title: Low-Rank Bilinear Pooling for Fine-Grained Classification

abstract: Pooling second-order local feature statistics to form a high-dimensional bilinear feature has been shown to achieve state-of-the-art performance on a variety of fine-grained classification tasks. To address the computational demands of high feature dimensionality, we propose to represent the covariance features as a matrix and apply a low-rank bilinear classifier. The resulting classifier can be evaluated without explicitly computing the bilinear feature map which allows for a large reduction in the compute time as well as decreasing the effective number of parameters to be learned. To further compress the model, we propose a classifier co-decomposition that factorizes the collection of bilinear classifiers into a common factor and compact per-class terms. The co-decomposition idea can be deployed through two convolutional layers and trained in an end-to-end architecture. We suggest a simple yet effective initialization that avoids explicitly first training and factorizing the larger bilinear classifiers. Through extensive experiments, we show that our model achieves state-of-the-art performance on several public datasets for fine-grained classification trained with only category labels. Importantly, our final model is an order of magnitude smaller than the recently proposed compact bilinear model [8], and three orders smaller than the standard bilinear CNN model [19].


title: A new contactless fault diagnosis approach for pantograph-catenary system

abstract: Railway transport is one of the most important mass transportation media in the worldwide. With the development of trains speed, safety and comfort levels of railways is getting more importance day by day. Besides high level of security requirement, detection of anomaly for rail and road shall be early identified for decreasing operation and maintenance expenditures. The pantograph-catenary system has an important role for collecting the current in electrical railways. The problem occurred in this system will affect the current collection performance of electrified trains. In this paper, a new image processing based technique is proposed to detect the arcing faults occurred between catenary and pantograph contact. The proposed method takes one frame from the digital camera and then the edge detection algorithm extracts the edges of pantograph. The arcing between contact wire and pantograph is detected by examining the position of contact wire of pantograph's edge.


title: Assessing the Mumbai metropolitan region: a governance perspective

abstract: The paper written from a governance perspective takes the position that a useful governance perspective must be informed by the socio-economic-cultural milieu and in the specific Indian context must sit on a tripod of ‘political reality’, ‘state of decentralization’ and ‘basic economic principles’. Mumbai Metropolitan Region (MMR) is one of the most important growth engines for India as it accrues tremendous revenues to the central and the Maharashtra (State) governments. It is thus crucial to ensure that the governance system in MMR is efficient for it to continue on its growth trajectory. We assess the performance of the governance system in MMR which resembles a polycentric governance system- a system that has been heralded as being greatly suited to metropolitan regions by several scholars. We recognize that Metropolitan governance is a concept since in reality it comprises a loose bundle of multiple actors and organizations. For sharper focus, we study the interactions between only two (dominant) public organizations- Municipal Corporation of Greater Mumbai (an Urban Local Body of Greater Mumbai) and the Mumbai Metropolitan Region Development Authority (a parastatal of the State Government). Our assessment reveals that the institutions – rules of the game – which are essential for the efficient functioning of polycentric governance system are missing in the region. Our findings emphasize the need to bring about micro reforms in the institutional framework for polycentric governance to be successful in MMR.


title: The effects of online social media on tourism websites

abstract: Web 2.0 and online social networking websites heavily affect today most of the online activities and their effect on tourism is obviously rather important. This paper aims at verifying the impact that online social networks (OSN) have on the popularity of tourism websites. Two OSNs have been considered: Facebook and Twitter. The pattern of visits to a sample of Italian tourism websites was analysed and the relationship between the total visits and those having the OSNs as referrals were measured. The analysis shows a clear correlation and confirms the starting hypothesis. Consequences and implications of these outcomes are discussed.


Applications of Dual Quaternions in Three Dimensional Transformation and Interpolation


title: Self-monitoring dietary intake: current and future practices.

abstract: This article reviews the literature on the use of paper diaries for self-monitoring food intake, identifies the strengths and limitations of paper-and-pencil diaries and their new counterpart, the electronic diary or personal digital assistant (PDA), and reports how participants were trained to use a PDA with dietary software in two pilot studies--one with hemodialysis patients and the other with participants in a weight loss study. The report of the pilot studies focuses on the practical issues encountered in training participants in the use of a PDA and addresses the pros and cons of different dietary software programs. Six hemodialysis patients were trained in the first study and seven participants attempting to lose or maintain their weight were trained in the second pilot study. The training focused on how to use a PDA and how to navigate the dietary software to self-monitor food intake. The goals of using the PDA were to improve adherence to the therapeutic diets and to self-monitoring. Lessons learned from the pilot studies are shared.


title: The impact of forecasting uncertainty on the performance of a predictive optimal controller for thermal energy storage systems

abstract: In this paper an investigation is presented to determine the effect of forecasting uncertainty on the cost savings performance of a predictive optimal controller for thermal energy storage (TES) systems. Specifically, this investigation analyzed four uncertainty models to predict future values for cooling loads, weather variables, and electrical rates. The uncertainty models considered are unbiased Gaussian noise, correlated Gaussian noise, unbiased uniform noise, and biased uniform noise. The results of the analysis summarized in this paper show that the predictive optimal controller is robust and does not require high levels of accuracy in predicting the cooling loads and the real-time pricing (RTP) rates.


title: Developmental psychopathology: concepts and challenges.

abstract: The defining features of developmental psychopathology concepts include attention to the ::: understanding of causal processes, appreciation of the role of developmental mechanisms, and ::: consideration of continuities and discontinuities between normality and psychopathology. ::: Accomplishments with respect to these issues are reviewed in relation to attachment disorders, ::: antisocial behavior, autism, depressive disorder, schizophrenia, and intellectual development. ::: Major research challenges remain in relation to measurement issues, comorbidity, gender ::: differences, cognitive processing, nature–nurture interplay, heterotypic continuity, ::: continuities between normal variations and disorders, developmental programming, and ::: therapeutic mechanisms in effective treatments.


title: Building classification models from imbalanced fraud detection data / Terence Yong Koon Beh, Swee Chuan Tan and Hwee Theng Yeo

abstract: Many real-world data sets exhibit imbalanced class distributions in which almost all instances are assigned to one class and far fewer instances to a smaller, yet usually interesting class. Building classification models from such imbalanced data sets is a relatively new challenge in the machine learning and data mining community because many traditional classification algorithms assume similar proportions of majority and minority classes. When the data is imbalanced, these algorithms generate models that achieve good classification accuracy for the majority class, but poor accuracy for the minority class. This paper reports our experience in applying data balancing techniques to develop a classifier for an imbalanced real-world fraud detection data set. We evaluated the models generated from seven classification algorithms with two simple data balancing techniques. Despite many ideas floating in the literature to tackle the imbalanced issue, ::: our study shows the simplest data balancing technique is all that is required to significantly improve the accuracy in identifying the primary class of interest (i.e., the minority class) in all the seven algorithms tested. Our results also show that precision and recall are useful and effective measures for evaluating models created from artificially balanced data. Hence, we advise data mining practitioners to try simple data balancing first before exploring more sophisticated techniques to tackle the class imbalance problem.


title: News recommendation via hypergraph learning: encapsulation of user behavior and news content

abstract: Personalized news recommender systems have gained increasing attention in recent years. Within a news reading community, the implicit correlations among news readers, news articles, topics and named entities, e.g., what types of named entities in articles are preferred by users, and why users like the articles, could be valuable for building an effective news recommender. In this paper, we propose a novel news personalization framework by mining such correlations. We use hypergraph to model various high-order relations among different objects in news data, and formulate news recommendation as a ranking problem on fine-grained hypergraphs. In addition, by transductive inference, our proposed algorithm is capable of effectively handling the so-called cold-start problem. Extensive experiments on a data set collected from various news websites have demonstrated the effectiveness of our proposed algorithm.


title: Enhancing Automated Decision Support across Medical and Oral Health Domains with Semantic Web Technologies

abstract: Research has shown that the general health and oral health of an individual are closely related. Accordingly, current practice of isolating the information base of medical and oral health domains can be dangerous and detrimental to the health of the individual. However, technical issues such as heterogeneous data collection and storage formats, limited sharing of patient information and lack of decision support over the shared information are the principal reasons for the current state of affairs. To address these issues, the following research investigates the development and application of a cross-domain ontology and rules to build an evidence-based and reusable knowledge base consisting of the inter-dependent conditions from the two domains. Through example implementation of the knowledge base in Protege, we demonstrate the effectiveness of our approach in reasoning over and providing decision support for cross-domain patient information.


title: Clustering transactions using large items

abstract: In traditional data clustering, similarity of a cluster of objects is measured by pairwise similarity of objects in that cluster. We argue that such measures are not appropriate for transactions that are sets of items. We propose the notion of large items , i.e., items contained in some minimum fraction of transactions in a cluster, to measure the similarity of a cluster of transactions. The intuition of our clustering criterion is that there should be many large items within a cluster and little overlapping of such items across clusters. We discuss the rationale behind our approach and its implication on providing a better solution to the clustering problem. We present a clustering algorithm based on the new clustering criterion and evaluate its effectiveness.


