The Fermi surface of underdoped high-T_c superconducting cuprates

The coexistence of $\pi$-flux state and d-wave RVB state is considered in this paper within the slave boson approach. A critical value of doping concentration $\delta_c$ is found, below which the coexisting $\pi$-flux and d-wave RVB state is favored in energy. The pseudo Fermi surface of spinons and the physical electron spectral function are calculated. A clear Fermi-level crossing is found along the (0,0) to ($\pi$, $\pi$) direction, but no such crossing is detected along the ($\pi$, 0) to ($\pi$, $\pi$) direction. Also, an energy gap of d-wave symmetry appears at the Fermi level in our calculation. The above results are in agreement with the angle-resolved photoemission experiments which indicate at a d-wave pseudo-gap and a half-pocket-like Fermi surface in underdoped cuprates.Comment: 18 pages RevTex, 6 figures in PS file

An integrated bayesian approach for effective multi-truth discovery

Truth-finding is the fundamental technique for corroborating reports from multiple sources in both data integration and collective intelligent applications. Traditional truthfinding methods assume a single true value for each data item and therefore cannot deal will multiple true values (i.e., the multi-truth-finding problem). So far, the existing approaches handle the multi-truth-finding problem in the same way as the single-truth-finding problems. Unfortunately, the multi-truth-finding problem has its unique features, such as the involvement of sets of values in claims, different implications of inter-value mutual exclusion, and larger source profiles. Considering these features could provide new opportunities for obtaining more accurate truthfinding results. Based on this insight, we propose an integrated Bayesian approach to the multi-truth-finding problem, by taking these features into account. To improve the truth-finding efficiency, we reformulate the multi-truthfinding problem model based on the mappings between sources and (sets of) values. New mutual exclusive relations are defined to reflect the possible co-existence of multiple true values. A finer-grained copy detection method is also proposed to deal with sources with large profiles. The experimental results on three real-world datasets show the effectiveness of our approach.Xianzhi Wang, Quan Z. Sheng, Xiu Susie Fang, Lina Yao, Xiaofei Xu, Xue L

Approximate truth discovery via problem scale reduction

Many real-world applications rely on multiple data sources to provide information on their interested items. Due to the noises and uncertainty in data, given a specific item, the information from different sources may conflict. To make reliable decisions based on these data, it is important to identify the trustworthy information by resolving these conflicts, i.e., the truth discovery problem. Current solutions to this problem detect the veracity of each value jointly with the reliability of each source for every data item. In this way, the efficiency of truth discovery is strictly confined by the problem scale, which in turn limits truth discovery algorithms from being applicable on a large scale. To address this issue, we propose an approximate truth discovery approach, which divides sources and values into groups according to a userspecified approximation criterion. The groups are then used for efficient inter-value influence computation to improve the accuracy. Our approach is applicable to most existing truth discovery algorithms. Experiments on real-world datasets show that our approach improves the efficiency compared to existing algorithms while achieving similar or even better accuracy. The scalability is further demonstrated by experiments on large synthetic datasets.Xianzhi Wang, Quan Z. Sheng, Xiu Susie Fang, Xue Li, Xiaofei Xu, and Lina Ya

Valosin-containing protein regulates the proteasome-mediated degradation of DNA-PKcs in glioma cells.

DNA-dependent protein kinase (DNA-PK) has an important role in the repair of DNA damage and regulates the radiation sensitivity of glioblastoma cells. The VCP (valosine-containing protein), a chaperone protein that regulates ubiquitin-dependent protein degradation, is phosphorylated by DNA-PK and recruited to DNA double-strand break sites to regulate DNA damage repair. However, it is not clear whether VCP is involved in DNA-PKcs (DNA-PK catalytic subunit) degradation or whether it regulates the radiosensitivity of glioblastoma. Our data demonstrated that DNA-PKcs was ubiquitinated and bound to VCP. VCP knockdown resulted in the accumulation of the DNA-PKcs protein in glioblastoma cells, and the proteasome inhibitor MG132 synergised this increase. As expected, this increase promoted the efficiency of DNA repair in several glioblastoma cell lines; in turn, this enhanced activity decreased the radiation sensitivity and prolonged the survival fraction of glioblastoma cells in vitro. Moreover, the VCP knockdown in glioblastoma cells reduced the survival time of the xenografted mice with radiation treatment relative to the control xenografted glioblastoma mice. In addition, the VCP protein was also downregulated in ~25% of GBM tissues from patients (WHO, grade IV astrocytoma), and the VCP protein level was correlated with patient survival (R(2)=0.5222, P<0.05). These findings demonstrated that VCP regulates DNA-PKcs degradation and increases the sensitivity of GBM cells to radiation

