Miracle-dz/UniFAR-FTUs huggingface dataset:
Do Credit Ratings Reflect Underlying Firm Characteristics? Evidence from the Utility Industry

The recent financial crisis has raised questions regarding the role credit rating agencies play in monitoring the quality of corporate debt. Utility industry deregulation serves as a natural testing ground for evaluating the prudence of rating agencies and their monitoring process. Following deregulation and the Enron scandal, the general opinion among industry professionals is that utilities are being punished by credit rating agencies. Contrary to this popular belief, we find that the utility credit ratings are significantly higher compared to those of other firms, and this significance is more pronounced in the post-deregulation period. We also do not find any evidence that the credit ratings of utilities are more likely to be downgraded (upgraded) following deregulation. Although rating agencies often cite regulatory reasons for placing utilities on negative credit watches, these firms‟ ratings are rarely downgraded after being placed on negative watches. We also find that while firms in other industries adjust their capital structures following rating changes, rating changes have insignificant impact on utilities. Thus, despite the statements often seen in popular press, credit ratings of utilities seem more of a product of interactions between utilities and rating agencies than of firm characteristics. In general, our evidence indicates that credit ratings might not always be reflective of the underlying firm characteristics.
Developing Control Systems : A Method and a Case Study

In this paper we present a systematic, seven-step development method for control systems. The first five steps are dedicated to correctly capturing the requirements of the control system into one, single specification. The last two steps focus on identifying a control system-specific structure from this specification. A central point in our method is the modeling of system components on two different levels: informally as UML diagrams and formally as action system-based specifications. The diagrams contribute to capturing requirements, defining the control structure, and documenting the final system. The formal specifications ensure the robustness of the method. We develop the formal specifications (using refinement and decomposition techniques) in parallel with the UML diagrams. Since the UML diagrams mirror the formal part of the development, we remark an informal refinement of such diagrams. The proposed method is demonstrated with a detailed case study on the sorting hub of a fast courier service.
THE ADAPTABILITY CHALLENGE FOR EMBEDDED CONTROL SYSTEM SOFTWARE

Abstract Software-related costs account for a growing share of total development costs for embedded control systems. In the control field, containment of software costs can be done either through the use of model-based tools (e.g. Matlab) or through a higher level of reuse. This paper argues that the second strategy is advantageous in the case of industrial control systems targeting niche markets where systems tend to be one-of-a-kind and where they can be organized in “families” of related applications. The paper then argues that progress in raising the level of software reuse in these fields depends on the adoption of better software adaptability techniques. The most promising such techniques are reviewed from the standpoint of control engineers.
PENGEMBANGAN PEMBELAJARAN JUDO TEKNIK BANTINGAN KYU - 4 DENGAN MEDIA VCD

Penelitian ini adalah untuk mengembangkan pembelajaran judo terutama kyu - 4 yaitu teknik membanting dengan media VCD yang berguna dan baik untuk semua atlet Judo. Dengan adanya pengembangan pembelajaran dengan media VCD ini, dapat membantu dan memfasilitasi atlet Judo dalam mempercepat aspek dominasi gerakan judo atau teknik terutama teknik kyu - 4 membanting. Jenis penelitian ini adalah penelitian pengembangan dengan menggunakan model Borg dan Gall. Lokasi penelitian di cabang PJSI Medan dengan jumlah atlet 30 subjek Judo. Dari penelitian ini dapat disimpulkan bahwa pengembangan yang telah dihasilkan terbukti dapat digunakan dan dipraktekkan oleh atlet Judo. Hasil penelitian diperoleh dengan media VCD pejudo dapat belajar materi pembelajaran judo teknik bantingan kyu 4 secara efektif dan efisien. Kata kunci: Judo, Teknik Bantingan Kyu-4
Waveform Interpolation Speech Coder at 4 kb/s

