Miracle-dz/UniFAR-FTUs huggingface dataset:
ASCOS: An Asymmetric network Structure COntext Similarity measure

Discovering similar objects in a social network has many interesting issues. Here, we present ASCOS, an Asymmetric Structure COntext Similarity measure that captures the similarity scores among any pairs of nodes in a network. The definition of ASCOS is similar to that of the well-known SimRank since both define score values recursively. However, we show that ASCOS outputs a more complete similarity score than SimRank because SimRank (and several of its variations, such as P-Rank and SimFusion) on average ignores half paths between nodes during calculation. To make ASCOS tractable in both computation time and memory usage, we propose two variations of ASCOS: a low rank approximation based approach and an iterative solver Gauss-Seidel for linear equations.When the target network is sparse, the run time and the required computing space of these variations are smaller than computing SimRank and ASCOS directly. In addition, the iterative solver divides the original network into several independent sub-systems so that a multi-core server or a distributed computing environment, such as MapReduce, can efficiently solve the problem. We compare the performance of ASCOS with other global structure based similarity measures, including SimRank, Katz, and LHN. The experimental results based on user evaluation suggest that ASCOS gives better results than other measures. In addition, the asymmetric property has the potential to identify the hierarchical structure of a network. Finally, variations of ASCOS (including one distributed variation) can also reduce computation both in space and time.
Glial Tissue Mechanics and Mechanosensing by Glial Cells

Understanding the mechanical behavior of human brain is critical to interpret the role of physical stimuli in both normal and pathological processes that occur in CNS tissue, such as development, inflammation, neurodegeneration, aging, and most common brain tumors. Despite clear evidence that mechanical cues influence both normal and transformed brain tissue activity as well as normal and transformed brain cell behavior, little is known about the links between mechanical signals and their biochemical and medical consequences. A multi-level approach from whole organ rheology to single cell mechanics is needed to understand the physical aspects of human brain function and its pathologies. This review summarizes the latest achievements in the field.
4.3.2 Assessment and modeling of NH3SnO2 interactions using individual nanowire sensors

In this work, DFT (density functional theory) calculations were applied to study the interaction between NH3 and the SnO2 surface. To that end, two types of scenarios were selected; (i) the clean stoichiometric SnO2 (110) surface and (ii) a SnO2 (110) surface with pre-adsorbed Oads. The NH3 adsorption mechanism and charge transfer to the metal oxide after the adsorption and dissociation steps of the molecule were simulated to gain a deeper insight into the NH3 sensing mechanism of this metal oxide. Making use of the intrinsic advantages of nanoscale prototypes, the analysis of the nanowires’ resistance modulation upon exposure to NH3 and their dynamic response, as function of temperature and gas concentration, allowed validating some of the theoretical results.
Weighted Optimization-Based Distributed Kalman Filter for Nonlinear Target Tracking in Collaborative Sensor Networks

The identification of the nonlinearity and coupling is crucial in nonlinear target tracking problem in collaborative sensor networks. According to the adaptive Kalman filtering (KF) method, the nonlinearity and coupling can be regarded as the model noise covariance, and estimated by minimizing the innovation or residual errors of the states. However, the method requires large time window of data to achieve reliable covariance measurement, making it impractical for nonlinear systems which are rapidly changing. To deal with the problem, a weighted optimization-based distributed KF algorithm (WODKF) is proposed in this paper. The algorithm enlarges the data size of each sensor by the received measurements and state estimates from its connected sensors instead of the time window. A new cost function is set as the weighted sum of the bias and oscillation of the state to estimate the “best” estimate of the model noise covariance. The bias and oscillation of the state of each sensor are estimated by polynomial fitting a time window of state estimates and measurements of the sensor and its neighbors weighted by the measurement noise covariance. The best estimate of the model noise covariance is computed by minimizing the weighted cost function using the exhaustive method. The sensor selection method is in addition to the algorithm to decrease the computation load of the filter and increase the scalability of the sensor network. The existence, suboptimality and stability analysis of the algorithm are given. The local probability data association method is used in the proposed algorithm for the multitarget tracking case. The algorithm is demonstrated in simulations on tracking examples for a random signal, one nonlinear target, and four nonlinear targets. Results show the feasibility and superiority of WODKF against other filtering algorithms for a large class of systems.
Optimal Regulation of Virtual Power Plants

