Eigenstates of the lattice RS model

For quantum field theories involving interaction between fermion and boson fields, the bare fermionic (bosonic) annihilation operators cannot annihilate the vacuum state, and the bare fermionic (bosonic) creation operators cannot create fermionic (bosonic) one-particle states. The actual bosonic particles contain fermion field components, and fermionic particles contain boson field components, while the vacuum state exhibits entanglement between the fermion and boson fields. We utilize the lattice Rothe-Stamatescu (RS) model to nonperturbatively and directly demonstrate this field mixing. We provide the Hamiltonian of the lattice RS model, calculate its correlation functions, and observe that the correlation functions in the continuum limit recover those of the original continuous RS model. Furthermore, we derive the equations of motion for the lattice RS model and compare them to those of the original RS model. Instead of employing the traditional Fock representation commonly used in discussions of field mixing, we define a special representation to present the vacuum state and one-particle states of the lattice RS model. These eigenstates not only reveal the entanglement between the boson and fermion fields but also allow us to directly observe the spatial entanglement structure.Comment: 59 pages, no figure

Content-based Unrestricted Adversarial Attack

Unrestricted adversarial attacks typically manipulate the semantic content of an image (e.g., color or texture) to create adversarial examples that are both effective and photorealistic, demonstrating their ability to deceive human perception and deep neural networks with stealth and success. However, current works usually sacrifice unrestricted degrees and subjectively select some image content to guarantee the photorealism of unrestricted adversarial examples, which limits its attack performance. To ensure the photorealism of adversarial examples and boost attack performance, we propose a novel unrestricted attack framework called Content-based Unrestricted Adversarial Attack. By leveraging a low-dimensional manifold that represents natural images, we map the images onto the manifold and optimize them along its adversarial direction. Therefore, within this framework, we implement Adversarial Content Attack based on Stable Diffusion and can generate high transferable unrestricted adversarial examples with various adversarial contents. Extensive experimentation and visualization demonstrate the efficacy of ACA, particularly in surpassing state-of-the-art attacks by an average of 13.3-50.4% and 16.8-48.0% in normally trained models and defense methods, respectively

Towards Automatic Boundary Detection for Human-AI Hybrid Essay in Education

Human-AI collaborative writing has been greatly facilitated with the help of modern large language models (LLM), e.g., ChatGPT. While admitting the convenience brought by technology advancement, educators also have concerns that students might leverage LLM to partially complete their writing assignment and pass off the human-AI hybrid text as their original work. Driven by such concerns, in this study, we investigated the automatic detection of Human-AI hybrid text in education, where we formalized the hybrid text detection as a boundary detection problem, i.e., identifying the transition points between human-written content and AI-generated content. We constructed a hybrid essay dataset by partially removing sentences from the original student-written essays and then instructing ChatGPT to fill in for the incomplete essays. Then we proposed a two-step detection approach where we (1) Separated AI-generated content from human-written content during the embedding learning process; and (2) Calculated the distances between every two adjacent prototypes (a prototype is the mean of a set of consecutive sentences from the hybrid text in the embedding space) and assumed that the boundaries exist between the two prototypes that have the furthest distance from each other. Through extensive experiments, we summarized the following main findings: (1) The proposed approach consistently outperformed the baseline methods across different experiment settings; (2) The embedding learning process (i.e., step 1) can significantly boost the performance of the proposed approach; (3) When detecting boundaries for single-boundary hybrid essays, the performance of the proposed approach could be enhanced by adopting a relatively large prototype size, leading to a $22$\% improvement (against the second-best baseline method) in the in-domain setting and an $18$\% improvement in the out-of-domain setting.Comment: 9 pages including references, 2 figure

Exploring Decision-based Black-box Attacks on Face Forgery Detection

Face forgery generation technologies generate vivid faces, which have raised public concerns about security and privacy. Many intelligent systems, such as electronic payment and identity verification, rely on face forgery detection. Although face forgery detection has successfully distinguished fake faces, recent studies have demonstrated that face forgery detectors are very vulnerable to adversarial examples. Meanwhile, existing attacks rely on network architectures or training datasets instead of the predicted labels, which leads to a gap in attacking deployed applications. To narrow this gap, we first explore the decision-based attacks on face forgery detection. However, applying existing decision-based attacks directly suffers from perturbation initialization failure and low image quality. First, we propose cross-task perturbation to handle initialization failures by utilizing the high correlation of face features on different tasks. Then, inspired by using frequency cues by face forgery detection, we propose the frequency decision-based attack. We add perturbations in the frequency domain and then constrain the visual quality in the spatial domain. Finally, extensive experiments demonstrate that our method achieves state-of-the-art attack performance on FaceForensics++, CelebDF, and industrial APIs, with high query efficiency and guaranteed image quality. Further, the fake faces by our method can pass face forgery detection and face recognition, which exposes the security problems of face forgery detectors

Boosting the Transferability of Adversarial Attacks with Global Momentum Initialization

Deep neural networks are vulnerable to adversarial examples, which attach human invisible perturbations to benign inputs. Simultaneously, adversarial examples exhibit transferability under different models, which makes practical black-box attacks feasible. However, existing methods are still incapable of achieving desired transfer attack performance. In this work, from the perspective of gradient optimization and consistency, we analyze and discover the gradient elimination phenomenon as well as the local momentum optimum dilemma. To tackle these issues, we propose Global Momentum Initialization (GI) to suppress gradient elimination and help search for the global optimum. Specifically, we perform gradient pre-convergence before the attack and carry out a global search during the pre-convergence stage. Our method can be easily combined with almost all existing transfer methods, and we improve the success rate of transfer attacks significantly by an average of 6.4% under various advanced defense mechanisms compared to state-of-the-art methods. Eventually, we achieve an attack success rate of 95.4%, fully illustrating the insecurity of existing defense mechanisms

