title: Digital video segmentation

abstract: The data driven, bottom up approach to video segmentation has ignored the inherent structure that exists in video. This work uses the model driven approach to digital video segmentation. Mathematical models of video based on video production techniques are formulated. These models are used to classify the edit effects used in video and film production. The classes and models are used to systematically design the feature detectors for detecting edit effects in digital video. Digital video segmentation is formulated as a feature based classification problem. Experimental results from segmenting cable television programming with cuts, fades, dissolves and page translate edits are presented.


title: Predictive Control of a Closed Grinding Circuit System in Cement Industry

abstract: This paper presents the development of a nonlinear model predictive controller (NMPC) applied to a closed grinding circuit system in the cement industry. A Markov chain model is used to characterize the cement grinding circuit by modeling the ball mill and the centrifugal dust separator. The probability matrices of the Markovian model are obtained through a combination of comminution principles and experimental data obtained from the particle size distribution of cement samples at specific stages of the system. The NMPC is designed as a supervisory controller to manage distributed controllers installed in the process. Both the model and the controller are validated online through the implementation of the proposed approach in the supervisory control and data acquisition (SCADA) system of an industrial plant. The results show a significant improvement in the performance of the grinding circuit in comparison to the operation of the system without the proposed controller.


title: The role of emotion in computer-mediated communication: A review

abstract: It has been argued that the communication of emotions is more difficult in computer-mediated communication (CMC) than in face-to-face (F2F) communication. The aim of this paper is to review the empirical evidence in order to gain insight in whether emotions are communicated differently in these different modes of communication. We review two types of studies: (1) studies that explicitly examine discrete emotions and emotion expressions, and (2) studies that examine emotions more implicitly, namely as self-disclosure or emotional styles. Our conclusion is that there is no indication that CMC is a less emotional or less personally involving medium than F2F. On the contrary, emotional communication online and offline is surprisingly similar, and if differences are found they show more frequent and explicit emotion communication in CMC than in F2F.


title: Distributionally Robust Logistic Regression

abstract: This paper proposes a distributionally robust approach to logistic regression. We use the Wasserstein distance to construct a ball in the space of probability distributions centered at the uniform distribution on the training samples. If the radius of this ball is chosen judiciously, we can guarantee that it contains the unknown data-generating distribution with high confidence. We then formulate a distributionally robust logistic regression model that minimizes a worst-case expected logloss function, where the worst case is taken over all distributions in the Wasserstein ball. We prove that this optimization problem admits a tractable reformulation and encapsulates the classical as well as the popular regularized logistic regression problems as special cases. We further propose a distributionally robust approach based on Wasserstein balls to compute upper and lower confidence bounds on the misclassification probability of the resulting classifier. These bounds are given by the optimal values of two highly tractable linear programs. We validate our theoretical out-of-sample guarantees through simulated and empirical experiments.


title: 地理網路爬蟲：具擴充及擴展性之地理網路資源爬行架構;GeoWeb Crawler: An Extensible and Scalable Web Crawling Framework for Discovering Geospatial Web Resources

abstract: With the advance of the World-Wide Web (WWW) technology, people can easily share content on the Web, including geospatial data and web services. Thus, the “big geospatial data management” issues start attracting attention. Among the big geospatial data issues, this research focuses on discovering distributed geospatial resources. As resources are scattered on the WWW, users cannot find resources of their interests efficiently. While the WWW has Web search engines addressing web resource discovery issues, we envision that the geospatial Web (i.e., GeoWeb) also requires GeoWeb search engines. To realize a GeoWeb search engine, one of the first steps is to proactively discover GeoWeb resources on the WWW. Hence, in this study, we propose the GeoWeb Crawler, an extensible Web crawling framework that can find various types of GeoWeb resources, such as Open Geospatial Consortium (OGC) web services, Keyhole Markup Language (KML) and Environmental Systems Research Institute, Inc (ESRI) Shapefiles. In addition, we apply the distributed computing concept to promote the performance of the GeoWeb Crawler. The result shows that for 10 targeted resources types, the GeoWeb Crawler discovered 7351 geospatial services and 194,003 datasets. As a result, the proposed GeoWeb Crawler framework is proven to be extensible and scalable to provide a comprehensive index of GeoWeb.


title: North-Holland ON GENERATING ALL MAXIMAL INDEPENDENT SETS

abstract: Abstract We present an algorithm that generates all maximal independent sets of a graph in lexicographic order, with only polynomial delay between the output of two successive independent sets. We also show that there is no polynomial-delay algorithm for generating all maximal independent sets in reverse lexicographic order, unless P=NP.


title: Sustainable Making? Balancing Optimism and Criticism in HCI Discourse

abstract: We examine the recent move from a rhetoric of “users” toward one of “makers,” “crafters,” and “hackers” within HCI discourse. Through our analysis, we make several contributions. First, we provide a general overview of the structure and common framings within research on makers. We discuss how these statements reconfigure themes of empowerment and progress that have been central to HCI rhetoric since the field's inception. In the latter part of the article, we discuss the consequences of these shifts for contemporary research problems. In particular, we explore the problem of designed obsolescence, a core issue for Sustainable Interaction Design (SID) research. We show how the framing of the maker, as an empowered subject, presents certain opportunities and limitations for this research discourse. Finally, we offer alternative framings of empowerment that can expand maker discourse and its use in contemporary research problems such as SID.


title: A Human-Airbag System for Hip Protection Using MEMS Motion Sensors: Experimental Feasibility Results

abstract: This paper describes the development of a human airbag system which is designed to reduce the impact force from slippage falling-down. A micro inertial measurement unit (muIMU) which is based on MEMS accelerometers and gyro sensors is developed as the motion sensing part of the system. A weightless recognition algorithm is used for real-time falling determination. With the algorithm, the microcontroller integrated with muIMU can discriminate falling-down motion from normal human motions and trigger an airbag system when a fall occurs. Our airbag system is designed to be fast response with moderate input pressure, i.e., the experimental response time is less than 0.3 second under 0.4 MPa (gage pressure). Also, we present our progress on development of the inflator and the airbags


title: Micromechanical characterisation of weld metal susceptibility to hydrogen-assisted cold cracking using instrumented indentation

abstract: Hydrogen-assisted cold cracking is generally accepted to be the consequence of a critical concentration of hydrogen trapped within a susceptible microstructure and subjected to a threshold level of stress. Traditionally, hardness has been used as a proxy for establishing the critical limits above which the risk of a hydrogen crack propagating is considered significant. However, developments in the steel-making process, in particular thermomechanically controlled processing, has brought into question the suitability of empirical hardness limits developed using older generation steels. In this paper, a safe welding boundary was established for single-pass root runs for API 5 L X70 steel welded with E6010 electrodes. Across this boundary, it was shown that hydrogen cracks were present in welds with hardness’s well below the traditionally accepted threshold of 350 HV. This paper explores the use of nanoindentation as means of quantifying the susceptibility of welds deposited on high-strength low-alloy steels, using shielded metal arc welding, to hydrogen-assisted cold cracking. It is suggested that the use of the hardness/elastic modulus (H/E) ratio, which is directly related to the yield strength of a material, is a more suitable parameter to predict weld metal hydrogen-assisted cold cracking (HACC) susceptibility than is the hardness alone.


title: Twitter ain't without frontiers: economic, social, and cultural boundaries in international communication

abstract: With the advent of Twitter and other lightweight social-networking services, one might think that it is easier than ever to maintain geographically dispersed, weaker social ties. By contrast, in this study we show that the international Twitter communication landscape is not only still largely predetermined by physical distance, but that it also depends on countries' social, economic, and cultural attributes. We describe a study of an international Twitter mention network of 13 million users across over 100 countries. We show that the Gravity Model, which hypothesizes that the flow between two areas is proportional to their masses (which we approximate using internet penetration) and inversely proportional to the distance between them, is correlated (r=0.68) with the international communication flow. Using this model, along with other social, economic, and cultural variables, we predict the communication volume at Adjusted R2 of $0.80$, with trade, language and racial intolerance especially impacting communication. We discuss the implications of these barriers to communication in the contexts of collaborative work, software design, and recommendation systems.


