Miracle-dz/UniFAR-FTUs huggingface dataset:
Long Short-Term Memory in Echo State Networks: Details of a Simulation Study

Echo State Networks (ESNs) is an approach to design and train recurrent neural networks in supervised learning tasks. An important objective in many such tasks is to learn to exploit long-time dependencies in the processed signals ("long short-term memory" performance). Here we expose ESNs to a series of synthetic benchmark tasks that have been used in the literature to study the learnability of long-range temporal dependencies. This report provides all the detail necessary to replicate these experiments. It is intended to serve as the technical companion to a journal submission paper where the findings are analysed and compared to results obtained elsewhere with other learning paradigms.
Illiquidity and All its Friends

The recent crisis was characterized by massive illiquidity. This paper reviews what we know and don't know about illiquidity and all its friends: market freezes, fire sales, contagion, and ultimately insolvencies and bailouts. It first explains why liquidity cannot easily be apprehended through a single statistics, and asks whether liquidity should be regulated given that a capital adequacy requirement is already in place. The paper then analyzes market breakdowns due to either adverse selection or shortages of financial muscle, and explains why such breakdowns are endogenous to balance sheet choices and to information acquisition. It then looks at what economics can contribute to the debate on systemic risk and its containment. Finally, the paper takes a macroeconomic perspective, discusses shortages of aggregate liquidity and analyses how market value accounting and capital adequacy should react to asset prices. It concludes with a topical form of liquidity provision, monetary bailouts and recapitalizations, and analyses optimal combinations thereof; it stresses the need for macro-prudential policies.
String and Membrane Gaussian Processes

In this paper we introduce a novel framework for making exact nonparametric Bayesian inference on latent functions, that is particularly suitable for Big Data tasks. Firstly, we introduce a class of stochastic processes we refer to as string Gaussian processes (string GPs), which are not to be mistaken for Gaussian processes operating on text. We construct string GPs so that their finite-dimensional marginals exhibit suitable local conditional independence structures, which allow for scalable, distributed, and flexible nonparametric Bayesian inference, without resorting to approximations, and while ensuring some mild global regularity constraints. Furthermore, string GP priors naturally cope with heterogeneous input data, and the gradient of the learned latent function is readily available for explanatory analysis. Secondly, we provide some theoretical results relating our approach to the standard GP paradigm. In particular, we prove that some string GPs are Gaussian processes, which provides a complementary global perspective on our framework. Finally, we derive a scalable and distributed MCMC scheme for supervised learning tasks under string GP priors. The proposed MCMC scheme has computational time complexity $\mathcal{O}(N)$ and memory requirement $\mathcal{O}(dN)$, where $N$ is the data size and $d$ the dimension of the input space. We illustrate the efficacy of the proposed approach on several synthetic and real-world datasets, including a dataset with $6$ millions input points and $8$ attributes.
T-cell-specific murine Ia antigens: serology of I-J and I-E subregion specificities

(B10 X B10.D2)F1 mice were immunized with B10.A(5R) concanavalin A- stimulated thymocyte blasts. The genetic disparity between donor and recipient was restricted to the I-J and I-E subregions of the murine major histocompatibility (H-2) complex. A high-titered, T-cell-specific anti I-JkEk serum was obtained. The antiserum lysed 27-30% of haplotype k, q, or s lymph node cells, 5.3 +/- 2% of haplotype k spleen cells, and did not lyse thymocytes. Nylon wool-passed lymph node or spleen cells (H-2k) showed considerable reactivity with anti-I-JkEk serum (35- 40% lysis); anti-Thy1.2 plus complement-treated spleen cells did not react (less than 5% lysis). I-Ek antibody was detected by B10.A(3R) lymph node cell reactivity (20% lysis), whereas reaction with H-2k lymph node cells after B10.A(3R) absorption demonstrated IJk antibody (12% lysis). Lymphocyte activation with alloantigen or mitogen led to increased anti-I-JkEk serum reactivity. These results, showing antibody production to at least two T-cell Ia antigenic determinants by concanavalin A thmocyte blast immunization, suggest that a group of I- region-encoded T-cell specificities may not have been detected using conventional immunization protocols because they would not have comprised a major antigenic component of the immunizing cell population. The existence of multiple Ia antigenic determinants unique to T lymphocytes would have important implications for serological and functional studies of T-cell subpopulations.
Glucocorticoid Receptor Mutants Demonstrate Increased Motility Inside the Nucleus of Living Cells: Time of Fluorescence Recovery After Photobleaching (FRAP) Is an Integrated Measure of Receptor Function

