title: CloudBLAST: Combining MapReduce and Virtualization on Distributed Resources for Bioinformatics Applications

abstract: This paper proposes and evaluates an approach to the parallelization, deployment and management of bioinformatics applications that integrates several emerging technologies for distributed computing. The proposed approach uses the MapReduce paradigm to parallelize tools and manage their execution, machine virtualization to encapsulate their execution environments and commonly used data sets into flexibly deployable virtual machines, and network virtualization to connect resources behind firewalls/NATs while preserving the necessary performance and the communication environment. An implementation of this approach is described and used to demonstrate and evaluate the proposed approach. The implementation integrates Hadoop, Virtual Workspaces, and ViNe as the MapReduce, virtual machine and virtual network technologies, respectively, to deploy the commonly used bioinformatics tool NCBI BLAST on a WAN-based test bed consisting of clusters at two distinct locations, the University of Florida and the University of Chicago. This WAN-based implementation, called CloudBLAST, was evaluated against both non-virtualized and LAN-based implementations in order to assess the overheads of machine and network virtualization, which were shown to be insignificant. To compare the proposed approach against an MPI-based solution, CloudBLAST performance was experimentally contrasted against the publicly available mpiBLAST on the same WAN-based test bed. Both versions demonstrated performance gains as the number of available processors increased, with CloudBLAST delivering speedups of 57 against 52.4 of MPI version, when 64 processors on 2 sites were used. The results encourage the use of the proposed approach for the execution of large-scale bioinformatics applications on emerging distributed environments that provide access to computing resources as a service.


title: The Value of Information Visualization

abstract: Researchers and users of Information Visualization are convinced that it has value. This value can easily be communicated to others in a face-to-face setting, such that this value is experienced in practice. To convince broader audiences, and also, to understand the intrinsic qualities of visualization is more difficult, however. In this paper we consider information visualization from different points of view, and gather arguments to explain the value of our field.


title: String stability of interconnected vehicles under communication constraints

abstract: In this paper, we present a novel modelling and string stability analysis method for an interconnected vehicle string in which information exchange takes place via wireless communication. The usage of wireless communication introduces time-varying sampling intervals, delays, and communication constraints of which the impact on string stability requires a careful analysis. In particular, we study a Cooperative Adaptive Cruise Control (CACC) system which regulates inter-vehicle distances in a vehicle string and utilizes information exchange between vehicles through wireless communication in addition to local sensor measurements. The propagation of disturbances through the interconnected vehicle string is inspected by using the notion of so-called string stability which is formulated here in terms of an ℒ 2 -gain requirement from disturbance inputs to controlled outputs. This paper provides conditions on the uncertain sampling intervals and delays under which string stability can still be guaranteed. These results support the design of CACC systems that are robust to uncertainties introduced by wireless communication.


EVALUATION OF BUCKET CAPACITY , DIGGING FORCE CALCULATIONS AND STATIC FORCE ANALYSIS OF MINI HYDRAULIC BACKHOE EXCAVATOR


title: Primality testing using elliptic curves

abstract: We present a primality proving algorithm—a probablistic primality test that produces short certificates of primality on prime inputs. We prove that the test runs in expected polynomial time for all but a vanishingly small fraction of the primes. As a corollary, we obtain an algorithm for generating large certified primes with distribution statistically close to uniform. Under the conjecture that the gap between consecutive primes is bounded by some polynomial in their size, the test is shown to run in expected polynomial time for all primes, yielding a Las Vegas primality test. ::: Our test is based on a new methodology for applying group theory to the problem of prime certification, and the application of this methodology using groups generated by elliptic curves over finite fields. ::: We note that our methodology and methods have been subsequently used and improved upon, most notably in the primality proving algorithm of Adleman and Huang using hyperelliptic curves and in practical primality provers using elliptic curves.


title: The mystery machine: end-to-end performance analysis of large-scale internet services

abstract: Current debugging and optimization methods scale poorly to deal with the complexity of modern Internet services, in which a single request triggers parallel execution of numerous heterogeneous software components over a distributed set of computers. The Achilles' heel of current methods is the need for a complete and accurate model of the system under observation: producing such a model is challenging because it requires either assimilating the collective knowledge of hundreds of programmers responsible for the individual components or restricting the ways in which components interact. ::: ::: Fortunately, the scale of modern Internet services offers a compensating benefit: the sheer volume of requests serviced means that, even at low sampling rates, one can gather a tremendous amount of empirical performance observations and apply "big data" techniques to analyze those observations. In this paper, we show how one can automatically construct a model of request execution from pre-existing component logs by generating a large number of potential hypotheses about program behavior and rejecting hypotheses contradicted by the empirical observations. We also show how one can validate potential performance improvements without costly implementation effort by leveraging the variation in component behavior that arises naturally over large numbers of requests to measure the impact of optimizing individual components or changing scheduling behavior. ::: ::: We validate our methodology by analyzing performance traces of over 1.3 million requests to Facebook servers. We present a detailed study of the factors that affect the end-to-end latency of such requests. We also use our methodology to suggest and validate a scheduling optimization for improving Facebook request latency.


title: A Review on Video-Based Human Activity Recognition

abstract: This review article surveys extensively the current progresses made toward video-based human activity recognition. Three aspects for human activity recognition are addressed including core technology, human activity recognition systems, and applications from low-level to high-level representation. In the core technology, three critical processing stages are thoroughly discussed mainly: human object segmentation, feature extraction and representation, activity detection and classification algorithms. In the human activity recognition systems, three main types are mentioned, including single person activity recognition, multiple people interaction and crowd behavior, and abnormal activity recognition. Finally the domains of applications are discussed in detail, specifically, on surveillance environments, entertainment environments and healthcare systems. Our survey, which aims to provide a comprehensive state-of-the-art review of the field, also addresses several challenges associated with these systems and applications. Moreover, in this survey, various applications are discussed in great detail, specifically, a survey on the applications in healthcare monitoring systems.


title: A tractable numerical strategy for robust MILP and application to energy management

abstract: Robust optimization has emerged as a tractable methodology for coping with parameter uncertainty in an optimization problem. In order to avoid conservative solutions, i.e. overly protective and expensive solutions, Ben-Tal and Nemirovski [1] introduced the notion of affine adaptability. However, their approach significantly increases the program size and threatens its tractability, especially in the context of mixed-integer programming. In this paper, we focus on robust mixed-integer linear programs. We propose a tractable numerical strategy for solving them and demonstrate the computational efficiency of our method when applied to a real energy management problem. In addition, we propose a practical data-driven methodology for designing the uncertainty set of robust programs.


title: YAGO : A Core of Semantic Knowledge Unifying WordNet and Wikipedia

abstract: We present YAGO, a light-weight and extensible ontology with high coverage and quality. YAGO builds on entities and relations and currently contains more than 1 million entities and 5 million facts. This includes the Is-A hierarchy as well as non-taxonomic relations between entities (such as HASONEPRIZE). The facts have been automatically extracted from Wikipedia and unified with WordNet, using a carefully designed combination of rule-based and heuristic methods described in this paper. The resulting knowledge base is a major step beyond WordNet: in quality by adding knowledge about individuals like persons, organizations, products, etc. with their semantic relationships - and in quantity by increasing the number of facts by more than an order of magnitude. Our empirical evaluation of fact correctness shows an accuracy of about 95%. YAGO is based on a logically clean model, which is decidable, extensible, and compatible with RDFS. Finally, we show how YAGO can be further extended by state-of-the-art information extraction techniques.


title: Distributively Computing Random Walk Betweenness Centrality in Linear Time

abstract: Betweenness centrality of a node represents its influence over the spread of information in the network. It is normally defined as the ratio of the number of shortest paths passing through the node among all shortest paths. However, the spread of information may not just pass through the shortest paths which is captured by a new measure of betweenness centrality based on random walks [1]. The random walk betweenness centrality of a node means how often it is traversed by a random walk between all pairs of other nodes. In this paper, we propose an O(n log n) time distributed randomized approximation algorithm for calculating each node's random walk betweenness centrality with an approximation ratio (1-∊ where n is the number of nodes and ∊ is an arbitrarily small constant between 0 and 1. Our distributed algorithm is designed under the widely used CONGEST model, where each edge can only transfer O(log n) bits in each round. To our best knowledge, this is the first distributed algorithm for computing the random walk betweenness centrality. Moreover, we give a non-trivial lower bound for distributively computing the exact random walk betweenness centrality under the CONGEST model, which is Ω(n\log n +D) where D is the network diameter. This means exactly computing random walk betweenness cannot be done in sublinear time.


title: Excitons in core-shell nanowires with polygonal cross sections

abstract: The distinctive prismatic geometry of semiconductor core–shell nanowires leads to complex localization patterns of carriers. Here, we describe the formation of optically active in-gap excitonic states induced by the interplay between localization of carriers in the corners and their mutual Coulomb interaction. To compute the energy spectra and configurations of excitons created in the conductive shell, we use a multielectron numerical approach based on the exact solution of the multiparticle Hamiltonian for electrons in the valence and conduction bands, which includes the Coulomb interaction in a nonperturbative manner. We expose the formation of well-separated quasidegenerate levels, and focus on the implications of the electron localization in the corners or on the sides of triangular, square, and hexagonal cross sections. We obtain excitonic in-gap states associated with symmetrically distributed electrons in the spin singlet configuration. They acquire large contributions due to Coulomb interaction, a...


