The folly of forecasting : The effects of a disaggregated sales forecasting system on sales forecast error , sales forecast positive bias , and inventory levels. In this study we provide field evidence of the role that sales forecasts play as the coordination mechanism between sales managers and production managers. An extensive body of operations research documents the negative consequences of sales forecast error and investigates how to respond to sales forecast error by optimizing inventory "buffer" stocks. In contrast, we focus on whether a change in the sales forecasting information environment implemented at our research site reduces forecast error and, hence, the need for those buffer stocks. The newly implemented sales forecast "contingency system" disaggregated the sales forecast into two components: (i) an official sales forecast that reflected relatively more certain expected demand, and (ii) a separate report that provided the probability of a contingent demand "event" occurring and the expected volume impact of that event. We predict and find that the system had the intended effect of a reduction in inventory levels, both through better timing of production via a production "postponement" strategy and through a decrease in absolute forecast error. We further consider the incentives of the self-interested sales managers and predict and find that the inventory reduction benefits of the sales forecast system gained through postponement and a decline in absolute forecast error were partially offset by an increase in positive sales forecast bias. Our study provides novel insights regarding the role of forecasting within the organizational context. While the operations literature uses analytic and simulation methods to examine sales and operations planning in a very mechanistic way, we examine the role that changes in the sales forecast information environment play and the opportunistic responses of self-interested managers. (266 words)

Question: What is the role of forecasting in the organization?


LED driver circuit with inherent PFC. A buck-boost topology operating in discontinuous conduction mode (DCM) is used as an off line LED driver for lighting applications. Operating from a full wave rectified ac-voltage with minimum input capacitance, with a constant switching frequency and constant on-time, the utility current has near unity power factor. This LED driver is suitable for cost sensitive applications because this circuit has a minimal parts count and a very simple control circuit. Disadvantages are that the LED drive current is modulated at twice the utility frequency and DCM operation increases component stress levels.

Question: What is the advantages of this circuit?


A novel soil measuring wireless sensor network. Emerging technologies have made low-power and low-cost wireless sensor networks feasible. This paper presents a hierarchical wireless sensor network for measuring soil parameters such as temperature and humidity. Specifically, we designed sensor nodes that are placed completely underground and are used to collect soil measurements. These nodes use their radios to deliver the collected measurements to one of multiple relay nodes located above ground. In turn, relay nodes that are capable of long-range communications forward the data collected from the network's sensor nodes to a base node, which is connected to a workstation. The proposed hierarchical wireless sensor network uses a probabilistic communication protocol to achieve a very low duty cycle and hence a long life-time for soil monitoring applications.

Question: What is a wireless sensor network?


Neurocognitive effects of clozapine, olanzapine, risperidone, and haloperidol in patients with chronic schizophrenia or schizoaffective disorder.. OBJECTIVE Newer antipsychotic drugs have shown promise in ameliorating neurocognitive deficits in patients with schizophrenia, but few studies have compared newer antipsychotic drugs with both clozapine and conventional agents, particularly in patients who have had suboptimal response to prior treatments.   METHOD The authors examined the effects of clozapine, olanzapine, risperidone, and haloperidol on 16 measures of neurocognitive functioning in a double-blind, 14-week trial involving 101 patients. A global score was computed along with scores in four neurocognitive domains: memory, attention, motor function, and general executive and perceptual organization.   RESULTS Global neurocognitive function improved with olanzapine and risperidone treatment, and these improvements were superior to those seen with haloperidol. Patients treated with olanzapine exhibited improvement in the general and attention domains but not more than that observed with other treatments. Patients treated with risperidone exhibited improvement in memory that was superior to that of both clozapine and haloperidol. Clozapine yielded improvement in motor function but not more than in other groups. Average effect sizes for change were in the small to medium range. More than half of the patients treated with olanzapine and risperidone experienced "clinically significant" improvement (changes in score of at least one-half standard deviation relative to baseline). These findings did not appear to be mediated by changes in symptoms, side effects, or blood levels of medications.   CONCLUSIONS Patients with a history of suboptimal response to conventional treatments may show cognitive benefits from newer antipsychotic drugs, and there may be differences between atypical antipsychotic drugs in their patterns of cognitive effects.

Question: What is the difference between clozapine and olanzapine?


Securing distributed storage: challenges, techniques, and systems. The rapid increase of sensitive data and the growing number of government regulations that require longterm data retention and protection have forced enterprises to pay serious attention to storage security. In this paper, we discuss important security issues related to storage and present a comprehensive survey of the security services provided by the existing storage systems. We cover a broad range of the storage security literature, present a critical review of the existing solutions, compare them, and highlight potential research issues.

Question: What is the size of the canadian military?


Integrated force and distance sensing using elastomer-embedded commodity proximity sensors. We describe a combined force and distance sensor using a commodity infrared distance sensor embedded in a transparent elastomer with applications in robotic manipulation. Prior to contact, the sensor works as a distance sensor (0–10 cm), whereas after contact the material doubles as a spring, with force proportional to the compression of the elastomer (0– 5 N). We describe its principle of operation and design parameters, including polymer thickness, mixing ratio, and emitter current, and show that the sensor response has an inflection point at contact that is independent of an object's surface properties. We then demonstrate how two arrays of eight sensors, each mounted on a standard Baxter gripper, can be used to (1) improve gripper alignment during grasping, (2) determine contact points with objects, and (3) obtain crude 3D models that can serve to determine possible grasp locations.

Question: How do you make a robot that can pick up an object?


A Compact Multipath Mitigating Ground Plane for Multiband GNSS Antennas. This paper presents the design of a novel multipath mitigating ground plane for global navigation satellite system (GNSS) antennas. First, the concept of a compact low multipath cross-plate reflector ground plane (CPRGP) is presented. In comparison with the choke ring and electromagnetic band gap (EBG) ground planes, the proposed CPRGP has compact size, low mass, wide operational bandwidth, and simple configuration. The proposed CPRGP is then integrated with a circularly polarized dual-band GNSS antenna in order to assess the multipath mitigating performance over two frequency bands. Measurement results of the proposed CPRGP with GNSS antenna achieves a front-to-back ratio (FBR) over 25 dB at L1 (1.575 GHz) and L2 (1.227 GHz) bands and maximum backward cross-polarization levels below -23 dB at both bands. Antenna phase center variation remains less than 2 mm across both L1 and L2 bands. Furthermore, the performance comparison of the proposed CPRGP with the commercially available pinwheel antenna and the shallow corrugated ground plane is presented, showing the advantages of CPRGP for high precision GNSS applications.

Question: What is a compact multipath mitigating ground plane?


Avoiding pathologies in very deep networks. Choosing appropriate architectures and regularization strategies of deep networks is crucial to good predictive performance. To shed light on this problem, we analyze the analogous problem of constructing useful priors on compositions of functions. Specifically, we study the deep Gaussian process, a type of infinitely-wide, deep neural network. We show that in standard architectures, the representational capacity of the network tends to capture fewer degrees of freedom as the number of layers increases, retaining only a single degree of freedom in the limit. We propose an alternate network architecture which does not suffer from this pathology. We also examine deep covariance functions, obtained by composing infinitely many feature transforms. Lastly, we characterize the class of models obtained by performing dropout on Gaussian processes.

Question: What is the deep Gaussian process?


Near Optimal Online Algorithms and Fast Approximation Algorithms for Resource Allocation Problems. We present prior robust algorithms for a large class of resource allocation problems where requests arrive one-by-one (online), drawn independently from an unknown distribution at every step. We design a single algorithm that, for every possible underlying distribution, obtains a 1−ε fraction of the profit obtained by an algorithm that knows the entire request sequence ahead of time. The factor ε approaches 0 when no single request consumes/contributes a significant fraction of the global consumption/contribution by all requests together. We show that the tradeoff we obtain here that determines how fast ε approaches 0, is near optimal: We give a nearly matching lower bound showing that the tradeoff cannot be improved much beyond what we obtain.  Going beyond the model of a static underlying distribution, we introduce the adversarial stochastic input model, where an adversary, possibly in an adaptive manner, controls the distributions from which the requests are drawn at each step. Placing no restriction on the adversary, we design an algorithm that obtains a 1−ε fraction of the optimal profit obtainable w.r.t. the worst distribution in the adversarial sequence. Further, if the algorithm is given one number per distribution, namely the optimal profit possible for each of the adversary's distribution, then we design an algorithm that achieves a 1−ε fraction of the weighted average of the optimal profit of each distribution the adversary picks.  In the offline setting we give a fast algorithm to solve very large linear programs (LPs) with both packing and covering constraints. We give algorithms to approximately solve (within a factor of 1+ε) the mixed packing-covering problem with O(γ m log (n/Δ)/ε2) oracle calls where the constraint matrix of this LP has dimension n× m, the success probability of the algorithm is 1−Δ, and γ quantifies how significant a single request is

Question: What is the difference between the online and offline setting?


