arXiv subjects

Yao Tian

Publications and source records attributed to Yao Tian.

At least 37 records · Page 2Linked to original sources

arXiv2024

A Dual-Path Framework with Frequency-and-Time Excited Network for Anomalous Sound Detection

In contrast to human speech, machine-generated sounds of the same type often exhibit consistent frequency characteristics and discernible temporal periodicity. However, leveraging these dual attributes in anomaly detection remains relatively under-explored. In this paper, we propose an automated dual-path framework that learns prominent frequency and temporal patterns for diverse machine types. One pathway uses a novel Frequency-and-Time Excited Network (FTE-Net) to learn the salient features across frequency and time axes of the spectrogram. It incorporates a Frequency-and-Time Chunkwise Encoder (FTC-Encoder) and an excitation network. The other pathway uses a 1D convolutional network for utterance-level spectrum. Experimental results on the DCASE 2023 task 2 dataset show the state-of-the-art performance of our proposed method. Moreover, visualizations of the intermediate feature maps in the excitation network are provided to illustrate the effectiveness of our method.

eess.AS↗

arXiv2024

Last Week with ChatGPT: A Weibo Study on Social Perspective Regarding ChatGPT for Education and Beyond

The application of AI-powered tools has piqued the interest of many fields, particularly in the academic community. This study uses ChatGPT, currently the most powerful and popular AI tool, as a representative example to analyze how the Chinese public perceives the potential of large language models (LLMs) for educational and general purposes. Although facing accessibility challenges, we found that the number of discussions on ChatGPT per month is 16 times that of Ernie Bot developed by Baidu, the most popular alternative product to ChatGPT in the mainland, making ChatGPT a more suitable subject for our analysis. The study also serves as the first effort to investigate the changes in public opinion as AI technologies become more advanced and intelligent. The analysis reveals that, upon first encounters with advanced AI that was not yet highly capable, some social media users believed that AI advancements would benefit education and society, while others feared that advanced AI, like ChatGPT, would make humans feel inferior and lead to problems such as cheating and a decline in moral principles. The majority of users remained neutral. Interestingly, with the rapid development and improvement of AI capabilities, public attitudes have tended to shift in a positive direction. We present a thorough analysis of the trending shift and a roadmap to ensure the ethical application of ChatGPT-like models in education and beyond.

cs.CY↗

arXiv2023

Detect Depression from Social Networks with Sentiment Knowledge Sharing

Social network plays an important role in propagating people's viewpoints, emotions, thoughts, and fears. Notably, following lockdown periods during the COVID-19 pandemic, the issue of depression has garnered increasing attention, with a significant portion of individuals resorting to social networks as an outlet for expressing emotions. Using deep learning techniques to discern potential signs of depression from social network messages facilitates the early identification of mental health conditions. Current efforts in detecting depression through social networks typically rely solely on analyzing the textual content, overlooking other potential information. In this work, we conduct a thorough investigation that unveils a strong correlation between depression and negative emotional states. The integration of such associations as external knowledge can provide valuable insights for detecting depression. Accordingly, we propose a multi-task training framework, DeSK, which utilizes shared sentiment knowledge to enhance the efficacy of depression detection. Experiments conducted on both Chinese and English datasets demonstrate the cross-lingual effectiveness of DeSK.

cs.CL↗

arXiv2022

BiFSMN: Binary Neural Network for Keyword Spotting

The deep neural networks, such as the Deep-FSMN, have been widely studied for keyword spotting (KWS) applications. However, computational resources for these networks are significantly constrained since they usually run on-call on edge devices. In this paper, we present BiFSMN, an accurate and extreme-efficient binary neural network for KWS. We first construct a High-frequency Enhancement Distillation scheme for the binarization-aware training, which emphasizes the high-frequency information from the full-precision network's representation that is more crucial for the optimization of the binarized network. Then, to allow the instant and adaptive accuracy-efficiency trade-offs at runtime, we also propose a Thinnable Binarization Architecture to further liberate the acceleration potential of the binarized network from the topology perspective. Moreover, we implement a Fast Bitwise Computation Kernel for BiFSMN on ARMv8 devices which fully utilizes registers and increases instruction throughput to push the limit of deployment efficiency. Extensive experiments show that BiFSMN outperforms existing binarization methods by convincing margins on various datasets and is even comparable with the full-precision counterpart (e.g., less than 3% drop on Speech Commands V1-12). We highlight that benefiting from the thinnable architecture and the optimized 1-bit implementation, BiFSMN can achieve an impressive 22.3x speedup and 15.5x storage-saving on real-world edge hardware. Our code is released at https://github.com/htqin/BiFSMN.

