Automatic query-based keyword and keyphrase extraction. Extracting keywords and keyphrases mainly for identifying content of a document, has an importance role in text processing tasks such as text summarization, information retrieval, and query expansion. In this research, we introduce a new keyword/keyphrase extraction approach in which both single and multi-document keyword/keyphrase extraction techniques are considered. The proposed approach is specifically practical when a user is interested in additional data such as keywords/keyphrases related to a topic or query. In the proposed approach, first a set of documents are retrieved based on user's query, then a single document keyword extraction method is applied to extract candidate keyword/keyphrases from each retrieved document. Finally, a new re-scoring scheme is introduced to extract final keywords/keyphrases. We have evaluated the proposed method based on the relationship between the final keyword/keyphrases with the initial user query, and based user's satisfaction. Our experimental results show how much the extracted keywords/keyphrases are relevant and wellmatched with user's need.

Question: What is the relationship between the extracted keywords and the initial user query?


The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation. This report surveys the landscape of potential security threats from malicious uses of AI, and proposes ways to better forecast, prevent, and mitigate these threats. After analyzing the ways in which AI may influence the threat landscape in the digital, physical, and political domains, we make four high-level recommendations for AI researchers and other stakeholders. We also suggest several promising areas for further research that could expand the portfolio of defenses, or make attacks less effective or harder to execute. Finally, we discuss, but do not conclusively resolve, the long-term equilibrium of attackers and defenders.

Question: What is the malicious use of artificial intelligence?


Cloud computing paradigms for pleasingly parallel biomedical applications. Cloud computing offers exciting new approaches for scientific computing that leverages the hardware and software investments on large scale data centers by major commercial players. Loosely coupled problems are very important in many scientific fields and are on the rise with the ongoing move towards data intensive computing. There exist several approaches to leverage clouds & cloud oriented data processing frameworks to perform pleasingly parallel computations. In this paper we present two pleasingly parallel biomedical applications, 1) assembly of genome fragments 2) dimension reduction in the analysis of chemical structures, implemented utilizing cloud infrastructure service based utility computing models of Amazon AWS and Microsoft Windows Azure as well as utilizing MapReduce based data processing frameworks, Apache Hadoop and Microsoft DryadLINQ. We review and compare each of the frameworks and perform a comparative study among them based on performance, efficiency, cost and the usability. Cloud service based utility computing model and the managed parallelism (MapReduce) exhibited comparable performance and efficiencies for the applications we considered. We analyze the variations in cost between the different platform choices (eg: EC2 instance types), highlighting the need to select the appropriate platform based on the nature of the computation.

Question: What is the difference between cloud computing and utility computing?


Vitamins, minerals, and mood.. In this article, the authors explore the breadth and depth of published research linking dietary vitamins and minerals (micronutrients) to mood. Since the 1920s, there have been many studies on individual vitamins (especially B vitamins and Vitamins C, D, and E), minerals (calcium, chromium, iron, magnesium, zinc, and selenium), and vitamin-like compounds (choline). Recent investigations with multi-ingredient formulas are especially promising. However, without a reasonable conceptual framework for understanding mechanisms by which micronutrients might influence mood, the published literature is too readily dismissed. Consequently, 4 explanatory models are presented, suggesting that mood symptoms may be expressions of inborn errors of metabolism, manifestations of deficient methylation reactions, alterations of gene expression by nutrient deficiency, and/or long-latency deficiency diseases. These models provide possible explanations for why micronutrient supplementation could ameliorate some mental symptoms.

Question: What is the relationship between vitamins and minerals and mood?


On the Mathematics of Flat Origamis∗. Origami is the art of folding pieces of paper into works of sculpture without the aid of scissors or glue. Modern advancements in the complexity of origami (e.g., the work of Montroll and Maekawa) reveal a rich geometric structure governing the possibilities of paperfolding. In this paper we initiate a mathematical study of this "origami geometry" and explore the possibilities of a graph theoretic model. In particular, we study the properties of origami models which fold flat (i.e., can be pressed in a book without crumpling). Necessary and sufficient conditions are given for an origami model to locally fold flat, and the problems encountered in trying to extend these results globally are discussed.

Question: What is the mathematics of origami?


AI Methods in Algorithmic Composition: A Comprehensive Survey. Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence.

Question: What is algorithmic composition?


A new ultrawideband printed monopole antenna: the planar inverted cone antenna (PICA). A new antenna, the planar inverted cone antenna (PICA), provides ultrawideband (UWB) performance with a radiation pattern similar to monopole disk antennas , but is smaller in size. Extensive simulations and experiments demonstrate that the PICA antenna provides more than a 10:1 impedance bandwidth (for VSWR<2) and supports a monopole type omnidirectional pattern over 4:1 bandwidth. A second version of the PICA with two circular holes changes the current flow on the metal disk and extends the high end of the operating frequency range, improving the pattern bandwidth to 7:1.

Question: What is the planar inverted cone antenna?


Adaptive configuration of lora networks for dense IoT deployments. Large-scale Internet of Things (IoT) deployments demand long-range wireless communications, especially in urban and metropolitan areas. LoRa is one of the most promising technologies in this context due to its simplicity and flexibility. Indeed, deploying LoRa networks in dense IoT scenarios must achieve two main goals: efficient communications among a large number of devices and resilience against dynamic channel conditions due to demanding environmental settings (e.g., the presence of many buildings). This work investigates adaptive mechanisms to configure the communication parameters of LoRa networks in dense IoT scenarios. To this end, we develop FLoRa, an open-source framework for end-to-end LoRa simulations in OMNeT++. We then implement and evaluate the Adaptive Data Rate (ADR) mechanism built into LoRa to dynamically manage link parameters for scalable and efficient network operations. Extensive simulations show that ADR is effective in increasing the network delivery ratio under stable channel conditions, while keeping the energy consumption low. Our results also show that the performance of ADR is severely affected by a highly-varying wireless channel. We thereby propose an improved version of the original ADR mechanism to cope with variable channel conditions. Our proposed solution significantly increases both the reliability and the energy efficiency of communications over a noisy channel, almost irrespective of the network size. Finally, we show that the delivery ratio of very dense networks can be further improved by using a network-aware approach, wherein the link parameters are configured based on the global knowledge of the network.

Question: How to configure the communication parameters of LoRa networks in dense IoT deployments?


Compact LTCC Bandpass Filter With Wide Stopband Using Discriminating Coupling. This paper presents a novel compact low-temperature cofired ceramic (LTCC) bandpass filter (BPF) with wide stopband and high selectivity. The proposed circuit consists of two coupled λg/4 transmission-line resonators. A special coupling region is selected to realize a novel discriminating coupling scheme for generating a transmission zero (TZ) at the third harmonic frequency. The mechanism is analyzed and the design guideline is described. The source-load coupling is introduced to generate two TZs near the passband and one in the stopband. Thus, wide stopband can be obtained without extra circuits. Due to the LTCC multilayer structures, the filter size is 0.058 λg×0.058 λg×0.011 λg, or 2.63 mm × 2.61 mm × 0.5 mm. The simulated and measured results of the demonstrated LTCC BPF are presented to validate the proposed design.

Question: What is the size of the filter?


A new retexturing method for virtual fitting room using Kinect 2 camera. This research work proposes a new method for garment retexturing using a single static image along with depth information obtained using the Microsoft Kinect 2 camera. First the garment is segmented out from the image and texture domain coordinates are computed for each pixel of the shirt using 3D information. After that shading is applied on the new colours from the texture image by applying linear stretching of the luminance of the segmented garment. The proposed method is colour and pattern invariant and results in to visually realistic retexturing. The proposed method has been tested on various images and it is shown that it generally performs better and produces more realistic results compared to the state-of-the-art methods. The proposed method can be an application for the virtual fitting room.

Question: What is the proposed method?


Machine learning in automated text categorization. The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.

Question: What is the difference between machine learning and knowledge engineering?


The HEXACO-60: a short measure of the major dimensions of personality.. We describe the HEXACO-60, a short personality inventory that assesses the 6 dimensions of the HEXACO model of personality structure. We selected the 10 items of each of the 6 scales from the longer HEXACO Personality Inventory-Revised (Ashton & Lee, 2008; Lee & Ashton, 2004, 2006), with the aim of representing the broad range of content that defines each dimension. In self-report data from samples of college students and community adults, the scales showed reasonably high levels of internal consistency reliability and rather low interscale correlations. Correlations of the HEXACO-60 scales with measures of the Big Five factors were consistent with theoretical expectations, and convergent correlations between self-reports and observer reports on the HEXACO-60 scales were high, averaging above .50. We recommend the HEXACO-60 for use in personality assessment contexts in which administration time is limited.

Question: What is the HEXACO model of personality structure?


