Intel MPX Explained: An Empirical Study of Intel MPX and Software-based Bounds Checking Approaches. Memory-safety violations are a prevalent cause of both reliability and security vulnerabilities in systems software written in unsafe languages like C/C++. Unfortunately, all the existing software-based solutions to this problem exhibit high performance overheads preventing them from wide adoption in production runs. To address this issue, Intel recently released a new ISA extension—Memory Protection Extensions (Intel MPX), a hardware-assisted full-stack solution to protect against memory safety violations. In this work, we perform an exhaustive study of the Intel MPX architecture to understand its advantages and caveats. We base our study along three dimensions: (a) performance overheads, (b) security guarantees, and (c) usability issues. To put our results in perspective, we compare Intel MPX with three prominent software-based approaches: (1) trip-wire—AddressSanitizer, (2) objectbased—SAFECode, and (3) pointer-based—SoftBound. Our main conclusion is that Intel MPX is a promising technique that is not yet practical for widespread adoption. Intel MPX's performance overheads are still high (~50% on average), and the supporting infrastructure has bugs which may cause compilation or runtime errors. Moreover, we showcase the design limitations of Intel MPX: it cannot detect temporal errors, may have false positives and false negatives in multithreaded code, and its restrictions on memory layout require substantial code changes for some programs. This paper presents only the general discussion and aggregated data; for the complete evaluation, please see the supporting website: https://Intel-MPX.github.io/. Evaluation plots and section headings have hyperlinks to the complete experimental description and results.

Question: What is the performance overhead of Intel MPX?


Cooperation between distributed agents through self-organisation. The paper discussu some antral irrw in the cooperation between disuibuted agcnu using lhe following case study: The objective is to explore a distant planet. more concretely to collect samples of a parricular typ of precious ruck. The location of the ruck .iamplu is unknown in advance but they arc rypiwlly clustered in cenain spm There i S a vehicle that can drive around on the planet and later reenter the s p x d (0 g0 back to ewlh There is no deuiled map of the t e m h although it h known hat the terrain is fuU of obstacles. hills. valleys. etc.

Question: What is the name of the vehicle?


Hidden Markov Models for Speech Recognition. Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

Question: How to use JSTOR?


A flexible coupling approach to multi-agent planning under incomplete information. Multi-agent planning (MAP) approaches are typically oriented at solving loosely coupled problems, being ineffective to deal with more complex, strongly related problems. In most cases, agents work under complete information, building complete knowledge bases. The present article introduces a general-purpose MAP framework designed to tackle problems of any coupling levels under incomplete information. Agents in our MAP model are partially unaware of the information managed by the rest of agents and share only the critical information that affects other agents, thus maintaining a distributed vision of the task. Agents solve MAP tasks through the adoption of an iterative refinement planning procedure that uses single-agent planning technology. In particular, agents will devise refinements through the partial-order planning paradigm, a flexible framework to build refinement plans leaving unsolved details that will be gradually completed by means of new refinements. Our proposal is supported with the implementation of a fully operative MAP system and we show various experiments when running our system over different types of MAP problems, from the most strongly related to the most loosely coupled.

Question: What is a flexible coupling approach to multi-agent planning under incomplete information?


Image statistics and the perception of surface qualities. The world is full of surfaces, and by looking at them we can judge their material qualities. Properties such as colour or glossiness can help us decide whether a pancake is cooked, or a patch of pavement is icy. Most studies of surface appearance have emphasized textureless matte surfaces, but real-world surfaces, which may have gloss and complex mesostructure, are now receiving increased attention. Their appearance results from a complex interplay of illumination, reflectance and surface geometry, which are difficult to tease apart given an image. If there were simple image statistics that were diagnostic of surface properties it would be sensible to use them. Here we show that the skewness of the luminance histogram and the skewness of sub-band filter outputs are correlated with surface gloss and inversely correlated with surface albedo (diffuse reflectance). We find evidence that human observers use skewness, or a similar measure of histogram asymmetry, in making judgements about surfaces. When the image of a surface has positively skewed statistics, it tends to appear darker and glossier than a similar surface with lower skewness, and this is true whether the skewness is inherent to the original image or is introduced by digital manipulation. We also find a visual after-effect based on skewness: adaptation to patterns with skewed statistics can alter the apparent lightness and glossiness of surfaces that are subsequently viewed. We suggest that there are neural mechanisms sensitive to skewed statistics, and that their outputs can be used in estimating surface properties.

Question: What is the relationship between skewness and albedo?


Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect spatial and temporal dependencies. In this paper, we propose a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction problem in traffic domain. Instead of applying regular convolutional and recurrent units, we formulate the problem on graphs and build the model with complete convolutional structures, which enable much faster training speed with fewer parameters. Experiments show that our model STGCN effectively captures comprehensive spatio-temporal correlations through modeling multi-scale traffic networks and consistently outperforms state-of-the-art baselines on various real-world traffic datasets.

Question: What is the time series prediction problem in traffic domain?


The mnist database of handwritten digits. please note that your browser may uncompress these files without telling you. If the files you downloaded have a larger size than the above, they have been uncompressed by your browser. Simply rename them to remove the .gz extension. Some people have asked me "my application can't open your image files". These files are not in any standard image format. You have to write your own (very simple) program to read them. The file format is described at the bottom of this page.

Question: How to read a file in a compressed format?


Results of syndactyly release using a modification of the Flatt technique.. The results of 144 congenital syndactyly releases over a 12-year period by a single surgeon using a modified Flatt technique (dorsal hourglass flap, interdigitating zigzag flaps, and full-thickness skin grafts) are analyzed considering the association of skin grafts and web creep. The mean follow-up was 5 years. There were seven cases of graft failure, only one of which developed web creep. Web creep occurred in 4.2% of web releases. The results suggest that avoiding longitudinal straight-line scars across the web space may be an important factor in avoiding web creep when performing the modified Flatt technique described.

Question: What is the modified Flatt technique?


Question: What is the stability of drugs and dosage forms?


An evidential approach to map-building for autonomous vehicles. In this work, we examine the problem of constructing and maintaining a map of an autonomous vehicle's environment for the purpose of navigation, using evidential reasoning. The inherent uncertainty in the origin of measurements of sensors demands a probabilistic approach to processing, or fusing, the new sensory information to build an accurate map. In this paper, the map is based on a two-dimensional (2-D) occupancy grid. The sensor readings are "fused" into the map using the Dempster–Shafer inference rule. This evidential approach with its multivalued hypotheses allows quantitative analysis of the quality of the data. The map building system is experimentally evaluated using sonar data from real environments.

Question: How to build a map of an autonomous vehicle's environment?


Observing Tutorial Dialogues Collaboratively: Insights About Human Tutoring Effectiveness From Vicarious Learning. The goals of this study are to evaluate a relatively novel learning environment, as well as to seek greater understanding of why human tutoring is so effective. This alternative learning environment consists of pairs of students collaboratively observing a videotape of another student being tutored. Comparing this collaboratively observing environment to four other instructional methods-one-on-one human tutoring, observing tutoring individually, collaborating without observing, and studying alone-the results showed that students learned to solve physics problems just as effectively from observing tutoring collaboratively as the tutees who were being tutored individually. We explain the effectiveness of this learning environment by postulating that such a situation encourages learners to become active and constructive observers through interactions with a peer. In essence, collaboratively observing combines the benefit of tutoring with the benefit of collaborating. The learning outcomes of the tutees and the collaborative observers, along with the tutoring dialogues, were used to further evaluate three hypotheses explaining why human tutoring is an effective learning method. Detailed analyses of the protocols at several grain sizes suggest that tutoring is effective when tutees are independently or jointly constructing knowledge: with the tutor, but not when the tutor independently conveys knowledge.

Question: What is the difference between tutoring and collaborating?


Deeper Inside PageRank. This paper serves as a companion or extension to the "Inside PageRank" paper by Bianchini et al. [19]. It is a comprehensive survey of all issues associated with PageRank, covering the basic PageRank model, available and recommended solution methods, storage issues, existence, uniqueness, and convergence properties, possible alterations to the basic model, suggested alternatives to the traditional solution methods, sensitivity and conditioning, and finally the updating problem. We introduce a few new results, provide an extensive reference list, and speculate about exciting areas of future research.

Question: What is the difference between the "Inside PageRank" paper and this paper?


First-Order Theorem Proving and Vampire. In this tutorial we give a short introduction in first-order theorem proving and the use of the theorem prover Vampire. The first part of the tutorial is intended for the audience with little or no knowledge in theorem proving. We will discuss the the resolution and superposition calculus, introduce the saturation principle, present various algorithms implementing redundancy elimination, preprocessing and clause form transformation and demonstrate how these concepts are implemented in Vampire. The second part will cover more advanced topics and features. Some of these features are implemented only in Vampire. This includes reasoning with theories, such as arithmetic, answering queries to very large knowledge bases, interpolation, and an original technique of symbol elimination, which allows one to automatically discover invariants in programs with loops. All the introduced concepts will be illustrated by running the firstorder theorem prover Vampire.

Question: What is the first-order theorem prover Vampire?


