arXiv subjects

Teng Zhang

Publications and source records attributed to Teng Zhang.

At least 109 records · Page 6Linked to original sources

arXiv2025

Cyclic-Shift Sparse Kronecker Tensor Classifier for Signal-Region Detection in Neuroimaging

This study proposes a cyclic-shift logistic sparse Kronecker product decomposition (SKPD) model for high-dimensional tensor data, enhancing the SKPD framework with a cyclic-shift mechanism for binary classification. The method enables interpretable and scalable analysis of brain MRI data, detecting disease-relevant regions through a structured low-rank factorization. By incorporating a second spatially shifted view of the data, the cyclic-shift logistic SKPD improves robustness to misalignment across subjects, a common challenge in neuroimaging. We provide asymptotic consistency guarantees under a restricted isometry condition adapted to logistic loss. Simulations confirm the model's ability to recover spatial signals under noise and identify optimal patch sizes for factor decomposition. Application to OASIS-1 and ADNI-1 datasets demonstrates that the model achieves strong classification accuracy and localizes estimated coefficients in clinically relevant brain regions, such as the hippocampus. A data-driven slice selection strategy further improves interpretability in 2D projections. The proposed framework offers a principled, interpretable, and computationally efficient tool for neuroimaging-based disease diagnosis, with potential extensions to multi-class settings and more complex transformations.

stat.ME↗

arXiv2025

Bias-preserving and error-detectable entangling operations in a superconducting dual-rail system

For useful quantum computation, error-corrected machines are required that can dramatically reduce the inevitable errors experienced by physical qubits. While significant progress has been made in approaching and exceeding the surface-code threshold in superconducting platforms, large gains in the logical error rate with increasing system size remain out of reach. This is due both to the large number of required physical qubits and the need to operate far below threshold. Importantly, by exploiting the biases and structure of the physical errors, this threshold can be raised. Erasure qubits achieve this by detecting certain errors at the hardware level. Dual-rail qubits encoded in superconducting cavities are a promising erasure qubit wherein the dominant error, photon loss, can be detected and converted to an erasure. In these approaches, the complete set of operations, including two qubit gates, must be high performance and preserve as much of the desirable hierarchy or bias in the errors as possible. Here, we design and realize a novel two-qubit gate for dual-rail erasure qubits based on superconducting microwave cavities. The gate is high-speed ($\sim$500 ns duration), and yields a residual gate infidelity after error detection below 0.1%. Moreover, we experimentally demonstrate that this gate largely preserves the favorable error structure of idling dual-rail qubits, making it ideal for error correction. We measure low erasure rates of $\sim$0.5% per gate, as well as low and asymmetric dephasing errors that occur at least three times more frequently on control qubits compared to target qubits. Bit-flip errors are practically nonexistent, bounded at the few parts per million level. This error asymmetry has not been well explored but is extremely useful in quantum error correction and flag-qubit contexts, where it can create a faster path to effective error-corrected systems.

quant-ph↗

arXiv2025

FHBench: Towards Efficient and Personalized Federated Learning for Multimodal Healthcare

Federated Learning (FL) has emerged as an effective solution for multi-institutional collaborations without sharing patient data, offering a range of methods tailored for diverse applications. However, real-world medical datasets are often multimodal, and computational resources are limited, posing significant challenges for existing FL approaches. Recognizing these limitations, we developed the Federated Healthcare Benchmark(FHBench), a benchmark specifically designed from datasets derived from real-world healthcare applications. FHBench encompasses critical diagnostic tasks across domains such as the nervous, cardiovascular, and respiratory systems and general pathology, providing comprehensive support for multimodal healthcare evaluations and filling a significant gap in existing benchmarks. Building on FHBench, we introduced Efficient Personalized Federated Learning with Adaptive LoRA(EPFL), a personalized FL framework that demonstrates superior efficiency and effectiveness across various healthcare modalities. Our results highlight the robustness of FHBench as a benchmarking tool and the potential of EPFL as an innovative approach to advancing healthcare-focused FL, addressing key limitations of existing methods.

cs.LG↗

arXiv2025

Controlling Droplets at the Tips of Fibers

Many complex wetting behaviors of fibrous materials are rooted in the behaviors of individual droplets attached to pairs of fibers. Here, we study the splitting of a droplet held between the tips of two cylindrical fibers. We discover a sharp transition between two post-rupture states, navigated by changing the angle between the rods, in agreement with our bifurcation analysis. Depinning of the bridge contact line can lead to a much larger asymmetry between the volume of liquid left on each rod. This second scenario enables the near-complete transfer of an aqueous glycerol droplet between two identical vinylpolysiloxane fibers. We leverage this response in a device that uses a ruck to pass a droplet along a train of fibers, a proof-of-concept for the geometric control of droplets on deformable, architected surfaces.

