arXiv subjects

David Jiang

Publications and source records attributed to David Jiang.

11 recordsLinked to original sources

arXiv2026

On-Detector Machine Learning for Beam-Induced Background Rejection at a 10 TeV Muon Collider

A 10 TeV Muon Collider is a compelling candidate for a future energy-frontier facility, offering unprecedented opportunities to explore the fundamental laws of particle physics. Muon decays in the collider ring produce intense beam-induced background (BIB) that can overwhelm detector occupancy and exceed readout bandwidth constraints. We investigate the potential of on-detector Machine Learning for BIB rejection in the vertex detector, exploiting pixel cluster shapes to distinguish background from collision products. We study three classes of lightweight neural-network architectures, and evaluate their implementation feasibility using high-level synthesis. Selected architectures achieve 88 to 90% data reduction at 99% signal efficiency, while requiring hardware resources compatible with potential ASIC implementation. These results demonstrate the potential of performing substantial BIB rejection directly in the pixel readout, providing a strategy for meeting the tracker readout requirements at a future Muon Collider.

hep-ex↗

arXiv2026

Evaluating Tool Cloning in Agentic-AI Ecosystems

Agent tools are becoming a core interface through which LLM agents access external data, services, and execution environments. As these tools are distributed through public marketplaces, raw tool counts may substantially overstate ecosystem diversity if many repositories are cloned, lightly modified, or derived from shared templates. Such hidden duplication can contaminate benchmark splits, propagate vulnerable implementations, bias measurements of tool-use generalization, and raise provenance, attribution, and intellectual-property concerns. We present, to our knowledge, the first large-scale measurement study of tool cloning in agentic AI ecosystems. We curate a unified dataset from multiple public platforms, covering 7,508 Model Context Protocol (MCP) repositories with 87,564 extracted tools and 1,353 Skills repositories with 12,447 tools, for a total of 8,861 repositories and 100,011 tool entries. To measure implementation-level duplication, we build a repository-level auditing pipeline using complementary lexical and fuzzy-structural similarity metrics, and compute pairwise similarity across MCP-to-MCP, Skills-to-Skills, and MCP-to-Skills repository pairs. We further manually verify 100 sampled pairs per MCP and Skills ecosystem across similarity-score buckets to calibrate how often high similarity reflects true code cloning. Our analysis shows that cloning is not an isolated artifact: high-similarity regions appear across comparison settings, and 60\% of high-Jaccard candidates and 85\% of high-ssdeep candidates in the MCP ecosystem are manually verified as clones. These results indicate that tool cloning is a pervasive and severe source of hidden duplication in agent-tool ecosystems. They further suggest that agent-tool datasets and benchmarks should account for repository provenance and implementation similarity when measuring tool diversity or constructing evaluation splits.

cs.SE↗

arXiv2026

On-chip probabilistic inference for charged-particle tracking at the sensor edge

Modern scientific instruments operate under increasingly extreme constraints on bandwidth, latency, and power. Inference at the sensor edge determines experimental data collection efficiency by deciding which information to save for further analysis. Particle tracking detectors at the Large Hadron Collider exemplify this challenge: pixelated silicon sensors generate rich spatiotemporal ionization patterns, yet most of this information is discarded due to data-rate limitations. Concurrently, advancements in co-design tools provide rapid turn-around for incorporating machine learning into application-specific integrated circuits, motivating designs for particle detectors with new integrated technologies. We demonstrate that neural networks embedded in the front-end electronics can infer charged-particle kinematic parameters from a single silicon layer. We regress hit positions and incident angles with calibrated uncertainties, while satisfying stringent constraints on numerical precision, latency, and silicon area. Our results establish a path toward probabilistic inference directly at the edge, opening new opportunities for intelligent sensing in high-rate scientific instruments.

