Pustulobullous variant of SDRIFE (symmetrical drug-related intertriginous and flexural exanthema).

We report on a 64-year-old patient from Sri Lanka who presented to the emergency room with a 24-hour history of pruritic, erythematous patches – with occasional pustules and blisters – in the groins, axillae, and antecubital fossae. The patient reported no pain or general symptoms. Six days earlier, he had started taking cefuroxime (500 mg BID) for the first time. The antibiotic had been prescribed following temporary ureteral stent placement due to nephrolithiasis. He denied any other medical or dermatological conditions, and no other drugs had been taken. At initial presentation, he exhibited sharply demarcated, symmetrically disseminated, dark-red patches in the groins, axillae, and antecubital fossae. Some lesions showed pustules and blisters (Figure 1a–c). The physical exam was otherwise unremarkable; in particular, there was no fever. Histology revealed a superficial perivascular, spongiform dermatitis with lymphocytes, eosinophils, and neutrophils. Focally, necrotic keratinocytes and neutrophils were noted in the epidermis. Overall, the findings were consistent with a drug reaction (Figure 2). Apart from slightly elevated CRP levels (5.7 mg/dL, normal range: < 0.5) and mild thrombocytopenia (111,000, normal range: 166,000–308,000), laboratory tests were within normal limits. The detection of Escherichia coli in a swab taken from a pustule in the inguinal region was thought to represent contamination; the pathological lab results were considered to be consistent with an inflammatory response to nephrolithiasis. Taking the history, clinical symptoms, and histological findings into account, the patient was diagnosed with a pustulobullous variant of SDRIFE (symmetrical drug-related intertriginous and flexural exanthema). Since 2004, the acronym SDRIFE has been used for the condition previously known as baboon syndrome. First reported in 1984, the term baboon syndrome referred to the acute development of erythematous lesions in the gluteal area following contact with mercury from broken fever thermometers [1, 2]. In particular, the new term SDRIFE takes into account that systemic drugs, too, may induce flexural skin lesions without prior sensitization [3–6]. In the present case, the development of skin lesions six days after the intake of cefuroxime suggests first exposure to the drug. The diagnosis of SDRIFE includes five clinical criteria that are summarized in Table 1. Atypical disease courses with pustules, papules, and blisters have also been described [4]. The onset of SDRIFE is independent of patient age, and occurs a few hours or even up to eight days after the administration/application of the triggering factor. Apart from systemic antibiotics – in particular β-lactam antibiotics – corticosteroids, psychopharmaceuticals, biologics, and many other drug classes are


High Efficient Interleaved Multi-channel dc/dc Converter Dedicated to Mobile Applications

In the transportation field, the empty weight of the vehicle has to be as light as possible in order to have the highest energetic efficiency. For onboard storage electrical systems, DC/DC converters are usually used to control the power flows in the vehicle. This paper focuses on two different DC/DC converter applications: high current/low voltage on the one hand and low current/high voltage on the other hand. For all power mobile applications, a standard dc/dc converter is not realizable due to mass and volume constraints. As the inductor is usually the biggest and heaviest component in the system, it is important to develop a new structure allowing the reduction of its size and weight. The chosen technology is based on interleaved channels operating in discontinuous mode, also using reverse conduction of power MOSFET transistors. For both of the cases described above, a prototype has been built composed of 8 and 4 channels respectively. The first converter is used for power assistance (250 A/18 V) using the MOS reverse conduction property. In this particular case, efficiency is up to 96.3 %. The second is developed for a mobile solar application (3.8 A/270 V). Efficiencies are up to 98.4 %


Face Detection: A Survey

In this paper we present a comprehensive and critical survey of face detection algorithms. Face detection is a necessary first-step in face recognition systems, with the purpose of localizing and extracting the face region from the background. It also has several applications in areas such as content-based image retrieval, video coding, video conferencing, crowd surveillance, and intelligent human–computer interfaces. However, it was not until recently that the face detection problem received considerable attention among researchers. The human face is a dynamic object and has a high degree of variability in its apperance, which makes face detection a difficult problem in computer vision. A wide variety of techniques have been proposed, ranging from simple edge-based algorithms to composite high-level approaches utilizing advanced pattern recognition methods. The algorithms presented in this paper are classified as either feature-based or image-based and are discussed in terms of their technical approach and performance. Due to the lack of standardized tests, we do not provide a comprehensive comparative evaluation, but in cases where results are reported on common datasets, comparisons are presented. We also give a presentation of some proposed applications and possible application areas. c © 2001 Academic Press


Greedy Sampling of Graph Signals

Sampling is a fundamental topic in graph signal processing, having found applications in estimation, clustering, and video compression. In contrast to traditional signal processing, the irregularity of the signal domain makes selecting a sampling set nontrivial and hard to analyze. Indeed, though conditions for graph signal interpolation from noiseless samples exist, they do not lead to a unique sampling set. The presence of noise makes choosing among these sampling sets a hard combinatorial problem. Although greedy sampling schemes are commonly used in practice, they have no performance guarantee. This work takes a twofold approach to address this issue. First, universal performance bounds are derived for the Bayesian estimation of graph signals from noisy samples. In contrast to currently available bounds, they are not restricted to specific sampling schemes and hold for any sampling sets. Second, this paper provides near-optimal guarantees for greedy sampling by introducing the concept of approximate submodularity and updating the classical greedy bound. It then provides explicit bounds on the approximate supermodularity of the interpolation mean-square error showing that it can be optimized with worst case guarantees using greedy search even though it is not supermodular. Simulations illustrate the derived bound for different graph models and show an application of graph signal sampling to reduce the complexity of kernel principal component analysis.


Development and Evaluation of a Bluetooth EKG Monitoring Sensor

With the adoption of 2G and 3G cellular network technologies, mobile phones now have the bandwidth capability to stream data back to monitoring stations in real-time. Our paper describes the design and evaluation of a Bluetooth electrocardiogram sensor that transmits medical data to a cell phone. This data is displayed and stored on the phone. Future development of the system will relay this data over a cellular GPRS network. The current system provides a low cost and lightweight alternative to existing EKG event monitors. The final GPRS connected system will provide continuous monitoring of a patient 's heart anywhere cellular coverage is available


Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning

The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class. The recently introduced meta-learning approaches tackle this problem by learning a generic classifier across a large number of multiclass classification tasks and generalizing the model to a new task. Yet, even with such meta-learning, the low-data problem in the novel classification task still remains. In this paper, we propose Transductive Propagation Network (TPN), a novel meta-learning framework for transductive inference that classifies the entire test set at once to alleviate the low-data problem. Specifically, we propose to learn to propagate labels from labeled instances to unlabeled test instances, by learning a graph construction module that exploits the manifold structure in the data. TPN jointly learns both the parameters of feature embedding and the graph construction in an end-to-end manner. We validate TPN on multiple benchmark datasets, on which it largely outperforms existing few-shot learning approaches and achieves the state-of-the-art results.


Biomedical Image Processing

medical fields has led to the automated processing ofpictorial data. Here, a device called the cytocomputer searches for genetic mutations. A computer revolution has occurred not only in technical fields but also in medicine, where vast amounts of information must be processed quickly and accurately. Nowhere is the need for image processing techniques more apparent than in clinical diagnosis or mass screening applications where data take the form of digital images. New high-resolution scanning techniques such as computed tomography, nuclear magnetic resonance, po-sitron emission tomography, and digital radiography produce images containing immense amounts of relevant information for medical analysis. But as these scanning techniques become more vital to clinical diagnosis, the work for specialists who must visually examine the resultant images increases. In many cases, quantitative data in the form of measurements and counts are needed to supplement nonimage patient data, and the manual extraction of these data is a time-consuming and costly step in an otherwise automated process. Furthermore, subtle variants of shade and shape can be the earliest clues to a diagnosis, placing the additional burden of complete thoroughness on the examining specialist. For the last five years, the University of Michigan and the Environmental Research Institute of Michigan have conducted a unique series of studies that involve the processing of biomedical imagery on a highly parallel computer specifically designed for image processing. System designers have incorporated the requirements of extracting a verifiable answer from an image in a reasonable time into an integrated approach to hardware and software design. The system includes a parallel pipelined image processor, called a cytocomputer, and a high-level language specifically created for image processing, C-3PL, the cytocomputer parallel picture processing language. These studies have involved a great many people from both the medical and engineering communities and have highlighted the interdisciplinary aspects of biomedical image processing. The methods have been tested in anatomy, developmental biology, nuclear medicine, car-diology, and transplant rejection. The general consensus is that quantification by automated image analysis not only increases diagnostic accuracy but also provides significant data not obtainable from qualitative analysis alone. One study in particular, on which descriptions in this article are based, involves a joint effort by the University of Michigan's human genetics and electrical and computer engineering departments and is supported by a grant from the National Cancer Institute. Basically, automated image analysis is being applied via sophisticated biochemical and computer techniques to derive …


A Review on Automated Brain Tumor Detection and Segmentation from MRI of Brain

Tumor segmentation from magnetic resonance imaging (MRI) data is an important but time consuming manual task performed by medical experts. Automating this process is a challenging task because of the high diversity in the appearance of tumor tissues among different patients and in many cases similarity with the normal tissues. MRI is an advanced medical imaging technique providing rich information about the human soft-tissue anatomy. There are different brain tumor detection and segmentation methods to detect and segment a brain tumor from MRI images. These detection and segmentation approaches are reviewed with an importance placed on enlightening the advantages and drawbacks of these methods for brain tumor detection and segmentation. The use of MRI image detection and segmentation in different procedures are also described. Here a brief review of different segmentation for detection of brain tumor from MRI of brain has been discussed.


