Tootfinder

A deep dive into the state of the tech job market in 2025, including an increase in demand for AI engineers and a rising average tenure at Big Tech companies (Gergely Orosz/The Pragmatic Engineer)
https://newsletter.pragmaticengineer.com/p/state-of-the-tech-market…

State of the software engineering job market in 2025
A deepdive into today’s tech jobs market, with exclusive data on tech jobs, AI engineering, Big Tech recruitment, the growing importance of location, and more

Key fantasy football questions for Week 1: Will the Chiefs' deep ball return? Pump the brakes on JCM? https://www.nytimes.com/athletic/6598115/2025/09/04/fantasy-football-week-1-key-questions-chiefs-deep-ball/

FOX News contributor: "Democrats didn't just win in blue places. They broke the supermajority in the Mississippi state legislature. They flipped seats in Texas. They won parts of Virginia that are deep red. Democrats won all over the country."
#FoxNews #democrats

Quantum Deep Learning for Massive MIMO User Scheduling
Xingyu Huang, Ruining Fan, Mouli Chakraborty, Avishek Nag, Anshu Mukherjee
https://arxiv.org/abs/2508.03327 https://

Quantum Deep Learning for Massive MIMO User Scheduling
We introduce a hybrid Quantum Neural Networks (QNN) architecture for the efficient user scheduling in 5G/Beyond 5G (B5G) massive Multiple Input Multiple Output (MIMO) systems, addressing the scalability issues of traditional methods. By leveraging statistical Channel State Information (CSI), our model reduces computational overhead and enhances spectral efficiency. It integrates classical neural networks with a variational quantum circuit kernel, outperforming classical Convolutional Neural Net…

BrainIB : Leveraging Graph Neural Networks and Information Bottleneck for Functional Brain Biomarkers in Schizophrenia
Tianzheng Hu, Qiang Li, Shu Liu, Vince D. Calhoun, Guido van Wingen, Shujian Yu
https://arxiv.org/abs/2510.03004

BrainIB++: Leveraging Graph Neural Networks and Information Bottleneck for Functional Brain Biomarkers in Schizophrenia
The development of diagnostic models is gaining traction in the field of psychiatric disorders. Recently, machine learning classifiers based on resting-state functional magnetic resonance imaging (rs-fMRI) have been developed to identify brain biomarkers that differentiate psychiatric disorders from healthy controls. However, conventional machine learning-based diagnostic models often depend on extensive feature engineering, which introduces bias through manual intervention. While deep learning…

LowDiff: Efficient Frequent Checkpointing via Low-Cost Differential for High-Performance Distributed Training Systems
Chenxuan Yao, Yuchong Hu, Feifan Liu, Zhengyu Liu, Dan Feng
https://arxiv.org/abs/2509.04084

LowDiff: Efficient Frequent Checkpointing via Low-Cost Differential for High-Performance Distributed Training Systems
Distributed training of large deep-learning models often leads to failures, so checkpointing is commonly employed for recovery. State-of-the-art studies focus on frequent checkpointing for fast recovery from failures. However, it generates numerous checkpoints, incurring substantial costs and thus degrading training performance. Recently, differential checkpointing has been proposed to reduce costs, but it is limited to recommendation systems, so its application to general distributed training …

Aleatoric Uncertainty from AI-based 6D Object Pose Predictors for Object-relative State Estimation
Thomas Jantos, Stephan Weiss, Jan Steinbrener
https://arxiv.org/abs/2509.01583

Aleatoric Uncertainty from AI-based 6D Object Pose Predictors for Object-relative State Estimation
Deep Learning (DL) has become essential in various robotics applications due to excelling at processing raw sensory data to extract task specific information from semantic objects. For example, vision-based object-relative navigation relies on a DL-based 6D object pose predictor to provide the relative pose between the object and the robot as measurements to the robot's state estimator. Accurately knowing the uncertainty inherent in such Deep Neural Network (DNN) based measurements is essential…

Paving the Way Towards Kinematic Assessment Using Monocular Video: A Preclinical Benchmark of State-of-the-Art Deep-Learning-Based 3D Human Pose Estimators Against Inertial Sensors in Daily Living Activities
Mario Medrano-Paredes, Carmen Fern\'andez-Gonz\'alez, Francisco-Javier D\'iaz-Pernas, Hichem Saoudi, Javier Gonz\'alez-Alonso, Mario Mart\'inez-Zarzuela

Paving the Way Towards Kinematic Assessment Using Monocular Video: A Preclinical Benchmark of State-of-the-Art Deep-Learning-Based 3D Human Pose Estimators Against Inertial Sensors in Daily Living Activities
Advances in machine learning and wearable sensors offer new opportunities for capturing and analyzing human movement outside specialized laboratories. Accurate assessment of human movement under real-world conditions is essential for telemedicine, sports science, and rehabilitation. This preclinical benchmark compares monocular video-based 3D human pose estimation models with inertial measurement units (IMUs), leveraging the VIDIMU dataset containing a total of 13 clinically relevant daily acti…

Optical characterization of deep level defects in n-type Al$_x$In$_y$Ga$_{1-x-y}$P for development of solid-state photomultiplier analogs
Andrew M. Armstrong, Evan M. Anderson, Lisa N. Caravello, Eduardo Garcia, Joseph P. Klesko, Samuel D. Hawkins, Eric A. Shaner, John F. Klem, Aaron J. Muhowski
https://arxiv.org/abs/2509.02880

Optical characterization of deep level defects in n-type Al$_x$In$_y$Ga$_{1-x-y}$P for development of solid-state photomultiplier analogs
Characterizing intrinsic defects is an important step in evaluating materials for new optoelectronic device applications. For photomultipliers, suppressing dark currents is critical, but there exists a tradeoff between maximizing the band gap while remaining sensitive to the wavelength of interest, and minimizing the incorporation of new defects by growing not-yet-optimized alloys. We present a series of capacitance-based measurements, including deep level optical spectroscopy, steady-state pho…

Linear RNNs for autoregressive generation of long music samples
Konrad Szewczyk, Daniel Gallo Fern\'andez, James Townsend
https://arxiv.org/abs/2510.02401 https://

Linear RNNs for autoregressive generation of long music samples
Directly learning to generate audio waveforms in an autoregressive manner is a challenging task, due to the length of the raw sequences and the existence of important structure on many different timescales. Traditional approaches based on recurrent neural networks, as well as causal convolutions and self-attention, have only had limited success on this task. However, recent work has shown that deep state space models, also referred to as linear RNNs, can be highly efficient in this context. In …

When we travel from deep-blue states to deep-red states,
the ‘Fox-Book’ media diet increases by about 10 percentage points.
Likewise, the diet of political ignorance (no political media consumption) increases by about 3.5 percentage points.
Summing these two diets, we get a roughly 14-point swing in media consumption across the state partisan spectrum.
Obviously, this 14-point swing is not Orwellian: there is no state population that receives all of its political infor…

Partisan Politics and the Road to Plutocracy – Economics from the Top Down
In this essay, I take a deep dive into the history of American plutocracy and its conspicuously Republican roots.

