Tootfinder

💗 Forest expansion increases agricultural output, new study shows
#india

Forest expansion increases agricultural output, new study shows
Agriculture is the largest cause of deforestation. So, it follows that forest expansion efforts would displace agriculture—but new research from Georgia Tech's School of Economics reports that that's not necessarily the case. And not only that, forest expansion actually increased agricultural output in the study.

Factored Output Feedback Controller Synthesis with Locality Constraints for Spatially-Invariant Systems
Walden Marshall
https://arxiv.org/abs/2509.14168 https://

Factored Output Feedback Controller Synthesis with Locality Constraints for Spatially-Invariant Systems
We consider H2 output feedback controller synthesis with pre-specified constraints on spatial communication distance (locality) for spatially-invariant systems using two factored controller frameworks: the system-level parameterization and the input-output parameterization. In our main result, we show that in both frameworks, output feedback controller synthesis with locality constraints can be formulated as a convex problem in finitely many transfer function variables, admitting the use of sta…

@… @… the keccak / random oracle thing i couldn’t remember was that it doesn’t make sense to compare a function with variable sized output against a random oracle, because in that model longer output implies better security…

Well, I've stayed up until 04:00 writing an essay on terrorism and genocide, and why I expect to be arrested this afternoon.
So now I can't chicken out.
I'd better get some sleep.
#GazaGenocide
#PalestineAction

so, i ported #GlasgowInterfaceExplorer to webusb

Insights Informed Generative AI for Design: Incorporating Real-world Data for Text-to-Image Output
Richa Gupta, Alexander Htet Kyaw
https://arxiv.org/abs/2506.15008

Insights Informed Generative AI for Design: Incorporating Real-world Data for Text-to-Image Output
Generative AI, specifically text-to-image models, have revolutionized interior architectural design by enabling the rapid translation of conceptual ideas into visual representations from simple text prompts. While generative AI can produce visually appealing images they often lack actionable data for designers In this work, we propose a novel pipeline that integrates DALL-E 3 with a materials dataset to enrich AI-generated designs with sustainability metrics and material usage insights. After t…

On the Gaussian Limit of the Output of IIR Filters
Yashaswini Murthy, Bassam Bamieh, R. Srikant
https://arxiv.org/abs/2508.12705 https://arxiv.org/pdf/2508…

On the Gaussian Limit of the Output of IIR Filters
We study the asymptotic distribution of the output of a stable Linear Time-Invariant (LTI) system driven by a non-Gaussian stochastic input. Motivated by longstanding heuristics in the stochastic describing function method, we rigorously characterize when the output process becomes approximately Gaussian, even when the input is not. Using the Wasserstein-1 distance as a quantitative measure of non-Gaussianity, we derive upper bounds on the distance between the appropriately scaled output and a …

📈 Russia’s Arctic LNG 2 Hits Record Output as Ice Conditions Ease
https://oilprice.com/Latest-Energy-News/World-News/Russias-Arctic-LNG-2-Hits-Record-Output-as-Ice-Conditions-Ease.html

Russia's Arctic LNG 2 Hits Record Output as Ice Conditions Ease
Russia's Arctic LNG 2 project lifted production to record levels in late August as summer ice conditions opened the Northern Sea Route, allowing additional cargoes to reach Asia

Another dice rolling post: checks are implemented! Probably not the most exciting thing for folks outside of #Pathfinder or #DnD but it's starting to look feature complete. I removed the floor for checks, added in advantage/disadvantage, and added a status that tells whether the first dic…

Creative Intelligence is the only book that will give you the foundation, the ability for you to create your own floor. While others scramble to hit AI usage metrics and come up with bland ideas, you'll be building the skills and capabilities that actually matter and set you apart.
https://

Been busy updating the #Trajectools archival records over at #SDSL2025

Noise-Level Diffusion Guidance: Well Begun is Half Done
Harvey Mannering, Zhiwu Huang, Adam Prugel-Bennett
https://arxiv.org/abs/2509.13936 https://arxiv.o…

Noise-Level Diffusion Guidance: Well Begun is Half Done
Diffusion models have achieved state-of-the-art image generation. However, the random Gaussian noise used to start the diffusion process influences the final output, causing variations in image quality and prompt adherence. Existing noise-level optimization approaches generally rely on extra dataset construction, additional networks, or backpropagation-based optimization, limiting their practicality. In this paper, we propose Noise Level Guidance (NLG), a simple, efficient, and general noise-le…

DSPC: Dual-Stage Progressive Compression Framework for Efficient Long-Context Reasoning
Yaxin Gao, Yao Lu, Zongfei Zhang, Jiaqi Nie, Shanqing Yu, Qi Xuan
https://arxiv.org/abs/2509.13723

DSPC: Dual-Stage Progressive Compression Framework for Efficient Long-Context Reasoning
Large language models (LLMs) have achieved remarkable success in many natural language processing (NLP) tasks. To achieve more accurate output, the prompts used to drive LLMs have become increasingly longer, which incurs higher computational costs. To address this prompt inflation problem, prompt compression has been proposed. However, most existing methods require training a small auxiliary model for compression, incurring a significant amount of additional computation. To avoid this, we propo…

Simulating Clinical AI Assistance using Multimodal LLMs: A Case Study in Diabetic Retinopathy
Nadim Barakat, William Lotter
https://arxiv.org/abs/2509.13234 https://

Simulating Clinical AI Assistance using Multimodal LLMs: A Case Study in Diabetic Retinopathy
Diabetic retinopathy (DR) is a leading cause of blindness worldwide, and AI systems can expand access to fundus photography screening. Current FDA-cleared systems primarily provide binary referral outputs, where this minimal output may limit clinical trust and utility. Yet, determining the most effective output format to enhance clinician-AI performance is an empirical challenge that is difficult to assess at scale. We evaluated multimodal large language models (MLLMs) for DR detection and thei…

I haven't actually written a new blog post about the #Tricycle project, but I have greatly amended and extended the last one.
#BikeTooter

EfficientUICoder: Efficient MLLM-based UI Code Generation via Input and Output Token Compression
Jingyu Xiao, Zhongyi Zhang, Yuxuan Wan, Yintong Huo, Yang Liu, Michael R. Lyu
https://arxiv.org/abs/2509.12159

EfficientUICoder: Efficient MLLM-based UI Code Generation via Input and Output Token Compression
Multimodal Large Language Models have demonstrated exceptional performance in UI2Code tasks, significantly enhancing website development efficiency. However, these tasks incur substantially higher computational overhead than traditional code generation due to the large number of input image tokens and extensive output code tokens required. Our comprehensive study identifies significant redundancies in both image and code tokens that exacerbate computational complexity and hinder focus on key UI…

Unitary causal decompositions: a combinatorial characterisation via lattice theory
Tein van der Lugt, Robin Lorenz
https://arxiv.org/abs/2508.11762 https://

