Miracle-dz/UniFAR-FTUs huggingface dataset:
Neurodevelopmental Hypothesis about the Etiology of Autism Spectrum Disorders

Previous models or hypotheses of autism spectral disorder (ASD) failed to take into full consideration the chronological and causal developmental trajectory, leading to the emergence of diverse phenotypes through a complex interaction between individual etiologies and environmental factors. Those phenotypes include persistent deficits in social communication and social interaction (criteria A in DSM-5), and restricted, repetitive patterns of behavior, interests, or activities (criteria B in DSM-5). In this article, we proposed a domain-general model that can explain criteria in DSM-5 based on the assumption that the same etiological mechanism would trigger the various phenotypes observed in different individuals with ASD. In the model, we assumed the following joint causes as the etiology of autism: (1) Hypoplasia of the pons in the brainstem, occurring immediately following neural tube closure; and (2) Deficiency in the GABA (γ-aminobutyric acid) developmental switch during the perinatal period. Microstructural abnormalities of the pons directly affect both the structural and functional development of the brain areas strongly connected to it, especially amygdala. The impairment of GABA switch could not only lead to the deterioration of inhibitory processing in the neural network, but could also cause abnormal cytoarchitecture. We introduced a perspective that atypical development in both brain structure and function can give full explanation of diverse phenotypes and pathogenetic mechanism of ASD. Finally, we discussed about neural mechanisms underlying the phenotypic characteristics of ASD that are not described in DSM-5 but should be considered as important foundation: sleep, global precedence, categorical perception, intelligence, interoception and motor control.
Comparing carbon nanotubes and graphene nanoplatelets as reinforcements in polyamide 12 composites

We investigate the influence of nanofillers including carbon nanotubes (CNTs) and graphene nanoplatelets on a thermoplastic engineering polymer, polyamide 12 (PA12). The comparison between these two important nanofillers as to how they influence the structure and properties of the polymer is systematically studied. The polymer–nanofiller composites were prepared using a twin-screw micro-extruder and the composite was thereafter hot pressed into thin films. The structure (using wide angle x-ray diffraction and differential scanning calorimetry) and properties (through tensile testing and conductivity measurement) of the thin films have been investigated. The composites incorporating surfactant showed the best CNT distribution and dispersion, causing an improvement of up to 80% in the toughness modulus over pure PA12. Electrical percolation could also be achieved at nanofiller concentrations of 1 to 2 wt%. In this study we observed that CNT fillers bring about more pronounced improvements in PA12 compared to graphene nanoplatelets, as far as mechanical and electrical properties are concerned.
Uplink Communication Efficient Differentially Private Sparse Optimization With Feature-Wise Distributed Data

Preserving differential privacy during empirical risk minimization model training has been extensively studied under centralized and sample-wise distributed dataset settings. This paper considers a nearly unexplored context with features partitioned among different parties under privacy restriction. Motivated by the nearly optimal utility guarantee achieved by centralized private Frank-Wolfe algorithm (Talwar, Thakurta, and Zhang 2015), we develop a distributed variant with guaranteed privacy, utility and uplink communication complexity. To obtain these guarantees, we provide a much generalized convergence analysis for block-coordinate Frank-Wolfe under arbitrary sampling, which greatly extends known convergence results that are only applicable to two specific block sampling distributions. We also design an active feature sharing scheme by utilizing private Johnson-Lindenstrauss transform, which is the key to updating local partial gradients in a differentially private and communication efficient manner.
Does the Environment Influence the Employment Growth of SMEs?

Abstract A great deal of empirical research has been devoted to the study of the impact of a firm's environmental characteristics on its growth. However, most of this work focused on one or a few predictors. Furthermore, no research has provided an exhaustive list of all variables previously studied. We have tried to fill this gap and have tested on a sample of Belgian SMEs the potential influence of 15 variables on employment growth. On the basis of Mintzberg's classification, we have grouped our variables into three sub-categories of determinants relating to the generosity or hostility of the environment, to its stability or dynamism and to its simplicity or complexity. Our results show that employment growth within SMEs is only influenced by two variables linked to the generosity sub-group. These also show that the environment, at least if it is studied independently, has only an extremely limited influence on the growth of SMEs. This leads us to question the validity of purely external approaches to firms’ employment growth, such as the theory of population ecology of organizations.
An Analysis of Thai Financial Reporting Practices and the Impact of the 1997 Economic Crisis

