title: Sex Differences and HIV Risk Behaviors: The Interaction between the Experience of Multiple Types of Abuse and Self-Restraint on HIV Risk Behaviors

abstract: Adolescents with abuse histories have been shown to be at increased risk to acquire human immunodeficiency virus and sexually transmitted infections. In addition, teens with lower levels of self-restraint or higher levels of distress, such as those with psychiatric concerns, have also demonstrated increased sexual risk behaviors. This study explored sex differences in sexual risk behaviors among a sample of adolescents in a therapeutic/alternative high school setting. Moderated regression analysis showed that a lower level of self-restraint was associated with sexual risk behaviors in boys but not in girls. Rather, the interaction of self-restraint and multiple types of abuse was associated with greater sex risk within girls in this sample. Results suggest that girls and boys with abuse histories and low levels of self-restraint may have different intervention needs related to sexual risk behaviors.


title: Automated detection of vulnerabilities in privileged programs by execution monitoring

abstract: Presents a method for detecting exploitations of vulnerabilities in privileged programs by monitoring their execution using audit trails, where the monitoring is with respect to specifications of the security-relevant behavior of the programs. Our work is motivated by the intrusion detection paradigm, but is an attempt to avoid ad hoc approaches to codifying misuse behavior. Our approach is based on the observation that although privileged programs can be exploited (due to errors) to cause security compromises in systems because of the privileges accorded to them, the intended behavior of privileged programs is, of course, limited and benign. The key, then, is to specify the intended behavior (i.e. the program policy) and to detect any action by a privileged program that is outside the intended behavior and that imperils security. We describe a program policy specification language, which is based on simple predicate logic and regular expressions. In addition, we present specifications of privileged programs in Unix, and a prototype execution monitor for analyzing audit trails with respect to these specifications. The program policies are surprisingly concise and clear, and in addition, capable of detecting exploitations of known vulnerabilities in these programs. Although our work has been motivated by the known vulnerabilities in Unix, we believe that by tightly restricting the behavior of all privileged programs, exploitations of unknown vulnerabilities can be detected. As a check on the specifications, work is in progress on verifying them with respect to an abstract security policy. >


title: The modification of cortical reorganization and chronic pain by sensory feedback.

abstract: Recent neuroscientific evidence has revealed that the adult brain is capable of substantial plastic change in areas such as the primary somatosensory cortex that were formerly thought to be modifiable only during early experience. We discuss research on phantom limb pain as well as chronic back pain that revealed functional reorganization in both the somatosensory and the motor system in these chronic pain states. In phantom limb pain patients, cortical reorganization is correlated with the amount of phantom limb pain; in low back pain patients the amount of reorganizational change increases with chronicity. We present a model of the development of chronic pain that assumes an important role of somatosensory pain memories. In phantom limb pain, we propose that those patients who experienced intense pain prior to the amputation will later likely develop enhanced cortical reorganization and phantom limb pain. We show that cortical plasticity related to chronic pain can be reduced by behavioral interventions that provide feedback to the brain areas that were altered by somatosensory pain memories.


title: Loads analysis of skid steer robot drive system

abstract: This paper presents the results of 3 tons skid steer 6×6 wheel robot drive system field tests. The aim of the investigation was verification of assumed data for drive train design and forces identification in the drivetrain system during maneuvering and crossing obstacles. Robot has hydraulics drive system with motors in the wheels and hydraulic suspension system. The needed drive torque on wheels was determined by pressure measuring on motors.


Approximate Gaussian Elimination


Development of a Virtual Open House to Market a Dietetic Internship Program


title: FlowBroker: A Software-Defined Network Controller Architecture for Multi-Domain Brokering and Reputation

abstract: Software defined networking (SDN) provides great flexibility by decoupling the control and the data planes, allowing for the implementation of new and innovative network control plane experiments. Presently, the reference implementation for SDN architecture is reliant upon a single controller to push flow rules to all SDN-enabled switches in the network, creating a performance bottleneck and single-point of failure in large networks. To provide a scalable yet efficient solution to distributed SDN network management, we propose FlowBroker, a hierarchical brokering agent layer which manages and coordinates among distributed SDN controllers, where each controller is charged with the flow-rule maintenance of the switches in its managed domain. This paper proposes the FlowBroker architecture as a collaborative multi-domain approach to load balancing and network performance enhancement in software-defined networks. Moreover, we introduce distributed machine learning agents to allow controllers to evaluate which brokers are more advantageous than others, from a performance-based reputation perspective. Simulation results show that the FlowBroker architecture, with broker-based collaborative load-balancing and controller-based distributed reputation, can significantly increase the network performance of a multi-domain software-defined network. FlowBroker yields significant reductions in traffic loss, end-to-end delay and maximum link utilization when cooperative brokering and reputation are utilized.


title: Dissociation of Cognitive and Emotional Empathy in Adults with Asperger Syndrome Using the Multifaceted Empathy Test (MET)

abstract: Empathy is a multidimensional construct consisting of cognitive (inferring mental states) and emotional (empathic concern) components. Despite a paucity of research, individuals on the autism spectrum are generally believed to lack empathy. In the current study we used a new, photo-based measure, the Multifaceted Empathy Test (MET), to assess empathy multidimensionally in a group of 17 individuals with Asperger syndrome (AS) and 18 well-matched controls. Results suggested that while individuals with AS are impaired in cognitive empathy, they do not differ from controls in emotional empathy. Level of general emotional arousability and socially desirable answer tendencies did not differ between groups. Internal consistency of the MET’s scales ranged from .71 to .92, and convergent and divergent validity were highly satisfactory.


title: Recognition by Linear Combinations of Models

abstract: An approach to visual object recognition in which a 3D object is represented by the linear combination of 2D images of the object is proposed. It is shown that for objects with sharp edges as well as with smooth bounding contours, the set of possible images of a given object is embedded in a linear space spanned by a small number of views. For objects with sharp edges, the linear combination representation is exact. For objects with smooth boundaries, it is an approximation that often holds over a wide range of viewing angles. Rigid transformations (with or without scaling) can be distinguished from more general linear transformations of the object by testing certain constraints placed on the coefficients of the linear combinations. Three alternative methods of determining the transformation that matches a model to a given image are proposed. >


title: On formalizing fairness in prediction with machine learning

abstract: Machine learning algorithms for prediction are increasingly being used in critical decisions affecting human lives. Various fairness formalizations, with no firm consensus yet, are employed to prevent such algorithms from systematically discriminating against people based on certain attributes protected by law. The aim of this article is to survey how fairness is formalized in the machine learning literature for the task of prediction and present these formalizations with their corresponding notions of distributive justice from the social sciences literature. We provide theoretical as well as empirical critiques of these notions from the social sciences literature and explain how these critiques limit the suitability of the corresponding fairness formalizations to certain domains. We also suggest two notions of distributive justice which address some of these critiques and discuss avenues for prospective fairness formalizations.


Obtaining Evidence for the International Criminal Court Using Data and Quantitative Analysis


title: Taslihan Virtual Reconstruction - Interactive Digital Story or a Serious Game

abstract: During the Ottoman period, Taslihan was the largest accommodation complex in Sarajevo, Bosnia and Herzegovina. Today, only one wall remains as a memento of its existence. In this paper, we compare user appreciation of an interactive digital story about this building and of a serious game about Taslihan to see which application offers more knowledge and immersion while bringing this monument to life in the collective memory of the people.


title: Multi-energy X-ray Techniques for NDT: a New Challenge

abstract: Digital X-ray imaging is a well-known technique for NDT. Based on the energetic dependence of X-ray material absorption, multi-energy techniques can provide mat erial information. Dual-energy techniques have been developed for that purpose. Proved to be efficient for materials differentiation, limits are encounter ed for materials with close attenuation. Beam hardening ef fect and detector imperfections may induce artefact s unacceptable for accurate quantification. For a few years, new types of detectors based on se miconductor technology have emerged with energy discrimination abilities. They reduce acquisition n oise and provide multi-energy information. But impe rfections due to charge sharing and induction signal cannot b e neglected. Dedicated data processing methods are required. So then the question of improving dual-energy techn iques with such detectors arises. Which problems ca n be solved, and which new problems appear? In this paper we give some indications on potential benefits of spectral X-ray imaging for NDT. We present preliminary evaluations of performance and limitations. 1. Introduction: from standard X-ray imaging to multi-energy techniques Conventional X-ray imaging exploits the absorption of radiation through the examined object. This information may be sufficient, for instance in defect detection task considering an homogeneous mono-material sample, but is often inadequate when material characterization is required. More precisely, radiographic imaging p rovides a representation of the object in terms of line-integrals of the attenuation coeffici ent. Tomography provides, through a multi- view acquisition, a reconstructed volume in terms o f absorption values, and gets rid of items superimposition. But in both cases, a single exposu re using a standard detector which provides a single value per pixel can scarcely allo w material identification. In the usual inspection energy range, the attenuati on for X-ray radiation is a combination of two photon-matter interactions: the photoelectric e ffect and Compton scattering. These interactions and their relative contribution to the total attenuation are energy dependant. This dependence can potentially be used to characterize materials - as long as the employed acquisition system permits to obtain this informati on. It is not the case of a conventional X- ray detector, which is composed of a scintillator l ayer coupled to photodiodes array and supplies the deposited energy integrated over the f ull energy range of the generator spectrum. Dual-energy techniques have been developed for that purpose. In terms of system, energy discrimination can be done at source level or at de tector one. In the first case two acquisitions are performed successively, the energy switch being realized by voltage tuning associated with the use of filters. This method is efficient i n terms of energy separation, but assumes the object being static. The implementation using a 2D detector, either scintillator screen or flat panel, allows an acquisition rate at the detector s peed but induces influence of scatter, problematic for numerical accuracy. Dual-energy may also be implemented within a single exposure technique. Adapted detectors consist in tw o receptor layers of scintillator- photodiode type separated by an intermediate filter . The front (resp. back) layer absorbs the low (resp. high) energy photons. The two images are acquired simultaneously, allowing to


title: Parallel Transportation Management and Control System and Its Applications in Building Smart Cities

abstract: Advancements in complexity, complex systems, and the intelligence sciences, particularly smart city technologies, have shown great potential in aiding to ease traffic congestion. The overall approach and the main ideas in building smart transportation for smart cities, particularly ACP (artificial system, computational experiment, and parallel execution)-based parallel transportation management and control systems (PTMS), are presented. PTMS can be expanded to the new generation of intelligent transportation systems. The main components of the proposed architecture include social signal and social traffic, ITS clouds and services, agent-based traffic control, and transportation knowledge automation. Some technical details of these components are discussed. Finally, one case study is introduced, and the effectiveness is analyzed.


