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Yao Zhu

Yao Zhu appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
Artificial IntelligenceDistributed, Parallel, and Cluster ComputingInformation Theorymath.ITNumerical AnalysisComputationComputer Visioncond-mat.softData Structures and Algorithmseess.SYMachine LearningSystems and Control

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Published work

9 published item(s)

preprint2026arXiv

Confusions and Erasures of Error-Bounded Block Decoders with Finite Blocklength

This paper investigates two distinct types of block errors - undetected errors (confusions) and erasures - in additive white Gaussian noise (AWGN) channels with error-bounded block decoders operating in the finite blocklength (FBL) regime. While block error rate (BLER) is a common metric, it does not distinguish between confusions and erasures, which can have significantly different impacts in cross-layer protocol design, despite upper-layer protocols universally assuming physical (PHY) errors manifest as packet erasures rather than undetected corruptions - an assumption lacking rigorous PHY-layer validation. We present a systematic analysis of confusions and erasures under BLER-constrained maximum likelihood (ML) decoding. Through sphere-packing analysis, we provide analytical bounds for both block confusion and erasure probabilities, and derive the sensitivities of these bounds to blocklength and signal-to-noise ratio (SNR). To the best of our knowledge, this is the first study on this topic in the FBL regime. Our findings provide theoretical validation for the block erasure channel abstraction commonly assumed in medium access control (MAC) and network layer protocols, confirming that, for practical FBL codes, block confusions are negligible compared to block erasures, especially at large blocklengths and high SNR.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Decomposed Vision-Language Alignment for Fine-Grained Open-Vocabulary Segmentation

Open-vocabulary segmentation models often struggle to generalize to unseen combinations of object categories and attributes, because fine-grained descriptions are typically encoded as holistic sentences that entangle multiple semantic units. We propose a Decomposed Vision-Language Alignment framework that explicitly factorizes textual prompts into a concept token and multiple attribute tokens, enabling separate cross-modal interactions for each semantic unit. At the feature level, we introduce a Feature-Gated Cross-Attention module that generates attribute-specific gating maps to fuse information in a multiplicative manner, effectively enforcing compositional semantics. At the scoring level, per-token similarities are aggregated in log-space, producing a stable and interpretable compositional matching. The method can be seamlessly integrated into existing transformer-based segmentation architectures and significantly improves generalization to unseen attribute-category compositions in fine-grained open-vocabulary segmentation benchmarks.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

DMH-HARQ: Reliable and Open Latency-Constrained Wireless Transport Network

The extreme requirements for high reliability and low latency in the upcoming Sixth Generation (6G) wireless networks are challenging the design of multi-hop wireless transport networks. Inspired by the advent of the virtualization concept in the wireless networks design and openness paradigm as fostered by the Open-Radio Access Network (O-RAN) Alliance, we target a revolutionary resource allocation scheme to improve the overall transmission efficiency. In this paper, we investigate the problem of automatic repeat request (ARQ) in multi-hop decode-and-forward (DF) relaying in the finite blocklength (FBL) regime, and propose a dynamic scheme of multi-hop hybrid ARQ (HARQ), which maximizes the end-to-end (E2E) communication reliability in the wireless transport network. We also propose an integer dynamic programming (DP) algorithm to efficiently solve the optimal Dynamic Multi-Hop HARQ (DMH-HARQ) strategy. Constrained within a certain time frame to accomplish E2E transmission, our proposed approach is proven to outperform the conventional listening-based cooperative ARQ, as well as any static HARQ strategy, regarding the E2E reliability. It is applicable without dependence on special delay constraint, and is particularly competitive for long-distance transport network with many hops.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Joint Communication Scheduling and Resource Allocation for Distributed Edge Learning: Seamless Integration in Next-Generation Wireless Networks

Distributed edge learning (DL) is considered a cornerstone of intelligence enablers, since it allows for collaborative training without the necessity for local clients to share raw data with other parties, thereby preserving privacy and security. Integrating DL into the 6G networks requires a coexistence design with existing services such as high-bandwidth (HB) traffic like eMBB. Current designs in the literature mainly focus on communication round-wise designs that assume a rigid resource allocation throughout each communication round (CR). However, rigid resource allocation within a CR is a highly inefficient and inaccurate representation of the system's realistic behavior, especially when CR duration far exceeds the channel coherence time due to large model size or limited resources. This is due to the heterogeneous nature of the system, as clients inherently may need to access the network at different time instants. This work zooms into one arbitrary CR, and demonstrates the importance of considering a time-dependent design for sharing the resource pool with HB traffic. We first formulate a timeslot-wise optimization problem to minimize the consumed time by DL within the CR while constrained by a DL energy budget. Due to its intractability, a session-based optimization problem is formulated assuming a CR lasts less than a large-scale coherence time. Some scheduling properties of such multi-server joint communication scheduling and resource allocation framework have been established. An iterative algorithm has been designed to solve such non-convex and non-block-separable-constrained problems. Simulation results confirm the importance of the efficient and accurate integration design proposed in this work.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

