S3C: Semi-Supervised VQA Natural Language Explanation via Self-Critical Learning

VQA Natural Language Explanation (VQA-NLE) task aims to explain the decision-making process of VQA models in natural language. Unlike traditional attention or gradient analysis, free-text rationales can be easier to understand and gain users' trust. Existing methods mostly use post-hoc or self-rationalization models to obtain a plausible explanation. However, these frameworks are bottlenecked by the following challenges: 1) the reasoning process cannot be faithfully responded to and suffer from the problem of logical inconsistency. 2) Human-annotated explanations are expensive and time-consuming to collect. In this paper, we propose a new Semi-Supervised VQA-NLE via Self-Critical Learning (S3C), which evaluates the candidate explanations by answering rewards to improve the logical consistency between answers and rationales. With a semi-supervised learning framework, the S3C can benefit from a tremendous amount of samples without human-annotated explanations. A large number of automatic measures and human evaluations all show the effectiveness of our method. Meanwhile, the framework achieves a new state-of-the-art performance on the two VQA-NLE datasets.Comment: CVPR202

Multi-layer reconstruction of skull base after endoscopic transnasal surgery for invasive pituitary adenomas

Objective. To explore the efficacy of multi-layer skull base reconstruction after endoscopic transnasal surgery for invasive pituitary adenomas (IPAs). Clinical rationale for the study. Skull base reconstruction for IPAs. Material and methods. This retrospective analysis involved 160 patients with IPAs who underwent operations from October 2018 to October 2020. All patients were diagnosed with IPAs by pituitary enhanced magnetic resonance imaging, and all tumours were confirmed to be Knosp grades 3a, 3b, or 4. The experimental group and the control group comprised 80 patients in each, and we used different methods to reconstruct the skull base in each group. The comparison indicators included cerebrospinal fluid leakage, sellar floor bone flap (or middle turbinate) shifting, delayed healing of the skull base reconstructed tissue, nasal discomfort, and epistaxis. We used the chi-square test, and p < 0.05 was considered statistically significant. Results. In the experimental group, cerebrospinal fluid leakage occurred intraoperatively in 73 patients, two of whom had cerebrospinal fluid leakage postoperatively. Brain CT 12 months postoperatively showed no sellar floor bone flap (or middle turbinate) shifting. Endoscopic transnasal checks performed seven days after surgery showed that the skull base reconstructed tissue had healed in 74 patients and had failed to heal in six. However, endoscopic transnasal checks showed that all six of these patients’ pedicled nasoseptal flaps had healed well by 14 days after surgery. Other sequelae comprised nasal discomfort in four patients, and epistaxis in four. In the control group, cerebrospinal fluid leakage occurred intraoperatively in 71 patients, 14 of whom had cerebrospinal fluid leakage postoperatively. Brain CT 12 months postoperatively showed floor bone flap (or middle turbinate) shifting in 12 patients. Endoscopic transnasal checks performed seven days after surgery showed that the skull base reconstructed tissue had healed in 65 patients. In 12 patients, pedicled nasoseptal flaps had healed well by 14 days after surgery, while the remaining three patients required reoperation. Other sequelae comprised nasal discomfort in five patients, and epistaxis in six. Conclusions. This new method of multi-layer skull base reconstruction could play an important role in endoscopic transnasal IPA surgery

MPHM: Model poisoning attacks on federal learning using historical information momentum

Federated learning(FL) development has grown increasingly strong with the increased emphasis on data for individuals and industry. Federated learning allows individual participants to jointly train a global model without sharing local data, which significantly enhances data privacy. However, federated learning is vulnerable to poisoning attacks by malicious participants. Since federated learning does not have access to the participants’ training process, i.e., attackers can compromise the global model by uploading elaborate malicious local updates to the server under the guise of normal participants. Current model poisoning attacks usually add small perturbations to the local model after it is trained to craft harmful local updates and the attacker finds the appropriate perturbation size to bypass robust detection methods and corrupt the global model as much as possible. In contrast, we propose a novel model poisoning attack based on the momentum of history information (MPHM), that is, the attacker makes new malicious updates by dynamically crafting perturbations using the historical information in the local training, which will make the new malicious updates more effective and stealthy. Our attack aims to indiscriminately reduce the testing accuracy of the global model with minimal information. Experiments show that in the classical defense case, our attack can significantly corrupt the accuracy of the global model compared to other advanced poisoning attacks

Association between ambient air pollution and hospital admissions, length of hospital stay and hospital cost for patients with cardiovascular diseases and comorbid diabetes mellitus: Base on 1,969,755 cases in Beijing, China, 2014–2019

