title: Analysis of the MED oscillation problem in BGP

abstract: The multi exit discriminator (MED) attribute of the border gateway protocol (BGP) is widely used to implement "cold potato routing" between autonomous systems. However, the use of MED in practice has led to BGP persistent oscillation. The MED oscillation problem has been described with example configurations and complicated, step-by-step evaluation of dynamic route computations performed at multiple routers. Our work presents the first rigorous analysis of the MED oscillation problem. We employ the stable paths problem (SPP) formalism that allows a static analysis of the interaction of routing policies. We give a formal definition of MED induced routing anomalies (MIRA) and show that, in general, they can span multiple autonomous systems. However, if we assume that the BGP configurations between autonomous systems follows a common model based on customer/provider and peer/peer relationships, then we show that the scope of any MIRA is always contained within a single autonomous system. Contrary to widely held assumptions, we show that a MIRA can occur even in a fully meshed IBGP configuration. We also show that a stable BGP routing may actually violate the stated semantics of the MED attribute.


title: ERP System Adoption - Does the Size Matter?

abstract: This paper investigates the relationship of enterprise size to the constraints and objectives of Enterprise Resource Planning (ERP) systems adoption. The survey data based on the responses of 44 companies indicates that significant differences exist between small, medium-sized and large enterprises in ERP system adoption. Specifically, the findings suggest that small companies experience more knowledge constraints than their larger counterparts in ERP adoption. Further, while being the most prevalent objective for ERP adoption in all the company groups, business development through ERP adoption is considered especially important by the medium-sized and large enterprises. Finally, the findings of the study suggest that instead of considering small and medium-sized enterprises as one homogenous group, the differences between these two groups of companies should be acknowledged in the future research.


title: Deep Learning Computed Tomography: Learning Projection-Domain Weights From Image Domain in Limited Angle Problems

abstract: In this paper, we present a new deep learning framework for 3-D tomographic reconstruction. To this end, we map filtered back-projection-type algorithms to neural networks. However, the back-projection cannot be implemented as a fully connected layer due to its memory requirements. To overcome this problem, we propose a new type of cone-beam back-projection layer, efficiently calculating the forward pass. We derive this layer's backward pass as a projection operation. Unlike most deep learning approaches for reconstruction, our new layer permits joint optimization of correction steps in volume and projection domain. Evaluation is performed numerically on a public data set in a limited angle setting showing a consistent improvement over analytical algorithms while keeping the same computational test-time complexity by design. In the region of interest, the peak signal-to-noise ratio has increased by 23%. In addition, we show that the learned algorithm can be interpreted using known concepts from cone beam reconstruction: the network is able to automatically learn strategies such as compensation weights and apodization windows.


title: Detecting Hate Speech on Twitter Using a Convolution-GRU Based Deep Neural Network

abstract: In recent years, the increasing propagation of hate speech on social media and the urgent need for effective counter-measures have drawn significant investment from governments, companies, and empirical research. Despite a large number of emerging scientific studies to address the problem, a major limitation of existing work is the lack of comparative evaluations, which makes it difficult to assess the contribution of individual works. This paper introduces a new method based on a deep neural network combining convolutional and gated recurrent networks. We conduct an extensive evaluation of the method against several baselines and state of the art on the largest collection of publicly available Twitter datasets to date, and show that compared to previously reported results on these datasets, our proposed method is able to capture both word sequence and order information in short texts, and it sets new benchmark by outperforming on 6 out of 7 datasets by between 1 and 13% in F1. We also extend the existing dataset collection on this task by creating a new dataset covering different topics.


title: DEPRESSION, LONELINESS, ANGER BEHAVIOURS AND INTERPERSONAL RELATIONSHIP STYLES IN MALE PATIENTS ADMITTED TO INTERNET ADDICTION OUTPATIENT CLINIC IN TURKEY

abstract: SUMMARY Background: ‘Internet addiction’ is excessive computer use that interferes with daily life of a person. We designed this study in order to evaluate the predictor effect of depression, loneliness, anger and interpersonal relationship styles for internet addiction as well as develop a model. Subjects and methods: Forty (40) male internet addicted patients were selected from our hospital’s internet Addiction Outpatient Clinic. During the study, the Internet Addiction Test (IAT), the Beck Depression Inventory (BDI), the State Trait Anger Expression Scale (STAXI), the UCLA-Loneliness Scale (UCLA-LS), and the Interpersonal Relationship Styles Scale (IRSS) were used for the evaluation of the patients. Results: The results of this study showed that the ‘duration of internet use’ (B=2.353, p=0.01) and STAXI ‘anger in’ subscale (B=1.487, p=0.01) were the predictors of internet addiction. Conclusion: When the clinicians suspect for the internet overuse, regulation of internet usage might be helpful. Psychiatric treatments for expressing anger and therapies that focus on validation of the feelings may be useful.


title: Connectionist Temporal Modeling for Weakly Supervised Action Labeling

abstract: We propose a weakly-supervised framework for action labeling in video, where only the order of occurring actions is required during training time. The key challenge is that the per-frame alignments between the input (video) and label (action) sequences are unknown during training. We address this by introducing the Extended Connectionist Temporal Classification (ECTC) framework to efficiently evaluate all possible alignments via dynamic programming and explicitly enforce their consistency with frame-to-frame visual similarities. This protects the model from distractions of visually inconsistent or degenerated alignments without the need of temporal supervision. We further extend our framework to the semi-supervised case when a few frames are sparsely annotated in a video. With less than 1 % of labeled frames per video, our method is able to outperform existing semi-supervised approaches and achieve comparable performance to that of fully supervised approaches.


title: Web-Based virtual learning environments: a research framekwork and a preliminary assessment of effectiveness in basic IT skills training

abstract: Internet technologies are having a significant impact on the learning industry. For-profit organizations and traditional institutions of higher education have developed and are using web-based courses, but little is known about their effectiveness compared to traditional classroom education. Our work focuses on the effectiveness of a web-based virtual learning environment (VLE) in the context of basic information technology skills training. ::: ::: This article provides three main contributions. First, it introduces and defines the concept of VLE, discussing how a VLE differs from the traditional classroom and differentiating it from the related, but narrower, concept of computer aided instruction (CAI). Second, it presents a framework of VLE effectiveness, grounded in the technology-mediated learning literature, which frames the VLE research domain, and addresses the relationship between the main constructs. Finally, it focuses on one essential VLE design variable, learner control, and compares a web-based VLE to a traditional classroom through a longitudinal experimental design. ::: ::: Our results indicate that, in the context of IT basic skills training in undergraduate education, there are no significant differences in performance between students enrolled in the two environments. However, the VLE leads to higher reported computer self-efficacy, while participants report being less satisfied with the learning process.


title: Implications of modified waterfall model to the roles and education of health IT professionals

abstract: Electronic Health Records (EHRs) are believed to have the potential to enhance efficiency and provide better health care. However, the benefits could be easily compromised if EHRs are not used appropriately. This paper applies a modified waterfall life cycle model to evaluate the roles of health IT professionals in the adoption and management of EHRs. We then present our development of a Masters' program in Medical Informatics for the education of health IT professionals. We conclude that health IT professionals serve key roles in addressing the problems and concerns and help fulfill envision of EHRs.


title: A survey of evidence for test-driven development in academia

abstract: University professors traditionally struggle to incorporate software testing into their course curriculum. Worries include double-grading for correctness of both source and test code and finding time to teach testing as a topic. Test-driven development (TDD) has been suggested as a possible solution to improve student software testing skills and to realize the benefits of testing. According to most existing studies, TDD improves software quality and student productivity. This paper surveys the current state of TDD experiments conducted exclusively at universities. Similar surveys compare experiments in both the classroom and industry, but none have focused strictly on academia.


title: Crowdsourcing Applications for Public Health

abstract: Abstract Crowdsourcing is an online, distributed, problem-solving, and production model that uses the collective intelligence of networked communities for specific purposes. Although its use has benefited many sectors of society, it has yet to be fully realized as a method for improving public health. This paper defines the core components of crowdsourcing and proposes a framework for understanding the potential utility of crowdsourcing in the domain of public health. Four discrete crowdsourcing approaches are described (knowledge discovery and management; distributed human intelligence tasking; broadcast search; and peer-vetted creative production types) and a number of potential applications for crowdsourcing for public health science and practice are enumerated.


title: A cooperative heterogeneous Unmanned Autonomous Systems solution for monitoring and inspecting power distribution system

abstract: This paper presents a comprehensive solution to monitoring and inspection of power-line distribution system. This involves two robotic platforms which are heterogeneous and cooperative in executing tasks as planned. One of them is Robonwire which is a three-arms robot which moves on the transmission lines while the other is an Octacopter UAV. The UAV inspects the transmission tower while the Robonwire performs both inspection and monitoring of the lines by travelling on them. The Robonwire is capable of moving across and along the transmission lines by expansion and retraction. This enables the robot to handle and avoid the obstacles with minimum effort. The UAV is a vertical take-off rotorcraft comprising eight propellers designed to carry a payload of camera platform and wireless telemetry. In addition to inspection, the octacopter serves as a wireless relay node establishing data transfer between Robonwire and the ground station. An UAV test-bed is proposed to speed up the controller design and tuning of the quadcopter in the laboratory. Both the UAV-platform and Robonwire together provide complementary inspection roles: horizontally and vertically enable a cooperative behaviour. Preliminary simulation and experimental investigations the individual system is presented.