title: Unsupervised learning of translation invariant occlusive components

abstract: We study unsupervised learning of occluding objects in images of visual scenes. The derived learning algorithm is based on a probabilistic generative model which parameterizes object shapes, object features and the background. No assumptions are made for the object orders in depth or the objects' planar positions. Parameter optimization is thus subject to the large combinatorics of depth orders and positions. Previous approaches constrained this combinatorics but were still only able to learn a very small number of objects. By applying a novel variational EM approach, we show that even without constraints on the object combinatorics, a relatively large number of objects can be learned. In different numerical experiments, our unsupervised approach extracts explicit object representations with object masks and object features closely aligned with the true objects in the scenes. We investigate the robustness of the approach and the use of the learned representations for inference. Furthermore, we demonstrate generality of the approach by applying it to grayscale images, color-vector images, and Gabor-vector images as well as to motion trajectory data for which the extracted components correspond to motion primitives.


title: Mind the tracker you wear: a security analysis of wearable health trackers

abstract: Wearable tracking devices have gained widespread usage and popularity because of the valuable services they offer, monitoring human's health parameters and, in general, assisting persons to take a better care of themselves. Nevertheless, the security risks associated with such devices can represent a concern among consumers, because of the sensitive information these devices deal with, like sleeping patterns, eating habits, heart rate and so on. In this paper, we analyse the key security and privacy features of two entry level health trackers from leading vendors (Jawbone and Fitbit), exploring possible attack vectors and vulnerabilities at several system levels. The results of the analysis show how these devices are vulnerable to several attacks (perpetrated with consumer-level devices equipped with just bluetooth and Wi-Fi) that can compromise users' data privacy and security, and eventually call the tracker vendors to raise the stakes against such attacks.


title: CT Angiography: Injection and Acquisition Technique

abstract: CT scanner technology is continuously evolving, with scan times becoming shorter with each scanner generation. Achieving adequate arterial opacification synchronized with CT data acquisition is becoming increasingly difficult. A fundamental understanding of early arterial contrast medium dynamics is thus of utmost importance for the design of CT scanning and injection protocols for current and future cardiovascular CT applications. Arterial enhancement is primarily controlled by the iodine flux (injection flow rate) and the injection duration versus a patient's cardiac output and local downstream physiology. The technical capabilities of modern CT equipment require precise scan timing. Together with automated tube current modulation and weight-based injection protocols, both radiation exposure and contrast medium enhancement can be individualized.


title: Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles

abstract: This paper describes the application of state-estimation techniques for the real-time prediction of the state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Specifically, approaches based on the well-known Kalman Filter (KF) and Extended Kalman Filter (EKF), are presented, using a generic cell model, to provide correction for offset, drift, and long-term state divergence-an unfortunate feature of more traditional coulomb-counting techniques. The underlying dynamic behavior of each cell is modeled using two capacitors (bulk and surface) and three resistors (terminal, surface, and end), from which the SoC is determined from the voltage present on the bulk capacitor. Although the structure of the model has been previously reported for describing the characteristics of lithium-ion cells, here it is shown to also provide an alternative to commonly employed models of lead-acid cells when used in conjunction with a KF to estimate SoC and an EKF to predict state-of-health (SoH). Measurements using real-time road data are used to compare the performance of conventional integration-based methods for estimating SoC with those predicted from the presented state estimation schemes. Results show that the proposed methodologies are superior to more traditional techniques, with accuracy in determining the SoC within 2% being demonstrated. Moreover, by accounting for the nonlinearities present within the dynamic cell model, the application of an EKF is shown to provide verifiable indications of SoH of the cell pack.


title: Class Rectification Hard Mining for Imbalanced Deep Learning

abstract: Recognising detailed facial or clothing attributes in images of people is a challenging task for computer vision, especially when the training data are both in very large scale and extremely imbalanced among different attribute classes. To address this problem, we formulate a novel scheme for batch incremental hard sample mining of minority attribute classes from imbalanced large scale training data. We develop an end-to-end deep learning framework capable of avoiding the dominant effect of majority classes by discovering sparsely sampled boundaries of minority classes. This is made possible by introducing a Class Rectification Loss (CRL) regularising algorithm. We demonstrate the advantages and scalability of CRL over existing state-of-the-art attribute recognition and imbalanced data learning models on two large scale imbalanced benchmark datasets, the CelebA facial attribute dataset and the X-Domain clothing attribute dataset.


title: Filtering noise from images with wavelet transforms.

abstract: A new method of filtering MR images is presented that uses wavelet transforms instead of Fourier transforms. The new filtering method does not reduce the sharpness of edges. However, the new method does eliminate any small structures that are similar in size to the noise eliminated. There are many possible extensions of the filter.


title: Animal-inspired design and aerodynamic stabilization of a hexapedal millirobot

abstract: The VelociRoACH is a 10 cm long, 30 gram hexapedal millirobot capable of running at 2.7 m/s, making it the fastest legged robot built to date, relative to scale. We present the design by dynamic similarity technique and the locomotion adaptations which have allowed for this highly dynamic performance. In addition, we demonstrate that rotational dynamics become critical for stability as the scale of a robotic system is reduced. We present a new method of experimental dynamic tuning for legged millirobots, aimed at finding stable limit cycles with minimal rotational energy. By implementing an aerodynamic rotational damper, we further reduced the rotational energy in the system, and demonstrated that stable limit cycles with lower rotational energy are more robust to disturbances. This method increased the stability of the system without detracting from forward speed.


title: Foundations of SPARQL query optimization

abstract: We study fundamental aspects related to the efficient processing of the SPARQL query language for RDF, proposed by the W3C to encode machine-readable information in the Semantic Web. Our key contributions are (i) a complete complexity analysis for all operator fragments of the SPARQL query language, which -- as a central result -- shows that the SPARQL operator Optional alone is responsible for the PSpace-completeness of the evaluation problem, (ii) a study of equivalences over SPARQL algebra, including both rewriting rules like filter and projection pushing that are well-known from relational algebra optimization as well as SPARQL-specific rewriting schemes, and (iii) an approach to the semantic optimization of SPARQL queries, built on top of the classical chase algorithm. While studied in the context of a theoretically motivated set semantics, almost all results carry over to the official, bag-based semantics and therefore are of immediate practical relevance.


title: Dynamic Local Search for the Maximum Clique Problem

abstract: In this paper, we introduce DLS-MC, a new stochastic local search algorithm for the maximum clique problem. DLS-MC alternates between phases of iterative improvement, during which suitable vertices are added to the current clique, and plateau search, during which vertices of the current clique are swapped with vertices not contained in the current clique. The selection of vertices is solely based on vertex penalties that are dynamically adjusted during the search, and a perturbation mechanism is used to overcome search stagnation. The behaviour of DLS-MC is controlled by a single parameter, penalty delay, which controls the frequency at which vertex penalties are reduced. We show empirically that DLS-MC achieves substantial performance improvements over state-of-the-art algorithms for the maximum clique problem over a large range of the commonly used DIMACS benchmark instances.


title: Local greyvalue invariants for image retrieval

abstract: This paper addresses the problem of retrieving images from large image databases. The method is based on local grayvalue invariants which are computed at automatically detected interest points. A voting algorithm and semilocal constraints make retrieval possible. Indexing allows for efficient retrieval from a database of more than 1,000 images. Experimental results show correct retrieval in the case of partial visibility, similarity transformations, extraneous features, and small perspective deformations.


title: A Padded Encoding Scheme to Accelerate Scans by Leveraging Skew

abstract: In-memory data analytic systems that use vertical bit-parallel scan methods generally use encoding techniques. We observe that in such environments, there is an opportunity to turn skew in both the data and predicate distributions (usually a problem for query processing) into a benefit that can be leveraged to encode the column values. This paper proposes a padded encoding scheme to address this opportunity. The proposed scheme creates encodings that map common attribute values to codes that can easily be distinguished from other codes by only examining a few bits in the full code. Consequently, scans on columns stored using the padded encoding scheme can safely prune the computation without examining all the bits in the code, thereby reducing the memory bandwidth and CPU cycles that are consumed when evaluating scan queries. Our padded encoding method results in a fixed-length encoding, as fixed-length encodings are easier to manage. However, the proposed padded encoding may produce longer (fixed-length) codes than those produced by popular order-preserving encoding methods, such as dictionary-based encoding. This additional space overhead has the potential to negate the gains from early pruning of the scan computation. However, as we demonstrate empirically, the additional space overhead is generally small, and the padded encoding scheme provides significant performance improvements.