cs.CL↗

arXiv2022

Orientably-Regular $π$-Maps and Regular $π$-Maps

Given a map with underlying graph $\mathcal{G}$, if the set of prime divisors of $|V(\mathcal{G}|$ is denoted by $π$, then we call the map a {\it $π$-map}. An orientably-regular (resp. A regular ) $π$-map is called {\it solvable} if the group $G^+$ of all orientation-preserving automorphisms (resp. the group $G$ of automorphisms) is solvable; and called {\it normal} if $G^+$ (resp. $G$) contains a normal $π$-Hall subgroup. In this paper, it will be proved that orientably-regular $π$-maps are solvable and normal if $2\notin π$ and regular $π$-maps are solvable if $2\notin π$ and $G$ has no sections isomorphic to ${\rm PSL}(2,q)$ for some prime power $q$. In particular, it's shown that a regular $π$-map with $2\notin π$ is normal if and only if $G/O_{2^{'}}(G)$ is isomorphic to a Sylow $2$-group of $G$. Moreover, nonnormal $π$-maps will be characterized and some properties and constructions of normal $π$-maps will be given in respective sections.

math.CO↗

arXiv2022

A Learned Index for Exact Similarity Search in Metric Spaces

Indexing is an effective way to support efficient query processing in large databases. Recently the concept of learned index, which replaces or complements traditional index structures with machine learning models, has been actively explored to reduce storage and search costs. However, accurate and efficient similarity query processing in high-dimensional metric spaces remains to be an open challenge. In this paper, we propose a novel indexing approach called LIMS that uses data clustering, pivot-based data transformation techniques and learned indexes to support efficient similarity query processing in metric spaces. In LIMS, the underlying data is partitioned into clusters such that each cluster follows a relatively uniform data distribution. Data redistribution is achieved by utilizing a small number of pivots for each cluster. Similar data are mapped into compact regions and the mapped values are totally ordinal. Machine learning models are developed to approximate the position of each data record on disk. Efficient algorithms are designed for processing range queries and nearest neighbor queries based on LIMS, and for index maintenance with dynamic updates. Extensive experiments on real-world and synthetic datasets demonstrate the superiority of LIMS compared with traditional indexes and state-of-the-art learned indexes.

cs.DB↗

arXiv2022

DB-LSH: Locality-Sensitive Hashing with Query-based Dynamic Bucketing

Among many solutions to the high-dimensional approximate nearest neighbor (ANN) search problem, locality sensitive hashing (LSH) is known for its sub-linear query time and robust theoretical guarantee on query accuracy. Traditional LSH methods can generate a small number of candidates quickly from hash tables but suffer from large index sizes and hash boundary problems. Recent studies to address these issues often incur extra overhead to identify eligible candidates or remove false positives, making query time no longer sub-linear. To address this dilemma, in this paper we propose a novel LSH scheme called DB-LSH which supports efficient ANN search for large high-dimensional datasets. It organizes the projected spaces with multi-dimensional indexes rather than using fixed-width hash buckets. Our approach can significantly reduce the space cost as by avoiding the need to maintain many hash tables for different bucket sizes. During the query phase of DB-LSH, a small number of high-quality candidates can be generated efficiently by dynamically constructing query-based hypercubic buckets with the required widths through index-based window queries. For a dataset of $n$ $d$-dimensional points with approximation ratio $c$, our rigorous theoretical analysis shows that DB-LSH achieves a smaller query cost ${O(n^{ρ^*} d\log n)}$, where ${ρ^*}$ is bounded by ${1/c^α}$ while the bound is ${1/c}$ in the existing work. An extensive range of experiments on real-world data demonstrates the superiority of DB-LSH over state-of-the-art methods on both efficiency and accuracy.

cs.DB↗

arXiv2022

Hamilton Cycles In Primitive Graphs of Order $2rs$

After long term efforts, it was recently proved in \cite{DKM2} that except for the Peterson graph, every connected vertex-transitive graph of order $rs$ has a Hamilton cycle, where $r$ and $s$ are primes. A natural topic is to solve the hamiltonian problem for connected vertex-transitive graphs of $2rs$. This topic is quite trivial, as the problem is still unsolved even for that of $r=3$. In this paper, it is shown that except for the Coxeter graph, every connected vertex-transitive graph of order $2rs$ contains a Hamilton cycle, provided the automorphism group acts primitively on vertices.