title: Binary Space Partitioning Layouts To Help Build Better Information Dashboards

abstract: Information dashboards are a widely used means to present data at-a-glance and their quality (i.e., their ability to communicate the data in a clear and efficient way) depends not only on the way to represent the data but also on how visual elements are placed on the screen - the dashboard layout. In many cases the dashboards are predefined by the application but we are now in an era where the user wants more freedom. Unfortunately this freedom is today bounded by the applications using layout managers which either do not offer the needed flexibility or are too complex to be used by non-experts. In this paper, we propose a novel layout manager more suitable for dashboards; it provides layouts based on a binary space partitioning (BSP) and we show how the user can interactively build and modify them. Then we present how we can compute an optimal solution for such layouts, i.e., how to actually compute the position and size of all visual elements in order for them to be as close as possible to their preferred size while respecting the arrangement decided by the user. This is achieved by automatically generating a set of constraints which are then solved to find the optimal solution. Finally we describe the actual implementation of a lightweight constraint solver in JavaScript - named QPSolver - that can be embedded in a web application and we compare its performance with Cassowary [4], a well-known solver for constraint-based GUI layouts notably used by Apple in OS X and iOS.


title: A three-dimensional kinematic analysis of the long jump take-off

abstract: The long jump has been widely studied in recent years. Two models exist in the literature which define the relationship between selected variables that affect performance. Both models suggest that the critical phase of the long jump event is the touch-down to take-off phase, as it is in this phase that the necessary vertical velocity is generated. Many three dimensional studies of the long jump exist, but the only studies to have reported detailed data on this phase were two-dimensional in nature. In these, the poor relationships obtained between key variables and performance led to the suggestion that there may be some relevant information in data in the third dimension. The aims of this study were to conduct a three-dimensional analysis of the touch-down to take-off phase in the long jump and to explore the interrelationships between key variables. Fourteen male long jumpers were filmed using three-dimensional methods during the finals of the 1994 (n = 8) and 1995 (n = 6) UK National Championships. ...


Identifying Necessary Conditions for Open-Ended Evolution through the Artificial Life World of Chromaria


title: Impact of abnormal cerebrovascular reactivity on BOLD fMRI: a preliminary investigation of moyamoya disease

abstract: Blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) studies of patients with cerebrovascular disease have largely ignored the confounds associated with abnormal cerebral blood flow, vascular reactivity and neurovascular coupling. We studied BOLD fMRI activation and cerebrovascular reactivity in moyamoya disease. To characterize the impact of remote vascular demands on BOLD fMRI measurements, we varied the vascular territories engaged by manipulating the experimental task performed by the participants. Vascular territories affected by disease were identified using BOLD cerebrovascular reactivity. Preliminary evidence from two patients pre- and postrevascularization surgery and four controls indicates that neurovascular coupling in affected brain regions can be modulated by the task-related vascular demands in unaffected regions. In one patient studied, we observed that brain regions with improved cerebrovascular reactivity after surgery demonstrated normalized neurovascular coupling, that is the degree to which neurovascular coupling was modulated by task-related vascular demands was decreased. We propose that variations in task-dependent neurovascular coupling in patients with moyamoya disease are likely related to vascular steal. While preliminary, our findings are a proof of concept of the limitations of BOLD fMRI in cerebrovascular disease and suggest a role for assessment of cerebrovascular reactivity to improve interpretation of task-related BOLD fMRI activation.


title: Deep Convolutional Neural Network on iOS Mobile Devices

abstract: Deep Convolutional Neural Network (CNN) draws significant attention in the computer vision community by facilitating machines with more intelligence in understanding visual signals, however, its computation complexity has also increased significantly. To achieve ubiquitous machine intelligence, deep CNN is required to be ported onto local devices rather than cloud-based solution due to low latency consideration. Hence, in this paper, we propose a method to explore the design space for porting deep CNN onto iOS mobile devices, with attempts in maximizing data reusability, which alleviates the high bandwidth burden in the convolution layers of CNN. Furthermore, effective data reuse also makes possible the parallelization of all computing threads without data loading latency. On the other hand, deep CNN is usually over-parametrized with many unused convolution kernels. Based on Algorithm/Architecture Co-Exploration, we introduced a method in pruning redundant kernels in deep CNN with ignorable performance degradation on validation dataset (0.06% loss). This reduces 29% of operations and 34% of storage size on a 16-layer CNN. We used iPhone 6s and iPad Pro for case studies, and ported 8-layer and 16-layer CNNs onto targeted devices. The data reusability strategy improves computation speed up to 1.3x, and redundant kernel removal increases computation speed to 1.43x. As a result, we achieved high computation efficiency and have thus enhanced the capability of machine intelligence on local mobile devices.


GUIDING CRITERIA FOR OPERATIONAL RISK REPORTING IN A CORPORATE ENVIRONMENT


title: Cyber Crime Assessment

abstract: In physical crime the concepts of crime scene assessment and offender profiling are well-accepted if occasionally controversial. Work by the FBI has put profiling into the mainstream of investigative theories. However, in the digital world no such progress has been made. Further, the notion of offender profiling addresses the psychological continuum whereas we are concerned with the criminological spectrum. The ongoing research reported in this paper will begin the process of translating physical crime scene assessment techniques into digital practice. The potential benefits of accomplishing this successfully are significant. Unfortunately, the translation between physical investigation and digital investigation is not straightforward. The addition of particular techniques pioneered by crime assessment experts in physical investigations has turned out to be far more promising than application of psychological profiling. This research-in-progress paper presents a new approach to cyber crime assessment. It views the computer as the crime scene and it applies a crime typology.


title: A Metric-based Framework for Automatic Taxonomy Induction

abstract: This paper presents a novel metric-based framework for the task of automatic taxonomy induction. The framework incrementally clusters terms based on ontology metric, a score indicating semantic distance; and transforms the task into a multi-criteria optimization based on minimization of taxonomy structures and modeling of term abstractness. It combines the strengths of both lexico-syntactic patterns and clustering through incorporating heterogeneous features. The flexible design of the framework allows a further study on which features are the best for the task under various conditions. The experiments not only show that our system achieves higher F1-measure than other state-of-the-art systems, but also reveal the interaction between features and various types of relations, as well as the interaction between features and term abstractness.


title: EyeTab: model-based gaze estimation on unmodified tablet computers

abstract: Despite the widespread use of mobile phones and tablets, hand-held portable devices have only recently been identified as a promising platform for gaze-aware applications. Estimating gaze on portable devices is challenging given their limited computational resources, low quality integrated front-facing RGB cameras, and small screens to which gaze is mapped. In this paper we present EyeTab, a model-based approach for binocular gaze estimation that runs entirely on an unmodified tablet. EyeTab builds on set of established image processing and computer vision algorithms and adapts them for robust and near-realtime gaze estimation. A technical prototype evaluation with eight participants in a normal indoors office setting shows that EyeTab achieves an average gaze estimation accuracy of 6.88° of visual angle at 12 frames per second.


title: Face Spoofing Detection Based on Multiple Descriptor Fusion Using Multiscale Dynamic Binarized Statistical Image Features

abstract: Face recognition has been the focus of attention for the past couple of decades and, as a result, a significant progress has been made in this area. However, the problem of spoofing attacks can challenge face biometric systems in practical applications. In this paper, an effective countermeasure against face spoofing attacks based on a kernel discriminant analysis approach is presented. Its success derives from different innovations. First, it is shown that the recently proposed multiscale dynamic texture descriptor based on binarized statistical image features on three orthogonal planes (MBSIF-TOP) is effective in detecting spoofing attacks, showing promising performance compared with existing alternatives. Next, by combining MBSIF-TOP with a blur-tolerant descriptor, namely, the dynamic multiscale local phase quantization (MLPQ-TOP) representation, the robustness of the spoofing attack detector can be further improved. The fusion of the information provided by MBSIF-TOP and MLPQ-TOP is realized via a kernel fusion approach based on a fast kernel discriminant analysis (KDA) technique. It avoids the costly eigen-analysis computations by solving the KDA problem via spectral regression. The experimental evaluation of the proposed system on different databases demonstrates its advantages in detecting spoofing attacks in various imaging conditions, compared with the existing methods.


title: Broadband planar wide-scan array employing tightly coupled elements and integrated balun

abstract: In this paper we present the design, fabrication and demonstration of an X-band phased array capable of wide-angle scanning. A new non-symmetric element for wideband tightly coupled dipole arrays is integrated with a low-profile microstrip balun printed on the array ground plane. The feed connects to the array aperture with vertical twin-wire transmission lines that concurrently perform impedance matching. The proposed element arms are identical near the center feed portion but dissimilar towards the ends, forming a ball-and-cup. A 64 element array prototype is verified experimentally and compared to numerical simulation. The array aperture is placed λ/7 (at 8 GHz) above a ground plane and shown to maintain a VSWR < 2 from 8–12.5 GHz while scanning up to 75° and 60° in E and H-plane, respectively.


title: Suicide With Inert Gases

abstract: Suicide by asphyxiation with a plastic bag placed over the head is a method that has been advocated by right-to-die groups. Recently, such groups have proposed the introduction of helium into the plastic bag as a means of hastening death. Helium is readily available at toy stores, where it is sold in tanks for balloon inflation. It produces asphyxiation by the exclusion of oxygen in enclosed spaces. We report 7 fatalities throughout an 18-month period involving plastic bag suffocation in conjunction with helium use. These fatalities coincide with publication of an update to a popular right-to-die text in which this method is described. Although right-to-die literature was absent from all scenes, this method was not previously observed in our jurisdiction, and the deaths likely reflect exposure to this information. Because of analytical difficulties in testing for helium in biologic specimens, death certification rests on scene investigation.


