arXiv subjects

Yu Shi

Publications and source records attributed to Yu Shi.

At least 91 records · Page 5Linked to original sources

arXiv2025

Mixture of Experts for Node Classification

Nodes in the real-world graphs exhibit diverse patterns in numerous aspects, such as degree and homophily. However, most existent node predictors fail to capture a wide range of node patterns or to make predictions based on distinct node patterns, resulting in unsatisfactory classification performance. In this paper, we reveal that different node predictors are good at handling nodes with specific patterns and only apply one node predictor uniformly could lead to suboptimal result. To mitigate this gap, we propose a mixture of experts framework, MoE-NP, for node classification. Specifically, MoE-NP combines a mixture of node predictors and strategically selects models based on node patterns. Experimental results from a range of real-world datasets demonstrate significant performance improvements from MoE-NP.

cs.SI↗

arXiv2025

SIDIS at small $x$ at next-to-leading order: transverse photon

We calculate the next-to-leading order corrections to single inclusive hadron production in deep inelastic scattering at small $x$ using the color glass condensate formalism, for the case when the exchanged photon is transversely polarized. We show all UV and soft divergences cancel while collinear and rapidity divergences result in scale evolution of quark-hadron fragmentation function and small-x evolution of dipole amplitudes, respectively.

hep-ph↗

arXiv2025

Chien-Shiung Wu as the experimental pioneer in quantum entanglement: a 2022 note

Advised by Prof. Chen Ning Yang, we review the early pioneering work by Chien-Shiung Wu on entangled photons created from the electron-positron annihilation. This paper formed a basis of the author's speech at International Symposium Commemorating the 110th Birth Anniversary of Chien-Shiung Wu, May 31, 2022, and of a few articles on the subject in Chinese.

physics.hist-ph↗

arXiv2025

Scientific Spirit of Chien-Shiung Wu: From Quantum Entanglement to Parity Nonconservation

In 1950, Chien-Shiung Wu and her student published a coincidence experiment on entangled photon pairs that were created in electron-positron annihilation. This experiment precisely verified the prediction of quantum electrodynamics. Additionally, it was also the first instance of a precisely controlled quantum entangled state of spatially separated particles, although Wu did not know about this at the time. In 1956, Wu initiated and led the so-called Wu experiment, which discovered parity nonconservation, becoming one of the greatest experiments of the 20th century. As Chen Ning Yang said, Wu's experiments were well known for their precision and accuracy. Experimental precision and accuracy manifested Wu's scientific spirit, which we investigate here in some detail. This paper is the translated transcript of the speech the author made at the International Symposium Commemorating the 110th Anniversary of the Birth of Chien-Shiung Wu, on May 31, 2022. The above abstract is the translation of the original abstract of the speech.

physics.hist-ph↗

arXiv2025

Speedup of high-order unconstrained binary optimization using quantum Z2 lattice gauge theory

An important and difficult problem in optimization is the high-order unconstrained binary optimization, which can represent many optimization problems more efficient than quadratic unconstrained binary optimization, but how to quickly solve it has remained difficult. Here we present an approach by mapping the high-order unconstrained binary optimization to quantum Z2 lattice gauge theory defined on the dual graph, and propose the gauged local quantum annealing, which is the local quantum annealing protected by the gauge symmetry. We present the quantum algorithm and its corresponding quantum-inspired classical algorithm for this problem, and achieve algorithmic speedup by using gauge symmetry. By running the quantum-inspired classical algorithm, we demonstrate that the gauged local quantum annealing reduces the computational time by one order of magnitude from that of the local quantum annealing.

quant-ph↗

arXiv2025

Fluctuations of topological charges in two-dimensional classical Heisenberg model

Binding and unbinding of vortices drives Kosterlitz-Thouless phase transition in two-dimensional XY model. Here we investigate whether similar mechanism works in two-dimensional Heisenberg model, by using the fluctuation of skyrmion number inside a loop to characterize the nature of binding versus unbinding of defects. Through Monte Carlo simulations, we find that the fluctuation is proportional to the perimeter of the loop at low temperatures while it is proportional to the area of the loop at high temperatures, implying binding of the defects at low temperatures and unbinding at high temperatures.

