Discrete Multi-modal Hashing with Canonical Views for Robust Mobile Landmark Search

Mobile landmark search (MLS) recently receives increasing attention for its great practical values. However, it still remains unsolved due to two important challenges. One is high bandwidth consumption of query transmission, and the other is the huge visual variations of query images sent from mobile devices. In this paper, we propose a novel hashing scheme, named as canonical view based discrete multi-modal hashing (CV-DMH), to handle these problems via a novel three-stage learning procedure. First, a submodular function is designed to measure visual representativeness and redundancy of a view set. With it, canonical views, which capture key visual appearances of landmark with limited redundancy, are efficiently discovered with an iterative mining strategy. Second, multi-modal sparse coding is applied to transform visual features from multiple modalities into an intermediate representation. It can robustly and adaptively characterize visual contents of varied landmark images with certain canonical views. Finally, compact binary codes are learned on intermediate representation within a tailored discrete binary embedding model which preserves visual relations of images measured with canonical views and removes the involved noises. In this part, we develop a new augmented Lagrangian multiplier (ALM) based optimization method to directly solve the discrete binary codes. We can not only explicitly deal with the discrete constraint, but also consider the bit-uncorrelated constraint and balance constraint together. Experiments on real world landmark datasets demonstrate the superior performance of CV-DMH over several state-of-the-art methods

Effect of Qilongtoutong granule on calcitonin gene-related peptide, beta-endorphin, serotonin, dopamine, and noradrenalin in migraine model rats and mice

AbstractObjectiveTo study the effect of Qilongtoutong granule (QLTT) on plasma calcitonin gene-related peptide (CGRP), beta-endorphin (β-EP), 5-HT, dopamine (DA), noradrenalin (NE), and blood viscosity in migraine model rats and mice.MethodsBoth the acute blood stasis model group and nitroglycerin-induced migraine model group included 60 Sprague-Dawley rats. The reserpine-reduced model group had 60 Kunming mice. Rats from each test were grouped into normal control group, model group, Zhengtian pill (ZTP) group, and high, moderate, or low-dose QLTT groups. In the acute blood stasis model test, after gavage for 7 days, rats were given 0.8 mL/kg adrenaline hydrochloride subcutaneously twice, and kept in ice water for 5 min. After fasting for 12 h, rats were anesthetized and blood samples were collected for detection of blood viscosity. In the nitroglycerin-induced migraine group, after gavage for 7 days, rats were intraperitoneally injected nitroglycerin (10 mg/kg), and 4 h later, blood samples were collected from postcava for measuring the plasma CGRP and β-EP levels. In the reserpine-reduced model test, except the normal control group, mice were administered reserpine (0.25 mg/kg, i.h.) for 9 days. Mice received intragastric administration from the third day to the ninth day. One hour after the last gavage, blood and brain tissue samples were obtained. Then, blood clotting time and the contents of neurotransmitters were determined.ResultsQLTT- (3.6, 1.8, and 0.9 g/kg) and ZTP-treated rats had lower blood viscosity than that in model rats under different shear rates (P< 0.01). QLTT- (3.6, 1.8 g/kg) and ZTP-treated rats had significantly lower plasma CGRP levels and higher plasma β-EP levels than those in model rats (P< 0.01). QLTT treatment at dose of 0.9 g/kg had lower plasma CGRP levels as well (P<0.05). QLTT- (5.2, 2.6 g/kg) and ZTP-treated mice had longer blood clotting time than that in model mice (P<0.01). QLTT- (2.6 g/kg) and ZTP-treated mice had higher plasma serotonin (5-HT) levels than those in model mice (P<0.05).ConclusionQLTT-treated animals had lower plasma CGRP level, higher plasma β-EP, 5-HT, higher brain tissue 5-HT, NE, DA levels, and lower blood viscosity than those in the migraine model animals

Sequential Recommendation with Diffusion Models

Generative models, such as Variational Auto-Encoder (VAE) and Generative Adversarial Network (GAN), have been successfully applied in sequential recommendation. These methods require sampling from probability distributions and adopt auxiliary loss functions to optimize the model, which can capture the uncertainty of user behaviors and alleviate exposure bias. However, existing generative models still suffer from the posterior collapse problem or the model collapse problem, thus limiting their applications in sequential recommendation. To tackle the challenges mentioned above, we leverage a new paradigm of the generative models, i.e., diffusion models, and present sequential recommendation with diffusion models (DiffRec), which can avoid the issues of VAE- and GAN-based models and show better performance. While diffusion models are originally proposed to process continuous image data, we design an additional transition in the forward process together with a transition in the reverse process to enable the processing of the discrete recommendation data. We also design a different noising strategy that only noises the target item instead of the whole sequence, which is more suitable for sequential recommendation. Based on the modified diffusion process, we derive the objective function of our framework using a simplification technique and design a denoise sequential recommender to fulfill the objective function. As the lengthened diffusion steps substantially increase the time complexity, we propose an efficient training strategy and an efficient inference strategy to reduce training and inference cost and improve recommendation diversity. Extensive experiment results on three public benchmark datasets verify the effectiveness of our approach and show that DiffRec outperforms the state-of-the-art sequential recommendation models