title: Music Education using Augmented Reality with a Head Mounted Display

abstract: Traditional music education places a large emphasis on individual practice. Studies have shown that individual practice is frequently not very productive due to limited feedback and students lacking interest and motivation. In this paper we explore the use of augmented reality to create an immersive experience to improve the efficiency of learning of beginner piano students. The objective is to stimulate development in notation literacy and to create motivation through presenting as a game the task that was perceived as a chore. This is done by identifying successful concepts from existing systems and merging them into a new system designed to be used with a head mounted display. The student is able to visually monitor their practice and have fun while doing so. An informal user study indicates that the system initially puts some pressure on users, but that participants find it helpful and believe that it improves learning.


title: Truncating Wide Networks Using Binary Tree Architectures

abstract: In this paper, we propose a binary tree architecture to truncate architecture of wide networks by reducing the width of the networks. More precisely, in the proposed architecture, the width is incrementally reduced from lower layers to higher layers in order to increase the expressive capacity of networks with a less increase on parameter size. Also, in order to ease the gradient vanishing problem, features obtained at different layers are concatenated to form the output of our architecture. By employing the proposed architecture on a baseline wide network, we can construct and train a new network with same depth but considerably less number of parameters. In our experimental analyses, we observe that the proposed architecture enables us to obtain better parameter size and accuracy trade-off compared to baseline networks using various benchmark image classification datasets. The results show that our model can decrease the classification error of a baseline from 20:43% to 19:22% on Cifar-100 using only 28% of parameters that the baseline has. Code is available at https://github.com/ZhangVision/bitnet.


AUTOMATIC BRAIN TUMOR DETECTION AND CLASSIFICATION USING SVM CLASSIFIER


title: A Hierarchical Approach to Neural Context-Aware Modeling

abstract: We present a new recurrent neural network topology to enhance state-of-the-art machine learning systems by incorporating a broader context. Our approach overcomes recent limitations with extended narratives through a multi-layered computational approach to generate an abstract context representation. Therefore, the developed system captures the narrative on word-level, sentence-level, and context-level. Through the hierarchical set-up, our proposed model summarizes the most salient information on each level and creates an abstract representation of the extended context. We subsequently use this representation to enhance neural language processing systems on the task of semantic error detection. To show the potential of the newly introduced topology, we compare the approach against a context-agnostic set-up including a standard neural language model and a supervised binary classification network. The performance measures on the error detection task show the advantage of the hierarchical context-aware topologies, improving the baseline by 12.75% relative for unsupervised models and 20.37% relative for supervised models.


KHARMA : A KML / HTML Architecture for Mobile Augmented Reality Applications


title: Anomaly-based intrusion detection through K-Means clustering and Naives Bayes classification

abstract: Intrusion detection systems (IDSs) effectively balance extra security appliance by identifying intrusive activities on a computer system, and their enhancement is emerging at an unexpected rate. Anomaly-based intrusion detection methods, which employ machine learning algorithms, are able to identify unforeseen attacks. Regrettably, the foremost challenge of this method is to minimize false alarm while maximizing detection and accuracy rate. We propose an integrated machine learning algorithm across K-Means clustering and Naive Bayes Classifier called KMC+NBC to overcome the aforesaid drawbacks. K-Means clustering is applied to labeling and gathers the entire data into corresponding cluster sets based on the data behavior, i.e. , i.e. normal and attack, while Naive Bayes Classifier (NBC) is applied to reorder the misclassified clustered data into correct categories. Experiments have been performed to evaluate the performance of KMC+NBC and NBC against ISCX 2012 Intrusion Detection Evaluation Dataset. The result shows that KMC+NBC significantly improves the accuracy, detection rate up to 99% and 98.8%, respectively, while decreasing the false alarm to 2.2%.


title: A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation

abstract: The convergence rate and final performance of common deep learning models have significantly benefited from heuristics such as learning rate schedules, knowledge distillation, skip connections, and normalization layers. In the absence of theoretical underpinnings, controlled experiments aimed at explaining these strategies can aid our understanding of deep learning landscapes and the training dynamics. Existing approaches for empirical analysis rely on tools of linear interpolation and visualizations with dimensionality reduction, each with their limitations. Instead, we revisit such analysis of heuristics through the lens of recently proposed methods for loss surface and representation analysis, viz., mode connectivity and canonical correlation analysis (CCA), and hypothesize reasons for the success of the heuristics. In particular, we explore knowledge distillation and learning rate heuristics of (cosine) restarts and warmup using mode connectivity and CCA. Our empirical analysis suggests that: (a) the reasons often quoted for the success of cosine annealing are not evidenced in practice; (b) that the effect of learning rate warmup is to prevent the deeper layers from creating training instability; and (c) that the latent knowledge shared by the teacher is primarily disbursed to the deeper layers.


title: Towards End-to-End Code-Switching Speech Recognition

abstract: Code-switching speech recognition has attracted an increasing interest recently, but the need for expert linguistic knowledge has always been a big issue. End-to-end automatic speech recognition (ASR) simplifies the building of ASR systems considerably by predicting graphemes or characters directly from acoustic input. In the mean time, the need of expert linguistic knowledge is also eliminated, which makes it an attractive choice for code-switching ASR. This paper presents a hybrid CTC-Attention based end-to-end Mandarin-English code-switching (CS) speech recognition system and studies the effect of hybrid CTC-Attention based models, different modeling units, the inclusion of language identification and different decoding strategies on the task of code-switching ASR. On the SEAME corpus, our system achieves a mixed error rate (MER) of 34.24%.


title: An FPGA implemented 24-bit audio DAC with 1-bit sigma-delta modulator

abstract: This paper presents the design and FPGA implementation of a 24-bit audio DAC incorporating a set of multiplier-free interpolation filters and a 4th-order 1-bit sigma-delta modulator (SDM). The whole system achieves a signal to noise ratio (SNR) as high as 139dB with idle tones and noise modulation virtually eliminated. For the fixed point interpolation filters, multiplier-free canonical signed digits (CSD) coefficients are chosen to sharply reduce FPGA resources utilization. And the zeros of SDM are psychoacoustically optimized to minimize the total perceived output noise power so that the resultant noise within the signal band becomes unobjectionable to the human ear. Furthermore, in order to eliminate the idle tones notorious in 1-bit SDM, single-bit dither of ±0.125 amplitude is added while guaranteeing a maximum stable input range of −5.19dBFS.


title: Depth of field rendering via adaptive recursive filtering

abstract: We present a new post-processing method for rendering high-quality depth-of-field effects in real time. Our method is based on a recursive filtering process, which adaptively smooths the image frame with local depth and circle of confusion information. Unlike previous post-filtering approaches that rely on various convolution kernels, the behavior of our filter is controlled by a weighting function defined between two neighboring pixels. By properly designing this weighting function, our method produces spatially-varying smoothed results, correctly handles the boundaries between in-focus and out-of-focus objects, and avoids rendering artifacts such as intensity leakage and blurring discontinuity. Additionally, our method works on the full frame without resorting to image pyramids. Our algorithms runs efficiently on graphics hardware. We demonstrate the effectiveness of the proposed method with several complex scenes.


Usability Metric Framework for Mobile Phone Application


title: Mindfulness-Based Stress Reduction for Older Adults: Effects on Executive Function, Frontal Alpha Asymmetry and Immune Function

abstract: Background/Aims: Mindfulness-based stress reduction (MBSR) has enhanced cognition, positive emotion, and immunity in younger and middle-aged samples; its benefits are less well known for older persons. Here we report on a randomized controlled trial of MBSR for older adults and its effects on executive function, left frontal asymmetry of the EEG alpha band, and antibody response. Methods: Older adults (n = 201) were randomized to MBSR or waiting list control. The outcome measures were: the Trail Making Test part B/A (Trails B/A) ratio, a measure of executive function; changes in left frontal alpha asymmetry, an indicator of positive emotions or approach motivation; depression, mindfulness, and perceived stress scores, and the immunoglobulin G response to a protein antigen, a measure of adaptive immunity. Results: MBSR participants had a lower Trails B/A ratio immediately after intervention (p lower antibody responses 24 weeks after antigen challenge (p Conclusions: MBSR produced small but significant changes in executive function, mindfulness, and sustained left frontal alpha asymmetry. The antibody findings at follow-up were unexpected. Further study of the effects of MBSR on immune function should assess changes in antibody responses in comparison to T-cell-mediated effector functions, which decline as a function of age.


title: Controller Synthesis for String Stability of Vehicle Platoons

abstract: Cooperative adaptive cruise control (CACC) allows for short-distance automatic vehicle following using intervehicle wireless communication in addition to onboard sensors, thereby potentially improving road throughput. In order to fulfill performance, safety, and comfort requirements, a CACC-equipped vehicle platoon should be string stable, attenuating the effect of disturbances along the vehicle string. Therefore, a controller design method is developed that allows for explicit inclusion of the string stability requirement in the controller synthesis specifications. To this end, the notion of string stability is introduced first, and conditions for L2 string stability of linear systems are presented that motivate the development of an H∞ controller synthesis approach for string stability. The potential of this approach is illustrated by its application to the design of controllers for CACC for one- and two-vehicle look-ahead communication topologies. As a result, L2 string-stable platooning strategies are obtained in both cases, also revealing that the two-vehicle look-ahead topology is particularly effective at a larger communication delay. Finally, the results are experimentally validated using a platoon of three passenger vehicles, illustrating the practical feasibility of this approach.


