Miracle-dz/UniFAR-FTUs huggingface dataset:
Time-dependent Numerical Model for the Emission of Radiation from Relativistic Plasma

We describe a numerical model constructed for the study of the emission of radiation from relativistic plasma under conditions characteristic of, e.g., gamma-ray bursts and active galactic nuclei. The model solves self-consistently the kinetic equations for e± and photons, describing cyclosynchrotron emission, direct Compton and inverse Compton scattering, and pair production and annihilation, including the evolution of high-energy electromagnetic cascades. The code allows calculations over a wide range of particle energies, spanning more than 15 orders of magnitude in energy and timescales. Our unique algorithm, which enables to follow the particle distributions over a wide energy range, allows us to accurately derive spectra at high energies, >100 TeV. We present the kinetic equations that are being solved, a detailed description of the equations describing the various physical processes, the solution method, and several examples of numerical results. Excellent agreement with analytical results of the synchrotron-synchrotron self-Compton model is found for parameter-space regions in which this approximation is valid, and several examples are presented of calculations for parameter-space regions for which analytic results are not available.
Fast data analytics with FPGAs

The rapidly increasing amount of data available for real-time analysis (i.e., so-called operational business intelligence) is creating an interesting opportunity for creative approaches to speeding up data processing algorithms. One such approach that is starting to become more common is using hardware accelerators for stream processing. Typically these accelerators are implemented on top of reconfigurable hardware, known as field-programmable gate arrays (FPGAs). Though the value of FPGAs for data warehouses is gradually recognized by the database community, their true potential for various business analytic tasks is yet unexplored. In this line of research, we investigate FPGA technology in the context of extreme data processing looking for opportunities where FPGAs can be exploited to improve over classical CPU-based architectures. We introduce a framework for FPGA-accelerated (real-time) analytics including a query-to-hardware compiler for static complex event detection, an XPath engine for dynamic query workloads, and templates for high-speed data mining operators in hardware.
A Maximum Entropy Model Application on Recognition of Metaphor Phenomena

Metaphor happening in our daily life language is a usual phenomenon, and recognizing them by the use of computer is becoming a valuable research task in the field of natural language processing, artificial intelligence, and even applied linguistics. This paper proposes a way to recognize metaphors based on maximum entropy model after analyzing the features of metaphor, and reasons the rationality to build a recognition model using the statistical methods. The results of the experiment show that the model performs well at high precision and recall, as well as the f value, thus we come to the conclusion that such metaphor recognizing model based on maximum entropy principle has a promising future
Thrombin Generation Capacity of Prothrombin Complex Concentrate in an In Vitro Dilutional Model

Background The use of PCC for the treatment of trauma-induced coagulopathy potentially increase the risk of thromboembolism and disseminated intravascular coagulation, which is addressed to an imbalance of both pro- and anticoagulants. As PCCs differ in composition, we used an in vitro dilutional approach to assess the overall thrombin generation of five different PCCs through various laboratory assays. Methods The vitamin K-dependent coagulation factors, heparin, and antithrombin were assessed in five commercially available PCCs. The procoagulant potential of the PCCs was assessed in plasma and whole blood from 4 healthy donors by means of classical coagulation assays, thrombin generation assay and thromboelastometry. In order to reflect coagulopathy, whole blood was diluted to 80, 60, 40, and 20% with Ringer’s lactate solution. Results The five different PCCs were characterised by comparable levels of factors II, VII, IX and X (all around 20–30 IU/mL), whereas the heparin (0 to 17.6 IU/mL) and antithrombin (0.06 to 1.29 IU/mL) levels were remarkably different between manufactures. In vitro dilution of blood induced a prolongation of the PT and aPTT, and attenuation of thrombin generation and ExTem induced thromboelastometry. Overall, non- or low-heparin containing PCCs restored the in vitro dilutional coagulopathy, whereas PCCs containing heparin have an anticoagulant effect. The thrombin generation assay showed to be the most sensitive method for assessment of PCC effects. Conclusions This study shows that most available PCCs are not balanced regarding their pro- and anticoagulants. The effect of measured differences in thrombin generation among different PCCs requires further investigations to elaborate the clinical meaning of this finding in the treatment of trauma induced coagulopathy.
ONLINE MAPS IN GEOMED Internet Mapping, online GIS and their application in Collaborative Spatial Decision-Making