physics.ins-det↗

arXiv2025

Sensor Co-design for $\textit{smartpixels}$

Pixel tracking detectors at upcoming collider experiments will see unprecedented charged-particle densities. Real-time data reduction on the detector will enable higher granularity and faster readout, possibly enabling the use of the pixel detector in the first level of the trigger for a hadron collider. This data reduction can be accomplished with a neural network (NN) in the readout chip bonded with the sensor that recognizes and rejects tracks with low transverse momentum (p$_T$) based on the geometrical shape of the charge deposition (``cluster''). To design a viable detector for deployment at an experiment, the dependence of the NN as a function of the sensor geometry, external magnetic field, and irradiation must be understood. In this paper, we present first studies of the efficiency and data reduction for planar pixel sensors exploring these parameters. A smaller sensor pitch in the bending direction improves the p$_T$ discrimination, but a larger pitch can be partially compensated with detector depth. An external magnetic field parallel to the sensor plane induces Lorentz drift of the electron-hole pairs produced by the charged particle, broadening the cluster and improving the network performance. The absence of the external field diminishes the background rejection compared to the baseline by $\mathcal{O}$(10%). Any accumulated radiation damage also changes the cluster shape, reducing the signal efficiency compared to the baseline by $\sim$ 30 - 60%, but nearly all of the performance can be recovered through retraining of the network and updating the weights. Finally, the impact of noise was investigated, and retraining the network on noise-injected datasets was found to maintain performance within 6% of the baseline network trained and evaluated on noiseless data.

physics.ins-det↗

arXiv2025

Characterization of a 28 nm $\textit{smartpixels}$ ASIC With On-Chip ML for Particle Tracking Detectors

We present a 28 nm CMOS pixel readout integrated circuit implementing in-pixel analog signal processing and on-chip machine learning data filtering for particle tracking detectors. Our ASIC comprises two $32 \times 8$ pixel matrices with a pixel pitch of $25 \times 25~\mu\mathrm{m}^2$, in which each pixel integrates a charge-sensitive amplifier with synchronous auto-zero offset cancellation and a 2-bit flash ADC with programmable thresholds. Two analog front-end architectures, single-ended and differential, are implemented and characterized. Digitized pixel data are combined into row-wise projections and processed by an on-chip, fully combinational neural network classifier for data reduction. Measurements at room temperature using charge injection demonstrate an equivalent noise charge of $54.6~\mathrm{e}^{-}$ and a threshold dispersion of $\sim$78.2~\unit{\electron} at nominal bias, linear response up to several~\unit{\kilo\electron}, and stable operation at a 10~MHz clock frequency. The neural network output is compared with offline RTL predictions and agrees for $99.06\%$ of $1.5 \times 10^{5}$ test inputs.

physics.ins-det↗

arXiv2025

Colored Vertex Models and Interacting Reverse Plane Partitions

We study the coupling of pairs of reverse plane partitions of the same shape by assigning a certain local interaction between the reverse plane partitions. We show that they are in bijection with a certain Yang-Baxter integrable colored vertex model. By utilizing the Yang-Baxter equation for this colored vertex model, we are able to compute the generating function for the interacting pairs of reverse plane partitions. We also give a bijection between the coupled pairs of reverse plane partitions with the interaction strength set to zero and a single reverse plane partition of the same shape.

math.CO↗

arXiv2024

Sychronous vs. asynchronous coalitions in multiplayer games, with applications to guts poker

We study the issue introduced by Buck-Lee-Platnick-Wheeler-Zumbrun of synchronous vs. asynchronous coalitions in multiplayer games, that is, the difference between coalitions with full and partial communication, with a specific interest in the context of continuous Guts poker where this problem was originally formulated. We observe for general symmetric multiplayer games, with players 2-n in coalition against player 1, that there are three values, corresponding to symmetric Nash equilibrium, optimal asynchronous, and optimal synchronous strategies, in that order, for which inequalities may for different examples be strict or nonstrict (i.e., equality) in any combination. Different from Nash equilibria and synchronous optima, which may be phrased as convex optimization problems, or classical 2-player games, determination of asynchronous optima is a nonconvex optimization problem. We discuss methods of numerical approximation of this optimum, and examine performance on 3-player rock-paper-scissors and discretized Guts poker. Finally, we present sufficient conditions guaranteeing different possibilities for behavior, based on concave/convexity properties of the payoff function. These answer in the affirmative the open problem posed by Buck-Lee-Platnick-Wheeler-Zumbrun whether the optimal asynchronous coalition value for 3-player guts is equal to the Nash equilibrium value zero. At the same time, we present a number of new results regarding synchronous coalition play for continuous $3$-player guts.

cs.GT↗

arXiv2024

Intelligent Pixel Detectors: Towards a Radiation Hard ASIC with On-Chip Machine Learning in 28 nm CMOS

