File Fragment Classification-The Case for Specialized Approaches. Increasingly advances in file carving, memory analysis and network forensics requires the ability to identify the underlying type of a file given only a file fragment. Work to date on this problem has relied on identification of specific byte sequences in file headers and footers, and the use of statistical analysis and machine learning algorithms taken from the middle of the file. We argue that these approaches are fundamentally flawed because they fail to consider the inherent internal structure in widely used file types such as PDF, DOC, and ZIP. We support our argument with a bottom-up examination of some popular formats and an analysis of TK PDF files. Based on our analysis, we argue that specialized methods targeted to each specific file type will be necessary to make progress in this area.

Question: What is the case for specialized approaches?


Reduction of unbalanced axial magnetic force in post-fault operation of a novel six-phase double-stator axial flux PM machine using model predictrive control. This paper investigated the post-fault operation of a novel six-phase double-stator axial flux permanent magnet machine with detached winding configuration, which was found to be superior to existing winding configuration in previous study. However, the unbalanced magnetic force problem still remains unsolved. In this paper, the axial force balancing control principle is proposed and a group of specific current waveforms are deduced. When applying these currents under post-fault condition, magnetic torque, axial magnetic force and rotor losses of the machine are calculated in finite element analysis. The results are compared with normal condition and commonly-used post-fault current waveforms. It is verified that this method reduced the unbalanced axial magnetic force immensely and the torque ripple was also kept at a low level. In order to achieve the proposed current waveform, finite control set model predictive control (FCS-MPC) is adopted. This paper proposed the post-fault model of dual three-phase permanent magnet machines and designed a cost function to track the desired current waveforms. The model of the machine is used to predict the future behavior of the controlled variables and the cost function decides the next step of the inverter by evaluating all the predictions. At last, it is verified by simulation results that the control strategy performs well in both dynamic and steady-state situations.

Question: How to reduce unbalanced axial magnetic force in post-fault operation of a novel six-phase double-stator axial flux PM machine using model predictive control?


Partitioning and Segment Organization Strategies for Real-Time Selective Search on Document Streams. The basic idea behind selective search is to partition a collection into topical clusters, and for each query, consider only a subset of the clusters that are likely to contain relevant documents. Previous work on web collections has shown that it is possible to retain high-quality results while considering only a small fraction of the collection. These studies, however, assume static collections where it is feasible to run batch clustering algorithms for partitioning. In this work, we consider the novel formulation of selective search on document streams (specifically, tweets), where partitioning must be performed incrementally. In our approach, documents are partitioned into temporal segments and selective search is performed within each segment: these segments can either be clustered using batch or online algorithms, and at different temporal granularities. For efficiency, we take advantage of word embeddings to reduce the dimensionality of the document vectors. Experiments with test collections from the TREC Microblog Tracks show that we are able to achieve precision indistinguishable from exhaustive search while considering only around 5% of the collection. Interestingly, we observe no significant effectiveness differences between batch vs. online clustering and between hourly vs. daily temporal segments, despite them being very different index organizations. This suggests that architectural choices should be primarily guided by efficiency considerations.

Question: What is the difference between a batch and an online clustering algorithm?


Theory and Application of Magnetic Flux Leakage Pipeline Detection. Magnetic flux leakage (MFL) detection is one of the most popular methods of pipeline inspection. It is a nondestructive testing technique which uses magnetic sensitive sensors to detect the magnetic leakage field of defects on both the internal and external surfaces of pipelines. This paper introduces the main principles, measurement and processing of MFL data. As the key point of a quantitative analysis of MFL detection, the identification of the leakage magnetic signal is also discussed. In addition, the advantages and disadvantages of different identification methods are analyzed. Then the paper briefly introduces the expert systems used. At the end of this paper, future developments in pipeline MFL detection are predicted.

Question: What is the main principle of MFL detection?


Thinking forensics: Cognitive science for forensic practitioners.. Human factors and their implications for forensic science have attracted increasing levels of interest across criminal justice communities in recent years. Initial interest centred on cognitive biases, but has since expanded such that knowledge from psychology and cognitive science is slowly infiltrating forensic practices more broadly. This article highlights a series of important findings and insights of relevance to forensic practitioners. These include research on human perception, memory, context information, expertise, decision-making, communication, experience, verification, confidence, and feedback. The aim of this article is to sensitise forensic practitioners (and lawyers and judges) to a range of potentially significant issues, and encourage them to engage with research in these domains so that they may adapt procedures to improve performance, mitigate risks and reduce errors. Doing so will reduce the divide between forensic practitioners and research scientists as well as improve the value and utility of forensic science evidence.

Question: What is the difference between cognitive science and psychology?


Phish Phinder: A Game Design Approach to Enhance User Confidence in Mitigating Phishing Attacks. Phishing is an especially challenging cyber security threat as it does not attack computer systems, but targets the user who works on that system by relying on the vulnerability of their decision-making ability. Phishing attacks can be used to gather sensitive information from victims and can have devastating impact if they are successful in deceiving the user. Several anti-phishing tools have been designed and implemented but they have been unable to solve the problem adequately. This failure is often due to security experts overlooking the human element and ignoring their fallibility in making trust decisions online. In this paper, we present Phish Phinder, a serious game designed to enhance the user's confidence in mitigating phishing attacks by providing them with both conceptual and procedural knowledge about phishing. The user is trained through a series of gamified challenges, designed to educate them about important phishing related concepts, through an interactive user interface. Key elements of the game interface were identified through an empirical study with the aim of enhancing user interaction with the game. We also adopted several persuasive design principles while designing Phish Phinder to enhance phishing avoidance behaviour among users.

Question: What is a phishing attack?


Skin lesions on yellowfin tuna Thunnus albacares from Gulf of Mexico outer continental shelf: Morphological, molecular, and histological diagnosis of infection by a capsalid monogenoid.. We characterize lesion-associated capsaline infections on yellowfin tuna, Thunnus albacares, in the Gulf of Mexico by comparing our specimens with published descriptions and museum specimens ascribed to Capsala biparasiticum and its synonyms: vouchers of C. biparasiticum from parasitic copepods; the holotype of Capsala neothunni; and vouchers of Capsala abidjani. Those from parasitic copepods differed by having a small, rounded body, large anterior attachment organs, closely spaced dorsomarginal body sclerites, small testes, and a short and wide testicular field. No morphometric feature in the holotype of C. neothunni ranged outside of that reported for the newly-collected specimens, indicating conspecificity of our specimens. The specimens of C. abidjani differed by having a large anterior attachment organ, few and dendritic testes, and a short, wide testicular field. Large subunit ribosomal DNA (28S) sequences grouped our specimens and Capsala sp. as sister taxa and indicated a phylogenetic affinity of Nasicola klawei. The haptoral attachment site comprised a crater-like depression surrounded by a blackish-colored halo of extensively rugose skin, with abundant pockmarked-like, irregularly-shaped oblong or semi-circular epidermal pits surrounding these attachment sites. Histology confirmed extensive folding of epidermis and underlying stratum laxum, likely epidermal hyperplasia, foci of weak cell-to-cell adhesions among apical malpighian cells as well as that between stratum germinativum and stratum laxum, myriad goblet cells in epidermis, rodlet cells in apical layer of epidermis, and lymphocytic infiltrates and melanin in dermis. The present study comprises (i) the first published report of this parasite from yellowfin tuna captured in the Gulf of Mexico-NW Atlantic Ocean Basin,

Question: What is the difference between Capsala biparasiticum and Capsala neothunni?


Exoskeleton robot for rehabilitation of elbow and forearm movements. To perform essential daily activities the movement of shoulder, elbow, and wrist play a vital role and therefore proper functioning of upper-limb is very much essential. We therefore have been developing an exoskeleton robot (ExoRob) to rehabilitate and to ease upper limb motion. Toward to make a complete (i.e., 7DOF) upper-arm motion assisted robotic exoskeleton this paper focused on the development of a 2DOF exoskeleton robot to rehabilitate the elbow and forearm movements. The proposed 2DOF ExoRob is supposed to be worn on the lateral side of forearm and provide naturalistic range movements of elbow (flexion/extension) and forearm (pronation/supination) motions. This paper also focuses on the modeling and control of the proposed ExoRob. A kinematic model of the ExoRob has been developed based on modified Denavit-Hartenberg notations. Nonlinear sliding mode control technique is employed in dynamic simulation of the proposed ExoRob, where trajectory tracking that corresponds to typical rehab (passive) exercises has been carried out to evaluate the effectiveness of the developed model and controller. Simulated results show that the controller is able to maneuver the ExoRob efficiently to track the desired trajectories, which in this case consisted in passive arm movements. These movements are widely used in rehab therapy and could be performed efficiently with the developed ExoRob and the controller.

Question: What is the difference between a robot and an exoskeleton?


Creativity, the Turing Test, and the (Better) Lovelace Test. The Turing Test (TT) is claimed by many to be a way to test for the presence, in computers, of such ``deep'' phenomena as thought and consciousness. Unfortunately, attempts to build computational systems able to pass TT (or at least restricted versions of this test) have devolved into shallow symbol manipulation designed to, by hook or by crook, trick. The human creators of such systems know all too well that they have merely tried to fool those people who interact with their systems into believing that these systems really have minds. And the problem is fundamental: the structure of the TT is such as to cultivate tricksters. A better test is one that insists on a certain restrictive epistemic relation between an artificial agent (or system) A, its output o, and the human architect H of A – a relation which, roughly speaking, obtains when H cannot account for how A produced o. We call this test the ``Lovelace Test'' in honor of Lady Lovelace, who believed that only when computers originate things should they be believed to have minds.