Survey on Cloud Computing Security

Cloud is a modern age computing paradigm which has potential to bring great benefits to information and Communication Technology (ICT) and ICT enabled business. The term Cloud comes from the graphic that was often used to show the heterogeneous networks and complex infrastructure. This graphic was adopted to describe the many aspects of Cloud Computing. In this paper, we aim to identify the security issues in cloud computing. Here we present an analysis of security issues in a cloud environment. Solutions exist to a certain extent for various issues. Keywords—Cloud computing, security challenges, threats


Review on Non-Volatile Memory with High-k Dielectrics: Flash for Generation Beyond 32 nm

Flash memory is the most widely used non-volatile memory device nowadays. In order to keep up with the demand for increased memory capacities, flash memory has been continuously scaled to smaller and smaller dimensions. The main benefits of down-scaling cell size and increasing integration are that they enable lower manufacturing cost as well as higher performance. Charge trapping memory is regarded as one of the most promising flash memory technologies as further down-scaling continues. In addition, more and more exploration is investigated with high-k dielectrics implemented in the charge trapping memory. The paper reviews the advanced research status concerning charge trapping memory with high-k dielectrics for the performance improvement. Application of high-k dielectric as charge trapping layer, blocking layer, and tunneling layer is comprehensively discussed accordingly.


A unified theory of development: a dialectic integration of nature and nurture.

The understanding of nature and nurture within developmental science has evolved with alternating ascendance of one or the other as primary explanations for individual differences in life course trajectories of success or failure. A dialectical perspective emphasizing the interconnectedness of individual and context is suggested to interpret the evolution of developmental science in similar terms to those necessary to explain the development of individual children. A unified theory of development is proposed to integrate personal change, context, regulation, and representational models of development.


A framework for automated measurement of the intensity of non-posed Facial Action Units

This paper presents a framework to automatically measure the intensity of naturally occurring facial actions. Naturalistic expressions are non-posed spontaneous actions. The facial action coding system (FACS) is the gold standard technique for describing facial expressions, which are parsed as comprehensive, nonoverlapping action units (Aus). AUs have intensities ranging from absent to maximal on a six-point metric (i.e., 0 to 5). Despite the efforts in recognizing the presence of non-posed action units, measuring their intensity has not been studied comprehensively. In this paper, we develop a framework to measure the intensity of AU12 (lip corner puller) and AU6 (cheek raising) in videos captured from infant-mother live face-to-face communications. The AU12 and AU6 are the most challenging case of infant's expressions (e.g., low facial texture in infant's face). One of the problems in facial image analysis is the large dimensionality of the visual data. Our approach for solving this problem is to utilize the spectral regression technique to project high dimensionality facial images into a low dimensionality space. Represented facial images in the low dimensional space are utilized to train support vector machine classifiers to predict the intensity of action units. Analysis of 18 minutes of captured video of non-posed facial expressions of several infants and mothers shows significant agreement between a human FACS coder and our approach, which makes it an efficient approach for automated measurement of the intensity of non-posed facial action units.


Facial Expression Analysis

The face is one of the most powerful channels of nonverbal communication. Facial expression provides cues about emotion, intention, alertness, pain, personality, regulates interpersonal behavior, and communicates psychiatric and biomedical status among other functions. Within the past 15 years, there has been increasing interest in automated facial expression analysis within the computer vision and machine learning communities. This chapter reviews fundamental approaches to facial measurement by behavioral scientists and current efforts in automated facial expression recognition. We consider challenges, review databases available to the research community, approaches to feature detection, tracking, and representation, and both supervised and unsupervised learning. keywords : Facial expression analysis, Action unit recognition, Active Appearance Models, temporal clustering.


A Wavelet Tour of Signal Processing, 2nd Edition

Bargaining with reading habit is no need. Reading is not kind of something sold that you can take or not. It is a thing that will change your life to life better. It is the thing that will give you many things around the world and this universe, in the real world and here after. As what will be given by this a wavelet tour of signal processing 2nd edition, how can you bargain with the thing that has many benefits for you?


Detecting Faces in Images: A Survey

Images containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face regardless of its three-dimensional position, orientation, and lighting conditions. Such a problem is challenging because faces are nonrigid and have a high degree of variability in size, shape, color, and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics, and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for


Switched Reluctance Motor Drive With External Rotor for Fan in Air Conditioner

This paper describes the developed three-phase 6/8 poles switched reluctance external rotor motor drive for a fan in air conditioner. The external rotor core structure and the internal stator core structure, the three-phase windings arrangement, the slotted claw, and the setting structure of the photoelectric transducers on the rotor position detector are illustrated. The electromagnetic field calculation results are given. The three-phase asymmetric bridge power converter was used in the drive system. The block diagram of the switched reluctance external rotor motor drive with closed-loop rotor speed control is given. The closed-loop rotor speed control is implemented using a fuzzy logic algorithm. The experimental tests of the developed prototype are made for driving the fan from 200 to 950 r/min. The comparison results of the two systems show that the input power and input phase current RMS value are lower in the developed three-phase 6/8 poles external rotor switched reluctance motor drive prototype with the fan than those in the induction motor variable-frequency variable-speed drive with the fan.


Reflective and Refractive Objects for Mixed Reality

In this paper, we present a novel rendering method which integrates reflective or refractive objects into a differential instant radiosity (DIR) framework usable for mixed-reality (MR) applications. This kind of objects are very special from the light interaction point of view, as they reflect and refract incident rays. Therefore they may cause high-frequency lighting effects known as caustics. Using instant-radiosity (IR) methods to approximate these high-frequency lighting effects would require a large amount of virtual point lights (VPLs) and is therefore not desirable due to real-time constraints. Instead, our approach combines differential instant radiosity with three other methods. One method handles more accurate reflections compared to simple cubemaps by using impostors. Another method is able to calculate two refractions in real-time, and the third method uses small quads to create caustic effects. Our proposed method replaces parts in light paths that belong to reflective or refractive objects using these three methods and thus tightly integrates into DIR. In contrast to previous methods which introduce reflective or refractive objects into MR scenarios, our method produces caustics that also emit additional indirect light. The method runs at real-time frame rates, and the results show that reflective and refractive objects with caustics improve the overall impression for MR scenarios.


GENETAG: a tagged corpus for gene/protein named entity recognition

Named entity recognition (NER) is an important first step for text mining the biomedical literature. Evaluating the performance of biomedical NER systems is impossible without a standardized test corpus. The annotation of such a corpus for gene/protein name NER is a difficult process due to the complexity of gene/protein names. We describe the construction and annotation of GENETAG, a corpus of 20K MEDLINE® sentences for gene/protein NER. 15K GENETAG sentences were used for the BioCreAtIvE Task 1A Competition. To ensure heterogeneity of the corpus, MEDLINE sentences were first scored for term similarity to documents with known gene names, and 10K high- and 10K low-scoring sentences were chosen at random. The original 20K sentences were run through a gene/protein name tagger, and the results were modified manually to reflect a wide definition of gene/protein names subject to a specificity constraint, a rule that required the tagged entities to refer to specific entities. Each sentence in GENETAG was annotated with acceptable alternatives to the gene/protein names it contained, allowing for partial matching with semantic constraints. Semantic constraints are rules requiring the tagged entity to contain its true meaning in the sentence context. Application of these constraints results in a more meaningful measure of the performance of an NER system than unrestricted partial matching. The annotation of GENETAG required intricate manual judgments by annotators which hindered tagging consistency. The data were pre-segmented into words, to provide indices supporting comparison of system responses to the "gold standard". However, character-based indices would have been more robust than word-based indices. GENETAG Train, Test and Round1 data and ancillary programs are freely available at ftp://ftp.ncbi.nlm.nih.gov/pub/tanabe/GENETAG.tar.gz . A newer version of GENETAG-05, will be released later this year.


Robust Driving Path Detection in Urban and Highway Scenarios Using a Laser Scanner and Online Occupancy Grids

Many driver assistant and safety systems depend on an accurate environmental model containing the positions of stationary objects, the states of dynamic objects and information about valid driving corridors. Therefore, a robust differentiation between moving and stationary objects is required. This is challenging for laser scanners, because these sensors are not able to measure the velocity of objects directly. Therefore, an advanced occupancy grid approach, the online map, is introduced, which enables the robust separation of moving and stationary objects. The online map is used for the robust detection of the road boundaries for the determination of driving corridors in urban and highway scenarios. An algorithm for the detection of arbitrary moving objects using the online map is proposed.


Aromatherapy positively affects mood, EEG patterns of alertness and math computations.

EEG activity, alertness, and mood were assessed in 40 adults given 3 minutes of aromatherapy using two aromas, lavender (considered a relaxing odor) or rosemary (considered a stimulating odor). Participants were also given simple math computations before and after the therapy. The lavender group showed increased beta power, suggesting increased drowsiness, they had less depressed mood (POMS) and reported feeling more relaxed and performed the math computations faster and more accurately following aromatherapy. The rosemary group, on the other hand, showed decreased frontal alpha and beta power, suggesting increased alertness. They also had lower state anxiety scores, reported feeling more relaxed and alert and they were only faster, not more accurate, at completing the math computations after the aromatherapy session.


