Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating Radioprotective Effect of Hesperidin on Acute Radiation Damage in the Lung Tissue of Rats

Background: Oxidative stress plays an important role in the pathogenesis and progression of γ-irradiation-induced cellular damage, Lung is a radiosensitive organ and its damage is a dose-limiting factor in radiotherapy. The administration of dietary antioxidants has been suggested to protect against the succeeding tissue damage. The present study aimed to evaluate the radioprotective efficacy of Hesperidin (HES) against γ-irradiation-induced tissue damage in the lung of male rats. Materials and Methods: Thirty two rats were divided into four groups. Rats in Group 1 received PBS and underwent sham irradiation. Rats in Group 2 received HES and underwent sham irradiation. Rats in Group 3 received PBS and underwent γ-irradiation. Rats in Group 4 received HES and underwent γ-irradiation. These rats were exposed to γ-radiation 18 Gy using a single fraction cobalt-60 unit, and were administered HES (100 mg/kg/d, b.w, orally) for 7 days prior to irradiation. Rats in each group were sacrificed 24 hours after radiotherapy (RT) for the determination of superoxide dismutase (SOD), glutathione (GSH), malondialdehyde (MDA) and histopathological evaluations. Results: Compared to group 1, the level of SOD and GSH significantly decreased and MDA level significantly increased in group 3 at 24 h following irradiation, (p=0.001, p<0.001, p=0.001), respectively. A statistically significant difference in all parameters was observed for rats in group 4 as compared to group 3 (p<0.05). Histopathological results 24 hours after RT showed that radiation has increased inflammation, lymphocyte, macrophage and neutrophil compared to group 1 ( p<0.0125). Oral administration of HES before RT significantly decreased macrophage and neutrophil when compared to group 3 (p<0.0125), but partly there was inflammation and lymphocyte that indicated there was no significant difference when compared to group 3 (p>0.0125). Conclusion: Oral administration of HES was found to offer protection against γ-irradiation- induced pulmonary damage and oxidative stress in rats, probably by exerting a protective effect against inflammatory disorders via its free radical scavenging and membrane stabilizing ability.
An efficient lung segmentation approach for interstitial lung disease

Interstitial lung disease, a heterogeneous group of parenchymal lung disorder is a great menace to human health. Segmentation is a major step in the estimation of interstitial lung disease using Computer Aided Diagnosis (CAD) system. In this paper, an automatic segmentation of lung field is presented which separates the exact lung region from the background CT slice. An active contour with distance regularized level set method is used for segmentation which clearly traces the right and left lung separately. When compared to the conventional methods, this method avoids the need for reinitialization and hence it produces accurate segmentation results.
Overrepresentation of Extreme Events in Decision Making Reflects Rational Use of Cognitive Resources

People’s decisions and judgments are disproportionately swayed by improbable but extreme eventualities, such as terrorism, that come to mind easily. This article explores whether such availability biases can be reconciled with rational information processing by taking into account the fact that decision makers value their time and have limited cognitive resources. Our analysis suggests that to make optimal use of their finite time decision makers should overrepresent the most important potential consequences relative to less important, put potentially more probable, outcomes. To evaluate this account, we derive and test a model we call utility-weighted sampling. Utility-weighted sampling estimates the expected utility of potential actions by simulating their outcomes. Critically, outcomes with more extreme utilities have a higher probability of being simulated. We demonstrate that this model can explain not only people’s availability bias in judging the frequency of extreme events but also a wide range of cognitive biases in decisions from experience, decisions from description, and memory recall.
On Endogenously Staggered Prices

064 Taylor's model of staggered contracts is an influential explanation for nominal inertia and the persistent real effects of nominal shocks. However, in standard imperfect competition models, if agents are allowed to choose the timing of pricing decisions, they will typically choose to synchronize. This paper provides a simple model of imperfect competition which produces stable staggering. Our argument relies on strategic interaction at two levels --- between firms within an industries, and across industries --- and produces a continuum of stable staggered price equilibria.
Multi-modal Sensing and Analysis of Poster Conversations: Toward Smart Posterboard

