arXiv subjects

Zhong Yin

Publications and source records attributed to Zhong Yin.

18 recordsLinked to original sources

arXiv2025

Attosecond-resolved probing of recolliding electron wave packets in liquids and aqueous solutions

High-harmonic spectroscopy (HHS) in liquids promises real-time access to ultrafast electronic dynamics in the native environment of chemical and biological processes. While electron recollision has been established as the dominant mechanism of high-harmonic generation (HHG) in liquids, resolving the underlying electron dynamics has remained elusive. Here we demonstrate attosecond-resolved measurements of recolliding electron wave packets, extending HHS from neat liquids to aqueous solutions. Using phase-controlled two-colour fields, we observe a linear scaling of the two-colour delay that maximizes even-harmonic emission with photon energy, yielding slopes of 208+/-55 as/eV in ethanol and 124+/-42 as/eV in water, the latter matching ab initio simulations (125+/-48 as/eV). In aqueous salt solutions, we uncover interference minima whose appearance depends on solute type and concentration, arising from destructive interference between solute and solvent emission. By measuring the relative phase of solvent and solute HHG, we retrieve a variation of electron transit time by 113+/-32 as/eV, consistent with our neat-liquid results. These findings establish HHS as a powerful attosecond-resolved probe of electron dynamics in disordered media, opening transformative opportunities for studying ultrafast processes such as energy transfer, charge migration, and proton dynamics in liquids and solutions.

physics.chem-ph↗

arXiv2025

Multi-plateau high-harmonic generation in liquids driven by off-site recombination

Non-perturbative high-harmonic generation (HHG) has recently been observed in the liquid phase, where it was demonstrated to have a different physical mechanism compared to gas and solid phases of matter. The currently best physical picture for liquid HHG eliminates scattered-electron contributions and identifies on-site recombination as the dominant contributor. This mechanism accurately predicts the cut-off energy and its independence of the driving laser wavelength and intensity. However, this implies that additional energy absorbed in the liquid as the driving laser intensity is increased does not result in higher-order non-linearities, which is in contrast to the conventional expectation from most nonlinear media. Here we experimentally observe the formation of a second plateau in HHG from multiple liquids (water, heavy water, propranol, and ethanol), thus explaining the conundrum of the missing higher-order response. We analyze this second plateau with a combination of experimental, state-of-the-art ab-initio numerical (in diverse systems of water, ammonia, and liquid methane), and semi-classical analytical, techniques, and elucidate its physical origin to electrons that recombine on neighboring water molecules rather than at the ionization site, leading to unique HHG ellipticity dependence. Remarkably, we find that the second plateau is dominated by electrons recombining at the second solvation shell, relying on wide hole delocalization. Theory also predicts the appearance of even higher plateaus, indicating a general trend. Our work establishes new physical phenomena in the highly non-linear optical response of liquids, paving the way to attosecond probing of electron dynamics in solutions.

physics.chem-ph↗

arXiv2025

Astra: Toward General-Purpose Mobile Robots via Hierarchical Multimodal Learning

Modern robot navigation systems encounter difficulties in diverse and complex indoor environments. Traditional approaches rely on multiple modules with small models or rule-based systems and thus lack adaptability to new environments. To address this, we developed Astra, a comprehensive dual-model architecture, Astra-Global and Astra-Local, for mobile robot navigation. Astra-Global, a multimodal LLM, processes vision and language inputs to perform self and goal localization using a hybrid topological-semantic graph as the global map, and outperforms traditional visual place recognition methods. Astra-Local, a multitask network, handles local path planning and odometry estimation. Its 4D spatial-temporal encoder, trained through self-supervised learning, generates robust 4D features for downstream tasks. The planning head utilizes flow matching and a novel masked ESDF loss to minimize collision risks for generating local trajectories, and the odometry head integrates multi-sensor inputs via a transformer encoder to predict the relative pose of the robot. Deployed on real in-house mobile robots, Astra achieves high end-to-end mission success rate across diverse indoor environments.

cs.RO↗

arXiv2025

Photo-generated charge-transfer excitons in NiO revealed by ultrafast time-resolved resonant inelastic x-ray scattering

