Miracle-dz/UniFAR-FTUs huggingface dataset:
DC-SIGN Binding Contributed by an Extra N-Linked Glycosylation on Japanese Encephalitis Virus Envelope Protein Reduces the Ability of Viral Brain Invasion

The major structural envelope (E) protein of Japanese encephalitis virus (JEV) facilitates cellular binding/entry and is the primary target of neutralizing antibodies. JEV E protein has one N-linked glycosylation site at N154 (G2 site), but the related dengue virus E protein has two glycosylation sites at N67 (G1 site) and N153 (G2 site). We generated three recombinant JEVs with different glycosylation patterns on the E protein. As compared with wild-type (WT) JEV with G2 glycosylation, viral growth in culture cells as well as neurovirulence and neuroinvasiveness in challenged mice were reduced when infected with the G1 mutant (E-D67N/N154A) with glycosylation shifted to G1 site, and the G0 mutant (E-N154A) with non-glycosylation. The G1G2 mutant (E-D67N), with E-glycosylation on both G1 and G2 sites, showed potent in vitro viral replication and in vivo neurovirulence, but reduced neuroinvasiveness. Furthermore, the JEV mutants with G1 glycosylation showed enhanced DC-SIGN binding, which may then lead to reduced brain invasion and explain the reason why WT JEV is devoid of this G1 site of glycosylation. Overall, the patterns of N-linked glycosylation on JEV E proteins may affect viral interaction with cellular lectins and contribute to viral replication and pathogenesis.
Resource Allocation for Energy Efficiency in 5G Wireless Networks

Resource allocation is one important mission in wireless communication systems. In 5G wireless networks, it is essential that the new systems be more dynamic and wiser to simultaneously satisfy various network demands, by using new wireless technologies and approaches. To this end, resource allocation is faced with many signiﬁcant challenges such as interference alignment, security attacks, or green communication. On the other hand, as one serious problem in 5G networks, the issue of energy is a ﬀ ected directly by the allocated resources in the system, i.e., bandwidth allocation, power control, association allocation, and deployment strategies. Consequently, together with the enhancement of spectral e ﬃ ciency performance, an emerging trend of 5G wireless networks is to approach green communication via energy e ﬃ ciency (EE) (bits/Hz/Joule), whose most signiﬁcant challenge is due to its belonging to the fractional programming in the optimization ﬁeld, i.e., nonconvex programming. This leaves many di ﬃ cult tasks for improving network EE performance. In this paper, we will tackle the critical EE in 5G wireless networks.
Real-time detection and mitigation of distributed denial of service (DDoS) attacks in software defined networking (SDN)

The emergence of Software Defined Network (SDN) and its promises in networking technology has gotten every stakeholder excited. However, it is believed that every technological development comes with its own challenges of which the most prominent in this case is security. This paper presents a real time detection of the distributed denial of service (DDoS) attacks on the SDN and a control method based on the sFlow mitigation technology. sFlow analyses samples of packets collected from the network traffic and generates handling rules to be sent to the controller in case of an attack detection. The implementation was done by emulating the network in Mininet which runs on a Virtual Machine (VM) and it was shown that the proposed method effectively detects and mitigates DDoS attacks.
Semi-parametric Image Inpainting

This paper introduces a semi-parametric approach to image inpainting for irregular holes. The nonparametric part consists of an external image database. During test time database is used to retrieve a supplementary image, similar to the input masked picture, and utilize it as auxiliary information for the deep neural network. Further, we propose a novel method of generating masks with irregular holes and present public dataset with such masks. Experiments on CelebA-HQ dataset show that our semi-parametric method yields more realistic results than previous approaches, which is confirmed by the user study.
Hybrid holiday traffic predictions in cellular networks

