Identifiability of Label Noise Transition Matrix

The noise transition matrix plays a central role in the problem of learning with noisy labels. Among many other reasons, a large number of existing solutions rely on access to it. Identifying and estimating the transition matrix without ground truth labels is a critical and challenging task. When label noise transition depends on each instance, the problem of identifying the instance-dependent noise transition matrix becomes substantially more challenging. Despite recent works proposing solutions for learning from instance-dependent noisy labels, the field lacks a unified understanding of when such a problem remains identifiable. The goal of this paper is to characterize the identifiability of the label noise transition matrix. Building on Kruskal's identifiability results, we are able to show the necessity of multiple noisy labels in identifying the noise transition matrix for the generic case at the instance level. We further instantiate the results to explain the successes of the state-of-the-art solutions and how additional assumptions alleviated the requirement of multiple noisy labels. Our result also reveals that disentangled features are helpful in the above identification task and we provide empirical evidence.Comment: Preprint. Under review. For questions please contact [email protected]

Interfacial thermal conductance in graphene/black phosphorus heterogeneous structures

Graphene, as a passivation layer, can be used to protect the black phosphorus from the chemical reaction with surrounding oxygen and water. However, black phosphorus and graphene heterostructures have low efficiency of heat dissipation due to its intrinsic high thermal resistance at the interfaces. The accumulated energy from Joule heat has to be removed efficiently to avoid the malfunction of the devices. Therefore, it is of significance to investigate the interfacial thermal dissipation properties and manipulate the properties by interfacial engineering on demand. In this work, the interfacial thermal conductance between few-layer black phosphorus and graphene is studied extensively using molecular dynamics simulations. Two critical parameters, the critical power Pcr to maintain thermal stability and the maximum heat power density Pmax with which the system can be loaded, are identified. Our results show that interfacial thermal conductance can be effectively tuned in a wide range with external strains and interracial defects. The compressive strain can enhance the interfacial thermal conductance by one order of magnitude, while interface defects give a two-fold increase. These findings could provide guidelines in heat dissipation and interfacial engineering for thermal conductance manipulation of black phosphorus-graphene heterostructure-based devices.Comment: 33 pages, 22 figure

Complete genome sequence of a Megalocytivirus (family Iridoviridae) associated with turbot mortality in China

<p>Abstract</p> <p>Background</p> <p>Turbot reddish body iridovirus (TRBIV) causes serious systemic diseases with high mortality in the cultured turbot, <it>Scophthalmus maximus</it>. We here sequenced and analyzed the complete genome of TRBIV, which was identified in Shandong province, China.</p> <p>Results</p> <p>The genome of TRBIV is a linear double-stranded DNA of 110,104 base pairs, comprising 55% G + C. Total 115 open reading frames were identified, encoding polypeptides ranging from 40 to 1168 amino acids. Amino acid sequences analysis revealed that 39 of the 115 potential gene products of TRBIV show significant homology to other iridovirus proteins. Phylogenetic analysis of conserved genes indicated that TRBIV is closely related to infectious spleen and kidney necrosis virus (ISKNV), rock bream iridovirus (RBIV), orange-spotted grouper iridovirus (OSGIV), and large yellow croaker iridovirus (LYCIV). The results indicated that TRBIV belongs to the genus <it>Megalocytivirus </it>(family Iridoviridae).</p> <p>Conclusions</p> <p>The determination of the genome of TRBIV will provide useful information for comparative study of Megalocytivirus and developing strategies to control outbreaks of TRBIV-induced disease.</p

Massive Wireless Energy Transfer without Channel State Information via Imperfect Intelligent Reflecting Surfaces

Intelligent Reflecting Surface (IRS) utilizes low-cost, passive reflecting elements to enhance the passive beam gain, improve Wireless Energy Transfer (WET) efficiency, and enable its deployment for numerous Internet of Things (IoT) devices. However, the increasing number of IRS elements presents considerable channel estimation challenges. This is due to the lack of active Radio Frequency (RF) chains in an IRS, while pilot overhead becomes intolerable. To address this issue, we propose a Channel State Information (CSI)-free scheme that maximizes received energy in a specific direction and covers the entire space through phased beam rotation. Furthermore, we take into account the impact of an imperfect IRS and meticulously design the active precoder and IRS reflecting phase shift to mitigate its effects. Our proposed technique does not alter the existing IRS hardware architecture, allowing for easy implementation in the current system, and enabling access or removal of any Energy Receivers (ERs) without additional cost. Numerical results illustrate the efficacy of our CSI-free scheme in facilitating large-scale IRS without compromising performance due to excessive pilot overhead. Furthermore, our scheme outperforms the CSI-based counterpart in scenarios involving large-scale ERs, making it a promising solution in the era of IoT

Guardauto: A Decentralized Runtime Protection System for Autonomous Driving

Due to the broad attack surface and the lack of runtime protection, potential safety and security threats hinder the real-life adoption of autonomous vehicles. Although efforts have been made to mitigate some specific attacks, there are few works on the protection of the self-driving system. This paper presents a decentralized self-protection framework called Guardauto to protect the self-driving system against runtime threats. First, Guardauto proposes an isolation model to decouple the self-driving system and isolate its components with a set of partitions. Second, Guardauto provides self-protection mechanisms for each target component, which combines different methods to monitor the target execution and plan adaption actions accordingly. Third, Guardauto provides cooperation among local self-protection mechanisms to identify the root-cause component in the case of cascading failures affecting multiple components. A prototype has been implemented and evaluated on the open-source autonomous driving system Autoware. Results show that Guardauto could effectively mitigate runtime failures and attacks, and protect the control system with acceptable performance overhead

2D-3D Interlaced Transformer for Point Cloud Segmentation with Scene-Level Supervision

We present a Multimodal Interlaced Transformer (MIT) that jointly considers 2D and 3D data for weakly supervised point cloud segmentation. Research studies have shown that 2D and 3D features are complementary for point cloud segmentation. However, existing methods require extra 2D annotations to achieve 2D-3D information fusion. Considering the high annotation cost of point clouds, effective 2D and 3D feature fusion based on weakly supervised learning is in great demand. To this end, we propose a transformer model with two encoders and one decoder for weakly supervised point cloud segmentation using only scene-level class tags. Specifically, the two encoders compute the self-attended features for 3D point clouds and 2D multi-view images, respectively. The decoder implements interlaced 2D-3D cross-attention and carries out implicit 2D and 3D feature fusion. We alternately switch the roles of queries and key-value pairs in the decoder layers. It turns out that the 2D and 3D features are iteratively enriched by each other. Experiments show that it performs favorably against existing weakly supervised point cloud segmentation methods by a large margin on the S3DIS and ScanNet benchmarks. The project page will be available at https://jimmy15923.github.io/mit_web/.Comment: ICCV 2023 (main + supp). Website: https://jimmy15923.github.io/mit_web