Miracle-dz/UniFAR-FTUs huggingface dataset:
Shadow detection for vehicles by locating the object-shadow boundary

We introduce in this paper a shadow detection method for vehicles in traffic video sequences. Our method approximates the boundary between vehicles and their associated shadows by one or more straight lines. These lines are located in the image by exploiting both local information (e.g. statistics in intensity differences) and global information (e.g. principal edge directions). The proposed method does not assume a particular lighting condition, and requires no human interaction nor parameter training. Experiments on practical real-world traffic video sequences demonstrate that our method is simple, robust and efficient under traffic scenes with different lighting conditions. Accurate positioning of target vehicles is thus achieved even in the presence of cast shadows.
Learning from Noisy Data under Distributional Assumptions

We study the framework of online learning, when individual examples are corrupted by random noise, and both examples and noise type can be chosen adversarially. Previous work has shown that without knowledge of the noise distribution, it is possible to learn using a random, potentially unbounded number of independent noisy copies of each example. Moreover, it is generally impossible to learn with just one noisy copy per example. In this paper, we explore the consequences of being given some side information on the noise distribution. We consider several settings, and show how one can learn linear and kernel-based predictors using just one or two noisy views of each example, depending on the side information provided.
Brain heterotrimeric Gαi2‐subunit protein‐gated pathways mediate central sympathoinhibition to maintain fluid and electrolyte homeostasis during stress

Fluid and electrolyte homeostasis is integral to blood pressure regulation. However, the central molecular mechanisms regulating the neural control of sodium excretion remain unclear. We have demonstrated that brain Gαi2‐subunit protein pathways mediate the natriuretic response to α2‐adrenoreceptor activation in vivo. Consequently, we examined the role of brain Gαi2 proteins in the neural mechanisms facilitating fluid and electrolyte homeostasis in response to acute [i.v. volume expansion (VE)] or chronic stressful stimuli (dietary sodium restriction vs. supplementation) in conscious Sprague‐Dawley rats. Selective oligodeoxynucleotide (ODN)‐mediated down‐regulation of brain Gαi2 proteins, but not a scrambled ODN, abolished the renal sympathoinhibitory response and attenuated the natriuresis to VE. In scrambled ODN‐treated rats, chronic changes in dietary sodium intake evoked an endogenous, hypothalamic paraventricular nucleus (PVN)‐specific, decrease (sodium deficiency) or increase (sodium excess) in PVN Gαi2 proteins; plasma norepinephrine levels were inversely related to dietary sodium content. Finally, in rats treated with an ODN to prevent high salt‐induced up‐regulation of brain Gαi2 proteins, animals exhibited sodium retention, global sympathoexcitation, and elevated blood pressure. Collectively, these data demonstrate that PVN Gαi2 protein pathways play an endogenous role in maintaining fluid and electrolyte balance by controlling the influence the sympathetic nervous system has on the renal handling of sodium. —Kapusta, D. R. , Pascale, C. L. , Wainford, R D. Brain heterotrimeric Gαi2‐subunit protein‐gated pathways mediate central sympathoinhibition to maintain fluid and electrolyte homeostasis during stress. FASEB J. 26, 2776–2787 (2012). www.fasebj. org
MVS-based semi-supervised clustering

Semi-supervised clustering is a popular machine learning technique, used for challenge data categorization tasks, when some prior knowledge is available to users. In this paper, we report the empirical studies on our newly proposed semi-supervised clustering framework, which utilizes multiple viewpoints for the similarity measure, with the help of the prior knowledge. Two different MVS-based approaches are developed for knowledge given in either class labels or pair-wise constraints, namely LMVS and PMVS respectively. Extensive experimental studies performed on a few benchmark datasets demonstrate the effectiveness of the proposed methods. Comparisons are also made between LMVS and PMVS, together with a few well-known semi-supervised clustering algorithms.
The rubella virus E1 glycoprotein is arrested in a novel post-ER, pre- Golgi compartment

Evidence is accumulating that a distinct compartment(s) exists in the secretory pathway interposed between the rough ER (RER) and the Golgi stack. In this study we have defined a novel post-RER, pre-Golgi compartment where unassembled subunits of rubella virus (RV) E1 glycoprotein accumulate. When RV E1 is expressed in CHO cells in the absence of E2 glycoprotein, transport of E1 to the Golgi complex is arrested. The compartment in which E1 accumulates consists of a tubular network of smooth membranes which is in continuity with the RER but has distinctive properties from either the RER, Golgi, or previously characterized intermediate compartments. It lacks RER and Golgi membrane proteins and is not disrupted by agents which disrupt either the RER (thapsigargin, ionomycin) or Golgi (nocodazole and brefeldin A). However, luminal ER proteins bearing the KDEL signal have access to this compartment. Kinetically the site of E1 arrest lies distal to or at the site where palmitylation occurs and proximal to the low temperature 15 degrees C block. Taken together the findings suggest that the site of E1 arrest corresponds to, or is located close to the exit site from the ER. This compartment could be identified morphologically because it is highly amplified in cells overexpressing unassembled E1 subunits, but it may have its counterpart among the transitional elements of non-transfected cells. We conclude that the site of E1 arrest may represent a new compartment or a differentiated proximal moiety of the intermediate compartment.
Sensual and Sexual Marital Contentment in Parents of Small Children—A Follow-Up Study When the First Child is Four Years Old

