The Effect of Music on the Human Stress Response. BACKGROUND Music listening has been suggested to beneficially impact health via stress-reducing effects. However, the existing literature presents itself with a limited number of investigations and with discrepancies in reported findings that may result from methodological shortcomings (e.g. small sample size, no valid stressor). It was the aim of the current study to address this gap in knowledge and overcome previous shortcomings by thoroughly examining music effects across endocrine, autonomic, cognitive, and emotional domains of the human stress response.   METHODS Sixty healthy female volunteers (mean age = 25 years) were exposed to a standardized psychosocial stress test after having been randomly assigned to one of three different conditions prior to the stress test: 1) relaxing music ('Miserere', Allegri) (RM), 2) sound of rippling water (SW), and 3) rest without acoustic stimulation (R). Salivary cortisol and salivary alpha-amylase (sAA), heart rate (HR), respiratory sinus arrhythmia (RSA), subjective stress perception and anxiety were repeatedly assessed in all subjects. We hypothesized that listening to RM prior to the stress test, compared to SW or R would result in a decreased stress response across all measured parameters.   RESULTS The three conditions significantly differed regarding cortisol response (p = 0.025) to the stressor, with highest concentrations in the RM and lowest in the SW condition. After the stressor, sAA (p=0.026) baseline values were reached considerably faster in the RM group than in the R group. HR and psychological measures did not significantly differ between groups.   CONCLUSION Our findings indicate that music listening impacted the psychobiological stress system. Listening to music prior to a standardized stressor predominantly affected the autonomic nervous system (in terms of a faster recovery), and to a lesser degree the endocrine and psychological stress response. These f

Question: What is the effect of music on the human stress response?


Cooperative (rather than autonomous) vehicle-highway automation systems. The recent DARPA-sponsored automated vehicle "Challenges" have generated strong interest in both the research community and the general public, raising consciousness about the possibilities for vehicle automation. Driverless vehicles make good subjects for the visually-oriented media, and they pose enough interesting research challenges to occupy generations of graduate students. However, automated vehicles also have the potential to help solve a variety of real-world problems. Engineers need to think carefully about which of those problems we are actually solving.A well-engineered system should be designed to satisfy specific needs, and those needs should be reflected in the definition of system requirements. Alternative technological approaches can then be evaluated and traded off based on their ability to meet the requirements. The article describes the rather different needs of the public road transportation system and the military transportation system, and then shows how those needs influence the requirements for automated vehicle systems. These requirements point toward significantly different technical approaches, but it is possible to find some limited areas of technical commonality.

Question: What are the different needs of the public road transportation system and the military transportation system?


Pancreas Segmentation in Abdominal CT Scan: A Coarse-to-Fine Approach. Deep neural networks have been widely adopted for automatic organ segmentation from CT-scanned images. However, the segmentation accuracy on some small organs (e.g., the pancreas) is sometimes below satisfaction, arguably because deep networks are easily distracted by the complex and variable background region which occupies a large fraction of the input volume. In this paper, we propose a coarse-to-fine approach to deal with this problem. We train two deep neural networks using different regions of the input volume. The first one, the coarse-scaled model, takes the entire volume as its input. It is used for roughly locating the spatial position of the pancreas. The second one, the fine-scaled model, only sees a small input region covering the pancreas, thus eliminating the background noise and providing more accurate segmentation especially around the boundary areas. At the testing stage, we first use the coarse-scaled model to roughly locate the pancreas, then adopt the fine-scaled model to refine the initial segmentation in an iterative manner to obtain increasingly better segmentation. We evaluate our algorithm on the NIH pancreas segmentation dataset with 82 volumes, and outperform the state-of-theart [18] by more than 4%, measured by the Dice-Sørensen Coefficient (DSC). In addition, we report 62.43% DSC for our worst case, significantly better than 34.11% reported in [18].

Question: How to segment the pancreas from abdominal CT scan?


Human posture recognition using curved segments for image retrieval. This paper presents a human posture recognition method from a single image. We rst segment an image into homogeneous regions and extract curve segments corresponding to human body parts. Each body part is considered as a 2D ribbon. From the smooth curve segments in skin regions, 2D ribbons are extracted and a human body model is constructed. We assign a predeened posture type to the image according to the constructed body model. For the user input query to retrieve images containing human of speciic posture, the system convert the query to a body model. The body model is compared to other body models saved in the local storage of target images and images of good matches are retrieved. When a face detection result is available for the given image, it is also used to increase the reliability of body model. For the query human posture, our system retrieves images of the corresponding posture. As another application, the proposed method provides an initial location of a human body to track in a video sequence.

Question: How to recognize human posture from a single image?


Information theory and neural coding. Information theory quantifies how much information a neural response carries about the stimulus. This can be compared to the information transferred in particular models of the stimulus–response function and to maximum possible information transfer. Such comparisons are crucial because they validate assumptions present in any neurophysiological analysis. Here we review information-theory basics before demonstrating its use in neural coding. We show how to use information theory to validate simple stimulus–response models of neural coding of dynamic stimuli. Because these models require specification of spike timing precision, they can reveal which time scales contain information in neural coding. This approach shows that dynamic stimuli can be encoded efficiently by single neurons and that each spike contributes to information transmission. We argue, however, that the data obtained so far do not suggest a temporal code, in which the placement of spikes relative to each other yields additional information.

Question: What is the difference between information theory and neural coding?


Defending continuous collision detection against errors. Numerical errors and rounding errors in continuous collision detection (CCD) can easily cause collision detection failures if they are not handled properly. A simple and effective approach is to use error tolerances, as shown in many existing CCD systems. Unfortunately, finding the optimal tolerance values is a difficult problem for users. Larger tolerance values will introduce false positive artifacts, while smaller tolerance values may cause collisions to be undetected. The biggest issue here is that we do not know whether or when CCD will fail, even though failures are extremely rare. In this paper, we demonstrate a set of simple modifications to make a basic CCD implementation failure-proof. Using error analysis, we prove the safety of this method and we formulate suggested tolerance values to reduce false positives. The resulting algorithms are safe, automatic, efficient, and easy to implement.

Question: How to defend against numerical errors in continuous collision detection?


Computer Vision - Algorithms and Applications. As known, adventure and experience about lesson, entertainment, and knowledge can be gained by only reading a book. Even it is not directly done, you can know more about this life, about the world. We offer you this proper and easy way to gain those all. We offer many book collections from fictions to science at all. One of them is this computer vision algorithms and applications that can be your partner.

Question: What is the name of this book?


Analysis and Optimization of Switched-Capacitor DC–DC Converters. Analysis methods are developed that fully determine a switched-capacitor (SC) dc-dc converter's steady-state performance through evaluation of its output impedance. This analysis method has been verified through simulation and experimentation. The simple formulation developed permits optimization of the capacitor sizes to meet a constraint such as a total capacitance or total energy storage limit, and also permits optimization of the switch sizes subject to constraints on total switch conductances or total switch volt-ampere (V-A) products. These optimizations then permit comparison among several switched-capacitor topologies, and comparisons of SC converters with conventional magnetic-based dc-dc converter circuits, in the context of various application settings. Significantly, the performance (based on conduction loss) of a ladder-type converter is found to be superior to that of a conventional magnetic-based converter for medium to high conversion ratios.

Question: What is a switched-capacitor dc-dc converter?


Adapting microsoft SQL server for cloud computing. Cloud SQL Server is a relational database system designed to scale-out to cloud computing workloads. It uses Microsoft SQL Server as its core. To scale out, it uses a partitioned database on a shared-nothing system architecture. Transactions are constrained to execute on one partition, to avoid the need for two-phase commit. The database is replicated for high availability using a custom primary-copy replication scheme. It currently serves as the storage engine for Microsoft's Exchange Hosted Archive and SQL Azure.

Question: What is the cloud sql server?


Minimizing Faulty Executions of Distributed Systems. When troubleshooting buggy executions of distributed systems, developers typically start by manually separating out events that are responsible for triggering the bug (signal) from those that are extraneous (noise). In this paper we present DEMi, a tool for automatically performing this minimization. We apply DEMi to buggy executions of two very different distributed systems, Raft and Spark, and find that it achieves up to 97% reduction of execution length within a small time budget.

Question: How to minimize faulty executions of distributed systems?


Determining the effectiveness of prompts for self-regulated learning in problem-solving scenarios. Cognitive scientists have studied internal cognitive structures, processes, and systems for decades in order to understand how they function in human learning. In order to solve challenging tasks in problem situations, learners not only have to perform cognitive activities, e.g., activating existing cognitive structures or organizing new information, they also have to set specific goals, plan their activities, monitor their performance during the problem-solving process, and evaluate the efficiency of their actions. This paper reports an experimental study with 98 participants where effective instructional interventions for self-regulated learning within problemsolving processes are investigated. Furthermore, an automated assessment and analysis methodology for determining the quality of learning outcomes is introduced. The results indicate that generic prompts are an important aid for developing cognitive structures while solving problems.

Question: What is the quality of learning outcomes?


