title: Link layer behavior of body area networks at 2.4 GHz

abstract: Body Area Networks (BANs) can perform the task of continuous, remote monitoring of a patient's physiological signals in diverse environments. Apart from providing healthcare professionals with extensive logs of a patient's physiological history, BANs can be used to identify and react to emergency situations. We identify three important factors that afflict wireless communication in BANs: impermeability of the human body to radio waves at frequencies commonly used in BANs, efficient operation in mobile and time-varying environments, and mission-critical requirements for quick response to emergencies. An understanding of the link layer behavior of wireless sensor nodes placed on the body is crucial to address these and other challenges such as reducing energy consumption and increasing network lifetime. In this paper, we investigate link layer behavior by placing nodes on the body and directly measuring metrics of interest to engineers such as packet delivery ratio (PDR) and RSSI. Emulating a possible real-life BAN operating at the 2.4 GHz band with 12 sensor nodes, we collect over 80 hours of data from 14 volunteers in $3$ different environments that BANs are expected to operate in. We analyze the data to reveal several link layer characteristics to provide insight and guidelines for the designing of BANs. We also evaluate the performance of common routing metrics on our data. Our analysis helps us make the following conclusions. Link PDR is highly affected by the environment and not significantly by the volunteer for the experiment. Routing between nodes on the same side of the body is preferred to routing between nodes on the opposite sides. For links with the same source, failure of packet transmission to a certain node, in some cases, implies the increased probability of reception for other nodes. Most errors occur in bursts of length 1, but a small fraction occur in longer periods ($40$ packets or more).


title: Fear of missing out, need for touch, anxiety and depression are related to problematic smartphone use

abstract: Problematic smartphone use is an important public health challenge and is linked with poor mental health outcomes. However, little is known about the mechanisms that maintain this behavior. We recruited a sample of 308 participants from Amazon's Mechanical Turk labor market. Participants responded to standardized measures of problematic smartphone use, and frequency of smartphone use, depression and anxiety and possible mechanisms including behavioral activation, need for touch, fear of missing out (FoMO), and emotion regulation. Problematic smartphone use was most correlated with anxiety, need for touch and FoMO. The frequency of use was most correlated (inversely) with depression. In regression models, problematic smartphone use was associated with FoMO, depression (inversely), anxiety, and need for touch. Frequency of use was associated with need for touch, and (inversely) with depressive symptoms. Behavioral activation mediated associations between smartphone use (both problematic and usage frequency) and depression and anxiety symptoms. Emotional suppression also mediated the association between problematic smartphone use and anxiety. Results demonstrate the importance of social and tactile need fulfillment variables such as FoMO and need for touch as critical mechanisms that can explain problematic smartphone use and its association with depression and anxiety. Problematic smartphone use was most related to the fear of missing out, depression (inversely), and a need for touch.Behavior activation mediated associations between smartphone use and both anxiety and depression.Results demonstrate the importance of social and tactile need fulfillment variables that explain problem smartphone use.


title: What happens when HTTP adaptive streaming players compete for bandwidth?

abstract: With an increasing demand for high-quality video content over the Internet, it is becoming more likely that two or more adaptive streaming players share the same network bottleneck and compete for available bandwidth. This competition can lead to three performance problems: player instability, unfairness between players, and bandwidth underutilization. However, the dynamics of such competition and the root cause for the previous three problems are not yet well understood. In this paper, we focus on the problem of competing video players and describe how the typical behavior of an adaptive streaming player in its Steady-State, which includes periods of activity followed by periods of inactivity (ON-OFF periods), is the main root cause behind the problems listed above. We use two adaptive players to experimentally showcase these issues. Then, focusing on the issue of player instability, we test how several factors (the ON-OFF durations, the available bandwidth and its relation to available bitrates, and the number of competing players) affect stability.


title: Long term monitoring in patients receiving treatment to lower blood pressure: analysis of data from placebo controlled randomised controlled trial

abstract: OBJECTIVE ::: To determine the value of monitoring blood pressure by quantifying the probability that observed changes in blood pressure reflect true changes. ::: ::: ::: DESIGN ::: Analysis of blood pressure measurements of patients in the perindopril protection against recurrent stroke study (PROGRESS). ::: ::: ::: SETTING ::: Randomised placebo controlled trial carried out in 172 centres in Asia, Australasia, and Europe. ::: ::: ::: PARTICIPANTS ::: 1709 patients with history of stroke or transient ischaemic attack randomised to fixed doses of perindopril and indapamide. Measurements Mean of two blood pressure measurements in patients receiving treatment recorded to the nearest 2 mm Hg with a standard mercury sphygmomanometer at baseline and then at three months, six months, nine months, and 15 months and then every six months to 33 months. ::: ::: ::: RESULTS ::: There was no change in the mean blood pressure of the cohort during the 33 month follow-up. Six months after blood pressure was stabilised on treatment, if systolic blood pressure was measured as having increased by >10 mm Hg, six of those measurements would be false positives for every true increase of >or=10 mm Hg. The corresponding value for an increase of 20 mm Hg was over 200. Values for 5 mm Hg and 10 mm Hg increases in diastolic blood pressure were 3.5 and 39, respectively. The likelihood that observed increases in blood pressure reflected true increases rose with the time between measurements such that the ratio of true positives to false positives reached parity at 21 months. ::: ::: ::: CONCLUSIONS ::: Usual clinical approaches to the monitoring of patients taking drugs to lower blood pressure have a low probability of yielding reliable information about true changes in blood pressure. Evidence based guidelines for monitoring treatment response are urgently required to guide clinical practice. Trial registration Australia and New Zealand Clinical Trial Registry.


title: SHINE+: A General Framework for Domain-Specific Entity Linking with Heterogeneous Information Networks

abstract: Heterogeneous information networks that consist of multi-type, interconnected objects are becoming increasingly popular, such as social media networks and bibliographic networks. The task of linking named entity mentions detected from unstructured Web text with their corresponding entities in a heterogeneous information network is of practical importance for the problem of information network population. This task is challenging due to name ambiguity and limited knowledge existing in the network. Most existing entity linking methods focus on linking entities with Wikipedia and cannot be applied to our task. In this paper, we present SHINE+, a general framework for linking named entitie S in Web free text with a H eterogeneous I nformation NE twork. We propose a probabilistic linking model, which unifies an entity popularity model with an entity object model. As the entity knowledge contained in the information network is insufficient, we propose a knowledge population algorithm to iteratively enrich the network entity knowledge by leveraging the context information of mentions mapped by the linking model with high confidence, which subsequently boosts the linking performance. Experimental results over two real heterogeneous information networks (i.e., DBLP and IMDb) demonstrate the effectiveness and efficiency of our proposed framework in comparison with the baselines.


Business Intelligence and Analytics: A Capabilities Dynamization View


title: Learning Robust Visual-Semantic Embeddings

abstract: Many of the existing methods for learning joint embedding of images and text use only supervised information from paired images and its textual attributes. Taking advantage of the recent success of unsupervised learning in deep neural networks, we propose an end-to-end learning framework that is able to extract more robust multi-modal representations across domains. The proposed method combines representation learning models (i.e., auto-encoders) together with cross-domain learning criteria (i.e., Maximum Mean Discrepancy loss) to learn joint embeddings for semantic and visual features. A novel technique of unsupervised-data adaptation inference is introduced to construct more comprehensive embeddings for both labeled and unlabeled data. We evaluate our method on Animals with Attributes and Caltech-UCSD Birds 200-2011 dataset with a wide range of applications, including zero and few-shot image recognition and retrieval, from inductive to transductive settings. Empirically, we show that our frame-work improves over the current state of the art on many of the considered tasks.


title: Chaskey: a MAC Algorithm for Microcontrollers – Status Update and Proposal of Chaskey-12 –

abstract: The Chaskey MAC algorithm was presented by Mouha et al. at SAC 2014. It is designed for real-world applications where 128-bit keys are required, but standard cryptographic algorithms cannot be implemented because of stringent requirements on speed, energy consumption, or code size. Shortly after its publication, Chaskey was considered for standardization by ISO/IEC JTC 1/SC 27/WG 2. At the October 2015 meeting, the ISO/IEC committee decided to terminate the study period on Chaskey, and to circulate a first working draft. Since Chaskey was introduced, many follow-up results were published, including improved cryptanalysis results, new security proofs and more efficient implementations. This paper gives a comprehensive overview of those results, and introduces a twelve-round variant of Chaskey: Chaskey-12. Although the original eight-round Chaskey remains unbroken, Chaskey-12 has a much more conservative design, while reducing the performance by only 15% to 30%, depending on the platform.


title: Extending the Numerov Process to the Semiconductor Transport Equations

abstract: Some classes of differential equations are amenable to a numerical solution based on the Numerov process (NP), whose accuracy can be up to two orders of magnitude superior with respect to the standard finite-difference or box-integration methods, with a negligible increase in the computational cost. The paper shows that the equations describing charge transport in solid-state devices can suitably be manipulated to make the application of NP possible. Also, thanks to a specifically-tailored algebraic solver, the 1D Poisson equation is fully decoupled from the transport equation, this reducing the procedure to the solution of a single non-linear equation. The example of an Ovonic device is considered, used as selector in phase-change memory applications.


title: A novel clustering-based image segmentation via density peaks algorithm with mid-level feature

abstract: Image segmentation is an important and fundamental task in computer vision. Its performance is mainly influenced by feature representations and segmentation algorithms. In this paper, we propose a novel clustering-based image segmentation approach which can be called ICDP algorithm. It is able to capture the inherent structure of image and detect the nonspherical clusters. Compared to the other segmentation methods based on clustering, there are several advantages as follows: (1) Integral channel features are used to clearly and comprehensively represent the input image by naturally integrating heterogeneous sources of information; (2) cluster number can be determined directly and cluster centers are able to be identified automatically; (3) hierarchical segmentation is easy to be achieved via ICDP algorithm. The PSNR and MSE are applied to quantitatively evaluate the segmentation performance. Experimental results clearly demonstrate the effectiveness of our novel image segmentation algorithm.