title: Decoding two-dimensional movement trajectories using electrocorticographic signals in humans

abstract: Signals from the brain could provide a non-muscular communication and control system, a brain–computer interface (BCI), for people who are severely paralyzed. A common BCI research strategy begins by decoding kinematic parameters from brain signals recorded during actual arm movement. It has been assumed that these parameters can be derived accurately only from signals recorded by intracortical microelectrodes, but the long-term stability of such electrodes is uncertain. The present study disproves this widespread assumption by showing in humans that kinematic parameters can also be decoded from signals recorded by subdural electrodes on the cortical surface (ECoG) with an accuracy comparable to that achieved in monkey studies using intracortical microelectrodes. A new ECoG feature labeled the local motor potential (LMP) provided the most information about movement. Furthermore, features displayed cosine tuning that has previously been described only for signals recorded within the brain. These results suggest that ECoG could be a more stable and less invasive alternative to intracortical electrodes for BCI systems, and could also prove useful in studies of motor function.


title: Cyber and Physical Security Vulnerability Assessment for IoT-Based Smart Homes

abstract: The Internet of Things (IoT) is an emerging paradigm focusing on the connection of devices, objects, or “things” to each other, to the Internet, and to users. IoT technology is anticipated to become an essential requirement in the development of smart homes, as it offers convenience and efficiency to home residents so that they can achieve better quality of life. Application of the IoT model to smart homes, by connecting objects to the Internet, poses new security and privacy challenges in terms of the confidentiality, authenticity, and integrity of the data sensed, collected, and exchanged by the IoT objects. These challenges make smart homes extremely vulnerable to different types of security attacks, resulting in IoT-based smart homes being insecure. Therefore, it is necessary to identify the possible security risks to develop a complete picture of the security status of smart homes. This article applies the operationally critical threat, asset, and vulnerability evaluation (OCTAVE) methodology, known as OCTAVE Allegro, to assess the security risks of smart homes. The OCTAVE Allegro method focuses on information assets and considers different information containers such as databases, physical papers, and humans. The key goals of this study are to highlight the various security vulnerabilities of IoT-based smart homes, to present the risks on home inhabitants, and to propose approaches to mitigating the identified risks. The research findings can be used as a foundation for improving the security requirements of IoT-based smart homes.


title: Combining Traditional Marketing and Viral Marketing with Amphibious Influence Maximization

abstract: In this paper, we propose the amphibious influence maximization (AIM) model that combines traditional marketing via content providers and viral marketing to consumers in social networks in a single framework. In AIM, a set of content providers and consumers form a bipartite network while consumers also form their social network, and influence propagates from the content providers to consumers and among consumers in the social network following the independent cascade model. An advertiser needs to select a subset of seed content providers and a subset of seed consumers, such that the influence from the seed providers passing through the seed consumers could reach a large number of consumers in the social network in expectation. We prove that the AIM problem is NP-hard to approximate to within any constant factor via a reduction from Feige's k-prover proof system for 3-SAT5. We also give evidence that even when the social network graph is trivial (i.e. has no edges), a polynomial time constant factor approximation for AIM is unlikely. However, when we assume that the weighted bi-adjacency matrix that describes the influence of content providers on consumers is of constant rank, a common assumption often used in recommender systems, we provide a polynomial-time algorithm that achieves approximation ratio of (1-1/e-e)3 for any (polynomially small) e > 0. Our algorithmic results still hold for a more general model where cascades in social network follow a general monotone and submodular function.


title: Familiarity, Confidence, Trust: Problems and Alternatives

abstract: Trust has never been a topic of mainstream sociology. Neither classical authors nor modern sociologists use the term in a theoretical context. For this reason the elaboration of theoretical frameworks, one of the main sources of conceptual clarification, has been relatively neglected. Furthermore, empirical research for example, research about trust and distrust in politics has relied on rather general and unspecified ideas, confusing problems of trust with positive or negative attitudes toward political leadership or political institutions, with alienation (itself a multidimensional concept), with hopes and worries, or with confidence. In their monograph on patrons, clients, and friends, Shmuel Eisenstadt and Luis Roniger (1984) use the concept of trust as roughly equivalent to solidarity, meaning, and participation. This makes it possible to show that unconditional trust is generated in families and small-scale societies and cannot be automatically transferred to complex societies based on the division of labour. Trust, then, needs for its reconstruction special social institutions; friendship networks and patron-client relations are examples for this adaptation. But this is merely to reiterate well-known statements about the division of labour and the need to reconstruct solidarity, about Gesellschaft and Gemeinschaft. It does not give any new insight into the particularities of trusting relations. To gain such insights we need further conceptual clarification.


title: Detection of New Vessels on the Optic Disc Using Retinal Photographs

abstract: Proliferative diabetic retinopathyis a rare condition likely to lead to severe visual impairment. It is characterized by the development of abnormal new retinal vessels. We describe a method for automatically detecting new vessels on the optic disc using retinal photography. Vessel-like candidate segments are first detected using a method based on watershed lines and ridge strength measurement. Fifteen feature parameters, associated with shape, position, orientation, brightness, contrast and line density are calculated for each candidate segment. Based on these features, each segment is categorized as normal or abnormal using a support vector machine (SVM) classifier. The system was trained and tested by cross-validation using 38 images with new vessels and 71 normal images from two diabetic retinal screening centers and one hospital eye clinic. The discrimination performance of the fifteen features was tested against a clinical reference standard. Fourteen features were found to be effective and used in the final test. The area under the receiver operator characteristic curve was 0.911 for detecting images with new vessels on the disc. This accuracy may be sufficient for it to play a useful clinical role in an automated retinopathy analysis system.


title: S3 - Guidelines on the treatment of psoriasis vulgaris (English version). Update.

abstract: Psoriasis vulgaris is a common and often chronic inflammatory skin disease. The incidence of psoriasis in Western industrialized countries ranges from 1.5% to 2%. Patients afflicted with severe psoriasis vulgaris may experience a significant reduction in quality of life. Despite the large variety of treatment options available, surveys have shown that patients still do not received optimal treatments. To optimize the treatment of psoriasis in Germany, the Deutsche Dermatologi sche Gesellschaft (DDG) and the Berufsverband Deutscher Dermatologen (BVDD) have initiated a project to develop evidence-based guidelines for the management of psoriasis. They were first published in 2006 and updated in 2011. The Guidelines focus on induction therapy in cases of mild, moderate and severe plaque-type psoriasis in adults including systemic therapy, UV therapy and topical therapies. The therapeutic recommendations were developed based on the results of a systematic literature search and were finalized during a consensus meeting using structured consensus methods (nominal group process).


title: Improved flicker removal through motion vectors compensation

abstract: Image flicker, the unnatural temporal fluctuation in perceived image intensity, is an annoying artifact existing in many old film sequences and its removal is an integral part in any old film restoration process. In this paper, we propose an improved flicker removal method based on flicker parameters estimation with the aide of motion vectors compensation. We tested the proposed method on sequences with artificially added global and local flicker, as well as on natural real flicker film material. Test results obtained showed the proposed method produced much better flicker correction quality than an established flicker removal method known as the "Roosmalen method".


Poster : Getting The Point ( er ) : On the Feasibility of Attacks on Code-Pointer Integrity


title: Metal-frame inverted-F antenna for the LTE metal-casing smartphone

abstract: A metal-frame inverted-F antenna for the metal-casing smartphone is presented. The antenna is formed by two inverted-F Antenna (IFA) structures to cover 824–960 MHz (low band), 1710–2690 MHz (middle band), and 3400–3600 MHz (high band). The antenna for the metal-casing smartphone has a narrow metal clearance of 2 mm. The feeding and shorting strips with matching networks for the two IFAs are disposed on a small dielectric substrate as the feed circuit board. Results of the fabricated antenna are presented to verify the simulation results.


title: SURFing the point clouds: Selective 3D spatial pyramids for category-level object recognition

abstract: This paper proposes a novel approach to recognize object categories in point clouds. By quantizing 3D SURF local descriptors, computed on partial 3D shapes extracted from the point clouds, a vocabulary of 3D visual words is generated. Using this codebook, we build a Bag-of-Words representation in 3D, which is used in conjunction with a SVM classification machinery. We also introduce the 3D Spatial Pyramid Matching Kernel, which works by partitioning a working volume into fine sub-volumes, and computing a hierarchical weighted sum of histogram intersections at each level of the pyramid structure. With the aim of increasing both the classification accuracy and the computational efficiency of the kernel, we propose selective hierarchical volume decomposition strategies, based on representative and discriminative (sub-)volume selection processes, which drastically reduce the pyramid to consider. Results on the challenging large-scale RGB-D object dataset show that our kernels significantly outperform the state-of-the-art results by using a single 3D shape feature type extracted from individual depth images.