title: Interpreting Image Databases by Region Classification

abstract: Abstract This paper addresses automatic interpretation of images of outdoor scenes. The method allows instances of objects from a number of generic classes to be identified: vegetation, buildings; vehicles; roads, etc., thereby enabling image databases to be queried on scene content. The feature set is based, in part, on psychophysical principles and includes measures of colour, texture and shape. Using a large database of ground-truth labelled images, a neural network is trained as a pattern classifier. The method is demonstrated on a large test set to provide highly accurate image interpretations, with over 90% of the image area labelled correctly.


title: ARP-P4: A Hybrid ARP-Path/P4Runtime Switch

abstract: This paper presents ARP-P4, a hybrid switch based on a local ARP-Path control plane written in P4, but maintaining legacy P4 Runtime control plane capabilities. Its main purpose is to study the readiness of P4 to perform directly local control actions at the data plane, such as the required ones for the ARP-Path protocol. Thus, ARP-P4 defines a data plane that forwards any ingress packet locally via ARP-Path or remotely via P4 Runtime installed rules through an SDN controller. Finally, we discuss the current limitations of P4 non-standard externs to interact with P4 Runtime to develop autonomous capabilities.


title: Langevin-gradient parallel tempering for Bayesian neural learning

abstract: Abstract Bayesian inference provides a rigorous approach for neural learning with knowledge representation via the posterior distribution that accounts for uncertainty quantification. Markov Chain Monte Carlo (MCMC) methods typically implement Bayesian inference by sampling from the posterior distribution. This not only provides point estimates of the weights, but the ability to propagate and quantify uncertainty in decision making. However, these techniques face challenges in convergence and scalability, particularly in settings with large datasets and neural network architectures. This paper addresses these challenges in two ways. First, parallel tempering MCMC sampling method is used to explore multiple modes of the posterior distribution and implemented in multi-core computing architecture. Second, we make within-chain sampling scheme more efficient by using Langevin gradient information for creating Metropolis–Hastings proposal distributions. We demonstrate the techniques using time series prediction and pattern classification applications. The results show that the method not only improves the computational time, but provides better decision making capabilities when compared to related methods.


title: Ethanol exposure reduces the density of the low-affinity nerve growth factor receptor (p75) on pheochromocytoma (PC12) cells

abstract: Although ethanol is detrimental to the developing nervous system, the mechanism(s) by which ethanol produces neuronal damage is (are) not clear. One potential mechanism is ethanol-induced inhibition of neurotrophic support. This study utilized an in vitro model, pheochromocytoma PC12 cells, to examine the effect of ethanol on the nerve growth factor (NGF) receptor. NGF binding studies indicated that ethanol exposure (400 mg/dl for 4 days) reduced the density of the low-affinity (p75) NGF receptor on PC12 cells, but had no effect on the density of the high-affinity NGF receptor. The equilibrium dissociation constants (Kd) for both the low-affinity and high-affinity NGF receptors were unaffected by ethanol. Low-affinity NGF binding is mediated by the p75 component of the NGF receptor. Quantification of p75 by immunoprecipitation revealed that ethanol reduced the level of p75 in PC12 cells. However, Northern analysis indicated that the p75 mRNA was not reduced by ethanol exposure, raising the possibilities that ethanol inhibited translation of p75 or incorporation of the p75 protein into the plasma membrane. This work is consistent with the hypothesis that ethanol's detrimental effects may be produced in part by inhibition of neurotrophic support at the receptor level.


title: Reduced DC-Link Voltage Active Power Filter Using Modified PUC5 Converter

abstract: In this letter, the five-level packed U-cell (PUC5) inverter is reconfigured with two identical dc links operating as an active power filter (APF). Generally, the peak voltage of an APF should be greater than the ac voltage at the point-of-common coupling (PCC) to ensure the boost operation of the converter in order to inject harmonic current into the system effectively; therefore, full compensation can be obtained. The proposed modified PUC5 (MPUC5) converter has two equally regulated separated dc links, which can operate at no load condition useful for APF application. Those divided dc terminals amplitudes are added at the input of the MPUC5 converter to generate a boosted voltage that is higher than the PCC voltage. Consequently, the reduced dc-links voltages are achieved since they do not individually need to be higher than the PCC voltage due to the mentioned fact that their summation has to be higher than PCC voltage. The voltage balancing unit is integrated into the modulation technique to be decoupled from the APF controller. The proposed APF is practically tested to validate its good dynamic performance in harmonic elimination, ac-side power factor correction, reactive power compensation, and power quality improvement.


Isoelastic behavior of parallel robots


title: Communication for Polio Eradication: Improving the Quality of Communication Programming Through Real-Time Monitoring and Evaluation

abstract: Communication is a critical component in assuring that children are fully immunized and that simultaneous immunity is attained and maintained across large geographic areas for disease eradication and control initiatives. If service delivery is of good quality and outreach to the population is active, effective communication--through advocacy, social mobilization, and program communication (including behavior change activities and interpersonal communication)--will assist in raising awareness, creating and sustaining demand, preventing or dispelling misinformation and doubts, encouraging acceptance of and participation in vaccination services, more rapid reporting of disease cases and outbreaks, and mobilizing financial resources to support immunization efforts. There is evidence of 12% to 20% or more increases in the absolute level of immunization coverage and 33% to 100% increases in relative coverage compared to baselines when communication is included as a key component of immunization strengthening. This article utilizes evidence from Afghanistan, India, Pakistan, and Nigeria to examine how the Global Polio Eradication Initiative has utilized monitoring and evaluation data to focus and improve the quality and impact of communication activities.


title: Modified greedy permutation algorithm for low complexity encoding in LDPC codes

abstract: The low-density parity-check (LDPC) codes are used to achieve excellent performance with low encoding and decoding complexity. One major criticism concerning LDPC codes has been their apparent high encoding complexity and memory inefficient nature due to large parity check matrix. More generally, we consider the encoding problem for codes specified by sparse parity-check matrices. We show how to exploit the sparseness of the parity-check matrix to obtain efficient encoders. A new technique for efficient encoding of LDP Codes based on the known concept of approximate lower triangulation (ALT) is introduced. The algorithm computes parity check symbols by solving a set of sparse equations, and the triangular factorization is employed to solve the equations efficiently. The key of the encoding method is to get the systematic approximate lower triangular (SALT) form of the Parity Check Matrix with minimum gap g, because the smaller the gap is, the more efficient encoding will be obtained. The functions are to be coded in MATLAB.


Machine Learning for Information Retrieval : Neural Networks , Symbolic Learning , and Genetic Algorithms


title: Low-loss 0.13-µm CMOS 50 – 70 GHz SPDT and SP4T switches

abstract: This paper presents 50–70 GHz single-pole double-throw (SPDT) and single-pole four-throw (SP4T) switches built using a low-cost 0.13-µm CMOS process. The switches are based on tuned λ/4 designs with output matching networks. High substrate resistance together with deep trenches and isolation moats are used for low insertion loss. The SPDT and SP4T switches result in a measured insertion loss of 2.0 and 2.3 dB at 60 GHz, with an isolation of ≫ 32 dB and ≫ 22 dB, respectively. The measured output port-to-port isolation is ≫ 27 dB for both designs. The P1dB is 13–14 dBm with a measured IIP3 of ≫ 23 dBm for both switches. Both designs have a return loss better than −10 dB at all ports from 50 to 70 GHz. The active chip area is 0.39×0.32 mm2 (SPDT) and 0.59×0.45 mm2 (SP4T). To our knowledge, this paper presents the lowest loss 60 GHz SPDT and SP4T switches and also the highest isolation SPDT switch in any CMOS technology to-date.


title: Bypassing Negative Epistasis on Yield in Tomato Imposed by a Domestication Gene

abstract: Selection for inflorescence architecture with improved flower production and yield is common to many domesticated crops. However, tomato inflorescences resemble wild ancestors, and breeders avoided excessive branching because of low fertility. We found branched variants carry mutations in two related transcription factors that were selected independently. One founder mutation enlarged the leaf-like organs on fruits and was selected as fruit size increased during domestication. The other mutation eliminated the flower abscission zone, providing "jointless" fruit stems that reduced fruit dropping and facilitated mechanical harvesting. Stacking both beneficial traits caused undesirable branching and sterility due to epistasis, which breeders overcame with suppressors. However, this suppression restricted the opportunity for productivity gains from weak branching. Exploiting natural and engineered alleles for multiple family members, we achieved a continuum of inflorescence complexity that allowed breeding of higher-yielding hybrids. Characterizing and neutralizing similar cases of negative epistasis could improve productivity in many agricultural organisms. VIDEO ABSTRACT.


title: The Attention System of the Human Brain

abstract: Abstract : The concept of attention as central to human performance extends back to the start of experimental psychology, yet even a few years ago, it would not have been possible to outline in even a preliminary form a functional anatomy of the human attentional system. New developments in neuroscience have opened the study of higher cognition to physiological analysis, and have revealed a system of anatomical areas that appear to be basic to the selection of information for focal (conscious) processing. The importance of attention is its unique role in connecting the mental level of description of processes used in cognitive science with the anatomical level common in neuroscience. Sperry describes the central role that mental concepts play in understanding brain function. As is the case for sensory and motor systems of the brain, our knowledge of the anatomy of attention is incomplete. Nevertheless, we can now begin to identify some principles of organization that allow attention to function as a unified system for the control of mental processing. Although many of our points are still speculative and controversial, we believe they constitute a basis for more detailed studies of attention from a cognitive-neuroscience viewpoint. Perhaps even more important for furthering future studies, multiple methods of mental chronometry, brain lesions, electrophysiology, and several types of neuro-imaging have converged on common findings.


