title: Control of Redundant Robots Under Hard Joint Constraints: Saturation in the Null Space

abstract: We present an efficient method for addressing online the inversion of differential task kinematics for redundant manipulators, in the presence of hard limits on joint space motion that can never be violated. The proposed Saturation in the Null Space (SNS) algorithm proceeds by successively discarding the use of joints that would exceed their motion bounds when using the minimum norm solution. When processing multiple tasks with priority, the SNS method realizes a preemptive strategy by preserving the correct order of priority in spite of the presence of saturations. In the single- and multitask case, the algorithm automatically integrates a least possible task-scaling procedure, when an original task is found to be unfeasible. The optimality properties of the SNS algorithm are analyzed by considering an associated quadratic programming problem. Its solution leads to a variant of the algorithm, which guarantees optimality even when the basic SNS algorithm does not. Numerically efficient versions of these algorithms are proposed. Their performance allows real-time control of robots executing many prioritized tasks with a large number of hard bounds. Experimental results are reported.


title: Advances in vegetation management for power line corridor monitoring using aerial remote sensing techniques

abstract: This paper presents a comprehensive discussion of vegetation management approaches in power line corridors based on aerial remote sensing techniques. We address three issues 1) strategies for risk management in power line corridors, 2) selection of suitable platforms and sensor suite for data collection and 3) the progress in automated data processing techniques for vegetation management. We present initial results from a series of experiments and, challenges and lessons learnt from our project.


Wearable Devices in Medical Internet of Things: Scientific Research and Commercially Available Devices


title: Voila: Visual Anomaly Detection and Monitoring with Streaming Spatiotemporal Data

abstract: The increasing availability of spatiotemporal data continuously collected from various sources provides new opportunities for a timely understanding of the data in their spatial and temporal context. Finding abnormal patterns in such data poses significant challenges. Given that there is often no clear boundary between normal and abnormal patterns, existing solutions are limited in their capacity of identifying anomalies in large, dynamic and heterogeneous data, interpreting anomalies in their multifaceted, spatiotemporal context, and allowing users to provide feedback in the analysis loop. In this work, we introduce a unified visual interactive system and framework, Voila, for interactively detecting anomalies in spatiotemporal data collected from a streaming data source. The system is designed to meet two requirements in real-world applications, i.e., online monitoring and interactivity. We propose a novel tensor-based anomaly analysis algorithm with visualization and interaction design that dynamically produces contextualized, interpretable data summaries and allows for interactively ranking anomalous patterns based on user input. Using the “smart city” as an example scenario, we demonstrate the effectiveness of the proposed framework through quantitative evaluation and qualitative case studies.


title: StreamingBandit: Developing Adaptive Persuasive Systems

abstract: This paper introduces StreamingBandit, a (back-end) solution for developing adaptive and personalized persuasive systems. Creating successful persuasive applications requires a combination of design, social science, and technology. StreamingBandit contributes to the required technology by providing a platform that can be used to adapt persuasive technologies in real-time and at large scales. We first introduce the design philosophy of StreamingBandit using a running example and highlight how a large number of adaptive persuasive systems can be regarded as solutions to (contextual) multi-armed bandit problems: a type of problem that StreamingBandit was built to address. Subsequently, we detail several scenarios of the use of StreamingBandit to create adaptive persuasive systems and detail its future developments.


title: Design and analysis of an outer rotor permanent magnet alternator for low-speed wind turbine

abstract: The outer rotor permanent magnet alternator (PMA) is superior for small wind turbine system due to its robust, little maintenance and low cost configuration. In this research, the design and performance analysis of a radial flux outer rotor type PMA used for gearless, extremely low-speed, and small capacity wind turbine system has been discussed. The design has been simulated to analyze this PMA. The elementary configuration like material types, magnet types and additional electrical and mechanical parameters are studied, and the performances parameters of PMA are improved by changing these elementary electrical and mechanical parameters or some other related factors such as reducing the cogging effect. Cogging torque has been reduced by employing fractional slot per pole, width of slot opening, mounting of the magnets. Also the slot shape has been custom-made to increase the efficiency. The slot fill factor was improved by using different wire diameter used as winding coil. This improvement has been played a great role to reduce copper losses thereby improving the thermal condition of the slot. Finite element analysis (FEA) method was used to analyze this PMA in order to test the real time performance simulation with loss and cogging torque calculation. By taking all these considerations into account, higher efficiency from the outer rotor arrangement with minimum losses has been achieved.


title: Nanocellulose electroconductive composites

abstract: Cellulose-based electroconductive composites can be prepared by combining conducting electroactive materials with hydrophilic biocompatible cellulose. Inorganic nanoparticles, such as metal ions and oxides, carbon nanotubes, graphene and graphene oxide, conducting polymers, and ionic liquids (through doping, blending or coating) can be introduced into the cellulose matrix. Such composites can form a biocompatible interface for microelectronic devices, and provide a biocompatible matrix or scaffold for electrically stimulated drug release devices, implantable biosensors, and neuronal prostheses. Here the benefits of combining conventional and bacterial cellulose with these electroactive composites are described and future applications are considered.


miR-129 b suppresses cell proliferation in the human lung cancer cell lines A 549 and H 1299


title: MS-CapsNet: A Novel Multi-Scale Capsule Network

abstract: Capsule network is a novel architecture to encode the properties and spatial relationships of the feature in an image, which shows encouraging results on image classification. However, the original capsule network is not suitable for some classification tasks, where the target objects are complex internal representations. Hence, we propose a multi-scale capsule network that is more robust and efficient for feature representation in image classification. The proposed multi-scale capsule network consists of two stages. In the first stage, structural and semantic information are obtained by multi-scale feature extraction. In the second stage, the hierarchy of features is encoded to multi-dimensional primary capsules. Moreover, we propose an improved dropout to enhance the robustness of the capsule network. Experimental results show that our method has a competitive performance on FashionMNIST and CIFAR10 datasets.


title: Abstract Curiosity and Interest: The Benefits of Thriving on Novelty and Challenge

abstract: Animbalance existsbetweentheroleofcuriosity asamotivational forceinnearlyallhumanendeavorsand thelack of scientific attentiongiventothe topic. Inrecent years, however, there has beena proliferation of concepts that capture the essence of curiosity-recognizing, seeking out, and showing a preference for the new. In this chapter, we combine this work to address the nature of curiosity, where it fits in the larger scheme of positive emotions, the advantages of being curious in social relationships, links between curiosity and elements of well-being, and how it has been used in interventions to improve people’s quality of life. Our emphasis is on methodologically sophisticated findings that show how curiosity operates in the laboratory and everyday life, and how, under certain conditions, curiosity can be a profound source of strengthoraliability.Peoplewhoareregularlycuriousandwillingtoembracethenovelty,uncertainty,and challenges that are inevitable as we navigate the shoals of everyday life are at an advantage in creating a fulfilling existence compared with their less curious peers. Our brief review is designed to bring further attention to this neglected, underappreciated, human universal.


title: A Logit Model of Brand Choice Calibrated on Scanner Data

abstract: A multinomial logit model of brand choice, calibrated on 32 weeks of purchases of regular ground coffee by 100 households, shows high statistical significance for the explanatory variables of brand loyalty, size loyalty, presence/absence of store promotion, regular shelf price and promotional price cut. The model is parsimonious in that the coefficients of these variables are modeled to be the same for all coffee brand-sizes. The calibrated model predicts remarkably well the share of purchases by brand-size in a hold-out sample of 100 households over the 32-week calibration period and a subsequent 20-week forecast period. The success of the model is attributed in part to the level of detail and completeness of the household panel data employed, which has been collected through optical scanning of the Universal Product Code in supermarkets. ::: ::: Three short-term market response measures are calculated from the model: regular (depromoted) price elasticity of share, percent increase in share for a promotion with a median price cut, and promotional price cut elasticity of share. Response varies across brand-sizes in a systematic way with large share brand-sizes showing less response in percentage terms but greater in absolute terms. On the basis of the model a quantitative picture emerges of groups of loyal customers who are relatively insensitive to marketing actions and a pool of switchers who are quite sensitive. ::: ::: This article was originally published in Marketing Science, Volume 2, Issue 3, pages 203--238, in 1983.


title: Chronos: Visualizing slices of source-code history

abstract: In this paper, we present CHRONOS-a tool that enables the querying, exploration, and discovery of historical change events to source code. Unlike traditional Revision-Control-System tools, CHRONOS allows queries across any subset of the code, down to the line-level, which can potentially be contiguous or disparate, even among multiple files. In addition, CHRONOS provides change history across all historical versions (i.e., it is not limited to a pairwise “diff”). The tool implements a zoom-able user interface as a visualization of the history of the queried code to provide both a high-level view of the changes, which supports pattern recognition and discovery, and a low-level view that supports semantic comprehension for tasks such as reverse engineering and identifying design rationale. In this paper, we describe use cases in which CHRONOS may be helpful, provide a motivating example to demonstrate the benefits brought by CHRONOS, and describe its visualization in detail.


title: Exact Regenerating Codes for Distributed Storage

abstract: The total porosity of a subterranean formation is calculated by summing the effective Porosity and the clay bound water (CBW) porosity. With the invention described in this patent, the distortion effects of clay bound water (CBW) porosity on the measurement of total porosity is filtered out. The effects will be more significant in slialy sand where the CBW porosity component of total porosity is abundant. The correct NMR transverse relaxation time (T2) spectra distribution is essential to a determination of capillary bound fluid (BVI) partial porosity and the Spectral Bulk Volume Irreducible (SBVI) model. The same data correction concept can be applied to make a meaningful comparison of well logs or laboratory core NMR data that are obtained with different interecho time (TE) sampling periods.


