title: Matching model of flow table for networked big data

abstract: Networking for big data has to be intelligent because it will adjust data transmission requirements adaptively during data splitting and merging. Software-defined networking (SDN) provides a workable and practical paradigm for designing more efficient and flexible networks. Matching strategy in the flow table of SDN switches is most crucial. In this paper, we use a classification approach to analyze the structure of packets based on the tuple-space lookup mechanism, and propose a matching model of the flow table in SDN switches by classifying packets based on a set of fields, which is called an F-OpenFlow. The experiment results show that the proposed F-OpenFlow effectively improves the utilization rate and matching efficiency of the flow table in SDN switches for networked big data.


title: CBEADS©: a framework to support meta-design paradigm

abstract: We have developed a meta-model for Business applications. To generate applications using this meta-model we created a Component Based EBusiness Application Development and Deployment Shell; CBEADS©. The meta-model we created was based on three abstraction levels: Shell, Applications and Functions. The Shell provides the functionality common to any Web-based Business Application, and a set of configurable components and tools to create functions that are specific to an application. By using CBEADS© we can rapidly develop Web-based Business Applications by creating instances of the meta-model based on the Meta Design Paradigm. The key aspect that underpinned this research work was the viewpoint that "software is a medium to capture knowledge rather than a product". The developer's knowledge is embedded into the shell and the tools. The End-user's knowledge is used to populate instances of the meta-model from which applications are generated within CBEADS©.


title: Design and control of an actuated thumb exoskeleton for hand rehabilitation following stroke

abstract: Chronic hand impairment is common following stroke. This paper presents an actuated thumb exoskeleton (ATX) to facilitate research in hand rehabilitation therapy. The ATX presented in this work permits independent bi-directional actuation in each of the 5 degrees-of-freedom (DOF) of the thumb using a mechanism that has 5 active DOF and 3 passive DOF. The ATX is able to provide considerable joint torques for the user while still allowing backdrivability through flexible shaft transmission. A prototype has been built and experiments were conducted to evaluate the closed-loop position control. Further improvement and future work are discussed.


title: Data Mining: A prediction for Student's Performance Using Classification Method

abstract: Currently the amount huge of data stored in educational database these database contain the useful information for predict of students performance. The most useful data mining techniques in educational database is classification. In this paper, the classification task is used to predict the final grade of students and as there are many approaches that are used for data classification, the decision tree (ID3) method is used here.


title: The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables

abstract: The reparameterization trick enables optimizing large scale stochastic computation graphs via gradient descent. The essence of the trick is to refactor each stochastic node into a differentiable function of its parameters and a random variable with fixed distribution. After refactoring, the gradients of the loss propagated by the chain rule through the graph are low variance unbiased estimators of the gradients of the expected loss. While many continuous random variables have such reparameterizations, discrete random variables lack useful reparameterizations due to the discontinuous nature of discrete states. In this work we introduce Concrete random variables -- continuous relaxations of discrete random variables. The Concrete distribution is a new family of distributions with closed form densities and a simple reparameterization. Whenever a discrete stochastic node of a computation graph can be refactored into a one-hot bit representation that is treated continuously, Concrete stochastic nodes can be used with automatic differentiation to produce low-variance biased gradients of objectives (including objectives that depend on the log-probability of latent stochastic nodes) on the corresponding discrete graph. We demonstrate the effectiveness of Concrete relaxations on density estimation and structured prediction tasks using neural networks.


title: Wireless in the enterprise: requirements, solutions and research directions

abstract: In the last few years, wireless networking has seen considerable interest among service providers, users, vendors and content developers. Significant advances have been made in devices, applications, middleware and networking infrastructure. With wireless becoming such a mainstream technology, there is a growing interest in increasing its usage in the enterprise environment. Before wireless solutions can be deployed widely, the requirements of the enterprise environment and capabilities and limitations of wireless technologies must be addressed. In this study, we analyse and discuss the enterprise requirements and how such requirements can be met by the current and emerging wireless infrastructure. In particular, the major requirements are dependability, security, coverage, ease-of-use, devices, network interoperability and cost. Some of these requirements are addressed in the paper, and we also present a dependable, secure and scalable solution for the emerging wireless enterprise. Since this is an emerging area, we present several possible approaches to conduct research in wireless enterprise.


title: Spine Controller for a Sprawling Posture Robot

abstract: More and more biologically inspired robots present spines with multiples degrees of freedom to resemble their biological counterparts. The function of the spine on both animal and robot can play an important role in locomotion. If it has enough degrees of freedom to allow independent control of both shoulder and pelvic girdle (i.e., the points of attachment of body and limb), it can be used to extend the reach of the legs or to increase turning capabilities. In this letter, we present the spine controller for a sprawling quadrupedal robot. The controller achieves a synchronization between spine and legs and allows a precise control of the girdle positions by solving the inverse kinematics of the spine. The controller is validated in simulation and experiments with the real robot showing an ease in the control of the locomotion and achieving a very good tracking between the girdle trajectories with a very small, noncumulative error (less than 6% of the inter-girdle distance). This controller enables both the human operation and the implementation of autonomous path planning and navigation algorithms for these type of amphibious robots, making them more suitable for scenarios with limited maneuverability such as inside pipes or in collapsed buildings.


title: What do usability evaluators do in practice?: an explorative study of think-aloud testing

abstract: Think-aloud testing is a widely employed usability evaluation method, yet its use in practice is rarely studied. We report an explorative study of 14 think-aloud sessions, the audio recordings of which were examined in detail. The study shows that immediate analysis of observations made in the think-aloud sessions is done only sporadically, if at all. When testing, evaluators seem to seek confirmation of problems that they are already aware of. During testing, evaluators often ask users about their expectations and about hypothetical situations, rather than about experienced problems. In addition, evaluators learn much about the usability of the tested system but little about its utility. The study shows how practical realities rarely discussed in the literature on usability evaluation influence sessions. We discuss implications for usability researchers and professionals, including techniques for fast-paced analysis and tools for capturing observations during sessions.


title: The role of dopamine in cognitive sequence learning: evidence from Parkinson’s disease

abstract: Electrophysiological and computational studies suggest that nigro-striatal dopamine may play an important role in learning about sequences of environmentally important stimuli, particularly when this learning is based upon step-by-step associations between stimuli, such as in second-order conditioning. If so, one would predict that disruption of the midbrain dopamine system — such as occurs in Parkinson’s disease — may lead to deficits on tasks that rely upon such learning processes. This hypothesis was tested using a “chaining” task, in which each additional link in a sequence of stimuli leading to reward is trained step-by-step, until a full sequence is learned. We further examined how medication (L-dopa) affects this type of learning. As predicted, we found that Parkinson’s patients tested ‘off’ L-dopa performed as well as controls during the first phase of this task, when required to learn a simple stimulus–response association, but were impaired at learning the full sequence of stimuli. In contrast, we found that Parkinson’s patients tested ‘on’ L-dopa performed better than those tested ‘off’, and no worse than controls, on all phases of the task. These findings suggest that the loss of dopamine that occurs in Parkinson’s disease can lead to specific learning impairments that are predicted by electrophysiological and computational studies, and that enhancing dopamine levels with L-dopa alleviates this deficit. This last result raises questions regarding the mechanisms by which midbrain dopamine modulates learning in Parkinson’s disease, and how L-dopa affects these processes. © 2004 Elsevier B.V. All rights reserved.


title: A Review on Cloud Computing Development

abstract: Cloud computing can provide critical services for business management, reducing IT costs and maintenance costs of hardware and software effectively. In the meanwhile, it can enable enterprises to access to professional IT solutions with less IT investment. Cloud computing is of great significance for the ICT industry of each country. It is now bringing enormous impact to the human society, especially the business world. In this paper, the basic concepts and the development of cloud computing were introduced, and then the current situation and development of cloud computing research from two aspects of technology and business were illustrated. Finally future trends of cloud computing were discussed.


title: Interactive Visual Calibration of Volumetric Head-Tracked 3D Displays

abstract: Head-tracked 3D displays can provide a compelling 3D effect, but even small inaccuracies in the calibration of the participant's viewpoint to the display can disrupt the 3D illusion. We propose a novel interactive procedure for a participant to easily and accurately calibrate a head-tracked display by visually aligning patterns across a multi-screen display. Head-tracker measurements are then calibrated to these known viewpoints. We conducted a user study to evaluate the effectiveness of different visual patterns and different display shapes. We found that the easiest to align shape was the spherical display and the best calibration pattern was the combination of circles and lines. We performed a quantitative camera-based calibration of a cubic display and found visual calibration outperformed manual tuning and generated viewpoint calibrations accurate to within a degree. Our work removes the usual, burdensome step of manual calibration when using head-tracked displays and paves the way for wider adoption of this inexpensive and effective 3D display technology.


title: Temperature Drift Compensation for Hemispherical Resonator Gyro Based on Natural Frequency

abstract: Temperature changes have a strong effect on Hemispherical Resonator Gyro (HRG) output; therefore, it is of vital importance to observe their influence and then make necessary compensations. In this paper, a temperature compensation model for HRG based on the natural frequency of the resonator is established and then temperature drift compensations are accomplished. To begin with, a math model of the relationship between the temperature and the natural frequency of HRG is set up. Then, the math model is written into a Taylor expansion expression and the expansion coefficients are calibrated through temperature experiments. The experimental results show that the frequency changes correspond to temperature changes and each temperature only corresponds to one natural frequency, so the output of HRG can be compensated through the natural frequency of the resonator instead of the temperature itself. As a result, compensations are made for the output drift of HRG based on natural frequency through a stepwise linear regression method. The compensation results show that temperature-frequency method is valid and suitable for the gyroscope drift compensation, which would ensure HRG's application in a larger temperature range in the future.