Conversations in poster sessions in academic events, referred to as poster conversations, pose interesting and challenging topics on multi-modal analysis of multi-party dialogue. This article gives an overview of our project on multi-modal sensing, analysis and "understanding" of poster conversations. We focus on the audience's feedback behaviors such as non-lexical backchannels (reactive tokens) and noddings as well as joint eye-gaze events by the presenter and the audience. We investigate whether we can predict when and who will ask what kind of questions, and also interest level of the audience. Based on these analyses, we design a smart posterboard which can sense human behaviors and annotate interactions and interest level during poster sessions.
Energy-Efficient Assignment of User Equipment to Cooperative Base Stations

Future radio access networks (RANs) will activate and deactivate base stations (BSs) depending on the activity of users to conserve energy. Reducing the fraction of time a BS is enabled will reduce the energy consumption of the RAN. Using cooperative transmissions from BSs allows to reach user equipment (UE) that is not in range of any single BS. When some BSs cooperate, other BSs can be deactivated. In this paper, we quantify the reduction in activity probability when BSs can cooperate compared to not cooperating. We assume the BSs are placed on a hexagonal grid and a spatial Poisson process places the UE. We calculate the probability that a BS has to be enabled depending on whether we allow BSs to cooperate or not. Allowing BSs to cooperate decreases the expected number of enabled BSs per area by up to 11%, depending on the user density. Additionally placing the BSs at the optimal distance for cooperation would reduce activity by additional 39%. We conclude that cooperation can extend the range of BSs and thereby allow other BSs to be disabled and, thus, conserve energy.
A New Sensitive Serological Assay for Detection of Lentivirus Infections in Small Ruminants

ABSTRACT Lentivirus infections in small ruminants represent an economic problem affecting several European countries with important sheep-breeding industries. Programs for control and eradication of these infections are being initiated and require reliable screening assays. This communication describes the construction and evaluation of a new serological screening enzyme-linked immunosorbent assay (ELISA) for the detection of antibodies to maedi-visna virus (MVV) in sheep and to caprine arthritis encephalitis virus (CAEV) in goats. The solid phase is sensitized with a combination of the major core protein p25 of MVV produced in Escherichia coli and a peptide derived from the immunodominant region of the viral transmembrane protein gp46. The peptide carries an N-terminal biotin residue and is complexed with streptavidin prior to being coated. The new assay was evaluated with 2,336 sheep serum samples from different European countries with large differences in the levels of prevalence of MVV infections, and the results have been compared to those of the standard agar gel immunodiffusion test. Discrepant samples were analyzed by Western blotting with viral lysate, and most sera could be classified unambiguously. The estimated overall sensitivity of the new ELISA was 99.4% (95% confidence interval [CI], 98.4 to 99.8%) and the specificity was 99.3% (95% CI, 98.7 to 99.6%). A limited set of goat sera (n = 212) was also analyzed, with similar results. These data indicate that the new assay is a reliable tool that can be used in control and eradication programs for small ruminant lentivirus infections.
The Relationship between Violent Video Games Exposure and School Behavior Problems in Qatari Schools

Research on violence in movies, television programs, music and video games indicates consistently that media violence increases the aggression and violence, in both immediate/short term and long term contexts. The relationship between violent video games and aggressive behavior is widely studied, but less is known regarding the relationship with school behavior problems. This study investigates the relationship between violent video games exposure and school behavior problems in a sample of 202 students. Multiple regression analysis showed that time spent playing violent video games was positively related to physical fights and antisocial behaviors. This is consistent with a meta-analytic review that found similar violent video game effects for people whether they lived in Western or Eastern countries.
Comprar The Aging Auditory System | Gordon-Salant, S. | 9781441909923 | Springer

