Miracle-dz/UniFAR-FTUs huggingface dataset:
Face puzzle—two new video-based tasks for measuring explicit and implicit aspects of facial emotion recognition

Recognizing others' emotional states is crucial for effective social interaction. While most facial emotion recognition tasks use explicit prompts that trigger consciously controlled processing, emotional faces are almost exclusively processed implicitly in real life. Recent attempts in social cognition suggest a dual process perspective, whereby explicit and implicit processes largely operate independently. However, due to differences in methodology the direct comparison of implicit and explicit social cognition has remained a challenge. Here, we introduce a new tool to comparably measure implicit and explicit processing aspects comprising basic and complex emotions in facial expressions. We developed two video-based tasks with similar answer formats to assess performance in respective facial emotion recognition processes: Face Puzzle, implicit and explicit. To assess the tasks' sensitivity to atypical social cognition and to infer interrelationship patterns between explicit and implicit processes in typical and atypical development, we included healthy adults (NT, n = 24) and adults with autism spectrum disorder (ASD, n = 24). Item analyses yielded good reliability of the new tasks. Group-specific results indicated sensitivity to subtle social impairments in high-functioning ASD. Correlation analyses with established implicit and explicit socio-cognitive measures were further in favor of the tasks' external validity. Between group comparisons provide first hints of differential relations between implicit and explicit aspects of facial emotion recognition processes in healthy compared to ASD participants. In addition, an increased magnitude of between group differences in the implicit task was found for a speed-accuracy composite measure. The new Face Puzzle tool thus provides two new tasks to separately assess explicit and implicit social functioning, for instance, to measure subtle impairments as well as potential improvements due to social cognitive interventions.
Articulation Prior in an Axial Representation

Local symmetry axis based schemes have been used for generic shape recognition as they lead to articulation insensitive representations. Despite their strengths, purely syntactic level of axial representations precludes the possibility of distinguishing a likely articulation from an unlikely one. In order to overcome this weakness, syntax should be combined with pragmatics and/or semantics. As a solution we propose a novel articulation space which enables inferences on the likelihood of possible articulations. Articulation priors can be constructed directly from examples (pragmatics) or set externally (semantics). We incorporate articulation priors to a skeletal matching scheme to arrive at an enriched axial representation which is sensitive to unlikely articulations but insensitive to likely ones.
Host Mutations (miaA and rpsL) Reduce Tetracycline Resistance Mediated by Tet(O) and Tet(M)

ABSTRACT The effects of mutations in host genes on tetracycline resistance mediated by the Tet(O) and Tet(M) ribosomal protection proteins, which originated in Campylobacter spp. andStreptococcus spp. , respectively, were investigated by using mutants of Salmonella typhimuriumand Escherichia coli. The miaA,miaB, and miaAB double mutants of S. typhimurium specify enzymes for tRNA modification at the adenosine at position 37, adjacent to the anticodon in tRNA. InS. typhimurium, this involves biosynthesis ofN 6-(4-hydroxyisopentenyl)-2-methylthioadenosine (ms2io6A). The miaA mutation reduced the level of tetracycline resistance mediated by both Tet(O) and Tet(M), but the latter showed a greater effect, which was ascribed to the isopentenyl (i6) group or to a combination of the methylthioadenosine (ms2) and i6 groups but not to the ms2 group alone (specified by miaB). In addition, mutations in E. coli rpsL genes, generating both streptomycin-resistant and streptomycin-dependent strains, were also shown to reduce the level of tetracycline resistance mediated by Tet(O) and Tet(M). The single-site amino acid substitutions present in the rpsL mutations were pleiotropic in their effects on tetracycline MICs. These mutants affect translational accuracy and kinetics and suggest that Tet(O) and Tet(M) binding to the ribosome may be reduced or slowed in theE. coli rpsL mutants in which the S12 protein is altered. Data from both the miaA and rpsL mutant studies indicate a possible link between stability of the aminoacyl-tRNA in the ribosomal acceptor site and tetracycline resistance mediated by the ribosomal protection proteins.
Gaussian interaction profile kernels for predicting drug-target interaction