BRAIN WATER MEASURED IN VOLUNTEERS AFTER ALCOHOL AND VASOPRESSIN


title: Non-invasive blood glucose measurement scheme based on near-infrared spectroscopy

abstract: Diabetes is a kind of common chronic diseases, seriously harmful to human health. Determination of blood glucose levels is the key link in diabetes care. The traditional measurement method firstly collects blood drops, and then uses the blood analyzer for electrochemical analysis. The traditional method is inconvenient, algesiogenic and easily cause infection. With the development of chemometric methods, infrared spectroscopy has been widely used in the field of blood glucose measurement and medicine care. The non-invasive blood glucose measurement scheme based on near-infrared spectroscopy is a way which is more efficiently, without damage and chemical reagents. In this paper, we proposed a non-invasive blood glucose detection and analysis the scheme based on near-infrared spectroscopy. In order to avoid the influence of the environment during the measuring process and considering the individual differences, we use the wave acquisition system to collect human pulse wave, and reduce the noise of the pulse wave signal first. By comparing the actual measured data with the data obtained in the traditional way, we can establish models to predict the blood glucose levels and achieve non-invasive blood glucose measurement using spectrum analysis technology. In the end, we improve the models by analyzing the relationship between individual factors and the pulse wave signal. The experimental results show that the method can effectively determine the blood glucose levels, and it is more convenient, safer and healthier than the traditional method, which can effectively reduce the time cost and the money cost.


title: A classification scheme for content analyses of YouTube video comments

abstract: Purpose – The video-sharing website YouTube encourages interaction between its users via the provision of a user comments facility. This was originally envisaged as a way for viewers to provide information about and reactions to videos, but is employed for other communicative purposes including sharing ideas, paying tributes, social networking, and question answering. This study seeks to examine and categorise the types of comments created by YouTube users to highlight the various ways in which this interactive feature has been employed as a means of communication and self-expression. Design/methodology/approach – By conducting a content analysis of 66,637 user comments on YouTube videos the authors created a classification schema which may be used to categorise the types of comments users leave. Findings – The schema reveals ten broad categories, and 58 subcategories which reflect the wide-ranging use of the YouTube comments facility. Research limitations/implications – As YouTube continues to evolve, ne...


title: Research on quality of life in female patients with congenital adrenal hyperplasia and issues in developing nations.

abstract: Congenital adrenal hyperplasia (CAH) is the commonest cause of ambiguous genitalia for female newborns and is one of the conditions under the umbrella term of "Disorders of Sex Development" (DSD). Management of these patients require multidisciplinary collaboration and is challenging because there are many aspects of care, such as the most appropriate timing and extent of feminizing surgery required and attention to psychosexual, psychological, and reproductive issues, which still require attention and reconsideration, even in developed nations. In developing nations, however, additional challenges prevail: poverty, lack of education, lack of easily accessible and affordable medical care, traditional beliefs on intersex, religious, and cultural issues, as well as poor community support. There is a paucity of long-term outcome studies on DSD and CAH to inform on best management to achieve optimal outcome. In a survey conducted on 16 patients with CAH and their parents in a Malaysian tertiary center, 31.3% of patients stated poor knowledge of their condition, and 37.5% did not realize that their medications were required for life. This review on the research done on quality of life (QOL) of female patients with CAH aims: to discuss factors affecting QOL of female patients with CAH, especially in the developing population; to summarize the extant literature on the quality of life outcomes of female patients with CAH; and to offer recommendations to improve QOL outcomes in clinical practice and research.


title: Automated Classification of Centroblast Cells using Morphological and Texture Features

abstract: The early detection of lymphoma in patients can greatly increase the probability of recovery. Centroblasts of lymphoma are conventionally identified under microscopes by expert doctor’s manually. cytogenetics and immunopheno typing are currently used. A novel method has been proposed to recognize centroblast (CB) cells from non centroblast (nonCB) cells for computer assisted evaluation of lymphocyte centroblasts. The novel aspects is to identify the centroblast cells with prior information. Geometric and texture features are extracted from the input image for identification and classification. Important geometric features like area, boundingbox, convex area, perimeter, major-axis, minor-axis, solidity are extracted. Texture features are extracted using Log-Gabor filter. Combined features of texture and morphology improve the performance of the system significantly. Supervised support vector machine (SVM) classifier is used to classify centroblast (CB) and non-CB cell.


title: RASL: Robust Alignment by Sparse and Low-Rank Decomposition for Linearly Correlated Images

abstract: This paper studies the problem of simultaneously aligning a batch of linearly correlated images despite gross corruption (such as occlusion). Our method seeks an optimal set of image domain transformations such that the matrix of transformed images can be decomposed as the sum of a sparse matrix of errors and a low-rank matrix of recovered aligned images. We reduce this extremely challenging optimization problem to a sequence of convex programs that minimize the sum of l1-norm and nuclear norm of the two component matrices, which can be efficiently solved by scalable convex optimization techniques with guaranteed fast convergence. We verify the efficacy of the proposed robust alignment algorithm with extensive experiments with both controlled and uncontrolled real data, demonstrating higher accuracy and efficiency than existing methods over a wide range of realistic misalignments and corruptions.


title: Toward Tense as a Clinical Marker of Specific Language Impairment

abstract: A critical clinical issue is the identification of a clinical marker, a linguistic form or principle that can be shown to be characteristic of children with Specific Language Impairment (SLI). In t...


title: Articulated Particle Filter for hand tracking

abstract: This paper proposes a new version of Particle Filter, called Articulated Particle Filter — ArPF —, which has been specifically designed for an efficient sampling of hierarchical spaces, generated by articulated objects. Our approach decomposes the articulated motion into layers for efficiency purposes, making use of a careful modeling of the diffusion noise along with its propagation through the articulations. This produces an increase of accuracy and prevent for divergences. The algorithm is tested on hand tracking due to its complex hierarchical articulated nature. With this purpose, a new dataset generation tool for quantitative evaluation is also presented in this paper.


title: Genetic and environmental influences on antisocial behavior: a meta-analysis of twin and adoption studies.

abstract: A meta-analysis of 51 twin and adoption studies was conducted to estimate the magnitude of genetic and environmental influences on antisocial behavior. The best fitting model included moderate proportions of variance due to additive genetic influences (.32), nonadditive genetic influences (.09), shared environmental influences (.16), and nonshared environmental influences (.43). The magnitude of familial influences (i.e., both genetic and shared environmental influences) was lower in parent-offspring adoption studies than in both twin studies and sibling adoption studies. Operationalization, assessment method, zygosity determination method, and age were significant moderators of the magnitude of genetic and environmental influences on antisocial behavior, but there were no significant differences in the magnitude of genetic and environmental influences for males and females.


title: Words or Characters? Fine-grained Gating for Reading Comprehension

abstract: Previous work combines word-level and character-level representations using concatenation or scalar weighting, which is suboptimal for high-level tasks like reading comprehension. We present a fine-grained gating mechanism to dynamically combine word-level and character-level representations based on properties of the words. We also extend the idea of fine-grained gating to modeling the interaction between questions and paragraphs for reading comprehension. Experiments show that our approach can improve the performance on reading comprehension tasks, achieving new state-of-the-art results on the Children's Book Test dataset. To demonstrate the generality of our gating mechanism, we also show improved results on a social media tag prediction task.


title: On the energy (in)efficiency of Hadoop clusters

abstract: Distributed processing frameworks, such as Yahoo!'s Hadoop and Google's MapReduce, have been successful at harnessing expansive datacenter resources for large-scale data analysis. However, their effect on datacenter energy efficiency has not been scrutinized. Moreover, the filesystem component of these frameworks effectively precludes scale-down of clusters deploying these frameworks (i.e. operating at reduced capacity). This paper presents our early work on modifying Hadoop to allow scale-down of operational clusters. We find that running Hadoop clusters in fractional configurations can save between 9% and 50% of energy consumption, and that there is a tradeoff between performance energy consumption. We also outline further research into the energy-efficiency of these frameworks.


title: Letters: Handwritten digit recognition using biologically inspired features

abstract: Image recognition problems are usually difficult to solve using raw pixel data. To improve the recognition it is often needed some form of feature extraction to represent the data in a feature space. We use the output of a biologically inspired model for visual recognition as a feature space. The output of the model is a binary code which is used to train a linear classifier for recognizing handwritten digits using the MNIST and USPS datasets. We evaluate the robustness of the approach to a variable number of training samples and compare its performance on these popular datasets to other published results. We achieve competitive error rates on both datasets while greatly improving relatively to related networks using a linear classifier.


title: Defining success factors to describe coordinated care in cancer

abstract: Providing coordinated care remains a challenge for cancer services globally. There is a lack of consensus in the literature about what constitutes successful coordinated care. This study aimed to define and prioritize a set of consensus-driven success factors that can lead to coordinated care. A mixed-methods approach was used that included literature review, a broad call for submissions from relevant stakeholders, and a priority-setting process based on a modified nominal group technique. Thirty articles that related to success factors in coordinated care were identified in the literature. Twenty submissions were received from a broad range of stakeholders. From these sources, a set of 20 success factors was derived. Seventy stakeholders attended a series of workshops across New South Wales, Australia, to review and prioritize these 20 success factors against significance and measurability. Clear consensus was reached on prioritizing two success factors linked to improving coordinated care from first presentation to diagnosis and ensuring that patients are routinely screened for physical and supportive care needs. Other highly ranked factors included the need for a comprehensive care plan and the identification of patients at higher risk for disjointed care. This study defines and prioritizes a set of success factors related to coordinated care in cancer. These success factors will be used to guide the development of interventions that target improving coordinated care as well as supporting the development of new funding models based on performance indicators derived from these factors.


