arXiv subjects

Yihao Wang

Publications and source records attributed to Yihao Wang.

At least 55 records · Page 3Linked to original sources

arXiv2024

Official-NV: An LLM-Generated News Video Dataset for Multimodal Fake News Detection

News media, especially video news media, have penetrated into every aspect of daily life, which also brings the risk of fake news. Therefore, multimodal fake news detection has recently garnered increased attention. However, the existing datasets are comprised of user-uploaded videos and contain an excess amounts of superfluous data, which introduces noise into the model training process. To address this issue, we construct a dataset named Official-NV, comprising officially published news videos. The crawl officially published videos are augmented through the use of LLMs-based generation and manual verification, thereby expanding the dataset. We also propose a new baseline model called OFNVD, which captures key information from multimodal features through a GLU attention mechanism and performs feature enhancement and modal aggregation via a cross-modal Transformer. Benchmarking the dataset and baselines demonstrates the effectiveness of our model in multimodal news detection.

cs.CV↗

arXiv2024

LLM4PR: Improving Post-Ranking in Search Engine with Large Language Models

Alongside the rapid development of Large Language Models (LLMs), there has been a notable increase in efforts to integrate LLM techniques in information retrieval (IR) and search engines (SE). Recently, an additional post-ranking stage is suggested in SE to enhance user satisfaction in practical applications. Nevertheless, research dedicated to enhancing the post-ranking stage through LLMs remains largely unexplored. In this study, we introduce a novel paradigm named Large Language Models for Post-Ranking in search engine (LLM4PR), which leverages the capabilities of LLMs to accomplish the post-ranking task in SE. Concretely, a Query-Instructed Adapter (QIA) module is designed to derive the user/item representation vectors by incorporating their heterogeneous features. A feature adaptation step is further introduced to align the semantics of user/item representations with the LLM. Finally, the LLM4PR integrates a learning to post-rank step, leveraging both a main task and an auxiliary task to fine-tune the model to adapt the post-ranking task. Experiment studies demonstrate that the proposed framework leads to significant improvements and exhibits state-of-the-art performance compared with other alternatives.

cs.IR↗

arXiv2024

State-of-the-art Advances of Deep-learning Linguistic Steganalysis Research

With the evolution of generative linguistic steganography techniques, conventional steganalysis falls short in robustly quantifying the alterations induced by steganography, thereby complicating detection. Consequently, the research paradigm has pivoted towards deep-learning-based linguistic steganalysis. This study offers a comprehensive review of existing contributions and evaluates prevailing developmental trajectories. Specifically, we first provided a formalized exposition of the general formulas for linguistic steganalysis, while comparing the differences between this field and the domain of text classification. Subsequently, we classified the existing work into two levels based on vector space mapping and feature extraction models, thereby comparing the research motivations, model advantages, and other details. A comparative analysis of the experiments is conducted to assess the performances. Finally, the challenges faced by this field are discussed, and several directions for future development and key issues that urgently need to be addressed are proposed.

cs.CL↗

arXiv2024

MODRL-TA:A Multi-Objective Deep Reinforcement Learning Framework for Traffic Allocation in E-Commerce Search

Traffic allocation is a process of redistributing natural traffic to products by adjusting their positions in the post-search phase, aimed at effectively fostering merchant growth, precisely meeting customer demands, and ensuring the maximization of interests across various parties within e-commerce platforms. Existing methods based on learning to rank neglect the long-term value of traffic allocation, whereas approaches of reinforcement learning suffer from balancing multiple objectives and the difficulties of cold starts within realworld data environments. To address the aforementioned issues, this paper propose a multi-objective deep reinforcement learning framework consisting of multi-objective Q-learning (MOQ), a decision fusion algorithm (DFM) based on the cross-entropy method(CEM), and a progressive data augmentation system(PDA). Specifically. MOQ constructs ensemble RL models, each dedicated to an objective, such as click-through rate, conversion rate, etc. These models individually determine the position of items as actions, aiming to estimate the long-term value of multiple objectives from an individual perspective. Then we employ DFM to dynamically adjust weights among objectives to maximize long-term value, addressing temporal dynamics in objective preferences in e-commerce scenarios. Initially, PDA trained MOQ with simulated data from offline logs. As experiments progressed, it strategically integrated real user interaction data, ultimately replacing the simulated dataset to alleviate distributional shifts and the cold start problem. Experimental results on real-world online e-commerce systems demonstrate the significant improvements of MODRL-TA, and we have successfully deployed MODRL-TA on an e-commerce search platform.

cs.LG↗

arXiv2024

Linguistic Steganalysis via LLMs: Two Modes for Efficient Detection of Strongly Concealed Stego

