Miracle-dz/UniFAR-FTUs huggingface dataset:
ECharts for SIP servlets: a state-machine programming environment for VoIP applications

The development of telecommunication applications that require multiple call legs is often complex due to their event-driven nature as well as the significant amount of state that must be maintained. In general, the state associated with different call legs within an application instance differs and must be separately maintained; in addition, (non-call related) application state is often required. As a means of managing all the required state information, application developers often implement ad-hoc state machine programming constructs within the application. This paper describes an alternate approach, wherein the application logic is written in ECharts, an open-source state-machine programming language, and translated into SIP Servlet applications which can be deployed and executed on any standards-compliant container. This approach can yield great benefits for complex applications, including reusability, increased maintainability, and the prospect of program analysis.
Improved delay prediction for on-chip buses

In this paper, we introduce a simple procedure to predict wiring delay in bi-directional buses and a way of properly sizing the driver for each of its port. In addition, we propose a simple calibration procedure to improve its delay prediction over the Elmore delay of the RC tree. The technique is fast, accurate, and ideal for implementation in floorplanner during behavioral synthesis.
ASR error detection using recurrent neural network language model and complementary ASR

Detecting automatic speech recognition (ASR) errors can play an important role for effective human-computer spoken dialogue system, as recognition errors can hinder accurate system understanding of user intents. Our goal is to locate errors in an utterance so that the dialogue manager can pose appropriate clarification questions to the users. We propose two approaches to improve ASR error detection: (1) using recurrent neural network language models to capture long-distance word context within and across previous utterances; (2) using a complementary ASR system. The intuition is that when two complementary ASR systems disagree on a region in an utterance, this region is most likely an error. We train a neural network predictor of errors using a variety of features. We performed experiments on both English and Iraqi Arabic ASR and observed significant improvement in error detection using the proposed methods.
Branch&Rank: Non-Linear Object Detection

Branch&rank is an object detection scheme that overcomes the inherent limitation of branch&bound: this method works with arbitrary (classifier) functions whereas tight bounds exist only for simple functions. Objects are usually detected with less than 100 classifier evaluation, which paves the way for using strong (and thus costly) classifiers: We utilize non-linear SVMs with RBF-c 2 kernels without a cascade-like approximation. Our approach features three key components: a ranking function that operates on sets of hypotheses and a grouping of these into different tasks. Detection efficiency results from adaptively sub-dividing the object search space into decreasingly smaller sets. This is inherited from branch&bound, while the ranking function supersedes a tight bound which is often unavailable (except for too simple function classes). The grouping makes the system effective: it separates image classification from object recognition, yet combines them in a single, structured SVM formulation. A novel aspect of branch&rank is that a better ranking function is expected to decrease the number of classifier calls during detection. We demonstrate the algorithmic properties using the VOC’07 dataset.
Comparing hierarchical dirichlet process with latent dirichlet allocation in bug report multiclass classification

Bug reports play essential roles in many software engineering tasks. Since validity and performance of these tasks definitely rely on the quality of bug reports, accurate information from bug reports is very important. However, as found in previous study, significant numbers of reports classified as bug are not really a bug. Recent studies proposed techniques to automatically classify bug reports into binary classes, yet there is still more to desire. These bug reports can be classified into multiple classes, which could help to identify what these reports are actually about. Moreover, previous study only looks into one possibility of topic modeling, that is, Latent Dirichlet Allocation (LDA). While LDA has its advantage, parameter tuning is required. In this paper, we propose a nonparametric approach to automatically classify bug reports with, another topic modeling method, Hierarchical Dirichlet Process (HDP). The result indicates that our nonparametric approach performance is comparable to the parametric one. We also examine various aspects of LDA to provide more thoroughly understanding of this process.
A signal processing framework for multimodal cardiac analysis

The heart is a complex organ in the cardiovascular system which its measurement and analysis system in clinical level should be realized in an integrated system including all cardiac vital signs. A previous study combined ECG and PCG analysis could detect murmur symptom. However, the heart mechanical activity could not be described. This study developed a multimodal analysis of cardiac signals consisting of ECG signals, carotid pulse, and PCG. The purpose of this study was to develop and test an appropriate signal processing framework to facilitate parameter extraction and to enhance understanding of underlying mechanisms in the cardiac physiology. Frequency and time-frequency domain analysis of cardiac signals were performed to design sophisticated digital filters. Recursive digital filters were chosen in realizing segmentation methods and the advanced signal processing techniques were performed in parameter extraction. Results show the proposed method was able to detect QRS complex, P and T waves in ECG signal with 88% sensitivity and also percussion wave with 85.62% sensitivity. Sistolic (S1) and diastolic (S2) heart sound also could be separated. Classification of normal and the disease type of heart based on the cardiac parameters resulted by the presented signal processing framework would be next research topic.
Neural Plasticity and Minimal Topologies for Reward-Based Learning

