Miracle-dz/UniFAR-FTUs huggingface dataset:
Divergence of the Response Induced by Xenogenic Immunization in the Sepsis Survival of Rats

We have previously described that boosted natural xenoantibodies in rats cross-react to bacteria by targeting carbohydrate antigens. This type of immunization is associated with reduced survival after cecal ligation and puncture (CLP). In the present study, we investigated further this phenomenon by immunizing Lewis rats with three intraperitoneal injections, every other day, of hamster blood compared to saline-injected control animals. One day after the last injection, CLP was performed to produce a low-grade sepsis. Induction of xenoantibodies was associated with a reduction in animal survival after CLP relative to controls (45% vs. 90%, p<0.01). No bacterial blood load was observed after CLP in this model either with or without xenoantibody enhancement, indicating that the augmented mortality was not mediated by a direct effect of boosted xenoantibodies over blood bacteria. Nevertheless, the xenoimmunization produced a systemic inflammatory response in all rats. Additionally, a lack of weight gain at the time of CLP was present in animals that died after the procedure, which was not observed in surviving rats and controls. The cytokine profile at the time of CLP in animals that died after the procedure was characterized by an increase in the serum level of several cytokines, particularly adipokines. In contrast, the cytokine profile at CLP of xenoimmunized rats that survived the procedure was characterized by a reduction in the level of cytokines. In conclusion, this study failed to show a direct effect of boosted xenoantibodies over blood bacterial isolates as cause for the decreased survival after CLP. However, it evidenced that non-infectious systemic inflammation may lead to a pattern of augmented cytokines, particularly adipokines, which impairs survival after subsequent CLP. Therefore, the profile of cytokines existing before the infectious insult appears more crucial than that resulting from the condition for the outcome of sepsis.
Quotients of products of curves, new surfaces with pg = 0 and their fundamental groups.

We construct many new surfaces of general type with q = pg = 0 whose canonical model is the quotient of the product of two curves by the action of a finite group G, constructing in this way many new interesting fundamental groups which distinguish connected components of the moduli space of surfaces of general type. We indeed classify all such surfaces whose canonical model is singular (the smooth case was classified in an earlier work). As an important tool we prove a structure theorem giving a precise description of the fundamental group of quotients of products of curves by the action of a finite group G.
A credibility measurement method of smart grid data

The requirement of the credibility is analyzed for smart grid data and the traditional theories of data measurement and bad data identification are studied in power system. On the basis of that, this paper mainly studies the credibility between data sources, the construction of the trusted network generation algorithm, and the hierarchical and dynamic trusted network model innovatively which is based on the relationship between data sources. The credibility evaluation of data sources, the credibility evaluation between data sources and the credibility evaluation of data are studied. The credibility between data sources is restricted by the credibility of data sources, the credibility of data sources is restricted both from the credibility of data and the credibility between data sources, the credibility of data is restricted by the credibility between data sources and the credibility of data sources, they are interrelated and restricted by each other and constitute to a whole. The simulation results show that the model can satisfy the requirement of the credibility of smart grid data better, and provide the ideas to solve the problem of the credibility measure for prospective research is feasible.
Vehicle Color Recognition With Spatial Pyramid Deep Learning

Color, as a notable and stable attribute of vehicles, can serve as a useful and reliable cue in a variety of applications in intelligent transportation systems. Therefore, vehicle color recognition in natural scenes has become an important research topic in this area. In this paper, we propose a deep-learning-based algorithm for automatic vehicle color recognition. Different from conventional methods, which usually adopt manually designed features, the proposed algorithm is able to adaptively learn representation that is more effective for the task of vehicle color recognition, which leads to higher recognition accuracy and avoids preprocessing. Moreover, we combine the widely used spatial pyramid strategy with the original convolutional neural network architecture, which further boosts the recognition accuracy. To the best of our knowledge, this is the first work that employs deep learning in the context of vehicle color recognition. The experiments demonstrate that the proposed approach achieves superior performance over conventional methods.
Performance assessment and rubrics in art education: A study of student perceptions

Performance assessment is widely applied in university arts courses. However, rubrics are only rarely used in this context. The introduction of new assessment methodologies in the field of arts education has met with some resistance. There is a concern that the specificity of these disciplines requires a kind of connoisseurship model which recently developed assessment methods are unable to provide. This article describes the implementation of a rubrics-based performance assessment in a course taught on the Fine Arts, Design and Conservation/Restoration undergraduate degrees. We performed an empirical study of 94 students’ opinions of the use of rubrics. The calculation of basic statistics and correlation coefficients showed that the rubrics helped students to understand the learning environment and to plan and develop their work, and promoted high cognitive learning. However, their usefulness in self-and peer assessment was rated less positively.
Monetary Policy Lag, Zero Lower Bound, and Inflation Targeting

