arXiv subjects

Qing Zhang

Publications and source records attributed to Qing Zhang.

At least 109 records · Page 6Linked to original sources

arXiv2024

Hierarchical Action Recognition: A Contrastive Video-Language Approach with Hierarchical Interactions

Video recognition remains an open challenge, requiring the identification of diverse content categories within videos. Mainstream approaches often perform flat classification, overlooking the intrinsic hierarchical structure relating categories. To address this, we formalize the novel task of hierarchical video recognition, and propose a video-language learning framework tailored for hierarchical recognition. Specifically, our framework encodes dependencies between hierarchical category levels, and applies a top-down constraint to filter recognition predictions. We further construct a new fine-grained dataset based on medical assessments for rehabilitation of stroke patients, serving as a challenging benchmark for hierarchical recognition. Through extensive experiments, we demonstrate the efficacy of our approach for hierarchical recognition, significantly outperforming conventional methods, especially for fine-grained subcategories. The proposed framework paves the way for hierarchical modeling in video understanding tasks, moving beyond flat categorization.

cs.CV↗

arXiv2024

On a refined local converse theorem for SO(4)

Recently, Hazeltine-Liu, and independently Haan-Kim-Kwon, proved a local converse theorem for $\mathrm{SO}_{2n}(F)$ over a $p$-adic field $F$, which says that, up to an outer automorphism of $\mathrm{SO}_{2n}(F)$, an irreducible generic representation of $\mathrm{SO}_{2n}(F)$ is uniquely determined by its twisted gamma factors by generic representations of $\mathrm{GL}_k(F)$ for $k=1,\dots,n$. It is desirable to remove the ``up to an outer automorphism" part in the above theorem using more twisted gamma factors, but this seems a hard problem. In this paper, we provide a solution to this problem for the group $\mathrm{SO}_4(F)$, namely, we show that a generic supercuspidal representation $π$ of $\mathrm{SO}_4(F)$ is uniquely determined by its $\mathrm{GL}_1$, $\mathrm{GL}_2$ twisted local gamma factors and a twisted exterior square local gamma factor of $π$.

math.RT↗

arXiv2024

Generic representations, open parameters and ABV-packets for $p$-adic groups

If $π$ is a representation of a $p$-adic group $G(F)$, and $ϕ$ is its Langlands parameter, can we use the moduli space of Langlands parameters to find a geometric property of $ϕ$ that will detect when $π$ is generic? In this paper we show that if $G$ is classical or if we assume the Kazhdan-Lusztig hypothesis for $G$, then the answer is yes, and the property is that the orbit of $ϕ$ is open. We also propose an adaptation of Shahidi's enhanced genericity conjecture to ABV-packets: for every Langlands parameter $ϕ$ for a $p$-adic group $G(F)$, the ABV-packet $Π^{\mathrm{ABV}}_ϕ(G(F))$ contains a generic representation if and only if the local adjoint L-function $L(s,ϕ,\mathop{\text{Ad}})$ is regular at $s=1$, and show that this condition is equivalent to the "open parameter" condition above. We show that this genericity conjecture for ABV-packets follows from other standard conjectures and we verify its validity with the same conditions on $G$. We show that, in this case, the ABV-packet for $ϕ$ coincides with its $L$-packet. Finally, we prove Vogan's conjecture on $A$-packets for tempered parameters.

math.RT↗

arXiv2024

The closure ordering conjecture on local Arthur packets of classical groups

In this paper, we prove the closure ordering conjecture on the local $L$-parameters of representations in local Arthur packets of $\mathrm{G}_n=\mathrm{Sp}_{2n}, \mathrm{SO}_{2n+1}$ over a non-Archimedean local field of characteristic zero. Precisely, given any representation $π$ in a local Arthur packet $Π_ψ$, the closure of the local $L$-parameter of $π$ in the Vogan variety must contain the local $L$-parameter corresponding to $ψ$. This conjecture reveals a geometric nature of local Arthur packets and is inspired by the work of Adams, Barbasch, and Vogan, and the work of Cunningham, Fiori, Moussaoui, Mracek, and Xu, on ABV-packets. As an application, for general quasi-split connected reductive groups, we show that the closure ordering conjecture implies the enhanced Shahidi conjecture, under certain reasonable assumptions. This provides a framework towards the enhanced Shahidi conjecture in general. We verify these assumptions for $\mathrm{G}_n$, hence give a new proof of the enhanced Shahidi conjecture. At last, we show that local Arthur packets cannot be fully contained in other ones, which is in contrast to the situation over Archimedean local fields and has its own interests.