Background: Evidence on the effects of the air pollutants on the hospital admissions, hospital cost and length of stay (LOS) among patients with comorbidities remains limited in China, particularly for patients with cardiovascular diseases and comorbid diabetes mellitus (CVD-DM). Methods: We collected daily data on CVD-DM patients from 242 hospitals in Beijing between 2014 and 2019. Generalized additive model was employed to quantify the associations between admissions, LOS, and hospital cost for CVD-DM patients and air pollutants. We further evaluated the attributable risk posed by air pollutants to CVD-DM patients, using both Chinese and WHO air quality guidelines as reference. Results: Per 10 ug/m3 increase of particles with an aerodynamic diameter \u3c 2.5 μm (PM2.5), particles with an aerodynamic diameter \u3c 10 μm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbonic oxide (CO) and ozone (O3) corresponded to a 0.64% (95% CI: 0.57 to 0.71), 0.52% (95% CI: 0.46 to 0.57), 0.93% (95% CI: 0.67 to 1.20), 0.98% (95% CI: 0.81 to 1.16), 1.66% (95% CI: 1.18 to 2.14) and 0.53% (95% CI: 0.45 to 0.61) increment for CVD-DM patients’ admissions. Among the six pollutants, particulate pollutants (PM2.5 and PM10) in most lag days exhibited adverse effects on LOS and hospital cost. For every 10 ug/m3 increase in PM2.5 and PM10, the absolute increase with LOS will increase 62.08 days (95% CI: 28.93 to 95.23) and 51.77 days (95% CI:22.88 to 80.66), respectively. The absolute increase with hospital cost will increase 105.04 Chinese Yuan (CNY) (95% CI: 49.27 to 160.81) and 81.76 CNY (95% CI: 42.01 to 121.51) in PM2.5 and PM10, respectively. Given WHO 2021 air quality guideline as the reference, PM2.5 had the maximum attributable fraction of 3.34% (95% CI: 2.94% to 3.75%), corresponding to an avoidable of 65,845 (95% CI: 57,953 to 73,812) patients with CVD-DM. Conclusion: PM2.5 and PM10 are positively associated with hospital admissions, hospital cost and LOS for patients with CVD-DM. Policy changes to reduce air pollutants exposure may reduce CVD-DM admissions and substantial savings in health care spending and LOS

Acute effect of particulate matter pollution on hospital admissions for cause-specific respiratory diseases among patients with and without type 2 diabetes in Beijing, China, from 2014 to 2020

BACKGROUND: Scientific studies have identified various adverse effects of particulate matter (PM) on respiratory disease (RD) and type 2 diabetes (T2D). However, whether short-term exposure to PM triggers the onset of RD with T2D, compared with RD without T2D, has not been elucidated. METHODS: A two-stage time-series study was conducted to evaluate the acute adverse effects of PM on admission for RD and for RD with and without T2D in Beijing, China, from 2014 to 2020. District-specific effects of PM and PM were estimated using the over-dispersed Poisson generalized addictive model after adjusting for weather conditions, day of the week, and long-term and seasonal trends. Meta-analyses were applied to pool the overall effects on overall and cause-specific RD, while the exposure-response (E-R) curves were evaluated using a cubic regression spline. RESULTS: A total of 1550,154 admission records for RD were retrieved during the study period. Meta-analysis suggested that per interquartile range upticks in the concentration of PM corresponded to 1.91% (95% CI: 1.33-2.49%), 2.16% (95% CI: 1.08-3.25%), and 1.92% (95% CI: 1.46-2.39%) increments in admission for RD, RD with T2D, and RD without T2D, respectively, at lag 0-8 days, lag 8 days, and lag 8 days. The effect size of PM was statistically significantly higher in the T2D group than in the group without T2D (z = 3.98, P \u3c 0.01). The effect sizes of PM were 3.86% (95% CI: 2.48-5.27%), 3.73% (95% CI: 1.72-5.79%), and 3.92% (95% CI: 2.65-5.21%), respectively, at lag 0-13 days, lag 13 days, and lag 13 days, respectively, and no statistically significant difference was observed between T2D groups (z = 0.24, P = 0.81). Significant difference was not observed between T2D groups for the associations of PM and different RD and could be found between three groups for effects of PM on RD without T2D. The E-R curves varied by sex, age and T2D condition subgroups for the associations between PM and daily RD admissions. CONCLUSIONS: Short-term PM exposure was associated with increased RD admission with and without T2D, and the effect size of PM was higher in patients with T2D than those without T2D

