title: Improved single-stage kickback-rejected comparator for high speed and low noise flash ADCs

abstract: In this paper, the conventional single-stage latched comparator is improved for both high speed and low noise flash ADCs. In the proposed method for high-speed applications, the common mode level of output voltage is preserved unchanged during both amplification and latch operations, to speed up the comparison of small voltage differences. Also, the amplitude of digital control signals is reduced in the modified low noise comparator by using a fully differential structure to remove the concern of digital noise coupling on analog section. Worst-Case simulation results for all corners, using the BSIM3 model of a 0.35μm CMOS process, confirm that a 5mv differential input can be simply detected and recovered to full range values, at 800MS/s and 500MS/s update rate, consuming around 780μW and 650μW in high speed and low noise comparators, respectively. This is equivalent to about 60% improvement in speed of the conventional single-stage comparator. Also, the amplitude of the control signals is reduced to about 18% of full range values, from 3.3v to 0.6v, by using the proposed low noise structure. High speed and low noise comparators can be implemented in 288μm2 and 480μm2 active area, respectively.


Cyber Security Analysis of Substation Automation System


title: Transforming a competency model to parameterised questions in assessment

abstract: The problem of comparing and matching different learners’ knowledge arises when assessment systems use a one-dimensional numerical value to represent “knowledge level”. Such assessment systems may measure inconsistently because they estimate this level differently and inadequately. The multidimensional competency model called COMpetence-Based learner knowledge for personalized Assessment (COMBA) is being developed to represent a learner’s knowledge in a multi-dimensional vector space. The heart of this model is to treat knowledge, not as possession, but as a contextualized space of capability either actual or potential. The paper discusses a system for automatically generating questions from the COMBA competency model as a “guideon-the–side”. The system’s novel design and implementation involves an ontological database that represents the intended learning outcome to be assessed across a number of dimensions, including level of cognitive ability and subject matter. The system generates all the questions that are possible from a given learning outcome, which may then be used to test for understanding, and so could determine the degree to which learners actually acquire the desired knowledge.


title: Mobile Robot Rough-Terrain Control (RTC) For Planetary Exploration

abstract: Mobile robots are increasingly being developed for highrisk missions in rough terrain situations, such as planetary exploration. Here a rough-terrain control (RTC) methodology is presented that exploits the actuator redundancy found in multi-wheeled mobile robot systems to improve ground traction and reduce power consumption. The methodology “chooses” an optimization criterion based on the local terrain profile. A key element of the method is to be able to estimate the wheel-ground contact angles. A method using an extended Kalman filter is presented for estimating these angles using simple on-board sensors. Simulation results for a wheeled micro-rover traversing Mars-like terrain demonstrate the effectiveness of the algorithms.


Building Streetview Datasets for Place Recognition and City Reconstruction


title: A Novel High Performance Low Power Universal Gate Implementation in Subthreshold Region

abstract: any integrated circuit power consumption plays a paramount role and is considered as one of the top challenges in International technology roadmap for semiconductors. In this paper, a low power circuit designed to operate in subthreshold region is proposed. Voltage scaling technique is incorporated to reduce dynamic power consumption while static or leakage power is greatly reduced with forced stack technique. The present technique (VS-STACK) features very low power dissipation as compared to its standard CMOS counterparts in subthreshold region. The power consumption is curtailed by 20% to 90% together with a better power delay product (PDP) over a supply voltage range. The technique is tested on a 2-input NOR gate in the 45nm process. Tanner Tool EDA 13.0v is used for simulation.


title: A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection

abstract: We review accuracy estimation methods and compare the two most common methods crossvalidation and bootstrap. Recent experimental results on artificial data and theoretical re cults in restricted settings have shown that for selecting a good classifier from a set of classifiers (model selection), ten-fold cross-validation may be better than the more expensive leaveone-out cross-validation. We report on a largescale experiment--over half a million runs of C4.5 and a Naive-Bayes algorithm--to estimate the effects of different parameters on these algrithms on real-world datasets. For crossvalidation we vary the number of folds and whether the folds are stratified or not, for bootstrap, we vary the number of bootstrap samples. Our results indicate that for real-word datasets similar to ours, The best method to use for model selection is ten fold stratified cross validation even if computation power allows using more folds.


title: Taming the beast: Free and open-source massive point cloud web visualization

abstract: Powered by WebGL, some renderers have recently become available for the visualization of point cloud data over the web, for example Plasio or Potree. We have extended Potree to be able to visualize massive point clouds and we have successfully used it with the second national Lidar survey of the Netherlands, AHN2, with 640 billion points. In addition to the visualization, the publicly available service at ttp://ahn2.pointclouds.nl/ also features a multi-resolution download tool, a geographic name search bar, a measurement toolkit, a 2D orientation map with field of view depiction, a demo mode and the tuning of the visualization parameters. Potree relies on reorganizing the point cloud data into an multi-resolution octree data structure. However, this reorganization is very time consuming for massive data sets. Hence, we have used a divide and conquer approach to decrease the octree creation time. To achieve such performance improvement we divided the entire space into smaller cells, generated an octree for each of them in a distributed manner and then we merged them into a single massive octree. The merging is possible because the extent of all the nodes of the octrees is known and fixed. All the developed tools are free and open-source (FOSS) and they can be used to visualize over the web other massive point clouds.


title: Exposure of young children working on Mexican tobacco plantations to organophosphorous and carbamic pesticides, indicated by cholinesterase depression

abstract: BACKGROUND ::: Organophosphorous (OP) and carbamic pesticides are used in large quantities on tobacco plantations in Nayarit State, Mexico, where up to 3000 children and their families work. OP and carbamic pesticides are easily inhaled or absorbed through the skin and children may be particularly vulnerable to pesticides because of their smaller body mass, their height and more regular hand-mouth contact. The aim of this study is to assess the effect of pesticide exposure on acetylcholinesterase levels of very young migrant Mexican tobacco workers and younger siblings. ::: ::: ::: METHODS ::: Blood samples were collected from 160 children aged 0-14 years during harvest (exposure) and from 62 children in their home communities 6-9 months after harvest (baseline). Samples were tested for cholinesterase corrected for haemoglobin and ambient temperature. ::: ::: ::: RESULTS ::: Fifteen per cent of children had depression scores ranging from -40% to 190% of their baseline levels. Thirty-three per cent of children had depression scores of at least 15% and 86% of children were anaemic. ::: ::: ::: CONCLUSION ::: These results suggest that very young children are exposed to potentially harmful and toxic amounts of pesticides while working. Further research is needed to assess the actual acute and chronic health impacts of such exposures.


title: Designing focused crawler based on improved genetic algorithm

abstract: As focused crawler intends to search the Internet conform to a specific topic, it has a good prospect in the field of vertical search. A good search strategy is the core to improve the accuracy and coverage of focused crawler. Best-First search strategy is often applied but easily falls into local optimization. In order to improve the global search capability, this paper proposes a focused crawler based on improved genetic algorithm. In this algorithm, fitness function considers both topic correlation and importance. Topic correlation is analyzed by vector space model and topic importance is calculated by improved PageRank algorithm. Genetic operations are optimized based on user browsing behavior. Selection operation chooses webpages with high fitness, crossover operation sorts links by topic importance and mutation operation searchs combined keywords through search engine. Compared with existing genetic algorithms, the experimental results show that improved genetic algorithm can enhance precision and recall of focused crawler and enlarge the search scope of the crawler.


title: Semantic Topic Models: Combining Word Distributional Statistics and Dictionary Definitions

abstract: In this paper, we propose a novel topic model based on incorporating dictionary definitions. Traditional topic models treat words as surface strings without assuming predefined knowledge about word meaning. They infer topics only by observing surface word co-occurrence. However, the co-occurred words may not be semantically related in a manner that is relevant for topic coherence. Exploiting dictionary definitions explicitly in our model yields a better understanding of word semantics leading to better text modeling. We exploit WordNet as a lexical resource for sense definitions. We show that explicitly modeling word definitions helps improve performance significantly over the baseline for a text categorization task.


title: A Novel Wireless Photoplethysmography Blood-Flow Volume Sensor for Assessing Arteriovenous Fistula of Hemodialysis Patients

abstract: A novel blood-flow volume (BFV) sensor is presented for assessing quality of arteriovenous fistula in hemodialysis patients via noninvasive reflectance-type photoplethysmography (PPG). BFV is nowadays in clinic practices evaluated using an ultrasound Doppler monitor, which is expensive, bulky, and can only be operated by well-trained medical personnels. This study is devoted to develop a low-cost, small-sized, portable, and easy-to-use PPG sensor that is capable of continuous measurement of BFV. New designs of front-end analog circuits, signal processing, and an intelligent neural network calibration method are employed to finally achieve high correlation ( R 2 = 0.7176), as opposed to the ultrasound Doppler monitor, with the root-mean-squared errors successfully controlled under 289 ml/min.


title: A neural mechanism of first impressions

abstract: Evaluating social others requires processing complex information. Nevertheless, we can rapidly form an opinion of an individual during an initial encounter. Moreover, people can vary in these opinions, even though the same information is provided. We investigated the brain mechanisms that give rise to the impressions that are formed on meeting a new person. Neuroimaging revealed that responses in the amygdala and the posterior cingulate cortex (PCC) were stronger while encoding social information that was consistent, relative to inconsistent, with subsequent evaluations. In addition, these responses scaled parametrically with the strength of evaluations. These findings provide evidence for encoding differences on the basis of subsequent evaluations, suggesting that the amygdala and PCC are important for forming first impressions.


