arXiv subjects

Peng Zhang

Publications and source records attributed to Peng Zhang.

At least 343 records · Page 19Linked to original sources

arXiv2024

CogVLM2: Visual Language Models for Image and Video Understanding

Beginning with VisualGLM and CogVLM, we are continuously exploring VLMs in pursuit of enhanced vision-language fusion, efficient higher-resolution architecture, and broader modalities and applications. Here we propose the CogVLM2 family, a new generation of visual language models for image and video understanding including CogVLM2, CogVLM2-Video and GLM-4V. As an image understanding model, CogVLM2 inherits the visual expert architecture with improved training recipes in both pre-training and post-training stages, supporting input resolution up to $1344 \times 1344$ pixels. As a video understanding model, CogVLM2-Video integrates multi-frame input with timestamps and proposes automated temporal grounding data construction. Notably, CogVLM2 family has achieved state-of-the-art results on benchmarks like MMBench, MM-Vet, TextVQA, MVBench and VCGBench. All models are open-sourced in https://github.com/THUDM/CogVLM2 and https://github.com/THUDM/GLM-4, contributing to the advancement of the field.

cs.CV↗

arXiv2024

ConsistencyTrack: A Robust Multi-Object Tracker with a Generation Strategy of Consistency Model

Multi-object tracking (MOT) is a critical technology in computer vision, designed to detect multiple targets in video sequences and assign each target a unique ID per frame. Existed MOT methods excel at accurately tracking multiple objects in real-time across various scenarios. However, these methods still face challenges such as poor noise resistance and frequent ID switches. In this research, we propose a novel ConsistencyTrack, joint detection and tracking(JDT) framework that formulates detection and association as a denoising diffusion process on perturbed bounding boxes. This progressive denoising strategy significantly improves the model's noise resistance. During the training phase, paired object boxes within two adjacent frames are diffused from ground-truth boxes to a random distribution, and then the model learns to detect and track by reversing this process. In inference, the model refines randomly generated boxes into detection and tracking results through minimal denoising steps. ConsistencyTrack also introduces an innovative target association strategy to address target occlusion. Experiments on the MOT17 and DanceTrack datasets demonstrate that ConsistencyTrack outperforms other compared methods, especially better than DiffusionTrack in inference speed and other performance metrics. Our code is available at https://github.com/Tankowa/ConsistencyTrack.

cs.CV↗

arXiv2024

Constraints on the axial-vector and pseudo-scalar mediated WIMP-nucleus interactions from PandaX-4T experiment

We present the constraints on the axial-vector and pseudo-scalar mediated WIMP-nucleus interactions from the PandaX-4T experiment, using the data set corresponding to a total exposure of 0.63~tonne$\cdot$year. No significant signal excess is observed, and the most stringent upper limits to date on the spin-dependent WIMP-neutron scattering cross section are set at 90\% confidence level with the minimum WIMP-neutron scattering cross section of 5.8$\times 10^{-42}$\si{\cm^{2}} for WIMP mass of 40~\si{\GeV/}$c^2$. Exclusion limits on the axial-vector and pseudo-scalar simplified models are also derived.

hep-ex↗

arXiv2024

Advancing Gamma-Ray Burst Identification through Transfer Learning with Convolutional Neural Networks

The Rapid and accurate identification of Gamma-Ray Bursts (GRBs) is crucial for unraveling their origins. However, current burst search algorithms frequently miss low-threshold signals or lack universality for observations. In this study, we propose a novel approach utilizing transfer learning experiment based on convolutional neural network (CNN) to establish a universal GRB identification method, which validated successfully using GECAM-B data. By employing data augmentation techniques, we enhance the diversity and quantity of the GRB sample. We develop a 1D CNN model with a multi-scale feature cross fusion module (MSCFM) to extract features from samples and perform classification. The comparative results demonstrated significant performance improvements following pre-training and transferring on a large-scale dataset. Our optimal model achieved an impressive accuracy of 96.41% on the source dataset of GECAM-B, and identified three previously undiscovered GRBs by contrast with manual analysis of GECAM-B observations. These innovative transfer learning and data augmentation methods presented in this work hold promise for applications in multi-satellite exploration scenarios characterized by limited data sets and a scarcity of labeled samples in high-energy astronomy.

astro-ph.HE↗

arXiv2024

Is There a Scaling Law in the Inviscid Coalescence of Unequal-size Droplets?