Detectors at future high energy colliders will face enormous technical challenges. Disentangling the unprecedented numbers of particles expected in each event will require highly granular silicon pixel detectors with billions of readout channels. With event rates as high as 40 MHz, these detectors will generate petabytes of data per second. To enable discovery within strict bandwidth and latency constraints, future trackers must be capable of fast, power efficient, and radiation hard data-reduction at the source. We are developing a radiation hard readout integrated circuit (ROIC) in 28nm CMOS with on-chip machine learning (ML) for future intelligent pixel detectors. We will show track parameter predictions using a neural network within a single layer of silicon and hardware tests on the first tape-outs produced with TSMC. Preliminary results indicate that reading out featurized clusters from particles above a modest momentum threshold could enable using pixel information at 40 MHz.

physics.ins-det↗

arXiv2024

Influence of Material Parameter Variability on the Predicted Coronary Artery Biomechanical Environment via Uncertainty Quantification

Central to the clinical adoption of patient-specific modeling strategies is demonstrating that simulation results are reliable and safe. Simulation frameworks must be robust to uncertainty in model input(s), and levels of confidence should accompany results. In this study we applied a coupled uncertainty quantification-finite element (FE) framework to understand the impact of uncertainty in vascular material properties on variability in predicted stresses. Univariate probability distributions were fit to material parameters derived from layer-specific mechanical behavior testing of human coronary tissue. Parameters were assumed to be probabilistically independent, allowing for efficient parameter ensemble sampling. In an idealized coronary artery geometry, a forward FE model for each parameter ensemble was created to predict tissue stresses under physiologic loading. An emulator was constructed within the UncertainSCI software using polynomial chaos techniques, and statistics and sensitivities were directly computed. Results demonstrated that material parameter uncertainty propagates to variability in predicted stresses across the vessel wall, with the largest dispersions in stress within the adventitial layer. Variability in stress was most sensitive to uncertainties in the anisotropic component of the strain energy function. Unary and binary interactions within the adventitial layer were the main contributors to stress variance, and the leading factor in stress variability was uncertainty in the stress-like material parameter summarizing contribution of the embedded fibers to the overall artery stiffness. Results from a patient-specific coronary model confirmed many of these findings. Collectively, this highlights the impact of material property variation on predicted artery stresses and presents a pipeline to explore and characterize uncertainty in computational biomechanics.

q-bio.TO↗

arXiv2023

Tiling Ferrers Diagrams

We will show that a necessary and sufficient condition for a Ferrers board (or Young Diagrams) to be fully tileable with 1x2 dominoes requires the board to be 2-colorable such that no color is adjacent to its own color using both induction and a graph theory approach. We will walk through all prerequisite knowledge and go through the failed attempts we tried while also providing supplementary exercises that fit the topics. Plenty of background content is included, so even if you don't know much about the subject, it should still be readable. If you do know most of the background content, feel free to skip around.

math.GM↗

arXiv2023

Rounded notch method of femoral endarterectomy offers mechanical advantages in finite element models

Objective: Use of a vascular punch to produce circular heel and toe arteriotomies for femoral endarterectomy with patch angioplasty is a novel technique. This study investigated the plausibility of this approach and the mechanical advantages of the technique using finite element models. Methods: The patient underwent a standard femoral endarterectomy. Prior to patch angioplasty, a 4.2 mm coronary vascular punch was used to created proximal and distal circular arteriotomies. The idealized artery was modeled as a 9 mm cylinder with a central slit. The vertices of the slit were modeled as: a sharp V consistent with traditional linear arteriotomy, circular punched hole, and beveled punched hole. The artery was pressurized to achieve displacement consistent with the size of a common femoral artery prior to patch angioplasty. Maximum von Mises stress, area-averaged stress, and stress concentration factors were evaluated for all three models. Results: Maximum von Mises stress was 0.098 MPa with 5 mm of displacement and increased to 0.26 MPa with 10 mm of displacement. Maximum stress in the uniform circular model was 0.019 MPa and 0.018 with a beveled notch. Average stress was lowest in the circular punch model at 0.006 MP and highest in the linear V notch arteriotomy at 0.010 MPa. Stress concentration factor was significantly lower in both circular models compared with the V notch. Conclusions: Femoral endarterectomy modified with the creation of circular arteriotomies is a safe and effective surgical technique. Finite element modeling revealed reduced maximum von Mises stress and average stress at the vertices of a circular or beveled punch arteriotomy compared with a linear, V shaped arteriotomy. Reduced vertex stress may promote lower risk of restenosis.

physics.med-ph↗