arXiv subjects

Xin Fang

Publications and source records attributed to Xin Fang.

At least 55 records · Page 3Linked to original sources

arXiv2022

Learning Contextually Fused Audio-visual Representations for Audio-visual Speech Recognition

With the advance in self-supervised learning for audio and visual modalities, it has become possible to learn a robust audio-visual speech representation. This would be beneficial for improving the audio-visual speech recognition (AVSR) performance, as the multi-modal inputs contain more fruitful information in principle. In this paper, based on existing self-supervised representation learning methods for audio modality, we therefore propose an audio-visual representation learning approach. The proposed approach explores both the complementarity of audio-visual modalities and long-term context dependency using a transformer-based fusion module and a flexible masking strategy. After pre-training, the model is able to extract fused representations required by AVSR. Without loss of generality, it can be applied to single-modal tasks, e.g. audio/visual speech recognition by simply masking out one modality in the fusion module. The proposed pre-trained model is evaluated on speech recognition and lipreading tasks using one or two modalities, where the superiority is revealed.

eess.IV↗

arXiv2022

DLMP of Competitive Markets in Active Distribution Networks: Models, Solutions, Applications, and Visions

Traditionally, the electric distribution system operates with uniform energy prices across all system nodes. However, as the adoption of distributed energy resources (DERs) propels a shift from passive to active distribution network (ADN) operation, a distribution-level electricity market has been proposed to manage new complexities efficiently. In addition, distribution locational marginal price (DLMP) has been established in the literature as the primary pricing mechanism. The DLMP inherits the LMP concept in the transmission-level wholesale market, but incorporates characteristics of the distribution system, such as high R/X ratios and power losses, system imbalance, and voltage regulation needs. The DLMP provides a solution that can be essential for competitive market operation in future distribution systems. This paper first provides an overview of the current distribution-level market architectures and their early implementations. Next, the general clearing model, model relaxations, and DLMP formulation are comprehensively reviewed. The state-of-the-art solution methods for distribution market clearing are summarized and categorized into centralized, distributed, and decentralized methods. Then, DLMP applications for the operation and planning of DERs and distribution system operators (DSOs) are discussed in detail. Finally, visions of future research directions and possible barriers and challenges are presented.

eess.SY↗

arXiv2022

Band degeneration and evolution in nonlinear triatomic chain superlattices

Nonlinear superlattices exhibit unique features allowing for wave manipulations. Despite the increasing attention received, the underlying physical mechanisms and the evolution process of the band structures and bandgaps in strongly nonlinear superlattices remain unclear. Here we establish and examine strongly nonlinear superlattice models (three triatomic models) to show the evolution process of typical nonlinear band structures based on analytical and numerical approaches. We find that the strongly nonlinear superlattices present particular band degeneration and bifurcation, accompanied with the vibration mode transfer in their unit cells. The evolution processes and the physical mechanisms of the band degeneration in different models are clarified with the consideration of the mode transfer. The observed degeneration may occur as the shifting, bifurcating, shortening, merging or disappearing of dispersion curves, all depending on the arrangement of the coupled nonlinear elements. Meanwhile, the dimension of the unit cell reduces, alongside changes in the frequency range and mechanisms (Bragg and local resonance) of the bandgaps. These findings answer some foundamental questions peritinent to the study of nonlinear periodic structures, nonlinear crystals and nonlinear metamaterials, which are of interest to the broad community of physics

cond-mat.other↗

arXiv2022

A Complementary Joint Training Approach Using Unpaired Speech and Text for Low-Resource Automatic Speech Recognition

Unpaired data has shown to be beneficial for low-resource automatic speech recognition~(ASR), which can be involved in the design of hybrid models with multi-task training or language model dependent pre-training. In this work, we leverage unpaired data to train a general sequence-to-sequence model. Unpaired speech and text are used in the form of data pairs by generating the corresponding missing parts in prior to model training. Inspired by the complementarity of speech-PseudoLabel pair and SynthesizedAudio-text pair in both acoustic features and linguistic features, we propose a complementary joint training~(CJT) method that trains a model alternatively with two data pairs. Furthermore, label masking for pseudo-labels and gradient restriction for synthesized audio are proposed to further cope with the deviations from real data, termed as CJT++. Experimental results show that compared to speech-only training, the proposed basic CJT achieves great performance improvements on clean/other test sets, and the CJT++ re-training yields further performance enhancements. It is also apparent that the proposed method outperforms the wav2vec2.0 model with the same model size and beam size, particularly in extreme low-resource cases.

