Miracle-dz/UniFAR-FTUs huggingface dataset:
A quasi‐exact method for the confidence intervals of the difference of two independent binomial proportions in small sample cases

In this paper we propose a quasi‐exact alternative to the exact unconditional method by Chan and Zhang (1999) estimating confidence intervals for the difference of two independent binomial proportions in small sample cases. The quasi‐exact method is an approximation to a modified version of Chan and Zhang's method, where the two‐sided p‐value of an observation is defined by adding to the one‐sided p‐value the sum of all probabilities of more ‘extreme’ events in the unobserved tail. We show that distinctively less conservative interval estimates can be derived following the modified definition of the two‐sided p‐value. The approximations applied in the quasi‐exact method help to simplify the computations greatly, while the resulting infringements to the nominal level are low. Compared with other approximate methods, including the mid‐p quasi‐exact methods and the Miettinen and Nurminen (M&N) asymptotic method, our quasi‐exact method demonstrates much better reliability in small sample cases. Copyright © 2002 John Wiley & Sons, Ltd.
Treatment of Depression in Drug-Dependent Patients : Effects on Mood and Drug Use

Symptoms of depression and anxiety are common in patients with substance use disorders (Meyer 1986; Schuckit 1986). In the general population, mood and anxiety disorders convey increased risk for substance use disorders (Regier et al. 1990). Further, mooddisordered substance abusers have poor prognoses (Rounsaville et al. 1982b, 1986b, 1987; Weissman et al. 1976; Kosten et al. 1986; LaPorte et al. 1981; Loosen et al. 1990; Carroll et al. 1993). Thus, evaluation and appropriate management of affective disorders should be a useful treatment adjunct with the potential of improving outcome of substance abuse. Nevertheless, controversy continues to surround this clinical problem, and approaches vary widely among clinicians.
Do the European Union’s Bilateral Investment Treaties Matter?

This working paper addresses a number of policy-relevant issues regarding the EU’s bilateral investment treaties (BITS), namely, whether the EU’s BITs have a significantly positive impact on outflows; and which member states and which BIT partners have had a significant experience after the implementation of the BIT. The author finds that both OECD BITs and EU BITs have a statistically significant and positive impact on FDI outflows. This result is robust to the inclusion of variables such as privatisation proceeds that control for the level of economic reform, the level of trade linkages, the level of democratic freedom and a measure of risk of expropriation among other standard controls. A number of policy implications of these findings are also considered.
Long-term neurocognitive function after critical illness.

BACKGROUND Until relatively recently, critical care practitioners have focused on the survival of their patients and not on long-term outcomes. The incidence of chronic neurocognitive dysfunction has been underestimated and underreported, and only recently has it been studied in critically ill patients. However, neurocognitive outcomes have been the subject of extensive investigation in other medical populations for many years. METHODS Review of the current literature regarding long-term neurocognitive outcomes following critical illness. RESULTS Data from studies to date indicate that critical illness can lead to significant neurocognitive impairments. The neurocognitive impairments persist for months and years, and may have important consequences for quality of life, the ability to return to work, overall functional ability, and substantial economic costs. The mechanisms of the neurocognitive impairments are not fully understood but likely include delirium, hypoxia, glucose dysregulation, metabolic derangements, inflammation, and the effects of sedatives and narcotics among other factors. The contributions of these factors may be particularly significant in patients with preexisting vulnerabilities for the development of cognitive impairments such as mild cognitive impairment, dementia, prior traumatic brain injury, or other comorbid disorders associated with neurocognitive impairments. CONCLUSIONS Current research indicates that neurocognitive sequelae following critical illness are common, may be permanent, and are associated with impairments in daily function, decreased quality of life, and an inability to return to work. Research needs to be done to better understand the prevalence, nature, risk factors, and nuances of the neurocognitive impairments observed in ICU survivors.
Real-Time Adaptive Radiometric Compensation

Recent radiometric compensation techniques make it possible to project images onto colored and textured surfaces. This is realized with projector-camera systems by scanning the projection surface on a per-pixel basis. Using the captured information, a compensation image is calculated that neutralizes geometric distortions and color blending caused by the underlying surface. As a result, the brightness and the contrast of the input image is reduced compared to a conventional projection onto a white canvas. If the input image is not manipulated in its intensities, the compensation image can contain values that are outside the dynamic range of the projector. These will lead to clipping errors and to visible artifacts on the surface. In this article, we present an innovative algorithm that dynamically adjusts the content of the input images before radiometric compensation is carried out. This reduces the perceived visual artifacts while simultaneously preserving a maximum of luminance and contrast. The algorithm is implemented entirely on the GPU and is the first of its kind to run in real time.
Therapeutic Effect of Yi-Chi-Tsung-Ming-Tang on Amyloid β 1−40-Induced Alzheimer's Disease-Like Phenotype via an Increase of Acetylcholine and Decrease of Amyloid β

