title: Reduction of interference in oscillometric arterial blood pressure measurement using fuzzy logic

abstract: In oscillometry, oscillation amplitudes (OAs) embedded in the cuff pressure are drastically affected by a variety of artifacts and cardiovascular diseases, leading to inaccurate arterial blood pressure (ABP) measurement. The purpose of this paper is to improve the accuracy in the arterial pressure measurement by reducing interference in the OAs using a recursive weighted regression algorithm (RWRA). This method includes a fuzzy logic discriminator (FLD) and a recursive regression algorithm. The FLD is used to reduce the effect of artifacts caused by measurement motion disturbance or cardiovascular diseases, and to determine the truthfulness of the oscillation pulse. According to the truth degree, the relationship between the cuff pressure and OA is reconstructed using the regression algorithm. Because the regression method must utilize inverse matrix operation, which will be difficult to implement in an automatic or ambulatory monitor, the recursive regression method is proposed to solve this problem. To test the performance of this RWRA, 47 subjects underwent the ABP measurement using both the auscultation and the oscillometry combined with the RWRA. It was found that the average difference between the pooled blood pressures measured by the auscultation and those by the oscillometry combined with the RWRA was found to be only 4.9 mmHg. Clinical results demonstrated that the proposed RWRA is more robust than the traditional curve fitting algorithm (TCFA). We conclude that the proposed RWRA can be applied to effectively improve the accuracy of the oscillometric blood pressure measurement.


title: An Efficient System for Non-transferable Anonymous Credentials with Optional Anonymity Revocation

abstract: A credential system is a system in which users can obtain credentials from organizations and demonstrate possession of these credentials. Such a system is anonymous when transactions carried out by the same user cannot be linked. An anonymous credential system is of significant practical relevance because it is the best means of providing privacy for users. In this paper we propose a practical anonymous credential system that is based on the strong RSA assumption and the decisional Diffie-Hellman assumption modulo a safe prime product and is considerably superior to existing ones: (1) We give the first practical solution that allows a user to unlinkably demonstrate possession of a credential as many times as necessary without involving the issuing organization. (2) To prevent misuse of anonymity, our scheme is the first to offer optional anonymity revocation for particular transactions. (3) Our scheme offers separability: all organizations can choose their cryptographic keys independently of each other. Moreover, we suggest more effective means of preventing users from sharing their credentials, by introducing all-or-nothing sharing: a user who allows a friend to use one of her credentials once, gives him the ability to use all of her credentials, i.e., taking over her identity. This is implemented by a new primitive, called circular encryption, which is of independent interest, and can be realized from any semantically secure cryptosystem in the random oracle model.


title: The experience of ethics: Evaluation of self harm risks in automated vehicles

abstract: Automated vehicles, but also safety or driver assistance systems for manually driven cars, will soon face situations where they have to choose between several options with negative or even lethal outcome for the one or the other party. Experimental ethics is an approach to evaluate expectations humans put into the morality of digital agents. With this work we present a personalized abstraction of the “Trolley Problem” evaluated in a driving simulator. The aim of the study was to assess drivers' individual attitudes in ethical decisions and derive common knowledge about how to solve such situations. In contrast to previous work, the study at hand looks at the problem from a holistic point of view, including uncertainty and accident risk (presented to subjects as their probability to survive). Furthermore, subjective scales and semi-structured interviews to determine subjects' justifications for ethical decisions complemented the setting. Our results (n=40) suggest that most drivers want their vehicles to act in an utilitarian way and opt for the more severe collision — even when their own probability to survive is substantially low. In addition, age and size of the injured party have a significant effect on the results. Qualitative data (interviews) indicate that the justification, in particular for decisions with the same outcome, strongly differs as most people have embodied their own moral concepts.


title: Detection of parking spots using 2D range data

abstract: This paper addresses the problem of reliably detecting parking spots in semi-filled parking lots using on-board laser line scanners. In order to identify parking spots, one needs to detect parked vehicles and interpret the parking environment. Our approach uses a supervised learning technique to achieve vehicle detection by identifying vehicle bumpers from laser range scans. In particular, we use AdaBoost to train a classifier based on relevant geometric features of data segments that correspond to car bumpers. Using the detected bumpers as landmarks of vehicle hypotheses, our algorithm constructs a topological graph representing the structure of the parking space. Spatial analysis is then performed on the topological graph to identify potential parking spots. Algorithm performance is evaluated through a series of experimental tests.


title: Siau et al. The Value of Mobile Commerce to Customers The Value of Mobile Commerce to Customers

abstract: Advances in wireless technology have stimulated rapid developments in electronic commerce via the use of mobile devices. This research studies the values of mobile commerce using a qualitative means-ends approach, called Value-Focused Thinking. We interviewed 30 subjects and captured their values of mobile commerce. The relationships among these values were illustrated using a means-ends objective network. For researchers, this research contributes to our understanding of mobile commerce and provides a foundation for future research. For practitioners, the results of this research highlight the concerns and issues of customers, and are valuable for strategy formulation in mobile commerce.


title: Noncontact Proximity Vital Sign Sensor Based on PLL for Sensitivity Enhancement

abstract: In this paper, a noncontact proximity vital sign sensor, using a phase locked loop (PLL) incorporated with voltage controlled oscillator (VCO) built-in planar type circular resonator, is proposed to enhance sensitivity in severe environments. The planar type circular resonator acts as a series feedback element of the VCO as well as a near-field receiving antenna. The frequency deviation of the VCO related to the body proximity effect ranges from 0.07 MHz/mm to 1.8 MHz/mm (6.8 mV/mm to 205 mV/mm in sensitivity) up to a distance of 50 mm, while the amount of VCO drift is about 21 MHz in the condition of 60 ° ::: C temperature range and discrete component tolerance of ±5%. Total frequency variation occurs in the capture range of the PLL which is 60 MHz. Thus, its loop control voltage converts the amount of frequency deviation into a difference of direct current (DC) voltage, which is utilized to extract vital signs regardless of the ambient temperature. The experimental results reveal that the proposed sensor placed 50 mm away from a subject can reliably detect respiration and heartbeat signals without the ambiguity of harmonic signals caused by respiration signal at an operating frequency of 2.4 GHz.


title: Ensemble Trees and CLTs : Statistical Inference for Supervised Learning

abstract: This work develops formal statistical inference procedures for machine learning ensemble methods. Ensemble methods based on bootstrapping, such as bagging and random forests, have improved the predictive accuracy of individual trees, but fail to provide a framework in which distributional results can be easily determined. Instead of aggregating full bootstrap samples, we consider predicting by averaging over trees built on subsamples of the training set and demonstrate that the resulting estimator takes the form of a U-statistic. As such, predictions for individual feature vectors are asymptotically normal, allowing for confidence intervals to accompany predictions. In practice, a subset of subsamples is used for computational speed; here our estimators take the form of incomplete U-statistics and equivalent results are derived. We further demonstrate that this setup provides a framework for testing the significance of features. Moreover, the internal estimation method we develop allows us to estimate the variance parameters and perform these inference procedures at no additional computational cost. Simulations and illustrations on a real dataset are provided.


title: Awareness requirements for adaptive systems

abstract: Recently, there has been a growing interest in self-adaptive systems. Roadmap papers in this area point to feedback loops as a promising way of operationalizing adaptivity in such systems. In this paper, we define a new type of requirement - called Awareness Requirement - that can refer to other requirements and their success/failures. We propose a way to elicit and formalize such requirements and offer a requirements monitoring framework to support them.


title: The Spy in the Sandbox: Practical Cache Attacks in JavaScript and their Implications

abstract: We present the first micro-architectural side-channel attack which runs entirely in the browser. In contrast to other works in this genre, this attack does not require the attacker to install any software on the victim’s machine ‐ to facilitate the attack, the victim needs only to browse to an untrusted webpage with attacker-controlled content. This makes the attack model highly scalable and extremely relevant and practical to today’s web, especially since most desktop browsers currently accessing the Internet are vulnerable to this attack. Our attack, which is an extension of the last-level cache attacks of Yarom et al. [23], allows a remote adversary recover information belonging to other processes, other users and even other virtual machines running on the same physical host as the victim web browser. We describe the fundamentals behind our attack, evaluate its performance using a high bandwidth covert channel and finally use it to construct a system-wide mouse/network activity logger. Defending against this attack is possible, but the required countermeasures can exact an impractical cost on other benign uses of the web browser and of the computer.


title: Rethinking security properties, threat models, and the design space in sensor networks: A case study in SCADA systems

abstract: In recent years we have witnessed the emergence and establishment of research in sensor network security. The majority of the literature has focused on discovering numerous vulnerabilities and attacks against sensor networks, along with suggestions for corresponding countermeasures. However, there has been little guidance for understanding the holistic nature of sensor network security for practical deployments. In this paper, we discuss these concerns and propose a taxonomy composed of the security properties of the sensor network, the threat model, and the security design space. In particular, we try to understand the application-layer goals of a sensor network, and provide a guide to research challenges that need to be addressed in order to prioritize our defenses against threats to application-layer goals.


title: Consumer Research, 129-134. Online Reviews: Do Consumers Use Them?

abstract: The use of the WWW as a venue for voicing opinions, complaints and recommendations on products and firms has been widely reported in the popular media. However little is known how consumers use these reviews and if they subsequently have any influence on evaluations and purchase intentions of products and retailers. This study examines the effect of negative reviews on retailer evaluation and patronage intention given that the consumer has already made a product/brand decision. Our results indicate that the extent of WOM search depends on the consumer's reasons for choosing an online retailer. Further the influence of negative WOM information on perceived reliability and purchase intentions is determined largely by familiarity with the retailer and differs based on whether the retailer is a pure-Internet or clicks-and-mortar firm. Managerial implications for positioning strategies to minimize the effect of negative word-of-mouth have been discussed.


