commercial PMDs using different technologies-resistive, elastomer-based capacitive, air-based capacitive-were assessed and compared with respect to absolute pressure measurements, hysteresis, creep and COP estimation. The static and dynamic pressure tests showed very high accuracy of capacitive, elastomer-based technology (RMSE<0.5%), and quite a good performance of capacitive, air-based technology (RMSE<5%). High accuracy was also found for the resistive technology by TEKSCAN (RMSE<2.5%), even though a complex ad hoc calibration was necessary.

Question: What is the difference between a resistive and a capacitive pressure measurement device?


What Does Classifying More Than 10, 000 Image Categories Tell Us?. Image classification is a critical task for both humans and computers. One of the challenges lies in the large scale of the semantic space. In particular, humans can recognize tens of thousands of object classes and scenes. No computer vision algorithm today has been tested at this scale. This paper presents a study of large scale categorization including a series of challenging experiments on classification with more than 10, 000 image classes. We find that a) computational issues become crucial in algorithm design; b) conventional wisdom from a couple of hundred image categories on relative performance of different classifiers does not necessarily hold when the number of categories increases; c) there is a surprisingly strong relationship between the structure of WordNet (developed for studying language) and the difficulty of visual categorization; d) classification can be improved by exploiting the semantic hierarchy. Toward the future goal of developing automatic vision algorithms to recognize tens of thousands or even millions of image categories, we make a series of observations and arguments about dataset scale, category density, and image hierarchy.

Question: What does classifying more than 10,000 image categories tell us?


Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies. D3EGF(FIH)J KMLONPEGQSRPETN UCV.WYX(Z R.[ V R6\M[ X N@]_^O\`JaNcb V RcQ W d EGKeL(^(QgfhKeLOE?i)^(QSj ETNPfPQkRl[ V R)m"[ X ^(KeLOEG^ npo qarpo m"[ X ^(KeLOEG^tsAu EGNPb V ^ v wyx zlwO{(|(}<~OC}€('(xp{aƒy".~A}†…ˆ‡_~ ‰CŠlƒ3‰#|<€Az†w#|l€6‡ ‹(| Œ JpfhL XVŽ EG^O QgJ ' ETFOR†'"] ^O\"J•NPb V RcQ—– X E)ETR ˜6EGKeLOETNcKMLOEš™ Fˆ› ETN V RcQgJp^(^OE ZgZ E i ^(Qkj EGNPfhQSRO› E œOE2m1Jp^ RcNY› E V•Z sOŸž! ¡ q.n sCD X KGKa'8¢EG^ RPNhE¤£ ¥¦Q ZgZ E•s m§J•^ RPNO› E V•Z s( ̈ X › EG©#EKas# V ^ V œ V s(H a «a•¬3 ®#|.€Y ̄y} xa°OC}l{x"‡ ‰ ƒyxl€Y~3{| " ±2‡Pz " ž V J Z J U N V fhKTJp^(Q ' ETFOR†' J•\ D vYf3RPEGb ́f V ^(œ§ˆJpbF X RPETN@D KTQ—EG^(KTE i ^(QSjpEGNPfhQSR4vμJ•\ U¶Z JaNPEG^(K·E jYQ V œ(Q ̧D V ^ R V m V N3R V aOs#1 o ¡Ga r U Q—NhE^OoTE1⁄4»,] R V•Z vC1⁄2 3⁄4 " x ± x  ‹#¿ }À‡ ‰3€t}l'C}2‡P}<~ ¬t[ X NP•E^§D KeL(b ́Qgœ(L X ©yETN ] ' DY]_Á ˆJ•NPfhJàZ j EToQ V a• rpopo2Ä X  V ^(J(sCD Å)QSRPoTEGN ZgV ^(œ Æ ‰#|•{3 ̄|.€(C}.‹C¿Y}p" ‡Pz†w

Question: What is the difficulty of learning long-term dependencies?


NEED FOR HUMAN DIRECTION OF DATA MINING CROSS-INDUSTRY STANDARD PROCESS: CRISP–DM CASE STUDY 1: ANALYZING AUTOMOBILE WARRANTY CLAIMS: EXAMPLE OF THE CRISP–DM INDUSTRY STANDARD PROCESS IN ACTION FALLACIES OF DATA MINING. WHAT IS DATA MINING? WHY DATA MINING? NEED FOR HUMAN DIRECTION OF DATA MINING CROSS-INDUSTRY STANDARD PROCESS: CRISP–DM CASE STUDY 1: ANALYZING AUTOMOBILE WARRANTY CLAIMS: EXAMPLE OF THE CRISP–DM INDUSTRY STANDARD PROCESS IN ACTION FALLACIES OF DATA MINING WHAT TASKS CAN DATA MINING ACCOMPLISH? CASE STUDY 2: PREDICTING ABNORMAL STOCK MARKET RETURNS USING NEURAL NETWORKS CASE STUDY 3: MINING ASSOCIATION RULES FROM LEGAL DATABASES CASE STUDY 4: PREDICTING CORPORATE BANKRUPTCIES USING DECISION TREES CASE STUDY 5: PROFILING THE TOURISM MARKET USING k-MEANS CLUSTERING ANALYSIS

Question: What is data mining?


