Learning Cross-modality Information Bottleneck Representation for Heterogeneous Person Re-Identification

Visible-Infrared person re-identification (VI-ReID) is an important and challenging task in intelligent video surveillance. Existing methods mainly focus on learning a shared feature space to reduce the modality discrepancy between visible and infrared modalities, which still leave two problems underexplored: information redundancy and modality complementarity. To this end, properly eliminating the identity-irrelevant information as well as making up for the modality-specific information are critical and remains a challenging endeavor. To tackle the above problems, we present a novel mutual information and modality consensus network, namely CMInfoNet, to extract modality-invariant identity features with the most representative information and reduce the redundancies. The key insight of our method is to find an optimal representation to capture more identity-relevant information and compress the irrelevant parts by optimizing a mutual information bottleneck trade-off. Besides, we propose an automatically search strategy to find the most prominent parts that identify the pedestrians. To eliminate the cross- and intra-modality variations, we also devise a modality consensus module to align the visible and infrared modalities for task-specific guidance. Moreover, the global-local feature representations can also be acquired for key parts discrimination. Experimental results on four benchmarks, i.e., SYSU-MM01, RegDB, Occluded-DukeMTMC, Occluded-REID, Partial-REID and Partial\_iLIDS dataset, have demonstrated the effectiveness of CMInfoNet

The Value of Backers’ Word-of-Mouth in Screening Crowdfunding Projects: An Empirical Investigation

Reward-based crowdfunding is an emerging financing channel for entrepreneurs to raise money for their innovative projects. How to screen the crowdfunding projects is critical for crowdfunding platform, project founder, and potential backers. This study aims to investigate whether backers’ word-of-mouth (WOM) is a valuable input to generate collective intelligence for project screening. Specially, we answer three questions. First, is backers’ WOM an effective signal for implementation performance of crowdfunding projects? Second, how do the WOM help screen projects during the fund-raising process? Third, which kind of comments (positive or negative) is more effective in screening crowdfunding projects? Research hypotheses were developed based on theories of collective intelligence and WOM communication. Using a cross section dataset and a panel dataset, we get the following findings. First, backers’ negative WOM can effectively predict project implementation performance, however positive WOM does not have that prediction power. The prediction power of positive and negative WOM differs significantly. One possible reason is that negative WOM does contain more information of project quality. Second, project with more accumulative negative WOM tend to attract fewer subsequent backers. However, accumulative positive WOM is not helpful for attracting more potential backers. We conclude that negative WOM is useful for project screening project, because it is a signal of project quality, and meanwhile it could prevent backers make subsequent investments

Quantitative Evaluation of the Environmental Quality of New Rural Communities-a Case Study of Henan Province, China

Abstract: The construction of new rural communities is an important measure to promote the integration of urban and rural areas. The environmental quality of new rural communities represents the residential suitability of the communities. The evaluation of the environmental quality can help promote the healthy development of new rural communities. The present study combines AHP (Analytic Hierarchy Process) and TOPSIS (Technique for Order Preference by Similarity to an Lead Solution) for the evaluation and ordering of the environmental quality of 28 new rural communities in Henan Province, China. The AHP model containing four hierarchies is constructed: objective hierarchy, principle hierarchy, index hierarchy and factor hierarchy. The principle hierarchy is composed of 3 factors: social environment, material environment and ecological environment; the index hierarchy consists of 7 factors: service environment, civilized environment, planning environment, architectural environment, facility environment, greening environment, sanitation environment; the factor hierarchy consists of 14 factors: life service, health service, education degree, neighborhood relationship, spatial layout, functional layout, architectural style, architectural functions, infrastructure, public facilities, percentage of green open space, leisure and entertainment facilities, garbage treatment rate and wastewater treatment rate. By AHP model, the weight of the factors in every hierarchy is obtained and TOPSIS is employed for the ordering of the environmental quality of the 28 new rural communities. The results show: in the environmental evaluation, spatial layout, functional layout, architectural functions, infrastructure and neighborhood relationship have a relatively higher weight and more importance should be attached to these respects. The ordering of environmental quality of new rural communities has a high discrimination. The five communities with the highest environmental quality (representing 17.8% of the total communities) are R 13 , R 6 , R 24 , R 23 and R 28 . The result can effectively reflect the environmental quality of new rural communities. On the one hand, this result can provide the basis for the transform and restructuring of the existing communities; on the other hand, it can be used as the reference for the quality control of newly-built communities, so that the objectives of new rural community construction will be met

