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Xin He

Publications and source records attributed to Xin He.

At least 127 records · Page 7Linked to original sources

Generalized Volterra-type integral operators between Bloch-type spaces

The Volterra-type integral operator plays an essential role in modern complex analysis and operator theory. Recently, Chalmoukis \cite{Cn} introduced a generalized integral operator, say $I_{g,a}$, defined by $$I_{g,a}f=I^n(a_0f^{(n-1)}g'+a_1f^{(n-2)}g''+\cdots+a_{n-1}fg^{(n)}),$$ where $g\in H(\mathbb{D})$ and $a=(a_0,a_1,\cdots,a_{n-1})\in \mathbb{C}^n$. $I^n$ is the $n$th iteration of the integral operator $I$. In this paper, we introduce a more generalized integral operators $I_{\mathbf{g}}^{(n)}$ that cover $I_{g,a}$ on the Bloch-type space $\mathcal{B}^α$, defined by $$I_{\mathbf{g}}^{(n)}f=I^n(fg_0+\cdots+f^{(n-1)}g_{n-1}).$$ We show the rigidity of the operator $I_{\mathbf{g}}^{(n)}$ and further the sum $\sum_{i=1}^nI_{g_i}^{N_i,k_i}$, where $I_{g_i}^{N_i,k_i}f=I^{N_i}(f^{(k_i)}g_i)$. Specifically, the boundedness and compactness of $\sum_{i=1}^nI_{g_i}^{N_i,k_i}$ are equal to those of each $I_{g_i}^{N_i,k_i}$. Moreover, the boundedness and compactness of $I^n((fg')^{(n-1)})$ are independent of $n$ when $α>1$.

math.FA↗

A Novel Class of Phase Space Representations for the Exact Population Dynamics of Two-State Quantum Systems and the Relation to Triangle Window Functions

Isomorphism of the two-state system is heuristic in understanding the dynamical or statistical behavior of the simplest yet most quantum system that has no classical counterpart. We use the constraint phase space developed in J. Chem. Phys. 2016, 145, 204105; 2019, 151, 024105 and J. Phys. Chem. Lett. 2021, 12, 2496-2501, non-covariant phase space functions, time-dependent weight functions, and time-dependent normalization factors to construct a novel class of phase space representations of the exact population dynamics of the two-state quantum system. The equations of motion of the trajectory on constraint phase space are isomorphic to the time-dependent Schrödinger equation. The contribution of each trajectory to the integral expression for the population dynamics is always positive semi-definite. We also prove that the triangle window function approach, albeit proposed as a heuristic empirical model in J. Chem. Phys. 2016, 145, 144108, is related to a special case of the novel class and leads to an isomorphic representation of the exact population dynamics of the two-state quantum system.

quant-ph↗

Nonadiabatic Field with Triangle Window Functions on Quantum Phase Space

The constraint coordinate-momentum phase space (CPS) formulation of finite-state quantum systems has recently revealed that the triangle window function approach is an isomorphic representation of the exact population-population correlation function of the two-state system. We use the triangle window (TW) function and the CPS mapping kernel element to formulate a novel useful representation of discrete electronic degrees of freedom (DOFs). When it is employed with nonadiabatic field (NaF) dynamics, a new variant of the NaF approach (i.e., NaF-TW) is proposed. Extensive benchmark tests of model systems in both the condensed phase and gas phase demonstrate that the NaF-TW approach is competent in faithfully capturing the dynamical interplay between electronic and nuclear DOFs. In comparison to the symmetrical quasi-classical (SQC) method where triangle window functions were originally proposed, the performance of NaF-TW is significantly better when the bifurcation characteristic of nuclear motion in the asymptotic region is important.

