Tootfinder

Wasser auf @… s Mühlen:
Dieser Aufsatz setzt sich mit Definitionen von 'reuse' auseinander und kommt zum Schluss, dass use = reuse, man also der Einfachheit halber schlicht auch von "use" sprechen kann. Also auch: Nutzungsszenarien, nutzbare Daten. Schade um das R in

gamedevs should reuse more assets actually

When I added the threat-actor @… galaxy type on Mar 4, 2016, I didn’t expect that, years later, vendors would still invent new names for already known threat actors, avoid using UUIDs, reuse similar names for different actors, and create confusing names by mixing tools or software used by the actors.
That’s why we contin…

Freeze and Conquer: Reusable Ansatz for Solving the Traveling Salesman Problem
Fabrizio Fagiolo, Nicolo' Vescera
https://arxiv.org/abs/2508.21730 https://

Freeze and Conquer: Reusable Ansatz for Solving the Traveling Salesman Problem
In this paper we present a variational algorithm for the Traveling Salesman Problem (TSP) that combines (i) a compact encoding of permutations, which reduces the qubit requirement too, (ii) an optimize-freeze-reuse strategy: where the circuit topology (``Ansatz'') is first optimized on a training instance by Simulated Annealing (SA), then ``frozen'' and re-used on novel instances, limited to a rapid re-optimization of only the circuit parameters. This pipeline eliminates costly structural resea…

Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming
Haruki Yokota, Hiroshi Higashi, Yuichi Tanaka, Gene Cheung
https://arxiv.org/abs/2506.01826

Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming
Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as anti-correlations in data. A balanced signed graph is a signed graph with no cycles containing an odd number of negative edges. Laplacian of a balanced signed graph has eigenvectors that map via a simple linear transform to ones in a corresponding positive graph Laplacian, thus enabling reuse of spectral filtering tools designed for positive graphs. We propose an efficient method t…

On the Future of Software Reuse in the Era of AI Native Software Engineering
Antero Taivalsaari, Tommi Mikkonen, Cesare Pautasso
https://arxiv.org/abs/2508.19834 https://…

On the Future of Software Reuse in the Era of AI Native Software Engineering
Software development is currently under a paradigm shift in which artificial intelligence and generative software reuse are taking the center stage in software creation. Earlier opportunistic software reuse practices and organic software development methods are rapidly being replaced by "AI Native" approaches in which developers place their trust on code that has been generated by artificial intelligence. This is leading to a new form of software reuse that is conceptually not all that differen…

Curate, Connect, Inquire: A System for Findable Accessible Interoperable and Reusable (FAIR) Human-Robot Centered Datasets
Xingru Zhou, Sadanand Modak, Yao-Cheng Chan, Zhiyun Deng, Luis Sentis, Maria Esteva
https://arxiv.org/abs/2506.00220

Curate, Connect, Inquire: A System for Findable Accessible Interoperable and Reusable (FAIR) Human-Robot Centered Datasets
The rapid growth of AI in robotics has amplified the need for high-quality, reusable datasets, particularly in human-robot interaction (HRI) and AI-embedded robotics. While more robotics datasets are being created, the landscape of open data in the field is uneven. This is due to a lack of curation standards and consistent publication practices, which makes it difficult to discover, access, and reuse robotics data. To address these challenges, this paper presents a curation and access system wi…

Creative Commons launches “CC Signals”. Quote:
“TL;DR – What are CC signals?
“CC signals are a proposed framework to help content stewards express how they want their works used in AI training—emphasizing reciprocity, recognition, and sustainability in machine reuse. They aim to preserve open knowledge by encouraging responsible AI behavior without limiting innovation.”

