arXiv subjects

Zhongyi Zhang

Publications and source records attributed to Zhongyi Zhang.

At least 37 records · Page 2Linked to original sources

arXiv2025

Fermi-surface diagnosis for topological superconductivity with $s$-wave-like pairing symmetries

Theoretical prediction of topological superconductivity is key to their discovery. Recently, it is proved that in 199 out of 230 space groups, topological superconductivity coexists with an $s$-wave-like pairing symmetry, raising the hope of finding more candidates for this exotic phase. However, a comprehensive and efficient method for diagnosing topological superconductivity in realistic materials remains elusive. Here, we derive Fermi-surface formulas for gapped and gapless topological phases of time-reversal symmetric superconductors with $s$-wave-like pairing symmetries in all layer and space groups, applicable to thin-film and bulk materials. Our diagnosis uses only the sign of the pairing and the Fermi velocity at several Fermi points, and yields complete (partial) diagnosis for gapped topological superconductivity in 159 (40) out of the 199 space groups. This provides a fundamental basis for the first-principles prediction of new topological superconductors.

cond-mat.supr-con↗

arXiv2025

LearnAFE: Circuit-Algorithm Co-design Framework for Learnable Audio Analog Front-End

This paper presents a circuit-algorithm co-design framework for learnable analog front-end (AFE) in audio signal classification. Designing AFE and backend classifiers separately is a common practice but non-ideal, as shown in this paper. Instead, this paper proposes a joint optimization of the backend classifier with the AFE's transfer function to achieve system-level optimum. More specifically, the transfer function parameters of an analog bandpass filter (BPF) bank are tuned in a signal-to-noise ratio (SNR)-aware training loop for the classifier. Using a co-design loss function LBPF, this work shows superior optimization of both the filter bank and the classifier. Implemented in open-source SKY130 130nm CMOS process, the optimized design achieved 90.5%-94.2% accuracy for 10-keyword classification task across a wide range of input signal SNR from 5 dB to 20 dB, with only 22k classifier parameters. Compared to conventional approach, the proposed audio AFE achieves 8.7% and 12.9% reduction in power and capacitor area respectively.

eess.AS↗

arXiv2025

UICopilot: Automating UI Synthesis via Hierarchical Code Generation from Webpage Designs

Automating the synthesis of User Interfaces (UIs) plays a crucial role in enhancing productivity and accelerating the development lifecycle, reducing both development time and manual effort. Recently, the rapid development of Multimodal Large Language Models (MLLMs) has made it possible to generate front-end Hypertext Markup Language (HTML) code directly from webpage designs. However, real-world webpages encompass not only a diverse array of HTML tags but also complex stylesheets, resulting in significantly lengthy code. The lengthy code poses challenges for the performance and efficiency of MLLMs, especially in capturing the structural information of UI designs. To address these challenges, this paper proposes UICopilot, a novel approach to automating UI synthesis via hierarchical code generation from webpage designs. The core idea of UICopilot is to decompose the generation process into two stages: first, generating the coarse-grained HTML hierarchical structure, followed by the generation of fine-grained code. To validate the effectiveness of UICopilot, we conduct experiments on a real-world dataset, i.e., WebCode2M. Experimental results demonstrate that UICopilot significantly outperforms existing baselines in both automatic evaluation metrics and human evaluations. Specifically, statistical analysis reveals that the majority of human annotators prefer the webpages generated by UICopilot over those produced by GPT-4V.

