Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations

In recent years, computer vision has made remarkable advancements in autonomous driving and robotics. However, it has been observed that deep learning-based visual perception models lack robustness when faced with camera motion perturbations. The current certification process for assessing robustness is costly and time-consuming due to the extensive number of image projections required for Monte Carlo sampling in the 3D camera motion space. To address these challenges, we present a novel, efficient, and practical framework for certifying the robustness of 3D-2D projective transformations against camera motion perturbations. Our approach leverages a smoothing distribution over the 2D pixel space instead of in the 3D physical space, eliminating the need for costly camera motion sampling and significantly enhancing the efficiency of robustness certifications. With the pixel-wise smoothed classifier, we are able to fully upper bound the projection errors using a technique of uniform partitioning in camera motion space. Additionally, we extend our certification framework to a more general scenario where only a single-frame point cloud is required in the projection oracle. This is achieved by deriving Lipschitz-based approximated partition intervals. Through extensive experimentation, we validate the trade-off between effectiveness and efficiency enabled by our proposed method. Remarkably, our approach achieves approximately 80% certified accuracy while utilizing only 30% of the projected image frames.Comment: 32 pages, 5 figures, 13 table

Certifying Out-of-Domain Generalization for Blackbox Functions

Certifying the robustness of model performance under bounded data distribution drifts has recently attracted intensive interest under the umbrella of distributional robustness. However, existing techniques either make strong assumptions on the model class and loss functions that can be certified, such as smoothness expressed via Lipschitz continuity of gradients, or require to solve complex optimization problems. As a result, the wider application of these techniques is currently limited by its scalability and flexibility -- these techniques often do not scale to large-scale datasets with modern deep neural networks or cannot handle loss functions which may be non-smooth such as the 0-1 loss. In this paper, we focus on the problem of certifying distributional robustness for blackbox models and bounded loss functions, and propose a novel certification framework based on the Hellinger distance. Our certification technique scales to ImageNet-scale datasets, complex models, and a diverse set of loss functions. We then focus on one specific application enabled by such scalability and flexibility, i.e., certifying out-of-domain generalization for large neural networks and loss functions such as accuracy and AUC. We experimentally validate our certification method on a number of datasets, ranging from ImageNet, where we provide the first non-vacuous certified out-of-domain generalization, to smaller classification tasks where we are able to compare with the state-of-the-art and show that our method performs considerably better.Comment: 39th International Conference on Machine Learning (ICML) 202

FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data

Federated learning (FL) allows agents to jointly train a global model without sharing their local data. However, due to the heterogeneous nature of local data, it is challenging to optimize or even define fairness of the trained global model for the agents. For instance, existing work usually considers accuracy equity as fairness for different agents in FL, which is limited, especially under the heterogeneous setting, since it is intuitively "unfair" to enforce agents with high-quality data to achieve similar accuracy to those who contribute low-quality data, which may discourage the agents from participating in FL. In this work, we propose a formal FL fairness definition, fairness via agent-awareness (FAA), which takes different contributions of heterogeneous agents into account. Under FAA, the performance of agents with high-quality data will not be sacrificed just due to the existence of large amounts of agents with low-quality data. In addition, we propose a fair FL training algorithm based on agent clustering (FOCUS) to achieve fairness in FL measured by FAA. Theoretically, we prove the convergence and optimality of FOCUS under mild conditions for linear and general convex loss functions with bounded smoothness. We also prove that FOCUS always achieves higher fairness in terms of FAA compared with standard FedAvg under both linear and general convex loss functions. Empirically, we show that on four FL datasets, including synthetic data, images, and texts, FOCUS achieves significantly higher fairness in terms of FAA while maintaining competitive prediction accuracy compared with FedAvg and state-of-the-art fair FL algorithms

