title: Non-Profiled Deep Learning-based Side-Channel attacks with Sensitivity Analysis.

abstract: Deep Learning has recently been introduced as a new alternative to perform Side-Channel analysis [MPP16]. Until now, studies have been focused on applying Deep Learning techniques to perform Profiled Side-Channel attacks where an attacker has a full control of a profiling device and is able to collect a large amount of traces for different key values in order to characterize the device leakage prior to the attack. In this paper we introduce a new method to apply Deep Learning techniques in a Non-Profiled context, where an attacker can only collect a limited number of side-channel traces for a fixed unknown key value from a closed device. We show that by combining key guesses with observations of Deep Learning metrics, it is possible to recover information about the secret key. The main interest of this method is that it is possible to use the power of Deep Learning and Neural Networks in a Non-Profiled scenario. We show that it is possible to exploit the translation-invariance property of Convolutional Neural Networks [CDP17] against de-synchronized traces also during Non-Profiled side-channel attacks. In this case, we show that this method can outperform classic Non-Profiled attacks such as Correlation Power Analysis. We also highlight that it is possible to break masked implementations in black-box, without leakages combination pre-preprocessing and with no assumptions nor knowledge about the masking implementation. To carry the attack, we introduce metrics based on Sensitivity Analysis that can reveal both the secret key value as well as points of interest, such as leakages and masks locations in the traces. The results of our experiments demonstrate the interests of this new method and show that this attack can be performed in practice.


title: Sampling Large Databases for Association Rules

abstract: Discovery of association rules .is an important database mining problem. Current algorithms for finding association rules require several passes over the analyzed database, and obviously the role of I/O overhead is very significant for very large databases. We present new algorithms that reduce the database activity considerably. The idea is to pick a Random sample, to find using this sample all association rules that probably hold in the whole database, and then to verify the results with the rest of the database. The algorithms thus produce exact association rules, not approximations based on a sample. The approach is, however, probabilistic, and in those rare cases where our sampling method does not produce all association rules, the missing rules can be found in a second pass. Our experiments show that the proposed algorithms can find association rules very efficiently in only one database


title: Enhancing LTE-Advanced network performance using active antenna systems

abstract: This work exploits the benefits of adaptive downtilt and vertical sectorization schemes for Long Term Evolution Advanced (LTE-A) networks equipped with active antenna systems (AAS). We highlight how the additional control in the elevation domain (via AAS) enables use of adaptive downtilt and vertical sectorization techniques, thereby improving system spectrum efficiency. Our results, based on a full 3 dimensional (3D) channel, demonstrate that adaptive downtilt achieves up to 11% cell edge and 5% cell average spectrum efficiency gains when compared to a baseline system utilizing fixed downtilt, without the need for complex coordination among cells. In addition, vertical sectorization, especially high-order vertical sectorization utilizing multiple vertical beams, which increases spatial reuse of time and frequency resources, is shown to provide even higher performance gains.


title: Imperforate Hymen - a rare cause of acute abdominal pain and tenesmus: case report and review of the literature

abstract: Imperforate hymen is a rare condition that presents with amenorrhea, cyclical abdominal pains and urine retention among pubertal girls. A 14 year old girl with imperforate hymen underwent hymenotomy for hematocolpometra, having presented with abdominal pains and tenesmus.


title: Consistent stylization and painterly rendering of stereoscopic 3D images

abstract: We present a method for stylizing stereoscopic 3D images that guarantees consistency between the left and right views. Our method decomposes the left and right views of an input image into discretized disparity layers and merges the corresponding layers from the left and right views into a single layer where stylization takes place. We then construct new stylized left and right views by compositing portions of the stylized layers. Because the left and right views come from the same source layers, our method eliminates common artifacts that cause viewer discomfort. We also present a stereoscopic 3D painterly rendering algorithm tailored to our layer-based approach. This method uses disparity information to assist in stroke creation so that strokes follow surface geometry without ignoring painted surface patterns. Finally, we conduct a user study that demonstrates that our approach to stereoscopic 3D image stylization leads to images that are more comfortable to view than those created using other techniques.


title: Follow the Sun through the Clouds: Application Migration for Geographically Shifting Workloads

abstract: Global cloud services have to respond to workloads that shift geographically as a function of time-of-day or in response to special events. While many such services have support for adding nodes in one region and removing nodes in another, we demonstrate that such mechanisms can lead to significant performance degradation. Yet other services do not support application-level migration at all. Live VM migration between availability zones or even across cloud providers would be ideal, but cloud providers do not support this flexible mechanism. This paper presents the Supercloud, a uniform cloud service that supports live VM migration between data centers of all major public cloud providers. The Supercloud also provides a scheduler that automatically determines when and where to move VMs for optimal performance. We demonstrate that live VM migration can support shifting workloads effectively, with low downtimes and transparently to both services and their clients. The Supercloud also addresses challenges for supporting cross-cloud storage and networking.


title: Mobile travel guide using image recognition and GPS/Geo tagging: A smart way to travel

abstract: In our current tourism system, whenever a tourist visits a monument or a famous spot, to know more about the place, he/she hires a guide. With the advancement in the technology, the role of the guide is being taken up by the different services and applications. The increase in the use of mobile phones has made it easier to provide different applications available to the tourists ‘on the go’. In this project, we are making an android based mobile application that can be used as a guide by the tourists. The main function of this application is to recognize a monument or a famous spot from the picture clicked/uploaded by the user and to provide detailed information regarding it. This is achieved by using open-cv for image recognition. It then displays the information that is associated with that monument. The information is available in the JSON database. It will also be able to point the location of that monument on the map.


title: Public Transit Routing with Unrestricted Walking

abstract: We study the problem of answering profile queries in public transportation networks that allow unrestricted walking. That is, finding all Pareto-optimal journeys regarding travel time and number of transfers in a given time interval. We introduce a novel algorithm that, unlike most state-of-the-art algorithms, can compute profiles efficiently in a setting that allows arbitrary walking. Using our algorithm, we show in an extensive experimental study that allowing unrestricted walking, significantly reduces travel times, compared to settings where walking is restricted. Beyond that, we publish the transportation networks of Switzerland that we used in our study, in order to encourage further research on this topic.


title: A Monolithic Phased Array Using 3-bit Distributed RF MEMS Phase Shifters

abstract: This paper presents a novel electronically scanning phased-array antenna with 128 switches monolithically implemented using RF microelectromechanical systems (MEMS) technology. The structure, which is designed at 15 GHz, consists of four linearly placed microstrip patch antennas, 3-bit distributed RF MEMS low-loss phase shifters, and a corporate feed network. MEMS switches and high-Q metal-air-metal capacitors are employed as loading elements in the phase shifter. The system is fabricated monolithically using an in-house surface micromachining process on a glass substrate and occupies an area of 6 cm times 5 cm. The measurement results show that the phase shifter can provide nearly 20deg/50deg/95deg phase shifts and their combinations at the expense of 1.5-dB average insertion loss at 15 GHz for eight combinations. It is also shown by measurements that the main beam can be steered to required directions by suitable settings of the RF MEMS phase shifters.


title: Poster Abstract: Resource Aware Placement of Data Stream Analytics Operators on Fog Infrastructure for Internet of Things Applications

abstract: While cloud computing led the path towards a revolutionary change in the modern day computing aspects, further developments gave way to the Internet of Things and its own range of highly interactive applications. While such a paradigm is more distributed in reach, it also brings forth its own set of challenges in the form of latency sensitive applications, where a quick response highly contributes to efficient usage and QoS (Quality of Service). Fog computing, which is the answer to all such challenges, is rapidly changing the distributed computing landscape by extending the cloud computing paradigm to include widespread resources located at the network edge. While the fog paradigm makes use of edge-ward devices capable of computing, networking and storage, one of the key impending challenges is to determine where to place the data analytic operators for maximum efficiency and least costs for the network and its traffic, the efficient algorithmic solution to which we seek to propose by way of this work underway.


title: Dynamic Sampling in Convolutional Neural Networks for Imbalanced Data Classification

abstract: Many multimedia systems stream real-time visual data continuously for a wide variety of applications. These systems can produce vast amounts of data, but few studies take advantage of the versatile and real-time data. This paper presents a novel model based on the Convolutional Neural Networks (CNNs) to handle such imbalanced and heterogeneous data and successfully identifies the semantic concepts in these multimedia systems. The proposed model can discover the semantic concepts from the data with a skewed distribution using a dynamic sampling technique. The paper also presents a system that can retrieve real-time visual data from heterogeneous cameras, and the run-time environment allows the analysis programs to process the data from thousands of cameras simultaneously. The evaluation results in comparison with several state-of-the-art methods demonstrate the ability and effectiveness of the proposed model on visual data captured by public network cameras.


title: A Hybrid Algorithm for Clustering of Time Series Data Based on Affinity Search Technique

abstract: Time series clustering is an important solution to various problems in numerous fields of research, including business, medical science, and finance. However, conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impracticality results in poor clustering accuracy in several systems. In this paper, a new hybrid clustering algorithm is proposed based on the similarity in shape of time series data. Time series data are first grouped as subclusters based on similarity in time. The subclusters are then merged using the k-Medoids algorithm based on similarity in shape. This model has two contributions: (1) it is more accurate than other conventional and hybrid approaches and (2) it determines the similarity in shape among time series data with a low complexity. To evaluate the accuracy of the proposed model, the model is tested extensively using syntactic and real-world time series datasets.


