arXiv subjects

Cong Li

Publications and source records attributed to Cong Li.

At least 109 records · Page 6Linked to original sources

arXiv2024

Self-reconfigurable Multifunctional Memristive Nociceptor for Intelligent Robotics

Artificial nociceptors, mimicking human-like stimuli perception, are of significance for intelligent robotics to work in hazardous and dynamic scenarios. One of the most essential characteristics of the human nociceptor is its self-adjustable attribute, which indicates that the threshold of determination of a potentially hazardous stimulus relies on environmental knowledge. This critical attribute has been currently omitted, but it is highly desired for artificial nociceptors. Inspired by these shortcomings, this article presents, for the first time, a Self-Directed Channel (SDC) memristor-based self-reconfigurable nociceptor, capable of perceiving hazardous pressure stimuli under different temperatures and demonstrates key features of tactile nociceptors, including 'threshold,' 'no-adaptation,' and 'sensitization.' The maximum amplification of hazardous external stimuli is 1000%, and its response characteristics dynamically adapt to current temperature conditions by automatically altering the generated modulation schemes for the memristor. The maximum difference ratio of the response of memristors at different temperatures is 500%, and this adaptability closely mimics the functions of biological tactile nociceptors, resulting in accurate danger perception in various conditions. Beyond temperature adaptation, this memristor-based nociceptor has the potential to integrate different sensory modalities by applying various sensors, thereby achieving human-like perception capabilities in real-world environments.

physics.app-ph↗

arXiv2024

Data Informed Residual Reinforcement Learning for High-Dimensional Robotic Tracking Control

The learning inefficiency of reinforcement learning (RL) from scratch hinders its practical application towards continuous robotic tracking control, especially for high-dimensional robots. This work proposes a data-informed residual reinforcement learning (DR-RL) based robotic tracking control scheme applicable to robots with high dimensionality. The proposed DR-RL methodology outperforms common RL methods regarding sample efficiency and scalability. Specifically, we first decouple the original robot into low-dimensional robotic subsystems; and further utilize one-step backward (OSBK) data to construct incremental subsystems that are equivalent model-free representations of the above decoupled robotic subsystems. The formulated incremental subsystems allow for parallel learning to relieve computation load and offer us mathematical descriptions of robotic movements for conducting theoretical analysis. Then, we apply DR-RL to learn the tracking control policy, a combination of incremental base policy and incremental residual policy, under a parallel learning architecture. The incremental residual policy uses the guidance from the incremental base policy as the learning initialization and further learns from interactions with environments to endow the tracking control policy with adaptability towards dynamically changing environments. Our proposed DR-RL based tracking control scheme is developed with rigorous theoretical analysis of system stability and weight convergence. The effectiveness of our proposed method is validated numerically on a 7-DoF KUKA iiwa robot manipulator and experimentally on a 3-DoF robot manipulator that would fail for other counterpart RL methods.

eess.SY↗

arXiv2024

Real-Time State Modulation and Acquisition Circuit in Neuromorphic Memristive Systems

Memristive neuromorphic systems are designed to emulate human perception and cognition, where the memristor states represent essential historical information to perform both low-level and high-level tasks. However, current systems face challenges with the separation of state modulation and acquisition, leading to undesired time delays that impact real-time performance. To overcome this issue, we introduce a dual-function circuit that concurrently modulates and acquires memristor state information. This is achieved through two key features: 1) a feedback operational amplifier (op-amp) based circuit that ensures precise voltage application on the memristor while converting the passing current into a voltage signal; 2) a division calculation circuit that acquires state information from the modulation voltage and the converted voltage, improving stability by leveraging the intrinsic threshold characteristics of memristors. This circuit has been evaluated in a memristor-based nociceptor and a memristor crossbar, demonstrating exceptional performance. For instance, it achieves mean absolute acquisition errors below 1 Ω during the modulation process in the nociceptor application. These results demonstrate that the proposed circuit can operate at different scales, holding the potential to enhance a wide range of neuromorphic applications.