Characterization and Implementation of Dual-SiC MOSFET Modules for Future Use in Traction Converters. Silicon (Si) insulated-gate bipolar transistors are widely used in railway traction converters. In the near future, silicon carbide (SiC) technology will push the limits of switching devices in three directions: higher blocking voltage, higher operating temperature, and higher switching speeds. The first silicon carbide (SiC) MOSFET modules are available on the market and look promising. Although they are still limited in breakdown voltage, these wide-bandgap components should improve traction-chain efficiency. Particularly, a significant reduction in the switching losses is expected which should lead to improvements in power-weight ratios. Nevertheless, because of the high switching speed and the high current levels required by traction applications, the implementation of these new modules is critical. An original method is proposed to compare, in terms of stray inductance, several dc bus-bar designs. To evaluate the potential of these new devices, a first set of measurements, based on a single-pulse test-bench, was obtained. The switching behavior of SiC devices was well understood at turn-off and turn-on. To complete this work, the authors use an opposition method to compare Si-IGBT and SiC-MOSFET modules in voltage source inverter operation. For this purpose, a second test-bench, allowing electrical and thermal measurements, was developed. Experimental results confirm the theoretical loss-calculation of the single-pulse tests and the correct operation of up to three modules directly connected in parallel. This analysis provides guidelines for a full SiC inverter design, and prospects for developments in traction applications are presented.

Question: What is the difference between a traction converter and a traction inverter?


Artificial Noise Aided Secure Cognitive Beamforming for Cooperative MISO-NOMA Using SWIPT. Cognitive radio (CR) and non-orthogonal multiple access (NOMA) have been deemed two promising technologies due to their potential to achieve high spectral efficiency and massive connectivity. This paper studies a multiple-input single-output NOMA CR network relying on simultaneous wireless information and power transfer conceived for supporting a massive population of power limited battery-driven devices. In contrast to most of the existing works, which use an ideally linear energy harvesting model, this study applies a more practical non-linear energy harvesting model. In order to improve the security of the primary network, an artificial-noise-aided cooperative jamming scheme is proposed. The artificial-noise-aided beamforming design problems are investigated subject to the practical secrecy rate and energy harvesting constraints. Specifically, the transmission power minimization problems are formulated under both perfect channel state information (CSI) and the bounded CSI error model. The problems formulated are non-convex, hence they are challenging to solve. A pair of algorithms either using semidefinite relaxation (SDR) or a cost function are proposed for solving these problems. Our simulation results show that the proposed cooperative jamming scheme succeeds in establishing secure communications and NOMA is capable of outperforming the conventional orthogonal multiple access in terms of its power efficiency. Finally, we demonstrate that the cost function algorithm outperforms the SDR-based algorithm.

Question: How to design a secure cognitive beamforming scheme for NOMA?


On battery State of Charge estimation: A new mixed algorithm. A new SoC estimation algorithm is presented. The estimation is performed mixing the Coulomb-Counting and the Model-Based estimation approaches. The algorithm was tested on a brand new lithium-ion cell which use a nanoscale technology able to grant a more powerful and safer lithium-ion battery than the batteries currently on the market. The cell, called A123-M1, was first characterized and then identified. The cell was identified with a equivalent electrical circuits model which is usually called 2th Randle model: the used test-bench and the identification process are described. Then the SoC estimation problem is described. A briefly review of the state of art of SoC estimation is given and the drawbacks of the methods which are usually used are presented. Finally the new mixed estimation algorithm is described: the algorithm scheme and the results are shown.

Question: How to estimate the state of charge of a battery?


Pitfalls in pediatric radiology. This essay depicts some of the diagnostic errors identified in a large academic pediatric imaging department during a 13-year period. Our aim is to illustrate potential situations in which errors are more likely to occur and more likely to cause harm, and to share our difficult cases so other radiologists might learn without having to experience those situations themselves.

Question: What is the difference between a pitfall and a pit?


A 0 . 5V Ultra-Low-Power OTA With Improved Gain and Bandwidth. In the context of ultra-low-power and ultra-low-voltage operational transconductance amplifiers (OTAs), there is the inconvenience of low unity-gain frequency and DC gain. In this paper, we presented a 0.5-V improved ultra-low-power OTA using the transistors operate in weak inversion. The proposed topology based on a bulk-driven input differential pair employed an under-weak-inversion gain-stage in the Miller capacitor feedback path to improve the "pole-splitting" effect. Simulations in a standard 0.18 μm CMOS process resulted on considerable enhancement in the unity-gain bandwidth and the DC gain as well. The topology presents rail-to-rail input and output swings and consumes only 1 μW.

Question: What is the unity-gain bandwidth of an OTA?


Automatic classification of citation function. The automatic recognition of the rhetorical function of citations in scientific text has many applications, from improvement of impact factor calculations to text summarisation and more informative citation indexers. Citation function is defined as the author's reason for citing a given paper (e.g. acknowledgement of the use of the cited method). We show that our annotation scheme for citation function is reliable, and present a supervised machine learning framework to automatically classify citation function, which uses several shallow and linguistically-inspired features. We find, amongst other things, a strong relationship between citation function and sentiment classification.

Question: What is the automatic classification of citation function?


A bibliometric analysis on rural studies in human geography and related disciplines. Although the world has experienced rapid urbanization, rural areas have always been and are still an important research field in human geography. This paper performed a bibliometric analysis on rural geography studies based on the peer-reviewed articles concerning rural geography published in the SSCI-listed journals from 1990 to 2012. Our analysis examines publication patterns (document types and publishing languages, article outputs and their categories, major journals and their publication, most productive authors, geographic distribution and international collaboration) and demonstrates the evolution of intellectual development of rural geography by studying highly cited papers and their citation networks and temporal evolution of keywords. Our research findings include: The article number has been increasing since the 1900s, and went through three phases, and the rural geography research is dominated in size by UK and USA. The USA is the most productive in rural geography, but the UK had more impact than other countries in the terms of the average citation of articles. Three distinct but loosely linked research streams of rural geography were identified and predominated by the UK rural geographers. The keywords frequencies evolved according to contexts of rural development and academic advances of human geography, but they were loosely and scattered since the rural researches in different regions or different systems faced with different problems.

Question: What is the bibliometric analysis on rural studies in human geography and related disciplines?


Low-Resolution Face Recognition of Multi-Scale Blocking CS-LBP and Weighted PCA. A novel method is proposed in this paper to improve the recognition accuracy of Local Binary Pattern (LBP) on low-resolution face recognition. More precise descriptors and e®ectively face features can be extracted by combining multi-scale blocking center symmetric local binary pattern (CS-LBP) based on Gaussian pyramids and weighted principal component analysis (PCA) on low-resolution condition. Firstly, the features statistical histograms of face images are calculated by multi-scale blocking CS-LBP operator. Secondly, the stronger classi ̄cation and lower dimension features can be got by applying weighted PCA algorithm. Finally, the di®erent classi ̄ers are used to select the optimal classi ̄cation categories of low-resolution face set and calculate the recognition rate. The results in the ORL human face databases show that recognition rate can get 89.38% when the resolution of face image drops to 12 10 pixel and basically satisfy the practical requirements of recognition. The further comparison of other descriptors and experiments from videos proved that the novel algorithm can improve recognition accuracy.

Question: How to improve the recognition accuracy of low-resolution face recognition?


Effects of Fitness Applications with SNS: How Do They Influence Physical Activity. Fitness applications with social network services (SNS) have emerged for physical activity management. However, there is little understanding of the effects of these applications on users' physical activity. Motivated thus, we develop a theoretical model based on social cognitive theory of self-regulation to explain the effects of goal setting, self-tracking, and SNS (through social comparison from browsing others' tracking data and social support from sharing one's own tracking data) on physical activity. The model was tested with objective data from 476 Runkeeper users. Our preliminary results show that goal setting, number of uses, the proportion of lower-performing friends, and number of likes positively influence users' physical activities, while the proportion of higher-performing friends has a negative effect. Moreover, the effect of the proportion of higher-performing friends is moderated by the average difference between the friends and the individual. The initial contributions of the study and remaining research plan are described.

Question: How do fitness applications with social network services (SNS) influence physical activity?


Research on autonomous stairs climbing for the shape-shifting robot. In this paper, we present an algorithm for a shape-shifting robot climbing stairs autonomously. The robot has been used several times for search and rescue missions. Because of the special environment of tasks, the robot must have the ability to climb stairs autonomously to help the operators. Due to the characters of robot in "T" configuration, the mathematical model of force is established and the whole process of climbing stairs is analyzed. Grouser-tread hooking, track-stair edge frictional force et al. are all considered during the process of climbing. A description of its dynamic model is also presented which is involved in the controller. The controller is comprised of center control and heading control, which can help the robot climb stairs safely and quickly. The effectiveness of the algorithms proposed are verified by experiment.

Question: How to climb stairs safely and quickly?


Real-Time Classification of Dance Gesturesfrom Skeleton Animation. We present a real-time gesture classification system for skeletal wireframe motion. Its key components include an angular representation of the skeleton designed for recognition robustness under noisy input, a cascaded correlation-based classifier for multivariate time-series data, and a distance metric based on dynamic time-warping to evaluate the difference in motion between an acquired gesture and an oracle for the matching gesture. While the first and last tools are generic in nature and could be applied to any gesture-matching scenario, the classifier is conceived based on the assumption that the input motion adheres to a known, canonical time-base: a musical beat. On a benchmark comprising 28 gesture classes, hundreds of gesture instances recorded using the XBOX Kinect platform and performed by dozens of subjects for each gesture class, our classifier has an average accuracy of 96:9%, for approximately 4-second skeletal motion recordings. This accuracy is remarkable given the input noise from the real-time depth sensor.