Mark Sanchez arrested in Indianapolis; Bills and Eagles no longer undefeated; Aces lead WNBA Finals 2-0

https://www.cbssports.com/nfl/news/mark-sa

CapsBeam: Accelerating Capsule Network based Beamformer for Ultrasound Non-Steered Plane Wave Imaging on Field Programmable Gate Array
Abdul Rahoof, Vivek Chaturvedi, Mahesh Raveendranatha Panicker, Muhammad Shafique
https://arxiv.org/abs/2509.03201

CapsBeam: Accelerating Capsule Network based Beamformer for Ultrasound Non-Steered Plane Wave Imaging on Field Programmable Gate Array
In recent years, there has been a growing trend in accelerating computationally complex non-real-time beamforming algorithms in ultrasound imaging using deep learning models. However, due to the large size and complexity these state-of-the-art deep learning techniques poses significant challenges when deploying on resource-constrained edge devices. In this work, we propose a novel capsule network based beamformer called CapsBeam, designed to operate on raw radio-frequency data and provide an en…

ShapeGen3DCP: A Deep Learning Framework for Layer Shape Prediction in 3D Concrete Printing
Giacomo Rizzieri, Federico Lanteri, Liberato Ferrara, Massimiliano Cremonesi
https://arxiv.org/abs/2510.02009 …

ShapeGen3DCP: A Deep Learning Framework for Layer Shape Prediction in 3D Concrete Printing
This work introduces ShapeGen3DCP, a deep learning framework for fast and accurate prediction of filament cross-sectional geometry in 3D Concrete Printing (3DCP). The method is based on a neural network architecture that takes as input both material properties in the fluid state (density, yield stress, plastic viscosity) and process parameters (nozzle diameter, nozzle height, printing and flow velocities) to directly predict extruded layer shapes. To enhance generalization, some inputs are refo…

Privacy Risks in Time Series Forecasting: User- and Record-Level Membership Inference
Nicolas Johansson (Chalmers University of Technology), Tobias Olsson (Chalmers University of Technology), Daniel Nilsson (AI Sweden), Johan \"Ostman (AI Sweden), Fazeleh Hoseini (AI Sweden)
https://arxiv.org/abs/2509.04169

Privacy Risks in Time Series Forecasting: User- and Record-Level Membership Inference
Membership inference attacks (MIAs) aim to determine whether specific data were used to train a model. While extensively studied on classification models, their impact on time series forecasting remains largely unexplored. We address this gap by introducing two new attacks: (i) an adaptation of multivariate LiRA, a state-of-the-art MIA originally developed for classification models, to the time-series forecasting setting, and (ii) a novel end-to-end learning approach called Deep Time Series (DT…

Benchmarking Deep Learning Convolutions on Energy-constrained CPUs
Enrique Galvez (ALSOC), Adrien Cassagne (ALSOC), Alix Munier (ALSOC), Manuel Bouyer
https://arxiv.org/abs/2509.26217

Benchmarking Deep Learning Convolutions on Energy-constrained CPUs
This work evaluates state-of-the-art convolution algorithms for CPU-based deep learning inference. While most prior studies focus on GPUs or NPUs, CPU implementations remain relatively underoptimized. We benchmark direct, GEMM-based, and Winograd convolutions across modern CPUs from ARM __ , Intel __ , AMD __ , Apple __ , and Nvidia __ , considering both latency and energy efficiency. Our results highlight the key architectural factors that govern CPU efficiency for convolution operations, prov…

Guten Morgen Zusammen,
heute geh ich nicht aus dem Haus, das Büro kommt heute zu mir :-) Neun Stunden Restsprechzeit bietet mir der Headsetakku an. 😱 Deep State, der steckt mit meinem Cheffchen unter einer Decke!
Anonsten, arbeitet auch schon, wir haben ja schon Mittwoch! :-) :coffeepot:

Trump's mass firings decimate a city's 'largest employer' in this deep-red state - Alternet.org
https://www.alternet.org/utah-trump-irs-doge/

An Efficient Deep Template Matching and In-Plane Pose Estimation Method via Template-Aware Dynamic Convolution
Ke Jia, Ji Zhou, Hanxin Li, Zhigan Zhou, Haojie Chu, Xiaojie Li
https://arxiv.org/abs/2510.01678

An Efficient Deep Template Matching and In-Plane Pose Estimation Method via Template-Aware Dynamic Convolution
In industrial inspection and component alignment tasks, template matching requires efficient estimation of a target's position and geometric state (rotation and scaling) under complex backgrounds to support precise downstream operations. Traditional methods rely on exhaustive enumeration of angles and scales, leading to low efficiency under compound transformations. Meanwhile, most deep learning-based approaches only estimate similarity scores without explicitly modeling geometric pose, making …

S2M2ECG: Spatio-temporal bi-directional State Space Model Enabled Multi-branch Mamba for ECG
Huaicheng Zhang, Ruoxin Wang, Chenlian Zhou, Jiguang Shi, Yue Ge, Zhoutong Li, Sheng Chang, Hao Wang, Jin He, Qijun Huang
https://arxiv.org/abs/2509.03066

S2M2ECG: Spatio-temporal bi-directional State Space Model Enabled Multi-branch Mamba for ECG
As one of the most effective methods for cardiovascular disease (CVD) diagnosis, multi-lead Electrocardiogram (ECG) signals present a characteristic multi-sensor information fusion challenge that has been continuously researched in deep learning domains. Despite the numerous algorithms proposed with different DL architectures, maintaining a balance among performance, computational complexity, and multi-source ECG feature fusion remains challenging. Recently, state space models (SSMs), particula…

Deep Learning Accelerated Algebraic Multigrid Methods for Polytopal Discretizations of Second-Order Differential Problems
Paola F. Antonietti, Matteo Caldana, Lorenzo Gentile, Marco Verani
https://arxiv.org/abs/2510.01442

Deep Learning Accelerated Algebraic Multigrid Methods for Polytopal Discretizations of Second-Order Differential Problems
Algebraic Multigrid (AMG) methods are state-of-the-art algebraic solvers for partial differential equations. Still, their efficiency depends heavily on the choice of suitable parameters and/or ingredients. Paradigmatic examples include the so-called strong threshold parameter $θ$, which controls the algebraic coarse-grid hierarchy, as well as the smoother, i.e., the relaxation methods used on the fine grid to damp out high-frequency errors. In AMG, since the coarse grids are constructed algebr…

Heading to Chicago this week. Italian beef, Vienna dogs, deep dish pizza. Art Institute. Billy Goat Tavern. I'm sure I can manage this without any help from red state militia nitwits or US Marines.

Exclusive: Wide-ranging group of US officials pursues Trump's fight against 'Deep State' (Reuters)
https://www.reuters.com/world/us/wide-ranging-group-us-officials-pursues-trumps-fight-against-deep-state-2025-10-20/
http://www.memeorandum.com/251020/p141#a251020p141

Perturbatively corrected ring-polymer instanton rate theory rigorously captures anharmonicity and deep tunneling
Jindra Du\v{s}ek, Joseph E. Lawrence, Jeremy O. Richardson
https://arxiv.org/abs/2509.01454

Perturbatively corrected ring-polymer instanton rate theory rigorously captures anharmonicity and deep tunneling
In this paper, we derive a perturbatively-corrected instanton rate theory in the ring-polymer framework (RPI+PC), which significantly enhances the accuracy of instanton theory by using third and fourth derivatives of the potential to capture anharmonic effects. Instanton theory is a rigorous semiclassical method that extends transition-state theory by including quantum tunneling along a well-defined optimal tunneling pathway. However, the standard leading-order instanton theory (RPI) neglects a…

Multi-Actor Multi-Critic Deep Deterministic Reinforcement Learning with a Novel Q-Ensemble Method
Andy Wu, Chun-Cheng Lin, Rung-Tzuo Liaw, Yuehua Huang, Chihjung Kuo, Chia Tong Weng
https://arxiv.org/abs/2510.01083

Multi-Actor Multi-Critic Deep Deterministic Reinforcement Learning with a Novel Q-Ensemble Method
Reinforcement learning has gathered much attention in recent years due to its rapid development and rich applications, especially on control systems and robotics. When tackling real-world applications with reinforcement learning method, the corresponded Markov decision process may have huge discrete or even continuous state/action space. Deep reinforcement learning has been studied for handling these issues through deep learning for years, and one promising branch is the actor-critic architectu…

Oh you thought the ordeal of being perceived was mortifying? Wait 'till you try being perceived by a state.
As its bureaucratic gaze lingers through the entrails of your medical, financial, scholastic, and social records, a deep disquiet oozes through your brain upon consideration of the labyrinthine antagonisms it may—or may not—inflict upon you. In service only to Capital, this entity has bequeathed immeasurable portions of suffering and death to those it threatens to protect, let alone to its ever-multiplying enemies, and now it is your turn to be found wanting, for in its incessant mechanical hunger, wanting is all that the state can conceive of.
#horror #capitalism #nationalism

Republican control of state legislatures is systematically associated with the rich being (and becoming) richer.
In short, American plutocracy seems to be a quintessentially Republican affair.
And that’s not all.
As we journey into the depths of US state politics, the plot will thicken.
We’ll find striking partisan differences in the language used in state bills.
We’ll see the many ways that Republicans help the rich and hurt workers.
We’ll see the impact pa…

Partisan Politics and the Road to Plutocracy – Economics from the Top Down
In this essay, I take a deep dive into the history of American plutocracy and its conspicuously Republican roots.