title: Hierarchical Additive Hough Transform for Lane Detection

abstract: Detection of lanes is an important problem in the upcoming field of vehicle safety and navigation, for which linear Hough transform (HT) is widely used. In order to meet real-time requirements, various attempts to accelerate the HT have been proposed in the past, including hierarchical HT. Existing hierarchical approaches involve the overhead of recomputing HT values at every level in the hierarchy. In this letter, we propose a novel, computationally efficient hierarchical HT by extending and applying the additive property of HT to multiple levels of hierarchies. This proposed approach, called hierarchical additive Hough transform (HAHT) is shown to lead to significant computational savings of up to 98-99% in the Hough voting process. The HAHT has been validated on a wide range of straight lane images and it is shown to successfully detect lanes.


title: Cognitive Vulnerability-Stress Theories of Depression: Examining Affective Specificity in the Prediction of Depression Versus Anxiety in Three Prospective Studies

abstract: Anxiety and depression overlap extensively at the level of symptoms and disorder. We tested the etiological factors from two cognitive vulnerability-stress models of depression (Hopelessness theory and Beck’s theory) for specificity in predicting depression compared with anxiety. Multiple symptom measures of anxiety and depression with good discriminant validity, diagnoses of anxiety and depression, cognitive vulnerability (negative cognitive style and dysfunctional attitudes), and negative events were assessed in three prospective studies: one with a short-term (5-weeks) follow-up, the second with a long-term (2-years) follow-up, and the third with an academic midterm design. Results show that negative events were a general risk factor for anxiety and depression. Cognitive vulnerability for depression interacted with negative events to predict future depression specifically but not anxiety. Comparison of the two theories suggests that their cognitive vulnerability-stress components overlap largely in the prediction of depression. Implications for the co-occurrence of anxiety and depression as well as for the cognitive vulnerability-stress theories of depression are discussed.


title: The MMR vaccination and autism controversy in United Kingdom 1998-2005: inevitable community outrage or a failure of risk communication?

abstract: BACKGROUND ::: The report of an hypothesised link between measles-mumps-rubella (MMR) vaccination and autism in 1998 became a major public health issue in the United Kingdom (UK), leaving most experts surprised by the overwhelming influence it had on public opinion about MMR vaccination. Coverage rates fell dramatically, and did not start to recover until 2004. Could this public reaction have been predicted? ::: ::: ::: METHODS ::: We used Sandman's model of components predicting community outrage to assess the MMR controversy. ::: ::: ::: RESULTS ::: The controversy fulfilled all of Sandman's 12 primary components and six of the eight additional components. ::: ::: ::: CONCLUSIONS ::: The Sandman model provided a useful framework to analyse this controversy and explained a significant portion of the community reaction and subsequent fall in vaccination coverage rates.


title: A Review on Flight Delay Prediction

abstract: Flight delays have a negative effect on airlines, airports and passengers. Their prediction is crucial during the decision-making process for all players of commercial aviation. Moreover, the development of accurate prediction models for flight delays became cumbersome due to the complexity of air transportation system, the amount of methods for prediction, and the deluge of data related to such system. In this context, this paper presents a thorough literature review of approaches used to build flight delay prediction models from the Data Science perspective. We propose a taxonomy and summarize the initiatives used to address the flight delay prediction problem, according to scope, data and computational methods, giving special attention to an increasing usage of machine learning methods. Besides, we also present a timeline of major works that depicts relationships between flight delay prediction problems and research trends to address them.


Overview of the RANSAC Algorithm


title: Investigating aspects of data visualization literacy using 20 information visualizations and 273 science museum visitors

abstract: In the information age, a person's ability to read and make data visualizations is nearly as important as being able to read and write text. This article reports the results of a multi-phase study conducted in informal learning environments in three US science museums. The goal of the study was to determine the familiarity of youth and adult museum visitors with different visualization types. To address this, a total of 273 visitors were shown 5 out of 20 different visualizations that included two charts, five maps, eight graphs, and five network layouts. They were asked to judge the familiarity of the visualization, provide information on how to read it, and provide a name and identify typical locations where they would encounter the data display and possible data sources that might be visualized in this way. The results show that while most participants have a strong interest in science, math, and art, many have a hard time naming and interpreting visualizations. Participants in this study commonly encounter visualizations in school, in books, at work, on the Internet, and in the news. Overall, they were more familiar with basic charts, maps, and graphs, but very few are familiar with network layouts and most have no ability in reading network visualizations. When asked how they would interpret the visualizations, most participants pointed to superficial features such as color, lines, or text as important to developing understanding. Overall, we found that participants were interested in the visualizations we presented to them, but had significant limitations in identifying and understanding them. The results substantiate intuitions shared by many regarding the rather low level of data visualization literacy of general audiences. We hope they will help catalyze novel research on the development of easy-to-use yet effective visualizations with standardized names and guaranteed properties that can be readily used by those interested to understand and solve real-world problems. The results also have implications for how information visualizations are taught and used in formal and informal education, the media, or in different professions.


title: Improving the Robustness of Neural Networks Using K-Support Norm Based Adversarial Training

abstract: It is of significant importance for any classification and recognition system, which claims near or better than human performance to be immune to small perturbations in the dataset. Researchers found out that neural networks are not very robust to small perturbations and can easily be fooled to persistently misclassify by adding a particular class of noise in the test data. This, so-called adversarial noise severely deteriorates the performance of neural networks, which otherwise perform really well on unperturbed dataset. It has been recently proposed that neural networks can be made robust against adversarial noise by training them using the data corrupted with adversarial noise itself. Following this approach, in this paper, we propose a new mechanism to generate a powerful adversarial noise model based on K-support norm to train neural networks. We tested our approach on two benchmark datasets, namely the MNIST and STL-10, using muti-layer perceptron and convolutional neural networks. Experimental results demonstrate that neural networks trained with the proposed technique show significant improvement in robustness as compared to state-of-the-art techniques.


title: Safety and Feasibility of using the EksoTM Bionic Exoskeleton to Aid Ambulation after Spinal Cord Injury

abstract: Objective: Evaluate feasibility and safety of EksoTM to aid ambulation in individuals with SCI. ::: Design: Prospective pilot study. ::: Setting: SCI Rehabilitation Center Outpatient Gym. ::: Participants: Eight individuals, at least 18 years of age, with complete T1 SCI or below, within 2 years of injury, completed initial inpatient rehabilitation. All participants signed informed consent, had been cleared from requiring spinal orthoses, met inclusion criteria, and were pre-screened based on device requirements and medical stability. ::: Intervention: Six weekly sessions with graduated time and less assistance in the EksoTM device. ::: Outcome Measures: Skin evaluation, blood pressure, pain level, spasticity, time and level of assistance needed to transfer into and donn device; time ambulating; time up in device; assistive devices used during ambulation; step length; distance walked; level of assistance during use; and level of assistance needed to doff and transfer out of device. ::: Results: No major skin effects, minimal pain reports, no known fractures, swelling, or other adverse events. Level of assistance ranged from dependent to moderate independent, average set up time was 18.13 minutes, loss of balance and falls were infrequent. ::: Conclusions: Bionic exoskeletons such as EksoTM are safe for those with complete thoracic SCI in a controlled environment, in the presence of experts, and may eventually enhance mobility in those without volitional lower extremity function. There appears to be a training effect in the device but further trials are needed. Future studies of bionic exoskeletons as gait training devices are warranted. Future studies of bionic exoskeletons as a clinical tool to alleviate secondary complications should be considered.


title: Exploring Feature Definition and Selection for Sentiment Classifiers

abstract: In this paper, we systematically explore feature definition and selection strategies for sentiment polarity classification. We begin by exploring basic questions, such as whether to use stemming, term frequency versus binary weighting, negation-enriched features, n-grams or phrases. We then move onto more complex aspects including feature selection using frequency-based vocabulary trimming, part-of-speech and lexicon selection (three types of lexicons), as well as using expected Mutual Information (MI). Using three product and movie review datasets of various sizes, we show, for example, that some techniques are more beneficial for larger datasets than the smaller. A classifier trained on only few features ranked high by MI outperformed one trained on all features in large datasets, yet in small dataset this did not prove to be true. Finally, we perform a space and computation cost analysis to further understand the merits of various feature types.


title: A Survey on Hand Gesture Recognition in Context of Soft Computing

abstract: Hand gestures recognition is the natural way of Human Machine interaction and today many researchers in the academia and industry are interested in this direction. It enables human being to interact with machine very easily and conveniently without wearing any extra device. It can be applied from sign language recognition to robot control and from virtual reality to intelligent home systems. In this paper we are discussing work done in the area of hand gesture recognition where focus is on the soft computing based methods like artificial neural network, fuzzy logic, genetic algorithms, etc. We also described hand detection methods in the preprocessed image for detecting the hand image. Most researchers used fingertips for hand detection in appearance based modeling. Finally we are comparing results given by different researchers after their implementation.


title: Simple BM25 extension to multiple weighted fields

abstract: This paper describes a simple way of adapting the BM25 ranking formula to deal with structured documents. In the past it has been common to compute scores for the individual fields (e.g. title and body) independently and then combine these scores (typically linearly) to arrive at a final score for the document. We highlight how this approach can lead to poor performance by breaking the carefully constructed non-linear saturation of term frequency in the BM25 function. We propose a much more intuitive alternative which weights term frequencies before the non-linear term frequency saturation function is applied. In this scheme, a structured document with a title weight of two is mapped to an unstructured document with the title content repeated twice. This more verbose unstructured document is then ranked in the usual way. We demonstrate the advantages of this method with experiments on Reuters Vol1 and the TREC dotGov collection.


title: Video key frame extraction through dynamic Delaunay clustering with a structural constraint

abstract: Key frame based video summarization has emerged as an important area of research for the multimedia community. Video key frames enable an user to access any video in a friendly and meaningful way. In this paper, we propose an automated method of video key frame extraction using dynamic Delaunay graph clustering via an iterative edge pruning strategy. A structural constraint in form of a lower limit on the deviation ratio of the graph vertices further improves the video summary. We also employ an information-theoretic pre-sampling where significant valleys in the mutual information profile of the successive frames in a video are used to capture more informative frames. Various video key frame visualization techniques for efficient video browsing and navigation purposes are incorporated. A comprehensive evaluation on 100 videos from the Open Video and YouTube databases using both objective and subjective measures demonstrate the superiority of our key frame extraction method.