math.RT↗

arXiv2024

NECA: Neural Customizable Human Avatar

Human avatar has become a novel type of 3D asset with various applications. Ideally, a human avatar should be fully customizable to accommodate different settings and environments. In this work, we introduce NECA, an approach capable of learning versatile human representation from monocular or sparse-view videos, enabling granular customization across aspects such as pose, shadow, shape, lighting and texture. The core of our approach is to represent humans in complementary dual spaces and predict disentangled neural fields of geometry, albedo, shadow, as well as an external lighting, from which we are able to derive realistic rendering with high-frequency details via volumetric rendering. Extensive experiments demonstrate the advantage of our method over the state-of-the-art methods in photorealistic rendering, as well as various editing tasks such as novel pose synthesis and relighting. The code is available at https://github.com/iSEE-Laboratory/NECA.

cs.CV↗

arXiv2024

Room-temperature continuous-wave pumped exciton polariton condensation in a perovskite microcavity

Microcavity exciton polaritons (polaritons) as part-light part-matter quasiparticles, garner significant attention for non-equilibrium Bose-Einstein condensation at elevated temperatures. Recently, halide perovskites have emerged as promising room-temperature polaritonic platforms thanks to their large exciton binding energies and superior optical properties. However, currently, inducing room-temperature non-equilibrium polariton condensation in perovskite microcavities requires optical pulsed excitations with high excitation densities. Herein, we demonstrate continuous-wave optically pumped polariton condensation with an exceptionally low threshold of ~0.6 W cm-2 and a narrow linewidth of ~1 meV. Polariton condensation is unambiguously demonstrated by characterizing the nonlinear behavior and coherence properties. We also identify a microscopic mechanism involving the potential landscape in the perovskite microcavity, where numerous discretized energy levels arising from the hybridization of adjacent potential minima enhance the polariton relaxation, facilitating polariton condensate formation. Our findings lay the foundation for the next-generation energy-efficient polaritonic devices operating at room temperature.

physics.optics↗

arXiv2023

Cross-Layer Optimization for Fault-Tolerant Deep Learning

Fault-tolerant deep learning accelerator is the basis for highly reliable deep learning processing and critical to deploy deep learning in safety-critical applications such as avionics and robotics. Since deep learning is known to be computing- and memory-intensive, traditional fault-tolerant approaches based on redundant computing will incur substantial overhead including power consumption and chip area. To this end, we propose to characterize deep learning vulnerability difference across both neurons and bits of each neuron, and leverage the vulnerability difference to enable selective protection of the deep learning processing components from the perspective of architecture layer and circuit layer respectively. At the same time, we observe the correlation between model quantization and bit protection overhead of the underlying processing elements of deep learning accelerators, and propose to reduce the bit protection overhead by adding additional quantization constrain without compromising the model accuracy. Finally, we employ Bayesian optimization strategy to co-optimize the correlated cross-layer design parameters at algorithm layer, architecture layer, and circuit layer to minimize the hardware resource consumption while fulfilling multiple user constraints including reliability, accuracy, and performance of the deep learning processing at the same time.

cs.AR↗

arXiv2023

Optimal Strategies for Round-Trip Pairs Trading Under Geometric Brownian Motions

This paper is concerned with an optimal strategy for simultaneously trading a pair of stocks. The idea of pairs trading is to monitor their price movements and compare their relative strength over time. A pairs trade is triggered by the divergence of their prices and consists of a pair of positions to short the strong stock and to long the weak one. Such a strategy bets on the reversal of their price strengths. A round-trip trading strategy refers to opening and closing such a pair of security positions. Typical pairs-trading models usually assume a difference of the stock prices satisfies a mean-reversion equation. However, we consider the optimal pairs-trading problem by allowing the stock prices to follow general geometric Brownian motions. The objective is to trade the pairs over time to maximize an overall return with a fixed commission cost for each transaction. Initially, we allow the initial pairs position to be either long or flat. We then consider the problem when the initial pairs position may be long, flat, or short. In each case, the optimal policy is characterized by threshold curves obtained by solving the associated HJB equations.

math.OC↗

arXiv2023

Towards High-Quality Specular Highlight Removal by Leveraging Large-Scale Synthetic Data

This paper aims to remove specular highlights from a single object-level image. Although previous methods have made some progresses, their performance remains somewhat limited, particularly for real images with complex specular highlights. To this end, we propose a three-stage network to address them. Specifically, given an input image, we first decompose it into the albedo, shading, and specular residue components to estimate a coarse specular-free image. Then, we further refine the coarse result to alleviate its visual artifacts such as color distortion. Finally, we adjust the tone of the refined result to match that of the input as closely as possible. In addition, to facilitate network training and quantitative evaluation, we present a large-scale synthetic dataset of object-level images, covering diverse objects and illumination conditions. Extensive experiments illustrate that our network is able to generalize well to unseen real object-level images, and even produce good results for scene-level images with multiple background objects and complex lighting.