Unitary causal decompositions: a combinatorial characterisation via lattice theory
If a unitary transformation has a decomposition into a quantum circuit with no directed path from input $a$ to output $b$, then $a$ does not influence $b$ through the overall unitary. Conversely, it is known that if $a$ does not influence $b$, one may always find a circuit decomposition lacking a path between these systems, thus making the no-influence condition directly apparent in the connectivity of the circuit. Causal decompositions are circuit decompositions in which, more generally, multi…

As I am still working through the ansible infoblox scripting, I have a new problem. Progress was made (Great!) but here is where I am stuck now and have been a few days. I THINK its both an Ansible AND an Infoblox WABI issue, but not sure either how to diagnose or solve. For some reason, ansible keeps trying to use WABI v 2.7.3 even though I specify v 2.13.6 in my code. I can see it in the error output. ""Failed on object search with url https://grid.master/wapi/v2.7.3/record%...…

Bayesian Modeling and Estimation of Linear Time-Variant Systems using Neural Networks and Gaussian Processes
Yaniv Shulman
https://arxiv.org/abs/2507.12878

Bayesian Modeling and Estimation of Linear Time-Variant Systems using Neural Networks and Gaussian Processes
The identification of Linear Time-Variant (LTV) systems from input-output data is a fundamental yet challenging ill-posed inverse problem. This work introduces a unified Bayesian framework that models the system's impulse response, $h(t, τ)$, as a stochastic process. We decompose the response into a posterior mean and a random fluctuation term, a formulation that provides a principled approach for quantifying uncertainty and naturally defines a new, useful system class we term Linear Time-Inva…

Pay-per-output? AI firms blindsided by beefed up robots.txt instructions. https://arstechnica.com/tech-policy/2025/09/pay-per-output-ai-firms-blindsided-by-beefed-up-robots-txt-instructions/

Pay-per-output? AI firms blindsided by beefed up robots.txt instructions.
“Really Simple Licensing” makes it easier for creators to get paid for AI scraping.

Fully Spiking Actor-Critic Neural Network for Robotic Manipulation
Liwen Zhang, Heng Deng, Guanghui Sun
https://arxiv.org/abs/2508.12038 https://arxiv.org/…

Fully Spiking Actor-Critic Neural Network for Robotic Manipulation
This study proposes a hybrid curriculum reinforcement learning (CRL) framework based on a fully spiking neural network (SNN) for 9-degree-of-freedom robotic arms performing target reaching and grasping tasks. To reduce network complexity and inference latency, the SNN architecture is simplified to include only an input and an output layer, which shows strong potential for resource-constrained environments. Building on the advantages of SNNs-high inference speed, low energy consumption, and spik…

LLM Jailbreak Detection for (Almost) Free!
Guorui Chen, Yifan Xia, Xiaojun Jia, Zhijiang Li, Philip Torr, Jindong Gu
https://arxiv.org/abs/2509.14558 https://

LLM Jailbreak Detection for (Almost) Free!
Large language models (LLMs) enhance security through alignment when widely used, but remain susceptible to jailbreak attacks capable of producing inappropriate content. Jailbreak detection methods show promise in mitigating jailbreak attacks through the assistance of other models or multiple model inferences. However, existing methods entail significant computational costs. In this paper, we first present a finding that the difference in output distributions between jailbreak and benign prompt…

Commanders' offense experienced its worst output with Jayden Daniels under center in loss to Packers https://www.nfl.com/news/commanders-offense-experienced-its-worst-output-with-jayden-daniels-under-center-in-loss-to…

Linear and Numerical SDoF Bounds of Active RIS-Assisted MIMO Wiretap Interference Channel
Su Linfan, Miao Yuhang, Song Yuxuan, Zheng Shuo, Zhang Tong, Xu Yinfei, Wang Shuai, Li Na
https://arxiv.org/abs/2506.15073

Linear and Numerical SDoF Bounds of Active RIS-Assisted MIMO Wiretap Interference Channel
The multiple-input multiple-output (MIMO) wiretap interference channel (IC) serves as a canonical model for information-theoretic security, where a multiple-antenna eavesdropper attempts to intercept communications in a two-user MIMO IC system. The secure degrees-of-freedom (SDoF) of an active reconfigurable intelligent surface (RIS)-assisted MIMO wiretap IC is with practical interests but remains unexplored. In this paper, we establish both sum-SDoF lower and upper bounds through linear beamfo…

Contribution of expert aggregation to temperature prediction part I
L\'eo Pfitzner (GMAP), Olivier Wintenberger (SU), Olivier Mestre (GMAP), Marion Riverain
https://arxiv.org/abs/2506.15217

Contribution of expert aggregation to temperature prediction part I
Many Numerical Weather Prediction (NWP) models and their associated Model Output Statistics (MOS) are available. Combining all of these predictions in an optimal way is however not straightforward. This can be achieved thanks to Expert Aggregation (EA) [Cesa-Bianchi and Lugosi, 2006, Gaillard et al., 2014, Wintenberger, 2024] which has many advantages, such as being online, being adaptive to model changes and having theoretical guarantees. Hence, in this paper, we propose a method for making de…

Active Learning of Symbolic Mealy Automata
Kengo Irie, Masaki Waga, Kohei Suenaga
https://arxiv.org/abs/2509.14694 https://arxiv.org/pdf/2509.14694

Active Learning of Symbolic Mealy Automata
We propose $Λ^*_M$-an active learning algorithm that learns symbolic Mealy automata, which support infinite input alphabets and multiple output characters. Each of these two features has been addressed separately in prior work. Combining these two features poses a challenge in learning the outputs corresponding to potentially infinite sets of input characters at each state. To address this challenge, we introduce the notion of essential input characters, a finite set of input characters that i…

Eek; I was expecting something more erm sinusoidal from the cassette output of the Epson.
#retrocomputing ocomputing

HOMI: Ultra-Fast EdgeAI platform for Event Cameras
Shankaranarayanan H, Satyapreet Singh Yadav, Adithya Krishna, Ajay Vikram P, Mahesh Mehendale, Chetan Singh Thakur
https://arxiv.org/abs/2508.12637

HOMI: Ultra-Fast EdgeAI platform for Event Cameras
Event cameras offer significant advantages for edge robotics applications due to their asynchronous operation and sparse, event-driven output, making them well-suited for tasks requiring fast and efficient closed-loop control, such as gesture-based human-robot interaction. Despite this potential, existing event processing solutions remain limited, often lacking complete end-to-end implementations, exhibiting high latency, and insufficiently exploiting event data sparsity. In this paper, we pres…

From Variability To Accuracy: Conditional Bernoulli Diffusion Models with Consensus-Driven Correction for Thin Structure Segmentation
Jinseo An, Min Jin Lee, Kyu Won Shim, Helen Hong
https://arxiv.org/abs/2507.12985

From Variability To Accuracy: Conditional Bernoulli Diffusion Models with Consensus-Driven Correction for Thin Structure Segmentation
Accurate segmentation of orbital bones in facial computed tomography (CT) images is essential for the creation of customized implants for reconstruction of defected orbital bones, particularly challenging due to the ambiguous boundaries and thin structures such as the orbital medial wall and orbital floor. In these ambiguous regions, existing segmentation approaches often output disconnected or under-segmented results. We propose a novel framework that corrects segmentation results by leveragin…

Holy cow 😅 Iterating on :nixos: #nixOS SD card images surely adds up...