Entity Linking with a Paraphrase Flavor.


title: A convergent dynamic window approach to obstacle avoidance

abstract: The dynamic window approach (DWA) is a well-known navigation scheme developed by Fox et al. and extended by Brock and Khatib. It is safe by construction, and has been shown to perform very efficiently in experimental setups. However, one can construct examples where the proposed scheme fails to attain the goal configuration. What has been lacking is a theoretical treatment of the algorithm's convergence properties. Here we present such a treatment by merging the ideas of the DWA with the convergent, but less performance-oriented, scheme suggested by Rimon and Koditschek. Viewing the DWA as a model predictive control (MPC) method and using the control Lyapunov function (CLF) framework of Rimon and Koditschek, we draw inspiration from an MPC/CLF framework put forth by Primbs to propose a version of the DWA that is tractable and convergent.


title: Spin glass approach to the feedback vertex set problem

abstract: A feedback vertex set (FVS) of an undirected graph is a set of vertices that contains at least one vertex of each cycle of the graph. The feedback vertex set problem consists of constructing a FVS of size less than a certain given value. This combinatorial optimization problem has many practical applications, but it is in the nondeterministic polynomial-complete class of worst-case computational complexity. In this paper we define a spin glass model for the FVS problem and then study this model on the ensemble of finite-connectivity random graphs. In our model the global cycle constraints are represented through the local constraints on all the edges of the graph, and they are then treated by distributed message-passing procedures such as belief propagation. Our belief propagation-guided decimation algorithm can construct nearly optimal feedback vertex sets for single random graph instances and regular lattices. We also design a spin glass model for the FVS problem on a directed graph. Our work will be very useful for identifying the set of vertices that contribute most significantly to the dynamical complexity of a large networked system.


title: A dual-band wireless power transfer and backscatter communication approach for implantable neuroprosthetic devices

abstract: We present a dual-band HF and UHF fully-integrated implantable neuroprosthetic testbed. This testbed includes a custom implanted device as well as an external system based on a commercially available USRP B210 software defined radio (SDR) platform. The implanted device integrates a BPSK backscatter uplink rate of 5 Mbps, an HF WPT efficiency of 47% with a power consumption of 1.332 milliwatt. The implanted device measures 25 mm diameter and has a total thickness of 2.8 mm including the printed circuit substrate, antenna, all circuitry, and silicone encapsulation. It supports up to 10 neural and 4 electromyogram (EMG) channels with a sampling rate of 26.1 kHz for the neural channels and 1.628 kHz for the EMG channels. The communication link is shown to have 0% packet error rate at an implant depth of up to 2.5 cm.


title: Self-Criticism and Self-Compassion: Risk and Resilience: Being Compassionate to Oneself Is Associated with Emotional Resilience and Psychological Well-Being

abstract: Once thought to only be associated with depression, self-criticism is a transdiagnostic risk factor for diverse forms of psychopathology. (1,2) However, research has shown that self-compassion is a robust resilience factor when faced with feelings of personal inadequacy. (3,4) Self-critical individuals experience feelings of unworthiness, inferiority, failure, and guilt. They engage in constant and harsh selfscrutiny and evaluation, and fear being disapproved and criticized and losing the approval and acceptance of others. (5) Self-compassion involves treating oneself with care and concern when confronted with personal inadequacies, mistakes, failures, and painful life situations. (6,7) Although self-criticism is the aspect of perfectionism most associated with maladjustment, (8) one can be harshly self-critical without being a perfectionist. Most studies of self-criticism have not measured shame; however, this self-conscious emotion has been implicated in diverse forms of psychopathology. (9) In contrast to guilt, which results from acknowledging bad behavior, shame results from seeing oneself as a bad or inadequate person. Although self-criticism is destructive across clinical disorders and interpersonal relationships, self-compassion is associated with healthy relationships, emotional well-being, and better treatment outcomes. Recent research shows how clinicians can teach their patients how to be less self-critical and more self-compassionate. Neff (6,7) proposes that self-compassion involves treating yourself with care and concern when being confronted with personal inadequacies, mistakes, failures, and painful life situations. It consists of 3 interacting components, each of which has a positive and negative pole: * self-kindness vs self-judgment * a sense of common humanity vs isolation * mindfulness vs over-identification. Self-kindness refers to being caring and understanding with oneself rather than harshly judgmental. Instead of attacking and berating oneself for personal shortcomings, the self is offered warmth and unconditional acceptance. Humanity involves recognizing that humans are imperfect, that all people fail, make mistakes, and have serious life challenges. By remembering that imperfection is part of life, we feel less isolated when we are in pain. Mindfulness in the context of self-compassion involves being aware of one's painful experiences in a balanced way that neither ignores and avoids nor exaggerates painful thoughts and emotions. Self-compassion is more than the absence of self-judgment, although a defining feature of self-compassion is the lack of self-judgment, and self-judgment overlaps with self-criticism. Rather, self-compassion provides several access points for reducing self-criticism. For example, being kind and understanding when confronting personal inadequacies (eg, "it's okay not to be perfect") can counter harsh self-talk (eg, "I'm not defective"). Mindfulness of emotional pain (eg, "this is hard") can facilitate a kind and warm response (eg, "what can I do to take care of myself right now?") and therefore lessen self-blame (eg, "blaming myself is just causing me more suffering"). Similarly, remembering that failure is part of the human experience (eg, "it's normal to mess up sometimes") can lessen egocentric feelings of isolation (eg, "it's not just me") and over-identification (eg, "it's not the end of the world"), resulting in lessened self-criticism (eg, "maybe it's not just because I'm a bad person"). Depression Several studies have found that self-criticism predicts depression. In 3 epidemiological studies, "feeling worthless" was among the top 2 symptoms predicting a depression diagnosis and later depressive episodes. (10) Self-criticism in fourth-year medical students predicted depression 2 years later, and--in males--10 years later in their medical careers better than a history of depression. …


title: Design of a Highly efficient bidirectional isolated LLC resonant converter

abstract: The isolated unidirectional LLC resonant converter is known for its outstanding efficiency and high power density. Little information has however been published about the possibility of transferring power in the reverse direction. This paper presents modulation schemes for making the LLC converter bidirectional. High efficiencies are predicted for both directions of power flow, though, as the behavior of the resonant tank is substantially different in the reverse direction, some of the inherent benefits of the conventional LLC converter are lost.


title: High Gain Monopole Antenna With Sleeve Ground Plane for WLAN Applications

abstract: In this letter, a compact monopole antenna is presented for wireless local area network (WLAN) applications. The proposed structure consists of a quarter-wavelength monopole, a ground plane, and an annular sleeve. The sleeve and the ground plane form a quarter-wavelength cavity. The open-ended cavity increases the radiation aperture and enhances the antenna gain. A small piece of substrate is used to support the monopole and match the input impedance. The simulated and measured results agree well. The measured impedance bandwidth is 160 MHz (2.38-2.54 GHz), which is sufficient for WLAN band. In the concerned band, the measured gain is above 3.6 dBi, which is 2.2 dB higher than that of a traditional monopole antenna.


title: Beagle: Automated Extraction and Interpretation of Visualizations from the Web

abstract: "How common is interactive visualization on the web?" "What is the most popular visualization design?" "How prevalent are pie charts really?" These questions intimate the role of interactive visualization in the real (online) world. In this paper, we present our approach (and findings) to answering these questions. First, we introduce Beagle, which mines the web for SVG-based visualizations and automatically classifies them by type (i.e., bar, pie, etc.). With Beagle, we extract over 41,000 visualizations across five different tools and repositories, and classify them with 86% accuracy, across 24 visualization types. Given this visualization collection, we study usage across tools. We find that most visualizations fall under four types: bar charts, line charts, scatter charts, and geographic maps. Though controversial, pie charts are relatively rare in practice. Our findings also indicate that users may prefer tools that emphasize a succinct set of visualization types, and provide diverse expert visualization examples.


title: Leveraging Augmented Reality Training Tool for Medical Education: a Case Study in Central Venous Catheterization

abstract: Central venous catheterization is a relatively common bedside medical procedure that involves the placement of a catheter into a patient»s internal jugular vein in order to administer medication or fluids. To learn this technique, medical students traditionally practice on training mannequins under the guidance of a clinical instructor. The objective of this project was to co-develop a standardized augmented reality solution for teaching medical students this procedure, which would enable them to practice independently and at their own pace. Following an iterative design and prototyping process, we compiled a comprehensive set of usability heuristics specific to augmented reality healthcare applications, used to identify unique usability issues associated with augmented reality software. This approach offers a better strategy to improve the usability of augmented reality system and increases the potential to standardize and render medical education more accessible. The benefits of applying augmented reality to simulated medical education come with heavy consequences in the event of poor learning outcomes. The usability of these systems is paramount to ensure the development of clinical competence is facilitated and not hindered.