title: CMUcam3: An Open Programmable Embedded Vision Sensor

abstract: In this paper we present CMUcam3, a low-cost, open source, embedded computer vision platform. The CMUcam3 is the third generation of the CMUcam system and is designed to provide a flexible and easy to use open source development environment along with a more powerful hardware platform. The goal of the system is to provide simple vision capabilities to small embedded systems in the form of an intelligent sensor that is supported by an open source community. The hardware platform consists of a color CMOS camera, a frame buffer, a low cost 32bit ARM7TDMI microcontroller, and an MMC memory card slot. The CMUcam3 also includes 4 servo ports, enabling one to create entire, working robots using the CMUcam3 board as the only requisite robot processor. Custom C code can be developed using an optimized GNU toolchain and executables can be flashed onto the board using a serial port without external downloading hardware. The development platform includes a virtual camera target allowing for rapid application development exclusively on a PC. The software environment comes with numerous open source example applications and libraries including JPEG compression, frame differencing, color tracking, convolutions, histogramming, edge detection, servo control, connected component analysis, FAT file system support, and a face detector.


title: A severity rating scale for body dysmorphic disorder: development, reliability, and validity of a modified version of the Yale-Brown Obsessive Compulsive Scale.

abstract: The authors developed the Yale Brown Obsessive Compulsive Scale Modified for Body Dysmorphic Disorder (BDD-YBOCS), a 12-item semistructured clinician-rated instrument designed to rate severity of body dysmorphic disorder (BDD). The scale was administered to 125 subjects with BDD, and interviews with 15 subjects were rated by 3 other raters. Test-retest reliability was assessed in 30 subjects. Other scales were administered to assess convergent and discriminant validity, and sensitivity to change was evaluated in a study of fluvoxamine. Each item was frequently endorsed across a range of severity. Good interrater reliability, test-retest reliability, and internal consistency were obtained. BDD-YBOCS scores correlated with global severity scores but not with a measure of general psychopathology; they were modestly positively correlated with depression severity scores. Three factors accounted for 59.6 percent of the variance. The scale was sensitive to change in BDD severity. The BDD-YBOCS appears to be a reliable and valid measure of BDD severity and is a suitable outcome measure in treatment studies of BDD.


title: Matlab-Simulink model of solid-state transformer realized with matrix converters

abstract: A three-phase to three-phase solid-state transformer (SST) is considered in the present paper. The SST realization is based on a matrix converter of three-phase line voltage to one-phase high-frequency voltage. A high-frequency single-phase transformer is used with center tapped secondary winding for galvanic isolation and for obtaining a high transformation coefficient. The converter in the secondary winding of the transformer operates in a mode of high-frequency rectifier, which modulates the rectified voltage according to sinusoidal law with the line frequency. The algorithm of operation is verified using simulation using the program Matlab-Simulink. The simulation results are presented in the paper.


title: Analytical equations for CT dose profiles derived using a scatter kernel of Monte Carlo parentage with broad applicability to CT dosimetry problems

abstract: Purpose: Knowledge of the complete axial dose profile f(z), including its long scatter tails, provides the most complete (and flexible) description of the accumulated dose in CT scanning. The CTDI paradigm (including CTDI{sub vol}) requires shift-invariance along z (identical dose profiles spaced at equal intervals), and is therefore inapplicable to many of the new and complex shift-variant scan protocols, e.g., high dose perfusion studies using variable (or zero) pitch. In this work, a convolution-based beam model developed by Dixon et al.[Med. Phys. 32, 3712-3728, (2005)] updated with a scatter LSF kernel (or DSF) derived from a Monte Carlo simulation by Boone [Med. Phys. 36, 4547-4554 (2009)] is used to create an analytical equation for the axial dose profile f(z) in a cylindrical phantom. Using f(z), equations are derived which provide the analytical description of conventional (axial and helical) dose, demonstrating its physical underpinnings; and likewise for the peak axial dose f(0) appropriate to stationary phantom cone beam CT, (SCBCT). The methodology can also be applied to dose calculations in shift-variant scan protocols. This paper is an extension of our recent work Dixon and Boone [Med. Phys. 37, 2703-2718 (2010)], which dealt only with the properties of the peak dose f(0),more » its relationship to CTDI, and its appropriateness to SCBCT. Methods: The experimental beam profile data f(z) of Mori et al.[Med. Phys. 32, 1061-1069 (2005)] from a 256 channel prototype cone beam scanner for beam widths (apertures) ranging from a = 28 to 138 mm are used to corroborate the theoretical axial profiles in a 32 cm PMMA body phantom. Results: The theoretical functions f(z) closely-matched the central axis experimental profile data{sup 11} for all apertures (a = 28 -138 mm). Integration of f(z) likewise yields analytical equations for all the (CTDI-based) dosimetric quantities of conventional CT (including CTDI{sub L} itself) in addition to the peak dose f(0) relevant to SCBCT (allowing direct cross-comparison between CT scan modes and mathematical proofs of several hypotheses of practical utility in CT dosimetry). A fast, analytical dose simulator{sup 6} is also demonstrated--successfully matching complex dose distributions measured using OSL and film dosimetry. Conclusions: The model described allows one to obtain analytical functions describing both the primary and scatter components of the axial dose profile. This model (using no empirical functions or adjustable fit parameters) provides a good match to the experimental data, as well as a complete analytical description of dose for both conventional (axial and helical) CT and cone beam CT. An efficient method whereby the complete data set for both modalities can be obtained from a single measurement of either CTDI{sub 100} or f(0) is illustrated. This method is also flexible--allowing calculation of heretofore unattainable doses for recently-introduced shift-variant protocols [e.g., variable pitch (irregular scan spacing), variable aperture, shuttle mode acquisition, and mA modulation schemes].« less


title: Cost effective video streaming using server push over HTTP 2.0

abstract: The Hypertext Transfer Protocol (HTTP) has been widely adopted and deployed as the key protocol for video streaming over the Internet. One of the consequences of leveraging traditional HTTP for video streaming is the significantly increased request overhead due to the segmentation of the video content into HTTP resources. The overhead becomes even more significant when non-multiplexed video and audio segments are deployed. In this paper, we investigate and address the request overhead problem by employing the server push technology in the new HTTP 2.0 protocol. In particular, we develop a set of push strategies that actively deliver video and audio content from the HTTP server without requiring a request for each individual segment. We evaluate our approach in a Dynamic Adaptive Streaming over HTTP (DASH) streaming system. We show that the request overhead can be significantly reduced by using our push strategies. Also, we validate that the server push based approach is compatible with the existing HTTP streaming features, such as adaptive bitrate switching.


title: Feature-driven visual analytics of soccer data

abstract: Soccer is one the most popular sports today and also very interesting from an scientific point of view. We present a system for analyzing high-frequency position-based soccer data at various levels of detail, allowing to interactively explore and analyze for movement features and game events. Our Visual Analytics method covers single-player, multi-player and event-based analytical views. Depending on the task the most promising features are semi-automatically selected, processed, and visualized. Our aim is to help soccer analysts in finding the most important and interesting events in a match. We present a flexible, modular, and expandable layer-based system allowing in-depth analysis. The integration of Visual Analytics techniques into the analysis process enables the analyst to find interesting events based on classification and allows, by a set of custom views, to communicate the found results. The feedback loop in the Visual Analytics pipeline helps to further improve the classification results. We evaluate our approach by investigating real-world soccer matches and collecting additional expert feedback. Several use cases and findings illustrate the capabilities of our approach.


"PageRank" for Argument Relevance


title: Memory access buffering in multiprocessors

abstract: In highly-pipelined machines, instructions and data are prefetched and buffered in both the processor and the cache. This is done to reduce the average memory access latency and to take advantage of memory interleaving. Lock-up free caches are designed to avoid processor blocking on a cache miss. Write buffers are often included in a pipelined machine to avoid processor waiting on writes. In a shared memory multiprocessor, there are more advantages in buffering memory requests, since each memory access has to traverse the memory- processor interconnection and has to compete with memory requests issued by different processors. Buffering, however, can cause logical problems in multiprocessors. These problems are aggravated if each processor has a private memory in which shared writable data may be present, such as in a cache-based system or in a system with a distributed global memory. In this paper, we analyze the benefits and problems associated with the buffering of memory requests in shared memory multiprocessors. We show that the logical problem of buffering is directly related to the problem of synchronization. A simple model is presented to evaluate the performance improvement resulting from buffering.


