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We study how to reduce the reconfiguration time in hybrid optical-electrical Datacenter Networks (DCNs). With a layer of Optical Circuit Switches (OCSes), hybrid optical-electrical DCNs can reconfigure its logical topologies to better match the on-going traffic patterns, but the reconfiguration time may directly affect the benefits of reconfigurability. The reconfiguration time consists of the topology solver running time and the network convergence time after triggering reconfiguration. However, existing topology solvers either incur high algorithmic complexity or fail to minimize the reconfiguration overhead. In this paper, we propose a novel algorithm that combines the ideas of bipartition and Minimum Cost Flow (MCF) to reduce the overall reconfiguration time. For the first time, we formulate the topology solving problem as a MCF problem with piecewise-linear cost, which strikes a better balance between solver complexity and solution optimality. Our evaluation shows that our algorithm can significantly reduce the network convergence time while consuming less topology solver running time, making its overall performance superior to existing algorithms. Our code and test cases are available at a public repository.

Recent years have witnessed a widespread adoption of containers in cloud computing. While containers simplify and accelerate application development, the existing container network technologies either incur significant overhead, which hurts performance for distributed applications, or lose flexibility or universality, which hinders the widespread deployment in production. We design and implement XMasq, an eBPF-based container overlay network, to eliminate the extra overhead while keeping flexibility and universality. We take full advantage of eBPF and design a cache-based network virtualization mechanism and a redirect-based intra-host data path in XMasq. XMasq closes the performance gap between overlay networks and host networks. Compared to standard overlay networks, XMasq improves the TCP throughput by 18% and the Request-Response transaction rate by 101%; XMasq also reduces the latency of Memcached by 28.3%, PostgreSQL by 14.6% and Nginx by 29%. Compared to container native-routing networks, XMasq does not require the underlay network being able to foward packets using container IPs. Compared to Slim, which only supports TCP traffic, XMasq is protocol independent and thus all the applications can benefit from XMasq. We deploy XMasq as a plugin of Antrea, which is a Container Network Interface (CNI).

Deterministic Networking (DetNet) is a rising technology that offers deterministic delay \& jitter and zero packet loss regardless of failures in large IP networks. In order to support DetNet, we must be able to find a set of low-cost routing paths for a given node pair subject to delay-range constraints. Unfortunately, the \textbf{Delay-Range} Constrained Routing (DRCR) problem is NP-Complete. Existing routing approaches either cannot support the delay-range constraints, or incur extremely high computational complexity. We propose Pulse$+$, a highly scalable and efficient DRCR problem solver. Pulse$+$ adopts a branch-and-bound methodology and optimizes its pruning strategies for higher efficiency. We also integrate Pulse$+$ with a divide-and-conquer approach and propose CoSE-Pulse$+$ to find a pair of active/backup paths that meet DetNet's delay-range and delay-diff constraints. Both Pulse$+$ and CoSE-Pulse$+$ offer optimality guarantee. Notably, although Pulse$+$ and CoSE-Pulse$+$ do not have a polynomial worst-case time complexity, their empirical performance is superior. We evaluate Pulse$+$ and CoSE-Pulse$+$ against the K-Shorst-Path and Lagrangian-dual based algorithms using synthetic test cases generated over networks with thousands of nodes and links. Both Pulse$+$ and CoSE-Pulse$+$ achieve significant speedup. To enable reproduction, we open source our code and test cases at [1].

Most existing 3D point cloud object detection approaches heavily rely on large amounts of labeled training data. However, the labeling process is costly and time-consuming. This paper considers few-shot 3D point cloud object detection, where only a few annotated samples of novel classes are needed with abundant samples of base classes. To this end, we propose Prototypical VoteNet to recognize and localize novel instances, which incorporates two new modules: Prototypical Vote Module (PVM) and Prototypical Head Module (PHM). Specifically, as the 3D basic geometric structures can be shared among categories, PVM is designed to leverage class-agnostic geometric prototypes, which are learned from base classes, to refine local features of novel categories.Then PHM is proposed to utilize class prototypes to enhance the global feature of each object, facilitating subsequent object localization and classification, which is trained by the episodic training strategy. To evaluate the model in this new setting, we contribute two new benchmark datasets, FS-ScanNet and FS-SUNRGBD. We conduct extensive experiments to demonstrate the effectiveness of Prototypical VoteNet, and our proposed method shows significant and consistent improvements compared to baselines on two benchmark datasets., NeurIPS 2022

Farnesoid X receptor (FXR) agonists would be considered as an important therapeutic strategy for several chronic liver and metabolic diseases. Here we have employed an integrated virtual screening by combining ligand-based pharmacophore mapping and molecular docking to identify novel nonsteroidal FXR agonists. Eighteen compounds were selected for in vitro FXR agonistic activity assay, and results showed five compounds exhibiting promising FXR agonistic activity. Among these compounds, compounds F4 and F17 were the most remarkable in vitro activity by using homogeneous time resolved fluorescence (HTRF) assay and the full-length FXR reporter gene assay in HepG2 cells. Real-time PCR assay was performed to measure the expression of FXR target genes. Compounds F4 and F17 increased small heterodimer partner (SHP), in turn, suppress mRNA levels of cholesterol 7-alpha-hydroxylase (CYP7A1). The obtained compounds F4 and F17 from this study may be potential leads for developing novel FXR agonists in the treatment of metabolic diseases.