math.CO↗

arXiv2022

The Volcspeech system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge

This paper describes our submission to ICASSP 2022 Multi-channel Multi-party Meeting Transcription (M2MeT) Challenge. For Track 1, we propose several approaches to empower the clustering-based speaker diarization system to handle overlapped speech. Front-end dereverberation and the direction-of-arrival (DOA) estimation are used to improve the accuracy of speaker diarization. Multi-channel combination and overlap detection are applied to reduce the missed speaker error. A modified DOVER-Lap is also proposed to fuse the results of different systems. We achieve the final DER of 5.79% on the Eval set and 7.23% on the Test set. For Track 2, we develop our system using the Conformer model in a joint CTC-attention architecture. Serialized output training is adopted to multi-speaker overlapped speech recognition. We propose a neural front-end module to model multi-channel audio and train the model end-to-end. Various data augmentation methods are utilized to mitigate over-fitting in the multi-channel multi-speaker E2E system. Transformer language model fusion is developed to achieve better performance. The final CER is 19.2% on the Eval set and 20.8% on the Test set.

cs.SD↗

arXiv2022

Orientably-Regular $p$-Maps and Regular $p$-Maps

A map is called a {\it $p$-map} if it has a prime $p$-power vertices. An orientably-regular (resp. A regular ) $p$-map is called {\it solvable} if the group $G^+$ of all orientation-preserving automorphisms (resp. the group $G$ of automorphisms) is solvable; and called {\it normal} if $G^+$ (resp. $G$) contains the normal Sylow $p$-subgroup. In this paper, it will be proved that both orientably-regular $p$-maps and regular $p$-maps are solvable and except for few cases that $p\in \{2, 3\}$, they are normal. Moreover, nonnormal $p$-maps will be characterized and some properties and constructions of normal $p$-maps will be given.

math.CO↗

arXiv2020

The range of non-Kitaev terms and fractional particles in RuCl$_3$

Significant efforts have focused on the magnetic excitations of relativistic Mott insulators, predicted to realize the Kitaev quantum spin liquid (QSL). This exactly solvable model involves a highly entangled state resulting from bond-dependent Ising interactions that produce excitations which are non-local in terms of spin flips. A key challenge in real materials is identifying the relative size of the non-Kitaev terms and their role in the emergence or suppression of fractional excitations. Here, we identify the energy and temperature boundaries of non-Kitaev interactions by direct comparison of the Raman susceptibility of RuCl3 with quantum Monte Carlo (QMC) results for the Kitaev QSLs. Moreover, we further confirm the fractional nature of the magnetic excitations, which is given by creating a pair of fermionic quasiparticles. Interestingly, this fermionic response remains valid in the non-Kitaev range. Our results and focus on the use of the Raman susceptibility provide a stringent new test for future theoretical and experimental studies of QSLs.

cond-mat.str-el↗

arXiv2019

Direct Evidence for Fermi Statistics from Proximity to the Kitaev Spin Liquid in RuCl3

A key characteristic of quantum spin liquids(QSL) is the presence of fractional excitations related to their entanglement properties, yet experimental verification of their statistics is missing. For example, in the potential Kitaev spin liquid, RuCl3 experiments uncovered signs of fractional particles, though not their Fermi statistics. Here we employ Raman scattering to reveal the true nature of the magnetic excitations, using improved experimental methods and analysis to remove the influence of laser heating and thermal fluctuations. Via the energy loss and gain spectra, we extract the energy and temperature dependence of the Raman susceptibility to prove RuCl3's magnetic response is given by pair creation of fermionic excitations. Furthermore, by comparing with quantum Monte Carlo (QMC) results for the exact Kitaev limit, we are able to discern the energy and temperature range where additional interaction terms are important. Our results open new directions in QSL research by providing a new way to investigate fractional excitations and the importance of terms causing spinon confinement.

cond-mat.str-el↗

arXiv2017

Comparison of Multiple Features and Modeling Methods for Text-dependent Speaker Verification

Text-dependent speaker verification is becoming popular in the speaker recognition society. However, the conventional i-vector framework which has been successful for speaker identification and other similar tasks works relatively poorly in this task. Researchers have proposed several new methods to improve performance, but it is still unclear that which model is the best choice, especially when the pass-phrases are prompted during enrollment and test. In this paper, we introduce four modeling methods and compare their performance on the newly published RedDots dataset. To further explore the influence of different frame alignments, Viterbi and forward-backward algorithms are both used in the HMM-based models. Several bottleneck features are also investigated. Our experiments show that, by explicitly modeling the lexical content, the HMM-based modeling achieves good results in the fixed-phrase condition. In the prompted-phrase condition, GMM-HMM and i-vector/HMM are not as successful. In both conditions, the forward-backward algorithm brings more benefits to the i-vector/HMM system. Additionally, we also find that even though bottleneck features perform well for text-independent speaker verification, they do not outperform MFCCs on the most challenging Imposter-Correct trials on RedDots.

