Miracle-dz/UniFAR-FTUs huggingface dataset:
Food-related tolerance in capuchin monkeys (Cebus apella) varies with knowledge of the partner's previous food-consumption

Capuchin monkeys, as well as several other primate species, show food-related tolerance in both captive and wild settings. Although researchers have revealed that past experience affects food-related tolerance, it is unclear if and how observing a partner’s previous food consumption affects tolerance. This question is important to determine the proximate mechanism of food-related tolerance, which may lead to food sharing, co-feeding, or tolerated taking. We investigated whether perception of another’s consumption of food affected the rate of tolerant food transfers among brown capuchin monkeys (Cebus apella). First, in the test condition, subjects observed their partner either eating (Eat-In View) or not eating food (No Eat-In View) through a window. In a control condition, the subjects could not observe the same partner behind an opaque screen, while the partner either ate (Eat-Out of View) or did not eat (No Eat-Out of View). After this, the subjects were provided with food to examine how well they tolerated their partner’s access to it through the mesh. Tolerant food transfers were sharply reduced after the subjects had observed their partner eat, but not in the control condition or after they had observed the partner not eating. We consider two possible hypotheses for this behavior, one relates to the internal state of the subject after having seen their partner eat (i.e., increased competitiveness, aggression, or food motivation). The other hypothesis relates to how the subject understands the partner’s motivational state after having witnessed food consumption, perhaps by grasping the partner’s need.
Modeling Robust Settlements to Civil War: Indivisible Stakes and Distributional Compromises

Why do some civil war settlements prove robust, while others fail? I show how a settlement’s robustness, defined in terms of the risk factor of the mutual-compromi se equilibrium, depends on the nature of the stakes of the conflict and the distributional terms of the settlement. I identify the distributional terms of the optimal settleme nt, namely, that most robust to exogenous shocks to the actors’ confidence that the other will continue to compromise. I introduce a measure of the degree of the perceived indivisibility of the stakes, an increase in which not only decreases the range of feasible distributional settlements, but decreases their robustness as well. I explore how intra-party heterogeneity and uncertainty regarding ex-post outcomes lessen the range and robustness of settle ments. In the conclusion, I compile the predictions of the model and briefly consider the policy implications.
Learning Linear Regression Models over Factorized Joins

We investigate the problem of building least squares regression models over training datasets defined by arbitrary join queries on database tables. Our key observation is that joins entail a high degree of redundancy in both computation and data representation, which is not required for the end-to-end solution to learning over joins. We propose a new paradigm for computing batch gradient descent that exploits the factorized computation and representation of the training datasets, a rewriting of the regression objective function that decouples the computation of cofactors of model parameters from their convergence, and the commutativity of cofactor computation with relational union and projection. We introduce three flavors of this approach: F/FDB computes the cofactors in one pass over the materialized factorized join; Favoids this materialization and intermixes cofactor and join computation; F/SQL expresses this mixture as one SQL query. Our approach has the complexity of join factorization, which can be exponentially lower than of standard joins. Experiments with commercial, public, and synthetic datasets show that it outperforms MADlib, Python StatsModels, and R, by up to three orders of magnitude.
The Virtual Token-Passing Ethernet Implementation and Experimental Results

This paper presents the design and first experimental results of VTPE (Virtual Token-passing Ethernet). VTPE is a deterministic protocol for real-time applications based on shared Ethernet, aimed to be used either in small processing power processors or in powerful ones. It is based on implicit token rotation similar to the virtual token-passing used in the P-NET protocol. The VTPE implementation leads to reduced program code, thus fitting in small microcontrollers’ memory and imposing low communication overhead.
Stress and Early Pregnancy in Sows Effect on endocrinology , ova transport and embryo development