On-Line Economic Optimization of Energy Systems Using Weather Forecast Information

We establish an on-line optimization framework to exploit weather forecast information in the operation of energy systems. We argue that anticipating the weather conditions can lead to more proactive and cost-effective operations. The framework is based on the solution of a stochastic dynamic real-time optimization (D-RTO) problem incorporating forecasts generated from a state-of-the-art weather prediction model. The necessary uncertainty information is extracted from the weather model using an ensemble approach. The accuracy of the forecast trends and uncertainty bounds are validated using real meteorological data. We present a numerical simulation study in a building system to demonstrate the developments.


An Ultra-Wide-Band 0.4–10-GHz LNA in 0.18-$\mu$m CMOS

A two-stage ultra-wide-band CMOS low-noise amplifier (LNA) is presented. With the common-gate configuration employed as the input stage, the broad-band input matching is obtained and the noise does not rise rapidly at higher frequency. By combining the common-gate and common-source stages, the broad-band characteristic and small area are achieved by using two inductors. This LNA has been fabricated in a 0.18-mum CMOS process. The measured power gain is 11.2-12.4 dB and noise figure is 4.4-6.5 dB with -3-dB bandwidth of 0.4-10 GHz. The measured IIP3 is -6 dBm at 6 GHz. It consumes 12 mW from a 1.8-V supply voltage and occupies only 0.42 mm2


Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula

Factorizing low-rank matrices has many applications in machine learning and statistics. For probabilistic models in the Bayes optimal setting, a general expression for the mutual information has been proposed using heuristic statistical physics computations, and proven in few specific cases. Here, we show how to rigorously prove the conjectured formula for the symmetric rank-one case. This allows to express the minimal mean-square-error and to characterize the detectability phase transitions in a large set of estimation problems ranging from community detection to sparse PCA. We also show that for a large set of parameters, an iterative algorithm called approximate message-passing is Bayes optimal. There exists, however, a gap between what currently known polynomial algorithms can do and what is expected information theoretically. Additionally, the proof technique has an interest of its own and exploits three essential ingredients: the interpolation method introduced in statistical physics by Guerra, the analysis of the approximate message-passing algorithm and the theory of spatial coupling and threshold saturation in coding. Our approach is generic and applicable to other open problems in statistical estimation where heuristic statistical physics predictions are available. Consider the following probabilistic rank-one matrix estimation problem: one has access to noisy observations w=(wij)i,j=1 of the pair-wise product of the components of a vector s=(s1, . . . , sn)∈R with i.i.d components distributed as Si∼P0, i=1, . . . , n. The entries of w are observed through a noisy element-wise (possibly non-linear) output probabilistic channel Pout(wij |sisj/ √ n). The goal is to estimate the vector s from w assuming that both P0 and Pout are known and independent of n (noise is symmetric so that wij =wji). Many important problems in statistics and machine learning can be expressed in this way, such as sparse PCA [Zou et al. (2006)], the Wigner spike model [Johnstone and Lu (2012); Deshpande and Montanari (2014)], community detection [Deshpande et al. (2015)] or matrix completion [Candès and Recht (2009)]. Proving a result initially derived by a heuristic method from statistical physics, we give an explicit expression for the mutual information and the information theoretic minimal mean-square-error (MMSE) in the asymptotic n→+∞ limit. Our results imply that for 1 ar X iv :1 60 6. 04 14 2v 1 [ cs .I T ] 1 3 Ju n 20 16 a large region of parameters, the posterior marginal expectations of the underlying signal components (often assumed intractable to compute) can be obtained in the leading order in n using a polynomial-time algorithm called approximate message-passing (AMP) [Rangan and Fletcher (2012); Deshpande and Montanari (2014); Deshpande et al. (2015); Lesieur et al. (2015b)]. We also demonstrate the existence of a region where both AMP and spectral methods [Baik et al. (2005)] fail to provide a good answer to the estimation problem, while it is nevertheless information theoretically possible to do so. We illustrate our theorems with examples and also briefly discuss the implications in terms of computational complexity. 1. Setting and main results 1.1 The additive white Gaussian noise setting A standard and natural setting is the case of additive white Gaussian noise (AWGN) of known variance ∆, wij = sisj √ n + zij √ ∆, (1) where z=(zij)i,j=1 is a symmetric matrix with i.i.d entries Zij∼N (0, 1), 1≤ i≤j≤n. Perhaps surprisingly, it turns out that this Gaussian setting is sufficient to completely characterize all the problems discussed in the introduction, even if these have more complicated output channels. This is made possible by a theorem of channel universality [Krzakala et al. (2016)] (already proven for community detection in [Deshpande et al. (2015)] and conjectured in [Lesieur et al. (2015a)]). This theorem states that given an output channel Pout(w|y), such that logPout(w|y = 0) is three times differentiable with bounded second and third derivatives, then the mutual information satisfies I(S;W)=I(S;SST/ √ n+Z √ ∆)+O( √ n), where ∆ is the inverse Fisher information (evaluated at y = 0) of the output channel: ∆−1 := EPout(w|0)[(∂y logPout(W |y)|y=0)]. Informally, this means that we only have to compute the mutual information for an AWGN channel to take care of a wide range of problems, which can be expressed in terms of their Fisher information. In this paper we derive rigorously, for a large class of signal distributions P0, an explicit one-letter formula for the mutual information per variable I(S;W)/n in the asymptotic limit n→+∞.


Passing a Hide-and-Seek Third-Person Turing Test

Hiding and seeking are cognitive abilities frequently demonstrated by humans in both real life and video games. To determine to which extent these abilities can be replicated with AI, we introduce a specialized version of the Turing test for hiding and seeking. We then develop a computer agent that passes the test by appearing indistinguishable from human behavior to a panel of human judges. We analyze the AI techniques that enable the agent to imitate human hide-and-seek behavior and their relative contribution to the agent's performance.


Altered salience network connectivity predicts macronutrient intake after sleep deprivation

Although insufficient sleep is a well-recognized risk factor for overeating and weight gain, the neural mechanisms underlying increased caloric (particularly fat) intake after sleep deprivation remain unclear. Here we used resting-state functional magnetic resonance imaging and examined brain connectivity changes associated with macronutrient intake after one night of total sleep deprivation (TSD). Compared to the day following baseline sleep, healthy adults consumed a greater percentage of calories from fat and a lower percentage of calories from carbohydrates during the day following TSD. Subjects also exhibited increased brain connectivity in the salience network from the dorsal anterior cingulate cortex (dACC) to bilateral putamen and bilateral anterior insula (aINS) after TSD. Moreover, dACC-putamen and dACC-aINS connectivity correlated with increased fat and decreased carbohydrate intake during the day following TSD, but not during the day following baseline sleep. These findings provide a potential neural mechanism by which sleep loss leads to increased fat intake.


Sentence Selection and Weighting for Neural Machine Translation Domain Adaptation

Neural machine translation NMT has been prominent in many machine translation tasks. However, in some domain-specific tasks, only the corpora from similar domains can improve translation performance. If out-of-domain corpora are directly added into the in-domain corpus, the translation performance may even degrade. Therefore, domain adaptation techniques are essential to solve the NMT domain problem. Most existing methods for domain adaptation are designed for the conventional phrase-based machine translation. For NMT domain adaptation, there have been only a few studies on topics such as fine tuning, domain tags, and domain features. In this paper, we have four goals for sentence level NMT domain adaptation. First, the NMT's internal sentence embedding is exploited and the sentence embedding similarity is used to select out-of-domain sentences that are close to the in-domain corpus. Second, we propose three sentence weighting methods, i.e., sentence weighting, domain weighting, and batch weighting, to balance the data distribution during NMT training. Third, in addition, we propose dynamic training methods to adjust the sentence selection and weighting during NMT training. Fourth, to solve the multidomain problem in a real-world NMT scenario where the domain distributions of training and testing data often mismatch, we proposed a multidomain sentence weighting method to balance the domain distributions of training data and match the domain distributions of training and testing data. The proposed methods are evaluated in international workshop on spoken language translation IWSLT English-to-French/German tasks and a multidomain English-to-French task. Empirical results show that the sentence selection and weighting methods can significantly improve the NMT performance, outperforming the existing baselines.


Amortized Efficiency of List Update and Paging Rules

In this article we study the amortized efficiency of the “move-to-front” and similar rules for dynamically maintaining a linear list. Under the assumption that accessing the ith element from the front of the list takes &thgr;(i) time, we show that move-to-front is within a constant factor of optimum among a wide class of list maintenance rules. Other natural heuristics, such as the transpose and frequency count rules, do not share this property. We generalize our results to show that move-to-front is within a constant factor of optimum as long as the access cost is a convex function. We also study paging, a setting in which the access cost is not convex. The paging rule corresponding to move-to-front is the “least recently used” (LRU) replacement rule. We analyze the amortized complexity of LRU, showing that its efficiency differs from that of the off-line paging rule (Belady's MIN algorithm) by a factor that depends on the size of fast memory. No on-line paging algorithm has better amortized performance.


