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Xin Liu

Publications and source records attributed to Xin Liu.

At least 613 records · Page 34Linked to original sources
arXiv2023

Transition metal single-atom anchored on MoSi2N4 monolayer as highly efficient electrocatalyst for hydrogen evolution reaction

Single-atom catalysts are considered as a promising method for efficient energy conversion, owing to their advantages of high atom utilization and low catalyst cost. However, finding a stable two-dimensional structure and high hydrogen evolution reaction (HER) performance is a current research hotspot. Herein, based on the first-principles calculations, we identify the HER properties of six catalysts (TM@MoSi2N4, TM = Sc, Ti, V, Fe, Co, and Ni) comprising transition metal atoms anchored on MoSi2N4 monolayer. The results show that the spin-polarized states appear around the Fermi level after anchoring TM atoms. Therefore, the energy level of the first available unoccupied state for accommodating hydrogen drops, regulating the bonding strength of hydrogen. Thus, the single transition metal atom activates the active site of the MoSi2N4 inert base plane, becoming a quite suitable site for the HER. Based on ΔGH*, the exchange current density and volcano diagram of the corresponding catalytic system were also calculated. Among them, V@MoSi2N4 (ΔGH* = -0.07 eV) and Ni@MoSi2N4 (ΔGH* = 0.06 eV) systems show efficient the HER property. Our study confirms that the transition metal atom anchoring is an effective means to improve the performance of electrocatalysis, and TM@MoSi2N4 has practical application potential as a high efficiency HER electrocatalyst.

cond-mat.mtrl-sci↗
arXiv2023

Big-model Driven Few-shot Continual Learning

Few-shot continual learning (FSCL) has attracted intensive attention and achieved some advances in recent years, but now it is difficult to again make a big stride in accuracy due to the limitation of only few-shot incremental samples. Inspired by distinctive human cognition ability in life learning, in this work, we propose a novel Big-model driven Few-shot Continual Learning (B-FSCL) framework to gradually evolve the model under the traction of the world's big-models (like human accumulative knowledge). Specifically, we perform the big-model driven transfer learning to leverage the powerful encoding capability of these existing big-models, which can adapt the continual model to a few of newly added samples while avoiding the over-fitting problem. Considering that the big-model and the continual model may have different perceived results for the identical images, we introduce an instance-level adaptive decision mechanism to provide the high-level flexibility cognitive support adjusted to varying samples. In turn, the adaptive decision can be further adopted to optimize the parameters of the continual model, performing the adaptive distillation of big-model's knowledge information. Experimental results of our proposed B-FSCL on three popular datasets (including CIFAR100, minilmageNet and CUB200) completely surpass all state-of-the-art FSCL methods.

cs.CV↗
arXiv2023

Federated Linear Bandit Learning via Over-the-Air Computation

In this paper, we investigate federated contextual linear bandit learning within a wireless system that comprises a server and multiple devices. Each device interacts with the environment, selects an action based on the received reward, and sends model updates to the server. The primary objective is to minimize cumulative regret across all devices within a finite time horizon. To reduce the communication overhead, devices communicate with the server via over-the-air computation (AirComp) over noisy fading channels, where the channel noise may distort the signals. In this context, we propose a customized federated linear bandits scheme, where each device transmits an analog signal, and the server receives a superposition of these signals distorted by channel noise. A rigorous mathematical analysis is conducted to determine the regret bound of the proposed scheme. Both theoretical analysis and numerical experiments demonstrate the competitive performance of our proposed scheme in terms of regret bounds in various settings.

cs.LG↗
arXiv2023

MSECNet: Accurate and Robust Normal Estimation for 3D Point Clouds by Multi-Scale Edge Conditioning

