arXiv subjects

Xiaoying Zheng

Publications and source records attributed to Xiaoying Zheng.

11 recordsLinked to original sources

arXiv2026

SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting

Modern power systems increasingly require probabilistic forecasts amid interacting uncertainties from renewable intermittency, flexible demand, market volatility, and weather-dependent generation. However, existing methods often treat multi-scale decomposition, exogenous-variable alignment, and probabilistic output as separate steps, obscuring how predictable structures and uncertainty-bearing fluctuations jointly shape the forecast distribution. This paper proposes a state-space exogenous-context and temporal-frequency resolution architecture for general probabilistic energy forecasting. Its central premise is that trend-periodic components primarily determine the baseline trajectory, whereas high-frequency residuals and external perturbations govern the spread and asymmetry of forecast uncertainty. Accordingly, the architecture adaptively separates deterministic and residual streams, aligns exogenous context with both, refines the deterministic backbone through multi-resolution spectral-temporal state-space modeling, and estimates ordered quantile boundaries from their complementary representations. Experiments on load, price, solar, and wind forecasting achieve the best continuous ranked probability score in 14 of 18 settings, reducing average CRPS by 5.74\% and upper-tail quantile risk by 7.27\% over the strongest baselines. These results support deterministic-stochastic separation as an effective design principle for general probabilistic energy forecasting.

stat.ML↗

arXiv2026

Coherent magnetic excitations in a topological Kondo semimetal

In Kondo insulators the many-body Kondo lattice effect drives the formation of bands containing heavy charge carriers with a hybridization gap, leading to insulating properties. These renormalized bands can host non-trivial topologies driven by strong electron-electron interactions, but probing narrow heavy bands at low temperatures is challenging. We use inelastic neutron scattering (INS) to probe the Kondo lattice CeNiSn, which hosts both semimetallic transport properties and a hybridization gap. The INS response exhibits momentum-dependent magnetic excitations and a spin-gap in the low-temperature Kondo coherent state, which electronic structure calculations corroborate as arising from the renormalized heavy band structure. Dynamical-mean field theory demonstrates that this renormalized band structure corresponds to a topological Kondo insulating state, and hence the INS probes bulk excitations of heavy topological bands. This identification of a Kondo insulator addresses the long-standing mystery of the electronic properties of CeNiSn, and demonstrates the manifestation of a topological many-body coherent state in spectroscopic measurements of strongly correlated narrow band materials.

cond-mat.str-el↗

arXiv2025

KARMA: A Multilevel Decomposition Hybrid Mamba Framework for Multivariate Long-Term Time Series Forecasting

Multivariate long-term and efficient time series forecasting is a key requirement for a variety of practical applications, and there are complex interleaving time dynamics in time series data that require decomposition modeling. Traditional time series decomposition methods are single and rely on fixed rules, which are insufficient for mining the potential information of the series and adapting to the dynamic characteristics of complex series. On the other hand, the Transformer-based models for time series forecasting struggle to effectively model long sequences and intricate dynamic relationships due to their high computational complexity. To overcome these limitations, we introduce KARMA, with an Adaptive Time Channel Decomposition module (ATCD) to dynamically extract trend and seasonal components. It further integrates a Hybrid Frequency-Time Decomposition module (HFTD) to further decompose Series into frequency-domain and time-domain. These components are coupled with multi-scale Mamba-based KarmaBlock to efficiently process global and local information in a coordinated manner. Experiments on eight real-world datasets from diverse domains well demonstrated that KARMA significantly outperforms mainstream baseline methods in both predictive accuracy and computational efficiency. Code and full results are available at this repository: https://github.com/yedadasd/KARMA

cs.LG↗

arXiv2025

HICD: Hallucination-Inducing via Attention Dispersion for Contrastive Decoding to Mitigate Hallucinations in Large Language Models

