Miracle-dz/UniFAR-FTUs huggingface dataset:
Compacting Discriminator Information for Spatial Trees

Cache-conscious behaviour of data structures becomes more important as memory sizes increase and whole databases fit into main memory. For spatial data, R-trees, originally designed for disk-based data, can be adopted for in-memory applications. In this paper, we will investigate how the small amount of space in an in-memory R-tree node can be used better to make R-trees more cache-conscious. We observe that many entries share sides with their parents, and introduce the partial R-tree which only stores information that is not given by the parent node. Our experiments showed that the partial R-tree shows up to 30 per cent better performance than the traditional R-tree. We also investigated if we could improve the search performance by storing more descriptive information instead of the standard minimum bounding box without decreasing the fanout of the R-tree. The partial static O-tree is based on the O-tree, but stores only the most important part of the information of an O-tree box. Experiments showed that this approach reduces the search time for line data by up to 60 per cent.
Normalized and classified feature selection in text categorization

Feature selection is a valid method to reduce the dimension of text vector in automatic text categorization system. The paper finds a defect among several normal evaluation functions based on experiments data and proposes that normalization should be taken into these methods as a necessary step. Furthermore, the paper also brings forward a new idea named classified feature selection that applies traditional evaluation function among each class. Experiments prove the validity of these two solutions.
Target Cascading for Design of Product Families

Abstract The target cascading methodology for product development is extended to product families with predefined platforms. The single-product formulation is modified to accommodate the presence of shared systems, subsystems, and/or components. Hierarchical optimization problems associated with each product variant are combined to formulate the product family multicriteria design problem, and common subproblems are identified based on the shared elements (i.e., the platform). The solution of the overall design problem is coordinated so that the shared elements are consistent with the performance and behavior of the product variants. A simple automotive design example is used to demonstrate the proposed methodology.
Diversity and Persistence of Salmonella enterica Strains in Rural Landscapes in the Southeastern United States

Salmonellosis cases in the in the United States show distinct geographical trends, with the southeast reporting among the highest rates of illness. In the state of Georgia, USA, non-outbreak associated salmonellosis is especially high in the southern low-lying coastal plain. Here we examined the distribution of Salmonella enterica in environmental waters and associated wildlife in two distinct watersheds, one in the Atlantic Coastal Plain (a high case rate rural area) physiographic province and one in the Piedmont (a lower case rate rural area). Salmonella were isolated from the two regions and compared for serovar and strain diversity, as well as distribution, between the two study areas, using both a retrospective and prospective design. Thirty-seven unique serovars and 204 unique strain types were identified by pulsed-field gel electrophoresis (PFGE). Salmonella serovars Braenderup, Give, Hartford, and Muenchen were dominant in both watersheds. Two serovars, specifically S. Muenchen and S. Rubislaw, were consistently isolated from both systems, including water and small mammals. Conversely, 24 serovars tended to be site-specific (64.8%, n = 37). Compared to the other Salmonella serovars isolated from these sites, S. Muenchen and S. Rubislaw exhibited significant genetic diversity. Among a subset of PFGE patterns, approximately half of the environmental strain types matched entries in the USA PulseNet database of human cases. Ninety percent of S. Muenchen strains from the Little River basin (the high case rate area) matched PFGE entries in PulseNet compared to 33.33% of S. Muenchen strains from the North Oconee River region (the lower case rate area). Underlying the diversity and turnover of Salmonella strains observed for these two watersheds is the persistence of specific Salmonella serovars and strain types that may be adapted to these watersheds and landscapes.
On campus network P2P and its link control

This paper, based on the survey of students' online behavior and the Netflow statistics which turned out to be the fact that P2P flow accounts for about 70% of the total compus netflow and that the Thunder connection greatly endangers the firewall load, analyzed the download principle of thunder P2P software and put forward some methods on how to control Netflow.
RAV genes: regulation of floral induction and beyond.