A quadruped robot with parallel mechanism legs. Summary form only given. The design and control of quadruped robots has become a fascinating research field because they have better mobility on unstructured terrains. Until now, many kinds of quadruped robots were developed, such as JROB-1 [1], BISAM [2], BigDog [3], LittleDog [4], HyQ [5] and Cheetah cub [6]. They have shown significant walking performance. However, most of them use serial mechanism legs and have animal like structure: the thigh and the crus. To swing the crus in swing phase and support the body's weight in stance phase, a linear actuator is attached on the thigh [2, 3, 5, 6], or instead, a rotational actuator is installed on the knee joint [1, 4]. To make the robot more useful in the wild environment, e.g., the detection or manipulation tasks, the payload capability is very important. To carry the sensors or tools, heavy load legged robot is very necessary. Thus the knee actuator should be lightweight, powerful and easy to maintain. However, this can be very costly and hard to satisfy at the same time.

Question: What is a quadruped robot?


How to derive priorities in AHP: a comparative study. A heated discussion has arisen over the "best" priorities derivation method. Using a Monte Carlo simulation this article compares and evaluates the solutions of four AHP ratio scaling methods: the right eigenvalue method, the left eigenvalue method, the geometric mean and the mean of normalized values. Matrices with different dimensions and degree of impurities are randomly constructed. We observe a high level of agreement between the different scaling techniques. The number of ranking contradictions increases with the dimension of the matrix and the inconsistencies. However these contradictions affect only close priorities.

Question: What is the best way to derive priorities?


Imaging of conjoined twins. The incidence of conjoined twins is estimated to be around 1 in 250,000 live births. There is a distinct female predominance. In this paper the imaging of conjoined twins both antenatally and postnatally is reviewed, in particular taking into consideration recent advances with multidetector CT. Accurate counselling of parents regarding the likely outcome of the pregnancy and the likelihood of successful separation is dependent on good prenatal imaging with ultrasound and MRI. Planning of postnatal surgical separation is aided by accurate preoperative imaging which, depending on the conjoined area, will encompass many imaging modalities, but often relies heavily on CT scanning.

Question: What is the incidence of conjoined twins?


Wideband and Unidirectional Cavity-Backed Folded Triangular Bowtie Antenna. In this paper, a cavity-backed folded triangular bowtie antenna (FTBA) is proposed and investigated. Comparisons show that it has much smaller fluctuation of input impedance and much larger bandwidth (BW) for stable radiation patterns than cavity-backed ordinary TBA. The proposed antenna is fed by a conventional balun composed of a transition from a microstrip line to a parallel stripline. It achieves an impedance BW of 92.2% for |S11| les -10 dB, stable gain of around 9.5 dBi and unidirectional radiation patterns over the whole operating band. A close examination of the antenna aperture efficiency and the electric field distribution in the aperture provides an explanation for the stable gain across such large frequency band. Comparisons are made against several conventional antennas, including short backfire and microstrip patch antennas, to illustrate the salient features of the proposed antenna. A design guideline is also given for practical applications.

Question: What is a cavity-backed folded triangular bowtie antenna?


Using institutional theory and dynamic simulation to understand complex e-Government phenomena. Available online 17 May 2011

Question: How to understand complex e-Government phenomena?


mmWave phased-array with hemispheric coverage for 5th generation cellular handsets. A first-of-the-kind 28 GHz antenna solution for the upcoming 5th generation cellular communication is presented in detail. Extensive measurements and simulations ascertain the proposed 28 GHz antenna solution to be highly effective for cellular handsets operating in realistic propagating environments.

Question: What is the first-of-the-kind 28 GHz antenna solution for the upcoming 5th generation cellular communication?


Comparing support vector machines with Gaussian kernels to radial basis function classifiers. The support vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights, and threshold that minimize an upper bound on the expected test error. The present study is devoted to an experimental comparison of these machines with a classical approach, where the centers are determined byk-means clustering, and the weights are computed using error backpropagation. We consider three machines, namely, a classical RBF machine, an SV machine with Gaussian kernel, and a hybrid system with the centers determined by the SV method and the weights trained by error backpropagation. Our results show that on the United States postal service database of handwritten digits, the SV machine achieves the highest recognition accuracy, followed by the hybrid system. The SV approach is thus not only theoretically wellfounded but also superior in a practical application.

Question: What is the difference between a support vector machine and a radial basis function classifier?


Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning. We propose a simple algorithm to train stochastic neural networks to draw samples from given target distributions for probabilistic inference. Our method is based on iteratively adjusting the neural network parameters so that the output changes along a Stein variational gradient (Liu & Wang, 2016) that maximumly decreases the KL divergence with the target distribution. Our method works for any target distribution specified by their unnormalized density function, and can train any black-box architectures that are differentiable in terms of the parameters we want to adapt. As an application of our method, we propose an amortized MLE algorithm for training deep energy model, where a neural sampler is adaptively trained to approximate the likelihood function. Our method mimics an adversarial game between the deep energy model and the neural sampler, and obtains realisticlooking images competitive with the state-of-the-art results.

Question: How to train a neural network to draw samples from a given target distribution?


Optimal assay design for determining the in vitro sensitivity of ring stage Plasmodium falciparum to artemisinins.. Recent reports demonstrate that failure of artemisinin-based antimalarial therapies is associated with an altered response of early blood stage Plasmodium falciparum. This has led to increased interest in the use of pulse assays that mimic clinical drug exposure for analysing artemisinin sensitivity of highly synchronised ring stage parasites. We report a methodology for the reliable execution of drug pulse assays and detail a synchronisation strategy that produces well-defined tightly synchronised ring stage cultures in a convenient time-frame.

Question: How to synchronise ring stage Plasmodium falciparum?


The Effect of Focal Loss in Semantic Segmentation of High Resolution Aerial Image. The semantic segmentation of High Resolution Remote Sensing (HRRS) images is the fundamental research area of the earth observation. Convolutional Neural Network (CNN), which has achieved superior performance in computer vision task, is also useful for semantic segmentation of HRRS images. In this work, focal loss is used instead of cross-entropy loss in training of CNN to handle the imbalance in training data. To evaluate the effect of focal loss, we train SegNet and FCN with focal loss and confirm improvement in accuracy in ISPRS 2D Semantic Labeling Contest dataset, especially when $\gamma$ is 0.5 in SegNet.

Question: What is the effect of focal loss in semantic segmentation?


A Design Procedure for All-Digital Phase-Locked Loops Based on a Charge-Pump Phase-Locked-Loop Analogy. In this brief, a systematic design procedure for a second-order all-digital phase-locked loop (PLL) is proposed. The design procedure is based on the analogy between a type-II second-order analog PLL and an all-digital PLL. The all-digital PLL design inherits the frequency response and stability characteristics of the analog prototype PLL

Question: What is a phase-locked loop?


Finding Structure in Time. Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory. In this approach, hidden unit patterns are fed back to themselves; the internal representations which develop thus reflect task demands in the context of prior internal states. A set of simulations is reported which range from relatively simple problems (temporal version of XOR) to discovering syntactic/semantic features for words. The networks are able to learn interesting internal representations which incorporate task demands with memory demands; indeed, in this approach the notion of memory is inextricably bound up with task processing. These representations reveal a rich structure, which allows them to be highly context-dependent while also expressing generalizations across classes of items. These representations suggest a method for representing lexical categories and the type/token distinction.

Question: How to represent time in connectionist models?


Benchmark of a large scale database for facial beauty prediction. Due to its indefinite evaluation criterion, facial beauty analysis has shown its challenges in pattern recognition and biometric recognition. Plenty of methods have been designed for solving this problem, but there are still various limitations. Most of the available research results in this field are achieved on a small-scale facial beauty database which is difficult in modeling the structure information for facial beauty. And majority of the presented facial beauty prediction algorithm requires burden landmark or expensive optimization procedure. In this paper, we have established a large scale facial beauty database named LSFBD, which is a new database for facial beauty analysis. The LSFBD database contains 20000 labeled images, which has 10000 unconstrained male subjects and 10000 unconstrained female subjects separately, and all images are verified with well-designed rating with average scores and standard deviation. In this database, manually adding the extreme beauty images make the distribution more reasonable and the result more accurate. CRBM or Eigenfaces results are presented in the paper as benchmark for evaluating the task of facial beauty prediction.

Question: What is the average score of the database?


Clustal W and Clustal X version 2.0. SUMMARY The Clustal W and Clustal X multiple sequence alignment programs have been completely rewritten in C++. This will facilitate the further development of the alignment algorithms in the future and has allowed proper porting of the programs to the latest versions of Linux, Macintosh and Windows operating systems.   AVAILABILITY The programs can be run on-line from the EBI web server: http://www.ebi.ac.uk/tools/clustalw2. The source code and executables for Windows, Linux and Macintosh computers are available from the EBI ftp site ftp://ftp.ebi.ac.uk/pub/software/clustalw2/

Question: How to run the programs on windows?