physics.flu-dyn↗

arXiv2025

Appearance of Multiple Spectral Gaps in Voltage-Biased Josephson Junctions Without Floquet Hybridization

A time-periodic drive enables the engineering of non-equilibrium quantum systems by hybridizing Floquet sidebands. We investigated DC voltage-biased planar Josephson junctions built upon epitaxial Al/InAs heterostructures in which the intrinsic AC Josephson effect is theoretically expected to provide a time-periodic drive leading to Floquet hybridization. Tunneling spectroscopy is performed using probes positioned at the ends of the junction to study the evolution of the local density of states. With applied drive, we observe multiple coherence peaks which are studied as a function of DC voltage bias and in-plane magnetic field. Our analysis suggests that these spectral gaps arise from a direct mesoscopic coupling between the tunneling probe and the superconducting leads rather than from a Floquet-driven gap opening. Our numerical simulations indicate that an increase in the ratio of junction width to coherence length will enhance the contribution of Floquet hybridization. This work lays a foundation for the exploration of Floquet physics utilizing voltage-biased hybrid superconductor-semiconductor Josephson junctions and provides means for distinguishing direct couplings from genuine Floquet effects.

cond-mat.mes-hall↗

arXiv2025

Frequency-dependent squeezing for gravitational-wave detection through quantum teleportation

Ground-based interferometric gravitational wave detectors are highly precise sensors for weak forces, limited in sensitivity across their detection band by quantum fluctuations of light. Current and future instruments address this limitation by injecting frequency-dependent squeezed vacuum into the detection port, utilizing narrow-band, low-loss optical cavities for optimal rotation of the squeezing ellipse at each signal frequency. This study introduces a novel scheme employing the principles of quantum teleportation and entangled states of light. It allows achieving broadband suppression of quantum noise in detuned signal recycled-Fabry-Perot--Michelson interferometers, which is the baseline design of the low-frequency detector within the Einstein Telescope xylophone detector, without requiring additional filter cavities or modifications to the core optics of the main interferometer.

quant-ph↗

arXiv2025

Ten Challenging Problems in Federated Foundation Models

Federated Foundation Models (FedFMs) represent a distributed learning paradigm that fuses general competences of foundation models as well as privacy-preserving capabilities of federated learning. This combination allows the large foundation models and the small local domain models at the remote clients to learn from each other in a teacher-student learning setting. This paper provides a comprehensive summary of the ten challenging problems inherent in FedFMs, encompassing foundational theory, utilization of private data, continual learning, unlearning, Non-IID and graph data, bidirectional knowledge transfer, incentive mechanism design, game mechanism design, model watermarking, and efficiency. The ten challenging problems manifest in five pivotal aspects: ``Foundational Theory," which aims to establish a coherent and unifying theoretical framework for FedFMs. ``Data," addressing the difficulties in leveraging domain-specific knowledge from private data while maintaining privacy; ``Heterogeneity," examining variations in data, model, and computational resources across clients; ``Security and Privacy," focusing on defenses against malicious attacks and model theft; and ``Efficiency," highlighting the need for improvements in training, communication, and parameter efficiency. For each problem, we offer a clear mathematical definition on the objective function, analyze existing methods, and discuss the key challenges and potential solutions. This in-depth exploration aims to advance the theoretical foundations of FedFMs, guide practical implementations, and inspire future research to overcome these obstacles, thereby enabling the robust, efficient, and privacy-preserving FedFMs in various real-world applications.

cs.LG↗

arXiv2025

Several combinatorial results generalized from one large subset of semigroups to infinitely many

In 2015, Phulara established a generalization of the famous central set theorem by an original idea. Roughly speaking, this idea extends a combinatorial result from one large subset of the given semigroup to countably many. In this paper, we apply this idea to other combinatorial results to obtain corresponding generalizations, and do some further investigation. Moreover, we find that Phulara's generalization can be generalized further that can deal with uncountably many C-sets.

