arXiv subjects

Chao Zhang

Publications and source records attributed to Chao Zhang.

At least 487 records · Page 27Linked to original sources

arXiv2024

Randomness versus Nonlocality in Multi-input and Multi-output Quantum Scenario

Device-independent randomness certification based on Bell nonlocality does not require any assumptions about the devices and therefore provides adequate security. Great effort has been made to demonstrate that nonlocality is necessary for generating quantum randomness, but the minimal resource required for random number generation has not been clarified. Here we first prove and experimentally demonstrate that violating any two-input Bell inequality is both necessary and sufficient for certifying randomness, however, for the multi-input cases, this sufficiency ceases to apply, leading to certain states exhibiting Bell nonlocality without the capability to certify randomness. We examine two typical classes of Bell inequalities with multi-input and multi-output, the facet inequalities and Salavrakos-Augusiak-Tura-Wittek-Acín-Pironio Bell inequalities, in the high-dimensional photonic system, and observe the violation of the latter one can always certify randomness which is not true for the former. The private randomness with a generation rate of 1.867\pm0.018 bits per photon pair is obtained in the scenario of Salavrakos-Augusiak-Tura-Wittek-Acín-Pironio Bell inequalities with 3-input and 4-output. Our work unravels the internal connection between randomness and nonlocality, and effectively enhances the performance of tasks such as device-independent random number generation.

quant-ph↗

arXiv2024

SOT Triggered Neural Clustering for Speaker Attributed ASR

This paper introduces a novel approach to speaker-attributed ASR transcription using a neural clustering method. With a parallel processing mechanism, diarisation and ASR can be applied simultaneously, helping to prevent the accumulation of errors from one sub-system to the next in a cascaded system. This is achieved by the use of ASR, trained using a serialised output training method, together with segment-level discriminative neural clustering (SDNC) to assign speaker labels. With SDNC, our system does not require an extra non-neural clustering method to assign speaker labels, thus allowing the entire system to be based on neural networks. Experimental results on the AMI meeting dataset demonstrate that SDNC outperforms spectral clustering (SC) by a 19% relative diarisation error rate (DER) reduction on the AMI Eval set. When compared with the cascaded system with SC, the parallel system with SDNC gives a 7%/4% relative improvement in cpWER on the Dev/Eval set.

eess.AS↗

arXiv2024

The constraint on modified black holes with extreme mass ratio inspirals

The low-energy effective action of String Theory introduces corrections to the dilaton-graviton sector, resulting in deformed black holes beyond general relativity. We analyze extreme mass-ratio inspiral systems (EMRIs), where a stellar-mass object spirals into a slowly rotating supermassive black hole including a distinct deviation parameter. This study examines the effects of this deformation on gravitational wave fluxes, orbital evolution, and phase dynamics, incorporating leading-order post-Newtonian corrections. With one-year observations of EMRIs, we employ the Fisher information matrix method to evaluate the potential for detecting deviations from general relativity through space-based gravitational wave detectors that utilize time-delay interferometry to suppress laser noise. The constraint on modified black holes, $Δα\preceq 10^{-5}$, is almost the same with and without the time-delay interferometry combination. This analysis enhances our understanding and underscores the crucial role of observations in advancing gravitational phenomena within String Theory.

gr-qc↗

arXiv2024

Regularity for a class of degenerate fully nonlinear nonlocal elliptic equations

We consider a wide class of fully nonlinear integro-differential equations that degenerate when the gradient of the solution vanishes. By using compactness and perturbation arguments, we give a complete characterization of the regularity of viscosity solutions according to different diffusion orders. More precisely, when the order of the fractional diffusion is sufficiently close to 2, we obtain Hölder continuity for the gradient of any viscosity solutions and further derive an improved gradient regularity estimate at the origin. For the order of the fractional diffusion in the interval $(1, 2)$, we prove that there is at least one solution of class $C^{1, α}_{\rm loc}$. Additionally, if the order of the fractional diffusion is in the interval $(0,1]$, the local Hölder continuity of solutions is inferred.

math.AP↗

arXiv2024

Whisper-PMFA: Partial Multi-Scale Feature Aggregation for Speaker Verification using Whisper Models

