arXiv subjects

Wenjie Zhou

Publications and source records attributed to Wenjie Zhou.

33 records · Page 2Linked to original sources

arXiv2025

Mimicking the Physicist's Eye:A VLM-centric Approach for Physics Formula Discovery

Automated discovery of physical laws from observational data in the real world is a grand challenge in AI. Current methods, relying on symbolic regression or LLMs, are limited to uni-modal data and overlook the rich, visual phenomenological representations of motion that are indispensable to physicists. This "sensory deprivation" severely weakens their ability to interpret the inherent spatio-temporal patterns within dynamic phenomena. To address this gap, we propose VIPER-R1, a multimodal model that performs Visual Induction for Physics-based Equation Reasoning to discover fundamental symbolic formulas. It integrates visual perception, trajectory data, and symbolic reasoning to emulate the scientific discovery process. The model is trained via a curriculum of Motion Structure Induction (MSI), using supervised fine-tuning to interpret kinematic phase portraits and to construct hypotheses guided by a Causal Chain of Thought (C-CoT), followed by Reward-Guided Symbolic Calibration (RGSC) to refine the formula structure with reinforcement learning. During inference, the trained VIPER-R1 acts as an agent: it first posits a high-confidence symbolic ansatz, then proactively invokes an external symbolic regression tool to perform Symbolic Residual Realignment (SR^2). This final step, analogous to a physicist's perturbation analysis, reconciles the theoretical model with empirical data. To support this research, we introduce PhysSymbol, a new 5,000-instance multimodal corpus. Experiments show that VIPER-R1 consistently outperforms state-of-the-art VLM baselines in accuracy and interpretability, enabling more precise discovery of physical laws. Project page: https://jiaaqiliu.github.io/VIPER-R1/

cs.AI↗

arXiv2025

On ordering of surjective cardinals

Let $\mathrm{Card}$ denote the class of cardinals. For all cardinals $\mathfrak{a}$ and $\mathfrak{b}$, $\mathfrak{a}\leqslant\mathfrak{b}$ means that there is an injection from a set of cardinality $\mathfrak{a}$ into a set of cardinality $\mathfrak{b}$, and $\mathfrak{a}\leqslant^\ast\mathfrak{b}$ means that there is a partial surjection from a set of cardinality $\mathfrak{b}$ onto a set of cardinality $\mathfrak{a}$. A doubly ordered set is a triple $\langle P,\preccurlyeq,\preccurlyeq^\ast\rangle$ such that $\preccurlyeq$ is a partial ordering on $P$, $\preccurlyeq^\ast$ is a preordering on $P$, and ${\preccurlyeq}\subseteq{\preccurlyeq^\ast}$. In 1966, Jech proved that for every partially ordered set $\langle P,\preccurlyeq\rangle$, there exists a model of $\mathsf{ZF}$ in which $\langle P,\preccurlyeq\rangle$ can be embedded into $\langle\mathrm{Card},\leqslant\rangle$. We generalize this result by showing that for every doubly ordered set $\langle P,\preccurlyeq,\preccurlyeq^\ast\rangle$, there exists a model of $\mathsf{ZF}$ in which $\langle P,\preccurlyeq,\preccurlyeq^\ast\rangle$ can be embedded into $\langle\mathrm{Card},\leqslant,\leqslant^\ast\rangle$.

math.LO↗

arXiv2025

The Superconformal Index and Black Hole Instabilities

The superconformal index of ${\cal N}=4$ supersymmetric Yang-Mills theory with gauge group $\mathrm{U}(N)$ has provided powerful insights into the entropy of supersymmetric black holes in AdS$_5\times S^5$, including some sub-leading logarithmic and non-perturbative corrections. Recently, the phase space of supersymmetric solutions has been argued to contain configurations other than the asymptotically AdS$_5$ black hole. Such configurations include the so-called grey galaxies where the black hole at the center is surrounded by a gas of gravitons. By numerically evaluating the superconformal index of ${\cal N}=4$ supersymmetric Yang-Mills at small values of $N$, we detect systematic deviations from the entropy of black holes with two distinct angular momenta. We find that the giant graviton expansion of the index is a numerically efficient way of evaluating the index that complements the direct character evaluation and allows for explicit access to $N\le 15$ with up to two giant gravitons in the expansion. We find it remarkable that a supersymmetric quantity in field theory, usually thought of as a rigid counting observable, indeed contains information about different phases in the space of supersymmetric solutions on the gravity side.