physics.app-ph↗

arXiv2024

Topological Weyl Altermagnetism in CrSb

Altermagnets constitute a novel, third fundamental class of collinear magnetic ordered materials, alongside with ferro- and antiferromagnets. They share with conventional antiferromagnets the feature of a vanishing net magnetization. At the same time they show a spin-splitting of electronic bands, just as in ferromagnets, caused by the atomic exchange interaction. On the other hand, topology has recently revolutionized our understanding of condensed matter physics, introducing new phases of matter classified by intrinsic topological order. Here we connect the worlds of altermagnetism and topology, showing that the electronic structure of the altermagnet CrSb is topological and hosts a novel Weyl semimetallic state. Using high-resolution and spin angleresolved photoemission spectroscopy, we observe a large momentum-dependent spin-splitting in CrSb, reaching up to 1 eV, that induces altermagnetic Weyl nodes with an associated magnetic quantum number. At the surface we observe their spin-polarized topological Fermi-arcs. This establishes that in altermagnets the large energy scale intrinsic to the spin-splitting - orders of magnitude larger than the relativistic spin-orbit coupling - creates its own realm of robust electronic topology.

cond-mat.mtrl-sci↗

arXiv2024

Discovery of Very-high-energy Gamma-ray Emissions from the Low Luminosity AGN NGC 4278 by LHAASO

The first source catalog of Large High Altitude Air Shower Observatory reported the detection of a very-high-energy gamma ray source, 1LHAASO J1219+2915. In this paper a further detailed study of the spectral and temporal behavior of this point-like source have been carried. The best-fit position of the TeV source ($\rm{RA}=185.05^{\circ}\pm0.04^{\circ}$, $\rm{Dec}=29.25^{\circ}\pm0.03^{\circ}$) is compatible with NGC 4278 within $\sim0.03$ degree. Variation analysis shows an indication of the variability at a few months level in the TeV band, which is consistent with low frequency observations. Based on these observations, we report the detection of TeV $γ$-ray emissions from this low-luminosity AGN NGC 4278. The observations by LHAASO-WCDA during active period has a significance level of 8.8\,$σ$ with best-fit photon spectral index $\varGamma=2.56\pm0.14$ and a flux $f_{1-10\,\rm{TeV}}=(7.0\pm1.1_{\rm{sta}}\pm0.35_{\rm{syst}})\times10^{-13}\,\rm{photons\,cm^{-2}\,s^{-1}}$, or approximately $5\%$ of the Crab Nebula. The discovery of VHE from NGC 4278 indicates that the compact, weak radio jet can efficiently accelerate particles and emit TeV photons.

astro-ph.HE↗

arXiv2024

MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis

Federated learning is widely used in medical applications for training global models without needing local data access. However, varying computational capabilities and network architectures (system heterogeneity), across clients pose significant challenges in effectively aggregating information from non-independently and identically distributed (non-IID) data. Current federated learning methods using knowledge distillation require public datasets, raising privacy and data collection issues. Additionally, these datasets require additional local computing and storage resources, which is a burden for medical institutions with limited hardware conditions. In this paper, we introduce a novel federated learning paradigm, named Model Heterogeneous personalized Federated Learning via Injection and Distillation (MH-pFLID). Our framework leverages a lightweight messenger model that carries concentrated information to collect the information from each client. We also develop a set of receiver and transmitter modules to receive and send information from the messenger model, so that the information could be injected and distilled with efficiency.

