arXiv subjects

Huan Zhao

Publications and source records attributed to Huan Zhao.

At least 91 records · Page 5Linked to original sources

arXiv2023

Photoconductive Effects in Single Crystals of BaZrS$_3$

Chalcogenide perovskites, such as BaZrS$_3$, are emerging semiconductors with potential for high photovoltaic power conversion efficiency. The role of defects in the efficiency of the generation and collection of photo-excited carriers has not been experimentally investigated extensively. We study the effect of processing-induced defects on the photoconductive properties of single crystals of BaZrS$_3$. We achieved ohmic contacts to single crystals of BaZrS$_3$ and observed positive surface photovoltage, which is typically observed in p-type semiconductors. However, mechanical polishing of BaZrS$_3$ to remove the surface oxide leads to dense deformation grain boundaries and leads to trap-dominated photoconductive response. In comparison, ohmic contacts achieved in cleaved crystals leave fewer deformation defects and greatly improve optoelectronic properties. Defect-controlled crystal growth and contact fabrication are potentially limiting factors for achieving high photon-to-excited electron conversion efficiency in BaZrS$_3$.

physics.app-ph↗

arXiv2023

TempEE: Temporal-Spatial Parallel Transformer for Radar Echo Extrapolation Beyond Auto-Regression

Meteorological radar reflectivity data (i.e. radar echo) significantly influences precipitation prediction. It can facilitate accurate and expeditious forecasting of short-term heavy rainfall bypassing the need for complex Numerical Weather Prediction (NWP) models. In comparison to conventional models, Deep Learning (DL)-based radar echo extrapolation algorithms exhibit higher effectiveness and efficiency. Nevertheless, the development of reliable and generalized echo extrapolation algorithm is impeded by three primary challenges: cumulative error spreading, imprecise representation of sparsely distributed echoes, and inaccurate description of non-stationary motion processes. To tackle these challenges, this paper proposes a novel radar echo extrapolation algorithm called Temporal-Spatial Parallel Transformer, referred to as TempEE. TempEE avoids using auto-regression and instead employs a one-step forward strategy to prevent cumulative error spreading during the extrapolation process. Additionally, we propose the incorporation of a Multi-level Temporal-Spatial Attention mechanism to improve the algorithm's capability of capturing both global and local information while emphasizing task-related regions, including sparse echo representations, in an efficient manner. Furthermore, the algorithm extracts spatio-temporal representations from continuous echo images using a parallel encoder to model the non-stationary motion process for echo extrapolation. The superiority of our TempEE has been demonstrated in the context of the classic radar echo extrapolation task, utilizing a real-world dataset. Extensive experiments have further validated the efficacy and indispensability of various components within TempEE.

eess.SP↗

arXiv2023

Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Models

After the inception of emotion recognition or affective computing, it has increasingly become an active research topic due to its broad applications. Over the past couple of decades, emotion recognition models have gradually migrated from statistically shallow models to neural network-based deep models, which can significantly boost the performance of emotion recognition models and consistently achieve the best results on different benchmarks. Therefore, in recent years, deep models have always been considered the first option for emotion recognition. However, the debut of large language models (LLMs), such as ChatGPT, has remarkably astonished the world due to their emerged capabilities of zero/few-shot learning, in-context learning, chain-of-thought, and others that are never shown in previous deep models. In the present paper, we comprehensively investigate how the LLMs perform in emotion recognition in terms of diverse aspects, including in-context learning, few-short learning, accuracy, generalisation, and explanation. Moreover, we offer some insights and pose other potential challenges, hoping to ignite broader discussions about enhancing emotion recognition in the new era of advanced and generalised large models.

cs.CL↗

arXiv2023

MASK-CNN-Transformer For Real-Time Multi-Label Weather Recognition

Weather recognition is an essential support for many practical life applications, including traffic safety, environment, and meteorology. However, many existing related works cannot comprehensively describe weather conditions due to their complex co-occurrence dependencies. This paper proposes a novel multi-label weather recognition model considering these dependencies. The proposed model called MASK-Convolutional Neural Network-Transformer (MASK-CT) is based on the Transformer, the convolutional process, and the MASK mechanism. The model employs multiple convolutional layers to extract features from weather images and a Transformer encoder to calculate the probability of each weather condition based on the extracted features. To improve the generalization ability of MASK-CT, a MASK mechanism is used during the training phase. The effect of the MASK mechanism is explored and discussed. The Mask mechanism randomly withholds some information from one-pair training instances (one image and its corresponding label). There are two types of MASK methods. Specifically, MASK-I is designed and deployed on the image before feeding it into the weather feature extractor and MASK-II is applied to the image label. The Transformer encoder is then utilized on the randomly masked image features and labels. The experimental results from various real-world weather recognition datasets demonstrate that the proposed MASK-CT model outperforms state-of-the-art methods. Furthermore, the high-speed dynamic real-time weather recognition capability of the MASK-CT is evaluated.