title: Mobile Apps for Weight Management: A Scoping Review

abstract: Background: Obesity remains a major public health concern. Mobile apps for weight loss/management are found to be effective for improving health outcomes in adults and adolescents, and are pursued as a cost-effective and scalable intervention for combating overweight and obesity. In recent years, the commercial market for ‘weight loss apps’ has expanded at rapid pace, yet little is known regarding the evidence-based quality of these tools for weight control. Objective: To characterize the inclusion of evidence-based strategies, health care expert involvement, and scientific evaluation of commercial mobile apps for weight loss/management. Methods: An electronic search was conducted between July 2014 and July 2015 of the official app stores for four major mobile operating systems. Three raters independently identified apps with a stated goal of weight loss/management, as well as weight loss/management apps targeted to pediatric users. All discrepancies regarding selection were resolved through discussion with a fourth rater. Metadata from all included apps were abstracted into a standard assessment criteria form and the evidence-based strategies, health care expert involvement, and scientific evaluation of included apps was assessed. Evidence-based strategies included: self-monitoring, goal-setting, physical activity support, healthy eating support, weight and/or health assessment, personalized feedback, motivational strategies, and social support. Results: A total of 393 apps were included in this review. Self-monitoring was most common (139/393, 35.3%), followed by physical activity support (108/393, 27.5%), weight assessment (100/393, 25.4%), healthy eating support (91/393, 23.2%), goal-setting (84/393, 21.4%), motivational strategies (28/393, 7.1%), social support (21/393, 5.3%), and personalized feedback (7/393, 1.8%). Of apps, 0.8% (3/393) underwent scientific evaluation and 0.3% (1/393) reported health care expert involvement. No apps were comprehensive in the assessment criteria, with the majority of apps meeting less than two criteria. Conclusions: Commercial mobile apps for weight loss/management lack important evidence-based features, do not involve health care experts in their development process, and have not undergone rigorous scientific testing. This calls into question the validity of apps’ claims regarding their effectiveness and safety, at a time when the availability and growth in adoption of these tools is rapidly increasing. Collaborative efforts between developers, researchers, clinicians, and patients are needed to develop and test high-quality, evidence-based mobile apps for weight loss/management before they are widely disseminated in commercial markets. [JMIR Mhealth Uhealth 2016;4(3):e87]


title: Possibilities and limitations of using Lemna minor, Hydrocharis morsus-ranae and Ceratophyllum demersum in removing metals with contaminated water

abstract: Abstract The paper presents the assessment of possibilities and limitations of the use of Lemna minor, Hydrocharis morsus-ranae and Ceratophyllum demersum in removing metals from contaminated waters. Synthetically discussed the role of these species in phytotechnology and their importance in the assessment of surface water status. The variability of concentration of selected metals in waters and the content of metals in the organs of the analysed plants are presented. Their advantages and disadvantages in removing metals from waters due to biological features have been characterized. Minimum and maximum efficiency of metal removal depending on the scale of water pollution was determined. It was found that analysed plants can be used for phytoremediation of metals from water, but the limitation of effectiveness of treatments is the toxicity of these metals to plants and the time of exposure. The highest removal efficiency can be obtained thanks to the use of sequences of single-species filtration systems.


title: Object Detection and Recognition for Assistive Robots: Experimentation and Implementation

abstract: Technological advances are being made to assist humans in performing ordinary tasks in everyday settings. A key issue is the interaction with objects of varying size, shape, and degree of mobility. Autonomous assistive robots must be provided with the ability to process visual data in real time so that they can react adequately for quickly adapting to changes in the environment. Reliable object detection and recognition is usually a necessary early step to achieve this goal. In spite of significant research achievements, this issue still remains a challenge when real-life scenarios are considered. In this article, we present a vision system for assistive robots that is able to detect and recognize objects from a visual input in ordinary environments in real time. The system computes color, motion, and shape cues, combining them in a probabilistic manner to accurately achieve object detection and recognition, taking some inspiration from vision science. In addition, with the purpose of processing the input visual data in real time, a graphical processing unit (GPU) has been employed. The presented approach has been implemented and evaluated on a humanoid robot torso located at realistic scenarios. For further experimental validation, a public image repository for object recognition has been used, allowing a quantitative comparison with respect to other state-of-the-art techniques when realworld scenes are considered. Finally, a temporal analysis of the performance is provided with respect to image resolution and the number of target objects in the scene.


title: Attribute learning in largescale datasets

abstract: We consider the task of learning visual connections between object categories using the ImageNet dataset, which is a large-scale dataset ontology containing more than 15 thousand object classes. We want to discover visual relationships between the classes that are currently missing (such as similar colors or shapes or textures). In this work we learn 20 visual attributes and use them in a zero-shot transfer learning experiment as well as to make visual connections between semantically unrelated object categories.


title: Open Set Fingerprint Spoof Detection Across Novel Fabrication Materials

abstract: A fingerprint spoof detector is a pattern classifier that is used to distinguish a live finger from a fake (spoof) one in the context of an automated fingerprint recognition system. Most spoof detectors are learning-based and rely on a set of training images. Consequently, the performance of any such spoof detector significantly degrades when encountering spoofs fabricated using novel materials not found in the training set. In real-world applications, the problem of fingerprint spoof detection must be treated as an open set recognition problem where incomplete knowledge of the fabrication materials used to generate spoofs is present at training time, and novel materials may be encountered during system deployment. To mitigate the security risk posed by novel spoofs, this paper introduces: 1) the use of the Weibull-calibrated SVM (W-SVM), which is relatively robust for open set recognition, as a novel-material detector and a spoof detector and 2) a scheme for the automatic adaptation of the W-SVM-based spoof detector to new spoof materials that leverages interoperability across classifiers. Experiments conducted on new partitions of the LivDet 2011 database designed for open set evaluation suggest: 1) a 97% increase in the error rate of the existing spoof detectors when tested using new spoof materials and 2) up to 44% improvement in spoof detection performance across spoof materials when the proposed adaptive approach is used.


title: A Survey of Usability Evaluation in Virtual Environments: Classification and Comparison of Methods

abstract: Virtual environments (VEs) are a relatively new type of human-computer interface in which users perceive and act in a three-dimensional world. The designers of such systems cannot rely solely on design guidelines for traditional two-dimensional interfaces, so usability evaluation is crucial for VEs. This paper presents an overview of VE usability evaluation to organize and critically analyze diverse work from this field. First, we discuss some of the issues that differentiate VE usability evaluation from evaluation of traditional user interfaces such as GUIs. We also present a review of some VE evaluation methods currently in use, and discuss a simple classification space for VE usability evaluation methods. This classification space provides a structured means for comparing evaluation methods according to three key characteristics: involvement of representative users, context of evaluation, and types of results produced. Finally, to illustrate these concepts, we compare two existing evaluation approaches: testbed evaluation (Bowman, Johnson, & Hodges, 1999) and sequential evaluation (Gabbard, Hix, & Swan, 1999).


title: Classifying phishing URLs using recurrent neural networks

abstract: As the technical skills and costs associated with the deployment of phishing attacks decrease, we are witnessing an unprecedented level of scams that push the need for better methods to proactively detect phishing threats. In this work, we explored the use of URLs as input for machine learning models applied for phishing site prediction. In this way, we compared a feature-engineering approach followed by a random forest classifier against a novel method based on recurrent neural networks. We determined that the recurrent neural network approach provides an accuracy rate of 98.7% even without the need of manual feature creation, beating by 5% the random forest method. This means it is a scalable and fast-acting proactive detection system that does not require full content analysis.


title: Incentive Mechanism Design for Computation Offloading in Heterogeneous Fog Computing: A Contract-Based Approach

abstract: Fog computing is a promising solution for new emerging applications requiring intensive computation resources and low latency. Devices at the edge of network can share idle resources and collaboratively accomplish the computing tasks in fog computing. Thus, task publishers have heterogeneous options when offloading computing tasks considering the quality of transmission links, energy consumption and other hardware constraints of fog nodes. To incentivize these devices to participate in computation offloading, effective incentive mechanisms are needed. In this paper, utilizing the framework of contract theory, we formulate the negotiation between task ublisher and fog nodes as an optimization problem. The optimal contract is the Nash equilibrium solution achieved by task publisher and fog nodes. Simulation results show that an optimal contract can maximize the utility of task publisher meanwhile guarantee the individual rationality and incentive compatibility of fog nodes. Therefore, edge devices can be incentivized effectively to involve in the computation offloading.


title: Real-time data-driven interactive rough sketch inking

abstract: We present an interactive approach for inking, or turning a pencil rough sketch into a clean line drawing, based on data-driven "smart" tools that intuitively react to user input.


title: Chi: A Scalable and Programmable Control Plane for Distributed Stream Processing Systems

abstract: Stream-processing workloads and modern shared cluster environments exhibit high variability and unpredictability. Combined with the large parameter space and the diverse set of user SLOs, this makes modern streaming systems very challenging to statically configure and tune. To address these issues, in this paper we investigate a novel control-plane design, Chi, which supports continuous monitoring and feedback, and enables dynamic re-configuration. Chi leverages the key insight of embedding control-plane messages in the data-plane channels to achieve a low-latency and flexible control plane for stream-processing systems. ::: ::: Chi introduces a new reactive programming model and design mechanisms to asynchronously execute control policies, thus avoiding global synchronization. We show how this allows us to easily implement a wide spectrum of control policies targeting different use cases observed in production. Large-scale experiments using production workloads from a popular cloud provider demonstrate the flexibility and efficiency of our approach.


title: Transformation Autoregressive Networks

abstract: The fundamental task of general density estimation $p(x)$ has been of keen interest to machine learning. In this work, we attempt to systematically characterize methods for density estimation. Broadly speaking, most of the existing methods can be categorized into either using: \textit{a}) autoregressive models to estimate the conditional factors of the chain rule, $p(x_{i}\, |\, x_{i-1}, \ldots)$; or \textit{b}) non-linear transformations of variables of a simple base distribution. Based on the study of the characteristics of these categories, we propose multiple novel methods for each category. For example we proposed RNN based transformations to model non-Markovian dependencies. Further, through a comprehensive study over both real world and synthetic data, we show for that jointly leveraging transformations of variables and autoregressive conditional models, results in a considerable improvement in performance. We illustrate the use of our models in outlier detection and image modeling. Finally we introduce a novel data driven framework for learning a family of distributions.


