Finding a Small Vertex Cut on Distributed Networks

We present an algorithm for distributed networks to efficiently find a small vertex cut in the CONGEST model. Given a positive integer κ, our algorithm can, with high probability, either find κ vertices whose removal disconnects the network or return that such κ vertices do not exist. Our algorithm takes κ3⋅Õ(D + √n) rounds, where n is the number of vertices in the network and D denotes the network's diameter. This implies κ3⋅Õ(D + √n) round complexity whenever κ=polylog(n).Prior to our result, a bound of Õ(D) is known only when κ = 1,2 [Parter, Petruschka DISC'22]. For κ ≥ 3, this bound can be obtained only by an O(logn)-approximation algorithm [Censor-Hillel, Ghaffari, Kuhn PODC'14], and the only known exact algorithm takes O((κΔD)O(κ)) rounds, where Δ is the maximum degree [Parter DISC'19]. Our result answers an open problem by Nanongkai, Saranurak, and Yingchareonthawornchai [STOC'19]

JALAD: Joint Accuracy- and Latency-Aware Deep Structure Decoupling for Edge-Cloud Execution

Recent years have witnessed a rapid growth of deep-network based services and applications. A practical and critical problem thus has emerged: how to effectively deploy the deep neural network models such that they can be executed efficiently. Conventional cloud-based approaches usually run the deep models in data center servers, causing large latency because a significant amount of data has to be transferred from the edge of network to the data center. In this paper, we propose JALAD, a joint accuracy- and latency-aware execution framework, which decouples a deep neural network so that a part of it will run at edge devices and the other part inside the conventional cloud, while only a minimum amount of data has to be transferred between them. Though the idea seems straightforward, we are facing challenges including i) how to find the best partition of a deep structure; ii) how to deploy the component at an edge device that only has limited computation power; and iii) how to minimize the overall execution latency. Our answers to these questions are a set of strategies in JALAD, including 1) A normalization based in-layer data compression strategy by jointly considering compression rate and model accuracy; 2) A latency-aware deep decoupling strategy to minimize the overall execution latency; and 3) An edge-cloud structure adaptation strategy that dynamically changes the decoupling for different network conditions. Experiments demonstrate that our solution can significantly reduce the execution latency: it speeds up the overall inference execution with a guaranteed model accuracy loss.Comment: conference, copyright transfered to IEE

Perfect Simulation of Las Vegas Algorithms via Local Computation

The notion of Las Vegas algorithms was introduced by Babai (1979) and can be defined in two ways: * In Babai's original definition, a randomized algorithm is called Las Vegas if it has a finitely bounded running time and certifiable random failure. * Another definition widely accepted today is that Las Vegas algorithms refer to zero-error randomized algorithms with random running times. The equivalence between the two definitions is straightforward. Specifically, for randomized algorithms with certifiable failures, repeatedly running the algorithm until no failure is encountered allows for faithful simulation of the correct output when it executes successfully. We show that a similar perfect simulation can also be achieved in distributed local computation. Specifically, in the LOCAL model, with polylogarithmic overhead in time complexity, any Las Vegas algorithm with finitely bounded running time and locally certifiable failures can be converted to a zero-error Las Vegas algorithm. This transformed algorithm faithfully reproduces the correct output of the original algorithm in successful executions

Progressive Feature Fusion Network for Enhancing Image Quality Assessment

Image compression has been applied in the fields of image storage and video broadcasting. However, it's formidably tough to distinguish the subtle quality differences between those distorted images generated by different algorithms. In this paper, we propose a new image quality assessment framework to decide which image is better in an image group. To capture the subtle differences, a fine-grained network is adopted to acquire multi-scale features. Subsequently, we design a cross subtract block for separating and gathering the information within positive and negative image pairs. Enabling image comparison in feature space. After that, a progressive feature fusion block is designed, which fuses multi-scale features in a novel progressive way. Hierarchical spatial 2D features can thus be processed gradually. Experimental results show that compared with the current mainstream image quality assessment methods, the proposed network can achieve more accurate image quality assessment and ranks second in the benchmark of CLIC in the image perceptual model track.Comment: Data Compression Conferenc

A tri-axial accelerometer with structure-based voltage operation by using series-connected piezoelectric elements

AbstractOutput-voltage operation on a sensor structure is proposed and a tri-axial accelerometer with low cross-axis sensitivities is designed. The output voltage between the electrodes sandwiching piezoelectric thin-films on a deforming structure is proportional to the in-plane stress of the piezoelectric thin-film. If the piezoelectric thin-film is processed to separated elements and the electrodes of the elements are connected in series, the output voltages from the series-connected piezoelectric elements are multiplied or canceled depending on the situations of the internal-stresses (i.e. compressive or tensile) of the elements. Proper design of the electrode connections by taking the deformation shape of structures into consideration can realize expected outputvoltage operations on the device structure. The principle of structure-based output-voltage operation is applied to the design of a tri-axial accelerometer with low cross-axes sensitivities. Finite-element-method (FEM) simulations of the tri-axial accelerometer revealed the cross-axis sensitivity of less than 1.5%