cs.SE↗

arXiv2025

Judge Anything: MLLM as a Judge Across Any Modality

Evaluating generative foundation models on open-ended multimodal understanding (MMU) and generation (MMG) tasks across diverse modalities (e.g., images, audio, video) poses significant challenges due to the complexity of cross-modal interactions. To this end, the idea of utilizing Multimodal LLMs (MLLMs) as automated judges has emerged, with encouraging results in assessing vision-language understanding tasks. Moving further, this paper extends MLLM-as-a-Judge across modalities to a unified manner by introducing two benchmarks, TaskAnything and JudgeAnything, to respectively evaluate the overall performance and judging capabilities of MLLMs across any-to-any modality tasks. Specifically, TaskAnything evaluates the MMU and MMG capabilities across 15 any-to-any modality categories, employing 1,500 queries curated from well-established benchmarks. Furthermore, JudgeAnything evaluates the judging capabilities of 5 advanced (e.g., GPT-4o and Gemini-2.0-Flash) from the perspectives of Pair Comparison and Score Evaluation, providing a standardized testbed that incorporates human judgments and detailed rubrics. Our extensive experiments reveal that while these MLLMs show promise in assessing MMU (i.e., achieving an average of 66.55% in Pair Comparison setting and 42.79% in Score Evaluation setting), they encounter significant challenges with MMG tasks (i.e., averaging only 53.37% in Pair Comparison setting and 30.05% in Score Evaluation setting), exposing cross-modality biases and hallucination issues. To address this, we present OmniArena, an automated platform for evaluating omni-models and multimodal reward models. Our work highlights the need for fairer evaluation protocols and stronger alignment with human preferences. The source code and dataset are publicly available at: https://urrealhero.github.io/judgeanythingweb/.

cs.CL↗

arXiv2025

An FPT Constant-Factor Approximation Algorithm for Correlation Clustering

The Correlation Clustering problem is one of the most extensively studied clustering formulations due to its wide applications in machine learning, data mining, computational biology and other areas. We consider the Correlation Clustering problem on general graphs, where given an undirected graph (maybe not complete) with each edge being labeled with $\langle + \rangle$ or $\langle - \rangle$, the goal is to partition the vertices into clusters to minimize the number of the disagreements with the edge labeling: the number of $\langle - \rangle$ edges within clusters plus the number of $\langle + \rangle$ edges between clusters. Hereby, a $\langle + \rangle$ (or $\langle - \rangle$) edge means that its end-vertices are similar (or dissimilar) and should belong to the same cluster (or different clusters), and ``missing'' edges are used to denote that we do not know if those end-vertices are similar or dissimilar. Correlation Clustering is NP-hard, even if the input graph is complete, and Unique-Games hard to obtain polynomial-time constant approximation on general graphs. With a complete graph as input, Correlation Clustering admits a $(1.994+\varepsilon )$-approximation. We investigate Correlation Clustering on general graphs from the perspective of parameterized approximability. We set the parameter $k$ as the minimum number of vertices whose removal results in a complete graph, and obtain the first FPT constant-factor approximation for Correlation Clustering on general graphs which runs in $2^{O(k^3)} \cdot \textrm{poly}(n)$ time.

cs.DS↗

arXiv2025

Universal Anomaly Detection at the LHC: Transforming Optimal Classifiers and the DDD Method

In this work, we present a novel approach to transform supervised classifiers into effective unsupervised anomaly detectors. The method we have developed, termed Discriminatory Detection of Distortions (DDD), enhances anomaly detection by training a discriminator model on both original and artificially modified datasets. We conducted a comprehensive evaluation of our models on the Dark Machines Anomaly Score Challenge channels and a search for 4-top quark events, demonstrating the effectiveness of our approach across various final states and beyond the Standard Model scenarios. We compare the performance of the DDD method with the Deep Robust One-Class Classification method (DROCC), which incorporates signals in the training process, and the Deep Support Vector Data Description (DeepSVDD) method, a well-established and well-performing method for anomaly detection. Results show that the effectiveness of each model varies by signal and channel, with DDD proving to be a very effective anomaly detector. We recommend the combined use of DeepSVDD and DDD for purely unsupervised applications, with the addition of flow models for improved performance when resources allow. Findings suggest that network architectures that excel in supervised contexts, such as the particle transformer with standard model interactions, also perform well as unsupervised anomaly detectors. We also show that with these methods, it is likely possible to recognize 4-top quark production as an anomaly without prior knowledge of the process. We argue that the Large Hadron Collider community can transform supervised classifiers into anomaly detectors to uncover potential new physical phenomena in each search.

hep-ph↗

arXiv2025

Superconducting Order Parameters in Spin Space Groups: Methodology and Application

We employ a systematic approach to construct superconducting order parameters based on the spin space group. Compared to magnetic space groups where spatial and spin rotation of elements are completely locked, the superconducting channels and the forms of basis functions in spin space groups can be different. Furthermore, we utilize the obtained basis functions to construct the Ginzburg-Landau free energy and discuss the possible superconducting ground states as well as the interplay between superconducting states and magnetic orders. By virtue of those results, we discuss the application of our theory to realistic systems. Our work provides a rigorous description of superconducting gap functions in the case of spin space group, and enables further study of the interplay between superconductivity and magnetism.

