LEAP: Efficient and Automated Test Method for NLP Software

The widespread adoption of DNNs in NLP software has highlighted the need for robustness. Researchers proposed various automatic testing techniques for adversarial test cases. However, existing methods suffer from two limitations: weak error-discovering capabilities, with success rates ranging from 0% to 24.6% for BERT-based NLP software, and time inefficiency, taking 177.8s to 205.28s per test case, making them challenging for time-constrained scenarios. To address these issues, this paper proposes LEAP, an automated test method that uses LEvy flight-based Adaptive Particle swarm optimization integrated with textual features to generate adversarial test cases. Specifically, we adopt Levy flight for population initialization to increase the diversity of generated test cases. We also design an inertial weight adaptive update operator to improve the efficiency of LEAP's global optimization of high-dimensional text examples and a mutation operator based on the greedy strategy to reduce the search time. We conducted a series of experiments to validate LEAP's ability to test NLP software and found that the average success rate of LEAP in generating adversarial test cases is 79.1%, which is 6.1% higher than the next best approach (PSOattack). While ensuring high success rates, LEAP significantly reduces time overhead by up to 147.6s compared to other heuristic-based methods. Additionally, the experimental results demonstrate that LEAP can generate more transferable test cases and significantly enhance the robustness of DNN-based systems.Comment: Accepted at ASE 202

Amplifying Non-Resonant Production of Dark Sector Particles in Scattering Dominance Regime

We investigate the enhancement of dark sector particle production within the scattering dominant regime. These particles typically exhibit a slight mixing with Standard Model particles through various portals, allowing for their generation through in-medium oscillation from Standard Model particle sources. Our analysis reveals that in the scattering dominance regime, with a significantly smaller scattering mean free path $\lambda_{\rm sca}$ compared to the absorption mean free path $\lambda_{\rm abs}$, the non-resonant production of sterile states can experience an enhancement by a factor of $\lambda_{\rm abs}/\lambda_{\rm sca}$. This phenomenon is demonstrated within the context of kinetic mixing dark photon production at a reactor, precisely satisfying this condition. By incorporating this collisional enhancement, we find that the current sensitivity to the mixing parameter $\epsilon$ for dark photons in the TEXONO experiment can be significantly improved across a range spanning from tens of eV to MeV. This advancement establishes the most stringent laboratory constraint within this mass spectrum for the dark photon. Sterile neutrino production, however, does not exhibit such enhancement, either due to the failure to meet the scattering dominance criterion or the neutrino damping in resonant production.Comment: 8 pages, 4 figure

CTC-based Non-autoregressive Speech Translation

Combining end-to-end speech translation (ST) and non-autoregressive (NAR) generation is promising in language and speech processing for their advantages of less error propagation and low latency. In this paper, we investigate the potential of connectionist temporal classification (CTC) for non-autoregressive speech translation (NAST). In particular, we develop a model consisting of two encoders that are guided by CTC to predict the source and target texts, respectively. Introducing CTC into NAST on both language sides has obvious challenges: 1) the conditional independent generation somewhat breaks the interdependency among tokens, and 2) the monotonic alignment assumption in standard CTC does not hold in translation tasks. In response, we develop a prediction-aware encoding approach and a cross-layer attention approach to address these issues. We also use curriculum learning to improve convergence of training. Experiments on the MuST-C ST benchmarks show that our NAST model achieves an average BLEU score of 29.5 with a speed-up of 5.67$\times$, which is comparable to the autoregressive counterpart and even outperforms the previous best result of 0.9 BLEU points.Comment: ACL 2023 Main Conferenc

Study on the relationship between bacterial biofilm and various microbial taxa

This paper reviews the effects on bacterial biofilms from perspectives of fungi,other bacteria,protozoa and bacteriophages. Between bacteria and fungi, the biofilms are not only means of antagonism of bacteria, but also intermediate coexistence of the two.When dealing with heterogeneous bacteria, it competes with intruders as a physical defender.When it suffers from predator's predation the density of biofilms increased to cope with the impact of protozoa, or the formation of biofilms reduced and then the uneasily predated microcolonies formed. In terms of resistance to bacteriophage's infection, the role of biofilms includes aligning the bacteria closer, regulating their chemical composition and their quorum sensing, to inhibit bacteriophage infection

Protocol to identify centrosome-associated transcription factors during mitosis in mammalian cell lines

Summary: During eukaryotic cell mitosis, the nuclear envelope disintegrates and transcription factors are dissociated from condensed chromosomes. Here, we describe a protocol to study centrosomal translocation of nuclear receptor RXRα. We detail procedures for HeLa cell synchronization followed by immunofluorescence, in situ proximity ligation assay, and centrosome isolation. This protocol can be used to identify other transcription factors associated with the centrosome or other subcellular structures during mitotic progression.For complete details on the use and execution of this protocol, please refer to Xie et al. (2020