Deep learning based automatic liver tumor segmentation in CT with shape-based post-processing


A Literature Review on Iris Segmentation Techniques for Iris Recognition Systems


title: Power quality improvement based on shunt active power filter connected to a photovoltaic array

abstract: This paper presents an analysis and simulation of a PV interactive Shunt Active power Filter topology that achieves simultaneously harmonic current damping and reactive power compensation. In the day-time with intensive sunlight, the PV interactive Shunt Active Filter system brings all its functions into operation. At night and during no sunlight periods, the power required by the loads is received from the distribution system while the inverter system only provides reactive power compensation and filter harmonic currents. For the Shunt Active Filter reference current computation. We used the synchronous d-q-o reference frame algorithm (SRF). Hysteresis Controllers is used to generate switching signals of the voltage source inverter. The Simulation results (using MATLAB/SIMULINK) are presented and discussed. They show the effectiveness of the photovoltaic compensation system, the sinusoidal form of the current and the effectiveness of the reactive power compensation. The proposed solution has achieved a low THD (Total Harmonic Distortion), demonstrating the effectiveness of the presented method.


title: Prolonged continuous exposure to high fine particulate matter associated with cardiovascular and respiratory disease mortality in Beijing, China

abstract: Abstract Although many studies examined the effects of fine particulate matter (PM 2.5 ) on the deaths of cardiovascular disease (CVD) and respiratory disease (RD), few research has paid attention to the effects of prolonged continuous exposure to high PM 2.5 pollution. This study estimated the excess risks (ER) of CVD and RD mortalities associated with prolonged continuous exposure to high PM 2.5 pollution for the whole population and specific subsociodemographic groups in Beijing, which is the capital city of China with over 20 million residents and having severe PM 2.5 pollution problems. Our results suggested that when high PM 2.5 pollution occurred continuously, at various thresholds and durations, the adverse effects on CVD and RD mortalities varied significantly. The CVD mortality risks in association with prolonged continuous high PM 2.5 pollution exposure were more serious for single individuals (including unmarried, divorced, and widowed), illiterate and outdoor workers than for other specific subsociodemographic groups. When the daily PM 2.5 concentration higher than 105 μg/m 3 consecutively occurs, at the ninth day, the ERs of CVD death for single individuals, illiterate and outdoor workers groups reached to 45% (95% CI: 22, 71), 51% (95% CI: 28, 79) and 53% (95% CI: 29, 82) respectively. On the other hand, prolonged continuous high PM 2.5 pollution level appeared to contribute a higher proportion of RD deaths among illiterate and outdoor workers, but less significant for the other specific subsociodemographic groups. When the duration with daily PM 2.5 pollution higher than 115 μg/m 3 reached to six days, the ERs for outdoor workers and illiterate attributed to prolonged continuous PM 2.5 pollution exposure increased 36% (95% CI: 5, 76) and 49% (95% CI: 16, 91) respectively.


Analyzing the contribution of cardiac pulsation to the variability of quantities derived from the diffusion tensor. ISMRM 11th annual mtg


title: Canonical Scattering Feature Models for 3D and Bistatic SAR

abstract: This paper develops three-dimensional (3D), bistatic parametric models that describe canonical radar scattering responses of several geometric objects. These models find use in inverse scattering-based processing of high-frequency radar returns. Canonical feature models are useful for extracting geometry from synthetic-aperture radar (SAR) scattering measurements and as feature primitives for automatic target recognition (ATR) and scene visualization. Previous work has considered monostatic feature models for two-dimensional (2D) radar processing; we extend this work to consider bistatic and 3D radar apertures. In the work presented here, we generalize geometric theory of diffraction (GTD) solutions for several scattering mechanisms in a plane. Products of these planar mechanisms in azimuth and elevation are used to produce 3D bistatic scattering models for six canonical shapes: a rectangular plate, dihedral, trihedral, cylinder, top-hat, and sphere. The derived models are characterized by a small number of parameters, and are shown to agree with results obtained from high-frequency, asymptotic scattering simulations.


Vehicle detection and tracking for the urban challenge


MANPY : AN OPEN SOURCE LIBRARY OF MANUFACTURING SIMULATION OBJECTS WRITTEN IN PYTHON


title: Face anti-spoofing using Haralick features

abstract: Face spoofing can be performed in a variety of ways such as replay attack, print attack, and mask attack to deceive an automated recognition algorithm. To mitigate the effect of spoofing attempts, face anti-spoofing approaches aim to distinguish between genuine samples and spoofed samples. The focus of this paper is to detect spoofing attempts via Haralick texture features. The proposed algorithm extracts block-wise Haralick texture features from redundant discrete wavelet transformed frames obtained from a video. Dimensionality of the feature vector is reduced using principal component analysis and two class classification is performed using support vector machine. Results on the 3DMAD database show that the proposed algorithm achieves state-of-the-art results for both frame-based and video-based approaches, including 100% accuracy on video-based spoofing detection. Further, the results are reported on existing benchmark databases on which the proposed feature extraction framework archives state-of-the-art performance.


title: Characterization of the Impact of Hardware Islands on OLTP

abstract: Modern hardware is abundantly parallel and increasingly heterogeneous. The numerous processing cores have non-uniform access latencies to the main memory and processor caches, which causes variability in the communication costs. Unfortunately, database systems mostly assume that all processing cores are the same and that microarchitecture differences are not significant enough to appear in critical database execution paths. As we demonstrate in this paper, however, non-uniform core topology does appear in the critical path and conventional database architectures achieve suboptimal and even worse, unpredictable performance. We perform a detailed performance analysis of OLTP deployments in servers with multiple cores per CPU (multicore) and multiple CPUs per server (multisocket). We compare different database deployment strategies where we vary the number and size of independent database instances running on a single server, from a single shared-everything instance to fine-grained shared-nothing configurations. We quantify the impact of non-uniform hardware on various deployments by (a) examining how efficiently each deployment uses the available hardware resources and (b) measuring the impact of distributed transactions and skewed requests on different workloads. We show that no strategy is optimal for all cases and that the best choice depends on the combination of hardware topology and workload characteristics. Finally, we argue that transaction processing systems must be aware of the hardware topology in order to achieve predictably high performance.


title: Cytochrome P4501A induction in oil-exposed pink salmon Oncorhynchus gorbuscha embryos predicts reduced survival potential

abstract: Cytochrome P4501A (CYP1A) induction in pink salmon Oncorhynchus gorbuscha embryos exposed to crude oil is linked to adverse effects at cellular, organism and population levels, and can be used to predict these responses. When combined with the results of an experiment designed to examine CYP1A induction during embryonic stages and growth after emergence, results from a series of experiments spanning 4 other brood years demonstrated that CYP1A induction is related to a variety of lethal and sublethal effects, including poorer marine survival, reduced growth and abnormalities. The lowest observed effective concentration of total polynuclear aromatic hydro- carbons (TPAH) in water that caused significant physiological responses, including reduced size 6 mo after exposure ended (<0.94 µg l -1 ), was less than the lowest concentration that caused significant CYP1A induction (<3.7 µg l -1 ). Thus, CYP1A induction is not only a biomarker, it can be considered a bioindicator; induction in early life stages implies long-term negative consequences for the individ- ual and the population.


title: An analytical model for multi-tier internet services and its applications

abstract: Since many Internet applications employ a multi-tier architecture, in this paper, we focus on the problem of analytically modeling the behavior of such applications. We present a model based on a network of queues, where the queues represent different tiers of the application. Our model is sufficiently general to capture (i) the behavior of tiers with significantly different performance characteristics and (ii) application idiosyncrasies such as session-based workloads, concurrency limits, and caching at intermediate tiers. We validate our model using real multi-tier applications running on a Linux server cluster. Our experiments indicate that our model faithfully captures the performance of these applications for a number of workloads and configurations. For a variety of scenarios, including those with caching at one of the application tiers, the average response times predicted by our model were within the 95% confidence intervals of the observed average response times. Our experiments also demonstrate the utility of the model for dynamic capacity provisioning, performance prediction, bottleneck identification, and session policing. In one scenario, where the request arrival rate increased from less than 1500 to nearly 4200 requests/min, a dynamic provisioning technique employing our model was able to maintain response time targets by increasing the capacity of two of the application tiers by factors of 2 and 3.5, respectively.


title: Sonar: Detecting SS7 Redirection Attacks with Audio-Based Distance Bounding

abstract: The global telephone network is relied upon by billions every day. Central to its operation is the Signaling System 7 (SS7) protocol, which is used for setting up calls, managing mobility, and facilitating many other network services. This protocol was originally built on the assumption that only a small number of trusted parties would be able to directly communicate with its core infrastructure. As a result, SS7 — as a feature — allows all parties with core access to redirect and intercept calls for any subscriber anywhere in the world. Unfortunately, increased interconnectivity with the SS7 network has led to a growing number of illicit call redirection attacks. We address such attacks with Sonar, a system that detects the presence of SS7 redirection attacks by securely measuring call audio round-trip times between telephony devices. This approach works because redirection attacks force calls to travel longer physical distances than usual, thereby creating longer end-to-end delay. We design and implement a distance bounding-inspired protocol that allows us to securely characterize the round-trip time between the two endpoints. We then use custom hardware deployed in 10 locations across the United States and a redirection testbed to characterize how distance affects round trip time in phone networks. We develop a model using this testbed and show Sonar is able to detect 70.9% of redirected calls between call endpoints of varying attacker proximity (300–7100 miles) with low false positive rates (0.3%). Finally, we ethically perform actual SS7 redirection attacks on our own devices with the help of an industry partner to demonstrate that Sonar detects 100% of such redirections in a real network (with no false positives). As such, we demonstrate that telephone users can reliably detect SS7 redirection attacks and protect the integrity of their calls.


title: Consistent resting-state networks across healthy subjects

abstract: Functional MRI (fMRI) can be applied to study the functional connectivity of the human brain. It has been suggested that fluctuations in the blood oxygenation level-dependent (BOLD) signal during rest reflect the neuronal baseline activity of the brain, representing the state of the human brain in the absence of goal-directed neuronal action and external input, and that these slow fluctuations correspond to functionally relevant resting-state networks. Several studies on resting fMRI have been conducted, reporting an apparent similarity between the identified patterns. The spatial consistency of these resting patterns, however, has not yet been evaluated and quantified. In this study, we apply a data analysis approach called tensor probabilistic independent component analysis to resting-state fMRI data to find coherencies that are consistent across subjects and sessions. We characterize and quantify the consistency of these effects by using a bootstrapping approach, and we estimate the BOLD amplitude modulation as well as the voxel-wise cross-subject variation. The analysis found 10 patterns with potential functional relevance, consisting of regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the so-called default-mode network, each with BOLD signal changes up to 3%. In general, areas with a high mean percentage BOLD signal are consistent and show the least variation around the mean. These findings show that the baseline activity of the brain is consistent across subjects exhibiting significant temporal dynamics, with percentage BOLD signal change comparable with the signal changes found in task-related experiments.


