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Glow reduction of ultra-low noise LmAPDs: towards photon counting infrared arrays
No full textSpectroscopy and direct-imaging of ultra-faint targets such as Earth-like exoplanets and high redshift galaxies are among the primary goals of upcoming large scale astronomy projects like the Habitable World Observatory (HWO). Such objectives pose extreme instrumental challenges, in particular on detectors where dark currents lower than 1 e-/pixel/kilosecond and read noise less than 1 e-/pixel/frame will have to be achieved on large format arrays. Some technologies meet these requirements at optical wavelengths, but none do in the infrared. With this goal in mind, the University of Hawaii has partnered with Leonardo to develop linear-mode avalanche photodiodes (LmAPDs). In this paper, we report recent tests performed on LmAPDs, where we measure a ROIC glow of approximately 0.01 e-/pixel/frame, without which the intrinsic dark current is essentally zero (< 0.1 e- /pixel/kilosecond). We show that at high gain, these devices are capable of detecting single photonsProceedings of SPIE Vol. 13103, 1310320 (2024
TelApart: Differentiating Network Faults from Customer-Premise Faults in Cable Broadband Networks
Get PDFTwo types of radio frequency (RF) impairments frequently occur in a cable broadband network: impairments that occur inside a cable network and impairments occur at the edge of the broadband network, i.e., in a subscriber\u27s premise. Differentiating these two types of faults is important, as different faults require different types of technical personnel to repair them. Presently, the cable industry lacks publicly available tools to automatically diagnose the type of fault. In this work, we present TelApart, a fault diagnosis system for cable broadband networks. TelApart uses telemetry data collected by the Proactive Network Maintenance (PNM) infrastructure in cable networks to effectively differentiate the type of fault. Integral to TelApart\u27s design is an unsupervised machine learning model that groups cable devices sharing similar anomalous patterns together. We use metrics derived from an ISP\u27s customer trouble tickets to programmatically tune the model\u27s hyper-parameters so that an ISP can deploy TelApart in various conditions without hand-tuning its hyper-parameters. We also address the data challenge that the telemetry data collected by the PNM system contain numerous missing, duplicated, and unaligned data points. Using real-world data contributed by a cable ISP, we show that TelApart can effectively identify different types of faults.14 pages. arXiv admin note: text overlap with arXiv:2412.0956
On Round-Off Errors and Gaussian Blur in Superresolution and in Image Registration
Get PDFSuperresolution theory and techniques seek to recover signals from samples in the presence of blur and noise. Discrete image registration can be an approach to fuse information from different sets of samples of the same signal. Quantization errors in the spatial domain are inherent to digital images. We consider superresolution and discrete image registration for one-dimensional spatially-limited piecewise constant functions which are subject to blur which is Gaussian or a mixture of Gaussians as well as to round-off errors. We describe a signal-dependent measurement matrix which captures both types of effects. For this setting we show that the difficulties in determining the discontinuity points from two sets of samples even in the absence of other types of noise. If the samples are also subject to statistical noise, then it is necessary to align and segment the data sequences to make the most effective inferences about the amplitudes and discontinuity points. Under some conditions on the blur, the noise, and the distance between discontinuity points, we prove that we can correctly align and determine the first samples following each discontinuity point in two data sequences with an approach based on dynamic programming
Importance Sampling With Stochastic Particle Flow and Diffusion Optimization
Get PDFParticle flow (PFL) is an effective method for overcoming particle degeneracy, the main limitation of particle filtering. In PFL, particles are migrated towards regions of high likelihood based on the solution of a partial differential equation. Recently proposed stochastic PFL introduces a diffusion term in the ordinary differential equation (ODE) that describes particle motion. This diffusion term reduces the stiffness of the ODE and makes it possible to perform PFL with a lower number of numerical integration steps compared to traditional deterministic PFL. In this work, we introduce a general approach to perform importance sampling (IS) based on stochastic PFL. Our method makes it possible to evaluate a flow-induced proposal probability density function (PDF) after the parameters of a Gaussian mixture model (GMM) have been migrated by stochastic PFL. Compared to conventional stochastic PFL, the resulting processing step is asymptotically optimal. Within our method, it is possible to optimize the diffusion matrix that describes the diffusion term of the ODE to improve the accuracy-computational complexity tradeoff. Our simulation results in a highly nonlinear 3-D source localization scenario showcase a reduced stiffness of the ODE and an improved estimating accuracy compared to state-of-the-art deterministic and stochastic PFL
Neural-Symbolic Reasoning over Knowledge Graphs: A Survey from a Query Perspective
No full textKnowledge graph reasoning is pivotal in various domains such as data mining, artificial intelligence, the Web, and social sciences. These knowledge graphs function as comprehensive repositories of human knowledge, facilitating the inference of new information. Traditional symbolic reasoning, despite its strengths, struggles with the challenges posed by incomplete and noisy data within these graphs. In contrast, the rise of Neural Symbolic AI marks a significant advancement, merging the robustness of deep learning with the precision of symbolic reasoning. This integration aims to develop AI systems that are not only highly interpretable and explainable but also versatile, effectively bridging the gap between symbolic and neural methodologies. Additionally, the advent of large language models (LLMs) has opened new frontiers in knowledge graph reasoning, enabling the extraction and synthesis of knowledge in unprecedented ways. This survey offers a thorough review of knowledge graph reasoning, focusing on various query types and the classification of neural symbolic reasoning. Furthermore, it explores the innovative integration of knowledge graph reasoning with large language models, highlighting the potential for groundbreaking advancements. This comprehensive overview is designed to support researchers and practitioners across multiple fields, including data mining, AI, the Web, and social sciences, by providing a detailed understanding of the current landscape and future directions in knowledge graph reasoning