Using Suffix Arrays to Compute Term Frequency and Document Frequency for All Substrings in a Corpus. Bigrams and trigrams are commonly used in statistical natural language processing; this paper will describe techniques for working with much longer n-grams. Suffix arrays (Manber and Myers 1990) were first introduced to compute the frequency and location of a substring (n-gram) in a sequence (corpus) of length N. To compute frequencies over all N(N+1)/2 substrings in a corpus, the substrings are grouped into a manageable number of equivalence classes. In this way, a prohibitive computation over substrings is reduced to a manageable computation over classes. This paper presents both the algorithms and the code that were used to compute term frequency (tf) and document frequency (df) for all n-grams in two large corpora, an English corpus of 50 million words of Wall Street Journal and a Japanese corpus of 216 million characters of Mainichi Shimbun. The second half of the paper uses these frequencies to find interesting substrings. Lexicographers have been interested in n-grams with high mutual information (MI) where the joint term frequency is higher than what would be expected by chance, assuming that the parts of the n-gram combine independently. Residual inverse document frequency (RIDF) compares document frequency to another model of chance where terms with a particular term frequency are distributed randomly throughout the collection. MI tends to pick out phrases with noncompositional semantics (which often violate the independence assumption) whereas RIDF tends to highlight technical terminology, names, and good keywords for information retrieval (which tend to exhibit nonrandom distributions over documents). The combination of both MI and RIDF is better than either by itself in a Japanese word extraction task.

Question: What is the difference between a suffix array and a suffix tree?


Effectiveness of fundamental frequency (F0) and strength of excitation (SOE) for spoofed speech detection. Current countermeasures used in spoof detectors (for speech synthesis (SS) and voice conversion (VC)) are generally phase-based (as vocoders in SS and VC systems lack phase-information). These approaches may possibly fail for non-vocoder or unit-selection-based spoofs. In this work, we explore excitation source-based features, i.e., fundamental frequency (F0) contour and strength of excitation (SoE) at the glottis as discriminative features using GMM-based classification system. We use F0 and SoE1 estimated from speech signal through zero frequency (ZF) filtering method. Further, SoE2 is estimated from negative peaks of derivative of glottal flow waveform (dGFW) at glottal closure instants (GCIs). On the evaluation set of ASVspoof 2015 challenge database, the F0 and SoEs features along with its dynamic variations achieve an Equal Error Rate (EER) of 12.41%. The source features are fused at score-level with MFCC and recently proposed cochlear filter cepstral coefficients and instantaneous frequency (CFCCIF) features. On fusion with MFCC (CFCCIF), the EER decreases from 4.08% to 3.26% (2.07% to 1.72%). The decrease in EER was evident on both known and unknown vocoder-based attacks. When MFCC, CFCCIF and source features are combined, the EER further decreased to 1.61%. Thus, source features captures complementary information than MFCC and CFCCIF used alone.

Question: How effective are the features for spoof detection?


Face liveness detection through face structure analysis. Liveness detection is a way to detect whether the person is live or not during submission of his/her biometric trait. It is mandatory in order to prevent face spoofing attacks. Therefore, in this paper, we proposed a robust face structure analysis mechanism to detect the liveness by exploiting face shape information. 3D structure/shape of the face is measured on the basis of disparity map between left and right image taken by stereoscopic vision. A gradient-based eight neighbour feature extraction technique has been proposed to extract unique features from these disparity images. It produces minimal computational cost by taking subset of the overall image. We have applied linear discriminant analysis (LDA), C-means algorithms on these features while principal component analysis (PCA) is applied on raw disparity images. We have achieved a recognition rate of 91.6%, 97.5% and 98.3% using PCA, LDA and C-means respectively, which strengthened the confidence of our proposed feature extraction technique.

Question: How to detect liveness in biometric traits?


Bit-Cell Level Optimization for Non-volatile Memories Using Magnetic Tunnel Junctions and Spin-Transfer Torque Switching. Spin-transfer torque magnetic random access memories (STT-MRAM), using magnetic tunnel junctions (MTJ), is a resistive memory technology that has spurred significant research interest due to its potential for on-chip, high-density, high-speed, low-power, and non-volatile memory. However, due to conflicting read and write requirements, there is a need to develop optimization techniques for designing STT-MRAM bit-cells to minimize read and write failures. We propose an optimization technique that minimizes read and write failures by proper selection of bit-cell configuration and by proper access transistor sizing. A mixed-mode simulation framework was developed to evaluate the effectiveness of our optimization technique. Our simulation framework captures the transport physics in the MTJ using Non-Equilibrium Green's Function method and self-consistently solves the MTJ magnetization dynamics using Landau-Lifshitz-Gilbert equation augmented with the full Slonczewski spin-torque term. The electrical parameters of the MTJ are then encapsulated in a Verilog-A model and used in HSPICE to perform bit-cell level optimization. The optimization technique is applied to STT-MRAM bit-cells designed using 45 nm bulk and 45 nm silicon-on-insulator CMOS technologies. Finally, predictions are made for optimized STT-MRAM bit-cells designed in 16 nm predictive technology.

Question: How to optimize the bit-cell level for non-volatile memories?


Illuminating the Effects of Dynamic Lighting on Student Learning. Light is universally understood as essential to the human condition. Yet light quality varies substantially both in nature and in controlled environments leading to questions of which artificial light characteristics facilitate maximum learning. Recent research has examined lighting variables of color temperature, and illuminance for impacting sleep, mood, focus, motivation, concentration and work and school performance. This has resulted in artificial light systems intended to support human beings in their actualization through dynamic lighting technology allowing for different lighting conditions per task. Eighty-four third graders were exposed to either focus (6000K-100fc average maintained) or normal lighting. Focus lighting led to a higher percentage increase in oral reading fluency performance (36%) than did control lighting (17%). No lighting effects were found for motivation or concentration, possibly attributable to the younger age level of respondents as compared to European studies. These findings illuminate the need for further research on artificial light and learning. The Effects of Lighting on Humans in General The human evolution is shaped by light. In the course of evolution, human beings have adapted and developed an internal clock that under natural light conditions is synchronized to the earth's 24-hour light-dark rotational cycle (Czeisler et al., 1999). Research reveals the mechanism for how light is essential for human functioning (Boyce, Hunter, & Howlett, 2003). Light is a strong enabler for visual performance (Grangaard, 1995), regulates a large variety of bodily processes such as sleep and alertness (Dijk et al., 1997; Wright et al., 2006; Takasu et al., 2006; Viola et al., 2008), is essential for cognition and mood (Veitch & McColl, 2001; Goven et al., 2011; Taras, 2005), enables production of important hormones such as melatonin and cortisol (Berson, 2002; dijk et al., 199

Question: What is the effect of lighting on humans?


Phase Current Sensor and Short-Circuit Detection based on Rogowski Coils Integrated on Gate Driver for 1.2 kV SiC MOSFET Half-Bridge Module. Silicon-carbide (SiC) MOSFETs are enabling electrical vehicle motor drives to meet the demands of higher power density, efficiency, and lower system cost. Hence, this paper seeks to explore the benefits that a gate-driver-level intelligence can contribute to SiC-based power inverters. The intelligence is brought by PCB-embedded Rogowski switch-current sensors (RSCS) integrated on the gate driver of a 1.2 kV, 300 A SiC MOSFET half-bridge module. They collect two MOSFET switch currents in a manner of high magnitude, high bandwidth, and solid signal isolation. The switch-current signals are used for short-circuit detection under various fault impedances, as well as for phase-current reconstruction by subtracting one switch current from another. The fundamentals and noise-immunity design of the gate driver containing the RSCS are presented in the paper and can be applied to any half-bridge power module. A three-phase inverter prototype has been built and operated in continuous PWM mode. On this setup, the performance and limitations of the short-circuit detection and phase-current reconstruction are experimentally validated by comparing with commercial current probes and Hall sensors.

Question: What is the difference between a gate driver and a gate driver-level intelligence?


Design science in information systems research.

Question: What is design science in information systems research?


Optical see-through head up displays' effect on depth judgments of real world objects. Recent research indicates that users consistently underestimate depth judgments to Augmented Reality (AR) graphics when viewed through optical see-through displays. However, to our knowledge, little work has examined how AR graphics may affect depth judgments of real world objects that have been overlaid or annotated with AR graphics. This study begins a preliminary analysis whether AR graphics have directional effects on users' depth perception of real-world objects, as might be experienced in vehicle driving scenarios (e.g., as viewed via an optical see-through head-up display or HUD). Twenty-four participants were asked to judge the depth of a physical pedestrian proxy figure moving towards them at a constant rate of 1 meter/second. Participants were shown an initial target location that varied in distance from 11 to 20 m and were then asked to press a button to indicate when the moving target was perceived to be at the previously specified target location. Each participant experienced three different display conditions: no AR visual display (control), a conformal AR graphic overlaid on the pedestrian via a HUD, and the same graphic presented on a tablet physically located on the pedestrian. Participants completed 10 trials (one for each target distance between 11 and 20 inclusive) per display condition for a total of 30 trials per participant. The judged distance from the correct location was recorded, and after each trial, participants' confidence in determining the correct distance was captured. Across all conditions, participants underestimated the distance of the physical object consistent with existing literature. Greater variability was observed in the accuracy of distance judgments under the AR HUD condition relative to the other two display conditions. In addition, participant confidence levels were considerably lower in the AR HUD condition.

Question: How does the AR HUD affect depth judgments of real world objects?


