Miracle-dz/UniFAR-FTUs huggingface dataset:
Research of camera calibration algorithm based on 3D re-projective error

According to the calibration of binocular vision, an improved two-stages camera calibration method involved with multi-distortion coefficients is introduced in this paper. It takes the sum of distance in the world coordinate between ground truth 3D points and calculated 3D points as cost function. The 3D points' coordinate are calculated by the imitated direct linear transformation (IDLT) triangulation based on distortion compensation. This strategy can allow the two cameras' all parameters being optimized at the same time. The experiment among IDLT method , Matlab calibration toolbox method and Tsai mehtod is made and the results show that the strategy of taking the 3D reconstruction errors as object function is feasible, and IDLT method possesses more higher calibration precision than those of the other two methods.
Spreading Behaviors on the Power-Law Growth Networks

This paper discusses the dynamics of the epidemic spreading behaviors for susceptible-infected-recovery (SIR) model on the power-law growth networks, characterized by variable value for exponent 7. We give the general generated algorithm for power-law growth networks, analyze the dynamics behavior for SIR model in complex networks, and introduce the interaction Markov chains mean-field equations as well as the stochastic numerical approach to examine the threshold (steady state) and time-independent behavior for the epidemic model on such network. We have done the simulation both for the BA networks and power-law growth networks. Analytical methods and simulated experiments show there exhibits a critical threshold for the pow-law growth networks below which it cannot diffuse in such type of the system. Meanwhile, results shows the stochastic numerical approach (SNA) can save memory and get the fast exploration, compared to the Monte Carlo calculations.
Enhancement of Single to Inter-clouds in Cloud Computing

Cloud computing promises to significantly change the way we use computers and access and store our personal and business information. With these new computing and communications paradigms arise new data security challenges. Existing data protection mechanisms such as encryption have failed in preventing data theft attacks, especially those perpetrated by an insider to the cloud provider. The use of cloud computing has increased rapidly in many organizations. Cloud computing provides many benefits in terms of low cost and accessibility of data. Ensuring the security of cloud computing is a major factor in the cloud computing environment, as users often store sensitive information with cloud storage providers but these providers may be untrusted. Dealing with “single cloud” providers is predicted to become less popular with customers due to risks of service availability failure and the possibility of malicious insiders in the single cloud. A movement towards “multi-clouds”, or in other words, “inter clouds” or “cloud-of-clouds” has emerged recently. This paper surveys recent research related to single and intercloud security and addresses possible solutions. It is found that the research into the use of inter-cloud providers to maintain security has received less attention from the research community than has the use of single clouds. This work aims to promote the use of inter-clouds due to its ability to reduce security risks that affect the cloud computing user. KeywordsPrivate clouds, Public clouds, Cloud service, IaaS, SaaS, PaaS, single cloud, inter-clouds, cloud storage, data integrity, data intrusion, service availability.
Provided for Non-commercial Research and Educational Use Only. Not for Reproduction, Distribution or Commercial Use. Medial Prefrontal Cortex and the Adaptive Regulation of Reinforcement Learning Parameters 22 Author's Personal Copy

