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Liming Wang

Publications and source records attributed to Liming Wang.

At least 37 records · Page 2Linked to original sources
arXiv2025

A Spatial-temporal Deep Probabilistic Diffusion Model for Reliable Hail Nowcasting with Radar Echo Extrapolation

Hail nowcasting is a considerable contributor to meteorological disasters and there is a great need to mitigate its socioeconomic effects through precise forecast that has high resolution, long lead times and local details with large landscapes. Existing medium-range weather forecasting methods primarily rely on changes in upper air currents and cloud layers to predict precipitation events, such as heavy rainfall, which are unsuitable for hail nowcasting since it is mainly caused by low-altitude local strong convection associated with terrains. Additionally, radar captures the status of low cloud layers, such as water vapor, droplets, and ice crystals, providing rich signals suitable for hail nowcasting. To this end, we introduce a Spatial-Temporal gEnerAtive Model called SteamCast for hail nowcasting with radar echo extrapolation, it is a deep probabilistic diffusion model based on spatial-temporal representations including radar echoes as well as their position/time embeddings, which we trained on historical reanalysis archive from Yan'an Meteorological Bureau in China, where the crop yield like apple suffers greatly from hail damage. Considering the short-term nature of hail, SteamCast provides 30-minute nowcasts at 6-minute intervals for a single radar reflectivity variable, across 9 different vertical angles, on a latitude-longitude grid with approximately 1 km * 1 km resolution per pixel in Yan'an City, China. By successfully fusing the spatial-temporal features of radar echoes, SteamCast delivers competitive, and in some cases superior, results compared to other deep learning-based models such as PredRNN and VMRNN.

cs.LG↗
arXiv2025

Towards Unsupervised Speech Recognition Without Pronunciation Models

Recent advancements in supervised automatic speech recognition (ASR) have achieved remarkable performance, largely due to the growing availability of large transcribed speech corpora. However, most languages lack sufficient paired speech and text data to effectively train these systems. In this article, we tackle the challenge of developing ASR systems without paired speech and text corpora by proposing the removal of reliance on a phoneme lexicon. We explore a new research direction: word-level unsupervised ASR, and experimentally demonstrate that an unsupervised speech recognizer can emerge from joint speech-to-speech and text-to-text masked token-infilling. Using a curated speech corpus containing a fixed number of English words, our system iteratively refines the word segmentation structure and achieves a word error rate of between 20-23%, depending on the vocabulary size, without parallel transcripts, oracle word boundaries, or a pronunciation lexicon. This innovative model surpasses the performance of previous unsupervised ASR models under the lexicon-free setting.

cs.CL↗
arXiv2024

Semiconductive and Ferromagnetic Lanthanide MXenes Derived from Carbon Intercalated Two-dimensional Halides

Two-dimensional (2D) magnetic semiconductors are a key focus in developing next-generation information storage technologies. MXenes, as emerging 2D early transition metal carbides and nitrides, offer versatile compositions and tunable chemical structures. Incorporating lanthanide metals, with their unique role of 4f-electrons in engineering physical properties, into MXenes holds potential for advancing technological applications. However, the scarcity of lanthanide-containing ternary MAX phase precursors and the propensity of lanthanides to oxidize pose significant challenges to obtain lanthanide MXenes (Ln2CT2) via the top-down etching method. Here, we propose a general bottom-up methodology for lanthanide MXenes, that derive from carbon intercalated van der Waals building blocks of 2D halides. Compared to conventional MXenes conductors, the synthesized Ln2CT2 exhibit tunable band gaps spanning 0.32 eV to 1.22 eV that cover typical semiconductors such as Si (1.12 eV) and Ge (0.67 eV). Additionally, the presence of unpaired f-electrons endows Ln2CT2 with intrinsic ferromagnetism, with Curie temperatures ranging between 36 K and 60 K. Theoretical calculations reveal that, in contrast to traditional MXenes, the number of d-electrons states around the Fermi level are largely diminishes in bare Ln2C MXenes, and the halogen terminals can further exhaust these electrons to open band gaps. Meanwhile, the Ln-4f electrons in Ln2CT2 are highly localized and stay away from the Fermi level, contributing to the spin splitting for the observed ferromagnetic behavior. Lanthanide MXenes hold immense promise for revolutionizing future applications in spintronic devices.