title: A Unified Approach for Learning the Parameters of Sum-Product Networks

abstract: We present a unified approach for learning the parameters of Sum-Product networks (SPNs). We prove that any complete and decomposable SPN is equivalent to a mixture of trees where each tree corresponds to a product of univariate distributions. Based on the mixture model perspective, we characterize the objective function when learning SPNs based on the maximum likelihood estimation (MLE) principle and show that the optimization problem can be formulated as a signomial program. We construct two parameter learning algorithms for SPNs by using sequential monomial approximations (SMA) and the concave-convex procedure (CCCP), respectively. The two proposed methods naturally admit multiplicative updates, hence effectively avoiding the projection operation. With the help of the unified framework, we also show that, in the case of SPNs, CCCP leads to the same algorithm as Expectation Maximization (EM) despite the fact that they are different in general.


title: Comparison of different classifier in WEKA for rheumatoid arthritis

abstract: Analyzing the hidden information from the images are helpful to identify the various causes. In general processing of images includes Pre-processing, segmentation, Feature extraction and Classification. Significance of classifier is essential since results are always based on the classifier. The ultimate aim is to explain how WEKA tool is used for rheumatoid arthritis and investigate the performance of the various classifiers for huge data. In our method we are distinctively give attention to the classification methods like ADTree, Best First Decision tree(BF), Decision stump, J48Pruned tree, J48 Graft Pruned tree, Least Absolute Deviation regression trees (LAD), Logistic Model Tree(LMT), Naïve-Bayes (NB), Random tree, Random forest tree, CART Decision tree. The features like Area, perimeter, circularity, integrated density, Median, Skewness, Raw integrated density, and Roundness and solidity are obtained from the Lymphocytes images and formed the data set. Different classifier is applied for RA facet for Validation. RA facet contains 108 rows and 10 columns. Using classifier to find out the various values like Relative Absolute Error, and Kappa Statistic, Mean Absolute Error, Root Mean Squared Error and Root Relative Squared Error. From those values compare with all the methods ADT classifier is suggested for use in huge data.


title: 7 Institutions and Organizational Socialization: Integrating Employees in Cross-Border Mergers and Acquisitions

abstract: There is a consensus within the Merger and Acquisition (M&A) literature that acquisitions fail during the integration phase, in large part due to neglect of social factors such as differences in rules, norms, and values across organizations. Thus, a crucial part of successful integration is whether firms can socialize employees effectively into the newly merged entity so that they learn accepted ways of behaving in that organization. This effect is exacerbated for cross-border M&As. Yet, there is little theory and practical advice on how to successfully manage post-M&A integration. We draw on institutional and socialization theories to develop a framework that identifies the most salient institutions involved in the socialization of employees during the post-acquisition integration process and apply this model to the international dimension. We conclude by presenting managerial implications of our model and suggestions for future research.


title: Speech probability distribution

abstract: It is demonstrated that the distribution of speech samples is well described by Laplacian distribution (LD). The widely known speech distributions, i.e., LD, Gaussian distribution (GD), generalized GD, and gamma distribution, are tested as four hypotheses, and it is proved that speech samples during voice activity intervals are Laplacian random variables. A decorrelation transformation is then applied to speech samples to approximate their multivariate distribution. To do this, speech is decomposed using an adaptive Karhunen-Loeve transform or a discrete cosine transform. Then, the distributions of speech components in decorrelated domains are investigated. Experimental evaluations prove that the statistics of speech signals are like a multivariate LD. All marginal distributions of speech are accurately described by LD in decorrelated domains. While the energies of speech components are time-varying, their distribution shape remains Laplacian.


title: Network analysis of supply chain systems: A systematic review and future research

abstract: Supply chains are continuously evolving and adapting systems driven by complex socio-technical interfirm interactions. Traditional engineering and operations management modeling approaches have primarily focused on technical issues and are not well suited to effectively capture the many complex structural and behavioral aspects of supply chain systems (SCSs). There is growing recognition by the supply chain community of the significant benefits a network analytic lens can provide to understand, design, and manage SCSs. We systematically review and analyze the relevant literature and, drawing on a multi-disciplinary theoretical foundation, develop an integrative framework. Our framework identifies three distinct, but interdependent themes that characterize the study of SCSs: SCS network structure (i.e. system architecture), SCS network dynamics (i.e. system behavior), and SCS network strategy (i.e. system policy and control). We elaborate on these themes, review key findings, identify the current limitations and knowledge gaps, and discuss the fundamental benefits derived from adopting an integrated SCSs perspective. We conclude with future research directions for network analysis in SCS design and management, in particular, and complex enterprise systems in general.


The Thing and I: Understanding the Relationship Between User and Product


title: Fluid Simulation For Computer Graphics: A Tutorial in Grid Based and Particle Based Methods

abstract: In this paper we present a tutorial on the implementation of both a grid based and a particle based fluid simulator for computer graphics applications. Many research papers on fluid simulation are readily available, but these papers often assume a very sophisticated mathematical background not held by many undergraduates. Furthermore, these papers tend to gloss over the implementation details, which are very important to people trying to implement a working system. Recently, Robert Bridson release the wonderful book, ”Fluid Simulation for Computer Graphics.[Bridson 2009]” We base a large portion of our own grid-based simulator off of this text. However, this text is very dense and theory intensive, and this document serves as easy version for those who want to implement a simulator quickly. Furthermore, Bridson’s text does not cover particle based methods, like SPH, which are quickly becoming commonplace within the graphics community. This work provides an introduction to SPH as well.


title: One Phase Commit: A Low Overhead Atomic Commitment Protocol for Scalable Metadata Services

abstract: As the number of client machines in high end computing clusters increases, the file system cannot keep up with the resulting volume of requests, using a centralized metadata server. This problem will be even more prominent with the advent of the exascale computing age. In this context, the centralized metadata server represents a bottleneck for the scaling of the file system performance as well as a single point of failure. To overcome this problem, file systems are evolving from centralized metadata services to distributed metadata services. The metadata distribution raises a number of additional problems that must be taken into account. In this paper we will focus on the problem of managing distributed namespace operations such as CREATE, DELETE and RENAME. Distributed namespace operations are a side effect of metadata distribution across the cluster of metadata servers. Available protocols for handling distributed namespace operations such as the two phase commitment protocol are expensive since they require the exchange of a large number of messages between metadata servers as well as synchronous writes to stable storage to log vital information. Moreover, such protocols adopt locking schemes to protect the resource during the operation, which force multiple operations on the same directory to be serialized. This severely impacts the performance of high performance computing applications in typical scenarios such as high rate of file create operations. We propose a one phase commit protocol that is tailored to the use for typical inter-metadata messages. We rely on a fast, highly available shared storage for metadata in order to minimize writes, messages, coordination overhead and recovery time in case of failing metadata servers. We present a formal description of the new protocol, a theoretical analysis of its capabilities, a proof of correctness and the evaluation of the protocol in a simulated environment that renders the protocol to be fast and reliable. In simulations the protocol achieved more than 50% better performance compared with the two phase commitment protocol.


title: Single underwater image enhancement using depth estimation based on blurriness

abstract: In this paper, we propose to use image blurriness to estimate the depth map for underwater image enhancement. It is based on the observation that objects farther from the camera are more blurry for underwater images. Adopting image blurriness with the image formation model (IFM), we can estimate the distance between scene points and the camera and thereby recover and enhance underwater images. Experimental results on enhancing such images in different lighting conditions demonstrate the proposed method performs better than other IFM-based enhancement methods.


title: HIGHLIGHTED TOPIC A Physiological Systems Approach to Human and Mammalian Thermoregulation Neuronal basis of Hammel's model for set-point thermoregulation

abstract: Boulant, Jack A. Neuronal basis of Hammel’s model for set-point thermoregulation. J Appl Physiol 100: 1347–1354, 2006; doi:10.1152/japplphysiol.01064.2005.—In 1965, H. T. Hammel proposed a neuronal model to explain set-point thermoregulation. His model was based on a synaptic network encompassing four different types of hypothalamic neurons: i.e., warm-sensitive and temperature-insensitive neurons and heat loss and heat production effector neurons. Although some modifications to this model are suggested, recent electrophysiological and morphological studies support many of the model’s major tenets. Hypothalamic warm-sensitive neurons integrate core and peripheral thermal information. These neurons sense changes in hypothalamic temperature, and they orient their dendrites medially and laterally to receive ascending afferent input from cutaneous thermoreceptors. Temperatureinsensitive neurons have a different dendritic orientation and may provide constant reference signals, which are important in determining thermoregulatory set points. In Hammel’s model, temperature-sensitive and -insensitive neurons send mutually antagonistic synaptic inputs to effector neurons controlling various thermoregulatory responses. The model predicts that warm-sensitive neurons synaptically excite heat loss effector neurons and inhibit heat production effector neurons. In recent studies, one counterpart of these effector neurons may be “excitatory postsynaptic potential-driven neurons,” the activity of which is dependent on synaptic excitation from nearby cells. Excitatory postsynaptic potential-driven neurons have sparse dendrites that appear to be specifically oriented, either medially or laterally, presumably to receive selective synaptic input from a discrete source. Another counterpart of effector neurons may be “silent neurons,” which have extensive dendritic branches that may receive synaptic excitation from remote sources. Because some silent neurons receive synaptic inhibition from nearby warmsensitive neurons, Hammel’s model would predict that they have a role in heat production or heat retention responses. hypothalamus; neuron; synaptic network; body temperature


title: Why Some Internet Users Don’t Buy Air Tickets Online

abstract: This work attempts to identify the determinant variables that make some Internet users not to buy airline tickets online. The Technology Acceptance Model (TAM) and the Theory of Planned Behaviour (TPB) have been used as the conceptual reference framework, with the addition of the influence of perceived risk and trust. The results of the empirical study of Internet users who never have purchased an airline ticket online suggest that both subjective norm and attitude have a direct influence on airline ticket purchase intention. Ease of use has also proved to be a significant variable, because it has an indirect influence on behaviour through perceived usefulness, trust and risk. Risk, trust and perceived behavioural control were found to influence purchase intention through attitude. The results also show that risk is a multidimensional variable and that the dimensions do not all exercise the same influence on airline ticket purchase intention. Managerial implications are provided.


