Miracle-dz/UniFAR-FTUs huggingface dataset:
Economic consequences of adopting English for annual reports

We investigate the economic consequences of increasing financial report visibility, measured by the use of English as a reporting language for firms from non-English-speaking countries. We sample 113 firms that started publishing their annual report in English (in addition to their local language) during 2004-2007 while not cross listing, not joining a major stock index or a stock index that requires external reporting in English and not engaging in major M&A activity. Taking into account the endogeneity of the reporting language, with a difference-in-differences setting and propensity score matching, and controlling for confounding factors, we find that adoption of English in the annual report is associated with lower information asymmetry, greater analyst following and more foreign investors. This suggests that language per se is an attribute of the firm's visibility.
Pain and depressive symptoms among financially disadvantaged women's health patients.

OBJECTIVES Women are at greater risk than men for both pain and depression, yet little is known about the frequency and implications of comorbid pain and depression among women in women's health settings. This study aimed to determine the frequency of comorbid depressive symptoms and pain among a sample of gynecology outpatients and to evaluate the associations of comorbid pain and depressive symptoms with physical, emotional, and social functioning and abuse experiences. METHODS A total of 242 low-income, primarily African American women presenting at an urban women's health clinic for routine gynecological care consented to participate. Subjects completed the Beck Depression Inventory, Brief Symptom Inventory, Graded Chronic Pain Scale, SF-36, Physical and Sexual Abuse Questionnaire, and Duke Social Support Index. Multivariate analyses were used to test pain and depressive symptoms in their associations with emotional, physical, and social functioning and abuse experience. Age, race, income, and education were controlled in all analyses. RESULTS Nearly 20% of participants reported comorbid high depressive symptoms and pain. Both depressive symptoms and pain were independently associated with emotional, physical, and social functioning domains. Depressive symptoms, but not pain, were associated with increased likelihood of history of abuse. CONCLUSIONS Comorbid depressive symptoms and pain are a substantial problem, with pervasive implications among financially disadvantaged women seeking routine gynecological care. Subsequent research will determine if psychosocial treatment can be adapted effectively to the needs of this patient population.
Characterization of Host-Level Application Traffic with Multi-Scale Gamma Model

In this paper, we discuss the validity of the multi-scale gamma model and characterize the differences in host-level application traffic with this model by using a real traffic trace collected on a 150-Mbps transpacific link. First, we investigate the dependency of the model (parameters α and β, and fitting accuracy e) on time scale Δ, then find suitable time scales for the model. Second, we inspect the relations among α, β, and e, in order to characterize the differences in the types of applications. The main findings of the paper are as follows. (1) Different types of applications show different dependencies of α, β, and e on Δ, and display different suitable Δs for the model. The model is more accurate if the traffic consists of intermittently-sent packets than other. (2) More appropriate models are obtained with specific α and β values (e.g., 0.1 < α < 1, and β < 2 for Δ = 500ms). Also, application-specific traffic presents specific ranges of α, β, and e for each Δ, so that these characteristics can be used in application identification methods such as anomaly detection and other machine learning methods.
Enhancing node connectivity by utilizing RSSI for ZigBee-based WSN

ZigBee-based Wireless Sensor Network (WSN) is formed by a large number of sensor nodes each having limited connectivity. As a result, some nodes may become disconnected to the network, especially during the network configuration stage. The existing node connection scheme for WSN minimize the number of isolated nodes, but the distance between the connected nodes was not considered. In this paper we improve the scheme by proposing two methods, Gaussian filtering with averaging and Median method based on RSSI samples. The first method aims to achieve high accuracy, while the second one aims to minimize the computational overhead in node connection. Computer simulation demonstrates that the proposed approach allows each node to connect to the nearest child node with higher accuracy than the existing scheme, and thus maximizes power efficiency of the entire network.
Interior-Point Methods for Massive Support Vector Machines

We investigate the use of interior-point methods for solving quadratic programming problems with a small number of linear constraints, where the quadratic term consists of a low-rank update to a positive semidefinite matrix. Several formulations of the support vector machine fit into this category. An interesting feature of these particular problems is the volume of data, which can lead to quadratic programs with between 10 and 100 million variables and, if written explicitly, a dense Q matrix. Our code is based on OOQP, an object-oriented interior-point code, with the linear algebra specialized for the support vector machine application. For the targeted massive problems, all of the data is stored out of core and we overlap computation and input/output to reduce overhead. Results are reported for several linear support vector machine formulations demonstrating that the method is reliable and scalable.
Chronic network stimulation enhances evoked action potentials

