title: Mechanics of Precurved-Tube Continuum Robots

abstract: This paper presents a new class of thin, dexterous continuum robots, which we call active cannulas due to their potential medical applications. An active cannula is composed of telescoping, concentric, precurved superelastic tubes that can be axially translated and rotated at the base relative to one another. Active cannulas derive bending not from tendon wires or other external mechanisms but from elastic tube interaction in the backbone itself, permitting high dexterity and small size, and dexterity improves with miniaturization. They are designed to traverse narrow and winding environments without relying on ldquoguidingrdquo environmental reaction forces. These features seem ideal for a variety of applications where a very thin robot with tentacle-like dexterity is needed. In this paper, we apply beam mechanics to obtain a kinematic model of active cannula shape and describe design tools that result from the modeling process. After deriving general equations, we apply them to a simple three-link active cannula. Experimental results illustrate the importance of including torsional effects and the ability of our model to predict energy bifurcation and active cannula shape.


title: Visual Data Augmentation through Learning

abstract: The rapid progress in machine learning methods has been empowered by i) huge datasets that have been collected and annotated, ii) improved engineering (e.g. data pre-processing/normalization). The existing datasets typically include several million samples, which constitutes their extension a colossal task. In addition, the state-of-the-art data-driven methods demand a vast amount of data, hence a standard engineering trick employed is artificial data augmentation for instance by adding into the data cropped and (affinely) transformed images. However, this approach does not correspond to any change in the natural 3D scene. ::: We propose instead to perform data augmentation through learning realistic local transformations. We learn a forward and an inverse transformation that maps an image from the high-dimensional space of pixel intensities to a latent space which varies (approximately) linearly with the latent space of a realistically transformed version of the image. Such transformed images can be considered two successive frames in a video. Next, we utilize these transformations to learn a linear model that modifies the latent spaces and then use the inverse transformation to synthesize a new image. We argue that the this procedure produces powerful invariant representations. We perform both qualitative and quantitative experiments that demonstrate our proposed method creates new realistic images.


title: A 20Gb/s full-rate linear CDR circuit with automatic frequency acquisition

abstract: A linear CDR circuit [1] manifests itself in easy modeling and minimal activity on phase adjustment under locked condition. However, linear PDs face a speed limitation at around 10Gb/s, primarily because of the required pulsewidth comparison and finite flip-flop CK-to-Q delay. Parallelism could relax the stringent speed requirement, but it also introduces other issues such as clock skews and jitters. Frequency acquisition without an external reference such as Pottbacker FD [2] and other similar approaches [3,4] require quadrature clocks, potentially leading to higher phase noise as well. This paper presents the design and analysis of a 20Gb/s full-rate CDR circuit in 90nm CMOS, which completely eliminates the conventional issues by using an alternative linear PD structure and a referenceless FD with automaticity.


Spin-orbital effects in metal-dichalcogenide semiconducting monolayers


title: High-dimensional Multivariate Mediation: with Application to Neuroimaging Data

abstract: Mediation analysis is an important tool in the behavioral sciences for investigating the role of intermediate variables that lie in the path between a treatment and an outcome variable. The influence of the intermediate variable on the outcome is often explored using a linear structural equation model (LSEM), with model coefficients interpreted as possible effects. While there has been significant research on the topic, little work has been done when the intermediate variable (mediator) is a high-dimensional vector. In this work, we introduce a novel method for identifying potential mediators in this setting called the directions of mediation (DMs). DMs linearly combine potential mediators into a smaller number of orthogonal components, with components ranked based on the proportion of the LSEM likelihood each accounts for. This method is well suited for cases when many potential mediators are measured. Examples of high-dimensional potential mediators are brain images composed of hundreds of thousands of voxels, genetic variation measured at millions of single nucleotide polymorphisms (SNPs), or vectors of thousands of variables in large-scale epidemiological studies. We demonstrate the method using a functional magnetic resonance imaging study of thermal pain where we are interested in determining which brain locations mediate the relationship between the application of a thermal stimulus and self-reported pain.


title: Influence maximization in complex networks through optimal percolation

abstract: A rigorous method to determine the most influential superspreaders in complex networks is presented—involving the mapping of the problem onto optimal percolation along with a scalable algorithm for big-data social networks—showing, unexpectedly, that many weak nodes can be powerful influencers.


title: Immuno-enhancement effects of ethanol extract from Cyrtomium macrophyllum (Makino) Tagawa on cyclophosphamide-induced immunosuppression in BALB/c mice.

abstract: ETHNOPHARMACOLOGICAL RELEVANCE ::: Cyrtomium macrophyllum (Makino) Tagawa has been traditionally used as a herbal medicine for the treatment of various infectious diseases such as tapeworm infestation, colds, and viral diseases. However, no systematic study of the immunity of Cyrtomium macrophyllum ethanol extracts (CM) has yet been reported. The present work evaluates these traits. ::: ::: ::: MATERIALS AND METHODS ::: 120 male BALB/c mice were divided into 6 groups of 20 mice each: (1) normal group (sterile physiological saline), which served as a blank control; (2) model group (Cyclophosphamide, CY) group (sterile physiological saline), which served as a negative control; (3) low-dose CM (50 mg/kg BW); (4) intermediate-dose CM (100 mg/kg BW); (5) high-dose CM (200 mg/kg BW); (6) CM group (200 mg/kg BW). CY (0.2 ml) was administered via intraperitoneal injection. The other regimens were administered via gavage in 0.2 ml solution. Phytochemical of CM was characterized by HPLC-LTQ-Orbitrap. The acute toxicity effect of the ethanol extract of Cyrtomium macrophyllum was also investigated. ::: ::: ::: RESULTS ::: The spleen and thymus indices of mice receiving low, intermediate, and high doses of CM recovered more quickly than those of CY mice, and they did so in a dose-dependent manner. These mice also showed higher T cell and B cell proliferation responses and macrophage function than those of CY mice, and their serum levels of interleukin-6 and interferon-γ had become normal. In acute toxicity test, CM exhibited no mortality and behavioral changes in mice. Quantitative phytochemical analysis showed flavonoids, polyphenols, and tannins to be the major compounds present in the extract, at 27.64%, 30.87%, and 11.22%, respectively. We found that 16 compounds were characterized by the interpretation of their mass spectra obtained by the MS/MS. ::: ::: ::: CONCLUSION ::: The current study demonstrates that Cyrtomium macrophyllum ethanol extract improved immune function in CY-treated mice.


title: A Generative Model for Image Segmentation Based on Label Fusion

abstract: We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms rely on pairwise registrations between the test image and individual training images. The training labels are then transferred to the test image and fused to compute the final segmentation of the test subject. Such label fusion methods have been shown to yield accurate segmentation, since the use of multiple registrations captures greater inter-subject anatomical variability and improves robustness against occasional registration failures. To the best of our knowledge, this manuscript presents the first comprehensive probabilistic framework that rigorously motivates label fusion as a segmentation approach. The proposed framework allows us to compare different label fusion algorithms theoretically and practically. In particular, recent label fusion or multiatlas segmentation algorithms are interpreted as special cases of our framework. We conduct two sets of experiments to validate the proposed methods. In the first set of experiments, we use 39 brain MRI scans - with manually segmented white matter, cerebral cortex, ventricles and subcortical structures - to compare different label fusion algorithms and the widely-used FreeSurfer whole-brain segmentation tool. Our results indicate that the proposed framework yields more accurate segmentation than FreeSurfer and previous label fusion algorithms. In a second experiment, we use brain MRI scans of 282 subjects to demonstrate that the proposed segmentation tool is sufficiently sensitive to robustly detect hippocampal volume changes in a study of aging and Alzheimer's Disease.


title: On the Use of Machine Learning to Predict the Time and Resources Consumed by Applications

abstract: Most data centers, clouds and grids consist of multiple generations of computing systems, each with different performance profiles, posing a challenge to job schedulers in achieving the best usage of the infrastructure. A useful piece of information for scheduling jobs, typically not available, is the extent to which applications will use available resources once they are executed. This paper comparatively assesses the suitability of several machine learning techniques for predicting spatio temporal utilization of resources by applications. Modern machine learning techniques able to handle large number of attributes are used, taking into account application- and system-specific attributes (e.g., CPU micro architecture, size and speed of memory and storage, input data characteristics and input parameters). The work also extends an existing classification tree algorithm, called Predicting Query Runtime (PQR), to the regression problem by allowing the leaves of the tree to select the best regression method for each collection of data on leaves. The new method (PQR2) yields the best average percentage error, predicting execution time, memory and disk consumption for two bioinformatics applications, BLAST and RAxML, deployed on scenarios that differ in system and usage. In specific scenarios where usage is a non-linear function of system and application attributes, certain configurations of two other machine learning algorithms, Support Vector Machine and k-nearest neighbors, also yield competitive results. In addition, experiments show that the inclusion of system performance and application-specific attributes also improves the performance of machine learning algorithms investigated.


