Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementing Evidence-Based Practice in Elite Team Sports: An Action Research Case Study

Research Objectives This research project is an intervention project in which the objective has been to integrate analytics (i.e. the use of statistical analysis to support decision making) into the day-to-day work of the coaching staff of an elite sports team. Little is known about the use of evidence-based approaches within elite sports teams and how statistical analysis is used to support decision making by coaches other than the well-publicised Moneyball story which tells how the Oakland Athletics in Major League Baseball use statistical analysis in player recruitment (Lewis, 2003). The project offers a unique insight into the process of developing evidence-based practice in an organisation since the researcher has become part of the organisation and has been an integral participant in the change process by undertaking the performance analysis and co-producing the reporting structure. Evidence-Based Practice Evidence-based practice refers to the conscientious, explicit and judicious use of information in making decisions. Practice, particularly when alternative courses of action exist, is usually justified by some form of evidence. What counts is the nature and quality of the evidence and how it is used to inform practice. An explicit evidence-based approach can be traced back to medicine and the reaction to an editorial in the British Medical Journal in 1991 citing a study that only 15% of medical interventions were supported by solid scientific evidence. The descriptor “evidence-based” is now widely used in many fields including, for example, evidence-based management (Briner et al, 2009), evidence-based policy and evidence-based coaching. Four general types of evidence can be identified across these various applications: (i) practitioner expertise; (ii) situational context; (iii) external research; and
Fermi–LAT Observations toward the Galactic Center

Abstract The inner region of the Milky Way is one of the most interesting and complex regions of the γ-ray sky. Intense interstellar emission and point sources contribute to it, as well as other potential components such as an unresolved population of point sources and dark matter. In recent years, claims have been made of an excess consistent with a dark matter annihilation signal in the data collected with the Fermi Large Area Telescope (Fermi–LAT). Although these results are intriguing, the complexity involved in modeling the foreground and background emission from conventional astrophysical sources of γ-rays makes a conclusive interpretation of these results challenging. In these proceedings, I discuss Fermi–LAT observations of the Galactic center region, the methodology for point source detection and treatment of the interstellar emission, the characterization of the GeV excess, and implications for dark matter.
On the Far‐Zone Electromagnetic Field of a Horizontal Electric Dipole Over an Imperfectly Conducting Half‐Space With Extensions to Plasmonics

Second‐order asymptotic formulas for the electromagnetic fields of a horizontal electric dipole over an imperfectly conducting half‐space are derived using the modified saddle‐point method. Application examples are presented for ordinary and plasmonic media, and the accuracy of the new formulation is assessed by comparisons with two alternative state‐of‐the‐art theories and with the rigorous results of numerical integration.
An Open Repository and analysis tools for fine-grained, longitudinal learner data

We introduce an open data repository and set of associated visualization and analysis tools. The Pittsburgh Science of Learning Center’s “DataShop” has data from thousands of students deriving from interactions with on-line course materials and intelligent tutoring systems. The data is finegrained, with student actions recorded roughly every 20 seconds, and it is longitudinal, spanning semester or yearlong courses. Currently over 110 datasets are stored including nearly 18 million student actions. Most student actions are “coded” meaning they are not only graded as correct or incorrect, but are categorized in terms of the hypothesized competencies or knowledge components neede to perform that action. Researchers have analyzed these data to better understand student cognitive and affective states and the results have been used to redesign instruction and demonstrably improve student learning.
Combining Federalism and Democracy in Developing Countries

This decade's geopolitical changes have given the governments of developing countries increased opportunities to shape their countries'future by designing appropriate institutions. Economic advisers today normally recommend these governments to strengthen economic competition by abandoning planning systems and implementing markets. This, however, is no guarantee for a steady development. Competition must also be installed in the political realm in order to make the governments observe the preferences of their citizens. Functional, Overlapping and Competing Jurisdictions (FOCJ) provide a system of dynamic federalism and democracy which breaks the monopoly of central government and shifts the power to local, functional and democratic units.
Two-sided space-time $L^1$ approximation and optimal control of polynomial systems

We study a two-sided space-time $L^1$ optimization problem and show how to reformulate the problem within the framework of optimal control theory for polynomial systems. This yields insight on the structure of the optimal solution. We prove existence and uniqueness of the optimal solution, and we characterize it by means of the Pontryagin maximum principle. The cost function and the control converge when the polynomial degree tends to $+∞$. We illustrate the theory with numerical simulations, which show that our optimal control interpretation leads to efficient algorithms.
Information Leakage in Encrypted Deduplication via Frequency Analysis