Natural mutations of the human glucocorticoid receptor (GR) isoform α cause the glucocorticoid resistance syndrome. Mutant receptors may have abnormal interactions with the ligand, target DNA sequences, and/or multiple intracellular proteins, as well as aberrant nucleocytoplasmic trafficking. Using fluorescence recovery after photobleaching (FRAP) analysis, all GR pathologic mutant receptors examined, as well as 2 synthetic GR mutants lacking the activation function (AF)-1 or the ligand-binding domain (and hence the AF-2), had defective transcriptional activity and dynamic motility defects inside the nucleus of living cells. In the presence of dexamethasone, these mutants displayed a curtailed 50% recovery time (t1/2) after photobleaching and, hence, significantly increased intranuclear motility and decreased “chromatin retention. ” The t1/2 values of the mutants correlated positively with their transcriptional activities and depended on the GR domain affected. GRβ, a natural splice variant of the GR gene, also demonstrated a shorter t1/2 than GRa. The motility responsiveness of the natural and artificial mutant receptors examined, and of GRβ, to the proteasomal inhibitor MG-132 also depended on the mutant domain. Thus, mutant glucocorticoid receptors possess dynamic motility defects in the nucleus, possibly caused by their inability to properly interact with all key partner nuclear molecules necessary for full activation of glucocorticoid-responsive genes.
Contribution of Amino Acid Region 659−663 of Factor Va Heavy Chain to the Activity of Factor Xa within Prothrombinase†,‡

Factor Va, the cofactor of prothrombinase, is composed of heavy and light chains associated noncovalently in the presence of divalent metal ions. The COOH-terminal region of the heavy chain contains acidic amino acid clusters that are important for cofactor activity. In this work, we have investigated the role of amino acid region 659−663, which contains five consecutive acidic amino acid residues, by site-directed mutagenesis. We have generated factor V molecules in which all residues were mutated to either lysine (factor V5K) or alanine (factor V5A). We have also constructed a mutant molecule with this region deleted (factor VΔ659−663). The recombinant molecules along with wild-type factor V (factor VWT) were transiently expressed in mammalian cells, purified, and assessed for cofactor activity. Two-stage clotting assays revealed that the mutant molecules had reduced clotting activities compared to that of factor VaWT. Kinetic analyses of prothrombinase assembled with the mutant molecules demonstrated diminished kcat values, while the affinity of all mutant molecules for factor Xa was similar to that for factor VaWT. Gel electrophoresis analyses of plasma-derived and recombinant mutant prothrombin activation demonstrated delayed cleavage of prothrombin at both Arg320 and Arg271 by prothrombinase assembled with the mutant molecules, resulting in meizothrombin lingering throughout the activation process. These results were confirmed after analysis of the cleavage of FPR-meizothrombin. Our findings provide new insights into the structural contribution of the acidic COOH-terminal region of factor Va heavy chain to factor Xa activity within prothrombinase and demonstrate that amino acid region 659−663 from the heavy chain of the cofactor contributes to the regulation of the rate of cleavage of prothrombin by prothrombinase.
Knowledge and attitude of male primary school teachers about attention deficit and hyperactivity disorder in Riyadh, Saudi Arabia

Objective: We assessed the level and source of knowledge and attitude of male primary school teachers about attention-deficit and hyperactivity disorder (ADHD). Methods: A cross-sectional study was conducted from January to November 2016 involving Saudi male teachers currently working at 17 primary schools in Khashm-Alaan district, Saudi Arabia. A prevalidated, self-administered, structured questionnaire was used for data collection. Teachers' mean knowledge score was measured in the following three categories: insufficient, good, and very good. Results: Only 141 out of 182 teachers completed the questionnaire. Mean age of participants was 36.16 ± 6.6 years with teaching experience of 12 ± 6.2 years. Bachelor's degree was the highest qualification reported by 86% of participants. Seventy-two percent of the teachers had either very good (13%) or good (59%) knowledge about ADHD. Almost half of them reported multiple sources such as internet, social media, and television for obtaining information about ADHD. However, internet and social media alone were reported as a source of information by 13% and 11.4%, respectively. Those who attended any course on ADHD had significantly (P = 0.006) more knowledge about the disorder. Furthermore, those teachers having taught a child with ADHD had more knowledge about the disorder. A significant correlation between mean knowledge and attitude scores was observed. Conclusion: Two-third of the teachers had knowledge about ADHD. Those who had either attended a course or taught a child with the ADHD had significantly more knowledge about the disorder. Most of the teachers learned about the disorder from multiple sources.
Causal and noncausal conditionals: An integrated model of interpretation and reasoning