Although the concept of monetary policy lag has historical roots deep in the monetary economics literature, relatively little attention has been paid to the idea. In this paper, we build on Svensson’s (1997) inflation targeting framework by explicitly taking into account the lagged effect of monetary policy and characterize the optimal monetary policy reaction function both in the absence and in the presence of the zero lower bound on the nominal interest rate. We numerically show the function to be more aggressive and more pre-emptive with the lagged effect than without it. We also characterize the long-run stabilization cost to the central bank by explicitly taking into account the lagged effect of monetary policy. It turns out that, in the presence of the zero lower bound constraint, the long-run stabilization cost is higher with the lagged effect than the case without it. This result suggests that the central bank and/or the government should set a relatively high inflation target when confronted with a relatively long monetary policy lag. This can be interpreted as another justification for targeting a positive inflation rate in the long-run.
Decomposing the Foster-Greer-Thorbecke Index of Vulnerability to Poverty

This paper applies the decomposition of the Foster-Greer-Thorbecke poverty index to the measurement of the individual vulnerability to poverty. I highlight that poverty risk can be expressed as a function of expected incidence, expected intensity and expected downward variability. An empirical illustration is provided using the British Household Panel Survey (BHPS) and the Survey on Household Income and Wealth (SHIW).
A Classification Framework for Approaches and Methodologies to make Web Services Compositions Reliable

Individual web services can be composed together to form composite services representing business process workflows. The value of such workflows is directly influenced by the reliability of the composite services. There is considerable research concerned with reliability of web services compositions. There is, however, no clear definition of reliability that establishes its scope in the context of a composite service. We propose a definition of composite service reliability in this paper that takes into account different aspects affecting reliability of a composite service. We also put this definition to use as the basis of a framework that can be used for classification of approaches and associated methodologies for making composite services reliable. The framework can be used to identify which aspect of composite service reliability is addressed by a particular approach. We will also reference some selected methodologies to classify them according to the aspect of reliability they address. A definition of composite service reliability and its use to classify methodologies for composite service reliability as described in this paper will prove useful for comparing and evaluating methodologies for making composite services reliable, and will also have a bearing on the quality of service aspects of architectural styles and methodologies of software solutions based on web services compositions.
Performance analysis of multinomial distribution of group assignment framed ALOHA for multiple RFID

In this paper, we present the analysis of the new transmission algorithm of RFID in form of multichannel which has been developed from frame slotted ALOHA protocol. By using concept of the tag transmission group division which differ in slot access ability and the multinomial distribution foundation for calculate each probability of successful transmission slots. And compare with the ordinary frame slotted ALOHA after.
Adaptive Control of a Production and Maintenance System with Unknown Deterioration and Obsolescence Rates

This paper deals with a continuous-time model of a production and maintenance system whose performance deteriorates over time. The problem of adaptive control of a production-maintenance system with unknown deterioration rate is presented. Using a feedback the scheduled production rate, preventive maintenance rate, and updating rule of deterioration and obsolescence rates will be derived.
Testing and Analysis of the HRV Signals from Wearable Smart HRV Sensors

The objective of the test procedure is to obtain bio signals from Photoplethysmograph and Electrocardiograph sensors on selected consumer devices and to statistically validate the data for use with a drowsiness estimation method.The method selected for validation uses LF/HF ratio calculated by a set of R-R interval data to estimate drowsiness state of a human. The value LF to HF ratio calculates balance between sympathetic and parasympathetic activity that can be measured from HRV (Heart rate variability) signals. The statistical data collected are processed by using Fast Fourier Transform and HRV frequency domain analysis on a set of test participants.There is a correlation between medical ECG equipment control output and Matlab tool’s HRVAS (Burg) output of data processed from ECG based wearable smart sensor when the LF/HF ratio is calculated in all observed volunteer data. The results for Photoplethysmograph sensors of this test correlate with other tested tools but level of the values is lower, and data from optical biosensor devices which are designed to measure HRV time-domain properties as pulse did not confirm with ECG equipment results for frequency-domain analysis required for use with selected drowsiness estimation method. The result affecting factors are sensor placement, motion artefacts and discrete vendor-specific signal pre-processing of wearable device output data.The following results confirm the use of consumer grade biosensor that produces discretely pre-processed R-R interval data for the frequency based HRV method and application validation against directly processed ECG data from certified medical equipment.
Improvement of the Data Mining Algorithm of Rough Set under the Framework of Map/Reduce

