TD^2-Net: Toward Denoising and Debiasing for Dynamic Scene Graph Generation

Dynamic scene graph generation (SGG) focuses on detecting objects in a video and determining their pairwise relationships. Existing dynamic SGG methods usually suffer from several issues, including 1) Contextual noise, as some frames might contain occluded and blurred objects. 2) Label bias, primarily due to the high imbalance between a few positive relationship samples and numerous negative ones. Additionally, the distribution of relationships exhibits a long-tailed pattern. To address the above problems, in this paper, we introduce a network named TD$^2$-Net that aims at denoising and debiasing for dynamic SGG. Specifically, we first propose a denoising spatio-temporal transformer module that enhances object representation with robust contextual information. This is achieved by designing a differentiable Top-K object selector that utilizes the gumbel-softmax sampling strategy to select the relevant neighborhood for each object. Second, we introduce an asymmetrical reweighting loss to relieve the issue of label bias. This loss function integrates asymmetry focusing factors and the volume of samples to adjust the weights assigned to individual samples. Systematic experimental results demonstrate the superiority of our proposed TD$^2$-Net over existing state-of-the-art approaches on Action Genome databases. In more detail, TD$^2$-Net outperforms the second-best competitors by 12.7 \% on mean-Recall@10 for predicate classification.Comment: Accepted by AAAI 202

Zero-Shot Wireless Indoor Navigation through Physics-Informed Reinforcement Learning

The growing focus on indoor robot navigation utilizing wireless signals has stemmed from the capability of these signals to capture high-resolution angular and temporal measurements. Prior heuristic-based methods, based on radio frequency propagation, are intuitive and generalizable across simple scenarios, yet fail to navigate in complex environments. On the other hand, end-to-end (e2e) deep reinforcement learning (RL), powered by advanced computing machinery, can explore the entire state space, delivering surprising performance when facing complex wireless environments. However, the price to pay is the astronomical amount of training samples, and the resulting policy, without fine-tuning (zero-shot), is unable to navigate efficiently in new scenarios unseen in the training phase. To equip the navigation agent with sample-efficient learning and {zero-shot} generalization, this work proposes a novel physics-informed RL (PIRL) where a distance-to-target-based cost (standard in e2e) is augmented with physics-informed reward shaping. The key intuition is that wireless environments vary, but physics laws persist. After learning to utilize the physics information, the agent can transfer this knowledge across different tasks and navigate in an unknown environment without fine-tuning. The proposed PIRL is evaluated using a wireless digital twin (WDT) built upon simulations of a large class of indoor environments from the AI Habitat dataset augmented with electromagnetic (EM) radiation simulation for wireless signals. It is shown that the PIRL significantly outperforms both e2e RL and heuristic-based solutions in terms of generalization and performance. Source code is available at \url{https://github.com/Panshark/PIRL-WIN}.Comment: 16 pages, 13 figures, 4 table

Generative Adversarial Mapping Networks

Generative Adversarial Networks (GANs) have shown impressive performance in generating photo-realistic images. They fit generative models by minimizing certain distance measure between the real image distribution and the generated data distribution. Several distance measures have been used, such as Jensen-Shannon divergence, $f$-divergence, and Wasserstein distance, and choosing an appropriate distance measure is very important for training the generative network. In this paper, we choose to use the maximum mean discrepancy (MMD) as the distance metric, which has several nice theoretical guarantees. In fact, generative moment matching network (GMMN) (Li, Swersky, and Zemel 2015) is such a generative model which contains only one generator network $G$ trained by directly minimizing MMD between the real and generated distributions. However, it fails to generate meaningful samples on challenging benchmark datasets, such as CIFAR-10 and LSUN. To improve on GMMN, we propose to add an extra network $F$, called mapper. $F$ maps both real data distribution and generated data distribution from the original data space to a feature representation space $\mathcal{R}$, and it is trained to maximize MMD between the two mapped distributions in $\mathcal{R}$, while the generator $G$ tries to minimize the MMD. We call the new model generative adversarial mapping networks (GAMNs). We demonstrate that the adversarial mapper $F$ can help $G$ to better capture the underlying data distribution. We also show that GAMN significantly outperforms GMMN, and is also superior to or comparable with other state-of-the-art GAN based methods on MNIST, CIFAR-10 and LSUN-Bedrooms datasets.Comment: 9 pages, 7 figure

Hepatitis C Seroprevalence and Associated Risk Factors, Anyang, China

Hepatitis C virus screening was conducted among 8,226 residents 25–65 years of age in 4 counties of China; virus prevalence was 0.9%. A subsequent case–control study indicated blood transfusion (odds ratio [OR] 4.55), esophageal balloon examination (OR 3.78), and intravenous injection (OR 5.83) were associated with infection

The Anyang Esophageal Cancer Cohort Study: Study Design, Implementation of Fieldwork, and Use of Computer-Aided Survey System

Background: Human papillomavirus (HPV) has been observed repeatedly in esophageal squamous cell carcinoma (ESCC) tissues. However, the causal relationship between HPV infection and the onset of ESCC remains unknown. A large cohort study focusing on this topic is being carried out in rural Anyang, China. Methodology/Principal Findings: The Anyang Esophageal Cancer Cohort Study (AECCS) is a population-based prospective endoscopic cohort study designed to investigate the association of HPV infection and ESCC. This paper provides information regarding the design and implementation of this study. In particular we describe the recruitment strategies and quality control procedures which have been put into place, and the custom designed computer-aided survey system (CASS) used for this project. This system integrates barcode technology and unique identification numbers, and has been developed to facilitate real-time data management throughout the workflow using a wireless local area network. A total of 8,112 (75.3%) of invited subjects participated in the baseline endoscopic examination; of those invited two years later to take part in the first cycle of follow-up, 91.9 % have complied. Conclusions/Significance: The AECCS study has high potential for evaluating the causal relationship between HPV infection and the occurrence of ESCC. The experience in setting up the AECCS may be beneficial for others planning to initiate simila