Food security, irrigation, climate change, and water scarcity in India


title: Impact of personal and social resources on parenting stress in mothers of children with autism spectrum disorder

abstract: This study examined the longitudinal associations between child behavior problems, coping strategies, social resources, and parenting stress in mothers of young children with autism spectrum disorder. Participants were 283 mothers who completed self- and child-report measures at the time of diagnosis and 2 years later. Hierarchical multiple regression was conducted to predict overall parenting stress. At diagnosis, the final model indicated that high levels of social support and mothers’ use of active engaged coping strategies were associated with lower levels of parenting stress. Conversely, high levels of child externalizing behavior problems, family dysfunction, and mothers’ use of disengaged coping strategies were associated with higher parenting stress. Two years later, high levels of parenting stress at diagnosis predicted increased parenting stress. In addition, high or increasing levels of social support predicted a decrease in parenting stress, while high or increasing levels of family dysfunctio...


title: A human motor behavior model for distal pointing tasks

abstract: Models of human motor behavior are well known as an aid in the design of user interfaces (UIs). Most current models apply primarily to desktop interaction, but with the development of non-desktop UIs, new types of motor behaviors need to be modeled. Distal pointing-pointing directly at a target that is remotely situated with respect to the input device-is such a motor behavior. A model of distal pointing would be particularly useful in the comparison of different interaction techniques, because the performance of such techniques is highly dependent on user strategy, making controlled studies difficult to perform. Inspired by Fitts' law, we studied four possible models and concluded that movement time for a distal pointing task is best described as a function of the angular amplitude of movement and the angular size of the target. Contrary to Fitts' law, our model shows that the angular size has a much larger effect on movement time than the angular amplitude and that the growth in the difficulty of the tasks is quadratic, rather than linear. We estimated the model's parameters experimentally with a correlation coefficient of 96%.


title: Fetal pulse oximetry: duration of desaturation and intrapartum outcome.

abstract: Abstract Objective: To analyze labor outcomes in relation to masked fetal arterial oxyhemoglobin saturation values above or below 30%. Methods: Consenting gravidas with uncomplicated pregnancies at or beyond 36 weeks’ gestation underwent continuous fetal pulse oximetry. Pregnancy outcomes were compared between two groups: women with fetuses with at least one epoch of arterial oxyhemoglobin saturation below 30% (10 seconds or longer) and women with fetuses without such an episode. We also attempted to ascertain whether duration of saturation below 30% correlated with fetal compromise. Results: We measured arterial oxyhemoglobin saturation in 129 fetuses, 69 (53%) of whom had at least one epoch of saturation below 30%. There were no statistically significant differences in labor and delivery outcomes between the high-saturation and low-saturation groups (eg, cesarean delivery: 13 versus 9%, P = .41; umbilical artery [UA] pH less than 7.20: 10 versus 9%, P > .999). However, as duration of fetal arterial oxyhemoglobin saturation below 30% increased from 10 seconds to longer than 9 consecutive minutes, the incidence of fetal compromise (considered present when at least one of the following criteria was met: cesarean delivery for nonreassuring fetal heart rate pattern, UA pH less than 7.20, admission to the special care nursery, or 5-minute Apgar score not more than 3) increased significantly ( P = .002). The threshold duration of fetal arterial oxyhemoglobin saturation below 30% associated with increased fetal compromise was 2 minutes. Conclusion: Transient fetal arterial oxyhemoglobin saturation values below 30% are common during normal labor and did not predict fetal compromise. Fetal arterial oxyhemoglobin saturation values less than 30% for 2 minutes or longer might be associated with fetal compromise.


title: Metcalfe ' s Law , Web 2 . 0 , and the Semantic Web

abstract: The power of the Web is enhanced through the network effect produced as resources link to each other with the value determined by Metcalfe's law. In Web 2.0 applications, much of that effect is delivered through social linkages realized via social networks online. Unfortunately, the associated semantics for Web 2.0 applications, delivered through tagging, is generally minimally hierarchical and sparsely linked. The Semantic Web suffers from the opposite problem. Semantic information, delivered through ontologies of varying amounts of expressivity, is linked to other terms (within or between resources) creating a link space in the semantic realm. However, the use of the Semantic Web has yet to fully realize the social schemes that provide the network of users. In this article, we discuss putting these together, with linked semantics coupled to linked social networks, to deliver a much greater effect.


title: A Review of Convolutional Neural Networks for Inverse Problems in Imaging

abstract: In this article, we review recent uses of convolutional neural networks (CNNs) to solve inverse problems in imaging. It has recently become feasible to train deep CNNs on large databases of images, and they have shown outstanding performance on object classification and segmentation tasks. Motivated by these successes, researchers have begun to apply CNNs to the resolution of inverse problems such as denoising, deconvolution, superresolution, and medical image reconstruction, and they have started to report improvements over state-of-the-art methods, including sparsity-based techniques such as compressed sensing. Here, we review the recent experimental work in these areas, with a focus on the critical design decisions.


title: The Correspondence Bias

abstract: The correspondence bias is the tendency to draw inferences about a person's unique and enduring dispositions from behaviors that can be entirely explained by the situations in which they occur. Although this tendency is one of the most fundamental phenomena in social psychology, its causes and consequences remain poorly understood. This article sketches an intellectual history of the correspondence bias as an evolving problem in social psychology, describes 4 mechanisms (lack of awareness, unrealistic expectations, inflated categorizations, and incomplete corrections) that produce distinct forms of correspondence bias, and discusses how the consequences of correspondence-biased inferences may perpetuate such inferences. One will seldom go wrong if one attributes extreme actions to vanity, average ones to habit, and petty ones to fear. (Friedrich Nietzsche, 1886/1984, p. 59) Despite the homilies of philosophers, no one has yet found a simple formula for understanding others. The problem, of course, is that a person's inner self is hidden from view. Character, motive, belief, desire, and intention play leading roles in people's construal of others, and yet none of these constructs can actually be observed. As such, people are forced into the difficult business of inferring these intangibles from that which is, in fact, observable: other people's words and deeds. When one infers the invisible from the visible, one risks making a mistake. Three decades of research in social psychology have shown that many of the mistakes people make are of a kind: When people observe behavior, they often conclude that the person who performed the behavior was predisposed to do so—that the person's behavior corresponds to the person's unique dispositions—and they draw such conclusions even when a logical analysis suggests they should not. In this article, we describe the causes and consequences of this particular mistake, which we call the correspondence bias. We do not attempt a complete review of the voluminous literature on this topic. Rather, we first define the correspondence


Rare presentation of 6q16.3 microdeletion syndrome with severe upper limb reduction defects and duodenal atresia


title: MySpiders: Evolve Your Own Intelligent Web Crawlers

abstract: The dynamic nature of the World Wide Web makes it a challenge to find information that is both relevant and recent. Intelligent agents can complement the power of search engines to meet this challenge. We present a Web tool called MySpiders, which implements an evolutionary algorithm managing a population of adaptive crawlers who browse the Web autonomously. Each agent acts as an intelligent client on behalf of the user, driven by a user query and by textual and linkage clues in the crawled pages. Agents autonomously decide which links to follow, which clues to internalize, when to spawn offspring to focus the search near a relevant source, and when to starve. The tool is available to the public as a threaded Java applet. We discuss the development and deployment of such a system.


title: Interacting Tracklets for Multi-Object Tracking

abstract: In this paper, we propose to exploit the interactions between non-associable tracklets to facilitate multi-object tracking. We introduce two types of tracklet interactions, close interaction and distant interaction. The close interaction imposes physical constraints between two temporally overlapping tracklets and more importantly, allows us to learn local classifiers to distinguish targets that are close to each other in the spatiotemporal domain. The distant interaction, on the other hand, accounts for the higher-order motion and appearance consistency between two temporally isolated tracklets. Our approach is modeled as a binary labeling problem and solved using the efficient Quadratic Pseudo-Boolean Optimization (QPBO). It yields promising tracking performance on the challenging PETS09 and MOT16 dataset. Our code will be made publicly available upon the acceptance of the manuscript.


title: Factors associated with IT audits by the internal audit function

abstract: Responses from a large sample of 1029 chief audit executives (CAEs) from Australia, Canada, New Zealand, the UK/Ireland, and the US are used to estimate the proportion of time spent by the internal audit functions (IAFs) on information technology (IT) audits. The sample is also used to investigate explanatory and control variables that are associated with the extent of IT audits by the IAFs. The results show that the proportion of the IAF time spent on IT audits was only 7.97% in 2003, 10.61% in 2006, and was projected to be 13.40% in 2009, indicating an approximately 1% increase per year. Multivariate regression indicates that four variables; the certified information system auditor (CISA) certification, IAF age, training, and the number of organizational employees are significantly and positively associated with IT audits by the IAFs. Other common certifications such as CIA, CPA, and CMA are not positively associated with the proportion of IT audits. Also, while CAE experience, education level, and the country of residence did not affect the results, an IS/CS (information system/computer science) was significant and positive in two of the four models tested. Implications for additional research and practice are discussed.


title: Enhanced 3GPP system for Machine Type Communications and Internet of Things

abstract: The Third Generation Partnership Project (3GPP) has been working on developing specifications on Machine Type Communications (MTC), also known as Machine to Machine Communications (M2M), which is all part of Internet of Things (IoT), a technology that enables machines and devices to be inter-connected via the internet. This paper presents recent M2M/IoT feature enhancements in 3GPP. These features include architectural enhancements for inclusion of a service exposure framework, group based IoT management, efficient monitoring mechanisms for IoT devices, optimization of IoT device power consumption, and high latency communications. These M2M/IoT related functionalities and enhancements are a continuing effort within 3GPP to make the mobile network fulfil the ever changing M2M/IoT requirements, and in so doing allow operators/service providers to offer unique services.