Artificial neural networks for online learning problems are often implemented with synaptic plasticity to achieve adaptive behaviour. A common problem is that the overall learning dynamics are emergent properties strongly dependent on the correct combination of neural architectures, plasticity rules and environmental features. Which complexity in architectures and learning rules is required to match specific control and learning problems is not clear. Here a set of homosynaptic plasticity rules is applied to topologically unconstrained neural controllers while operating and evolving in dynamic reward-based scenarios. Performances are monitored on simulations of bee foraging problems and T-maze navigation. Varying reward locations compel the neural controllers to adapt their foraging strategies over time, fostering online reward-based learning. In contrast to previous studies, the results here indicate that reward-based learning in complex dynamic scenarios can be achieved with basic plasticity rules and minimal topologies.
Racial segregation and the limits of international undergraduate student diversity

Abstract This study challenges the assertion that the influx of Asian international undergraduate students in universities across the United States creates richer educational and social environments. Drawing on qualitative research at a public university with a large number of Asian international students, this article examines how Asian American student leaders and their organization took on the difficult institutional task of actualizing the diversity of these new students in a racially segregated campus. We found that instead of viewing racial segregation practices as possibly tied to racial discrimination and privileges of normative whiteness, students expressed both support and resistance to Asian international students in race-neutral language of comfort and organizational differences that reflects the dominant ideology of colorblindness. We argue that any claims to the benefits of international student diversity must take serious account of colorblind racism and the experiences of racial marginalization and racial segregation among domestic minority and international students.
Towards the game critical moments in basketball: a grounded theory approach

Exploring the practical knowledge of expert basketball coaches, the present study aims to analyse the basketball game criticality through a grounded theory approach. Data from semi-structured interviews with 17 expert basketball coaches were used to explore the conceptual understanding of game critical moments. Results demonstrated that expert coaches identify different game occurrences as precipitating events. They can be characterized according the basketball game nature. Game tempo, point’s difference and balance/unbalance perception emerged as three situational variables that allowed us to define a preliminary coaching model to explain how expert coaches deal with the critical game situations. This preliminary model allows us to access a whole range of information about game development and to a better understanding of the coaching decision-making. Further studies are needed to investigate and generalize these results and to have a more valid explorative model of the sport games criticality.
Finite element surrogate model for electric machines with revolving field — application to IPM motors

The model allows the ultra-fast simulation of the steady-state performance of synchronous machines and is particularly suitable for brushless motors with non-overlapping windings having coils concentrated around the teeth. Finite element analysis (FEA) is employed only for calculating the magnetic vector potential in the coils. For the example IPM motors presented, as little as one magnetostatic FE solution was used for fundamental flux linkage and average torque computation. Two FE solutions were employed for core flux density waveforms and power loss estimation. A minimum of three solutions is recommended for torque ripple, back emf and induced voltage. A substantial reduction of one to two orders of magnitude was achieved for the solving time as compared with detailed time-stepping FEA. The surrogate FE model can also be tuned for increased speed, comparable with that of magnetic equivalent circuit solvers. The general applicability of the model is discussed and recommendations are provided. Successful validation was performed against detailed FEA and experiments.
The Crustal Rigidity of a Neutron Star and Implications for PSR B1828–11 and Other Precession Candidates

Like the Earth, a neutron star (NS) can undergo torque-free precession because some piece ΔId of its inertia tensor remains tied to the crust's principal axes, as opposed to following the crust's angular velocity vector. The (body frame) precession frequency νp is νsΔId/IC, where νs is the NS's spin frequency and IC is the moment of inertia associated with the crustal nuclei, plus any component of the star tightly coupled to the crust over a timescale less than the spin period. For a spinning NS with a relaxed crust, ΔId = bΔIΩ, where ΔIΩ is the rotational oblateness of a fluid star rotating at spin frequency Ω and b is the NS's rigidity parameter. A previous estimate of b by Baym & Pines gives b ~ 10-5 for typical NS parameters. Here we calculate the rigidity parameter b and show that it is ~40 times smaller than the Baym-Pines estimate. We apply this result to PSR B1828-11, an isolated pulsar whose correlated timing residuals and pulse shape variations provide strong evidence for precession with a 511 day period. We show that this precession period is ~250 times shorter than one would expect, assuming that (1) the crust is relaxed (except for the stresses induced by the precession itself) and (2) the NS possesses no other source of stress that would deform its figure (e.g., a strong magnetic field). We conclude that the crust must be under significant stress to explain the precession period of PSR B1828-11; such stress arises naturally as the star spins down. Assuming that crustal shear stresses do set the precession period, the star's reference angular velocity (roughly, the spin at which the crust is most relaxed) is ≈40 Hz (i.e., ≈ times faster than today's spin), and the weighted average of the crust's present spin-down strain is ≃ 5 × 10-5. We briefly describe the implications of our improved b calculation for other precession candidates.
Public Debt & Sovereign Ratings - Do Industrialized Countries Enjoy a Privilege?

