arXiv subjects

Xin Hu

Publications and source records attributed to Xin Hu.

At least 55 records · Page 3Linked to original sources

arXiv2024

Spin Seebeck Effect in Graphene

We develop a microscopic theory of the spin Seebeck effect (SSE) at the interface of a bilayer system of a ferromagnetic insulator and graphene. We compare the tunneling spin current at the interface because of the SSE and the spin pumping (SP), where the SSE and SP are induced by the temperature gradient and the microwave irradiation, respectively. We demonstrate that the thermally driven SSE exhibits a quantum oscillation pattern similar to that predicted in coherently driven SP. Additionally, we show a peak shift of the quantum oscillation owing to the contribution of thermally excited magnons with higher frequencies, which becomes particularly pronounced at higher temperatures.

cond-mat.mes-hall↗

arXiv2024

Degradation-Aware Residual-Conditioned Optimal Transport for Unified Image Restoration

All-in-one image restoration has emerged as a practical and promising low-level vision task for real-world applications. In this context, the key issue lies in how to deal with different types of degraded images simultaneously. In this work, we present a Degradation-Aware Residual-Conditioned Optimal Transport (DA-RCOT) approach that models (all-in-one) image restoration as an optimal transport (OT) problem for unpaired and paired settings, introducing the transport residual as a degradation-specific cue for both the transport cost and the transport map. Specifically, we formalize image restoration with a residual-guided OT objective by exploiting the degradation-specific patterns of the Fourier residual in the transport cost. More crucially, we design the transport map for restoration as a two-pass DA-RCOT map, in which the transport residual is computed in the first pass and then encoded as multi-scale residual embeddings to condition the second-pass restoration. This conditioning process injects intrinsic degradation knowledge (e.g., degradation type and level) and structural information from the multi-scale residual embeddings into the OT map, which thereby can dynamically adjust its behaviors for all-in-one restoration. Extensive experiments across five degradations demonstrate the favorable performance of DA-RCOT as compared to state-of-the-art methods, in terms of distortion measures, perceptual quality, and image structure preservation. Notably, DA-RCOT delivers superior adaptability to real-world scenarios even with multiple degradations and shows distinctive robustness to both degradation levels and the number of degradations.

cs.CV↗

arXiv2024

Toeplitz Operators with Positive Measures on Harmonic Fock Spaces

In this paper, we study the basic properties of Toeplitz Operators with positive measures $μ$ on harmonic Fock spaces. We prove equivalent conditions for boundedness, compactness and Schatten classes $S_{p}$ of $T_μ$ by using the methods of Berezin transform of operators.

math.FA↗

arXiv2024

Smart Navigation System for Parking Assignment at Large Events: Incorporating Heterogeneous Driver Characteristics

Parking challenges escalate significantly during large events such as concerts and sports games, yet few studies address dynamic parking lot assignments in these occasions. This paper introduces a smart navigation system designed to optimize parking assignments efficiently during major events, employing a mixed search algorithm that considers diverse drivers characteristics. We validated our system through simulations conducted in Berkeley, CA during the "Big Game" showcasing the advantages of our novel parking assignment approach.

cs.HC↗

arXiv2024

Airport Delay Prediction with Temporal Fusion Transformers

Since flight delay hurts passengers, airlines, and airports, its prediction becomes crucial for the decision-making of all stakeholders in the aviation industry and thus has been attempted by various previous research. However, previous delay predictions are often categorical and at a highly aggregated level. To improve that, this study proposes to apply the novel Temporal Fusion Transformer model and predict numerical airport arrival delays at quarter hour level for U.S. top 30 airports. Inputs to our model include airport demand and capacity forecasts, historic airport operation efficiency information, airport wind and visibility conditions, as well as enroute weather and traffic conditions. The results show that our model achieves satisfactory performance measured by small prediction errors on the test set. In addition, the interpretability analysis of the model outputs identifies the important input factors for delay prediction.

cs.LG↗

arXiv2024

Adversarial Attacks to Multi-Modal Models

Multi-modal models have gained significant attention due to their powerful capabilities. These models effectively align embeddings across diverse data modalities, showcasing superior performance in downstream tasks compared to their unimodal counterparts. Recent study showed that the attacker can manipulate an image or audio file by altering it in such a way that its embedding matches that of an attacker-chosen targeted input, thereby deceiving downstream models. However, this method often underperforms due to inherent disparities in data from different modalities. In this paper, we introduce CrossFire, an innovative approach to attack multi-modal models. CrossFire begins by transforming the targeted input chosen by the attacker into a format that matches the modality of the original image or audio file. We then formulate our attack as an optimization problem, aiming to minimize the angular deviation between the embeddings of the transformed input and the modified image or audio file. Solving this problem determines the perturbations to be added to the original media. Our extensive experiments on six real-world benchmark datasets reveal that CrossFire can significantly manipulate downstream tasks, surpassing existing attacks. Additionally, we evaluate six defensive strategies against CrossFire, finding that current defenses are insufficient to counteract our CrossFire.