Optimal, Smooth, Nonholonomic Mobile Robot Motion Planning in State Lattices


title: Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis.

abstract: PURPOSE ::: Glaucoma is the leading cause of global irreversible blindness. Present estimates of global glaucoma prevalence are not up-to-date and focused mainly on European ancestry populations. We systematically examined the global prevalence of primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG), and projected the number of affected people in 2020 and 2040. ::: ::: ::: DESIGN ::: Systematic review and meta-analysis. ::: ::: ::: PARTICIPANTS ::: Data from 50 population-based studies (3770 POAG cases among 140,496 examined individuals and 786 PACG cases among 112 398 examined individuals). ::: ::: ::: METHODS ::: We searched PubMed, Medline, and Web of Science for population-based studies of glaucoma prevalence published up to March 25, 2013. Hierarchical Bayesian approach was used to estimate the pooled glaucoma prevalence of the population aged 40-80 years along with 95% credible intervals (CrIs). Projections of glaucoma were estimated based on the United Nations World Population Prospects. Bayesian meta-regression models were performed to assess the association between the prevalence of POAG and the relevant factors. ::: ::: ::: MAIN OUTCOME MEASURES ::: Prevalence and projection numbers of glaucoma cases. ::: ::: ::: RESULTS ::: The global prevalence of glaucoma for population aged 40-80 years is 3.54% (95% CrI, 2.09-5.82). The prevalence of POAG is highest in Africa (4.20%; 95% CrI, 2.08-7.35), and the prevalence of PACG is highest in Asia (1.09%; 95% CrI, 0.43-2.32). In 2013, the number of people (aged 40-80 years) with glaucoma worldwide was estimated to be 64.3 million, increasing to 76.0 million in 2020 and 111.8 million in 2040. In the Bayesian meta-regression model, men were more likely to have POAG than women (odds ratio [OR], 1.36; 95% CrI, 1.23-1.52), and after adjusting for age, gender, habitation type, response rate, and year of study, people of African ancestry were more likely to have POAG than people of European ancestry (OR, 2.80; 95% CrI, 1.83-4.06), and people living in urban areas were more likely to have POAG than those in rural areas (OR, 1.58; 95% CrI, 1.19-2.04). ::: ::: ::: CONCLUSIONS ::: The number of people with glaucoma worldwide will increase to 111.8 million in 2040, disproportionally affecting people residing in Asia and Africa. These estimates are important in guiding the designs of glaucoma screening, treatment, and related public health strategies.


title: Organic Foods: Health and Environmental Advantages and Disadvantages

abstract: The US market for organic foods has grown from $3.5 billion in 1996 to $28.6 billion in 2010, according to the Organic Trade Association. Organic products are now sold in specialty stores and conventional supermarkets. Organic products contain numerous marketing claims and terms, only some of which are standardized and regulated. In terms of health advantages, organic diets have been convincingly demonstrated to expose consumers to fewer pesticides associated with human disease. Organic farming has been demonstrated to have less environmental impact than conventional approaches. However, current evidence does not support any meaningful nutritional benefits or deficits from eating organic compared with conventionally grown foods, and there are no well-powered human studies that directly demonstrate health benefits or disease protection as a result of consuming an organic diet. Studies also have not demonstrated any detrimental or disease-promoting effects from an organic diet. Although organic foods regularly command a significant price premium, well-designed farming studies demonstrate that costs can be competitive and yields comparable to those of conventional farming techniques. Pediatricians should incorporate this evidence when discussing the health and environmental impact of organic foods and organic farming while continuing to encourage all patients and their families to attain optimal nutrition and dietary variety consistent with the US Department of Agriculture’s MyPlate recommendations. This clinical report reviews the health and environmental issues related to organic food production and consumption. It defines the term “organic,” reviews organic food-labeling standards, describes organic and conventional farming practices, and explores the cost and environmental implications of organic production techniques. It examines the evidence available on nutritional quality and production contaminants in conventionally produced and organic foods. Finally, this report provides guidance for pediatricians to assist them in advising their patients regarding organic and conventionally produced food choices.


title: Dynamic Scene Occlusion Culling

abstract: Large, complex 3D scenes are best rendered in an output-sensitive way, i.e., in time largely independent of the entire scene model's complexity. Occlusion culling is one of the key techniques for output-sensitive rendering. We generalize existing occlusion culling algorithms, intended for static scenes, to handle dynamic scenes having numerous moving objects. The data structure used by an occlusion culling method is updated to reflect the objects' possible positions. To avoid updating the structure for every dynamic object at each frame, a temporal bounding volume (TBV) is created for each occluded dynamic object, using some known constraints on the object's motion. The TBV is inserted into the structure instead of the object. Subsequently, the object is ignored as long as the TBV is occluded and guaranteed to contain the object. The generalized algorithms' rendering time is linearly affected only by the scene's visible parts, not by hidden parts or by occluded dynamic objects. Our techniques also save communications in distributed graphic systems, e.g., multiuser virtual environments, by eliminating update messages for hidden dynamic objects. We demonstrate the adaptation of two occlusion culling algorithms to dynamic scenes: hierarchical Z-buffering and BSP tree projection.


title: Design and experiments of advanced leg module (HRP-2L) for humanoid robot (HRP-2) development

abstract: This paper presents an advanced leg module developed for HRP-2, a new humanoid robotics platform, which has been developed in phase two of the Humanoid Robotics Project (HRP), a five year program sponsored by the Ministry of Economy, Trade and Industry of Japan (METI) from 1998FY to 2002FY. The biped locomotion ability of HRP-1, the humanoid robotics platform developed in phase I is to be improved so that HRP-2 can cope with rough terrain and can prevent possible damage to the robot body caused by tipping over. In this paper, the mechanisms and specifications of the leg module, the electrical system, the simulation results utilized for deciding specifications, and some experimental results are presented.


title: NOTE Fast Euclidean Distance Transformation by Propagation Using Multiple Neighborhoods

abstract: We propose a new exact Euclidean distance transformation (DT) by propagation, using bucket sorting. A fast but approximate DT is first computed using a coarse neighborhood. A sequence of larger neighborhoods is then used to gradually improve this approximation. Computations are kept short by restricting the use of these large neighborhoods to the tile borders in the Voronoi diagram of the image. We assess the computational cost of this new algorithm and show that it is both smaller and less image-dependent than all other DTs recently proposed. Contrary to all other propagation DTs, it appears to remain o(n2) even in the worst-case scenario.


title: Hardware Intrinsic Security from Physically Unclonable Functions

abstract: Counterfeiting of goods in general and of electronic goods in particular is a growing concern with a huge impact on the global economy, the society, and the security of its critical infrastructure. Various examples are known where companies suffer from economic and brand damage due to competition with counterfeit goods. In some cases the use of counterfeit components has even led to tragic accidents in which lives were lost. It has also recently become clear that counterfeit products can penetrate the critical and security infrastructure of our modern societies and hence cause a threat to national security.


title: On Selection of Rotor Support Material for a Ferrite Magnet Spoke-Type Traction Motor

abstract: Interior permanent magnet motors with ferrite magnets and distributed windings can be a cost effective alternative to rare-earth magnet based motors for demanding applications such as automotive traction. Among different rotor topologies, the spoke type may be preferred, due to its advantages for high flux concentration and resistance to demagnetization, when carefully designed. When high speed operation is required, to increase the power density of the motor, the spoke type rotor must be comprised of two sections: a) the ferromagnetic rotor pole to provide the path for the magnetic flux, and b) the non-magnetic rotor support to provide the structural integrity. In this paper, the multiphysics and cost implications of the rotor support material, as part of a high performance ferrite magnet traction motor, are reviewed, and an optimal selection with respect to those criteria is proposed.


title: Uncovering the Bitcoin Blockchain: An Analysis of the Full Users Graph

abstract: BITCOIN is a novel decentralized cryptocurrency system which has recently received a great attention from a wider audience. An interesting and unique feature of this system is that the complete list of all the transactions occurred from its inception is publicly available. This enables the investigation of funds movements to uncover interesting properties of the BITCOIN economy. In this paper we present a set of analyses of the user graph, i.e. the graph obtained by an heuristic clustering of the graph of BITCOIN transactions. Our analyses consider an up-to-date BITCOIN blockchain, as in December 2015, after the exponential explosion of the number of transactions occurred in the last two years. The set of analyses we defined includes, among others, the analysis of the time evolution of BITCOIN network, the verification of the "rich get richer" conjecture and the detection of the nodes which are critical for the network connectivity.


