title: Sparse representation of terrains for procedural modeling

abstract: In this paper, we present a simple and efficient method to represent terrains as elevation functions built from linear combinations of landform features (atoms). These features can be extracted either from real world data-sets or procedural primitives, or from any combination of multiple terrain models. Our approach consists in representing the elevation function as a sparse combination of primitives, a concept which we call Sparse Construction Tree, which blends the different landform features stored in a dictionary. The sparse representation allows us to represent complex terrains using combinations of atoms from a small dictionary, yielding a powerful and compact terrain representation and synthesis tool. Moreover, we present a method for automatically learning the dictionary and generating the Sparse Construction Tree model. We demonstrate the efficiency of our method in several applications: inverse procedural modeling of terrains, terrain amplification and synthesis from a coarse sketch.


title: Communication-optimal parallel and sequential QR and LU factorizations

abstract: We present parallel and sequential dense QR factorization algorithms that are both optimal (up to polylogarithmic factors) in the amount of communication they perform, and just as stable as Householder QR. We prove optimality by extending known lower bounds on communication bandwidth for sequential and parallel matrix multiplication to provide latency lower bounds, and show these bounds apply to the LU and QR decompositions. We not only show that our QR algorithms attain these lower bounds (up to polylogarithmic factors), but that existing LAPACK and ScaLAPACK algorithms perform asymptotically more communication. We also point out recent LU algorithms in the literature that attain at least some of these lower bounds.


title: Accuracy and reliability of forensic latent fingerprint decisions

abstract: The interpretation of forensic fingerprint evidence relies on the expertise of latent print examiners. The National Research Council of the National Academies and the legal and forensic sciences communities have called for research to measure the accuracy and reliability of latent print examiners’ decisions, a challenging and complex problem in need of systematic analysis. Our research is focused on the development of empirical approaches to studying this problem. Here, we report on the first large-scale study of the accuracy and reliability of latent print examiners’ decisions, in which 169 latent print examiners each compared approximately 100 pairs of latent and exemplar fingerprints from a pool of 744 pairs. The fingerprints were selected to include a range of attributes and quality encountered in forensic casework, and to be comparable to searches of an automated fingerprint identification system containing more than 58 million subjects. This study evaluated examiners on key decision points in the fingerprint examination process; procedures used operationally include additional safeguards designed to minimize errors. Five examiners made false positive errors for an overall false positive rate of 0.1%. Eighty-five percent of examiners made at least one false negative error for an overall false negative rate of 7.5%. Independent examination of the same comparisons by different participants (analogous to blind verification) was found to detect all false positive errors and the majority of false negative errors in this study. Examiners frequently differed on whether fingerprints were suitable for reaching a conclusion.


title: Target-Dependent Sentiment Classification with Long Short Term Memory.

abstract: Target-dependent sentiment classification remains a challenge: modeling the semantic relatedness of a target with its context words in a sentence. Different context words have different influences on determining the sentiment polarity of a sentence towards the target. Therefore, it is desirable to integrate the connections between target word and context words when building a learning system. In this paper, we develop two target dependent long short-term memory (LSTM) models, where target information is automatically taken into account. We evaluate our methods on a benchmark dataset from Twitter. Empirical results show that modeling sentence representation with standard LSTM does not perform well. Incorporating target information into LSTM can significantly boost the classification accuracy. The target-dependent LSTM models achieve state-of-the-art performances without using syntactic parser or external sentiment lexicons.


title: DUC in context

abstract: Recent years have seen increased interest in text summarization with emphasis on evaluation of prototype systems. Many factors can affect the design of such evaluations, requiring choices among competing alternatives. This paper examines several major themes running through three evaluations: SUMMAC, NTCIR, and DUC, with a concentration on DUC. The themes are extrinsic and intrinsic evaluation, evaluation procedures and methods, generic versus focused summaries, single- and multi-document summaries, length and compression issues, extracts versus abstracts, and issues with genre.


title: Issues in Building General Letter to Sound Rules

abstract: In general text-to-speech systems, it is not possible to guarantee that a lexicon will contain all words found in a text, therefore some system for predicting pronunciation from the word itself is necessary. Here we present a general framework for building letter to sound (LTS) rules from a word list in a language. The technique can be fully automatic, though a small amount of hand seeding can give better results. We have applied this technique to English (UK and US), French and German. The generated models achieve, 75%, 58%, 93% and 89%, respectively, words correct for held out data from the word lists. To test our models on more typical data we also analyzed general text, to find which words do not appear in our lexicon. These unknown words were used as a more realistic test corpus for our models. We also discuss the distribution and type of such unknown words.


title: Combining Human Computing and Machine Learning to Make Sense of Big (Aerial) Data for Disaster Response

abstract: Aerial imagery captured via unmanned aerial vehicles (UAVs) is playing an increasingly important role in disaster response. Unlike satellite imagery, aerial imagery can be captured and processed within hours rather than days. In addition, the spatial resolution of aerial imagery is an order of magnitude higher than the imagery produced by the most sophisticated commercial satellites today. Both the United States Federal Emergency Management Agency (FEMA) and the European Commission's Joint Research Center (JRC) have noted that aerial imagery will inevitably present a big data challenge. The purpose of this article is to get ahead of this future challenge by proposing a hybrid crowdsourcing and real-time machine learning solution to rapidly process large volumes of aerial data for disaster response in a time-sensitive manner. Crowdsourcing can be used to annotate features of interest in aerial images (such as damaged shelters and roads blocked by debris). These human-annotated features can then be used to train a supervised machine learning system to learn to recognize such features in new unseen images. In this article, we describe how this hybrid solution for image analysis can be implemented as a module (i.e., Aerial Clicker) to extend an existing platform called Artificial Intelligence for Disaster Response (AIDR), which has already been deployed to classify microblog messages during disasters using its Text Clicker module and in response to Cyclone Pam, a category 5 cyclone that devastated Vanuatu in March 2015. The hybrid solution we present can be applied to both aerial and satellite imagery and has applications beyond disaster response such as wildlife protection, human rights, and archeological exploration. As a proof of concept, we recently piloted this solution using very high-resolution aerial photographs of a wildlife reserve in Namibia to support rangers with their wildlife conservation efforts (SAVMAP project, http://lasig.epfl.ch/savmap ). The results suggest that the platform we have developed to combine crowdsourcing and machine learning to make sense of large volumes of aerial images can be used for disaster response.


title: A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features

abstract: BACKGROUND ::: Automatic sleep scoring is essential owing to the fact that conventionally a large volume of data have to be analyzed visually by the physicians which is onerous, time-consuming and error-prone. Therefore, there is a dire need of an automated sleep staging scheme. ::: ::: ::: NEW METHOD ::: In this work, we decompose sleep-EEG signal segments using tunable-Q factor wavelet transform (TQWT). Various spectral features are then computed from TQWT sub-bands. The performance of spectral features in the TQWT domain has been determined by intuitive and graphical analyses, statistical validation, and Fisher criteria. Random forest is used to perform classification. Optimal choices and the effects of TQWT and random forest parameters have been determined and expounded. ::: ::: ::: RESULTS ::: Experimental outcomes manifest the efficacy of our feature generation scheme in terms of p-values of ANOVA analysis and Fisher criteria. The proposed scheme yields 90.38%, 91.50%, 92.11%, 94.80%, 97.50% for 6-stage to 2-stage classification of sleep states on the benchmark Sleep-EDF data-set. In addition, its performance on DREAMS Subjects Data-set is also promising. ::: ::: ::: COMPARISON WITH EXISTING METHODS ::: The performance of the proposed method is significantly better than the existing ones in terms of accuracy and Cohen's kappa coefficient. Additionally, the proposed scheme gives high detection accuracy for sleep stages non-REM 1 and REM. ::: ::: ::: CONCLUSIONS ::: Spectral features in the TQWT domain can discriminate sleep-EEG signals corresponding to various sleep states efficaciously. The proposed scheme will alleviate the burden of the physicians, speed-up sleep disorder diagnosis, and expedite sleep research.


title: Injectable, Cellular-Scale Optoelectronics with Applications for Wireless Optogenetics

abstract: Successful integration of advanced semiconductor devices with biological systems will accelerate basic scientific discoveries and their translation into clinical technologies. In neuroscience generally, and in optogenetics in particular, the ability to insert light sources, detectors, sensors, and other components into precise locations of the deep brain yields versatile and important capabilities. Here, we introduce an injectable class of cellular-scale optoelectronics that offers such features, with examples of unmatched operational modes in optogenetics, including completely wireless and programmed complex behavioral control over freely moving animals. The ability of these ultrathin, mechanically compliant, biocompatible devices to afford minimally invasive operation in the soft tissues of the mammalian brain foreshadow applications in other organ systems, with potential for broad utility in biomedical science and engineering.


title: Airgapless Electric Motors With an External Rotor

abstract: This paper proposes a new family of electric motors based on the concept of an airgapless device. In this type of device, the rotor is allowed to touch the stator at a contact point, which maximizes the internal flux and, therefore, the electromagnetic torque. A higher torque density motor is proposed in this paper due to a reduced reluctance caused by zero airgap situation. A comparison with other high-torque-density electric motors demonstrates the advantages of the proposed airgapless motors. The proposed motor will maximize the generated torque, allowing these types of motors to be competitive in applications where hydraulic motors are prevalent, i.e., low-speed and high-torque requirements. Hydraulic motor systems face two major problems with their braking system and with low efficiency due to a large number of energy conversion stages (i.e., a motor pump, hydraulic connections, and the hydraulic motor itself). The proposed electric motor, unlike hydraulic motors, converts electrical energy directly to mechanical energy with no extra braking system necessary and with higher efficiency. The modeling of this machine with a minimum number of poles is discussed before a generalization is presented. A proof-of-concept electric motor with nine magnetic bipoles is built to validate the theoretical assumptions proposed in this paper.