D Region Proposal U-Net with Dense and Residual Learning for Lung Nodule Detection


Algorithms for Cable Network Design on Large-scale Wind Farms


title: Artificial Neural Network Application in the Diagnosis of Disease Conditions with Liver Ultrasound Images

abstract: The preliminary study presented within this paper shows a comparative study of various texture features extracted from liver ultrasonic images by employing Multilayer Perceptron (MLP), a type of artificial neural network, to study the presence of disease conditions. An ultrasound (US) image shows echo-texture patterns, which defines the organ characteristics. Ultrasound images of liver disease conditions such as “fatty liver,” “cirrhosis,” and “hepatomegaly” produce distinctive echo patterns. However, various ultrasound imaging artifacts and speckle noise make these echo-texture patterns difficult to identify and often hard to distinguish visually. Here, based on the extracted features from the ultrasonic images, we employed an artificial neural network for the diagnosis of disease conditions in liver and finding of the best classifier that distinguishes between abnormal and normal conditions of the liver. Comparison of the overall performance of all the feature classifiers concluded that “mixed feature set” is the best feature set. It showed an excellent rate of accuracy for the training data set. The gray level run length matrix (GLRLM) feature shows better results when the network was tested against unknown data.


title: Modeling local and global deformations in Deep Learning: Epitomic convolution, Multiple Instance Learning, and sliding window detection

abstract: Deep Convolutional Neural Networks (DCNNs) achieve invariance to domain transformations (deformations) by using multiple ‘max-pooling’ (MP) layers. In this work we show that alternative methods of modeling deformations can improve the accuracy and efficiency of DCNNs. First, we introduce epitomic convolution as an alternative to the common convolution-MP cascade of DCNNs, that comes with the same computational cost but favorable learning properties. Second, we introduce a Multiple Instance Learning algorithm to accommodate global translation and scaling in image classification, yielding an efficient algorithm that trains and tests a DCNN in a consistent manner. Third we develop a DCNN sliding window detector that explicitly, but efficiently, searches over the object's position, scale, and aspect ratio. We provide competitive image classification and localization results on the ImageNet dataset and object detection results on Pascal VOC2007.


title: Taxonomy for description of cross-domain attacks on CPS

abstract: The pervasiveness of Cyber-Physical Systems (CPS) in various aspects of the modern society grows rapidly. This makes CPS to increasingly attractive targets for various kinds of attacks. We consider cyber-security as an integral part of CPS security. Additionally, the necessity exists to investigate the CPS-specific aspects which are out of scope of cyber-security. Most importantly, attacks capable to cross the cyber-physical domain boundary should be analyzed. The vulnerability of CPS to such cross-domain attacks has been practically proven by numerous examples, e.g., by the currently most famous Stuxnet attack. In this paper, we propose taxonomy for description of attacks on CPS. The proposed taxonomy is capable of representing both conventional cyber-attacks as well as cross-domain attacks on CPS. Furthermore, based on the proposed taxonomy, we define the attack categorization. Several possible application areas of the proposed taxonomy are extensively discussed. Among others, it can be used to establish a knowledge base about attacks on CPS known in the literature. Furthermore, the proposed description structure will foster the quantitative and qualitative analysis of these attacks, both of which are necessarily to improve CPS security.


title: SPMF: a Java open-source pattern mining library

abstract: We present SPMF, an open-source data mining library offering implementations of more than 55 data mining algorithms. SPMF is a cross-platform library implemented in Java, specialized for discovering patterns in transaction and sequence databases such as frequent itemsets, association rules and sequential patterns. The source code can be integrated in other Java programs. Moreover, SPMF offers a command line interface and a simple graphical interface for quick testing. The source code is available under the GNU General Public License, version 3. The website of the project offers several resources such as documentation with examples of how to run each algorithm, a developer's guide, performance comparisons of algorithms, data sets, an active forum, a FAQ and a mailing list.


title: Clinical Decision Support System Based Virtual Telemedicine

abstract: Telemedicine is a blessing for the people of remote areas because it provides high level medical facilities in an efficient way at low cost. Store-and-Forward method of telemedicine suits more to the progressive countries like Pakistan as not only because it is easy to set up but also due to its cheap operating cost. However, the high response time taken by store & forward telemedicine becomes a critical factor in emergency cases, where each minute has a price. The response time factor can be overcome by using virtual telemedicine approach. In virtual telemedicine, a Clinical Decision Support System (CDSS) is deployed at rural station. The CDSS is intelligent enough to diagnose a patient's disease and prescribe proper medication. In a case, the CDSS can not answer a query, the CDSS immediately sends an e-mail to a medical expert (doctor) and when the response is received the CDSS knowledge-base is updated for the future queries. In this research paper, we not only report a NL-based CDSS that can answer NL queries but also present a complete architecture of a virtual telemedicine setup.


PLASMA WAKEFIELD EXPERIMENTS WITH 28.5 GeV ELECTRON AND POSITRON BEAMS


title: Spectral characteristics and motion-compensated restoration of composite frames

abstract: The practice of superimposing the fields of a frame is applied in various fields, for example, thermographic and biomedical imaging. The pictures obtained in this way, which are termed composite frames, are severely degraded if the scene's objects are not perfectly still. The restoration of composite frames affected by motion-induced blurring requires the ability to estimate the field displacement from composite frames. The frequency domain analysis of composite frames proposed in the paper suggests a displacement estimation technique of a phase-correlation type that can be applied to composite frames. >


title: Random Regression Forests for Acoustic Event Detection and Classification

abstract: Despite the success of the automatic speech recognition framework in its own application field, its adaptation to the problem of acoustic event detection has resulted in limited success. In this paper, instead of treating the problem similar to the segmentation and classification tasks in speech recognition, we pose it as a regression task and propose an approach based on random forest regression. Furthermore, event localization in time can be efficiently handled as a joint problem. We first decompose the training audio signals into multiple interleaved superframes which are annotated with the corresponding event class labels and their displacements to the temporal onsets and offsets of the events. For a specific event category, a random-forest regression model is learned using the displacement information. Given an unseen superframe, the learned regressor will output the continuous estimates of the onset and offset locations of the events. To deal with multiple event categories, prior to the category-specific regression phase, a superframe-wise recognition phase is performed to reject the background superframes and to classify the event superframes into different event categories. While jointly posing event detection and localization as a regression problem is novel, the superior performance on two databases ITC-Irst and UPC-TALP demonstrates the efficiency and potential of the proposed approach.


title: A Deep Convolutional Neural Network for Background Subtraction

abstract: In this work, we present a novel background subtraction system that uses a deep Convolutional Neural Network (CNN) to perform the segmentation. With this approach, feature engineering and parameter tuning become unnecessary since the network parameters can be learned from data by training a single CNN that can handle various video scenes. Additionally, we propose a new approach to estimate background model from video. For the training of the CNN, we employed randomly 5 percent video frames and their ground truth segmentations taken from the Change Detection challenge 2014(CDnet 2014). We also utilized spatial-median filtering as the post-processing of the network outputs. Our method is evaluated with different data-sets, and the network outperforms the existing algorithms with respect to the average ranking over different evaluation metrics. Furthermore, due to the network architecture, our CNN is capable of real time processing.


title: Application of Deep Belief Networks for Natural Language Understanding

abstract: Applications of Deep Belief Nets (DBN) to various problems have been the subject of a number of recent studies ranging from image classification and speech recognition to audio classification. In this study we apply DBNs to a natural language understanding problem. The recent surge of activity in this area was largely spurred by the development of a greedy layer-wise pretraining method that uses an efficient learning algorithm called Contrastive Divergence (CD). CD allows DBNs to learn a multi-layer generative model from unlabeled data and the features discovered by this model are then used to initialize a feed-forward neural network which is fine-tuned with backpropagation. We compare a DBN-initialized neural network to three widely used text classification algorithms: Support Vector Machines (SVM), boosting and Maximum Entropy (MaxEnt). The plain DBN-based model gives a call-routing classification accuracy that is equal to the best of the other models. However, using additional unlabeled data for DBN pre-training and combining DBN-based learned features with the original features provides significant gains over SVMs, which, in turn, performed better than both MaxEnt and Boosting.


title: Approaches to strategic alignment of business and information systems: A study on application service acquisitions

abstract: Purpose – Technology‐related issues have an increasing impact on business. The alignment between overall business objectives and technology‐related decisions, such as decisions on application service acquisitions, resurfaces occasionally in the intense academic discussion on information systems (IS). Prior research indicates that the alignment of business and IS decisions remains a major concern for business practitioners. The purpose of this paper is to analyze the factors that drive the consistency in technology‐related decisions and result in diverse modes of business‐IS alignment.Design/methodology/approach – This study investigates application service acquisitions among the top 200 firms in Finland. In this quantitative empirical study, principal component analysis with varimax rotation method is used to examine the companies' drivers for business‐IS alignment. Moreover, the firms are categorized using the cluster analysis method.Findings – This study identifies four factors that drive the achievemen...


title: Automatic detection of motion artifacts in MR images using CNNS

abstract: Considerable practical interest exists in being able to automatically determine whether a recorded magnetic resonance image is affected by motion artifacts caused by patient movements during scanning. Existing approaches usually rely on the use of navigators or external sensors to detect and track patient motion during image acquisition. In this work, we present an algorithm based on convolutional neural networks that enables fully automated detection of motion artifacts in MR scans without special hardware requirements. The approach is data driven and uses the magnitude of MR images in the spatial domain as input. We evaluate the performance of our algorithm on both synthetic and real data and observe adequate performance in terms of accuracy and generalization to different types of data. Our proposed approach could potentially be used in clinical practice to tag an MR image as motion-free or motion-corrupted immediately after a scan is finished. This process would facilitate the acquisition of high-quality MR images that are often indispensable for accurate medical diagnosis.