Estimating surface normals from 3D point clouds is critical for various applications, including surface reconstruction and rendering. While existing methods for normal estimation perform well in regions where normals change slowly, they tend to fail where normals vary rapidly. To address this issue, we propose a novel approach called MSECNet, which improves estimation in normal varying regions by treating normal variation modeling as an edge detection problem. MSECNet consists of a backbone network and a multi-scale edge conditioning (MSEC) stream. The MSEC stream achieves robust edge detection through multi-scale feature fusion and adaptive edge detection. The detected edges are then combined with the output of the backbone network using the edge conditioning module to produce edge-aware representations. Extensive experiments show that MSECNet outperforms existing methods on both synthetic (PCPNet) and real-world (SceneNN) datasets while running significantly faster. We also conduct various analyses to investigate the contribution of each component in the MSEC stream. Finally, we demonstrate the effectiveness of our approach in surface reconstruction.

cs.CV↗
arXiv2023

Parity-protected superconducting qubit based on topological insulators

We propose a novel architecture that utilizes two 0-$π$ qubits based on topological Josephson junctions to implement a parity-protected superconducting qubit. The topological Josephson junctions provides protection against fabrication variations, which ensures the identical Josephson junctions required to implement the0-$π$ qubit. By viewing the even and odd parity ground states of a 0-$π$ qubit as spin-$\frac{1}{2}$ states, we construct the logic qubit states using the total parity odd subspace of two 0-$π$ qubits. This parity-protected qubit exhibits robustness against charge noise, similar to a singlet-triplet qubit's immunity to global magnetic field fluctuations. Meanwhile, the flux noise cannot directly couple two states with the same total parity and therefore is greatly suppressed. Benefiting from the simultaneous protection from both charge and flux noise, we demonstrate a dramatic enhancement of both $T_1$ and $T_2$ coherence times. Our work presents a new approach to engineer symmetry-protected superconducting qubits.

cond-mat.mes-hall↗
arXiv2023

Video BagNet: short temporal receptive fields increase robustness in long-term action recognition

Previous work on long-term video action recognition relies on deep 3D-convolutional models that have a large temporal receptive field (RF). We argue that these models are not always the best choice for temporal modeling in videos. A large temporal receptive field allows the model to encode the exact sub-action order of a video, which causes a performance decrease when testing videos have a different sub-action order. In this work, we investigate whether we can improve the model robustness to the sub-action order by shrinking the temporal receptive field of action recognition models. For this, we design Video BagNet, a variant of the 3D ResNet-50 model with the temporal receptive field size limited to 1, 9, 17 or 33 frames. We analyze Video BagNet on synthetic and real-world video datasets and experimentally compare models with varying temporal receptive fields. We find that short receptive fields are robust to sub-action order changes, while larger temporal receptive fields are sensitive to the sub-action order.

cs.CV↗
arXiv2023

Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks

With the growing popularity of electric vehicles (EVs), maintaining power grid stability has become a significant challenge. To address this issue, EV charging control strategies have been developed to manage the switch between vehicle-to-grid (V2G) and grid-to-vehicle (G2V) modes for EVs. In this context, multi-agent deep reinforcement learning (MADRL) has proven its effectiveness in EV charging control. However, existing MADRL-based approaches fail to consider the natural power flow of EV charging/discharging in the distribution network and ignore driver privacy. To deal with these problems, this paper proposes a novel approach that combines multi-EV charging/discharging with a radial distribution network (RDN) operating under optimal power flow (OPF) to distribute power flow in real time. A mathematical model is developed to describe the RDN load. The EV charging control problem is formulated as a Markov Decision Process (MDP) to find an optimal charging control strategy that balances V2G profits, RDN load, and driver anxiety. To effectively learn the optimal EV charging control strategy, a federated deep reinforcement learning algorithm named FedSAC is further proposed. Comprehensive simulation results demonstrate the effectiveness and superiority of our proposed algorithm in terms of the diversity of the charging control strategy, the power fluctuations on RDN, the convergence efficiency, and the generalization ability.

eess.SY↗
arXiv2023

Multi-scale Promoted Self-adjusting Correlation Learning for Facial Action Unit Detection