Speech coding at bit rates near 4 kbps is expected to be widely deployed in applications such as visual telephony, mobile and personal communications. This research focuses on developing a speech coder based on the waveform interpolation (WI) scheme, with an attempt to deliver near toll-quality speech at rates around 4 kbps. A WI coder has been simulated in floating-point using the C programming language. The high performance of the WI model has been confirmed by subjective listening tests in which the unquantized coder outperforms the 32 kbps G.726 standard (ADPCM) 98% of the time under clean input speech conditions; the reconstructed speech is perceived to be essentially indistinguishable from the original. When fully quantized, the speech quality of the WI coder at 4.25 kbps has been judged to be equivalent to or better than that of G.729 (the ITU-T toll-quality 8 kbps standard) for 45% of the test sentences. Further refinements of the quantization techniques are warranted to bring the coder closer to the toll-quality benchmark. Yet, the existing implementation has produced good quality coded speech with a high degree of intelligibility and naturalness when compared to the conventional coding schemes operating in the neighbourhood of 4 kbps.
Unstable Particles

Unstable particles cannot be treated as asymptotic external states in $S$-matrix theory and when they occur as resonant states cannot be described by finite-order perturbation theory. The known facts concerning unstable particles are reviewed and it is shown how to construct gauge-invariant expressions for matrix elements containing intermediate unstable particles and physically meaningful production cross-sections for unstable particles. The results and methodology presented are relevant for $Z^0$ resonance physics, $W^+W^-$ and $Z^0Z^0$ pair production and can be straightforwardly applied to other processes.
The Noise Reduction of Speech Signals Based on RBFN

The speech signals in the transmission are often accompanied with a lot of unnecessary disturbing noise. Usually, sound-absorbing cotton or directional microphones are applied on the hardware to suppress the background noise that caused by the unwanted signals, such as sound of wind and human voice. In addition, the channel effects are generated among various voice-processing systems when voice signals are transferred. This paper takes advantages of the Radial Basis Function Networks (RBFN), in which the noise signals can be figured simply in time period without the transfer through frequency domain, by matching the features of the sources of speech signals within the samples speech frame, the authentic speech signals can be restored. The simulation results show that the propose RBFN method works well to reduce the noise signals and provides the same quality as the original speech signals under Additive White Gaussian Noise (AWGN).
Epistasis can accelerate adaptive diversification in haploid asexual populations

A fundamental goal of the biological sciences is to determine processes that facilitate the evolution of diversity. These processes can be separated into ecological, physiological, developmental and genetic. An ecological process that facilitates diversification is frequency-dependent selection caused by competition. Models of frequency-dependent adaptive diversification have generally assumed a genetic basis of phenotype that is non-epistatic. Here, we present a model that indicates diversification is accelerated by an epistatic basis of phenotype in combination with a competition model that invokes frequency-dependent selection. Our model makes use of a genealogical model of epistasis and insights into the effects of balancing selection on the genealogical structure of a population to understand how epistasis can facilitate diversification. The finding that epistasis facilitates diversification may be informative with respect to empirical results that indicate an epistatic basis of phenotype in experimental bacterial populations that experienced adaptive diversification.
Effect of Friction Stir Welding Parameters on the Mechanical and Microstructure Properties of the Al-Cu Butt Joint

Friction Stir Welding (FSW) is a solid-state welding process used for welding similar and dissimilar materials. FSW is especially suitable to join sheet Al alloys, and this technique allows different material couples to be welded continuously. In this study, 1050 Al alloys and commercially pure Cu were produced at three different tool rotation speeds (630, 1330, 2440 rpm) and three different tool traverse speeds (20, 30, 50 mm/min) with four different tool position (0, 1, 1.5, 2 mm) by friction stir welding. The influence of the welding parameters on the microstructure and mechanical properties of the joints was investigated. Tensile and bending tests and microhardness measurements were used to determine the mechanical properties. The microstructures of the weld zone were investigated by optical microscope and scanning electron microscope (SEM) and were analyzed in an energy dispersed spectrometer (EDS). Intermetallic phases were detected based on the X-ray diffraction (XRD) analysis results that evaluated the formation of phases in the weld zone. When the welding performance of the friction stir welded butt joints was evaluated, the maximum value obtained was 89.55% with a 1330 rpm tool rotational speed, 20 mm/min traverse speed and a 1 mm tool position configuration. The higher tensile strength is attributed to the dispersion strengthening of the fine Cu particles distributed over the Al material in the stir zone region.
Lightweight Authentication and Privacy-Preserving Scheme for V2G Connections

