Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Cognition in Williams Syndrome: Genotype/Phenotype Insights from Partial Deletion Patients

Identifying genotype/phenotype relations in human social cognition has been enhanced by the study of Williams syndrome (WS). Indeed, individuals with WS present with a particularly strong social drive, and researchers have sought to link deleted genes in the WS critical region (WSCR) of chromosome 7q11.23 to this unusual social profile. In this paper, we provide details of two case studies of children with partial genetic deletions in the WSCR: an 11-year-old female with a deletion of 24 of the 28 WS genes, and a 14-year-old male who presents with the opposite profile, i.e., the deletion of only four genes at the telomeric end of the WSCR. We tested these two children on a large battery of standardized and experimental social perception and social cognition tasks – both implicit and explicit – as well as standardized social questionnaires and general psychometric measures. Our findings reveal a partial WS socio-cognitive profile in the female, contrasted with a more autistic-like profile in the male. We discuss the implications of these findings for genotype/phenotype relations, as well as the advantages and limitations of animal models and of case study approaches.
Non-Atomistic Wage Setters and Monetary Policy ∗

The purpose of the paper is to study the monetary policy implications of unionized labor markets in a New Keynesian framework. It is shown that, once the presence of large wage setters is taken into account, the degree of wage setting centralization and the aggressiveness of the central bank in stabilizing inflation jointly affect the equilibrium level of real economic activity in the long-run. The classical neutrality result is not challenged: a temporary shock to the policy instrument dies off in the long-run. A change in the policy rule, however, has a permanent real effect since it alters the steady state equilibrium level of employment. In addition, the central bank can control the degree of inefficiency of the economy pushing output towards Pareto efficiency without creating inflation. JEL Classification: E24, E52
Comparative Analysis of CNV Calling Algorithms: Literature Survey and a Case Study Using Bovine High-Density SNP Data

Copy number variations (CNVs) are gains and losses of genomic sequence between two individuals of a species when compared to a reference genome. The data from single nucleotide polymorphism (SNP) microarrays are now routinely used for genotyping, but they also can be utilized for copy number detection. Substantial progress has been made in array design and CNV calling algorithms and at least 10 comparison studies in humans have been published to assess them. In this review, we first survey the literature on existing microarray platforms and CNV calling algorithms. We then examine a number of CNV calling tools to evaluate their impacts using bovine high-density SNP data. Large incongruities in the results from different CNV calling tools highlight the need for standardizing array data collection, quality assessment and experimental validation. Only after careful experimental design and rigorous data filtering can the impacts of CNVs on both normal phenotypic variability and disease susceptibility be fully revealed.
Performance analysis of MIMO-OFDM systems on Nakagami-m fading channels

Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) has become one of the key technologies for the B3G & 4G broadband wireless communications. As MIMO could increase the capacity of the wireless communication system considerably compared with Single Input Single Output (SISO) and OFDM could obtain good performances in multipath frequency-selective fading channels, we combine them together with the Space-Time Block Coding (STBC) scheme and the Nakagami-m fading model. The Nakagami-m (0.5≤m ∞) fading model, covering a series of channel conditions in different fading parameters, brings greater flexibility and accuracy than normal, logistic and other distributions. From the simulation figures, we can draw the conclusion that the scheme could better performances of the system's bit error rate (BER) and capacity greatly, thus improving the whole system's performances.
Convergence analysis of run-to-run control for a class of nonlinear systems

In run-to-run control, measurements from previous runs are used to push the outputs of the current run towards desired set points. From a run-to-run perspective, the classical dynamics get integrated by each run, thereby leading to static nonlinear input-output map. This paper shows that, when successive linearization of this nonlinear map is used to adapt the run-to-run controller, convergence may not be achieved. However, convergence can be guaranteed of the controller is based on a linear approximation for which the outputs are in-phase (i.e., within 90/spl deg/) with the true outputs. A convergence proof based on Lyapunov approach is provided. The theoretical aspects are illustrated through the simulated meal-to-meal control of blood glucose concentration in diabetic patients.
Robust Models in Information Retrieval