MOTIVATION The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of all drug-target pairs in current datasets are experimentally validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. RESULTS We show that a simple machine learning method that uses the drug-target network as the only source of information is capable of predicting true interaction pairs with high accuracy. Specifically, we introduce interaction profiles of drugs (and of targets) in a network, which are binary vectors specifying the presence or absence of interaction with every target (drug) in that network. We define a kernel on these profiles, called the Gaussian Interaction Profile (GIP) kernel, and use a simple classifier, (kernel) Regularized Least Squares (RLS), for prediction drug-target interactions. We test comparatively the effectiveness of RLS with the GIP kernel on four drug-target interaction networks used in previous studies. The proposed algorithm achieves area under the precision-recall curve (AUPR) up to 92.7, significantly improving over results of state-of-the-art methods. Moreover, we show that using also kernels based on chemical and genomic information further increases accuracy, with a neat improvement on small datasets. These results substantiate the relevance of the network topology (in the form of interaction profiles) as source of information for predicting drug-target interactions. AVAILABILITY Software and Supplementary Material are available at http://cs.ru. nl/~tvanlaarhoven/drugtarget2011/. CONTACT tvanlaarhoven@cs.ru. nl; elenam@cs.ru. nl. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Development and Assessment of Shelf-Stable Spicy Snacks Prepared from Buffalo Tripe by Incorporating Sichuan Pepper (Timur)

A study was conducted for the preparation of the shelf stable spicy tripe snack from buffalo tripe with two different spices mixture. The main difference in the spice mixture is that one mix did not contain the Sichuan Pepper (timur) which is called as control batch and another contained it which is called as treated batch. Trial of product development was performed for three times where the combinations of the spices were altered till best combination was obtained. The prepared products were compared to sensory, microbiological and chemical analysis. The timur treated sample was found significantly superior in terms of taste and overall acceptability. The results of microbiological and physiochemical analysis showed that the timur-added snack more shelf-stable than un-added snack. Proximate composition of the best formulation moisture, crude protein, crude fat, crude fiber, total ash, salt and carbohydrates by difference are 43.915±1.5, 10.447±0.5, 15.203±0.5, 1.263±0.7, 2.7±0.7, 5.554±0.6 and 20.919±2.3, respectively.
Robust control for buck voltage converter under resistive and inductive varying load

Voltage converter circuits are fundamental components in power electronics applications. However, their paramount role on various appliances due to their well-known benefits comes together with difficulties and restrictions for their modeling and control. A buck converter is naturally subjected to non-linearities, time-dependency, noise, and parameter variations. Altogether, it is difficult to provide a fully-descriptive model so linear approximations are commonly used, delegating those burdens to dependable controllers. Traditional models (and resulting controllers) are derived considering a simple resistance as converter's load. A non-resistive load actually requires the converter's model to be modified, many works have dealt only with resistive loads for sake of simplicity. As this behavior is seldom seen in real applications, this work focuses on a resistive-inductive varying loads. The robust controller design is able to face load variations, so cascaded or hierarchical approaches are avoided, resulting in a low-order single-loop controller. Proposal validation is provided through simulation tests over the actual circuit components' models, incorporated in Simulink's Simscape™ library.
Genetic-based neuro-fuzzy Design of FACTS Controller in Power System

This paper introduces a critical assessment for the effectiveness of thyristor controlled reactor (TCR) and voltage source inverter (VSI) based FACTS devices on multi-machine power system oscillation damping. The oscillation problem is analyzed from the point of view of damping and synchronizing torque components. An eigenanalysis is adopted to study different controllers, their location, and use of various control signals for the effective damping of these oscillations. To improve system damping over a wide range of operating conditions, it is desirable to adapt the parameters of each damping controller in the system. In order to do this, on-line measurements of local system signals at converter are chosen as input signals to an adaptive neuro-fuzzy inference system (ANFIS). The outputs of each neuro-fuzzy controller are the desired parameters of system damping controllers.
Speaking and acting - Interacting language and action for an expressive character

We discuss the FearNot! application demonstrator, currently being developed for the EU framework V project VICTEC. It details the language structure, content, interactions management and design of the FearNot! Demonstrator, as well as presenting the VICTEC project and its motivations. This paper also focuses on the different sets of Speech Act inspired language action lists developed for the project and discusses their use for an interactive language and action system for the elaboration of expressive characters.
Creative or Not? Birds and Ants Draw with Muscles

In this work, a novel approach of merging two swarm intelligence algorithms is considered – one mimicking the behaviour of ants foraging (Stochastic Diffusion Search [5]) and the other algorithm simulating the behaviour of birds flocking (Particle Swarm Optimisation [17]). This hybrid algorithm is assisted by a mechanism inspired from the behaviour of skeletal muscles activated by motor neurons. The operation of the swarm intelligence algorithms is first introduced via metaphor before the new hybrid algorithm is defined. Next, the novel behaviour of the hybrid algorithm is reflected through a cooperative attempt to make a drawing, followed by a discussion about creativity in general and the ’computational creativity’ of the swarm.
Sense of Presence in Emotional Virtual Environments

