A Robust Algorithm for Shadow Removal of Foreground Detection

Abstract-We proposed an accurate algorithm to prevent moving shadows from being misclassified as part of moving objects in video target segmentation in this paper. Firstly, moving objects were achieved through background subtraction using adaptive Gaussian mixture models. Then, moving shadows were eliminated by a shadow detection algorithm. Finally, we performed a morphological reconstruction algorithm to recover the foreground distorted after shadow removal process. The experimental results proved its validity and accuracy in various fixed outdoor video scenes

MAFW: A Large-scale, Multi-modal, Compound Affective Database for Dynamic Facial Expression Recognition in the Wild

Dynamic facial expression recognition (FER) databases provide important data support for affective computing and applications. However, most FER databases are annotated with several basic mutually exclusive emotional categories and contain only one modality, e.g., videos. The monotonous labels and modality cannot accurately imitate human emotions and fulfill applications in the real world. In this paper, we propose MAFW, a large-scale multi-modal compound affective database with 10,045 video-audio clips in the wild. Each clip is annotated with a compound emotional category and a couple of sentences that describe the subjects' affective behaviors in the clip. For the compound emotion annotation, each clip is categorized into one or more of the 11 widely-used emotions, i.e., anger, disgust, fear, happiness, neutral, sadness, surprise, contempt, anxiety, helplessness, and disappointment. To ensure high quality of the labels, we filter out the unreliable annotations by an Expectation Maximization (EM) algorithm, and then obtain 11 single-label emotion categories and 32 multi-label emotion categories. To the best of our knowledge, MAFW is the first in-the-wild multi-modal database annotated with compound emotion annotations and emotion-related captions. Additionally, we also propose a novel Transformer-based expression snippet feature learning method to recognize the compound emotions leveraging the expression-change relations among different emotions and modalities. Extensive experiments on MAFW database show the advantages of the proposed method over other state-of-the-art methods for both uni- and multi-modal FER. Our MAFW database is publicly available from https://mafw-database.github.io/MAFW.Comment: This paper has been accepted by ACM MM'2

CO2 dissociation activated through electron attachment on reduced rutile TiO2(110)-1x1 surface

Converting CO$_2$ to useful compounds through the solar photocatalytic reduction has been one of the most promising strategies for artificial carbon recycling. The highly relevant photocatalytic substrate for CO$_2$ conversion has been the popular TiO$_2$ surfaces. However, the lack of accurate fundamental parameters that determine the CO$_2$ reduction on TiO$_2$ has limited our ability to control these complicated photocatalysis processes. We have systematically studied the reduction of CO2 at specific sites of the rutile TiO$_2$(110)-1x1 surface using scanning tunneling microscopy at 80 K. The dissociation of CO2 molecules is found to be activated by one electron attachment process and its energy threshold, corresponding to the CO$_2^{\dot-}$/CO$_2$ redox potential, is unambiguously determined to be 2.3 eV higher than the onset of the TiO$_2$ conduction band. The dissociation rate as a function of electron injection energy is also provided. Such information can be used as practical guidelines for the design of effective catalysts for CO$_2$ photoreduction

Neuroendocrine pathways and breast cancer progression : a pooled analysis of somatic mutations and gene expression from two large breast cancer cohorts