cs.LG↗

arXiv2024

Nyonic Technical Report

This report details the development and key achievements of our latest language model designed for custom large language models. The advancements introduced include a novel Online Data Scheduler that supports flexible training data adjustments and curriculum learning. The model's architecture is fortified with state-of-the-art techniques such as Rotary Positional Embeddings, QK-LayerNorm, and a specially crafted multilingual tokenizer to enhance stability and performance. Moreover, our robust training framework incorporates advanced monitoring and rapid recovery features to ensure optimal efficiency. Our Wonton 7B model has demonstrated competitive performance on a range of multilingual and English benchmarks. Future developments will prioritize narrowing the performance gap with more extensively trained models, thereby enhancing the model's real-world efficacy and adaptability.GitHub: \url{https://github.com/nyonicai/nyonic-public}

cs.CL↗

arXiv2024

LHAASO-KM2A detector simulation using Geant4

KM2A is one of the main sub-arrays of LHAASO, working on gamma ray astronomy and cosmic ray physics at energies above 10 TeV. Detector simulation is the important foundation for estimating detector performance and data analysis. It is a big challenge to simulate the KM2A detector in the framework of Geant4 due to the need to track numerous photons from a large number of detector units (>6000) with large altitude difference (30 m) and huge coverage (1.3 km^2). In this paper, the design of the KM2A simulation code G4KM2A based on Geant4 is introduced. The process of G4KM2A is optimized mainly in memory consumption to avoid memory overffow. Some simpliffcations are used to signiffcantly speed up the execution of G4KM2A. The running time is reduced by at least 30 times compared to full detector simulation. The particle distributions and the core/angle resolution comparison between simulation and experimental data of the full KM2A array are also presented, which show good agreement.

astro-ph.IM↗

arXiv2024

Molecular intercalation in the van der Waals antiferromagnets FePS3 and NiPS3

We have performed electrochemical treatment of the van der Waals antiferromagnetic materials FePS$_3$ and NiPS$_3$ with the ionic liquid EMIM-BF$_4$, achieving significant molecular intercalation. Mass analysis of the intercalated compounds, EMIM$_x$-FePS$_3$ and EMIM$_x$-NiPS$_3$, indicated respective intercalation levels, $x$, of approximately 27\% and 37\%, and X-ray diffraction measurements demonstrated a massive (over 50\%) enhancement of the $c$-axis lattice parameters. To investigate the consequences of these changes for the magnetic properties, we performed magnetic susceptibility and $^{31}$P nuclear magnetic resonance (NMR) studies of both systems. For EMIM$_x$-FePS$_3$, intercalation reduces the magnetic ordering temperature from $T_N = 120$~K to 78~K, and we find a spin gap in the antiferromagnetic phase that drops from 45~K to 30~K. For EMIM$_x$-NiPS$_3$, the ordering temperature is almost unaffected (changing from 148~K to 145~K), but a change towards nearly isotropic spin fluctuations suggests an alteration of the magnetic Hamiltonian. Such relatively modest changes, given that the huge extension of the $c$ axes is expected to cause a very strong suppression any interlayer interactions, point unequivocally to the conclusion that the magnetic properties of both parent compounds are determined solely by two-dimensional (2D), intralayer physics. The changes in transition temperatures and low-temperature spin dynamics in both compounds therefore indicate that intercalation also results in a significant modulation of the intralayer magnetic interactions, which we propose is due to charge doping and localization on the P sites. Our study offers chemical intercalation with ionic liquids as an effective method to control not only the interlayer but also the intralayer interactions in quasi-2D magnetic materials.

cond-mat.str-el↗

arXiv2024

scInterpreter: Training Large Language Models to Interpret scRNA-seq Data for Cell Type Annotation

Despite the inherent limitations of existing Large Language Models in directly reading and interpreting single-cell omics data, they demonstrate significant potential and flexibility as the Foundation Model. This research focuses on how to train and adapt the Large Language Model with the capability to interpret and distinguish cell types in single-cell RNA sequencing data. Our preliminary research results indicate that these foundational models excel in accurately categorizing known cell types, demonstrating the potential of the Large Language Models as effective tools for uncovering new biological insights.

q-bio.GN↗

arXiv2024

NMR evidence of spinon localization in kagome antiferromagnet YCu$_3$(OH)$_6$Br$_2$[Br$_{1-x}$(OH)$_x$]