Antidiabetic retinopathy effect of Fufang Danshen Mingmu in rats

Purpose: To investigate the effect of Fufang Danshen Mingmu (FDM) on streptozotocin-induced diabetic retinopathy rats.Methods: Diabetic retinopathy model rats were prepared using a single intraperitoneal injection of a freshly prepared solution of streptozotocin (50 mg/kg). The rats were randomly divided into 6 groups of ten rats each: negative control group, control group, reference group (glibenclamide, 1 mg/kg) as well as FDM groups, (50, 100 and 200 mg/kg body weight). Blood glucose and plasma insulin levels were determined. Oxidative stress was evaluated in liver and kidney as lipid peroxidation (LPO), superoxide dismutase (SOD), reduced glutathione (GSH), glutathione peroxidase (GPx) and catalase (CAT). Blood serum levels of creatinine and urea were determined in both diabetic control and treated rats.Results: Compared with diabetic rats, oral administration of FDM at a dose of 200 mg/kg daily for 30 days resulted in a significant decrease in fasting blood glucose (120.21 ± 3.37 mg/dL, p < 0.05) and increased insulin level (13.31 ± 0.67 uU/mL, p < 0.05).  Furthermore, it significantly reduced biochemical parameters (serum creatinine, 0.86 ±0.24 mg/dL, p < 0.05) and serum urea 41.86±1.59 mg/dL, p <0.05).Conclusion: The results indicate that FDM normalizes impaired antioxidant status in streptozotocin induced diabetic retinopathy rats, and also exerts a protective effect against lipid peroxidation by scavenging free radicals

Reversible shear thickening at low shear rates of electrorheological fluids under electric fields

Shear thickening is a phenomenon of significant viscosity increase of colloidal suspensions. While electrorheological (ER) fluids can be turned into a solid-like material by applying an electric field, their shear strength is widely represented by the attractive electrostatic interaction between ER particles. By shearing ER fluids between two concentric cylinders, we show a reversible shear thickening of ER fluids above a low critical shear rate (<1 s-1) and a high critical electric field strength (>100 V/mm), which could be characterized by a modified Mason number. Shear thickening and electrostatic particle interaction-induced inter-particle friction forces is considered to be the real origin of the high shear strength of ER fluids, while the applied electric field controls the extent of shear thickening. The electric field-controlled reversible shear thickening has implications for high-performance ER/magnetorheological (MR) fluid design, clutch fluids with high friction forces triggered by applying local electric field, other field-responsive materials and intelligent systems.Comment: 29pages, 9 figure

Dual-factor Synergistically Activated ESIPT-based Probe:Differential Fluorescence Signals to Simultaneously Detect α-Naphthyl Acetate and Acid α-Naphthyl Acetate Esterase

[Image: see text] α-Naphthyl acetate esterase (α-NAE) and acid α-naphthyl acetate esterase (ANAE), a class of special esterases, are important for lymphocyte typing and immunocompetence-monitoring. As such, the simultaneous detection of α-NAE and ANAE has become a target to effectively improve the accuracy in lymphocyte typing. Therefore, we developed a dual-factor synergistically activated ESIPT-based probe (HBT-NA) to detect α-NAE and ANAE sensitively, rapidly, and simultaneously in a differential manner. HBT-NA exhibits differential fluorescence signal outputs toward small changes of α-NAE and ANAE activities. HBT-NA displays a weak fluorescence signal at 392 nm over a pH range from 6.0 to 7.4. However, when it interacts with α-NAE (0–25 U) at pH = 7.4, the fluorescence intensity at 392 nm enhanced linearly within 60 s (F(392 nm)/F0(392 nm) = 0.042 C(α-NAE) + 1.1, R(2) = 0.99). Furthermore, HBT-NA emits ratiometric fluorescence signals (F(505 nm)/F(392 nm)) for ANAE (0–25 U) at pH = 6.0 within 2.0 min, exhibiting a good linear relationship (F(505 nm)/F(392 nm) = 0.83C(ANAE) – 1.75, R(2) = 0.99). The differential fluorescence signals can be used to simultaneously detect the activities of α-NAE and ANAE in solutions and complex living organisms. More importantly, based on the differential fluorescence signals toward α-NAE and ANAE, T lymphocytes and B lymphocytes could be successfully typed and differentiated among nontyped lymphocytes, facilitating the real-time evaluation of their immune functions using flow cytometry. Hence, HBT-NA could be used for the ultrasensitive detection of the enzyme activities of α-NAE and ANAE, the real-time precise typing of lymphocytes, and the monitoring of immunocompetence

Hydrothermal Preparation of Visible-Light-Driven N-Br-Codoped TiO

Using a facile hydrothermal method, N-Br-codoped TiO2 photocatalyst that had intense absorption in visible region was prepared at low temperature (100°C), through a direct reaction between nanocrystalline anatase TiO2 solution and cetyltrimethylammonium bromide (CTAB). The results of X-ray photoelectron spectroscopy (XPS) showed the existence of N-Ti-N, O-Ti-N-R, Ti3+ (attribute to the doped Br atoms by charge compensation), and TiOxNy species, indicating the successful codoping of N and Br atoms, which were substituted for lattice oxygen without any influence on the crystalline phase of TiO2. In contrast to the N-doped sample, the N-Br-codoped TiO2 photocatalyst could more readily photodegrade methylene blue (MB) under visible-light irradiation. The visible-light catalytic activity of thus-prepared photocatalyst resulted from the synergetic effect of the doped nitrogen and bromine, which not only gave high absorbance in the visible-light range, but also reduced electron-hole recombination rate