title: 2MA: Verifying Voice Commands via Two Microphone Authentication

abstract: Voice controlled interfaces have vastly improved the usability of many devices (e.g., headless IoT systems). Unfortunately, the lack of authentication for these interfaces has also introduced command injection vulnerabilities - whether via compromised IoT devices, television ads or simply malicious nearby neighbors, causing such devices to perform unauthenticated sensitive commands is relatively easy. We address these weaknesses with Two Microphone Authentication (2MA), which takes advantage of the presence of multiple ambient and personal devices operating in the same area. We develop an embodiment of 2MA that combines approximate localization through Direction of Arrival (DOA) techniques with Robust Audio Hashes (RSHs). Our results show that our 2MA system can localize a source to within a narrow physical cone ($


title: Users of the world, unite! The challenges and opportunities of Social Media

abstract: The concept of Social Media is top of the agenda for many business executives today. Decision makers, as well as consultants, try to identify ways in which firms can make profitable use of applications such as Wikipedia, YouTube, Facebook, Second Life, and Twitter. Yet despite this interest, there seems to be very limited understanding of what the term ''Social Media'' exactly means; this article intends to provide some clarification. We begin by describing the concept of Social Media, and discuss how it differs from related concepts such as Web 2.0 and User Generated Content. Based on this definition, we then provide a classification of Social Media which groups applications currently subsumed under the generalized term into more specific categories by characteristic: collaborative projects, blogs, content communities, social networking sites, virtual game worlds, and virtual social worlds. Finally, we present 10 pieces of advice for companies which decide to utilize Social Media.


title: Incentivizing users for balancing bike sharing systems

abstract: Bike sharing systems have been recently adopted by a growing number of cities as a new means of transportation offering citizens a flexible, fast and green alternative for mobility. Users can pick up or drop off the bicycles at a station of their choice without prior notice or time planning. This increased flexibility comes with the challenge of unpredictable and fluctuating demand as well as irregular flow patterns of the bikes. As a result, these systems can incur imbalance problems such as the unavailability of bikes or parking docks at stations. In this light, operators deploy fleets of vehicles which re-distribute the bikes in order to guarantee a desirable service level. Can we engage the users themselves to solve the imbalance problem in bike sharing systems? ::: ::: In this paper, we address this question and present a crowdsourcing mechanism that incentivizes the users in the bike repositioning process by providing them with alternate choices to pick or return bikes in exchange for monetary incentives. We design the complete architecture of the incentives system which employs optimal pricing policies using the approach of regret minimization in online learning. We investigate the incentive compatibility of our mechanism and extensively evaluate it through simulations based on data collected via a survey study. Finally, we deployed the proposed system through a smartphone app among users of a large scale bike sharing system operated by a public transport company, and we provide results from this experimental deployment. To our knowledge, this is the first dynamic incentives system for bikes re-distribution ever deployed in a real-world bike sharing system.


title: CamOdoCal: Automatic intrinsic and extrinsic calibration of a rig with multiple generic cameras and odometry

abstract: Multiple cameras are increasingly prevalent on robotic and human-driven vehicles. These cameras come in a variety of wide-angle, fish-eye, and catadioptric models. Furthermore, wheel odometry is generally available on the vehicles on which the cameras are mounted. For robustness, vision applications tend to use wheel odometry as a strong prior for camera pose estimation, and in these cases, an accurate extrinsic calibration is required in addition to an accurate intrinsic calibration. To date, there is no known work on automatic intrinsic calibration of generic cameras, and more importantly, automatic extrinsic calibration of a rig with multiple generic cameras and odometry. We propose an easy-to-use automated pipeline that handles both intrinsic and extrinsic calibration; we do not assume that there are overlapping fields of view. At the begining, we run an intrinsic calibration for each generic camera. The intrinsic calibration is automatic and requires a chessboard. Subsequently, we run an extrinsic calibration which finds all camera-odometry transforms. The extrinsic calibration is unsupervised, uses natural features, and only requires the vehicle to be driven around for a short time. The intrinsic parameters are optimized in a final bundle adjustment step in the extrinsic calibration. In addition, the pipeline produces a globally-consistent sparse map of landmarks which can be used for visual localization. The pipeline is publicly available as a standalone C++ package.


title: Max - a Multimodal Assistant in Virtual Reality Construction. KI-Künstliche Intelligenz

abstract: In the Collaborative Research Center SFB 360 ``Situated Artificial Communicators'' the anthropomorphic agent Max is under development. Max inhabits a CAVE-like Virtual Reality environment where he is visualized in human size. Engaged in cooperative construction tasks, the human user meets a multimodal communication partner that employs synthetic speech, gaze, facial expression, and gesture. Combining deliberative and reactive conversational behaviors with the capability to initiate assembly actions, the agent guides the user through interactive construction procedures. This paper describes the overall architecture of Max with a focus on dialog and interaction management and the generation of synchronized multimodal utterances.


title: Protocol for a new concept of no-prep ultrathin ceramic veneers

abstract: Objectives ::: No-prep veneers, although ideally considered the best option because of tooth structure maximum preservation, have been frequently criticized for some potential limitations including esthetic outcomes and periodontal complications. ::: ::: Clinical considerations ::: A new protocol to optimize no-prep veneers restorations is presented. A key point of the proposed technique is to identify optimal margins’ positions: margin is positioned in the point of maximum convexity of teeth, avoiding the over contouring of traditional no-prep veneers. ::: ::: Conclusion ::: The procedure can be appreciated for the marginal accuracy and the resulting aesthetic stability. The case reports show that properly managed no-prep veneers can have biologically healthy and aesthetically pleasant tooth-restoration transitions and emergence profiles. ::: ::: Clinical Significance ::: High-quality no-prep veneers can be more challenging to realize than conventional veneers and the success seems to depend on a combination of good case selection, margins’ position, sound adhesive principles, clinical, and laboratory experience.


title: Dual Band Microstrip Patch Antenna for Sar Applications

abstract: Microstrip patch antennas offer the advantages of thin profile, light weight, low cost, ease of fabrication and compatibility with integrated circuitry, so the antennas are widely used to satisfy demands for polarization diversity and dual-frequency. This paper presents a coaxilly-fed single-layer compact dual band (C- and X-band) microstrip patch antenna for achieving dual-polarized radiation suitable for applications in airborne synthetic aperture radar (SAR) systems. The designed antenna consists of three rectangular patches which are overlapped along their diagonals. Full-wave electromagnetic simulations are performed to accurately predict the frequency response of the antenna. The fabricated antenna achieves an impedance bandwidth of 154 MHz (f0 = 6.83 GHz) and 209 MHz (f0 = 9.73 GHz) for VSWR < 2. Simultaneous use of both frequencies should drastically improve data collection and knowledge of the targets in the SAR system.


title: Effects of cannabidiol in the treatment of patients with Parkinson’s disease: An exploratory double-blind trial

abstract: Introduction:Parkinson’s disease (PD) has a progressive course and is characterized by the degeneration of dopaminergic neurons. Although no neuroprotective treatments for PD have been found to date, the endocannabinoid system has emerged as a promising target.Methods:From a sample of 119 patients consecutively evaluated in a specialized movement disorders outpatient clinic, we selected 21 PD patients without dementia or comorbid psychiatric conditions. Participants were assigned to three groups of seven subjects each who were treated with placebo, cannabidiol (CBD) 75 mg/day or CBD 300 mg/day. One week before the trial and in the last week of treatment participants were assessed in respect to (i) motor and general symptoms score (UPDRS); (ii) well-being and quality of life (PDQ-39); and (iii) possible neuroprotective effects (BDNF and H1-MRS).Results:We found no statistically significant differences in UPDRS scores, plasma BDNF levels or H1-MRS measures. However, the groups treated with placebo and CBD 3...


title: Design of a TV white space converter prototype towards cognitive radio for WLAN routers

abstract: We report on the development of a frequency converter frontend that enables 2.4 GHz IEEE 802.11 WLAN devices to operate in unused parts of the UHF spectrum, the so-called TV whitespaces. The new frontend features separate TX and RX paths with automatic directionality switching and in-hardware energy detection. Equipped with a standard antenna port at the input side, and a fully configurable transmit chain controlled through a user-accessible microcontroller, this whitespace converter (WSC) with commercial off-the-shelf components is particularly suitable for cognitive radio (CR) research. We discuss the specific challenges in the design of a WSC for 802.11 WLAN routers. The complete architecture is presented, and preliminary performance results from a level plan optimization are discussed.


title: Salt and Pepper Noise Detection and removal by Modified Decision based Unsymmetrical Trimmed Median Filter for Image Restoration

abstract: In this paper, six different image filtering algorithms are compared based on their ability to reconstruct noise affected images. The purpose of these algorithms is to remove noise from a signal that might occur through the transmission of an image. A new algorithm, the Spatial Median Filter, is introduced and compared with current image smoothing techniques. Experimental results demonstrate that the proposed algorithm is comparable to these techniques.. This proposed algorithm shows better results than the Standard Median Filter (MF), Decision Based Algorithm (DBA), Modified Decision Based Algorithm (MDBA), and Progressive Switched Median Filter (PSMF). The proposed algorithm is tested against different grayscale and color images and it gives better Peak Signal-to-Noise Ratio (PSNR) and Image Enhancement Factor (IEF).


