Miracle-dz/UniFAR-FTUs huggingface dataset:
Credit Ratings and the Cross-Section of Stock Returns

Low credit risk firms realize higher returns than high credit risk firms. This is puzzling because investors seem to pay a premium for bearing credit risk. The credit risk effect manifests itself due to the poor performance of low-rated stocks (which account for 4.2% of total market capitalization) during periods of financial distress. Around rating downgrades, low-rated firms experience considerable negative returns amid strong institutional selling, whereas returns do not differ across credit risk groups in stable or improving credit conditions. The evidence for the credit risk effect points towards mispricing generated by retail investors and sustained by illiquidity and short sell constraints.
Prosodic classification of discourse markers

The first contribution of this study is the description of the prosodic behavior of discourse markers present in two speech corpora of European Portuguese (EP) in different domains (university lectures, and map-task dialogues). The second contribution is a multiclass classification to verify, given their prosodic features, which words in both corpora are classified as discourse markers, which are disfluencies, and which correspond to words that are neither markers nor disfluencies (chunks). Our goal is to automatically predict discourse markers and include them in rich transcripts, along with other structural metadata events (e.g., disfluencies and punctuation marks) that are already encompassed in the language models of our in-house speech recognizer. Results show that the automatic classification of discourse markers is better for the lectures corpus (87%) than for the dialogue corpus (84%). Nonetheless, in both corpora, discourse markers are more easily confused with chunks than with disfluencies.
The Reconstruction of Transcriptional Networks Reveals Critical Genes with Implications for Clinical Outcome of Multiple Myeloma

Purpose: The combined use of microarray technologies and bioinformatics analysis has improved our understanding of biological complexity of multiple myeloma (MM). In contrast, the application of the same technology in the attempt to predict clinical outcome has been less successful with the identification of heterogeneous molecular signatures. Herein, we have reconstructed gene regulatory networks in a panel of 1,883 samples from MM patients derived from publicly available gene expression sets, to allow the identification of robust and reproducible signatures associated with poor prognosis across independent data sets. Experimental Design: Gene regulatory networks were reconstructed by using Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) and microarray data from seven MM data sets. Critical analysis of network components was applied to identify genes playing an essential role in transcriptional networks, which are conserved between data sets. Results: Network critical analysis revealed that (i) CCND1 and CCND2 were the most critical genes; (ii) CCND2, AIF1, and BLNK had the largest number of connections shared among the data sets; (iii) robust gene signatures with prognostic power were derived from the most critical transcripts and from shared primary neighbors of the most connected nodes. Specifically, a critical-gene model, comprising FAM53B, KIF21B, WHSC1, and TMPO, and a neighbor-gene model, comprising BLNK shared neighbors CSGALNACT1 and SLC7A7, predicted survival in all data sets with follow-up information. Conclusions: The reconstruction of gene regulatory networks in a large panel of MM tumors defined robust and reproducible signatures with prognostic importance, and may lead to identify novel molecular mechanisms central to MM biology. Clin Cancer Res; 17(23); 7402–12. ©2011 AACR.
Remote Sensing Image Classification With Large-Scale Gaussian Processes

Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources. Upcoming missions will soon provide large data streams that will make land cover/use classification difficult. Machine-learning classifiers can help at this, and many methods are currently available. A popular kernel classifier is the Gaussian process classifier (GPC), since it approaches the classification problem with a solid probabilistic treatment, thus yielding confidence intervals for the predictions as well as very competitive results to the state-of-the-art neural networks and support vector machines. However, its computational cost is prohibitive for large-scale applications, and constitutes the main obstacle precluding wide adoption. This paper tackles this problem by introducing two novel efficient methodologies for GP classification. We first include the standard random Fourier features approximation into GPC, which largely decreases its computational cost and permits large-scale remote sensing image classification. In addition, we propose a model which avoids randomly sampling a number of Fourier frequencies and alternatively learns the optimal ones within a variational Bayes approach. The performance of the proposed methods is illustrated in complex problems of cloud detection from multispectral imagery and infrared sounding data. Excellent empirical results support the proposal in both computational cost and accuracy.
MODELING MOLECULAR HYDROGEN AND STAR FORMATION IN COSMOLOGICAL SIMULATIONS

