Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Active Recovery on Power Performance During the Bench Press Exercise

Abstract The objective of this study was to verify the effect of active and passive recovery on blood lactate concentration and power performance. Twelve male subjects were submitted to a maximal strength test in the the bench press, a maximal aerobic test in the bench step, and to four sets of bench press exercise performed as fast and as long as possible, using 80% of maximal strength when active or passive recovery was performed. The maximum number of repetitions, mean and peak power in eccentric and concentric phases were computed and blood lactate concentration was measured. Comparisons for the variables were made using a two-way variance analysis (recovery type and set numer) with repeated measures in the second factor. When significant differences were detected (p < 0.05), a Tukey post-hoc test was used. There was a main effect of set number on maximum number of repetitions (p < 0.05) (1 > 2, 3, and 4; 2 > 3 and 4; 3 > 4). Mean and peak power in both eccentric and concentric phases also differed across sets (1 > 2, 3, and 4; 2 > 4). There was also a main effect for the recovery type, with lower values (p < 0.05) observed for the active recovery compared to the passive one. It can be concluded that active recovery resulted in lower lactate concentration, but did not improve power performance in the bench press exercise.
Building high accuracy bloom filters using partitioned hashing

The growing importance of operations such as packet-content inspection, packet classification based on non-IP headers, maintaining flow-state, etc. has led to increased interest in the networking applications of Bloom filters. This is because Bloom filters provide a relatively easy method for hardware implementation of set-membership queries. However, the tradeoff is that Bloom filters only provide a probabilistic test and membership queries can result in false positives. Ideally, we would like this false positive probability to be very low. The main contribution of this paper is a method for significantly reducing this false positive probability in comparison to existing schemes. This is done by developing a partitioned hashing method which results in a choice of hash functions that set far fewer bits in the Bloom filter bit vector than would be the case otherwise. This lower fill factor of the bit vector translates to a much lower false positive probability. We show experimentally that this improved choice can result in as much as a ten-fold increase in accuracy over standard Bloom filters. We also show that the scheme performs much better than other proposed schemes for improving Bloom filters.
Base Rate Pass-Through: Evidence from Banks' and Building Societies' Retail Rates

Official interest rate changes are intended to influence short rates on money market instruments and retail products, such as deposit accounts and mortgages, and complete pass-through is often simply taken for granted. In this paper a theoretical and econometric framework for assessing the evidence for this assumption is provided, using 14 years of monthly data for interest rates on deposit and mortgage products offered by UK banks and building societies. The method employed allows for asymmetries and non-linearities in adjustment and the results show that the speed of adjustment in retail rates depends on whether the perceived 'gap' between retail and base rates is widening or narrowing.
Impact of star performance ratings in English acute hospital trusts

Objective To explore some of the impacts of star performance ratings in acute hospital trusts in England. Methods A multiple case study design was used which incorporated purposeful sampling of ‘low’ and ‘high’ performing trusts using the star rating system. In each case study site, data collection comprised semi-structured interviews and documentary analysis. Between eight and 12 senior managers and senior clinicians were interviewed in each organisation. Results There was a general view that the star ratings as presently constituted did not represent a rounded or balanced scorecard of their own organisation's performance and a widespread belief that the information used to calculate the ratings was often incomplete and inaccurate. The star ratings were viewed by some managers as useful, in that they gave added weight to their trust's modernisation agenda. In addition to driving beneficial change, the ratings were also sometimes reported to have inadvertently induced a range of unintended and dysfunctional consequences, including tunnel vision and a distortion of clinical priorities, bullying and intimidation, erosion of public trust and reduced staff morale, and ghettoisation. Conclusions Set in the context of an international body of research, this study highlights some important gaps in knowledge and failings in current policy and practice. In particular, the many dysfunctional consequences of publishing star ratings indicate a need for a re-examination of performance management policies.
Diversity Measurement for the Email Content of Information Workers

Since the introduction of computers and the Internet, information processing has evolved to be a major part of many businesses, but factors that contribute to the productivity of information workers are still understudied. Some social network studies claim that diverse information that passes through workers in the positions with diverse sources of information drives performance. However, such claims are rarely verified by empirical data. This study develops a measurement methodology for the diversity of the email content processed by information workers. The diversity values will be used for future productivity studies along with the results from social network analysis. Erik Brynjolfsson George and Sandi Schussel Professor of Management and Director, Center for Digital Business, MIT Sloan School of Management Thesis supervisor Sinan Aral PhD Candidate, Sloan School of Management Thesis co-supervisor Marshall Van Alstyne Associate Professor, Boston University School of Management Visiting Professor, MIT Sloan School of Management Thesis co-supervisor This research was funded by the National Science Foundation under grant number IIS-0085725.
From Natural Language Requirements to Formal Specification Using an Ontology