hep-th↗

arXiv2025

Two-dimensional ferroelectric crystal with temperature-invariant ultralow thermal conductivity

We report the discovery of temperature-invariant ultralow thermal conductivity ($κ$) in monolayer $β'$-In$_2$Se$_3$, a two-dimensional ferroelectric crystal with in-plane polarization. Using a combination of generalized Wigner transport equation theory and machine-learning-assisted molecular dynamics simulations, we reveal that the balance between particle-like phonon propagating and wave-like tunneling transport mechanisms results in a propagating-tunneling-invariant (PTI) ultralow thermal conductivity of approximately 0.6 W/mK (comparable to that of glass) over a broad temperature range ($150<T<800$~K). This behavior stems from intrinsic strong lattice anharmonicity driven by ferroelectric dipolar fluctuations, eliminating the need for extrinsic structural modifications. In contrast, the $α$-In$_2$Se$_3$~monolayer, which shares the same stoichiometry, exhibits a conventional temperature-dependent thermal conductivity, $κ(T) \propto T^{-1}$, typical of simple crystals. Furthermore, we demonstrate that the anharmonicity in $β'$-In$_2$Se$_3$~can be precisely modulated by an external electric field, enabling on-demand control of thermal transport properties, including modifying the temperature scaling behavior of heat conductivity and achieving a large thermal switching ratio of $\approx$2.5. These findings provide fundamental insights into the interplay between field-tunable lattice anharmonicity, phonon dynamics, and thermal transport mechanisms.

cond-mat.mtrl-sci↗

arXiv2024

GOVERN: Gradient Orientation Vote Ensemble for Multi-Teacher Reinforced Distillation

Pre-trained language models have become an integral component of question-answering systems, achieving remarkable performance. However, for practical deployment, it is crucial to perform knowledge distillation to maintain high performance while operating under computational constraints. In this paper, we address a key question: given the importance of unsupervised distillation for student model performance, how can knowledge from multiple teacher models be effectively ensemble during this stage without the guidance of labels? We propose a novel algorithm, GOVERN, to tackle this issue. GOVERN has demonstrated significant improvements in both offline and online experiments, enabling the student model to achieve results comparable to that of teacher ensembles. Our experiments show that GOVERN remarkably requires a mere 1\% of the ensemble method's inference budget to achieve 99.5\% of performance. The proposed algorithm has been successfully deployed in a real-world commercial question-answering system, demonstrating its real-world applicability.

cs.CL↗

arXiv2024

Polycatenated Architected Materials

Architected materials derive their properties from the geometric arrangement of their internal structural elements. Their designs rely on continuous networks of members to control the global mechanical behavior of the bulk. Here, we introduce a class of materials that consist of discrete concatenated rings or cage particles interlocked in three-dimensional networks, forming polycatenated architected materials (PAMs). We propose a general design framework that translates arbitrary crystalline networks into particles' concatenations and geometries. In response to small external loads, PAMs behave like non-Newtonian fluids, showing both shear-thinning and shear-thickening responses. At larger strains, PAMs behave like lattices and foams, with a nonlinear stress-strain relation. At microscale, we demonstrate that PAMs can change their shapes in response to applied electrostatic charges. PAM's unique properties pave the path for developing stimuli-responsive materials, energy-absorbing systems and morphing architectures.

cond-mat.soft↗

arXiv2024

The first-order phase transition in the neutron star from the deep neural network

This study investigates the first-order phase transition within neutron stars, leveraging the deep neural network (DNN) framework alongside contemporary astronomical measurements. The equation of state (EOS) for neutron stars is delineated in a piecewise polytropic form, with the speed of sound ($c_s$) serving as a pivotal determinant. In the phase transition region, $c_s$ is presumed to be zero, while in other intervals, it is optimized utilizing the DNN. Various onset energy densities of phase transition ($\varepsilon_{pt}$), spanning from $2\varepsilon_0$ to $3\varepsilon_0$ (where $\varepsilon_0$ denotes the energy density at nuclear saturation density), as well as phase transition widths ($Δ\varepsilon$) ranging from $0.5\varepsilon_0$ to $\varepsilon_0$, are examined. Our findings underscore that smaller values of $\varepsilon_{pt}$ lead to a more substantial impact of $Δ\varepsilon$ on neutron star properties, encompassing maximum mass, corresponding radius, tidal deformability, phase transition mass, and trace anomaly. Conversely, when $\varepsilon_{pt}$ exceeds $2.5\varepsilon_0$, the influence of $Δ\varepsilon$ diminishes, resulting in a stiffer EOS compared to scenarios lacking a phase transition. Furthermore, the trace anomaly at high density shifts to negative values upon the commencement of the phase transition. It is noteworthy that the correlations between the average speed of sound at different energy density segments demonstrate a notably weak connection. The discernment of whether a phase transition has occurred with the present observables of neutron stars poses a challenging task.