Strong electronic correlation can lead to insulating behavior and to the opening of large optical gaps, even in materials with partly filled valence shells. Although the non-equilibrium optical response encodes both local (quasi atomic) and collective (long range) responses, optical spectroscopy is usually more sensitive to the latter. Resonant x-ray techniques are better suited to investigate the quasi-atomic properties of correlated solids. Using time-resolved resonant inelastic x-ray scattering (RIXS), here we study the ultrafast non-equilibrium processes in NiO following photo-excitation by ultraviolet photons with energy exceeding the optical gap. We observe the creation of charge-transfer excitons that decay with a time constant of about 2\,ps, while itinerant photo-doping persists for tens of picoseconds. Following our discovery, which establishes time-resolved high-resolution RIXS as a powerful tool for the study of transient phenomena in condensed matter, the possible presence of charge-transfer excitons will need to be considered when interpreting optical pump-probe experiments on correlated quantum materials.

cond-mat.str-el↗

arXiv2025

Ultrafast decoupling of polarization and strain in ferroelectric BaTiO$_3$

A fundamental understanding of the interplay between lattice structure, polarization and electrons is pivotal to the optical control of ferroelectrics. The interaction between light and matter enables the remote and wireless control of the ferroelectric polarization on the picosecond timescale, while inducing strain, i.e., lattice deformation. At equilibrium, the ferroelectric polarization is proportional to the strain, and is typically assumed to be so also out of equilibrium. Decoupling the polarization from the strain would remove the constraint of sample design and provide an effective knob to manipulate the polarization by light. Here, upon an above-bandgap laser excitation of the prototypical ferroelectric BaTiO$_3$, we induce and measure an ultrafast decoupling between polarization and strain that begins within 350 fs, by softening Ti-O bonds via charge transfer, and lasts for several tens of picoseconds. We show that the ferroelectric polarization out of equilibrium is mainly determined by photoexcited electrons, instead of the strain. This excited state could serve as a starting point to achieve stable and reversible polarization switching via THz light. Our results demonstrate a light-induced transient and reversible control of the ferroelectric polarization and offer a pathway to control by light both electric and magnetic degrees of freedom in multiferroics.

cond-mat.mtrl-sci↗

arXiv2025

Intermolecular Radiative Decay: A non-local decay mechanism providing an insider's view of the solvation shell

Aqueous solutions are crucial in chemistry, biology, environmental science, and technology. The chemistry of solutes is influenced by the surrounding solvation shell of water molecules, which have different chemical properties than bulk water due to their different electronic and geometric structure. It is an experimental challenge to selectively investigate this property-determining electronic and geometric structure. Here, we report experimental results on a novel non-local X-ray emission process, Intermolecular Radiative Decay (IRD), for the prototypical ions Na$^{+}$ and Mg$^{2+}$ in water. We show that, in IRD, an electron from the solvation shell fills a core hole in the solute, and the released energy is emitted as an X-ray photon. We analyze the underlying mechanism using theoretical calculations, and show how IRD will allow us to meet the challenge of chemically selective probing of solvation shells from within.

physics.chem-ph↗

arXiv2024

Electronic dynamics created at conical intersections and its dephasing in aqueous solution

A dynamical rearrangement in the electronic structure of a molecule can be driven by different phenomena, including nuclear motion, electronic coherence or electron correlation. Recording such electronic dynamics and identifying their fate in aqueous solution has remained a challenge. Here, we reveal the electronic dynamics induced by electronic relaxation through conical intersections in pyrazine molecules using X-ray spectroscopy. We show that the ensuing created dynamics corresponds to a cyclic rearrangement of the electronic structure around the aromatic ring. Furthermore, we find that such electronic dynamics are entirely suppressed when pyrazine is dissolved in water. Our observations confirm that conical intersections can create electronic dynamics that are not directly excited by the pump pulse and that aqueous solvation can dephase them in less than 40 fs. These results have implications for the investigation of electronic dynamics created during light-induced molecular dynamics and shed light on their susceptibility to aqueous solvation.

physics.chem-ph↗

arXiv2024

A Ducted Fan UAV for Safe Aerial Grabbing and Transfer of Multiple Loads Using Electromagnets