cs.SD↗

arXiv2016

Magneto-Elastic Coupling in a potential ferromagnetic 2D Atomic Crystal

Cr2Ge2Te6 has been of interest for decades, as it is one of only a few naturally forming ferromagnetic semiconductors. Recently, this material has been revisited due to its potential as a 2 dimensional semiconducting ferromagnet and a substrate to induce anomalous quantum Hall states in topological insulators. However, many relevant properties of Cr2Ge2Te6 still remain poorly understood, especially the spin-phonon coupling crucial to spintronic, multiferrioc, thermal conductivity, magnetic proximity and the establishment of long range order on the nanoscale. We explore the interplay between the lattice and magnetism through high resolution micro-Raman scattering measurements over the temperature range from 10 K to 325 K. Strong spin-phonon coupling effects are confirmed from multiple aspects: two low energy modes splits in the ferromagnetic phase, magnetic quasielastic scattering in paramagnetic phase, the phonon energies of three modes show clear upturn below Tc, and the phonon linewidths change dramatically below Tc as well. Our results provide the first demonstration of spin-phonon coupling in a potential 2 dimensional atomic crystal.

cond-mat.mtrl-sci↗

arXiv2016

Low vibration high numerical aperture automated variable temperature Raman microscope

Raman micro-spectroscopy is well suited for studying a variety of properties and has been applied to wide- ranging areas. Combined with tuneable temperature, Raman spectra can offer even more insights into the properties of materials. However, previous designs of variable temperature Raman microscopes have made it extremely challenging to measure samples with low signal levels due to thermal and positional instability as well as low collection efficiencies. Thus, contemporary Raman microscope has found limited applicability to probing the subtle physics involved in phase transitions and hysteresis. This paper describes a new design of a closed-cycle, Raman microscope with full polarization rotation. High collection efficiency, thermal and mechanical stability are ensured by both deliberate optical, cryogenic, and mechanical design. Measurements on two samples, Bi2Se3 and V2O3, which are known as challenging due to low thermal conductivities, low signal levels and/or hysteretic effects, are measured with previously undemonstrated temperature resolution.

cond-mat.mtrl-sci↗

arXiv2016

Spin-orbit excitations and electronic structure of the putative Kitaev magnet $α$-RuCl$_3$

Mott insulators with strong spin-orbit coupling have been proposed to host unconventional magnetic states, including the Kitaev quantum spin liquid. The 4$d$ system $α$-RuCl$_3$ has recently come into view as a candidate Kitaev system, with evidence for unusual spin excitations in magnetic scattering experiments. We apply a combination of optical spectroscopy and Raman scattering to study the electronic structure of this material. Our measurements reveal a series of orbital excitations involving localized total angular momentum states of the Ru ion, implying that strong spin-orbit coupling and electron-electron interactions coexist in this material. Analysis of these features allows us to estimate the spin-orbit coupling strength, as well as other parameters describing the local electronic structure, revealing a well-defined hierarchy of energy scales within the Ru $d$ states. By comparing our experimental results with density functional theory calculations, we also clarify the overall features of the optical response. Our results demonstrate that $α$-RuCl$_3$ is an ideal material system to study spin-orbit coupled magnetism on the honeycomb lattice.

cond-mat.str-el↗

arXiv2016

Local phonon mode in thermoelectric Bi2Te2Se from charge neutral antisites

Local modes caused by defects play a significant role in the thermal transport properties of thermoelectrics. Of particular interest are charge-neutral defects that suppress thermal conductivity, without significantly reducing electrical transport. Here we report a temperature dependent Raman study, that identifies such a mode in a standard thermoelectric material, Bi2Te2Se. One of the modes observed, whose origin has been debated for decades, was shown mostly likely to be an antisite defect induced local mode. The anomalous temperature independent broadening of the local mode is ascribed to the random arrangement of Se atoms. The temperature renormalization of all modes are well explained by an anharmonic model-Klemens's model.

cond-mat.mtrl-sci↗

arXiv2015

Sn-doped Bi1.1Sb0.9Te2S, a bulk topological insulator with ideal properties

A long-standing issue in topological insulator research has been to find a material that provides an ideal platform for characterizing topological surface states without interference from bulk electronic states and can reliably be fabricated as bulk crystals. This material would be a bulk insulator, have a surface state Dirac point energy well isolated from the bulk valence and conduction bands, have high surface state electronic mobility, and be growable as large, high quality bulk single crystals. Here we show that this major materials obstacle in the field is overcome by crystals of lightly Sn-doped Bi1.1Sb0.9Te2S (Sn-BSTS) grown by the Vertical Bridgeman method, which we characterize here via angle-resolved photoemission spectroscopy, scanning tunneling microscopy, transport studies of the bulk and surface states, and X-ray diffraction and Raman scattering. We present this new material as a bulk topological insulator that can be reliably grown and studied in many laboratories around the world.

cond-mat.str-el↗