Funding Information: Open access funding provided by Karolinska Institute. This work was supported by grants awarded to KH by the China Scholarship Council (No. 201806240005); to FF by the Swedish Cancer Society (20 0846 PjF); to DL by the National Natural Science Foundation of China (No. 8187111500) and the Swedish Research Council (2018–00648). The funding bodies did not play any role in the design of the study and collection, analysis, or interpretation of data or in writing the manuscript. Funding Information: We thank the West China Biobank, Department of Clinical Research Management, West China Hospital, Sichuan University for the bio-sample storage. We thank Dr. Jianming Zeng (University of Macau) and his team biotrainee for generously sharing their experiences and codes. The results shown here are in part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga. This work was presented as an e-Poster (215P) in ESMO Congress 2021, 16-21 September 2021. Publisher Copyright: © 2022, The Author(s).Background: Experimental studies indicate that neuroendocrine pathways might play a role in progression of breast cancer. We aim to test the hypothesis that somatic mutations in the genes of neuroendocrine pathways influence breast cancer prognosis, through dysregulated gene expression in tumor tissue. Methods: We conducted an extreme case–control study including 208 breast cancer patients with poor invasive disease-free survival (iDFS) and 208 patients with favorable iDFS who were individually matched on molecular subtype from the Breast Cancer Cohort at West China Hospital (WCH; N = 192) and The Cancer Genome Atlas (TCGA; N = 224). Whole exome sequencing and RNA sequencing of tumor and paired normal breast tissues were performed. Adrenergic, glucocorticoid, dopaminergic, serotonergic, and cholinergic pathways were assessed for differences in mutation burden and gene expression in relation to breast cancer iDFS using the logistic regression and global test, respectively. Results: In the pooled analysis, presence of any somatic mutation (odds ratio = 1.66, 95% CI: 1.07–2.58) of the glucocorticoid pathway was associated with poor iDFS and a two-fold increase of tumor mutation burden was associated with 17% elevated odds (95% CI: 2–35%), after adjustment for cohort membership, age, menopausal status, molecular subtype, and tumor stage. Differential expression of genes in the glucocorticoid pathway in tumor tissue (P = 0.028), but not normal tissue (P = 0.701), was associated with poor iDFS. Somatic mutation of the adrenergic and cholinergic pathways was significantly associated with iDFS in WCH, but not in TCGA. Conclusion: Glucocorticoid pathway may play a role in breast cancer prognosis through differential mutations and expression. Further characterization of its functional role may open new avenues for the development of novel therapeutic targets for breast cancer.Peer reviewe

Evaluating Shipping Efficiency in Chinese Port Cities: Four-Stage Bootstrap DEA Model

This study examines shipping efficiency and its influencing factors in 19 Chinese port cities using a four-stage bootstrap DEA approach. Infrastructure, asset investment, and labor are selected as its inputs; cargo throughput and cargo turnover are selected as the outputs. First, in the three-stage DEA model, the SFA regression method is used to eliminate the impact of external environmental factors and random factors on shipping efficiency. Furthermore, the Bootstrap DEA method is applied to correct deviation to solve the problem of the traditional DEA method being sensitive to the number of variables of a chosen sample. Finally, the real shipping efficiency of the port cities is measured. The empirical results show that the shipping efficiency of each port city is affected by the factors of foreign trade, population size, economic development, consumption level, and government support. Additionally, the average efficiency values of port cities in the eastern region is higher than the general average at each stage; on the contrary, the average efficiency values in the central and western regions are lower than the general average. Finally, the study provides policy implications for the future improvement of shipping efficiency

Evaluating Shipping Efficiency in Chinese Port Cities: Four-Stage Bootstrap DEA Model

This study examines shipping efficiency and its influencing factors in 19 Chinese port cities using a four-stage bootstrap DEA approach. Infrastructure, asset investment, and labor are selected as its inputs; cargo throughput and cargo turnover are selected as the outputs. First, in the three-stage DEA model, the SFA regression method is used to eliminate the impact of external environmental factors and random factors on shipping efficiency. Furthermore, the Bootstrap DEA method is applied to correct deviation to solve the problem of the traditional DEA method being sensitive to the number of variables of a chosen sample. Finally, the real shipping efficiency of the port cities is measured. The empirical results show that the shipping efficiency of each port city is affected by the factors of foreign trade, population size, economic development, consumption level, and government support. Additionally, the average efficiency values of port cities in the eastern region is higher than the general average at each stage; on the contrary, the average efficiency values in the central and western regions are lower than the general average. Finally, the study provides policy implications for the future improvement of shipping efficiency

Inventory policy for deteriorating seasonal products with price and ramp-type time dependent demand

In this paper, we investigate the problem of simultaneously determining the order quantity and optimal prices for deteriorating products with price and ramp-type time dependent demand. We assume that a retailer has the opportunity to adjust prices before the end of the sales season to increase demand, decrease deterioration, and improve revenues. A mathematical model is developed to jointly optimize the order quantity, time interval for any two successive price changes, and the corresponding prices. An algorithm is provided to find the optimal solution to the proposed model. Finally, we use a numerical example to verify the availability of this model

Optimization of Spoken Term Detection System

Generally speaking, spoken term detection system will degrade significantly because of mismatch between acoustic model and spontaneous speech. This paper presents an improved spoken term detection strategy, which integrated with a novel phoneme confusion matrix and an improved word-level minimum classification error (MCE) training method. The first technique is presented to improve spoken term detection rate while the second one is adopted to reject false accepts. On mandarin conversational telephone speech (CTS), the proposed methods reduce the equal error rate (EER) by 8.4% in relative