title: Center of attention: How facebook users allocate attention across friends

abstract: An individual's personal network — their set of social contacts — is a basic object of study in sociology. Studies of personal networks have focused on their size (the number of contacts) and their composition (in terms of categories such as kin and co-workers). Here we propose a new measure for the analysis of personal networks, based on the way in which an individual divides his or her attention across contacts. This allows us to contrast people who focus a large fraction of their interactions on a small set of close friends with people who disperse their attention more widely. Using data from Facebook, we find that this balance of attention is a relatively stable property of an individual over time, and that it displays interesting variation across both different groups of people and different modes of interaction. In particular, activities based on communication involve a much higher focus of attention than activities based simply on observation, and these two types of modalities also exhibit different forms of variation in interaction patterns both within and across groups. Finally, we contrast the amount of attention paid by individuals to their most frequent contacts with the rate of change in the identities of these contacts, providing a measure of churn for this set.


title: Predicting Engagement Intensity in the Wild Using Temporal Convolutional Network

abstract: Engagement is the holy grail of learning whether it is in a classroom setting or an online learning platform. Studies have shown that engagement of the student while learning can benefit students as well as the teacher if the engagement level of the student is known. It is difficult to keep track of the engagement of each student in a face-to-face learning happening in a large classroom. It is even more difficult in an online learning platform where, the user is accessing the material at different instances. Automatic analysis of the engagement of students can help to better understand the state of the student in a classroom setting as well as online learning platforms and is more scalable. In this paper we propose a framework that uses Temporal Convolutional Network (TCN) to understand the intensity of engagement of students attending video material from Massive Open Online Courses (MOOCs). The input to the TCN network is the statistical features computed on 10 second segments of the video from the gaze, head pose and action unit intensities available in OpenFace library. The ability of the TCN architecture to capture long term dependencies gives it the ability to outperform other sequential models like LSTMs. On the given test set in the EmotiW 2018 sub challenge-"Engagement in the Wild", the proposed approach with Dilated-TCN achieved an average mean square error of 0.079.


title: An enabling waveform for 5G — QAM-FBMC: Initial analysis

abstract: In this paper, we identified the challenges and requirements for the waveform design of the fifth generation mobile communication networks (5G) and compared Orthogonal frequency-division multiplexing (OFDM) based waveforms with Filter Bank Multicarrier (FBMC) based ones. Recently it has been shown that Quadrature-Amplitude Modulation (QAM) transmission and reception can be enabled in FBMC by using multiple prototype filters, resulting in a new waveform: QAM-FBMC. Here, the transceiver architecture and signal model of QAM-FBMC are presented and channel estimation error and RF impairment, e.g., phase noise, are modeled. In addition, initial evaluation is made in terms of out-of-band (OOB) emission and complexity. The simulation results show that QAM-FBCM can achieve the same BER performance as cyclic-prefix (CP) OFDM without spectrum efficiency reduction due to the adding of CP. Different equalization schemes are evaluated and the effect of channel estimation error is investigated. Moreover, effects of the phase noise are evaluated and QAM-FBMC is shown to be robust to the phase noise.


title: Periodontal conditions in patients with Marfan syndrome – a multicenter case control study

abstract: BackgroundMarfan syndrome (MFS) is a disorder of the connective tissues. Alterations of the elastic fibers may manifest in different tissues especially in the skeletal, cardiovascular and ocular system. Oral manifestations like orthodontic or skeletal anomalies and fragility of the temporomandibular joint have been well described by various authors. However, no data are available regarding a possible periodontal involvement of MFS. Hence, the aim of the present study was to investigate for the first time if MFS may increase the susceptibility to periodontitis.MethodsA comprehensive periodontal examination including documentation of probing pocket depth, gingival recession, clinical attachment level, and bleeding on probing was conducted in all patients. In addition, dental conditions were assessed by determining the Index for Decayed, Missing and Filled Teeth (DMFT) and a self-administered questionnaire was filled out by patients. For statistical analysis, the unpaired t-Test was applied (level of significance: p < 0.05). Both groups were matched concerning well known periodontal risk factors like age, gender and smoking habits.Results82 participants, 51 patients with MFS (30 female and 21 male, mean age: 40.20 ± 15.35 years) and 31 sound controls (17 female and 14 male, mean age: 40.29 ± 13.94 years), were examined. All assessed periodontal and dental parameters were not significantly different between groups.ConclusionsBased on our data, patients with MFS did not reveal a higher prevalence of periodontitis compared to the control group. However, Marfan patients showed a tendency to more inflammation signs, which can be explained by the crowded teeth. Therefore, a regular professional cleaning of the teeth is recommendable (i.e., 6 months intervals) in order to reduce the bacterial biofilm in the oral cavity and thus resulting in a decreased risk of systemic diseases, specifically endocarditis.


title: Using corpora in machine-learning chatbot systems

abstract: A chatbot is a machine conversation system which interacts with human users via natural conversational language. Software to machine-learn conversational patterns from a transcribed dialogue corpus has been used to generate a range of chatbots speaking various languages and sublanguages including varieties of English, as well as French, Arabic and Afrikaans. This paper presents a program to learn from spoken transcripts of the Dialogue Diversity Corpus of English, the Minnesota French Corpus, the Corpus of Spoken Afrikaans, the Qur’an Arabic-English parallel corpus, and the British National Corpus of English; we discuss the problems which arose during learning and testing. Two main goals were achieved from the automation process. One was the ability to generate different versions of the chatbot in different languages, bringing chatbot technology to languages with few if any NLP resources: the corpus-based learning techniques transferred straightforwardly to develop chatbots for Afrikaans and Qur’anic Arabic. The second achievement was the ability to learn a very large number of categories within a short time, saving effort and errors in doing such work manually: we generated more than one million AIML categories or conversation-rules from the BNC corpus, 20 times the size of existing AIML rule-sets, and probably the biggest AI Knowledge-Base ever.


title: Robust Website Fingerprinting Through the Cache Occupancy Channel

abstract: Website fingerprinting attacks, which use statistical analysis on network traffic to compromise user privacy, have been shown to be effective even if the traffic is sent over anonymity-preserving networks such as Tor. The classical attack model used to evaluate website fingerprinting attacks assumes an on-path adversary, who can observe all traffic traveling between the user's computer and the Tor network. In this work we investigate these attacks under a different attack model, inwhich the adversary is capable of running a small amount of unprivileged code on the target user's computer. Under this model, the attacker can mount cache side-channel attacks, which exploit the effects of contention on the CPU's cache, to identify the website being browsed. In an important special case of this attack model, a JavaScript attack is launched when the target user visits a website controlled by the attacker. The effectiveness of this attack scenario has never been systematically analyzed,especially in the open-world model which assumes that the user is visiting a mix of both sensitive and non-sensitive sites. In this work we show that cache website fingerprinting attacks in JavaScript are highly feasible, even when they are run from highly restrictive environments, such as the Tor Browser .Specifically, we use machine learning techniques to classify traces of cache activity. Unlike prior works, which try to identify cache conflicts, our work measures the overall occupancy of the last-level cache. We show that our approach achieves high classification accuracy in both the open-world and the closed-world models. We further show that our techniques are resilient both to network-based defenses and to side-channel countermeasures introduced to modern browsers as a response to the Spectre attack.


title: Question Answering over Freebase with Multi-Column Convolutional Neural Networks

abstract: Answering natural language questions over a knowledge base is an important and challenging task. Most of existing systems typically rely on hand-crafted features and rules to conduct question understanding and/or answer ranking. In this paper, we introduce multi-column convolutional neural networks (MCCNNs) to understand questions from three different aspects (namely, answer path, answer context, and answer type) and learn their distributed representations. Meanwhile, we jointly learn low-dimensional embeddings of entities and relations in the knowledge base. Question-answer pairs are used to train the model to rank candidate answers. We also leverage question paraphrases to train the column networks in a multi-task learning manner. We use FREEBASE as the knowledge base and conduct extensive experiments on the WEBQUESTIONS dataset. Experimental results show that our method achieves better or comparable performance compared with baseline systems. In addition, we develop a method to compute the salience scores of question words in different column networks. The results help us intuitively understand what MCCNNs learn.


title: Ultra-wideband implementation of printed rectangular monopole antenna

abstract: To meet the ultra-wideband (UWB) radio applications, a microstrip-fed printed monopole antenna is designed with a rectangular radiation patch of the golden section ratio. To maximize the impedance bandwidth and miniaturize the antenna size, a concave ground plane and a feed gap are introduced into the printed antenna. The simulation results show that the impedance bandwidth defined by the reflection coefficient S11 less than −10 dB is from 2.9 GHz to 13.3 GHz with a ration of about 1∶4.59. These results are confirmed by the experimental measure. And a nearly omnidirectional radiation pattern is exhibited


title: Input-series output-parallel AC/AC converter

abstract: Input-series and output-parallel (ISOP) converters are suitable for the high input voltage and low output voltage conversion applications. An ISOP current mode AC/AC converter with high frequency link is proposed. The control strategy and operation principle of the ISOP AC/AC converter are investigated. The control strategy ensures the proper sharing of the input voltage and the proper sharing of the output current among the ISOP AC/AC converter. By simulation, the correctness of the operation principle and the control strategy of the ISOP AC/AC converter are proved.


title: Backside illuminated SPAD image sensor with 7.83μm pitch in 3D-stacked CMOS technology

abstract: We present the first 3D-stacked backside illuminated (BSI) single photon avalanche diode (SPAD) image sensor capable of both single photon counting (SPC) intensity, and time resolved imaging. The 128×120 prototype has a pixel pitch of 7.83 μm making it the smallest pixel reported for SPAD image sensors. A low power, high density 40nm bottom tier hosts the quenching front end and processing electronics while an imaging specific 65nm top tier hosts the photo-detectors with a 1-to-1 hybrid bond connection [1]. The SPAD exhibits a median dark count rate (DCR) below 200cps at room temperature and 1V excess bias, and has a peak photon detection probability (PDP) of 27.5% at 640nm and 3 V excess bias.


