Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating the internal and external training loads in soccer.

PURPOSE This study aimed to assess the relationships of fitness in soccer players with a novel integration of internal and external training load (TL). DESIGN Ten amateur soccer players performed a lactate threshold (LT) test followed by a soccer simulation (Ball-Sport Endurance and Sprint Test [BEAST90mod]). METHODS The results from the LT test were used to determine velocity at lactate threshold (vLT), velocity at onset of blood lactate accumulation (vOBLA), maximal oxygen uptake (VO2max), and the heart rate-blood lactate profile for calculation of internal TL (individualized training impulse, or iTRIMP). The total distance (TD) and high intensity distance (HID) covered during the BEAST90mod were measured using GPS technology that allowed measurement of performance and external TL. The internal TL was divided by the external TL to form TD:iTRIMP and HID:iTRIMP ratios. Correlation analyses assessed the relationships between fitness measures and the ratios to performance in the BEAST90mod. RESULTS vLT, vOBLA, and VO2max showed no significant relationship to TD or HID. HID:iTRIMP significantly correlated with vOBLA (r = .65, P = .04; large), and TD:iTRIMP showed a significant correlation with vLT (r = .69, P = .03; large). CONCLUSIONS The results suggest that the integrated use of ratios may help in the assessment of fitness, as performance alone showed no significant relationships with fitness.
Discovering Mappings Between Ontologies

Knowledge Representation is important part of AI. The purpose is to reveal best possible representation of the Universe of Discourse (UoD) by capturing entities, concepts and relations among them. With increased understanding of various scientific and technological disciplines, it is possible to derive rules that governs the behaviour and outcome of the entities in the UoD. In certain cases, it is not possible to establish any explicit rule, yet through experience or observation, some experts can define rules from their tacit knowledge in specific domain. Knowledge representation techniques are focused on techniques that allows externalization of implicit and explicit knowledge of expert(s) with a goal of reuse in absence of physical presence of such expertise. To ease this task, two parallel dimensions have developed over period of time. One dimension is focused on investigating more efficient methods that best suit the knowledge representation requirement resulting in theories and tools that allows capturing the domain knowledge (Brachman & Levesque, 2004). Another development has taken place in harmonization of tools and techniques that allows standard based representation of knowledge (Davies, Studer, & Warren, 2006). Various languages are proposed for representation of the knowledge. Reasoning and classification algorithms are also realized. As an outcome of standardization process, standards like DAML-OIL (Horrocks & PatelSchneider, 2001), RDF (Manola & Miller, 2004) and OWL(Antoniou & Harmelen, 2004) are introduced. Capturing the benefit of both developments, the tooling is also came in to existence that allows creation of knowledgebase. As a result of these developments, the amount of publicly shared knowledge is continuously increasing. At the time of this writing, a search engine like Swoogle (Ding et al., 2004)-developed to index publicly available Ontologies, is handling over 2,173,724 semantic web documents containing 431,467,096 triples. While the developments are yielding positive results by such a huge amount of knowledge available for reuse, it have become difficult to select and reuse required knowledge from this vast pool. The concepts and their relations that are important to the given problem could have already been defined in multiple Ontologies with different perspectives with specific level of details. It is very likely that to get complete representation of the knowledge, multiple Ontologies must be utilized. This requirement has introduced a new discipline within the domain of knowledge representation that is focused on investigation of techniques and tools that allows integration of multiple shared Ontologies.
Assessment of Prediction Confidence and Domain Extrapolation of Two Structure–Activity Relationship Models for Predicting Estrogen Receptor Binding Activity

Quantitative structure–activity relationship (QSAR) methods have been widely applied in drug discovery, lead optimization, toxicity prediction, and regulatory decisions. Despite major advances in algorithms and software, QSAR models have inherent limitations associated with a size and chemical-structure diversity of the training set, experimental error, and many characteristics of structure representation and correlation algorithms. Whereas excellent fit to the training data may be readily attainable, often models fail to predict accurately chemicals that are outside their domain of applicability. A QSAR’s utility and, in the case of regulatory decisions, justification for usage increasingly depend on the ability to quantify a model’s potential for predicting unknown chemicals with some known degree of certainty. It is never possible to predict an unknown chemical with absolute certainty. Here we report on two QSAR models based on different data sets for classification of chemicals according to their ability to bind to the estrogen receptor. The models were developed by using a novel QSAR method, Decision Forest, which combines the results of multiple heterogeneous but comparable Decision Tree models to produce a consensus prediction. We used an extensive cross-validation process to define an applicability domain for model predictions based on two quantitative measures: prediction confidence and domain extrapolation. Together, these measures quantify the accuracy of each prediction within and outside of the training domain. Despite being based on large and diverse training sets, both QSAR models had poor accuracy for chemicals within the domain of low confidence, whereas good accuracy was obtained for those within the domain of high confidence. For prediction in the high confidence domain, accuracy was inversely proportional to the degree of domain extrapolation. The model with a larger training set of 1,092, compared with 232 for the other, was more accurate in predicting chemicals at larger domain extrapolation, and could be particularly useful for rapidly prioritizing potential endocrine disruptors from large chemical universe.
SystemCoDesigner—an automatic ESL synthesis approach by design space exploration and behavioral synthesis for streaming applications