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder characterized by amyloid accumulation, neuronal death, and cognitive impairments. Yi-Chi-Tsung-Ming-Tang (YCTMT) is a traditional Chinese medicine and has never been used to enhance cognitive function and treat neurodegenerative disorders such as senile dementia. Whether YCTMT has a beneficial role in improving learning and memory in AD patients remains unclear. The present study showed that oral administration of YCTMT ameliorated amyloid-β- (Aβ 1−40) injection-induced learning and memory impairments in rats, examined using passive avoidance and Morris water-maze tests. Immunostaining and Western Blot results showed that continuous Aβ 1−40 infusion caused amyloid accumulation and decreased acetylcholine level in hippocampus. Oral administration of medium and high dose of YCTMT 7 days after the Aβ 1−40 infusion decreased amyloid accumulation area and reversed acetylcholine decline in the Aβ 1−40-injected hippocampus, suggesting that YCTMT might inhibit Aβ plague accumulation and rescue reduced acetylcholine expression. This study has provided evidence on the beneficial role of YCTMT in ameliorating amyloid-induced AD-like symptom, indicating that YCTMT may offer an alternative strategy for treating AD.
Efficient Exploration for Reinforcement Learning Based Distributed Spectrum Sharing in Cognitive Radio System

3 ABSTRACT: In this paper, we investigate how distributed reinforcement learning-based resource assignment algorithms can be used to improve the performance of a cognitive radio system. Today's decision making in most wireless systems include cognitive radio systems in development, depends purely on instantaneous measurement. Two system architectures have been investigated in this paper. A point-to-point architecture is examined first in an open spectrum scenario. Then, the distributed reinforcement learning-based algorithms are developed by modifying the traditional reinforcement learning model in order to be applied to a fully distributed cognitive radio system.
Firewall Policy Queries

Firewalls are crucial elements in network security, and have been widely deployed in most businesses and institutions for securing private networks. The function of a firewall is to examine each incoming and outgoing packet and decide whether to accept or to discard the packet based on its policy. Due to the lack of tools for analyzing firewall policies, most firewalls on the Internet have been plagued with policy errors. A firewall policy error either creates security holes that will allow malicious traffic to sneak into a private network or blocks legitimate traffic and disrupts normal business processes, which in turn could lead to irreparable, if not tragic, consequences. Because a firewall may have a large number of rules and the rules often conflict, understanding and analyzing the function of a firewall has been known to be notoriously difficult. An effective way to assist firewall administrators to understand and analyze the function of their firewalls is by issuing queries. An example of a firewall query is "Which computers in the private network can receive packets from a known malicious host in the outside Internet? rdquo Two problems need to be solved in order to make firewall queries practically useful: how to describe a firewall query and how to process a firewall query. In this paper, we first introduce a simple and effective SQL-like query language, called the Structured Firewall Query Language (SFQL), for describing firewall queries. Second, we give a theorem, called the Firewall Query Theorem, as the foundation for developing firewall query processing algorithms. Third, we present an efficient firewall query processing algorithm, which uses decision diagrams as its core data structure. Fourth, we propose methods for optimizing firewall query results. Finally, we present methods for performing the union, intersect, and minus operations on firewall query results. Our experimental results show that our firewall query processing algorithm is very efficient: it takes less than 10 milliseconds to process a query over a firewall that has up to 10,000 rules.
GPU-ABiSort: optimal parallel sorting on stream architectures

In this paper, we present a novel approach for parallel sorting on stream processing architectures. It is based on adaptive bitonic sorting. For sorting n values utilizing p stream processor units, this approach achieves the optimal time complexity O((n log n)/p). While this makes our approach competitive with common sequential sorting algorithms not only from a theoretical viewpoint, it is also very fast from a practical viewpoint. This is achieved by using efficient linear stream memory accesses (and by combining the optimal time approach with algorithms optimized for small input sequences). We present an implementation on modern programmable graphics hardware (GPUs). On GPUs, our optimal parallel sorting approach has shown to be remarkably faster than sequential sorting on the CPU, and it is also faster than previous non-optimal sorting approaches on the GPU for sufficiently large input sequences. Because of the excellent scalability of our algorithm with the number of stream processor units p (up to n/log/sup 2/ n or even n/log n units, depending on the stream architecture), our approach profits heavily from the trend of increasing number of fragment processor units on GPUs, so that we can expect further speed improvement with upcoming GPU generations.
Discrete-time closed-form solution of time-optimal seek control for hard disk drives

