Machine learning in cardiovascular medicine: are we there yet?. Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform. In this review article, we discuss the basics of machine learning algorithms and what potential data sources exist; evaluate the need for machine learning; and examine the potential limitations and challenges of implementing machine in the context of cardiovascular medicine. The most promising avenues for AI in medicine are the development of automated risk prediction algorithms which can be used to guide clinical care; use of unsupervised learning techniques to more precisely phenotype complex disease; and the implementation of reinforcement learning algorithms to intelligently augment healthcare providers. The utility of a machine learning-based predictive model will depend on factors including data heterogeneity, data depth, data breadth, nature of modelling task, choice of machine learning and feature selection algorithms, and orthogonal evidence. A critical understanding of the strength and limitations of various methods and tasks amenable to machine learning is vital. By leveraging the growing corpus of big data in medicine, we detail pathways by which machine learning may facilitate optimal development of patient-specific models for improving diagnoses, intervention and outcome in cardiovascular medicine.

Question: What is machine learning?


Clarifying Hypotheses by Sketching Data. Discussions between data analysts and colleagues or clients with no statistical background are difficult, as the analyst often has to teach and explain their statistical and domain knowledge. We investigate work practices of data analysts who collaborate with non-experts, and report findings regarding types of analysis, collaboration and availability of data. Based on these, we have created a tool to enhance collaboration between data analysts and their clients in the initial stages of the analytical process. Sketching time series data allows analysts to discuss expectations for later analysis. We propose function composition rather than freehand sketching, in order to structure the analyst-client conversation by independently expressing expected features in the data. We evaluate the usability of our prototype through two small studies, and report on user feedback for future iterations.

Question: How do data analysts collaborate with non-experts?


Measuring the impact of opening the London shared bicycle scheme to casual users. 0968-090X/$ see front matter 2011 Published b doi:10.1016/j.trc.2011.12.004 ⇑ Corresponding author. Tel.: +44 20 7679 7214. E-mail address: n.lathia@cs.ucl.ac.uk (N. Lathia). The increasing availability of sensor data in urban areas now offers the opportunity to perform continuous evaluations of transport systems and measure the effects of policy changes, in an empirical, large-scale, and non-invasive way. In this paper, we study one such example: the effect of changing the user-access policy in the London Barclays Cycle Hire scheme. When the scheme was launched in July 2010, users were required to apply for a key to access to the system. By December 2010, this policy was overridden in order to allow for ''casual'' usage, so that anyone in possession of a debit or credit card could gain access. While the transport authority measured the policy shift's success by the increased number of trips, we set out to investigate how the change affected the system's usage throughout the city. We present an extensive analysis of station data collected from the scheme's web site both preand post-policy change, showing how differences in both global and local behaviour can be measured, and how the policy change correlates with a variety of effects observed around the city. We find that, as expected, quicker access to the system correlates with greater week end usage; it also reinforces the week-day commuting trend. In both the preand post-change periods, the geographic distribution of activity at individual stations forms concentric circles around central London. However, upon policy change, a number of stations undergo a complete usage change, now exhibiting an opposite trend with respect to that which they had prior to the policy change. 2011 Published by Elsevier Ltd.

Question: How does the policy change affect the usage of the system?


Vertical and horizontal elasticity for dynamic virtual machine reconfiguration. Today, cloud computing applications are rapidly constructed by services belonging to different cloud providers and service owners. This work presents the inter-cloud elasticity framework, which focuses on cloud load balancing based on dynamic virtual machine reconfiguration when variations on load or on user requests volume are observed. We design a dynamic reconfiguration system, called inter-cloud load balancer (ICLB), that allows scaling up or down the virtual resources (thus providing automatized elasticity), by eliminating service downtimes and communication failures. It includes an inter-cloud load balancer for distributing incoming user HTTP traffic across multiple instances of inter-cloud applications and services and we perform dynamic reconfiguration of resources according to the real time requirements. The experimental analysis includes different topologies by showing how real-time traffic variation (using real world workloads) affects resource utilization and by achieving better resource usage in inter-cloud.

Question: What is the inter-cloud elasticity framework?


Cross-Validation and the Estimation of Conditional Probability Densities. Many practical problems, especially some connected with forecasting, require nonparametric estimation of conditional densities from mixed data. For example, given an explanatory data vector X for a prospective customer, with components that could include the customer's salary, occupation, age, sex, marital status, and address, a company might wish to estimate the density of the expenditure, Y , that could be made by that person, basing the inference on observations of (X,Y) for previous clients. Choosing appropriate smoothing parameters for this problem can be tricky, not in the least because plug-in rules take a particularly complex form in the case of mixed data. An obvious difficulty is that there exists no general formula for the optimal smoothing parameters. More insidiously, and more seriously, it can be difficult to determine which components of X are relevant to the problem of conditional inference. For example, if the jth component of X is independent of Y , then that component is irrelevant to estimating the density of Y given X, and ideally should be dropped before conducting inference. In this article we show that cross-validation overcomes these difficulties. It automatically determines which components are relevant and which are not, through assigning large smoothing parameters to the latter and consequently shrinking them toward the uniform distribution on the respective marginals. This effectively removes irrelevant components from contention, by suppressing their contribution to estimator variance; they already have very small bias, a consequence of their independence of Y . Cross-validation also yields important information about which components are relevant; the relevant components are precisely those that cross-validation has chosen to smooth in a traditional way, by assigning them smoothing parameters of conventional size. Indeed, cross-validation produces asymptotically

Question: How to estimate the conditional density of Y given X?


Joint torques during sit-to-stand in healthy subjects and people with Parkinson's disease.. OBJECTIVES To compare lower limb joint torques during sit-to-stand in normal elderly subjects and people with Parkinson's disease, using a developed biomechanical model simulating all phases of sit-to-stand.Design. A cross-sectional study utilizing a Parkinsonian and a control group.   BACKGROUND Subjects with Parkinson's disease were observed to experience difficulty in performing sit-to-stand. The developed model was used to calculate the lower limb joint torques in normal elderly subjects and subjects with Parkinson's disease, to delineate possible causes underlying difficulties in initiating sit-to-stand task.   METHODS Six normal elderly subjects and seven age-matched subjects with Parkinson's disease performed five sit-to-stand trials at their self-selected speed. Anthropometric data, two-dimensional kinematic and foot-ground and thigh-chair reactive forces were used to calculate, via inverse dynamics, the joint torques during sit-to-stand in both before and after seat-off phases. The difference between the control and Parkinson's disease group was analysed using independent t-tests.   RESULTS Both control and Parkinson's disease groups had a similar joint kinematic pattern, although the Parkinson's disease group demonstrated a slower angular displacement. The latter subjects produced significantly smaller normalized hip flexion torque and presented a slower torque build-up rate than the able-bodied subjects (P<0.05).   CONCLUSION Slowness of sit-to-stand in people with Parkinson's disease could be due to a reduced hip flexion joint torque and a prolonged rate of torque production.

Question: What is the difference between a joint torque and a joint torque?


Global climate change and terrestrial net primary production. A process-based model was used to estimate global patterns of net primary production and soil nitrogen cycling for contemporary climate conditions and current atmospheric C02 concentration. Over half of the global annual net primary production was estimated to occur in the tropics, with most of the production attributable to tropical evergreen forest. The effects of C02 doubling and associated climate changes were also explored. The responses in tropical and dry temperate ecosystems were dominated by C02, but those in northern and moist temperate ecosystems reflected the effects of temperature on nitrogen availability.

Question: What is the global net primary production?


Polygonal boundary approximation for a 2D general sweep based on envelope and boolean operations. This paper presents an algorithm that approximates (using polygons) the boundary of a general sweep for an arbitrary 2D curved object (possibly with holes). Based on set-theoretic properties of the general sweep, our algorithm generates the polygonal sweep boundary incrementally, where envelope approximations and union operations are repeatedly applied to intermediate boundaries of the sweep and consecutive instances of the moving object at sampled locations of the motion. For approximation, each instance of the object is polygonized along the motion, where the object may experience dynamic shape transformation with topological changes such as creating and /or destroying internal holes. The incremental nature of the proposed algorithm makes the boundary construction of a general sweep useful for applications in interactive shape design, collision detection, and mechanical part design. Our algorithm generates a precise approximation of the boundary of a general sweep with real-time performance in computing unsweeps, Minkowski sums and differences, and constant radius offsets. Some experimental results are also given in this paper.

Question: What is a general sweep?


Tagging Ingush - Language Technology For Low-Resource Languages Using Resources From Linguistic Field Work. This paper presents on-going work on creating NLP tools for under-resourced languages from very sparse training data coming from linguistic field work. In this work, we focus on Ingush, a Nakh-Daghestanian language spoken by about 300,000 people in the Russian republics Ingushetia and Chechnya. We present work on morphosyntactic taggers trained on transcribed and linguistically analyzed recordings and dependency parsers using English glosses to project annotation for creating synthetic treebanks. Our preliminary results are promising, supporting the goal of bootstrapping efficient NLP tools with limited or no task-specific annotated data resources available.

Question: What is the goal of this work?


