Miracle-dz/UniFAR-FTUs huggingface dataset:
DCSYNTH: Guided Reactive Synthesis with Soft Requirements for Robust Controller and Shield Synthesis

DCSYNTH is a tool for the synthesis of controllers from safety and bounded liveness requirements given in interval temporal logic QDDC. It investigates the role of soft requirements (with priorities) in obtaining high quality controllers. A QDDC formula specifies past time properties. In DCSYNTH synthesis, hard requirements must be invariantly satisfied whereas soft requirements may be satisfied "as much as possible" in a best effort manner by the controller. Soft requirements provide an invaluable ability to guide the controller synthesis. In the paper, using DCSYNTH, we show the application of soft requirements in obtaining robust controllers with various specifiable notions of robustness. We also show the use of soft requirements to specify and synthesize efficient runtime enforcement shields which can correct burst errors. Finally, we discuss the use of soft requirements in improving the latency of controlled system.
Regression-based neural network training for the solution of ordinary differential equations

In this paper, we have introduced a method which is based on the use of unsupervised type of regression-based algorithm (RBA) for solving ordinary differential equations (ODEs) with initial or boundary conditions. Approximate solution of differential equation is differentiable and closed analytic. Here we have used error back propagation method for minimising the error function and modification of the parameters without direct use of other optimisation techniques. Initial weights are taken as combination of random as well as by proposed regression-based method. We present the method for solving a variety of problems and the results with arbitrary and regression-based initial weights are compared. Here the number of nodes in hidden layer has been fixed according to the degree of polynomial in the regression. The present model demonstrates to get also the approximate solutions for the differential equation inside and outside of the training domain.
Statistical Performance Prediction of Generalized Monopulse Estimation

Monopulse is an established array processing technique for fast and accurate angle estimation. This technique has been generalized to space-time array processing of any dimension. The statistical performance of this generalized monopulse parameter estimation has been characterized for several target fluctuation models but not for all cases. This gap is filled by this paper. We derive the mean and variance of the complex and real and imaginary part of the averaged monopulse ratio for all Swerling target models, deterministic targets (Swerling 0 case), χ2 distributed targets with 4 degrees of freedom (Swerling 3 or 4 case) including a given detection threshold, for an arbitrary number of difference beams, and for an arbitrary number of noncoherent averaging of time snapshots. For completeness we give also the already known results for Rayleigh targets (Swerling 1 or 2 case) in the same notation. From these means and variances, the performance of all kinds of parameter estimates with the generalized monopulse formula can be calculated. Applications of these statistical descriptions are presented for planar arrays and adaptive beamforming and for space-time adaptive processing (STAP) for broadband interference suppression. From these examples some interesting conclusions can be drawn.
Experimental Study on Two-phase Spray Cooling for the Cooling of High-heat-flux Electronic Chip

With the development of the electronic technology, the cooling issue of electronic equipment is more and more severe. The traditional way of air cooling can't well meet the requirement. Spray cooling, especially spray cooling with phase change, is an efficient solution to high-heat-flux cooling. It will probably be applied extensively to the cooling of large scale electronic equipment. This paper firstly introduces the design of the experimental device, which is used to study the spray cooling of electronic chips. Then the experiment process is described in detail. It generally demonstrates the feasibility of two-phase spray cooling being applied to large-scale electronic equipment.
Promoting Positive Attitudes Through Aging Education

More Americans than ever before are living into old age, yet many are not educationally, socially, or emotionally prepared for these later years. Research has shown that children tend to mirror our society's ageist attitudes. The purpose of this study was to investigate whether instruction about age and aging would have a significantly positive impact on preschool children's attitudes toward the elderly. An aging education curriculum with the teaching units on aging was developed and implemented in three preschool classes while three other preschool classes served as a control. Results showed that preschoolers who participated in the teaching units had significantly more positive attitudes about older people than the preschoolers who did not.
Climate change underlies global demographic, genetic, and cultural transitions in pre-Columbian southern Peru