cs.CV↗

arXiv2023

Pairs Trading: An Optimal Selling Rule with Constraints

The focus of this paper is on identifying the most effective selling strategy for pairs trading of stocks. In pairs trading, a long position is held in one stock while a short position is held in another. The goal is to determine the optimal time to sell the long position and repurchase the short position in order to close the pairs position. The paper presents an optimal pairs-trading selling rule with trading constraints. In particular, the underlying stock prices evolve according to a two dimensional geometric Brownian motion and the trading permission process is given in terms of a two-state {trading allowed, trading not allowed} Markov chain. It is shown that the optimal policy can be determined by a threshold curve which is obtained by solving the associated HJB equations (quasi-variational inequalities). A closed form solution is obtained. A verification theorem is provided. Numerical experiments are also reported to demonstrate the optimal policies and value functions.

q-fin.MF↗

arXiv2023

An Equivalent Graph Reconstruction Model and its Application in Recommendation Prediction

Recommendation algorithm plays an important role in recommendation system (RS), which predicts users' interests and preferences for some given items based on their known information. Recently, a recommendation algorithm based on the graph Laplacian regularization was proposed, which treats the prediction problem of the recommendation system as a reconstruction issue of small samples of the graph signal under the same graph model. Such a technique takes into account both known and unknown labeled samples information, thereby obtaining good prediction accuracy. However, when the data size is large, solving the reconstruction model is computationally expensive even with an approximate strategy. In this paper, we propose an equivalent reconstruction model that can be solved exactly with extremely low computational cost. Finally, a final prediction algorithm is proposed. We find in the experiments that the proposed method significantly reduces the computational cost while maintaining a good prediction accuracy.

cs.IR↗

arXiv2023

Debiasing Recommendation by Learning Identifiable Latent Confounders

Recommendation systems aim to predict users' feedback on items not exposed to them. Confounding bias arises due to the presence of unmeasured variables (e.g., the socio-economic status of a user) that can affect both a user's exposure and feedback. Existing methods either (1) make untenable assumptions about these unmeasured variables or (2) directly infer latent confounders from users' exposure. However, they cannot guarantee the identification of counterfactual feedback, which can lead to biased predictions. In this work, we propose a novel method, i.e., identifiable deconfounder (iDCF), which leverages a set of proxy variables (e.g., observed user features) to resolve the aforementioned non-identification issue. The proposed iDCF is a general deconfounded recommendation framework that applies proximal causal inference to infer the unmeasured confounders and identify the counterfactual feedback with theoretical guarantees. Extensive experiments on various real-world and synthetic datasets verify the proposed method's effectiveness and robustness.

cs.LG↗

arXiv2023

Influence of slope angle on deposit morphology and propagation of laboratory landslides

Landslide deposits often exhibit surface features, such as transverse ridges and X-shaped conjugate troughs, whose physical formation origins are not well understood. To study the deposit morphology, laboratory studies typically focus on the simplest landslide geometry: an inclined plane accelerating the sliding mass immediately followed by its deceleration on a horizontal plane. However, existing experiments have been conducted only for a limited range of the slope angle $θ$. Here, we study the effect of $θ$ on the kinematics and deposit morphology of laboratory landslides along a low-friction base, measured using an advanced 3D scanner. At low $θ$ (30°-35°), we find transverse ridges formed by overthrusting on the landslide deposits. At moderate $θ$ (40°-55°), conjugate troughs form. A Mohr-Coulomb failure model predicts the angle enclosed by the X-shaped troughs as 90°-$ϕ$, with $ϕ$ the internal friction angle, in agreement with our experiments and a natural landslide. This supports the speculation that conjugate troughs form due to failure associated with a triaxial shear stress. At high $θ$ (60°-85°), a double-upheaval morphology forms because the rear of the sliding mass impacts the front during the transition from the slope to the horizontal plane. The overall surface area of the landslides increases during their downslope motion and then decreases during their runout.

physics.geo-ph↗

arXiv2023

On near-group centers and super-modular categories

The construction and classification of super-modular categories is an ongoing project, of interest in algebra, topology and physics. In a recent paper, Cho, Kim, Seo and You produced two mysterious families of super-modular data, with no known realization. We show that these data are realized by modifying the Drinfeld centers of near-group fusion categories associated with the groups $\mathbb Z/6$ and $\mathbb Z/2\times \mathbb Z/4$. The methods we develop have wider applications and we describe some of these, with a view towards understanding when near-group centers provide super-modular categories.

