Half-Metallic Silicon Nanowires: Multiple Surface Dangling Bonds and Nonmagnetic Doping

By means of first-principles density functional theory calculations, we find that hydrogen-passivated ultrathin silicon nanowires (SiNWs) along [100] direction with symmetrical multiple surface dangling bonds (SDBs) and boron doping can have a half-metallic ground state with 100% spin polarization, where the half-metallicity is shown quite robust against external electric fields. Under the circumstances with various SDBs, the H-passivated SiNWs can also be ferromagnetic or antiferromagnetic semiconductors. The present study not only offers a possible route to engineer half-metallic SiNWs without containing magnetic atoms but also sheds light on manipulating spin-dependent properties of nanowires through surface passivation.Comment: 4 pages, 5 figure

Thermoelectric Properties of Silicon Carbide Nanowires with Nitrogen Dopants and Vacancies

The thermoelectric properties of cubic zincblend silicon carbide nanowires (SiCNWs) with nitrogen impurities and vacancies along [111] direction are theoretically studied by means of atomistic simulations. It is found that the thermoelectric figure of merit ZT of SiCNWs can be significantly enhanced by doping N impurities together with making Si vacancies. Aiming at obtaining a large ZT, we study possible energetically stable configurations, and disclose that, when N dopants locate at the center, a small number of Si vacancies at corners are most favored for n-type nanowires, while a large number of Si vacancies spreading into the flat edge sites are most favored for p-type nanowires. For the SiCNW with a diameter of 1.1 nm and a length of 4.6 nm, the ZT value for the n-type is shown capable of reaching 1.78 at 900K. The conditions to get higher ZT values for longer SiCNWs are also addressed.Comment: 9 pages, 10 figure

Dense-Coding Attack on Three-Party Quantum Key Distribution Protocols

Cryptanalysis is an important branch in the study of cryptography, including both the classical cryptography and the quantum one. In this paper we analyze the security of two three-party quantum key distribution protocols (QKDPs) proposed recently, and point out that they are susceptible to a simple and effective attack, i.e. the dense-coding attack. It is shown that the eavesdropper Eve can totally obtain the session key by sending entangled qubits as the fake signal to Alice and performing collective measurements after Alice's encoding. The attack process is just like a dense-coding communication between Eve and Alice, where a special measurement basis is employed. Furthermore, this attack does not introduce any errors to the transmitted information and consequently will not be discovered by Alice and Bob. The attack strategy is described in detail and a proof for its correctness is given. At last, the root of this insecurity and a possible way to improve these protocols are discussed.Comment: 6 pages, 3 figure

Vibration model of a multi-supported guide bar and analysis on the effect of supports location

Two methods (equivalent force method and segmental mode assuming method) of calculating the natural frequencies and mode shapes of a free-free-multi-supported beam subjected to an axial load is found, considering the structure characteristic of the guide bar, which has long length but small section, and supported by many bearings. The calculation shows that these two methods are convenient for computer programing and have the same results in obtaining the natural frequencies and mode shapes of a free-free-multi-supported beam subjected to an axial load, solving the problem that the vibration function of this kind of beam is hard to deal with because it cannot be simplified with the boundary condition of two ends. Then the segmental mode assuming method is used to analyze the impact of the support location on the natural frequencies and mode shapes of the guide bar. The relation graphs of the natural frequencies with support location, as well as the support locations where the natural frequencies reached the maximum and the minimum are found, providing a reference for the support location selection for the guide bar. The changing curves of the mode shapes with support location are plotted, which show that the bending deformation is homogeneous when the length of each segment is approximately equal, avoiding the phenomenon that bending stresses concentrates at the large-amplitude segments and cause breakage while less stress exists in small-amplitude segments and hinder the exploiting of their performance, providing a reference for the structure design of the guide bar

DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text

With the rapid progress of large language models (LLMs) and the huge amount of text they generated, it becomes more and more impractical to manually distinguish whether a text is machine-generated. Given the growing use of LLMs in social media and education, it prompts us to develop methods to detect machine-generated text, preventing malicious usage such as plagiarism, misinformation, and propaganda. Previous work has studied several zero-shot methods, which require no training data. These methods achieve good performance, but there is still a lot of room for improvement. In this paper, we introduce two novel zero-shot methods for detecting machine-generated text by leveraging the log rank information. One is called DetectLLM-LRR, which is fast and efficient, and the other is called DetectLLM-NPR, which is more accurate, but slower due to the need for perturbations. Our experiments on three datasets and seven language models show that our proposed methods improve over the state of the art by 3.9 and 1.75 AUROC points absolute. Moreover, DetectLLM-NPR needs fewer perturbations than previous work to achieve the same level of performance, which makes it more practical for real-world use. We also investigate the efficiency--performance trade-off based on users preference on these two measures and we provide intuition for using them in practice effectively. We release the data and the code of both methods in https://github.com/mbzuai-nlp/DetectLLMComment: machine-generated text, large language models, LLMs, zero-sho

High-order wavelet reconstruction for multi-scale edge aware tone mapping

This paper presents a High Order Reconstruction (HOR) method for improved multi-scale edge aware tone mapping. The study aims to contribute to the improvement of edge-aware techniques for smoothing an input image, while keeping its edges intact. The proposed HOR methods circumvent limitations of the existing state of the art methods, e.g., altering the image structure due to changes in contrast; remove artefacts around edges; as well as reducing computational complexity in terms of implementation and associated computational costs. In particular, the proposed method aims at reducing the changes in the image structure by intrinsically enclosing an edge-stop mechanism whose computational cost is comparable to the state-of-the-art multi-scale edge aware techniques

Realistic Full-Body Tracking from Sparse Observations via Joint-Level Modeling

To bridge the physical and virtual worlds for rapidly developed VR/AR applications, the ability to realistically drive 3D full-body avatars is of great significance. Although real-time body tracking with only the head-mounted displays (HMDs) and hand controllers is heavily under-constrained, a carefully designed end-to-end neural network is of great potential to solve the problem by learning from large-scale motion data. To this end, we propose a two-stage framework that can obtain accurate and smooth full-body motions with the three tracking signals of head and hands only. Our framework explicitly models the joint-level features in the first stage and utilizes them as spatiotemporal tokens for alternating spatial and temporal transformer blocks to capture joint-level correlations in the second stage. Furthermore, we design a set of loss terms to constrain the task of a high degree of freedom, such that we can exploit the potential of our joint-level modeling. With extensive experiments on the AMASS motion dataset and real-captured data, we validate the effectiveness of our designs and show our proposed method can achieve more accurate and smooth motion compared to existing approaches.Comment: Accepted to ICCV 2023. Project page: https://zxz267.github.io/AvatarJL