title: Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

abstract: Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%. Our approach combines two key insights: (1) one can apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost. Since we combine region proposals with CNNs, we call our method R-CNN: Regions with CNN features. We also present experiments that provide insight into what the network learns, revealing a rich hierarchy of image features. Source code for the complete system is available at http://www.cs.berkeley.edu/~rbg/rcnn.


title: Cluster-driven model for improved word and text embedding

abstract: Most of the existing word embedding models only consider the relationships between words and their local contexts (e.g. ten words around the target word). However, information beyond local contexts (global contexts), which reflect the rich semantic meanings of words, are usually ignored. In this paper, we present a general framework for utilizing global information to learn word and text representations. Our models can be easily integrated into existing local word embedding models, and thus introduces global information of varying degrees according to different downstream tasks. Moreover, we view our models in the co-occurrence matrix perspective, based on which a novel weighted term-document matrix is factorized to generate text representations. We conduct a range of experiments to evaluate word and text representations learned by our models. Experimental results show that our models outperform or compete with state-of-the-art models. Source code of the paper is available at https://github.com/zhezhaoa/cluster-driven.


title: CPW-Fed Penta-Band Slot Dipole Antenna Based on Comb-Like Metal Sheets

abstract: A penta-band coplanar waveguide (CPW)-fed slot dipole antenna with two comb-like metal sheets is proposed in this letter. Each operation band is controlled by a respective resonant path, and this results in high design freedom. The penta-band antenna operates at 1.88 ( $f_{1}$ ), 2.34 ( $f_{2}$ ), 2.70 ( $f_{3}$ ), 3.08 ( $f_{4}$ ), and 3.60 GHz ( $f_{5}$ ). The 6-dB fractional bandwidths are 17.68%, 8.66%, 5.59%, 11.61%, and 5.27%, respectively. The proposed antenna achieves a nearly omnidirectional radiation H -plane pattern, and the antenna peak gain is from 5.37 to 6.46 dBi. Although this letter describes a general design idea, it should be noted that the operation band frequencies could be easily tuned for commercial wireless communication systems, such as PCS, WLAN, WiMAX, and LTE.


title: Renewing and improving the business model toward sustainability in theory and practice

abstract: The paper aims to analyse and discuss the evolution towards a sustainable business model and to focus on the motivations and the implications on the mission, the governance and the accountability of companies. Moreover, the theoretical framework of values-based, ethical and sustainable leadership has been used as interpretative key for the case-study analysis. After presenting the literature review, the second part of the work is based on the inductive approach applied to the analysis of the experience of an Italian medium-sized company (SGR Group) belonging to the energy sector (oil & gas). Recently this company has started a new project aimed at renewing its business model and deepening its sustainability orientation, which has already been experienced for years. The project is finalized at identifying and assessing vices and virtues which affect the governance and the leadership model, as well as the company’s stakeholders engagement processes. The case exemplifies a resilience capability derived from a sustainable business model activated through relationships among internal and external stakeholders and supported by an ethical-based transformational leadership model which, in turn, derives from and nurtures the coherence among the mission-governance and accountability model. Specifically, the analysis confirms the propositions drawn from the literature review relative to the fact that the coherence among mission, governance and accountability is a key driver for effective business model and that sustainable business models derive from and are characterized by sustainable leadership models, which include transformational, ethical-based and values-virtues-driven leadership.


title: Learning action models with minimal observability

abstract: Abstract This paper presents FAMA , a novel approach for learning Strips action models from observations of plan executions that compiles the learning task into a classical planning task. Unlike all existing learning systems, FAMA is able to learn when the actions of the plan executions are partially or totally unobservable and information on intermediate states is partially provided. This flexibility makes FAMA an ideal learning approach in domains where only sensoring data are accessible. Additionally, we leverage the compilation scheme and extend it to come up with an evaluation method that allows us to assess the quality of a learned model syntactically, that is, with respect to the actual model; and, semantically, that is, with respect to a set of observations of plan executions. We also show that the extended compilation scheme can be used to lay the foundations of a framework for action model comparison. FAMA is exhaustively evaluated over a wide range of IPC domains and its performance is compared to ARMS , a state-of-the-art benchmark in action model learning.


title: Analysis of an Offloading Scheme for Data Centers in the Framework of Fog Computing

abstract: In the context of fog computing, we consider a simple case where data centers are installed at the edge of the network and assume that if a request arrives at an overloaded data center, then it is forwarded to a neighboring data center with some probability. Data centers are assumed to have a large number of servers, and traffic at some of them is assumed to cause saturation. In this case, the other data centers may help to cope with this saturation regime by accepting some of the rejected requests. Our aim is to qualitatively estimate the gain achieved via cooperation between neighboring data centers. After proving some convergence results related to the scaling limits of loss systems for the process describing the number of free servers at both data centers, we show that the performance of the system can be expressed in terms of the invariant distribution of a random walk in the quarter plane. By using and developing existing results in the technical literature, explicit formulas for the blocking rates of such a system are derived.


EVALUATION OF THE SOCKET SHIELD TECHNIQUE FOR IMMEDIATE IMPLANTATION


title: An Efficient Kernel Transformation Architecture for Binary- and Ternary-Weight Neural Network Inference

abstract: While deep convolutional neural networks (CNNs) have emerged as the driving force of a wide range of domains, their computationally and memory intensive natures hinder the further deployment in mobile and embedded applications. Recently, CNNs with low-precision parameters have attracted much research attention. Among them, multiplier-free binary- and ternary-weight CNNs are reported to be of comparable recognition accuracy with full-precision networks, and have been employed to improve the hardware efficiency. However, even with the weights constrained to binary and ternary values, large-scale CNNs still require billions of operations in a single forward propagation pass. In this paper, we introduce a novel approach to maximally eliminate redundancy in binary- and ternary-weight CNN inference, improving both the performance and energy efficiency. The initial kernels are transformed into much fewer and sparser ones, and the output feature maps are rebuilt from the immediate results. Overall, the number of total operations in convolution is reduced. To find an efficient transformation solution for each already trained network, we propose a searching algorithm, which iteratively matches and eliminates the overlap in a set of kernels. We design a specific hardware architecture to optimize the implementation of kernel transformation. Specialized dataflow and scheduling method are proposed. Tested on SVHN, AlexNet, and VGG-16, our architecture removes 43.4%--79.9% operations, and speeds up the inference by 1.48--3.01 times.


title: Faster title and abstract screening? Evaluating Abstrackr, a semi-automated online screening program for systematic reviewers

abstract: Citation screening is time consuming and inefficient. We sought to evaluate the performance of Abstrackr, a semi-automated online tool for predictive title and abstract screening. Four systematic reviews (aHUS, dietary fibre, ECHO, rituximab) were used to evaluate Abstrackr. Citations from electronic searches of biomedical databases were imported into Abstrackr, and titles and abstracts were screened and included or excluded according to the entry criteria. This process was continued until Abstrackr predicted and classified the remaining unscreened citations as relevant or irrelevant. These classification predictions were checked for accuracy against the original review decisions. Sensitivity analyses were performed to assess the effects of including case reports in the aHUS dataset whilst screening and the effects of using larger imbalanced datasets with the ECHO dataset. The performance of Abstrackr was calculated according to the number of relevant studies missed, the workload saving, the false negative rate, and the precision of the algorithm to correctly predict relevant studies for inclusion, i.e. further full text inspection. Of the unscreened citations, Abstrackr’s prediction algorithm correctly identified all relevant citations for the rituximab and dietary fibre reviews. However, one relevant citation in both the aHUS and ECHO reviews was incorrectly predicted as not relevant. The workload saving achieved with Abstrackr varied depending on the complexity and size of the reviews (9 % rituximab, 40 % dietary fibre, 67 % aHUS, and 57 % ECHO). The proportion of citations predicted as relevant, and therefore, warranting further full text inspection (i.e. the precision of the prediction) ranged from 16 % (aHUS) to 45 % (rituximab) and was affected by the complexity of the reviews. The false negative rate ranged from 2.4 to 21.7 %. Sensitivity analysis performed on the aHUS dataset increased the precision from 16 to 25 % and increased the workload saving by 10 % but increased the number of relevant studies missed. Sensitivity analysis performed with the larger ECHO dataset increased the workload saving (80 %) but reduced the precision (6.8 %) and increased the number of missed citations. Semi-automated title and abstract screening with Abstrackr has the potential to save time and reduce research waste.


title: Advanced single-phase AC-DC converter using single-phase matrix converter topology

abstract: This paper proposes advanced single-phase AC-DC converter using single-phase matrix converter (SPMC) topology incorporating with active power filter function. The topology is also capable of incorporating many other features for controlled rectifier operation that includes; bidirectional, regenerative and dual converter capabilities. The PWM technique was used to calculate the switch duty ratio to synthesize the output. Switch commutation arrangements were developed that allows dead time to avoid current spikes of non-ideal switches whilst providing a current path for the inductive load to avoid voltage spikes. Selected simulation and experimental results are presented to verify proposed operation.