title: Variance Reduction for Distributed Stochastic Gradient Descent

abstract: Variance reduction (VR) methods boost the performance of stochastic gradient descent (SGD) by enabling the use of larger stepsizes and preserving linear convergence rates. However, current variance reduced SGD methods require either high memory usage or require a full pass over the (large) data set at the end of each epoch to calculate the exact gradient of the objective function. This makes current VR methods impractical in distributed or parallel settings. In this paper, we propose a variance reduction method, called VR-lite, that does not require full gradient computations or extra storage. We explore distributed synchronous and asynchronous variants with both high and low communication latency. We find that our distributed algorithms scale linearly with the number of local workers and remain stable even with low communication frequency. We empirically compare both the sequential and distributed algorithms to state-of-the-art stochastic optimization methods, and find that our proposed algorithms consistently converge faster than other stochastic methods.


title: Development of an instrument in Portuguese to identify barriers for physical activity in children

abstract: The aim of this study was to develop an instrument to identify barriers to PA in children. This is a validation study with observational and cross-sectional design. The population consisted of students aged 06-09 years from the city of Uruguaiana/RS. The instrument content was obtained by theoretical matrix, constructed from the literature review and the assessment of barriers through open questions applied by proxy report (n=100) and focus groups conducted with parents/guardians and children. It is a questionnaire consisting of 29 closed questions. After testing the instrument clarity, its implementation was carried out (n = 293) to verify the construct validity and internal consistency. After one week, parents/guardians were asked to answer the questionnaire retest reliability (n = 168) to verify the agreement. Exploratory factor analysis distributed the 29 questions in six factors, with total variance of 55.6%. All items that have defined the factors obtained load greater than 0.4 and eingenvalues above 1.0. In the internal consistency analysis, item-total correlations ranged from 0.23 to 0.64 and the total alpha value was 0.90. Reproducibility values ranged from 0.43 to 0.78. Twenty-four questions met all the criteria adopted for the study. It was concluded that the instrument developed has adequate validity and reliability for the identification of barriers to PA in schoolchildren aged 06-09 years.


title: Self-regulation of frontal-midline theta facilitates memory updating and mental set shifting

abstract: Frontal-midline (fm) theta oscillations as measured via the electroencephalogram (EEG) have been suggested as neural "working language" of executive functioning. Their power has been shown to increase when cognitive processing or task performance is enhanced. Thus, the question arises whether learning to increase fm-theta amplitudes would functionally impact the behavioral performance in tasks probing executive functions (EFs). Here, the effects of neurofeedback (NF), a learning method to self-up-regulate fm-theta over fm electrodes, on the four most representative EFs, memory updating, set shifting, conflict monitoring, and motor inhibition are presented. Before beginning and after completing an individualized, eight-session gap-spaced NF intervention, the three-back, letter/number task-switching, Stroop, and stop-signal tasks were tested while measuring the EEG. Self-determined up-regulation of fm-theta and its putative role for executive functioning were compared to an active control group, the so-called pseudo-neurofeedback group. Task-related fm-theta activity after training differed significantly between groups. More importantly, though, after NF significantly enhanced behavioral performance was observed. The training group showed higher accuracy scores in the three-back task and reduced mixing and shifting costs in letter/number task-switching. However, this specific protocol type did not affect performance in tasks probing conflict monitoring and motor inhibition. Thus, our results suggest a modulation of proactive but not reactive mechanisms of cognitive control. Furthermore, task-related EEG changes show a distinct pattern for fm-theta after training between the NF and the pseudo-neurofeedback group, which indicates that NF training indeed tackles EFs-networks. In sum, the modulation of fm-theta via NF may serve as potent treatment approach for executive dysfunctions.


title: Time Series Epenthesis: Clustering Time Series Streams Requires Ignoring Some Data

abstract: Given the pervasiveness of time series data in all human endeavors, and the ubiquity of clustering as a data mining application, it is somewhat surprising that the problem of time series clustering from a single stream remains largely unsolved. Most work on time series clustering considers the clustering of individual time series, e.g., gene expression profiles, individual heartbeats or individual gait cycles. The few attempts at clustering time series streams have been shown to be objectively incorrect in some cases, and in other cases shown to work only on the most contrived datasets by carefully adjusting a large set of parameters. In this work, we make two fundamental contributions. First, we show that the problem definition for time series clustering from streams currently used is inherently flawed, and a new definition is necessary. Second, we show that the Minimum Description Length (MDL) framework offers an efficient, effective and essentially parameter-free method for time series clustering. We show that our method produces objectively correct results on a wide variety of datasets from medicine, zoology and industrial process analyses.


title: Effects of psychotherapy for depression in children and adolescents: A meta-analysis.

abstract: Serious sequelae of youth depression, plus recent concerns over medication safety, prompt growing interest in the effects of youth psychotherapy. In previous meta-analyses, effect sizes (ESs) have averaged .99, well above conventional standards for a large effect and well above mean ES for other conditions. The authors applied rigorous analytic methods to the largest study sample to date and found a mean ES of .34, not superior but significantly inferior to mean ES for other conditions. Cognitive treatments (e.g., cognitive-behavioral therapy) fared no better than noncognitive approaches. Effects showed both generality (anxiety was reduced) and specificity (externalizing problems were not), plus short- but not long-term holding power. Youth depression treatments appear to produce effects that are significant but modest in their strength, breadth, and durability.


title: An Efficient Approach to Identifying and Validating Clusters in Multivariate Datasets with Applications in Gene Expression Analysis

abstract: Gene expression data analysis has become an important topic in bioinformatics due to its wide application in the biomedical industry. Effective analysis of gene expression data is an essential pail of various data mining methods, especially the clustering techniques. Various kinds of clustering methods have been proposed, yet they do not satisfy for the requirements of high efficiency, high quality and automation in the mining of gene expression data. In this paper, we propose an efficient and automatic clustering approach that is suitable for gene expression analysis. The proposed approach primarily employs similarity-matrix based clustering techniques, complemented by new heuristics for reducing the computation cost. In particular, a novel validation technique is incorporated for evaluating the quality of the discovered gene expression patterns. Because it includes empirical evaluation of different gene expression datum, the proposed approach is able perform better than other methods in terms of efficiency, clustering quality and automation.


title: Smart manufacturing, manufacturing intelligence and demand-dynamic performance

abstract: Abstract Smart Manufacturing is the dramatically intensified and pervasive application of networked information-based technologies throughout the manufacturing and supply chain enterprise. The defining technical threads are time, synchronization, integrated performance metrics and cyber-physical–workforce requirements. Smart Manufacturing responds and leads to a dramatic and fundamental business transformation to demand-dynamic economics keyed on customers, partners and the public; enterprise performance and variability management; real-time integrated computational materials engineering and rapid qualification, demand-driven supply chain services; and broad-based workforce involvement. IT-enabled Smart factories and supply networks can better respond to national interests and strategic imperatives and can revitalize the industrial sector by facilitating global competitiveness and exports, providing sustainable jobs, radically improving performance, and facilitating manufacturing innovation.


title: Factors influencing satisfaction and loyalty in online shopping: an integrated model

abstract: Purpose – Taking into consideration external (technology acceptance factors, website service quality) as well as internal (specific holdup cost) factors, this paper aims to explore how internet customer satisfaction and loyalty can be associated with each other and how they are affected by these dynamics.Design/methodology/approach – This study adopts structural equation modelling (SEM) as the main analytical tool. It investigates the shopping experiences of users of the major shopping websites of Taiwan.Findings – The research results point to the following: first, customer e‐satisfaction will positively influence customer e‐loyalty directly; second, technology acceptance factors will positively influence customer e‐satisfaction and e‐loyalty directly; third, website service quality can positively influence customer e‐satisfaction and e‐loyalty directly; and fourth, specific holdup cost can positively influence customer e‐loyalty directly, but cannot positively influence customer e‐satisfaction directly....


title: PRESENT: An Ultra-Lightweight Block Cipher

abstract: With the establishment of the AES the need for new block ciphers has been greatly diminished; for almost all block cipher applications the AES is an excellent and preferred choice. However, despite recent implementation advances, the AES is not suitable for extremely constrained environments such as RFID tags and sensor networks. In this paper we describe an ultra-lightweight block cipher, present . Both security and hardware efficiency have been equally important during the design of the cipher and at 1570 GE, the hardware requirements for present are competitive with today's leading compact stream ciphers.


title: Plant root-microbe communication in shaping root microbiomes

abstract: A growing body of research is highlighting the impacts root-associated microbial communities can have on plant health and development. These impacts can include changes in yield quantity and quality, timing of key developmental stages and tolerance of biotic and abiotic stresses. With such a range of effects it is clear that understanding the factors that contribute to a plant-beneficial root microbiome may prove advantageous. Increasing demands for food by a growing human population increases the importance and urgency of understanding how microbiomes may be exploited to increase crop yields and reduce losses caused by disease. In addition, climate change effects may require novel approaches to overcoming abiotic stresses such as drought and salinity as well as new emerging diseases. This review discusses current knowledge on the formation and maintenance of root-associated microbial communities and plant–microbe interactions with a particular emphasis on the effect of microbe–microbe interactions on the shape of microbial communities at the root surface. Further, we discuss the potential for root microbiome modification to benefit agriculture and food production.


title: Arm Motion Estimation Algorithm Using MYO Armband

abstract: In this paper, we proposes an algorithm to estimate the overall arm motion using the MYO armband attached to the upper arm. Thus, we can get EMG data of biceps brachii and triceps brachii when the human stretch or bend his/her arm. The motion of the lower arm is estimated through the EMG signal of the upper arm. The motion of the upper arm is detected through the IMU sensor. To increase the reliability of the data, experimental environment is set up and process of filtering and initialization are performed before motion estimation. Finally, we propose an algorithm with average, sum and rate sequence based on the signals measured on 8 EMG channels of MYO. If the user takes an arbitrary action based on motion in this paper, it is shown by a simulation after algorithm processing.