UHRF1 is required for basal stem cell proliferation in response to airway injury

Cellular senescence is a cell fate characterized by an irreversible cell cycle arrest, but the molecular mechanism underlying this senescence hallmark remains poorly understood. Through an unbiased search for novel senescence regulators in airway basal cells, we discovered that the epigenetic regulator ubiquitin-like with PHD and ring finger domain-containing protein 1 (UHRF1) is critical for regulating cell cycle progression. Upon injury, basal cells in the mouse airway rapidly induce the expression of UHRF1 in order to stimulate stem cell proliferation and tissue repair. Targeted depletion of Uhrf1 specifically in airway basal cells causes a profound defect in cell cycle progression. Consistently, cultured primary human basal cells lacking UHRF1 do not exhibit cell death or differentiation phenotypes but undergo a spontaneous program of senescence. Mechanistically, UHRF1 loss induces G1 cell cycle arrest by abrogating DNA replication factory formation as evidenced by loss of proliferating cell nuclear antigen (PCNA) puncta and an inability to enter the first cell cycle. This proliferation defect is partially mediated by the p15 pathway. Overall, our study provides the first evidence of an indispensable role of UHRF1 in somatic stem cells proliferation during the process of airway regeneration

Research into the Relationship between Personality and Behavior in Video Games, Based on Mining Association Rules

Nowadays, people have started to spend more and more time using the Internet, which has a crucial impact on people’s lives. Individual personality type is often the main factor dictating the various behaviors that people carry out, and it dominates their activities when socializing, communicating, and making choices in the virtual world. This study is dedicated to uncovering how the six dimensions of personality traits relate to players’ in-game behavior. This research is divided into two studies. Study 1 uses the K-means method to classify players in “Clash of Kings”, an online strategy video game, according to their activities. Using apriori algorithm, this research analyzes the correlation between in-game behavior and personality. In Study 2, the correlations are validated. In conclusion, not all personality traits are related to in-game behaviors. Players with high extraversion demonstrate more killings and attacks in games. Conscientiousness is negatively related to deaths. Emotionality shows strong extremes. The highest or lowest emotionality scores are associated with killings and attacks, while players with moderate emotionality will behave irregularly. Honesty/humility, agreeableness, and openness to experience are not predictive of in-game behaviors. For game manufacturers, players’ personality traits can be inferred through their corresponding in-game behaviors, to use in order to carry out targeted promotions

Double Prior Network for Multidegradation Remote Sensing Image Super-Resolution

Image super-resolution (SR) is widely used in remote sensing because it can effectively increase image details. Neural networks have shown remarkable performance in recent years, benefitting from their end-to-end training. However, remote sensing images contain a variety of degradation factors. Neural networks lack flexibility in dealing with these complex issues compared with reconstruction-based approaches. Traditional neural network methods cannot take advantage of prior knowledge and lack interpretability. To develop a flexible, accurate, and interpretable algorithm for remote sensing SR, we proposed an effective SR network called YSRNet. It is performed by unfolding a traditional optimization process into a learnable network. Combining conventional reconstruction-based methods and neural networks can significantly improve the algorithm's performance. Since the gradient features of remote sensing images contain valuable information, the total variation constraints and the deep prior constraints are introduced into the objective function for image SR. Furthermore, we propose an enhanced version called YSRNet+, which can apply attention weights to different prior terms and channels. Compared with the YSRNet, the YSRNet+ enables networks to focus more on useful prior information and improve the interpretability of networks. Experiments on three remote sensing datasets are performed to evaluate the algorithm's effectiveness. The experimental results demonstrate that the proposed algorithm performs better than some state-of-the-art neural network algorithms, especially in the scenario of the multidegradation factors

Multisource Information Fusion Network for Optical Remote Sensing Image Super-Resolution

The super-resolution algorithms based on deep learning can effectively increase optical remote sensing image (ORSI) details for further analysis tasks. Deep unfolding methods have been studied in recent years to bridge the gap between optimization-based and learning-based methods. However, these unfolding methods usually ignore the utilization of intermediate network features between different iteration stages, thereby limiting the performance of super-resolution results. We propose a multi-source information fusion network (MSFNet) for ORSI super-resolution to address this problem. We mainly consider three strategies to enhance the image super-resolution performance, including feature extraction strategy, information fusion strategy, and the structure of the unfolding network. Firstly, image information of various scales is helpful for mining potential features of images for image super-resolution. Therefore, we introduce multi-scale implicit constraints to the objective function. Secondly, we unfold the optimization process into a neural network by alternating direction method of multipliers (ADMM). This unfolding strategy can effectively utilize the prior information for image reconstruction. Thirdly, we propose a row-column decoupling Transformer module for feature fusion. Specifically, the row Transformer block completes the feature fusion of various scales, and the column Transformer block completes the feature fusion of various channels. The fused features are transmitted to the next iteration stage for feature enhancement. We perform experiments on three remote sensing image datasets to fully demonstrate the algorithm's effectiveness. Experiment results show that the proposed algorithm can achieve better image reconstruction performance