title: WSDM Cup 2016: Entity Ranking Challenge

abstract: In this paper, we describe the WSDM Cup entity ranking challenge held in conjunction with the 2016 Web Search and Data Mining conference (WSDM 2016). Participants in the challenge were provided access to the Microsoft Academic Graph (MAG), a large heterogeneous graph of academic entities, and were invited to calculate the query-independent importance of each publication in the graph. Submissions for the challenge were open from August through November 2015, and a public leaderboard displayed teams? progress against a set of training judgements. Final evaluations were performed against a separate, withheld portion of the evaluation judgements. The top eight performing teams were then invited to submit papers to the WSDM Cup workshop, held at the WSDM 2016 conference.


title: Learning to Sample

abstract: Processing large point clouds is a challenging task. Therefore, the data is often sampled to a size that can be processed more easily. The question is how to sample the data? A popular sampling technique is Farthest Point Sampling (FPS). However, FPS is agnostic to a downstream application (classification, retrieval, etc.). The underlying assumption seems to be that minimizing the farthest point distance, as done by FPS, is a good proxy to other objective functions. We show that it is better to learn how to sample. To do that, we propose a deep network to simplify 3D point clouds. The network, termed S-NET, takes a point cloud and produces a smaller point cloud that is optimized for a particular task. The simplified point cloud is not guaranteed to be a subset of the original point cloud. Therefore, we match it to a subset of the original points in a post-processing step. We contrast our approach with FPS by experimenting on two standard data sets and show significantly better results for a variety of applications. Our code is publicly available.


title: How to learn a graph from smooth signals

abstract: We propose a framework that learns the graph structure underlying a set of smooth signals. Given $X\in\mathbb{R}^{m\times n}$ whose rows reside on the vertices of an unknown graph, we learn the edge weights $w\in\mathbb{R}_+^{m(m-1)/2}$ under the smoothness assumption that $\text{tr}{X^\top LX}$ is small. We show that the problem is a weighted $\ell$-1 minimization that leads to naturally sparse solutions. We point out how known graph learning or construction techniques fall within our framework and propose a new model that performs better than the state of the art in many settings. We present efficient, scalable primal-dual based algorithms for both our model and the previous state of the art, and evaluate their performance on artificial and real data.


title: A Human--Exoskeleton Interface Utilizing Electromyography

abstract: This paper presents a human--machine interface to control exoskeletons that utilizes electrical signals from the muscles of the operator as the main means of information transportation. These signals are recorded with electrodes attached to the skin on top of selected muscles and reflect the activation of the observed muscle. They are evaluated by a sophisticated but simplified biomechanical model of the human body to derive the desired action of the operator. A support action is computed in accordance to the desired action and is executed by the exoskeleton. The biomechanical model fuses results from different biomechanical and biomedical research groups and performs a sensible simplification considering the intended application. Some of the model parameters reflect properties of the individual human operator and his or her current body state. A calibration algorithm for these parameters is presented that relies exclusively on sensors mounted on the exoskeleton. An exoskeleton for knee joint support was designed and constructed to verify the model and to investigate the interaction between operator and machine in experiments with force support during everyday movements.


title: Extrasensory perception: odorant and taste receptors beyond the nose and mouth.

abstract: G protein-coupled receptors (GPCRs) represent the largest family of transmembrane receptors and are prime therapeutic targets. The odorant and taste receptors account for over half of the GPCR repertoire, yet they are generally excluded from large-scale, drug candidate analyses. Accumulating molecular evidence indicates that the odorant and taste receptors are widely expressed throughout the body and functional beyond the oronasal cavity - with roles including nutrient sensing, autophagy, muscle regeneration, regulation of gut motility, protective airway reflexes, bronchodilation, and respiratory disease. Given this expanding array of actions, the restricted perception of these GPCRs as mere mediators of smell and taste is outdated. Moreover, delineation of the precise actions of odorant and taste GPCRs continues to be hampered by the relative paucity of selective and specific experimental tools, as well as the lack of defined receptor pharmacology. In this review, we summarize the evidence for expression and function of odorant and taste receptors in tissues beyond the nose and mouth, and we highlight their broad potential in physiology and pathophysiology.


title: Four Switch Buck-Boost Converter for Telecom DC-DC power supply applications

abstract: Converters for telecom DC/DC power supply applications often require an output voltage somewhere within a wide range of input voltages. While the design of traditional converters will come with a heavy penalty in terms of component stresses and losses, and with the restrictions on the output voltage. Besides that, the high efficiency around the nominal input is another restriction for traditional converters. A controlling scheme for the four switch buck-boost converter is proposed to achieve high efficiency within the line range and the highest efficiency around the nominal input. A 48 V(36-75 V) input 12 V@25 A output two-stage prototype composed of the proposed converter and a full bridge converter is built in the lab. The experimental results verified the analysis.


title: Efficient sparse matrix-vector multiplication on GPUs using the CSR storage format

abstract: The performance of sparse matrix vector multiplication (SpMV) is important to computational scientists. Compressed sparse row (CSR) is the most frequently used format to store sparse matrices. However, CSR-based SpMV on graphics processing units (GPUs) has poor performance due to irregular memory access patterns, load imbalance, and reduced parallelism. This has led researchers to propose new storage formats. Unfortunately, dynamically transforming CSR into these formats has significant runtime and storage overheads. We propose a novel algorithm, CSR-Adaptive, which keeps the CSR format intact and maps well to GPUs. Our implementation addresses the aforementioned challenges by (i) efficiently accessing DRAM by streaming data into the local scratchpad memory and (ii) dynamically assigning different numbers of rows to each parallel GPU compute unit. CSR-Adaptive achieves an average speedup of 14.7 × over existing CSR-based algorithms and 2.3× over clSpMV cocktail, which uses an assortment of matrix formats.


A Parallel Recurrent Neural Network for Language Modeling with POS Tags.


title: Resource Sharing Optimization for Device-to-Device Communication Underlaying Cellular Networks

abstract: We consider Device-to-Device (D2D) communication underlaying cellular networks to improve local services. The system aims to optimize the throughput over the shared resources while fulfilling prioritized cellular service constraints. Optimum resource allocation and power control between the cellular and D2D connections that share the same resources are analyzed for different resource sharing modes. Optimality is discussed under practical constraints such as minimum and maximum spectral efficiency restrictions, and maximum transmit power or energy limitation. It is found that in most of the considered cases, optimum power control and resource allocation for the considered resource sharing modes can either be solved in closed form or searched from a finite set. The performance of the D2D underlay system is evaluated in both a single-cell scenario, and a Manhattan grid environment with multiple WINNER II A1 office buildings. The results show that by proper resource management, D2D communication can effectively improve the total throughput without generating harmful interference to cellular networks.


title: Tiling in Interactive Panoramic Video: Approaches and Evaluation

abstract: Interactive panoramic systems are currently on the rise. However, one of the major challenges in such a system is the overhead involved in transferring a full-quality panorama to the client when only a part of the panorama is used to extract a virtual view. Thus, such a system should maximize the user experience while simultaneously minimizing the bandwidth required. In this paper, we apply tiling to deliver different quality levels for different parts of the panorama. Tiling has traditionally been applied to the delivery of very high-resolution content to clients. Here, we apply similar ideas in a real-time interactive panoramic video system. A major challenge lies in the movement of such a virtual view, for which clients’ regions of interest change dynamically and independently from each other. We show that our algorithms, which progressively increase in quality toward the point of the view, manage to (i) reduce the bandwidth requirement and (ii) provide a similar quality of experience (QoE) compared to a full panorama system.


title: Evidence that emotional intelligence is related to job performance and affect and attitudes at work.

abstract: The relation between emotional intelligence, assessed with a performance measure, and positive workplace outcomes was examined in 44 analysts and clerical employees from the finance department of a Fortune 400 insurance company. Emotionally intelligent individuals received greater merit increases and held higher company rank than their counterparts. They also received better peer and/or supervisor ratings of interpersonal facilitation and stress tolerance than their counterparts. With few exceptions, these associations remained statistically significant after controlling for other predictors, one at a time, including age, gender, education, verbal ability, the Big Five personality traits, and trait affect.


title: Optical interconnects for extreme scale computing systems

abstract: Face-to-face comparison of major large scale HPC interconnects.Review of challenges and solutions for increased use of optics in HPC.Description of optical switching, in terms of principle, limitations, technical requirements and benefits. Large-scale high performance computing is permeating nearly every corner of modern applications spanning from scientific research and business operations, to medical diagnostics, and national security. All these communities rely on computer systems to process vast volumes of data quickly and efficiently, yet progress toward increased computing power has experienced a slowdown in the last number of years. The sheer cost and scale, stemming from the need for extreme parallelism, are among the reasons behind this stall. In particular, very large-scale, ultra-high bandwidth interconnects, essential for maintaining computation performance, represent an increasing portion of the total cost budget.Photonic systems are often cited as ways to break through the energy-bandwidth limitations of conventional electrical wires toward drastically improving interconnect performance. This paper presents an overview of the challenges associated with large-scale interconnects, and reviews how photonic technologies can contribute to addressing these challenges. We review some important aspects of photonics that should not be underestimated in order to truly reap the benefits of cost and power reduction.