title: Project Lachesis: parsing and modeling location histories

abstract: A datatype with increasing importance in GIS is what we call the location history–a record of an entity’s location in geographical space over an interval of time. This paper proposes a number of rigorously defined data structures and algorithms for analyzing and generating location histories. Stays are instances where a subject has spent some time at a single location, and destinations are clusters of stays. Using stays and destinations, we then propose two methods for modeling location histories probabilistically. Experiments show the value of these data structures, as well as the possible applications of probabilistic models of location histories.


title: The RUSH Exam 2012: Rapid Ultrasound in Shock in the Evaluation of the Critically Ill Patient

abstract: Care of the patient with shock can be one of the most challenging issues in Emergency Medicine and Critical Care. Even the most seasoned clinician, standing at the bedside of the patient in extremis, can be unclear about the cause of shock and the optimal initial therapeutic approach. Traditional physical examination techniques can be misleading given the complex physiology of shock. 1 Patients in shock have high mortality


title: The beliefs behind the teacher that influences their ICT practices

abstract: This paper explores teacher beliefs that influence the ways Information and Communications Technologies (ICT) are used in learning contexts. Much has been written about the impact of teachers' beliefs and attitudes to ICT as 'barriers' to ICT integration (Ertmer, Ottenbreit-Leftwich, & York, 2007; Higgins & Moseley, 2001; Loveless, 2003). This paper takes a closer look at the types of beliefs that influence ICT practices in classrooms and the alignment of these beliefs to current pedagogical reform in Australia. The paper draws on data collected through the initial phase of a research project that involved an Industry Collaborative of four Catholic primary schools (prep - grade 7). Data are drawn from teacher surveys, interviews and document analysis. The results present specific links between ICT beliefs that are informing teachers' practices. ICT beliefs and practices are aligned to reform agenda for digital pedagogies. The findings of this research inform teacher ICT practice and requirements for ICT professional development.


title: Characteristics and Recovery Performance of VSC-HVDC DC Transmission Line Fault

abstract: As a new approach of DC transmission scheme, Voltage Sourced Converter based HVDC (VSC-HVDC) transmits electric energy by connecting rectifier and inverter through DC transmission line, which is the same as LCC-HVDC. However, the fault performance and relevant restart demand are different due to that VSC-HVDC is based on voltage sourced converter. This paper analyzes the mechanism of DC line-to-ground fault and line-to-line fault in VSC-HVDC scheme, and then proposes the recovery demand of temporary faults at overhead line scheme. By establishing an electromagnetic transient model under PSCAD/EMTDC, the performance under DC transmission line transients has been thoroughly investigated. According to the characteristics under simulation, the solutions of key points during recovery process are proposed, which include the grounding design, adopting monopolar topology and modifying the transformer type etc.


title: Integrated Fault Location and Power-Quality Analysis in Electric Power Distribution Systems

abstract: This paper presents a methodology for automated disturbance analysis and fault location on electric power distribution systems using a combination of modern techniques for network analysis, signal processing, and intelligent systems. New algorithms to detect, classify, and locate power-quality disturbances are developed. The continuous process of detecting these disturbances is accomplished through statistical analysis and multilevel signal analysis in the wavelet domain. The behavioral indices of the current and voltage signals are extracted by employing the discrete wavelet transform, multiresolution analysis, and the concept of signal energy. These indices are used by a number of independent Fuzzy-ARTMAP neural networks, which aim to classify the fault type and the power-quality events. The fault location is performed after the classification process. A real life three-phase distribution system with 134 nodes—13.8 kV and 7.065 MVA—was used to test the proposed algorithms, providing satisfactory results, attesting that the proposed algorithms are efficient, fast, and, above all, intelligent.


title: Online trajectory generation using clothoid segments

abstract: In this paper, a trajectory generation algorithm within an integrated flight guidance and control system is presented. The approach uses clothoids to deal with the problem of curvature steps during the transition phase between straight line and arc flight. The algorithm is designed in interaction with a trajectory controller for 2nd order error dynamics, which hence requires more trajectory information than a conventional approach. The presented test results were obtained via in-flight tests with an experimental CS 23 aircraft.


title: Metasone furoate induced acneiform eruption

abstract: Acne is the chronic inflammatory disease of the pilosebaceous unit, characterized by the formation of comedones, erythematous papules and pustules. Steroid induced acne is an acneiform eruption characterized by sudden onset of follicular papules and pustules shortly after starting topical or oral corticosteroid. Here, we report a case of a 25-year-old female patient who presented herself to the Dermatology out-patient department with the complaints of acne all over the face after the application of mometasone furoate ointment.


title: Unifying Models of Churn and Resilience for Unstructured P2P Graphs

abstract: Previous analytical results on the resilience of unstructured P2P systems have not explicitly modeled heterogeneity of user churn (i.e., difference in online behavior) or the impact of in-degree on system resilience. To overcome these limitations, we introduce a unifying model of heterogeneous user churn and derive the distribution of the various metrics observed in prior experimental studies. We also show that the arrival process of in-edges to each user converges to Poisson when system size tends to infinity, model transient behavior of in-degree, and apply these results to obtain the joint in/out-degree isolation probability.


title: Hierarchical deep hashing for image retrieval

abstract: We present a new method to generate efficient multi-level hashing codes for image retrieval based on the deep siamese convolutional neural network (DSCNN). Conventional deep hashing methods trade off the capability of capturing highly complex and nonlinear semantic information of images against very compact hash codes, usually leading to high retrieval efficiency but with deteriorated accuracy. We alleviate the restrictive compactness requirement of hash codes by extending them to a two-level hierarchical coding scheme, in which the first level aims to capture the high-level semantic information extracted by the deep network using a rich encoding strategy, while the subsequent level squeezes them to more global and compact codes. At running time, we adopt an attention-based mechanism to select some of its most essential bits specific to each query image for retrieval instead of using the full hash codes of the first level. The attention-based mechanism is based on the guides of hash codes generated by the second level, taking advantage of both local and global properties of deep features. Experimental results on various popular datasets demonstrate the advantages of the proposed method compared to several state-of-the-art methods.


title: Advanced LSTM: A Study About Better Time Dependency Modeling in Emotion Recognition

abstract: Long short-term memory (LSTM) is normally used in recurrent neural network (RNN) as basic recurrent unit. However,conventional LSTM assumes that the state at current time step depends on previous time step. This assumption constraints the time dependency modeling capability. In this study, we propose a new variation of LSTM, advanced LSTM (A-LSTM), for better temporal context modeling. We employ A-LSTM in weighted pooling RNN for emotion recognition. The A-LSTM outperforms the conventional LSTM by 5.5% relatively. The A-LSTM based weighted pooling RNN can also complement the state-of-the-art emotion classification framework. This shows the advantage of A-LSTM.


title: The relationship between motivation, learning strategies and choice of environment whether traditional or including an online component

abstract: This study examined how students’ achievement goals, self-efficacy and learning strategies influenced their choice of an online, hybrid or traditional learning environment. One hundred thirty-two post-secondary students completed surveys soliciting their preferences for learning environments, reasons for their preference, their motivational orientation towards learning and learning strategies used. Findings indicated that most students preferred traditional learning environments. This preference was based on how well the environment matched their personal learning style and engaged them as students. Discriminant analyses indicated significant differences in motivational beliefs and learning strategies; students who preferred traditional environments showed a mastery goal orientation and greater willingness to apply effort while learning. Students who preferred less traditional environments presented as more confident that they could manage a non-traditional class. These findings have implications for understanding students’ motivation for learning in diverse educational settings.


title: Contributions of memory circuits to language: the declarative/procedural model

abstract: The structure of the brain and the nature of evolution suggest that, despite its uniqueness, language likely depends on brain systems that also subserve other functions. The declarative/procedural (DP) model claims that the mental lexicon of memorized word-specific knowledge depends on the largely temporal-lobe substrates of declarative memory, which underlies the storage and use of knowledge of facts and events. The mental grammar, which subserves the rule-governed combination of lexical items into complex representations, depends on a distinct neural system. This system, which is composed of a network of specific frontal, basal-ganglia, parietal and cerebellar structures, underlies procedural memory, which supports the learning and execution of motor and cognitive skills, especially those involving sequences. The functions of the two brain systems, together with their anatomical, physiological and biochemical substrates, lead to specific claims and predictions regarding their roles in language. These predictions are compared with those of other neurocognitive models of language. Empirical evidence is presented from neuroimaging studies of normal language processing, and from developmental and adult-onset disorders. It is argued that this evidence supports the DP model. It is additionally proposed that “language” disorders, such as specific language impairment and non-fluent and fluent aphasia, may be profitably viewed as impairments primarily affecting one or the other brain system. Overall, the data suggest a new neurocognitive framework for the study of lexicon and grammar. q 2004 Elsevier B.V. All rights reserved.


title: Cultural variations in the clinical presentation of depression and anxiety: implications for diagnosis and treatment.

abstract: This article reviews cultural variations in the clinical presentation of depression and anxiety. Culture-specific symptoms may lead to underrecognition or misidentification of psychological distress. Contrary to the claim that non-Westerners are prone to somatize their distress, recent research confirms that somatization is ubiquitous. Somatic symptoms serve as cultural idioms of distress in many ethnocultural groups and, if misinterpreted by the clinician, may lead to unnecessary diagnostic procedures or inappropriate treatment. Clinicians must learn to decode the meaning of somatic and dissociative symptoms, which are not simply indices of disease or disorder but part of a language of distress with interpersonal and wider social meanings. Implications of these findings for the recognition and treatment of depressive disorders among culturally diverse populations in primary care and mental health settings are discussed.