title: Mechanisms of secondary structure breakers in soluble proteins

abstract: Breaking signals of secondary structure put strong limitations on the tertiary structures of proteins. In addition to proline and glycine clusters, which are well-known secondary structure breakers, clusters of amphiphilic residues were found to be a novel type of secondary structure breaker. These secondary structure breakers were found to depend on specific environmental factors. Such conditions included the average hydrophobicity, the helical periodicity, the density of serine and threonine residues, and the presence of tryptophan and tyrosine clusters. Principal component analysis of environmental factors was conducted in order to identify candidate breakers in the secondary structure breaking regions. Predicted breakers were located in breaking regions with an accuracy of 72%. Taking the loop core into consideration, almost 90% of the predicted breakers were located in the loop segments. When the migration effect of the breaking point was taken into account, the loop segments with the predicted breakers covered two thirds of all loop segments. Herein, the possibility of secondary structure prediction based on secondary structure breakers is discussed. The system of the present method is available at the URL: http://bp.nuap.nagoya-u. ac.jp/sosui/sosuibreaker/sosuibreaker_submit.html.


title: An Analysis of Logistic Models: Exponential Family Connections and Online Performance

abstract: Logistic models are arguably one of the most widely used data analysis techniques. In this paper, we present analyses focussing on two important aspects of logistic models—its relationship with exponential family based generative models, and its performance in online and potentially adversarial settings. In particular, we present two new theoretical results on logistic models focusing on the above two aspects. First, we establish an exact connection between logistic models and exponential family based generative models, resolving a long-standing ambiguity over their relationship. Second, we show that online Bayesian logistic models are competitive to the best batch models, even in potentially adversarial settings. Further, we discuss relevant connections of our analysis to the literature on integral transforms, and also present a new optimality result for Bayesian models. The analysis makes a strong case for using logistic models and partly explains the success of such models for a wide range of practical problems.


title: WordRank: Learning Word Embeddings via Robust Ranking

abstract: Embedding words in a vector space has gained a lot of attention in recent years. While state-of-the-art methods provide efficient computation of word similarities via a low-dimensional matrix embedding, their motivation is often left unclear. In this paper, we argue that word embedding can be naturally viewed as a ranking problem due to the ranking nature of the evaluation metrics. Then, based on this insight, we propose a novel framework WordRank that efficiently estimates word representations via robust ranking, in which the attention mechanism and robustness to noise are readily achieved via the DCG-like ranking losses. The performance of WordRank is measured in word similarity and word analogy benchmarks, and the results are compared to the state-of-the-art word embedding techniques. Our algorithm is very competitive to the state-of-the- arts on large corpora, while outperforms them by a significant margin when the training set is limited (i.e., sparse and noisy). With 17 million tokens, WordRank performs almost as well as existing methods using 7.2 billion tokens on a popular word similarity benchmark. Our multi-node distributed implementation of WordRank is publicly available for general usage.


title: Audio Visual Speech Recognition using Deep Recurrent Neural Networks

abstract: In this work, we propose a training algorithm for an audio-visual automatic speech recognition (AV-ASR) system using deep recurrent neural network (RNN). First, we train a deep RNN acoustic model with a Connectionist Temporal Classification (CTC) objective function. The frame labels obtained from the acoustic model are then used to perform a non-linear dimensionality reduction of the visual features using a deep bottleneck network. Audio and visual features are fused and used to train a fusion RNN. The use of bottleneck features for visual modality helps the model to converge properly during training. Our system is evaluated on GRID corpus. Our results show that presence of visual modality gives significant improvement in character error rate (CER) at various levels of noise even when the model is trained without noisy data. We also provide a comparison of two fusion methods: feature fusion and decision fusion.


title: Exploiting the synergy between fractal dimension and lacunarity for improved texture recognition

abstract: Fractal dimension measures the geometrical complexity of images. Lacunarity being a measure of spatial heterogeneity can be used to differentiate between images that have similar fractal dimensions but different appearances. This paper presents a method to combine fractal dimension (FD) and lacunarity for better texture recognition. For the estimation of the fractal dimension an improved algorithm is presented. This algorithm uses new box-counting measure based on the statistical distribution of the gray levels of the ''boxes''. Also for the lacunarity estimation, new and faster gliding-box method is proposed, which utilizes summed area tables and Levenberg-Marquardt method. Methods are tested using Brodatz texture database (complete set), a subset of the Oulu rotation invariant texture database (Brodatz subset), and UIUC texture database (partial). Results from the tests showed that combining fractal dimension and lacunarity can improve recognition of textures.


title: Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering

abstract: Context has been recognized as an important factor to consider in personalized Recommender Systems. However, most model-based Collaborative Filtering approaches such as Matrix Factorization do not provide a straightforward way of integrating context information into the model. In this work, we introduce a Collaborative Filtering method based on Tensor Factorization, a generalization of Matrix Factorization that allows for a flexible and generic integration of contextual information by modeling the data as a User-Item-Context N-dimensional tensor instead of the traditional 2D User-Item matrix. In the proposed model, called Multiverse Recommendation, different types of context are considered as additional dimensions in the representation of the data as a tensor. The factorization of this tensor leads to a compact model of the data which can be used to provide context-aware recommendations. We provide an algorithm to address the N-dimensional factorization, and show that the Multiverse Recommendation improves upon non-contextual Matrix Factorization up to 30% in terms of the Mean Absolute Error (MAE). We also compare to two state-of-the-art context-aware methods and show that Tensor Factorization consistently outperforms them both in semi-synthetic and real-world data - improvements range from 2.5% to more than 12% depending on the data. Noticeably, our approach outperforms other methods by a wider margin whenever more contextual information is available.


title: A miniature digital current sensor with differential Hall probes using enhanced chopping techniques and mechanical stress compensation

abstract: A 10kHz bandwidth 50Amax current sensor using a Hall effect gradiometer without magnetic core provides 80kHz update rate with a digital interface. Very low un-calibrated offset of 30mA (1σ) and after calibration typical 10mA over temperature is accomplished by a chopped multi-bit feedback continuous-time 3rd order ΔΣ-ADC. This also realizes low noise of 13mA rms in 1kHz signal bandwidth. The ADC uses enhanced chopping techniques and additional digital feedback loops to avoid chopper ripple. New analog and digital stress-compensation circuits with lateral and vertical n-doped resistors achieve lifetime gain drifts below 1% and temperature compensation. Auto-zeroing ping-pong comparators offer a fast over-current detection of 1…2μs on a dedicated output pin. The monolithic integrated sensor chip and the 4kV galvanic isolated current rail fit into a very small 7×7×1mm3 package.


title: Coarse to Fine : Diffusing Categories in Wikipedia

abstract: Automatic taxonomy construction aims to build a categorization system without human efforts. Traditional textual pattern based methods extract hyponymy relation in raw texts. However, these methods usually yield low precision and recall. In this paper, we propose a method to automatically find diffusing attributes to a category from Wikipedia infoboxes. We use the diffusing attribute to diffuse a coarse-grained category into several fine-grained subcategories and generate a finer-grained taxonomy. Experiments show our method can find proper diffusing attributes to categories across various domains.


title: How, and why, process metrics are better

abstract: Defect prediction techniques could potentially help us to focus quality-assurance efforts on the most defect-prone files. Modern statistical tools make it very easy to quickly build and deploy prediction models. Software metrics are at the heart of prediction models; understanding how and especially why different types of metrics are effective is very important for successful model deployment. In this paper we analyze the applicability and efficacy of process and code metrics from several different perspectives. We build many prediction models across 85 releases of 12 large open source projects to address the performance, stability, portability and stasis of different sets of metrics. Our results suggest that code metrics, despite widespread use in the defect prediction literature, are generally less useful than process metrics for prediction. Second, we find that code metrics have high stasis; they don't change very much from release to release. This leads to stagnation in the prediction models, leading to the same files being repeatedly predicted as defective; unfortunately, these recurringly defective files turn out to be comparatively less defect-dense.