quant-ph↗

RSDehamba: Lightweight Vision Mamba for Remote Sensing Satellite Image Dehazing

Remote sensing image dehazing (RSID) aims to remove nonuniform and physically irregular haze factors for high-quality image restoration. The emergence of CNNs and Transformers has taken extraordinary strides in the RSID arena. However, these methods often struggle to demonstrate the balance of adequate long-range dependency modeling and maintaining computational efficiency. To this end, we propose the first lightweight network on the mamba-based model called RSDhamba in the field of RSID. Greatly inspired by the recent rise of Selective State Space Model (SSM) for its superior performance in modeling linear complexity and remote dependencies, our designed RSDehamba integrates the SSM framework into the U-Net architecture. Specifically, we propose the Vision Dehamba Block (VDB) as the core component of the overall network, which utilizes the linear complexity of SSM to achieve the capability of global context encoding. Simultaneously, the Direction-aware Scan Module (DSM) is designed to dynamically aggregate feature exchanges over different directional domains to effectively enhance the flexibility of sensing the spatially varying distribution of haze. In this way, our RSDhamba fully demonstrates the superiority of spatial distance capture dependencies and channel information exchange for better extraction of haze features. Extensive experimental results on widely used benchmarks validate the surpassing performance of our RSDehamba against existing state-of-the-art methods.

cs.CV↗

UAV-Rain1k: A Benchmark for Raindrop Removal from UAV Aerial Imagery

Raindrops adhering to the lens of UAVs can obstruct visibility of the background scene and degrade image quality. Despite recent progress in image deraining methods and datasets, there is a lack of focus on raindrop removal from UAV aerial imagery due to the unique challenges posed by varying angles and rapid movement during drone flight. To fill the gap in this research, we first construct a new benchmark dataset for removing raindrops from UAV images, called UAV-Rain1k. In this letter, we provide a dataset generation pipeline, which includes modeling raindrop shapes using Blender, collecting background images from various UAV angles, random sampling of rain masks and etc. Based on the proposed benchmark, we further present a comprehensive evaluation of existing representative image deraining algorithms, and reveal future research opportunities worth exploring. The proposed dataset is publicly available at https://github.com/cschenxiang/UAV-Rain1k.

cs.CV↗

Nonadiabatic Field on Quantum Phase Space: A Century after Ehrenfest

Nonadiabatic transition dynamics lies at the core of many electron/hole transfer, photoactivated, and vacuum field-coupled processes. About a century after Ehrenfest proposed "Phasenraum" and the Ehrenfest theorem, we report a conceptually novel trajectory-based nonadiabatic dynamics approach, nonadiabatic field (NaF), based on a generalized exact coordinate-momentum phase space formulation of quantum mechanics. It does not employ the conventional Born-Oppenheimer or Ehrenfest trajectory in the nonadiabatic coupling region. Instead, in NaF the equations of motion of the independent trajectory involve a nonadiabatic nuclear force term in addition to an adiabatic nuclear force term of a single electronic state. A few benchmark tests for gas phase and condensed phase systems indicate that NaF offers a practical tool to capture the correct correlation of electronic and nuclear dynamics for processes where the states remain coupled all the time as well as for the asymptotic region where the coupling of electronic states vanishes.

quant-ph↗

Efficient Post-training Quantization with FP8 Formats

Recent advances in deep learning methods such as LLMs and Diffusion models have created a need for improved quantization methods that can meet the computational demands of these modern architectures while maintaining accuracy. Towards this goal, we study the advantages of FP8 data formats for post-training quantization across 75 unique network architectures covering a wide range of tasks, including machine translation, language modeling, text generation, image classification, generation, and segmentation. We examine three different FP8 representations (E5M2, E4M3, and E3M4) to study the effects of varying degrees of trade-off between dynamic range and precision on model accuracy. Based on our extensive study, we developed a quantization workflow that generalizes across different network architectures. Our empirical results show that FP8 formats outperform INT8 in multiple aspects, including workload coverage (92.64% vs. 65.87%), model accuracy and suitability for a broader range of operations. Furthermore, our findings suggest that E4M3 is better suited for NLP models, whereas E3M4 performs marginally better than E4M3 on computer vision tasks. The code is publicly available on Intel Neural Compressor: https://github.com/intel/neural-compressor.