research and educational use including without limitation use in instruction at your institution, sending it to specific colleagues who know you, and providing a copy to your institution's administrator. All other uses, reproduction and distribution, including without limitation commercial reprints, selling or licensing copies or access, or posting on open internet sites, your personal or institution's website or repository, are prohibited. For exceptions, permission may be sought for such use through Elsevier's permissions site at: Medial prefrontal cortex and the adaptive regulation of reinforcement learning parameters. Abstract Converging evidence suggest that the medial prefrontal cortex (MPFC) is involved in feedback categorization, performance monitoring, and task monitoring, and may contribute to the online regulation of reinforcement learning (RL) parameters that would affect decision-making processes in the lateral prefrontal cortex (LPFC). Previous neurophysiological experiments have shown MPFC activities encoding error likelihood, uncertainty, reward volatility, as well as neu-ral responses categorizing different types of feedback, for instance, distinguishing between choice errors and execution errors. Rushworth and colleagues have proposed that the involvement of MPFC in tracking the volatility of the task could contribute to the regulation of one of RL parameters called the learning rate. We extend this hypothesis by proposing that MPFC could contribute to the regulation of other RL parameters such as the exploration rate and default action values in case of task shifts. Here, we analyze the sensitivity to RL parameters of behavioral performance in two monkey decision-making tasks, one with a deterministic reward schedule and the other with a stochastic one. We show that there exist optimal parameter values specific to each of these tasks, that need to be found for optimal performance and that are usually hand-tuned in computational models. In contrast, automatic online regulation of these parameters using some heuristics can help producing a good, although non-optimal, behavioral performance in each task. We finally describe our computational model of MPFC–LPFC interaction used for online regulation of the exploration rate and its application to a human–robot interaction scenario. There, unexpected uncertainties are produced by the human introducing cued task changes or by cheating. The model enables the robot to autonomously learn to reset exploration in response to such uncertain cues and events. The combined results provide concrete evidence specifying how prefrontal cortical subregions may cooperate to regulate RL parameters. It also shows how such neurophysiologically inspired mechanisms can control advanced robots in the real world. Finally, the model's …
Droop controller comparison for AC microgrids

The aim of this study is to analyze and compare three droop controller schemes to regulate the operation of a microgrid. Three droop control schemes are selected because of their capability to regulate voltage, frequency and power sharing without using communication links and complex measurement systems. Specifically, the conventional droop controller and two of its variants are compared, the angular and robust controllers. An analysis regarding their differences is given in terms of voltage, frequency, and power sharing performance.
Investigation of mixture splitting concept for training linear bottlenecks of deep neural network acoustic models

A Gaussian or log-linear mixture model trained by maximum likelihood may be trained further using discriminative training. It is desirable that the mixture splitting is also done during the discriminative training, to achieve better mixture density distribution. In previous work such a discriminative splitting approach was presented. Similarly, the resolution of a deep neural network may also be increased by splitting. In this paper, discriminative splitting is applied as a way of initializing a linear bottleneck between two layers of a DNN. Experiments for a single hidden layer and six hidden layer cases show the potential of this approach as an alternative method of pre-training for linear bottlenecks for MLP hidden layers.
Intercellular coupling mediated by potassium accumulation in peg-and-socket junctions

Coupling of smooth muscle cells is important for coordination of gastrointestinal motility. Small structures called peg-and-socket junctions (PSJs) have been found between muscle cells and may play a role in electrical coupling due to extracellular potassium accumulation in the narrow cleft between the muscle cells. A model was developed in which an electrical boundary element model of the cell morphology is used in conjunction with a finite difference model which described ionic fluxes and diffusion of extracellular potassium in the PSJ. The boundary element model used a combination of triangular and cylindrical elements to reduce computational demand while ensuring accuracy. Barrier kinetics were used to model the underlying ionic transport mechanisms. Seven ionic transport mechanisms were used to create the transmembrane voltage waveform. Results indicate that PSJs may produce significant coupling between smooth muscle cells under appropriate conditions. Coupling increased exponentially with increasing length and with decreasing intercellular gap.
Brave Induction Revisited

Sakama and Inoue introduced brave induction as a novel logic framework for concept-learning. They showed that brave induction has potential applications for problem solving in many domains. In this paper, motivated from Shapiro’s deﬁnition of model inference problems, we provide an optimization of brave induction called proper brave induction, which prefers hypotheses resulting fewer minimal models. We ﬁrst propose formal deﬁnitions of proper brave induction for clausal theories and nonmonotonic logic programs, then investigate corresponding properties and develop an optimization procedure. At last, we analyze computational complexity of decision problems for proper brave induction in propositional case.
Reduced fear and aggression and altered serotonin metabolism in Gtf2ird1‐targeted mice