cs.SD↗

arXiv2022

LS Algebras, Valuations and Schubert Varieties

In this paper, we propose an algebraic approach via Lakshmibai-Seshadri (LS) algebras to establish a link between standard monomial theories, Newton-Okounkov bodies and valuations. This is applied to Schubert varieties, where this approach is compatible with the one using Seshadri stratifications by the same authors (arXiv:2112.03776), showing that LS paths encode vanishing multiplicities with respect to the web of Schubert varieties.

math.AG↗

arXiv2022

A Noise-Robust Self-supervised Pre-training Model Based Speech Representation Learning for Automatic Speech Recognition

Wav2vec2.0 is a popular self-supervised pre-training framework for learning speech representations in the context of automatic speech recognition (ASR). It was shown that wav2vec2.0 has a good robustness against the domain shift, while the noise robustness is still unclear. In this work, we therefore first analyze the noise robustness of wav2vec2.0 via experiments. We observe that wav2vec2.0 pre-trained on noisy data can obtain good representations and thus improve the ASR performance on the noisy test set, which however brings a performance degradation on the clean test set. To avoid this issue, in this work we propose an enhanced wav2vec2.0 model. Specifically, the noisy speech and the corresponding clean version are fed into the same feature encoder, where the clean speech provides training targets for the model. Experimental results reveal that the proposed method can not only improve the ASR performance on the noisy test set which surpasses the original wav2vec2.0, but also ensure a tiny performance decrease on the clean test set. In addition, the effectiveness of the proposed method is demonstrated under different types of noise conditions.

eess.AS↗

arXiv2021

Effective Parallelism for Equation and Jacobian Evaluation in Power Flow Calculation

This letter investigates parallelism approaches for equation and Jacobian evaluations in large-scale power flow calculation. Two levels of parallelism are proposed and analyzed: inter-model parallelism, which evaluates models in parallel, and intra-model parallelism, which evaluates calculations within each model in parallel. Parallelism techniques such as multi-threading and single instruction multiple data (SIMD) vectorization are discussed, implemented, and benchmarked as six calculation workflows. Case studies on the 70,000-bus synthetic grid show that equation evaluations can be accelerated by ten times, and the overall Newton power flow advances the state of the art by 20%.

eess.SY↗

arXiv2021

Chaotic time-delay signature suppression using quantum noise

Time-delay signature (TDS) suppression of semiconductor lasers with external optical feedback is necessary to ensure the security of chaos-based secure communications. Here we numerically and experimentally demonstrate a technique to effectively suppress the TDS of chaotic lasers using quantum noise. The TDS and dynamical complexity are quantified using the autocorrelation function and normalized permutation entropy at the feedback delay time, respectively. Quantum noise from quadrature fluctuations of vacuum state is prepared through balanced homodyne measurement. The effects of strength and bandwidth of quantum noise on chaotic TDS suppression and complexity enhancement are investigated numerically and experimentally. Compared to the original dynamics, the TDS of this quantum-noise improved chaos is suppressed up to 94% and the bandwidth suppression ratio of quantum noise to chaotic laser is 1:25. The experiment agrees well with the theory. The improved chaotic laser is potentially beneficial to chaos-based random number generation and secure communication.

quant-ph↗

arXiv2021

Impact of DER Communication Delay in AGC: Cyber-Physical Dynamic Simulation

Distributed energy resource (DER) frequency regulations are promising technologies for future grid operation. Unlike conventional generators, DERs might require open communication networks to exchange signals with control centers, possibly through DER aggregators; therefore, the impacts of the communication variations on the system stability need to be investigated. This paper develops a cyber-physical dynamic simulation model based on the Hierarchical Engine for Large-Scale Co-Simulation (HELICS) to evaluate the impact of the communication variations, such as delays in DER frequency regulations. The feasible delay range can be obtained under different parameter settings. The results show that the risk of instability generally increases with the communication delay.

