Miracle-dz/UniFAR-FTUs huggingface dataset:
Net Costs Due to Seasonal Influenza Vaccination — United States, 2005–2009

Background Seasonal influenza causes considerable morbidity and mortality across all age groups, and influenza vaccination was recommended in 2010 for all persons aged 6 months and above. We estimated the averted costs due to influenza vaccination, taking into account the seasonal economic burden of the disease. Methods We used recently published values for averted outcomes due to influenza vaccination for influenza seasons 2005-06, 2006-07, 2007-08, and 2008-09, and age cohorts 6 months-4 years, 5-19 years, 20-64 years, and 65 years and above. Costs were calculated according to a payer and societal perspective (in 2009 US$), and took into account medical costs and productivity losses. Results When taking into account direct medical costs (payer perspective), influenza vaccination was cost saving only for the older age group (65≥) in seasons 2005-06 and 2007-08. Using the same perspective, influenza vaccination resulted in total costs of $US 1.7 billion (95%CI: $US 0.3–4.0 billion) in 2006-07 and $US 1.8 billion (95%CI: $US 0.1–4.1 billion) in 2008-09. When taking into account a societal perspective (and including the averted lost earnings due to premature death) averted deaths in the older age group influenced the results, resulting in cost savings for all ages combined in season 07-08. Discussion Influenza vaccination was cost saving in the older age group (65≥) when taking into account productivity losses and, in some seasons, when taking into account medical costs only. Averted costs vary significantly per season; however, in seasons where the averted burden of deaths is high in the older age group, averted productivity losses due to premature death tilt overall seasonal results towards savings. Indirect vaccination effects and the possibility of diminished case severity due to influenza vaccination were not considered, thus the averted burden due to influenza vaccine may be even greater than reported.
Low Power Clock Network Design

Power is a primary concern in modern circuits. Clock distribution networks, in particular, are an essential element of a synchronous digital circuit and a significant power consumer. Clock distribution networks are subject to clock skew due to process, voltage, and temperature (PVT) variations and load imbalances. A target skew between sequentially-adjacent registers can be obtained in a balanced low power clock tree using techniques such as buffer and wire sizing. Existing skew mitigation techniques in tree-based clock distribution networks, however, are not efficient in coping with post design variations; whereas the latest non-tree mesh-based solutions reliably handle skew variations, albeit with a significant increase in dissipated power. Alternatively, crosslink-based methods provide low power and variation-efficient skew solutions. Existing crosslink-based methods, however, only address skew at the network topology level and do not target low power consumption. Different methods to manage skew and skew variations within tree and non-tree clock distribution networks are reviewed and compared in this paper. Guidelines for inserting crosslinks within a buffered low power clock tree are provided. Metrics to determine the most power efficient technique for a given circuit are discussed and verified with simulation.
Mining the emotional words from Chinese reviews based on part of speech and syntax

In the reviews, customers use emotional words to express their own views about the products. Consequently, mining the emotional words automatically become one of the hot spots in reviews mining. For the emotional words abstraction of Chinese product reviews, we approach from the two aspects of part of speech and syntax in this paper. We mine the part of speech templates of emotional words from training set firstly, then extract emotion words set based on the templates, and prune the emotional words set through the dependent relationship and stop words set at last. The final extraction results not only include the emotional words which evaluate the dominant features, but also include the ones about the invisible features and overall evaluations. The method is not limited to certain categories of products, so it has the extensive applicability. The experimental results show the effectiveness of this method.
Effects of hunting on cougar spatial organization

The effects of increased mortality on the spatial dynamics of solitary carnivores are not well understood. We examined the spatial ecology of two cougar populations that differed in hunting intensity to test whether increased mortality affected home range size and overlap. The stability hypothesis predicts that home range size and overlap will be similar for both sexes among the two areas. The instability hypothesis predicts that home range size and overlap will be greater in the heavily hunted population, although may differ for males versus females due to behavior strategies. We marked 22 adult resident cougars in the lightly hunted population and 20 in the heavily hunted population with GPS collars from 2002 to 2008. Cougar densities and predation rates were similar among areas, suggesting no difference in per capita resources. We compared home range size, two-dimensional home range overlap, and three-dimensional utilization distribution overlap index (UDOI) among annual home ranges for male and female cougars. Male cougars in the heavily hunted area had larger sized home ranges and greater two-dimensional and three-dimensional UDOI overlap than those in the lightly hunted area. Females showed no difference in size and overlap of home range areas between study populations – further suggesting that differences in prey quantity and distribution between study areas did not explain differences in male spatial organization. We reject the spatial stability hypothesis and provide evidence to support the spatial instability hypothesis. Increased hunting and ensuing increased male home range size and overlap may result in negative demographic effects for cougars and potential unintended consequences for managers.
Withstand Capability Testing and Limitation Measurements on Du/dt and Di/dt of Thyristor Valve of the Controllable Metal Oxide Surge Arrester

