Formal Specification with the Java Modeling Language. This text is a general, self contained, and tool independent introduction into the Java Modeling Language, JML. It appears in a book about the KeY approach and tool, because JML is the dominating starting point of KeY style Java verification. However, this chapter does not depend on KeY, nor any other specific tool, nor on any specific verification methodology. With this text, the authors aim to provide, for the time being, the definitive, general JML tutorial. Other chapters in this book discuss the particular usage of JML in KeY style verification.1 In this chapter, however, we only refer to KeY in very few places, without relying on it. This introduction is written for all readers with an interest in formal specification of software in general, and anyone who wants to learn about the JML approach to specification in particular. A preliminary version of this chapter appeared as a technical report [Huisman et al., 2014].

Question: What is the KeY approach?


Stereotypes of individuals with learning disabilities: views of college students with and without learning disabilities.. To explore possible reasons for low self-identification rates among undergraduates with learning disabilities (LD), we asked students (38 with LD, 100 without LD) attending two large, public, research-intensive universities to respond to a questionnaire designed to assess stereotypes about individuals with LD and conceptions of ability. Responses were coded into six categories of stereotypes about LD (low intelligence, compensation possible, process deficit, nonspecific insurmountable condition, working the system, and other), and into three categories of conceptions of intelligence (entity, incremental, neither). Consistent with past findings, the most frequent metastereotype reported by individuals in both groups related to generally low ability. In addition, students with LD were more likely to espouse views of intelligence as a fixed trait. As a whole, the study's findings have implications for our understanding of factors that influence self-identification and self-advocacy at the postsecondary level.

Question: What are the most common stereotypes of individuals with learning disabilities?


Designed Interdependence, Growing Social Capital and Knowledge Sharing in IS Project Teams. This study brings in the task interdependence as the antecedent for growing team social capital and increasing knowledge sharing among team members in IS project context in which heterogeneous members need to interact very closely. Data collected from 203 IS projects largely confirm the proposed research model. Between the task interdependence and team performance, the team social capital as well as the knowledge sharing among members fully mediates the relationship. Interestingly, direct path from task interdependence onto performance is found to be insignificant, rebutting the suggestions of the work design theory which says well designed interdependence directly increase performance in heterogeneous teams. Findings of our study signify the criticality of the mediating roles of social capital and knowledge sharing. Additionally, it is also confirmed that the social capital grows with time. Also, longer term projects revealed a bit different mechanism than shorter ones. Implications are discussed with limitations and further studies.

Question: How does the task interdependence affect the team performance?


Model-Based Probabilistic Collision Detection in Autonomous Driving. The safety of the planned paths of autonomous cars with respect to the movement of other traffic participants is considered. Therefore, the stochastic occupancy of the road by other vehicles is predicted. The prediction considers uncertainties originating from the measurements and the possible behaviors of other traffic participants. In addition, the interaction of traffic participants, as well as the limitation of driving maneuvers due to the road geometry, is considered. The result of the presented approach is the probability of a crash for a specific trajectory of the autonomous car. The presented approach is efficient as most of the intensive computations are performed offline, which results in a lean online algorithm for real-time application.

Question: How to predict the occupancy of the road by other vehicles?


Side gate HiGT with low dv/dt noise and low loss. This paper presents a novel side gate HiGT (High-conductivity IGBT) that incorporates historical changes of gate structures for planar and trench gate IGBTs. Side gate HiGT has a side-wall gate, and the opposite side of channel region for side-wall gate is covered by a thick oxide layer to reduce Miller capacitance (Cres). In addition, side gate HiGT has no floating p-layer, which causes the excess Vge overshoot. The proposed side gate HiGT has 75% smaller Cres than the conventional trench gate IGBT. The excess Vge overshoot during turn-on is effectively suppressed, and Eon + Err can be reduced by 34% at the same diode's recovery dv/dt. Furthermore, side gate HiGT has sufficiently rugged RBSOA and SCSOA.

Question: What is the difference between a planar and a trench gate IGBT?


Marginalized Denoising Dictionary Learning With Locality Constraint. Learning good representation for images is always a hot topic in machine learning and pattern recognition fields. Among the numerous algorithms, dictionary learning is a well-known strategy for effective feature extraction. Recently, more discriminative sub-dictionaries have been built by Fisher discriminative dictionary learning with specific class labels. Different types of constraints, such as sparsity, low rankness, and locality, are also exploited to make use of global and local information. On the other hand, as the basic building block of deep structure, the auto-encoder has demonstrated its promising performance in extracting new feature representation. To this end, we develop a unified feature learning framework by incorporating the marginalized denoising auto-encoder into a locality-constrained dictionary learning scheme, named marginalized denoising dictionary learning. Overall, we deploy low-rank constraint on each sub-dictionary and locality constraint instead of sparsity on coefficients, in order to learn a more concise and pure feature spaces meanwhile inheriting the discrimination from sub-dictionary learning. Finally, we evaluate our algorithm on several face and object data sets. Experimental results have demonstrated the effectiveness and efficiency of our proposed algorithm by comparing with several state-of-the-art methods.

Question: What is the difference between Fisher discriminative dictionary learning and marginalized denoising dictionary learning?


Insights into Human Behavior from Lesions to the Prefrontal Cortex. The prefrontal cortex (PFC), a cortical region that was once thought to be functionally insignificant, is now known to play an essential role in the organization and control of goal-directed thought and behavior. Neuroimaging, neurophysiological, and modeling techniques have led to tremendous advances in our understanding of PFC functions over the last few decades. It should be noted, however, that neurological, neuropathological, and neuropsychological studies have contributed some of the most essential, historical, and often prescient conclusions regarding the functions of this region. Importantly, examination of patients with brain damage allows one to draw conclusions about whether a brain area is necessary for a particular function. Here, we provide a broad overview of PFC functions based on behavioral and neural changes resulting from damage to PFC in both human patients and nonhuman primates.

Question: What is the function of the prefrontal cortex?


Bayesian Rose Trees. Hierarchical structure is ubiquitous in data across many domains. There are many hierarchical clustering methods, frequently used by domain experts, which strive to discover this structure. However, most of these methods limit discoverable hierarchies to those with binary branching structure. This limitation, while computationally convenient, is often undesirable. In this paper we explore a Bayesian hierarchical clustering algorithm that can produce trees with arbitrary branching structure at each node, known as rose trees. We interpret these trees as mixtures over partitions of a data set, and use a computationally efficient, greedy agglomerative algorithm to find the rose trees which have high marginal likelihood given the data. Lastly, we perform experiments which demonstrate that rose trees are better models of data than the typical binary trees returned by other hierarchical clustering algorithms.

Question: What is a rose tree?


Passivity-Based Controller Design of Grid-Connected VSCs for Prevention of Electrical Resonance Instability. The time delay in the current control loop of a grid-connected voltage-source converter (VSC) may cause destabilization of electrical resonances in the grid or in the VSC's input filter. Instability is prevented if the input admittance of the VSC can be made passive. This paper presents an analytical controller design method for obtaining passivity. The method is equally applicable to single- and three-phase systems, i.e., in the latter case, for both stationary- and synchronous-frame control. Simulations and experiments verify the theoretical results.

Question: How to design a passive controller for a grid-connected VSC?


Barycentric Lagrange Interpolation. Barycentric interpolation is a variant of Lagrange polynomial interpolation that is fast and stable. It deserves to be known as the standard method of polynomial interpolation.

Question: What is barycentric interpolation?


Transparent, conductive graphene electrodes for dye-sensitized solar cells.. Transparent, conductive, and ultrathin graphene films, as an alternative to the ubiquitously employed metal oxides window electrodes for solid-state dye-sensitized solar cells, are demonstrated. These graphene films are fabricated from exfoliated graphite oxide, followed by thermal reduction. The obtained films exhibit a high conductivity of 550 S/cm and a transparency of more than 70% over 1000-3000 nm. Furthermore, they show high chemical and thermal stabilities as well as an ultrasmooth surface with tunable wettability.

Question: What is the conductivity of graphene?


The multilayer perceptron as an approximation to a Bayes optimal discriminant function. The multilayer perceptron, when trained as a classifier using backpropagation, is shown to approximate the Bayes optimal discriminant function. The result is demonstrated for both the two-class problem and multiple classes. It is shown that the outputs of the multilayer perceptron approximate the a posteriori probability functions of the classes being trained. The proof applies to any number of layers and any type of unit activation function, linear or nonlinear.

Question: What is a multilayer perceptron?


Narrowband Cooperative Spectrum Sensing in Cognitive Networks. Narrowband Cooperative Spectrum Sensing in Cognitive Networks

Question: What is the narrowband cooperative spectrum sensing in cognitive networks?