title: A 60-GHz CMOS Transceiver With On-Chip Antenna and Periodic Near Field Directors for Multi-Gb/s Contactless Connector

abstract: A 60-GHz-based millimeter-wave (mm-wave)-wireless-interconnect (M2W2-I) with noncoherent amplitude shift-key (ASK) modulation is presented for applications in multigigabit ultrashort distance contactless connector. M2W2-I modulates data at mm-wave frequencies and transmits over the air in a very short distance (5 mm or less). A folded-dipole antenna is designed on-chip for a compact package form-factor, and a periodic near field director (PNFD) is inserted between transmitter (TX) and receiver (RX) to enhance a communication distance. Designed and fabricated in 65-nm CMOS, the M2W2-I achieves 5 Gb/s with 0.5 mm separation of air-coupling and 5.7-Gb/s 5-mm separation through a PNFD inserted channel. The measured energy efficiency is 7 pJ/bit using a PNFD.


title: CBA: Cloud-Based Bigdata Analytics

abstract: The requirement to perform complicated statistic analysis of big data by institutions of engineering, scientific research, health care, commerce, banking and computer research is immense. However, the limitations of the widely used current desktop software like R, excel, minitab and spss gives a researcher limitation to deal with big data. The big data analytic tools like IBM Big Insight, Revolution Analytics, and tableau software are commercial and heavily license. Still, to deal with big data, client has to invest in infrastructure, installation and maintenance of hadoop cluster to deploy these analytical tools. Apache Hadoop is an open source distributed computing framework that uses commodity hardware. With this project, I intend to collaborate Apache Hadoop and R software over the on the Cloud. Objective is to build a SaaS (Software-as-a-Service) analytic platform that stores a analyzes big data using open source Apache Hadoop and open source R software. The benefits of this cloud based big data analytical service are user friendliness a cost as it is developed using open-source software. The system is cloud based so users have their own space in cloud where user can store there data. User can browse data, files, folders using browser and arrange datasets. User can select dataset and analyze required dataset and store result back to cloud storage. Enterprise with a cloud environment can save cost of hardware, upgrading software, maintenance or network configuration, thus it making it more economical.


title: Extraction and classification of unstructured data in WebPages for structured multimedia database via XML

abstract: Nowadays, there is a vast amount of information available in the internet. The useful data must be captured and stored for future purposes. One of the major unsolved problems in the information technology (IT) industry is the management of unstructured data. The unstructured data such as multimedia files, documents, spreadsheets, news, emails, memorandums, reports and web pages are difficult to capture and store in the common database storage. The underlying reason is due to the tools and techniques that proved to be so successful in transforming structured data into business intelligence and actionable information, simply do not work when it comes to unstructured data. As a result, new approaches are necessary. Attempts have been undertaken by several researchers to deal with unstructured data, but, so far it is hard to find a tool that can store and retrieve the extracted and classified unstructured data into a structured database system. This paper is to present our research on unstructured data identification, extraction and classification of web pages, which is then transformed into structured format in Extensible Markup Language (XML) document, and later stored into a multimedia database. The contribution of this research is in the approach of capturing the unstructured data and the efficiency of a multimedia database to handle this kind of data. The stored data could give benefits to various communities such as students, lecturers, researchers and IT managers because it can be used for any planning, decision-making, day-today operations, and other future purposes.


title: Object-Extraction And Question-Parsing Using CCG

abstract: Accurate dependency recovery has recently been reported for a number of wide-coverage statistical parsers using Combinatory Categorial Grammar (CCG). However, overall figures give no indication of a parser’s performance on specific constructions, nor how suitable a parser is for specific applications. In this paper we give a detailed evaluation of a CCG parser on object extraction dependencies found in WSJ text. We also show how the parser can be used to parse questions for Question Answering. The accuracy of the original parser on questions is very poor, and we propose a novel technique for porting the parser to a new domain, by creating new labelled data at the lexical category level only. Using a supertagger to assign categories to words, trained on the new data, leads to a dramatic increase in question parsing accuracy.


Open-Domain Attribute-Value Acquisition from Semi-Structured Texts


title: A Fully Printable Chipless RFID Tag With Detuning Correction Technique

abstract: This article presents a new chipless RFID tag operating in the frequency span 2 to 4 GHz. In particular the tag does not require any ground plane and it is made of 20 scatterers giving 20 b as coding capacity, for a compact size of 70 25 , compatible with a credit-card format. Its fabrication process is potentially very cheap because it needs only one conductive layer, so that it can be fully printed directly on the product. To overcome the detuning effect inherent to a single layer tag and make this design robust to the environment versatility, a simple compensation technique is introduced and experimented for the first time. Measurements have been performed frequency domain, using amplitude and the group delay response. The exploitation of group delay appears to be very reliable and promising way to retrieve the coded information.


The discrete Plateau problem: algorithm and numerics


title: Multimode antenna feed system for an x-band monopulse radar

abstract: In this paper, a 3-channel multimode feed system for a dielectric loaded E-plane rectangular waveguide horn antenna is proposed. A detailed modal analysis for this multimode monopulse feed system is presented. Simulations for the complete system are performed to optimize isolations between sum, azimuth and elevation channels and enhance the matching of each, within a wideband of operation. The feed system exhibits very good amplitude and phase control of the radar signals over a 10-percent bandwidth. Measurements for different target positions in the 9–10 GHz frequency range are carried out to verify the modal analysis and the simulation results. Good agreement between measurements and simulations is achieved.


title: May 2005 CLARIFYING BUSINESS MODELS : ORIGINS , PRESENT , AND FUTURE OF THE CONCEPT

abstract: Modern Information and Communication Technology open a door for innovations that can improve the functioning of companies. Many innovations can come from the analysis of business processes. Today modeling is widely used for the analysis of business processes. In these work we propose a process modeling technique based on role modeling. To specify a process where one business object may play several roles, a synthesis operation (the composition of two base roles in a third role) has to be specified. All role-based techniques have difficulties specifying role synthesis: synthesis is never specified without the description of actual messages passing between business roles. Such implementation details complicate the understanding of the model and semantics of synthesis become implicit. To specify a business process of a complex system at a higher level of abstraction requires the proper understanding of relationships between roles, when they are put together in one common context. In this paper we define the concept of “synthesis constraints” that shows relations between roles. Using “synthesis constraints” allows a business modeler to make explicit his decisions about how the synthesis is done in an abstract and implementation independent way. This approach can be used for building a BPR case tool that enables the discovery of new business processes by means of different disassembling and assembling of roles.


title: High-mobility transport anisotropy and linear dichroism in few-layer black phosphorus

abstract: Two-dimensional crystals are emerging materials for nanoelectronics. Development of the field requires candidate systems with both a high carrier mobility and, in contrast to graphene, a sufficiently large electronic bandgap. Here we present a detailed theoretical investigation of the atomic and electronic structure of few-layer black phosphorus (BP) in order to predict its electrical and optical properties. This system has a direct bandgap, tunable from 1.51 eV for a monolayer to 0.59 eV for a 5-layer sample. We predict that the mobilities are hole-dominated, rather high and highly anisotropic. The monolayer is exceptional in having an extremely high hole-mobility (of order 10000 cm$^{2}$ V$^{-1}$ s$^{-1}$) and anomalous elastic properties which reverse the anisotropy. Light absorption spectra indicate linear dichroism between perpendicular in-plane directions, which allows optical determination of the crystalline orientation and optical activation of the anisotropic transport properties. These results make few-layer BP a promising candidate for future electronics.


title: Sexually responsive vascular tissue of the vulva

abstract: OBJECTIVE ::: ::: To better understand the genital changes that occur during the female sexual response, using a gross anatomical and histological study of the vascular tissue of the vulva, supplemented with magnetic resonance imaging (MRI). ::: ::: ::: ::: MATERIALS AND METHODS ::: ::: Seven cadaveric vulvectomy specimens were used; they were serially sectioned in coronal, sagittal, and axial planes, and stained with haematoxylin and eosin. Selected blocks were stained with elastic Masson's trichrome. Axial MR images were taken of two healthy women with intact sexual functioning using a gadolinium-based blood-pool contrast agent. A 1.5 T system was used for all MRI studies, with images taken at baseline and during sexual arousal while viewing an erotic videotape. ::: ::: ::: ::: RESULTS ::: ::: There are five vascular compartments of the female external genitalia, found in the clitoris, clitoral bulbs, labia minora, urethra, and vestibule/vagina. Of these five compartments, two distinct types of vascular tissue were identified, i.e. erectile and non-erectile/specialized genital. The erectile tissue compartments had the greatest change in blood volume during sexual arousal, as assessed by MRI. ::: ::: ::: ::: CONCLUSIONS ::: ::: The vulva contains a substantial amount of vascular tissue. These specialized tissues show a variable, but unified response to sexual arousal.


title: An inductorless noise-cancelling CMOS LNA using wideband linearization technique

abstract: An inductorless noise-cancelling CMOS low-noise amplifier (LNA) with wideband linearization technique is proposed. The complementary configuration by stacked NMOS/PMOS is employed to compensate second-order nonlinearity of the circuit. The third-order distortion of the auxiliary stage is also mitigated by that of the weak inversion transistors in the main path. The bias and scaling size combined by digital control words is further tuned to obtain enhanced linearity over the desired band. Implemented in a 0.18-μm CMOS process, simulated results show that the proposed LNA provides a voltage gain of 16.1 dB and a NF of 2.8∼3.4 dB from 0.1 to 1.4 GHz. The IIP3 and IIP2 of 13∼18.9 and 24∼40 dBm are obtained, respectively. The circuit core consumes 19 mW from a 1.8 V supply.


title: Communication Complexity of Distributed Convex Learning and Optimization

abstract: We study the fundamental limits to communication-efficient distributed methods for convex learning and optimization, under different assumptions on the information available to individual machines, and the types of functions considered. We identify cases where existing algorithms are already worst-case optimal, as well as cases where room for further improvement is still possible. Among other things, our results indicate that without similarity between the local objective functions (due to statistical data similarity or otherwise) many communication rounds may be required, even if the machines have unbounded computational power.