MiR-103a targeting Piezo1 is involved in acute myocardial infarction through regulating endothelium function

Background: Acute myocardial infarction (AMI) is commonly known as the heart attack. The molecular events involved in the development of AMI remain unclear. This study was to investigate the expression of miR-103a in patients with high blood pressure (HBP) and AMI patients with and without HBP, as well as its effect on endothelial cell functions. Methods: MiR-103a expression in plasma and peripheral blood mononuclear cells (PBMCs) was measured by real-time polymerase chain reaction (PCR). The regulatory effect of miR-103a on Piezo1 gene was identified by a luciferase reporter system. The role of miR-103a in endothelial cells was evaluated by the capillary tube formation ability and cell viability of human umbilical vein endothelial cells (HUVECs). Results: The plasma miR-103a concentration was significantly elevated in patients with HBP alone, AMI alone, and comorbidity of AMI and HBP. The miR-103a expression in PBMCs in patients with AMI and HBP was significantly higher than the one in healthy controls (p &lt; 0.05), however miR-103a expression in PBMCs was not significantly different among patients with HBP alone, patients with AMI alone, and healthy controls. MiR-103a targeted Piezo1 and inhibited Piezo1 protein expression, which subsequently reduced capillary tube formation ability and cell viability of HUVECs. Conclusions: MiR-103a might be a potential biomarker of myocardium infarction and could be used as an index for the diagnosis of AMI. It may be involved in the development of HBP and onset of AMI through regulating the Piezo1 expression.

A Learned Index for Exact Similarity Search in Metric Spaces

Indexing is an effective way to support efficient query processing in large databases. Recently the concept of learned index has been explored actively to replace or supplement traditional index structures with machine learning models to reduce storage and search costs. However, accurate and efficient similarity query processing in high-dimensional metric spaces remains to be an open challenge. In this paper, a novel indexing approach called LIMS is proposed to use data clustering and pivot-based data transformation techniques to build learned indexes for efficient similarity query processing in metric spaces. The underlying data is partitioned into clusters such that each cluster follows a relatively uniform data distribution. Data redistribution is achieved by utilizing a small number of pivots for each cluster. Similar data are mapped into compact regions and the mapped values are totally ordinal. Machine learning models are developed to approximate the position of each data record on the disk. Efficient algorithms are designed for processing range queries and nearest neighbor queries based on LIMS, and for index maintenance with dynamic updates. Extensive experiments on real-world and synthetic datasets demonstrate the superiority of LIMS compared with traditional indexes and state-of-the-art learned indexes.Comment: 14 pages, 14 figures, submitted to Transactions on Knowledge and Data Engineerin

PRIVACY PRESERVATION FOR TRANSACTION INITIATORS: STRONGER KEY IMAGE RING SIGNATURE AND SMART CONTRACT-BASED FRAMEWORK

Recently, blockchain technology has garnered support. However, an attenuating factor to its global adoption in certain use cases is privacy-preservation owing to its inherent transparency. A widely explored cryptographic option to address this challenge has been ring signature which aside its privacy guarantee must be double spending resistant. In this paper, we identify and prove a catastrophic flaw for double-spending attack in a Lightweight Ring Signature scheme and proceed to construct a new, fortified commitment scheme using the signer’s entire private key. Subsequently, we compute a stronger key image to yield a double-spending-resistant signature scheme solidly backed by formal proof. Inherent in our solution is a novel, zero-knowledge-based, secured and cost-effective smart contract for public key aggregation. We test our solution on a private blockchain as well as Kovan testnet along with performance analysis attesting to efficiency and usability and make the code publicly available on GitHub

Deep Reinforcement Learning-based Multi-objective Path Planning on the Off-road Terrain Environment for Ground Vehicles

Due to the energy-consumption efficiency between up-slope and down-slope is hugely different, a path with the shortest length on a complex off-road terrain environment (2.5D map) is not always the path with the least energy consumption. For any energy-sensitive vehicles, realizing a good trade-off between distance and energy consumption on 2.5D path planning is significantly meaningful. In this paper, a deep reinforcement learning-based 2.5D multi-objective path planning method (DMOP) is proposed. The DMOP can efficiently find the desired path with three steps: (1) Transform the high-resolution 2.5D map into a small-size map. (2) Use a trained deep Q network (DQN) to find the desired path on the small-size map. (3) Build the planned path to the original high-resolution map using a path enhanced method. In addition, the imitation learning method and reward shaping theory are applied to train the DQN. The reward function is constructed with the information of terrain, distance, border. Simulation shows that the proposed method can finish the multi-objective 2.5D path planning task. Also, simulation proves that the method has powerful reasoning capability that enables it to perform arbitrary untrained planning tasks on the same map