Du/dt is voltage rise rate in off-state of thyristor. Di/dt is current rise rate in on-state of thyristor. Du/dt and di/dt are key parameters that determine working security of thyristor valve of the controllable metal oxide surge arrester (CMOA, shortly). Combining with the Jindongnan via Nan yang to Jingmen UHV AC pilot project of China, the maximal values of du/dt and di/dt of thyristor were simulated. Required parameters were put forward. The maximal du/dt was 28.3kV/μs and the maximal di/dt was 87kA/μs. Du/dt and di/dt mustn't exceed critical value du/dtcrit and di/dtcrit. Though du/dtcrit and di/dtcrit could be provided by factory, they should be tested by actual application condition because actual application condition may be different from the testing condition. So, by testing, the maximal withstand capacity of du/dt and di/dt was tested by actual application condition. Comparing test results with required parameters indicated that the maximal withstand capacity of du/dt of 2.0 to 3.0 inch thyristor was not less than 45kV/μs which was more than required 28.3kV/μs. Limiting measurement was not needed. The maximal withstand capacity of di/dt was between 865 A/μs and 910A/μs which was much less than the required 87kA/μs. Limiting measures were needed. Study indicated that di/dt could be limited within allowable range by current limited reactor. However reactor may affect limiting switching over voltage of CMOA. So, appropriate values of reactor should be selected. Study indicated that reactor value might be between 3 and 5 mH. In such range, the current limited reactor has wee influence on CMOA limiting effect on switching over voltage.
Design and Analysis of Low Power Master Slave Flip-Flops Imran

In this paper, a comparative analysis of existing architectures for flip-flop along with the proposed design is made. The comparison is done on the basis of power, delay, PDP and transistor count. Due to continuous increase in integration of transistors and growing needs of portable equipments, low power design is of prime importance. All simulations are performed on TSpice using BSIM models in 130 nm process node. The simulation results show that for all supply voltages, all clock frequencies and all data activities proposed flip-flop consumes the lowest power. Proposed flip-flop has the second shortest delay and the second lowest PDP and also occupies low area. So this design is best suited for low power and high performance applications.
Joint Design of Transmit Waveforms and Receive Filters for MIMO Radar Space-Time Adaptive Processing

This paper considers the joint design of transmit waveforms and receive filters for airborne multiple-input-multiple-output (MIMO) radar systems. The aim is to maximize the output signal-to-interference-plus-noise ratio (SINR) such that we can achieve enhanced detection performance for a slow-moving target that might be obscured by clutter and jamming. We devise a cyclic algorithm to tackle the non-convex joint design problem. Considering practical implementation issues, we extend the algorithm to deal with constrained designs (i.e., the design of waveforms under a constant-envelope constraint and a similarity constraint, respectively). The convergence of all the devised algorithms is guaranteed. We provide several numerical examples to demonstrate the effectiveness of the proposed algorithms.
Downregulation of VAPB expression in motor neurons derived from induced pluripotent stem cells of ALS8 patients

Amyotrophic lateral sclerosis (ALS) is an incurable neuromuscular disease that leads to a profound loss of life quality and premature death. Around 10% of the cases are inherited and ALS8 is an autosomal dominant form of familial ALS caused by mutations in the vamp-associated protein B/C (VAPB) gene. The VAPB protein is involved in many cellular processes and it likely contributes to the pathogenesis of other forms of ALS besides ALS8. A number of successful drug tests in ALS animal models could not be translated to humans underscoring the need for novel approaches. The induced pluripotent stem cells (iPSC) technology brings new hope, since it can be used to model and investigate diseases in vitro. Here we present an additional tool to study ALS based on ALS8-iPSC. Fibroblasts from ALS8 patients and their non-carrier siblings were successfully reprogrammed to a pluripotent state and differentiated into motor neurons. We show for the first time that VAPB protein levels are reduced in ALS8-derived motor neurons but, in contrast to over-expression systems, cytoplasmic aggregates could not be identified. Our results suggest that optimal levels of VAPB may play a central role in the pathogenesis of ALS8, in agreement with the observed reduction of VAPB in sporadic ALS.
Estimation Error of the Lasso

This paper presents an upper bound for the estimation error of the constrained lasso, under the high-dimensional (n < p) setting. In contrast to existing results, the error bound in this paper is sharp, is valid when the parameter to be estimated is not exactly sparse (e.g., when the parameter is weakly sparse), and shows explicitly the effect of over-estimating the `1-norm of the parameter to be estimated on the estimation performance of the lasso. The results of this paper show that the lasso is minimax optimal for estimating a parameter with bounded `1-norm, and if the exact value of the `1-norm of the parameter to be estimated is accessible, the lasso is also minimax optimal for estimating a weakly sparse parameter.
Die storage improvement with k-means clustering algorithm: A case of paper packaging business

