arXiv subjects

Jiaming Lu

Publications and source records attributed to Jiaming Lu.

17 recordsLinked to original sources

arXiv2025

IrisNet: Infrared Image Status Awareness Meta Decoder for Infrared Small Targets Detection

Infrared Small Target Detection (IRSTD) faces significant challenges due to low signal-to-noise ratios, complex backgrounds, and the absence of discernible target features. While deep learning-based encoder-decoder frameworks have advanced the field, their static pattern learning suffers from pattern drift across diverse scenarios (\emph{e.g.}, day/night variations, sky/maritime/ground domains), limiting robustness. To address this, we propose IrisNet, a novel meta-learned framework that dynamically adapts detection strategies to the input infrared image status. Our approach establishes a dynamic mapping between infrared image features and entire decoder parameters via an image-to-decoder transformer. More concretely, we represent the parameterized decoder as a structured 2D tensor preserving hierarchical layer correlations and enable the transformer to model inter-layer dependencies through self-attention while generating adaptive decoding patterns via cross-attention. To further enhance the perception ability of infrared images, we integrate high-frequency components to supplement target-position and scene-edge information. Experiments on NUDT-SIRST, NUAA-SIRST, and IRSTD-1K datasets demonstrate the superiority of our IrisNet, achieving state-of-the-art performance.

cs.CV↗

arXiv2025

Dual-Granularity Semantic Prompting for Language Guidance Infrared Small Target Detection

Infrared small target detection remains challenging due to limited feature representation and severe background interference, resulting in sub-optimal performance. While recent CLIP-inspired methods attempt to leverage textual guidance for detection, they are hindered by inaccurate text descriptions and reliance on manual annotations. To overcome these limitations, we propose DGSPNet, an end-to-end language prompt-driven framework. Our approach integrates dual-granularity semantic prompts: coarse-grained textual priors (e.g., 'infrared image', 'small target') and fine-grained personalized semantic descriptions derived through visual-to-textual mapping within the image space. This design not only facilitates learning fine-grained semantic information but also can inherently leverage language prompts during inference without relying on any annotation requirements. By fully leveraging the precision and conciseness of text descriptions, we further introduce a text-guide channel attention (TGCA) mechanism and text-guide spatial attention (TGSA) mechanism that enhances the model's sensitivity to potential targets across both low- and high-level feature spaces. Extensive experiments demonstrate that our method significantly improves detection accuracy and achieves state-of-the-art performance on three benchmark datasets.

cs.CV↗

arXiv2025

Jahn-Teller-like Distortion in a One-dimensional {\pi}-Conjugated Polymer

Structurally distorting low-dimensional {\pi}-conjugated systems can profoundly influence their electronic properties, but controlling such behavior in extended-width systems remains challenging. Here we demonstrate that a one-dimensional conjugated polymer, poly-(difluorenoheptalene-ethynylene) (PDFHE), undergoes a pronounced out-of-plane backbone distortion, equivalent to a spontaneous symmetry breaking (SSB) of its mirror symmetry. We synthesized PDFHE on noble metal surfaces and characterized its structure and electronic states using low-temperature scanning tunneling microscopy. Rather than adopting a planar, high-symmetry conformation, PDFHE relaxes into non-planar isomers stabilized by a Jahn-Teller-like mechanism that relieves an electronic instability relative to the gapped planar structure. Density functional theory calculations corroborate these findings, revealing that distortion lowers the total polymer energy and enlarges the bandgap, providing a microscopic explanation for the SSB. Our results show that even in mechanically robust extended {\pi}-systems, subtle electron-lattice coupling can spontaneously drive significant structural rearrangements.

cond-mat.mes-hall↗

arXiv2024

High dimensional Bayesian Optimization via Condensing-Expansion Projection

In high-dimensional settings, Bayesian optimization (BO) can be expensive and infeasible. The random embedding Bayesian optimization algorithm is commonly used to address high-dimensional BO challenges. However, this method relies on the effective subspace assumption on the optimization problem's objective function, which limits its applicability. In this paper, we introduce Condensing-Expansion Projection Bayesian optimization (CEPBO), a novel random projection-based approach for high-dimensional BO that does not reply on the effective subspace assumption. The approach is both simple to implement and highly practical. We present two algorithms based on different random projection matrices: the Gaussian projection matrix and the hashing projection matrix. Experimental results demonstrate that both algorithms outperform existing random embedding-based algorithms in most cases, achieving superior performance on high-dimensional BO problems. The code is available in \url{https://anonymous.4open.science/r/CEPBO-14429}.

