arXiv subjects

Xi Zhu

Publications and source records attributed to Xi Zhu.

At least 55 records · Page 3Linked to original sources

arXiv2024

BezierFormer: A Unified Architecture for 2D and 3D Lane Detection

Lane detection has made significant progress in recent years, but there is not a unified architecture for its two sub-tasks: 2D lane detection and 3D lane detection. To fill this gap, we introduce BézierFormer, a unified 2D and 3D lane detection architecture based on Bézier curve lane representation. BézierFormer formulate queries as Bézier control points and incorporate a novel Bézier curve attention mechanism. This attention mechanism enables comprehensive and accurate feature extraction for slender lane curves via sampling and fusing multiple reference points on each curve. In addition, we propose a novel Chamfer IoU-based loss which is more suitable for the Bézier control points regression. The state-of-the-art performance of BézierFormer on widely-used 2D and 3D lane detection benchmarks verifies its effectiveness and suggests the worthiness of further exploration.

cs.CV↗

arXiv2024

REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language Models

The integration of multimodal Electronic Health Records (EHR) data has significantly improved clinical predictive capabilities. Leveraging clinical notes and multivariate time-series EHR, existing models often lack the medical context relevent to clinical tasks, prompting the incorporation of external knowledge, particularly from the knowledge graph (KG). Previous approaches with KG knowledge have primarily focused on structured knowledge extraction, neglecting unstructured data modalities and semantic high dimensional medical knowledge. In response, we propose REALM, a Retrieval-Augmented Generation (RAG) driven framework to enhance multimodal EHR representations that address these limitations. Firstly, we apply Large Language Model (LLM) to encode long context clinical notes and GRU model to encode time-series EHR data. Secondly, we prompt LLM to extract task-relevant medical entities and match entities in professionally labeled external knowledge graph (PrimeKG) with corresponding medical knowledge. By matching and aligning with clinical standards, our framework eliminates hallucinations and ensures consistency. Lastly, we propose an adaptive multimodal fusion network to integrate extracted knowledge with multimodal EHR data. Our extensive experiments on MIMIC-III mortality and readmission tasks showcase the superior performance of our REALM framework over baselines, emphasizing the effectiveness of each module. REALM framework contributes to refining the use of multimodal EHR data in healthcare and bridging the gap with nuanced medical context essential for informed clinical predictions.

cs.AI↗

arXiv2023

Model Selection for Exposure-Mediator Interaction

In mediation analysis, the exposure often influences the mediating effect, i.e., there is an interaction between exposure and mediator on the dependent variable. When the mediator is high-dimensional, it is necessary to identify non-zero mediators (M) and exposure-by-mediator (X-by-M) interactions. Although several high-dimensional mediation methods can naturally handle X-by-M interactions, research is scarce in preserving the underlying hierarchical structure between the main effects and the interactions. To fill the knowledge gap, we develop the XMInt procedure to select M and X-by-M interactions in the high-dimensional mediators setting while preserving the hierarchical structure. Our proposed method employs a sequential regularization-based forward-selection approach to identify the mediators and their hierarchically preserved interaction with exposure. Our numerical experiments showed promising selection results. Further, we applied our method to ADNI morphological data and examined the role of cortical thickness and subcortical volumes on the effect of amyloid-beta accumulation on cognitive performance, which could be helpful in understanding the brain compensation mechanism.

stat.ME↗

arXiv2023

A survey on deep learning approaches for data integration in autonomous driving system

The perception module of self-driving vehicles relies on a multi-sensor system to understand its environment. Recent advancements in deep learning have led to the rapid development of approaches that integrate multi-sensory measurements to enhance perception capabilities. This paper surveys the latest deep learning integration techniques applied to the perception module in autonomous driving systems, categorizing integration approaches based on "what, how, and when to integrate". A new taxonomy of integration is proposed, based on three dimensions: multi-view, multi-modality, and multi-frame. The integration operations and their pros and cons are summarized, providing new insights into the properties of an "ideal" data integration approach that can alleviate the limitations of existing methods. After reviewing hundreds of relevant papers, this survey concludes with a discussion of the key features of an optimal data integration approach.

cs.RO↗

arXiv2023

NeMO: Neural Map Growing System for Spatiotemporal Fusion in Bird's-Eye-View and BDD-Map Benchmark

