Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Dietary Levels of Glycine, Threonine and Protein on Threonine Efficiency and Threonine Dehydrogenase Activity in Hepatic Mitochondria of Chicks

This study was carried out to evaluate the relationship between threonine (Thr) efficiency and Thr dehydrogenase (TDG) activity as an indicator of Thr oxidation on chicks fed with levels of diets (CP [17.5% and 21.5%] and Thr [3.8 and 4.7 g/100 g CP]; glycine [Gly][0.64% and 0.98%] and true digestible Thr [dThr] [0.45% and 0.60%]). Calculation of the Thr efficiency was based on N-balance data and an exponential N-utilization model, and TDG activity was determined as accumulation of aminoacetone and Gly during incubation of hepatic mitochondria. This study found that in the liver of chicks who received a diet containing up to 0.79% Thr (4.7 g Thr/100 g of CP) in the 17.5% CP diet, no significant (p>0.05) effect on TDG activity was observed. However, significantly (p = 0.014) increased TDG activity was observed with a diet containing 21.5% CP (4.7 g Thr/100 g of CP) and the efficiency of Thr utilization showed a significant (p = 0.001) decrease, indicating the end of the Thr limiting range. No significant (p>0.05) effect on the total TDG activity and accumulation of Gly was observed with addition of Gly to a diet containing 0.45% dThr. In addition, addition of Gly to a diet containing 0.60% dThr also did not result in a change in accumulation of Gly. Due to an increase in accumulation of aminoacetone, an elevated effect on total TDG activity was also observed. No significant (p>0.05) reduction in the efficiency of Thr utilization was observed after addition of Gly at the level of 0.45% dThr. However, significantly (p<0.001) reduced efficiency of Thr utilization was observed after addition of Gly at the level of 0.60% dThr. Collectively, we found that TDG was stimulated not only by addition of Thr and protein to the diet, but also by addition of Gly, and efficiency of Thr utilization was favorably affected by addition of Gly at the level near to the optimal Thr concentration. In addition, no metabolic requirement of Gly through the TDG pathway was observed with almost the same accumulation of Gly and a slight increase in TDG activity by addition of Gly. Thus, our findings suggest that determination of TDG activity and parameter of efficiency of Thr utilization may be useful for evaluation of dietary Thr level.
An Integrated QFD-DEA Framework with Imprecise Data for Supplier Selection

— Supplier selection possesses the need to evaluate multiple criteria incorporating vagueness and imprecision with the involvement of a group of experts, and is considered as a crucial multi-criteria group decision making problem. In this paper, a novel fuzzy multi-criteria group decision making approach integrating quality function deployment (QFD), fuzzy weighted average (FWA), and data envelopment analysis (DEA) is developed for supplier selection. The proposed methodology enables to consider the impacts of inner dependence among supplier assessment criteria. The upper and the lower bounds of the weights of supplier assessment criteria are identified by using FWA method that allows for the fusion of imprecise and subjective information expressed as linguistic variables. DEA is implemented for supplier selection utilizing the data from the house of quality (HOQ) and the weights of supplier assessment criteria obtained using FWA. The proposed decision making framework is illustrated using a data set from a previously reported supplier selection problem. The proposed approach is a sound and effective decision aid that considers qualitative as well as quantitative aspects, and thus improves the quality of complex supplier selection decisions.
Down to hades and back — Experiences gained in comprehending a distributed legacy system

Software engineering principles and practices face a lot of challenges. Among them, debugging and comprehending systems is still demanding. Furthermore, comprehending distributed systems, even though there are several papers and approaches to be found in literature, is a challenge of a completely different dimension. This paper describes the experiences gained when trying to comprehend (and debug) a distributed legacy system and it takes a closer look at the different options somebody has in similar situations. It does not propose specific tools or methodologies, but tries to provide guidelines to developers who might be challenged by comparable problems.
Microscopic traffic simulation with reactive driving agents

Microscopic traffic simulators can model traffic flow in a realistic manner and are ideal for agent-based vehicle control. In this paper we describe a model of a reactive agent that is used to control a simulated vehicle. The agent is capable of tactical-level driving and has different driving styles. To ensure fast reaction times, the agent's driving task is divided into several competing and reactive behavior rules. The agent is implemented in and tested with a prototype traffic simulator program. The simulator consists of an urban environment with multi-lane roads, intersections, traffic lights, and vehicles. Every vehicle is controlled by a driving agent and all agents have individual behavior settings. Preliminary experiments show that the agents exhibit human-like behavior ranging from slow and careful to fast and aggressive driving behavior.
Modeling spectral envelopes using restricted Boltzmann machines for statistical parametric speech synthesis