We study how to design edge server placement and server scheduling policies under workload uncertainty for 5G networks. We introduce a new metric called resource pooling factor to handle unexpected workload bursts. Maximizing this metric offers a strong enhancement on top of robust optimization against workload uncertainty. Using both real traces and synthetic traces, we show that the proposed server placement and server scheduling policies not only demonstrate better robustness against workload uncertainty than existing approaches, but also significantly reduce the cost of service providers. Specifically, in order to achieve close-to-zero workload rejection rate, the proposed server placement policy reduces the number of required edge servers by about 25% compared with the state-of-the-art approach; the proposed server scheduling policy reduces the energy consumption of edge servers by about 13% without causing much impact on the service quality., The extended version of (IWQoS 2021) accepted paper: Design of Robust and Efficient Edge Server Placement and Server Scheduling Policies

Takeda G-protein-coupled receptor 5 (TGR5) is emerging as an important and promising target for the development of anti-diabetic drugs. To understand the structural characteristics of TGR5 agonists, the common feature pharmacophore models were generated and molecular docking was performed. The ligand-based virtual screening combined with pharmacophore mapping and molecular docking was performed to identify novel nonsteroidal TGR5 agonists. Finally, 20 compounds were screened for in vitro TGR5 agonistic activity assay, and results showed most compounds exhibiting TGR5 agonistic activity at 40 μM. Among these compounds, V12 and V14 displayed obvious TGR5 agonist activity, with the EC50 values of 19.5 μM and 7.7 μM, respectively. Compounds V12 and V14 could be considered potential TGR5 agonist candidates and also may be used as initial hits for developing novel TGR5 agonists.

Although Person Re-Identification has made impressive progress, difficult cases like occlusion, change of view-pointand similar clothing still bring great challenges. Besides overall visual features, matching and comparing detailed information is also essential for tackling these challenges. This paper proposes two key recognition patterns to better utilize the detail information of pedestrian images, that most of the existing methods are unable to satisfy. Firstly, Visual Clue Alignment requires the model to select and align decisive regions pairs from two images for pair-wise comparison, while existing methods only align regions with predefined rules like high feature similarity or same semantic labels. Secondly, the Conditional Feature Embedding requires the overall feature of a query image to be dynamically adjusted based on the gallery image it matches, while most of the existing methods ignore the reference images. By introducing novel techniques including correspondence attention module and discrepancy-based GCN, we propose an end-to-end ReID method that integrates both patterns into a unified framework, called CACE-Net((C)lue(A)lignment and (C)onditional (E)mbedding). The experiments show that CACE-Net achieves state-of-the-art performance on three public datasets.

In the conventional person Re-ID setting, it is widely assumed that cropped person images are for each individual. However, in a crowded scene, off-shelf-detectors may generate bounding boxes involving multiple people, where the large proportion of background pedestrians or human occlusion exists. The representation extracted from such cropped images, which contain both the target and the interference pedestrians, might include distractive information. This will lead to wrong retrieval results. To address this problem, this paper presents a novel deep network termed Pedestrian-Interference Suppression Network (PISNet). PISNet leverages a Query-Guided Attention Block (QGAB) to enhance the feature of the target in the gallery, under the guidance of the query. Furthermore, the involving Guidance Reversed Attention Module and the Multi-Person Separation Loss promote QGAB to suppress the interference of other pedestrians. Our method is evaluated on two new pedestrian-interference datasets and the results show that the proposed method performs favorably against existing Re-ID methods., Accepted by ECCV 2020

