Miracle-dz/UniFAR-FTUs huggingface dataset:
Using knowledge about the opponent in game-tree search

Ever since Shannon proposed his view on programming a computer to play chess, computer chess researchers have respected his basic assumptions. This thesis relaxes some of these assumptions, in particular the tenet that the two players should be assumed identical or similar. During at least part of any interesting game, one of the players (the 'agent') attempts to achieve a result larger than the game-theoretic value (e.g. tries to convert a drawn position into a win, or to salvage a loss). This is only possible if the opponent makes a game-theoretic mistake the agent can exploit. Hence the agent will try, based on knowledge of the opponent, to bring about a situation in which the likelihood that the latter commits an error is increased ('speculative play'). An error by the opponent may be due to (1) factors pertaining to the domain, (2) factors pertaining to the opponent, and (3) factors pertaining to the interaction of the opponent with the domain. This thesis investigates factors contributing to error both in the structure of game domains and in properties of the opponent's search strategy. The first part of the thesis considers properties of random games, as defined by their game graphs. The execution of the retrograde analysis algorithm for the solution of games imposes an implicit structure on the game. From this structure the thesis derives several interesting properties of random games, including difficulty-related characteristics. These properties are investigated both in an empirical analysis of actual random games and through a mathematical analysis of an approximate statistical model of random games. Several properties exhibited by both analyses are shown to be relevant, in fact explanatory, of properties of actual games. In the second part of the thesis, some properties defined in part I are discussed for the chess endgame of King and Queen vs. King and Rook (KQKR). Human play in KQKR is analyzed, and common heuristics evaluated. This information is used to construct a 'heuristic program' for database play, which is shown to induce an increased average error in human subjects in comparison to a standard database program. This result demonstrates feasibility and utility of speculative strategies in actual game playing situations.
On a class of switched, robustly stable, adaptive systems

A class of switched algorithms for adaptive control of SISO linear systems is described. The systems considered are assumed to belong to one among a finite number of classes of admissible process models, and each class is robustly stabilizable by some linear time-invariant controller. The control used is chosen in real time by a tuner or supervisor, according to observations of suitably defined "identification errors. " The method preserves the robustness properties of the linear control design in an adaptive context. One motivating application is fault-tolerant control.
Traitement des cancers du bas rectum

La definition des cancers du bas rectum doit etre de plus en plus precise pour affiner les choix therapeutiques. La marge de securite circonferentielle, evaluee par l’imagerie par resonance magnetique, est aussi, voire plus, importante que la marge distale pour choisir le type de chirurgie.La technique de resection intersphincterienne permet d’eviter l’amputation abdominoperineale chez la majorite des patients porteurs d’un cancer du bas rectum.La chirurgie conservatrice sphincterienne des cancers du bas rectum ne compromet pas le pronostic local ni la survie, preserve mieux la sexualite mais expose a des troubles fonctionnels digestifs dont l’impact sur la qualite de vie est dependant du patient.Les traitements neoadjuvants ameliorent le controle local et sont utilises pour selectionner les patients pour une epargne sphincterienne et meme une epargne rectale.La specialite chirurgicale joue un role preponderant dans la strategie et les resultats du traitement des cancers du bas rectum.
Mental health and Labour Force Status: Panel Estimates With Four Waves of HILDA

In Australia it is estimated that mental illness accounts for around 13 per cent of the total years of healthy life lost to disease yet only 5 per cent of the annual health budget is allocated to services for the mentally ill (Butterworth 2003; Andrews, Hall Teeson and Henderson 1999). There is growing concern regarding the effect of employment, including stress, low-quality work and difficulties in balancing work and family life, and of the effect of unemployment on mental health. This paper uses a summary measure of individuals’ mental health to examine the links between mental health and labour market experiences. The measure is based on the summary score typically derived from the SF-36 instrument included in the first 4 waves of the Household Income and Labour Dynamics Australia survey (HILDA), but is adjusted to correct for seemingly undesirable properties of that measure. Simple analysis of labour force transitions over the first four waves of HILDA suggests that entering or remaining in unemployment does not have the deleterious impact upon mental health that might have been expected. Greater changes in mental health are associated with movements into and out of the labour force altogether. The estimation of multivariate panel models does find evidence of unemployment contributing to lower mental health for those persons who are participating in the labour force. However, larger differences in mental health are identified among those in employment conditional upon characteristics of their work or attitudes towards their jobs, than are found between those in and out of work.
Monitoring a Hypothetical Channelopathy in Chronic Fatigue Syndrome: Preliminary Observations

