Small polaron with generic open boundary conditions revisit: exact solution via the off-diagonal Bethe ansatz

The small polaron, an one-dimensional lattice model of interacting spinless fermions, with generic non-diagonal boundary terms is studied by the off-diagonal Bethe ansatz method. The presence of the Grassmann valued non-diagonal boundary fields gives rise to a typical $U(1)$-symmetry-broken fermionic model. The exact spectra of the Hamiltonian and the associated Bethe ansatz equations are derived by constructing an inhomogeneous $T-Q$ relation.Comment: 12 pages, no figure, published versio

Advances and trends in plastic forming technologies for welded tubes

AbstractWith the implementation of environmental protection, sustainable development and conservation-oriented policies, components and parts of thin-walled welded tubes have gained increasing application in the aircraft and automotive industries because of their advantages: easily achieving forming and manufacturing process at low cost and in a short time. The current research on welded tube plastic forming is mainly concentrated on tube internal high-pressure forming, tube bending forming, and tube spinning forming. The focuses are on the material properties and characterization of welded tubes, finite element modeling for welded tube forming, and inhomogeneous deformation behavior and the mechanism and rules of deformation coordination in welded tube plastic forming. This paper summarizes the research progress in welded tube plastic forming from these aspects. Finally, with a focus on the urgent demand of the aviation, aerospace and automotive industries for high-strength and light-weight tubes, this paper discusses the development trends and challenges in the theory and technology of welded tube plastic forming in the future. Among them, laser tailor-welded technology will find application in the manufacture of high-strength steel tubes. Tube-end forming technology, such as tube flaring and flanging technology, will expand its application in welded tubes. Therefore, future studies will focus on the FE modeling regarding how to consider effects of welding on residual stresses, welding distortions and microstructure, the inhomogeneous deformation and coordination mechanism of the plastic forming process of tailor-welded tubes, and some end-forming processes of welded tubes, and more comprehensive research on the forming mechanism and limit of welded tubes

Defense Against Adversarial Attacks for Neural Representations of Text

In this paper, we focus on defending against adversarial attacks for privacy-preserving Natural Language Processing (NLP) under a model partitioning scenario, where the model splits into a local, on-device part and a remote, cloud-based part. Model partitioning improves the scalability and protects the privacy of inputs into the model. However, we argue that privacy protection breaks during inference with model partitioning. In this paper, an adversary eavesdrops on the hidden representations output from the local devices and tries to use the representations to obtain private information from the input text. We study two types of adversarial attacks, i.e., adversarial classification and adversarial generation. Based on these two attack models, we correspondingly propose two defenses: defending the adversarial classification (DAC) and defending the adversarial generation (DAG). Specifically, the DAC and DAG approaches are both bilevel optimization-based defense methods. Both methods optimally modify a subpopulation of the neural representations that are subject to maximally decreasing the adversary’s ability. The representations trained with this bilevel optimization protect sensitive information from the adversary attack while maintaining their utility for downstream tasks. Our experiments show that both DAC and DAG approaches improve the performance of the main text classifier and achieve even higher privacy of neural representations compared with the current state-of-the-art methods

New Threats to Privacy-preserving Text Representations

The users’ privacy concerns mandate data publishers to protect privacy by anonymizing the data before sharing it with data consumers. Thus, the ultimate goal of privacy-preserving representation learning is to protect user privacy while ensuring the utility, e.g., the accuracy of the published data, for future tasks and usages. Privacy-preserving embeddings are usually functions that are encoded to low-dimensional vectors to protect privacy while preserving important semantic information about an input text. We demonstrate that these embeddings still leak private information, even though the low dimensional embeddings encode generic semantics. We develop two classes of attacks, i.e., adversarial classification attack and adversarial generation attack, to study the threats for these embeddings. In particular, the threats are (1) these embeddings may reveal sensitive attributes letting alone if they explicitly exist in the input text, and (2) the embedding vectors can be partially recovered via generation models. Besides, our experimental results show that our approach can produce higher-performing adversary models than other adversary baselines

Determinant representations for scalar products of the XXZ Gaudin model with general boundary terms

We obtain the determinant representations of the scalar products for the XXZ Gaudin model with generic non-diagonal boundary terms.Comment: Latex file, 17 page