title: MBSE driven approach for defining problem domain

abstract: This paper continues introduction of the MBSE Grid framework. As the name reveals, the framework is intended for model-based systems engineering (MBSE). MBSE is an emerging approach that applies modeling to support complex system requirements, design, analysis, and verification and validation activities. Being a synthesis of widely known MBSE methodologies (IBM Harmony, OOSEM, etc.) and frameworks (DoDAF, NAF, etc.), the MBSE Grid proposes an unambiguous workflow for modeling systems by iterations going from the highest to the lowest layers of abstraction. This paper mainly focuses on the Concept and Problem definition layers, describing in detail each step of the workflow in the range of these two layers and revealing presentation artifacts each step produces, also, giving clues for elements traceability. The case study demonstrates and proves adaptability of the MBSE Grid on a real-world problem domain using MagicDraw CASE tool.


title: Cooperative Spectrum Sharing in Cognitive Radio Networks: A Game-Theoretic Approach

abstract: We consider the problem of cooperative spectrum sharing among a primary user (PU) and multiple secondary users (SUs), where the PU selects a proper set of secondary users to serve as the cooperative relays for its transmission. In return, the PU leases portion of channel access time to the selected SUs for their own transmission. The PU decides the portion of channel access time it will leave for the selected SUs (i.e., the cooperative relays), and the cooperative relays decide their respective power level used to help PU's transmission in order to achieve proportional access time to the channel. We assume that the PU and SUs are rational and selfish, i.e., they only aim at maximizing their own utility. As SU's utility is in term of their own transmission rate and the power cost for PU's transmission, so they will choose a proper power level to meet the tradeoff between transmission rate and power cost. PU will choose a proper portion of channel access time for the cooperative relays to attract them to employ higher power level. We formulate the problem as a non-cooperative game between PU and SUs, and prove that the proposed game converges to a unique Stackelberg equilibrium. By employing an iterative updating algorithm, we can achieve the unique equilibrium point.


title: Stress Detection and Management: A Survey of Wearable Smart Health Devices

abstract: In today's world, many people feel stressed out from school, work, or other life events. Therefore, it is important to detect stress and manage it to reduce the risk of damage to an individual's well being. With the emergence of the Internet of Things (IoT), devices can be made to detect stress and manage it effectively by using cloudbased services and smartphone apps to aggregate and compute large data sets that track stress behavior over long periods of time. Additionally, there is added convenience via the connectivity and portability of these IoT devices. They allow individuals to seek intervention prior to elevated health risks and achieve a less stressful life.


title: Network Slicing for 5G with SDN/NFV: Concepts, Architectures and Challenges

abstract: The fifth generation of mobile communications is anticipated to open up innovation opportunities for new industries such as vertical markets. However, these verticals originate myriad use cases with diverging requirements that future 5G networks have to efficiently support. Network slicing may be a natural solution to simultaneously accommodate, over a common network infrastructure, the wide range of services that vertical- specific use cases will demand. In this article, we present the network slicing concept, with a particular focus on its application to 5G systems. We start by summarizing the key aspects that enable the realization of so-called network slices. Then we give a brief overview on the SDN architecture proposed by the ONF and show that it provides tools to support slicing. We argue that although such architecture paves the way for network slicing implementation, it lacks some essential capabilities that can be supplied by NFV. Hence, we analyze a proposal from ETSI to incorporate the capabilities of SDN into the NFV architecture. Additionally, we present an example scenario that combines SDN and NFV technologies to address the realization of network slices. Finally, we summarize the open research issues with the purpose of motivating new advances in this field.


title: Graphene Oxide a New Carrier for Slow Release of Plant Micronutrients

abstract: The environmental problems and low efficiency associated with conventional fertilizers provides an impetus to develop advanced fertilizers with slower release and better performances. Here, we report of development of a new carrier platform based on graphene oxide (GO) sheets that can provide a high loading of plant micronutrients with controllable slow release. To prove this concept, two micronutrients, zinc (Zn) and copper (Cu), were used to load on GO sheets and hence formulate GO-based micronutrients fertilizer. The chemical composition and successful loading of both nutrients on GO sheets were confirmed by X-ray photoelectron spectroscopy, thermogravimetric analysis, and X-ray diffraction (XRD). The prepared Zn-graphene oxide (Zn-GO) and Cu-graphene oxide (Cu-GO) fertilizers showed a biphasic dissolution behavior compared to that of commercial zinc sulfate and copper sulfate fertilizer granules, displaying desirable fast and slow micronutrient release. A visualization method and chemical analysis were used to assess the release and diffusion of Cu and Zn in soil from GO-based fertilizers compared with commercial soluble fertilizers to demonstrate the advantages of GO carriers and show their capability to be used as a generic platform for macro- and micronutrients delivery. A pot trial demonstrated that Zn and Cu uptake by wheat was higher when using GO-based fertilizers compared to that when using standard zinc or copper salts. This is the first report on the agronomic performance of GO-based slow-release fertilizer.


title: SBGAR: Semantics Based Group Activity Recognition

abstract: Activity recognition has become an important function in many emerging computer vision applications e.g. automatic video surveillance system, human-computer interaction application, and video recommendation system, etc. In this paper, we propose a novel semantics based group activity recognition scheme, namely SBGAR, which achieves higher accuracy and efficiency than existing group activity recognition methods. SBGAR consists of two stages: in stage I, we use a LSTM model to generate a caption for each video frame; in stage II, another LSTM model is trained to predict the final activity categories based on these generated captions. We evaluate SBGAR using two well-known datasets: the Collective Activity Dataset and the Volleyball Dataset. Our experimental results show that SBGAR improves the group activity recognition accuracy with shorter computation time compared to the state-of-the-art methods.


title: Background calibration techniques for multistage pipelined ADCs with digital redundancy

abstract: The proposed digital background calibration scheme, applicable to multistage (pipelined or algorithmic/cyclic) analog-to-digital converters (ADCs), corrects the linearity errors resulting from capacitor mismatches and finite opamp gain. A high-accuracy calibration is achieved by recalculating the digital output based on each stage's equivalent radix. The equivalent radices are extracted in the background by using a digital correlation method. The proposed calibration technique takes advantage of the digital redundancy architecture inherent to most pipelined ADCs. In the proposed method, the SNR is not degraded from the pseudorandom noise sequence injected into the system. A two-channel ADC architecture with negligible overhead is also proposed to significantly improve the efficiency of the digital correlation. Simulation results confirm that 16-bit linearity can be achieved after calibration for an ADC with /spl sigma/=0.1% capacitor mismatches and 60 dB opamp gain.


title: MyCS: CS for middle-years students and their teachers

abstract: This paper describes the MyCS curriculum, which aims to encourage middle-years students, i.e., grades 4-10, to engage not only as consumers of CS, but as composers of computational artifacts. MyCS draws from many successful online tools and curricula, including Exploring Computer Science (ECS) and Scratch. MyCS's 18-week curriculum balances a broad treatment of the modern role of the computer with deepening students' hands-on skills through computational problem-solving, both unplugged and via programming. This paper summarizes assessments from several MyCS professional-development workshops, with an emphasis on eliciting teachers' needs and goals for their classrooms. In addition, we discuss the project's future in the format of a teacher-mediated MOOC that will enable a larger group of teachers to engage their students in CS.


title: Lower lip reconstruction with Abbe–Estlander flap modification: preserving the same side vascular pedicle

abstract: A 74-year-old man was referred to our clinic with a nonhealing crusted lower lip ulcer. On examination, an ulcerated lower lip lesion measuring 1.5 9 1.5 cm was detected at a location 1 cm from the left oral commissure approaching the midline. There was no palpable lymphadenopathy in the neck. An incisional biopsy was carried out and the lesion was reported as squamous cell carcinoma. Supra-omohyoid bilateral neck dissections were performed and the tumor was excised. Vertical full-thickness incisions were made so as to leave a safety margin of 1 cm. The defect measuring 3.5 cm was reconstructed utilizing an ipsilateral upper lip Abbe–Estlander flap, which was prepared with 3-cm length and 1.5-cm base, preserving the superior labial artery. The left upper labial artery sank deeply into the submucosal plan of the mobilized flap (Fig. 1, 2). The upper lip defect was reconstructed by primary closure. Histological examination of the specimen confirmed no metastasis in the neck, with a negative surgical margin. No complications developed in the following 6 months with satisfactory esthetic and functional results. According to the defect location and size, various lip reconstruction methods have been described. ‘’ V ‘’ or ‘’ W ‘’ shaped excision and primary closure are sufficient for smaller than 30 % lower lip defects. In 35–70 % of lower lip defects, the Karapandzic, Abbe, Estlander, McGregor or Gillies’ fan flaps, the Nakajima or Schuchardt flaps or their modifications can be used. In cases in which there is insufficient tissue in the lower lip, cheek tissue is used in Webster, Bernard and Dieffenbach advancement flaps and their various modifications. Deltopectoral or radial forearm free flaps can be options for large defects of the lip extending to the mandible [1–5]. The Abbe–Estlander flap, which is fed by the opposite superior labial artery, is a full-thickness triangular-shaped upper lip flap that creates the oral commissure and the lateral part of the lower lip. This flap begins from the medial part of the oral commissure and is approximately 1.5 9 3 cm in size. Direct rotation to the defect is completed at one stage. In our case, the lower lip was reconstructed using the modified Abbe–Estlander flap following tumor resection. We describe a method that is different from conventional methods, which protects the ipsilateral superior labial artery. It is not necessary to sacrifice the superior labial artery, which is encountered in the incision line when creating the flap. Additional dissection to expose the artery, such as that in facial arterial dissection, is not required. Therefore, no co-morbidity of the facial muscles occurs. At first, the mucosal layer of the flap is sutured to the defect area after having released a small part of the muscle. Thereafter, the flap is sutured to the defect area while preserving the artery, which is between the mucosa and the muscular layer of the flap. Protecting the ipsilateral superior labial artery does not complicate the mobility and the processing of the flap. Our modification, which goes with an intact contralateral facial artery in terms of flap feeding, may not have superiority to the classical Abbe– Estlander flap for patients who have never undergone a surgical procedure to the neck. However, in patients who This manuscript had been presented at 33rd. National Congress of Oto-Rhino-Laryngology Head and Neck Surgery, Antalya, 26–29 October 2011.