We performed nuclear magnetic resonance studies on a kagome antiferromagnet YCu$_3$(OH)$_6$Br$_2$[Br$_{1-x}$(OH)$_{x}$]. No significant NMR spectral broadening is found in the Br center peak from 1 K down to 0.05 K, indicating absence of static antiferromagnetic ordering. In contrast to signatures of dominant 2D kagome antiferromagnetic fluctuations at temperature above 30 K, both the Knight shift $K_{\rm{n}}$ and the spin-lattice relaxation rate $1/T_{1}$ increase when the sample is cooled from 30 K to 8 K, which can be attributed to the scattering of spin excitations by strong non-magnetic impurities. Unusually, a hump is observed in $K_{\rm{n}}$ and $1/T_{2}$ close to 2 K (far below the exchange energy), which indicates the existence of excitations with a large density of states close to zero energy. These phenomena are reproduced by a mean-field simulation of Heisenberg model with bond-dependent exchange interactions, where the sign fluctuations in the spinon kinetic terms caused by impurities result in localization of spinons and an almost flat band close to the Fermi energy.

cond-mat.str-el↗

arXiv2024

Field-induced phase transitions and quantum criticality in a honeycomb antiferromagnet Na3Co2SbO6

We performed 23Na NMR measurements on a single-domain crystal of the Kitaev material Na3Co2SbO6, with magnetic field applied along the crystalline a axis. A positive Curie-Weiss constant is obtained from the NMR Knight shift, which suggests the existence of ferromagnetic exchange couplings. The antiferromagnetic ordering is found to be suppressed at a field of 1.9 T. Inside the ordered phase, our data reveal two additional phase transitions. At 1.9 T, the spin-lattice relaxation rate 1/23T1 establishes a quantum critical behavior at high temperatures. However, at low temperatures, a gapped behavior is observed at the critical field, which suggests a weakly first-order transition instead and a possible field-induced quantum spin liquid. Our results reveal complex microscopic interactions in the system, which may help to search for possible quantum spin liquids.

cond-mat.str-el↗

arXiv2024

Factorization of Coupling Constant in Background Field

Due to color confinement, hadrons are inseparable when studying strong interactions. The paper mainly studies the scattering in the background field, especially the scattering of quarks in the hadronic background field. We discovered that the coupling constant within the background field is made up of the distribution function of background field. The finding provides a foundational comprehension of coupling constants, which significantly contributes to our understanding of running coupling constant and the perturbative nature of theory.

hep-th↗

arXiv2024

TRLS: A Time Series Representation Learning Framework via Spectrogram for Medical Signal Processing

Representation learning frameworks in unlabeled time series have been proposed for medical signal processing. Despite the numerous excellent progresses have been made in previous works, we observe the representation extracted for the time series still does not generalize well. In this paper, we present a Time series (medical signal) Representation Learning framework via Spectrogram (TRLS) to get more informative representations. We transform the input time-domain medical signals into spectrograms and design a time-frequency encoder named Time Frequency RNN (TFRNN) to capture more robust multi-scale representations from the augmented spectrograms. Our TRLS takes spectrogram as input with two types of different data augmentations and maximizes the similarity between positive ones, which effectively circumvents the problem of designing negative samples. Our evaluation of four real-world medical signal datasets focusing on medical signal classification shows that TRLS is superior to the existing frameworks.

eess.SP↗

arXiv2023

Intelligent machines work in unstructured environments by differential neuromorphic computing