title: Go-Explore: a New Approach for Hard-Exploration Problems

abstract: A grand challenge in reinforcement learning is intelligent exploration, especially when rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard-exploration domains: Montezuma's Revenge and Pitfall. On both games, current RL algorithms perform poorly, even those with intrinsic motivation, which is the dominant method to improve performance on hard-exploration domains. To address this shortfall, we introduce a new algorithm called Go-Explore. It exploits the following principles: (1) remember previously visited states, (2) first return to a promising state (without exploration), then explore from it, and (3) solve simulated environments through any available means (including by introducing determinism), then robustify via imitation learning. The combined effect of these principles is a dramatic performance improvement on hard-exploration problems. On Montezuma's Revenge, Go-Explore scores a mean of over 43k points, almost 4 times the previous state of the art. Go-Explore can also harness human-provided domain knowledge and, when augmented with it, scores a mean of over 650k points on Montezuma's Revenge. Its max performance of nearly 18 million surpasses the human world record, meeting even the strictest definition of "superhuman" performance. On Pitfall, Go-Explore with domain knowledge is the first algorithm to score above zero. Its mean score of almost 60k points exceeds expert human performance. Because Go-Explore produces high-performing demonstrations automatically and cheaply, it also outperforms imitation learning work where humans provide solution demonstrations. Go-Explore opens up many new research directions into improving it and weaving its insights into current RL algorithms. It may also enable progress on previously unsolvable hard-exploration problems in many domains, especially those that harness a simulator during training (e.g. robotics).


title: Face liveness detection with feature discrimination between sharpness and blurriness

abstract: Face recognition has been extensively used in a wide variety of security systems for identity authentication for years. However, many security systems are vulnerable to spoofing face attacks (e.g., 2D printed photo, replayed video). Consequently, a number of anti-spoofing approaches have been proposed. In this study, we introduce a new algorithm that addresses the face liveness detection based on the digital focus technique. The proposed algorithm relies on the property of digital focus with various depths of field (DOFs) while shooting. Two features of the blurriness level and the gradient magnitude threshold are computed on the nose and the cheek subimages. The differences of these two features between the nose and the cheek in real face images and spoofing face images are used to facilitate detection. A total of 75 subjects with both real and spoofing face images were used to evaluate the proposed framework. Preliminary experimental results indicated that this new face liveness detection system achieved a high recognition rate of 94.67% and outperformed many state-of-the-art methods. The computation speed of the proposed algorithm was the fastest among the tested methods.


title: Protein Nutrition, Exercise and Aging

abstract: Aging is associated with remarkable changes in body composition. Loss of skeletal muscle, a process called sarcopenia, is a prominent feature of these changes. In addition, gains in total body fat and visceral fat content continue into late life. The cause of sarcopenia is likely a result of a number of changes that also occur with aging. These include reduced levels of physical activity, changing endocrine function (reduced testosterone, growth hormone, and estrogen levels), insulin resistance, and increased dietary protein needs. Healthy free-living elderly men and women have been shown to accommodate to the Recommended Dietary Allowance (RDA) for protein of 0.8 g kg 1 d 1 with a continued decrease in urinary nitrogen excretion and reduced muscle mass. While many elderly people consume adequate amounts of protein, many older people have a reduced appetite and consume less than the protein RDA, likely resulting in an accelerated rate of sarcopenia. One important strategy that counters sarcopenia is strength conditioning. Strength conditioning will result in an increase in muscle size and this increase in size is largely the result of increased contractile proteins. The mechanisms by which the mechanical events stimulate an increase in RNA synthesis and subsequent protein synthesis are not well understood. Lifting weight requires that a muscle shorten as it produces force (concentric contraction). Lowering the weight, on the other hand, forces the muscle to lengthen as it produces force (eccentric contraction). These lengthening muscle contractions have been shown to produce ultrastructural damage (microscopic tears in contractile proteins muscle cells) that may stimulate increased muscle protein turnover. This muscle damage produces a cascade of metabolic events which is similar to an acute phase response and includes complement activation, mobilization of neutrophils, increased circulating an skeletal muscle interleukin-1, macrophage accumulation in muscle, and an increase in muscle protein synthesis and degradation. While endurance exercise increases the oxidation of essential amino acids and increases the requirement for dietary protein, resistance exercise results in a decrease in nitrogen excretion, lowering dietary protein needs. This increased efficiency of protein use may be important for wasting diseases such as HIV infection and cancer and particularly in elderly people suffering from sarcopenia. Research has indicated that increased dietary protein intake (up to 1.6 g protein kg 1 d 1 ) may enhance the hypertrophic response to resistance exercise. It has also been demonstrated that in very old men and women the use of a protein-calorie supplement was associated with greater strength and muscle mass gains than did the use of placebo.


title: Face Detection without Bells and Whistles

abstract: Face detection is a mature problem in computer vision. While diverse high performing face detectors have been proposed in the past, we present two surprising new top performance results. First, we show that a properly trained vanilla DPM reaches top performance, improving over commercial and research systems. Second, we show that a detector based on rigid templates - similar in structure to the Viola&Jones detector - can reach similar top performance on this task. Importantly, we discuss issues with existing evaluation benchmark and propose an improved procedure.


title: Conditional tail behaviour and Value at Risk

abstract: In this paper we study the tail behaviour of eight major market indexes stratifying data according to the violation of a high threshold on the previous day. The distributional differences found can be exploited to improve VaR calculations in several settings, giving rise to what we call 'MCVaR'. We compare the performance of MCVaR with unconditioned VaR calculation methods and with GARCH VaR by means of several back-testing techniques that take into account not only the number of violations but also their magnitude and clustering.


title: Simultaneous Use of Selective Neurectomy With Liposuction for Calf Reduction in Asians

abstract: Background ::: Calf contouring continues to be popular among Northeast Asians. Calf hypertrophy and distorted leg contours are stressful to many women. Several calf reduction techniques such as a selective neurectomy or calf muscle resection have been attempted, but have inconsistent results. ::: ::: ::: Objectives ::: This study was designed to demonstrate improved outcomes when combining a selective neurectomy with simultaneous liposuction. ::: ::: ::: Methods ::: A total of 780 patients with hypertrophic calves underwent calf reduction from January 2002 to December 2010. Of these, 193 patients were treated by selective neurectomy with simultaneous liposuction. Calf hypertrophy with a circumference below 34 cm was defined as mild, calves with a circumference of 34 to 38 cm were defined as moderate, and a calf circumference above 38 cm was defined as severe. In all groups, patients whose pinch test was above 2 cm underwent a simultaneous liposuction. ::: ::: ::: Results ::: Twenty-eight cases (14.5%) were defined as mild, 72 (37.3%) were moderate, and 93 (48.2%) were severe. Over an average of 8.7 months of postoperative follow up, the reduction in calf circumference averaged 3.7 cm in the mild group (11.1%), 4.0 cm in the moderate group (10.7 %), and 4.3 cm in the severe group (10.7%). Overall, 97.5% of patients were satisfied with the results. There were no severe complications including functional problem of lower extremity reported. ::: ::: ::: Conclusions ::: The shape, type, and fat distribution of the hypertrophic calves were considered in our patient analysis. A selective neurectomy with liposuction was performed on 193 patients. This technique allowed for a successful calf reduction and improved the patient's aesthetic satisfaction without any reported functional complications. ::: ::: ::: Level of Evidence 4


title: Classical Structured Prediction Losses for Sequence to Sequence Learning

abstract: There has been much recent work on training neural attention models at the sequence-level using either reinforcement learning-style methods or by optimizing the beam. In this paper, we survey a range of classical objective functions that have been widely used to train linear models for structured prediction and apply them to neural sequence to sequence models. Our experiments show that these losses can perform surprisingly well by slightly outperforming beam search optimization in a like for like setup. We also report new state of the art results on both IWSLT 2014 German-English translation as well as Gigaword abstractive summarization.


title: Universal Intelligence: A Definition of Machine Intelligence

abstract: A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: we take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines.


title: Caffe con Troll: Shallow Ideas to Speed Up Deep Learning

abstract: We present Caffe con Troll (CcT), a fully compatible end-to-end version of the popular framework Caffe with rebuilt internals. We built CcT to examine the performance characteristics of training and deploying general-purpose convolutional neural networks across different hardware architectures. We find that, by employing standard batching optimizations for CPU training, we achieve a 6:3× throughput improvement over Caffe on popular networks like CaffeNet. Moreover, with these improvements, the end-to-end training time for CNNs is directly proportional to the FLOPS delivered by the CPU, which enables us to efficiently train hybrid CPU-GPU systems for CNNs.


title: Symbiotic Bacteria Direct Expression of an Intestinal Bactericidal Lectin

abstract: The mammalian intestine harbors complex societies of beneficial bacteria that are maintained in the lumen with minimal penetration of mucosal surfaces. Microbial colonization of germ-free mice triggers epithelial expression of RegIIIgamma, a secreted C-type lectin. RegIIIgamma binds intestinal bacteria but lacks the complement recruitment domains present in other microbe-binding mammalian C-type lectins. We show that RegIIIgamma and its human counterpart, HIP/PAP, are directly antimicrobial proteins that bind their bacterial targets via interactions with peptidoglycan carbohydrate. We propose that these proteins represent an evolutionarily primitive form of lectin-mediated innate immunity, and that they reveal intestinal strategies for maintaining symbiotic host-microbial relationships.