Facial Action Unit (AU) detection is a crucial task in affective computing and social robotics as it helps to identify emotions expressed through facial expressions. Anatomically, there are innumerable correlations between AUs, which contain rich information and are vital for AU detection. Previous methods used fixed AU correlations based on expert experience or statistical rules on specific benchmarks, but it is challenging to comprehensively reflect complex correlations between AUs via hand-crafted settings. There are alternative methods that employ a fully connected graph to learn these dependencies exhaustively. However, these approaches can result in a computational explosion and high dependency with a large dataset. To address these challenges, this paper proposes a novel self-adjusting AU-correlation learning (SACL) method with less computation for AU detection. This method adaptively learns and updates AU correlation graphs by efficiently leveraging the characteristics of different levels of AU motion and emotion representation information extracted in different stages of the network. Moreover, this paper explores the role of multi-scale learning in correlation information extraction, and design a simple yet effective multi-scale feature learning (MSFL) method to promote better performance in AU detection. By integrating AU correlation information with multi-scale features, the proposed method obtains a more robust feature representation for the final AU detection. Extensive experiments show that the proposed method outperforms the state-of-the-art methods on widely used AU detection benchmark datasets, with only 28.7\% and 12.0\% of the parameters and FLOPs of the best method, respectively. The code for this method is available at \url{https://github.com/linuxsino/Self-adjusting-AU}.

cs.CV↗
arXiv2023

Dwarf AGNs from Variability for the Origins of Seeds (DAVOS): Intermediate-mass black hole demographics from optical synoptic surveys

We present a phenomenological forward Monte Carlo model for forecasting the population of active galactic nuclei (AGNs) in dwarf galaxies observable via their optical variability. Our model accounts for expected changes in the spectral energy distribution of AGNs in the intermediate-mass black hole (IMBH) mass range and uses observational constraints on optical variability as a function of black hole (BH) mass to generate mock light curves. Adopting several different models for the BH occupation function, including one for off-nuclear IMBHs, we quantify differences in the predicted local AGN mass and luminosity functions in dwarf galaxies. As a result, we are able to model the variable fraction of AGNs as a function of physical host properties, such as host galaxy stellar mass, in the presence of complex selection effects. We find that our adopted occupation fractions for the "heavy" and "light" initial BH seeding scenarios can be distinguished with variability data at the $2-3 σ$ level for galaxy host stellar masses below $\sim 10^8 M_\odot$ with the Vera C. Rubin Observatory. We demonstrate the prevalence of a selection bias whereby recovered IMBH masses fall, on average, above the predicted value from the local host galaxy - BH mass scaling relation with the strength of the bias dependent on the survey sensitivity. The methodology developed in this work can be used more broadly to forecast and correct for selection effects for AGN demographic studies in synoptic surveys. Finally, we show that a targeted $\sim$ hourly cadence program over a few nights with the Rubin Observatory can provide strong constraints on IMBH masses given their expected rapid variability timescales.

astro-ph.GA↗
arXiv2023

Objects do not disappear: Video object detection by single-frame object location anticipation

Objects in videos are typically characterized by continuous smooth motion. We exploit continuous smooth motion in three ways. 1) Improved accuracy by using object motion as an additional source of supervision, which we obtain by anticipating object locations from a static keyframe. 2) Improved efficiency by only doing the expensive feature computations on a small subset of all frames. Because neighboring video frames are often redundant, we only compute features for a single static keyframe and predict object locations in subsequent frames. 3) Reduced annotation cost, where we only annotate the keyframe and use smooth pseudo-motion between keyframes. We demonstrate computational efficiency, annotation efficiency, and improved mean average precision compared to the state-of-the-art on four datasets: ImageNet VID, EPIC KITCHENS-55, YouTube-BoundingBoxes, and Waymo Open dataset. Our source code is available at https://github.com/L-KID/Videoobject-detection-by-location-anticipation.