Question: How can we classify dance gestures?


IEC 61850 impact on substation design. IEC 61850 communication networks and systems for utility automation is a standard for communications that creates an environment that will allow dramatic changes in the design and operation of substations. The paper describes the different components of such systems and the distribution of functions between the devices included in it. Multiple Merging Units communicating over a 100 Mb Ethernet with multiple devices and/or a central computer that receives and processes current and voltage samples with a rate of 256 samples/cycle allow the implementation of all typical substation protection, automation, control, monitoring and recording functions in a very efficient way. The concepts also allow to change the design of layout of the high voltage switchgear.

Question: What is the IEC 61850 standard?


Cyberbullying classification using text mining. Cyberbully is a misuse of technology advantage to bully a person. Cyberbully and its impact have occurred around the world and now the number of cases are increasing. Cyberbullying detection is very important because the online information is too large so it is not possible to be tracked by humans. The purpose of this research is to construct a classification model with optimal accuracy in identifying cyberbully conversation using Naive Bayes method and Support Vector Machine (SVM) then applying n-gram 1 to 5 for the number of class 2, 4, and 11 for each method. Naive Bayes yields an average accuracy of 92.81%, SVM with a poly kernel yields an average accuracy of 97.11% It can be concluded that SVM with poly kernel yields higher accuracy than SVM with other kernels, Naive Bayes, and Kelly Reynolds research method of decision tree (J48) and k-NN.

Question: What is cyberbullying classification using text mining?


An IoT-Aware Architecture for Smart Healthcare Systems. Over the last few years, the convincing forward steps in the development of Internet of Things (IoT)-enabling solutions are spurring the advent of novel and fascinating applications. Among others, mainly radio frequency identification (RFID), wireless sensor network (WSN), and smart mobile technologies are leading this evolutionary trend. In the wake of this tendency, this paper proposes a novel, IoT-aware, smart architecture for automatic monitoring and tracking of patients, personnel, and biomedical devices within hospitals and nursing institutes. Staying true to the IoT vision, we propose a smart hospital system (SHS), which relies on different, yet complementary, technologies, specifically RFID, WSN, and smart mobile, interoperating with each other through a Constrained Application Protocol (CoAP)/IPv6 over low-power wireless personal area network (6LoWPAN)/representational state transfer (REST) network infrastructure. The SHS is able to collect, in real time, both environmental conditions and patients' physiological parameters via an ultra-low-power hybrid sensing network (HSN) composed of 6LoWPAN nodes integrating UHF RFID functionalities. Sensed data are delivered to a control center where an advanced monitoring application (MA) makes them easily accessible by both local and remote users via a REST web service. The simple proof of concept implemented to validate the proposed SHS has highlighted a number of key capabilities and aspects of novelty, which represent a significant step forward compared to the actual state of the art.

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


Solving ill-posed inverse problems using iterative deep neural networks. We propose a partially learned approach for the solution of ill posed inverse problems with not necessarily linear forward operators. The method builds on ideas from classical regularization theory and recent advances in deep learning to perform learning while making use of prior information about the inverse problem encoded in the forward operator, noise model and a regularizing functional. The method results in a gradient-like iterative scheme, where the "gradient" component is learned using a convolutional network that includes the gradients of the data discrepancy and regularizer as input in each iteration. We present results of such a partially learned gradient scheme on a non-linear tomographic inversion problem with simulated data from both the Sheep-Logan phantom as well as a head CT. The outcome is compared against filtered backprojection and total variation reconstruction and the proposed method provides a 5.4 dB PSNR improvement over the total variation reconstruction while being significantly faster, giving reconstructions of 512× 512 pixel images in about 0.4 s using a single graphics processing unit (GPU).

Question: How to solve ill-posed inverse problems using deep learning?


Exploring the physical layer frontiers of cellular uplink. Communication systems in practice are subject to many technical/technological constraints and restrictions. Multiple input, multiple output (MIMO) processing in current wireless communications, as an example, mostly employs codebook-based pre-coding to save computational complexity at the transmitters and receivers. In such cases, closed form expressions for capacity or bit-error probability are often unattainable; effects of realistic signal processing algorithms on the performance of practical communication systems rather have to be studied in simulation environments. The Vienna LTE-A Uplink Simulator is a 3GPP LTE-A standard compliant MATLAB-based link level simulator that is publicly available under an academic use license, facilitating reproducible evaluations of signal processing algorithms and transceiver designs in wireless communications. This paper reviews research results that have been obtained by means of the Vienna LTE-A Uplink Simulator, highlights the effects of single-carrier frequency-division multiplexing (as the distinguishing feature to LTE-A downlink), extends known link adaptation concepts to uplink transmission, shows the implications of the uplink pilot pattern for gathering channel state information at the receiver and completes with possible future research directions.

Question: What is the difference between the uplink and downlink of LTE-A?


Knowledge distillation across ensembles of multilingual models for low-resource languages. This paper investigates the effectiveness of knowledge distillation in the context of multilingual models. We show that with knowledge distillation, Long Short-Term Memory(LSTM) models can be used to train standard feed-forward Deep Neural Network (DNN) models for a variety of low-resource languages. We then examine how the agreement between the teacher's best labels and the original labels affects the student model's performance. Next, we show that knowledge distillation can be easily applied to semi-supervised learning to improve model performance. We also propose a promising data selection method to filter un-transcribed data. Then we focus on knowledge transfer among DNN models with multilingual features derived from CNN+DNN, LSTM, VGG, CTC and attention models. We show that a student model equipped with better input features not only learns better from the teacher's labels, but also outperforms the teacher. Further experiments suggest that by learning from each other, the original ensemble of various models is able to evolve into a new ensemble with even better combined performance.

Question: What is knowledge distillation?


WhatsApp Usage Patterns and Prediction Models. This paper presents an extensive study of the usage of the WhatsApp social network, an Internet messaging application that is quickly replacing SMS messaging. It is based on the analysis of over 4 million messages from nearly 100 users that we collected in order to understand people's use of the network. We believe that this is the first in-depth study of the properties of WhatsApp messages with an emphasis of noting differences across different age and gender demographic groups. It is also the first to use statistical and data analytic tools in this analysis. We found that different genders and age demographics had significantly different usage habits in almost all message and group attributes. These differences facilitate the development of user prediction models based on data mining tools. We illustrate this by developing several prediction models such as for a person's gender or approximate age. We also noted differences in users' group behavior. We created group behaviorial models including the likelihood a given group would have more file attachments, if a group would contain a larger number of participants, a higher frequency of activity, quicker response times and shorter messages. We present a detailed discussion about the specific attributes that were contained in all predictive models and suggest possible applications based on these results.

Question: What is the difference between genders and age demographics?


Planar MIMO antennas for IoT and CR applications. In this paper, a planar, compact, single-substrate, multiband 2 sets of 2-elements each multiple-input-multiple-output (MIMO) antenna system is presented. The MIMO antenna system consists of a tunable 2-element meandered and folded MIMO antenna to cover the LTE Band (698 MHz–813 MHz) and a compact 2-element modified truncated cube wideband antenna to cover 754 MHz–971 MHz, 1.65–1.83 GHz and 2–3.66 GHz, respectively. The ground plane of this antenna behaves as a sensing antenna operating in 0.76–1.92 GHz, and 3.0–5.2 GHz. The upper band antennas operate in 0.728–1.08 GHz, 1.64–1.84 GHz, 2.1–3.69 GHz, and 5.01–5.55 GHz range to develop a complete antenna platform for cognitive radios (CR) and Internet of Things (IoT) applications. The antenna is fabricated on a low cost FR-4 substrate (ε<inf>r</inf>=4.4 tanδ=0.02) of dimensions 65 ×120 ×1.56 mm3.

Question: What is a planar MIMO antenna?


SUN database: Large-scale scene recognition from abbey to zoo. Scene categorization is a fundamental problem in computer vision. However, scene understanding research has been constrained by the limited scope of currently-used databases which do not capture the full variety of scene categories. Whereas standard databases for object categorization contain hundreds of different classes of objects, the largest available dataset of scene categories contains only 15 classes. In this paper we propose the extensive Scene UNderstanding (SUN) database that contains 899 categories and 130,519 images. We use 397 well-sampled categories to evaluate numerous state-of-the-art algorithms for scene recognition and establish new bounds of performance. We measure human scene classification performance on the SUN database and compare this with computational methods. Additionally, we study a finer-grained scene representation to detect scenes embedded inside of larger scenes.

Question: What is the largest scene database?


Chinese Image Text Recognition on grayscale pixels. This paper presents a novel scheme for Chinese text recognition in images and videos. It's different from traditional paradigms that binarize text images, fed the binarized text to an OCR engine and get the recognized results. The proposed scheme, named grayscale based Chinese Image Text Recognition (gCITR), implements the recognition directly on grayscale pixels via the following steps: image text over-segmentation, building recognition graph, Chinese character recognition and beam search determination. The advantages of gCITR lie in: (1) it does not heavily rely on the performance of binarization, which is not robust in practical and thus severely affects the performance of OCR, (2) grayscale image retains more information of the text thus facilitates the recognition. Experimental results on text from 13 TV news videos demonstrate the effectiveness of the proposed gCITR, from which significant performance gains are observed.