cs.CV↗

arXiv2023

The FlySpeech Audio-Visual Speaker Diarization System for MISP Challenge 2022

This paper describes the FlySpeech speaker diarization system submitted to the second \textbf{M}ultimodal \textbf{I}nformation Based \textbf{S}peech \textbf{P}rocessing~(\textbf{MISP}) Challenge held in ICASSP 2022. We develop an end-to-end audio-visual speaker diarization~(AVSD) system, which consists of a lip encoder, a speaker encoder, and an audio-visual decoder. Specifically, to mitigate the degradation of diarization performance caused by separate training, we jointly train the speaker encoder and the audio-visual decoder. In addition, we leverage the large-data pretrained speaker extractor to initialize the speaker encoder.

cs.SD↗

arXiv2023

Automated 3D Pre-Training for Molecular Property Prediction

Molecular property prediction is an important problem in drug discovery and materials science. As geometric structures have been demonstrated necessary for molecular property prediction, 3D information has been combined with various graph learning methods to boost prediction performance. However, obtaining the geometric structure of molecules is not feasible in many real-world applications due to the high computational cost. In this work, we propose a novel 3D pre-training framework (dubbed 3D PGT), which pre-trains a model on 3D molecular graphs, and then fine-tunes it on molecular graphs without 3D structures. Based on fact that bond length, bond angle, and dihedral angle are three basic geometric descriptors corresponding to a complete molecular 3D conformer, we first develop a multi-task generative pre-train framework based on these three attributes. Next, to automatically fuse these three generative tasks, we design a surrogate metric using the \textit{total energy} to search for weight distribution of the three pretext task since total energy corresponding to the quality of 3D conformer.Extensive experiments on 2D molecular graphs are conducted to demonstrate the accuracy, efficiency and generalization ability of the proposed 3D PGT compared to various pre-training baselines.

q-bio.QM↗

arXiv2023

Correlated Excitonic Signatures in a Nanoscale van der Waals Antiferromagnet

Composite quasi-particles with emergent functionalities in spintronic and quantum information science can be realized in correlated materials due to entangled charge, spin, orbital, and lattice degrees of freedom. Here we show that by reducing the lateral dimension of correlated antiferromagnet NiPS3 flakes to tens of nanometers, we can switch-off the bulk spin-orbit entangled exciton in the near-infrared (1.47 eV) and activate visible-range (1.8 to 2.2 eV) transitions with charge-transfer character. These ultra-sharp lines (<120 ueV at 4.2 K) share the spin-correlated nature of the bulk exciton by displaying a Neel temperature dependent linear polarization. Furthermore, exciton photoluminescence lineshape analysis reveals a polaronic character via coupling with at-least 3 phonon modes and a comb-like Stark effect through discretization of charges in each layer. These findings augment the knowledge on the many-body nature of excitonic quasi-particles in correlated antiferromagnets and also establish the nanoscale platform as promising for maturing integrated magneto-optic devices.

cond-mat.mtrl-sci↗

arXiv2023

Iterative fluctuation ghost imaging

We present a new technique, iterative fluctuation ghost imaging (IFGI) which dramatically enhances the resolution of ghost imaging (GI). It is shown that, by the fluctuation characteristics of the second-order correlation function, the imaging information with the narrower point spread function (PSF) than the original information can be got. The effects arising from the PSF and the iteration times also be discussed.