The development of a new test of agility for rugby league.. Agility requires change of direction speed (CODS) and also perceptual and decision-making skills and reaction speed. The purpose of this study was to develop a reliable and valid agility test for rugby league, which stressed all those dimensions. Players from a subelite rugby league team were tested twice on a sport-specific reactive agility test (RAT) and CODS test. Data were analyzed for reliability. For validity results from the subelite groups, first test was compared with data from an elite group. The RAT required participants to run toward an unpredictable life-size video of an attacking opponent and react to that video by changing direction. The CODS test required the same movement patterns however direction changes were preplanned. The subelite group's mean time to complete the CODS test and RAT on their first test was 1.67 ± 0.15 and 1.98 ± 0.16 seconds, respectively, and 1.62 ± 0.14 and 1.91 ± 0.17 seconds, respectively, on their second test (results are ± σ). Statistical analyses revealed no significant difference in means (p < 0.05) and good correlation (intraclass correlation coefficient = 0.87 and 0.82, respectively). The elite group's mean time to complete the tests was 1.65 ± 0.09 and 1.79 ±0.12 seconds, respectively. Statistical analyses revealed a significant difference in mean RAT time between the elite group and the subelite group (p < 0.05). The RAT was reliable and valid. Performance differences on the RAT were attributed to differences in perceptual skills and/or reaction ability. Testing and training agility should therefore stress those dimensions of agility and not just CODS.

Question: What is the difference between a subelite and an elite rugby league player?


Alternative Splicing: New Insights from Global Analyses. Recent analyses of sequence and microarray data have suggested that alternative splicing plays a major role in the generation of proteomic and functional diversity in metazoan organisms. Efforts are now being directed at establishing the full repertoire of functionally relevant transcript variants generated by alternative splicing, the specific roles of such variants in normal and disease physiology, and how alternative splicing is coordinated on a global level to achieve cell- and tissue-specific functions. Recent progress in these areas is summarized in this review.

Question: What is the full repertoire of functionally relevant transcript variants generated by alternative splicing?


Make new friends, but keep the old: recommending people on social networking sites. This paper studies people recommendations designed to help users find known, offline contacts and discover new friends on social networking sites. We evaluated four recommender algorithms in an enterprise social networking site using a personalized survey of 500 users and a field study of 3,000 users. We found all algorithms effective in expanding users' friend lists. Algorithms based on social network information were able to produce better-received recommendations and find more known contacts for users, while algorithms using similarity of user-created content were stronger in discovering new friends. We also collected qualitative feedback from our survey users and draw several meaningful design implications.

Question: What is the difference between social networking sites and social media?


Numerical Synthesis of Six-Bar Linkages for Mechanical Computation. This paper presents a design procedure for six-bar linkages that use eight accuracy points to approximate a specified input-output function. In the kinematic synthesis of linkages, this is known as the synthesis of a function generator to perform mechanical computation. Our formulation uses isotropic coordinates to define the loop equations of the Watt II, Stephenson II, and Stephenson III six-bar linkages. The result is 22 polynomial equations in 22 unknowns that are solved using the polynomial homotopy software Bertini. The bilinear structure of the system yields a polynomial degree of 705,432. Our first run of Bertini generated 92,736 nonsingular solutions, which were used as the basis of a parameter homotopy solution. The algorithm was tested on the design of the Watt II logarithmic function generator patented by Svoboda in 1944. Our algorithm yielded his linkage and 64 others, in 129 minutes of parallel computation on a Mac Pro with 12⇥2.93 GHz processors. Three additional examples are provided as well.

Question: What is the purpose of the paper?


Constructing a Hermitian Matrix from Its Diagonal Entries and Eigenvalues. Given two vectors a R the Schur Horn theorem states that a majorizes if and only if there exists a Hermitian matrix H with eigenvalues and diagonal entries a While the theory is regarded as classical by now the known proof is not constructive To construct a Hermitian matrix from its diagonal entries and eigenvalues therefore becomes an interesting and challenging inverse eigenvalue problem Two algorithms for determining the matrix numerically are proposed in this paper The lift and pro jection method is an iterative method which involves an interesting application of the Wielandt Ho man theorem The projected gradient method is a continuous method which besides its easy implementation o ers a new proof of existence because of its global convergence property

Question: How to construct a hermitian matrix from its diagonal entries and eigenvalues?


A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images. BACKGROUND AND OBJECTIVES Highly accurate classification of biomedical images is an essential task in the clinical diagnosis of numerous medical diseases identified from those images. Traditional image classification methods combined with hand-crafted image feature descriptors and various classifiers are not able to effectively improve the accuracy rate and meet the high requirements of classification of biomedical images. The same also holds true for artificial neural network models directly trained with limited biomedical images used as training data or directly used as a black box to extract the deep features based on another distant dataset. In this study, we propose a highly reliable and accurate end-to-end classifier for all kinds of biomedical images via deep learning and transfer learning.   METHODS We first apply domain transferred deep convolutional neural network for building a deep model; and then develop an overall deep learning architecture based on the raw pixels of original biomedical images using supervised training. In our model, we do not need the manual design of the feature space, seek an effective feature vector classifier or segment specific detection object and image patches, which are the main technological difficulties in the adoption of traditional image classification methods. Moreover, we do not need to be concerned with whether there are large training sets of annotated biomedical images, affordable parallel computing resources featuring GPUs or long times to wait for training a perfect deep model, which are the main problems to train deep neural networks for biomedical image classification as observed in recent works.   RESULTS With the utilization of a simple data augmentation method and fast convergence speed, our algorithm can achieve the best accuracy rate and outstanding classification ability for biomedical images. We

Question: What is the best accuracy rate for biomedical images?


User practice in password security: An empirical study of real-life passwords in the wild. Due to increasing security awareness of password from the public and little attention on the characteristics of real-life passwords, it is thus natural to understand the current state of characteristics of real-life passwords, and to explore how password characteristics change over time and how earlier password practice is understood in current context. In this work, we attempt to present an in-depth and comprehensive understanding of user practice in real-life passwords, and to see whether the previous observations can be confirmed or reversed, based on large-scale measurements rather than anecdotal experiences or user surveys. Specifically, we measure password characteristics on over 6 million passwords, in terms of password length, password composition, and password selection. We then make informed comparisons of the findings between our investigation and previously reported results. Our general findings include: (1) average password length is at least 12% longer than previous results, and 85% of our passwords have the length between 8 and 11 characters;

Question: What is the average length of a password?


Improving the intrinsic calibration of a Velodyne LiDAR sensor. LiDAR (Light detection and ranging) sensors are widely used in research and development. As such, they build the base for the evaluation of newly developed ADAS (Advanced Driver Assistance Systems) functions in the automotive field where they are used for ground truth establishment. However, the factory calibration provided for the sensors is not able to satisfy the high accuracy requirements by such applications. In this paper we propose a concept to easily improve the existing calibration of a Velodyne LiDAR sensor without the need for special calibration setups which can even be used to enhance already recorded data.

Question: How to improve the intrinsic calibration of a Velodyne LiDAR sensor?


A portable powered ankle-foot orthosis for rehabilitation.. Innovative technological advancements in the field of orthotics, such as portable powered orthotic systems, could create new treatment modalities to improve the functional out come of rehabilitation. In this article, we present a novel portable powered ankle-foot orthosis (PPAFO) to provide untethered assistance during gait. The PPAFO provides both plantar flexor and dorsiflexor torque assistance by way of a bidirectional pneumatic rotary actuator. The system uses a portable pneumatic power source (compressed carbon dioxide bottle) and embedded electronics to control the actuation of the foot. We collected pilot experimental data from one impaired and three nondisabled subjects to demonstrate design functionality. The impaired subject had bilateral impairment of the lower legs due to cauda equina syndrome. We found that data from nondisabled walkers demonstrated the PPAFO's capability to provide correctly timed plantar flexor and dorsiflexor assistance during gait. Reduced activation of the tibialis anterior during stance and swing was also seen during assisted nondisabled walking trials. An increase in the vertical ground reaction force during the second half of stance was present during assisted trials for the impaired subject. Data from nondisabled walkers demonstrated functionality, and data from an impaired walker demonstrated the ability to provide functional plantar flexor assistance.

Question: What is the difference between a portable powered ankle-foot orthosis and a powered ankle-foot orthosis?


Segmenting Two-Sided Markets. Recent years have witnessed the rise of many successful e-commerce marketplace platforms like the Amazon marketplace, AirBnB, Uber/Lyft, and Upwork, where a central platform mediates economic transactions between buyers and sellers. A common feature of many of these two-sided marketplaces is that the platform has full control over search and discovery, but prices are determined by the buyers and sellers. Motivated by this, we study the algorithmic aspects of market segmentation via directed discovery in two-sided markets with endogenous prices. We consider a model where an online platform knows each buyer/seller's characteristics, and associated demand/supply elasticities. Moreover, the platform can use discovery mechanisms (search, recommendation, etc.) to control which buyers/sellers are visible to each other. We develop efficient algorithms for throughput (i.e. volume of trade) and welfare maximization with provable guarantees under a variety of assumptions on the demand and supply functions. We also test the validity of our assumptions on demand curves inferred from NYC taxicab log-data, as well as show the performance of our algorithms on synthetic experiments.

Question: What is the difference between a two-sided market and a one-sided market?


KidCAD: digitally remixing toys through tangible tools. Children have great facility in the physical world, and can skillfully model in clay and draw expressive illustrations. Traditional digital modeling tools have focused on mouse, keyboard and stylus input. These tools may be complicated and difficult for young users to easily and quickly create exciting designs. We seek to bring physical interaction to digital modeling, to allow users to use existing physical objects as tangible building blocks for new designs. We introduce KidCAD a digital clay interface for children to remix toys. KidCAD allows children to imprint 2.5D shapes from physical objects into their digital models by deforming a malleable gel input device, deForm. Users can mashup existing objects, edit and sculpt or draw new designs on a 2.5D canvas using physical objects, hands and tools as well as 2D touch gestures. We report on a preliminary user study with 13 children, ages 7 to 10, which provides feedback for our design and helps guide future work in tangible modeling for children.