This work examines the coalescence of two unequal-size spherical liquid droplets in the inviscid regime, with an emphasis on exploring the scaling of the liquid bridge evolution. Our experiment suggests that the classical 1/2 power-law scaling for equal-size droplets still holds for the unequal-size situation of small size ratios, but it diverges as the size ratio increases. Employing an energy balance analysis, we develop a theoretical model to collapse the experimental data of different droplet size ratios. The model reveals an exponential dependence of the bridge's radial growth on time, implying an intrinsic breaking of scaling law. The scale-free evolution behavior is evident only at late coalescence time and large size ratio, which can be explained using the length and time scales obtained from the theory.

physics.flu-dyn↗

arXiv2024

Giant Hall Switching by Surface-State-Mediated Spin-Orbit Torque in a Hard Ferromagnetic Topological Insulator

Topological insulators (TI) and magnetic topological insulators (MTI) can apply highly efficient spin-orbit torque (SOT) and manipulate the magnetization with their unique topological surface states with ultra-high efficiency. Here, we demonstrate efficient SOT switching of a hard MTI, V-doped (Bi,Sb)2Te3 (VBST) with a large coercive field that can prevent the influence of an external magnetic field. A giant switched anomalous Hall resistance of 9.2 $kΩ$ is realized, among the largest of all SOT systems, which makes the Hall channel a good readout and eliminates the need to fabricate complicated magnetic tunnel junction (MTJ) structures. The SOT switching current density can be reduced to $2.8\times10^5 A/cm^2$. Moreover, as the Fermi level is moved away from the Dirac point by both gate and composition tuning, VBST exhibits a transition from edge-state-mediated to surface-state-mediated transport, thus enhancing the SOT effective field to $1.56\pm 0.12 T/ (10^6 A/cm^2)$ and the interfacial charge-to-spin conversion efficiency to $3.9\pm 0.3 nm^{-1}$. The findings establish VBST as an extraordinary candidate for energy-efficient magnetic memory devices.

cond-mat.mes-hall↗

arXiv2024

Computational Realization of Popping Impinging Sprays of Hypergolic Bipropellants by a Eulerian-Lagrangian Approach

This work adopts a Eulerian-Lagrangian approach to numerically simulate the spray impingement of MMH (Monomethyl hydrazine)/NTO (nitrogen tetroxide), which are prevalent rocket engine bipropellants for deep space missions and satellite orbital maneuvers. The emphasis of the work is to computationally realize the popping phenomenon and to study its parametric dependence on liquid and gas-phase reaction rates. The liquid-phase reaction of MMH/NTO is realized based on the extended spray equation, incorporating the additional independent variable, propellant mass fraction, to account for the mixing of droplets. The spray popping can be computationally reproduced over wide ranges of Damköhler numbers for both liquid- and gas-phase reactions. Furthermore, the computational results have been validated through qualitative comparison with experimental images and quantitative comparison with experimental frequencies. The present results verify our hypothesis that the heat release from the liquid-phase reaction enhances the evaporation of MMH and NTO so that the intense gas-phase reaction zone around the spray impingement point periodically separates the MMH and NTO impinging sprays to cause the popping phenomenon. Furthermore, it was found that the popping phenomenon can be suppressed by reducing the Damköhler numbers of liquid-phase reaction and therefore to suppress the evaporation of the propellants. This work is believed to provide valuable understanding for avoiding the off-design popping phenomenon that may reduce combustion efficiency and increase the risk of combustion instability in rocket engines.

physics.flu-dyn↗

arXiv2024

Computational Identification and Stuart-Landau Modeling of Collective Dynamical Behaviors of Octuple Laminar Diffusion Flame Oscillators