Towards Accurate Duplicate Bug Retrieval Using Deep Learning Techniques. Duplicate Bug Detection is the problem of identifying whether a newly reported bug is a duplicate of an existing bug in the system and retrieving the original or similar bugs from the past. This is required to avoid costly rediscovery and redundant work. In typical software projects, the number of duplicate bugs reported may run into the order of thousands, making it expensive in terms of cost and time for manual intervention. This makes the problem of duplicate or similar bug detection an important one in Software Engineering domain. However, an automated solution for the same is not quite accurate yet in practice, in spite of many reported approaches using various machine learning techniques. In this work, we propose a retrieval and classification model using Siamese Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) for accurate detection and retrieval of duplicate and similar bugs. We report an accuracy close to 90% and recall rate close to 80%, which makes possible the practical use of such a system. We describe our model in detail along with related discussions from the Deep Learning domain. By presenting the detailed experimental results, we illustrate the effectiveness of the model in practical systems, including for repositories for which supervised training data is not available.

Question: What is the accuracy of duplicate bug detection?


Reconfiguring the Imaging Pipeline for Computer Vision. Advancements in deep learning have ignited an explosion of research on efficient hardware for embedded computer vision. Hardware vision acceleration, however, does not address the cost of capturing and processing the image data that feeds these algorithms. We examine the role of the image signal processing (ISP) pipeline in computer vision to identify opportunities to reduce computation and save energy. The key insight is that imaging pipelines should be be configurable: to switch between a traditional photography mode and a low-power vision mode that produces lower-quality image data suitable only for computer vision. We use eight computer vision algorithms and a reversible pipeline simulation tool to study the imaging system's impact on vision performance. For both CNN-based and classical vision algorithms, we observe that only two ISP stages, demosaicing and gamma compression, are critical for task performance. We propose a new image sensor design that can compensate for these stages. The sensor design features an adjustable resolution and tunable analog-to-digital converters (ADCs). Our proposed imaging system's vision mode disables the ISP entirely and configures the sensor to produce subsampled, lower-precision image data. This vision mode can save ~75% of the average energy of a baseline photography mode with only a small impact on vision task accuracy.

Question: How can we reduce the energy consumption of computer vision?


Enhancing Test Compression With Dependency Analysis for Multiple Expansion Ratios. Scan test data compression is widely used in industry to reduce test data volume (TDV) and test application time (TAT). This paper shows how multiple scan chain expansion ratios can help to obtain high test data compression in system-on-chips. Scan chains are partitioned with a higher expansion ratio than normal in scan compression mode and then are gradually concatenated based on a cost function to detect any faults that could not be detected at the higher expansion ratios. It improves the overall test compression ratio since it potentially allows faults to be detected at the highest expansion ratio. This paper introduces a new cost function to choose scan chain concatenation candidates for concatenation for multiple expansion ratios. To avoid TDV and TAT increase by scan concatenation, the proposed method takes a logic structure and scan chain length into consideration. Experiment results show the proposed method reduces TAT and TDV by 53%–64% compared with a traditional scan compression method.

Question: How to improve test compression?


GEOSTATISTICS : PAST , PRESENT , AND FUTURE. Geostatistical analyses were first developed in the 1950's as a result of interest in areal or block averages for ore reserves in the mining industry. Today, variogram estimation and spatial prediction (kriging) span all sciences where data exhibit spatial correlation. Theoretical properties of the spatial process are addressed under the distribution-free and likelihood-based perspectives. Strengths and weaknesses of these two dominant methodologies for modeling spatial correlation and predicting responses at unsampled sites and areal units are explored. Examples of hot spot detection and areal prediction show the flexibility of the Bayesian paradigm. Current bottlenecks and future directions in the field of geostatistics are discussed.

Question: What is the difference between variogram estimation and spatial prediction?


On-line Q-learning using connectionist systems. Reinforcement learning algorithms are a powerful machine learning technique. However, much of the work on these algorithms has been developed with regard to discrete nite-state Markovian problems, which is too restrictive for many real-world environments. Therefore, it is desirable to extend these methods to high dimensional continuous state-spaces, which requires the use of function approximation to generalise the information learnt by the system. In this report, the use of back-propagation neural networks (Rumelhart, Hinton and Williams 1986) is considered in this context. We consider a number of di erent algorithms based around Q-Learning (Watkins 1989) combined with the Temporal Di erence algorithm (Sutton 1988), including a new algorithm (Modi ed Connectionist Q-Learning), and Q( ) (Peng and Williams 1994). In addition, we present algorithms for applying these updates on-line during trials, unlike backward replay used by Lin (1993) that requires waiting until the end of each trial before updating can occur. On-line updating is found to be more robust to the choice of training parameters than backward replay, and also enables the algorithms to be used in continuously operating systems where no end of trial conditions occur. We compare the performance of these algorithms on a realistic robot navigation problem, where a simulated mobile robot is trained to guide itself to a goal position in the presence of obstacles. The robot must rely on limited sensory feedback from its surroundings, and make decisions that can be generalised to arbitrary layouts of obstacles. These simulations show that on-line learning algorithms are less sensitive to the choice of training parameters than backward replay, and that the alternative update rules of MCQ-L and Q( ) are more robust than standard Q-learning updates. 1

Question: What is a robot navigation problem?