An Approach to Generate Topic Similar Document by Seed Extraction-Based SeqGAN Training for Bait Document. In recent years, topic similar document generation has drawn more and more attention in both academia and industry. Especially, bait document generation is very important for security. For more-like and fast bait document generation, we proposed the topic similar document generation model based on SeqGAN model (TSDG-SeqGAN). In the training phrase, we used jieba word segmentation tool for training text to greatly reduce the training time. In the generation phrase, we extract keywords and key sentence from the subject document as seeds, and then enter the seeds into the trained generation network. Next, we get keyword-based documents and documents based on key sentences from generation network. Finally, we output documents that are most similar to the subject document as the final result. Experiments show the effectiveness of our model.

Question: What is the topic similar document generation model based on SeqGAN?


Acoustic Modeling for Google Home. This paper describes the technical and system building advances made to the Google Home multichannel speech recognition system, which was launched in November 2016. Technical advances include an adaptive dereverberation frontend, the use of neural network models that do multichannel processing jointly with acoustic modeling, and Grid-LSTMs to model frequency variations. On the system level, improvements include adapting the model using Google Home specific data. We present results on a variety of multichannel sets. The combination of technical and system advances result in a reduction of WER of 8-28% relative compared to the current production system.

Question: What is the WER of the google home?


Latent dirichlet allocation based diversified retrieval for e-commerce search. Diversified retrieval is a very important problem on many e-commerce sites, e.g. eBay and Amazon. Using IR approaches without optimizing for diversity results in a clutter of redundant items that belong to the same products. Most existing product taxonomies are often too noisy, with overlapping structures and non-uniform granularity, to be used directly in diversified retrieval. To address this problem, we propose a Latent Dirichlet Allocation (LDA) based diversified retrieval approach that selects diverse items based on the hidden user intents. Our approach first discovers the hidden user intents of a query using the LDA model, and then ranks the user intents by making trade-offs between their relevance and information novelty. Finally, it chooses the most representative item for each user intent to display. To evaluate the diversity in the search results on e-commerce sites, we propose a new metric, average satisfaction, measuring user satisfaction with the search results. Through our empirical study on eBay, we show that the LDA model discovers meaningful user intents and the LDA-based approach provides significantly higher user satisfaction than the eBay production ranker and three other diversified retrieval approaches.

Question: What is the average satisfaction of the search results on eBay?


TWO-WAY COMMUNICATION CHANNELS. input at terminal 2 and Y2 the corresponding output. Once each second, say, new inputs xi and x2 may be chosen from corresponding input alphabets and put into the channel; outputs yi and Y2 may then be observed. These outputs will be related statistically to the inputs and perhaps historically to previous inputs and outputs if the channel has memory. The problem is to communicate in both directions through the channel as effectively as possible. Particularly, we wish to determine what pairs of signalling rates R1 and R2 for the two directions can be approached with arbitrarily small error probabilities.

Question: What is the channel capacity?


A Machine Learning Approach to Anomaly Detection. Much of the intrusion detection research focuses on signature (misuse) detection, where models are built to recognize known attacks. However, signature detection, by its nature, cannot detect novel attacks. Anomaly detection focuses on modeling the normal behavior and identifying significant deviations, which could be novel attacks. In this paper we explore two machine learning methods that can construct anomaly detection models from past behavior. The first method is a rule learning algorithm that characterizes normal behavior in the absence of labeled attack data. The second method uses a clustering algorithm to identify outliers.

Question: What is anomaly detection?


Clique percolation in random networks.. The notion of k-clique percolation in random graphs is introduced, where k is the size of the complete subgraphs whose large scale organizations are analytically and numerically investigated. For the Erdos-Rényi graph of N vertices we obtain that the percolation transition of k-cliques takes place when the probability of two vertices being connected by an edge reaches the threshold p(c) (k) = [(k - 1)N](-1/(k - 1)). At the transition point the scaling of the giant component with N is highly nontrivial and depends on k. We discuss why clique percolation is a novel and efficient approach to the identification of overlapping communities in large real networks.

Question: What is the scaling of the giant component at the transition point?


Anomaly detection: A survey. Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

Question: What is anomaly detection?


Visual attention measures for multi-screen TV. We introduce a set of nine measures to characterize viewers' visual attention patterns for multi-screen TV. We apply our measures during an experiment involving nine screen layouts with two, three, and four TV screens, for which we report new findings on visual attention. For example, we found that viewers need an average discovery time up to 4.5 seconds to visually fixate four screens, and their perceptions of how long they watched each screen are substantially accurate, i.e., we report Pearson correlations up to .892 with measured eye tracking data. We hope our set of new measures (and the companion toolkit to compute them automatically) will benefit the community as a first step toward understanding visual attention for emerging multi-screen TV applications.

Question: What is the visual attention of a viewer for multi-screen TV?


A Multiple Instance Learning Framework for Identifying Key Sentences and Detecting Events. State-of-the-art event encoding approaches rely on sentence or phrase level labeling, which are both time consuming and infeasible to extend to large scale text corpora and emerging domains. Using a multiple instance learning approach, we take advantage of the fact that while labels at the sentence level are difficult to obtain, they are relatively easy to gather at the document level. This enables us to view the problems of event detection and extraction in a unified manner. Using distributed representations of text, we develop a multiple instance formulation that simultaneously classifies news articles and extracts sentences indicative of events without any engineered features. We evaluate our model in its ability to detect news articles about civil unrest events (from Spanish text) across ten Latin American countries and identify the key sentences pertaining to these events. Our model, trained without annotated sentence labels, yields performance that is competitive with selected state-of-the-art models for event detection and sentence identification. Additionally, qualitative experimental results show that the extracted event-related sentences are informative and enhance various downstream applications such as article summarization, visualization, and event encoding.

Question: What is a multiple instance learning framework for identifying key sentences and detecting events?


Nutrition Therapy Recommendations for the Management of Adults With Diabetes. There is no standard meal plan or eating pattern that works universally for all people with diabetes. In order to be effective, nutrition therapy should be individualized for each patient/client based on his or her individual health goals; personal and cultural preferences; health literacy and numeracy; access to healthful choices; and readiness, willingness, and ability to change. Nutrition interventions should emphasize a variety of minimally processed nutrient dense foods in appropriate portion sizes as part of a healthful eating pattern and provide the individual with diabetes with practical tools for day-to-day food plan and behavior change that can be maintained over the long term.

Question: What is the difference between a meal plan and a nutrition plan?


Abstractive Sentence Summarization with Attentive Recurrent Neural Networks. Abstractive Sentence Summarization generates a shorter version of a given sentence while attempting to preserve its meaning. We introduce a conditional recurrent neural network (RNN) which generates a summary of an input sentence. The conditioning is provided by a novel convolutional attention-based encoder which ensures that the decoder focuses on the appropriate input words at each step of generation. Our model relies only on learned features and is easy to train in an end-to-end fashion on large data sets. Our experiments show that the model significantly outperforms the recently proposed state-of-the-art method on the Gigaword corpus while performing competitively on the DUC-2004 shared task.ive Sentence Summarization generates a shorter version of a given sentence while attempting to preserve its meaning. We introduce a conditional recurrent neural network (RNN) which generates a summary of an input sentence. The conditioning is provided by a novel convolutional attention-based encoder which ensures that the decoder focuses on the appropriate input words at each step of generation. Our model relies only on learned features and is easy to train in an end-to-end fashion on large data sets. Our experiments show that the model significantly outperforms the recently proposed state-of-the-art method on the Gigaword corpus while performing competitively on the DUC-2004 shared task.

Question: How to generate a summary of a sentence?


A Robust Digital Image Watermarking Scheme Using Hybrid DWT-DCT-SVD Technique. Protection of digital multimedia content has become an increasingly important issue for content owners and service providers. As watermarking is identified as a major technology to achieve copyright protection, the relevant literature includes several distinct approaches for embedding data into a multimedia element (primarily images, audio, and video). In this paper, we present a hybrid watermarking scheme based on Discrete Wavelet Transform – Discrete Cosine Transform – Singular Value Decomposition (DWT-DCT-SVD). Robustness is achieved by taking DCT of the DWT coefficients of the HL band of DWT. After applying DCT we map the DCT coefficients in a zig – zag order into four quadrants and apply the SVD to each quadrant. These four quadrants represent frequency bands from the lowest to the highest. The singular values in each quadrant are then modified by the singular values of the DWT-DCT transformed visual watermark. We show that embedding data in lowest frequencies is resistant to most of the attacks and some attacks are resistant to other frequency bands.

Question: How to protect digital multimedia content?


AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling. In vision-enabled autonomous systems such as robots and autonomous cars, video object detection plays a crucial role, and both its speed and accuracy are important factors to provide reliable operation. The key insight we show in this paper is that speed and accuracy are not necessarily a trade-off when it comes to image scaling. Our results show that re-scaling the image to a lower resolution will sometimes produce better accuracy. Based on this observation, we propose a novel approach, dubbed AdaScale, which adaptively selects the input image scale that improves both accuracy and speed for video object detection. To this end, our results on ImageNet VID and mini YouTube-BoundingBoxes datasets demonstrate 1.3 points and 2.7 points mAP improvement with 1.6× and 1.8× speedup, respectively. Additionally, we improve state-of-the-art video acceleration work by an extra 1.25× speedup with slightly better mAP on ImageNet VID dataset.