In this paper, we explore the institutional investors' assessment of relative creditworthiness across selected country groups with a special focus on the impact of public debt on the perception of sovereign risk. Our results show that general government debt is among the most important determinants of credit risk in industrialized countries and emerging markets alike. When using a multivariate framework, we further find that the inuence of debt on ratings do es not differ between both groups. Also, our results point towards a rating penalty for highly-indebted advanced countries when their debt ratio is associated with a growing one. By contrast, a high debt level alone does not lead to an additional rating decline. Finally, we show that peripheral euro area economies (GIIPS) received a rating privilege before the financial crisis that turned into a penalty after 2008.
The Derivational Complexity Induced by the Dependency Pair Method

We study the derivational complexity induced by the (basic) dependency pair method. Suppose the derivational complexity induced by a termination method is closed under elementary functions. We show that the derivational complexity induced by the dependency pair method based on this termination technique is the same as for the direct technique. Therefore, the derivational complexity induced by the dependency pair method based on lexicographic path orders or multiset path orders is multiple recursive or primitive recursive, respectively. Moreover for the dependency pair method based on Knuth-Bendix orders, we obtain that the derivational complexity function is majorised by the Ackermann function. These characterisations are essentially optimal.
Journal of Economic Behavior & Organization Myopic Loss Aversion and Market Experience

We probe the boundaries of myopic loss aversion (MLA) theory through market treatments designed to reduce the MLA effect. Our market settings separate investment commitment from information frequency, display a running average asset value and explore the influence of participant experience. The market-based results suggest MLA persists with inexperienced participants despite efforts to mitigate MLA. Prices in markets with returning participants do not display an MLA effect. However, the same experienced participants individually succumb to MLA in an allocation setting immediately following the market. Overall, our results suggest that, while market experience mitigates the MLA effect, participants do not transfer these results to other settings.
Convergence of Markov Processes August

The aim of this minicourse is to provide a number of tools that allow one to determine at which speed (if at all) the law of a diffusion process, or indeed a rather general Markov process, approaches its stationary distribution. Of particular interest will be cases where this speed is subexponential. After an introduction to the general ergodic theory of Markov processes, the first part of the course is devoted to Lyapunov function techniques. The second part is then devoted to an elementary introduction to Malliavin calculus and to a proof of Hörmander’s famous ”sums of squares” regularity theorem. 1 General (ergodic) theory of Markov processes In this note, we are interested in the long-time behaviour of Markov processes, both in discrete and continuous time. Recall that a discrete-time Markov process x on a state space X is described by a transition kernel P , which we define as a measurable map from X into the space of probability measures on X . In all that follows, X will always be assumed to be a Polish space, that is a complete, separable metric space. When viewed as a measurable space, we will always endow it with its natural Borel σ-algebra, that is the smallest σ-algebra containing all open sets. This ensures that X endowed with any probability measure is a Lebesgue space and that basic intuitive results of probability and measure theory (Fubini’s theorem, regular conditional probabilities, etc) are readily available. We will sometimes consider P as a linear operator on the space of signed measures on X and / or the space of bounded measurable functions by
Diabetic retinopathy: morphometric analysis of basement membrane thickening of capillaries in different retinal layers within arterial and venous environments.

AIMS--To assess quantitatively variations in the extent of capillary basement membrane (BM) thickening between different retinal layers and within arterial and venous environments during diabetes. METHODS--One year after induction of experimental (streptozotocin) diabetes in rats, six diabetic animals together with six age-matched control animals were sacrificed and the retinas fixed for transmission electron microscopy (TEM). Blocks of retina straddling the major arteries and veins in the central retinal were dissected out, embedded in resin, and sectioned. Capillaries in close proximity to arteries or veins were designated as residing in either an arterial (AE) or a venous (VE) environment respectively, and the retinal layer in which each capillary was located was also noted. The thickness of the BM was then measured on an image analyser based two dimensional morphometric analysis system. RESULTS--In both diabetics and controls the AE capillaries had consistently thicker BMs than the VE capillaries. The BMs of both AE and VE capillaries from diabetics were thicker than those of capillaries in the corresponding retinal layer from the normal rats (p < or = 0.005). Also, in normal AE and VE capillaries and diabetic AE capillaries the BM in the nerve fibre layer (NFL) was thicker than that in either the inner (IPL) or outer (OPL) plexiform layers (p < or = 0.001). However, in diabetic VE capillaries the BMs of capillaries in the NFL were thicker than those of capillaries in the IPL (p < or = 0.05) which, in turn, had thicker BMs than capillaries in the OPL (p < or = 0.005). CONCLUSIONS--The variation in the extent of capillary BM thickening between different retinal layers within AE and VE environments may be related to differences in levels of oxygen tension and oxidative stress in the retina around arteries compared with that around veins.
Supervisor–Subordinate Relationship Conflict Asymmetry and Subordinate Turnover Intentions: The Mediating Roles of Stress and Counterproductive Work Behaviors