In recent years, research on aerial grasping, manipulation, and transportation of objects has garnered significant attention. These tasks often require UAVs to operate safely close to environments or objects and to efficiently grasp payloads. However, current widely adopted flying platforms pose safety hazards: unprotected high-speed rotating propellers can cause harm to the surroundings. Additionally, the space for carrying payloads on the fuselage is limited, and the restricted position of the payload also hinders efficient grasping. To address these issues, this paper presents a coaxial ducted fan UAV which is equipped with electromagnets mounted externally on the fuselage, enabling safe grasping and transfer of multiple loads in midair without complex additional actuators. It also has the capability to achieve direct human-UAV cargo transfer in the air. The forces acting on the loads during magnetic attachment and their influencing factors were analyzed. An ADRC controller is utilized to counteract disturbances during grasping and achieve attitude control. Finally, flight tests are conducted to verify the UAV's ability to directly grasp multiple loads from human hands in flight while maintaining attitude tracking.

cs.RO↗

arXiv2024

The Heisenberg-RIXS instrument at the European XFEL

Resonant Inelastic X-ray Scattering (RIXS) is an ideal X-ray spectroscopy method to push the combination of energy and time resolutions to the Fourier transform ultimate limit, because it is unaffected by the core-hole lifetime energy broadening. And in pump-probe experiments the interaction time is made very short by the same core-hole lifetime. RIXS is very photon hungry so it takes great advantage from high repetition rate pulsed X-ray sources like the European XFEL. The hRIXS instrument is designed for RIXS experiments in the soft X-ray range with energy resolution approaching the Fourier and the Heisenberg limits. It is based on a spherical grating with variable line spacing (VLS) and a position-sensitive 2D detector. Initially, two gratings are installed to adequately cover the whole photon energy range. With optimized spot size on the sample and small pixel detector the energy resolution can be better than 40 meV at any photon energy below 1000 eV. At the SCS instrument of the European XFEL the spectrometer can be easily positioned thanks to air-pads on a high-quality floor, allowing the scattering angle to be continuously adjusted over the 65-145 deg range. It can be coupled to two different sample interaction chamber, one for liquid jets and one for solids, each equipped at the state-of-the-art and compatible for optical laser pumping in collinear geometry. The measured performances, in terms of energy resolution and count rate on the detector, closely match design expectations. hRIXS is open to public users since the summer of 2022.

cond-mat.str-el↗

arXiv2023

SCVCNet: Sliding cross-vector convolution network for cross-task and inter-individual-set EEG-based cognitive workload recognition

This paper presents a generic approach for applying the cognitive workload recognizer by exploiting common electroencephalogram (EEG) patterns across different human-machine tasks and individual sets. We propose a neural network called SCVCNet, which eliminates task- and individual-set-related interferences in EEGs by analyzing finer-grained frequency structures in the power spectral densities. The SCVCNet utilizes a sliding cross-vector convolution (SCVC) operation, where paired input layers representing the theta and alpha power are employed. By extracting the weights from a kernel matrix's central row and column, we compute the weighted sum of the two vectors around a specified scalp location. Next, we introduce an inter-frequency-point feature integration module to fuse the SCVC feature maps. Finally, we combined the two modules with the output-channel pooling and classification layers to construct the model. To train the SCVCNet, we employ the regularized least-square method with ridge regression and the extreme learning machine theory. We validate its performance using three databases, each consisting of distinct tasks performed by independent participant groups. The average accuracy (0.6813 and 0.6229) and F1 score (0.6743 and 0.6076) achieved in two different validation paradigms show partially higher performance than the previous works. All features and algorithms are available on website:https://github.com/7ohnKeats/SCVCNet.

eess.SP↗

arXiv2022

Electron Dynamics at High-Energy Densities in Nickel from Non-linear Resonant X-ray Absorption Spectra

The pulse intensity from X-ray free-electron lasers (FELs) can create extreme excitation densities in solids, entering the regime of non-linear X-ray-matter interactions. We show L3-edge absorption spectra of metallic nickel thin films with fluences entering a regime where several X-ray photons are incident per absorption cross-section. Main features of the observed non-linear spectral changes are described with a predictive rate model for electron population dynamics during the pulse, utilizing a fixed density of states and tabulated ground-state properties.

cond-mat.mtrl-sci↗

arXiv2022

Temporal-spatial Representation Learning Transformer for EEG-based Emotion Recognition