Vision-centric Bird's-Eye View (BEV) representation is essential for autonomous driving systems (ADS). Multi-frame temporal fusion which leverages historical information has been demonstrated to provide more comprehensive perception results. While most research focuses on ego-centric maps of fixed settings, long-range local map generation remains less explored. This work outlines a new paradigm, named NeMO, for generating local maps through the utilization of a readable and writable big map, a learning-based fusion module, and an interaction mechanism between the two. With an assumption that the feature distribution of all BEV grids follows an identical pattern, we adopt a shared-weight neural network for all grids to update the big map. This paradigm supports the fusion of longer time series and the generation of long-range BEV local maps. Furthermore, we release BDD-Map, a BDD100K-based dataset incorporating map element annotations, including lane lines, boundaries, and pedestrian crossing. Experiments on the NuScenes and BDD-Map datasets demonstrate that NeMO outperforms state-of-the-art map segmentation methods. We also provide a new scene-level BEV map evaluation setting along with the corresponding baseline for a more comprehensive comparison.

cs.CV↗

arXiv2023

Anchor Free remote sensing detector based on solving discrete polar coordinate equation

As the rapid development of depth learning, object detection in aviatic remote sensing images has become increasingly popular in recent years. Most of the current Anchor Free detectors based on key point detection sampling directly regression and classification features, with the design of object loss function based on the horizontal bounding box. It is more challenging for complex and diverse aviatic remote sensing object. In this paper, we propose an Anchor Free aviatic remote sensing object detector (BWP-Det) to detect rotating and multi-scale object. Specifically, we design a interactive double-branch(IDB) up-sampling network, in which one branch gradually up-sampling is used for the prediction of Heatmap, and the other branch is used for the regression of boundary box parameters. We improve a weighted multi-scale convolution (WmConv) in order to highlight the difference between foreground and background. We extracted Pixel level attention features from the middle layer to guide the two branches to pay attention to effective object information in the sampling process. Finally, referring to the calculation idea of horizontal IoU, we design a rotating IoU based on the split polar coordinate plane, namely JIoU, which is expressed as the intersection ratio following discretization of the inner ellipse of the rotating bounding box, to solve the correlation between angle and side length in the regression process of the rotating bounding box. Ultimately, BWP-Det, our experiments on DOTA, UCAS-AOD and NWPU VHR-10 datasets show, achieves advanced performance with simpler models and fewer regression parameters.

cs.CV↗

arXiv2021

Unraveling the Excitonic Transition and Associated Dynamics in Confined Long Linear Carbon-chains with Time-Resolved Resonance Raman Scattering

Long linear carbon-chains have been attracting intense interest arising from the remarkable properties predicted and their potential applications in future nanotechnology. Here we comprehensively interrogate the excitonic transitions and the associated relaxation dynamics of nanotube confined long linear carbon-chains by using steady state and time-resolved Raman spectroscopies. The exciton relaxation dynamics on the confined carbon-chains occurs on a hundreds of picoseconds timescale, in strong contrast to the host dynamics that occurs on a few picosecond timescale. A prominent time-resolved Raman response is observed over a broad energy range extending from 1.2 to 2.8 eV, which includes the strong Raman resonance region around 2.2 eV. Evidence for a strong coupling between the chain and the nanotube host is found from the dynamics at high excitation energies which provides a clear evidence for an efficient energy transfer from the host carbon nanotube to the chain. Our experimental study presents the first unique characterization of the long linear carbon-chain exciton dynamics, providing indispensable knowledge for the understanding of the interactions between different carbon allotropes.

cond-mat.mtrl-sci↗

arXiv2021

Tensor Network for Supervised Learning at Finite Temperature

The large variation of datasets is a huge barrier for image classification tasks. In this paper, we embraced this observation and introduce the finite temperature tensor network (FTTN), which imports the thermal perturbation into the matrix product states framework by placing all images in an environment with constant temperature, in analog to energy-based learning. Tensor network is chosen since it is the best platform to introduce thermal fluctuation. Different from traditional network structure which directly takes the summation of individual losses as its loss function, FTTN regards it as thermal average loss computed from the entanglement with the environment. The temperature-like parameter can be automatically optimized, which gives each database an individual temperature. FTTN obtains improvement in both test accuracy and convergence speed in several datasets. The non-zero temperature automatically separates similar features, avoiding the wrong classification in previous architecture. The thermal fluctuation may give a better improvement in other frameworks, and we may also implement the temperature of database to improve the training effect.

cs.LG↗

arXiv2021

Effect of Varying the TD-lc-DFTB Range-Separation Parameter on Charge and Energy Transfer in a Model Pentacene/Buckminsterfullerene Heterojunction