title: Trust Network Analysis with Subjective Logic

abstract: Trust networks consist of transitive trust relationships between people, organisations and software agents connected through a medium for communication and interaction. By formalising trust relationships, e.g. as reputation scores or as subjective trust measures, trust between parties within the community can be derived by analysing the trust paths linking the parties together. This article describes a method for trust network analysis using subjective logic (TNA-SL). It provides a simple notation for expressing transitive trust relationships, and defines a method for simplifying complex trust networks so that they can be expressed in a concise form and be computationally analysed. Trust measures are expressed as beliefs, and subjective logic is used to compute trust between arbitrary parties in the network. We show that TNA-SL is efficient, and illustrate possible applications with examples.


title: Tasks scheduling and resource allocation in heterogeneous cloud for delay-bounded mobile edge computing

abstract: Mobile edge computing is a novel technique in which mobile devices offload computation-intensive tasks with stringent delay requirements to the edge cloud. However, the limited computational resource in the edge cloud may result in the Quality of Service degradation. In this paper, we address this issue by coordinating the heterogeneous cloud which includes the edge cloud and the remote cloud. Considering the offloading of delay-bounded tasks, we study into the scheduling of heterogeneous cloud in order to maximize the probability that tasks can have the delay requirements met. The problem formulation is proved to be concave, and an optimal algorithm is proposed accordingly. The optimal policy with heterogeneous cloud is notably different from the policy merely using the edge cloud. With only the edge cloud, the system serves tasks with loose delay bounds and drops tasks with stringent delay bounds when the traffic load is heavy. However, with the heterogeneous cloud, tasks with stringent delay bounds are offloaded to the edge cloud and tasks with loose delay bounds are offloaded to the remote cloud. In numerical results, the probability that the delay bounds of tasks are satisfied can be improved by about 40% with the assistance of the remote cloud.


title: Orchestrating Tree-Type VNF Forwarding Graphs in Inter-DC Elastic Optical Networks

abstract: It is known that by incorporating network function virtualization (NFV) in inter-datacenter (inter-DC) networks, service providers can use their network resources more efficiently and adaptively and expedite the deployment of new services. This paper studies the provisioning algorithms to realize tree-type virtual network function forwarding graphs (VNF-FGs), i.e., multicast NFV trees (M-NFV-Ts), in inter-DC elastic optical networks (IDC-EONs) cost-effectively. Specifically, we try to optimize the VNF placement and multicast routing and spectrum assignment jointly for orchestrating M-NFV-Ts in an IDC-EON with the lowest cost. Our study addresses both static network planning and dynamic network provisioning. For network planning, we first formulate a mixed integer linear programming (MILP) model to solve the problem exactly, and then propose three heuristic algorithms, namely, auxiliary frequency slot matrix (AFM)-MILP, AFM-GS, and RB. Extensive simulations show that AFM-MILP and AFM-GS can approximate the MILP's performance on low-cost M-NFV-T provisioning with much shorter running time. For network provisioning, we design two additional online algorithms based on AFM-GS and RB to serve M-NFV-Ts in a dynamic IDC-EON, with the consideration of spectrum fragmentation.


title: BIOTIC INVASIONS: CAUSES, EPIDEMIOLOGY, GLOBAL CONSEQUENCES, AND CONTROL

abstract: Biotic invaders are species that establish a new range in which they proliferate, spread, and persist to the detriment of the environment. They are the most important ecological outcomes from the unprecedented alterations in the distribution of the earth's biota brought about largely through human transport and commerce. In a world without borders, few if any areas remain sheltered from these im- migrations. The fate of immigrants is decidedly mixed. Few survive the hazards of chronic and stochastic forces, and only a small fraction become naturalized. In turn, some naturalized species do become invasive. There are several potential reasons why some immigrant species prosper: some escape from the constraints of their native predators or parasites; others are aided by human-caused disturbance that disrupts native communities. Ironically, many biotic invasions are apparently facilitated by cultivation and husbandry, unintentional actions that foster immigrant populations until they are self-perpetuating and uncontrollable. Whatever the cause, biotic invaders can in many cases inflict enormous environmental damage: (1) Animal invaders can cause extinctions of vulnerable native species through predation, grazing, competition, and habitat alteration. (2) Plant invaders can completely alter the fire regime, nutrient cycling, hydrology, and energy budgets in a native ecosystem and can greatly diminish the abundance or survival of native species. (3) In agriculture, the principal pests of temperate crops are nonindigenous, and the combined expenses of pest control and crop losses constitute an onerous "tax" on food, fiber, and forage production. (4) The global cost of virulent plant and animal diseases caused by parasites transported to new ranges and presented with susceptible new hosts is currently incalculable. Identifying future invaders and taking effective steps to prevent their dispersal and establishment con- stitutes an enormous challenge to both conservation and international commerce. Detection and management when exclusion fails have proved daunting for varied reasons: (1) Efforts to identify general attributes of future invaders have often been inconclusive. (2) Predicting susceptible locales for future invasions seems even more problematic, given the enormous differences in the rates of arrival among potential invaders. (3) Eradication of an established invader is rare, and control efforts vary enormously in their efficacy. Successful control, however, depends more on commitment and continuing diligence than on the efficacy of specific tools themselves. (4) Control of biotic invasions is most effective when it employs a long-term, ecosystem- wide strategy rather than a tactical approach focused on battling individual invaders. (5) Prevention of invasions is much less costly than post-entry control. Revamping national and international quarantine laws by adopting a "guilty until proven innocent" approach would be a productive first step. Failure to address the issue of biotic invasions could effectively result in severe global consequences, including wholesale loss of agricultural, forestry, and fishery resources in some regions, disruption of the ecological processes that supply natural services on which human enterprise depends, and the creation of homogeneous, impoverished ecosystems composed of cosmopolitan species. Given their current scale, biotic invasions have taken their place alongside human-driven atmospheric and oceanic alterations as major agents of global change. Left unchecked, they will influence these other forces in profound but still unpredictable ways.


title: Are test cases needed? Replicated comparison between exploratory and test-case-based software testing

abstract: Manual software testing is a widely practiced verification and validation method that is unlikely to fade away despite the advances in test automation. In the domain of manual testing, many practitioners advocate exploratory testing (ET), i.e., creative, experience-based testing without predesigned test cases, and they claim that it is more efficient than testing with detailed test cases. This paper reports a replicated experiment comparing effectiveness, efficiency, and perceived differences between ET and test-case-based testing (TCT) using 51 students as subjects, who performed manual functional testing on the jEdit text editor. Our results confirm the findings of the original study: 1) there is no difference in the defect detection effectiveness between ET and TCT, 2) ET is more efficient by requiring less design effort, and 3) TCT produces more false-positive defect reports than ET. Based on the small differences in the experimental design, we also put forward a hypothesis that the effectiveness of the TCT approach would suffer more than ET from time pressure. We also found that both approaches had distinctive issues: in TCT, the problems were related to correct abstraction levels of test cases, and the problems in ET were related to test design and logging of the test execution and results. Finally, we recognize that TCT has other benefits over ET in managing and controlling testing in large organizations.


title: pyGPs: a Python library for Gaussian process regression and classification

abstract: We introduce pyGPs, an object-oriented implementation of Gaussian processes (gps) for machine learning. The library provides a wide range of functionalities reaching from simple gp specification via mean and covariance and gp inference to more complex implementations of hyperparameter optimization, sparse approximations, and graph based learning. Using Python we focus on usability for both "users" and "researchers". Our main goal is to offer a user-friendly and flexible implementation of GPs for machine learning.


title: A self-aligning knee joint for walking assistance devices

abstract: This paper presents a novel self-aligning knee mechanism for walking assistance devices for the elderly to provide physical gait assistance. Self-aligning knee joints can assist in flexion/extension motions of the knee joint and compensate the knee's transitional movements in the sagittal plane. In order to compensate the center of rotation, which moves with the flexion/extension motion of the human knee joint, a self-aligning knee joint is proposed that adds redundant degrees of freedom (i.e., 2-DoF) to the 1-DoF revolute joint. The key idea of the proposed mechanism is to decouple joint rotations and translations for use in lower-extremity wearable devices. This paper describes the mechanical design of this self-aligning knee mechanism and its implementation on a wearable robot and in preliminary experiments. The performance of the proposed mechanism is verified by simulations and experiments.


title: Blocked recursive image composition

abstract: Presentations that feature arrangements of photos (e.g., collages, photobooks and slideshows) are a popular means of sharing and communication. In the prevalent framework for creating such presentations, layouts are supplied to the user in the form of designed templates. However, a template library may not support a desired number of photos, combination of aspect ratios or range of areas. To alleviate this issue, we present BRIC, a method for arranging practically any number of photos on a rectangular canvas. Primary constraints ensure respect of photo aspect ratios and specified gutter thickness; while secondary constraints encourage layouts in which photo areas correspond to desired relative area values. Photos are arranged based on a recursive partition of the page, with photo areas computed as the solution to a linear system implied by the partition. We present a detailed description of BRIC along with examples that demonstrate its versatility.


title: A Geometric Analysis of Phase Retrieval

abstract: Can we recover a complex signal from its Fourier magnitudes? More generally, given a set of $m$ measurements, $y_k = |\mathbf a_k^* \mathbf x|$ for $k = 1, \dots, m$, is it possible to recover $\mathbf x \in \mathbb{C}^n$ (i.e., length-$n$ complex vector)? This **generalized phase retrieval** (GPR) problem is a fundamental task in various disciplines, and has been the subject of much recent investigation. Natural nonconvex heuristics often work remarkably well for GPR in practice, but lack clear theoretical explanations. In this paper, we take a step towards bridging this gap. We prove that when the measurement vectors $\mathbf a_k$'s are generic (i.i.d. complex Gaussian) and the number of measurements is large enough ($m \ge C n \log^3 n$), with high probability, a natural least-squares formulation for GPR has the following benign geometric structure: (1) there are no spurious local minimizers, and all global minimizers are equal to the target signal $\mathbf x$, up to a global phase; and (2) the objective function has a negative curvature around each saddle point. This structure allows a number of iterative optimization methods to efficiently find a global minimizer, without special initialization. To corroborate the claim, we describe and analyze a second-order trust-region algorithm.