Learning Multi-agent Communication under Limited-bandwidth Restriction for Internet Packet Routing

Communication is an important factor for the big multi-agent world to stay organized and productive. Recently, the AI community has applied the Deep Reinforcement Learning (DRL) to learn the communication strategy and the control policy for multiple agents. However, when implementing the communication for real-world multi-agent applications, there is a more practical limited-bandwidth restriction, which has been largely ignored by the existing DRL-based methods. Specifically, agents trained by most previous methods keep sending messages incessantly in every control cycle; due to emitting too many messages, these methods are unsuitable to be applied to the real-world systems that have a limited bandwidth to transmit the messages. To handle this problem, we propose a gating mechanism to adaptively prune unprofitable messages. Results show that the gating mechanism can prune more than 80% messages with little damage to the performance. Moreover, our method outperforms several state-of-the-art DRL-based and rule-based methods by a large margin in both the real-world packet routing tasks and four benchmark tasks.


Assessing the Potential of Classical Q-learning in General Game Playing

After the recent groundbreaking results of AlphaGo and AlphaZero, we have seen strong interests in deep reinforcement learning and artificial general intelligence (AGI) in game playing. However, deep learning is resource-intensive and the theory is not yet well developed. For small games, simple classical table-based Q-learning might still be the algorithm of choice. General Game Playing (GGP) provides a good testbed for reinforcement learning to research AGI. Q-learning is one of the canonical reinforcement learning methods, and has been used by (Banerjee & Stone, IJCAI 2007) in GGP. In this paper we implement Q-learning in GGP for three small-board games (Tic-Tac-Toe, Connect Four, Hex), to allow comparison to Banerjee et al.. We find that Q-learning converges to a high win rate in GGP. For the -greedy strategy, we propose a first enhancement, the dynamic algorithm. In addition, inspired by (Gelly & Silver, ICML 2007) we combine online search (Monte Carlo Search) to enhance offline learning, and propose QM-learning for GGP. Both enhancements improve the performance of classical Q-learning. In this work, GGP allows us to show, if augmented by appropriate enhancements, that classical table-based Q-learning can perform well in small games.


A new adaptive frequency oscillator for gait assistance

To control exoskeletons for walking gait assistance, it is of primary importance to control them to act synchronously with the gaits of users. To effectively estimate the gait cycle (or the phase within a stride) of users, we propose a new adaptive frequency oscillator (AFO). While previous AFOs successfully estimated the walking frequency from joint angles as inputs, the new AFO, called particularly-shaped adaptive oscillator (PSAO) can estimate gait cycle from the same inputs, which would have required foot contact sensors in previous approaches. To predict the effects of PSAO-based gait assistance on human walking, it has been tested with neuromuscular walking simulation. In the simulation, the gait assistance system reduced the metabolic cost of walking for some assistance patterns. The walk ratio (step length per step rate) also changed as assistance patterns shifted in phase, which is meaningful because metabolic cost of walking in general is minimal at specific walk ratio. For a prototype exoskeleton we developed, the effect of gait assistance was experimented on a human subject walking on level ground and inclining slopes to verify the predictions from the simulation: (1) physiological cost index computed from heart rate significantly decreased indicating reduction in metabolic energy expenditure; (2) walk ratio was in fact controllable to an extent.


Education 4.0 for tall thin engineer in a data driven society

The rise of digital technology creates disruptive innovation, new opportunities in communication, it transforms industrial production and it changes the way we work, learn and live. That leads to the fourth Industrial Revolution, which changes the working world enormously. New skills, future-ready curricula, new learning and training concepts as well as the flexibility in education are necessary. To deliver that, it needs a fourth revolution in the educational system with a strong active presence and most important Artificial Intelligence (AI) driven features for better performance. Education 4.0 needs also a strong partnership between industry and academicals environment in the formation of human resources for Industry 4.0. We present in this paper our answers of education regarding the challenges of fourth revolution in Industry and the seven AI driven didactic methods identified for the University of Future.


Fusion of Keypoint Tracking and Facial Landmark Detection for Real-Time Head Pose Estimation

In this paper, we address the problem of extreme head pose estimation from intensity images, in a monocular setup. We introduce a novel fusion pipeline to integrate into a dedicated Kalman Filter the pose estimated from a tracking scheme in the prediction stage and the pose estimated from a detection scheme in the correction stage. To that end, the measurement covariance of the Kalman Filter is updated in every frame. The tracking scheme is performed using a set of keypoints extracted in the area of the head along with a simple 3D geometric model. The detection scheme, on the other hand, relies on the alignment of facial landmarks in each frame combined with 3D features extracted on a head mesh. The head pose in each scheme is estimated by minimizing the reprojection error from the 3D-2D correspondences. By combining both frameworks, we extend the applicability of head pose estimation from facial landmarks to cases where these features are no longer visible. We compared the proposed method to other related approaches, showing that it can achieve state-of-the-art performance. We also demonstrate that our approach is suitable for cases with extreme head rotations and (self-) occlusions, besides being suitable for real time applications.


Mux-Kmeans: multiplex kmeans for clustering large-scale data set

Kmeans clustering algorithm is widely used in a number of scientific applications due to its simple iterative nature and ease of implementation. The quality of clustering result highly depends on the selection of initial centroids. Different selections of initial centroids result in different clustering results. In practice, people run a series of Kmeans processes with multiple initial centroid groups serially and return the best clustering result among them. However, in the era of big data, a Kmeans process is implemented on MapReduce to scale to large data sets. Even a single Kmeans process on MapReduce requires considerable long runtime. This paper proposes Mux-Kmeans. Rather than executing multiple Kmeans processes serially, Mux-Kmeans launches these Kmeans processes concurrently with multiple centroid groups. In each iteration, Mux-Kmeans (i) evaluates these Kmeans processes, (ii) prunes the low-quality Kmeans processes, and (iii) incubates new Kmeans processes. After a certain number of iterations, it finally obtains the best among these local optimal results. We implement Mux-Kmeans on MapReduce and evaluate it on Amazon EC2. The experimental results show that starting from the same initial centroid groups, the clustering result of Mux-Kmeans is always non-worse than the best of a series of Kmeans processes. Mux-Kmeans also saves elapsed time than serial multiple Kmeans processes.


A Clinical Study of 35 Cases of Pincer Nails

BACKGROUND
Pincer nail is a nail deformity characterized by transverse overcurvature of the nail plate. Pincer nail can affect a patient's quality of life due to its chronic, recurrent course; however, there have been no clinical studies on the pincer nail condition in Korean patients.

OBJECTIVE
The purpose of this study was to characterize the clinical findings and treatment of pincer nail. In addition, possible etiological factors were considered, and treatment efficacy was evaluated.

METHODS
The medical records and clinical photographs of 35 patients (12 males, 23 females) who were diagnosed with pincer nail between August 1, 2005 and July 31, 2009 were studied.

RESULTS
Patient age ranged from 10 to 77 (52.09±17.26) years, and there was a predominance of female (23 out of 35 patients, F:M=2:1). The mean duration of the disorder was 7.45 years (range 0.25~40); 85% had pincer nail for at least 1 year. In addition, 40% had a history of previous treatment and recurrence. There were 82.8% patients with the common type of pincer nails. The most commonly involved nails were both great toenails. Among 35 patients, nail grinding was started in 30 patients, and 25 patients showed clinical improvement with nail grinding. The width index increased and the height index decreased after treatment. The mean follow up period was 8.42 months (range 1~27), and 7 patients showed recurrence after 8.8 months (range 2~20). Among 35 patients, 5 patients were treated with nail extraction with matricectomy, and the symptoms resolved immediately. The mean follow up period was 7.6 months (range 0~19), and recurrence was not observed. Onychomycosis was also present in 37.1% of patients, and itraconazole pulse therapy for 3 months was added.

CONCLUSION
The results of this study demonstrate the clinical features of pincer nail in Korean patients. The findings show that the common type of pincer nail was most common, and nail grinding as a conservative treatment greatly improved pincer nails despite a risk of recurrence. When onychomycosis was also present, oral antifungal therapy added to nail grinding resulted in a more rapid change in nail thickness and clinical improvement.


Applying a tendency to be well retweeted to false information detection

While a lot of useful information can be found in SNS, false information also diffuses through it, thereby confusing many people sometimes. In this paper, we predict a tendency of tweets to be well retweeted and consider applying the tendency to false information detection. The tendency prediction can be implemented with simple features of tweets. We examine the effect of the tendency when it is used in false information detection empirically. Our experimental results indicate that it would be valuable to take the tendency into account for the detection. We also discuss findings when applying them to tweets in Japanese.


COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking

In this paper, we consider collaborative filtering as a ranking problem. We present a method which uses Maximum Margin Matrix Factorization and optimizes ranking instead of rating. We employ structured output prediction to optimize directly for ranking scores. Experimental results show that our method gives very good ranking scores and scales well on collaborative filtering tasks.


EFFICIENT CONSTRAINED PATH PLANNING VIA SEARCH IN STATE LATTICES

We propose a novel approach to constrained path planning that is based on a special search space which efficiently encodes feasible paths. The paths are encoded implicitly as connections between states, but only feasible and local connections are included. Once this search space is developed, we systematically generate a near-minimal set of spatially distinct path primitives. This set expresses the local connectivity of constrained motions and also eliminates redundancies. The set of primitives is used to define heuristic search, and thereby create a very efficient path planner at the chosen resolution. We also discuss a wide variety of space and terrestrial robotics applications where this motion planner can be especially useful.