Large Language Models (LLMs) often generate hallucinations, producing outputs that are contextually inaccurate or factually incorrect. We introduce HICD, a novel method designed to induce hallucinations for contrastive decoding to mitigate hallucinations. Unlike existing contrastive decoding methods, HICD selects attention heads crucial to the model's prediction as inducing heads, then induces hallucinations by dispersing attention of these inducing heads and compares the hallucinated outputs with the original outputs to obtain the final result. Our approach significantly improves performance on tasks requiring contextual faithfulness, such as context completion, reading comprehension, and question answering. It also improves factuality in tasks requiring accurate knowledge recall. We demonstrate that our inducing heads selection and attention dispersion method leads to more "contrast-effective" hallucinations for contrastive decoding, outperforming other hallucination-inducing methods. Our findings provide a promising strategy for reducing hallucinations by inducing hallucinations in a controlled manner, enhancing the performance of LLMs in a wide range of tasks.

cs.CL↗

arXiv2024

Enhancing Management of Large-Scale Optical Networks through RFID Technology Integration

Managing large-scale optical distribution networks is a daunting task. This paper introduces a novel solution using radio frequency identification (RFID) technology to transform the procedure we monitor and manage the complex optical network dumb resources (ONDR). By implementing and deploying removable RFID tag pairing based on the serial peripheral interface (SPI) communication protocol, the system identifies 30 pairs of tags within one second, even at densities of up to 5.1 tagged components per square inch of patch panel surface area. The integration of light-emitting diode (LED) navigation aids in indicating correctly matched interfaces, effectively addressing the complexities associated with large-scale fiber matching.

cs.NI↗

arXiv2023

Bitformer: An efficient Transformer with bitwise operation-based attention for Big Data Analytics at low-cost low-precision devices

In the current landscape of large models, the Transformer stands as a cornerstone, playing a pivotal role in shaping the trajectory of modern models. However, its application encounters challenges attributed to the substantial computational intricacies intrinsic to its attention mechanism. Moreover, its reliance on high-precision floating-point operations presents specific hurdles, particularly evident in computation-intensive scenarios such as edge computing environments. These environments, characterized by resource-constrained devices and a preference for lower precision, necessitate innovative solutions. To tackle the exacting data processing demands posed by edge devices, we introduce the Bitformer model, an inventive extension of the Transformer paradigm. Central to this innovation is a novel attention mechanism that adeptly replaces conventional floating-point matrix multiplication with bitwise operations. This strategic substitution yields dual advantages. Not only does it maintain the attention mechanism's prowess in capturing intricate long-range information dependencies, but it also orchestrates a profound reduction in the computational complexity inherent in the attention operation. The transition from an $O(n^2d)$ complexity, typical of floating-point operations, to an $O(n^2T)$ complexity characterizing bitwise operations, substantiates this advantage. Notably, in this context, the parameter $T$ remains markedly smaller than the conventional dimensionality parameter $d$. The Bitformer model in essence endeavors to reconcile the indomitable requirements of modern computing landscapes with the constraints posed by edge computing scenarios. By forging this innovative path, we bridge the gap between high-performing models and resource-scarce environments, thus unveiling a promising trajectory for further advancements in the field.

cs.LG↗

arXiv2023

Electronic band reconstruction across the insulator-metal transition in colossal magnetoresistive EuCd2P2

While colossal magnetoresistance (CMR) in Eu-based compounds is often associated with strong spin-carrier interactions, the underlying reconstruction of the electronic bands is much less understood from spectroscopic experiments. Here using angle-resolved photoemission, we directly observe an electronic band reconstruction across the insulator-metal (and magnetic) transition in the recently discovered CMR compound EuCd2P2. This transition is manifested by a large magnetic band splitting associated with the magnetic order, as well as unusual energy shifts of the valence bands: both the large ordered moment of Eu and carrier localization in the paramagnetic phase are crucial. Our results provide spectroscopic evidence for an electronic structure reconstruction underlying the enormous CMR observed in EuCd2P2, which could be important for understanding Eu-based CMR materials, as well as designing CMR materials based on large-moment rare-earth magnets.

cond-mat.mtrl-sci↗

arXiv2019

Exploring Mixed Integer Programming Reformulations for Virtual Machine Placement with Disk Anti-Colocation Constraints