physics.optics↗

arXiv2023

Exploring the Feasibility of ChatGPT for Event Extraction

Event extraction is a fundamental task in natural language processing that involves identifying and extracting information about events mentioned in text. However, it is a challenging task due to the lack of annotated data, which is expensive and time-consuming to obtain. The emergence of large language models (LLMs) such as ChatGPT provides an opportunity to solve language tasks with simple prompts without the need for task-specific datasets and fine-tuning. While ChatGPT has demonstrated impressive results in tasks like machine translation, text summarization, and question answering, it presents challenges when used for complex tasks like event extraction. Unlike other tasks, event extraction requires the model to be provided with a complex set of instructions defining all event types and their schemas. To explore the feasibility of ChatGPT for event extraction and the challenges it poses, we conducted a series of experiments. Our results show that ChatGPT has, on average, only 51.04% of the performance of a task-specific model such as EEQA in long-tail and complex scenarios. Our usability testing experiments indicate that ChatGPT is not robust enough, and continuous refinement of the prompt does not lead to stable performance improvements, which can result in a poor user experience. Besides, ChatGPT is highly sensitive to different prompt styles.

cs.CL↗

arXiv2023

Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification

In recent years, Graph Neural Networks (GNNs) have been popular in the graph classification task. Currently, shallow GNNs are more common due to the well-known over-smoothing problem facing deeper GNNs. However, they are sub-optimal without utilizing the information from distant nodes, i.e., the long-range dependencies. The mainstream methods in the graph classification task can extract the long-range dependencies either by designing the pooling operations or incorporating the higher-order neighbors, while they have evident drawbacks by modifying the original graph structure, which may result in information loss in graph structure learning. In this paper, by justifying the smaller influence of the over-smoothing problem in the graph classification task, we evoke the importance of stacking-based GNNs and then employ them to capture the long-range dependencies without modifying the original graph structure. To achieve this, two design needs are given for stacking-based GNNs, i.e., sufficient model depth and adaptive skip-connection schemes. By transforming the two design needs into designing data-specific inter-layer connections, we propose a novel approach with the help of neural architecture search (NAS), which is dubbed LRGNN (Long-Range Graph Neural Networks). Extensive experiments on five datasets show that the proposed LRGNN can achieve the best performance, and obtained data-specific GNNs with different depth and skip-connection schemes, which can better capture the long-range dependencies.

cs.LG↗

arXiv2023

Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extraction

We present Mask-then-Fill, a flexible and effective data augmentation framework for event extraction. Our approach allows for more flexible manipulation of text and thus can generate more diverse data while keeping the original event structure unchanged as much as possible. Specifically, it first randomly masks out an adjunct sentence fragment and then infills a variable-length text span with a fine-tuned infilling model. The main advantage lies in that it can replace a fragment of arbitrary length in the text with another fragment of variable length, compared to the existing methods which can only replace a single word or a fixed-length fragment. On trigger and argument extraction tasks, the proposed framework is more effective than baseline methods and it demonstrates particularly strong results in the low-resource setting. Our further analysis shows that it achieves a good balance between diversity and distributional similarity.

cs.CL↗

arXiv2022

Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach

In recent years, Graph Neural Networks (GNNs) have been popular in graph representation learning which assumes the homophily property, i.e., the connected nodes have the same label or have similar features. However, they may fail to generalize into the heterophilous graphs which in the low/medium level of homophily. Existing methods tend to address this problem by enhancing the intra-class information extraction, i.e., either by designing better GNNs to improve the model effectiveness, or re-designing the graph structures to incorporate more potential intra-class nodes from distant hops. Despite the success, we observe two aspects that can be further improved: (a) enhancing the ego feature information extraction from node itself which is more reliable in extracting the intra-class information; (b) designing node-wise GNNs can better adapt to the nodes with different homophily ratios. In this paper, we propose a novel method IIE-GNN (Intra-class Information Enhanced Graph Neural Networks) to achieve two improvements. A unified framework is proposed based on the literature, in which the intra-class information from the node itself and neighbors can be extracted based on seven carefully designed blocks. With the help of neural architecture search (NAS), we propose a novel search space based on the framework, and then provide an architecture predictor to design GNNs for each node. We further conduct experiments to show that IIE-GNN can improve the model performance by designing node-wise GNNs to enhance intra-class information extraction.

cs.LG↗

arXiv2022

TSUP Speaker Diarization System for Conversational Short-phrase Speaker Diarization Challenge

This paper describes the TSUP team's submission to the ISCSLP 2022 conversational short-phrase speaker diarization (CSSD) challenge which particularly focuses on short-phrase conversations with a new evaluation metric called conversational diarization error rate (CDER). In this challenge, we explore three kinds of typical speaker diarization systems, which are spectral clustering(SC) based diarization, target-speaker voice activity detection(TS-VAD) and end-to-end neural diarization(EEND) respectively. Our major findings are summarized as follows. First, the SC approach is more favored over the other two approaches under the new CDER metric. Second, tuning on hyperparameters is essential to CDER for all three types of speaker diarization systems. Specifically, CDER becomes smaller when the length of sub-segments setting longer. Finally, multi-system fusion through DOVER-LAP will worsen the CDER metric on the challenge data. Our submitted SC system eventually ranks the third place in the challenge.