AiDE-Q: Synthetic Labeled Datasets Can Enhance Learning Models for Quantum Property Estimation
Xinbiao Wang, Yuxuan Du, Zihan Lou, Yang Qian, Kaining Zhang, Yong Luo, Bo Du, Dacheng Tao
https://arxiv.org/abs/2509.26109

AiDE-Q: Synthetic Labeled Datasets Can Enhance Learning Models for Quantum Property Estimation
Quantum many-body problems are central to various scientific disciplines, yet their ground-state properties are intrinsically challenging to estimate. Recent advances in deep learning (DL) offer potential solutions in this field, complementing prior purely classical and quantum approaches. However, existing DL-based models typically assume access to a large-scale and noiseless labeled dataset collected by infinite sampling. This idealization raises fundamental concerns about their practical uti…

Deep Learning Inductive Biases for fMRI Time Series Classification during Resting-state and Movie-watching
Behdad Khodabandehloo, Reza Rajimehr
https://arxiv.org/abs/2509.16973 …

Deep Learning Inductive Biases for fMRI Time Series Classification during Resting-state and Movie-watching
Deep learning has advanced fMRI analysis, yet it remains unclear which architectural inductive biases are most effective at capturing functional patterns in human brain activity. This issue is particularly important in small-sample settings, as most datasets fall into this category. We compare models with three major inductive biases in deep learning including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and Transformers for the task of biological sex classific…

Evaluating the Impact of Radiographic Noise on Chest X-ray Semantic Segmentation and Disease Classification Using a Scalable Noise Injection Framework
Derek Jiu, Kiran Nijjer, Nishant Chinta, Ryan Bui, Ben Liu, Kevin Zhu
https://arxiv.org/abs/2509.25265

Evaluating the Impact of Radiographic Noise on Chest X-ray Semantic Segmentation and Disease Classification Using a Scalable Noise Injection Framework
Deep learning models are increasingly used for radiographic analysis, but their reliability is challenged by the stochastic noise inherent in clinical imaging. A systematic, cross-task understanding of how different noise types impact these models is lacking. Here, we evaluate the robustness of state-of-the-art convolutional neural networks (CNNs) to simulated quantum (Poisson) and electronic (Gaussian) noise in two key chest X-ray tasks: semantic segmentation and pulmonary disease classificati…

Efficient Large-Scale Cross-Domain Sequential Recommendation with Dynamic State Representations
Manuel V. Loureiro, Steven Derby, Aleksei Medvedev, Alejandro Ariza-Casabona, Gonzalo Fiz Pontiveros, Tri Kurniawan Wijaya
https://arxiv.org/abs/2508.20945

Efficient Large-Scale Cross-Domain Sequential Recommendation with Dynamic State Representations
Recently, autoregressive recommendation models (ARMs), such as Meta's HSTU model, have emerged as a major breakthrough over traditional Deep Learning Recommendation Models (DLRMs), exhibiting the highly sought-after scaling law behaviour. However, when applied to multi-domain scenarios, the transformer architecture's attention maps become a computational bottleneck, as they attend to all items across every domain. To tackle this challenge, systems must efficiently balance inter and intra-domain…

CausalKANs: interpretable treatment effect estimation with Kolmogorov-Arnold networks
Alejandro Almod\'ovar, Patricia A. Apell\'aniz, Santiago Zazo, Juan Parras
https://arxiv.org/abs/2509.22467

CausalKANs: interpretable treatment effect estimation with Kolmogorov-Arnold networks
Deep neural networks achieve state-of-the-art performance in estimating heterogeneous treatment effects, but their opacity limits trust and adoption in sensitive domains such as medicine, economics, and public policy. Building on well-established and high-performing causal neural architectures, we propose causalKANs, a framework that transforms neural estimators of conditional average treatment effects (CATEs) into Kolmogorov--Arnold Networks (KANs). By incorporating pruning and symbolic simpli…

Comparative study of Wavelet transform and Fourier domain filtering for medical image denoising
M. Ali Saif, Bassam M. Mughalles, Ibrahim G. H. Loqman
https://arxiv.org/abs/2509.26608

Comparative study of Wavelet transform and Fourier domain filtering for medical image denoising
Denoising of images is a crucial preprocessing step in medical imaging, essential for improving diagnostic clarity. While deep learning methods offer state-of-the-art performance, their computational complexity and data requirements can be prohibitive. In this study we present a comprehensive comparative analysis of two classical, computationally efficient transform-domain techniques: Discrete Wavelet Transform (DWT) and Discrete Fourier Cosine Transform (DFCT) filtering. We evaluated their eff…

Opposition leader Benny Gantz proclaims long-term multiparty rejection of a Palestinian state by Israeli Jewish political society. https://www.nytimes.com/2025/09/24/opinion/israel-war-security.html?un…

Opinion | What the World Gets Wrong About Israel
There are deep political divisions and disagreements in Israel, but the nation’s core security interests are not partisan property.

Deep prior-based denoising for state-of-the-art scientific imaging and metrology
Yuichi Yokoyama, Kohei Yamagami, Yuta Sumiya, Hayaru Shouno, Masaichiro Mizumaki
https://arxiv.org/abs/2510.09410

Deep prior-based denoising for state-of-the-art scientific imaging and metrology
Deep learning has revolutionized computer vision, yet a major gap persists between complex, data-hungry deep learning models and the practical demands of state-of-the-art scientific measurements. To fundamentally bridge this gap, we propose deep prior-based denoising, a robust deep learning model that requires no training data. We demonstrate its effectiveness by removing grid artifacts in angle-resolved photoemission spectroscopy (ARPES), a long-standing and critical data analysis challenge in…

High-Resolution Sensing in Communication-Centric ISAC: Deep Learning and Parametric Methods
Salmane Naoumi, Ahmad Bazzi, Roberto Bomfin, Marwa Chafii
https://arxiv.org/abs/2509.02137

High-Resolution Sensing in Communication-Centric ISAC: Deep Learning and Parametric Methods
This paper introduces two novel algorithms designed to address the challenge of super-resolution sensing parameter estimation in bistatic configurations within communication-centric integrated sensing and communication (ISAC) systems. Our approach leverages the estimated channel state information derived from reference symbols originally intended for communication to achieve super-resolution sensing parameter estimation. The first algorithm, IFFT-C2VNN, employs complex-valued convolutional neur…

Linear Power System Modeling and Analysis Across Wide Operating Ranges: A Hierarchical Neural State-Space Equation Approach
Weicheng Liu, Di Liu, Songyan Zhang, Chao Lu
https://arxiv.org/abs/2508.17774

Linear Power System Modeling and Analysis Across Wide Operating Ranges: A Hierarchical Neural State-Space Equation Approach
Developing a unified small-signal model for modern, large-scale power systems that remains accurate across a wide range of operating ranges presents a formidable challenge. Traditional methods, spanning mechanistic modeling, modal identification, and deep learning, have yet to fully overcome persistent limitations in accuracy, universal applicability, and interpretability. In this paper, a novel hierarchical neural state-space equation approach is proposed to overcome these obstacles, achieving…

SatDINO: A Deep Dive into Self-Supervised Pretraining for Remote Sensing
Jakub Straka, Ivan Gruber
https://arxiv.org/abs/2508.21402 https://arxiv.org/pdf/2…

SatDINO: A Deep Dive into Self-Supervised Pretraining for Remote Sensing
Self-supervised learning has emerged as a powerful tool for remote sensing, where large amounts of unlabeled data are available. In this work, we investigate the use of DINO, a contrastive self-supervised method, for pretraining on remote sensing imagery. We introduce SatDINO, a model tailored for representation learning in satellite imagery. Through extensive experiments on multiple datasets in multiple testing setups, we demonstrate that SatDINO outperforms other state-of-the-art methods base…