Emotional Voice Conversion for Mandarin using Tone Nucleus Model – Small Corpus and High Efficiency


title: Semantic sentence embeddings for paraphrasing and text summarization

abstract: This paper introduces a sentence to vector encoding framework suitable for advanced natural language processing. Our latent representation is shown to encode sentences with common semantic information with similar vector representations. The vector representation is extracted from an encoder-decoder model which is trained on sentence paraphrase pairs. We demonstrate the application of the sentence representations for two different tasks -- sentence paraphrasing and paragraph summarization, making it attractive for commonly used recurrent frameworks that process text. Experimental results help gain insight how vector representations are suitable for advanced language embedding.


title: Trauma oblique radiographs of the knee.

abstract: Special trauma oblique radiographs of the knee were designed for the purpose of demonstrating the patella radiographically in a manner that would free it from the shadow of the distal end of the femur. These radiographs are made by directing the x-ray tube at an angle of 45 degrees and making two exposures, with the film placed first on the lateral and then on the medial side of the injured knee. These two radiographs have been found to be useful for delineating the extent of fractures of the patella as well as abnormalities of the tibial plateau.


title: Negative Link Prediction in Social Media

abstract: Signed network analysis has attracted increasing attention in recent years. This is in part because research on signed network analysis suggests that negative links have added value in the analytical process. A major impediment in their effective use is that most social media sites do not enable users to specify them explicitly. In other words, a gap exists between the importance of negative links and their availability in real data sets. Therefore, it is natural to explore whether one can predict negative links automatically from the commonly available social network data. In this paper, we investigate the novel problem of negative link prediction with only positive links and content-centric interactions in social media. We make a number of important observations about negative links, and propose a principled framework NeLP, which can exploit positive links and content-centric interactions to predict negative links. Our experimental results on real-world social networks demonstrate that the proposed NeLP framework can accurately predict negative links with positive links and content-centric interactions. Our detailed experiments also illustrate the relative importance of various factors to the effectiveness of the proposed framework.


title: Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data

abstract: Summary: The two main functions of bioinformatics are the organization and analysis of biological data using computational resources. Geneious Basic has been designed to be an easy-to-use and flexible desktop software application framework for the organization and analysis of biological data, with a focus on molecular sequences and related data types. It integrates numerous industry-standard discovery analysis tools, with interactive visualizations to generate publication-ready images. One key contribution to researchers in the life sciences is the Geneious public application programming interface (API) that affords the ability to leverage the existing framework of the Geneious Basic software platform for virtually unlimited extension and customization. The result is an increase in the speed and quality of development of computation tools for the life sciences, due to the functionality and graphical user interface available to the developer through the public API. Geneious Basic represents an ideal platform for the bioinformatics community to leverage existing components and to integrate their own specific requirements for the discovery, analysis and visualization of biological data. ::: ::: Availability and implementation: Binaries and public API freely available for download at http://www.geneious.com/basic, implemented in Java and supported on Linux, Apple OSX and MS Windows. The software is also available from the Bio-Linux package repository at http://nebc.nerc.ac.uk/news/geneiousonbl. ::: ::: Contact: peter@biomatters.com


title: An Analysis of Multi-Criteria Decision Making Methods

abstract: Multi-Criteria Decision Making (MCDM) methods have evolved to accommodate various types of applications. Dozens of methods have been developed, with even small variations to existing methods causing the creation of new branches of research. This paper performs a literature review of common Multi-Criteria Decision Making methods, examines the advantages and disadvantages of the identified methods, and explains how their common applications relate to their relative strengths and weaknesses. The analysis of MCDM methods performed in this paper provides a clear guide for how MCDM methods should be used in particular situations.


title: Approach for Semantic Interoperability Testing in Internet of Things

abstract: The Internet of Things (IoT) systems must support deployment of new services capable of supporting multiple, scalable and cross-domain applications. But the current IoT applications and services are highly fragmented in terms of platform development, data and security. Semantic interoperability is widely acknowledged to be the key to address the challenges and achievethe full potential of the IoT systems. Although semantic interoperability has received increased attention in the recent days, the current IoT systems lack means for testing that. This paper describes our approach for semantic interoperability testing in the IoT. We have identifiedthree main requirements for semantic tests - (i) lexical check, (ii) syntactic check and (iii)semantic checks. To examine semantic interoperability between IoT systems, we define two typesof tests - (i) semantic conformance test and (ii) interoperability test. The operational stepsare presented along with detailed test scenarios.


title: Generalized distributed rate limiting

abstract: The Distributed Rate Limiting (DRL) paradigm is a recently proposed mechanism for decentralized control of cloud-based services. DRL is a simple and efficient approach to resolve the issues of pricing and resource control/engineering of cloud based services. The existing DRL schemes focus on very specific performance metrics (such as loss rate and fair-share) and their design heavily depends on the assumption that the traffic is generated by elastic TCP sources. In this paper we tackle the DRL problem for general workloads and performance metrics and propose an analytic framework for the design of stable DRL algorithms. The closed-form nature of our results allows simple design rules which, together with extremely low communication overhead, makes the presented algorithms practical and easy to deploy with guaranteed convergence properties under a wide range of possible scenarios.


k - RDF-Neighbourhood Anonymity: Combining Structural and Attribute-based Anonymisation for Linked Data


title: Epigastric heteropagus conjoined twins: two case studies and associated DNA analysis

abstract: Conjoined twins are rarely observed, but heteropagus conjoined twins occur even less frequently. Heteropagus conjoined twins are asymmetrically conjoined twins, in which one twin is nearly anatomically normal (autosite), and the other twin remains anatomically incomplete (parasite) but physically attached to the autosite. Epigastric heteropagus conjoined twins (EHTs), referring to a parasitic twin who is attached to the epigastrium of the autosite, are extremely rare (1). ::: ::: Due to the rare incidence of EHTs, there are few publications on this subject, which has resulted in a general lack of knowledge among surgeons with respect to how to approach the management of EHTs. Furthermore, EHTs generally manifest as more medically complex situations than typical conjoined twins. Therefore, the etiology, manifestation, and therapeutic outcomes of EHTs remain controversial. In this article, we report two cases of EHTs encountered in our department between May 2007 and October 2007. One EHT case involved a large ventral hernia near the omphalocele, which has not been previously reported. Moreover, DNA analysis of case number 2 suggested a monozygotic origin of the EHTs.


title: A dataset for computer-vision-based PCB analysis

abstract: We present a public dataset with the aim to facilitate research on computer-vision-based Printed Circuit Board (PCB) analysis, with a focus on recycling-related applications. The dataset contains 748 images of PCBs from a recycling facility, captured under representative conditions using a professional DSLR camera. For all these images we provide accurate segmentation information for the depicted PCBs as well as bounding box information for all Integrated Circuit (IC) chips (9313 samples). Furthermore, we provide textual information of the labels for a subset of 1740 IC samples. By including these different aspects of information, our dataset is useful for designing and testing a variety of methods for PCB analysis, from PCB classification over IC chip localization to the detection of specific chips. We discuss the benefits of PCB analysis for recycling, present dataset statistics, and use the dataset to evaluate two example methods, one for detecting specific PCBs, and one for recognizing mainboards.


title: A Differential Digitally Controlled Crystal Oscillator With a 14-Bit Tuning Resolution and Sine Wave Outputs for Cellular Applications

abstract: This paper describes the design topologies and considerations of a differential sinusoidal-output digitally controlled crystal oscillator (DCXO) intended for use in cellular applications. The oscillator has a fine-tuning range of ±44 ppm, approximately 14 bits of resolution, and an average step size of 0.005 ppm. All signals connecting externally to I/O pins are sine waves for reducing noise, interference, and spurs couplings. The 26 MHz DCXO fabricated in 65 nm CMOS achieves a phase noise of -149.1 dBc/Hz at 10 kHz offset measured at the sine wave buffer output. The DCXO is capable of meeting the stringent phase noise requirements for IEEE 802.11n 5 GHz WLAN devices. A typical frequency pulling of 0.01 ppm due to turning on/off the sine wave buffer is measured. The DCXO dissipates 1.2 mA of current, whereas each sine wave output buffer draws 1.4 mA. The DXCO occupies a total silicon area of 0.15 mm2 .


title: Build Your Own Quadrotor: Open-Source Projects on Unmanned Aerial Vehicles

abstract: This article presents a survey on publicly available open-source projects (OSPs) on quadrotor unmanned aerial vehicles (UAVs). Recently, there has been increasing interest in quadrotor UAVs. Exciting videos have been published on the Internet by many research groups and have attracted much attention from the public [1][7]. Relatively simple structures of quadrotors has promoted interest from academia, UAV industries, and radio-control (RC) hobbyists alike. Unlike conventional helicopters, swashplates, which are prone to failure without constant maintenance, are not required. Furthermore, the diameter of individual rotors can be reduced as a result of the presence of four actuators [8].


title: A 1-MHz 5-kV Power Supply Applying SiC Diodes and GaN HEMT Cascode MOSFETs in Soft Switching

abstract: A new high-voltage (HV) power supply based on the half-wave Cockcroft-Walton voltage multiplier topology is introduced. The proposed power supply operates at 1 MHz (at least ten times faster than conventional HV multipliers) and exhibits a very stable output voltage with minimal ripple ( 90%). The high operation frequency and efficiency were facilitated by a critical design that applies softly switched wide-bandgap devices (silicon carbide diodes and gallium nitride high electron mobility transistor cascode MOSFETS) in full bridge cascaded by a voltage multiplier topology. Experimental evidence is provided from a prototype unit that raises the voltage from 300 V to 5 kV, with a 93% efficiency.


title: A new alarming system for an underground mining environment using visible light communications

abstract: Visible light communication (VLC) system has been widely used in underground mines, to support communication-blind areas, due to its superior benefits compared to Wi-Fi. These benefits include low energy consumption, wide frequency spectrum (430–790) THZ, higher data rates and small-sized infrastructure. Moreover, radio waves have poor underground communication capabilities, due to irregular data propagation and limited frequency ranges. Therefore, cannot be used underground for affective communication required for health and safety measures in mines environment. In this paper, we introduce VLC based communication system for underground mines, for reducing the probability of accidents, and improving safety for mines work environment. We model the communication channel considering the shadowing and mines dust effects on optical signals. Simulation results show the effectiveness of the proposed model in terms of BER and improved received power, specially for small distance between the transmitter and receiver.