title: Frontal to profile face verification in the wild

abstract: We have collected a new face data set that will facilitate research in the problem of frontal to profile face verification ‘in the wild’. The aim of this data set is to isolate the factor of pose variation in terms of extreme poses like profile, where many features are occluded, along with other ‘in the wild’ variations. We call this data set the Celebrities in Frontal-Profile (CFP) data set. We find that human performance on Frontal-Profile verification in this data set is only slightly worse (94.57% accuracy) than that on Frontal-Frontal verification (96.24% accuracy). However we evaluated many state-of-the-art algorithms, including Fisher Vector, Sub-SML and a Deep learning algorithm. We observe that all of them degrade more than 10% from Frontal-Frontal to Frontal-Profile verification. The Deep learning implementation, which performs comparable to humans on Frontal-Frontal, performs significantly worse (84.91% accuracy) on Frontal-Profile. This suggests that there is a gap between human performance and automatic face recognition methods for large pose variation in unconstrained images.


title: A large-scale study of the ultrawideband microwave dielectric properties of normal, benign and malignant breast tissues obtained from cancer surgeries

abstract: The development of microwave breast cancer detection and treatment techniques has been driven by reports of substantial contrast in the dielectric properties of malignant and normal breast tissues. However, definitive knowledge of the dielectric properties of normal and diseased breast tissues at microwave frequencies has been limited by gaps and discrepancies across previously published studies. To address these issues, we conducted a large-scale study to experimentally determine the ultrawideband microwave dielectric properties of a variety of normal, malignant and benign breast tissues, measured from 0.5 to 20 GHz using a precision open-ended coaxial probe. Previously, we reported the dielectric properties of normal breast tissue samples obtained from reduction surgeries. Here, we report the dielectric properties of normal (adipose, glandular and fibroconnective), malignant (invasive and non-invasive ductal and lobular carcinomas) and benign (fibroadenomas and cysts) breast tissue samples obtained from cancer surgeries. We fit a one-pole Cole-Cole model to the complex permittivity data set of each characterized sample. Our analyses show that the contrast in the microwave-frequency dielectric properties between malignant and normal adipose-dominated tissues in the breast is considerable, as large as 10:1, while the contrast in the microwave-frequency dielectric properties between malignant and normal glandular/fibroconnective tissues in the breast is no more than about 10%.


title: Improving Generalization Performance by Switching from Adam to SGD

abstract: Despite superior training outcomes, adaptive optimization methods such as Adam, Adagrad or RMSprop have been found to generalize poorly compared to Stochastic gradient descent (SGD). These methods tend to perform well in the initial portion of training but are outperformed by SGD at later stages of training. We investigate a hybrid strategy that begins training with an adaptive method and switches to SGD when appropriate. Concretely, we propose SWATS, a simple strategy which switches from Adam to SGD when a triggering condition is satisfied. The condition we propose relates to the projection of Adam steps on the gradient subspace. By design, the monitoring process for this condition adds very little overhead and does not increase the number of hyperparameters in the optimizer. We report experiments on several standard benchmarks such as: ResNet, SENet, DenseNet and PyramidNet for the CIFAR-10 and CIFAR-100 data sets, ResNet on the tiny-ImageNet data set and language modeling with recurrent networks on the PTB and WT2 data sets. The results show that our strategy is capable of closing the generalization gap between SGD and Adam on a majority of the tasks.


title: WaveletNet: Logarithmic Scale Efficient Convolutional Neural Networks for Edge Devices

abstract: We present a logarithmic-scale efficient convolutional neural network architecture for edge devices, named WaveletNet. Our model is based on the well-known depthwise convolution, and on two new layers, which we introduce in this work: a wavelet convolution and a depthwise fast wavelet transform. By breaking the symmetry in channel dimensions and applying a fast algorithm, WaveletNet shrinks the complexity of convolutional blocks by an O(logD/D) factor, where D is the number of channels. Experiments on CIFAR-10 and ImageNet classification show superior and comparable performances of WaveletNet compared to state-of-the-art models such as MobileNetV2.


title: RADical Strategies for Engineering Web-Scale Cloud Solutions

abstract: There's a large push toward offering solutions and services in the cloud due to its numerous advantages. However, there are no clear guidelines for designing and deploying cloud solutions that can seamlessly operate to handle Web-scale traffic. The authors review industry best practices and identify principles for operating Web-scale cloud solutions by deriving design patterns that enable each principle in cloud solutions. In addition, using a seemingly straightforward cloud service as an example, they explain the application of the identified patterns.


title: Dual-band bandpass filter designed by exploiting the second-order degenerated modes of the SIW-cavity

abstract: This paper presents a novel technique to design a dual-band bandpass filter by exploiting the second order degenerated modes of the SIW-cavity. The second order filter has been designed by using the proposed technique, at Ka-band of microwave frequencies; the ratio of two bands center frequencies — frequency ratio (FR) — is 1.073. Rogers RT/Duroid 5880 — with substrate height of 0.508mm — has been used as the substrate material for design purpose. The measured return loss of the filter in both bands is better than −15 dB, moreover, the isolation between two bands is also better than 40 dB.


title: Weakly nonlinear analysis on the Kelvin-Helmholtz instability

abstract: A weakly nonlinear model is proposed for the Kelvin-Helmholtz instability in two-dimensional incompressible fluids. The second- and third-harmonic generation effects of single-mode perturbation, as well as the nonlinear correction to the exponential growth of the fundamental modulation are analyzed. An important resonance in the mode-coupling process is found. The nonlinear saturation time depends on the initial perturbation amplitude and the density ratio of the two fluids, but the nonlinear saturation amplitude depends only on the initial perturbation amplitude. The weakly nonlinear result is supported by numerical simulation. The practical system of boundary layer containing thermal conductivity is analyzed. Their nonlinear saturation amplitude can be predicted by our weakly nonlinear model.


title: Quantifying the value of pronunciation lexicons for keyword search in lowresource languages

abstract: This paper quantifies the value of pronunciation lexicons in large vocabulary continuous speech recognition (LVCSR) systems that support keyword search (KWS) in low resource languages. State-of-the-art LVCSR and KWS systems are developed for conversational telephone speech in Tagalog, and the baseline lexicon is augmented via three different grapheme-to-phoneme models that yield increasing coverage of a large Tagalog word-list. It is demonstrated that while the increased lexical coverage - or reduced out-of-vocabulary (OOV) rate - leads to only modest (ca 1%-4%) improvements in word error rate, the concomitant improvements in actual term weighted value are as much as 60%. It is also shown that incorporating the augmented lexicons into the LVCSR system before indexing speech is superior to using them post facto, e.g., for approximate phonetic matching of OOV keywords in pre-indexed lattices. These results underscore the disproportionate importance of automatic lexicon augmentation for KWS in morphologically rich languages, and advocate for using them early in the LVCSR stage.


title: Minoxidil upregulates the expression of vascular endothelial growth factor in human hair dermal papilla cells.

abstract: The hair follicle dermal papilla which controls hair growth, is characterized in the anagen phase by a highly developed vascular network. We have demonstrated in a previous study that the expression of an angiogenic growth factor called vascular endothelial growth factor (VEGF) mRNA varied during the hair cycle. VEGF mRNA is strongly expressed in dermal papilla cells (DPC) in the anagen phase, but during the catagen and telogen phases. VEGF mRNA is less strongly expressed. This involvement of VEGF during the hair cycle allowed us to determine whether VEGF mRNA expression by DPC was regulated by minoxidil. In addition, the effect of minoxidil on VEGF protein synthesis in both cell extracts and DPC-conditioned medium, was investigated immunoenzymatically. Both VEGF mRNA and protein were significantly elevated in treated DPC compared with controls. DPC incubated with increasing minoxidil concentrations (0.2, 2, 6, 12 and 24 mumol/L) induced a dose-dependent expression of VEGF mRNA. Quantification of transcripts showed that DPC stimulated with 24 mumol/L minoxidil express six times more VEGF mRNA than controls. Similarly, VEGF protein production increases in cell extracts and conditioned media following minoxidil stimulation. These studies strongly support the likely involvement of minoxidil in the development of dermal papilla vascularization via a stimulation of VEGF expression, and support the hypothesis that minoxidil has a physiological role in maintaining a good vascularization of hair follicles in androgenetic alopecia.