cond-mat.mtrl-sci↗
arXiv2024

Contextual Bandit with Herding Effects: Algorithms and Recommendation Applications

Contextual bandits serve as a fundamental algorithmic framework for optimizing recommendation decisions online. Though extensive attention has been paid to tailoring contextual bandits for recommendation applications, the "herding effects" in user feedback have been ignored. These herding effects bias user feedback toward historical ratings, breaking down the assumption of unbiased feedback inherent in contextual bandits. This paper develops a novel variant of the contextual bandit that is tailored to address the feedback bias caused by the herding effects. A user feedback model is formulated to capture this feedback bias. We design the TS-Conf (Thompson Sampling under Conformity) algorithm, which employs posterior sampling to balance the exploration and exploitation tradeoff. We prove an upper bound for the regret of the algorithm, revealing the impact of herding effects on learning speed. Extensive experiments on datasets demonstrate that TS-Conf outperforms four benchmark algorithms. Analysis reveals that TS-Conf effectively mitigates the negative impact of herding effects, resulting in faster learning and improved recommendation accuracy.

cs.LG↗
arXiv2024

Automatic Prediction of Amyotrophic Lateral Sclerosis Progression using Longitudinal Speech Transformer

Automatic prediction of amyotrophic lateral sclerosis (ALS) disease progression provides a more efficient and objective alternative than manual approaches. We propose ALS longitudinal speech transformer (ALST), a neural network-based automatic predictor of ALS disease progression from longitudinal speech recordings of ALS patients. By taking advantage of high-quality pretrained speech features and longitudinal information in the recordings, our best model achieves 91.0\% AUC, improving upon the previous best model by 5.6\% relative on the ALS TDI dataset. Careful analysis reveals that ALST is capable of fine-grained and interpretable predictions of ALS progression, especially for distinguishing between rarer and more severe cases. Code is publicly available.

cs.SD↗
arXiv2024

NTIRE 2024 Challenge on Night Photography Rendering

This paper presents a review of the NTIRE 2024 challenge on night photography rendering. The goal of the challenge was to find solutions that process raw camera images taken in nighttime conditions, and thereby produce a photo-quality output images in the standard RGB (sRGB) space. Unlike the previous year's competition, the challenge images were collected with a mobile phone and the speed of algorithms was also measured alongside the quality of their output. To evaluate the results, a sufficient number of viewers were asked to assess the visual quality of the proposed solutions, considering the subjective nature of the task. There were 2 nominations: quality and efficiency. Top 5 solutions in terms of output quality were sorted by evaluation time (see Fig. 1). The top ranking participants' solutions effectively represent the state-of-the-art in nighttime photography rendering. More results can be found at https://nightimaging.org.

cs.CV↗
arXiv2023

Unsupervised Speech Recognition with N-Skipgram and Positional Unigram Matching

Training unsupervised speech recognition systems presents challenges due to GAN-associated instability, misalignment between speech and text, and significant memory demands. To tackle these challenges, we introduce a novel ASR system, ESPUM. This system harnesses the power of lower-order N-skipgrams (up to N=3) combined with positional unigram statistics gathered from a small batch of samples. Evaluated on the TIMIT benchmark, our model showcases competitive performance in ASR and phoneme segmentation tasks. Access our publicly available code at https://github.com/lwang114/GraphUnsupASR.

cs.CL↗
arXiv2023

Prototypes-oriented Transductive Few-shot Learning with Conditional Transport

Transductive Few-Shot Learning (TFSL) has recently attracted increasing attention since it typically outperforms its inductive peer by leveraging statistics of query samples. However, previous TFSL methods usually encode uniform prior that all the classes within query samples are equally likely, which is biased in imbalanced TFSL and causes severe performance degradation. Given this pivotal issue, in this work, we propose a novel Conditional Transport (CT) based imbalanced TFSL model called {\textbf P}rototypes-oriented {\textbf U}nbiased {\textbf T}ransfer {\textbf M}odel (PUTM) to fully exploit unbiased statistics of imbalanced query samples, which employs forward and backward navigators as transport matrices to balance the prior of query samples per class between uniform and adaptive data-driven distributions. For efficiently transferring statistics learned by CT, we further derive a closed form solution to refine prototypes based on MAP given the learned navigators. The above two steps of discovering and transferring unbiased statistics follow an iterative manner, formulating our EM-based solver. Experimental results on four standard benchmarks including miniImageNet, tieredImageNet, CUB, and CIFAR-FS demonstrate superiority of our model in class-imbalanced generalization.