This paper derives a discrete-time closed-form solution of time-optimal seek control based on the 3rd-order hard disk drive model including the back-EMF effect and coil inductance. Using the proposed method, online reference signal generation for 2-DOF control structure is discussed for short and long seeking cases. Simulation results show that the reference signals generated are good for smooth settling. Modification of the proposed method for improving acoustics during seeking is demonstrated. A feasible implementation of the proposed method is investigated for efficient usage of digital signal processor and limited memory available. The feed-forward current for seek control is obtained considering the power amplifier saturation so the proposed method has better features of adaptation for varying operating conditions.
Automatic reward shaping in Reinforcement Learning using graph analysis

Reinforcement Learning is a popular context of machine learning that aims at improving the behavior of autonomous agents that learn from interactions with the environment. However, it is often costly, time consuming, and even dangerous. To deal with these problems, reward shaping has been used as a powerful method to accelerate the learning speed of the agent. The principle idea is to incorporate a numerical feedback, other than environment reward, for the learning agent. However, finding an efficient potential function to shape the reward is still an interesting area of research. In this paper, a new algorithm has been proposed that receives the environment graph, performs some new analysis, and provides the extracted information for the learning agent to accelerate the speed of learning. This information includes sub goals, bad states, and sub environments with different exploration, or reward, values. To evaluate this algorithm an experimental study has been conducted on two benchmark environments, Six Rooms and Maze. The obtained results demonstrate the effectiveness of the proposed algorithm.
Use of gravitational stiffness in an attractor-based Whole-Body Motion Control approach

This paper presents an analysis of the relation between effort and gravitational stiffness, two physical measures that depend on the configuration of the robot. It is shown that whenever the gravitational stiffness is maximized, the effort is indirectly minimized. A minimum effort attractor that controls the gravitational stiffness is presented. This attractor together with an attractor to zero joint momentum guarantee balance maintenance. The novel implementation of the attractor-based Whole-body Motion Control (WBMC) System is experimentally tested: first with simple models, and finally with the full-body humanoid robot COMAN in a physical simulation.
Study of stochastic resonance in hierarchical IF neural networks

This paper studies the stochastic resonance effect in a hierarchical integrate-and-fire (IF) neural networks characterized by the measure of average mutual information. It is shown that, as the internal noise intensity increases, the average mutual information can be optimized at an optimal non-zero noise level. It is also noted that the maximum of mutual information obtained in the resonant region can be further enhanced for a suitable signal frequency. This leads to the more efficient transduction of signals in the IF neural network. Correspondingly, the interval time of spikes, regularly established at the optimal noise level, is in accord with the input signal period. The present results are meaningful to the study of signal transduction through hierarchical IF neural networks.
Determination of HLA‐G Expression and Evaluation of Its Role as a Prognostic Factor in Chronic Lymphocytic Leukemia

In recent years, the clinical and biological features governing the clinical course of chronic lymphocytic leukemia (CLL) have been most extensively studied. Human leukocyte antigen‐G (HLA‐G) allows tumor cells to escape from the antitumor effect of the immune system. Recent studies have shown that various tumor cells show an increased HLA‐G expression. Data regarding HLA‐G expression in CLL are limited and controversial. The aim of this work is to evaluate flow cytometry study of HLA‐G expression on cell surface and assess its relationship with other prognostic factors (CD38, ZAP70, beta 2 microglobulin [β2MG]) in patients with CLL.
Hierarchical Spherical Hashing for Compressing High Dimensional Vectors

We present a hierarchical approach to compress large dimensional vectors using hyper spherical hashing functions. We provide a practical solution for learning hyper spherical hashing functions by partitioning the vectors and learning hyper spheres in subspaces. Our method is an efficient way to preserve the hashing properties of sub-space hashing functions to generate the full-hashing functions in a divide and conquer fashion. We demonstrate the performance of our approach on the ILSVRC2010 Validation dataset and two large scale datasets: ILSVRC2010 Train and Holidays+Flickr 1M with high dimensional representations of size 128000, 25600 and 12800 respectively. Our results highlight the compact nature of hyper spherical hashing functions which significantly outperform the state-of-the art methods at compression ratios of 512, 256 and 128. Furthermore, we boost the retrieval performance by introducing an as symetric distance for spherical hashing functions.
Low-Complexity Saliency Detection Algorithm for Fast Perceptual Video Coding

A low-complexity saliency detection algorithm for perceptual video coding is proposed; low-level encoding information is adopted as the characteristics of visual perception analysis. Firstly, this algorithm employs motion vector (MV) to extract temporal saliency region through fast MV noise filtering and translational MV checking procedure. Secondly, spatial saliency region is detected based on optimal prediction mode distributions in I-frame and P-frame. Then, it combines the spatiotemporal saliency detection results to define the video region of interest (VROI). The simulation results validate that the proposed algorithm can avoid a large amount of computation work in the visual perception characteristics analysis processing compared with other existing algorithms; it also has better performance in saliency detection for videos and can realize fast saliency detection. It can be used as a part of the video standard codec at medium-to-low bit-rates or combined with other algorithms in fast video coding.
Relationship between Students' Emotional Intelligence, Social Bond, and Interactions in Online Learning