Network traffic forecasting during holidays is important for efficient congestion management and capacity planning. Unfortunately, there is not always enough relevant historical data to forecast traffic at base stations (BSes) individually - this is true in real systems where BSes may be newly built and the data acquisition centers are updated regularly. Hence, it is preferable to forecast holidays in groups of similar traffic patterns, so that data from different BSes can be gathered together to train a single model, and consistent prediction accuracy can be obtained when the context varies. This paper introduces a decomposed model consisting of trend, seasonality, and holiday components of traffic. Then, we focus on developing the holiday sub-models based on residuals that are not covered by trend and seasonality components. A modified k-means algorithm is proposed to cluster residual holiday data. We evaluate our hybrid holiday traffic prediction algorithm on real cellular network data and compare it with the open-source Prophet model developed by Facebook. The evaluation shows that the hybrid prediction method considerably boosts the performance of holiday predictions.
Modeling the superheated steam temperature with a data-driven based approach

Superheated steam temperature is a vital factor that affects the power generation efficiency. A data-driven based approach is proposed to modeling the superheated steam temperature. The ReliefF algorithm is employed to select the input features. In addition, a back propagation neural network(BP) model with parameters optimized by genetic algorithm (GA) is proposed to constructed the prediction model. Experiment results demonstrate that the proposed method can get better forecasting results in comparison with the PSO-BP(particle swarm optimized back propagation neural network), linear regression approach and the MLP(multi-layer perceptron) approach.
Social robot navigation in human-robot interactive environments: Social force model approach

The aim of this paper is to discover strategies focusing on maintaining humans' physical safety and mental comfort in human-robot interactive social environments. Our work builds on the ideas for attaining human-like collision avoidance behavior for mobile robots that share the same social environment with people. More explicitly, this paper intends to find out a social motion planning algorithm that enables the robot to have from “regular collision avoidance” to “human-like collision avoidance”. To achieve this goal, we use a variant of a pedestrian model called Collision Prediction based Social Force model that is particularly developed for low or average density environments and also benefits from human motion during navigation. Once the model parameters have been calibrated, the model is then adopted to be used as a local planner to generate a socially aware path planning in a hallway scenario. The system is tested in simulation environment, and results are reported.
3D dubins net-recovery path planning for fixed wing UAV

Dubins is a well-known path-planning algorithm for planning the minimum-distance path between two configurations subjects. In this paper, basic Three-Dimension-Dubins (3D Dubins) is used to generate the unmanned aerial vehicle(UAV) net-recovery path for the first time. Following the path, the UAV can bump the net smoothly, fast and precisely. Considering the mobility of the UAV, as well as the safety and efficiency of net recovery, a new net recovery path generation method with 3D Dubins is proposed. Simulation examples with various height differences are carried out to demonstrate the applicability and performance of the improved algorithm. The simulation results prove that the new method is more adaptive to the mobility of UAV and avoids the continuous and long-term turning effectively. It is also promising to be applied in the planning of the guidance path for small fixed-wing UAV net recovery.
Comparative molecular characterization of typical and exceptional responders in glioblastoma

Glioblastoma (GBM) is the most common and the deadliest type of primary brain tumor, with a median survival time of only 15 months despite aggressive treatment. Although most patients have an extremely poor prognosis, a relatively small number of patients survive far beyond the median survival time. Investigation of these exceptional responders has sparked a great deal of interest and is becoming an important focus in the field of cancer research. To investigate the molecular differences between typical and exceptional responders in GBM, comparative analyses of somatic mutations, copy number, methylation, and gene expression datasets from The Cancer Genome Atlas were performed, and the results of these analyses were integrated via gene ontology and pathway analyses to assess the functional significance of the differential aberrations. Less severe copy number loss of CDKN2A, lower expression of CXCL8, and FLG mutations are all associated with an exceptional response. Typical responders are characterized by upregulation of NF-κB signaling and of pro-inflammatory cytokines, while exceptional responders are characterized by upregulation of Alzheimer’s and Parkinson’s disease pathways as well as of genes involved in synaptic transmission. The upregulated pathways and processes in typical responders are consistently associated with more aggressive tumor phenotypes, while those in the exceptional responders suggest a retained ability in tumor cells to undergo cell death in response to treatment. With the upcoming launch of the National Cancer Institute’s Exceptional Responders Initiative, similar studies with much larger sample sizes will likely become possible, hopefully providing even more insight into the molecular differences between typical and exceptional responders.
ASYMPTOTIC ENUMERATION OF SYMMETRIC INTEGER MATRICES WITH UNIFORM ROW SUMS