cs.LG↗

arXiv2024

CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control

Effective multi-intersection collaboration is pivotal for reinforcement-learning-based traffic signal control to alleviate congestion. Existing work mainly chooses neighboring intersections as collaborators. However, quite an amount of congestion, even some wide-range congestion, is caused by non-neighbors failing to collaborate. To address these issues, we propose to separate the collaborator selection as a second policy to be learned, concurrently being updated with the original signal-controlling policy. Specifically, the selection policy in real-time adaptively selects the best teammates according to phase- and intersection-level features. Empirical results on both synthetic and real-world datasets provide robust validation for the superiority of our approach, offering significant improvements over existing state-of-the-art methods. The code is available at https://github.com/bonaldli/CoSLight.

cs.MA↗

arXiv2024

X-Light: Cross-City Traffic Signal Control Using Transformer on Transformer as Meta Multi-Agent Reinforcement Learner

The effectiveness of traffic light control has been significantly improved by current reinforcement learning-based approaches via better cooperation among multiple traffic lights. However, a persisting issue remains: how to obtain a multi-agent traffic signal control algorithm with remarkable transferability across diverse cities? In this paper, we propose a Transformer on Transformer (TonT) model for cross-city meta multi-agent traffic signal control, named as X-Light: We input the full Markov Decision Process trajectories, and the Lower Transformer aggregates the states, actions, rewards among the target intersection and its neighbors within a city, and the Upper Transformer learns the general decision trajectories across different cities. This dual-level approach bolsters the model's robust generalization and transferability. Notably, when directly transferring to unseen scenarios, ours surpasses all baseline methods with +7.91% on average, and even +16.3% in some cases, yielding the best results.

cs.AI↗

arXiv2024

Regioselective On-Surface Synthesis of [3]Triangulene Graphene Nanoribbons

The integration of low-energy states into bottom-up engineered graphene nanoribbons (GNRs) is a robust strategy for realizing materials with tailored electronic band structure for nanoelectronics. Low-energy zero-modes (ZMs) can be introduced into nanographenes (NGs) by creating an imbalance between the two sublattices of graphene. This phenomenon is exemplified by the family of [n]triangulenes. Here, we demonstrate the synthesis of [3]triangulene-GNRs, a regioregular one-dimensional (1D) chain of [3]triangulenes linked by five-membered rings. Hybridization between ZMs on adjacent [3]triangulenes leads to the emergence of a narrow band gap, Eg = 0.7 eV, and topological end states that are experimentally verified using scanning tunneling spectroscopy (STS). Tight-binding and first-principles density functional theory (DFT) calculations within the local spin density approximation (LSDA) corroborate our experimental observations. Our synthetic design takes advantage of a selective on-surface head-to-tail coupling of monomer building blocks enabling the regioselective synthesis of [3]triangulene-GNRs. Detailed ab initio theory provides insight into the mechanism of on-surface radical polymerization, revealing the pivotal role of Au-C bond formation/breakage in driving selectivity.

cond-mat.mes-hall↗

arXiv2023

DuaLight: Enhancing Traffic Signal Control by Leveraging Scenario-Specific and Scenario-Shared Knowledge

Reinforcement learning has been revolutionizing the traditional traffic signal control task, showing promising power to relieve congestion and improve efficiency. However, the existing methods lack effective learning mechanisms capable of absorbing dynamic information inherent to a specific scenario and universally applicable dynamic information across various scenarios. Moreover, within each specific scenario, they fail to fully capture the essential empirical experiences about how to coordinate between neighboring and target intersections, leading to sub-optimal system-wide outcomes. Viewing these issues, we propose DuaLight, which aims to leverage both the experiential information within a single scenario and the generalizable information across various scenarios for enhanced decision-making. Specifically, DuaLight introduces a scenario-specific experiential weight module with two learnable parts: Intersection-wise and Feature-wise, guiding how to adaptively utilize neighbors and input features for each scenario, thus providing a more fine-grained understanding of different intersections. Furthermore, we implement a scenario-shared Co-Train module to facilitate the learning of generalizable dynamics information across different scenarios. Empirical results on both real-world and synthetic scenarios show DuaLight achieves competitive performance across various metrics, offering a promising solution to alleviate traffic congestion, with 3-7\% improvements. The code is available under: https://github.com/lujiaming-12138/DuaLight.