title: An EEG artifact identification embedded system using ICA and multi-instance learning

abstract: Electroencephalogram (EEG) data is used for a variety of purposes, including brain-computer interfaces, disease diagnosis, and determining cognitive states. Yet EEG signals are susceptible to noise from many sources, such as muscle and eye movements, and motion of electrodes and cables. Traditional approaches to this problem involve supervised training to identify signal components corresponding to noise so that they can be removed. However these approaches are artifact specific. In this paper, we present a novel software-hardware system that uses a weak supervisory signal to indicate that some noise is occurring, but not what the source of the noise is or how it is manifested in the EEG signal. The EEG data is decomposed into independent components using ICA, and these components form bags that are labeled and classified by a multi-instance learning algorithm that can identify the noise components for removal to reconstruct a clean EEG signal. We also performed extensive hyperparameter optimization for the model with the goal of improving accuracy without increasing execution time. This resulted the execution time to be reduced from 282 s to 8.8 s when running the model on an embedded ARM CPU processor at 1.6 GHz clock frequency. In this paper, we present the overall system which includes ICA, SAX and MIL, along with preliminary results for software and hardware implementation when using real EEG data from 64 electrodes. The proposed system consumes 909 mW power during processing above a baseline of 2.32 W idle, while achieving 91.2% artifact identification accuracy.


title: Aesthetic Inference for Smart Mobile Devices

abstract: The convenience of cell-phone cameras has made them one of the most common ways by which people document their lives, whether it is everyday pleasures or celebrations. With thousands of images, it might prove to be a daunting task to organize them by hand. When applying automated algorithms to help us, we would like to have both images that are dear to us but are also of good quality. In this paper we explore the performance of the MobileNet CNN architecture, and the different design (inputs size, and layer depth) choices, in their ability in solving various aesthetic inference task: binary classification, regression, image cropping. We show that the baseline MobileNet architecture achieves near state-of-the-art results for binary classification on the AVA dataset while being more than 10 times smaller and compute efficient. We further show that these models, when trained for fine-grained aesthetics inference, achieve better cropping performance than other aestheticsbased croppers.


title: A Survey On Different Approaches Used In Image Quality Assessment

abstract: Image quality assessment means estimating the quality of an image and it is used for many image processing applications. Image quality can be measured in two ways, subjective and objective method. Objective method is more preferable than subjective because most of the time the original image is not available for the comparison and it is not that much expensive like the subjective method. These methods are used to predict the visual quality by comparing a distorted image against a reference image. In this paper we are comparing the different approaches of image quality assessment.


title: UniBic: Sequential row-based biclustering algorithm for analysis of gene expression data

abstract: Biclustering algorithms, which aim to provide an effective and efficient way to analyze gene expression data by finding a group of genes with trend-preserving expression patterns under certain conditions, have been widely developed since Morgan et al. pioneered a work about partitioning a data matrix into submatrices with approximately constant values. However, the identification of general trend-preserving biclusters which are the most meaningful substructures hidden in gene expression data remains a highly challenging problem. We found an elementary method by which biologically meaningful trend-preserving biclusters can be readily identified from noisy and complex large data. The basic idea is to apply the longest common subsequence (LCS) framework to selected pairs of rows in an index matrix derived from an input data matrix to locate a seed for each bicluster to be identified. We tested it on synthetic and real datasets and compared its performance with currently competitive biclustering tools. We found that the new algorithm, named UniBic, outperformed all previous biclustering algorithms in terms of commonly used evaluation scenarios except for BicSPAM on narrow biclusters. The latter was somewhat better at finding narrow biclusters, the task for which it was specifically designed.


title: Influence of fly ash and its mean particle size on certain engineering properties of cement composite mortars

abstract: An experimental investigation on the effects of incorporating large volumes of fly ash on the early engineering properties and long-term strength of masonry mortars is reported. The effect of fly ash and its mean particle size (PD) on the variation of workability and strength has been studied. It was found that fly ash and its mean particle size play a very significant role on the strength of masonry mortars. It has been observed that the early-term strength, except the mortars incorporating coarse fly ash (CFA), was slightly influenced by the replacement with fly ash. The long-term strength (both the bond strength and the compressive strength) will significantly increase, especially for the bond strength of mortars incorporating coarse fly ash. It was also found that the bond strength significantly increased as the mean particle size of fly ash decreases after 28 days curing. However, the 7-day strength was little influenced by fly ash particle size. The fluidity of composite mortar enhanced due to replace cement and lime with fly ash, and the mean PD of fly ash significantly influenced the workability.


title: Evolving Artificial Neural Networks through L-system and evolutionary computation

abstract: The aim of this paper is to present a biologically inspired Neuro Evolutive Algorithm (NEA) able to generate modular, hierarchical and recurrent neural structures as those often found in the nervous system of live beings, and that enable them to solve intricate survival problems. In our approach we consider that a nervous system design and organization is a constructive process carried out by genetic information encoded in DNA. Our NEA evolves Artificial Neural Networks (ANNs) using a Lindenmayer System with memory that implements the principles of organization, modularity, repetition (multiple use of the same sub-structure), hierarchy (recursive composition of sub-structures), as a metaphor for development of neurons and its connections. In our method, this basic neural codification is integrated to a Genetic Algorithm (GA) that implements the constructive approach found in the evolutionary process, making it closest to the biological ones. Our method was initially tested on a simple, but non-trivial, XOR problem. We also submit our method to two other problems of increasing complexity: Prediction of the effect of a new drug on Breast Cancer and KDDCUP'99 benchmark intrusion detection dataset. Some advantages of the proposed methodology are that it increases the level of implicit parallelism of the GA and seems to be capable of generating minimal satisfactory neural architectures, resulting in a reduction of project costs and increasing the performance of the evolved ANN, suggesting a promising potential for future applications.


title: Reversible data hiding in encrypted images using interpolation-based distributed space reservation

abstract: Reversible data hiding (RDH) in encrypted images has attained more attention recently in research community. Privacy protection of additional data as well as cover media makes it attractive for applications in medical imaging, cloud storage, forensics etc. In this paper, a new method for reversible data hiding in encrypted images (RDH-EI), is proposed. Our method adopts the approach of reserving sufficient space for the additional data before encrypting the cover image. First we identify suitable blocks for hiding data from various parts of the image. Before encrypting the image, one or more LSB-planes of these blocks are backed-up into remaining parts of the image using a high-performing traditional RDH method that works on unencrypted images. After encrypting the image, those LSB-planes are used to hide additional data. Recovery of original cover image and error-free extraction of additional data is guaranteed always. Moreover, the proposed method is simple and intuitive. Experimentally results show that our method outperforms the state-of-the-art methods for reversible data hiding in encrypted images.


title: Design of joint torque sensor and joint structure of a robot arm to minimize crosstalk and torque ripple

abstract: Torque control of a robot arm often requires joint torques which are measured using joint torque sensors. However, a joint torque sensor installed at a robot joint suffers from crosstalk errors and torque ripple. The crosstalk error generated by a moment load induces an offset to the torque sensor output. Furthermore, torque ripple noise generated by a harmonic drive interferes with accurate torque measurement as its amplitude is much higher than electric noise. In this study, novel methods are proposed to reduce crosstalk and torque ripple. The crosstalk is dealt with by specially designing the structure of a robot joint. Torque ripple is reduced by taking an average of two Wheatstone bridges outputs. The performance of the proposed robot joint was verified through various experiments.


Correlation optimized warping and dynamic time warping as preprocessing methods for chromatographic data


title: Misperceiving Bullshit as Profound Is Associated with Favorable Views of Cruz, Rubio, Trump and Conservatism

abstract: The present research investigates the associations between holding favorable views of potential Democratic or Republican candidates for the US presidency 2016 and seeing profoundness in bullshit statements. In this contribution, bullshit is used as a technical term which is defined as communicative expression that lacks content, logic, or truth from the perspective of natural science. We used the Bullshit Receptivity scale (BSR) to measure seeing profoundness in bullshit statements. The BSR scale contains statements that have a correct syntactic structure and seem to be sound and meaningful on first reading but are actually vacuous. Participants (N = 196; obtained via Amazon Mechanical Turk) rated the profoundness of bullshit statements (using the BSR) and provided favorability ratings of three Democratic (Hillary Clinton, Martin O'Malley, and Bernie Sanders) and three Republican candidates for US president (Ted Cruz, Marco Rubio, and Donald Trump). Participants also completed a measure of political liberalism/conservatism. Results revealed that favorable views of all three Republican candidates were positively related to judging bullshit statements as profound. The smallest correlation was found for Donald Trump. Although we observe a positive association between bullshit and support for the three Democrat candidates, this relationship is both substantively small and statistically insignificant. The general measure of political liberalism/conservatism was also related to judging bullshit statements as profound in that individuals who were more politically conservative had a higher tendency to see profoundness in bullshit statements. Of note, these results were not due to a general tendency among conservatives to see profoundness in everything: Favorable views of Republican candidates and conservatism were not significantly related to profoundness ratings of mundane statements. In contrast, this was the case for Hillary Clinton and Martin O'Malley. Overall, small-to-medium sized correlations were found, indicating that far from all conservatives see profoundness in bullshit statements.