math.QA↗

arXiv2023

Pyramid Texture Filtering

We present a simple but effective technique to smooth out textures while preserving the prominent structures. Our method is built upon a key observation -- the coarsest level in a Gaussian pyramid often naturally eliminates textures and summarizes the main image structures. This inspires our central idea for texture filtering, which is to progressively upsample the very low-resolution coarsest Gaussian pyramid level to a full-resolution texture smoothing result with well-preserved structures, under the guidance of each fine-scale Gaussian pyramid level and its associated Laplacian pyramid level. We show that our approach is effective to separate structure from texture of different scales, local contrasts, and forms, without degrading structures or introducing visual artifacts. We also demonstrate the applicability of our method on various applications including detail enhancement, image abstraction, HDR tone mapping, inverse halftoning, and LDR image enhancement.

cs.CV↗

arXiv2023

Airy-like hyperbolic shear polariton in high symmetry van der Waals crystals

Controlling light at the nanoscale by exploiting ultra-confined polaritons - hybrid light and matter waves - in various van der Waals (vdW) materials empowers unique opportunities for many nanophotonic on-chip technologies. So far, mainstream approaches have relied interfacial techniques (e.g., refractive optics, meta-optics and moire engineering) to manipulate polariton wavefront. Here, we propose that orbital angular momentum (OAM) of incident light could offer a new degree of freedom to structure vdW polaritons. With vortex excitations, we observed a new class of accelerating polariton waves - Airy-like hyperbolic phonon polaritons (PhPs) in high-symmetry orthorhombic vdW crystal α-MoO3. In analogous to the well-known Airy beams in free space, such Airy-like PhPs also exhibit self-accelerating, nonspreading and self-healing characteristics. Interestingly, the helical phase gradient of vortex beam leads to asymmetry excitation of polaritons, as a result, the Airy-like PhPs possess asymmetric propagation feature even with a symmetric mode, analogous to the asymmetry hyperbolic shear polaritons in low-symmetry crystals. Our finding highlights the potential of OAM to manipulate polaritons in vdW materials, which could be further extended into a variety of applications such as active structured polaritonic devices.

physics.optics↗

arXiv2023

PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layout

Content-aware visual-textual presentation layout aims at arranging spatial space on the given canvas for pre-defined elements, including text, logo, and underlay, which is a key to automatic template-free creative graphic design. In practical applications, e.g., poster designs, the canvas is originally non-empty, and both inter-element relationships as well as inter-layer relationships should be concerned when generating a proper layout. A few recent works deal with them simultaneously, but they still suffer from poor graphic performance, such as a lack of layout variety or spatial non-alignment. Since content-aware visual-textual presentation layout is a novel task, we first construct a new dataset named PosterLayout, which consists of 9,974 poster-layout pairs and 905 images, i.e., non-empty canvases. It is more challenging and useful for greater layout variety, domain diversity, and content diversity. Then, we propose design sequence formation (DSF) that reorganizes elements in layouts to imitate the design processes of human designers, and a novel CNN-LSTM-based conditional generative adversarial network (GAN) is presented to generate proper layouts. Specifically, the discriminator is design-sequence-aware and will supervise the "design" process of the generator. Experimental results verify the usefulness of the new benchmark and the effectiveness of the proposed approach, which achieves the best performance by generating suitable layouts for diverse canvases.

cs.CV↗

arXiv2023

Particle production at large $p_T$ in Xe+Xe collisions with jet quenching using the higher twist approach

The production of $π^0$, $η$, and $ϕ$ in the most central (0%-5%) Xe+Xe collisions at $\sqrt{s_{NN}}$ = 5.44 TeV is investigated in the framework of the perturbative QCD (pQCD) improved parton model at an accuracy of next-to-leading order (NLO). The jet quenching effect is effectively incorporated by medium-modified fragmentation functions via the higher-twist approach. Predictions of the nuclear modification factors of $π^0$, $η$, and $ϕ$ as functions of the transverse momentum $p_{\rm T}$ are made with the jet transport parameter $\hat{q}_0$, which is extracted from the available experimental data of charged hadrons provided by ALICE and CMS. The particle ratios $η/π^0,\ ϕ/π^0$ as functions of $p_{\rm T}$ in Xe+Xe collisions at $\sqrt{s_{NN}}$ = 5.44 TeV as well as in 0%-5% Pb+Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV are also presented. The numerical simulations of the scaled ratios of charged hadron production in the Xe+Xe 5.44 TeV system over those in the Pb+Pb 5.02 TeV system give a good description of the CMS data, and the scaled ratios of $π^0$, $η$, and $ϕ$ production coincide with the curve of charged hadron production.

hep-ph↗