This paper develops a real-time algorithmic framework for aggregations of distributed energy resources (DERs) in distribution networks to provide regulation services in response to transmission-level requests. Leveraging online primal-dual-type methods for time-varying optimization problems and suitable linearizations of the nonlinear AC power-flow equations, this work establishes a system-theoretic foundation to realize the vision of distribution-level virtual power plants. The optimization framework controls the output powers of dispatchable DERs such that, in aggregate, they respond to automatic generation control and/or regulation-services commands. This is achieved while concurrently regulating voltages within the feeder and maximizing customers’ and utility's performance objectives. Convergence and tracking capabilities are analytically established under suitable modeling assumptions. Simulations are provided to validate the proposed approach.
Transparent GPU memory management for DNNs

Modern DNN frameworks exploit GPU acceleration by default to achieve high performance. The limitation of GPU memory capacity becomes a serious problem because DNNs are becoming deeper and larger. This paper proposes a purely software-based transparent solution, called tvDNN, to the GPU memory capacity problem. It is based on GPU memory swapping and memory object sectioning techniques. It also provides an efficient memory-object swapping schedule based on ILP (optimal) and heuristics (suboptimal). The experimental results show that tvDNN enables Caffe to build VGG-16 with a large batch size, such as 256 or 512, using a few GB of GPU memory without significant performance degradation.
GeoBurst+

The real-time discovery of local events (e.g., protests, disasters) has been widely recognized as a fundamental socioeconomic task. Recent studies have demonstrated that the geo-tagged tweet stream serves as an unprecedentedly valuable source for local event detection. Nevertheless, how to effectively extract local events from massive geo-tagged tweet streams in real time remains challenging. To bridge the gap, we propose a method for effective and real-time local event detection from geo-tagged tweet streams. Our method, named GeoBurst+, first leverages a novel cross-modal authority measure to identify several pivots in the query window. Such pivots reveal different geo-topical activities and naturally attract similar tweets to form candidate events. GeoBurst+ further summarizes the continuous stream and compares the candidates against the historical summaries to pinpoint truly interesting local events. Better still, as the query window shifts, GeoBurst+ is capable of updating the event list with little time cost, thus achieving continuous monitoring of the stream. We used crowdsourcing to evaluate GeoBurst+ on two million-scale datasets and found it significantly more effective than existing methods while being orders of magnitude faster.
The 745.5 issue in code‐based, adult congenital heart disease population studies: Relevance to current and future ICD‐9‐CM and ICD‐10‐CM studies

OBJECTIVE Although the ICD-9-CM code 745.5 is widely used to indicate the presence of a secundum atrial septal defect (ASD), it is also used for patent foramen ovale (PFO) which is a normal variant and for "rule-out" congenital heart disease (CHD). The ICD-10-CM code Q21.1 perpetuates this issue. The objective of this study was to assess whether code 745.5 in isolation or in combination with unspecified CHD codes 746.9 or 746.89 miscodes for CHD, and if true CHD positives decrease with age. DESIGN Echocardiograms of patients with an ICD-9-CM code of 745.5 in isolation or in combination with unspecified CHD codes 746.9 or 746.89 were reviewed to validate the true incidence of an ASD. This observational, cross-sectional record review included patients between 11 and 64 years of age. RESULTS Medical charts and echocardiograms of 190 patients (47.9% males) were reviewed. The number of falsely coded patients with 745.5 (no ASD) was high (76.3%). Forty-five (23.7%) patients had a true ASD. Among the 145 patients without an ASD, 100 (52.6%) were classified as having a PFO, 37 (19.5%) had a normal non-CHD echocardiogram, and 8 (4.2%) had some other CHD anomaly. The likelihood that 745.5 coded for a true ASD was higher in children aged 11-20 (64.3%) than adults aged 21-64 years (20.6%). CONCLUSIONS This validation study demonstrates that 745.5 performed poorly across all ages. As 745.5 is widely used in population-level investigations and ICD-10-CM perpetuates the problem, future analyses utilizing CHD codes should consider separate analysis of those identified only through code 745.5.
A novel method for retinoic acid administration reveals differential and dose‐dependent downregulation of Fgf3 in the developing inner ear and anterior CNS