Abstract We investigate the number of symmetric matrices of nonnegative integers with zero diagonal such that each row sum is the same. Equivalently, these are zero-diagonal symmetric contingency tables with uniform margins, or loop-free regular multigraphs. We determine the asymptotic value of this number as the size of the matrix tends to infinity, provided the row sum is large enough. We conjecture that one form of our answer is valid for all row sums. An example appears in Figure 1.
Vaccinium bracteatum Leaf Extract Reverses Chronic Restraint Stress-Induced Depression-Like Behavior in Mice: Regulation of Hypothalamic-Pituitary-Adrenal Axis, Serotonin Turnover Systems, and ERK/Akt Phosphorylation

The leaves of Vaccinium bracteatum Thunb. are a source of traditional herbal medicines found in East Asia. The present study aimed to evaluate the mechanisms underlying the antidepressant-like effects of water extract of V. bracteatum Thunb. leaves (VBLW) in a mouse model of chronic restraint stress (CRS) and to identify the possible molecular in vitro mechanisms of the neuroprotective effects. The CRS-exposed mice were orally administered VBLW (100 and 200 mg/kg) daily for 21 days consecutively. The behavioral effects of VBLW were assessed through the forced swim test (FST) and the open field test (OFT). The levels of serum corticosterone (CORT), corticotropin releasing hormone (CRH), and adrenocorticotropin hormone (ACTH), brain monoamines, such as serotonin, dopamine, and norepinephrine, and serotonin turnover by tryptophan hydroxylase 2 (TPH2), serotonin reuptake (SERT), and monoamine oxidase A (MAO-A) were evaluated, in addition to the extracellular signal-regulated kinases (ERKs)/protein kinase B (Akt) signaling pathway. CRS-exposed mice treated with VBLW (100 and 200 mg/kg) showed significantly reduced immobility time and increased swimming and climbing times in the FST, and increased locomotor activity in the OFT. Moreover, CRS mice treated with VBLW exhibited significantly decreased CORT and ACTH, but enhanced brain monoamine neurotransmitters. In addition, CRS mice treated with VBLW had dramatically decreased protein levels of MAO-A and SERT, but increased TPH2 protein levels in the hippocampus and the PFC. Similarly, VBLW significantly upregulated the ERKs/Akt signaling pathway in the hippocampus and the PFC. Furthermore, VBLW showed neuroprotective effects via increased CREB phosphorylation in CORT-induced cell injury that were mediated through the ERK/Akt/mTOR signaling pathways. These results suggested that the antidepressant-like effects of VBLW might be mediated by the regulation of the HPA axis, glucocorticoids, and serotonin turnover, such as TPH2, SERT, and MAO-A, as well as the concentration of monoamine neurotransmitters, and the activities of ERK and Akt phosphorylation, which were possibly associated with neuroprotective effects.
C-ABAC: An ABAC based Model for Collaboration in Multi-tenant Environment

Collaborative systems allow a group of users to collaborate through distributed platforms in order to perform a common task. Collaborators usually use cloud-based solutions to outsource their data and to benefit from the cloud capabilities. Ensuring access control in a cloud-based collaborative session is an important problem that should be addressed, especially in a multi-tenant configuration. In this paper, we present C-ABAC, a Collaboration Attributes Based Access Control model that ensures access control in multi-tenant cloud environments. C-ABAC supports the workflow concept, preserves the tenants autonomy in defining their local policies and preserves the confidentiality of the object attributes. The implementation of C-ABAC in the SwiftStack environment demonstrates the feasibility of the suggested model. Received on 15 December 2017; accepted on 18 April 2018; published on 26 June 2018
Monocular dense reconstruction by depth estimation fusion