Significance It has long been assumed that climate played a major role in the population history of the Central Andes. Although adaptations of the Andean populations to climatic changes such as the intensification of agriculture have been inferred from the archaeological record, evidence for demographic adaptations such as migration is missing so far. In this paper, ancient DNA data from populations that lived in southern Peru between 840 BC and 1450 AD provide evidence for two large-scale migrations in the Central Andes coincident with episodes of drought and increased climatic variability. These migrations led to a successive genetic homogenization of southern Peruvian populations generally attributed to intrusions by the late pre-Columbian highland empires such as the Wari, Tiwanaku, or Inca. Several archaeological studies in the Central Andes have pointed at the temporal coincidence of climatic fluctuations (both long- and short-term) and episodes of cultural transition and changes of socioeconomic structures throughout the pre-Columbian period. Although most scholars explain the connection between environmental and cultural changes by the impact of climatic alterations on the capacities of the ecosystems inhabited by pre-Columbian cultures, direct evidence for assumed demographic consequences is missing so far. In this study, we address directly the impact of climatic changes on the spatial population dynamics of the Central Andes. We use a large dataset of pre-Columbian mitochondrial DNA sequences from the northern Rio Grande de Nasca drainage (RGND) in southern Peru, dating from ∼840 BC to 1450 AD. Alternative demographic scenarios are tested using Bayesian serial coalescent simulations in an approximate Bayesian computational framework. Our results indicate migrations from the lower coastal valleys of southern Peru into the Andean highlands coincident with increasing climate variability at the end of the Nasca culture at ∼640 AD. We also find support for a back-migration from the highlands to the coast coincident with droughts in the southeastern Andean highlands and improvement of climatic conditions on the coast after the decline of the Wari and Tiwanaku empires (∼1200 AD), leading to a genetic homogenization in the RGND and probably southern Peru as a whole.
Stochastic Differential Dynamic Programming

Although there has been a significant amount of work in the area of stochastic optimal control theory towards the development of new algorithms, the problem of how to control a stochastic nonlinear system remains an open research topic. Recent iterative linear quadratic optimal control methods iLQG handle control and state multiplicative noise while they are derived based on first order approximation of dynamics. On the other hand, methods such as Differential Dynamic Programming expand the dynamics up to the second order but so far they can handle nonlinear systems with additive noise. In this work we present a generalization of the classic Differential Dynamic Programming algorithm. We assume the existence of state and control multiplicative process noise, and proceed to derive the second-order expansion of the cost-to-go. We find the correction terms that arise from the stochastic assumption. Despite having quartic and cubic terms in the initial expression, we show that these vanish, leaving us with the same quadratic structure as standard DDP.
Using UPPAAL for the secure and optimal control of AGV fleets

The design and realization of an on line control system for automated guided vehicles (AGV) is addressed. A synthesis method is proposed based on the use of the model checking tool for timed automata UPPAAL. This system has to route the vehicles while ensuring the system safeness, a good coordination between vehicles and the optimization of performance criteria. This problem is like synthesizing a Ramadge and Wonham supervisor combined with routing and optimizing functions, that is an ongoing problem within the supervisory control theory. The proposed concepts are validated through a software tool suite based on UPPAAL in order to generate optimal traces and interact with an AGV system emulated with Arena.
Executive Functions Are Employed to Process Episodic and Relational Memories in Children With Autism Spectrum Disorders

Objective: Long-term memory functioning in autism spectrum disorders (ASDs) is marked by a characteristic pattern of impairments and strengths. Individuals with ASD show impairment in memory tasks that require the processing of relational and contextual information, but spared performance on tasks requiring more item-based, acontextual processing. Two experiments investigated the cognitive mechanisms underlying this memory profile. Method: A sample of 14 children with a diagnosis of high-functioning ASD (age: M = 12.2 years), and a matched control group of 14 typically developing (TD) children (age: M = 12.1 years), participated in a range of behavioral memory tasks in which we measured both relational and item-based memory abilities. They also completed a battery of executive function measures. Results: The ASD group showed specific deficits in relational memory, but spared or superior performance in item-based memory, across all tasks. Importantly, for ASD children, executive ability was significantly correlated with relational memory but not with item-based memory. No such relationship was present in the control group. This suggests that children with ASD atypically employed effortful, executive strategies to retrieve relational (but not item-specific) information, whereas TD children appeared to use more automatic processes. Conclusions: The relational memory impairment in ASD may result from a specific impairment in automatic associative retrieval processes with an increased reliance on effortful and strategic retrieval processes. Our findings allow specific neural predictions to be made regarding the interactive functioning of the hippocampus, prefrontal cortex, and posterior parietal cortex in ASD as a neural network supporting relational memory processing.
The role of the theoretical drive in maintaining validity in mixed-method research

In this article we focus on the role of the theoretical drive in maintaining validity in mixed-method research. Methodological problems often occur when the incomplete supplementary component is from a contrasting paradigm to the core component–for instance, if the core component's theoretical drive is quantitative and the supplemental component is qualitative (ie, QUAN-qual designs) or, conversely, if the theoretical drive of the core component is qualitative and the supplemental component is quantitative (ie, QUAL-quan designs). The principles and assumptions of the overall theoretical drive have profound implications for sampling, analysis and the interpretation of data for the supplementary component. Attention to the mechanics of decision-making throughout a mixed-method research project is crucial for avoiding pitfalls posing a serious threat to validity, and a researcher's advanced knowledge of both qualitative and quantitative methods is essential.
Bluetooth smart: An enabling technology for the Internet of Things