Balanced-to-Unbalanced Bandpass Filters and the Antenna Application. Novel balanced-to-unbalanced (balun) bandpass filters are presented and carefully examined. By properly converting a symmetric four-port balanced-to-balanced bandpass filter to a three-port device, with two ports forming a balanced output and one port remaining unbalanced, a novel three-port balun bandpass filter with both functions of balun and bandpass filter may be realized. Specifically, a balun bandpass filter is implemented with its center frequency at 0.99 GHz and a 3-dB fractional bandwidth (Bomega) of 10.1%. The implemented balun bandpass filter presents an excellent in-band balance performance with common-mode rejection ratio (Rcm) better than 41 dB (the corresponding amplitude and phase imbalances are within 0.003deg and 1deg) over the pass- band. In this study, a wideband balun bandpass filter is also implemented so as to improve the wideband performance of the quasi- Yagi antenna. Being integrated with the proposed wideband balun bandpass filter, the implemented quasi-Yagi antenna achieves a bandwidth of 48% for IS11 | < -10 dB, with front-to-back ratio better than 13 dB, cross-polarization smaller than -20 dB, and peak gain of 3-5 dBi within the wide operating bandwidth.

Question: What is a balun bandpass filter?


Sequence to sequence learning for unconstrained scene text recognition. In this work we present a state-of-the-art approach for unconstrained natural scene text recognition. We propose a cascade approach that incorporates a convolutional neural network (CNN) architecture followed by a long short term memory model (LSTM). The CNN learns visual features for the characters and uses them with a softmax layer to detect sequence of characters. While the CNN gives very good recognition results, it does not model relation between characters, hence gives rise to false positive and false negative cases (confusing characters due to visual similarities like"g"and"9", or confusing background patches with characters; either removing existing characters or adding non-existing ones) To alleviate these problems we leverage recent developments in LSTM architectures to encode contextual information. We show that the LSTM can dramatically reduce such errors and achieve state-of-the-art accuracy in the task of unconstrained natural scene text recognition. Moreover we manually remove all occurrences of the words that exist in the test set from our training set to test whether our approach will generalize to unseen data. We use the ICDAR 13 test set for evaluation and compare the results with the state of the art approaches [11, 18]. We finally present an application of the work in the domain of for traffic monitoring.

Question: How to recognize text in a scene?


A Hierarchical Neural Autoencoder for Paragraphs and Documents. Natural language generation of coherent long texts like paragraphs or longer documents is a challenging problem for recurrent networks models. In this paper, we explore an important step toward this generation task: training an LSTM (Longshort term memory) auto-encoder to preserve and reconstruct multi-sentence paragraphs. We introduce an LSTM model that hierarchically builds an embedding for a paragraph from embeddings for sentences and words, then decodes this embedding to reconstruct the original paragraph. We evaluate the reconstructed paragraph using standard metrics like ROUGE and Entity Grid, showing that neural models are able to encode texts in a way that preserve syntactic, semantic, and discourse coherence. While only a first step toward generating coherent text units from neural models, our work has the potential to significantly impact natural language generation and summarization1.

Question: How does the neural model generate coherent text?


' s personal copy A comparative study on feature reduction approaches in Hindi and Bengali named entity recognition. Features used for named entity recognition (NER) are often high dimensional in nature. These cause overfitting when training data is not sufficient. Dimensionality reduction leads to performance enhancement in such situations. There are a number of approaches for dimensionality reduction based on feature selection and feature extraction. In this paper we perform a comprehensive and comparative study on different dimensionality reduction approaches applied to the NER task. To compare the performance of the various approaches we consider two Indian languages namely Hindi and Bengali. NER accuracies achieved in these languages are comparatively poor as yet, primarily due to scarcity of annotated corpus. For both the languages dimensionality reduction is found to improve performance of the classifiers. A Comparative study of the effectiveness of several dimensionality reduction techniques is presented in detail in this paper. 2011 Elsevier B.V. All rights reserved.

Question: What is the dimensionality of the features?


Data-free parameter pruning for Deep Neural Networks. Deep Neural nets (NNs) with millions of parameters are at the heart of many stateof-the-art computer vision systems today. However, recent works have shown that much smaller models can achieve similar levels of performance. In this work, we address the problem of pruning parameters in a trained NN model. Instead of removing individual weights one at a time as done in previous works, we remove one neuron at a time. We show how similar neurons are redundant, and propose a systematic way to remove them. Our experiments in pruning the densely connected layers show that we can remove upto 85% of the total parameters in an MNIST-trained network, and about 35% for AlexNet without significantly affecting performance. Our method can be applied on top of most networks with a fully connected layer to give a smaller network.

Question: How to prune parameters in a neural network?


Automated Verification of Electrum Wallet. We introduce a formal modeling in ASLan++ of the twofactor authentication protocol used by the Electrum Bitcoin wallet. This allows us to perform an automatic analysis of the wallet and show that it is secure for standard scenarios in Dolev Yao model [Dolev 1981]. The result could be derived thanks to some advanced features of the protocol analyzer such as the possibility to specify i) new intruder deduction rules with clauses and ii) non-deducibility constraints.

Question: How to model the two-factor authentication protocol used by the Electrum Bitcoin wallet?


Physical and mental health effects of intimate partner violence for men and women.. BACKGROUND Few population-based studies have assessed the physical and mental health consequences of both psychological and physical intimate partner violence (IPV) among women or men victims. This study estimated IPV prevalence by type (physical, sexual, and psychological) and associated physical and mental health consequences among women and men.   METHODS The study analyzed data from the National Violence Against Women Survey (NVAWS) of women and men aged 18 to 65. This random-digit-dial telephone survey included questions about violent victimization and health status indicators.   RESULTS A total of 28.9% of 6790 women and 22.9% of 7122 men had experienced physical, sexual, or psychological IPV during their lifetime. Women were significantly more likely than men to experience physical or sexual IPV (relative risk [RR]=2.2, 95% confidence interval [CI]=2.1, 2.4) and abuse of power and control (RR=1.1, 95% CI=1.0, 1.2), but less likely than men to report verbal abuse alone (RR=0.8, 95% CI=0.7, 0.9). For both men and women, physical IPV victimization was associated with increased risk of current poor health; depressive symptoms; substance use; and developing a chronic disease, chronic mental illness, and injury. In general, abuse of power and control was more strongly associated with these health outcomes than was verbal abuse. When physical and psychological IPV scores were both included in logistic regression models, higher psychological IPV scores were more strongly associated with these health outcomes than were physical IPV scores.   CONCLUSIONS Both physical and psychological IPV are associated with significant physical and mental health consequences for both male and female victims.

Question: What is the difference between physical and psychological IPV?


Towards reducing the attack surface of software backdoors. Backdoors in software systems probably exist since the very first access control mechanisms were implemented and they are a well-known security problem. Despite a wave of public discoveries of such backdoors over the last few years, this threat has only rarely been tackled so far.  In this paper, we present an approach to reduce the attack surface for this kind of attacks and we strive for an automated identification and elimination of backdoors in binary applications. We limit our focus on the examination of server applications within a client-server model. At the core, we apply variations of the delta debugging technique and introduce several novel heuristics for the identification of those regions in binary application that backdoors are typically installed in (i.e., authentication and command processing functions). We demonstrate the practical feasibility of our approach on several real-world backdoors found in modified versions of the popular software tools ProFTPD and OpenSSH. Furthermore, we evaluate our implementation not only on common instruction set architectures such as x86-64, but also on commercial off-the-shelf embedded devices powered by a MIPS32 processor.

Question: What is the attack surface of software backdoors?


A New Rectenna With All-Polarization-Receiving Capability for Wireless Power Transmission. This letter presents a new rectenna with all-polarization-receiving capability for wireless power transmission applications. By utilizing a dual linearly-polarized antenna, the incident wave of arbitrary polarization can be totally collected at its two ports. To be connected with the antenna as a rectenna, a dual-input rectifier is designed so that the RF power supplied by the two ports of the antenna can be efficiently rectified. With a proper polarization of the incident wave, the proposed rectenna exhibits a maximum efficiency of 78% under the input power density of 295.3 μW/cm2. Moreover, under the same power density, the rectenna's efficiency can always stay higher than 61% regardless of the incident wave's polarization.

Question: What is a rectenna?


Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing. ÐEfficient application scheduling is critical for achieving high performance in heterogeneous computing environments. The application scheduling problem has been shown to be NP-complete in general cases as well as in several restricted cases. Because of its key importance, this problem has been extensively studied and various algorithms have been proposed in the literature which are mainly for systems with homogeneous processors. Although there are a few algorithms in the literature for heterogeneous processors, they usually require significantly high scheduling costs and they may not deliver good quality schedules with lower costs. In this paper, we present two novel scheduling algorithms for a bounded number of heterogeneous processors with an objective to simultaneously meet high performance and fast scheduling time, which are called the Heterogeneous Earliest-Finish-Time (HEFT) algorithm and the Critical-Path-on-a-Processor (CPOP) algorithm. The HEFT algorithm selects the task with the highest upward rank value at each step and assigns the selected task to the processor, which minimizes its earliest finish time with an insertion-based approach. On the other hand, the CPOP algorithm uses the summation of upward and downward rank values for prioritizing tasks. Another difference is in the processor selection phase, which schedules the critical tasks onto the processor that minimizes the total execution time of the critical tasks. In order to provide a robust and unbiased comparison with the related work, a parametric graph generator was designed to generate weighted directed acyclic graphs with various characteristics. The comparison study, based on both randomly generated graphs and the graphs of some real applications, shows that our scheduling algorithms significantly surpass previous approaches in terms of both quality and cost of schedules, which are mainly presented with sc

Question: What is the difference between the HEFT and CPOP algorithms?