Dense and accurate reconstruction plays a fundamental role in mobile robot's environment perception and navigation. It's also necessary for obstacle avoidance and path planning of mobile robots. We propose a method to incrementally reconstruct the scene from monocular sequence by fusing the depth from geometry computation and generative adversarial networks (GAN) prediction. The depth from geometry triangulation is precise but sparse, while the depth from GAN is dense but unscaled. In this paper, we combine the advantages from two methods with a linear model optimized by graph structure. Experiments showed that our proposed method gives precise dense reconstruction in real time.
Temporal graph-based clustering for historical record linkage

Research in the social sciences is increasingly based on large and complex data collections, where individual data sets from different domains are linked and integrated to allow advanced analytics. A popular type of data used in such a context are historical censuses, as well as birth, death, and marriage certificates. Individually, such data sets however limit the types of studies that can be conducted. Specifically, it is impossible to track individuals, families, or households over time. Once such data sets are linked and family trees spanning several decades are available it is possible to, for example, investigate how education, health, mobility, employment, and social status influence each other and the lives of people over two or even more generations. A major challenge is however the accurate linkage of historical data sets which is due to data quality and commonly also the lack of ground truth data being available. Unsupervised techniques need to be employed, which can be based on similarity graphs generated by comparing individual records. In this paper we present initial results from clustering birth records from Scotland where we aim to identify all births of the same mother and group siblings into clusters. We extend an existing clustering technique for record linkage by incorporating temporal constraints that must hold between births by the same mother, and propose a novel greedy temporal clustering technique. Experimental results show improvements over non-temporary approaches, however further work is needed to obtain links of high quality.
VLASE: Vehicle Localization by Aggregating Semantic Edges

We propose VLASE, a framework to use semantic edge features from images to achieve on-road localization. Semantic edge features denote edge contours that separate pairs of distinct objects such as building-sky, road-sidewalk, and building-ground. While prior work has shown promising results by utilizing the boundary between prominent classes such as sky and building using skylines, we generalize this to consider 19 semantic classes. We extract semantic edge features using CASENet architecture and utilize VLAD framework to perform image retrieval. We achieve improvement over state-of-the-art localization algorithms such as SIFT-VLAD and its deep variant NetVLAD. Ablation study shows the importance of different semantic classes, and our unified approach achieves better performance compared to individual prominent features such as skylines. We also introduce SLC Marathon dataset, a challenging dataset covering most of Salt Lake City with sufficient lighting variations.
NS-based live attenuated H 1 N 1 pandemic vaccines protect mice and ferrets ZHOU

Although vaccines against influenza A virus are the most effective method to combat infection, it is clear that their production needs to be accelerated and their efficacy improved. We generated live attenuated human influenza A vaccines (LAIVs) by rationally engineering mutations directly into the genome of a pandemic-H1N1 virus. Two LAIVs (NS1-73 and NS1-126) were based on the success of LAIVs for animal influenza A viruses. A third candidate (NSΔ5) is a unique NS-mutant that has never been used as a LAIV. The vaccine potential of each LAIV was determined through analysis of attenuation, interferon production, immunogenicity, and their ability to protect mice and ferrets. This study demonstrates that NSΔ5 is an ideal LAIV candidate, provides important information on the effects that different NS mutations have on the pandemic-H1N1 virus and shows that LAIVs can be engineered directly from the genomes of emerging/circulating influenza A viruses. ZHOU, Bin, et al. NS-based live attenuated H1N1 pandemic vaccines protect mice and ferrets. Vaccine, 2010, vol. 28, no. 50, p. 8015-8025 DOI : 10.1016/j.vaccine. 2010.08. 106
Finite time control for generic hypersonic vehicle