title: Case-based reasoning for knowledge-intensive template selection during text generation

abstract: The present paper describes a case-based reasoning solution for solving the task of selecting adequate templates for realizing messages describing actions in a given domain. This solution involves the construction of a case base from a corpus of example texts, using information from WordNet to group related verbs together. A case retrieval net is used as a memory model. A taxonomy of the concepts involved in the texts is used to compute similarity between concepts. The set of data to be converted into text acts as a query to the system. The process of solving a given query may involve several retrieval processes – to obtain a set of cases that together constitute a good solution for transcribing the data in the query as text messages – and a process of knowledge-intensive adaptation which resorts to a knowledge base to identify appropriate substitutions and completions for the concepts that appear in the cases, using the query as a source. We describe this case-based solution, and we present examples of how it solves the task of selecting an appropriate set of templates to render a given set of data as text.


title: TetGen, a Delaunay-Based Quality Tetrahedral Mesh Generator

abstract: TetGen is a Cpp program for generating good quality tetrahedral meshes aimed to support numerical methods and scientific computing. The problem of quality tetrahedral mesh generation is challenged by many theoretical and practical issues. TetGen uses Delaunay-based algorithms which have theoretical guarantee of correctness. It can robustly handle arbitrary complex 3D geometries and is fast in practice. The source code of TetGen is freely available. This article presents the essential algorithms and techniques used to develop TetGen. The intended audience are researchers or developers in mesh generation or other related areas. It describes the key software components of TetGen, including an efficient tetrahedral mesh data structure, a set of enhanced local mesh operations (combination of flips and edge removal), and filtered exact geometric predicates. The essential algorithms include incremental Delaunay algorithms for inserting vertices, constrained Delaunay algorithms for inserting constraints (edges and triangles), a new edge recovery algorithm for recovering constraints, and a new constrained Delaunay refinement algorithm for adaptive quality tetrahedral mesh generation. Experimental examples as well as comparisons with other softwares are presented.


title: Electrolyte effects in the adsorption and desorption of a cationic polyacrylamide on cellulose fibers

abstract: Adsorption isotherms for a cationic derivative polyacrylamide (QCPAM) on cellulose fibers were determined as functions of aluminum sulfate (alum) concentration and pH, and QCPAM desorption measurements in alum, NaCl, CaCl2, and LaCl3 were also made. The isotherms had a steep, but not infinite, initial slope leading to a rounded knee followed by a linear interval with a small positive slope. Polymer adsorption increased with pH due to increased dissociation of cellulose carboxyl groups. Aluminum sorption made the electrophoretic mobility of the fines less negative and lowered the capacity of the fibers to adsorb QCPAM. Exposure of QCPAM-saturated fibers to electrolyte solution resulted in polymer desorption, most of which occurred within 1 h. Most desorption in alum occurred at pH 4 with less at pH 4.5 and very little at pH 5. The molarities of LaCl3, CaCl2, and NaCl required to desorb 50% of the adsorbed QCPAM were 6.0 × 10−4, 2.1 × 10−3, and 8.3 × 10−3 M, respectively.


title: Patient Safety Climate in US Hospitals: Variation by Management Level

abstract: BACKGROUND ::: Strengthening hospital safety culture offers promise for reducing adverse events, but efforts to improve culture may not succeed if hospital managers perceive safety differently from frontline workers. ::: ::: ::: OBJECTIVES ::: To determine whether frontline workers and supervisors perceive a more negative patient safety climate (ie, surface features, reflective of the underlying safety culture) than senior managers in their institutions. To ascertain patterns of variation within management levels by professional discipline. ::: ::: ::: RESEARCH DESIGN ::: A safety climate survey was administered from March 2004 to May 2005 in 92 US hospitals. Individual-level cross sectional comparisons related safety climate to management level. Hierarchical and hospital-fixed effects modeling tested differences in perceptions. ::: ::: ::: SUBJECTS ::: Random sample of hospital personnel (18,361 respondents). ::: ::: ::: MEASURES ::: Frequency of responses indicating absence of safety climate (percent problematic response) overall and for 8 survey dimensions. ::: ::: ::: RESULTS ::: Frontline workers' safety climate perceptions were 4.8 percentage points (1.4 times) more problematic than were senior managers', and supervisors' perceptions were 3.1 percentage points (1.25 times) more problematic than were senior managers'. Differences were consistent among 7 safety climate dimensions. Differences by management level depended on discipline: senior manager versus frontline worker discrepancies were less pronounced for physicians and more pronounced for nurses, than they were for other disciplines. ::: ::: ::: CONCLUSIONS ::: Senior managers perceived patient safety climate more positively than nonsenior managers overall and across 7 discrete safety climate domains. Patterns of variation by management level differed by professional discipline. Continuing efforts to improve patient safety should address perceptual differences, both among and within groups by management level.


title: A framework for classifying and comparing software architecture evaluation methods

abstract: Software architecture evaluation has been proposed as a means to achieve quality attributes such as maintainability and reliability in a system. The objective of the evaluation is to assess whether or not the architecture lead to the desired quality attributes. Recently, there have been a number of evaluation methods proposed. There is, however, little consensus on the technical and nontechnical issues that a method should comprehensively address and which of the existing methods is most suitable for a particular issue. We present a set of commonly known but informally described features of an evaluation method and organizes them within a framework that should offer guidance on the choice of the most appropriate method for an evaluation exercise. We use this framework to characterise eight SA evaluation methods.


title: Is trust robust?: an analysis of trust-based recommendation

abstract: Systems that adapt to input from users are susceptible to attacks from those same users. Recommender systems are common targets for such attacks since there are financial, political and many other motivations for influencing the promotion or demotion of recommendable items [2].Recent research has shown that incorporating trust and reputation models into the recommendation process can have a positive impact on the accuracy and robustness of recommendations. In this paper we examine the effect of using five different trust models in the recommendation process on the robustness of collaborative filtering in an attack situation. In our analysis we also consider the quality and accuracy of recommendations. Our results caution that including trust models in recommendation can either reduce or increase prediction shift for an attacked item depending on the model-building process used, while highlighting approaches that appear to be more robust.


title: People tracking in multi-camera systems: a review

abstract: Ubiquitousness of multiple cameras in surveillance systems is very beneficial for studying peoples behavior. The multiple views of the observed scene permit the management of dynamic occlusions and failures that may affect any sensor. The multi-camera tracking of objects is considered as the basic step in the design of intelligent surveillance applications. This thematic had been addressed in several researches. Various methods had been proposed to achieve an accurate tracking in the most challenging conditions as occlusions and lighting variations. These methods are addressed in two main research lines: the centralized and the distributed tracking approaches. In this paper, we propose an overview of the multi-camera tracking of objects which summarizes and classifies the most used existing methods.


title: A survey of memory deduplication approaches for intelligent urban computing

abstract: Limited memory size is considered as a major bottleneck in data centers for intelligent urban computing. It is shown that there exist a large number of duplicated pages when various processes working with big data are hosted in data centers. Memory deduplication aims to automatically eliminate duplicate data in memory. It is an efficient technique to reduce memory requirement. In memory deduplication, pages with same content are detected and merged into a single copy. Recently, several system-level techniques have been proposed to address this issue, in which content-based page sharing (CBPS) is most widely used, since CBPS can be performed transparently in the hypervisor layer without any modification to guest operating systems of data center. In this paper, we survey several techniques for memory deduplication. We also classify these techniques based on their characteristics to highlight their similarities and differences. The aim of this paper is to provide insights to researchers into working of memory deduplication techniques and also to motivate them to propose better intelligent urban computing systems.


title: Crowdsourced Comprehension: Predicting Prerequisite Structure in Wikipedia

abstract: The growth of open-access technical publications and other open-domain textual information sources means that there is an increasing amount of online technical material that is in principle available to all, but in practice, incomprehensible to most. We propose to address the task of helping readers comprehend complex technical material, by using statistical methods to model the "prerequisite structure" of a corpus --- i.e., the semantic impact of documents on an individual reader's state of knowledge. Experimental results using Wikipedia as the corpus suggest that this task can be approached by crowd-sourcing the production of ground-truth labels regarding prerequisite structure, and then generalizing these labels using a learned classifier which combines signals of various sorts. The features that we consider relate pairs of pages by analyzing not only textual features of the pages, but also how the containing corpora is connected and created.


title: Deep learning for automatic cell detection in wide-field microscopy zebrafish images

abstract: The zebrafish has become a popular experimental model organism for biomedical research. In this paper, a unique framework is proposed for automatically detecting Tyrosine Hydroxylase-containing (TH-labeled) cells in larval zebrafish brain z-stack images recorded through the wide-field microscope. In this framework, a supervised max-pooling Convolutional Neural Network (CNN) is trained to detect cell pixels in regions that are preselected by a Support Vector Machine (SVM) classifier. The results show that the proposed deep-learned method outperforms hand-crafted techniques and demonstrate its potential for automatic cell detection in wide-field microscopy z-stack zebrafish images.


title: Power-Efficient Resource Allocation for MC-NOMA with Statistical Channel State Information

abstract: In this paper, we study the power-efficient resource allocation for multicarrier non-orthogonal multiple access (MC-NOMA) systems. The resource allocation algorithm design is formulated as a non-convex optimization problem which takes into account the statistical channel state information at transmitter and quality of service (QoS) constraints. To strike a balance between system performance and computational complexity, we propose a suboptimal power allocation and user scheduling with low computational complexity to minimize the total power consumption. The proposed design exploits the heterogeneity of QoS requirement to determine the successive interference cancellation decoding order. Simulation results demonstrate that the proposed scheme achieves a close-to-optimal performance and significantly outperforms a conventional orthogonal multiple access (OMA) scheme.