Knowledge Transfer and Opinion Detection in the TREC 2006 Blog Track


title: Universal Recognition of Three Basic Emotions in Music

abstract: Department of PsychologyPevensey BuildingUniversity of SussexFalmer BN1 9QHUKSummaryIt has long been debated which aspects of music perceptionareuniversalandwhicharedevelopedonlyafterexposuretoa speciﬁc musical culture [1–5]. Here, we report a crosscul-turalstudywithparticipantsfromanativeAfricanpopulation(Mafa) and Western participants, with both groups beingnaive to the music of the other respective culture. Experi-ment 1 investigated the ability to recognize three basicemotions (happy, sad, scared/fearful) expressed in Westernmusic. Results show that the Mafas recognized happy, sad,and scared/fearful Western music excerpts above chance,indicating that the expression of these basic emotions inWestern music can be recognized universally. Experiment2 examined how a spectral manipulation of original, natural-istic music affects the perceived pleasantness of music inWestern as well as in Mafa listeners. The spectral manipula-tion modiﬁed, among other factors, the sensory dissonanceofthemusic.Thedata showthatboth groupspreferred orig-inal Western music and also original Mafa music over theirspectrally manipulated versions. It is likely that the sensorydissonance produced by the spectral manipulation was atleast partly responsible for this effect, suggesting thatconsonanceandpermanentsensorydissonanceuniversallyinﬂuence the perceived pleasantness of music.Results and DiscussionThe expression of emotions is a basic feature of Westernmusic, and the capacity of music to convey emotional expres-sions is often regarded as a prerequisite to its appreciation inWestern cultures. This is not necessarily the case in non-Western music cultures, many of which do not similarlyemphasize emotional expressivity, but rather may appreciatemusic for qualities such as group coordination in rituals. Toour knowledge, there has not yet been a conclusive investiga-tion into the universals of the recognition of emotional expres-sion in music and music appreciation. The investigation ofmusical universals with Western music stimuli requires partic-ipants who are completely naive to Western music. Even indi-viduals from non-Western cultures who have only listened toWestern music occasionally, and perhaps without payingexplicit attention to it (e.g., while listening to the radio orwatching a movie), do not qualify as participants becausemusical knowledge is usually acquired implicitly and is thuseven shaped through inattentive listening experience [6]. Theindividuals investigated in our study belonged to the Mafa,one of approximately 250 ethnic groups that make up thepop-ulation of Cameroon. They are located in the Extreme North inthe Mandara mountain range, an area culturally isolated asa result of a high regional density of endemic illnesses. Themore remote Mafa settlements do not have electrical supplyand are still inhabited by many individuals who pursue a tradi-tionallifestyleandhaveneverbeenexposedtoWesternmusic.The investigation of the recognition of emotional expres-sions conveyed by the music of other cultures has only beenaddressed in three previous studies [1, 7, 8]. These studiesaimed to investigate cues that transcend cultural boundaries,and the authors made an effort to include listeners with littleprior exposure to the music presented (e.g., Westernerslistening to Hindustani music). Although these studies havesigniﬁcantly enhanced our understanding of how culturalexperience may inﬂuence music perception, the participantsin these studies were exposed to the mass media and thusalso inadvertently to emotional cues of the respective foreignmusic(forexample,bytheassociationofthismusicwithﬁlms).To draw clear conclusions about music universals, however, itis necessary to address music listeners who are completelyculturally isolated from one another. Here, we employeda research paradigm to investigate the recognition of musicalemotion in two groups: Mafa listeners naive to Western musicand a group of Western listeners naive to Mafa music. Exper-iment 1 was designed to examine the recognition of threebasic emotions as expressed by Western music (happy, sad,and scared/fearful), using music pieces that had been usedpreviously to investigate the recognition of these emotions inbrain-damaged patients [9, 10].Data from experiment 1 showed that all three emotionalexpressions (happy, sad, and scared/fearful) were recognizedabove chance level by both Western and Mafa listeners(Figure 1A, see also Supplemental Data available online forstatistical evaluation; note that the Mafa listeners had neverbeen exposed to Western music before). However, the Mafalisteners showed considerable variability in their performance,and 2 of the 21 Mafa participants performed at chancelevel. The mechanism underlying the universal recognition ofemotional expressions conveyed by Western musical phrasesappears to be quite similar for both Western listeners and theMafas:ananalysisofratingtendenciesrevealedthatbothMafasand Westerners relied on temporal cues and on mode for theirjudgment of emotional expressions, although this pattern wasmore marked in Western listeners (see Supplemental Results


title: Development of a New Approach for the Therapy of Prostate Cancer with SPOP Mutations

abstract: Advanced prostate cancer is treated with androgen deprivation, but most ::: patients eventually progress and need new therapy. Recent genomic/exomic ::: sequencing identified SPOP as the most frequently mutated gene in 6% - 15% of ::: prostate cancer. Based on the function of SPOP as a ubiquitin ligase in protein ::: degradation, it was hypothesized that loss-of-function mutations of SPOP led to ::: accumulation of SPOP substrates that enhance androgen receptor activity and ::: facilitate prostate cancer formation. SPOP ::: substrates could thus be potential targets for treatment of androgen-sensitive ::: prostate cancer. PubMed and BLAST search identified that Gli, SRC-3, and AWP1 ::: are SPOP substrates, and that inhibition of PRK-1, a binding partner of AWP1, ::: by lestaurtinib suppressed androgen receptor activity. LNCaP, PC3, DU145 and ::: 22RV1 prostate cancer cells were used to evaluate the effect of lestaurtinib. ::: LNCaP cells, an androgen-sensitive prostate cancer cell line, were the most ::: sensitive. SRC-3 protein decreased when LNCaP cells were treated with ::: lestaurtinib; whereas PRK-1 increased in nucleus after lestaurtinib treatment. ::: These data suggest that lestaurtinib modulates SRC-3 and PRK-1 to ::: induced cell death in androgen-sensitive prostate cancer, and could be a useful ::: agent for future development for prostate cancer with SPOP mutations.


title: Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates

abstract: Reinforcement learning holds the promise of enabling autonomous robots to learn large repertoires of behavioral skills with minimal human intervention. However, robotic applications of reinforcement learning often compromise the autonomy of the learning process in favor of achieving training times that are practical for real physical systems. This typically involves introducing hand-engineered policy representations and human-supplied demonstrations. Deep reinforcement learning alleviates this limitation by training general-purpose neural network policies, but applications of direct deep reinforcement learning algorithms have so far been restricted to simulated settings and relatively simple tasks, due to their apparent high sample complexity. In this paper, we demonstrate that a recent deep reinforcement learning algorithm based on off-policy training of deep Q-functions can scale to complex 3D manipulation tasks and can learn deep neural network policies efficiently enough to train on real physical robots. We demonstrate that the training times can be further reduced by parallelizing the algorithm across multiple robots which pool their policy updates asynchronously. Our experimental evaluation shows that our method can learn a variety of 3D manipulation skills in simulation and a complex door opening skill on real robots without any prior demonstrations or manually designed representations.


40Ar-39Ar dating of Proterozoic basaltic and granitic rocks in the Nepal Himalaya and their comparison with those in Singbhum area, peninsular India


When does a compliment become sexist? Analysis and classification of ambivalent sexism using twitter data


title: Measuring e-learning systems success in an organizational context: Scale development and validation

abstract: Electronic learning (e-learning) has been widely adopted as a promising solution by many companies to offer learning-on-demand opportunities to individual employees in order to reduce training time and cost. While information systems (IS) success models have received much attention among researchers, little research has been conducted to assess the success and/or effectiveness of e-learning systems in an organizational context. Whether traditional IS success models can be extended to investigate e-learning systems success has been scarcely addressed. Based on previous IS success literature, this study developed and validated a multi-dimensional model for assessing e-learning systems success (ELSS) from the perspective of the employee (e-learner). The procedures used in conceptualizing an ELSS construct, generating items, collecting data, and validating a multiple-item scale for measuring ELSS are described. This paper presents evidence of the scale's factor structure, reliability, content validity, criterion-related validity, convergent validity, and discriminant validity on the basis of analyzing data from a sample of 206 respondents. Theoretical and managerial implications of our results are discussed. This empirically validated instrument will be useful to researchers in developing and testing e-learning systems theories, as well as to organizations in implementing successful e-learning systems.


title: 3D modelling and simulation of a crawler robot in ROS/Gazebo

abstract: Modelling and animation of crawler UGV's caterpillars is a complicated task, which has not been completely resolved in ROS/Gazebo simulators. In this paper, we proposed an approximation of track-terrain interaction of a crawler UGV, perform modelling and simulation of Russian crawler robot "Engineer" within ROS/Gazebo and visualize its motion in ROS/RViz software. Finally, we test the proposed model in heterogeneous robot group navigation scenario within uncertain Gazebo environment.


title: The hitchhiker's guide to successful residential sensing deployments

abstract: Homes are rich with information about people's energy consumption, medical health, and personal or family functions. In this paper, we present our experiences deploying large-scale residential sensing systems in over 20 homes. Deploying small-scale systems in homes can be deceptively easy, but in our deployments we encountered a phase transition in which deployment effort increases dramatically as residential deployments scale up in terms of 1) the number of nodes, 2) the length of time, and 3) the number of houses. In this paper, we distill our experiences down to a set of guidelines and design principles to help future deployments avoid the potential pitfalls of large-scale sensing in homes.


title: An analysis of unreliability and asymmetry in low-power wireless links

abstract: Experimental studies have demonstrated that the behavior of real links in low-power wireless networks (such as wireless sensor networks) deviates to a large extent from the ideal binary model used in several simulation studies. In particular, there is a large transitional region in wireless link quality that is characterized by significant levels of unreliability and asymmetry, significantly impacting the performance of higher-layer protocols. We provide a comprehensive analysis of the root causes of unreliability and asymmetry. In particular, we derive expressions for the distribution, expectation, and variance of the packet reception rate as a function of distance, as well as for the location and extent of the transitional region. These expressions incorporate important environmental and radio parameters such as the path loss exponent and shadowing variance of the channel, and the modulation, encoding, and hardware variance of the radios.