CC Signals: A New Social Contract for the Age of AI - Creative Commons
TL;DR – What are CC signals? CC signals are a proposed framework to help content stewards express how they want their works used in AI training—emphasizing reciprocity, recognition, and sustainability in machine reuse. They aim to preserve open knowledge by encouraging responsible AI behavior without limiting innovation. 💗Love it! How can I show my support?…

The hunt for research data: Development of an open-source workflow for tracking institutionally-affiliated research data publications
Bryan M. Gee
https://arxiv.org/abs/2507.01228

The hunt for research data: Development of an open-source workflow for tracking institutionally-affiliated research data publications
The ability to find data is central to the FAIR principles underlying research data stewardship. As with the ability to reuse data, efforts to ensure and enhance findability have historically focused on discoverability of data by other researchers, but there is a growing recognition of the importance of stewarding data in a fashion that makes them FAIR for a wide range of potential reusers and stakeholders. Research institutions are one such stakeholder and have a range of motivations for disco…

Graph Neural Networks to Predict Coercivity of Hard Magnetic Microstructures
Heisam Moustafa, Alexander Kovacs, Johann Fischbacher, Markus Gusenbauer, Qais Ali, Leoni Breth, Thomas Schrefl, Harald Oezelt
https://arxiv.org/abs/2506.23615

Graph Neural Networks to Predict Coercivity of Hard Magnetic Microstructures
Graph neural networks (GNN) are a promising tool to predict magnetic properties of large multi-grain structures, which can speed up the search for rare-earth free permanent magnets. In this paper, we use our magnetic simulation data to train a GNN to predict coercivity of hard magnetic microstructures. We evaluate the performance of the trained GNN and quantify its uncertainty. Subsequently, we reuse the GNN architecture for predicting the maximum energy product. Out-of-distribution predictions…

"The Hague Declaration on Knowledge Discovery in the Digital Age" was published around 10 years ago. Very interesting, how things have changed since then. TDM for research is still in its infancy, and reuse in the context of #ai has caused a backlash against most of the 6 principles of the Declaration. Tellingly, the Declaration is only accessible via the internet archive:

So #Gentoo #Python eclasses are pretty modern, in the sense that they tend to follow the best practices and standards, and eventually deal with deprecations. Nevertheless, they have a long history and carry quite some historical burden, particularly regarding to naming.
The key point is that the eclasses were conceived as a replacement for the old eclasses: "distutils" and "python". Hence, much like we revision ebuilds, I've named the matching eclasses "distutils-r1" and "python-r1". For consistency, I've also used the "-r1" suffix for the remaining eclasses introduced at the time: "python-any-r1", "python-single-r1" and "python-utils-r1" — even though there were never "r0"s.
It didn't take long to realize my first mistake. I've made the multi-impl eclass effectively the "main" eclass, probably largely inspired by the previous Gentoo recommendations. However, in the end I've found out that for the most use cases (i.e. where "distutils-r1" is not involved), there is no real need for multi-impl, and it makes things much harder. So if I were naming them today, I would have named it "python-multi", to indicate the specific use case — and either avoid designating a default at all, or made "python-single" the default.
What aged even worse is the "distutils-r1" eclass. Admittedly, back when it was conceived, distutils was still largely a thing — and there were people (like me) who avoided unnecessary dependency on setuptools. Of course, nowadays it has been entirely devoured by setuptools, and with #PEP517 even "setuptools" wouldn't be a good name anymore. Nowadays, people are getting confused why they are supposed to use "distutils-r1" for, say, Hatchling.
Admittedly, this is something I could have done differently — PEP517 support was a major migration, and involved an explicit switch. Instead of adding DISTUTILS_USE_PEP517 (what a self-contradictory name) variable, I could have forked the eclass. Why didn't I do that? Because there used to be a lot of code shared between the two paths. Of course, over time they diverged more, and eventually I've dropped the legacy support — but the opportunity to rename was lost.
In fact, as a semi-related fact, I've recognized another design problem with the eclass earlier — I should have gone for two eclasses rather than one: a "python-phase" eclass with generic sub-phase support, and a "distutils" (or later "python-pep517") implementing default sub-phases for the common backends. And again, this is precisely how I could have solved the code reuse problem when I introduced PEP517 support.
But then, I didn't anticipate how the eclasses would end up looking like in the end — and I can't really predict what new challenges the Python ecosystem is going to bring us. And I think it's too late to rename or split stuff — too much busywork on everyone.

