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Yong Yang

Publications and source records attributed to Yong Yang.

At least 127 records · Page 7Linked to original sources
arXiv2024

Not All Prompts Are Secure: A Switchable Backdoor Attack Against Pre-trained Vision Transformers

Given the power of vision transformers, a new learning paradigm, pre-training and then prompting, makes it more efficient and effective to address downstream visual recognition tasks. In this paper, we identify a novel security threat towards such a paradigm from the perspective of backdoor attacks. Specifically, an extra prompt token, called the switch token in this work, can turn the backdoor mode on, i.e., converting a benign model into a backdoored one. Once under the backdoor mode, a specific trigger can force the model to predict a target class. It poses a severe risk to the users of cloud API, since the malicious behavior can not be activated and detected under the benign mode, thus making the attack very stealthy. To attack a pre-trained model, our proposed attack, named SWARM, learns a trigger and prompt tokens including a switch token. They are optimized with the clean loss which encourages the model always behaves normally even the trigger presents, and the backdoor loss that ensures the backdoor can be activated by the trigger when the switch is on. Besides, we utilize the cross-mode feature distillation to reduce the effect of the switch token on clean samples. The experiments on diverse visual recognition tasks confirm the success of our switchable backdoor attack, i.e., achieving 95%+ attack success rate, and also being hard to be detected and removed. Our code is available at https://github.com/20000yshust/SWARM.

cs.CV↗
arXiv2024

Adversarial Robustness for Visual Grounding of Multimodal Large Language Models

Multi-modal Large Language Models (MLLMs) have recently achieved enhanced performance across various vision-language tasks including visual grounding capabilities. However, the adversarial robustness of visual grounding remains unexplored in MLLMs. To fill this gap, we use referring expression comprehension (REC) as an example task in visual grounding and propose three adversarial attack paradigms as follows. Firstly, untargeted adversarial attacks induce MLLMs to generate incorrect bounding boxes for each object. Besides, exclusive targeted adversarial attacks cause all generated outputs to the same target bounding box. In addition, permuted targeted adversarial attacks aim to permute all bounding boxes among different objects within a single image. Extensive experiments demonstrate that the proposed methods can successfully attack visual grounding capabilities of MLLMs. Our methods not only provide a new perspective for designing novel attacks but also serve as a strong baseline for improving the adversarial robustness for visual grounding of MLLMs.

cs.CV↗
arXiv2024

Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding

We present Hunyuan-DiT, a text-to-image diffusion transformer with fine-grained understanding of both English and Chinese. To construct Hunyuan-DiT, we carefully design the transformer structure, text encoder, and positional encoding. We also build from scratch a whole data pipeline to update and evaluate data for iterative model optimization. For fine-grained language understanding, we train a Multimodal Large Language Model to refine the captions of the images. Finally, Hunyuan-DiT can perform multi-turn multimodal dialogue with users, generating and refining images according to the context. Through our holistic human evaluation protocol with more than 50 professional human evaluators, Hunyuan-DiT sets a new state-of-the-art in Chinese-to-image generation compared with other open-source models. Code and pretrained models are publicly available at github.com/Tencent/HunyuanDiT

cs.CV↗
arXiv2024

Searching for Two-Neutrino and Neutrinoless Double Beta Decay of $^{134}$Xe with the PandaX-4T Experiment

$^{134}$Xe is a candidate isotope for neutrinoless double beta decay~($0νββ$) search. In addition, the two-neutrino case ($2νββ$) allowed by the Standard Model of particle physics has not yet been observed. Utilizing the 10.4% of $^{134}$Xe in the natural xenon in the PandaX-4T detector and its first 94.9-day exposure, we have established the most stringent constraints on $2νββ$ and $0νββ$ of $^{134}$Xe half-lives, with limits of $2.8\times10^{22}$ yr and $3.0\times10^{23}$ yr at 90% confidence level, respectively. The $2νββ$ ($0νββ$) limit surpasses the previously reported best result by a factor of 32 (2.7), highlighting the potential of large monolithic natural xenon detectors.