title: Design of a 750,000 rpm switched reluctance motor for micro machining

abstract: This paper presents a detailed design process of an ultra-high speed, switched reluctance machine for micro machining. The performance goal of the machine is to reach a maximum rotation speed of 750,000 rpm with an output power of 100 W. The design of the rotor involves reducing aerodynamic drag, avoiding mechanical resonance, and mitigating excessive stress. The design of the stator focuses on meeting the torque requirement while minimizing core loss and copper loss. The performance of the machine and the strength of the rotor structure are both verified through finite-element simulations The final design is a 6/4 switched reluctance machine with a 6mm diameter rotor that is wrapped in a carbon fiber sleeve and exhibits 13.6 W of viscous loss. The stator has shoeless poles and exhibits 19.1 W of electromagnetic loss.


title: Online dating recommendations: matching markets and learning preferences

abstract: Recommendation systems for online dating have recently attracted much attention from the research community. In this paper we propose a two-side matching framework for online dating recommendations and design an Latent Dirichlet Allocation (LDA) model to learn the user preferences from the observed user messaging behavior and user profile features. Experimental results using data from a large online dating website shows that two-sided matching improves the rate of successful matches by as much as 45%. Finally, using simulated matching, we show that the LDA model can correctly capture user preferences.


title: Beobot 2.0: Cluster architecture for mobile robotics

abstract: With the recent proliferation of robust but computationally demanding robotic algorithms, there is now a need for a mobile robot platform equipped with powerful computing facilities. In this paper, we present the design and implementation of Beobot 2.0, an affordable research-level mobile robot equipped with a cluster of 16 2.2-GHz processing cores. Beobot 2.0 uses compact Computer on Module (COM) processors with modest power requirements, thus accommodating various robot design constraints while still satisfying the requirement for computationally intensive algorithms. We discuss issues involved in utilizing multiple COM Express modules on a mobile platform, such as interprocessor communication, power consumption, cooling, and protection from shocks, vibrations, and other environmental hazards such as dust and moisture. We have applied Beobot 2.0 to the following computationally demanding tasks: laser-based robot navigation, scale-invariant feature transform (SIFT) object recognition, finding objects in a cluttered scene using visual saliency, and vision-based localization, wherein the robot has to identify landmarks from a large database of images in a timely manner. For the last task, we tested the localization system in three large-scale outdoor environments, which provide 3,583, 6,006, and 8,823 test frames, respectively. The localization errors for the three environments were 1.26, 2.38, and 4.08 m, respectively. The per-frame processing times were 421.45, 794.31, and 884.74 ms respectively, representing speedup factors of 2.80, 3.00, and 3.58 when compared to a single dual-core computer performing localization. © 2010 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.


title: A Novel Approach for Edge Detection of Images

abstract: Edge detection is a problem of fundamental importance in image analysis. Many approaches for edge detection have already revealed more are waiting to be. But edge detection using K-means algorithm is the most heuristic and unique approach. In this paper, we have proposed an algorithmic technique to detect the edge of any kind of true gray scale images considering the artificial features of the image as the feature set which is fed to K-Means algorithm for clustering the image and there to detect clearly the edges of the objects present in the considered image. The artificial features, which we have considered here, are mean, standard deviation, entropy and busyness of pixel intensity values.


title: Probabilistic traversability map generation using 3D-LIDAR and camera

abstract: Estimating the traversability of rough terrain is a critical task for an outdoor mobile robot. While classifying structured environment can be learned from large number of training data, it is an extremely difficult task to learn and estimate traversability of unstructured rough terrain. Moreover, in many cases information from a single sensor may not be sufficient for estimating traversability reliably in the absence of artificial landmarks such as lane markings or curbs. Our approach estimates traversability of the terrain and build a 2D probabilistic grid map online using 3D-LIDAR and camera. The combination of LIDAR and camera is favoured in many robotic application because they provide complementary information. Our approach assumes the data captured by these two sensors are independent and build separate traversability maps, each with information captured from one sensor. Traversability estimation with vision sensor autonomously collects training data and update classifier without human intervention as the vehicle traverse the terrain. Traversability estimation with 3D-LIDAR measures the slopes of the ground to predict the traversability. Two independently built probabilistic maps are fused using Bayes' rule to improve the detection performance. This is in contrast with other methods in which each sensor performs different tasks. We have implemented the algorithm on a UGV(Unmanned Ground Vehicle) and tested our approach on a rough terrain to evaluate the detection performance.


title: Fractional order difference filters and edge detection

abstract: In order to reduce the effect of the noise in edge detection, a kind of fractional-order difference filters were designed, whose theory was worked out and amplitude-frequency characteristic were analyzed. The coefficients of these filters can vary with its orders. The appropriate orders filters have the capability of smooth. At the same time, it overcomes the shift of the edge using fractional order differentiation directly. We use these filters to detect the edge of the objective. The results show that there are the same results for the objective without noise, but it can obtain better results for the objective with noise.


title: Socioeconomic disadvantage, social participation and networks and the self-rated health of English men and women with mild and moderate intellectual disabilities: cross sectional survey

abstract: Background: Extremely high rates of mortality and morbidity have been reported among people with intellectual disabilities. Virtually no research has addressed the potential social determinants of health status within this very vulnerable population. Method: Cross-sectional survey of self-reported health status and indicators of socioeconomic disadvantage and social connectedness in 1273 English adults with mild or moderate intellectual disabilities. Results: Indicators of socioeconomic disadvantage accounted for a statistically significant proportion of variation in health status, over and above any variation attributable to the personal characteristics and living circumstances of participants. Indicators of social participation and networks did not add to the explanatory power of the model. Among the indicators of socioeconomic disadvantage, hardship was more strongly associated with variation in health status than either employment status or area-level deprivation. Conclusion: As in the general population, self-reported health was associated with indicators of socioeconomic disadvantage, especially hardship. In contrast, there was no evidence of any association between health status and social participation and networks.


title: An improved deep learning approach for detection of thyroid papillary cancer in ultrasound images

abstract: Unlike daily routine images, ultrasound images are usually monochrome and low-resolution. In ultrasound images, the cancer regions are usually blurred, vague margin and irregular in shape. Moreover, the features of cancer region are very similar to normal or benign tissues. Therefore, training ultrasound images with original Convolutional Neural Network (CNN) directly is not satisfactory. In our study, inspired by state-of-the-art object detection network Faster R-CNN, we develop a detector which is more suitable for thyroid papillary carcinoma detection in ultrasound images. In order to improve the accuracy of the detection, we add a spatial constrained layer to CNN so that the detector can extract the features of surrounding region in which the cancer regions are residing. In addition, by concatenating the shallow and deep layers of the CNN, the detector can detect blurrier or smaller cancer regions. The experiments demonstrate that the potential of this new methodology can reduce the workload for pathologists and increase the objectivity of diagnoses. We find that 93:5% of papillary thyroid carcinoma regions could be detected automatically while 81:5% of benign and normal tissue could be excluded without the use of any additional immunohistochemical markers or human intervention.


title: Heterogeneous defect prediction

abstract: Software defect prediction is one of the most active research areas in software engineering. We can build a prediction model with defect data collected from a software project and predict defects in the same project, i.e. within-project defect prediction (WPDP). Researchers also proposed cross-project defect prediction (CPDP) to predict defects for new projects lacking in defect data by using prediction models built by other projects. In recent studies, CPDP is proved to be feasible. However, CPDP requires projects that have the same metric set, meaning the metric sets should be identical between projects. As a result, current techniques for CPDP are difficult to apply across projects with heterogeneous metric sets. To address the limitation, we propose heterogeneous defect prediction (HDP) to predict defects across projects with heterogeneous metric sets. Our HDP approach conducts metric selection and metric matching to build a prediction model between projects with heterogeneous metric sets. Our empirical study on 28 subjects shows that about 68% of predictions using our approach outperform or are comparable to WPDP with statistical significance.


title: Binary increase congestion control (BIC) for fast long-distance networks

abstract: High-speed networks with large delays present a unique environment where TCP may have a problem utilizing the full bandwidth. Several congestion control proposals have been suggested to remedy this problem. The existing protocols consider mainly two properties: TCP friendliness and bandwidth scalability. That is, a protocol should not take away too much bandwidth from standard TCP flows while utilizing the full bandwidth of high-speed networks. This work presents another important constraint, namely, RTT (round trip time) unfairness where competing flows with different RTTs may consume vastly unfair bandwidth shares. Existing schemes have a severe RTT unfairness problem because the congestion window increase rate gets larger as the window grows ironically the very reason that makes them more scalable. RTT unfairness for high-speed networks occurs distinctly with drop tail routers for flows with large congestion windows where packet loss can be highly synchronized. After identifying the RTT unfairness problem of existing protocols, This work presents a new congestion control scheme that alleviates RTT unfairness while supporting TCP friendliness and bandwidth scalability. The proposed congestion control algorithm uses two window size control policies called additive increase and binary search increase. When the congestion window is large, additive increase with a large increment ensures square RTT unfairness as well as good scalability. Under small congestion windows, binary search increase supports TCP friendliness. The simulation results confirm these properties of the protocol.


title: Antioxidant properties of ferulic acid and its related compounds.