title: Dynamic Multi-Scale Semantic Segmentation based on Dilated Convolutional Networks.

abstract: Semantic segmentation requires methods capable of learning high-level features while dealing with large volume of data. Towards such goal, Convolutional Networks can learn specific and adaptable features based on the data. However, these networks are not capable of processing a whole remote sensing image, given its huge size. To overcome such limitation, the image is processed using fixed size patches. The definition of the input patch size is usually performed empirically (evaluating several sizes) or imposed (by network constraint). Both strategies suffer from drawbacks and could not lead to the best patch size. To alleviate this problem, several works exploited multi-scale information by combining networks or layers. This process increases the number of parameters resulting in a more difficult model to train. In this work, we propose a novel technique to perform semantic segmentation of remote sensing images that exploits a multi-scale paradigm without increasing the number of parameters while defining, in training time, the best patch size. The main idea is to train a dilated network with distinct patch sizes, allowing it to capture multi-scale characteristics from heterogeneous contexts. While processing these varying patches, the network provides a score for each patch size, helping in the definition of the best size for the current scenario. A systematic evaluation of the proposed algorithm is conducted using four high-resolution remote sensing datasets with very distinct properties. Our results show that the proposed algorithm provides improvements in pixelwise classification accuracy when compared to state-of-the-art methods.


title: Detecting Anomaly in Big Data System Logs Using Convolutional Neural Network

abstract: Nowadays, big data systems are being widely adopted by many domains for offering effective data solutions, such as manufacturing, healthcare, education, and media. Big data systems produce tons of unstructured logs that contain buried valuable information. However, it is a daunting task to manually unearth the information and detect system anomalies. A few automatic methods have been developed, where the cutting-edge machine learning technique is one of the most promising ways. In this paper, we propose a novel approach for anomaly detection from big data system logs by leveraging Convolutional Neural Networks (CNN). Different from other existing statistical methods or traditional rule-based machine learning approaches, our CNN-based model can automatically learn event relationships in system logs and detect anomaly with high accuracy. Our deep neural network consists of logkey2vec embeddings, three 1D convolutional layers, dropout layer, and max-pooling. According to our experiment, our CNN-based approach has better accuracy(reaches to 99%) compared to other approaches using Long Short term memory (LSTM) and Multilayer Perceptron (MLP) on detecting anomaly in Hadoop Distributed File System (HDFS) logs.


title: Lazy selection: a scribble-based tool for smart shape elements selection

abstract: This paper presents Lazy Selection, a scribble-based tool for quick selection of one or more desired shape elements by roughly stroking through the elements. Our algorithm automatically refines the selection and reveals the user's intention. To give the user maximum flexibility but least ambiguity, our technique first extracts selection candidates from the scribble-covered elements by examining the underlying patterns and then ranks them based on their location and shape with respect to the user-sketched scribble. Such a design makes our tool tolerant to imprecise input systems and applicable to touch systems without suffering from the fat finger problem. A preliminary evaluation shows that compared to the standard click and lasso selection tools, which are the most commonly used, our technique provides significant improvements in efficiency and flexibility for many selection scenarios.


title: Persistent Navigation and Mapping using a Biologically Inspired SLAM System

abstract: The challenge of persistent navigation and mapping is to develop an autonomous robot system that can simultaneously localize, map and navigate over the lifetime of the robot with little or no human intervention. Most solutions to the simultaneous localization and mapping (SLAM) problem aim to produce highly accurate maps of areas that are assumed to be static. In contrast, solutions for persistent navigation and mapping must produce reliable goal-directed navigation outcomes in an environment that is assumed to be in constant flux. We investigate the persistent navigation and mapping problem in the context of an autonomous robot that performs mock deliveries in a working office environment over a two-week period. The solution was based on the biologically inspired visual SLAM system, RatSLAM. RatSLAM performed SLAM continuously while interacting with global and local navigation systems, and a task selection module that selected between exploration, delivery, and recharging modes. The robot performed 1,143 delivery tasks to 11 different locations with only one delivery failure (from which it recovered), traveled a total distance of more than 40 km over 37 hours of active operation, and recharged autonomously a total of 23 times.


title: Risk-Based Static Authentication in Web Applications with Behavioral Biometrics and Session Context Analytics

abstract: In order to improve the security of password-based authentication in web applications, it is a common industry practice to profile users based on their sessions context, such as IP ranges and Browser type. On the other hand, behavioral dynamics such as mouse and keyword features have been proposed in order to improve authentication, but have been shown most effective only in continuous authentication scenarios. In this paper we propose to combine both fingerprinting and behavioral dynamics (for mouse and keyboard) in order to increase security of login mechanisms. We do this by using machine learning techniques that aim at high accuracy, and only occasionally raise alarms for manual inspection. Our combined approach achieves an AUC of 0.957. We discuss the practicality of our approach in industrial contexts.


title: A Comprehensive Survey of Data Mining-based Fraud Detection Research

abstract: This survey paper categorises, compares, and summarises from almost all published technical and review articles in automated fraud detection within the last 10 years. It defines the professional fraudster, formalises the main types and subtypes of known fraud, and presents the nature of data evidence collected within affected industries. Within the business context of mining the data to achieve higher cost savings, this research presents methods and techniques together with their problems. Compared to all related reviews on fraud detection, this survey covers much more technical articles and is the only one, to the best of our knowledge, which proposes alternative data and solutions from related domains.


title: Wearable Privacy: Skeletons in The Data Closet

abstract: Equipped with sensors that are capable of collecting physiological and environmental data continuously, wearable technologies have the potential to become a valuable component of personalized healthcare and health management. However, in addition to the potential benefits of wearable devices, the widespread and continuous use of wearables also poses many privacy challenges. In some instances, users may not be aware of the risks associated with wearable devices, while in other cases, users may be aware of the privacy-related risks, but may beunable to negotiate complicated privacy settings to meet their needs and preferences. This lack of awareness could have an adverse impact on users in the future, even becoming a "skeleton in the closet." In this work, we conducted 32 semi-structured interviews to understand how users perceive privacy in wearable computing. Results suggest that user concerns toward wearable privacy have different levels of variety ranging from no concern to highly concerned. In addition, while user concerns and benefits are similar among participants in our study, these variablesshould be investigated more extensively for the development of privacy enhanced wearable technologies.


title: A Study on the Usage of Material Aesthetics in Car Design

abstract: The purpose of this paper is to explore how the aesthetics of material is applied to car design. Based on a series of current material research with substantive examples, the paper will encourage car designers to be more experimental in their choice of materials and designs. It covers four physiologically sections including visual, tactile, auditory, and olfactory perceptions of materials. In addition, four typical case studies will showcase the present status and development trends of materials science within the automobile industry.


title: Topography of the nasolacrimal duct on the lateral nasal wall in Koreans

abstract: Purpose ::: The aim of this study was to describe in detail the gross anatomy of the nasolacrimal apparatus in relation to the lateral nasal wall by the various steps of cadaver dissection and to measure the distances of surgically important landmarks from relevant structures to improve the safety and efficiency of endoscopic surgical procedures.


OpenSGX: An Open Platform for SGX Research


FoxP 3 Diseases following In Vivo Depletion of Early Interpretation of Fatal Inflammatory Locus in Epithelial Cells : A Caution against FoxP 3 Cutting Edge : Broad Expression of the Pan


title: Efficient energy-based embedding models for link prediction in knowledge graphs

abstract: We focus on the problem of link prediction in Knowledge Graphs, with the goal of discovering new facts. To this purpose, Energy-Based Models for Knowledge Graphs that embed entities and relations in continuous vector spaces have been largely used. The main limitation in their applicability lies in the parameter learning phase, which may require a large amount of time for converging to optimal solutions. In this article, we first propose an unified view on different Energy-Based Embedding Models. Hence, for improving the model training phase, we propose the adoption of adaptive learning rates. We show that, by adopting adaptive learning rates during training, we can improve the efficiency of the parameter learning process by an order of magnitude, while leading to more accurate link prediction models in a significantly lower number of iterations. We extensively evaluate the proposed learning procedure on a variety of new models: our result show a significant improvement over state-of-the-art link prediction methods on two large Knowledge Graphs, namely WordNet and Freebase.


title: Analysis of self-efficacy theory of behavioral change

abstract: This article reports the findings of two experimental tests of self-efficacy theory of behavioral change. The first study investigated the hypothesis that systematic desensitization effects changes in avoidance behavior by creating and strengthening expectations of personal efficacy. Thorough extinction of anxiety arousal to visualized threats by desensitization treatment produced differential increases in self-efficacy. In accord with prediction, microanalysis of congruence between self-efficacy and performance showed self-efficacy to be a highly accurate predictor of degree of behavioral change following complete desensitization. The findings also lend support to the view that perceived self-efficacy mediates anxiety arousal. The second experiment investigated the process of efficacy and behavioral change during the course of treatment by participant modeling. Self-efficacy proved to be a superior predictor of amount of behavioral improvement phobics gained from partial mastery of threats at different phases of treatment.