Research on employee turnover suggests that divergent perceptions of relationship conflict between supervisors and subordinates may motivate voluntary turnover. However, despite evidence of asymmetric perceptions of conflict within groups, little research has examined the effects of conflict asymmetry between supervisors and subordinates on behavioral outcomes. Drawing on a sample of 178 supervisor-subordinate dyads, we test whether subordinate turnover intentions are associated with the relationship conflict asymmetry. We further examine subordinate stress and counterproductive work behaviors directed towards supervisors as mediators of this relationship. Implications for practice and future research are elaborated.
Species mtDNA genetic diversity explained by infrapopulation size in a host‐symbiont system

Abstract Understanding what shapes variation in genetic diversity among species remains a major challenge in evolutionary ecology, and it has been seldom studied in parasites and other host‐symbiont systems. Here, we studied mtDNA variation in a host‐symbiont non‐model system: 418 individual feather mites from 17 feather mite species living on 17 different passerine bird species. We explored how a surrogate of census size, the median infrapopulation size (i.e., the median number of individual parasites per infected host individual), explains mtDNA genetic diversity. Feather mite species genetic diversity was positively correlated with mean infrapopulation size, explaining 34% of the variation. As expected from the biology of feather mites, we found bottleneck signatures for most of the species studied but, in particular, three species presented extremely low mtDNA diversity values given their infrapopulation size. Their star‐like haplotype networks (in contrast with more reticulated networks for the other species) suggested that their low genetic diversity was the consequence of severe bottlenecks or selective sweeps. Our study shows for the first time that mtDNA diversity can be explained by infrapopulation sizes, and suggests that departures from this relationship could be informative of underlying ecological and evolutionary processes.
Detecting Risks in the Banking System by Sentiment Analysis

In November 2014, the European Central Bank (ECB) started to directly supervise the largest banks in the Eurozone via the Single Supervisory Mechanism (SSM). While supervisory risk assessments are usually based on quantitative data and surveys, this work explores whether sentiment analysis is capable of measuring a bank’s attitude and opinions towards risk by analyzing text data. For realizing this study, a collection consisting of more than 500 CEO letters and outlook sections extracted from bank annual reports is built up. Based on these data, two distinct experiments are conducted. The evaluations find promising opportunities, but also limitations for risk sentiment analysis in banking supervision. At the level of individual banks, predictions are relatively inaccurate. In contrast, the analysis of aggregated figures revealed strong and significant correlations between uncertainty or negativity in textual disclosures and the quantitative risk indicator’s future evolution. Risk sentiment analysis should therefore rather be used for macroprudential analyses than for assessments of individual banks.
Combined and Relative Effect Levels of Perceived Risk, Knowledge, Optimism, Pessimism, and Social Trust on Anxiety among Inhabitants Concerning Living on Heavy Metal Contaminated Soil

This research aims at combined and relative effect levels on anxiety of: (1) perceived risk, knowledge, optimism, pessimism, and social trust; and (2) four sub-variables of social trust among inhabitants concerning living on heavy metal contaminated soil. On the basis of survey data from 499 Chinese respondents, results suggest that perceived risk, pessimism, optimism, and social trust have individual, significant, and direct effects on anxiety, while knowledge does not. Knowledge has significant, combined, and interactive effects on anxiety together with social trust and pessimism, respectively, but does not with perceived risk and optimism. Social trust, perceived risk, pessimism, knowledge, and optimism have significantly combined effects on anxiety; the five variables as a whole have stronger predictive values than each one individually. Anxiety is influenced firstly by social trust and secondly by perceived risk, pessimism, knowledge, and optimism. Each of four sub-variables of social trust has an individual, significant, and negative effect on anxiety. When introducing four sub-variables into one model, trust in social organizations and in the government have significantly combined effects on anxiety, while trust in experts and in friends and relatives do not; anxiety is influenced firstly by trust in social organization, and secondly by trust in the government.