title: Behavior Analysis Using a Multilevel Motion Pattern Learning Framework

abstract: The increasing availability of video data, through existing traffic cameras or dedicated field data collection, and the development of computer vision techniques pave the way for the collection of massive data sets about the microscopic behavior of road users. Analysis of such data sets helps in understanding normal road user behavior and can be used for realistic prediction of motion and computation of surrogate safety indicators. A multilevel motion pattern learning framework was developed to enable automated scene interpretation, anomalous behavior detection, and surrogate safety analysis. First, points of interest (POIs) were learned on the basis of the Gaussian mixture model and the expectation maximization algorithm and then used to form activity paths (APs). Second, motion patterns, represented by trajectory prototypes, were learned from road users’ trajectories in each AP by using a two-stage trajectory clustering method based on spatial then temporal (speed) information. Finally, motion predictio...


title: The business model concept: theoretical underpinnings and empirical illustrations

abstract: The business model concept is becoming increasingly popular within IS, management and strategy literature. It is used within many fields of research, including both traditional strategy theory and in the emergent body of literature on e-business. However, the concept is often used independently from theory, meaning model components and their interrelations are relatively obscure. Nonetheless, we believe that the business model concept is useful in explaining the relation between IS and strategy. This paper offers an outline for a conceptual business model, and proposes that it should include customers and competitors, the offering, activities and organisation, resources and factor market interactions. The causal inter-relations and the longitudinal processes by which business models evolve should also be included. The model criticises yet draws on traditional strategy theory and on the literature that addresses business models directly. The business model is illustrated by an ERP implementation in a European multi-national company.


title: Handling imbalanced datasets: A review

abstract: Learning classifiers from imbalanced or skewed datasets is an impor- tant topic, arising very often in practice in classification problems. In such problems, almost all the instances are labelled as one class, while far fewer in- stances are labelled as the other class, usually the more important class. It is obvious that traditional classifiers seeking an accurate performance over a full range of instances are not suitable to deal with imbalanced learning tasks, since they tend to classify all the data into the majority class, which is usually the less important class. This paper describes various techniques for handling im- balance dataset problems. Of course, a single article cannot be a complete re- view of all the methods and algorithms, yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting re- search directions and suggesting possible bias combinations that have yet to be explored.


title: High accurate robotic drilling with external sensor and compliance model-based compensation

abstract: High accurate absolute robot positioning is a requirement, and still a challenge, in many applications, such as drilling in the aerospace industry. The accuracy is affected due to many sources of errors from robot model, tool calibration, sensor and product uncertainties. While model-based error compensation cannot reach the desired accuracy, sensor-based compensation appears as the practical solution to increase the robot positioning accuracy. A structured analysis of the error sources in robotic manufacturing processes can facilitate error identification and further compensation. This paper describes an error source breaking down approach for analyzing robotic manufacturing processes. Moreover, an external sensor-based compensation is proposed for error reduction and error identification. Comparison with a compliance model-based compensation is performed. The proposed approach is applied to a robotic drilling process for aircraft manufacturing, considered a general and real industrial application. Further validation through experimentation is performed. The validation revealed a clear improvement in robot positioning accuracy and the benefits of the proposed error source structure for analysis.


title: Arbitrary Category Classification of Websites Based on Image Content

abstract: This paper presents a comprehensive methodology for general large-scale image-based classification tasks. It addresses the Big Data challenge in arbitrary image classification and more specifically, filtering of millions of websites with abstract target classes and high levels of label noise. Our approach uses local image features and their color descriptors to build image representations with the help of a modified k-NN algorithm. Image representations are refined into image and website class predictions by a two-stage classifier method suitable for a very large-scale real dataset. A modification of an Extreme Learning Machine is found to be a suitable classifier technique. The methodology is robust to noise and can learn abstract target categories; website classification accuracy surpasses 97% for the most important categories considered in this study.


title: Microwave ablation technique (MWA) for cancer treatment: Simulation of single Slot MCA for different slot position

abstract: One of the effective options to kill cancer cell is Microwave ablation (MWA). In this process radiation is focused on the desired areas, ensuring appropriate temperature distribution without damaging the surrounding tissues. To effectively treat cancer, Slot coaxial antennas can be used for producing a heat absorption and temperature distribution model. In this work, it is has been demonstrated that when the antenna slot position is at 50% of the tumor higher SAR value (about 5.78 kW/kg peak SAR) and larger heat ablation area is achieved; while changing the slot position more good cells get destroyed but changing slot position also decreases temperature (The maximum temperature within the breast tissue is 203°C for 50% slot position for t = 2 min and for the same duration of time 156°C and 185°C were found for 25% and 75% slot position respectively). Lastly the results reveals that for the presented Slot Coaxial Antenna at 2.45GHz and 10W power; for optimized and efficient impact, the microwave heating should be continued for 2 min at 50% slot position; less time would not cover all the tumor cell and changing the slot position will result in damaging more good cells.


title: PACHI: State of the Art Open Source Go Program

abstract: We present a state of the art implementation of the Monte Carlo Tree Search algorithm for the game of Go. Our Pachi software is currently one of the strongest open source Go programs, competing at the top level with other programs and playing evenly against advanced human players. We describe our implementation and choice of published algorithms as well as three notable original improvements: (1) an adaptive time control algorithm, (2) dynamic komi, and (3) the usage of the criticality statistic. We also present new methods to achieve efficient scaling both in terms of multiple threads and multiple machines in a cluster.


title: Predicting survival , reproduction and abundance of polar bears under climate change

abstract: Polar bear (Ursus maritimus) populations are predicted to be negatively affected by climate warming, but the timeframe and manner in which change to polar bear populations will occur remains unclear. Predictions incorporating climate change effects are necessary for proactive population management, the setting of optimal harvest quotas, and conservation status decisions. Such predictions are difficult to obtain from historic data directly because past and predicted environmental conditions differ substantially. Here, we explore how models can be used to predict polar bear population responses under climate change. We suggest the development of mechanistic models aimed at predicting reproduction and survival as a function of the environment. Such models can often be developed, parameterized, and tested under current environmental conditions. Model predictions for reproduction and survival under future conditions could then be input into demographic projection models to improve abundance predictions under climate change. We illustrate the approach using two examples. First, using an individual-based dynamic energy budget model, we estimate that 3–6% of adult males in Western Hudson Bay would die of starvation before the end of a 120 day summer fasting period but 28–48% would die if climate warming increases the fasting period to 180 days. Expected changes in survival are non-linear (sigmoid) as a function of fasting period length. Second, we use an encounter rate model to predict changes in female mating probability under sea ice area declines and declines in mate-searching efficiency due to habitat fragmentation. The model predicts that mating success will decline non-linearly if searching efficiency declines faster than habitat area, and increase non-linearly otherwise. Specifically for the Lancaster Sound population, we predict that female mating success would decline from 99% to 91% if searching efficiency declined twice as fast as sea ice area, and to 72% if searching efficiency declined four times as fast as area. Sea ice is a complex and dynamic habitat that is rapidly changing. Failure to incorporate climate change effects into population projections can result in flawed conservation assessments and management decisions.


title: Representing and Using Non-Functional Requirements: A Process-Oriented Approach

abstract: A comprehensive framework for representing and using nonfunctional requirements during the development process is proposed. The framework consists of five basic components which provide the representation of nonfunctional requirements in terms of interrelated goals. Such goals can be refined through refinement methods and can be evaluated in order to determine the degree to which a set of nonfunctional requirements is supported by a particular design. Evidence for the power of the framework is provided through the study of accuracy and performance requirements for information systems. >


title: Towards scalable streaming to SAGE2 video walls

abstract: Video walls are useful to display large visualizations. The SAGE2 web-based system allows easy programming of scalable visualization applications. However, it is not easy to stream high resolution video to SAGE2 powered video walls. We proposed several methods of high resolution video streaming to LCD walls, evaluated their performance and discuss their scalability and properties.


title: The ParallelEye Dataset: Constructing Large-Scale Artificial Scenes for Traffic Vision Research

abstract: Video image datasets are playing an essential role in design and evaluation of traffic vision algorithms. Nevertheless, a longstanding inconvenience concerning image datasets is that manually collecting and annotating large-scale diversified datasets from real scenes is time-consuming and prone to error. For that virtual datasets have begun to function as a proxy of real datasets. In this paper, we propose to construct large-scale artificial scenes for traffic vision research and generate a new virtual dataset called "ParallelEye". First of all, the street map data is used to build 3D scene model of Zhongguancun Area, Beijing. Then, the computer graphics, virtual reality, and rule modeling technologies are utilized to synthesize large-scale, realistic virtual urban traffic scenes, in which the fidelity and geography match the real world well. Furthermore, the Unity3D platform is used to render the artificial scenes and generate accurate ground-truth labels, e.g., semantic/instance segmentation, object bounding box, object tracking, optical flow, and depth. The environmental conditions in artificial scenes can be controlled completely. As a result, we present a viable implementation pipeline for constructing large-scale artificial scenes for traffic vision research. The experimental results demonstrate that this pipeline is able to generate photorealistic virtual datasets with low modeling time and high accuracy labeling.