A Comprehensive approach For Brain Waves Exploitation To Control A Robotic Arm


title: Glycosylation of Residue 141 of Subtype H7 Influenza A Hemagglutinin (HA) Affects HA-Pseudovirus Infectivity and Sensitivity to Site A Neutralizing Antibodies

abstract: Human infections with H7 subtype influenza virus have been reported, including an H7N7 outbreak in Netherlands in 2003 and H7N9 infections in China in 2013. Previously, we reported murine monoclonal antibodies (mAbs) that recognize the antigenic site A of H7 hemagglutinin (HA). To better understand protective immunity of H7 vaccines and vaccine candidate selection, we used these mAbs to assess the antigenic relatedness among two H7 HA isolated from past human infections and determine residues that affect susceptibility to neutralization. We found that these mAbs neutralize pseudoviruses bearing HA of A/Shanghai/02/2013(H7N9), but not A/Netherlands/219/2003(H7N7). Glycosylation of the asparagine residue at position 141 (N141) (N133, H3 HA numbering) in the HA of A/Netherlands/219/2003 HA is responsible for this resistance, and it affects the infectivity of HA-pseudoviruses. The presence of threonine at position 143 (T135, H3 HA numbering) in the HA of A/Netherlands/219/2003, rather than an alanine found in the HA of A/Shanghai/02/2013(H7N9), accounts for these differences. These results demonstrate a key role for glycosylation of residue N141 in affecting H7 influenza HA-mediated entry and sensitivity to neutralizing antibodies, which have implications for candidate vaccine design.


title: Identification of miR‐305, a microRNA that promotes aging, and its target mRNAs in Drosophila

abstract: MicroRNAs (miRNAs) are involved in the regulation of important biological processes. Here, we describe a novel Drosophila miRNAs involved in aging. We selected eight Drosophila miRNAs, displaying high homology with seed sequences of aging-related miRNAs characterized in other species, and investigated whether the over-expression of these miRNAs affected aging in Drosophila adult flies. The lifespan of adults over-expressing miR-305, a miRNA showing high homology with miR-239 in C. elegans, was significantly shorter. Conversely, a reduction in miR-305 expression led to a longer lifespan than that in control flies. miR-305 over-expression accelerated the impairment of locomotor activity and promoted the age-dependent accumulation of poly-ubiquitinated protein aggregates in the muscle, as flies aged. Thus, we show that the ectopic expression of miR-305 has a deleterious effect on aging in Drosophila. To identify the targets of miR-305, we performed RNA-Seq. We discovered several mRNAs encoding antimicrobial peptides and insulin-like peptides, whose expression changed in adults upon miR-305 over-expression. We further confirmed, by qRT-PCR, that miR-305 over-expression significantly decreases the mRNA levels of four antimicrobial peptides. As these mRNAs contain multiple sequences matching the seed sequence of miR-305, we speculate that a reduction in target mRNA levels, caused by ectopic miRNA expression, promotes aging.


title: Integration of Ni-Cu-Zn Ferrite in LTCC-Modules

abstract: Integration of magnetic functional components in LTCC circuit boards calls for co-firing of dielectric and ferrite tapes. Ni-Cu-Zn ferrites with permeability of μ =900 were developed which can be fully densified at the standard LTCC sintering temperature of 900 °C. Successful co-firing of this ferrite with dielectric tapes requires the adaptation of the shrinkage behavior of the materials as well as the thermal expansion during the cooling period - especially in the temperature range below the transformation point of the glassy phase of the dielectric tape. To match these preconditions, a new dielectric LTCC material with steep sintering curve and high thermal expansion coefficient was designed. Sintered multilayer composed of Ni-Cu-Zn ferrite and tailored dielectric tapes are free of cracks and possess no open porosity. No significant interdiffusion between the ferrite and dielectric tapes was found by EDX measurements. Compared to pure ferrite laminates the permeability of co-sintered Ni-Cu-Zn ferrite l...


title: Benefits of Cloud Computing: Literature Review in a Maturity Model Perspective

abstract: Cloud computing is drawing attention from both practitioners and researchers, and its adoption among organizations is on the rise. The focus has mainly been on minimizing fixed IT costs and using the IT resource flexibility offered by the cloud. However, the promise of cloud computing is much greater. As a disruptive technology, it enables innovative new services and business models that decrease time to market, create operational efficiencies and engage customers and citizens in new ways. However, we are still in the early days of cloud computing, and, for organizations to exploit the full potential, we need knowledge of the potential applications and pitfalls of cloud computing. Maturity models provide effective methods for organizations to assess, evaluate, and benchmark their capabilities as bases for developing roadmaps for improving weaknesses. Adopting the business-IT maturity model by Pearlson & Saunders (2007) as analytical framework, we synthesize the existing literature, identify levels of cloud computing benefits, and establish propositions for practice in terms of how to realize these benefits.


title: A study of interferences inside an RF switch array in 45nm SOI CMOS

abstract: This paper presents a study of interferences inside an RF switch array, aiming to understand the design influences on interference characteristics. The 2×2 single-pole double/triple-throw (SP2T/SP3T) Tx/Rx band switch array, featuring a series-shunt topology with gate resistance and feed-forward capacitance (FFC) and covering low (699–894MHz) and high (1710–2155MHz) bands, was designed and fabricated in a 45nm SOI CMOS. The inter-band and inner-band interferences were characterized, which reveals that existing noise isolation techniques, e.g., substrate isolation and layout floor planning, are insufficient for interference reduction. It therefore calls for novel in-die interference elimination techniques.


title: Sizing Ratios for Stormwater Treatment Facilities

abstract: Wet weather effluents in both combined and separate sewer systems contain significant loads of pollutants that have negative acute and cumulative impacts on receiving waters depending on the observation time scale. Based on long data series and calibrated models, sizing ratios, with confidence intervals and/or associated with specific return periods, have been evaluated for 2 types of stormwater treatment facilities (storage-settling tanks and on-line settlers), for event, annual and inter-annual time scales. These ratios have been calculated for an interception efficiency E defined as the mass of pollutant intercepted by the facility divided by the mass of pollutant transported in the sewer system. Calculations have been carried out for two representative catchments (one separate, one combined) with 1362 rainfall events from a 17 years long rainfall data series. Results are highly variable for event time scale, but mean values for annual and inter-annual time scales are relatively stable. Storage-settling tanks with specific volumes of 100 m 3 /active ha intercept approximately 80 % of the annual TSS pollutant load, and on-line settlers designed for 8 L/s/active ha can intercept 82 to 85 % of this load. Differences exist between combined and separate systems but are usually less than 10–15 %.


title: AND: Autoregressive Novelty Detectors.

abstract: We propose an unsupervised model for novelty detection. The subject is treated as a density estimation problem, in which a deep neural network is employed to learn a parametric function that maximizes probabilities of training samples. This is achieved by equipping an autoencoder with a novel module, responsible for the maximization of compressed codes' likelihood by means of autoregression. We illustrate design choices and proper layers to perform autoregressive density estimation when dealing with both image and video inputs. Despite a very general formulation, our model shows promising results in diverse one-class novelty detection and video anomaly detection benchmarks.


title: Smart cities in the era of artificial intelligence and internet of things: literature review from 1990 to 2017

abstract: The concept of smart city is considered as the new engine for economic and social growths since it is supported by the rapid development of information and communication technologies. However, each technology not only brings its advantages, but also the challenges that cities have to face in order to implement it. So, this paper addresses two research questions : « What are the most important technologies that drive the development of smart cities ?» and « what are the challenges that cities will face when adopting these technologies ? » Relying on a literature review of studies published between 1990 and 2017, the ensuing results show that Artificial Intelligence and Internet of Things represent the most used technologies for smart cities. So, the focus of this paper will be on these two technologies by showing their advantages and their challenges.


title: Topic-driven reader comments summarization

abstract: Readers of a news article often read its comments contributed by other readers. By reading comments, readers obtain not only complementary information about this news article but also the opinions from other readers. However, the existing ranking mechanisms for comments (e.g., by recency or by user rating) fail to offer an overall picture of topics discussed in comments. In this paper, we first propose to study Topic-driven Reader Comments Summarization (Torcs) problem. We observe that many news articles from a news stream are related to each other; so are their comments. Hence, news articles and their associated comments provide context information for user commenting. To implicitly capture the context information, we propose two topic models to address the Torcs problem, namely, Master-Slave Topic Model (MSTM) and Extended Master-Slave Topic Model (EXTM). Both models treat a news article as a master document and each of its comments as a slave document. MSTM model constrains that the topics discussed in comments have to be derived from the commenting news article. On the other hand, EXTM model allows generating words of comments using both the topics derived from the commenting news article, and the topics derived from all comments themselves. Both models are used to group comments into topic clusters. We then use two ranking mechanisms Maximal Marginal Relevance (MMR) and Rating & Length (RL) to select a few most representative comments from each comment cluster. To evaluate the two models, we conducted experiments on 1005 Yahoo! News articles with more than one million comments. Our experimental results show that EXTM significantly outperforms MSTM by perplexity. Through a user study, we also confirm that the comment summary generated by EXTM achieves better intra-cluster topic cohesion and inter-cluster topic diversity.


title: A probabilistic retrieval model for semistructured data

abstract: Retrieving semistructured (XML) data typically requires either a structured query such as XPath, or a keyword query that does not take structure into account. In this paper, we infer structural information automatically from keyword queries and incorporate this into a retrieval model. More specifically, we propose the concept of a mapping probability, which maps each query word into a related field (or XML element). This mapping probability is used as a weight to combine the language models estimated from each field. Experiments on two test collections show that our retrieval model based on mapping probabilities outperforms baseline techniques significantly.


title: Specificity of genetic and environmental risk factors for use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates in male twins

abstract: OBJECTIVE: Data on use and misuse of six classes of illicit substances by male twin pairs were used to examine whether genetic and shared environmental risk factors for substance use disorders are substance-specific or -nonspecific in their effect. METHOD: Lifetime history of use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates was assessed at personal interview in both members of 1,196 male-male twin pairs ascertained by the Virginia Twin Registry. Multivariate twin modeling of substance-nonspecific (common) and substance-specific genetic, shared environmental, and unique environmental risk factors was performed by using the program Mx. RESULTS: High levels of comorbidity involving the different substance categories were observed for both use and abuse/dependence. One common genetic factor was found to have a strong influence on risk for illicit use and abuse/dependence for all six substance classes. A modest influence of substance-specific genetic factors was ...