math.CO↗

arXiv2025

Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge

Multi-class segmentation of the aorta in computed tomography angiography (CTA) scans is essential for diagnosing and planning complex endovascular treatments for patients with aortic dissections. However, existing methods reduce aortic segmentation to a binary problem, limiting their ability to measure diameters across different branches and zones. Furthermore, no open-source dataset is currently available to support the development of multi-class aortic segmentation methods. To address this gap, we organized the AortaSeg24 MICCAI Challenge, introducing the first dataset of 100 CTA volumes annotated for 23 clinically relevant aortic branches and zones. This dataset was designed to facilitate both model development and validation. The challenge attracted 121 teams worldwide, with participants leveraging state-of-the-art frameworks such as nnU-Net and exploring novel techniques, including cascaded models, data augmentation strategies, and custom loss functions. We evaluated the submitted algorithms using the Dice Similarity Coefficient (DSC) and Normalized Surface Distance (NSD), highlighting the approaches adopted by the top five performing teams. This paper presents the challenge design, dataset details, evaluation metrics, and an in-depth analysis of the top-performing algorithms. The annotated dataset, evaluation code, and implementations of the leading methods are publicly available to support further research. All resources can be accessed at https://aortaseg24.grand-challenge.org.

eess.IV↗

arXiv2025

Outlier Synthesis via Hamiltonian Monte Carlo for Out-of-Distribution Detection

Out-of-distribution (OOD) detection is crucial for developing trustworthy and reliable machine learning systems. Recent advances in training with auxiliary OOD data demonstrate efficacy in enhancing detection capabilities. Nonetheless, these methods heavily rely on acquiring a large pool of high-quality natural outliers. Some prior methods try to alleviate this problem by synthesizing virtual outliers but suffer from either poor quality or high cost due to the monotonous sampling strategy and the heavy-parameterized generative models. In this paper, we overcome all these problems by proposing the Hamiltonian Monte Carlo Outlier Synthesis (HamOS) framework, which views the synthesis process as sampling from Markov chains. Based solely on the in-distribution data, the Markov chains can extensively traverse the feature space and generate diverse and representative outliers, hence exposing the model to miscellaneous potential OOD scenarios. The Hamiltonian Monte Carlo with sampling acceptance rate almost close to 1 also makes our framework enjoy great efficiency. By empirically competing with SOTA baselines on both standard and large-scale benchmarks, we verify the efficacy and efficiency of our proposed HamOS.

cs.LG↗

arXiv2025

On partition and almost disjoint properties of combinatorial notions

It is known that there are many notions of largeness in a semigroup that own rich combinatorial properties. In this paper, we focus on partition and almost disjoint properties of these notions. One of the most remarkable results with respect to this topic is that in an infinite very weakly cancellative semigroup of size κ, every central set can be split into κdisjoint central subsets. Moreover, if κcontains λalmost disjoint subsets, then every central set contains a family of λalmost disjoint central subsets. And many other combinatorial notions are found successively to have analogous properties, among these are thick sets, piecewise syndetic sets, J-sets and C-sets. In this paper, we mainly study four other notions: IP sets, combinatorially rich sets, Cp-sets and PP-rich sets. Where the latter two are known in (N, +), related to the polynomial extension of the central sets theorem. We lift them up to commutative cancellative semigroups and obtain an uncountable version of the polynomial extension of the central sets theorem incidentally. And we finally find that the infinite partition and almost disjoint properties hold for Cp-sets in commutative cancellative semigroups and for other three notions in (N, +).

math.CO↗

arXiv2025

Zaptos: Towards Optimal Blockchain Latency

End-to-end blockchain latency has become a critical topic of interest in both academia and industry. However, while modern blockchain systems process transactions through multiple stages, most research has primarily focused on optimizing the latency of the Byzantine Fault Tolerance consensus component. In this work, we identify key sources of latency in blockchain systems and introduce Zaptos, a parallel pipelined architecture designed to minimize end-to-end latency while maintaining the high-throughput of pipelined blockchains. We implemented Zaptos and evaluated it against the pipelined architecture of the Aptos blockchain in a geo-distributed environment. Our evaluation demonstrates a 25\% latency reduction under low load and over 40\% reduction under high load. Notably, Zaptos achieves a throughput of 20,000 transactions per second with sub-second latency, surpassing previously reported blockchain throughput, with sub-second latency, by an order of magnitude.

cs.DC↗

arXiv2024

Merging L-shaped resonator with Michelson configuration for kilohertz gravitational-wave detection

Detection of gravitational waves in kilohertz frequency range is crucial for understanding the physical processes of binary neutron star mergers. In Ref. [Phys. Rev. X {\bf 13}, 021019 (2023)], a new interferometric configuration has been proposed, employing an L-shaped optical resonant cavity as arm cavity. This alteration enhances the detector's response to kHz signals. However, the departure from conventional Michelson configuration necessitates a redesign of its sensing and control scheme, which is currently under study. In this article, we propose replacing linear arm cavities in the conventional Michelson by the L-shaped resonator. This hybrid configuration features an enhanced response at kHz while retaining the same sensing and control scheme as the Michelson setup. At the conceptual level, it exhibits higher sensitivity in the 2-4 kHz range compared to existing configurations.