title: High frequency ring oscillator using capacitor based level shift circuits

abstract: This paper proposes a novel high frequency ring oscillator. The proposed ring oscillator consists of CMOS inverters which use simple capacitor based level shift circuits. The level shift circuit consists of only a capacitor and a MOSFET. Its power consumption is ideally zero. Thanks to the level shift circuits, transconductance and drain conductance of MOSFETs in CMOS inverters increase and high oscillation frequency is achieved. Validity of the proposed method is confirmed by simulations. The simulation results show that oscillation frequency of the proposed circuit is 2.7 times as high as that of conventional circuit.


title: A stochastic neighborhood search approach for airport gate assignment problem

abstract: An appropriate and efficient gate assignment is of great importance in airports since it plays a major role in the revenue obtained from the airport operations. In this study, we have focused mainly on maximum gate employment, or in other words minimize the total duration of un-gated flights. Here, we propose a method that combines the benefits of heuristic approaches with some stochastic approach instead of using a purely probabilistic approach to top-down solution of the problem. The heuristic approaches are usually used in order to provide a fast solution of the problem and later stochastic searches are used in order to ameliorate the previous results of the heuristic approach whenever possible. The proposed method generates an assignment order for the whole planes that corresponds to assignment priority. The ordering process is followed by the allocation step. Since, in practice, each airport has its own physical architecture, there have been arisen many constraints mainly concerning airplane types and parking lots in this step. Sequentially handling the plane ordering and allocation phases provides us great modularity in handling the constraints. The effectiveness of the proposed methodology has been tried to be illustrated firstly on fictively generated flight schedule data and secondly on the real world data obtained from a real world application developed for Istanbul Ataturk Airport.


title: Analogical mapping by constraint satisfaction

abstract: A theory of analogical mapping between source and target analogs based upon interacting structural, semantic, and pragmatic constraints is proposed here. The structural constraint of isomorphism encourages mappings that maximize the consistency of relational corresondences between the elements of the two analogs. The constraint of semantic similarity supports mapping hypotheses to the degree that mapped predicates have similar meanings. The constraint of pragmatic centrality favors mappings involving elements the analogist believes to be important in order to achieve the purpose for which the analogy is being used. The theory is implemented in a computer program called ACME (Analogical Constraint Mapping Engine), which represents constraints by means of a network of supporting and competing hypotheses regarding what elements to map. A cooperative algorithm for parallel constraint satisfaction identities mapping hypotheses that collectively represent the overall mapping that best fits the interacting constraints. ACME has been applied to a wide range of examples that include problem analogies, analogical arguments, explanatory analogies, story analogies, formal analogies, and metaphors. ACME is sensitive to semantic and pragmatic information if it is available, and yet able to compute mappings between formally isomorphic analogs without any similar or identical elements. The theory is able to account for empirical findings regarding the impact of consistency and similarity on human processing of analogies.


title: PSYCHOLOGICAL SCIENCE Research Article CRITICAL EVIDENCE: A Test of the Critical-Period Hypothesis for Second-Language Acquisition

abstract: The critical-period hypothesis for second-language acqui- sition was tested on data from the 1990 U.S. Census using responses from 2.3 million immigrants with Spanish or Chinese language back- grounds. The analyses tested a key prediction of the hypothesis, namely, that the line regressing second-language attainment on age of immigration would be markedly different on either side of the critical- age point. Predictions tested were that there would be a difference in slope, a difference in the mean while controlling for slope, or both. The results showed large linear effects for level of education and for age of immigration, but a negligible amount of additional variance was accounted for when the parameters for difference in slope and difference in means were estimated. Thus, the pattern of decline in second-language acquisition failed to produce the discontinuity that is an essential hallmark of a critical period.


title: Developing multiagent systems: The Gaia methodology

abstract: Systems composed of interacting autonomous agents offer a promising software engineering approach for developing applications in complex domains. However, this multiagent system paradigm introduces a number of new abstractions and design/development issues when compared with more traditional approaches to software development. Accordingly, new analysis and design methodologies, as well as new tools, are needed to effectively engineer such systems. Against this background, the contribution of this article is twofold. First, we synthesize and clarify the key abstractions of agent-based computing as they pertain to agent-oriented software engineering. In particular, we argue that a multiagent system can naturally be viewed and architected as a computational organization, and we identify the appropriate organizational abstractions that are central to the analysis and design of such systems. Second, we detail and extend the Gaia methodology for the analysis and design of multiagent systems. Gaia exploits the aforementioned organizational abstractions to provide clear guidelines for the analysis and design of complex and open software systems. Two representative case studies are introduced to exemplify Gaia's concepts and to show its use and effectiveness in different types of multiagent system.


Simulated Binary Crossover for Continuous Search Space.


title: Perfect Fingerprint Orientation Fields by Locally Adaptive Global Models

abstract: Fingerprint recognition is widely used for verification and identification in many commercial, governmental and forensic applications. The orientation field (OF) plays an important role at various processing stages in fingerprint recognition systems. OFs are used for image enhancement, fingerprint alignment, for fingerprint liveness detection, fingerprint alteration detection and fingerprint matching. In this study, a novel approach is presented to globally model an OF combined with locally adaptive methods. The authors show that this model adapts perfectly to the ‘true OF’ in the limit. This perfect OF is described by a small number of parameters with straightforward geometric interpretation. Applications are manifold: Quick expert marking of very poor quality (for instance latent) OFs, high-fidelity low parameter OF compression and a direct road to ground truth OFs markings for large databases, say. In this contribution, they describe an algorithm to perfectly estimate OF parameters automatically or semi-automatically, depending on image quality, and they establish the main underlying claim of high-fidelity low parameter OF compression.


title: Internet visual media processing: a survey with graphics and vision applications

abstract: In recent years, the computer graphics and computer vision communities have devoted significant attention to research based on Internet visual media resources. The huge number of images and videos continually being uploaded by millions of people have stimulated a variety of visual media creation and editing applications, while also posing serious challenges of retrieval, organization, and utilization. This article surveys recent research as regards processing of large collections of images and video, including work on analysis, manipulation, and synthesis. It discusses the problems involved, and suggests possible future directions in this emerging research area.


title: Use of machine learning techniques for educational proposes: a decision support system for forecasting students’ grades

abstract: Use of machine learning techniques for educational proposes (or educational data mining) is an emerging field aimed at developing methods of exploring data from computational educational settings and discovering meaningful patterns. The stored data (virtual courses, e-learning log file, demographic and academic data of students, admissions/registration info, and so on) can be useful for machine learning algorithms. In this article, we cite the most current articles that use machine learning techniques for educational proposes and we present a case study for predicting students' marks. Students' key demographic characteristics and their marks in a small number of written assignments can constitute the training set for a regression method in order to predict the student's performance. Finally, a prototype version of software support tool for tutors has been constructed.


title: The problem of information overload in business organisations: a review of the literature

abstract: This paper reviews the literature on the problem of information overload, with particular reference to business organisations. The literature reveals that although the problem of information overload has existed for many years, in recent years the problem has become more widely recognised and experienced. Both perceptions and the actual effects of information overload have been exacerbated by the rapid advances made in information and communication technology, although it is not clear cut as to whether the Internet has worsened or improved the situation. A theme stressed in the literature is the paradoxical situation that, although there is an abundance of information available, it is often difficult to obtain useful, relevant information when it is needed. Some solutions put forward to reduce information overload are: a reduction in the duplication of information found in the professional literature; the adoption of personal information management strategies, together with the integration of software solutions such as push technology and intelligent agents; and the provision of value-added information (filtered by software or information specialists). An emphasis is placed on technology as a tool and not the driver, while increased information literacy may provide the key to reducing information overload.


title: Comparison of various methods for handling incomplete data in software engineering databases

abstract: Increasing the awareness of how missing data affects software predictive accuracy has led to increasing numbers of missing data techniques (MDTs). This paper investigates the robustness and accuracy of eight popular techniques for tolerating incomplete training and test data using tree-based models. MDTs were compared by artificially simulating different proportions, patterns, and mechanisms of missing data. A 4-way repeated measures design was employed to analyze the data. The simulation results suggest important differences. Listwise deletion is substantially inferior while multiple imputation (MI) represents a superior approach to handling missing data. Decision tree single imputation and surrogate variables splitting are more severely impacted by missing values distributed among all attributes. MI should be used if the data contain many missing values. If few values are missing, any of the MDTs might be considered. Choice of technique should be guided by pattern and mechanisms of missing data.


title: Discovering data quality rules

abstract: Dirty data is a serious problem for businesses leading to incorrect decision making, inefficient daily operations, and ultimately wasting both time and money. Dirty data often arises when domain constraints and business rules, meant to preserve data consistency and accuracy, are enforced incompletely or not at all in application code. ::: ::: In this work, we propose a new data-driven tool that can be used within an organization's data quality management process to suggest possible rules, and to identify conformant and non-conformant records. Data quality rules are known to be contextual, so we focus on the discovery of context-dependent rules. Specifically, we search for conditional functional dependencies (CFDs), that is, functional dependencies that hold only over a portion of the data. The output of our tool is a set of functional dependencies together with the context in which they hold (for example, a rule that states for CS graduate courses, the course number and term functionally determines the room and instructor). Since the input to our tool will likely be a dirty database, we also search for CFDs that almost hold. We return these rules together with the non-conformant records (as these are potentially dirty records). ::: ::: We present effective algorithms for discovering CFDs and dirty values in a data instance. Our discovery algorithm searches for minimal CFDs among the data values and prunes redundant candidates. No universal objective measures of data quality or data quality rules are known. Hence, to avoid returning an unnecessarily large number of CFDs and only those that are most interesting, we evaluate a set of interest metrics and present comparative results using real datasets. We also present an experimental study showing the scalability of our techniques.


title: Can you tell the difference between DL-Lite ontologies

abstract: We develop a formal framework for comparing different versions of DL-Lite ontologies. Four notions of difference and entailment between ontologies are introduced and their applications in ontology development and maintenance discussed. These notions are obtained by distinguishing between differences that can be observed among concept inclusions, answers to queries over ABoxes, and by taking into account additional context ontologies. We compare these notions, study their meta-properties, and determine the computational complexity of the corresponding reasoning tasks. Moreover, we show that checking difference and entailment can be automated by means of encoding into QBF satisfiability and using off-the-shelf QBF solvers. Finally, we explore the relationship between the notion of forgetting (or uniform interpolation) and our notions of difference between ontologies.