Path Planning using a Dynamic Vehicle Model

This paper addresses the problem of path planning using a dynamic vehicle model. Previous works which include a basic kinematic model generate paths that are only realistic at very low speed. By considering higher vehicle speed during navigation, the vehicle can significantly deviate from the planned trajectory. Consequently, the planned path becomes unusable for the mission achievement. So, to bridge a gap between planning and navigation, we propose a realistic path planner based on a dynamic vehicle model


Autonomous local path planning for a mobile robot using a genetic algorithm

This work presents results of our work in development of a genetic algorithm based path-planning algorithm for local obstacle avoidance (local feasible path) of a mobile robot in a given search space. The method tries to find not only a valid path but also an optimal one. The objectives are to minimize the length of the path and the number of turns. The proposed path-planning method allows a free movement of the robot in any direction so that the path-planner can handle complicated search spaces.


Reactive Nonholonomic Trajectory Generation via Parametric Optimal Control

There are many situations for which a feasible nonholonomic motion plan must be generated immediately based on real-time perceptual information. Parametric trajectory representations limit computation because they reduce the search space for solutions (at the cost of potentially introducing suboptimality). The use of any parametric trajectory model converts the optimal control formulation into an equivalent nonlinear programming problem. In this paper, curvature polynomials of arbitrary order are used as the assumed form of solution. Polynomials sacrifice little in terms of spanning the set of feasible controls while permitting an expression of the general solution to the system dynamics in terms of decoupled quadratures. These quadratures are then readily linearized to express the necessary conditions for optimality. Resulting trajectories are convenient to manipulate and execute in vehicle controllers and they can be computed with a straightforward numerical procedure in real time. KEY WORDS—mobile robots, car-like robots, trajectory generation, curve generation, nonholonomic, clothoid, cornu spiral, optimal control


MINDFULNESS AND TRAUMA: IMPLICATIONS FOR TREATMENT

Mindfulness, originally a construct used in Eastern spiritual and philosophical traditions, has found new utility in psychotherapy practice. Mindfulness practice has been recently applied to treatments of several psychological and health related problems, and research is showing successful outcomes in psychological interventions incorporating mindfulness practices. Several schools of psychotherapy have theorized why mindfulness may be an effective intervention. One population which would theoretically be benefited by mindfulness practice in treatment consists of those individuals who have experienced traumatic events and are exhibiting post-traumatic stress disorder and/or related correlates of past trauma. The present paper gives a general review of the application of mindfulness to clinical psychology interventions. Additionally, we explain how mindfulness is applicable to our integrative behavioral approach to treating trauma and its sequelae. Specifically, this paper will (a) give a general overview of the conceptions and applications of mindfulness to psychology and psychotherapy and provide a brief account of the concepts origins in eastern traditions; (b) discuss the theoretical conceptualization of clinical problems that may relate to the longterm correlates of trauma; (c) describe how mindfulness, acceptance and the therapeutic relationship can address trauma symptoms and discuss a modified treatment approach for trauma survivors that incorporates mindfulness and acceptance practices into traditional exposure treatment. Address correspondence to Victoria Follette, Department of Psychology/296, University of Nevada, Reno, NV, 89557, USA; e-mail: vmf@unr.edu. Journal of Rational-Emotive & Cognitive-Behavior Therapy, Vol. 24, No. 1, Spring 2006 ( 2006) DOI: 10.1007/s10942-006-0025-2 Published Online: August 2, 2006 45 2006 Springer Science+Business Media, Inc. Understanding the long-term effects of traumatic experiences and identifying appropriate treatment for trauma survivors continue to be complex and at times controversial tasks (Brewin, 2003). While cognitive-behavioral treatments are generally considered to have the most significant empirical support for the treatment of trauma (American Psychiatric Association Work Group on ASD and PTSD, 2004; Foa, Keane, & Friedman, 2000), practices associated with the treatment of the broader range of trauma related problems continue to evolve. Our understanding of trauma symptoms has expanded to include the knowledge that outcomes associated with traumatic experiences are not limited to Post-traumatic Stress Disorder (PTSD) but rather extend across a range of intraand interpersonal problems. Therefore, in our work with trauma survivors, we have conceptualized trauma symptomology in terms of functional classes of behavior, rather than focusing on a syndromal classification of symptoms (Follette & Naugle, 2006). While a great deal is known about the treatment of basic anxiety related phenomena associated with traumatic experiences, there are a number of lacunae in our knowledge about the treatment of the broader range of problems associated with a trauma history. Certainly the current repertoire of cognitive behavioral treatments for trauma survivors has demonstrated efficacy. However, we believe that the addition of mindfulness related treatment interventions will enhance our ability to treat people who have difficulties associated with having experienced a traumatic event. The theoretical work arising from our conceptualization of functional domains of behavior has informed our present integrative behavioral treatment approach. In our approach, we work to influence behavior change by increasing both acceptance strategies and skillful behavior. Mindfulness is a core component of both of these approaches. In the present paper, we will briefly address the historical roots of mindfulness, with its origins in eastern religious traditions. We will present a short review of the application of mindfulness to clinical psychology interventions. Finally, we will discuss our view of the utility of mindfulness in our integrative behavioral approach to treating trauma and its sequelae. MINDFULNESS IN EASTERN RELIGIOUS TRADITIONS In discussing the integration of Buddhist teachings with Western psychological practices, it should be noted that there are 46 Journal of Rational-Emotive & Cognitive-Behavior Therapy


Measuring User Engagement

User engagement refers to the quality of the user experience that emphasizes the positive aspects of interacting with an online application and, in particular, the desire to use that application longer and repeatedly. User engagement is a key concept in the design of online applications (whether for desktop, tablet or mobile), motivated by the observation that successful applications are not just used, but are engaged with. Users invest time, attention, and emotion in their use of technology, and seek to satisfy pragmatic and hedonic needs. Measurement is critical for evaluating whether online applications are able to successfully engage users, and may inform the design of and use of applications. User engagement is a multifaceted, complex phenomenon; this gives rise to a number of potential measurement approaches. Common ways to evaluate user engagement include using self-report measures, e.g., questionnaires; observational methods, e.g. facial expression analysis, speech analysis; neuro-physiological signal processing methods, e.g., respiratory and cardiovascular accelerations and decelerations, muscle spasms; and web analytics, e.g., number of site visits, click depth. These methods represent various trade-offs in terms of the setting (laboratory versus “in the wild”), object of measurement (user behaviour, affect or cognition) and scale of data collected. For instance, small-scale user studies are deep and rich, but limited in terms of generalizability, whereas large-scale web analytic studies are powerful but negate users’ motivation and context. The focus of this book is how user engagement is currently being measured and various considerations for its measurement. Our goal is to leave readers with an appreciation of the various ways in which to measure user engagement, and their associated strengths and weaknesses. We emphasize the multifaceted nature of user engagement and the unique contextual constraints that come to bear upon attempts to measure engagement in different settings, and across different user groups and web domains. At the same time, this book advocates for the development of “good” measures and good measurement practices that will advance the study of user engagement and improve our understanding of this construct, which has become so vital in our wired world.


A 95% Efficient Normally-Off GaN-on-Si HEMT Hybrid-IC Boost-Converter with 425-W Output Power at 1 MHz

A 2:1 351 V hard-switched boost converter was constructed using high-voltage GaN high-electron-mobility transistors grown on Si substrates and GaN Schottky diodes grown on Sapphire substrates. The high speed and low on-resistance of the GaN devices enables extremely fast switching times and low losses, resulting in a high conversion efficiency of 95% with 425-W output power at 1 MHz. The boost converter has a power density of 175 W/in3. To our knowledge, these results are the best reported on GaN devices, and the highest for 1MHz switching.


Episodic Memory and Beyond: The Hippocampus and Neocortex in Transformation.

The last decade has seen dramatic technological and conceptual changes in research on episodic memory and the brain. New technologies, and increased use of more naturalistic observations, have enabled investigators to delve deeply into the structures that mediate episodic memory, particularly the hippocampus, and to track functional and structural interactions among brain regions that support it. Conceptually, episodic memory is increasingly being viewed as subject to lifelong transformations that are reflected in the neural substrates that mediate it. In keeping with this dynamic perspective, research on episodic memory (and the hippocampus) has infiltrated domains, from perception to language and from empathy to problem solving, that were once considered outside its boundaries. Using the component process model as a framework, and focusing on the hippocampus, its subfields, and specialization along its longitudinal axis, along with its interaction with other brain regions, we consider these new developments and their implications for the organization of episodic memory and its contribution to functions in other domains.


Solving sybil attacks using evolutionary game theory

Recommender systems have become quite popular recently. However, such systems are vulnerable to several types of attacks that target user ratings. One such attack is the Sybil attack where an entity masquerades as several identities with the intention of diverting user ratings. In this work, we propose evolutionary game theory as a possible solution to the Sybil attack in recommender systems. After modeling the attack, we use replicator dynamics to solve for evolutionary stable strategies. Our results show that under certain conditions that are easily achievable by a system administrator, the probability of an attack strategy drops to zero implying degraded fitness for Sybil nodes that eventually die out.


Design of a smart parking system using wireless sensor network

In this article it is included the design of a smart parking system, the base was the current system "Sistema integrado de Estacionamiento (SIMERT)" and the mobility in Loja - Ecuador, with the use of a sensor network and wireless technology based in Zigbee 900 and Digimesh 2.4 GHz. To the feasibility of the project it was considered a technological platform that could be adapted to the city conditions and that be friendly with the environment, furthermore the suggested solution gives us connectivity with a Web application for integrated parking and usability of administrators and final users.