In order to check requirement specifications written in natural language, we have chosen to model domain knowledge through an ontology and to formally represent user requirements by its population. Our approach of ontology population focuses on instance property identification from texts. We do so using extraction rules automatically acquired from a training corpus and a bootstrapping terminology. These rules aim at identifying instance property mentions represented by triples of terms, using lexical, syntactic and semantic levels of analysis. They are generated from recurrent syntactic paths between terms denoting instances of concepts and properties. We show how focusing on instance property identification allows us to precisely identify concept instances explicitly or implicitly mentioned in texts.
Managing Disruptions in Decentralized Supply Chains with Endogenous Supply Process Reliability

Supply disruptions are all too common in supply chains. To mitigate delivery risk, buyers may either source from multiple suppliers or offer incentives to their preferred supplier to improve its process reliability. These incentives can be either direct (investment subsidy) or indirect (inflated order quantity). In this study, we present a series of models to highlight buyers’ and suppliers’ optimal parameter choices. Our base‐case model has deterministic buyer demand and two possibilities for the supplier yield outcomes: all‐or‐nothing supply or partial disruption. For the all‐or‐nothing model, we show that the buyer prefers to only use the subsidy option, which obviates the need to inflate order quantity. However, in the partial disruption model, both incentives—subsidy and order inflation—may be used at the same time. Although single sourcing provides greater indirect incentive to the selected supplier because that avoids order splitting, we show that the buyer may prefer the diversification strategy under certain circumstances. We also quantify the amount by which the wholesale price needs to be discounted (if at all) to ensure that dual sourcing strategy dominates sole sourcing. Finally, we extend the model to the case of stochastic demand. Structural properties of ordering/subsidy decisions are derived for the all‐or‐nothing model, and in contrast to the deterministic demand case, we establish that the buyer may increase use of subsidy and order quantity at the same time.
Grounding speech utterances in robotics affordances: An embodied statistical language model

We propose an embodied statistical language model for learning the meaning of word sequences, which incorporate action words (i.e. push, pull, tap, etc.,). The model attempts to unify embodied grounding and dis-embodied processing by representing the meaning of word sequences in both the sensorimotor and linguistic knowledge of a humanoid robot. The modeling of word sequences enables to distinguish the meaning of utterances containing the same categories and number of words but different structures. Once the robot has learned the semantics of word sequences and derived the affordances from words physical referents, has at its disposal a grounded language model that enables the mapping of linguistic commands provided by a human tutor into the appropriate behaviors. Moreover, the affordance model enables the robot to solve inference queries for filling in the information missing in the verbal description of the task.
Monadic exploration: seeing the whole through its parts

Monadic exploration is a new approach to interacting with relational information spaces that challenges the distinction between the whole and its parts. Building on the work of sociologists Gabriel Tarde and Bruno Latour we turn to the concept of the monad as a useful lens on online communities and collections that expands the possibility for creating meaning in their navigation. While existing interfaces tend to emphasize either the structure of the whole or details of a part, monadic exploration brings these opposing perspectives closer together in continuous movements between partially overlapping points of view. We present a visualization that reflects a given node's relative position within a network using radial displacements and visual folding. To investigate the potential of monadic exploration we report on an iterative design process of a web-based visualization of a highly cross-referenced book and its six-month deployment.
Hidden Markov Models for Image and Video Retrieval Using Textual Queries

A novel method is introduced for automatic annotation of images with keywords from a generic vocabulary of concepts or objects for the purpose of content-based image retrieval. An image, represented as sequence of featurevectors characterizing low-level visual features such as color, texture or orientededges, is modeled as having been stochastically generated by a hidden Markov model, whose states represent concepts. The parameters of the model are estimated from a set of manually annotated training-images. Each image in a large test-collection is then automatically labeled with the a posteriori probability of concepts present in it. This annotation supports content-based search of the testcollection via keywords. Various aspects of model parameterization, parameter estimation, and image annotation are dicussed, and empirical retrieval results are presented on the COREL and TRECVID data-sets. Comparisons are made with two other recently developed techniques on the same data-sets.
Learning Radial Basis Function Model with Matching Score Quality for Person Authentication in Multimodal Biometrics

