arXiv subjects

Shicheng Ma

Publications and source records attributed to Shicheng Ma.

9 recordsLinked to original sources

arXiv2026

Fractional Chern Insulators in Twisted Bilayer Optical Lattices

Twisted bilayer materials provide a versatile platform for realizing novel topological states. Motivated by recent experimental realization of atomic Bose-Einstein condensates in twisted bilayer optical lattices, we theoretically investigate topological states for cold atoms trapped in such a system. At single-particle level, the system hosts nearly flat moir\'{e} bands under appropriate experimental parameters. Although these noninteracting bands are topologically trivial in the Altland-Zirnbauer classification, we find that atomic interactions can induce a flat Chern band based on self-consistent Hartree-Fock calculations. Furthermore, exact diagonalization identifies a fractional Chern insulator phase at fractional filling. Our work thus paves the way for exploring strongly correlated topological phases in highly tunable twisted bilayer optical lattices.

cond-mat.quant-gas↗

arXiv2026

SpeechSense: A Paralinguistic-Focused Dataset for Fine-Grained Speech Sentiment Analysis

Recent advances in AI have revolutionized speech processing, yet effective speech understanding requires discerning not just what is said, but how it is said. Speech Sentiment Analysis plays a critical role in decoding these paralinguistic cues for diverse real-world applications such as recruitment and customer service. However, existing Speech Sentiment Analysis research faces two primary limitations. First, dominant approaches rely on text-centric pipelines that cascade Automatic Speech Recognition with text analysis. This process inevitably discards essential acoustic features like prosody and tone, failing to capture attitudinal meanings in acoustically ambiguous utterances. Second, current benchmarks suffer from a mismatch in label granularity, prioritizing basic emotions (e.g., happy, sad) over the nuanced interpersonal stances (e.g., confident, impatient) necessary for social sensitivity. To address these limitations, we propose a novel dataset, SpeechSense, for fine-grained speech sentiment analysis. Specifically, we define a specialized 8-class taxonomy of interpersonal stances detectable primarily through prosodic cues beyond lexical content alone. We then construct a curated dataset based on this taxonomy, built from high-fidelity speech synthesis and rigorous human validation. Comprehensive experiments across multi-modal LLMs, text-only LLMs, and speech encoders demonstrate that models with acoustic access consistently outperform text-only baselines. These results empirically validate the primacy of acoustic cues in detecting subtle speaker attitudes, highlighting the necessity of SpeechSense. Dataset and supplementary materials are available at https://github.com/Sher13cked/SpeechSense.

cs.CL↗

arXiv2026

Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security

Agentic AI systems -- Large Language Models (LLMs) augmented with planning, tool use, memory, and long-horizon interactions -- can execute complex tasks autonomously, but their multi-step trajectories introduce new failure modes that challenge trustworthiness. This survey provides a focused examination of trustworthy agentic AI through two core dimensions that are critical for high-risk deployments: Safety and Robustness, and Privacy and System Security. For each dimension, we clarify key concepts, identify where risks emerge along the agent workflow, and summarize stage-targeted mitigation strategies. Other trustworthiness aspects (value alignment, transparency, fairness, and accountability) are discussed as relevant context rather than parallel chapters. To support consistent comparison and deployment decisions, we consolidate evaluation into a unified metrics-and-benchmarks hub, emphasizing both outcome and process signals (e.g., constraint violations, trace completeness, and adversarial success rates) and offering scenario-to-metric guidance for release gating. We conclude by outlining open challenges such as self-evolving agents, runtime monitoring and verification, privacy-preserving personalization, and the trust-utility trade-off, and present a case study of real-world security failures in open-source agentic systems. Our goal is to serve as a practical reference for researchers and practitioners building trustworthy agentic systems in high-stakes environments.

cs.AI↗

arXiv2026

Chiral Spin Liquid in Rydberg Atom Arrays

Despite long-standing theoretical interest, the chiral spin liquid, a topologically ordered phase, has yet to be observed experimentally. Here we surprisingly find its emergence in an experimentally realized dipolar $\text{XY}$ model when Rydberg atoms are arranged in a breathing kagome lattice. Using the infinite density matrix renormalization group, we numerically calculate the ground state's chiral order parameter, spin-spin correlations, Chern number, and entanglement spectrum. Our numerical results provide strong evidence for the chiral spin liquid phase. Furthermore, we identify a quantum phase transition from a Dirac spin liquid to a chiral spin liquid as the lattice geometry is tuned from the isotropic kagome to the breathing kagome lattice.