Vagal influence on working memory and attention.. The aim of the present study was to investigate the effect of vagal tone on performance during executive and non-executive tasks, using a working memory and a sustained attention test. Reactivity to cognitive tasks was also investigated using heart rate (HR) and heart rate variability (HRV). Fifty-three male sailors from the Royal Norwegian Navy participated in this study. Inter-beat-intervals were recorded continuously for 5 min of baseline, followed by randomized presentation of a working memory test (WMT) based on Baddeley and Hitch's research (1974) and a continuous performance test (CPT). The session ended with a 5-min recovery period. High HRV and low HRV groups were formed based on a median split of the root mean squared successive differences during baseline. The results showed that the high HRV group showed more correct responses than the low HRV group on the WMT. Furthermore, the high HRV group showed faster mean reaction time (mRT), more correct responses and less error, than the low HRV group on the CPT. Follow-up analysis revealed that this was evident only for components of the CPT where executive functions were involved. The analyses of reactivity showed a suppression of HRV and an increase in HR during presentation of cognitive tasks compared to recovery. This was evident for both groups. The present results indicated that high HRV was associated with better performance on tasks involving executive function.

Question: What is the effect of vagal tone on working memory and attention?


An Analysis of Anonymity in the Bitcoin System. Anonymity in Bit coin, a peer-to-peer electronic currency system, is a complicated issue. Within the system, users are identified by public-keys only. An attacker wishing to de-anonymize its users will attempt to construct the one to-many mapping between users and public-keys and associate information external to the system with the users. Bitcoinfrustrates this attack by storing the mapping of a user to his or her public-keys on that user's node only and by allowing each user to generate as many public-keys as required. In this paper we consider the topological structure of two networks derived from Bitcoin's public transaction history. We show that the two networks have a non-trivial topological structure, provide complementary views of the Bit coin system and have implications for anonymity. We combine these structures with external information and techniques such as context discovery and flow analysis to investigate an alleged theft of Bit coins, which, at the time of the theft, had a market value of approximately half a million U.S. dollars.

Question: What is the topological structure of the bitcoin network?


An Examination of Undergraduate Student's Perceptions and Predilections of the Use of YouTube in the Teaching and Learning Process. Pervasive social networking and media sharing technologies have augmented perceptual understanding and information gathering and, while text-based resources have remained the standard for centuries, they do not appeal to the hyper-stimulated visual learners of today. In particular, the research suggests that targeted YouTube videos enhance student engagement, depth of understanding, and overall satisfaction in higher education courses. In order to investigate student perceptions and preferences regarding the implications of YouTube, a study was conducted at a Mid-Atlantic minority serving institution that examined student opinions regarding the usage of YouTube videos to augment instruction in online and classroombased courses. According to the findings, use of YouTube in the teaching and learning process enhances instruction with students most likely to visit video sharing services from mobile devices. Further, length has an impact on student decisions whether or not to watch a video, and course delivery format impacts length and audio preferences. Finally, there is no relationship between personal use of social media and the perceived value of the use of YouTube in the instructional process.

Question: What is the relationship between the use of social media and the use of YouTube in the teaching and learning process?


A New Distance Measure for Face Recognition System. This paper proposes a new powerful distance measure called Normalized Unmatched Points (NUP). This measure can be used in a face recognition system to discriminate facial images. It works by counting the number of unmatched pixels between query and database images. A face recognition system has been proposed which makes use of this proposed distance measure for taking the decision on matching. This system has been tested on four publicly available databases, viz. ORL, YALE, BERN and CALTECH databases. Experimental results show that the proposed measure achieves recognition rates more than 98.66% for the first five likely matched faces. It is observed that the NUP distance measure performs better than other existing similar variants on these databases.

Question: What is the distance measure for face recognition?


High speed true random number generator based on open loop structures in FPGAs. Most hardware ''True'' Random Number Generators (TRNG) take advantage of the thermal agitation around a flip-flop metastable state. In Field Programmable Gate Arrays (FPGA), the classical TRNG structure uses at least two oscillators, build either from PLL or ring oscillators. This creates good TRNG albeit limited in frequency by the interference rate which cannot exceed a few Mbit/s. This article presents an architecture allowing higher bit rates while maintaining provable unconditional security. This speed requirement becomes stringent for secure communication applications such as the cryptographic quantum key distribution protocols. The proposed architecture is very simple and generic as it is based on an open loop structure with no specific component such as PLL. & 2009 Elsevier Ltd. All rights reserved.

Question: What is a true random number generator?


Improving Duckworth lewis method by using machine learning. There has been lot of research has been done in the area of sports with prediction. Machine learning algorithm has been applied on most of the data to predict its outcome. In cricket also there are many research has been done to find out the outcome of the match. But there are a special case in the cricket where statistic plays an important role. When the cricket match is interrupted by the rain and then again it starts during the shortage of the time it is difficult to predict the run and overs remaining in the cricket match.To over come this issue Duck worth lewis method has been introduced. In this paper we are going to discuss the short come of the method of Duck worth lewis method. In this research We are going to improve the model of duck worth lewis method with machine learning models. At end we are going to compare the Duck worth lewis method, Duck worth lewis method with run rate as a variable and Improved version of duck worth lewis method.

Question: How to improve duckworth lewis method?


Low-rank matrix completion using alternating minimization. Alternating minimization represents a widely applicable and empirically successful approach for finding low-rank matrices that best fit the given data. For example, for the problem of low-rank matrix completion, this method is believed to be one of the most accurate and efficient, and formed a major component of the winning entry in the Netflix Challenge [17].  In the alternating minimization approach, the low-rank target matrix is written in a bi-linear form, i.e. X = UV†; the algorithm then alternates between finding the best U and the best V. Typically, each alternating step in isolation is convex and tractable. However the overall problem becomes non-convex and is prone to local minima. In fact, there has been almost no theoretical understanding of when this approach yields a good result.  In this paper we present one of the first theoretical analyses of the performance of alternating minimization for matrix completion, and the related problem of matrix sensing. For both these problems, celebrated recent results have shown that they become well-posed and tractable once certain (now standard) conditions are imposed on the problem. We show that alternating minimization also succeeds under similar conditions. Moreover, compared to existing results, our paper shows that alternating minimization guarantees faster (in particular, geometric) convergence to the true matrix, while allowing a significantly simpler analysis.

Question: What is the difference between alternating minimization and other methods?


Filtering of Azimuth Ambiguity in Stripmap Synthetic Aperture Radar Images. Due to the specific characteristics of the SAR system, peculiar artifacts can appear on SAR images. In particular, finite pulse repetition frequency (PRF) and nonideal antenna pattern give rise to azimuth ambiguity, with the possible presence of "ghosts" on the image. They are due to the replica of strong targets located outside of the antenna main beam, superposed onto low intensity areas of the imaged scene. In this paper, we propose a method for the filtering of azimuth ambiguities on stripmap SAR images, that we name "asymmetric mapping and selective filtering" (AM&SF) method. Our framework is based on the theory of selective filtering and on a two-step procedure. In the first step, two asymmetric filters are used to suppress ambiguities due to each sidelobe of the antenna pattern, and the ratios between the original and filtered images are used to produce two maps of the ambiguity-affected areas (one for each sidelobe). In the second step, these maps are used to produce a final image in which only the areas affected by the ambiguities are replaced by their filtered (via the proper of the two filters) versions. The proposed method can be employed in situations in which similar approaches fail, and it has a smaller computational burden. The framework is positively tested on TerraSAR-X and COSMO/SkyMed SAR images of different marine scenes.

Question: How to filter azimuth ambiguities in stripmap SAR images?


Detection and Localization of Curbs and Stairways Using Stereo Vision. We present algorithms to detect and precisely localize curbs and stairways for autonomous navigation. These algorithms combine brightness information (in the form of edgels) with 3-D data from a commercial stereo system. The overall system (including stereo computation) runs at about 4 Hz on a 1 GHz laptop. We show experimental results and discuss advantages and shortcomings of our approach.

Question: How to detect and localize curbs and stairways for autonomous navigation?