cs.MA↗

arXiv2023

Little Exploration is All You Need

The prevailing principle of "Optimism in the Face of Uncertainty" advocates for the incorporation of an exploration bonus, generally assumed to be proportional to the inverse square root of the visit count ($1/\sqrt{n}$), where $n$ is the number of visits to a particular state-action pair. This approach, however, exclusively focuses on "uncertainty," neglecting the inherent "difficulty" of different options. To address this gap, we introduce a novel modification of standard UCB algorithm in the multi-armed bandit problem, proposing an adjusted bonus term of $1/n^\tau$, where $\tau > 1/2$, that accounts for task difficulty. Our proposed algorithm, denoted as UCB$^\tau$, is substantiated through comprehensive regret and risk analyses, confirming its theoretical robustness. Comparative evaluations with standard UCB and Thompson Sampling algorithms on synthetic datasets demonstrate that UCB$^\tau$ not only outperforms in efficacy but also exhibits lower risk across various environmental conditions and hyperparameter settings.

cs.LG↗

arXiv2023

Uncertainty-Aware Cross-Modal Transfer Network for Sketch-Based 3D Shape Retrieval

In recent years, sketch-based 3D shape retrieval has attracted growing attention. While many previous studies have focused on cross-modal matching between hand-drawn sketches and 3D shapes, the critical issue of how to handle low-quality and noisy samples in sketch data has been largely neglected. This paper presents an uncertainty-aware cross-modal transfer network (UACTN) that addresses this issue. UACTN decouples the representation learning of sketches and 3D shapes into two separate tasks: classification-based sketch uncertainty learning and 3D shape feature transfer. We first introduce an end-to-end classification-based approach that simultaneously learns sketch features and uncertainty, allowing uncertainty to prevent overfitting noisy sketches by assigning different levels of importance to clean and noisy sketches. Then, 3D shape features are mapped into the pre-learned sketch embedding space for feature alignment. Extensive experiments and ablation studies on two benchmarks demonstrate the superiority of our proposed method compared to state-of-the-art methods.

cs.CV↗

arXiv2019

Real-Time FPGA-based Digital Signal Processing and Correction for a Small Animal PET

Small animal Positron Emission Tomography (PET) is dedicated to small animal imaging, which requires high position and energy precision, as well as good flexibility and efficiency of the electronics. This paper presents the design of a digital signal processing logic for a marmoset brain PET system based on LYSO crystal arrays, SiPMs, and the resistive network readout method. We implement 32-channel signal processing in a single Xilinx Artix-7 Field-Programmable Gate Array (FPGA). The logic is designed to support four online modes which are regular data processing mode, flood map construction mode, energy spectrum construction mode, and raw data mode. Several functions are integrated, including two-dimensional (2D) raw position calculation, crystal locating, events filtering, and synchronization detection. Furthermore, a series of online corrections is also integrated, such as photon peak correction to 511 keV and time measurement result correction with crystal granularity. A Gigabit Ethernet interface is utilized for data transfer, Look-Up Tables (LUTs) configuration, and command issuing. The pipeline logic works at 125 MHz with a signal processing capability beyond the required data rate of 1,000,000 events/s/channel. A series of initial tests are conducted. The results indicate that the logic design meets the application requirement.

physics.ins-det↗

arXiv2019

Prototype design of singles processing unit for the small animal PET

Position Emission Tomography (PET) is an advanced clinical diagnostic imaging technique for nuclear medicine. Small animal PET is increasingly uesd for studying the animal model of disease, new drugs and new therapies. A prototype of Singles Processing Unit (SPU) for a small animal PET system was designed to obtain the time, energy, and position information. The energy and position is actually calculated through high precison charge measurement, which is based on amplification, shaping, A/D conversion and area calculation in digital signal processing domian. Analysis and simulations were also conducted to optimize the key parameters in system design. Initial tests indicate that the charge and time precision is better than 0.3% FWHM and 350 ps FWHM respectively, while the position resolution is better than 0.35% FWHM. Commination tests of the SPU prototype with the PET detector indicate that the system time precision is better than 2.5 ns, while the flood map and energy spectra concored well with the expected.

physics.ins-det↗

arXiv2019

A Time-to-Digital Converter-based Correction Method for Charge Measurement through Area Integration