title: Integrating mobile and cloud for PPG signal selection to monitor heart rate during intensive physical exercise

abstract: Heart rate monitoring has become increasingly popular in the industry through mobile phones and wearable devices. However current determination of heart rate through mobile applications suffer from high corruption of signals during intensive physical exercise. In this paper, we present a novel technique for accurately determining heart rate during intensive motion by classifying PPG signals obtained from smartphones or wearable devices combined with motion data obtained from accelerometer sensors. Our approach utilizes the Internet of Things (IoT) cloud connectivity of smartphones for PPG signals selection using deep learning. The technique is validated using the TROIKA dataset and is accurately able to predict heart rate with a 10-fold cross validation error margin of 4.88%.


title: Design and Control of a Four Steered Wheeled Mobile Robot

abstract: This paper presents the kinematical analysis of AZIMUT-2, a four steered wheeled mobile robot. The utilization of a new wheel concept called the AZIMUT wheel allowed us to create an innovative omnidirectional non-holonomic robot. Novelty of this wheel concept resides in the non-conventional positioning of the steering axis and the wheel axis. We propose a kinematical model based on the geometrical constraints of these wheels. The degree of mobility, steerability and maneuverability are studied. Additionally, we describe a special design implementation of the wheel mechanism to overcome a hyper-motorization issue inherent to the wheel's geometrical properties. Finally, we describe AZIMUT-2's two operational and seven locomotion modes, along with a control algorithm based on the kinematical model of the robot


Artificial Mitochondria Transfer: Current Challenges, Advances, and Future Applications


title: A Correspondence Between Random Neural Networks and Statistical Field Theory

abstract: A number of recent papers have provided evidence that practical design questions about neural networks may be tackled theoretically by studying the behavior of random networks. However, until now the tools available for analyzing random neural networks have been relatively ad-hoc. In this work, we show that the distribution of pre-activations in random neural networks can be exactly mapped onto lattice models in statistical physics. We argue that several previous investigations of stochastic networks actually studied a particular factorial approximation to the full lattice model. For random linear networks and random rectified linear networks we show that the corresponding lattice models in the wide network limit may be systematically approximated by a Gaussian distribution with covariance between the layers of the network. In each case, the approximate distribution can be diagonalized by Fourier transformation. We show that this approximation accurately describes the results of numerical simulations of wide random neural networks. Finally, we demonstrate that in each case the large scale behavior of the random networks can be approximated by an effective field theory.


Indexes of spherical codes.


AdaCost: Misclassification Cost-Sensitive Boosting


Writing meta-analytic reviews.


title: An Approach for Weakly-Supervised Deep Information Retrieval

abstract: Recent developments in neural information retrieval models have been promising, but a problem remains: human relevance judgments are expensive to produce, while neural models require a considerable amount of training data. In an attempt to fill this gap, we present an approach that---given a weak training set of pseudo-queries, documents, relevance information---filters the data to produce effective positive and negative query-document pairs. This allows large corpora to be used as neural IR model training data, while eliminating training examples that do not transfer well to relevance scoring. The filters include unsupervised ranking heuristics and a novel measure of interaction similarity. We evaluate our approach using a news corpus with article headlines acting as pseudo-queries and article content as documents, with implicit relevance between an article's headline and its content. By using our approach to train state-of-the-art neural IR models and comparing to established baselines, we find that training data generated by our approach can lead to good results on a benchmark test collection.


title: Sterile abscess formation associated with depot leuprorelin acetate therapy for central precocious puberty.

abstract: We describe a case of an 8 year old girl with central precocious puberty. She was commenced on 3 monthly intramuscular depot Leuprorelin acetate therapy, as a result of which she developed sterile abscesses. She was converted to daily subcutaneous Leuprorelin acetate therapy with no recurrence of the abscesses. The possible mechanisms for this reaction are described in the article.


title: Mining Event-Oriented Topics in Microblog Stream with Unsupervised Multi-View Hierarchical Embedding

abstract: This article presents an unsupervised multi-view hierarchical embedding (UMHE) framework to sufficiently reveal the intrinsic topical knowledge in social events. Event-oriented topics are highly related to such events as it can provide explicit descriptions of what have happened in social community. In many real-world cases, however, it is difficult to include all attributes of microblogs, more often, textual aspects only are available. Traditional topic modelling methods have failed to generate event-oriented topics with the textual aspects, since the inherent relations between topics are often overlooked in these methods. Meanwhile, the metrics in original word vocabulary space might not effectively capture semantic distances. Our UMHE framework overcomes the severe information deficiency and poor feature representation. The UMHE first develops a multi-view Bayesian rose tree to preliminarily generate prior knowledge for latent topics and their relations. With such prior knowledge, we design an unsupervised translation-based hierarchical embedding method to make a better representation of these latent topics. By applying self-adaptive spectral clustering on the embedding space and the original space concomitantly, we eventually extract event-oriented topics in word distributions to express social events. Our framework is purely data-driven and unsupervised, without any external knowledge. Experimental results on TREC Tweets2011 dataset and Sina Weibo dataset demonstrate that the UMHE framework can construct hierarchical structure with high fitness, but also yield topic embeddings with salient semantics; therefore, it can derive event-oriented topics with meaningful descriptions.


title: Can I Do That? Discovering Domain Axioms Using Declarative Programming and Relational Reinforcement Learning

abstract: Robots deployed to assist humans in complex, dynamic domains need the ability to represent, reason with, and learn from, different descriptions of incomplete domain knowledge and uncertainty. This paper presents an architecture that integrates declarative programming and relational reinforcement learning to support cumulative and interactive discovery of previously unknown axioms governing domain dynamics. Specifically, Answer Set Prolog (ASP), a declarative programming paradigm, is used to represent and reason with incomplete commonsense domain knowledge. For any given goal, unexplained failure of plans created by inference in the ASP program is taken to indicate the existence of unknown domain axioms. The task of learning these axioms is formulated as a Reinforcement Learning problem, and decision-tree regression with a relational representation is used to generalize from specific axioms identified over time. The new axioms are added to the ASP-based representation for subsequent inference. We demonstrate and evaluate the capabilities of our architecture in two simulated domains: Blocks World and Simple Mario.


title: Understanding inertial and frequency response of wind power plants

abstract: The objective of this paper is to analyze and quantify the inertia and frequency responses of wind power plants with different wind turbine technologies (particularly those of fixed speed, variable slip with rotor-resistance controls, and variable speed with vector controls).


Interpregnancy Interval and Pregnancy Outcomes


title: A high frequency power factor correction converter with soft switching

abstract: Power factor correction (PFC) converters are typically operated at low frequency to mitigate switching losses and to simplify control; this in turn requires large passive components. Soft switching techniques could permit higher frequency operation, but most soft-switched converters do not maintain high performance across the wide voltage and power ranges required for PFC applications. Here we present a PFC converter which enables high frequency operation by maintaining soft switching and by using a control scheme which requires no current sensing. These advantages are verified with a prototype which achieves power factors above 0.996 (THD < 10%) while maintaining ZVS across voltage and power for efficiencies ∼ 97 %. By using increased switching frequency (∼ 10x over conventional designs), this converter can take advantage of greatly reduced passive component values for power conversion and EMI filtering.


title: Velocity estimation from monocular video for automotive applications using convolutional neural networks

abstract: We aim to determine the speed of ego-vehicle motion from a video stream. Previous work by Konda et al. [1] has shown that motion can be detected and quantified with the help of a synchrony autoencoder, which has multiplicative gating interactions introduced between its hidden units, and hence, across video frames. In this work we modify their synchrony autoencoder method to achieve a ”real time” performance in a wide variety of driving environments. Our modifications led to a model which is 1.5 times faster and uses only half of the total memory by comparison with the original. We also benchmark the speed estimation performance against a model based on CaffeNet. CaffeNet is known for visual classification and localization but we employ its architecture with a little tweak for speed determination using sequential video frames and blur patterns. We evaluate our models on self-collected data, KITTI, and other standard sets.


title: PixelLink: Detecting Scene Text via Instance Segmentation

abstract: Most state-of-the-art scene text detection algorithms are deep learning based methods that depend on bounding box regression and perform at least two kinds of predictions: text/non-text classification and location regression. Regression plays a key role in the acquisition of bounding boxes in these methods, but it is not indispensable because text/non-text prediction can also be considered as a kind of semantic segmentation that contains full location information in itself. However, text instances in scene images often lie very close to each other, making them very difficult to separate via semantic segmentation. Therefore, instance segmentation is needed to address this problem. In this paper, PixelLink, a novel scene text detection algorithm based on instance segmentation, is proposed. Text instances are first segmented out by linking pixels within the same instance together. Text bounding boxes are then extracted directly from the segmentation result without location regression. Experiments show that, compared with regression-based methods, PixelLink can achieve better or comparable performance on several benchmarks, while requiring many fewer training iterations and less training data.


title: Characterization of an fdxN mutant of Rhodobacter capsulatus indicates that ferredoxin I serves as electron donor to nitrogenase

abstract: Abstract A mutant of Rhodobacter capsulatus , carrying an insertion into the fdxN gene encoding ferredoxin I (FdI), has been studied by biochemical analysis and genetic complementation experiments. When compared to the wild-type strain, the fdxN mutant exhibited altered nitrogen fixing ability and 20-fold lower levels of nitrogenase activity as assayed in vivo. When assayed in vitro with an artificial reductant, nitrogenase activity was only 3- to 4-fold lower than in the wild type. These results suggested that the FdI-deleted mutant had impaired electron transport to nitrogenase. Immunochemical assay of both nitrogenase components showed that the fdxN mutant contained about 4-fold less enzyme than wild-type cells. Results of pulse-chase labeling experiments using [ 35 S]methionine indicated that nitrogenase was significantly less stable in the FdI-deleted mutant. When a copy of fdxN was introduced in the mutant in trans , the resulting strain appeared to be fully complemented with respect to both diazotrophic growth and nitrogenase activity. Depending on whether fdxN expression was driven by a nif promoter or a fructose-inducible promoter. FdI was synthesized either at wild-type level or in 10-fold lower amounts. The strain producing 10-fold less FdI did, however, display normal N 2 -fixing ability. Analysis of cytosolic proteins by bidimensional electrophoresis revealed that the fdxN mutant produced a 14 kDa polypeptide in amounts about 3-fold greater than wild-type cells. This protein was identified by N-terminal microsequencing as a recently purified [2Fe-2S] ferredoxin, called FdV, which cannot reduce nitrogenase. It is concluded that FdI serves as the main electron donor to nitrogenase in R. capsulatus and that an ancillary electron carrier, distinct of FdV, is responsible for the residual nitrogenase activity observed in the FdI-deleted mutant.