cond-mat.stat-mech↗

arXiv2025

Stabilizer Entanglement Distillation and Efficient Fault-Tolerant Encoders

Entanglement is essential for quantum information processing, but is limited by noise. We address this by developing high-yield entanglement distillation protocols with several advancements. (1) We extend the 2-to-1 recurrence entanglement distillation protocol to higher-rate n-to-(n-1) protocols that can correct any single-qubit errors. These protocols are evaluated through numerical simulations focusing on fidelity and yield. We also outline a method to adapt any classical error-correcting code for entanglement distillation, where the code can correct both bit-flip and phase-flip errors by incorporating Hadamard gates. (2) We propose a constant-depth decoder for stabilizer codes that transforms logical states into physical ones using single-qubit measurements. This decoder is applied to entanglement distillation protocols, reducing circuit depth and enabling protocols derived from high-performance quantum error-correcting codes. We demonstrate this by evaluating the circuit complexity for entanglement distillation protocols based on surface codes and quantum convolutional codes. (3) Our stabilizer entanglement distillation techniques advance quantum computing. We propose a fault-tolerant protocol for constant-depth encoding and decoding of arbitrary states in surface codes, with potential extensions to more general quantum low-density parity-check codes. This protocol is feasible with state-of-the-art reconfigurable atom arrays and surpasses the limits of conventional logarithmic depth encoders. Overall, our study integrates stabilizer formalism, measurement-based quantum computing, and entanglement distillation, advancing both quantum communication and computing.

quant-ph↗

arXiv2025

Unlocking a New Rust Programming Experience: Fast and Slow Thinking with LLMs to Conquer Undefined Behaviors

To provide flexibility and low-level interaction capabilities, the unsafe tag in Rust is essential in many projects, but undermines memory safety and introduces Undefined Behaviors (UBs) that reduce safety. Eliminating these UBs requires a deep understanding of Rust's safety rules and strong typing. Traditional methods require depth analysis of code, which is laborious and depends on knowledge design. The powerful semantic understanding capabilities of LLM offer new opportunities to solve this problem. Although existing large model debugging frameworks excel in semantic tasks, limited by fixed processes and lack adaptive and dynamic adjustment capabilities. Inspired by the dual process theory of decision-making (Fast and Slow Thinking), we present a LLM-based framework called RustBrain that automatically and flexibly minimizes UBs in Rust projects. Fast thinking extracts features to generate solutions, while slow thinking decomposes, verifies, and generalizes them abstractly. To apply verification and generalization results to solution generation, enabling dynamic adjustments and precise outputs, RustBrain integrates two thinking through a feedback mechanism. Experimental results on Miri dataset show a 94.3% pass rate and 80.4% execution rate, improving flexibility and Rust projects safety.

cs.SE↗

arXiv2025

Lighting up the Photon Wigner Distribution via Dilepton Productions

We present a systematic investigation of lepton pair production through photon-photon fusion processes in heavy-ion collisions. It is demonstrated that the dilepton production at a given impact parameter ($b_\perp$) with a fixed transverse momentum imbalance ($q_\perp$) can be factorized into a unified formula in terms of the Wigner photon distribution of heavy nuclei. We show that this framework provides a comprehensive description of all the relevant data from RHIC to the LHC, with a strong evidence that the quasi-real photon can be radiated not only from the nucleus as a whole, standing for the coherent contribution, but also from the sub-structures inside the nucleus, representing the incoherent contribution. Further predictions are made for the anisotropies in the correlations between $q_\perp$, $b_\perp$, and the dilepton transverse momentum ($P_\perp$). This will help us to constrain the photon Wigner distribution which plays a crucial role to study the gluonic matter of nucleus at small-$x$ through the diffractive photoproduction processes in heavy ion collision.

hep-ph↗

arXiv2025

Entanglement-Assisted Coding for Arbitrary Linear Computations Over a Quantum MAC

We study a linear computation problem over a quantum multiple access channel (LC-QMAC), where $S$ servers share an entangled state and separately store classical data streams $W_1,\cdots, W_S$ over a finite field $\mathbb{F}_d$. A user aims to compute $K$ linear combinations of these data streams, represented as $Y = \mathbf{V}_1 W_1 + \mathbf{V}_2 W_2 + \cdots + \mathbf{V}_S W_S \in \mathbb{F}_d^{K \times 1}$. To this end, each server encodes its classical information into its local quantum subsystem and transmits it to the user, who retrieves the desired computations via quantum measurements. In this work, we propose an achievable scheme for LC-QMAC based on the stabilizer formalism and the ideas from entanglement-assisted quantum error-correcting codes (EAQECC). Specifically, given any linear computation matrix, we construct a self-orthogonal matrix that can be implemented using the stabilizer formalism. Also, we apply precoding matrices to minimize the number of auxiliary qudits required. Our scheme achieves more computations per qudit, i.e., a higher computation rate, compared to the best-known methods in the literature, and attains the capacity in certain cases.