Question: What is the Turing Test?


Discovering affective regions in deep convolutional neural networks for visual sentiment prediction. In this paper, we address the problem of automatically recognizing emotions in still images. While most of current work focus on improving whole-image representations using CNNs, we argue that discovering affective regions and supplementing local features will boost the performance, which is inspired by the observation that both global distributions and salient objects carry massive sentiments. We propose an algorithm to discover affective regions via deep framework, in which we use an off-the-shelf tool to generate N object proposals from a query image and rank these proposals with their objectness scores. Then, each proposal's sentiment score is computed using a pre-trained and fine-tuned CNN model. We combine both scores and select top K regions from the N candidates. These K regions are regarded as the most affective ones of the input image. Finally, we extract deep features from the whole-image and the selected regions, respectively, and sentiment label is predicted. The experiments show that our method is able to detect the affective local regions and achieve state-of-the-art performances on several popular datasets.

Question: What is the name of the algorithm?


Design and simulation of a Sierpinski carpet fractal antenna for 5G commercial applications. This article presents the design and simulation of a multi-band microstrip patch antenna, using Sierpinski carpet fractal concept. The proposed antenna is designed on a FR-4 substrate with a relative permittivity of 4.4 and excited using a microstrip feed line at the edge of the patch. Sierpinski carpet fractal with iteration-3 is applied to the radiating patch. After the application of fractal, this patch antenna resonates at 14.80 GHz, 17.92 GHz, 23.12 GHz, and 27.92 GHz with impedance bandwidth of 5800 MHz, 3040 MHz, 2960 MHz, and 3840 MHz respectively. With these four bands, this antenna finds application in commercial 5G wireless communications. To illustrate the effectiveness of the fractal concept, four patch antennas are designed independently at those four resonating frequencies and compared with the single fractal antenna in terms of their size and other performance parameters. All the antennas proposed here, are designed using HFSS15 and results are analyzed in terms of return loss, VSWR, gain, and bandwidth.

Question: What is a fractal antenna?


Brain-computer interfaces based on the steady-state visual-evoked response.. The Air Force Research Laboratory has implemented and evaluated two brain-computer interfaces (BCI's) that translate the steady-state visual evoked response into a control signal for operating a physical device or computer program. In one approach, operators self-regulate the brain response; the other approach uses multiple evoked responses.

Question: What is the steady-state visual-evoked response?


A novel hybrid MCDM model combining the SAW, TOPSIS and GRA methods based on experimental design. Multiple criteria decision-making (MCDM) is a difficult task because the existing alternatives are frequently in conflict with each other. This study presents a hybrid MCDM method combining simple additive weighting (SAW), techniques for order preference by similarity to an ideal solution (TOPSIS) and grey relational analysis (GRA) techniques. A feature of this method is that it employs an experimental design technique to assign attribute weights and then combines different MCDM evaluation methods to construct the hybrid decision-making model. This model can guide a decision maker in making a reasonable judgment without requiring professional skills or extensive experience. The ranking results agreed upon by multiple MCDM methods are more trustworthy than those generated by a single MCDM method. The proposed method is illustrated in a practical application scenario involving an IC packaging company. Four additional numerical examples are provided to demonstrate the applicability of the proposed method. In all of the cases, the results obtained using the proposed method were highly similar to those derived by previous studies, thus proving the validity and capability of this method to solve real-life MCDM problems. © 2016 Elsevier Inc. All rights reserved.

Question: What is a hybrid MCDM model?


Educating the Disagreeable Extravert : Narcissism , the Big Five Personality Traits , and Achievement Goal Orientation. Despite the fact that longitudinal data have been compiled over the past 30 years among undergraduate students in higher education settings regarding narcissism, the literature is devoid of empirical investigations that explore the relationships between narcissism and learning. Because the data suggest that narcissism scores are increasing each year among this population, an exploration of the relationship between narcissism and learning is timely and warranted. Sampling from university undergraduate students, this study uses the Narcissistic Personality Inventory, the Big Five Inventory, and the Achievement Goal Questionnaire to verify the known relationships between narcissism and the Big Five personality traits of extraversion and agreeableness; to verify the known relationships between the Big Five personality traits of extraversion and agreeableness and goal orientation; and to explore a previously undocumented empirical relationship between narcissism and performance goal orientation. Results of this exploratory study indicate that while narcissism does contribute to a performance goal orientation, it is not a substantial variable in determining achievement goal orientation in general. The study addresses the implications and limitations of this research in addition to areas for additional investigation.

Question: What is the relationship between narcissism and learning?


A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts. Sentiment analysis seeks to identify the viewpoint(s) underlying a text span; an example application is classifying a movie review as "thumbs up" or "thumbs down". To determine this sentiment polarity, we propose a novel machine-learning method that applies text-categorization techniques to just the subjective portions of the document. Extracting these portions can be implemented using efficient techniques for finding minimum cuts in graphs; this greatly facilitates incorporation of cross-sentence contextual constraints. Publication info: Proceedings of the ACL, 2004.

Question: What is the minimum cut?


On the dimensionality of organizational justice: a construct validation of a measure.. This study explores the dimensionality of organizational justice and provides evidence of construct validity for a new justice measure. Items for this measure were generated by strictly following the seminal works in the justice literature. The measure was then validated in 2 separate studies. Study 1 occurred in a university setting, and Study 2 occurred in a field setting using employees in an automobile parts manufacturing company. Confirmatory factor analyses supported a 4-factor structure to the measure, with distributive, procedural, interpersonal, and informational justice as distinct dimensions. This solution fit the data significantly better than a 2- or 3-factor solution using larger interactional or procedural dimensions. Structural equation modeling also demonstrated predictive validity for the justice dimensions on important outcomes, including leader evaluation, rule compliance, commitment, and helping behavior.

Question: What is the dimensionality of organizational justice?


Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape. The problem of path planning for an automaton moving in a two-dimensional scene filled with unknown obstacles is considered. The automaton is presented as a point; obstacles can be of an arbitrary shape, with continuous boundaries and of finite size; no restriction on the size of the scene is imposed. The information available to the automaton is limited to its own current coordinates and those of the target position. Also, when the automaton hits an obstacle, this fact is detected by the automaton's "tactile sensor." This information is shown to be sufficient for reaching the target or concluding in finite time that the target cannot be reached. A worst-case lower bound on the length of paths generated by any algorithm operating within the framework of the accepted model is developed; the bound is expressed in terms of the perimeters of the obstacles met by the automaton in the scene. Algorithms that guarantee reaching the target (if the target is reachable), and tests for target reachability are presented. The efficiency of the algorithms is studied, and worst-case upper bounds on the length of generated paths are produced.

Question: What is the worst case for a path planning algorithm?


Kinematics analysis of a novel 4-dof stabilized platform. This paper presents a novel stabilized platform with three UPS-type and one RPU-type active limbs based on the shipboard helicopter's landing process and its kinematics are studied systematically. First, a prototype of this stabilized platform is constructed and its dof is analyzed, Second, analytic formulas for solving the inverse displacement kinematics are derived Third, analytic formulas for solving the forward displacement kinematics are derived and verified. Theoretical formulas and results provide a basis for the further optimization, control, and manufacturing.

Question: What is a stabilized platform?


Speaker identification on the SCOTUS corpus. This paper reports the results of our experiments on speaker identification in the SCOTUS corpus, which includes oral arguments from the Supreme Court of the United States. Our main findings are as follows: 1) a combination of Gaussian mixture models and monophone HMM models attains near-100% textindependent identification accuracy on utterances that are longer than one second; 2) the sampling rate of 11025 Hz achieves the best performance (higher sampling rates are harmful); and a sampling rate as low as 2000 Hz still achieves more than 90% accuracy; 3) a distance score based on likelihood numbers was used to measure the variability of phones among speakers; we found that the most variable phone is the phone UH (as in good), and the velar nasal NG is more variable than the other two nasal sounds M and N; 4.) our models achieved "perfect" forced alignment on very long speech segments (40 minutes). These findings and their significance are discussed.

Question: How to identify speakers in the supreme court corpus?