Recently multimodal biometrics technology that employs more than two types of biometrics data has been popularly used for person authentication and verification. In particular, the score-level fusion approach which combines matching scores from unimodal systems to make final decision has gained lots of attentions. In most of these works, however, they assume all the matching scores to be of the same quality. This assumption may cause the problem not to reflect such situation that the qualities of the matching scores from certain unimodal systems are relatively low. To deal with this problem, we propose the RBF based score-level fusion approach which incorporates the quality information of the scores in developing classification models. According to our experimental results, the proposed method using quality information showed its superiority in the performance of person authentication to the usual RBF based score-level fusion without using quality information.
User – Transparent Run – Time Performance Optimization

Abstract –– High–performance embedded systems– High–performance embedded systems are being implemented as parallel and distributed systems with increasing frequency. There is strong motivation to make such systems adaptive and/or dynamic in order to obtain maximum performance and maximum reliability. One important component of an adaptive or dynamic parallel system is the ability to monitor the performance of running programs and make some effort to optimize execution. The goal of this research, part of a project named Hector, is to monitor the performance of parallel programs ‘‘behind the user’s back’’ to try to minimize run time on available resources.
Tuning of Multiple Parameter Sets in Evolutionary Algorithms

Evolutionary optimization algorithms typically use one or more parameters that control their behavior. These parameters, which are often kept constant, can be tuned to improve the performance of the algorithm on specific problems. However, past studies have indicated that the performance can be further improved by adapting the parameters during runtime. A limitation of these studies is that they only control, at most, a few parameters, thereby missing potentially beneficial interactions between them. Instead of finding a direct control mechanism, the novel approach in this paper is to use different parameter sets in different stages of an optimization. These multiple parameter sets, which remain static within each stage, are tuned through extensive bi-level optimization experiments that approximate the optimal adaptation of the parameters. The algorithmic performance obtained with tuned multiple parameter sets is compared against that obtained with a single parameter set. For the experiments in this paper, the parameters of NSGA-II are tuned when applied to the ZDT, DTLZ and WFG test problems. The results show that using multiple parameter sets can significantly increase the performance over a single parameter set.
Design and evaluation of a variable-flux flux-intensifying interior permanent magnet machine

This paper presents a design approach for interior permanent magnet (IPM) machines with variable-flux characteristics using low coercive force magnet material for improved efficiency and extended operating speed range. A flux-intensifying interior permanent (FI-IPM) type having Ld >; Lq is used in the design due to positive Id operation and reduced load Iq effects. Design consideration of machine structures and variable-flux machine attributes are discussed. In addition, leakage flux in a rotor is specially designed to obtain another degree-of-freedom in variable-flux control as well. Evaluation of the designed machine is provided using FEA simulations and experimental evaluation of a proof-of-principle prototype machine. The designed machine shows benefits in improving efficiency and extending the range of the torque-speed map when magnetization level of the low coercive force magnet is operated at the optimal levels.
Inherited Thrombophilia: Part 1

From the Sharing Cross and Westminster Medical School, London, UK; 2IRCCS Maggiore Hospital, Milan, Italy; 3Beth Israel Hospital, Boston USA; 4University Hospital Leiden, The Netherlands, 5Research Centre of Medical Genetics, Moscow, The Russian Federation, 6WHO, Switzerland; 7Christian Medical College Hospital, Vellore, India; 8University of Lund, Malmo, Sweden; ’ Institute of Clinical Genetics, Moscow, The Russian Federation; 10Washington University, St Louis, USA; 11 Institute of Thrombosis and Hemostasis, Tel Hashomer, Israel •
Is intellectual capital‐based strategy market‐based or resource‐based?: On sustainable strategy in a knowledge‐based economy

Purpose – The purpose of this paper is to construct a strategy model based on Intellectual Capital (IC) theory and to demonstrate that it is not purely resource‐based (RBV), but includes many elements that are rooted in the market based view (MBV). The authors' analysis indicates that only strategies which lead to both tangible and intangible revenues are sustainable in a knowledge‐based economy.Design/methodology/approach – The paper takes the form of an extensive review of IC and strategy literature, and in‐depth comparative analysis of IC concept and the strategy management frameworks, particularly Porter's framework.Findings – It is found that the IC‐based view (ICBV) is much closer to the MBV than what one would expect and the ICBV is more appropriate for a knowledge‐based economy than both the MBV and the RBV in general.Originality/value – It is widely assumed that IC theory is strongly related to resource‐based strategy. The authors question this simple view and maintain that the IC‐based view rela.
Research of Improving the Accuracy of License Plate Character Segmentation