We describe a phenomenological model for molecular hydrogen formation suited for applications in galaxy formation simulations, which includes nonequilibrium formation of H2 on dust and approximate treatment of both its self-shielding and shielding by dust from the dissociating UV radiation. The model is applicable in simulations in which individual star-forming regions—the giant molecular complexes—can be identified (resolution of tens of parsecs) and their mean internal density estimated reliably, even if internal structure is not resolved. In agreement with previous studies, calculations based on our model show that the transition from atomic to fully molecular phase depends primarily on the metallicity, which we assume is directly related to the dust abundance, and clumpiness of the interstellar medium. The clumpiness simply boosts the formation rate of molecular hydrogen, while dust serves both as a catalyst of H2 formation and as an additional shielding from dissociating UV radiation. The upshot is that it is difficult to form fully shielded giant molecular clouds while gas metallicity is low. However, once the gas is enriched to Z ∼ 0.01–0.1 Z☉, the subsequent star formation and enrichment can proceed at a much faster rate. This may keep star formation efficiency in the low-mass, low-metallicity progenitors of galaxies very low for a certain period of time with the effect similar to a strong “feedback” mechanism. The effect may help explain the steep increase of the mass-to-light ratio toward smaller masses observed in the local galaxy population. We apply the model and star formation recipes based on the local amount of molecular gas to an output from a cosmological simulation of galaxy formation and show that resulting global correlations between star formation and gas, and H2 surface densities are in good agreement with observations.
Efficient Handoff Routing (EHR) in WMN

In WMN, handoff is a significant parameter of research. Whenever a mesh client leaves the range of serving mesh router and searches for accessing a new router based on good SNR (signal to Noise) ratio, a handoff procedure takes place. During mobility, packet transmission may enhance the number of security threats and network performance degradation issues which needs a secure routing protocol to be considered. In order to overwhelm over these hitches, this manuscript proposes a technique called Efficient Handoff Routing EHR. The suggested technique is compared and evaluated over network metrics i. e. handoff latency, throughput, end-to-end delay and packet delivery ratio. Further the approach is proved by
A review of the optical properties of biological tissues

The known optical properties (absorption, scattering, total attenuation, effective attenuation, and/or anisotropy coefficients) of various biological tissues at a variety of wavelengths are reviewed. The theoretical foundations for most experimental approaches are outlined. Relations between Kubelka-Munk parameters and transport coefficients are listed. The optical properties of aorta, liver, and muscle at 633 nm are discussed in detail. An extensive bibliography is provided. >
Writer recognition by characters, words and sentences

The methods developed in the research project “Herbar Digital” are to help plant taxonomists to master the great amount of material of about 3.5 million dried plants on paper sheets belonging to the Botanic Museum Berlin in Germany. Frequently the collector of the plant is unknown. So a procedure had to be developed in order to determine the writer of the handwriting on the sheet. In the present work the static character is transformed into a dynamic form. This is done with the model of an inert ball which is rolled through the written character. During this off-line writer recognition, different mathematical procedures are used such as the reproduction of the write line of individual characters by Legendre polynomials. When only one character is used, a recognition rate of about 40% is obtained. By combining multiple characters, the recognition rate rises considerably and reaches 98.7% with 13 characters and 93 writers (chosen randomly from the international IAMdatabase [3]). Another approach tries to identify the writer by handwritten words. The word is cut out and transformed into a 6-dimensional time series and compared e.g. by means of DTW-methods. A global statistical approach using the whole handwritten sentences results in a similar recognition rate of more than 98%. By combining the methods, a recognition rate of 99.5% is achieved.
Collaboration and human factors in software development: Teaching agile methodologies based on industrial insight

Recent studies show that many companies have successfully adopted agile methodologies. In this paper the authors present results of their quantitative and qualitative studies, showing that only experienced companies apply agile collaboration practices properly. The studies also suggest that successful professional agile teams tend to use more collaboration practices and consciously live the agile values. This leads to the conclusion that applying the collaborative practices and living the agile values is difficult. Thus we educators should pay special attention to teaching these practices and values in courses on agile software development. This paper presents how agile collaboration is being taught in the classroom in a fourth semester software engineering module and explains the underlying assumptions. We use an agile coaching game as introduction to Scrum and discuss the mechanics of agile teams in the classroom. We present the setup of a hands-on agile student project with large student teams and the observations we made. Last but not least, we show and discuss how modern online collaboration tools act as enablers for agile collaboration in the classroom.
A Modified Remote User Authentication and Key Agreement Scheme Using Smart Cards

In 2000, Hwang and Li proposed a new remote user authentication scheme using smart cards. Chan-Cheng, Shen-Lin-Hwang and then Chang-Hwang pointed out some attacks on Hwang-Li's scheme. Shen-Lin-Hwang also proposed a modified scheme. In 2003, Leung et al showed that the modified scheme is still vulnerable to some previous attacks. In 2004, Kumar proposed a new scheme to solve these problems. In this paper, we find out that some weaknesses still exist in Kumar's scheme and present a modified remote user authentication and key agreement scheme using smart cards to conquer them. The proposed scheme also generates a common session key.
Analysis of random processes via And-Or tree evaluation