title: Differential mode (DM) current ripple EMI noise analysis for three-phase Vienna type rectifiers

abstract: For a Vienna rectifier, the phase-leg's pulsating voltage is the source for the input current ripple and Electromagnetic Interference (EMI) noise. Double Fourier integral transform is commonly used to determine the harmonic spectrum of the phase-leg voltage so as to predict EMI noise. For three-phase, three-level rectifiers, like the Vienna rectifier, this process can be very cumbersome and can vary depending on the modulation scheme used. Moreover it doesn't give direct estimate of the current spectrum which is needed for filter design as the limits of noise current spectra are given in many EMI standards. Hence, for designing the filter for the DM noise, detailed analysis of the DM ripple current is very important. In this paper, the DM ripple current in Vienna type rectifiers using a dual stacked carrier based modulation scheme is investigated and the technique to predict the worst current ripple is developed. Based on this worst current ripple, the method of finding the attenuation requirements for EMI filter design is also described. Simulations were conducted to validate the worst current ripple predicted using the proposed technique.


title: Automatic detection system for printed fabric defects

abstract: Printed fabrics are high value-added artifacts with rich colors and various patterns. Flawed products occur owing to uncertainties during the manufacturing process. Such defects waste not only raw materials and machine operating time, but also large amounts of labor to inspect, sift and sort. Hence, if the detection process for printed fabric defects could be automated, the product quality of printed fabrics could be increased, and industry efficiency could also be improved by reducing the requirement for manpower. So this study aims to develop such a defect detecting system to investigate printed fabrics with repeated patterns, locate flaw sites by the minimum repeated zone of repeated patterns, and finally find out the most common flaw type. The novelty of this technique is the introduction of an image processing technology known as the RGB accumulative average method (RGBAAM) to test and locate flawed zones, then use fuzzy logic to discern the flaw types. The RGBAAM has the merits of compactness and hi...


title: Low temperature Cu-Cu direct bonding for 3D-IC by using fine crystal layer

abstract: In this paper, we report a method of low temperature solid diffusion bonding. To investigate bondability of solid diffusion, we examined the effect of bump metals and bump planarization methods. Cu and Au bump were used for bump metals and CMP and ultra-precision cutting were used for bump planarization methods. We found that fine crystal layer could be formed on only cut Cu and Au bumps, and especially cut Cu bumps had a thick fine crystal layer on the surface. The layer on cut Cu bump was found to be easily to recrystallize at low temperature condition of 150 degree C. Moreover, the bonding interface of cut Cu bump disappeared at 200 degree C for 30 min, which means solid diffusion across the interface was realized with the contribution of fine crystal layer. In addition, for Cu-Cu direct bonding, formic acid treatment before bonding is effective because formic acid can react at low temperature without destroying fine crystal layer. That led to achieve high bonding strength between cut Cu bumps.


title: A comparative study of microneedling with platelet-rich plasma plus topical minoxidil (5%) and topical minoxidil (5%) alone in androgenetic alopecia

abstract: Context: There are very few studies evaluating efficacy of platelet-rich plasma (PRP) in hair restoration and its combination with microneedling. As far as ascertained, there is no study to evaluate efficacy of microneedling with PRP plus topical minoxidil (5%) versus topical minoxidil (5%) alone in androgenetic alopecia (AGA). Aims: This study aims (1) to compare the efficacy of (a) topical minoxidil (5%) alone and (b) topical minoxidil (5%) + microneedling with PRP in men between 18 and 50 years with AGA Grade III to V vertex (Norwood–Hamilton scale) and (2) to perform objective and subjective evaluation based on clinical improvement and photographic evidence. Settings and Design: The study was conducted in the outpatient department of dermatology, venereology, and leprology in tertiary care hospital. It was open, prospective study. Subjects and Methods: Fifty patients with AGA were selected on the basis of inclusion and exclusion criteria. These patients were randomly divided into two groups of 25 patients each and were given following treatment: (i) Group A: topical minoxidil (5%) alone and (ii) Group B: topical minoxidil (5%) + microneedling with platelet-rich plasma (PRP). Statistical Analysis Used: Patients were assessed before starting the treatment and at the end of 6 months on the basis of (a) Patient's self-assessment based on standardized seven-point scale compared with baseline (b) Physician's assessment based on standardized seven-point scale of hair growth compared with baseline. Results: There was a significant improvement (P < 0.05) in both patients' assessment and investigator's assessment in Group B as compared to Group A at the end of 6 months. Conclusions: Microneedling with PRP is safe, effective, and a promising tool for the management of AGA.


title: Augmenting the NEAT algorithm to improve its temporal processing capabilities

abstract: This paper is concerned with the incorporation of new time processing capacities to the Neuroevolution of Augmenting Topologies (NEAT) algorithm. This algorithm is quite popular within the robotics community for the production of trained neural networks without having to determine a priori their size and topology. However, and even though the algorithm can address temporal processing issues through its capacity of establishing feedback synaptic connections, that is, through recurrences, there are still instances where more precise time processing may go beyond its limits. In order to address these cases, in this paper we describe a new implementation of the NEAT algorithm where trainable synaptic time delays are incorporated into its toolbox. This approach is shown to improve the behavior of neural networks obtained using NEAT in many instances. Here, we provide some of these results using a series of typical complex time processing tasks related to chaotic time series modeling and consider an example of the integration of this new approach within a robotic cognitive architecture.


title: Natural Language Processing Service Based on Stroke-Level Convolutional Networks for Chinese Text Classification

abstract: With the development of deep learning and artificial intelligence, more and more research apply neural networks to natural language processing tasks. However, while the majority of these research take English corpus as the dataset, few studies have been done using Chinese corpus. Meanwhile, Existing Chinese processing algorithms typically regard Chinese word or Chinese character as the basic unit but ignore the deeper information into the Chinese character. In Chinese linguistic, strokes are the basic unit of Chinese character who are similar to letters of the English word. Inspired by the recent success of deep learning at character-level, we delve deeper to Chinese stroke level for Chinese language processing and developed it into service for Chinese text classification. In this paper, we dig the basic feature of the strokes considering the similar Chinese character components and propose a new method to leverage Chinese stroke for learning the continuous representation of Chinese character and develop it into a service for Chinese text classification. We develop a dedicated neural architecture based on the convolutional neural network to effectively learn character embedding and apply it to Chinese word similarity judgment and Chinese text classification. Both experiments results show that the stroke level method is effective for Chinese language processing.


title: Estimation of Toxic Metals in Milk Collected from Lactating Mothers in Karak, Khyber Pakhtunkhwa Pakistan.

abstract: The objective of the study was to assess the concentration of heavy metals like Zn, Ni, Fe, Cd and Pb in human milk. Human milk was obtained from lactating mothers. Breast milk samples were collected from thirty lactating mothers below the age of forty who patronize the post natal clinic of Karak Government district hospital. The samples were obtained by self-milking into sterilized polyethylene bottles and well labeled. Few drops of 0.1M trichloroacetic acid were added to the sample and the aqueous layer was heated at 500 ° C for one hour. After ashing, it was digested with 0.5 M HNO3 and the metals were analyzed using an AAS (Perkin Elmer 400).The study showed that most of the samples were found to have Pb below detection limit, and ranged between 0.006 -0.269. Cadmium was not detected throughout the study. The mean Nickel concentration was found to be 0.545μg/L and ranged between 0.004-0.745μg/. From the analysis it was concluded that heavy metals in human milk do not exceed the permissible limit, so it is safe to lactate child in the selected community.


title: Tracking-Learning-Detection Adopted Unsupervised Learning Algorithm

abstract: In this paper, we research the real-time object tracking technology. The object tracking algorithm discussed in this paper is developed based on the Tracking-Learning-Detection(TLD) and the Centroid Neural Network(CNN). The object is unknown ahead of tracking, the model of the object is composed of objects transformed geometrically immediately after tracking. The TLD framework is useful for long-term object tracking in a video stream because the TLD framework applies a novel learning algorithm called P-N learning. We propose a method that applies the CNN algorithm to the TLD framework. The CNN algorithm is an unsupervised learning algorithm that provides a stable result, regardless of initial values of learning coefficients and neurons. The object tracking algorithm discussed in this paper has a higher accuracy than that of TLD in terms of detection. Additionally, it exhibits better processing performance than that of TLD.


title: The his and hers of prosocial behavior: An examination of the social psychology of gender.

abstract: Prosocial behavior consists of behaviors regarded as beneficial to others, including helping, sharing, comforting, guiding, rescuing, and defending others. Although women and men are similar in engaging in extensive prosocial behavior, they are different in their emphasis on particular classes of th


title: Capacitively coupled ECG sensor using a single electrode with adaptive power-line noise cancellation

abstract: This report describes a small heartbeat monitoring system using capacitively coupled ECG sensors. Capacitively coupled sensors using an insulated electrode have been proposed to obtain ECG signals without pasting electrodes directly onto the skin. Although the sensors have better usability than conventional ECG sensors, it is difficult to remove noise contamination. Power-line noise can be a severe noise source that increases when only a single electrode is used. However, a multiple electrode system degrades usability. To address this problem, we propose a noise cancellation technique using an adaptive noise feedback approach, which can improve the availability of the capacitive ECG sensor using a single electrode. An instrumental amplifier is used in the proposed method for the first stage amplifier instead of voltage follower circuits. A microcontroller predicts the noise waveform from an ADC output. To avoid saturation caused by power-line noise, the predicted noise waveform is fed back to an amplifier input through a DAC. We implemented the prototype sensor system to evaluate the noise reduction performance. Measurement results using a prototype board show that the proposed method can suppress 28-dB power-line noise.