ChartMark: A Structured Grammar for Chart Annotation
Yiyu Chen, Yifan Wu, Shuyu Shen, Yupeng Xie, Leixian Shen, Hui Xiong, Yuyu Luo
https://arxiv.org/abs/2507.21810 https://

ChartMark: A Structured Grammar for Chart Annotation
Chart annotations enhance visualization accessibility but suffer from fragmented, non-standardized representations that limit cross-platform reuse. We propose ChartMark, a structured grammar that separates annotation semantics from visualization implementations. ChartMark features a hierarchical framework mapping onto annotation dimensions (e.g., task, chart context), supporting both abstract intents and precise visual details. Our toolkit demonstrates converting ChartMark specifications into V…

InterfO-RAN: Real-Time In-band Cellular Uplink Interference Detection with GPU-Accelerated dApps
Neagin Neasamoni Santhi, Davide Villa, Michele Polese, Tommaso Melodia
https://arxiv.org/abs/2507.23177 …

InterfO-RAN: Real-Time In-band Cellular Uplink Interference Detection with GPU-Accelerated dApps
Ultra-dense fifth generation (5G) and beyond networks leverage spectrum sharing and frequency reuse to enhance throughput, but face unpredictable in-band uplink (UL) interference challenges that significantly degrade Signal to Interference plus Noise Ratio (SINR) at affected Next Generation Node Bases (gNBs). This is particularly problematic at cell edges, where overlapping regions force User Equipments (UEs) to increase transmit power, and in directional millimeter wave systems, where beamform…

Looked at @…'s Signals page (https://creativecommons.org/ai-and-the-commons/cc-signals/) again.
I think this…

CC Signals: A New Social Contract for the Age of AI - Creative Commons
TL;DR – What are CC signals? CC signals are a proposed framework to help content stewards express how they want their works used in AI training—emphasizing reciprocity, recognition, and sustainability in machine reuse. They aim to preserve open knowledge by encouraging responsible AI behavior without limiting innovation. 💗Love it! How can I show my support?…

How many times do you reuse a teabag?

"A solar panel recycling scheme would help reduce waste, but please repair and reuse first"
#SolarPower #Energy #Renewables

A solar panel recycling scheme would help reduce waste, but please repair and reuse first
Older rooftop solar systems are being scrapped – often well before the end of their useful life. Establishing a product stewardship scheme is part of the solution.

Software Reuse in the Generative AI Era: From Cargo Cult Towards AI Native Software Engineering
Tommi Mikkonen, Antero Taivalsaari
https://arxiv.org/abs/2506.17937

Software Reuse in the Generative AI Era: From Cargo Cult Towards AI Native Software Engineering
Software development is currently under a paradigm shift in which artificial intelligence and generative software reuse are taking the center stage in software creation. Consequently, earlier software reuse practices and methods are rapidly being replaced by AI-assisted approaches in which developers place their trust on code that has been generated by artificial intelligence. This is leading to a new form of software reuse that is conceptually not all that different from cargo cult development…

OmniCache: A Trajectory-Oriented Global Perspective on Training-Free Cache Reuse for Diffusion Transformer Models
Huanpeng Chu, Wei Wu, Guanyu Fen, Yutao Zhang
https://arxiv.org/abs/2508.16212

OmniCache: A Trajectory-Oriented Global Perspective on Training-Free Cache Reuse for Diffusion Transformer Models
Diffusion models have emerged as a powerful paradigm for generative tasks such as image synthesis and video generation, with Transformer architectures further enhancing performance. However, the high computational cost of diffusion Transformers-stemming from a large number of sampling steps and complex per-step computations-presents significant challenges for real-time deployment. In this paper, we introduce OmniCache, a training-free acceleration method that exploits the global redundancy inhe…

ProForm: Solder-Free Circuit Assembly Using Thermoforming
Narjes Pourjafarian, Zhenming Yang, Jeffrey I. Lipton, Benyamin Davaji, Gregory D. Abowd
https://arxiv.org/abs/2507.20933

ProForm: Solder-Free Circuit Assembly Using Thermoforming
Electronic waste (e-waste) is a growing global challenge, with millions of functional components discarded due to the difficulty of repair and reuse. Traditional circuit assembly relies on soldering, which creates semi-permanent bonds that limit component recovery and contribute to unnecessary waste. We introduce ProForm, a thermoforming approach for solder-free circuit prototyping. By encapsulating electronic components with pressure-formed thermoplastics, ProForm enables secure, reversible mo…

