arXiv subjects

Shixiong Zhang

Publications and source records attributed to Shixiong Zhang.

15 recordsLinked to original sources

arXiv2026

Decomposing the Delta: What Do Models Actually Learn from Preference Pairs?

Preference optimization methods such as DPO and KTO are widely used for aligning language models, yet little is understood about what properties of preference data drive downstream reasoning gains. We ask: what aspects of a preference pair improve a reasoning model's performance on general reasoning tasks? We investigate two distinct notions of quality delta in preference data: generator-level delta, arising from the differences in capability between models that generate chosen and rejected reasoning traces, and sample-level delta, arising from differences in judged quality differences within an individual preference pair. To study generator-level delta, we vary the generator's scale and model family, and to study sample-level delta, we employ an LLM-as-a-judge to rate the quality of generated traces along multiple reasoning-quality dimensions. We find that increasing generator-level delta steadily improves performance on out-of-domain reasoning tasks and filtering data by sample-level delta can enable more data-efficient training. Our results suggest a twofold recipe for improving reasoning performance through preference optimization: maximize generator-level delta when constructing preference pairs and exploit sample-level delta to select the most informative training examples.

cs.CL↗

arXiv2025

LADSG: Label-Anonymized Distillation and Similar Gradient Substitution for Label Privacy in Vertical Federated Learning

Vertical Federated Learning (VFL) has emerged as a promising paradigm for collaborative model training across distributed feature spaces, which enables privacy-preserving learning without sharing raw data. However, recent studies have confirmed the feasibility of label inference attacks by internal adversaries. By strategically exploiting gradient vectors and semantic embeddings, attackers-through passive, active, or direct attacks-can accurately reconstruct private labels, leading to catastrophic data leakage. Existing defenses, which typically address isolated leakage vectors or are designed for specific types of attacks, remain vulnerable to emerging hybrid attacks that exploit multiple pathways simultaneously. To bridge this gap, we propose Label-Anonymized Defense with Substitution Gradient (LADSG), a unified and lightweight defense framework for VFL. LADSG first anonymizes true labels via soft distillation to reduce semantic exposure, then generates semantically-aligned substitute gradients to disrupt gradient-based leakage, and finally filters anomalous updates through gradient norm detection. It is scalable and compatible with standard VFL pipelines. Extensive experiments on six real-world datasets show that LADSG reduces the success rates of all three types of label inference attacks by 30-60% with minimal computational overhead, demonstrating its practical effectiveness.

cs.CR↗

arXiv2025

Double quantum dots with quenched charging energy in PbTe nanowires

We investigate double quantum dots defined by electrostatic gating in semiconductor PbTe nanowire devices. We perform transport measurements to obtain charge stability diagrams distinguished by negligible separation between paired triple points and by the spin degeneracy of all transport resonances at zero magnetic field. We show a fourfold splitting of high-bias stability diagram triangles in an applied magnetic field to illustrate the lifting of this spin degeneracy. We also identify patterns of narrow transport resonances in these high-bias triangles and discuss their possible physical origins. Our results represent a step towards the realization of PbTe-based spin qubits.

cond-mat.mes-hall↗

arXiv2025

Probing Stress and Magnetism at High Pressures with Two-Dimensional Quantum Sensors

Pressure serves as a fundamental tuning parameter capable of drastically modifying all properties of matter. The advent of diamond anvil cells (DACs) has enabled a compact and tabletop platform for generating extreme pressure conditions in laboratory settings. However, the limited spatial dimensions and ultrahigh pressures within these environments present significant challenges for conventional spectroscopy techniques. In this work, we integrate optical spin defects within a thin layer of two-dimensional (2D) materials directly into the high-pressure chamber, enabling an in situ quantum sensing platform for mapping local stress and magnetic environments up to 4~GPa. Compared to nitrogen-vacancy (NV) centers embedded in diamond anvils, our 2D sensors exhibit around three times stronger response to local stress and provide nanoscale proximity to the target sample in heterogeneous devices. We showcase the versatility of our approach by imaging both stress gradients within the high-pressure chamber and a pressure-driven magnetic phase transition in a room-temperature self-intercalated van der Waals ferromagnet, Cr$_{1+δ}$Te$_2$. Our work demonstrates an integrated quantum sensing device for high-pressure experiments, offering potential applications in probing pressure-induced phenomena such as superconductivity, magnetism, and mechanical deformation.

cond-mat.mes-hall↗

arXiv2024

LoRA-Whisper: Parameter-Efficient and Extensible Multilingual ASR

Recent years have witnessed significant progress in multilingual automatic speech recognition (ASR), driven by the emergence of end-to-end (E2E) models and the scaling of multilingual datasets. Despite that, two main challenges persist in multilingual ASR: language interference and the incorporation of new languages without degrading the performance of the existing ones. This paper proposes LoRA-Whisper, which incorporates LoRA matrix into Whisper for multilingual ASR, effectively mitigating language interference. Furthermore, by leveraging LoRA and the similarities between languages, we can achieve better performance on new languages while upholding consistent performance on original ones. Experiments on a real-world task across eight languages demonstrate that our proposed LoRA-Whisper yields a relative gain of 18.5% and 23.0% over the baseline system for multilingual ASR and language expansion respectively.