cond-mat.mes-hall↗

arXiv2025

Double Majorana Vortex Flat Bands in the Topological Dirac Superconductor

Vortex lines, known as topological defects, are cable of trapping Majorana modes in superconducting topological materials. Previous studies have primarily focused on topological bands with conventional s-wave pairing. However, topological Dirac semimetals exhibiting a unique orbital texture can favor unconventional pairing when electronic correlations are significant. The topology of vortices in these systems has yet to be explored. In this work, we investigate the vortex bound states in superconducting Dirac semimetals, with a particular focus on the orbital-singlet unconventional pairing, which generates higher-order Majorana hinge modes. Remarkably, we identify robust double Majorana vortex flat bands at zero energy. In type-I Dirac semimetals, these Majorana flat bands are located between the projections of two superconduting Dirac points. In contrast, in type-II Dirac semimetals, they extend across the entire 1D Brillouin zone. These double flat bands arise from a nontrivial $\mathbb{Z}_2$ topology defined by an effective particle-hole symmetry and are protected by the four-fold rotational symmetry. Additionally, we observe that moving the vortex line close to a hinge can trivialize the higher-order Majorana arc on the hinge, leaving a single Majorana mode at the vortex core due to the hybridization of Majorana modes. Finally, we discuss the potential experimental implications for correlated Dirac semimetals, such as electron-doped iron-based superconductors.

cond-mat.supr-con↗

arXiv2024

Topological Superconductivity from Unconventional Band Degeneracy with Conventional Pairing

We present a new scheme for Majorana modes in systems with nonsymmporhic-symmetry-protected band degeneracy. We reveal that when the gapless fermionic excitations are encoded with conventional superconductivity and magnetism, which can be intrinsic or induced by proximity effect, topological superconductivity and Majorana modes can be obtained. We illustrate this outcome in a system which respects the space group P4/nmm and features a fourfold-degenerate fermionic mode at (π, π) in the Brillouin zone. We show that in the presence of conventional superconductivity, different types of topological superconductivity, i.e. first-order and second-order topological superconductivity, with coexisting fragile Wannier obstruction in the latter case, can be generated in accordance with the different types of magnetic orders; Majorana modes are shown to exist on the boundary, at the corner and in the vortices. To further demonstrate the effectiveness of our approach, another example related to the space group P4/ncc based on this scheme is also provided. Our study offers insights into constructing topological superconductors based on bulk energy bands and conventional superconductivity, and helps to find new material candidates and design new platforms for realizing Majorana modes.

cond-mat.supr-con↗

arXiv2024

Chiral kagome superconductivity modulations with residual Fermi arcs in KV3Sb5 and CsV3Sb5

Superconductivity involving finite momentum pairing can lead to spatial gap and pair density modulations, as well as Bogoliubov Fermi states within the superconducting gap. However, the experimental realization of their intertwined relations has been challenging. Here, we detect chiral kagome superconductivity modulations with residual Fermi arcs in KV3Sb5 and CsV3Sb5 by normal and Josephson scanning tunneling microscopy down to 30mK with resolved electronic energy difference at microelectronvolt level. We observe a U-shaped superconducting gap with flat residual in-gap states. This gap exhibits chiral 2 by 2 spatial modulations with magnetic field tunable chirality, which align with the chiral 2 by 2 pair density modulations observed through Josephson tunneling. These findings demonstrate a chiral pair density wave (PDW) that breaks time-reversal symmetry. Quasiparticle interference imaging of the in-gap zero-energy states reveals segmented arcs, with high-temperature data linking them to parts of the reconstructed V d-orbital states within the charge order. The detected residual Fermi arcs can be explained by the partial suppression of these d-orbital states through an interorbital 2 by 2 PDW and thus serve as candidate Bogoliubov Fermi states. Additionally, we differentiate the observed PDW order from impurity-induced gap modulations. Our observations not only uncover a chiral PDW order with orbital-selectivity, but also illuminate the fundamental space-momentum correspondence inherent in finite momentum paired superconductivity.