title: How do People Make Sense of Unfamiliar Visualizations?: A Grounded Model of Novice's Information Visualization Sensemaking

abstract: In this paper, we would like to investigate how people make sense of unfamiliar information visualizations. In order to achieve the research goal, we conducted a qualitative study by observing 13 participants when they endeavored to make sense of three unfamiliar visualizations (i.e., a parallel-coordinates plot, a chord diagram, and a treemap) that they encountered for the first time. We collected data including audio/video record of think-aloud sessions and semi-structured interview; and analyzed the data using the grounded theory method. The primary result of this study is a grounded model of NOvice's information Vlsualization Sensemaking (NOVIS model), which consists of the five major cognitive activities: encountering visualization , constructing a frame , exploring visualization , questioning the frame , and floundering on visualization . We introduce the NOVIS model by explaining the five activities with representative quotes from our participants. We also explore the dynamics in the model. Lastly, we compare with other existing models and share further research directions that arose from our observations.


title: Your click decides your fate: Inferring Information Processing and Attrition Behavior from MOOC Video Clickstream Interactions

abstract: In this work, we explore video lecture interaction in Massive Open Online Courses (MOOCs), which is central to student learning experience on these educational platforms. As a research contribution, we operationalize video lecture clickstreams of students into cognitively plausible higher level behaviors, and construct a quantitative information processing index, which can aid instructors to better understand MOOC hurdles and reason about unsatisfactory learning outcomes. Our results illustrate how such a metric inspired by cognitive psychology can help answer critical questions regarding students' engagement, their future click interactions and participation trajectories that lead to in-video & course dropouts. Implications for research and practice are discussed


title: Differentiation of the Cholesky decomposition

abstract: We review strategies for differentiating matrix-based computations, and derive symbolic and algorithmic update rules for differentiating expressions containing the Cholesky decomposition. We recommend new `blocked' algorithms, based on differentiating the Cholesky algorithm DPOTRF in the LAPACK library, which uses `Level 3' matrix-matrix operations from BLAS, and so is cache-friendly and easy to parallelize. For large matrices, the resulting algorithms are the fastest way to compute Cholesky derivatives, and are an order of magnitude faster than the algorithms in common usage. In some computing environments, symbolically-derived updates are faster for small matrices than those based on differentiating Cholesky algorithms. The symbolic and algorithmic approaches can be combined to get the best of both worlds.


title: Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package

abstract: Dynamic time warping is a popular technique for comparing time series, providing both a distance measure that is insensitive to local compression and stretches and the warping which optimally deforms one of the two input series onto the other. A variety of algorithms and constraints have been discussed in the literature. The dtw package provides an unification of them; it allows R users to compute time series alignments mixing freely a variety of continuity constraints, restriction windows, endpoints, local distance definitions, and so on. The package also provides functions for visualizing alignments and constraints using several classic diagram types.


title: Diff/TS: A Tool for Fine-Grained Structural Change Analysis

abstract: This paper reports on a tool for fine-grained analysis of structural changes made between revisions of programs. The tool, called Diff/TS, calculates, visualizes and classifies edit operations including "moves'' that will change one revision into another by means of detailed tree structural analysis on source code. Such analysis tends to be time consuming and inflexible. We have extended a general tree comparison algorithm with heuristics driven control configurable for multiple programming languages and have achieved both processing speed and analysis precision needed for investigating large-scale software projects. The tool is capable of processing Python, Java, C and C++ projects. We present several applications including software "archaeology'' on a widely known open source software project and automated "phylogenetic'' malware classification based on control flows. These examples suggest that tree differencing is useful for measuring distance or dissimilarity between tree structured artifacts, and offer good precision tests of the method.


title: Skin antisepsis with chlorhexidine–alcohol versus povidone iodine–alcohol, with and without skin scrubbing, for prevention of intravascular-catheter-related infection (CLEAN): an open-label, multicentre, randomised, controlled, two-by-two factorial trial

abstract: Summary Background Intravascular-catheter-related infections are frequent life-threatening events in health care, but incidence can be decreased by improvements in the quality of care. Optimisation of skin antisepsis is essential to prevent short-term catheter-related infections. We hypothesised that chlorhexidine–alcohol would be more effective than povidone iodine–alcohol as a skin antiseptic to prevent intravascular-catheter-related infections. Methods In this open-label, randomised controlled trial with a two-by-two factorial design, we enrolled consecutive adults (age ≥18 years) admitted to one of 11 French intensive-care units and requiring at least one of central-venous, haemodialysis, or arterial catheters. Before catheter insertion, we randomly assigned (1:1:1:1) patients via a secure web-based random-number generator (permuted blocks of eight, stratified by centre) to have all intravascular catheters prepared with 2% chlorhexidine–70% isopropyl alcohol (chlorhexidine–alcohol) or 5% povidone iodine–69% ethanol (povidone iodine–alcohol), with or without scrubbing of the skin with detergent before antiseptic application. Physicians and nurses were not masked to group assignment but microbiologists and outcome assessors were. The primary outcome was the incidence of catheter-related infections with chlorhexidine–alcohol versus povidone iodine–alcohol in the intention-to-treat population. This study is registered with ClinicalTrials.gov, number NCT01629550 and is closed to new participants. Findings Between Oct 26, 2012, and Feb 12, 2014, 2546 patients were eligible to participate in the study. We randomly assigned 1181 patients (2547 catheters) to chlorhexidine–alcohol (594 patients with scrubbing, 587 without) and 1168 (2612 catheters) to povidone iodine–alcohol (580 patients with scrubbing, 588 without). Chlorhexidine–alcohol was associated with lower incidence of catheter-related infections (0·28 vs 1·77 per 1000 catheter-days with povidone iodine–alcohol; hazard ratio 0·15, 95% CI 0·05–0·41; p=0·0002). Scrubbing was not associated with a significant difference in catheter colonisation (p=0·3877). No systemic adverse events were reported, but severe skin reactions occurred more frequently in those assigned to chlorhexidine–alcohol (27 [3%] patients vs seven [1%] with povidone iodine–alcohol; p=0·0017) and led to chlorhexidine discontinuation in two patients. Interpretation For skin antisepsis, chlorhexidine–alcohol provides greater protection against short-term catheter-related infections than does povidone iodine–alcohol and should be included in all bundles for prevention of intravascular catheter-related infections. Funding University Hospital of Poitiers, CareFusion.


Personal Attributes Extraction in Chinese Text Based on Recurrent Neural Network


title: A fully pipelined hardware architecture for convolutional neural network with low memory usage and DRAM bandwidth

abstract: As a typical deep learning model, Convolutional Neural Network (CNN) has shown excellent ability in solving complex classification problems. To apply CNN models in mobile ends and wearable devices, a fully pipelined hardware architecture adopting a Row Processing Tree (RPT) structure with small memory resource consumption between convolutional layers is proposed. A modified Row Stationary (RS) dataflow is implemented to evaluate the RPT architecture. Under the the same work frequency requirement for these two architectures, the experimental results show that the RPT architecture reduces 91% on-chip memory and 75% DRAM bandwidth compared with the modified RS dataflow, but the throughput of the modified RS dataflow is 3 times higher than the our proposed RPT architecture. The RPT architecture can achieve 121fps at 100MHZ while processing a CNN including 4 convolutional layers.


title: A framework and risk assessment approaches for risk-based access control in the cloud

abstract: Cloud computing is advantageous for customers and service providers. However, it has specific security requirements that are not captured by traditional access control models, e.g., secure information sharing in dynamic and collaborative environments. Risk-based access control models try to overcome these limitations, but while there are well-known enforcement mechanisms for traditional access control, this is not the case for risk-based policies. In this paper, we motivate the use of risk-based access control in the cloud and present a framework for enforcing risk-based policies that is based on an extension of XACML. We also instantiate this framework using a new ontology-based risk assessment approach, as well as other models from related work, and present experimental results of the implementation of our work.


title: Pose-robust face signature for multi-view face recognition

abstract: Despite the great progress achieved in unconstrained face recognition, pose variations still remain a challenging and unsolved practical issue. We propose a novel framework for multi-view face recognition based on extracting and matching pose-robust face signatures from 2D images. Specifically, we propose an efficient method for monocular 3D face reconstruction, which is used to lift the 2D facial appearance to a canonical texture space and estimate the self-occlusion. On the lifted facial texture we then extract various local features, which are further enhanced by the occlusion encodings computed on the self-occlusion mask, resulting in a pose-robust face signature, a novel feature representation of the original 2D facial image. Extensive experiments on two public datasets demonstrate that our method not only simplifies the matching of multi-view 2D facial images by circumventing the requirement for pose-adaptive classifiers, but also achieves superior performance.


title: Toward sustainable networking: Storage area networks with network coding

abstract: This manuscript provides a model to characterize the energy savings of network coded storage (NCS) in storage area networks (SANs). We consider blocking probability of drives as our measure of performance. A mapping technique to analyze SANs as independent M/G/K/K queues is presented, and blocking probabilities for uncoded storage schemes and NCS are derived and compared. We show that coding operates differently than the amalgamation of file chunks and energy savings are shown to scale well with striping number. We illustrate that for enterprise-level SANs energy savings of 20–50% can be realized.


title: ECAST: A Benchmark Framework for Renewable Energy Forecasting Systems

abstract: The increasing capacities of renewable energy sources and the opportunities emerging from the smart grid technology lead to new challenges for energy forecasters. Energy output fluctuates stronger compared to conventional power produc- tion. More time series data is available through the usage of sensor technology. New supply forecasting approaches are developed to better address those characteristics, but meaningful benchmarks of such solutions are rare. Conduct- ing detailed evaluations is time-intensive and unattractive to customers as this is mostly handwork. We define and discuss requirements for ecient and reliable benchmarks of renewable energy supply forecasting tools. To cope with those requirements, we introduce the automated benchmark framework ECAST as our proposed solution. The system's capability is demonstrated on a real-world scenario compar- ing the performance of di↵erent prediction tools against a naive method.