title: Lions and Tigers and Bears: Capturing Non-rigid, 3D, Articulated Shape from Images

abstract: Animals are widespread in nature and the analysis of their shape and motion is important in many fields and industries. Modeling 3D animal shape, however, is difficult because the 3D scanning methods used to capture human shape are not applicable to wild animals or natural settings. Consequently, we propose a method to capture the detailed 3D shape of animals from images alone. The articulated and deformable nature of animals makes this problem extremely challenging, particularly in unconstrained environments with moving and uncalibrated cameras. To make this possible, we use a strong prior model of articulated animal shape that we fit to the image data. We then deform the animal shape in a canonical reference pose such that it matches image evidence when articulated and projected into multiple images. Our method extracts significantly more 3D shape detail than previous methods and is able to model new species, including the shape of an extinct animal, using only a few video frames. Additionally, the projected 3D shapes are accurate enough to facilitate the extraction of a realistic texture map from multiple frames.


title: Potentials, Trends, and Prospects in Edge Technologies: Fog, Cloudlet, Mobile Edge, and Micro Data Centers

abstract: Abstract Advancements in smart devices, wearable gadgets, sensors, and communication paradigm have enabled the vision of smart cities, pervasive healthcare, augmented reality and interactive multimedia, Internet of Every Thing (IoE), and cognitive assistance, to name a few. All of these visions have one thing in common, i.e., delay sensitivity and instant response. Various new technologies designed to work at the edge of the network, such as fog computing, cloudlets, mobile edge computing, and micro data centers have emerged in the near past. We use the name ``edge computing'' for this set of emerging technologies. Edge computing is a promising paradigm to offer the required computation and storage resources with minimal delays because of ``being near'' to the users or terminal devices. Edge computing aims to bring cloud resources and services at the edge of the network, as a middle layer between end user and cloud data centers, to offer prompt service response with minimal delay. Two major aims of edge computing can be denoted as: (a) minimize response delay by servicing the users’ request at the network edge instead of servicing it at far located cloud data centers, and (b) minimize downward and upward traffic volumes in the network core. Minimization of network core traffic inherently brings energy efficiency and data cost reductions. Downward network traffic can be minimized by servicing set of users at network edge instead of service provider's data centers (e.g., multimedia and shared data) Content Delivery Networks (CDNs), and upward traffic can be minimized by processing and filtering raw data (e.g., sensors monitored data) and uploading the processed information to cloud. This survey presents a detailed overview of potentials, trends, and challenges of edge computing. The survey illustrates a list of most significant applications and potentials in the area of edge computing. State of the art literature on edge computing domain is included in the survey to guide readers towards the current trends and future opportunities in the area of edge computing.


title: Toward a No-Reference Image Quality Assessment Using Statistics of Perceptual Color Descriptors

abstract: Analysis of the statistical properties of natural images has played a vital role in the design of no-reference (NR) image quality assessment (IQA) techniques. In this paper, we propose parametric models describing the general characteristics of chromatic data in natural images. They provide informative cues for quantifying visual discomfort caused by the presence of chromatic image distortions. The established models capture the correlation of chromatic data between spatially adjacent pixels by means of color invariance descriptors. The use of color invariance descriptors is inspired by their relevance to visual perception, since they provide less sensitive descriptions of image scenes against viewing geometry and illumination variations than luminances. In order to approximate the visual quality perception of chromatic distortions, we devise four parametric models derived from invariance descriptors representing independent aspects of color perception: 1) hue; 2) saturation; 3) opponent angle; and 4) spherical angle. The practical utility of the proposed models is examined by deploying them in our new general-purpose NR IQA metric. The metric initially estimates the parameters of the proposed chromatic models from an input image to constitute a collection of quality-aware features (QAF). Thereafter, a machine learning technique is applied to predict visual quality given a set of extracted QAFs. Experimentation performed on large-scale image databases demonstrates that the proposed metric correlates well with the provided subjective ratings of image quality over commonly encountered achromatic and chromatic distortions, indicating that it can be deployed on a wide variety of color image processing problems as a generalized IQA solution.


Review on Content Based Image Retrieval : From Its Origin to the New Age


title: Function-based, biologically inspired concept generation

abstract: The natural world provides numerous cases for inspiration in engineering design. Biological organisms, phenomena, and strategies, which we refer to as biological systems, provide a rich set of analogies. These systems provide insight into sustainable and adaptable design and offer engineers billions of years of valuable experience, which can be used to inspire engineering innovation. This research presents a general method for functionally representing biological systems through systematic design techniques, leading to the conceptualization of biologically inspired engineering designs. Functional representation and abstraction techniques are used to translate biological systems into an engineering context. The goal is to make the biological information accessible to engineering designers who possess varying levels of biological knowledge but have a common understanding of engineering design. Creative or novel engineering designs may then be discovered through connections made between biology and engineering. To assist with making connections between the two domains concept generation techniques that use biological information, engineering knowledge, and automatic concept generation software are employed. Two concept generation approaches are presented that use a biological model to discover corresponding engineering components that mimic the biological system and use a repository of engineering and biological information to discover which biological components inspire functional solutions to fulfill engineering requirements. Discussion includes general guidelines for modeling biological systems at varying levels of fidelity, advantages, limitations, and applications of this research. The modeling methodology and the first approach for concept generation are illustrated by a continuous example of lichen.


title: Smart fabrics and interactive textile enabling wearable personal applications: R&D state of the art and future challenges

abstract: Smart fabrics and interactive textiles (SFIT) are fibrous structures that are capable of sensing, actuating, generating/storing power and/or communicating. Research and development towards wearable textile-based personal systems allowing e.g. health monitoring, protection & safety, and healthy lifestyle gained strong interest during the last 10 years. Under the Information and Communication Programme of the European Commission, a cluster of R&D projects dealing with smart fabrics and interactive textile wearable systems regroup activities along two different and complementary approaches i.e. “application pull” and “technology push”. This includes projects aiming at personal health management through integration, validation, and use of smart clothing and other networked mobile devices as well as projects targeting the full integration of sensors/actuators, energy sources, processing and communication within the clothes to enable personal applications such as protection/safety, emergency and healthcare. The integration part of the technologies into a real SFIT product is at present stage on the threshold of prototyping and testing. Several issues, technical as well user-centred, societal and business, remain to be solved. The paper presents on going major R&D activities, identifies gaps and discuss key challenges for the future.


title: A SHORT INTRODUCTION TO SUBJECTIVE WELL-BEING : ITS MEASUREMENT , CORRELATES AND POLICY USES Prepared for the international conference Is happiness measurable and what do those measures mean for policy ?

abstract: Since the emergence of the field over five decades ago, the subjective well-being (SWB; ::: e.g. self-reported happiness or life satisfaction) literature has progressed rapidly. This ::: overview discusses the state of the art in the measurement of SWB, the understanding of ::: the factors determining SWB, and some uses of SWB in policy. Three broad conclusions ::: emerge. First, SWB measures are meaningful in the sense that they are able to provide ::: valid and reliable information on how well people and societies as a whole are doing. ::: Second, a broad range of factors is found to correlate with or be a causal factor in SWB, ::: both at the level of individuals and that of countries. Finally, SWB data can help shape ::: and appraise policy. In several areas our knowledge is still limited, but the most recent ::: and future research promises to secure further the usefulness of SWB research, not least ::: in policy applications.


title: GPU based non-overlapping multi-camera vehicle tracking

abstract: Vehicle tracking and surveillance is an area which is having a considerable attention in the context of security and safety. The detection and tacking of moving vehicles through multiple cameras is considered as a method of vehicle surveillance. This work addresses a problem of detecting and matching vehicles through multiple cameras. The power of GPUs are used to increase the number of video streams which can be processed using a single computer. In the detection process the Gabor filter is used as a directional filter and the SURF is used by the matcher to uniquely represent the vehicle.


title: Unifying Maximum Cut and Minimum Cut of a Planar Graph

abstract: The real-weight maximum cut of a planar graph is considered. Given an undirected planar graph with real-value weights associated with its edges, the problem is to find a partition of the vertices into two nonempty sets such that the sum of the weights of the edges connecting the two sets is maximum. The conventional maximum cut and minimum cut problems assume nonnegative edge weights, and thus are special cases of the real-weight maximum cut. An O(n/sup 3/2/ log n) algorithm for finding a real-weight maximum cut of a planar graph where n is the number of vertices in the graph is developed. The best maximum cut algorithm previously known for planar graphs has running time of O(n/sup 3/). >


title: Ensemble GradientBoost for increasing classification accuracy of credit scoring

abstract: The method for Credit Scoring has been developed to select a better model in predicting credit risk. Data mining methods are superior to the statistical methods of dealing with Credit Scoring issues, especially for nonlinear relationships between variables. By flashing the ensemble method with statistical methods, proven to achieve a higher level of accuracy than the method of data mining. This paper proposes a credit scoring algorithm using Ensemble Logistic Regression by boosting the method using the GradientBoost algorithm. Two datasets for implementing the algorithm, i.e., German and Australian Dataset. The results showed that GradientBoost Ensemble managed to improve the performance of a single classification Logistic Regression and achieve the highest level of accuracy in both datasets. The proposed method produces accuracy of 81% for German datasets and 88.4% for Australian datasets.