This paper gives an overview of Internet Mapping and discusses its use for Geographical Mediation. Internet Mapping summarizes a variety of tools, ranging from geodata and map servers up to online Geographic Information Systems (GIS). First, a classification is proposed, according to the services offered and to the techniques used. Then, the requirements of Collaborative Spatial Decision-Making (CSDM) for online maps are discussed in more detail with reference to the European Union research project "Geographical Mediation System" (GeoMed).
Multiway Tree-Based Group Key Management Using Chinese Remainder Theorem for Multi-Privileged Group Communications

Key management becomes more difficult in multi-privileged communications due to the dynamic membership and the complex relations between users and resources. In this paper, we propose a novel key management scheme in which the key graph is based on multiway trees. Chinese remainder theorem is employed to combine some encrypted rekeying materials into a short message for rekeying. As a result, users can update affected keys through the rekeying material and one-way function by themselves. The security analysis shows forward and backward security can be maintained. Compared with some existing schemes, our proposed scheme can reduce rekeying overhead efficiently.
Ultrasensitive flow-based immunoassays using single-molecule counting.

BACKGROUND Immunoassay (IA) technology has expanded the clinical utility of protein biomarkers, but demands for increased sensitivity, dynamic reporting ranges, and small sample volumes have limited the potential clinical usefulness of many biomarkers. We assessed the performance, including limits of detection (LODs) and the dynamic reporting range, of an IA-based technology, Erenna Immunoassay System, for a series of biomarkers, including cardiac troponin I (cTnI). METHODS Erenna IAs were used with 10 different and clinically important biomarkers to ascertain the LOD with various sample sizes (10 microL to 200 microL). RESULTS The Erenna Immunoassay System generated LODs of 10-100 pg/L using 100 microL of sample. For cTnI, the LOD was 0.2 ng/L and a 10% CV was seen between 0.78 and 1.6 ng/L. CONCLUSIONS The Erenna IA-based technology reproducibly measures protein biomarkers with detection limits of 10-100 pg/L, with a dynamic range of >4.5 logs in sample volumes of 50-150 microL.
Finding Density Functionals with Machine Learning

Machine learning is used to approximate density functionals. For the model problem of the kinetic energy of noninteracting fermions in 1D, mean absolute errors below 1 kcal/mol on test densities similar to the training set are reached with fewer than 100 training densities. A predictor identifies if a test density is within the interpolation region. Via principal component analysis, a projected functional derivative finds highly accurate self-consistent densities. The challenges for application of our method to real electronic structure problems are discussed.
Toward Combining Speed, Efficiency, Versatility, and Robustness in an Autonomous Quadruped

This paper provides an overview about StarlETH: a compliant quadrupedal robot that is designed to study fast, efficient, versatile, and robust locomotion. The platform is driven by highly compliant series elastic actuation, which makes the system fully torque controllable, energetically efficient, and well suited for dynamic maneuvers. Using model-based control strategies, this medium dog-sized machine is capable of various gaits ranging from static walking to dynamic running over challenging terrain. StarlETH is equipped with an onboard PC, batteries, and various sensor equipment that enables enduring autonomous operation. In this paper, we provide an overview about the underlying locomotion control algorithms, outline a real-time control and simulation environment, and conclude the work with a number of experiments to demonstrate the performance of the presented hardware and controllers.
Towards Affordable Disclosure of Spoken Heritage Archives