A nonlinear finite time sliding mode control (FTSMC) for generic hypersonic vehicle (GHV) is proposed in this paper. Firstly, a control-oriented model of GHV is introduced for sake of simplicity. Secondly, Finite time controllers for velocity and altitude subsystems are designed independently by sliding mode theory to achieve tracking of velocity and altitude trajectories. Total disturbances including parametric perturbations, nonlinear dynamics and external disturbances in GHV model are estimated by utilizing finite time disturbance observer (FTDO) method. Chattering problem in sliding mode is settled by using a smooth approximate function. Finally, numerical simulations are provided to show that the proposed composite control scheme allows an excellent tracking performance.
Research on robust decision of emergency material distribution for chain retail supply chain

The challenges to the dispatch of emergency material distribution have been posed based on the dynamic of emergency logistics environment, the diversity of emergency material demand and the time-varying of emergency material demand and supplies in chain retail supply chain. The urgent of emergency logistics requires to furthest assure material supplies of the disaster-affected area. It is required that the distribution cost of emergency logistics is minimized in chain retail supply chain. Therefore, this paper created a nonlinear dynamic multi-objective programming model for the emergency material distribution of chain retail supply chain with multi-point rescue, multi-point disaster and multimaterial types. It proposed a robust decision-making algorithm for emergency supplies distribution in chain retail supply chain based on CS-GA to minimize the transportation costs and demand costs of emergency supplies. The feasibility and effectiveness of the models and algorithms are verified by the numerical experiments. The results of the numerical experiments show that the robust decision-making model and algorithm for emergency material distribution improve the efficiency and satisfaction rate of distribution and reduce the emergency distribution cost of chain retail supply chain.
Evaluating the use of local search strategies for a memetic algorithm for the protein-ligand docking problem

Molecular docking aims to predict the best conformation of a molecule when it is attached to another, to form a stable complex. This problem is considered to be of a high computational complexity because of the large number of factors that must be taken into account in the binding process. In this paper we present three different local search strategies that were implemented as part of a memetic algorithm for the molecular docking problem. The algorithms were tested with real complexes obtained from Protein Data Bank. The results were evaluated according to two criteria: minimum energy and RMSD respect to the crystallographic structure of the complex. The results generated by the algorithm are satisfactory, since it was possible to generate real-like conformations in 75% of cases. Finally the results showed that one of the techniques achieved the best free energy values in 90% of the tests.
Tensor ring decomposition

Tensor decompositions such as the canonical format and the tensor train format have been widely utilized to reduce storage costs and operational complexities for high-dimensional data, achieving linear scaling with the input dimension instead of exponential scaling. In this paper, we investigate even lower storage-cost representations in the tensor ring format, which is an extension of the tensor train format with variable end-ranks. Firstly, we introduce two algorithms for converting a tensor in full format to tensor ring format with low storage cost. Secondly, we detail a rounding operation for tensor rings and show how this requires new definitions of common linear algebra operations in the format to obtain storage-cost savings. Lastly, we introduce algorithms for transforming the graph structure of graph-based tensor formats, with orders of magnitude lower complexity than existing literature. The efficiency of all algorithms is demonstrated on a number of numerical examples, and we achieve up to more than an order of magnitude higher compression ratios than previous approaches to using the tensor ring format.
Efficient network configuration verification using optimized datalog

Network correctness and reliability are often the greatest concerns. Users use low-level configurations to realize sophisticated policies, particularly in enterprise and datacenter network, which inevitably makes errors. Existing solutions that verify whether the configuration satisfies intended policies either suffer hardcode models, absence of specification languages or scale poorly. In this paper, we propose a new framework, BEEP, which has a high-level specification and modeling language. BEEP takes configuration files as inputs, models control plane via a compact way of sharing information, and then verifies using optimized Datalog rules/quires. Finally, it either indicates success or returns counterexamples that help to debug the configuration.