Creating Clinical Trial Summary Tables Containing P-Values : A Practical Approach Using Standard SAS ® Macros


title: Beyond developable: computational design and fabrication with auxetic materials

abstract: We present a computational method for interactive 3D design and rationalization of surfaces via auxetic materials, i.e., flat flexible material that can stretch uniformly up to a certain extent. A key motivation for studying such material is that one can approximate doubly-curved surfaces (such as the sphere) using only flat pieces, making it attractive for fabrication. We physically realize surfaces by introducing cuts into approximately inextensible material such as sheet metal, plastic, or leather. The cutting pattern is modeled as a regular triangular linkage that yields hexagonal openings of spatially-varying radius when stretched. In the same way that isometry is fundamental to modeling developable surfaces, we leverage conformal geometry to understand auxetic design. In particular, we compute a global conformal map with bounded scale factor to initialize an otherwise intractable non-linear optimization. We demonstrate that this global approach can handle non-trivial topology and non-local dependencies inherent in auxetic material. Design studies and physical prototypes are used to illustrate a wide range of possible applications.


title: Generative Topic Embedding: a Continuous Representation of Documents (Extended Version with Proofs)

abstract: Word embedding maps words into a low-dimensional continuous embedding space by exploiting the local word collocation patterns in a small context window. On the other hand, topic modeling maps documents onto a low-dimensional topic space, by utilizing the global word collocation patterns in the same document. These two types of patterns are complementary. In this paper, we propose a generative topic embedding model to combine the two types of patterns. In our model, topics are represented by embedding vectors, and are shared across documents. The probability of each word is influenced by both its local context and its topic. A variational inference method yields the topic embeddings as well as the topic mixing proportions for each document. Jointly they represent the document in a low-dimensional continuous space. In two document classification tasks, our method performs better than eight existing methods, with fewer features. In addition, we illustrate with an example that our method can generate coherent topics even based on only one document.


title: Early sex differences in spatial skill.

abstract: This study investigated sex differences in young children's spatial skill. The authors developed a spatialtransformation task, which showed a substantial male advantage by age 4 years 6 months. The size of thisadvantage was no more robust for rotation items than for translation items. This finding contrasts withstudies of older children and adults, which report that sex differences are largest on mental rotation tasks.Comparable performance of boys and girls on a vocabulary task indicated that the male advantage on thespatial task was not attributable to an overall intellectual advantage of boys in the sample.


title: Vygotsky in a TeamRoom: an exploratory study on collective concept formation in electronic environments

abstract: The effective use of distributed collaboration environments requires shared mental models that guide users in sense-making and categorization. In Lotus Notes-based collaboration systems, such shared models are usually implemented as views and document types. TeamRoom, developed at Lotus Institute, implements in its design a theory of effective social process that creates a set of team-specific categories, which can then be used as a basis for knowledge sharing collaboration, and team memory. The paper reports an exploratory study in collective concept formation in the TeamRoom environment. The study was run in an ecological setting, while the team members used the system for their everyday work. The author applies theory developed by Lev Vygotsky, and use a modified version of an experiment on concept formation, devised by Lev Sakharov, and discussed by Vygotsky (1986). Vygotsky emphasized the role of language, cognitive artifacts, and historical and social sources in the development of thought processes. Within the Vygotskian framework it becomes clear that development of thinking does not end in adolescence. In teams of adults, learning and knowledge creation are continuous processes. New concepts are created shared, and developed into systems. The question, then, becomes how spontaneous concepts are collectively generated in teams, how they become integrated as systems, and how computer mediated collaboration environments affect these processes.


title: Learning Setting-Generalized Activity Models for Smart Spaces

abstract: Smart home activity recognition systems can learn generalized models for common activities that span multiple environment settings and resident types.


title: Superior Thermal Conductivity of Single-Layer Graphene

abstract: We report the measurement of the thermal conductivity of a suspended single-layer graphene. The room temperature values of the thermal conductivity in the range ∼(4.84 ± 0.44) × 103 to (5.30 ± 0.48) × 103 W/mK were extracted for a single-layer graphene from the dependence of the Raman G peak frequency on the excitation laser power and independently measured G peak temperature coefficient. The extremely high value of the thermal conductivity suggests that graphene can outperform carbon nanotubes in heat conduction. The superb thermal conduction property of graphene is beneficial for the proposed electronic applications and establishes graphene as an excellent material for thermal management.


title: Malassezia Folliculitis versus Truncal Acne Vulgaris (Clinical and Histopathological Study)

abstract: Background: Malassezia folliculitis is an infection of the hair follicle, which commonly affects young adult, and is often misdiagnosed as truncal acne. Objectives: To shed light on different clinical, histopathological, and microbiological aspects of Malassezia folliculitis and to be compared with truncal acne. Patients and Methods: This is a descriptive comparative cross sectional, out patient based study, which was conducted at the Department of Dermatology, Basrah Teaching Hospital from October 2008 to October 2009. Total number of 113 patients with papular and/or pustular follicular skin lesions on the trunk were enrolled in this study. They were divided according to the clinical features, Wood’s light examination, direct microscopic examination, PAS stained skin biopsy and the response to a trial of antifungal drugs into two groups: group 1: patient with Malassezia folliculitis and group 2: patient with truncal acne vulgaris. Results: Group 1: They were 53(46.9%) out of a total 113, 33(62.3%) males and 20(37.7%) females, their ages ranged from 19 - 40 years. Group 2 consisted of 60(53.1%) patients, 25(41.6%) males and 35(58.4%) females, whose ages ranged from 14 - 29 years. The ages were earlier in the acne group in comparison with MF group (p Malassezia folliculitis was more common among males than females, while in truncal acne, females predominated males. The center of the back was the predominant site among MF group affecting (90.6%) of patients with sparing the face, in contrast to truncal acne where the periphery of the back was involved in (91.7%) and the face in (75%) (p 0.0001). Wood’s light examination, skin scrapping and PAS stained skin biopsies were also positive in all patients with MF in contrast to acne group where all these tests were negative. All MF patients showed marked response to a two weeks trial of antifungal while the reverse is true for the acne patients, whereas the antibiotic was the second most common leading aggravating factor in 20 (37.7%) of cases with MF, which was significantly different from acne group (p 0.0001). Conclusion: Malassezia folliculitis should be considered in young adults with an itchy papulopustulr follicular eruptions affecting the trunk that should be differentiated from truncal acne by their characteristic clinical, histopathological features and its remarkable response to antifungal therapy.


title: Wearable technologies for smart environments: A review with emphasis on BCI

abstract: In recent years, there has been a growing demand for the development of smart environments to improve quality of life. A major role in this process have wearable devices capable of monitoring and recording psychophysiological measures/signals required for variety of applications including remote health monitoring, physical activity monitoring and general user interaction. This paper surveys the current state of technology, models and requirements of wearable devices with emphasis on Brain Computer Interface (BCI). BCI technology is a rapidly growing scientific field with a number of practical applications providing useful and accurate information and has a promising future. The paper also summarizes the current applications of BCI and presents their limitations and signal-processing algorithms. This area will be the biggest challenge of future research and development process. Other challenges include connectivity, data input, security, design, robustness, low energy consumption and energy harvesting.


title: Spatiotemporal urbanization processes in the megacity of Mumbai, India: A Markov chains-cellular automata urban growth model

abstract: Several factors contribute to on-going challenges of spatial planning and urban policy in megacities, including rapid population shifts, less organized urban areas, and a lack of data with which to monitor urban growth and land use change. To support Mumbai's sustainable development, this research was conducted to examine past urban land use changes on the basis of remote sensing data collected between 1973 and 2010. An integrated Markov Chains–Cellular Automata (MC–CA) urban growth model was implemented to predict the city's expansion for the years 2020–2030. To consider the factors affecting urban growth, the MC–CA model was also connected to multi-criteria evaluation to generate transition probability maps. The results of the multi-temporal change detection show that the highest urban growth rates, 142% occurred between 1973 and 1990. In contrast, the growth rates decreased to 40% between 1990 and 2001 and decreased to 38% between 2001 and 2010. The areas most affected by this degradation were open land and croplands. The MC–CA model predicts that this trend will continue in the future. Compared to the reference year, 2010, increases in built-up areas of 26% by 2020 and 12% by 2030 are forecast. Strong evidence is provided for complex future urban growth, characterized by a mixture of growth patterns. The most pronounced of these is urban expansion toward the north along the main traffic infrastructure, linking the two currently non-affiliated main settlement ribbons. Additionally, urban infill developments are expected to emerge in the eastern areas, and these developments are expected to increase urban pressure.