cond-mat.supr-con↗

arXiv2023

Multi--lepton Signatures from $U(1)_{L_μ-L_τ}$ at the LHC

The $U(1)_{L_μ-L_τ}$ extended Standard Model (SM) is anomaly free, and contains a massive $Z^\prime$ boson. The associated Higgs, which generates the $Z'$'s mass via spontaneous symmetry breaking (SSB) can mix with the SM Higgs. The new parameters relating to extra Higgs cannot be probed at the LHC with final states containing no more than $4$ leptons. Therefore, we use signatures with at least $6$ leptons to probe the parameter space through LHC experiments. Since SM predicts a lower cross section for a final state with at least $6$ leptons than is currently visible at the LHC, the background is negligible, thereby making this channel an extremely sensitive probe. We find that in a limited region of the parameter space this channel can strongly constrain the associated $U(1)_{L_μ-L_τ}$ coupling constant, even more so than for a $4$ lepton or fewer final state.

hep-ph↗

arXiv2023

New energy spectra in neutrino and photon detectors to reveal hidden dark matter signals

Neutral particles capable of travelling cosmic distances from a source to detectors on Earth are limited to photons and neutrinos. Examination of the Dark Matter annihilation/decay spectra for these particles reveals the presence of continuum spectra (e.g. due to fragmentation and W or Z decay) and peaks (due to direct annihilations/decays). However, when one explores extensions of the Standard Model (BSM), unexplored spectra emerge that differ significantly from those of the Standard Model (SM) for both neutrinos and photons. In this paper, we argue for the inclusion of important spectra that include peaks as well as previously largely unexplored entities such as boxes and combinations of box, peak and continuum decay spectra.

hep-ph↗

arXiv2023

Pairing Symmetries of Unconventional High Temperature Superconductivity in a Zinc-Blende Structure

We classify the pairing symmetries of three-dimensional superconductivity in the zinc-blende structure which can support an electronic environment to host unconventional high temperature superconductivity, and calculate the pairing symmetry in the presence of strong electron-electron correlation by the slave boson mean-field approach. We find that the $d_{2z^2-x^2-y^2} \pm id_{x^2-y^2}$ pairing state, a three dimensional analogy of the $d\pm id$ pairing in a two dimensional square lattice, is ubiquitously favored near half filling upon hole doping in both single-orbital and three-orbital models. However, unlike the two dimensional counterpart, the Bogoliubov quasiparticle spectrum of the three dimensional state upholds the full $T_d$ point group symmetry and encompasses point nodes along certain high symmetric lines.

cond-mat.supr-con↗

arXiv2023

On degenerate fractional Schrödinger-Kirchhoff-Poisson equations with upper critical nonlinearity and electromagnetic fields

We investigate the degenerate fractional Schrödinger-Kirchhoff-Poisson equation in $\mathbb{R}^3$ with critical nonlinearity and electromagnetic fields $\varepsilon^{2s} M([u]_{s,A}^2)(-Δ)_{A}^su + V(x)u + ϕu = k(x)|u|^{r-2}u + \left(\mathcal{I}_μ*|u|^{2_s^\sharp}\right)|u|^{2_s^\sharp-2}u$ and $(-Δ)^tϕ= u^2,$ where $\varepsilon > 0$ is a parameter, $3/4<s<1$, $0 < t < 1$, $V$ is an electric potential satisfying some suitable assumptions, $0 < k_\ast \leq k(x) \leq k^\ast$, $\mathcal{I}_μ(x) = |x|^{3-μ}$ with $0<μ<3$, $2_s^\sharp =\frac{3+μ}{3-2s},$ and $2 < r < 2_s^\sharp$. With the help of the concentration compactness principle and variational methods, together with some fine analytical tools, we establish the existence and multiplicity of solutions for the above problem when $\varepsilon \rightarrow 0$ in the degenerate cases, i.e. when the Kirchhoff term $M$ vanishes at zero.

math.AP↗

arXiv2023

M2GNN: Metapath and Multi-interest Aggregated Graph Neural Network for Tag-based Cross-domain Recommendation