abstract: Antioxidant activity of 24 ferulic acid related compounds together with 6 gallic acid related compounds was evaluated using several different physical systems as well as their radical scavenging activity. The radical scavenging activity on 1,1-diphenyl-2-picrylhydrazyl (DPPH) decreased in the order caffeic acid > sinapic acid > ferulic acid > ferulic acid esters > p-coumaric acid. In bulk methyl linoleate, test hydroxycinnamic acids and ferulic acid esters showed antioxidant activity in parallel with their radical scavenging activity. In an ethanol−buffer solution of linoleic acid, the activity of test compounds was not always associated with their radical scavenging activity. Ferulic acid was most effective among the tested phenolic acids. Esterification of ferulic acid resulted in increasing activity. The activity of alkyl ferulates was somewhat influenced by the chain length of alcohol moiety. When the inhibitory effects of alkyl ferulates against oxidation of liposome induced by AAPH were tested, hexy...


title: A forest fire monitoring system based on GPRS and ZigBee wireless sensor network

abstract: The significance of forest fire monitoring was determined by the importance of forest resource and the destructive of forest fire. In the paper, according to the limitation of traditional forest fire monitoring schemes, a new wireless network implementation scheme oriented to forest fire monitoring was presented based on GPRS communication technology and ZigBee technology. The related hardware schemes and software program flows were given. The forest environmental information was collected by ZigBee network and transmitted to FTP server with public network IP on the internet through GPRS network by GPRS module which was controlled by coordinator node. The monitoring center got the data, which was provided for relative experts and decision maker, from FTP server to implement the achievement of remote data from monitoring region. Through the analysis of historical data and real-time data, correct judges and decisions were made. It had strategic significance to improve the level of modernization of forest fire monitoring.


title: 3D teleimmersion for collaboration and interaction of geographically distributed users

abstract: Teleimmersion is an emerging technology that enables users to collaborate remotely by generating realistic 3D avatars in real time and rendering them inside a shared virtual space. The teleimmersive environment thus provides a venue for collaborative work on 3D data such as medical imaging, scientific data and models, archaeological datasets, architectural or mechanical designs, remote training (e.g., oil rigs, military applications), and remote teaching of physical activities (e.g., rehabilitation, dance). In this paper, we present our research work performed over the course of several years in developing the teleimmersive technology using image-based stereo and more recently Kinect. We outline the issues pertaining to the capture, transmission, rendering, and interaction. We describe several applications where we have explored the use of the 3D teleimmersion for remote interaction and collaboration among professional and scientific users. We believe the presented findings are relevant for future developers in teleimmersion and apply across various 3D video capturing technologies.


title: An Overview of Genome Organization and How We Got There: from FISH to Hi-C

abstract: SUMMARY In humans, nearly two meters of genomic material must be folded to fit inside each micrometer-scale cell nucleus while remaining accessible for gene transcription, DNA replication, and DNA repair. This fact highlights the need for mechanisms governing genome organization during any activity and to maintain the physical organization of chromosomes at all times. Insight into the functions and three-dimensional structures of genomes comes mostly from the application of visual techniques such as fluorescence in situ hybridization (FISH) and molecular approaches including chromosome conformation capture (3C) technologies. Recent developments in both types of approaches now offer the possibility of exploring the folded state of an entire genome and maybe even the identification of how complex molecular machines govern its shape. In this review, we present key methodologies used to study genome organization and discuss what they reveal about chromosome conformation as it relates to transcription regulation across genomic scales in mammals.


title: ModelHub: Deep Learning Lifecycle Management

abstract: Deep learning has improved the state-of-the-art results in many domains, leading to the development of several systems for facilitating deep learning. Current systems, however, mainly focus on model building and training phases, while the issues of lifecycle management are largely ignored. Deep learning modeling lifecycle contains a rich set of artifacts and frequently conducted tasks, dealing with them is cumbersome and left to the users. To address these issues in a comprehensive manner, we demonstrate ModelHub, which includes a novel model versioning system (dlv), a domain-specific language for searching through model space (DQL), and a hosted service (ModelHub). Video: https://youtu.be/4JVehm5Ohg4.


title: My Guardian: A Personal Safety Mobile Application

abstract: Smartphones have become somewhat essential and most people have one on hand at all times. Smartphones have been considered a blessing as it has many capabilities and is not just limited to calling and text messaging unlike the regular mobile phone. It can be utilised by converting it into an emergency safety device that can be used when users are placed in a potentially unsafe and dangerous situation. It will ease the process of getting help by allowing users to quickly notify people of an emergency situation with a press of a button. My Guardian, a personal safety application developed for smartphones, intends to help allow users to notify a set of predefined contacts when they feel they are in an unsafe situation or is simply nervous about travelling alone. With a press of a button, the application will send a text message to these contacts with their location coordinates and a personalized emergency alert message.


title: Improving Join Reorderability with Compensation Operators

abstract: A critical task in query optimization is the join reordering problem which is to find an efficient evaluation order for the join operators in a query plan. While the join reordering problem is well studied for queries with only inner-joins, the problem becomes considerably harder when outerjoins/antijoins are involved as such operators are generally not associative. The existing solutions for this problem do not enumerate the complete space of join orderings due to various restrictions on the query rewriting rules considered. In this paper, we present a novel approach for this problem for the class of queries involving inner-joins, single-sided outerjoins, and/or antijoins. Our work is able to support complete join reorderability for this class of queries which supersedes the state-of-the-art approaches.


title: Very Broadband Extended Hemispherical Lenses: Role of Matching Layers for Bandwidth Enlargement

abstract: The design and optimization of very broadband integrated lens antennas (ILAs) constitutes one of the future trends in lens antenna field. To this end we investigate numerically the radiation performance of millimeter wave ILAs coated with multiple anti reflection layers. We propose lens structures of moderate size (four wavelengths in diameter at the center frequency) and made from a dense dielectric material (ceramic). They are illuminated by two kinds of on-axis primary sources, namely a dielectric-loaded metallic waveguide and a patch antenna. This enables to assess the role of the lens illumination law on the performance of broadband ILAs. In particular, we demonstrate that ILAs coated with three stacked quarter wavelength matching layers exhibit a very broadband promising features. First their radiation characteristics remain very stable over a large frequency band: a 36% relative bandwidth is achieved using dielectric-loaded waveguide feeds. Secondly very high values of aperture efficiencies (beyond 91% over a 21% bandwidth) are obtained using printed feeds. The truncation effects of the ground plane and substrate of planar feeds upon the beam characteristics are also studied. We conclude that they must be taken into account at the very first stages of the design process of ILAs.


title: Selection of appropriate candidates for a type position using C4.5 decision tree

abstract: Recruitment and selection of new employees rank to the important processes of human potential management and development. Especially the process of employee selection prepares proper conditions for a successful work performance and decides on a future progress-ability of the organizations. In a unique sector of private security, the precise realization of employee selection can solve one of the most frequent problem of the private security organizations: high fluctuation/employee turnover. The paper focuses on an experimental possibility to assign systematically, and with the high measure of exactness, the required competences of candidates to the specification of clients' protected interests or objects. In presented experiment study, built on the basis of benchmarking, the paper presents the concept of well-known decision trees to choose best candidates. Demonstration of proposed system and method is done by public available data, where we achieved 97.27% accuracy of classification.


title: Which imaging method should be used for genicular nerve radio frequency thermocoagulation in chronic knee osteoarthritis?

abstract: This study aimed to perform genicular nerve RF neurotomy using two different imaging methods, fluoroscopy and ultrasound, and to compare the clinical effects and reliability of the two methods. Fifty patients with osteoarthritis were included in this study. Patients were randomly allocated into group 1 (fluoroscopy imaging) and group 2 (ultrasound imaging). Outcomes were measured using a pain scale (visual analog scale; VAS) and the Western Ontario and McMaster Universities (WOMAC) Index of Osteoarthritis. The application time in the ultrasound group (20.2 ± 6.4 min) was shorter than in the fluoroscopy group (25 ± 4.8 min) (p < 0.05). There was no difference in pain relief and functional status between the ultrasound and fluoroscopy groups. Decrease in VAS score and WOMAC total score in the first and third months was significant in both groups (p < 0.001). GNRFT under ultrasound guidance was easily applicable, safe and dynamic, and required no radiation to achieve the same benefit as the fluoroscopy-guided interventions.


Achieving Your Vision of Professional Development: How To Assess Your Needs and Get What You Want. Hot Topics.


title: Matrix Trees

abstract: We propose a new data representation for octrees and kd-trees that improves upon memory size and algorithm speed of existing techniques. While pointerless approaches exploit the regular structure of the tree to facilitate efficient data access, their memory footprint becomes prohibitively large as the height of the tree increases. Pointerbased trees require memory consumption proportional to the number of tree nodes, thus exploiting the typical sparsity of large trees. Yet, their traversal is slowed by the need to follow explicit pointers across the different levels. Our solution is a pointerless approach that represents each tree level with its own matrix, as opposed to traditional pointerless trees that use only a single vector. This novel data organization allows us to fully exploit the tree's regular structure and improve the performance of tree operations. By using a sparse matrix data structure we obtain a representation that is suited for sparse and dense trees alike. In particular, it uses less total memory than pointer-based trees even when the data set is extremely sparse. We show how our approach is easily implemented on the GPU and illustrate its performance in typical visualization scenarios.


title: LTSG: Latent Topical Skip-Gram for Mutually Learning Topic Model and Vector Representations

abstract: Topic models have been widely used in discovering latent topics which are shared across documents in text mining. Vector representations, word embeddings and topic embeddings, map words and topics into a low-dimensional and dense real-value vector space, which have obtained high performance in NLP tasks. However, most of the existing models assume the results trained by one of them are perfect correct and used as prior knowledge for improving the other model. Some other models use the information trained from external large corpus to help improving smaller corpus. In this paper, we aim to build such an algorithm framework that makes topic models and vector representations mutually improve each other within the same corpus. An EM-style algorithm framework is employed to iteratively optimize both topic model and vector representations. Experimental results show that our model outperforms state-of-the-art methods on various NLP tasks.