The potential role of RNA N6-methyladenosine in primary Sjögren’s syndrome

ObjectiveThe pathogenesis of primary Sjögren’s syndrome (pSS) remains incompletely understood. The N6-methyladenosine (m6A) RNA modification, the most abundant internal transcript modification, has close associations with multiple diseases. This study aimed to investigate the role of m6A in patients with pSS.Materials and methodsThis study enrolled 44 patients with pSS, 50 age- and gender-matched healthy controls (HCs), and 11 age- and gender-matched patients with non-SS sicca. We detected the messenger RNA (mRNA) levels of m6A elements (including METTL3, WTAP, RBM15, ALKBH5, FTO, YTHDF1, YTHDF2, YTHDF3, YTHDC1, and YTHDC2), ISG15, and USP18 in peripheral blood mononuclear cells (PBMCs) from patients with pSS, patients with non-SS sicca, and HCs. The clinical characteristics and laboratory findings of patients with pSS and patients with non-SS sicca were also collected. We used binary logistic regression to determine if m6A elements were risk factors for pSS.ResultsThe mRNA levels of m6A writers (METTL3 and RBM15), erasers (ALKBH5 and FTO), and readers (YTHDF1, YTHDF2, YTHDF3, YTHDC1, and YTHDC2) were all significantly higher in PBMCs from patients with pSS than in HCs. The mRNA levels of m6A writers (METTL3 and WTAP) and readers (YTHDF2, YTHDF3, and YTHDC2) were lower in PBMCs from patients with pSS compared to patients with non-SS sicca. The expression of METTL3, RBM15, FTO, YTHDF1, YTHDF2, YTHDC1, and YTHDC2 was positively correlated with the level of C-reactive protein (CRP) of patients with pSS. The mRNA level of YTHDF1 in PBMCs from patients with pSS was negatively correlated with the EULAR Sjögren’s syndrome disease activity index (ESSDAI) score. In patients with pSS, FTO, YTHDC1, and YTHDC2 were also related to white blood cells (WBCs), neutrophils, lymphocytes, and monocytes. Increased mRNA level of ALKBH5 in PBMCs was a risk factor for pSS, as determined by binary logistic regression analysis. The mRNA level of ISG15 was positively correlated with that of FTO, YTHDF2, YTHDF3, and YTHDC2 in patients with pSS.ConclusionCompared with HCs, the expression of METTL3, RBM15, ALKBH5, FTO, YTHDF1, YTHDF2, YTHDF3, YTHDC1, and YTHDC2 was considerably higher in PBMCs from patients with pSS. In comparison with patients with non-SS sicca, the expression of METTL3, WTAP, YTHDF2, YTHDF3, and YTHDC2 was reduced in PBMCs from patients with pSS. The m6A elements correlating with clinical variables may indicate the disease activity and inflammation status of pSS. Elevated expression of ALKBH5 was a risk factor for pSS. The dynamic process of m6A modification is active in pSS. m6A elements (FTO, YTHDF2, YTHDF3, or YTHDC2) might target ISG15, stimulate the expression of ISG15, and activate the type I IFN signaling pathway, playing an active role in initiating the autoimmunity in pSS

Improving Certified Robustness via Statistical Learning with Logical Reasoning

Intensive algorithmic efforts have been made to enable the rapid improvements of certificated robustness for complex ML models recently. However, current robustness certification methods are only able to certify under a limited perturbation radius. Given that existing pure data-driven statistical approaches have reached a bottleneck, in this paper, we propose to integrate statistical ML models with knowledge (expressed as logical rules) as a reasoning component using Markov logic networks (MLN, so as to further improve the overall certified robustness. This opens new research questions about certifying the robustness of such a paradigm, especially the reasoning component (e.g., MLN). As the first step towards understanding these questions, we first prove that the computational complexity of certifying the robustness of MLN is #P-hard. Guided by this hardness result, we then derive the first certified robustness bound for MLN by carefully analyzing different model regimes. Finally, we conduct extensive experiments on five datasets including both high-dimensional images and natural language texts, and we show that the certified robustness with knowledge-based logical reasoning indeed significantly outperforms that of the state-of-the-art

Job burnout among primary healthcare workers during COVID-19 pandemic: cross-sectional study in China