Generative Adversarial Reviews: When LLMs Become the Critic
No full textThe peer review process is fundamental to scientific progress, determining which papers meet the quality standards for publication. Yet, the rapid growth of scholarly production and increasing specialization in knowledge areas strain traditional scientific feedback mechanisms. In light of this, we introduce Generative Agent Reviewers (GAR), leveraging LLM-empowered agents to simulate faithful peer reviewers. To enable generative reviewers, we design an architecture that extends a large language model with memory capabilities and equips agents with reviewer personas derived from historical data. Central to this approach is a graph-based representation of manuscripts, condensing content and logically organizing information - linking ideas with evidence and technical details. GAR\u27s review process leverages external knowledge to evaluate paper novelty, followed by detailed assessment using the graph representation and multi-round assessment. Finally, a meta-reviewer aggregates individual reviews to predict the acceptance decision. Our experiments demonstrate that GAR performs comparably to human reviewers in providing detailed feedback and predicting paper outcomes. Beyond mere performance comparison, we conduct insightful experiments, such as evaluating the impact of reviewer expertise and examining fairness in reviews. By offering early expert-level feedback, typically restricted to a limited group of researchers, GAR democratizes access to transparent and in-depth evaluation
CUPS: Improving Human Pose-Shape Estimators with Conformalized Deep Uncertainty
No full textWe introduce CUPS, a novel method for learning sequence-to-sequence 3D human shapes and poses from RGB videos with uncertainty quantification. To improve on top of prior work, we develop a method to generate and score multiple hypotheses during training, effectively integrating uncertainty quantification into the learning process. This process results in a deep uncertainty function that is trained end-to-end with the 3D pose estimator. Post-training, the learned deep uncertainty model is used as the conformity score, which can be used to calibrate a conformal predictor in order to assess the quality of the output prediction. Since the data in human pose-shape learning is not fully exchangeable, we also present two practical bounds for the coverage gap in conformal prediction, developing theoretical backing for the uncertainty bound of our model. Our results indicate that by taking advantage of deep uncertainty with conformal prediction, our method achieves state-of-the-art performance across various metrics and datasets while inheriting the probabilistic guarantees of conformal prediction
Novel 3D Binary Indexed Tree for Volume Computation of 3D Reconstructed Models from Volumetric Data
No full textIn the burgeoning field of medical imaging, precise computation of 3D volume holds a significant importance for subsequent qualitative analysis of 3D reconstructed objects. Combining multivariate calculus, marching cube algorithm, and binary indexed tree data structure, we developed an algorithm for efficient computation of intrinsic volume of any volumetric data recovered from computed tomography (CT) or magnetic resonance (MR). We proposed the 30 configurations of volume values based on the polygonal mesh generation method. Our algorithm processes the data in scan-line order simultaneously with reconstruction algorithm to create a Fenwick tree, ensuring query time much faster and assisting users\u27 edition of slicing or transforming model. We tested the algorithm\u27s accuracy on simple 3D objects (e.g., sphere, cylinder) to complicated structures (e.g., lungs, cardiac chambers). The result deviated within and there is still room for further improvement.8 pages, 7 figure
Explaining Model Overfitting in CNNs via GMM Clustering
No full textConvolutional Neural Networks (CNNs) have demonstrated remarkable prowess in the field of computer vision. However, their opaque decision-making processes pose significant challenges for practical applications. In this study, we provide quantitative metrics for assessing CNN filters by clustering the feature maps corresponding to individual filters in the model via Gaussian Mixture Model (GMM). By analyzing the clustering results, we screen out some anomaly filters associated with outlier samples. We further analyze the relationship between the anomaly filters and model overfitting, proposing three hypotheses. This method is universally applicable across diverse CNN architectures without modifications, as evidenced by its successful application to models like AlexNet and LeNet-5. We present three meticulously designed experiments demonstrating our hypotheses from the perspectives of model behavior, dataset characteristics, and filter impacts. Through this work, we offer a novel perspective for evaluating the CNN performance and gain new insights into the operational behavior of model overfitting
Discretization anisotropy in micromagnetic simulations
No full textFinite difference based micromagnetic simulations are a powerful tool for the computational investigation of magnetic structures. In this paper, we demonstrate how the discretization of continuous micromagnetic equations introduces a numerical \u27discretization anisotropy\u27. We demonstrate that, in certain scenarios, this anisotropy operates on an energy scale comparable to that of intrinsic physical phenomena. Furthermore, we illustrate that selecting appropriate finite difference stencils and minimizing the size of the discretization cells are effective strategies to mitigate discretization anisotropy.5 pages, 4 figures, Supplementary Material https://gitlab.mpcdf.mpg.de/samholt/paper_discretization_anisotropy_supplementary_materia