Both the temporal dynamics and spatial correlations of Electroencephalogram (EEG), which contain discriminative emotion information, are essential for the emotion recognition. However, some redundant information within the EEG signals would degrade the performance. Specifically,the subjects reach prospective intense emotions for only a fraction of the stimulus duration. Besides, it is a challenge to extract discriminative features from the complex spatial correlations among a number of electrodes. To deal with the problems, we propose a transformer-based model to robustly capture temporal dynamics and spatial correlations of EEG. Especially, temporal feature extractors which share the weight among all the EEG channels are designed to adaptively extract dynamic context information from raw signals. Furthermore, multi-head self-attention mechanism within the transformers could adaptively localize the vital EEG fragments and emphasize the essential brain regions which contribute to the performance. To verify the effectiveness of the proposed method, we conduct the experiments on two public datasets, DEAP and MAHNOBHCI. The results demonstrate that the proposed method achieves outstanding performance on arousal and valence classification.

eess.SP↗

arXiv2022

High-harmonic generation in liquids with few-cycle pulses: effect of laser-pulse duration on the cut-off energy

High-harmonic generation (HHG) in liquids is opening new opportunities for attosecond light sources and attosecond time-resolved studies of dynamics in the liquid phase. In gas-phase HHG, few-cycle pulses are routinely used to create isolated attosecond pulses and to extend the cut-off energy. Here, we study the properties of HHG in liquids, including water and several alcohols, by continuously tuning the pulse duration of a mid-infrared driver from the multi- to the sub-two-cycle regime. Similar to the gas phase, we observe the transition from discrete odd-order harmonics to continuous extreme-ultraviolet emission. However, the cut-off energy is shown to be entirely independent of the pulse duration. This observation is confirmed by ab-initio simulations of HHG in large clusters. Our results support the notion that the cut-off energy is a fundamental property of the liquid, independent of the driving-pulse properties. Combined with the recently reported wavelength-independence of the cutoff, these results confirm the direct sensitivity of HHG to the mean-free paths of slow electrons in liquids. Our results additionally imply that few-cycle mid-infrared laser pulses are suitable drivers for generating isolated attosecond pulses from liquids.

physics.optics↗

arXiv2022

Probing the low-energy electron-scattering dynamics in liquids with high-harmonic spectroscopy

High-harmonic spectroscopy (HHS) is a nonlinear all-optical technique with inherent attosecond temporal resolution, which has been applied successfully to a broad variety of systems in the gas phase and solid state. Here, we extend HHS to the liquid phase, and uncover the mechanism of high-harmonic generation (HHG) for this phase of matter. Studying HHG over a broad range of wavelengths and intensities, we show that the cut-off ($E_c$) is independent of the wavelength beyond a threshold intensity, and find that $E_c$ is a characteristic property of the studied liquid. We explain these observations within an intuitive semi-classical model based on electron trajectories that are limited by scattering to a characteristic length, which is connected to the electron mean-free path. Our model is validated against rigorous multi-electron time-dependent density-functional theory calculations in, both, supercells of liquid water with periodic boundary conditions, and large clusters of a variety of liquids. These simulations confirm our interpretation and thereby clarify the mechanism of HHG in liquids. Our results demonstrate a new, all-optical access to effective mean-free paths of slow electrons ($\leq$10 eV) in liquids, in a regime that is inaccessible to accurate calculations, but is critical for the understanding of radiation damage to living tissue. Our work also establishes the possibility of resolving sub-femtosecond electron dynamics in liquids, which offers a novel, all-optical approach to attosecond spectroscopy of chemical processes in their native liquid environment.

physics.chem-ph↗

arXiv2022

Locally temporal-spatial pattern learning with graph attention mechanism for EEG-based emotion recognition