OpenCV on TI ' s DSP + ARM ® platforms : Mitigating the challenges of porting OpenCV to embedded platforms Introduction. In today's advancing market, the growing performance and decreasing price of embedded processors are opening many doors for developers to design highly sophisticated solutions for different end applications. The complexities of these systems can create bottlenecks for developers in the form of longer development times, more complicated development environments and issues with application stability and quality. Developers can address these problems using sophisticated software packages such as OpenCV, but migrating this software to embedded platforms poses its own set of challenges. This paper will review how to mitigate some of these issues, including C++ implementation, memory constraints, floating-point support and opportunities to maximize performance using vendor-optimized libraries and integrated accelerators or co-processors. Finally, we will introduce a new effort by Texas Instruments (TI) to optimize vision systems by running OpenCV on the C6000TM digital signal processor (DSP) architecture. Benchmarks will show the advantage of using the DSP by comparing the performance of a DSP+ARM® system-on-chip (SoC) processor against an ARM-only device. OpenCV on TI's DSP+ARM platforms: Mitigating the challenges of porting OpenCV to embedded platforms Introduction OpenCV is a free and open-source computer vision library that offers a broad range of functionality under the permissive Berkeley Software Distribution (BSD) license. The library itself is written in C++ and is also usable through C or Python language applications. Thousands of developers use OpenCV to power their own specialized applications, making it the most widely-used library of its kind. The OpenCV project is under active development, with regular updates to eliminate bugs and add new functionality. The mainline development effort targets the x86 architecture and supports acceleratio

Question: What is the most widely used computer vision library?


From phonemes to images: levels of representation in a recurrent neural model of visually-grounded language learning. We present a model of visually-grounded language learning based on stacked gated recurrent neural networks which learns to predict visual features given an image description in the form of a sequence of phonemes. The learning task resembles that faced by human language learners who need to discover both structure and meaning from noisy and ambiguous data across modalities. We show that our model indeed learns to predict features of the visual context given phonetically transcribed image descriptions, and show that it represents linguistic information in a hierarchy of levels: lower layers in the stack are comparatively more sensitive to form, whereas higher layers are more sensitive to meaning.

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


Initial construction and validation of the Pathological Narcissism Inventory.. The construct of narcissism is inconsistently defined across clinical theory, social-personality psychology, and psychiatric diagnosis. Two problems were identified that impede integration of research and clinical findings regarding narcissistic personality pathology: (a) ambiguity regarding the assessment of pathological narcissism vs. normal narcissism and (b) insufficient scope of existing narcissism measures. Four studies are presented documenting the initial derivation and validation of the Pathological Narcissism Inventory (PNI). The PNI is a 52-item self-report measure assessing 7 dimensions of pathological narcissism spanning problems with narcissistic grandiosity (Entitlement Rage, Exploitativeness, Grandiose Fantasy, Self-sacrificing Self-enhancement) and narcissistic vulnerability (Contingent Self-esteem, Hiding the Self, Devaluing). The PNI structure was validated via confirmatory factor analysis. The PNI correlated negatively with self-esteem and empathy, and positively with shame, interpersonal distress, aggression, and borderline personality organization. Grandiose PNI scales were associated with vindictive, domineering, intrusive, and overly-nurturant interpersonal problems, and vulnerable PNI scales were associated with cold, socially avoidant, and exploitable interpersonal problems. In a small clinical sample, PNI scales exhibited significant associations with parasuicidal behavior, suicide attempts, homicidal ideation, and several aspects of psychotherapy utilization.

Question: What is the difference between narcissism and pathological narcissism?


Robust Automatic Measurement of 3D Scanned Models for the Human Body Fat Estimation. In this paper, we present an automatic tool for estimating geometrical parameters from 3-D human scans independent on pose and robustly against the topological noise. It is based on an automatic segmentation of body parts exploiting curve skeleton processing and ad hoc heuristics able to remove problems due to different acquisition poses and body types. The software is able to locate body trunk and limbs, detect their directions, and compute parameters like volumes, areas, girths, and lengths. Experimental results demonstrate that measurements provided by our system on 3-D body scans of normal and overweight subjects acquired in different poses are highly correlated with the body fat estimates obtained on the same subjects with dual-energy X-rays absorptiometry (DXA) scanning. In particular, maximal lengths and girths, not requiring precise localization of anatomical landmarks, demonstrate a good correlation (up to 96%) with the body fat and trunk fat. Regression models based on our automatic measurements can be used to predict body fat values reasonably well.

Question: How to estimate body fat from 3D scans?


Global, Dense Multiscale Reconstruction for a Billion Points. We present a variational approach for surface reconstruction from a set of oriented points with scale information. We focus particularly on scenarios with nonuniform point densities due to images taken from different distances. In contrast to previous methods, we integrate the scale information in the objective and globally optimize the signed distance function of the surface on a balanced octree grid. We use a finite element discretization on the dual structure of the octree minimizing the number of variables. The tetrahedral mesh is generated efficiently with a lookup table which allows to map octree cells to the nodes of the finite elements. We optimize memory efficiency by data aggregation, such that robust data terms can be used even on very large scenes. The surface normals are explicitly optimized and used for surface extraction to improve the reconstruction at edges and corners.

Question: How to reconstruct a surface from a set of points?


Cost-Aware WWW Proxy Caching Algorithms. Difference between Web Caching & Conventional Paging Problems: Web caching is variable-size caching (web documents vary dramatically in size depending on the information they carry (text, image, video, etc.). Web pages take different amounts of time to download, even if they are of the same size (download latency). Access streams seen by the proxy cache are the union of web access streams from tens to thousands of users (instead of coming from a few programmed sources as in the case of virtual memory paging)

Question: What is the difference between web caching and conventional paging?


Cobalt fill for advanced interconnects. Cobalt as a material promises to change the conductor landscape in many areas, particularly logic contact and interconnect. In this paper, we focus on Cobalt as the conductor for logic interconnect — a potential Cu replacement. We demonstrate Co fill capability down to 10nm CD, and show that Co-Cu resistivity cross-over will occur below 14nm CD. Single damascene Co-ULK structures are used to establish an optimized metallization stack with robust CMP performance that has electromigration and TDDB reliability better than copper interconnect.

Question: Cobalt as a conductor for logic interconnect


Definition Extraction with LSTM Recurrent Neural Networks. Definition extraction is the task to identify definitional sentences automatically from unstructured text. The task can be used in the aspects of ontology generation, relation extraction and question answering. Previous methods use handcraft features generated from the dependency structure of a sentence. During this process, only part of the dependency structure is used to extract features, thus causing information loss. We model definition extraction as a supervised sequence classification task and propose a new way to automatically generate sentence features using a Long Short-Term Memory neural network model. Our method directly learns features from raw sentences and corresponding part-ofspeech sequence, which makes full use of the whole sentence. We experiment on the Wikipedia benchmark dataset and obtain 91.2% on F1 score which outperforms the current state-of-the-art methods by 5.8%. We also show the effectiveness of our method in dealing with other languages by testing on a Chinese dataset and obtaining 85.7% on F1 score.

Question: What is the definition of a neural network?


Analysis of political discourse on twitter in the context of the 2016 US presidential elections. Social media now plays a pivotal role in electoral campaigns. Rapid dissemination of information through platforms such as Twitter has enabled politicians to broadcast their message to a wide audience. In this paper, we investigated the sentiment of tweets by the two main presidential candidates, Hillary Clinton and Donald Trump, along with almost 2.9 million tweets by Twitter users during the 2016 US Presidential Elections. We analyzed these short texts to evaluate how accurately Twitter represented the public opinion and real world events of significance related with the elections. We also analyzed the behavior of over a million distinct Twitter users to identify whether the platform was used to share original opinions and to interact with other users or whether few opinions were repeated over and over again with little inter-user dialogue. Finally, we wanted to assess the sentiment of tweets by both candidates and their impact on the election related discourse on Twitter. Some of our findings included the discovery that little original content was created by users and Twitter was primarily used for rebroadcasting already present opinions in the form of retweets with little communication between users. Also of significance was the finding that sentiment and topics expressed on Twitter can be a good proxy of public opinion and important election related events. Moreover, we found that Donald Trump offered a more optimistic and positive campaign message than Hillary Clinton and enjoyed better sentiment when mentioned in messages by Twitter users.

Question: What is the impact of social media on the 2016 US presidential elections?


Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control. The Differentiable Neural Computer (DNC) can learn algorithmic and question answering tasks. An analysis of its internal activation patterns reveals three problems: Most importantly, the lack of key-value separation makes the address distribution resulting from content-based look-up noisy and flat, since the value influences the score calculation, although only the key should. Second, DNC's deallocation of memory results in aliasing, which is a problem for content-based look-up. Thirdly, chaining memory reads with the temporal linkage matrix exponentially degrades the quality of the address distribution. Our proposed fixes of these problems yield improved performance on arithmetic tasks, and also improve the mean error rate on the bAbI question answering dataset by 43%.

Question: How does the DNC work?


History and State of the Art in Commercial Electric Ship Propulsion, Integrated Power Systems, and Future Trends. Electric propulsion has emerged as one of the most efficient propulsion arrangements for several vessel types over the last decades. Even though examples can be found in the history at the end of 19th century, and further into the 20th century, the modern use of electric propulsion started in the 1980s along with the development of semiconductor switching devices to be used in high power drives (dc drives and later ac-to-ac drives). This development opened up for full rpm control of propellers and thrusters, and thereby enabling a simplification of the mechanical structure. However, the main reason for using electric propulsion in commercial ship applications is the potential for fuel savings compared to equivalent mechanical alternatives, except for icebreakers where the performance of an electric powered propeller is superior to a combustion engine powered propeller. The fuel saving potential lies within the fact that the applicable vessels have a highly varying operation profile and are seldom run at full power. This favors the power plant principle in which electric power can be produced at any time with optimum running of prime movers, e.g., diesel engines, by turning on and off units depending on the power demand for propulsion and other vessel loads. Icebreakers were among the first vessels to take advantage of this technology later followed by cruise vessel, and the offshore drilling vessels operating with dynamic positioning (DP). The converter technology was rapidly developing and soon the dc drives were replaced with ac drives. In the same period electric propulsion emerged as basic standard for large cruise liners, and DP operated drilling vessels, but also found its way into other segments as shuttle tankers, ferries, and other special vessels. At the same time podded propulsion were introduced, where the electric motor was mounted directly

Question: What is the history of electric propulsion?