Interplay of blowups in a differential kinetic model of gravity wave turbulence
Daniel Schubring, Vladimir Rosenhaus, Simon Thalabard
https://arxiv.org/abs/2509.23199 https://…

Interplay of blowups in a differential kinetic model of gravity wave turbulence
We describe the blowup scenarios in a novel phase-parametrized differential approximation model of deep water surface gravity waves that was recently derived in the limit of a large number of multicomponent fields. Previous work showed that upon precisely tuning the phase parameter $ϕ$ to $π$, the model formally supports steady-state solutions transitioning from Kolmogorov-Zakharov $\propto ω^{-4}$ to Phillips' critical balance $\propto ω^{-5}$ at small scales, a feature qualitatively consi…

The 'deep state' is proving to Trump it's a worthy foe (Erin Schumaker/Politico)
https://www.politico.com/news/2025/09/14/trump-federal-workers-deep-state-civil-service-00558940
http://www.memeorandum.com/250914/p14#a250914p14

FuXi-TC: A generative framework integrating deep learning and physics-based models for improved tropical cyclone forecasts
Shan Guo, Lei Chen, Zeyi Niu, Ziyao Sun, Xinyan Zhang, Yangyang Zhao, Xiaohui Zhong, Hao Li
https://arxiv.org/abs/2508.16168

FuXi-TC: A generative framework integrating deep learning and physics-based models for improved tropical cyclone forecasts
Tropical cyclones (TCs) are among the most devastating natural hazards, yet their intensity remains notoriously difficult to predict. Numerical weather prediction (NWP) models are constrained by both computational demands and intrinsic predictability, while state-of-the-art deep learning-based weather forecasting models tend to underestimate TC intensity due to biases in reanalysis-based training data. Here, we present FuXi-TC, a diffusion-based generative forecasting framework that combines th…

Drop-Muon: Update Less, Converge Faster
Kaja Gruntkowska, Yassine Maziane, Zheng Qu, Peter Richt\'arik
https://arxiv.org/abs/2510.02239 https://arxiv.o…

Drop-Muon: Update Less, Converge Faster
Conventional wisdom in deep learning optimization dictates updating all layers at every step-a principle followed by all recent state-of-the-art optimizers such as Muon. In this work, we challenge this assumption, showing that full-network updates can be fundamentally suboptimal, both in theory and in practice. We introduce a non-Euclidean Randomized Progressive Training method-Drop-Muon-a simple yet powerful framework that updates only a subset of layers per step according to a randomized sche…

Computationally Tractable Offline Quantum Experimental Design for Nuclear Spin Detection
B. Varona-Uriarte, F. Belliardo, T. H. Taminiau, C. Bonato, E. Garrote, J. Casanova
https://arxiv.org/abs/2508.21450

Computationally Tractable Offline Quantum Experimental Design for Nuclear Spin Detection
The characterization of nuclear spin environments in solid-state devices plays an important role in advancing quantum technologies, yet traditional methods often demand long measurement times. To address this challenge, we extend our recently developed deep-learning-based SALI model (Signal-to-image ArtificiaL Intelligence) by introducing the surrogate information gain (SIG) to optimize the selection of data points in the measurements. This approach significantly reduces time requirements in ex…

Investigating the Impact of Rational Dilated Wavelet Transform on Motor Imagery EEG Decoding with Deep Learning Models
Marco Siino, Giuseppe Bonomo, Rosario Sorbello, Ilenia Tinnirello
https://arxiv.org/abs/2510.09242

Investigating the Impact of Rational Dilated Wavelet Transform on Motor Imagery EEG Decoding with Deep Learning Models
The present study investigates the impact of the Rational Discrete Wavelet Transform (RDWT), used as a plug-in preprocessing step for motor imagery electroencephalographic (EEG) decoding prior to applying deep learning classifiers. A systematic paired evaluation (with/without RDWT) is conducted on four state-of-the-art deep learning architectures: EEGNet, ShallowConvNet, MBEEG\_SENet, and EEGTCNet. This evaluation was carried out across three benchmark datasets: High Gamma, BCI-IV-2a, and BCI-I…

Weekend Reads
* Fingerprinting DPI devices
https://arxiv.org/abs/2509.09081
* Exportation of China's GFW
https://

Fingerprinting Deep Packet Inspection Devices by Their Ambiguities
Users around the world face escalating network interference such as censorship, throttling, and interception, largely driven by the commoditization and growing availability of Deep Packet Inspection (DPI) devices. Once reserved for a few well-resourced nation-state actors, the ability to interfere with traffic at scale is now within reach of nearly any network operator. Despite this proliferation, our understanding of DPIs and their deployments on the Internet remains limited -- being network i…

A Deep Learning Framework for Joint Channel Acquisition and Communication Optimization in Movable Antenna Systems
Ruizhi Zhang, Yuchen Zhang, Lipeng Zhu, Ying Zhang, Rui Zhang
https://arxiv.org/abs/2509.10487

A Deep Learning Framework for Joint Channel Acquisition and Communication Optimization in Movable Antenna Systems
This paper presents an end-to-end deep learning framework in a movable antenna (MA)-enabled multiuser communication system. In contrast to the conventional works assuming perfect channel state information (CSI), we address the practical CSI acquisition issue through the design of pilot signals and quantized CSI feedback, and further incorporate the joint optimization of channel estimation, MA placement, and precoding design. The proposed mechanism enables the system to learn an optimized transm…

Replaced article(s) found for cs.CR. https://arxiv.org/list/cs.CR/new
[1/1]:
- Evaluating The Explainability of State-of-the-Art Deep Learning-based Network Intrusion Detection...
Ayush Kumar, Vrizlynn L. L. Thing

T-ILR: a Neurosymbolic Integration for LTLf
Riccardo Andreoni, Andrei Buliga, Alessandro Daniele, Chiara Ghidini, Marco Montali, Massimiliano Ronzani
https://arxiv.org/abs/2508.15943

T-ILR: a Neurosymbolic Integration for LTLf
State-of-the-art approaches for integrating symbolic knowledge with deep learning architectures have demonstrated promising results in static domains. However, methods to handle temporal logic specifications remain underexplored. The only existing approach relies on an explicit representation of a finite-state automaton corresponding to the temporal specification. Instead, we aim at proposing a neurosymbolic framework designed to incorporate temporal logic specifications, expressed in Linear Te…

Choosing to Be Green: Advancing Green AI via Dynamic Model Selection
Emilio Cruciani, Roberto Verdecchia
https://arxiv.org/abs/2509.19996 https://arxiv.org…

Choosing to Be Green: Advancing Green AI via Dynamic Model Selection
Artificial Intelligence is increasingly pervasive across domains, with ever more complex models delivering impressive predictive performance. This fast technological advancement however comes at a concerning environmental cost, with state-of-the-art models - particularly deep neural networks and large language models - requiring substantial computational resources and energy. In this work, we present the intuition of Green AI dynamic model selection, an approach based on dynamic model selection…

The Bolton Raid Feels Like a Warning (Shane Harris/The Atlantic)
https://www.theatlantic.com/national-security/archive/2025/08/bolton-raid-trump-retribution-deep-state/683990/
http://www.memeorandum.com/250823/p2#a250823p2

Towards Robust LiDAR Localization: Deep Learning-based Uncertainty Estimation
Minoo Dolatabadi, Fardin Ayar, Ehsan Javanmardi, Manabu Tsukada, Mahdi Javanmardi
https://arxiv.org/abs/2509.18954

Improving Deep Learning for Accelerated MRI With Data Filtering
Kang Lin, Anselm Krainovic, Kun Wang, Reinhard Heckel
https://arxiv.org/abs/2508.13822 https://

Improving Deep Learning for Accelerated MRI With Data Filtering
Deep neural networks achieve state-of-the-art results for accelerated MRI reconstruction. Most research on deep learning based imaging focuses on improving neural network architectures trained and evaluated on fixed and homogeneous training and evaluation data. In this work, we investigate data curation strategies for improving MRI reconstruction. We assemble a large dataset of raw k-space data from 18 public sources consisting of 1.1M images and construct a diverse evaluation set comprising 48…

Database Views as Explanations for Relational Deep Learning
Agapi Rissaki, Ilias Fountalis, Wolfgang Gatterbauer, Benny Kimelfeld
https://arxiv.org/abs/2509.09482 https://