Truth discovery via exploiting implications from multi-source data

Data veracity is a grand challenge for various tasks on the Web. Since the web data sources are inherently unreliable and may provide con icting information about the same real-world entities, truth discovery is emerging as a counter- measure of resolving the con icts by discovering the truth, which conforms to the reality, from the multi-source data. A major challenge related to truth discovery is that different data items may have varying numbers of true values (or multi-truth), which counters the assumption of existing truth discovery methods that each data item should have exactly one true value. In this paper, we address this challenge by exploiting and leveraging the implications from multi-source data. In particular, we exploit three types of implications, namely the implicit negative claims, the distribution of positive/negative claims, and the co-occurrence of values in sources' claims, to facilitate multi-truth discovery. We propose a probabilistic approach with improvement measures that incorporate the three implications in all stages of truth discovery process. In particular, incorporating the negative claims enables multi-truth discovery, considering the distribution of positive/negative claims relieves truth discovery from the impact of sources' behavioral features in the specific datasets, and considering values' co-occurrence relationship compensates the information lost from evaluating each value in the same claims individually. Experimental results on three real-world datasets demonstrate the effectiveness of our approach.Xianzhi Wang, Quan Z. Sheng, Lina Yao, Xue Li, Xiu Susie Fang, Xiaofei Xu, and Boualem Benatalla

Empowering truth discovery with multi-truth prediction

Truth discovery is the problem of detecting true values from the con icting data provided by multiple sources on the same data items. Since sources' reliability is unknown a priori, a truth discovery method usually estimates sources' reliability along with the truth discovery process. A major limitation of existing truth discovery methods is that they commonly assume exactly one true value on each data item and therefore cannot deal with the more general case that a data item may have multiple true values (or multi-truth). Since the number of true values may vary from data item to data item, this requires truth discovery methods being able to detect varying numbers of truth values from the multi source data. In this paper, we propose a multi-truth discovery approach, which addresses the above challenges by providing a generic framework for enhancing existing truth discovery methods. In particular, we redeem the numbers of true values as an important clue for facilitating multi-truth discovery. We present the procedure and components of our approach, and propose three models, namely the byproduct model, the joint model, and the synthesis model to implement our approach. We further propose two extensions to enhance our approach, by leveraging the implications of similar numerical values and values' co-occurrence informa- tion in sources' claims to improve the truth discovery accuracy. Experimental studies on real-world datasets demonstrate the effectiveness of our approach.Xianzhi Wang, Quan Z. Sheng, Lina Yao, Xue Li, Xiu Susie Fang, Xiaofei Xu, and Boualem Benatalla

Exposure to hazardous volatile organic compounds, PM 10 and CO while walking along streets in urban Guangzhou, China

Toxic air pollutants in street canyons are important issues concerning public health especially in some large Asian cities like Guangzhou. In 1998 <18% of Guangzhou citizens used public transportation modes, with a majority commuting on foot (42%) or by bicycle (22%). Of the pedestrians, 57% were either senior citizens or students. In the present study, we measured toxic air pollutants while walking along urban streets in Guangzhou to evaluate pedestrian exposure. Volatile organic compounds (VOCs) were collected with sorbent tubes, and PM 10 and CO were measured simultaneously with portable analyzers. Our results showed that pedestrian exposure to PM 10 (with an average of 303 μg m -3 for all samples) and some toxic VOCs (for example, benzene) was relatively high. Monocyclic aromatic hydrocarbons were found to be the most abundant VOCs, and 71% of the samples had benzene levels higher than 30 μg m -3. Benzene, PM 10 and CO in walk-only streets were significantly lower (p<0.05) than in traffic streets, and the differences in exposure levels between new urban streets and old urban streets were highly significant (p<0.01). Pedestrian exposure to toxic VOCs and PM 10 was higher than those reported in other public transportation modes (bus and subway). The good correlations between BTEX, PM 10 and CO in the streets indicated that automotive emission might be their major source. Our study also showed that the risk to pedestrians due to air pollution was misinterpreted by the reported air quality index based on measurement of SO 2, NO x and PM 10 in the government monitoring stations. An urban roadside monitoring station might be needed by air quality monitoring networks in large Asian cities like Guangzhou, in order to survey exposure to air toxics in urban roadside microenvironments. © 2004 Elsevier Ltd. All rights reserved

Manipulation of heat current by the interface between graphene and white graphene

We investigate the heat current flowing across the interface between graphene and hexagonal boron nitride (so-called white graphene) using both molecular dynamics simulation and nonequilibrium Green's function approaches. These two distinct methods discover the same phenomena that the heat current is reduced linearly with increasing interface length, and the zigzag interface causes stronger reduction of heat current than the armchair interface. These phenomena are interpreted by both the lattice dynamics analysis and the transmission function explanation, which both reveal that the localized phonon modes at interfaces are responsible for the heat management. The room temperature interface thermal resistance is about $7\times10^{-10}$m$^{2}$K/W in zigzag interface and $3.5\times10^{-10}$m$^{2}$K/W in armchair interface, which directly results in stronger heat reduction in zigzag interface. Our theoretical results provide a specific route for experimentalists to control the heat transport in the graphene and hexagonal boron nitride compound through shaping the interface between these two materials.Comment: accepted by EP

Negative Magnetoresistance in the Nearest-neighbor Hopping Conduction

We propose a size effect which leads to the negative magnetoresistance in granular metal-insulator materials in which the hopping between two nearest neighbor clusters is the main transport mechanism. We show that the hopping probability increases with magnetic field. This is originated from the level crossing in a few-electron cluster. Thus, the overlap of electronic states of two neighboring clusters increases, and the negative magnetoresistance is resulted.Comment: Latex file, no figur