Identifying and Accounting for Task-Dependent Bias in Crowdsourcing. Models for aggregating contributions by crowd workers have been shown to be challenged by the rise of taskspecific biases or errors. Task-dependent errors in assessment may shift the majority opinion of even large numbers of workers to an incorrect answer. We introduce and evaluate probabilistic models that can detect and correct task-dependent bias automatically. First, we show how to build and use probabilistic graphical models for jointly modeling task features, workers' biases, worker contributions and ground truth answers of tasks so that task-dependent bias can be corrected. Second, we show how the approach can perform a type of transfer learning among workers to address the issue of annotation sparsity. We evaluate the models with varying complexity on a large data set collected from a citizen science project and show that the models are effective at correcting the task-dependent worker bias. Finally, we investigate the use of active learning to guide the acquisition of expert assessments to enable automatic detection and correction of worker bias.

Question: How to identify and correct task-dependent bias in crowdsourcing?


Induced pluripotent stem cell-based modeling of neurodegenerative diseases: a focus on autophagy. The advent of cell reprogramming has enabled the generation of induced pluripotent stem cells (iPSCs) from patient skin fibroblasts or blood cells and their subsequent differentiation into tissue-specific cells, including neurons and glia. This approach can be used to recapitulate disease-specific phenotypes in classical cell culture paradigms and thus represents an invaluable asset for disease modeling and drug validation in the framework of personalized medicine. The autophagy pathway is a ubiquitous eukaryotic degradation and recycling system, which relies on lysosomal degradation of unwanted and potentially cytotoxic components. The relevance of autophagy in the pathogenesis of neurodegenerative diseases is underlined by the observation that disease-linked genetic variants of susceptibility factors frequently result in dysregulation of autophagic-lysosomal pathways. In particular, disrupted autophagy is implied in the accumulation of potentially neurotoxic products such as protein aggregates and their precursors and defective turnover of dysfunctional mitochondria. Here, we review the current state of iPSC-based assessment of autophagic dysfunction in the context of neurodegenerative disease modeling. The collected data show that iPSC technology is capable to reveal even subtle alterations in subcellular homeostatic processes, which form the molecular basis for disease manifestation.

Question: What is the relevance of autophagy in the pathogenesis of neurodegenerative diseases?


LaserStroke: Mid-air Tactile Experiences on Contours Using Indirect Laser Radiation. This demonstration presents a novel form of mid-air tactile display, LaserStroke, that makes use of a laser irradiated on the elastic medium attached to the skin. LaserStroke extends a laser device with an orientation control platform and a magnetic tracker so that it can elicit tapping and stroking sensations to a user's palm from a distance. LaserStroke offers unique tactile experiences while a user freely moves his/her hand in midair.

Question: What is a mid-air tactile display?


Fast Hydraulic and Thermal Erosion on the GPU Balázs Jákó. Computer games, TV series, movies, simulators, and many other computer graphics applications use external scenes where a realistic looking terrain is a vital part of the viewing experience. Creating such terrains is a challenging task. In this paper we propose a method that generates realistic virtual terrains by simulation of hydraulic and thermal erosion on a predefined height field terrain. The model is designed to be executed interactively on parallel architectures like graphics processors.

Question: How to simulate hydraulic and thermal erosion on a predefined height field terrain?


Question: What is an enzyme-enhanced microbial fuel cell?


Mean and Variance of the Sampling Distribution of Particle Swarm Optimizers During Stagnation. Several theoretical analyses of the dynamics of particle swarms have been offered in the literature over the last decade. Virtually all rely on substantial simplifications, often including the assumption that the particles are deterministic. This has prevented the exact characterization of the sampling distribution of the particle swarm optimizer (PSO). In this paper we introduce a novel method that allows us to exactly determine all the characteristics of a PSO sampling distribution and explain how it changes over any number of generations, in the presence stochasticity. The only assumption we make is stagnation, i.e., we study the sampling distribution produced by particles in search for a better personal best. We apply the analysis to the PSO with inertia weight, but the analysis is also valid for the PSO with constriction and other forms of PSO.

Question: What is the mean and variance of the sampling distribution of the particle swarm optimizer?


Multilayer networks: an architecture framework. We present an architecture framework for the control and management of multilayer networks and associated advanced network services. This material is identified as an "architecture framework" to emphasize its role in providing guidance and structure to our subsequent detailed architecture, design, and implementation activities. Our work is motivated by requirements from the Department of Energy science application community for real-time on-demand science-domain-specific network services and resource provisioning. We also summarize the current state of deployments and use of network services based on this multilayer network architecture framework.

Question: What is a multilayer network?


Vaginoplasty using autologous in vitro cultured vaginal tissue in a patient with Mayer-von-Rokitansky-Kuster-Hauser syndrome.. Mayer-von-Rokitansky-Küster-Hauser syndrome (MRKHS) is characterized by vaginal agenesis with variable Müllerian duct abnormalities. The Abbè-McIndoe technique is considered a valid treatment option for vaginoplasty but no consensus has been reached on what material should be used for the neovagina canal wall lining. We report the first case of autologous vaginal tissue transplantation in a 28-year-old women with MRKHS. The patient was subjected to a 1 cm2 full-thickness mucosal biopsy from the vaginal vestibule. Following enzymatic dissociation, cells were inoculated onto collagen IV-coated plates and cultured for 2 weeks. The patient was subjected to a vaginoplasty with a modified Abbè-McIndoe vaginoplasty with 314 cm2 autologous in vitro cultured vaginal tissue for the canal lining. At 1 month from surgery, the vagina appeared normal in length and depth and a vaginal biopsy revealed normal vaginal tissue. The use of autologous in vitro cultured vaginal tissue to create a neovagina appears as an easy, minimally invasive and useful method.

Question: What is the Abbè-McIndoe vaginoplasty?


Adaptation of Word Vectors using Tree Structure for Visual Semantics. We propose a framework of word-vector adaptation, which makes vectors of visually similar concepts close to each other. Here, word vectors are real-valued vector representation of words, e.g., word2vec representation. Our basic idea is to assume that each concept has some hypernyms that are important to determine its visual features. For example, for a concept Swallow with hypernyms Bird, Animal and Entity, we believe Bird is the most important since birds have common visual features with their feathers etc. Adapted word vectors are obtained for each word by taking a weighted sum of a given original word vector and its hypernym word vectors. Our weight optimization makes vectors of visually similar concepts close to each other, by giving a large weight for such important hypernyms. We apply the adapted word vectors to zero-shot learning on the TRECVID 2014 semantic indexing dataset. We achieved 0.083 of Mean Average Precision, which is the best performance without using TRECVID training data to the best of our knowledge.

Question: What is the best performance without using TRECVID training data?


Gaze-Adaptive Above and On-Surface Interaction. We explore the combination of above-surface sensing with eye tracking to facilitate concurrent interaction with multiple regions on touch screens. Conventional touch input relies on positional accuracy, thereby requiring tight visual monitoring of one's own motor action. In contrast, above-surface sensing and eye tracking provides information about how user's hands and gaze are distributed across the interface. In these situations we facilitate interaction by 1) showing the visual feedback of the hand hover near user's gaze point and 2) decrease the requisite of positional accuracy by employing gestural information. We contribute input and visual feedback techniques that combine these modalities and demonstrate their use in example applications. A controlled study showed the effectiveness of our techniques for manipulation tasks against conventional touch, while the effectiveness in acquisition tasks depended on the amount of mid-air motion, leading to our conclusion that the techniques can benefit interacting with multiple interface regions.

Question: How to make a touch screen more comfortable for the user?


A method for learning matching errors for stereo computation. This paper describes a novel learning-based approach for improving the performance of stereo computation. It is based on the observation that whether the image matching scores lead to true or erroneous depth values is dependent on the original stereo images and the underlying scene structure. This function is learned from training data and is integrated into a depth estimation algorithm using the MAP-MRF framework. Because the resultant likelihood function is dependent on the states of a large neighboring region around each pixel, we propose to solve the high-order MRF inference problem using the simulated annealing algorithm combined with a Metropolis-Hastings sampler. A segmentation-based approach is proposed to accelerate the computational speed and improve the performance. Preliminary experimental results show that the learning process captures common errors in SSD matching including the fattening effect, the aperture effect, and mismatches in occluded or low texture regions. It is also demonstrated that the proposed approach significantly improves the accuracy of the depth computation.

Question: What is the aperture effect?


The Mini Nutritional Assessment (MNA) review of the literature--What does it tell us?. UNLABELLED To review the literature on the MNA to Spring 2006, we searched MEDLINE, Web of Science and Scopus, and did a manual search in J Nutr Health Aging, Clin Nutr, Eur J Clin Nutr and free online available publications.   VALIDATION AND VALIDITY The MNA was validated against two principal criteria, clinical status and comprehensive nutrition assessment using principal component and discriminant analysis. The MNA shortform (MNA-SF) was developed and validated to allow a 2-step screening process. The MNA and MNA-SF are sensitive, specific, and accurate in identifying nutrition risk.   NUTRITIONAL SCREENING The prevalence of malnutrition in community-dwelling elderly (21 studies, n = 14149 elderly) is 2 +/- 0.1% (mean +/- SE, range 0- 8%) and risk of malnutrition is 24 +/- 0.4% (range 8-76%). A similar pattern is seen in out-patient and home care elderly (25 studies, n = 3119 elderly) with prevalence of undernutrition 9 +/- 0.5% (mean +/- SE, range 0-30%) and risk of malnutrition 45 +/- 0.9% (range 8-65%). A high prevalence of undernutrition has been reported in hospitalized and institutionalized elderly patients: prevalence of malnutrition is 23 +/- 0.5% (mean +/- SE, range 1- 74%) in hospitals (35 studies, n = 8596) and 21 +/- 0.5% (mean +/- SE, range 5-71%) in institutions (32 studies, n = 6821 elderly). An even higher prevalence of risk of malnutrition was observed in the same populations, with 46 +/- 0.5% (range 8-63%) and 51 +/- 0.6% (range 27-70%), respectively. In cognitively impaired elderly subjects (10 studies, n = 2051 elderly subjects), detection using the MNA, prevalence of malnutrition was 15 +/- 0.8% (mean +/- SE, range 0-62%), and 44 +/- 1.1% (range 19-87%) of risk of malnutrition.   CHARACTERISTICS The large variability is due to differences in level of dependence and health status among the elderly. In hospital settings, a low MNA score is associated with an

Question: What is the prevalence of malnutrition in elderly?