cs.LG↗

ONE-SA: Enabling Nonlinear Operations in Systolic Arrays for Efficient and Flexible Neural Network Inference

The computation and memory-intensive nature of DNNs limits their use in many mobile and embedded contexts. Application-specific integrated circuit (ASIC) hardware accelerators employ matrix multiplication units (such as the systolic arrays) and dedicated nonlinear function units to speed up DNN computations. A close examination of these ASIC accelerators reveals that the designs are often specialized and lack versatility across different networks, especially when the networks have different types of computation. In this paper, we introduce a novel systolic array architecture, which is capable of executing nonlinear functions. By encompassing both inherent linear and newly enabled nonlinear functions within the systolic arrays, the proposed architecture facilitates versatile network inferences, substantially enhancing computational power and energy efficiency. Experimental results show that employing this systolic array enables seamless execution of entire DNNs, incurring only a negligible loss in the network inference accuracy. Furthermore, assessment and evaluation with FPGAs reveal that integrating nonlinear computation capacity into a systolic array does not introduce extra notable (less than 1.5%) block memory memories (BRAMs), look-up-tables (LUTs), or digital signal processors (DSPs) but a mere 13.3% - 24.1% more flip flops (FFs). In comparison to existing methodologies, executing the networks with the proposed systolic array, which enables the flexibility of different network models, yields up to 25.73x, 5.21x, and 1.54x computational efficiency when compared to general-purpose CPUs, GPUs, and SoCs respectively, while achieving comparable (83.4% - 135.8%) performance with the conventional accelerators which are designed for specific neural network models.

cs.AR↗

Incorporating Visual Experts to Resolve the Information Loss in Multimodal Large Language Models

Multimodal Large Language Models (MLLMs) are experiencing rapid growth, yielding a plethora of noteworthy contributions in recent months. The prevailing trend involves adopting data-driven methodologies, wherein diverse instruction-following datasets are collected. However, a prevailing challenge persists in these approaches, specifically in relation to the limited visual perception ability, as CLIP-like encoders employed for extracting visual information from inputs. Though these encoders are pre-trained on billions of image-text pairs, they still grapple with the information loss dilemma, given that textual captions only partially capture the contents depicted in images. To address this limitation, this paper proposes to improve the visual perception ability of MLLMs through a mixture-of-experts knowledge enhancement mechanism. Specifically, we introduce a novel method that incorporates multi-task encoders and visual tools into the existing MLLMs training and inference pipeline, aiming to provide a more comprehensive and accurate summarization of visual inputs. Extensive experiments have evaluated its effectiveness of advancing MLLMs, showcasing improved visual perception achieved through the integration of visual experts.

cs.CV↗

Superconductivity above 30 K achieved in dense scandium

Superconductivity is one of most intriguing quantum phenomena, and the quest for elemental superconductors with high critical temperature (Tc) is of great scientific significance due to their relatively simple material composition and the underlying mechanism. Here we report the experimental discovery of densely compressed scandium (Sc) becoming the first elemental superconductor with Tc breaking into 30 K range, which is comparable to the Tc values of the classic La-Ba-Cu-O or LaFeAsO superconductors. Our results show that Tconset of Sc increases from ~3 K at around 43 GPa to ~32 K at about 283 GPa (Tczero ~ 31 K), which is well above liquid neon temperature. Interestingly measured Tc shows no sign of saturation up to the maximum pressure achieved in our experiments, indicating that Tc might be even higher upon further compression.

cond-mat.supr-con↗

Fundamental Electron and Potential Relations in Dilute Plasma Flows

In this short note, we present some work on investigating electron temperatures and potentials in steady or unsteady dilute plasma flows. The analysis is based on the detailed fluid model for electrons. Ionization, normalized electron number density gradients, and magnetic fields are neglected. The transport properties are assumed as local constants. With these treatments, the partial differential equation for electron temperature degenerates as an ordinary differential equation. Along an electron streamline, fundamental formulas for electron temperature and plasma potential are obtained. These formulas offer significant insights, 1). for steady flow, the electron temperature and plasma potential distributions along an electron streamline include two exponential functions, and the one for plasma potential includes an extra linear distribution function; 2). for unsteady flows, both the temporal and spatical parts include potential functions.