Endogenous retinoic acid plays critical roles in normal vertebrate development, but can be teratogenic in excess. In mice, additional retinoic acid is administered by oral gavage or intraperitoneal injection. Here we evaluate a novel non‐invasive system for administering retinoic acid via chocolate/sugar pellets. We use this delivery system to examine the role of retinoic acid in regulating the expression of the fibroblast growth factor Fgf3, and find that the timing of retinoic acid treatment is critical for its effects on Fgf3 expression. Administration of increasing amounts of retinoic acid at 7.75 dpc leads to dose‐dependent downregulation of Fgf3 in the otocyst and changes in spatial expression in the hindbrain. Detailed analysis of the developing inner ear also reveals a lateralisation of Fgf3 expression with increasing retinoic acid dose that is dependent on timing of administration. We discuss how these data impact on current models of retinoic acid patterning of the otocyst. Developmental Dynamics 241:741–758, 2012. © 2012 Wiley Periodicals, Inc.
2D nanosphere lithography by using surface plasmon-enhanced optical trapping

A two-dimensional (2D) surface plasmon (SP)-enhanced optical trapping system based on a single high numerical aperture objective has been developed. The system can be utilized to trap dielectric particles and simultaneously provide imaging. The 40-fold electric field enhancement, and hence strong 2D trapping force distribution with SP excitation through a gold film with a thickness of 45 nm in the near infrared region, was analyzed. The strong trapping force and high-resolution trapping image of nanoparticles can be concurrently achieved via the same high NA objective. The developed SP-enhanced trapping system was successfully applied to efficiently trap dielectric particles with a size down to 350 nm on a cover slip surface and allows for real-time imaging observation. Also, in order to further increase the penetration depth and the electric field of the evanescent wave, a coupled-waveguide surface plasmon resonance configuration consisting of a five-layer structure of Bk7/Au/SiO2/Au/H2O for two-dimensional optical trapping has been developed. Theoretical analysis shows that the maximum enhancement of the local electric field intensity is about 60-fold while the penetration depth is about 1 μm at the resonance angle. The trapped and aligned dielectric single layer particles were spread over a large area with a reduction in feature size to form a hexagonally close-packed (HCP) pattern on a cover slip surface. The HCP pattern has the potential for well-ordered 2D nanosphere lithography.
Light-weight Domain-based Form Assistant: Querying Web Databases On the Fly

The Web has been rapidly "deepened" by myriad searchable databases online, where data are hidden behind query forms. Helping users query alternative "deep Web" sources in the same domain (e.g., Books, Airfares) is an important task with broad applications. As a core component of those applications, dynamic query translation (i.e., translating a user's query across dynamically selected sources) has not been extensively explored. While existing works focus on isolated subproblems (e.g., schema matching, query rewriting) to study, we target at building a complete query translator and thus face new challenges: 1) To complete the translator, we need to solve the predicate mapping problem (i.e., map a source predicate to target predicates), which is largely unexplored by existing works; 2) To satisfy our application requirements, we need to design a customizable system architecture to assemble various components addressing respective subproblems (i.e., schema matching, predicate mapping, query rewriting). Tackling these challenges, we develop a light-weight domain-based form assistant, which can generally handle alternative sources in the same domain and is easily customizable to new domains. Our experiment shows the effectiveness of our form assistant in translating queries for real Web sources.
Effects of human rhinovirus on epithelial barrier integrity and function in children with asthma