We present an integrated model for the understanding of and the reasoning from conditional statements. Central assumptions from several approaches are integrated into a causal path model. According to the model, the cognitive availability of exceptions to a conditional reduces the subjective conditional probability of the consequent, given the antecedent. This conditional probability determines people's degree of belief in the conditional, which in turn affects their willingness to accept logically valid inferences. In addition to this indirect pathway, the model contains a direct pathway: Availability of exceptional situations directly reduces the endorsement of valid inferences. We tested the integrated model with three experiments using conditional statements embedded in pseudonaturalistic cover stories. An explicitly mentioned causal link between antecedent and consequent was either present (causal conditionals) or absent (arbitrary conditionals). The model was supported for the causal but not for the arbitrary conditional statements.
An Analysis of the Technology Acceptance Model in Understanding University Students' Behavioral Intention to Use e-Learning

Many universities implement e-learning for various reasons. It is obvious that the number of e-learning opportunities provided by higher educational institutes continues to grow in Korea. Yet little research has been done to verify the process of how university students adopt and use e-learning. A sample of 628 university students took part in the research. The structural equation modeling (SEM) technique was employed with the LISREL program to explain the adoption process. The general structural model, which included e-learning selfefficacy, subjective norm, system accessibility, perceived usefulness, perceived ease of use, attitude, and behavioral intention to use e-learning, was developed based on the technology acceptance model (TAM). The result proved TAM to be a good theoretical tool to understand users’ acceptance of e-learning. E-learning selfefficacy was the most important construct, followed by subjective norm in explicating the causal process in the model.
BCILAB: a platform for brain–computer interface development

Objective. The past two decades have seen dramatic progress in our ability to model brain signals recorded by electroencephalography, functional near-infrared spectroscopy, etc., and to derive real-time estimates of user cognitive state, response, or intent for a variety of purposes: to restore communication by the severely disabled, to effect brain-actuated control and, more recently, to augment human–computer interaction. Continuing these advances, largely achieved through increases in computational power and methods, requires software tools to streamline the creation, testing, evaluation and deployment of new data analysis methods. Approach. Here we present BCILAB, an open-source MATLAB-based toolbox built to address the need for the development and testing of brain-computer interface (BCI) methods by providing an organized collection of over 100 pre-implemented methods and method variants, an easily extensible framework for the rapid prototyping of new methods, and a highly automated framework for systematic testing and evaluation of new implementations. Main results. To validate and illustrate the use of the framework, we present two sample analyses of publicly available data sets from recent BCI competitions and from a rapid serial visual presentation task. We demonstrate the straightforward use of BCILAB to obtain results compatible with the current BCI literature. Significance. The aim of the BCILAB toolbox is to provide the BCI community a powerful toolkit for methods research and evaluation, thereby helping to accelerate the pace of innovation in the field, while complementing the existing spectrum of tools for real-time BCI experimentation, deployment and use.
Deep Keyphrase Generation

Keyphrase provides highly-summative information that can be effectively used for understanding, organizing and retrieving text content. Though previous studies have provided many workable solutions for automated keyphrase extraction, they commonly divided the to-be-summarized content into multiple text chunks, then ranked and selected the most meaningful ones. These approaches could neither identify keyphrases that do not appear in the text, nor capture the real semantic meaning behind the text. We propose a generative model for keyphrase prediction with an encoder-decoder framework, which can effectively overcome the above drawbacks. We name it as deep keyphrase generation since it attempts to capture the deep semantic meaning of the content with a deep learning method. Empirical analysis on six datasets demonstrates that our proposed model not only achieves a significant performance boost on extracting keyphrases that appear in the source text, but also can generate absent keyphrases based on the semantic meaning of the text. Code and dataset are available at https://github.com/memray/seq2seq-keyphrase.
Traditional Institutions Meet the Modern World: Caste, Gender and Schooling Choice in a Globalizing Economy

This paper addresses the question of how traditional institutions interact with the forces of globalization to shape the economic mobility and welfare of particular groups of individuals in the new economy. We explore the role of one such traditional institution? the caste system? in shaping career choices by gender in Bombay using new survey data on school enrollment and income over the past 20 years. We find that male working-class? lower-caste? networks continue to channel boys into local language schools that lead to the traditional occupation, despite the fact that returns to nontraditional white-collar occupations rose substantially in the 1990s, suggesting the possibility of a dynamic inefficiency. In contrast, lower-caste girls, who historically had low labor market participation rates and so did not benefit from the network, are taking full advantage of the opportunities that became available in the new economy by switching rapidly to English schools. (JEL I21, J16, O15, Z13)
Environmental services in watersheds with small declivity: fluvial marine plains