Question: How does the proposed scheme work?


Semantic Representation. In recent years, there has been renewed interest in the NLP community in genuine language understanding and dialogue. Thus the long-standing issue of how the semantic content of language should be represented is reentering the communal discussion. This paper provides a brief "opinionated survey" of broadcoverage semantic representation (SR). It suggests multiple desiderata for such representations, and then outlines more than a dozen approaches to SR–some longstanding, and some more recent, providing quick characterizations, pros, cons, and some comments on imple-

Question: What is a semantic representation?


Fine-tuning Deep Convolutional Networks for Plant Recognition. This paper describes the participation of the ECOUAN team in the LifeCLEF 2015 challenge. We used a deep learning approach in which the complete system was learned without hand-engineered components. We pre-trained a convolutional neural network using 1.8 million images and used a fine-tuning strategy to transfer learned recognition capabilities from general domains to the specific challenge of Plant Identification task. The classification accuracy obtained by our method outperformed the best result obtained in 2014. Our group obtained the 4th position among all teams and the 10th position among 18 runs.

Question: What is the best result in the plant recognition challenge?


On deep learning-based channel decoding. We revisit the idea of using deep neural networks for one-shot decoding of random and structured codes, such as polar codes. Although it is possible to achieve maximum a posteriori (MAP) bit error rate (BER) performance for both code families and for short codeword lengths, we observe that (i) structured codes are easier to learn and (ii) the neural network is able to generalize to codewords that it has never seen during training for structured, but not for random codes. These results provide some evidence that neural networks can learn a form of decoding algorithm, rather than only a simple classifier. We introduce the metric normalized validation error (NVE) in order to further investigate the potential and limitations of deep learning-based decoding with respect to performance and complexity.

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


The effect of active video games on cognitive functioning in clinical and non-clinical populations: A meta-analysis of randomized controlled trials. Physically-active video games ('exergames') have recently gained popularity for leisure and entertainment purposes. Using exergames to combine physical activity and cognitively-demanding tasks may offer a novel strategy to improve cognitive functioning. Therefore, this systematic review and meta-analysis was performed to establish effects of exergames on overall cognition and specific cognitive domains in clinical and non-clinical populations. We identified 17 eligible RCTs with cognitive outcome data for 926 participants. Random-effects meta-analyses found exergames significantly improved global cognition (g=0.436, 95% CI=0.18-0.69, p=0.001). Significant effects still existed when excluding waitlist-only controlled studies, and when comparing to physical activity interventions. Furthermore, benefits of exergames where observed for both healthy older adults and clinical populations with conditions associated with neurocognitive impairments (all p<0.05). Domain-specific analyses found exergames improved executive functions, attentional processing and visuospatial skills. The findings present the first meta-analytic evidence for effects of exergames on cognition. Future research must establish which patient/treatment factors influence efficacy of exergames, and explore neurobiological mechanisms of action.

Question: What is the effect of exergames on cognitive functioning in clinical and non-clinical populations?


Image Deblurring via Enhanced Low-Rank Prior. Low-rank matrix approximation has been successfully applied to numerous vision problems in recent years. In this paper, we propose a novel low-rank prior for blind image deblurring. Our key observation is that directly applying a simple low-rank model to a blurry input image significantly reduces the blur even without using any kernel information, while preserving important edge information. The same model can be used to reduce blur in the gradient map of a blurry input. Based on these properties, we introduce an enhanced prior for image deblurring by combining the low rank prior of similar patches from both the blurry image and its gradient map. We employ a weighted nuclear norm minimization method to further enhance the effectiveness of low-rank prior for image deblurring, by retaining the dominant edges and eliminating fine texture and slight edges in intermediate images, allowing for better kernel estimation. In addition, we evaluate the proposed enhanced low-rank prior for both the uniform and the non-uniform deblurring. Quantitative and qualitative experimental evaluations demonstrate that the proposed algorithm performs favorably against the state-of-the-art deblurring methods.

Question: Image Deblurring via Enhanced Low-Rank Prior.


Eye Contact Detection via Deep Neural Networks. With the presence of ubiquitous devices in our daily lives, effectively capturing and managing user attention becomes a critical device requirement. While gaze-tracking is typically employed to determine the user's focus of attention, gaze-lock detection to sense eyecontact with a device is proposed in [16]. This work proposes eye contact detection using deep neural networks, and makes the following contributions: (1) With a convolutional neural network (CNN) architecture, we achieve superior eye-contact detection performance as compared to [16] with minimal data pre-processing ; our algorithm is furthermore validated on multiple datasets, (2) Gaze-lock detection is improved by combining head pose and eye-gaze information consistent with social attention literature, and (3) We demonstrate gaze-locking on an Android mobile platform via CNN model compression.

Question: How to detect eye contact?


Development of next-generation system-on-package (SOP) technology based on silicon carriers with fine-pitch chip interconnection. of next-generation system-on-package (SOP) technology based on silicon carriers with fine-pitch chip interconnection J. U. Knickerbocker P. S. Andry L. P. Buchwalter A. Deutsch R. R. Horton K. A. Jenkins Y. H. Kwark G. McVicker C. S. Patel R. J. Polastre C. Schuster A. Sharma S. M. Sri-Jayantha C. W. Surovic C. K. Tsang B. C. Webb S. L. Wright S. R. McKnight E. J. Sprogis B. Dang System-on-Package (SOP) technology based on silicon carriers has the potential to provide modular design flexibility and highperformance integration of heterogeneous chip technologies and to support robust chip manufacturing with high-yield/low-cost chips for a wide range of twoand three-dimensional product applications. Key technology enablers include silicon through-vias, high-density wiring, high-I/O chip interconnection, and supporting test and assembly technologies. The silicon through-vias are a key feature permitting efficient area array signal, power, and ground interconnection through these thinned silicon packages. Highdensity wiring and high-density chip I/O interconnection can enable tight integration of heterogeneous chip technologies which approximate the performance of an integrated system-on-chip with a ''virtual chip'' using the silicon package for integration. Silicon carrier fabrication leverages existing manufacturing capability and mid-UV lithography to provide very dense package wiring following CMOS back-end-of-line design rules. Further, the thermal expansion of the silicon carrier package matches the chip, which helps maintain reliability even as the high-density chip microbump interconnections scale to smaller size. In addition to heterogeneous chip integration, SOP products may leverage the integration of passive components, active devices, and electrooptic structures to enhance system-level performance while also maintaining functional

Question: What is the potential of silicon carriers for next-generation system-on-package (SOP) technology?


The All Relevant Feature Selection using Random Forest. In this paper we examine the application of the random forest classifier for the all relevant feature selection problem. To this end we first examine two recently proposed all relevant feature selection algorithms, both being a random forest wrappers, on a series of synthetic data sets with varying size. We show that reasonable accuracy of predictions can be achieved and that heuristic algorithms that were designed to handle the all relevant problem, have performance that is close to that of the reference ideal algorithm. Then, we apply one of the algorithms to four families of semi-synthetic data sets to assess how the properties of particular data set influence results of feature selection. Finally we test the procedure using a well-known gene expression data set. The relevance of nearly all previously established important genes was confirmed, moreover the relevance of several new ones is discovered.

Question: What is the all relevant feature selection using random forest?


Usability Engineering for Augmented Reality: Employing User-Based Studies to Inform Design. A major challenge, and thus opportunity, in the field of human-computer interaction and specifically usability engineering (UE) is designing effective user interfaces for emerging technologies that have no established design guidelines or interaction metaphors or introduce completely new ways for users to perceive and interact with technology and the world around them. Clearly, augmented reality (AR) is one such emerging technology. We propose a UE approach that employs user-based studies to inform design by iteratively inserting a series of user-based studies into a traditional usability-engineering life cycle to better inform initial user interface designs. We present an exemplar user-based study conducted to gain insight into how users perceive text in outdoor AR settings and to derive implications for design in outdoor AR. We also describe "lessons learned" from our experiences, conducting user-based studies as part of the design process.

Question: How do users perceive text in outdoor AR settings?


Planning-based prediction for pedestrians. We present a novel approach for determining robot movements that efficiently accomplish the robot's tasks while not hindering the movements of people within the environment. Our approach models the goal-directed trajectories of pedestrians using maximum entropy inverse optimal control. The advantage of this modeling approach is the generality of its learned cost function to changes in the environment and to entirely different environments. We employ the predictions of this model of pedestrian trajectories in a novel incremental planner and quantitatively show the improvement in hindrance-sensitive robot trajectory planning provided by our approach.

Question: How does the robot plan its movements?


Face Photo-Sketch Synthesis and Recognition. Today in Modern Society Face Recognition has gained much attention in the field of network multimedia access. After the 9/11 tragedy in India, the need for technologies for identification, detection and recognition of suspects has increased. One of the most common biometric recognition techniques is face recognition since face is the convenient way used by the people to identify each other. In this paper we are going to study a method for representing face which is based on the features which uses geometric relationship among the facial features like mouth, nose and eyes .Feature based face representation is done by independently matching templates of three facial regions i.e eyes, mouth and nose .Principal Component Analysis method which is also called Eigen faces is appearance based technique used widely for the dimensionality reduction and recorded a greater performance in face recognition. Here we are going to study about PCA followed by Feed Forward Neural Network called PCA-NN.

