Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles

The technology of dynamic map fusion among networked vehicles has been developed to enlarge sensing ranges and improve sensing accuracies for individual vehicles. This paper proposes a federated learning (FL) based dynamic map fusion framework to achieve high map quality despite unknown numbers of objects in fields of view (FoVs), various sensing and model uncertainties, and missing data labels for online learning. The novelty of this work is threefold: (1) developing a three-stage fusion scheme to predict the number of objects effectively and to fuse multiple local maps with fidelity scores; (2) developing an FL algorithm which fine-tunes feature models (i.e., representation learning networks for feature extraction) distributively by aggregating model parameters; (3) developing a knowledge distillation method to generate FL training labels when data labels are unavailable. The proposed framework is implemented in the Car Learning to Act (CARLA) simulation platform. Extensive experimental results are provided to verify the superior performance and robustness of the developed map fusion and FL schemes.Comment: 12 pages, 5 figures, to appear in 2021 IEEE International Conference on Robotics and Automation (ICRA

Revisiting the Concrete Hardness of SelfTargetMSIS in CRYSTALS-Dilithium

In this paper, we reconsider the security for CRYSTALS-Dilithium, a lattice-based post-quantum signature scheme standardized by NIST. In their documentation, the authors proved that the security of the signature scheme can be based on the hardness of the following three assumptions: MLWE, MSIS and SelfTargetMSIS. While the first two are standard lattice assumptions with hardness well studied, the authors claimed that the third assumption SelfTargetMSIS can be estimated by the hardness of MSIS (and further into SIS). However, we point out that this is in fact not the case. We give a new algorithm for solving SelfTargetMSIS, by both experimental results and asymptotic complexities, we prove that under specific parameters, solving SelfTargetMSIS might be faster than MSIS. Although our algorithm does not propose a real threat to parameters used in Dilithium, we successfully show that solving SelfTargetMSIS cannot be turned into solving MSIS or MISIS. Furthermore, we define a new variant of MISIS, called sel-MISIS, and show that solving SelfTargetMSIS can only be turned into solving sel-MISIS. We believe that in order to fully understand the concrete hardness of SelfTargetMSIS and prevent potential attacks to Dilithium, the hardness of this new problem needs to be further studied

VOProof: Efficient zkSNARKs from Vector Oracle Compilers

The design of zkSNARKs is increasingly complicated and requires familiarity with a broad class of cryptographic and algebraic tools. This complexity in zkSNARK design also increases the difficulty in zkSNARK implementation, analysis, and optimization. To address this complexity, we develop a new workflow for designing and implementing zkSNARKs, called $\mathsf{VOProof}$. In $\mathsf{VOProof}$, the designer only needs to construct a \emph{Vector Oracle (VO) protocol} that is intuitive and straightforward to design, and then feeds this protocol to our \emph{VO compiler} to transform it into a fully functional zkSNARK. This new workflow conceals most algebraic and cryptographic operations inside the compiler, so that the designer is no longer required to understand these cumbersome and error prone procedures. Moreover, our compiler can be fine-tuned to compile one VO protocol into multiple zkSNARKs with different tradeoffs. We apply $\mathsf{VOProof}$ to construct three general-purpose zkSNARKs targeting three popular representations of arithmetic circuits: the Rank-1 Constraint System (R1CS), the Hadamard Product Relation (HPR), and the $\mathsf{PLONK}$ circuit. These zkSNARKs have shorter and more intuitive descriptions, thus are easier to implement and optimize compared to prior works. To evaluate their performance, we implement a Python framework for describing VO protocols and compiling them into working Rust code of zkSNARKs. Our evaluation shows that the $\mathsf{VOProof}$-based zkSNARKs have competitive performance, especially in proof size and verification time, e.g., both reduced by roughly $50\%$ compared to $\mathsf{Marlin}$ (Chiesa et al., EUROCRYPT 2020). These improvements make the $\mathsf{VOProof}$-based zkSNARKs more preferable in blockchain scenarios where the proof size and verification time are critical

Poly(lactic acid)/lignin blends prepared with the Pickering emulsion template method

In this work, the Pickering emulsion template method was used to introduce lignin nanoparticles into poly(lactic acid) (PLA) with improved dispersion. The effect of lignin as the stabilizer of Pickering emulsions was studied in this paper, including the thermal, rheological and mechanical properties of the blends. The PLA/lignin films had reduced light transmission in the UV light region, and Young’s modulus of PLA/lignin blends increased, while their tensile strength and elongation-at-break decreased as compared to neat PLA film. The introduction of lignin improved crystallinity of PLA from 7.5% to over 15%, and increased its decomposition temperature by about 10 °C. The lignin in the blends prepared using the Pickering emulsion template approach had much larger load bearing capacity than the dispersed lignin particles in the usual melt blended material. All the results indicated that the Pickering emulsion template method improves the dispersion of lignin (over 5.0 wt%) in PLA and improves UV protection, crystallinity, decomposition temperature and Young’s modulus of PLA

Polymorphisms in the Perilipin Gene May Affect Carcass Traits of Chinese Meat-type Chickens

Improved meat quality and greater muscle yield are highly sought after in high-quality chicken breeding programs. Past studies indicated that polymorphisms of the Perilipin gene (PLIN1) are highly associated with adiposity in mammals and are potential molecular markers for improving meat quality and carcass traits in chickens. In the present study, we screened single nucleotide polymorphisms (SNPs) in all exons of the PLIN1 gene with a direct sequencing method in six populations with different genetic backgrounds (total 240 individuals). We evaluated the association between the polymorphisms and carcass and meat quality traits. We identified three SNPs, located on the 5′ flanking region and exon 1 of PLIN1 on chromosome 10 (rs315831750, rs313726543, and rs80724063, respectively). Eight main haplotypes were constructed based on these SNPs. We calculated the allelic and genotypic frequencies, and genetic diversity parameters of the three SNPs. The polymorphism information content (PIC) ranged from 0.2768 to 0.3750, which reflected an intermediate genetic diversity for all chickens. The CC, CT, and TT genotypes influenced the percentage of breast muscle (PBM), percentage of leg muscle (PLM) and percentage of abdominal fat at rs315831750 (p<0.05). Diplotypes (haplotype pairs) affected the percentage of eviscerated weight (PEW) and PBM (p<0.05). Compared with chickens carrying other diplotypes, H3H7 had the greatest PEW and H2H2 had the greatest PBM, and those with diplotype H7H7 had the smallest PEW and PBM. We conclude that PLIN1 gene polymorphisms may affect broiler carcass and breast muscle yields, and diplotypes H3H7 and H2H2 could be positive molecular markers to enhance PEW and PBM in chickens

Robotic Wireless Energy Transfer in Dynamic Environments: System Design and Experimental Validation

Wireless energy transfer (WET) is a ground-breaking technology for cutting the last wire between mobile sensors and power grids in smart cities. However, WET only offers effective transmission of energy over a short distance. Robotic WET is an emerging paradigm that mounts the energy transmitter on a mobile robot and navigates the robot through different regions in a large area to charge remote energy harvesters. However, it is challenging to determine the robotic charging strategy in an unknown and dynamic environment due to the uncertainty of obstacles. This article proposes a hardware-in-the-loop joint optimization framework that offers three distinctive features: efficient model updates and re-optimization based on the last-round experimental data; iterative refinement of the anchor list for adaptation to different environments; and verification of algorithms in a high-fidelity Gazebo simulator and a multi-robot testbed. Experimental results show that the proposed framework significantly saves WET mission completion time while satisfying energy harvesting and collision avoidance constraints