Factorizations of almost simple orthogonal groups of minus type

This is the fourth one in a series of papers classifying the factorizations of almost simple groups with nonsolvable factors. In this paper we deal with orthogonal groups of minus type.

MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers

Developing automatic Math Word Problem (MWP) solvers has been an interest of NLP researchers since the 1960s. Over the last few years, there are a growing number of datasets and deep learning-based methods proposed for effectively solving MWPs. However, most existing methods are benchmarked soly on one or two datasets, varying in different configurations, which leads to a lack of unified, standardized, fair, and comprehensive comparison between methods. This paper presents MWPToolkit, the first open-source framework for solving MWPs. In MWPToolkit, we decompose the procedure of existing MWP solvers into multiple core components and decouple their models into highly reusable modules. We also provide a hyper-parameter search function to boost the performance. In total, we implement and compare 17 MWP solvers on 4 widely-used single equation generation benchmarks and 2 multiple equations generation benchmarks. These features enable our MWPToolkit to be suitable for researchers to reproduce advanced baseline models and develop new MWP solvers quickly. Code and documents are available at https://github.com/LYH-YF/MWPToolkit.

The Fragile Points Method, with an interface damage model, to simulate damage and fracture of U-notched structures

Notched components are commonly used in engineering structures, where stress concentration may easily lead to crack initiation and development. The main goal of this work is to develop a simple numerical method to predict the structural strength and crack-growth-path of U-notched specimens made of brittle materials. For this purpose, the Fragile Points Method (FPM), as previously proposed by the authors, has been augmented by an interface damage model at the interfaces of the FPM domains, to simulate crack initiation and development. The formulations of FPM are based on a discontinuous Galerkin weak form where point-based piece-wise-continuous polynomial test and trial functions are used instead of element-based basis functions. In this work, the numerical fluxes introduced across interior interfaces between subdomains are postulated as the tractions acting on the interface derived from an interface damage model. The interface damage is triggered when the numerical flux reaches the interface strength, and the process of crack-surface separation is governed by the fracture energy. In this way, arbitrary crack initiation and propagation can be naturally simulated without the need for knowing the fracture-patch before-hand. Additionally, a small penalty parameter is sufficient to enforce the weak-form continuity condition before damage initiation, without causing problems such as artificial compliance and numerical ill-conditioning. As validations, the proposed FPM method with the interface damage model is used to predict the structural strength and crack-development from U-notched structures made of brittle materials, which is useful but challenging in engineering structural design practices.

Ultrahigh-Efficiency Second Harmonic Generation in a Reverse-Polarization Dual-Layer Crystalline Thin Film Nanophotonic Waveguide

Second harmonic generation (SHG), as one of the most significant c{hi}(2) nonlinear optical processes, plays crucial roles in a broad variety of optical and photonic applications. Designing various delicate schemes to achieve highly efficient SHG has become a long standing and challenging topic in field of nonlinear optics. Despite numerous success on SHG based on birefringent phase matching and quasi-phase matching, so far, modal phase matching (MPM) for SHG in tightly light-confined structures has still in its infancy. Here, we propose a new scheme to realize highly-efficient SHG via MPM by using a nanophotonic LiNbO3 thin-film waveguide consists of two bonded layers with internally reversed polarizations. In such a dual-layer ridge waveguide based on lithium niobate on insulator, upon optical excitation at 1574.6 nm, we observe SHG at 787.3 nm with ultrahigh conversion efficiency of 5,540% /W/cm/cm experimentally. This work advances our understanding on modal-phase-matched SHG and other quadratic optical nonlinear process, offering additional strategies for development of high-performance nonlinear photonic devices in on-chip platforms.

Asymmetric parametric generation of images with nonlinear dielectric metasurfaces

Subwavelength dielectric resonators assembled into metasurfaces have become versatile tools to miniaturise optical components towards the nanoscale. An important class of such functionalities is associated with asymmetries in both generation and propagation of light with respect to reversals of the positions of transmitters and receivers. A promising pathway towards miniaturisation of asymmetric light control is via nonlinear light-matter interactions. Here we demonstrate asymmetric parametric generation of light at the level of individual subwavelength resonators. We assemble thousands of dissimilar nonlinear dielectric resonators into translucent metasurfaces that produce images in the visible spectral range when illuminated by infrared radiation. By design, these nonlinear metasurfaces produce different and completely independent images for the reversed directions of illumination, that is when the positions of the infrared transmitter and the visible light receiver are exchanged. Nonlinearity-enabled asymmetric control of light at the level of individual subwavelength resonators opens an untapped potential for developing novel nanophotonic components via dense integration of large quantities of nonlinear resonators into compact metasurfaces.