Bronchial epithelial tight junctions (TJ) have been extensively assessed in healthy airway epithelium. However, no studies have yet assessed the effect of human rhinovirus (HRV) infection on the expression and resultant barrier function in epithelial tight junctions (TJ) in childhood asthma.
Mertk on tumor macrophages is a therapeutic target to prevent tumor recurrence following radiation therapy

Radiation therapy provides a means to kill large numbers of cancer cells in a controlled location resulting in the release of tumor-specific antigens and endogenous adjuvants. However, by activating pathways involved in apoptotic cell recognition and phagocytosis, irradiated cancer cells engender suppressive phenotypes in macrophages. We demonstrate that the macrophage-specific phagocytic receptor, Mertk is upregulated in macrophages in the tumor following radiation therapy. Ligation of Mertk on macrophages results in anti-inflammatory cytokine responses via NF-kB p50 upregulation, which in turn limits tumor control following radiation therapy. We demonstrate that in immunogenic tumors, loss of Mertk is sufficient to permit tumor cure following radiation therapy. However, in poorly immunogenic tumors, TGFb inhibition is also required to result in tumor cure following radiation therapy. These data demonstrate that Mertk is a highly specific target whose absence permits tumor control in combination with radiation therapy.
High Energy Milling of Zirconia: A Systematic Critical Review on the Phase Transformation

The phase transformation of zirconia from monoclinic to tetragonal polymorph at room temperature under mechanical processing has been a subject of a great interest due to technological importance of this material. Mechanism of this transformation has been widely investigated and plenty of explanation theories of zirconia stabilisation have been developed as well. This article critically reviews the systematic development regarding this transformation under mechanical processing and includes the summarised results of key-publications on this topic.
Association of the GLB1 rs4678680 genetic variant with risk of HBV-related hepatocellular carcinoma

Accumulated evidences demonstrated that GLB1 is involved in cell senescence and cancer development. The GLB1 rs4678680 single nucleotide polymorphism (SNP) has been identified as a hepatocellular carcinoma (HCC) susceptibility polymorphism by a genome-wide association study in Korean population previously. However, little or nothing was known about its involvement and functional significance in hepatitis B viruses (HBV)-related HCC in Chinese. Therefore, we investigated the association between the GLB1 rs4678680 SNP and HBV-related HCC risk as well as its biological function in vivo. Genotypes were determined in two independent case-control sets from two medical centers of China. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated by logistic regression. The potential regulation role the rs4678680 genetic variant on GLB1 expression was examined with HCC and normal liver tissues. We found that The rs4678680 G allele was showed to be risk allele; individuals with the TG genotype had an OR of 1.51 (95% CI = 1.10–2.07, P = 0.010, Shandong set) or 1.49 (95% CI = 1.11–1.99, P = 0.008, Jiangsu set) for developing HBV-related HCC, respectively, compared with individuals with the TT genotype. This association was more pronounced in males, individuals aged older than 57 years and drinkers (all P < 0.05). In the genotype-phenotype correlation analyses of fifty-six human liver tissue samples, rs4678680 TG or GG was associated with a statistically significant increase of GLB1 mRNA expression (P < 0.05). Our data indicated that the GLB1 rs4678680 SNP contributes to susceptibility to develop HBV-related HCC, highlighting the involvement of GLB1 and cell senescence in etiology of HCC.
A Mobile Data Gathering Framework for Wireless Rechargeable Sensor Networks with Vehicle Movement Costs and Capacity Constraints