Density-functional tight binding (DFTB) has become a popular form of approximate density-functional theory (DFT) based upon a minimal valence basis set and neglect of all but two center integrals. We report the results of our tests of a recent long-range correction (lc) for time-dependent (TD) lc-DFTB by carrying out TD-lc-DFTB fewest switches surface hopping (FSSH) calculations of energy and charge transfer times using the relatively new DFTBaby program. An advantage of this method is the ability to run enough trajectories to get meaningful ensemble averages. Our interest in the present work is less in determining exact energy and charge transfer rates than in understanding how the results of these calculations vary with the value of the range-separation parameter (Rlc = 1/μ) for a model organic solar cell heterojunction consisting of a van der Waals complex P/F made up of single pentacene (P) molecule together with a single buckminsterfullerene (F) molecule. The default value of Rlc = 3.03 a0 is found to be much too small as neither energy nor charge transfer is observed until Rlc ~ 10 a0. Tests at a single geometry show that best agreement with high-quality ab-initio spectra is obtained in the limit of no lc (i.e., very large Rlc.) A plot of energy and charge transfer rates as a function of Rlc is provided which suggests that a value of Rlc ~ 15 a0 yields the typical literature charge transfer time of about 100 fs. However, energy and charge transfer times become as high as ~ 300 fs for Rlc ~ 25 a0. A closer examination of the charge transfer process P*/F to P+/F- shows that the initial electron transfer is accompanied by a partial delocalization of the P hole onto F which then relocalizes back onto P, consistent with a polaron-like picture in which the nuclei relax to stabilize the resultant redistribution of charges.

physics.chem-ph↗

arXiv2020

Overcoming Language Priors with Self-supervised Learning for Visual Question Answering

Most Visual Question Answering (VQA) models suffer from the language prior problem, which is caused by inherent data biases. Specifically, VQA models tend to answer questions (e.g., what color is the banana?) based on the high-frequency answers (e.g., yellow) ignoring image contents. Existing approaches tackle this problem by creating delicate models or introducing additional visual annotations to reduce question dependency while strengthening image dependency. However, they are still subject to the language prior problem since the data biases have not been even alleviated. In this paper, we introduce a self-supervised learning framework to solve this problem. Concretely, we first automatically generate labeled data to balance the biased data, and propose a self-supervised auxiliary task to utilize the balanced data to assist the base VQA model to overcome language priors. Our method can compensate for the data biases by generating balanced data without introducing external annotations. Experimental results show that our method can significantly outperform the state-of-the-art, improving the overall accuracy from 49.50% to 57.59% on the most commonly used benchmark VQA-CP v2. In other words, we can increase the performance of annotation-based methods by 16% without using external annotations.

cs.CV↗

arXiv2019

Symmetrical Graph Neural Network for Quantum Chemistry, with Dual R/K Space

Most of current neural network models in quantum chemistry (QC) exclude the molecular symmetry, separate the well-correlated real space (R space), and momenta space (K space) into two individuals, which lack the essential physics in molecular chemistry. In this work, by endorsing the molecular symmetry and elementals of group theory, we propose a comprehensive method to apply symmetry in the graph neural network (SY-GNN), which extends the property-predicting coverage to all the orbital symmetry for both ground and excited states. SY-GNN shows excellent performance in predicting both the absolute and relative of R and K spaces quantities. Besides the numerical properties, SY-GNN also can predict the orbitals distributions in real space, providing the active regions of chemical reactions. We believe the symmetry endorsed deep learning scheme covers the significant physics inside and is essential for the application of neural networks in QC and many other research fields in the future.

physics.chem-ph↗

arXiv2019

Fixed-Size Ordinally Forgetting Encoding Based Word Sense Disambiguation

In this paper, we present our method of using fixed-size ordinally forgetting encoding (FOFE) to solve the word sense disambiguation (WSD) problem. FOFE enables us to encode variable-length sequence of words into a theoretically unique fixed-size representation that can be fed into a feed forward neural network (FFNN), while keeping the positional information between words. In our method, a FOFE-based FFNN is used to train a pseudo language model over unlabelled corpus, then the pre-trained language model is capable of abstracting the surrounding context of polyseme instances in labelled corpus into context embeddings. Next, we take advantage of these context embeddings towards WSD classification. We conducted experiments on several WSD data sets, which demonstrates that our proposed method can achieve comparable performance to that of the state-of-the-art approach at the expense of much lower computational cost.

cs.CL↗

arXiv2018

The Newton Scheme for Deep Learning

We introduce a neural network (NN) strictly governed by Newton's Law, with the nature required basis functions derived from the fundamental classic mechanics. Then, by classifying the training model as a quick procedure of 'force pattern' recognition, we developed the Newton physics-based NS scheme. Once the force pattern is confirmed, the neuro network simply does the checking of the 'pattern stability' instead of the continuous fitting by computational resource consuming big data-driven processing. In the given physics's law system, once the field is confirmed, the mathematics bases for the force field description actually are not diverged but denumerable, which can save the function representations from the exhaustible available mathematics bases. In this work, we endorsed Newton's Law into the deep learning technology and proposed Newton Scheme (NS). Under NS, the user first identifies the path pattern, like the constant acceleration movement.The object recognition technology first loads mass information, then, the NS finds the matched physical pattern and describe and predict the trajectory of the movements with nearly zero error. We compare the major contribution of this NS with the TCN, GRU and other physics inspired 'FIND-PDE' methods to demonstrate fundamental and extended applications of how the NS works for the free-falling, pendulum and curve soccer balls.The NS methodology provides more opportunity for the future deep learning advances.