The solution for the branching factor of the alpha-beta pruning algorithm and its optimality


title: Geo-spatial event detection in the twitter stream

abstract: The rise of Social Media services in the last years has created huge streams of information that can be very valuable in a variety of scenarios. What precisely these scenarios are and how the data streams can efficiently be analyzed for each scenario is still largely unclear at this point in time and has therefore created significant interest in industry and academia. In this paper, we describe a novel algorithm for geo-spatial event detection on Social Media streams. We monitor all posts on Twitter issued in a given geographic region and identify places that show a high amount of activity. In a second processing step, we analyze the resulting spatio-temporal clusters of posts with a Machine Learning component in order to detect whether they constitute real-world events or not. We show that this can be done with high precision and recall. The detected events are finally displayed to a user on a map, at the location where they happen and while they happen.


title: Compositional program synthesis from natural language and examples

abstract: Compositionality is a fundamental notion in computation whereby complex abstractions can be constructed from simpler ones, yet this property has so far escaped the paradigm of end-user programming from examples or natural language. Existing approaches restrict end users to only give holistic specifications of tasks, which limits the expressivity and scalability of these approaches to relatively simple programs in very restricted domains. In this paper we propose Compositional Program Synthesis (CPS): an approach in which tasks can be specified in a compositional manner through a combination of natural language and examples. We present a domain-agnostic program synthesis algorithm and demonstrate its application to an expressive string manipulation language. We evaluate our approach on complex tasks from online help forums that are beyond the scope of current state-ofthe-art methods.


title: The Indian Buffet Process: An Introduction and Review

abstract: The Indian buffet process is a stochastic process defining a probability distribution over equivalence classes of sparse binary matrices with a finite number of rows and an unbounded number of columns. This distribution is suitable for use as a prior in probabilistic models that represent objects using a potentially infinite array of features, or that involve bipartite graphs in which the size of at least one class of nodes is unknown. We give a detailed derivation of this distribution, and illustrate its use as a prior in an infinite latent feature model. We then review recent applications of the Indian buffet process in machine learning, discuss its extensions, and summarize its connections to other stochastic processes.


title: Generative and Discriminative Algorithms for Spoken Language Understanding

abstract: Spoken Language Understanding (SLU) for conversational systems (SDS) aims at extracting concept and their relations from spontaneous speech. Previous approaches to SLU have modeled concept relations as stochastic semantic networks ranging from generative approach to discriminative. As spoken dialog systems complexity increases, SLU needs to perform understanding based on a richer set of features ranging from a-priori knowledge, long dependency, dialog history, system belief, etc. This paper studies generative and discriminative approaches to modeling the sentence segmentation and concept labeling. We evaluate algorithms based on Finite State Transducers (FST) as well as discriminative algorithms based on Support Vector Machine sequence classifier based and Conditional Random Fields (CRF). We compare them in terms of concept accuracy, generalization and robustness to annotation ambiguities. We also show how non-local non-lexical features (e.g. a-priori knowledge) can be modeled with CRF which is the best performing algorithm across tasks. The evaluation is carried out on two SLU tasks of different complexity, namely ATIS and MEDIA corpora.


title: Dry Gangrene of the Extremities Associated with Salmonella dublin Infection in a Calf

abstract: 3 Abstract: An orphan disease was observed in a young calf presenting dry gangrene of the extremities with necrosis of the hind legs, ears and tail. Careful clinical examination and differential diagnosis with other causes of dry gangrene have been made including frostbite, bovine virus diarrhea and poisoning by fescue and ergotism. Deep investigation and laboratory research revealed the occurrence of Salmonella infection and further identification according to ISO 6579 method in calf feces and visceras led to isolation of Salmonella sp subsequently serotyped as Salmonella dublin. A relationship has been suggested between the occurrence of this disease and the cold agglutination associated with the autoimmune hemolytic anemia leading to ischemia of tissues of the terminal extremities.


title: Deep Canonical Time Warping for Simultaneous Alignment and Representation Learning of Sequences

abstract: Machine learning algorithms for the analysis of time-series often depend on the assumption that utilised data are temporally aligned. Any temporal discrepancies arising in the data is certain to lead to ill-generalisable models, which in turn fail to correctly capture properties of the task at hand. The temporal alignment of time-series is thus a crucial challenge manifesting in a multitude of applications. Nevertheless, the vast majority of algorithms oriented towards temporal alignment are either applied directly on the observation space or simply utilise linear projections-thus failing to capture complex, hierarchical non-linear representations that may prove beneficial, especially when dealing with multi-modal data (e.g., visual and acoustic information). To this end, we present Deep Canonical Time Warping (DCTW), a method that automatically learns non-linear representations of multiple time-series that are (i) maximally correlated in a shared subspace, and (ii) temporally aligned. Furthermore, we extend DCTW to a supervised setting, where during training, available labels can be utilised towards enhancing the alignment process. By means of experiments on four datasets, we show that the representations learnt significantly outperform state-of-the-art methods in temporal alignment, elegantly handling scenarios with heterogeneous feature sets, such as the temporal alignment of acoustic and visual information.


title: Malware clustering using image processing hashes

abstract: Malware clustering is a big challenge and because of the large amount of samples, it is a problem in Big Data processing. Since the number of Malwares is huge, general clustering algorithms are not applicable to Malware clustering for its time complexity. The time complexity is high since we need to compute similarity between every pair of samples that costs O (n2). In this paper, we apply image processing perceptual hashing methods that do not need any comparison between samples and are really fast and applicable to a huge number of samples. Comparison results of our method to other methods indicate better scalability and good accuracy.


title: A high voltage Dickson charge pump in SOI CMOS

abstract: An improved charge pump that utilizes a MOSFET body diode as a charge transfer switch is discussed. The body diode is characterized and a body diode model is developed for simulating the charge pump circuit. An increase in voltage pumping gain for a silicon-on-insulator (SOI) Dickson charge pump is demonstrated when compared with a traditional bulk CMOS Dickson charge pump. A 6-stage Dickson charge pump was designed to produce a 20 V output from a 3.3 V supply, using a 4 MHz, two-phase non-overlapping clock signal driving the charge pump. The design was fabricated in a 0.35 /spl mu/m partially depleted SOI CMOS process. An efficiency of 72% is achieved at a load current of approximately 20 /spl mu/A.


title: Optimal Variable Weighting for Ultrametric and Additive Trees and K-means Partitioning: Methods and Software

abstract: -means partitioning. We also describe some new features and improvements to the algorithm proposed by De Soete. Monte Carlo simulations have been conducted using different error conditions. In all cases (i.e., ultrametric or additive trees, or K-means partitioning), the simulation results indicate that the optimal weighting procedure should be used for analyzing data containing noisy variables that do not contribute relevant information to the classification structure. However, if the data involve error-perturbed variables that are relevant to the classification or outliers, it seems better to cluster or partition the entities by using variables with equal weights. A new computer program, OVW, which is available to researchers as freeware, implements improved algorithms for optimal variable weighting for ultrametric and additive tree clustering, and includes a new algorithm for optimal variable weighting for K-means partitioning.


title: Object volume estimation based on 3D point cloud

abstract: An approach to estimating the volume of an object based on 3D point cloud is proposed in this paper. Firstly, 3D point cloud is cut into slices of equal thickness along the z-axis. Bisect each slice along the y-axis and cut each slice into sub-intervals along the x-axis. Using the minimum and maximum coordinates in each sub-interval, two surface curve functions can be fitted accordingly for the bisected slices. Then, the curves are integrated to estimate the area of each slice and further integrated along the z-axis for an estimated volume of the object.


title: Comparison between Incompressible SPH Solvers

abstract: In this paper, we present two high performing and efficient Smoothed Particle Hydrodynamics (SPH) methods used to physically simulate incompressible fluids: Implicit Incompressible SPH (IISPH) and Divergence-Free SPH (DFSPH). At the moment, the most stable SPH methods employ density correctors that reduce the density error of the fluid particles to achieve very low levels of compressibility. We analyze the performance of the correctors used for the methods previously mentioned. We also compare IISPH against DFSPH to study several characteristics such as the stability of the particles, the compressibility levels of the fluid achieved during the simulations, the execution speed of each corrector, the maximum possible increase of the time steps, etc.


title: Reservoir Competence of Native North American Birds for the Lyme Disease Spirochete, Borrelia burgdorferi

abstract: Abstract Reservoir competence for the Lyme disease spirochete, Borrelia burgdorferi, was tested for six species of native North American birds: American robin, gray catbird, brown thrasher, eastern towhee, song sparrow, and northern cardinal. Wild birds collected by mist netting on Fire Island, NY, were held in a field laboratory in cages over water and locally collected larval ticks were placed on the birds, harvested from the water after engorgement, and tested for infection by direct fluorescent-antibody staining after molting to the nymphal stage. American robins were competent reservoirs, infecting 16.1% of larvae applied to wild-caught birds, compared with 0% of control ticks placed on uninfected laboratory mice. Robins that were previously infected in the laboratory by nymphal feeding infected 81.8% of applied larvae. Wild-caught song sparrows infected 4.8% of applied larvae and 21.1% when infected by nymphal feeding. Results suggest moderate levels of reservoir competence for northern cardinals, lower levels for gray catbirds, and little evidence of reservoir competence for eastern towhees or brown thrashers. Lower infection rates in larvae applied to wild-caught birds compared with birds infected in the laboratory suggest that infected birds display temporal variability in infectiousness to larval ticks. Engorged larvae drop from birds abundantly during daylight, so the abundance of these bird species in the peridomestic environment suggests that they might contribute infected ticks to lawns and gardens.