This paper presents and discusses ongoing work aiming at affordable disclosure of real-world spoken heritage archives in general, and in particular of a collection of recorded interviews with Dutch survivors of World War II concentration camp Buchenwald. Given such collections, we at least want to provide search at different levels and a flexible way of presenting results. Strategies for automatic annotation based on speech recognition - supporting e.g., within-document search - are outlined and discussed with respect to the Buchenwald interview collection. In addition, usability aspects of the spoken word search are discussed on the basis of our experiences with the online Buchenwald web portal. It is concluded that, although user feedback is generally fairly positive, automatic annotation performance is not yet satisfactory, and requires additional research.
A genetic algorithm for minimax optimization problems

Robust discrete optimization is a technique for structuring uncertainty in the decision-making process. The objective is to find a robust solution that has the best worst-case performance over a set of possible scenarios. However, this is a difficult optimization problem. This paper proposes a two-space genetic algorithm as a general technique to solve minimax optimization problems. This algorithm maintains two populations. The first population represents solutions. The second population represents scenarios. An individual in one population is evaluated with respect to the individuals in the other population. The populations evolve simultaneously, and they converge to a robust solution and its worst-case scenario. Since minimax optimization problems occur in many areas, the algorithm will have a wide variety of applications. To illustrate its potential, we use the two-space genetic algorithm to solve a parallel machine scheduling problem with uncertain processing times. Experimental results show that the two-space genetic algorithm can find robust solutions.
Battery aware dynamic scheduling for periodic task graphs

Battery lifetime, a primary design constraint for mobile embedded systems, has been shown to depend heavily on the load current profile. This paper explores how scheduling guidelines from battery models can help in extending battery capacity. It then presents a 'battery-aware scheduling' methodology for periodically arriving task graphs with real time deadlines and precedence constraints. Scheduling of even a single taskgraph while minimizing the weighted sum of a cost function has been shown to be NP-hard (Lawler, 1978). The presented methodology divides the problem in to two steps. First, a good DVS algorithm dynamically determines the minimum frequency of execution. Then, a greedy algorithm allows a near optimal priority function (Gruian, 2002) to choose the task which would maximize slack recovery. The methodology also ensures adherence of real time deadlines independent of the choice of the DVS algorithm and priority function used, while following battery guidelines to maximize battery lifetime. Battery simulations carried out on the profile generated by our methodology for a large set of taskgraphs show that battery life time is extended up to 23.3% as compared to existing dynamic scheduling schemes
Coordinating CPU and Memory Elasticity Controllers to Meet Service Response Time Constraints

Vertical elasticity is recognized as a key enabler for efficient resource utilization of cloud infrastructure through fine-grained resource provisioning, e.g., allowing CPU cycles to be leased for as short as a few seconds. However, little research has been done to support vertical elasticity where the focus is mostly on a single resource, either CPU or memory, while an application may need arbitrary combinations of these resources at different stages of its execution. Nonetheless, the existing techniques cannot be readily used as-is without proper orchestration since they may lead to either under-or over-provisioning of resources and consequently result in undesirable behaviors such as performance disparity. The contribution of this paper is the design of an autonomic resource controller using a fuzzy control approach as a coordination technique. The novel controller dynamically adjusts the right amount of CPU and memory required to meet the performance objective of an application, namely its response time. We perform a thorough experimental evaluation using three different interactive benchmark applications, RUBiS, RUBBoS, and Olio, under workload traces generated based on open and closed system models. The results show that the coordination of memory and CPU elasticity controllers using the proposed fuzzy control provisions the right amount of resources to meet the response time target without over-committing any of the resource types. In contrast, with no coordinating between controllers, the behaviour of the system is unpredictable e.g., the application performance may be met but at the expense of over-provisioning of one of the resources, or application crashing due to severe resource shortage as a result of conflicting decisions.
Usage of GPM Portable Equipment for Determination of Corrosion Stage of Concrete Structures