Tienda online donde Comprar The Aging Auditory System al precio 109,67 € de Gordon-Salant, S. | Frisina, R.D. | Fay, R.R. | Popper, A.N. , tienda de Libros de Medicina, Libros de Neurologia - Neurologia general
Identi cation of Scenes in News Video from Image Features of Background Region

In this paper, we will introduce a content identi cation method based on character region segmentation, which will be used for automatic news video indexing. As a result of a preliminary scene identi cation experiment based on background region image feature analysis, the proposed method showed higher relativeness between the evaluation data and the most related training data.
Distributed Frequent Itemsets Mining in Heterogeneous Platforms

Huge amounts of datasets with different sizes are naturally distributed over the network. In this paper we propose a distributed algorithm for frequent itemsets generation on heterogeneous clus- ters and grid environments. In addition to the disparity in the performance and the workload capacity in these environments, other constraints are related to the datasets distribution and their nature, and the middleware structure and overheads. The proposed approach uses a dynamic workload manage- ment through a block-based partitioning, and takes into account inherent characteristics of the Apriori algorithm related to the candidate sets generation. The proposed technique greatly enhances the per- formance and achieves high scalability compared to the existing distributed Apriori-based approaches. This approach is evaluated on large scale datasets distributed over a heterogeneous cluster. Since the generation task of a distributed implementation of frequent itemsets mining is very com- putationally expensive, any unbalanced workload and multiple synchronization constraints will signifi- cantly degrade the performance. In this paper, we propose a new efficient distributed approach to deal with the heterogeneity and data distributions. This technique is based on an asynchronous dynamic
Concepts and Techniques in Working with Juvenile Sex Offenders

Abstract This article deals with alternative techniques in working with juvenile sex offenders in a group modality. Our data is based primarily on work in the Saint Anne Institute Juvenile Sex Offender Project (JSOP), which currently provides three ongoing groups serving clients in the cognitive developmental age range of ten to nineteen. Each candidate for group is selected following a detailed assessment procedure in which psychosocial development, family processes, and past involvement with legal systems are examined. Each potential group member is interviewed to assess his/her level of admittance to the crime, ability to portray empathy for the victim, ability to identify a problem related to the sexual abuse, and ability to develop a plan to prevent further abuse. These criteria not only allow the individual into JSOP, but are also utilized throughout treatment to assess progress in the program. A major focus of this article is the theoretical framework noted to be effective in working with juvenile .
Managing the Adaptive Processing of Distributed Multimedia Information

htmlabstractThe term multimedia conjures up visions of desktop computers reproducing digital movies, high-resolution images and stereo sound. While many current systems support such functionality, none do so elegantly-especially when data is fetched and synchronized from dissimilar sources distributed across resource-limited networks. Our research investigates general approaches for managing the flow of multimedia information in a distributed computing environment, providing adaptive support for time-sensitive retrieval and presentation based on multimedia document specifications. The benefit of our approach is that it provides flexible, content-based utilization of resources without overburdening the application author/developer.
Towards an Assessment Method for Software Reuse Capability (Short Paper)

The growing interest of software reuse by software organizations makes adoption and evaluation of reuse an essential activity. Many organizations struggle in their attempts to select appropriate reuse practices (methods, techniques and tools support) in their processes. In this way, we propose a reuse assessment method to evaluate the organization's capability of software reuse in an efficient way.
Multiscale representation for partial face recognition under near infrared illumination