Question: AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling


Structured Approaches for Exploring Interpersonal Relationships in Natural Language Text. Title of dissertation: STRUCTURED APPROACHES FOR EXPLORING INTERPERSONAL RELATIONSHIPS IN NATURAL LANGUAGE TEXT Snigdha Chaturvedi, Doctor of Philosophy, 2016 Dissertation directed by: Dr. Hal Daumé III Department of Computer Science Human relationships have long been studied by scientists from domains like sociology, psychology, literature, etc. for understanding people's desires, goals, actions and expected behaviors. In this dissertation we study inter-personal relationships as expressed in natural language text. Modeling inter-personal relationships from text finds application in general natural language understanding, as well as real-world domains such as social networks, discussion forums, intelligent virtual agents, etc. We propose that the study of relationships should incorporate not only linguistic cues in text, but also the contexts in which these cues appear. Our investigations, backed by empirical evaluation, support this thesis, and demonstrate that the task benefits from using structured models that incorporate both types of information. We present such structured models to address the task of modeling the nature of relationships between any two given characters from a narrative. To begin with, we assume that relationships are of two types: cooperative and non-cooperative. We first describe an approach to jointly infer relationships between all characters in the narrative, and demonstrate how the task of characterizing the relationship between two characters can benefit from including information about their relationships with other characters in the narrative. We next formulate the relationshipmodeling problem as a sequence prediction task to acknowledge the evolving nature of human relationships, and demonstrate the need to model the history of a relationship in predicting its evolution. Thereafter, we present a data-driven method to automatically discover vari

Question: What is the difference between a dissertation and a dissertation?


Quantitative Analysis of Human-Model Agreement in Visual Saliency Modeling: A Comparative Study. Visual attention is a process that enables biological and machine vision systems to select the most relevant regions from a scene. Relevance is determined by two components: 1) top-down factors driven by task and 2) bottom-up factors that highlight image regions that are different from their surroundings. The latter are often referred to as "visual saliency." Modeling bottom-up visual saliency has been the subject of numerous research efforts during the past 20 years, with many successful applications in computer vision and robotics. Available models have been tested with different datasets (e.g., synthetic psychological search arrays, natural images or videos) using different evaluation scores (e.g., search slopes, comparison to human eye tracking) and parameter settings. This has made direct comparison of models difficult. Here, we perform an exhaustive comparison of 35 state-of-the-art saliency models over 54 challenging synthetic patterns, three natural image datasets, and two video datasets, using three evaluation scores. We find that although model rankings vary, some models consistently perform better. Analysis of datasets reveals that existing datasets are highly center-biased, which influences some of the evaluation scores. Computational complexity analysis shows that some models are very fast, yet yield competitive eye movement prediction accuracy. Different models often have common easy/difficult stimuli. Furthermore, several concerns in visual saliency modeling, eye movement datasets, and evaluation scores are discussed and insights for future work are provided. Our study allows one to assess the state-of-the-art, helps to organizing this rapidly growing field, and sets a unified comparison framework for gauging future efforts, similar to the PASCAL VOC challenge in the object recognition and detection domains.

Question: What is the state of the art in visual saliency modeling?


Conceptual data structures for personal knowledge management. Structured Abstract Purpose – Designing a model and tools that are (a) capable of representing and handling personal knowledge in different degrees of structuredness and formalisation and (b) usable and extensible by end-users. Design/methodology/approach – This paper presents the result of analysing literature and various data-models and formalism used to structure information on the desktop. Findings – The unified data model (CDS) is capable of representing structures from various information tools, e. g. documents, file system, hypertext, tagging, and mind maps. The five knowledge axes of CDS are: identity, order, hierarchy, annotation and linking. Research limitations / implications – The CDS model is based on text. Extensions for multimedia annotations have not been investigated. Practical implications – Future PKM tools should take the mentioned shortcoming of existing PKM tools into account. Implementing the CDS model can be a way to make PKM tools interoperable. Originality/value – This paper presents research combining cognitive psychology, personal knowledge management and semantic web technologies. The CDS model shows a way to let end-users work on different levels of granularity and different levels of formality in one environment. Introduction " The most important contribution of management in the 20th century was to increase manual worker productivity fifty-fold. The most important contribution of management in the 21st century will be to increase knowledge worker productivity – hopefully by the same percentage. [...] The methods, however, are totally different from those that increased the productivity of manual workers. " Drucker (1999a, p. 79) What might these methods be? The field of knowledge management investigates since 1995 (Stankosky, 2005) how people and knowledge work together. North (2007) defines knowledge work as work based on knowledge with an immaterial result; value creation

Question: What is knowledge management?


Finding Steiner Forests in Planar Graphs. Several routing problems such as VLSI river routing and single-layer routing can be formulated as a problem of finding a Steiner forest in a planar (grid) graph. Given an unweighted planar graph G together with nets of terminals, our problem is to find a Steiner forest, i.e., vertex-disjoint trees, each of which interconnects all the terminals of a net. This paper gives an efficient algorithm to solve the problem for the case all terminals lie on two face boundaries of G. Also obtained is an algorithm for finding a maximum number of internally vertexdisjoint paths connecting two specified vertices in a planar graph G. Both run in 0( nlog n) time if G has n vertices.

Question: What is a Steiner forest?


Harmony: A Hand Wash Monitoring and Reminder System using Smart Watches. Hand hygiene compliance is extremely important in hospitals, clinics and food businesses. Caregivers' compliance with hand hygiene is one of the most effective tools in preventing healthcare associated infections (HAIs) in hospitals and clinics. In food businesses, hand hygiene compliance is essential to prevent food contamination, and thus food borne illness. Washing hands properly is the cornerstone of hand hygiene. However, the hand wash compliance rate by the workers (care givers, waiters, chefs, food processors and so on) is not up to the mark. Monitoring hand wash compliance along with a reminder system increases the compliance rate significantly. Quality of a hand wash is also important which can be achieved by washing hands in accordance with standard guidelines. In this paper, we present Harmony, a hand wash monitoring and reminder system that monitors hand wash events and their quality, provides real time feedback, reminds the person of interest when he/she is required to wash hands, and stores related data in a server for further use. Worker worn smart watches are the key components of Harmony that can differentiate hand wash gestures from other gestures with an average accuracy of about 88%. Harmony is robust, scalable, and easy to install, and it overcomes most of the problems of existing related systems.

Question: What is a hand wash monitoring and reminder system?


Prognostics and health management using physics-of-failure. This paper presents a physics-of-failure based prognostics and health management approach for effective reliability prediction. This method permits in-situ assessment of system reliability under actual application conditions. The method uses sensor data with models that enable in-situ assessment of the deviation or degradation of a product from an expected normal operating condition (i.e., the system's ldquohealthrdquo) and the prediction of the future state of reliability. The implementation procedure of this approach includes failure modes, mechanisms, and effects analysis, data reduction and feature extraction from the life-cycle loads, damage accumulation, and assessment of uncertainty.

Question: What is the health of a product?


Gaussian process factorization machines for context-aware recommendations. Context-aware recommendation (CAR) can lead to significant improvements in the relevance of the recommended items by modeling the nuanced ways in which context influences preferences. The dominant approach in context-aware recommendation has been the multidimensional latent factors approach in which users, items, and context variables are represented as latent features in low-dimensional space. An interaction between a user, item, and a context variable is typically modeled as some linear combination of their latent features. However, given the many possible types of interactions between user, items and contextual variables, it may seem unrealistic to restrict the interactions among them to linearity.  To address this limitation, we develop a novel and powerful non-linear probabilistic algorithm for context-aware recommendation using Gaussian processes. The method which we call Gaussian Process Factorization Machines (GPFM) is applicable to both the explicit feedback setting (e.g. numerical ratings as in the Netflix dataset) and the implicit feedback setting (i.e. purchases, clicks). We derive stochastic gradient descent optimization to allow scalability of the model. We test GPFM on five different benchmark contextual datasets. Experimental results demonstrate that GPFM outperforms state-of-the-art context-aware recommendation methods.

Question: What is the difference between a factorization machine and a Gaussian process?