nucl-ex↗
arXiv2024

Detecting Neutrinos from Supernova Bursts in PandaX-4T

Neutrinos from core-collapse supernovae are essential for the understanding of neutrino physics and stellar evolution. The dual-phase xenon dark matter detectors can provide a way to track explosions of galactic supernovae by detecting neutrinos through coherent elastic neutrino-nucleus scatterings. In this study, a variation of progenitor masses as well as explosion models are assumed to predict the neutrino fluxes and spectra, which result in the number of expected neutrino events ranging from 6.6 to 13.7 at a distance of 10 kpc over a 10-second duration with negligible backgrounds at PandaX-4T. Two specialized triggering alarms for monitoring supernova burst neutrinos are built. The efficiency of detecting supernova explosions at various distances in the Milky Way is estimated. These alarms will be implemented in the real-time supernova monitoring system at PandaX-4T in the near future, providing the astronomical communities with supernova early warnings.

hep-ex↗
arXiv2024

Lateral Control of Brain-Controlled Vehicle Based on SVM Probability Output Model

The non-stationary characteristics of EEG signal and the individual differences of brain-computer interfaces (BCIs) lead to poor performance in the control process of the brain-controlled vehicles (BCVs). In this paper, by combining steady-state visual evoked potential (SSVEP) interactive interface, brain instructions generation module and vehicle lateral control module, a probabilistic output model based on support vector machine (SVM) is proposed for BCV lateral control to improve the driving performance. Firstly, a filter bank common spatial pattern (FBCSP) algorithm is introduced into the brain instructions generation module, which can improve the off-line decoding performance. Secondly, a sigmod-fitting SVM (SF-SVM) is trained based on the sigmod-fitting method and the lateral control module is developed, which can produce all commands in the form of probability instead of specific single command. Finally, a pre-experiment and two road-keeping experiments are conducted. In the pre-experiment, the experiment results show that, the average highest off-line accuracy among subjects is 95.64\%, while for those in the online stage, the average accuracy is only 84.44\%. In the road-keeping experiments, the task completion rate in the two designed scenes increased by 25.6\% and 20\%, respectively.

q-bio.NC↗
arXiv2024

RCoCo: Contrastive Collective Link Prediction across Multiplex Network in Riemannian Space

Link prediction typically studies the probability of future interconnection among nodes with the observation in a single social network. More often than not, real scenario is presented as a multiplex network with common (anchor) users active in multiple social networks. In the literature, most existing works study either the intra-link prediction in a single network or inter-link prediction among networks (a.k.a. network alignment), and consider two learning tasks are independent from each other, which is still away from the fact. On the representation space, the vast majority of existing methods are built upon the traditional Euclidean space, unaware of the inherent geometry of social networks. The third issue is on the scarce anchor users. Annotating anchor users is laborious and expensive, and thus it is impractical to work with quantities of anchor users. Herein, in light of the issues above, we propose to study a challenging yet practical problem of Geometry-aware Collective Link Prediction across Multiplex Network. To address this problem, we present a novel contrastive model, RCoCo, which collaborates intra- and inter-network behaviors in Riemannian spaces. In RCoCo, we design a curvature-aware graph attention network ($κ-$GAT), conducting attention mechanism in Riemannian manifold whose curvature is estimated by the Ricci curvatures over the network. Thereafter, we formulate intra- and inter-contrastive loss in the manifolds, in which we augment graphs by exploring the high-order structure of community and information transfer on anchor users. Finally, we conduct extensive experiments with 14 strong baselines on 8 real-world datasets, and show the effectiveness of RCoCo.

cs.SI↗
arXiv2024

Simulation Studies for the First Pathfinder of the CATCH Space Mission

The Chasing All Transients Constellation Hunters (CATCH) space mission is an intelligent constellation consisting of 126 micro-satellites in three types (A, B, and C), designed for X-ray observation with the objective of studying the dynamic universe. Currently, we are actively developing the first Pathfinder (CATCH-1) for the CATCH mission, specifically for type-A satellites. CATCH-1 is equipped with Micro Pore Optics (MPO) and a 4-pixel Silicon Drift Detector (SDD) array. To assess its scientific performance, including the effective area of the optical system, on-orbit background, and telescope sensitivity, we employ the Monte Carlo software Geant4 for simulation in this study. The MPO optics exhibit an effective area of $41$ cm$^2$ at the focal spot for 1 keV X-rays, while the entire telescope system achieves an effective area of $29$ cm$^2$ at 1 keV when taking into account the SDD detector's detection efficiency. The primary contribution to the background is found to be from the Cosmic X-ray Background. Assuming a 625 km orbit with an inclination of $29^\circ$, the total background for CATCH-1 is estimated to be $8.13\times10^{-2}$ counts s$^{-1}$ in the energy range of 0.5--4 keV. Based on the background within the central detector and assuming a Crab-like source spectrum, the estimated ideal sensitivity could achieve $1.9\times10^{-12}$ erg cm$^{-2}$ s$^{-1}$ for an exposure of 10$^4$ s in the energy band of 0.5--4 keV. Furthermore, after simulating the background caused by low-energy charged particles near the geomagnetic equator, we have determined that there is no need to install a magnetic deflector.