title: Pain sensitivity and tactile spatial acuity are altered in healthy musicians as in chronic pain patients

abstract: Extensive training of repetitive and highly skilled movements, as it occurs in professional classical musicians, may lead to changes in tactile sensitivity and corresponding cortical reorganization of somatosensory cortices. It is also known that professional musicians frequently experience musculoskeletal pain and pain-related symptoms during their careers. The present study aimed at understanding the complex interaction between chronic pain and music training with respect to somatosensory processing. For this purpose, tactile thresholds (mechanical detection, grating orientation, twopoint discrimination) and subjective ratings to thermal and pressure pain stimuli were assessed in 17 professional musicians with chronic pain, 30 pain-free musicians, 20 nonmusicians with chronic pain, and 18 pain-free non-musicians. We found that pain-free musicians displayed greater touch sensitivity (i.e., lower mechanical detection thresholds), lower tactile spatial acuity (i.e., higher grating orientation thresholds) and increased pain sensitivity to pressure and heat compared to pain-free non-musicians. Moreover, we also found that musicians and non-musicians with chronic pain presented lower tactile spatial acuity and increased pain sensitivity to pressure and heat compared to pain-free non-musicians. The significant increment of pain sensitivity together with decreased spatial discrimination in pain-free musicians and the similarity of results found in chronic pain patients, suggests that the extensive training of repetitive and highly skilled movements in classical musicians could be considered as a risk factor for developing chronic pain, probably due to use-dependent plastic changes elicited in somatosensory pathways.


title: Visual Snow: a Potential Cortical Hyperexcitability Syndrome

abstract: The purpose of this review is to provide an overview of visual snow (VS) and provide information regarding current treatment options for VS. Visual snow (VS) is a rare disorder manifesting with a persistent visual phenomenon of seeing numerous tiny snow-like dots throughout the visual field, and it can cause debilitating visual and psychological consequences. It is emerging as a disorder separate from, but associated with, migraine visual aura, and neuronal cortical hyperexcitability is being considered as a theoretical mechanism for the persistent-positive visual symptoms. There are few studies that have investigated the treatment of VS, but as our understanding of this entity begins to change, we expect that new treatment approaches and treatment trials will emerge in the next decade. Currently, our approach is to consider pharmacologic treatment for all patients with VS who report decreased quality of life as a result of VS. Resolution of the disorder is difficult to accomplish with treatment, but in our experience, even when symptom intensity is simply reduced, many patients find that there is an improvement in their quality of life that is beneficial. Our preferred treatment options include: (1) oral lamotrigine with a slow increase from 25 mg daily to a maintenance dose of 200–300 mg daily in divided doses as tolerated, and this is typically achieved by advancing the dose in increments of 25–50 mg weekly following the first 2 weeks of therapy; (2) oral acetazolamide with an initial dose of 250 mg daily followed by a slow increase over 1–2 weeks to a total of 1000 mg daily in divided doses, and higher doses can be tolerated by some without increasing the risk-benefit ratio; or (3) oral verapamil long-acting at 120–240 mg daily, and if side effects limit the dose the can be initiated, then lower doses with short-acting verapamil two or three times daily can be substituted until higher doses with the long-acting formula can be tolerated. By initiating drug treatments with low doses and slowly increasing over 1 to 4 weeks, tolerability and compliance improves and allows patients to realize the full benefits of treatment. The proposed mechanisms of microstructural cortical abnormalities and hyperexcitability as a cause of VS may lead to new treatment approaches in the future. Until such a time, medications reported to relieve persistent visual phenomena of migraine and visual aura of migraine are treatment options worth considering and these are reviewed for that purpose. Although clinical trials for the treatment of visual snow are lacking due to the rarity of the disorder, medications reviewed here should be considered for use in patients with VS who experience an impact on their quality of life. Theoretical mechanisms that lead to cortical hyperexcitability are being investigated and could lead to new treatment options. In the meantime, medications may provide benefits in this disabling condition.


title: Design of modulated metasurface antennas

abstract: A review of recent achievements obtained in Modulated Metasurface Antennas is presented. These antennas are based on a transformation between a cylindrical surface-wave (SW) and a leaky-wave (LW) through interaction with a periodically modulated MTS. The latter is realized by using sub-wavelength patches of different dimensions printed on a grounded slab. A general design process is described, followed by a description of the main challenges and open problems in practical implementation.


title: Molecular activity prediction using deep learning software library

abstract: In order to know how work deep learning method in chemoinformatics and bioinformatics problems, we have attempted to predict the molecular activities using the molecular fingerprints (chemical descriptor vectors) provided by the “Merck molecular activity challenge” competition and an open source deep learning library Chainer. Our result has been able to reproduce almost identical increase-decrease tendencies with the correlation R s 2 of the champion group in the competition. GPU performance was also examined and the speed gain were more than 11 times than only CPU computation.


title: 2nd generation 600V SiC Schottky diodes use merged pn/Schottky structure for surge overload protection

abstract: A new generation of silicon carbide Schottky diodes has been developed which address surge current overload thermal runaway and lack of avalanche clamping by the use of a merged pn/Schottky diode structure. A PN structure with low ohmic contact is used in parallel with Schottky structure to greatly extend peak current handling and also provide avalanche energy capability without compromising dynamic performance.


title: Estimating worst-case latency of on-chip interconnects with formal simulation

abstract: Latency is a major issue in the design and validation of a Network-on-Chip (NoC). Various techniques for establishing latency bounds exist. Formal and mathematical methods, such as network calculus, can be used to analyze an NoC model. Simulation-based methods can be used to estimate latency bounds by exploring reachable states. Both have their advantages and disadvantages. This paper presents an approach that finds a middle ground between these two worlds. Our approach is based on simulation of high-level formal models. In contrast to traditional formal methods for worst-case latency, we do not require error-prone manual computation or the absence of cycles. In contrast to traditional simulation-based methods, we leverage the high level of abstraction to explore up to billions of states within a couple of hours. We apply our approach on an 8 core case study where a simple cache protocol runs on top of a ring-based Spidergon architecture. We show that deadlocks or starvations are easily found, and that for live networks a worst-case bound estimation can be produced within reasonable time.


title: Predicting Students ’ Academic Performance using Artificial Neural Network : A Case Study of an Engineering Course

abstract: The observed poor quality of graduates of some Nigerian Universities in recent times has been partly traced to inadequacies of the National University Admission Examination System. In this study an Artificial Neural Network (ANN) model, for predicting the likely performance of a candidate being considered for admission into the university was developed and tested. Various factors that may likely influence the performance of a student were identified. Such factors as ordinary level subjects’ scores and subjects’ combination, matriculation examination scores, age on admission, parental background, types and location of secondary school attended and gender, among others, were then used as input variables for the ANN model. A model based on the Multilayer Perceptron Topology was developed and trained using data spanning five generations of graduates from an Engineering Department of University of Ibadan, Nigeria’s first University.


title: Smartphone-Based Recognition of States and State Changes in Bipolar Disorder Patients

abstract: Today's health care is difficult to imagine without the possibility to objectively measure various physiological parameters related to patients' symptoms (from temperature through blood pressure to complex tomographic procedures). Psychiatric care remains a notable exception that heavily relies on patient interviews and self-assessment. This is due to the fact that mental illnesses manifest themselves mainly in the way patients behave throughout their daily life and, until recently there were no "behavior measurement devices." This is now changing with the progress in wearable activity recognition and sensor enabled smartphones. In this paper, we introduce a system, which, based on smartphone-sensing is able to recognize depressive and manic states and detect state changes of patients suffering from bipolar disorder. Drawing upon a real-life dataset of ten patients, recorded over a time period of 12 weeks (in total over 800 days of data tracing 17 state changes) by four different sensing modalities, we could extract features corresponding to all disease-relevant aspects in behavior. Using these features, we gain recognition accuracies of 76% by fusing all sensor modalities and state change detection precision and recall of over 97%. This paper furthermore outlines the applicability of this system in the physician-patient relations in order to facilitate the life and treatment of bipolar patients.


title: The Infinite PCFG Using Hierarchical Dirichlet Processes

abstract: We present a nonparametric Bayesian model of tree structures based on the hierarchical Dirichlet process (HDP). Our HDP-PCFG model allows the complexity of the grammar to grow as more training data is available. In addition to presenting a fully Bayesian model for the PCFG, we also develop an efficient variational inference procedure. On synthetic data, we recover the correct grammar without having to specify its complexity in advance. We also show that our techniques can be applied to full-scale parsing applications by demonstrating its effectiveness in learning state-split grammars.


title: Normalizing microtext

abstract: The use of computer mediated communication has resulted in a new form of written text--Microtext--which is very different from well-written text. Tweets and SMS messages, which have limited length and may contain misspellings, slang, or abbreviations, are two typical examples of microtext. Microtext poses new challenges to standard natural language processing tools which are usually designed for well-written text. The objective of this work is to normalize microtext, in order to produce text that could be suitable for further treatment. ::: ::: We propose a normalization approach based on the source channel model, which incorporates four factors, namely an orthographic factor, a phonetic factor, a contextual factor and acronym expansion. Experiments show that our approach can normalize Twitter messages reasonably well, and it outperforms existing algorithms on a public SMS data set.