ABSTRACTThis study was aimed at monitoring of a previously suggested channelopathy in Chronic Fatigue Syndrome, and at searching for possible explanations by means of immune system characteristics. Twenty-seven CFS patients and 20 age and sex matched healthy volunteers were recruited. RNase L-ratio, percent of the norm of whole body potassium content, serum electrolytes (sodium, calcium and potassium), immune cells, blood cell count and erythrocyte sedimentation rate were determined. More than fifty percent of our patients presented with abnormal whole body potassium content. Eight patients had increased, while six had depleted potassium content. Discriminant function analysis revealed that the CFS patients and control subjects could be differentiated on immunophenotyping with the predominant cell differences being the increase in CD19+ CD5+ (mature B-) cells and the decrease in CD3-CD16+ CD56+ (NK) cells in both the percentage and count distributions. The fall in NK-cells was very strongly associated wit.
: Exploratory data analysis with MATLAB ®

This book presents a broad panoply of data-analytical methods implemented in MATLAB. According to the Preface “this text is not focused on the theoretical aspects of the methods. Rather, its main focus is on the use of the EDA methods”, its purpose being to “make EDA techniques available to a wide range of users”. The required background is “basic linear algebra, and introductory probability and statistics courses”; although actually, some familiarity with regression and multivariate analysis would be desirable. Chapter 1 is a general introduction. Chapter 2 is on linear dimensionality reduction and deals with principal components, nonnegative matrix factorization, factor analysis, estimation of intrinsic dimensionality and Fisher’s discriminant function. Chapter 3 on nonlinear dimensionality reduction treats multidimensional scaling, manifold learning and neural networks. Chapter 4 on data tours describes the “Grand tour”, projection pursuit and independent components analysis. Chapter 5 is on finding clusters and deals with hierarchical methods, k-means, spectral clustering, document clustering and evaluation of clusters. Chapter 6 on modelbased clustering deals with mixtures, the EM algorithm, hierarchical agglomerative model-based clustering, and the use of model-based clustering for density estimation and discriminant analysis. Chapter 7 deals with scatterplots smoothing, and treats Loess and smoothing splines. The last three chapters are devoted to graphical methods. Chapter 8 is on cluster visualization. Chapter 9 on distribution shapes deals with box-, quantileand bag-plots. Chapter 10 is on multivariate visualization and describes glyphs, dynamic graphics, linking, brushing, coplots, Andrews’ plots and biplots.
The Vanishing Ramadan Effect: A Structural Time-Series Test

This study investigates whether religious belief creates stock market return seasonality, focusing on the Muslim holy month “Ramadan�? We use data from 12 stock markets in countries with a Muslim majority, and employ both deterministic and stochastic seasonality tests. We find there is no Ramadan return seasonality in the majority of these stock markets in neither deterministic nor stochastic tests. However, further analysis of the risk-adjusted returns reveals that the significant drop in market volatility during Ramadan leads to higher risk-adjusted returns.
Estimation of distance between thumb and forefinger from hand dorsal image using deep learning

A three-dimensional virtual object can be manipulated by hand and finger movements with an optical hand tracking device where it is necessary to recognize a posture of one's hand. Conventional hand posture recognition is based on three-dimensional coordinates of fingertips and a skeletal model of the hand [1]. It is difficult for conventional methods to estimate a posture of the hand when a fingertip is hidden from an optical camera. This study, therefore, proposes the estimation of a posture of a hand on the basis of a hand-dorsal image that can be taken even when the hand occludes its fingertips. A regression model that estimates a distance between fingertips of the thumb and forefinger was constructed using a convolution neural network (CNN) [2]. This work evaluated the root mean squared error (RMSE) of estimation. The RMSE of estimation based on a model on the same day was less than 1.8 mm, which shows that the proposed method could be an effective method where self-occlusion is a problem. This study also evaluates the robustness of the learning model to time-variation.
Efficient training procedures for adaptive kernel classifiers

The authors investigate two training schemes for adapting the locations and receptive field widths of the centroids in radial basis function classifiers. The adaptive kernel classifier is able to adjust the responses of the hidden units during training using an extension of the Delta rule, thus leading to improved performance and reduced network size. The rapid kernel classifier, on the other hand, uses the faster learned vector quantization algorithm to adapt the centroids. This network shows a remarkable reduction in training time with little compromise in accuracy. The performance of these two networks is evaluated using underwater acoustic transient signals. <<ETX>>
Implementing a Scalable Music Recommender System