Challenges and prospects of lithium-sulfur batteries.. Electrical energy storage is one of the most critical needs of 21st century society. Applications that depend on electrical energy storage include portable electronics, electric vehicles, and devices for renewable energy storage from solar and wind. Lithium-ion (Li-ion) batteries have the highest energy density among the rechargeable battery chemistries. As a result, Li-ion batteries have proven successful in the portable electronics market and will play a significant role in large-scale energy storage. Over the past two decades, Li-ion batteries based on insertion cathodes have reached a cathode capacity of ∼250 mA h g(-1) and an energy density of ∼800 W h kg(-1), which do not meet the requirement of ∼500 km between charges for all-electric vehicles. With a goal of increasing energy density, researchers are pursuing alternative cathode materials such as sulfur and O2 that can offer capacities that exceed those of conventional insertion cathodes, such as LiCoO2 and LiMn2O4, by an order of magnitude (>1500 mA h g(-1)). Sulfur, one of the most abundant elements on earth, is an electrochemically active material that can accept up to two electrons per atom at ∼2.1 V vs Li/Li(+). As a result, sulfur cathode materials have a high theoretical capacity of 1675 mA h g(-1), and lithium-sulfur (Li-S) batteries have a theoretical energy density of ∼2600 W h kg(-1). Unlike conventional insertion cathode materials, sulfur undergoes a series of compositional and structural changes during cycling, which involve soluble polysulfides and insoluble sulfides. As a result, researchers have struggled with the maintenance of a stable electrode structure, full utilization of the active material, and sufficient cycle life with good system efficiency. Although researchers have made significant progress on rechargeable Li-S batteries in the last decade, these cycle life and efficiency problems prevent their use in commercial cells. To ove

Question: What is the capacity of a lithium-sulfur battery?


Restoration of Endodontically Treated Molars Using All Ceramic Endocrowns. Clinical success of endodontically treated posterior teeth is determined by the postendodontic restoration. Several options have been proposed to restore endodontically treated teeth. Endocrowns represent a conservative and esthetic restorative alternative to full coverage crowns. The preparation consists of a circular equigingival butt-joint margin and central retention cavity into the entire pulp chamber constructing both the crown and the core as a single unit. The case reports discussed here are moderately damaged endodontically treated molars restored using all ceramic endocrowns fabricated using two different systems, namely, CAD/CAM and pressed ceramic.

Question: What is the difference between a crown and an endocrown?


A systematic literature review of nurse shortage and the intention to leave.. AIM To present the findings of a literature review regarding nurses' intention to leave their employment or the profession.   BACKGROUND The nursing shortage is a problem that is being experienced worldwide. It is a problem that, left unresolved, could have a serious impact on the provision of quality health care. Understanding the reasons why nurses leave their employment or the profession is imperative if efforts to increase retention are to be successful.   EVALUATION Electronic databases were systematically searched to identify English research reports about nurses' intention to leave their employment or the profession. Key results concerning the issue were extracted and synthesized.   KEY ISSUES The diversified measurement instruments, samples and levels of intention to leave caused difficulties in the attempt to compare or synthesize findings. The factors influencing nurses' intention to leave were identified and categorized into organizational and individual factors.   CONCLUSIONS The reasons that trigger nurses' intention to leave are complex and are influenced by organizational and individual factors. Further studies should be conducted to investigate how external factors such as job opportunities correlate with nurses' intention to leave.   IMPLICATIONS FOR NURSING MANAGEMENT The review provides insight that can be useful in designing and implementing strategies to maintain a sustainable workforce in nursing.

Question: What is the difference between a systematic literature review and a meta-analysis?


DAISY Filter Flow: A Generalized Discrete Approach to Dense Correspondences. Establishing dense correspondences reliably between a pair of images is an important vision task with many applications. Though significant advance has been made towards estimating dense stereo and optical flow fields for two images adjacent in viewpoint or in time, building reliable dense correspondence fields for two general images still remains largely unsolved. For instance, two given images sharing some content exhibit dramatic photometric and geometric variations, or they depict different 3D scenes of similar scene characteristics. Fundamental challenges to such an image or scene alignment task are often multifold, which render many existing techniques fall short of producing dense correspondences robustly and efficiently. This paper presents a novel approach called DAISY filter flow (DFF) to address this challenging task. Inspired by the recent PatchMatch Filter technique, we leverage and extend a few established methods: DAISY descriptors, filter-based efficient flow inference, and the PatchMatch fast search. Coupling and optimizing these modules seamlessly with image segments as the bridge, the proposed DFF approach enables efficiently performing dense descriptor-based correspondence field estimation in a generalized high-dimensional label space, which is augmented by scales and rotations. Experiments on a variety of challenging scenes show that our DFF approach estimates spatially coherent yet discontinuity-preserving image alignment results both robustly and efficiently.

Question: What is the difference between a dense correspondence field and a dense correspondence?


Optimizing 360 video delivery over cellular networks. As an important component of the virtual reality (VR) technology, 360-degree videos provide users with panoramic view and allow them to freely control their viewing direction during video playback. Usually, a player displays only the visible portion of a 360 video. Thus, fetching the entire raw video frame wastes bandwidth. In this paper, we consider the problem of optimizing 360 video delivery over cellular networks. We first conduct a measurement study on commercial 360 video platforms. We then propose a cellular-friendly streaming scheme that delivers only 360 videos' visible portion based on head movement prediction. Using viewing data collected from real users, we demonstrate the feasibility of our approach, which can reduce bandwidth consumption by up to 80% based on a trace-driven simulation.

Question: How to optimize 360 video delivery over cellular networks?


Can Scientific Impact Be Predicted?. A widely used measure of scientific impact is citations. However, due to their heavy-tailed distribution, citations are fundamentally difficult to predict. Instead, to characterize scientific impact, we address two analogous questions asked by many scientific researchers: "How will my h-index evolve over time, and which of my previously or newly published papers will contribute to it?" To answer these questions, we perform two related tasks. First, we develop a model to predict authors' future h-indices based on their current scientific impact. Second, we examine the factors that drive papers-either previously or newly published-to increase their authors' predicted future h-indices. By leveraging relevant factors, we can predict an author's h-index in five years with an R2 value of 0.92 and whether a previously (newly) published paper will contribute to this future h-index with an F1 score of 0.99 (0.77). We find that topical authority and publication venue are crucial to these effective predictions, while topic popularity is surprisingly inconsequential. Further, we develop an online tool that allows users to generate informed h-index predictions. Our work demonstrates the predictability of scientific impact, and can help researchers to effectively leverage their scholarly position of "standing on the shoulders of giants".

Question: How can we predict the future h-index of an author?


seL4: formal verification of an OS kernel. Complete formal verification is the only known way to guarantee that a system is free of programming errors.  We present our experience in performing the formal, machine-checked verification of the seL4 microkernel from an abstract specification down to its C implementation. We assume correctness of compiler, assembly code, and hardware, and we used a unique design approach that fuses formal and operating systems techniques. To our knowledge, this is the first formal proof of functional correctness of a complete, general-purpose operating-system kernel. Functional correctness means here that the implementation always strictly follows our high-level abstract specification of kernel behaviour. This encompasses traditional design and implementation safety properties such as the kernel will never crash, and it will never perform an unsafe operation. It also proves much more: we can predict precisely how the kernel will behave in every possible situation.  seL4, a third-generation microkernel of L4 provenance, comprises 8,700 lines of C code and 600 lines of assembler. Its performance is comparable to other high-performance L4 kernels.

Question: What is the formal verification of an OS kernel?


Design of Boost-Flyback Single-Stage PFC converter for LED power supply without electrolytic capacitor for energy-storage. Light emitting diodes (LEDs) are likely to be used for general lighting applications due to their high efficiency and longer life. This paper presents the concept of applying large voltage ripple for energy storage into the Boost-Flyback Single -Stage PFC converter for the elimination of the electrical capacitor. Following proposed design procedure, the single stage PFC circuit with small energy storage capacitance can still achieve good output voltage regulation while preserving desired input power factor. The detailed theoretical analysis and design procedure of the Single-Stage PFC converter is presented. The experimental results obtained on a 60W prototype converter along with waveforms are presented.

Question: How to design a single stage PFC converter for LED power supply without electrolytic capacitor?


Design of cavity-backed spiral antennas. In this paper, the design procedure of the 2-18 GHz and the 18-40 GHz cavity-backed spiral antenna is described. By varying the antenna parameters the VSWR and the axial ratio characteristics of the antenna are investigated. For the 2-18 GHz spiral antenna, the solid absorber and the Marchand balun are applied. The measured results of the fabricated antenna are compared with the simulated ones. For the 18-40 GHz antenna, the honeycomb absorber having variable loss tangent is considered in simulation to investigate for improvement of the VSWR and the axial ratio characteristics.

Question: How to design a cavity-backed spiral antenna?


Nonlinear dimensionality reduction by locally linear embedding.. Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how to discover compact representations of high-dimensional data. Here, we introduce locally linear embedding (LLE), an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs. Unlike clustering methods for local dimensionality reduction, LLE maps its inputs into a single global coordinate system of lower dimensionality, and its optimizations do not involve local minima. By exploiting the local symmetries of linear reconstructions, LLE is able to learn the global structure of nonlinear manifolds, such as those generated by images of faces or documents of text.

Question: What is the difference between clustering and dimensionality reduction?


On Laser Ranging Based on High-Speed/Energy Laser Diode Pulses and Single-Photon Detection Techniques. This paper discusses the construction principles and performance of a pulsed time-of-flight (TOF) laser radar based on high-speed (FWHM ~100 ps) and high-energy (~1 nJ) optical transmitter pulses produced with a specific laser diode working in an "enhanced gain-switching" regime and based on single-photon detection in the receiver. It is shown by analysis and experiments that single-shot precision at the level of 2W3 cm is achievable. The effective measurement rate can exceed 10 kHz to a noncooperative target (20% reflectivity) at a distance of > 50 m, with an effective receiver aperture size of 2.5 cm2. The effect of background illumination is analyzed. It is shown that the gating of the SPAD detector is an effective means to avoid the blocking of the receiver in a high-level background illumination case. A brief comparison with pulsed TOF laser radars employing linear detection techniques is also made.