Vehicle-to-grid (V2G) connection allows a two-way electricity transmission between electric vehicles (EVs) and the power grid for achieving many known benefits. However, V2G connections suffer from certain security threats, such as to the EV's privacy and in authenticating the EV to the grid. In this paper, we propose a lightweight secure and privacy-preserving V2G connection scheme in which the power grid assures the confidentiality and integrity of exchanged information during (dis)charging electricity sessions and overcomes the EVs' authentication problem. The proposed scheme guarantees the financial profits of the grid and prevents EVs from acting maliciously. Meanwhile, EVs preserve their private information by generating their own pseudonym identities. In addition, the scheme maintains accountability for the electricity exchange trade. Furthermore, the proposed scheme provides these security requirements by lightweight overhead, as it diminishes the number of exchanged messages during (dis)charging sessions. Simulation results demonstrate that the proposed scheme significantly reduces the total communication and computation load for V2G connection, particularly for EVs.
-(S)-Alpha-phenyl-2-pyridine-ethanamine Dihydrochloride-, a low affinity uncompetitive N-methyl-D-aspartic acid antagonist, is effective in rodent models of global and focal ischemia.

[(S)-Alpha-phenyl-2-pyridine-ethanamine dihydrochloride] (ARL 15896AR) is a low affinity uncompetitive N-methyl-D-aspartic acid receptor antagonist that was tested in animal models of anoxia and ischemia. Pretreatment of rodents with ARL 15896AR extended survival time during exposure to hypoxia. With the rat four-vessel occlusion model of global ischemia (20 min), oral dosing commencing at reflow, resulted in significant protection of the CA1 hippocampal neurons. ARL 15896AR was, however, ineffective in the rat two-vessel occlusion model and in the gerbil models of forebrain ischemia, the latter due to an inability to attain suitable plasma levels. In the spontaneously hypertensive rat model of middle cerebral artery occlusion (MCAO) (2 hr plus 22 hr reflow), acute dosing with ARL 15896AR (i.p.) beginning from 30 min before or up to 1 hr post-MCAO significantly reduced cortical infarct volume. The ability of ARL 15896AR to influence infarct size, as well as functional correlates was examined in SHR after 90 min of MCAO. T2 weighted magnetic resonance images taken at 2 and 6 days post-MCAO revealed significantly smaller lesion sizes in the group receiving injections with ARL 15896AR beginning 30 min after occlusion. Spontaneously hypertensive rats were subsequently tested (30-42 days post-MCAO) and found to be deficient in skilled use of the forepaws (staircase test). The contralateral forepaw was most severely impaired, however, ARL 15896AR treatment prevented motor impairment in only the ipsilateral forepaw. Histopathological examination of cortical infarct size was unremarkable between treated and control rats. The findings indicate that ARL 15896AR exhibits neuroprotection in global and focal models of ischemia
Impact of Professional Development on a Teacher and Her Students: A Case Study

One of the most difficult challenges in research on teacher learning is identifying and documenting linkages between teacher learning, classroom practice, and student learning (Loucks-Horsley & Matsumoto, 1999; Wilson & Berne, 1999). Demonstrating convincing evidence of this proposed linkage is important, especially in a policy climate that emphasizes accountability in terms of student test performance (e.g., U.S. Department of Education, 2001). The development of rigorous academic standards (e.g., American Association for the Advancement of Science, 1993) and an increased emphasis on systemic reform (Smith & O'Day, 1991) as a means of fostering educational change pre-date the current accountability emphasis, but operate in combination with accountability to create a greater demand for high-quality teachers of science. Professional development is frequently cited as the cornerstone of any reform effort that hopes to create systemic changes leading to improved student performance on academic standards (Committee on Science and Mathematics Teacher Preparation, 2001), and many recent funding efforts have sought to focus attention on teacher learning and its role in fostering improved student learning.
Heuristically Creating Test Cases for Program Verification Systems