To detect stego (steganographic text) in complex scenarios, linguistic steganalysis (LS) with various motivations has been proposed and achieved excellent performance. However, with the development of generative steganography, some stegos have strong concealment, especially after the emergence of LLMs-based steganography, the existing LS has low detection or cannot detect them. We designed a novel LS with two modes called LSGC. In the generation mode, we created an LS-task "description" and used the generation ability of LLM to explain whether texts to be detected are stegos. On this basis, we rethought the principle of LS and LLMs, and proposed the classification mode. In this mode, LSGC deleted the LS-task "description" and used the "causalLM" LLMs to extract steganographic features. The LS features can be extracted by only one pass of the model, and a linear layer with initialization weights is added to obtain the classification probability. Experiments on strongly concealed stegos show that LSGC significantly improves detection and reaches SOTA performance. Additionally, LSGC in classification mode greatly reduces training time while maintaining high performance.

cs.CL↗

arXiv2024

UP4LS: User Profile Constructed by Multiple Attributes for Enhancing Linguistic Steganalysis

Linguistic steganalysis (LS) tasks aim to detect whether a text contains secret information. Existing LS methods focus on the deep-learning model design and they achieve excellent results in ideal data. However, they overlook the unique user characteristics, leading to weak performance in social networks. And a few stegos here that further complicate detection. We propose the UP4LS, a framework with the User Profile for enhancing LS in realistic scenarios. Three kinds of user attributes like writing habits are explored to build the profile. For each attribute, the specific feature extraction module is designed. The extracted features are mapped to high-dimensional user features via the deep-learning model of the method to be improved. The content feature is extracted by the language model. Then user and content features are integrated. Existing methods can improve LS results by adding the UP4LS framework without changing their deep-learning models. Experiments show that UP4LS can significantly enhance the performance of LS-task baselines in realistic scenarios, with the overall Acc increased by 25%, F1 increased by 51%, and SOTA results. The improvement is especially pronounced in fewer stegos. Additionally, UP4LS also sets the stage for the related-task SOTA methods to efficient LS.

cs.CL↗

arXiv2023

Current-Controlled Skyrmion Number in Confined Ferromagnetic Nanostripes

Skyrmions are vortex-like localized magnetic structures that possess an integer-valued topological index known as the skyrmion number or topological charge. Skyrmion number determines the topology-related emergent magnetism, which is highly desirable for advanced storage and computing devices. In order to achieve device functions, it is necessary to manipulate the skyrmion number in confined nanostructured geometries using electrical methods. Here, we report the reliable current-controlled operations for manipulating the skyrmion number through reversible topological transformations between skyrmion chains and stripe domains in confined Fe3Sn2 nanostripes. The results of micromagnetic simulations are successful in numerically reproducing our experiments and explaining them through the combined effect of current-induced Joule heating and magnetic hysteresis. These findings hold the potential to advance the development of topological spintronic devices.

cond-mat.mtrl-sci↗

arXiv2023

Observation of Hybrid Magnetic Skyrmion Bubbles in Fe3Sn2 Nanodisks

It is well known that there are two types of magnetic bubbles in uniaxial magnets. Here, using Lorentz-transimission electronic microscopy magnetic imaging, we report the direct experimental observation of 3D type-III hybrid bubbles, which comprise Néel-twisted skyrmion bubbles with topological charge Q = -1 in near-surface layers and type-II bubbles with Q = 0 in interior layers, in Fe3Sn2 nanodisks. Using the tilted magnetic field, we further show the controlled topological magnetic transformations of three types of bubbles in a confined ferromagnetic nanodisk. Our observations are well reproduced using micromagnetic simulations based on measured magnetic parameters. Our results advance fundamental classification and understanding of magnetic bubbles, which could propel the applications of three-dimensional magnetism.

cond-mat.mtrl-sci↗

arXiv2023

Propagate And Calibrate: Real-time Passive Non-line-of-sight Tracking

Non-line-of-sight (NLOS) tracking has drawn increasing attention in recent years, due to its ability to detect object motion out of sight. Most previous works on NLOS tracking rely on active illumination, e.g., laser, and suffer from high cost and elaborate experimental conditions. Besides, these techniques are still far from practical application due to oversimplified settings. In contrast, we propose a purely passive method to track a person walking in an invisible room by only observing a relay wall, which is more in line with real application scenarios, e.g., security. To excavate imperceptible changes in videos of the relay wall, we introduce difference frames as an essential carrier of temporal-local motion messages. In addition, we propose PAC-Net, which consists of alternating propagation and calibration, making it capable of leveraging both dynamic and static messages on a frame-level granularity. To evaluate the proposed method, we build and publish the first dynamic passive NLOS tracking dataset, NLOS-Track, which fills the vacuum of realistic NLOS datasets. NLOS-Track contains thousands of NLOS video clips and corresponding trajectories. Both real-shot and synthetic data are included. Our codes and dataset are available at https://againstentropy.github.io/NLOS-Track/.