title: An empirical study on the use of i* by non-technical stakeholders: the case of strategic dependency diagrams

abstract: Early phases of information systems engineering include the understanding of the enterprise’s context and the construction of models at different levels of decomposition, required to design the system architecture. These time-consuming activities are usually conducted by relatively large teams, composed of groups of non-technical stakeholders playing mostly an informative role (i.e. not involved in documentation and even less in modelling), led by few experienced technical consultants performing most of the documenting and modelling effort. This paper evaluates the ability of non-technical stakeholders to create strategic dependency diagrams written with the i* language in the design of the context model of a system architecture, and find out which difficulties they may encounter and what the quality of the models they build is. A case study involving non-technical stakeholders from 11 organizational areas in an Ecuadorian university held under the supervision and coordination of the two authors acting as consultants. The non-technical stakeholders identified the majority of the dependencies that should appear in the case study’s context model, although they experienced some difficulties in declaring the type of dependency, representing such dependencies graphically and applying the description guidelines provided in the training. Managers were observed to make more mistakes than other more operational roles. From the observations of these results, a set of methodological advices were compiled for their use in future, similar endeavours. It is concluded that non-technical stakeholders can take an active role in the construction of the context model. This conclusion is relevant for both researchers and practitioners involved in technology transfer actions with use of i*.


title: Frequency-Domain Turbo Equalization With Iterative Channel Estimation for MIMO Underwater Acoustic Communications

abstract: This paper proposes a new iterative receiver for single-carrier multiple-input–multiple-output (SC-MIMO) underwater acoustic (UWA) communications, which utilizes frequency-domain turbo equalization (FDTE) and iterative channel estimation. Soft-decision symbols are not only fed back to the equalizer to cancel the intersymbol interference (ISI) and cochannel interference (CCI), but also used as training signals in the channel estimator to update the estimated channel state information (CSI) after each turbo iteration. This iterative channel estimation scheme helps to combat the problem commonly suffered by block-processing receivers in fast time-varying channels. Compared with time-domain turbo equalization, FDTE achieves comparable performance with significantly reduced computational complexity. Using soft-decision symbols to reestimate the time-varying channels, iterative channel estimation further improves the accuracy of the estimated CSI. The proposed iterative receiver has been verified through undersea experimental data collected in the Surface Processes and Acoustic Communications Experiment 2008 (SPACE08).


title: A generalized maximum entropy approach to bregman co-clustering and matrix approximation

abstract: Co-clustering, or simultaneous clustering of rows and columns of a two-dimensional data matrix, is rapidly becoming a powerful data analysis technique. Co-clustering has enjoyed wide success in varied application domains such as text clustering, gene-microarray analysis, natural language processing and image, speech and video analysis. In this paper, we introduce a partitional co-clustering formulation that is driven by the search for a good matrix approximation---every co-clustering is associated with an approximation of the original data matrix and the quality of co-clustering is determined by the approximation error. We allow the approximation error to be measured using a large class of loss functions called Bregman divergences that include squared Euclidean distance and KL-divergence as special cases. In addition, we permit multiple structurally different co-clustering schemes that preserve various linear statistics of the original data matrix. To accomplish the above tasks, we introduce a new minimum Bregman information (MBI) principle that simultaneously generalizes the maximum entropy and standard least squares principles, and leads to a matrix approximation that is optimal among all generalized additive models in a certain natural parameter space. Analysis based on this principle yields an elegant meta algorithm, special cases of which include most previously known alternate minimization based clustering algorithms such as kmeans and co-clustering algorithms such as information theoretic (Dhillon et al., 2003b) and minimum sum-squared residue co-clustering (Cho et al., 2004). To demonstrate the generality and flexibility of our co-clustering framework, we provide examples and empirical evidence on a variety of problem domains and also describe novel co-clustering applications such as missing value prediction and compression of categorical data matrices.


title: Landscape of Big Medical Data: A Pragmatic Survey on Prioritized Tasks

abstract: Big medical data poses great challenges to life scientists, clinicians, computer scientists, and engineers. In this paper, a group of life scientists, clinicians, computer scientists and engineers sit together to discuss several fundamental issues. First, what are the unique characteristics of big medical data different from those of the other domains? Second, what are the prioritized tasks in clinician research and practices utilizing big medical data? And do we have enough publicly available data sets for performing those tasks? Third, do the state-of-the-practice and state-of-the-art algorithms perform good jobs? Fourth, are there any benchmarks for measuring algorithms and systems for big medical data? Fifth, what are the performance gaps of state-of-the-practice and state-of-the-art systems handling big medical data currently or in future? Finally but not least, are we, life scientists, clinicians, computer scientists and engineers, ready for working together? We believe answering the above issues will help define and shape the landscape of big medical data.


title: Fast multidimensional image processing with OpenCL

abstract: Multidimensional image data, i.e., images with three or more dimensions, are used in many areas of science. Multidimensional image processing is supported in Python and MATLAB. VisionGL is an open source library that provides a set of image processing functions and can help the programmer by automatically generating code. The objective of this work is to augment VisionGL by adding multidimensional image processing support with OpenCL for high performance through use of GPUs. Benchmarking experiments were run with window and point operations to compare Python, MATLAB and VisionGL when processing 1D to 5D images. As a result, speedups of up to two orders of magnitude were obtained.


title: Flexible Paxos: Quorum intersection revisited

abstract: Distributed consensus is integral to modern distributed systems. The widely adopted Paxos algorithm uses two phases, each requiring majority agreement, to reliably reach consensus. In this paper, we demonstrate that Paxos, which lies at the foundation of many production systems, is conservative. Specifically, we observe that each of the phases of Paxos may use non-intersecting quorums. Majority quorums are not necessary as intersection is required only across phases. ::: Using this weakening of the requirements made in the original formulation, we propose Flexible Paxos, which generalizes over the Paxos algorithm to provide flexible quorums. We show that Flexible Paxos is safe, efficient and easy to utilize in existing distributed systems. We conclude by discussing the wide reaching implications of this result. Examples include improved availability from reducing the size of second phase quorums by one when the number of acceptors is even and utilizing small disjoint phase-2 quorums to speed up the steady-state.


Do foreign language learners also have constructions ? Evidence from priming , sorting , and corpora *


title: The Use of Likert Scales With Children

abstract: Tasks A 25-item questionnaire was compiled using questions taken from scales used in previous research projects. The items were chosen to cover a range of constructs rather than a single construct, and that would most likely vary in their level of abstractness. Further, they were chosen on the basis of being amenable for wording that would be consistent with the various scale anchor formats. The constructs include body satisfaction, peer relations, adjustment, and perceptions of teacher interactions. Examples of questions included ‘‘I sleep very well,’’ ‘‘I feel lonely,’’ ‘‘Spiders make me frightened,’’ ‘‘I feel that parents listen to what their children have to say,’’ and ‘‘I am able to do things as well as most other kids.’’ All questions are shown in Table II. We also asked a group of 22 elementary school teachers to rate the items in the questionnaire as either concrete or abstract because we wanted to understand better the response patterns. While the objective task using the sweets described above was considered to be concrete in nature, it was based on a physical observation and judgment task of a different quality to the languagebased tasks in the proposed question sets. The teachers agreed that 7 items were concrete in nature and 18 were abstract (Table II). Five versions of the questionnaire, each containingTable II). Five versions of the questionnaire, each containing the same items but using a different response format, were created. The response formats, following the stem this statement is like me, were 1. Yes/no (Scale A). 2. Five points marked by the numbers 1–5 with a key indicating that 1 represented not like me at all, to 5 very much like me (Scale B). Table II. Items Used in the Questionnaire and Complexity Rating by Teachers (n1⁄422)


title: Mollifying Networks

abstract: The optimization of deep neural networks can be more challenging than the traditional convex optimization problems due to highly non-convex nature of the loss function, e.g. it can involve pathological landscapes such as saddle-surfaces that can be difficult to escape from for algorithms based on simple gradient descent. In this paper, we attack the problem of optimization of highly non-convex neural networks objectives by starting with a smoothed -- or mollified -- objective function which becomes more complex as the training proceeds. Our proposition is inspired by the recent studies in continuation methods: similarly to curriculum methods, we begin by learning an easier (possibly convex) objective function and let it evolve during training until it eventually becomes the original, difficult to optimize objective function. The complexity of the mollified networks is controlled by a single hyperparameter that is annealed during training. We show improvements on various difficult optimization tasks and establish a relationship between recent works on continuation methods for neural networks and mollifiers.