Understanding Norm Change : An Evolutionary Game-Theoretic Study


title: Structuring Legacy Pathology Reports by openEHR Archetypes to Enable Semantic Querying.

abstract: BACKGROUND ::: Clinical information is often stored as free text, e.g. in discharge summaries or pathology reports. These documents are semi-structured using section headers, numbered lists, items and classification strings. However, it is still challenging to retrieve relevant documents since keyword searches applied on complete unstructured documents result in many false positive retrieval results. ::: ::: ::: OBJECTIVES ::: We are concentrating on the processing of pathology reports as an example for unstructured clinical documents. The objective is to transform reports semi-automatically into an information structure that enables an improved access and retrieval of relevant data. The data is expected to be stored in a standardized, structured way to make it accessible for queries that are applied to specific sections of a document (section-sensitive queries) and for information reuse. ::: ::: ::: METHODS ::: Our processing pipeline comprises information modelling, section boundary detection and section-sensitive queries. For enabling a focused search in unstructured data, documents are automatically structured and transformed into a patient information model specified through openEHR archetypes. The resulting XML-based pathology electronic health records (PEHRs) are queried by XQuery and visualized by XSLT in HTML. ::: ::: ::: RESULTS ::: Pathology reports (PRs) can be reliably structured into sections by a keyword-based approach. The information modelling using openEHR allows saving time in the modelling process since many archetypes can be reused. The resulting standardized, structured PEHRs allow accessing relevant data by retrieving data matching user queries. ::: ::: ::: CONCLUSIONS ::: Mapping unstructured reports into a standardized information model is a practical solution for a better access to data. Archetype-based XML enables section-sensitive retrieval and visualisation by well-established XML techniques. Focussing the retrieval to particular sections has the potential of saving retrieval time and improving the accuracy of the retrieval.


title: Activity-Based Sleep-Wake Identification: An Empirical Test of Methodological Issues

abstract: The effects of actigraph placement and device sensitivity on actigraphic automatic sleep-wake scoring were assessed using concomitant polysomnographic and wrist actigraphic data from dominant and nondominant hands of 20 adults and 16 adolescents during 1 laboratory night. Although activity levels differed between dominant and nondominant wrists during periods of sleep (F = 4.57; p < 0.05) and wake (F = 15.5; p < 0.0005), resulting sleep-wake scoring algorithms were essentially the same and were equally explanatory (R2 = 0.64; p < 0.0001). When the sleep-wake scoring algorithm derived from the nondominant hand was used to score the nondominant data for sleep-wake, overall agreement rates with polysomnography scoring ranged between 91 and 93% for the calibration and validation samples. Results obtained with the same algorithm for the dominant-wrist data were within the same range. Agreement for sleep scoring was consistently higher than for wake scoring. Statistical manipulation of activity levels before applying the scoring algorithm indicated that this algorithm is quite robust toward moderate changes in activity level. Use of "twin-wrist actigraphy" enables identification of artifacts that may result from breathing-related motions.


title: Snooping and Sexting : Digital Media as a Context for Dating Aggression and Abuse Among College Students

abstract: Digital dating abuse (DDA) is a pattern of behaviors that control, pressure, or threaten a dating partner using a cell phone or the Internet. A survey of 365 college students was conducted, finding that digital monitoring behaviors were especially common. There were no gender differences in number of DDA behaviors experienced, but women reported more negative hypothetical reactions to sexual messaging than men. DDA was associated with measures of physical, sexual, and psychological dating violence. Results suggest that digital media are a context for potentially harmful dating behaviors, and the experience of DDA may differ by gender for sexual behaviors.


title: Games and Agents: Designing Intelligent Gameplay

abstract: There is an attention shift within the gaming industry toward more natural (long-term) behavior of nonplaying characters (NPCs). Multiagent system research offers a promising technology to implement cognitive intelligent NPCs. However, the technologies used in game engines and multiagent platforms are not readily compatible due to some inherent differences of concerns. Where game engines focus on real-time aspects and thus propagate efficiency and central control, multiagent platforms assume autonomy of the agents. Increased autonomy and intelligence may offer benefits for a more compelling gameplay and may even be necessary for serious games. However, it raises problems when current game design techniques are used to incorporate state-of-the-art multiagent system technology. In this paper, we will focus on three specific problem areas that arise from this difference of view: synchronization, information representation, and communication. We argue that the current attempts for integration still fall short on some of these aspects. We show that to fully integrate intelligent agents in games, one should not only use a technical solution, but also a design methodology that is amenable to agents. The game design should be adjusted to incorporate the possibilities of agents early on in the process.


Similarity metrics for clustering PubMed abstracts for evidence based medicine


title: How do Team Leaders Communicate to Develop Situation Awareness?: An Exploratory Study in a Nuclear Submarine Simulator

abstract: This exploratory study aimed to identify how team leaders' verbal statements support the development of team situation awareness, in a highly dynamic and risky environment. The study was conducted during seven training sessions in a nuclear submarine simulator. Trainees were Diving-Safety Teams, who are responsible for controlling the ship and ensuring safety. Team leaders' verbal statements were coded according to Endsley's model of situation awareness (SA) in order to determine their underlying intentions. Results showed that operational performance improved when team leaders shared their understanding of the current situation and its potential evolution. They also highlighted the importance of sharing evaluations of data, actions and goals. The key finding was that two-thirds of verbal statements expressed by team leaders support the development of SA. This confirms its crucial role in the context of complex sociotechnical systems in risky environments.


title: Automatic Image Cropping using Visual Composition, Boundary Simplicity and Content Preservation Models

abstract: Cropping is one of the most common tasks in image editing for improving the aesthetic quality of a photograph. In this paper, we propose a new, aesthetic photo cropping system which combines three models: visual composition, boundary simplicity, and content preservation. The visual composition model measures the quality of composition for a given crop. Instead of manually defining rules or score functions for composition, we learn the model from a large set of well-composed images via discriminative classifier training. The boundary simplicity model measures the clearness of the crop boundary to avoid object cutting-through. The content preservation model computes the amount of salient information kept in the crop to avoid excluding important content. By assigning a hard lower bound constraint on the content preservation and linearly combining the scores from the visual composition and boundary simplicity models, the resulting system achieves significant improvement over recent cropping methods in both quantitative and qualitative evaluation.


title: Children learning with a social robot

abstract: We used a social robot as a teaching assistant in a class for children's collaborative learning. In the class, a group of 6th graders learned together using Lego Mindstorms. The class consisted of seven lessons with Robovie, a social robot, followed by one lesson to test their individual achievement. Robovie managed the class and explained how to use Lego Mindstorms. In addition to such basic management behaviors for the class, we prepared social behaviors for building relationships with the children and encouraging them. The result shows that the social behavior encouraged children to work more in the first two lessons, but did not affect them in later lessons. On the other hand, social behavior contributed to building relationships and attaining better social acceptance.


title: Disassortative Degree Mixing and Information Diffusion for Overlapping Community Detection in Social Networks (DMID)

abstract: In this paper we propose a new two-phase algorithm for overlapping community detection (OCD) in social networks. In the first phase, called disassortative degree mixing, we identify nodes with high degrees through a random walk process on the row-normalized disassortative matrix representation of the network. In the second phase, we calculate how closely each node of the network is bound to the leaders via a cascading process called network coordination game. We implemented the algorithm and four additional ones as a Web service on a federated peer-to-peer infrastructure. Comparative test results for small and big real world networks demonstrated the correct identification of leaders, high precision and good time complexity. The Web service is available as open source software.


title: Meta-Learning with Adaptive Layerwise Metric and Subspace

abstract: Recent advances in meta-learning demonstrate that deep representations combined with the gradient descent method have sufficient capacity to approximate any learning algorithm. A promising approach is the model-agnostic meta-learning (MAML) which embeds gradient descent into the meta-learner. It optimizes for the initial parameters of the learner to warm-start the gradient descent updates, such that new tasks can be solved using a small number of examples. In this paper we elaborate the gradient-based meta-learning, developing two new schemes. First, we present a feedforward neural network, referred to as T-net, where the linear transformation between two adjacent layers is decomposed as T W such that W is learned by task-specific learners and the transformation T, which is shared across tasks, is meta-learned to speed up the convergence of gradient updates for task-specific learners. Second, we present MT-net where gradient updates in the T-net are guided by a binary mask M that is meta-learned, restricting the updates to be performed in a subspace. Empirical results demonstrate that our method is less sensitive to the choice of initial learning rates than existing meta-learning methods, and achieves the state-of-the-art or comparable performance on few-shot classification and regression tasks.