Adaptive Contextual Biasing for Transducer Based Streaming Speech Recognition

By incorporating additional contextual information, deep biasing methods have emerged as a promising solution for speech recognition of personalized words. However, for real-world voice assistants, always biasing on such personalized words with high prediction scores can significantly degrade the performance of recognizing common words. To address this issue, we propose an adaptive contextual biasing method based on Context-Aware Transformer Transducer (CATT) that utilizes the biased encoder and predictor embeddings to perform streaming prediction of contextual phrase occurrences. Such prediction is then used to dynamically switch the bias list on and off, enabling the model to adapt to both personalized and common scenarios. Experiments on Librispeech and internal voice assistant datasets show that our approach can achieve up to 6.7% and 20.7% relative reduction in WER and CER compared to the baseline respectively, mitigating up to 96.7% and 84.9% of the relative WER and CER increase for common cases. Furthermore, our approach has a minimal performance impact in personalized scenarios while maintaining a streaming inference pipeline with negligible RTF increase

The ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge (ICSRC): Dataset, Tracks, Baseline and Results

This paper summarizes the outcomes from the ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge (ICSRC). We first address the necessity of the challenge and then introduce the associated dataset collected from a new-energy vehicle (NEV) covering a variety of cockpit acoustic conditions and linguistic contents. We then describe the track arrangement and the baseline system. Specifically, we set up two tracks in terms of allowed model/system size to investigate resource-constrained and -unconstrained setups, targeting to vehicle embedded as well as cloud ASR systems respectively. Finally we summarize the challenge results and provide the major observations from the submitted systems.Comment: Accepted by ISCSLP202

Expert Consensus on Microtransplant for Acute Myeloid Leukemia in Elderly Patients -Report From the International Microtransplant Interest Group

Recent studies have shown that microtransplant (MST) could improve outcome of patients with elderly acute myeloid leukemia (EAML). To further standardize the MST therapy and improve outcomes in EAML patients, based on analysis of the literature on MST, especially MST with EAML from January 1st, 2011 to November 30th, 2022, the International Microtransplant Interest Group provides recommendations and considerations for MST in the treatment of EAML. Four major issues related to MST for treating EAML were addressed: therapeutic principle of MST (1), candidates for MST (2), induction chemotherapy regimens (3), and post-remission therapy based on MST (4). Others included donor screening, infusion of donor cells, laboratory examinations, and complications of treatment

Three-dimensional ambient noise tomography across the Taiwan Strait: The structure of a magma-poor rifted margin

Rifting along southeastern Eurasia in the Late Cenozoic led to the formation of a magma-poor rifted margin facing the South China Sea to the southeast and the Philippine Sea to the east. Further rifting along the outer part of the margin during the middle to late Miocene was accompanied by an extensive episode of intraplate flood volcanism that formed the Penghu Archipelago. Previous geophysical studies in the area of the strait have focused primarily on the shallow structures of the rift basins and the depth to the Moho. In this study we present the regional-scale 3-D S wave structure of the Taiwan Strait that is derived from a joint Chinese and Taiwanese 3-D ambient noise tomography study. The S wave model shows a thinning of the crust beneath the rift basins where, locally, thin high-velocity layers suggest the presence of intrusive bodies. The rift basin and the foreland basin along the west coast of Taiwan are imaged as low-velocity zones with thicknesses between 5 and 10 km and extending eastward beneath the Taiwan mountain belt. In the upper 10 km of the crust, the basaltic rocks of the Penghu Archipelago are imaged as a high-velocity zone that, with depth, becomes a relatively low-velocity zone. We interpret this low-velocity zone in the lower crust and upper mantle beneath the Penghu Archipelago to image a thermal anomaly related to the still cooling magma feeding system and the melt reservoir area that fed the flood basalts at the surface. ©2016. American Geophysical Union. All Rights Reserved.H.K-C. is supported by Ministry of Science and Technology (grant MOST104-2628-M-008-005-MY3). D. Brown acknowledges funding by MINECO grant-GL2013-43877-P. Q. Li and Z. Ye acknowledge funding by Sinoprobe-02-03. S wave tomography model presented in all figures can be obtained from the lead author (Hao Kuo-Chen, [email protected]).Peer reviewe

Copper Incorporated Molybdenum Trioxide Nanosheet Realizing High-Efficient Performance for Hydrogen Production

The development of highly active non-precious metal electrocatalysts is crucial for advancing the practical application of hydrogen evolution reaction (HER). Doping engineering is one of the important strategies to optimize the electrocatalytic activity of electrocatalysts. Herein, we put forward a simple strategy to optimize the catalytic activity of MoO3 material by incorporating the Cu atoms into the interlayer (denoted as Cu-MoO3). The prepared Cu-MoO3 nanosheet has a larger surface area, higher conductivity, and strong electron interactions, which contributes to optimal reaction kinetics of the HER process. As a result, the Cu-MoO3 nanosheet only needs a small overpotential of 106 mV to reach the geometric current density of 10 mA cm−2. In addition, it also delivers a low Tafel slope of 83 mV dec−1, as well as high stability and Faraday efficiency. Notably, when using the Cu-MoO3 as a cathode to construct the water electrolyzer, it only needs 1.55 V to reach the 10 mA cm−2, indicating its promising application in hydrogen generation. This work provides a novel type of design strategy for a highly active electrocatalyst for an energy conversion system