title: A quantitative study of irregular programs on GPUs

abstract: GPUs have been used to accelerate many regular applications and, more recently, irregular applications in which the control flow and memory access patterns are data-dependent and statically unpredictable. This paper defines two measures of irregularity called control-flow irregularity and memory-access irregularity, and investigates, using performance-counter measurements, how irregular GPU kernels differ from regular kernels with respect to these measures. For a suite of 13 benchmarks, we find that (i) irregularity at the warp level varies widely, (ii) control-flow irregularity and memory-access irregularity are largely independent of each other, and (iii) most kernels, including regular ones, exhibit some irregularity. A program's irregularity can change between different inputs, systems, and arithmetic precision but generally stays in a specific region of the irregularity space. Whereas some highly tuned implementations of irregular algorithms exhibit little irregularity, trading off extra irregularity for better locality or less work can improve overall performance.


title: The state of the art in ontology design: A survey and comparative review

abstract: In this article, we develop a framework for comparing ontologies and place a number of the more prominent ontologies into it. We have selected 10 specific projects for this study, including general ontologies, domain-specific ones, and one knowledge representation system. The comparison framework includes general characteristics, such as the purpose of an ontology, its coverage (general or domain specific), its size, and the formalism used. It also includes the design process used in creating an ontology and the methods used to evaluate it. Characteristics that describe the content of an ontology include taxonomic organization, types of concept covered, top-level divisions, internal structure of concepts, representation of part-whole relations, and the presence and nature of additional axioms. Finally, we consider what experiments or applications have used the ontologies. Knowledge sharing and reuse will require a common framework to support interoperability of independently created ontologies. Our study shows there is great diversity in the way ontologies are designed and the way they represent the world. By identifying the similarities and differences among existing ontologies, we clarify the range of alternatives in creating a standard framework for ontology design.


title: PFC: Privacy Preserving FPGA Cloud - A Case Study of MapReduce

abstract: Privacy is one of the critical concerns that hinder the adoption of public cloud. For storage, encryption can be used to protect user's data. But for outsourced data processing, for example MapReduce, there is no satisfying solution. Users have to trust the cloud service providers totally. In this work, we propose PFC, a FPGA cloud for privacy preserving computation in the public cloud environment. PFC leverages the security feature of the existing FPGAs originally designed for bitstream IP protection and proxy re-encryption for preserving user data privacy. In PFC, cloud service providers are not necessarily trusted, and during outsourced computation, user's data is protected by a data encryption key only accessible by trusted FPGA devices. As an important application of cloud computing, we apply PFC to the popular MapReduce programming model and extend the FPGA based MapReduce pipeline with privacy protection capabilities. Proxy re-encryption is employed to support dynamic allocations of trusted FPGA devices as mappers and reducers. Finally, we conduct evaluation to demonstrate the effectiveness of PFC.


title: A GaN MMIC Modified Doherty PA With Large Bandwidth and Reconfigurable Efficiency

abstract: In this paper, we further develop the concept of a wideband Doherty power-amplifier topology with reconfigurable efficiency. This is done by presenting new theory that-in contrast to previous work-makes it possible to analyze how the performance of the amplifier depends on the input network properties and the choice of gate bias voltages. The utility of the presented theory is demonstrated by the design and characterization of a gallium-nitride monolithic-microwave integrated-circuit amplifier designed in close agreement with the theoretical findings. Continuous-wave (CW) measurements show that the fabricated amplifier provides more than 31% power-added efficiency (PAE) at 9-dB output power back-off over a 5.8-8.8-GHz frequency range, and that the efficiency can be reconfigured as predicted by the presented theory. Modulated measurements-employing digital pre-distortion (DPD) and a 20-MHz signal with 8.5-dB peak-to-average power ratio-show more than 32% average PAE across the same 5.8-8.8-GHz frequency range. Both the CW and the modulated measurements thereby verify the wideband performance predicted by the theory. In addition, modulated measurements at 7.0 GHz without DPD show that the amplifier can provide excellent raw linearity by demonstrating -41.0-dBc adjacent channel power ratio and -34.2-dB normalized mean square error.


title: A change of view: homologous recombination at single-molecule resolution

abstract: Genetic recombination occurs in all organisms and is vital for genome stability. Indeed, in humans, aberrant recombination can lead to diseases such as cancer. Our understanding of homologous recombination is built upon more than a century of scientific inquiry, but achieving a more complete picture using ensemble biochemical and genetic approaches is hampered by population heterogeneity and transient recombination intermediates. Recent advances in single-molecule and super-resolution microscopy methods help to overcome these limitations and have led to new and refined insights into recombination mechanisms, including a detailed understanding of DNA helicase function and synaptonemal complex structure. The ability to view cellular processes at single-molecule resolution promises to transform our understanding of recombination and related processes.


title: A Multiple Radar Approach for Automatic Target Recognition of Aircraft Using Inverse Synthetic Aperture Radar

abstract: Following the recent advancements in radar technologies, research on automatic target recognition using Inverse Synthetic Aperture Radar (ISAR) has correspondingly seen more attention and activity. ISAR automatic target recognition researchers aim to fully automate recognition and classification of military vehicles, but because radar images often do not present a clear image of what they detect, it is considered a challenging process to do this. Here we present a novel approach to fully automate a system with Convolutional Neural Networks (CNNs) that results in better target recognition and requires less training time. Specifically, we developed a simulator to generate images with complex values to train our CNN. The simulator is capable of accurately replicating real ISAR configurations and thus can be used to determine the optimal number of radars needed to detect and classify targets. Testing with seven distinct targets, we achieve higher recognition accuracy while reducing the time constraints that the training and testing processes traditionally entail.


title: Deep neural networks for short-term load forecasting in ERCOT system

abstract: Short-term load forecasting plays a major role in the operation of electric power systems to ensure instantaneous balance between electricity generation and demand. The accuracy of the forecast generated by a neural network (NN) has several factors, including but not limited to, the algorithm used to train the network, how much and what kind of data are used in the network's training set, how many hidden layers are in the NN, and the size of the hidden layer(s). We investigate the best combination of these factors to decrease the mean absolute percent error (MAPE) and to give the best forecast possible. Based on system load data from the Electric Reliability Council of Texas (ERCOT), this paper focuses on a comprehensive understanding of the accuracy of forecasts generated by NN's with different algorithms, while varying the length of the network's training set, hidden layer size, number of hidden layers, and the addition of data sets when training to create a forecast with the highest accuracy possible.


title: Developing a Virtual Piano Playing Environment

abstract: In this paper the framework for a multimedia platform called ADRI is described which aids in playing the piano. A 3D-piano with visualisation of notes was built in a virtual environment and connected with a real synthesizer using Midi to interact with a user. Multiple users can play together on the virtual piano in this 3D environment from different locations. Multiple agents were made, each with its own independent functionality within the multi-agent platform. Using this system an artificial piano teacher can be developed further to give the environment more educational value and to aid a real piano teacher with his lessons. The environment can also be used as an administrative tool or knowledge base with lessons, songs or other information needed for piano playing.


title: A real-time tracking method based on SURF

abstract: Two important problems in real-time tracking are: 1) how to discriminate an object from clutter environments, and 2) how to meet the real-time requirement in practical applications. In real-time tracking application scenario, neither priori information about targets nor background model is known, which makes many traditional methods fail. In this paper, we propose an object detection and tracking method based on Speeded-Up Robust Features (SURF). A region of interests is set up to reduce computation burden and an adaptive reference library is built and updated by reusing the extracted feature points and past object location. The advantages of this method lies in its robustness while its calculation is light. Our experiments show that our method is robust under camera wobble, background clutter and illumination changes. It can reach real-time processing in various occasions.


title: Reconfigurable 4 channel carrier aggregation receiver using harmonic recombination technique

abstract: This paper presents reconfigurable 4-band carrier aggregation receiver using only one frequency source. The receiver targets not only for intra-band but also for inter-band carrier aggregation by employing an harmonic recombination technique. By using an 8-phase mixer for an harmonic recombination, 1st and 3rd order bands can be used for an inter-band carrier aggregation. Using 28-nm CMOS process, an wide-band high performance LNA [5] is designed and an 8-phase differential mixer is designed with a non-overlapping LO generator. The harmonic recombination stage is implemented with a base band poly phase filter and an active RC complex Band Pass Filter (BPF). The receiver covers the input frequency range of 0.5 GHz–3.6 GHz with the 3.7 dB NF and 65 dB voltage gain. The center frequency of one band can be tuned anywhere between 1–50 MHz. The receiver also rejects opposite channel by 45 dB and the 3rd order channel by 30 dB.


title: A performance evaluation of Apache Kafka in support of big data streaming applications

abstract: Stream computing is becoming a more and more popular paradigm as it enables the real-time promise of data analytics. Apache Kafka is currently the most popular framework used to ingest the data streams into the processing platforms. However, how to tune Kafka and how much resources to allocate for it remains a challenge for most users, who now rely mainly on empirical approaches to determine the best parameter settings for their deployments. In this poster, we make a through evaluation of several configurations and performance metrics of Kafka in order to allow users avoid bottlenecks, reach its full potential and avoid bottlenecks and eventually leverage some good practice for efficient stream processing.


title: Simple resistance calculation in litz-wire planar windings for induction cooking appliances

abstract: We present a simple method to calculate the winding resistance of a litz-wire inductor in an induction cooking appliance, taking into account eddy-current losses, including conduction losses and proximity-effect losses. The method, which uses numerical calculation of the magnetic field, can be used to determine the optimum number of strands in the litz wire. The effects of the temperature on the winding total resistance are included in the calculation. We have built a prototype to verify the calculation method and its limitations. We consider the accuracy limits of the method.