title: Basic principles of ROC analysis

abstract: The limitations of diagnostic "accuracy" as a measure of decision performance require introduction of the concepts of the "sensitivity" and "specificity" of a diagnostic test. These measures and the related indices, "true positive fraction" and "false positive fraction", are more meaningful than "accuracy", yet do not provide a unique description of diagnostic performance because they depend on the arbitrary selection of a decision threshold. The receiver operating characteristic (ROC) curve is shown to be a simple yet complete empirical description of this decision threshold effect, indicating all possible combinations of the relative frequencies of the various kinds of correct and incorrect decisions. Practical experimental techniques for measuring ROC curves are described, and the issues of case selection and curve-fitting are discussed briefly. Possible generalizations of conventional ROC analysis to account for decision performance in complex diagnostic tasks are indicated. ROC analysis is shown to be related in a direct and natural way to cost/benefit analysis of diagnostic decision making. The concepts of "average diagnostic cost" and "average net benefit" are developed and used to identify the optimal compromise among various kinds of diagnostic error. Finally, the way in which ROC analysis can be employed to optimize diagnostic strategies is suggested.


title: Family transitions following the birth of a sibling: An empirical review of changes in the firstborn's adjustment

abstract: Nearly 80% of children in the U.S. have at least one sibling, indicating that the birth of a baby sibling is a normative ecological transition for most children. Many clinicians and theoreticians believe the transition is stressful, constituting a developmental crisis for most children. Yet, a comprehensive review of the empirical literature on children’s adjustment over the transition to siblinghood (TTS) has not been done for several decades. The current review summarized research examining change in firstborns’ adjustment to determine whether there was evidence that the TTS was disruptive for most children. Thirty studies addressing the transition to siblinghood were found and of those studies, the evidence did not support a crisis model of developmental transitions, nor was there overwhelming evidence of consistent changes in firstborn adjustment. Although there were decreases in children’s affection and responsiveness toward mothers, the results were more equivocal for many other behaviors (e.g., sleep problems, anxiety, aggression, regression). An inspection of the scientific literature indicated there are large individual differences in children’s adjustment and that the TTS can be a time of disruption, an occasion for developmental advances, or a period of quiescence with no noticeable changes. The TTS may be a developmental turning point for some children that portends future psychopathology or growth depending on the transactions between children and the changes in the ecological context over time. A developmental ecological systems framework guided the discussion of how child, parent, and contextual factors may contribute to the prediction of firstborn children’s successful adaptation to the birth of a sibling.


title: Compact multi-band planar monopole antenna for LTE terminals

abstract: This article introduces a compact multi-band planar monopole antenna which is made of a driven monopole antenna and an additional strip. It is etched in a no-ground part, which is small compared to the Printed Circuit Board (PCB) size. The antenna which can operate in GSM/UMTS bands and most of LTE bands (LTE700, LTE2500, LTE3400 and LTE3600 bands) has the size suitable for mobile phones. A prototype has been realized and there is a good agreement between simulation and measurement.


title: Theories Used in Information Systems Research: Identifying Theory Networks in Leading IS Journals

abstract: Though use of theory is critical in Information Systems (IS) research, the theoretical foundations of IS research have been understudied. Using Social Network Analysis, we analyze theory usage in IS research published in MIS Quarterly and Information Systems Research from 1998 to 2006. We find Technology Acceptance Model, ResourceBased View and Game Theory to be the three most frequently used theories. While strong dominance is found in research focusing on Information Technology (IT) for individuals, organizations and markets, no theoretical dominance is found in IT for groups and IS development. Psychology, Economics and Sociology are disciplines IS researchers most frequently leverage for theories. Psychology contributes several theories representing a large fraction of the long tail of theories. Our analysis suggests that IS consists of a few distinctive clusters of research instead of a single core. Our results provide insights on theoretical foundations of IS and suggest research opportunities for scholars.


title: Pilot-assisted channel estimation for coexisting heterogeneous wireless personal area networks

abstract: Wireless personal area networks (WPANs) are drawing more and more attention recently, specially with the proposal of high rate technologies for its physical (PHY) layer. Multiband orthogonal frequency division multiplexing (MB-OFDM) ultra wideband (UWB) and direct-sequence (DS) UWB are the two technologies proposed for the PHY-layer in WPANs. Coexistence issue of the two aforementioned technologies while operating simultaneously in collocated WPANs, is a matter of concern, as they may induce mutual interference. In this paper, we address the channel estimation issue for DS-UWB receiver DEVs, in the presence of multi-user interference (MUI) and MB-OFDM interference. In fact, we use the minimum mean square error estimation (MMSE) to estimate the channel based on a pilot transmission scheme. Later, we employ the maximum likelihood (ML) criteria to detect the data symbols.


title: Global grasp planning using triangular meshes

abstract: In this paper we present an algorithm to determine the location of contact points to obtain force closure grasps on tree dimensional objects. The shape of the object is assumed to be given by a triangle mesh - a format widely used in CAD software. Our algorithm can handle an arbitrary number of contact points and does nor require any prior information about their initial locations. Through an iterative process, contact point locations are updated aiming at improving a commonly used grasp quality metric. The process is global in the sense that during the process the whole surface of the object can be explored, and contact point locations can cross sharp edges that usually represent a problem for optimization algorithms relying on smooth surface representations. Extensive simulation results illustrate the performance of the proposed method, outlining strengths and directions for further research.


title: On the Integration of Space, Time, and Memory

abstract: The hippocampus is famous for mapping locations in spatially organized environments, and several recent studies have shown that hippocampal networks also map moments in temporally organized experiences. Here I consider how space and time are integrated in the representation of memories. The brain pathways for spatial and temporal cognition involve overlapping and interacting systems that converge on the hippocampal region. There is evidence that spatial and temporal aspects of memory are processed somewhat differently in the circuitry of hippocampal subregions but become fully integrated within CA1 neuronal networks as independent, multiplexed representations of space and time. Hippocampal networks also map memories across a broad range of abstract relations among events, suggesting that the findings on spatial and temporal organization reflect a generalized mechanism for organizing memories.


title: Average interradicular sites for miniscrew insertion: should dental crowding be considered?

abstract: OBJECTIVE ::: To define a map of interradicular spaces where miniscrew can be likely placed at a level covered by attached gingiva, and to assess if a correlation between crowding and availability of space exists. ::: ::: ::: METHODS ::: Panoramic radiographs and digital models of 40 patients were selected according to the inclusion criteria. Interradicular spaces were measured on panoramic radiographs, while tooth size-arch length discrepancy was assessed on digital models. Statistical analysis was performed to evaluate if interradicular spaces are influenced by the presence of crowding. ::: ::: ::: RESULTS ::: In the mandible, the most convenient sites for miniscrew insertion were in the spaces comprised between second molars and first premolars; in the maxilla, between first molars and second premolars as well as between canines and lateral incisors and between the two central incisors. The interradicular spaces between the maxillary canines and lateral incisors, and between mandibular first and second premolars revealed to be influenced by the presence of dental crowding. ::: ::: ::: CONCLUSIONS ::: The average interradicular sites map hereby proposed can be used as a general guide for miniscrew insertion at the very beginning of orthodontic treatment planning. Then, the clinician should consider the amount of crowding: if this is large, the actual interradicular space in some areas might be significantly different from what reported on average. Individualized radiographs for every patient are still recommended.


title: Inverted microstrip-fed cavity-backed slot antennas

abstract: This article presents a study of microstrip-fed cavity-backed slot (CBS) antennas using a new inverted configuration. The inverted structure places the metallic cavity in the slot side of the substrate, which renders a number of advantages. Two inverted microstrip-fed cavity-backed antennas, including single CBS antenna and an eight-element CBS array, are simulated, fabricated, and tested. Measured data verify the assertion that the inverted configuration is a better arrangement for designing microstrip-fed CBS antennas and arrays.


title: Fasting: molecular mechanisms and clinical applications.

abstract: Fasting has been practiced for millennia, but, only recently, studies have shed light on its role in adaptive cellular responses that reduce oxidative damage and inflammation, optimize energy metabolism, and bolster cellular protection. In lower eukaryotes, chronic fasting extends longevity, in part, by reprogramming metabolic and stress resistance pathways. In rodents intermittent or periodic fasting protects against diabetes, cancers, heart disease, and neurodegeneration, while in humans it helps reduce obesity, hypertension, asthma, and rheumatoid arthritis. Thus, fasting has the potential to delay aging and help prevent and treat diseases while minimizing the side effects caused by chronic dietary interventions.