Most definitions on presence have been based on cognitive or environmental aspects. However, we think that presence, like all human experiences, is influenced by emotions. EMMA project (IST-2001-39192) is aimed to study the nature of this relationship between emotions and presence. One of the main hypotheses proposed by EMMA is that emotions may enhance presence. In this line, the main objective of the present paper is to study the differences in presence between “emotional” environments and “neutral” environments. In order to achieve this objective, we have designed a Mood Induction Procedure using VR (VR-MIP, to induce different moods (sadness, joy, anxiety and relax) in experimental subjects. Our results point out that VR-MIPs are able to induce different moods in the users. Regarding the role of mood on the sense of presence, our results show differences between emotional and neutral environments in some presence measurements.
5'-noncoding region sacR is the target of all identified regulation affecting the levansucrase gene in Bacillus subtilis

The regulation of the levansucrase gene sacB was studied in Bacillus subtilis strains. Fusions were constructed in which genes of cytoplasmic proteins such as lacZ were placed immediately downstream from sacR, the regulatory region located upstream from sacB. These fusions were introduced in mutants affected in sacB regulation. In all cases the marker gene was affected in the same way as sacB by the genetic context. This result is of particular interest for the sacU pleiotropic mutations, which affect sacB expression and other cellular functions such as the synthesis of several exocellular enzymes. We also showed that strains harboring sacU+ or sacU-hyperproducing alleles contained different amounts of sacB mRNA, which was proportional to their levansucrase secretion. We concluded that the sacU gene does not affect sacB expression at the level of secretion but acts on a target within sacR. We discuss the possibility that sacU acts on a part of sacR, a homologous copy of which was found upstream from the gene of another sacU-dependent secreted enzyme of B. subtilis, beta-glucanase.
Complete temporal characterisation of highly chirped ultrabroadband optical pulses

Chirped pulses have important applications in [optical parametric] chirped pulse amplification ([OP]CPA), telecommunications, molecular alignment, chirp-assisted sum frequency generation, dispersive Fourier transform spectroscopy and coherent control [1, 2]. Knowledge of the nonlinear dispersion of these pulses is required for accurate extraction of information from experiments, as well as enabling optimal operation of [OP]CPA systems.
Darwin's Cathedral: Evolution, Religion, and the Nature of Society

One of the great intellectual battles of modern times is between evolution and religion. Until now, they have been considered completely irreconcilable theories of origin and existence. David Sloan Wilson's "Darwin's Cathedral" takes the radical step of joining the two, in the process proposing an evolutionary theory of religion that shakes both evolutionary biology and social theory at their foundations. The key, argues Wilson, is to think of society as an organism, an old idea that has received new life based on recent developments in evolutionary biology. If society is an organism, can we then think of morality and religion as biologically and culturally evolved adaptations that enable human groups to function as single units rather than mere collections of individuals? Wilson brings a variety of evidence to bear on this question, from both the biological and social sciences. From Calvinism in 16th-century Geneva to Balinese water temples, from hunter-gatherer societies to urban America, Wilson demonstrates how religions have enabled people to achieve by collective action what they never could do alone. He also includes a chapter considering forgiveness from an evolutionary perspective and concludes by discussing how all social organizations, even science, could benefit by incoporating elements of religion. Religious believers often compare their communities to single organisms and even to insect colonies. Astoundingly, Wilson shows that they might be literally correct. Intended for any educated reader, "Darwin's Cathedral" should change forever the way we view the relations among evolution, religion and human society.
Relationships between angiographic findings and National Institutes of Health stroke scale score in cases of hyperacute carotid ischemic stroke.