Neurons cultured on multielectrode arrays almost always lack external stimulation except during the acute experimental phase. We have investigated the effects of chronic stimulation during the course of development in cultured hippocampal neural networks by applying paired pulses at half of the electrodes for 0, 1 or 3 r/day for 8 days. Spike latencies increased from 4 to 16 ms as the distance from the stimulus increased from 200 to 1700 µm, suggesting an average of four synapses over this distance. Compared to no chronic stimulation, our results indicate that chronic stimulation increased evoked spike counts per stimulus by 50% at recording sites near the stimulating electrode and increased the instantaneous firing rate. On trials where both pulses elicited responses, spike count was 40–80% higher than when only one of the pulses elicited a response. In attempts to identify spike amplitude plasticity, we found mainly amplitude variation with different latencies suggesting recordings from neurons with different identities. These data suggest plastic network changes induced by chronic stimulation that enhance the reliability of information transmission and the efficiency of multisynaptic network communication.
The Effect of Refinement on Student Achievement in a Fundamental Motor Skill in Grades K through 6

This study investigated whether the teacher behavior of refinement would affect boys’ and girls’ achievement in the standing broad jump across grade levels kindergarten through 6th grade. The study took place in a public school rural setting using intact classes involving 529 students. A nonequivalent control-group design was used for the experiment. All subjects were pretested and posttested. A posttest took place immediately following the experiment and after a time span of 7 months. The posttest scores were analyzed using an analysis of covariance at each grade level. From the analysis of the data, it was concluded that the teacher behavior of refinement did have both immediate and long-term positive effects on student achievement in performing the standing broad jump.
3D-TV Production From Conventional Cameras for Sports Broadcast

3DTV production of live sports events presents a challenging problem involving conflicting requirements of maintaining broadcast stereo picture quality with practical problems in developing robust systems for cost effective deployment. In this paper we propose an alternative approach to stereo production in sports events using the conventional monocular broadcast cameras for 3D reconstruction of the event and subsequent stereo rendering. This approach has the potential advantage over stereo camera rigs of recovering full scene depth, allowing inter-ocular distance and convergence to be adapted according to the requirements of the target display and enabling stereo coverage from both existing and `virtual' camera positions without additional cameras. A prototype system is presented with results of sports TV production trials for rendering of stereo and free-viewpoint video sequences of soccer and rugby.
Joint Top-K Spatial Keyword Query Processing

Web users and content are increasingly being geopositioned, and increased focus is being given to serving local content in response to web queries. This development calls for spatial keyword queries that take into account both the locations and textual descriptions of content. We study the efficient, joint processing of multiple top-k spatial keyword queries. Such joint processing is attractive during high query loads and also occurs when multiple queries are used to obfuscate a user's true query. We propose a novel algorithm and index structure for the joint processing of top-k spatial keyword queries. Empirical studies show that the proposed solution is efficient on real data sets. We also offer analytical studies on synthetic data sets to demonstrate the efficiency of the proposed solution.
A Probabilistic Embedding Clustering Method for Urban Structure Detection

Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by learning via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.
Reliable crosstalk-driven interconnect optimization

As technology advances apace, crosstalk becomes a design metric of comparable importance to area and delay. This article focuses mainly on the crosstalk issue, specifically on the impacts of physical design and process variation on crosstalk. While the feature size shrinks below 0. 25μm, the impact of process variation on crosstalk increases rapidly. Hence, a crosstalk insensitive design is desirable in the deep submicron regime. In this article, crosstalk sensitivity is referred to as the influence of process variation on crosstalk in a circuit. We show that the lower bound of crosstalk sensitivity grows quadratically, while that of crosstalk increases linearly. Therefore, designers should also consider crosstalk sensitivity, when optimizing other design objectives such as crosstalk, area, and delay. According to our modeling, these objectives are all in posynomial forms, and thus the multi-objective optimization problem can optimally be solved by Lagrangian relaxation. Experimental results show that our method is effective and efficient. For instance, a circuit of 2856 gates and 5272 wires is optimized using 13-minute runtime and 2.8-MB memory on a Pentium III 1.0 GHz PC with 256-MB memory. In particular, by relaxing Lagrange multipliers to the critical paths, it takes only two iterations for all solutions to converge to the global optimal, which is much more efficient than related previous work. This relaxation scheme provides a key insight into the rapid convergence in Lagrangian relaxation.
Inference of R 0 and Transmission Heterogeneity from the Size Distribution of Stuttering Chains