A high-precision charge measurement can be achieved by the area integration of a digitized quasi-Gaussian signal after the signal passes through the shaper and analog-to-digital converter (ADC). The charge measurement contains an error due to the uncertainty of the first sampled point of a signal waveform. To reduce the error, we employ a time-to-digital converter (TDC) to measure the uncertainty precisely, and we design correction algorithms to improve the resolution of the charge measurement. This work includes analysis and simulations of the proposed algorithms and implementation of them in an FPGA device. Besides, the tests are also conducted to evaluate the performance of the correction method. Test results indicate that the resolution of the charge measurement is successfully improved from 0.231% to 0.126% by using a signal from the shaping circuit (with the amplitude of 2 V, and leading and trailing edges of about 80 ns and 280 ns, respectively) digitized at the sampling rate of 62.5 Msps.

physics.ins-det↗

arXiv2019

Extensive beam test study of prototype MRPCs for the T0 detector at the CSR external-target experiment

The CSR External-target Experiment (CEE) will be the first large-scale nuclear physics experiment device at the Cooling Storage Ring (CSR) of the Heavy-Ion Research Facility in Lanzhou (HIRFL) in China. A new T0 detector has been proposed to measure the multiplicity, angular distribution and timing information of charged particles produced in heavy-ion collisions at the target region. Multi-gap resistive plate chamber (MRPC) technology was chosen as part of the construction of the T0 detector, which provides precision event collision times (T0) and collision geometry information. The prototype was tested with hadron and heavy-ion beams to study its performance. By comparing the experimental results with a Monte Carlo simulation, the time resolution of the MRPCs are found to be $\sim$ 50 ps or better. The timing performance of the T0 detector, including both detector and readout electronics, we found to fulfil the requirements of the CEE.

physics.ins-det↗

arXiv2019

Readout Electronics of T0 Detector in the External Target Experiment of CSR in HIRFL

T0 detector, based on Multi-gap Resistive Plate Chambers (MRPC) technology, is one of the key components in the External Target Experiment. Through precision measurements of the MRPC signals, timing of the beam impact on target can be obtained and used as the start time for other detectors. A readout electronics system was designed for the T0 detector. Based on the NINO ASIC, front-end-electronics (FEE) circuits which can achieve high precision leading-edge discrimination and Charge-to-Time Conversion (QTC) were designed for the internal and external MRPCs of the T0 detector. The output pulse of the FEE is then digitized by high precision time digitization modules with Time-to-Digital Converters (TDCs), trigger matching and other control logic integrated within Field Programmable Gate Array (FPGA) devices. To evaluate the functionality and performance, we also conducted a series of tests of the electronics. Results indicate that the system functions well and the time precision of the electronics is better than 21 ps, which satisfies the application requirement.

physics.ins-det↗

arXiv2018

A Design of FPGA Based Small Animal PET Real Time Digital Signal Processing and Correction Logic

Small animal Positron Emission Tomography (PET) is dedicated to small animal imaging. Animals used in experiments, such as rats and monkeys, are often much smaller than human bodies, which requires higher position and energy precision of the PET imaging system. Besides, Flexibility, high efficiency are also the major demands of a practical PET system. These requires a high-quality analog front-end and a digital signal processing logic with high efficiency and compatibility of multiple data processing modes. The digital signal processing logic of the small animal PET system presented in this paper implements 32-channel signal processing in a single Xilinx Artix-7 family of Field-Programmable Gate Array (FPGA). The logic is designed to support three online modes which are regular package mode, flood map and energy spectrum histogram. Several functions are integrated, including two-dimensional (2D) raw position calculation, crystal identification, events filtering, etc. Besides, a series of online corrections are also integrated, such as photon peak correction to 511 keV and timing offset correction with crystal granularity. A Gigabit Ethernet interface is utilized for data transfer, Look-Up Tables (LUTs) configuration and commands issuing. The pipe-line logic processes the signals at 125 MHz with a rate of 1,000,000 events/s. A series of initial tests are conducted. The results indicate that the digital processing logic achieves the expectations.

eess.SP↗

arXiv2016

A Comparative Study of Algorithms for Realtime Panoramic Video Blending

Unlike image blending algorithms, video blending algorithms have been little studied. In this paper, we investigate 6 popular blending algorithms---feather blending, multi-band blending, modified Poisson blending, mean value coordinate blending, multi-spline blending and convolution pyramid blending. We consider in particular realtime panoramic video blending, a key problem in various virtual reality tasks. To evaluate the performance of the 6 algorithms on this problem, we have created a video benchmark of several videos captured under various conditions. We analyze the time and memory needed by the above 6 algorithms, for both CPU and GPU implementations (where readily parallelizable). The visual quality provided by these algorithms is also evaluated both objectively and subjectively. The video benchmark and algorithm implementations are publicly available.

cs.CV↗