Enhancing Knowledge Graph Completion By Embedding Correlations. Despite their large sizes, modern Knowledge Graphs (KGs) are still highly incomplete. Statistical relational learning methods can detect missing links by "embedding" the nodes and relations into latent feature tensors. Unfortunately, these methods are unable to learn good embeddings if the nodes are not well-connected. Our proposal is to learn embeddings for correlations between subgraphs and add a post-prediction phase to counter the lack of training data. This technique, applied on top of methods like TransE or HolE, can significantly increase the predictions on realistic KGs.

Question: How to improve knowledge graph completion?


New Comparative Study Between DES, 3DES and AES within Nine Factors. --With the rapid development of various multimedia technologies, more and more multimedia data are generated and transmitted in the medical, also the internet allows for wide distribution of digital media data. It becomes much easier to edit, modify and duplicate digital information .Besides that, digital documents are also easy to copy and distribute, therefore it will be faced by many threats. It is a big security and privacy issue, it become necessary to find appropriate protection because of the significance, accuracy and sensitivity of the information. , which may include some sensitive information which should not be accessed by or can only be partially exposed to the general users. Therefore, security and privacy has become an important. Another problem with digital document and video is that undetectable modifications can be made with very simple and widely available equipment, which put the digital material for evidential purposes under question. Cryptography considers one of the techniques which used to protect the important information. In this paper a three algorithm of multimedia encryption schemes have been proposed in the literature and description. The New Comparative Study between DES, 3DES and AES within Nine Factors achieving an efficiency, flexibility and security, which is a challenge of researchers.

Question: What is the difference between DES, 3DES and AES?


Deep Neural Networks for Learning Graph Representations. In this paper, we propose a novel model for learning graph representations, which generates a low-dimensional vector representation for each vertex by capturing the graph structural information. Different from other previous research efforts, we adopt a random surfing model to capture graph structural information directly, instead of using the samplingbased method for generating linear sequences proposed by Perozzi et al. (2014). The advantages of our approach will be illustrated from both theorical and empirical perspectives. We also give a new perspective for the matrix factorization method proposed by Levy and Goldberg (2014), in which the pointwise mutual information (PMI) matrix is considered as an analytical solution to the objective function of the skipgram model with negative sampling proposed by Mikolov et al. (2013). Unlike their approach which involves the use of the SVD for finding the low-dimensitonal projections from the PMI matrix, however, the stacked denoising autoencoder is introduced in our model to extract complex features and model non-linearities. To demonstrate the effectiveness of our model, we conduct experiments on clustering and visualization tasks, employing the learned vertex representations as features. Empirical results on datasets of varying sizes show that our model outperforms other stat-of-the-art models in such tasks.

Question: What is the difference between the random surfing model and the samplingbased model?


Fast single image fog removal using the adaptive Wiener filter. We present in this paper a fast single image defogging method that uses a novel approach to refining the estimate of amount of fog in an image with the Locally Adaptive Wiener Filter. We provide a solution for estimating noise parameters for the filter when the observation and noise are correlated by decorrelating with a naively estimated defogged image. We demonstrate our method is 50 to 100 times faster than existing fast single image defogging methods and that our proposed method subjectively performs as well as the Spectral Matting smoothed Dark Channel Prior method.

Question: How to estimate noise parameters for the filter when the observation and noise are correlated?


A HOG-based Real-time and Multi-scale Pedestrian Detector Demonstration System on FPGA. Pedestrian detection will play a major role in future driver assistance and autonomous driving. One powerful algorithm in this field uses HOG features to describe the specific properties of pedestrians in images. To determine their locations, features are extracted and classified window-wise from different scales of an input image. The results of the classification are finally merged to remove overlapping detections. The real-time execution of this method requires specific FPGA- or ASIC-architectures. Recent work focused on accelerating the feature extraction and classification. Although merging is an important step in the algorithm, it is only rarely considered in hardware implementations. A reason for that could be its complexity and irregularity that is not trivial to implement in hardware. In this paper, we present a new bottom-up FPGA architecture that maps the full HOG-based algorithm for pedestrian detection including feature extraction, SVM classification, and multi-scale processing in combination with merging. For that purpose, we also propose a new hardware-optimized merging method. The resulting architecture is highly efficient. Additionally, we present an FPGA-based full real-time and multi-scale pedestrian detection demonstration system.

Question: What is the size of the canadian military?


A Practical Message Falsification Attack on WPA. In 2008, Beck and Tews have proposed a practical attack on WPA. Their attack (called the Beck-Tews attack) can recover plaintext from an encrypted short packet, and can falsify it. The execution time of the Beck-Tews attack is about 12-15 minutes. However, the attack has the limitation, namely, the targets are only WPA implementations those support IEEE802.11e QoS features. In this paper, we propose a practical message falsification attack on any WPA implementation. In order to ease targets of limitation of wireless LAN products, we apply the Beck-Tews attack to the man-in-the-middle attack. In the man-inthe-middle attack, the user's communication is intercepted by an attacker until the attack ends. It means that the users may detect our attack when the execution time of the attack is large. Therefore, we give methods for reducing the execution time of the attack. As a result, the execution time of our attack becomes about one minute in the best case.

Question: What is the practical message falsification attack on WPA?


Applying Deep Learning to Better Predict Cryptocurrency Trends. In this paper, we will create deep learning models using the Python library, Keras, to make predictions on Bitcoin. We designed two neural networks to make similar predictions with important differences. In our first approach, we created a simple neural net with one input layer and an additional dropout layer to generate a continuous value. This continuous value is the predicted price of the Bitcoin a week from the given input. For the second model, we attempted to predict whether or not the price of the Bitcoin would go up 3%, stay within 3%, or go down 3% a week from a given date. Instead of returning a single continuous value, the result contains an array of three values. Each value is a percentage of the likelihood that it will either go up 3%, stay within 3%, or go down 3%. We approached designing the models using many different optimizers, activation functions, number of neurons, and various quantities of layers before settling on the final models. We also compared three different optimizers for each problem: Stochastic Gradient Descent (SGD), Adam, and RMSProp. We compared the results of all three optimizers to determine which one was the most effective at creating the most accurate model.

Question: How to predict cryptocurrency trends?


Describing objects by their attributes. We propose to shift the goal of recognition from naming to describing. Doing so allows us not only to name familiar objects, but also: to report unusual aspects of a familiar object ("spotty dog", not just "dog"); to say something about unfamiliar objects ("hairy and four-legged", not just "unknown"); and to learn how to recognize new objects with few or no visual examples. Rather than focusing on identity assignment, we make inferring attributes the core problem of recognition. These attributes can be semantic ("spotty") or discriminative ("dogs have it but sheep do not"). Learning attributes presents a major new challenge: generalization across object categories, not just across instances within a category. In this paper, we also introduce a novel feature selection method for learning attributes that generalize well across categories. We support our claims by thorough evaluation that provides insights into the limitations of the standard recognition paradigm of naming and demonstrates the new abilities provided by our attribute-based framework.

Question: What is the difference between naming and describing?


Software Testing Research: Achievements, Challenges, Dreams. Software engineering comprehends several disciplines devoted to prevent and remedy malfunctions and to warrant adequate behaviour. Testing, the subject of this paper, is a widespread validation approach in industry, but it is still largely ad hoc, expensive, and unpredictably effective. Indeed, software testing is a broad term encompassing a variety of activities along the development cycle and beyond, aimed at different goals. Hence, software testing research faces a collection of challenges. A consistent roadmap of the most relevant challenges to be addressed is here proposed. In it, the starting point is constituted by some important past achievements, while the destination consists of four identified goals to which research ultimately tends, but which remain as unreachable as dreams. The routes from the achievements to the dreams are paved by the outstanding research challenges, which are discussed in the paper along with interesting ongoing work.

Question: What is the roadmap of software testing research?


Finding Interesting Rules from Large Sets of Discovered Association Rules. Association rules, introduced by Agrawal, Imielinski, and Swami, are rules of the form "for 90% of the rows of the relation, if the row has value 1 in the columns in set W, then it has 1 also in column B". Efficient methods exist for discovering association rules from large collections of data. The number of discovered rules can, however, be so large that browsing the rule set and finding interesting rules from it can be quite difficult for the user. We show how a simple formalism of rule templates makes it possible to easily describe the structure of interesting rules. We also give examples of visualization of rules, and show how a visualization tool interfaces with rule templates.

Question: How to find interesting rules from large sets of discovered association rules?