title: Audio Spectrogram Representations for Processing with Convolutional Neural Networks

abstract: One of the decisions that arise when designing a neural network for any application is how the data should be represented in order to be presented to, and possibly generated by, a neural network. For audio, the choice is less obvious than it seems to be for visual images, and a variety of representations have been used for different applications including the raw digitized sample stream, hand-crafted features, machine discovered features, MFCCs and variants that include deltas, and a variety of spectral representations. This paper reviews some of these representations and issues that arise, focusing particularly on spectrograms for generating audio using neural networks for style transfer.


title: Signal Processing for FMCW SAR

abstract: The combination of frequency-modulated continuous-wave (FMCW) technology and synthetic aperture radar (SAR) techniques leads to lightweight cost-effective imaging sensors of high resolution. One limiting factor to the use of FMCW sensors is the well-known presence of nonlinearities in the transmitted signal. This results in contrast- and range-resolution degradation, particularly when the system is intended for high-resolution long-range applications, as it is the case for SAR. This paper presents a novel processing solution, which solves the nonlinearity problem for the whole range profile. Additionally, the conventional stop-and-go approximation used in pulse-radar algorithms is not valid in FMCW SAR applications under certain circumstances. Therefore, the motion within the sweep needs to be taken into account. Analytical development of the FMCW SAR signal model, starting from the deramped signal and without using the stop-and-go approximation, is presented in this paper. The model is then applied to stripmap, spotlight, and single-transmitter/multiple-receiver digital-beamforming SAR operational mode. The proposed algorithms are verified by processing real FMCW SAR data collected with the demonstrator system built at the Delft University of Technology.


title: Brain-computer interfaces for control of neuroprostheses: from synchronous to asynchronous mode of operation.

abstract: Transferring a brain-computer interface (BCI) from the laboratory environment into real world applications is directly related to the problem of identifying user intentions from brain signals without any additional information in real time. From the perspective of signal processing, the BCI has to have an uncued or asynchronous design. Based on the results of two clinical applications, where 'thought' control of neuroprostheses based on movement imagery in tetraplegic patients with a high spinal cord injury has been established, the general steps from a synchronous or cue-guided BCI to an internally driven asynchronous brain-switch are discussed. The future potential of BCI methods for various control purposes, especially for functional rehabilitation of tetraplegics using neuroprosthetics, is outlined.


title: Desloratadine in combination with montelukast in the treatment of chronic urticaria: a randomized, double-blind, placebo-controlled study

abstract: BACKGROUND ::: Chronic urticaria (CU) is a common skin condition. It is frequently a disabling disease due to the persistency of clinical symptoms, the unpredictable course and negative influence on the quality of life. ::: ::: ::: OBJECTIVE ::: The aim of this study is to determine whether montelukast, a LTD4 receptor antagonist, plus desloratadine, is more efficacious than desloratadine alone in the treatment of chronic urticaria. ::: ::: ::: MATERIALS ::: A randomized, double-blind, placebo-controlled study was conducted on 81 patients with a diagnosis of CU. A 1-week single-blind placebo run-in period (baseline) was followed by a 6-weeks double blind active treatment period. The patients were randomized to receive the following treatment once daily: (a) oral desloratadine (5 mg) plus placebo; (b) desloratadine (5 mg) plus montelukast (10 mg); (c) oral placebo alone. The study ended after another 1-week single-blind placebo washout period. ::: ::: ::: RESULTS ::: The evaluable population thus consisted of 76 patients. Both desloratadine alone and desloratadine plus montelukast administered once daily yielded improvements with respect to the baseline assessment as regards pruritus, number of separate episodes, size and number of weals, visual analogue score and patients' quality of life and with respect to the placebo group both in the active treatment period and in the run-out period. However, desloratadine plus montelukast was shown to improve the symptoms and patients' quality of life significantly more than desloratadine alone, although it did not have a significant effect on the number of urticarial episodes. ::: ::: ::: CONCLUSION ::: The combination of desloratadine plus montelukast is effective in the treatment of CU. It may therefore be a valid alternative in patients with relatively mild CU, in view of its efficacy and the lack of adverse events.


title: Structural-RNN: Deep Learning on Spatio-Temporal Graphs

abstract: Deep Recurrent Neural Network architectures, though remarkably capable at modeling sequences, lack an intuitive high-level spatio-temporal structure. That is while many problems in computer vision inherently have an underlying high-level structure and can benefit from it. Spatio-temporal graphs are a popular tool for imposing such high-level intuitions in the formulation of real world problems. In this paper, we propose an approach for combining the power of high-level spatio-temporal graphs and sequence learning success of Recurrent Neural Networks~(RNNs). We develop a scalable method for casting an arbitrary spatio-temporal graph as a rich RNN mixture that is feedforward, fully differentiable, and jointly trainable. The proposed method is generic and principled as it can be used for transforming any spatio-temporal graph through employing a certain set of well defined steps. The evaluations of the proposed approach on a diverse set of problems, ranging from modeling human motion to object interactions, shows improvement over the state-of-the-art with a large margin. We expect this method to empower new approaches to problem formulation through high-level spatio-temporal graphs and Recurrent Neural Networks.


title: Understanding BGP misconfiguration

abstract: It is well-known that simple, accidental BGP configuration errors can disrupt Internet connectivity. Yet little is known about the frequency of misconfiguration or its causes, except for the few spectacular incidents of widespread outages. In this paper, we present the first quantitative study of BGP misconfiguration. Over a three week period, we analyzed routing table advertisements from 23 vantage points across the Internet backbone to detect incidents of misconfiguration. For each incident we polled the ISP operators involved to verify whether it was a misconfiguration, and to learn the cause of the incident. We also actively probed the Internet to determine the impact of misconfiguration on connectivity.Surprisingly, we find that configuration errors are pervasive, with 200-1200 prefixes (0.2-1.0% of the BGP table size) suffering from misconfiguration each day. Close to 3 in 4 of all new prefix advertisements were results of misconfiguration. Fortunately, the connectivity seen by end users is surprisingly robust to misconfigurations. While misconfigurations can substantially increase the update load on routers, only one in twenty five affects connectivity. While the causes of misconfiguration are diverse, we argue that most could be prevented through better router design.


title: Massive mining of publicly available RNA-seq data from human and mouse

abstract: RNA sequencing (RNA-seq) is the leading technology for genome-wide transcript quantification. However, publicly available RNA-seq data is currently provided mostly in raw form, a significant barrier for global and integrative retrospective analyses. ARCHS4 is a web resource that makes the majority of published RNA-seq data from human and mouse available at the gene and transcript levels. For developing ARCHS4, available FASTQ files from RNA-seq experiments from the Gene Expression Omnibus (GEO) were aligned using a cloud-based infrastructure. In total 187,946 samples are accessible through ARCHS4 with 103,083 mouse and 84,863 human. Additionally, the ARCHS4 web interface provides intuitive exploration of the processed data through querying tools, interactive visualization, and gene pages that provide average expression across cell lines and tissues, top co-expressed genes for each gene, and predicted biological functions and protein–protein interactions for each gene based on prior knowledge combined with co-expression.


title: Anytime parallel density-based clustering

abstract: The density-based clustering algorithm DBSCAN is a state-of-the-art data clustering technique with numerous applications in many fields. However, DBSCAN requires neighborhood queries for all objects and propagation of labels from object to object. This scheme is time consuming and thus limits its applicability for large datasets. In this paper, we propose a novel anytime approach to cope with this problem by reducing both the range query and the label propagation time of DBSCAN. Our algorithm, called AnyDBC, compresses the data into smaller density-connected subsets called primitive clusters and labels objects based on connected components of these primitive clusters to reduce the label propagation time. Moreover, instead of passively performing range queries for all objects as in existing techniques, AnyDBC iteratively and actively learns the current cluster structure of the data and selects a few most promising objects for refining clusters at each iteration. Thus, in the end, it performs substantially fewer range queries compared to DBSCAN while still satisfying the cluster definition of DBSCAN. Moreover, by processing queries in block and merging the results into the current cluster structure, AnyDBC can be efficiently parallelized on shared memory architectures to further accelerate the performance, uniquely making it a parallel and anytime technique at the same time. Experiments show speedup factors of orders of magnitude compared to DBSCAN and its fastest variants as well as a high parallel scalability on multicore processors for very large real and synthetic complex datasets.