For many infectious disease processes such as emerging zoonoses and vaccine-preventable diseases, and infections occur as self-limited stuttering transmission chains. A mechanistic understanding of transmission is essential for characterizing the risk of emerging diseases and monitoring spatio-temporal dynamics. Thus methods for inferring and the degree of heterogeneity in transmission from stuttering chain data have important applications in disease surveillance and management. Previous researchers have used chain size distributions to infer , but estimation of the degree of individual-level variation in infectiousness (as quantified by the dispersion parameter, ) has typically required contact tracing data. Utilizing branching process theory along with a negative binomial offspring distribution, we demonstrate how maximum likelihood estimation can be applied to chain size data to infer both and the dispersion parameter that characterizes heterogeneity. While the maximum likelihood value for is a simple function of the average chain size, the associated confidence intervals are dependent on the inferred degree of transmission heterogeneity. As demonstrated for monkeypox data from the Democratic Republic of Congo, this impacts when a statistically significant change in is detectable. In addition, by allowing for superspreading events, inference of shifts the threshold above which a transmission chain should be considered anomalously large for a given value of (thus reducing the probability of false alarms about pathogen adaptation). Our analysis of monkeypox also clarifies the various ways that imperfect observation can impact inference of transmission parameters, and highlights the need to quantitatively evaluate whether observation is likely to significantly bias results.
HIGH-RESOLUTION NONPARAMETRIC SPECTRAL ANALYSIS: THEORY AND APPLICATIONS

Spectral estimation can be defined as the art of recovering the frequency content in a measured signal, and is a highly relevant problem in practice. In particular, many engineering problems, ranging from synthetic aperture radar (SAR) imaging to the analysis of time-series observed in seismology or astronomy, can be cast as a spectral analysis problem. A nonparametric spectral estimator is a method that, unlike parametric methods, attempts to compute the spectral content of a signal without using any a priori information or making any explicit assumption about it. While many smoothed versions of the discrete Fourier transform can be interpreted as nonparametric methods, a body of recent work has suggested more advanced such methods that are based on adaptive filterbanks. This chapter provides a review of some of the existing work in the area of nonparametric spectral estimation, including fast implementations of the most successful estimators as well as various extensions to spectral analysis of incomplete data. Numerical examples are provided to exemplify the various algorithms.
Determinism Is Not Enough : Making Parallel Programs Reliable with Stable Multithreading

Our accelerating computational demand and the rise of multicore hardware have made parallel programs, especially shared-memory multithreaded programs, increasingly pervasive and critical. Yet, these programs remain extremely difficult to write, test, analyze, debug, and verify. Conventional wisdom has attributed these difficulties to nondeterminism (i.e., repeated executions of the same program on the same input may show different behaviors), and researchers have recently dedicated much effort to bringing determinism into multithreading. In this article, we argue that determinism is not as useful as commonly perceived: it is neither sufficient nor necessary for reliability. We present our view on why multithreaded programs are difficult to get right, describe a promising approach we call stable multithreading to dramatically improve reliability, and summarize our last four years’ research on building and applying stable multithreading systems.
Object Localization based on Structural SVM using Privileged Information

We propose a structured prediction algorithm for object localization based on Support Vector Machines (SVMs) using privileged information. Privileged information provides useful high-level knowledge for image understanding and facilitates learning a reliable model even with a small number of training examples. In our setting, we assume that such information is available only at training time since it may be difficult to obtain from visual data accurately without human supervision. Our goal is to improve performance by incorporating privileged information into ordinary learning framework and adjusting model parameters for better generalization. We tackle object localization problem based on a novel structural SVM using privileged information, where an alternating loss-augmented inference procedure is employed to handle the term in the objective function corresponding to privileged information. We apply the proposed algorithm to the Caltech-UCSD Birds 200-2011 dataset, and obtain encouraging results suggesting further investigation into the benefit of privileged information in structured prediction.
Analysis of a Vaccinia Virus Mutant Expressing a Nonpalmitylated Form of p37, a Mediator of Virion Envelopment