Input/Output Devices and Interaction Techniques. The computing literature often draws a sharp distinction between input and output; computer scientists are used to regarding a screen as a passive output device and a mouse as a pure input device. However, nearly all examples of human-computer interaction require both input and output to do anything useful. For example, what good would a mouse be without the corresponding feedback embodied by the cursor on the screen, as well as the sound and feel of the buttons when they are clicked? The distinction between output devices and input devices becomes even more blurred in the real world. A sheet of paper can be used to both record ideas (input) and display them (output). Clay reacts to the sculptor's fingers yet also provides feedback through the curvature and texture of its surface. Indeed, the complete and seamless integration of input and output is becoming a common research theme in advanced computer interfaces such as ubiquitous computing (Weiser, 1991) and tangible interaction (Ishii & Ullmer, 1997).

Question: What is the difference between input and output?


ERP evidence for conceptual mappings and comparison processes during the comprehension of conventional and novel metaphors. Cognitive linguists suggest that understanding metaphors requires activation of conceptual mappings between the involved concepts. We tested whether mappings are indeed in use during metaphor comprehension, and what mapping means as a cognitive process with Event-Related Potentials. Participants read literal, conventional metaphorical, novel metaphorical, and anomalous target sentences preceded by primes with related or unrelated mappings. Experiment 1 used sentence-primes to activate related mappings, and Experiment 2 used simile-primes to induce comparison thinking. In the unprimed conditions of both experiments, metaphors elicited N400s more negative than the literals. In Experiment 1, related sentence-primes reduced the metaphor-literal N400 difference in conventional, but not in novel metaphors. In Experiment 2, related simile-primes reduced the metaphor-literal N400 difference in novel, but not clearly in conventional metaphors. We suggest that mapping as a process occurs in metaphors, and the ways in which it can be facilitated by comparison differ between conventional and novel metaphors.

Question: What is the difference between conventional and novel metaphors?


Regression Shrinkage and Selection via the Lasso. We propose a new method for estimation in linear models. The 'lasso' minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients hat are exactly 0 and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models like subset selection and exhibits the stability of ridge regression. There is also an interesting relationship with recent work in adaptive function estimation by Donoho and Johnstone. The lasso idea is quite general and can be applied in a variety of statistical models: extensions to generalized regression models and tree-based models are briefly described.

Question: What is the lasso?


Threat detection in online discussions. This paper investigates the effect of various types of linguistic features (lexical, syntactic and semantic) for training classifiers to detect threats of violence in a corpus of YouTube comments. Our results show that combinations of lexical features outperform the use of more complex syntactic and semantic features for this task.

Question: What is the effect of various types of linguistic features for training classifiers to detect threats of violence in a corpus of YouTube comments?


Automating hierarchical document classification for construction management information systems. The widespread use of information technologies for construction is considerably increasing the number of electronic text documents stored in construction management information systems. Consequently, automated methods for organizing and improving the access to the information contained in these types of documents become essential to construction information management. This paper describes a methodology developed to improve information organization and access in construction management information systems based on automatic hierarchical classification of construction project documents according to project components. A prototype system for document classification is presented, as well as the experiments conducted to verify the feasibility of the proposed approach. D 2003 Elsevier Science B.V. All rights reserved.

Question: How to classify documents?


Action recognition based on a bag of 3D points. This paper presents a method to recognize human actions from sequences of depth maps. Specifically, we employ an action graph to model explicitly the dynamics of the actions and a bag of 3D points to characterize a set of salient postures that correspond to the nodes in the action graph. In addition, we propose a simple, but effective projection based sampling scheme to sample the bag of 3D points from the depth maps. Experimental results have shown that over 90% recognition accuracy were achieved by sampling only about 1% 3D points from the depth maps. Compared to the 2D silhouette based recognition, the recognition errors were halved. In addition, we demonstrate the potential of the bag of points posture model to deal with occlusions through simulation.

Question: What is the bag of 3D points?


Design and Sensorless Control of a Novel Axial-Flux Permanent Magnet Machine for In-Wheel Applications. This paper proposes an axial-flux-modulation permanent magnet (PM) machine and its sensorless control strategy for in-wheel application in electrical vehicles. A Vernier structure is integrated with the axial-flux PM machine to include the magnetic gear effect and improve output torque. The sensorless control strategy of the proposed machine, including initial rotor position estimation and rotating position estimation, is proposed for flux-modulation motors in this paper. The initial rotor position estimation is based on the technique of rectangular pulse voltage injection and the rotating rotor position estimation is based on the sliding mode observer (SMO). The saturation effect on inductances, which is the theoretical basis of the rectangular pulse voltage injection, makes the stator parameter variation in different loads and affects the SMO estimation. To overcome this problem, a novel online parameter self-adjustment procedure for the SMO is introduced. The machine design and its sensorless control performance are verified by simulation and prototype experiments.

Question: How does the sensorless control work?


BCD8sP: An advanced 0.16 μm technology platform with state of the art power devices. Advanced 0.16 μm BCD technology platform offering dense logic transistors (1.8 V-5 V CMOS) and high performance analog features has been developed. Thanks to dedicated field plate optimization, body and drain engineering, state of the art power devices (8 V to 42 V rated) have been obtained ensuring large Safe Operating Areas with best RONXAREA-BVDSS tradeoff.

Question: What is the safe operating area of a transistor?


A Decomposed Dual-Cross Generative Adversarial Network for Image Rain Removal. Rain removal is important for many computer vision applications, such as surveillance, autonomous car, etc. Traditionally, rain removal is regarded as a signal removal problem which usually causes over-smoothing by removing texture details in non-rain background regions. This paper considers the issue of rain removal from a completely different perspective, to treat rain removal as a signal decomposition problem. Specifically, we decompose the rain image into two components, namely non-rain background image and rain streaks image. Then, we introduce an adversarial training mechanism to synthesize non-rain background image and rain streaks image in a Dual-Cross manner, which makes the two adversarial branches interact with each other, archiving a win-win result ultimately. The proposed Decomposed Dual-Cross Generative Adversarial Network (DDC-GAN) shows significantly performance improvement compared with stateof-the-art methods on both synthetic and real-world images in terms of qualitative and quantitative measures (over 3dB gains in PSNR).

Question: What is the difference between a generative adversarial network and a generative model?


Additive Manufacturing of Metals: A Review. Over the past 20 years, additive manufacturing (AM) has evolved from 3D printers used for rapid prototyping to sophisticated rapid manufacturing that can create parts directly without the use of tooling. AM technologies build near net shape components layer by layer using 3D model data. AM could revolutionize many sectors of manufacturing by reducing component lead time, material waste, energy usage, and carbon footprint. Furthermore, AM has the potential to enable novel product designs that could not be fabricated using conventional processes. This proceedings is a review that assesses available AM technologies for direct metal fabrication. Included is an outline of the definition of AM, a review of the commercially available and under development technologies for direct metal fabrication, possibilities, and an overall assessment of the state of the art. Perspective on future research opportunities is also be presented.

Question: What is additive manufacturing?


Algebraic gossip: a network coding approach to optimal multiple rumor mongering. The problem of simultaneously disseminating k messages in a large network of n nodes, in a decentralized and distributed manner, where nodes only have knowledge about their own contents, is studied. In every discrete time-step, each node selects a communication partner randomly, uniformly among all nodes and only one message can be transmitted. The goal is to disseminate rapidly, with high probability, all messages to all nodes. It is shown that a random linear coding (RLC) based protocol disseminates all messages to all nodes in time ck + O (√k ln(k) ln(n)), where c < 3.46 using pull-based dissemination and c < 5.96 using push-based dissemination. Simulations suggest that c < 2 might be a tighter bound. Thus, if k ≫ (ln(n))3, the time for simultaneous dissemination RLC is asymptotically at most ck, versus the Ω(k log<inf>2</inf>(n))3 time of sequential dissemination. Furthermore, when k ≫ (ln(n))3, the dissemination time is order optimal. When k ≪ (ln(n))2, RLC reduces dissemination time by a factor of Ω(√k/ln k) over sequential dissemination. The overhead of the RLC protocol is negligible for messages of reasonable size. A store-and-forward mechanism without coding is also considered. It is shown that this approach performs no better than a sequential approach when k=∞ n. Owing to the distributed nature of the system, the proof requires analysis of an appropriate time-varying Bernoulli process.

Question: What is the time complexity of disseminating k messages in a network of n nodes?


Toward autonomous mapping and exploration for mobile robots through deep supervised learning. We consider an autonomous mapping and exploration problem in which a range-sensing mobile robot is guided by an information-based controller through an a priori unknown environment, choosing to collect its next measurement at the location estimated to yield the maximum information gain within its current field of view. We propose a novel and time-efficient approach to predict the most informative sensing action using a deep neural network. After training the deep neural network on a series of thousands of randomly-generated "dungeon maps", the predicted optimal sensing action can be computed in constant time, with prospects for appealing scalability in the testing phase to higher dimensional systems. We evaluated the performance of deep neural networks on the autonomous exploration of two-dimensional workspaces, comparing several different neural networks that were selected due to their success in recent ImageNet challenges. Our computational results demonstrate that the proposed method provides high efficiency as well as accuracy in selecting informative sensing actions that support autonomous mobile robot exploration.

Question: What is the most informative sensing action?


ON THE PRICING OF CORPORATE DEBT : THE RISK STRUCTURE OF INTEREST RATES. On the Pricing of Corporate Debt: The Risk Structure of Interest Rates Author(s): Robert C. Merton Source: The Journal of Finance, Vol. 29, No. 2, Papers and Proceedings of the Thirty-Second Annual Meeting of the American Finance Association, New York, New York, December 28-30, 1973 (May, 1974), pp. 449-470 Published by: Blackwell Publishing for the American Finance Association Stable URL: http://www.jstor.org/stable/2978814 Accessed: 16/10/2008 10:09

Question: What is the risk structure of interest rates?