Experiments With Scalable Gradient-based Hyperparameter Optimization for Deep Neural Networks by


title: Analytical Modeling and Mitigation Techniques for the Energy Hole

abstract: In this paper we investigate the problem of uneven energy consumption in a large class of many-to-one sensor networks. In a many-to-one sensor network, all sensor nodes generate constant bit rate (CBR) data and send them to a single sink via multihop transmissions. This type of sensor network has many potential applications such as environmental monitoring and data gathering. Based on the observation that sensor nodes sitting around the sink need to relay more traffic compared to other nodes in outer sub-regions, our analysis verifies that nodes in inner rings suffer much faster energy consumption rates (ECR) and thus have much shorter expected lifetimes. We term this phenomenon of uneven energy consumption rates as the ''energy hole'' problem, which may result in severe consequences such as early dysfunction of the entire network. We proposed analytical modeling for this problem, which can help understand the relevance of different factors on energy consumption rates. Using this model, we study the effectiveness of several existing approaches towards mitigating the ''energy hole'' problem, including deployment assistance, traffic compression and aggregation. We have used simulation results to validate our analysis.


title: A novel planar four-quad antenna

abstract: So-called bi-quad antennas fabricated from a simple piece of wire have found their use in applications for terrestrial digital TV (DVB-T) and wireless LANs (WLAN). A realization of such antennas in planar form and the extension to a novel four-quad antenna are described in this contribution. A bandwidth of more than 40% and a gain of around 8 dB of this antenna are demonstrated; narrower beamwidths and a further increase of gain can be achieved using a reflector.


title: Review spam detection using machine learning

abstract: Prior to buying a product, people usually inform themselves by reading online reviews. To make more profit sellers often try to fake user experience. As customers are being deceived this way, recognizing and removing fake reviews is of great importance. This paper analyzes spam detection methods, based on machine learning, and presents their overview and results.


title: Unsupervised Neural Machine Translation with Weight Sharing

abstract: Unsupervised neural machine translation (NMT) is a recently proposed approach for machine translation which aims to train the model without using any labeled data. The models proposed for unsupervised NMT often use only one shared encoder to map the pairs of sentences from different languages to a shared-latent space, which is weak in keeping the unique and internal characteristics of each language, such as the style, terminology, and sentence structure. To address this issue, we introduce an extension by utilizing two independent encoders but sharing some partial weights which are responsible for extracting high-level representations of the input sentences. Besides, two different generative adversarial networks (GANs), namely the local GAN and global GAN, are proposed to enhance the cross-language translation. With this new approach, we achieve significant improvements on English-German, English-French and Chinese-to-English translation tasks.


title: A new adaptive multi-start technique for combinatorial global optimizations

abstract: We analyze relationships among local minima for the traveling salesman and graph bisection problems under standard neighborhood structures. Our work reveals surprising correlations that suggest a globally convex, or ''big valley'' structure in these optimization cost surfaces. In conjunction with combinatorial results that sharpen previous analyses, our analysis directly motivates a new adaptive multi-start paradigm for heuristic global optimization, wherein starting points for greedy descent are adaptively derived from the best previously found local minima. We test a simple instance of this method for the traveling salesman problem and obtain very significant speedups over previous multi-start implementations.


Music Transcription Using Deep Learning


title: Seeking strategic advantage in the post-net era: viewing ERP systems from the resource-based perspective

abstract: Abstract The purpose of this research is to explore whether enterprise resource planning (ERP) systems can provide an organization with a sustained competitive advantage. Using the VRIO framework of the resource-based model of competitive advantage, four questions are posed to consider this issue. Is the ERP system valuable? Is the ERP system a resource that is heterogeneously distributed across competing firms? Is the ERP system imperfectly mobile? And, is the firm organized to exploit the full potential of its ERP system? An examination of the existing research suggests that ERP systems may not provide a competitive advantage based upon the premises of system value, distribution, and imitability. This is largely due to the ‘common systems’ approach used for the implementation of most ERP systems. Instead, the source of competitive advantage may lie in the careful planning and successful management of ERP projects, refinement of the reengineering of the organization, and the post-implementation alignment of the ERP system with the organization's strategic direction. Suggestions for future research are offered.


title: An Exploratory Study of Oral and Dental Health in Canada

abstract: Healthcare practitioners agree that good oral health is a critical indicator of general health and wellness of a population. The lack of access to mandatory coverage for common issues such as cavities and non-surgical periodontal care often lead not only to medical problems, but also to loss of productivity. This trend is especially evident for older individuals and lower-income families. This paper discusses the results of our exploration of the annual Canadian Community Health Survey (CCHS), in order to further study the interplay between socio-economic factors and oral and dental health. To this end, we present the results when applying a number of machine learning algorithms to a CCHS data mart. Our results reaffirm that individuals' levels and sources of income are strong indicators of the number of dental visits per year. In addition, we found that younger adults and youth, who usually live in larger households, visit the dentist less frequently than all other survey respondents.


title: A faster approximation algorithm for the Steiner tree problem in graphs

abstract: Abstract The Steiner tree problem requires a shortest tree spanning a given vertex subset S within graph G =( V , E ). Two heuristics finding approximate Steiner trees no longer than 11/6 times of the optimal length appeared recently. They run in time O ( α + VS 2 + S 4 ) and O ( α + VS 2 + S 3.5 ), respectively, where α is the time complexity of the all-pairs-shortest-paths problem. We reduce the runtime of the first heuristic to O ( S ( E + VS + V log V )).


title: Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

abstract: Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.


title: The effects of wall slide and sling slide exercises on scapular alignment and pain in subjects with scapular downward rotation

abstract: [Purpose] The present study was performed to evaluate the changes in the scapular alignment, pressure pain threshold and pain in subjects with scapular downward rotation after 4 weeks of wall slide exercise or sling slide exercise. [Subjects and Methods] Twenty-two subjects with scapular downward rotation participated in this study. The alignment of the scapula was measured using radiographic analysis (X-ray). Pain and pressure pain threshold were assessed using visual analogue scale and digital algometer. Patients were assessed before and after a 4 weeks of exercise. [Results] In the within-group comparison, the wall slide exercise group showed significant differences in the resting scapular alignment, pressure pain threshold, and pain after four weeks. The between-group comparison showed that there were significant differences between the wall slide group and the sling slide group after four weeks. [Conclusion] The results of this study found that the wall slide exercise may be effective at reducing pain and improving scapular alignment in subjects with scapular downward rotation.


title: Motivational determinants among physicians in Lahore, Pakistan

abstract: IntroductionHuman resource crises in developing countries have been identified as a critical aspect of poor quality and low accessibility in health care. Worker motivation is an important facet of this issue. Specifically, motivation among physicians, who are an important bridge between health systems and patients, should be considered. This study aimed to identify the determinants of job motivation among physicians, a neglected perspective, especially in developing countries.MethodsA stratified random sample of 360 physicians was selected from public primary, public secondary and public and private tertiary health facilities in the Lahore district, Pakistan. Pretested, semi-structured, self-administered questionnaires were used. For the descriptive part of this study, physicians were asked to report their 5 most important work motivators and demotivators within the context of their current jobs and in general. Responses were coded according to emergent themes and frequencies calculated. Of the 30 factors identified, 10 were classified as intrinsic, 16 as organizational and 4 as socio-cultural.ResultsIntrinsic and socio-cultural factors like serving people, respect and career growth were important motivators. Conversely, demotivators across setups were mostly organizational, especially in current jobs. Among these, less pay was reported the most frequently. Fewer opportunities for higher qualifications was a demotivator among primary and secondary physicians. Less personal safety and poor working conditions were important in the public sector, particularly among female physicians. Among private tertiary physicians financial incentives other than pay and good working conditions were motivators in current jobs. Socio-cultural and intrinsic factors like less personal and social time and the inability to financially support oneself and family were more important among male physicians.ConclusionMotivational determinants differed across different levels of care, sectors and genders. Nonetheless, the important motivators across setups in this study were mostly intrinsic and socio-cultural, which are difficult to affect while the demotivators were largely organizational. Many can be addressed even at the facility level such as less personal safety and poor working conditions. Thus, in resource limited settings a good strategic starting point could be small scale changes that may markedly improve physicians' motivation and subsequently the quality of health care.


title: CIMDS: Adapting Postprocessing Techniques of Associative Classification for Malware Detection

abstract: Malware is software designed to infiltrate or damage a computer system without the owner's informed consent (e.g., viruses, backdoors, spyware, trojans, and worms). Nowadays, numerous attacks made by the malware pose a major security threat to computer users. Unfortunately, along with the development of the malware writing techniques, the number of file samples that need to be analyzed, named "gray list," on a daily basis is constantly increasing. In order to help our virus analysts, quickly and efficiently pick out the malicious executables from the "gray list," an automatic and robust tool to analyze and classify the file samples is needed. In our previous work, we have developed an intelligent malware detection system (IMDS) by adopting associative classification method based on the analysis of application programming interface (API) execution calls. Despite its good performance in malware detection, IMDS still faces the following two challenges: (1) handling the large set of the generated rules to build the classifier; and (2) finding effective rules for classifying new file samples. In this paper, we first systematically evaluate the effects of the postprocessing techniques (e.g., rule pruning, rule ranking, and rule selection) of associative classification in malware detection, and then, propose an effective way, i.e., CIDCPF, to detect the malware from the "gray list." To the best of our knowledge, this is the first effort on using postprocessing techniques of associative classification in malware detection. CIDCPF adapts the postprocessing techniques as follows: first applying Chi-square testing and Insignificant rule pruning followed by using Database coverage based on the Chi-square measure rule ranking mechanism and Pessimistic error estimation, and finally performing prediction by selecting the best First rule. We have incorporated the CIDCPF method into our existing IMDS system, and we call the new system as CIMDS system. Case studies are performed on the large collection of file samples obtained from the Antivirus Laboratory at Kingsoft Corporation and promising experimental results demonstrate that the efficiency and ability of detecting malware from the "gray list" of our CIMDS system outperform popular antivirus software tools, such as McAfee VirusScan and Norton Antivirus, as well as previous data-mining-based detection systems, which employed Naive Bayes, support vector machine, and decision tree techniques. In particular, our CIMDS system can greatly reduce the number of generated rules, which makes it easy for our virus analysts to identify the useful ones.