Parametric and nonparametric linkage analysis: a unified multipoint approach.. In complex disease studies, it is crucial to perform multipoint linkage analysis with many markers and to use robust nonparametric methods that take account of all pedigree information. Currently available methods fall short in both regards. In this paper, we describe how to extract complete multipoint inheritance information from general pedigrees of moderate size. This information is captured in the multipoint inheritance distribution, which provides a framework for a unified approach to both parametric and nonparametric methods of linkage analysis. Specifically, the approach includes the following: (1) Rapid exact computation of multipoint LOD scores involving dozens of highly polymorphic markers, even in the presence of loops and missing data. (2) Non-parametric linkage (NPL) analysis, a powerful new approach to pedigree analysis. We show that NPL is robust to uncertainty about mode of inheritance, is much more powerful than commonly used nonparametric methods, and loses little power relative to parametric linkage analysis. NPL thus appears to be the method of choice for pedigree studies of complex traits. (3) Information-content mapping, which measures the fraction of the total inheritance information extracted by the available marker data and points out the regions in which typing additional markers is most useful. (4) Maximum-likelihood reconstruction of many-marker haplotypes, even in pedigrees with missing data. We have implemented NPL analysis, LOD-score computation, information-content mapping, and haplotype reconstruction in a new computer package, GENEHUNTER. The package allows efficient multipoint analysis of pedigree data to be performed rapidly in a single user-friendly environment.

Question: What is the difference between parametric and nonparametric linkage analysis?


Substrate Integrated Waveguide Loaded by Complementary Split-Ring Resonators and Its Applications to Miniaturized Waveguide Filters. A substrate integrated waveguide with square complementary split-ring resonators (CSRRs) etched on the waveguide surface is investigated in this paper. The proposed structures allow the implementation of a forward-wave passband propagating below the characteristic cutoff frequency of the waveguide. By changing the orientations of the CSRRs, which are incorporated in the waveguide surface and can be interpreted in terms of electric dipoles, varied passband characteristics are observed. A detailed explanation for the generation and variations of the passbands has been illuminated. The application of this waveguide and CSRR combination technique to the design of miniaturized waveguide bandpass filters characterized by transmission zeros is then illustrated. Filter design methodology is examined. These proposed filters exhibit high selectivity and compact size due to the employment of the subwavelength resonators and an evanescent-wave transmission. By slightly altering the configuration of the CSRRs, we find that the propagation of the TE10 mode can be suppressed and filters with improved selectivity and stopband rejection can be obtained. To verify the presented concept, three different types of filters are fabricated based on the standard printed circuit board process. The measured results are in good agreement with the simulation.

Question: What is the difference between a waveguide and a waveguide filter?


Clustering to Find Exemplar Terms for Keyphrase Extraction. Keyphrases are widely used as a brief summary of documents. Since manual assignment is time-consuming, various unsupervised ranking methods based on importance scores are proposed for keyphrase extraction. In practice, the keyphrases of a document should not only be statistically important in the document, but also have a good coverage of the document. Based on this observation, we propose an unsupervised method for keyphrase extraction. Firstly, the method finds exemplar terms by leveraging clustering techniques, which guarantees the document to be semantically covered by these exemplar terms. Then the keyphrases are extracted from the document using the exemplar terms. Our method outperforms sate-of-the-art graphbased ranking methods (TextRank) by 9.5% in F1-measure.

Question: How to find keyphrases?


Corporate Sustainability : First Evidence on Materiality. An increasing number of companies make sustainability investments, and an increasing number of investors integrate sustainability performance data in their capital allocation decisions. To date however, the prior academic literature has not distinguished between investments in material versus immaterial sustainability issues. We develop a novel dataset by hand-mapping data on sustainability investments classified as material for each industry into firm-specific performance data on a variety of sustainability investments. This allows us to present new evidence on the value implications of sustainability investments. Using calendar-time portfolio stock return regressions we find that firms with good performance on material sustainability issues significantly outperform firms with poor performance on these issues, suggesting that investments in sustainability issues are shareholder-value enhancing. Further, firms with good performance on sustainability issues not classified as material do not underperform firms with poor performance on these same issues, suggesting investments in sustainability issues are at a minimum not value-destroying. Finally, firms with good performance on material issues and concurrently poor performance on immaterial issues perform the best. These results speak to the efficiency of firms' sustainability investments, and also have implications for asset managers who have committed to the integration of sustainability factors in their capital allocation decisions.  Mozaffar Khan is a visiting Associate Professor at Harvard Business School. George Serafeim is the Jakurski Family Associate Professor of Business Administration at Harvard Business School. Aaron Yoon is a doctoral student at Harvard Business School. We are grateful for comments from seminar participants at National University of Singapore and Harvard Business School. We are grateful for financial support from the Division of F

Question: What is the value of material sustainability issues?


CAST: Collaborative Agents for Simulating Teamwork. Psychological studies on teamwork have shown that an effective team often can anticipate information needs of teammates based on a shared mental model. Existing multi-agent models for teamwork are limited in their ability to support proactive information exchange among teammates. To address this issue, we have developed and implemented a multi-agent architecture called CAST that simulates teamwork and supports proactive information exchange in a dynamic environment. We present a formal model for proactive information exchange. Knowledge regarding the structure and process of a team is described in a language called MALLET. Beliefs about shared team processes and their states are represented using Petri Nets. Based on this model, CAST agents offer information proactively to those who might need it using an algorithm called DIARG. Empirical evaluations using a multi-agent synthetic testbed application indicate that CAST enhances the effectiveness of teamwork among agents without sacrificing a high cost for communications.

Question: What is a multi-agent architecture?


A cross-platform analysis of bugs and bug-fixing in open source projects: desktop vs. Android vs. iOS. As smartphones continue to increase in popularity, understanding how software processes associated with the smartphone platform differ from the traditional desktop platform is critical for improving user experience and facilitating software development and maintenance. In this paper we focus specifically on differences in bugs and bug-fixing processes between desktop and smartphone software. Our study covers 444,129 bug reports in 88 open source projects on desktop, Android, and iOS. The study has two main thrusts: a quantitative analysis to discover similarities and differences between desktop and smartphone bug reports/processes; and a qualitative analysis where we extract topics from bug reports to understand bugs' nature, categories, and differences between platforms. Our findings include: during 2011--2013, iOS bugs were fixed three times faster compared to Android and desktop; top smartphone bug fixers are more involved in reporting bugs than top desktop bug fixers; and most frequent high-severity bugs are due to build issues on desktop, concurrency on Android, and application logic on iOS. Our study, findings, and recommendations are potentially useful to smartphone researchers and practitioners.

Question: What is the difference between desktop and smartphone bug fixing?


Applying WinWin to Quality Requirements: A Case Study. This paper describes the application of the WinWin paradigm to identify and resolve conflicts in a series of real-client, student-developer digital library projects. The paper is based on a case study of the statistical analysis of 15 projects and an in-depth analysis of one representative project. These analyses focus on the conflict resolution process, stakeholders' roles and their relationships to quality artifacts, and tool effectiveness. We show that stakeholders tend to accept satisfactory rather than optimal resolutions. Users and customers are more proactive in stating win conditions, whereas developers are more active in working toward resolutions. Further, we suggest that knowledge-based automated aids have potential to significantly enhance process effectiveness and efficiency. Finally, we conclude that such processes and tools have theoretical and practical implications in the quest for better software requirements elicitation.

Question: What is the difference between a win condition and a requirement?


Functional cultures and health benefits. A number of health benefits have been claimed for probiotic bacteria such as Lactobacillus acidophilus, Bifidobacterium spp., and L. casei. These benefits include antimutagenic effects, anticarcinogenic properties, improvement in lactose metabolism, reduction in serum cholesterol, and immune system stimulation. Because of the potential health benefits, these organisms are increasingly being incorporated into dairy foods, particularly yoghurt. In addition to yoghurt, fermented functional foods with health benefits based on bioactive peptides released by probiotic organisms, including Evolus and Calpis, have been introduced in the market. To maximize effectiveness of bifidus products, prebiotics are used in probiotic foods. Synbiotics are products that contain both prebiotics and probiotics. r 2007 Elsevier Ltd. All rights reserved.

Question: What are the health benefits of probiotics?


Algorithms for Infinitely Many-Armed Bandits. We consider multi-armed bandit problems where the number of arms is larger than the possible number of experiments. We make a stochastic assumption on the mean-reward of a new selected arm which characterizes its probability of being a near-optimal arm. Our assumption is weaker than in previous works. We describe algorithms based on upper-confidence-bounds applied to a restricted set of randomly selected arms and provide upper-bounds on the resulting expected regret. We also derive a lower-bound which matches (up to a logarithmic factor) the upper-bound in some cases.

Question: What is the probability of a near-optimal arm?


Paragon: QoS-aware scheduling for heterogeneous datacenters. Large-scale datacenters (DCs) host tens of thousands of diverse applications each day. However, interference between colocated workloads and the difficulty to match applications to one of the many hardware platforms available can degrade performance, violating the quality of service (QoS) guarantees that many cloud workloads require. While previous work has identified the impact of heterogeneity and interference, existing solutions are computationally intensive, cannot be applied online and do not scale beyond few applications.  We present Paragon, an online and scalable DC scheduler that is heterogeneity and interference-aware. Paragon is derived from robust analytical methods and instead of profiling each application in detail, it leverages information the system already has about applications it has previously seen. It uses collaborative filtering techniques to quickly and accurately classify an unknown, incoming workload with respect to heterogeneity and interference in multiple shared resources, by identifying similarities to previously scheduled applications. The classification allows Paragon to greedily schedule applications in a manner that minimizes interference and maximizes server utilization. Paragon scales to tens of thousands of servers with marginal scheduling overheads in terms of time or state.  We evaluate Paragon with a wide range of workload scenarios, on both small and large-scale systems, including 1,000 servers on EC2. For a 2,500-workload scenario, Paragon enforces performance guarantees for 91% of applications, while significantly improving utilization. In comparison, heterogeneity-oblivious, interference-oblivious and least-loaded schedulers only provide similar guarantees for 14%, 11% and 3% of workloads. The differences are more striking in oversubscribed scenarios where resource efficiency is more critical.