HAROOD: A Benchmark for Out-of-distribution Generalization in Sensor-based Human Activity Recognition

Sensor-based human activity recognition (HAR) mines activity patterns from the time-series sensory data. In realistic scenarios, variations across individuals, devices, environments, and time introduce significant distributional shifts for the same activities. Recent efforts attempt to solve this challenge by applying or adapting existing out-of-distribution (OOD) algorithms, but only in certain distribution shift scenarios (e.g., cross-device or cross-position), lacking comprehensive insights on the effectiveness of these algorithms. For instance, is OOD necessary to HAR? Which OOD algorithm performs the best? In this paper, we fill this gap by proposing HAROOD, a comprehensive benchmark for HAR in OOD settings. We define 4 OOD scenarios: cross-person, cross-position, cross-dataset, and cross-time, and build a testbed covering 6 datasets, 16 comparative methods (implemented with CNN-based and Transformer-based architectures), and two model selection protocols. Then, we conduct extensive experiments and present several findings for future research, e.g., no single method consistently outperforms others, highlighting substantial opportunity for advancement. Our codebase is highly modular and easy to extend for new datasets, algorithms, comparisons, and analysis, with the hope to facilitate the research in OOD-based HAR. Our implementation is released and can be found at https://github.com/AIFrontierLab/HAROOD.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Demonstration of liquid crystal for barocaloric cooling application

Current vapor-compression technology is based on the gas-liquid transition of hazardous gas. The alternative cooling technology focuses on the solid caloric material. A new liquid barocaloric material, i.e. the liquid crystal, is proved to be potential for cooling application primarily (based on other literatures). Its phase transition needs a small stress (tens of MPa) and can be operated near room temperature. It has a large entropy change of ~200 J.K-1.kg-1 (0.2 J.K-1.cm-1). There is no breakage problem due to its liquid form. It will provide the advantage for some unique cooling application cases comparing with solid caloric material. Moreover, this material is sustainable and non-toxic, which can meet the original intention of the alternative cooling technology.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

A Parallel Min-Cut Algorithm using Iteratively Reweighted Least Squares

We present a parallel algorithm for the undirected $s,t$-mincut problem with floating-point valued weights. Our overarching algorithm uses an iteratively reweighted least squares framework. This generates a sequence of Laplacian linear systems, which we solve using parallel matrix algorithms. Our overall implementation is up to 30-times faster than a serial solver when using 128 cores.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Erasure coding for fault oblivious linear system solvers

Dealing with hardware and software faults is an important problem as parallel and distributed systems scale to millions of processing cores and wide area networks. Traditional methods for dealing with faults include checkpoint-restart, active replicas, and deterministic replay. Each of these techniques has associated resource overheads and constraints. In this paper, we propose an alternate approach to dealing with faults, based on input augmentation. This approach, which is an algorithmic analog of erasure coded storage, applies a minimally modified algorithm on the augmented input to produce an augmented output. The execution of such an algorithm proceeds completely oblivious to faults in the system. In the event of one or more faults, the real solution is recovered using a rapid reconstruction method from the augmented output. We demonstrate this approach on the problem of solving sparse linear systems using a conjugate gradient solver. We present input augmentation and output recovery techniques. Through detailed experiments, we show that our approach can be made oblivious to a large number of faults with low computational overhead. Specifically, we demonstrate cases where a single fault can be corrected with less than 10% overhead in time, and even in extreme cases (fault rates of 20%), our approach is able to compute a solution with reasonable overhead. These results represent a significant improvement over the state of the art.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

EigenGP: Sparse Gaussian process models with data-dependent eigenfunctions

Gaussian processes (GPs) provide a nonparametric representation of functions. However, classical GP inference suffers from high computational cost and it is difficult to design nonstationary GP priors in practice. In this paper, we propose a sparse Gaussian process model, EigenGP, based on the Karhunen-Loeve (KL) expansion of a GP prior. We use the Nystrom approximation to obtain data dependent eigenfunctions and select these eigenfunctions by evidence maximization. This selection reduces the number of eigenfunctions in our model and provides a nonstationary covariance function. To handle nonlinear likelihoods, we develop an efficient expectation propagation (EP) inference algorithm, and couple it with expectation maximization for eigenfunction selection. Because the eigenfunctions of a Gaussian kernel are associated with clusters of samples - including both the labeled and unlabeled - selecting relevant eigenfunctions enables EigenGP to conduct semi-supervised learning. Our experimental results demonstrate improved predictive performance of EigenGP over alternative state-of-the-art sparse GP and semisupervised learning methods for regression, classification, and semisupervised classification.

0 citations0 reviewsNo code linkedOpen access