cs.LG↗

arXiv2017

The Evolution of Alq3 Films Exposure to Air

Small molecular solar cell becomes more stable when a thin tris-(8-hydroxyquinoline) aluminum (Alq3) buffer layer instead of bathocuproine (BCP) is inserted between the active layer and electrode. In this work, we introduce a single layer device (ITO / Alq3 / Au) exposure to air to investigate the role of Alq3 in organic solar cells. The large PL intensity and undetectable Raman peaks of 8-hydroxyquinoline (8-Hq), a degradation product of Alq3 through chemical reaction, indicate that the degradation of Alq3 is not severe even after a few hundred hours exposure. Thus the Alq3 buffer layer gives a good protection of the active layers against oxygen\water permeation comparing with BCP. However, the small quantity of degraded Alq3 products can enhance the current density (after 15 h) in single layer Alq3 device, which is reflected in the decrease of parallel resistance Rp. Therefore, due to the enhanced charge transport and the long term better protection from ambient oxygen/water, the lifetime of small molecular solar cell with Alq3 buffer layer is improved.

cond-mat.mtrl-sci↗

arXiv2017

A Saw-tooth Scaling of Work Function in Hydrogenated Graphene

The saw-tooth scaling investigations of work functions (WF) in hydrogenated graphene are theoretically performed by density functional theory. The positions of hydrogen atoms and the H/C ratios affect WF. With the comparison of different graphene structures by adsorbed two hydrogen atoms, WF is larger with less negative charges of the graphene. With different H/C ratios, WF is controlled by two parts: the graphene surface and hydrogen groups. When the ratio is smaller than 0.5, the work func-tion is more dependent on the Mulliken charge of graphene. With more hydrogen atoms, the surface of graphene is more negative, resulting in a smaller work function. When the ratio is larger than 0.5, the effect of hydrogen groups cannot be ignored. When two sides of graphene have different numbers of hy-drogen atoms, a dipole exists, making the movement of electron from Fermi level to vacuum level more difficult, thus WF is larger than that with the same hydrogen atoms of each side. Therefore the scaling of work function acts like a saw-tooth dependent on H/C ratio.

cond-mat.mtrl-sci↗

arXiv2017

The Reversible Spin Switch by External Control of Interval Distance of CuPc and C59N with the investigation of DFT

In this paper, we introduce a new kind of spin switch based on a joint system of copper phthalocyanine (CuPc) and C59N. Using density functional theory, we investigate the total magnetic moment of this system when gradually changing the interval distance between two molecules. The spin hopping happens during the critical distance with very low energy. This phenomenon shows a possible reality of reversible spin switch by external control of the interval distance. With orbital analysis and electron transfer consideration, the form of C59N+- CuPc- ion pair support this spin hopping phenomenon.

cond-mat.mtrl-sci↗

arXiv2017

Magnetic properties of quasi one-dimensional vanadium-benzene nanowire affected by gas molecules: a first-principle study

Magnetic properties of quasi one-dimensional vanadium-benzene nanowires (VBNW) are investigated theoretically with the absorption of gas molecules-NO and NO2. With the increase adsorption of NO on VBNW, the phase transition from half metal to ferromagnetic metal and last to paramagnetic semiconductor can be observed. With the increase of NO2 on VBNW, half metallic property can be enhanced at first and decreased later. Thus, the electronic and magnetic properties of VBNW can be sensitive and selective to NO and NO2, revealing the potential applications in spintronic sensors of these two kinds of molecules.

cond-mat.mtrl-sci↗

arXiv2017

A Robust Fiber-based Frequency Synchronization System Immune to Dramatic Temperature Fluctuation

Fiber-based frequency synchronization system is sensitive to temperature change because of the limited isolation and nonlinear effect of RF components in the system. In order to make it suitable for the use of large-scale scientific and engineering projects in which the ambient temperature of the fiber link changes dramatically, we designed a non-harmonic frequency dissemination system immune to temperature fluctuation. After the lab tests in which the ambient temperature of fiber fluctuates 40 degrees centigrade per day and 20 degrees centigrade per hour respectively, the relative frequency stabilities of this system reached 4.0 E-14/s and 3.0 E-16/1E4 s. It is demonstrated that the proposed non-harmonic scheme shows a strong robustness to the complicated working environment with dramatic temperature fluctuation.

physics.ins-det↗