In this paper, Whisper, a large-scale pre-trained model for automatic speech recognition, is proposed to apply to speaker verification. A partial multi-scale feature aggregation (PMFA) approach is proposed based on a subset of Whisper encoder blocks to derive highly discriminative speaker embeddings.Experimental results demonstrate that using the middle to later blocks of the Whisper encoder keeps more speaker information. On the VoxCeleb1 and CN-Celeb1 datasets, our system achieves 1.42% and 8.23% equal error rates (EERs) respectively, receiving 0.58% and 1.81% absolute EER reductions over the ECAPA-TDNN baseline, and 0.46% and 0.97% over the ResNet34 baseline. Furthermore, our results indicate that using Whisper models trained on multilingual data can effectively enhance the model's robustness across languages. Finally, the low-rank adaptation approach is evaluated, which reduces the trainable model parameters by approximately 45 times while only slightly increasing EER by 0.2%.

cs.SD↗

arXiv2024

Mapping Hydrogen Evolution Activity Trends of V-based A15 Superconducting Alloys

Exploring high-efficiency and low-cost electrocatalysts is valuable for water-splitting technologies. Recently, Si-group compounds have attracted increasing attention in electrocatalysis, considering the abundant Si-group elements on Earth. However, Si-group compounds for HER electrocatalysis have not been systematically studied. In this study, we unveil the activity trends of non-noble metal catalyst A15-type V3M (i.e., V3Si, V3Ge, and V3Sn) superconductors and show that V3Si is the most efficient HER catalyst because of the high electronic conductivity and suitable d-band center. Among them, the V3Si only requires 33.4 mV to reach 10 mA cm-2, and only 57.6 mV and 114.6 mV are required to attain a high current density of 100 mA cm-2 and 500 mA cm-2, respectively. These low overpotentials are close to the 34.3 mV at 10 mA cm-2 of state-of-art Pt/C (20 %) but superior to 168.5 mV of Pt/C (20 %) at 100 mA cm-2. Furthermore, the V3Si illustrates exceptional durability with no obvious decay in the 120 h at the different current densities (i.e., 10 - 250 mA cm-2). The excellent HER activity of V3Si alloy can be ascribed to the synergies of superior electronic conductivity and suitable d-band center. Moreover, DFT calculations reveal that the absolute hydrogen adsorption Gibbs free energy is decreased after introducing the V to Si. Beyond offering a stable and high-performance electrocatalyst in an acidic medium, this work inspires the rational design of desirable silicide electrocatalysts.

cond-mat.mtrl-sci↗

arXiv2024

Excellent and CO$_2$$_{0.85}$Nd$_{0.1}$Cu$_{0.05}$O$_{2-δ}$-Nd$_x$Sr$_{1-x}$Fe$_{1-y}$Cu$_y$O$_{3-δ}$ dual-phase oxygen transport membranes

Oxygen transport membranes(OTMs)have provided great opportunities in the last decades but are suffering from the trade-off effect between stability and oxygen permeability. Here, we report a group of new planar dual-phase mixed ionic-electronic conducting (MIEC) OTMs consisting of CO$_2$$_{0.85}$Nd$_{0.1}$Cu$_{0.05}$O$_2$ (CNCO) and Nd$_x$Sr$_{1-x}$Fe$_{1-y}$Cu$_y$O$_3$(NSFCO; $x = 0.4, 0.6$; $y = 0.05, 0.1$) phases, showing excellent oxygen permeability while comparable CO$_2$-resistant stability. The substitution of Cu as a bifunctional additive decreases the sintering temperature and enhances bulk diffusion and oxygen permeability with the co-doping of Nd.The oxygen permeation fluxes reached 2.62 and 1.52 mL min$^{-1}$ cm$^{-2}$ at 1000$^\circ$C through the optimal 60wt%Ce0.85Nd0.1Cu0.05O2-40wt%Nd0.4Sr0.6Fe0.9Cu0.1O3 (CNCO-NSFCO41) composition with He and CO$_2$ sweeping, respectively, higher than all reported dense dual-phase OTMs. Such excellent CO$_2$-tolerant permeability meets the needs of potential industrial applications. Analysis with Zhu's oxygen permeation model shows lower bulk diffusion resistance of CNCO-NSFCO41 than that of reported 60wt%Ce0.85Pr0.1Cu0.05O2-40wt%Pr0.4Sr0.6Fe0.9Cu0.1O3(CPCO-PSFCO41)and more limitation by the interfacial exchange at high temperature. All the prepared OTMs also show good long-term stability over 100 hours in both atmospheres. Our results confirm the excellent oxygen permeability and stability under a high-concentration CO2 atmosphere, providing a material candidate for CO2 capture in oxyfuel combustion.