title: Laser voltage imaging and probing, efficient techniques for scan chain verification in advanced node

abstract: Laser voltage imaging (LVI) and laser voltage probing (LVP) are laser stimulation techniques to verify a device under test (DUT) and have been widely used for scan chain circuit debugging and various frequency-dependent failure modes. In the case of complex logic failures in advanced technology nodes, defect localization continues to be a challenge in the failure analysis field. Dynamic electrical failure analysis (D-EFA) techniques and their derivatives can increase efficiency for defect localization techniques. In this paper, several scan chain failure studies will be demonstrated by using LVI and LVP techniques, and the waveform analytic data will also be described by further physical failure analysis (PFA).


title: Demonstrating the safety and performance of a velocity sourced series elastic actuator

abstract: Actuators with deliberately added compliant elements in the transmission system are often described as improving the safety of the actuator at the detriment of the performance. We show that our variant of the series elastic actuator topology, the velocity sourced series elastic actuator, has well defined performance characteristics that make for improvements in safety and performance over conventional high impedance actuators. The improvement in performance was principally achieved by having tight velocity control of the DC motor that acts as the mechanical power source for the actuator. Results for performance are given for point to point transition times, while results for safety are based on empirical assessment of the head injury criterion during collisions.


title: Large-scale Semantic Parsing via Schema Matching and Lexicon Extension

abstract: Supervised training procedures for semantic parsers produce high-quality semantic parsers, but they have difficulty scaling to large databases because of the sheer number of logical constants for which they must see labeled training data. We present a technique for developing semantic parsers for large databases based on a reduction to standard supervised training algorithms, schema matching, and pattern learning. Leveraging techniques from each of these areas, we develop a semantic parser for Freebase that is capable of parsing questions with an F1 that improves by 0.42 over a purely-supervised learning algorithm.


title: Flexible and Transparent MoS2Field-Effect Transistors on Hexagonal Boron Nitride-Graphene Heterostructures

abstract: Atomically thin forms of layered materials, such as conducting graphene, insulating hexagonal boron nitride (hBN), and semiconducting molybdenum disulfide (MoS2), have generated great interests recently due to the possibility of combining diverse atomic layers by mechanical “stacking” to create novel materials and devices. In this work, we demonstrate field-effect transistors (FETs) with MoS2 channels, hBN dielectric, and graphene gate electrodes. These devices show field-effect mobilities of up to 45 cm2/Vs and operating gate voltage below 10 V, with greatly reduced hysteresis. Taking advantage of the mechanical strength and flexibility of these materials, we demonstrate integration onto a polymer substrate to create flexible and transparent FETs that show unchanged performance up to 1.5% strain. These heterostructure devices consisting of ultrathin two-dimensional (2D) materials open up a new route toward high-performance flexible and transparent electronics.


title: A review on powder-based additive manufacturing for tissue engineering: selective laser sintering and inkjet 3D printing

abstract: Since most starting materials for tissue engineering are in powder form, using powder-based additive manufacturing methods is attractive and practical. The principal point of employing additive manufacturing (AM) systems is to fabricate parts with arbitrary geometrical complexity with relatively minimal tooling cost and time. Selective laser sintering (SLS) and inkjet 3D printing (3DP) are two powerful and versatile AM techniques which are applicable to powder-based material systems. Hence, the latest state of knowledge available on the use of AM powder-based techniques in tissue engineering and their effect on mechanical and biological properties of fabricated tissues and scaffolds must be updated. Determining the effective setup of parameters, developing improved biocompatible/bioactive materials, and improving the mechanical/biological properties of laser sintered and 3D printed tissues are the three main concerns which have been investigated in this article.


title: Wideband True-Time-Delay Microwave Lenses Based on Metallo-Dielectric and All-Dielectric Lowpass Frequency Selective Surfaces

abstract: We present a new technique for designing microwave lenses with broadband, true-time-delay response types. The lenses examined in this work are planar structures with circular apertures populated with numerous spatial time-delay units (TDUs). Each TDU is the unit cell of an appropriately designed lowpass frequency selective surface (FSS) that provides a desired time delay over a wide frequency range. The lowpass FSSs used in this paper are either metallo-dielectric or all-dielectric type multi-layer structures. A metallo-dielectric lowpass FSS is composed of a number of capacitive patch layers separated from each other by thin dielectric substrates. An all-dielectric lowpass FSS, on the other hand, is composed of high- er and low-er dielectric substrates cascaded sequentially. Two metallo-dielectric lowpass FSS-based true-time-delay (TTD) lens prototypes and one all-dielectric lowpass FSS-based TTD lens prototype with focal length to aperture diameter ratios (f/D) of 1, 1.5 and 1.3 are designed, fabricated, and experimentally characterized. They respectively operate over a bandwidth of 30%, 50% and 40% without any chromatic aberrations. This is demonstrated experimentally by characterizing the responses of these lenses both in frequency domain and in time domain. Moreover, all of these lenses demonstrates excellent scanning performances with fields of views of ±60°.


title: Bioactive Chemical Constituents of Cladiella Species

abstract: From the methanolic extract of Cladiella sp., collected from the Andaman Island, India, a new sesquiterpene, cladidiol (2), and three known diterpenes, cladiellaperoxide (3), (6E)-2α,9α-epoxyeunicella-6,11(12)-dien-3β-ol (4), and polyanthellin A (5) were isolated. The structures of these compounds were established by extensive spectroscopic studies. Compound 2 exhibited modest acetylcholinesterase-inhibition activity, and compounds 3–5 showed antibacterial activities against Streptococcus pyogenes, Escherichia coli, and Pseudomonas aeruginosa.


A Goal-oriented Autonomous Controller for Space Exploration


title: Development an interactive VR training for CNC machining

abstract: The article introduces the basic features of a VR training system for Computer Numerical Control (CNC). The system is designed and implemented based on the World ToolKit (WTK) software to support the interactive training for workpiece machining.


title: SkinMarks: Enabling Interactions on Body Landmarks Using Conformal Skin Electronics

abstract: The body provides many recognizable landmarks due to the underlying skeletal structure and variations in skin texture, elasticity, and color. The visual and spatial cues of such body landmarks can help in localizing on-body interfaces, guide input on the body, and allow for easy recall of mappings. Our main contribution are SkinMarks, novel skin-worn I/O devices for precisely localized input and output on fine body landmarks. SkinMarks comprise skin electronics on temporary rub-on tattoos. They conform to fine wrinkles and are compatible with strongly curved and elastic body locations. We identify five types of body landmarks and demonstrate novel interaction techniques that leverage SkinMarks' unique touch, squeeze and bend sensing with integrated visual output. Finally, we detail on the conformality and evaluate sub-millimeter electrodes for touch sensing. Taken together, SkinMarks expands the on-body interaction space to more detailed, highly curved and challenging areas on the body.


title: The use of unlabeled data to improve supervised learning for text summarization

abstract: With the huge amount of information available electronically, there is an increasing demand for automatic text summarization systems. The use of machine learning techniques for this task allows one to adapt summaries to the user needs and to the corpus characteristics. These desirable properties have motivated an increasing amount of work in this field over the last few years. Most approaches attempt to generate summaries by extracting sentence segments and adopt the supervised learning paradigm which requires to label documents at the text span level. This is a costly process, which puts strong limitations on the applicability of these methods. We investigate here the use of semi-supervised algorithms for summarization. These techniques make use of few labeled data together with a larger amount of unlabeled data. We propose new semi-supervised algorithms for training classification models for text summarization. We analyze their performances on two data sets - the Reuters news-wire corpus and the Computation and Language (cmp_lg) collection of TIPSTER SUMMAC. We perform comparisons with a baseline - non learning - system, and a reference trainable summarizer system.


title: Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement Learning and Multi-task Reinforcement Learning

abstract: Reinforcement learning (RL), while often powerful, can suffer from slow learning speeds, particularly in high dimensional spaces. The autonomous decomposition of tasks and use of hierarchical methods hold the potential to significantly speed up learning in such domains. This paper proposes a novel practical method that can autonomously decompose tasks, by leveraging association rule mining, which discovers hidden relationship among entities in data mining. We introduce a novel method called ARM-HSTRL (Association Rule Mining to extract Hierarchical Structure of Tasks in Reinforcement Learning). It extracts temporal and structural relationships of sub-goals in RL, and multi-task RL. In particular,it finds sub-goals and relationship among them. It is shown the significant efficiency and performance of the proposed method in two main topics of RL.


title: Market, Hierarchy, and Trust: The Knowledge Economy and the Future of Capitalism

abstract: Recent conceptualizations of trends in the structure of U.S. industry have focused on the relative importance of markets, hierarchies, and hybrid intermediate forms. This paper advances the discussion by distinguishing three ideal-typical forms of organization and their corresponding coordination mechanisms: market/price, hierarchy/authority, and community/trust. Different institutions combine the three forms/mechanisms in different proportions. Economic and organizational theory have shown that, compared to trust, price and authority are relatively ineffective means of dealing with knowledge-based assets. Therefore, as knowledge becomes increasingly important in our economy, we should expect high-trust institutional forms to proliferate.A review of trends in employment relations, interdivisional relations, and interfirm relations finds evidence suggesting that the effect of growing knowledge-intensity may indeed be a trend toward greater reliance on trust. There is also reason to believe that the form of trust most effective in this context is a distinctively modern kind--"reflective trust"--as opposed to traditionalistic, "blind" trust. Such a trend to reflective trust appears to threaten the privileges of currently dominant social actors, and these actors' resistance, in combination with the complex interdependencies between price, authority, and trust mechanisms, imparts a halting character to the trend. But the momentum of this trend nevertheless appears to be self-reinforcing, which suggests that it may ultimately challenge the foundations of our capitalist form of society while simultaneously creating the foundations of a new, postcapitalist form.


