Insecurity of detector-device-independent quantum key distribution

Detector-device-independent quantum key distribution (ddiQKD) held the promise of being robust to detector side-channels, a major security loophole in QKD implementations. In contrast to what has been claimed, however, we demonstrate that the security of ddiQKD is not based on post-selected entanglement, and we introduce various eavesdropping strategies that show that ddiQKD is in fact insecure against detector side-channel attacks as well as against other attacks that exploit device's imperfections of the receiver. Our attacks are valid even when the QKD apparatuses are built by the legitimate users of the system themselves, and thus free of malicious modifications, which is a key assumption in ddiQKD.Comment: 7 pages, 5 figures, 1 tabl

Wave breaking for the generalized Fornberg-Whitham equation

This paper aims to show that the Cauchy problem of the Burgers equation with a weakly dispersive perturbation involving the Bessel potential (generalization of the Fornberg-Whitham equation) can exhibit wave breaking for initial data with large slope. We also comment on the dispersive properties of the equation

EdgeYOLO: An Edge-Real-Time Object Detector

This paper proposes an efficient, low-complexity and anchor-free object detector based on the state-of-the-art YOLO framework, which can be implemented in real time on edge computing platforms. We develop an enhanced data augmentation method to effectively suppress overfitting during training, and design a hybrid random loss function to improve the detection accuracy of small objects. Inspired by FCOS, a lighter and more efficient decoupled head is proposed, and its inference speed can be improved with little loss of precision. Our baseline model can reach the accuracy of 50.6% AP50:95 and 69.8% AP50 in MS COCO2017 dataset, 26.4% AP50:95 and 44.8% AP50 in VisDrone2019-DET dataset, and it meets real-time requirements (FPS>=30) on edge-computing device Nvidia Jetson AGX Xavier. We also designed lighter models with less parameters for edge computing devices with lower computing power, which also show better performances. Our source code, hyper-parameters and model weights are all available at https://github.com/LSH9832/edgeyolo

Experimental quantum key distribution with source flaws

Decoy-state quantum key distribution (QKD) is a standard technique in current quantum cryptographic implementations. Unfortunately, existing experiments have two important drawbacks: the state preparation is assumed to be perfect without errors and the employed security proofs do not fully consider the finite-key effects for general attacks. These two drawbacks mean that existing experiments are not guaranteed to be secure in practice. Here, we perform an experiment that for the first time shows secure QKD with imperfect state preparations over long distances and achieves rigorous finite-key security bounds for decoy-state QKD against coherent attacks in the universally composable framework. We quantify the source flaws experimentally and demonstrate a QKD implementation that is tolerant to channel loss despite the source flaws. Our implementation considers more real-world problems than most previous experiments and our theory can be applied to general QKD systems. These features constitute a step towards secure QKD with imperfect devices.Comment: 12 pages, 4 figures, updated experiment and theor

A Blockchain Based Certificate Revocation Scheme For Vehicular Communication Systems

Both the academy and industry believe that Intelligent Transportation System (ITS) would be achievable in one decade since modern vehicle and communication technologies advanced apace. Vehicular Communication System (VCS) introduces information technology to the ITS and aims to improve road safety and traffic efficiency. In recent year, security and privacy schemes in VCS are becoming important. However, recovery mechanisms to eliminate the negative effect of security and privacy attacks are still an important topic for research. Therefore, the certificate revocation scheme is considered as a feasible technique to prevent the system from potential attacks. The major challenge of the certificate revocation scheme is to achieve low-cost operation since the communication resources must be capable of carrying various applications apart from the security and privacy purposes. In this paper, we propose an efficient certificate revocation scheme in VCS. The Blockchain concept is introduced to simplify the network structure and distributed maintenance of the Certificate Revocation List (CRL). The proposed scheme embeds part of the certificate revocation functions within the security and privacy applications, aiming to reduce the communication overhead and shorten the processing time cost. Extensive simulations and analysis show the effectiveness and efficiency of the proposed scheme, in which the Blockchain structure costs fewer network resources and gives a more economic solution to against further cybercrime attacks

Genetic prediction of the causal relationship between schizophrenia and tumors: a Mendelian randomized study

BackgroundPatients with schizophrenia are at a higher risk of developing cancer. However, the causal relationship between schizophrenia and different tumor types remains unclear.MethodsUsing a two-sample, two-way Mendelian randomization method, we used publicly available genome-wide association analysis (GWAS) aggregate data to study the causal relationship between schizophrenia and different cancer risk factors. These tumors included lung adenocarcinoma, lung squamous cell carcinoma, small-cell lung cancer, gastric cancer, alcohol-related hepatocellular cancer, tumors involving the lungs, breast, thyroid gland, pancreas, prostate, ovaries and cervix, endometrium, colon and colorectum, and bladder. We used the inverse variance weighting (IVW) method to determine the causal relationship between schizophrenia and different tumor risk factors. In addition, we conducted a sensitivity test to evaluate the effectiveness of the causality.ResultsAfter adjusting for heterogeneity, evidence of a causal relationship between schizophrenia and lung cancer risk was observed (odds ratio [OR]=1.001, 95% confidence interval [CI], 1.000–1.001; P=0.0155). In the sensitivity analysis, the causal effect of schizophrenia on the risk of lung cancer was consistent in both direction and degree. However, no evidence of causality or reverse causality between schizophrenia and other tumors was found.ConclusionThis study elucidated a causal relationship between the genetic predictors of schizophrenia and the risk of lung cancer, thereby providing a basis for the prevention, pathogenesis, and treatment of schizophrenia in patients with lung cancer