nucl-th↗

arXiv2024

A Shock Flash Breaking Out of a Dusty Red Supergiant

Shock breakout emission is light that arises when a shockwave, generated by core-collapse explosion of a massive star, passes through its outer envelope. Hitherto, the earliest detection of such a signal was at several hours after the explosion, though a few others had been reported. The temporal evolution of early light curves should reveal insights into the shock propagation, including explosion asymmetry and environment in the vicinity, but this has been hampered by the lack of multiwavelength observations. Here we report the instant multiband observations of a type II supernova (SN 2023ixf) in the galaxy M101 (at a distance of 6.85+/-0.15 Mpc), beginning at about 1.4 hours after the explosion. The exploding star was a red supergiant with a radius of about 440 solar radii. The light curves evolved rapidly, on timescales of 1-2 hours, and appeared unusually fainter and redder than predicted by models within the first few hours, which we attribute to an optically thick dust shell before it was disrupted by the shockwave. We infer that the breakout and perhaps the distribution of the surrounding dust were not spherically symmetric.

astro-ph.HE↗

arXiv2023

Smith-Purcell radiation from time grating

Smith-Purcell radiation (SPR) occurs when an electron skims above a spatial grating, but the fixed momentum compensation from the static grating imposes limitations on the emission wavelength. It has been discovered that a temporally periodic system can provide energy compensation to generate light emissions in free space. Here, we introduce temporal SPR (t-SPR) emerging from a time grating and propose a generalized t-SPR dispersion equation to predict the relationship between radiation frequency, direction, electron velocity, modulation period, and harmonic orders. Compared to conventional SPR, t-SPR can: 1) Provide a versatile platform for manipulating SPR emission through temporal modulation (e.g., period, amplitude, wave shape). 2) Exhibit strong robustness to the electron-grating separation, alleviating the constraints associated with extreme electron near-field excitation. 3) Introduce additional energy channels through temporal modulation, enhancing and amplifying emission.

physics.optics↗

arXiv2023

TransientViT: A novel CNN - Vision Transformer hybrid real/bogus transient classifier for the Kilodegree Automatic Transient Survey

The detection and analysis of transient astronomical sources is of great importance to understand their time evolution. Traditional pipelines identify transient sources from difference (D) images derived by subtracting prior-observed reference images (R) from new science images (N), a process that involves extensive manual inspection. In this study, we present TransientViT, a hybrid convolutional neural network (CNN) - vision transformer (ViT) model to differentiate between transients and image artifacts for the Kilodegree Automatic Transient Survey (KATS). TransientViT utilizes CNNs to reduce the image resolution and a hierarchical attention mechanism to model features globally. We propose a novel KATS-T 200K dataset that combines the difference images with both long- and short-term images, providing a temporally continuous, multidimensional dataset. Using this dataset as the input, TransientViT achieved a superior performance in comparison to other transformer- and CNN-based models, with an overall area under the curve (AUC) of 0.97 and an accuracy of 99.44%. Ablation studies demonstrated the impact of different input channels, multi-input fusion methods, and cross-inference strategies on the model performance. As a final step, a voting-based ensemble to combine the inference results of three NRD images further improved the model's prediction reliability and robustness. This hybrid model will act as a crucial reference for future studies on real/bogus transient classification.

astro-ph.IM↗

arXiv2023

Nonparametric model for the equations of state of neutron star from deep neural network