eess.SY↗

arXiv2021

USTC-NELSLIP System Description for DIHARD-III Challenge

This system description describes our submission system to the Third DIHARD Speech Diarization Challenge. Besides the traditional clustering based system, the innovation of our system lies in the combination of various front-end techniques to solve the diarization problem, including speech separation and target-speaker based voice activity detection (TS-VAD), combined with iterative data purification. We also adopted audio domain classification to design domain-dependent processing. Finally, we performed post processing to do system fusion and selection. Our best system achieved DERs of 11.30% in track 1 and 16.78% in track 2 on evaluation set, respectively.

cs.SD↗

arXiv2021

XLST: Cross-lingual Self-training to Learn Multilingual Representation for Low Resource Speech Recognition

In this paper, we propose a weakly supervised multilingual representation learning framework, called cross-lingual self-training (XLST). XLST is able to utilize a small amount of annotated data from high-resource languages to improve the representation learning on multilingual un-annotated data. Specifically, XLST uses a supervised trained model to produce initial representations and another model to learn from them, by maximizing the similarity between output embeddings of these two models. Furthermore, the moving average mechanism and multi-view data augmentation are employed, which are experimentally shown to be crucial to XLST. Comprehensive experiments have been conducted on the CommonVoice corpus to evaluate the effectiveness of XLST. Results on 5 downstream low-resource ASR tasks shows that our multilingual pretrained model achieves relatively 18.6% PER reduction over the state-of-the-art self-supervised method, with leveraging additional 100 hours of annotated English data.

eess.AS↗

arXiv2021

Bidirectional elastic diode with frequency-preserved nonreciprocity

The study of nonreciprocal wave propagation is of great interests for both fundamental research and engineering applications. Here we demonstrate theoretically and experimentally a bidirectional, nonreciprocal, and high-quality diode that can rectify elastic waves in both forward and backward directions in an elastic metamaterial designed to exhibit enhanced nonlinearity of resonances. This diode can preserve or vary frequency, rectify low-frequency long wave with small system size, offer high-quality insulation, can be modulated by amplitude, and break reciprocity of both the total energy and fundamental wave. We report three mechanisms to break reciprocity: the amplitude-dependent bandgap combining interface reflection, chaotic response combining linear bandgap, amplitude-dependent attenuation rate in damping diode. The bidirectional diode paves ways for mutually controlling information/energy transport between two sources, which can be used as new wave insulators.

physics.app-ph↗

arXiv2021

Two-Stage Stochastic Optimization Frameworks to Aid in Decision-Making Under Uncertainty for Variable Resource Generators Participating in a Sequential Energy Market

Decisions for a variable renewable resource generators commitment in the energy market are typically made in advance when little information is obtainable about wind availability and market prices. Much research has been published recommending various frameworks for addressing this issue. However, these frameworks are limited as they do not consider all markets a producer can participate in. Moreover, current stochastic programming models do not allow for uncertainty data to be updated as more accurate information becomes available. This work proposes two decision-making frameworks for a wind energy generator participating in day-ahead, intraday, reserve, and balancing markets. The first framework is a two-stage stochastic convex optimization approach, where both scenario-independent and scenario-dependent decisions are made concurrently. The second framework is a series of four two-stage stochastic optimization models wherein the results from each model feed into each subsequent model allowing for scenarios to be updated as more information becomes available to the decision-maker. In the simulation experiments, the multi-phase framework performs better than the single-phase in every run, and results in an average profit increase of 7%. The proposed optimization frameworks aid in better decision-making while addressing uncertainty related to variable resource generators and maximize the return on investment.

math.OC↗

arXiv2021

Transmission-and-Distribution Frequency Dynamic Co-Simulation Framework for Distributed Energy Resources Frequency Response

The rapid deployment of distributed energy resources (DERs) in distribution networks has brought challenges to balance the system and stabilize frequency. DERs have the ability to provide frequency regulation; however, existing dynamic frequency simulation tools-which were developed mainly for the transmission system-lack the capability to simulate distribution network dynamics with high penetrations of DERs. Although electromagnetic transient (EMT) simulation tools can simulate distribution network dynamics, the computation efficiency limits their use for large-scale transmission-and-distribution (T&D) simulations. This paper presents an efficient T&D dynamic frequency co-simulation framework for DER frequency response based on the HELICS platform and existing off-the-shelf simulators. The challenge of synchronizing frequency between the transmission network and DERs hosted in the distribution network is approached by detailed modeling of DERs in frequency dynamic models while DER phasor models are also preserved in the distribution networks. Thereby, local voltage constraints can be respected when dispatching the DER power for frequency response. The DER frequency responses (primary and secondary)-are simulated in case studies to validate the proposed framework. Lastly, fault-induced delayed voltage recovery (FIDVR) event of a large system is presented to demonstrate the efficiency and effectiveness of the overall framework.