The past couple of years have seen a heightened interest in the Internet of Things (IoT), transcending industry, academia and government. As with new ideas that hold immense potential, the optimism of IoT has also exaggerated the underlying technologies well before they can mature into a sustainable ecosystem. While 6LoWPAN has emerged as a disruptive technology that brings IP capability to networks of resource constrained devices, a suitable radio technology for this device class is still debatable. In the recent past, Bluetooth Low Energy (LE) - a subset of the Bluetooth v4.0 stack - has surfaced as an appealing alternative that provides a low-power and loosely coupled mechanism for sensor data collection with ubiquitous units (e.g., smartphones and tablets). When Bluetooth 4.0 was first released, it was not targeted for IP-connected devices but for communication between two neighboring peers. However, the latest release of Bluetooth 4.2 offers features that makes Bluetooth LE a competitive candidate among the available low-power communication technologies in the IoT space. In this paper, we discuss the novel features of Bluetooth LE and its applicability in 6LoWPAN networks. We also highlight important research questions and pointers for potential improvement for its greater impact.
Theories and practices for rural sociology in a 'new' Europe.

Dans la premiere conference donnee aux jeunes chercheurs lors du recent Congres Europeen de Sociologie Rurale, il a semble pertinent de relever les demandes qui sont adressees a la sociologie rurale par les nouveaux mouvements sociaux europeens, mouvements qui ont particulierement reussi a attirer les energies et les aspirations des jeunes. Cet article est une version revisee de cette conference. Il se centre sur les defis theoriques et methodologiques qui emergent des luttes contemporaines pour l'environnement et l'emancipation des femmes, en montrant que les concepts de nature et de sexe (gender) en viennent a occuper une place centrale dans les perspectives de recherche
Niche Partitioning of the N Cycling Microbial Community of an Offshore Oxygen Deficient Zone

Microbial communities in marine oxygen deficient zones (ODZs) are responsible for up to half of marine N loss through conversion of nutrients to N2O and N2. This N loss is accomplished by a consortium of diverse microbes, many of which remain uncultured. Here, we characterize genes for all steps in the anoxic N cycle in metagenomes from the water column and >30 μm particles from the Eastern Tropical North Pacific (ETNP) ODZ. We use an approach that allows for both phylogenetic identification and semi-quantitative assessment of gene abundances from individual organisms, and place these results in context of chemical measurements and rate data from the same location. Denitrification genes were enriched in >30 μm particles, even in the oxycline, while anammox bacteria were not abundant on particles. Many steps in denitrification were encoded by multiple phylotypes with different distributions. Notably three N2O reductases (nosZ), each with no cultured relative, inhabited distinct niches; one was free-living, one dominant on particles and one had a C terminal extension found in autotrophic S-oxidizing bacteria. At some depths >30% of the community possessed nitrite reductase nirK. A nirK OTU linked to SAR11 explained much of this abundance. The only bacterial gene found for NO reduction to N2O in the ODZ was a form of qnorB related to the previously postulated “nitric oxide dismutase,” hypothesized to produce N2 directly while oxidizing methane. However, similar qnorB-like genes are also found in the published genomes of many bacteria that do not oxidize methane, and here the qnorB-like genes did not correlate with the presence of methane oxidation genes. Correlations with N2O concentrations indicate that these qnorB-like genes likely facilitate NO reduction to N2O in the ODZ. In the oxycline, qnorB-like genes were not detected in the water column, and estimated N2O production rates from ammonia oxidation were insufficient to support the observed oxycline N2O maximum. However, both qnorB-like and nosZ genes were present within particles in the oxycline, suggesting a particulate source of N2O and N2. Together, our analyses provide a holistic view of the diverse players in the low oxygen nitrogen cycle.
Articular cartilage: in vivo diffusion-tensor imaging.

PURPOSE To investigate technical feasibility, test-retest reproducibility, and the ability to differentiate healthy subjects from subjects with osteoarthritis (OA) with diffusion-tensor (DT) imaging parameters and T2 relaxation time. MATERIALS AND METHODS This study was approved by the institutional review board and was HIPAA compliant. All subjects provided written informed consent. DT imaging parameters and T2 (resolution=0.6×0.6×2 mm) of patellar cartilage were measured at 7.0 T in 16 healthy volunteers and 10 patients with OA with subtle inhomogeneous signal intensity but no signs of cartilage erosion at clinical magnetic resonance (MR) imaging. Ten volunteers were imaged twice to determine test-retest reproducibility. After cartilage segmentation, maps of mean apparent diffusion coefficient (ADC), fractional anisotropy (FA), and T2 relaxation time were calculated. Differences for ADC, FA, and T2 between the healthy and OA populations were assessed with nonparametric tests. The ability of each MR imaging parameter to help discriminate healthy subjects from subjects with OA was assessed by using receiver operating characteristic curve analysis. RESULTS Test-retest reproducibility was better than 10% for mean ADC (8.1%), FA (9.7%), and T2 (5.9%). Mean ADC and FA differed significantly (P<.01) between the OA and healthy populations, but T2 did not. For ADC, the optimal threshold to differentiate both populations was 1.2×10(-3) mm2/sec, achieving specificity of 1.0 (16 of 16) and sensitivity of 0.80 (eight of 10). For FA, the optimal threshold was 0.25, yielding specificity of 0.88 (14 of 16) and sensitivity of 0.80 (eight of 10). T2 showed poor differentiation between groups (optimal threshold=22.9 msec, specificity=0.69 [11 of 16], sensitivity=0.60 [six of 10]). CONCLUSION In vivo DT imaging of patellar cartilage is feasible, has good test-retest reproducibility, and may be accurate in discriminating healthy subjects from subjects with OA. ADC and FA are two promising biomarkers for early OA.
Stress-Related Cognitive Dysfunction in Dementia Caregivers