cond-mat.mtrl-sci↗

arXiv2024

Non-trivial Topological Surface States Regulation of 1T-OsCoTe$_2$ Enables Selective C-C Coupling for Highly Efficient Photochemical CO$_2$ Reduction Toward C$_{2+}$ hydrocarbons

Despite ongoing research, the rational design of nontrivial topological semimetal surface states for the selective photocatalytic CO$_2$ conversion into valuable products remains full of challenges. Herein, we present the synthesis of 1T-OsCoTe$_2$ for the photoreduction upgrading of CO$_2$ to tricarbon alkane C$_3$H$_8$,by the integration of experimental work and theory calculation. Experimental studies suggested a high electron based selectivity of 71.2% for C$_3$H$_8$ and an internal quantum efficiency of 54.6% at 380 nm. In-situ X-ray photoelectron spectroscopy and X-ray absorption fine structure spectroscopy demonstrated that Co and Os atoms coordinated with Te atoms enable an efficient Os-Te-Co electron transfer to activate the generation of *CH$_3$,*CHOCO and *CH$_2$OCOCO. Density functional theory calculations further confirmed Os-Te-Co electron bridging on the improved CO$_2$ conversion kinetics. To our knowledge, this is the first report suggesting the role of Os atoms in accelerating the photocatalytic CO$_2$ conversion activity of the topological semimetal 1T-OsCoTe$_2$.

cond-mat.mtrl-sci↗

arXiv2024

Revealing the nontrivial topological surface states of catalysts for effective photochemical carbon dioxide conversion

Topological semimetals with protected surface states mark a new paradigm of research beyond the early landmarks of band-structure engineering, allowing fabrication of efficient catalyst to harness the rich metallic surface states to activate specific chemical processes. Herein, we demonstrate a facile solid-phase method for in-situ doping of Ir at the Os sites in the Os3Sn7, an alloy with topological states, which significantly improves the photocatalytic performance for the reduction of CO2 to CO and CH4. Experimental evidence combined with theoretical calculations reveal that the nontrivial topological surface states greatly accelerate charge-separation/electron-enrichment and adsorption/activation of CO2 molecules, rendering highly efficient reaction channels to stimulate the formation of *COOH and *CO, as well CHO*. This work shows the promise of achieving high photocatalytic performances with synthesizing topological catalysts and provides hints on the design of novel topological catalysts with superior photoactivity towards the CO2 reduction reaction.

cond-mat.mtrl-sci↗

arXiv2024

Structural and Superconducting Properties in the Te-doped Spinel CuRh2Se4

In this paper, we discuss the impact of tellurium (Te) doping on the spinel superconductor CuRh2Se4. We conducted a comprehensive evaluation of the structural and superconducting properties of the system using various techniques, including X-ray diffraction (XRD), resistivity, magnetization, and specific heat measurements. Based on our XRD analysis, we found that the spinel superconductor CuRh2Se4-xTex crystallizes in the space group Fd3m(227) with x in the region of 0 to 0.28, while the layered compound CuRh2Se4-xTex crystallizes in the space group P3m1 (164) with x in the region of 2.8 to 4.0. The upper critical magnetic field can be increased from 0.95(2) T for CuRh2Se4 to 3.44(1) T for CuRh2Se3.72Te0.28 by doping with elemental Te. However, the layered compound CuRh2Se4-xTex did not exhibit superconducting properties. Besides, the specific heat measurements of CuRh2Se4-xTex (x = 0, 0.1, 0.28) indicate that the Te element doping affects the electronic structure and interactions of the material and breaks the stability of the superconducting pairing, which leads to a decrease in the Tc. Finally, we show the electronic phase diagram of Tc with Te doping to summarise our findings.