cs.CV↗

arXiv2022

Combined Magnetic Imaging and Anisotropic Magnetoresistance Detection of Dipolar Skyrmions

Magnetic skyrmions are localized particle-like nontrivial swirls that are promising in building high-performance topological spintronic devices. The read-out functions in skyrmionic devices require the translation of magnetic skyrmions to electrical signals. Here, we report combined real-space magnetic imaging and anisotropic magnetoresistance studies on dipolar skyrmions. A single skyrmion chain and single skyrmion are observed using Lorentz transmission electron microscopy imaging. Meanwhile, the field, helicity, and skyrmion count dependence of anisotropic magnetoresistance of the Fe3Sn2 nanostructures are obtained simultaneously. Our results demonstrate that the anisotropic magnetoresistance of skyrmions is independent of the helicity and proportional to the skyrmion count. Our work could promote read-out operations in skyrmion-based spintronic devices.

cond-mat.mes-hall↗

arXiv2022

Anomalous quasiparticles in the zone center electron pocket of the kagomé ferromagnet Fe3Sn2

One material containing kagome bilayers and featuring both exceptional magnetism and electron transport is the ferromagnetic metal Fe3Sn2. Notwithstanding the widespread interest in Fe3Sn2, crystal twinning, difficulties in distinguishing surface from bulk states, and a large unit cell have until now prevented the synchrotron-based spectroscopic observation of sharply resolved quasiparticles near the Fermi surface which could be responsible for the anomalous properties appearing at low temperatures for the material. Here we report microfocused laser-based angle-resolved photoemission spectroscopy (micro-ARPES), which offers the first look at such quasiparticles. The high spatial resolution allows individual crystal twin domains to be examined in isolation, resulting in the discovery of three-fold symmetric electron pockets at the Brillouin zone (BZ) center, not predicted by early tight-binding descriptions but in agreement with density functional theory (DFT) calculations, which also feature Weyl nodes. The quasiparticles in these pockets have remarkably long mean free paths, and their Fermi surface area is consistent with reported quantum oscillations. At the same time, though, the best-defined Fermi surface is reduced at low temperature, and the quasiparticles generally are marginal in the sense that their wavelength uncertainty is of order the deviation of the quasiparticle wavelength from the Fermi vector. We attribute these manifestations of strong electron-electron interactions to a flat band predicted by our DFT to lie just above the dispersive bands seen in this experiment. Thus, beyond demonstrating the impact of twin averaging for ARPES measurements of band structures, our experiments reveal many-body physics unaccounted for by current theories of metallic kagome ferromagnets.

cond-mat.str-el↗

arXiv2022

Current-Controlled Topological Magnetic Transformations in a Nanostructured Kagome Magnet

Topological magnetic charge Q is a fundamental parameter that describes the magnetic domains and determines their intriguing electromagnetic properties. The ability to switch Q in a controlled way by electrical methods allows for flexible manipulation of electromagnetic behavior in future spintronic devices. Here we report the room-temperature current-controlled topological magnetic transformations between Q = -1 skyrmions and Q = 0 stripes or type-II bubbles in a kagome crystal Fe$_3$Sn$_2$. We show that the reproducible and reversible skyrmion-bubble and skyrmion-stripe transformations can be achieved by tuning the density of nanosecond pulsed current of the order of ~10$^{10}$ A$^{-2}$. Further numerical simulations suggest that spin-transfer torque combined with Joule thermal heating effects determine the current-induced topological magnetic transformations.

cond-mat.mes-hall↗

arXiv2022

Two-dimensional characterization of three-dimensional magnetic bubbles in Fe$_3$Sn$_2$ nanostructures

We report differential phase contrast scanning transmission electron microscopy (TEM) of nanoscale magnetic objects in Kagome ferromagnet Fe$_3$Sn$_2$ nanostructures. This technique can directly detect the deflection angle of a focused electron beam, thus allowing clear identification of the real magnetic structures of two magnetic objects including three-ring and complex arch-shaped vortices in Fe$_3$Sn$_2$ by Lorentz transmission electron microscopy imaging. Numerical calculations based on real material-specific parameters well reproduced the experimental results, showing that the magnetic objects can be attributed to integral magnetizations of two types of complex three-dimensional (3D) magnetic skyrmion bubbles with depth-modulated spin twisting. Magnetic configurations obtained using the high-resolution TEM are generally considered as two-dimensional (2D) magnetic objects previously. Our results imply the importance of the integral magnetizations of underestimated 3D magnetic structures in 2D TEM magnetic characterizations.

cond-mat.mtrl-sci↗

arXiv2022

Exploring the Impact of Negative Samples of Contrastive Learning: A Case Study of Sentence Embedding