Rated aspect summarization of short comments. Web 2.0 technologies have enabled more and more people to freely comment on different kinds of entities (e.g. sellers, products, services). The large scale of information poses the need and challenge of automatic summarization. In many cases, each of the user-generated short comments comes with an overall rating. In this paper, we study the problem of generating a ``rated aspect summary'' of short comments, which is a decomposed view of the overall ratings for the major aspects so that a user could gain different perspectives towards the target entity. We formally define the problem and decompose the solution into three steps. We demonstrate the effectiveness of our methods by using eBay sellers' feedback comments. We also quantitatively evaluate each step of our methods and study how well human agree on such a summarization task. The proposed methods are quite general and can be used to generate rated aspect summary automatically given any collection of short comments each associated with an overall rating.

Question: What is the rated aspect summarization of short comments?


Twitter sentiment analysis using deep learning methods. The social media has Immense and popularity among all the services today. Data from SNS (Social Network Service) can be used for a lot of objectives such as prediction or sentiment analysis. Twitter is a SNS that has a huge data with user posting, with this significant amount of data, it has the potential of research related to text mining and could be subjected to sentiment analysis. But handling such a huge amount of unstructured data is a difficult task, machine learning is needed for handling such huge of data. Deep learning is of the machine learning method that use the deep feed forward neural network with many hidden layers in the term of neural network with the result of the experiment about 75%.

Question: What is the difference between deep learning and machine learning?


Vector Space Representations of Documents in Classifying Finnish Social Media Texts.

Question: What is a vector space representation of documents?


Control of Grid Connected PV Array Using P&O MPPT Algorithm. The renewable energy is becoming moremainstream and accessible. This has been made possible due toan increase in environmental awareness coupled with thepopular demand to cut back on the greenhouse emissions. We inthis project propose a grid connected PV system. The aim of theproject is to implement a complete distributed energy resourcesystem (DER). The project will feature a PV module, which willbe controlled and optimized by means of a maximum powerpoint tracking (MPPT) algorithm. A boost converter along witha single phase grid tie inverter will be used to increase theoutput voltage and to convert it to AC. A phase locked loopcircuit will be used to integrate the single phase inverter withthe grid. A control methodology consisting of PI controllers isemployed for operating the PV at the MPPT point bycontrolling the switching of the boost converter and also for theoperation of the single phase inverter and its integration withthe grid. The parameters of these controllers are tuned to givethe best results. This will be followed by a detailed mathematicaland engineering analysis for the simulated results. The validityof the proposed scheme will be verified by simulation using thePSIM software.

Question: How to control a PV array using MPPT algorithm?


Face Description with Local Binary Patterns: Application to Face Recognition. This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed

Question: What is the face recognition problem?


A 5-MHz 91% Peak-Power-Efficiency Buck Regulator With Auto-Selectable Peak- and Valley-Current Control. This paper presents a multi-MHz buck regulator for portable applications using an auto-selectable peak- and valley-current control (ASPVCC) scheme. The proposed ASPVCC scheme can enable the current-mode buck regulator to reduce the settling-time requirement of the current sensing by two times. In addition, the dynamically biased shunt feedback technique is employed to improve the sensing speed and the sensing accuracy of both the peak and valley current sensors. With both ASPVCC scheme and advanced current sensors, the buck regulator can thus operate at high switching frequencies with a wide range of duty ratios for reducing the required off-chip inductance. The proposed current-mode buck regulator was fabricated in a standard 0.35-μm CMOS process and occupies a small chip area of 0.54 mm2. Measurement results show that the buck regulator can deliver a maximum output current of 500 mA, operate at 5 MHz with a wide duty-ratio range of about 0.6, use a small-value off-chip inductor of 1 μH, and achieve the peak power efficiency of 91%.

Question: What is the purpose of this paper?


Studying Evolution with Self-Replicating Computer Programs. A critical discussion is presented on the use of self-replicating program systems as tools for the formulation of generalised theories of evolution. Results generated by such systems must be treated with caution, but, if used properly, they can ooer us unprecedented opportunities for empirical , comparative studies. A new system called Cosmos is introduced, which is based upon Ray's Tierra 15]. The major diierence between Cosmos and previous systems is that individual self-replicating programs in Cosmos are modelled (in a very simpliied fashion) on cellular organisms. Previous systems have generally used simpler self-replicators. The hope is that Cosmos may be better able to address questions concerning the sudden emergence of complex multicellular biological organisms during the Cambrian explosion. Results of initial exploratory runs are presented, which are somewhat diierent to those of similar runs on Tierra. These diierences were expected , and indicate the sensitivity of such systems to the precise details of the language in which the self-replicating programs are written. With the strengths and weaknesses of the methodology in mind, some directions for future research with Cosmos are discussed. Within the last decade, computers have become powerful and aaordable enough to enable a number of research groups to study the evolution of life in a new way. Rather than following the traditional approach of trying to capture properties of whole populations in mathematical models, the new approach models a large number of individual self-replicating entities which are competing against each other for resources required for replication. This is achieved by creating a computer which can run a large number of self-replicating programs in parallel 1. 1 In practice, a virtual computer is created (i.e. implemented in software), with parallelism simulated by time-slicing between Tom Ray pioneered this approach with h

Question: What is the difference between Tierra and Cosmos?


Review on Printed Log Periodic and Yagi MSA. The log periodic antenna and Yagi-Uda antenna are used in the applications where very high directivity is required. They also give very high gain in the range of 17-20dBi. This paper presents a review on various configurations of log periodic and Yagi antennas, their advantages and problems. One problem encountered with Yagi-Uda antenna is relatively less bandwidth. This problem is solved by log periodic antenna which can operate over high bandwidth and providing high gain at the same time. In this paper, a review of various techniques to realize printed Yagi-Uda and log periodic antenna is discussed. They are realized by using different feeding techniques like microstrip feeding, co-axial feeding etc. They are also realized by using modifying the shape of directors and reflectors. The high bandwidth (log periodic antenna) has also been realized by increasing the number of reflectors.

Question: What is the difference between log periodic and Yagi-Uda antenna?


AR typing interface for mobile devices. We propose a new user interface system for mobile devices. By using augmented reality (AR) technology, the system overlays virtual objects on real images captured by a camera attached to the back of a mobile device, and the user can operate the mobile device by manipulating the virtual objects with his/her hand in the space behind the mobile device. This system allows the user to operate the device in a wide three-dimensional space and to select small objects easily. Also, the AR technology provides the user with a sense of reality in operating the device. We developed a typing application using our system and verified the effectiveness by user studies. The results showed that more than half of the subjects felt that the operation area of the proposed system is larger than that of a smartphone and that both AR and unfixed key-plane are effective for improving typing speed.

Question: What is the size of the proposed system?


Leveraging Augmented Reality Training Tool for Medical Education: a Case Study in Central Venous Catheterization. Central venous catheterization is a relatively common bedside medical procedure that involves the placement of a catheter into a patient»s internal jugular vein in order to administer medication or fluids. To learn this technique, medical students traditionally practice on training mannequins under the guidance of a clinical instructor. The objective of this project was to co-develop a standardized augmented reality solution for teaching medical students this procedure, which would enable them to practice independently and at their own pace. Following an iterative design and prototyping process, we compiled a comprehensive set of usability heuristics specific to augmented reality healthcare applications, used to identify unique usability issues associated with augmented reality software. This approach offers a better strategy to improve the usability of augmented reality system and increases the potential to standardize and render medical education more accessible. The benefits of applying augmented reality to simulated medical education come with heavy consequences in the event of poor learning outcomes. The usability of these systems is paramount to ensure the development of clinical competence is facilitated and not hindered.

Question: How to teach medical students to place a central venous catheter?


The DL-Lite Family and Relations. The recently introduced series of description logics under the common moniker 'DLLite' has attracted attention of the description logic and semantic web communities due to the low computational complexity of inference, on the one hand, and the ability to represent conceptual modeling formalisms, on the other. The main aim of this article is to carry out a thorough and systematic investigation of inference in extensions of the original DL-Lite logics along five axes: by (i) adding the Boolean connectives and (ii) number restrictions to concept constructs, (iii) allowing role hierarchies, (iv) allowing role disjointness, symmetry, asymmetry, reflexivity, irreflexivity and transitivity constraints, and (v) adopting or dropping the unique name assumption. We analyze the combined complexity of satisfiability for the resulting logics, as well as the data complexity of instance checking and answering positive existential queries. Our approach is based on embedding DL-Lite logics in suitable fragments of the one-variable first-order logic, which provides useful insights into their properties and, in particular, computational behavior.

Question: What is the data complexity of instance checking and answering positive existential queries?


Text mining in healthcare. Applications and opportunities.. Healthcare information systems collect massive amounts of textual and numeric information about patients, visits, prescriptions, physician notes and more. The information encapsulated within electronic clinical records could lead to improved healthcare quality, promotion of clinical and research initiatives, fewer medical errors and lower costs. However, the documents that comprise the health record vary in complexity, length and use of technical vocabulary. This makes knowledge discovery complex. Commercial text mining tools provide a unique opportunity to extract critical information from textual data archives. In this paper, we share our experience of a collaborative research project to develop predictive models by text mining electronic clinical records. We provide an overview of the text mining process, examples of existing studies, experiences of our collaborative project and future opportunities.