title: Multi-frame compression: theory and design

abstract: This paper consist of two parts. The first part concerns approximation capabilities in using an overcomplete dictionary, a frame, for block coding. A frame design technique for use with vector selection algorithms, for example matching pursuits (MP), is presented. We call the technique method of optimal directions (MOD). It is iterative and requires a training set of signal vectors. Experiments demonstrate that the approximation capabilities of the optimized frames are significantly better than those obtained using frames designed by ad hoc techniques or chosen in an ad hoc fashion. Experiments show typical reduction in mean squared error (MSE) by 30–80% for speech and electrocardiogram (ECG) signals. The second part concerns a complete compression scheme using a set of optimized frames, and evaluates both the use of fixed size and variable size frames. A signal compression scheme using frames optimized with the MOD technique is proposed. The technique, called multi-frame compression (MFC) uses several different frames, each optimized for a fixed number of selected frame vectors in each approximation. We apply the MOD and the MFC scheme to ECG signals. The coding results are compared with results obtained when using transform-based compression schemes like the discrete cosine transform (DCT) in combination with run-length and entropy coding. The experiments demonstrate improved rate-distortion performance by 2–4 dB for the MFC scheme when compared to the DCT at low bit-rates. They also show that variable sized frames in the compression scheme perform better than fixed sized frames.


Cloud Federation


title: MIMO-OFDM spatial multiplexing technique implementation for GNU radio

abstract: Multiple-Input Multiple-Output (MIMO) is a wireless technology allowing a significant increasing of the throughput without the extension of the bandwidth but by means of the use of multiple antennas both in transmission and reception. Currently, Orthogonal Frequency Division Multiplexing (OFDM) is used in conjuction with MIMO to achieve better performance. With OFDM, instead of a single carrier, the main signal is split in a sub-set of independently modulated signals on orthogonal sub-carriers. In this paper, we provide a description of our MIMONet SDR platform for network-level exploitation of MIMO technology. We present the implementation of our OFDM transceiver, developed following the structure of IEEE 802.11n standard and implementing one of the most powerful MIMO technique: spatial multiplexing. In this technique, two (or more) different data streams are simultaneously transmitted over two (or more) antennas. Starting from the original GNU Radio code, we modified and added blocks to achieve a complete implementation of MIMO-OFDM spatial multiplexing. We added some features, such as the concatenation of Forward Error Correction (FEC) in the packet construction, and the use of pilot sub-carriers for channel estimation. We also developed a new synchronization algorithm derived by extending the Van de Beek algorithm to the MIMO setting. We build the framework of the standard IEEE 802.11n. In particular, we put all the preambles needed for synchronization and channel estimation. We have also designed and implemented a fine grained signal-to-noise ratio (SNR) estimation, bit error rate (BER) and packet error rate (PER) computations, that allow us to evaluate the channel conditions and validate performance of the software implementation.


title: Position of the American Dietetic Association and Dietitians of Canada:Vegetarian Diets

abstract: It is the position of the American Dietetic Association and Dietitians of Canada that appropriately planned vegetarian diets are healthful, nutritionally adequate, and provide health benefits in the prevention and treatment of certain diseases. Approximately 2.5% of adults in the United States and 4% of adults in Canada follow vegetarian diets. A vegetarian diet is defined as one that does not include meat, fish, or fowl. Interest in vegetarianism appears to be increasing, with many restaurants and college foodservices offering vegetarian meals routinely. Substantial growth in sales of foods attractive to vegetarians has occurred and these foods appear in many supermarkets. This position paper reviews the current scientific data related to key nutrients for vegetarians including protein, iron, zinc, calcium, vitamin D, riboflavin, vitamin B-12, vitamin A, n-3 fatty acids, and iodine. A vegetarian, including vegan, diet can meet current recommendations for all of these nutrients. In some cases, use of fortified foods or supplements can be helpful in meeting recommendations for individual nutrients. Well-planned vegan and other types of vegetarian diets are appropriate for all stages of the life-cycle including during pregnancy, lactation, infancy, childhood, and adolescence. Vegetarian diets offer a number of nutritional benefits including lower levels of saturated fat, cholesterol, and animal protein as well as higher levels of carbohydrates, fibre, magnesium, potassium, folate, antioxidants such as vitamins C and E, and phytochemicals. Vegetarians have been reported to have lower body mass indices than non-vegetarians, as well as lower rates of death from ischemic heart disease, lower blood cholesterol levels, lower blood pressure, and lower rates of hypertension, type 2 diabetes, and prostate and colon cancer. While a number of federally funded and institutional feeding programs can accommodate vegetarians, few have foods suitable for vegans at this time. Because of the variability of dietary practices among vegetarians, individual assessment of dietary intakes of vegetarians is required. Dietetics professionals have a responsibility to support and encourage those who express an interest in consuming a vegetarian diet. They can play key roles in educating vegetarian clients about food sources of specific nutrients, food purchase and preparation, and any dietary modifications that may be necessary to meet individual needs. Menu planning for vegetarians can be simplified by use of a food guide that specifies food groups and serving sizes.


title: Printed Omni-Directional UWB Monopole Antenna With Very Compact Size

abstract: A compact rectangular monopole with an equal-width ground plane is presented. This novel structural configuration could significantly improve its radiation performance and decrease its size. The feeding structure composed of a trident-shaped strip and a tapered impedance transformer is presented and discussed. Its impedance bandwidth, defined by measured VSWR less than 2, is from 2.75 to 16.2 GHz with a ratio of about 5.9:1. Both numerical and experimental results show that the proposed monopole antenna has stably omni-directional H-plane radiation patterns with low cross-polarization level within its impedance bandwidth. This novel monopole antenna has ultrawide impedance bandwidth, very compact size (30 mm*8 mm), low fabrication lost, and omni-directional H-plane radiation patterns, which are suitable for various broadband applications.


title: Rough sets: Some extensions

abstract: In this article, we present some extensions of the rough set approach and we outline a challenge for the rough set based research.


title: Online decision making in crowdsourcing markets: theoretical challenges

abstract: Over the past decade, crowdsourcing has emerged as a cheap and efficient method of obtaining solutions to simple tasks that are difficult for computers to solve but possible for humans. The popularity and promise of crowdsourcing markets has led to both empirical and theoretical research on the design of algorithms to optimize various aspects of these markets, such as the pricing and assignment of tasks. Much of the existing theoretical work on crowdsourcing markets has focused on problems that fall into the broad category of online decision making; task requesters or the crowdsourcing platform itself make repeated decisions about prices to set, workers to filter out, problems to assign to specific workers, or other things. Often these decisions are complex, requiring algorithms that learn about the distribution of available tasks or workers over time and take into account the strategic (or sometimes irrational) behavior of workers. ::: As human computation grows into its own field, the time is ripe to address these challenges in a principled way. However, it appears very difficult to capture all pertinent aspects of crowdsourcing markets in a single coherent model. In this paper, we reflect on the modeling issues that inhibit theoretical research on online decision making for crowdsourcing, and identify some steps forward. This paper grew out of the authors' own frustration with these issues, and we hope it will encourage the community to attempt to understand, debate, and ultimately address them. ::: The authors welcome feedback for future revisions of this paper.


title: Influence of pole-arc width on cogging torque in permanent magnet motors

abstract: This work investigates the effect of the pole-arc width on the cogging torque of the permanent magnet motors based on the assumption of distributed force instead of the previous concentrated one. On the basis of the motors' mechanical and electrical symmetry, the superposition method is employed to examine the above effect. And the phase tuning relationships between the pole-arc width and the cogging torque is obtained. This study presents a tuning method for the prediction and suppression of the magnetically induced vibration, which in turn provides a theoretical foundation for the dynamic design of the permanent magnet motors. The theoretical conclusions are verified by the Finite Element method.


title: Implementation of a Touch Screen Panel With Triangle Twist Sensor Pattern and Controller IC

abstract: In this paper, a cost-effective touch screen panel with triangle twist (TAT) sensor pattern and its calibration algorithm are proposed. For touch detection, the proposed touch screen panel consists of one drive line to receive sensing information from two sensing lines. The proposed touch screen panel reduces the number of signal lines and bonding pads by about 42% compared to conventional touch screen panels, which decrease both the manufacturing difficulty and costs. By a simple calibration algorithm for the proposed touch screen panel with a TAT sensor pattern in a micro controller unit, the touch performance with respect to accuracy, precision, and linearity is considerably enhanced. The proposed touch screen panel shows reliable touch accuracy with errors less than 2 mm. As shown in the implementation results, the proposed touch screen panel is very effective in reducing manufacturing costs of touch devices, which suffer from implementation issues of signal lines caused by adopting a huge number of touch sensors.