title: Predicting High-Risk Preterm Birth Using Artificial Neural Networks

abstract: A reengineered approach to the early prediction of preterm birth is presented as a complimentary technique to the current procedure of using costly and invasive clinical testing on high-risk maternal populations. Artificial neural networks (ANNs) are employed as a screening tool for preterm birth on a heterogeneous maternal population; risk estimations use obstetrical variables available to physicians before 23 weeks gestation. The objective was to assess if ANNs have a potential use in obstetrical outcome estimations in low-risk maternal populations. The back-propagation feedforward ANN was trained and tested on cases with eight input variables describing the patient's obstetrical history; the output variables were: 1) preterm birth; 2) high-risk preterm birth; and 3) a refined high-risk preterm birth outcome excluding all cases where resuscitation was delivered in the form of free flow oxygen. Artificial training sets were created to increase the distribution of the underrepresented class to 20%. Training on the refined high-risk preterm birth model increased the network's sensitivity to 54.8%, compared to just over 20% for the nonartificially distributed preterm birth model


title: Real-time simulation of hybrid microgrid for islanding detection analysis

abstract: This paper investigates passive islanding detection methods in hybrid microgrid having both rotating machine and converter-based distributed energy units (DEUs). The converter control schemes of distributed energy units are simulated using Real Time Digital Simulator (RTDS). Frequency, voltage, phase jump detection and active power based passive islanding techniques are analyzed and compared with sequence component based techniques to improve the accuracy during power imbalance situations. A hybrid micro grid system is simulated using Real Time Digital Simulator (RTDS). Several test cases are generated to examine the performance of the passive islanding detection methods.


title: National Academy of Clinical Biochemistry Laboratory Medicine Practice Guidelines for use of tumor markers in testicular, prostate, colorectal, breast, and ovarian cancers

abstract: Background: Updated National Academy of Clinical Biochemistry (NACB) Laboratory Medicine Practice Guidelines for the use of tumor markers in the clinic have been developed. ::: ::: Methods: Published reports relevant to use of tumor markers for 5 cancer sites—testicular, prostate, colorectal, breast, and ovarian—were critically reviewed. ::: ::: Results: For testicular cancer, α-fetoprotein, human chorionic gonadotropin, and lactate dehydrogenase are recommended for diagnosis/case finding, staging, prognosis determination, recurrence detection, and therapy monitoring. α-Fetoprotein is also recommended for differential diagnosis of nonseminomatous and seminomatous germ cell tumors. Prostate-specific antigen (PSA) is not recommended for prostate cancer screening, but may be used for detecting disease recurrence and monitoring therapy. Free PSA measurement data are useful for distinguishing malignant from benign prostatic disease when total PSA is <10 μg/L. In colorectal cancer, carcinoembryonic antigen is recommended (with some caveats) for prognosis determination, postoperative surveillance, and therapy monitoring in advanced disease. Fecal occult blood testing may be used for screening asymptomatic adults 50 years or older. For breast cancer, estrogen and progesterone receptors are mandatory for predicting response to hormone therapy, human epidermal growth factor receptor-2 measurement is mandatory for predicting response to trastuzumab, and urokinase plasminogen activator/plasminogen activator inhibitor 1 may be used for determining prognosis in lymph node–negative patients. CA15-3/BR27–29 or carcinoembryonic antigen may be used for therapy monitoring in advanced disease. CA125 is recommended (with transvaginal ultrasound) for early detection of ovarian cancer in women at high risk for this disease. CA125 is also recommended for differential diagnosis of suspicious pelvic masses in postmenopausal women, as well as for detection of recurrence, monitoring of therapy, and determination of prognosis in women with ovarian cancer. ::: ::: Conclusions: Implementation of these recommendations should encourage optimal use of tumor markers.


title: Multi-resolution stacking for speech separation based on boosted DNN.

abstract: Recent progress in speech separation shows that deep neural networks (DNN) based supervised methods can improve the performance in difficult noise conditions and exhibit good generalization to unseen noise scenarios. However, existing approaches do not explore contextual information sufficiently. In this paper, we focus on exploring contextual information using DNN. The proposed method has two parts—a multi-resolution stacking (MRS) framework and a boosted DNN (bDNN) classifier. The MRS framework trains a stack of classifier ensembles, where each classifier in an ensemble concatenates the raw acoustic feature and the outputs of its bottom ensemble as a new feature, and different classifiers in an ensemble work with different window lengths. The bDNN classifier first generates multiple base predictions for a frame from a given window that is centered on the frame and contains multiple neighboring frames, and then aggregates the base predictions for the final prediction. Our experimental comparison with DNN based speech separation in difficult noise scenarios demonstrates the effectiveness of the proposed method in terms of both prediction accuracy and objective speech intelligibility.


title: Asymmetric numeral systems: entropy coding combining speed of Huffman coding with compression rate of arithmetic coding

abstract: The modern data compression is mainly based on two approaches to entropy coding: Huffman (HC) and arithmetic/range coding (AC). The former is much faster, but approximates probabilities with powers of 2, usually leading to relatively low compression rates. The latter uses nearly exact probabilities - easily approaching theoretical compression rate limit (Shannon entropy), but at cost of much larger computational cost. Asymmetric numeral systems (ANS) is a new approach to accurate entropy coding, which allows to end this trade-off between speed and rate: the recent implementation [1] provides about $50\%$ faster decoding than HC for 256 size alphabet, with compression rate similar to provided by AC. This advantage is due to being simpler than AC: using single natural number as the state, instead of two to represent a range. Beside simplifying renormalization, it allows to put the entire behavior for given probability distribution into a relatively small table: defining entropy coding automaton. The memory cost of such table for 256 size alphabet is a few kilobytes. There is a large freedom while choosing a specific table - using pseudorandom number generator initialized with cryptographic key for this purpose allows to simultaneously encrypt the data. This article also introduces and discusses many other variants of this new entropy coding approach, which can provide direct alternatives for standard AC, for large alphabet range coding, or for approximated quasi arithmetic coding.


title: Faster Fuzzing: Reinitialization with Deep Neural Models

abstract: We improve the performance of the American Fuzzy Lop (AFL) fuzz testing framework by using Generative Adversarial Network (GAN) models to reinitialize the system with novel seed files. We assess performance based on the temporal rate at which we produce novel and unseen code paths. We compare this approach to seed file generation from a random draw of bytes observed in the training seed files. The code path lengths and variations were not sufficiently diverse to fully replace AFL input generation. However, augmenting native AFL with these additional code paths demonstrated improvements over AFL alone. Specifically, experiments showed the GAN was faster and more effective than the LSTM and out-performed a random augmentation strategy, as measured by the number of unique code paths discovered. GAN helps AFL discover 14.23% more code paths than the random strategy in the same amount of CPU time, finds 6.16% more unique code paths, and finds paths that are on average 13.84% longer. Using GAN shows promise as a reinitialization strategy for AFL to help the fuzzer exercise deep paths in software.


Envelopes generated by circular-arc coupler-bars in James Watt’s four-bar linkages


title: Autonomous transfer for reinforcement learning

abstract: Recent work in transfer learning has succeeded in making reinforcement learning algorithms more efficient by incorporating knowledge from previous tasks. However, such methods typically must be provided either a full model of the tasks or an explicit relation mapping one task into the other. An autonomous agent may not have access to such high-level information, but would be able to analyze its experience to find similarities between tasks. In this paper we introduce Modeling Approximate State Transitions by Exploiting Regression (MASTER), a method for automatically learning a mapping from one task to another through an agent's experience. We empirically demonstrate that such learned relationships can significantly improve the speed of a reinforcement learning algorithm in a series of Mountain Car tasks. Additionally, we demonstrate that our method may also assist with the difficult problem of task selection for transfer.


title: Customer relationship management (CRM) in business‐to‐business (B2B) e‐commerce

abstract: Customer relationship management (CRM) and business‐to‐business (B2B) are essential to the success of modern business. Although they are two different modules, they share many similarities. The integration of CRM and B2B will benefit all related parties in business processes, including sales, marketing, customer service, and information support. This article discusses the characteristics, similarities, and differences of B2B and CRM. It also explores the CRM‐B2B integration strategies, the current issues and their future development trends.


title: ANTHER AND POLLEN DEVELOPMENT IN RICE (ORYZA SATIVA)

abstract: Investigations of the growth of anthers and ontogeny of pollen grains of Oryza sativa (rice) IR-30 were undertaken for the purpose of 1) providing a set of growth measurements and 2) describing stable cytological features of anther and pollen development. Correlations exist between elongation of the floret and growth parameters of the anther such as its length, width, fresh and dry weights and cytological stage of pollen development. In the early ontogeny of the anther, hypodermal archesporial initials divide periclinally to form primary parietal cells and primary sporogenous cells. Each ofthe latter divides twice mitotically to generate four microspore mother cells, which undergo meiosis. The anther wall is formed by anticlinal and periclinal divisions of the primary parietal cells as well as of cells surrounding the primary sporogenous cells. Subsequent cytological features in the development of anther and pollen grains of rice have much in common with anther and pollen developmental biology of other members of


title: Dapper: Data Plane Performance Diagnosis of TCP

abstract: With more applications moving to the cloud, cloud providers need to diagnose performance problems in a timely manner. Offline processing of logs is slow and inefficient, and instrumenting the end-host network stack would violate the tenants' rights to manage their own virtual machines (VMs). Instead, our system Dapper analyzes TCP performance in real time near the end-hosts (e.g., at the hypervisor, NIC, or top-of-rack switch). Dapper determines whether a connection is limited by the sender (e.g., a slow server competing for shared resources), the network (e.g., congestion), or the receiver (e.g., small receive buffer). Emerging edge devices now offer flexible packet processing at high speed on commodity hardware, making it possible to monitor TCP performance in the data plane, at line rate. We use P4 to prototype Dapper and evaluate our design on real and synthetic traffic. To reduce the data-plane state requirements, we perform lightweight detection for all connections, followed by heavier-weight diagnosis just for the troubled connections.