Near infrared (NIR) partial face images acquired in iris recognition in less constrained environments contain plentiful identity information which have not been fully exploited. In this paper, a NIR partial face recognition (PFR) algorithm is designed according to the characteristics of NIR partial face images acquired in iris recognition systems. In the preprocessing stage, the eye corners are utilized for alignment because the eye regions are usually visible. In the feature representation stage, highly compact and discriminatory features are extracted based on Multiscale Double Supervision Convolutional Neural Network (MD-SCNN) from several multiscale patches which are cropped from the aligned images. Weights of each multiscale patch are learned to improve the performance of PFR. Finally, the dissimilarity between two partial face images is calculated as the weighted t2 distance between corresponding patches. A new NIR partial face (NIR-PF) database is constructed for extensive evaluation, which includes 5300 images acquired from 276 subjects when they walk towards an iris imaging device. Experimental results on the NIR-PF and CASIA-IrisV4-Distance databases demonstrate the effectiveness and efficiency of the proposed algorithm.
Impact of restricted forward greedy feature selection technique on bug prediction

Several change metrics and source code metrics have been introduced and proved to be effective in bug prediction. Researchers performed comparative studies of bug prediction models built using the individual metrics as well as combination of these metrics. In this paper, we investigate the impact of feature selection in bug prediction models by analyzing the misclassification rates of these models with and without feature selection in place. We conduct our experiments on five open source projects by considering numerous change metrics and source code metrics. And this study aims to figure out the reliable subset of metrics that are common amongst all projects.
Global data association for the Probability Hypothesis Density filter using network flows

The Probability Hypothesis Density (PHD) filter is an efficient formulation of multi-target state estimation that circumvents the combinatorial explosion of the multi-target posterior by operating on single-target space without maintaining target identities. In this paper, we propose a multi-target tracker based on the PHD filter that provides instantaneous state estimation and delayed decision on data association. For this purpose, we reformulate the PHD recursion in terms of single-target track hypotheses and solve a min-cost flow network for trajectory estimation where measurement likelihoods and transition probabilities are based on multi-target state estimates. In this manner, the presented approach combines global data association with efficient multi-target filtering. We evaluate the approach on a publicly available pedestrian tracking dataset to present state estimation and data association capabilities.
Beyond Service Production: Volunteering for Social Innovation

Building on theories from different fields, we discuss the roles that volunteers can play in the generation, implementation, and diffusion of social innovations. We present a study relying on 26 interviews with volunteer managers, other professionals, volunteers, and one former volunteer in 17 (branches of) third sector organizations in eight European countries. We identify organizational factors that help and hinder volunteer contributions to social innovation. While volunteer contributions to social innovations are encouraged by decentralized organizational structures, systematic “scaling up” of ideas, providing training, and giving a sense of ownership, they are hindered by a reluctant attitude and a lack of resources . This rich, explorative study makes it a fruitful start for further research on the relationship between volunteering and social innovation.
A Simple New Method for Analyzing Gapped Time Series: Search for a High-frequency Cutoff in the X-Ray Power Spectrum of the Seyfert Galaxy MCG –6–30–15

We present a new, simple method for analyzing gapped time series that is particularly suited to probe the X-ray power spectra of active galactic nuclei (AGNs). The method is applied to a 4 day observation of the Seyfert 1 galaxy MCG -6-30-15 with ASCA (Advanced Satellite for Cosmology and Astrophysics). MCG -6-30-15 is well known for rapid, large-amplitude X-ray variability and was one of the first AGNs to show evidence of relativistic effects in its Fe-K line profile. In this source, our method probes the power spectrum in the range 10-3-5×10−2 Hz, thus extending the high-frequency coverage by a factor of over 50 compared to previous studies. The ASCA data rule out a cessation of variability up to 1.6×10−2 Hz at greater than 90% confidence. The power-law index of the power spectrum in this range is consistent with ~-1.4. At 2.1×10−2 Hz we can place an upper limit of 7% on the intrinsic fluctuations of the source luminosity at greater than 99% confidence. Above this frequency Poisson noise becomes dominant, but we can place an upper limit of 10% on intrinsic fluctuations up to 5×10−2 Hz at 98.9% confidence. The data argue against orbital modulation as the origin of the variability since it would imply upper limits on the mass of the putative black hole of less than 5×105 M☉, and therefore super-Eddington luminosities.
Hysteresis in the linearized Landau-Lifshitz equation