cond-mat.str-el↗

arXiv2024

Left-left-right-right magnetic order in spin-1/2 Kitaev-Heisenberg chain

In this work, we perform a combination of analytical and numerical studies on the phase diagram of the spin-1/2 Kitaev-Heisenberg chain in the region of negative Kitaev and positive Heisenberg couplings. Apart from the antiferromagnetic phase, we find a magnetically ordered phase with left-left-right-right order and a gapless phase with central charge value $c=1$, resolving the existing contradictory results in literature regarding this parameter region. In particular, the origin of the left-left-right-right order is clarified based on a perturbative Luttinger liquid analysis. The left-left-right-right phase is further shown to persist in the Kitaev-Heisenberg-Gamma model when a small nonzero Gamma interaction is introduced. Using a coupled-chain method, we also demonstrate that the one-dimensional (1D) left-left-right-right order gives a quasi-1D explanation for the 2D stripy order of the same model on the honeycomb lattice.

cond-mat.str-el↗

arXiv2024

Imaginary gap-closed points and dynamics in a class of dissipative systems

We investigate imaginary gap-closed (IGC) points and their associated dynamics in dissipative systems. In a general non-Hermitian model, we derive the equation governing the IGC points of the energy spectrum, establishing that these points are only determined by the Hermitian part of the Hamiltonian. Focusing on a class of one-dimensional dissipative chains, we explore quantum walks across different scenarios and various parameters, showing that IGC points induce a power-law decay scaling in bulk loss probability and trigger a boundary phenomenon referred to as "edge burst". This observation underscores the crucial role of IGC points under periodic boundary conditions (PBCs) in shaping quantum walk dynamics. Finally, we demonstrate that the damping matrices of these dissipative chains under PBCs possess Liouvillian gapless points, implying an algebraic convergence towards the steady state in long-time dynamics.

quant-ph↗

arXiv2023

Self-assembly of Colloids with Competing Interactions Confined in Spheres

At low temperatures, colloidal particles with short-range attractive and long-range repulsive interactions can form various periodic microphases in bulk.In this paper, we investigate the self-assembly behaviour of colloids with competing interactions under spherical confinement by conducting molecular dynamics simulations. We find that the cluster, mixture, cylindrical, perforated lamellar and lamellar structures can be obtained, but the details of the ordered structures are different from those in bulk systems. Interestingly, the system tends to form more perforated structures when confined in smaller spheres. The mechanism behind this phenomenon is the relationship between the energy of the ordered structures and the bending of the confinement wall, which is different from the mechanism in copolymer systems.

cond-mat.soft↗

arXiv2022

Emergent SU(2)$_1$ conformal symmetry in the spin-1/2 Kitaev-Gamma chain with a Dzyaloshinskii-Moriya interaction

We study the one-dimensional spin-1/2 Kitaev-Gamma model with a bond-dependent Dzyaloshinskii-Moriya (DM) interaction, which can be induced by an electric field applied in the third direction where the first and second directions refer to the two bond directions in the model. By a combination of field theory and symmetry analysis, an extended gapless phase with an emergent SU(2)$_1$ conformal symmetry is found in the phase diagram of the spin-1/2 Kitaev-Gamma-DM chain. The analytic predictions are in good agreements with numerical results obtained from density matrix renormalization group simulations.

cond-mat.str-el↗

arXiv2021

A Literature Review of Recent Graph Embedding Techniques for Biomedical Data

With the rapid development of biomedical software and hardware, a large amount of relational data interlinking genes, proteins, chemical components, drugs, diseases, and symptoms has been collected for modern biomedical research. Many graph-based learning methods have been proposed to analyze such type of data, giving a deeper insight into the topology and knowledge behind the biomedical data, which greatly benefit to both academic research and industrial application for human healthcare. However, the main difficulty is how to handle high dimensionality and sparsity of the biomedical graphs. Recently, graph embedding methods provide an effective and efficient way to address the above issues. It converts graph-based data into a low dimensional vector space where the graph structural properties and knowledge information are well preserved. In this survey, we conduct a literature review of recent developments and trends in applying graph embedding methods for biomedical data. We also introduce important applications and tasks in the biomedical domain as well as associated public biomedical datasets.

cs.AI↗