Multi-level bootstrap analysis of stable clusters in resting-state fMRI. A variety of methods have been developed to identify brain networks with spontaneous, coherent activity in resting-state functional magnetic resonance imaging (fMRI). We propose here a generic statistical framework to quantify the stability of such resting-state networks (RSNs), which was implemented with k-means clustering. The core of the method consists in bootstrapping the available datasets to replicate the clustering process a large number of times and quantify the stable features across all replications. This bootstrap analysis of stable clusters (BASC) has several benefits: (1) it can be implemented in a multi-level fashion to investigate stable RSNs at the level of individual subjects and at the level of a group; (2) it provides a principled measure of RSN stability; and (3) the maximization of the stability measure can be used as a natural criterion to select the number of RSNs. A simulation study validated the good performance of the multi-level BASC on purely synthetic data. Stable networks were also derived from a real resting-state study for 43 subjects. At the group level, seven RSNs were identified which exhibited a good agreement with the previous findings from the literature. The comparison between the individual and group-level stability maps demonstrated the capacity of BASC to establish successful correspondences between these two levels of analysis and at the same time retain some interesting subject-specific characteristics, e.g. the specific involvement of subcortical regions in the visual and fronto-parietal networks for some subjects.

Question: How many stable clusters can be identified in resting-state fMRI?


Does Competition Destroy Ethical Behavior?. This paper shows that conduct described as unethical and blamed on "greed" is sometimes a consequence of market competition. I illustrate this observation using examples of five censured activities: employment of children, corruption, excessive executive pay, corporate earnings manipulation, and involvement of universities in commercial activities. In all these examples, I show how competitive pressures lead to the spread of the censured behavior. My point is not to excuse or condemn any or all of these practices, but merely to pinpoint the crucial role of competition, as opposed to greed, in their spread. I focus here on ethics, not efficiency. The relationship between the two is complex. In many cases, ethical norms evolve to sustain cooperative behavior and thus to promote successful functioning of social institutions. For example, ethical condemnation of corruption is based on the idea that officials should not selfishly abuse public trust, and that a society functions better when its government works fairly. Likewise, the ethical norm against the employment of children is driven in part by the more general concern with abuse of the weak by the strong. When ethics promote social cooperation, ethical behavior and efficient behavior typically go together. In other instances, there is a mismatch between ethics and efficiency, either because ethical standards which might have had efficiency justifications long ago no longer do or because the behavior that is ethical in some idealized society makes matters worse in the real world. For example, the ethical norm against debt or interest, which might have been justifiable a millennium ago, is clearly no longer efficient. And while child labor might be a bad idea in a world with good access to capital markets and educational opportunities, for many families in the developing world the alternative to child labor is malnutrition and disease. These examples of a mismatch suggest t

Question: What is the relationship between ethics and efficiency?


Sculpting: an interactive volumetric modeling technique. We present a new interactive modeling technique based on the notion of sculpting a solid material. A sculpting tool is controlled by a 3D input device and the material is represented by voxel data; the tool acts by modifying the values in the voxel array, much as a "paint" program's "paintbrush" modifies bitmap values. The voxel data is converted to a polygonal surface using a "marching-cubes" algorithm; since the modifications to the voxel data are local, we accelerate this computation by an incremental algorithm and accelerate the display by using a special data structure for determining which polygons must be redrawn in a particular screen region. We provide a variety of tools: one that cuts away material, one that adds material, a "sandpaper" tool, a "heat gun," etc. The technique provides an intuitive direct interaction, as if the user were working with clay or wax. The models created are free-form and may have complex topology; however, they are not precise, so the technique is appropriate for modeling a boulder or a tooth but not for modeling a crankshaft.

Question: What is a sculpting tool?


Optimization Using Simulation of Traffic Light Signal Timings. Traffic congestion has become a great challenge and a large burden on both the governments and the citizens in vastly populated cities. The main problem, originally initiated by several factors, continues to threaten the stability of modern cities and the livelihood of its habitants. Hence, improving the control strategies that govern the traffic operations is a powerful solution that can solve the congestion problem. These improvements can be achieved by enhancing the traffic control performance through adjusting the traffic signal timings. This paper focuses on finding various solutions for the addressed problem through the optimization using simulation of traffic signal timings under oversaturated conditions. The system under study is an actual road network in Alexandria, Egypt; where, numerous data have been collected in different time intervals. A series of computer simulation models to represent the actual system as well as proposed solutions have been developed using the ExtendSim simulation environment. Furthermore, an evolutionary optimizer is utilized to attain a set of optimum/near-optimum signal timings to minimize the total time in system of the vehicles, resulting in an improved performance of the road network. Analysis of experimentation results shows that the adopted methodology optimizes the vehicular flow in a multiple-junction urban traffic network.

Question: How to optimize traffic signal timings?


Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images. We introduce Embed to Control (E2C), a method for model learning and control of non-linear dynamical systems from raw pixel images. E2C consists of a deep generative model, belonging to the family of variational autoencoders, that learns to generate image trajectories from a latent space in which the dynamics is constrained to be locally linear. Our model is derived directly from an optimal control formulation in latent space, supports long-term prediction of image sequences and exhibits strong performance on a variety of complex control problems.

Question: What is a locally linear latent dynamics model?


Ontology based classification system for online job offers. The significance of employment in a setup of society is quite evident. Publishing jobs online opened the research opportunities to study different methods for the automation of job offers retrieval — from the internet. We need a classification mechanism, from machine learning or some other discipline, to automate job offers retrieval. In this paper, we devised an ontology-based classifier for job offers classification. More than 5000 job offers were collected from different job offers websites. The ontology-based classification works in three stages, concepts are extracted from job offers text description as feature vectors, the minimum threshold for the classification is calculated, and a classification model is developed. Rather using machine learning algorithms, we used an ontology for classification. We evaluated this classifier according to machine learning evaluation model, training, and test dataset. Our classifier showed more than 0.9 accuracy, precision, and recall for both training and test dataset. Our finding paves the way to automate the job offers classification and retrieval.

Question: What is the significance of employment in a setup of society?


Sentence Selection and Evaluation Metrics. Human-quality text summarization systems are di cult to design, and even more di cult to evaluate, in part because documents can di er along several dimensions, such as length, writing style and lexical usage. Nevertheless, certain cues can often help suggest the selection of sentences for inclusion in a summary. This paper presents our analysis of news-article summaries generated by sentence selection. Sentences are ranked for potential inclusion in the summary using a weighted combination of statistical and linguistic features. The statistical features were adapted from standard IR methods. The potential linguistic ones were derived from an analysis of news-wire summaries. To evaluate these features we use a normalized version of precision-recall curves, with a baseline of random sentence selection, as well as analyze the properties of such a baseline. We illustrate our discussions with empirical results showing the importance of corpus-dependent baseline summarization standards, compression ratios and carefully crafted long queries.

Question: What is the importance of corpus-dependent baseline summarization standards?


Precision SAR processing using chirp scaling. Abstmct-A space-variant interpolation is required to compensate for the migration of signal energy through range resolution cells when processing synthetic aperture radar (SAR) data, using either the classical rangelDoppler (RID) algorithm or related frequency domain techniques. In general, interpolation requires significant computation time, and leads to loss of image quality, especially in the complex image. The new chirp scaling algorithm avoids interpolation, yet performs range cell migration correction accurately. The algorithm requires only complex multiplies and Fourier transforms to implement, is inherently phase preserving, and is suitable for wide-swath, largebeamwidth, and large-squint applications. This paper describes the chirp scaling algorithm, summarizes simulation results, presents imagery processed with the algorithm, and reviews quantitative measures of its performance. Based on quantitative comparison, the chirp scaling algorithm provides image quality equal to or better than the precision rangel Doppler processor. Over the range of parameters tested, image quality results approach the theoretical limit, as defined by the system bandwidth.

Question: What is chirp scaling?


A Ka-Band CMOS Wilkinson Power Divider Using Synthetic Quasi-TEM Transmission Lines. This work presents a Ka-band two-way 3 dB Wilkinson power divider using synthetic quasi-transverse electromagnetic (TEM) transmission lines (TLs). The synthetic quasi-TEM TL, also called complementary-conducting-strip TL (CCS TL), is theoretically analyzed. The equivalent TL model, whose production is based on the extracted results, is applied to the power divider design. The prototype is fabricated by the standard 0.18 mum 1P6M CMOS technology, showing the circuit size of 210.0 mumtimes390.0 mum without contact pads. The measurement results, which match the 50 Omega system, reveal perfect agreements with those of the simulations. The comparison reveals the following characteristics. The divider exhibits an equal power-split with the insertion losses (S21 and S31) of 3.65 dB. The return losses (S11, S22 and S33) of the prototype are higher than 10.0 dB from 30.0 to 40.0 GHz.

Question: What is the difference between a Wilkinson power divider and a power divider?


Anterior cingulate cortex, error detection, and the online monitoring of performance.. An unresolved question in neuroscience and psychology is how the brain monitors performance to regulate behavior. It has been proposed that the anterior cingulate cortex (ACC), on the medial surface of the frontal lobe, contributes to performance monitoring by detecting errors. In this study, event-related functional magnetic resonance imaging was used to examine ACC function. Results confirm that this region shows activity during erroneous responses. However, activity was also observed in the same region during correct responses under conditions of increased response competition. This suggests that the ACC detects conditions under which errors are likely to occur rather than errors themselves.