It is of great interest to understand the equation of state (EOS) of the neutron star (NS), whose core includes highly dense matter. However, there are large uncertainties in the theoretical predictions for the EOS of NS. It is useful to develop a new framework, which is flexible enough to consider the systematic error in theoretical predictions and to use them as a best guess at the same time. We employ a deep neural network to perform a non-parametric fit of the EOS of NS using currently available data. In this framework, the Gaussian process is applied to represent the EOSs and the training set data required to close physical solutions. Our model is constructed under the assumption that the true EOS of NS is a perturbation of the relativistic mean-field model prediction. We fit the EOSs of NS using two different example datasets, which can satisfy the latest constraints from the massive neutron stars, NICER, and the gravitational wave of the binary neutron stars. Given our assumptions, we find that a maximum neutron star mass is $2.38^{+0.15}_{-0.13} M_\odot$ or $2.41^{+0.15}_{-0.14}$ at $95\%$ confidence level from two different example datasets. It implies that the $1.4 M_\odot$ radius is $12.31^{+0.29}_{-0.31}$ km or $12.30^{+0.35}_{-0.37}$ km. These results are consistent with results from previous studies using similar priors. It has demonstrated the recovery of the EOS of NS using a nonparametric model.

nucl-th↗

arXiv2020

EQG-RACE: Examination-Type Question Generation

Question Generation (QG) is an essential component of the automatic intelligent tutoring systems, which aims to generate high-quality questions for facilitating the reading practice and assessments. However, existing QG technologies encounter several key issues concerning the biased and unnatural language sources of datasets which are mainly obtained from the Web (e.g. SQuAD). In this paper, we propose an innovative Examination-type Question Generation approach (EQG-RACE) to generate exam-like questions based on a dataset extracted from RACE. Two main strategies are employed in EQG-RACE for dealing with discrete answer information and reasoning among long contexts. A Rough Answer and Key Sentence Tagging scheme is utilized to enhance the representations of input. An Answer-guided Graph Convolutional Network (AG-GCN) is designed to capture structure information in revealing the inter-sentences and intra-sentence relations. Experimental results show a state-of-the-art performance of EQG-RACE, which is apparently superior to the baselines. In addition, our work has established a new QG prototype with a reshaped dataset and QG method, which provides an important benchmark for related research in future work. We will make our data and code publicly available for further research.

cs.CL↗

arXiv2020

Automating App Review Response Generation Based on Contextual Knowledge

User experience of mobile apps is an essential ingredient that can influence the audience volumes and app revenue. To ensure good user experience and assist app development, several prior studies resort to analysis of app reviews, a type of app repository that directly reflects user opinions about the apps. Accurately responding to the app reviews is one of the ways to relieve user concerns and thus improve user experience. However, the response quality of the existing method relies on the pre-extracted features from other tools, including manually-labelled keywords and predicted review sentiment, which may hinder the generalizability and flexibility of the method. In this paper, we propose a novel end-to-end neural network approach, named CoRe, with the contextual knowledge naturally incorporated and without involving external tools. Specifically, CoRe integrates two types of contextual knowledge in the training corpus, including official app descriptions from app store and responses of the retrieved semantically similar reviews, for enhancing the relevance and accuracy of the generated review responses. Experiments on practical review data show that CoRe can outperform the state-of-the-art method by 11.53% in terms of BLEU-4, an accuracy metric that is widely used to evaluate text generation systems.

cs.SE↗

arXiv2019

Question-type Driven Question Generation

Question generation is a challenging task which aims to ask a question based on an answer and relevant context. The existing works suffer from the mismatching between question type and answer, i.e. generating a question with type $how$ while the answer is a personal name. We propose to automatically predict the question type based on the input answer and context. Then, the question type is fused into a seq2seq model to guide the question generation, so as to deal with the mismatching problem. We achieve significant improvement on the accuracy of question type prediction and finally obtain state-of-the-art results for question generation on both SQuAD and MARCO datasets.

cs.CL↗

arXiv2019

Multi-Task Learning with Language Modeling for Question Generation

This paper explores the task of answer-aware questions generation. Based on the attention-based pointer generator model, we propose to incorporate an auxiliary task of language modeling to help question generation in a hierarchical multi-task learning structure. Our joint-learning model enables the encoder to learn a better representation of the input sequence, which will guide the decoder to generate more coherent and fluent questions. On both SQuAD and MARCO datasets, our multi-task learning model boosts the performance, achieving state-of-the-art results. Moreover, human evaluation further proves the high quality of our generated questions.

cs.CL↗