Question: What is KidCAD?


A multi-structural framework for adaptive supply chain planning and operations control with structure dynamics considerations. A trend in up-to-date developments in supply chain management (SCM) is to make supply chains more agile, flexible, and responsive. In supply chains, different structures (functional, organizational, informational, financial etc.) are (re)formed. These structures interrelate with each other and change in dynamics. The paper introduces a new conceptual framework for multi-structural planning and operations of adaptive supply chains with structure dynamics considerations. We elaborate a vision of adaptive supply chain management (A-SCM), a new dynamic model and tools for the planning and control of adaptive supply chains. SCM is addressed from perspectives of execution dynamics under uncertainty. Supply chains are modelled in terms of dynamic multi-structural macro-states, based on simultaneous consideration of the management as a function of both states and structures. The research approach is theoretically based on the combined application of control theory, operations research, and agent-based modelling. The findings suggest constructive ways to implement multi-structural supply chain management and to transit from a " one-way " partial optimization to the feedback-based, closed-loop adaptive supply chain optimization and execution management for value chain adaptability, stability and crisis-resistance. The proposed methodology enhances managerial insight into advanced supply chain management.

Question: What is a multi-structural framework for adaptive supply chain planning and operations control with structure dynamics considerations?


Advanced Beamformers for Cochlear Implant Users: Acute Measurement of Speech Perception in Challenging Listening Conditions. OBJECTIVE To investigate the performance of monaural and binaural beamforming technology with an additional noise reduction algorithm, in cochlear implant recipients.   METHOD This experimental study was conducted as a single subject repeated measures design within a large German cochlear implant centre. Twelve experienced users of an Advanced Bionics HiRes90K or CII implant with a Harmony speech processor were enrolled. The cochlear implant processor of each subject was connected to one of two bilaterally placed state-of-the-art hearing aids (Phonak Ambra) providing three alternative directional processing options: an omnidirectional setting, an adaptive monaural beamformer, and a binaural beamformer. A further noise reduction algorithm (ClearVoice) was applied to the signal on the cochlear implant processor itself. The speech signal was presented from 0° and speech shaped noise presented from loudspeakers placed at ±70°, ±135° and 180°. The Oldenburg sentence test was used to determine the signal-to-noise ratio at which subjects scored 50% correct.   RESULTS Both the adaptive and binaural beamformer were significantly better than the omnidirectional condition (5.3 dB±1.2 dB and 7.1 dB±1.6 dB (p<0.001) respectively). The best score was achieved with the binaural beamformer in combination with the ClearVoice noise reduction algorithm, with a significant improvement in SRT of 7.9 dB±2.4 dB (p<0.001) over the omnidirectional alone condition.   CONCLUSIONS The study showed that the binaural beamformer implemented in the Phonak Ambra hearing aid could be used in conjunction with a Harmony speech processor to produce substantial average improvements in SRT of 7.1 dB. The monaural, adaptive beamformer provided an averaged SRT improvement of 5.3 dB.

Question: What is the difference between a monaural and a binaural beamformer?


Seasonal Effect on Tree Species Classification in an Urban Environment Using Hyperspectral Data, LiDAR, and an Object-Oriented Approach. The objective of the current study was to analyze the seasonal effect on differentiating tree species in an urban environment using multi-temporal hyperspectral data, Light Detection And Ranging (LiDAR) data, and a tree species database collected from the field. Two Airborne Imaging Spectrometer for Applications (AISA) hyperspectral images were collected, covering the Summer and Fall seasons. In order to make both datasets spatially and spectrally compatible, several preprocessing steps, including band reduction and a spatial degradation, were performed. An object-oriented classification was performed on both images using training data collected randomly from the tree species database. The seven dominant tree species (Gleditsia triacanthos, Acer saccharum, Tilia Americana, Quercus palustris, Pinus strobus and Picea glauca) were used in the classification. The results from this analysis did not show any major difference in overall accuracy between the two seasons. Overall accuracy was approximately 57% for the Summer dataset and 56% for the Fall dataset. However, the Fall dataset provided more consistent results for all tree species while the Summer dataset had a few higher individual class accuracies. Further, adding LiDAR into the classification improved the results by 19% for both fall and summer. This is mainly due to the removal of shadow effect and the addition of elevation data to separate low and high vegetation.

Question: What is the difference between the summer and fall datasets?


Autonomous robotic system for bridge deck data collection and analysis. Bridge deck inspection is conducted to identify bridge condition deterioration and, thus, to facilitate implementation of appropriate maintenance or rehabilitation procedures. In this paper, we report the development of a robotic system for bridge deck data collection and analysis. The robotic system accurately localizes itself and autonomously maneuvers on the bridge deck to collect visual images and conduct nondestructive evaluation (NDE) measurements. The developed robotic system can reduce the cost and time of the bridge deck data collection. Crack detection and mapping algorithm to build the deck crack maps is presented in detail. The electrical resistivity (ER), impact-echo (IE) and ultrasonic surface waves (USW) data collected by the robot are analyzed to generate the corrosion, delamination and concrete elastic modulus maps of the deck. The presented robotic system has been successfully deployed to inspect numerous bridges.

Question: What is the cost of the robotic system?


Automated concolic testing of smartphone apps. We present an algorithm and a system for generating input events to exercise smartphone apps. Our approach is based on concolic testing and generates sequences of events automatically and systematically. It alleviates the path-explosion problem by checking a condition on program executions that identifies subsumption between different event sequences. We also describe our implementation of the approach for Android, the most popular smartphone app platform, and the results of an evaluation that demonstrates its effectiveness on five Android apps.

Question: How to test smartphone apps?


A Robust Single-Phase PLL System With Stable and Fast Tracking. Phase, frequency, and amplitude of single-phase voltages are the most important and basic information required for single-phase grid-connected applications. This paper proposes a method for instantly and robustly estimating the phase, frequency, and amplitude of frequency-varying single-phase signals for such applications, which is a phase-locked loop (PLL) method based on a structure. The proposed method has the following attractive features: 1) the estimation system results in a nonlinear system, but it can be stabilized; 2) all subsystems constructing the system can be easily designed; 3) "two-phase signal generator"; can autotune a single system parameter in response to varying frequency of the injected signal; 4) high-order "PLL-Controllers" allowing fast tracking can be stably used; and 5) even in hostile envelopments, favorable instant estimates can be obtained. This paper proposes the estimation method and verifies its usefulness by extensive numerical experiments.

Question: What is a robust single-phase PLL system?


An O(sqrt(|v|) |E|) Algorithm for Finding Maximum Matching in General Graphs.

Question: What is the complexity of the algorithm?


Inferring international and internal migration patterns from Twitter data. Data about migration flows are largely inconsistent across countries, typically outdated, and often inexistent. Despite the importance of migration as a driver of demographic change, there is limited availability of migration statistics. Generally, researchers rely on census data to indirectly estimate flows. However, little can be inferred for specific years between censuses and for recent trends. The increasing availability of geolocated data from online sources has opened up new opportunities to track recent trends in migration patterns and to improve our understanding of the relationships between internal and international migration. In this paper, we use geolocated data for about 500,000 users of the social network website "Twitter". The data are for users in OECD countries during the period May 2011- April 2013. We evaluated, for the subsample of users who have posted geolocated tweets regularly, the geographic movements within and between countries for independent periods of four months, respectively. Since Twitter users are not representative of the OECD population, we cannot infer migration rates at a single point in time. However, we proposed a difference-in-differences approach to reduce selection bias when we infer trends in out-migration rates for single countries. Our results indicate that our approach is relevant to address two longstanding questions in the migration literature. First, our methods can be used to predict turning points in migration trends, which are particularly relevant for migration forecasting. Second, geolocated Twitter data can substantially improve our understanding of the relationships between internal and international migration. Our analysis relies uniquely on publicly available data that could be potentially available in real time and that could be used to monitor migration trends. The Web Science community is well-positioned to address, in future work

Question: What is the difference between internal and international migration?


Learning with Local and Global Consistency. We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problems and demonstrates effective use of unlabeled data.

Question: What is the difference between semi-supervised learning and transductive inference?


Branch Prediction Strategies and Branch Target Buffer Design.

Question: What is branch prediction?


Virtual garments : A Fully Geometric Approach for Clothing Design. Modeling dressed characters is known as a very tedious process. It usually requires specifying 2D fabric patterns, positioning and assembling them in 3D, and then performing a physically-based simulation. The latter accounts for gravity and collisions to compute the rest shape of the garment, with the adequate folds and wrinkles. This paper presents a more intuitive way to design virtual clothing. We start with a 2D sketching system in which the user draws the contours and seam-lines of the garment directly on a virtual mannequin. Our system then converts the sketch into an initial 3D surface using an existing method based on a precomputed distance field around the mannequin. The system then splits the created surface into different panels delimited by the seam-lines. The generated panels are typically not developable. However, the panels of a realistic garment must be developable, since each panel must unfold into a 2D sewing pattern. Therefore our system automatically approximates each panel with a developable surface, while keeping them assembled along the seams. This process allows us to output the corresponding sewing patterns. The last step of our method computes a natural rest shape for the 3D garment, including the folds due to the collisions with the body and gravity. The folds are generated using procedural modeling of the buckling phenomena observed in real fabric. The result of our algorithm consists of a realistic looking 3D mannequin dressed in the designed garment and the 2D patterns which can be used for distortion free texture mapping. As we demonstrate, the patterns we create allow us to sew real replicas of the virtual garments.