Comparative study on the physicochemical properties, functional components, color and anthocyanins profile of Aronia melanocarpa juice using different sterilization methods

Investigating the influences of different sterilization methods on overall juice quality is essential for the production of high-quality juice. The effects of ultra-high temperature instantaneous sterilization (UHT), thermosonication (TS), high hydrostatic pressure sterilization (HHP), and irradiation sterilization (IS) on the physicochemical properties, functional components, and color of Aronia melanocarpa juice (AMJ) were investigated. In addition, anthocyanin target metabolomics were used to explore the influences of different sterilization methods on the AMJ anthocyanin profile. All sterilization treatments effectively ensured the microbial safety of AMJ, and the AMJ viscosity was noticeably declined after sterilization (p < 0.05). Except for HHP, the other treatments aggravated AMJ browning (p < 0.05). Both TS and HHP treatments significantly enhanced or preserved the total polyphenols, flavonoids, and anthocyanins in AMJ and retained the original juice color, whereas UHT and IS treatments were not conducive to maintaining these characteristics. TS treatment significantly increased cyanidin-3-O-galactoside (C-3-O-gal) and cyanidin-3-O-arabinoside (C-3-O-ara) contents in AMJ by 7.98% and 5.90%, while IS resulted in a significant decrease of 15.74% and 10.46% (p < 0.05). C-3-O-gal and C-3-O-ara were the major reasons for the significant upregulation and downregulation of the total monomeric anthocyanins contents (TMAC) in the AMJ after TS and IS treatment, respectively. Malvidin-3-O-glucoside (M-3-O-glu), Cyanidin-3-O-(6-O-malonyl-β-D-glucoside) and Kaempferol-3-O-rutinoside (K-3-O-rut) might be markers of differential metabolites produced by the TS, HHP, and IS treatments, respectively. Correlation analysis indicated that Cyanidin-3-O-xyloside (C-3-O-xyl), C-3-O-ara, and Pelargonidin-3-O-arabinoside (P-3-O-ara) might be the principal contributed to the antioxidant capacity of AMJ. The research results are anticipated to supply technical reference for AMJ processing

Pharmacologic inhibition of the Menin-MLL interaction blocks progression of MLL leukemia in vivo

Chromosomal translocations affecting mixed lineage leukemia gene (MLL) result in acute leukemias resistant to therapy. The leukemogenic activity of MLL fusion proteins is dependent on their interaction with menin, providing basis for therapeutic intervention. Here we report the development of highly potent and orally bioavailable small-molecule inhibitors of the menin-MLL interaction, MI-463 and MI-503, and show their profound effects in MLL leukemia cells and substantial survival benefit in mouse models of MLL leukemia. Finally, we demonstrate the efficacy of these compounds in primary samples derived from MLL leukemia patients. Overall, we demonstrate that pharmacologic inhibition of the menin-MLL interaction represents an effective treatment for MLL leukemias in vivo and provide advanced molecular scaffold for clinical lead identification

The 3rd Anti-UAV Workshop & Challenge: Methods and Results

The 3rd Anti-UAV Workshop & Challenge aims to encourage research in developing novel and accurate methods for multi-scale object tracking. The Anti-UAV dataset used for the Anti-UAV Challenge has been publicly released. There are two main differences between this year's competition and the previous two. First, we have expanded the existing dataset, and for the first time, released a training set so that participants can focus on improving their models. Second, we set up two tracks for the first time, i.e., Anti-UAV Tracking and Anti-UAV Detection & Tracking. Around 76 participating teams from the globe competed in the 3rd Anti-UAV Challenge. In this paper, we provide a brief summary of the 3rd Anti-UAV Workshop & Challenge including brief introductions to the top three methods in each track. The submission leaderboard will be reopened for researchers that are interested in the Anti-UAV challenge. The benchmark dataset and other information can be found at: https://anti-uav.github.io/.Comment: Technical report for 3rd Anti-UAV Workshop and Challenge. arXiv admin note: text overlap with arXiv:2108.0990

Secrets of RLHF in Large Language Models Part I: PPO

Large language models (LLMs) have formulated a blueprint for the advancement of artificial general intelligence. Its primary objective is to function as a human-centric (helpful, honest, and harmless) assistant. Alignment with humans assumes paramount significance, and reinforcement learning with human feedback (RLHF) emerges as the pivotal technological paradigm underpinning this pursuit. Current technical routes usually include \textbf{reward models} to measure human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize policy model outputs, and \textbf{process supervision} to improve step-by-step reasoning capabilities. However, due to the challenges of reward design, environment interaction, and agent training, coupled with huge trial and error cost of large language models, there is a significant barrier for AI researchers to motivate the development of technical alignment and safe landing of LLMs. The stable training of RLHF has still been a puzzle. In the first report, we dissect the framework of RLHF, re-evaluate the inner workings of PPO, and explore how the parts comprising PPO algorithms impact policy agent training. We identify policy constraints being the key factor for the effective implementation of the PPO algorithm. Therefore, we explore the PPO-max, an advanced version of PPO algorithm, to efficiently improve the training stability of the policy model. Based on our main results, we perform a comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT. The absence of open-source implementations has posed significant challenges to the investigation of LLMs alignment. Therefore, we are eager to release technical reports, reward models and PPO code