The Landau-Lifshitz equation describes the behaviour of magnetization inside a ferromagnetic object. It is known that the Landau-Lifshitz equation has an infinite number of stable equilibrium points. The existence of multiple stable equilibria is closely related to hysteresis. This is a phenomenon that is often characterized by a looping behaviour; however, the existence of a loop is not sufficient to identify hysteretic systems, but is defined more precisely as the presence of looping as the frequency of the input goes to zero. We describe these two approaches to identification of hysteresis and demonstrate that both the linear and nonlinear Landau-Lifshitz equations exhibit hysteresis. The presence of hysteresis in the linear Landau-Lifshitz equation, as well as in a simpler system also described here, indicates that nonlinearity is not necessary for hysteresis to exist.
Modélisation conjointe de l'infrastructure et des processus pour l'administration pro-active de l'entreprise distribuée

Dans un contexte economique instable, les organisations doivent s'adapter rapidement pour rester competitives. Pour cela, l'infrastructure de fonctionnement et les modeles d'entreprise de l'organisation doivent etre optimises pour repondre aux besoins reels de l'organisation. Pour parvenir a cette optimisation, une demarche envisageable consiste a creer des boucles de retro-action entre les vues theoriques de l'entreprise et son fonctionnement reel. Il est necessaire pour cela d'exploiter et de correler les informations provenant de l'activite de modelisation de l'entreprise et de l'activite d'administration de son infrastructure. Nous avons etabli dans ce but un modele de donnees global permettant de structurer les donnees collectees. Il permet d'arriver a une complementarite de la modelisation d'entreprise et de la gestion de son infrastructure de fonctionnement pour atteindre deux objectifs : l'administration proactive des infrastructures et la reconstruction des modeles d'entreprise en vue de leur optimisation. Le second probleme est de collecter les donnees pertinentes dans le SI pour effectuer la propre administration de ce-dernier et reconstruire les modeles d'entreprise. Nous avons pour cela utilise une architecture de collecte et d'exploitation des donnees a base d'agents mobiles convenant a un systeme informatique fortement distribue, heterogene, de grande envergure et de connectivite instable. Les resultats de l'application du systeme de collecte sur un prototypage d'organisation virtuelle montre qu'il est possible, grâce a un parametrage judicieux des agents, de detecter et de reconstruire les pratiques collaboratives reelles entre les organisations, ce qui permet par la suite de re-parametrer leurs infrastructures en consequence. Ainsi, nous avons montre qu'il etait possible de mettre en place des boucles de retro-action entre les modeles d'entreprise et les infrastructures de fonctionnement, ce qui permet d'effectuer une optimisation globale de l'organisation.
End-to-End Neural Sentence Ordering Using Pointer Network

Sentence ordering is one of important tasks in NLP. Previous works mainly focused on improving its performance by using pair-wise strategy. However, it is nontrivial for pair-wise models to incorporate the contextual sentence information. In addition, error prorogation could be introduced by using the pipeline strategy in pair-wise models. In this paper, we propose an end-to-end neural approach to address the sentence ordering problem, which uses the pointer network (Ptr-Net) to alleviate the error propagation problem and utilize the whole contextual information. Experimental results show the effectiveness of the proposed model. Source codes and dataset of this paper are available.
Human-Robot communication based on sensor information

The paper proposes a new communication system named SOBAR which employs environmental sensors for achieving human-robot interaction with physical world objects or environmental affairs. However, there is the uncertainty of human’s attention when he/she interacts with the objects. The uncertainty causes misinterpretation of sensor information. The mechanism resolves the uncertainty by drawing human’s attention to an aspect of a selected object with a robot’s utterance and its gesture. After drawing his/her attention, it determines the interpretation of sensor information. Moreover, the interpretation based on established attention achieves variety of interaction with the same sensor data.