This follow-up study explored parents' (N = 452) intimate relationships when their firstborn child was 6 months old and then 4 years later. The measurement used was a modified version of the Dyadic Adjustment Scale, DAS. The present results show that most parents responding were content sensually at 6 months postpartum in 2002 and 4 years later, in 2006. More parents had changed from being sensually content in 2002 to discontent in 2006, than the contrary, however, and the sexual contentment remained low. The average sexual frequency was low both at 6 months and at 4 years for both parents with and without additional children. Experiencing tiredness as a hindrance to having sex was perceived as a greater problem among those parents without additional children. Prospective regression analyses indicated that sexuality was significantly related to both sensual and sexual contentment 4 years later. Sexuality was also one of the most significant covariates in the cross-sectional regression analysis for sensual contentment. Since sexuality plays a central role in partner well-being, it may be an important target for helping parents with small children.
Climate-Related Local Extinctions Are Already Widespread among Plant and Animal Species

Current climate change may be a major threat to global biodiversity, but the extent of species loss will depend on the details of how species respond to changing climates. For example, if most species can undergo rapid change in their climatic niches, then extinctions may be limited. Numerous studies have now documented shifts in the geographic ranges of species that were inferred to be related to climate change, especially shifts towards higher mean elevations and latitudes. Many of these studies contain valuable data on extinctions of local populations that have not yet been thoroughly explored. Specifically, overall range shifts can include range contractions at the “warm edges” of species’ ranges (i.e., lower latitudes and elevations), contractions which occur through local extinctions. Here, data on climate-related range shifts were used to test the frequency of local extinctions related to recent climate change. The results show that climate-related local extinctions have already occurred in hundreds of species, including 47% of the 976 species surveyed. This frequency of local extinctions was broadly similar across climatic zones, clades, and habitats but was significantly higher in tropical species than in temperate species (55% versus 39%), in animals than in plants (50% versus 39%), and in freshwater habitats relative to terrestrial and marine habitats (74% versus 46% versus 51%). Overall, these results suggest that local extinctions related to climate change are already widespread, even though levels of climate change so far are modest relative to those predicted in the next 100 years. These extinctions will presumably become much more prevalent as global warming increases further by roughly 2-fold to 5-fold over the coming decades.
Multi-filtering Method Based Cross-lingual Link Discovery

paper describes cross-lingual link discovery method of ISTIC used in the system evaluation task at NTCIR-9. In this year's evaluation, we participated in cross-lingual link discovery task from English to Chinese. In this paper, we mainly describe our understanding for CLLD, the key techniques of our system, and the evaluation results.
Visual latencies in areas V1 and V2 of the macaque monkey

Abstract Latencies to small flashing spots of light were measured in different layers of areas V1 and V2 in anesthetized and paralyzed macaque monkeys. The shortest latencies were found in layers 4Cα and 4B of area V1. Latencies in layer 4Cβ were on average 20 ms longer than those in 4Cα and 4B. The shortest latencies in area V2 were observed in the infragranular layers and they did not differ significantly from those found in the infragranular layers in V1. Similarly, latencies in the supragranular layers of V2 were not significantly different from those measured in the supragranular layers of V1. These results show that, in area V1, neurons of the magnocellular pathway are activated on average 20 ms earlier than those of the parvocellular pathway. Our data also suggest that much processing begins simultaneously in areas V1 and V2.
Research on the Shanghai Cooperation Organization network architecture from the big data perspective

The Shanghai Cooperation Organization (SCO) is playing an increasingly important role in many respects, such as the economic, political and security cooperation. Research results aiming at this regional organization have gained lots of attentions for a long time. The Social Network Analysis (SNA) is considered to be an effective method in the studies of international relation, especially from the Big Data perspective. Our research is mainly based on the economic and trade data among the member states of SCO in 2012. All the results have significantly shown that both Russia and China have occupied the central roles, no matter in the tables or in the figures. Due to the limitation of essential data, the importance of Russia has not been fully reflected. Based on the results, we suggests that China should pay more attention to the affairs of the region. And then the Chinese government may enhance her influence in the SCO.
ELiRF at MediaEval 2015: Query by Example Search on Speech Task (QUESST)