This paper presents a new spectral modeling method for statistical parametric speech synthesis. In contrast to the conventional methods in which high-level spectral parameters, such as mel-cepstra or line spectral pairs, are adopted as the features for hidden Markov model (HMM) based parametric speech synthesis, our new method directly models the distribution of the lower-level, un-transformed or raw spectral envelopes. Instead of using single Gaussian distributions, we adopt restricted Boltzmann machines (RBM) to represent the distribution of the spectral envelopes at each HMM state. We anticipate these will give superior performance in modeling the joint distribution of high-dimensional stochastic vectors. The spectral parameters are derived from the spectral envelope corresponding to the estimated mode of each context-dependent RBM and act as the Gaussian mean vector in the parameter generation procedure at synthesis time. Our experimental results show that the RBM is able to model the distribution of the spectral envelopes with better accuracy and generalization ability than the Gaussian mixture model. As a result, our proposed method can significantly improve the naturalness of the conventional HMM-based speech synthesis system using mel-cepstra.
Agile Practices Reduce Distance in Global Software Development

Abstract This article explores how agile practices can reduce three kinds of “distance” – temporal, geographical, and sociocultural – in global software development (GSD). On the basis of two in-depth case studies, specific Scrum and eXtreme Programming (XP) practices are found to be useful for reducing communication, coordination, and control problems that have been associated with GSD.
Nutrition Metabolism Plays an Important Role in the Alternate Bearing of the Olive Tree (Olea europaea L.)

The olive tree (Olea europaea L.) is widely known for its strong tendency for alternate bearing, which severely affects the fruit yield from year to year. Microarray based gene expression analysis using RNA from olive samples (on-off years leaves and ripe-unripe fruits) are particularly useful to understand the molecular mechanisms influencing the periodicity in the olive tree. Thus, we carried out genome wide transcriptome analyses involving different organs and temporal stages of the olive tree using the NimbleGen Array containing 136,628 oligonucleotide probe sets. Cluster analyses of the genes showed that cDNAs originated from different organs could be sorted into separate groups. The nutritional control had a particularly remarkable impact on the alternate bearing of olive, as shown by the differential expression of transcripts under different temporal phases and organs. Additionally, hormonal control and flowering processes also played important roles in this phenomenon. Our analyses provide further insights into the transcript changes between ”on year” and “off year” leaves along with the changes from unrpipe to ripe fruits, which shed light on the molecular mechanisms underlying the olive tree alternate bearing. These findings have important implications for the breeding and agriculture of the olive tree and other crops showing periodicity. To our knowledge, this is the first study reporting the development and use of an olive array to document the gene expression profiling associated with the alternate bearing in olive tree.
Segregation, Stereotypes, and STEM

Scientific, technical, engineering, and mathematical (STEM) occupations are strongholds of gender segregation in the contemporary United States. While many Americans regard this segregation as natural and inevitable, closer examination reveals a great deal of variability in the gendering of STEM fields across time, space, and demographic groups. This article assesses how different theoretical accounts accord with the available evidence on the gender composition of scientific and technical fields. We find most support for accounts that allow for a dynamic interplay between individual-level traits and the broader sociocultural environments in which they develop. The existing evidence suggests, in particular, that Western cultural stereotypes about the nature of STEM work and STEM workers and about the intrinsic qualities of men and women can be powerful drivers of individual aptitudes, aspirations, and affinities. We offer an illustrative catalog of stereotypes that support women’s STEM-avoidance and men’s STEM-affinity, and we conclude with some thoughts on policy implications.
Image Reconstruction Using an Improved MAP-EM Method in X-ray CT

An improved MAP-EM algorithm is proposed for Bayesian reconstruction in X-ray CT based upon Markov random field priors and the Poisson data model. The improved algorithm can yield better reconstruction than MAP-EM algorithm, and its convergence is faster. The improved method is verified by applications to computer simulation data and real X-ray CT data from two aluminous tubes scans. Experiments results show this method is effective. Reconstructed slice images of the improved algorithm are accurate and clear.
Development of robot which recognizes user's false beliefs using view estimation

It is desired that a human-symbiotic robot behaves in consideration for a difference in recognition of situations between a user and the robot itself by putting itself in the user's position. This ability will be especially strongly necessary when the user has a false belief, that is, when he or she recognizes a situation wrongly. In such a case, it is desired that the robot understands the user's false belief and behaves in consideration for his or her mistake and its consequence. We anticipate that such a “considerate” robot will be friendlier and more reliable for users. In this paper, we pro-pose a robot that can recognize a user's false belief in scenes of the maintenance of joint attention and carry out experimental subjective evaluations of impressions that the proposed robot gives to humans during a human-robot interaction to statistically test our anticipation. Participants are asked to observe the interaction and to subjectively evaluate their impressions of the proposed robot's behaviors. The experimental results show that the robot that can recognize a user's false belief can give “familiar” and “intelligent” impressions to humans. This fact is expected to be one of fundamental recommendations for designing much friendlier interactions with robots and other intelligent systems.
Hybrid fusion scheme for pedestrian detection based on laser scanner and far infrared camera