title: A Simplified, Reliable Approach for Advancement Genioplasty

abstract: IMPORTANCE ::: Advancement genioplasty is a versatile procedure able to address both functional and cosmetic issues. It is commonly performed by oral surgeons and less so by facial plastic and plastic surgeons, presumably because of its inherent risks. ::: ::: ::: OBJECTIVES ::: To present our technique for advancement genioplasty and our aesthetic and functional outcomes. ::: ::: ::: DESIGN, SETTING, AND PARTICIPANTS ::: A retrospective review was conducted of the medical records of 126 patients who had undergone advancement genioplasty with our modified technique in a private practice setting from September 1997, through September 2014. Our analysis was performed between January and March 2015. ::: ::: ::: MAIN OUTCOMES AND MEASURES ::: Description of technique and evaluation of complication rates, improvement in sleep apnea symptoms, and patient satisfaction. ::: ::: ::: RESULTS ::: The 126 patients had a mean age of 39.8 years. There were 81 male and 45 female patients. The overall complication rate was 6.3% (8 of the 126 patients). Complications included plate extrusion in 2 patients, infection in 2 patients, mental nerve injury in 3 patients, and tooth root injury in 1 patient. Patients were satisfied with the aesthetic result of the procedure, which also produced an improvement in obstructive sleep apnea in 82 of 89 (92%) patients with sleep apnea. ::: ::: ::: CONCLUSIONS AND RELEVANCE ::: Advancement genioplasty is a valuable tool that can address both functional and aesthetic issues. The technique presented here allows safe, reliable, and effective execution of the procedure. ::: ::: ::: LEVEL OF EVIDENCE ::: 3.


title: Abstractive Text Classification Using Sequence-to-convolution Neural Networks

abstract: We propose a new deep neural network model and its training scheme for text classification. Our model Sequence-to-convolution Neural Networks(Seq2CNN) consists of two blocks: Sequential Block that summarizes input texts and Convolution Block that receives summary of input and classifies it to a label. Seq2CNN is trained end-to-end to classify various-length texts without preprocessing inputs into fixed length. We also present Gradual Weight Shift(GWS) method that stabilizes training. GWS is applied to our model's loss function. We compared our model with word-based TextCNN trained with different data preprocessing methods. We obtained significant improvement in classification accuracy over word-based TextCNN without any ensemble or data augmentation.


title: Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Perspective

abstract: Integrating the unmanned aerial vehicles (UAVs) into the cellular network is envisioned to be a promising technology to significantly enhance the communication performance of both UAVs and existing terrestrial users. In this paper, we first provide an overview on the two main research paradigms in cellular UAV communications, namely, cellular-enabled UAV communication with UAVs as new aerial users served by the ground base stations (GBSs), and UAV-assisted cellular communication with UAVs as new aerial communication platforms serving the terrestrial users. Then, we focus on the former paradigm and study a new UAV trajectory design problem subject to practical communication connectivity constraints with the GBSs. Specifically, we consider a cellular-connected UAV in the mission of flying from an initial location to a final location that are given, during which it needs to maintain reliable communication with the cellular network by associating with one of the available GBSs at each time instant that has the best line-of-sight channel (or shortest distance) with it. We aim to minimize the UAV’s mission completion time by optimizing its trajectory, subject to a quality-of-connectivity constraint of the GBS-UAV link specified by a minimum receive signal-to-noise ratio target, which needs to be satisfied throughout its mission. To tackle this challenging non-convex optimization problem, we first propose an efficient method to verify its feasibility via checking the connectivity between two given vertices on an equivalent graph. Next, by examining the GBS-UAV association sequence over time, we obtain useful structural results on the optimal UAV trajectory, based on which two efficient methods are proposed to find high-quality approximate trajectory solutions by leveraging the techniques from graph theory and convex optimization. The proposed methods are analytically shown to be capable of achieving a flexible tradeoff between complexity and performance, and yielding a solution in polynomial time with the performance arbitrarily close to that of the optimal solution. Numerical results further validate the effectiveness of our proposed designs against benchmark schemes. Finally, we make concluding remarks and point out promising directions for future work.


title: Face anti-spoofing with multifeature videolet aggregation

abstract: Biometric systems can be attacked in several ways and the most common being spoofing the input sensor. Therefore, anti-spoofing is one of the most essential prerequisite against attacks on biometric systems. For face recognition it is even more vulnerable as the image capture is non-contact based. Several anti-spoofing methods have been proposed in the literature for both contact and non-contact based biometric modalities often using video to study the temporal characteristics of a real vs. spoofed biometric signal. This paper presents a novel multi-feature evidence aggregation method for face spoofing detection. The proposed method fuses evidence from features encoding of both texture and motion (liveness) properties in the face and also the surrounding scene regions. The feature extraction algorithms are based on a configuration of local binary pattern and motion estimation using histogram of oriented optical flow. Furthermore, the multi-feature windowed videolet aggregation of these orthogonal features coupled with support vector machine-based classification provides robustness to different attacks. We demonstrate the efficacy of the proposed approach by evaluating on three standard public databases: CASIA-FASD, 3DMAD and MSU-MFSD with equal error rate of 3.14%, 0%, and 0%, respectively.


title: Flexible MXene/Carbon Nanotube Composite Paper with High Volumetric Capacitance

abstract: Dr. M.-Q. Zhao, C. E. Ren, Z. Ling, M. R. Lukatskaya, C. F. Zhang, K. L. Van Aken, Prof. M. W. Barsoum, Prof. Y. Gogotsi Department of Materials Science and Engineering and A. J. Drexel Nanomaterials Institute Drexel University 3141 Chestnut Street , Philadelphia , PA 19104 , USA E-mail: gogotsi@drexel.edu Z. Ling Carbon Research Laboratory Liaoning Key Lab for Energy Materials and Chemical Engineering State Key Lab of Fine Chemicals Dalian University of Technology Dalian 116024 , China C. F. Zhang State Key Laboratory of Chemical Engineering East China University of Science and Technology Shanghai 200237 , China


title: An evaluation framework for Health Information Systems: human, organization and technology-fit factors (HOT-fit)

abstract: BACKGROUND AND PURPOSE: The realization of Health Information Systems (HIS) requires rigorous evaluation that addresses technology, human and organization issues. Our review indicates that current evaluation methods evaluate different aspects of HIS and they can be improved upon. A new evaluation framework, human, organization and technology-fit (HOT-fit) was developed after having conducted a critical appraisal of the findings of existing HIS evaluation studies. HOT-fit builds on previous models of IS evaluation--in particular, the IS Success Model and the IT-Organization Fit Model. This paper introduces the new framework for HIS evaluation that incorporates comprehensive dimensions and measures of HIS and provides a technological, human and organizational fit. METHODS: Literature review on HIS and IS evaluation studies and pilot testing of developed framework. The framework was used to evaluate a Fundus Imaging System (FIS) of a primary care organization in the UK. The case study was conducted through observation, interview and document analysis. RESULTS: The main findings show that having the right user attitude and skills base together with good leadership, IT-friendly environment and good communication can have positive influence on the system adoption. CONCLUSIONS: Comprehensive, specific evaluation factors, dimensions and measures in the new framework (HOT-fit) are applicable in HIS evaluation. The use of such a framework is argued to be useful not only for comprehensive evaluation of the particular FIS system under investigation, but potentially also for any Health Information System in general.


title: Motion of the shoulder complex during multiplanar humeral elevation.

abstract: BACKGROUND ::: Many prior studies have evaluated shoulder motion, yet no three-dimensional analysis comparing the combined clavicular, scapular, and humeral motion during arm elevation has been done. We aimed to describe and compare dynamic three-dimensional motion of the shoulder complex during raising and lowering the arm across three distinct elevation planes (flexion, scapular plane abduction, and coronal plane abduction). ::: ::: ::: METHODS ::: Twelve subjects without a shoulder abnormality were enrolled. Transcortical pin placement into the clavicle, scapula, and humerus allowed electromagnetic motion sensors to be rigidly fixed. The subjects completed two repetitions of raising and lowering the arm in flexion, scapular, and abduction planes. Three-dimensional angles were calculated for sternoclavicular, acromioclavicular, scapulothoracic, and glenohumeral joint motions. Joint angles between humeral elevation planes and between raising and lowering of the arm were compared. ::: ::: ::: RESULTS ::: General patterns of shoulder motion observed during humeral elevation were clavicular elevation, retraction, and posterior axial rotation; scapular internal rotation, upward rotation, and posterior tilting relative to the clavicle; and glenohumeral elevation and external rotation. Clavicular posterior rotation predominated at the sternoclavicular joint (average, 31 degrees). Scapular posterior tilting predominated at the acromioclavicular joint (average, 19 degrees). Differences between flexion and abduction planes of humerothoracic elevation were largest for the glenohumeral joint plane of elevation (average, 46 degrees). ::: ::: ::: CONCLUSIONS ::: Overall shoulder motion consists of substantial angular rotations at each of the four shoulder joints, enabling the multiple-joint interaction required to elevate the arm overhead.


title: Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning

abstract: We present an approach for automatic diagnosis of tissue biopsies. Our methodology consists of a quantitative phase imaging tissue scanner and machine learning algorithms to process these data. We illustrate the performance by automatic Gleason grading of prostate specimens. The imaging system operates on the principle of interferometry and, as a result, reports on the nanoscale architecture of the unlabeled specimen. We use these data to train a random forest classifier to learn textural behaviors of prostate samples and classify each pixel in the image into different classes. Automatic diagnosis results were computed from the segmented regions. By combining morphological features with quantitative information from the glands and stroma, logistic regression was used to discriminate regions with Gleason grade 3 versus grade 4 cancer in prostatectomy tissue. The overall accuracy of this classification derived from a receiver operating curve was 82%, which is in the range of human error when interobserver variability is considered. We anticipate that our approach will provide a clinically objective and quantitative metric for Gleason grading, allowing us to corroborate results across instruments and laboratories and feed the computer algorithms for improved accuracy.