Encrypted deduplication seamlessly combines encryption and deduplication to simultaneously achieve both data security and storage efficiency. State-of-the-art encrypted deduplication systems mostly adopt a deterministic encryption approach that encrypts each plaintext chunk with a key derived from the content of the chunk itself, so that identical plaintext chunks are always encrypted into identical ciphertext chunks for deduplication. However, such deterministic encryption inherently reveals the underlying frequency distribution of the original plaintext chunks. This allows an adversary to launch frequency analysis against the resulting ciphertext chunks, and ultimately infer the content of the original plaintext chunks. In this paper, we study how frequency analysis practically affects information leakage in encrypted deduplication storage, from both attack and defense perspectives. We first propose a new inference attack that exploits chunk locality to increase the coverage of inferred chunks. We conduct trace-driven evaluation on both real-world and synthetic datasets, and show that the new inference attack can infer a significant fraction of plaintext chunks under backup workloads. To protect against frequency analysis, we borrow the idea of existing performance-driven deduplication approaches and consider an encryption scheme called MinHash encryption, which disturbs the frequency rank of ciphertext chunks by encrypting some identical plaintext chunks into multiple distinct ciphertext chunks. Our trace-driven evaluation shows that MinHash encryption effectively mitigates the inference attack, while maintaining high storage efficiency.
Evaluating the Differential Effects of Alternative Welfare-to-Work Training Components : A Re-Analysis of the California GAIN Program * by

In this paper, we propose and implement non-experimental matching/regression-adjustment methods in an attempt to isolate one of the potentially important reasons for across-program differences in training effects, namely that programs differ in the mix in and assignment of different types of training to the participants in its programs. The latter source of treatment heterogeneity and its potential consequences for across-program differences in training effects has been noted by Hotz, Imbens and Mortimer (2005) and others who have evaluated the effectiveness of training programs. We show how non-experimental matching and/or regression-adjustment methods can be used to estimate such differential effects and how data for a control group generated by random assignment can be use to partially assess the validity of such methods. Substantively, we use these methods and tests to re-analyze data from the MDRC Evaluation of California’s welfare-to-work program in the 1990s, namely the Greater Avenues to Independence (GAIN) program. This experimental Evaluation found that, compared to other counties that stressed Human Capital Development (HCD) training strategies, Riverside county’s GAIN program, which stressed getting program participants into jobs quickly through a Labor Force Attachment (LFA) training strategy, had relatively larger effects on post-random assignment employment, labor market earnings, and welfare participation. We apply matching/regression-adjustment methods and implement the partial tests of these methods noted above to these data to directly assess the shortand long-term differential impacts of these two training strategies and, thus, previous conclusions about the reasons for the success of the Riverside GAIN program.
Investigation of the neuroprotective effect of kefir in experimental spinal cord injury

In this study, the antioxidant, anti-inflammatory, and neuroprotective effects of kefir were investigated in spinal cord injury that was experimentally created on rats with a compression trauma model. A total of 56 Wistar-Albino male rats were used in the study. Daily freshly prepared 18 ml/kg/day of kefir was given by oral gavage to animals 7 days before and during the trauma and during the trauma. Spinal cord injury was created according to the weight drop method. On the 1 st and 7 th days before euthanasia, intracardiac blood was collected for analysis, and then they were sacrificed. The damaged spinal cord segments were examined biochemically, immunohistochemically, and histopathologically. When compared to the sham groups, kefir had a positive effect in the preconditioning and treatment groups by decreasing spinal cord bleeding, edema, myelin sheath damage, liquefactive necrosis, neuronal necrosis, selectivity of canalis centralis, and gitter cell levels significantly. When compared to the sham groups, kefir was found to have a positive effect in the treatment groups by decreasing the neuron specific enolase (NSE), ionized calcium binding adapter molecule 1 (IBA-1), inducible nitric oxide synthase (INOS), cyclooxygenase 2 (COX-2) and myelin basic protein (MBP) levels significantly on the 1 st and 7 th days, and by increasing the glial fibrillary acidic protein (GFAP) level significantly. As a result, it was demonstrated that kefir had a protective and therapeutic effect on spinal cord injury.
Dimension reductio via Sliced Inverse Regression : ideas and extensions

This thesis proposes three extensions of Sliced Inverse Regression namely: Collaborative SIR, Student SIR and Knockoff SIR.One of the weak points of SIR is the impossibility to check if the Linearity Design Condition (LDC) holds. It is known that if X follows an elliptic distribution thecondition holds true, in case of a mixture of elliptic distributions there are no guaranties that the condition is satisfied globally, but locally holds. Starting from this consideration an extension is proposed. Given the predictor variable X, Collaborative SIR performs initially a clustering. In each cluster, SIR is applied independently. The result from each component collaborates to give the final solution.Our second contribution, Student SIR, comes from the need to robustify SIR. Since SIR is based on the estimation of the covariance, and contains a PCA step, it is indeed sensitive to noise. To extend SIR, an approach based on a inverse formulation of SIR proposed by R.D. Cook has been used.Finally Knockoff SIR is an extension of SIR to perform variable selection and give sparse solution that has its foundations in a recently published paper by R. F. Barber and E. J. Candes that focuses on the false discovery rate in the regression framework. The underlying idea of this paper is to construct copies of the original variables that have some properties. It is shown that SIR is robust to this copies and a strategy is proposed to use this result for variable selection and to generate sparse solutions.
Compact grid layouts of multi-level networks