Online End-of-Turn Detection from Speech Based on Stacked Time-Asynchronous Sequential Networks


title: The Relationship Between Online Social Networking and Depression: A Systematic Review of Quantitative Studies.

abstract: Online social networking sites (SNSs) such as Facebook, Twitter, and MySpace are used by billions of people every day to communicate and interact with others. There has been increasing interest in the potential impact of online social networking on wellbeing, with a broadening body of new research into factors associated with both positive and negative mental health outcomes such as depression. This systematic review of empirical studies (n = 30) adds to existing research in this field by examining current quantitative studies focused on the relationship between online social networking and symptoms of depression. The academic databases PsycINFO, Web of Science, CINAHL, MEDLINE, and EMBASE were searched systematically using terms related to online social networking and depression. Reporting quality was critically appraised and the findings discussed with reference to their wider implications. The findings suggest that the relationship between online social networking and symptoms of depression may be complex and associated with multiple psychological, social, behavioral, and individual factors. Furthermore, the impact of online social networking on wellbeing may be both positive and negative, highlighting the need for future research to determine the impact of candidate mediators and moderators underlying these heterogeneous outcomes across evolving networks.


title: Activity Recognition in Egocentric Life-Logging Videos

abstract: With the increasing availability of wearable cameras, research on first-person view videos (egocentric videos) has received much attention recently. While some effort has been devoted to collecting various egocentric video datasets, there has not been a focused effort in assembling one that could capture the diversity and complexity of activities related to life-logging, which is expected to be an important application for egocentric videos. In this work, we first conduct a comprehensive survey of existing egocentric video datasets. We observe that existing datasets do not emphasize activities relevant to the life-logging scenario. We build an egocentric video dataset dubbed LENA (Life-logging EgoceNtric Activities) (http://people.sutd.edu.sg/~1000892/dataset) which includes egocentric videos of 13 fine-grained activity categories, recorded under diverse situations and environments using the Google Glass. Activities in LENA can also be grouped into 5 top-level categories to meet various needs and multiple demands for activities analysis research. We evaluate state-of-the-art activity recognition using LENA in detail and also analyze the performance of popular descriptors in egocentric activity recognition.


title: Creating a nursing residency: decrease turnover and increase clinical competence.

abstract: New graduates are the largest source of registered nurses available for recruitment, and graduates are expected to transition quickly into professional practice. Stress of this transition can lead to high turnover within the first year. The design and goals of a graduate nurse residency program to increase competence, leadership, and job satisfaction, and ultimately decrease turnover are reported.


title: Survey on Unmanned Aerial Vehicle Networks for Civil Applications: A Communications Viewpoint

abstract: The days where swarms of unmanned aerial vehicles (UAVs) will occupy our skies are fast approaching due to the introduction of cost-efficient and reliable small aerial vehicles and the increasing demand for use of such vehicles in a plethora of civil applications. Governments and industry alike have been heavily investing in the development of UAVs. As such it is important to understand the characteristics of networks with UAVs to enable the incorporation of multiple, coordinated aerial vehicles into the air traffic in a reliable and safe manner. To this end, this survey reports the characteristics and requirements of UAV networks for envisioned civil applications over the period 2000–2015 from a communications and networking viewpoint. We survey and quantify quality-of-service requirements, network-relevant mission parameters, data requirements, and the minimum data to be transmitted over the network. Furthermore, we elaborate on general networking related requirements such as connectivity, adaptability, safety, privacy, security, and scalability. We also report experimental results from many projects and investigate the suitability of existing communication technologies for supporting reliable aerial networking.


Knowledge-based identification of sleep stages based on two forehead electroencephalogram channels


title: Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography

abstract: In a social network, nodes correspond topeople or other social entities, and edges correspond to social links between them. In an effort to preserve privacy, the practice of anonymization replaces names with meaningless unique identifiers. We describe a family of attacks such that even from a single anonymized copy of a social network, it is possible for an adversary to learn whether edges exist or not between specific targeted pairs of nodes.


title: A systemic and cognitive view on collaborative knowledge building with wikis

abstract: Wikis provide new opportunities for learning and for collaborative knowledge building as well as for understanding these processes. This article presents a theoretical framework for describing how learning and collaborative knowledge building take place. In order to understand these processes, three aspects need to be considered: the social processes facilitated by a wiki, the cognitive processes of the users, and how both processes influence each other mutually. For this purpose, the model presented in this article borrows from the systemic approach of Luhmann as well as from Piaget’s theory of equilibration and combines these approaches. The model analyzes processes which take place in the social system of a wiki as well as in the cognitive systems of the users. The model also describes learning activities as processes of externalization and internalization. Individual learning happens through internal processes of assimilation and accommodation, whereas changes in a wiki are due to activities of external assimilation and accommodation which in turn lead to collaborative knowledge building. This article provides empirical examples for these equilibration activities by analyzing Wikipedia articles. Equilibration activities are described as being caused by subjectively perceived incongruities between an individuals’ knowledge and the information provided by a wiki. Incongruities of medium level cause cognitive conflicts which in turn activate the described processes of equilibration and facilitate individual learning and collaborative knowledge building.


title: Generating Responses Expressing Emotion in an Open-domain Dialogue System

abstract: Neural network-based Open-ended conversational agents automatically generate responses based on predictive models learned from a large number of pairs of utterances. The generated responses are typically acceptable as a sentence but are often dull, generic, and certainly devoid of any emotion. In this paper, we present neural models that learn to express a given emotion in the generated response. We propose four models and evaluate them against 3 baselines. An encoder-decoder framework-based model with multiple attention layers provides the best overall performance in terms of expressing the required emotion. While it does not outperform other models on all emotions, it presents promising results in most cases.


title: Parkinson's Disease Digital Biomarker Discovery with Optimized Transitions and Inferred Markov Emissions

abstract: We search for digital biomarkers from Parkinson's Disease by observing approximate repetitive patterns matching hypothesized step and stride periodic cycles. These observations were modeled as a cycle of hidden states with randomness allowing deviation from a canonical pattern of transitions and emissions, under the hypothesis that the averaged features of hidden states would serve to informatively characterize classes of patients/controls. We propose a Hidden Semi-Markov Model (HSMM), a latent-state model, emitting 3D-acceleration vectors. Transitions and emissions are inferred from data. We fit separate models per unique device and training label. Hidden Markov Models (HMM) force geometric distributions of the duration spent at each state before transition to a new state. Instead, our HSMM allows us to specify the distribution of state duration. This modified version is more effective because we are interested more in each state's duration than the sequence of distinct states, allowing inclusion of these durations the feature vector.


Scaling Up and Zooming In: Big Data and Personalization in Language Learning.


title: An automated method of segmentation for tumor detection by neutrosophic sets and morphological operations using MR images

abstract: Brain tumor is the most life undermining sickness and its recognition is the most challenging task for radio logistics by manual detection due to varieties in size, shape and location and sort of tumor. So, detection ought to be quick and precise and can be obtained by automated segmentation methods on MR images. In this paper, neutrosophic sets based segmentation is performed to detect the tumor. MRI is an intense apparatus over CT to analyze the interior segments of the body and the tumor. Tumor is detected and true, false and indeterminacy values of tumor are determined by this technique and the proposed method produce the beholden results.


title: North-South terms of trade and growth: A macroeconomic framework on Kaldorian lines

abstract: Abstract In the Presidential address to the Royal Economic Society (1976), Nicholas Kaldor presented an informal model of the world economy. On this Kaldorian line, this paper presents a North-South growth model. It has been observed that there is no relationship between the monopoly power of the Northern capitalists and the North-South terms of trade. Technical progress in the South has a declining impact on the Southern terms of trade while technical progress in the North does not have a similar impact on the Northern terms of trade. Furthermore, this model shows that the South as a whole cannot speed up its long-term growth rate through export promotion; rather it faces a terms of trade loss.


title: Inertial Motion Sensing Glove for Sign Language Gesture Acquisition and Recognition

abstract: The most popular systems for automatic sign language recognition are based on vision. They are user-friendly, but very sensitive to changes in regard to recording conditions. This paper presents a description of the construction of a more robust system-an accelerometer glove-as well as its application in the recognition of sign language gestures. The basic data regarding inertial motion sensors and the design of the gesture acquisition system as well as project proposals are presented. The evaluation of the solution presents the results of the gesture recognition attempt by using a selected set of sign language gestures with a described method based on Hidden Markov Model (HMM) and parallel HMM approaches. The proposed usage of parallel HMM for sensor-fusion modeling reduced the equal error rate by more than 60%, while preserving 99.75% recognition accuracy.


title: Reverberation robust acoustic modeling using i-vectors with time delay neural networks.

abstract: In reverberant environments there are long term interactions between speech and corrupting sources. In this paper a time delay neural network (TDNN) architecture, capable of learning long term temporal relationships and translation invariant representations, is used for reverberation robust acoustic modeling. Further, iVectors are used as an input to the neural network to perform instantaneous speaker and environment adaptation, providing 10% relative improvement in word error rate. By subsampling the outputs at TDNN layers across time steps, training time is reduced. Using a parallel training algorithm we show that the TDNN can be trained on ∼ 5500 hours of speech data in 3 days using up to 32 GPUs. The TDNN is shown to provide results competitive with state of the art systems in the IARPA ASpIRE challenge, with 27.7% WER on the dev test set.


title: New Routes from Minimal Approximation Error to Principal Components

abstract: We introduce two new methods of deriving the classical PCA in the framework of minimizing the mean square error upon performing a lower-dimensional approximation of the data. These methods are based on two forms of the mean square error function. One of the novelties of the presented methods is that the commonly employed process of subtraction of the mean of the data becomes part of the solution of the optimization problem and not a pre-analysis heuristic. We also derive the optimal basis and the minimum error of approximation in this framework and demonstrate the elegance of our solution in comparison with a recent solution in the framework.