Near-Field SWIPT with gMIMO in the Upper Mid-Band: Opportunities, Challenges, and the Way Forward
\"Ozlem Tugfe Demir, Mustafa Ozger, Ferdi Kara, Woong-Hee Lee, Emil Bj\"ornson
https://arxiv.org/abs/2506.15670

Near-Field SWIPT with gMIMO in the Upper Mid-Band: Opportunities, Challenges, and the Way Forward
This paper explores the integration of simultaneous wireless information and power transfer (SWIPT) with gigantic multiple-input multiple-output (gMIMO) technology operating in the upper mid-band frequency range (7-24 GHz). The near-field propagation achieved by gMIMO introduces unique opportunities for energy-efficient, high-capacity communication systems that cater to the demands of 6G wireless networks. Exploiting spherical wave propagation, near-field SWIPT with gMIMO enables precise energy…

Fedi -Power, do your magic, please!
I lost a source. I remember that one fedi being destroyed the concept of "field-weighted citation impact (FWCI)" on the occasion of the Elsevier-Report "Germany as a science nation", published in May, 2025, I think this figure was shown in the toot. But I can't find it anymore. Can anybody help me? Thank you!

Okay, added a few more features and I'm really liking the results. I added the `-` operator for `dice_groups` which was a bit more tricky than I thought it'd be. I also added the ability to roll a dice group multiple times via a `x`|`*` operator. I removed the requirement that a dice group has to have a damage type associated with it since the only way you can roll something like `1d4-1` is in a dice group since there is a minimum value of 1 associated with it.
The code liv…

MATPAC : Enhanced Masked Latent Prediction for Self-Supervised Audio Representation Learning
Aurian Quelennec, Pierre Chouteau, Geoffroy Peeters, Slim Essid
https://arxiv.org/abs/2508.12709

MATPAC++: Enhanced Masked Latent Prediction for Self-Supervised Audio Representation Learning
Masked latent prediction has emerged as a leading paradigm in self-supervised learning (SSL), especially for general audio and music representation learning. While recent methods have demonstrated strong performance, the role of the predictor module used at the output of such SSL systems remains mainly overlooked, despite being crucial for solving the pretext task at hand. In particular, this module should be able to deal with the ambiguity inherent in audio content, especially when it is compo…

Observable Optimization for Precision Theory: Machine Learning Energy Correlators
Arindam Bhattacharya, Katherine Fraser, Matthew D. Schwartz
https://arxiv.org/abs/2508.10988 ht…

Observable Optimization for Precision Theory: Machine Learning Energy Correlators
The practice of collider physics typically involves the marginalization of multi-dimensional collider data to uni-dimensional observables relevant for some physics task. In any cases, such as classification or anomaly detection, the observable can be arbitrarily complicated, such as the output of a neural network. However, for precision measurements, the observable must correspond to something computable systematically beyond the level of current simulation tools. In this work, we demonstrate t…

Cooperative Sensing-Assisted Predictive Beam Tracking for MIMO-OFDM Networked ISAC Systems
Xiaoyu Yang, Zhiqing Wei, Jie Xu, Huici Wu, Zhiyong Feng
https://arxiv.org/abs/2508.12723

Cooperative Sensing-Assisted Predictive Beam Tracking for MIMO-OFDM Networked ISAC Systems
This paper studies a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) networked integrated sensing and communication (ISAC) system, in which multiple base stations (BSs) perform beam tracking to communicate with a mobile device. In particular, we focus on the beam tracking over a number of tracking time slots (TTSs) and suppose that these BSs operate at non-overlapping frequency bands to avoid the severe inter-cell interference. Under this setup, we propos…

Easier debugging of piped analyses in R: https://github.com/MilesMcBain/breakerofchains by @…

Modeling wind farm noise emission and propagation: effects of flow layout
J. Colas, A. Emmanuelli, D. Dragna, R. J. A. M. Stevens
https://arxiv.org/abs/2508.13128 https://

Modeling wind farm noise emission and propagation: effects of flow layout
This study demonstrates how wind farm flow physics influence noise generation and downstream propagation through numerical simulations. The flow field is modeled using large-eddy simulations (LES), and the time-averaged output serves as input to acoustic models that predict wind turbine noise. In the first turbine row, turbulence-induced noise (TIN) and trailing edge noise (TEN) contribute equally, with TIN dominating at low frequencies and TEN at higher frequencies. Downstream, TEN decreases d…

Output-Sparse Matrix Multiplication Using Compressed Sensing
Huck Bennett, Karthik Gajulapalli, Alexander Golovnev, Evelyn Warton
https://arxiv.org/abs/2508.10250 https://

Output-Sparse Matrix Multiplication Using Compressed Sensing
We give two algorithms for output-sparse matrix multiplication (OSMM), the problem of multiplying two $n \times n$ matrices $A, B$ when their product $AB$ is promised to have at most $O(n^δ)$ many non-zero entries for a given value $δ\in [0, 2]$. We then show how to speed up these algorithms in the fully sparse setting, where the input matrices $A, B$ are themselves sparse. All of our algorithms work over arbitrary rings. Our first, deterministic algorithm for OSMM works via a two-pass redu…

The radial memory effect in multimode optical fibres
Ulas Gokay, Robert J. Kilpatrick, Simon A. R. Horsley, David B. Phillips, Jacopo Bertolotti
https://arxiv.org/abs/2508.11389

The radial memory effect in multimode optical fibres
We present a study of a novel memory effect in multimode optical fibres, which manifests itself as an output ring of excess energy at the same radius as an input focussed spot. This effect is robust against fibre perturbations, and we discuss its possible use for spatial multiplexing.

Coarse non-wandering sets and their filtration
Tomoo Yokoyama
https://arxiv.org/abs/2509.13697 https://arxiv.org/pdf/2509.13697

Coarse non-wandering sets and their filtration
This paper investigates recurrence properties of dynamical systems under the restriction that control is available only through inputs and outputs. We introduce the concept of ``coarse non-wandering'', a generalization of the classical non-wandering concept, and construct an associated filtration based on levels that quantify the closeness of recurrence behavior under input/output-only control. The forward direction of this filtration describes how the level of control relates to recurrence pro…

SPG: Style-Prompting Guidance for Style-Specific Content Creation
Qian Liang, Zichong Chen, Yang Zhou, Hui Huang
https://arxiv.org/abs/2508.11476 https://a…

SPG: Style-Prompting Guidance for Style-Specific Content Creation
Although recent text-to-image (T2I) diffusion models excel at aligning generated images with textual prompts, controlling the visual style of the output remains a challenging task. In this work, we propose Style-Prompting Guidance (SPG), a novel sampling strategy for style-specific image generation. SPG constructs a style noise vector and leverages its directional deviation from unconditional noise to guide the diffusion process toward the target style distribution. By integrating SPG with Clas…