Database Views as Explanations for Relational Deep Learning
In recent years, there has been significant progress in the development of deep learning models over relational databases, including architectures based on heterogeneous graph neural networks (hetero-GNNs) and heterogeneous graph transformers. In effect, such architectures state how the database records and links (e.g., foreign-key references) translate into a large, complex numerical expression, involving numerous learnable parameters. This complexity makes it hard to explain, in human-underst…

A Chaotic Dynamics Framework Inspired by Dorsal Stream for Event Signal Processing
Yu Chen, Jing Lian, Zhaofei Yu, Jizhao Liu, Jisheng Dang, Gang Wang
https://arxiv.org/abs/2509.26085

A Chaotic Dynamics Framework Inspired by Dorsal Stream for Event Signal Processing
Event cameras are bio-inspired vision sensor that encode visual information with high dynamic range, high temporal resolution, and low latency.Current state-of-the-art event stream processing methods rely on end-to-end deep learning techniques. However, these models are heavily dependent on data structures, limiting their stability and generalization capabilities across tasks, thereby hindering their deployment in real-world scenarios. To address this issue, we propose a chaotic dynamics event …

Deep Reinforcement Learning in Factor Investment
Junlin Liu
https://arxiv.org/abs/2509.16206 https://arxiv.org/pdf/2509.16206

Deep Reinforcement Learning in Factor Investment
Deep reinforcement learning has shown promise in trade execution, yet its use in low-frequency factor portfolio construction remains under-explored. A key obstacle is the high-dimensional, unbalanced state space created by stocks that enter and exit the investable universe. We introduce Conditional Auto-encoded Factor-based Portfolio Optimisation (CAFPO), which compresses stock-level returns into a small set of latent factors conditioned on 94 firm-specific characteristics. The factors feed a D…

HCQA: Hybrid Classical-Quantum Agent for Generating Optimal Quantum Sensor Circuits
Ahmad Alomari, Sathish A. P. Kumar
https://arxiv.org/abs/2508.21246 https://

HCQA: Hybrid Classical-Quantum Agent for Generating Optimal Quantum Sensor Circuits
This study proposes an HCQA for designing optimal Quantum Sensor Circuits (QSCs) to address complex quantum physics problems. The HCQA integrates computational intelligence techniques by leveraging a Deep Q-Network (DQN) for learning and policy optimization, enhanced by a quantum-based action selection mechanism based on the Q-values. A quantum circuit encodes the agent current state using Ry gates, and then creates a superposition of possible actions. Measurement of the circuit results in prob…

Semi-inclusive deep inelastic scattering off a tensor-polarized spin-1 target
Jing Zhao, Alessandro Bacchetta, Shunzo Kumano, Tianbo Liu, Ya-jin Zhou
https://arxiv.org/abs/2508.06134

Semi-inclusive deep inelastic scattering off a tensor-polarized spin-1 target
We investigate the semi-inclusive deep inelastic scattering off a tensor-polarized spin-1 target, focusing on the production of an unpolarized hadron. The complete differential cross section is expressed in terms of 23 structure functions, which depend on the spin states of the target and the azimuthal modulations of the final-state hadron. Within the transverse momentum dependent (TMD) factorization framework, we derive the hadronic tensor using quark-quark correlators and quark-gluon-quark co…

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels
Lucas Rakotoarivony
https://arxiv.org/abs/2508.21424 https://arxiv.org/pdf/2508.2142…

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels
Deep learning models have achieved state-of-the-art performance in many computer vision tasks. However, in real-world scenarios, novel classes that were unseen during training often emerge, requiring models to acquire new knowledge incrementally. Class-Incremental Learning (CIL) methods enable a model to learn novel classes while retaining knowledge of previous classes. However, these methods make the strong assumption that the incremental dataset is fully labeled, which is unrealistic in pract…

Fingerprinting Deep Packet Inspection Devices by Their Ambiguities
Diwen Xue, Armin Huremagic, Wayne Wang, Ram Sundara Raman, Roya Ensafi
https://arxiv.org/abs/2509.09081 https:…

Fingerprinting Deep Packet Inspection Devices by Their Ambiguities
Users around the world face escalating network interference such as censorship, throttling, and interception, largely driven by the commoditization and growing availability of Deep Packet Inspection (DPI) devices. Once reserved for a few well-resourced nation-state actors, the ability to interfere with traffic at scale is now within reach of nearly any network operator. Despite this proliferation, our understanding of DPIs and their deployments on the Internet remains limited -- being network i…

Analyzing Finnish Inflectional Classes through Discriminative Lexicon and Deep Learning Models
Alexandre Nikolaev, Yu-Ying Chuang, R. Harald Baayen
https://arxiv.org/abs/2509.04813

Analyzing Finnish Inflectional Classes through Discriminative Lexicon and Deep Learning Models
Descriptions of complex nominal or verbal systems make use of inflectional classes. Inflectional classes bring together nouns which have similar stem changes and use similar exponents in their paradigms. Although inflectional classes can be very useful for language teaching as well as for setting up finite state morphological systems, it is unclear whether inflectional classes are cognitively real, in the sense that native speakers would need to discover these classes in order to learn how to p…

Reconstruction of the Equations of State (EoSs) of Compact Stars using machine and deep learning regression techniques
Ioannis Stergakis
https://arxiv.org/abs/2509.13037 https:/…

Reconstruction of the Equations of State (EoSs) of Compact Stars using machine and deep learning regression techniques
This dissertation focuses on the reconstruction of Equations of State (EoSs) describing the interior of compact stars, using modern machine learning and deep learning methods. The pipeline is based on data from mass-radius (M-R) curves, obtained by numerically solving the Tolman-Oppenheimer-Volkoff equations for a wide range of admissible EoSs. The manuscript is divided into a Theoretical Part (Chs. 1-4) and a Computational Part (Chs. 5-7). The theoretical chapters analyze the properties of neu…

I hold deep contempt for the adult, powerful, wealthy politicians who saw the opportunity to single out four children in the entire state of #Utah and keep them from enjoying their sport.
This is a story that radicalized me, and whenever I'm reminded by the bullies gloating over the pain of literal children, I'm going to share it again.

STDiff: A State Transition Diffusion Framework for Time Series Imputation in Industrial Systems
Gary Simethy, Daniel Ortiz-Arroyo, Petar Durdevic
https://arxiv.org/abs/2508.19011

STDiff: A State Transition Diffusion Framework for Time Series Imputation in Industrial Systems
Most deep learning methods for imputing missing values treat the task as completing patterns within a fixed time window. This assumption often fails in industrial systems, where dynamics are driven by control actions, are highly non-stationary, and can experience long, uninterrupted gaps. We propose STDiff, which reframes imputation as learning how the system evolves from one state to the next. STDiff uses a conditional denoising diffusion model with a causal bias aligned to control theory, gen…

T-ILR: a Neurosymbolic Integration for LTLf
Riccardo Andreoni, Andrei Buliga, Alessandro Daniele, Chiara Ghidini, Marco Montali, Massimiliano Ronzani
https://arxiv.org/abs/2508.15943

T-ILR: a Neurosymbolic Integration for LTLf
State-of-the-art approaches for integrating symbolic knowledge with deep learning architectures have demonstrated promising results in static domains. However, methods to handle temporal logic specifications remain underexplored. The only existing approach relies on an explicit representation of a finite-state automaton corresponding to the temporal specification. Instead, we aim at proposing a neurosymbolic framework designed to incorporate temporal logic specifications, expressed in Linear Te…

Matsuo Basho (1644–1694) lived his peculiar life on the conviction that
art could create an awareness that allowed one to see into and communicate the essence of experience.
Throughout his life he searched for the state of being one with the object of his poems,
something he believed a poet needed to reach in order to write truthfully.
This life-long search brought Basho to wandering.
He thought that travelling would lead to a state of karumi (lightness), ess…

In Basho’s Footsteps: Hiking the Narrow Road to the Deep North Three Centuries Later
Matsuo Basho (1644–1694) lived his peculiar life on the conviction that art could create an awareness that allowed one to see into and communicate the essence of experience.