title: mrrobust: a Stata package for MR-Egger regression type analyses

abstract: MR-Egger regression analyses are becoming increasingly common in Mendelian randomization studies (MR) (Bowden et al. 2015). MR-Egger analyses use summary level data, as reported by genome-wide association studies. Such data is conveniently available from the MR-base platform (Hemani et al., 2016). MR-Egger and related methods treat a multiple instrument MR analysis as a meta-analysis across the multiple genotypes. In the MR-Egger approach, bias from the pleiotropic effects of the multiple genotypes is treated as small study reporting bias in meta-analysis. They represent an important quality control check for any MR analysis incorporating multiple genotypes. We implemented several of these methods (inverse-variance weighted [IVW], MR-Egger and weighted median approaches, as well as a relevant plot) in a package for Stata called mrrobust (pleiotropy robust methods for MR). There are also implementations of these methods in R (Yavorska and Burgess 2016). mrrobust is freely available from https://github.com/remlapmot/mrrobust, which includes instructions on how to install the package from within Stata. We plan to add features over time.


title: On the Recursive Neural Networks for Relation Extraction and Entity Recognition

abstract: Recently there has been a surge of interest in neural architectures for complex structured learning tasks. Along this track, we are addressing the supervised task of relation extraction and named-entity recognition via recursive neural structures and deep unsupervised feature learning. Our models are inspired by several recent works in deep learning for natural language. We have extended the previous models, and evaluated them in various scenarios, for relation extraction and namedentity recognition. In the models, we avoid using any external features, so as to investigate the power of representation instead of feature engineering. We implement the models and proposed some more general models for future work. We will briefly review previous works on deep learning and give a brief overview of recent progresses relation extraction and named-entity recognition.


title: Structure mapping in analogy and similarity

abstract: ions Keil, 1989 ; Rips, 1989) . For example, bats have the perceptual and behavioral characteristics of birds (they are similar to birds in this sense), but they are classified as mammals, because of important (though nonobvious) properties, such as giving birth to live young. On the basis of examples like this, similarity's role in categorization has been challenged ; it has been argued that category membership judgments are theory based rather than similarity based (Keil, 1989 ; Murphy & Medin, 1985) . The process of alignment and mapping points the way to a reconciliation of similarity-based and theorybased accounts (see also Goldstone, 1994a) . If we focus purely on perceptual similarity among objects, we are led to conclude that bats should be categorized with birds . On this view, theory-based knowledge (such as why bats are mammals) must intervene from elsewhere to overrule this assignment . However, if the similarity computation is assumed to be that ofstructural alignment, then the similarity between two instances will be based riot only on object-level commonalities but also on common relations such as common causal relations and common origins . Assuming that our representations include information about theory-based relations, such as that bats bear live young, as well as information about features, then the schism between similarity-based and theory-based categorization may be more apparent than real . Developmentally, if we assume that theoretical knowledge is acquired gradually, this view would account for the characteristic-to-defining shift (Keil & Batterman, 1984) in children's interpretations of word meaning from local object features (e.g ., a taxi is bright yellow and has a checkered sign) to deeper relational commonalities (e.g ., a taxi is a vehicle that may be hired to transport people) . Choice and decision . Structural alignment also sheds light on the processes underlying choice behavior. Medin, Goldstone, and Markman (1995) reviewed paral lels between phenomena in decision processing and phenomena in comparison processing that suggest an important role for structural alignment in decision making . Structural alignment influences which features to pay attention to in choice options . Research suggests that alignable differences are given more weight in choice situations than are nonalignable differences (Lindemann & Markman, 1996 ; Markman & Medin, 1995 ; Slovic & MacPhillamy, 1974) . For example, Markman and Medin (1995) asked participants to choose between video games and to justify their choices. Their justifications were more likely to contain alignable differences than nonalignable differences . As another example, Kahneman and Z'versky (1984) described to participants a hypothetical store in which a jacket could be bought for $125 and a calculator for $15 . They offered participants the opportunity to go to another store and save $5 on the total purchase. Participants who were offered ajacket for $125 and a calculator for $10 were more willing to make the effort to go to another store than those offered a jacket for $120 and a calculator for $15 . Even though the monetary reward for going to the other store was the same for both groups, participants were influenced by the alignable difference .


ARCHITECTURAL SURVEY OF CONTEXT-AWARE SYSTEMS IN PERVASIVE COMPUTING ENVIRONMENT


title: A Frame Tracking Model for Memory-Enhanced Dialogue Systems

abstract: Recently, resources and tasks were proposed to go beyond state tracking in dialogue systems. An example is the frame tracking task, which requires recording multiple frames, one for each user goal set during the dialogue. This allows a user, for instance, to compare items corresponding to different goals. This paper proposes a model which takes as input the list of frames created so far during the dialogue, the current user utterance as well as the dialogue acts, slot types, and slot values associated with this utterance. The model then outputs the frame being referenced by each triple of dialogue act, slot type, and slot value. We show that on the recently published Frames dataset, this model significantly outperforms a previously proposed rule-based baseline. In addition, we propose an extensive analysis of the frame tracking task by dividing it into sub-tasks and assessing their difficulty with respect to our model.


Enhancement of MD5 Algorithm for Secured Web Development


title: The inducible form of nitric oxide synthase (iNOS) in insulin-producing cells.

abstract: The enzyme nitric oxide synthase catalyzes the conversion of L-arginine to citrulline and the radical nitric oxide, a short-lived mediator which can be produced in a variety of cell types. Overproduction of nitric oxide is probably implicated in the pathogenesis of several immunologically mediated diseases, including insulin-dependent diabetes mellitus (Type 1). Insulin-producing cells exposed to cytokines, especially interleukin-1, express an inducible form of nitric oxide synthase which is similar to that observed in activated macrophages. Induction of this enzyme mRNA in these cells depends on protein synthesis, and it is probably modulated by protein products of early response genes, such as C-fos. Cytokines seem to activate beta-cell inducible-nitric oxide synthase mostly by stimulating mRNA transcription, but drugs such as nicotinamide and dexamethasone inhibit interleukin 1 induced nitric oxide production by posttranscriptional mechanisms. Considering the potential role for nitric oxide in beta-cell damage during the early stages of Type 1 diabetes, it is of high relevance to further characterize the regulation of this enzyme in insulin-producing cells.


title: Performance evaluation and design trade-offs for network-on-chip interconnect architectures

abstract: Multiprocessor system-on-chip (MP-SoC) platforms are emerging as an important trend for SoC design. Power and wire design constraints are forcing the adoption of new design methodologies for system-on-chip (SoC), namely, those that incorporate modularity and explicit parallelism. To enable these MP-SoC platforms, researchers have recently pursued scaleable communication-centric interconnect fabrics, such as networks-on-chip (NoC), which possess many features that are particularly attractive for these. These communication-centric interconnect fabrics are characterized by different trade-offs with regard to latency, throughput, energy dissipation, and silicon area requirements. In this paper, we develop a consistent and meaningful evaluation methodology to compare the performance and characteristics of a variety of NoC architectures. We also explore design trade-offs that characterize the NoC approach and obtain comparative results for a number of common NoC topologies. To the best of our knowledge, this is the first effort in characterizing different NoC architectures with respect to their performance and design trade-offs. To further illustrate our evaluation methodology, we map a typical multiprocessing platform to different NoC interconnect architectures and show how the system performance is affected by these design trade-offs.


title: Serverless: beyond the cloud

abstract: Serverless computing is widely known as an event-driven cloud execution model. In this model, the client provides the code and the cloud provider manages the life-cycle of the execution environment of that code. The idea is based on reducing the life span of the program to execute functionality in response to an event. Hence, the program's processes are born when an event is triggered and are killed after the event is processed. This model has proved its usefulness in the cloud as it reduced the operational cost and complexity of executing event-driven workloads. In this paper we argue that the serverless model does not have to be limited the to the cloud. We show how the same model can be applied at the micro-level of a single machine. In such model, certain operating system commands are treated as events that trigger a serverless reaction. This reaction consists of deploying and running code only in response to those events. Thus, reducing the attack surface and complexity of managing single machines.