title: Sparse array design for millimeter-wave imaging

abstract: In this paper, the design of an efficient sparse array, used to reconstruct the image from millimeter-wave signals, is presented. The proposed array takes advantage of the sparsity concept of the image in the spatial Fourier domain to reduce the number of array elements such that the beam pattern of the designed array meets the desired beamwidth and sidelobe level. Simulation and measured results confirm the effectiveness of the proposed design on high-quality reconstruction of the image, while 85% reduction in the number of elements is achieved as well as the reduction in scan time and system complexity.


title: Automatic detection and correction of web application vulnerabilities using data mining to predict false positives

abstract: Web application security is an important problem in today's internet. A major cause of this status is that many programmers do not have adequate knowledge about secure coding, so they leave applications with vulnerabilities. An approach to solve this problem is to use source code static analysis to find these bugs, but these tools are known to report many false positives that make hard the task of correcting the application. This paper explores the use of a hybrid of methods to detect vulnerabilities with less false positives. After an initial step that uses taint analysis to flag candidate vulnerabilities, our approach uses data mining to predict the existence of false positives. This approach reaches a trade-off between two apparently opposite approaches: humans coding the knowledge about vulnerabilities (for taint analysis) versus automatically obtaining that knowledge (with machine learning, for data mining). Given this more precise form of detection, we do automatic code correction by inserting fixes in the source code. The approach was implemented in the WAP tool and an experimental evaluation was performed with a large set of open source PHP applications.


English Language Proficiency and Academic Performance of Philippine Science High School Students


title: Advanced Low Power Spintronic Memories beyond STT-MRAM

abstract: Until now, spin transfer torque magnetic random access memory (STT-MRAM) has drawn considerable R&D interest worldwide. A number of companies and universities are currently involved in this promising technology. In 2016, Everspin released the first 256M STT-MRAM chip, indicating the commercialization and application of STT-MRAM. Nevertheless, STT-MRAM still has some intrinsic limitations, such as dynamic write power and speed, compared with CMOS-based memory technologies. Following the technical evolution process from toggle-MRAM to STT-MRAM, the continuous pursuit of high performance, high density, low power and scalability, drives the intensive R&D of new memory technologies. In this paper, we will show the recent progress in advanced spintronic memories beyond STT-MRAM, such as the spin Hall effect (SHE)-driven and voltage-driven MRAMs. These advanced MRAM technologies do have some unique advantages compared with STT-MRAM, but they also suffer from new design and fabrication challenges. In addition, we will present the latest research in emerging spintronic devices, e.g., magnetic skyrmions, which are potential as information carriers in future spintronic memories, e.g., racetrack memory.


The Therapeutic Potentials of Ayahuasca: Possible Effects against Various Diseases of Civilization


title: Role-Dynamics: Fast Mining of Large Dynamic Networks

abstract: To understand the structural dynamics of a large-scale social, biological or technological network, it may be useful to discover behavioral roles representing the main connectivity patterns present over time. In this paper, we propose a scalable non-parametric approach to automatically learn the structural dynamics of the network and individual nodes. Roles may represent structural or behavioral patterns such as the center of a star, peripheral nodes, or bridge nodes that connect different communities. Our novel approach learns the appropriate structural role dynamics for any arbitrary network and tracks the changes over time. In particular, we uncover the specific global network dynamics and the local node dynamics of a technological, communication, and social network. We identify interesting node and network patterns such as stationary and non-stationary roles, spikes/steps in role-memberships (perhaps indicating anomalies), increasing/decreasing role trends, among many others. Our results indicate that the nodes in each of these networks have distinct connectivity patterns that are non-stationary and evolve considerably over time. Overall, the experiments demonstrate the effectiveness of our approach for fast mining and tracking of the dynamics in large networks. Furthermore, the dynamic structural representation provides a basis for building more sophisticated models and tools that are fast for exploring large dynamic networks.


title: 25 Challenges of Semantic Process Modeling

abstract: Process modeling has become an essential part of many organizations for documenting, analyzing and redesigning their business operations and to support them with suitable information systems. In order to serve this purpose, it is important for process models to be well grounded in formal and precise semantics. While behavioural semantics of process models are well understood, there is a considerable gap of research into the semantic aspects of their text labels and natural language descriptions. The aim of this paper is to make this research gap more transparent. To this end, we clarify the role of textual content in process models and the challenges that are associated with the interpretation, analysis, and improvement of their natural language parts. More specifically, we discuss particular use cases of semantic process modeling to identify 25 challenges. For each challenge, we identify prior research and discuss directions for addressing them.


title: Online Parameter Estimation Technique for Adaptive Control Applications of Interior PM Synchronous Motor Drives

abstract: This paper proposes an online parameter estimation method based on a discrete-time dynamic model for the interior permanent-magnet synchronous motors (IPMSMs). The proposed estimation technique, which takes advantage of the difference in dynamics of motor parameters, consists of two affine projection algorithms. The first one is designed to accurately estimate the stator inductances, whereas the second one is designed to precisely estimate the stator resistance, rotor flux linkage, and load torque. In this paper, the adaptive decoupling proportional-integral (PI) controllers with the maximum torque per ampere operation, which utilize the previously identified parameters in real time, are chosen to verify the effectiveness of the proposed parameter estimation scheme. The simulation results via MATLAB/Simulink and the experimental results via a prototype IPMSM drive system with a TI TMS320F28335 DSP are presented under various conditions. A comparative study with the conventional decoupling PI control method is carried out to demonstrate the better performances (i.e., faster dynamic response, less steady-state error, more robustness, etc.) of the adaptive decoupling PI control scheme based on the proposed online parameter estimation technique.


title: Optimality Properties and Driver Input Parameterization for Trail-braking Cornering

abstract: In this work we present an analysis of rally-racing driving techniques using numerical optimization. We provide empirical guidelines for executing a Trail- Braking (TB) maneuver, one of the common cornering techniques used in rally-racing. These guidelines are verified via experimental data collected from TB maneuvers performed by an expert rally driver. We show that a TB maneuver can be generated as a special case of the minimum-time cornering problem subject to specific boundary conditions. We propose simple parameterizations of the driver's steering, throttle and braking commands that lead to an alternative formulation of the optimization problem with a reduced search space. We show that the proposed parametrization of the driver's commands can reproduce TB maneuvers along a variety of corner geometries, requiring only a small number of parameters.


title: Managing smart grid information in the cloud: opportunities, model, and applications

abstract: Smart grid (SG), regarded as the next-generation electric grid, will use advanced power, communication, and information technologies to create an automated, intelligent, and widely distributed energy delivery network. In this article, we explore how cloud computing (CC), a next-generation computing paradigm, can be used for information management of the SG and present a novel SG information management paradigm, called Cloud Service-Based SG Information Management (CSSGIM). We analyze the benefits and opportunities from the perspectives of both the SG and CC domains. We further propose a model for CSSGIM and present four motivating applications.


title: Real-time Human Pose and Shape Estimation for Virtual Try-On Using a Single Commodity Depth Camera

abstract: We present a system that allows the user to virtually try on new clothes. It uses a single commodity depth camera to capture the user in 3D. Both the pose and the shape of the user are estimated with a novel real-time template-based approach that performs tracking and shape adaptation jointly. The result is then used to drive realistic cloth simulation, in which the synthesized clothes are overlayed on the input image. The main challenge is to handle missing data and pose ambiguities due to the monocular setup, which captures less than 50 percent of the full body. Our solution is to incorporate automatic shape adaptation and novel constraints in pose tracking. The effectiveness of our system is demonstrated with a number of examples.


title: Molecular dynamics simulations of dynamic force microscopy: applications to the Si(111)-7×7 surface

abstract: Molecular dynamics simulations have been performed to understand true atomic resolution, which has been observed on the Si(111)-7 X 7 surface by dynamic force microscopy (DFM) in ultra high vacuum (UHV). Stable atomic-scale contrast is reproduced in simulations at constant mean height above a critical tip-sample separation when monitoring the interaction force between tip and sample. Missing or additional adatoms can be recognized in such scans, although they are less well resolved than native adatoms. The resonance frequency shift, as well as arbitrary scans, e.g., at constant force, can be computed from a series of force-distance characteristics. By means of dynamic simulations, we show how energy losses induced by interaction with an oscillating tip can be monitored, and that they occur even in the non-contact range. (C) 2000 Elsevier Science B.V. All rights reserved.


title: Adaptive Batch Normalization for Practical Domain Adaptation

abstract: Abstract Deep neural networks (DNN) have shown unprecedented success in various computer vision applications such as image classification and object detection. However, it is still a common annoyance during the training phase, that one has to prepare at least thousands of labeled images to fine-tune a network to a specific domain. Recent study (Tommasi et al., 2015) shows that a DNN has strong dependency towards the training dataset, and the learned features cannot be easily transferred to a different but relevant task without fine-tuning. In this paper, we propose a simple yet powerful remedy, called Adaptive Batch Normalization (AdaBN) to increase the generalization ability of a DNN. By modulating the statistics from the source domain to the target domain in all Batch Normalization layers across the network, our approach achieves deep adaptation effect for domain adaptation tasks. In contrary to other deep learning domain adaptation methods, our method does not require additional components, and is parameter-free. It archives state-of-the-art performance despite its surprising simplicity. Furthermore, we demonstrate that our method is complementary with other existing methods. Combining AdaBN with existing domain adaptation treatments may further improve model performance.