title: Graph-analytic technique for data routing in nonlinear holographic associative memories

abstract: We derive the graph-analytic representation of influence of the higher-order nonlinearities (including the third and fourth ones) of holographic recording on the associative properties of second-order holograms defined as ‘holograms between two holograms’. It is shown that the higher-order nonlinearities of the amplitude response of a hologram do not only cause the noise contribution into conjugate associative response, but in some scenarios of formation of the second-order hologram just predetermine reconstruction of this response. Using the proposed technique, we analyze various cases of imposed (sequential) record of partial signals within the framework of the model of multiple diffraction at hologram structure. We discuss the cases when reconstruction of the conjugate associative response is provided by the presence of the hierarchy of combination pseudogratings, rather than by the ‘direct’ interference mechanism that lies in the base of conventional holographic associative storage, using ghost-image holograms. The represented results expand considerably functional feasibilities of the phase-conjugation associative memories on the base of static holograms.


title: Receptors for luteinizing hormone-releasing hormone (GnRH) as therapeutic targets in triple negative breast cancers (TNBC)

abstract: Triple negative breast cancers express receptors for gonadotropin-releasing hormone (GnRH) in more than 50 % of the cases, which can be targeted with peptidic analogs of GnRH, such as triptorelin. The current study investigates cytotoxic activity of triptorelin as a monotherapy and in treatment combinations with chemotherapeutic agents and inhibitors of the PI3K and the ERK pathways in in vitro models of triple negative breast cancers (TNBC). GnRH receptor expression of TNBC cell lines MDA-MB-231 and HCC1806 was investigated. Cells were treated with triptorelin, chemotherapeutic agents (cisplatin, docetaxel, AEZS-112), PI3K/AKT inhibitors (perifosine, AEZS-129), an ERK inhibitor (AEZS-134), and dual PI3K/ERK inhibitor AEZS-136 applied as single agent therapies and in combinations. MDA-MB-231 and HCC1806 TNBC cells both expressed receptors for GnRH on messenger (m)RNA and protein level and were found sensitive to triptorelin with a respective median effective concentration (EC50) of 31.21 ± 0.21 and 58.50 ± 19.50. Synergistic effects occurred when triptorelin was combined with cisplatin. In HCC1806 cells, synergy occurred when triptorelin was applied with PI3K/AKT inhibitors perifosine and AEZS-129. In MDA-MB-231 cells, synergy was observed after co-treatment with triptorelin and ERK inhibitor AEZS-134 and dual PI3K/ERK inhibitor AEZS-136. GnRH receptors on TNBC cells can be used for targeted therapy of these cancers with GnRH agonist triptorelin. Treatment combinations based on triptorelin and PI3K and ERK inhibitors and chemotherapeutic agent cisplatin have synergistic effects in in vitro models of TNBC. If confirmed in vivo, clinical trials based on triptorelin and cisplatin could be quickly carried out, as triptorelin is FDA approved for other indications and known to be well tolerated.


title: Fear Memory

abstract: Fear memory is the best-studied form of memory. It was thoroughly investigated in the past 60 years mostly using two classical conditioning procedures (contextual fear conditioning and fear conditioning to a tone) and one instrumental procedure (one-trial inhibitory avoidance). Fear memory is formed in the hippocampus (contextual conditioning and inhibitory avoidance), in the basolateral amygdala (inhibitory avoidance), and in the lateral amygdala (conditioning to a tone). The circuitry involves, in addition, the pre- and infralimbic ventromedial prefrontal cortex, the central amygdala subnuclei, and the dentate gyrus. Fear learning models, notably inhibitory avoidance, have also been very useful for the analysis of the biochemical mechanisms of memory consolidation as a whole. These studies have capitalized on in vitro observations on long-term potentiation and other kinds of plasticity. The effect of a very large number of drugs on fear learning has been intensively studied, often as a prelude to the investigation of effects on anxiety. The extinction of fear learning involves to an extent a reversal of the flow of information in the mentioned structures and is used in the therapy of posttraumatic stress disorder and fear memories in general.


Design of EMG Acquisition Circuit to Control an Antagonistic Mechanism Actuated by Pneumatic Artificial Muscles PAMs


title: Maltreated Children's Memory: Accuracy, Suggestibility, and Psychopathology.

abstract: Memory, suggestibility, stress arousal, and trauma-related psychopathology were examined in 328 3- to 16-year-olds involved in forensic investigations of abuse and neglect. Children's memory and suggestibility were assessed for a medical examination and venipuncture. Being older and scoring higher in cognitive functioning were related to fewer inaccuracies. In addition, cortisol level and trauma symptoms in children who reported more dissociative tendencies were associated with increased memory error, whereas cortisol level and trauma symptoms were not associated with increased error for children who reported fewer dissociative tendencies. Sexual and/or physical abuse predicted greater accuracy. The study contributes important new information to scientific understanding of maltreatment, psychopathology, and eyewitness memory in children. Language: en


title: Premature Peripheral Vascular Disease: Clinical Profile and Abnormal Lipid Peroxidation

abstract: The aim of this study was to determine any biochemical differences between early-onset peripheral vascular disease and typical onset atherosclerosis, and age-matched controls. A subset of patients present at a young age (< 50 years) with peripheral vascular disease which pursues an aggressive course. As lipid oxidation seems important in atherosclerosis, total lipid peroxides, oxidized subfractions, and Trolox equivalent antioxidant capacity (TEAC) were studied in patients with premature peripheral vascular disease. Charts were reviewed of patients operated on for vascular occlusive disease over a 5-year period. Patients with early-onset peripheral vascular disease (group I) were evaluated for biochemical abnormalities and compared with typical onset atherosclerotics (group II) and age-matched controls (group III). Sixteen patients with early-onset peripheral vascular disease underwent biochemical evaluation. Conventional lipid profiles did not differ statistically from those of age-matched controls, except for mild elevations in LDL and VLDL in patients with vascular occlusive disease (207 and 195 mg/dl in groups I and II versus 157 mg/dl in group III). Total oxidative potential was significantly elevated (P = 0.006) 3.04, 2.15 and 2.04 nmol/ml in groups I, II and III, respectively. Levels of oxidized LDL and VLDL were even more significantly elevated (P = 0.0009) for premature peripheral vascular disease, (1.2, 0.58 and 0.47 nmol/ml in groups I-III). TEAC values did not differ significantly between groups (0.83, 0.82, 0.82 nmol/ml) and did not correlate with total lipid peroxide values for individual patients. In conclusion, lipid peroxides were significantly elevated in patients with premature peripheral vascular disease, the most marked changes being seen in oxidized LDL and VLDL subfractions. Lipid peroxides were elevated when standard lipid profiles were only mildly abnormal. The poor long-term prognosis in these patients suggests the need for aggressive evaluation and treatment of lipid abnormalities.


title: Performance comparison between brushless PM and induction motors for hybrid electric vehicle applications

abstract: Hybrid electric vehicles are becoming more popular due to environmental consciousness. Hybrid electric vehicles consist of a combustion and an electric motor. In practice, the effect of selecting appropriate electric motor is observable on all the main indices of vehicle performance such as maximum speed, gradeability, pollutant emission, fuel consumption, etc. Different studies have been done to compare various electric machines operating as motors or generators. However, investigating the performance of electric motors as electric traction systems in hybrid electric vehicles has not been emphasized in literature. In this paper, the performance of two induction and PM motors have been investigated in hybrid vehicle application through simulation results, benefiting ADVISOR software.


title: Evolution of co-operative communication signals in artificial societies

abstract: Experimental issues arise when scientists attempt to directly study emergent behaviour brought on by the evolutionary process. Recently, algorithms that simulate artificial evolution in robotic societies have been used to circumvent such issues. This study attempts to investigate and interpret emergent signals used by artificial agents when evolved through a simple genetic algorithm setup. A multiagent simulation environment is used to model foraging behaviour of artificial agents. Results identify the importance of communication in facilitating co-operative behaviour and reveal interesting convergence in the use of communication signals. Future work is suggested to amend some of the model's drastic simplifications.


title: Sustainable Food Supply Chains: The Role of Collaboration and Sustainable Relationships

abstract: The life cycle approach is widely used in the analysis of sustainability. Its application to supply chains is necessary since the product flows, from processing of raw materials to the final customer, are considered. The role of the organizational aspects, expressed in terms of relationships between the supply chain agents, is little considered in the life cycle analysis approach. The aim of this paper is to extend the scope of the food chain life cycle analysis by adding the organizational dimension to the environmental, economic and social ones. Within this context, Collaboration and Sustainable Relationships concepts have been explored based on a literature survey. A theoretical framework, describing their role in assessing the organizational dimension in the life cycle analysis of the food supply chains, is defined. A hypothesis on their joint influence on the supply chains performances is formulated.


title: Bidirectional LiFi Attocell Access Point Slicing Scheme

abstract: LiFi attocell access networks will be deployed everywhere to support diverse applications and service provisioning to various end-users. The LiFi infrastructure providers will need to offer LiFi access points (APs) resources as a service. This, however, requires a research challenge to be solved to dynamically and effectively allocate resources among service providers (SPs) while guaranteeing performance isolation among them and their respective users. This paper introduces an autonomic resource slicing (virtualization) scheme, which realizes autonomic management and configuration of virtual APs, in a LiFi attocell access network, based on SPs and their users service requirements. The developed scheme comprises of traffic analysis and classification, a local AP controller, downlink and uplink slice resources manager, traffic measurement, and information collection modules. It also contains a hybrid medium access protocol and an extended token bucket fair queueing algorithm to support uplink access virtualization and spectrum slicing. The proposed resource slicing scheme collects and analyzes the traffic statistics of the different applications supported on the slices defined in each LiFi AP and distributes the available resources fairly and proportionally among them. It uses a control algorithm to adjust the minimum contention window of user devices to achieve the target throughput and ensure airtime fairness among SPs and their users. The developed scheme has been extensively evaluated using OMNeT++. The obtained results show various resource slicing capabilities to support differentiated services and performance isolation.