We consider the problem of generating layouts of multilevel networks, in particular, switching, sorting, and interconnection networks, as compactly as possible on VLSI grids. Besides traditional interest in these problems motivated by interconnection topologies in parallel computing and switching circuits in telecommunications, there is renewed interest in such layouts in the context of ATM (Asynchronous Transfer Mode) switches. Our results improve on the existing area bounds for these networks by factors of up to three.
DROP: Dimensionality Reduction Optimization for Time Series

Dimensionality reduction is critical in analyzing increasingly high-volume, high-dimensional time series. In this paper, we revisit a now-classic study of time series dimensionality reduction operators and find that for a given quality constraint, Principal Component Analysis (PCA) uncovers representations that are over 2× smaller than those obtained via alternative techniques favored in the literature. However, as classically implemented via Singular Value Decomposition (SVD), PCA is incredibly expensive for large datasets. Therefore, we present DROP, a dimensionality reduction optimizer for high-dimensional analytics pipelines that greatly reduces the cost of the PCA operation over time series datasets. We show that many time series are highly structured, hence a small number of data points are sufficient to characterize the data set, which permits aggressive sampling during dimensionality reduction. This sampling allows DROP to uncover high quality low-dimensional bases in running time proportional to the dataset’s intrinsic dimensionality— independent of the actual dataset size—without requiring the user to specify this intrinsic dimensionality a priori. DROP further enables downstream-operation-aware optimization by coupling sampling with online progress estimation, trading-off degree of dimensionality reduction with the combined runtime of DROP and downstream analytics tasks. By progressively sampling its input, computing a candidate basis for transformation, and terminating once it finds a sufficiently high quality basis in a reasonable running time, DROP provides speedups of up to 50× over PCA via SVD and 33× in end-to-end high-dimensional analytics pipelines.
Multi-block Bregman proximal alternating linearized minimization and its application to sparse orthogonal nonnegative matrix factorization

We introduce and analyze BPALM and A-BPALM, two multi-block proximal alternating linearized minimization algorithms using Bregman distances for solving structured nonconvex problems. The objective function is the sum of a multi-block relatively smooth function (i.e., relatively smooth by fixing all the blocks except one \cite{bauschke2016descent,lu2018relatively}) and block separable (nonsmooth) nonconvex functions. It turns out that the sequences generated by our algorithms are subsequentially convergent to critical points of the objective function, while they are globally convergent under KL inequality assumption. The rate of convergence is further analyzed for functions satisfying the {\L}ojasiewicz's gradient inequality. We apply this framework to orthogonal nonnegative matrix factorization (ONMF) and sparse ONMF (SONMF) that both satisfy all of our assumptions and the related subproblems are solved in closed forms.
The Estimation of Renewal Functions Using the Mean Value Theorem for Integrals (MeVTI) Method

In the analysis of warranty, renewal functions are important in acquiring the expected number of failures of a nonrepairable component in a time interval. It is very difficult and complicated -if at all possible- to obtain a renewal function analytically. This paper proposes a numerical integration method for estimating renewal functions in the terms of renewal integral equations. The estimation is done through the Mean Value Theorem for Integrals (MeVTI) method after modifying the variable of the renewal integral equations. The accuracy of the estimation is measured by its comparison against the existing analytical approach of renewal functions, those are for Exponential, Erlang, Gamma, and Normal baseline failure distributions. The estimation of the renewal function for a Weibull baseline failure distribution as the results of the method is compared to that of the well-known numerical integration approaches, the Riemann-Stieljies and cubic spline methods. Keywords : Mean Value Theorem for Integrals, Renewal Functions, Renewal Integral Equations.
Equilibrium Approximation Quality of Current No-Limit Poker Bots

Approximating a Nash equilibrium is currently the best performing approach for creating poker-playing programs. While for the simplest variants of the game, it is possible to evaluate the quality of the approximation by computing the value of the best response strategy, this is currently not computationally feasible for larger variants of the game, such as heads-up no-limit Texas hold'em. In this paper, we present a simple and computationally inexpensive Local Best Response method for computing an approximate lower bound on the value of the best response strategy. Using this method, we show that existing poker-playing programs, based on solving abstract games, are remarkably poor Nash equilibrium approximations.
Dynamic Predictions from Time Series Data — An Artificial Neural Network Approach