We focus on listed Thai companies between 1993 and 2002 to ascertain whether the 1997 economic crisis, which we refer to as an economic disturbance, had an impact on financial reporting practices. Both changes in measurement and disclosure practices are considered and the period of study is divided into three sub-periods: the pre-economic crisis period (1993–96), the economic crisis period (1996–98) and the post-economic crisis period (1998–2002). The results show that there were significant increases in disclosure levels over the ten years but no substantial changes in measurement methods. This work takes on added relevance in the light of the recent (2007–08) economic bubble and subsequent financial crisis worldwide.
Parents’ assessment of circadian preference in elementary school-aged children: Validity and relations to educational outcomes

ABSTRACT Meta-analyses suggest that morning-oriented students obtain better school grades than evening-oriented students. This finding has generally been found for students in high school using self-report data for the assessment of circadian preference. Two studies (N = 2718/192) investigated whether these findings generalize across samples (i.e. elementary school-aged students) and methods (i.e. parent reports). These studies also explored whether the relation between circadian preference and school achievement could be explained within an expectancy-value framework. To this end, the Lark-Owl Chronotype Indicator (LOCI) was modified to obtain parents’ evaluations of their children’s circadian preference, while students completed a battery of assessments designed to explore the test-criterion evidence. Structural equation modeling and correlational analyses revealed: (1) morning and evening orientation were two separable factors of children’s circadian preference; (2) correlations with behavioral (e.g. sleep and eating times) and psychological (e.g. cognitive ability) data supported the test-criterion validity of both factors; (3) morning orientation was positively related to school achievement and (4) consistent with an expectancy-value framework this relation was mediated by children’s academic self-concept (ASC). These findings have important research and policy implications for considering circadian preference in the schooling of elementary students.
Masseter inhibitory reflex in amyotrophic lateral sclerosis

OBJECTIVES: To elucidate underlying abnormalities of the masseter motor system in amyotrophic lateral sclerosis (ALS). METHODS: We recorded biphasic EMG suppressions of the masseter muscle (silent or inhibitory periods 1 and 2) after electrical stimulation of the mental nerve during maximum clenching of the teeth in 16 ALS patients and 15 healthy control subjects. We also examined the recovery cycle of the silent periods by delivering paired stimuli with an interstimulus interval of 250 ms. RESULTS: Silent period 1 in the patients was not significantly different from that in control subjects, whereas silent period 2 (as measured by its area on the graph) was increased in the patients, especially with exaggerated jaw jerk. Only two patients showed absence of silent period 2. The recovery cycle of silent period 2 in the subgroup of patients with exaggerated jaw jerk was significantly lower than the control value. CONCLUSIONS: The abnormalities of the masseter inhibitory reflex in ALS could be ascribable to involvement of the corticobulbar or corticoreticular tract projecting to the inhibitory interneurons mediating silent period 2.
Deep-Treat: Learning Optimal Personalized Treatments From Observational Data Using Neural Networks

We propose a novel approach for constructing effective treatment policies when the observed data is biased and lacks counterfactual information. Learning in settings where the observed data does not contain all possible outcomes for all treatments is difficult since the observed data is typically biased due to existing clinical guidelines. This is an important problem in the medical domain as collecting unbiased data is expensive and so learning from the wealth of existing biased data is a worthwhile task. Our approach separates the problem into two stages: first we reduce the bias by learning a representation map using a novel auto-encoder network---this allows us to control the trade-off between the bias-reduction and the information loss---and then we construct effective treatment policies on the transformed data using a novel feedforward network. Separation of the problem into these two stages creates an algorithm that can be adapted to the problem at hand---the bias-reduction step can be performed as a preprocessing step for other algorithms. We compare our algorithm against state-of-art algorithms on two semi-synthetic datasets and demonstrate that our algorithm achieves a significant improvement in performance.
Earnings management in China: an exploratory study

Purpose - In recent years, China has been making progress in internationalizing its financial reporting system. However, it is believed that earnings management from legitimate accounting choices to fraud that violates generally accepted accounting principles, is common in the mainland. The purpose of this study is to identify the most frequently used earnings management techniques in China and the underlying factors that motivate firms to engage in earnings management. Design/methodology/approach - Data were gained through a questionnaire sent to managers and accountants in mainland Chinese companies. Findings - The results show that the size and form of ownership of companies materially influence earnings management incentives and techniques in China. Public ownership companies have stronger incentives to manage earnings for management compensation, while private ownership companies pay more attention to tax expense savings. Also, several popular techniques employed in China are revealed. Originality/value - This study presents a general picture of earnings management in China by surveying the opinions of accountants and financial managers in Chinese companies.
Predictive Coding Machine for Compressed Sensing and Image Denoising