Efficient operation of intelligent machines in the real world requires methods that allow them to understand and predict the uncertainties presented by the unstructured environments with good accuracy, scalability and generalization, similar to humans. Current methods rely on pretrained networks instead of continuously learning from the dynamic signal properties of working environments and suffer inherent limitations, such as data-hungry procedures, and limited generalization capabilities. Herein, we present a memristor-based differential neuromorphic computing, perceptual signal processing and learning method for intelligent machines. The main features of environmental information such as amplification (>720%) and adaptation (<50%) of mechanical stimuli encoded in memristors, are extracted to obtain human-like processing in unstructured environments. The developed method takes advantage of the intrinsic multi-state property of memristors and exhibits good scalability and generalization, as confirmed by validation in two different application scenarios: object grasping and autonomous driving. In the former, a robot hand experimentally realizes safe and stable grasping through fast learning (in ~1 ms) the unknown object features (e.g., sharp corner and smooth surface) with a single memristor. In the latter, the decision-making information of 10 unstructured environments in autonomous driving (e.g., overtaking cars, pedestrians) is accurately (94%) extracted with a 40*25 memristor array. By mimicking the intrinsic nature of human low-level perception mechanisms, the electronic memristive neuromorphic circuit-based method, presented here shows the potential for adapting to diverse sensing technologies and helping intelligent machines generate smart high-level decisions in the real world.

eess.SP↗

arXiv2023

Noise-induced stochastic Nash equilibrium

In order to better understand the impact of environmental stochastic fluctuations on the evolution of animal behavior, we introduce the concept of a stochastic Nash equilibrium (SNE) that extends the classical concept of a Nash equilibrium (NE). Based on a stochastic stability analysis of a linear evolutionary game with temporally varying payoffs, we address the question of the existence of a SNE, either weak when the geometric mean payoff against it is the same for all other strategies or strong when it is strictly smaller for all other strategies, and its relationship with a stochastically evolutionarily stable (SES) strategy. While a strong SNE is always SES, this is not necessarily the case for a weak SNE. We give conditions for a completely mixed weak SNE not to be SES and to coexist with at least two strong SNE. More importantly, we show that a pair of two completely mixed strong SNE can emerge as the noise level increases. This not only indicates that a noise-induced SNE may possess some properties that a NE cannot possess, such as being completely mixed and strong, but also illustrates the complexity of evolutionary game dynamics in a stochastic environment.

q-bio.PE↗

arXiv2023

Analysis of double-$J/ψ$ production in $Z$ decay at next-to-leading-order QCD accuracy

In this article, we study in detail the double-$J/ψ$ yield through $Z$ decay at the next-to-leading-order (NLO) QCD accuracy within the nonrelativistic QCD factorization. At the tree level, the pure QCD diagrams predict a branching ratio of $\mathcal{B}_{Z \to J/ψ+J/ψ} \sim 10^{-12}$; however, the inclusion of the QED diagrams would augment this prediction by approximately 2-3 orders of magnitude. After incorporating the QCD corrections, the QCD results exhibit a considerable increase, whereas the QED results undergo a substantial reduction. Combining the QCD and QED contributions at NLO in $α_s$, it is observed that the prediction of $\mathcal{B}_{Z \to J/ψ+J/ψ}=(1.110^{+0.334+0.054}_{-0.241-0.001})\times 10^{-10}$, which displays a fairly steady dependence on the renormalization scale, is significantly lower than the upper limits released by CMS.

hep-ph↗

arXiv2023

MDVSC -- Wireless Model Division Video Semantic Communication

This paper introduces a novel method for transmitting video data over noisy wireless channels with high efficiency and controllability. The method derivates from model division multiple access (MDMA) to extract common semantic features from video frames. It also uses deep joint source-channel coding (JSCC) as the main framework to establish communication links and deal with channel noise. An entropy-based variable length coding scheme is developed to adjust the data amount accurately and explicitly. We name our method as model division video semantic communication (MDVSC). The main steps of our approach are as follows: first, video frames are transformed into a latent space to reduce computational complexity and redistribute data. Then, common features and individual features are extracted, and variable length coding is applied to further eliminate redundant semantic information under the communication bandwidth constraint. We evaluate our method on standard video test sequences and compare it with traditional wireless video coding methods. The results show that MDVSC generally surpasses the conventional methods in terms of quality metrics and has the capability to control code length precisely. Moreover, additional experiments and ablation studies are conducted to demonstrate its potential for various tasks.

cs.CE↗