We introduce a new set of probabilistic analysis tools based on the analysis of And-Or trees with random inputs. These tools provide a unifying, intuitive, and powerful framework for carrying out the analysis of several previously studied random processes of interest, including random loss-resilient codes, solving random k-SAT formula using the pure literal rule, and the greedy algorithm for matchings in random graphs. In addition, these tools allow generalizations of these problems not previously analyzed to be analyzed in a straightforward manner. We illustrate our methodology on the three problems listed above
Improving the Usability of HL7 Information Models by Automatic Filtering

The amount of knowledge represented in the Health Level 7 International (HL7) information models is very large. The sheer size of those models makes them very useful for the communities for which they are developed. However, the size of the models and their overall organization makes it difficult to manually extract knowledge from them. We propose to extract that knowledge by using a novel filtering method that we have developed. Our method is based on the concept of class interest as a combination of class importance and class closeness. The application of our method automatically obtains a filtered information model of the whole HL7 models according to the user preferences. We show that the use of a prototype tool that implements that method and produces such filtered model improves the usability of the HL7 models due to its high precision and low computational time.
An FPGA Based Motion Control IC and Its Application to Robotic Manipulators

This paper presents an FPGA based motion control IC by integrating most of the motion control functions into one FPGA chip. The hardware system of the FPGA executes quickly in dedicated parallel hardware without timing overhead penalty of a serial processor. The update rates of the current control loop and position/velocity control loop can be up to 120 kHz and 40 kHz, respectively. The new designed motion control IC can be incorporated with a general-purpose microcontroller or DSP to provide a simple, compact, low-cost, and effective solution for high sampling rate motion control system. A fully digital 3-axis motion controller/amplifier is designed by using the proposed motion control IC. In order to increase the sampling frequency of the system when using dynamic control algorithm for robotic control, the dynamic control algorithm can be partitioned into a linear portion and a nonlinear portion. The linear portion with position/velocity feedback represents the major control loop and is implemented in the FPGA based IC. The nonlinear portion acts as dynamic compensation to the linear portion to perform complex modeling related calculations, and is implemented in the DSP. Experimental results demonstrate that the new proposed system is successful and it exhibits much improved motion performance especially during high-speed motions
Transient Receptor Potential Canonical 3 (TRPC3) Is Required for IgG Immune Complex-Induced Excitation of the Rat Dorsal Root Ganglion Neurons

Chronic pain may accompany immune-related disorders with an elevated level of serum IgG immune complex (IgG-IC), but the underlying mechanisms are obscure. We previously demonstrated that IgG-IC directly excited a subpopulation of dorsal root ganglion (DRG) neurons through the neuronal Fc-gamma receptor I (FcγRI). This might be a mechanism linking IgG-IC to pain and hyperalgesia. The purpose of this study was to investigate the signaling pathways and transduction channels activated downstream of IgG-IC and FcγRI. In whole-cell recordings, IgG-IC induced a nonselective cation current (IIC) in the rat DRG neurons, carried by Ca2+ and Na+. The IIC was potentiated or attenuated by, respectively, lowering or increasing the intracellular Ca2+ buffering capacity, suggesting that this current was regulated by intracellular calcium. Single-cell RT-PCR revealed that transient receptor potential canonical 3 (TRPC3) mRNA was always coexpressed with FcγRI mRNA in the same DRG neuron. Moreover, ruthenium red (a general TRP channel blocker), BTP2 (a general TRPC channel inhibitor), and pyrazole-3 (a selective TRPC3 blocker) each potently inhibited the IIC. Specific knockdown of TRPC3 using small interfering RNA attenuated the IgG-IC-induced Ca2+ response and the IIC. Additionally, the IIC was blocked by the tyrosine kinase Syk inhibitor OXSI-2, the phospholipase C (PLC) inhibitor neomycin, and either the inositol triphosphate (IP3) receptor antagonist 2-aminoethyldiphenylborinate or heparin. These results indicated that the activation of neuronal FcγRI triggers TRPC channels through the Syk–PLC–IP3 pathway and that TRPC3 is a key molecular target for the excitatory effect of IgG-IC on DRG neurons.
Utilizing the Wikidata System to Improve the Quality of Medical Content in Wikipedia in Diverse Languages: A Pilot Study