One of the important problems for datacenter resource management is to place virtual machines (VMs) to physical machines (PMs) such that certain cost, profit or performance objective is optimized, subject to various constraints. In this paper, we consider an interesting and difficult VM placement problem with disk anti-colocation constraints: a VM's virtual disks should be spread out across the physical disks of its assigned PM. For solutions, we use the mixed integer programming (MIP) formulations and algorithms. However, a challenge is the potentially long computation time of the MIP algorithms. In this paper, we explore how reformulation of the problem can help to reduce the computation time. We develop two reformulations, by redefining the variables, for our VM placement problem and evaluate the computation time of all three formulations. We show that they have vastly different computation time. All three formulations can be useful, but for different problem instances. They all should be kept in the toolbox for tackling the problem. Out of the three, formulation COMB is especially flexible and versatile, and it can solve large problem instances.

cs.DC↗

arXiv2019

Workflow Scheduling in the Cloud with Weighted Upward-rank Priority Scheme Using Random Walk and Uniform Spare Budget Splitting

We study a difficult problem of how to schedule complex workflows with precedence constraints under a limited budget in the cloud environment. We first formulate the scheduling problem as an integer programming problem, which can be optimized and used as the baseline of performance. We then consider the traditional approach of scheduling jobs in a prioritized order based on the upward-rank of each job. For those jobs with no precedence constraints among themselves, the plain upward-rank priority scheme assigns priorities in an arbitrary way. We propose a job prioritization scheme that uses Markovian chain stationary probabilities as a measure of importance of jobs. The scheme keeps the precedence order for the jobs that have precedence constraints between each other, and assigns priorities according to the jobs' importance for the jobs without precedence constraints. We finally design a uniform spare budget splitting strategy, which splits the spare budget uniformly across all the jobs. We test our algorithms on a variety of workflows, including FFT, Gaussian elimination, typical scientific workflows, randomly generated workflows and workflows from an in-production cluster of an online streaming service company. We compare our algorithms with the-state-of-art algorithms. The empirical results show that the uniform spare budget splitting scheme outperforms the splitting scheme in proportion to extra demand in average for most cases, and the Markovian based prioritization further improves the workflow makespan.

cs.DC↗

arXiv2013

Content Distribution by Multiple Multicast Trees and Intersession Cooperation: Optimal Algorithms and Approximations

In traditional massive content distribution with multiple sessions, the sessions form separate overlay networks and operate independently, where some sessions may suffer from insufficient resources even though other sessions have excessive resources. To cope with this problem, we consider the universal swarming approach, which allows multiple sessions to cooperate with each other. We formulate the problem of finding the optimal resource allocation to maximize the sum of the session utilities and present a subgradient algorithm which converges to the optimal solution in the time-average sense. The solution involves an NP-hard subproblem of finding a minimum-cost Steiner tree. We cope with this difficulty by using a column generation method, which reduces the number of Steiner-tree computations. Furthermore, we allow the use of approximate solutions to the Steiner-tree subproblem. We show that the approximation ratio to the overall problem turns out to be no less than the reciprocal of the approximation ratio to the Steiner-tree subproblem. Simulation results demonstrate that universal swarming improves the performance of resource-poor sessions with negligible impact to resource-rich sessions. The proposed approach and algorithm are expected to be useful for infrastructure-based content distribution networks with long-lasting sessions and relatively stable network environment.

cs.NI↗

arXiv2013

Algorithms and Stability Analysis for Content Distribution over Multiple Multicast Trees

The paper investigates theoretical issues in applying the universal swarming technique to efficient content distribution. In a swarming session, a file is distributed to all the receivers by having all the nodes in the session exchange file chunks. By universal swarming, not only all the nodes in the session, but also some nodes outside the session may participate in the chunk exchange to improve the distribution performance. We present a universal swarming model where the chunks are distributed along different Steiner trees rooted at the source and covering all the receivers. We assume chunks arrive dynamically at the sources and focus on finding stable universal swarming algorithms. To achieve the throughput region, universal swarming usually involves a tree-selection subproblem of finding a min-cost Steiner tree, which is NP-hard. We propose a universal swarming scheme that employs an approximate tree-selection algorithm. We show that it achieves network stability for a reduced throughput region, where the reduction ratio is no more than the approximation ratio of the tree-selection algorithm. We propose a second universal swarming scheme that employs a randomized tree-selection algorithm. It achieves the throughput region, but with a weaker stability result. The proposed schemes and their variants are expected to be useful for infrastructure-based content distribution networks with massive content and relatively stable network environment.

cs.NI↗