Question: What is the dimensionality reduction technique used in face recognition?


An overview of audio information retrieval. The problem of audio information retrieval is familiar to anyone who has returned from vacation to find an answering machine full of messages. While there is not yet an "AltaVista" for the audio data type, many workers are finding ways to automatically locate, index, and browse audio using recent advances in speech recognition and machine listening. This paper reviews the state of the art in audio information retrieval, and presents recent advances in automatic speech recognition, word spotting, speaker and music identification, and audio similarity with a view towards making audio less "opaque". A special section addresses intelligent interfaces for navigating and browsing audio and multimedia documents, using automatically derived information to go beyond the tape recorder metaphor.

Question: What is the state of the art in audio information retrieval?


Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems. To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226 × 370 image, whereas the original selective search method extracted approximately 10 6 × n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset.

Question: What is the difference between object detection and object classification?


Parametric Exponential Linear Unit for Deep Convolutional Neural Networks. Object recognition is an important task for improving the ability of visual systems to perform complex scene understanding. Recently, the Exponential Linear Unit (ELU) has been proposed as a key component for managing bias shift in Convolutional Neural Networks (CNNs), but defines a parameter that must be set by hand. In this paper, we propose learning a parameterization of ELU in order to learn the proper activation shape at each layer in the CNNs. Our results on the MNIST, CIFAR-10/100 and ImageNet datasets using the NiN, Overfeat, All-CNN and ResNet networks indicate that our proposed Parametric ELU (PELU) has better performances than the non-parametric ELU. We have observed as much as a 7.28% relative error improvement on ImageNet with the NiN network, with only 0.0003% parameter increase. Our visual examination of the non-linear behaviors adopted by Vgg using PELU shows that the network took advantage of the added flexibility by learning different activations at different layers.

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


Climbing gaits of a modular biped climbing robot. For high-rise work in fields such as agriculture, forestry and architecture, and inspired by the climbing motion of inchworms, chimpanzees, and sloths, we have developed a biped climbing robot - Climbot. Consisting of five 1-DOF joint modules in series and two special grippers at the ends, Climbot is capable of grasping objects and climbing poles, trees and trusses. In this paper, we first introduce this novel robot, and then present three climbing gaits. We perform simulation to illustrate, verify and compare the proposed climbing gaits.

Question: What is a modular biped climbing robot?


Cache-oblivious streaming B-trees. A streaming B-tree is a dictionary that efficiently implements insertions and range queries. We present two cache-oblivious streaming B-trees, the shuttle tree, and the cache-oblivious lookahead array (COLA).  For block-transfer size B and on N elements, the shuttle tree implements searches in optimal O(log B+1N) transfers, range queries of L successive elements in optimal O(log B+1N +L/B) transfers, and insertions in O((log B+1N)/BΘ(1/(log log B)2)+(log2N)/B) transfers, which is an asymptotic speedup over traditional B-trees if B ≥ (log N)1+c log log log2 N for any constant c >1.  A COLA implements searches in O(log N) transfers, range queries in O(log N + L/B) transfers, and insertions in amortized O((log N)/B) transfers, matching the bounds for a (cache-aware) buffered repository tree. A partially deamortized COLA matches these bounds but reduces the worst-case insertion cost to O(log N) if memory size M = Ω(log N). We also present a cache-aware version of the COLA, the lookahead array, which achieves the same bounds as Brodal and Fagerberg's (cache-aware) Bε-tree.  We compare our COLA implementation to a traditional B-tree. Our COLA implementation runs 790 times faster for random inser-tions, 3.1 times slower for insertions of sorted data, and 3.5 times slower for searches.

Question: What is the difference between a shuttle tree and a cache-oblivious lookahead array?


LINE-mediated retrotransposition of marked Alu sequences. Alu elements are the most successful transposons in humans. They are 300-bp non-coding sequences transcribed by RNA polymerase III (Pol III) and are expected to retrotranspose with the aid of reverse transcriptases of cellular origin. We previously showed that human LINEs can generate cDNA copies of any mRNA transcript by means of a retroposition process involving reverse transcription and integration by the LINE-encoded endonuclease and reverse transcriptase. Here we show mobility of marked Alu sequences in human HeLa cells with the canonical features of a retrotransposition process, including splicing out of an autocatalytic intron introduced into the marked sequence, target site duplications of varying lengths and integrations into consensus A-rich sequences. We further show that the poly-A stretch at the Alu 3′ end is essential for mobility, that LINEs are required for transposition and that the rate of retroposition is 100–1,000 times higher for Alu transcripts than for control mRNAs, thus accounting for the high mutational activity of these elements observed in humans.

Question: What is LINE-mediated retrotransposition of marked Alu sequences?


A New Algorithm of Fully-automatic Red-eye Removal. the redeye effect is typically formed in consumer photos taken with a built-in camera flash. The paper presents a new algorithm of the automatic removal of redeye from digital photos. The proposed algorithm can remove red-eye automatically without manual intervention. First, Detecting faces by using the AdaBoost algorithm. Second, the red-eyes are located by using operations of segmentation, morphology and geometric constraint. Finally, correcting the located red-eyes completely. The experimental results are satisfied with high correction rates, relatively low computational complexity and robustness.

Question: What is the algorithm of red-eye removal?


Time-Frequency Masking in the Complex Domain for Speech Dereverberation and Denoising. In real-world situations, speech is masked by both background noise and reverberation, which negatively affect perceptual quality and intelligibility. In this paper, we address monaural speech separation in reverberant and noisy environments. We perform dereverberation and denoising using supervised learning with a deep neural network. Specifically, we enhance the magnitude and phase by performing separation with an estimate of the complex ideal ratio mask. We define the complex ideal ratio mask so that direct speech results after the mask is applied to reverberant and noisy speech. Our approach is evaluated using simulated and real room impulse responses, and with background noises. The proposed approach improves objective speech quality and intelligibility significantly. Evaluations and comparisons show that it outperforms related methods in many reverberant and noisy environments.

Question: How to perform speech separation in noisy environments?


Multiclass Posterior Probability Support Vector Machines. Tao et. al. have recently proposed the posterior probability support vector machine (PPSVM) which uses soft labels derived from estimated posterior probabilities to be more robust to noise and outliers. Tao et. al.'s model uses a window-based density estimator to calculate the posterior probabilities and is a binary classifier. We propose a neighbor-based density estimator and also extend the model to the multiclass case. Our bias-variance analysis shows that the decrease in error by PPSVM is due to a decrease in bias. On 20 benchmark data sets, we observe that PPSVM obtains accuracy results that are higher or comparable to those of canonical SVM using significantly fewer support vectors.

Question: What is a support vector machine?


A global perspective on the use, sales, exposure pathways, occurrence, fate and effects of veterinary antibiotics (VAs) in the environment.. Veterinary antibiotics (VAs) are widely used in many countries worldwide to treat disease and protect the health of animals. They are also incorporated into animal feed to improve growth rate and feed efficiency. As antibiotics are poorly adsorbed in the gut of the animals, the majority is excreted unchanged in faeces and urine. Given that land application of animal waste as a supplement to fertilizer is often a common practice in many countries, there is a growing international concern about the potential impact of antibiotic residues on the environment. Frequent use of antibiotics has also raised concerns about increased antibiotic resistance of microorganisms. We have attempted in this paper to summarize the latest information available in the literature on the use, sales, exposure pathways, environmental occurrence, fate and effects of veterinary antibiotics in animal agriculture. The review has focused on four important groups of antibiotics (tylosin, tetracycline, sulfonamides and, to a lesser extent, bacitracin) giving a background on their chemical nature, fate processes, occurrence, and effects on plants, soil organisms and bacterial community. Recognising the importance and the growing debate, the issue of antibiotic resistance due to the frequent use of antibiotics in food-producing animals is also briefly covered. The final section highlights some unresolved questions and presents a way forward on issues requiring urgent attention.

Question: What is the fate of antibiotics in the environment?


Plant pathogens and integrated defence responses to infection. Plants cannot move to escape environmental challenges. Biotic stresses result from a battery of potential pathogens: fungi, bacteria, nematodes and insects intercept the photosynthate produced by plants, and viruses use replication machinery at the host's expense. Plants, in turn, have evolved sophisticated mechanisms to perceive such attacks, and to translate that perception into an adaptive response. Here, we review the current knowledge of recognition-dependent disease resistance in plants. We include a few crucial concepts to compare and contrast plant innate immunity with that more commonly associated with animals. There are appreciable differences, but also surprising parallels.

Question: What is the difference between plant and animal immunity?