Explain and Monitor Deep Learning Models for Computer Vision using Obz AI
Neo Christopher Chung, Jakub Binda
https://arxiv.org/abs/2508.18188 https://arxiv…

Explain and Monitor Deep Learning Models for Computer Vision using Obz AI
Deep learning has transformed computer vision (CV), achieving outstanding performance in classification, segmentation, and related tasks. Such AI-based CV systems are becoming prevalent, with applications spanning from medical imaging to surveillance. State of the art models such as convolutional neural networks (CNNs) and vision transformers (ViTs) are often regarded as ``black boxes,'' offering limited transparency into their decision-making processes. Despite a recent advancement in explaina…

Wavelet-Assisted Mamba for Satellite-Derived Sea Surface Temperature Super-Resolution
Wankun Chen, Feng Gao, Yanhai Gan, Jingchao Cao, Junyu Dong, Qian Du
https://arxiv.org/abs/2509.24334

Wavelet-Assisted Mamba for Satellite-Derived Sea Surface Temperature Super-Resolution
Sea surface temperature (SST) is an essential indicator of global climate change and one of the most intuitive factors reflecting ocean conditions. Obtaining high-resolution SST data remains challenging due to limitations in physical imaging, and super-resolution via deep neural networks is a promising solution. Recently, Mamba-based approaches leveraging State Space Models (SSM) have demonstrated significant potential for long-range dependency modeling with linear complexity. However, their ap…

On the nature and charge state of the X-Defect, a radiation-induced Silicon defect with field-enhanced charge carrier emission
Niels Sorgenfrei, Yana Gurimskaya, Anja Himmerlich, Michael Moll, Ulrich Parzefall, Ioana Pintilie, Joern Schwandt
https://arxiv.org/abs/2508.16197

On the nature and charge state of the X-Defect, a radiation-induced Silicon defect with field-enhanced charge carrier emission
The elusive X-Defect, a defect found in low-resistivity $p$-type Silicon after irradiation, observed as a low-temperature shoulder of the $\mathrm{B}_\mathrm{i}\mathrm{O}_\mathrm{i}$ defect (Boron-interstitial-Oxygen-interstitial complex) in Thermally Stimulated Current (TSC) measurements, was investigated to determine its properties, matching them with those of a previously identified defect. Through a combination of TSC, Deep-Level Transient Spectroscopy (DLTS), Difference-DLTS (DDLTS), numer…

Optimal Condition for Initialization Variance in Deep Neural Networks: An SGD Dynamics Perspective
Hiroshi Horii (SU), Sothea Has (KHM)
https://arxiv.org/abs/2508.12834 https://…

Optimal Condition for Initialization Variance in Deep Neural Networks: An SGD Dynamics Perspective
Stochastic gradient descent (SGD), one of the most fundamental optimization algorithms in machine learning (ML), can be recast through a continuous-time approximation as a Fokker-Planck equation for Langevin dynamics, a viewpoint that has motivated many theoretical studies. Within this framework, we study the relationship between the quasi-stationary distribution derived from this equation and the initial distribution through the Kullback-Leibler (KL) divergence. As the quasi-steady-state distr…

Pre-Manipulation Alignment Prediction with Parallel Deep State-Space and Transformer Models
Motonari Kambara, Komei Sugiura
https://arxiv.org/abs/2509.13839 https://

Pre-Manipulation Alignment Prediction with Parallel Deep State-Space and Transformer Models
In this work, we address the problem of predicting the future success of open-vocabulary object manipulation tasks. Conventional approaches typically determine success or failure after the action has been carried out. However, they make it difficult to prevent potential hazards and rely on failures to trigger replanning, thereby reducing the efficiency of object manipulation sequences. To overcome these challenges, we propose a model, which predicts the alignment between a pre-manipulation egoc…

Digital Twin Aided Massive MIMO CSI Feedback: Exploring the Impact of Twinning Fidelity
Hao Luo, Shuaifeng Jiang, Saeed R. Khosravirad, Ahmed Alkhateeb
https://arxiv.org/abs/2509.25793

Digital Twin Aided Massive MIMO CSI Feedback: Exploring the Impact of Twinning Fidelity
Deep learning (DL) techniques have demonstrated strong performance in compressing and reconstructing channel state information (CSI) while reducing feedback overhead in massive MIMO systems. A key challenge, however, is their reliance on extensive site-specific training data, whose real-world collection incurs significant overhead and limits scalability across deployment sites. To address this, we propose leveraging site-specific digital twins to assist the training of DL-based CSI compression …

High Utilization Energy-Aware Real-Time Inference Deep Convolutional Neural Network Accelerator
Kuan-Ting Lin, Ching-Te Chiu, Jheng-Yi Chang, Shi-Zong Huang, Yu-Ting Li
https://arxiv.org/abs/2509.05688

High Utilization Energy-Aware Real-Time Inference Deep Convolutional Neural Network Accelerator
Deep convolution Neural Network (DCNN) has been widely used in computer vision tasks. However, for edge devices even inference has too large computational complexity and data access amount. The inference latency of state-of-the-art models are impractical for real-world applications. In this paper, we propose a high utilization energy-aware real-time inference deep convolutional neural network accelerator, which improves the performance of the current accelerators. First, we use the 1x1 size con…

STAFF: Stateful Taint-Assisted Full-system Firmware Fuzzing
Alessio Izzillo, Riccardo Lazzeretti, Emilio Coppa
https://arxiv.org/abs/2509.18039 https://arx…

STAFF: Stateful Taint-Assisted Full-system Firmware Fuzzing
Modern embedded Linux devices, such as routers, IP cameras, and IoT gateways, rely on complex software stacks where numerous daemons interact to provide services. Testing these devices is crucial from a security perspective since vendors often use custom closed- or open-source software without documenting releases and patches. Recent coverage-guided fuzzing solutions primarily test individual processes, ignoring deep dependencies between daemons and their persistent internal state. This article…

Recent Advancements in Microscopy Image Enhancement using Deep Learning: A Survey
Debasish Dutta, Neeharika Sonowal, Risheraj Barauh, Deepjyoti Chetia, Sanjib Kr Kalita
https://arxiv.org/abs/2509.15363

Recent Advancements in Microscopy Image Enhancement using Deep Learning: A Survey
Microscopy image enhancement plays a pivotal role in understanding the details of biological cells and materials at microscopic scales. In recent years, there has been a significant rise in the advancement of microscopy image enhancement, specifically with the help of deep learning methods. This survey paper aims to provide a snapshot of this rapidly growing state-of-the-art method, focusing on its evolution, applications, challenges, and future directions. The core discussions take place aroun…

Verify Distributed Deep Learning Model Implementation Refinement with Iterative Relation Inference
Zhanghan Wang, Ding Ding, Hang Zhu, Haibin Lin, Aurojit Panda
https://arxiv.org/abs/2508.09505

Verify Distributed Deep Learning Model Implementation Refinement with Iterative Relation Inference
Distributed machine learning training and inference is common today because today's large models require more memory and compute than can be provided by a single GPU. Distributed models are generally produced by programmers who take a sequential model specification and apply several distribution strategies to distribute state and computation across GPUs. Unfortunately, bugs can be introduced in the process, and a distributed model implementation's outputs might differ from the sequential model'…

Deep Feedback Models
David Calhas, Arlindo L. Oliveira
https://arxiv.org/abs/2509.15905 https://arxiv.org/pdf/2509.15905

Deep Feedback Models
Deep Feedback Models (DFMs) are a new class of stateful neural networks that combine bottom up input with high level representations over time. This feedback mechanism introduces dynamics into otherwise static architectures, enabling DFMs to iteratively refine their internal state and mimic aspects of biological decision making. We model this process as a differential equation solved through a recurrent neural network, stabilized via exponential decay to ensure convergence. To evaluate their ef…

HuMam: Humanoid Motion Control via End-to-End Deep Reinforcement Learning with Mamba
Yinuo Wang, Yuanyang Qi, Jinzhao Zhou, Gavin Tao
https://arxiv.org/abs/2509.18046 https://…