In order to solve the problem that there is a shortage of space and computing power of the traditional spatial data mining algorithm during the processing for massive spatial data information, a combination of Rough set and distributed framework is used in the process of spatial data mining. In this paper, parallel improvement is taken into the algorithm of the traditional Rough set for spatial data mining based on the basic theory of rough set and the Map/Reduce framework, which is efficient and cheap. Then, a spatial data example is utilized to show the feasibility of the improved parallel algorithm. Empirical results show that the improved parallel algorithm of Rough set for spatial data mining can not only effectively improve the efficiency of the algorithm but also meet the need of people to deal with massive spatial data which is hardly to the algorithm of traditional Rough set. Improved Rough set parallel algorithm for spatial data mining can effectively solve the problem of shortage for massive spatial data storage and computing power mining.
Analytical Design of Compact Dual-Band Filters Using Dual Composite Right-/Left-Handed Resonators

A new kind of compact dual-mode dual-composite right-/left-handed resonators is proposed. Dual-band bandpass filters (BPFs) are consequently designed with over 88% size reduction. To assist the filter design, an analytical design method is further presented, with which one filter can be designed flexibly according to the desired frequencies and bandwidths (BWs). To alleviate the design difficulties, a synthesis design technique and a feedback-based optimization process are both employed. In addition, the independent control of frequencies, BWs, and coupling coefficients are also realized, which provide large freedom for designing BPFs. To ensure the feasibility of the design method, the valid ranges of center frequency and BW ratios are first determined. Furthermore, four compact BPFs are designed with a large/small center frequency ratio (3.11/1.6) and a large/small BW ratio (2.52/0.52). Good agreement between the measurements and simulations are observed, indicating the validity of the proposed design method.
Cross channel optimized marketing by reinforcement learning

The issues of cross channel integration and customer life time value modeling are two of the most important topics surrounding customer relationship management (CRM) today. In the present paper, we describe and evaluate a novel solution that treats these two important issues in a unified framework of Markov Decision Processes (MDP). In particular, we report on the results of a joint project between IBM Research and Saks Fifth Avenue to investigate the applicability of this technology to real world problems. The business problem we use as a testbed for our evaluation is that of optimizing direct mail campaign mailings for maximization of profits in the store channel. We identify a problem common to cross-channel CRM, which we call the Cross-Channel Challenge, due to the lack of explicit linking between the marketing actions taken in one channel and the customer responses obtained in another. We provide a solution for this problem based on old and new techniques in reinforcement learning. Our in-laboratory experimental evaluation using actual customer interaction data show that as much as 7 to 8 per cent increase in the store profits can be expected, by employing a mailing policy automatically generated by our methodology. These results confirm that our approach is valid in dealing with the cross channel CRM scenarios in the real world.
Tiled Display System for Improved Communication Efficiency

Visual information such as images, graphs and figures are a very efficient way to share information between members of a team. Tiled display is one-way of gathering and presenting a large amount of information simultaneously to multiple users. In this paper we introduce a new method of using tiled display system for information sharing between users to improve communication efficiency
Use the palmprint for identification and authentication of persons: a new real-time method of data treatment based on graph construction of spatial interest points

The identification and authentication of individuals by their palmprints is a recent approach in the family of biometric modalities, very interesting in appearance and non-contact non-intrusive. It was recently studied in topic research over the last decade. The proposed approaches are mostly based on methods of classification and learning. However, the complexity of the calculations leads to an inappropriate application in real time. In our work, we investigated the use of basic primitives pictures more precisely the Space Interest Points (SIO) in a real-time process of identification and authentication of palmprint. This process is based on construction and matching of graph. By setting few constraints and working with matching methods and matching specific, experimental results suggest a robust real-time solution as good as the best methods with an error rate authentication below 1% for a population of 20 individuals.
The association between borderline personality disorder and somatoform disorders: A systematic review and meta‐analysis