Razdan, P. 2003. Stress and early pregnancy in sows Effect on endocrinology, ova transport and embryo development Doctoral dissertation. Mixing unfamiliar breeding female pigs creates stressful situations due to aggressive interactions, which can lead to food deprivation of the more submissive animals. The aim of this thesis was to study the type of stress occurring when sows are regrouped into unfamiliar groups and to determine the effect of this type of stress during two different stages of pregnancy in multiparous sows. Ova transport rate, embryo cleavage rate, the number of spermatozoa attached to the zona pellucida and the hormonal patterns were studied in sows subjected to food deprivation or ACTH-stimulation for 48 h, immediately after ovulation in their second oestrus after weaning. In sows subjected to the same treatments during the time of maternal recognition of pregnancy, the effect on hormonal concentrations in the blood plasma and allantoic fluid was examined, as well as the conceptus growth and foetal survival rate at day 30 of pregnancy. Food deprivation decreased ova transport rate, which might be due to an impact of the elevated levels of PGF 2α-metabolite on oviductal motility. Sows stimulated with ACTH had an increased level of cortisol and a decreased baseline level of PGF 2α-metabolite. There was no difference in ova transport rate but ACTH-stimulated sows had decreased embryonic cleavage rate and a lower number of spermatozoa attached to the zona pellucida compared with controls, indicating an alteration in the oviductal environment. Food deprivation and ACTH-stimulation during days 13 and 14 of pregnancy had no effect on embryo survival at day 30 of pregnancy, but both treatments increased the plasma levels of cortisol. ACTH-stimulation caused a two-day delay in the increase of plasma oestrone concentration seen at day 19 of pregnancy in control sows. This delay might reflect an impeded development of the embryos. Plasma levels of cortisol, progesterone, and free fatty acids increased concomitantly with a decrease in insulin in food-deprived sows, indicating an adrenal activation as well as a catabolic metabolism with decreased metabolic clearance rate in the liver. Progesterone concentration in allantoic fluid among food-deprived sows was elevated. This increase in progesterone concentration was positively correlated with the weight of the placentas, suggesting an influence of progesterone on placenta size. Post-ovulatory stress appears to alter the oviductal environment and post-ovulatory food deprivation also induces a decrease in ova transport rate. However, multiparous, well-nursed sows appear …
Using machine learning techniques for advanced passive operating system fingerprinting

TCP/IP fingerprinting is the active or passive collection of information usually extracted from a remote computer’s network stack. The combination of such information can be then used to infer the remote operating system (OS fingerprinting). OS fingerprinting is traditionally based on a database of “signatures”. A signature comprises several features (i.e., pairs attribute/value) extracted from network packets generated by a known operating system. Signatures are manually generated (and updated) by ob- serving several operating systems. There are two types of fingerprinting: active and passive. In this work, we focus on automating the generation and updating of the signatures for passive fingerprinting. By using classification algorithms we deal with fingerprints which do not have an exact match with an already known signature.
Stochastic Optimization Assisted Joint Channel Estimation and Multi-User Detection for OFDM/SDMA

Stochastic optimization assisted joint Channel Estimation (CE) and Multi-User Detection (MUD) were conceived and compared in the context of multi-user Multiple-Input Multiple-Output (MIMO) aided Orthogonal Frequency-Division Multiplexing/Space Division Multiple Access (OFDM/SDMA) systems. The development of stochastic optimization algorithms, such as Genetic Algorithms (GA), Repeated Weighted Boosting Search (RWBS), Particle Swarm Optimization (PSO) and Differential Evolution (DE) has stimulated wide interests in the signal processing and communication research community. However, the quantitative performance versus complexity comparison of GA, RWBS, PSO and DE techniques applied to joint CE and MUD is a challenging open issue at the time of writing, which has to consider both the continuous-valued CE optimization problem and the discrete-valued MUD optimization problem. In this study we fill this gap in the open literature. Our simulation results demonstrated that stochastic optimization assisted joint CE and MUD is capable of approaching both the Cramer-Rao Lower Bound (CRLB) and the Bit Error Ratio (BER) performance of the optimal ML-MUD, respectively, despite the fact that its computational complexity is only a fraction of the optimal ML complexity.
Development of a neuro-fuzzy integrating multi-tendency indices system for countering driver fatigue

In this study a neuro-fuzzy integrating system, a possible more economical solution for detecting and combating driver fatigue, was developed. First, we surveyed and selected adaptive tendency indices (i.e. Relative Strength Index (RSI), Stochastic Oscillators (KDL) and Moving Average Convergence Divergence (MACD)) based actual driving performance to predict driver fatigue and found that RSI, KDL and MACD of vehicle speed indicated significantly the different momentum before and after fatigue occurring, RSI and MACD were also significant in lateral position, but not significant in steering wheel angle. Second, a neuro-fuzzy technology was used to integrate multi-tendency indices for exactly predicting fatigue and control alarm for combating fatigue. The experiment results showed that the neuro-fuzzy system could efficiently alert subjects for keeping concentration on traffic conditions. The alarm reducing some symptoms of fatigue is marginally significant.
Characterization of Suicidal Behaviour with Self-Organizing Maps

The study of the variables involved in suicidal behavior is important from a social, medical, and economical point of view. Given the high number of potential variables of interest, a large population of subjects must be analysed in order to get conclusive results. In this paper, we describe a method based on self-organizing maps (SOMs) for finding the most relevant variables even when their relation to suicidal behavior is strongly nonlinear. We have applied the method to a cohort with more than 8,000 subjects and 600 variables and discovered four groups of variables involved in suicidal behavior. According to the results, there are four main groups of risk factors that characterize the population of suicide attempters: mental disorders, alcoholism, impulsivity, and childhood abuse. The identification of specific subpopulations of suicide attempters is consistent with current medical knowledge and may provide a new avenue of research to improve the management of suicidal cases.
An effective feature extraction method for facial expression recognition using adaptive Gabor wavelet