title: A Convolutional Neural Network Approach for Assisting Avalanche Search and Rescue Operations with UAV Imagery

abstract: Following an avalanche, one of the factors that affect victims’ chance of survival is the speed with which they are located and dug out. Rescue teams use techniques like trained rescue dogs and electronic transceivers to locate victims. However, the resources and time required to deploy rescue teams are major bottlenecks that decrease a victim’s chance of survival. Advances in the field of Unmanned Aerial Vehicles (UAVs) have enabled the use of flying robots equipped with sensors like optical cameras to assess the damage caused by natural or manmade disasters and locate victims in the debris. In this paper, we propose assisting avalanche search and rescue (SAR) operations with UAVs fitted with vision cameras. The sequence of images of the avalanche debris captured by the UAV is processed with a pre-trained Convolutional Neural Network (CNN) to extract discriminative features. A trained linear Support Vector Machine (SVM) is integrated at the top of the CNN to detect objects of interest. Moreover, we introduce a pre-processing method to increase the detection rate and a post-processing method based on a Hidden Markov Model to improve the prediction performance of the classifier. Experimental results conducted on two different datasets at different levels of resolution show that the detection performance increases with an increase in resolution, while the computation time increases. Additionally, they also suggest that a significant decrease in processing time can be achieved thanks to the pre-processing step.


title: Employee training and development and the learning organization

abstract: The global competition and swiftness of changes emphasize the importance of human capital within organizations, as well as the swiftness and ways of knowledge gaining of that capital. In the economy where uncertainty is the only certainty, knowledge is becoming a reliable source of sustained competitive advantage. Knowledge is becoming basic capital and the trigger of development. Previously built on foundations of possessing specific resources and low costs, present day competition is based on knowledge possessing and efficient knowledge management. Modern organizations therefore use their resources (money, time, energy, information, etc) for permanent training and advancement of their employees. Organizations which are constantly creating new knowledge, extending it through the entire organization and implementing it quickly inside the new technologies develop good products and excellent services. These activities determine the company as a learning organization with constant innovation being its sole business. These are organizations which realize that learning and new knowledge are becoming the key of success, and that education is crucial for abundance.


title: How to assess diabetes distress: comparison of the Problem Areas in Diabetes Scale (PAID) and the Diabetes Distress Scale (DDS)

abstract: AIMS ::: To compare the properties of the two most commonly used assessment tools for diabetes distress, the Problem Areas in Diabetes Scale (PAID) and the Diabetes Distress Scale (DDS), in order to discriminate their psychometric capabilities and functions. ::: ::: ::: METHODS ::: Six hundred and twenty-eight people with diabetes (67% Type 1, 33% Type 2) were cross-sectionally assessed with the PAID, the DDS and further self-report scales regarding coping, quality of life, depressive symptoms and self-care, and medical data were gained. We analysed the PAID and DDS for areas of contentual/psychometric divergence in assessing diabetes distress and compared their associations with criteria of interest. ::: ::: ::: RESULTS ::: Content analysis: The PAID covers a greater variety of emotional concerns and shows a stronger focus on food-related problems and complications. The DDS is more reflective of physician-related distress and problems concerning diabetes self-management. Psychometric analysis: Exploratory factor analyses revealed four-factor structures of both scales, explaining 60% (PAID) and 67% (DDS) of variance. Confirmatory factor analyses confirmed that single-factor and four-factor models fit the data. Total scales proved high and subscales mostly satisfactory reliability. Associations with criteria of interest: The PAID was significantly more strongly associated with dysfunctional coping styles, quality of life and depressive symptoms. The DDS showed significantly stronger associations with diabetes self-care and metabolic outcomes. ::: ::: ::: CONCLUSION ::: Our results support both PAID and DDS as good self-report measures of diabetes distress. The observed contentual/psychometric differences suggest that a justified choice with regard to the intended clinical or scientific purpose can improve the acquisition of the required data.


title: Optimal Peak Shaving Using Batteries at Datacenters: Charging Risk and Degradation Model

abstract: A datacenter’s power consumption is a major contributor to its operational expenditures (op-ex) as determined by peak-demand-over-billing-period based pricing which is often employed by major electric utility providers. There is a growing interest in reducing a datacenters electricity costs by using demand-throttling techniques and/or energy storage devices (batteries which are readily available at most datacenters as a backup energy source). For the latter, we present a Markov Decision Process framework based on power-demand uncertainty and a linearized battery degradation model. This framework also explicitly considers risk of over or under charging the battery resulting in higher cost-savings (up to 2×) with tractable risk. We show the complete characterization of risk-cost trade-off and cost-per-risk as a function of datacenter’s workload characteristics. We also, study a linearized battery degradation model empirically, and show the accuracy of this model for bursty workloads, however there is some discrepancy between the linearized model and reality for workloads with lower variability.


title: A comparison of index-based lempel-Ziv LZ77 factorization algorithms

abstract: Since 1977, when Lempel and Ziv described a kind of string factorization useful for text compression, there has been a succession of algorithms proposed for computing “LZ factorization”. In particular, there have been several recent algorithms proposed that extend the usefulness of LZ factorization, for example, to the calculation of maximal repetitions. In this article, we provide an overview of these new algorithms and compare their efficiency in terms of their usage of time and space.


title: Scale-space theory: A basic tool for analysing structures at different scales

abstract: An inherent property of objects in the world is that they only exist as meaningful entities over certain ranges of scale. If one aims to describe the structure of unknown real-world signals, then a multi-scale representation of data is of crucial importance. This paper gives a tutorial review of a special type of multi-scale representation—linear scale-space representation—which has been developed by the computer vision community to handle image structures at different scales in a consistent manner. The basic idea is to embed the original signal into a one-parameter family of gradually smoothed signals in which the fine-scale details are successively suppressed. Under rather general conditions on the type of computations that are to be performed at the first stages of visual processing, in what can be termed ‘the visual front-end’, it can be shown that the Gaussian kernel and its derivatives are singled out as the only possible smoothing kernels. The conditions that specify the Gaussian kernel are, basica...


Document-level Sentiment Inference with Social, Faction, and Discourse Context


title: A qualitative evaluation of evolution of a learning analytics tool

abstract: LOCO-Analyst is a learning analytics tool we developed to provide educators with feedback on students learning activities and performance. Evaluation of the first version of the tool led to the enhancement of the tool's data visualization, user interface, and supported feedback types. The second evaluation of the improved tool allowed us to see how the improvements affected the users' perceived value of the tool. Here, we present the qualitative results of our two evaluations and discuss important lessons learned stemming from the comparison of the two studies. The results show that educators find the kinds of feedback implemented in the tool informative and they value the mix of textual and graphical representations of different kinds of feedback provided by the tool.


title: BinMatch: A Semantics-Based Hybrid Approach on Binary Code Clone Analysis

abstract: Binary code clone analysis is an important technique which has a wide range of applications in software engineering (e.g., plagiarism detection, bug detection). The main challenge of the topic lies in the semantics-equivalent code transformation (e.g., optimization, obfuscation) which would alter representations of binary code tremendously. Another challenge is the trade-off between detection accuracy and coverage. Unfortunately, existing techniques still rely on semantics-less code features which are susceptible to the code transformation. Besides, they adopt merely either a static or a dynamic approach to detect binary code clones, which cannot achieve high accuracy and coverage simultaneously. In this paper, we propose a semantics-based hybrid approach to detect binary clone functions. We execute a template binary function with its test cases, and emulate the execution of every target function for clone comparison with the runtime information migrated from that template function. The semantic signatures are extracted during the execution of the template function and emulation of the target function. Lastly, a similarity score is calculated from their signatures to measure their likeness. We implement the approach in a prototype system designated as BinMatch which analyzes IA-32 binary code on the Linux platform. We evaluate BinMatch with eight real-world projects compiled with different compilation configurations and commonly-used obfuscation methods, totally performing over 100 million pairs of function comparison. The experimental results show that BinMatch is robust to the semantics-equivalent code transformation. Besides, it not only covers all target functions for clone analysis, but also improves the detection accuracy comparing to the state-of-the-art solutions.


title: Circulating mutant DNA to assess tumor dynamics

abstract: The measurement of circulating nucleic acids has transformed the management of chronic viral infections such as HIV. The development of analogous markers for individuals with cancer could similarly enhance the management of their disease. DNA containing somatic mutations is highly tumor specific and thus, in theory, can provide optimum markers. However, the number of circulating mutant gene fragments is small compared to the number of normal circulating DNA fragments, making it difficult to detect and quantify them with the sensitivity required for meaningful clinical use. In this study, we applied a highly sensitive approach to quantify circulating tumor DNA (ctDNA) in 162 plasma samples from 18 subjects undergoing multimodality therapy for colorectal cancer. We found that ctDNA measurements could be used to reliably monitor tumor dynamics in subjects with cancer who were undergoing surgery or chemotherapy. We suggest that this personalized genetic approach could be generally applied to individuals with other types of cancer (pages 914–915).


title: A Study on Digital Forensics Standard Operation Procedure for Wireless Cybercrime

abstract: With the rapid development of information technology, computer and networking, it has brought convenience to the whole world, it has changed the way we live our daily life, interactions with people are no longer just face to face, but can be transmitted through the use of internet. With the frequent use of internet technology, Cyber crime became the new threat of our society. Amongst all advance technology, wireless network is the number one threat, with high mobility, no wired required and easy installation, it's the most common way to access the internet these days. Wireless signals travels through air rather than a traditional wired network which can be much more vulnerable for those who has intentions pursuing illegal activities. With the increases growth of internet users and the development of new technologies, current legislations and security has faced difficulties in trying to keep up, hence Cyber crime numbers are increased rapidly. Although wireless Cyber crime is a new threat, but with the detail tracking and investigation effort, ultimately the professional will find some kind of digital evidence, and its often required to identify and preserve in order to be recognized and restoring the truth, hence the establishment of trainings in digital evidence through to forensic requires immediate implementation, with increase of the standards and knowledge it will strength the competence and credibility of the forensics unit's professional ability in assistance to fight again all crime.