Question: How to design virtual clothing?


Diagnosis, prevention, and treatment of catheter-associated urinary tract infection in adults: 2009 International Clinical Practice Guidelines from the Infectious Diseases Society of America.. Guidelines for the diagnosis, prevention, and management of persons with catheter-associated urinary tract infection (CA-UTI), both symptomatic and asymptomatic, were prepared by an Expert Panel of the Infectious Diseases Society of America. The evidence-based guidelines encompass diagnostic criteria, strategies to reduce the risk of CA-UTIs, strategies that have not been found to reduce the incidence of urinary infections, and management strategies for patients with catheter-associated asymptomatic bacteriuria or symptomatic urinary tract infection. These guidelines are intended for use by physicians in all medical specialties who perform direct patient care, with an emphasis on the care of patients in hospitals and long-term care facilities.

Question: What is the difference between a catheter and a urinary tract infection?


A Customer Loyalty Model for E-Service Context. While the importance of customer loyalty has been recognized in the marketing literature for at least three decades, the conceptualization and empirical validation of a customer loyalty model for e-service context has not been addressed. This paper describes a theoretical model for investigating the three main antecedent influences on loyalty (attitudinal commitment and behavioral loyalty) for e-service context: trust, customer satisfaction, and perceived value. Based on the theoretical model, a comprehensive set of hypotheses were formulated and a methodology for testing them was outlined. These hypotheses were tested empirically to demonstrate the applicability of the theoretical model. The results indicate that trust, customer satisfaction, perceived value, and commitment are separate constructs that combine to determine the loyalty, with commitment exerting a stronger influence than trust, customer satisfaction, and perceived value. Customer satisfaction and perceived value were also indirectly related to loyalty through commitment. Finally, the authors discuss the managerial and theoretical implications of these results.

Question: What is a customer loyalty model for e-service context?


The Digital Sin City: An Empirical Study of Craigslist's Impact on Prostitution Trends. Online prostitution has flourished due to presence of online intermediaries. Despite the illegality of selling sexual services online, the Section 230 of Communications Decency Act shields websites from liability for unlawful postings by third parties. Consequently, the websites like Craigslist have become a haven for prostitution-related ads. In this paper, we quantify the economic impact of Craigslist's entry on prostitution incidence, and identify potential pathways in which the website affects the sex industry. Using a national panel data for 1,796 U.S. counties from 1999 to 2008, our results suggest that entry of Craigslist to a county is related to 26.61 percent increase in prostitution cases. In addition, our analyses investigates four key market mechanisms, namely the relationship to commercial vice, geographical trends, spillover effects, and enhanced policing efficacy. Our results contributes broadly to the emerging literature that researches the societal challenges associated with online intermediaries and Internet penetration.

Question: What is the economic impact of Craigslist on prostitution?


Ant supervised by PSO and 2-Opt algorithm, AS-PSO-2Opt, applied to Traveling Salesman Problem. AS-PSO-2Opt is a new enhancement of the AS-PSO method. In the classical AS-PSO, the Ant heuristic is used to optimize the tour length of a Traveling Salesman Problem, TSP, and PSO is applied to optimize three parameters of ACO, (α, β, ρ). The AS-PSO-2Opt consider a post processing resuming path redundancy, helping to improve local solutions and to decrease the probability of falling in local minimum. Applied to TSP, the method allowed retrieving a valuable path solution and a set of fitted parameters for ACO. The performance of the AS-PSO-2Opt is tested on nine different TSP test benches. Experimental results based on a statistical analysis showed that the new proposal performs better than key state of art methods using Genetic algorithm, Neural Network and ACO algorithm. The AS-PSO-2Opt performs better than close related methods such as PSO-ACO-3Opt [9] and ACO with ABC [19] for various test benches.

Question: What is the difference between AS-PSO-2Opt and AS-PSO?


Trust in leadership: meta-analytic findings and implications for research and practice.. In this study, the authors examined the findings and implications of the research on trust in leadership that has been conducted during the past 4 decades. First, the study provides estimates of the primary relationships between trust in leadership and key outcomes, antecedents, and correlates (k = 106). Second, the study explores how specifying the construct with alternative leadership referents (direct leaders vs. organizational leadership) and definitions (types of trust) results in systematically different relationships between trust in leadership and outcomes and antecedents. Direct leaders (e.g., supervisors) appear to be a particularly important referent of trust. Last, a theoretical framework is offered to provide parsimony to the expansive literature and to clarify the different perspectives on the construct of trust in leadership and its operation.

Question: What is the difference between trust in leadership and trust in organization?


Software Cost Estimation Meets Software Diversity. The previous goal of having a one-size-fits-all software cost (and schedule) estimation model is no longer achievable. Sources of wide variation in the nature of software development and evolution processes, products, properties, and personnel (PPPPs) require a variety of estimation models and methods best fitting their situations. This Technical Briefing will provide a short history of pattern-breaking changes in software estimation methods, a summary of the sources of variation in software PPPPs and their estimation implications, a summary of the types of estimation methods being widely used or emerging, a summary of the best estimation-types for the various PPPP-types, and a process for guiding an organization's choices of estimation methods as their PPPP-types evolve.

Question: What is the history of software cost estimation?


Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding. One of the key problems in spoken language understanding (SLU) is the task of slot filling. In light of the recent success of applying deep neural network technologies in domain detection and intent identification, we carried out an in-depth investigation on the use of recurrent neural networks for the more difficult task of slot filling involving sequence discrimination. In this work, we implemented and compared several important recurrent-neural-network architectures, including the Elman-type and Jordan-type recurrent networks and their variants. To make the results easy to reproduce and compare, we implemented these networks on the common Theano neural network toolkit, and evaluated them on the ATIS benchmark. We also compared our results to a conditional random fields (CRF) baseline. Our results show that on this task, both types of recurrent networks outperform the CRF baseline substantially, and a bi-directional Jordantype network that takes into account both past and future dependencies among slots works best, outperforming a CRFbased baseline by 14% in relative error reduction.

Question: What is the difference between a recurrent neural network and a conditional random fields?


Mapping the U.S. Political Blogosphere: Are Conservative Bloggers More Prominent?. Weblogs are now a key part of online culture, and social scientists are interested in characterising the networks formed by bloggers and measuring their extent and impact in areas such as politics. However, researchers wishing to conduct quantitative social science analysis of the blogging phenomenon are faced with the challenge of using new methods of data collection and analysis largely derived from fields outside of the social sciences, such as the information sciences. This paper presents an overview of one new approach for collecting and analysing weblog data, and illustrates this approach in the context of a preliminary quantitative analysis of online networks formed by a sample of North-American "A-list" political bloggers. There are two aims to this paper. First is to assess (using different data and methods) the conclusion of Adamic and Glance (2005) that there are significant differences in the behaviour of liberal and conservative bloggers, with the latter forming more dense patterns of linkages. We find broad support for this conclusion, and empirically assess the implications of differences in conservative/liberal linking behaviour for the online visibility of different political messages or ideologies. The second aim is to highlight the role of web mining and data visualisation in the analysis of weblogs, and the opportunities and challenges inherent in this new field of research.

Question: What is the difference between liberal and conservative bloggers?


Exploring Deep Learning Methods for Discovering Features in Speech Signals. Exploring Deep Learning Methods for discovering features in speech signals. Navdeep Jaitly Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2014 This thesis makes three main contributions to the area of speech recognition with Deep Neural Network Hidden Markov Models (DNN-HMMs). Firstly, we explore the effectiveness of features learnt from speech databases using Deep Learning for speech recognition. This contrasts with prior works that have largely confined themselves to using traditional features such as Mel Cepstral Coefficients and Mel log filter banks for speech recognition. We start by showing that features learnt on raw signals using Gaussian-ReLU Restricted Boltzmann Machines can achieve accuracy close to that achieved with the best traditional features. These features are, however, learnt using a generative model that ignores domain knowledge. We develop methods to discover features that are endowed with meaningful semantics that are relevant to the domain using capsules. To this end, we extend previous work on transforming autoencoders and propose a new autoencoder with a domain-specific decoder to learn capsules from speech databases. We show that capsule instantiation parameters can be combined with Mel log filter banks to produce improvements in phone recognition on TIMIT. On WSJ the word error rate does not improve, even though we get strong gains in classification accuracy. We speculate this may be because of the mismatched objectives of word error rate over an utterance and frame error rate on the sub-phonetic class for a frame. Secondly, we develop a method for data augmentation in speech datasets. Such methods result in strong gains in object recognition, but have largely been ignored in speech recognition. Our data augmentation encourages the learning of invariance to vocal tract length of speakers. The method is shown to improve the phone

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


Web Content Analysis: Expanding the Paradigm. Are established methods of content analysis (CA) adequate to analyze web content, or should new methods be devised to address new technological developments? This chapter addresses this question by contrasting narrow and broad interpretations of the concept of web content analysis. The utility of a broad interpretation that subsumes the narrow one is then illustrated with reference to research on weblogs (blogs), a popular web format in which features of HTML documents and interactive computer-mediated communication converge. The chapter concludes by proposing an expanded Web Content Analysis (WebCA) paradigm in which insights from paradigms such as discourse analysis and social network analysis are operationalized and implemented within a general content analytic framework.