The lack of trustworthy sensors makes ADAS applications a tough task. In this paper a fusion approach that uses two sensors: far infrared camera vision and a laser scanner to detect pedestrian for ADAS applications is presented. Both sensors have different field of view thus different detection zones are created according to the number of sensors available. The proposed algorithm combines low and high level information, providing a hybrid fusion scheme. Experimental results show that pedestrian detections are improved thanks to the use of several sensors.
Using a cross-language information retrieval approach to improve Computer-Aided Translation performance

Translation memories play a vital role in Computer-Aided Translation (CAT). Approaches to align sentences from parallel corpora are generally used to construct translation memories. In this paper, we present, on the one hand, a hybrid approach to align sentences combining different information sources (bilingual lexicon, sentence length and sentence position, semantic similarity), and on the other hand, a Computer-Aided Translation prototype based on a cross-language information retrieval approach. This approach consists in building a database of source and target sentences augmented with grammatical information of words and considering the sentence to translate as a "query" to that database. The Computer-Aided Translation prototype has been experimented on a set of examples and the experimental results we obtained are encouraging and outperform the current CAT tools.
New technics based on the wavelet transform for the restoration of old recordings

Digital techniques used for the restoration of old recordings are presented in this paper. Three different flaws can be present in old recordings, these being, harmonic distortion, impulsive noise, and background noise. Only the cancellation of the impulsive noise and the reduction of the background noise are considered. In order to cancel the impulsive noise, the corrupted samples are replaced by interpolated samples. An interpolator that uses the information located near the impulsive noise must be achieved. In this paper, two different methods of interpalation are compared. For the reduction of the background noise, we have used a method worked out by Ephraim and Malah that dces riot crzate musical noise. In order to improve the filtering of transcients, a decomposition of the signal in several frequency channels is beforehand perf ormed.
New pathways: affective labor and distributed expertise in peer-supported Learning Circles

ABSTRACT This paper explores learners’ and facilitators’ participation in Peer 2 Peer University’s Learning Circles (face-to-face study groups for people who are taking massive open online courses together). While digital technologies and open educational resources bring new possibilities for learning in informal settings, few empirical studies explore how adults take advantage of them. This research employs ethnography to investigate how learners and facilitators participated in eight different Learning Circles over a six-week period. My thematic analysis reveals that participants in these study groups expressed care, shared learning resources, enacted self-motivated learning strategies, and took risks by leaving comfort zones. Participants leveraged affective labor to create group cohesion, strengthen accountability, and enable peer interactions that supported self-guided learning. I argue that these virtues can open new pathways for adult learning in the twenty-first century.
On combining information from modulation spectra and mel-frequency cepstral coefficients for automatic detection of pathological voices

Abstract This work presents a novel approach for the automatic detection of pathological voices based on fusing the information extracted by means of mel-frequency cepstral coefficients (MFCC) and features derived from the modulation spectra (MS). The system proposed uses a two-stepped classification scheme. First, the MFCC and MS features were used to feed two different and independent classifiers; and then the outputs of each classifier were used in a second classification stage. In order to establish the best configuration which provides the highest accuracy in the detection, the fusion of information was carried out employing different classifier combination strategies. The experiments were carried out using two different databases: the one developed by The Massachusetts Eye and Ear Infirmary Voice Laboratory, and a database recorded by the Universidad Politécnica de Madrid. The results show that the combination of MFCC and MS features employing the proposed approach yields an improvement in the detection accuracy, demonstrating that both methods of parameterization are complementary.
A Trainable Tokenizer, solution for multilingual texts and compound expression tokenization

Tokenization is one of the initial steps done for almost any text processing task. It is not particularly recognized as a challenging task for English monolingual systems but it rapidly increases in complexity for systems that apply it for different languages. This article proposes a supervised learning approach to perform the tokenization task. The method presented in this article is based on character transitions representation, a representation that allows compound expressions to be recognized as a single token. Compound tokens are identified independent of the character that creates the expression. The method automatically learns tokenization rules from a pre-tokenized corpus. The results obtained using the trainable system show that for Romanian and English a statistical significant improvement is obtained over a baseline system that tokenizes texts on every non-alphanumeric character.
On small sample inference for common mean in heteroscedastic one-way model