title: Association of an Educational Program in Mindful Communication With Burnout, Empathy, and Attitudes Among Primary Care Physicians

abstract: Context Primary care physicians report high levels of distress, which is linked to burnout, attrition, and poorer quality of care. Programs to reduce burnout before it results in impairment are rare; data on these programs are scarce. Objective To determine whether an intensive educational program in mindfulness, communication, and self-awareness is associated with improvement in primary care physicians' well-being, psychological distress, burnout, and capacity for relating to patients. Design, Setting, and Participants Before-and-after study of 70 primary care physicians in Rochester, New York, in a continuing medical education (CME) course in 2007-2008. The course included mindfulness meditation, self-awareness exercises, narratives about meaningful clinical experiences, appreciative interviews, didactic material, and discussion. An 8-week intensive phase (2.5 h/wk, 7-hour retreat) was followed by a 10-month maintenance phase (2.5 h/mo). Main Outcome Measures Mindfulness (2 subscales), burnout (3 subscales), empathy (3 subscales), psychosocial orientation, personality (5 factors), and mood (6 subscales) measured at baseline and at 2, 12, and 15 months. Results Over the course of the program and follow-up, participants demonstrated improvements in mindfulness (raw score, 45.2 to 54.1; raw score change [Δ], 8.9; 95% confidence interval [CI], 7.0 to 10.8); burnout (emotional exhaustion, 26.8 to 20.0; Δ = −6.8; 95% CI, −4.8 to −8.8; depersonalization, 8.4 to 5.9; Δ = −2.5; 95% CI, −1.4 to −3.6; and personal accomplishment, 40.2 to 42.6; Δ = 2.4; 95% CI, 1.2 to 3.6); empathy (116.6 to 121.2; Δ = 4.6; 95% CI, 2.2 to 7.0); physician belief scale (76.7 to 72.6; Δ = −4.1; 95% CI, −1.8 to −6.4); total mood disturbance (33.2 to 16.1; Δ = −17.1; 95% CI, −11 to −23.2), and personality (conscientiousness, 6.5 to 6.8; Δ = 0.3; 95% CI, 0.1 to 5 and emotional stability, 6.1 to 6.6; Δ = 0.5; 95% CI, 0.3 to 0.7). Improvements in mindfulness were correlated with improvements in total mood disturbance (r = −0.39, P Conclusions Participation in a mindful communication program was associated with short-term and sustained improvements in well-being and attitudes associated with patient-centered care. Because before-and-after designs limit inferences about intervention effects, these findings warrant randomized trials involving a variety of practicing physicians.


title: Role of Flavonoids in DNA Damage and Carcinogenesis Prevention

abstract: High level of ionizing radiation and various other mutagenic agents can damage DNA and induce carcinogenesis ::: which may be transferred to offspring’s. Flavonoids are major antioxidant compounds obtained from plants and form ::: a significant proportion of most diets. In this review, we have summarized the present understanding of the roles of ::: various flavonoids as preventive approaches against DNA damage and carcinogenesis.


title: Demonstration of 2µm RDL wiring using dry film photoresists and 5µm RDL via by projection lithography for low-cost 2.5D panel-based glass and organic interposers

abstract: High-density packages and 2.5D interposers require 2µm trace widths and gaps, and less than 10µm ultra-small microvias to achieve 20–40µm I/O pitch interconnections. Silicon interposers with through-silicon-vias (TSVs) have been used for such ultra-high density interconnections between logic and memory chips with sub-micron multi-layer copper wiring. However, the high cost of silicon interposers coming from back end of line (BEOL) processes have limited their applicability to mobile systems like smart phones and wearables. Glass and organic interposers have been investigated as a lower cost solution coming from large panel processes and dry film lithography for semi-additive copper metallization. However, achieving high wiring density with low-cost package substrate processes remains a challenge. This paper presents the first demonstration of high resolution photo-lithography processes to achieve 2µm copper line widths and 5–10µm microvias with panel-based processes using newly developed large field projection lithography tools and advanced dry film photoresists. A two-metal layer redistribution layer (RDL) structure integrating 2µm line and space wiring and less than 10µm ultra-small microvias was demonstrated on ultra-thin glass and organic substrates.


title: An Improved velocity Energy-efficient and Link-aware Cluster-Tree based data collection scheme for mobile networks

abstract: Data Collection schemes fail to supply a secure reliable network in terms of quality, traffic, and end-to-end affiliation. during this paper, a Improved rate Energy-efficient and Link-aware Cluster-Tree (IVELCT) theme for knowledge assortment in WSNs is projected which might effectively mitigate the issues of coverage distance, mobility, delay, traffic, tree intensity, and end-to-end affiliation. The projected IVELCT constructs the information assortment Tree (DCT) supported the cluster head location. the information assortment node within the DCT doesn't participate in sensing on this explicit spherical, however, it merely collects the information packet from the cluster head and delivers it to the sink. The designed IVELCT theme minimizes the energy exploitation, reduces the end-to-end delay and traffic in cluster head in WSNs by effective usage of the DCT. The strength of the IVELCT algorithmic rule is to construct an easy tree structure, thereby reducing the energy consumption of the cluster head and avoids frequent cluster formation. It additionally maintains the cluster for a substantial quantity of your time. Simulation results have incontestable that IVELCT provides higher QoS in terms of energy consumption, throughput, end-to-end delay, and network time period for mobility-based WSNs.


Probabilistic Logic Programming for Natural Language Processing


title: Landmark Placement for Accurate Mobile Robot Navigation

abstract: Being able to navigate accurately is one of the basic capabilities a mobile robot needs to effectively execute a variety of tasks, including collision avoidance, docking, manipulation, and path following. One popular approach to achieve the desired accuracy is to use artificial landmarks for a sufficiently accurate localization. In this paper, we consider the problem of optimally placing landmarks for robots navigating repeatedly along a given set of trajectories. Our method aims at minimizing the number of landmarks for which a bound on the maximum deviation of the robot from its desired trajectory can be guaranteed with high confidence. The proposed approach incrementally places landmarks utilizing linearized versions of the system dynamics of the robot, thus allowing for an efficient computation of the deviation guarantee. To verify this guarantee for the real and possibly non-linear system dynamics of the robot, our method then performs a Monte Carlo simulation. We evaluate our algorithm in extensive experiments carried out both in simulation and with a real robot. The experiments demonstrate that our method requires substantially fewer landmarks than other approaches to achieve the desired accuracy.


title: CD-HIT: accelerated for clustering the next-generation sequencing data

abstract: Summary: CD-HIT is a widely used program for clustering biological sequences to reduce sequence redundancy and improve the performance of other sequence analyses. In response to the rapid increase in the amount of sequencing data produced by the next-generation sequencing technologies, we have developed a new CD-HIT program accelerated with a novel parallelization strategy and some other techniques to allow efficient clustering of such datasets. Our tests demonstrated very good speedup derived from the parallelization for up to ~24 cores and a quasi-linear speedup for up to ~8 cores. The enhanced CD-HIT is capable of handling very large datasets in much shorter time than previous versions. ::: ::: Availability: http://cd-hit.org. ::: ::: Contact: [email protected] ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online.


title: A Data Mining Approach to Identify Key Factors of Traffic Injury Severity

abstract: Seventy percent of the traffic crash fatalities of Iran happen on rural roads, and a significant proportion of the rural roads network of this country is constituted of the main two-lane, two-way roads. The purpose of this study is to identify the most important factors which affect injury severity of drivers involved in traffic crashes on these roads, so that by eliminating or controlling such factors an overall safety improvement can be accomplished. Using the Classification and Regression Tree (CART), one of the powerful data mining tools, the crash data pertaining to the last three years (2006-2008) were analyzed. The variable selection procedure was carried out on the basis of Variable Importance Measure (VIM) which is one of the CART method outputs. The results revealed that not using the seat belt, improper overtaking and speeding are the most important factors associated with injury severity. ::: KEYWORDS: injury severity; traffic safety; data mining; Classification and Regression Trees (CART); Variable Importance Measure (VIM)


title: Design and construction of a realistic digital brain phantom

abstract: After conception and implementation of any new medical image processing algorithm, validation is an important step to ensure that the procedure fulfils all requirements set forth at the initial design stage. Although the algorithm must be evaluated on real data, a comprehensive validation requires the additional use of simulated data since it is impossible to establish ground truth with in vivo data. Experiments with simulated data permit controlled evaluation over a wide range of conditions (e.g., different levels of noise, contrast, intensity artefacts, or geometric distortion). Such considerations have become increasingly important with the rapid growth of neuroimaging, i.e., computational analysis of brain structure and function using brain scanning methods such as positron emission tomography and magnetic resonance imaging. Since simple objects such as ellipsoids or parallelepipedes do not reflect the complexity of natural brain anatomy, the authors present the design and creation of a realistic, high-resolution, digital, volumetric phantom of the human brain. This three-dimensional digital brain phantom is made up of ten volumetric data sets that define the spatial distribution for different tissues (e.g., grey matter, white matter, muscle, skin, etc.), where voxel intensity is proportional to the fraction of tissue within the voxel. The digital brain phantom can be used to simulate tomographic images of the head. Since the contribution of each tissue type to each voxel in the brain phantom is known, it can be used as the gold standard to test analysis algorithms such as classification procedures which seek to identify the tissue "type" of each image voxel. Furthermore, since the same anatomical phantom may be used to drive simulators for different modalities, it is the ideal tool to test intermodality registration algorithms. The brain phantom and simulated MR images have been made publicly available on the Internet (http://www.bic.mni.mcgill.ca/brainweb).


title: Pictorial essay: Anatomical variations of paranasal sinuses on multidetector computed tomography-How does it help FESS surgeons?

abstract: With the advent of multidetector computed tomography (MDCT), imaging of paranasal sinuses prior to functional endoscopic sinus surgery (FESS) has become mandatory. Multiplanar imaging, particularly coronal reformations, offers precise information regarding the anatomy of the sinuses and its variations, which is an essential requisite before surgery.


title: Calculating Statistical Similarity between Sentences

abstract: Sentence similarity plays an important role in text-related research and applications. It is closely related to word similarity and document similarity. The statistical similarity measures between sentences, based on symbolic characteristics and structural information, could measure the similarity between sentences without any prior knowledge but only on the statistical information of sentences. This paper presents several approaches to calculating statistical similarity between sentences on a test corpus of 40 sentences. These measures can be used in short text related applications such as corpus construction and title/abstract based document recommendation. The evaluation results show the differences of these measures.