Question: What is the difference between a laser radar and a laser range finder?


Expectancy-Value Theory of Achievement Motivation.. We discuss the expectancy-value theory of motivation, focusing on an expectancy-value model developed and researched by Eccles, Wigfield, and their colleagues. Definitions of crucial constructs in the model, including ability beliefs, expectancies for success, and the components of subjective task values, are provided. These definitions are compared to those of related constructs, including self-efficacy, intrinsic and extrinsic motivation, and interest. Research is reviewed dealing with two issues: (1) change in children's and adolescents' ability beliefs, expectancies for success, and subjective values, and (2) relations of children's and adolescents' ability-expectancy beliefs and subjective task values to their performance and choice of activities. Copyright 2000 Academic Press.

Question: What is the expectancy-value theory of achievement motivation?


ITIL in small to medium-sized enterprises software companies: towards an implementation sequence. Information Technology Infrastructure Library, ITIL framework, is a set of comprehensive publications providing descriptive guidance on the management of IT processes, functions, roles and responsibilities related to Information Technology Service Management. However, and in spite of its repercussion and popularity, the ITIL framework does not suggest an implementation order for their processes. This decision constitutes the first challenge that an organization must overcome when starting an ITIL implementation, the enterprise size being one of the leading factors to be considered in the decision making process. In the scenario of Small and Medium Enterprises dedicated to producing software, this paper is devoted to investigating which processes are the most selected to start the implementation of ITIL in these organizations. This is done by means of two different instruments. Firstly, a systematic literature review on the topic and secondly, a survey conducted among experts and practitioners. Results show in both cases that Incident Management Process should be the first process when implementing ITIL framework.

Question: Which process should be implemented first?


GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks. Facial landmarks constitute the most compressed representation of faces and are known to preserve information such as pose, gender and facial structure present in the faces. Several works exist that attempt to perform high-level face-related analysis tasks based on landmarks alone without the aid of face images. In contrast, in this work, an attempt is made to tackle the inverse problem of synthesizing faces from their respective landmarks. The primary aim of this work is to demonstrate that information preserved by landmarks (gender in particular) can be further accentuated by leveraging generative models to synthesize corresponding faces. Though the problem is particularly challenging due to its ill-posed nature, we believe that successful synthesis will enable several applications such as boosting performance of high-level face related tasks using landmark points and performing dataset augmentation. To this end, a novel face-synthesis method known as Gender Preserving Generative Adversarial Network (GP-GAN) that is guided by adversarial loss, perceptual loss and a gender preserving loss is presented. Further, we propose a novel generator sub-network UDeNet for GP-GAN that leverages advantages of U-Net and DenseNet architectures. Extensive experiments and comparison with recent methods are performed to verify the effectiveness of the proposed method. Our code is available at: https://github.com/DetionDXlGP-GAN-Gender-Preserving-GAN-for-Synthesizing-Faces-from-Landmarks

Question: What is the gender preserving loss?


Knowledge Management: An Introduction and Perspective. In the mid-1980s, individuals and organizations began to appreciate the increasingly important role of knowledge in the emerging competitive environment. International competition was changing to increasingly emphasize product and service quality, responsiveness, diversity and customization. Some organizations, such as USbased Chaparral Steel, had been pursuing a knowledge focus for some years, but during this period it started to become a more wide-spread business concern. These notions appeared in many places throughout the world – almost simultaneously in the way bubbles appear in a kettle of superheated water! Over a brief period from 1986 to 1989, numerous reports appeared in the public domain concerning how to manage knowledge explicitly. There were studies, results of corporate efforts, and conferences on the topic.

Question: What is knowledge management?


Implicit Association Test 1 The Implicit Association Test at Age 7 : A Methodological and Conceptual Review. A mong earthly organisms, humans have a unique propensity to introspect or look inward into the contents of their own minds, and to share those observations with others. With the ability to introspect comes the palpable feeling of " knowing, " of being objective or certain, of being mentally in control of one's thoughts, aware of the causes of one's thoughts, feelings, and actions, and of making decisions deliberately and rationally. Among the noteworthy discoveries of 20th century psychology was a challenge posed to this assumption of rationality. From the groundbreaking theorizing of Herbert Simon (1955) and the mind-boggling problems posed by Kahneman, Slovik, and Tversky (1982) to striking demonstrations of illusions of control (Wegner, 2002), the paucity of introspection (Nisbett and Wilson, 1977), and the automaticity of everyday thought (Bargh, 1997), psychologists have shown the frailties of the minds of their species. As psychologists have come to grips with the limits of the mind, there has been an increased interest in measuring aspects of thinking and feeling that may not be easily accessed or available to consciousness. Innovations in measurement have been undertaken with the purpose of bringing under scrutiny new forms of cogni-tion and emotion that were previously undiscovered and especially by asking if traditional concepts such as attitude and preference, belief and stereotype, self-concept and self-esteem can be rethought based on what the new measures reveal. These newer measures do not require introspection on the part of the subject. For many constructs this is considered a valuable, if not essential, feature of measurement; for others, avoiding introspection is greeted with suspicion and skepticism. For example, one approach to measuring math ability would be to ask " how good are you at math? " whereas an alternative approach is to infe

Question: What is the difference between the two?


An Augmented Reality museum guide. Recent years have seen advances in many enabling augmented reality technologies. Furthermore, much research has been carried out on how augmented reality can be used to enhance existing applications. This paper describes our experiences with an AR-museum guide that combines some of the latest technologies. Amongst other technologies, markerless tracking, hybrid tracking, and an ultra-mobile-PC were used. Like existing audio guides, the AR-guide can be used by any museum visitor, during a six-month exhibition on Islamic art. We provide a detailed description of the museumpsilas motivation for using AR, of our experiences in developing the system, and the initial results of user surveys. Taking this information into account, we can derive possible system improvements.

Question: What is an augmented reality museum guide?


Grammatical Constructions and Linguistic Generalizations : the What ' s X doing Y ? Construction 1. To adopt a constructional approach is to undertake a commitment in principle to account for the entirety of each language. This means that the relatively general patterns of the language, such as the one licensing the ordering of a finite auxiliary verb before its subject in English as illustrated in (1), and the more idiomatic patterns, such as those exemplified in (2), stand on an equal footing as data for which the grammar must provide an account.

Question: What is a constructional approach?


Neural-Network-Based Adaptive Leader-Following Control for Multiagent Systems With Uncertainties. A neural-network-based adaptive approach is proposed for the leader-following control of multiagent systems. The neural network is used to approximate the agent's uncertain dynamics, and the approximation error and external disturbances are counteracted by employing the robust signal. When there is no control input constraint, it can be proved that all the following agents can track the leader's time-varying state with the tracking error as small as desired. Compared with the related work in the literature, the uncertainty in the agent's dynamics is taken into account; the leader's state could be time-varying; and the proposed algorithm for each following agent is only dependent on the information of its neighbor agents. Finally, the satisfactory performance of the proposed method is illustrated by simulation examples.

Question: What is the difference between the neural network and the adaptive control?


Variance Reduction for Stochastic Gradient Optimization. Stochastic gradient optimization is a class of widely used algorithms for training machine learning models. To optimize an objective, it uses the noisy gradient computed from the random data samples instead of the true gradient computed from the entire dataset. However, when the variance of the noisy gradient is large, the algorithm might spend much time bouncing around, leading to slower convergence and worse performance. In this paper, we develop a general approach of using control variate for variance reduction in stochastic gradient. Data statistics such as low-order moments (pre-computed or estimated online) is used to form the control variate. We demonstrate how to construct the control variate for two practical problems using stochastic gradient optimization. One is convex—the MAP estimation for logistic regression, and the other is non-convex—stochastic variational inference for latent Dirichlet allocation. On both problems, our approach shows faster convergence and better performance than the classical approach.

Question: How to construct a control variate for stochastic gradient optimization?


Statistics of Patch Offsets for Image Completion. Image completion involves filling missing parts in images. In this paper we address this problem through the statistics of patch offsets. We observe that if we match similar patches in the image and obtain their offsets (relative positions), the statistics of these offsets are sparsely distributed. We further observe that a few dominant offsets provide reliable information for completing the image. With these offsets we fill the missing region by combining a stack of shifted images via optimization. A variety of experiments show that our method yields generally better results and is faster than existing state-of-the-art methods.

Question: What is the statistics of patch offsets for image completion?


LATIONS FOR KNOWLEDGE BASE COMPLETION. In this work we present a novel approach for the utilization of observed relations between entity pairs in the task of triple argument prediction. The approach is based on representing observations in a shared, continuous vector space of structured relations and text. Results on a recent benchmark dataset demonstrate that the new model is superior to existing sparse feature models. In combination with state-of-the-art models, we achieve substantial improvements when observed relations are available.