CASE REPORT: Vagus Nerve Injury with Severe Diarrhea After Laparoscopic Antireflux Surgery


Overcoming language barriers in healthcare: A protocol for investigating safe and effective communication when patients or clinicians use a second language


Fusion of Leap Motion and Kinect Sensors for Improved Field of View and Accuracy for VR Applications


title: Low voltage ride through (LVRT) ability of DFIG based wind energy conversion system II

abstract: Doubly Fed Induction Generators (DFIGs) are nowadays extensively used in variable speed wind power plants. Doubly fed induction generators (DFIG) offer many advantages such as reduced converter rating, low cost and reduced losses with an improved efficiency, easy implementation of power factor correction schemes, variable speed operation and four quadrants active and reactive power control capabilities. Due to variable speed operation total energy output is much more in case of DFIG based WECS so capacity utilization factor is improved and cost of per unit energy is reduced. But the main disadvantage of DFIG is that it is very sensitive to grid disturbance/fault, especially for the voltage dip. Since the doubly-fed induction generator (DFIG) has been widely used in wind energy conversion, the low voltage ride through (LVRT) technology of the DFIG has been investigated extensively in recent times. This paper focuses on the fault ride-through capability of doubly fed induction generator (DFIG) based WECSs Under three phase symmetrical fault. The paper also provides an overview on the interaction between variable-speed DFIG based WECSs and the power system subjected to three phase symmetrical fault. The Steady state behaviour under normal condition and dynamic behaviour of DFIG wind turbines during grid faults is simulated and assessed.


title: Haar-Like Filtering for Human Activity Recognition Using 3D Accelerometer

abstract: In this paper, novel 2 one-dimensional (1D) Haar-like filtering techniques are proposed as a new and low calculation cost feature extraction method suitable for 3D acceleration signals based human activity recognition. Proposed filtering method is a simple difference filter with variable filter parameters. Our method holds a strong adaptability to various classification problems which no previously studied features (mean, standard deviation, etc.) possessed. In our experiment on human activity recognition, the proposed method achieved both the highest recognition accuracy of 93.91% while reducing calculation cost to 21.22% compared to previous method.


title: Cell type specific genetic regulation of gene expression across human tissues

abstract: The Genotype-Tissue Expression (GTEx) project has identified expression and splicing quantitative trait loci (cis-QTLs) for the majority of genes across a wide range of human tissues. However, the interpretation of these QTLs has been limited by the heterogeneous cellular composition of GTEx tissue samples. Here, we map interactions between computational estimates of cell type abundance and genotype to identify cell type interaction QTLs for seven cell types and show that cell type interaction eQTLs provide finer resolution to tissue specificity than bulk tissue cis-eQTLs. Analyses of genetic associations to 87 complex traits show a contribution from cell type interaction QTLs and enables the discovery of hundreds of previously unidentified colocalized loci that are masked in bulk tissue. One Sentence Summary Estimated cell type abundances from bulk RNA-seq across tissues reveal the cellular specificity of quantitative trait loci.


title: The genotoxic and cytotoxic effects of 1-butyl-3-methylimidazolium chloride in soil on Vicia faba seedlings.

abstract: Although considered as "green" solvents, the toxic effects of ionic liquids (ILs) on organisms have been widely investigated in recent years. However, studies on the toxic effects of ILs on plants all focus on toxicity in nutrient solution. In the present paper, the toxic effects of 1-butyl-3-methylimidazolium chloride ([C4mim]Cl) on Vicia faba seedlings in soil at 0, 500, 1500, and 2500 mg kg(-1) on day 10 were studied. The present results showed that the growth of V. faba seedlings may be seriously inhibited when the IL concentrations were higher than 500 mg kg(-1). The EC50 values for shoot length, root length, and dry weight were 3886, 2483, and 3359 mg kg(-1), respectively. In addition, [C4mim]Cl caused lipid peroxidation and DNA damage at 500 mg kg(-1) and oxidative stress at 1500 mg kg(-1), which indicated that [C4mim]Cl may have genotoxicity and cytotoxicity on V. faba seedlings. Moreover, oxidative damage may be the main toxic mechanism of ILs.


title: How to stop spread of misinformation on social media: Facebook plans vs. right-click authenticate approach

abstract: One of the key features of social networks is that users are able to share information, and through cascades of sharing information, this information may reach a large number of individuals. The high availability of user-provided contents on online social media facilitates people aggregation around shared beliefs, interests, worldviews and narratives. With lack of means to verify information, social media has been accused of becoming a hot bed for sharing of misinformation. Facebook, as one of the largest social networking services, has been facing widespread criticism on how its newsfeed algorithm is designed thus amplifying dissemination of misinformation. In late 2016, Facebook revealed plans to address fake news on Facebook newsfeeds. In this work, we study the methods Facebook has proposed to combat the spread of misinformation and compare it with our previously proposed approach called ‘Right-click Authenticate’. By analyzing the Business Process Modeling and Notation of both approaches, this paper suggests some key weaknesses and improvements social media companies need to consider when tackling the spread of misinformation online.


title: Denser than the densest subgraph: extracting optimal quasi-cliques with quality guarantees

abstract: Finding dense subgraphs is an important graph-mining task with many applications. Given that the direct optimization of edge density is not meaningful, as even a single edge achieves maximum density, research has focused on optimizing alternative density functions. A very popular among such functions is the average degree, whose maximization leads to the well-known densest-subgraph notion. Surprisingly enough, however, densest subgraphs are typically large graphs, with small edge density and large diameter. In this paper, we define a novel density function, which gives subgraphs of much higher quality than densest subgraphs: the graphs found by our method are compact, dense, and with smaller diameter. We show that the proposed function can be derived from a general framework, which includes other important density functions as subcases and for which we show interesting general theoretical properties. To optimize the proposed function we provide an additive approximation algorithm and a local-search heuristic. Both algorithms are very efficient and scale well to large graphs. We evaluate our algorithms on real and synthetic datasets, and we also devise several application studies as variants of our original problem. When compared with the method that finds the subgraph of the largest average degree, our algorithms return denser subgraphs with smaller diameter. Finally, we discuss new interesting research directions that our problem leaves open.


title: Bilingualism protects anterior temporal lobe integrity in aging

abstract: Abstract Cerebral gray-matter volume (GMV) decreases in normal aging but the extent of the decrease may be experience-dependent. Bilingualism may be one protective factor and in this article we examine its potential protective effect on GMV in a region that shows strong age-related decreases—the left anterior temporal pole. This region is held to function as a conceptual hub and might be expected to be a target of plastic changes in bilingual speakers because of the requirement for these speakers to store and differentiate lexical concepts in 2 languages to guide speech production and comprehension processes. In a whole brain comparison of bilingual speakers (n = 23) and monolingual speakers (n = 23), regressing out confounding factors, we find more extensive age-related decreases in GMV in the monolingual brain and significantly increased GMV in left temporal pole for bilingual speakers. Consistent with a specific neuroprotective effect of bilingualism, region of interest analyses showed a significant positive correlation between naming performance in the second language and GMV in this region. The effect appears to be bilateral though because there was a nonsignificantly different effect of naming performance on GMV in the right temporal pole. Our data emphasize the vulnerability of the temporal pole to normal aging and the value of bilingualism as both a general and specific protective factor to GMV decreases in healthy aging.


title: Level lines based disocclusion

abstract: Object recognition, robotic vision, occluding noise removal or photograph design require the ability to perform disocclusion. We call disocclusion the recovery of hidden parts of objects in a digital image by interpolation from the vicinity of the occluded area. It is shown in this paper how disocclusion can be performed by means of a level lines structure, which offers a reliable, complete and contrast-invariant representation of an image, in contrast to edges. Level lines based disocclusion yields a solution that may have strong discontinuities, which is not possible with PDE-based interpolation. Moreover, the proposed method is fully compatible with Kanizsa's (1996) theory of "amodal completion".


The place of English in the Tanzanian education system: the political and economic dimensions to current and alternative language policy options


title: Cardiopulmonary Demand of 16-kg Kettlebell Snatches in Simulated Girevoy Sport.

abstract: Chan, M, MacInnis, MJ, Koch, S, MacLeod, KE, Lohse, KR, Gallo, ME, Sheel, AW, and Koehle, MS. Cardiopulmonary demand of 16-kg kettlebell snatches in simulated Girevoy Sport. J Strength Cond Res XX(X): 000-000, 2018-Kettlebell lifting has become popular both as a strength and conditioning training tool and as a sport in and of itself: Girevoy Sport (GS). Although several kettlebell multimovement protocols have been analyzed, little research has attempted to quantify the aerobic stimulus of the individual events in GS, which could better inform kettlebell-related exercise prescription. The purpose of this study was to quantify the cardiopulmonary demand, assessed primarily by oxygen consumption (V[Combining Dot Above]O2) and heart rate (HR), of continuous high-intensity kettlebell snatches-under conditions relevant to GS-and to compare this demand with a more traditional graded rowing ergometer maximal exercise test. Ten male participants (age = 28.4 ± 4.6 years, height = 185 ± 7 cm, body mass = 95.1 ± 14.9 kg) completed (a) a graded-exercise test on a rowing ergometer to determine maximal oxygen consumption (V[Combining Dot Above]O2max) and maximal heart rate (HRmax) and (b) a graded-exercise test consisting of continuous 16-kg kettlebell snatches to determine peak oxygen consumption (V[Combining Dot Above]O2peak) and peak heart rate (HRpeak) during a simulated GS snatch event. Subjects achieved a V[Combining Dot Above]O2max of 45.7 ± 6.7 ml·kg·min and HRmax of 177 ± 8.3 b·min on the rowing ergometer. The kettlebell snatch test produced a V[Combining Dot Above]O2peak of 37.6 ± 4.4 ml·kg·min (82.7 ± 6.5% V[Combining Dot Above]O2max) and a HRpeak of 174 ± 10 b·min (98.0 ± 3.4% HRmax). These findings suggest that GS kettlebell snatches with 16-kg can provide an adequate aerobic stimulus to improve cardiorespiratory fitness in those with a V[Combining Dot Above]O2max of ≤51 ml·kg·min, according to aerobic training recommendations from the American College of Sports Medicine.