STDP-based spiking deep convolutional neural networks for object recognition. Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in spiking neural networks (SNN) to extract visual features of low or intermediate complexity in an unsupervised manner. These studies, however, used relatively shallow architectures, and only one layer was trainable. Another line of research has demonstrated - using rate-based neural networks trained with back-propagation - that having many layers increases the recognition robustness, an approach known as deep learning. We thus designed a deep SNN, comprising several convolutional (trainable with STDP) and pooling layers. We used a temporal coding scheme where the most strongly activated neurons fire first, and less activated neurons fire later or not at all. The network was exposed to natural images. Thanks to STDP, neurons progressively learned features corresponding to prototypical patterns that were both salient and frequent. Only a few tens of examples per category were required and no label was needed. After learning, the complexity of the extracted features increased along the hierarchy, from edge detectors in the first layer to object prototypes in the last layer. Coding was very sparse, with only a few thousands spikes per image, and in some cases the object category could be reasonably well inferred from the activity of a single higher-order neuron. More generally, the activity of a few hundreds of such neurons contained robust category information, as demonstrated using a classifier on Caltech 101, ETH-80, and MNIST databases. We also demonstrate the superiority of STDP over other unsupervised techniques such as random crops (HMAX) or auto-encoders. Taken together, our results suggest that the combination of STDP with latency coding may be a key to understanding the way that the primate visual system learns, its remarkable processing speed and its low energy consumption. These mechanisms are a

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


A 3D printed low profile magnetic dipole antenna. An electrically small, low profile magnetic dipole antenna is presented. The proposed antenna is composed of multiple small twisted loops connected in parallel. The simulated radiation efficiency is 87 % at the resonant frequency of 222.5 MHz. The corresponding electrical size ka is 0.276 with the height of 0.0016λ. The prototype is built using a selective laser sintering technology and silver paste painting. The measured result is discussed.

Question: What is the resonant frequency of the antenna?


Reinforcement Learning for Humanoid Robotics. Reinforcement learning offers one of the most general framework to take traditional robotics towards true autonomy and versatility. However, applying reinforcement learning to high dimensional movement systems like humanoid robots remains an unsolved problem. In this paper, we discuss different approaches of reinforcement learning in terms of their applicability in humanoid robotics. Methods can be coarsely classified into three different categories, i.e., greedy methods, 'vanilla' policy gradient methods, and natural gradient methods. We discuss that greedy methods are not likely to scale into the domain humanoid robotics as they are problematic when used with function approximation. 'Vanilla' policy gradient methods on the other hand have been successfully applied on real-world robots including at least one humanoid robot [3]. We demonstrate that these methods can be significantly improved using the natural policy gradient instead of the regular policy gradient. A derivation of the natural policy gradient is provided, proving that the average policy gradient of Kakade [10] is indeed the true natural gradient. A general algorithm for estimating the natural gradient, the Natural Actor-Critic algorithm, is introduced. This algorithm converges to the nearest local minimum of the cost function with respect to the Fisher information metric under suitable conditions. The algorithm outperforms non-natural policy gradients by far in a cart-pole balancing evaluation, and for learning nonlinear dynamic motor primitives for humanoid robot control. It offers a promising route for the development of reinforcement learning for truly high-dimensionally continuous state-action systems.

Question: What is the difference between a natural policy gradient and a regular policy gradient?


Evolving a Heuristic Function for the Game of Tetris.

Question: What is a heuristic function?


Pustulobullous variant of SDRIFE (symmetrical drug-related intertriginous and flexural exanthema).. We report on a 64-year-old patient from Sri Lanka who presented to the emergency room with a 24-hour history of pruritic, erythematous patches – with occasional pustules and blisters – in the groins, axillae, and antecubital fossae. The patient reported no pain or general symptoms. Six days earlier, he had started taking cefuroxime (500 mg BID) for the first time. The antibiotic had been prescribed following temporary ureteral stent placement due to nephrolithiasis. He denied any other medical or dermatological conditions, and no other drugs had been taken. At initial presentation, he exhibited sharply demarcated, symmetrically disseminated, dark-red patches in the groins, axillae, and antecubital fossae. Some lesions showed pustules and blisters (Figure 1a–c). The physical exam was otherwise unremarkable; in particular, there was no fever. Histology revealed a superficial perivascular, spongiform dermatitis with lymphocytes, eosinophils, and neutrophils. Focally, necrotic keratinocytes and neutrophils were noted in the epidermis. Overall, the findings were consistent with a drug reaction (Figure 2). Apart from slightly elevated CRP levels (5.7 mg/dL, normal range: < 0.5) and mild thrombocytopenia (111,000, normal range: 166,000–308,000), laboratory tests were within normal limits. The detection of Escherichia coli in a swab taken from a pustule in the inguinal region was thought to represent contamination; the pathological lab results were considered to be consistent with an inflammatory response to nephrolithiasis. Taking the history, clinical symptoms, and histological findings into account, the patient was diagnosed with a pustulobullous variant of SDRIFE (symmetrical drug-related intertriginous and flexural exanthema). Since 2004, the acronym SDRIFE has been used for the condition previously known as baboon syndrome. First reported in 1984, the term baboon syndro

Question: What is the difference between SDRIFE and baboon syndrome?


Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. A review and meta-analysis was performed of seventy-five articles concerned with innovation characteristics and their relationship to innovation adoption and implementation. One part of the analysis consisted of constructing a methodological profile of the existing studies, and contrasting this with a hypothetical optimal approach. A second part of the study employed meta-analytic statistical techniques to assess the generality and consistency of existing empirical findings. Three innovation characteristics (compatibility, relative advantage, and complexity) had the most consistent significant relationship to innovation adoption. Suggestions for future research in the area were made.

Question: What are the characteristics of innovation?


SAR automatic target recognition based on a visual cortical system. Human Vision system is the most complex and accurate system. In order to extract better features about Synthetic Aperture Radar (SAR) targets, a SAR automatic target recognition (ATR) algorithm based on human visual cortical system is proposed. This algorithm contains three stages: (1) Image preprocessing (we use a Kuan filter to do the enhancement and an adaptive Intersecting Cortical Model (ICM) to do the segmentation) (2) Feature extraction using a sparse autoencoder. (3) Classification using a softmax regression classifier. Experiment result of MSTAR public data shows a better performance of recognition.

Question: What is the SAR automatic target recognition based on a visual cortical system?


Selection of the number of clusters via the bootstrap method. Here the problem of selecting the number of clusters in cluster analysis is considered. Recently, the concept of clustering stability, which measures the robustness of any given clustering algorithm, has been utilized in Wang (2010) for selecting the number of clusters through cross validation. In this manuscript, an estimation scheme for clustering instability is developed based on the bootstrap, and then the number of clusters is selected so that the corresponding estimated clustering instability is minimized. The proposed selection criterion's effectiveness is demonstrated on simulations and real examples.

Question: How to select the number of clusters in cluster analysis?


Active-feedback frequency-compensation technique for low-power multistage amplifiers. An active-feedback frequency-compensation (AFFC) technique for low-power operational amplifiers is presented in this paper. With an active-feedback mechanism, a high-speed block separates the low-frequency high-gain path and high-frequency signal path such that high gain and wide bandwidth can be achieved simultaneously in the AFFC amplifier. The gain stage in the active-feedback network also reduces the size of the compensation capacitors such that the overall chip area of the amplifier becomes smaller and the slew rate is improved. Furthermore, the presence of a left-half-plane zero in the proposed AFFC topology improves the stability and settling behavior of the amplifier. Three-stage amplifiers based on AFFC and nested-Miller compensation (NMC) techniques have been implemented by a commercial 0.8m CMOS process. When driving a 120-pF capacitive load, the AFFC amplifier achieves over 100-dB dc gain, 4.5-MHz gain-bandwidth product (GBW) , 65 phase margin, and 1.5-V/ s average slew rate, while only dissipating 400W power at a 2-V supply. Compared to a three-stage NMC amplifier, the proposed AFFC amplifier provides improvement in both the GBW and slew rate by 11 times and reduces the chip area by 2.3 times without significant increase in the power consumption.

Question: What is the active-feedback frequency-compensation technique for low-power multistage amplifiers?


Off-policy evaluation for slate recommendation. This paper studies the evaluation of policies that recommend an ordered set of items (e.g., a ranking) based on some context—a common scenario in web search, ads, and recommendation. We build on techniques from combinatorial bandits to introduce a new practical estimator that uses logged data to estimate a policy's performance. A thorough empirical evaluation on real-world data reveals that our estimator is accurate in a variety of settings, including as a subroutine in a learningto-rank task, where it achieves competitive performance. We derive conditions under which our estimator is unbiased—these conditions are weaker than prior heuristics for slate evaluation—and experimentally demonstrate a smaller bias than parametric approaches, even when these conditions are violated. Finally, our theory and experiments also show exponential savings in the amount of required data compared with general unbiased estimators.

Question: What is off-policy evaluation?


T-bar reconstruction of constricted ears and a new classification.. For the correction of constricted ears, many techniques are described in the literature, the majority based on Tanzer's classification of 1975. The improvements in ear reconstruction published by Brent, Nagata, Firmin and Park make better outcomes possible. It is therefore that a new classification for constricted ears is proposed, together with an alternative technique for correction of group IIA and IIB deformities, using a T-strut of costal cartilage to reconstruct the underdeveloped or missing superior crus of the antihelix.

Question: What is the new classification for constricted ears?


Biologically Plausible, Human-Scale Knowledge Representation. Several approaches to implementing symbol-like representations in neurally plausible models have been proposed. These approaches include binding through synchrony (Shastri & Ajjanagadde, ), "mesh" binding (van der Velde & de Kamps, ), and conjunctive binding (Smolensky, ). Recent theoretical work has suggested that most of these methods will not scale well, that is, that they cannot encode structured representations using any of the tens of thousands of terms in the adult lexicon without making implausible resource assumptions. Here, we empirically demonstrate that the biologically plausible structured representations employed in the Semantic Pointer Architecture (SPA) approach to modeling cognition (Eliasmith, ) do scale appropriately. Specifically, we construct a spiking neural network of about 2.5 million neurons that employs semantic pointers to successfully encode and decode the main lexical relations in WordNet, which has over 100,000 terms. In addition, we show that the same representations can be employed to construct recursively structured sentences consisting of arbitrary WordNet concepts, while preserving the original lexical structure. We argue that these results suggest that semantic pointers are uniquely well-suited to providing a biologically plausible account of the structured representations that underwrite human cognition.