Novel Two-Dimensional Layered MSi$_2$N$_4$ (M = Mo, W): New Promising Thermal Management Materials

With the miniaturization and integration of nanoelectronic devices, efficient heat removal becomes a key factor affecting the reliable operation of the nanoelectronic device. With the high intrinsic thermal conductivity, good mechanical flexibility, and precisely controlled growth, two-dimensional (2D) materials are widely accepted as ideal candidates for thermal management materials. In this work, by solving the phonon Boltzmann transport equation (BTE) based on first-principles calculations, we comprehensively investigated the thermal conductivity of novel 2D layered MSi$_2$N$_4$ (M = Mo, W). Our results point to competitive thermal conductivities (162 W/mK) of monolayer MoSi$_2$N$_4$, which is around two times larger than that of WSi$_2$N$_4$ and seven times larger than that of silicene despite their similar non-planar structures. It is revealed that the high thermal conductivity arises mainly from its large group velocity and low anharmonicity. Our result suggests that MoSi$_2$N$_4$ could be a potential candidate for 2D thermal management materials.

Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Adaptive Refined Labeling

Price movement forecasting aims at predicting the future trends of financial assets based on the current market conditions and other relevant information. Recently, machine learning(ML) methods have become increasingly popular and achieved promising results for price movement forecasting in both academia and industry. Most existing ML solutions formulate the forecasting problem as a classification(to predict the direction) or a regression(to predict the return) problem in the entire set of training data. However, due to the extremely low signal-to-noise ratio and stochastic nature of financial data, good trading opportunities are extremely scarce. As a result, without careful selection of potentially profitable samples, such ML methods are prone to capture the patterns of noises instead of real signals. To address the above issues, we propose a novel framework-LARA(Locality-Aware Attention and Adaptive Refined Labeling), which contains the following three components: 1)Locality-aware attention automatically extracts the potentially profitable samples by attending to their label information in order to construct a more accurate classifier on these selected samples. 2)Adaptive refined labeling further iteratively refines the labels, alleviating the noise of samples. 3)Equipped with metric learning techniques, Locality-aware attention enjoys task-specific distance metrics and distributes attention on potentially profitable samples in a more effective way. To validate our method, we conduct comprehensive experiments on three real-world financial markets: ETFs, the Chinas A-share stock market, and the cryptocurrency market. LARA achieves superior performance compared with the time-series analysis methods and a set of machine learning based competitors on the Qlib platform. Extensive ablation studies and experiments demonstrate that LARA indeed captures more reliable trading opportunities.

Bilateral Control of Teleoperators with Closed Architecture and Time-Varying Delay

This paper investigates bilateral control of teleoperators with closed architecture and subjected to arbitrary bounded time-varying delay. A prominent challenge for bilateral control of such teleoperators lies in the closed architecture, especially in the context not involving interaction force/torque measurement. This yields the long-standing situation that most bilateral control rigorously developed in the literature is hard to be justified as applied to teleoperators with closed architecture. With a new class of dynamic feedback, we propose kinematic and adaptive dynamic controllers for teleoperators with closed architecture, and we show that the proposed kinematic and dynamic controllers are robust with respect to arbitrary bounded time-varying delay. In addition, by exploiting the input-output properties of an inverted form of the dynamics of robot manipulators with closed architecture, we remove the assumption of uniform exponential stability of a linear time-varying system due to the adaptation to the gains of the inner controller in demonstrating stability of the presented adaptive dynamic control. The application of the proposed approach is illustrated by the experimental results using a Phantom Omni and a UR10 robot.

Dancing Synchronization in Coupled Spin-Torque Nano-Oscillators

We are reporting a new type of synchronization, termed dancing synchronization, between two spin-torque nano-oscillators (STNOs) coupled through spin waves. Different from the known synchronizations in which two STNOs are locked with various fixed relative phases, in this new synchronized state two STNOs have the same frequency, but their relative phase varies periodically within the common period, resulting in a dynamic waving pattern. The amplitude of the oscillating relative phase depends on the coupling strength of two STNOs, as well as the driven currents. The dancing synchronization turns out to be universal, and can exist in two nonlinear Van der Pol oscillators coupled both reactively and dissipativly. Our findings open doors for new functional STNO-based devices.

Self-Supervised Metric Learning in Multi-View Data: A Downstream Task Perspective

Self-supervised metric learning has been a successful approach for learning a distance from an unlabeled dataset. The resulting distance is broadly useful for improving various distance-based downstream tasks, even when no information from downstream tasks is utilized in the metric learning stage. To gain insights into this approach, we develop a statistical framework to theoretically study how self-supervised metric learning can benefit downstream tasks in the context of multi-view data. Under this framework, we show that the target distance of metric learning satisfies several desired properties for the downstream tasks. On the other hand, our investigation suggests the target distance can be further improved by moderating each directions weights. In addition, our analysis precisely characterizes the improvement by self-supervised metric learning on four commonly used downstream tasks: sample identification, two-sample testing, $k$-means clustering, and $k$-nearest neighbor classification. As a by-product, we propose a simple spectral method for self-supervised metric learning, which is computationally efficient and minimax optimal for estimating target distance. Finally, numerical experiments are presented to support the theoretical results in the paper.