Distraction method in the restoration of the thumb


title: Fatty acids as uncouplers of oxidative phosphorylation.

abstract: This review summarizes data on the uncoupling effect of fatty acids on oxidative phosphorylation in mitochondria of various animal and plant tissues.


title: A fast-transient quasi-V2 switching buck regulator using AOT control

abstract: A switching regulator with quasi-V2 adaptive on-time (AOT) control, that provides a fast load transient response is proposed. A feed-forward path network allows the proposed switching regulator to achieve a fast transient response and stable operation without requiring an output capacitor with a large equivalent series resistance. The proposed AOT controller makes the switching frequency pseudo fixed in continuous conduction mode and works in pulse-frequency modulation mode under ultra-light load conditions. The AOT controller adjusts the on-time according to the supply voltage load current conditions. The measurement results verify that the switching regulator can operate under load currents of 5–800 mA for supply voltages of 3.3–4.2 V and an output voltage of 1.2 V. The recovery time is 3.6μs and the voltage drop is 55 mV when the load current is increased from 5 to 750 mA.


title: Concept analysis: resilience.

abstract: Abstract This paper will systematically analyze the concept of resilience using an integrated review of literature. The historical perspective, attributes, antecedents, and consequences of resilience will be reviewed. A theoretical and operational definition will be provided. The Walker and Avant method will be used to describe the cases. Finally, the use of concept map will capture the relationships among the attributes, antecedents, consequences, and empirical indicators through clustering and chaining.


title: Microwave Voltage-Controlled Oscillator With Harmonic-Suppressed Stepped-Impedance-Resonator Filter

abstract: This brief proposes a new low-phase-noise microwave voltage-controlled oscillator (VCO) with a harmonic-suppressed stepped-impedance-resonator (SIR) filter. Since the filter is synthesized by multiple resonators, it can provide a high-quality factor to improve the phase-noise performance. In addition, the harmonic-suppressed property of the filter adopted in this brief can alleviate the noise contributed from the oscillation harmonics. By applying the two techniques mentioned, the proposed VCO is designed at 2.4 GHz with a three-pole Butterworth filter, which is synthesized by the parallel transmission-line SIRs to reduce the circuit size. The proposed VCO using a three-pole filter has a measured phase noise of −147 dBc/Hz at 1-MHz offset frequency with a corresponding figure-of-merit of −203.18 dBc/Hz.


title: Comparative analysis of profitability of honey production using traditional and box hives

abstract: Information on the profitability and productivity of box hives is important to encourage beekeepers to adopt the technology. However, comparative analysis of profitability and productivity of box and traditional hives is not adequately available. The study was carried out on 182 beekeepers using cross sectional survey and employing a random sampling technique. The data were analyzed using descriptive statistics, analysis of variance (ANOVA), the Cobb-Douglas (CD) production function and partial budgeting. The CD production function revealed that supplementary bee feeds, labor and medication were statistically significant for both box and traditional hives. Generally, labor for bee management, supplementary feeding, and medication led to productivity differences of approximately 42.83%, 7.52%, and 5.34%, respectively, between box and traditional hives. The study indicated that productivity of box hives were 72% higher than traditional hives. The average net incomes of beekeepers using box and traditional hives were 33,699.7 SR/annum and 16,461.4 SR/annum respectively. The incremental net benefit of box hives over traditional hives was nearly double. Our study results clearly showed the importance of adoption of box hives for better productivity of the beekeeping subsector.


title: Multiple sensors based fire extinguisher robot based on DTMF, bluetooth and GSM technology with multiple mode of operation

abstract: In this paper, design and development of a multiple sensors based fire extinguisher robot is proposed and implementation is demonstrated with a brief discussion of construction and operation. The developed fire extinguisher robot can be operated in multiple modes using the DTMF and Bluetooth remote control as well as GSM and GPS technology. Basically, three different sensors of flame sensor, temperature sensor, and smoke sensor have been used to ensure proper detection of fire. The robot can be controlled using both DTMF remote control and Android smartphone and can be operated in three different modes. The first mode allows full autonomous operation of the robot which can be activated by the user or by the robot itself based on the situation. The second mode is a line following mode where robot follows a black drawn line to detect fire and the third mode is complete manual operation using remote control.


title: An unsupervised object-level image segmentation method based on foreground and background priors

abstract: Conventional unsupervised image segmentation methods return many superpixels or object parts and thus tend to over-segmentation. In this paper, we present a novel post-processing approach for unsupervised object-level image segmentation (UnOLIS). Starting with the results of any conventional unsupervised segmentation method, we first combine a global region-based saliency and a robust background feature to cluster the pre-segmented regions into foreground and background. We then design a region growing process, encoded with several object priors, to generate a high quality foreground object segmentation. In parallel, we group the background regions into different stuffs by clustering. We test our method on the Berkeley Segmentation Dataset (BSDS500). Our approach significantly improves conventional unsupervised segmentation methods and achieves almost comparable results as the state-of-the-art supervised image segmentation methods.


title: Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images

abstract: In this paper, we study the challenging problem of predicting the dynamics of objects in static images. Given a query object in an image, our goal is to provide a physical understanding of the object in terms of the forces acting upon it and its long term motion as response to those forces. Direct and explicit estimation of the forces and the motion of objects from a single image is extremely challenging. We define intermediate physical abstractions called Newtonian scenarios and introduce Newtonian Neural Network ($N^3$) that learns to map a single image to a state in a Newtonian scenario. Our experimental evaluations show that our method can reliably predict dynamics of a query object from a single image. In addition, our approach can provide physical reasoning that supports the predicted dynamics in terms of velocity and force vectors. To spur research in this direction we compiled Visual Newtonian Dynamics (VIND) dataset that includes 6806 videos aligned with Newtonian scenarios represented using game engines, and 4516 still images with their ground truth dynamics.


title: Fundamental properties and optimization of broadside radiation from uniform leaky-wave antennas

abstract: In this paper, radiation at broadside is studied for a general class of leaky-wave antennas (LWAs) comprised of a grounded slab covered with a partially reflecting surface, on the basis of a simple transverse equivalent network model of the structure. The analysis of the one-dimensional (1-D) version of such a LWA excited by a line source shows that a central role in establishing the features of broadside radiation is played by the condition that the phase and attenuation constants of the leaky mode responsible for radiation are equal. When this happens, a beam with a single peak at broadside is on the verge of splitting into two distinct peaks, and maximum power density is radiated at broadside. Design formulas to achieve such an optimized condition, as well as approximate expressions for the frequency bandwidth and pattern beamwidth of the antenna and for the leaky-wave phase and attenuation constants are derived, both in the absence and in the presence of losses; in addition, an optimal-beamwidth condition (which gives the narrowest broadside beam) is derived. Finally, all the results are extended to the practical case of a 2-D LWA excited by a horizontal dipole


title: Using Argumentative Structure to Interpret Debates in Online Deliberative Democracy and eRulemaking

abstract: Governments around the world are increasingly utilising online platforms and social media to engage with, and ascertain the opinions of, their citizens. Whilst policy makers could potentially benefit from such enormous feedback from society, they first face the challenge of making sense out of the large volumes of data produced. In this article, we show how the analysis of argumentative and dialogical structures allows for the principled identification of those issues that are central, controversial, or popular in an online corpus of debates. Although areas such as controversy mining work towards identifying issues that are a source of disagreement, by looking at the deeper argumentative structure, we show that a much richer understanding can be obtained. We provide results from using a pipeline of argument-mining techniques on the debate corpus, showing that the accuracy obtained is sufficient to automatically identify those issues that are key to the discussion, attracting proportionately more support than others, and those that are divisive, attracting proportionately more conflicting viewpoints.


title: Aligot: cryptographic function identification in obfuscated binary programs

abstract: Analyzing cryptographic implementations has important applications, especially for malware analysis where they are an integral part both of the malware payload and the unpacking code that decrypts this payload. These implementations are often based on well-known cryptographic functions, whose description is publicly available. While potentially very useful for malware analysis, the identification of such cryptographic primitives is made difficult by the fact that they are usually obfuscated. Current state-of-the-art identification tools are ineffective due to the absence of easily identifiable static features in obfuscated code. However, these implementations still maintain the input-output (I/O) relationship of the original function. In this paper, we present a tool that leverages this fact to identify cryptographic functions in obfuscated programs, by retrieving their I/O parameters in an implementation-independent fashion, and comparing them with those of known cryptographic functions. In experimental evaluation, we successfully identified the cryptographic functions TEA, RC4, AES and MD5 both in synthetic examples protected by a commercial-grade packer (AsProtect), and in several obfuscated malware samples (Sality, Waledac, Storm Worm and SilentBanker). In addition, our tool was able to recognize basic operations done in asymmetric ciphers such as RSA.