The correctness of program verification systems is of great importance, as they are used to formally prove that safety- and security-critical programs follow their specification. This correctness needs to be checked and demonstrated to users and certification agencies. One of the contributing factors to the correctness of the whole verification system is the correctness of the background axiomatization, which captures the semantics of the target program language. We present an optimisation framework for the maximization of the proportion of the axiomatization that is used (“covered”) during testing of the verification tool. We show how test cases for regression tests can be created based on existing ones, as the initial creation of test cases is a very time consuming process. Our study shows that the combination of different heuristics leads to a diverse set of test cases, which in turn increases the axiomatization coverage. This translates into a significant increase in trust in the program verification system.
Adaptive-scale determining for edge detecion in correlated texture noise

The selection of an appropriate filter size or scale is an important problem for edge detection. Using a single fixed scale over the entire image cannot produce desirable results: small-scaled filters are sensitive to step edges but have little control over noise, whereas large-scale filters have superior noise suppression characteristics, but they suffer from wide response around edges. This problem becomes more severe when the input images are corrupted by spatial correlated texture noise. In order to make the detection filter robust to texture noise, we tend to collect color information from neighbors as much as possible in homogeneous color areas and to reduce the number of neighbors when the locations close to edges. We introduce an adaptive scheme in this paper to determine an appropriate filter size or scale for each pixel base on local color statistics and hypothesis test on confidence intervals. This adaptive-scale determining scheme is integrated into a Canny edge detector, and the experimental results show the ability of the new procedure to detect both salient and weak edges from color textile images.
Automatic Fire Smoke Detection Based on Image Visual Features

For open spaces, this paper proposes a novel method for automatic fire smoke detection based on image visual features. The greatest characteristic of the method is that both static and dynamic features of fire smoke are investigated. And the basic strategy is that we extract features of the moving target including growth, disorder, frequent flicker in boundaries, self- similarity and local wavelet energy as a joint feature vector which will be normalized, and then a BP artificial neural network is trained to recognize fire smoke. Experimental results show that this method can achieve early detection of fire accident with high accuracy and stronger anti-jamming ability.
Person Re-Identification Using Group Information

After first observing a person, the task of person re-identification involves recognising an individual at different locations across a network of cameras at a later time. Traditionally, this task has been performed by first extracting appearance features of an individual and then matching these features to the previous observation. However, identifying an individual based solely on appearance can be ambiguous, particularly when people wear similar clothing (i.e. people dressed in uniforms in sporting and school settings). This task is made more difficult when the resolution of the input image is small as is typically the case in multi-camera networks. To circumvent these issues, we need to use other contextual cues. In this paper, we use "group" information as our contextual feature to aid in the re-identification of a person, which is heavily motivated by the fact that people generally move together as a collective group. To encode group context, we learn a linear mapping function to assign each person to a "role" or position within the group structure. We then combine the appearance and group context cues using a weighted summation. We demonstrate how this improves performance of person re-identification in a sports environment over appearance based-features.
A micro/macro algorithm to accelerate Monte Carlo simulation of stochastic differential equations