Summary In a cross-sectional study, 31 dementia caregivers were compared to a group of 25 noncaregiving controls to evaluate whether the stress of being the primary caregiver of a person with dementia produces cognitive dysfunction. Cognitive differences were examined to evaluate the relationships between cognitive function and stress-related physiological and psychological measures to contribute information regarding its potential mechanism. The cognitive assessments were 2 measures of attention-executive function and 1 word list memory task. Physiological and self-rated stress-related measurements included cortisol, perceived stress, depression, self-efficacy, mindfulness, sleep quality, fatigue, and neuroticism. Caregivers performed worse than noncaregivers on the 2 attention tasks but not on the word list memory test. There was no interaction of caregiver status and age on cognitive performance. The caregivers and noncaregivers differed in morning salivary cortisol and most of the self-rated stress-related measures with the caregiver values reflecting greater stress. Of note, impaired sleep was the only potential mediator of the caregiver effect on cognitive performance in our small sample.
Based on Fractal Dimension Calculation by the Area Cover for Extraction of Shift Invariant Multiwavelet Features of Texture Images

In this paper, we propose an effective method based on Fractal Dimension Calculation by the Area Cover for extraction of shift invariant multiwavelet Features of Texture Images. The feature extraction process involves a normalization followed by a shift invariant multiwavelet packet transform. The normalization converts a given image into a size invariant image which is then passed to the shift invariant multiwavelet packet transform to generate subbands of shift invariant wavelet coefficients. Then we convert the multiwavelet coefficients matrix to a smaller dimension correlation matrix. Then we employ a technique based on the fractal dimension (FD) and a new fractal dimension estimating method is proposed by taking the area instead of the volume covering in box-counting to estimate the FD. Three FD features are based on the original image, the above average/high gray level image, the below average/low gray level image. An energy signature is computed for each subband of these FD coefficients. In order to reduce feature dimensionality, only the most dominant wavelet energy signatures are selected as feature vector for classification.
Optimal management of β thalassaemia intermedia

Our understanding of the molecular and pathophysiological mechanisms underlying the disease process in patients with β thalassaemia intermedia (TI) has substantially increased over the past decade. The hallmark of disease process in patients with TI includes ineffective erythropoiesis, chronic haemolytic anaemia, and iron overload. There are a number of options currently available for managing patients with TI including splenectomy, transfusion therapy, iron chelation therapy, modulation of fetal haemoglobin production, and several other agents targeting specific clinical complications. Limited studies assessed the efficacy and safety of these modalities; hence, there are currently no clear guidelines for managing patients with TI. Until solid evidence‐based guidelines are available, individualised treatment should be entertained.
A discrete-time battery model for high-level power estimation

In this paper, we introduce a discrete-time model for the complete power supply sub-system that closely approximates the behavior of its circuit-level (i.e., HSpice), continuous-time counterpart. The model is abstract and efficient enough to enable event-driven simulation of digital systems described at a very high level of abstraction and that include, among their components, also the power supply. Therefore, it can be successfully used for the purpose of battery life-time estimation during design optimization, as shown by the results we have collected on a meaningful case study. Experiments prove also that the accuracy of our model is very close to that provided by the corresponding Spice-level model.
Unsupervised image-set clustering using an information theoretic framework

In this paper, we combine discrete and continuous image models with information-theoretic-based criteria for unsupervised hierarchical image-set clustering. The continuous image modeling is based on mixture of Gaussian densities. The unsupervised image-set clustering is based on a generalized version of a recently introduced information-theoretic principle, the information bottleneck principle. Images are clustered such that the mutual information between the clusters and the image content is maximally preserved. Experimental results demonstrate the performance of the proposed framework for image clustering on a large image set. Information theoretic tools are used to evaluate cluster quality. Particular emphasis is placed on the application of the clustering for efficient image search and retrieval.