Question: What is a weblog?


Absorptive Frequency-Selective Transmission Structure With Square-Loop Hybrid Resonator. A novel design of an absorptive frequency-selective transmission structure (AFST) is proposed. This structure is based on the design of a frequency-dependent lossy layer with square-loop hybrid resonator (SLHR). The parallel resonance provided by the hybrid resonator is utilized to bypass the lossy path and improve the insertion loss. Meanwhile, the series resonance of the hybrid resonator is used for expanding the upper absorption bandwidth. Furthermore, the absorption for out-of-band frequencies is achieved by using four metallic strips with lumped resistors, which are connected with the SLHR. The quantity of lumped elements required in a unit cell can be reduced by at least 50% compared to previous structures. The design guidelines are explained with the aid of an equivalent circuit model. Both simulation and experiment results are presented to demonstrate the performance of our AFST. It is shown that an insertion loss of 0.29 dB at 6.1 GHz and a 112.4% 10 dB reflection reduction bandwidth are obtained under the normal incidence.

Question: What is the absorption bandwidth of the absorptive frequency-selective transmission structure?


A study of attention-based neural machine translation model on Indian languages. Neural machine translation (NMT) models have recently been shown to be very successful in machine translation (MT). The use of LSTMs in machine translation has significantly improved the translation performance for longer sentences by being able to capture the context and long range correlations of the sentences in their hidden layers. The attention model based NMT system has become state-of-the-art, performing equal or better than other statistical MT approaches. In this paper, we studied the performance of the attention-model based NMT system on the Indian language pair, Hindi and Bengali. We analysed the types of errors that occur in morphologically rich languages when there is a scarcity of large parallel training corpus. We then carried out certain post-processing heuristic steps to improve the quality of the translated statements and suggest further measures.

Question: What is the attention model based neural machine translation system?


Separate face and body selectivity on the fusiform gyrus.. Recent reports of a high response to bodies in the fusiform face area (FFA) challenge the idea that the FFA is exclusively selective for face stimuli. We examined this claim by conducting a functional magnetic resonance imaging experiment at both standard (3.125 x 3.125 x 4.0 mm) and high resolution (1.4 x 1.4 x 2.0 mm). In both experiments, regions of interest (ROIs) were defined using data from blocked localizer runs. Within each ROI, we measured the mean peak response to a variety of stimulus types in independent data from a subsequent event-related experiment. Our localizer scans identified a fusiform body area (FBA), a body-selective region reported recently by Peelen and Downing (2005) that is anatomically distinct from the extrastriate body area. The FBA overlapped with and was adjacent to the FFA in all but two participants. Selectivity of the FFA to faces and FBA to bodies was stronger for the high-resolution scans, as expected from the reduction in partial volume effects. When new ROIs were constructed for the high-resolution experiment by omitting the voxels showing overlapping selectivity for both bodies and faces in the localizer scans, the resulting FFA* ROI showed no response above control objects for body stimuli, and the FBA* ROI showed no response above control objects for face stimuli. These results demonstrate strong selectivities in distinct but adjacent regions in the fusiform gyrus for only faces in one region (the FFA*) and only bodies in the other (the FBA*).

Question: What is the fusiform body area?


A route planning method based on improved artificial potential field algorithm. The route planning method based on artificial potential field is an important branch and the hot research focus of assignment planning. The traditional artificial potential field method is easy to fall into the local minimum, and the paper analyzed the causes, and then did some research on the improved artificial potential field method, and then proposed a method modifying the repulsion direction based on the improved artificial potential field to solve the three typical local minimum problem. The simulation results show that the proposed method is effective.

Question: What is the improved artificial potential field method?


Anticipating Visual Representations from Unlabeled Video. Anticipating actions and objects before they start or appear is a difficult problem in computer vision with several real-world applications. This task is challenging partly because it requires leveraging extensive knowledge of the world that is difficult to write down. We believe that a promising resource for efficiently learning this knowledge is through readily available unlabeled video. We present a framework that capitalizes on temporal structure in unlabeled video to learn to anticipate human actions and objects. The key idea behind our approach is that we can train deep networks to predict the visual representation of images in the future. Visual representations are a promising prediction target because they encode images at a higher semantic level than pixels yet are automatic to compute. We then apply recognition algorithms on our predicted representation to anticipate objects and actions. We experimentally validate this idea on two datasets, anticipating actions one second in the future and objects five seconds in the future.

Question: How can we anticipate visual representations from unlabeled video?


A Survey of Game Theory as Applied to Network Security. Network security is a complex and challenging problem. The area of network defense mechanism design is receiving immense attention from the research community for more than two decades. However, the network security problem is far from completely solved. Researchers have been exploring the applicability of game theoretic approaches to address the network security issues and some of these approaches look promising. This paper surveys the existing game theoretic solutions which are designed to enhance network security and presents a taxonomy for classifying the proposed solutions. This taxonomy should provide the reader with a better understanding of game theoretic solutions to a variety of cyber security problems.

Question: What is a taxonomy for classifying game theoretic solutions?


A Two-Stage Iterative Decoding of LDPC Codes for Lowering Error Floors. In iterative decoding of LDPC codes, trapping sets often lead to high error floors. In this work, we propose a two-stage iterative decoding to break trapping sets. Simulation results show that the error floor performance can be significantly improved with this decoding scheme.

Question: How to break trapping sets?


Additive Logistic Regression : a Statistical. Boosting (Freund & Schapire 1996, Schapire & Singer 1998) is one of the most important recent developments in classiication methodology. The performance of many classiication algorithms can often be dramatically improved by sequentially applying them to reweighted versions of the input data, and taking a weighted majority vote of the sequence of classiiers thereby produced. We show that this seemingly mysterious phenomenon can be understood in terms of well known statistical principles, namely additive modeling and maximum likelihood. For the two-class problem, boosting can be viewed as an approximation to additive modeling on the logistic scale using maximum Bernoulli likelihood as a criterion. We develop more direct approximations and show that they exhibit nearly identical results to boosting. Direct multi-class generalizations based on multinomial likelihood are derived that exhibit performance comparable to other recently proposed multi-class generalizations of boosting in most situations, and far superior in some. We suggest a minor modiication to boosting that can reduce computation, often by factors of 10 to 50. Finally, we apply these insights to produce an alternative formulation of boosting decision trees. This approach, based on best-rst truncated tree induction , often leads to better performance, and can provide interpretable descriptions of the aggregate decision rule. It is also much faster com-putationally making it more suitable to large scale data mining applications .

Question: What is the difference between additive logistic regression and boosting?


What happens when HTTP adaptive streaming players compete for bandwidth?. With an increasing demand for high-quality video content over the Internet, it is becoming more likely that two or more adaptive streaming players share the same network bottleneck and compete for available bandwidth. This competition can lead to three performance problems: player instability, unfairness between players, and bandwidth underutilization. However, the dynamics of such competition and the root cause for the previous three problems are not yet well understood. In this paper, we focus on the problem of competing video players and describe how the typical behavior of an adaptive streaming player in its Steady-State, which includes periods of activity followed by periods of inactivity (ON-OFF periods), is the main root cause behind the problems listed above. We use two adaptive players to experimentally showcase these issues. Then, focusing on the issue of player instability, we test how several factors (the ON-OFF durations, the available bandwidth and its relation to available bitrates, and the number of competing players) affect stability.

Question: What is the root cause of the problems listed above?


Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces. Large multi-label datasets contain labels that occur thousands of times (frequent group), those that occur only a few times (few-shot group), and labels that never appear in the training dataset (zero-shot group). Multi-label few- and zero-shot label prediction is mostly unexplored on datasets with large label spaces, especially for text classification. In this paper, we perform a fine-grained evaluation to understand how state-of-the-art methods perform on infrequent labels. Furthermore, we develop few- and zero-shot methods for multi-label text classification when there is a known structure over the label space, and evaluate them on two publicly available medical text datasets: MIMIC II and MIMIC III. For few-shot labels we achieve improvements of 6.2% and 4.8% in R@10 for MIMIC II and MIMIC III, respectively, over prior efforts; the corresponding R@10 improvements for zero-shot labels are 17.3% and 19%.

Question: How do we evaluate multi-label text classification?


Design of a triangular fractal patch antenna with slit for IRNSS and GAGAN applications. In this paper a triangular fractal patch antenna with slit is designed for IRNSS and GAGAN applications using ADS software. India is intended to develop a satellite based navigation systems known as Indian Regional Navigational Satellite System (IRNSS) for positioning applications. Design of IRNSS antenna at user sector is indispensable. GPS Aided and Geo Augmented Navigation (GAGAN), a satellite based augmentation system for India, erect over the GPS system is anticipated to provide the flawless navigation support over the Asia-Pacific regions. The desired antenna has been deliberate on dielectric constant εr = 4.8 and substrate thickness h = 3.05 mm. The feed location of the antenna has been selected to produce the circular polarization. The self-similar property in antenna exhibits multi-band resonant frequencies. These specifications should be satisfied at the frequency L5 (1175 MHz), L1 (1575.42 MHz) and S (2492.08 MHz).