A hybrid approach, incorporating concepts of nonlinear dynamics in artificial neural networks (ANN), is proposed to model a time series generated by complex dynamic systems. We introduce well-known features used in the study of dynamic systems — time delay τ and embedding dimension d — for ANN modeling of time series. These features provide a theoretical basis for selecting the optimal size for the number of neurons in the input layer. The main outcome of the new approach for such problems is that to a large extent it defines the ANN architecture, models the time series and gives good prediction. As a consequence, we have an integrated and systematic data-driven scheme for modeling time series data. We illustrate our method by considering computer generated periodic and chaotic time series. The ANN model developed gave excellent quality of fit for the training and test sets as well as for iterative dynamic predictions for future values of the two time series. Further, computer experiments were conducted by introducing Gaussian noise of various degrees in the two time series, to simulate real world effects. We find that up to a limit introduction of noise leads to a smaller network with good generalizing capability.
Greedy versus Map-based Optimized Adaptive Algorithms for random-telegraph-noise mitigation by spector qubits

In a scenario where data-storage qubits are kept in isolation as far as possible, with minimal measurements and controls, noise mitigation can still be done using additional noise probes, with corrections applied only when needed. Motivated by the case of solid-state qubits, we consider dephasing noise arising from a two-state ﬂuctuator, described by random telegraph process, and a noise probe which is also a qubit, a so-called spectator qubit (SQ). We construct the theoretical model assuming projective measurements on the SQ, and derive the performance of diﬀerent measurement and control strategies in the regime where the noise mitigation works well. We start with the Greedy algorithm; that is, the strategy that always maximizes the data qubit coherence in the immediate future. We show numerically that this algorithm works very well, and ﬁnd that its adaptive strategy can be well approximated by a simpler algorithm with just a few parameters. Based on this, and an analytical construction using Bayesian maps, we design a one-parameter (Θ) family of algorithms. In the asymptotic regime of high noise-sensitivity of the SQ, we show analytically that this Θ-family of algorithms reduces the data qubit decoherence rate by a divisor scaling as the square of this sensitivity. Setting Θ equal to its optimal value, Θ ⋆ , yields the Map-based Optimized Adaptive Algorithm for Asymptotic Regime (MOAAAR). We show, analytically and numerically, that MOAAAR outperforms the Greedy algorithm, especially in the regime of high noise sensitivity of SQ.
Journal of Emerging Trends in Computing and Information Sciences Ocr Post-processing Error Correction Algorithm Using Google's Online Spelling Suggestion

With the advent of digital optical scanners, a lot of paper-based books, textbooks, magazines, articles, and documents are being transformed into an electronic version that can be manipulated by a computer. For this purpose, OCR, short for Optical Character Recognition was developed to translate scanned graphical text into editable computer text. Unfortunately, OCR is still imperfect as it occasionally mis-recognizes letters and falsely identifies scanned text, leading to misspellings and linguistics errors in the OCR output text. This paper proposes a post-processing context-based error correction algorithm for detecting and correcting OCR non-word and real-word errors. The proposed algorithm is based on Google's online spelling suggestion which harnesses an internal database containing a huge collection of terms and word sequences gathered from all over the web, convenient to suggest possible replacements for words that have been misspelled during the OCR process. Experiments carried out revealed a significant improvement in OCR error correction rate. Future research can improve upon the proposed algorithm so much so that it can be parallelized and executed over multiprocessing platforms.
Long-range coulomb interactions in small silicon devices: Transconductance and mobility degradation

In small silicon devices, conduction electrons in the channel are subject to long-range Coulomb interactions with electrons in the heavily-doped drain, source, and gate regions. We show that for devices with channel lengths shorter than about 40 nm and oxides thinner than 2.5 nm these interactions cause a reduction of the electron velocity. We present results obtained using both semiclassical two-dimensional self-consistentMonte Carlo-Poisson simulations and a quantum-mechanical model based on electron scattering from gate-oxide interface plasmons.
Optimization‐based adaptive sliding mode control with application to vehicle dynamics control

This paper presents the design of a new adaptive optimization‐based second‐order sliding mode control algorithm for uncertain nonlinear systems. It is designed on the basis of a second‐order sliding mode control with optimal reaching, with the aim of reducing the control effort while maintaining all the positive aspects in terms of finite‐time convergence and robustness in front of matched uncertainties. These features are beneficial to guarantee good performance in case of vehicle dynamics control, a crucial topic in the light of the increasing demand of semiautonomous and autonomous driving capabilities in commercial vehicles. The new proposal is theoretically analyzed, as well as verified relying on an extensive comparative study, carried out on a realistic simulator of a 4‐wheeled vehicle, in the case of a lateral stability control system.