Classification tasks in information retrieval deal with document collections of enormous size, which makes the ratio between the document set underlying the learning process and the set of unseen documents very small. With a ratio close to zero, the evaluation of a model-classifier-combination's generalization ability with leave-n-out-methods or cross-validation becomes unreliable: The generalization error of a complex model (with a more complex hypothesis structure) might underestimated compared to the generalization error of a simple model (with a less complex hypothesis structure). Given this situation, optimizing the bias-variance-tradeoff to select among these models will lead one astray. To address this problem we introduce the idea of robust models, where one intentionally restricts the hypothesis structure within the model formation process. We observe that -- despite the fact that such a robust model entails a higher test error -- its efficiency "in the wild" outperforms the model that would have been chosen normally, under the perspective of the best bias-variance-tradeoff. We present two case studies: (1) a categorization task, which demonstrates that robust models are more stable in retrieval situations when training data is scarce, and (2) a genre identification task, which underlines the practical relevance of robust models.
SZTAKI @ ResPubliQA 2010

This paper summarizes the results of our rst participation at ResPubliQA. Lacking a true question answering system, we relied on our traditional search engine to nd the answers. We submitted two runs: a pure IR baseline and one where denition question identication and corpus-specic techniques were employed to improve IR performance. Both runs performed well, and our second run obtained better results than the baseline. However, the drawbacks of relying solely on an IR phase are clearly seen in our runs.
Burnout among physical education teachers in primary and secondary schools

The present study examined whether physical education teachers working in primary and secondary schools experience the same burnout levels. Four hundred and thirty seven full-time Greek physical education teachers from primary and secondary public schools filled in the “educator’s” version of Maslach Burnout Inventory. Two hundred and seven where teaching in primary schools and 230 in secondary. Multivariate analysis of variance showed that physical education teachers working in the primary schools reported significantly and meaningfully higher levels on the core burnout dimension, namely emotional exhaustion” in comparison to their colleagues in the secondary schools. Moreover, the strength of association among the three burnout components was more prominent in primary physical educators than in secondary. The paper shows that the education level in which physical education teachers are working represents an important job characteristic that influences burnout levels and should be taken into consideration when this syndrome is examined, at least within the Greek educational system.
Mixture model label propagation

Usually, we can use a classification or clustering machine learning algorithm to manage knowledge and information retrieval. If we have a small size of known information with a large scale of unknown data, a semi-supervised learning (SSL) algorithm is often preferred. Under the cluster or manifold assumption, usually, the larger amount of unlabeled data are used for learning, the bigger gains of the SSL approaches are achieved. In the paper, we adopt the graph-based SSL algorithm to solve the problem. However the graph-based SSL algorithms are unable to be learnt with large-scale unlabeled samples and originally can only work in a transductive setting. In the paper, we propose a scalable graph-based SSL algorithm to attack the problems aforementioned by Gaussian mixture model label propagation. Experiments conducted on the real dataset illustrate the effectiveness of the proposed algorithm.
On the Use of Computer Programs as Money

Money is a technology for promoting economic prosperity. Over history money has become increasingly abstract, it used to be hardware, gold coins and the like, now it is mostly software, data structures located in banks. Here I propose the logical conclusion of the abstraction of money: to use as money the most general form of information - computer programs. The key advantage that using programs for money (program-money) adds to the technology of money is agency. Program-money is active and thereby can fully participate in economics as economic agents. I describe the three basic technologies required to implement program-money: computational languages/logics to unambiguously describe the actions and interactions of program-money; computational cryptography to ensure that only the correct actions and interactions are performed; and a distributed computational environment in which the money can execute. I demonstrate that most of the technology for program-money has already been developed. The adoption of program-money transfers responsibility from human economic agents to money itself and has great potential economic advantages over the current passive form of money. For example in microeconomics, adding agency to money will simplify the exchange of ownership, ensure money is only used legally, automate the negotiation and forming of contracts, etc. Similar advantages occur in macroeconomics, where for example the control of the money supply could be transferred from central banks to money. It is also possible to envisage money that is not owned by any external human agent or corporation. One motivation for this is to force economic systems to behave more rationally and/or more like a specific economic theory, thereby increasing the success of economic forecasting.
Fuzzy rule interpolation for multidimensional input space with petroleum engineering application

Fuzzy rule based systems have been very popular in many engineering applications. In petroleum engineering, fuzzy rules are normally constructed using some fuzzy rule extraction techniques to establish the petrophysical property prediction model. However, when generating fuzzy rules from the available information, it may result in a sparse fuzzy rule base. The use of more than one input variable is also common in petroleum engineering. The paper examines the application of fuzzy interpolation to resolve problems using sparse fuzzy rule bases, and perform analysis of fuzzy interpolation in multidimensional input space.
Variational Random Walk Autoencoders