title: Fast Feature Pyramids for Object Detection

abstract: Multi-resolution image features may be approximated via extrapolation from nearby scales, rather than being computed explicitly. This fundamental insight allows us to design object detection algorithms that are as accurate, and considerably faster, than the state-of-the-art. The computational bottleneck of many modern detectors is the computation of features at every scale of a finely-sampled image pyramid. Our key insight is that one may compute finely sampled feature pyramids at a fraction of the cost, without sacrificing performance: for a broad family of features we find that features computed at octave-spaced scale intervals are sufficient to approximate features on a finely-sampled pyramid. Extrapolation is inexpensive as compared to direct feature computation. As a result, our approximation yields considerable speedups with negligible loss in detection accuracy. We modify three diverse visual recognition systems to use fast feature pyramids and show results on both pedestrian detection (measured on the Caltech, INRIA, TUD-Brussels and ETH data sets) and general object detection (measured on the PASCAL VOC). The approach is general and is widely applicable to vision algorithms requiring fine-grained multi-scale analysis. Our approximation is valid for images with broad spectra (most natural images) and fails for images with narrow band-pass spectra (e.g., periodic textures).


title: The Aphasia Database on the Web: Description of a Model for Problems of Classification in Medicine

abstract: In aphasiology many inconsistencies exist in the definition and interpretation of aphasic syndromes. These syndromes are the co-occurrence of a set of symptoms. Thus, ambiguities in these clinical, aphasic categories are suited to be generalized to many problems of classification in medicine. In this paper the aphasia database is launched as a model for data mining in medicine. Nominal and topological data about 265 aphasic patients is collected in this database, which is a model for benchmarking different methods of soft computing.


Muscanone: A 3‐O‐(1′′,8′′,14′′‐Trimethylhexadecanyl)naringenin from Commiphora wightii


Study of Deep Learning Techniques for Side-Channel Analysis and Introduction to ASCAD Database


title: Train faster, generalize better: Stability of stochastic gradient descent

abstract: We show that parametric models trained by a stochastic gradient method (SGM) with few iterations have vanishing generalization error. We prove our results by arguing that SGM is algorithmically stable in the sense of Bousquet and Elisseeff. Our analysis only employs elementary tools from convex and continuous optimization. We derive stability bounds for both convex and non-convex optimization under standard Lipschitz and smoothness assumptions. ::: Applying our results to the convex case, we provide new insights for why multiple epochs of stochastic gradient methods generalize well in practice. In the non-convex case, we give a new interpretation of common practices in neural networks, and formally show that popular techniques for training large deep models are indeed stability-promoting. Our findings conceptually underscore the importance of reducing training time beyond its obvious benefit.


title: Frequence: interactive mining and visualization of temporal frequent event sequences

abstract: Extracting insights from temporal event sequences is an important challenge. In particular, mining frequent patterns from event sequences is a desired capability for many domains. However, most techniques for mining frequent patterns are ineffective for real-world data that may be low-resolution, concurrent, or feature many types of events, or the algorithms may produce results too complex to interpret. To address these challenges, we propose Frequence, an intelligent user interface that integrates data mining and visualization in an interactive hierarchical information exploration system for finding frequent patterns from longitudinal event sequences. Frequence features a novel frequent sequence mining algorithm to handle multiple levels-of-detail, temporal context, concurrency, and outcome analysis. Frequence also features a visual interface designed to support insights, and support exploration of patterns of the level-of-detail relevant to users. Frequence's effectiveness is demonstrated with two use cases: medical research mining event sequences from clinical records to understand the progression of a disease, and social network research using frequent sequences from Foursquare to understand the mobility of people in an urban environment.


title: The dark side of the sharing economy … and how to lighten it

abstract: Improving the sharing economy will require addressing myriad problems.


Cognitive Biases in Visualizations


title: Towards zero unknown word in neural machine translation

abstract: Neural Machine translation has shown promising results in recent years. In order to control the computational complexity, NMT has to employ a small vocabulary, and massive rare words outside the vocabulary are all replaced with a single unk symbol. Besides the inability to translate rare words, this kind of simple approach leads to much increased ambiguity of the sentences since meaningless unks break the structure of sentences, and thus hurts the translation and reordering of the in-vocabulary words. To tackle this problem, we propose a novel substitution-translation-restoration method. In substitution step, the rare words in a testing sentence are replaced with similar in-vocabulary words based on a similarity model learnt from monolingual data. In translation and restoration steps, the sentence will be translated with a model trained on new bilingual data with rare words replaced, and finally the translations of the replaced words will be substituted by that of original ones. Experiments on Chinese-to-English translation demonstrate that our proposed method can achieve more than 4 BLEU points over the attention-based NMT. When compared to the recently proposed method handling rare words in NMT, our method can also obtain an improvement by nearly 3 BLEU points.


An Unexpected Product of an Indium-Mediated Carbon—Carbon Coupling: 2-(1-Phenyl-1,3-butadien-2-yl)benzoic Acid.


title: ExploreKit: Automatic Feature Generation and Selection

abstract: Feature generation is one of the challenging aspects of machine learning. We present ExploreKit, a framework for automated feature generation. ExploreKit generates a large set of candidate features by combining information in the original features, with the aim of maximizing predictive performance according to user-selected criteria. To overcome the exponential growth of the feature space, ExploreKit uses a novel machine learning-based feature selection approach to predict the usefulness of new candidate features. This approach enables efficient identification of the new features and produces superior results compared to existing feature selection solutions. We demonstrate the effectiveness and robustness of our approach by conducting an extensive evaluation on 25 datasets and 3 different classification algorithms. We show that ExploreKit can achieve classification-error reduction of 20% overall. Our codeis available at https://github.com/giladkatz/ExploreKit.


title: Assessing the Relationship between Technology Readiness and Continuance Intention in an E-Appointment System: Relationship Quality as a Mediator

abstract: Numerous types of self-service technologies have prevailed due to innovations in network and information technology. To hospitals, patient intentions to continue to use the e-appointment system are crucial. Previous investigations discussed only the relationships between the technology readiness of users and their continuance intentions, and ignored the most important mediator, relationship quality. This study explored the relationships among technology readiness, relationship quality, and continuance intention. The research results demonstrated that both optimism and innovativeness significantly and positively influenced continuance intention through the mediating effect of relationship quality. However, discomfort and insecurity hid not significantly influence relationship quality or continuance intention. Finally, theoretical contributions, managerial implications and future research directions were discussed.


title: Geometric skinning with approximate dual quaternion blending

abstract: Skinning of skeletally deformable models is extensively used for real-time animation of characters, creatures and similar objects. The standard solution, linear blend skinning, has some serious drawbacks that require artist intervention. Therefore, a number of alternatives have been proposed in recent years. All of them successfully combat some of the artifacts, but none challenge the simplicity and efficiency of linear blend skinning. As a result, linear blend skinning is still the number one choice for the majority of developers. In this article, we present a novel skinning algorithm based on linear combination of dual quaternions. Even though our proposed method is approximate, it does not exhibit any of the artifacts inherent in previous methods and still permits an efficient GPU implementation. Upgrading an existing animation system from linear to dual quaternion skinning is very easy and has a relatively minor impact on runtime performance.


title: A Brazilian survey on UML and model-driven practices for embedded software development

abstract: This paper brings statistical findings from a survey about the use of UML modeling and model-driven approaches for the design of embedded software in Brazil. The survey provides evidences regarding the maturity of use of UML and model-driven approaches, how they are employed, and which and where the professionals who use them are. Technical, organizational, and social aspects were investigated and documented by making use of a descriptive research method. Such aspects seemingly reflect the opinions of software engineers on how they perceive the impact of using UML and model-driven approaches on productivity and quality in embedded software development. Results show that most participants are clearly aware of the modeling approach value, even though they practice it only to a limited degree. Most respondents who make use of model-driven approaches attest that productivity and portability are the key advantages of their use.


title: Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy, and Comparative Study

abstract: The classification problem can be addressed by numerous techniques and algorithms which belong to different paradigms of machine learning. In this paper, we are interested in evolutionary algorithms, the so-called genetics-based machine learning algorithms. In particular, we will focus on evolutionary approaches that evolve a set of rules, i.e., evolutionary rule-based systems, applied to classification tasks, in order to provide a state of the art in this field. This paper has a double aim: to present a taxonomy of the genetics-based machine learning approaches for rule induction, and to develop an empirical analysis both for standard classification and for classification with imbalanced data sets. We also include a comparative study of the genetics-based machine learning (GBML) methods with some classical non-evolutionary algorithms, in order to observe the suitability and high potential of the search performed by evolutionary algorithms and the behavior of the GBML algorithms in contrast to the classical approaches, in terms of classification accuracy.


title: DOG I : an annotation system for images of dog breeds

abstract: DOGI ( D og O ntolo G y I mage annotator) is a complete and fully automated semantic annotation system for images of dog breeds. Annotation relies on feature extraction and on associating low-level features with image concepts in an ontology. Because general purpose ontologies for all image types are not yet available, we choose the problem of annotating images of dog breeds as a case study and for the evaluation of our methodology. Nevertheless, DOGI can be adapted to more image types provided that an ontology for a new image domain becomes available. Therefore, DOGI offers an ideal test-bed for experimentation and sets the grounds for the annotation and evaluation of virtually any image type. Evaluation results are realized using images collected from the Web. Almost 95% of the test images is correctly annotated (i.e., DOGI identified their class correctly). DOGIis accessible on the Internet.