BACKGROUND Transcription factors of the RAV (RELATED TO ABI3 AND VP1) family are plant-specific and possess two DNA-binding domains. In Arabidopsis thaliana, the family comprises six members, including TEMPRANILLO 1 (TEM1) and TEM2. Arabidopsis RAV1 and TEM1 have been shown to bind bipartite DNA sequences, with the consensus motif C(A/C/G)ACA(N)2-8(C/A/T)ACCTG. Through direct binding to DNA, RAV proteins act as transcriptional repressors, probably in complexes with other co-repressors. SCOPE AND CONCLUSIONS In this review, a summary is given of current knowledge of the regulation and function of RAV genes in diverse plant species, paying particular attention to their roles in the control of flowering in arabidopsis. TEM1 and TEM2 delay flowering by repressing the production of two florigenic molecules, FLOWERING LOCUS T (FT) and gibberellins. In this way, TEM1 and TEM2 prevent precocious flowering and postpone floral induction until the plant has accumulated enough reserves or has reached a growth stage that ensures survival of the progeny. Recent results indicate that TEM1 and TEM2 are regulated by genes acting in several flowering pathways, suggesting that TEMs may integrate information from diverse pathways. However, flowering is not the only process controlled by RAV proteins. Family members are involved in other aspects of plant development, such as bud outgrowth in trees and leaf senescence, and possibly in general growth regulation. In addition, they respond to pathogen infections and abiotic stresses, including cold, dehydration, high salinity and osmotic stress.
Posture recognition of elbow flexion and extension using sEMG signal based on multi-scale entropy

Recognition for human elbow motion with surface electromyographic signal (sEMG) research receives more and more attention, especially in some fields like human machine interaction and measurement of human motor function due to the reason the EMG can reflect the activation of human muscle. However, continuous recognition for human elbow motion without load is still difficult due to the low signal noise ratio (SNR). In this paper, we utilized the multi-scale entropy and moving-window method to reveal the elbow motion information hidden in the filtered sEMG signals from the biceps muscle with good performance compared to the angel record derived from an inertia sensor.
Addressee identification for human-human-agent multiparty conversations in different proxemics

This paper proposes a method for identifying the addressee based on speech and gaze information, and shows that the proposed method can be applicable to human-human-agent multiparty conversations in different proxemics. First, we collected human-human-agent interaction in different proxemics, and by analyzing the data, we found that people spoke with a higher tone of voice and more loudly and slowly when they talked to the agent. We also confirmed that this speech style was consistent regardless of the proxemics. Then, by employing SVM, we proposed a general addressee estimation model that can be used in different proxemics, and the model achieved over 80% accuracy in 10-fold cross-validation.
Experimental Investigation into the Transmembrane Electrical Potential of the Forward Osmosis Membrane Process in Electrolyte Solutions

The transmembrane electrical potential (TMEP) in a forward osmosis membrane process with a single electrolyte solution as the draw and feed solutions was investigated by experiments. The effects of membrane orientation, the electrolyte species (KCl, NaCl, MgCl2, and CaCl2), concentration and concentration ratio of solutions at both sides of membrane on water flux and TMEP were investigated. The results showed that the TMEPs at different membrane orientation cannot completely coincide, which confirmed the effect of membrane asymmetry. The ion diffusion coefficients significantly affected the TMEP across the membrane, with different patterns for different electrolytes and concentrations.
Linking News Value Theory With Online Deliberation: How News Factors and Illustration Factors in News Articles Affect the Deliberative Quality of User Discussions in SNS’ Comment Sections