Failures to Surface Harmful Contents in Video Large Language Models
Yuxin Cao, Wei Song, Derui Wang, Jingling Xue, Jin Song Dong
https://arxiv.org/abs/2508.10974 https://…

Failures to Surface Harmful Contents in Video Large Language Models
Video Large Language Models (VideoLLMs) are increasingly deployed on numerous critical applications, where users rely on auto-generated summaries while casually skimming the video stream. We show that this interaction hides a critical safety gap: if harmful content is embedded in a video, either as full-frame inserts or as small corner patches, state-of-the-art VideoLLMs rarely mention the harmful content in the output, despite its clear visibility to human viewers. A root-cause analysis reveal…

Geometric Theory of Ising Machines
Andrew G. Moore, Zachary Richey, Isaac K. Martin
https://arxiv.org/abs/2507.12626 https://arxiv.or…

Geometric Theory of Ising Machines
We contribute to the mathematical theory of the design of low temperature Ising machines, a type of experimental probabilistic computing device implementing the Ising model. Encoding the output of a function in the ground state of a physical system allows efficient and distributed computation, but the design of the energy function is a difficult puzzle. We introduce a diagrammatic device that allows us to visualize the decision boundaries for Ising circuits. It is then used to prove two results…

Some day I'm going to understand why ffprobe writes all its output to stderr and why it's so terribly spammy.

Data-Driven Deepfake Image Detection Method -- The 2024 Global Deepfake Image Detection Challenge
Xiaoya Zhu, Yibing Nan, Shiguo Lian
https://arxiv.org/abs/2508.11464 https://…

Data-Driven Deepfake Image Detection Method -- The 2024 Global Deepfake Image Detection Challenge
With the rapid development of technology in the field of AI, deepfake technology has emerged as a double-edged sword. It has not only created a large amount of AI-generated content but also posed unprecedented challenges to digital security. The task of the competition is to determine whether a face image is a Deepfake image and output its probability score of being a Deepfake image. In the image track competition, our approach is based on the Swin Transformer V2-B classification network. And o…

GEMMAS: Graph-based Evaluation Metrics for Multi Agent Systems
Jisoo Lee, Raeyoung Chang, Dongwook Kwon, Harmanpreet Singh, Nikhil Verma
https://arxiv.org/abs/2507.13190

GEMMAS: Graph-based Evaluation Metrics for Multi Agent Systems
Multi-agent systems built on language models have shown strong performance on collaborative reasoning tasks. However, existing evaluations focus only on the correctness of the final output, overlooking how inefficient communication and poor coordination contribute to redundant reasoning and higher computational costs. We introduce GEMMAS, a graph-based evaluation framework that analyzes the internal collaboration process by modeling agent interactions as a directed acyclic graph. To capture col…

A Framework for Nonstationary Gaussian Processes with Neural Network Parameters
Zachary James, Joseph Guinness
https://arxiv.org/abs/2507.12262 https://

A Framework for Nonstationary Gaussian Processes with Neural Network Parameters
Gaussian processes have become a popular tool for nonparametric regression because of their flexibility and uncertainty quantification. However, they often use stationary kernels, which limit the expressiveness of the model and may be unsuitable for many datasets. We propose a framework that uses nonstationary kernels whose parameters vary across the feature space, modeling these parameters as the output of a neural network that takes the features as input. The neural network and Gaussian proce…

Overcoming Knowledge Discrepancies: Structuring Reasoning Threads through Knowledge Balancing in Interactive Scenarios
Daniel Burkhardt, Xiangwei Cheng
https://arxiv.org/abs/2508.12100

Overcoming Knowledge Discrepancies: Structuring Reasoning Threads through Knowledge Balancing in Interactive Scenarios
Reasoning in interactive problem solving scenarios requires models to construct reasoning threads that reflect user understanding and align with structured domain knowledge. However, current reasoning models often lack explicit semantic hierarchies, user-domain knowledge alignment, and principled mechanisms to prune reasoning threads for effectiveness. These limitations result in lengthy generic output that does not guide users through goal-oriented reasoning steps. To address this, we propose …

Improving photovoltaics by adding extra terminals to extract hot carriers
Bruno Bertin-Johannet, Thibaut Thu\'egaz, Janine Splettstoesser, Robert S. Whitney
https://arxiv.org/abs/2507.13279

Improving photovoltaics by adding extra terminals to extract hot carriers
Photovoltaic cells usually have two terminals, one collecting electrons and the other collecting holes. Can more terminals improve such solar cells? Energy-filtering terminals could collect "hot" carriers (electrons or holes not yet relaxed to the band edge), with other terminals for low-energy carriers. For collection faster than carrier-phonon relaxation, we predict four-terminal cells with higher power output than optimal two-terminal cells -- more than 40% higher for fast carrier collection…

QSpark: Towards Reliable Qiskit Code Generation
Kiana Kheiri, Aamna Aamir, Andriy Miranskyy, Chen Ding
https://arxiv.org/abs/2507.12642 https://

QSpark: Towards Reliable Qiskit Code Generation
Quantum circuits must be error-resilient, yet LLMs like Granite-20B-Code and StarCoder often output flawed Qiskit code. We fine-tuned a 32 B model with two RL methods, Group Relative Policy Optimization (GRPO) and Odds-Ratio Preference Optimization (ORPO), using a richly annotated synthetic dataset. On the Qiskit HumanEval benchmark, ORPO reaches 56.29\% Pass@1 ($\approx+10$ pp over Granite-8B-QK) and GRPO hits 49\%, both beating all general-purpose baselines; on the original HumanEval they sco…

Mitigating sloppiness in joint estimation of successive squeezing parameters
Priyanka Sharma, Stefano Olivares, Devendra Kumar Mishra, Matteo G. A. Paris
https://arxiv.org/abs/2506.15638

Mitigating sloppiness in joint estimation of successive squeezing parameters
When two successive squeezing operations with the same phase are applied to a field mode, reliably estimating the amplitude of each is impossible because the output state depends solely on their sum. In this case, the quantum statistical model becomes sloppy, and the quantum Fisher information matrix turns singular. However, estimation of both parameters becomes feasible if the quantum state is subjected to an appropriate scrambling operation between the two squeezing operations. In this work, …

Design of MIMO Lur'e oscillators via dominant system theory with application in multi-agent rhythm synchronization
Yu Kawano, Fulvio Forni
https://arxiv.org/abs/2508.12141 h…

Design of MIMO Lur'e oscillators via dominant system theory with application in multi-agent rhythm synchronization
This paper presents a new design framework for dynamic output-feedback controllers for Lur'e oscillation in a multiple-input multiple-output setting. We first revisit and extend dominant system theory to state-dependent rates, with the goal of deriving conditions based on linear matrix inequalities. Then, we introduce a separation principle for Lur'e oscillator design, which allows for the independent design of a state-feedback oscillator and an observer. Our proposed control synthesis is demon…