Feature extraction is an important and challenging phase of facial expression recognition problem. In this paper, an effective feature extraction method is proposed. Our facial feature representation method is based on an adaptive Gabor wavelet transform. In this method, we used a fuzzy controller for tuning the orientation parameter of filter. This filter can detect the most significant edges of facial images. Furthermore, the proposed adaptive filter improves the drawbacks of conventional Gabor filters. Nearest neighbor and multi-class Support Vector Machine (SVM) classifiers are applied for classification task. Experimental results on Japanese Female Facial Expression (JAFFE) database show that the proposed method can provide high recognition rate. The main advantage of proposed method over other methods is its flexibility.
Novel Biotechnological Approaches for the Recovery of Metals from Primary and Secondary Resources

Microorganisms have developed various mechanisms to deal with metals, thus providing numerous tools that can be used in biohydrometallurgical processes. “Biomining” processes—including bioleaching and biooxidation processes—facilitate the degradation of minerals, accompanied by a release of metals. These processes are especially attractive for low-grade ores and are used on an industrial scale mainly for sulfidic ores. In biosorption processes, biomass or certain biomolecules are used to bind and concentrate selected ions or other molecules from aqueous solutions. Biosorptive materials can be an environmentally friendly and efficient alternative to conventional materials, such as ion exchange resins. Other interesting mechanisms are bioaccumulation, bioflotation, bioprecipitation, and biomineralisation. Although these processes are well-known and have been studied in detail during the last decades, the recent strong progress of biotechnologies (e.g., genetic engineering and molecule design), as well as their combination with novel developments in material sciences (e.g., nanotechnologies) facilitate new strategies for the application of biotechnologies in mineral processing. The article gives a summary of current activities in this field that are being performed in our group.
Neuroprotective effects of physical activity on the brain: a closer look at trophic factor signaling

While the relationship between increased physical activity and cognitive ability has been conjectured for centuries, only recently have the mechanisms underlying this relationship began to emerge. Convergent evidence suggests that physical activity offers an affordable and effective method to improve cognitive function in all ages, particularly the elderly who are most vulnerable to neurodegenerative disorders. In addition to improving cardiac and immune function, physical activity alters trophic factor signaling and, in turn, neuronal function and structure in areas critical for cognition. Sustained exercise plays a role in modulating anti-inflammatory effects and may play a role in preserving cognitive function in aging and neuropathological conditions. Moreover, recent evidence suggests that myokines released by exercising muscles affect the expression of brain-derived neurotrophic factor synthesis in the dentate gyrus of the hippocampus, a finding that could lead to the identification of new and therapeutically important mediating factors. Given the growing number of individuals with cognitive impairments worldwide, a better understanding of how these factors contribute to cognition is imperative, and constitutes an important first step toward developing non-pharmacological therapeutic strategies to improve cognition in vulnerable populations.
Batch Process Monitoring Based on Multilinear Principal Component Analysis

A new batch process monitoring based on Multilinear Principal Component Analysis (MLPCA) is proposed in this paper. In the existing vector-based method on batch process monitoring such as Multiway Principal Component Analysis (MPCA), a batch data is represented as a vector in high-dimensional space. But vectorizing the batch data will lead to large storage requirements and information loss. MLPCA can be used to deal with the three-way data (or tensor) directly instead of performing vectorizing procedure. Hence, MLPCA has some advantages such as low memory and storage requirements for Normal Operation Condition (NOC) model. Furthermore, the MLPCA is able to extract more meaningful information from the batch dataset. The MLPCA monitoring approach is tested with the data from a Reactor of Thermal Anneal (RTA) batch process. Simulation results show that MLPCA early finds the process fault and improves the accuracy of process monitoring compared with MPCA.
Particle Filtering in Geophysical Systems

The application of particle filters in geophysical systems is reviewed. Some background on Bayesian filtering is provided, and the existing methods are discussed. The emphasis is on the methodology, and not so much on the applications themselves. It is shown that direct application of the basic particle filter (i.e., importance sampling using the prior as the importance density) does not work in high-dimensional systems, but several variants are shown to have potential. Approximations to the full problem that try to keep some aspects of the particle filter beyond the Gaussian approximation are also presented and discussed.
Sliding mode control for a two-wheeled inverted pendulum mobile robot driving on uniform slopes

In this paper, we consider a robust controller design for a two-wheeled inverted pendulum mobile robot driving on uniform slopes. For stabilizing the body posture of the mobile robot on slopes, we use a 2 degree-of-freedom (DOF) model which is obtained by restricting the spinning motion in a 3 DOF motion dynamic equation. In order to design the robust controller guaranteeing stability of the robot driving on slopes, we propose a sliding mode control based on the theory of variable structure systems and design a sliding surface using the theory of the linear quadratic regulation (LQR). From simulation results, we show that the proposed controller effectively controls the robot driving on slopes.
Criticality and the Pedagogical Reconstruction of Leadership Standards in an Educational Leadership Classroom