Annular chambers, consisting of multiple flame nozzles, are frequently used in many industrial processes, for example, rocket engines and gas turbines. In the study, we proposed a novel approach to the problem of annular combustion with emphasis on the collective dynamical behaviors that its individuals do not have. A series of circular arrays of octuple flickering laminar buoyant diffusion flames were investigated computationally and theoretically. Five distinct dynamical modes, such as the merged, in-phase mode, rotation, flickering death, partially flickering death, and anti-phase modes, were computationally identified and interpreted from the perspective of vortex dynamics. A unified regime diagram was obtained in terms of the normalized flame frequency f/f_0 and the combined parameter (α-1)Gr^1/2, where α=l/D is the ratio of the flame separation distance l to the flame nozzle diameter D and Gr is the Grashof number. The bifurcation transition from the in-phase mode and the anti-phase mode to the totally or partially flickering death occurs at (α-1)Gr^1/2=655+-55. In addition, a Stuart-Landau model with a time-delay coupling was utilized to reproduce the general features and collective modes of the octuple oscillators flame systems.

physics.flu-dyn↗

arXiv2024

Role Identification based Method for Cyberbullying Analysis in Social Edge Computing

Over the past few years, many efforts have been dedicated to studying cyberbullying in social edge computing devices, and most of them focus on three roles: victims, perpetrators, and bystanders. If we want to obtain a deep insight into the formation, evolution, and intervention of cyberbullying in devices at the edge of the Internet, it is necessary to explore more fine-grained roles. This paper presents a multi-level method for role feature modeling and proposes a differential evolution-assisted K-means (DEK) method to identify diverse roles. Our work aims to provide a role identification scheme for cyberbullying scenarios for social edge computing environments to alleviate the general safety issues that cyberbullying brings. The experiments on ten real-world datasets obtained from Weibo and five public datasets show that the proposed DEK outperforms the existing approaches on the method level. After clustering, we obtained nine roles and analyzed the characteristics of each role and their evolution trends under different cyberbullying scenarios. Our work in this paper can be placed in devices at the edge of the Internet, leading to better real-time identification performance and adapting to the broad geographic location and high mobility of mobile devices.

cs.HC↗

arXiv2024

A Comprehensive Summarization and Evaluation of Feature Refinement Modules for CTR Prediction

Click-through rate (CTR) prediction is widely used in academia and industry. Most CTR tasks fall into a feature embedding \& feature interaction paradigm, where the accuracy of CTR prediction is mainly improved by designing practical feature interaction structures. However, recent studies have argued that the fixed feature embedding learned only through the embedding layer limits the performance of existing CTR models. Some works apply extra modules on top of the embedding layer to dynamically refine feature representations in different instances, making it effective and easy to integrate with existing CTR methods. Despite the promising results, there is a lack of a systematic review and summarization of this new promising direction on the CTR task. To fill this gap, we comprehensively summarize and define a new module, namely \textbf{feature refinement} (FR) module, that can be applied between feature embedding and interaction layers. We extract 14 FR modules from previous works, including instances where the FR module was proposed but not clearly defined or explained. We fully assess the effectiveness and compatibility of existing FR modules through comprehensive and extensive experiments with over 200 augmented models and over 4,000 runs for more than 15,000 GPU hours. The results offer insightful guidelines for researchers, and all benchmarking code and experimental results are open-sourced. In addition, we present a new architecture of assigning independent FR modules to separate sub-networks for parallel CTR models, as opposed to the conventional method of inserting a shared FR module on top of the embedding layer. Our approach is also supported by comprehensive experiments demonstrating its effectiveness.

cs.IR↗

arXiv2024

AOTree: Aspect Order Tree-based Model for Explainable Recommendation

Recent recommender systems aim to provide not only accurate recommendations but also explanations that help users understand them better. However, most existing explainable recommendations only consider the importance of content in reviews, such as words or aspects, and ignore the ordering relationship among them. This oversight neglects crucial ordering dimensions in the human decision-making process, leading to suboptimal performance. Therefore, in this paper, we propose Aspect Order Tree-based (AOTree) explainable recommendation method, inspired by the Order Effects Theory from cognitive and decision psychology, in order to capture the dependency relationships among decisive factors. We first validate the theory in the recommendation scenario by analyzing the reviews of the users. Then, according to the theory, the proposed AOTree expands the construction of the decision tree to capture aspect orders in users' decision-making processes, and use attention mechanisms to make predictions based on the aspect orders. Extensive experiments demonstrate our method's effectiveness on rating predictions, and our approach aligns more consistently with the user' s decision-making process by displaying explanations in a particular order, thereby enhancing interpretability.