title: Simultaneous Preconcentration of Cd(II) and Pb(II) with N,N′-bis(4-methoxysalicylidene) Ethylenediamine Coated Silica Gel Prior to Determination by Flame Atomic Absorption Spectrometry

abstract: In this paper, a silica gel-based new sorbent was prepared with a Schiff base, called as N,N′-bis(4-methoxysalicylidene) ethylenediamine (MSE). The modified silica gel (Si-MSE) was used for simultaneous separation and preconcentration of Cd(II) and Pb(II) from aqueous solutions. Determination of analyte concentrations in eluates were achieved by flame atomic absorption spectrometry (FAAS). The analytical variables including pH, flow rate, and sample volume for sorption and flow rate, eluent concentration, and eluent volume for elution, were optimized by central composite design (CCD). HNO3 was used as eluent and maximum preconcentration factor was found to be 200. Limit of detections (LODs) were found to be 49.6 ng L−1 and 1.3 μg L−1 for Cd(II) and Pb(II), respectively. Validation of the developed method was performed by using certified reference material (TMDA-53.3) and mixed metal standard. The methodology was applied for determination of Cd(II) and Pb(II) in natural water samples and satisfactory results were obtained.


title: A Survey on Cyber Security for Smart Grid Communications

abstract: A smart grid is a new form of electricity network with high fidelity power-flow control, self-healing, and energy reliability and energy security using digital communications and control technology. To upgrade an existing power grid into a smart grid, it requires significant dependence on intelligent and secure communication infrastructures. It requires security frameworks for distributed communications, pervasive computing and sensing technologies in smart grid. However, as many of the communication technologies currently recommended to use by a smart grid is vulnerable in cyber security, it could lead to unreliable system operations, causing unnecessary expenditure, even consequential disaster to both utilities and consumers. In this paper, we summarize the cyber security requirements and the possible vulnerabilities in smart grid communications and survey the current solutions on cyber security for smart grid communications.


title: EyeSee: beyond reality with Microsoft HoloLens

abstract: Head-mounted Augmented Reality (AR) devices allow overlaying digital information on the real world, where objects may be out of view. Visualizing these out-of-view objects is useful under certain scenarios. To address this, we developed EyeSee360[1] in our previous work. However, our implementation of EyeSee360 was limited to video-see-through devices. These devices suffer from a delayed looped camera image and are decreasing the human field-of-view. In this demo, we present our EyeSee360 transferred to optical-see-through Augmented Reality to overcome these limitations.


title: Hippocampus-independent phase precession in entorhinal grid cells

abstract: Theta-phase precession in hippocampal place cells is one of the best-studied experimental models of temporal coding in the brain. Theta-phase precession is a change in spike timing in which the place cell fires at progressively earlier phases of the extracellular theta rhythm as the animal crosses the spatially restricted firing field of the neuron. Within individual theta cycles, this phase advance results in a compressed replication of the firing sequence of consecutively activated place cells along the animal's trajectory, at a timescale short enough to enable spike-time-dependent plasticity between neurons in different parts of the sequence. The neuronal circuitry required for phase precession has not yet been established. The fact that phase precession can be seen in hippocampal output stuctures such as the prefrontal cortex suggests either that efferent structures inherit the precession from the hippocampus or that it is generated locally in those structures. Here we show that phase precession is expressed independently of the hippocampus in spatially modulated grid cells in layer II of medial entorhinal cortex, one synapse upstream of the hippocampus. Phase precession is apparent in nearly all principal cells in layer II but only sparsely in layer III. The precession in layer II is not blocked by inactivation of the hippocampus, suggesting that the phase advance is generated in the grid cell network. The results point to possible mechanisms for grid formation and raise the possibility that hippocampal phase precession is inherited from entorhinal cortex.


title: BCSAT : A Benchmark Corpus for Sentiment Analysis in Telugu Using Word-level Annotations

abstract: The presented work aims at generating a systematically annotated corpus that can support the enhancement of sentiment analysis tasks in Telugu using word-level sentiment annotations. From OntoSenseNet, we extracted 11,000 adjectives, 253 adverbs, 8483 verbs and sentiment annotation is being done by language experts. We discuss the methodology followed for the polarity annotations and validate the developed resource. This work aims at developing a benchmark corpus, as an extension to SentiWordNet, and baseline accuracy for a model where lexeme annotations are applied for sentiment predictions. The fundamental aim of this paper is to validate and study the possibility of utilizing machine learning algorithms, word-level sentiment annotations in the task of automated sentiment identification. Furthermore, accuracy is improved by annotating the bi-grams extracted from the target corpus.


title: Raspberry Pi based laser spot detection

abstract: This paper presents an application of the Raspberry Pi for detecting laser spot using the OpenCV library. To capture the image, a Web-Camera connected to the USB port of the Raspberry Pi is employed. Since the Raspberry Pi is small in size, the module could be mounted on the gun for laser shooting. Compared to the existing laser shooting, our method has benefit in the flexibility of laser shooting mechanism, where the shooter could move the gun freely without the restriction of the camera's cable connection. The image processing tasks and interfacing to the camera could be accomplished by the Raspberry Pi mounted on the gun. Experimental results show that the proposed system could detect the laser spots under the varying backgrounds, lighting conditions, and shooting distances effectively. The processing speed of 18 fps could be achieved for real time implementation.


title: Improving Reliability of Word Similarity Evaluation by Redesigning Annotation Task and Performance Measure

abstract: We suggest a new method for creating and using gold-standard datasets for word similarity evaluation. Our goal is to improve the reliability of the evaluation, and we do this by redesigning the annotation task to achieve higher inter-rater agreement, and by defining a performance measure which takes the reliability of each annotation decision in the dataset into account.


title: Online Naive Bayes classification for network intrusion detection

abstract: Intrusion detection system (IDS) is an important component to ensure network security. In this paper we build an online Naive Bayes classifier to discriminate normal and bad (intrusion) connections on KDD 99 dataset for network intrusion detection. The classifier starts with a small number of training examples of normal and bad classes; then, as it classifies the rest of the samples one at a time, it continuously updates the mean and the standard deviations of the features (IDS variables). We present experimental results of parameter updating methods and their parameters for the online Naive Bayes classifier. The obtained results show that our proposed method performs comparably to the simple incremental update.


title: Determinants of healthy eating among low-income Canadians.

abstract: This paper draws on four bodies of literature to consider the determinants of healthy eating for low-income Canadians: a) the social determinants of health; b) socio-economic gradients in diet; c) food security; and d) the sociology of food. Though there is a paucity of data for Canada, it is very likely that, as in other industrialized countries, there are socio-economic gradients in diet such that those who are better off consume healthier diets than those less well-to-do. The available evidence suggests that income affects food intake both directly and indirectly through the dispositions associated with particular social class locations. Thus, there may be both economic and cultural thresholds for some food groups or particular foods in food groups. Understanding these thresholds is especially important in addressing the issues facing those who are the most vulnerable among Canadians with low incomes: the food insecure. The literature reviewed suggests that improved nutrition for low-income Canadians may be difficult to achieve a) in isolation from other changes to improve their lives; b) without improvement in the nutrition of the general population of Canadians; and c) without some combination of these two changes. Four major areas of research need were identified: a) national data on socio-economic gradients in diet; b) sociological research on the interaction of income and class with other factors affecting food practices; c) sociological research on Canadian food norms and cultures; and d) research on the costs of healthy eating.


SAR System for UAV Operation with Motion Error Compensation beyond the Resolution Cell


title: Interactive multi-frame reconstruction for mobile devices

abstract: The small size of handheld devices, their video capabilities and multiple cameras are under-exploited assets. Properly combined, the features can be used for creating novel applications that are ideal for pocket-sized devices, but may not be useful in laptop computers, such as interactively capturing and analyzing images on the fly. In this paper we consider building mosaic images of printed documents and natural scenes from low resolution video frames. High interactivity is provided by giving a real-time feedback on the video quality, while simultaneously guiding the user's actions. In our contribution, we analyze and compare means to reach interactivity and performance with sensor signal processing and GPU assistance. The viability of the concept is demonstrated on a mobile phone. The achieved usability benefits suggest that combining interactive imaging and energy efficient high performance computing could enable new mobile applications and user interactions.


title: Momentum acceleration of least-squares support vector machines

abstract: Least-Squares Support Vector Machines (LS-SVMs) have been a successful alternative model for classification and regression Support Vector Machines (SVMs), and used in a wide range of applications. In spite of this, only a limited effort has been realized to design efficient algorithms for the training of this class of models, in clear contrast to the vast amount of contributions of this kind in the field of classic SVMs. In this work we propose to combine the popular Sequential Minimal Optimization (SMO) method with a momentum strategy that manages to reduce the number of iterations required for convergence, while requiring little additional computational effort per iteration, especially in those situations where the standard SMO algorithm for LS-SVMs fails to obtain fast solutions.


title: Network modelling methods for FMRI.

abstract: There is great interest in estimating brain “networks” from FMRI data. This is often attempted by identifying a set of functional “nodes” (e.g., spatial ROIs or ICA maps) and then conducting a connectivity analysis between the nodes, based on the FMRI timeseries associated with the nodes. Analysis methods range from very simple measures that consider just two nodes at a time (e.g., correlation between two nodes' timeseries) to sophisticated approaches that consider all nodes simultaneously and estimate one global network model (e.g., Bayes net models). Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data. In this work we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity estimation approaches. Our results show that in general correlation-based approaches can be quite successful, methods based on higher-order statistics are less sensitive, and lag-based approaches perform very poorly. More specifically: there are several methods that can give high sensitivity to network connection detection on good quality FMRI data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve, though Patel's τ can be reasonably successful. With respect to the various confounds added to the data, the most striking result was that the use of functionally inaccurate ROIs (when defining the network nodes and extracting their associated timeseries) is extremely damaging to network estimation; hence, results derived from inappropriate ROI definition (such as via structural atlases) should be regarded with great caution.