Question: What is the spiking neural network?


BGPmon: A Real-Time, Scalable, Extensible Monitoring System. This paper presents a new system, called BGPmon, for monitoring the Border Gateway Protocol (BGP). BGP is the routing protocol for the global Internet. Monitoring BGP is important for both operations and research; a number of public and private BGP monitors are deployed and widely used. These existing monitors typically collect data using a full implementation of a BGP router. In contrast, BGPmon eliminates the unnecessary functions of route selection and data forwarding to focus solely on the monitoring function. BGPmon uses a publish/subscribe overlay network to provide real-time access to vast numbers of peers and clients. All routing events are consolidated into a single XML stream. XML allows us to add additional features such as labeling updates to allow easy identification of useful data by clients. Clients subscribe to BGPmon and receive the XML stream, performing tasks such as archiving, filtering, or real-time data analysis. BGPmon enables scalable real-time monitoring data distribution by allowing monitors to peer with each other and form an overlay network to provide new services and features without modifying the monitors. We illustrate the effectiveness of the BGPmon data using the Cyclops route monitoring system.

Question: What is the difference between a BGP monitor and a BGP router?


Dialectic Perspective on Innovation 1 A Dialectic Perspective on Innovation : Conflicting Demands , Multiple Pathways , and Ambidexterity. Innovation, the development and intentional introduction of new and useful ideas by individuals, teams, and organizations, lies at the heart of human adaptation. Decades of research in different disciplines and at different organizational levels have produced a wealth of knowledge about how innovation emerges and the factors that facilitate and inhibit innovation. We propose that this knowledge needs integration. In an initial step toward this goal, we apply a dialectic perspective on innovation to overcome limitations of dichotomous reasoning and to gain a more valid account of innovation. We point out that individuals, teams, and organizations need to self-regulate and manage conflicting demands of innovation and that multiple pathways can lead to idea generation and innovation. By scrutinizing the current use of the concept of organizational ambidexterity and extending it to individuals and teams, we develop a framework to help guide and facilitate future research and practice. Readers expecting specific and universal prescriptions of how to innovate will be disappointed as current research does not allow such inferences. Rather, we think innovation research should focus on developing and testing principles of innovation management in addition to developing decision aids for organizational practice. To this end, we put forward key propositions and action principles of innovation management. Dialectic Perspective on Innovation 3 A Dialectic Perspective on Innovation: Conflicting Demands, Multiple Pathways, and Ambidexterity Throughout this paper we use the term creativity for the generation of new and useful ideas and that of innovation to include both creative ideas and their implementation. Innovation can take different forms, including technological innovation, product and service innovation, and process innovation. The impo

Question: What is the difference between innovation and creativity?


Effective Multiple Feature Hashing for Large-Scale Near-Duplicate Video Retrieval. Near-duplicate video retrieval (NDVR) has recently attracted much research attention due to the exponential growth of online videos. It has many applications, such as copyright protection, automatic video tagging and online video monitoring. Many existing approaches use only a single feature to represent a video for NDVR. However, a single feature is often insufficient to characterize the video content. Moreover, while the accuracy is the main concern in previous literatures, the scalability of NDVR algorithms for large scale video datasets has been rarely addressed. In this paper, we present a novel approach-Multiple Feature Hashing (MFH) to tackle both the accuracy and the scalability issues of NDVR. MFH preserves the local structural information of each individual feature and also globally considers the local structures for all the features to learn a group of hash functions to map the video keyframes into the Hamming space and generate a series of binary codes to represent the video dataset. We evaluate our approach on a public video dataset and a large scale video dataset consisting of 132,647 videos collected from YouTube by ourselves. This dataset has been released (http://itee.uq.edu.au/shenht/UQ_VIDEO/). The experimental results show that the proposed method outperforms the state-of-the-art techniques in both accuracy and efficiency.

Question: How to hash a video?


MIMO-Net: A multi-input multi-output convolutional neural network for cell segmentation in fluorescence microscopy images. We propose a novel multiple-input multiple-output convolution neural network (MIMO-Net) for cell segmentation in fluorescence microscopy images. The proposed network trains the network parameters using multiple resolutions of the input image, connects the intermediate layers for better localization and context and generates the output using multi-resolution deconvolution filters. The MIMO-Net allows us to deal with variable intensity cell boundaries and highly variable cell size in the mouse pancreatic tissue by adding extra convolutional layers which bypass the max-pooling operation. The results show that our method outperforms state-of-the-art deep learning based approaches for segmentation.

Question: What is a convolutional neural network?


Study on Virtual Control of a Robotic Arm via a Myo Armband for the Self-Manipulation of a Hand Amputee. This paper proposes a Myo device that has electromyography (EMG) sensors for detecting electrical activities from different parts of the forearm muscles; it also has a gyroscope and an accelerometer. EMG sensors detect and provide very clear and important data from muscles compared with other types of sensors. The Myo armband sends data from EMG, gyroscope, and accelerometer sensors to a computer via Bluetooth and uses these data to control a virtual robotic arm which was built in Unity 3D. Virtual robotic arms based on EMG, gyroscope, and accelerometer sensors have different features. A robotic arm based on EMG is controlled by using the tension and relaxation of muscles. Consequently, a virtual robotic arm based on EMG is preferred for a hand amputee to a virtual robotic arm based on a gyroscope and an accelerometer

Question: What is the difference between a virtual robotic arm based on EMG and a virtual robotic arm based on a gyroscope and an accelerometer?


Fully integrated CMOS power amplifier design using the distributed active-transformer architecture. A novel on-chip impedance matching and powercombining method, the distributed active transformer is presented. It combines several low-voltage push–pull amplifiers efficiently with their outputs in series to produce a larger output power while maintaining a 50match. It also uses virtual ac grounds and magnetic couplings extensively to eliminate the need for any offchip component, such as tuned bonding wires or external inductors. Furthermore, it desensitizes the operation of the amplifier to the inductance of bonding wires making the design more reproducible. To demonstrate the feasibility of this concept, a 2.4-GHz 2-W 2-V truly fully integrated power amplifier with 50input and output matching has been fabricated using 0.35m CMOS transistors. It achieves a power added efficiency (PAE) of 41% at this power level. It can also produce 450 mW using a 1-V supply. Harmonic suppression is 64 dBc or better. This new topology makes possible a truly fully integrated watt-level gigahertz range low-voltage CMOS power amplifier for the first time.

Question: What is the power added efficiency?


To have or not to have Internet at home: Implications for online shopping. This paper analyzes the individual decision of online shopping, in terms of socioeconomic characteristics, internet related variables and location factors. Since online shopping is only observed for internet users, we use the two-step Heckman's model to correct sample selection. We argue that one of the relevant variables to explain online shopping, the existence of a home internet connection, can be endogenous. To account for this potential endogeneity, we jointly estimate the probability of online shopping and the probability of having internet at home. The dataset used in this paper comes from the Household Survey of ICT Equipment and Usage, conducted by the Spanish Statistical O¢ ce on an annual basis. Our analysis covers the period 2004-2009. Our results show that not accounting for the endogeneity of having internet at home, yields to overestimate its e¤ect on the probability of buying online. This …nding can be important in the design of public policies aimed at enhancing e-commerce through providing households with internet connection at home. We also show that, compared to other variables that are also relevant for online shopping, the e¤ect of internet at home is quite small. JEL Classi…cation: D12, C35, C25

Question: What is the e¤ect of internet at home on the probability of buying online?


A Monocephalus Diprosopus Fetus: Antenatal Sonographic Findings -. Monocephalus Diprosopus is the rarest form of conjoined twins. The etiology of this anomaly is stil obscure. We herein report a monocephalus diprosopus case that was diagnosed in week 19 of pregnancy was presented due to its rarity and the significance of its prenatal diagnosis. Recommended a pregnancy termination since it does not have a definitive treatment today, prenatal diagnosis made for such fetuses at an early stage bears importance in terms of lowering the severity of psychological trauma.

Question: What is the significance of prenatal diagnosis?


Simulation-based optimization of Markov reward processes. We propose a simulation based algorithm for optimizing the average reward in a Markov Reward Process that depends on a set of parameters As a special case the method applies to Markov Decision Processes where optimization takes place within a parametrized set of policies The algorithm involves the simulation of a single sample path and can be implemented on line A convergence result with probability is provided This research was supported by contracts with Siemens AG Munich Germany and Alcatel Bell Belgium and by contract DMI with the National Science Foundation Introduction Markov Decision Processes and the associated dynamic programming DP methodology Ber a Put provide a general framework for posing and analyzing problems of se quential decision making under uncertainty DP methods rely on a suitably de ned value function that has to be computed for every state in the state space However many inter esting problems involve very large state spaces curse of dimensionality In addition DP assumes the availability of an exact model in the form of transition probabilities In many practical situations such a model is not available and one must resort to simulation or experimentation with an actual system For all of these reasons dynamic programming in its pure form may be inapplicable The e orts to overcome the aforementioned di culties involve two main ideas The use of simulation to estimate quantities of interest thus avoiding model based computations The use of parametric representations to overcome the curse of dimensionality Parametric representations and the associated algorithms can be broadly classi ed into three main categories a Parametrized value functions Instead of associating a value V i with each state i one uses a parametric form V i r where r is a vector of tunable parameters weights and V is a so called approximation architecture For example V i r could be the output of a multilayer perceptron with

Question: What is a parametrized value function?