title: CATCH-919 Hand: Design of a 9-actuator 19-DOF Anthropomorphic Robotic Hand

abstract: To achieve human-like dexterity for anthropomorphic robotic hands, it is essential to understand the biomechanics and control strategies of the human hand, in order to reduce the number of actuators being used without loosing hand flexibility. To this end, in this article, we propose a new interpretation about the working mechanism of the metacarpal (MCP) joint's extension and the underlying control strategies of the human hand, based on which we further propose a highly flexible finger design to achieve independent movements of interphalangeal (IP) joints and MCP joint. Besides, we consider the hyperextension of fingertip into our design which helps robotic finger present compliant and adaptive posture for touching and pinching. In addition, human thumb muscle functions are reconstructed in the proposed robotic hand design, by replacing 9 human muscle tendons with 3 cables in the proposed task-oriented design, realizing all 33 static and stable grasping postures. Videos are available at this https URL


title: A new simulation-based model for calculating post-mortem intervals using developmental data for Lucilia sericata (Dipt.: Calliphoridae)

abstract: Homicide investigations often depend on the determination of a minimum post-mortem interval (PMI$_{min}$) by forensic entomologists. The age of the most developed insect larvae (mostly blow fly larvae) gives reasonably reliable information about the minimum time a person has been dead. Methods such as isomegalen diagrams or ADH calculations can have problems in their reliability, so we established in this study a new growth model to calculate the larval age of \textit{Lucilia sericata} (Meigen 1826). This is based on the actual non-linear development of the blow fly and is designed to include uncertainties, e.g. for temperature values from the crime scene. We used published data for the development of \textit{L. sericata} to estimate non-linear functions describing the temperature dependent behavior of each developmental state. For the new model it is most important to determine the progress within one developmental state as correctly as possible since this affects the accuracy of the PMI estimation by up to 75%. We found that PMI calculations based on one mean temperature value differ by up to 65% from PMIs based on an 12-hourly time temperature profile. Differences of 2\degree C in the estimation of the crime scene temperature result in a deviation in PMI calculation of 15 - 30%.


Arrested Eruption of First Permanent Molar – Frequency , Clinical Problems and Analysis


title: Learning high-dimensional directed acyclic graphs with latent and selection variables

abstract: We consider the problem of learning causal information between random variables in directed acyclic graphs (DAGs) when allowing arbitrarily many latent and selection variables. The FCI (Fast Causal Inference) algorithm has been explicitly designed to infer conditional independence and causal information in such settings. However, FCI is computationally infeasible for large graphs. We therefore propose the new RFCI algorithm, which is much faster than FCI. In some situations the output of RFCI is slightly less informative, in particular with respect to conditional independence information. However, we prove that any causal information in the output of RFCI is correct in the asymptotic limit. We also define a class of graphs on which the outputs of FCI and RFCI are identical. We prove consistency of FCI and RFCI in sparse high-dimensional settings, and demonstrate in simulations that the estimation performances of the algorithms are very similar. All software is implemented in the R-package pcalg.


title: A Performance Comparison of Recent Network Simulators

abstract: A widespread methodology for performance analysis in the field of communication systems engineering is network simulation. While ns-2 has established itself as virtually the standard network simulation tool, other network simulators have gained more and more attention during the last years. In this paper, we briefly survey new developments in the field of network simulation and conduct a performance comparison study by implementing an identical simulation set-up in five simulators, namely ns-2, OMNet++, ns-3, SimPy and JiST/SWANS. Our results reveal large differences according to both run-time performance and memory usage.


title: A bio-inspired swimming robot for marine aquaculture applications: From concept-design to simulation

abstract: This paper presents the development of a bio-inspired swimming robot from concept design to simulation for marine aquaculture applications. Based on investigation of several fish motions, the Manta ray is found to be the most suitable mock object since the flapping pectoral fin features long-endurance, low noise, high payload capacity, good stability and maneuverability. Through a comprehensive analysis of the structure of Manta ray, the shape proportional relationship between the body and the pectoral fins is obtained. Even though the concept design simplifies the structure, major functional components are retained. By applying two degrees of freedoms to each segment of the pectoral fin, the propulsion mechanism allow the robotic fish to swim in 3D. In addition, a thrust analysis is performed for a good understanding of the fish's aquatic locomotion principle. The flapping motion is decomposed into two orthogonal waves and realized on the robotic fish, taking advantages of sine generators. Simulation experiments including motion comparison, speed and turning tests verify the correctness of the robotic fish's structure and its propulsion mechanisms.


title: Web evaluation: heuristic evaluation vs. user testing.

abstract: Abstract It is very important that designers recognize the benefits and limitations of different usability inspection methods. This is because the quality of the usability evaluation is dependent on the method used. Two of the most popular usability evaluation techniques are user testing and heuristic analysis. The main objective of this study was to compare the efficiency and effectiveness between user testing and heuristic analysis in evaluating four different commercial web sites. The results showed that both user testing and heuristic analysis addressed different usability problems. Analysis by severity of problems found and diminishing return analysis model on the relationship between the number of new problems discovered with users and evaluators used showed that both methods are equally efficient and effective in addressing different categories of usability problems. These significant differences found between these two methods suggested that the two methods are complimentary and should not be competing. In order for better evaluation results, both user testing and heuristic analysis are still needed. Relevance to industry The research findings from this study will be of particular value to the web development industry and communities. Knowledge regarding the differences between user testing and heuristic evaluation will enable appropriate business decisions to be made on when and how to apply these methods to improve the overall efficiency of the design process.


title: Food Intake Detection Using Ultrasonic Doppler Sonar

abstract: Reliable, user-friendly and convenient sensing is highly desirable when the continuous monitoring of food intake is necessary. In this paper, food intake monitoring was during the processes of chewing and swallowing. Acoustic Doppler sonar (ADS) detected chewing and swallowing events that were non-contact and free from acoustic interference. When a 40 kHz ultrasonic beam was focused on the lower jaw and neck, movements of the chin and neck cause Doppler frequency shifts and an amplitude envelope modulation of ultrasonic signals. Hence, it was possible to detect chewing and swallowing events using Doppler frequency shifts in the received ultrasound signals. To prevent suspicious chew events caused by talking from being recognized as food intake events, the log-filter bank energy of the voice band was also taken into consideration. Automatic detection of chewing and swallowing events was achieved via an artificial neural network. The experimental results showed that the proposed ADS-based food intake detection method yielded promising results with maximum recognition rates of 91.4% and 78.4% for chewing and swallowing, respectively. As a result, it was confirmed that the proposed food intake detection method using ultrasonic Doppler yielded high rates of recognition without discomfort to the user from continuous skin contact.


title: Women's Violence to Men in Intimate Relationships: Working on a Puzzle

abstract: Different notions among researchers about the nature of intimate partner violence have long been the subjects of popular and academic debate. Research findings are contradictory and point in two directions, with some revealing that women are as likely as men to perpetrate violence against an intimate partner (symmetry) and others showing that it is overwhelmingly men who perpetrate violence against women partners (asymmetry). The puzzle about who perpetrates intimate partner violence not only concerns researchers but also policy makers and community advocates who, in differing ways, have a stake in the answer to this question, since it shapes the focus of public concern, legislation, public policy and interventions for victims and offenders. The question of who are the most usual victims and perpetrators rests, to a large extent, on 'what counts' as violence. It is here that we begin to try to unravel the puzzle, by focusing on concept formation, definitions, forms of measurement, context, consequences and approaches to claim-making, in order better to understand how researchers have arrived at such apparently contradictory findings and claims. The question also turns on having more detailed knowledge about the nature, extent and consequences of women's violence, in order to consider the veracity of these contradictory findings. To date, there has been very little in-depth research about women's violence to male partners and it is difficult, if not impossible, to consider this debate without such knowledge. We present quantitative and qualitative findings from 190 interviews with 95 couples in which men and women reported separately upon their own violence and upon that of their partner. Men's and women's violence are compared. The findings suggest that intimate partner violence is primarily an asymmetrical problem of men's violence to women, and women's violence does not equate to men's in terms of frequency, severity, consequences and the victim's sense of safety and well-being. But why bother about the apparent contradictions in findings of research? For those making and implementing policies and expending public and private resources, the apparent contradiction about the very nature of this problem has real consequences for what might be done for those who are its victims and those who are its perpetrators. Worldwide, legislators, policy makers and advocates have developed responses that conceive of the problem as primarily one of men's violence to women, and these findings provide support for such efforts and suggest that the current general irection of public policy and expenditure is appropriate.


title: VELNET (Virtual Environment for Learning Networking)

abstract: The problems of providing a real, physical specialist laboratory to teach computer networking such as, the lack of funding and physical space and the risks and threats to the network environment and infrastructure, can be solved by the use of a virtual learning environment. Velnet is such a virtual learning environment that we have developed and used successfully. Velnet consists of one or more host machines and operating systems, commercial virtual machine software, virtual machines and their operating systems, a virtual network connecting the virtual machines, and remote desktop display software. In order to be able to present more computer-networking concepts and to improve on our original version of Velnet we have been developing a virtual reality overlay. This virtual reality overlay allows students to build virtual networking topologies in a virtual lab. This paper describes Velnet our virtual environment for learning networking and the virtual reality overlay under development.