eess.AS↗

arXiv2023

SECap: Speech Emotion Captioning with Large Language Model

Speech emotions are crucial in human communication and are extensively used in fields like speech synthesis and natural language understanding. Most prior studies, such as speech emotion recognition, have categorized speech emotions into a fixed set of classes. Yet, emotions expressed in human speech are often complex, and categorizing them into predefined groups can be insufficient to adequately represent speech emotions. On the contrary, describing speech emotions directly by means of natural language may be a more effective approach. Regrettably, there are not many studies available that have focused on this direction. Therefore, this paper proposes a speech emotion captioning framework named SECap, aiming at effectively describing speech emotions using natural language. Owing to the impressive capabilities of large language models in language comprehension and text generation, SECap employs LLaMA as the text decoder to allow the production of coherent speech emotion captions. In addition, SECap leverages HuBERT as the audio encoder to extract general speech features and Q-Former as the Bridge-Net to provide LLaMA with emotion-related speech features. To accomplish this, Q-Former utilizes mutual information learning to disentangle emotion-related speech features and speech contents, while implementing contrastive learning to extract more emotion-related speech features. The results of objective and subjective evaluations demonstrate that: 1) the SECap framework outperforms the HTSAT-BART baseline in all objective evaluations; 2) SECap can generate high-quality speech emotion captions that attain performance on par with human annotators in subjective mean opinion score tests.

cs.SD↗

arXiv2023

Nanotube spin defects for omnidirectional magnetic field sensing

Optically addressable spin defects in three-dimensional (3D) crystals and two-dimensional (2D) van der Waals (vdW) materials are revolutionizing nanoscale quantum sensing. Spin defects in one-dimensional (1D) vdW nanotubes will provide unique opportunities due to their small sizes in two dimensions and absence of dangling bonds on side walls. However, optically detected magnetic resonance of localized spin defects in a nanotube has not been observed. Here, we report the observation of single spin color centers in boron nitride nanotubes (BNNTs) at room temperature. Our findings suggest that these BNNT spin defects possess a spin $S=1/2$ ground state without an intrinsic quantization axis, leading to orientation-independent magnetic field sensing. We harness this unique feature to observe anisotropic magnetization of a 2D magnet in magnetic fields along orthogonal directions, a challenge for conventional spin $S=1$ defects such as diamond nitrogen-vacancy centers. Additionally, we develop a method to deterministically transfer a BNNT onto a cantilever and use it to demonstrate scanning probe magnetometry. Further refinement of our approach will enable atomic scale quantum sensing of magnetic fields in any direction.

cond-mat.mes-hall↗

arXiv2023

Survey on Controlable Image Synthesis with Deep Learning

Image synthesis has attracted emerging research interests in academic and industry communities. Deep learning technologies especially the generative models greatly inspired controllable image synthesis approaches and applications, which aim to generate particular visual contents with latent prompts. In order to further investigate low-level controllable image synthesis problem which is crucial for fine image rendering and editing tasks, we present a survey of some recent works on 3D controllable image synthesis using deep learning. We first introduce the datasets and evaluation indicators for 3D controllable image synthesis. Then, we review the state-of-the-art research for geometrically controllable image synthesis in two aspects: 1) Viewpoint/pose-controllable image synthesis; 2) Structure/shape-controllable image synthesis. Furthermore, the photometrically controllable image synthesis approaches are also reviewed for 3D re-lighting researches. While the emphasis is on 3D controllable image synthesis algorithms, the related applications, products and resources are also briefly summarized for practitioners.

cs.CV↗

arXiv2021

MetricNet: Towards Improved Modeling For Non-Intrusive Speech Quality Assessment

The objective speech quality assessment is usually conducted by comparing received speech signal with its clean reference, while human beings are capable of evaluating the speech quality without any reference, such as in the mean opinion score (MOS) tests. Non-intrusive speech quality assessment has attracted much attention recently due to the lack of access to clean reference signals for objective evaluations in real scenarios. In this paper, we propose a novel non-intrusive speech quality measurement model, MetricNet, which leverages label distribution learning and joint speech reconstruction learning to achieve significantly improved performance compared to the existing non-intrusive speech quality measurement models. We demonstrate that the proposed approach yields promisingly high correlation to the intrusive objective evaluation of speech quality on clean, noisy and processed speech data.

eess.AS↗

arXiv2020

WPD++: An Improved Neural Beamformer for Simultaneous Speech Separation and Dereverberation

This paper aims at eliminating the interfering speakers' speech, additive noise, and reverberation from the noisy multi-talker speech mixture that benefits automatic speech recognition (ASR) backend. While the recently proposed Weighted Power minimization Distortionless response (WPD) beamformer can perform separation and dereverberation simultaneously, the noise cancellation component still has the potential to progress. We propose an improved neural WPD beamformer called "WPD++" by an enhanced beamforming module in the conventional WPD and a multi-objective loss function for the joint training. The beamforming module is improved by utilizing the spatio-temporal correlation. A multi-objective loss, including the complex spectra domain scale-invariant signal-to-noise ratio (C-Si-SNR) and the magnitude domain mean square error (Mag-MSE), is properly designed to make multiple constraints on the enhanced speech and the desired power of the dry clean signal. Joint training is conducted to optimize the complex-valued mask estimator and the WPD++ beamformer in an end-to-end way. The results show that the proposed WPD++ outperforms several state-of-the-art beamformers on the enhanced speech quality and word error rate (WER) of ASR.