We describe the implementation of a highly scalable recommender system, with excellent algorithmic complexities, capable of delivering personalized recommendations in realtime. The system was built to be used at the Swedish company Spotify, which is a novel application where users can listen to a vast collection of several million tracks, streaming over the internet. In the report, we discuss how the logs can be efficiently analyzed by a computer cluster in order to find structures and patterns corresponding to different musical tastes among the users. We introduce several algorithms with high capability of suggesting relevant new music to users. Referat Personliga musikrekommendationer i realtid utifrån enorma datamängder Vi beskriver implementationen av ett skalbart rekommenderingssystem, vars algoritmiska komplexitet gör det möjligt att tillämpa på mycket stora datamängder, samtidigt som det kan presentera relevanta musikrekommendationer i realtid. Systemet utvecklades för det svenska företaget Spotify, som tagit fram en klient för att strömma musik över internet från ett utbud av flera miljoner spår. Vi berör hur systemet effektivt kan hitta strukturer och mönster i användares musiksmak genom att analysera loggfiler på ett datorkluster. Vi presenterar flera algoritmer som på ett skalbart och snabbt sätt kan presentera ny relevant musik till användare.
An expert system application for network intrusion detection

The paper describes the design of a prototype intrusion detection system for the Los Alamos National Laboratory's Integrated Computing Network (ICN). The Network Anomaly Detection and Intrusion Reporter (NADIR) differs in one respect from most intrusion detection systems. It tries to address the intrusion detection problem on a network, as opposed to a single operating system. NADIR design intent was to copy and improve the audit record review activities normally done by security auditors. We wished to replace the manual review of audit logs with a near realtime expert system. NADIR compares network activity, as summarized in user profiles, against expert rules that define network security policy, improper or suspicious network activities, and normal network and user activity. When it detects deviant (anomalous) behavior, NADIR alerts operators in near realtime, and provides tools to aid in the investigation of the anomalous event. 15 refs., 2 figs.
Supporting the use of algorithmic design in architecture: An empirical study of reuse of design knowledge

This thesis tests the reuse of design knowledge as a method to support learning and use of algorithmic design in architecture. The use of algorithmic design systems and programming environments offer architects immense opportunities, providing a powerful means to create geometries and allowing dynamic design exploration, but it can also impose substantial challenges. Architects often struggle with adopting algorithmic design methods (translating a design idea into an algorithm of actions), as well as with the implementation of programming languages, the latter often proving frustrating and creating barriers for both novice and advanced software users. The proposition explored in this thesis is that the reuse of design knowledge can improve architects’ ability to use algorithmic design systems, and reduce the barriers for using programming. This study explores and compares two approaches as a means of accessing and reusing existing design solutions. The first approach is the reuse of abstract algorithmic ‘Design Patterns’. The second is the reuse of algorithmic solutions from specific design cases (Case-Based Design). The research was set up as an experimental comparative study between three test groups: one group using Design Patterns, a second group using Case-Based Design, and the control group. A total of 126 designers participated in the study providing sufficient numbers within each group to permit rigorous studies of the statistical significance of the observed differences. Results of this study illustrate that the systematic inclusion of the Design Patterns approach to the learning strategy of programming in architecture and design, proves to be highly beneficial. The use of abstract solutions improves designers’ ability to overcome programming barriers, and helps architects to adopt algorithmic design methods. The use of Design Patterns also encourages design exploration and experimentation. The use of the Case-Based Design approach seems to be more effective after designers and architects, who are novices in programming, gain more experience with the tool. It encourages more focused reasoning, oriented to the realisation of a particular (originally intended) design outcome. The contribution of this research is to provide empirical evidence that the reuse of abstract and case-based algorithmic solutions can be very beneficial. Results of this study illustrate that both reuse methods can be strategically integrated into design education and architectural practice, supporting learning and use of algorithmic design systems in architecture. The study also identifies potential weaknesses of each approach, proposing areas which could be addressed by future studies.
Um framework para análise de agrupamento baseado na combinação multi-objetivo de algoritmos de agrupamento

This Thesis presents a framework for exploratory data analysis via clustering techniques. The goal is to facilitate the work of the experts in the data domain. The core of the framework is a multi-objective clustering ensemble algorithm, the MOCLE algorithm, complemented by a method for integrated visualization of a set of partitions. By applying together the ideas of clustering ensemble and multi-objective clustering, MOCLE automatically performs important steps of cluster analysis: run several conceptually different clustering algorithms with various parameter configuration, combine the partitions resulting from these algorithms, and select the partitions with the best trade-offs for different validation measures. MOCLE is a robust approach to deal with different types of structures that can be present in a dataset. It results in a concise and stable set of high quality alternative structures, without the need of previous knowledge about the data or deep knowledge on cluster analysis. Furthermore, in order to facilitate the discovery of more complex structures, MOCLE allows the automatic integration of previous knowledge of a simple structure via their objective functions. Finally, the visualization method proposed allows the simultaneous observation of a set of partitions. This helps in the analysis of MOCLE results.
The relationship between dietary inflammatory index (DII) and muscle mass and strength in Chinese children aged 6-9 years.