Question: What is the text mining process?


What is principal component analysis?. Several measurement techniques used in the life sciences gather data for many more variables per sample than the typical number of samples assayed. For instance, DNA microarrays and mass spectrometers can measure levels of thousands of mRNAs or proteins in hundreds of samples. Such high-dimensionality makes visualization of samples difficult and limits simple exploration of the data. Principal component analysis (PCA) is a mathematical algorithm that reduces the dimensionality of the data while retaining most of the variation in the data set1. It accomplishes this reduction by identifying directions, called principal components, along which the variation in the data is maximal. By using a few components, each sample can be represented by relatively few numbers instead of by values for thousands of variables. Samples can then be plotted, making it possible to visually assess similarities and differences between samples and determine whether samples can be grouped. Saal et al.2 used microarrays to measure the expression of 27,648 genes in 105 breast tumor samples. I will use this gene expression data set, which is available through the Gene Expression Omnibus database (accession no. GSE5325), to illustrate how PCA can be used to represent samples with a smaller number of variables, visualize samples and genes, and detect dominant patterns of gene expression. My aim with this example is to leave you with an idea of how PCA can be used to explore data sets in which thousands of variables have been measured.

Question: What is principal component analysis?


Simple, Fast, and Practical Non-Blocking and Blocking Concurrent Queue Algorithms. Drawing ideas from previous authors, we present a new non-blocking concurrent queue algorithm and a new twolock queue algorithm in which one enqueue and one dequeue can proceed concurrently. Both algorithms are simple, fast, and practical; we were surprised not to find them in the literature. Experiments on a 12-node SGI Challenge multiprocessor indicate that the new non-blocking queue consistently outperforms the best known alternatives; it is the clear algorithm of choice for machines that provide a universal atomic primitive (e.g. compare and swap or load linked/store conditional). The two-lock concurrent queue outperforms a single lock when several processes are competing simultaneously for access; it appears to be the algorithm of choice for busy queues on machines with non-universal atomic primitives (e.g. test and set). Since much of the motivation for non-blocking algorithms is rooted in their immunity to large, unpredictable delays in process execution,we report experimental results both for systems with dedicated processors and for systems with several processes multiprogrammed on each processor.

Question: What is the difference between a non-blocking and a blocking queue?


Friendship and mobility: user movement in location-based social networks. Even though human movement and mobility patterns have a high degree of freedom and variation, they also exhibit structural patterns due to geographic and social constraints. Using cell phone location data, as well as data from two online location-based social networks, we aim to understand what basic laws govern human motion and dynamics. We find that humans experience a combination of periodic movement that is geographically limited and seemingly random jumps correlated with their social networks. Short-ranged travel is periodic both spatially and temporally and not effected by the social network structure, while long-distance travel is more influenced by social network ties. We show that social relationships can explain about 10% to 30% of all human movement, while periodic behavior explains 50% to 70%. Based on our findings, we develop a model of human mobility that combines periodic short range movements with travel due to the social network structure. We show that our model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance than present models of human mobility.

Question: What is the order of magnitude of the percentage of human movement explained by social ties?


Sum-Rate Analysis for High Altitude Platform (HAP) Drones With Tethered Balloon Relay. High altitude platform (HAP) drones can provide broadband wireless connectivity to ground users in rural areas by establishing line-of-sight links and exploiting effective beamforming techniques. However, at high altitudes, acquiring the channel state information (CSI) for HAPs, which is a key requirement to perform beamforming, is challenging. In this paper, by exploiting an interference alignment (IA) technique, a novel method for achieving the maximum sum-rate in HAP-based communications without CSI is proposed. In particular, to realize IA, a multiple-antenna tethered balloon is used as a relay between multiple HAP drones and ground stations (GSs). Here, a multiple-input multiple-output X network system is considered. The capacity of the considered  $M \times N$  X network with a tethered balloon relay is derived in closed-form. Simulation results corroborate the theoretical findings and show that the proposed approach yields the maximum sum-rate in multiple HAPs-GSs communications in absence of CSI. The results also show the existence of an optimal balloon's altitude for which the sum-rate is maximized.

Question: How to achieve maximum sum-rate in HAP-based communications without CSI?


A ug 2 01 7 Reinforced Video Captioning with Entailment Rewards. Sequence-to-sequence models have shown promising improvements on the temporal task of video captioning, but they optimize word-level cross-entropy loss during training. First, using policy gradient and mixed-loss methods for reinforcement learning, we directly optimize sentence-level task-based metrics (as rewards), achieving significant improvements over the baseline, based on both automatic metrics and human evaluation on multiple datasets. Next, we propose a novel entailment-enhanced reward (CIDEnt) that corrects phrase-matching based metrics (such as CIDEr) to only allow for logically-implied partial matches and avoid contradictions, achieving further significant improvements over the CIDEr-reward model. Overall, our CIDEnt-reward model achieves the new state-of-the-art on the MSR-VTT dataset.

Question: What is the new state-of-the-art on the MSR-VTT dataset?


Power laws , Pareto distributions and Zipf ' s law. When the probability of measuring a particular value of some quantity varies inversely as a power of that value, the quantity is said to follow a power law, also known variously as Zipf's law or the Pareto distribution. Power laws appear widely in physics, biology, earth and planetary sciences, economics and finance, computer science, demography and the social sciences. For instance, the distributions of the sizes of cities, earthquakes, forest fires, solar flares, moon craters and people's personal fortunes all appear to follow power laws. The origin of power-law behaviour has been a topic of debate in the scientific community for more than a century. Here we review some of the empirical evidence for the existence of power-law forms and the theories proposed to explain them.

Question: What is the origin of power-law behaviour?


Consciousness and anesthesia.. When we are anesthetized, we expect consciousness to vanish. But does it always? Although anesthesia undoubtedly induces unresponsiveness and amnesia, the extent to which it causes unconsciousness is harder to establish. For instance, certain anesthetics act on areas of the brain's cortex near the midline and abolish behavioral responsiveness, but not necessarily consciousness. Unconsciousness is likely to ensue when a complex of brain regions in the posterior parietal area is inactivated. Consciousness vanishes when anesthetics produce functional disconnection in this posterior complex, interrupting cortical communication and causing a loss of integration; or when they lead to bistable, stereotypic responses, causing a loss of information capacity. Thus, anesthetics seem to cause unconsciousness when they block the brain's ability to integrate information.

Question: What is the difference between consciousness and unconsciousness?


Real-Time Standard Scan Plane Detection and Localisation in Fetal Ultrasound Using Fully Convolutional Neural Networks. Fetal mid-pregnancy scans are typically carried out according to fixed protocols. Accurate detection of abnormalities and correct biometric measurements hinge on the correct acquisition of clearly defined standard scan planes. Locating these standard planes requires a high level of expertise. However, there is a worldwide shortage of expert sonographers. In this paper, we consider a fully automated system based on convolutional neural networks which can detect twelve standard scan planes as defined by the UK fetal abnormality screening programme. The network design allows real-time inference and can be naturally extended to provide an approximate localisation of the fetal anatomy in the image. Such a framework can be used to automate or assist with scan plane selection, or for the retrospective retrieval of scan planes from recorded videos. The method is evaluated on a large database of 1003 volunteer mid-pregnancy scans. We show that standard planes acquired in a clinical scenario are robustly detected with a precision and recall of 69% and 80%, which is superior to the current state-of-the-art. Furthermore, we show that it can retrospectively retrieve correct scan planes with an accuracy of 71% for cardiac views and 81% for non-cardiac views.

Question: What is the UK fetal abnormality screening programme?


Related Key Chosen IV Attack on Grain-128a Stream Cipher. The well-known stream cipher Grain-128 is a variant version of Grain v1 with 128-bit secret key. Grain v1 is a stream cipher which has successfully been chosen as one of seven finalists by European eSTREAM project. Yet Grain-128 is vulnerable against some recently introduced attacks. A new version of Grain-128 with authentication, named Grain-128a, is proposed by Ågren, Hell, Johansson, and Meier. The designers claimed that Grain-128a is strengthened against all known attacks and observations on the original Grain-128. So far there exists no attack on Grain-128a except a differential fault attack by Banik, Maitra, and Sarkar. In this paper, we give some observations on Grain-128a, and then propose a related key chosen IV attack on Grain-128a based on these observations. Our attack can recover the 128-bit secret key of Grain-128a with a computational complexity of <formula formulatype="inline"> <tex Notation="TeX">$2^{96.322} $</tex></formula>, requiring <formula formulatype="inline"> <tex Notation="TeX">$2^{96} $</tex></formula> chosen IVs and <formula formulatype="inline"><tex Notation="TeX">$2^{103.613} $</tex> </formula> keystream bits. The success probability of our attack is 0.632. This related key attack is "minimal" in the sense that it only requires two related keys. The result shows that our attack is much better than an exhaustive key search in the related key setting.

Question: What is the security of Grain-128a?