Looking up Christina Chong on #Google after watching an episode of #STStrangeNewWorlds. 2 questions in Google changes the output to Christina Chang, a completely different person, age and nationality. If a human did that we'd call them racist for thinking all Asian people look al…

Shapley Values: Paired-Sampling Approximations
Michael Mayer, Mario V. W\"uthrich
https://arxiv.org/abs/2508.12947 https://arxiv.org/pdf/2508.12947

Shapley Values: Paired-Sampling Approximations
Originally introduced in cooperative game theory, Shapley values have become a very popular tool to explain machine learning predictions. Based on Shapley's fairness axioms, every input (feature component) gets a credit how it contributes to an output (prediction). These credits are then used to explain the prediction. The only limitation in computing the Shapley values (credits) for many different predictions is of computational nature. There are two popular sampling approximations, sampling K…

Preference-restricted parking functions
Jasper Bown, Peter Kagey, Alan Kappler, Michael E. Orrison, Jayden Thadani
https://arxiv.org/abs/2507.11701 https:/…

Preference-restricted parking functions
A parking function is a function $π:[n]\to [n]$ whose $i$th-smallest output is at most $i,$ corresponding to a parking procedure for $n$ cars on a one-way street. We refine this concept by introducing preference-restricted parking functions, which are parking functions with codomain restricted to some $S\subseteq[n]$. Particular choices of $S$ yield new combinatorial interpretations of previous results about variant parking procedures, and new results too. In particular we consider prime parki…

Analysis Theory of Data Economy: Dataization, Technological Progress and Dynamic General Equilibrium
Yongheng Hu
https://arxiv.org/abs/2507.13274 https://

Analysis Theory of Data Economy: Dataization, Technological Progress and Dynamic General Equilibrium
This paper constructs a clean and efficient representative agent model of the data economy from a macroeconomics perspective, in order to analyze the impact of datatization and technological progress on the dynamic general equilibrium of 'consumption-capital', and the catalysis effect of datatization on technological progress. We first set the data in production comes from datatization of the total output of the society and is exponentially functionally related to the technology. Secondly, the …

Finally got around to visualising the output of our combined drone mapping, collecting street-level imagery, and on-the-ground mapping a couple of weeks ago (c.f. #openstreetmap

A Crowdsensing Intrusion Detection Dataset For Decentralized Federated Learning Models
Chao Feng, Alberto Huertas Celdran, Jing Han, Heqing Ren, Xi Cheng, Zien Zeng, Lucas Krauter, Gerome Bovet, Burkhard Stiller
https://arxiv.org/abs/2507.13313

A Crowdsensing Intrusion Detection Dataset For Decentralized Federated Learning Models
This paper introduces a dataset and experimental study for decentralized federated learning (DFL) applied to IoT crowdsensing malware detection. The dataset comprises behavioral records from benign and eight malware families. A total of 21,582,484 original records were collected from system calls, file system activities, resource usage, kernel events, input/output events, and network records. These records were aggregated into 30-second windows, resulting in 342,106 features used for model trai…

Do Governments React to Public Debt Accumulation? A Cross-Country Analysis
Paolo Canofari, Alessandro Piergallini, Marco Tedeschi
https://arxiv.org/abs/2507.13084

Do Governments React to Public Debt Accumulation? A Cross-Country Analysis
Do governments adjust budgetary policy to rising public debt, precluding fiscal unsustainability? Using budget data for 52 industrial and emerging economies since 1990, we apply panel methods accounting for cross-sectional dependence and heterogeneous fiscal conduct. We find that a primary-balance rule with tax-smoothing motives and responsiveness to debt has robust explanatory power in describing fiscal behavior. Controlling for temporary output, temporary spending, and the current account bal…

from my link log —
You know more Finnish than you think.
https://dannybate.com/2025/08/03/you-know-more-finnish-than-you-think/
saved 2025-08-07

Asymptotic Analysis of Nonlinear One-Bit Precoding in Massive MIMO Systems via Approximate Message Passing
Zheyu Wu, Junjie Ma, Ya-Feng Liu, Bruno Clerckx
https://arxiv.org/abs/2509.13955

Asymptotic Analysis of Nonlinear One-Bit Precoding in Massive MIMO Systems via Approximate Message Passing
Massive multiple-input multiple-output (MIMO) systems employing one-bit digital-to-analog converters offer a hardware-efficient solution for wireless communications. However, the one-bit constraint poses significant challenges for precoding design, as it transforms the problem into a discrete and nonconvex optimization task. In this paper, we investigate a widely adopted ``convex-relaxation-then-quantization" approach for nonlinear symbol-level one-bit precoding. Specifically, we first solve a …

From Hard Refusals to Safe-Completions: Toward Output-Centric Safety Training
Yuan Yuan, Tina Sriskandarajah, Anna-Luisa Brakman, Alec Helyar, Alex Beutel, Andrea Vallone, Saachi Jain
https://arxiv.org/abs/2508.09224

From Hard Refusals to Safe-Completions: Toward Output-Centric Safety Training
Large Language Models used in ChatGPT have traditionally been trained to learn a refusal boundary: depending on the user's intent, the model is taught to either fully comply or outright refuse. While this is a strong mitigation for explicitly malicious prompts, focusing safety training on refusals can lead to brittleness for prompts with obscured user intent. Binary refusal boundaries are especially ill-suited for dual-use cases (such as biology or cybersecurity), where a user request can be an…

MELA-TTS: Joint transformer-diffusion model with representation alignment for speech synthesis
Keyu An, Zhiyu Zhang, Changfeng Gao, Yabin Li, Zhendong Peng, Haoxu Wang, Zhihao Du, Han Zhao, Zhifu Gao, Xiangang Li
https://arxiv.org/abs/2509.14784

MELA-TTS: Joint transformer-diffusion model with representation alignment for speech synthesis
This work introduces MELA-TTS, a novel joint transformer-diffusion framework for end-to-end text-to-speech synthesis. By autoregressively generating continuous mel-spectrogram frames from linguistic and speaker conditions, our architecture eliminates the need for speech tokenization and multi-stage processing pipelines. To address the inherent difficulties of modeling continuous features, we propose a representation alignment module that aligns output representations of the transformer decoder …

Two years ago I took 7 `<blockquote>` variations (W3C & WHATWG HTML) and reported their output across 7 screen readers, for a measly 49 variations:
https://adrianroselli.com/2023/07/blockquotes-in-screen-readers.html
I made one anti-…

Blockquotes in Screen Readers
TL;DR: This post does not assert the correct way to code blockquotes, it will only demonstrate how screen readers announce some existing patterns. Test Details The first four examples are lifted from WHATWG HTML’s

entry. The next three are from W3C HTML’s 2019

guidance (the W3C HTML spec…

I’m pretty sure this is a joke, and if so it’s a good one.
But I’m not 100% sure because *hand gestures at everything*.
https://github.com/abyesilyurt/vibesort

GitHub - abyesilyurt/vibesort: GPT powered sorting using structured output
GPT powered sorting using structured output. Contribute to abyesilyurt/vibesort development by creating an account on GitHub.