cond-mat.supr-con↗

arXiv2024

An Augmented Lagrangian Method for Training Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are widely used to model sequential data in a wide range of areas, such as natural language processing, speech recognition, machine translation, and time series analysis. In this paper, we model the training process of RNNs with the ReLU activation function as a constrained optimization problem with a smooth nonconvex objective function and piecewise smooth nonconvex constraints. We prove that any feasible point of the optimization problem satisfies the no nonzero abnormal multiplier constraint qualification (NNAMCQ), and any local minimizer is a Karush-Kuhn-Tucker (KKT) point of the problem. Moreover, we propose an augmented Lagrangian method (ALM) and design an efficient block coordinate descent (BCD) method to solve the subproblems of the ALM. The update of each block of the BCD method has a closed-form solution. The stop criterion for the inner loop is easy to check and can be stopped in finite steps. Moreover, we show that the BCD method can generate a directional stationary point of the subproblem. Furthermore, we establish the global convergence of the ALM to a KKT point of the constrained optimization problem. Compared with the state-of-the-art algorithms, numerical results demonstrate the efficiency and effectiveness of the ALM for training RNNs.

math.OC↗

arXiv2024

ViC: Virtual Compiler Is All You Need For Assembly Code Search

Assembly code search is vital for reducing the burden on reverse engineers, allowing them to quickly identify specific functions using natural language within vast binary programs. Despite its significance, this critical task is impeded by the complexities involved in building high-quality datasets. This paper explores training a Large Language Model (LLM) to emulate a general compiler. By leveraging Ubuntu packages to compile a dataset of 20 billion tokens, we further continue pre-train CodeLlama as a Virtual Compiler (ViC), capable of compiling any source code of any language to assembly code. This approach allows for virtual compilation across a wide range of programming languages without the need for a real compiler, preserving semantic equivalency and expanding the possibilities for assembly code dataset construction. Furthermore, we use ViC to construct a sufficiently large dataset for assembly code search. Employing this extensive dataset, we achieve a substantial improvement in assembly code search performance, with our model surpassing the leading baseline by 26%.

cs.SE↗

arXiv2024

Speaker Adaptation for Quantised End-to-End ASR Models

End-to-end models have shown superior performance for automatic speech recognition (ASR). However, such models are often very large in size and thus challenging to deploy on resource-constrained edge devices. While quantisation can reduce model sizes, it can lead to increased word error rates (WERs). Although improved quantisation methods were proposed to address the issue of performance degradation, the fact that quantised models deployed on edge devices often target only on a small group of users is under-explored. To this end, we propose personalisation for quantised models (P4Q), a novel strategy that uses speaker adaptation (SA) to improve quantised end-to-end ASR models by fitting them to the characteristics of the target speakers. In this paper, we study the P4Q strategy based on Whisper and Conformer attention-based encoder-decoder (AED) end-to-end ASR models, which leverages a 4-bit block-wise NormalFloat4 (NF4) approach for quantisation and the low-rank adaptation (LoRA) approach for SA. Experimental results on the LibriSpeech and the TED-LIUM 3 corpora show that, with a 7-time reduction in model size and 1% extra speaker-specific parameters, 15.1% and 23.3% relative WER reductions were achieved on quantised Whisper and Conformer AED models respectively, comparing to the full precision models.

cs.SD↗

arXiv2024

Affect Recognition in Conversations Using Large Language Models

Affect recognition, encompassing emotions, moods, and feelings, plays a pivotal role in human communication. In the realm of conversational artificial intelligence, the ability to discern and respond to human affective cues is a critical factor for creating engaging and empathetic interactions. This study investigates the capacity of large language models (LLMs) to recognise human affect in conversations, with a focus on both open-domain chit-chat dialogues and task-oriented dialogues. Leveraging three diverse datasets, namely IEMOCAP (Busso et al., 2008), EmoWOZ (Feng et al., 2022), and DAIC-WOZ (Gratch et al., 2014), covering a spectrum of dialogues from casual conversations to clinical interviews, we evaluate and compare LLMs' performance in affect recognition. Our investigation explores the zero-shot and few-shot capabilities of LLMs through in-context learning as well as their model capacities through task-specific fine-tuning. Additionally, this study takes into account the potential impact of automatic speech recognition errors on LLM predictions. With this work, we aim to shed light on the extent to which LLMs can replicate human-like affect recognition capabilities in conversations.