Previous research suggests that distinct characteristics of news articles, such as their news factors, account for the different participation rates in comment sections as well as the degree of interactivity among the discussants. In this study, this assumption is tested in the Facebook environment and extended to the analysis of how news factors (i.e., event characteristics) and illustration factors (i.e., characteristics resulting from a specific journalistic editing) of news articles predict the inclusiveness of discussions, as well as the occurrence of civility, rationality, and deliberative interactivity in user comments. A content analysis of 619 news articles and 11,218 related user comments on nine nation-wide Facebook news pages reveals that the news factors controversy, latent conflict, contravention, obtrusiveness, and impact particularly account for specific discussion qualities. The results also show that the illustration factors emotional language/visualizations, slant, and conversational prompts affect the deliberative quality of the discussions.
From Teleoperation to Autonomy : “ Autonomizing ” Non-Autonomous Robots

Many complex tasks (search and rescue, explosive ordinance disposal, and more) that eventually will be performed by autonomous robots are still performed by human operators via teleoperation. Since the requirements of teleoperation are different from those of autonomous operation, design decisions of teleoperation platforms can make it difficult to convert such platforms for autonomous use. In this paper, we discuss the differences and the potential difficulties involved in this conversion, as well as strategies for overcoming them. And we demonstrate these strategies by “autonomizing” a fully teleoperated robot designed for tasks such as bomb disposal, using an autonomous architecture that has the requisite capabilities.
Mesenchymal Stem Cell-Derived Microparticles: A Promising Therapeutic Strategy

Mesenchymal stem cells (MSCs) are multipotent stem cells that give rise to various cell types of the mesodermal germ layer. Because of their unique ability to home in on injured and cancerous tissues, MSCs are of great potential in regenerative medicine. MSCs also contribute to reparative processes in different pathological conditions, including cardiovascular diseases and cancer. However, many studies have shown that only a small proportion of transplanted MSCs can actually survive and be incorporated into host tissues. The effects of MSCs cannot be fully explained by their number. Recent discoveries suggest that microparticles (MPs) derived from MSCs may be important for the physiological functions of their parent. Though the physiological role of MSC-MPs is currently not well understood, inspiring results indicate that, in tissue repair and anti-cancer therapy, MSC-MPs have similar pro-regenerative and protective properties as their cellular counterparts. Thus, MSC-MPs represent a promising approach that may overcome the obstacles and risks associated with the use of native or engineered MSCs.
Evaluación de los déficits cognitivos asociados al traumatismo craneoencefálico

En la primera parte se revisan de modo general las caracteristicas epidemiologicas y fisiopatologicas de los traumatismos craneoencefalicos (T.C.E.), los fundamentos conceptuales y metodologicas de la neuropsicologia clinica, los principales deficit cognitivos y alteraciones emocionales postraumaticos y los instrumentos de evaluacion mas empleados. Se presentan las investigaciones realizadas para valorar la utilidad de procedimientos de evaluacion neuropsicologica novedosos en nuestro pais (Bateria de evaluacion neuropsicologica Luria-Nebraska y Escala de registro neuroconductual revisada), asi como la relacion existente entre las alteraciones encontradas y los criterios empleados para estimar la gravedad de los T.C. E. Entre los resultados obtenidos cabe destacar: a) A pesar de la variabilidad de deficit cognitivos encontrados es posible plantear un modelo general de afectacion neuropsicologica en los T.C. E. graves, constituido por deficit de atencion, lentitud en el procesamiento de la informacion y funcion motora, trastornos de memoria anterograda y dificultades en el funcionamiento ejecutivo. b) La relacion entre la severidad de las alteraciones cognitivas y emocionales no es lineal. Por contra, cuanto mayor es la gravedad del T.C. E. menores son las posibilidades de desarrollar trastornos de ansiedad y depresion, lo que esta determinado en gran medida por la menor conciencia de las limitaciones existentes.
A Test of Bargaining Theory in the Auto Retailing Industry ∗