title: CHIC: a combination-based recommendation system

abstract: Current recommender systems are focused largely on recommending items based on similarity. For instance, Netflix can recommend movies similar to previously viewed movies, and Amazon can recommend items based on ratings of similar users. Although similarity-based recommendation works well for books and movies, it provides an incomplete solution for items such as clothing or furniture which are inherently used in combination with other items of the same type, e.g., shirt with pants, and desk with a chair. As a result, the decision to buy a clothing or furniture item depends not only on the item itself, but also on how well it works with other items of that type. Recommending such items therefore requires a combination-based recommendation system that given an item, can suggest interesting and diverse combinations containing that item. This problem is challenging because features affecting combination quality are often difficult to identify; quality, being a function of all items in the combination, cannot be computed independently; and there are an exponential number of combinations to explore. In this demonstration, we present CHIC, a first-of-its-kind, combination-based recommendation system for clothing. The audience will interact with our system through the CHIC mobile app which allows the user to take a picture of a clothing item and search for interesting combinations containing the item instantly. The audience can also compete with CHIC to create alternate ensembles and compare quality. Finally, we highlight via visualizations the core modules of CHIC including model building and our novel search and classification algorithm, C-Search.


title: Inter-block consistent soft decoding of JPEG images with sparsity and graph-signal smoothness priors

abstract: Given the prevalence of JPEG compressed images on the Internet, image reconstruction from the compressed format remains an important and practical problem. Instead of simply reconstructing a pixel block from the centers of assigned DCT coefficient quantization bins (hard decoding), we propose to jointly reconstruct a neighborhood group of pixel patches using two image priors while satisfying the quantization bin constraints. First, we assume that a pixel patch can be approximated as a sparse linear combination of atoms from an offline-learned over-complete dictionary. Second, we assume that a patch, when interpreted as a graph-signal, is smooth with respect to an appropriately defined graph that captures the estimated structure of the target image. Finally, neighboring patches in the optimization have sufficient overlaps and are forced to be consistent, so that blocking artifacts typical in JPEG decoded images are avoided. To find the optimal group of patches, we formulate a constrained optimization problem and propose a fast alternating algorithm to find locally optimal solutions. Experimental results show that our proposed algorithm outperforms state-of-the-art soft decoding algorithms by up to 1.47dB in PSNR.


title: Analysis of Common-Mode Noise for Weakly Coupled Differential Serpentine Delay Microstrip Line in High-Speed Digital Circuits

abstract: This study investigates the mechanisms of generation of the transient transmission common-mode noise in a differential serpentine delay line under weak coupling condition. The generation mechanism and the frequency of common-mode noise are investigated with reference to the time-domain transmission waveform and the differential-to-common mode conversion mixed-mode S-parameters using the circuit solver HSPICE and 3-D full-wave simulator HFSS, respectively. The generation mechanisms of common-mode noise include length mismatch between vertical-turn-coupled traces, the length effect of parallel-coupled traces, and the crosstalk noise effect. Moreover, a graphical method based on wave tracing is presented to illustrate the cancellation mechanism of near-end common-mode noise for the symmetrical differential serpentine delay line. Some practical, commonly used layout routings of the differential serpentine delay line are investigated. Some important design guidelines are provided to help design differential serpentine delay line with low common-mode noise. A comparison between simulated and measured results validates the equivalent circuit model and analytical approach.


title: A follow-up study of girls with gender identity disorder.

abstract: This study provided information on the natural histories of 25 girls with gender identity disorder (GID). Standardized assessment data in childhood (mean age, 8.88 years; range, 3-12 years) and at follow-up (mean age, 23.24 years; range, 15-36 years) were used to evaluate gender identity and sexual orientation. At the assessment in childhood, 60% of the girls met the Diagnostic and Statistical Manual of Mental Disorders criteria for GID, and 40% were subthreshold for the diagnosis. At follow-up, 3 participants (12%) were judged to have GID or gender dysphoria. Regarding sexual orientation, 8 participants (32%) were classified as bisexual/homosexual in fantasy, and 6 (24%) were classified as bisexual/homosexual in behavior. The remaining participants were classified as either heterosexual or asexual. The rates of GID persistence and bisexual/homosexual sexual orientation were substantially higher than base rates in the general female population derived from epidemiological or survey studies. There was some evidence of a "dosage" effect, with girls who were more cross-sex typed in their childhood behavior more likely to be gender dysphoric at follow-up and more likely to have been classified as bisexual/homosexual in behavior (but not in fantasy).


title: Probabilistic AND-OR Attribute Grouping for Zero-Shot Learning

abstract: In zero-shot learning (ZSL), a classifier is trained to recognize visual classes without any image samples. Instead, it is given semantic information about the class, like a textual description or a set of attributes. Learning from attributes could benefit from explicitly modeling structure of the attribute space. Unfortunately, learning of general structure from empirical samples is hard with typical dataset sizes. Here we describe LAGO, a probabilistic model designed to capture natural soft and-or relations across groups of attributes. We show how this model can be learned end-to-end with a deep attribute-detection model. The soft group structure can be learned from data jointly as part of the model, and can also readily incorporate prior knowledge about groups if available. The soft and-or structure succeeds to capture meaningful and predictive structures, improving the accuracy of zero-shot learning on two of three benchmarks. Finally, LAGO reveals a unified formulation over two ZSL approaches: DAP (Lampert et al., 2009) and ESZSL (Romera-Paredes&Torr, 2015). Interestingly, taking only one singleton group for each attribute, introduces a new soft-relaxation of DAP, that outperforms DAP by ~40.


title: Treetopes and their Graphs

abstract: We define treetopes, a generalization of the three-dimensional roofless polyhedra (Halin graphs) to arbitrary dimensions. Like roofless polyhedra, treetopes have a designated base facet such that every face of dimension greater than one intersects the base in more than one point. We prove an equivalent characterization of the 4-treetopes using the concept of clustered planarity from graph drawing, and we use this characterization to recognize the graphs of 4-treetopes in polynomial time. This result provides one of the first classes of 4-polytopes, other than pyramids and stacked polytopes, that can be recognized efficiently from their graphs.


IRON IN NUTRITION V. THE AVAILABILITY OF THE RAT FOR STUDIES IN ANEMIA


title: LHCP and RHCP Substrate Integrated Waveguide Antenna Arrays for Millimeter-Wave Applications

abstract: Left-hand and right-hand circularly polarized (LHCP and RHCP) substrate integrated waveguide (SIW) antenna arrays are presented at the 28-GHz band for millimeter-wave (mm-wave) applications. Two types of circularly polarized (CP) antenna elements are designed to achieve respective LHCP and RHCP performance. Eight-element LHCP and RHCP antenna arrays have been implemented with feeding networks and measured. Based on the measurement, the LHCP and RHCP antenna arrays have impedance bandwidths of 1.54 and 1.7 GHz within $| S_{\rm{11}}| , whereas 3-dB axial-ratio bandwidths are 1.1 and 1.3 GHz, respectively. Each of the fabricated LHCP and RHCP antenna arrays is accomplished with a gain up to 13.09 and 13.52 dBi. Most of the measured results are validated with the simulated ones. The proposed CP antenna arrays can provide low-cost, broadband characteristics, and high-gain radiation performance with CP properties for mm-wave applications.


title: Boeing Disc Resonator Gyroscope

abstract: As microelectromechanical system (MEMS) gyros were being developed for automotive safety and military tactical applications, in 1994 Boeing selected a conventionally-machined hemispherical resonator gyroscope (HRG) for high performance, continuous space pointing applications. In that same year research was begun into high performance MEMS gyros for compact, low-cost space pointing applications. Collaboration with several national MEMS research labs and operational experience with the HRG led to an understanding of the benefits of high Q, symmetrical resonator designs in MEMS. Early post resonator designs led to closed loop, tuned, low-noise electronics design and operation with capacitive sensing but required undesirable 3D assembly of the post onto the micro-machined flexures. High dynamic loading and imprecision of the bonded joints led to gyro bias that was not stable over the long run. This led to the conception of the Disc Resonator Gyroscope (DRG) which yielded a compact planar micro-machined design with central support and carrying no critical loads. Successive optimization of the layout, scale, material selection and fabrication design as well as the operational electronics has led to progressively more stable performance. A recent fixed orientation laboratory run demonstrated a stable rate within 0.01 ::: o ::: /h over a week of continual measurement, believed to be a record for a MEMS gyroscope. This research background behind the DRG and its principle of operation will be presented along with the latest test results which promise high performance, as well as compact, low-cost MEMS gyroscopes for space applications.


title: Biochemistry and molecular cell biology of diabetic complications

abstract: Diabetes-specific microvascular disease is a leading cause of blindness, renal failure and nerve damage, and diabetes-accelerated atherosclerosis leads to increased risk of myocardial infarction, stroke and limb amputation. Four main molecular mechanisms have been implicated in glucose-mediated vascular damage. All seem to reflect a single hyperglycaemia-induced process of overproduction of superoxide by the mitochondrial electron-transport chain. This integrating paradigm provides a new conceptual framework for future research and drug discovery.