Agenda-Based User Simulation for Bootstrapping a POMDP Dialogue System. This paper investigates the problem of bootstrapping a statistical dialogue manager without access to training data and proposes a new probabilistic agenda-based method for simulating user behaviour. In experiments with a statistical POMDP dialogue system, the simulator was realistic enough to successfully test the prototype system and train a dialogue policy. An extensive study with human subjects showed that the learned policy was highly competitive, with task completion rates above 90%. 1 Background and Introduction 1.1 Bootstrapping Statistical Dialogue Managers One of the key advantages of a statistical approach to Dialogue Manager (DM) design is the ability to formalise design criteria as objective reward functions and to learn an optimal dialogue policy from real dialogue data. In cases where a system is designed from scratch, however, it is often the case that no suitable in-domain data is available for training the DM. Collecting dialogue data without a working prototype is problematic, leaving the developer with a classic chicken-and-egg problem. Wizard-of-Oz (WoZ) experiments can be carried out to record dialogues, but they are often time-consuming and the recorded data may show characteristics of humanhuman conversation rather than typical human-computer dialogue. Alternatively, human-computer dialogues can be recorded with a handcrafted DM prototype but neither of these two methods enables the system designer to test the implementation of the statistical DM and the learning algorithm. Moreover, the size of the recorded corpus (typically 10 dialogues) usually falls short of the requirements for training a statistical DM (typically 10 dialogues). 1.2 User Simulation-Based Training In recent years, a number of research groups have investigated the use of a two-stage simulation-based setup. A statistical user model is first trained on a limited amount of dialogue data and the model is t

Question: How to bootstrap a dialogue system?


Big Data and the Internet of Things. Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.

Question: What is the Internet of Things?


Creating natural dialogs in the carnegie mellon communicator system. The Carnegie Mellon Communicator system helps users create complex travel itineraries through a conversational interface. Itineraries consist of (multi-leg) flights, hotel and car reservations and are built from actual travel information for North America, obtained from the Web. The system manages dialog using a schema-based approach. Schemas correspond to major units of task information (such as a flight leg) and define conversational topics, or foci of interaction, meaningful to the user.

Question: How do you create natural dialogs in the carnegie mellon communicator system?


Closing the Loop: Evaluating a Measurement Instrument for Maturity Model Design. To support the systematic improvement of business intelligence (BI) in organizations, we have designed and refined a BI maturity model (BIMM) and a respective measurement instrument (MI) in prior research. In this study, we devise an evaluation strategy, and evaluate the validity of the designed measurement artifact. Through cluster analysis of maturity assessments of 92 organizations, we identify characteristic BI maturity scenarios and representative cases for the relevant scenarios. For evaluating the designed instrument, we compare its results with insights obtained from in-depth interviews in the respective companies. A close match between our model's quantitative maturity assessments and the maturity levels from the qualitative analyses indicates that the MI correctly assesses BI maturity. The applied evaluation approach has the potential to be re-used in other design research studies where the validity of utility claims is often hard to prove.

Question: How do you evaluate a measurement instrument?


Modelling memory functions with recurrent neural networks consisting of input compensation units: I. Static situations. Humans are able to form internal representations of the information they process—a capability which enables them to perform many different memory tasks. Therefore, the neural system has to learn somehow to represent aspects of the environmental situation; this process is assumed to be based on synaptic changes. The situations to be represented are various as for example different types of static patterns but also dynamic scenes. How are neural networks consisting of mutually connected neurons capable of performing such tasks? Here we propose a new neuronal structure for artificial neurons. This structure allows one to disentangle the dynamics of the recurrent connectivity from the dynamics induced by synaptic changes due to the learning processes. The error signal is computed locally within the individual neuron. Thus, online learning is possible without any additional structures. Recurrent neural networks equipped with these computational units cope with different memory tasks. Examples illustrate how information is extracted from environmental situations comprising fixed patterns to produce sustained activity and to deal with simple algebraic relations.

Question: How can neural networks learn to represent aspects of the environmental situation?


Keeping track of time: evidence for episodic-like memory in great apes. Episodic memory, as defined by Tulving, can be described in terms of behavioural elements (what, where and when information) but it is also accompained by an awareness of one's past (chronesthesia) and a subjective conscious experience (autonoetic awareness). Recent experiments have shown that corvids and rodents recall the where, what and when of an event. This capability has been called episodic-like memory because it only fulfils the behavioural criteria for episodic memory. We tested seven chimpanzees, three orangutans and two bonobos of various ages by adapting two paradigms, originally developed by Clayton and colleagues to test scrub jays. In Experiment 1, subjects were fed preferred but perishable food (frozen juice) and less preferred but non-perishable food (grape). After the food items were hidden, subjects could choose one of them either after 5 min or 1 h. The frozen juice was still available after 5 min but melted after 1 h and became unobtainable. Apes chose the frozen juice significantly more after 5 min and the grape after 1 h. In Experiment 2, subjects faced two baiting events happening at different times, yet they formed an integrated memory for the location and time of the baiting event for particular food items. We also included a memory task that required no temporal encoding. Our results showed that apes remember in an integrated fashion what, where and when (i.e., how long ago) an event happened; that is, apes distinguished between different events in which the same food items were hidden in different places at different times. The temporal control of their choices was not dependent on the familiarity of the platforms where the food was hidden. Chimpanzees' and bonobos' performance in the temporal encoding task was age-dependent, following an inverted U-shaped distribution. The age had no effect on the performance of the subjects in the task that required no temporal enco

Question: What is the difference between episodic and semantic memory?


Individual Tree Segmentation from LiDAR Point Clouds for Urban Forest Inventory. The objective of this study is to develop new algorithms for automated urban forest inventory at the individual tree level using LiDAR point cloud data. LiDAR data contain three-dimensional structure information that can be used to estimate tree height, base height, crown depth, and crown diameter. This allows precision urban forest inventory down to individual trees. Unlike most of the published algorithms that detect individual trees from a LiDAR-derived raster surface, we worked directly with the LiDAR point cloud data to separate individual trees and estimate tree metrics. Testing results in typical urban forests are encouraging. Future works will be oriented to synergize LiDAR data and optical imagery for urban tree characterization through data fusion techniques.

Question: How to detect individual trees from a LiDAR point cloud?


Genetic programming - on the programming of computers by means of natural selection. Page ii Complex Adaptive Systems John H. Holland, Christopher Langton, and Stewart W. Wilson, advisors Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, MIT Press edition John H. Holland Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life edited by Francisco J. Varela and Paul Bourgine Genetic Programming: On the Programming of Computers by Means of Natural Selection John R. Koza

Question: What is genetic programming?


Watershed Cuts: Minimum Spanning Forests and the Drop of Water Principle. We study the watersheds in edge-weighted graphs. We define the watershed cuts following the intuitive idea of drops of water flowing on a topographic surface. We first establish the consistency of these watersheds: they can be equivalently defined by their "catchment basinsrdquo (through a steepest descent property) or by the "dividing linesrdquo separating these catchment basins (through the drop of water principle). Then, we prove, through an equivalence theorem, their optimality in terms of minimum spanning forests. Afterward, we introduce a linear-time algorithm to compute them. To the best of our knowledge, similar properties are not verified in other frameworks and the proposed algorithm is the most efficient existing algorithm, both in theory and in practice. Finally, the defined concepts are illustrated in image segmentation, leading to the conclusion that the proposed approach improves, on the tested images, the quality of watershed-based segmentations.

Question: What is the drop of water principle?


The effect of shame and shame memories on paranoid ideation and social anxiety.. BACKGROUND Social wariness and anxiety can take different forms. Paranoid anxiety focuses on the malevolence of others, whereas social anxiety focuses on the inadequacies in the self in competing for social position and social acceptance. This study investigates whether shame and shame memories are differently associated with paranoid and social anxieties.   METHOD Shame, traumatic impact of shame memory, centrality of shame memory, paranoia and social anxiety were assessed using self-report questionnaires in 328 participants recruited from the general population.   RESULTS Results from path analyses show that external shame is specifically associated with paranoid anxiety. In contrast, internal shame is specifically associated with social anxiety. In addition, shame memories, which function like traumatic memories, or that are a central reference point to the individual's self-identity and life story, are significantly associated with paranoid anxiety, even when current external and internal shame are considered at the same time. Thus, traumatic impact of shame memory and centrality of shame memory predict paranoia (but not social anxiety) even when considering for current feelings of shame.   CONCLUSION Our study supports the evolutionary model suggesting there are two different types of 'conspecific' anxiety, with different evolutionary histories, functions and psychological processes. Paranoia, but less so social anxiety, is associated with traumatic impact and the centrality of shame memories. Researchers and clinicians should distinguish between types of shame memory, particularly those where the self might have felt vulnerable and subordinate and perceived others as threatening and hostile, holding malevolent intentions towards the self.