Risk and parameter convergence of logistic regression. The logistic loss is strictly convex and does not attain its infimum; consequently the solutions of logistic regression are in general off at infinity. This work provides a convergence analysis of stochastic and batch gradient descent for logistic regression. Firstly, under the assumption of separability, stochastic gradient descent minimizes the population risk at rate O(ln(t)/t) with high probability. Secondly, with or without separability, batch gradient descent minimizes the empirical risk at rate O(ln(t)/t). Furthermore, parameter convergence can be characterized along a unique pair of complementary subspaces defined by the problem instance: one subspace along which strong convexity induces parameters to converge at rate O(ln(t)/ √ t), and its orthogonal complement along which separability induces parameters to converge in direction at rate O(ln ln(t)/ ln(t)). 1 Overview Logistic regression is the task of finding a vector w ∈ R which approximately minimizes the population or empirical logistic risk, namely Rlog(w) := E [ `log(〈w,−yx〉) ] or R̂log(w) := 1 n n ∑ i=1 `log(〈w,−yixi〉), where `log(r) := ln ( 1 + exp(r) ) is the logistic loss. A traditional way to minimize Rlog or R̂log is to pick an arbitrary w0, and from there recursively construct stochastic gradient descent (SGD) or gradient descent (GD) iterates (wj)j≥0 via wj+1 := wj − ηjgj , where (ηj)j≥0 are step sizes, and gj := ` ′ log (〈 wj ,−yjxj 〉) (−yjxj) for SGD or gj := ∇R̂`log(wj) for GD. A common assumption is that the distribution on (x, y) is separable (Novikoff, 1962; Soudry et al., 2017). Under this assumption, the first result here is that SGD with constant step sizes minimizes the population risk Rlog at rate Õ(1/t) with high probability. Theorem 1.1 (Simplification of Theorem 2.1). Suppose there exists a unit vector ū with 〈ū, yx〉 ≥ γ, and |yx| ≤ 1 almost surely. Consider SGD iterates (wj)j≥0 as above, with w0 = 0 and ηj = 1. Then for any t

Question: What is the rate of convergence of SGD?


Learning Over Long Time Lags. The advantage of recurrent neural networks (RNNs) in learni ng dependencies between time-series data has distinguished RNNs from other deep learning models. Recent ly, many advances are proposed in this emerging field. However, there is a lack of comprehensive review on mem ory models in RNNs in the literature. This paper provides a fundamental review on RNNs and long short te rm memory (LSTM) model. Then, provides a surveys of recent advances in different memory enhancement s and learning techniques for capturing long term dependencies in RNNs.

Question: What is the difference between recurrent neural networks and long short term memory?


Taming Google-Scale Continuous Testing. Growth in Google's code size and feature churn rate has seen increased reliance on continuous integration (CI) and testing to maintain quality. Even with enormous resources dedicated to testing, we are unable to regression test each code change individually, resulting in increased lag time between code check-ins and test result feedback to developers. We report results of a project that aims to reduce this time by: (1) controlling test workload without compromising quality, and (2) distilling test results data to inform developers, while they write code, of the impact of their latest changes on quality. We model, empirically understand, and leverage the correlations that exist between our code, test cases, developers, programming languages, and code-change and test-execution frequencies, to improve our CI and development processes. Our findings show: very few of our tests ever fail, but those that do are generally "closer" to the code they test, certain frequently modified code and certain users/tools cause more breakages, and code recently modified by multiple developers (more than 3) breaks more often.

Question: How do we tame google-scale continuous testing?


Unique Signatures of Histograms for Local Surface Description. This paper deals with local 3D descriptors for surface matching. First, we categorize existing methods into two classes: Signatures and Histograms. Then, by discussion and experiments alike, we point out the key issues of uniqueness and repeatability of the local reference frame. Based on these observations, we formulate a novel comprehensive proposal for surface representation, which encompasses a new unique and repeatable local reference frame as well as a new 3D descriptor. The latter lays at the intersection between Signatures and Histograms, so as to possibly achieve a better balance between descriptiveness and robustness. Experiments on publicly available datasets as well as on range scans obtained with Spacetime Stereo provide a thorough validation of our proposal.

Question: What is a local reference frame?


Test anxiety and direction of attention.. The literature reviewed suggests an attentional interpretation, of the adverse effects which test anxiety has on task performance. During task performance the highly test-anxious person divides his attention between self-relevant and task-relevant variables, in contrast to the low-test-anxious person who focuses his attention more fully on the task. This interpretation was supported by literature from diverse areas suggesting that (a) highly anxious persons are generally more self-preoccupied than are people low in anxiety; (b) the selffocusing tendencies of highly test-anxious persons are activated in testing situations; (c) those situational conditions in which the greatest performance differences occur are ones which elicit the self-focusing tendencies of highly test-anxious subjects, and the task-focusing tendencies of low-anxious subjects; (d) research examining the relationship between anxiety and task variables suggests that anxiety reduces the range of task cues utilized in performance; (e) "worry," an attentionally demanding cognitive activity, is more debilitating of task performance than is autonomic arousal. Treatment and research implications of this attentional interpretation of test anxiety are briefly discussed.

Question: What is test anxiety?


Zero-Current Switching Switched-Capacitor Zero-Voltage-Gap Automatic Equalization System for Series Battery String. The series battery string or supercapacitor string automatic equalization system based on quasi-resonant switched-capacitor converter is presented in this paper. It realizes the zero-voltage gap between cells and allows maximum energy recovery in a series battery system or supercapacitor system. It not only inherits the advantage of conventional switched-capacitor battery cell balancing system, but also overcomes the drawback of conduction loss, switching loss, and finite voltage difference among battery cells. All switches are MOSFET and controlled by just a pair of complementary signals in synchronous trigger pattern and the resonant tanks operate alternatively between the two states of charging and discharging. Zero-current switching and zero-voltage gap are achieved in this paper. Different resonant tank designs can meet the needs of different balancing time to meet the needs of different energy storage devices. Experimental results indicate that the efficiency of the system is high exceeding 98%. The system is very suitable for balancing used in battery management system.

Question: What is the difference between a supercapacitor and a battery?


Digitalism: The New Realism?. Today's society is increasingly digitalised, with mobile smartphones being routinely carried and used by a significant percentage of the population. This provides an augmented experience for the individual that does not depend on their geographical separation with respect to their community of friends and other contacts. This changes the nature of relationships between people. Individuals may live in a "digital bubble", close to others physically, but far away from them in their digital world. More specifically, digital images can be generated and shared with ever greater ease. Sometimes the digital image takes on an important part of the individual's experience of reality. This paper explores examples of the phenomenon, within the context of the arts in particular and culture in general. We also consider the assortment of terms used in a variety of ways by researchers in different fields with regard to our ever more digital society, such as digitalism, digitality, digitalisation, digital culture, digital philosophy, etc. We survey these terms, exploring them from alternative viewpoints, including sociological and philosophical aspects, and attempt to pinpoint some of these terms more precisely, especially in a cultural and artistic context.

Question: What is the new realism?


A Supervised Named-Entity Extraction System for Medical Text. TOKEN LEMMA POS Chunk Capitalized Token Class Dependency Sem Group Doc Type Section CUI TUI SemGroup UMLS Categ LEXIQUE SG LEXIQUE CUI DISO TOKEN ANAT TOKEN Wiki Gold label Nails nail NNS B-NP Capitalized Word ROOT DIS heent B-C1 B-T023 B-ANAT B DISO B-ANAT B-C0027342 1 1 – O : : : O – Punct P ANAT DIS heent – – – O O O 0 0 – O No no DT B-NP Capitalized Word NMOD #NA DIS heent – – – O O O 0 0 – O bed bed NN I-NP – Word NMOD #NA DIS heent – – – O O O 1 1 – B-DISORDER abnormalities abnormality NNS I-NP – Word NMOD DISO DIS heent – – – O O O 1 0 – I-DISORDER , , , O – Punct P #NA DIS heent – – – O O O 0 0 – O lunulas lunula NNS B-NP – Word AMOD #NA DIS heent – – – O O O 0 0 – O present present JJ B-ADJP – Word APPO #NA DIS heent – – – O O O 1 1 – O , , , O – Punct P ANAT DIS heent – – – O O O 0 0 – O no no DT B-NP – Word NMOD DISO DIS heent – – – O O O 0 0 – O splinters splinter NNS I-NP – Word ROOT DIS heent B-C2 C3 B-T037 B-DISO O O O 1 0 B-DISO B-DISORDER , , , I-NP – Punct P DISO DIS heent – – – O O O 0 0 – O pulses pulse NNS I-NP – Word COORD DISO DIS heent – – – O B-PHYS B-C0391850 0 0 – O I Lexical and morphological features (e.g., capitalization, lemma)

Question: What is the difference between a splinter and a pulse?


Language Identification in the Limit. Language learnabi l i ty has been inves t igated . This refers to the following s i tua t ion: A class of possible languages is specified, t oge the r wi th a method of present ing informat ion to the learner about an unknown language, which is to be chosen from the class. The quest ion is now asked, " I s the informat ion sufficient to determine which of the possible languages is the unknown language?" M a n y definitions of l ea rnab i l i ty are possible, bu t only the following is considered here: Time is quant ized and has a finite s t a r t ing time. At each t ime the learner receives a un i t of in format ion and is to make a guess as to the i den t i t y of the unknown language on the basis of the informat ion received so far. This process continues forever. The class of languages will be considered lea~nabIe with respect to the specified me thod of in format ion p resen ta t ion if there is an a lgor i thm t h a t the learner can use to make his guesses, the a lgor i thm hav ing the following p roper ty : Given any language of the class, there is some finite t ime af te r which the guesses will all be the same and they will be correct . In this pre l iminary invest igat ion, a language is t aken to be a set of s tr ings on some finite a lphabet . The a lphabe t is the same for all languages of tile class. Several var ia t ions of each of the following two basic methods of informat ion presenta t ion are inves t iga ted: A text for a language generates the s t r ings of the language in any order such t h a t every s t r ing of the language occurs a t least once. An informant for a language tells whe the r a s t r ing is in the language, and chooses the s t r ings in some order such t h a t every s t r ing occurs at least once. I t was found t h a t the class of context-sensi t ive languages is learnable from an informant, but that not even the class of regular languages is learnable from a text.