title: Optimizing Scientific Workflows in the Cloud: A Montage Example

abstract: As scientific workflows are increasingly deployed in clouds, a myriad of studies have been conducted-including the development of workflow execution systems and scheduling/resource-management algorithms-for optimizing the execution of these workflows. However, the efficacy of most, if not all, of these previous works is limited by the original design and structure of workflow, i.e., Sequential code and few bottleneck tasks. In this paper, we address the optimization of scientific workflow execution in clouds by exploiting multi-core systems with the parallelization of bottleneck tasks. To this end, we develop a workflow visualization toolkit to synthetize resource consumption and data transfer patterns, as well as to identify the bottleneck, of the workflow being studied. Parallelization techniques are then applied to the module that is identified as the bottleneck in order to take full advantage of the underlying multicore computing environment. Testing results with a 6.0-degree Montage example on Amazon EC2 with various configurations show that our optimization of workflows (bottleneck tasks in particular) reduces completion time (or make span) by 21% to 43% depending on the instance type being used to run the workflow, without any impact on the cost.


title: Structure and morphology of sol-gel prepared polymer-ceramic composite thin films

abstract: Abstract Poly( p -phenylene vinylene) (PPV)-silica composite thin films were prepared via the sol-gel process incorporating a water-soluble PPV precursor. After solvent evaporation, the spin-coated or quiescently cast films were cured and the PPV precursor and the silica sol were completely converted into the PPV-silica composites. Quiescently cast films show an improved wave-guiding behaviour with a χ 3 value only slightly smaller than pure PPV films. Their structure consists of a nanophase-separated morphology composed of amorphous silica and paracrystalline PPV. Small-angle X-ray scattering and bright field transmission electron microscopy images indicate a random arrangement of the nanoscale domains with average domain size of approximately 4 nm. In contrast, spin-coated films show clusters and cluster-cluster aggregates approximately 100 nm in diameter. The clusters consist of particulate silica and pores, each about 3 nm in size, with the PPV contained in the pores and between the clusters. The observed morphologies are related to film preparation conditions in terms of sol-gel processing, taking the presence of the polymer into account.


atalytic pyrolysis of recalcitrant , insoluble humin byproducts from 6 sugar biorefineries


title: A low power TDC with 0.5ps resolution for ADPLL in 40nm CMOS

abstract: A low power time-to-digital converter (TDC) with high resolution is presented in this paper. The TDC employs a digital-to-time converter (DTC) to reduce the dynamic range based on a phase-prediction technique. A snapshot circuit is used to reduce the sampling rate from digitally-controlled oscillator (DCO) frequency to reference frequency, thus greatly saving power. In addition, the proposed digital architecture adopts a time-amplifier based TDC (TA-TDC) to achieve high resolution. The proposed TDC is implemented in SMIC 40nm CMOS. Simulation results show that it can achieve a resolution about 0.5ps while totally consuming only 163uW.


title: Partial light field tomographic reconstruction from a fixed-camera focal stack

abstract: This paper describes a novel approach to partially reconstruct high-resolution 4D light fields from a stack of differently focused photographs taken with a fixed camera. First, a focus map is calculated from this stack using a simple approach combining gradient detection and region expansion with graph-cut. Then, this focus map is converted into a depth map thanks to the calibration of the camera. We proceed after this with the tomographic reconstruction of the epipolar images by back-projecting the focused regions of the scene only. We call it masked back-projection. The angles of back-projection are calculated from the depth map. Thanks to the high angular resolution we achieve by suitably exploiting the image content captured over a large interval of focus distances, we are able to render puzzling perspective shifts although the original photographs were taken from a single fixed camera at a fixed position.


title: Composing Measures for Computing Text Similarity

abstract: We present a comprehensive study of computing similarity between texts. We start from the observation that while the concept of similarity is well grounded in psychology, text similarity is much less well-defined in the natural language processing community. We thus define the notion of text similarity and distinguish it from related tasks such as textual entailment and near-duplicate detection. We then identify multiple text dimensions, i.e. characteristics inherent to texts that can be used to judge text similarity, for which we provide empirical evidence. ::: We discuss state-of-the-art text similarity measures previously proposed in the literature, before continuing with a thorough discussion of common evaluation metrics and datasets. Based on the analysis, we devise an architecture which combines text similarity measures in a unified classification framework. We apply our system in two evaluation settings, for which it consistently outperforms prior work and competing systems: (a) an intrinsic evaluation in the context of the Semantic Textual Similarity Task as part of the Semantic Evaluation (SemEval) exercises, and (b) an extrinsic evaluation for the detection of text reuse. ::: As a basis for future work, we introduce DKPro Similarity, an open source software package which streamlines the development of text similarity measures and complete experimental setups.


title: Aspect-oriented programming with C# and .NET

abstract: Microsoft has introduced the .NET architecture as a new component-based programming environment, which allows for easy integration of classical distributed programming techniques with Web computing. NET defines a type system and introduces notions such as component, object, and interface, which are building blocks for distributed multi-language component-based applications. As many other component frameworks, .NET mainly focuses on functional interfaces of components. Non-functional aspects of components, such as resource usage (CPU, memory), timing behavior, fault-tolerance, or security requirements are currently not expressed in .NET's component interfaces. We discuss the usage of aspect-oriented programming techniques in context of the .NET framework. We focus on the fault-tolerance aspect and discuss the expression of non-functional component properties (aspects) as C# custom attributes. Our approach uses reflection to generate replicated objects based on settings of a special "fault-tolerance" attribute for C# components. We have implemented an aspect-weaver for integration of aspect-code and component-code, which uses the mechanisms of the language-neutral .NET type system. Therefore, our approach is not restricted to the C# language but works for any of the .NET programming languages.


title: Towards a neural basis of music perception

abstract: Music perception involves complex brain functions underlying acoustic analysis, auditory memory, auditory scene analysis, and processing of musical syntax and semantics. Moreover, music perception potentially affects emotion, influences the autonomic nervous system, the hormonal and immune systems, and activates (pre)motor representations. During the past few years, research activities on different aspects of music processing and their neural correlates have rapidly progressed. This article provides an overview of recent developments and a framework for the perceptual side of music processing. This framework lays out a model of the cognitive modules involved in music perception, and incorporates information about the time course of activity of some of these modules, as well as research findings about where in the brain these modules might be located.


title: Detection Algorithms for V-BLAST Systems

abstract: Vertical Bell Laboratories Layered Space-Time(V-BLAST) is a novel Multi-Input Multi-Output(MIMO) antenna scheme.This paper focuses on the detection algorithms for V-BLAST.Firstly the structure of the system is introduced.And then various detection algorithms are analyzed.Finally some conclusions are drawn on the complexity and performance of different algorithms based on the simulation results.


title: Evaluation of focus measures in multi-focus image fusion

abstract: Several focus measures were studied in this paper as the measures of image clarity, in the field of multi-focus image fusion. All these focus measures are defined in the spatial domain and can be implemented in real-time fusion systems with fast response and robustness. This paper proposed a method to assess focus measures according to focus measures' capability of distinguishing focused image blocks from defocused image blocks. Experiments were conducted on several sets of images and results show that sum-modified-Laplacian (SML) can provide better performance than other focus measures, when the execution time is not included in the evaluation.


title: Reflective afocal broadband adaptive optics scanning ophthalmoscope

abstract: A broadband adaptive optics scanning ophthalmoscope (BAOSO) consisting of four afocal telescopes, formed by pairs of off-axis spherical mirrors in a non-planar arrangement, is presented. The non-planar folding of the telescopes is used to simultaneously reduce pupil and image plane astigmatism. The former improves the adaptive optics performance by reducing the root-mean-square (RMS) of the wavefront and the beam wandering due to optical scanning. The latter provides diffraction limited performance over a 3 diopter (D) vergence range. This vergence range allows for the use of any broadband light source(s) in the 450-850 nm wavelength range to simultaneously image any combination of retinal layers. Imaging modalities that could benefit from such a large vergence range are optical coherence tomography (OCT), multi- and hyper-spectral imaging, single- and multi-photon fluorescence. The benefits of the non-planar telescopes in the BAOSO are illustrated by resolving the human foveal photoreceptor mosaic in reflectance using two different superluminescent diodes with 680 and 796 nm peak wavelengths, reaching the eye with a vergence of 0.76 D relative to each other.


title: Morphological and molecular characterization of Colletotrichum species from herbaceous plants in Thailand

abstract: Thirty-four isolates of Colletotrichum spp. were isolated from banana, ginger, Euphatorium thymifolia, soybean, longan, mango and Draceana sanderiana. They included endophytes from healthy plants and probable pathogens from disease lesions. Isolates were identified and grouped based on colony morphology, and size and shape of appressoria and conidia. Molecular analysis based on sequences of the rDNA internal transcribed spacers (ITS1 and ITS2), indicated that the Colletotrichum isolates comprised four clades that paralleled the morphological groupings. Most isolates clustered within three distinct clades which potentially represented different species. Endophytes isolated from different hosts are more likely to be the same species. Colletotrichum musae was positioned close to the C. gloeosporioides clades. Morphological and phylogenetic analysis of Colletotrichum pathogens and endophytes showed that endophytic isolates were most similar to C. gloeosporioides however, no pathogenic isolates clustered with endophytic isolates. The correlation between morphological and molecular-based clustering demonstrated the genetic relationships among the isolates and species of Colletotrichum and indicated that ITS rDNA sequence data were potentially useful in taxonomic species determination.


title: Factors influencing the adoption of personalisation mobile services: empirical evidence from young Australians

abstract: The adoption of mobile services is often studied at a generic level and limited research has addressed personalisation mobile services. The purpose of this paper is to develop a model that attempts to predict the adoption of personalisation mobile services amongst young Australians. Employing quantitative empirical evidence, it was found that perceived enjoyment and perceived usefulness are the most important factors in predicting the adoption of these services. These findings provide theoretical and practical implications and insights for the development, design and marketing of personalisation mobile services. Knowledge and appreciation of these factors may help mobile operators and service providers to both design novel personalisation services or enhance existing ones in order to gain competitive advantage.