Question: What is the difference between social anxiety and paranoia?


Digital Infrastructures : The Missing IS Research Agenda. S the inauguration of information systems research (ISR) two decades ago, the information systems (IS) field's attention has moved beyond administrative systems and individual tools. Millions of users log onto Facebook, download iPhone applications, and use mobile services to create decentralized work organizations. Understanding these new dynamics will necessitate the field paying attention to digital infrastructures as a category of IT artifacts. A state-of-the-art review of the literature reveals a growing interest in digital infrastructures but also confirms that the field has yet to put infrastructure at the centre of its research endeavor. To assist this shift we propose three new directions for IS research: (1) theories of the nature of digital infrastructure as a separate type of IT artifact, sui generis; (2) digital infrastructures as relational constructs shaping all traditional IS research areas; (3) paradoxes of change and control as salient IS phenomena. We conclude with suggestions for how to study longitudinal, large-scale sociotechnical phenomena while striving to remain attentive to the limitations of the traditional categories that have guided IS research.

Question: What is the missing IS research agenda?


Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation. The recent advances in deep learning have made it possible to generate photorealistic images by using neural networks and even to extrapolate video frames from an input video clip. In this paper, for the sake of both furthering this exploration and our own interest in a realistic application, we study image-to-video translation and particularly focus on the videos of facial expressions. This problem challenges the deep neural networks by another temporal dimension comparing to the imageto-image translation. Moreover, its single input image fails most existing video generation methods that rely on recurrent models. We propose a user-controllable approach so as to generate video clips of various lengths from a single face image. The lengths and types of the expressions are controlled by users. To this end, we design a novel neural network architecture that can incorporate the user input into its skip connections and propose several improvements to the adversarial training method for the neural network. Experiments and user studies verify the effectiveness of our approach. Especially, we would like to highlight that even for the face images in the wild (downloaded from the Web and the authors' own photos), our model can generate high-quality facial expression videos of which about 50% are labeled as real by Amazon Mechanical Turk workers. Please see our project page1 for more demonstrations.

Question: How to generate videos of facial expressions?


Peak-End Effects on Player Experience in Casual Games. The peak-end rule is a psychological heuristic observing that people's retrospective assessment of an experience is strongly influenced by the intensity of the peak and final moments of that experience. We examine how aspects of game player experience are influenced by peak-end manipulations to the sequence of events in games that are otherwise objectively identical. A first experiment examines players' retrospective assessments of two games (a pattern matching game based on Bejeweled and a point-and-click reaction game) when the sequence of difficulty is manipulated to induce positive, negative and neutral peak-end effects. A second experiment examines assessments of a shootout game in which the balance between challenge and skill is similarly manipulated. Results across the games show that recollection of challenge was strongly influenced by peak-end effects; however, results for fun, enjoyment, and preference to repeat were varied -- sometimes significantly in favour of the hypothesized effects, sometimes insignificant, but never against the hypothesis.

Question: What is the peak-end rule?


Phonetic posteriorgrams for many-to-one voice conversion without parallel data training. This paper proposes a novel approach to voice conversion with non-parallel training data. The idea is to bridge between speakers by means of Phonetic PosteriorGrams (PPGs) obtained from a speaker-independent automatic speech recognition (SI-ASR) system. It is assumed that these PPGs can represent articulation of speech sounds in a speaker-normalized space and correspond to spoken content speaker-independently. The proposed approach first obtains PPGs of target speech. Then, a Deep Bidirectional Long Short-Term Memory based Recurrent Neural Network (DBLSTM) structure is used to model the relationships between the PPGs and acoustic features of the target speech. To convert arbitrary source speech, we obtain its PPGs from the same SI-ASR and feed them into the trained DBLSTM for generating converted speech. Our approach has two main advantages: 1) no parallel training data is required; 2) a trained model can be applied to any other source speaker for a fixed target speaker (i.e., many-to-one conversion). Experiments show that our approach performs equally well or better than state-of-the-art systems in both speech quality and speaker similarity.

Question: How to convert speech from one speaker to another?


Discovery of significant parameters in kidney dialysis data sets by K-means algorithm. The contributing factors for kidney dialysis such as creatinine, sodium, urea plays an important role in deciding the survival prediction of the patients as well as the need for undergoing kidney transplantation. Several attempts have been made to derive automated decision making procedure for earlier prediction. This preliminary study investigates the importance of clustering technique for identifying the influence of kidney dialysis parameters. A simple K-means algorithm is used to elicit knowledge about the interaction between many of these measured parameters and patient survival. The clustering procedure predicts the survival period of the patients who is undergoing the dialysis procedure.

Question: What is the K-means algorithm?


Environmental Detectives--The Development of an Augmented Reality Platform for Environmental Simulations. This research was supported with a grant from Microsoft MIT iCampus as a part of the Games-to-Teach Project. The authors would like to thank the PIs of the Games-to-Teach Project, Randy Hinrichs at Microsoft Research and Henry Jenkins, Director MIT Comparative Media Studies for their support of this project, as well as Kodjo Hesse, Gunnar Harboe, and Walter Holland for their hard work in the development of Environmental Detectives.

Question: What is the development of an augmented reality platform for environmental simulations?


A Cluster Separation Measure. A measure is presented which indicates the similarity of clusters which are assumed to have a data density which is a decreasing function of distance from a vector characteristic of the cluster. The measure can be used to infer the appropriateness of data partitions and can therefore be used to compare relative appropriateness of various divisions of the data. The measure does not depend on either the number of clusters analyzed nor the method of partitioning of the data and can be used to guide a cluster seeking algorithm.

Question: What is a cluster separation measure?


Deep Neural Architecture for Multi-Modal Retrieval based on Joint Embedding Space for Text and Images. Recent advances in deep learning and distributed representations of images and text have resulted in the emergence of several neural architectures for cross-modal retrieval tasks, such as searching collections of images in response to textual queries and assigning textual descriptions to images. However, the multi-modal retrieval scenario, when a query can be either a text or an image and the goal is to retrieve both a textual fragment and an image, which should be considered as an atomic unit, has been significantly less studied. In this paper, we propose a gated neural architecture to project image and keyword queries as well as multi-modal retrieval units into the same low-dimensional embedding space and perform semantic matching in this space. The proposed architecture is trained to minimize structured hinge loss and can be applied to both cross- and multi-modal retrieval. Experimental results for six different cross- and multi-modal retrieval tasks obtained on publicly available datasets indicate superior retrieval accuracy of the proposed architecture in comparison to the state-of-art baselines.

Question: What is the state of the art in cross-modal retrieval?


Graviola: a novel promising natural-derived drug that inhibits tumorigenicity and metastasis of pancreatic cancer cells in vitro and in vivo through altering cell metabolism.. Pancreatic tumors are resistant to conventional chemotherapies. The present study was aimed at evaluating the potential of a novel plant-derived product as a therapeutic agent for pancreatic cancer (PC). The effects of an extract from the tropical tree Annona Muricata, commonly known as Graviola, was evaluated for cytotoxicity, cell metabolism, cancer-associated protein/gene expression, tumorigenicity, and metastatic properties of PC cells. Our experiments revealed that Graviola induced necrosis of PC cells by inhibiting cellular metabolism. The expression of molecules related to hypoxia and glycolysis in PC cells (i.e. HIF-1α, NF-κB, GLUT1, GLUT4, HKII, and LDHA) were downregulated in the presence of the extract. In vitro functional assays further confirmed the inhibition of tumorigenic properties of PC cells. Overall, the compounds that are naturally present in a Graviola extract inhibited multiple signaling pathways that regulate metabolism, cell cycle, survival, and metastatic properties in PC cells. Collectively, alterations in these parameters led to a decrease in tumorigenicity and metastasis of orthotopically implanted pancreatic tumors, indicating promising characteristics of the natural product against this lethal disease.

Question: What is the potential of Graviola as a therapeutic agent for pancreatic cancer?


Graph-based Approach to Automatic Taxonomy Generation (GraBTax). We propose a novel graph-based approach for constructing concept hierarchy from a large text corpus. Our algorithm, GraBTax, incorporates both statistical co-occurrences and lexical similarity in optimizing the structure of the taxonomy. To automatically generate topic-dependent taxonomies from a large text corpus, GraBTax first extracts topical terms and their relationships from the corpus. The algorithm then constructs a weighted graph representing topics and their associations. A graph partitioning algorithm is then used to re-cursively partition the topic graph into a tax-onomy. For evaluation, we apply GraBTax to articles, primarily computer science, in the CiteSeerX digital library and search engine. The quality of the resulting concept hierarchy is assessed by both human judges and comparison with Wikipedia categories.

Question: What is a graph-based approach to automatic taxonomy generation?