Question: What is the best approach for utilizing observed relations in the task of triple argument prediction?


HMM-Based Word Alignment in Statistical Translation. In this paper, we describe a new model for word alignment in statistical translation and present experimental results. The idea of the model is to make the alignment probabilities dependent on the differences in the alignment positions rather than on the absolute positions. To achieve this goal, the approach uses a first-order Hidden Markov model (HMM) for the word alignment problem as they are used successfully in speech recognition for the time alignment problem. The difference to the time alignment HMM is that there is no monotony constraint for the possible word orderings. We describe the details of the model and test the model on several bilingual corpora.

Question: What is a first-order HMM?


Benign violations: making immoral behavior funny.. Humor is an important, ubiquitous phenomenon; however, seemingly disparate conditions seem to facilitate humor. We integrate these conditions by suggesting that laughter and amusement result from violations that are simultaneously seen as benign. We investigated three conditions that make a violation benign and thus humorous: (a) the presence of an alternative norm suggesting that the situation is acceptable, (b) weak commitment to the violated norm, and (c) psychological distance from the violation. We tested the benign-violation hypothesis in the domain of moral psychology, where there is a strong documented association between moral violations and negative emotions, particularly disgust. Five experimental studies show that benign moral violations tend to elicit laughter and amusement in addition to disgust. Furthermore, seeing a violation as both wrong and not wrong mediates behavioral displays of humor. Our account is consistent with evolutionary accounts of laughter, explains humor across many domains, and suggests that humor can accompany negative emotion.

Question: What is the difference between benign and non-benign violations?


Mapping and Localization in 3D Environments Using a 2D Laser Scanner and a Stereo Camera. 2D laser scanners have been widely used for accomplishing a number of challenging AI and robotics tasks such as mapping of large environments and localization in highly dynamic environments. However, using only one 2D laser scanner could be insufficient and less reliable for accomplishing tasks in 3D environments. The problem could be solved using multiple 2D laser scanners or a 3D laser scanner for performing 3D perception. Unfortunately, the cost of such 3D sensing systems is still too high for enabling AI and robotics applications. In this paper, we propose to use a 2D laser scanner and a stereo camera for accomplishing simultaneous localization and mapping (SLAM) in 3D indoor environments in which the 2D laser scanner is used for SLAM and the stereo camera is used for 3D mapping. The experimental results demonstrate that the proposed system is lower cost yet effective, and the obstacle detection rate is significant improved compares to using one 2D laser scanner for mapping.

Question: How to map and localize in 3D environments using a 2D laser scanner and a stereo camera?


The maternal brain and its plasticity in humans. This article is part of a Special Issue "Parental Care". Early mother-infant relationships play important roles in infants' optimal development. New mothers undergo neurobiological changes that support developing mother-infant relationships regardless of great individual differences in those relationships. In this article, we review the neural plasticity in human mothers' brains based on functional magnetic resonance imaging (fMRI) studies. First, we review the neural circuits that are involved in establishing and maintaining mother-infant relationships. Second, we discuss early postpartum factors (e.g., birth and feeding methods, hormones, and parental sensitivity) that are associated with individual differences in maternal brain neuroplasticity. Third, we discuss abnormal changes in the maternal brain related to psychopathology (i.e., postpartum depression, posttraumatic stress disorder, substance abuse) and potential brain remodeling associated with interventions. Last, we highlight potentially important future research directions to better understand normative changes in the maternal brain and risks for abnormal changes that may disrupt early mother-infant relationships.

Question: What is the maternal brain?


Joint Inference for Event Timeline Construction. This paper addresses the task of constructing a timeline of events mentioned in a given text. To accomplish that, we present a novel representation of the temporal structure of a news article based on time intervals. We then present an algorithmic approach that jointly optimizes the temporal structure by coupling local classifiers that predict associations and temporal relations between pairs of temporal entities with global constraints. Moreover, we present ways to leverage knowledge provided by event coreference to further improve the system performance. Overall, our experiments show that the joint inference model significantly outperformed the local classifiers by 9.2% of relative improvement in F1. The experiments also suggest that good event coreference could make remarkable contribution to a robust event timeline construction system.

Question: How to construct a timeline of events mentioned in a given text?


Multimaterial 3D Printing of Graphene-Based Electrodes for Electrochemical Energy Storage Using Thermoresponsive Inks.. The current lifestyles, increasing population, and limited resources result in energy research being at the forefront of worldwide grand challenges, increasing the demand for sustainable and more efficient energy devices. In this context, additive manufacturing brings the possibility of making electrodes and electrical energy storage devices in any desired three-dimensional (3D) shape and dimensions, while preserving the multifunctional properties of the active materials in terms of surface area and conductivity. This paves the way to optimized and more efficient designs for energy devices. Here, we describe how three-dimensional (3D) printing will allow the fabrication of bespoke devices, with complex geometries, tailored to fit specific requirements and applications, by designing water-based thermoresponsive inks to 3D-print different materials in one step, for example, printing the active material precursor (reduced chemically modified graphene (rCMG)) and the current collector (copper) for supercapacitors or anodes for lithium-ion batteries. The formulation of thermoresponsive inks using Pluronic F127 provides an aqueous-based, robust, flexible, and easily upscalable approach. The devices are designed to provide low resistance interface, enhanced electrical properties, mechanical performance, packing of rCMG, and low active material density while facilitating the postprocessing of the multicomponent 3D-printed structures. The electrode materials are selected to match postprocessing conditions. The reduction of the active material (rCMG) and sintering of the current collector (Cu) take place simultaneously. The electrochemical performance of the rCMG-based self-standing binder-free electrode and the two materials coupled rCMG/Cu printed electrode prove the potential of multimaterial printing in energy applications.

Question: How to print electrodes for energy storage devices?


Characteristics of cross (bypass) coupling through higher/lower order modes and their applications in elliptic filter design. This paper presents a new set of results concerning the use of higher/lower order modes as a means to implement bypass or cross coupling for applications in elliptic filter design. It is shown that the signs of the coupling coefficients to produce a transmission zero (TZ) either below or above the passband are, in certain situations, reversed from the predictions of simpler existing models. In particular, the bypass coupling to higher/lower order modes must be significantly stronger than the coupling to the main resonance in order to generate TZs in the immediate vicinity of the passband. Planar (H-plane) singlets are used to illustrate the derived results. This study should provide very important guidelines in selecting the proper main and bypass couplings for sophisticated filtering structures. Example filters are designed, built, and measured to demonstrate the validity of the introduced theory.

Question: What is a singleton?


Naive Bayes for Text Classification with Unbalanced Classes. Abstract. Multinomial naive Bayes (MNB) is a popular method for document classification due to its computational efficiency and relatively good predictive performance. It has recently been established that predictive performance can be improved further by appropriate data transformations [1, 2]. In this paper we present another transformation that is designed to combat a potential problem with the application of MNB to unbalanced datasets. We propose an appropriate correction by adjusting attribute priors. This correction can be implemented as another data normalization step, and we show that it can significantly improve the area under the ROC curve. We also show that the modified version of MNB is very closely related to the simple centroid-based classifier and compare the two methods empirically.

Question: Naive Bayes for Text Classification with Unbalanced Classes


Learning to Rank for Consumer Health Search: A Semantic Approach. For many internet users, searching for health advice online is the first step in seeking treatment. We present a Learning to Rank system that uses a novel set of syntactic and semantic features to improve consumer health search. Our approach was evaluated on the 2016 CLEF eHealth dataset, outperforming the best method by 26.6% in NDCG@10.

Question: What is the best way to rank for consumer health search?


A COMPRESSED SENSING VIEW OF UNSUPERVISED TEXT EMBEDDINGS , BAG-OF-n-GRAMS , AND LSTM S. Low-dimensional vector embeddings, computed using LSTMs or simpler techniques, are a popular approach for capturing the "meaning" of text and a form of unsupervised learning useful for downstream tasks. However, their power is not theoretically understood. The current paper derives formal understanding by looking at the subcase of linear embedding schemes. Using the theory of compressed sensing we show that representations combining the constituent word vectors are essentially information-preserving linear measurements of Bag-of-n-Grams (BonG) representations of text. This leads to a new theoretical result about LSTMs: low-dimensional embeddings derived from a low-memory LSTM are provably at least as powerful on classification tasks, up to small error, as a linear classifier over BonG vectors, a result that extensive empirical work has thus far been unable to show. Our experiments support these theoretical findings and establish strong, simple, and unsupervised baselines on standard benchmarks that in some cases are state of the art among word-level methods. We also show a surprising new property of embeddings such as GloVe and word2vec: they form a good sensing matrix for text that is more efficient than random matrices, the standard sparse recovery tool, which may explain why they lead to better representations in practice.

Question: What is the difference between a compressed sensing view of unsupervised text embeddings and a compressed sensing view of supervised text embeddings?