The purpose of the study was to investigate the relationship between students’ emotional intelligence, social bond, and their interactions in an online learning environment. The research setting in this study was a 100% online master’s degree program within a university located in the Midwest of the United States. Eighty-four students participated in the study. Using canonical correlation analysis, statistically significant relationships were found between students’ emotional intelligence, social bond, and the interactions that occurred naturally in the educational setting. The results showed that students’ ability to perceive emotion by facial expression was negatively related to the number of text and audio messages sent during synchronous interaction. Additionally, the ability of students to perceive emotion was positively related to peer bonding. Lastly, students’ bond to their online program was associated with management type interaction during synchronous discussion sessions. Several implications for online learning practitioners and researchers are discussed. Keyword Emotional intelligence, Social bond, Online interactions
Turbo Source Extraction Algorithm and Noncancellation Source Separation Algorithms by Kurtosis Maximization

The kurtosis maximization criterion has been effectively used for blind spatial extraction of one source from an instantaneous mixture of multiple non-Gaussian sources, such as the kurtosis maximization algorithm by Ding and Nguyen, and the fast kurtosis maximization algorithm (FKMA) by Chi and Chen. By empirical studies, we found that the smaller the normalized kurtosis magnitude of the extracted source signal, the worse the performance of these algorithms. In this paper, with the assumption that each source is a non-Gaussian linear process, a novel blind source extraction algorithm, called turbo source extraction algorithm (TSEA), is proposed. The ideas of the TSEA are to exploit signal temporal properties for increasing the normalized kurtosis magnitude, and to apply spatial and temporal processing in a cyclic fashion to improve the signal extraction performance. The proposed TSEA not only outperforms the FKMA, but also shares the convergence and computation advantages enjoyed by the latter. This paper also considers the extraction of multiple sources, also known as source separation, by incorporating the proposed TSEA into the widely used multistage successive cancellation (MSC) procedure. A problem with the MSC procedure is its susceptibility to error propagation accumulated at each stage. We propose two noncancellation multistage (NCMS) algorithms, referred to as NCMS-FKMA and NCMS-TSEA, that are free from the error propagation effects. Simulation results are presented to show that the NCMS-TSEA yields substantial performance gain compared with some existing blind separation algorithms, together with a computational complexity comparison. Finally, we draw some conclusions
Dictionary based action video classification with action bank

Classifying action videos became challenging problem in computer vision community. In this work, action videos are represented by dictionaries which are learned by online dictionary learning (ODL). Here, we have used two simple measures to classify action videos, reconstruction error and projection. Sparse approximation algorithm LASSO is used to reconstruct test video and reconstruction error is calculated for each of the dictionaries. To get another discriminative measure projection, the test vector is projected onto the atoms in the dictionary. Minimum reconstruction error and maximum projection give information regarding the action category of the test vector. With action bank as a feature vector, our best performance is 59.3% on UCF50 (benchmark is 57.9%), 97.7% on KTH (benchmark is 98.2%)and 23.63% on HMDB51 (benchmark is 26.9%).
Process Variation Tolerant Low Power DCT Architecture

2D discrete cosine transform (DCT) is widely used as the core of digital image and video compression. In this paper, the authors present a novel DCT architecture that allows aggressive voltage scaling by exploiting the fact that not all intermediate computations are equally important in a DCT system to obtain "good" image quality with peak signal to noise ratio (PSNR) > 30 dB. This observation has led us to propose a DCT architecture where the signal paths that are less contributive to PSNR improvement are designed to be longer than the paths that are more contributive to PSNR improvement It should also be noted that robustness with respect to parameter variations and low power operation typically impose contradictory requirements in terms of architecture design. However, the proposed architecture lends itself to aggressive voltage scaling for low-power dissipation even under process parameter variations. Under a scaled supply voltage and/or variations in process parameters, any possible delay errors would only appear from the long paths that are less contributive towards PSNR improvement, providing large improvement in power dissipation with small PSNR degradation. Results show that even under large process variation and supply voltage scaling (0.8V), there is a gradual degradation of image quality with considerable power savings (62.8%) for the proposed architecture when compared to existing implementations in 70 nm process technology
Identifying the L1 of non-native writers: the CMU-Haifa system

We show that it is possible to learn to identify, with high accuracy, the native language of English test takers from the content of the essays they write. Our method uses standard text classification techniques based on multiclass logistic regression, combining individually weak indicators to predict the most probable native language from a set of 11 possibilities. We describe the various features used for classification, as well as the settings of the classifier that yielded the highest accuracy.