physics.plasm-ph↗

Multiplayer Homicidal Chauffeur Reach-Avoid Games: A Pursuit Enclosure Function Approach

This paper presents a multiplayer Homicidal Chauffeur reach-avoid differential game, which involves Dubins-car pursuers and simple-motion evaders. The goal of the pursuers is to cooperatively protect a planar convex region from the evaders, who strive to reach the region. We propose a cooperative strategy for the pursuers based on subgames for multiple pursuers against one evader and optimal task allocation. We introduce pursuit enclosure functions (PEFs) and propose a new enclosure region pursuit (ERP) winning approach that supports forward analysis for the strategy synthesis in the subgames. We show that if a pursuit coalition is able to defend the region against an evader under the ERP winning, then no more than two pursuers in the coalition are necessarily needed. We also propose a steer-to-ERP approach to certify the ERP winning and synthesize the ERP winning strategy. To implement the strategy, we introduce a positional PEF and provide the necessary parameters, states, and strategies that ensure the ERP winning for both one pursuer and two pursuers against one evader. Additionally, we formulate a binary integer program using the subgame outcomes to maximize the captured evaders in the ERP winning for the pursuit task allocation. Finally, we propose a multiplayer receding-horizon strategy where the ERP winnings are checked in each horizon, the task is allocated, and the strategies of the pursuers are determined. Numerical examples are provided to illustrate the results.

eess.SY↗

Search Optimization with Query Likelihood Boosting and Two-Level Approximate Search for Edge Devices

We present a novel search optimization solution for approximate nearest neighbor (ANN) search on resource-constrained edge devices. Traditional ANN approaches fall short in meeting the specific demands of real-world scenarios, e.g., skewed query likelihood distribution and search on large-scale indices with a low latency and small footprint. To address these limitations, we introduce two key components: a Query Likelihood Boosted Tree (QLBT) to optimize average search latency for frequently used small datasets, and a two-level approximate search algorithm to enable efficient retrieval with large datasets on edge devices. We perform thorough evaluation on simulated and real data and demonstrate QLBT can significantly reduce latency by 15% on real data and our two-level search algorithm successfully achieve deployable accuracy and latency on a 10 million dataset for edge devices. In addition, we provide a comprehensive protocol for configuring and optimizing on-device search algorithm through extensive empirical studies.

cs.IR↗

SemanticBoost: Elevating Motion Generation with Augmented Textual Cues

Current techniques face difficulties in generating motions from intricate semantic descriptions, primarily due to insufficient semantic annotations in datasets and weak contextual understanding. To address these issues, we present SemanticBoost, a novel framework that tackles both challenges simultaneously. Our framework comprises a Semantic Enhancement module and a Context-Attuned Motion Denoiser (CAMD). The Semantic Enhancement module extracts supplementary semantics from motion data, enriching the dataset's textual description and ensuring precise alignment between text and motion data without depending on large language models. On the other hand, the CAMD approach provides an all-encompassing solution for generating high-quality, semantically consistent motion sequences by effectively capturing context information and aligning the generated motion with the given textual descriptions. Distinct from existing methods, our approach can synthesize accurate orientational movements, combined motions based on specific body part descriptions, and motions generated from complex, extended sentences. Our experimental results demonstrate that SemanticBoost, as a diffusion-based method, outperforms auto-regressive-based techniques, achieving cutting-edge performance on the Humanml3D dataset while maintaining realistic and smooth motion generation quality.