Question: How does the brain monitor performance?


Enforcing k-anonymity in Web Mail Auditing. We study the problem of k-anonymization of mail messages in the realistic scenario of auditing mail traffic in a major commercial Web mail service. Mail auditing is necessary in various Web mail debugging and quality assurance activities, such as anti-spam or the qualitative evaluation of novel mail features. It is conducted by trained professionals, often referred to as "auditors", who are shown messages that could expose personally identifiable information. We address here the challenge of k-anonymizing such messages, focusing on machine generated mail messages that represent more than 90% of today's mail traffic. We introduce a novel message signature Mail-Hash, specifically tailored to identifying structurally-similar messages, which allows us to put such messages in a same equivalence class. We then define a process that generates, for each class, masked mail samples that can be shown to auditors, while guaranteeing the k-anonymity of users. The productivity of auditors is measured by the amount of non-hidden mail content they can see every day, while considering normal working conditions, which set a limit to the number of mail samples they can review. In addition, we consider k-anonymity over time since, by definition of k-anonymity, every new release places additional constraints on the assignment of samples. We describe in details the results we obtained over actual Yahoo mail traffic, and thus demonstrate that our methods are feasible at Web mail scale. Given the constantly growing concern of users over their email being scanned by others, we argue that it is critical to devise such algorithms that guarantee k-anonymity, and implement associated processes in order to restore the trust of mail users.

Question: How to anonymize mail traffic?


Missing value estimation methods for DNA microarrays. MOTIVATION Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clustering are not robust to missing data, and may lose effectiveness even with a few missing values. Methods for imputing missing data are needed, therefore, to minimize the effect of incomplete data sets on analyses, and to increase the range of data sets to which these algorithms can be applied. In this report, we investigate automated methods for estimating missing data.   RESULTS We present a comparative study of several methods for the estimation of missing values in gene microarray data. We implemented and evaluated three methods: a Singular Value Decomposition (SVD) based method (SVDimpute), weighted K-nearest neighbors (KNNimpute), and row average. We evaluated the methods using a variety of parameter settings and over different real data sets, and assessed the robustness of the imputation methods to the amount of missing data over the range of 1--20% missing values. We show that KNNimpute appears to provide a more robust and sensitive method for missing value estimation than SVDimpute, and both SVDimpute and KNNimpute surpass the commonly used row average method (as well as filling missing values with zeros). We report results of the comparative experiments and provide recommendations and tools for accurate estimation of missing microarray data under a variety of conditions.

Question: What is the difference between the SVDimpute and KNNimpute methods?


Real-time human gesture grading based on OpenPose. In this paper, we presented a real-time 2D human gesture grading system from monocular images based on OpenPose, a library for real-time multi-person keypoint detection. After capturing 2D positions of a person's joints and skeleton wireframe of the body, the system computed the equation of motion trajectory for every joint. Similarity metric was defined as distance between motion trajectories of standard and real-time videos. A modifiable scoring formula was used for simulating the gesture grading scenario. Experimental results showed that the system worked efficiently with high real-time performance, low cost of equipment and strong robustness to the interference of noise.

Question: How does the system work?


Confidence intervals in within-subject designs: A simpler solution to Loftus and Masson's method. Within-subject ANOVAs are a powerful tool to analyze data because the variance associated to differences between the participants is removed from the analysis. Hence, small differences, when present for most of the participants, can be significant even when the participants are very different from one another. Yet, graphs showing standard error or confidence interval bars are misleading since these bars include the between-subject variability. Loftus and Masson (1994) noticed this fact and proposed an alternate method to compute the error bars. However, i) their approach requires that the ANOVA be performed first, which is paradoxical since a graph is an aid to decide whether to perform analyses or not; ii) their method provides a single error bar for all the conditions, masking information such as the heterogeneity of variances across conditions; iii) the method proposed is difficult to implement in commonly-used graphing software. Here we propose a simple alternative and sow how it can be implemented in SPSS.

Question: How to compute confidence intervals in within-subject designs?


e-health first impressions and visual evaluations: key design principles for attention and appeal. Design plays a critical role in the development of e-health, greatly impacting the outreach potential for pertinent health communication. Design influences viewers' initial evaluations of electronic displays of health information, as well as directly impacting the likelihood one will attend to and favorably evaluate the information, essential actions for processing the health concepts presented. Individuals with low health literacy, representing a hard-to-reach audience susceptible to worsened health outcomes, will benefit greatly from the application of theory-based design principles. Design principles that have been shown to appeal and engage audiences are the necessary first step for effective message delivery. Design principles, which directly impact increased attention, favorable evaluations, and greater information processing abilities, include: web aesthetics, visual complexity, affordances, prototypicality, and persuasive imagery. These areas of theory-driven design research should guide scholars in e-health investigation with research goals of broader outreach, reduction of disparities, and potential avenues for reduced health care costs. Improving design by working with this hard-to-reach audience will simultaneously improve practice, as the applications of key design principles through theory-driven design research will allow practitioners to create effective e-health that will benefit people more broadly.

Question: What is the first large-scale Canadian peacekeeping mission?


Agent-based modeling: methods and techniques for simulating human systems.. Agent-based modeling is a powerful simulation modeling technique that has seen a number of applications in the last few years, including applications to real-world business problems. After the basic principles of agent-based simulation are briefly introduced, its four areas of application are discussed by using real-world applications: flow simulation, organizational simulation, market simulation, and diffusion simulation. For each category, one or several business applications are described and analyzed.

Question: What is agent-based modeling?


Nonstationary Function Optimization Using Genetic Algorithms with Dominance and Diploidy. Specifically, we apply genetic algorithms that include diploid genotypes and dominance operators to a simple nonstationary problem in function optimization: an oscillating, blind knapsack problem. In doing this, we find that diploidy and dominance induce a form of long term distributed memory that stores and occasionally remembers good partial solutions that were once desirable. This memory permits faster adaptation to drastic environmental shifts than is possible without the added structures and operators. This paper investigates the use of diploid representations and dominance operators in genetic algorithms (GAs) to improve performance in environments that vary with time. The mechanics of diploidy and dominance in natural genetics are briefly discussed, and the usage of these structures and operators in other GA investigations is reviewed. An extension of the schema theorem is developed which illustrates the ability of diploid GAs with dominance to hold alternative alleles in abeyance. Both haploid and diploid GAs are applied to a simple time varying problem: an oscillating, blind knapsack problem. Simulation results show that a diploid GA with an evolving dominance map adapts more quickly to the sudden changes in this problem environment than either a haploid GA or a diploid GA with a fixed dominance map. These proof-of-principle results indicate that diploidy and dominance can be used to induce a form of long term distributed memory within a population of structures. In the remainder of this paper, we explore the mechanism, theory, and implementation of dominance and diploidy in artificial genetic search. We start by examining the role of diploidy and dominance in natural genetics, and we briefly review examples of their usage in genetic algorithm circles. We extend the schema theorem to analyze the effect of these structures and mechanisms. We present results from computa

Question: What is the difference between diploid and haploid genetic algorithms?


Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models. Advanced Driver Assistance Systems (ADAS) have made driving safer over the last decade. They prepare vehicles for unsafe road conditions and alert drivers if they perform a dangerous maneuver. However, many accidents are unavoidable because by the time drivers are alerted, it is already too late. Anticipating maneuvers beforehand can alert drivers before they perform the maneuver and also give ADAS more time to avoid or prepare for the danger. In this work we anticipate driving maneuvers a few seconds before they occur. For this purpose we equip a car with cameras and a computing device to capture the driving context from both inside and outside of the car. We propose an Autoregressive Input-Output HMM to model the contextual information alongwith the maneuvers. We evaluate our approach on a diverse data set with 1180 miles of natural freeway and city driving and show that we can anticipate maneuvers 3.5 seconds before they occur with over 80% F1-score in real-time.

Question: How do you anticipate maneuvers?


Semantics of context-free languages. "Meaning" may be assigned to a string in a context-free language by defining "attributes" of the symbols in a derivation tree for that string. The attributes can be defined by functions associated with each production in the grammar. This paper examines the implications of this process when some of the attributes are "synthesized", i.e., defined solely in terms of attributes of thedescendants of the corresponding nonterminal symbol, while other attributes are "inherited", i.e., defined in terms of attributes of theancestors of the nonterminal symbol. An algorithm is given which detects when such semantic rules could possibly lead to circular definition of some attributes. An example is given of a simple programming language defined with both inherited and synthesized attributes, and the method of definition is compared to other techniques for formal specification of semantics which have appeared in the literature.

Question: What is a context-free language?


A 2.4 GHz Interferer-Resilient Wake-Up Receiver Using A Dual-IF Multi-Stage N-Path Architecture. A 2.4 GHz interferer-resilient wake-up receiver for ultra-low power wireless sensor nodes uses an uncertain-IF dualconversion topology, combining a distributed multi-stage N-path filtering technique with an unlocked low-Q resonator-referred local oscillator. This structure provides narrow-band selectivity and strong immunity against interferers, while avoiding expensive external resonant components such as BAW resonators or crystals. The 65 nm CMOS receiver prototype provides a sensitivity of -97 dBm and a carrier-to-interferer ratio better than -27 dB at 5 MHz offset, for a data rate of 10 kb/s at a 10-3 bit error rate, while consuming 99 μW from a 0.5 V voltage supply under continuous operation.