Hardware-Efficient Cognitive Radar: Multi-Target Detection with RL-Driven Transmissive RIS
Adam Umra, Aya Mostafa Ahmed, Stefan Roth, Aydin Sezgin
https://arxiv.org/abs/2509.14160

Hardware-Efficient Cognitive Radar: Multi-Target Detection with RL-Driven Transmissive RIS
Cognitive radar has emerged as a key paradigm for next-generation sensing, enabling adaptive, intelligent operation in dynamic and complex environments. Yet, conventional cognitive multiple-input multiple-output (MIMO) radars offer strong detection performance but suffer from high hardware complexity and power demands. To overcome these limitations, we develop a reinforcement learning (RL)-based framework that leverages a transmissive reconfigurable intelligent surface (TRIS) for adaptive beamf…

Cross-Modal Knowledge Distillation with Multi-Level Data Augmentation for Low-Resource Audio-Visual Sound Event Localization and Detection
Qing Wang, Ya Jiang, Hang Chen, Sabato Marco Siniscalchi, Jun Du, Jianqing Gao
https://arxiv.org/abs/2508.12334

Cross-Modal Knowledge Distillation with Multi-Level Data Augmentation for Low-Resource Audio-Visual Sound Event Localization and Detection
This work presents a cross-modal knowledge distillation (CMKD) framework combined with multi-level data augmentation for low-resource audio-visual (AV) sound event localization and detection (SELD). An audio-only SELD model acts as the teacher, transferring knowledge to an AV student model through both output responses and intermediate feature representations. To enhance learning, data augmentation is applied by mixing features randomly selected from multiple network layers and associated loss …

Stability Analysis of the Newton-Raphson Controller for a Class of Differentially Flat Systems
Kaicheng Niu, Yorai Wardi, Chaouki T. Abdallah
https://arxiv.org/abs/2508.12694 ht…

Stability Analysis of the Newton-Raphson Controller for a Class of Differentially Flat Systems
The Newton-Raphson Controller, established on the output prediction and the Newton-Raphson algorithm, is shown to be effective in a variety of control applications. Although the stability condition of the controller for linear systems has already been established, such condition for nonlinear systems remains unexplored. In this paper, we study the stability of the Newton-Raphson controller for a class of differentially flat nonlinear systems in the context of output regulation and tracking cont…

Reinforced Context Order Recovery for Adaptive Reasoning and Planning
Long Ma, Fangwei Zhong, Yizhou Wang
https://arxiv.org/abs/2508.13070 https://arxiv.or…

Reinforced Context Order Recovery for Adaptive Reasoning and Planning
Modern causal language models, followed by rapid developments in discrete diffusion models, can now produce a wide variety of interesting and useful content. However, these families of models are predominantly trained to output tokens with a fixed (left-to-right) or random order, which may deviate from the logical order in which tokens are generated originally. In this paper, we observe that current causal and diffusion models encounter difficulties in problems that require adaptive token gener…

Dual-Stage Value-Guided Inference with Margin-Based Reward Adjustment for Fast and Faithful VLM Captioning
Ankan Deria, Adinath Madhavrao Dukre, Feilong Tang, Sara Atito, Sudipta Roy, Muhammad Awais, Muhammad Haris Khan, Imran Razzak
https://arxiv.org/abs/2506.15649

Dual-Stage Value-Guided Inference with Margin-Based Reward Adjustment for Fast and Faithful VLM Captioning
Despite significant advances in inference-time search for vision-language models (VLMs), existing approaches remain both computationally expensive and prone to unpenalized, low-confidence generations which often lead to persistent hallucinations. We introduce \textbf{Value-guided Inference with Margin-based Reward (ViMaR)}, a two-stage inference framework that improves both efficiency and output fidelity by combining a temporal-difference value model with a margin-aware reward adjustment. In th…

Clean Code, Better Models: Enhancing LLM Performance with Smell-Cleaned Dataset
Zhipeng Xue, Xiaoting Zhang, Zhipeng Gao, Xing Hu, Shan Gao, Xin Xia, Shanping Li
https://arxiv.org/abs/2508.11958

Clean Code, Better Models: Enhancing LLM Performance with Smell-Cleaned Dataset
The Large Language Models (LLMs) have demonstrated great potential in code-related tasks. However, most research focuses on improving the output quality of LLMs (e.g., correctness), and less attention has been paid to the LLM input (e.g., the training code quality). Given that code smells are widely existed in practice and can negatively impact software maintainability and readability, this study takes the first systematic research to assess and improve dataset quality in terms of code smells. …

High-Efficiency Quantum Memory of Full-Bandwidth Squeezed Light
Jinxian Guo, Meihong Wang, Zeliang Wu, Chenyu Qiao, Fengyi Xu, Xiaoran Zhang, Xiaolong Su, Liqing Chen, Weiping Zhang
https://arxiv.org/abs/2506.15399

High-Efficiency Quantum Memory of Full-Bandwidth Squeezed Light
In continuous-variable quantum information processing, it is crucial to develop high-efficiency and broadband quantum memory of squeezed light, which enables the storage of full-bandwidth information. Here, we present a quantum memory of squeezed light with up to 24 MHz bandwidth, which is at least 12 times that of previous narrowband resonant memory systems, via a far-off resonant Raman process. We achieve output squeezing of as high as 1.0 dB with fidelity above 92% and a memory efficiency of…

MIMO Systems Aided by Microwave Linear Analog Computers: Capacity-Achieving Architectures with Reduced Circuit Complexity
Matteo Nerini, Bruno Clerckx
https://arxiv.org/abs/2506.15052

MIMO Systems Aided by Microwave Linear Analog Computers: Capacity-Achieving Architectures with Reduced Circuit Complexity
To meet the demands of future wireless networks, antenna arrays must scale from massive multiple-input multiple-output (MIMO) to gigantic MIMO, involving even larger numbers of antennas. To address the hardware and computational cost of gigantic MIMO, several strategies are available that shift processing from the digital to the analog domain. Among them, microwave linear analog computers (MiLACs) offer a compelling solution by enabling fully analog beamforming through reconfigurable microwave …

Probing for Arithmetic Errors in Language Models
Yucheng Sun, Alessandro Stolfo, Mrinmaya Sachan
https://arxiv.org/abs/2507.12379 https://

Probing for Arithmetic Errors in Language Models
We investigate whether internal activations in language models can be used to detect arithmetic errors. Starting with a controlled setting of 3-digit addition, we show that simple probes can accurately decode both the model's predicted output and the correct answer from hidden states, regardless of whether the model's output is correct. Building on this, we train lightweight error detectors that predict model correctness with over 90% accuracy. We then extend our analysis to structured chain-of…

Since the Black Magic HDMI capture card seems to be the only card that reliably captures audio/video I grabbed an HDMI splitter. Works great, hasn't caught fire...two thumbs up so far.
#hdmi #linux #CaptureCard