title: Side-Channel Attacks: Ten Years After Its Publication and the Impacts on Cryptographic Module Security Testing

abstract: Side-channel attacks are easy-to-implement whilst powerful attacks against cryptographic implementations, and their targets range from primitives, protocols, modules, and devices to even systems. These attacks pose a serious threat to the security of cryptographic modules. In consequence, cryptographic implementations have to be evaluated for their resistivity against such attacks and the incorporation of different countermeasures has to be considered. This paper surveys the methods and techniques employed in these attacks, the destructive effects of such attacks, the countermeasures against such attacks and evaluation of their feasibility and applicability. Finally, the necessity and feasibility of adopting this kind of physical security testing and evaluation in the development of FIPS 140-3 standard are explored. This paper is not only a survey paper, but also more a position paper.


title: "SOFT" EXOSKELETONS FOR UPPER AND LOWER BODY REHABILITATION — DESIGN, CONTROL AND TESTING

abstract: The basic concepts for exoskeletal systems have been suggested for some time with applications ranging from construction, manufacturing and mining to rescue and emergency services. In recent years, research has been driven by possible uses in medical/rehabilitation and military applications. Yet there are still significant barriers to the effective use and exploitation of this technology. Among the most pertinent of these factors is the power and actuation system and its impact of control, strength, speed and, perhaps most critically, safety. This work describes the design, construction and testing of an ultra low-mass, full-body exoskeleton system having seven degrees of freedom (DOFs) for the upper limbs and five degrees of freedom (DOFs) for each of the lower limbs. This low mass is primarily due to the use of a new range of pneumatic muscle actuators as the power source for the system. The work presented will show how the system takes advantage of the inherent controllable compliance to produce a unit...


title: Sindice.com: A document-oriented lookup index for open linked data

abstract: Data discovery on the Semantic Web requires crawling and indexing of statements, in addition to the 'linked-data' approach of de-referencing resource URIs. Existing Semantic Web search engines are focused on database-like functionality, compromising on index size, query performance and live updates. We present Sindice, a lookup index over Semantic Web resources. Our index allows applications to automatically locate documents containing information about a given resource. In addition, we allow resource retrieval through inverse-functional properties, offer a full-text search and index SPARQL endpoints. Finally, we extend the sitemap protocol to efficiently index large datasets with minimal impact on data providers.


title: Fast Adaptive Bilateral Filtering

abstract: In the classical bilateral filter, a fixed Gaussian range kernel is used along with a spatial kernel for edge-preserving smoothing. We consider a generalization of this filter, the so-called adaptive bilateral filter, where the center and width of the Gaussian range kernel are allowed to change from pixel to pixel. Though this variant was originally proposed for sharpening and noise removal, it can also be used for other applications, such as artifact removal and texture filtering. Similar to the bilateral filter, the brute-force implementation of its adaptive counterpart requires intense computations. While several fast algorithms have been proposed in the literature for bilateral filtering, most of them work only with a fixed range kernel. In this paper, we propose a fast algorithm for adaptive bilateral filtering, whose complexity does not scale with the spatial filter width. This is based on the observation that the concerned filtering can be performed purely in range space using an appropriately defined local histogram. We show that by replacing the histogram with a polynomial and the finite range-space sum with an integral, we can approximate the filter using analytic functions. In particular, an efficient algorithm is derived using the following innovations: the polynomial is fitted by matching its moments to those of the target histogram (this is done using fast convolutions), and the analytic functions are recursively computed using integration-by-parts. Our algorithm can accelerate the brute-force implementation by at least $20 \times $ , without perceptible distortions in the visual quality. We demonstrate the effectiveness of our algorithm for sharpening, JPEG deblocking, and texture filtering.


title: Designing digital tooling for business model exploration for the Internet‑of‑Things

abstract: As digital technologies are transforming enterprises, the interest in business models is increasing. Technological disruptions like the Internet of Things (IoT) drive enterprises to redefine their business models to create and capture value, and eventually, to stay competitive. The need for business model innovation may be urgent, yet it is not always clear what to change in a business model. In these cases, business model exploration is needed. Within academia and practice, business model tools are mainly focused on formalizing single business model designs rather than facilitating systematic exploration of alter-native business models. In this study, we present the design and prototype of a digital tool created to facilitate business model exploration. We use Design Sci-ence Research (DSR) as our research approach. In this paper, we present the re-sults from the first cycle evaluation of the design and prototype.


title: Generative and Discriminative Voxel Modeling with Convolutional Neural Networks

abstract: When working with three-dimensional data, choice of representation is key. We explore voxel-based models, and present evidence for the viability of voxellated representations in applications including shape modeling and object classification. Our key contributions are methods for training voxel-based variational autoencoders, a user interface for exploring the latent space learned by the autoencoder, and a deep convolutional neural network architecture for object classification. We address challenges unique to voxel-based representations, and empirically evaluate our models on the ModelNet benchmark, where we demonstrate a 51.5% relative improvement in the state of the art for object classification.


THE DANGERS OF GAMIFICATION Why We Shouldn't Build a Game Layer on Top of the World


title: Bilingual Word Embeddings from Non-Parallel Document-Aligned Data Applied to Bilingual Lexicon Induction

abstract: We propose a simple yet effective approach to learning bilingual word embeddings (BWEs) from non-parallel document-aligned data (based on the omnipresent skip-gram model), and its application to bilingual lexicon induction (BLI). We demonstrate the utility of the induced BWEs in the BLI task by reporting on benchmarking BLI datasets for three language pairs: (1) We show that our BWE-based BLI models significantly outperform the MuPTM-based and context-counting models in this setting, and obtain the best reported BLI results for all three tested language pairs; (2) We also show that our BWE-based BLI models outperform other BLI models based on recently proposed BWEs that require parallel data for bilingual training.


title: Fast Localization of Autonomous Vehicles Using Discriminative Metric Learning

abstract: In this paper, we report a novel algorithm for localization of autonomous vehicles in an urban environment using orthographic ground reflectivity map created with a three-dimensional (3D) laser scanner. It should be noted that the road paint (lane markings, zebra crossing, traffic signs etc.) constitute the distinctive features in the surface reflectivity map which are generally sparse as compared to the non-interesting asphalt and the off-road portion of the map. Therefore, we propose to project the reflectivity map to a lower dimensional space, that captures the useful features of the map, and then use these projected feature maps for localization. We use discriminative metric learning technique to obtain this lower dimensional space of feature maps. Experimental evaluation of the proposed method on real data shows that it is better than the standard image matching techniques in terms of accuracy. Moreover, the proposed method is computationally fast and can be executed at real-time (10 Hz) on a standard CPU.


title: Mental health morbidity among people subject to immigration detention in the UK: a feasibility study

abstract: Aims. The UK has one of the largest systems of immigration detention in Europe.. Those detained include asylum-seekers and foreign national prisoners, groups with a higher prevalence of mental health vulnerabilities compared with the general population. In light of little published research on the mental health status of detainees in immigration removal centres (IRCs), the primary aim of this study was to explore whether it was feasible to conduct psychiatric research in such a setting. A secondary aim was to compare the mental health of those seeking asylum with the rest of the detainees. Methods. Cross-sectional study with simple random sampling followed by opportunistic sampling. Exclusion criteria included inadequate knowledge of English and European Union nationality. Six validated tools were used to screen for mental health disorders including developmental disorders like Personality Disorder, Attention Deficit Hyperactivity Disorder (ADHD), Autistic Spectrum Disorder (ASD) and Intellectual Disability, as well as for needs assessment. These were the MINI v6, SAPAS, AQ-10, ASRS, LDSQ and CANFOR. Demographic data were obtained using a participant demographic sheet. Researchers were trained in the use of the screening battery and inter-rater reliability assessed by joint ratings. Results. A total of 101 subjects were interviewed. Overall response rate was 39%. The most prevalent screened mental disorder was depression (52.5%), followed by personality disorder (34.7%) and post-traumatic stress disorder (20.8%). 21.8% were at moderate to high suicidal risk. 14.9 and 13.9% screened positive for ASD and ADHD, respectively. The greatest unmet needs were in the areas of intimate relationships (76.2%), psychological distress (72.3%) and sexual expression (71.3%). Overall presence of mental disorder was comparable with levels found in prisons. The numbers in each group were too small to carry out any further analysis. Conclusion. It is feasible to undertake a psychiatric morbidity survey in an IRC. Limitations of the study include potential selection bias, use of screening tools, use of single-site study, high refusal rates, the lack of interpreters and lack of women and children in study sample. Future studies should involve the in-reach team to recruit participants and should be run by a steering group consisting of clinicians from the IRC as well as academics.


title: Teachers' and education students' perceptions of and reactions to children with and without the diagnostic label 'ADHD'

abstract: Thirty-four elementary school teachers and 32 education students from Canada rated their reactions towards vignettes describing children who met attention-deficit/hyperactivity disorder (ADHD) symptom criteria that included or did not include the label “ADHD.” “ADHD”-labeled vignettes elicited greater perceptions of the child's impairment as well as more negative emotions and less confidence in the participants, although it also increased participants' willingness to implement treatment interventions. Ratings were similar to vignettes of boys versus girls; however, important differences in ratings between teachers and education students emerged and are discussed. Finally, we investigated the degree to which teachers' professional backgrounds influenced bias based on the label “ADHD.” Training specific to ADHD consistently predicted label bias, whereas teachers' experience working with children with ADHD did not.


title: Deep Learning for Single-View Instance Recognition

abstract: Deep learning methods have typically been trained on large datasets in which many training examples are available. However, many real-world product datasets have only a small number of images available for each product. We explore the use of deep learning methods for recognizing object instances when we have only a single training example per class. We show that feedforward neural networks outperform state-of-the-art methods for recognizing objects from novel viewpoints even when trained from just a single image per object. To further improve our performance on this task, we propose to take advantage of a supplementary dataset in which we observe a separate set of objects from multiple viewpoints. We introduce a new approach for training deep learning methods for instance recognition with limited training data, in which we use an auxiliary multi-view dataset to train our network to be robust to viewpoint changes. We find that this approach leads to a more robust classifier for recognizing objects from novel viewpoints, outperforming previous state-of-the-art approaches including keypoint-matching, template-based techniques, and sparse coding.


title: Face recognition based on convolutional neural network

abstract: Face recognition is of great importance to real world applications such as video surveillance, human machine interaction and security systems. As compared to traditional machine learning approaches, deep learning based methods have shown better performances in terms of accuracy and speed of processing in image recognition. This paper proposes a modified Convolutional Neural Network (CNN) architecture by adding two normalization operations to two of the layers. The normalization operation which is batch normalization provided acceleration of the network. CNN architecture was employed to extract distinctive face features and Softmax classifier was used to classify faces in the fully connected layer of CNN. In the experiment part, Georgia Tech Database showed that the proposed approach has improved the face recognition performance with better recognition results.