cs.IR↗

arXiv2024

Electronic Correlations and Hund's Rule Coupling in Trilayer Nickelate La4Ni3O10

Trilayer Ruddlesden-Popper phase La4Ni3O10 has been observed with Tc of about 30 K at high pressure in recent experiment, which further expanded the family of nickelate superconductors. In this study, we explored the effects of electronic correlations in La4Ni3O10 using density function theory plus dynamical mean-field theory at ambient and high pressures. Our derived spectral functions and Fermi surface of ambient pressure phase are nicely consistent with the experimental results by angle-resolved photoemission spectroscopy, which emphasized the importance of electronic correlations in La4Ni3O10. We also found the electronic correlations in pressured La4Ni3O10 are both orbital-dependent and layer-dependent due to the presence of Hund's rule coupling. There is a competition between the Hund's rule coupling and the crystal-field splitting, and therefore the Ni-O layers with weaker crystal-field splitting energy would have stronger electronic correlations.

cond-mat.str-el↗

arXiv2024

Focus, Distinguish, and Prompt: Unleashing CLIP for Efficient and Flexible Scene Text Retrieval

Scene text retrieval aims to find all images containing the query text from an image gallery. Current efforts tend to adopt an Optical Character Recognition (OCR) pipeline, which requires complicated text detection and/or recognition processes, resulting in inefficient and inflexible retrieval. Different from them, in this work we propose to explore the intrinsic potential of Contrastive Language-Image Pre-training (CLIP) for OCR-free scene text retrieval. Through empirical analysis, we observe that the main challenges of CLIP as a text retriever are: 1) limited text perceptual scale, and 2) entangled visual-semantic concepts. To this end, a novel model termed FDP (Focus, Distinguish, and Prompt) is developed. FDP first focuses on scene text via shifting the attention to the text area and probing the hidden text knowledge, and then divides the query text into content word and function word for processing, in which a semantic-aware prompting scheme and a distracted queries assistance module are utilized. Extensive experiments show that FDP significantly enhances the inference speed while achieving better or competitive retrieval accuracy compared to existing methods. Notably, on the IIIT-STR benchmark, FDP surpasses the state-of-the-art model by 4.37% with a 4 times faster speed. Furthermore, additional experiments under phrase-level and attribute-aware scene text retrieval settings validate FDP's particular advantages in handling diverse forms of query text. The source code will be publicly available at https://github.com/Gyann-z/FDP.

cs.CV↗

arXiv2024

Fractional AC Josephson effect in a topological insulator proximitized by a self-formed superconductor

A lateral Josephson junction in which the surface of a 3D topological insulator serves as the weak link should support topologically protected excitations related to Majorana fermions. The resulting $4π$-periodic current-phase relationship could be detected under high-frequency excitation by the suppression of odd Shapiro steps. Here, we demonstrate such devices through the self-formation of a Pd-Te superconducting layer from a telluride topological insulator, and observe suppressed first and third Shapiro steps. Other devices, including those where the Pd-Te layer is bolstered by an additional Al layer, show no suppression of Shapiro steps, a difference supported by simulations. Though we rule out the known trivial causes of suppressed Shapiro steps in our devices, we nevertheless argue that corroborating measurements and disorder-aware theoretical descriptions of these systems are needed before confidently claiming the observation of Majorana states.

cond-mat.mes-hall↗

arXiv2024

QQQ: Quality Quattuor-Bit Quantization for Large Language Models

Quantization is a proven effective method for compressing large language models. Although popular techniques like W8A8 and W4A16 effectively maintain model performance, they often fail to concurrently speed up the prefill and decoding stages of inference. W4A8 is a promising strategy to accelerate both of them while usually leads to a significant performance degradation. To address these issues, we present QQQ, a Quality Quattuor-bit Quantization method with 4-bit weights and 8-bit activations. QQQ employs adaptive smoothing and Hessian-based compensation, significantly enhancing the performance of quantized models without extensive training. Furthermore, we meticulously engineer W4A8 GEMM kernels to increase inference speed. Our specialized per-channel W4A8 GEMM and per-group W4A8 GEMM achieve impressive speed increases of 3.67$\times$ and 3.29 $\times$ over FP16 GEMM. Our extensive experiments show that QQQ achieves performance on par with existing state-of-the-art LLM quantization methods while significantly accelerating inference, achieving speed boosts up to 2.24 $\times$, 2.10$\times$, and 1.25$\times$ compared to FP16, W8A8, and W4A16, respectively.