title: The Gaggle: An open-source software system for integrating bioinformatics software and data sources

abstract: BackgroundSystems biologists work with many kinds of data, from many different sources, using a variety of software tools. Each of these tools typically excels at one type of analysis, such as of microarrays, of metabolic networks and of predicted protein structure. A crucial challenge is to combine the capabilities of these (and other forthcoming) data resources and tools to create a data exploration and analysis environment that does justice to the variety and complexity of systems biology data sets. A solution to this problem should recognize that data types, formats and software in this high throughput age of biology are constantly changing.ResultsIn this paper we describe the Gaggle -a simple, open-source Java software environment that helps to solve the problem of software and database integration. Guided by the classic software engineering strategy of separation of concerns and a policy of semantic flexibility, it integrates existing popular programs and web resources into a user-friendly, easily-extended environment.We demonstrate that four simple data types (names, matrices, networks, and associative arrays) are sufficient to bring together diverse databases and software. We highlight some capabilities of the Gaggle with an exploration of Helicobacter pylori pathogenesis genes, in which we identify a putative ricin-like protein -a discovery made possible by simultaneous data exploration using a wide range of publicly available data and a variety of popular bioinformatics software tools.ConclusionWe have integrated diverse databases (for example, KEGG, BioCyc, String) and software (Cytoscape, DataMatrixViewer, R statistical environment, and TIGR Microarray Expression Viewer). Through this loose coupling of diverse software and databases the Gaggle enables simultaneous exploration of experimental data (mRNA and protein abundance, protein-protein and protein-DNA interactions), functional associations (operon, chromosomal proximity, phylogenetic pattern), metabolic pathways (KEGG) and Pubmed abstracts (STRING web resource), creating an exploratory environment useful to 'web browser and spreadsheet biologists', to statistically savvy computational biologists, and those in between. The Gaggle uses Java RMI and Java Web Start technologies and can be found at http://gaggle.systemsbiology.net.


title: An improved illumination model for shaded display

abstract: To accurately render a scene, global illumination information that affects the intensity of each pixel of the image must be known at the time the intensity is calculated. In a simplified form, this information is stored in a tree of “rays” extending from the viewer to the first surface encountered and from there to other surfaces and to the light sources. The visible surface algorithm creates this tree for each pixel of the display and passes it to the shader. The shader then traverses the tree to determine the intensity of the light received by the viewer. Consideration of all of these factors allows the shader to accurately simulate true reflection, shadows, and refraction as well as the effects simulated by conventional shaders. Anti-aliasing is included as an integral part of the visibility calculations. Surfaces displayed include curved as well as polygonal surfaces.


title: ArticulatedFusion: Real-time Reconstruction of Motion, Geometry and Segmentation Using a Single Depth Camera

abstract: This paper proposes a real-time dynamic scene reconstruction method capable of reproducing the motion, geometry, and segmentation simultaneously given live depth stream from a single RGB-D camera. Our approach fuses geometry frame by frame and uses a segmentation-enhanced node graph structure to drive the deformation of geometry in registration step. A two-level node motion optimization is proposed. The optimization space of node motions and the range of physically-plausible deformations are largely reduced by taking advantage of the articulated motion prior, which is solved by an efficient node graph segmentation method. Compared to previous fusion-based dynamic scene reconstruction methods, our experiments show robust and improved reconstruction results for tangential and occluded motions.


title: Process optimizations for NBTI/PBTI for future replacement metal gate technologies

abstract: Bias Temperature Instability (BTI) is a tremendous reliability concern for deeply scaled CMOS technologies, and is the limiting mechanism for further inversion layer thickness (Tinv) scaling for future nodes [1]. Replacement Metal Gate technologies are of particular concern, since the gate stack is not exposed to the high temperature source/drain anneals. We have identified four strategies for reducing BTI in Replacement metal gate technologies: Rapid Thermal Anneal (RTA) optimization, optimization of the HfO2 layer thickness, introducing a gate dielectric dopant for NBTI reduction, and effective Work Function tuning. Judiciously combining these four techniques enable further Tinv scaling for 10 nm and below.


title: Smooth transition and ripple reduction in 4-switch non-inverting buck-boost power converter for WCDMA RF power amplifier

abstract: In this paper, smooth transition between buck and boost modes in 4-switch non-inverting buck-boost power converter is investigated. It is shown, both theoretically and on an experimental converter prototype, how pulse-width modulator (PWM) maximum and minimum duty cycle limitations can result in pulse skipping at the mode transition and substantial increases in the output voltage ripple. This is a very significant problem when the converter is used as a power supply for WCDMA RF power amplifiers. An approach is proposed to achieve smooth mode transition by overlapping buck and boost modes of operation. The approach results in much reduced output voltage ripple, at the expense of reduced efficiency, as demonstrated on the experimental prototype.


title: SAR implementation using LFM signal

abstract: Synthetic Aperture Radar is a coherent airborne or space borne sidelooking radar system which is regarded as one of the most recent advancement in the field of research and defence and used extensively for mapping of a terrain. With the help of this technology we are capable of obtaining 3-D images of objects such as landscapes, terrain images with very high precision and accuracy. From RADAR basics, if the aperture of RADAR is increased then the image resolution of the terrain which is under consideration also increases, this concept is true for all RADAR whether if it is actually having a large physical area or if it is of ‘synthetic nature’ that is here the large physical area is basically simulated artificially even though if it's not available in reality [2]. With the help of this technology a resolution of about 10cm can be achieved and research has also been made wherein the resolution was further improvised to be in the order of few millimetres. In this paper the authors describe the simulation of a SAR system.


title: Power allocation for device-to-device communication underlaying massive MIMO multicasting networks

abstract: Device-to-device communication (D2D) is an emerging technology aiming at enhancing the performance of next generation wireless communication systems. D2D communication enables two mobile stations to communicate directly without traversing the Base Station (BS). Smart techniques should be applied to manage the interference between the BS and the D2D communicating devices. In this paper, we investigate the problem of BS precoder design and Power Allocation (PA) to deploy D2D communication in a single-cell network. We assume that the BS uses a very large number of antennas, referred to as Massive Multiple-Input-Multiple-Output (MIMO). We propose solution algorithms to maximize the sum of the achievable data rates of the D2D pairs, or their minimum, while maintaining some Quality of Service (QoS) constraints on the Cellular User Equipments (CUEs) which communicate with the BS. Simulation results show that the proposed solutions are superior to the conventional power allocation schemes.


title: Simulating Drone-be-Gone: Agile Low-Cost Cyber-Physical UAV Testbed (Demonstration)

abstract: In this paper, we demonstrate Drone-Be-Gone (DbeG): a general-purpose, inexpensive, and agile UAV Cyber-Physical System (CPS) testbed. We implement on our testbed 2-D vision-based localization within 5 cm precision, controllability of multiple UAVs, a simulation environment of our testbed in 2-D (named TeSLA), and external processing units (EPU). The testbed has the ability to switch between centralized or distributed control and processing due to the addition of EPUs. The simulator will allow users to interact with our testbed virtually and observe its behavior.


title: Ultrasonic pretreatment in lipase-catalyzed synthesis of structured lipids with high 1,3-dioleoyl-2-palmitoylglycerol content

abstract: Abstract Production of structured lipid 1,3-dioleoyl-2-palmitoylglycerol (OPO), from tripalmitin (PPP) and oleic acid (OA) using lipases and ultrasonic pretreatment was conducted. Factors influencing both the ultrasonic conditions and enzymatic reaction were investigated. Optimum conditions could be attained with 6 min pretreatment time, 50% ultrasonic power, 3 s/9 s (work/pause) cycle of ultrasonic pulse, 1:8 PPP/OA molar ratio, 12% enzyme dosage and 50 °C temperature of. At the optimum conditions, the OPO yield of 51.8% could be achieved in 4 h. Studies showed that the OPO content increased to 35.9% in 1 h with ultrasonic pretreatment, in comparison to 4 h without ultrasonic pretreatment. Reuse of Lipozyme RM IM for 10 cycles under ultrasonic irradiation did not cause essential damage to its lipase activity. Reaction kinetic model fitted well with the proposed Ping-Pong mechanism. The apparent kinetic constant (Vm′/K2) of ultrasound pretreatment reaction was 2.52 times higher than the conventional mechanical stirring, indicating that ultrasound pretreatment enhanced the substrates affinity to the enzyme. This study confirmed that ultrasonic pretreatment was more efficient in OPO production than conventional mechanical agitation.


title: The Pricing of Options on Assets with Stochastic Volatilities

abstract: One option-pricing problem which has hitherto been unsolved is the pricing of European call on an asset which has a stochastic volatility. This paper examines this problem. The option price is determined in series form for the case in which the stochastic volatility is independent of the stock price. Numerical solutions are also produced for the case in which the volatility is correlated with the stock price. It is found that the Black-Scholes price frequently overprices options and that the degree of overpricing increases with the time to maturity. Copyright 1987 by American Finance Association.


title: Proteus: a topology malleable data center network

abstract: Full-bandwidth connectivity between all servers of a data center may be necessary for all-to-all traffic patterns, but such interconnects suffer from high cost, complexity, and energy consumption. Recent work has argued that if all-to-all traffic is uncommon, oversubscribed network architectures that can adapt the topology to meet traffic demands, are sufficient. In line with this work, we propose Proteus, an all-optical architecture targeting unprecedented topology-flexibility, lower complexity and higher energy efficiency.


title: Stochastic Negative Mining for Learning with Large Output Spaces

abstract: We consider the problem of retrieving the most relevant labels for a given input when the size of the output space is very large. Retrieval methods are modeled as set-valued classifiers which output a small set of classes for each input, and a mistake is made if the label is not in the output set. Despite its practical importance, a statistically principled, yet practical solution to this problem is largely missing. To this end, we first define a family of surrogate losses and show that they are calibrated and convex under certain conditions on the loss parameters and data distribution, thereby establishing a statistical and analytical basis for using these losses. Furthermore, we identify a particularly intuitive class of loss functions in the aforementioned family and show that they are amenable to practical implementation in the large output space setting (i.e. computation is possible without evaluating scores of all labels) by developing a technique called Stochastic Negative Mining. We also provide generalization error bounds for the losses in the family. Finally, we conduct experiments which demonstrate that Stochastic Negative Mining yields benefits over commonly used negative sampling approaches.