Deep reinforcement learning for efficient exploration of combinatorial structural design spaces
Chloe S. H. Hong, Keith J. Lee, Caitlin T. Mueller
https://arxiv.org/abs/2507.22804

Deep reinforcement learning for efficient exploration of combinatorial structural design spaces
This paper proposes a reinforcement learning framework for performance-driven structural design that combines bottom-up design generation with learned strategies to efficiently search large combinatorial design spaces. Motivated by the limitations of conventional top-down approaches such as optimization, the framework instead models structures as compositions of predefined elements, aligning form finding with practical constraints like constructability and component reuse. With the formulation …

I propose a law that forbids companies from claiming copyright, patents or anything else once they end support for their products.
Then we can do a community rebrand of any hardware and reuse it as we please.

Data Gluttony: Epistemic Risks, Dependent Testing and Data Reuse in Large Datasets
Reid Dale, Jordan Rodu, Maria E. Currie, Mike Baiocchi
https://arxiv.org/abs/2508.16552 https:…

Data Gluttony: Epistemic Risks, Dependent Testing and Data Reuse in Large Datasets
Large-scale registries have collected vast amounts of data which has enabled investigators to efficiently conduct studies of observational data. Common practice is for investigators to use all data meeting the inclusion criteria of their study to perform their analysis. We term this common practice data gluttony. It has apparent formal justification insofar as this approach maximizes per-study power. But this comes at a cost: data reuse affects the shape of the tail distribution of inferential …

"The Seven Capital Sins of Open Science"
1. Worshiping the 'age factor'
2. Ignoring the value of data reuse and complexity
3. Disrespecting other disciplines
4. Publishing data without a supplementary paper
5. Creating and maintaining a nightmare for machines
6. Refusing to support investment in general infrastructure
7. Creating data without a FAIR and explicit data stewardship plan.

The Seven Capital Sins of Open Science
Years ago I made a set of cartoons depicting what I consider the seven major roadblocks to effectively realise an Open Science (rather FAIR science actually). These were distilled from my intensive work on the European Open Science Cloud, GO FAIR and also my presidency of CODATA.

This https://arxiv.org/abs/2407.03883 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Protecting Deep Learning Model Copyrights with Adversarial Example-Free Reuse Detection
Model reuse techniques can reduce the resource requirements for training high-performance deep neural networks (DNNs) by leveraging existing models. However, unauthorized reuse and replication of DNNs can lead to copyright infringement and economic loss to the model owner. This underscores the need to analyze the reuse relation between DNNs and develop copyright protection techniques to safeguard intellectual property rights. Existing white-box testing-based approaches cannot address the common…

Hierarchical Multi-Agent Reinforcement Learning-based Coordinated Spatial Reuse for Next Generation WLANs
Jiaming Yu, Le Liang, Hao Ye, Shi Jin
https://arxiv.org/abs/2506.14187

Hierarchical Multi-Agent Reinforcement Learning-based Coordinated Spatial Reuse for Next Generation WLANs
High-density Wi-Fi deployments often result in significant co-channel interference, which degrades overall network performance. To address this issue, coordination of multi access points (APs) has been considered to enable coordinated spatial reuse (CSR) in next generation wireless local area networks. This paper tackles the challenge of downlink spatial reuse in Wi-Fi networks, specifically in scenarios involving overlapping basic service sets, by employing hierarchical multi-agent reinforceme…

Heterogeneous Ensemble Enables a Universal Uncertainty Metric for Atomistic Foundation Models
Kai Liu, Zixiong Wei, Wei Gao, Poulumi Dey, Marcel H. F. Sluiter, Fei Shuang
https://arxiv.org/abs/2507.21297

Heterogeneous Ensemble Enables a Universal Uncertainty Metric for Atomistic Foundation Models
Universal machine learning interatomic potentials (uMLIPs) are reshaping atomistic simulation as foundation models, delivering near \textit{ab initio} accuracy at a fraction of the cost. Yet the lack of reliable, general uncertainty quantification limits their safe, wide-scale use. Here we introduce a unified, scalable uncertainty metric \(U\) based on a heterogeneous model ensemble with reuse of pretrained uMLIPs. Across chemically and structurally diverse datasets, \(U\) shows a strong correl…