cs.CV↗
arXiv2023

CasCIFF: A Cross-Domain Information Fusion Framework Tailored for Cascade Prediction in Social Networks

Existing approaches for information cascade prediction fall into three main categories: feature-driven methods, point process-based methods, and deep learning-based methods. Among them, deep learning-based methods, characterized by its superior learning and representation capabilities, mitigates the shortcomings inherent of the other methods. However, current deep learning methods still face several persistent challenges. In particular, accurate representation of user attributes remains problematic due to factors such as fake followers and complex network configurations. Previous algorithms that focus on the sequential order of user activations often neglect the rich insights offered by activation timing. Furthermore, these techniques often fail to holistically integrate temporal and structural aspects, thus missing the nuanced propagation trends inherent in information cascades.To address these issues, we propose the Cross-Domain Information Fusion Framework (CasCIFF), which is tailored for information cascade prediction. This framework exploits multi-hop neighborhood information to make user embeddings robust. When embedding cascades, the framework intentionally incorporates timestamps, endowing it with the ability to capture evolving patterns of information diffusion. In particular, the CasCIFF seamlessly integrates the tasks of user classification and cascade prediction into a consolidated framework, thereby allowing the extraction of common features that prove useful for all tasks, a strategy anchored in the principles of multi-task learning.

cs.SI↗
arXiv2023

Client Selection in Federated Learning: Principles, Challenges, and Opportunities

As a privacy-preserving paradigm for training Machine Learning (ML) models, Federated Learning (FL) has received tremendous attention from both industry and academia. In a typical FL scenario, clients exhibit significant heterogeneity in terms of data distribution and hardware configurations. Thus, randomly sampling clients in each training round may not fully exploit the local updates from heterogeneous clients, resulting in lower model accuracy, slower convergence rate, degraded fairness, etc. To tackle the FL client heterogeneity problem, various client selection algorithms have been developed, showing promising performance improvement. In this paper, we systematically present recent advances in the emerging field of FL client selection and its challenges and research opportunities. We hope to facilitate practitioners in choosing the most suitable client selection mechanisms for their applications, as well as inspire researchers and newcomers to better understand this exciting research topic.

cs.LG↗
arXiv2023

BrainCog: A Spiking Neural Network based Brain-inspired Cognitive Intelligence Engine for Brain-inspired AI and Brain Simulation

Spiking neural networks (SNNs) have attracted extensive attentions in Brain-inspired Artificial Intelligence and computational neuroscience. They can be used to simulate biological information processing in the brain at multiple scales. More importantly, SNNs serve as an appropriate level of abstraction to bring inspirations from brain and cognition to Artificial Intelligence. In this paper, we present the Brain-inspired Cognitive Intelligence Engine (BrainCog) for creating brain-inspired AI and brain simulation models. BrainCog incorporates different types of spiking neuron models, learning rules, brain areas, etc., as essential modules provided by the platform. Based on these easy-to-use modules, BrainCog supports various brain-inspired cognitive functions, including Perception and Learning, Decision Making, Knowledge Representation and Reasoning, Motor Control, and Social Cognition. These brain-inspired AI models have been effectively validated on various supervised, unsupervised, and reinforcement learning tasks, and they can be used to enable AI models to be with multiple brain-inspired cognitive functions. For brain simulation, BrainCog realizes the function simulation of decision-making, working memory, the structure simulation of the Neural Circuit, and whole brain structure simulation of Mouse brain, Macaque brain, and Human brain. An AI engine named BORN is developed based on BrainCog, and it demonstrates how the components of BrainCog can be integrated and used to build AI models and applications. To enable the scientific quest to decode the nature of biological intelligence and create AI, BrainCog aims to provide essential and easy-to-use building blocks, and infrastructural support to develop brain-inspired spiking neural network based AI, and to simulate the cognitive brains at multiple scales. The online repository of BrainCog can be found at https://github.com/braincog-x.