Word-Formation in English. published by the press syndicate of the university of cambridge A catalogue record for this book is available from the British Library Library of Congress Cataloguing in Publication data Plag, Ingo. Word-formation in English / Ingo Plag. p. cm. – (Cambridge textbooks in linguistics) Includes bibliographical references (p.) and indexes. Contents Preface page xi Abbreviations and notational conventions xiii Introduction 1 1 Basic concepts 4 1.1 What is a word? 4 1.2 Studying word-formation 9 1.3 Inflection and derivation 14 1.4 Summary 17 Further reading 18 Exercises 18 2 Studying complex words 20 2.1 Identifying morphemes 20 2.1.1 The morpheme as the minimal linguistic sign 20 2.1.2 Problems with the morpheme: the mapping of form and meaning 22 2.2 Allomorphy 27 2.3 Establishing word-formation rules 30 2.4 Multiple affixation 38 2.5 Summary 41 Further reading 41 Exercises 41

Question: What is a word?


A Unified Passivity Based Control Framework for Position, Torque and Impedance Control of Flexible Joint Robots. In this paper we describe a general passivity based framework for the control of flexible joint robots. Herein the recent DLR results on torque-, position-, as well as impedance control of flexible joint robots are summarized, and the relations between the individual contributions are highlighted. It is shown that an inner torque feedback loop can be incorporated into a passivity based analysis by interpreting torque feedback in terms of shaping of the motor inertia. This result, which implicitly was already included in our earlier works on torqueand position control, can also be seized for the design of impedance controllers. For impedance control, furthermore, potential shaping is of special interest. It is shown how, based only on the motor angles, a potential function can be designed which simultaneously incorporates gravity compensation and a desired Cartesian stiffness relation for the link angles. All the presented controllers were experimentally evaluated on the DLR light-weight robots and proved their performance and robustness with respect to uncertain model parameters. Herein, an impact experiment is presented briefly, and an overview of several applications is given in which the controllers have been applied.

Question: What is a light-weight robot?


An Access Control scheme for Big Data processing. Access Control (AC) systems are among the most critical of network security components. A system's privacy and security controls are more likely to be compromised due to the misconfiguration of access control policies rather than the failure of cryptographic primitives or protocols. This problem becomes increasingly severe as software systems become more and more complex, such as Big Data (BD) processing systems, which are deployed to manage a large amount of sensitive information and resources organized into a sophisticated BD processing cluster. Basically, BD access control requires the collaboration among cooperating processing domains to be protected as computing environments that consist of computing units under distributed AC managements. Many BD architecture designs were proposed to address BD challenges; however, most of them were focused on the processing capabilities of the "three Vs" (Velocity, Volume, and Variety). Considerations for security in protecting BD are mostly ad hoc and patch efforts. Even with some inclusion of security in recent BD systems, a critical security component, AC (Authorization), for protecting BD processing components and their users from the insider attacks, remains elusive. This paper proposes a general purpose AC scheme for distributed BD processing clusters.

Question: What is the difference between a BD processing cluster and a BD processing system?


Low-Temperature Sintering of Nanoscale Silver Paste for Attaching Large-Area $({>}100~{\rm mm}^{2})$ Chips. A low-temperature sintering technique enabled by a nanoscale silver paste has been developed for attaching large-area (>100 mm2) semiconductor chips. This development addresses the need of power device or module manufacturers who face the challenge of replacing lead-based or lead-free solders for high-temperature applications. The solder-reflow technique for attaching large chips in power electronics poses serious concern on reliability at higher junction temperatures above 125°C. Unlike the soldering process that relies on melting and solidification of solder alloys, the low-temperature sintering technique forms the joints by solid-state atomic diffusion at processing temperatures below 275°C with the sintered joints having the melting temperature of silver at 961°C. Recently, we showed that a nanoscale silver paste could be used to bond small chips at temperatures similar to soldering temperatures without any externally applied pressure. In this paper, we extend the use of the nanomaterial to attach large chips by introducing a low pressure up to 5 MPa during the densification stage. Attachment of large chips to substrates with silver, gold, and copper metallization is demonstrated. Analyses of the sintered joints by scanning acoustic imaging and electron microscopy showed that the attachment layer had a uniform microstructure with micrometer-sized porosity with the potential for high reliability under high-temperature applications.

Question: How does the sintering process work?


STREAM: A First Programming Process. Programming is recognized as one of seven grand challenges in computing education. Decades of research have shown that the major problems novices experience are composition-based---they may know what the individual programming language constructs are, but they do not know how to put them together. Despite this fact, textbooks, educational practice, and programming education research hardly address the issue of teaching the skills needed for systematic development of programs.  We provide a conceptual framework for incremental program development, called Stepwise Improvement, which unifies best practice in modern software development such as test-driven development and refactoring with the prevailing perspective of programming methodology, stepwise refinement. The conceptual framework enables well-defined characterizations of incremental program development.  We utilize the conceptual framework to derive a programming process, STREAM, designed specifically for novices. STREAM is a carefully down-scaled version of a full and rich agile software engineering process particularly suited for novices learning object-oriented programming. In using it we hope to achieve two things: to help novice programmers learn faster and better while at the same time laying the foundation for a more thorough treatment of more advanced aspects of software engineering. In this article, two examples demonstrate the application of STREAM.  The STREAM process has been taught in the introductory programming courses at our universities for the past three years and the results are very encouraging. We report on a small, preliminary study evaluating the learning outcome of teaching STREAM. The study indicates a positive effect on the development of students' process competences.

Question: What is a programming process?


An Experiment of Influences of Facebook Posts in Other Users. People usually present similar behavior of their friends due to the social influence that is produced by group pressure. This factor is also present in online social networks such as Facebook. In this paper, we make a study on social influence produced by the contents published on Facebook. First, we have executed a systematic literature review of the previous works to understand better this field of research. Then, by executing the Asch experiment, we have illustrated how such social influence can change the behavior of users in Facebook. Through this experiment, we could identify how the social influence type called "conformity" is present in online social network platforms and how this influence can make changes in the behavior of people.

Question: What is the Asch experiment?


ClusType: Effective Entity Recognition and Typing by Relation Phrase-Based Clustering. Entity recognition is an important but challenging research problem. In reality, many text collections are from specific, dynamic, or emerging domains, which poses significant new challenges for entity recognition with increase in name ambiguity and context sparsity, requiring entity detection without domain restriction. In this paper, we investigate entity recognition (ER) with distant-supervision and propose a novel relation phrase-based ER framework, called ClusType, that runs data-driven phrase mining to generate entity mention candidates and relation phrases, and enforces the principle that relation phrases should be softly clustered when propagating type information between their argument entities. Then we predict the type of each entity mention based on the type signatures of its co-occurring relation phrases and the type indicators of its surface name, as computed over the corpus. Specifically, we formulate a joint optimization problem for two tasks, type propagation with relation phrases and multi-view relation phrase clustering. Our experiments on multiple genres---news, Yelp reviews and tweets---demonstrate the effectiveness and robustness of ClusType, with an average of 37% improvement in F1 score over the best compared method.

Question: What is the average of 37% improvement in F1 score over the best compared method?


Sequential Optimization and Reliability Assessment Method for Efficient Probabilistic Design. Probabilistic design, such as reliability-based design and robust design, offers tool making reliable decisions with the consideration of uncertainty associated with de variables/parameters and simulation models. Since a probabilistic optimization ofte volves a double-loop procedure for the overall optimization and iterative probabili assessment, the computational demand is extremely high. In this paper, the seq optimization and reliability assessment (SORA) is developed to improve the efficien probabilistic optimization. The SORA method employs a single-loop strategy with a s of cycles of deterministic optimization and reliability assessment. In each cycle, op zation and reliability assessment are decoupled from each other; the reliability asses is only conducted after the deterministic optimization to verify constraint feasibility un uncertainty. The key to the proposed method is to shift the boundaries of violated straints (with low reliability) to the feasible direction based on the reliability informati obtained in the previous cycle. The design is quickly improved from cycle to cycle an computational efficiency is improved significantly. Two engineering applications, reliability-based design for vehicle crashworthiness of side impact and the integr reliability and robust design of a speed reducer, are presented to demonstrate the tiveness of the SORA method. @DOI: 10.1115/1.1649968 #

Question: What is the difference between deterministic and probabilistic optimization?


Generic decoding of seen and imagined objects using hierarchical visual features. Object recognition is a key function in both human and machine vision. While brain decoding of seen and imagined objects has been achieved, the prediction is limited to training examples. We present a decoding approach for arbitrary objects using the machine vision principle that an object category is represented by a set of features rendered invariant through hierarchical processing. We show that visual features, including those derived from a deep convolutional neural network, can be predicted from fMRI patterns, and that greater accuracy is achieved for low-/high-level features with lower-/higher-level visual areas, respectively. Predicted features are used to identify seen/imagined object categories (extending beyond decoder training) from a set of computed features for numerous object images. Furthermore, decoding of imagined objects reveals progressive recruitment of higher-to-lower visual representations. Our results demonstrate a homology between human and machine vision and its utility for brain-based information retrieval.

Question: How does the brain decode objects?


Temperature insensitive current reference circuit using standard CMOS devices. In this paper, temperature insensitive current reference circuit is proposed. The reference current value is determined by using the threshold voltage controlled circuit. The main difference from the previous work (Georglou and Toumazou, 2002) is that the circuit can be fabricated by the standard CMOS process. The resistor and the transistor physical parameter temperature dependences are compensated with each other to determine the output reference current. The detail temperature performance is analyzed, and is evaluated by simulations.