We present and analyze a micro/macro acceleration technique for the Monte Carlo simulation of stochastic differential equations (SDEs) in which there is a separation between the (fast) time-scale on which individual trajectories of the SDE need to be simulated and the (slow) time-scale on which we want to observe the (macroscopic) function of interest. The method performs short bursts of microscopic simulation using an ensemble of SDE realizations, after which the ensemble is restricted to a number of macroscopic state variables. The resulting macroscopic state is then extrapolated forward in time and the ensemble is projected onto the extrapolated macroscopic state. We provide a first analysis of its convergence in terms of extrapolation time step and number of macroscopic state variables. The effects of the different approximations on the resulting error are illustrated via numerical experiments.
Gene Expression Profiles Characterize Inflammation Stages in the Acute Lung Injury in Mice

Acute Lung Injury (ALI) carries about 50 percent mortality and is frequently associated with an infection (sepsis). Life-support treatment with mechanical ventilation rescues many patients, although superimposed infection or multiple organ failure can result in death. The outcome of a patient developing sepsis depends on two factors: the infection and the pre-existing inflammation. In this study, we described each stage of the inflammation process using a transcriptional approach and an animal model. Female C57BL6/J mice received an intravenous oleic acid injection to induce an acute lung injury (ALI). Lung expression patterns were analyzed using a 9900 cDNA mouse microarray (MUSV29K). Our gene-expression analysis revealed marked changes in the immune and inflammatory response metabolic pathways, notably lipid metabolism and transcription. The early stage (1 hour–1.5 hours) is characterized by a pro-inflammatory immune response. Later (3 hours–4 hours), the immune cells migrate into inflamed tissues through interaction with vascular endothelial cells. Finally, at late stages of lung inflammation (18 hours–24 hours), metabolism is deeply disturbed. Highly expressed pro-inflammatory cytokines activate transcription of many genes and lipid metabolism. In this study, we described a global overview of critical events occurring during lung inflammation which is essential to understand infectious pathologies such as sepsis where inflammation and infection are intertwined. Based on these data, it becomes possible to isolate the impact of a pathogen at the transcriptional level from the global gene expression modifications resulting from the infection associated with the inflammation.
Speech Recognition in Unknown Noisy Conditions

This chapter describes our recent advances in automatic speech recognition, with a focus on improving the robustness against environmental noise. In particular, we investigate a new approach for performing recognition using noisy speech samples without assuming prior information about the noise. The research is motivated in part by the increasing deployment of speech recognition technologies on handheld devices or the Internet. Due to the mobile nature of such systems, the acoustic environments and hence the noise sources can be highly time-varying and potentially unknown. This raises the requirement for noise robustness in the absence of information about the noise. Traditional approaches for noisy speech recognition include noise filtering or noise compensation. Noise filtering aims to remove the noise from the speech signal. Typical techniques include spectral subtraction (Boll, 1979), Wiener filtering (Macho et al., 2002) and RASTA filtering (Hermansky & Morgan, 1994), each assuming a priori knowledge of the noise spectra. Noise compensation aims to construct a new acoustic model to match the noisy environment thereby reducing the mismatch between the training and testing data. Typical approaches include parallel model combination (PMC) (Gales & Young, 1993), multicondition training (Lippmann et al., 1987; Pearce & Hirsch, 2000), and SPLICE (Deng et al., 2001). PMC composes a noisy acoustic model from a clean model by incorporating a statistical model of the noise; multicondition training constructs acoustic models suitable for a number of noisy environments through the use of training data from each of the environments; SPLICE improves noise robustness by assuming that stereo training data exist for estimating the corruption characteristics. More recent studies are focused on the approaches requiring less information about the noise, since this information can be difficult to obtain in mobile environments subject to time-varying, unpredictable noise. For example, recent studies on missing-feature theory suggest that, when knowledge of the noise is insufficient for cleaning up the speech features, one may alternatively ignore the severely corrupted features and focus the recognition only on the features with little or no contamination. This can effectively reduce the influence of noise while requiring less knowledge than usually needed for noise filtering or compensation (e.g., Lippmann & Carlson, 1997; Raj et al., 1998; Cooke et al., 2001; Ming et al., 2002). However, missing-feature theory is only effective given partial feature corruption, i.e., the noise only affects part of the speech representation and the remaining part not