eess.AS↗

arXiv2020

Review of Single-cell RNA-seq Data Clustering for Cell Type Identification and Characterization

In recent years, the advances in single-cell RNA-seq techniques have enabled us to perform large-scale transcriptomic profiling at single-cell resolution in a high-throughput manner. Unsupervised learning such as data clustering has become the central component to identify and characterize novel cell types and gene expression patterns. In this study, we review the existing single-cell RNA-seq data clustering methods with critical insights into the related advantages and limitations. In addition, we also review the upstream single-cell RNA-seq data processing techniques such as quality control, normalization, and dimension reduction. We conduct performance comparison experiments to evaluate several popular single-cell RNA-seq clustering approaches on two single-cell transcriptomic datasets.

q-bio.GN↗

arXiv2019

RKKY Interactions in Graphene Landau Levels

We study RKKY interactions for magnetic impurities on graphene in situations where the electronic spectrum is in the form of Landau levels. Two such situations are considered: non-uniformly strained graphene, and graphene in a real magnetic field. RKKY interactions are enhanced by the lowest Landau level, which is shown to form electron states binding with the spin impurities and add a strong non-perturbative contribution to pairwise impurity spin interactions when their separation $R$ no more than the magnetic length. Beyond this interactions are found to fall off as $1/R^3$ due to perturbative effects of the negative energy Landau levels. Based on these results, we develop simple mean-field theories for both systems, taking into account the fact that typically the density of states in the lowest Landau level is much smaller than the density of spin impurities. For the strain field case, we find that the system is formally ferrimagnetic, but with very small net moment due to the relatively low density of impurities binding electrons. The transition temperature is nevertheless enhanced by them. For real fields, the system forms a canted antiferromagnet if the field is not so strong as to pin the impurity spins along the field. The possibility that the system in this latter case supports a Kosterlitz-Thouless transition is discussed.

cond-mat.mes-hall↗

arXiv2017

Surface Magnetism in Topological Crystalline Insulators

We study topological crystalline insulators doped with magnetic impurities, in which ferromagnetism at the surface lowers the electronic energy by spontaneous breaking of a crystalline symmetry. The number of energetically equivalent ground states is sensitive to the crystalline symmetry of the surface, as well as the precise density of electrons at the surface. We show that for a SnTe model in the topological state, magnetic states can have twofold symmetry, sixfold symmetry, or eightfold degenerate minima. We compute spin stiffnesses within the model to demonstrate the stability of ferromagnetic states, and consider their ramifications for thermal disordering. Possible experimental consequences of the surface magnetism are discussed.

cond-mat.str-el↗

arXiv2017

Epitaxial thin films of pyrochlore iridate Bi_{2+x}Ir_{2-y}O_{7-delta}: structure, defects and transport properties

While pyrochlore iridate thin films are theoretically predicted to possess a variety of emergent topological properties, experimental verification of these predictions can be obstructed by the challenge in thin film growth. Here we report on the pulsed laser deposition and characterization of thin films of a representative pyrochlore compound Bi2Ir2O7. The films were epitaxially grown on yttria-stabilized zirconia substrates and have lattice constants that are a few percent larger than that of the bulk single crystals. The film composition shows a strong dependence on the oxygen partial pressure. Density-functional-theory calculations indicate the existence of Bi_Ir antisite defects, qualitatively consistent with the high Bi: Ir ratio found in the films. Both Ir and Bi have oxidation states that are lower than their nominal values, suggesting the existence of oxygen deficiency. The iridate thin films show a variety of intriguing transport characteristics, including multiple charge carriers, logarithmic dependence of resistance on temperature, antilocalization corrections to conductance due to spin-orbit interactions, and linear positive magnetoresistance.

cond-mat.mtrl-sci↗

arXiv2008

Frequency- and electric-field-dependent conductivity of single-walled carbon nanotube networks of varying density

We present measurements of the frequency and electric field dependent conductivity of single walled carbon nanotube(SWCNT) networks of various densities. The ac conductivity as a function of frequency is consistent with the extended pair approximation model and increases with frequency above an onset frequency $ω_0$ which varies over seven decades with a range of film thickness from sub-monolayer to 200 nm. The nonlinear electric field-dependent DC conductivity shows strong dependence on film thickness as well. Measurement of the electric field dependence of the resistance R(E) allows for the determination of a length scale $L_{E}$ possibly characterizing the distance between tube contacts, which is found to systematically decrease with increasing film thickness. The onset frequency $ω_0$ of ac conductivity and the length scale $L_{E}$ of SWCNT networks are found to be correlated, and a physically reasonable empirical formula relating them has been proposed. Such studies will help the understanding of transport properties and benefit the applications of this material system.

cond-mat.mtrl-sci↗