The GTF2IRD1 general transcription factor is a candidate for involvement in the varied cognitive and neurobehavioral symptoms of the microdeletion disorder, Williams–Beuren syndrome (WBS). We show that mice with heterozygous or homozygous disruption of Gtf2ird1 exhibit decreased fear and aggression and increased social behaviors. These findings are reminiscent of the hypersociability and diminished fear of strangers that are hallmarks of WBS. Other core features of WBS, such as increased anxiety and problems with spatial learning were not present in the targeted mice. Investigation of a possible neurochemical basis for the altered behaviors in these mice using high‐performance liquid chromatography analysis showed increased levels of serotonin metabolites in several brain regions, including the amygdala, frontal cortex and parietal cortex. Serotonin levels have previously been implicated in fear and aggression, through modulation of the neural pathway connecting the prefrontal cortex and amygdala. These results suggest that hemizygosity for GTF2IRD1 may play a role in the complex behavioral phenotype seen in patients with WBS, either individually, or in combination with other genes, and that the GTF2I transcription factors may influence fear and social behavior through the alteration of neurochemical pathways.
From Structure to Catalysis: Recent Developments in the Biotechnological Applications of Lipases

Microbial lipases are highly appreciated as biocatalysts due to their peculiar characteristics such as the ability to utilize a wide range of substrates, high activity and stability in organic solvents, and regio- and/or enantioselectivity. These enzymes are currently being applied in a variety of biotechnological processes, including detergent preparation, cosmetics and paper production, food processing, biodiesel and biopolymer synthesis, and the biocatalytic resolution of pharmaceutical derivatives, esters, and amino acids. However, in certain segments of industry, the use of lipases is still limited by their high cost. Thus, there is a great interest in obtaining low-cost, highly active, and stable lipases that can be applied in several different industrial branches. Currently, the design of specific enzymes for each type of process has been used as an important tool to address the limitations of natural enzymes. Nowadays, it is possible to “order” a “customized” enzyme that has ideal properties for the development of the desired bioprocess. This review aims to compile recent advances in the biotechnological application of lipases focusing on various methods of enzyme improvement, such as protein engineering (directed evolution and rational design), as well as the use of structural data for rational modification of lipases in order to create higher active and selective biocatalysts.
The Impact of Early Life Stress on Growth and Cardiovascular Risk: A Possible Example for Autonomic Imprinting?

Introduction Early life stress is imprinting regulatory properties with life-long consequences. We investigated heart rate variability in a group of small children with height below the third percentile, who experienced an episode of early life stress due to heart failure or intra uterine growth retardation. These children appear to develop autonomic dysfunction in later life. Results Compared to the healthy control group heart rate variability (HRV) is reduced on average in a group of 101 children with short stature. Low HRV correlates to groups of children born small for gestational age (SGA), children with cardiac growth failure and children with congenital syndromes, but not to those with constitutional growth delay (CGD), who had normal HRV. Reduced HRV indicated by lower RMSSD and High Frequency (HF)-Power is indicating reduced vagal activity as a sign of autonomic imbalance. Conclusion It is not short stature itself, but rather the underlying diseases that are the cause for reduced HRV in children with height below the third percentile. These high risk children—allocated in the groups with an adverse autonomic imprinting in utero or infancy (SGA, congenital heart disease and congenital syndromes)—have the highest risk for ‘stress diseases’ such as cardiovascular disease in later life. The incidence of attention deficit disorder is remarkably high in our group of short children.
Predictors of Low Birth Weight in Newborn Delivered in Public Hospital of Gurage Zone, Southern Ethiopia: A Case-Control Study