title: Manifold SLIC: A Fast Method to Compute Content-Sensitive Superpixels

abstract: Superpixels are perceptually meaningful atomic regions that can effectively capture image features. Among various methods for computing uniform superpixels, simple linear iterative clustering (SLIC) is popular due to its simplicity and high performance. In this paper, we extend SLIC to compute content-sensitive superpixels, i.e., small superpixels in content-dense regions (e.g., with high intensity or color variation) and large superpixels in content-sparse regions. Rather than the conventional SLIC method that clusters pixels in R5, we map the image I to a 2-dimensional manifold M ⊂ R5, whose area elements are a good measure of the content density in I. We propose an efficient method to compute restricted centroidal Voronoi tessellation (RCVT) — a uniform tessellation — on M, which induces the content-sensitive superpixels in I. Unlike other algorithms that characterize content-sensitivity by geodesic distances, manifold SLIC tackles the problem by measuring areas of Voronoi cells on M, which can be computed at a very low cost. As a result, it runs 10 times faster than the state-of-the-art content-sensitive superpixels algorithm. We evaluate manifold SLIC and seven representative methods on the BSDS500 benchmark and observe that our method outperforms the existing methods.


title: A review of EEG, ERP, and neuroimaging studies of creativity and insight.

abstract: Creativity is a cornerstone of what makes us human, yet the neural mechanisms underlying creative thinking are poorly understood. A recent surge of interest into the neural underpinnings of creative behavior has produced a banquet of data that is tantalizing but, considered as a whole, deeply self-contradictory. We review the emerging literature and take stock of several long-standing theories and widely held beliefs about creativity. A total of 72 experiments, reported in 63 articles, make up the core of the review. They broadly fall into 3 categories: divergent thinking, artistic creativity, and insight. Electroencephalographic studies of divergent thinking yield highly variegated results. Neuroimaging studies of this paradigm also indicate no reliable changes above and beyond diffuse prefrontal activation. These findings call into question the usefulness of the divergent thinking construct in the search for the neural basis of creativity. A similarly inconclusive picture emerges for studies of artistic performance, except that this paradigm also often yields activation of motor and temporoparietal regions. Neuroelectric and imaging studies of insight are more consistent, reflecting changes in anterior cingulate cortex and prefrontal areas. Taken together, creative thinking does not appear to critically depend on any single mental process or brain region, and it is not especially associated with right brains, defocused attention, low arousal, or alpha synchronization, as sometimes hypothesized. To make creativity tractable in the brain, it must be further subdivided into different types that can be meaningfully associated with specific neurocognitive processes.


title: Sequence Modeling via Segmentations

abstract: Segmental structure is a common pattern in many types of sequences such as phrases in human languages. In this paper, we present a probabilistic model for sequences via their segmentations. The probability of a segmented sequence is calculated as the product of the probabilities of all its segments, where each segment is modeled using existing tools such as recurrent neural networks. Since the segmentation of a sequence is usually unknown in advance, we sum over all valid segmentations to obtain the final probability for the sequence. An efficient dynamic programming algorithm is developed for forward and backward computations without resorting to any approximation. We demonstrate our approach on text segmentation and speech recognition tasks. In addition to quantitative results, we also show that our approach can discover meaningful segments in their respective application contexts.


title: Binary recursive partitioning: background, methods, and application to psychology.

abstract: Binary recursive partitioning (BRP) is a computationally intensive statistical method that can be used in situations where linear models are often used. Instead of imposing many assumptions to arrive at a tractable statistical model, BRP simply seeks to accurately predict a response variable based on values of predictor variables. The method outputs a decision tree depicting the predictor variables that were related to the response variable, along with the nature of the variables' relationships. No significance tests are involved, and the tree's 'goodness' is judged based on its predictive accuracy. In this paper, we describe BRP methods in a detailed manner and illustrate their use in psychological research. We also provide R code for carrying out the methods.


Towards a UX Manifesto


title: Sexuality and sexual health of Canadian adolescents: Yesterday, today and tomorrow

abstract: A profile of the sexual health and behaviours of contemporary Canadian adolescents is developed based on current research and compared to adolescents in the latter half of the 20 th century. While notable changes occurred in the sexual lives of youth between the late 1950s and the early 1990s, the patterns of behaviour established in the latter part of the 20 th century, have continued into recent years. There is strong evidence that today's youth are experiencing better sexual health and taking more measures to protect their sexual health than prior generations of youth did. However, problems remain. Canadian teens and young adults continue to be challenged by STIs; many GLBTQ youth continue to face homonegativity and discrimination in their schools and communities; youth living in poverty, in rural areas and aboriginal youth carry the greatest burdens of poor sexual health and are the most poorly served by sexuality education and sexual health care. Recommendations are made to strengthen both sexuality education and sexual health services to meet the needs of all Canadian youth.


title: Reduced epithelial suppressor of cytokine signalling 1 in severe eosinophilic asthma

abstract: Severe asthma represents a major unmet clinical need. Eosinophilic inflammation persists in the airways of many patients with uncontrolled asthma, despite high-dose inhaled corticosteroid therapy. Suppressors of cytokine signalling (SOCS) are a family of molecules involved in the regulation of cytokine signalling via inhibition of the Janus kinase-signal transducers and activators of transcription pathway. We examined SOCS expression in the airways of asthma patients and investigated whether this is associated with persistent eosinophilia.Healthy controls, mild/moderate asthmatics and severe asthmatics were studied. Whole genome expression profiling, quantitative PCR and immunohistochemical analysis were used to examine expression of SOCS1, SOCS2 and SOCS3 in bronchial biopsies. Bronchial epithelial cells were utilised to examine the role of SOCS1 in regulating interleukin (IL)-13 signalling in vitroSOCS1 gene expression was significantly lower in the airways of severe asthmatics compared with mild/moderate asthmatics, and was inversely associated with airway eosinophilia and other measures of T-helper type 2 (Th2) inflammation. Immunohistochemistry demonstrated SOCS1 was predominantly localised to the bronchial epithelium. SOCS1 overexpression inhibited IL-13-mediated chemokine ligand (CCL) 26 (eotaxin-3) mRNA expression in bronchial epithelial cells.Severe asthma patients with persistent airway eosinophilia and Th2 inflammation have reduced airway epithelial SOCS1 expression. SOCS1 inhibits epithelial IL-13 signalling, supporting its key role in regulating Th2-driven eosinophilia in severe asthma.


title: A Subjective Study of Viewer Navigation Behaviors When Watching 360-Degree Videos on Computers

abstract: Virtual reality (VR) applications have become popular recently and rapidly commercialized. The behaviors of users watching 360-degree omni-directional videos have not been fully investigated. In this paper, a dataset of view trajectories for users watching 360-degree videos over computer (desktop/laptop) environment is presented. The dataset includes view center trajectory data collected from viewers watching twelve 360-degree videos over the computer, using mouse to navigate and explore the environment. The selected videos cover a variety of contents, leading to different navigation patterns and behaviors. Based on the dataset, we demonstrate that the viewers share similar viewing patterns over certain 360-degree video category. We also compare the view motion patterns and statistics with prior datasets captured using head-mounted display (HMD). The dataset has been made available online, to facilitate the studies on 360-degree video view prediction, content saliency analysis and VR streaming, etc.


title: Incidence and clearance of genital human papillomavirus infection in men (HIM): a cohort study

abstract: Summary Background Human papillomaviruses (HPVs) cause genital warts and cancers in men. The natural history of HPV infection in men is largely unknown, and that information is needed to inform prevention strategies. The goal in this study was to estimate incidence and clearance of type-specific genital HPV infection in men, and to assess the associated factors. Methods Men (aged 18–70 years), residing in Brazil, Mexico, and the USA, who were HIV negative and reported no history of cancer were recruited from the general population, universities, and organised health-care systems. They were assessed every 6 months for a median follow-up of 27·5 months (18·0–31·2). Specimens from the coronal sulcus, glans penis, shaft, and scrotum were obtained for the assessment of the status of HPV genotypes. Findings In 1159 men, the incidence of a new genital HPV infection was 38·4 per 1000 person months (95% CI 34·3–43·0). Oncogenic HPV infection was significantly associated with having a high number of lifetime female sexual partners (hazard ratio 2·40, 1·38–4·18, for at least 50 partners vs not more than one partner), and number of male anal-sexual partners (2·57, 1·46–4·49, for at least three male partners vs no recent partners). Median duration of HPV infection was 7·52 months (6·80–8·61) for any HPV and 12·19 months (7·16–18·17) for HPV 16. Clearance of oncogenic HPV infection decreased in men with a high number of lifetime female partners (0·49, 0·31–0·76, for at least 50 female partners vs not more than one partner), and in men in Brazil (0·71, 0·56–0·91) and Mexico (0·73, 0·57–0·94) compared with the USA. Clearance of oncogenic HPV was more rapid with increasing age (1·02, 1·01–1·03). Interpretation The data from this study are useful for the development of realistic cost-effectiveness models for male HPV vaccination internationally. Funding National Cancer Institute.