Many optical circuit switched data center networks (DCN) have been proposed in the past to attain higher capacity and topology reconfigurability, though commercial adoption of these architectures have been minimal. One major challenge these architectures face is the difficulty of handling uncertain traffic demands using commercial optical circuit switches (OCS) with high switching latency. Prior works have generally focused on developing fast-switching OCS prototypes to quickly react to traffic changes through frequent reconfigurations. This approach, however, adds tremendous complexity to the control plane, and raises the barrier for commercial adoption of optical circuit switched data center networks. We propose COUDER, a robust topology and routing optimization framework for reconfigurable optical circuit switched data centers. COUDER optimizes topology and routing based on a convex set of traffic matrices, and offers strict throughput guarantees for any future traffic matrices bounded by the convex set. For the bursty traffic demands that are unbounded by the convex set, we employ a desensitization technique to reduce performance hit. This enables COUDER to generate topology and routing solutions capable of handling unexpected traffic changes without relying on frequent topology reconfigurations. Our extensive evaluations based on Facebook's production DCN traces show that, even with daily reconfiguration, COUDER achieves about 20\% higher throughput, and about 32\% lower average hop count compared to cost-equivalent static topologies. Our work shows that adoption of reconfigurable topologies in commercial DCNs is feasible even without fast OCSs.

Numerous optical circuit switched data center networks have been proposed over the past decade for higher capacity, though commercial adoption of these architectures have been minimal so far. One major challenge commonly facing these architectures is the difficulty of handling bursty traffic with optical circuit switches (OCS) with high switching latency. Prior works generally rely on fast-switching OCS prototypes to better react to traffic changes via frequent reconfigurations. This approach, unfortunately, adds further complexity to the control plane. We propose METTEOR, an easily deployable solution for optical circuit switched data centers, that is designed for the current capabilities of commercial OCSs. Using multiple predicted traffic matrices, METTEOR designs data center topologies that are less sensitive to traffic changes, thus eliminating the need of frequently reconfiguring OCSs upon traffic changes. Results based on extensive evaluations using production traces show that METTEOR increases the percentage of direct-hop traffic by about 80% over a fat tree at comparable cost, and by about 30% over a uniform mesh, at comparable maximum link utilizations. Compared to ideal solutions that reconfigure OCSs on every traffic matrix, METTEOR achieves close-to-optimal bandwidth utilization even with biweekly reconfiguration. This drastically lowers the controller and management complexity needed to perform METTEOR in commercial settings.

TGR5 is emerging as an important and promising target for the treatment of diabetes, obesity and other metabolic syndromes. A series of novel 1-benzyl-1H-imidazole-5-carboxamide derivatives was designed, synthesized and evaluated in vitro and in vivo. The most potent compounds 19d and 19e exhibited excellent agonistic activities against hTGR5, which was superior to those of the reference drugs INT-777 and LCA. In addition, compounds 19d and 19e exhibited good selectivity against FXR and presented significant glucose-lowering effects in vivo. Compound 19d could stimulate GLP-1 secretion by activating of TGR5.

An extensive array of chemicals are used in industry and commerce, many produced in large volumes, but their fate in the environment and corresponding exposure to organisms (including humans) has only been measured for a limited number of substances. Modelling tools have been demonstrated to be an economical alternative to measurement data for the assessment of chemical fate and exposure. Therefore, many fate and bioaccumulation models have been developed in Western countries while progress on the development of tools and legislation are relatively far behind in China. In this thesis, the application of modelling tools in environmental fate and exposure assessment of organic pollutants has been explored in China. The first aspect of this research was the adaptability of Western dietary exposure models to the Chinese population. In Paper I, three established Western-based exposure models were used for China using specific Chinese dietary scenarios and modified human characteristics to explore their potential adaptability to be used for the Chinese population. The second aspect was to explore the possibility of extending existing bioaccumulation models to a wider range of organic pollutants by incorporation of newly developed poly-parameter linear free energy relationships (pp-LFERs) for individual biological phases in Paper II. Compared to traditional single parameter(sp) KOW-based (sp-LFERs) methods, the pp-LFERs only indicated limited advantages when evaluated with measurements, implying that the choice of approach should be based on other factors beyond methodology of calculating partitioning coefficients (e.g.,accuracy of input data and uncertainty from biotransformation). The studies described in Paper III and Paper IV took polychlorinated biphenyls (PCBs) as a case study, to comprehensively evaluate and demonstrate the ability of a global dynamic fate model （BETR-Global） linked to a bioaccumulation model (ACC-HUMAN) in the reconstruction of historical trends and predicting future of emission trends and exposure profiles for the Chinese population. Meanwhile, controlling sources of intentional and unintentional emissions were thoroughly explored within China in Paper III. Paper IV more focused on human exposure under the combined effect of emission trends and dietary transition for the Chinese population. The delayed peak time of the human body burden of PCBs has mainly been caused by rapid dietary shifts and on-going emissions from sources, such as imported e-waste in China. The Large uncertainty in the prediction of human body burdens suggests that the choice of model system could be relevant for exposure assessment and that the model should be tailored to the system of interest. Finally, preliminary suggestions to conduct effective controlling measures were also made for the policy makers.