MEDL-U: Uncertainty-aware 3D Automatic Annotation based on Evidential Deep Learning

Advancements in deep learning-based 3D object detection necessitate the availability of large-scale datasets. However, this requirement introduces the challenge of manual annotation, which is often both burdensome and time-consuming. To tackle this issue, the literature has seen the emergence of several weakly supervised frameworks for 3D object detection which can automatically generate pseudo labels for unlabeled data. Nevertheless, these generated pseudo labels contain noise and are not as accurate as those labeled by humans. In this paper, we present the first approach that addresses the inherent ambiguities present in pseudo labels by introducing an Evidential Deep Learning (EDL) based uncertainty estimation framework. Specifically, we propose MEDL-U, an EDL framework based on MTrans, which not only generates pseudo labels but also quantifies the associated uncertainties. However, applying EDL to 3D object detection presents three primary challenges: (1) relatively lower pseudolabel quality in comparison to other autolabelers; (2) excessively high evidential uncertainty estimates; and (3) lack of clear interpretability and effective utilization of uncertainties for downstream tasks. We tackle these issues through the introduction of an uncertainty-aware IoU-based loss, an evidence-aware multi-task loss function, and the implementation of a post-processing stage for uncertainty refinement. Our experimental results demonstrate that probabilistic detectors trained using the outputs of MEDL-U surpass deterministic detectors trained using outputs from previous 3D annotators on the KITTI val set for all difficulty levels. Moreover, MEDL-U achieves state-of-the-art results on the KITTI official test set compared to existing 3D automatic annotators.Comment: 6 pages Main, 1 page Reference, 5 pages Appendi

KD-EKF: A Consistent Cooperative Localization Estimator Based on Kalman Decomposition

In this paper, we revisit the inconsistency problem of EKF-based cooperative localization (CL) from the perspective of system decomposition. By transforming the linearized system used by the standard EKF into its Kalman observable canonical form, the observable and unobservable components of the system are separated. Consequently, the factors causing the dimension reduction of the unobservable subspace are explicitly isolated in the state propagation and measurement Jacobians of the Kalman observable canonical form. Motivated by these insights, we propose a new CL algorithm called KD-EKF which aims to enhance consistency. The key idea behind the KD-EKF algorithm involves perform state estimation in the transformed coordinates so as to eliminate the influencing factors of observability in the Kalman observable canonical form. As a result, the KD-EKF algorithm ensures correct observability properties and consistency. We extensively verify the effectiveness of the KD-EKF algorithm through both Monte Carlo simulations and real-world experiments. The results demonstrate that the KD-EKF outperforms state-of-the-art algorithms in terms of accuracy and consistency

BAGEL: Backdoor Attacks against Federated Contrastive Learning

Federated Contrastive Learning (FCL) is an emerging privacy-preserving paradigm in distributed learning for unlabeled data. In FCL, distributed parties collaboratively learn a global encoder with unlabeled data, and the global encoder could be widely used as a feature extractor to build models for many downstream tasks. However, FCL is also vulnerable to many security threats (e.g., backdoor attacks) due to its distributed nature, which are seldom investigated in existing solutions. In this paper, we study the backdoor attack against FCL as a pioneer research, to illustrate how backdoor attacks on distributed local clients act on downstream tasks. Specifically, in our system, malicious clients can successfully inject a backdoor into the global encoder by uploading poisoned local updates, thus downstream models built with this global encoder will also inherit the backdoor. We also investigate how to inject backdoors into multiple downstream models, in terms of two different backdoor attacks, namely the \textit{centralized attack} and the \textit{decentralized attack}. Experiment results show that both the centralized and the decentralized attacks can inject backdoors into downstream models effectively with high attack success rates. Finally, we evaluate two defense methods against our proposed backdoor attacks in FCL, which indicates that the decentralized backdoor attack is more stealthy and harder to defend

Contamination of per- and polyfluoroalkyl substances in the water source from a typical agricultural area in North China

There is limited information on contaminations of per- and polyfluoroalkyl substances (PFASs) in the water source affected by agricultural activities. The contamination of PFASs was investigated in the sources of agricultural groundwater and nearby surface water from an important vegetable production base supply for Beijing and Tianjin, located in North China. Σ13PFAS concentrations ranged from 0.321 to 8.285 ng/L, with an average concentration of 2.022 ng/L in the groundwater and 19.673 ng/L in the surface water. With the increase in the carbon chain length, the mean concentrations of PFASs in groundwater generally showed a decreasing trend. The dominated congeners of short-chain perfluorobutanesulfonate and perfluorooctanoate acid (PFOA) were detected in all groundwater samples, with mean concentrations of 0.944 and 0.654 ng/L. The difference was that PFOA and perfluorooctanesulfonate (PFOS) were the dominant congeners in nearby surface water, with concentrations of 7.585 and 3.421 ng/L. Thus, the concentrations of PFOA and PFOS in the surface water were about 8.5 times higher than those in the groundwater, indicating that Σ13PFAS concentrations might decrease with the water migration from the overground to the underground. In addition, significant correlations were observed between PFASs and DOC/TN related to agricultural activities, suggesting a certain relationship existed between PFAS conger distributions and organic carbon/nutrients in water. Health risk assessment indicated that the PFAS exposure caused insignificantly immediate harm to residents in the studied area. This survey provided information on the sources, spatial distribution, and potential migration characteristics of PFASs in a typical agricultural area of North China