BACKGROUND AND PURPOSE Stroke severity in cases of hyperacute carotid ischemic stroke may be related to site of arterial occlusion. We evaluated the relationships between National Institutes of Health Stroke Scale (NIHSS) scores and findings on intra-arterial digital subtraction angiograms (IA-DSA) of patients with ischemic stroke within 6 hr of stroke onset. METHODS A total of 43 consecutive patients (38 men and five women; mean age, 69.4 +/- 8.7 years) with ischemic stroke in the carotid territory underwent IA-DSA within 6 hr of stroke onset. Baseline NIHSS score was assessed immediately before IA-DSA. Patients were divided into four groups according to site of arterial occlusion: 1) the internal carotid artery (ICA group, n = 10); 2) stem of the middle cerebral artery or stem of the anterior cerebral artery (Stem group, n = 14); 3) branches of middle cerebral artery or anterior cerebral artery (Branch group, n = 11); and 4) no arterial occlusion (Normal group, n = 8). RESULTS Mean (+/-SD) NIHSS score was 14.7 +/- 7.4. The interval from stroke onset to IA-DSA study was 205 +/- 76 min. NIHSS score was higher in the ICA group (median, 23; range, 6-32) than in the Branch (median, 17; range, 11-25; P =.02) or Normal (median, 15; range, 2-17; P <.001) groups but was not higher than in the Stem group (median, 6; range, 1-11; P =.73). Sensitivity-specificity curve analysis suggested an NIHSS score > or = 10 as indicative of arterial occlusion of the carotid system. A total of 96.9% of patients with NIHSS scores > or = 10 displayed arterial occlusion, and 63.6% of patients with NIHSS scores <10 displayed no arterial occlusion. CONCLUSION NIHSS score is related to site of arterial occlusion in cases of hyperacute carotid ischemic stroke. An NIHSS score of 10 seems to represent the cut-off for discriminating between patients with arterial occlusion and patients without.
Evolutionary Selection of Modular Decision Architectures

We study the evolutionary properties of decision processes. We show that a population of agents possessing decision architectures with hierarchically organized modules will have a strictly higher asymptotic growth factor than a population of agents with unitary decision architectures in which the modules are fully connected. Furthermore, we show that internal conict within agents and behavioral heterogeneity across agents are properties of evolutionary equilibrium. We interpret these results as supporting economic models of multiple decision processes like e.g., planner-doer models.
Multidimensional beyond Worst-Case and Almost-Sure Problems for Mean-Payoff Objectives

The beyond worst-case threshold problem (BWC), recently introduced by Bruyère et al., asks given a quantitative game graph for the synthesis of a strategy that i) enforces some minimal level of performance against any adversary, and ii) achieves a good expectation against a stochastic model of the adversary. They solved the BWC problem for finite-memory strategies and unidimensional mean-payoff objectives and they showed membership of the problem in NP∩coNP. They also noted that infinite-memory strategies are more powerful than finite-memory ones, but the respective threshold problem was left open. We extend these results in several directions. First, we consider multidimensional mean-payoff objectives. Second, we study both finite-memory and infinite-memory strategies. We show that the multidimensional BWC problem is coNPc in both cases. Third, in the special case when the worst-case objective is unidimensional (but the expectation objective is still multidimensional) we show that the complexity decreases to NP∩coNP. This solves the infinite-memory threshold problem left open by Bruyère et al., and this complexity cannot be improved without improving the currently known complexity of classical mean-payoff games. Finally, we introduce a natural relaxation of the BWC problem, the beyond almost-sure threshold problem (BAS), which asks for the synthesis of a strategy that ensures some minimal level of performance with probability one and a good expectation against the stochastic model of the adversary. We show that the multidimensional BAS threshold problem is solvable in P.
Analysis of High-Power Switched-Capacitor Converter Regulation Based on Charge-Balance Transient-Calculation Method

Switched-capacitor (SC) converters were initially introduced for low-power applications and monolithic integration. In recent years, SC converter has found high-power applications. However, voltage regulation remains an issue. This paper addresses regulation challenges for high-power SC converters, based on charge-balance transient-calculation (CT) modeling method and peak current stress estimation. With the help of CT model, due to its accuracy and comprehensive relationship, circuit and control parameters' impacts on regulation become straightforward and concerns on components stresses can be addressed quantitatively. The suitability of CT method for regulation analysis is confirmed by comparison with traditional modeling methods. The CT method is used in a 1-kW 3X two-switch boosting switched-capacitor converter (TBSC) circuit for steady-state analysis and current stress estimation. The soft rising input current and nature interleaving properties of 3X TBSC make it well suited for high-power application. Finally, the small-signal model of the 3X TBSC is developed and a closed-loop operation is achieved under 1 kW power rating.
Grayscale Image Authentication using Neural Hashing

Many different approaches for neural network based hash functions have been proposed. Statistical analysis must correlate security of them. This paper proposes novel neural hashing approach for gray scale image authentication. The suggested system is rapid, robust, useful and secure. Proposed hash function generates hash values using neural network one-way property and non-linear techniques. As a result security and performance analysis are performed and satisfying results are achieved. These features are dominant reasons for preferring against traditional ones.