DREAMS and DREAMING The Effect of Dreams on Waking Life


title: Neuromuscular Adaptations to Multimodal Injury Prevention Programs in Youth Sports: A Systematic Review with Meta-Analysis of Randomized Controlled Trials

abstract: Objective: Neuromuscular injury prevention programs (IPP) can reduce injury rate by about 40% in youth sport. Multimodal IPP include, for instance, balance, strength, power, and agility exercises. Our systematic review and meta-analysis aimed to evaluate the effects of multimodal IPP on neuromuscular performance in youth sports. Methods: We conducted a systematic literature search including selected search terms related to youth sports, injury prevention, and neuromuscular performance. Inclusion criteria were: (i) the study was a (cluster-)randomized controlled trial (RCT), and (ii) investigated healthy participants, up to 20 years of age and involved in organized sport, (iii) an intervention arm performing a multimodal IPP was compared to a control arm following a common training regime, and (iv) neuromuscular performance parameters (e.g., balance, power, strength, sprint) were assessed. Furthermore, we evaluated IPP effects on sport-specific skills. Results: Fourteen RCTs (comprising 704 participants) were analyzed. Eight studies included only males, and 5 only females. Seventy-one percent of all studies investigated soccer players with basketball, field hockey, futsal, Gaelic football and hurling being the remaining sports. The average age of the participants ranged from 10 years up to 19 years and the level of play from recreational to professional. Intervention durations ranged from 4 weeks to 4.5 months with a total of 12 to 57 training sessions. We observed a small overall effect in favor of IPP for balance/stability (Hedges´g=0.37; 95%CI 0.17,0.58), leg power (g=0.22; 95%CI 0.07,0.38) and isokinetic hamstring and quadriceps strength as well as hamstrings-to-quadriceps ratio (g=0.38; 95%CI 0.21,0.55). We found a large overall effect for sprint abilities (g=0.80; 95%CI 0.50,1.09) and sport-specific skills (g=0.83; 95%CI 0.34,1.32). Subgroup analyses revealed larger effects in high-level (g=0.34-1.18) compared to low-level athletes (g=0.22-0.75), in boys (g=0.27-1.02) compared to girls (g=0.09-62 0.38), in older (g=0.32-1.16) compared to younger athletes (g=0.18-0.51) and in studies with high (g=0.35-1.16) compared to low (g=0.12-0.38) overall number of training sessions. Conclusion: Multimodal IPP beneficially affect neuromuscular performance. These improvements may substantiate the preventative efficacy of IPP and may support the wide-spread implementation and dissemination of IPP.


title: On Inferring Browsing Activity on Smartphones via USB Power Analysis Side-Channel

abstract: In this paper, we show that public USB charging stations pose a significant privacy risk to smartphone users even when no data communication is possible between the station and the user’s mobile device. We present a side-channel attack that allows a charging station to identify which Webpages are loaded while the smartphone is charging. To evaluate this side-channel, we collected power traces of Alexa top 50 Websites on multiple smartphones under several conditions, including battery charging level, browser cache enabled/disabled, taps on the screen, Wi-Fi/LTE, TLS encryption enabled/disabled, time elapsed between collection of training and testing data, and location of the Website. The results of our evaluation show that the attack is highly successful: in many settings, we were able to achieve over 90% Webpage identification accuracy. On the other hand, our experiments also show that this side-channel is sensitive to some of the aforementioned conditions. For instance, when training and testing traces were collected 70 days apart, accuracies were as low as 2.2%. Although there are studies that show that power-based side-channels can predict browsing activity on laptops, this paper is unique, because it is the first to study this side-channel on smartphones, under smartphone-specific constraints. Further, we demonstrate that Websites can be correctly identified within a short time span of $2\times6$ seconds, which is in contrast with prior work, which uses 15-s traces. This is important, because users typically spend less than 15 s on a Webpage.


Survey on the Techniques of FP-Growth Tree for Efficient Frequent Item-set Mining


title: From tweets to polls: linking text sentiment to public opinion time series

abstract: We connect measures of public opinion measured from polls with sentiment measured from text. We analyze several surveys on consumer confidence and political opinion over the 2008 to 2009 period, and find they correlate to sentiment word frequencies in contemporaneous Twitter messages. While our results vary across datasets, in several cases the correlations are as high as 80%, and capture important large-scale trends. The results highlight the potential of text streams as a substitute and supplement for traditional polling.


title: Calibration of RGB camera with velodyne LiDAR

abstract: Calibration of the LiDAR sensor with RGB camera finds its usage in many application fields from enhancing ::: image classification to the environment perception and mapping. This paper presents a pipeline for mutual pose ::: and orientation estimation of the mentioned sensors using a coarse to fine approach. Previously published methods ::: use multiple views of a known chessboard marker for computing the calibration parameters, or they are limited to ::: the calibration of the sensors with a small mutual displacement only. Our approach presents a novel 3D marker for ::: coarse calibration which can be robustly detected in both the camera image and the LiDAR scan. It also requires ::: only a single pair of camera-LiDAR frames for estimating large sensors displacement. Consequent refinement step ::: searches for more accurate calibration in small subspace of calibration parameters. The paper also presents a novel ::: way for evaluation of the calibration precision using projection error.


title: Post-Stroke Rehabilitation with the Rutgers Ankle System: A Case Study

abstract: The “Rutgers Ankle” is a Stewart platform-type haptic interface designed for use in rehabilitation. The system supplies six-degree-of-freedom (DOF) resistive forces on the patient's foot, in response to virtual reality-based exercises. The Rutgers Ankle controller contains an embedded Pentium board, pneumatic solenoid valves, valve controllers, and associated signal conditioning electronics. The rehabilitation exercise used in our case study consists of piloting a virtual airplane through loops. The exercise difficulty can be selected based on the number and placement of loops, the airplane speed in the virtual environment, and the degree of resistance provided by the haptic interface. Exercise data is stored transparently, in real time, in an Oracle database. These data consist of ankle position, forces, and mechanical work during an exercise, and over subsequent rehabilitation sessions. The number of loops completed and the time it took to do that are also stored online. A case study is presented of a patient nine months post-stroke using this system. Results showed that, over six rehabilitation sessions, the patient improved on clinical measures of strength and endurance, which corresponded well with torque and power output increases measured by the Rutgers Ankle. There were also substantial improvements in task accuracy and coordination during the simulation and the patient's walking and stair-climbing ability.


title: Camera calibration using spheres: a semi-definite programming approach

abstract: Vision algorithms utilizing camera networks with a common field of view are becoming increasingly feasible and important. Calibration of such camera networks is a challenging and cumbersome task. The current approaches for calibration using planes or a known 3D target may not be feasible as these objects may not be simultaneously visible in all the cameras. In this paper, we present a new algorithm to calibrate cameras using occluding contours of spheres. In general, an occluding contour of a sphere projects to an ellipse in the image. Our algorithm uses the projection of the occluding contours of three spheres and solves for the intrinsic parameters and the locations of the spheres. The problem is formulated in the dual space and the parameters are solved for optimally and efficiently using semidefinite programming. The technique is flexible, accurate and easy to use. In addition, since the contour of a sphere is simultaneously visible in all the cameras, our approach can greatly simplify calibration of multiple cameras with a common field of view. Experimental results from computer simulated data and real world data, both for a single camera and multiple cameras, are presented.


title: A Single-Phase Photovoltaic Inverter Topology With a Series-Connected Energy Buffer

abstract: Module integrated converters (MICs) have been under rapid development for single-phase grid-tied photovoltaic applications. The capacitive energy storage implementation for the double-line-frequency power variation represents a differentiating factor among existing designs. This paper introduces a new topology that places the energy storage block in a series-connected path with the line interface block. This design provides independent control over the capacitor voltage, soft-switching for all semiconductor devices, and the full four-quadrant operation with the grid. The proposed approach is analyzed and experimentally demonstrated.


title: Hybrid Analog-Digital Beamforming for Multiuser MIMO Millimeter Wave Relay Systems

abstract: This paper proposed new hybrid, analog-digital, beamforming for a multiuser millimeter wave (mm-wave) relay system. For this system, we consider a sum rate maximization problem. The proposed hybrid beamforming is designed indirectly by considering a sum mean square error (MSE) minimization problem while utilizing the solution of digital beamforming. To this end, we assume that the digital beamforming utilizes the well known block diagonalization (BD) approach. Under this assumption, we solve our problem as follows: First, we formulate the sum rate maximization problem as the minimization of the MSE between the received signal of the hybrid and digital beam-forming designs. Then, we design the hybrid beamformings of the source, relay and each destination by leveraging compressive sensing techniques. Simulation results confirm that the proposed hybrid beamforming design achieves performance very close to that of the digital one. Furthermore, we have examined the effects of the number of radio frequency (RF) chains and paths together, and the accuracy of angle of arrival (AoA) and angle of departure (AoD) estimators on the sum rate of the hybrid beamforming designs. Computer simulations reveal that the total sum rate of the hybrid beamforming increases when the number of RF chains and paths increase (or the accuracy of the AoA (AoD) estimator improves).