Software Fault Prediction: A Systematic Mapping Study.


title: PERSONALITY PROCESSES AND INDIVIDUAL DIFFERENCES Interpersonal Evaluations Following Threats to Self: Role of Self-Esteem

abstract: In 2 studies, the authors used dyadic interactions to assess the influence of ego threat on likability as a function of self-esteem. In both studies, 2 naive participants engaged in a structured conversation; in half of the dyads, 1 participant received an ego threat prior to the interaction. In the 1st study, threatened high self-esteem participants were rated as less likable than were threatened low self-esteem participants. The 2nd study confirmed that ego threats are associated with decreased liking for those with high self-esteem and with increased liking for those with low self-esteem. A mediational analysis demonstrated that decreased liking among high self-esteem participants was due to being perceived as antagonistic. Study 2 also indicated that the findings could not be explained by trait levels of narcissism. These patterns are interpreted in terms of differential sensitivity to potential interpersonal rejection.


title: TABLA: A unified template-based framework for accelerating statistical machine learning

abstract: A growing number of commercial and enterprise systems increasingly rely on compute-intensive Machine Learning (ML) algorithms. While the demand for these compute-intensive applications is growing, the performance benefits from general-purpose platforms are diminishing. Field Programmable Gate Arrays (FPGAs) provide a promising path forward to accommodate the needs of machine learning algorithms and represent an intermediate point between the efficiency of ASICs and the programmability of general-purpose processors. However, acceleration with FPGAs still requires long development cycles and extensive expertise in hardware design. To tackle this challenge, instead of designing an accelerator for a machine learning algorithm, we present TABLA, a framework that generates accelerators for a class of machine learning algorithms. The key is to identify the commonalities across a wide range of machine learning algorithms and utilize this commonality to provide a high-level abstraction for programmers. TABLA leverages the insight that many learning algorithms can be expressed as a stochastic optimization problem. Therefore, learning becomes solving an optimization problem using stochastic gradient descent that minimizes an objective function over the training data. The gradient descent solver is fixed while the objective function changes for different learning algorithms. TABLA provides a template-based framework to accelerate this class of learning algorithms. Therefore, a developer can specify the learning task by only expressing the gradient of the objective function using our high-level language. Tabla then automatically generates the synthesizable implementation of the accelerator for FPGA realization using a set of hand-optimized templates. We use Tabla to generate accelerators for ten different learning tasks targeted at a Xilinx Zynq FPGA platform. We rigorously compare the benefits of FPGA acceleration to multi-core CPUs (ARM Cortex A15 and Xeon E3) and many-core GPUs (Tegra K1, GTX 650 Ti, and Tesla K40) using real hardware measurements. TABLA-generated accelerators provide 19.4x and 2.9x average speedup over the ARM and Xeon processors, respectively. These accelerators provide 17.57x, 20.2x, and 33.4x higher Performance-per-Watt in comparison to Tegra, GTX 650 Ti and Tesla, respectively. These benefits are achieved while the programmers write less than 50 lines of code.


title: On Rooted Node-Connectivity Problems

abstract: Let G be a graph which is k -outconnected from a specified root node r , that is, G has k openly disjoint paths between r and v for every node v . We give necessary and sufficient conditions for the existence of a pair rv,rw of edges for which replacing these edges by a new edge vw gives a graph that is k -outconnected from r . This generalizes a theorem of Bienstock et al. on splitting off edges while preserving k -node-connectivity.


title: Competent Geometric Semantic Genetic Programming for Symbolic Regression and Boolean Function Synthesis

abstract: Program semantics is a promising recent research thread in Genetic Programming (GP). Over a dozen semantic-aware search, selection, and initialization operators for GP have been proposed to date. S...


title: Determination of D-penicillamine in serum by fluorescence derivatization and liquid column chromatography.

abstract: A simple and fast method for the determination of D-penicillamine in serum is described. The analysis is based o a fluorescence derivatization of the sulfhydryl group combined with a reversed-phase liquid chromatograhic separation and fluorescence detection. Before derivatization the serum proteins are precipitated with ethanol and removed by centrifugation. As derivatizing agent 5-dimethylaminonaphthaline-1-sulfonylaziridine is used which reacts selectively with thiols under defined reaction conditions. The detection limit is in the pmol range; 50-300 ng of D-penicillamine can be determined with a relative standard deviation of 7-8%. Thus the method permits a simple determination of D-penicillamine in serum at therapeutic levels.


title: The effect of two plyometric training techniques on muscular power and agility in youth soccer players

abstract: The aim of this study was to compare the effects of two plyometric training techniques on power and agility in youth soccer players. Twelve males from a semiprofessional football club's academy (age = 17.3 ± 0.4 years, stature = 177.9 ± 5.1 cm, mass = 68.7 ± 5.6 kg) were randomly assigned to 6 weeks of depth jump (DJ) or countermovement jump (CMJ) training twice weekly. Participants in the DJ group performed drop jumps with instructions to minimize ground-contact time while maximizing height. Participants in the CMJ group performed jumps from a standing start position with instructions to gain maximum jump height. Posttraining, both groups experienced improvements in vertical jump height (p 0.05). There were no differences between the treatment groups (p > 0.05). The study concludes that both DJ and CMJ plyometrics are worthwhile training activities for improving power and agility in youth soccer players.


title: Towards Motion Aware Light Field Video for Dynamic Scenes

abstract: Current Light Field (LF) cameras offer fixed resolution in space, time and angle which is decided a-priori and is independent of the scene. These cameras either trade-off spatial resolution to capture single-shot LF or tradeoff temporal resolution by assuming a static scene to capture high spatial resolution LF. Thus, capturing high spatial resolution LF video for dynamic scenes remains an open and challenging problem. We present the concept, design and implementation of a LF video camera that allows capturing high resolution LF video. The spatial, angular and temporal resolution are not fixed a-priori and we exploit the scene-specific redundancy in space, time and angle. Our reconstruction is motion-aware and offers a continuum of resolution tradeoff with increasing motion in the scene. The key idea is (a) to design efficient multiplexing matrices that allow resolution tradeoffs, (b) use dictionary learning and sparse representations for robust reconstruction, and (c) perform local motion-aware adaptive reconstruction. We perform extensive analysis and characterize the performance of our motion-aware reconstruction algorithm. We show realistic simulations using a graphics simulator as well as real results using a LCoS based programmable camera. We demonstrate novel results such as high resolution digital refocusing for dynamic moving objects.


Less is more ? How demographic sample weights can improve public opinion estimates based on Twitter data


title: The blood supply to the femoral head after posterior fracture/dislocation of the hip, assessed by CT angiography

abstract: The femoral head receives blood supply mainly ::: from the deep branch of the medial femoral circumflex artery (MFCA). ::: In previous studies we have performed anatomical dissections of ::: 16 specimens and subsequently visualised the arteries supplying ::: the femoral head in ::: 55 healthy individuals. In this further radiological study we compared ::: the arterial supply of the femoral head in 35 patients (34 men and ::: one woman, mean age 37.1 years (16 to 64)) with a fracture/dislocation ::: of the hip with a historical control group of 55 hips. Using CT ::: angiography, we identified the three main arteries supplying the femoral ::: head: the deep branch and the postero-inferior nutrient artery both ::: arising from the MFCA, and the piriformis branch of the inferior ::: gluteal artery. It was possible to visualise changes in blood flow ::: after fracture/dislocation. Our results suggest that blood flow is present after reduction ::: of the dislocated hip. The deep branch of the MFCA was patent and ::: contrast-enhanced in 32 patients, and the diameter of this branch ::: was significantly larger in the fracture/dislocation group than ::: in the control group (p = 0.022). In a subgroup of ten patients ::: with avascular necrosis (AVN) of the femoral head, we found a contrast-enhanced ::: deep branch of the MFCA in eight hips. Two patients with no blood ::: flow in any of the three main arteries supplying the femoral head ::: developed AVN. Cite this article: Bone Joint J 2013;95-B:1453–7.


title: From Theory to Experimental Evaluation: Resource Management in Software-Defined Vehicular Networks

abstract: Managing resources in dynamic vehicular environments is a tough task, which is becoming more challenging with the increased number of access technologies today available in connected cars (e.g., IEEE 802.11, LTE), in the variety of applications provided on the road (e.g., safety, traffic efficiency, and infotainment), in the amount of driving awareness/coordination required (e.g., local, context, and cooperative awareness), and in the level of automation toward zero-accident driving (e.g., platooning and autonomous driving). The open programmability and logically centralized control features of the software–defined networking (SDN) paradigm offer an attractive means to manage communication and networking resources in the vehicular environment and promise improved performance. In this paper, we enumerate the potentials of software-defined vehicular networks, analyze the need to rethink the traditional SDN approach from theoretical and practical standpoints when applied in this application context, and present an emulation approach based on the proposed node car architecture in Mininet-WiFi to showcase the applicability and some expected benefits of SDN in a selected use case scenario.