Semantic Annotations For Wsdl And Xml Schema


title: Steady-State Thermoelastic Behavior of a Two-Anisotropic Layer Thick Plate Strip

abstract: A steady-state thermoelastic analysis in a two-anisotropic layer plate strip is investigated. The thermal behavior in the form of the temperature distribution is determined based on the steady-state heat conduction model. The thermoelastic equations for the displacements and stresses are derived from the Duhamel-Neuman constitutive equations for anisotropic bodies. Interfacial thermal and mechanical continuity at the common boundary of the two layers are satisfied. The temperatures, displacements, and stresses are computed numerically using a stable, accurate finite difference scheme. The thermal behavior in terms of the temperature variations is found to be the main factor affecting the elastic quantities (displacements and stresses). The most significant temperature gradients are observed to take place in the first layer close to the hot lower surface of the plate.


title: An extensive experimental comparison of methods for multi-label learning

abstract: Multi-label learning has received significant attention in the research community over the past few years: this has resulted in the development of a variety of multi-label learning methods. In this paper, we present an extensive experimental comparison of 12 multi-label learning methods using 16 evaluation measures over 11 benchmark datasets. We selected the competing methods based on their previous usage by the community, the representation of different groups of methods and the variety of basic underlying machine learning methods. Similarly, we selected the evaluation measures to be able to assess the behavior of the methods from a variety of view-points. In order to make conclusions independent from the application domain, we use 11 datasets from different domains. Furthermore, we compare the methods by their efficiency in terms of time needed to learn a classifier and time needed to produce a prediction for an unseen example. We analyze the results from the experiments using Friedman and Nemenyi tests for assessing the statistical significance of differences in performance. The results of the analysis show that for multi-label classification the best performing methods overall are random forests of predictive clustering trees (RF-PCT) and hierarchy of multi-label classifiers (HOMER), followed by binary relevance (BR) and classifier chains (CC). Furthermore, RF-PCT exhibited the best performance according to all measures for multi-label ranking. The recommendation from this study is that when new methods for multi-label learning are proposed, they should be compared to RF-PCT and HOMER using multiple evaluation measures.


title: Toward a theory of knowledge reuse: Types of knowledge reuse situations and factors in reuse success

abstract: This paper represents a step toward a theory of knowledge reusability with emphasis on knowledge management systems and repositories, often called organizational memory systems. Synthesis of evidence from a wide variety of sources suggests four distinct types of knowledge reuse situations according to the knowledge reuser and the purpose of knowledge reuse. The types involve shared work producers, who produce knowledge they later reuse; shared work practitioners, who reuse each other's knowledge contributions; expertise-seeking novices; and secondary knowledge miners. Each type of knowledge reuser has different requirements for knowledge repositories. Owing to how repositories are created, reusers'requirements often remain unmet. Repositories often require considerable rework to be useful for new reusers, but knowledge producers rarely have the resources and incentives to do a good job of repurposing knowledge. Solutions include careful use of incentives and human and technical intermediaries.


title: Power Cycle Testing of Power Switches: A Literature Survey

abstract: Reliability of power converters and lifetime prediction has been a major topic of research in the last few decades, especially for traction applications. The main failures in high power semiconductors are caused by thermomechanical fatigue. Power cycling and temperature cycling are the two most common thermal acceleration tests used in assessing reliability. The objective of this paper is to study the various power cycling tests found in the literature and to develop generalized steps in planning application specific power cycling tests. A comparison of different tests based on the failures, duration, test circuits, and monitored electrical parameters is presented.


abstract: This paper explores a simple regularizer for reinforcement learning by proposing Generative Adversarial Self-Imitation Learning (GASIL), which encourages the agent to imitate past good trajectories via generative adversarial imitation learning framework. Instead of directly maximizing rewards, GASIL focuses on reproducing past good trajectories, which can potentially make long-term credit assignment easier when rewards are sparse and delayed. GASIL can be easily combined with any policy gradient objective by using GASIL as a learned shaped reward function. Our experimental results show that GASIL improves the performance of proximal policy optimization on 2D Point Mass and MuJoCo environments with delayed reward and stochastic dynamics.


title: The Role of Morphology of the Thumb in Anthropomorphic Grasping: A Review

abstract: The unique musculoskeletal structure of the human hand brings in wider dexterous capabilities to grasp and manipulate a repertoire of objects than the non-human primates. It has been widely accepted that the orientation and the position of the thumb plays an important role in this characteristic behavior. There have been numerous attempts to develop anthropomorphic robotic hands with varying levels of success. Nevertheless, manipulation ability in those hands is to be ameliorated even though they can grasp objects successfully. An appropriate model of the thumb is important to manipulate the objects against the fingers and to maintain the stability. Modeling these complex interactions about the mechanical axes of the joints and how to incorporate these joints in robotic thumbs is a challenging task. This paper presents a review of the biomechanics of the human thumb and the robotic thumb designs to identify opportunities for future anthropomorphic robotic hands.


title: Detecting Hate Speech on the World Wide Web

abstract: We present an approach to detecting hate speech in online text, where hate speech is defined as abusive speech targeting specific group characteristics, such as ethnic origin, religion, gender, or sexual orientation. While hate speech against any group may exhibit some common characteristics, we have observed that hatred against each different group is typically characterized by the use of a small set of high frequency stereotypical words; however, such words may be used in either a positive or a negative sense, making our task similar to that of words sense disambiguation. In this paper we describe our definition of hate speech, the collection and annotation of our hate speech corpus, and a mechanism for detecting some commonly used methods of evading common "dirty word" filters. We describe pilot classification experiments in which we classify anti-semitic speech reaching an accuracy 94%, precision of 68% and recall at 60%, for an F1 measure of. 6375.


title: A unifying framework for detecting outliers and change points from time series

abstract: We are concerned with the issue of detecting outliers and change points from time series. In the area of data mining, there have been increased interest in these issues since outlier detection is related to fraud detection, rare event discovery, etc., while change-point detection is related to event/trend change detection, activity monitoring, etc. Although, in most previous work, outlier detection and change point detection have not been related explicitly, this paper presents a unifying framework for dealing with both of them. In this framework, a probabilistic model of time series is incrementally learned using an online discounting learning algorithm, which can track a drifting data source adaptively by forgetting out-of-date statistics gradually. A score for any given data is calculated in terms of its deviation from the learned model, with a higher score indicating a high possibility of being an outlier. By taking an average of the scores over a window of a fixed length and sliding the window, we may obtain a new time series consisting of moving-averaged scores. Change point detection is then reduced to the issue of detecting outliers in that time series. We compare the performance of our framework with those of conventional methods to demonstrate its validity through simulation and experimental applications to incidents detection in network security.


title: Two-way string-matching

abstract: A new string-matching algorithm is presented, which can be viewed as an intermediate between the classical algorithms of Knuth, Morris, and Pratt on the one hand and Boyer and Moore, on the other hand. The algorithm is linear in time and uses constant space as the algorithm of Galil and Seiferas. It presents the advantage of being remarkably simple which consequently makes its analysis possible. The algorithm relies on a previously known result in combinatorics on words, called the Critical Factorization Theorem,which relates the global period of a word to Its local repetitions of blocks


title: Discriminative Correlation Filter with Channel and Spatial Reliability

abstract: Short-term tracking is an open and challenging problem for which discriminative correlation filters (DCF) have shown excellent performance. We introduce the channel and spatial reliability concepts to DCF tracking and provide a novel learning algorithm for its efficient and seamless integration in the filter update and the tracking process. The spatial reliability map adjusts the filter support to the part of the object suitable for tracking. This allows tracking of non-rectangular objects as well as extending the search region. Channel reliability reflects the quality of the learned filter and it is used as a feature weighting coefficient in localization. Experimentally, with only two simple standard features, HOGs and Colornames, the novel CSR-DCF method – DCF with Channel and Spatial Reliability – achieves state-of-the-art results on VOT 2016, VOT 2015 and OTB. The CSR-DCF runs in real-time on a CPU.


title: Solution of a Classical Cryptarithmetic Problem by using parallel genetic algorithm

abstract: The term CryptArithm: it is a mathematical puzzle which involves the replacement of digits with alphabets, symbols and letters. The art and science of solving CryptArithms is known as Cryptarithmetic. CryptArithmetic is related to constraint satisfaction problems (CSP) which uses Operators (Arithmetic) e.g +, −, between relevant words to make mathematical relations in a way that final outcome is logically true. It also ensures that the assigning of values every letter of each word and numbers will produce the accurate result. The numbers are assigned in such a way that no letter should have two numbers (0…9), each letter should contain different number from 0 to 9. Solving CryptArithmetic generally includes a mixed approach of derivation and huge possibility tests. This paper is an effort to find a solution of a Classical CryptArithmetic Problem (CAT+RAN=AWAY) which is computationally expensive and time consuming by using parallel genetic algorithm.


title: Face Recognition Using Active Appearance Models

abstract: We present a new framework for interpreting face images and image sequences using an Active Appearance Model (AAM). The AAM contains a statistical, photo-realistic model of the shape and grey-level appearance of faces. This paper demonstrates the use of the AAM's efficient iterative matching scheme for image interpretation. We use the AAM as a basis for face recognition, obtain good results for difficult images. We show how the AAM framework allows identity information to be decoupled from other variation, allowing evidence of identity to be integrated over a sequence. The AAM approach makes optimal use of the evidence from either a single image or image sequence. Since we derive a complete description of a given image our method can be used as the basis for a range of face image interpretation tasks.