Required Expertise


title: Transformational Marketing: Linking Marketing and Sustainability

abstract: Current marketing practices promote goods and services irrespective as to what their impacts on the environment and human society are. As this often has serious negative ecological and social consequences, change in practices and better education are required. The paper argues for the need for transformational marketing which links marketing with sustainability values in order to contribute towards mitigating or reversing the environmental and other harm caused by humans because of their poor consumption choices. Such new sustainability approach to marketing would allow to: (1) change the way marketing is perceived; (2) improve its role within society and (3) emphasize the long-term responsibility of business and consumers in relation to the marketed products, services or activities. Incorporating sustainability into education for marketing would inspire marketers to develop a new business ethics that puts global human and ecological values ahead of short-term business profits. Most importantly, such ethics would build the foundations of trust and respect allowing marketing to support only moral and responsible businesses, the benefits from which would be long-term and to broader society.


Review Optimal path planning of mobile robots: A review


title: Cluster labelling using chi-square-based keyword ranking and mutual information score: a hybrid approach

abstract: Cluster labelling is a technique which provides useful information about the cluster to the end users. In this paper, we propose a novel approach which is the follow-up of our previous work. Our earlier approach generates clusters of web documents by using a modified apriori approach which is more efficient and faster than the traditional apriori approach. To label the clusters, the proposed approach used an effective feature selection technique which selects the top features of a cluster. Rather than labelling the cluster with 'bag of words', a concept driven mechanism has been developed which uses the Wikipedia that takes the top features of a cluster as input to generate the possible candidate labels. Mutual information (MI) score technique has been used for ranking the candidate labels and then the topmost candidates are considered as potential labels of a cluster. Experimental results on two benchmark datasets demonstrate the efficiency of our approach.


title: Geometric Scene Parsing with Hierarchical LSTM

abstract: This paper addresses the problem of geometric scene parsing, i.e. simultaneously labeling geometric surfaces (e.g. sky, ground and vertical plane) and determining the interaction relations (e.g. layering, supporting, siding and affinity) between main regions. This problem is more challenging than the traditional semantic scene labeling, as recovering geometric structures necessarily requires the rich and diverse contextual information. To achieve these goals, we propose a novel recurrent neural network model, named Hierarchical Long Short-Term Memory (H-LSTM). It contains two coupled sub-networks: the Pixel LSTM (P-LSTM) and the Multi-scale Super-pixel LSTM (MS-LSTM) for handling the surface labeling and relation prediction, respectively. The two sub-networks provide complementary information to each other to exploit hierarchical scene contexts, and they are jointly optimized for boosting the performance. Our extensive experiments show that our model is capable of parsing scene geometric structures and outperforming several state-of-the-art methods by large margins. In addition, we show promising 3D reconstruction results from the still images based on the geometric parsing.


title: Energy Efficient Task Caching and Offloading for Mobile Edge Computing

abstract: While augment reality applications are becoming popular, more and more data-hungry and computation-intensive tasks are delay-sensitive. Mobile edge computing is expected to an effective solution to meet the low latency demand. In contrast to previous work on mobile edge computing, which mainly focus on computation offloading, this paper introduces a new concept of task caching. Task caching refers to the caching of completed task application and their related data in edge cloud. Then, we investigate the problem of joint optimization of task caching and offloading on edge cloud with the computing and storage resource constraint. We formulate this problem as mixed integer programming which is hard to solve. To solve the problem, we propose efficient algorithm, called task caching and offloading (TCO), based on alternating iterative algorithm. Finally, the simulation experimental results show that our proposed TCO algorithm outperforms others in terms of less energy cost.


title: Efficient Parallel Skyline Query Processing for High-Dimensional Data

abstract: Given a set of multidimensional data points, skyline queries retrieve those points that are not dominated by any other points in the set. Due to the ubiquitous se of skyline queries, such as in preference-based query answering and decision making, and the large amount of data that these queries have to deal with, enabling their scalable processing is of critical importance. However, there are several outstanding challenges that have not been well addressed. More specifically, in this paper, we are tackling the data straggler and data skew challenges introduced by distributed skyline query processing, as well as the ensuing high computation cost of merging skyline candidates. We thus introduce a new efficient three-phase approach for large scale processing of skyline queries. In the first preprocessing phase, the data is partitioned along the Z-order curve. We utilize a novel data partitioning approach that formulates data partitioning as an optimization problem to minimize the size of intermediate data. In the second phase, each compute node partitions the input data points into disjoint subsets, and then performs the skyline computation on each subset to produce skyline candidates in parallel. In the final phase, we build an index and employ an efficient algorithm to merge the generated skyline candidates. Extensive experiments demonstrate that the proposed skyline algorithm achieves more than one order of magnitude enhancement in performance compared to existing state-of-the-art approaches.


title: System-of-Systems Viewpoint for System Architecture Documentation

abstract: Context: The systems comprising a system of systems (SoS) are inde- pendently acquired, operated, and managed. Frequently, the architecture documentation of these existing systems addresses only a stand-alone perspective, and must be augmented to address concerns that arise in the integrated SoS. Objective: We evaluated an architecture documentation viewpoint to address the concerns of a SoS architect about a constituent system, to support SoS design and analysis involving that constituent system. Method: We performed an expert review of documentation produced by applying the viewpoint to a system, using the active review method. Results: The expert panel was able to used a view constructed using the baseline version of the viewpoint to answer questions related to all SoS architect concerns about a constituent system, except for questions concerning the interaction of the constituent system with the platform and network infrastructure. Conclusions: We found that the expert panel was unable to answer certain questions because the baseline version of the viewpoint had a gap in coverage related to relationship of software units of execution (e.g., processes or services) to computers and networks. The viewpoint was revised to add a Deployment Model to address these concerns, and is included in an appendix.


title: Power-Loss Analysis and Efficiency Maximization of a Silicon-Carbide MOSFET-Based Three-Phase 10-kW Bidirectional EV Charger Using Variable-DC-Bus Control

abstract: It is expected that wide-bandgap devices like silicon-carbide MOSFETs and gallium-nitride HEMTs could replace Si devices in power electronics converters to reach higher system efficiency. This paper adopts the conventional half-bridge LLC topology to realize a 10-kW all-SiC bidirectional charger used in electric vehicles. Though it is a well-known topology for the unidirectional charger, it has not been comprehensively explored for the bidirectional energy flow yet. A double-pulse-test (DPT) platform is utilized to provide accurate power losses. A state-space model is built to obtain accurate switching current waveforms, which is eventually combined with the DPT results to accurately predict the system efficiency. Based on this model, to further enhance the system efficiency, the dc-bus voltage is varied with LLC dc/dc converter running at the resonant frequency through the whole power range. Experimental results validated that the proposed approach could realize the bidirectional power flow. By varying the dc-bus voltage, the V2G and G2V modes reach $\sim 96$ % wall-to-battery efficiency.


title: ABMOF: A Novel Optical Flow Algorithm for Dynamic Vision Sensors

abstract: Dynamic Vision Sensors (DVS), which output asynchronous log intensity change events, have potential applications in high-speed robotics, autonomous cars and drones. The precise event timing, sparse output, and wide dynamic range of the events are well suited for optical flow, but conventional optical flow (OF) algorithms are not well matched to the event stream data. This paper proposes an event-driven OF algorithm called adaptive block-matching optical flow (ABMOF). ABMOF uses time slices of accumulated DVS events. The time slices are adaptively rotated based on the input events and OF results. Compared with other methods such as gradient-based OF, ABMOF can efficiently be implemented in compact logic circuits. Results show that ABMOF achieves comparable accuracy to conventional standards such as Lucas-Kanade (LK). The main contributions of our paper are new adaptive time-slice rotation methods that ensure the generated slices have sufficient features for matching,including a feedback mechanism that controls the generated slices to have average slice displacement within the block search range. An LK method using our adapted slices is also implemented. The ABMOF accuracy is compared with this LK method on natural scene data including sparse and dense texture, high dynamic range, and fast motion exceeding 30,000 pixels per second.The paper dataset and source code are available from http://sensors.ini.uzh.ch/databases.html.


title: A Survey on Consensus Mechanisms and Mining Management in Blockchain Networks.