ABSTRACT Vaccinia virus encodes a 37-kDa palmitylated protein (p37) that is required for envelopment, translocation, and cell-to-cell spread of virions. We have analyzed the biological significance of the palmitate modification by constructing a recombinant vaccinia virus that expresses a nonpalmitylated p37 and comparing its biological activity to that of the wild-type virus. The mutant virus is inefficient at cell-to-cell spread and does not produce or release enveloped virions, although it produces normal amounts of nonenveloped virions. Furthermore, the mutant virus is not able to nucleate actin to propel itself through and out of the cell, a function requiring the indirect participation of p37. The deficiency in protein function appears to result from a lack of appropriate targeting to the membranes of thetrans-Golgi network (TGN) which leaves p37 soluble in the cytoplasm. We conclude that the palmitate moiety is necessary for targeting or anchoring p37 to the TGN membrane, where, along with other vaccinia virus-encoded proteins, p37 is involved in the complex process of virion envelopment and release.
A unified computation model for functional and logic programming

We propose a new computation model which combines the operational principles of functional languages (reduction), logic languages (non-deterministic search for solutions), and integrated functional logic languages (residuation and narrowing). This computation model combines efficient evaluation principles of functional languages with the problem-solving capabilities of logic programming. Since the model allows the delay of function calls which are not sufficiently instantiated, it also supports a concurrent style of programming. We provide soundness and completeness results and show that known evaluation principles of functional logic languages are particular instances of this model. Thus, our model is a suitable basis for future declarative programming languages.
Fine-grained record integration and linkage tool.

BACKGROUND As part of the surveillance program to monitor the occurrence of birth defects in the metropolitan Atlanta area, we developed a record linkage software tool that provides latitude in the choice of linkage parameters, allows for efficient and accurate linkages, and enables objective assessments of the quality of the linked data. METHODS We developed and implemented a Java-based fine-grained probabilistic record integration and linkage tool (FRIL) that incorporates a rich collection of record distance metrics, search methods, and analysis tools. Along its workflow, FRIL provides a rich set of user-tunable parameters augmented with graphic visualization tools to assist users in understanding the effects of parameter choices. We used this software tool to link data from vital records (n = 1.25 million) with birth defects surveillance records (n = 12,700) from the metropolitan Atlanta Congenital Defects Program (MACDP) for the birth years 1967-2006. RESULTS Compared with the data linkage performed by conventional algorithms, the data linkage of birth certificates with birth defect records in MACDP using FRIL was more efficient. The linkage based on FRIL was also accurate, showing 99% precision and 95% recall. Based on positive user feedback, new features continue to be developed, and the tool is being adopted in several other data linkage projects in MACDP. CONCLUSIONS A software tool that allows significant user interaction and control, such as FRIL, can provide accurate data linkages for birth defect surveillance programs and allows an objective assessment of the quality of linked data.
Anawake : Signal-Based Power Management For Digital Signal Processing Systems

Single-chip, low-power, programmable digital signal processing systems are capable of hosting complete speech processing applications, while consuming a few milliwatts of average power. We present a power management architecture that decreases the average power consumption of these systems to 3–10 microwatts, in applications where speech signals are present with a sufficiently low duty cycle. In this architecture, a micropower analog signal processing system, Anawake, continuously analyzes the incoming signal, and controls the power consumption of the DSP system in a signal-dependent way. We estimate system power consumption for Anawake designs optimized for different peak-speech-signal to average-background-noise ratios.
Multiscale products for the detection of an electrocardiogram R wave: MIT-BIH polysomnographic

In this paper, we propose a method for detecting R wave of an electrocardiogram (ECG) based on the detection of singularities through the local maxima. This method is based on the detection of singularities of the product of three successive levels of decomposition of the wavelet transform. We tested the algorithm of detection on various signals from the MIT / BIH polysomnographic database containing various types of pathologies.