Leakage Analysis and Solution of the RFID Analog Front-END

The identification and modeling of different leakage components are very important for estimation and reduction of leakage power, especially low-power applications, such as RFID chip. This paper proposes a theory about leakage mechanism of RFID chip and proves the theory. The one contribution of the paper is the proposed theory about leakage mechanism of RFID chip. The other contribution is that it proves the differences between tape-out verification results and computer simulation results and that to what degree the differences occur for different circuits. And when the source potential is much lower than the substrate potential, tape-out verification results and computer simulation results have larger differences. The test results show that the actual leakage power increases 26.3 times compares with the computer simulation results’ when the source potential is -750mV

Order-Disorder: Imitation Adversarial Attacks for Black-box Neural Ranking Models

Neural text ranking models have witnessed significant advancement and are increasingly being deployed in practice. Unfortunately, they also inherit adversarial vulnerabilities of general neural models, which have been detected but remain underexplored by prior studies. Moreover, the inherit adversarial vulnerabilities might be leveraged by blackhat SEO to defeat better-protected search engines. In this study, we propose an imitation adversarial attack on black-box neural passage ranking models. We first show that the target passage ranking model can be transparentized and imitated by enumerating critical queries/candidates and then train a ranking imitation model. Leveraging the ranking imitation model, we can elaborately manipulate the ranking results and transfer the manipulation attack to the target ranking model. For this purpose, we propose an innovative gradient-based attack method, empowered by the pairwise objective function, to generate adversarial triggers, which causes premeditated disorderliness with very few tokens. To equip the trigger camouflages, we add the next sentence prediction loss and the language model fluency constraint to the objective function. Experimental results on passage ranking demonstrate the effectiveness of the ranking imitation attack model and adversarial triggers against various SOTA neural ranking models. Furthermore, various mitigation analyses and human evaluation show the effectiveness of camouflages when facing potential mitigation approaches. To motivate other scholars to further investigate this novel and important problem, we make the experiment data and code publicly available.Comment: 15 pages, 4 figures, accepted by ACM CCS 2022, Best Paper Nominatio

RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction

Universal Information Extraction (UIE) is an area of interest due to the challenges posed by varying targets, heterogeneous structures, and demand-specific schemas. However, previous works have only achieved limited success by unifying a few tasks, such as Named Entity Recognition (NER) and Relation Extraction (RE), which fall short of being authentic UIE models particularly when extracting other general schemas such as quadruples and quintuples. Additionally, these models used an implicit structural schema instructor, which could lead to incorrect links between types, hindering the model's generalization and performance in low-resource scenarios. In this paper, we redefine the authentic UIE with a formal formulation that encompasses almost all extraction schemas. To the best of our knowledge, we are the first to introduce UIE for any kind of schemas. In addition, we propose RexUIE, which is a Recursive Method with Explicit Schema Instructor for UIE. To avoid interference between different types, we reset the position ids and attention mask matrices. RexUIE shows strong performance under both full-shot and few-shot settings and achieves State-of-the-Art results on the tasks of extracting complex schemas

Regression-based approach for testing the association between multi-region haplotype configuration and complex trait

<p>Abstract</p> <p>Background</p> <p>It is quite common that the genetic architecture of complex traits involves many genes and their interactions. Therefore, dealing with multiple unlinked genomic regions simultaneously is desirable.</p> <p>Results</p> <p>In this paper we develop a regression-based approach to assess the interactions of haplotypes that belong to different unlinked regions, and we use score statistics to test the null hypothesis of non-genetic association. Additionally, multiple marker combinations at each unlinked region are considered. The multiple tests are settled via the <it>minP </it>approach. The <it>P </it>value of the "best" multi-region multi-marker configuration is corrected via Monte-Carlo simulations. Through simulation studies, we assess the performance of the proposed approach and demonstrate its validity and power in testing for haplotype interaction association.</p> <p>Conclusion</p> <p>Our simulations showed that, for binary trait without covariates, our proposed methods prove to be equal and even more powerful than htr and hapcc which are part of the FAMHAP program. Additionally, our model can be applied to a wider variety of traits and allow adjustment for other covariates. To test the validity, our methods are applied to analyze the association between four unlinked candidate genes and pig meat quality.</p