During a Brite/Euram Project several European partners developed and produced an integrated monitoring system. So the inspection and maintenance costs and the traffic impairments can be reduced. One part of this project was concerned with evaluation of portable techniques for evaluation of reinforcement corrosion. During this work a newly developed, hand held, easily to handle, portable equipment based on the galvanostatic pulse method (GPM) was developed. This paper deals with the results and analysis of the GPM measurements performed on a highwaybridge exposed to de-icing salts as well as the results of laboratory tests. Results of average corrosion rates determined by weight loss and galvanostatic pulse technique were compared. Special attention was paid on the comparability of instrument readings to real behavior. Finally the necessary precautions which need to be taken when the on site data are used for life prediction of structures are discussed.
Implementation of arbitrary circuits using modified constant delay technique

This paper perceives Pre evaluation of output before the arrival of inputs from the preceding stagesis ready becomes an added advantage of MCD logic style. Besides adjusting the width of timing window, clock allocation and its distribution are considered as crucial design factors. Power consumption is drasticallyreduced, but the pre-charge propagation path delay affects the speed performances and limits the energy-delay product (EDP) improvement. Using 45-nm general purpose CMOS technology, MCD logic is evaluated for single cycle multi-staged circuit block. Simulation results unveil that the MCD logic achieves meliorate performance and is more energy efficient than the other logic styles for the implementation on arbitrary circuits.
Regulação emocional, bem-estar psicológico e bem-estar subjetivo

O artigo analisa as relacoes entre modos de regulacao emocional e tres dimensoes de bem-estar subjetivo, a partir de dois modelos: um de predicao e outro de mediacao, que envolve efeitos indiretos da autonomia e do dominio do ambiente. Foram utilizadas tres escalas, uma para avaliar dois modos de regulacao emocional (up regulation e down regulation), outra para avaliar tres dimensoes de bem-estar subjetivo (afetos positivos, afetos negativos, satisfacao com a vida) e outra para avaliar dois componentes do bem-estar psicologico (autonomia, dominio do ambiente). Tambem se incluiu um questionario sociodemografico para caracterizacao da amostra. Participaram da pesquisa 231 brasileiros. Os resultados indicaram que os modos de regulacao emocional foram capazes de prever os niveis de autonomia e de dominio do ambiente. O modelo de mediacao indicou que a autonomia sozinha, foi capaz de mediar a relacao entre os modos de regulacao emocional e as dimensoes de bem-estar subjetivo.
Exploring input-output characteristics of the cerebellar granule neuron: role of synaptic inhibition

Understanding functional role of bursts firing is vital in understanding coding of sensory information [1]. Regulating the burst is related to stimulus properties and neural heterogeneities [2]. The granule cells form the largest neuronal population in the mammalian brain and regulate information transfer along the major afferent systems to the cerebellum. Understanding how the granule cell processes information appears critical to understand the cerebellar function. We used a mathematical model of cerebellar granule cell to explore information transmission in mossy fibre - granule cell synapse of the cerebellum. The impact of plasticity changes in excitatory synaptic release probability and variation in intrinsic excitability of granule cell was studied combining the modulatory effects of inhibition. We explored the changes in pre and post synaptic factors to study spiking properties and report their influence on first spike latency and spike amplitude, revealing the indicators of information encoding in individual neurons [3].
Soluble Forms and Ligands of the Receptor for Advanced Glycation End-Products in Patients with Acute Respiratory Distress Syndrome: An Observational Prospective Study