Question: What is the difference between a heterogeneous and a homogeneous datacenter?


Second-order cone programming. Second-order cone programming (SOCP) problems are convex optimization problems in which a linear function is minimized over the intersection of an affine linear manifold with the Cartesian product of second-order (Lorentz) cones. Linear programs, convex quadratic programs and quadratically constrained convex quadratic programs can all be formulated as SOCP problems, as can many other problems that do not fall into these three categories. These latter problems model applications from a broad range of fields from engineering, control and finance to robust optimization and combinatorial optimization. On the other hand semidefinite programming (SDP)—that is the optimization problem over the intersection of an affine set and the cone of positive semidefinite matrices—includes SOCP as a special case. Therefore, SOCP falls between linear (LP) and quadratic (QP) programming and SDP. Like LP, QP and SDP problems, SOCP problems can be solved in polynomial time by interior point methods. The computational effort per iteration required by these methods to solve SOCP problems is greater than that required to solve LP and QP problems but less than that required to solve SDP's of similar size and structure. Because the set of feasible solutions for an SOCP problem is not polyhedral as it is for LP and QP problems, it is not readily apparent how to develop a simplex or simplex-like method for SOCP. While SOCP problems can be solved as SDP problems, doing so is not advisable both on numerical grounds and computational complexity concerns. For instance, many of the problems presented in the survey paper of Vandenberghe and Boyd [VB96] as examples of SDPs can in fact be formulated as SOCPs and should be solved as such. In §2, 3 below we give SOCP formulations for four of these examples: the convex quadratically constrained quadratic programming (QCQP) problem, problems involving fractional quadratic functions such as those that arise in structural optimi

Question: What is the difference between a convex quadratic program and a second order cone program?


Improving Neural Knowledge Base Completion with Cross-Lingual Projections. In this paper we present a cross-lingual extension of a neural tensor network model for knowledge base completion. We exploit multilingual synsets from BabelNet to translate English triples to other languages and then augment the reference knowledge base with cross-lingual triples. We project monolingual embeddings of different languages to a shared multilingual space and use them for network initialization (i.e., as initial concept embeddings). We then train the network with triples from the cross-lingually augmented knowledge base. Results on WordNet link prediction show that leveraging cross-lingual information yields significant gains over exploiting only monolingual triples.

Question: How to improve neural knowledge base completion with cross-lingual projections?


A chaincode based scheme for fingerprint feature extraction. A feature extraction method using the chaincode representation of fingerprint ridge contours is presented for use by Automatic Fingerprint Identification Systems. The representation allows efficient image quality enhancement and detection of fine minutiae feature points. For image enhancement a given fingerprint image is first binarized after a quick averaging to generate its chaincode representation. The direction field is estimated from a set of selected chaincodes. The original gray scale image is then enhanced using connected component analysis and a dynamic filtering scheme that takes advantage of the knowledge gained from the estimated direction flow of the contours. For feature extraction, the enhanced fingerprint image is carefully binarized using a local-global binarization algorithm for generating the chaincode representation. The minutiae are generated using a sophisticated ridge contour following procedure. Visual inspection of the experiment images shows that the method is very effective.

Question: What is a fingerprint feature extraction method?


Language and life history: a new perspective on the development and evolution of human language.. It has long been claimed that Homo sapiens is the only species that has language, but only recently has it been recognized that humans also have an unusual pattern of growth and development. Social mammals have two stages of pre-adult development: infancy and juvenility. Humans have two additional prolonged and pronounced life history stages: childhood, an interval of four years extending between infancy and the juvenile period that follows, and adolescence, a stage of about eight years that stretches from juvenility to adulthood. We begin by reviewing the primary biological and linguistic changes occurring in each of the four pre-adult ontogenetic stages in human life history. Then we attempt to trace the evolution of childhood and juvenility in our hominin ancestors. We propose that several different forms of selection applied in infancy and childhood; and that, in adolescence, elaborated vocal behaviors played a role in courtship and intrasexual competition, enhancing fitness and ultimately integrating performative and pragmatic skills with linguistic knowledge in a broad faculty of language. A theoretical consequence of our proposal is that fossil evidence of the uniquely human stages may be used, with other findings, to date the emergence of language. If important aspects of language cannot appear until sexual maturity, as we propose, then a second consequence is that the development of language requires the whole of modern human ontogeny. Our life history model thus offers new ways of investigating, and thinking about, the evolution, development, and ultimately the nature of human language.

Question: What is the difference between the stages of human development?


X-band microstrip narrowband BPF composed of split ring resonator. The paper deals with the development of X-band microstrip narrowband bandpass filter (BPF) composed of split ring resonator (SRR). The proposed filter is designed on a grounded 0.508mm thick Roger RT/Duroid® 5880 dielectric substrate with the dimension of 25mm length and 20mm width. Some parametric studies to achieve an optimum design of microstrip narrowband BPF are investigated prior realization by varying the physical parameters of SRR including the width of rings and the gap between rings. From the experimental characterization, it shows that the realized microstrip narrowband BPF has a center frequency of 9.02 GHz with the values of return loss and insertion loss of 15.231 dB and 2.768 dB, respectively. The measured result is 20MHz lower than the simulated one which has center frequency of 9 GHz with the values of return loss and insertion loss of 15.025 dB and 2.242 dB, respectively.

Question: What is the center frequency of the filter?


Language, cognition, and short-term memory in individuals with Down syndrome.. The developmentally emerging phenotype of language and cognition in individuals with Down syndrome is summarized on the basis of the project's prior work. Identified are a) the emerging divergence of expressive and receptive language, b) the emerging divergence of lexical and syntactic knowledge in each process, and c) the emerging divergence within cognitive skills of auditory short-term memory and visuospatial short-term memory from other visuospatial skills. Expressive syntax and auditory short-term memory are identified as areas of particular difficulty. Evidence for the continued acquisition of language skills in adolescence is presented. The role of the two components of working memory, auditory and visual, in language development is investigated in studies of narrative and longitudinal change in language skills. Predictors of individual differences during six years of language development are evaluated through hierarchical linear modelling. Chronological age, visuospatial short-term memory, and auditory-short term memory are identified as key predictors of performance at study entry, but not individual change over time, for expressive syntax. The same predictors account for variation in comprehension skill at study outset; and change over the six years can be predicted by chronological age and the change in visuospatial short-term memory skills.

Question: What is the difference between expressive and receptive language?


Automatically Categorizing Written Texts by Author Gender. The problem of automatically determining the gender of a document's author would appear to be a more subtle problem than those of categorization by topic or authorship attribution. Nevertheless, it is shown that automated text categorization techniques can exploit combinations of simple lexical and syntactic features to infer the gender of the author of an unseen formal written document with approximately 80% accuracy. The same techniques can be used to determine if a document is fiction or non-fiction with approximately 98% accuracy.

Question: How can we automatically categorize written text?


Digital Dead Time Auto-Tuning for Maximum Efficiency Operation of Isolated DC-DC Converters. The rapidly growing use of digital control in Distributed Power Architectures (DPA) has been primarily driven by the increasing need of sophisticated Power Management functions. However, specifically designed control algorithms can also be exploited for optimizing the system performance. In this paper we present a new auto-tuning method and system that makes it possible to operate an isolated DC-DC converter at maximum efficiency. The auto-tuning performs an optimum adjustment of both primary side dead time and secondary side conduction times based on the measurement of the input current. It does not require any additional external components since current sensing functions are already implemented for power management purposes. Experimental measurements performed on an asymmetrical driven half-bridge DC-DC converter demonstrate the effectiveness of our solution and its robustness to component variations.

Question: How to optimize the performance of a DC-DC converter?


Stacked Convolutional Denoising Auto-Encoders for Feature Representation. Deep networks have achieved excellent performance in learning representation from visual data. However, the supervised deep models like convolutional neural network require large quantities of labeled data, which are very expensive to obtain. To solve this problem, this paper proposes an unsupervised deep network, called the stacked convolutional denoising auto-encoders, which can map images to hierarchical representations without any label information. The network, optimized by layer-wise training, is constructed by stacking layers of denoising auto-encoders in a convolutional way. In each layer, high dimensional feature maps are generated by convolving features of the lower layer with kernels learned by a denoising auto-encoder. The auto-encoder is trained on patches extracted from feature maps in the lower layer to learn robust feature detectors. To better train the large network, a layer-wise whitening technique is introduced into the model. Before each convolutional layer, a whitening layer is embedded to sphere the input data. By layers of mapping, raw images are transformed into high-level feature representations which would boost the performance of the subsequent support vector machine classifier. The proposed algorithm is evaluated by extensive experimentations and demonstrates superior classification performance to state-of-the-art unsupervised networks.