Intelligent Widgets for Intuitive Interaction and Coordination in Smart Home Environments. The intelligent home environment is a well-established example of the Ambient Intelligence application domain. A variety of sensors and actuators can be used to have the home environment adapt towards changing circumstances and user preferences. However, the complexity of how these intelligent home automation systems operate is often beyond the comprehension of non-technical users, and adding new technology to an existing infrastructure is often a burden. In this paper, we present a home automation framework designed based on smart widgets with a model driven methodology that raises the level of abstraction to configure home automation equipment. It aims to simplify user-level home automation management by mapping high-level home automation concepts onto a low-level composition and configuration of the automation building blocks with a reverse mapping to simplify the integration of new equipment into existing home automation systems. Experiments have shown that the mappings we proposed are sufficient to represent household appliances to the end user in a simple way and that new mappings can easily be added to our framework.

Question: What is the difference between a smart home and a home automation system?


Matching-CNN meets KNN: Quasi-parametric human parsing. Both parametric and non-parametric approaches have demonstrated encouraging performances in the human parsing task, namely segmenting a human image into several semantic regions (e.g., hat, bag, left arm, face). In this work, we aim to develop a new solution with the advantages of both methodologies, namely supervision from annotated data and the flexibility to use newly annotated (possibly uncommon) images, and present a quasi-parametric human parsing model. Under the classic K Nearest Neighbor (KNN)-based nonparametric framework, the parametric Matching Convolutional Neural Network (M-CNN) is proposed to predict the matching confidence and displacements of the best matched region in the testing image for a particular semantic region in one KNN image. Given a testing image, we first retrieve its KNN images from the annotated/manually-parsed human image corpus. Then each semantic region in each KNN image is matched with confidence to the testing image using M-CNN, and the matched regions from all KNN images are further fused, followed by a superpixel smoothing procedure to obtain the ultimate human parsing result. The M-CNN differs from the classic CNN [12] in that the tailored cross image matching filters are introduced to characterize the matching between the testing image and the semantic region of a KNN image. The cross image matching filters are defined at different convolutional layers, each aiming to capture a particular range of displacements. Comprehensive evaluations over a large dataset with 7,700 annotated human images well demonstrate the significant performance gain from the quasi-parametric model over the state-of-the-arts [29, 30], for the human parsing task.

Question: What is the difference between parametric and non-parametric approaches?


Metaphoric structuring: understanding time through spatial metaphors. The present paper evaluates the claim that abstract conceptual domains are structured through metaphorical mappings from domains grounded directly in experience. In particular, the paper asks whether the abstract domain of time gets its relational structure from the more concrete domain of space. Relational similarities between space and time are outlined along with several explanations of how these similarities may have arisen. Three experiments designed to distinguish between these explanations are described. The results indicate that (1) the domains of space and time do share conceptual structure, (2) spatial relational information is just as useful for thinking about time as temporal information, and (3) with frequent use, mappings between space and time come to be stored in the domain of time and so thinking about time does not necessarily require access to spatial schemas. These findings provide some of the first empirical evidence for Metaphoric Structuring. It appears that abstract domains such as time are indeed shaped by metaphorical mappings from more concrete and experiential domains such as space.

Question: What is metaphoric structuring?


Deep learning for health informatics: Recent trends and future directions. Health informatics has emerged as a growing domain of interest among researchers world-wide owing to its major implications on society. Applications of machine learning in healthcare range from disease prediction to patient-level personalized services. The prevalence of big data in healthcare has paved the way for applications based on deep learning techniques in the past few years. This paper reviews recent trends and applications of deep learning applied to the healthcare domain. We highlight recent research work, identify challenges and suggest possible future directions that could be pursued further in this domain.

Question: What is deep learning?


Gesture recognition using kinect for sign language translation. Sign Language is a widely used method of communication among the community of deaf-mute people. It contains some series of body gestures, which enables a person to interact without the need of spoken words. Although the use of sign language is very popular among the deaf-mute people but the other communities don't even try to learn it, this creates a gulf of communication and hence becomes a cause of the isolation of physically impaired people. The problem creates a requirement of a system which can facilitate a way of communication between these two communities. This paper presents a novel method for identification of an isolated sign language gesture using Microsoft Kinect. This paper presents the way of extracting some highly robust features from the depth image provided by Kinect and to use them in creating a robust and accurate gesture recognition system, for the purpose of sign language translation. Apart from translation, the proposed system also opens the doors of endless applications in the field of Human Computer Interaction. The proposed algorithm helps in translating a sign language gesture performed by a user, which in-turn can be used as an input for different types of applications.

Question: How to recognize a gesture using Kinect?


Student Privacy and Educational Data Mining: Perspectives from Industry. While the field of educational data mining (EDM) has generated many innovations for improving educational software and student learning, the mining of student data has recently come under a great deal of scrutiny. Many stakeholder groups, including public officials, media outlets, and parents, have voiced concern over the privacy of student data and their efforts have garnered national attention. The momentum behind and scrutiny of student privacy has made it increasingly difficult for EDM applications to transition from academia to industry. Based on experience as academic researchers transitioning into industry, we present three primary areas of concern related to student privacy in practice: policy, corporate social responsibility, and public opinion. Our discussion will describe the key challenges faced within these categories, strategies for overcoming them, and ways in which the academic EDM community can support the adoption of innovative technologies in large-scale production.

Question: What is the privacy of student data?


Deep Relative Attributes. Visual attributes are great means of describing images or scenes, in a way both humans and computers understand. In order to establish a correspondence between images and to be able to compare the strength of each property between images, relative attributes were introduced. However, since their introduction, hand-crafted and engineered features were used to learn increasingly complex models for the problem of relative attributes. This limits the applicability of those methods for more realistic cases. We introduce a deep neural network architecture for the task of relative attribute prediction. A convolutional neural network (ConvNet) is adopted to learn the features by including an additional layer (ranking layer) that learns to rank the images based on these features. We adopt an appropriate ranking loss to train the whole network in an end-to-end fashion. Our proposed method outperforms the baseline and state-of-the-art methods in relative attribute prediction on various coarse and fine-grained datasets. Our qualitative results along with the visualization of the saliency maps show that the network is able to learn effective features for each specific attribute. Source code of the proposed method is available at https://github.com/yassersouri/ghiaseddin.

Question: What is the difference between deep relative attributes and deep neural networks?


Signal Processing and Machine Learning with Differential Privacy: Algorithms and Challenges for Continuous Data. Private companies, government entities, and institutions such as hospitals routinely gather vast amounts of digitized personal information about the individuals who are their customers, clients, or patients. Much of this information is private or sensitive, and a key technological challenge for the future is how to design systems and processing techniques for drawing inferences from this large-scale data while maintaining the privacy and security of the data and individual identities. Individuals are often willing to share data, especially for purposes such as public health, but they expect that their identity or the fact of their participation will not be disclosed. In recent years, there have been a number of privacy models and privacy-preserving data analysis algorithms to answer these challenges. In this article, we will describe the progress made on differentially private machine learning and signal processing.

Question: What is the difference between differential privacy and differential privacy?


An ESPAR Antenna for Beamspace-MIMO Systems Using PSK Modulation Schemes. In this paper the use of electronically steerable passive array radiator (ESPAR) antennas is introduced for achieving increased spectral efficiency characteristics in multiple-input, multiple-output (MIMO) systems using a single active element, and compact antennas. The proposed ESPAR antenna is capable of mapping phase-shift-keying (PSK) modulated symbols to be transmitted onto orthogonal basis functions on the wavevector domain of the multi-element antenna (MEA), instead of the traditional approach of sending different symbol streams in different locations on the antenna domain. In this way, different symbols are transmitted simultaneously towards different angles of departure at the transmitter side. We show that the proposed system, called beamspace-MIMO (BS-MIMO) can achieve performance characteristics comparable to traditional MIMO systems while using a single radio-frequency (RF) front-end and ESPAR antenna arrays of total length equal to lambda/8, for popular PSK modulation schemes such as binary-PSK (BPSK), quaternary-PSK (QPSK), as well as for their offset-PSK and differential-PSK variations.

Question: What is the difference between beamspace-MIMO and traditional MIMO?


A High-Performance FPGA-Based Implementation of the LZSS Compression Algorithm. The increasing growth of embedded networking applications has created a demand for high-performance logging systems capable of storing huge amounts of high-bandwidth, typically redundant data. An efficient way of maximizing the logger performance is doing a real-time compression of the logged stream. In this paper we present a flexible high-performance implementation of the LZSS compression algorithm capable of processing up to 50 MB/s on a Virtex-5 FPGA chip. We exploit the independently addressable dual-port block RAMs inside the FPGA chip to achieve an average performance of 2 clock cycles per byte. To make the compressed stream compatible with the ZLib library [1] we encode the LZSS algorithm output using a fixed Huffman table defined by the Deflate specification [2]. We also demonstrate how changing the amount of memory allocated to various internal tables impacts the performance and compression ratio. Finally, we provide a cycle-accurate estimation tool that allows finding a trade-off between FPGA resource utilization, compression ratio and performance for a specific data sample.