Background Wikipedia is an important source of medical information for both patients and medical professionals. Given its wide reach, improving the quality, completeness, and accessibility of medical information on Wikipedia could have a positive impact on global health. Objective We created a prototypical implementation of an automated system for keeping drug-drug interaction (DDI) information in Wikipedia up to date with current evidence about clinically significant drug interactions. Our work is based on Wikidata, a novel, graph-based database backend of Wikipedia currently in development. Methods We set up an automated process for integrating data from the Office of the National Coordinator for Health Information Technology (ONC) high priority DDI list into Wikidata. We set up exemplary implementations demonstrating how the DDI data we introduced into Wikidata could be displayed in Wikipedia articles in diverse languages. Finally, we conducted a pilot analysis to explore if adding the ONC high priority data would substantially enhance the information currently available on Wikipedia. Results We derived 1150 unique interactions from the ONC high priority list. Integration of the potential DDI data from Wikidata into Wikipedia articles proved to be straightforward and yielded useful results. We found that even though the majority of current English Wikipedia articles about pharmaceuticals contained sections detailing contraindications, only a small fraction of articles explicitly mentioned interaction partners from the ONC high priority list. For 91.30% (1050/1150) of the interaction pairs we tested, none of the 2 articles corresponding to the interacting substances explicitly mentioned the interaction partner. For 7.21% (83/1150) of the pairs, only 1 of the 2 associated Wikipedia articles mentioned the interaction partner; for only 1.48% (17/1150) of the pairs, both articles contained explicit mentions of the interaction partner. Conclusions Our prototype demonstrated that automated updating of medical content in Wikipedia through Wikidata is a viable option, albeit further refinements and community-wide consensus building are required before integration into public Wikipedia is possible. A long-term endeavor to improve the medical information in Wikipedia through structured data representation and automated workflows might lead to a significant improvement of the quality of medical information in one of the world’s most popular Web resources.
Test generation and optimization for DRAM cell defects using electrical simulation

Although electrical simulation has become a vital tool in the design process of memory devices, memory testing has not yet been able to employ electrical simulation as an integral part of the test generation and optimization process. This is due to the exponential complexity of the simulation-based fault analysis, a complexity that made such an analysis impractical. This paper describes new methods to reduce the complexity of the fault analysis from exponential to constant with respect to the number of analyzed operations, thereby making it possible: 1) to use electrical simulation to generate test patterns; and 2) to perform simulation-based stress optimization of tests. The paper also discusses ways to analyze the impact of idle time on the faulty behavior. In addition, results of a fault analysis study performed to verify the new analysis method are shown, where the new analysis reduces the analysis time by a factor of 30.
Network State Awareness and Proactive Anomaly Detection in Self-Organizing Networks

Inference of network state and detection of anomaly network behavior based on the available data play important roles in the big data empowered self-organizing networks for enabling 5G. In this paper, we propose a novel framework of efficient network monitoring and proactive cell anomaly detection based on dimension reduction and fuzzy classification techniques. The enhanced semi-supervised classification algorithm allows adaptation of new behavior patterns, while incorporating a priori knowledge. The experimental results suggest that (i) our proposed method proactively detects the network anomalies associated with various fault classes, and (ii) the trajectory of the network states moving toward or away from a safe or fault class can be visualized, using the projected data onto a low-dimensional subspace.
Affective Encounters and Spatial Engagements: Pedagogies of Desire in e-Learning

How do students engage in e-learning environments? What are the affective encounters and spatial engagements of students in these environments? These questions are considered by viewing affectivity and spatial engagements in terms of hybridity of the subject-object (humanmaterial) embrace to consider not only people but also the vitality of objects and their materiality. Two poststructuralist transdisciplinary practice-focused frameworks are used: 1) the material semiotic lens of Actor-Network Theory (ANT) (Latour, 2005; Law, 2008a, 2008b) which accents material agency, and 2) Non-Representational Theory (Thrift 2008) which draws on Deleuzean notions (Deleuze & Guattari 1988) to consider affectivity as ―charged‖ (Navaro-Yashin, 2009) intensities. This paper draws on student data from a larger ethnographic study of four fully online postgraduate subjects at an Australian university to trace participant e-learning experiences. By exploring the salience of student affective encounters and spatial engagements through three contrasting vignettes, I open up questions to address pedagogies of desire‘ (Zembylas, 2007a, 2007b, 2007c) to explore how subjectivities and desires are (per)formed in a more than human way‘ and how places of (e-)learning are ―affectively charged‖ (Leander, Phillips & Taylor, 2010, p. 336, original emphasis). These insights can open up new ways to (re)think e-learning design and pedagogy, in theory and in practice.
Sideslip angle, lateral tire force and road friction estimation in simulations and experiments

Transversal tire forces and sideslip angle are essential data for improving vehicle safety, handling, steerability, comfort and performance. This paper proposes and compares four observers designed to calculate vehicle sideslip angle and lateral tire forces. The different observers are derived from the extended Kalman filter (EKF), the single-track model, and use different on tire-force models. The first three tire-force models are the linear model, the Burckhardt model and the Pacejka model. The remaining tire-force model, the "linear adaptive" model, is proposed in this paper in order to construct an adaptive observer. The "linear adaptive" model is based on the linear model, but with an additional variable used to correct wheel cornering stiffness errors. The paper describes models and observers, along with a road-friction identification method. Observers are first compared in a simulation context using a professional vehicle simulator. Subsequently, observers are applied to real experimental data acquired on the Heudiasyc Laboratory car