Question: What is a 2.4 GHz interferer-resilient wake-up receiver?


Validating a Measurement Tool of Presen ce in Online Communities of Inquiry. This article examines work related to the development and validation of a measurement tool for the Community of Inquiry (CoI) framework in online settings. The framework consists of three elements: social presence, teaching presence and cognitive presence, each of which is integral to the instrument. The 34 item instrument, and thus framework, was tested after being administered at four institutions in the Summer of 2007. The article also includes a discussion of implications for the future use of the CoI survey and the CoI framework itself.

Question: What is the Community of Inquiry framework?


The architecture of subterranean ant nests: beauty and mystery underfoot. Over the 100 million years of their evolution, ants have constructed or occupied nests in a wide range of materials and situations. A large number of ant species excavate nests in the soil, and these subterranean nests have evolved into a wide range of sizes and architectures. On the basis of casts made of such nests, this variation and the patterns that govern it are described. The possible functions of architectural features are discussed, as are the behavioral "rules" through which the nests are created by worker ants.

Question: What is the architecture of subterranean ant nests?


SMOTE: Synthetic Minority Over-sampling Technique. An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of "normal" examples with only a small percentage of "abnormal" or "interesting" examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.

Question: What is the difference between SMOTE and under-sampling?


A deep learning method for classifying mammographic breast density categories.. PURPOSE Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI-RADS) breast density categories. It is particularly difficult for radiologists to consistently distinguish the two most common and most variably assigned BI-RADS categories, i.e., "scattered density" and "heterogeneously dense". The aim of this work was to investigate a deep learning-based breast density classifier to consistently distinguish these two categories, aiming at providing a potential computerized tool to assist radiologists in assigning a BI-RADS category in current clinical workflow.   METHODS In this study, we constructed a convolutional neural network (CNN)-based model coupled with a large (i.e., 22,000 images) digital mammogram imaging dataset to evaluate the classification performance between the two aforementioned breast density categories. All images were collected from a cohort of 1,427 women who underwent standard digital mammography screening from 2005 to 2016 at our institution. The truths of the density categories were based on standard clinical assessment made by board-certified breast imaging radiologists. Effects of direct training from scratch solely using digital mammogram images and transfer learning of a pretrained model on a large nonmedical imaging dataset were evaluated for the specific task of breast density classification. In order to measure the classification performance, the CNN classifier was also tested on a refined version of the mammogram image dataset by removing some potentially inaccurately labeled images. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to measure the accuracy of the classifier.   RESULTS The AUC was 0.9421 when the CNN-model was trained from scratch on our

Question: What is the area under the curve?


Very Deep Convolutional Networks for Large-Scale Image Recognition. In this work we investigate the effect of the convolutional n etwork depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth, which shows that a significant improvement on the prior-art configurations can be achi eved by pushing the depth to 16–19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first a nd he second places in the localisation and classification tracks respec tively. We also show that our representations generalise well to other datasets, whe re t y achieve the stateof-the-art results. Importantly, we have made our two bestp rforming ConvNet models publicly available to facilitate further research o n the use of deep visual representations in computer vision.

Question: What is the state of the art in large-scale image recognition?


Two decades of statistical language modeling: where do we go from here?. Statistical language models estimate the distribution of various natural language phenomena for the purpose of speech recognition and other language technologies. Since the first significant model was proposed in 1980, many attempts have been made to improve the state of the art. We review them, point to a few promising directions, and argue for a Bayesian approach to integration of linguistic theories with data.

Question: What is a statistical language model?


A new exact method for the two-dimensional orthogonal packing problem. The two-dimensional orthogonal packing problem (2OPP ) consists of determining if a set of rectangles (items) can be packed into one rectangle of fixed size (bin). In this paper we propose two exact algorithms for solving this problem. The first algorithm is an improvement on a classical branch&bound method, whereas the second algorithm is based on a two-step enumerative method. We also describe reduction procedures and lower bounds which can be used within the branch&bound method. We report computational experiments for randomly generated benchmarks, which demonstrate the efficiency of both methods.

Question: What is the exact method for the two-dimensional orthogonal packing problem?


Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks. Deep learning classifiers are known to be inherently vulnerable to manipulation by intentionally perturbed inputs, named adversarial examples. In this work, we establish that reinforcement learning techniques based on Deep Q-Networks (DQNs) are also vulnerable to adversarial input perturbations, and verify the transferability of adversarial examples across different DQN models. Furthermore, we present a novel class of attacks based on this vulnerability that enable policy manipulation and induction in the learning process of DQNs. We propose an attack mechanism that exploits the transferability of adversarial examples to implement policy induction attacks on DQNs, and demonstrate its efficacy and impact through experimental study of a game-learning scenario.

Question: What is the vulnerability of deep reinforcement learning to policy induction attacks?


Supernumerary nostril: a case report. BACKGROUND Supernumerary nostril is a congenital anomaly that contains additional nostril with or without accessory cartilage. These rare congenital nasal deformities result from embryological defects. Since 1906, Lindsay (Trans Pathol Soc Lond. 57:329-330, 1906) has published the first research of bilateral supernumerary nostrils, and only 34 cases have been reported so far in the English literature.   CASE PRESENTATION A 1-year-old female baby was brought to our department group for the treatment of an accessory opening above the left nostril which had been presented since her birth. Medical history was non-specific and her birth was normal. The size of a supernumerary nostril was about 0.2 cm diameter and connected to the left nostril. The right one was normal. Minimal procedure was operated for the anomaly. After 1 year, rhinoplasty was performed for the nostril asymmetry.   CONCLUSIONS At 1 year follow-up, the functional and cosmetic result was satisfactory. In this case, it is important that we have early preoperative diagnosis. Also, it is desirable that we should perform a corrective surgery as soon as possible for the patient's psychosocial growth.

Question: What is the treatment for supernumerary nostril?


A comparative study on content-based music genre classification. Content-based music genre classification is a fundamental component of music information retrieval systems and has been gaining importance and enjoying a growing amount of attention with the emergence of digital music on the Internet. Currently little work has been done on automatic music genre classification, and in addition, the reported classification accuracies are relatively low. This paper proposes a new feature extraction method for music genre classification, DWCHs. DWCHs stands for Daubechies Wavelet Coefficient Histograms. DWCHs capture the local and global information of music signals simultaneously by computing histograms on their Daubechies wavelet coefficients. Effectiveness of this new feature and of previously studied features are compared using various machine learning classification algorithms, including Support Vector Machines and Linear Discriminant Analysis. It is demonstrated that the use of DWCHs significantly improves the accuracy of music genre classification.

Question: What is a feature extraction method for music genre classification?


It's always April fools' day!: On the difficulty of social network misinformation classification via propagation features. Given the huge impact that Online Social Networks (OSN) had in the way people get informed and form their opinion, they became an attractive playground for malicious entities that want to spread misinformation, and leverage their effect. In fact, misinformation easily spreads on OSN, and this is a huge threat for modern society, possibly influencing also the outcome of elections, or even putting people's life at risk (e.g., spreading "anti-vaccines" misinformation). Therefore, it is of paramount importance for our society to have some sort of "validation" on information spreading through OSN. The need for a wide-scale validation would greatly benefit from automatic tools. In this paper, we show that it is difficult to carry out an automatic classification of misinformation considering only structural properties of content propagation cascades. We focus on structural properties, because they would be inherently difficult to be manipulated, with the the aim of circumventing classification systems. To support our claim, we carry out an extensive evaluation on Facebook posts belonging to conspiracy theories (representative of misinformation), and scientific news (representative of fact-checked content). Our findings show that conspiracy content reverberates in a way which is hard to distinguish from scientific content: for the classification mechanism we investigated, classification F-score never exceeds 0.7.

Question: What is the difference between fact and fiction?


Bullying and victimization in adolescence: concurrent and stable roles and psychological health symptoms.. From an initial sample of 1278 Italian students, the authors selected 537 on the basis of their responses to a self-report bully and victim questionnaire. Participants' ages ranged from 13 to 20 years (M = 15.12 years, SD = 1.08 years). The authors compared the concurrent psychological symptoms of 4 participant groups (bullies, victims, bully/victims [i.e., bullies who were also victims of bullying], and uninvolved students). Of participants, 157 were in the bullies group, 140 were in the victims group, 81 were in the bully/victims group, and 159 were in the uninvolved students group. The results show that bullies reported a higher level of externalizing problems, victims reported more internalizing symptoms, and bully/victims reported both a higher level of externalizing problems and more internalizing symptoms. The authors divided the sample into 8 groups on the basis of the students' recollection of their earlier school experiences and of their present role. The authors classified the participants as stable versus late bullies, victims, bully/victims, or uninvolved students. The authors compared each stable group with its corresponding late group and found that stable victims and stable bully/victims reported higher degrees of anxiety, depression, and withdrawal than did the other groups. The authors focus their discussion on the role of chronic peer difficulties in relation to adolescents' symptoms and well-being.