Classification Filtering
Ilker Bayram
https://arxiv.org/abs/2509.13975 https://arxiv.org/pdf/2509.13975

Classification Filtering
We consider a streaming signal in which each sample is linked to a latent class. We assume that multiple classifiers are available, each providing class probabilities with varying degrees of accuracy. These classifiers are employed following a straightforward and fixed policy. In this setting, we consider the problem of fusing the output of the classifiers while incorporating the temporal aspect to improve classification accuracy. We propose a state-space model and develop a filter tailored for…

Dataset-Adaptive Dimensionality Reduction
Hyeon Jeon, Jeongin Park, Soohyun Lee, Dae Hyun Kim, Sungbok Shin, Jinwook Seo
https://arxiv.org/abs/2507.11984 h…

Dataset-Adaptive Dimensionality Reduction
Selecting the appropriate dimensionality reduction (DR) technique and determining its optimal hyperparameter settings that maximize the accuracy of the output projections typically involves extensive trial and error, often resulting in unnecessary computational overhead. To address this challenge, we propose a dataset-adaptive approach to DR optimization guided by structural complexity metrics. These metrics quantify the intrinsic complexity of a dataset, predicting whether higher-dimensional s…

Automatically describe data and models as text using the {report} package. #rstats

PredGen: Accelerated Inference of Large Language Models through Input-Time Speculation for Real-Time Speech Interaction
Shufan Li, Aditya Grover
https://arxiv.org/abs/2506.15556

PredGen: Accelerated Inference of Large Language Models through Input-Time Speculation for Real-Time Speech Interaction
Large Language Models (LLMs) are widely used in real-time voice chat applications, typically in combination with text-to-speech (TTS) systems to generate audio responses. However, their large size often leads to noticeable latency between the end of user input and the start of audio output, resulting in suboptimal user experiences. This latency is particularly evident when LLMs are deployed as single-user voice assistants on consumer-grade hardware with limited computing capacity. We discovered…

Age of Semantic Information-Aware Wireless Transmission for Remote Monitoring Systems
Xue Han, Biqian Feng, Yongpeng Wu, Xiang-Gen Xia, Wenjun Zhang, Shengli Sun
https://arxiv.org/abs/2508.12248

Age of Semantic Information-Aware Wireless Transmission for Remote Monitoring Systems
Semantic communication is emerging as an effective means of facilitating intelligent and context-aware communication for next-generation communication systems. In this paper, we propose a novel metric called Age of Incorrect Semantics (AoIS) for the transmission of video frames over multiple-input multiple-output (MIMO) channels in a monitoring system. Different from the conventional age-based approaches, we jointly consider the information freshness and the semantic importance, and then formul…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/8]:
- Another Turn, Better Output? A Turn-Wise Analysis of Iterative LLM Prompting
Shashidhar Reddy Javaji, Bhavul Gauri, Zining Zhu

Enabling low-power massive MIMO with ternary ADCs for AIoT sensing
Shengheng Liu, Ningning Fu
https://arxiv.org/abs/2508.11234 https://arxiv.org/pdf/2508.1…

Enabling low-power massive MIMO with ternary ADCs for AIoT sensing
The proliferation of networked devices and the surging demand for ubiquitous intelligence have given rise to the artificial intelligence of things (AIoT). However, the utilization of high-resolution analog-to-digital converters (ADCs) and numerous radio frequency chains significantly raises power consumption. This paper explores a cost-effective solution using ternary ADCs (T-ADCs) in massive multiple-input-multiple-output (MIMO) systems for low-power AIoT and specifically addresses channel sen…

Early Stopping Chain-of-thoughts in Large Language Models
Minjia Mao, Bowen Yin, Yu Zhu, Xiao Fang
https://arxiv.org/abs/2509.14004 https://arxiv.org/pdf/2…

Early Stopping Chain-of-thoughts in Large Language Models
Reasoning large language models (LLMs) have demonstrated superior capacities in solving complicated problems by generating long chain-of-thoughts (CoT), but such a lengthy CoT incurs high inference costs. In this study, we introduce ES-CoT, an inference-time method that shortens CoT generation by detecting answer convergence and stopping early with minimal performance loss. At the end of each reasoning step, we prompt the LLM to output its current final answer, denoted as a step answer. We then…

rCamInspector: Building Reliability and Trust on IoT (Spy) Camera Detection using XAI
Priyanka Rushikesh Chaudhary, Manan Gupta, Jabez Christopher, Putrevu Venkata Sai Charan, Rajib Ranjan Maiti
https://arxiv.org/abs/2509.09989

rCamInspector: Building Reliability and Trust on IoT (Spy) Camera Detection using XAI
The classification of network traffic using machine learning (ML) models is one of the primary mechanisms to address the security issues in IoT networks and/or IoT devices. However, the ML models often act as black-boxes that create a roadblock to take critical decision based on the model output. To address this problem, we design and develop a system, called rCamInspector, that employs Explainable AI (XAI) to provide reliable and trustworthy explanations to model output. rCamInspector adopts t…

Bayesian Learning for Pilot Decontamination in Cell-Free Massive MIMO
Christian Forsch, Zilu Zhao, Dirk Slock, Laura Cottatellucci
https://arxiv.org/abs/2508.11791 https://

Bayesian Learning for Pilot Decontamination in Cell-Free Massive MIMO
Pilot contamination (PC) arises when the pilot sequences assigned to user equipments (UEs) are not mutually orthogonal, eventually due to their reuse. In this work, we propose a novel expectation propagation (EP)-based joint channel estimation and data detection (JCD) algorithm specifically designed to mitigate the effects of PC in the uplink of cell-free massive multiple-input multiple-output (CF-MaMIMO) systems. This modified bilinear-EP algorithm is distributed, scalable, demonstrates strong…

Replaced article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[2/2]:
- Another Turn, Better Output? A Turn-Wise Analysis of Iterative LLM Prompting
Shashidhar Reddy Javaji, Bhavul Gauri, Zining Zhu

Path Integral Approach to Input-Output Theory
Aaron Daniel, Matteo Brunelli, Aashish A. Clerk, Patrick P. Potts
https://arxiv.org/abs/2509.07563 https://ar…

Path Integral Approach to Input-Output Theory
Input-output theory is a well-known tool in quantum optics and ubiquitous in the description of quantum systems probed by light. Owing to the generality of the setup it describes, the theory finds application in a wide variety of experiments in circuit and cavity QED. We present an approach to input-output theory using the Schwinger-Keldysh path integral formalism that gives us direct access to the full output field statistics such as the first and second order coherence functions. By making th…

Towards Reliable Multi-Agent Systems for Marketing Applications via Reflection, Memory, and Planning
Lorenzo Jaime Yu Flores, Junyi Shen, Xiaoyuan Gu
https://arxiv.org/abs/2508.11120