Question: How to train a deep network?


BSSync: Processing Near Memory for Machine Learning Workloads with Bounded Staleness Consistency Models. Parallel machine learning workloads have become prevalent in numerous application domains. Many of these workloads are iterative convergent, allowing different threads to compute in an asynchronous manner, relaxing certain read-after-write data dependencies to use stale values. While considerable effort has been devoted to reducing the communication latency between nodes by utilizing asynchronous parallelism, inefficient utilization of relaxed consistency models within a single node have caused parallel implementations to have low execution efficiency. The long latency and serialization caused by atomic operations have a significant impact on performance. The data communication is not overlapped with the main computation, which reduces execution efficiency. The inefficiency comes from the data movement between where they are stored and where they are processed. In this work, we propose Bounded Staled Sync (BSSync), a hardware support for the bounded staleness consistency model, which accompanies simple logic layers in the memory hierarchy. BSSync overlaps the long latency atomic operation with the main computation, targeting iterative convergent machine learning workloads. Compared to previous work that allows staleness for read operations, BSSync utilizes staleness for write operations, allowing stale-writes. We demonstrate the benefit of the proposed scheme for representative machine learning workloads. On average, our approach outperforms the baseline asynchronous parallel implementation by 1.33x times.

Question: What is the bounded staleness consistency model?


Question: What is the fast effective rule induction algorithm?


Role of the amygdala in decision-making.. The somatic marker hypothesis proposes that both the amygdala and the orbitofrontal cortex are parts of a neural circuit critical for judgment and decision-making. Although both structures couple exteroceptive sensory information with interoceptive information concerning somatic/emotional states, they do so at different levels, thus making different contributions to the process. We define "primary inducers" as stimuli that unconditionally, or through learning (e.g., conditioning and semantic knowledge), can (perceptually or subliminally) produce states that are pleasurable or aversive. Encountering a fear object (e.g., a snake), a stimulus predictive of a snake, or semantic information such as winning or losing a large sum of money are all examples of primary inducers. "Secondary inducers" are entities generated by the recall of a personal or hypothetical emotional event or perceiving a primary inducer that generates "thoughts" and "memories" about the inducer, all of which, when they are brought to memory, elicit a somatic state. The episodic memory of encountering a snake, losing a large sum of money, imagining the gain of a large sum of money, or hearing or looking at primary inducers that bring to memory "thoughts" pertaining to an emotional event are all examples of secondary inducers. We present evidence in support of the hypothesis that the amygdala is a critical substrate in the neural system necessary for triggering somatic states from primary inducers. The ventromedial cortex is a critical substrate in the neural system necessary for the triggering of somatic states from secondary inducers. The amygdala system is a priori a necessary step for the normal development of the orbitofrontal system for triggering somatic states from secondary inducers. However, once this orbitofrontal system is developed, the induction of somatic states by secondary inducers via the orbitofrontal system is less dependent on the amygdala

Question: What is the amygdala?


Analyzing the effects of disk-pointer corruption. The long-term availability of data stored in a file system depends on how well it safeguards on-disk pointers used to access the data. Ideally, a system would correct all pointer errors. In this paper, we examine how well corruption-handling techniques work in reality. We develop a new technique called type-aware pointer corruption to systematically explore how a file system reacts to corrupt pointers. This approach reduces the exploration space for corruption experiments and works without source code. We use type-aware pointer corruption to examine Windows NTFS and Linux ext3. We find that they rely on type and sanity checks to detect corruption, and NTFS recovers using replication in some instances. However, NTFS and ext3 do not recover from most corruptions, including many scenarios for which they possess sufficient redundant information, leading to further corruption, crashes, and unmountable file systems. We use our study to identify important lessons for handling corrupt pointers.

Question: How does a file system recover from corruption?


Input-series output-parallel AC/AC converter. Input-series and output-parallel (ISOP) converters are suitable for the high input voltage and low output voltage conversion applications. An ISOP current mode AC/AC converter with high frequency link is proposed. The control strategy and operation principle of the ISOP AC/AC converter are investigated. The control strategy ensures the proper sharing of the input voltage and the proper sharing of the output current among the ISOP AC/AC converter. By simulation, the correctness of the operation principle and the control strategy of the ISOP AC/AC converter are proved.

Question: What is the input voltage and output voltage of an ISOP AC/AC converter?


A novel ripple-based constant on-time control with virtual inductance and offset cancellation for DC power converters. In recent years, there has been a growing trend of mandating high power conversion efficiency, for both the heavy-load and the light-load conditions. To achieve this purpose, a ripple-based constant on-time (RBCOT) control for DC-DC converters has received wide attentions because of its natural characteristic of switching frequency reduction under light-load condition. However, a RBCOT converter suffers from output-voltage offset problem and sub-harmonic instability problem. In this paper, a modified RBCOT buck converter circuit will be proposed to solve both problems using the concept of virtual inductor current to stabilize the feedback and an offset cancellation circuit to cancel out the output DC offset. A control model based on describing function is also developed for the modified converter. From the model, it's found out that it's much easier to accomplish adaptive-voltage-positioning (AVP) using the proposed modified RBCOT scheme compared to a conventional constant-frequency controller. Simulation and experimental results are also given to verify the proposed scheme.

Question: What is a ripple-based constant on-time control?


Uneasy lies the head that wears the crown: the link between guilt proneness and leadership.. We propose that guilt proneness is a critical characteristic of leaders and find support for this hypothesis across 3 studies. Participants in the first study rated a set of guilt-prone behaviors as more indicative of leadership potential than a set of less guilt-prone behaviors. In a follow-up study, guilt-prone participants in a leaderless group task engaged in more leadership behaviors than did less guilt-prone participants. In a third, and final, study, we move to the field and analyze 360° feedback from a group of young managers working in a range of industries. The results indicate that highly guilt-prone individuals were rated as more capable leaders than less guilt-prone individuals and that a sense of responsibility for others underlies the positive relationship between guilt proneness and leadership evaluations.

Question: What is the link between guilt proneness and leadership?


Celeb-500K: A Large Training Dataset for Face Recognition. In this paper, we propose a large training dataset named Celeb-500K for face recognition, which contains 50M images from 500K persons. To better facilitate academic research, we clean Celeb-500K to obtain Celeb-500K-2R, which contains 25M aligned face images from 365K persons. Based on the developed dataset, we achieve state-of-the-art face recognition performance and reveal two important observations on face recognition study. First, metric learning methods have limited performance gain when the training dataset contains a large number of identities. Second, in order to develop an efficient training dataset, the number of identities is more important than the average image number of each identity from the perspective of face recognition performance. Extensive experimental results show the superiority of Celeb-500K and provide a strong support to the two observations.

Question: What is the size of the dataset?


Vertex-Centric Graph Processing: The Good, the Bad, and the Ugly. We study distributed graph algorithms that adopt an iterati ve vertexcentric framework for graph processing, popularized by Goo gle's Pregel system. Since then, there are several attempts to imp lement many graph algorithms in a vertex-centric framework, a s well as efforts to design optimization techniques for improving the efficiency. However, to the best of our knowledge, there has not been any systematic study to compare these vertex-centric i mplementations with their sequential counterparts. Our paper a dd esses this gap in two ways. (1) We analyze the computational complexity of such implementations with the notion of time-pro cessor product, and benchmark several vertex-centric graph algor ithms whether they perform more work with respect to their best-kn ow sequential solutions. (2) Employing the concept of balance d practical Pregel algorithms, we study if these implementations suffer from imbalanced workload and large number of iterations. Ou r findings illustrate that with the exception of Euler tour tre e algorithm, all other algorithms either perform asymptotically more work than their best-known sequential approach, or suffer from i mbalanced workload/ large number of iterations, or even both. We also emphasize on graph algorithms that are fundamentally diffic ult to be expressed in vertex-centric frameworks, and conclude by discussing the road ahead for distributed graph processing.

Question: What is the difference between vertex-centric and sequential graph algorithms?


BIM: Enabling Sustainability and Asset Management through Knowledge Management. Building Information Modeling (BIM) is the use of virtual building information models to develop building design solutions and design documentation and to analyse construction processes. Recent advances in IT have enabled advanced knowledge management, which in turn facilitates sustainability and improves asset management in the civil construction industry. There are several important qualifiers and some disadvantages of the current suite of technologies. This paper outlines the benefits, enablers, and barriers associated with BIM and makes suggestions about how these issues may be addressed. The paper highlights the advantages of BIM, particularly the increased utility and speed, enhanced fault finding in all construction phases, and enhanced collaborations and visualisation of data. The paper additionally identifies a range of issues concerning the implementation of BIM as follows: IP, liability, risks, and contracts and the authenticity of users. Implementing BIM requires investment in new technology, skills training, and development of new ways of collaboration and Trade Practices concerns. However, when these challenges are overcome, BIM as a new information technology promises a new level of collaborative engineering knowledge management, designed to facilitate sustainability and asset management issues in design, construction, asset management practices, and eventually decommissioning for the civil engineering industry.

Question: What is BIM?


Comparison of main control strategies for DC/DC stage of bidirectional vehicle charger. This paper presents comparison of two control algorithms for DC/DC converters in vehicle chargers. It presents operation in phase-shifting algorithm and resonant LLC algorithm, their implementation, and test results.

Question: How to compare two control algorithms?