Question: What is the frequency of the IRNSS satellite?


Using Linear Algebra for Intelligent Information Retrieval. Currently most approaches to retrieving textual materials from scienti c databases depend on a lexical match between words in users requests and those in or assigned to documents in a database Because of the tremendous diversity in the words people use to describe the same document lexical methods are necessarily incomplete and imprecise Using the singular value decomposition SVD one can take advantage of the implicit higher order structure in the association of terms with documents by determining the SVD of large sparse term by document matrices Terms and documents represented by of the largest singular vectors are then matched against user queries We call this retrieval method Latent Semantic Indexing LSI because the subspace represents important associative relationships between terms and documents that are not evident in individual documents LSI is a completely automatic yet intelligent indexing method widely applicable and a promising way to improve users access to many kinds of textual materials or to documents and services for which textual descriptions are available A survey of the computational requirements for managing LSI encoded databases as well as current and future applications of LSI is presented

Question: What is the computational requirements for managing LSI encoded databases?


Practical Stabilization of a Skid-steering Mobile Robot - A Kinematic-based Approach. This paper presents kinematic control problem of skid-steering mobile robot using practical smooth and time-varying stabilizer. The stability result is proved using Lyapunov analysis and takes into account both input signal saturation and uncertainty of kinematics. In order to ensure stable motion of the robot the condition of permissible velocities is formulated according to dynamic model and wheel-surface interaction. Theoretical considerations are illustrated by simulation results

Question: How to stabilize a skid-steering mobile robot?


DesnowNet: Context-Aware Deep Network for Snow Removal. Existing learning-based atmospheric particle-removal approaches such as those used for rainy and hazy images are designed with strong assumptions regarding spatial frequency, trajectory, and translucency. However, the removal of snow particles is more complicated because they possess additional attributes of particle size and shape, and these attributes may vary within a single image. Currently, hand-crafted features are still the mainstream for snow removal, making significant generalization difficult to achieve. In response, we have designed a multistage network named DesnowNet to in turn deal with the removal of translucent and opaque snow particles. We also differentiate snow attributes of translucency and chromatic aberration for accurate estimation. Moreover, our approach individually estimates residual complements of the snow-free images to recover details obscured by opaque snow. Additionally, a multi-scale design is utilized throughout the entire network to model the diversity of snow. As demonstrated in the qualitative and quantitative experiments, our approach outperforms state-of-the-art learning-based atmospheric phenomena removal methods and one semantic segmentation baseline on the proposed Snow100K dataset. The results indicate our network would benefit applications involving computer vision and graphics.

Question: What is the size of the canadian military?


Automated personalized feedback for physical activity and dietary behavior change with mobile phones: a randomized controlled trial on adults.. BACKGROUND A dramatic rise in health-tracking apps for mobile phones has occurred recently. Rich user interfaces make manual logging of users' behaviors easier and more pleasant, and sensors make tracking effortless. To date, however, feedback technologies have been limited to providing overall statistics, attractive visualization of tracked data, or simple tailoring based on age, gender, and overall calorie or activity information. There are a lack of systems that can perform automated translation of behavioral data into specific actionable suggestions that promote healthier lifestyle without any human involvement.   OBJECTIVE MyBehavior, a mobile phone app, was designed to process tracked physical activity and eating behavior data in order to provide personalized, actionable, low-effort suggestions that are contextualized to the user's environment and previous behavior. This study investigated the technical feasibility of implementing an automated feedback system, the impact of the suggestions on user physical activity and eating behavior, and user perceptions of the automatically generated suggestions.   METHODS MyBehavior was designed to (1) use a combination of automatic and manual logging to track physical activity (eg, walking, running, gym), user location, and food, (2) automatically analyze activity and food logs to identify frequent and nonfrequent behaviors, and (3) use a standard machine-learning, decision-making algorithm, called multi-armed bandit (MAB), to generate personalized suggestions that ask users to either continue, avoid, or make small changes to existing behaviors to help users reach behavioral goals. We enrolled 17 participants, all motivated to self-monitor and improve their fitness, in a pilot study of MyBehavior. In a randomized two-group trial, investigators randomly assigned participants to rec

Question: How do you measure the success of a mobile app?


Improved Forms for Controlling the Acrobot with Motors of Atypical Size Using Artificial Intelligence Techniques. An acrobot is a planar robot with a passive actuator in its first joint. The control problem of the acrobot tries to make it rise from the rest position to the inverted pendulum position. This control problem can be divided in the swing-up problem, when the robot has to rise itself through swinging up as a human acrobat does, and the balancing problem, when the robot has to maintain itself on the inverted pendulum position. We have developed three controllers for the swing-up problem applied to two types of motors: small and big. For small motors, we used the SARSA controller and the PD with a trajectory generator. For big motors, we propose a new controller to control the acrobot, a PWM controller. All controllers except SARSA are tuned using a Genetic Algorithm.

Question: What is the difference between a SARSA controller and a PD controller?


A Customizable Matrix Multiplication Framework for the Intel HARPv2 Xeon+FPGA Platform: A Deep Learning Case Study. General Matrix to Matrix multiplication (GEMM) is the cornerstone for a wide gamut of applications in high performance computing (HPC), scientific computing (SC) and more recently, deep learning. In this work, we present a customizable matrix multiplication framework for the Intel HARPv2 CPU+FPGA platform that includes support for both traditional single precision floating point and reduced precision workloads. Our framework supports arbitrary size GEMMs and consists of two parts: (1) a simple application programming interface (API) for easy configuration and integration into existing software and (2) a highly customizable hardware template. The API provides both compile and runtime options for controlling key aspects of the hardware template including dynamic precision switching; interleaving and block size control; and fused deep learning specific operations. The framework currently supports single precision floating point (FP32), 16, 8, 4 and 2 bit Integer and Fixed Point (INT16, INT8, INT4, INT2) and more exotic data types for deep learning workloads: INT16xTernary, INT8xTernary, BinaryxBinary.  We compare our implementation to the latest NVIDIA Pascal GPU and evaluate the performance benefits provided by optimizations built into the hardware template. Using three neural networks (AlexNet, VGGNet and ResNet) we illustrate that reduced precision representations such as binary achieve the best performance, and that the HARPv2 enables fine-grained partitioning of computations over both the Xeon and FPGA. We observe up to 50x improvement in execution time compared to single precision floating point, and that runtime configuration options can improve the efficiency of certain layers in AlexNet up to 4x, achieving an overall 1.3x improvement over the entire network.

Question: What is the best way to multiply matrices?


Design for auto-tuning PID controller based on genetic algorithms. This paper presents a method of designing proportional-integral-derivative (PID) controller based on genetic algorithms (GA). Ziegler-Nichols tuning formula has been used for predicting the range of gain coefficients of GA-PID controller. The use of GA for optimizing the gain coefficients of PID controller has considerably improved the performance of PID controller. Simulation studies on a second-order and a third-order control system demonstrate that the proposed controller provides high performance of dynamic and static characteristics.

Question: How to design a PID controller based on genetic algorithms?


Audio-Visual Speech Enhancement based on Multimodal Deep Convolutional Neural Network. Speech enhancement (SE) aims to reduce noise in speech signals. Most SE techniques focus on addressing audio information only. In this work, inspired by multimodal learning, which utilizes data from different modalities, and the recent success of convolutional neural networks (CNNs) in SE, we propose an audio-visual deep CNN (AVDCNN) SE model, which incorporates audio and visual streams into a unified network model. In the proposed AVDCNN SE model, audio and visual features are first processed using individual CNNs, and then, fused into a joint network to generate enhanced speech at an output layer. The AVDCNN model is trained in an end-toend manner, and parameters are jointly learned through backpropagation. We evaluate enhanced speech using five objective criteria. Results show that the AVDCNN yields notably better performance as compared to an audio-only CNN-based SE model, confirming the effectiveness of integrating visual information into the SE process.

Question: How does audio-visual speech enhancement work?


Sensitivity and Generalization in Neural Networks: an Empirical Study. In practice it is often found that large over-parameterized neural networks generalize better than their smaller counterparts, an observation that appears to conflict with classical notions of function complexity, which typically favor smaller models. In this work, we investigate this tension between complexity and generalization through an extensive empirical exploration of two natural metrics of complexity related to sensitivity to input perturbations. Our experiments survey thousands of models with various fully-connected architectures, optimizers, and other hyper-parameters, as well as four different image classification datasets. We find that trained neural networks are more robust to input perturbations in the vicinity of the training data manifold, as measured by the norm of the input-output Jacobian of the network, and that it correlates well with generalization. We further establish that factors associated with poor generalization – such as full-batch training or using random labels – correspond to lower robustness, while factors associated with good generalization – such as data augmentation and ReLU non-linearities – give rise to more robust functions. Finally, we demonstrate how the input-output Jacobian norm can be predictive of generalization at the level of individual test points.

Question: What is the norm of the input-output Jacobian of a neural network?


Least-Squares Fitting of Two 3-D Point Sets. Two point sets {pi} and {p'i}; i = 1, 2,..., N are related by p'i = Rpi + T + Ni, where R is a rotation matrix, T a translation vector, and Ni a noise vector. Given {pi} and {p'i}, we present an algorithm for finding the least-squares solution of R and T, which is based on the singular value decomposition (SVD) of a 3 × 3 matrix. This new algorithm is compared to two earlier algorithms with respect to computer time requirements.