Sensing Fractional Power Spectrum of Nonstationary Signals with Coprime Filter Banks

The coprime discrete Fourier transform (DFT) filter banks provide an effective scheme of spectral sensing for wide-sense stationary (WSS) signals in case that the sampling rate is far lower than the Nyquist sampling rate. And the resolution of the coprime DFT filter banks in the Fourier domain (FD) is 2π/MN, where M and N are coprime. In this work, a digital fractional Fourier transform- (DFrFT-) based coprime filter banks spectrum sensing method is suggested. Our proposed method has the same sampling principle as the coprime DFT filter banks but has some advantages compared to the coprime DFT filter banks. Firstly, the fractional power spectrum of the chirp-stationary signals which are nonstationary in the FD can be sensed effectively by the coprime DFrFT filter banks because of the linear time-invariant (LTI) property of the proposed system in discrete-time Fourier domain (DTFD), while the coprime DFT filter banks can only sense the power spectrum of the WSS signals. Secondly, the coprime DFrFT filter banks improve the resolution from 2π/MN to 2π sin α/MN by combining the fractional filter banks theory with the coprime theory. Simulation results confirm the obtained analytical results

Sparse Bayes Tensor and DOA Tracking Inspired Channel Estimation for V2X Millimeter Wave Massive MIMO System

Efficient vehicle-to-everything (V2X) communications improve traffic safety, enable autonomous driving, and help to reduce environmental impacts. To achieve these objectives, accurate channel estimation in highly mobile scenarios becomes necessary. However, in the V2X millimeter-wave massive MIMO system, the high mobility of vehicles leads to the rapid time-varying of the wireless channel and results in the existing static channel estimation algorithms no longer applicable. In this paper, we propose a sparse Bayes tensor and DOA tracking inspired channel estimation for V2X millimeter wave massive MIMO system. Specifically, by exploiting the sparse scattering characteristics of the channel, we transform the channel estimation into a sparse recovery problem. In order to reduce the influence of quantization errors, both the receiving and transmitting angle grids should have super-resolution. We obtain the measurement matrix to increase the resolution of the redundant dictionary. Furthermore, we take the low-rank characteristics of the received signals into consideration rather than singly using the traditional sparse prior. Motivated by the sparse Bayes tensor, a direction of arrival (DOA) tracking method is developed to acquire the DOA at the next moment, which equals the sum of the DOA at the previous moment and the offset. The obtained DOA is expected to provide a significant angle information update for tracking fast time-varying vehicular channels. The proposed approach is evaluated over the different speeds of the vehicle scenarios and compared to the other methods. Simulation results validated the theoretical analysis and demonstrate that the proposed solution outperforms a number of state-of-the-art researches

Low-carbon transportation scheduling of open-pit mine based on GWO-NSGA-Ⅱ hybrid algorithm

In order to improve truck transport efficiency, reduce carbon emissions and save transport costs in open-pit mines, pure electric trucks are taken as the research object. The objective function is transportation cost, total queuing time (including truck charging time, operation time and maintenance waiting time in the production process), and ore grade deviation. The constraints include the crushing capacity of the crushing site, mining capacity of the mining site, loading capacity, ore grade error limit, vehicle charging pile selection and charging limit. The optimization model of low carbon transportation scheduling of open-pit is established. The gray wolf optimization (GWO) and non-dominated sorting genetic algorithm-II (NSGA-II) have been used to solve the low-carbon transportation scheduling model for pure electric mining trucks in open-pit mines. The former is prone to get trapped in local optimum while the latter is likely to achieve a global optimum but converges slowly. In order to solve the above problems, a GWO-NSGA-II hybrid algorithm is proposed. The hybrid algorithm introduces three genetic operations of NSGA-II, selection, crossover and mutation, into the GWO algorithm to effectively prevent the algorithm from falling into local optimum. In order to improve the stability of the global convergence of the algorithm, hunting and attack operations are introduced into the elite retention strategy of NSGA-II. Five standard test functions are used to verify that the hybrid algorithm improves the stability while ensuring the convergence. The example analysis shows that, compared with NSGA-II and GWO, the hybrid algorithm improves the optimization speed by 48.7% and 27.1% respectively. The hybrid algorithm improves the optimization precision by 17.1% and 9.3% respectively. The hybrid algorithm reduces the number of trucks, carbon emissions, transportation distance and transportation costs

Low skeletal muscle mass index and all-cause mortality risk in adults: A systematic review and meta-analysis of prospective cohort studies.

