104 research outputs found
Protecting Temporal Fingerprints with Synchronized Chaotic Circuits
In recent years, connected autonomous vehicles (CAVs) feature an increasing number of Ethernet-enabled electronic control units (ECUs), thereby creating more threat vectors that provide access to the Controller Area Network (CAN) Bus. Currently, mitigation techniques to protect the CAN bus from compromised ECU units in vehicle ad hoc networks (VANET) often utilize classical cryptographic techniques. However, ECUs often have temporal signatures that leak internal state information to eavesdropping attackers who can leverage temporal properties for longitudinal attacks. Unfortunately, these types of attacks are difficult to defend against using classical encryption schemes and intrusion detection systems (IDS) due to their high computational demands and ineffectiveness at protecting CAVs throughout the duration of their long lifespans. In order to address these problems, we propose a novel cryptographic framework that protects information embedded in ECU network communications by delivering an encryption system that periodically salts the temporal dynamics of individual ECU units with chaotic signals that are difficult to learn. We demonstrate the framework on two datasets, and our results show that the underlying temporal signatures cannot be approximated by state-of-the-art learning algorithms over finite time horizons
Verification of {\Gamma} symmetry assignment for the top valence band of ZnO by magneto-optical studies of the free A exciton state
The circularly-polarized and angular-resolved magneto-photoluminescence
spectroscopy was carried out to study the free A exciton 1S state in wurtzite
ZnO at 5 K.Comment: 4 figures, 16 pages. arXiv admin note: substantial text overlap with
arXiv:0706.396
AutoAssign+: Automatic Shared Embedding Assignment in Streaming Recommendation
In the domain of streaming recommender systems, conventional methods for
addressing new user IDs or item IDs typically involve assigning initial ID
embeddings randomly. However, this practice results in two practical
challenges: (i) Items or users with limited interactive data may yield
suboptimal prediction performance. (ii) Embedding new IDs or low-frequency IDs
necessitates consistently expanding the embedding table, leading to unnecessary
memory consumption. In light of these concerns, we introduce a reinforcement
learning-driven framework, namely AutoAssign+, that facilitates Automatic
Shared Embedding Assignment Plus. To be specific, AutoAssign+ utilizes an
Identity Agent as an actor network, which plays a dual role: (i) Representing
low-frequency IDs field-wise with a small set of shared embeddings to enhance
the embedding initialization, and (ii) Dynamically determining which ID
features should be retained or eliminated in the embedding table. The policy of
the agent is optimized with the guidance of a critic network. To evaluate the
effectiveness of our approach, we perform extensive experiments on three
commonly used benchmark datasets. Our experiment results demonstrate that
AutoAssign+ is capable of significantly enhancing recommendation performance by
mitigating the cold-start problem. Furthermore, our framework yields a
reduction in memory usage of approximately 20-30%, verifying its practical
effectiveness and efficiency for streaming recommender systems
Combination of Neutrophil Count and Gensini Score as a Prognostic Marker in Patients with ACS and Uncontrolled T2DM Undergoing PCI
Background: Several biomarkers have been studied as prognostic indicators among people with diabetes and coronary artery disease (CAD). The purpose of this study was to determine the prognostic value of neutrophil counts and the Gensini score in patients with diabetes and ACS undergoing percutaneous coronary intervention (PCI). Methods: A total of 694 people with ACS and T2DM who simultaneously had elevated HBA1c received PCI. Spearman rank correlation estimates were used for correlation evaluation. Multivariate Cox regression and Kaplan-Meier analysis were used to identify characteristics associated with major adverse cardiovascular and cerebrovascular events (MACCEs) and patient survival. The effects of single- and multi-factor indices on MACCEs were evaluated through receiver operating characteristic curve analysis. Results: The Gensini score and neutrophil count significantly differed between the MACCE and non-MACCE groups among patients receiving PCI who had concomitant ACS and T2DM with elevated HBA1c (P<0.001). The Gensini score and neutrophil count were strongly associated with MACCEs (log-rank, P<0.001). The Gensini score and neutrophil count, alone or in combination, were predictors of MACCEs, according to multivariate Cox regression analysis (adjusted hazard ratio [HR], 1.005; 95% confidence interval [CI], 1.002–1.008; P=0.002; adjusted HR, 1.512; 95% CI, 1.005–2.274; P=0.047, respectively). The Gensini score was strongly associated with neutrophil count (variance inflation factor ≥ 5). Area under the curve analysis revealed that the combination of multivariate factors predicted the occurrence of MACCEs better than any single variable. Conclusion: In patients with T2DM and ACS with elevated HBA1c who underwent PCI, both the Gensini score and neutrophil count were independent predictors of outcomes. The combination of both predictors has a higher predictability
Potential of Core-Collapse Supernova Neutrino Detection at JUNO
JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve
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