Question: What is the difference between bullies and victims?


Artificial Bee Colony algorithm for optimization of truss structures. The main goal of the structural optimization is to minimize the weight of structures while satisfying all design requirements imposed by design codes. In this paper, the Artificial Bee Colony algorithm with an adaptive penalty function approach (ABC-AP) is proposed to minimize the weight of truss structures. The ABC algorithm is swarm intelligence based optimization technique inspired by the intelligent foraging behavior of honeybees. Five truss examples with fixed-geometry and up to 200 elements were studied to verify that the ABC algorithm is an effective optimization algorithm in the creation of an optimal design for truss structures. The results of the ABC-AP compared with results of other optimization algorithms from the literature show that this algorithm is a powerful search and optimization technique for structural design. © 2010 Elsevier B.V. All rights reserved.

Question: What is the weight of a truss?


The Malmo Platform for Artificial Intelligence Experimentation. We present Project Malmo – an AI experimentation platform built on top of the popular computer game Minecraft, and designed to support fundamental research in artificial intelligence. As the AI research community pushes for artificial general intelligence (AGI), experimentation platforms are needed that support the development of flexible agents that learn to solve diverse tasks in complex environments. Minecraft is an ideal foundation for such a platform, as it exposes agents to complex 3D worlds, coupled with infinitely varied game-play. Project Malmo provides a sophisticated abstraction layer on top of Minecraft that supports a wide range of experimentation scenarios, ranging from navigation and survival to collaboration and problem solving tasks. In this demo we present the Malmo platform and its capabilities. The platform is publicly released as open source software at IJCAI, to support openness and collaboration in AI research.

Question: What is the malmo platform?


Alphasort: A cache-sensitive parallel external sort. A new sort algorithm, called AlphaSort, demonstrates that commodity processors and disks can handle commercial batch workloads. Using commodity processors, memory, and arrays of SCSI disks, AlphaSort runs the industrystandard sort benchmark in seven seconds. This beats the best published record on a 32-CPU 32-disk Hypercube by 8:1. On another benchmark, AlphaSort sorted more than a gigabyte in one minute. AlphaSort is a cache-sensitive, memoryintensive sort algorithm. We argue that modern architectures require algorithm designers to re-examine their use of the memory hierarchy. AlphaSort uses clustered data structures to get good cache locality, file striping to get high disk bandwidth, QuickSort to generate runs, and replacement-selection to merge the runs. It uses shared memory multiprocessors to break the sort into subsort chores. Because startup times are becoming a significant part of the total time, we propose two new benchmarks: (1) MinuteSort: how much can you sort in one minute, and (2) PennySort: how much can you sort for one penny.

Question: What is a cache-sensitive sort?


The neurobiology of social cognition. Recent studies have begun to elucidate the roles played in social cognition by specific neural structures, genes, and neurotransmitter systems. Cortical regions in the temporal lobe participate in perceiving socially relevant stimuli, whereas the amygdala, right somatosensory cortices, orbitofrontal cortices, and cingulate cortices all participate in linking perception of such stimuli to motivation, emotion, and cognition. Open questions remain about the domain-specificity of social cognition, about its overlap with emotion and with communication, and about the methods best suited for its investigation.

Question: What is the neurobiology of social cognition?


Efficient Name Disambiguation for Large-Scale Databases. Name disambiguation can occur when one is seeking a list of publications of an author who has used different name variations and when there are multiple other authors with the same name. We present an efficient integrative framework for solving the name disambiguation problem: a blocking method retrieves candidate classes of authors with similar names and a clustering method, DBSCAN, clusters papers by author. The distance metric between papers used in DBSCAN is calculated by an online active selection support vector machine algorithm (LASVM), yielding a simpler model, lower test errors and faster prediction time than a standard SVM. We prove that by recasting transitivity as density reachability in DBSCAN, transitivity is guaranteed for core points. For evaluation, we manually annotated 3,355 papers yielding 490 authors and achieved 90.6% pairwise-F1. For scalability, authors in the entire CiteSeer dataset, over 700,000 papers, were readily disambiguated.

Question: How to disambiguate authors with similar names?


A Fully Convolutional Neural Network for Speech Enhancement. In hearing aids, the presence of babble noise degrades hearing intelligibility of human speech greatly. However, removing the babble without creating artifacts in human speech is a challenging task in a low SNR environment. Here, we sought to solve the problem by finding a 'mapping' between noisy speech spectra and clean speech spectra via supervised learning. Specifically, we propose using fully Convolutional Neural Networks, which consist of lesser number of parameters than fully connected networks. The proposed network, Redundant Convolutional Encoder Decoder (R-CED), demonstrates that a convolutional network can be 12 times smaller than a recurrent network and yet achieves better performance, which shows its applicability for an embedded system: the hearing aids.

Question: How to remove babble noise from speech?


Multi objective outbound logistics network design for a manufacturing supply chain. Outbound logistics network (OLN) in the downstream supply chain of a firm plays a dominant role in the success or failure of that firm. This paper proposes the design of a hybrid and flexible OLN in multi objective context. The proposed distribution network for a manufacturing supply chain consists of a set of customer zones (CZs) at known locations with known demands being served by a set of potential manufacturing plants, a set of potential central distribution centers (CDCs), and a set of potential regional distribution centers (RDCs). Three variants of a single product classified based on nature of demand are supplied to CZs through three different distribution channels. The decision variables include number of plants, CDCs, RDCs, and quantities of each variant of product delivered to CZs through a designated distribution channel. The goal is to design the network with multiple objectives so as to minimize the total cost, maximize the unit fill rates, and maximize the resource utilization of the facilities in the network. The problem is formulated as a mixed integer linear programming problem and a multiobjective genetic algorithm (MOGA) called non-dominated sorting genetic algorithm—II (NSGA-II) is employed to solve the resulting NP-hard combinatorial optimization problem. Computational experiments conducted on randomly generated data sets are presented and analyzed showing the effectiveness of the solution algorithm for the proposed network. N. C. Hiremath · S. Sahu · M. K. Tiwari (B) Department of Industrial Engineering and Management, Indian Institute of Technology Kharagpur, Kharagpur, 721302 West Bengal, India e-mail: mkt09@hotmail.com N. C. Hiremath e-mail: nchiremath@gmail.com S. Sahu e-mail: sahus@mech.iitkgp.ernet.in

Question: What is the difference between a hybrid and a flexible OLN?


The Language of Place: Semantic Value from Geospatial Context. There is a relationship between what we say and where we say it. Word embeddings are usually trained assuming that semantically-similar words occur within the same textual contexts. We investigate the extent to which semantically-similar words occur within the same geospatial contexts. We enrich a corpus of geolocated Twitter posts with physical data derived from Google Places and OpenStreetMap, and train word embeddings using the resulting geospatial contexts. Intrinsic evaluation of the resulting vectors shows that geographic context alone does provide useful information about semantic relatedness.

Question: How can we use geospatial context to enrich word embeddings?


XFI: Software Guards for System Address Spaces. XFI is a comprehensive protection system that offers both flexible access control and fundamental integrity guarantees, at any privilege level and even for legacy code in commodity systems. For this purpose, XFI combines static analysis with inline software guards and a two-stack execution model. We have implemented XFI for Windows on the x86 architecture using binary rewriting and a simple, stand-alone verifier; the implementation's correctness depends on the verifier, but not on the rewriter. We have applied XFI to software such as device drivers and multimedia codecs. The resulting modules function safely within both kernel and user-mode address spaces, with only modest enforcement overheads.

Question: What is XFI?


A High Efficiency two-stage ZVS AC/DC converter with all SiC MOSFET. A High Efficiency two-stage AC/DC converter is presented and discussed in this paper, which can be applied in on-board charger for electric vehicles. The front-end converter is a high efficiency ZVS half bridge power factor correction (PFC) topology with only one device in the current flowing path. Owing to the high voltage stress of the half bridge rectifier, the SiC MOSFET is applied to reduce the conduction and switching losses and achieve high frequency. The downstream stage is a LLC resonant DC/DC converter. The peak efficiency reaches 97.5%.

Question: What is the efficiency of the converter?


Nonlinear Component Analysis as a Kernel Eigenvalue Problem. A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear mapfor instance, the space of all possible five-pixel products in 16 16 images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.

Question: How to compute principal components in high-dimensional feature spaces?


Learning to Grade Short Answer Questions using Semantic Similarity Measures and Dependency Graph Alignments. In this work we address the task of computerassisted assessment of short student answers. We combine several graph alignment features with lexical semantic similarity measures using machine learning techniques and show that the student answers can be more accurately graded than if the semantic measures were used in isolation. We also present a first attempt to align the dependency graphs of the student and the instructor answers in order to make use of a structural component in the automatic grading of student answers.