Worldwide, low birth weight is one of the most common causes of neonatal morbidity and mortality. Though factors associated with low birth weight vary from one region to another, in Gurage Zone, there is no sound analytical study done to examine the local determinants of low birth weight. Because of this, the study aimed to elucidate the predictors of low birth weight in public hospital of Gurage zone. Methods: A facility based unmatched case-control study design was employed to detect the existing exposure difference between cases and controls. The study included a total of 270 controls (normal weight) and 134 cases (low birth weight baby). Control and case participants were selected through consecutively. To collect the data, structured interviewer administered questionnaires and anthropometric measurement were used. The data were entered through Epi-data 3.1 computer program and analyzed through Statistical package for Social Sciences version 21 (SPSS v. 21). Univariate, Bivariate and Multivariate analysis were done. Result: Multiple logistic regression model analysis indicated that low birth weight was independently associated with maternal mid upper arm circumference less than 23 cm (AOR 1.79 (95% CI = 1.01 - 3.16)), gestational age less than 37 weeks (AOR 24.94 (95% CI = 12.38 - 50.27)), presence of malaria infection during period of pregnancy (AOR 3.02 (95% CI = 1.39 - 6.51)), presence of preclampsia, multiparity (AOR 2.19 (95% CI = 1.21 - 3.96)) and twining (AOR 5.42 95% CI = 2.01 - 14.59). Conclusion: Gestational age < 37 weeks, twining, malaria infection during pregnancy and maternal undernutrition were a significant predictor of LBW in the region of Gurage zone. Hence, to effectively prevent low birth weight, Gurage zone health departments should work on promotion of maternal nutrition during pregnancy, prevention of malaria during pregnancy.
El Trabajo Social ante el reto de la transformación digital

La investigacion de Joaquin Castillo de Mesa trata uno de los temas mas actuales e importantes en el campo del trabajo social. Ademas, puede resultar de interes tanto a los debutantes en la materia, como a los expertos. La obra abarca una gama amplia de asuntos, analizandolos desde diferentes perspectivas.
Comparing Real and Intended System Usages: A Case for Web Portal

Regarding the complexity of actual software systems, including web portals, it is becoming more and more difficult to develop software systems such that their real usage will satisfy their intended usage. To tackle this problem, we can compare the a priori assumptions about how the system should be used with the actual user behavior in order to decide how the system could be improved. For this aim, we propose to employ the same formalism to express the intended usage, the web portal model and the real usage extracted from system usage traces by data mining algorithms. Inspired from BioCham, we propose to use temporal logic and Kripke structure as such a common formalism.
Personality Trait Based Simulation Model of the E-mail System

Within the area of criminal and terrorist social network analysis, there is little research being done on analysing the communication behavior of criminal and terrorist groups. In this paper, we describe the development of a conceptual simulation model of the e-mail system, which is based on the use of personality trait dimensions to model the e-mail traffic behavior of e-mail users. This conceptual simulation model is being used as a first step for further development in simulating criminal and terrorist communication behavior. We also describe the development of an e-mail traffic analyser system, which uses a decision tree to search for interesting e-mail traffic behavioral patterns, and uses social network and time-series visualisation to provide the details of the interesting traffic patterns. We demonstrate that the personality trait based e-mail system model is useful as a tool for generating e-mail traffic behavioral patterns and that decision trees are useful for finding interesting patterns in the e-mail traffic data.
Joint velocity redistribution for fault tolerant manipulators

If the end-effector of a robotic manipulator moves on a specified trajectory, then for the fault tolerant operation, it is required that the end-effector continues the trajectory with a minimum velocity jump when a fault occurs within a joint. This problem is addressed in the paper. A way to tolerate the fault is to find new joint velocities for the faulty manipulator in which results into the same end-effector velocity provided by the healthy manipulator. The aim of this study is to find a strategy which optimally redistributes the joint velocities for the remained healthy joints of the manipulators. The optimality is defined by the minimum end-effector velocity jump. A solution of the problem is presented and it is applied to a robotics manipulator. Then through a case study and a simulation study it is validated. The paper shows that if would be possible the joint velocity redistribution results into a zero velocity jump.
Dense visual-inertial odometry for tracking of aggressive motions