cs.CV↗
arXiv2023

A Theory of Unsupervised Speech Recognition

Unsupervised speech recognition (ASR-U) is the problem of learning automatic speech recognition (ASR) systems from unpaired speech-only and text-only corpora. While various algorithms exist to solve this problem, a theoretical framework is missing from studying their properties and addressing such issues as sensitivity to hyperparameters and training instability. In this paper, we proposed a general theoretical framework to study the properties of ASR-U systems based on random matrix theory and the theory of neural tangent kernels. Such a framework allows us to prove various learnability conditions and sample complexity bounds of ASR-U. Extensive ASR-U experiments on synthetic languages with three classes of transition graphs provide strong empirical evidence for our theory (code available at cactuswiththoughts/UnsupASRTheory.git).

eess.AS↗
arXiv2023

A Smart Switch Configuration and Reliability Assessment Method for Large-Scale Offshore Wind Farm Electrical Collector System

With the development of offshore wind farms (OWFs) in far-offshore and deep-sea areas, each OWF could contain more and more wind turbines and cables, making it imperative to study high-reliability electrical collector system (ECS) for OWF. Enlightened by active distribution network, for OWF, we propose an ECS switch configuration that enables post-fault network recovery, along with a reliability assessment (RA) method based on optimization models. It can also determine the optimal normal state and network reconfiguration strategies to maximize ECS reliability. Case studies on several OWFs demonstrate that the proposed RA method is more computationally efficient and accurate than the traditional sequential Monte-Carlo simulation method. Moreover, the proposed switch configuration, in conjunction with the network reconfiguration strategy and proper topology, provides significant benefits to ECS reliability.

eess.SY↗
arXiv2023

Interactive Log Parsing via Light-weight User Feedback

Template mining is one of the foundational tasks to support log analysis, which supports the diagnosis and troubleshooting of large scale Web applications. This paper develops a human-in-the-loop template mining framework to support interactive log analysis, which is highly desirable in real-world diagnosis or troubleshooting of Web applications but yet previous template mining algorithms fails to support it. We formulate three types of light-weight user feedbacks and based on them we design three atomic human-in-the-loop template mining algorithms. We derive mild conditions under which the outputs of our proposed algorithms are provably correct. We also derive upper bounds on the computational complexity and query complexity of each algorithm. We demonstrate the versatility of our proposed algorithms by combining them to improve the template mining accuracy of five representative algorithms over sixteen widely used benchmark datasets.

cs.AI↗
arXiv2023

Optimal Planning for Electrical Collector System of Offshore Wind Farm with Double-sided Ring Topology

We propose a planning method for offshore wind farm electrical collector system (OWF-ECS) with double-sided ring topology meeting the "N-1" criterion on cable faults, in which the submarine cables layout of OWF is optimized considering cable length and power losses. The proposed mixed-integer quadratic programming (MIQP) model is based on the Capacitated Vehicle Routing Problem (CVRP) formulation and power network expansion planning, which could approximate the power losses in OWF-ECS. In addition, cross-avoidance constraints are proposed to avoid crossing cables, and the minimum k-degree center tree model is included to improve the convergence. Case studies on OWFs with 30 and 62 WTs demonstrate the effectiveness of the proposed method. Considering the potential outage cost in the radial topology, the total cost of the planning result is reduced by up to 25.9% with reliability improvement. The cable investment is reduced by 4%~8% with the proposed method compared with conventional heuristic methods and Google OR-tools. The proposed method/model can also achieve acceptable computation efficiency and OWF-ECS planning results with good optimality. Moreover, it could be solved by modern commercial solvers/optimization software, thus it's easy to use even for large-scale OWF.

eess.SY↗
arXiv2022

Exploring Graph-aware Multi-View Fusion for Rumor Detection on Social Media

Automatic detecting rumors on social media has become a challenging task. Previous studies focus on learning indicative clues from conversation threads for identifying rumorous information. However, these methods only model rumorous conversation threads from various views but fail to fuse multi-view features very well. In this paper, we propose a novel multi-view fusion framework for rumor representation learning and classification. It encodes the multiple views based on Graph Convolutional Networks (GCN), and leverages Convolutional Neural Networks (CNN) to capture the consistent and complementary information among all views and fuse them together. Experimental results on two public datasets demonstrate that our method outperforms state-of-the-art approaches.