title: Modeling of the Latent Embedding of Music using Deep Neural Network

abstract: While both the data volume and heterogeneity of the digital music content is huge, it has become increasingly important and convenient to build a recommendation or search system to facilitate surfacing these content to the user or consumer community. Most of the recommendation models fall into two primary species, collaborative filtering based and content based approaches. Variants of instantiations of collaborative filtering approach suffer from the common issues of so called"cold start"and"long tail"problems where there is not much user interaction data to reveal user opinions or affinities on the content and also the distortion towards the popular content. Content-based approaches are sometimes limited by the richness of the available content data resulting in a heavily biased and coarse recommendation result. In recent years, the deep neural network has enjoyed a great success in large-scale image and video recognitions. In this paper, we propose and experiment using deep convolutional neural network to imitate how human brain processes hierarchical structures in the auditory signals, such as music, speech, etc., at various timescales. This approach can be used to discover the latent factor models of the music based upon acoustic hyper-images that are extracted from the raw audio waves of music. These latent embeddings can be used either as features to feed to subsequent models, such as collaborative filtering, or to build similarity metrics between songs, or to classify music based on the labels for training such as genre, mood, sentiment, etc.


title: Compressed Synthetic Aperture Radar

abstract: In this paper, we introduce a new synthetic aperture radar (SAR) imaging modality which can provide a high-resolution map of the spatial distribution of targets and terrain using a significantly reduced number of needed transmitted and/or received electromagnetic waveforms. This new imaging scheme, requires no new hardware components and allows the aperture to be compressed. It also presents many new applications and advantages which include strong resistance to countermesasures and interception, imaging much wider swaths and reduced on-board storage requirements.


title: Frequent pattern mining with uncertain data

abstract: This paper studies the problem of frequent pattern mining with uncertain data. We will show how broad classes of algorithms can be extended to the uncertain data setting. In particular, we will study candidate generate-and-test algorithms, hyper-structure algorithms and pattern growth based algorithms. One of our insightful observations is that the experimental behavior of different classes of algorithms is very different in the uncertain case as compared to the deterministic case. In particular, the hyper-structure and the candidate generate-and-test algorithms perform much better than tree-based algorithms. This counter-intuitive behavior is an important observation from the perspective of algorithm design of the uncertain variation of the problem. We will test the approach on a number of real and synthetic data sets, and show the effectiveness of two of our approaches over competitive techniques.


title: Dagster: Censorship-resistant publishing without replication

abstract: In this paper we present Dagster, a new censorship-resistant publishing scheme. Unlike previous censorship-resistant schemes, Dagster does not rely on the widespread replication of data and can even be used in a single server setting. It accomplishes this by “intertwining” legitimate and illegitimate data, so that a censor can not remove objectionable content without simultaneously removing legally protected content. The Dagster system was designed to be as simple and efficient as possible. It increases required network traffic by a contant (but tunable) factor, but otherwise has a very low cost for both clients and servers, making it easy to scale.


title: Bayesian probabilistic model for context-aware recommendations

abstract: Context-aware recommender systems that provide better recommendations for users by using their rating history in different situations have been proposed. Because incorporating all contextual information can make the data sparser and degrade the prediction accuracy, most context-aware methods focus on detecting and using only the most effective contextual factors. However, in addition to accuracy, the diversity of the recommendation is also a key to improving users' satisfaction with recommendation results. Moreover, most context-aware techniques have not considered directly the relationships among context, users, and items before predicting the ratings. In the real world, different contextual factors tend to affect users and items differently. This paper proposes a latent probabilistic model to incorporate the contextual information. By adopting a binary particle-swarm optimization technique, the relevant contextual factors for user classes and item classes are identified and incorporated into the model. We optimize our model for two cases, namely considering accuracy alone and considering the trade-off between accuracy and diversity. An evaluation shows that our proposed model performs better than 1) a model that considers only the relation of context to users alone or items alone, 2) a model that exploits all contextual factors, and 3) the traditional context-aware recommendation method.


title: Insulin-Like Growth Factor I (IGF-1) Ec/Mechano Growth Factor – A Splice Variant of IGF-1 within the Growth Plate

abstract: Human insulin-like growth factor 1 Ec (IGF-1Ec), also called mechano growth factor (MGF), is a splice variant of insulin-like growth factor 1 (IGF-1), which has been shown in vitro as well as in vivo to induce growth and hypertrophy in mechanically stimulated or damaged muscle. Growth, hypertrophy and responses to mechanical stimulation are important reactions of cartilaginous tissues, especially those in growth plates. Therefore, we wanted to ascertain if MGF is expressed in growth plate cartilage and if it influences proliferation of chondrocytes, as it does in musculoskeletal tissues. MGF expression was analyzed in growth plate and control tissue samples from piglets aged 3 to 6 weeks. Furthermore, growth plate chondrocyte cell culture was used to evaluate the effects of the MGF peptide on proliferation. We showed that MGF is expressed in considerable amounts in the tissues evaluated. We found the MGF peptide to be primarily located in the cytoplasm, and in some instances, it was also found in the nucleus of the cells. Addition of MGF peptides was not associated with growth plate chondrocyte proliferation.


title: The Biology of Forgetting—A Perspective

abstract: Pioneering research studies, beginning with those using Drosophila , have identified several molecular and cellular mechanisms for active forgetting. The currently known mechanisms for active forgetting include neurogenesis-based forgetting, interference-based forgetting, and intrinsic forgetting, the latter term describing the brain's chronic signaling systems that function to slowly degrade molecular and cellular memory traces. The best-characterized pathway for intrinsic forgetting includes "forgetting cells" that release dopamine onto engram cells, mobilizing a signaling pathway that terminates in the activation of Rac1/Cofilin to effect changes in the actin cytoskeleton and neuron/synapse structure. Intrinsic forgetting may be the default state of the brain, constantly promoting memory erasure and competing with processes that promote memory stability like consolidation. A better understanding of active forgetting will provide insights into the brain's memory management system and human brain disorders that alter active forgetting mechanisms.


title: Not‐so‐supervised: A survey of semi‐supervised, multi‐instance, and transfer learning in medical image analysis

abstract: Abstract Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. While medical imaging datasets have been growing in size, a challenge for supervised ML algorithms that is frequently mentioned is the lack of annotated data. As a result, various methods that can learn with less/other types of supervision, have been proposed. We give an overview of semi-supervised, multiple instance, and transfer learning in medical imaging, both in diagnosis or segmentation tasks. We also discuss connections between these learning scenarios, and opportunities for future research. A dataset with the details of the surveyed papers is available via https://figshare.com/articles/Database_of_surveyed_literature_in_Not-so-supervised_a_survey_of_semi-supervised_multi-instance_and_transfer_learning_in_medical_image_analysis_/7479416 .


title: Personality & video game genre preferences

abstract: This research has been conducted to ascertain whether people with certain personality types exhibit preferences for particular game genres. Four hundred and sixty-six participants completed an online survey in which they described their preference for various game genres and provided measures of personality. Personality types were measured using the five-factor model of personality. Significant relationships between personality types and game genres were found. The results are interpreted in the context of the features of particular game genres and possible matches between personality traits and these features.


title: Link Prediction Using Supervised Learning

abstract: Social network analysis has attracted much attention in recent years. Link prediction is a key research directions within this area. In this research, we study link prediction as a supervised learning task. Along the way, we identify a set of features that are key to the superior performance under the supervised learning setup. The identified features are very easy to compute, and at the same time surprisingly effective in solving the link prediction problem. We also explain the effectiveness of the features from their class density distribution. Then we compare different classes of supervised learning algorithms in terms of their prediction performance using various performance metrics, such as accuracy, precision-recall, F-values, squared error etc. with a 5-fold cross validation. Our results on two practical social network datasets shows that most of the well-known classification algorithms (decision tree, k-nn,multilayer perceptron, SVM, rbf network) can predict link with surpassing performances, but SVM defeats all of them with narrow margin in all different performance measures. Again, ranking of features with popular feature ranking algorithms shows that a small subset of features always plays a significant role in the link prediction job.


title: Concept of wearable folded half-mode cavity antenna

abstract: The half-mode cavity antenna is a typical design of half-size resonator antennas suitable for wearable applications. Based on a classical cavity model, a basic rectangular half-mode cavity antenna is designed for operation at 2.45 GHz. Interestingly, the simulation results show that the antenna has stable performance even when subjected to 180° bending around an axis parallel the direction of the radiating aperture. On that basis, the concept of a folded rectangular half-mode cavity antenna is proposed with a practical feeding method. The Su curves, internal E-field distributions and radiation patterns of the planar and folded rectangular half-mode cavity antennas are compared using full-wave electromagnetic simulations.


title: SMART-L multibeam radar

abstract: This paper highlights some aspects of the SMART-L three-dimensional long-range surveillance radar system. In particular attention is paid to the architecture of the digital beamforming-on-receive that makes this radar one of the first operational systems based on such a beamforming procedure. Measuring the elevation of aircraft flying low over sea requires special precautions to deal with multipsth propagation effects. Aspects of miniaturishg the hardware intensive solution and processing of the antenna arrays are discussed. Also another feature of prime relevance of the design of the radar, being the medium PRF waveform, is addressed.