title: Ischiofemoral impingement

abstract: Femoroacetabular impingement is a well-documented cause of hip pain. There is, however, increasing evidence for the presence of a previously unrecognised impingement-type condition around the hip - ischiofemoral impingement. This is caused by abnormal contact between the lesser trochanter of the femur and the ischium, and presents as atypical groin and/or posterior buttock pain. The symptoms are gradual in onset and may be similar to those of iliopsoas tendonitis, hamstring injury or bursitis. The presence of ischiofemoral impingement may be indicated by pain caused by a combination of hip extension, adduction and external rotation. Magnetic resonance imaging demonstrates inflammation and oedema in the ischiofemoral space and quadratus femoris, and is distinct from an acute tear. To date this has only appeared in the specialist orthopaedic literature as a problem that has developed after total hip replacement, not in the unreplaced joint.


title: Fixed point iteration for local strictly pseudo-contractive mapping

abstract: A fixed point of the local strictly pseudo-contractive mapping is obtained as the limit of an iteratively constructed sequence with an error estimation in uniformly smooth Banach spaces. This paper has been motivated by a paper by C. E. Chidume [2], in which an iterative approximation of the fixed point of a Lipschitzian strictly pseudocontractive mapping in Lp (2 1 such that the inequality llx yll 0. We denote by J the normalized duality mapping from X to 2x given by Jx = {f* E X*: If*112 = lxll2 = Re(x, f*)} where (, *) denotes the generalized duality pairing. If X is uniformly smooth, then J is single-valued and uniformly continuous on bounded set. A mapping T is said to be local strongly accretive if given x E D(T) there exists a positive number kx such that for each y E D(T) there is i E J(x y) such that (Tx Ty,~ j) > kxllx yll 2 . In [3] T. Kato discovered the relationship between pseudo-contractive mappings Received by the editors April 23, 1990. 1980 Mathematics Subject Classification (1985 Revision). Primary 47H15; Secondary 47H05.


title: Mining cross-frequency coupling microstates from resting state MEG: An application to mild traumatic brain injury

abstract: Recent studies have investigated the possible role of dynamic functional connectivity and the role of cross-frequency coupling (CFC) to provide the substrate for reliable biomarkers of brain disorders. In this study, we analyzed time-varying CFC profiles from resting state Magnetoencephal-ographic recordings of 30 mild Traumatic Brain Injury (mTBI) patients and 50 normal controls. Interactions among sensors at specific pairs of frequency bands were computed via estimation of phase-to-amplitude couplings. We then computed time-varying functional connectivity graphs that were described in terms of segregation (local efficiency, LE) and integration (global efficiency, GE) and mapped those graphs to time series of GE/LE estimates. The resulting dynamic network revealed transitions between a limited number of microstates for mTBI subjects compared to controls. The significant differences in transition probability between the two groups, along with the limited repertoire of possible states, can form the basis for a robust dynamic connectomic biomarker for the diagnosis of mTBI.


title: MU-MIMO MAC Protocols for Wireless Local Area Networks: A Survey

abstract: As wireless devices boom and bandwidth-hungry applications (e.g., video and cloud uploading) get popular, today's wireless local area networks (WLANs) become not only crowded but also stressed at throughput. Multiuser multiple-input–multiple-output (MU-MIMO), an advanced form of MIMO, has gained attention due to its huge potential in improving the performance of WLANs. This paper surveys random access-based medium access control (MAC) protocols for MU-MIMO-enabled WLANs. It first provides background information about the evolution and the fundamental MAC schemes of IEEE 802.11 Standards and Amendments, and then identifies the key requirements of designing MU-MIMO MAC protocols for WLANs. After this, the most representative MU-MIMO MAC proposals in the literature are overviewed by benchmarking their MAC procedures and examining the key components, such as the channel state information acquisition, decoding/precoding, and scheduling schemes. Classifications and discussions on important findings of the surveyed MAC protocols are provided, based on which, the research challenges for designing effective MU-MIMO MAC protocols, as well as the envisaged MAC's role in the future heterogeneous networks, are highlighted.


Beyond the productivity paradox


title: Scene Labeling using Gated Recurrent Units with Explicit Long Range Conditioning

abstract: Recurrent neural network (RNN), as a powerful contextual dependency modeling framework, has been widely applied to scene labeling problems. However, this work shows that directly applying traditional RNN architectures, which unfolds a 2D lattice grid into a sequence, is not sufficient to model structure dependencies in images due to the "impact vanishing" problem. First, we give an empirical analysis about the "impact vanishing" problem. Then, a new RNN unit named Recurrent Neural Network with explicit long range conditioning (RNN-ELC) is designed to alleviate this problem. A novel neural network architecture is built for scene labeling tasks where one of the variants of the new RNN unit, Gated Recurrent Unit with Explicit Long-range Conditioning (GRU-ELC), is used to model multi scale contextual dependencies in images. We validate the use of GRU-ELC units with state-of-the-art performance on three standard scene labeling datasets. Comprehensive experiments demonstrate that the new GRU-ELC unit benefits scene labeling problem a lot as it can encode longer contextual dependencies in images more effectively than traditional RNN units.


Using An Expanded Morris-Lecar Model to Determine Neuronal Dynamics In the Event of Traumatic Brain Injury


title: A Study of AI Population Dynamics with Million-agent Reinforcement Learning

abstract: We conduct an empirical study on discovering the ordered collective dynamics obtained by a population of intelligence agents, driven by million-agent reinforcement learning. Our intention is to put intelligent agents into a simulated natural context and verify if the principles developed in the real world could also be used in understanding an artificially-created intelligent population. To achieve this, we simulate a large-scale predator-prey world, where the laws of the world are designed by only the findings or logical equivalence that have been discovered in nature. We endow the agents with the intelligence based on deep reinforcement learning (DRL). In order to scale the population size up to millions agents, a large-scale DRL training platform with redesigned experience buffer is proposed. Our results show that the population dynamics of AI agents, driven only by each agent's individual self-interest, reveals an ordered pattern that is similar to the Lotka-Volterra model studied in population biology. We further discover the emergent behaviors of collective adaptations in studying how the agents' grouping behaviors will change with the environmental resources. Both of the two findings could be explained by the self-organization theory in nature.


title: Cost-benefit analysis; an ethical critique

abstract: An ethical perspective applied to the use of cost/benefit analyses for environmental, safety, and health regulations reaches the following conclusions: (1) in areas of environmental, safety, and health regulation, there may be many instances where a certain decision might be right even though its benefits do not outweigh its costs; (2) there are good reasons to oppose efforts to put dollar values on non-marketed benefits and costs; (3) given the relative frequency of occasions in the areas of environmental, safety, and health regulation where one would not wish to use a benefits-outweigh-costs test as a decision rule, and given the reasons to oppose the monetizing of non-marketed benefits or costs that is a prerequisite for cost/benefit analysis, it is not justifiable to devote major resources to the generation of data for cost/benefit calculations or to undertake efforts to spread the gospel of cost/benefit analysis further. 5 references, 2 tables.


title: Differentiating between successful and unsuccessful dieters. Validity and reliability of the Perceived Self-Regulatory Success in Dieting Scale

abstract: Dieting and the wish to lose weight are ubiquitous in our society. Only few people, however, are successful in long-term weight loss and -maintenance. The Perceived Self-Regulatory Success in Dieting Scale (PSRS) is a succinct 3-item measure that can be used to differentiate between successful and unsuccessful dieters. However, psychometric properties of the scale have not yet been investigated. We re-analyzed data from several studies and demonstrate validity and reliability of the PSRS. Internal consistency was reasonably high (Cronbach’s alpha = .72–.79). Item means were medium and item-total-correlations were high. Validity of the scale was demonstrated such that it correlated negatively with body-mass-index (BMI), concern for dieting, rigid dietary control, food cravings, food addiction symptoms, and binge eating. Flexible dietary control was positively associated with the PSRS. We conclude that the PSRS constitutes a valid and reliable measure to efficiently assess dieting success.


title: If You Measure It, Can You Improve It?: Exploring The Value of Energy Disaggregation

abstract: Over the past few years, dozens of new techniques have been proposed for more accurate energy disaggregation, but the jury is still out on whether these techniques can actually save energy and, if so, whether higher accuracy translates into higher energy savings. In this paper, we explore both of these questions. First, we develop new techniques that use disaggregated power data to provide actionable feedback to residential users. We evaluate these techniques using power traces from 240 homes and find that they can detect homes that need feedback with as much as 84% accuracy. Second, we evaluate whether existing energy disaggregation techniques provide power traces with sufficient fidelity to support the feedback techniques that we created and whether more accurate disaggregation results translate into more energy savings for the users. Results show that feedback accuracy is very low even while disaggregation accuracy is high. These results indicate a need to revisit the metrics by which disaggregation is evaluated.


title: Scapular positioning and movement in unimpaired shoulders, shoulder impingement syndrome, and glenohumeral instability

abstract: The purpose of this manuscript is to review the knowledge of scapular positioning at rest and scapular movement in different anatomic planes in asymptomatic subjects and patients with shoulder impingement syndrome (SIS) and glenohumeral shoulder instability. We reviewed the literature for all biomechanical and kinematic studies using keywords for impingement syndrome, shoulder instability, and scapular movement published in peer reviewed journal. Based on the predefined inclusion and exclusion criteria, 30 articles were selected for inclusion in the review. The literature is inconsistent regarding the scapular resting position. At rest, the scapula is positioned approximately horizontal, 35° of internal rotation and 10° anterior tilt. During shoulder elevation, most researchers agree that the scapula tilts posteriorly and rotates both upward and externally. It appears that during shoulder elevation, patients with SIS demonstrate a decreased upward scapular rotation, a decreased posterior tilt, and a decrease in external rotation. In patients with glenohumeral shoulder instability, a decreased scapular upward rotation and increased internal rotation is seen. This literature overview provides clinicians with insight into scapular kinematics in unimpaired shoulders and shoulders with impingement syndrome and instability.