Variational autoencoders (VAEs) have become one of the most popular deep learning approaches to unsupervised learning and data generation. However, traditional VAEs suffer from the constraint that the latent space must distributionally match a simple prior (e.g. normal, uniform), independent of the initial data distribution. This leads to a number of issues around modeling manifold data, as there is no function with a bounded Jacobian that maps a normal distribution to certain manifolds (e.g. a hypersphere). Similarly, there are not many theoretical guarantees on the encoder and decoder created by the VAE. In this work, we propose a variational autoencoder that maps manifold valued data to its diffusion map coordinates in the latent space, resamples in a neighborhood around a given point in the latent space, and learns a decoder that maps the newly resampled points back to the manifold. The framework is built off of SpectralNet [Shaham et al., 2018a] and is capable of learning this data dependent latent space without computing the eigenfunction of the Laplacian explicitly. We prove that our method is capable of learning a locally bi-Lipschitz map between the manifold and the latent space, and that our resampling method around a point in the latent space $\psi(x)$ maps points back to the manifold around the point $x$, specifically into a neighborbood on the tangent space at the point $x$ on the manifold. We also provide empirical evidence of the benefits of using a diffusion map latent space on manifold data.
Development of a fuzzy logic based intelligent system for autonomous guidance of post-stroke rehabilitation exercise

This paper presents preliminary studies in developing a fuzzy logic based intelligent system for autonomous post-stroke upper-limb rehabilitation exercise. The intelligent system autonomously varies control parameters to generate different haptic effects on the robotic device. The robotic device is able to apply both resistive and assistive forces for guiding the patient during the exercise. The fuzzy logic based decision-making system estimates muscle fatigue of the patient using exercise performance and generates a combination of resistive and assistive forces so that the stroke survivor can exercise for longer durations with increasing control. The fuzzy logic based system is initially developed using a study with healthy subjects and preliminary results are also presented to validate the developed system with healthy subjects. The next stage of this work will collect data from stroke survivors for further development of the system.
State and parameters observation for accurate off-road navigation of wheeled mobile robots

This paper addresses the problem of accurate and reliable path following of a heavy industrial mobile robot evolving with centimetric accuracy requirement. For this purpose sliding estimation is a crucial issue, even more in off-road context with uneven and slippery terrains. So, we focus on position and sliding observers design in function of the distance covered, thus allowing to carry on maneuvers at any operational speed with constant accuracy. Furthermore, these observers are not affected by path tracking errors. Implementation in simulation demonstrates efficiency of such algorithms.
Coping With Conflict: Examining the Influence of PSM on Perceptions of Workplace Stressors

While the presence and degree of workplace stress poses a significant problem for organizations in all sectors, scholarship frequently acknowledges that responses to workplace stress vary significantly across individuals. However, public sector human resource management (HRM) research, relative to generic HRM research, invests comparatively less attention toward understanding individual differences in response to perceived stressors. We employ the relational model of stress developed by Matteson and Ivancevich and Lazarus to examine how one dispositional characteristic commonly examined in public sector HRM research, public service motivation (PSM), influences the stress process. Results obtained using data from the 2010 U.S. Merit Principles Survey reveal that individuals with higher than average PSM experience more pronounced negative emotions when they perceive heightened workplace conflict, which subsequently increases their intent to separate from the organization.
Forcing as a Program Transformation

This paper is a study of the forcing translation through the proofs as programs correspondence in classical logic, following the methodology introduced by Krivine in [Kri08, Kri10]. For that, we introduce an extension of (classical) higher-order arithmetic suited to express the forcing translation, called PA omega+. We present the proof system of PA omega+ - based on Curry-style proof terms with call/cc - as well as the corresponding classical realizability semantics. Then, given a poset of conditions (represented in PA omega+ as an upwards closed subset of a fixed meet semi-lattice), we define the forcing translation A |-> (p\forces A) (where A ranges over propositions) and show that the corresponding transformation of proofs is induced by a simple program transformation t |-> t* defined on raw proof-terms (i.e. independently from the derivation). From an analysis of the computational behavior of transformed programs, we show how to avoid the cost of the transformation by introducing an extension of Krivine's abstract machine devoted to the execution of proofs constructed by forcing. We show that this machine induces new classical realizability models and present the corresponding adequacy results.
Spiritual Emotional Freedom Technique (SEFT) Sebagai Metode Terapi Sufistik