Question: How to make a temperature insensitive current reference circuit?


Joint Multimodal Learning with Deep Generative Models. We investigate deep generative models that can exchange mul tiple modalities bidirectionally, e.g., generating images from correspondin g texts and vice versa. Recently, some studies handle multiple modalities on deep gen erative models, such as variational autoencoders (VAEs). However, these models typically assume that modalities are forced to have a conditioned relation, i.e., we can only generate modalities in one direction. To achieve our objective, we sh ould extract a joint representation that captures high-level concepts among al l modalities and through which we can exchange them bi-directionally. As described h rein, we propose a joint multimodal variational autoencoder (JMVAE), in whicall modalities are independently conditioned on joint representation. In other words, it models a joint distribution of modalities. Furthermore, to be able to gene rate missing modalities from the remaining modalities properly, we develop an additional method, JMVAE-kl, that is trained by reducing the divergence betwee n JMVAE's encoder and prepared networks of respective modalities. Our experi ments show that our proposed method can obtain appropriate joint representati o from multiple modalities and that it can generate and reconstruct them more prop erly than conventional VAEs. We further demonstrate that JMVAE can generate multip le modalities bidirectionally.

Question: How can we generate multiple modalities bidirectionally?


The Quest Data Mining System. The goal of the Quest project at the IBM Almaden Research center is to develop technology to enable a new breed of da&intensive decision-support applications. This paper is a capsule summary of the current functionality and architecture of the Quest data mining System. Our overall approach has been to identify basic data mining operations that cut across applications and develop fast, scalable algorithms for their execution (Agrawal, Imielinski, & Swami 1993a). We wanted our algorithms to:

Question: What is the goal of the quest project?


Enhancing the RGR Routing Protocol for Unmanned Aeronautical Ad-hoc Networks. The design of a routing protocol for Unmanned Aeronautical Ad-hoc Networks (UAANETs) is quite challenging, especially due to the high mobility of Unmanned Aerial Vehicles (UAVs) and the low network density. Among all routing protocols that have been designed for UAANETs, the Reactive-Greedy-Reactive (RGR) protocol has been proposed as a promising routing protocol in high mobility and density-variable scenarios. While prior results have shown that RGR is competitive to AODV or other purely reactive routing protocols, it is not fully exploiting all the unique features of UAANETs and the knowledge maintained by intermediate nodes. This report presents a number of different enhancements, namely scoped flooding, delayed route request, and mobility prediction, in order to improve the performance of RGR in terms of overhead, packet delivery ratio and delay respectively. These modifications were implemented in Opnet, and simulation results show that we can reduce the protocol overhead by about 30%, while at the same time increasing PDR by about 3-5% and reducing packet latency. The results further show that there is still significant scope for further performance improvements.

Question: What is the overhead of RGR?


An Attribute-Based High-Level Image Representation for Scene Classification. Scene classification is increasingly popular due to its extensive usage in many real-world applications such as object detection, image retrieval, and so on. Traditionally, the low-level hand-crafted image representations are adopted to describe the scene images. However, they usually fail to detect semantic features of visual concepts, especially in handling complex scenes. In this paper, we propose a novel high-level image representation which utilizes image attributes as features for scene classification. More specifically, the attributes of each image are firstly extracted by a deep convolution neural network (CNN), which is trained to be a multi-label classifier by minimizing an element-wise logistic loss function. The process of generating attributes can reduce the "semantic gap" between the low-level feature representation and the high level scene meaning. Based on the attributes, we then build a system to discover semantically meaningful descriptions of the scene classes. Extensive experiments on four large-scale scene classification datasets show that our proposed algorithm considerably outperforms other state-of-the-art methods.

Question: What is the semantic gap?


Performance on Raven ' s Advanced Progressive Matrices by African , East Indian , and White engineering students in South Africa. The hypothesis is tested that the Raven's Advanced Progressive Matrices (APM) has the same construct validity in African university students as it does in non-African university students. Analyses were made of scores from 294 highly select 17–23-year-olds in the Faculties of Engineering and the Built Environment at the University of the Witwatersrand (187 Africans, 40 East Indians, 67 Whites; 70 women, 224 men). Out of a total of 36 problems, the African students solved an average of 22, the East Indian students, 25, and the White students, 29 (P < .001), placing them at the 57th, 64th, and 86th percentiles, respectively, and yielding IQ equivalents of 103, 106, and 117 on the 1993 US norms. Four months earlier, they had completed the Standard Progressive Matrices. The two tests correlated .60 or higher for both the Africans and the non-Africans, and both tests predicted final end-of-year grades with mean r's = .30 (P's < .05). Items found difficult by one group were difficult for the others; items found easy by one group were easy for the others (mean r's = .90, P < .001). The African–East Indian–White differences were ''Jensen Effects,'' being most pronounced on the general factor of intelligence (measured in this instance by items with the highest item-total correlations). Indeed, the g loadings showed cross-cultural generality: For example, item-total correlations calculated on the East Indian students predicted the magnitude of the African–White differences. When each of the 0160-2896/03/$ – see front matter D 2003 Elsevier Science Inc. All rights reserved. PII: S0160 -2896 (02 )00140 -X * Corresponding author. Tel.: +1-519-661-3685; fax: +1-519-850-2302. E-mail addresses: Rushton@uwo.ca (J.P. Rushton), 135skuy@mentor.edcm.wits.ac.za (M. Skuy). 1 Tel.: +27-11-716-5286; fax: +27-11-339-3844. Intelligence 31 (2003) 123–137 36 g loading

Question: What is the difference between the APM and the SPM?


Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-to-Image Translation. The applicability of computer vision to real paintings and artworks has been rarely investigated, even though a vast heritage would greatly benefit from techniques which can understand and process data from the artistic domain. This is partially due to the small amount of annotated artistic data, which is not even comparable to that of natural images captured by cameras. In this paper, we propose a semantic-aware architecture which can translate artworks to photo-realistic visualizations, thus reducing the gap between visual features of artistic and realistic data. Our architecture can generate natural images by retrieving and learning details from real photos through a similarity matching strategy which leverages a weakly-supervised semantic understanding of the scene. Experimental results show that the proposed technique leads to increased realism and to a reduction in domain shift, which improves the performance of pre-trained architectures for classification, detection, and segmentation. Code will be made publicly available.

Question: How can we generate realistic images from artworks?


Hierarchical Grouping to Optimize an Objective Function. Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

Question: How to optimize an objective function?


Windows driver memory analysis: A reverse engineering methodology. In a digital forensics examination, the capture and analysis of volatile data provides significant information on the state of the computer at the time of seizure. Memory analysis is a premier method of discovering volatile digital forensic information. While much work has been done in extracting forensic artifacts from Windows kernel structures, less focus has been paid to extracting information from Windows drivers. There are two reasons for this: (1) source code for one version of the Windows kernel (but not associated drivers) is available for educational use and (2) drivers are generally called asynchronously and contain no exported functions. Therefore, finding the handful of driver functions of interest out of the thousands of candidates makes reverse code engineering problematic at best. Developing a methodology to minimize the effort of analyzing these drivers, finding the functions of interest, and extracting the data structures of interest is highly desirable. This paper provides two contributions. First, it describes a general methodology for reverse code engineering of Windows drivers memory structures. Second it applies the methodology to tcpip.sys, a Windows driver that controls network connectivity. The result is the extraction from tcpip.sys of the data structures needed to determine current network connections and listeners from the 32 and 64 bit versions of Windows Vista and Windows 7.

Question: How to extract the data structures from a driver?


Web Caching with Consistent Hashing. A key performance measure for the World Wide Web is the speed with which content is served to users. As traffic on the Web increases, users are faced with increasing delays and failures in data delivery. Web caching is one of the key strategies that has been explored to improve performance. An important issue in many caching systems is how to decide what is cached where at any given time. Solutions have included multicast queries and directory schemes. In this paper, we offer a new Web caching strategy based on consistent hashing. Consistent hashing provides an alternative to multicast and directory schemes, and has several other advantages in load balancing and fault tolerance. Its performance was analyzed theoretically in previous work; in this paper we describe the implementation of a consistent-hashing-based system and experiments that support our thesis that it can provide performance improvements.  1999 Published by Elsevier Science B.V. All rights reserved.

Question: What is the performance of consistent hashing?


A tactile sensor for the fingertips of the humanoid robot iCub. In order to successfully perform object manipulation, humanoid robots must be equipped with tactile sensors. However, the limited space that is available in robotic fingers imposes severe design constraints. In [1] we presented a small prototype fingertip which incorporates a capacitive pressure system. This paper shows an improved version, which has been integrated on the hand of the humanoid robot iCub. The fingertip is 14.5 mm long and 13 mm wide. The capacitive pressure sensor system has 12 sensitive zones and includes the electronics to send the 12 measurements over a serial bus with only 4 wires. Each synthetic fingertip is shaped approximately like a human fingertip. Furthermore, an integral part of the capacitive sensor is soft silicone foam, and therefore the fingertip is compliant. We describe the structure of the fingertip, their integration on the humanoid robot iCub and present test results to show the characteristics of the sensor.

Question: What is the size of the capacitive sensor?