title: Privacy-preserving data mining

abstract: A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models without access to precise information in individual data records? We consider the concrete case of building a decision-tree classifier from training data in which the values of individual records have been perturbed. The resulting data records look very different from the original records and the distribution of data values is also very different from the original distribution. While it is not possible to accurately estimate original values in individual data records, we propose a novel reconstruction procedure to accurately estimate the distribution of original data values. By using these reconstructed distributions, we are able to build classifiers whose accuracy is comparable to the accuracy of classifiers built with the original data.


title: Characterization of PicoGreen interaction with dsDNA and the origin of its fluorescence enhancement upon binding.

abstract: PicoGreen is a fluorescent probe that binds dsDNA and forms a highly luminescent complex when compared to the free dye in solution. This unique probe is widely used in DNA quantitation assays but has limited application in biophysical analysis of DNA and DNA-protein systems due to limited knowledge pertaining to its physical properties and characteristics of DNA binding. Here we have investigated PicoGreen binding to DNA to reveal the origin and mode of PicoGreen/DNA interactions, in particular the role of electrostatic and nonelectrostatic interactions in formation of the complex, as well as demonstrating minor groove binding specificity. Analysis of the fluorescence properties of free PicoGreen, the diffusion properties of PG/DNA complexes, and the excited-state lifetime changes upon DNA binding and change in solvent polarity, as well as the viscosity, reveal that quenching of PicoGreen in the free state results from its intramolecular dynamic fluctuations. On binding to DNA, intercalation and electrostatic interactions immobilize the dye molecule, resulting in a >1000-fold enhancement in its fluorescence. Based on the results of this study, a model of PicoGreen/DNA complex formation is proposed.


Frustratingly Easy Neural Domain Adaptation


Comparing Template-based, Feature-based and Supervised Classification of Facial Expressions from Static Images


Multimodal Biometric Personal Identification System Based On IRIS & Fingerprint


title: Improvement of Torque Capability of Permanent-Magnet Motor by Using Hybrid Rotor Configuration

abstract: This paper proposes a novel hybrid rotor permanent-magnet (HRPM) motor to improve torque capability, including average torque and torque ripple. The key of the proposed motor is that it can utilize high PM torque of surface-inserted PM (SPM) rotor module and high reluctance torque of interior PM (IPM) rotor module at the same time. Meanwhile, the shifting angle of SPMs and open angle of air barriers are introduced to improve the average torque and reduce the torque ripple, respectively. The two variables are employed to create circumferential asymmetry of the rotor for generating an angular difference of the electromagnetic torque between adjacent N-pole and S-pole, thus minimizing torque ripple. In order to evaluate the proposed motor, an IPM motor and a SPM motor with the same dimension and volume of PM are used as benchmarks. Through theoretical analysis and experimental test, it is verified that the proposed HRPM motor can offer higher torque density and lower torque ripple simultaneously.


title: Chemical composition and oxidative stability of flax, safflower and poppy seed and seed oils.

abstract: Abstract Three seeds of Turkish origin, flax, poppy and safflower were analyzed for their proximate, fatty acids, tocols (tocopherols and tocotrienols) and total phenolic composition, and oxidative stability of their oil. The major fatty acid in the flax oil was α-linolenic acid, comprising 58.3% of total fatty acids, whereas poppy and safflower oils were rich in linoleic acid at 74.5% and 70.5% level, respectively. The amount of total tocols was 14.6 mg/100 g flax, 11.0 mg/100 g poppy and 12.1 mg/100 g safflower seed. Flax and poppy oil were rich in γ-tocopherol as 79.4 mg/100 g oil and 30.9 mg/100 g oil, respectively, while α-tocopherol (44.1 g/100 g oil) was dominant in safflower oil. Only α- and γ-tocotrienol were found in the oils. Oxidative stability of oils was measured at 110 °C at the rate of 20 L/h air flow rate, and poppy oil (5.56 h) was most stabile oil followed by safflower oil (2.87 h) and flax oil (1.57). There were no correlation between oxidative stability and unsaturation degree of fatty acids and tocol levels of the oils. All of the seeds investigated provide a healthy oil profile and may have potential as a source of specialty oils on a commercial scale.


title: Fast Graph Laplacian Regularized Kernel Learning via Semidefinite–Quadratic–Linear Programming

abstract: Kernel learning is a powerful framework for nonlinear data modeling. Using the kernel trick, a number of problems have been formulated as semidefinite programs (SDPs). These include Maximum Variance Unfolding (MVU) (Weinberger et al., 2004) in nonlinear dimensionality reduction, and Pairwise Constraint Propagation (PCP) (Li et al., 2008) in constrained clustering. Although in theory SDPs can be efficiently solved, the high computational complexity incurred in numerically processing the huge linear matrix inequality constraints has rendered the SDP approach unscalable. In this paper, we show that a large class of kernel learning problems can be reformulated as semidefinite-quadratic-linear programs (SQLPs), which only contain a simple positive semidefinite constraint, a second-order cone constraint and a number of linear constraints. These constraints are much easier to process numerically, and the gain in speedup over previous approaches is at least of the order m2.5, where m is the matrix dimension. Experimental results are also presented to show the superb computational efficiency of our approach.


title: On Improving TCP Performance over Mobile Data Networks

abstract: Mobile data networks such as 3G and LTE exhibit properties that are fundamentally different from those of fixed networks. These differences result in severe performance bottlenecks in current Transmission Control Protocol (TCP) implementations, which are the foundation for most of today's mobile applications. This work addresses this challenge by developing a transparent protocol optimization device to perform on-the-fly protocol optimization to improve TCP's throughput performance while maintaining full compatibility with current end-host TCP implementations. The proposed protocol optimizations can achieve near optimal bandwidth utilization. This was verified and confirmed in production 3G and LTE networks using a prototype implementation. Compared to current TCP implementations, the proposed protocol optimization device can raise throughput by 48 to 163 percent. In contrast to inventing a new transport protocol or modifying an existing TCP implementation, the proposed approach does not require any modification to the existing TCP implementation at the client/server hosts, does not require any reconfiguration of the server or client, and hence can be readily deployed in today's 3G and 4G mobile networks, raising the throughput performance of all existing network applications running atop TCP.


title: Higher Graphical Fidelity Decreases Players' Access to Aggressive Concepts in Violent Video Games

abstract: Several features of violent video games (hence VVGs) may cause gamers to access aggressive concepts during play. Two of these features are graphical fidelity and use of narrative. In particular, the increased graphical fidelity of modern VVGs is widely theorised to cause increased access to aggressive concepts. However, despite this theoretical speculation, there is little empirical evidence of whether these formal features of VVGs actually do influence players to access aggressive concepts during play. Therefore, a 2x2 between-subjects factorial online experiment (N = 710) was employed. This examined the effects of graphical fidelity and narrative content on the extent to which players accessed aggression-related concepts when playing a VVG. Results indicated that the presence of realistic graphics in a video game representing aggression caused players to access aggressive concepts less (p = 0.014). The presence of narrative content was not found to have a statistically significant effect. Under an influential (though controversial) model of VVG effects known as the General Aggression Model, increased access to aggressive concepts causally contributes to anti-social behaviour by players of VVGs. These findings suggest that even if playing modern VVGs did lead to these negative effects, they would not be influenced by the increased graphical fidelity of modern VVGs.


title: Learning monocular visual odometry with dense 3D mapping from dense 3D flow

abstract: This paper introduces a fully deep learning approach to monocular SLAM, which can perform simultaneous localization using a neural network for learning visual odometry (L-VO) and dense 3D mapping. Dense 2D flow and a depth image are generated from monocular images by sub-networks, which are then used by a 3D flow associated layer in the L-VO network to generate dense 3D flow. Given this 3D flow, the dual-stream L-VO network can then predict the 6DOF relative pose and furthermore reconstruct the vehicle trajectory. In order to learn the correlation between motion directions, the Bivariate Gaussian modelling is employed in the loss function. The L-VO network achieves an overall performance of 2.68% for average translational error and 0.0143 deg/m for average rotational error on the KITTI odometry benchmark. Moreover, the learned depth is fully leveraged to generate a dense 3D map. As a result, an entire visual SLAM system, that is, learning monocular odometry combined with dense 3D mapping, is achieved.


title: A Cartesian grid projection method for the incompressible Euler equations in complex geometries

abstract: Many problems in fluid dynamics require the representation of complicated internal or external boundaries of the flow. Here we present a method for calculating time-dependent incompressible inviscid flow which combines a projection method with a "Cartesian grid" approach for representing geometry. In this approach, the body is represented as an interface embedded in a regular Cartesian mesh. The advection step is based on a Cartesian grid algorithm for compressible flow, in which the discretization of the body near the flow uses a volume-of-fluid representation. A redistribution procedure is used to eliminate time-step restrictions due to small cells where the boundary intersects the mesh. The projection step uses an approximate projection based on a Cartesian grid method for potential flow. The method incorporates knowledge of the body through volume and area fractions along with certain other integrals over the mixed cells. Convergence results are given for the projection itself and for the time-dependent algorithm in two dimensions. The method is also demonstrated on flow past a half-cylinder with vortex shedding.


title: Boosting image classification scheme

abstract: Image classification is very active and promising research domain in image retrieval and management. We propose a boosting image classification scheme with automatic selection of discriminative features. Firstly, we present an image feature called the orientational color correlogram (OCC) and apply it to image classification. OCC extends the color correlogram by adding in orientational information which can take into account both the local color correlation and the global context structure of an image. Secondly, we give a solution to feature selection for the very high dimensionality of OCC by using a boosting classification scheme which can select the most discriminative features automatically. In our experiments, only a small number of elements of OCC are selected, which can reduce the storage space of classifier models and speed up the classification process. The experimental results suggest the proposed method has preferable performances