cs.NE↗
arXiv2023

Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches

Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically-developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3-16 years of age underwent eight walking/running activities, including five 25 meters walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-minute walk test (6MWT), a 100 meters fast-walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens.

eess.SP↗
arXiv2023

Theoretical proposal to obtain strong Majorana evidence from scanning tunneling spectroscopy of a vortex with a dissipative environment

It is predicted that a vortex in a topological superconductor contains a Majorana zero mode (MZM). The confirmative Majorana signature, i.e., the $2e^2/h$ quantized conductance, however is easily sabotaged by unavoidable interruptions, e.g. instrument broadening, non-Majorana signal, and extra particle channels. We propose to avoid the signal interruption by introducing disorder-induced dissipation that couples to the tip-sample tunneling. With dissipation involved, we highlight three features, each of which alone can provide a strong evidence to identify MZM. Firstly, dissipation suppresses a finite-energy Caroli-de Gennes-Matricon (CdGM) conductance peak into a valley, while it does not split MZM zero-bias conductance peak. Secondly, we predict a dissipation-dependent scaling feature of the zero-bias conductance peak. Thirdly, the introduced dissipation manifests the MZM signal by suppressing non-topological CdGM modes. Importantly, the observation of these features does not require a quantized conductance value $2e^2/h$.

cond-mat.supr-con↗
arXiv2023

The Eighteenth Data Release of the Sloan Digital Sky Surveys: Targeting and First Spectra from SDSS-V

The eighteenth data release of the Sloan Digital Sky Surveys (SDSS) is the first one for SDSS-V, the fifth generation of the survey. SDSS-V comprises three primary scientific programs, or "Mappers": Milky Way Mapper (MWM), Black Hole Mapper (BHM), and Local Volume Mapper (LVM). This data release contains extensive targeting information for the two multi-object spectroscopy programs (MWM and BHM), including input catalogs and selection functions for their numerous scientific objectives. We describe the production of the targeting databases and their calibration- and scientifically-focused components. DR18 also includes ~25,000 new SDSS spectra and supplemental information for X-ray sources identified by eROSITA in its eFEDS field. We present updates to some of the SDSS software pipelines and preview changes anticipated for DR19. We also describe three value-added catalogs (VACs) based on SDSS-IV data that have been published since DR17, and one VAC based on the SDSS-V data in the eFEDS field.

astro-ph.GA↗
arXiv2023

Causal Explanation for Reinforcement Learning: Quantifying State and Temporal Importance

Explainability plays an increasingly important role in machine learning. Furthermore, humans view the world through a causal lens and thus prefer causal explanations over associational ones. Therefore, in this paper, we develop a causal explanation mechanism that quantifies the causal importance of states on actions and such importance over time. We also demonstrate the advantages of our mechanism over state-of-the-art associational methods in terms of RL policy explanation through a series of simulation studies, including crop irrigation, Blackjack, collision avoidance, and lunar lander.

cs.AI↗
arXiv2023

Discovery and Characterization of Galactic-scale Dual Supermassive Black Holes Across Cosmic Time

The hierarchical structure formation paradigm predicts the formation of pairs of supermassive black holes in merging galaxies. When both (or one) members of the SMBH pair are unobscured AGNs, the system can be identified as a dual (or offset) AGN. Quantifying the abundance of these AGN pairs as functions of separation, redshift and host properties is crucial to understanding SMBH formation and AGN fueling in the broad context of galaxy formation. The High Latitude Wide Area Survey with Roman, with its unprecedented combination of sensitivity, spatial resolution, area and NIR wavelength coverage, will revolutionize the study of galactic-scale environments of SMBH pairs. This white paper summarizes the science opportunities and technical requirements on the discovery and characterization of SMBH pairs down to galactic scales (i.e., less than tens of kpc) over broad ranges of redshift (1 1E42 erg/s).

astro-ph.GA↗