This paper presents die storage improvement for a case study company, who is a manufacturer of made-to-order paper packaging product. One of the critical equipment used to produce paper packaging is the dies used in die cutting machine. These dies are stored in the separate storage room and they are placed on any available shelf slot. Due to the wide variety of product design, number of die stored in die storage room is large and continues to grows every year due to the increasing number of customers. Die storage room has become untidy and packed, which make the die retrieve process become more difficult. K-means clustering, one of the data mining algorithms, was applied to cluster dies into groups based on their size, price and frequency of use. Then the layout of storage room was re-designed based on the new cluster to improve space utilization. After improvement, the time used for die retrieval was significantly reduced.
Fibre optic acoustic emission sensor system for hydrogen induced cold crack monitoring in welding applications

A fibre optic acoustic emission method to monitor hydrogen induced cold crack activities during welding is presented in this paper. A high sensitivity fiber Bragg grating (FBG) based acoustic emission (AE) measurement system is developed to monitor the cold crack induced AE events during a titanium aluminide alloy welding using gas tungsten arc welding method Frequency ranges of the AE where cold cracking occurs in the metal also has been evaluated. The measured frequency components of the AE events were within 270 kHz. The results of this feasibility study shows the potential of FBG based AE detection method to accurately predict the defects in welding, which will improve the quality of the final weld product in applications where higher welding standards are required.
Familiarity breeds contempt: perceived service and sales complacency in business-to-business relationships

Relationship marketing and its strategic focus on long-term exchanges is an engrained practice in today’s business-to-business (B2B) environment. B2B relationships are important given the reciprocating benefits they offer to offset the resource-intensive investments necessary for relationship initiation and maintenance. However, the reality of the marketplace shows customer defection rates remain a concern, dark side relationship mechanisms emerge as exchanges persist, and suppliers shirk on investing the effort necessary to service their customers. Research is needed to better understand customer defections and the nuanced assessment of perceived supplier complacency in B2B service relationships. To inform this research agenda, the authors assess depth interviews with 56 executive participants across 35 post-mortem customer defection cases. Findings identify thematic representations of perceived supplier complacency within the customer-supplier service provision (i.e. service complacency), as well as during the sales renewal process (i.e. sales complacency).
A New Approach for Heparin Standardization: Combination of Scanning UV Spectroscopy, Nuclear Magnetic Resonance and Principal Component Analysis

The year 2007 was marked by widespread adverse clinical responses to heparin use, leading to a global recall of potentially affected heparin batches in 2008. Several analytical methods have since been developed to detect impurities in heparin preparations; however, many are costly and dependent on instrumentation with only limited accessibility. A method based on a simple UV-scanning assay, combined with principal component analysis (PCA), was developed to detect impurities, such as glycosaminoglycans, other complex polysaccharides and aromatic compounds, in heparin preparations. Results were confirmed by NMR spectroscopy. This approach provides an additional, sensitive tool to determine heparin purity and safety, even when NMR spectroscopy failed, requiring only standard laboratory equipment and computing facilities.
Improved One-Class SVM for Pattern Denoising

One-class support vector machine (OCSVM) is one of the most well-known kernel methods, in which one attempts to use the hyperplane defined on feature space to separate the normal data from all other possible abnormal data. However, the standard OCSVM ignores the importance of the data distribution. With respect to the drawback of OCSVM, the information of data distribution is introduced to improve the robustness of algorithm and descript the normal data more reasonably. Meanwhile, combining with the solution of pre-image problem, we propose a novel method which takes the characteristic of data into full consideration for pattern denoising. In the paper, denoising procedures are completely presented and experimental results on a toy dataset and several real-word datasets explicitly indicate the applicability and effectiveness by comparing with some former pattern denoising methods.
Calibration of kinematic body sensor networks: Kinect-based gauging of data gloves “in the wild”

Our hands generic precision and agility is yet unmatched by technology, hence the quantitative study of its daily life kinematics is fundamental to neurology/prosthetics & robotics and creative industries. State-of-the-art solutions capturing hand movements ‘in the wild’ requires wearable body sensor networks: data gloves. Yet, fast-accurate calibration is challenging due to variability in hand anatomy and complexity of finger joints. We present here novel methods for calibration using streaming information from depth cameras (Microsoft Kinect). Our low-cost system calibrates the data glove by observing a user wiggling their hands while wearing data gloves. Using inverse kinematics we reconstruct in real-time hand configuration, enabling augmented reality by superimposing the virtual and real hand veridically. We achieve accuracies of ±5 degrees RMSE over all 21 joints, almost 20% more accurate than standard calibration methods and accurately capture touching of fingertips and thumb — our benchmark test unmatched by other calibration methods.
Job recommendation in AskStory: experiences, methods, and evaluation