ObjectiveThis study evaluated job burnout among primary healthcare workers (PHCWs) in China during the COVID-19 pandemic, explored its influencing factors, and examined PHCWs' preferences for reducing job burnout.MethodWe conducted a multicenter cross-sectional study in Heilongjiang, Sichuan, Anhui, Gansu, and Shandong Provinces. An electronic questionnaire survey was conducted through convenience sampling in communities from May to July 2022. We collected sociodemographic characteristics, job burnout level, job satisfaction, and preferred ways to reduce job burnout among PHCWs.ResultsThe job burnout rate among PHCWs in China was 59.87% (937/1565). Scores for each dimension of job burnout were lower among PHCWs who had a better work environment (emotional exhaustion OR: 0.60; depersonalization OR: 0.73; personal accomplishment OR: 0.76) and higher professional pride (emotional exhaustion OR: 0.63; depersonalization OR: 0.70; personal accomplishment OR: 0.44). PHCWs with higher work intensity (emotional exhaustion OR: 2.37; depersonalization OR: 1.34; personal accomplishment OR: 1.19) had higher scores in all job burnout dimensions. Improving work environments and raising salaries were the preferred ways for PHCWs to reduce job burnout.ConclusionStrategies should be developed to improve job satisfaction among PHCWs, enhance their professional identity, and alleviate burnout to ensure the effective operation of the healthcare system, especially during periods of overwork

Flotation separation of poly (ethylene terephthalate and vinyl chloride) mixtures based on clean corona modification: Optimization using response surface methodology

Postconsumer polyethylene terephthalate (PET) has potential applications in many areas of manufacturing, but contamination by hazardous polyvinyl chloride (PVC) in common waste streams can reduce its recyclable value. Separating collected PET-PVC mixtures before recycling remains very challenging because of the similar physicochemical properties of PET and PVC. Herein, we describe a novel flotation process with corona modification pretreatment to facilitate the separation of PET-PVC mixtures. Through water contact angle, surface free energy, X-ray photoelectron and FT-IR characterization, we found that polar hydroxyl groups can be more easily introduced on the PVC surface than on the PET surface induced by corona modification. This selective wetting can suppress the floatability of PVC, leading to the separation of PET as floating product. A reliable mechanism including two different hydrogen-abstraction pathways was established. Response surface methodology consisting of Plackett-Burman and Box-Behnken designs was adopted for optimization of the combined process, and control parameters were solved based on high-quality prediction models, with fitting from significant variables and interactions. For physical or chemical circulation strategies with PET purity prioritization, the validated purity of the product reached 96.05% at a 626 W corona power, 5.42 m/min passing speed, 24.78 mg/L frother concentration and 286 L/h air flow rate. For the energy recuperation strategy with PET recovery prioritization, the factual recovery reached 98.08% under a 601 W corona power, 6.04 m/min passing speed, 27.55 mg/L frother concentration and 184 L/h air flow rate. The current work provides technological insights into the cleaner disposal of waste plastics

Characterization of auxin transporter AUX, PIN and PILS gene families in pineapple and evaluation of expression profiles during reproductive development and under abiotic stresses

Polar auxin transport in plant is mediated by influx and efflux transporters, which are encoded by AUX/LAX, PIN and PILS genes, respectively. The auxin transporter gene families have been characterized in several species from monocots and eudicots. However, a genome-wide overview of auxin transporter gene families in pineapple is not yet available. In this study, we identified a total of threeAcAUX genes, 12 AcPIN genes, and seven AcPILS genes in the pineapple genome, which were variably located on 15 chromosomes. The exon-intron structure of these genes and properties of deduced proteins were relatively conserved within the same family. Most protein motifs were widespread in the AUX, PIN or PILS proteins, whereas a few motifs were absent in only one or two proteins. Analysis of the expression profiles of these genes elucidated that several genes exhibited either preferential or tissue-specific expression patterns in vegetative and/or reproductive tissues. AcAUX2 was specifically expressed in the early developmental ovules, while AcPIN1b and AcPILS2 were strongly expressed in stamens and ovules. AcPIN9b, AcPILS1, AcPILS6a, 6b and 6c were abundantly expressed in stamens. Furthermore, qRT-PCR results showed that several genes in these families were responsive to various abiotic stresses. Comparative analysis indicated that the genes with close evolutionary relationships among pineapple, rice and Arabidopsis exhibited similar expression patterns. Overexpression of the AcAUX1 in Arabidopsis rescued the phenotype in aux1-T, and resulted in increased lateral roots in WT. These results will provide new insights into auxin transporter genes of pineapple and facilitate our understanding of their roles in pineapple growth and development