Depression and memory impairment: a meta-analysis of the association, its pattern, and specificity.. The existing evidence paints an unclear picture of whether an association exists between depression and memory impairment. The purpose of this investigation was to determine whether depression is associated with memory impairment, whether moderator variables determine the extent of this association, and whether any obtained association is unique to depression. Meta-analytic techniques were used to synthesize data from 99 studies on recall and 48 studies on recognition in clinically depressed and nondepressed samples. Associations between memory impairment and other psychiatric disorders (e.g., schizophrenia, dementia) were also examined. A significant, stable association between depression and memory impairment was revealed. Further analyses indicated, however, that it is likely that depression is linked to particular aspects of memory, the linkage is found in particular subsets of depressed individuals, and memory impairment is not unique to depression.

Question: What is the association between depression and memory impairment?


Forecasting Nike's sales using Facebook data. This paper tests whether accurate sales forecasts for Nike are possible from Facebook data and how events related to Nike affect the activity on Nike's Facebook pages. The paper draws from the AIDA sales framework (Awareness, Interest, Desire, and Action) from the domain of marketing and employs the method of social set analysis from the domain of computational social science to model sales from Big Social Data. The dataset consists of (a) selection of Nike's Facebook pages with the number of likes, comments, posts etc. that have been registered for each page per day and (b) business data in terms of quarterly global sales figures published in Nike's financial reports. An event study is also conducted using the Social Set Visualizer (SoSeVi). The findings suggest that Facebook data does have informational value. Some of the simple regression models have a high forecasting accuracy. The multiple regressions have a lower forecasting accuracy and cause analysis barriers due to data set characteristics such as perfect multicollinearity. The event study found abnormal activity around several Nike specific events but inferences about those activity spikes, whether they are purely event-related or coincidences, can only be determined after detailed case-by-case text analysis. Our findings help assess the informational value of Big Social Data for a company's marketing strategy, sales operations and supply chain.

Question: What is the forecasting accuracy of the simple regression models?


A Fully Integrated Reconfigurable Self-Startup RF Energy-Harvesting System With Storage Capability. This paper introduces a fully integrated RF energy-harvesting system. The system can simultaneously deliver the current demanded by external dc loads and store the extra energy in external capacitors, during periods of extra output power. The design is fabricated in 0.18- $\mu \text{m}$  CMOS technology, and the active chip area is 1.08 mm2. The proposed self-startup system is reconfigurable with an integrated LC matching network, an RF rectifier, and a power management/controller unit, which consumes 66–157 nW. The required clock generation and the voltage reference circuit are integrated on the same chip. Duty cycle control is used to operate for the low input power that cannot provide the demanded output power. Moreover, the number of stages of the RF rectifier is reconfigurable to increase the efficiency of the available output power. For high available power, a secondary path is activated to charge an external energy storage element. The measured RF input power sensitivity is −14.8 dBm at a 1-V dc output.

Question: What is the name of the system?


A Bandelet-Based Inpainting Technique for Clouds Removal From Remotely Sensed Images. It is well known that removing cloud-contaminated portions of a remotely sensed image and then filling in the missing data represent an important photo editing cumbersome task. In this paper, an efficient inpainting technique for the reconstruction of areas obscured by clouds or cloud shadows in remotely sensed images is presented. This technique is based on the Bandelet transform and the multiscale geometrical grouping. It consists of two steps. In the first step, the curves of geometric flow of different zones of the image are determined by using the Bandelet transform with multiscale grouping. This step allows an efficient representation of the multiscale geometry of the image's structures. Having well represented this geometry, the information inside the cloud-contaminated zone is synthesized by propagating the geometrical flow curves inside that zone. This step is accomplished by minimizing a functional whose role is to reconstruct the missing or cloud contaminated zone independently of the size and topology of the inpainting domain. The proposed technique is illustrated with some examples on processing aerial images. The obtained results are compared with those obtained by other clouds removal techniques.

Question: How to remove clouds from remotely sensed images?


2D Color Barcodes for Mobile Phones. We propose a new high capacity color barcode, named HCC2D (High Capacity Colored 2-Dimensional), which use colors to increase the barcode data density. The introduction and recognition of colored modules poses some new and non-trivial computer vision challenges, such as handling the color distortions introduced by the hardware equipment that realizes the Print&Scan process. We developed a prototype for generating and reading the HCC2D code format, both on desktops (Linux and Windows platforms) and on mobile phones (Android platforms). We tested this prototype in many experiments considering different operating scenarios and data densities, and compared it to known 2-dimensional barcodes.

Question: What is the difference between a barcode and a QR code?


A Ka-band power amplifier based on the traveling-wave power-dividing/combining slotted-waveguide circuit. An eight-device Ka-band solid-state power amplifier has been designed and fabricated using a traveling-wave power-dividing/combining technique. The low-profile slotted-waveguide structure employed in this design provides not only a high power-combining efficiency over a wide bandwidth, but also efficient heat sinking for the active devices. The measured maximum small-signal gain of the eight-device power amplifier is 19.4 dB at 34 GHz with a 3-dB bandwidth of 3.2 GHz (f/sub L/=31.8 GHz, f/sub H/=35 GHz). The measured maximum output power at 1-dB compression (P/sub out/ at 1 dB) from the power amplifier is 33 dBm (/spl sim/2 W) at 32.2 GHz, with a power-combining efficiency of 80%. Furthermore, performance degradation of this power amplifier due to device failures has also been simulated and measured.

Question: What is the maximum output power of the amplifier?


Formal Query Generation for Question Answering over Knowledge Bases. Question answering (QA) systems often consist of several components such as Named Entity Disambiguation (NED), Relation Extraction (RE), and Query Generation (QG). In this paper, we focus on the QG process of a QA pipeline on a large-scale Knowledge Base (KB), with noisy annotations and complex sentence structures. We therefore propose SQG, a SPARQL Query Generator with modular architecture, enabling easy integration with other components for the construction of a fully functional QA pipeline. SQG can be used on large open-domain KBs and handle noisy inputs by discovering a minimal subgraph based on uncertain inputs, that it receives from the NED and RE components. This ability allows SQG to consider a set of candidate entities/relations, as opposed to the most probable ones, which leads to a significant boost in the performance of the QG component. The captured subgraph covers multiple candidate walks, which correspond to SPARQL queries. To enhance the accuracy, we present a ranking model based on Tree-LSTM that takes into account the syntactical structure of the question and the tree representation of the candidate queries to find the one representing the correct intention behind the question. SQG outperforms the baseline systems and achieves a macro F1-measure of 75% on the LC-QuAD dataset.

Question: What is the name of the paper?


Strabismus Recognition Using Eye-Tracking Data and Convolutional Neural Networks. Strabismus is one of the most common vision diseases that would cause amblyopia and even permanent vision loss. Timely diagnosis is crucial for well treating strabismus. In contrast to manual diagnosis, automatic recognition can significantly reduce labor cost and increase diagnosis efficiency. In this paper, we propose to recognize strabismus using eye-tracking data and convolutional neural networks. In particular, an eye tracker is first exploited to record a subject's eye movements. A gaze deviation (GaDe) image is then proposed to characterize the subject's eye-tracking data according to the accuracies of gaze points. The GaDe image is fed to a convolutional neural network (CNN) that has been trained on a large image database called ImageNet. The outputs of the full connection layers of the CNN are used as the GaDe image's features for strabismus recognition. A dataset containing eye-tracking data of both strabismic subjects and normal subjects is established for experiments. Experimental results demonstrate that the natural image features can be well transferred to represent eye-tracking data, and strabismus can be effectively recognized by our proposed method.

Question: How to recognize strabismus using eye-tracking data and convolutional neural networks?


The Most Dangerous Code in the Browser. Browser extensions are ubiquitous. Yet, in today's browsers, extensions are the most dangerous code to user privacy. Extensions are third-party code, like web applications, but run with elevated privileges. Even worse, existing browser extension systems give users a false sense of security by considering extensions to be more trustworthy than web applications. This is because the user typically has to explicitly grant the extension a series of permissions it requests, e.g., to access the current tab or a particular website. Unfortunately, extensions developers do not request minimum privileges and users have become desensitized to install-time warnings. Furthermore, permissions offered by popular browsers are very broad and vague. For example, over 71% of the top-500 Chrome extensions can trivially leak the user's data from any site. In this paper, we argue for new extension system design, based on mandatory access control, that protects the user's privacy from malicious extensions. A system employing this design can enable a range of common extensions to be considered safe, i.e., they do not require user permissions and can be ensured to not leak information, while allowing the user to share information when desired. Importantly, such a design can make permission requests a rarity and thus more meaningful.

Question: What is the most dangerous code in the browser?


A current-starved inverter-based differential amplifier design for ultra-low power applications. As silicon feature sizes decrease, more complex circui try arrays can now be contrived on a single die. This increase in the number of on-chip devices per unit area results in increased power dissipation per unit area. In order to meet certain power and operating temperature specifications, circuit design necessitates a focus on power efficiency, which is especially important in systems employing hundreds or thousands of instances of the same device. In large arrays, a slight increase in the power efficiency of a single component is heightened by the number of instances of the device in the system. This paper proposes a fully differential, low-power current-starving inverter-based amplifier topology designed in a commercial 0.18μm process. This design achieves 46dB DC gain and a 464 kHz uni ty gain frequency with a power consumption of only 145.32nW at 700mV power supply vol tage for ultra-low power, low bandwidth applications. Higher bandwidth designs are also proposed, including a 48dB DC gain, 2.4 MHz unity-gain frequency amplifier operating at 900mV wi th only 3.74μW power consumption.