ObjectiveThe relationship between low skeletal muscle mass index (SMI) and all-cause mortality risk in the general adults remains unclear. Our study was conducted to examine and quantify the associations between low SMI and all-cause mortality risks.MethodsPubMed, Web of Science, and Cochrane Library for primary data sources and references to relevant publications retrieved until 1 April 2023. A random-effect model, subgroup analyses, meta-regression, sensitivity analysis, and publication bias were conducted using STATA 16.0.ResultsSixteen prospective studies were included in the meta-analysis of low SMI and the risk of all-cause mortality. A total of 11696 deaths were ascertained among 81358 participants during the 3 to 14.4 years follow-up. The pooled RR of all-cause mortality risk was 1.57 (95% CI, 1.25 to 1.96, P ConclusionsLow SMI was significantly associated with the increased risk of all-cause mortality, and the risk of all-cause mortality associated with low SMI was higher in adults with a higher BMI. Low SMI Prevention and treatment might be significant for reducing mortality risk and promoting healthy longevity

Echinatin inhibits the growth and metastasis of human osteosarcoma cells through Wnt/β-catenin and p38 signaling pathways

Osteosarcoma (OS) is a highly aggressive malignant bone tumor that mainly occurs in adolescents. At present, chemotherapy is the most commonly used method in clinical practice to treat OS. However, due to drug resistance, toxicity and long-term side effects, chemotherapy can’t always provide sufficient benefits for OS patients, especially those with metastasis and recurrence. Natural products have long been an excellent source of anti-tumor drug development. In the current study, we evaluated the anti-OS activity of Echinatin (Ecn), a natural active component from the roots and rhizomes of licorice, and explored the possible mechanism. We found that Ecn inhibited the proliferation of human OS cells and blocked cell cycle at S phase. In addition, Ecn suppressed the migration and invasion, while induced the apoptosis of human OS cells. However, Ecn had less cytotoxicity against normal cells. Moreover, Ecn inhibited the xenograft tumor growth of OS cells in vivo. Mechanistically, Ecn inactivated Wnt/β-catenin signaling pathway while activated p38 signaling pathway. β-catenin over-expression and the p38 inhibitor SB203580 both attenuated the inhibitory effect of Ecn on OS cells. Notably, we demonstrated that Ecn exhibited synergistic inhibitory effect with cisplatin (DDP) on OS cells in vitro and in vivo. Therefore, our results suggest that Ecn may exert anti-OS effects at least partly through regulating Wnt/β-catenin and p38 signaling pathways. Most meaningfully, the results obtained suggest a potential strategy to improve the DDP-induced tumor-killing effect on OS cells by combining with Ecn

The Feasibility of Maintaining Biological Phosphorus Removal in A-Stage via the Short Sludge Retention Time Approach: System Performance, Functional Genus Abundance, and Methanogenic Potential

The increasing concerns on resource and energy recovery call for the modification of the current wastewater treatment strategy. This study synthetically evaluates the feasibility of the short sludge retention time approach to improve the energy recovery potential, but keeping steady biological phosphorus removal and system stability simultaneously. SBRS-SRT and SBRcontrol that simulated the short sludge retention time and conventional biological phosphorus removal processes, respectively, were set up to treat real domestic sewage for 120 d. SBRS-SRT achieved an efficient COD (91.5 ± 3.5%), PO43−-P (95.4 ± 3.8%), and TP (93.5 ± 3.7%) removal and maintained the settling volume index around 50 mL/gSS when the sludge retention time was 3 d, indicating steady operational stability. The poor ammonia removal performance (15.7 ± 7.7%) and a few sequences detected in samples collected in SBRS-SRT indicated the washout of nitrifiers. The dominant phosphorus accumulating organisms Tetrasphaera and Hydrogenophaga, which were enriched with the shortened sludge retention time, was in line with the excellent phosphorus performance of SBRS-SRT. The calculated methanogenic efficiency of SBRS-SRT increased significantly, which was in line with the higher sludge yield. This study proved that the short sludge retention time is a promising and practical approach to integrate biological phosphorus removal in A-stage when re-engineering a biological nutrient removal process