CA-Jaccard: Camera-aware Jaccard Distance for Person Re-identification

Person re-identification (re-ID) is a challenging task that aims to learn discriminative features for person retrieval. In person re-ID, Jaccard distance is a widely used distance metric, especially in re-ranking and clustering scenarios. However, we discover that camera variation has a significant negative impact on the reliability of Jaccard distance. In particular, Jaccard distance calculates the distance based on the overlap of relevant neighbors. Due to camera variation, intra-camera samples dominate the relevant neighbors, which reduces the reliability of the neighbors by introducing intra-camera negative samples and excluding inter-camera positive samples. To overcome this problem, we propose a novel camera-aware Jaccard (CA-Jaccard) distance that leverages camera information to enhance the reliability of Jaccard distance. Specifically, we design camera-aware k-reciprocal nearest neighbors (CKRNNs) to find k-reciprocal nearest neighbors on the intra-camera and inter-camera ranking lists, which improves the reliability of relevant neighbors and guarantees the contribution of inter-camera samples in the overlap. Moreover, we propose a camera-aware local query expansion (CLQE) to mine reliable samples in relevant neighbors by exploiting camera variation as a strong constraint and assign these samples higher weights in overlap, further improving the reliability. Our CA-Jaccard distance is simple yet effective and can serve as a general distance metric for person re-ID methods with high reliability and low computational cost. Extensive experiments demonstrate the effectiveness of our method.Comment: This paper is accepted by CVPR 202

Intelligent Offloading in Blockchain-Based Mobile Crowdsensing Using Deep Reinforcement Learning

Mobile Crowdsensing (MCS) utilizes sensing data collected from users' mobile devices (MDs) to provide high-quality and personalized services, such as traffic monitoring, weather prediction, and service recommendation. In return, users who participate in crowdsensing (i.e., MCS participants) get payment from cloud service providers (CSPs) according to the quality of their shared data. Therefore, it is vital to guarantee the security of payment transactions between MCS participants and CSPs. As a distributed ledger, the blockchain technology is effective in providing secure transactions among users without a trusted third party, which has found many promising applications such as virtual currency and smart contract. In a blockchain, the proof-of-work (PoW) executed by users plays an essential role in solving consensus issues. However, the complexity of PoW severely obstructs the application of blockchain in MCS due to the limited computational capacity of MDs. To solve this issue, we propose a new framework based on Deep Reinforcement Learning (DRL) for offloading computation-intensive tasks of PoW to edge servers in a blockchain-based MCS system. The proposed framework can be used to obtain the optimal offloading policy for PoW tasks under the complex and dynamic MCS environment. Simulation results demonstrate that our method can achieve a lower weighted cost of latency and power consumption compared to benchmark methods

PSO-GA Based Resource AllocationStrategy for Cloud-Based SoftwareServices with Workload-Time Windows

Cloud-based software services necessitate adaptive resource allocation with the promise of dynamic resource adjustment for guaranteeing the Quality-of-Service (QoS) and reducing resource costs. However, it is challenging to achieve adaptive resource allocation for software services in complex cloud environments with dynamic workloads. To address this essential problem, we propose an adaptive resource allocation strategy for cloud-based software services with workload-time windows. Based on the QoS prediction, the proposed strategy first brings the current and future workloads into the process of calculating resource allocation plans. Next, the particle swarm optimization and genetic algorithm (PSO-GA) is proposed to make run time decisions for exploring the objective resource allocation plan. Using the RUBiS benchmark, the extensive simulation experiments are conducted to validate the effectiveness of the proposed strategy on improving the performance of resource allocation for cloud-based software services.The simulation results show that the proposed strategy can obtain a better trade-off between the QoS and resource costs than two classic resource allocation methods.publishedVersio

Imaging through multimode fibres with physical prior

Imaging through perturbed multimode fibres based on deep learning has been widely researched. However, existing methods mainly use target-speckle pairs in different configurations. It is challenging to reconstruct targets without trained networks. In this paper, we propose a physics-assisted, unsupervised, learning-based fibre imaging scheme. The role of the physical prior is to simplify the mapping relationship between the speckle pattern and the target image, thereby reducing the computational complexity. The unsupervised network learns target features according to the optimized direction provided by the physical prior. Therefore, the reconstruction process of the online learning only requires a few speckle patterns and unpaired targets. The proposed scheme also increases the generalization ability of the learning-based method in perturbed multimode fibres. Our scheme has the potential to extend the application of multimode fibre imaging