title: How do consumers search for and appraise health information on the world wide web? Qualitative study using focus groups, usability tests, and in-depth interviews

abstract: Abstract Objectives: To describe techniques for retrieval and appraisal used by consumers when they search for health information on the internet. Design: Qualitative study using focus groups, naturalistic observation of consumers searching the world wide web in a usability laboratory, and in-depth interviews. Participants: A total of 21 users of the internet participated in three focus group sessions. 17 participants were given a series of health questions and observed in a usability laboratory setting while retrieving health information from the web; this was followed by in-depth interviews. Setting: Heidelberg, Germany. Results: Although their search technique was often suboptimal, internet users successfully found health information to answer questions in an average of 5 minutes 42 seconds (median 4 minutes 18 seconds) per question. Participants in focus groups said that when assessing the credibility of a website they primarily looked for the source, a professional design, a scientific or official touch, language, and ease of use. However, in the observational study, no participants checked any “about us” sections of websites, disclaimers, or disclosure statements. In the post-search interviews, it emerged that very few participants had noticed and remembered which websites they had retrieved information from. Conclusions: Further observational studies are needed to design and evaluate educational and technological innovations for guiding consumers to high quality health information on the web. What is already known on this topic Little is known about how consumers retrieve and assess the quality of health information on the internet Qualitative data are needed to design educational and technological innovations to guide consumers to high quality health information What this study adds Users of the internet explore only the first few links on general search engines when seeking health information Consumers say that when assessing the credibility of a site they primarily look for the source, a professional design, and a variety of other criteria In practice, internet users do not check the “about us” sections of websites, try to find out who authors or owners of the site are, or read disclaimers or disclosure statements Very few internet users later remember from which websites they retrieved information or who stood behind the sites


title: Anti-spyware Security Design Patterns

abstract: Spyware is deemed a big danger to the privacy and secrecy of both individuals and firms, it frequently results in the damage and loss of electronic data stored on all types of computers. The main challenges in designing anti-spyware systems are the need for updating data describing spywares and changing the structure of old programs to detect new or unknown spywares. In this paper, we aimed to propose a security pattern for spyware detection. We achieved our objective by introducing a framework for anti-spyware system that combine data mining and design patterns in creating security patterns able to detect and classify spyware efficiently. The proposed anti-spyware system is characterized by its reusability, in sense that it can modify itself for any new or unknown spyware. The experimental results of the proposed approach outperform all the previous competing approaches.


title: Cursive Character Segmentation Using Neural Network Techniques

abstract: The segmentation of cursive and mixed scripts persists to be a difficult problem in the area of handwriting recognition. This research details advances for segmenting characters in off-line cursive script. Specifically, a heuristic algorithm and a neural network-based technique, which uses a structural feature vector representation, are proposed and combined for identifying incorrect segmentation points. Following the location of appropriate anchorage points, a character extraction technique, using segmentation paths, is employed to complete the segmentation process. Results are presented for neural-based heuristic segmentation, segmentation point validation, character recognition, segmentation path detection and overall segmentation accuracy.


title: Pop-routing: Centrality-based tuning of control messages for faster route convergence

abstract: Fast and efficient recovery from node failure, with minimal disruption of routes and the consequent traffic loss is of the utmost importance for any routing protocol. Link-state protocols, albeit preferred to distance vector ones because of faster convergence, still suffer from a trade-off between control message overhead and performance. This work formalizes the routes' disruption following a node failure as an optimization problem depending on the nodes' centrality in the topology, constrained to a constant signaling overhead. Next, it shows that the solution can be found using Lagrange Multipliers. The solution complexity is low enough to be computed on-line on the network routers, thus obtaining the optimal setting of control message timers that minimize the traffic loss following a node failure. The gain obtained is quantified in power-law synthetic topologies, and it is also tested on real network topologies extending the OLSR protocol to use the modified timers, showing that the inevitable approximations introduced in the analysis do not hamper the very good results achievable through this novel approach. The technique can be applied to any link state protocol, including OSPF, and improves route convergence not only upon failures but on every topology modification.


title: Data-driven exploration of care plans for patients

abstract: CareFlow is a novel visual analytics tool designed to help doctors devise a care plan for their patient. Using historical outcomes from clinically similar patients, CareFlow allows doctors to analyze which treatments have been effective for patients like theirs.


title: Burstiness and memory in complex systems

abstract: The dynamics of a wide range of real systems, from email patterns to earthquakes, display a bursty, intermittent nature, characterized by short timeframes of intense activity followed by long times of no or reduced activity. The understanding of the origin of such bursty patterns is hindered by the lack of tools to compare different systems using a common framework. Here we propose to characterize the bursty nature of real signals using orthogonal measures quantifying two distinct mechanisms leading to burstiness: the interevent time distribution and the memory. We find that while the burstiness of natural phenomena is rooted in both the interevent time distribution and memory, for human dynamics memory is weak, and the bursty character is due to the changes in the interevent time distribution. Finally, we show that current models lack in their ability to reproduce the activity pattern observed in real systems, opening up avenues for future work. Copyright c �EPLA, 2008


title: Acknowledgments Local multidimensional scaling

abstract: In a visualization task, every nonlinear projection method needs to make a compromise between trustworthiness and continuity. In a trustworthy projection the visualized proximities hold in the original data as well, whereas a continuous projection visualizes all proximities of the original data. We show experimentally that one of the multidimensional scaling methods, curvilinear components analysis, is good at maximizing trustworthiness. We then extend it to focus on local proximities both in the input and output space, and to explicitly make a user-tunable parameterized compromise between trustworthiness and continuity. The new method compares favorably to alternative nonlinear projection methods.


title: Design and control of a variable footprint mechanism for holonomic omnidirectional vehicles and its application to wheelchairs

abstract: A reconfigurable mechanism for varying the footprint of a four-wheeled omnidirectional vehicle is developed and applied to wheelchairs. The variable footprint mechanism consists of a pair of beams intersecting at a pivotal point in the middle. Two pairs of bad wheels at the diagonal positions of the vehicle chassis are mounted, respectively, on the two beams intersecting in the middle. The angle between the two beams varies actively so that the ratio of the wheel base to the tread may change. Four independent servo motors driving the four ball wheels allow the vehicle to move in an arbitrary direction from an arbitrary configuration as well as to change the angle between the two beams and thereby change the footprint. The objective of controlling the beam angle is threefold. One is to augment static stability by varying the footprint so that the mass centroid of the vehicle may be kept within the footprint at all times. The second is to reduce the width of the vehicle when going through a narrow doorway. The third is to apparently change the gear ratio relating the vehicle speed to individual actuator speeds. First the concept of the varying footprint mechanism is described, and its kinematic behavior is analyzed, followed by the three control algorithms for varying the footprint. A prototype vehicle for an application as a wheelchair platform is designed, built, and tested.


title: Nearly-tight VC-dimension bounds for piecewise linear neural networks

abstract: We prove new upper and lower bounds on the VC-dimension of deep neural networks with the ReLU activation function. These bounds are tight for almost the entire range of parameters. Letting $W$ be the number of weights and $L$ be the number of layers, we prove that the VC-dimension is $O(W L \log(W))$, and provide examples with VC-dimension $\Omega( W L \log(W/L) )$. This improves both the previously known upper bounds and lower bounds. In terms of the number $U$ of non-linear units, we prove a tight bound $\Theta(W U)$ on the VC-dimension. All of these results generalize to arbitrary piecewise linear activation functions.


title: A biologically-inspired multi-joint soft exosuit that can reduce the energy cost of loaded walking

abstract: BackgroundCarrying load alters normal walking, imposes additional stress to the musculoskeletal system, and results in an increase in energy consumption and a consequent earlier onset of fatigue. This phenomenon is largely due to increased work requirements in lower extremity joints, in turn requiring higher muscle activation. The aim of this work was to assess the biomechanical and physiological effects of a multi-joint soft exosuit that applies assistive torques to the biological hip and ankle joints during loaded walking.MethodsThe exosuit was evaluated under three conditions: powered (EXO_ON), unpowered (EXO_OFF) and unpowered removing the equivalent mass of the device (EXO_OFF_EMR). Seven participants walked on an instrumented split-belt treadmill and carried a load equivalent to 30 % their body mass. We assessed their metabolic cost of walking, kinetics, kinematics, and lower limb muscle activation using a portable gas analysis system, motion capture system, and surface electromyography.ResultsOur results showed that the exosuit could deliver controlled forces to a wearer. Net metabolic power in the EXO_ON condition (7.5 ± 0.6 W kg−1) was 7.3 ± 5.0 % and 14.2 ± 6.1 % lower than in the EXO_OFF_EMR condition (7.9 ± 0.8 W kg−1; p = 0.027) and in the EXO_OFF condition (8.5 ± 0.9 W kg−1; p = 0.005), respectively. The exosuit also reduced the total joint positive biological work (sum of hip, knee and ankle) when comparing the EXO_ON condition (1.06 ± 0.16 J kg−1) with respect to the EXO_OFF condition (1.28 ± 0.26 J kg−1; p = 0.020) and to the EXO_OFF_EMR condition (1.22 ± 0.21 J kg−1; p = 0.007).ConclusionsThe results of the present work demonstrate for the first time that a soft wearable robot can improve walking economy. These findings pave the way for future assistive devices that may enhance or restore gait in other applications.


title: Hot Topic Extraction Based on Timeline Analysis and Multidimensional Sentence Modeling

abstract: With the vast amount of digitized textual materials now available on the Internet, it is almost impossible for people to absorb all pertinent information in a timely manner. To alleviate the problem, we present a novel approach for extracting hot topics from disparate sets of textual documents published in a given time period. Our technique consists of two steps. First, hot terms are extracted by mapping their distribution over time. Second, based on the extracted hot terms, key sentences are identified and then grouped into clusters that represent hot topics by using multidimensional sentence vectors. The results of our empirical tests show that this approach is more effective in identifying hot topics than existing methods.