Background The main soluble form of the receptor for advanced glycation end-products (sRAGE) is elevated during acute respiratory distress syndrome (ARDS). However other RAGE isoforms and multiple ligands have been poorly reported in the clinical setting, and their respective contribution to RAGE activation during ARDS remains unclear. Our goal was therefore to describe main RAGE isoforms and ligands levels during ARDS. Methods 30 ARDS patients and 30 mechanically ventilated controls were prospectively included in this monocenter observational study. Arterial, superior vena cava and alveolar fluid levels of sRAGE, endogenous-secretory RAGE (esRAGE), high mobility group box-1 protein (HMGB1), S100A12 and advanced glycation end-products (AGEs) were measured in duplicate ELISA on day 0, day 3 and day 6. In patients with ARDS, baseline lung morphology was assessed with computed tomography. Results ARDS patients had higher arterial, central venous and alveolar levels of sRAGE, HMGB1 and S100A12, but lower levels of esRAGE and AGEs, than controls. Baseline arterial sRAGE, HMGB1 and S100A12 were correlated with nonfocal ARDS (AUC 0.79, 0.65 and 0.63, respectively). Baseline arterial sRAGE, esRAGE, S100A12 and AGEs were associated with severity as assessed by PaO2/FiO2. Conclusions This is the first kinetics study of levels of RAGE main isoforms and ligands during ARDS. Elevated sRAGE, HMGB1 and S100A12, with decreased esRAGE and AGEs, were found to distinguish patients with ARDS from those without. Our findings should prompt future studies aimed at elucidating RAGE/HMGB1/S100A12 axis involvement in ARDS. Trial Registration clinicaltrials.gov Identifier: NCT01270295.
Global annotation on georeferenced photographs

We present an efficient world-scale system for providing automatic annotation on collections of geo-referenced photos. As a user uploads a photograph a place of origin is estimated from visual features which the user can refine. Once the correct location is provided, tags are suggested based on geographic and image similarity retrieved from a large database of 1.2 million images crawled from Flickr. The system effectively mines geographically relevant terms and ranks potential suggestion terms by their posterior probability given observed visual and geocoordinate features. A series of experiments analyzes the geocoordinate prediction accuracy and precision-recall metric of tags suggestions based on information retrieval techniques. The system is novel in that it fuses geographic and visual information to provide annotations for uploaded photographs taken anywhere in the world in a matter of seconds.
Underwater stereo SLAM with refraction correction

This work presents a method for underwater stereo localization and mapping for detailed inspection tasks. The method generates dense, geometrically accurate reconstructions of underwater environments by compensating for image distortions due to refraction. A refractive model of the camera and enclosure is calculated offline using calibration images and produces non-linear epipolar curves for use in stereo matching. An efficient block matching algorithm traverses the precalculated epipolar curves to find pixel correspondences and depths are calculated using pixel ray tracing. Finally the depth maps are used to perform dense simultaneous localization and mapping to generate a 3D model of the environment. The localization and mapping algorithm incorporates refraction corrected ray tracing to improve map quality. The method is shown to improve overall depth map quality over existing methods and to generate high quality 3-D reconstructions.
Liver Toxicity of Cadmium Telluride Quantum Dots (CdTe QDs) Due to Oxidative Stress in Vitro and in Vivo

With the applications of quantum dots (QDs) expanding, many studies have described the potential adverse effects of QDs, yet little attention has been paid to potential toxicity of QDs in the liver. The aim of this study was to investigate the effects of cadmium telluride (CdTe) QDs in mice and murine hepatoma cells alpha mouse liver 12 (AML 12). CdTe QDs administration significantly increased the level of lipid peroxides marker malondialdehyde (MDA) in the livers of treated mice. Furthermore, CdTe QDs caused cytotoxicity in AML 12 cells in a dose- and time-dependent manner, which was likely mediated through the generation of reactive oxygen species (ROS) and the induction of apoptosis. An increase in ROS generation with a concomitant increase in the gene expression of the tumor suppressor gene p53, the pro-apoptotic gene Bcl-2 and a decrease in the anti-apoptosis gene Bax, suggested that a mitochondria mediated pathway was involved in CdTe QDs’ induced apoptosis. Finally, we showed that NF-E2-related factor 2 (Nrf2) deficiency blocked induced oxidative stress to protect cells from injury induced by CdTe QDs. These findings provide insights into the regulatory mechanisms involved in the activation of Nrf2 signaling that confers protection against CdTe QDs-induced apoptosis in hepatocytes.