Comparative Analysis Of Risk Management Strategies For Electricity Retailers


title: Writer identification using a probabilistic model of handwritten digits and approximate Bayesian computation

abstract: Writer identification is a classification problem where the classes correspond to a group of writers and the data points are their handwriting samples. This paper proposes an approach to offline text-sensitive writer identification on the basis of a probabilistic generative model of isolated handwritten digits. The model parameters are learned separately for each writer, and the writer of query samples is identified via Bayes decision rule. We deal with a scenario where just a few handwritten instances of a certain Latin digit exist as the training set for each writer. To handle the data sparseness problem, the parameters are learned in a fully Bayesian fashion instead of point estimators through a Markov chain Monte Carlo sampling algorithm. In addition, the hyperparameters are obtained from a background set which contains samples from various writers. Due to computational concerns, an explicit likelihood function is not defined in the model. Instead, the ideas of approximate Bayesian computation have been used in performing inference. Experimental results support the key ideas of the proposed approach.


title: Design of an Efficient X-Band Waveguide-Fed Microstrip Patch Array

abstract: The design and fabrication of a 10.5 GHz microstrip patch array fed by a waveguide is presented. The gain of this antenna is 29 dB and its efficiency is 65%. Commonly, the efficiency of conventional microstrip arrays at X-band is not more than 50%. This antenna demonstrates the ability to accomplish a very high efficiency at X-band in a simple structure. This is achieved by using a slotted waveguide to feed the planar array. To allow symmetrical feed of this antenna, the waveguide is a center-fed slotted waveguide, the two ends of which are shorted. The design procedure is expatiated, which contains the study and design of the waveguide-fed subarray structure and of the coax-to- waveguide transition structure. For designing this entire antenna, a three-dimensional electromagnetic field simulation software CST Microwave Studio is applied. Good agreement is achieved between measurement and simulation results.


title: Stock trend prediction using news sentiment analysis

abstract: Efficient Market Hypothesis is the popular theory about stock prediction. With its failure much research has been carried in the area of prediction of stocks. This project is about taking non quantifiable data such as financial news articles about a company and predicting its future stock trend with news sentiment classification. Assuming that news articles have impact on stock market, this is an attempt to study relationship between news and stock trend. To show this, we created three different classification models which depict polarity of news articles being positive or negative. Observations show that RF and SVM perform well in all types of testing. Na\"ive Bayes gives good result but not compared to the other two. Experiments are conducted to evaluate various aspects of the proposed model and encouraging results are obtained in all of the experiments. The accuracy of the prediction model is more than 80% and in comparison with news random labeling with 50% of accuracy; the model has increased the accuracy by 30%.


title: Gut Microbiota and Brain Function: An Evolving Field in Neuroscience

abstract: There is a growing appreciation of the importance of gut microbiota to health and disease. This has been driven by advances in sequencing technology and recent findings demonstrating the important role of microbiota in common health disorders such as obesity. Moreover, the potential role of gut microbiota in influencing brain function, behavior, and mental health has attracted the attention of neuroscientists and psychiatrists. At the 29(th) International College of Neuropsychopharmacology (CINP) World Congress held in Vancouver, Canada, in June 2014, a group of experts presented the symposium, "Gut microbiota and brain function: Relevance to psychiatric disorders" to review the latest findings in how gut microbiota may play a role in brain function, behavior, and disease. The symposium covered a broad range of topics, including gut microbiota and neuroendocrine function, the influence of gut microbiota on behavior, probiotics as regulators of brain and behavior, and imaging the gut-brain axis in humans. This report provides an overview of these presentations.


title: A SEMILINEAR BLACK AND SCHOLES PARTIAL DIFFERENTIAL EQUATION FOR VALUING AMERICAN OPTIONS

abstract: Using the dynamic programming principle in optimal stopping theory, we derive a semilinear Black and Scholes type partial differential equation set in a fixed domain for the value of an American (call/put) option. The nonlinearity in the semilinear Black and Scholes equation depends discontinuously on the Amer- ican option value, so that standard theory for partial differential equation does not apply. In fact, it is not clear what one should mean by a solution to the semilinear Black and Scholes equation. Guided by the dynamic programming principle, we suggest an appropriate definition of a viscosity solution. Our main results imply that there exists exactly one such viscosity solution of the semilinear Black and Scholes equation, namely the American option value. In other words, we provide herein a new formulation of the American option valuation problem. Our formulation con- stitutes a starting point for designing and analyzing "easy to implement" numerical algorithms for computing the value of an American option. The numerical aspects of the semilinear Black and Scholes equation are addressed in (7).


Anomaly-Based Intrusion Detection for SCADA Systems


title: Bidding Strategy for Microgrid in Day-Ahead Market Based on Hybrid Stochastic/Robust Optimization

abstract: This paper proposes an optimal bidding strategy in the day-ahead market of a microgrid consisting of intermittent distributed generation (DG), storage, dispatchable DG, and price responsive loads. The microgrid coordinates the energy consumption or production of its components, and trades electricity in both day-ahead and real-time markets to minimize its operating cost as a single entity. The bidding problem is challenging due to a variety of uncertainties, including power output of intermittent DG, load variation, and day-ahead and real-time market prices. A hybrid stochastic/robust optimization model is proposed to minimize the expected net cost, i.e., expected total cost of operation minus total benefit of demand. This formulation can be solved by mixed-integer linear programming. The uncertain output of intermittent DG and day-ahead market price are modeled via scenarios based on forecast results, while a robust optimization is proposed to limit the unbalanced power in real-time market taking account of the uncertainty of real-time market price. Numerical simulations on a microgrid consisting of a wind turbine, a photovoltaic panel, a fuel cell, a micro-turbine, a diesel generator, a battery, and a responsive load show the advantage of stochastic optimization, as well as robust optimization.


title: A differential evolution based approach to generate test data for data-flow coverage

abstract: Software testing is a vital and an effort intensive phase of the software development process. Testing efficacy relies upon optimal test data from the input domain of the problem in accordance with a test adequacy criterion. Search-based evolutionary algorithms have been widely applied for automated test data generation; Genetic Algorithm (GA) and its variants being the choice of researchers. Other highly-adaptive evolutionary algorithms such as Particle Swarm Optimization (PSO) and Differential Evolution (DE) have been shown to be more accurate and efficient in comparison to GA. This paper presents a DE-based approach to generate optimal test data in accordance to the data-flow coverage test adequacy criterion. Fitness function is designed based on the concepts of dominance relations and branch distance metric. Measures such as average number of generations and average percentage coverage are collected and analysed to evaluate the performance of the proposed approach and for comparison with Random search, GA and PSO techniques on a set of benchmark programs. The results obtained have shown that the proposed DE-based approach is competent and have better performance than random search, GA and PSO with respect to optimal test data generation in accordance to the data-flow coverage test adequacy criterion.


title: A tale of two cities

abstract: An improved understanding of human mobility patterns would yield insights into a variety of important societal issues such as the environmental impact of daily commutes. Location information from cellular wireless networks has great potential as a tool for studying these patterns. In this work, we use anonymous and aggregate statistics of the approximate locations of hundreds of thousands of cell phones in Los Angeles and New York City to demonstrate different mobility patterns in the two cities. For example, we show that Angelenos have median daily travel distances two times greater than New Yorkers, but that the most mobile 25% of New Yorkers travel six times farther than their Los Angeles counterparts.


title: 90% High Efficiency and 100-W/cm$^{\bm 3}$ High Power Density Integrated DC - DC Converter for Cellular Phones

abstract: This paper describes a small-size buck-type dc–dc converter for cellular phones. Output power MOSFETs and control circuitry are monolithically integrated. The newly developed pulse frequency modulation control integrated circuit, mounted on a planar inductor within the converter package, has a low quiescent current below 10 μA and a small chip size of 1.4 mm × 1.1 mm in a 0.35-μm CMOS process. The converter achieves a maximum efficiency of 90% and a power density above 100 W/cm $^3$ .


title: AllenNLP: A Deep Semantic Natural Language Processing Platform

abstract: This paper describes AllenNLP, a platform for research on deep learning methods in natural language understanding. AllenNLP is designed to support researchers who want to build novel language understanding models quickly and easily. It is built on top of PyTorch, allowing for dynamic computation graphs, and provides (1) a flexible data API that handles intelligent batching and padding, (2) high-level abstractions for common operations in working with text, and (3) a modular and extensible experiment framework that makes doing good science easy. It also includes reference implementations of high quality approaches for both core semantic problems (e.g. semantic role labeling (Palmer et al., 2005)) and language understanding applications (e.g. machine comprehension (Rajpurkar et al., 2016)). AllenNLP is an ongoing open-source effort maintained by engineers and researchers at the Allen Institute for Artificial Intelligence.


title: Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network

abstract: We propose a straightforward method that simultaneously reconstructs the 3D facial structure and provides dense alignment. To achieve this, we design a 2D representation called UV position map which records the 3D shape of a complete face in UV space, then train a simple Convolutional Neural Network to regress it from a single 2D image. We also integrate a weight mask into the loss function during training to improve the performance of the network. Our method does not rely on any prior face model, and can reconstruct full facial geometry along with semantic meaning. Meanwhile, our network is very light-weighted and spends only 9.8ms to process an image, which is extremely faster than previous works. Experiments on multiple challenging datasets show that our method surpasses other state-of-the-art methods on both reconstruction and alignment tasks by a large margin.


title: Virtual reality rehabilitation of spatial abilities after brain damage.

abstract: Current rehabilitation of navigation and spatial orientation ability after brain damage is generally focused on training within the rehabilitation hospital or the patient's home as part of common physio- and occupational therapy sessions. To further promote generalization of gained abilities and to quantify functional improvements, this project aims at developing a Virtual Reality (VR) application that can be used for training and assessment of spatial orientation and navigation skills in brain-damaged patients. The training is administered after the standard hospital rehabilitation training is completed. Additionally, the program will be used as an assessment tool to quantify the participants' wayfinding performance. The data will be compared with real-world navigation performance in tasks of similar complexity to evaluate real-world transfer of the VR training. Currently, the application is under development and basic functionality and data acquisition are being implemented.