title: Discriminative score normalization for keyword search decision

abstract: Many keyword search (KWS) systems make “hit/false alarm (FA)” decisions based on the lattice-based posterior probability, which is incomparable across keywords. Therefore, score normalization is essential for a KWS system. In this paper, we investigate the integration of two novel features, ranking-score and relative-to-max, into a discriminative score normalization method. These features are extracted by considering all competing hypotheses of a putative detection. A metric-based normalization method is also applied as a post-processing step to further optimize the term-weighted value (TWV) evaluation metric. We report empirical improvements over standard baselines using the Vietnamese data from IARPA's Babel program in the NIST OpenKWS13 Evaluation setup.


title: Computational interlocking furniture assembly

abstract: Furniture typically consists of assemblies of elongated and planar parts that are connected together by glue, nails, hinges, screws, or other means that do not encourage disassembly and re-assembly. An alternative approach is to use an interlocking mechanism, where the component parts tightly interlock with one another. The challenge in designing such a network of interlocking joints is that local analysis is insufficient to guarantee global interlocking, and there is a huge number of joint combinations that require an enormous exploration effort to ensure global interlocking. In this paper, we present a computational solution to support the design of a network of interlocking joints that form a globally-interlocking furniture assembly. The key idea is to break the furniture complex into an overlapping set of small groups, where the parts in each group are immobilized by a local key, and adjacent groups are further locked with dependencies. The dependency among the groups saves the effort of exploring the immobilization of every subset of parts in the assembly, thus allowing the intensive interlocking computation to be localized within each small group. We demonstrate the effectiveness of our technique on many globally-interlocking furniture assemblies of various shapes and complexity.


title: Feature selection based on loss-margin of nearest neighbor classification

abstract: The problem of selecting a subset of relevant features is classic and found in many branches of science including-examples in pattern recognition. In this paper, we propose a new feature selection criterion based on low-loss nearest neighbor classification and a novel feature selection algorithm that optimizes the margin of nearest neighbor classification through minimizing its loss function. At the same time, theoretical analysis based on energy-based model is presented, and some experiments are also conducted on several benchmark real-world data sets and facial data sets for gender classification to show that the proposed feature selection method outperforms other classic ones.


title: Quality, trust, and utility of scientific data on the web: towards a joint model

abstract: In science, quality is paramount. As scientists increasingly look to the Web to share and discover scientific data, there is a growing need to support the scientist in assessing the quality of that data. However, quality is an ambiguous and overloaded term. In order to support the scientific user in discovering useful data we have systematically examined the nature of "quality" by exploiting three, prevalent properties of scientific data sets: (1) that data quality is commonly defined objectively; (2) the provenance and lineage in its production has a well understood role; and (3) "fitness-for-use" is a definition of utility rather than quality or trust, where the quality and trust-worthiness of the data and the entities that produced that data inform its utility. Our study is presented in two stages. First we review existing information quality dimensions and detail an assessment-oriented classification. We introduce definitions for quality, trust and utility in terms of the entities required in their assessment; producer, provider, consumer, process, artifact and quality standard. Next we detail a novel and experimental approach to assessment by modelling the causal relationships between quality, trust, and utility dimensions through the construction of decision networks informed by provenance graphs. To ground and motivate our discussion throughout we draw on the European Bioinformatics Institute's Gene Ontology Annotations database. We present an initial demonstration of our approach with an example for ranking results from the Gene Ontology Annotation database using an emerging objective quality measure, the Gene Ontology Annotation Quality score.


title: Learning a Scene Background Model via Classification

abstract: Learning to efficiently construct a scene background model is crucial for tracking techniques relying on background subtraction. Our proposed method is motivated by criteria leading to what a general and reasonable background model should be, and realized by a practical classification technique. Specifically, we consider a two-level approximation scheme that elegantly combines the bottom-up and top-down information for deriving a background model in real time. The key idea of our approach is simple but effective: If a classifier can be used to determine which image blocks are part of the background, its outcomes can help to carry out appropriate blockwise updates in learning such a model. The quality of the solution is further improved by global validations of the local updates to maintain the interblock consistency. And a complete background model can then be obtained based on a measurement of model completion. To demonstrate the effectiveness of our method, various experimental results and comparisons are included.


title: CMOS Low Noise Amplifier with Capacitive Feedback Matching

abstract: A capacitive shunt feedback LNA input matching network is demonstrated which offers matching, good noise performance, low component count and low area consumption. A prototype 9 GHz LNA vehicle amplifier is designed and fabricated in a 130 nm RF-CMOS process. The measured amplifier has 20.5 dB of gain at 8.8 GHz with input and output match of -15 dB and -8 dB respectively. It has 1.4 GHz of 3 dB-bandwidth around 9 GHz with the noise figure of 1.7 dB at the center frequency and below 2 dB across the band. Large signal measurements reveal that the amplifier can deliver -2 dBm of power to the 50Omega output load at its 1 dB compression point. It draws 23 mA of current from a 1.2 V supply. The chip occupies an area of 0.64 mm2.


title: Style Transfer Via Image Component Analysis

abstract: Example-based stylization provides an easy way of making artistic effects for images and videos. However, most existing methods do not consider the content and style separately. In this paper, we propose a style transfer algorithm via a novel component analysis approach, based on various image processing techniques. First, inspired by the steps of drawing a picture, an image is decomposed into three components: draft, paint and edge, which describe the content, main style, and strengthened strokes along the boundaries. Then the style is transferred from the template image to the source image in the paint and edge components. Style transfer is formulated as a global optimization problem by using Markov random fields, and a coarse-to-fine belief propagation algorithm is used to solve the optimization problem. To combine the draft component and the obtained style information, the final artistic result can be achieved via a reconstruction step. Compared to other algorithms, our method not only synthesizes the style, but also preserves the image content well. We also extend our algorithm from single image stylization to video personalization, by maintaining the temporal coherence and identifying faces in video sequences. The results indicate that our approach performs excellently in stylization and personalization for images and videos.


title: Social ties and mental health

abstract: It is generally agreed that social ties play a beneficial role in the maintenance of psychological well-being. In this targeted review, we highlight four sets of insights that emerge from the literature on social ties and mental health outcomes (defined as stress reactions, psychological well-being, and psychological distress, including depressive symptoms and anxiety). First, the pathways by which social networks and social supports influence mental health can be described by two alternative (although not mutually exclusive) causal models—the main effect model and the stress-buffering model. Second, the protective effects of social ties on mental health are not uniform across groups in society. Gender differences in support derived from social network participation may partly account for the higher prevalence of psychological distress among women compared to men. Social connections may paradoxically increase levels of mental illness symptoms among women with low resources, especially if such connections entail role strain associated with obligations to provide social support to others. Third, egocentric networks are nested within a broader structure of social relationships. The notion of social capital embraces the embeddedness of individual social ties within the broader social structure. Fourth, despite some successes reported in social support interventions to enhance mental health, further work is needed to deepen our understanding of the design, timing, and dose of interventions that work, as well as the characteristics of individuals who benefit the most.


title: A fluorescent perylene-assembled polyvinylpyrrolidone film: synthesis, morphology and nanostructure

abstract: A fluorescent polyvinylpyrrolidone (PVP) film with assembled nanostructures is successfully prepared in one pot by using perylene-3,4,9,10-tetracarboxylic acid dianhydride (PDA) as the fluorophore. The reactive anhydride groups in PDA play an important role in the covalent bonding of fluorescent molecules in the fluorescent PVP film, which has been demonstrated by a small-molecule model reaction. Interestingly, rod-like structures are found on the surface of the fluorescent film, which is attributed to the π–π interaction of PDA that occurred in the crosslinked PVP film. Further polarized optical microscopy (POM), X-ray diffraction (XRD) and optical analyses demonstrate the stable existence of π-conjugated nanostructures in the fluorescent PDA-based PVP film. The reactive anhydrides and the π–π interaction of perylene molecules are essential for the fabrication of the fluorescent perylene-assembled PVP film. This method could be extended to the preparation of the fluorescent PVP film with self-assembled nanostructures.


title: Let's Play My Way: Investigating Audience Influence in User-Generated Gaming Live-Streams

abstract: We investigate how the audience of gaming live-streams can influence the content. We conducted two case studies on streams in which audience influence is central and in which the audience can directly participate: First, we review an existing format of the Rocket Beans TV channel and describe how the audience can influence its course of action. With this, we illustrate current practices for integrating the audience. Second, we report the results of our investigation of a "Twitch Plays Pokemon" (TPP)-like setting in which the audience shares the control of the main character through aggregated chat messages. We explored a wider range of techniques than the original TPP offered and found that this can help the audience to organize itself in more nuanced ways. From both case studies, we synthesize results that are of relevance for streams that want to give the audience more influence.


title: Deep Neural Ensemble for Retinal Vessel Segmentation in Fundus Images towards Achieving Label-free Angiography

abstract: Automated segmentation of retinal blood vessels in label-free fundus images entails a pivotal role in computed aided diagnosis of ophthalmic pathologies, viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases. The challenge remains active in medical image analysis research due to varied distribution of blood vessels, which manifest variations in their dimensions of physical appearance against a noisy background. In this paper we formulate the segmentation challenge as a classification task. Specifically, we employ unsupervised hierarchical feature learning using ensemble of two level of sparsely trained denoised stacked autoencoder. First level training with bootstrap samples ensures decoupling and second level ensemble formed by different network architectures ensures architectural revision. We show that ensemble training of auto-encoders fosters diversity in learning dictionary of visual kernels for vessel segmentation. SoftMax classifier is used for fine tuning each member autoencoder and multiple strategies are explored for 2-level fusion of ensemble members. On DRIVE dataset, we achieve maximum average accuracy of 95.33% with an impressively low standard deviation of 0.003 and Kappa agreement coefficient of 0.708. Comparison with other major algorithms substantiates the high efficacy of our model.