HuMam: Humanoid Motion Control via End-to-End Deep Reinforcement Learning with Mamba
End-to-end reinforcement learning (RL) for humanoid locomotion is appealing for its compact perception-action mapping, yet practical policies often suffer from training instability, inefficient feature fusion, and high actuation cost. We present HuMam, a state-centric end-to-end RL framework that employs a single-layer Mamba encoder to fuse robot-centric states with oriented footstep targets and a continuous phase clock. The policy outputs joint position targets tracked by a low-level PD loop a…

Motion-Based User Identification across XR and Metaverse Applications by Deep Classification and Similarity Learning
Lukas Schach, Christian Rack, Ryan P. McMahan, Marc Erich Latoschik
https://arxiv.org/abs/2509.08539

Motion-Based User Identification across XR and Metaverse Applications by Deep Classification and Similarity Learning
This paper examines the generalization capacity of two state-of-the-art classification and similarity learning models in reliably identifying users based on their motions in various Extended Reality (XR) applications. We developed a novel dataset containing a wide range of motion data from 49 users in five different XR applications: four XR games with distinct tasks and action patterns, and an additional social XR application with no predefined task sets. The dataset is used to evaluate the per…

'It would just be wrong': Ex-governor of deep-red state rails against [redistricting before it's time] - Alternet.org
https://www.alternet.org/trump-republicans-indiana/?u=4a62dc4696c8b502adcf685229f4b216116b8811f49a25ead5941d682f61c8b1&utm_source=Iterable&utm_medium=email&utm_campaign=Aug.7.2025_8.53pm

Constrained Sensing and Reliable State Estimation with Shallow Recurrent Decoders on a TRIGA Mark II Reactor
Stefano Riva, Carolina Introini, Jos\`e Nathan Kutz, Antonio Cammi
https://arxiv.org/abs/2510.12368

Constrained Sensing and Reliable State Estimation with Shallow Recurrent Decoders on a TRIGA Mark II Reactor
Shallow Recurrent Decoder networks are a novel data-driven methodology able to provide accurate state estimation in engineering systems, such as nuclear reactors. This deep learning architecture is a robust technique designed to map the temporal trajectories of a few sparse measures to the full state space, including unobservable fields, which is agnostic to sensor positions and able to handle noisy data through an ensemble strategy, leveraging the short training times and without the need for …

Value Function Initialization for Knowledge Transfer and Jump-start in Deep Reinforcement Learning
Soumia Mehimeh
https://arxiv.org/abs/2508.09277 https://…

Value Function Initialization for Knowledge Transfer and Jump-start in Deep Reinforcement Learning
Value function initialization (VFI) is an effective way to achieve a jumpstart in reinforcement learning (RL) by leveraging value estimates from prior tasks. While this approach is well established in tabular settings, extending it to deep reinforcement learning (DRL) poses challenges due to the continuous nature of the state-action space, the noisy approximations of neural networks, and the impracticality of storing all past models for reuse. In this work, we address these challenges and intro…

Unveiling the Lithium-Ion Transport Mechanism in Li2ZrCl6 Solid-State Electrolyte via Deep Learning-Accelerated Molecular Dynamics Simulations
Hanzeng Guo, Volodymyr Koverga, Selva Chandrasekaran Selvaraj, Anh T. Ngo
https://arxiv.org/abs/2508.05598

Unveiling the Lithium-Ion Transport Mechanism in Li2ZrCl6 Solid-State Electrolyte via Deep Learning-Accelerated Molecular Dynamics Simulations
Lithium zirconium chlorides (LZCs) present a promising class of cost-effective solid electrolyte for next-generation all-solid-state batteries. The unique crystal structure of LZCs plays a crucial role in facilitating lithium-ion mobility, which is central to their electrochemical performance. To understand the underlying mechanism governing ion transport, we employed deep learning-accelerated molecular dynamics simulation on Li2ZrCl6 (trigonal α- and monoclinic \b{eta}-LZC), focusing specific…

Adaptive Science Operations in Deep Space Missions Using Offline Belief State Planning
Grace Ra Kim, Hailey Warner, Duncan Eddy, Evan Astle, Zachary Booth, Edward Balaban, Mykel J. Kochenderfer
https://arxiv.org/abs/2510.08812

Adaptive Science Operations in Deep Space Missions Using Offline Belief State Planning
Deep space missions face extreme communication delays and environmental uncertainty that prevent real-time ground operations. To support autonomous science operations in communication-constrained environments, we present a partially observable Markov decision process (POMDP) framework that adaptively sequences spacecraft science instruments. We integrate a Bayesian network into the POMDP observation space to manage the high-dimensional and uncertain measurements typical of astrobiology missions…

Tomographic reconstruction of free-electron quantum states
Hao Jeng, Claus Ropers
https://arxiv.org/abs/2508.17594 https://arxiv.org/pdf/2508.17594

Tomographic reconstruction of free-electron quantum states
We give several algorithms for reconstructing quantum states of swift electrons, using maximum likelihood estimation, Bayesian inversion, and deep learning. We apply these algorithms to data previously recorded for an attosecond electron pulse-train to retrieve the density matrix and to analyse its physical properties. Based on the reconstructed quantum state, we obtain pulse-durations of about 245as and predict a degree of coherence of 36 per cent for radiations and excitations produced by the…

When recalling in-context, Transformers are not SSMs
Destiny Okpekpe, Antonio Orvieto
https://arxiv.org/abs/2508.19029 https://arxiv.org/pdf/2508.19029

When recalling in-context, Transformers are not SSMs
Despite the advantageous subquadratic complexity of modern recurrent deep learning models -- such as state-space models (SSMs) -- recent studies have highlighted their potential shortcomings compared to transformers on reasoning and memorization tasks. In this paper, we dive deeper into one of such benchmarks: associative recall (AR), which has been shown to correlate well with language modeling performance, and inspect in detail the effects of scaling and optimization issues in recently propos…

Fired DOJ employee could face prison for throwing sandwich at officer
After his arrest, Charles Dunn allegedly told one of the officers: “I did it. I threw a sandwich.”

“If you touch any law enforcement officer, we will come after you,” Bondi posted on X Thursday.
“This is an example of the Deep State we have been up against for seven months as we work to refocus DOJ.”

Fired DOJ employee could face prison for throwing sandwich at officer
Police allege that the man approached law enforcement officers, including Metro Transit Police and U.S. Customs and Border Protection officers, and began yelling obscenities.

Optimizaci\'on de la Transmisi\'on de Estados Cu\'anticos en Cadenas de Qubits usando Deep Reinforcement Learning y Algoritmos Gen\'eticos
Sof\'ia Per\'on Santana, Ariel Fiuri, Omar Osenda, Mart\'in Dom\'inguez
https://arxiv.org/abs/2510.05010

Optimización de la Transmisión de Estados Cuánticos en Cadenas de Qubits usando Deep Reinforcement Learning y Algoritmos Genéticos
Quantum state transfer (QST) via homogeneous spin chains plays a crucial role in building scalable quantum hardware. A basic quantum state transmission protocol prepares a state in one qubit and transfers it to another through a channel, seeking to minimize the time and avoid information loss. The fidelity of the process is measured by functions proportional to the transition probability between both states. We approach this optimization problem using constant magnetic pulses and two complement…

Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities
Murat Temiz, Yongwei Zhang, Yanwei Fu, Chi Zhang, Chenfeng Meng, Orhan Kaplan, Christos Masouros
https://arxiv.org/abs/2509.06968

Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities
This article comprehensively reviews recent developments and research on deep learning-based (DL-based) techniques for integrated sensing and communication (ISAC) systems. ISAC, which combines sensing and communication functionalities, is regarded as a key enabler for 6G and beyond networks, as many emerging applications, such as vehicular networks and industrial robotics, necessitate both sensing and communication capabilities for effective operation. A unified platform that provides both func…

Unveiling the Actual Performance of Neural-based Models for Equation Discovery on Graph Dynamical Systems
Riccardo Cappi, Paolo Frazzetto, Nicol\`o Navarin, Alessandro Sperduti
https://arxiv.org/abs/2508.18173

Unveiling the Actual Performance of Neural-based Models for Equation Discovery on Graph Dynamical Systems
The ``black-box'' nature of deep learning models presents a significant barrier to their adoption for scientific discovery, where interpretability is paramount. This challenge is especially pronounced in discovering the governing equations of dynamical processes on networks or graphs, since even their topological structure further affects the processes' behavior. This paper provides a rigorous, comparative assessment of state-of-the-art symbolic regression techniques for this task. We evaluate …

States face massive new costs under Trump budget cuts
States are scrambling to prepare for an unprecedented shift of costs and responsibilities under Trump’s sweeping tax and spending plan,
which will force them to make difficult decisions about cuts to state programs to offset the new financial burdens.