cs.CV↗

Implicit Regularization in Over-Parameterized Support Vector Machine

In this paper, we design a regularization-free algorithm for high-dimensional support vector machines (SVMs) by integrating over-parameterization with Nesterov's smoothing method, and provide theoretical guarantees for the induced implicit regularization phenomenon. In particular, we construct an over-parameterized hinge loss function and estimate the true parameters by leveraging regularization-free gradient descent on this loss function. The utilization of Nesterov's method enhances the computational efficiency of our algorithm, especially in terms of determining the stopping criterion and reducing computational complexity. With appropriate choices of initialization, step size, and smoothness parameter, we demonstrate that unregularized gradient descent achieves a near-oracle statistical convergence rate. Additionally, we verify our theoretical findings through a variety of numerical experiments and compare the proposed method with explicit regularization. Our results illustrate the advantages of employing implicit regularization via gradient descent in conjunction with over-parameterization in sparse SVMs.

math.ST↗

Towards a Unified Analysis of Kernel-based Methods Under Covariate Shift

Covariate shift occurs prevalently in practice, where the input distributions of the source and target data are substantially different. Despite its practical importance in various learning problems, most of the existing methods only focus on some specific learning tasks and are not well validated theoretically and numerically. To tackle this problem, we propose a unified analysis of general nonparametric methods in a reproducing kernel Hilbert space (RKHS) under covariate shift. Our theoretical results are established for a general loss belonging to a rich loss function family, which includes many commonly used methods as special cases, such as mean regression, quantile regression, likelihood-based classification, and margin-based classification. Two types of covariate shift problems are the focus of this paper and the sharp convergence rates are established for a general loss function to provide a unified theoretical analysis, which concurs with the optimal results in literature where the squared loss is used. Extensive numerical studies on synthetic and real examples confirm our theoretical findings and further illustrate the effectiveness of our proposed method.

stat.ML↗

Structural transfer learning of non-Gaussian DAG

Directed acyclic graph (DAG) has been widely employed to represent directional relationships among a set of collected nodes. Yet, the available data in one single study is often limited for accurate DAG reconstruction, whereas heterogeneous data may be collected from multiple relevant studies. It remains an open question how to pool the heterogeneous data together for better DAG structure reconstruction in the target study. In this paper, we first introduce a novel set of structural similarity measures for DAG and then present a transfer DAG learning framework by effectively leveraging information from auxiliary DAGs of different levels of similarities. Our theoretical analysis shows substantial improvement in terms of DAG reconstruction in the target study, even when no auxiliary DAG is overall similar to the target DAG, which is in sharp contrast to most existing transfer learning methods. The advantage of the proposed transfer DAG learning is also supported by extensive numerical experiments on both synthetic data and multi-site brain functional connectivity network data.

stat.ML↗

Proton-mediated reversible switching of metastable ferroelectric phases with low operation voltages

The exploration of ferroelectric phase transitions enables an in-depth understanding of ferroelectric switching and promising applications in information storage. However, controllably tuning the dynamics of ferroelectric phase transitions remains challenging owing to inaccessible hidden phases. Here, using protonic gating technology, we create a series of metastable ferroelectric phases and demonstrate their reversible transitions in van der Waals ferroelectric α-In2Se3 transistors. By varying the gate bias, protons can be incrementally injected or extracted, achieving controllable tuning of the ferroelectric α-In2Se3 protonic dynamics across the channel and obtaining numerous intermediate phases. Combining piezoelectric force microscopy and electrical hysteresis measurements, we surprisingly discover that the gate tuning of α-In2Se3 protonation is volatile, unlike the conventional gating effect, and the created new phases remain polar. Their origin, revealed by first-principles calculations, is related to the formation of metastable hydrogen-stabilized α-In2Se3 phases with switchable dipoles. Furthermore, our approach enables ultralow gate-voltage switching of different phases (below 0.4 V), and easy integration with transistor components. This work provides fundamental insights into the interactions between protons and ferroelectric phase transitions, providing a possible avenue for accessing hidden phases in ferroelectric switching and for developing energy-efficient, multilevel-switching memory devices.

cond-mat.mtrl-sci↗