Question: What is the difference between a text and an informant?


Cedalion: a language for language oriented programming. Language Oriented Programming (LOP) is a paradigm that puts domain specific programming languages (DSLs) at the center of the software development process. Currently, there are three main approaches to LOP: (1) the use of internal DSLs, implemented as libraries in a given host language; (2) the use of external DSLs, implemented as interpreters or compilers in an external language; and (3) the use of language workbenches, which are integrated development environments (IDEs) for defining and using external DSLs. In this paper, we contribute: (4) a novel language-oriented approach to LOP for defining and using internal DSLs. While language workbenches adapt internal DSL features to overcome some of the limitations of external DSLs, our approach adapts language workbench features to overcome some of the limitations of internal DSLs. We introduce Cedalion, an LOP host language for internal DSLs, featuring static validation and projectional editing. To validate our approach we present a case study in which Cedalion was used by biologists in designing a DNA microarray for molecular Biology research.

Question: What is language oriented programming?


Crowdsourcing for on-street smart parking. Crowdsourcing has inspired a variety of novel mobile applications. However, identifying common practices across different applications is still challenging. In this paper, we use smart parking as a case study to investigate features of crowdsourcing that may apply to other mobile applications. Based on this we derive principles for efficiently harnessing crowdsourcing. We draw three key guidelines: First, we suggest that that the organizer can play an important role in coordinating participants', a key factor to successful crowdsourcing experience. Second, we suggest that the expected participation rate is a key factor when designing the crowdsourcing system: a system with a lower expected participation rate will place a higher burden in individual participants (e.g., through more complex interfaces that aim to improve the accuracy of the collected data). Finally, we suggest that not only above certain threshold of contributors, a crowdsourcing-based system is resilient to freeriding but, surprisingly, that including freeriders (i.e., actors that do not participate in system effort but share its benefits in terms of coordination) benefits the entire system.

Question: What is crowdsourcing?


The SWAL-QOL Outcomes Tool for Oropharyngeal Dysphagiain Adults: II. Item Reduction and Preliminary Scaling. The SWAL-QOL outcomes tool was constructed for use in clinical research for patients with oropharyngeal dysphagia. The SWAL-QOL was constructed a priori to enable preliminary psychometric analyses of items and scales before its final validation. This article describes data analysis from a pretest of the SWAL-QOL. We evaluated the different domains of the SWAL-QOL for respondent burden, data quality, item variability, item convergent validity, internal consistency reliability as measured by Cronbach's alpha, and range and skewness of scale scores upon aggregation and floor and ceiling effects. The item reduction techniques outlined reduced the SWAL-QOL from 185 to 93 items. The pretest of the SWAL-QOL afforded us the opportunity to select items for the ongoing validation study which optimally met our a priori psychometric criteria of high data quality, normal item distributions, and robust evidence of item convergent validity.

Question: What is the SWAL-QOL?


DoS & DDoS in Named Data Networking. With the growing realization that current Internet protocols are reaching the limits of their senescence, several ongoing research efforts aim to design potential next-generation Internet architectures. Although they vary in maturity and scope, in order to avoid past pitfalls, these efforts seek to treat security and privacy as fundamental requirements. Resilience to Denialof-Service (DoS) attacks that plague today's Internet is a major issue for any new architecture and deserves full attention. In this paper, we focus on DoS in Named Data Networking (NDN) – a specific candidate for next-generation Internet architecture designs. By naming data instead of its locations, NDN transforms data into a first-class entity and makes itself an attractive and viable approach to meet the needs for many current and emerging applications. It also incorporates some basic security features that mitigate classes of attacks that are commonly seen today. However, NDN's resilience to DoS attacks has not been analyzed to-date. This paper represents a first step towards assessment and possible mitigation of DoS in NDN. After identifying and analyzing several new types of attacks, it investigates their variations, effects and counter-measures. This paper also sheds some light on the debate about relative virtues of self-certifying, as opposed to human-readable, names in the context of content-centric networking.

Question: What is the difference between a DoS attack and a DDoS attack?


Menu-Match: Restaurant-Specific Food Logging from Images. Logging food and calorie intake has been shown to facilitate weight management. Unfortunately, current food logging methods are time-consuming and cumbersome, which limits their effectiveness. To address this limitation, we present an automated computer vision system for logging food and calorie intake using images. We focus on the "restaurant" scenario, which is often a challenging aspect of diet management. We introduce a key insight that addresses this problem specifically: restaurant plates are often both nutritionally and visually consistent across many servings. This insight provides a path to robust calorie estimation from a single RGB photograph: using a database of known food items together with restaurant-specific classifiers, calorie estimation can be achieved through identification followed by calorie lookup. As demonstrated on a challenging Menu-Match dataset and an existing third party dataset, our approach outperforms previous computer vision methods and a commercial calorie estimation app. Our Menu-Match dataset of realistic restaurant meals is made publicly available.

Question: How to estimate calorie intake from a single image?


Life Cycle of Smart Contracts in Blockchain Ecosystems. This paper discusses the life cycle of decentralized smart contracts, i.e. digital and executable representations of rights and obligations in a multi-party environment. The life cycle relies on blockchain technology, i.e. a distributed digital ledger, to ensure proper implementation and integrity of the smart contracts. The life cycle consists of four subsequent phases: Creation, freezing, execution, and finalization. For each phase actors and technological services are identified and explained in detail. With the life cycle at hand, risks and limitations of smart contracts and the underlying blockchain technology are briefly discussed.

Question: What is the life cycle of smart contracts?


Automated Transplantation and Differential Testing for Clones. Code clones are common in software. When applying similar edits to clones, developers often find it difficult to examine the runtime behavior of clones. The problem is exacerbated when some clones are tested, while their counterparts are not. To reuse tests for similar but not identical clones, Grafter transplants one clone to its counterpart by (1) identifying variations in identifier names, types, and method call targets, (2) resolving compilation errors caused by such variations through code transformation, and (3) inserting stub code to transfer input data and intermediate output values for examination. To help developers examine behavioral differences between clones, Grafter supports fine-grained differential testing at both the test outcome level and the intermediate program state level. In our evaluation on three open source projects, Grafter successfully reuses tests in 94% of clone pairs without inducing build errors, demonstrating its automated code transplantation capability. To examine the robustness of G RAFTER, we systematically inject faults using a mutation testing tool, Major, and detect behavioral differences induced by seeded faults. Compared with a static cloning bug finder, Grafter detects 31% more mutants using the test-level comparison and almost 2X more using the state-level comparison. This result indicates that Grafter should effectively complement static cloning bug finders.

Question: How does Grafter compare to static cloning bug finders?


An Introduction to Variational Methods for Graphical Models. This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models (Bayesian networks and Markov random fields). We present a number of examples of graphical models, including the QMR-DT database, the sigmoid belief network, the Boltzmann machine, and several variants of hidden Markov models, in which it is infeasible to run exact inference algorithms. We then introduce variational methods, which exploit laws of large numbers to transform the original graphical model into a simplified graphical model in which inference is efficient. Inference in the simpified model provides bounds on probabilities of interest in the original model. We describe a general framework for generating variational transformations based on convex duality. Finally we return to the examples and demonstrate how variational algorithms can be formulated in each case.

Question: What is a graphical model?


Data Change Exploration Using Time Series Clustering. Analysis of static data is one of the best studied research areas. However, data changes over time. These changes may reveal patterns or groups of similar values, properties, and entities. We study changes in large, publicly available data repositories by modelling them as time series and clustering these series by their similarity. In order to perform change exploration on real-world data we use the publicly available revision data of Wikipedia Infoboxes and weekly snapshots of IMDB. The changes to the data are captured as events, which we call change records. In order to extract temporal behavior we count changes in time periods and propose a general transformation framework that aggregates groups of changes to numerical time series of different resolutions. We use these time series to study different application scenarios of unsupervised clustering. Our explorative results show that changes made to collaboratively edited data sources can help find characteristic behavior, distinguish entities or properties and provide insight into the respective domains.

Question: How to extract temporal behavior from data?


Truncating Temporal Differences: On the Efficient Implementation of TD(lambda) for Reinforcement Learning. Temporal diierence (TD) methods constitute a class of methods for learning predictions in multi-step prediction problems, parameterized by a recency factor. Currently the most important application of these methods is to temporal credit assignment in reinforcement learning. Well known reinforcement learning algorithms, such as AHC or Q-learning, may be viewed as instances of TD learning. This paper examines the issues of the eecient and general implementation of TD() for arbitrary , for use with reinforcement learning algorithms optimizing the discounted sum of rewards. The traditional approach, based on eligibility traces, is argued to suuer from both ineeciency and lack of generality. The TTD (Truncated Temporal Diierences) procedure is proposed as an alternative, that indeed only approximates TD(), but requires very little computation per action and can be used with arbitrary function representation methods. The idea from which it is derived is fairly simple and not new, but probably unexplored so far. Encouraging experimental results are presented, suggesting that using > 0 with the TTD procedure allows one to obtain a signiicant learning speedup at essentially the same cost as usual TD(0) learning.

Question: What is the discount factor?