BACKGROUND AND OBJECTIVES The dietary inflammatory index (DII®) is a measure of the overall inflammatory potential of a person's diet. However, there have been no studies looking at the effect of DII on measures of muscle mass and strength. We aimed to examine the association between DII and skeletal muscle mass and strength in Chinese children. METHODS AND STUDY DESIGN A total of 466 children aged 6-9 years completed the study. Total body skeletal muscle mass (TSM), appendicular skeletal mass (ASM) and appendicular lean mass (ALM) were determined using Dual-energy X-ray absorptiometry. TSM/Height², TSM/Weight, ASM/Height² and ASM/Weight were calculated. The residual method was applied to compute ALM index (ALMI) adjusted for height and body fat. Hand grip strength was measured using hand dynamometer. DII scores were calculated from a 79-item food frequency questionnaire. RESULTS Fully adjusted linear regression models showed a statistically significant negative relationship between DII and ASM, ASM/Height², ASM/Weight, ALMI, TSM, TSM/Height², and TSM/Weight (p: 0.019‒0.014). The analysis of covariance indicated that the percentage differences in the extreme quartiles (Q4 vs Q1) of DII for the above-mentioned measures ranged from -1.04% to -4.36% (p-trend: <0.001‒0.013). When boys and girls were analyzed separately, similar findings were observed for boys but not for girls. No significant associations were detected between DII and hand grip strength. CONCLUSIONS DII score was inversely associated with skeletal muscle mass in boys but not in girls aged 6-9 years old. No significant associations were observed between DII and hand grip strength.
A deep learning approach to automatic detection of early glaucoma from visual fields

Purpose To investigate the suitability of multi-scale spatial information in 30o visual fields (VF), computed from a Convolutional Neural Network (CNN) classifier, for early-glaucoma vs. control discrimination. Method Two data sets of VFs acquired with the OCTOPUS 101 G1 program and the Humphrey Field Analyzer 24–2 pattern were subdivided into control and early-glaucomatous groups, and converted into a new image using a novel voronoi representation to train a custom-designed CNN so to discriminate between control and early-glaucomatous eyes. Saliency maps that highlight what regions of the VF are contributing maximally to the classification decision were computed to provide classification justification. Model fitting was cross-validated and average precision (AP) score performances were computed for our method, Mean Defect (MD), square-root of Loss Variance (sLV), their combination (MD+sLV), and a Neural Network (NN) that does not use convolutional features. Results CNN achieved the best AP score (0.874±0.095) across all test folds for one data set compared to others (MD = 0.869±0.064, sLV = 0.775±0.137, MD+sLV = 0.839±0.085, NN = 0.843±0.089) and the third best AP score (0.986 ±0.019) on the other one with slight difference from the other methods (MD = 0.986±0.023, sLV = 0.992±0.016, MD+sLV = 0.987±0.017, NN = 0.985±0.017). In general, CNN consistently led to high AP across different data sets. Qualitatively, computed saliency maps appeared to provide clinically relevant information on the CNN decision for individual VFs. Conclusion The proposed CNN offers high classification performance for the discrimination of control and early-glaucoma VFs when compared with standard clinical decision measures. The CNN classification, aided by saliency visualization, may support clinicians in the automatic discrimination of early-glaucomatous and normal VFs.
Meshless voronoi on the GPU

We propose a GPU algorithm that computes a 3D Voronoi diagram. Our algorithm is tailored for applications that solely make use of the geometry of the Voronoi cells, such as Lloyd's relaxation used in meshing, or some numerical schemes used in fluid simulations and astrophysics. Since these applications only require the geometry of the Voronoi cells, they do not need the combinatorial mesh data structure computed by the classical algorithms (Bowyer-Watson). Thus, by exploiting the specific spatial distribution of the point-sets used in this type of applications, our algorithm computes each cell independently, in parallel, based on its nearest neighbors. In addition, we show how to compute integrals over the Voronoi cells by decomposing them on the fly into tetrahedra, without needing to compute any global combinatorial information. The advantages of our algorithm is that it is fast, very simple to implement, has constant memory usage per thread and does not need any synchronization primitive. These specificities make it particularly efficient on the GPU: it gains one order of magnitude as compared to the fastest state-of-the-art multi-core CPU implementations. To ease the reproducibility of our results, the full documented source code is included in the supplemental material.
How Did a Grade 5 Science Community Co-Construct Collective Structures of Inquiry?

This study investigates how a Grade 5 science community co-constructed collective structures of inquiry in the form of “research cycles” to support sustained inquiry in a whole school year. Qualitative analysis of field notes, classroom videos, and student notebooks documented the evolution of research cycles. Analysis of student interviews showed how this structure was used and adapted by individual student to position and monitor knowledge progress and plan for further inquiry. Content analyses of student online discourse in Knowledge Forum indicated that students made more purposeful contributions aligning with the research cycle after formation.