cs.LG↗

arXiv2024

ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

We introduce ChatGLM, an evolving family of large language models that we have been developing over time. This report primarily focuses on the GLM-4 language series, which includes GLM-4, GLM-4-Air, and GLM-4-9B. They represent our most capable models that are trained with all the insights and lessons gained from the preceding three generations of ChatGLM. To date, the GLM-4 models are pre-trained on ten trillions of tokens mostly in Chinese and English, along with a small set of corpus from 24 languages, and aligned primarily for Chinese and English usage. The high-quality alignment is achieved via a multi-stage post-training process, which involves supervised fine-tuning and learning from human feedback. Evaluations show that GLM-4 1) closely rivals or outperforms GPT-4 in terms of general metrics such as MMLU, GSM8K, MATH, BBH, GPQA, and HumanEval, 2) gets close to GPT-4-Turbo in instruction following as measured by IFEval, 3) matches GPT-4 Turbo (128K) and Claude 3 for long context tasks, and 4) outperforms GPT-4 in Chinese alignments as measured by AlignBench. The GLM-4 All Tools model is further aligned to understand user intent and autonomously decide when and which tool(s) touse -- including web browser, Python interpreter, text-to-image model, and user-defined functions -- to effectively complete complex tasks. In practical applications, it matches and even surpasses GPT-4 All Tools in tasks like accessing online information via web browsing and solving math problems using Python interpreter. Over the course, we have open-sourced a series of models, including ChatGLM-6B (three generations), GLM-4-9B (128K, 1M), GLM-4V-9B, WebGLM, and CodeGeeX, attracting over 10 million downloads on Hugging face in the year 2023 alone. The open models can be accessed through https://github.com/THUDM and https://huggingface.co/THUDM.

cs.CL↗

arXiv2024

Computational Investigation on the formation of liquid-fueled oblique detonation waves

Utilizing a two-phase supersonic chemically reacting flow solver with the Eulerian-Lagrangian method implemented in OpenFOAM, this study computationally investigates the formation of liquid-fueled oblique detonation waves (ODWs) within a pre-injection oblique detonation wave engine operating at an altitude of 30 km and a velocity of Mach 9. The inflow undergoes two-stage compression, followed by uniform mixing with randomly distributed n-heptane droplets before entering the combustor. The study examines the effects of droplet breakup models, gas-liquid ratios, and on-wedge strips on the ODW formation. Results indicate that under the pure-droplet condition, the ODW fails to form within the combustor, irrespective of the breakup models used. However, increasing the proportion of n-heptane vapor in the fuel/air mixture facilitates the ODW formation, because the n-heptane vapor rapidly participates in the gaseous reactions, producing heat and accelerating the transition from low- to intermediate-temperature chemistry. Additionally, the presence of on-wedge strips enhances ODW formation by inducing a bow shock wave within the combustor, which significantly increases the temperature, directly triggering intermediate-temperature chemistry and subsequent heat-release reactions, thereby facilitating the formation of ODW.

physics.flu-dyn↗

arXiv2024

Matter Asymmetries in the $Z_N$ Dark matter -companion Models

A class of $Z_{N\geq 3}$-symmetric WIMP dark matter models that are characterized by the semi-annihilation into the companion of dark matter has been proposed in Ref.~\cite{Guo:2021rre}, providing a mechanism to evade the stringent direct detection constraint. In this work, we point out that such models naturally provide the three Sakharov elements necessary for dark matter asymmetry, and moreover this asymmetry can be transferred to the visible sector with a proper link to the leptonic or quark sector. In our minimal $Z_3$ example, the migration to the leptonic sector is via the asymmetric companion decay into neutrinos, and the lepton asymmetry can be further transferred to the quark sector. The CP violation parameter is restrained in this model. Thus, we explore the thermal motion effect of dark matter and find that it gives an enhancement to the CP violation parameter, which is studied for the first time. A preliminary numerical analysis based on the Boltzmann equations shows that both correct relic density of dark matter and baryon asymmetry can be accommodated.

hep-ph↗