cs.IT↗

arXiv2024

Lévy distributed fluctuations in the living cell cortex

The actomyosin cortex is an active material that provides animal cells with a strong but flexible exterior, whose mechanics, including non-Gaussian fluctuations and occasional large displacements or cytoquakes, have defied explanation. We study the active fluctuations of the cortex using nanoscale tracking of arrays of flexible microposts adhered to multiple cultured cell types. When the confounding effects of static heterogeneity and tracking error are removed, the fluctuations are found to be heavy-tailed and well-described by a truncated Lévy alpha-stable distribution over a wide range of timescales, in multiple cell types. The largest random displacements closely resemble the earlier-reported cytoquakes, but notably, we find these cytoquakes are not due to earthquake-like cooperative rearrangement of many cytoskeletal elements. Rather, they are indistinguishable from chance large excursions of a super-diffusive random process driven by heavy-tailed noise. The non-cooperative microscopic events driving these fluctuations need not be larger than the expected elastic energy of single tensed cortical actin filaments, and the implied distribution of microscopic event energies will need to be accounted for by future models of the cytoskeleton.

physics.bio-ph↗

arXiv2024

Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning

In recent years, machine learning has demonstrated impressive capability in handling molecular science tasks. To support various molecular properties at scale, machine learning models are trained in the multi-task learning paradigm. Nevertheless, data of different molecular properties are often not aligned: some quantities, e.g. equilibrium structure, demand more cost to compute than others, e.g. energy, so their data are often generated by cheaper computational methods at the cost of lower accuracy, which cannot be directly overcome through multi-task learning. Moreover, it is not straightforward to leverage abundant data of other tasks to benefit a particular task. To handle such data heterogeneity challenges, we exploit the specialty of molecular tasks that there are physical laws connecting them, and design consistency training approaches that allow different tasks to exchange information directly so as to improve one another. Particularly, we demonstrate that the more accurate energy data can improve the accuracy of structure prediction. We also find that consistency training can directly leverage force and off-equilibrium structure data to improve structure prediction, demonstrating a broad capability for integrating heterogeneous data.

cs.LG↗

arXiv2024

From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning

Accurate prediction of protein-ligand binding affinities is an essential challenge in structure-based drug design. Despite recent advances in data-driven methods for affinity prediction, their accuracy is still limited, partially because they only take advantage of static crystal structures while the actual binding affinities are generally determined by the thermodynamic ensembles between proteins and ligands. One effective way to approximate such a thermodynamic ensemble is to use molecular dynamics (MD) simulation. Here, an MD dataset containing 3,218 different protein-ligand complexes is curated, and Dynaformer, a graph-based deep learning model is further developed to predict the binding affinities by learning the geometric characteristics of the protein-ligand interactions from the MD trajectories. In silico experiments demonstrated that the model exhibits state-of-the-art scoring and ranking power on the CASF-2016 benchmark dataset, outperforming the methods hitherto reported. Moreover, in a virtual screening on heat shock protein 90 (HSP90) using Dynaformer, 20 candidates are identified and their binding affinities are further experimentally validated. Dynaformer displayed promising results in virtual drug screening, revealing 12 hit compounds (two are in the submicromolar range), including several novel scaffolds. Overall, these results demonstrated that the approach offer a promising avenue for accelerating the early drug discovery process.

q-bio.BM↗

arXiv2024

How a simple pendulum inside a running elevator oscillates

We propose to effectively realize a time-dependent gravitational acceleration by using a running elevator, so that a simple pendulum inside it effectively becomes one with a time-dependent gravitational acceleration. We did such an experiment using a realistic elevator, and analyzed the data. The acceleration of an elevator is much smaller than the gravitational acceleration, and is time-dependent only when the elevator starts and stops. However, we have managed to establish the effect on the oscillation of the pendulum. The effect becomes pronounced if the simple pendulum is put in a container vertically accelerating, and the acceleration is time-dependent, while its magnitude is comparable with that of the gravitational acceleration.

physics.class-ph↗

arXiv2024

Measuring the acceleration of an elevator by using the apparent weight of an object inside it

An accelerating elevator changes the apparent weight of any object inside it from the original weight, as measured inside the elevator, because the acceleration causes an inertial force on it. For any object in a running elevator, the variation of the acceleration of the elevator causes the variation of the apparent weight of the object. We have studied the time dependence of the apparent weight of the object and thus the acceleration of the elevator. For chosen initial and final floors, we measured the apparent weight of an object by using an electronic scale inside the elevator, and shot the readings of the scale and a watch during the movement of the elevator. Then we analyzed the data collected from the recorded video. If the initial and final floors are exchanged, the variations of the weight and acceleration are, respectively, same in magnitudes and opposite in signs. The experiments indicate that for the elevator to go directly from a floor to another, the process consists of periods with variable acceleration, constant acceleration, uniform motion, variable deceleration, constant deceleration and variable deceleration consecutively. If there are pauses during the movement, each pause causes an additional process consisting of periods with deceleration, stop and acceleration, replacing the original period of constant motion. Depending on the distance to the destination, the elevator reduces or diminishes the periods of constant acceleration and uniform motion.