Question: How to grade short answer questions using semantic similarity measures and dependency graph alignments.


Ocular Pain and Impending Blindness During Facial Cosmetic Injections: Is Your Office Prepared?. Soft tissue filler injections are the second most common non-surgical procedure performed by the plastic surgeon. Embolization of intravascular material after facial injection is a rare but terrifying outcome due to the high likelihood of long-term sequela such as blindness and cerebrovascular accident. The literature is replete with examples of permanent blindness caused by injection with autologous fat, soft tissue fillers such as hyaluronic acid, PLLA, calcium hydroxyl-apatite, and even corticosteroid suspensions. However, missing from the discussion is an effective treatment algorithm that can be quickly and safely followed by injecting physicians in the case of an intravascular injection with impending blindness. In this report, we present the case of a 64-year-old woman who suffered from blindness and hemiparesis after facial cosmetic injections performed by a family physician. We use this case to create awareness that this complication has become more common as the number of injectors and patients seeking these treatments have increased exponentially over the past few years. We share in this study our experience with the incorporation of a "blindness safety kit" in each of our offices to promptly initiate treatment in someone with embolization and impending blindness. The kit contains a step-by-step protocol to follow in the event of arterial embolization of filler material associated with ocular pain and impending loss of vision. 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 most common facial cosmetic injection?


Maximizing Air Gap and Efficiency of Magnetic Resonant Coupling for Wireless Power Transfer Using Equivalent Circuit and Neumann Formula. The progress in the field of wireless power transfer in the last few years is remarkable. With recent research, transferring power across large air gaps has been achieved. Both small and large electric equipment have been proposed, e.g., wireless power transfer for small equipment (mobile phones and laptops) and for large equipment (electric vehicles). Furthermore, replacing every cord with wireless power transfer is proposed. The coupled mode theory was proposed in 2006 and proven in 2007. Magnetic and electric resonant couplings allow power to traverse large air gaps with high efficiency. This technology is closely related to electromagnetic induction and has been applied to antennas and resonators used for filters in communication technology. We have studied these phenomena and technologies using equivalent circuits, which is a more familiar format for electrical engineers than the coupled mode theory. In this paper, we analyzed the relationship between maximum efficiency air gap using equivalent circuits and the Neumann formula and proposed equations for the conditions required to achieve maximum efficiency for a given air gap. The results of these equations match well with the results of electromagnetic field analysis and experiments.

Question: How to maximize the efficiency of wireless power transfer?


ADAM: Detecting Intrusions by Data Mining. Intrusion detection systems have traditionally been based on the characterization of an attack and the tracking of the activity on the system to see if it matches that characterization. Recently, new intrusion detection systems based on data mining are making their appearance in the field. This paper describes the design and experiences with the ADAM ( Audit Data Analysis and Mining) system, which we use as a testbed to study how useful data mining techniques can be in intrusion detection. Keywords—Intrusion Detection, Data Mining, Association Rules, Classifiers.

Question: What is the difference between data mining and data analysis?


Linked Data and multimedia: the state of affairs. Linked Data is a way of exposing and sharing data as resources on the Web and interlinking them with semantically related resources. In the last three years significant amounts of data have been generated, increasingly forming a globally connected, distributed data space. For multimedia content, metadata are a key factor for efficient management, organization, and retrieval. However, the relationship between multimedia and Linked Data has been rarely studied, leading to a lack of mutual awareness and, as a consequence thereof, technological deficiencies. This article introduces the basic concepts of Linked Data in the context of multimedia metadata, and discusses techniques to generate, expose, discover, and consume Linked Data. It shows that a large amount of data sources exist, which are ready to be exploited by multimedia applications. The benefit of Linked Data in two multimedia-related applications is discussed and open research issues are outlined with the goal of bringing the research fields of multimedia and Linked Data closer together in order to facilitate mutual benefit.

Question: What is the state of affairs?


Survey on LBP based texture descriptors for image classification. The aim of this work is to find the best way for describing a given texture using a local binary pattern (LBP) based approach. First several different approaches are compared, then the best fusion approach is tested on different datasets and compared with several approaches proposed in the literature (for fair comparisons, when possible we have used code shared by the original authors). Our experiments show that a fusion approach based on uniform local quinary pattern (LQP) and a rotation invariant local quinary pattern, where a bin selection based on variance is performed and Neighborhood Preserving Embedding (NPE) feature transform is applied, obtains a method that performs well on all tested datasets. As the classifier, we have tested a stand-alone support vector machine (SVM) and a random subspace ensemble of SVM. We compare several texture descriptors and show that our proposed approach coupled with random subspace ensemble outperforms other recent state-of-the-art approaches. This conclusion is based on extensive experiments conducted in several domains using six benchmark databases. 2011 Elsevier Ltd. All rights reserved.

Question: What is the best way for describing a given texture using a local binary pattern (LBP) based approach?


A Novel Secure Architecture for the Internet of Things. The "Internet of Things"(IoT) opens opportunities for devices and softwares to share information on an unprecedented scale. However, such a large interconnected network poses new challenges to system developers and users. In this article, we propose a layered architecture of IoT system. Using this model, we try to identify and assess each layer's challenges. We also discuss several existing technologies that can be used make this architecture secure.

Question: What is the layered architecture of IoT?


Confirmatory factor analysis of the WAIS-IV/WMS-IV.. The Wechsler Adult Intelligence Scale-fourth edition (WAIS-IV) and the Wechsler Memory Scale-fourth edition (WMS-IV) were co-developed to be used individually or as a combined battery of tests. The independent factor structure of each of the tests has been identified; however, the combined factor structure has yet to be determined. Confirmatory factor analysis was applied to the WAIS-IV/WMS-IV Adult battery (i.e., age 16-69 years) co-norming sample (n = 900) to test 13 measurement models. The results indicated that two models fit the data equally well. One model is a seven-factor solution without a hierarchical general ability factor: Verbal Comprehension, Perceptual Reasoning, Processing Speed, Auditory Working Memory, Visual Working Memory, Auditory Memory, and Visual Memory. The second model is a five-factor model composed of Verbal Comprehension, Perceptual Reasoning, Processing Speed, Working Memory, and Memory with a hierarchical general ability factor. Interpretative implications for each model are discussed.

Question: What is the difference between the two models?


A Conceptual Framework for Supporting a Rapid Design of Web Applications for Data Analysis of Electrical Quality Assurance Data for the LHC. The Large Hadron Collider (LHC) is one of the most complex machines ever build. It is composed of many components which constitute a large system. The tunnel and the accelerator is just one of a very critical fraction of the whole LHC infrastructure. Hardware comissioning as one of the critical processes before running the LHC is implemented during the Long Shutdown (LS) states of the macine, where Electrical Quality Assurance (ELQA) is one of its key components. Here a huge data is collected when implementing various ELQA electrical tests. In this paper we present a conceptual framework for supporting a rapid design of web applications for ELQA data analysis. We show a framework's main components, their possible integration with other systems and machine learning algorithms and a simple use case of prototyping an application for Electrical Quality Assurance of the LHC.

Question: What is a conceptual framework for supporting a rapid design of web applications for data analysis of electrical quality assurance data for the LHC?


A framework of sensor-cloud integration opportunities and challenges. In the past few years, wireless sensor networks (WSNs) have been gaining increasing attention because of their potential of enabling of novel and attractive solutions in areas such as industrial automation, environmental monitoring, transportation business, health-care etc. If we add this collection of sensor derived data to various Web-based social networks or virtual communities, blogs etc., we can have a remarkable transformation in our ability to "see" ourselves and our planet. Our primary goal is to facilitate connecting sensors, people and software objects to build community-centric sensing applications. However, the computational tools needed to launch this exploration may be more appropriately built from the data center "Cloud" computing model than the traditional HPC approaches. In this paper, we propose a framework to enable this exploration by integrating sensor networks to the emerging data center "cloud" model of computing. But there are many challenges to enable this framework. We propose a pub-sub based model which simplifies the integration of sensor networks with cloud based community-centric applications. Also there is a need for internetworking cloud providers in case of violation of service level agreement with users. We discussed these issues and proposed reasonable solutions.

Question: What is the difference between a sensor network and a sensor cloud?


The Statistical Recurrent Unit. Sophisticated gated recurrent neural network architectures like LSTMs and GRUs have been shown to be highly effective in a myriad of applications. We develop an un-gated unit, the statistical recurrent unit (SRU), that is able to learn long term dependencies in data by only keeping moving averages of statistics. The SRU's architecture is simple, un-gated, and contains a comparable number of parameters to LSTMs; yet, SRUs perform favorably to more sophisticated LSTM and GRU alternatives, often outperforming one or both in various tasks. We show the efficacy of SRUs as compared to LSTMs and GRUs in an unbiased manner by optimizing respective architectures' hyperparameters in a Bayesian optimization scheme for both synthetic and realworld tasks.

Question: What is the statistical recurrent unit?