cs.CL↗
arXiv2022

Sub-10-micron thick Ge thin films from bulk-Ge substrates via a wet etching method

Low-defect-density Ge thin films are critical in Ge based optical devices (optical detectors, LEDs and Lasers) integrated with Si electronic devices for low-cost, highly integrated photonic circuits. In this work, Ge thin films prepared by wet etching with four different solutions were studied in terms of the surface morphology, defect density and achievable thickness. Both nanostrip-based solution (1:1:10) and HCl-based solution (1:1:5) were able to wet-etch 535 micron thick bulk-Ge substrates to Ge films thinner than 10 micron within 53 hours. The corresponding RMS surface roughness was 32 nm for the nanostrip-based solution and 10 nm for the HCl-based solution. The good quality of bulk-Ge was preserved before and after the etching process according to the HRXRD results. The low threading dislocation density of 6000-7000 cm-2 was maintained in the process of wet etching without introducing extra defects. This approach provides an inexpensive and convenient way to prepare sub-10-micron thick Ge thin films, enabling future studies of low-defect-density Ge-based devices such as photodetectors, LEDs, and lasers.

physics.app-ph↗
arXiv2022

Dzyaloshinskii-Moriya interaction and magnetic skyrmions induced by curvature

Realizing sizeable Dzyaloshinskii-Moriya interaction (DMI) in intrinsic two-dimensional (2D) magnets without any manipulation will greatly enrich potential application of spintronics devices. The simplest and most desirable situation should be 2D magnets with intrinsic DMI and intrinsic chiral spin textures. Here, we propose to realize DMI by designing periodic ripple structures with different curvatures in low-dimensional magnets and demonstrate the concept in both one-dimensional (1D) CrBr2 and two-dimensional (2D) MnSe2 magnets by using first-principles calculations. We find that DMIs in curved CrBr2 and MnSe2 can be efficiently controlled by varying the size of curvature c, where c is defined as the ratio between the height h and the length l of curved structure. Moreover, we unveil that the dependence of first-principles calculated DMI on size of curvature c can be well described by the three-site Fert-Lévy model. At last, we uncover that field-free magnetic skyrmions can be realized in curved MnSe2 by using atomistic spin model simulations based on first-principles calculated magnetic parameters. The work will open a new avenue for inducing DMI and chiral spin textures in simple 2D magnets via curvature.

cond-mat.mtrl-sci↗
arXiv2022

Unsupervised Text-to-Speech Synthesis by Unsupervised Automatic Speech Recognition

An unsupervised text-to-speech synthesis (TTS) system learns to generate speech waveforms corresponding to any written sentence in a language by observing: 1) a collection of untranscribed speech waveforms in that language; 2) a collection of texts written in that language without access to any transcribed speech. Developing such a system can significantly improve the availability of speech technology to languages without a large amount of parallel speech and text data. This paper proposes an unsupervised TTS system based on an alignment module that outputs pseudo-text and another synthesis module that uses pseudo-text for training and real text for inference. Our unsupervised system can achieve comparable performance to the supervised system in seven languages with about 10-20 hours of speech each. A careful study on the effect of text units and vocoders has also been conducted to better understand what factors may affect unsupervised TTS performance. The samples generated by our models can be found at https://cactuswiththoughts.github.io/UnsupTTS-Demo, and our code can be found at https://github.com/lwang114/UnsupTTS.

eess.AS↗
arXiv2022

Theoretical study of small signal modulation behavior of Fabry-Perot Germanium-on-Silicon lasers

This work investigated the small signal performance of Fabry-Perot Ge-on-Si lasers by modeling and simulations. The 3dB bandwidth dependence on the structure parameters such as poly-Si cladding thickness, Ge cavity width and thickness, and minority carrier lifetime were studied. A 3dB bandwidth of 33.94 GHz at a biasing current of 270.5 mA is predicted after Ge laser structure optimization with a defect limited carrier lifetime of 1 ns.

physics.optics↗