cs.SD↗

arXiv2022

Backend Ensemble for Speaker Verification and Spoofing Countermeasure

This paper describes the NPU system submitted to Spoofing Aware Speaker Verification Challenge 2022. We particularly focus on the \textit{backend ensemble} for speaker verification and spoofing countermeasure from three aspects. Firstly, besides simple concatenation, we propose circulant matrix transformation and stacking for speaker embeddings and countermeasure embeddings. With the stacking operation of newly-defined circulant embeddings, we almost explore all the possible interactions between speaker embeddings and countermeasure embeddings. Secondly, we attempt different convolution neural networks to selectively fuse the embeddings' salient regions into channels with convolution kernels. Finally, we design parallel attention in 1D convolution neural networks to learn the global correlation in channel dimensions as well as to learn the important parts in feature dimensions. Meanwhile, we embed squeeze-and-excitation attention in 2D convolutional neural networks to learn the global dependence among speaker embeddings and countermeasure embeddings. Experimental results demonstrate that all the above methods are effective. After fusion of four well-trained models enhanced by the mentioned methods, the best SASV-EER, SPF-EER and SV-EER we achieve are 0.559\%, 0.354\% and 0.857\% on the evaluation set respectively. Together with the above contributions, our submission system achieves the fifth place in this challenge.

cs.SD↗

arXiv2022

A General End-to-end Diagnosis Framework for Manufacturing Systems

The manufacturing sector is envisioned to be heavily influenced by artificial intelligence-based technologies with the extraordinary increases in computational power and data volumes. A central challenge in manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications. Here we propose a general data-driven, end-to-end framework for the monitoring of manufacturing systems. This framework, derived from deep learning techniques, evaluates fused sensory measurements to detect and even predict faults and wearing conditions. This work exploits the predictive power of deep learning to automatically extract hidden degradation features from noisy, time-course data. We have experimented the proposed framework on ten representative datasets drawn from a wide variety of manufacturing applications. Results reveal that the framework performs well in examined benchmark applications and can be applied in diverse contexts, indicating its potential use as a critical corner stone in smart manufacturing.

cs.LG↗

arXiv2022

Understanding the degradation of a model Si-anode in Li-ion battery at the atomic-scale

Si-anodes have long been candidates thanks to an expected ten-fold increase in capacity compared to graphite. However, details of the mechanisms governing their degradation remain elusive, hindering science-guided development of long-lived Si-based anodes. Here we demonstrate how the latest developments in cryo-atom probe tomography enable the in-depth analysis of the electrode and electrolyte, and their interface at atomic-level.

cond-mat.mtrl-sci↗

arXiv2022

Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture Search

Aiming at two molecular graph datasets and one protein association subgraph dataset in OGB graph classification task, we design a graph neural network framework for graph classification task by introducing PAS(Pooling Architecture Search). At the same time, we improve it based on the GNN topology design method F2GNN to further design the feature selection and fusion strategies, so as to further improve the performance of the model in the graph property prediction task while overcoming the over smoothing problem of deep GNN training. Finally, a performance breakthrough is achieved on these three datasets, which is significantly better than other methods with fixed aggregate function. It is proved that the NAS method has high generalization ability for multiple tasks and the advantage of our method in processing graph property prediction tasks.

cs.LG↗

arXiv2022

Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network

Heterogeneous Graph Neural Network (HGNN) has been successfully employed in various tasks, but we cannot accurately know the importance of different design dimensions of HGNNs due to diverse architectures and applied scenarios. Besides, in the research community of HGNNs, implementing and evaluating various tasks still need much human effort. To mitigate these issues, we first propose a unified framework covering most HGNNs, consisting of three components: heterogeneous linear transformation, heterogeneous graph transformation, and heterogeneous message passing layer. Then we build a platform Space4HGNN by defining a design space for HGNNs based on the unified framework, which offers modularized components, reproducible implementations, and standardized evaluation for HGNNs. Finally, we conduct experiments to analyze the effect of different designs. With the insights found, we distill a condensed design space and verify its effectiveness.

cs.LG↗