title: Path integral guided policy search

abstract: 3Sergey Levine is with Google Brain, Mountain View, CA 94043, USA. We present a policy search method for learning complex feedback control policies that map from high-dimensional sensory inputs to motor torques, for manipulation tasks with discontinuous contact dynamics. We build on a prior technique called guided policy search (GPS), which iteratively optimizes a set of local policies for specific instances of a task, and uses these to train a complex, high-dimensional global policy that generalizes across task instances. We extend GPS in the following ways: (1) we propose the use of a model-free local optimizer based on path integral stochastic optimal control (PI2), which enables us to learn local policies for tasks with highly discontinuous contact dynamics; and (2) we enable GPS to train on a new set of task instances in every iteration by using on-policy sampling: this increases the diversity of the instances that the policy is trained on, and is crucial for achieving good generalization. We show that these contributions enable us to learn deep neural network policies that can directly perform torque control from visual input. We validate the method on a challenging door opening task and a pick-and-place task, and we demonstrate that our approach substantially outperforms the prior LQR-based local policy optimizer on these tasks. Furthermore, we show that on-policy sampling significantly increases the generalization ability of these policies.


title: Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

abstract: Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems.


title: Now it's personal: on abusive game design

abstract: In this paper, we introduce the concept of abusive game design as an attitude towards creating games -- an aesthetic practice that operates as a critique of certain conventionalisms in popular game design wisdom. We emphasize that abusive game design is, at its core, about spotlighting the dialogic relation between player and designer.


title: Face Detection for Pulse Rate Measurement

abstract: Monitoring vital signals, including pulse rate is a crucial process to detect cardiovascular disorders and body liveness. Some of the clinically used techniques might cause irritation or discomfort for patients. This paper presents a contactless, non-invasive camera-based method of extracting pulse rate from a video stream without attaching electrodes to the skin. The introduced method is a colour-based approach that can estimate heart rate through amplifying subtle changes in skin colour which caused by blood circulation. When evaluated on 7 subjects, this approach reported similar heart rates comparing to a reference oximeter system, however, this method is sensitive to motion artefact and light noise.


title: The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories

abstract: Abstract The psychometric properties of the Depression Anxiety Stress Scales (DASS) were evaluated in a normal sample of N = 717 who were also administered the Beck Depression Inventory (BDI) and the Beck Anxiety Inventory (BAI). The DASS was shown to possess satisfactory psychometric properties, and the factor structure was substantiated both by exploratory and confirmatory factor analysis. In comparison to the BDI and BAI, the DASS scales showed greater separation in factor loadings. The DASS Anxiety scale correlated 0.81 with the BAI, and the DASS Depression scale correlated 0.74 with the BDI. Factor analyses suggested that the BDI differs from the DASS Depression scale primarily in that the BDI includes items such as weight loss, insomnia, somatic preoccupation and irritability, which fail to discriminate between depression and other affective states. The factor structure of the combined BDI and BAI items was virtually identical to that reported by Beck for a sample of diagnosed depressed and anxious patients, supporting the view that these clinical states are more severe expressions of the same states that may be discerned in normals. Implications of the results for the conceptualisation of depression, anxiety and tension/stress are considered, and the utility of the DASS scales in discriminating between these constructs is discussed.


Usability Evaluation of Google Classroom : Basis for the Adaptation of GSuite E-Learning Platform


title: A sex difference in features that elicit genital response

abstract: Previous research suggests that women's genital arousal is an automatic response to sexual stimuli, whereas men's genital arousal is dependent upon stimulus features specific to their sexual interests. In this study, we tested the hypothesis that a nonhuman sexual stimulus would elicit a genital response in women but not in men. Eighteen heterosexual women and 18 heterosexual men viewed seven sexual film stimuli, six human films and one nonhuman primate film, while measurements of genital and subjective sexual arousal were recorded. Women showed small increases in genital arousal to the nonhuman stimulus and large increases in genital arousal to both human male and female stimuli. Men did not show any genital arousal to the nonhuman stimulus and demonstrated a category-specific pattern of arousal to the human stimuli that corresponded to their stated sexual orientation. These results suggest that stimulus features necessary to evoke genital arousal are much less specific in women than in men.


title: Application of DIP to Detect Power Transformers Axial Displacement and Disk Space Variation Using FRA Polar Plot Signature

abstract: While frequency response analysis (FRA) technique has been successfully used to assess the mechanical integrity of active parts within power transformers, it still exhibits some drawbacks including its inability to detect incipient and minor winding deformations and the requirement for an expert to analyze the results. Although several papers have investigated the impact of various faults on the transformer FRA signature, no attempt was made to automate and improve the fault detection accuracy of the current technique. The main contribution of this paper is the presentation of a new approach for the FRA technique through incorporating the magnitude and phase angle plots that can be measured using any commercial frequency response analyzer into one polar plot. Contrary to the current industry practice that only relies on the magnitude of the measured FRA signature for fault identification and quantification, the proposed polar plot that comprises more features than the magnitude plot will facilitate the use of digital image processing (DIP) techniques to improve the detection accuracy, standardize, and automate the FRA interpretation process. In this regard, three-dimensional models for two three-phase power transformers of different ratings, sizes, and windings structures are modeled using a finite element analysis technique to simulate various levels of axial displacement (AD) and disk space variation (DSV) at different locations of the transformer windings. Impact of minor fault levels on the proposed polar plot signature is investigated through the application of various DIP techniques. Simulation results are validated through practical measurements on a scaled-down transformer. Results show that the proposed polar plot along with the DIP technique is able to detect minor fault levels of AD and DSV with high accuracy.


A.: Cognitive Context Models and Discourse


title: Overcoming resource underutilization in spatial CNN accelerators

abstract: Convolutional neural networks (CNNs) are revolutionizing a variety of machine learning tasks, but they present significant computational challenges. Recently, FPGA-based accelerators have been proposed to improve the speed and efficiency of CNNs. Current approaches construct an accelerator optimized to maximize the overall throughput of iteratively computing the CNN layers. However, this approach leads to dynamic resource underutilization because the same accelerator is used to compute CNN layers of radically varying dimensions. We present a new CNN accelerator design that improves the dynamic resource utilization. Using the same FPGA resources, we build multiple accelerators, each specialized for specific CNN layers. Our design achieves 1.3× higher throughput than the state of the art when evaluating the convolutional layers of the popular AlexNet CNN on a Xilinx Virtex-7 FPGA.


title: Frequent Pattern Mining in Web Log Data

abstract: Frequent pattern mining is a heavily researched area in the field of data mining with wide range of applications. One of them is to use frequent pattern discovery methods in Web log data. Discovering hidden information from Web log data is called Web usage mining. The aim of discovering frequent patterns in Web log data is to obtain information about the navigational behavior of the users. This can be used for advertising purposes, for creating dynamic user profiles etc. In this paper three pattern mining approaches are investigated from the Web usage mining point of view. The different patterns in Web log mining are page sets, page sequences and page graphs. The expansion of the World Wide Web (Web for short) has resulted in a large amount of data that is now in general freely available for user access. The different types of data have to be managed and organized in such a way that they can be accessed by different users efficiently. Therefore, the application of data mining techniques on the Web is now the focus of an increasing number of researchers. Several data mining methods are used to discover the hidden information in the Web. However, Web mining does not only mean applying data mining techniques to the data stored in the Web. The algorithms have to be modified such that they better suit the demands of the Web. New approaches should be used which better fit the properties of Web data. Furthermore, not only data mining algorithms, but also artificial intelligence, information retrieval and natural language processing techniques can be used efficiently. Thus, Web mining has been developed into an autonomous research area.


title: High Sensitivity and Wide Dynamic Range Analog Front-End Circuits for Pulsed TOF 4-D Imaging LADAR Receiver

abstract: Analog front-end (AFE) circuits, which mainly consist of a transimpedance amplifier (TIA) with wide dynamic range and a timing discriminator with double threshold voltage, were designed and implemented for a pulsed time-of-fight 4-D imaging LADAR receiver. The preamplifier of the proposed TIA adopts a shunt-feedback topology to amplify weak echo signal, and a current-mirror topology to amplify strong one, respectively. The proposed AFE can capture directly the pulsed echo amplitude with wide dynamic range through programmable gain control switches. The proposed AFE circuits, which achieve a high gain of 106 dB $\Omega $ , a linear dynamic range of 80 dB, an averaged input-referred noise density of 0.89 pA/Hz0.5 and a minimum detectable signal of $0.36~\mu \text{A}$ at SNR = 5, and a sensitivity of 8 nW with APD of 45 A/W, were designed with 3.3 V devices and fabricated in a 0.18- $\mu \text{m}$ standard CMOS process. The total area of AFE, which includes the circuit core, bandgap and bias circuits, and I/O PAD, is approximately equal to $1.20\times 1.13$ mm2.