s were read for evidence that the studies met inclusion criteria, which were as follows: (1) The study was empirical, (2) a sample of human participants was used (i.e., it was not a case study), (3) there were references to BPD and one or more somatoform disorders, (4) there was evidence that the cooccurrence of BPD and somatoform disorders was examined, and (5) the article was in English or French. Studies were excluded that (1) employed non-DSM or non-ICD criteria for BPD or somatoform disorders, (2) used samples of children or youth because of the difficulties diagnosing BPD before adulthood, or (3) used samples limited by additional comorbidities or selection criteria, such as those with substance use disorders or prisoners. The abstracts were reviewed for their consistency with the inclusion criteria, absence of exclusion criteria, and were judged as exclude, or inconclusive, to await examination of the entire article. One author reviewed all of the abstracts, and the other author reviewed a randomly selected 10% of the PsycINFO abstracts (n = 37): They achieved 100% agreement on their judgments. 4.1. 2 | Full article inclusion and exclusion criteria In addition to the inclusion and exclusion criteria at the level of the abstract reviews, full articles were reviewed for 4 of 17 | SCHMALING AND FALES
Energy preserving matching of sensor network topology to dependency graph of the observed field

The goal of this paper is to design the topology of a wireless sensor network observing a Markov random field in order to match the structure of the dependency graph of the observed field, under constraints on the energy spent to maintain the sensor network connectivity. The approach is important to enable the implementation of belief propagation algorithms at the network level, to achieve optimal decisions requiring simple message passing mechanisms among nearby nodes. Our main task is to recover the sparsity of the dependency graph and to replicate it at the sensor network level, under the constraint of limiting the transmit power necessary to establish the link among the nodes. To avoid the computational burden of the combinatorial problem associated to the topology design, we devise ad hoc relaxation techniques that allow us to achieve the solution through efficient algorithms based on difference of convex problems.
Reconstructing ecological networks with hierarchical Bayesian regression and Mondrian processes

Ecological systems consist of complex sets of interactions among species and their environment, the understanding of which has implications for predicting environmental response to perturbations such as invading species and climate change. However, the revelation of these interactions is not straightforward, nor are the interactions necessarily stable across space. Machine learning can enable the recovery of such complex, spatially varying interactions from relatively easily obtained species abundance data. Here, we describe a novel Bayesian regression and Mondrian process model (BRAMP) for reconstructing species interaction networks from observed field data. BRAMP enables robust inference of species interactions considering autocorrelation in species abundances and allowing for variation in the interactions across space. We evaluate the model on spatially explicit simulated data, produced using a trophic niche model combined with stochastic population dynamics. We compare the model’s performance against L1-penalized sparse regression (LASSO) and non-linear Bayesian networks with the BDe scoring scheme. Finally, we apply BRAMP to real ecological data.
Hardware support for Local Memory Transactions on GPU Architectures

Graphics Processing Units (GPUs) are popular hardware accelerators for data-parallel applications, enabling the execution of thousands of threads in a Single Instruction Multiple Thread (SIMT) fashion. However, the SIMT execution model is not efficient when code includes critical sections to protect the access to data shared by the running threads. In addition, GPUs offer two shared spaces to the threads, local memory and global memory. Typical solutions to thread synchronization include the use of atomics to implement locks, the serialization of the execution of the critical section, or delegating the execution of the critical section to the host CPU, leading to suboptimal performance. In the multi-core CPU world, transactional memory (TM) was proposed as an alternative to locks to coordinate concurrent threads. Some solutions for GPUs started to appear in the literature. In contrast to these earlier proposals, our approach is to design hardware support for TM in two levels. The first level is a fast and lightweight solution for coordinating threads that share the local memory, while the second level coordinates threads through the global memory. In this paper we present GPU-LocalTM as a hardware TM (HTM) support for the first level. GPU-LocalTM offers simple conflict detection and version management mechanisms that minimize the hardware resources required for its implementation. For the workloads studied, GPU-LocalTM provides between 1.25-80X speedup over serialized critical sections, while the overhead introduced by transaction management is lower than 20%.
Entropy in Primary Sensory Areas Lower than in Associative Ones: The Brain Lies in Higher Dimensions than the Environment

The brain, rather than integrate sensory inputs and concentrate them into concepts as currently believed, appears to increase the complexity from the perceived object to the idea of it. Topological models predict indeed an increase in dimensions and symmetries from the environment to the higher activities of the brain. Models predict that informational entropy in the primary sensory areas must be lower than in the higher associative ones. In order to demonstrate the novel hypothesis, we introduce a method for the measurement of information in fMRI neuroimages, i.e., nucleus clustering’s Rényi entropy derived from strong proximities in feature-based Voronoï tessellations, e.g., maximal nucleus clustering. The technique facilitates the objective detection of entropy/information in zones of fMRI images generally not taken into account. We found that the Rényi entropy is higher in associative cortices than in the visual primary ones. It suggests that the brain lies in higher dimensions than the environment and that it does not concentrate, but rather dilutes the message coming from external inputs.