Fluvial marine plains harbor environments with reduced declivity, but with different environmental attributes that may be relevant ecosystem services in their watersheds, which can be transformed into opportunities for the development of environmental services payments. This study digitally spatialized part of the ecosystem services related to water availability in the microbasins of the Guandu Basin Hydrographic Basin, the main source of water supply for the metropolitan region of Rio de Janeiro, based on geo-environmental factors and the effects of transposition of watersheds, in order to facilitate the formulation of public policies regarding environmental services. The excerpts with the highest potential for producing environmental services from subsurface water flows, Topographic Wetness Index-ITU (ITU > 11), flat formation and low altimetry (< 40 m), are close to the old silted thalwegs, which can be potentialized by the effects of transposition, notably in the APA Guandu conservation unit. These areas should be prioritized in public and private water systems’ preservation programs. The areas near the transposition canals feature environmental services that depend directly on the transposition, and may be subject to management, as found in Guandu conservation unit. Flat areas with (7<ITU< 11) have their ecosystem services transferred from subsurface water flows to surface water flows and are dependent more on meteorological phenomena and on ways to manage heavy rain showers through their use, especially as altimetry and slope increase.
Density Nowcasts and Model Combination: Nowcasting Euro‐Area GDP Growth Over the 2008–09 Recession

Combined density nowcasts for quarterly Euro-area GDP growth are produced based on the real-time performance of component models. Components are distinguished by their use of ‘hard’ and ‘soft’, aggregate and disaggregate, indicators. We consider the accuracy of the density nowcasts as within-quarter indicator data accumulate. We find that the relative utility of ‘soft’ indicators surged during the recession. But as this instability was hard to detect in real-time it helps, when producing density nowcasts unknowing any within-quarter ‘hard’ data, to weight the different indicators equally. On receipt of ‘hard’ data for the second month in the quarter better calibrated densities are obtained by giving a higher weight in the combination to ‘hard’ indicators.
Probabilistic sequence learning in mild cognitive impairment

Mild Cognitive Impairment (MCI) causes slight but noticeable disruption in cognitive systems, primarily executive and memory functions. However, it is not clear if the development of sequence learning is affected by an impaired cognitive system and, if so, how. The goal of our study was to investigate the development of probabilistic sequence learning, from the initial acquisition to consolidation, in MCI and healthy elderly control groups. We used the Alternating Serial Reaction Time task (ASRT) to measure probabilistic sequence learning. Individuals with MCI showed weaker learning performance than the healthy elderly group. However, using the reaction times only from the second half of each learning block—after the reactivation phase—we found intact learning in MCI. Based on the assumption that the first part of each learning block is related to reactivation/recall processes, we suggest that these processes are affected in MCI. The 24-h offline period showed no effect on sequence-specific learning in either group but did on general skill learning: the healthy elderly group showed offline improvement in general reaction times while individuals with MCI did not. Our findings deepen our understanding regarding the underlying mechanisms and time course of sequence acquisition and consolidation.
Binary Aggregation by Selection of the Most Representative Voters

In binary aggregation, each member of a group expresses yes/no choices regarding several correlated issues and we need to decide on a collective choice that accurately reflects the views of the group. A good collective choice will minimise the distance to each of the individual choices, but using such a distance-based aggregation rule is computationally intractable. Instead, we explore a class of low-complexity aggregation rules that select the most representative voter in any given situation and return that voter's choice as the outcome.
Testing for symmetry in the conditional discriminations of language-trained chimpanzees.

If subjects are taught to match Stimulus A to B and then, without further training, match B to A, they have passed a test of symmetry. It has been suggested that non-humans' lack of success on symmetry tests might be overcome by giving them a history of symmetry exemplar training, that is, by directly teaching a large number of conditional relations (e.g., AB, CD, EF,.) and also directly training the "reverse" of these relations (e.g., BA, DC, FE,.). The chimpanzee subjects of the present study, Sherman, Austin, and Lana, had already received extensive symmetry exemplar training as a result of attempts to teach a selection-based language system of lexigrams. The present study systematically subjected 2 of these chimps (Sherman and Lana), for the first time, to standard symmetry tests in controlled conditions. Both chimps failed the tests, even when their correct responses on test trials were reinforced. The findings do not support the exemplar training hypothesis, and cast doubt upon whether the chimps can pass tests of stimulus equivalence.
Increasing Play Complexity in a Young Child With Autism

Young children with autism spectrum disorder (ASD) demonstrate fewer and less varied play behaviors than children with typical development. This article describes two studies designed to increase two aspects of play complexity—diverse play actions and sequential play actions—in a child with ASD. In Study 1, we use a multitreatment single-case design to compare the use of the system of least prompts with contingent reinforcement for diverse play or all play. In Study 2, we used a multiple probe design to examine the relation between the system of least prompts with contingent reinforcement and the child’s use of play sequences. Across both studies, the complexity of play increased when specific aspects—diversity or sequences—were prompted and reinforced; however, the maintenance of effects were variable.