physics.ed-ph↗

arXiv2024

Improved Esophageal Varices Assessment from Non-Contrast CT Scans

Esophageal varices (EV), a serious health concern resulting from portal hypertension, are traditionally diagnosed through invasive endoscopic procedures. Despite non-contrast computed tomography (NC-CT) imaging being a less expensive and non-invasive imaging modality, it has yet to gain full acceptance as a primary clinical diagnostic tool for EV evaluation. To overcome existing diagnostic challenges, we present the Multi-Organ-cOhesion-Network (MOON), a novel framework enhancing the analysis of critical organ features in NC-CT scans for effective assessment of EV. Drawing inspiration from the thorough assessment practices of radiologists, MOON establishes a cohesive multiorgan analysis model that unifies the imaging features of the related organs of EV, namely esophagus, liver, and spleen. This integration significantly increases the diagnostic accuracy for EV. We have compiled an extensive NC-CT dataset of 1,255 patients diagnosed with EV, spanning three grades of severity. Each case is corroborated by endoscopic diagnostic results. The efficacy of MOON has been substantiated through a validation process involving multi-fold cross-validation on 1,010 cases and an independent test on 245 cases, exhibiting superior diagnostic performance compared to methods focusing solely on the esophagus (for classifying severe grade: AUC of 0.864 versus 0.803, and for moderate to severe grades: AUC of 0.832 versus 0.793). To our knowledge, MOON is the first work to incorporate a synchronized multi-organ NC-CT analysis for EV assessment, providing a more acceptable and minimally invasive alternative for patients compared to traditional endoscopy.

eess.IV↗

arXiv2024

LIDIA: Precise Liver Tumor Diagnosis on Multi-Phase Contrast-Enhanced CT via Iterative Fusion and Asymmetric Contrastive Learning

The early detection and precise diagnosis of liver tumors are tasks of critical clinical value, yet they pose significant challenges due to the high heterogeneity and variability of liver tumors. In this work, a precise LIver tumor DIAgnosis network on multi-phase contrast-enhance CT, named LIDIA, is proposed for real-world scenario. To fully utilize all available phases in contrast-enhanced CT, LIDIA first employs the iterative fusion module to aggregate variable numbers of image phases, thereby capturing the features of lesions at different phases for better tumor diagnosis. To effectively mitigate the high heterogeneity problem of liver tumors, LIDIA incorporates asymmetric contrastive learning to enhance the discriminability between different classes. To evaluate our method, we constructed a large-scale dataset comprising 1,921 patients and 8,138 lesions. LIDIA has achieved an average AUC of 93.6% across eight different types of lesions, demonstrating its effectiveness. Besides, LIDIA also demonstrated strong generalizability with an average AUC of 89.3% when tested on an external cohort of 828 patients.

cs.CV↗

arXiv2024

Automated identification and quantification of myocardial inflammatory infiltration in digital histological images to diagnose myocarditis

This study aims to develop a new computational pathology approach that automates the identification and quantification of myocardial inflammatory infiltration in digital HE-stained images to provide a quantitative histological diagnosis of myocarditis.898 HE-stained whole slide images (WSIs) of myocardium from 154 heart transplant patients diagnosed with myocarditis or dilated cardiomyopathy (DCM) were included in this study. An automated DL-based computational pathology approach was developed to identify nuclei and detect myocardial inflammatory infiltration, enabling the quantification of the lymphocyte nuclear density (LND) on myocardial WSIs. A cutoff value based on the quantification of LND was proposed to determine if the myocardial inflammatory infiltration was present. The performance of our approach was evaluated with a five-fold cross-validation experiment, tested with an internal test set from the myocarditis group, and confirmed by an external test from a double-blind trial group. An LND of 1.02/mm2 could distinguish WSIs with myocarditis from those without. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) in the five-fold cross-validation experiment were 0.899 plus or minus 0.035, 0.971 plus or minus 0.017, 0.728 plus or minus 0.073 and 0.849 plus or minus 0.044, respectively. For the internal test set, the accuracy, sensitivity, specificity, and AUC were 0.887, 0.971, 0.737, and 0.854, respectively. The accuracy, sensitivity, specificity, and AUC for the external test set reached 0.853, 0.846, 0.858, and 0.852, respectively. Our new approach provides accurate and reliable quantification of the LND of myocardial WSIs, facilitating automated quantitative diagnosis of myocarditis with HE-stained images.

physics.med-ph↗