In order to improve the accuracy of license plate character segmentation, this paper strengthens preprocessings before segmentation. In character binarization, use improved Bernsen method to reduce noise interference; use line-shape filter to remove plate frames. Then character segmentation approach combines projection and inherent characteristics of character, can better solve character adhesion and fracture problem. For tilted characters only in horizontal direction, even without tilt correction, this method can achieve better segmentation. Experiment results shows the accuracy of character segmentation reaches 97%.
Using Domain Speciﬁc Languages for Software Process Modeling

This paper presents the use of Domain-Specific Modeling (DSM) technologies to tackle the complexity associated to the definition and improvement of software process models. This complexity arises because these tasks involve the description of different activities as well as the relationships with other processes. In this work, we propose the use of viewpoint models to represent, in a modular and non-intrusive way, concerns expressed on a software process model. We developed a Domain-Specific Aspect Language, called AspectViewpoint, to create viewpoint models using a vocabulary based on the workflow control patterns.
NRPSNR: No-Reference Peak Signal-to-Noise Ratio for JPEG2000

The aim of this work is to define a no-referenced perceptual image quality estimator applying the perceptual concepts of the Chromatic Induction Model. The approach consists in comparing the received image, presumably degraded, against the perceptual versions (different distances) of this image degraded by means of a Model of Chromatic Induction, which uses some of the human visual system properties. Also we compare our model with a original estimator in image quality assessment, PSNR. Results are highly correlated with the ones obtained by PSNR for image (99.32% Lenna and 96.95% for image Baboon), but this proposal does not need an original image or a reference one in order to give an estimation of the quality of the degraded image.
Deep Learning Network Models to Categorize Texts According to Author's Gender and to Identify Text Sentiment

In the present article, we consider a problem to evaluate the gain in accuracy of using deep learning network for two language tasks: the automatic text classification according to the authors gender and to identify text sentiment. A preexisting corpus of Russian-language texts RusPersonality labeled with information on their authors (gender, age, psychological testing and so on) has been used for gender task along with the materials of the SentiRuEval competition for evaluating the sentiment of tweets. We have performed the comparative study of machine learning techniques for both tasks on the Russian-language texts. In case of gender tasks the bias in topics and genre was deliberately removed. The obtained neuronet models of deep learning demonstrate accuracy close to the state-of-the-art and even higher: for the gender identification up to 0.86 +/- 0.03 in Accuracy, 0.86 in F1-score, for sentiment classification the best model demonstrates F1 scores with micro average of 0.57 and macro average of 0.61 for banks dataset, and F1-micro of 0.61 and F1-macro of 0.74 for telekom.
Density and population structure of owl monkeys (Aotus azarai) in the Argentinean chaco

Owl monkeys are small monogamous primates ranging over a wide area extending from Panamá to the Chaco region of northern Argentina. The Chaco, an alluvial plain covering over one million km2 of Argentina, Bolivia, Brazil, and Paraguay, consists of a mosaic of grasslands, savannas, xeric thorn forests, and gallery forests. The region shows significant seasonal variation in climate, rainfall, and food availability. The goal of this study was to determine the density, size, and structure of a population of Aotus azarai in the seasonal gallery forests of the eastern Argentinean Chaco. Reported population density, as well as group size and composition are based on data collected from 11 groups contacted on approximately 900 occasions, and observed for over 2,000 hours during a three‐year period. Group and individual densities were 16 groups/km2 and 64 individuals/km2, respectively. Approximately half of the groups (n = 5) were small groups which had three individuals most of the time and never more than four, whereas the remaining groups were large groups composed of four or five individuals, and sometimes even six or seven individuals. This is the first study of A. azarai based on monitoring of a relatively large number of distinct groups. Our data suggest that owl monkeys in the seasonal subtropical forests of Formosa live at a density as high as those reported for owl monkey populations observed in tropical forests. The data also show that the social groups in the owl monkey population are of comparable size and composition to those characteristic of populations in the tropics. Am. J. Primatol. 53:99–108, 2001. © 2001 Wiley‐Liss, Inc.