Effect of lysine demethylase 5C on renal carcinoma metastasis

Objective·To investigate the effects of lysine demethylase 5C (KDM5C) on the migration and invasion of renal clear cell carcinoma, analyze the genes regulated by KDM5C and explore the mechanisms of promoting renal cell carcinoma after its inactivation.Methods·Lentivirus was used to construct human renal clear cell carcinoma 786-O and Caki-1 cell lines with KDM5C stable knockout. The changes of migration and invasion abilities of renal carcinoma cells were observed by Transwell assay. Epithelial-mesenchymal transition (EMT) protein expression was detected by Western blotting. Differential gene analysis and functional pathway enrichment analysis were performed by using 786-O Control-sg, KDM5C-sg 2 group cell RNA sequencing data and The Cancer Genome Atlas (TCGA) public database to further explore the cancer-promoting mechanisms after KDM5C deletion. The up-regulated EMT-related genes in sequencing data were screened out according to the EMT gene set, and the genes were screened by survival analysis and univariate COX analysis. Finally, LASSO regression analysis and risk forest model were used to further screen genes highly related to the phenotype after KDM5C knockout.Results·After the deletion of KDM5C, both 786-O and Caki-1 cells showed significant migration and invasion phenotypes compared with control groups. Analysis of TCGA database indicated that mutation of KDM5C in renal cancer patients led to poor prognosis (P=0.042). RNA sequencing analysis showed that KDM5C knockout may affect cell adhesion molecules and upregulate epithelial-mesenchymal transition-related genes. Western blotting detected the increased expression levels of β-catenin, Vimentin and Snail proteins in two kinds of cells after KDM5C knockout. Finally, ten up-regulated EMT genes were obtained by survival analysis and univariate COX analysis for LASSO regression analysis and risk forest model prediction. The results showed that KDM5C may have a regulatory effect on PLAUR gene.Conclusion·The mutation of KDM5C can promote the development of renal carcinoma by enhancing migration and invasion

Advances in Fiber-Optic Extrinsic Fabry-Perot Interferometric Physical and Mechanical Sensors: A Review

Fabry-Perot Interferometers Have Found a Multitude of Scientific and Industrial Applications Ranging from Gravitational Wave Detection, High-Resolution Spectroscopy, and Optical Filters to Quantum Optomechanics. Integrated with Optical Fiber Waveguide Technology, the Fiber-Optic Fabry-Perot Interferometers Have Emerged as a Unique Candidate for High-Sensitivity Sensing and Have Undergone Tremendous Growth and Advancement in the Past Two Decades with their Successful Applications in an Expansive Range of Fields. the Extrinsic Cavity-Based Devices, I.e., the Fiber-Optic Extrinsic Fabry-Perot Interferometers (EFPIs), Enable Great Flexibility in the Design of the Sensitive Fabry-Perot Cavity Combined with State-Of-The-Art Micromachining and Conventional Mechanical Fabrication, Leading to the Development of a Diverse Array of EFPI Sensors Targeting at Different Physical Quantities. Here, We Summarize the Recent Progress of Fiber-Optic EFPI Sensors, Providing an overview of Different Physical and Mechanical Sensors based on the Fabry-Perot Interferometer Principle, with a Special Focus on Displacement-Related Quantities, Such as Strain, Force, Tilt, Vibration and Acceleration, Pressure, and Acoustic. the Working Principle and Signal Demodulation Methods Are Shown in Brief. Perspectives on Further Advancement of EFPI Sensing Technologies Are Also Discussed

MRI characteristics of lumbosacral dural arteriovenous fistulas

Background and purposeSpinal dural arteriovenous fistulas located in the lumbosacral region are rare and present with nonspecific clinical signs. The purpose of this study was to find out the specific radiologic features of these fistulas.MethodsWe retrospectively reviewed the clinical and radiological data of 38 patients diagnosed with lumbosacral spinal dural arteriovenous fistulas in our institution from September 2016 to September 2021. All patients underwent time-resolved contrast-enhanced three-dimensional MRA and DSA examinations, and were treated with either endovascular or neurosurgical strategies.ResultsMost of the patients (89.5%) had motor or sensory disorders in both lower limbs as the first symptoms. On MRA, the dilated filum terminale vein or radicular vein was seen in 23/30 (76.7%) patients with lumbar spinal dural arteriovenous fistulas and 8/8 (100%) patients with sacral spinal dural arteriovenous fistulas. T2W intramedullary abnormally high signal intensity areas were found in all lumbosacral spinal dural arteriovenous fistula patients, with involvement of the conus present in 35/38 (92.1%) patients. The “missing piece sign” in the intramedullary enhancement area was seen in 29/38 (76.3%) patients.ConclusionDilatation of the filum terminale vein or radicular vein is powerful evidence for diagnosis of lumbosacral spinal dural arteriovenous fistulas, especially for sacral spinal dural arteriovenous fistulas. T2W intramedullary hyperintensity in the thoracic spinal cord and conus, and the missing-piece sign could be indicative of lumbosacral spinal dural arteriovenous fistula