Unleashing Use-Before-Initialization Vulnerabilities in the Linux Kernel Using Targeted Stack Spraying


title: Obstacle avoidance embedded system for a smart wheelchair with a multimodal navigation interface

abstract: Many users with motor disabilities, such as quadriplegics are unable to handle a power wheelchair securely, without causing harm to others, to themselves or their surroundings. Smart wheelchairs usually have been instrumented with a collection of sensors and computers using systems and algorithms that have been designed to provide safe navigation assistance through collision avoidance. This paper proposes a real-time obstacle avoidance embedded system adapted to work with a multimodal navigation interface. 26 Ultrasonic sensors (Sonars) were used to provide feedback of the distance between the wheelchair and the obstacles.


title: Algorithm for Implicitizing Rational Parametric Surfaces

abstract: Abstract Many current geometric modeling systems use the rational parametric form to represent surfaces. Although the parametric representation is useful for tracing, rendering and surface fitting, many operations like surface intersection desire one of the surfaces to be represented implicitly. Moreover, the implicit representation can be used for testing whether a point lies on the surface boundary and to represent an object as a semi-algebraic set. Previously resultants and Grobner bases have been used to implicitize parametric surfaces. In particular, different formulations of resultants have been used to implicitize tensor product surfaces and triangular patches and in many cases the resulting expression contains and extraneous factor. The separation of these extraneous factors can be a time consuming task involving multivariate factorization. Furthermore, these algorithms fail altogether if the given parametrization has base points. In this paper we present an algorithm to implicitize parametric surfaces. One of the advantages of the algorithm is that we do not need multivariate factorization. If a parametrization has no base points, the implicit representation can be represented as a determinant of a matrix, otherwise we use perturbation techniques. In the latter case we make use of GCD operation to compute the implicit representation. We also describe an efficient implementation of the algorithm based on interpolation techniques.


The role of leaders in influencing unethical behavior in the workplace


title: Effect of body mass and clothing on decomposition of pig carcasses

abstract: Carcass mass and carcass clothing are factors of potential high forensic importance. In casework, corpses differ in mass and kind or extent of clothing; hence, a question arises whether methods for post-mortem interval estimation should take these differences into account. Unfortunately, effects of carcass mass and clothing on specific processes in decomposition and related entomological phenomena are unclear. In this article, simultaneous effects of these factors are analysed. The experiment followed a complete factorial block design with four levels of carcass mass (small carcasses 5-15 kg, medium carcasses 15.1-30 kg, medium/large carcasses 35-50 kg, large carcasses 55-70 kg) and two levels of carcass clothing (clothed and unclothed). Pig carcasses (N = 24) were grouped into three blocks, which were separated in time. Generally, carcass mass revealed significant and frequently large effects in almost all analyses, whereas carcass clothing had only minor influence on some phenomena related to the advanced decay. Carcass mass differently affected particular gross processes in decomposition. Putrefaction was more efficient in larger carcasses, which manifested itself through earlier onset and longer duration of bloating. On the other hand, active decay was less efficient in these carcasses, with relatively low average rate, resulting in slower mass loss and later onset of advanced decay. The average rate of active decay showed a significant, logarithmic increase with an increase in carcass mass, but only in these carcasses on which active decay was driven solely by larval blowflies. If a blowfly-driven active decay was followed by active decay driven by larval Necrodes littoralis (Coleoptera: Silphidae), which was regularly found in medium/large and large carcasses, the average rate showed only a slight and insignificant increase with an increase in carcass mass. These results indicate that lower efficiency of active decay in larger carcasses is a consequence of a multi-guild and competition-related pattern of this process. Pattern of mass loss in large and medium/large carcasses was not sigmoidal, but rather exponential. The overall rate of decomposition was strongly, but not linearly, related to carcass mass. In a range of low mass decomposition rate increased with an increase in mass, then at about 30 kg, there was a distinct decrease in rate, and again at about 50 kg, the rate slightly increased. Until about 100 accumulated degree-days larger carcasses gained higher total body scores than smaller carcasses. Afterwards, the pattern was reversed; moreover, differences between classes of carcasses enlarged with the progress of decomposition. In conclusion, current results demonstrate that cadaver mass is a factor of key importance for decomposition, and as such, it should be taken into account by decomposition-related methods for post-mortem interval estimation.


title: Microbiota and neurologic diseases: potential effects of probiotics

abstract: BACKGROUND ::: The microbiota colonizing the gastrointestinal tract have been associated with both gastrointestinal and extra-gastrointestinal diseases. In recent years, considerable interest has been devoted to their role in the development of neurologic diseases, as many studies have described bidirectional communication between the central nervous system and the gut, the so-called "microbiota-gut-brain axis". Considering the ability of probiotics (i.e., live non-pathogenic microorganisms) to restore the normal microbial population and produce benefits for the host, their potential effects have been investigated in the context of neurologic diseases. The main aims of this review are to analyse the relationship between the gut microbiota and brain disorders and to evaluate the current evidence for the use of probiotics in the treatment and prevention of neurologic conditions. ::: ::: ::: DISCUSSION ::: Overall, trials involving animal models and adults have reported encouraging results, suggesting that the administration of probiotic strains may exert some prophylactic and therapeutic effects in a wide range of neurologic conditions. Studies involving children have mainly focused on autism spectrum disorder and have shown that probiotics seem to improve neuro behavioural symptoms. However, the available data are incomplete and far from conclusive. ::: ::: ::: CONCLUSIONS ::: The potential usefulness of probiotics in preventing or treating neurologic diseases is becoming a topic of great interest. However, deeper studies are needed to understand which formulation, dosage and timing might represent the optimal regimen for each specific neurologic disease and what populations can benefit. Moreover, future trials should also consider the tolerability and safety of probiotics in patients with neurologic diseases.


IoT and Robotics: a synergy


Cross-View People Tracking by Scene-Centered Spatio-Temporal Parsing.


title: Learning to Read Irregular Text with Attention Mechanisms

abstract: We present a robust end-to-end neural-based model to attentively recognize text in natural images. Particularly, we focus on accurately identifying irregular (perspectively distorted or curved) text, which has not been well addressed in the previous literature. Previous research on text reading often works with regular (horizontal and frontal) text and does not adequately generalize to processing text with perspective distortion or curving effects. Our work proposes to overcome this difficulty by introducing two learning components: (1) an auxiliary dense character detection task that helps to learn text specific visual patterns, (2) an alignment loss that provides guidance to the training of an attention model. We show with experiments that these two components are crucial for achieving fast convergence and high classification accuracy for irregular text recognition. Our model outperforms previous work on two irregular-text datasets: SVT-Perspective and CUTE80, and is also highly-competitive on several regular-text datasets containing primarily horizontal and frontal text.


Emerging strategies for the clinical application of rapamycin


title: IoT Security Challenges and Ways Forward

abstract: Today 2.9 billion people, or 40% of the world's population are online. By 2020, at least 40 billion more devices will become smart via embedded processors. The impact of such Internet of Things (IoT) on our society will be extraordinary. It will influence most consumer and business sectors, impact education, healthcare and safety. However, it certainly will also pose a challenge from a security point of view. Not only will the devices themselves become more complex, also the interaction between devices, the networks and the variance in topology will grow. Finally, with increasing amounts of data and assets at stake the incentive for attackers will increase. The costs of cyber attacks in such setting are estimated to reach about 2 trillion USD by 2020. Today, the IoT is just beginning to emerge. Unfortunately, when looking at its security, there is lots of room for improvement. Exploits reported at a steady pace clearly suggest that security is a major challenge when the world wants to successfully switch from an IoT hype to a real IoT deployment. Security, and security risk awareness, insufficiently present in today's consumer and developer mindset, are only a starting point. Once the requirement for strong security is widely accepted, there will be still the economical question of who is going to pay for security and its maintenance. Without enforcing certain standards by means of third party evaluation this problem is expected to be hard to get under control.


title: Energy Cloud: Real-Time Cloud-Native Energy Management System to Monitor and Analyze Energy Consumption in Multiple Industrial Sites

abstract: Industrial organizations use Energy Management Systems (EMS) to monitor, control, and optimize their energy consumption. Industrial EMS are complex and expensive systems due to the unique requirements of performance, reliability, and interoperability. Moreover, industry is facing challenges with current EMS implementations such as cross-site monitoring of energy consumption and CO2 emissions, integration between energy and production data, and meaningful energy efficiency benchmarking. Additionally, big data has emerged because of recent advances in field instrumentation that led to the generation of large quantities of machine data, with much more detail and higher sampling rates. This created a challenge for real-time analytics. In order to address all these needs and challenges, we propose a cloud-native industrial EMS solution with cloud computing capabilities. Through this innovative approach we expect to generate useful knowledge in a shorter time period, enabling organizations to react quicker to changes of events and detect hidden patterns that compromise efficiency.


title: Pattern Classification: A Unified View of Statistical and Neural Approaches

abstract: Statistical Decision Theory. Need for Approximations: Fundamental Approaches. Classification Based on Statistical Models Determined by First-and-Second Order Statistical Moments. Classification Based on Mean-Square Functional Approximations. Polynomial Regression. Multilayer Perceptron Regression. Radial Basis Functions. Measurements, Features, and Feature Section. Reject Criteria and Classifier Performance. Combining Classifiers. Conclusion. STATMOD Program: Description of ftp Package. References. Index.