A Hierarchical Neural Model for Learning Sequences of Dialogue Acts. We propose a novel hierarchical Recurrent Neural Network (RNN) for learning sequences of Dialogue Acts (DAs). The input in this task is a sequence of utterances (i.e., conversational contributions) comprising a sequence of tokens, and the output is a sequence of DA labels (one label per utterance). Our model leverages the hierarchical nature of dialogue data by using two nested RNNs that capture long-range dependencies at the dialogue level and the utterance level. This model is combined with an attention mechanism that focuses on salient tokens in utterances. Our experimental results show that our model outperforms strong baselines on two popular datasets, Switchboard and MapTask; and our detailed empirical analysis highlights the impact of each aspect of our model.

Question: What is a hierarchical neural model for learning sequences of dialogue acts?


Massive parallelization of approximate nearest neighbor search on KD-tree for high-dimensional image descriptor matching.

Question: What is the approximate nearest neighbor search on KD-tree for high-dimensional image descriptor matching?


Using neural networks and GIS to forecast land use changes : a Land Transformation Model. The Land Transformation Model (LTM), which couples geographic information systems (GIS) with artificial neural networks (ANNs) to forecast land use changes, is presented here. A variety of social, political, and environmental factors contribute to the model's predictor variables of land use change. This paper presents a version of the LTM parameterized for Michigan's Grand Traverse Bay Watershed and explores how factors such as roads, highways, residential streets, rivers, Great Lakes coastlines, recreational facilities, inland lakes, agricultural density, and quality of views can influence urbanization patterns in this coastal watershed. ANNs are used to learn the patterns of development in the region and test the predictive capacity of the model, while GIS is used to develop the spatial, predictor drivers and perform spatial analysis on the results. The predictive ability of the model improved at larger scales when assessed using a moving scalable window metric. Finally, the individual contribution of each predictor variable was examined and shown to vary across spatial scales. At the smallest scales, quality views were the strongest predictor variable. We interpreted the multi-scale influences of land use change, illustrating the relative influences of site (e.g. quality of views, residential streets) and situation (e.g. highways and county roads) variables at different scales. # 2002 Elsevier Science Ltd. All rights reserved.

Question: How can neural networks be used to forecast land use changes?


Electrick: Low-Cost Touch Sensing Using Electric Field Tomography. Current touch input technologies are best suited for small and flat applications, such as smartphones, tablets and kiosks. In general, they are too expensive to scale to large surfaces, such as walls and furniture, and cannot provide input on objects having irregular and complex geometries, such as tools and toys. We introduce Electrick, a low-cost and versatile sensing technique that enables touch input on a wide variety of objects and surfaces, whether small or large, flat or irregular. This is achieved by using electric field tomography in concert with an electrically conductive material, which can be easily and cheaply added to objects and surfaces. We show that our technique is compatible with commonplace manufacturing methods, such as spray/brush coating, vacuum forming, and casting/molding enabling a wide range of possible uses and outputs. Our technique can also bring touch interactivity to rapidly fabricated objects, including those that are laser cut or 3D printed. Through a series of studies and illustrative example uses, we show that Electrick can enable new interactive opportunities on a diverse set of objects and surfaces that were previously static.

Question: How does Electrick work?


Curing regular expressions matching algorithms from insomnia, amnesia, and acalculia. The importance of network security has grown tremendously and a collection of devices have been introduced, which can improve the security of a network. Network intrusion detection systems (NIDS) are among the most widely deployed such system; popular NIDS use a collection of signatures of known security threats and viruses, which are used to scan each packet's payload. Today, signatures are often specified as regular expressions; thus the core of the NIDS comprises of a regular expressions parser; such parsers are traditionally implemented as finite automata. Deterministic Finite Automata (DFA) are fast, therefore they are often desirable at high network link rates. DFA for the signatures, which are used in the current security devices, however require prohibitive amounts of memory, which limits their practical use.  In this paper, we argue that the traditional DFA based NIDS has three main limitations: first they fail to exploit the fact that normal data streams rarely match any virus signature; second, DFAs are extremely inefficient in following multiple partially matching signatures and explodes in size, and third, finite automaton are incapable of efficiently keeping track of counts. We propose mechanisms to solve each of these drawbacks and demonstrate that our solutions can implement a NIDS much more securely and economically, and at the same time substantially improve the packet throughput.

Question: What is the difference between a finite automaton and a regular expression?


Crowd evacuation planning using Cartesian Genetic Programming and agent-based crowd modeling. This paper proposes a new evolutionary algorithm-based methodology for optimal crowd evacuation planning. In the proposed methodology, a heuristic-based evacuation scheme is firstly introduced. The key idea is to divide the region into a set of sub-regions and use a heuristic rule to dynamically recommend an exit to agents in each sub-region. Then, an evolutionary framework based on the Cartesian Genetic Programming algorithm and an agent-based crowd simulation model is developed to search for the optimal heuristic rule. By considering dynamic environment features to construct the heuristic rule and using multiple scenarios for training, the proposed methodology aims to find generic and efficient heuristic rules that perform well on different scenarios. The proposed methodology is applied to guide people's evacuation behaviors in six different scenarios. The simulation results demonstrate that the heuristic rule offered by the proposed method is effective to reduce the crowd evacuation time on different scenarios.

Question: How to evacuate a crowd?


Multi-modal Attention Mechanisms in LSTM and Its Application to Acoustic Scene Classification. Neural network architectures such as long short-term memory (LSTM) have been proven to be powerful models for processing sequences including text, audio and video. On the basis of vanilla LSTM, multi-modal attention mechanisms are proposed in this paper to synthesize the time and semantic information of input sequences. First, we reconstruct the forget and input gates of the LSTM unit from the perspective of attention model in the temporal dimension. Then the memory content of the LSTM unit is recalculated using a cluster-based attention mechanism in semantic space. Experiments on acoustic scene classification tasks show performance improvements of the proposed methods when compared with vanilla LSTM. The classification errors on LITIS ROUEN dataset and DCASE2016 dataset are reduced by 16.5% and 7.7% relatively. We get a second place in the Kaggle's YouTube-8M video understanding challenge, and multi-modal attention based LSTM model is one of our bestperforming single systems.

Question: What is the best performing single system in the Kaggle's YouTube-8M video understanding challenge?


Budgeted Batch Bayesian Optimization With Unknown Batch Sizes. Parameter settings profoundly impact the performance of machine learning algorithms and laboratory experiments. The classical grid search or trial-error methods are exponentially expensive in large parameter spaces, and Bayesian optimization (BO) offers an elegant alternative for global optimization of black box functions. In situations where the black box function can be evaluated at multiple points simultaneously, batch Bayesian optimization is used. Current batch BO approaches are restrictive in that they fix the number of evaluations per batch, and this can be wasteful when the number of specified evaluations is larger than the number of real maxima in the underlying acquisition function. We present the Budgeted Batch Bayesian Optimization (B3O) for hyper-parameter tuning and experimental design we identify the appropriate batch size for each iteration in an elegant way. To set the batch size flexible, we use the infinite Gaussian mixture model (IGMM) for automatically identifying the number of peaks in the underlying acquisition functions. We solve the intractability of estimating the IGMM directly from the acquisition function by formulating the batch generalized slice sampling to efficiently draw samples from the acquisition function. We perform extensive experiments for both synthetic functions and two real world applications machine learning hyper-parameter tuning and experimental design for alloy hardening. We show empirically that the proposed B3O outperforms the existing fixed batch BO approaches in finding the optimum whilst requiring a fewer number of evaluations, thus saving cost and time.

Question: How to find the optimum of a function?


Multiple PRP injections are more effective than single injections and hyaluronic acid in knees with early osteoarthritis: a randomized, double-blind, placebo-controlled trial. To compare the effectiveness of intraarticular (IA) multiple and single platelet-rich plasma (PRP) injections as well as hyaluronic acid (HA) injections in different stages of osteoarthritis (OA) of the knee. A total of 162 patients with different stages of knee OA were randomly divided into four groups receiving 3 IA doses of PRP, one dose of PRP, one dose of HA or a saline injection (control). Then, each group was subdivided into two groups: early OA (Kellgren–Lawrence grade 0 with cartilage degeneration or grade I–III) and advanced OA (Kellgren–Lawrence grade IV). The patients were evaluated before the injection and at the 6-month follow-ups using the EuroQol visual analogue scale (EQ-VAS) and International Knee Documentation Committee (IKDC) subjective scores. Adverse events and patient satisfaction were recorded. There was a statistically significant improvement in the IKDC and EQ-VAS scores in all the treatment groups compared with the control group. The knee scores of patients treated with three PRP injections were significantly better than those patients of the other groups. There was no significant difference in the scores of patients injected with one dose of PRP or HA. In the early OA subgroups, significantly better clinical results were achieved in the patients treated with three PRP injections, but there was no significant difference in the clinical results of patients with advanced OA among the treatment groups. The clinical results of this study suggest IA PRP and HA treatment for all stages of knee OA. For patients with early OA, multiple (3) PRP injections are useful in achieving better clinical results. For patients with advanced OA, multiple injections do not significantly improve the results of patients in any group. I.

Question: What is the difference between PRP and HA?