Modeling Cellular-to-UAV Path-Loss for Suburban Environments. Operating unmanned aerial vehicle (UAV) over cellular networks would open the barriers of remote navigation and far-flung flying by combining the benefits of UAVs and the ubiquitous availability of cellular networks. In this letter, we provide an initial insight on the radio propagation characteristics of cellular-to-UAV (CtU) channel. In particular, we model the statistical behavior of the path-loss from a cellular base station toward a flying UAV. Where we report the value of the path-loss as a function of the depression angle and the terrestrial coverage beneath the UAV. The provided model is derived based on extensive experimental data measurements conducted in a typical suburban environment for both terrestrial (by drive test) and aerial coverage (using a UAV). The model provides simple and accurate prediction of CtU path-loss that can be useful for both researchers and network operators alike.

Question: How to model the path loss of a cellular-to-UAV channel?


Efficient Parallel Graph Exploration on Multi-Core CPU and GPU. Graphs are a fundamental data representation that has been used extensively in various domains. In graph-based applications, a systematic exploration of the graph such as a breadth-first search (BFS) often serves as a key component in the processing of their massive data sets. In this paper, we present a new method for implementing the parallel BFS algorithm on multi-core CPUs which exploits a fundamental property of randomly shaped real-world graph instances. By utilizing memory bandwidth more efficiently, our method shows improved performance over the current state-of-the-art implementation and increases its advantage as the size of the graph increases. We then propose a hybrid method which, for each level of the BFS algorithm, dynamically chooses the best implementation from: a sequential execution, two different methods of multicore execution, and a GPU execution. Such a hybrid approach provides the best performance for each graph size while avoiding poor worst-case performance on high-diameter graphs. Finally, we study the effects of the underlying architecture on BFS performance by comparing multiple CPU and GPU systems, a high-end GPU system performed as well as a quad-socket high-end CPU system.

Question: What is the best way to implement a breadth-first search?


FOREX Rate prediction using Chaos and Quantile Regression Random Forest. This paper presents a hybrid of chaos modeling and Quantile Regression Random Forest (QRRF) for Foreign Exchange (FOREX) Rate prediction. The exchange rates data of US Dollar (USD) versus Japanese Yen (JPY), British Pound (GBP), and Euro (EUR) are used to test the efficacy of proposed model. Based on the experiments conducted, we conclude that the proposed model yielded accurate predictions compared to Chaos + Quantile Regression (QR), Chaos+Random Forest (RF) and that of Pradeepkumar and Ravi [12] in terms of both Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE).

Question: What is the difference between chaos modeling and quantile regression?


Area-efficient cross-coupled charge pump for on-chip solar cell. In this paper, an area-efficient cross-coupled charge pump for standalone chip using on-chip solar cell is proposed. The proposed cross-coupled charge pump outputs higher voltage than conventional cross-coupled charge pump on the same area by adding capacitors to drive MOS transistors. The proposed circuit is fabricated in 0.18um standard CMOS process, and outputs 80mV higher than the conventional circuit with 500mV input and 100kHz clock frequency.

Question: How to make a cross-coupled charge pump?


SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing. With the recent advance of wearable devices and Internet of Things (IoTs), it becomes attractive to implement the Deep Convolutional Neural Networks (DCNNs) in embedded and portable systems. Currently, executing the software-based DCNNs requires high-performance servers, restricting the widespread deployment on embedded and mobile IoT devices. To overcome this obstacle, considerable research efforts have been made to develop highly-parallel and specialized DCNN accelerators using GPGPUs, FPGAs or ASICs.  Stochastic Computing (SC), which uses a bit-stream to represent a number within [-1, 1] by counting the number of ones in the bit-stream, has high potential for implementing DCNNs with high scalability and ultra-low hardware footprint. Since multiplications and additions can be calculated using AND gates and multiplexers in SC, significant reductions in power (energy) and hardware footprint can be achieved compared to the conventional binary arithmetic implementations. The tremendous savings in power (energy) and hardware resources allow immense design space for enhancing scalability and robustness for hardware DCNNs.  This paper presents SC-DCNN, the first comprehensive design and optimization framework of SC-based DCNNs, using a bottom-up approach. We first present the designs of function blocks that perform the basic operations in DCNN, including inner product, pooling, and activation function. Then we propose four designs of feature extraction blocks, which are in charge of extracting features from input feature maps, by connecting different basic function blocks with joint optimization. Moreover, the efficient weight storage methods are proposed to reduce the area and power (energy) consumption. Putting all together, with feature extraction blocks carefully selected, SC-DCNN is holistically optimized to minimize area and power (energy) consumption while maintaining high netwo

Question: What is the area and power (energy) consumption of a deep convolutional neural network?