AskStory is an e-recruitment site that maintains a large number of resumes and job openings. Job seekers in AskStory have difficulty in finding proper job openings that she/he is likely to be interested in. We discuss an approach to recommend job openings to jobs seekers. We identify the properties of the dataset used in job recommendation, discover the problems caused by the properties, and propose the methods for alleviating the problems. We evaluate our approach through extensive experiments. The results show that our approach is effective in alleviating the problems and provides recommendation accuracy satisfactory to job seekers.
Reinforcement of the Brain's Rich-Club Architecture Following Early Neurodevelopmental Disruption Caused by Very Preterm Birth

The second half of pregnancy is a crucial period for the development of structural brain connectivity, and an abrupt interruption of the typical processes of development during this phase caused by the very preterm birth (<33 weeks of gestation) is likely to result in long-lasting consequences. We used structural and diffusion imaging data to reconstruct the brain structural connectome in very preterm-born adults. We assessed its rich-club organization and modularity as 2 characteristics reflecting the capacity to support global and local information exchange, respectively. Our results suggest that the establishment of global connectivity patterns is prioritized over peripheral connectivity following early neurodevelopmental disruption. The very preterm brain exhibited a stronger rich-club architecture than the control brain, despite possessing a relative paucity of white matter resources. Using a simulated lesion approach, we also investigated whether putative structural reorganization takes place in the very preterm brain in order to compensate for its anatomical constraints. We found that connections between the basal ganglia and (pre-) motor regions, as well as connections between subcortical regions, assumed an altered role in the structural connectivity of the very preterm brain, and that such alterations had functional implications for information flow, rule learning, and verbal IQ.
Adaptive feedback cancellation in hearing aids with linear prediction of the desired signal

The standard continuous adaptation feedback cancellation algorithm for feedback suppression in hearing aids suffers from a large model error or bias if the received sound signal is spectrally colored. To reduce the bias in the feedback path estimate, we propose adaptive feedback cancellation techniques that are based on a closed-loop identification of the feedback path as well as the (auto-regressive) modeling of the desired signal. In general, both models are not simultaneously identifiable in the closed-loop system at hand. We show that-under certain conditions, e.g., if a delay is inserted in the forward path-identification of both models is indeed possible. Two classes of adaptive procedures for identifying the desired signal model and the feedback path are derived: a two-channel identification method as well as a prediction error method. In contrast to the two-channel identification method, the prediction error method allows use of different adaptation schemes for the feedback path and for the desired signal model and, hence, is found to be preferable for highly nonstationary sound signals. Simulation results demonstrate that the proposed techniques outperform the standard continuous adaptation algorithm if the conditions for identifiability are satisfied.
Analgesic and anti-inflammatory effects of ethanol extracted leaves of selected medicinal plants in animal model -

Aim: The research was carried out to investigate the analgesic and anti-inflammatory effects of ethanol extract of Desmodium pauciflorum, Mangifera indica and Andrographis paniculata leaves. Materials and Methods: In order to assess the analgesic and anti-inflammatory effects acetic acid induced writhing response model and carrageenan induced paw edema model were used in Swiss albino mice and Wistar albino rats, respectively. In both cases, leaves extract were administered (2gm/kg body weight) and the obtained effects were compared with commercially available analgesic and anti-inflammatory drug Dclofenac sodium (40mg/kg body weight). Distilled water (2ml/kg body weight) was used as a control for the study. Results: In analgesic bioassay, oral administration of the ethanol extract of leaves were significantly (p<0.01) reduced the writhing response. The efficacy of leaves extract were almost 35% in Desmodium pauciflorum, 56% in Mangifera indica and 34% in Andrographis paniculata which is found comparable to the effect of standard analgesic drug diclofenac sodium (76%). Leaves extract reduced paw edema in variable percentages but they did not show any significant difference among the leaves. Conclusion: We recommend further research on these plant leaves for possible isolation and characterization of the various active chemical substances which has the toxic and medicinal values.
Experiments with an ensemble of Spanish dependency parsers

This article presents an ensemble system for dependency parsing of Spanish that combines three machine-learning-based dependency parsers. The system operates in two stages. In the first stage, each of the three parsers analyzes an input sentence and produces a dependency graph. In the second stage, a voting system distills a final dependency graph out of the three first-stage dependency graphs.
Competence research: teaching embedded micro/nano systems

In this paper, we present the development and first empirical refinement of a normative competence structure model for higher education in embedded micro systems and prospective embedded nanosystems. This kind of competence model is necessary to progress from a subjective and content oriented design of courses to traceable and outcome-oriented courses. In addition, we show a flexible alternative for teaching embedded systems in cost-intensive courses by building a FPGA laboratory offering remote access. Furthermore, we sketch out a possible way to overcome and teach fault-prone nanodevices in a Bottom-Up approach.