Optimizing memory transactions. Atomic blocks allow programmers to delimit sections of code as 'atomic', leaving the language's implementation to enforce atomicity. Existing work has shown how to implement atomic blocks over word-based transactional memory that provides scalable multi-processor performance without requiring changes to the basic structure of objects in the heap. However, these implementations perform poorly because they interpose on all accesses to shared memory in the atomic block, redirecting updates to a thread-private log which must be searched by reads in the block and later reconciled with the heap when leaving the block.This paper takes a four-pronged approach to improving performance: (1) we introduce a new 'direct access' implementation that avoids searching thread-private logs, (2) we develop compiler optimizations to reduce the amount of logging (e.g. when a thread accesses the same data repeatedly in an atomic block), (3) we use runtime filtering to detect duplicate log entries that are missed statically, and (4) we present a series of GC-time techniques to compact the logs generated by long-running atomic blocks.Our implementation supports short-running scalable concurrent benchmarks with less than 50\% overhead over a non-thread-safe baseline. We support long atomic blocks containing millions of shared memory accesses with a 2.5-4.5x slowdown.

Question: How to optimize memory transactions?


Course signals at Purdue: using learning analytics to increase student success. In this paper, an early intervention solution for collegiate faculty called Course Signals is discussed. Course Signals was developed to allow instructors the opportunity to employ the power of learner analytics to provide real-time feedback to a student. Course Signals relies not only on grades to predict students' performance, but also demographic characteristics, past academic history, and students' effort as measured by interaction with Blackboard Vista, Purdue's learning management system. The outcome is delivered to the students via a personalized email from the faculty member to each student, as well as a specific color on a stoplight -- traffic signal -- to indicate how each student is doing. The system itself is explained in detail, along with retention and performance outcomes realized since its implementation. In addition, faculty and student perceptions will be shared.

Question: What is a learning analytics solution?


MobiCloud: A geo-distributed mobile cloud computing platform. In a cloud computing environment, users prefer to migrate their locally processing workloads onto the cloud where more resources with better performance can be expected. ProtoGENI [1] and PlanetLab [17] have further improved the current Internet-based resource outsourcing by allowing end users to construct a virtual network system through virtualization and programmable networking technologies. However, to the best of our knowledge, there is no such general service or resource provisioning platform designated for mobile devices. In this paper, we present a new design and implementation of MobiCloud that is a geo-distributed mobile cloud computing platform. The discussions of the system components, infrastructure, management, implementation flow, and service scenarios are followed by an example on how to experience the MobiCloud system.

Question: What is a geo-distributed mobile cloud computing platform?


A SiC-based isolated DC/DC converter for high density data center applications. Data centers are increasing in number and size at astounding rates, while operational cost, thermal management, size, and performance continue to be the driving metrics for the power subsystems in the associated computing equipment. This paper presents a SiC-based phase-shifted full bridge (PSFB) converter designed for 10kW output power and targeted at data center applications. The design approach focused on tuning of the converter efficiency and minimizing the thermal management system, resulting in a high-density converter. A unique thermal management system has also been employed, resulting in both increased power density and better thermal management. In this paper, the implementation of this converter is described in detail, along with empirical results, both electrical and thermal.

Question: What is a phase-shifted full bridge converter?


A Comparison Among ARIMA, BP-NN, and MOGA-NN for Software Clone Evolution Prediction. Software evolution continues throughout the life cycle of the software. During the evolution of software system, it has been observed that the developers have a tendency to copy the modules completely or partially and modify them. This practice gives rise to identical or very similar code fragments called software clones. This paper examines the evolution of clone components by using advanced time series analysis. In the first phase, software clone components are extracted from the source repository of the software application by using the abstract syntax tree approach. Then, the evolution of software clone components is analyzed. In this paper, three models, Autoregressive Integrated Moving Average, back propagation neural network, and multi-objective genetic algorithm-based neural network, have been compared for the prediction of the evolution of software clone components. Evaluation is performed on the large open-source software application, ArgoUML. The ability to predict the clones helps the software developer to reduce the effort during software maintenance activities.

Question: What is the difference between autoregressive integrated moving average, back propagation neural network, and multi-objective genetic algorithm-based neural network?


Implementing Efficient, Portable Computations for Machine Learning. Implementing E cient, Portable Computations for Machine Learning

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


Quality of life in the anxiety disorders: a meta-analytic review.. There has been significant interest in the impact of anxiety disorders on quality of life. In this meta-analytic review, we empirically evaluate differences in quality of life between patients with anxiety disorders and nonclinical controls. Thirty-two patient samples from 23 separate studies (N=2892) were included in the analysis. The results yielded a large effect size indicating poorer quality of life among anxiety disorder patients vs. controls and this effect was observed across all anxiety disorders. Compared to control samples, no anxiety disorder diagnosis was associated with significantly poorer overall quality of life than was any other anxiety disorder diagnosis. Examination of specific domains of QOL suggests that impairments may be particularly prominent among patients with post-traumatic stress disorder. QOL domains of mental health and social functioning were associated with the highest levels of impairment among anxiety disorder patients. These findings are discussed in the context of future research on the assessment of quality of life in the anxiety disorders.

Question: What is the quality of life in the anxiety disorders?


Predicting NDUM Student's Academic Performance Using Data Mining Techniques. The ability to predict the students' academic performance is very important in institution educational system. Recently some researchers have been proposed data mining techniques for higher education. In this paper, we compare two data mining techniques which are: Artificial Neural Network (ANN) and the combination of clustering and decision tree classification techniques for predicting and classifying students' academic performance. The data set used in this research is the student data of Computer Science Department, Faculty of Science and Defence Technology, National Defence University of Malaysia (NDUM).

Question: What is the difference between clustering and decision tree classification?


Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features. Viewpoint invariant pedestrian recognition is an important yet under-addressed problem in computer vision. This is likely due to the difficulty in matching two objects with unknown viewpoint and pose. This paper presents a method of performing viewpoint invariant pedestrian recognition using an efficiently and intelligently designed object representation, the ensemble of localized features (ELF). Instead of designing a specific feature by hand to solve the problem, we define a feature space using our intuition about the problem and let a machine learning algorithm find the best representation. We show how both an object class specific representation and a discriminative recognition model can be learned using the AdaBoost algorithm. This approach allows many different kinds of simple features to be combined into a single similarity function. The method is evaluated using a viewpoint invariant pedestrian recognition dataset and the results are shown to be superior to all previous benchmarks for both recognition and reacquisition of pedestrians.

Question: What is a viewpoint invariant pedestrian recognition?


Suppressing the azimuth ambiguities in synthetic aperture radar images. This paper proposes a method for removing the azimuth ambiguities from synthetic aperture radar ( S A R ) images. The basic idea is to generate a two-dimensional reference function for S A R processing which provides, in addition to the matched filtering for the unaliased part of the received signal, the deconvolution of the azimuth ambiguities. This approach corresponds to an ideal filter concept, where an ideal impulse response function is obtained even in the presence of severe phase and amplitude errors. Modeling the sampled azimuth signal shows that the absolute phase value of the ambiguities cannot easily be determined due to their undersampling. The concept of the ideal filter is then extended to accommodate the undefined phase of the ambiguities and also the fading of the azimuth signal. Raw data from the E-SAR system have been used to verify the improvement in image quality by using the new method. It has a substantial significance in enabling the pulse-repetition frequency (PRF) constraints in the S A R system design to be relaxed and also for improving S A R image quality and interpretation.

Question: How to suppress azimuth ambiguities in synthetic aperture radar images?


Automatic Mapping Clinical Notes to Medical Terminologies. Automatic mapping of key concepts from clinical notes to a terminology is an important task to achieve for extraction of the clinical information locked in clinical notes and patient reports. The present paper describes a system that automatically maps free text into a medical reference terminology. The algorithm utilises Natural Language Processing (NLP) techniques to enhance a lexical token matcher. In addition, this algorithm is able to identify negative concepts as well as performing term qualification. The algorithm has been implemented as a web based service running at a hospital to process real-time data and demonstrated that it worked within acceptable time limits and accuracy limits for them. However broader acceptability of the algorithm will require comprehensive evaluations.

Question: How to map clinical notes to a medical terminology?


A Highly Parallel Framework for HEVC Coding Unit Partitioning Tree Decision on Many-core Processors. High Efficiency Video Coding (HEVC) uses a very flexible tree structure to organize coding units, which leads to a superior coding efficiency compared with previous video coding standards. However, such a flexible coding unit tree structure also places a great challenge for encoders. In order to fully exploit the coding efficiency brought by this structure, huge amount of computational complexity is needed for an encoder to decide the optimal coding unit tree for each image block. One way to achieve this is to use parallel computing enabled by many-core processors. In this paper, we analyze the challenge to use many-core processors to make coding unit tree decision. Through in-depth understanding of the dependency among different coding units, we propose a parallel framework to decide coding unit trees. Experimental results show that, on the Tile64 platform, our proposed method achieves averagely more than 11 and 16 times speedup for 1920x1080 and 2560x1600 video sequences, respectively, without any coding efficiency degradation.

Question: How to use many-core processors to make coding unit tree decision?


Automatic drift calibration for EOG-based gaze input interface. A drift calibration technique for DC-coupled EOG (electrooculogram) systems is proposed. It assumes a non-linear relationship between EOG and eye angle and estimates the absolute eye angle by the EOG differences during saccade. Drift is calibrated every saccade without user's explicit action, so it is especially suitable for long-term gaze input interfaces. An experiment confirms that it can estimate horizontal absolute eye angle with an error of about 5° in addition to accurate eye movement.

Question: How to calibrate the drift of the EOG?