In this paper, we present the systems that the Natural Language Engineering and Pattern Recognition group (ELiRF) has submitted to the MediaEval 2014 Query by Example Search on Speech task. All of them are based on a Subsequence Dynamic Time Warping algorithm and do not use any other information from outside the task (zero-resources systems).
Data Aggregation for group communication in Machine-to-Machine environments

The energy resources of Machine-to-Machine (M2M) devices need to last as much as possible. Data aggregation is a suitable solution to prolong the network lifetime, since it allows the devices to reduce the amount of data traffic. In M2M systems, the M2M platform and the Constrained Application Protocol (CoAP) enable multiple entities to send concurrent data-requests to the same capillary network. For example, in a Smart Metering scenario, there are devices measuring the electricity consumption of an entire building. The supplier company requests all devices to send the data updates every 1800 seconds (i.e., 30 minutes). On the other hand, a resident requests his/her devices to communicate every 600 seconds (i.e., 10 minutes). These concurrent data-requests create heterogeneous groups over the same capillary network, since each group might be able to execute different in-network functions and to have a unique temporal-frequency of communication. However, the traditional data aggregation solutions designed for periodic monitoring assume the execution of a single static data-request during all network lifetime. This makes the traditional data aggregation solutions not suitable for M2M environments. To fill this gap, this paper presents Data Aggregation for Multiple Groups (DAMiG), which is designed to provide Data Aggregation for heterogeneous and concurrent sets of CoAP data-requests. DAMiG explores the group communication periodicity to perform internal and external-group traffic aggregation. To achieve that, DAMiG computes a suitable aggregation structure and applies statistical and merger aggregation functions along the path. DAMiG is able to reduce the energy consumption in scenarios with single or several concurrent CoAP data-requests. Moreover, the selection of internal and external-group paths takes into account the residual energy of the nodes, avoiding the paths with low residual energy.
Face liveness detection based on texture and frequency analyses

This paper proposes a single image-based face liveness detection method for discriminating 2-D paper masks from the live faces. Still images taken from live faces and 2-D paper masks were found to bear the differences in terms of shape and detailedness. In order to effectively employ such differences, we exploit frequency and texture information by using power spectrum and Local Binary Pattern (LBP), respectively. In the experiments, three liveness detectors utilizing the power spectrum, LBP, and fusion of the two were trained and tested with two databases which consist of images taken from live and four types of 2-D paper masks. One database was acquired from a web camera while the other was from the camera on the automated teller machine. Experimental results show that the proposed methods can efficiently classify 2-D paper masks and live faces.
Dependence of correct pronunciation of Chinese aspirated sounds on power during voice onset time

The length of voice onset time (VOT) in uttering Chinese aspirated sounds, which are difficult for Japanese to pronounce, is an important factor in evaluating the quality of pronunciation. In this paper, both the length of the VOT and the power used during the VOT for 21 single-vowel syllables of six different Chinese aspirates were measured for 40 Japanese students and nine native speakers of Chinese. The quality of the students' pronunciation was evaluated using a hearing test judged by eight native Chinese. The results indicated that the correlation between the quality of the students' pronunciation and the power used in uttering a sound was greater than to the VOT within a certain range of VOT which varied for different syllables. Thus, we conclude that power is also an important factor in evaluating the quality of pronunciation.
Two-dimensional model simulation of Martian atmospheric convection with condensation of the major component under fixed thermal forcing

We perform a numerical simulation of cloud convection by using two-dimensional nonhydrostatic model for the purpose of investigating the structure of cloud convection with condensation of the major component when significant high supersaturation is not allowed. In quasi-equilibrium state, horizontally uniform cloud layer is formed above the condensation level, and the dry adiabatic layer is formed below the level. The updraft and downdraft associated with convection extends from near the surface to the top of the model atmosphere. The updraft and downdraft in the cloud layer seem to be maintained by buoyancy in the dry adiabatic layer.
Eurographics/ Ieee-vgtc Symposium on Visualization (2007) Visualization of Uncertainty in Lattices to Support Decision-making

Lattice graphs are used as underlying data structures in many statistical processing systems, including natural language processing. Lattices compactly represent multiple possible outputs and are usually hidden from users. We present a novel visualization intended to reveal the uncertainty and variability inherent in statistically-derived lattice structures. Applications such as machine translation and automated speech recognition typically present users with a best-guess about the appropriate output, with apparent complete confidence. Through case studies we show how our visualization uses a hybrid layout along with varying transparency, colour, and size to reveal the lattice structure, expose the inherent uncertainty in statistical processing, and help users make better-informed decisions about statistically-derived outputs.
Towards reliable and lightweight source switching for datacenter networks