Read through @…'s "signals" proposal and that's ... really weak.
Feels like it's just a bit of window dressing to keep to community busy while AI companies take everything they can find.
Like, why is that kind of signalling not part of the licenses? The promise of CC licenses was reuse by others (that is _people_…

Demonstrator Testbed for Effective Precoding in MEO Multibeam Satellites
Jorge L. Gonz\'alez-Rios, Liz Mart\'inez Marrero, Juan Duncan, Luis M. Garc\'es-Socarr\'as, Raudel Cuiman Marquez, Juan A. V\'asquez Peralvo, Jevgenij Krivochiza, Symeon Chatzinotas, Bj\"orn Ottersten
https://arxiv.org/abs/2508.19657

Demonstrator Testbed for Effective Precoding in MEO Multibeam Satellites
The use of communication satellites in medium Earth orbit (MEO) is foreseen to provide quasi-global broadband Internet connectivity in the coming networking ecosystems. Multi-user multiple-input single-output (MU-MISO) digital signal processing techniques, such as precoding, emerge as appealing technological enablers in the forward link of multi-beam satellite systems operating in full frequency reuse (FFR). However, the orbit dynamics of MEO satellites pose additional challenges that must be c…

Improving Wi-Fi 8 Latency with Coordinated Spatial Reuse
David Nunez, Francesc Wilhelmi, Lorenzo Galati-Giordano, Giovanni Geraci, Boris Bellalta
https://arxiv.org/abs/2507.18480

Improving Wi-Fi 8 Latency with Coordinated Spatial Reuse
IEEE 802.11 networks continuously adapt to meet the stringent requirements of emerging applications like cloud gaming, eXtended Reality (XR), and video streaming services, which require high throughput, low latency, and high reliability. To address these challenges, Coordinated Spatial Reuse (Co-SR) can potentially contribute to optimizing spectrum resource utilization. This mechanism is expected to enable simultaneous transmissions, thereby boosting spectral efficiency in dense environments an…

Conditional cross-fitting for unbiased machine-learning-assisted covariate adjustment in randomized experiments
Xin Lu, Lei Shi, Hanzhong Liu, Peng Ding
https://arxiv.org/abs/2508.15664

Conditional cross-fitting for unbiased machine-learning-assisted covariate adjustment in randomized experiments
Randomized experiments are the gold standard for estimating the average treatment effect (ATE). While covariate adjustment can reduce the asymptotic variances of the unbiased Horvitz-Thompson estimators for the ATE, it suffers from finite-sample biases due to data reuse in both prediction and estimation. Traditional sample-splitting and cross-fitting methods can address the problem of data reuse and obtain unbiased estimators. However, they require that the data are independently and identicall…

D\'ej\`a Vu: Efficient Video-Language Query Engine with Learning-based Inter-Frame Computation Reuse
Jinwoo Hwang, Daeun Kim, Sangyeop Lee, Yoonsung Kim, Guseul Heo, Hojoon Kim, Yunseok Jeong, Tadiwos Meaza, Eunhyeok Park, Jeongseob Ahn, Jongse Park
https://arxiv.org/abs/2506.14107

Déjà Vu: Efficient Video-Language Query Engine with Learning-based Inter-Frame Computation Reuse
Recently, Video-Language Models (VideoLMs) have demonstrated remarkable capabilities, offering significant potential for flexible and powerful video query systems. These models typically rely on Vision Transformers (ViTs), which process video frames individually to extract visual embeddings. However, generating embeddings for large-scale videos requires ViT inferencing across numerous frames, posing a major hurdle to real-world deployment and necessitating solutions for integration into scalabl…

ONLINE WORKSHOP> Navigating Indra’s Net: Digital Approaches to Text Reuse-based Inter-textuality in Pre-Modern East Asian Texts https://networks.h-net.org/group/announcements/20069984/navigating-indras-net-digital-approache…