In this paper we consider and compare several approximate methods for making small-sample statistical inference on the common mean in the heteroscedastic one-way random effects model. The topic of the paper was motivated by the problem of interlaboratory comparisons and is also known as the (traditional) common mean problem. It is also closely related to the problem of multicenter clinical trials and meta-analysis. Based on our simulation study we suggest to use the approach proposed by Kenward & Roger (1997) as an optimal choice for construction of the interval estimates of the common mean in the heteroscedastic one-way model.
On the Characterization of a Class of Fisher-Consistent Loss Functions and its Application to Boosting

Accurate classification of categorical outcomes is essential in a wide range of applications. Due to computational issues with minimizing the empirical 0/1 loss, Fisher consistent losses have been proposed as viable proxies. However, even with smooth losses, direct minimization remains a daunting task. To approximate such a minimizer, various boosting algorithms have been suggested. For example, with exponential loss, the AdaBoost algorithm (Freund and Schapire, 1995) is widely used for two-class problems and has been extended to the multi-class setting (Zhu et al., 2009). Alternative loss functions, such as the logistic and the hinge losses, and their corresponding boosting algorithms have also been proposed (Zou et al., 2008; Wang, 2012). In this paper we demonstrate that a broad class of losses, including non-convex functions, achieve Fisher consistency, and in addition can be used for explicit estimation of the conditional class probabilities. Furthermore, we provide a generic boosting algorithm that is not loss-specific. Extensive simulation results suggest that the proposed boosting algorithms could outperform existing methods with properly chosen losses and bags of weak learners.
An Experimental and Empirical Analysis of Consumer Response to Stockouts

Modern retailers typically manage thousands of individual stock keeping units (skus) and are faced with the complex task of stocking, pricing, promoting and maintaining an appropriate product assortment. This task is further complicated by the substantial heterogeneity in consumer preference for di®erent product o®erings. In this environment, it is critical that retail management has some understanding of how consumers respond to product unavailability. The goal of this paper is to develop an understanding of the process of consumer response to product unavailability (stockouts). We attack this problem using two disparate methods. First, we conduct a controlled laboratory experiment to gain insight into what consumers perceive to be the \cost" of choosing from a restricted set of product alternatives. We ̄nd that not only do consumers react negatively to the omission of preferred brands, but also that they experience dissatisfaction when previously available (but unchosen) brands are removed from the choice set. We subsequently use the results of the experiment to develop a random utility model of consumer choice in a product category, and implement this model using \natural experiment" scanner panel data. A key feature of our utility model is that we link a formalization of choice among items and assessment of a collection of items to represent consumer response to product unavailability in frequently-purchased product categories. We ̄nd: (1) that consumers switch in and out of brand/size sku combinations as assortment in terms of a third attribute (e.g., °avor) is increased and decreased, (2) utility is concave in the number of brands available in a category, (3) consumers have a mild preference for variety within a particular brand assortment, and (4) they defer category purchases when confronted with a stockout. The implications for retailers and manufacturers of consumer packaged goods are discussed.
Fairness and Convergence Results for Additive-Increase Multiplicative-Decrease Multiple-Bottleneck Networks

We examine the behavior of the additive-increase multiplicative-decrease (AIMD) congestion control algorithm. We present a variant of a proposed matrix model that allows us to obtain previous results for competition via a single bottleneck link. We then extend these results to the case of multiple bottleneck links paying particular attention to some aspects of fairness and convergence properties for multiple bottleneck systems. We examine both the synchronous (deterministic) and asynchronous (stochastic) cases. A simple simulation example illustrates the results
Derivation of Green's function of a spin Calogero–Sutherland model by Uglov's method

The hole propagator of a spin 1/2 Calogero–Sutherland model is derived using Uglov's method, which maps the exact eigenfunctions of the model, called the Yangian Gelfand-Zetlin basis, to a limit of Macdonald polynomials (gl2-Jack polynomials). To apply this mapping method to the calculation of 1-particle Green's function, we confirm that the sum of the field annihilation operator ψ↑ + ψ↓ on a Yangian Gelfand-Zetlin basis is transformed to the field annihilation operator ψ on gl2-Jack polynomials by the mapping. The resultant expression for the hole propagator for a finite-size system is written in terms of renormalized momenta and spin of quasi-holes, and the expression in the thermodynamic limit coincides with the earlier result derived by another method. We also discuss the singularity of the spectral function for a specific coupling parameter where the hole propagator of the spin Calogero–Sutherland model becomes equivalent to the dynamical colour correlation function of an SU(3) Haldane–Shastry model.