Question: How to find the least-squares solution of a rotation matrix and a translation vector?


Time scales in motor learning and development.. A theoretical framework based on the concepts and tools of nonlinear dynamical systems is advanced to account for both the persistent and transitory changes traditionally shown for the learning and development of motor skills. The multiple time scales of change in task outcome over time are interpreted as originating from the system's trajectory on an evolving attractor landscape. Different bifurcations between attractor organizations and transient phenomena can lead to exponential, power law, or S-shaped learning curves. This unified dynamical account of the functions and time scales in motor learning and development offers several new hypotheses for future research on the nature of change in learning theory.

Question: What is the time scale of motor learning?


Visualizing LSTM decisions. Long Short-Term Memory (LSTM) recurrent neural networks are renowned for being uninterpretable "black boxes". In the medical domain where LSTMs have shown promise, this is specifically concerning because it is imperative to understand the decisions made by machine learning models in such acute situations. This study employs techniques used in the Convolutional Neural Network domain to elucidate the operations that LSTMs perform on time series. The visualization techniques include input saliency by means of occlusion and derivatives, class mode visualization, and temporal outputs. Moreover, we demonstrate that LSTMs appear to extract features similar to those extracted by wavelets. It was found that deriving the inputs for saliency is a poor approximation and occlusion is a better approach. Moreover, analyzing LSTMs on different sets of data provide novel interpretations.

Question: What is the difference between LSTM and CNN?


Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access. We consider the problem of dynamic spectrum access for network utility maximization in multichannel wireless networks. The shared bandwidth is divided into  $K$  orthogonal channels. In the beginning of each time slot, each user selects a channel and transmits a packet with a certain transmission probability. After each time slot, each user that has transmitted a packet receives a local observation indicating whether its packet was successfully delivered or not (i.e., ACK signal). The objective is a multi-user strategy for accessing the spectrum that maximizes a certain network utility in a distributed manner without online coordination or message exchanges between users. Obtaining an optimal solution for the spectrum access problem is computationally expensive, in general, due to the large-state space and partial observability of the states. To tackle this problem, we develop a novel distributed dynamic spectrum access algorithm based on deep multi-user reinforcement leaning. Specifically, at each time slot, each user maps its current state to the spectrum access actions based on a trained deep-Q network used to maximize the objective function. Game theoretic analysis of the system dynamics is developed for establishing design principles for the implementation of the algorithm. The experimental results demonstrate the strong performance of the algorithm.

Question: What is the objective of the algorithm?


Vestibular papillomatosis as a normal vulvar anatomical condition.. At the beginning of the nineteen-eighties, vulvar vestibular papillomatosis (VVP) was thought to be a human papilloma virus (HPV) disease. Based upon these findings many clinicians have been treating this condition with laser ablation or by topical application of podophyllin or trichloroacetic acid. Currently, most authors believe that VVP should be considered an anatomical variant of the vestibular mucosa and not HPV related. We present a case of VVP in which there was no histological or molecular evidence of HPV; unnecessary treatment should be avoided.

Question: What is the difference between vestibular papillomatosis and vestibular papillomatosis?


Teaching and working with robots as a collaboration. New applications for autonomous robots bring them into the human environment where they are to serve as helpful assistants to untrained users in the home or office, or work as capable members of human-robot teams for security, military, and space efforts. These applications require robots to be able to quickly learn how to perform new tasks from natural human instruction, and to perform tasks collaboratively with human teammates. Using joint intention theory as our theoretical framework, our approach integrates learning and collaboration through a goal based task structure. Specifically, we use collaborative discourse with accompanying gestures and social cues to teach a humanoid robot a structurally complex task. Having learned the representation for the task, the robot then performs it shoulder-to-shoulder with a human partner, using social communication acts to dynamically mesh its plans with those of its partner, according to the relative capabilities of the human and the robot.

Question: What is the canadian military?


Stability of Causal Inference. We consider the sensitivity of causal identi cation to small perturbations in the input. A long line of work culminating in papers by Shpitser and Pearl (2006) and Huang and Valtorta (2008) led to a complete procedure for the causal identi cation problem. In our main result in this paper, we show that the identi cation function computed by these procedures is in some cases extremely unstable numerically. Speci cally, the "condition number" of causal identi cation can be of the order of Ω(exp(n0.49)) on an identi able semi-Markovian model with n visible nodes. That is, in order to give an output accurate to d bits, the empirical probabilities of the observable events need to be obtained to accuracy d +Ω(n0.49) bits.

Question: What is the condition number of causal inference?


Linear cross-modal hashing for efficient multimedia search. Most existing cross-modal hashing methods suffer from the scalability issue in the training phase. In this paper, we propose a novel cross-modal hashing approach with a linear time complexity to the training data size, to enable scalable indexing for multimedia search across multiple modals. Taking both the intra-similarity in each modal and the inter-similarity across different modals into consideration, the proposed approach aims at effectively learning hash functions from large-scale training datasets. More specifically, for each modal, we first partition the training data into $k$ clusters and then represent each training data point with its distances to $k$ centroids of the clusters. Interestingly, such a k-dimensional data representation can reduce the time complexity of the training phase from traditional O(n2) or higher to O(n), where $n$ is the training data size, leading to practical learning on large-scale datasets. We further prove that this new representation preserves the intra-similarity in each modal. To preserve the inter-similarity among data points across different modals, we transform the derived data representations into a common binary subspace in which binary codes from all the modals are "consistent" and comparable. nThe transformation simultaneously outputs the hash functions for all modals, which are used to convert unseen data into binary codes. Given a query of one modal, it is first mapped into the binary codes using the modal's hash functions, followed by matching the database binary codes of any other modals. Experimental results on two benchmark datasets confirm the scalability and the effectiveness of the proposed approach in comparison with the state of the art.

Question: How large is the canadian military?


Non-Orthogonal Multiple Access Based Integrated Terrestrial-Satellite Networks. In this paper, we investigate the downlink transmission of a non-orthogonal multiple access (NOMA)-based integrated terrestrial-satellite network, in which the NOMA-based terrestrial networks and the satellite cooperatively provide coverage for ground users while reusing the entire bandwidth. For both terrestrial networks and the satellite network, multi-antennas are equipped and beamforming techniques are utilized to serve multiple users simultaneously. A channel quality-based scheme is proposed to select users for the satellite, and we then formulate the terrestrial user pairing as a max–min problem to maximize the minimum channel correlation between users in one NOMA group. Since the terrestrial networks and the satellite network will cause interference to each other, we first investigate the capacity performance of the terrestrial networks and the satellite networks separately, which can be decomposed into the designing of beamforming vectors and the power allocation schemes. Then, a joint iteration algorithm is proposed to maximize the total system capacity, where we introduce the interference temperature limit for the satellite since the satellite can cause interference to all base station users. Finally, numerical results are provided to evaluate the user paring scheme as well as the total system performance, in comparison with some other proposed algorithms and existing algorithms.

Question: What is the capacity of the satellite network?


A packing algorithm for non-Manhattan hexagon/triangle placement design by using an adaptive O-tree representation. A non-Manhattan Hexagon/Triangle Placement (HTP for short) paradigm is proposed in the present paper. Main feature of this paradigm lies in adapting to the Y- architecture which is one of the promising non-Manhattan VLSI circuit layout architectures. Aim of the HTP is to place a set of equilateral triangles with given size onto a hexagonal chip with maximal chip area usage. Based on the O-tree representation, some adaptive packing rules are adopted to develop an effective placement algorithm for solving the HTP problem in BBL mode. Two examples with benchmark data transformed from the Manhattan BBL mode placement (ami33/49) are presented to justify the feasibility and effectiveness of our algorithms. Experiment results demonstrate that the chip area usage of 94% is achieved through simulated annealing optimization.

Question: What is the size of the chip?


Multilevel Analysis Techniques and Applications. The purpose of this series is to present methodological techniques to investigators and students from all functional areas of business, although individuals from other disciplines will also find the series useful. Each volume in the series will focus on a specific method The goal is to provide an understanding and working knowledge of each method with a minimum of mathematical derivations. Proposals are invited from all interested authors. Each proposal should consist of the following: (i) a brief description of the volume's focus and intended market, (ii) a table of contents with an outline of each chapter, and (iii) a curriculum vita. Materials may be sent to Dr. Contents Preface ix 1. Introduction to multilevel analysis 1 1.1. Why do we need special multilevel analysis techniques? 5 1.2. Multilevel theories 7 1.3. Models described in this book 8

Question: What is a multilevel analysis?


Visual tracking with online Multiple Instance Learning. In this paper, we address the problem of learning an adaptive appearance model for object tracking. In particular, a class of tracking techniques called "tracking by detection" have been shown to give promising results at real-time speeds. These methods train a discriminative classifier in an online manner to separate the object from the background. This classifier bootstraps itself by using the current tracker state to extract positive and negative examples from the current frame. Slight inaccuracies in the tracker can therefore lead to incorrectly labeled training examples, which degrades the classifier and can cause further drift. In this paper we show that using Multiple Instance Learning (MIL) instead of traditional supervised learning avoids these problems, and can therefore lead to a more robust tracker with fewer parameter tweaks. We present a novel online MIL algorithm for object tracking that achieves superior results with real-time performance.