Sparse and low rank coding has widely received much attention in machine learning, multimedia and computer vision. Unfortunately, expensive inference restricts the power of coding models in real-world applications, e.g., compressed sensing and image deblurring. In order to avoid the expensive inference, we propose a predictive coding machine (PCM) which aims to train a deep neural network (DNN) encoder to approximate the codes. By this means, a test sample can be fast approximated by the well-trained DNN. However, DNN leads PCM to be a non-convex and non-smooth optimization problem, which is extremely hard to solve. To address this challenge, we extend accelerated proximal gradient for PCM by steering gradient descent of DNN. To the best of our knowledge, we are the first to propose a gradient descent algorithm guided by accelerated proximal gradient for solving the PCM problem. Besides, a sufficient condition is provided to ensure the convergence to a critical point. Moreover, when the coding models are convex in PCM, the convergence rate O(1/(m2√t)) can be held in which m is the iteration number of accelerated proximal gradient, and t is the epoch of training DNN. Numerical results verify the promising advantages of PCM in terms of effectiveness, efficiency and robustness.
Learning a Wavelet-like Auto-Encoder to Accelerate Deep Neural Networks

Accelerating deep neural networks (DNNs) has been attracting increasing attention as it can benefit a wide range of applications, e.g., enabling mobile systems with limited computing resources to own powerful visual recognition ability. A practical strategy to this goal usually relies on a two-stage process: operating on the trained DNNs (e.g., approximating the convolutional filters with tensor decomposition) and fine-tuning the amended network, leading to difficulty in balancing the trade-off between acceleration and maintaining recognition performance. In this work, aiming at a general and comprehensive way for neural network acceleration, we develop a Wavelet-like Auto-Encoder (WAE) that decomposes the original input image into two low-resolution channels (sub-images) and incorporate the WAE into the classification neural networks for joint training. The two decomposed channels, in particular, are encoded to carry the low-frequency information (e.g., image profiles) and high-frequency (e.g., image details or noises), respectively, and enable reconstructing the original input image through the decoding process. Then, we feed the low-frequency channel into a standard classification network such as VGG or ResNet and employ a very lightweight network to fuse with the high-frequency channel to obtain the classification result. Compared to existing DNN acceleration solutions, our framework has the following advantages: i) it is tolerant to any existing convolutional neural networks for classification without amending their structures; ii) the WAE provides an interpretable way to preserve the main components of the input image for classification.
Cerebral cavernous malformations: natural history and prognosis after clinical deterioration with or without hemorrhage.

Despite recent studies of the natural history of cavernous malformations, there remains significant uncertainty concerning hemorrhage rates and the importance of lesion location. Controversy arises over varying definitions of "hemorrhage. " What is ultimately important to the patient is the occurrence of a neurological event, which may or may not be associated with radiologically documented hemorrhage, as well as the chance of recovery after such an event. The purpose of this study was to determine the rates of occurrence and sequelae of neurological events in 173 patients referred to our vascular malformation clinic with cavernous malformations. All patient data were entered into a database. The mean age at presentation for the 173 patients was 37.5 years. The lesion location was deep (brainstem, cerebellar nuclei, thalamus, or basal ganglia) in 64 patients (37%) and superficial in 109 (63%). Thirty-one patients (18%) had multiple lesions. Disease presentation was due to seizures in 62 patients (36%), hemorrhage in 44 (25%), focal neurological deficit without documented hemorrhage in 35 (20%), headache alone in 11 (6%), and incidental findings in 21 patients (12%). The results obtained in the 110 patients eligible for follow-up review were used to derive information on the rates of hemorrhage and neurological events. An interval event (neurological deterioration) required both symptoms and signs. The total mean follow-up period was 46 months, the majority (65%) of which was prospective. There were 18 interval events in 427 patient-years of follow-up review, for an overall annual event rate of 4.2%. Location was the most important factor for predicting interval event occurrence, with significantly higher rates for deeply located (10.6%/year) compared with superficially located lesions (0%/year) (p = 0.0001). Of patients suffering a neurological event, only 37% had complete resolution of their deficits. This largely prospective study indicates that deep cavernous malformations carry a worse prognosis than superficial lesions with respect to annual rates of neurological deterioration. The alarming rate of adverse clinical events occurring in patients with deep lesions is punctuated by the fact that less than one-half of them recover fully during long-term follow-up review.
The Impact of Accounting Conservatism on the Magnitude of the Differential Information Content of Cash Flows and Accruals