Towards Reliable Multi-Agent Systems for Marketing Applications via Reflection, Memory, and Planning
Recent advances in large language models (LLMs) enabled the development of AI agents that can plan and interact with tools to complete complex tasks. However, literature on their reliability in real-world applications remains limited. In this paper, we introduce a multi-agent framework for a marketing task: audience curation. To solve this, we introduce a framework called RAMP that iteratively plans, calls tools, verifies the output, and generates suggestions to improve the quality of the audie…

Replaced article(s) found for eess.SY. https://arxiv.org/list/eess.SY/new
[1/1]:
- Input-Output Extension of Underactuated Nonlinear Systems
Mirko Mizzoni, Amr Afifi, Antonio Franchi

Metasurfaces-Integrated Doubly-Dispersive MIMO: Channel Modeling and Optimization
Kuranage Roche Rayan Ranasinghe, Hyeon Seok Rou, Iv\'an Alexander Morales Sandoval, Giuseppe Thadeu Freitas de Abreu, George C. Alexandropoulos
https://arxiv.org/abs/2506.14985

Metasurfaces-Integrated Doubly-Dispersive MIMO: Channel Modeling and Optimization
The doubly-dispersive (DD) channel structure has played a pivotal role in wireless communications, particularly in high-mobility scenarios and integrated sensing and communications (ISAC), due to its ability to capture the key fading effects experienced by a transmitted signal as it propagates through a dynamic medium. However, extending the DD framework to multiple-input multiple-output (MIMO) systems, especially in environments artificially enhanced by reconfigurable intelligent surfaces (RIS…

Improving Accuracy and Efficiency of Implicit Neural Representations: Making SIREN a WINNER
Hemanth Chandravamsi, Dhanush V. Shenoy, Steven H. Frankel
https://arxiv.org/abs/2509.12980

Improving Accuracy and Efficiency of Implicit Neural Representations: Making SIREN a WINNER
We identify and address a fundamental limitation of sinusoidal representation networks (SIRENs), a class of implicit neural representations. SIRENs Sitzmann et al. (2020), when not initialized appropriately, can struggle at fitting signals that fall outside their frequency support. In extreme cases, when the network's frequency support misaligns with the target spectrum, a 'spectral bottleneck' phenomenon is observed, where the model yields to a near-zero output and fails to recover even the fr…

A Data-Integrated Framework for Learning Fractional-Order Nonlinear Dynamical Systems
Bahram Yaghooti, Chengyu Li, Bruno Sinopoli
https://arxiv.org/abs/2506.15665

A Data-Integrated Framework for Learning Fractional-Order Nonlinear Dynamical Systems
This paper presents a data-integrated framework for learning the dynamics of fractional-order nonlinear systems in both discrete-time and continuous-time settings. The proposed framework consists of two main steps. In the first step, input-output experiments are designed to generate the necessary datasets for learning the system dynamics, including the fractional order, the drift vector field, and the control vector field. In the second step, these datasets, along with the memory-dependent prop…

Jailbreak-Tuning: Models Efficiently Learn Jailbreak Susceptibility
Brendan Murphy, Dillon Bowen, Shahrad Mohammadzadeh, Julius Broomfield, Adam Gleave, Kellin Pelrine
https://arxiv.org/abs/2507.11630

Jailbreak-Tuning: Models Efficiently Learn Jailbreak Susceptibility
AI systems are rapidly advancing in capability, and frontier model developers broadly acknowledge the need for safeguards against serious misuse. However, this paper demonstrates that fine-tuning, whether via open weights or closed fine-tuning APIs, can produce helpful-only models. In contrast to prior work which is blocked by modern moderation systems or achieved only partial removal of safeguards or degraded output quality, our jailbreak-tuning method teaches models to generate detailed, high…

Looks like today should be the last day of NES work as the NESRGB mod is coming in today. Even better, the EuroSCART cable is coming in so I can test out the composite, S-Video, and RGB output from the console as well. I'm genuinely curious to see how different the PPU's composite output compares to the NESRGB's version. After everything is done, I'll get the blog post updated and published. Should be a fun day.

Model Predictive Black Start for Dynamic Formation of DER-Led Microgrids with Inrush Current Impacts
Cong Bai, Salish Maharjan, Zhaoyu Wang
https://arxiv.org/abs/2507.12569

Model Predictive Black Start for Dynamic Formation of DER-Led Microgrids with Inrush Current Impacts
Black start (BS) of the distribution system (DS) with high penetration of distributed energy resources (DERs) requires advanced control frameworks to ensure secure and efficient restoration. This paper proposes a model predictive black start (MPBS) framework incorporating an inrush current feasibility module to dynamically generate real-time feasible and optimal restoration sequences. Short-term forecasts of DER output and transmission grid (TG) availability are utilized to construct adaptive c…

FAS-ARIS: Turning Multipath Challenges Into Localization Opportunities
Hua Chen, Tao Gong, Tuo Wu, Maged Elkashlan, Baiyang Liu, Chan-Byoung Chae, Kin-Fai Tong, Kai-Kit Wong
https://arxiv.org/abs/2509.12348

FAS-ARIS: Turning Multipath Challenges Into Localization Opportunities
Traditional single-input single-output (SISO) systems face fundamental limitations in achieving accurate three-dimensional (3D) localization due to limited spatial degrees of freedom (DoF) and the adverse impact of multipath propagation. This paper proposes a novel fluid antenna system (FAS)-active reconfigurable intelligent surface (ARIS) framework that transforms multipath effects from a hindrance into a resource for enhanced localization. By synergistically combining the signal amplification…

Fundamental limitations of sensitivity metrics for anomaly impact analysis in LTI systems
Jingwei Dong, Kangkang Zhang, Anh Tung Nguyen, Andr\'e M. H. Teixeira
https://arxiv.org/abs/2509.11194

Fundamental limitations of sensitivity metrics for anomaly impact analysis in LTI systems
This study establishes a connection between the output-to-output gain (OOG), a sensitivity metric quantifying the impact of stealthy attacks, and a novel input-to-input gain (IIG) introduced to evaluate fault sensitivity under disturbances, and investigates their fundamental performance limitations arising from the transmission zeros of the underlying dynamical system. Inspired by the OOG, which characterizes the maximum performance loss caused by stealthy attacks, the IIG is proposed as a new …

NEFT: A Unified Transformer Framework for Efficient Near-Field CSI Feedback in XL-MIMO Systems
Haiyang Li, Tianqi Mao, Pengyu Wang, Ruiqi Liu, Shunyu Li, Zhaocheng Wang
https://arxiv.org/abs/2509.12748

NEFT: A Unified Transformer Framework for Efficient Near-Field CSI Feedback in XL-MIMO Systems
Extremely large-scale multiple-input multiple-output (XL-MIMO) systems, operating in the near-field region due to their massive antenna arrays, are a key enabler of next-generation wireless communications but face significant challenges in channel state information (CSI) feedback. Deep learning has emerged as a powerful tool by learning compact CSI representations for feedback. However, existing methods struggle to capture the intricate structure of near-field CSI while incurring prohibitive co…