This paper tests predictions from bargaining theory about how private information, patience, and bargaining disutility affect the division of surplus between negotiating parties. We do so using the responses to a novel survey of 1,500 new car buyers in California that asks buyers detailed questions about their bargaining behavior, their search for information, and their individual characteristics. We match the answers to this survey with detailed transaction data on the purchased car and its negotiated price. Consistent with theoretical predictions we find, first, that consumers who know the invoice price of the vehicle (a measure of the dealer’s reservation price) pay on average 0.6% less than consumers who do not. Second, we find that consumers who have an offer from a competing dealer (a measure of how much consumers know about their outside options) pay 0.6% less. Third, we find that buyers who indicated that they were patient during search paid 0.5% less than other consumers. Finally, we find that a buyer who according to our measures had the lowest bargaining disutility paid on average 1.5% less than a buyer who had the highest bargaining disutility. This paper confirms that the factors predicted to matter by bargaining theory really do—including knowledge of the other party’s position, knowledge of one’s own outside options, patience, and bargaining disutility. To the best of our knowledge, this paper is among the first to analyze how private information affects price negotiations between individuals outside the laboratory.
Development of a Continuous Phytoplankton Culture System for Ocean Acidification Experiments

Around one third of all anthropogenic CO2 emissions have been absorbed by the oceans, causing changes in seawater pH and carbonate chemistry. These changes have the potential to affect phytoplankton, which are critically important for marine food webs and the global carbon cycle. However, our current knowledge of how phytoplankton will respond to these changes is limited to a few laboratory and mesocosm experiments. Long-term experiments are needed to determine the vulnerability of phytoplankton to enhanced pCO2. Maintaining phytoplankton cultures in exponential growth for extended periods of time is logistically difficult and labour intensive. Here we describe a continuous culture system that greatly reduces the time required to maintain phytoplankton cultures, and minimises variation in experimental pCO2 treatments over time. This system is simple, relatively cheap, flexible, and allows long-term experiments to be performed to further our understanding of chronic responses and adaptation by phytoplankton species to future ocean acidification.
Role of Brain-Derived Neurotrophic Factor in Target Invasion in the Gustatory System

Brain-derived neurotrophic factor (BDNF) is a survival factor for different classes of neurons, including gustatory neurons. We have studied innervation and development of the gustatory system in transgenic mice overexpressing BDNF under the control of regulatory sequences from the nestin gene, an intermediate filament gene expressed in precursor cells of the developing nervous system and muscle. In transgenic mice, the number and size of gustatory papillae were decreased, circumvallate papillae had a deranged morphology, and there was also a severe loss of lingual taste buds. Paradoxically, similar deficits have been found in BDNF knock-out mice, which lack gustatory neurons. However, the number of neurons in gustatory ganglia was increased in BDNF-overproducing mice. Although gustatory fibers reached the tongue in normal numbers, the amount and density of nerve fibers in gustatory papillae were reduced in transgenic mice compared with wild-type littermates. Gustatory fibers appeared stalled at the base of the tongue, a site of ectopicBDNF expression, where they formed abnormal branches and sprouts. Interestingly, palatal taste buds, which are innervated by gustatory neurons whose afferents do not traverse sites of ectopic BDNF expression, appeared unaffected. We suggest that lingual gustatory deficits in BDNF overexpressing mice are a consequence of the failure of their BDNF-dependent afferents to reach their targets because of the effects of ectopically expressed BDNF on fiber growth. Our findings suggest that mammalian taste buds and gustatory papillae require proper BDNF-dependent gustatory innervation for development and that the correct spatial expression of BDNF in the tongue epithelium is crucial for appropriate target invasion and innervation.
Optic Neuritis and Myelopathy in Systemic Lupus Erythematosus

ABSTRACT: The optic neuritis of systemic lupus erythematosus (S.L.E.) more frequently results in the persistence of a central scotoma or complete blindness after a single attack than demyelinating optic neuritis, although the initial clinical presentations may be identical. A significant number of patients, however, recover normal vision. Optic neuritis may be the presenting symptom of S.L. E. and as myelopathy may also occur in the course of the disease, confusion with multiple sclerosis may result, especially if there are no arthritic, cutaneous nor visceral manifestations. We report a case of lupus optic neuritis associated with anticardiolipin antibodies and a circulating lupus anticoagulant and suggest these may be a marker for vasculitic optic neuritis and play a role in its aetiology.
Pedalvatar: An IMU-based real-time body motion capture system using foot rooted kinematic model