This research aims to know the application of Spiritual Emotional Freedom Technique (SEFT) as method of sufistic therapy in overcoming mental disorders in Rumah Perlindungan Sosial Berbasis Masyarakat (RPSBM) Pekalongan City. This research is a qualitative with case study approach. The population of this study is all patient of mental disorder, with sample 4 patient of mental disorder, taken by purposive sampling. Data collecting in research using library study and field study. Analysis of data using data reduction, data publishing and conclusion. The result showed that SEFT is a combination of spiritual power and energy psychology, of which 90% emphasizes spirituality. With SEFT, the subject is brought to the belief that healing comes from God so that it can be sincere and resigned to the mental problems that it undergoes. Keywords: mental disorders, SEFT, sufistic therapy
Theory of giant skew scattering by spin cluster

Skew scattering of electrons induced by a spin cluster is studied theoretically focusing on metals with localized magnetic moments. The scattering probability is calculated by a non-perturbative $T$ matrix method; this method is valid for arbitrary strength of electron-spin coupling. We show the scattering of electrons by a three-spin cluster produces a skew angle of order $0.1\pi$ rad when the electron-spin coupling is comparable to the bandwidth. This is one or two orders of magnitude larger than the usual skew angle by an impurity with spin-orbit interaction. Systematic analysis of the scattering probability of one-, two-, and three-spin clusters show that three spins are necessary for skew scattering. We also discuss the relation between anomalous/spin Hall effects and the spin chiralities; we find that the spin Hall effect requires three spins while it is related to the vector spin chirality defined by a pair of spins. The relevance of these results to the large extrinsic anomalous and spin Hall effects in noncentrosymmetric and/or frustrated magnets is also discussed.
Workshop of the International Precipitation Working Group EXPERIMENTAL LIGHT RAINFALL AND SNOWFALL PRODUCTS FOR CLOUDSAT

When it launches next spring CloudSat, a component of the A-Train formation of satellites, will carry the first millimeter wavelength cloud radar in space. In addition to its stated objective of making a global survey of cloud microphysical properties, the CloudSat 94 GHz Cloud Profiling Radar (CPR) is also well-suited to the problem of determining the distribution of light rain and snow that can be difficult to detect using current space-borne instrumentation. This presentation will provide an overview of the CloudSat mission and describe two experimental precipitation algorithms being developed for retrieving light liquid rainfall and snow from CPR reflectivity observations. These algorithms have been applied to cloud radar data from the Advanced Microwave Scanning Radiometer (AMSR) validation experiment that took place in Wakasa Bay, Japan in 2003. While preliminary in nature, examples from this dataset illustrate the potential of CloudSat observations for studying the global distribution of drizzle, light rainfall, and snow. Ultimately, it is anticipated that CloudSat’s experimental rainfall products may shed light on the importance of light rainfall and snow observations for establishing closure in the global energy budget and water cycle.
A Hidden Markov Model Approach to Testing Multiple Hypotheses on a Gene Ontology Graph

Gene category testing problems involve testing hundreds of null hypotheses that correspond to nodes in a directed acyclic graph. The logical relationships among the nodes in the graph imply that only some configurations of true and false null hypotheses are possible and that a test for a given node should depend on data from neighboring nodes. We developed a method based on a hidden Markov model that takes the whole graph into account and provides coherent decisions in this structured multiple hypothesis testing problem. The method is illustrated by testing Gene Ontology terms for evidence of differential expression.
Weakly Supervised Visual Dictionary Learning by Harnessing Image Attributes

Bag-of-features (BoFs) representation has been extensively applied to deal with various computer vision applications. To extract discriminative and descriptive BoF, one important step is to learn a good dictionary to minimize the quantization loss between local features and codewords. While most existing visual dictionary learning approaches are engaged with unsupervised feature quantization, the latest trend has turned to supervised learning by harnessing the semantic labels of images or regions. However, such labels are typically too expensive to acquire, which restricts the scalability of supervised dictionary learning approaches. In this paper, we propose to leverage image attributes to weakly supervise the dictionary learning procedure without requiring any actual labels. As a key contribution, our approach establishes a generative hidden Markov random field (HMRF), which models the quantized codewords as the observed states and the image attributes as the hidden states, respectively. Dictionary learning is then performed by supervised grouping the observed states, where the supervised information is stemmed from the hidden states of the HMRF. In such a way, the proposed dictionary learning approach incorporates the image attributes to learn a semantic-preserving BoF representation without any genuine supervision. Experiments in large-scale image retrieval and classification tasks corroborate that our approach significantly outperforms the state-of-the-art unsupervised dictionary learning approaches.