Contributions to High-Performance Big Data Computing


title: Jade: a high-level, machine-independent language for parallel programming

abstract: Jade, a high-level parallel programming language for managing coarse-grained parallelism, is discussed. Jade simplifies programming by providing sequential-execution and shared-address-space abstractions. It is also platform-independent; the same Jade program runs on uniprocessors, multiprocessors, and heterogeneous networks of machines. An example that illustrates how Jade programmers express irregular, dynamically determined concurrency and how the implementation exploits this source of concurrency is presented. A digital video imaging program that runs on a high-resolution video system and several other examples of Jade applications are described. >


title: Anomaly Detection and Attribution Using Bayesian Networks

abstract: Abstract : We present a novel approach to anomaly detection in Bayesian networks, enabling both the detection and explanation of anomalous cases in a dataset. By exploiting the structure of a Bayesian network, our algorithm is able to efficiently search for local maxima of data conflict between closely related variables. Benchmark tests using data simulated from complex Bayesian networks show that our approach provides a significant improvement over techniques that search for anomalies using the entire network, rather than its subsets. We conclude with demonstrations of the unique explanatory power of our approach in determining the observation(s) responsible for an anomaly.


title: Electronic customer relationship management: Revisiting the general principles of usability and resistance – an integrative implementation framework

abstract: Electronic customer relationship management (eCRM) has become the latest paradigm in the world of customer relationship management. Recent business surveys suggest that up to 50 per cent of such implementations do not yield measurable returns on investment. A secondary analysis of 13 case studies suggests that many of these limited success implementations can be attributed to usability and resistance factors. The objective of this paper is to review the general usability and resistance principles in order to build an integrative framework for analyzing eCRM case studies. The conclusions suggest that if organizations want to get the most from their eCRM implementations they need to revisit the general principles of usability and resistance and apply them thoroughly and consistently.


title: Distributed Topological Convex Hull Estimation of Event Region in Wireless Sensor Networks without Location Information

abstract: In critical event (e.g., fire or gas) monitoring applications of wireless sensor networks (WSNs), convex hull of the event region is an efficient tool in handling the usual tasks like event report, routes reconstruction and human motion planning. Existing works on estimating convex hull of event region usually require location information of sensor nodes, which needs high communication cost or hardware cost. In this paper, to avoid the requirement of location information, we define topological convex hull (T-convex hull) which presents the convex contour of an event region directly with a route passing by nodes, and hence becomes more efficient in handling the above tasks. To obtain the T-convex hull of event region in the absence of locations, we propose a low-weight (in terms of computation and storage resource requirement) distributed algorithm, with which sensor nodes just need to count the hop counts from some nodes. The communication cost of the algorithm is also low and independent of the network size. Comprehensive and large-scale simulations are conducted, showing the effectiveness and much lower communication cost of the proposed algorithm, compared with related method.


title: Quadrotor helicopter trajectory tracking control

abstract: The Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC), a eet of quadrotor helicopters, has been developed as a testbed for novel algorithms that enable autonomous operation of aerial vehicles. This paper develops an autonomous vehicle trajectory tracking algorithm through cluttered environments for the STARMAC platform. A system relying on a single optimization must trade o the complexity of the planned path with the rate of update of the control input. In this paper, a trajectory tracking controller for quadrotor helicopters is developed to decouple the two problems. By accepting as inputs a path of waypoints and desired velocities, the control input can be updated frequently to accurately track the desired path, while the path planning occurs as a separate process on a slower timescale. To enable the use of planning algorithms that do not consider dynamic feasibility or provide feedforward inputs, a computationally ecient algorithm using space-indexed waypoints is presented to modify the speed prole of input paths to guarantee feasibility of the planned trajectory and minimum time traversal of the planned. The algorithm is an ecient alternative to formulating a nonlinear optimization or mixed integer program. Both indoor and outdoor ight test results are presented for path tracking on the STARMAC vehicles.


title: A Design Procedure for High-Efficiency and Compact-Size 5–10-W MMIC Power Amplifiers in GaAs pHEMT Technology

abstract: This paper presents the design procedure of monolithic microwave integrated circuit (MMIC) high-power amplifiers (HPAs) as well as implementation of high-efficiency and compact-size HPAs in a 0.25- μm AlGaAs-InGaAs pHEMT technology. Presented design techniques used to extend bandwidth, improve efficiency, and reduce chip area of the HPAs are described in detail. The first HPA delivers 5 W of output power with 40% power-added efficiency (PAE) in the frequency band of 8.5-12.5 GHz, while providing 20 dB of small-signal gain. The second HPA delivers 8 W of output power with 35% PAE in the frequency band of 7.5-12 GHz, while maintaining a small-signal gain of 17.5 dB. The 8-W HPA chip area is 8.8 mm2, which leads to the maximum power/area ratio of 1.14 W/mm2. These are the lowest area and highest power/area ratio reported in GaAs HPAs operating within the same frequency band.


title: Transfer of dysbiotic gut microbiota has beneficial effects on host liver metabolism

abstract: Gut microbiota dysbiosis has been implicated in a variety of systemic disorders, notably metabolic diseases including obesity and impaired liver function, but the underlying mechanisms are uncertain. To investigate this question, we transferred caecal microbiota from either obese or lean mice to antibiotic-free, conventional wild-type mice. We found that transferring obese-mouse gut microbiota to mice on normal chow (NC) acutely reduces markers of hepatic gluconeogenesis with decreased hepatic PEPCK activity, compared to non-inoculated mice, a phenotypic trait blunted in conventional NOD2 KO mice. Furthermore, transferring of obese-mouse microbiota changes both the gut microbiota and the microbiome of recipient mice. We also found that transferring obese gut microbiota to NC-fed mice then fed with a high-fat diet (HFD) acutely impacts hepatic metabolism and prevents HFD-increased hepatic gluconeogenesis compared to non-inoculated mice. Moreover, the recipient mice exhibit reduced hepatic PEPCK and G6Pase activity, fed glycaemia and adiposity. Conversely, transfer of lean-mouse microbiota does not affect markers of hepatic gluconeogenesis. Our findings provide a new perspective on gut microbiota dysbiosis, potentially useful to better understand the aetiology of metabolic diseases.


title: Design of a High-Speed LVDS I/O Interface Using Telescopic Amplifier

abstract: This paper presents the design and the implementation of input/output (I/O) interface circuits for 2.5 Gbps operation in a 3.3V 0.35um CMOS technology. Due to the differential transmission technique and low voltage swing, LVDS(low-voltage differential signaling) has been widely used for high speed transmission with low power consumption. This interface circuit is fully compatible with the LVDS standard. The LVDS proposed in this paper utilizes a telescopic amplifier. This circuit is operated up to 2.3 Gbps. The circuit has a power consumption of 25. 5mW. This circuit is designed with Samsung CMOS process. The validity and effectiveness are verified through the HSPICE simulation.


title: MOSOCO: a mobile assistive tool to support children with autism practicing social skills in real-life situations

abstract: MOSOCO is a mobile assistive application that uses augmented reality and the visual supports of a validated curriculum, the Social Compass, to help children with autism practice social skills in real-life situations. In this paper, we present the results of a seven-week deployment study of MOSOCO in a public school in Southern California with both students with autism and neurotypical students. The results of our study demonstrate that MOSOCO facilitates practicing and learning social skills, increases both quantity and quality of social interactions, reduces social and behavioral missteps, and enables the integration of children with autism in social groups of neurotypical children. The findings from this study reveal emergent practices of the uses of mobile assistive technologies in real-life situations.


title: Agency and communion from the perspective of self versus others.

abstract: On the basis of previous research, the authors hypothesize that (a) person descriptive terms can be organized into the broad dimensions of agency and communion of which communion is the primary one; (b) the main distinction between these dimensions pertains to their profitability for the self (agency) vs. for other persons (communion); hence, agency is more desirable and important in the self-perspective, and communion is more desirable and important in the other-perspective; (c) self–other outcome dependency increases importance of another person’s agency. Study 1 showed that a large number of trait names can be reduced to these broad dimensions, that communion comprises more item variance, and that agency is predicted by self-profitability and communion by other-profitability. Studies 2 and 3 showed that agency is more relevant and desired for self, and communion is more relevant and desired for others. Study 4 showed that agency is more important in a close friend than an unrelated peer, and this difference is completely mediated by the perceived outcome dependency.