cs.CR↗

arXiv2024

Multidimensional Nonhomogeneous Quasi-Linear Systems and Their Hamiltonian Structure

In this paper, we investigate multidimensional first-order quasi-linear systems and find necessary conditions for them to admit Hamiltonian formulation. The insufficiency of the conditions is related to the Poisson cohomology of the admissible Hamiltonian operators. We present in detail the examples of two-dimensional, two-components systems of hydrodynamic type and of a real reduction of the 3-waves system.

nlin.SI↗

arXiv2024

The incompressible Navier-Stokes limit from the lattice BGK Boltzmann equation

In this paper, we prove that a local weak solution to the $d$-dimensional incompressible Navier-Stokes equations ($d \geq 2$) can be constructed by taking the hydrodynamic limit of a velocity-discretized Boltzmann equation with a simplified BGK collision operator. Moreover, in the case when the dimension is $d=2,3$, we characterize the combinations of finitely many particle velocities and probabilities that lead to the incompressible Navier-Stokes equations in the hydrodynamic limit. Numerical computations conducted in 2D provide information about the rate with which this hydrodynamic limit is achieved when the Knudsen number tends to zero.

math.AP↗

arXiv2024

Anomalous smoothing effect on the incompressible Navier-Stokes-Fourier limit from Boltzmann with periodic velocity

Adding some nontrivial terms composed from a microstructure, we prove the existence of a global-in-time weak solution, whose enstrophy is bounded for all the time, to an incompressible 3D Navier-Stokes-Fourier system for arbitrary initial data. It cannot be expected to directly derive the energy inequality for this new system of equations. The main idea is to employ the hydrodynamic limit from the Boltzmann equation with periodic velocity and a specially designed collision operator.

math.AP↗

arXiv2024

Stabilization and dynamics of magnetic antivortices in a nanodisk with anisotropic Dzyaloshinskii-Moriya interaction

We theoretically investigate the antivortex stabilized by anisotropic Dzyaloshinskii-Moriya interaction (DMI) in nanodisks. It is remarkably found that the antivortex remains stable even when the nanodisk radius is reduced to 15 nm, owing to the short-range nature of the DMI. We also investigate the antivortex dynamics under a static in-plane magnetic field, which shows that the displacement of the antivortex core depends on its vorticity and helicity, providing a fundamental basic for distinguishing different vortex types. Additionally, spin-polarized currents can trigger a self-sustained gyration of the antivortex at low current densities, while inducing polarity switching at high current densities. Our findings offer valuable insights into the DMI role in stabilizing topological solitons and their potential applications in spin-torque nano-oscillators and magnetic memories.

cond-mat.mes-hall↗

arXiv2024

Residual-Conditioned Optimal Transport: Towards Structure-Preserving Unpaired and Paired Image Restoration

Deep learning-based image restoration methods generally struggle with faithfully preserving the structures of the original image. In this work, we propose a novel Residual-Conditioned Optimal Transport (RCOT) approach, which models image restoration as an optimal transport (OT) problem for both unpaired and paired settings, introducing the transport residual as a unique degradation-specific cue for both the transport cost and the transport map. Specifically, we first formalize a Fourier residual-guided OT objective by incorporating the degradation-specific information of the residual into the transport cost. We further design the transport map as a two-pass RCOT map that comprises a base model and a refinement process, in which the transport residual is computed by the base model in the first pass and then encoded as a degradation-specific embedding to condition the second-pass restoration. By duality, the RCOT problem is transformed into a minimax optimization problem, which can be solved by adversarially training neural networks. Extensive experiments on multiple restoration tasks show that RCOT achieves competitive performance in terms of both distortion measures and perceptual quality, restoring images with more faithful structures as compared with state-of-the-art methods.

cs.CV↗

arXiv2024

Language-Specific Representation of Emotion-Concept Knowledge Causally Supports Emotion Inference

Humans no doubt use language to communicate about their emotional experiences, but does language in turn help humans understand emotions, or is language just a vehicle of communication? This study used a form of artificial intelligence (AI) known as large language models (LLMs) to assess whether language-based representations of emotion causally contribute to the AI's ability to generate inferences about the emotional meaning of novel situations. Fourteen attributes of human emotion concept representation were found to be represented by the LLM's distinct artificial neuron populations. By manipulating these attribute-related neurons, we in turn demonstrated the role of emotion concept knowledge in generative emotion inference. The attribute-specific performance deterioration was related to the importance of different attributes in human mental space. Our findings provide a proof-in-concept that even a LLM can learn about emotions in the absence of sensory-motor representations and highlight the contribution of language-derived emotion-concept knowledge for emotion inference.

cs.AI↗

arXiv2024

GPT-4 Technical Report

We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior. A core component of this project was developing infrastructure and optimization methods that behave predictably across a wide range of scales. This allowed us to accurately predict some aspects of GPT-4's performance based on models trained with no more than 1/1,000th the compute of GPT-4.