Automated Detection of Logical Errors in Programs

Research and industrial experience reveal that code reviews as a part of software inspection might be the most cost-effective technique a team can use to reduce defects. Tools that automate code inspection mostly focus on the detection of a priori known defect patterns and security vulnerabilities. Automated detection of logical errors, due to a faulty implementation of applications’ functionality is a relatively uncharted territory. Automation can be based on profiling the intended behavior behind the source code. In this paper, we present a new code profiling method that combines an information flow analysis, the crosschecking of dynamic invariants with symbolic execution, and the use of fuzzy logic. Our goal is to detect logical errors and exploitable vulnerabilities. The theoretical underpinnings and the practical implementation of our approach are discussed. We test the APP_LogGIC tool that implements the proposed analysis on two realworld applications. The results show that profiling the intended program behavior is feasible in diverse applications. We discuss the heuristics used to overcome the problem of state space explosion and of the large data sets. Code metrics and test results are provided to demonstrate the effectiveness of the approach.


An algorithm to determine peer-reviewers

The peer-review process is the most widely accepted certification mechanism for officially accepting the written results of researchers within the scientific community. An essential component of peer-review is the identification of competent referees to review a submitted manuscript. This article presents an algorithm to automatically determine the most appropriate reviewers for a manuscript by way of a co-authorship network data structure and a relative-rank particle-swarm algorithm. This approach is novel in that it is not limited to a pre-selected set of referees, is computationally efficient, requires no human-intervention, and, in some instances, can automatically identify conflict of interest situations. A useful application of this algorithm would be to open commentary peer-review systems because it provides a weighting for each referee with respects to their expertise in the domain of a manuscript. The algorithm is validated using referee bid data from the 2005 Joint Conference on Digital Libraries.


Health information systems: a survey of frameworks for developing countries.

OBJECTIVES
The objective of this paper is to perform a survey of excellent research on health information systems (HIS) analysis and design, and their underlying theoretical frameworks. It classifies these frameworks along major themes, and analyzes the different approaches to HIS development that are practical in resource-constrained environments.

METHOD
Literature review based on PubMed citations and conference proceedings, as well as Internet searches on information systems in general, and health information systems in particular.

RESULTS
The field of health information systems development has been studied extensively. Despite this, failed implementations are still common. Theoretical frameworks for HIS development are available that can guide implementers.

CONCLUSION
As awareness, acceptance, and demand for health information systems increase globally, the variety of approaches and strategies will also follow. For developing countries with scarce resources, a trial-and-error approach can be very costly. Lessons from the successes and failures of initial HIS implementations have been abstracted into theoretical frameworks. These frameworks organize complex HIS concepts into methodologies that standardize techniques in implementation. As globalization continues to impact healthcare in the developing world, demand for more responsive health systems will become urgent. More comprehensive frameworks and practical tools to guide HIS implementers will be imperative.


Applying Deep Learning to the Newsvendor Problem

The newsvendor problem is one of the most basic and widely applied inventory models. There are numerous extensions of this problem. If the probability distribution of the demand is known, the problem can be solved analytically. However, approximating the probability distribution is not easy and is prone to error; therefore, the resulting solution to the newsvendor problem may be not optimal. To address this issue, we propose an algorithm based on deep learning that optimizes the order quantities for all products based on features of the demand data. Our algorithm integrates the forecasting and inventory-optimization steps, rather than solving them separately, as is typically done, and does not require knowledge of the probability distributions of the demand. Numerical experiments on real-world data suggest that our algorithm outperforms other approaches, including data-driven and machine learning approaches, especially for demands with high volatility. Finally, in order to show how this approach can be used for other inventory optimization problems, we provide an extension for (r,Q) policies.


Arches: a Framework for Modeling Complex Terrains

In this paper, we present a framework for representing complex terrains with such features as overhangs, arches and caves and including different materials such as sand and rocks. Our hybrid model combines a volumetric discrete data structure that stores the different materials and an implicit representation for sculpting and reconstructing the surface of the terrain. Complex scenes can be edited and sculpted interactively with high level tools. We also propose an original rock generation technique that enables us to automatically generate complex rocky sceneries with piles of rocks without any computationally demanding physically-based simulation.


BERDS-BERkeley EneRgy Disaggregation Data Set

We first review some of the suggested methods for energy disaggregation. We then provide a real-life data set for researchers to help developing novel energy disaggregation algorithms. We also present a publicly available experimental data set captured from a typical building on the University of California, Berkeley campus.


Action Search: Learning to Search for Human Activities in Untrimmed Videos

Traditional approaches for action detection use trimmed data to learn sophisticated action detector models. Although these methods have achieved great success at detecting human actions, we argue that huge information is discarded when ignoring the process, through which this trimmed data is obtained. In this paper, we propose Action Search, a novel approach that mimics the way people annotate activities in video sequences. Using a Recurrent Neural Network, Action Search can efficiently explore a video and determine the time boundaries during which an action occurs. Experiments on the THUMOS14 dataset reveal that our model is not only able to explore the video efficiently but also accurately find human activities, outperforming stateof-the-art methods.


Utility-Preserving Differentially Private Data Releases Via Individual Ranking Microaggregation

Being able to release and exploit open data gathered in information systems is crucial for researchers, enterprises and the overall society. Yet, these data must be anonymized before release to protect the privacy of the subjects to whom the records relate. Differential privacy is a privacy model for anonymization that offers more robust privacy guarantees than previous models, such as k-anonymity and its extensions. However, it is often disregarded that the utility of differentially private outputs is quite limited, either because of the amount of noise that needs to be added to obtain them or because utility is only preserved for a restricted type and/or a limited number of queries. On the contrary, k-anonymity-like data releases make no assumptions on the uses of the protected data and, thus, do not restrict the number and type of doable analyses. Recently, some authors have proposed mechanisms to offer general-purpose differentially private data releases. This paper extends such works with a specific focus on the preservation of the utility of the protected data. Our proposal builds on microaggregation-based anonymization, which is more flexible and utility-preserving than alternative anonymization methods used in the literature, in order to reduce the amount of noise needed to satisfy differential privacy. In this way, we improve the utility of differentially private data releases. Moreover, the noise reduction we achieve does not depend on the size of the data set, but just on the number of attributes to be protected, which is a more desirable behavior for large data sets. The utility benefits brought by our proposal are empirically evaluated and compared with related works for several data sets and metrics.


The neuropsychiatry of the cerebellum — insights from the clinic

A central aspect of the cerebellar cognitive affective syndrome is the dysregulation of affect that occurs when lesions involve the ‘limbic cerebellum’ (vermis and fastigial nucleus). In this case series we describe neuropsychiatric disturbances in adults and children with congenital lesions including cerebellar agenesis, dysplasia, and hypoplasia, and acquired conditions including cerebellar stroke, tumor, cerebellitis, trauma, and neurodegenerative disorders. The behaviors that we witnessed and that were described by patients and families included distractibility and hyperactivity, impulsiveness, disinhibition, anxiety, ritualistic and stereotypical behaviors, illogical thought and lack of empathy, as well as aggression and irritability. Ruminative and obsessive behaviors, dysphoria and depression, tactile defensiveness and sensory overload, apathy, childlike behavior, and inability to appreciate social boundaries and assign ulterior motives were also evident. We grouped these disparate neurobehavioral profiles into five major domains, characterized broadly as disorders of attentional control, emotional control, and social skill set as well as autism spectrum disorders, and psychosis spectrum disorders. Drawing on our dysmetria of thought hypothesis, we conceptualized the symptom complexes within each putative domain as reflecting either exaggeration (overshoot, hypermetria) or diminution (hypotonia, or hypometria) of responses to the internal or external environment. Some patients fluctuated between these two states. We consider the implications of these neurobehavioral observations for the care of patients with ataxia, discuss the broader role of the cerebellum in the pathogenesis of these neuropsychiatric symptoms, and revisit the possibility of using cerebellar stimulation to treat psychiatric disorders by enhancing cerebellar modulation of cognition and emotion.


Neuronal Specificity of Acupuncture in Alzheimer's Disease and Mild Cognitive Impairment Patients: A Functional MRI Study

Although acupuncture is considered to be effective and safe for Alzheimer's disease (AD) and mild cognitive impairment (MCI), the mechanism underlying its therapeutic effect is still unknown. Most studies clarifying the neuronal pathway produced by acupuncture were still applied to healthy subjects with limited single acupuncture point stimulation, which was inconsistency with clinical practice. Thus, in our present study, we investigate the differences between brain activity changes in AD and MCI patients caused by multi-acupuncture point Siguan (four gates), in order to provide visualized evidence for neuronal specificity of clinical acupuncture. Forty-nine subjects were recruited, including 21 AD patients, 14 MCI patients, and 14 healthy controls (HC). AD and MCI patients were randomly divided into two groups, respectively: real acupuncture point group (14 AD and 8 MCI) and sham acupuncture point group (7 AD and 6 MCI). We adopted a 16-minute, single-block, experimental design for acquiring functional MRI images. We found, in AD and MCI patients, Siguan (four gates) elicited extensive activations and deactivations in cognitive-related areas, visual-related areas, the sensorimotor-related area, basal ganglia, and cerebellum. Compared with HC, AD and MCI patients showed similar activations in cognitive-related brain areas (inferior frontal gyrus, supramarginal gyrus, and rolandic operculum) as well as deactivations in cognitive-related areas, visual-related areas, basal ganglia, and cerebellum, which were not found in HC. Compared with sham acupuncture points, real acupuncture points produced more specific brain changes with both activated and deactivated brain activities in AD and MCI. The preliminary results in our study verified the objective evidence for neuronal specificity of acupuncture in AD and MCI patients.