With increasing design complexity, the gap from ESL (Electronic System Level) design to RTL synthesis becomes more and more crucial to many industrial projects. Although several behavioral synthesis tools exist to automatically generate synthesizable RTL code from C/C++/SystemC-based input descriptions and software generation for embedded processors is automated as well, an efficient ESL synthesis methodology combining both is still missing. This article presents SystemCoDesigner, a novel SystemC-based ESL tool to automatically optimize a hardware/software SoC (System on Chip) implementation with respect to several objectives. Starting from a SystemC behavioral model, SystemCoDesigner automatically extracts the mathematical model, performs a behavioral synthesis step, and explores the multiobjective design space using state-of-the-art multiobjective optimization algorithms. During design space exploration, a single design point is evaluated by simulating highly accurate performance models, which are automatically generated from the SystemC behavioral model and the behavioral synthesis results. Moreover, SystemCoDesigner permits the automatic generation of bit streams for FPGA targets from any previously optimized SoC implementation. Thus SystemCoDesigner is the first fully automated ESL synthesis tool providing a correct-by-construction generation of hardware/software SoC implementations. As a case study, a model of a Motion-JPEG decoder was automatically optimized and implemented using SystemCoDesigner. Several synthesized SoC variants based on this model show different tradeoffs between required hardware costs and achieved system throughput, ranging from software-only solutions to pure hardware implementations that reach real-time performance for QCIF streams on a 50MHz FPGA.
A Hybrid Cellular Automaton Model of Solid Tumor Growth and Bioreductive Drug Transport

Bioreductive drugs are a class of hypoxia selective drugs that are designed to eradicate the hypoxic fraction of solid tumors. Their activity depends upon a number of biological and pharmacological factors and we used a mathematical modeling approach to explore the dynamics of tumor growth, infusion, and penetration of the bioreductive drug Tirapazamine (TPZ). An in-silico model is implemented to calculate the tumor mass considering oxygen and glucose as key microenvironmental parameters. The next stage of the model integrated extra cellular matrix (ECM), cell-cell adhesion, and cell movement parameters as growth constraints. The tumor microenvironments strongly influenced tumor morphology and growth rates. Once the growth model was established, a hybrid model was developed to study drug dynamics inside the hypoxic regions of tumors. The model used 10, 50 and 100 μM as TPZ initial concentrations and determined TPZ pharmacokinetic (PK) (transport) and pharmacodynamics (cytotoxicity) properties inside hypoxic regions of solid tumor. The model results showed that diminished drug transport is a reason for TPZ failure and recommend the optimization of the drug transport properties in the emerging TPZ generations. The modeling approach used in this study is novel and can be a step to explore the behavioral dynamics of TPZ.
Ieee Transactions on Software Engineering 1 Reducing Masking Effects in Combinatorial Interaction Testing: a Feedback Driven Adaptive Approach

—The configuration spaces of modern software systems are too large to test exhaustively. Combinatorial interaction testing (CIT) approaches, such as covering arrays, systematically sample the configuration space and test only the selected configurations. The basic justification for CIT approaches is that they can cost-effectively exercise all system behaviors caused by the settings of t or fewer options. We conjecture, however, that in practice some of these behaviors are not actually tested because of unanticipated masking effects – test case failures that perturb system execution so as to prevent some behaviors from being exercised. While prior research has identified this problem, most solutions require knowing the masking effects a priori. In practice this is impractical, if not impossible. In this work, we reduce the harmful consequences of masking effects. First we define a novel interaction testing criterion, which aims to ensure that each test case has a fair chance to test all valid t-way combinations of option settings. We then introduce a feedback driven adaptive combinatorial testing process (FDA-CIT) to materialize this criterion in practice. At each iteration of FDA-CIT, we detect potential masking effects, heuristically isolate their likely causes (i.e., fault characterization), and then generate new samples that allow previously masked combinations to be tested in configurations that avoid the likely failure causes. The iterations end when the new interaction testing criterion has been satisfied. This paper compares two different fault characterization approaches – an integral part of the proposed approach, and empirically assesses their effectiveness and efficiency in removing masking effects on two widely used open source software systems. It also compares FDA-CIT against error locating arrays, a state of the art approach for detecting and locating failures. Furthermore, the scalability of the proposed approach is evaluated by comparing it with perfect test scenarios, in which all masking effects are known a priori. Our results suggest that masking effects do exist in practice, and that our approach provides a promising and efficient way to work around them, without requiring that masking effects be known a priori.
The rational planning model in forest planning : planning in the light of ambivalence