Question: What is the difference between tracking by detection and tracking by detection?


Compressed matching for feature vectors. The problem of compressing a large collection of feature vectors is investigated, so that object identification can be processed on the compressed form of the features. The idea is to perform matching of a query image against an image database, using directly the compressed form of the descriptor vectors, without decompression. Specifically, we concentrate on the Scale Invariant Feature Transform (SIFT), a known object detection method, as well as on Dense SIFT and PHOW features, that contain, for each image, about 300 times as many vectors as the original SIFT. Given two feature vectors, we suggest achieving our goal by compressing them using a lossless encoding by means of a Fibonacci code, for which the pairwise matching can be done directly on the compressed files. In our experiments, this approach improves the processing time and incurs only a small loss in compression efficiency relative to standard compressors requiring a decoding phase.

Question: What is a feature vector?


HF , VHF , and UHF Systems and Technology. A wide variety of unique systems and components inhabits the HF, VHF, and UHF bands. Many communication systems (ionospheric, meteor-burst, and troposcatter) provide beyond-line-of-sight coverage and operate independently of external infrastructure. Broadcasting and over-the-horizon radar also operate in these bands. Magnetic-resonance imaging uses HF/VHF signals to see the interior of a human body, and RF heating is used in a variety of medical and industrial applications. Receivers typically employ a mix of analog and digital-signal-processing techniques. Systems for these frequencies make use of RF-power MOSFETs, p-i-n diodes, and ferrite-loaded transmission-line transformers.

Question: What is the difference between HF, VHF, and UHF?


Emergence of 3D Printed Fashion: Navigating the Ambiguity of Materiality Through Collective Design. The emergence of 3D printing technology is being embraced by an increasing number of fashion designers. Due to the nascent and evolving nature of the technology, however, there is significant ambiguity around this technology's implications for the practices of fashion design. Based on the theoretical perspective of sociomateriality and the concept of translation, and drawing on archives, interviews, and other forms of qualitative data, this study's preliminary findings show that fashion designers navigate this ambiguity by pursuing a collective design process with diverse stakeholders to actively perceive and leverage the affordances and constraints of the technology. The ongoing interaction among a network of heterogeneous actors gives rise to innovative perceptions, practices, and products, which collectively shape the emergence of the field of 3D printed fashion.

Question: How does 3D printing affect fashion design?


3D human face description: landmarks measures and geometrical features. Morphometric measures and geometrical features are widely used to descript faces. Generally, they are extracted punctually from landmarks, namely anthropometric reference points. The aims are various, such as face recognition, facial expression recognition, face detection, study of changes in facial morphology due to growth, or dysmorphologies. Most of the time, landmarks were extracted with the help of an algorithm or manually located on the faces. Then, measures are computed or geometrical features are extracted to perform the scope of the study. This paper is intended as a survey collecting and explaining all these features, in order to provide a structured user database of the potential parameters and their charateristics. Firstly, facial soft-tissue landmarks are defined and contextualized; then the various morphometric measures are introduced and some results are given; lastly, the most important measures are compared to identify the best one for face recognition applications.

Question: What is a facial soft-tissue landmark?


Traffic monitoring and accident detection at intersections. Among the most important research in Intelligent Transportation Systems (ITS) is the development of systems that automatically monitor traffic flow at intersections. Rather than being based on global flow analysis as is currently done, these automatic monitoring systems should be based on local analysis of the behavior of each vehicle at the intersection. The systems should be able to identify each vehicle and track its behavior, and to recognize situations or events that are likely to result from a chain of such behavior. The most difficult problem associated with vehicle tracking is the occlusion effect among vehicles. In order to solve this problem we have developed an algorithm, referred to as spatio-temporal Markov random field (MRF), for traffic images at intersections. This algorithm models a tracking problem by determining the state of each pixel in an image and its transit, and how such states transit along both the – image axes as well as the time axes. Vehicles, of course, are of various shapes and they move in random fashion, thereby leading to full or partial occlusion at intersections. Despite these complications, our algorithm is sufficiently robust to segment and track occluded vehicles at a high success rate of 93%–96%. This success has led to the development of an extendable robust event recognition system based on the hidden Markov model (HMM). The system learns various event behavior patterns of each vehicle in the HMM chains and then, using the output from the tracking system, identifies current event chains. The current system can recognize bumping, passing, and jamming. However, by including other event patterns in the training set, the system can be extended to recognize those other events, e.g., illegal U-turns or reckless driving. We have implemented this system, evaluated it using the tracking results, and demonstrated its effectiveness.

Question: What is the occlusion effect?


Classroom Response Systems: A Review of the Literature.. As the frequency with which Classroom Response Systems (CRSs) are used is increasing, it becomes more and more important to define the affordances and limitations of these tools. Currently existing literature is largely either anecdotal or focuses on comparing CRS and non-CRS environments that are unequal in other aspects as well. In addition, the literature primarily describes situations in which the CRS is used to provide an individual as opposed to a group response. This article points to the need for a concerted research effort, one that rigorously explores conditions of use across diverse settings and pedagogies.

Question: What is the difference between a classroom response system and a classroom response system?


The KeY Platform for Verification and Analysis of Java Programs. The KeY system offers a platform of software analysis tools for sequential Java. Foremost, this includes full functional verification against contracts written in the Java Modeling Language. But the approach is general enough to provide a basis for other methods and purposes: (i) complementary validation techniques to formal verification such as testing and debugging, (ii) methods that reduce the complexity of verification such as modularization and abstract interpretation, (iii) analyses of non-functional properties such as information flow security, and (iv) sound program transformation and code generation. We show that deductive technology that has been developed for full functional verification can be used as a basis and framework for other purposes than pure functional verification. We use the current release of the KeY system as an example to explain and prove this claim.

Question: What is the key platform?


Gamification solutions for software acceptance: A comparative study of Requirements Engineering and Organizational Behavior techniques. Gamification is a powerful paradigm and a set of best practices used to motivate people carrying out a variety of ICT-mediated tasks. Designing gamification solutions and applying them to a given ICT system is a complex and expensive process (in time, competences and money) as software engineers have to cope with heterogeneous stakeholder requirements on one hand, and Acceptance Requirements on the other, that together ensure effective user participation and a high level of system utilization. As such, gamification solutions require significant analysis and design as well as suitable supporting tools and techniques. In this work, we compare concepts, tools and techniques for gamification design drawn from Software Engineering and Human and Organizational Behaviors. We conduct a comparison by applying both techniques to the specific Meeting Scheduling exemplar used extensively in the Requirements Engineering literature.

Question: What is gamification?


On the benefits of explaining herd immunity in vaccine advocacy. Most vaccines protect both the vaccinated individual and the community at large by building up herd immunity. Even though reaching disease-specific herd immunity thresholds is crucial for eliminating or eradicating certain diseases1,2, explanation of this concept remains rare in vaccine advocacy3. An awareness of this social benefit makes vaccination not only an individual but also a social decision. Although knowledge of herd immunity can induce prosocial vaccination in order to protect others, it can also invite free-riding, in which individuals profit from the protection provided by a well-vaccinated society without contributing to herd immunity themselves. This cross-cultural experiment assesses whether people will be more or less likely to be vaccinated when they know more about herd immunity. Results show that in cultures that focus on collective benefits, vaccination willingness is generally higher. Communicating the concept of herd immunity improved willingness to vaccinate, especially in cultures lacking this prosocial cultural background. Prosocial nudges can thus help to close these immunity gaps.

Question: What is the prosocial cultural background?


Image Thresholding for Optical Character Recognition and Other Applications Requiring Character Image Extraction. Two new, cost-effective thresholding algorithms for use in extracting binary images of characters from machineor hand-printed documents are described. The creation of a binary representation from an analog image requires such algorithms to determine whether a point is converted into a binary one because it falls within a character stroke or a binary zero because it does not. This thresholding is a critical step in Optical Character Recognition (OCR). I t is also essential for other Character Image Extraction (CIEJ applications, such as the processing of machine-printed or handwritten characters from carbon copy forms or bank checks, where smudges and scenic backgrounds, for example, may have to be suppressed. The first algorithm, a nonlinear, adaptive procedure, is implemented with a minimum of hardware and is intended for many CIE applications. The second is a more aggressive approach directed toward specialized, high-volume applications which justify extra complexity.

Question: What is the difference between a nonlinear and an adaptive algorithm?


SWEET: Serving the Web by Exploiting Email Tunnels. Open communications over the Internet pose serious threats to countries with repressive regimes, leading them to develop and deploy censorship mechanisms within their networks. Unfortunately, existing censorship circumvention systems do not provide high availability guarantees to their users, as censors can easily identify, hence disrupt, the traffic belonging to these systems using today's advanced censorship technologies. In this paper, we propose Serving the Web by Exploiting Email Tunnels SWEET, a highly available censorship-resistant infrastructure. SWEET works by encapsulating a censored user's traffic inside email messages that are carried over public email services like Gmail and Yahoo Mail. As the operation of SWEET is not bound to any specific email provider, we argue that a censor will need to block email communications all together in order to disrupt SWEET, which is unlikely as email constitutes an important part of today's Internet. Through experiments with a prototype of our system, we find that SWEET's performance is sufficient for Web browsing. In particular, regular Websites are downloaded within couple of seconds.

Question: How does SWEET work?