cs.CL↗

arXiv2023

Bounds on the closeness centrality of a graph

We present new values and bounds on the (normalised) closeness centrality $\bar{\mathsf{C}}_C$ of connected graphs and on its product $\bar{l}\bar{\mathsf{C}}_C$ with the mean distance $\bar{l}$ of these graphs. Our main result presents the fundamental bounds $1\leq \bar{l}\bar{\mathsf{C}}_C<2$. The lower bound is tight and the upper bound is asymptotically tight. Combining the lower bound with known upper bounds on the mean distance, we find ten new lower bounds for the closeness centrality of graphs. We also present explicit expressions for $\bar{\mathsf{C}}_C$ and $\bar{l}\bar{\mathsf{C}}_C$ for specific families of graphs. Elegantly and perhaps surprisingly, the asymptotic values $n\bar{\mathsf{C}}_C\big(P_n\big)$ and of $n\bar{\mathsf{C}}_C\big(L_n\big)$ both equal $π$, and the asymptotic limits of $\bar{l}\bar{\mathsf{C}}_C$ for these families of graphs are both equal to $π/3$. We conjecture that the set of values $\bar{l}\bar{\mathsf{C}}_C$ for all connected graphs is dense in the interval $[1,2)$.

math.CO↗

arXiv2023

Opportunities and challenges of ChatGPT for design knowledge management

Recent advancements in Natural Language Processing have opened up new possibilities for the development of large language models like ChatGPT, which can facilitate knowledge management in the design process by providing designers with access to a vast array of relevant information. However, integrating ChatGPT into the design process also presents new challenges. In this paper, we provide a concise review of the classification and representation of design knowledge, and past efforts to support designers in acquiring knowledge. We analyze the opportunities and challenges that ChatGPT presents for knowledge management in design and propose promising future research directions. A case study is conducted to validate the advantages and drawbacks of ChatGPT, showing that designers can acquire targeted knowledge from various domains, but the quality of the acquired knowledge is highly dependent on the prompt.

cs.IR↗

arXiv2023

A Multi-purpose Realistic Haze Benchmark with Quantifiable Haze Levels and Ground Truth

Imagery collected from outdoor visual environments is often degraded due to the presence of dense smoke or haze. A key challenge for research in scene understanding in these degraded visual environments (DVE) is the lack of representative benchmark datasets. These datasets are required to evaluate state-of-the-art vision algorithms (e.g., detection and tracking) in degraded settings. In this paper, we address some of these limitations by introducing the first realistic hazy image benchmark, from both aerial and ground view, with paired haze-free images, and in-situ haze density measurements. This dataset was produced in a controlled environment with professional smoke generating machines that covered the entire scene, and consists of images captured from the perspective of both an unmanned aerial vehicle (UAV) and an unmanned ground vehicle (UGV). We also evaluate a set of representative state-of-the-art dehazing approaches as well as object detectors on the dataset. The full dataset presented in this paper, including the ground truth object classification bounding boxes and haze density measurements, is provided for the community to evaluate their algorithms at: https://a2i2-archangel.vision. A subset of this dataset has been used for the ``Object Detection in Haze'' Track of CVPR UG2 2022 challenge at http://cvpr2022.ug2challenge.org/track1.html.

cs.CV↗

arXiv2022

GPTR: Gestalt-Perception Transformer for Diagram Object Detection

Diagram object detection is the key basis of practical applications such as textbook question answering. Because the diagram mainly consists of simple lines and color blocks, its visual features are sparser than those of natural images. In addition, diagrams usually express diverse knowledge, in which there are many low-frequency object categories in diagrams. These lead to the fact that traditional data-driven detection model is not suitable for diagrams. In this work, we propose a gestalt-perception transformer model for diagram object detection, which is based on an encoder-decoder architecture. Gestalt perception contains a series of laws to explain human perception, that the human visual system tends to perceive patches in an image that are similar, close or connected without abrupt directional changes as a perceptual whole object. Inspired by these thoughts, we build a gestalt-perception graph in transformer encoder, which is composed of diagram patches as nodes and the relationships between patches as edges. This graph aims to group these patches into objects via laws of similarity, proximity, and smoothness implied in these edges, so that the meaningful objects can be effectively detected. The experimental results demonstrate that the proposed GPTR achieves the best results in the diagram object detection task. Our model also obtains comparable results over the competitors in natural image object detection.

cs.CV↗

arXiv2022

E^2TAD: An Energy-Efficient Tracking-based Action Detector

Video action detection (spatio-temporal action localization) is usually the starting point for human-centric intelligent analysis of videos nowadays. It has high practical impacts for many applications across robotics, security, healthcare, etc. The two-stage paradigm of Faster R-CNN inspires a standard paradigm of video action detection in object detection, i.e., firstly generating person proposals and then classifying their actions. However, none of the existing solutions could provide fine-grained action detection to the "who-when-where-what" level. This paper presents a tracking-based solution to accurately and efficiently localize predefined key actions spatially (by predicting the associated target IDs and locations) and temporally (by predicting the time in exact frame indices). This solution won first place in the UAV-Video Track of 2021 Low-Power Computer Vision Challenge (LPCVC).

cs.CV↗