Roombots—Towards decentralized reconfiguration with self-reconfiguring modular robotic metamodules

This paper presents our work towards a decentralized reconfiguration strategy for self-reconfiguring modular robots, assembling furniture-like structures from Roombots (RB) metamodules. We explore how reconfiguration by locomotion from a configuration A to a configuration B can be controlled in a distributed fashion. This is done using Roombots metamodules—two Roombots modules connected serially—that use broadcast signals, lookup tables of their movement space, assumptions about their neighborhood, and connections to a structured surface to collectively build desired structures without the need of a centralized planner.


Social Persuasion in Online and Physical Networks

Social persuasion to influence the actions, beliefs, and behaviors of individuals, embedded in a social network, has been widely studied. It has been applied to marketing, healthcare, sustainability, political campaigns, and public policy. Traditionally, there has been a separation between physical (offline) and cyber (online) worlds. While persuasion methods in the physical world focused on strong interpersonal trust and design principles, persuasion methods in the online world were rich on data-driven analysis and algorithms. Recent trends including Internet of Things, “big data,” and smartphone adoption point to the blurring divide between the cyber world and the physical world in the following ways. Fine grained data about each individual's location, situation, social ties, and actions are collected and merged from different sources. The messages for persuasion can be transmitted through both worlds at suitable times and places. The impact of persuasion on each individual is measurable. Hence, we posit that the social persuasion will soon be able to span seamlessly across these worlds and will be able to employ computationally and empirically rigorous methods to understand and intervene in both cyber and physical worlds. Several early examples indicate that this will impact the fundamental facets of persuasion including who, how, where, and when, and pave way for multiple opportunities as well as research challenges.


Improving Interactional Organizational Research : A Model of Person-Organization Fit

In order for researchers to understand and predict behavior, they must consider both person and situation factors and how these factors interact. Even though organization researchers have developed interactional models, many have overemphasized either person or situation components, and most have failed to consider the effects that persons have on situations. This paper presents criteria for improving interactional models and a model of person-organization fit, which satisfies these criteria. Using a Q-sort methodology, individual value profiles are compared to organizational value profiles to determine fit and to predict changes in values, norms, and behaviors.


Constraint Programming in OPL

OPL is a modeling language for mathematical programming and combinatorial optimization problems. It is the rst modeling language to combine high-level algebraic and set notations from model-ing languages with a rich constraint language and the ability to specify search procedures and strategies that is the essence of constraint programming. In addition, OPL models can be controlled and composed using OPLSCRIPT, a script language that simpliies the development of applications that solve sequences of models, several instances of the same model, or a combination of both as in column-generation applications. This paper illustrates some of the functionalities of OPL for constraint programming using frequency allocation, sport-scheduling, and job-shop scheduling applications. It also illustrates how OPL models can be composed using OPLSCRIPT on a simple connguration example.


Recycling ambient microwave energy with broad-band rectenna arrays

This paper presents a study of reception and rectification of broad-band statistically time-varying low-power-density microwave radiation. The applications are in wireless powering of industrial sensors and recycling of ambient RF energy. A 64-element dual-circularly-polarized spiral rectenna array is designed and characterized over a frequency range of 2-18 GHz with single-tone and multitone incident waves. The integrated design of the antenna and rectifier, using a combination of full-wave electromagnetic field analysis and harmonic balance nonlinear circuit analysis, eliminates matching and filtering circuits, allowing for a compact element design. The rectified dc power and efficiency is characterized as a function of dc load and dc circuit topology, RF frequency, polarization, and incidence angle for power densities between 10/sup -5/-10/sup -1/ mW/cm/sup 2/. In addition, the increase in rectenna efficiency for multitone input waves is presented.


Dynamic Difficulty Adjustment (DDA) in Computer Games: A Review

Dynamic difficulty adjustment (DDA) is a method of automatically modifying a game’s features, behaviors, and scenarios in realtime, depending on the player’s skill, so that the player, when the game is very simple, does not feel bored or frustrated, when it is very difficult. The intent of the DDA is to keep the player engrossed till the end and to provide him/her with a challenging experience. In traditional games, difficulty levels increase linearly or stepwise during the course of the game. The features such as frequency, starting levels, or rates can be set only at the beginning of the game by choosing a level of difficulty. This can, however, result in a negative experience for players as they try to map a predecided learning curve. DDA attempts to solve this problem by presenting a customized solution for the gamers. This paper provides a review of the current approaches to DDA.


Scale-Space and Edge Detection Using Anisotropic Diffusion

Abstracf-The scale-space technique introduced by Witkin involves generating coarser resolution images by convolving the original image with a Gaussian kernel. This approach has a major drawback: it is difficult to obtain accurately the locations of the “semantically meaningful” edges at coarse scales. In this paper we suggest a new definition of scale-space, and introduce a class of algorithms that realize it using a diffusion process. The diffusion coefficient is chosen to vary spatially in such a way as to encourage intraregion smoothing in preference to interregion smoothing. It is shown that the “no new maxima should be generated at coarse scales” property of conventional scale space is preserved. As the region boundaries in our approach remain sharp, we obtain a high quality edge detector which successfully exploits global information. Experimental results are shown on a number of images. The algorithm involves elementary, local operations replicated over the image making parallel hardware implementations feasible.


Segmentation using Eigenvectors: A Unifying View

Automatic grouping and segmentation of images remains a challenging problem in computer vision. Recently, a number of authors have demonstrated good performance on this task using methods that are based on eigenvectors of the a nity matrix. These approaches are extremely attractive in that they are based on simple eigendecomposition algorithms whose stability is well understood. Nevertheless, the use of eigendecompositions in the context of segmentation is far from well understood. In this paper we give a unied treatment of these algorithms, and show the close connections between them while highlighting their distinguishing features. We then prove results on eigenvectors of block matrices that allow us to analyze the performance of these algorithms in simple grouping settings. Finally, we use our analysis to motivate a variation on the existing methods that combines aspects from di erent eigenvector segmentation algorithms. We illustrate our analysis with results on real and synthetic images. Human perceiving a scene can often easily segment it into coherent segments or groups. There has been a tremendous amount of e ort devoted to achieving the same level of performance in computer vision. In many cases, this is done by associating with each pixel a feature vector (e.g. color, motion, texture, position) and using a clustering or grouping algorithm on these feature vectors. Perhaps the cleanest approach to segmenting points in feature space is based on mixture models in which one assumes the data were generated by multiple processes and estimates the parameters of the processes and the number of components in the mixture. The assignment of points to clusters can then be easily performed by calculating the posterior probability of a point belonging to a cluster. Despite the elegance of this approach, the estimation process leads to a notoriously di cult optimization. The frequently used EM algorithm [3] often converges to a local maximum that depends on the initial conditions.


An Iterative Image Registration Technique with an Application to Stereo Vision

Image registration finds a variety of applications in computer vision. Unfortunately, traditional image registration techniques tend to be costly. We present a new image registration technique that makes use of the spatial intensity gradient of the images to find a good match using a type of Newton-Raphson iteration. Our technique is faster because it examines far fewer potential matches between the images than existing techniques. Furthermore, this registration technique can be generalized to handle rotation, scaling and shearing. We show show our technique can be adapted for use in a stereo vision system.


Normalized Cuts and Image Segmentation

w e propose Q novel approach for solving the perceptual grouping problem in vision. Rather than focusing on local features and their consistencies in the amage data, our approach aims a t extracting the global impression of an image. We treat image segmentation QS (I graph partitioning problem and propose Q novel global criterion, the normalized cut, for segmenting the graph. The normalized cut craterion measures both the total dissimilarity between the different groups QS well as the total similarity within the groups. We show that an eficient computational technique based on a generaked eigenvalue problem can be used to optimize this criterion. w e have applied this approach to segmenting static images and found results very enco u raging.


A First Look at a Telepresence System with Room-Sized Real-Time 3 D Capture and Life-Sized Tracked Display Wall

This paper provides a first look at a telepresence system offering room-sized, fully dynamic real-time 3D scene capture and continuous-viewpoint head-tracked display on a life-sized tiled display wall. The system is an expansion of a previous system, based an array of commodity depth sensors. We describe adjustments and improvements made to camera calibration, sensor data processing, data merger, rendering, and display, as required to scale the earlier system to room-sized.


Weighted extreme learning machine for imbalance learning

Extreme learning machine (ELM) is a competitive machine learning technique, which is simple in theory and fast in implementation. The network types are ‘‘generalized’’ single hidden layer feedforward networks, which are quite diversified in the form of variety in feature mapping functions or kernels. To deal with data with imbalanced class distribution, a weighted ELM is proposed which is able (1) it is simple in theory and convenient in implementation; (2) a wide type of feature mapping functions or kernels are available for the proposed framework; (3) the proposed method can be applied directly into multiclass classification tasks. In addition, after integrating with the weighting scheme, (1) the weighted ELM is able to deal with data with imbalanced class distribution while maintain the good performance on well balanced data as unweighted ELM; (2) by assigning different weights for each example according to users’ needs, the weighted ELM can be generalized to cost sensitive learning. & 2012 Elsevier B.V. All rights reserved.