Question: How to design a low power amplifier?


Attacking DDoS at the Source. Distributed denial-of-service (DDoS) attacks present an Internet-wide threat. We propose D-WARD, a DDoS defense system deployed at source-end networks that autonomously detects and stops attacks originating from these networks. Attacks are detected by the constant monitoring of two-way traffic flows between the network and the rest of the Internet and periodic comparison with normal flow models. Mismatching flows are rate-limited in proportion to their aggressiveness. D-WARD offers good service to legitimate traffic even during an attack, while effectively reducing DDoS traffic to a negligible level. A prototype of the system has been built in a Linux router. We show its effectiveness in various attack scenarios, discuss motivations for deployment, and describe associated costs.

Question: What is a DDoS attack?


The task-based approach : some questions and suggestions. This article >rst addresses the question of what tasks are. It suggests that rather than accept the common 'communicative' de>nition, we should return to a broader de>nition and then focus on key dimensions that distinguish (from the learner's perspective) di=erent types of task, notably degrees of taskinvolvement and degrees of focus on form or meaning. This approach helps us to conceptualize the complementary roles of form-focused and meaningfocused tasks in our methodology. It also shows the continuity between taskbased language teaching and the broader communicative approach within which it is a development. Finally the article asks whether 'task-based approach' is really the most appropriate term at all for describing these developments in language pedagogy.

Question: What is the task-based approach?


Feature based Summarization of Customers' Reviews of Online Products. With the growing availability and popularity of opinion-rich resources such as review forums for the product sold online, choosing the right product from a large number of products have become difficult for the user. For trendy product, the number of customers' opinions available can be in the thousands. It becomes hard for the customers to read all the reviews and if he reads only a few of those reviews, then he may get a biased view about the product. Makers of the products may also feel difficult to maintain, keep track and understand the customers' views for the products. Several research works have been proposed in the past to address these issues, but they have certain limitations: The systems implemented are completely opaque, the reviews are not easier to perceive and are time consuming to analyze because of large irrelevant information apart from actual opinions about the features, the feature based summarization system that are implemented are more generic ones and static in nature. In this research, we proposed a dynamic system for feature based summarization of customers' opinions for online products, which works according to the domain of the product. We are extracting online reviews for a product on periodic bases, each time after extraction, we carry out the following work: Firstly, identification of features of a product from customers' opinions is done. Next, for each feature, its corresponding opinions' are extracted and their orientation or polarity (positive/negative) is detected. The final polarity of feature-opinions pairs is calculated. At last, feature based summarizations of the reviews are generated, by extracting the relevant excerpts with respect to each feature-opinions pair and placing it into their respective feature based cluster. These feature based excerpts can easily be digested by the user. © 2013 The Authors. Published by Elsevier B.V. Selection and peer-review

Question: What is a feature based summarization of customers' opinions for online products?


Graph Partitioning using Quantum Annealing on the D-Wave System. Graph partitioning (GP) applications are ubiquitous throughout mathematics, computer science, chemistry, physics, bio-science, machine learning, and complex systems. Post Moore's era supercomputing has provided us an opportunity to explore new approaches for traditional graph algorithms on quantum computing architectures. In this work, we explore graph partitioning using quantum annealing on the D-Wave 2X machine. Motivated by a recently proposed graph-based electronic structure theory applied to quantum molecular dynamics (QMD) simulations, graph partitioning is used for reducing the calculation of the density matrix into smaller subsystems rendering the calculation more computationally efficient. Unconstrained graph partitioning as community clustering based on the modularity metric can be naturally mapped into the Hamiltonian of the quantum annealer. On the other hand, when constraints are imposed for partitioning into equal parts and minimizing the number of cut edges between parts, a quadratic unconstrained binary optimization (QUBO) reformulation is required. This reformulation may employ the graph complement to fit the problem in the Chimera graph of the quantum annealer. Partitioning into 2 parts and k parts concurrently for arbitrary k are demonstrated with benchmark graphs, random graphs, and small material system density matrix based graphs. Results for graph partitioning using quantum and hybrid classical-quantum approaches are shown to be comparable to current "state of the art" methods and sometimes better.

Question: What is the D-Wave 2X machine?


Predicting and analyzing secondary education placement-test scores: A data mining approach. Understanding the factors that lead to success (or failure) of students at placement tests is an interesting and challenging problem. Since the centralized placement tests and future academic achievements are considered to be related concepts, analysis of the success factors behind placement tests may help understand and potentially improve academic achievement. In this study using a large and feature rich dataset from Secondary Education Transition System in Turkey we developed models to predict secondary education placement test results, and using sensitivity analysis on those prediction models we identified the most important predictors. The results showed that C5 decision tree algorithm is the best predictor with 95% accuracy on hold-out sample, followed by support vector machines (with an accuracy of 91%) and artificial neural networks (with an accuracy of 89%). Logistic regression models came out to be the least accurate of the four with and overall accuracy of 82%. The sensitivity analysis revealed that previous test experience, whether a student has a scholarship, student's number of siblings, previous years' grade point average are among the most important predictors of the placement test scores. 2012 Elsevier Ltd. All rights reserved.

Question: What is the most important predictor of placement test scores?


Design for reliability of power electronics modules. 0026-2714/$ see front matter 2009 Published by doi:10.1016/j.microrel.2009.07.055 * Corresponding author. Tel.: +44 (0)20 8331 8660 E-mail address: c.bailey@gre.ac.uk (C. Bailey). Power electronics uses semiconductor technology to convert and control electrical power. Demands for efficient energy management, conversion and conservation, and the increasing take-up of electronics in transport systems has resulted in tremendous growth in the use of power electronics devices such as Insulated Gate Bipolar Transistors (IGBT's). The packaging of power electronics devices involves a number of challenges for the design engineer in terms of reliability. For example, IGBT modules will contain a number of semiconductor dies within a small footprint bonded to substrates with aluminum wires and wide area solder joints. To a great extent, the reliability of the package will depend on the thermo-mechanical behavior of these materials. This paper details a physics of failure approach to reliability predictions of IGBT modules. It also illustrates the need for a probabilistic approach to reliability predictions that include the effects of design variations. Also discussed are technologies for predicting the remaining life of the package when subjected to qualification stresses or in service stresses using prognostics methods. 2009 Published by Elsevier Ltd.

Question: What is the difference between a physics of failure approach and a probabilistic approach?


Preventing Shoulder-Surfing Attack with the Concept of Concealing the Password Objects' Information. Traditionally, picture-based password systems employ password objects (pictures/icons/symbols) as input during an authentication session, thus making them vulnerable to "shoulder-surfing" attack because the visual interface by function is easily observed by others. Recent software-based approaches attempt to minimize this threat by requiring users to enter their passwords indirectly by performing certain mental tasks to derive the indirect password, thus concealing the user's actual password. However, weaknesses in the positioning of distracter and password objects introduce usability and security issues. In this paper, a new method, which conceals information about the password objects as much as possible, is proposed. Besides concealing the password objects and the number of password objects, the proposed method allows both password and distracter objects to be used as the challenge set's input. The correctly entered password appears to be random and can only be derived with the knowledge of the full set of password objects. Therefore, it would be difficult for a shoulder-surfing adversary to identify the user's actual password. Simulation results indicate that the correct input object and its location are random for each challenge set, thus preventing frequency of occurrence analysis attack. User study results show that the proposed method is able to prevent shoulder-surfing attack.

Question: How to prevent shoulder-surfing attack?


Interactive Perception: Leveraging Action in Perception and Perception in Action. Recent approaches in robot perception follow the insight that perception is facilitated by interaction with the environment. These approaches are subsumed under the term Interactive Perception (IP). This view of perception provides the following benefits. First, interaction with the environment creates a rich sensory signal that would otherwise not be present. Second, knowledge of the regularity in the combined space of sensory data and action parameters facilitates the prediction and interpretation of the sensory signal. In this survey, we postulate this as a principle for robot perception and collect evidence in its support by analyzing and categorizing existing work in this area. We also provide an overview of the most important applications of IP. We close this survey by discussing remaining open questions. With this survey, we hope to help define the field of Interactive Perception and to provide a valuable resource for future research.

Question: What is the difference between perception and action?


Linear SLAM: A linear solution to the feature-based and pose graph SLAM based on submap joining. This paper presents a strategy for large-scale SLAM through solving a sequence of linear least squares problems. The algorithm is based on submap joining where submaps are built using any existing SLAM technique. It is demonstrated that if submaps coordinate frames are judiciously selected, the least squares objective function for joining two submaps becomes a quadratic function of the state vector. Therefore, a linear solution to large-scale SLAM that requires joining a number of local submaps either sequentially or in a more efficient Divide and Conquer manner, can be obtained. The proposed Linear SLAM technique is applicable to both feature-based and pose graph SLAM, in two and three dimensions, and does not require any assumption on the character of the covariance matrices or an initial guess of the state vector. Although this algorithm is an approximation to the optimal full nonlinear least squares SLAM, simulations and experiments using publicly available datasets in 2D and 3D show that Linear SLAM produces results that are very close to the best solutions that can be obtained using full nonlinear optimization started from an accurate initial value. The C/C++ and MATLAB source codes for the proposed algorithm are available on OpenSLAM.