CDC20 facilitates the proliferation of esophageal carcinoma cell by stabilizing NLRP3 expression

Background and purpose: Esophageal carcinoma (ESCA) is one of the malignant tumors with high mortality rate, and the underlying mechanism of its development is largely unknown. CDC20 plays an important role in tumorigenesis, and its dysregulated expression is closely related to tumor occurrence and development. The expression of CDC20 is increased in a variety of tumors, and knocking down CDC20 can inhibit tumor cell proliferation. NLRP3 is the main component of the inflammasome, and inflammasome is also closely related to tumor occurrence and development. Here, our study aimed to investigate whether CDC20 promotes the proliferation of ESCA cells through NLRP3 and its regulatory mechanism. Methods: The expression levels of CDC20 and NLRP3 genes in ESCA patients were analyzed using The Cancer Genome Atlas (TCGA) detabase and GTEx public database. We collected clinical and pathological data and tissues from 80 ESCA patients at the First Affiliated Hospital of Xinxiang Medical College, and detected the protein expression of NLRP3 in ESCA patients through immunohistochemistry staining. This study was approved by the Ethics Committee of the First Affiliated Hospital of Xinxiang Medical College (Number: EC-021-137). We studied the effects of knocking down CDC20 and NLRP3 gene on the proliferation ability of esophageal squamous cell carcinoma cells EC9706 and KYSE150 using short hairpin RNA (shRNA) technology. Co-immunoprecipitation (Co-IP), proteasome inhibitors and ubiquitination experiments were used to detect whether CDC20 interacts with NLRP3, and to elucidate whether CDC20 regulates NLRP3 expression through the ubiquitination pathway. This study was approved by the Ethics Committee of the First Affiliated Hospital of Xinxiang Medical College (Number: EC-021-137). Results: The TCGA database analysis showed that the expression levels of CDC20 and NLRP3 mRNA were significantly higher in the cancer tissues of ESCA patients than in the adjacent tissues. The immunohistochemistry results further showed that compared with adjacent tissues, the protein expression levels of CDC20 and NLRP3 were increased in ESCA tissues. Knocking down CDC20 and NLRP3 genes inhibited the proliferation of ESCA cells. Co-IP, proteasome inhibitors and ubiquitination experiments confirmed that CDC20 interacted with NLRP3 through its leucine-rich repeat (LRR), and CDC20 stabilized its expression by promoting NLRP3 ubiquitination. Conclusion: CDC20 and NLRP3 are upregulated in ESCA tissues, and CDC20 stabilizes their expression through ubiquitination of NLRP3, promoting ESCA cell proliferation. This suggests that CDC20 and NLRP3 may be potential diagnostic targets for ESCA

Oral microbiome and risk of malignant esophageal lesions in a high-risk area of China: A nested case-control study.

OBJECTIVE: We aimed to prospectively evaluate the association of oral microbiome with malignant esophageal lesions and its predictive potential as a biomarker of risk. METHODS: We conducted a case-control study nested within a population-based cohort with up to 8 visits of oral swab collection for each subject over an 11-year period in a high-risk area for esophageal cancer in China. The oral microbiome was evaluated with 16S ribosomal RNA (rRNA) gene sequencing in 428 pre-diagnostic oral specimens from 84 cases with esophageal lesions of severe squamous dysplasia and above (SDA) and 168 matched healthy controls. DESeq analysis was performed to identify taxa of differential abundance. Differential oral species together with subject characteristics were evaluated for their potential in predicting SDA risk by constructing conditional logistic regression models. RESULTS: A total of 125 taxa including 37 named species showed significantly different abundance between SDA cases and controls (all P0.84. CONCLUSIONS: The oral microbiome may play an etiological and predictive role in esophageal cancer, and it holds promise as a non-invasive early warning biomarker for risk stratification for esophageal cancer screening programs

Effect of external beam radiation therapy versus transcatheter arterial chemoembolization for non-diffuse hepatocellular carcinoma (≥ 5 cm): a multicenter experience over a ten-year period

BackgroundThe optimal local treatment for HCC with tumor diameter ≥ 5 cm is not well established. This research evaluated the effectiveness of external beam radiation therapy (EBRT) versus transcatheter arterial chemoembolization (TACE) for HCC with tumor diameter ≥ 5 cm.MethodsA total of 1210 HCC patients were enrolled in this study, including 302 and 908 patients that received EBRT and TACE, respectively. Propensity score matching (PSM) was used to identify patient pairs with similar baseline characteristics. Overall survival (OS) was the primary study endpoint.ResultsWe identified 428 patients using 1:1 PSM for survival comparison. Compared with the TACE group, the EBRT group had a significantly longer median OS (mOS) before (14.9 vs. 12.3 months, p = 0.0085) and after (16.8 vs. 11.4 months, p = 0.0026) matching. In the subgroup analysis, compared with the TACE group, the EBRT group had a significantly longer mOS for HCC with tumor diameters of 5-7 cm (34.1 vs. 14.3 months, p = 0.04) and 7-10 cm (34.4 vs. 10 months, p = 0.00065), whereas for HCC with tumor diameters ≥ 10 cm, no significant difference in mOS was observed (11.2 vs. 11.2 months, p = 0.83). In addition, the multivariable Cox analysis showed that Child-A, alkaline phosphatase < 125 U/L, and EBRT were independent prognostic indicators for longer survival.ConclusionEBRT is more effective than TACE as the primary local treatment for HCC with tumor diameter ≥ 5 cm, especially for HCC with tumor diameter of 5-10 cm