Frappé: Functional Reactive Programming in Java. Functional Reactive Programming (FRP) is a declarative programming model for constructing interactive applications based on a continuous model of time. FRP programs are described in terms of behaviors (continuous, timevarying, reactive values), and events (conditions that occur at discrete points in time). This paper presents Frappé, an implementation of FRP in the Java progamming language. The primary contribution of Frappé is its integration of the FRP event/behavior model with the Java Beans event/property model. At the interface level, any Java Beans component may be used as a source or sink for the FRP event and behavior combinators. This provides a mechanism for extending Frappé with new kinds of I/O connections and allows FRP to be used as a high-level declarative model for composing applications from Java Beans components. At the implementation level, the Java Beans event model is used internally by Frappé to propagate FRP events and changes to FRP behaviors. This allows Frappé applications to be packaged as Java Beans components for use in other applications, and yields an implementation of FRP well-suited to the requirements of event-driven applications (such as graphical user interfaces).

Question: What is functional reactive programming?


Spaceborne bi-and multistatic SAR : potential and challenges. Biand multistatic synthetic aperture radar (SAR) operates with distinct transmit and receive antennas that are mounted on separate platforms. Such a spatial separation has several operational advantages, which will increase the capability, reliability and flexibility of future SAR missions. Various spaceborne biand multistatic SAR configurations are introduced, and their potential for different applications such as frequent monitoring, wide-swath imaging, scene classification, single pass cross-track interferometry and resolution enhancement is compared. Furthermore, some major challenges such as phase and time synchronisation, biand multistatic SAR processing, satellite orbit selection and relative position sensing are addressed.

Question: What is the potential of biand multistatic SAR?


Data integration with uncertainty. This paper reports our first set of results on managing uncertainty in data integration. We posit that data-integration systems need to handle uncertainty at three levels and do so in a principled fashion. First, the semantic mappings between the data sources and the mediated schema may be approximate because there may be too many of them to be created and maintained or because in some domains (e.g., bioinformatics) it is not clear what the mappings should be. Second, the data from the sources may be extracted using information extraction techniques and so may yield erroneous data. Third, queries to the system may be posed with keywords rather than in a structured form. As a first step to building such a system, we introduce the concept of probabilistic schema mappings and analyze their formal foundations. We show that there are two possible semantics for such mappings: by-table semantics assumes that there exists a correct mapping but we do not know what it is; by-tuple semantics assumes that the correct mapping may depend on the particular tuple in the source data. We present the query complexity and algorithms for answering queries in the presence of probabilistic schema mappings, and we describe an algorithm for efficiently computing the top-k answers to queries in such a setting. Finally, we consider using probabilistic mappings in the scenario of data exchange.

Question: What is the difference between by-table and by-tuple semantics?


EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks. In recent years, there has been a growing interest in wireless sensor networks. One of the major issues in wireless sensor network is developing an energy-efficient clustering protocol. Hierarchical clustering algorithms are very important in increasing the network's life time. Each clustering algorithm is composed of two phases, the setup phase and steady state phase. The hot point in these algorithms is the cluster head selection. In this paper, we study the impact of heterogeneity of nodes in terms of their energy in wireless sensor networks that are hierarchically clustered. We assume that a percentage of the population of sensor nodes is equipped with the additional energy resources. We also assume that the sensor nodes are randomly distributed and are not mobile, the coordinates of the sink and the dimensions of the sensor field are known. Homogeneous clustering protocols assume that all the sensor nodes are equipped with the same amount of energy and as a result, they cannot take the advantage of the presence of node heterogeneity. Adapting this approach, we introduce an energy efficient heterogeneous clustered scheme for wireless sensor networks based on weighted election probabilities of each node to become a cluster head according to the residual energy in each node. Finally, the simulation results demonstrate that our proposed heterogeneous clustering approach is more effective in prolonging the network lifetime compared with LEACH. 2008 Elsevier B.V. All rights reserved.

Question: What is the impact of heterogeneity of nodes in terms of their energy in wireless sensor networks that are hierarchically clustered?


Colony of NPUs : Scaling the Efficiency of Neural Accelerators. Trading small amounts of output accuracy to obtain significant performance improvement or reduce power consumption is a promising research direction. Many modern applications from different domains such as image and signal processing, computer vision, data mining, machine learning and speech recognition are amenable to approximate computing. In many cases, their output is interpreted by humans, so infrequent variation in outputs are not noticeable by users. Moreover, the input of these applications is noisy which prevents strict constraints on the output of the system. Hence, these domains have significant potential for the aforementioned trade-offs, and these applications are ideal targets for approximate computing. Existing approaches for approximate computing include modifying the ISA [6], compiler [1], programming language [2, 4], underlying hardware [14] or the entire framework [3, 8, 10]. Approximation accelerators [5, 7, 13] use some of these methods to trade off accuracy for higher performance or energy savings. These accelerators require the programmer to annotate code sections which are amenable for approximation. Approximate parts are offloaded to the accelerator and other parts are executed by the CPU at run time. Esmaeilzadeh et. al. [7] proposed a Neural Processing Unit (NPU) as a programmable approximate accelerator. The key idea is to train a neural network (nn) to mimic an approximable region of original code and replace that with an efficient computation of the learned model. Although the proposed approximate accelerators such as NPU demonstrate acceptable results on the benchmarks from different domains, they have some shortcomings. Firstly, different invocations of a program might produce varying output qualities because the output quality is dependent on the input values. Consequently, using an NPU with fixed configuration for a wide range of inputs may produce outputs with poor acc

Question: What is the difference between a neural network and a neural accelerator?


Achieving Secure, Universal, and Fine-Grained Query Results Verification for Secure Search Scheme over Encrypted Cloud Data. Secure search techniques over encrypted cloud data allow an authorized user to query data files of interest by submitting encrypted query keywords to the cloud server in a privacy-preserving manner. However, in practice, the returned query results may be incorrect or incomplete in the dishonest cloud environment. For example, the cloud server may intentionally omit some qualified results to save computational resources and communication overhead. Thus, a well-functioning secure query system should provide a query results verification mechanism that allows the data user to verify results. In this paper, we design a secure, easily integrated, and fine-grained query results verification mechanism, by which, given an encrypted query results set, the query user not only can verify the correctness of each data file in the set but also can further check how many or which qualified data files are not returned if the set is incomplete before decryption. The verification scheme is loose-coupling to concrete secure search techniques and can be very easily integrated into any secure query scheme. We achieve the goal by constructing secure verification object for encrypted cloud data. Furthermore, a short signature technique with extremely small storage cost is proposed to guarantee the authenticity of verification object and a verification object request technique is presented to allow the query user to securely obtain the desired verification object. Performance evaluation shows that the proposed schemes are practical and efficient.

Question: What is the size of the Canadian military?


Scale-Aware Fast R-CNN for Pedestrian Detection. In this paper, we consider the problem of pedestrian detection in natural scenes. Intuitively, instances of pedestrians with different spatial scales may exhibit dramatically different features. Thus, large variance in instance scales, which results in undesirable large intracategory variance in features, may severely hurt the performance of modern object instance detection methods. We argue that this issue can be substantially alleviated by the divide-and-conquer philosophy. Taking pedestrian detection as an example, we illustrate how we can leverage this philosophy to develop a Scale-Aware Fast R-CNN (SAF R-CNN) framework. The model introduces multiple built-in subnetworks which detect pedestrians with scales from disjoint ranges. Outputs from all of the subnetworks are then adaptively combined to generate the final detection results that are shown to be robust to large variance in instance scales, via a gate function defined over the sizes of object proposals. Extensive evaluations on several challenging pedestrian detection datasets well demonstrate the effectiveness of the proposed SAF R-CNN. Particularly, our method achieves state-of-the-art performance on Caltech [P. Dollar, C. Wojek, B. Schiele, and P. Perona, "Pedestrian detection: An evaluation of the state of the art," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 4, pp. 743–761, Apr. 2012], and obtains competitive results on INRIA [N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2005, pp. 886–893], ETH [A. Ess, B. Leibe, and L. V. Gool, "Depth and appearance for mobile scene analysis," in Proc. Int. Conf. Comput. Vis., 2007, pp. 1–8], and KITTI [A. Geiger, P. Lenz, and R. Urtasun, "Are we ready for autonomous driving? The KITTI vision benchmark suite," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2012, pp. 3354–3361].

Question: What is the scale of a pedestrian?


Facing facts: neuronal mechanisms of face perception.. The face is one of the most important stimuli carrying social meaning. Thanks to the fast analysis of faces, we are able to judge physical attractiveness and features of their owners' personality, intentions, and mood. From one's facial expression we can gain information about danger present in the environment. It is obvious that the ability to process efficiently one's face is crucial for survival. Therefore, it seems natural that in the human brain there exist structures specialized for face processing. In this article, we present recent findings from studies on the neuronal mechanisms of face perception and recognition in the light of current theoretical models. Results from brain imaging (fMRI, PET) and electrophysiology (ERP, MEG) show that in face perception particular regions (i.e. FFA, STS, IOA, AMTG, prefrontal and orbitofrontal cortex) are involved. These results are confirmed by behavioral data and clinical observations as well as by animal studies. The developmental findings reviewed in this article lead us to suppose that the ability to analyze face-like stimuli is hard-wired and improves during development. Still, experience with faces is not sufficient for an individual to become an expert in face perception. This thesis is supported by the investigation of individuals with developmental disabilities, especially with autistic spectrum disorders (ASD).