EFFECT ON BODY TEMPERATURE OF MORPHINE AND ERGOTAMINE INJECTED INTO THE CEREBRAL VENTRICLES OF CATS


title: Painting style transfer for head portraits using convolutional neural networks

abstract: Head portraits are popular in traditional painting. Automating portrait painting is challenging as the human visual system is sensitive to the slightest irregularities in human faces. Applying generic painting techniques often deforms facial structures. On the other hand portrait painting techniques are mainly designed for the graphite style and/or are based on image analogies; an example painting as well as its original unpainted version are required. This limits their domain of applicability. We present a new technique for transferring the painting from a head portrait onto another. Unlike previous work our technique only requires the example painting and is not restricted to a specific style. We impose novel spatial constraints by locally transferring the color distributions of the example painting. This better captures the painting texture and maintains the integrity of facial structures. We generate a solution through Convolutional Neural Networks and we present an extension to video. Here motion is exploited in a way to reduce temporal inconsistencies and the shower-door effect. Our approach transfers the painting style while maintaining the input photograph identity. In addition it significantly reduces facial deformations over state of the art.


title: Fast and Simple Optimization for Poisson Likelihood Models

abstract: Poisson likelihood models have been prevalently used in imaging, social networks, and time series analysis. We propose fast, simple, theoretically-grounded, and versatile, optimization algorithms for Poisson likelihood modeling. The Poisson log-likelihood is concave but not Lipschitz-continuous. Since almost all gradient-based optimization algorithms rely on Lipschitz-continuity, optimizing Poisson likelihood models with a guarantee of convergence can be challenging, especially for large-scale problems. ::: We present a new perspective allowing to efficiently optimize a wide range of penalized Poisson likelihood objectives. We show that an appropriate saddle point reformulation enjoys a favorable geometry and a smooth structure. Therefore, we can design a new gradient-based optimization algorithm with $O(1/t)$ convergence rate, in contrast to the usual $O(1/\sqrt{t})$ rate of non-smooth minimization alternatives. Furthermore, in order to tackle problems with large samples, we also develop a randomized block-decomposition variant that enjoys the same convergence rate yet more efficient iteration cost. ::: Experimental results on several point process applications including social network estimation and temporal recommendation show that the proposed algorithm and its randomized block variant outperform existing methods both on synthetic and real-world datasets.


title: The Case Against Specialized Graph Analytics Engines

abstract: Graph analytic processing has started to become a nearly ubiquitous component in the enterprise data analytics ecosystem. In response to this growing need, various specialized graph processing engines have been created in recent years. Sadly, the use of relational database management systems (RDBMSs) for graph processing is largely ignored in most enterprise settings. This oversight is surprising since in most enterprise settings, RDBMSs are already present and used for a variety of other analytic tasks. This situation then begs the question of whether the use of RDBMS for graph processing is fundamentally lacking in some respect compared to the specialized graph processing engines. In this paper, we aim to address this question both from the programmer productivity perspective and from the performance perspective. We present Grail { a syntactic layer for querying graph in a vertex-centric way in an RDBMS, which can be compiled to translate graph queries to SQL. In a single node setting, we also compare Grail to GraphLab and Giraph, and examine the performance implications of using Grail, showing that the RDBMS engine is competitive to these specialized engines. Given that RDBMSs are ubiquitous in enterprise settings, and have a robust and mature technology that has been hardened over decades, and are part of existing administrative methods in place, we argue that it is time to reconsider if specialized graph engines have a role to play in most enterprises.


title: DeepHash: Getting Regularization, Depth and Fine-Tuning Right

abstract: This work focuses on representing very high-dimensional global image descriptors using very compact 64-1024 bit binary hashes for instance retrieval. We propose DeepHash: a hashing scheme based on deep networks. Key to making DeepHash work at extremely low bitrates are three important considerations -- regularization, depth and fine-tuning -- each requiring solutions specific to the hashing problem. In-depth evaluation shows that our scheme consistently outperforms state-of-the-art methods across all data sets for both Fisher Vectors and Deep Convolutional Neural Network features, by up to 20 percent over other schemes. The retrieval performance with 256-bit hashes is close to that of the uncompressed floating point features -- a remarkable 512 times compression.


title: Audio watermarking based on Fibonacci numbers

abstract: This paper presents a novel high-capacity audio watermarking system to embed data and extract them in a bit-exact manner by changing some of the magnitudes of the FFT spectrum. The key idea is to divide the FFT spectrum into short frames and change the magnitude of the selected FFT samples using Fibonacci numbers. Taking advantage of Fibonacci numbers, it is possible to change the frequency samples adaptively. In fact, the suggested technique guarantees and proves, mathematically, that the maximum change is less than 61% of the related FFT sample and the average error for each sample is 25%. Using the closest Fibonacci number to FFT magnitudes results in a robust and transparent technique. On top of very remarkable capacity, transparency and robustness, this scheme provides two parameters which facilitate the regulation of these properties. The experimental results show that the method has a high capacity (700 bps to 3 kbps), without significant perceptual distortion (ODG is about 1) and provides robustness against common audio signal processing such as echo, added noise, filtering, and MPEG compression (MP3). In addition to the experimental results, the fidelity of suggested system is proved mathematically.


title: Automated surface deformations detection and marking on automotive body panels

abstract: This paper proposes an integrated solution for automated surface deformations detection and marking on automotive body panels in the context of quality control in industrial manufacturing. Starting from a 3D image of the surface of the panel, deformations are extracted and classified automatically. The positions of the surface defects are provided to a robotic marking station that handles pose and motion estimation of the part on an assembly line using passive vision. The integrated system is validated with an experimental setup, using an automobile car door panel.


title: Network Virtualization: Technologies, Perspectives, and Frontiers

abstract: Network virtualization refers to a broad set of technologies. Commercial solutions have been offered by the industry for years, while more recently the academic community has emphasized virtualization as an enabler for network architecture research, deployment, and experimentation. We review the entire spectrum of relevant approaches with the goal of identifying the underlying commonalities. We offer a unifying definition of the term “network virtualization” and examine existing approaches to bring out this unifying perspective. We also discuss a set of challenges and research directions that we expect to come to the forefront as network virtualization technologies proliferate.


title: K-Local Hyperplane and Convex Distance Nearest Neighbor Algorithms

abstract: Guided by an initial idea of building a complex (non linear) decision surface with maximal local margin in input space, we give a possible geometrical intuition as to why K-Nearest Neighbor (KNN) algorithms often perform more poorly than SVMs on classification tasks. We then propose modified K-Nearest Neighbor algorithms to overcome the perceived problem. The approachis similar in spirit to Tangent Distance, but with invariances inferred from the local neighborhood rather than prior knowledge. Experimental results on real world classification tasks suggest that the modified KNN algorithms often give a dramatic improvement over standard KNN and perform as well or better than SVMs.


title: The Internet of Things: A Survey from the Data-Centric Perspective, Managing and Mining Sensor Data

abstract: Advances in sensor data collection technology, such as pervasive and embedded devices, and RFID Technology have lead to a large number of smart devices which are connected to the net and continuously transmit their data over time. It has been estimated that the number of internet connected devices has overtaken the number of humans on the planet, since 2008. The collection and processing of such data leads to unprecedented challenges in mining and processing such data. Such data needs to be processed in real-time and the processing may be highly distributed in nature. Even in cases, where the data is stored offline, the size of the data is often so large and distributed, that it requires the use of big data analytical tools for processing. In addition, such data is often sensitive, and brings a number of privacy challenges associated


title: Caregiver burden and coping strategies in caregivers of patients with Alzheimer’s disease

abstract: Background ::: Alzheimer’s disease (AD) causes considerable distress in caregivers who are continuously required to deal with requests from patients. Coping strategies play a fundamental role in modulating the psychologic impact of the disease, although their role is still debated. The present study aims to evaluate the burden and anxiety experienced by caregivers, the effectiveness of adopted coping strategies, and their relationships with burden and anxiety.


A Machine Learning based Music Retrieval and Recommendation System.


title: Unscented Kalman filter design for curvilinear motion models suitable for automotive safety applications

abstract: Research in automotive safety leads to the conclusion that modern vehicle should utilize active and passive sensors for the recognition of the environment surrounding them. Thus, the development of tracking systems utilizing efficient state estimators is very important. In this case, problems such as moving platform carrying the sensor and maneuvering targets could introduce large errors in the state estimation and in some cases can lead to the divergence of the filter. In order to avoid sub-optimal performance, the unscented Kalman filter is chosen, while a new curvilinear model is applied which takes into account both the turn rate of the detected object and its tangential acceleration, leading to a more accurate modeling of its movement. The performance of the unscented filter using the proposed model in the case of automotive applications is proven to be superior compared to the performance of the extended and linear Kalman filter.