Technique of emotion recognition enables computers to classify human affective states into discrete categories. However, the emotion may fluctuate instead of maintaining a stable state even within a short time interval. There is also a difficulty to take the full use of the EEG spatial distribution due to its 3-D topology structure. To tackle the above issues, we proposed a locally temporal-spatial pattern learning graph attention network (LTS-GAT) in the present study. In the LTS-GAT, a divide-and-conquer scheme was used to examine local information on temporal and spatial dimensions of EEG patterns based on the graph attention mechanism. A dynamical domain discriminator was added to improve the robustness against inter-individual variations of the EEG statistics to learn robust EEG feature representations across different participants. We evaluated the LTS-GAT on two public datasets for affective computing studies under individual-dependent and independent paradigms. The effectiveness of LTS-GAT model was demonstrated when compared to other existing mainstream methods. Moreover, visualization methods were used to illustrate the relations of different brain regions and emotion recognition. Meanwhile, the weights of different time segments were also visualized to investigate emotion sparsity problems.

eess.SP↗

arXiv2021

Temperature Measurements of Liquid Flat Jets in Vacuum

Sub-μm thin samples are essential for spectroscopic purposes. The development of flat micro-jets enabled novel spectroscopic and scattering methods for investigating molecular systems in the liquid phase. However characterization of the temperature of these ultra-thin liquid sheets in vacuum has not been systematically investigated. Here we present a comprehensive temperature characterization of two methods producing sub-micron flatjets, using optical Raman spectroscopy: colliding of two cylindrical jets and a cylindrical jet compressed by a high pressure gas. Our results reveal the dependence of the cooling rate on the material properties and the source characteristics, i.e. nozzle orifice size,flowrate, pressure. We show that materials with higher vapour pressures exhibit faster cooling rates which is illustrated by comparing the temperature profile of liquid water and ethanol flatjets. In a sub-μm liquid sheet, the temperature of the water sample reaches around 268 K and the ethanol around 253 K.

physics.chem-ph↗

arXiv2021

Cross-individual Recognition of Emotions by a Dynamic Entropy based on Pattern Learning with EEG features

Use of the electroencephalogram (EEG) and machine learning approaches to recognize emotions can facilitate affective human computer interactions. However, the type of EEG data constitutes an obstacle for cross-individual EEG feature modelling and classification. To address this issue, we propose a deep-learning framework denoted as a dynamic entropy-based pattern learning (DEPL) to abstract informative indicators pertaining to the neurophysiological features among multiple individuals. DEPL enhanced the capability of representations generated by a deep convolutional neural network by modelling the interdependencies between the cortical locations of dynamical entropy based features. The effectiveness of the DEPL has been validated with two public databases, commonly referred to as the DEAP and MAHNOB-HCI multimodal tagging databases. Specifically, the leave one subject out training and testing paradigm has been applied. Numerous experiments on EEG emotion recognition demonstrate that the proposed DEPL is superior to those traditional machine learning (ML) methods, and could learn between electrode dependencies w.r.t. different emotions, which is meaningful for developing the effective human-computer interaction systems by adapting to human emotions in the real world applications.

cs.LG↗

arXiv2021

Identification of mental fatigue in language comprehension tasks based on EEG and deep learning

Mental fatigue increases the risk of operator error in language comprehension tasks. In order to prevent operator performance degradation, we used EEG signals to assess the mental fatigue of operators in human-computer systems. This study presents an experimental design for fatigue detection in language comprehension tasks. We obtained EEG signals from a 14-channel wireless EEG detector in 15 healthy participants. Each participant was given a cognitive test of a language comprehension task, in the form of multiple choice questions, in which pronoun references were selected between nominal and surrogate sentences. In this paper, the 2400 EEG fragments collected are divided into three data sets according to different utilization rates, namely 1200s data set with 50% utilization rate, 1500s data set with 62.5% utilization rate, and 1800s data set with 75% utilization rate. In the aspect of feature extraction, different EEG features were extracted, including time domain features, frequency domain features and entropy features, and the effects of different features and feature combinations on classification accuracy were explored. In terms of classification, we introduced the Convolutional Neural Network (CNN) method as the preferred method, It was compared with Least Squares Support Vector Machines(LSSVM),Support Vector Machines(SVM),Logistic Regression (LR), Random Forest(RF), Naive Bayes (NB), K-Nearest Neighbor (KNN) and Decision Tree(DT).According to the results, the classification accuracy of convolutional neural network (CNN) is higher than that of other classification methods. The classification results show that the classification accuracy of 1200S dataset is higher than the other two datasets. The combination of Frequency and entropy feature and CNN has the highest classification accuracy, which is 85.34%.

cs.AI↗