title: ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection

abstract: Decentralized cryptocurrencies feature the use of blockchain to transfer values among peers on networks without central agency. Smart contracts are programs running on top of the blockchain consensus protocol to enable people make agreements while minimizing trusts. Millions of smart contracts have been deployed in various decentralized applications. The security vulnerabilities within those smart contracts pose significant threats to their applications. Indeed, many critical security vulnerabilities within smart contracts on Ethereum platform have caused huge financial losses to their users. In this work, we present ContractFuzzer, a novel fuzzer to test Ethereum smart contracts for security vulnerabilities. ContractFuzzer generates fuzzing inputs based on the ABI specifications of smart contracts, defines test oracles to detect security vulnerabilities, instruments the EVM to log smart contracts runtime behaviors, and analyzes these logs to report security vulnerabilities. Our fuzzing of 6991 smart contracts has flagged more than 459 vulnerabilities with high precision. In particular, our fuzzing tool successfully detects the vulnerability of the DAO contract that leads to $60 million loss and the vulnerabilities of Parity Wallet that have led to the loss of $30 million and the freezing of $150 million worth of Ether.


title: Realtime Fraud Detection in the Banking Sector Using Data Mining Techniques/Algorithm

abstract: The banking sector is a very important sector in our present day generation where almost every human has to deal with the bank either physically or online. In dealing with the banks, the customers and the banks face the chances of been trapped by fraudsters. Examples of fraud include insurance fraud, credit card fraud, accounting fraud, etc. Detection of fraudulent activity is thus critical to control these costs. This paper hereby addresses bank fraud detection via the use of data-mining techniques, association, clustering, forecasting, and classification to analyze the customer data in order to identify the patterns that can lead to frauds. Upon identification of the patterns, adding a higher level of verification/authentication to banking processes can be addedKeywords: Data mining techniques, banking sector, fraud, and authentication.


title: Rule extraction from neural networks — A comparative study

abstract: Though neural networks have achieved highest classification accuracy for many classification problems, the obtained results may not be interpretable as they are often considered as black box. To overcome this drawback researchers have developed many rule extraction algorithms. This paper has discussed on various rule extraction algorithms based on three different rule extraction approaches namely decompositional, pedagogical and eclectic. Also it evaluates the performance of those approaches by comparing different algorithms with these three approaches on three real datasets namely Wisconsin breast cancer, Pima Indian diabetes and Iris plants.


title: (Meta-) Evaluation of Machine Translation

abstract: This paper evaluates the translation quality of machine translation systems for 8 language pairs: translating French, German, Spanish, and Czech to English and back. We carried out an extensive human evaluation which allowed us not only to rank the different MT systems, but also to perform higher-level analysis of the evaluation process. We measured timing and intra- and inter-annotator agreement for three types of subjective evaluation. We measured the correlation of automatic evaluation metrics with human judgments. This meta-evaluation reveals surprising facts about the most commonly used methodologies.


title: The Nature of Statistical Learning Theory

abstract: Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.


title: The endocrinology of sexual arousal

abstract: The relevance of testosterone, oestradiol and certain peptides (oxytocin (OT), beta-endorphin and prolactin (PRL)) to sexual arousal in humans is reviewed. In addition to behavioural studies, evidence of distribution of gonadal steroid receptors in the brain and the limited evidence from brain imaging are also considered. Testosterone plays a key role in the adult male, with clear, consistent evidence from studies of hypogonadal and eugonadal men. The roles of testosterone in the development of sexual arousability, and in the aging male, are less clear. The relevance of aromatization and of non-sexual effects of testosterone which might indirectly influence sexual arousal are not well understood. Testosterone in the female presents a more complex, less consistent picture. One possible explanation is a much greater variability across women in responsiveness to testosterone. A 'desensitization hypothesis' to account for the striking gender differences is offered. There is limited evidence of a direct effect of oestradiol on sexual arousability in women. The extent to which testosterone in women acts by conversion to oestradiol or by increase of free oestradiol is not yet clear. The role of peptides in sexual arousal remains uncertain, partly because of the multiple roles and sites of action of most peptides. OT and beta-endorphin appear to have both excitatory and inhibitory effects. PRL has been proposed as an inhibitory factor via direct inhibition of dopaminergic activity, but the evidence for this is inconclusive. Whereas the traditional concept of 'hormone' continues to apply to the role of testosterone and oestradiol in sexual arousal, peptides present a more complex role.


title: Rapid Modeling of Chinese Huizhou Traditional Vernacular Houses

abstract: In this paper, we propose an automatic modeling method based on constructive grammar for building Chinese Huizhou traditional vernacular houses, which are famous and important examples of Chinese traditional architecture. The proposed method includes two steps: layout generation and feature generation. In the layout generation step, layout guidelines based on 2-D geometric rules are used to evaluate the layout result. In the feature generation step, a probabilistic network is used to generate the features for a single house while feature conflict penalty functions are used to generate the reasonable features for multiple houses. Both the layout generation and feature generation steps use the Metropolis–Hastings algorithm to search for the optimal solution. To increase the computational efficiency of the proposed system, the two steps are performed on a graphics processing unit device using a Monte Carlo sampler. Moreover, a dynamic probability strategy is used to perform the layout perturbation. The proposed method is easy to implement and extend. The experimental results show that the proposed method is practical and efficient. Non-expert users can rapidly generate reasonable Huizhou vernacular houses via simple and intuitive interactions with the system.


title: Oral manifestations of systemic disease.

abstract: Careful examination of the oral cavity may reveal findings indicative of an underlying systemic condition, and allow for early diagnosis and treatment. Examination should include evaluation for mucosal changes, periodontal inflammation and bleeding, and general condition of the teeth. Oral findings of anemia may include mucosal pallor, atrophic glossitis, and candidiasis. Oral ulceration may be found in patients with lupus erythematosus, pemphigus vulgaris, or Crohn disease. Additional oral manifestations of lupus erythematosus may include honeycomb plaques (silvery white, scarred plaques); raised keratotic plaques (verrucous lupus erythematosus); and nonspecific erythema, purpura, petechiae, and cheilitis. Additional oral findings in patients with Crohn disease may include diffuse mucosal swelling, cobblestone mucosa, and localized mucogingivitis. Diffuse melanin pigmentation may be an early manifestation of Addison disease. Severe periodontal inflammation or bleeding should prompt investigation of conditions such as diabetes mellitus, human immunodeficiency virus infection, thrombocytopenia, and leukemia. In patients with gastroesophageal reflux disease, bulimia, or anorexia, exposure of tooth enamel to acidic gastric contents may cause irreversible dental erosion. Severe erosion may require dental restorative treatment. In patients with pemphigus vulgaris, thrombocytopenia, or Crohn disease, oral changes may be the first sign of disease.


title: A Real-Time SRP-PHAT Source Location Implementation using Stochastic Region Contraction(SRC) on a Large-Aperture Microphone Array

abstract: In most microphone array applications, it is essential to localize sources in a noisy, reverberant environment. It has been shown that computing the steered response power (SRP) is more robust than faster, two-stage, direct time-difference of arrival methods. The problem with computing SRP is that the SRP space has many local maxima and thus computationally-intensive grid-search methods are used to find a global maximum. Grid search is too expensive for a real-time system. Several papers have addressed this issue. In this paper we propose using stochastic region contraction (SRC) to make computing the SRP practical. We discuss one important SRP method, computing it from the phase transform (SRP-PHAT), review SRC, and show the computational saving. Using real data from human talkers, we show that SRC saves computation by more than two orders of magnitude with almost no loss in accuracy.


title: Prefrontal cortex and basal ganglia control access to working memory

abstract: Our capacity to store information in working memory might be determined by the degree to which only relevant information is remembered. The question remains as to how this selection of relevant items to be remembered is accomplished. Here we show that activity in the prefrontal cortex and basal ganglia preceded the filtering of irrelevant information and that activity, particularly in the globus pallidus, predicted the extent to which only relevant information is stored. The preceding frontal and basal ganglia activity were also associated with inter-individual differences in working memory capacity. These findings reveal a mechanism by which frontal and basal ganglia activity exerts attentional control over access to working memory storage in the parietal cortex in humans, and makes an important contribution to inter-individual differences in working memory capacity.


title: DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution

abstract: The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants. However, contemporary Earth System Models (ESM) are run at spatial resolutions too coarse for assessing effects this localized. Local scale projections can be obtained using statistical downscaling, a technique which uses historical climate observations to learn a low-resolution to high-resolution mapping. Depending on statistical modeling choices, downscaled projections have been shown to vary significantly terms of accuracy and reliability. The spatio-temporal nature of the climate system motivates the adaptation of super-resolution image processing techniques to statistical downscaling. In our work, we present DeepSD, a generalized stacked super resolution convolutional neural network (SRCNN) framework for statistical downscaling of climate variables. DeepSD augments SRCNN with multi-scale input channels to maximize predictability in statistical downscaling. We provide a comparison with Bias Correction Spatial Disaggregation as well as three Automated-Statistical Downscaling approaches in downscaling daily precipitation from 1 degree (~100km) to 1/8 degrees (~12.5km) over the Continental United States. Furthermore, a framework using the NASA Earth Exchange (NEX) platform is discussed for downscaling more than 20 ESM models with multiple emission scenarios.


title: Automatic Correction of Non-uniform Illumination in Transmission Electron Microscopy Images

abstract: Transmission electron microscopy (TEM) provides resolutions on the order of a nanometer. Hence, it is a critical imaging modality for biomedical analysis at the cellular level. One of the problems associated with TEM images is variations in brightness due to electron imaging defects or nonuniform support films and specimen staining. These variations render image processing operations such as segmentation more dicult. The correction requires estimation of the global illumination field. In this paper, we propose an automatic method for estimating the illumination field using only image intensity gradients. The closed-form solution is very fast to compute.