Cross-domain recommendation (CDR) is an effective way to alleviate the data sparsity problem. Content-based CDR is one of the most promising branches since most kinds of products can be described by a piece of text, especially when cold-start users or items have few interactions. However, two vital issues are still under-explored: (1) From the content modeling perspective, sufficient long-text descriptions are usually scarce in a real recommender system, more often the light-weight textual features, such as a few keywords or tags, are more accessible, which is improperly modeled by existing methods. (2) From the CDR perspective, not all inter-domain interests are helpful to infer intra-domain interests. Caused by domain-specific features, there are part of signals benefiting for recommendation in the source domain but harmful for that in the target domain. Therefore, how to distill useful interests is crucial. To tackle the above two problems, we propose a metapath and multi-interest aggregated graph neural network (M2GNN). Specifically, to model the tag-based contents, we construct a heterogeneous information network to hold the semantic relatedness between users, items, and tags in all domains. The metapath schema is predefined according to domain-specific knowledge, with one metapath for one domain. User representations are learned by GNN with a hierarchical aggregation framework, where the intra-metapath aggregation firstly filters out trivial tags and the inter-metapath aggregation further filters out useless interests. Offline experiments and online A/B tests demonstrate that M2GNN achieves significant improvements over the state-of-the-art methods and current industrial recommender system in Dianping, respectively. Further analysis shows that M2GNN offers an interpretable recommendation.

cs.IR↗

arXiv2022

Non-Standard Neutrino Spectra From Annihilating Neutralino Dark Matter

Neutrino telescope experiments are rapidly becoming more competitive in indirect detection searches for dark matter. Neutrino signals arising from dark matter annihilations are typically assumed to originate from the hadronisation and decay of Standard Model particles. Here we showcase a supersymmetric model, the BLSSMIS, that can simultaneously obey current experimental limits while still providing a potentially observable non-standard neutrino spectrum from dark matter annihilation.

hep-ph↗

arXiv2022

Fully Automated Deep Learning-enabled Detection for Hepatic Steatosis on Computed Tomography: A Multicenter International Validation Study

Despite high global prevalence of hepatic steatosis, no automated diagnostics demonstrated generalizability in detecting steatosis on multiple international datasets. Traditionally, hepatic steatosis detection relies on clinicians selecting the region of interest (ROI) on computed tomography (CT) to measure liver attenuation. ROI selection demands time and expertise, and therefore is not routinely performed in populations. To automate the process, we validated an existing artificial intelligence (AI) system for 3D liver segmentation and used it to purpose a novel method: AI-ROI, which could automatically select the ROI for attenuation measurements. AI segmentation and AI-ROI method were evaluated on 1,014 non-contrast enhanced chest CT images from eight international datasets: LIDC-IDRI, NSCLC-Lung1, RIDER, VESSEL12, RICORD-1A, RICORD-1B, COVID-19-Italy, and COVID-19-China. AI segmentation achieved a mean dice coefficient of 0.957. Attenuations measured by AI-ROI showed no significant differences (p = 0.545) and a reduction of 71% time compared to expert measurements. The area under the curve (AUC) of the steatosis classification of AI-ROI is 0.921 (95% CI: 0.883 - 0.959). If performed as a routine screening method, our AI protocol could potentially allow early non-invasive, non-pharmacological preventative interventions for hepatic steatosis. 1,014 expert-annotated liver segmentations of patients with hepatic steatosis annotations can be downloaded here: https://drive.google.com/drive/folders/1-g_zJeAaZXYXGqL1OeF6pUjr6KB0igJX.

eess.IV↗

arXiv2022

Diversified Ruderman-Kittel-Kasuya-Yosida Interactions in a Nonsymmorphic Crystal

We show that there are diversified Ruderman-Kittel-Kasuya-Yosida (RKKY) interactions between magnetic impurities, mediated by itinerant electrons, in a centrosymmetric crystal respecting a nonsymmorphic space group. We take the $P4/nmm$ space group as an example. We demonstrate that the different type of interactions, including the Heisenberg-type, the Dzyaloshinskii-Moriya (DM)-type, the Ising-type and the anisotropic interactions, can appear in accordance with the positions of the impurities in the real space. Their strengths strongly depend on the location of the itinerant electrons in the reciprocal space. The diversity stems from the position-dependent site groups and the momentum-dependent electronic structures guaranteed by the nonsymmorphic symmetries. Our study unveils the role of the nonsymmorphic symmetries in affecting magnetism, and suggests that the nonsymmorphic crystals can be promising platforms to design magnetic interactions.

cond-mat.mes-hall↗