Modeling dual role preferences for trust-aware recommendation

Unlike in general recommendation scenarios where a user has only a single role, users in trust rating network, e.g. Epinions, are associated with two different roles simultaneously: as a truster and as a trustee. With different roles, users can show distinct preferences for rating items, which the previous approaches do not involve. Moreover, based on explicit single links between two users, existing methods can not capture the implicit correlation between two users who are similar but not socially connected. In this paper, we propose to learn dual role preferences (truster/trustee-specific preferences) for trust-aware recommendation by modeling explicit interactions (e.g., rating and trust) and implicit interactions. In particular, local links structure of trust network are exploited as two regularization terms to capture the implicit user correlation, in terms of truster/trustee-specific preferences. Using a real-world and open dataset, we conduct a comprehensive experimental study to investigate the performance of the proposed model, RoRec. The results show that RoRec outperforms other trust-aware recommendation approaches, in terms of prediction accuracy. Copyright 2014 ACM

SoRank: incorporating social information into learning to rank models for recommendation

Most existing learning to rank based recommendation methods only use user-item preferences to rank items, while neglecting social relations among users. In this paper, we propose a novel, effective and efficient model, SoRank, by integrating social information among users into listwise ranking model to improve quality of ranked list of items. In addition, with linear complexity to the number of observed ratings, SoRank is able to scale to very large dataset. Experimental results on publicly available dataset demonstrate the effectiveness of SoRank

Large tumor suppressor kinase 2 overexpression attenuates 5-FU-resistance in colorectal cancer via activating the JNK-MIEF1-mitochondrial division pathway

Abstract Background 5-Fluorouracil (5-FU) is a standard treatment for colorectal cancer, but most patients develop 5-FU resistance. Here, we conducted experiments to identify an effective approach to augment 5-FU-based treatment in colorectal cancer in vitro. Methods SW480 cells were in the present study and treated with 5-FU. Besides, LATS2 adenovirus vectors were infected into SW480 cells. Western blotting, immunofluorescence and ELISA were used to evaluate cell death and mitochondrial function. Pathway blocker was used to verify the role of MAPK-JNK pathway in SW480 cell death. Results An obvious drop in large tumor suppressor kinase 2 (LATS2) expression was observed in SW480 cells after treatment with 5-FU. In addition, upregulation of LATS2 expression through infection with LATS2 adenovirus further increased the reduction of SW480 cell viability induced by 5-FU. Functional exploration showed that 5-FU treatment suppressed mitochondrial membrane potential, enhanced cyt-c release into the nucleus, induced an oxidative injury environment by promoting ROS production, and eventually upregulated Bax-related mitochondrial apoptosis. Besides, LATS2 overexpression in combination with 5-FU treatment further perturbed mitochondrial homeostasis, and this effect was achieved by elevating mitochondrial division. Mechanistically, LATS2 overexpression and 5-FU co-treatment amplified mitochondrial division by upregulating MIEF1 expression in a manner dependent on MAPK-JNK axis. Knockdown of MIEF1 using an siRNA-mediated loss of function assay and/or inhibition of the MAPK-JNK pathway using the specific inhibitor SP600125 abolished LATS2/5-FU-mediated deleterious effects on mitochondrial performance and SW480 cell viability. Conclusions In light of the above findings, LATS2 downregulation could be a potential mechanism of low response to 5-FU treatment. Overexpression of LATS2 to further disrupt mitochondrial function via the JNK-MIEF1 signalling pathway might be a method to optimize 5-FU-based chemotherapy

Anisotropic percolation of SiC−Carbon nanotube hybrids: a new route toward thermally conductive High ‑k polymer composites

International audiencePercolation of carbon nanotubes (CNT) has been widely exploited in various polymer matrices to largely improve thedielectric constant or thermal conductivity of heterogeneous polymer composites. However, so far it is still very challenging tosimultaneously enhance both while maintaining the low losses of polymers. Herein, we demonstrate a thermally conductive high-k material with low losses by establishing anisotropic percolation of multiscale SiC−CNT hybrids within poly(vinylidenefluoride) (PVDF) matrix. Indeed, the SiC−CNT/PVDF composite exhibits a much lower electrical percolation threshold (1.23 wt %) along the in-plane direction than that (1.89 wt %) perpendicular to it. By locating CNT content (1.5 wt %) between them, the composite displays unprecedented dielectric properties in the out-of-plane direction with a dielectric constant as high as 714 and the loss tangent of 0.49, while the thermal conductivity improved by 200% as compared with the virgin polymer along the in-plane direction. The true anisotropy in electrical, dielectric, and thermal properties is elucidated by invoking percolation theory on the basis of the rod geometry and spatial orientation of the hybrids

A graph-based model for context-aware recommendation using implicit feedback data

Recommender systems have been successfully dealing with the problem of information overload. However, most recommendation methods suit to the scenarios where explicit feedback, e.g. ratings, are available, but might not be suitable for the most common scenarios with only implicit feedback. In addition, most existing methods only focus on user and item dimensions and neglect any additional contextual information, such as time and location. In this paper, we propose a graph-based generic recommendation framework, which constructs a Multi-Layer Context Graph (MLCG) from implicit feedback data, and then performs ranking algorithms in MLCG for context-aware recommendation. Specifically, MLCG incorporates a variety of contextual information into a recommendation process and models the interactions between users and items. Moreover, based on MLCG, two novel ranking methods are developed: Context-aware Personalized Random Walk (CPRW) captures user preferences and current situations, and Semantic Path-based Random Walk (SPRW) incorporates semantics of paths in MLCG into random walk model for recommendation. The experiments on two real-world datasets demonstrate the effectiveness of our approach