The Rate and Risk Factors for Anemia among Pregnant Mothers in Jerteh Terengganu, Malaysia


title: Design and development of image defogging system

abstract: The proposed work presents a prototype of novel method that comprises of a complete embedded system for the process of restoring the foggy images. Foggy conditions are considered to be a major source of accidents for vehicles. Use of a novel ‘Mean Channel Prior (MCP)’ algorithm for defogging gave more accurate results as compared with the earlier existing methodologies. The prototype of proposed system works with use of raspberry pi, camera and display screen. Use of low cost, low power processing core raspberry pi made it low power, portable and stand alone system. Results of MCP image defogging algorithm are compared with Dark Channel Prior (DCP), Median channel Prior (MCP) and Improved single image dehazing using dark channel prior (ISIDDCP) on the basis of two parameters: Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR).


title: Scaling limits in batch-fabricated silicon pressure sensors

abstract: The scaling properties of silicon capacitive and piezoresistive pressure sensors are described. An evaluation of the various noise mechanisms and pressure offsets in the scaled devices is presented, including Brownian noise, electrical noise, electrostatic pressure variations and pressure offset due to resistor mismatch. The analysis of diaphragm deflection includes the effects of intrinsic stress and the transition from plate theory to membrane theory. Both ultraminiature and ultrasensitive sensors are considered. Ultraminiature piezoresistive sensors with diaphragms measuring 100 µm in length and resolving 1 mmHg should be possible using present technology as well as ultrasensitive capacitive sensors that resolve 1 µmHg.


title: A particle swarm optimization based hyper-heuristic algorithm for the classic resource constrained project scheduling problem

abstract: Abstract In this paper, we propose a particle swarm optimization (PSO) based hyper-heuristic algorithm for solving the resource constrained project scheduling problem (RCPSP). To the best of our knowledge, this is the first attempt to develop a PSO hyper-heuristic and apply to the classic RCPSP. The hyper-heuristic works as an upper-level algorithm that controls several low-level heuristics which operate to the solution space. The solution representation is based on random keys. Active schedules are constructed by the serial scheduling generation scheme using the priorities of the activities which are modified by the low-level heuristics of the algorithm. Also, the double justification operator, i.e. a forward–backward improvement procedure, is applied to all solutions. The proposed approach was tested on a set of standard problem instances of the well-known library PSPLIB and compared with other approaches from the literature. The promising computational results validate the effectiveness of the proposed approach.


title: Utilization of healthcare services in postpartum women in the Philippines who delivered at home and the effects on their health: a cross-sectional analytical study

abstract: BACKGROUND ::: A low ratio of utilization of healthcare services in postpartum women may contribute to maternal deaths during the postpartum period. The maternal mortality ratio is high in the Philippines. The aim of this study was to examine the current utilization of healthcare services and the effects on the health of women in the Philippines who delivered at home. ::: ::: ::: METHODS ::: This was a cross-sectional analytical study, based on a self-administrated questionnaire, conducted from March 2015 to February 2016 in Muntinlupa, Philippines. Sixty-three postpartum women who delivered at home or at a facility were enrolled for this study. A questionnaire containing questions regarding characteristics, utilization of healthcare services, and abnormal symptoms during postpartum period was administered. To analyze the questionnaire data, the sample was divided into delivery at home and delivery at a facility. Chi-square test, Fisher's exact test, and Mann-Whitney U test were used. ::: ::: ::: RESULTS ::: There were significant differences in the type of birth attendant, area of residence, monthly income, and maternal and child health book usage between women who delivered at home and those who delivered at a facility (P<0.01). There was significant difference in the utilization of antenatal checkup (P<0.01) during pregnancy, whilst there was no significant difference in utilization of healthcare services during the postpartum period. Women who delivered at home were more likely to experience feeling of irritated eyes and headaches, and continuous abdominal pain (P<0.05). ::: ::: ::: CONCLUSION ::: Financial and environmental barriers might hinder the utilization of healthcare services by women who deliver at home in the Philippines. Low utilization of healthcare services in women who deliver at home might result in more frequent abnormal symptoms during postpartum.


title: Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control

abstract: We propose a plan online and learn offline (POLO) framework for the setting where an agent, with an internal model, needs to continually act and learn in the world. Our work builds on the synergistic relationship between local model-based control, global value function learning, and exploration. We study how local trajectory optimization can cope with approximation errors in the value function, and can stabilize and accelerate value function learning. Conversely, we also study how approximate value functions can help reduce the planning horizon and allow for better policies beyond local solutions. Finally, we also demonstrate how trajectory optimization can be used to perform temporally coordinated exploration in conjunction with estimating uncertainty in value function approximation. This exploration is critical for fast and stable learning of the value function. Combining these components enable solutions to complex simulated control tasks, like humanoid locomotion and dexterous in-hand manipulation, in the equivalent of a few minutes of experience in the real world.


title: The Social Brain Hypothesis

abstract: Conventional wisdom over the past 160 years in the cognitive and neurosciences has assumed that brains evolved to process factual information about the world. Most attention has therefore been focused on such features as pattern recognition, color vision, and speech perception. By extension, it was assumed that brains evolved to deal with essentially ecological problem-solving tasks. © 1998 Wiley-Liss, Inc.


title: MDL for Causal Inference on Discrete Data

abstract: The algorithmic Markov condition states that the most likely causal direction between two random variables X and Y can be identified as the direction with the lowest Kolmogorov complexity. This notion is very powerful as it can detect any causal dependency that can be explained by a physical process. However, due to the halting problem, it is also not computable. In this paper we propose an computable instantiation that provably maintains the key aspects of the ideal. We propose to approximate Kolmogorov complexity via the Minimum Description Length (MDL) principle, using a score that is mini-max optimal with regard to the model class under consideration. This means that even in an adversarial setting, the score degrades gracefully, and we are still maximally able to detect dependencies between the marginal and the conditional distribution. As a proof of concept, we propose CISC, a linear-time algorithm for causal inference by stochastic complexity, for pairs of univariate discrete variables. Experiments show that CISC is highly accurate on synthetic, benchmark, as well as real-world data, outperforming the state of the art by a margin, and scales extremely well with regard to sample and domain sizes.


title: Active appearance model and deep learning for more accurate prostate segmentation on MRI

abstract: Prostate segmentation on 3D MR images is a challenging task due to image artifacts, large inter-patient prostate shape and texture variability, and lack of a clear prostate boundary specifically at apex and base levels. We propose a supervised machine learning model that combines atlas based Active Appearance Model (AAM) with a Deep Learning model to segment the prostate on MR images. The performance of the segmentation method is evaluated on 20 unseen MR image datasets. The proposed method combining AAM and Deep Learning achieves a mean Dice Similarity Coefficient (DSC) of 0.925 for whole 3D MR images of the prostate using axial cross-sections. The proposed model utilizes the adaptive atlas-based AAM model and Deep Learning to achieve significant segmentation accuracy.


title: Medical nanobots

abstract: Researchers working in medical nanorobotics are creating technologies that could lead to novel health-care applications, such as new ways of accessing areas of the human body that would otherwise be unreachable without invasive surgery.


title: Quantifying the changing role of past publications

abstract: Our current societies increasingly rely on electronic repositories of collective knowledge. An archetype of these databases is the Web of Science (WoS) that stores scientific publications. In contrast to several other forms of knowledge--e.g., Wikipedia articles--a scientific paper does not change after its "birth". Nonetheless, from the moment a paper is published it exists within the evolving web of other papers, thus, its actual meaning to the reader changes. To track how scientific ideas (represented by groups of scientific papers) appear and evolve, we apply a novel combination of algorithms explicitly allowing for papers to change their groups. We (1) identify the overlapping clusters of the undirected yearly co-citation networks of the WoS (1975---2008) and (2) match these yearly clusters (groups) to form group timelines. After visualizing the longest lived groups of the entire data set we assign topic labels to all groups. We find that in the entire WoS multidisciplinarity is clearly over-represented among cutting edge ideas. In addition, we provide detailed examples for papers that (1) change their topic labels and (2) move between groups.


title: Fast and accurate scan registration through minimization of the distance between compact 3D NDT representations

abstract: Registration of range sensor measurements is an important task in mobile robotics and has received a lot of attention. Several iterative optimization schemes have been proposed in order to align th ...


title: Toward Information Diffusion Model for Viral Marketing in Business

abstract: Current obstacles in the study of social media marketing include dealing with massive data and real-time updates have motivated to contribute solutions that can be adopted for viral marketing. Since information diffusion and social networks are the core of viral marketing, this article aims to investigate the constellation of diffusion methods for viral marketing. Studies on diffusion methods for viral marketing have applied different computational methods, but a systematic investigation of these methods has limited. Most of the literature have focused on achieving objectives such as influence maxi-mization or community detection. Therefore, this article aims to conduct an in-depth review of works related to diffusion for viral marketing. Viral marketing has applied to business-to-consumer transactions but has seen limited adoption in business-to-business transactions. The literature review reveals a lack of new diffusion methods, especially in dynamic and large-scale networks. It also offers insights into applying various mining methods for viral marketing. It discusses some of the challenges, limitations, and future research directions of information diffusion for viral marketing. The article also introduces a viral marketing informa-tion diffusion model. The proposed model attempts to solve the dynamicity and large-scale data of social networks by adopting incremental clustering and a stochastic differential equation for business-to-business transactions.