gr-qc↗

arXiv2024

Fréchet Sufficient Dimension Reduction for Metric Space-Valued Data via Distance Covariance

We propose a novel Fréchet sufficient dimension reduction (SDR) method based on kernel distance covariance, tailored for metric space-valued responses such as count data, probability densities, and other complex structures. The method leverages a kernel-based transformation to map metric space-valued responses into a feature space, enabling efficient dimension reduction. By incorporating kernel distance covariance, the proposed approach offers enhanced flexibility and adaptability for datasets with diverse and non-Euclidean characteristics. The effectiveness of the method is demonstrated through synthetic simulations and several real-world applications. In all cases, the proposed method runs faster and consistently outperforms the existing Fréchet SDR approaches, demonstrating its broad applicability and robustness in addressing complex data challenges.

stat.ME↗

arXiv2024

Hyperbolic Hypergraph Neural Networks for Multi-Relational Knowledge Hypergraph Representation

Knowledge hypergraphs generalize knowledge graphs using hyperedges to connect multiple entities and depict complicated relations. Existing methods either transform hyperedges into an easier-to-handle set of binary relations or view hyperedges as isolated and ignore their adjacencies. Both approaches have information loss and may potentially lead to the creation of sub-optimal models. To fix these issues, we propose the Hyperbolic Hypergraph Neural Network (H2GNN), whose essential component is the hyper-star message passing, a novel scheme motivated by a lossless expansion of hyperedges into hierarchies. It implements a direct embedding that consciously incorporates adjacent entities, hyper-relations, and entity position-aware information. As the name suggests, H2GNN operates in the hyperbolic space, which is more adept at capturing the tree-like hierarchy. We compare H2GNN with 15 baselines on knowledge hypergraphs, and it outperforms state-of-the-art approaches in both node classification and link prediction tasks.

cs.LG↗

arXiv2024

Strengthening of Clarkson-McCarthy inequalities with several operators

Strengthening of two Clarkson-McCarthy inequalities with several operators is established. These not only confirm a conjecture of the author in [Israel J. Math. 2024], but also improve results of Hirazallah-Kittaneh in [Integral Equations Operator Theory 60 (2008)] and Bhatia-Kittaneh in [Bull. London Math. Soc. 36 (2004)]. We also give a generalization of a result for pairs $(A_i,B_i), 1\le i\le n$ and obtain another form of another inequality. The infinite cases of these inequalities are also discussed. The method is an extension of the result obtained by Bourin and Lee in [Linear Algebra Appl. 601 (2020)]. Some related eigenvalue inequalities are also obtained.

math.FA↗

arXiv2024

From Clarkson-McCarthy inequality to Ball-Carlen-Lieb inequality

In this paper, we give two new generalizations of Clarkson-McCarthy with several operators, which depends on the unitary orbit technique developed by Bourin, Hadamard Three-lines Theorem and the duality argument developed by Ball, Carlen and Lieb. Moreover, we complete the optimal 2-uniform convexity inequality established by Ball, Carlen and Lieb in [Invent. Math. 115 (1994) 463-482.]. Some open problems are presented.

math.FA↗

arXiv2024

EUFormer: Learning Driven 3D Spine Deformity Assessment with Orthogonal Optical Images

In clinical settings, the screening, diagnosis, and monitoring of adolescent idiopathic scoliosis (AIS) typically involve physical or radiographic examinations. However, physical examinations are subjective, while radiographic examinations expose patients to harmful radiation. Consequently, we propose a pipeline that can accurately determine scoliosis severity. This pipeline utilizes posteroanterior (PA) and lateral (LAT) RGB images as input to generate spine curve maps, which are then used to reconstruct the three-dimensional (3D) spine curve for AIS severity grading. To generate the 2D spine curves accurately and efficiently, we further propose an Efficient U-shape transFormer (EUFormer) as the generator. It can efficiently utilize the learned feature across channels, therefore producing consecutive spine curves from both PA and LAT views. Experimental results demonstrate superior performance of EUFormer on spine curve generation against other classical U-shape models. This finding demonstrates that the proposed method for grading the severity of AIS, based on a 3D spine curve, is more accurate when compared to using a 2D spine curve.

cs.GR↗