Computation offloading in blockchain-enabled MCS systems : A scalable deep reinforcement learning approach

In Mobile Crowdsensing (MCS) systems, cloud service providers (CSPs) pay for and analyze the sensing data collected by mobile devices (MDs) to enhance the Quality-of-Service (QoS). Therefore, it is necessary to guarantee security when CSPs and users conduct transactions. Blockchain can secure transactions between two parties by using the Proof-of-Work (PoW) to confirm transactions and add new blocks to the chain. Nevertheless, the complex PoW seriously hinders applying Blockchain into MCS since MDs are equipped with limited resources. To address these challenges, we first design a new consortium blockchain framework for MCS, aiming to assure high reliability in complex environments, where a novel Credit-based Proof-of-Work (C-PoW) algorithm is developed to relieve the complexity of PoW while keeping the reliability of blockchain. Next, we propose a new scalable Deep Reinforcement learning based Computation Offloading (DRCO) method to handle the computation-intensive tasks of C-PoW. By combining Proximal Policy Optimization (PPO) and Differentiable Neural Computer (DNC), the DRCO can efficiently make the optimal/near-optimal offloading decisions for C-PoW tasks in blockchain-enabled MCS systems. Extensive experiments demonstrate that the DRCO reaches a lower total cost (weighted sum of latency and power consumption) than state-of-the-art methods under various scenarios

A new perspective on proteinuria and drug therapy for diabetic kidney disease

Diabetic kidney disease (DKD) is one of the leading causes of end-stage renal disease worldwide and significantly increases the risk of premature death due to cardiovascular diseases. Elevated urinary albumin levels are an important clinical feature of DKD. Effective control of albuminuria not only delays glomerular filtration rate decline but also markedly reduces cardiovascular disease risk and all-cause mortality. New drugs for treating DKD proteinuria, including sodium-glucose cotransporter two inhibitors, mineralocorticoid receptor antagonists, and endothelin receptor antagonists, have shown significant efficacy. Auxiliary treatment with proprietary Chinese medicine has also yielded promising results; however, it also faces a broader scope for development. The mechanisms by which these drugs treat albuminuria in patients with DKD should be described more thoroughly. The positive effects of combination therapy with two or more drugs in reducing albuminuria and protecting the kidneys warrant further investigation. Therefore, this review explores the pathophysiological mechanism of albuminuria in patients with DKD, the value of clinical diagnosis and prognosis, new progress and mechanisms of treatment, and multidrug therapy in patients who have type 2 diabetic kidney disease, providing a new perspective on the clinical diagnosis and treatment of DKD

Influence of sodium/glucose cotransporter-2 inhibitors on the incidence of acute kidney injury: a meta-analysis

BackgroundSodium/glucose cotransporter-2 inhibitors (SGLT2i) are associated with cardiovascular benefits. The aim of this systematic review and meta-analysis is to summarize the influence of SGLT2i on the incidence of acute kidney injury (AKI), and to ascertain whether it is affected by confounding variables such as age, baseline renal function and concurrent use of renin-angiotensin-aldosterone system inhibitors (RAASi) or mineralocorticoid receptor antagonists (MRA).MethodsPubMed, Embase, and Cochrane Library databases were searched for randomized controlled trials comparing the influence of SGLT2i versus placebo/blank treatment on AKI in the adult population. A fixed-effect model was used if the heterogeneity was not significant; otherwise, a randomized-effect model was used.ResultsEighteen studies comprising 98,989 patients were included. Compared with placebo/blank treatment, treatment with SGLT2i significantly reduced the risk of AKI (risk ratio [RR]: 0.78, 95% confidence interval [CI]: 0.71 to 0.84, p < 0.001; I2 = 0%). Subgroup analysis suggested consistent results in patients with diabetes, chronic kidney disease, and heart failure (for subgroup difference, p = 0.32). Finally, univariate meta-regression suggested that the influence of SGLT2i on the risk of AKI was not significantly modified by variables such as age (coefficient: 0.011, p = 0.39), baseline estimated glomerular filtration rate (coefficient: −0.0042, p = 0.13) or concomitant use of RAASi (coefficient: 0.0041, p = 0.49) or MRA (coefficient: −0.0020, p = 0.34).ConclusionSGLT2i may be effective in reducing the risk of AKI, and the effect might not be modified by age, baseline renal function and concurrent use of RAASi or MRA