astro-ph.IM↗
arXiv2024

Formaldehyde sensing by Co3O4 hollow spheres at room temperature

Formaldehyde is a ubiquitous and high toxicity gas. It is an essential task to efficient detect owing to their toxicity and diffusion. In this work, we studied on the detection of trace amount of formaldehyde based on hollow Co3O4 nanostructure. It is found that Co3O4 hollow spheres with different structures shows distinct sensing performance towards formaldehyde at room temperature, the response value of nanosheet modified Co3O4 towards 100 ppm formaldehyde will reach 35 in 18 second, and the nanoparticle modified Co3O4 hollow sphere will reach 2.1 in 18 second, 17 in 300 second. The nanosheet modified and nanoparticle modified Co3O4 hollow sphere will reach 4 and 20 in 10 second towards 100 ppm formaldehyde at room temperature. As room temperature, the sensors do not response towards NH3, CO, etc. The sensing mechanism was proposed based on the theoretical and experimental results. The Co3O4 sensor shows that potential utility in CH2O quick sensing at room temperature.

physics.chem-ph↗
arXiv2024

Classifying Primitive Solvable Permutation Groups of Rank 5 and 6

Let $G$ be a finite solvable permutation group acting faithfully and primitively on a finite set $Ω$. Let $G_0$ be the stabilizer of a point $α\in Ω$ The rank of $G$ is defined as the number of orbits of $G_0$ in $Ω$, including the trivial orbit $\{α\}$. In this paper, we completely classify the cases where $G$ has rank 5 and 6, continuing the previous works on classifying groups of rank 4 or lower.

math.GR↗
arXiv2024

DRSM: efficient neural 4d decomposition for dynamic reconstruction in stationary monocular cameras

With the popularity of monocular videos generated by video sharing and live broadcasting applications, reconstructing and editing dynamic scenes in stationary monocular cameras has become a special but anticipated technology. In contrast to scene reconstructions that exploit multi-view observations, the problem of modeling a dynamic scene from a single view is significantly more under-constrained and ill-posed. Inspired by recent progress in neural rendering, we present a novel framework to tackle 4D decomposition problem for dynamic scenes in monocular cameras. Our framework utilizes decomposed static and dynamic feature planes to represent 4D scenes and emphasizes the learning of dynamic regions through dense ray casting. Inadequate 3D clues from a single-view and occlusion are also particular challenges in scene reconstruction. To overcome these difficulties, we propose deep supervised optimization and ray casting strategies. With experiments on various videos, our method generates higher-fidelity results than existing methods for single-view dynamic scene representation.

cs.CV↗
arXiv2024

Contrastive Sequential Interaction Network Learning on Co-Evolving Riemannian Spaces

The sequential interaction network usually find itself in a variety of applications, e.g., recommender system. Herein, inferring future interaction is of fundamental importance, and previous efforts are mainly focused on the dynamics in the classic zero-curvature Euclidean space. Despite the promising results achieved by previous methods, a range of significant issues still largely remains open: On the bipartite nature, is it appropriate to place user and item nodes in one identical space regardless of their inherent difference? On the network dynamics, instead of a fixed curvature space, will the representation spaces evolve when new interactions arrive continuously? On the learning paradigm, can we get rid of the label information costly to acquire? To address the aforementioned issues, we propose a novel Contrastive model for Sequential Interaction Network learning on Co-Evolving RiEmannian spaces, CSINCERE. To the best of our knowledge, we are the first to introduce a couple of co-evolving representation spaces, rather than a single or static space, and propose a co-contrastive learning for the sequential interaction network. In CSINCERE, we formulate a Cross-Space Aggregation for message-passing across representation spaces of different Riemannian geometries, and design a Neural Curvature Estimator based on Ricci curvatures for modeling the space evolvement over time. Thereafter, we present a Reweighed Co-Contrast between the temporal views of the sequential network, so that the couple of Riemannian spaces interact with each other for the interaction prediction without labels. Empirical results on 5 public datasets show the superiority of CSINCERE over the state-of-the-art methods.