Cognition and motivation in emotion.. The role of cognition--and to some extent motivation--in emotion, the ways meaning is generated, unconscious appraising, and the implications of this way of thinking for life-span development are addressed. It is argued that appraisal is a necessary as well as sufficient cause of emotion and that knowledge is necessary but not sufficient. This position is examined in light of what is known about emotions in infants and young children, the effects of drugs on acute emotions and moods, and recent patterns of thought about the brain in emotions. The discussion of how meaning is generated is the core of the article. Automatic processing without awareness is contrasted with deliberate and conscious processing, and the concept of resonance between an animal's needs and what is encountered in the environment is examined. The idea that there is more than one way meaning is achieved strengthens and enriches the case for the role of appraisal in emotion and allows the consideration of what is meant by unconscious and preconscious appraisal and the examination of how they might work.

Question: What is the role of cognition in emotion?


Use of AI Techniques for Residential Fire Detection in Wireless Sensor Networks. Early residential fire detection is important for prompt extinguishing and reducing damages and life losses. To detect fire, one or a combination of sensors and a detection algorithm are needed. The sensors might be part of a wireless sensor network (WSN) or work independently. The previous research in the area of fire detection using WSN has paid little or no attention to investigate the optimal set of sensors as well as use of learning mechanisms and Artificial Intelligence (AI) techniques. They have only made some assumptions on what might be considered as appropriate sensor or an arbitrary AI technique has been used. By closing the gap between traditional fire detection techniques and modern wireless sensor network capabilities, in this paper we present a guideline on choosing the most optimal sensor combinations for accurate residential fire detection. Additionally, applicability of a feed forward neural network (FFNN) and Naïve Bayes Classifier is investigated and results in terms of detection rate and computational complexity are analyzed.

Question: What is the optimal set of sensors for fire detection?


Asymmetry-Aware Link Quality Services in Wireless Sensor Networks. Recent study in wireless sensor networks (WSN) has found that the irregular link quality is a common phenomenon. The irregular link quality, especially link asymmetry, has significant impacts on the design of WSN protocols, such as MAC protocols, neighborhood and topology discovery protocols, and routing protocols. In this paper, we propose asymmetryaware link quality services, including the neighborhood link quality service (NLQS) and the link relay service (LRS), to provide the timeliness link quality information of neighbors, and build a relay framework to alleviate effects of the link asymmetry. To demonstrate proposed link quality services, we design and implement two example applications, the shortest hops routing tree (SHRT) and the best path reliability routing tree (BRRT), in the TinyOS platform. To evaluate proposed link quality services, we have conducted both static analysis and simulation through the TOSSIM simulator, in terms of four performance metrics. We found that the performance of two example applications was improved substantially. More than 40% of nodes identify more outbound neighbors and the percentage of increased outbound neighbors is between 14% and 100%. In SHRT, more than 15% of nodes reduce hops of the routing tree and the percentage of reduced hops is between 14% and 100%. In BRRT, more than 16% of nodes improve the path reliability of the routing tree and the percentage of the improved path reliability is between 2% to 50%.

Question: How to improve the performance of the routing tree in wireless sensor networks?


What does it take to generate natural textures. Natural image generation is currently one of the most actively explored fields in Deep Learning. Many approaches, e.g. for state-of-the-art artistic style transfer or natural texture synthesis, rely on the statistics of hierarchical representations in supervisedly trained deep neural networks. It is, however, unclear what aspects of this feature representation are crucial for natural image generation: is it the depth, the pooling or the training of the features on natural images? We here address this question for the task of natural texture synthesis and show that none of the above aspects are indispensable. Instead, we demonstrate that natural textures of high perceptual quality can be generated from networks with only a single layer, no pooling and random filters.

Question: What is the difference between a single layer and a pooling layer?


Generalized Semi-Orthogonal Multiple-Access for Massive MIMO. We propose a novel framework to enable concurrent transmission of multiple users to an access node equipped with a massive multiple-input multiple-output (mMIMO), which encompasses and extends the conventional time-division duplex (TDD) protocol and the recently proposed semi-orthogonal multiple-access (SOMA). The new solution is referred to generalized semi-orthogonal multiple-access (GSOMA). To enable GSOMA, the users are grouped such that the users within each group are assigned mutually orthogonal pilot sequences. However the pilot sequences can be reused in different groups and the users in different group are coordinated such that their pilot sequences do not interfere by a semi-orthogonal mechanism enabled by a resource-offset sequence. We describe the general framework and analyze a case of GSOMA and show that a properly designed GSOMA can outperform the conventional TDD and SOMA.

Question: What is the difference between the conventional time-division duplex (TDD) protocol and the recently proposed semi-orthogonal multiple-access (SOMA)?


Understanding the Factors Driving NFC-Enabled Mobile Payment Adoption: an Empirical Investigation. Though NFC mobile payment offers both convenience and benefits to consumers, its adoption rate is still very low. This study aims to explore the factors determining consumers' adoption of NFC mobile payment. In this study, building on TAM, an extended adoption intention model is developed by incorporating three sorts of variables into it, namely mobile payment system factors (compatibility and perceived complementarity), user characteristics (mobile payment knowledge), and a risk-related factor (perceived risk). The model is empirically tested with a sample of 377 validated respondents. Compatibility, perceived ease of use and mobile payment knowledge are found to be the factors determining individuals' intention to use NFC mobile payment. Against our expectations, perceived usefulness and perceived risk do not affect use intention significantly in adopting NFC mobile payments among consumers. We discuss the theoretical and practical implications of these findings, and point out the limitations and need for future research.

Question: What are the factors driving NFC-enabled mobile payment adoption?


Finger ECG signal for user authentication: Usability and performance. Over the past few years, the evaluation of Electrocardio-graphic (ECG) signals as a prospective biometric modality has revealed promising results. Given the vital and continuous nature of this information source, ECG signals offer several advantages to the field of biometrics; yet, several challenges currently prevent the ECG from being adopted as a biometric modality in operational settings. These arise partially due to ECG signal's clinical tradition and intru-siveness, but also from the lack of evidence on the permanence of the ECG templates over time. The problem of in-trusiveness has been recently overcome with the "off-the-person" approach for capturing ECG signals. In this paper we provide an evaluation of the permanence of ECG signals collected at the fingers, with respect to the biometric authentication performance. Our experimental results on a small dataset suggest that further research is necessary to account for and understand sources of variability found in some subjects. Despite these limitations, "off-the-person" ECG appears to be a viable trait for multi-biometric or standalone biometrics, low user throughput, real-world scenarios.

Question: How to evaluate the permanence of ECG signals?


Compressive coded aperture imaging. Nonlinear image reconstruction based upon sparse representations of images has recently received widespread attention with the emerging framework of compressed sensing (CS). This theory indicates that, when feasible, judicious selection of the type of distortion induced by measurement systems may dramatically improve our ability to perform image reconstruction. However, applying compressed sensing theory to practical imaging systems poses a key challenge: physical constraints typically make it infeasible to actually measure many of the random projections described in the literature, and therefore, innovative and sophisticated imaging systems must be carefully designed to effectively exploit CS theory. In video settings, the performance of an imaging system is characterized by both pixel resolution and field of view. In this work, we propose compressive imaging techniques for improving the performance of video imaging systems in the presence of constraints on the focal plane array size. In particular, we describe a novel yet practical approach that combines coded aperture imaging to enhance pixel resolution with superimposing subframes of a scene onto a single focal plane array to increase field of view. Specifically, the proposed method superimposes coded observations and uses wavelet-based sparsity recovery algorithms to reconstruct the original subframes. We demonstrate the effectiveness of this approach by reconstructing with high resolution the constituent images of a video sequence.

Question: How to improve the performance of video imaging systems in the presence of constraints on the focal plane array size?


Asynchronous Distributed Semi-Stochastic Gradient Optimization. Lemma 2 At a specific stage s and for a worker p, let g∗ i = ∇fi(w)−∇fi(w̃) +∇F (w̃), i ∈ Dp, then the following inequality holds, Ep [ Ei‖g i ‖ ] ≤ 2L [F (w̃)− F (w∗)] , Proof 2 Ei‖g i ‖ =Ei‖∇fi(w̃)−∇fi(w)−∇F (w̃)‖ =Ei‖∇fi(w̃)−∇fi(w)‖ − 2Ei〈∇F (w̃),∇fi(w̃)−∇fi(w)〉+ ‖∇F (w̃)‖ ≤2L (Fp(w̃)− Fp(w)− 〈∇Fp(w), w̃ − w∗〉) − 2〈∇F (w̃),∇Fp(w̃)−∇Fp(w)〉+ ‖∇F (w̃)‖, where the inequality is the result of applying Lemma 1. Further, taking expectation on both sides w.r.t. worker p, we get

Question: What is the difference between gradient descent and gradient descent with momentum?