cs.CL↗

arXiv2024

Strategic Federated Learning: Application to Smart Meter Data Clustering

Federated learning (FL) involves several clients that share with a fusion center (FC), the model each client has trained with its own data. Conventional FL, which can be interpreted as an estimation or distortion-based approach, ignores the final use of model information (MI) by the FC and the other clients. In this paper, we introduce a novel FL framework in which the FC uses an aggregate version of the MI to make decisions that affect the client's utility functions. Clients cannot choose the decisions and can only use the MI reported to the FC to maximize their utility. Depending on the alignment between the client and FC utilities, the client may have an individual interest in adding strategic noise to the model. This general framework is stated and specialized to the case of clustering, in which noisy cluster representative information is reported. This is applied to the problem of power consumption scheduling. In this context, utility non-alignment occurs, for instance, when the client wants to consume when the price of electricity is low, whereas the FC wants the consumption to occur when the total power is the lowest. This is illustrated with aggregated real data from Ausgrid \cite{ausgrid}. Our numerical analysis clearly shows that the client can increase his utility by adding noise to the model reported to the FC. Corresponding results and source codes can be downloaded from \cite{source-code}.

cs.LG↗

arXiv2024

Quantifying subspace entanglement with geometric measures

Determining whether a subspace spanned by certain quantum states is entangled and its entanglement dimensionality remains a fundamental challenge in quantum information science. This paper introduces a geometric measure of $r$-bounded rank, $E_r(\mathcal{S})$, for a given subspace $\mathcal{S}$. Derived from the established geometric measure of entanglement, this measure is specifically designed to assess the entanglement within $\mathcal{S}$. It not only serves as a tool for determining the entanglement dimensionality but also illuminates the subspace's capacity to preserve such entanglement. By employing developed non-convex optimization techniques utilized in machine learning area, we can accurately calculate $E_r(\mathcal{S})$ within the manifold optimization framework. Our approach demonstrates notable advantages over existing hierarchical methods, PPT relaxation techniques, and the seesaw strategy, particularly by combining computational efficiency with broad applicability. More importantly, it paves the way for high-dimensional entanglement certification, which is crucial for numerous quantum information tasks. We showcase its effectiveness in validating high-dimensional entangled subspaces in bipartite systems, determining the border rank of multipartite pure states, and identifying genuinely or completely entangled subspaces.

quant-ph↗

arXiv2024

Confidence Estimation for Automatic Detection of Depression and Alzheimer's Disease Based on Clinical Interviews

Speech-based automatic detection of Alzheimer's disease (AD) and depression has attracted increased attention. Confidence estimation is crucial for a trust-worthy automatic diagnostic system which informs the clinician about the confidence of model predictions and helps reduce the risk of misdiagnosis. This paper investigates confidence estimation for automatic detection of AD and depression based on clinical interviews. A novel Bayesian approach is proposed which uses a dynamic Dirichlet prior distribution to model the second-order probability of the predictive distribution. Experimental results on the publicly available ADReSS and DAIC-WOZ datasets demonstrate that the proposed method outperforms a range of baselines for both classification accuracy and confidence estimation.

cs.CL↗

arXiv2024

ChipExpert: The Open-Source Integrated-Circuit-Design-Specific Large Language Model

The field of integrated circuit (IC) design is highly specialized, presenting significant barriers to entry and research and development challenges. Although large language models (LLMs) have achieved remarkable success in various domains, existing LLMs often fail to meet the specific needs of students, engineers, and researchers. Consequently, the potential of LLMs in the IC design domain remains largely unexplored. To address these issues, we introduce ChipExpert, the first open-source, instructional LLM specifically tailored for the IC design field. ChipExpert is trained on one of the current best open-source base model (Llama-3 8B). The entire training process encompasses several key stages, including data preparation, continue pre-training, instruction-guided supervised fine-tuning, preference alignment, and evaluation. In the data preparation stage, we construct multiple high-quality custom datasets through manual selection and data synthesis techniques. In the subsequent two stages, ChipExpert acquires a vast amount of IC design knowledge and learns how to respond to user queries professionally. ChipExpert also undergoes an alignment phase, using Direct Preference Optimization, to achieve a high standard of ethical performance. Finally, to mitigate the hallucinations of ChipExpert, we have developed a Retrieval-Augmented Generation (RAG) system, based on the IC design knowledge base. We also released the first IC design benchmark ChipICD-Bench, to evaluate the capabilities of LLMs across multiple IC design sub-domains. Through comprehensive experiments conducted on this benchmark, ChipExpert demonstrated a high level of expertise in IC design knowledge Question-and-Answer tasks.

cs.AR↗