This study documents the relation between accounting conservatism and the differential response to operating accruals. Feltham and Ohlson's (1995) valuation framework predicts a positive association between the valuation multiple on operating assets and accounting conservatism. The relation between operating assets and operating accruals provides the basis for Feltham and Ohlson's (1995) further prediction of a positive association between the response coefficient to operating accruals and accounting conservatism. Stober (1996) and Ahmed, Morton, and Schaefer (2000) present evidence that the valuation multiple on operating assets is increasing in conservatism. Hence, by investigating the relation between conservatism and the differential response to operating accruals, this study extends the findings of Stober (1996) and Ahmed, Morton, and Schaefer (2000) and provides additional evidence on the agreement of the Feltham and Ohlson (1995) framework with the data. Results are presented for both cross-sectional and firm-specific designs as well as alternative measures of conservatism. When firms are partitioned on conservatism, evidence is generally consistent with Feltham and Ohlson's (1995) prediction that as conservatism increases so does the magnitude of the response coefficient on operating accruals.
Use of Multispectral Airborne Images to Improve In-Season Nitrogen Management, Predict Grain Yield and Estimate Economic Return of Maize in Irrigated High Yielding Environments

Vegetation indices (VIs) derived from active or passive sensors have been used for maize growth monitoring and real-time nitrogen (N) management at field scale. In the present multilocation two-year study, multispectral VIs (green- and red-based), chlorophyll meter (SPAD) and plant height (PltH) measured at V12–VT stage of maize development, were used to distinguish among the N status of maize, to predict grain yield and economic return in high yielding environments. Moreover, linear plateau-models were performed with VIs, SPAD and PltH measurements to determine the amount of N needed to achieve maximum maize grain yields and economic return. The available N in the topsoil (0–30 cm) was measured, and its relationship with VIs, maize yield and maize N requirements was analyzed. Green-based VIs were the most accurate indices to predict grain yield and to estimate the grain yield optimum N rate (GYONr) (216.8 kg N ha−1), but underestimated the grain yield optimum N available (GYONa) (248.6 kg N ha−1). Red-based VIs slightly overestimated the GYONr and GYONa, while SPAD highly underestimated both of them. The determination of the available N did not improve the accuracy of the VIs to determine the grain yield. The green chlorophyll index (GCI) distinguished maize that would yield less than 84% of the maximum yield, showing a high potential to detect and correct maize N deficiencies at V12 stage. The economic optimum nitrogen rate (EONr) and economic optimum nitrogen available (EONa) were determined below the GYONr and the GYONa, demonstrating that maximum grain yield strategies in maize are not normally the most profitable for farmers. Further research is needed to fine-tune the response of maize to N applications when deficiencies are detected at V12 stage, but airborne imagery could be useful for practical farming implementation in irrigated high yielding environments.
The take‐over market for privately held companies: the US experience

The volume of acquisitions involving privately held firms has far surpassed that of publicly traded firms in recent years; yet this segment of the take-over market remains largely unexplored. In analysing the unique features of private target take-overs, we compare the mergers and acquisitions markets for unquoted and quoted firms on several dimensions including methods of payment, offer premiums and bidder wealth effects. Our results show that these mergers provide positive shareholder wealth benefits for bidders and relatively high premiums for privately held targets. Also, the market's reaction depends on whether it perceives the price paid for the target as too high or too low, a rational response given the sentiment at the time of the announcement. Copyright 2001 by Oxford University Press.
Voting Against Your Constituents? How Lobbying Affects Representation

Citizens delegate the representation of their political preferences to members of Parliament (MPs), who are supposed to represent their interests in the legislature. However, MPs are exposed to a variety of interest groups seeking to influence their voting behavior. We argue that interest groups influence how MPs cast their vote in Parliament, but that this effect varies across groups. While lobbying by sectional groups provides incentives for MPs to defect from their constituents, we expect that cause groups in fact strengthen the link between MPs and their voters. We test our argument based on an innovative study of 118 Swiss public referenda, which allows for directly comparing voter preferences with legislative voting of 448 MPs on these issues. Drawing on a multilevel regression analysis, this study shows that interest groups considerably affect the link between MPs and their voters. Our findings have important implications for our understanding of political representation.
Currency Crises and Collapses

THE COLLAPSE of the Mexican peso in late 1994 remains a topic of controversy. To some observers it appeared that the bungling of a new administration had brought about a disaster where there was no problem and every reason to expect stability and prosperity. In fact, some even argue that the damage could be undone by raising the peso to its initial level. The notion that the devaluation was a blunder or worse was shared by the Wall Street Journal's editorial board, some members of the U.S. Congress, and other advocates of hard money, including exposed investors. In the Financial Times Jude Wanniski referred to "currency devaluationists," and "currency debauchery" at the hands of Lawrence Summers and Stanley Fischer. ' To others, the ultimate collapse seemed inevitable and the only issue was one of timing. In this view currency alignment-devaluation or floating-was long overdue and had it been accomplished earlier, much