In this paper, we present a low-cost IMU-based system, Pedalvatar, which can capture the full-body motion of users in real-time. Unlike the prior approaches using the hip-joint as the root of forward kinematic model, a foot-rooted kinematic model is developed in this work. A state change mechanism has also been investigated to allow dynamically switching the root of kinematic trees between the left and the right foot. Benefitted from this, full-body motions can be well captured in our system as long as there is at least one static foot in the movement. The `floating' artifact of hip-joint rooted methods has been eliminated in our approach, and more complicated motions such as climbing stairs can be successfully captured in real-time. Comparing to those vision-based systems, this IMU-based system provides more flexibility on capturing outdoor motions that are important for many robotic applications.
A Multi-objective Evolutionary Methodology for an Interdependent Transportation Project Selection Problem

Most transportation planning evaluation practices involve the aggregation of impacts into a single utility function in order to be optimized. Techniques that translate different units of measure into monetary terms are highly controversial. The multi-objective transportation infrastructure project selection problem (MTPSP) can be mathematically modeled as a multi-objective 0-1 knapsack problem with some additional constraints. We solved a MTPSP for the Mexican environment using an evolutionary approach, while considering explicit modeling of interdependent projects effects (quadratic objectives). Such inter dependency relationships were estimated using a gravity model. Due to the distinctive "steps-shape" of the Pareto frontier, an evolutionary-guided search over knee points was used to avoid keeping non-attractive solutions in the final decision stage. Using context-dependent preferences and having explicit knowledge of the filtered Pareto solutions, the final choice was made using a systematic tradeoff procedure to find the one that best represented the decision maker's interests under the existing context
Deep architectures for super-symmetric particle classification with noise labelling

Deep learning algorithms modelled with deep architectures have demonstrated impressive results to alleviate common problems of Feedforward Neural Networks, i.e. escaping poor solutions of local minima. Basic architecture concepts behind these powerful models have been set up by preliminary research on Neural Networks, including the application of unsupervised pretraining algorithms and greedy layer-wise strategy and on small scale benchmark datasets. We present practical experiments to evaluate deep classifiers on large scale Super-symmetric (SUSY) particle data sets with variations of noise labelling to examine the robustness of its regularizer. Deep architecture models that are compared in this study include one single hidden layer Multilayer Perceptrons (MLP), Deep stacked Neural Networks (DNN), Stacked Denoising Autoencoders (SDAE), and Deep Belief Networks (DBN). The result shows that deep architecture models (DNN and SDAE) can reduce error gap between low-level and complete feature learning up to 78%, as compared to shallow model (MLP). In our experiment, SDAE can also maintain its superiority in less noisy data set and exhibits nearly linear convergence through the benefits of unsupervised pretraining layers and early stopping of hyperparameter learning.
The Datacatcher: Batch Deployment and Documentation of 130 Location-Aware, Mobile Devices That Put Sociopolitically-Relevant Big Data in People's Hands: Polyphonic Interpretation at Scale

This paper reports the results of a field trial of 130 bespoke devices as well as our methodological approach to the undertaking. Datacatchers are custom-built, location-aware devices that stream messages about the area they are in. Derived from a large number of 'big data' sources, the messages simultaneously draw attention to the socio-political topology of the lived environment and to the nature of big data itself. We used a service design consultancy to deploy the devices, and two teams of documentary filmmakers to capture participants' experiences. Here we discuss the development of this approach and how people responded to the Datacatchers as products, as revealing sociopolitical issues, and as purveyors of big data that might be open to question.