A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. The use of flanking marker methods has proved to be a powerful tool for the mapping of quantitative trait loci (QTL) in the segregating generations derived from crosses between inbred lines. Methods to analyse these data, based on maximum-likelihood, have been developed and provide good estimates of QTL effects in some situations. Maximum-likelihood methods are, however, relatively complex and can be computationally slow. In this paper we develop methods for mapping QTL based on multiple regression which can be applied using any general statistical package. We use the example of mapping in an F2 population and show that these regression methods produce very similar results to those obtained using maximum likelihood. The relative simplicity of the regression methods means that models with more than a single QTL can be explored and we give examples of two linked loci and of two interacting loci. Other models, for example with more than two QTL, with environmental fixed effects, with between family variance or for threshold traits, could be fitted in a similar way. The ease, speed of application and generality of regression methods for flanking marker analyses, and the good estimates they obtain, suggest that they should provide the method of choice for the analysis of QTL mapping data from inbred line crosses.

Question: What is a flanking marker?


AmbiMax: Autonomous Energy Harvesting Platform for Multi-Supply Wireless Sensor Nodes. AmbiMax is an energy harvesting circuit and a supercapacitor based energy storage system for wireless sensor nodes (WSN). Previous WSNs attempt to harvest energy from various sources, and some also use supercapacitors instead of batteries to address the battery aging problem. However, they either waste much available energy due to impedance mismatch, or they require active digital control that incurs overhead, or they work with only one specific type of source. AmbiMax addresses these problems by first performing maximum power point tracking (MPPT) autonomously, and then charges supercapacitors at maximum efficiency. Furthermore, AmbiMax is modular and enables composition of multiple energy harvesting sources including solar, wind, thermal, and vibration, each with a different optimal size. Experimental results on a real WSN platform, Eco, show that AmbiMax successfully manages multiple power sources simultaneously and autonomously at several times the efficiency of the current state-of-the-art for WSNs

Question: What is the name of the company that makes the AmbiMax?


Model-Driven Data Acquisition in Sensor Networks. Declarative queries are proving to be an attractive paradigm for interacting with networks of wireless sensors. The metaphor that "the sensornet is a database" is problematic, however, because sensors do not exhaustively represent the data in the real world. In order to map the raw sensor readings onto physical reality, a modelof that reality is required to complement the readings. In this paper, we enrich interactive sensor querying with statistical modeling techniques. We demonstrate that such models can help provide answers that are both more meaningful, and, by introducing approximations with probabilistic confidences, significantly more efficient to compute in both time and energy. Utilizing the combination of a model and live data acquisition raises the challenging optimization problem of selecting the best sensor readings to acquire, balancing the increase in the confidence of our answer against the communication and data acquisition costs in the network. We describe an exponential time algorithm for finding the optimal solution to this optimization problem, and a polynomial-time heuristic for identifying solutions that perform well in practice. We evaluate our approach on several real-world sensor-network data sets, taking into account the real measured data and communication quality, demonstrating that our model-based approach provides a high-fidelity representation of the real phenomena and leads to significant performance gains versus traditional data acquisition techniques.

Question: What is the difference between a model and a database?


A 27.6 MHz 297 μW 33 ppm/°C CMOS relaxation oscillator with an adjustable temperature compensation scheme. A 27.6 MHz 297 μW relaxation oscillator is presented in this paper by using an 180-nm CMOS technology. The proposed oscillator employs an adjustable temperature compensation feedforward scheme, in which the charging current can be set steady by a four-bit digital trimming signal. We have demonstrated a frequency variation lower than 33.5 ppm/°C which could be close to 0 ppm/°C in theory if the precision is high enough. In practical production, it is effective to calibrate the mismatching and deviation of fabrication because of the novel adjustable temperature compensation scheme.

Question: What is a relaxation oscillator?


A Wideband Dual-Polarized L-Probe Stacked Patch Antenna Array. A 2 times 1 dual-polarized L-probe stacked patch antenna array is presented. It has employed a novel technique to achieve high isolation between the two input ports. The proposed antenna has a 14-dB return loss bandwidth of 19.8%, which is ranged from 0.808 to 0.986 GHz, for both ports. Also, it has an input port isolation of more than 30 dB and an average gain of 10.5 dBi over this bandwidth. Moreover, its radiation patterns in the two principal planes have cross-polarization level of less than -15 dB within the 3-dB beamwidths across the passband. Due to these features, this antenna array is highly suitable for the outdoor base station that is required to cover the operating bandwidths of both CDMA800 and GSM900 mobile communication systems.

Question: What is a dual-polarized L-probe stacked patch antenna array?


Vaginal Labiaplasty: Defense of the Simple "Clip and Snip" and a New Classification System. Vaginal labiaplasty has become a more frequently performed procedure as a result of the publicity and education possible with the internet. Some of our patients have suffered in silence for years with large, protruding labia minora and the tissue above the clitoris that is disfiguring and uncomfortable and makes intercourse very difficult and painful. We propose four classes of labia protrusion based on size and location: Class 1 is normal, where the labia majora and minora are about equal. Class 2 is the protrusion of the minora beyond the majora. Class 3 includes a clitoral hood. Class 4 is where the large labia minora extends to the perineum. There are two principal means of reconstructing this area. Simple amputation may be possible for Class 2 and Class 4. Class 2 and Class 3 may be treated with a wedge resection and flap advancement that preserves the delicate free edge of the labia minora (Alter, Ann Plast Surg 40:287, 1988). Class 4 may require a combination of both amputation of the clitoral hood and/or perineal extensions and rotation flap advancement over the labia minora. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Question: What is the difference between a wedge resection and a rotation flap?


A Neural Schema Architecture for Autonomous Robots. As autonomous robots become more complex in their behavior, more sophisticated software architectures are required to support the ever more sophisticated robotics software. These software architectures must support complex behaviors involving adaptation and learning, implemented, in particular, by neural networks. We present in this paper a neural based schema [2] software architecture for the development and execution of autonomous robots in both simulated and real worlds. This architecture has been developed in the context of adaptive robotic agents, ecological robots [6], cooperating and competing with each other in adapting to their environment. The architecture is the result of integrating a number of development and execution systems: NSL, a neural simulation language; ASL, an abstract schema language; and MissionLab, a schema-based mission-oriented simulation and robot system. This work contributes to modeling in Brain Theory (BT) and Cogniti ve Psychology, with applications in Distributed Artificial Intell igence (DAI), Autonomous Agents and Robotics. Areas: Robotics, Agent-oriented programming, Neural Nets

Question: What is a neural schema architecture?


A low-voltage, low quiescent current, low drop-out regulator. The demand for low voltage, low drop-out (LDO) regulators is increasing because of the growing demand for portable electronics, i.e., cellular phones, pagers, laptops, etc. LDOs are used coherently with dc-dc converters as well as standalone parts. In power supply systems, they are typically cascaded onto switching regulators to suppress noise and provide a low noise output. The need for low voltage is innate to portable low power devices and corroborated by lower breakdown voltages resulting from reductions in feature size. Low quiescent current in a battery operated system is an intrinsic performance parameter because it partially determines battery life. This paper discusses some techniques that enable the practical realizations of low quiescent current LDOs at low voltages and in existing technologies. The proposed circuit exploits the frequency response dependence on load-current to minimize quiescent current flow. Moreover, the output current capabilities of MOS power transistors are enhanced and drop-out voltages are decreased for a given device size. Other applications, like dc-dc converters, can also reap the benefits of these enhanced MOS devices. An LDO prototype incorporating the aforementioned techniques was fabricated. The circuit was operable down to input voltages of 1 V with a zero-load quiescent current flow of 23 μA. Moreover, the regulator provided 18 and 50 mA of output current at input voltages of 1 and 1.2 V respectively.

Question: What is a low voltage, low drop-out regulator?


Customer Churn Prediction Modelling Based on Behavioural Patterns Analysis using Deep Learning. Customer churn refers to when a customer ceases their relationship with a company. A churn rate, used to estimate growth, is now considered as important a metric as financial profit. With growing competition in the market, companies are desperate to keep the churn rate as low as possible. Thus, churn prediction has gained critical importance, not just for existing customers, but also for predicting trends of future customers. This paper demonstrates prediction of churn on a Telco dataset using a Deep Learning Approach. A multilayered Neural Network was designed to build a non-linear classification model. The churn prediction model works on customer features, support features, usage features and contextual features. The possibility of churn as well as the determining factors are predicted. The trained model then applies the final weights on these features and predict the possibility of churn for that customer. An accuracy of 80.03% was achieved. Since the model also provides the churn factors, it can be used by companies to analyze the reasons for these factors and take steps to eliminate them.

Question: What is a churn prediction model?


Lane-Level Integrity Provision for Navigation and Map Matching With GNSS, Dead Reckoning, and Enhanced Maps. Lane-level positioning and map matching are some of the biggest challenges for navigation systems. Additionally, in safety applications or in those with critical performance requirements (such as satellite-based electronic fee collection), integrity becomes a key word for the navigation community. In this scenario, it is clear that a navigation system that can operate at the lane level while providing integrity parameters that are capable of monitoring the quality of the solution can bring important benefits to these applications. This paper presents a pioneering novel solution to the problem of combined positioning and map matching with integrity provision at the lane level. The system under consideration hybridizes measurements from a global navigation satellite system (GNSS) receiver, an odometer, and a gyroscope, along with the road information stored in enhanced digital maps, by means of a multiple-hypothesis particle-filter-based algorithm. A set of experiments in real environments in France and Germany shows the very good results obtained in terms of positioning, map matching, and integrity consistency, proving the feasibility of our proposal.

Question: What is the difference between a navigation system and a positioning system?