Question: What is a high-performance FPGA-based implementation of the LZSS compression algorithm?


Dynamic simulation of die pickup from wafer tape by a multi-disc ejector using peel-energy to peel-velocity coupling. A 2D simulation of thin die peeling from wafer tape is presented for a Multi Disc ejection system. The simulation models the dynamics of peeling, and visualizes time-dependency of peel front propagation and target die stress. It is based on a series of static snapshots, stringed together like a movie. A coupling of peel energy and peel velocity is defined. This allows to calculate the geometry of the actual snapshot by the peel energy of the preceding one. As a result, experimental data of a peel process can be verified. It is shown, why an increase in disc velocity leads to a slow-down of peel front propagation, and thus to a pickup failure.

Question: What is a multi-disc ejector?


REDUNDANT IMUS FOR PRECISE TRAJECTORY DETERMINATION. A redundant inertial measurement unit (IMU) is an inertial sensing device composed by more than three accelerometers and three gyroscopes. This paper analyses the performance of redundant IMUs and their potential benefits and applications in airborne remote sensing and photogrammetry. The theory of redundant IMUs is presented through two different algorithmic approaches. The first approach is to combine the inertial observations, in the observation space, to generate a "synthetic" non-redundant IMU. The second approach modifies the INS mechanization equations so that they directly account for the observational redundancy. The paper ends with an empirical assesment of the concept. For this purpose, redundant IMU data was generated by combining two IMUs in a non-orthogonal configuration and flying them. Preliminary results of this flight are presented.

Question: What is the difference between redundant and non-redundant IMUs?


Prediction of Web Users Browsing Behavior : A Review. In Web Usage Mining (WUM), Classification technique is useful in web user prediction, where user's browsing behavior is predicted. In Web prediction, classification is done first to predict the next Web pages set that a user may visit based on history of previously visited pages. Predicting user's behavior effectively is important in various applications like recommendation systems, smart phones etc. Currently various prediction systems are available based on Markov model, Association Rule Mining (ARM), classification techniques, etc. In Markov prediction model it cannot predict for a session that was not observed previously in the training set and prediction time is also constant. In ARM, Apriori algorithm requires more time to generate item sets. This paper presents review of various prediction models used to predict web users browsing behavior. Key Words— WUM, classification, Browsing behavior, prediction, ARM, session

Question: What is the prediction of web users browsing behavior?


BranchGAN: Branched Generative Adversarial Networks for Scale-Disentangled Learning and Synthesis of Images.. We introduce BranchGAN , a novel training method that enables unconditioned generative adversarial networks (GANs) to learn image manifolds at multiple scales. The key novel feature of BranchGAN is that it is trained in multiple branches, progressively covering both the breadth and depth of the network, as resolutions of the training images increase to reveal finer-scale features. Specifically, each noise vector, as input to the generator network, is explicitly split into several sub-vectors, each corresponding to, and is trained to learn, image representations at a particular scale. During training, we progressively "defreeze" the sub-vectors, one at a time, as a new set of higher-resolution images is employed for training and more network layers are added. A consequence of such an explicit sub-vector designation is that we can directly manipulate and even combine latent (sub-vector) codes which model different feature scales. Experiments demonstrate the effectiveness of our training method in scale-disentangled learning of image manifolds and synthesis, without any extra labels and without compromising quality of the synthesized high-resolution images. We further demonstrate three applications enabled or improved by BranchGAN.

Question: BranchGAN: Branched Generative Adversarial Networks for Scale-Disentangled Learning and Synthesis of Images..


How do I communicate my emotions on SNS and IMs?. With influx of new devices and applications over the past few years, computer mediated communication has developed as an alternate to face-to-face communication. Through our study we attempt to understand how individuals communicate in mediated settings in emotion-laden situations. We explore their preferred medium of communication in different situations along with analysis of the content of communication. Spatial arrays, lexical surrogates, vocal spellings and grammatical markers were used as strategies by individuals for communicating non-verbal cues in text based communication. We also look at how messages sent on IMs or posts on social networks are interpreted by readers in terms of the emotional state of the sender. We found that while valence of the sender gets easily and accurately communicated, arousal is misinterpreted in most situations. In this paper, we present findings from our study which can be valuable for technology companies looking to better the current communication experience across different media.

Question: How do I communicate my emotions on SNS and IMs?


Tangible Query Interfaces: Physically Constrained Tokens for Manipulating Database Queries. We present a new approach for using physically constrained tokens to express, manipulate, and visualize parameterized database queries. This method extends tangible interfaces to enable interaction with large aggregates of information. We describe two interface prototypes that use physical tokens to represent database parameters. These tokens are manipulated upon physical constraints, which map compositions of tokens onto interpretations including database queries, views, and Boolean operations. We propose a framework for " token + constraint" interfaces, and compare one of our prototypes with a comparable graphical interface in a preliminary user study. † Current affiliation: Visualization Dept., Zuse Institute Berlin (ZIB), ullmer@zib.de †† Current affiliation: Computer Science Dept., Tufts University, jacob@cs.tufts.edu

Question: What is the Zuse Institute Berlin?


Exploiting Matrix Dependency for Efficient Distributed Matrix Computation. Distributed matrix computation is a popular approach for many large-scale data analysis and machine learning tasks. However existing distributed matrix computation systems generally incur heavy communication cost during the runtime, which degrades the overall performance. In this paper, we propose a novel matrix computation system, named DMac, which exploits the matrix dependencies in matrix programs for efficient matrix computation in the distributed environment. We decompose each matrix program into a sequence of operations, and reveal the matrix dependencies between operations in the program. We next design a dependency-oriented cost model to select an optimal execution strategy for each operation, and generate a communication efficient execution plan for the matrix computation program. To facilitate the matrix computation in distributed systems, we further divide the execution plan into multiple un-interleaved stages which can run in a distributed cluster with efficient local execution strategy on each worker. The DMac system has been implemented on a popular general-purpose data processing framework, Spark. The experimental results demonstrate that our techniques can significantly improve the performance of a wide range of matrix programs.

Question: How to exploit matrix dependencies for efficient distributed matrix computation?


A Three-Phase Current-Fed DC/DC Converter With Active Clamp for Low-DC Renewable Energy Sources. This paper focuses on a new three-phase high power current-fed DC/DC converter with an active clamp. A three-phase DC/DC converter with high efficiency and voltage boosting capability is designed for use in the interface between a low-voltage fuel-cell source and a high-voltage DC bus for inverters. Zero-voltage switching in all active switches is achieved through using a common active clamp branch, and zero current switching in the rectifier diodes is achieved through discontinuous current conduction in the secondary side. Further, the converter is capable of increased power transfer due to its three-phase power configuration, and it reduces the RMS current per phase, thus reducing conduction losses. Moreover, a delta-delta connection on the three-phase transformer provides parallel current paths and reduces conduction losses in the transformer windings. An efficiency of above 93% is achieved through both improvements in the switching and through reducing conduction losses. A high voltage ratio is achieved by combining inherent voltage boost characteristics of the current-fed converter and the transformer turns ratio. The proposed converter and three-phase PWM strategy is analyzed, simulated, and implemented in hardware. Experimental results are obtained on a 500-W prototype unit, with all of the design verified and analyzed.

Question: What is a current-fed DC/DC converter?


Audiovisual Fusion: Challenges and New Approaches. In this paper, we review recent results on audiovisual (AV) fusion. We also discuss some of the challenges and report on approaches to address them. One important issue in AV fusion is how the modalities interact and influence each other. This review will address this question in the context of AV speech processing, and especially speech recognition, where one of the issues is that the modalities both interact but also sometimes appear to desynchronize from each other. An additional issue that sometimes arises is that one of the modalities may be missing at test time, although it is available at training time; for example, it may be possible to collect AV training data while only having access to audio at test time. We will review approaches to address this issue from the area of multiview learning, where the goal is to learn a model or representation for each of the modalities separately while taking advantage of the rich multimodal training data. In addition to multiview learning, we also discuss the recent application of deep learning (DL) toward AV fusion. We finally draw conclusions and offer our assessment of the future in the area of AV fusion.

Question: What is audiovisual fusion?


Challenges for efficient communication in underwater acoustic sensor networks. Ocean bottom sensor nodes can be used for oceanographic data collection, pollution monitoring, offshore exploration and tactical surveillance applications. Moreover, Unmanned or Autonomous Underwater Vehicles (UUVs, AUVs), equipped with sensors, will find application in exploration of natural undersea resources and gathering of scientific data in collaborative monitoring missions. Underwater acoustic networking is the enabling technology for these applications. Underwater Networks consist of a variable number of sensors and vehicles that are deployed to perform collaborative monitoring tasks over a given area.In this paper, several fundamental key aspects of underwater acoustic communications are investigated. Different architectures for two-dimensional and three-dimensional underwater sensor networks are discussed, and the underwater channel is characterized. The main challenges for the development of efficient networking solutions posed by the underwater environment are detailed at all layers of the protocol stack. Furthermore, open research issues are discussed and possible solution approaches are outlined.