title: GUN: Gradual Upsampling Network for Single Image Super-Resolution

abstract: In this paper, an efficient super-resolution (SR) method based on deep convolutional neural network (CNN) is proposed, namely gradual upsampling network (GUN). Recent CNN-based SR methods often preliminarily magnify the low-resolution (LR) input to high-resolution (HR) input and then reconstruct the HR input, or directly reconstruct the LR input and then recover the HR result at the last layer. The proposed GUN utilizes a gradual process instead of these two commonly used frameworks. The GUN consists of an input layer, multiple upsampling and convolutional layers, and an output layer. By means of the gradual process, the proposed network can simplify the direct SR problem to multistep easier upsampling tasks with very small magnification factor in each step. Furthermore, a gradual training strategy is presented for the GUN. In the proposed training process, an initial network can be easily trained with edgelike samples, and then, the weights are gradually tuned with more complex samples. The GUN can recover fine and vivid results and is easy to be trained. The experimental results on several image sets demonstrate the effectiveness of the proposed network.


title: Generic Axiomatization of Families of Noncrossing Graphs in Dependency Parsing

abstract: We present a simple encoding for unlabeled noncrossing graphs and show how its latent counterpart helps us to represent several families of directed and undirected graphs used in syntactic and semantic parsing of natural language as context-free languages. The families are separated purely on the basis of forbidden patterns in latent encoding, eliminating the need to differentiate the families of non-crossing graphs in inference algorithms: one algorithm works for all when the search space can be controlled in parser input.


title: Decreasing Popularity Of Ceramic Tourism Village Of Dinoyo Malang In New Media Age

abstract: Cyber media and social media applications become the key of many tourist attractions in Indonesia. In Malang city, East Java, the local government strengthens the tourism sector by building its tourism village. Utilizing the new media network, Malang is now known as the pioneer of Tourism Village. Unfortunately, as the number of tourism villages becomes famous, there is one famous tourism village that was previously famous, even falling in popularity, namely ‘Kampung Wisata Keramik Dinoyo’. This study aimed to determine the causes of decreasing popularity of the village using public relations theory and descriptive qualitative research methods. In-depth interview were administered to the Chairman of Paguyuban Kampung Wisata Keramik Dinoyo, government, and shop owners in Dinoyo Village. The results showed that Kampung Keramik Dinoyo removed many sectors in the field of public relations, especially the use of new media. It was suggested to tourism village to fully use the new media access in order to improve its image so its popularity also increased.


title: LDT: a language definition technique

abstract: We establish a semantics for building grammars from a modularised specification in which modules are able to delete individual productions from imported nonterminals. Our goal is to allow precise answers to the question: 'what character level language does this grammar generate' in the face of difficult issues such as the mutual embedding of languages that have different whitespace and commenting conventions. Our technique is to automatically generate a character level grammar from grammars written at token level in the conventional way; the grammars is constructed from modules each of which may have its own whitespace convention. Modules have import and export lists, and productions may be deleted nonterminals upon import. We conclude with a discussion of a concrete syntax which we use as the input language to our parser generator.


title: Transformed Representations for Convolutional Neural Networks in Diabetic Retinopathy Screening

abstract: Convolutional neural networks (CNNs) are flexible, biologically-inspired variants of multi-layer perceptrons that have proven themselves to be exceptionally suited to discriminative vision tasks. However, relatively little is known on whether they can be made both more efficient and more accurate, by introducing suitable transformations that exploit general knowledge of the target classes. We demonstrate this functionality through pre-segmentation of input images with a fast and robust but loose segmentation step, to obtain a set of candidate objects. These objects then undergo a spatial transformation into a reduced space, retaining but a compact high-level representation of their appearance. Additional attributes may be abstracted as raw features that are incorporated after the convolutional phase of the network. Finally, we compare its performance against existing approaches on the challenging problem of detecting lesions in retinal images.


title: Agile product line engineering—a systematic literature review

abstract: Software Product Line Engineering (SPLE) demands upfront long-term investment in (i) designing a common set of core-assets and (ii) managing variability across the products from the same family. When anticipated changes in these core-assets have been predicted with certain accuracy, SPLE has proved significant improvements. However, when large/complex software product line projects have to deal with changing market conditions, alternatives to supplement SPLE are required. Agile Software Development (ASD) may be an alternative, as agile processes harness change for the customer's competitive advantage. However, when the aim is to scale Agile projects up to effectively manage reusability and variability across the products from the same family, alternatives to supplement agility are also required. As a result, a new approach called Agile Product Line Engineering (APLE) advocates integrating SPLE and ASD with the aim of addressing these gaps. APLE is an emerging approach, which implies that organizations have to face several barriers to achieve its adoption. This paper presents a systematic literature review of experiences and practices on APLE, in which the key findings uncover important challenges about how to integrate the SPLE model with an agile iterative approach to fully put APLE into practice. Copyright © 2011 John Wiley & Sons, Ltd.


title: MVR: An Architecture for Computation Offloading in Mobile Edge Computing

abstract: As communication and sensing capabilities of mobile devices increase, mobile applications are becoming increasingly complex. The ability of computation offloading, which is one of the main features of mobile edge computing gains relevance as a technique to improve the battery lifetime of mobile devices and increase the performance of applications. In this paper, we describe the offloading system model and present an innovative architecture, called "MVR", contributing to computation offloading in mobile edge computing.


title: Spectral Learning for Supervised Topic Models

abstract: Supervised topic models simultaneously model the latent topic structure of large collections of documents and a response variable associated with each document. Existing inference methods are based on variational approximation or Monte Carlo sampling, which often suffers from the local minimum defect. Spectral methods have been applied to learn unsupervised topic models, such as latent Dirichlet allocation (LDA), with provable guarantees. This paper investigates the possibility of applying spectral methods to recover the parameters of supervised LDA (sLDA). We first present a two-stage spectral method, which recovers the parameters of LDA followed by a power update method to recover the regression model parameters. Then, we further present a single-phase spectral algorithm to jointly recover the topic distribution matrix as well as the regression weights. Our spectral algorithms are provably correct and computationally efficient. We prove a sample complexity bound for each algorithm and subsequently derive a sufficient condition for the identifiability of sLDA. Thorough experiments on synthetic and real-world datasets verify the theory and demonstrate the practical effectiveness of the spectral algorithms. In fact, our results on a large-scale review rating dataset demonstrate that our single-phase spectral algorithm alone gets comparable or even better performance than state-of-the-art methods, while previous work on spectral methods has rarely reported such promising performance.


title: A multi-sensor fusion system for moving object detection and tracking in urban driving environments

abstract: A self-driving car, to be deployed in real-world driving environments, must be capable of reliably detecting and effectively tracking of nearby moving objects. This paper presents our new, moving object detection and tracking system that extends and improves our earlier system used for the 2007 DARPA Urban Challenge. We revised our earlier motion and observation models for active sensors (i.e., radars and LIDARs) and introduced a vision sensor. In the new system, the vision module detects pedestrians, bicyclists, and vehicles to generate corresponding vision targets. Our system utilizes this visual recognition information to improve a tracking model selection, data association, and movement classification of our earlier system. Through the test using the data log of actual driving, we demonstrate the improvement and performance gain of our new tracking system.


title: V2X communication protocol in VANET for co-operative intelligent transportation system

abstract: Modern vehicles equipped with driver assistance systems feel by sensors, can see by cameras and in future speak (by communication systems). The new technology of cooperative Intelligent Transport Systems and Services (C-ITS) enables communication between vehicles and traffic infrastructure. Vehicular Ad-hoc Networks (VANETs) using a connected-car technology can support safety-critical applications in Cooperative Intelligent Transportation Systems (C-ITS). Services and applications being mainly subjected to safety critical systems. In this paper, we discussed an alternative to support V2X communication instead of using WAVE/DSRC. We used flexible mix of cellular network, wifi and ZigBee, to offload cellular traffic and alleviate additional cost, Wi-Fi Direct is used as a subsidiary method to enable communication by using local links without the help of infrastructure. Zifbee is used for connecting road side sensors. Our proposed system provides connectivity and monitoring vehicles through a cellular network and with the usage of wifi, local data transmission is established.


title: Characterization of evoked tactile sensation in forearm amputees with transcutaneous electrical nerve stimulation

abstract: Objective. The goal of this study is to characterize the phenomenon of evoked tactile sensation (ETS) on the stump skin of forearm amputees using transcutaneous electrical nerve stimulation (TENS). Approach. We identified the projected finger map (PFM) of ETS on the stump skin in 11 forearm amputees, and compared perceptual attributes of the ETS in nine forearm amputees and eight able-bodied subjects using TENS. The profile of perceptual thresholds at the most sensitive points (MSPs) in each finger-projected area was obtained by modulating current amplitude, pulse width, and frequency of the biphasic, rectangular current stimulus. The long-term stability of the PFM and the perceptual threshold of the ETS were monitored in five forearm amputees for a period of 11 months. Main results. Five finger-specific projection areas can be independently identified on the stump skin of forearm amputees with a relatively long residual stump length. The shape of the PFM was progressively similar to that of the hand with more distal amputation. Similar sensory modalities of touch, pressure, buzz, vibration, and numb below pain sensation could be evoked both in the PFM of the stump skin of amputees and in the normal skin of able-bodied subjects. Sensory thresholds in the normal skin of able-bodied subjects were generally lower than those in the stump skin of forearm amputees, however, both were linearly modulated by current amplitude and pulse width. The variation of the MSPs in the PFM was confined to a small elliptical area with 95% confidence. The perceptual thresholds of thumb-projected areas were found to vary less than 0.99 × 10−2 mA cm−2. Significance. The stable PFM and sensory thresholds of ETS are desirable for a non-invasive neural interface that can feed back finger-specific tactile information from the prosthetic hand to forearm amputees.


The incentive structure in an online information market