The Ecosystem Workforce Program Working Papers series offers in-depth reports on applied research, analysis and findings about a variety of areas associated with the effort to build quality jobs in ecosystem management. The target audience includes policy and administrative leaders, academics, leaders in community forestry, community-based organization leaders, and local community officials. Since its inception in the late nineteenth century, United States forest planning has been inextricably linked to a specific planning structure, that of the rational model. The current debates concerning not only the value of forest ecological sustainability, but ways to characterize, measure, and monitor it call some of the premises of the rational model into question. This report examine these tensions through a discussion of the foundations of the rational model, its application to forest planning, and the conflicts that have arisen due to the public demand that evaluative criteria not entirely amenable to the rational model be incorporated into forest plans. Recommendations for planning regulations devised by the 1999 Committee of Scientists Report (some of which were adopted as final planning rules in November 2000) are used as a lens to examine the possibility that planning may proceed despite the uncertainties associated with our knowledge of ecological processes. James Luzzi is a graduate student in Community and Regional Planning at the University of Oregon. Prior to returning to school, he worked in the woods for more than 20 years as a contractor.
Evolutionary Algorithms in Natural Language Processing

Natural language processing grapples with an ever-changing and moving target. The focus of study, natural language, is natural because it changes, interacts and evolves in various directions. The bio-inspired computational methods described as evolutionary computation create computational models that evolve a population of individuals to find a solution to a given problem. This paper investigates how evolutionary computation has been employed in natural language processing, ranging from efforts to induce grammars to models of language development through parameter optimization and search.
Finger-Aware Shortcuts

We evaluate and demonstrate finger, hand, and posture identification as keyboard shortcuts. By detecting the hand and finger used to press a key, and open or closed hand postures, a key press can have multiple command mappings. A formative study reveals performance and preference patterns when using different fingers and postures to press a key. The results are used to develop a computer vision algorithm to identify fingers and hands on a keyboard captured by a built-in lap top camera and reflector. This algorithm is built into a background service to enable system-wide finger-aware shortcut keys in any application. A controlled experiment uses the service to compare the performance of Finger-Aware Shortcuts with existing methods. The results show Finger-Aware Shortcuts are comparable with a common class of shortcuts using multiple modifier keys. Finally, application demonstrations illustrate different use cases and mappings for Finger-Aware Shortcuts and extend the idea to two-handed key presses, continuous parameter control, and menu selection.
Hypothesis-generating research and predictive medicine

Genomics has profoundly changed biology by scaling data acquisition, which has provided researchers with the opportunity to interrogate biology in novel and creative ways. No longer constrained by low-throughput assays, researchers have developed hypothesis-generating approaches to understand the molecular basis of nature—both normal and pathological. The paradigm of hypothesis-generating research does not replace or undermine hypothesis-testing modes of research; instead, it complements them and has facilitated discoveries that may not have been possible with hypothesis-testing research. The hypothesis-generating mode of research has been primarily practiced in basic science but has recently been extended to clinical-translational work as well. Just as in basic science, this approach to research can facilitate insights into human health and disease mechanisms and provide the crucially needed data set of the full spectrum of genotype–phenotype correlations. Finally, the paradigm of hypothesis-generating research is conceptually similar to the underpinning of predictive genomic medicine, which has the potential to shift medicine from a primarily population- or cohort-based activity to one that instead uses individual susceptibility, prognostic, and pharmacogenetic profiles to maximize the efficacy and minimize the iatrogenic effects of medical interventions.
A multidisciplinary study of the final episode of the Manda Hararo dyke sequence, Ethiopia, and implications for trends in volcanism during the rifting cycle

Abstract The sequence of dyke intrusions between 2005 and 2010 in the Manda Hararo rift segment, Ethiopia, provided an opportunity to test conceptual models of continental rifting. Based on trends up to dyke 13 in the sequence, it was anticipated that, should magma supply continue, dykes would shorten in length and eruptions would increase in size and decrease in distance from the segment centre as extensional stress was progressively released. In this paper we revisit these predictions by presenting a comprehensive overview of the May 2010 dyke and fissure eruption, the 14th and last in the sequence, from InSAR, seismicity, satellite thermal data, ultraviolet SO2 retrievals and multiple LiDAR surveys. We find the dyke is longer than other eruptive dykes in the sequence, propagating in two directions from the segment centre, but otherwise fairly typical in terms of opening, propagation speed and geodetic and seismic moment. However, though the eruption is located closer to the segment centre, it is much smaller than previous events. We interpret this as indicating that either the Manda Hararo rifting event was magma limited, or that extensional stress varies north and south of the segment centre.
Exploring the design space of interactive link curvature in network diagrams