title: Laser-Assisted Cartilage Reshaping (LACR) for Treating Ear Protrusions: A Clinical Study in 24 Patients

abstract: BACKGROUND ::: Protruding ears are the most common congenital ear deformity, with a frequency of 13.5%. Since 1845 and the first works of Dieffenbach, over 170 classical surgical methods have been proposed to correct it. We introduced laser-assisted cartilage reshaping (LACR) in 2004 as an alternative to invasive surgical otoplasty. ::: ::: ::: METHODS ::: Between January 2008 and June 2008, 24 patients underwent LACR for treatment of bilateral ear protrusion. Fourteen adults and ten children were treated (mean age = 16.0 years, range = 6-45 years). No anesthesia was used. Both sides of the entire helix and the concha were irradiated using a 1540-nm laser connected to a 4-mm spot handpiece with integrated cooling. Fluences varying from 70 up to 84 J/cm(2) were applied. Immediately after the irradiation, silicone elastomer was inserted inside the helix to give it the desired shape. After 3 minutes a solid mold was obtained. Patients were asked to keep this mold in place at all times with a bandage wrapped around the head for the first 3 weeks and then for an additional 3 weeks only during the night. A non-steroidal anti-inflammatory drug (NSAID) was prescribed to the patients for 3 days. At 1, 30, 60, and 90 days after the procedure, ears were checked and photographs were taken. Clinical follow-up at 1 year was obtained via direct patient contact or over the telephone. ::: ::: ::: RESULTS ::: The treatment was well tolerated. No hematomas or skin necrosis occurred. Contact dermatitis was observed in four children and two adults as a result of inappropriate mold design. These patients stopped wearing the mold and the shape of their ear did not improve. For the remaining 18 patients (6 children and 9 adults), the expected ear reshaping was obtained (fluence was 84 J/cm(2)). For three other adults, incomplete reshaping of the ears was observed and can be correlated to a lower fluence (70 J/cm(2)). ::: ::: ::: CONCLUSION ::: LACR, performed without any anesthesia, is a safe and less morbid approach to surgical otoplasty.


Analytics in the Pursuit of Knowledge: Adapting the Knowledge Pyramid


title: Efficient and Robust Specular Highlight Removal

abstract: A robust and effective specular highlight removal method is proposed in this paper. It is based on a key observation—the maximum fraction of the diffuse colour component in diffuse local patches in colour images changes smoothly. The specular pixels can thus be treated as noise in this case. This property allows the specular highlights to be removed in an image denoising fashion: an edge-preserving low-pass filter (e.g., the bilateral filter) can be used to smooth the maximum fraction of the colour components of the original image to remove the noise contributed by the specular pixels. Recent developments in fast bilateral filtering techniques enable the proposed method to run over $200\times$ faster than state-of-the-art techniques on a standard CPU and differentiates it from previous work.


title: Capability challenges in transforming government through open and big data: tales of two cities

abstract: Hyper-connected and digitized governments are increasingly advancing a vision of data-driven government as producers and consumers of big data in the big data ecosystem. Despite the growing interests in the potential power of big data, we found paucity of empirical research on big data use in government. This paper explores organizational capability challenges in transforming government through big data use. Using systematic literature review approach we developed initial framework for examining impacts of socio-political, strategic change, analytical, and technical capability challenges in enhancing public policy and service through big data. We then applied the framework to conduct case study research on two large-size city governments’ big data use. The findings indicate the framework’s usefulness, shedding new insights into the unique government context. Consequently, the framework was revised by adding big data public policy, political leadership structure, and organizational culture to further explain impacts of organizational capability challenges in transforming government.


title: A Study on Immersion and Presence of a Portable Hand Haptic System for Immersive Virtual Reality

abstract: This paper proposes a portable hand haptic system using Leap Motion as a haptic interface that can be used in various virtual reality (VR) applications. The proposed hand haptic system was designed as an Arduino-based sensor architecture to enable a variety of tactile senses at low cost, and is also equipped with a portable wristband. As a haptic system designed for tactile feedback, the proposed system first identifies the left and right hands and then sends tactile senses (vibration and heat) to each fingertip (thumb and index finger). It is incorporated into a wearable band-type system, making its use easy and convenient. Next, hand motion is accurately captured using the sensor of the hand tracking system and is used for virtual object control, thus achieving interaction that enhances immersion. A VR application was designed with the purpose of testing the immersion and presence aspects of the proposed system. Lastly, technical and statistical tests were carried out to assess whether the proposed haptic system can provide a new immersive presence to users. According to the results of the presence questionnaire and the simulator sickness questionnaire, we confirmed that the proposed hand haptic system, in comparison to the existing interaction that uses only the hand tracking system, provided greater presence and a more immersive environment in the virtual reality.


title: Architecting complex embedded systems: An industrial case study

abstract: This paper studies the current state of architecting practices in three different industrial segments which are characterized by being software-intensive. The context of the six different companies as well as the architecting practices are compared and analyzed. The methods used to solve the tasks within the architecting process are mapped to the context where it has been used in industry. An analysis of the case study indicates how different methods are more suitable in different environments. Many of the successful practices found in the study can be explained by external factors related to the context of the different companies. Others relate to the internal structure of the organization, including its maturity which is measured by assessment through an adaption of the Capability Maturity Model Integration (CMMI).


title: Electrical Safety, Electrical Hazards, and the 2018 NFPA 70E: Time to Update Annex K?

abstract: Sharing the same goals and overlapping membership, IEEE and NFPA are committed to improving electrical safety in the workplace. Knowledge about electrical hazards, especially arc flash hazards, has expanded greatly since the first, 2002 edition of the IEEE Standard 1584, IEEE Guide for Performing Arc-Flash Hazard Calculations. In the NFPA 70E Standard, 2004 edition, the 1584 arc-flash hazard calculations were included in the recommended methods for quantifying the potential incident energy exposure. In 2004 IEEE and NFPA initiated the IEEE/NFPA Arc Flash Collaboration Project to conduct research and testing which would lead to a greater scientific understanding of the arc flash hazard. Presently, IEEE Standard 1584 is in the process of major revision. Subsequent editions of NFPA 70E - the 2009, 2012 and the draft 2015 - have greatly expanded the coverage of the arc flash hazard. However, NFPA 70E Annex K, which addresses the general categories of electrical hazards, has changed little since the 2004 edition, except for the addition of a short paragraph on arc blast hazards in 2009. This paper suggests content for change in the 2018 NFPA 70E Annex K.


title: Uplift Modeling with ROC: An SRL Case Study

abstract: Uplift modeling is a classification method that determines the incremental impact of an action on a given population. Uplift modeling aims at maximizing the area under the uplift curve, which is the difference between the subject and control sets’ area under the lift curve. Lift and uplift curves are seldom used outside of the marketing domain, whereas the related ROC curve is frequently used in multiple areas. Achieving a good uplift using an ROC-based model instead of lift may be more intuitive in several areas, and may help uplift modeling reach a wider audience. We alter SAYL, an uplift-modeling statistical relational learner, to use ROC instead of lift. We test our approach on a screening mammography dataset. SAYL-ROC outperforms SAYL on our data, though not significantly, suggesting that ROC can be used for uplift modeling. On the other hand, SAYL-ROC returns larger models, reducing interpretability.


title: Automatic Patient Pose Estimation Using Pressure Sensing Mattresses

abstract: We present a system to automatically estimate the body pose of a reclined patient, based on measurement data from a pressure sensing mattress. It can be used to replace or reduce manual input in clinical imaging procedures and thus improve the workflow. The proposed method consists of two stages. First, the body posture is classified into prone, supine, and left and light lateral orientation by a k-nearestneighbor classifier. In the second algorithmic stage, a modified optimization scheme based on Powell’s direction set method fits a model of the human body to the observed pressure distribution. Thus, the position of important body landmarks is estimated. For our database of 143 measurements from 16 subjects, a mean classification rate of 96.0 % was achieved for the posture, and an average localization error of 6.95 cm for the body parts.


Analogical Learning and Reasoning


Inverse Visual Question Answering


title: Poisonings in Poland reported to the Polish National Health Fund in the years 2009–2011

abstract: BACKGROUND ::: Poisonings constitute a significant medical, social and economic problem worldwide. In Poland there is no nationwide registry of poisonings, which results in a lack of accurate epidemiological data. Few publications dealing with the problem are based on data obtained from toxicology units and therefore do not include information about cases treated at emergency departments and other non-toxicology units. ::: ::: ::: METHODS ::: We analyzed all admissions due to poisonings reported to the Polish National Health Fund by all hospital units in Poland in the 2009-2011 period. Diagnoses were encoded according to the ICD-10 classification. ::: ::: ::: RESULTS ::: A total of 254,425 admissions were reported, 85,398 in 2009, 85,230 in 2010 and 83,797 in 2011. The male to female ratios were 1.88, 1.75 and 1.80 respectively. The most frequent causes of admissions were poisonings with ethanol (n = 121,874; 47.9%), carbon monoxide (n = 17,179; 6.8%) and benzodiazepines (n = 10,340; 4.1%). Alcohols were the reason for 104,680 admissions in men (63.2%) and 22,612 admissions in women (25.5%; p < 0.01). Poisonings with pharmaceuticals and other drugs were reported in 34,616 men (20.9%) and 45,238 women (51%; p < 0.01). There were 1680 cases of fatal poisonings in the analyzed period. The hospital mortality due to poisonings increased from 1.1% in 2009 to 1.5% in 2011 (p < 0.01). The mortality in general Intensive Care Units increased from 14.4% in 2009 to 22.3% in 2011 (p < 0.01). The etiology of fatal poisonings was highly dependent on the type of hospital unit. ::: ::: ::: CONCLUSIONS ::: The overall number of admissions due to poisonings decreased slightly during the study period, but they remained a significant cause of morbidity and mortality. Alcohols were the main cause of admissions in the analyzed period. Alcohol intoxications were more frequent in men while poisonings with pharmaceuticals were more frequent in women. Carbon monoxide exposures were a significant cause of morbidity and mortality in the studied period in Poland. A national poison information and toxicovigilance system should be created in Poland, ideally allowing for near real-time monitoring of cases of poisonings.