Unlike the federal government, states must balance their budgets each year.
That means deep cuts and changes to Medicaid and the Supplemental Nutrition Assistance …

States face massive new costs under Trump budget cuts
Governors are already making budgetary decisions in anticipation of added costs and responsibilities they will have to take on.

Progressive Flow-inspired Unfolding for Spectral Compressive Imaging
Xiaodong Wang, Ping Wang, Zijun He, Mengjie Qin, Xin Yuan
https://arxiv.org/abs/2509.12079 https://

Progressive Flow-inspired Unfolding for Spectral Compressive Imaging
Coded aperture snapshot spectral imaging (CASSI) retrieves a 3D hyperspectral image (HSI) from a single 2D compressed measurement, which is a highly challenging reconstruction task. Recent deep unfolding networks (DUNs), empowered by explicit data-fidelity updates and implicit deep denoisers, have achieved the state of the art in CASSI reconstruction. However, existing unfolding approaches suffer from uncontrollable reconstruction trajectories, leading to abrupt quality jumps and non-gradual re…

Ensemble of Pre-Trained Models for Long-Tailed Trajectory Prediction
Divya Thuremella, Yi Yang, Simon Wanna, Lars Kunze, Daniele De Martini
https://arxiv.org/abs/2509.13914 http…

Ensemble of Pre-Trained Models for Long-Tailed Trajectory Prediction
This work explores the application of ensemble modeling to the multidimensional regression problem of trajectory prediction for vehicles in urban environments. As newer and bigger state-of-the-art prediction models for autonomous driving continue to emerge, an important open challenge is the problem of how to combine the strengths of these big models without the need for costly re-training. We show how, perhaps surprisingly, combining state-of-the-art deep learning models out-of-the-box (withou…

Deep Visual Odometry for Stereo Event Cameras
Sheng Zhong, Junkai Niu, Yi Zhou
https://arxiv.org/abs/2509.08235 https://arxiv.org/pdf/2509.08235

Deep Visual Odometry for Stereo Event Cameras
Event-based cameras are bio-inspired sensors with pixels that independently and asynchronously respond to brightness changes at microsecond resolution, offering the potential to handle state estimation tasks involving motion blur and high dynamic range (HDR) illumination conditions. However, the versatility of event-based visual odometry (VO) relying on handcrafted data association (either direct or indirect methods) is still unreliable, especially in field robot applications under low-light HD…

DeepEN: Personalized Enteral Nutrition for Critically Ill Patients using Deep Reinforcement Learning
Daniel Jason Tan, Jiayang Chen, Dilruk Perera, Kay Choong See, Mengling Feng
https://arxiv.org/abs/2510.08350

DeepEN: Personalized Enteral Nutrition for Critically Ill Patients using Deep Reinforcement Learning
We introduce DeepEN, a deep reinforcement learning (RL) framework for personalized enteral nutrition (EN) in critically ill patients. Trained offline on over 11,000 ICU patients from the MIMIC-IV database, DeepEN generates 4-hourly recommendations for caloric, protein, and fluid intake tailored to each patient's evolving physiology. The model integrates a curated, clinically informed state space with a custom reward function that balances short-term physiological and nutrition-related goals wit…

Minkowski-MambaNet: A Point Cloud Framework with Selective State Space Models for Forest Biomass Quantification
Jinxiang Tu, Dayong Ren, Fei Shi, Zhenhong Jia, Yahong Ren, Jiwei Qin, Fang He
https://arxiv.org/abs/2510.09367

Minkowski-MambaNet: A Point Cloud Framework with Selective State Space Models for Forest Biomass Quantification
Accurate forest biomass quantification is vital for carbon cycle monitoring. While airborne LiDAR excels at capturing 3D forest structure, directly estimating woody volume and Aboveground Biomass (AGB) from point clouds is challenging due to difficulties in modeling long-range dependencies needed to distinguish trees.We propose Minkowski-MambaNet, a novel deep learning framework that directly estimates volume and AGB from raw LiDAR. Its key innovation is integrating the Mamba model's Selective …

Efficient Environment Design for Multi-Robot Navigation via Continuous Control
Jahid Chowdhury Choton, John Woods, William Hsu
https://arxiv.org/abs/2508.14105 https://

Efficient Environment Design for Multi-Robot Navigation via Continuous Control
Multi-robot navigation and path planning in continuous state and action spaces with uncertain environments remains an open challenge. Deep Reinforcement Learning (RL) is one of the most popular paradigms for solving this task, but its real-world application has been limited due to sample inefficiency and long training periods. Moreover, the existing works using RL for multi-robot navigation lack formal guarantees while designing the environment. In this paper, we introduce an efficient and high…

Greener Deep Reinforcement Learning: Analysis of Energy and Carbon Efficiency Across Atari Benchmarks
Jason Gardner, Ayan Dutta, Swapnoneel Roy, O. Patrick Kreidl, Ladislau Boloni
https://arxiv.org/abs/2509.05273

Greener Deep Reinforcement Learning: Analysis of Energy and Carbon Efficiency Across Atari Benchmarks
The growing computational demands of deep reinforcement learning (DRL) have raised concerns about the environmental and economic costs of training large-scale models. While algorithmic efficiency in terms of learning performance has been extensively studied, the energy requirements, greenhouse gas emissions, and monetary costs of DRL algorithms remain largely unexplored. In this work, we present a systematic benchmarking study of the energy consumption of seven state-of-the-art DRL algorithms, …

SelfAdapt: Unsupervised Domain Adaptation of Cell Segmentation Models
Fabian H. Reith, Jannik Franzen, Dinesh R. Palli, J. Lorenz Rumberger, Dagmar Kainmueller
https://arxiv.org/abs/2508.11411

SelfAdapt: Unsupervised Domain Adaptation of Cell Segmentation Models
Deep neural networks have become the go-to method for biomedical instance segmentation. Generalist models like Cellpose demonstrate state-of-the-art performance across diverse cellular data, though their effectiveness often degrades on domains that differ from their training data. While supervised fine-tuning can address this limitation, it requires annotated data that may not be readily available. We propose SelfAdapt, a method that enables the adaptation of pre-trained cell segmentation model…

Vejde: A Framework for Inductive Deep Reinforcement Learning Based on Factor Graph Color Refinement
Jakob Nyberg, Pontus Johnson
https://arxiv.org/abs/2509.09219 https://…

Vejde: A Framework for Inductive Deep Reinforcement Learning Based on Factor Graph Color Refinement
We present and evaluate Vejde; a framework which combines data abstraction, graph neural networks and reinforcement learning to produce inductive policy functions for decision problems with richly structured states, such as object classes and relations. MDP states are represented as data bases of facts about entities, and Vejde converts each state to a bipartite graph, which is mapped to latent states through neural message passing. The factored representation of both states and actions allows …

N-BEATS-MOE: N-BEATS with a Mixture-of-Experts Layer for Heterogeneous Time Series Forecasting
Ricardo Matos, Luis Roque, Vitor Cerqueira
https://arxiv.org/abs/2508.07490 https:…

N-BEATS-MOE: N-BEATS with a Mixture-of-Experts Layer for Heterogeneous Time Series Forecasting
Deep learning approaches are increasingly relevant for time series forecasting tasks. Methods such as N-BEATS, which is built on stacks of multilayer perceptrons (MLPs) blocks, have achieved state-of-the-art results on benchmark datasets and competitions. N-BEATS is also more interpretable relative to other deep learning approaches, as it decomposes forecasts into different time series components, such as trend and seasonality. In this work, we present N-BEATS-MOE, an extension of N-BEATS based…