When exploiting the power of node-link diagrams to represent real-world data such as web structures, airline routes, electrical, telecommunication and social networks, link congestion frequently arises. Such areas in the diagram---with dense, overlapping links---are not readable connectivity, node shapes, labels, and contextual information are obscured. In response, graph-layout research has begun to consider the modification of link shapes with techniques such as link routing and bundling. In this paper, we delve into the interactive techniques afforded by variant use of link curvature, delineating a six-dimensional design space that is populated by four families of interactive techniques: bundling, fanning, magnets, and legends. Our taxonomy encompasses existing techniques and reveals several novel link interactions. We describe the implementation of these techniques and illustrate their potential for exploring dense graphs with multiple types of links.
Embedded modified Euler method: an efficient and accurate model

In this paper, an efficient and accurate method for the extended period simulation of pipe networks under dynamic loading is proposed. The method uses the modified Euler method to discretise the ordinary differential equation governing the variation of water level in the reservoirs to yield a non-linear algebraic equation in terms of the reservoir head and inflow. These equations are then embedded into the non-linear system of equations describing the steady-state flow in the pipe network to obtain the final system of equations. A Newton–Raphson method of linearisation is used for the equation governing the tank water level variation along with a gradient formulation of the pipe networks. The solution of the resulting system of equations automatically yields the distribution of nodal heads along with the reservoir head at the end of each period. The method uses only one steady-state simulation per period to predict accurately and efficiently the reservoir water elevation. Application of the proposed metho.
A Physics-Based, Neurobiologically-Inspired Stochastic Framework for Activity Recognition

We present a multi-disciplinary framework for motion modeling and recognition in machine vision. Building upon the neurobiological model of motion recognition, we propose computational equivalents for the Motion Energy and Form Pathways. We derive the Hamiltonian Energy Signature (HES) from first-principles in physics as the basis for the Motion Energy Pathway. The Form Pathway is modeled using existing low-level feature descriptors based on shape, appearance, and gradients. We propose an Integration methodology to combine both pathways using a variant of the Feature Integration and Biased Competition neurobiological models, which we implement via statistical hypothesis testing using the bootstrap. We also show the extensibility of our physics-based approach by proposing a physically-significant, compact representation for the gait of a person called the Gait Action Image (GAI), which is based on core physics principles employed in the HES formulation. We then show the generalizability of our neurobiologically-inspired integration framework by casting the GAI within this infrastructure. Since Motion and image analysis are both important for activity recognition in video, we also develop a new approach that extends the Hamiltonian Monte Carlo (HMC) to allow us to simultaneously search over the combined motion and image space in a concerted manner using well-known Markov Chain Monte Carlo (MCMC) techniques. For motion analysis in video, we use tracks generated from the video to calculate the Hamiltonian equations of motion for the systems under study, thus utilizing analytical Hamiltonian dynamics to derive a physically significant HMC algorithm which can be used for activity analysis. We then use image analysis to help explore both the motion energy space and the image space by integrating the Hamiltonian energy-based approach with an image-based data-driven proposal to drive the HMC, thereby yielding a Data Driven HMC (DDHMC). We reduce the enormity of the search space by driving the Hamiltonian dynamics-based MCMC with image data in this DDHMC. We also develop the reverse algorithm, which uses motion energy proposals to search the image space. While HMC has been used in other contexts, this is possibly the first work that shows how it can be used for activity recognition in video, taking into account the image analysis results and using the physical motion information of the system. In addition, the DDHMC framework has potential application to other domains where statistical sampling techniques are useful, as we outline in the section on future work.Experimental validation of the theory is provided on the well-known KTH, Weizmann, and USF Gait datasets with very promising results.
A Generic Collaborative Platform for Multilingual Lexical Database Development

The motivation of the Papillon project is to encourage the development of freely accessible Multilingual Lexical Resources by way of online collaborative work on the Internet. For this, we developed a generic community website originally dedicated to the diffusion and the development of a particular acception based multilingual lexical database. The generic aspect of our platform allows its use for the development of other lexical databases. Adapting it to a new lexical database is a matter of description of its structures and interfaces by way of XML files. In this paper, we show how we already adapted it to other very different lexical databases. We also show what future developments should be done in order to gather several lexical databases developers in a common network.