Automatic Segmentation, Classification and Clustering of Broadcast News Audio


title: End-to-End Prediction of Buffer Overruns from Raw Source Code via Neural Memory Networks

abstract: Detecting buffer overruns from a source code is one of the most common and yet challenging tasks in program analysis. Current approaches based on rigid rules and handcrafted features are limited in terms of flexible applicability and robustness due to diverse bug patterns and characteristics existing in sophisticated real-world software programs. In this paper, we propose a novel, data-driven approach that is completely end-to-end without requiring any hand-crafted features, thus free from any program language-specific structural limitations. In particular, our approach leverages a recently proposed neural network model called memory networks that have shown the state-of-the-art performances mainly in question-answering tasks. Our experimental results using source code samples demonstrate that our proposed model is capable of accurately detecting different types of buffer overruns. We also present in-depth analyses on how a memory network can learn to understand the semantics in programming languages solely from raw source codes, such as tracing variables of interest, identifying numerical values, and performing their quantitative comparisons.


title: CODAM: A neural network model of consciousness

abstract: We present a review of the CODAM neural network control model of consciousness and develop it to arrive at a functional account of consciousness. The main feature is as a speed-up and error-correcting mechanism known, in engineering control theory, to be efficient in improving the speed of response and accuracy of any control system. We use the CODAM model to generate a set of predictions as to how such speed-up is achieved, as well as relate to previous explanations by CODAM of various attention-based phenomena.


title: FloodGuard: A DoS Attack Prevention Extension in Software-Defined Networks

abstract: This paper addresses one serious SDN-specific attack, i.e., data-to-control plane saturation attack, which overloads the infrastructure of SDN networks. In this attack, an attacker can produce a large amount of table-miss packet_in messages to consume resources in both control plane and data plane. To mitigate this security threat, we introduce an efficient, lightweight and protocol-independent defense framework for SDN networks. Our solution, called FloodGuard, contains two new techniques/modules: proactive flow rule analyzer and packet migration. To preserve network policy enforcement, proactive flow rule analyzer dynamically derives proactive flow rules by reasoning the runtime logic of the SDN/OpenFlow controller and its applications. To protect the controller from being overloaded, packet migration temporarily caches the flooding packets and submits them to the OpenFlow controller using rate limit and round-robin scheduling. We evaluate FloodGuard through a prototype implementation tested in both software and hardware environments. The results show that FloodGuard is effective with adding only minor overhead into the entire SDN/OpenFlow infrastructure.


title: Patching. Restitching business portfolios in dynamic markets.

abstract: In turbulent markets, businesses and opportunities are constantly falling out of alignment. New technologies and emerging markets create fresh opportunities. Converging markets produce more. And of course, some markets fade. In this landscape of continuous flux, it's more important to build corporate-level strategic processes that enable dynamic repositioning than it is to build any particular defensible position. That's why smart corporate strategists use patching, a process of mapping and remapping business units to create a shifting mix of highly focused, tightly aligned businesses that can respond to changing market opportunities. Patching is not just another name for reorganizing; patchers have a distinctive mindset. Traditional managers see structure as stable; patching managers believe structure is inherently temporary. Traditional managers set corporate strategy first, but patching managers keep the organization focused on the right set of business opportunities and let strategy emerge from individual businesses. Although the focus of patching is flexibility, the process itself follows a pattern. Patching changes are usually small in scale and made frequently. Patching should be done quickly; the emphasis is on getting the patch about right and fixing problems later. Patches should have a test drive before they're formalized but then be tightly scripted after they've been announced. And patching won't work without the right infrastructure: modular business units, fine-grained and complete unit-level metrics, and companywide compensation parity. The authors illustrate how patching works and point out some common stumbling blocks.


title: Independent rise and fall edge dead time generator based on monostable multivibrator circuit

abstract: In this paper, the independent rise and fall edge dead time generator for switching control signal is presented. The scheme of the proposed circuit is based on the monostable multivibrator circuit together with the toggle circuit that use JK flip-flop to operate, OR Gate, AND Gate and NOT Gate. The obtained results from simulation and experiment affirm that the dead time at rise and fall edge of switching obtained from the proposed scheme can be independently controlled. The control ability is achieved by adjusting the resistance of the resistor or capacitance of the capacitor that are combined in monostable multivibrator circuit. The proposed circuit is easily implemented and provides the precise dead time duration, at least in nanosecond level. Furthermore, it can operate effectively at high frequency condition. It hence can be employed in various applications.


title: A Framework for Occupational Fraud Detection by Social Network Analysis

abstract: This paper explores issues related to occupational fraud detection. We observe over the past years, a broad use of network research across social and physical sciences including but not limited to social sharing and filtering, recommendation systems, marketing and customer intelligence, counter intelligence and law enforcement. However, the rate of social network analysis adoption in organizations by control professionals or even by academics for insider fraud detection purpose is still very low. This paper introduces the OFD – Occupational Fraud Detection framework, based on formal social network analysis and semantic reasoning principles by taking a design science research perspective.


title: Are AES x86 cache timing attacks still feasible?

abstract: We argue that five recent software and hardware developments - the AES-NI instructions, multicore processors with per-core caches, complex modern software, sophisticated prefetchers, and physically tagged caches - combine to make it substantially more difficult to mount data-cache side-channel attacks on AES than previously realized. We propose ways in which some of the challenges posed by these developments might be overcome. We also consider scenarios where side-channel attacks are attractive, and whether our proposed workarounds might be applicable to these scenarios.


title: Online extrinsic multi-camera calibration using ground plane induced homographies

abstract: This paper presents an approach for online estimation of the extrinsic calibration parameters of a multi-camera rig. Given a coarse initial estimate of the parameters, the relative poses between cameras are refined through recursive filtering. The approach is purely vision based and relies on plane induced homographies between successive frames. Overlapping fields of view are not required. Instead, the ground plane serves as a natural reference object. In contrast to other approaches, motion, relative camera poses, and the ground plane are estimated simultaneously using a single iterated extended Kalman filter. This reduces not only the number of parameters but also the computational complexity. Furthermore, an arbitrary number of cameras can be incorporated. Several experiments on synthetic as well as real data were conducted using a setup of four synchronized wide angle fisheye cameras, mounted on a moving platform. Results were obtained, using both, a planar and a general motion model with full six degrees of freedom. Additionally, the effects of uncertain intrinsic parameters and nonplanar ground were evaluated experimentally.


title: Seawater desalination using renewable energy sources

abstract: Abstract The origin and continuation of mankind is based on water. Water is one of the most abundant resources on earth, covering three-fourths of the planet's surface. However, about 97% of the earth's water is salt water in the oceans, and a tiny 3% is fresh water. This small percentage of the earth's water—which supplies most of human and animal needs—exists in ground water, lakes and rivers. The only nearly inexhaustible sources of water are the oceans, which, however, are of high salinity. It would be feasible to address the water-shortage problem with seawater desalination; however, the separation of salts from seawater requires large amounts of energy which, when produced from fossil fuels, can cause harm to the environment. Therefore, there is a need to employ environmentally-friendly energy sources in order to desalinate seawater. After a historical introduction into desalination, this paper covers a large variety of systems used to convert seawater into fresh water suitable for human use. It also covers a variety of systems, which can be used to harness renewable energy sources; these include solar collectors, photovoltaics, solar ponds and geothermal energy. Both direct and indirect collection systems are included. The representative example of direct collection systems is the solar still. Indirect collection systems employ two sub-systems; one for the collection of renewable energy and one for desalination. For this purpose, standard renewable energy and desalination systems are most often employed. Only industrially-tested desalination systems are included in this paper and they comprise the phase change processes, which include the multistage flash, multiple effect boiling and vapour compression and membrane processes, which include reverse osmosis and electrodialysis. The paper also includes a review of various systems that use renewable energy sources for desalination. Finally, some general guidelines are given for selection of desalination and renewable energy systems and the parameters that need to be considered.


[A case of giant fetal hepatic hemangioma].


title: Discovering microRNAs from deep sequencing data using miRDeep

abstract: The capacity of highly parallel sequencing technologies to detect small RNAs at unprecedented depth suggests their value in systematically identifying microRNAs (miRNAs). However, the identification of miRNAs from the large pool of sequenced transcripts from a single deep sequencing run remains a major challenge. Here, we present an algorithm, miRDeep, which uses a probabilistic model of miRNA biogenesis to score compatibility of the position and frequency of sequenced RNA with the secondary structure of the miRNA precursor. We demonstrate its accuracy and robustness using published Caenorhabditis elegans data and data we generated by deep sequencing human and dog RNAs. miRDeep reports altogether approximately 230 previously unannotated miRNAs, of which four novel C. elegans miRNAs are validated by northern blot analysis.


title: A Survey on Deep Learning Based Approaches for Action and Gesture Recognition in Image Sequences

abstract: The interest in action and gesture recognition has grown considerably in the last years. In this paper, we present a survey on current deep learning methodologies for action and gesture recognition in image sequences. We introduce a taxonomy that summarizes important aspects of deep learning for approaching both tasks. We review the details of the proposed architectures, fusion strategies, main datasets, and competitions. We summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, discussing their main features and identify opportunities and challenges for future research.


Image Retrieval : Importance and Applications


title: Design and Fabrication of Integrated Magnetic MEMS Energy Harvester for Low Frequency Applications

abstract: An integrated vibration-to-electrical MEMS electromagnetic energy harvester with low resonant frequency is designed, simulated, fabricated and tested. Special structures and magnet arrays of the device are designed and simulated for favorable low frequency performance. Both FEM simulations and system-level simulations are conducted to investigate the induced voltage of the device. Two types of harvester are fabricated with different magnet arrays and the magnetic field of the two harvesters is investigated. With the combination of the microfabricated metal structures and the electroplated CoNiMnP permanent micro magnets, the designed harvesters are of small size (5 mm ×5 mm × 0.53 mm) without manual assembly of conventional bulk magnets. The output power density is 0.03 μW/cm3 with optimized resistance load at 64 Hz. This magnetic harvester is suitable for batch fabrication through MEMS process and can be utilized to drive microelectronic devices, such as micro implantable and portable devices.