Question: What is the underwater channel?


Optimizing Java Bytecode Using the Soot Framework: Is It Feasible?. This paper presents Soot, a framework for optimizing Java bytecode. The framework is implemented in Java and supports three intermediate representations for representing Java bytecode: Baf, a streamlined representation of Java's stack-based bytecode; Jimple, a typed three-address intermediate representation suitable for optimization; and Grimp, an aggregated version of Jimple. Our approach to class le optimization is to rst convert the stack-based bytecode into Jimple, a three-address form more amenable to traditional program optimization, and then convert the optimized Jimple back to bytecode. In order to demonstrate that our approach is feasible, we present experimental results showing the e ects of processing class les through our framework. In particular, we study the techniques necessary to effectively translate Jimple back to bytecode, without losing performance. Finally, we demonstrate that class le optimization can be quite e ective by showing the results of some basic optimizations using our framework. Our experiments were done on ten benchmarks, including seven SPECjvm98 benchmarks, and were executed on ve di erent Java virtual machine implementations.

Question: How to optimize java bytecode?


An unusual case of asymptomatic spontaneous umbilical endometriosis treated with skin-sparing excision. Spontaneous umbilical endometriosis is a rare extrapelvic manifestation of endometriosis. Very few such cases have been previously reported, almost always associated with a variety of symptoms, usually during menstruation. We present a case of asymptomatic umbilical endometriosis treated with skin-sparing excision. Differential diagnoses relevant to the clinician, as well as treatment options, are also presented. Surgeons should always consider umbilical endometriosis in their diagnostic approach when confronted with atypical umbilical nodules, regardless of whether they are symptomatic or not.

Question: What is the treatment for umbilical endometriosis?


Walking away from the Desktop Computer: Distributed Collaboration and Mobility in a Product Design Team. A study of a spatially distributed product design team shows that most members are rarely at their individual desks. Mobility is essential for the use of shared resources and for communication. It facilitates informal interactions and awareness unavailable to colleagues at remote sites. Implications for technology design include portable and distributed computing resources, in particular, moving beyond individual workstation-centric CSCW applications.

Question: What is the difference between a desktop computer and a laptop?


Event-Centric Clustering of News Articles. Entertainity AB plans to build a news service to provide news to end-users in an innovative way. The service must include a way to automatically group series of news from different sources and publications, based on the stories they are covering. This thesis include three contributions: a survey of known clustering methods, an evaluation of human versus human results when grouping news articles in an event-centric manner, and last an evaluation of an incremental clustering algorithm to see if it is possible to consider a reduced input size and still get a sufficient result. The conclusions are that the result of the human evaluation indicates that users are different enough to warrant a need to take that into account when evaluating algorithms. It is also important that this difference is considered when conducting cluster analysis to avoid overfitting. The evaluation of an incremental event-centric algorithm shows it is desirable to adjust the similarity threshold, depending on what result one want. When running tests with different input sizes, the result implies that a short summary of a news article is a natural feature selection when performing cluster analysis.

Question: What is the difference between a clustering algorithm and a clustering method?


SARL: A General-Purpose Agent-Oriented Programming Language. Complex software systems development require appropriate high-level features to better and easily tackle the new requirements in terms of interactions, concurrency and distribution. This requires a paradigm change in software engineering and corresponding programming languages. We are convinced that agent-oriented programming may be the support for this change by focusing on a small corpus of commonly accepted concepts and the corresponding programming language in line with the current developers' programming practices. This papers introduces SARL, a new general-purpose agent-oriented programming language undertaking this challenge. SARL comes with its full support in the Eclipse IDE for compilation and debugging, and a new version 2.0 of the Janus platform for execution purposes. The main perspective that guided the creation of SARL is the establishment of an open and easily extensible language. Our expectation is to provide the community with a common forum in terms of a first working test bed to study and compare various programming alternatives and associated metamodels.

Question: What is an agent-oriented programming language?


Affordable Self Driving Cars and Robots with Semantic Segmentation. Image semantic segmentation is of immense interest for self-driving car research. Accurate and efficient segmentation mechanisms are required. We have evaluated Intersection over Union (IoU) metric over Cityscapes and KITTI datasets. We designed baseline softmax regression and maximum likelihood estimation, which performs quite poorly for the image segmentation task. We ran fully convolutional networks (FCN), which performed almost perfectly over the KITTI dataset. We found overfitting problem for the more complex Cityscape dataset. We conducted several experiments with regularization, dropout, data augmentation, image scaling and newer architectures. We are able to successfully mitigate overfitting by data augmentation. We also generated a confusion matrix and conducted error ablative analysis to get a deeper understanding of FCNs.

Question: What is the best way to segment an image?


Big data: the next frontier for innovation in therapeutics and healthcare.. Advancements in genomics and personalized medicine not only effect healthcare delivery from patient and provider standpoints, but also reshape biomedical discovery. We are in the era of the '-omics', wherein an individual's genome, transcriptome, proteome and metabolome can be scrutinized to the finest resolution to paint a personalized biochemical fingerprint that enables tailored treatments, prognoses, risk factors, etc. Digitization of this information parlays into 'big data' informatics-driven evidence-based medical practice. While individualized patient management is a key beneficiary of next-generation medical informatics, this data also harbors a wealth of novel therapeutic discoveries waiting to be uncovered. 'Big data' informatics allows for networks-driven systems pharmacodynamics whereby drug information can be coupled to cellular- and organ-level physiology for determining whole-body outcomes. Patient '-omics' data can be integrated for ontology-based data-mining for the discovery of new biological associations and drug targets. Here we highlight the potential of 'big data' informatics for clinical pharmacology.

Question: What is the next frontier for innovation in therapeutics and healthcare?


Computation and Computational Thinking. ion of reality in such a way that the neglected details in the model make it executable by a machine." [2] As we shall see, finding or devising appropriate models of computation to formulate problems is a central and often nontrivial part of computational thinking.

Question: What is computational thinking?


CACHE MISSING FOR FUN AND PROFIT. Simultaneous multithreading — put simply, the sharing of the execution resources of a superscalar processor between multiple execution threads — has recently become widespread via its introduction (under the name "Hyper-Threading") into Intel Pentium 4 processors. In this implementation, for reasons of efficiency and economy of processor area, the sharing of processor resources between threads extends beyond the execution units; of particular concern is that the threads share access to the memory caches. We demonstrate that this shared access to memory caches provides not only an easily used high bandwidth covert channel between threads, but also permits a malicious thread (operating, in theory, with limited privileges) to monitor the execution of another thread, allowing in many cases for theft of cryptographic keys. Finally, we provide some suggestions to processor designers, operating system vendors, and the authors of cryptographic software, of how this attack could be mitigated or eliminated entirely.

Question: How does hyperthreading work?


Implementation of multiport dc-dc converter-based Solid State Transformer in smart grid system. A solid-state transformer (SST) would be at least as efficient as a conventional version but would provide other benefits as well, particularly as renewable power sources become more widely used. Among its more notable strong points are on-demand reactive power support for the grid, better power quality, current limiting, management of distributed storage devices and a dc bus. Most of the nation's power grid currently operates one way - power flows from the utility to the consumer - and traditional transformers simply change voltage from one level to another. But smart transformers, based on power semiconductor switches, are more versatile. Not only can they change voltage levels, but also can effectively control the power flow in both directions. The development of a Solid State Transformer (SST) that incorporates a DC-DC multiport converter to integrate both photovoltaic (PV) power generation and battery energy storage is presented in this paper. The DC-DC stage is based on a quad active-bridge (QAB) converter which not only provides isolation for the load, but also for the PV and storage. The AC-DC stage is implemented with a pulse-width-modulated (PWM) single phase rectifier. A novel technique that complements the SISO controller by taking into account the cross coupling characteristics of the QAB converter is also presented herein. Cascaded SISO controllers are designed for the AC-DC stage. The QAB demanded power is calculated at the QAB controls and then fed into the rectifier controls in order to minimize the effect of the interaction between the two SST stages. The dynamic performance of the designed control loops based on the proposed control strategies are verified through extensive simulation of the SST average and switching models.

Question: What is a solid state transformer?


Phased array systems and technologies in SELEX-Sistemi Integrati: State of art and new challenges. This paper is a follow-on of [1] and [2], presented at 1996 and 2003 IEEE Intl. Symposium on Phased Array Systems and Technology, respectively. After a brief recall of 3D Long Range phased array concepts, already presented in [2], the focus is on the G-band multifunction radar systems and on the technological capabilities that SELEX-Sistemi Integrati is developing by taking advantage of its Research and Development facilities. At the end, future trends about new challenging applications and technological issues are presented and discussed.

Question: What is the state of art of phased array systems?