Robust motion detection using histogram of oriented gradients for illumination variations

This paper proposes a robust motion detection method for illumination variations which uses histogram of oriented gradients. The detection process is divided into two phases: coarse detection and refinement. In the coarse detection phase, first, a texture-based background model is built which implements a group of adaptive histograms of oriented gradients; then, by comparing the histogram of oriented gradients of each pixel between current frame and background model, a foreground is segmented based on texture feature which is not susceptible to illumination variations, whereas some missing foreground regions exist; finally, the result based on texture is optimized by combining the pixel-wise detection result produced by Gaussian Mixture Model (GMM) algorithm, which greatly improves the detection performance by incorporating efficient morphological operations. In the refinement phase, the above detection result is refined based on the distinction in color feature to eliminate errors like shadows, noises, redundant contour, etc. Experimental results show the effectiveness and robustness of our approach in detecting moving objects in varying illumination conditions.


Renewable energy strategies for sustainable development

This paper discusses the perspective of renewable energy (wind, solar, wave and biomass) in the making of strategies for a sustainable development. Such strategies typically involve three major technological changes: energy savings on the demand side, efficiency improvements in the energy production, and replacement of fossil fuels by various sources of renewable energy. Consequently, large-scale renewable energy implementation plans must include strategies of how to integrate the renewable sources in coherent energy systems influenced by energy savings and efficiency measures. Based on the case of Denmark, this paper discusses the problems and perspectives of converting present energy systems into a 100 percent renewable energy system. The conclusion is that such development will be possible. The necessary renewable energy sources are present, if further technological improvements of the energy system are achieved. Especially technologies of converting the transportation and the introduction of flexible energy system technologies are crucial.


DeepSynth : Synthesizing A Musical Instrument With Video

This paper introduces DeepSynth, an end-to-end neural network model for generating the sound of a musical instrument based on a silent video of it being played. We specifically focus on building a synthesizer for the piano, but the ideas proposed in this paper are applicable to a wide range of musical instruments. At a high level, the model consists of a convolutional neural network (CNN) to extract features from the raw video frames and two stacked neural autoregressive models, one to encode the spatiotemporal features of the video and the other to generate the raw audio waveform.


Implementation of page rank algorithm in Hadoop MapReduce framework

One of the most popular algorithm in processing internet data i.e. webpages is page rank algorithm which is intended to decide the importance of a webpage by assigning a weighting value based on any incoming link to those webpage. However, the large amount of internet data may lead into computational burden in processing those page rank algorithm. To take into account those burden, in this paper we present a page rank processing algorithm over distributed system using Hadoop MapReduce framework called MR PageRank. Our algorithm can be decomposed into three processes, each of which is implemented in one Map and Reduce job. We first parse the raw webpage input to produce title of page and its outgoing links as key and value pair, respectively, as well as total dangling node's weight and total amount of pages. We next calculate the probability of each page and distribute this probability to each of outgoing link evenly. Each of the outgoing weight is shuffled and aggregated based on similarity of page title to update a new weighting value of each page. Notice that in the calculation we consider the dangling node and jumping factor. In the end, all of the page are descendingly sorted based on their weighting values. From the experimental result, we show that our implementation has output with reasonable ordering result. This electronic document is a “live” template and already defines the components of your paper.


Big Data Analytics with Spark

What’s more, Big Data Analytics with Spark provides an introduction to other big data technologies that are commonly used along with Spark, such as HDFS, Avro, Parquet, Ka a, Cassandra, HBase, Mesos, and so on. It also provides an introduction to machine learning and graph concepts. So the book is self-suffi cient; all the technologies that you need to know to use Spark are covered. The only thing that you are expected to have is some programming knowledge in any language.


Variation in the impact of social network characteristics on physical functioning in elderly persons: MacArthur Studies of Successful Aging.

OBJECTIVES
Social support and social networks have been shown to exert significant effects on health and functioning among elderly persons. Although theorists have speculated that the strength of these effects may differ as a function of sociodemographic characteristics and prior health status, few studies have directly tested the moderating effects of these variables.

METHODS
Longitudinal data from the MacArthur Study of Successful Aging were used to examine the effects of structural and functional social support on changes in physical functioning over a 7-year period, measured by the Nagi scale, in a sample of initially high-functioning men and women aged 70 to 79 years. Multiple regression analyses were used to test the main effects of social support and social network variables, as well as their interactions with gender, income, and baseline physical performance.

RESULTS
After controlling for potential confounding effects, respondents with more social ties showed less functional decline. The beneficial effects of social ties were stronger for respondents who were male or had lower levels of baseline physical performance.

DISCUSSION
The effects of social support and social networks may vary according to the individual's gender and baseline physical capabilities. Studies of functional decline among elderly persons should not ignore this population variation in the effects of social networks.


Design and Implementation of Autonomous Car using Raspberry Pi

The project aims to build a monocular vision autonomous car prototype using Raspberry Pi as a processing chip. An HD camera along with an ultrasonic sensor is used to provide necessary data from the real world to the car. The car is capable of reaching the given destination safely and intelligently thus avoiding the risk of human errors. Many existing algorithms like lane detection, obstacle detection are combined together to provide the necessary control to the car.


Routing and Wavelength Assignment in Optical WDM Networks

This article discusses the routing and wavelength assignment (RWA) problem in optical networks employing wavelenength division multiplexing (WDM) technology. Two variants of the problem are studied: static RWA, whereby the traffic requirements are known in advance, and dynamic RWA in which connection requests arrive in some random fashion. Both point-topoint and multicast traffic demands are considered.


Low-Power High-Efficiency Class D Audio Power Amplifiers

The architecture, design, and implementation of two clock-free analog class D audio power amplifiers using 0.5 ¿m CMOS standard technology are introduced. The amplifiers are designed to consume significantly less power than former implementations. Both designs operate with a 2.7 V single voltage supply and deliver a maximum output power of 250 mW into an 8 ¿ speaker. The two class-D amplifiers are based on a hysteretic sliding mode controller, which avoids the complex task of generating the highly linear triangle carrier signal used in conventional architectures. The first design generates a two-level modulated signal, known as binary modulated amplifier (BMA); the second topology implements a three-level modulated signal, also called ternary modulated amplifier (TMA). The architectures and implementations are simple and compact, providing very low quiescent power consumption. Experimental results of the BMA/TMA yield an efficiency of 89/90% and a total harmonic distortion plus noise (THD+N) of 0.02/0.03%, respectively. The efficiency and linearity is comparable to state-of-the-art amplifiers but the static power consumption is less than one tenth of previous proposed architectures. Both class D amplifiers achieve a power supply rejection ratio greater than 75 dB at 217 Hz, and a signal-to-noise ratio (SNR) higher than 90 dB within the whole audio band. Each amplifier occupies less than 1.5 mm2.


Incremental Clustering of Mobile Objects

Moving objects are becoming increasingly attractive to the data mining community due to continuous advances in technologies like GPS, mobile computers, and wireless communication devices. Mining spatio-temporal data can benefit many different functions: marketing team managers for identifying the right customers at the right time, cellular companies for optimizing the resources allocation, web site administrators for data allocation matters, animal migration researchers for understanding migration patterns, and meteorology experts for weather forecasting. In this research we use a compact representation of a mobile trajectory and define a new similarity measure between trajectories. We also propose an incremental clustering algorithm for finding evolving groups of similar mobile objects in spatio-temporal data. The algorithm is evaluated empirically by the quality of object clusters (using Dunn and Rand indexes), memory space efficiency, execution times, and scalability (run time vs. number of objects).


Filling Knowledge Base Gaps for Distant Supervision of Relation Extraction

Distant supervision has attracted recent interest for training information extraction systems because it does not require any human annotation but rather employs existing knowledge bases to heuristically label a training corpus. However, previous work has failed to address the problem of false negative training examples mislabeled due to the incompleteness of knowledge bases. To tackle this problem, we propose a simple yet novel framework that combines a passage retrieval model using coarse features into a state-of-the-art relation extractor using multi-instance learning with fine features. We adapt the information retrieval technique of pseudorelevance feedback to expand knowledge bases, assuming entity pairs in top-ranked passages are more likely to express a relation. Our proposed technique significantly improves the quality of distantly supervised relation extraction, boosting recall from 47.7% to 61.2% with a consistently high level of precision of around 93% in the experiments.


Common-Specific Multimodal Learning for Deep Belief Network

Multimodal Deep Belief Network has been widely used to extract representations for multimodal data by fusing the high-level features of each data modality into common representations. Such straightforward fusion strategy can benefit the classification and information retrieval tasks. However, it may introduce noise in case the high-level features are not naturally common hence non-fusable for different modalities. Intuitively, each modality may have its own specific features and corresponding representation capabilities thus should not be simply fused. Therefore, it is more reasonable to fuse only the common features and represent the multimodal data by both the fused features and the modality-specific features. To distinguish common features from modal-specific features is a challenging task for traditional DBN models where all features are crudely mixed. This paper proposes the Common-Specific Multimodal Deep Belief Network (CSDBN) to solve the problem. CS-DBN automatically separates common features from modal-specific features and fuses only the common ones for data representation. Experimental results demonstrate the superiority of CS-DBN for classification tasks compared with the baseline approaches.