title: Sentiment Classification with Word Attention based on Weakly Supervised Learning with a Convolutional Neural Network

abstract: In order to maximize the applicability of sentiment analysis results, it is necessary to not only classify the overall sentiment (positive/negative) of a given document but also to identify the main words that contribute to the classification. However, most datasets for sentiment analysis only have the sentiment label for each document or sentence. In other words, there is no information about which words play an important role in sentiment classification. In this paper, we propose a method for identifying key words discriminating positive and negative sentences by using a weakly supervised learning method based on a convolutional neural network (CNN). In our model, each word is represented as a continuous-valued vector and each sentence is represented as a matrix whose rows correspond to the word vector used in the sentence. Then, the CNN model is trained using these sentence matrices as inputs and the sentiment labels as the output. Once the CNN model is trained, we implement the word attention mechanism that identifies high-contributing words to classification results with a class activation map, using the weights from the fully connected layer at the end of the learned CNN model. In order to verify the proposed methodology, we evaluated the classification accuracy and inclusion rate of polarity words using two movie review datasets. Experimental result show that the proposed model can not only correctly classify the sentence polarity but also successfully identify the corresponding words with high polarity scores.


title: Histogram of Oriented Uniform Patterns for robust place recognition and categorization

abstract: This paper presents a novel context-based scene recognition method that enables mobile robots to recognize previously observed topological places in known environments or categorize previously unseen places in new environments. We achieve this by introducing the Histogram of Oriented Uniform Patterns (HOUP), which provides strong discriminative power for place recognition, while offering a significant level of generalization for place categorization. HOUP descriptors are used for image representation within a subdivision framework, where the size and location of sub-regions are determined using an informative feature selection method based on kernel alignment. Further improvement is achieved by developing a similarity measure that accounts for perceptual aliasing to eliminate the effect of indistinctive but visually similar regions that are frequently present in outdoor and indoor scenes. An extensive set of experiments reveals the excellent performance of our method on challenging categorization and recognition tasks. Specifically, our proposed method outperforms the current state of the art on two place categorization datasets with 15 and 5 place categories, and two topological place recognition datasets, with 5 and 27 places.


title: Tesla Motors, Inc.: Pioneer towards a new strategic approach in the automobile industry along the open source movement?

abstract: In many industries, we observe a paradigm shift from traditional value creation towards co-creation and open production approaches. The boundaries of companies dissolve and many more players (suppliers, customers, community members, etc.) are integrated into the value creation process. This also implies the share of knowledge to set industry-wide standards and to advance new technologies. Tesla Motors, Inc. recently announced that it would give away all their patents to anyone who in good faith wants to use them. They say their aim was to foster the advancement of electric vehicles to compete with conventional vehicles and give the zero-emission mobility a push. Nevertheless, what about the traditional automobile industry with its big players where even the slightest growth in market share is crucial and the intellectual property (IP) of a company is kept secret like the Holy Grail as it ensures competitive advantages? Based on a Tesla case study our research focusses on product-, company-, market- and industry-specific factors that might enable even small players to start an industry-wide revolution by applying strategic aspects of openness in their business model.


title: Deep convolutional neural networks for multi-modality isointense infant brain image segmentation.

abstract: Abstract The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development in health and disease. In the isointense stage (approximately 6–8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, making the tissue segmentation very challenging. Only a small number of existing methods have been designed for tissue segmentation in this isointense stage; however, they only used a single T1 or T2 images, or the combination of T1 and T2 images. In this paper, we propose to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images. CNNs are a type of deep models in which trainable filters and local neighborhood pooling operations are applied alternatingly on the raw input images, resulting in a hierarchy of increasingly complex features. Specifically, we used multi-modality information from T1, T2, and fractional anisotropy (FA) images as inputs and then generated the segmentation maps as outputs. The multiple intermediate layers applied convolution, pooling, normalization, and other operations to capture the highly nonlinear mappings between inputs and outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense stage brain images. Results showed that our proposed model significantly outperformed prior methods on infant brain tissue segmentation. In addition, our results indicated that integration of multi-modality images led to significant performance improvement.


title: A Survey on Wireless Security: Technical Challenges, Recent Advances and Future Trends

abstract: This paper examines the security vulnerabilities and threats imposed by the inherent open nature of wireless communications and to devise efficient defense mechanisms for improving the wireless network security. We first summarize the security requirements of wireless networks, including their authenticity, confidentiality, integrity and availability issues. Next, a comprehensive overview of security attacks encountered in wireless networks is presented in view of the network protocol architecture, where the potential security threats are discussed at each protocol layer. We also provide a survey of the existing security protocols and algorithms that are adopted in the existing wireless network standards, such as the Bluetooth, Wi-Fi, WiMAX, and the long-term evolution (LTE) systems. Then, we discuss the state-of-the-art in physical-layer security, which is an emerging technique of securing the open communications environment against eavesdropping attacks at the physical layer. We also introduce the family of various jamming attacks and their counter-measures, including the constant jammer, intermittent jammer, reactive jammer, adaptive jammer and intelligent jammer. Additionally, we discuss the integration of physical-layer security into existing authentication and cryptography mechanisms for further securing wireless networks. Finally, some technical challenges which remain unresolved at the time of writing are summarized and the future trends in wireless security are discussed.


title: Leveraging Wikipedia Table Schemas for Knowledge Graph Augmentation

abstract: General solutions to augment Knowledge Graphs (KGs) with facts extracted from Web tables aim to associate pairs of columns from the table with a KG relation based on the matches between pairs of entities in the table and facts in the KG. These approaches suffer from intrinsic limitations due to the incompleteness of the KGs. In this paper we investigate an alternative solution, which leverages the patterns that occur on the schemas of a large corpus of Wikipedia tables. Our experimental evaluation, which used DBpedia as reference KG, demonstrates the advantages of our approach over state-of-the-art solutions and reveals that we can extract more than 1.7M of facts with an estimated accuracy of 0.81 even from tables that do not expose any fact on the KG.


title: Constrained Hough Transforms for Curve Detection

abstract: This paper describes techniques to perform fast and accurate curve detection using constrained Hough transforms, in which localization error can be propagated efficiently into the parameter space. We first review a formal definition of Hough transform and modify it to allow the formal treatment localization error. We then analyze current Hough transform techniques with respect to this definition. It is shown that the Hough transform can be subdivided into many small subproblems without a decrease in performance, where each subproblem is constrained to consider only those curves that pass through some subset of the edge pixels up to the localization error. This property allows us to accurately and efficiently propagate localization error into the parameter space such that curves are detected robustly without finding false positives. The use of randomization techniques yields an algorithm with a worst-case complexity ofO(n), wherenis the number of edge pixels in the image, if we are only required to find curves that are significant with respect to the complexity of the image. Experiments are discussed that indicate that this method is superior to previous techniques for performing curve detection and results are given showing the detection of lines and circles in real images.


title: FlashStore: High Throughput Persistent Key-Value Store

abstract: We present FlashStore, a high throughput persistent key-value store, that uses flash memory as a non-volatile cache between RAM and hard disk. FlashStore is designed to store the working set of key-value pairs on flash and use one flash read per key lookup. As the working set changes over time, space is made for the current working set by destaging recently unused key-value pairs to hard disk and recycling pages in the flash store. FlashStore organizes key-value pairs in a log-structure on flash to exploit faster sequential write performance. It uses an in-memory hash table to index them, with hash collisions resolved by a variant of cuckoo hashing. The in-memory hash table stores compact key signatures instead of full keys so as to strike tradeoffs between RAM usage and false flash read operations. ::: ::: FlashStore can be used as a high throughput persistent key-value storage layer for a broad range of server class applications. We compare FlashStore with BerkeleyDB, an embedded key-value store application, running on hard disk and flash separately, so as to bring out the performance gain of FlashStore in not only using flash as a cache above hard disk but also in its use of flash aware algorithms. We use real-world data traces from two data center applications, namely, Xbox LIVE Primetime online multi-player game and inline storage deduplication, to drive and evaluate the design of FlashStore on traditional and low power server platforms. FlashStore outperforms BerkeleyDB by up to 60x on throughput (ops/sec), up to 50x on energy efficiency (ops/Joule), and up to 85x on cost efficiency (ops/sec/dollar) on the evaluated datasets.


title: Instance-Level Segmentation for Autonomous Driving with Deep Densely Connected MRFs

abstract: Our aim is to provide a pixel-wise instance-level labeling of a monocular image in the context of autonomous driving. We build on recent work [Zhang et al., ICCV15] that trained a convolutional neural net to predict instance labeling in local image patches, extracted exhaustively in a stride from an image. A simple Markov random field model using several heuristics was then proposed in [Zhang et al., ICCV15] to derive a globally consistent instance labeling of the image. In this paper, we formulate the global labeling problem with a novel densely connected Markov random field and show how to encode various intuitive potentials in a way that is amenable to efficient mean field inference [Kr\"ahenb\"uhl et al., NIPS11]. Our potentials encode the compatibility between the global labeling and the patch-level predictions, contrast-sensitive smoothness as well as the fact that separate regions form different instances. Our experiments on the challenging KITTI benchmark [Geiger et al., CVPR12] demonstrate that our method achieves a significant performance boost over the baseline [Zhang et al., ICCV15].


DOES THE BALANCED SCORECARD WORK : AN EMPIRICAL