Several recent works have studied mobile vehicle scheduling to recharge sensor nodes via wireless energy transfer technologies. Unfortunately, most of them overlooked important factors of the vehicles' moving energy consumption and limited recharging capacity, which may lead to problematic schedules or even stranded vehicles. In this paper, we consider the recharge scheduling problem under such important constraints. To balance energy consumption and latency, we employ one dedicated data gathering vehicle and multiple charging vehicles. We first organize sensors into clusters for easy data collection, and obtain theoretical bounds on latency. Then we establish a mathematical model for the relationship between energy consumption and replenishment, and obtain the minimum number of charging vehicles needed. We formulate the scheduling into a Profitable Traveling Salesmen Problem that maximizes profit - the amount of replenished energy less the cost of vehicle movements, and prove it is NP-hard. We devise and compare two algorithms: a greedy one that maximizes the profit at each step; an adaptive one that partitions the network and forms Capacitated Minimum Spanning Trees per partition. Through extensive evaluations, we find that the adaptive algorithm can keep the number of nonfunctional nodes at zero. It also reduces transient energy depletion by 30-50 percent and saves 10-20 percent energy. Comparisons with other common data gathering methods show that we can save 30 percent energy and reduce latency by two orders of magnitude.
Animating sand as a fluid

We present a physics-based simulation method for animating sand. To allow for efficiently scaling up to large volumes of sand, we abstract away the individual grains and think of the sand as a continuum. In particular we show that an existing water simulator can be turned into a sand simulator with only a few small additions to account for inter-grain and boundary friction.We also propose an alternative method for simulating fluids. Our core representation is a cloud of particles, which allows for accurate and flexible surface tracking and advection, but we use an auxiliary grid to efficiently enforce boundary conditions and incompressibility. We further address the issue of reconstructing a surface from particle data to render each frame.
Integrin-dependent actomyosin contraction regulates epithelial cell scattering

The scattering of Madin-Darby canine kidney cells in vitro mimics key aspects of epithelial–mesenchymal transitions during development, carcinoma cell invasion, and metastasis. Scattering is induced by hepatocyte growth factor (HGF) and is thought to involve disruption of cadherin-dependent cell–cell junctions. Scattering is enhanced on collagen and fibronectin, as compared with laminin1, suggesting possible cross talk between integrins and cell–cell junctions. We show that HGF does not trigger any detectable decrease in E-cadherin function, but increases integrin-mediated adhesion. Time-lapse imaging suggests that tension on cell–cell junctions may disrupt cell–cell adhesion. Varying the density and type of extracellular matrix proteins shows that scattering correlates with stronger integrin adhesion and increased phosphorylation of the myosin regulatory light chain. To directly test the role of integrin-dependent traction forces, substrate compliance was varied. Rigid substrates that produce high traction forces promoted scattering, in comparison to more compliant substrates. We conclude that integrin-dependent actomyosin traction force mediates the disruption of cell–cell adhesion during epithelial cell scattering.
High precision road segmentation for cover level of forward view estimation via stereo camera

To develop a safe Intelligent Transportation System (ITS) while driving on unpredictable curves or road regions, high precision road segmentation and cover level of forward view estimation for drivers is necessary. Cover level of forward view is defined as the level of difficulty in predicting the dangerousness of road edge or incoming object near road edge especially at a curve due to the obstacles at the surrounding. We focus on road segmentation as it is one of the fundamental steps in developing ITS. According to the previous studies, road region is not segmented precisely; hence a new method of road segmentation is introduced. Input images had undergone illuminant invariant space conversion to remove shadow regions effectively. Next, the bottom part of illuminant invariant image (assumed to be road surface) is sampled to get a road model, which is then applied to obtain the road probability image. Lastly, dilation and erosion using mathematical morphology operation is applied to obtain road region. Our method of road segmentation shows precision of 0.73, recall as 0.82 and F measure as 0.81. High Precision road segmentation is very important for better cover level of forward view estimation. Segmented road region can be fully utilized for curve estimation and obstacles detection and hence lead to a better performance of cover level of forward view. Based on the results obtained, further improvement in several aspects, especially from the input image or illuminant invariant image to road probability image, has to be done in order to obtain perfectly segmented road region.