We propose a sliding window-based dense visual-inertial fusion method for real-time tracking of challenging aggressive motions. Our method combines recent advances in direct dense visual odometry, inertial measurement unit (IMU) preintegration, and graph-based optimization. At the front-end, direct dense visual odometry provides camera pose tracking that is resistant to motion blur. At the back-end, a sliding window optimization-based fusion framework with efficient IMU preintegration generates smooth and high-accuracy state estimates, even with occasional visual tracking failures. A local loop closure that is integrated into the back-end further eliminates drift after extremely aggressive motions. Our system runs real-time at 25 Hz on an off-the-shelf laptop. Experimental results show that our method is able to accurately track motions with angular velocities up to 1000 degrees/s and velocities up to 4 m/s. We also compare our method with state-of-the-art systems, such as Google Tango, and show superior performance during challenging motions. We show that our method achieves reliable tracking results, even if we throw the sensor suite during experiments.
Selection Signature Analysis Implicates the PC1/PCSK1 Region for Chicken Abdominal Fat Content

We conducted a selection signature analysis using the chicken 60k SNP chip in two chicken lines that had been divergently selected for abdominal fat content (AFC) for 11 generations. The selection signature analysis used multiple signals of selection, including long-range allele frequency differences between the lean and fat lines, long-range heterozygosity changes, linkage disequilibrium, haplotype frequencies, and extended haplotype homozygosity. Multiple signals of selection identified ten signatures on chromosomes 1, 2, 4, 5, 11, 15, 20, 26 and Z. The 0.73 Mb PC1/PCSK1 region of the Z chromosome at 55.43-56.16 Mb was the most heavily selected region. This region had 26 SNP markers and seven genes, Mar-03, SLC12A2, FBN2, ERAP1, CAST, PC1/PCSK1 and ELL2, where PC1/PCSK1 are the chicken/human names for the same gene. The lean and fat lines had two main haplotypes with completely opposite SNP alleles for the 26 SNP markers and were virtually line-specific, and had a recombinant haplotype with nearly equal frequency (0.193 and 0.196) in both lines. Other haplotypes in this region had negligible frequencies. Nine other regions with selection signatures were PAH-IGF1, TRPC4, GJD4-CCNY, NDST4, NOVA1, GALNT9, the ESRP2-GALR1 region with five genes, the SYCP2-CADH4 with six genes, and the TULP1-KIF21B with 14 genes. Genome-wide association analysis showed that nearly all regions with evidence of selection signature had SNP effects with genome-wide significance (P<10–6) on abdominal fat weight and percentage. The results of this study provide specific gene targets for the control of chicken AFC and a potential model of AFC in human obesity.
Addition of Resection of Temporal Muscle and Fascia in Decompressive Craniectomy in the Treatment of Traumatic Brain Injury

Objective Decompressive craniectomy (DC) is a widely used surgical procedure for control of severely increased intracranial pressure in various conditions. The goal of this study is to evaluate the effectiveness of the addition of resection of temporalis muscle and fascia in DC particularly in the treatment of traumatic brain injury. Methods Twenty patients underwent temporalis muscle and fascia resection in addition to conventional DC and duroplasty due to massive brain swelling in a single tertiary hospital from 2013 to 2015 were enrolled. Twenty other patients who received the standard techniques by other neurosurgeons in the same period were gathered for the control group. Postoperative computed tomography (CT) as well as functional outcome in both groups were analyzed retrospectively. Results CT volumetry showed a significant increase of 85.19 mL (p<0.001) of extracranial herniation volume in the research group compared with the control group. Using modified Rankin Scale and Glasgow Outcome Scale, there was no statistically significant difference in functional outcome between the two groups. Conclusion Although preliminary, the procedure appears to show a meaningful increase in extracranial herniation volume with minimal masticatory and cosmetic impairment.