Question: What is the difference between the face and the face-like stimuli?


Automatic Transliteration of Proper Nouns from Arabic to English. After providing a brief introduction to the transliteration problem, and highlighting some issues specific to Arabic to English translation, a three phase algorithm is introduced as a computational solution to the problem. The algorithm is based on a Hidden Markov Model approach, but also leverages information available in on-line databases. The algorithm is then evaluated, and shown to achieve accuracy approaching 80%.

Question: What is the algorithm for automatic transliteration of proper nouns from Arabic to English?


A Review of Productivity Factors and Strategies on Software Development. Since the late seventies, efforts to catalog factors that influences productivity, as well as actions to improve it, has been a huge concern for both academy and software development industry. Despite numerous studies, software organizations still do not know which the most significant factors are and what to do with it. Several studies present the factors in a very superficial way, some others address only the related factors or there are those that describe only a single factor. Actions to deal with the factors are spread and frequently were not mapped. Through a literature review, this paper presents a consolidated view of the main factors that have affected productivity over the years, and the strategies to deal with these factors nowadays. This research aims to support software development industry on the selection of their strategies to improve productivity by maximizing the positive factors and minimizing or avoiding the impact of the negative ones.

Question: What are the main factors that affect software development productivity?


Ratings meet reviews, a combined approach to recommend. Most existing recommender systems focus on modeling the ratings while ignoring the abundant information embedded in the review text. In this paper, we propose a unified model that combines content-based filtering with collaborative filtering, harnessing the information of both ratings and reviews. We apply topic modeling techniques on the review text and align the topics with rating dimensions to improve prediction accuracy. With the information embedded in the review text, we can alleviate the cold-start problem. Furthermore, our model is able to learn latent topics that are interpretable. With these interpretable topics, we can explore the prior knowledge on items or users and recommend completely "cold"' items. Empirical study on 27 classes of real-life datasets show that our proposed model lead to significant improvement compared with strong baseline methods, especially for datasets which are extremely sparse where rating-only methods cannot make accurate predictions.

Question: What is the unified model for recommendation?


Randomized Block Coordinate Descent for Online and Stochastic Optimization. Two types of low cost-per-iteration gradient descent metho ds have been extensively studied in parallel. One is online or stochastic gradient descent ( OGD/SG ), and the other is randomzied coordinate descent (RBCD). In this paper, we combine the two types o f methods together and propose online randomized block coordinate descent (ORBCD). At each itera t on, ORBCD only computes the partial gradient of one block coordinate of one mini-batch samples. ORBCD is well suited for the composite minimization problem where one function is the average of th e losses of a large number of samples and the other is a simple regularizer defined on high dimensional variables. We show that the iteration complexity of ORBCD has the same order as OGD or SGD. For strongly convex functions, by reducing the variance of stochastic gradients, we show that ORBCD can con verge at a geometric rate in expectation, matching the convergence rate of SGD with variance reductio n and RBCD.

Question: What is the difference between online and stochastic gradient descent?


Controllability of complex networks via pinning.. We study the problem of controlling a general complex network toward an assigned synchronous evolution by means of a pinning control strategy. We define the pinning controllability of the network in terms of the spectral properties of an extended network topology. The roles of the control and coupling gains, as well as of the number of pinned nodes, are also discussed.

Question: How to control a complex network?


Increased social fear and decreased fear of objects in monkeys with neonatal amygdala lesions. The amygdala has been implicated in the mediation of emotional and species-specific social behavior (Kling et al., 1970; Kling and Brothers, 1992; Kluver and Bucy, 1939; Rosvold et al., 1954). Humans with bilateral amygdala damage are impaired in judging negative emotion in facial expressions and making accurate judgements of trustworthiness (Adolphs et al., 1998, 1994). Amygdala dysfunction has also been implicated in human disorders ranging from social anxiety (Birbaumer et al., 1998) to depression (Drevets, 2000) to autism (Bachevalier, 1994; Baron-Cohen et al., 2000; Bauman and Kemper, 1993). We produced selective amygdala lesions in 2-week-old macaque monkeys who were returned to their mothers for rearing. At 6-8 months of age, the lesioned animals demonstrated less fear of novel objects such as rubber snakes than age-matched controls. However, they displayed substantially more fear behavior than controls during dyadic social interactions. These results suggest that neonatal amygdala lesions dissociate a system that mediates social fear from one that mediates fear of inanimate objects. Furthermore, much of the age-appropriate repertoire of social behavior was present in amygdala-lesioned infants indicating that these lesions do not produce autistic-like behavior in monkeys. Finally, amygdala lesions early in development have different effects on social behavior than lesions produced in adulthood.

Question: What is the difference between social fear and fear of objects?


Automatic categorization of medical images for content-based retrieval and data mining.. Categorization of medical images means selecting the appropriate class for a given image out of a set of pre-defined categories. This is an important step for data mining and content-based image retrieval (CBIR). So far, published approaches are capable to distinguish up to 10 categories. In this paper, we evaluate automatic categorization into more than 80 categories describing the imaging modality and direction as well as the body part and biological system examined. Based on 6231 reference images from hospital routine, 85.5% correctness is obtained combining global texture features with scaled images. With a frequency of 97.7%, the correct class is within the best ten matches, which is sufficient for medical CBIR applications.

Question: How many categories can be distinguished?


Parallel Training of Neural Networks for Speech Recognition. In this paper we describe parallel implementation of ANN training procedure based on block mode back-propagation learning algorithm. Two different approaches to parallelization were implemented. The first is data parallelization using POSIX threads, it is suitable for multi-core computers. The second is node parallelization using high performance SIMD architecture of GPU with CUDA, suitable for CUDA enabled computers. We compare the speed-up of both approaches by learning typically-sized network on the real-world phoneme-state classification task, showing nearly 10 times reduction when using CUDA version, while the 8-core server with multi-thread version gives only 4 times reduction. In both cases we compared to an already BLAS optimized implementation. The training tool will be released as Open-Source software under project name TNet.

Question: How to parallelize neural network training?


Three-axis NC milling simulation based on adaptive triangular mesh. 0360-8352/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.cie.2010.06.016 * Corresponding author. Tel.: +86 21 67791413; fax E-mail address: mainpowers@gmail.com (J. Mao). NC milling simulation has become an important step in computer aided manufacturing (CAM). To achieve real-time simulation, the total number of polygons has to be reduced, which results in poor image quality. This paper presents an adaptive triangular mesh algorithm to reduce the number of polygons while image quality remains high. Binary tree is used to represent the milling surface, and the optimization of the mesh is performed dynamically in the process of simulation. In this algorithm, the resolution of triangles is automatically updated according to local surface flatness, thus greatly reducing the number of triangles at planar regions. By doing this, real-time and high quality of visual presentation is insured and the translation, rotation and zooming operations are still applicable. When machining precision is evaluated, or overcut, undercut and interference are inspected, full resolution model stored in memory is automatically loaded to ensure the accuracy and correctness of these inspections. Finally, an example is presented to illustrate the validity of proposed algorithm. 2010 Elsevier Ltd. All rights reserved.

Question: How to reduce the number of polygons while image quality remains high?


Iterative Procedures for Nonlinear Integral Equations. The numerical solution of nonlinear integral equations involves the iterative soIutioon of finite systems of nonlinear algebraic or transcendental equations. Certain corwent i o n a l techniqucs for treating such systems are reviewed in the context of a particular class of n o n l i n e a r equations. A procedure is synthesized to offset some of the disadvantages of these t e c h n i q u e s in this context; however, the procedure is not restricted to this pt~rticular class of s y s t e m s of nonlinear equations.

Question: What is a nonlinear integral equation?


Graph Convolutional Networks With Argument-Aware Pooling for Event Detection. The current neural network models for event detection have only considered the sequential representation of sentences. Syntactic representations have not been explored in this area although they provide an effective mechanism to directly link words to their informative context for event detection in the sentences. In this work, we investigate a convolutional neural network based on dependency trees to perform event detection. We propose a novel pooling method that relies on entity mentions to aggregate the convolution vectors. The extensive experiments demonstrate the benefits of the dependencybased convolutional neural networks and the entity mentionbased pooling method for event detection. We achieve the state-of-the-art performance on widely used datasets with both perfect and predicted entity mentions.

Question: What is the state-of-the-art event detection method?


On Different Facets of Regularization Theory. This review provides a comprehensive understanding of regularization theory from different perspectives, emphasizing smoothness and simplicity principles. Using the tools of operator theory and Fourier analysis, it is shown that the solution of the classical Tikhonov regularization problem can be derived from the regularized functional defined by a linear differential (integral) operator in the spatial (Fourier) domain. State-ofthe-art research relevant to the regularization theory is reviewed, covering Occam's razor, minimum length description, Bayesian theory, pruning algorithms, informational (entropy) theory, statistical learning theory, and equivalent regularization. The universal principle of regularization in terms of Kolmogorov complexity is discussed. Finally, some prospective studies on regularization theory and beyond are suggested.

Question: What is the universal principle of regularization?