eess.SY↗

arXiv2020

Domain-Embeddings Based DGA Detection with Incremental Training Method

DGA-based botnet, which uses Domain Generation Algorithms (DGAs) to evade supervision, has become a part of the most destructive threats to network security. Over the past decades, a wealth of defense mechanisms focusing on domain features have emerged to address the problem. Nonetheless, DGA detection remains a daunting and challenging task due to the big data nature of Internet traffic and the potential fact that the linguistic features extracted only from the domain names are insufficient and the enemies could easily forge them to disturb detection. In this paper, we propose a novel DGA detection system which employs an incremental word-embeddings method to capture the interactions between end hosts and domains, characterize time-series patterns of DNS queries for each IP address and therefore explore temporal similarities between domains. We carefully modify the Word2Vec algorithm and leverage it to automatically learn dynamic and discriminative feature representations for over 1.9 million domains, and develop an simple classifier for distinguishing malicious domains from the benign. Given the ability to identify temporal patterns of domains and update models incrementally, the proposed scheme makes the progress towards adapting to the changing and evolving strategies of DGA domains. Our system is evaluated and compared with the state-of-art system FANCI and two deep-learning methods CNN and LSTM, with data from a large university's network named TUNET. The results suggest that our system outperforms the strong competitors by a large margin on multiple metrics and meanwhile achieves a remarkable speed-up on model updating.

cs.CR↗

arXiv2020

Exact structures and degeneration of Hall algebras

We study degenerations of the Hall algebras of exact categories induced by degree functions on the set of isomorphism classes of indecomposable objects. We prove that each such degeneration of the Hall algebra $\mathcal{H}(\mathcal{E})$ of an exact category $\mathcal{E}$ is the Hall algebra of a smaller exact structure $\mathcal{E}' < \mathcal{E}$ on the same additive category $\mathcal{A}.$ When $\mathcal{E}$ is admissible in the sense of Enomoto, for any $\mathcal{E}' < \mathcal{E}$ satisfying suitable finiteness conditions, we prove that $\mathcal{H}(\mathcal{E}')$ is a degeneration of $\mathcal{H}(\mathcal{E})$ of this kind. In the additively finite case, all such degree functions form a simplicial cone whose face lattice reflects properties of the lattice of exact structures. For the categories of representations of Dynkin quivers, we recover degenerations of the negative part of the corresponding quantum group, as well as the associated polyhedral structure studied by Fourier, Reineke and the first author. Along the way, we give minor improvements to certain results of Enomoto and Brüstle-Langford-Hassoun-Roy concerning the classification of exact structures on an additive category. We prove that for each idempotent complete additive category $\mathcal{A}$, there exists an abelian category whose lattice of Serre subcategories is isomorphic to the lattice of exact structures on $\mathcal{A}$. We show that every Krull-Schmidt category admits a unique maximal admissible exact structure and that the lattice of smaller exact structures of an admissible exact structure is Boolean.

math.RT↗

arXiv2020

Lusztig polytopes and FFLV polytopes

In this paper we prove that in type $\tt A_n$, the Feigin-Fourier-Littelmann-Vinberg (FFLV) polytope coincides with the Minkowski sum of Lusztig polytopes arising from various reduced decompositions. Using this result, we formulate a conjecture about the crystal structures on FFLV polytopes.

math.RT↗

arXiv2020

Weighted PBW degenerations and tropical flag varieties

We study algebraic, combinatorial and geometric aspects of weighted PBW-type degenerations of (partial) flag varieties in type $A$. These degenerations are labeled by degree functions lying in an explicitly defined polyhedral cone, which can be identified with a maximal cone in the tropical flag variety. Varying the degree function in the cone, we recover, for example, the classical flag variety, its abelian PBW degeneration, some of its linear degenerations and a particular toric degeneration.

math.RT↗