Diffusion-on-Manifold Aggregation of Local Features for Shape-based 3D Model Retrieval. Aggregating a set of local features has become one of the most common approaches for representing a multi-media data such as 2D image and 3D model. The success of Bag-of-Features (BF) aggregation [2] prompted several extensions to BF, that are, VLAD [12], Fisher Vector (FV) coding [22] and Super Vector (SV) coding [34]. They all learn small number of codewords, or representative local features, by clustering a set of large number of local features. The set of local features extracted from a media data (e.g., an image) is encoded by considering distribution of features around the codewords; BF uses frequency, VLAD and FV uses displacement vector, and SV uses a combination of both. In doing so, these encoding algorithms assume linearity of feature space about a codeword. Consequently, even if the set of features form a non-linear manifold, its non-linearity would be ignored, potentially degrading quality of aggregated features. In this paper, we propose a novel feature aggregation algorithm called Diffusion-on-Manifold (DM) that tries to take into account, via diffusion distance, structure of non-linear manifold formed by the set of local features. In view of 3D shape retrieval, we also propose a local 3D shape feature defined for oriented point set. Experiments using shape-based 3D model retrieval scenario show that the DM aggregation results in better retrieval accuracy than the existing aggregation algorithms we've compared against, that are, VLAD, FV, and SV, etc..

Question: What is the difference between diffusion distance and Euclidean distance?


Personality Neuroscience and the Biology of Traits. Personality neuroscience involves the use of neuroscience methods to study individual differences in behavior, motivation, emotion, and cognition. Personality psychology has contributed much to identifying the important dimensions of personality, but relatively little to understanding the biological sources of those dimensions. However, the rapidly expanding field of personality neuroscience is increasingly shedding light on this topic. This article provides a survey of progress in the use of neuroscience to study personality traits, organized using a hierarchical model of traits based on the Big Five dimensions: Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness ⁄ Intellect. Evidence is reviewed for hypotheses about the biological systems involved in each trait. The mission of personality psychology is ''to provide an integrative framework for understanding the whole person'' (McAdams & Pals, 2006; p. 204), and many different methods must be brought to bear to accomplish such an ambitious goal. The field of personality neuroscience rests on the premise that the whole person cannot be understood without understanding the brain. In this article, I discuss the role that neuroscience can play in personality research and review the progress of this rapidly expanding field. (For more in depth review of the field and its influential theories see DeYoung & Gray, 2009; and Zuckerman, 2005.) Personality psychology's attempt to understand the whole person calls for a broad conception of personality itself, such as the one provided by McAdams and Pals (2006, p. 212): Personality is an individual's unique variation on the general evolutionary design for human nature, expressed as a developing pattern of dispositional traits, characteristic adaptations, and integrative life stories, complexly and differentially situated in culture. This definition describes three levels at which personality can be analyze

Question: What is the role of neuroscience in personality research?


Weld : A Common Runtime for High Performance Data Analytics. Modern analytics applications combine multiple functions from different libraries and frameworks to build increasingly complex workflows. Even though each function may achieve high performance in isolation, the performance of the combined workflow is often an order of magnitude below hardware limits due to extensive data movement across the functions. To address this problem, we propose Weld, a runtime for data-intensive applications that optimizes across disjoint libraries and functions. Weld uses a common intermediate representation to capture the structure of diverse dataparallel workloads, including SQL, machine learning and graph analytics. It then performs key data movement optimizations and generates efficient parallel code for the whole workflow. Weld can be integrated incrementally into existing frameworks like TensorFlow, Apache Spark, NumPy and Pandas without changing their user-facing APIs. We show that Weld can speed up these frameworks, as well as applications that combine them, by up to 30×.

Question: What is a common intermediate representation?


MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets. A recent technical breakthrough in the domain of machine learning is the discovery and the multiple applications of Generative Adversarial Networks (GANs). Those generative models are computationally demanding, as a GAN is composed of two deep neural networks, and because it trains on large datasets. A GAN is generally trained on a single server. In this paper, we address the problem of distributing GANs so that they are able to train over datasets that are spread on multiple workers. MD-GAN is exposed as the first solution for this problem: we propose a novel learning procedure for GANs so that they fit this distributed setup. We then compare the performance of MD-GAN to an adapted version of Federated Learning to GANs, using the MNIST and CIFAR10 datasets. MD-GAN exhibits a reduction by a factor of two of the learning complexity on each worker node, while providing better performances than federated learning on both datasets. We finally discuss the practical implications of distributing GANs.

Question: How to train a GAN on multiple workers?


Joint Optimization of Service Function Chaining and Resource Allocation in Network Function Virtualization. Network function virtualization (NFV) has already been a new paradigm for network architectures. By migrating NFs from dedicated hardware to virtualization platform, NFV can effectively improve the flexibility to deploy and manage service function chains (SFCs). However, resource allocation for requested SFC in NFV-based infrastructures is not trivial as it mainly consists of three phases: virtual network functions (VNFs) chain composition, VNFs forwarding graph embedding, and VNFs scheduling. The decision of these three phases can be mutually dependent, which also makes it a tough task. Therefore, a coordinated approach is studied in this paper to jointly optimize NFV resource allocation in these three phases. We apply a general cost model to consider both network costs and service performance. The coordinate NFV-RA is formulated as a mixed-integer linear programming, and a heuristic-based algorithm (JoraNFV) is proposed to get the near optimal solution. To make the coordinated NFV-RA more tractable, JoraNFV is divided into two sub-algorithms, one-hop optimal traffic scheduling and a multi-path greedy algorithm for VNF chain composition and VNF forwarding graph embedding. Last, extensive simulations are performed to evaluate the performance of JoraNFV, and results have shown that JoraNFV can get a solution within 1.25 times of the optimal solution with reasonable execution time, which indicates that JoraNFV can be used for online NFV planning.

Question: What is the difference between a virtual network function and a virtual machine?


Learning from Simulated and Unsupervised Images through Adversarial Training. With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations. However, learning from synthetic images may not achieve the desired performance due to a gap between synthetic and real image distributions. To reduce this gap, we propose Simulated+Unsupervised (S+U) learning, where the task is to learn a model to improve the realism of a simulators output using unlabeled real data, while preserving the annotation information from the simulator. We develop a method for S+U learning that uses an adversarial network similar to Generative Adversarial Networks (GANs), but with synthetic images as inputs instead of random vectors. We make several key modifications to the standard GAN algorithm to preserve annotations, avoid artifacts, and stabilize training: (i) a self-regularization term, (ii) a local adversarial loss, and (iii) updating the discriminator using a history of refined images. We show that this enables generation of highly realistic images, which we demonstrate both qualitatively and with a user study. We quantitatively evaluate the generated images by training models for gaze estimation and hand pose estimation. We show a significant improvement over using synthetic images, and achieve state-of-the-art results on the MPIIGaze dataset without any labeled real data.

Question: How to generate realistic images?


The B2C group-buying model on the Internet. Internet-based group-buying has become a new growth point for China's e-commerce. By studying B2C group-buying model, this paper has collected data of 20 major Chinese group-buying websites, studied factors that might influence Internet users' group-buying behavior, proposed a new model for evaluating the value of Internet enterprises, and provided a feasible reference model for the evaluation and performance of Internet enterprises.

Question: What is the B2C group-buying model?


A planar probe double ladder waveguide power divider. The successful demonstration of a 1:4 power divider using microstrip probes and a WR-430 rectangular waveguide is presented. The 15-dB return loss bandwidth of the nonoptimized structure is demonstrated to be 22% and its 0.5-dB insertion loss bandwidth 26%. While realized through conventional machining, such a structure is assembled in a fashion consistent with proven millimeter and submillimeter-wave micromachining techniques. Thus, the structure presents a potential power dividing and power combining architecture, which through micromachining, may be used for applications well above 100GHz.

Question: What is a planar probe double ladder waveguide power divider?


Multi-Modal Fusion for Moment in Time Video Classification. Action recognition in videos remains a challenging problem in the machine learning community. Particularly challenging is the differing degree of intra-class variation between actions: While background information is enough to distinguish certain classes, many others are abstract and require fine-grained knowledge for discrimination. To approach this problem, in this work we evaluate different modalities on the recently published Moments in Time dataset, a collection of one million videos of short length.

Question: What is the Moments in Time dataset?


Deep learning and process understanding for data-driven Earth system science. Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning. Complex Earth system challenges can be addressed by incorporating spatial and temporal context into machine learning, especially via deep learning, and further by combining with physical models into hybrid models.

Question: What is deep learning?


Improving Machine Learning Approaches to Coreference Resolution. Wepresentanounphrasecoreferencesystem that extends the work of Soon et al. (2001) and, to our knowledge, producesthebestresultsto dateontheMUC6 andMUC-7 coreferenceresolutiondata sets— F-measuresof 70.4 and63.4, respecti vely. Improvementsarisefrom two sources: extra-linguistic changesto the learningframework anda large-scaleexpansionof thefeaturesetto includemore sophisticatedlinguisticknowledge.

Question: What is the best result to date?


Learning Progressions: Supporting Instruction and Formative Assessment Margaret Heritage the Council of Chief State School Officers T He Fast Scass ƒ Formative Assessment for Teachers and Learners Learning Progressions: Supporting Instruction and Formative Assessment. By its very nature, learning involves progression. To assist in its emergence, teachers need to understand the pathways along which students are expected to progress. These pathways or progressions ground both instruction and assessment. Yet, despite a plethora of standards and curricula, many teachers are unclear about how learning progresses in specific domains. This is an undesirable situation for teaching and learning, and one that particularly affects teachers' ability to engage in formative assessment.

Question: What is formative assessment?