The authors describe their attempt to develop students’ critical perspectives on the content and assumptions in the 2008 Florida Principal Leadership Standards (FPLS) through a reflective essay assignment in a class titled Ethics, Equity, and Leadership in Education. The authors conducted textual analysis of 92 student essays (a total of 922 pages) submitted in 5 sections of a class from the summer of 2008 through the spring of 2009. The authors describe ways students analyze knowledge claims in the FPLS in relation to the standards’ considerable silence around issues of ethics and equity, which were central to the class: a) Standards are sufficient and helpful; b) Standards are insufficient, but helpful; and c) Standards are insufficient and lack critical elements. The article concludes with a discussion of how university professors and other professional development leaders might consider working within and, when strategically appropriate, against notions of standards representing comprehensive legitimate knowledge through six specific pedagogical and professional stances.
Raising Student Aspirations: The Need to Share a Vision

The term aspirations has been deraise student aspirations. If school fined in a number of ways. Words— counselors and principals develop such as goals, dreams, ambitions, an understanding of and respect for drives have all been used intertheir roles, as well as a positive com changeably when discussing stumunication pattern, they will be bet dent aspirations. We believe that aster a^[e WOrk together to meet the pirations drive students to be all needs of the students, they can be. As educators it is our At the Maine Aspirations Confer responsibility to provide an environence in n igg7 the k e ment in our school system that will ej. John EMns President of n I I/Mir CTii/ionrp tci Hn rv\ omH Ku 4
Pinpointing and Hiding Surprising Fragments in an Obfuscated Program

In this paper, we propose a pinpoint-hide defense method, which aims to improve the stealth of obfuscated code. In the pinpointing process, we scan the obfuscated code in a few small code fragment level and identify all surprising fragments, that is, very unusual fragments which may draw the attention of an attacker to the obfuscated code. In the hiding process, we transform the pinpointed surprising fragments into unsurprising ones while preserving semantics. The obfuscated code transformed by our method consists only by unsurprising code fragments, therefore is more difficult for attackers to be distinguished from unobfuscated code than the original. In the case study, we apply our pinpoint-hide method to some programs transformed by well-known obfuscation techniques. The result shows our method can pinpoint surprising fragments such as dummy code that does not fit in the context of the program, and instructions used in a complicated arithmetic expression. We also confirm that instruction camouflage can make the pinpointed surprising fragments unsurprising ones, and that it runs correctly.
Monotonicity of quantum relative entropy and recoverability

The relative entropy is a principal measure of distinguishability in quantum information theory, with its most important property being that it is non-increasing with respect to noisy quantum operations. Here, we establish a remainder term for this inequality that quantifies how well one can recover from a loss of information by employing a rotated Petz recovery map. The main approach for proving this refinement is to combine the methods of [Fawzi and Renner, arXiv:1410.0664] with the notion of a relative typical subspace from [Bjelakovic and Siegmund-Schultze, arXiv:quant-ph/0307170]. Our paper constitutes partial progress towards a remainder term which features just the Petz recovery map (not a rotated Petz map), a conjecture which would have many consequences in quantum information theory. A well known result states that the monotonicity of relative entropy with respect to quantum operations is equivalent to each of the following inequalities: strong subadditivity of entropy, concavity of conditional entropy, joint convexity of relative entropy, and monotonicity of relative entropy with respect to partial trace. We show that this equivalence holds true for refinements of all these inequalities in terms of the Petz recovery map. So either all of these refinements are true or all are false.
Causal Analysis of Student's Characteristics of Note-Taking Activities and Learning Performance during a Fully Online Course

A fully online learning environment requires support procedures in order to promote pro-active learning. This paper presents the causal relationships between student's characteristics, note-taking skills, learning experience, note assessment and test scores. A structural equation modeling technique is used to trace student's learning activity as note-taking occurs. The results of this modeling technique suggest that key factors and their contributions to course performance can be measured. Also, the factors which contribute to note-taking activity were examined.
Social Preferences or Collusion ? Experimental Evidence

We study relative performance schemes in light of social preferences. To the extent players are other regarding, they internalize the negative externality they impose on other players by exerting lower e¤ort. Thus, players with other regarding preferences are more likely to initiate and sustain collusive behavior, as traditionally de ned. We nd this is indeed the case experimentally. We also nd that when communication is allowed, a sel sh leader may further increase collusion amongst an otherwise other regarding group. Finally, we nd a relationship between gender and both social preferences and leadership. This suggests studies using gender as a control could actually be noisily proxying for leadership and social preferences. However, after controlling for leadership and social preferences, in contrast to past studies, we nd little gender e¤ect.