Contrastive learning is emerging as a powerful technique for extracting knowledge from unlabeled data. This technique requires a balanced mixture of two ingredients: positive (similar) and negative (dissimilar) samples. This is typically achieved by maintaining a queue of negative samples during training. Prior works in the area typically uses a fixed-length negative sample queue, but how the negative sample size affects the model performance remains unclear. The opaque impact of the number of negative samples on performance when employing contrastive learning aroused our in-depth exploration. This paper presents a momentum contrastive learning model with negative sample queue for sentence embedding, namely MoCoSE. We add the prediction layer to the online branch to make the model asymmetric and together with EMA update mechanism of the target branch to prevent the model from collapsing. We define a maximum traceable distance metric, through which we learn to what extent the text contrastive learning benefits from the historical information of negative samples. Our experiments find that the best results are obtained when the maximum traceable distance is at a certain range, demonstrating that there is an optimal range of historical information for a negative sample queue. We evaluate the proposed unsupervised MoCoSE on the semantic text similarity (STS) task and obtain an average Spearman's correlation of $77.27\%$. Source code is available at https://github.com/xbdxwyh/mocose.

cs.CL↗

arXiv2022

Magnetotransport due to conductivity fluctuations in non-magnetic ZrTe2 nanoplates

Transition metal dichalcogenides with nontrivial band structures exhibit various fascinating physical properties and have sparked intensively research interest. Here, we performed systematic magnetotransport measurements on mechanical exfoliation prepared ZrTe2 nanoplates. We revealed that the negative longitudinal magnetoresistivity observed at high field region in the presence of parallel electric and magnetic fields could stem from the conductivity fluctuations due to the excess Zr in the nanoplates. In addition, the parametric plot, the planar Hall resistivity as function of the in-plane anisotropic magnetoresistivity, has an ellipse-shaped pattern with shifted orbital center, which further strengthen the evidence for the conductivity fluctuations. Our work provides some useful insights into transport phenomena in topological materials.

cond-mat.mes-hall↗

arXiv2021

Tuning the electronic band structure in a kagome ferromagnetic metal via magnetization

Materials with zero energy band gap display intriguing properties including high sensitivity of the electronic band structure to external stimulus such as pressure or magnetic field. An interesting candidate for zero energy band gap are Weyl nodes at the Fermi level EF. A prerequisite for the existence of Weyl nodes is to either have inversion or time reversal symmetry broken. Weyl nodes in systems with broken time reversal symmetry are ideal to realize the tunability of the electronic band structure by magnetic field. Theoretically, it has been shown that in ferromagnetic Weyl materials, the band structure is dependent upon the magnetization direction and thus the electronic bands can be tuned by controlling the magnetization direction. Here, we demonstrate tuning of the band structure in a kagome Weyl ferromagnetic metal Fe3Sn2 with magnetization and magnetic field. Owing to spin-orbit coupling, we observe changes in the band structure depending on the magnetization direction that amount to a decrease in the carrier density by a factor of four when the magnetization lies in the kagome plane as compared to when the magnetization is along the c axis. Our discovery opens a way for tuning the carrier density in ferromagnetic materials.

cond-mat.str-el↗

arXiv2021

ESOD:Edge-based Task Scheduling for Object Detection

Object Detection on the mobile system is a challenge in terms of everything. Nowadays, many object detection models have been designed, and most of them concentrate on precision. However, the computation burden of those models on mobile systems is unacceptable. Researchers have designed some lightweight networks for mobiles by sacrificing precision. We present a novel edge-based task scheduling framework for object detection (termed as ESOD). In detail, we train a DNN model (termed as pre-model) to predict which object detection model to use for the coming task and offloads to which edge servers by physical characteristics of the image task (e.g., brightness, saturation). The results show that ESOD can reduce latency and energy consumption by an average of 22.13% and 29.60% and improve the mAP to 45.8(with 0.9 mAP better), respectively, compared with the SOTA DETR model.

cs.CV↗

arXiv2021

Improving Adversarial Robustness for Free with Snapshot Ensemble

Adversarial training, as one of the few certified defenses against adversarial attacks, can be quite complicated and time-consuming, while the results might not be robust enough. To address the issue of lack of robustness, ensemble methods were proposed, aiming to get the final output by weighting the selected results from repeatedly trained processes. It is proved to be very useful in achieving robust and accurate results, but the computational and memory costs are even higher. Snapshot ensemble, a new ensemble method that combines several local minima in a single training process to make the final prediction, was proposed recently, which reduces the time spent on training multiple networks and the memory to store the results. Based on the snapshot ensemble, we present a new method that is easier to implement: unlike original snapshot ensemble that seeks for local minima, our snapshot ensemble focuses on the last few iterations of a training and stores the sets of parameters from them. Our algorithm is much simpler but the results are no less accurate than the original ones: based on different hyperparameters and datasets, our snapshot ensemble has shown a 5% to 30% increase in accuracy when compared to the traditional adversarial training.

cs.LG↗