Question: What is a linear solution to the feature-based and pose graph SLAM based on submap joining?


An Evaluation of Popular Copy-Move Forgery Detection Approaches. A copy-move forgery is created by copying and pasting content within the same image, and potentially postprocessing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features Sift and Surf, as well as the block-based DCT, DWT, KPCA, PCA, and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.

Question: Which feature sets perform best in copy-move forgery detection?


Towards Text Generation with Adversarially Learned Neural Outlines. Recent progress in deep generative models has been fueled by two paradigms – autoregressive and adversarial models. We propose a combination of both approaches with the goal of learning generative models of text. Our method first produces a high-level sentence outline and then generates words sequentially, conditioning on both the outline and the previous outputs. We generate outlines with an adversarial model trained to approximate the distribution of sentences in a latent space induced by general-purpose sentence encoders. This provides strong, informative conditioning for the autoregressive stage. Our quantitative evaluations suggests that conditioning information from generated outlines is able to guide the autoregressive model to produce realistic samples, comparable to maximum-likelihood trained language models, even at high temperatures with multinomial sampling. Qualitative results also demonstrate that this generative procedure yields natural-looking sentences and interpolations.

Question: How do you generate text?


Design of a wideband dual-polarized stacked patch antenna with high isolation and low cross polarization for X-band applications. The design and realization of a low-profile wideband dual-polarized patch antenna with high isolation and low cross polarization has been presented. The aperture-coupled antenna element has four layers of substrates. Two microstrip lines terminated with circular patches are printed on the backside of the bottom substrate for feeding the radiating patches, while on the other side is a ground plane with two offset H-shaped slots. One port is excited for horizontally polarized wave and the other one is for vertical polarization. Two slots are arranged in a `T' configuration to achieve a high isolation of over 35 dB. A square radiating patch is printed on the top layer of a Duroid substrate which is placed above the ground plane. A piece of Rohacell foam with a thickness of 3mm and a permittivity of 1.06 is located above the Duroid substrate. A top radiating patch is printed on the bottom side of a top Duroid substrate. Thus, a wideband performance can be obtained due to the multilayer structure and the impedance matching can be improved by adjusting the dimensions of the circular patches and the slots. Further to reduce the cross polarization, two identical elements with one rotated by 180° are combined and each pair of single linearly polarized elements are fed out of phase. In addition, a four-element array with two mirrored two-element arrays has been investigated. The obtained results can confirm that, the proposed dual-polarized patch array can provide a wideband performance across 8.5-11 GHz with VSWR <; 2. The isolation between the two ports is over 35 dB and the cross polarization level is less than -35 dB over the operating frequency band. Therefore, the proposed design technique is promising for X-band SAR applications.

Question: What is the design of a wideband dual-polarized stacked patch antenna with high isolation and low cross polarization for X-band applications?


OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks. We present an integrated framework for using Convolutional Networks for classification , localization and detection. We show how a multiscale and sliding window approach can be efficiently implemented within a ConvNet. We also introduce a novel deep learning approach to localization by learning to predict object boundaries. Bounding boxes are then accumulated rather than suppressed in order to increase detection confidence. We show that different tasks can be learned simultaneously using a single shared network. This integrated framework is the winner of the localization task of the ImageNet Large Scale Visual Recognition Challenge 2013 (ILSVRC2013) and obtained very competitive results for the detection and classifications tasks. In post-competition work, we establish a new state of the art for the detection task. Finally, we release a feature extractor from our best model called OverFeat.

Question: What is the canadian military?


The evolution of Crew Resource Management training in commercial aviation.. In this study, we describe changes in the nature of Crew Resource Management (CRM) training in commercial aviation, including its shift from cockpit to crew resource management. Validation of the impact of CRM is discussed. Limitations of CRM, including lack of cross-cultural generality are considered. An overarching framework that stresses error management to increase acceptance of CRM concepts is presented. The error management approach defines behavioral strategies taught in CRM as error countermeasures that are employed to avoid error, to trap errors committed, and to mitigate the consequences of error.

Question: What is the evolution of crew resource management training in commercial aviation?


Motivation and job satisfaction. The movement of workers to act in a desired manner has always consumed the thoughts of managers. In many ways, this goal has been reached through incentive programs, corporate pep talks, and other types of conditional administrative policy. However, as the workers adjust their behaviour in response to one of the aforementioned stimuli, is job satisfaction actualized? The instilling of satisfaction within workers is a crucial task of management. Satisfaction creates confidence, loyalty and ultimately improved quality in the output of the employed. Satisfaction, though, is not the simple result of an incentive program. Employees will most likely not take any more pride in their work even if they win the weekend getaway for having the highest sales. This paper reviews the literature of motivational theorists and draws from their approaches to job satisfaction and the role of motivation within job satisfaction. The theories of Frederick Herzberg and Edwin Locke are presented chronologically to show how Locke's theory was a response to Herzberg's theory. By understanding these theories, managers can focus on strategies of creating job satisfaction. This is followed by a brief examination of Kenneth Blanchard and Paul Hersey's theory on leadership within management and how this art is changing through time. Herzberg and job satisfaction

Question: What is the difference between Herzberg and Locke?


On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems. The ability to compute an accurate reward function is essential for optimising a dialogue policy via reinforcement learning. In real-world applications, using explicit user feedback as the reward signal is often unreliable and costly to collect. This problem can be mitigated if the user's intent is known in advance or data is available to pre-train a task success predictor off-line. In practice neither of these apply for most real world applications. Here we propose an on-line learning framework whereby the dialogue policy is jointly trained alongside the reward model via active learning with a Gaussian process model. This Gaussian process operates on a continuous space dialogue representation generated in an unsupervised fashion using a recurrent neural network encoder-decoder. The experimental results demonstrate that the proposed framework is able to significantly reduce data annotation costs and mitigate noisy user feedback in dialogue policy learning.

Question: How do you train a reward model?


A droop control strategy of parallel-inverter-based microgrid. In this paper, the control strategy for a parallel-inverters-based microgrid is presented. The control strategy includes outer active and reactive power control loop based on frequency and voltage droop method to avoid critical communications among distributed generation units (DGs). The inner inverter control loop is employed to produce the required inverter output voltage provided by the outer power loop. In addition, two inverter control schemes are introduced and compared. This microgrid can operate at both grid-connected and islanded mode with proper power sharing capability between parallel DG systems. Moreover, smooth transition between operating modes is achieved without causing negative effect on the utility and critical loads. The performance of this control strategy is verified in simulation using Matlab/Simulink.

Question: How to control a microgrid?


Provably Secure Active IC Metering Techniques for Piracy Avoidance and Digital Rights Management. In the horizontal semiconductor business model where the designer's intellectual property (IP) is transparent to foundry and to other entities on the production chain, integrated circuits (ICs) overbuilding and IP piracy are prevalent problems. Active metering is a suite of methods enabling the designers to control their chips postfabrication. We provide a comprehensive description of the first known active hardware metering method and introduce new formal security proofs. The active metering method uniquely and automatically locks each IC upon manufacturing, such that the IP rights owner is the only entity that can provide the specific key to unlock or otherwise control each chip. The IC control mechanism exploits: 1) the functional description of the design, and 2) unique and unclonable IC identifiers. The locks are embedded by modifying the structure of the hardware computation model, in the form of a finite state machine (FSM). We show that for each IC hiding the locking states within the modified FSM structure can be constructed as an instance of a general output multipoint function that can be provably efficiently obfuscated. The hidden locks within the FSM may also be used for remote enabling and disabling of chips by the IP rights owner during the IC's normal operation. An automatic synthesis method for low overhead hardware implementation is devised. Attacks and countermeasures are addressed. Experimental evaluations demonstrate the low overhead of the method. Proof-of-concept implementation on the H.264 MPEG decoder automatically synthesized on a Xilinix Virtex-5 field-programmable gate array (FPGA) further shows the practicality, security, and the low overhead of the new method.

Question: How to implement a secure IC locking mechanism?


Analysis of WEKA Data Mining Algorithm REPTree, Simple Cart and RandomTree for Classification of Indian News. The amount of data in the world and in our lives seems everincreasing and there's no end to it. The Weka workbench is an organized collection of state-of-the-art machine learning algorithms and data pre-processing tools. The basic way of interacting with these methods is by invoking them from the command line. However, convenient interactive graphical user interfaces are provided for data exploration, for setting up largescale experiments on distributed computing platforms, and for designing configurations for streamed data processing. These interfaces constitute an advanced environment for experimental data mining. Classification is an important data mining technique with broad applications. It classifies data of various kinds. This paper has been carried out to make a performance evaluation of REPTree, Simple Cart and RandomTree classification algorithm. The paper sets out to make comparative evaluation of classifiers REPTree, Simple Cart and RandomTree in the context of dataset of Indian news to maximize true positive rate and minimize false positive rate. For processing Weka API were used. The results in the paper on dataset of Indian news also show that the efficiency and accuracy of RandomTree is good than REPTree, and Simple Cart. Keywords— Simple Cart, RandomTree, REPTree, Weka, WWW

Question: What is the difference between REPTree, Simple Cart and RandomTree?