abstract: The past decade has witnessed the rapid evolution in blockchain technologies, which has attracted tremendous interests from both the research communities and the industry. The blockchain network was originated in the Internet finical sector as a decentralized, immutable ledger system for transactional data ordering. Nowadays, it is envisioned as a powerful backbone/framework for decentralized data processing and data-driven self-organization in flat, open-access networks. In particular, the plausible characteristics of decentralization, immutability and self-organization are primarily owing to the unique decentralized consensus mechanisms introduced by blockchain networks. This survey is motivated by the lack of a comprehensive literature review on the development of decentralized consensus mechanisms in blockchain networks. In this survey, we provide a systematic vision of the organization of blockchain networks. By emphasizing the unique characteristics of incentivized consensus in blockchain networks, our in-depth review of the state-of-the-art consensus protocols is focused on both the perspective of distributed consensus system design and the perspective of incentive mechanism design. From a game-theoretic point of view, we also provide a thorough review on the strategy adoption for self-organization by the individual nodes in the blockchain backbone networks. Consequently, we provide a comprehensive survey on the emerging applications of the blockchain networks in a wide range of areas. We highlight our special interest in how the consensus mechanisms impact these applications. Finally, we discuss several open issues in the protocol design for blockchain consensus and the related potential research directions.


title: SoftMC: Practical DRAM Characterization Using an FPGA-Based Infrastructure

abstract: This paper summarizes the SoftMC DRAM characterization infrastructure, which was published in HPCA 2017, and examines the work's significance and future potential. ::: SoftMC (Soft Memory Controller) is the first publicly-available DRAM testing infrastructure that can flexibly and efficiently test DRAM chips in a manner accessible to both software and hardware developers. SoftMC is an FPGA-based testing platform that can control and test memory modules designed for the commonly-used DDR (Double Data Rate) interface. SoftMC has two key properties: (i) it provides flexibility to thoroughly control memory behavior or to implement a wide range of mechanisms using DDR commands; and (ii) it is easy to use as it provides a simple and intuitive high-level programming interface for users, completely hiding the low-level details of the FPGA. ::: We demonstrate the capability, flexibility, and programming ease of SoftMC with two example use cases. First, we implement a test that characterizes the retention time of DRAM cells. Second, we show that the expected latency reduction of two recently-proposed mechanisms, which rely on accessing recently-refreshed or recently-accessed DRAM cells faster than other DRAM cells, is not observable in existing DRAM chips. ::: Various versions of the SoftMC platform have enabled many of our other DRAM characterization studies. We discuss several other use cases of SoftMC, including the ability to characterize emerging non-volatile memory modules that obey the DDR standard. We hope that our open-source release of SoftMC fills a gap in the space of publicly-available experimental memory testing infrastructures and inspires new studies, ideas, and methodologies in memory system design.


title: 3D Human Pose Estimation = 2D Pose Estimation + Matching

abstract: We explore 3D human pose estimation from a single RGB image. While many approaches try to directly predict 3D pose from image measurements, we explore a simple architecture that reasons through intermediate 2D pose predictions. Our approach is based on two key observations (1) Deep neural nets have revolutionized 2D pose estimation, producing accurate 2D predictions even for poses with self-occlusions (2) Big-datasets of 3D mocap data are now readily available, making it tempting to lift predicted 2D poses to 3D through simple memorization (e.g., nearest neighbors). The resulting architecture is straightforward to implement with off-the-shelf 2D pose estimation systems and 3D mocap libraries. Importantly, we demonstratethatsuchmethodsoutperformalmostallstate-of-theart 3D pose estimation systems, most of which directly try to regress 3D pose from 2D measurements.


Web Based Application for Hindi Question Answering System


Rational Proofs of Space-Time


title: Lightning Surge Analysis on a Large Scale Grid-Connected Solar Photovoltaic System

abstract: Solar photovoltaic (PV) farms currently play a vital role in the generation of electrical power in different countries, such as Malaysia, which is moving toward the use of renewable energy. Malaysia is one of the countries with abundant sunlight and thus can use solar PV farms as alternative sources for electricity generation. However, lightning strikes frequently occur in the country. Being installed in open and flat areas, solar PV farms, especially their electronic components, are at great risk of damage caused by lightning. In this paper, the effects of lightning currents with different peak currents and waveshapes on grid-connected solar PV farms were determined to approximate the level of transient effect that can damage solar PV modules, inverters and transformers. Depending on the location of the solar PV farm, engineer can obtain information on the peak current and median current of the site from the lightning location system (LLS) and utilise the results obtained in this study to appropriately assign an SPD to protect the solar panel, inverter and the main panel that connected to the grid. Therefore, the simulation results serve as the basis for controlling the effects of lightning strikes on electrical equipment and power grids where it provides proper justification on the ‘where to be installed’ and ‘what is the rating’ of the SPD. This judgment and decision will surely reduce the expensive cost of repair and replacement of electrical equipment damages due to the lightning.


title: A complex network based feature extraction for image retrieval

abstract: In this paper, we propose a complex network based low-level feature for image retrieval systems based on the graph representation of the image and the mathematical theory of diffusion over manifolds. We show that the proposed image feature is invariant to non-structural changes on images and performs well in hand written digits classification task. We also show that performance of image retrieval with existing low-level features could be improved by combining with the proposed feature.


title: Geometric Network Comparisons

abstract: Network analysis needs tools to compare networks and assess the significance of differences between networks. We propose a principled statistical approach to network comparison that approximates networks as probability distributions on negatively curved manifolds. We outline the theory, as well as implement the approach on simulated networks, where its accuracy can be confirmed.


title: On the half-Cauchy prior for a global scale parameter

abstract: This paper argues that the half-Cauchy distribution should replace the inverse-Gamma distribution as a default prior for a top-level scale parameter in Bayesian hierarchical models, at least for cases where a proper prior is necessary. Our arguments involve a blend of Bayesian and frequentist reasoning, and are intended to complement the original case made by Gelman (2006) in support of the folded-t family of priors. First, we generalize the half-Cauchy prior to the wider class of hypergeometric inverted-beta priors. We derive expressions for posterior moments and marginal densities when these priors are used for a top-level normal variance in a Bayesian hierarchical model. We go on to prove a proposition that, together with the results for moments and marginals, allows us to characterize the frequentist risk of the Bayes estimators under all global-shrinkage priors in the class. These theoretical results, in turn, allow us to study the frequentist properties of the half-Cauchy prior versus a wide class of alternatives. The half-Cauchy occupies a sensible 'middle ground' within this class: it performs very well near the origin, but does not lead to drastic compromises in other parts of the parameter space. This provides an alternative, classical justification for the repeated, routine use of this prior. We also consider situations where the underlying mean vector is sparse, where we argue that the usual conjugate choice of an inverse-gamma prior is particularly inappropriate, and can lead to highly distorted posterior inferences. Finally, we briefly summarize some open issues in the specification of default priors for scale terms in hierarchical models.


title: Introduction to Computational Advertising

abstract: Web advertising is the primary driving force behind many Web activities, including Internet search as well as publishing of online content by third-party providers. Even though the notion of online advertising barely existed a decade ago, the topic is so complex that it attracts attention of a variety of established scientific disciplines, including computational linguistics, computer science, economics, psychology, and sociology, to name but a few. Consequently, a new discipline --- Computational Advertising --- has emerged, which studies the process of advertising on the Internet from a variety of angles. A successful advertising campaign should be relevant to the immediate user's information need as well as more generally to user's background and personalized interest profile, be economically worthwhile to the advertiser and the intermediaries (e.g., the search engine), as well as be aesthetically pleasant and not detrimental to user experience.


title: The synthesis and rendering of eroded fractal terrains

abstract: In standard fractal terrain models based on fractional Brownian motion the statistical character of the surface is, by design, the same everywhere. A new approach to the synthesis of fractal terrain height fields is presented which, in contrast to previous techniques, features locally independent control of the frequencies composing the surface, and thus local control of fractal dimension and other statistical characteristics. The new technique, termed noise synthesis, is intermediate in difficulty of implementation, between simple stochastic subdivision and Fourier filtering or generalized stochastic subdivision, and does not suffer the drawbacks of creases or periodicity. Varying the local crossover scale of fractal character or the fractal dimension with altitude or other functions yields more realistic first approximations to eroded landscapes. A simple physical erosion model is then suggested which simulates hydraulic and thermal erosion processes to create gloabl stream/valley networks and talus slopes. Finally, an efficient ray tracing algorithm for general height fields, of which most fractal terrains are a subset, is presented.


title: High-Performance Arsine Removal Using CuOx/TiO2 Sorbents under Low-Temperature Conditions

abstract: In this work, copper-based titanium sorbents (CuOx/TiO2) synthesized by sol–gel method were used to remove arsine (AsH3) at low temperatures. The Cu content, calcination temperature, pH, oxygen concentration, and reaction temperature were the five key factors that were determined and analyzed in this work. The optimum breakthrough adsorption capacity for arsine was promising and had the value of 534.3 mg/g for the Cu content of 20 wt %, calcination temperature of 400 °C, pH of 10, oxygen concentration of 2%, and reaction temperature of 120 °C. The chemical and structural features of the initial and arsine-exposed sorbents were analyzed using the Brunauer–Emmett–Teller method, X-ray diffraction analysis, temperature-programmed desorption of carbon dioxide, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, and scanning electron microscopy. It was found that the contents of anatase and rutile TiO2 in sorbents could be regulated by changing the pH of sorbents. Additionally, at pH of 1...