cs.LG↗
arXiv2023

Single-shot pump-probe technique by the combination of an echelon and a grating with a time window of 109 ps

In this study, using only a single pulse, pump-probe measurement with a large time window of more than 100 ps is implemented. A commercial grating is used to encode a time window of ~ 56 ps in a single pulse; therefore, there is no need for machining customization. In addition, in this technique, the grating surface is accurately imaged, eliminating the image blur problem caused by phase differences in previous echelon-based techniques. Moreover, to make full use of the grating surface and obtain a larger time window, a simple reflection echelon is combined that matches the grating in the time window. This combination encoding strategy results in a total time window of ~ 109 ps and maintains accurate imaging of the grating surface. This time window is an order of magnitude greater than the maximum reported values of the echelon encoding strategy and the angle beam encoding strategy. To demonstrate this single-shot pump-probe technique, the two-photon absorption process of ZnSe and the excited-state absorption process of a symmetrical phenoxazinium bromine salt were studied. The possibility of further improving the experimental setup is also discussed.

physics.optics↗
arXiv2023

Search for Dark-Matter-Nucleon Interactions with a Dark Mediator in PandaX-4T

We report results of a search for dark-matter-nucleon interactions via a dark mediator using optimized low-energy data from the PandaX-4T liquid xenon experiment. With the ionization-signal-only data and utilizing the Migdal effect, we set the most stringent limits on the cross section for dark matter masses ranging from 30~$\rm{MeV/c^2}$ to 2~$\rm{GeV/c^2}$. Under the assumption that the dark mediator is a dark photon that decays into scalar dark matter pairs in the early Universe, we rule out significant parameter space of such thermal relic dark-matter model.

hep-ex↗
arXiv2023

Penetration of Arbitrary Double Potential Barriers with Probability Unity: Implications for Testing the Existence of a Minimum Length

Quantum tunneling across double potential barriers is studied. With the assumption that the real space is a continuum, it is rigorously proved that large barriers of arbitrary shapes can be penetrated by low-energy particles with a probability of unity, i.e., realization of resonant tunneling (RT), by simply tuning the inter-barrier spacing. The results are demonstrated by tunneling of electrons and protons, in which resonant and sequential tunneling are distinguished. The critical dependence of tunneling probabilities on the barrier positions not only demonstrates the crucial role of phase factors, but also points to the possibility of ultrahigh accuracy measurements near resonance. By contrast, the existence of a nonzero minimum length puts upper bounds on the barrier size and particle mass, beyond which effective RT ceases. A scheme is suggested for dealing with the practical difficulties arising from the delocalization of particle position due to the uncertainty principle. This work opens a possible avenue for experimental tests of the existence of a minimum length based on atomic systems.

cond-mat.mes-hall↗
arXiv2023

Electronic Structure of Ta$_2$O$_5$ Polymorphs: Variation Trend of Band Gap and the Role of Oxygen Vacancies

We provide a systematic study on the electronic structure of a series of Ta$_2$O$_5$ polymorphs using standard density functional theory (DFT) calculations as well as the more accurate many-body perturbation theory within the GW approximation. For the defect-free polymorphs, the variation trend of band gap can be microscopically related to the strength of orbital hybridization, and to the macroscopic bulk formation energy. The presence of oxygen vacancies is found to have profound effects on the electronic properties near the Fermi level, notably the reduction of band gap and gap closure (insulator-to-metal transition). Furthermore, depending on the vacancy sites, the band gap of some defective system may be enlarged with comparison to its defect-free counterpart. Such an anomalous behavior originates from the unexpected formation of Ta-Ta bonds.

cond-mat.mtrl-sci↗
arXiv2023

Bounding the number of p'-degrees from below

Let $G$ be a finite group of order divisible by a prime $p$ and let $P\in\Syl_p(G)$. We prove a recent conjecture by Hung stating that $|\Irr_{p'}(G)|\geq \frac{\exp(P/P')-1}{p-1}+2\sqrt{p-1}-1.$ Let $a\geq 2$ be an integer and suppose that $p^a$ does not exceed the exponent of the center of $P$. We then also show that the number of conjugacy classes of elements of $G$ for which $p^a$ is the exact $p$-part of their order is at least $p^{a-1}$.

math.GR↗