Modelling the interaction of electromagnetic fields (10 MHz-10 GHz) with the human body: methods and applications.. Numerical modelling of the interaction between electromagnetic fields (EMFs) and the dielectrically inhomogeneous human body provides a unique way of assessing the resulting spatial distributions of internal electric fields, currents and rate of energy deposition. Knowledge of these parameters is of importance in understanding such interactions and is a prerequisite when assessing EMF exposure or when assessing or optimizing therapeutic or diagnostic medical applications that employ EMFs. In this review, computational methods that provide this information through full time-dependent solutions of Maxwell's equations are summarized briefly. This is followed by an overview of safety- and medical-related applications where modelling has contributed significantly to development and understanding of the techniques involved. In particular, applications in the areas of mobile communications, magnetic resonance imaging, hyperthermal therapy and microwave radiometry are highlighted. Finally, examples of modelling the potentially new medical applications of recent technologies such as ultra-wideband microwaves are discussed.

Question: What is the difference between modelling and simulation?


An Over-110-GHz-Bandwidth 2:1 Analog Multiplexer in 0.25-μm InP DHBT Technology. This paper presents an over-110-GHz-bandwidth 2:1 analog multiplexer (AMUX) for ultra-broadband digital-to-analog (D/A) conversion subsystems. The AMUX was designed and fabricated by using newly developed $\pmb{0.25-\mu \mathrm{m}}$ -emitter-width InP double heterojunction bipolar transistors (DHBTs), which have a peak $\pmb{f_{\mathrm{T}}}$ and $\pmb{ f\displaystyle \max}$ of 460 and 480 GHz, respectively. The AMUX IC consists of lumped building blocks, including data-input linear buffers, a clock-input limiting buffer, an AMUX core, and an output linear buffer. The measured 3-dB bandwidth for data and clock paths are both over 110 GHz. In addition, it measures and obtains time-domain large-signal sampling operations of up to 180 GS/s. A 224-Gb/s (112-GBaud) four-level pulse-amplitude modulation (PAM4) signal was successfully generated by using this AMUX. To the best of our knowledge, this AMUX IC has the broadest bandwidth and the fastest sampling rate compared with any other previously reported AMUXes.

Question: What is the maximum bandwidth of the AMUX?


Dynamic Resource Allocation in Ad-Hoc Mobile Cloud Computing. Mobile Cloud Computing (MCC) has draw extensive research attentions due to the increasingly demand on the energy consumption and execution time constraints on Mobile Devices (MDs). MCC has been well investigated in the scenarios when workload is offloaded to a remote cloud or a cloudlet. However, in those scenarios, infrastructure is required to provide connections between MDs and the cloud#x002F;cloudlet. To facilitate MCC for environment when infrastructure is not available, Ad-hoc MCC which allows MDs to share and process workload coordinately is discussed [1]. In this study, the detailed resource allocation problem in Ad-hoc MCC, including how to assign tasks to MDs, how tasks are executed on each MD, and how to arrange task and task result return transmissions so that interference among different MDs can be avoided are carefully studied. Since the problem is NP-hard, a heuristic algorithm is then introduced. As shown in the evaluation section, when no infrastructure is available, compared with the case when an application is executed solely on a single MD, the proposed algorithm can reduce an application's response time significantly.

Question: How to allocate resources in ad-hoc mobile cloud computing?


T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-Less Objects. We introduce T-LESS, a new public dataset for estimating the 6D pose, i.e. translation and rotation, of texture-less rigid objects. The dataset features thirty industry-relevant objects with no significant texture and no discriminative color or reflectance properties. The objects exhibit symmetries and mutual similarities in shape and/or size. Compared to other datasets, a unique property is that some of the objects are parts of others. The dataset includes training and test images that were captured with three synchronized sensors, specifically a structured-light and a time-of-flight RGB-D sensor and a high-resolution RGB camera. There are approximately 39K training and 10K test images from each sensor. Additionally, two types of 3D models are provided for each object, i.e. a manually created CAD model and a semi-automatically reconstructed one. Training images depict individual objects against a black background. Test images originate from twenty test scenes having varying complexity, which increases from simple scenes with several isolated objects to very challenging ones with multiple instances of several objects and with a high amount of clutter and occlusion. The images were captured from a systematically sampled view sphere around the object/scene, and are annotated with accurate ground truth 6D poses of all modeled objects. Initial evaluation results indicate that the state of the art in 6D object pose estimation has ample room for improvement, especially in difficult cases with significant occlusion. The T-LESS dataset is available online at cmp:felk:cvut:cz/t-less.

Question: What is the size of the dataset?


Structural Health Monitoring Framework Based on Internet of Things: A Survey. Internet of Things (IoT) has recently received a great attention due to its potential and capacity to be integrated into any complex system. As a result of rapid development of sensing technologies such as radio-frequency identification, sensors and the convergence of information technologies such as wireless communication and Internet, IoT is emerging as an important technology for monitoring systems. This paper reviews and introduces a framework for structural health monitoring (SHM) using IoT technologies on intelligent and reliable monitoring. Specifically, technologies involved in IoT and SHM system implementation as well as data routing strategy in IoT environment are presented. As the amount of data generated by sensing devices are voluminous and faster than ever, big data solutions are introduced to deal with the complex and large amount of data collected from sensors installed on structures.

Question: What is the difference between IoT and SHM?


Verification of an Implementation of Tomasulo's Algorithm by Compositional Model Checking. An implementation of an out-of-order processing unit based on Tomasulo's algorithm is formally verified using compositional model checking techniques. This demonstrates that finite-state methods can be applied to such algorithms, without recourse to higher-order proof systems. The paper introduces a novel compositional system that supports cyclic environment reasoning and multiple environment abstractions per signal. A proof of Tomasulo's algorithm is outlined, based on refinement maps, and relying on the novel features of the compositional system. This proof is fully verified by the SMV verifier, using symmetry to reduce the number of assertions that must be verified.

Question: What is a compositional system?


Distribution Free Prediction Bands. We study distribution free, nonparametric prediction bands with a special focus on their finite sample behavior. First we investigate and develop different notions of finite sample coverage guarantees. Then we give a new prediction band estimator by combining the idea of "conformal prediction" (Vovk et al., 2009) with nonparametric conditional density estimation. The proposed estimator, called COPS (Conformal Optimized Prediction Set), always has finite sample guarantee in a stronger sense than the original conformal prediction estimator. Under regularity conditions the estimator converges to an oracle band at a minimax optimal rate. A fast approximation algorithm and a data driven method for selecting the bandwidth are developed. The method is illustrated first in simulated data. Then, an application shows that the proposed method gives desirable prediction intervals in an automatic way, as compared to the classical linear regression modeling.

Question: What is the difference between a prediction band and a prediction interval?


THE IMPACT OF CHANGE MANAGEMENT IN ERP SYSTEM : A CASE STUDY OF MADAR. This paper discusses Enterprise Resource Planning (ERP) change management. A review of the research literature has been presented that focuses on the ERP change management factors. The literature is further classified and the major outcomes of each study have been addressed in this paper. The discussion is supported by a practical ERP project called Madar. The paper is targeted to investigate and identify the reasons for resistance to diffusion and why individuals within an organization resist the changes. This paper also suggests strategies to minimize the resistance if not overcome completely.

Question: What is the impact of change management in ERP system?


The challenge of simultaneous object detection and pose estimation: A comparative study. Detecting objects and estimating their pose remains as one of the major challenges of the computer vision research community. There exists a compromise between localizing the objects and estimating their viewpoints. The detector ideally needs to be viewinvariant, while the pose estimation process should be able to generalize towards the category-level. This work is an exploration of using deep learning models for solving both problems simultaneously. For doing so, we propose three novel deep learning architectures, which are able to perform a joint detection and pose estimation, where we gradually decouple the two tasks. We also investigate whether the pose estimation problem should be solved as a classification or regression problem, being this still an open question in the computer vision community. We detail a comparative analysis of all our solutions and the methods that currently define the state of the art for this problem. We use PASCAL3D+ and ObjectNet3D datasets to present the thorough experimental evaluation and main results. With the proposed models we achieve the state-of-the-art performance in both datasets.

Question: What is the state of the art for simultaneous object detection and pose estimation?


An Efficient Synchronization Mechanism for Mirrored Game Architectures. Existing online multiplayer games typically use a client-server model, which introduces added latency as well as a single bottleneck and single point of failure to the game. Distributed multiplayer games minimize latency and remove the bottleneck, but require special synchronization mechanisms to provide a consistent game for all players. Current synchronization methods have been borrowed from distributed military simulations and are not optimized for the requirements of fast-paced multiplayer games. In this paper we present a new synchronization mechanism, trailing state synchronization (TSS), which is designed around the requirements of distributed first-person shooter games. We look at TSS in the environment of a mirrored game architecture, which is a hybrid between traditional centralized architectures and the more scalable peer-to-peer architectures. Mirrored architectures allow for improved performance compared to client-server architectures while at the same time allowing for a greater degree of centralized administration than peer-to-peer architectures. We evaluate the performance of TSS and other synchronization methods through simulation and examine heuristics for selecting the synchronization delays needed for TSS.

Question: What is a mirrored game architecture?


Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models. We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Generative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, flexible interactions. In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art neural language models and backoff n-gram models. We investigate the limitations of this and similar approaches, and show how its performance can be improved by bootstrapping the learning from a larger questionanswer pair corpus and from pretrained word embeddings.

Question: How to build a dialogue system?