A low-latency and reliable message switching network is critical for constructing high-speed datacenter networks. In this paper, we present the design, implementation, and evaluation of a novel Location basEd Source Switching (LESS) for datacenter networks. LESS enables lightweight source switching through a location-based addressing scheme. Each switch and host can independently derive a source route to reach a destination without requiring the full knowledge of the network topology. We demonstrate that using location-based source routes as forwarding labels allows LESS to eliminate the need for routing tables and integrate with minimum required functionality for packet forwarding. Moreover, we propose a fast rerouting solution to address the issue of fault tolerance in source routing. Each switch can locally derive an alternative source route during a failure. The paper evaluates the performance of LESS. Our evaluation results suggest that LESS improves the performance of datacenter networks in terms of latency, throughput, and reliability.
Video Steganography Based on Optimized Motion Estimation Perturbation

In this paper, a novel motion vector-based video steganographic scheme is proposed, which is capable of withstanding the current best statistical detection method. With this scheme, secret message bits are embedded into motion vector (MV) values by slightly perturbing their motion estimation (ME) processes. In general, two measures are taken for steganographic security (statistical undetectability) enhancement. First, the ME perturbations are optimized ensuring the modified MVs are still local optimal, which essentially makes targeted detectors ineffective. Secondly, to minimize the overall embedding impact under a given relative payload, a double-layered coding structure is used to control the ME perturbations. Experimental results demonstrate that the proposed scheme achieves a much higher level of security compared with other existing MV-based approaches. Meanwhile, the reconstructed visual quality and the coding efficiency are slightly affected as well.
MetaRanker 2.0: a web server for prioritization of genetic variation data

MetaRanker 2.0 is a web server for prioritization of common and rare frequency genetic variation data. Based on heterogeneous data sets including genetic association data, protein–protein interactions, large-scale text-mining data, copy number variation data and gene expression experiments, MetaRanker 2.0 prioritizes the protein-coding part of the human genome to shortlist candidate genes for targeted follow-up studies. MetaRanker 2.0 is made freely available at www.cbs. dtu.dk/services/MetaRanker-2.0.
Leveraging Environmental Research and Observation Networks to Advance Soil Carbon Science

Soil organic matter (SOM) is a critical ecosystem variable regulated by interacting physical, chemical, and biological processes. Collaborative efforts to integrate perspectives, data, and models from interdisciplinary research and observation networks can significantly advance predictive understanding of SOM. We outline how integrating three networks—the Long‐Term Ecological Research with a focus on ecological dynamics, the Critical Zone Observatories with strengths in landscape/geologic context, and the National Ecological Observatory Network with standardized multiscale measurements—can advance SOM knowledge. This integration requires improved data dissemination and sharing, coordinated data collection activities, and enhanced collaboration between empiricists and modelers within and across networks.
Bioluminescence-Activated Deep-Tissue Photodynamic Therapy of Cancer

Optical energy can trigger a variety of photochemical processes useful for therapies. Owing to the shallow penetration of light in tissues, however, the clinical applications of light-activated therapies have been limited. Bioluminescence resonant energy transfer (BRET) may provide a new way of inducing photochemical activation. Here, we show that efficient bioluminescence energy-induced photodynamic therapy (PDT) of macroscopic tumors and metastases in deep tissue. For monolayer cell culture in vitro incubated with Chlorin e6, BRET energy of about 1 nJ per cell generated as strong cytotoxicity as red laser light irradiation at 2.2 mW/cm2 for 180 s. Regional delivery of bioluminescence agents via draining lymphatic vessels killed tumor cells spread to the sentinel and secondary lymph nodes, reduced distant metastases in the lung and improved animal survival. Our results show the promising potential of novel bioluminescence-activated PDT.
Deep feature learning versus shallow feature learning systems for joint use of airborne thermal hyperspectral and visible remote sensing data

ABSTRACT Recently, the development of various remote sensing sensors has provided more reliable information and data for identification of different ground classes. Accordingly, multisensory fusion techniques are applied to enhance the process of information extraction from complementary airborne and spaceborne remote sensing data. Most of previous research in the literature has focused on the extraction of shallow features from a specific sensor and on classification of the resulted feature space using decision fusion systems. In recent years, Deep Learning (DL) algorithms have drawn a lot of attention in the machine learning area and have had different remote sensing applications, especially on data fusion. This study presents two different feature-learning strategies for the fusion of hyperspectral thermal infrared (HTIR) and visible remote sensing data. First, a Deep Convolutional Neural Network (DCNN)-Support Vector Machine (SVM) was utilized on the features of two datasets to provide the class labels. To validate the results with other learning strategies, a shallow feature model was used, as well. This model was based on feature fusion and decision fusion that classified and fused the two datasets. A co-registered thermal infrared hyperspectral (HTIR) and Fine Resolution Visible (Vis) RGB imagery was available from Quebec of Canada to examine the effectiveness of the proposed method. Experimental results showed that, except for the computational time, the proposed deep learning model outperformed shallow feature-based strategies in the classification performance that was based on its accuracy.