Recycling Algebraic Proof Certificates
Daniela Kaufmann, Clemens Hofstadler
https://arxiv.org/abs/2507.20267 https://arxiv.org/pdf/2507.20267

Recycling Algebraic Proof Certificates
Proof certificates can be used to validate the correctness of algebraic derivations. However, in practice, we frequently observed that the exact same proof steps are repeated for different sets of variables, which leads to unnecessarily large proofs. To overcome this issue we extend the existing Practical Algebraic Calculus with linear combinations (LPAC) with two new proof rules that allow us to capture and reuse parts of the proof to derive a more condensed proof certificate. We integrate the…

Repeated ancilla reuse for logical computation on a neutral atom quantum computer
J. A. Muniz, D. Crow, H. Kim, J. M. Kindem, W. B. Cairncross, A. Ryou, T. C. Bohdanowicz, C. -A. Chen, Y. Ji, A. M. W. Jones, E. Megidish, C. Nishiguchi, M. Urbanek, L. Wadleigh, T. Wilkason, D. Aasen, K. Barnes, J. M. Bello-Rivas, I. Bloomfield, G. Booth, A. Brown, M. O. Brown, K. Cassella, G. Cowan, J. Epstein, M. Feldkamp, C. Griger, Y. Hassan, A. Heinz, E. Halperin, T. Hofler, F. Hummel, M. Jaffe, E. …

Repeated ancilla reuse for logical computation on a neutral atom quantum computer
Quantum processors based on neutral atoms trapped in arrays of optical tweezers have appealing properties, including relatively easy qubit number scaling and the ability to engineer arbitrary gate connectivity with atom movement. However, these platforms are inherently prone to atom loss, and the ability to replace lost atoms during a quantum computation is an important but previously elusive capability. Here, we demonstrate the ability to measure and re-initialize, and if necessary replace, a …

Some things I don't know and would like to know:
1. What is the life expectancy of current generation photovoltaic #solarpower panels?
2. What is the physical process which causes degradation?
3. At the end of economic life, could they be refurbished for reuse, and if not, why not?
4. If they cannot be refurbished, what proportion of the components can be recycled?

Mind the Language Gap in Digital Humanities: LLM-Aided Translation of SKOS Thesauri
Felix Kraus, Nicolas Blumenr\"ohr, Danah Tonne, Achim Streit
https://arxiv.org/abs/2507.19537

Mind the Language Gap in Digital Humanities: LLM-Aided Translation of SKOS Thesauri
We introduce WOKIE, an open-source, modular, and ready-to-use pipeline for the automated translation of SKOS thesauri. This work addresses a critical need in the Digital Humanities (DH), where language diversity can limit access, reuse, and semantic interoperability of knowledge resources. WOKIE combines external translation services with targeted refinement using Large Language Models (LLMs), balancing translation quality, scalability, and cost. Designed to run on everyday hardware and be easi…

Water reuse is the future! Just like electric lights, it's becoming a necessity. #climatechange #climatesolutions #climate

Like electric lights, water reuse is destined to become a necessity
A looming U.S. water crisis will lead more cities to make water reuse mandatory.

FastMesh:Efficient Artistic Mesh Generation via Component Decoupling
Jeonghwan Kim, Yushi Lan, Armando Fortes, Yongwei Chen, Xingang Pan
https://arxiv.org/abs/2508.19188 https:/…

FastMesh:Efficient Artistic Mesh Generation via Component Decoupling
Recent mesh generation approaches typically tokenize triangle meshes into sequences of tokens and train autoregressive models to generate these tokens sequentially. Despite substantial progress, such token sequences inevitably reuse vertices multiple times to fully represent manifold meshes, as each vertex is shared by multiple faces. This redundancy leads to excessively long token sequences and inefficient generation processes. In this paper, we propose an efficient framework that generates ar…

ReLink: Computational Circular Design of Planar Linkage Mechanisms Using Available Standard Parts
Maxime Escande, Kristina Shea
https://arxiv.org/abs/2506.19657

ReLink: Computational Circular Design of Planar Linkage Mechanisms Using Available Standard Parts
The Circular Economy framework emphasizes sustainability by reducing resource consumption and waste through the reuse of components and materials. This paper presents ReLink, a computational framework for the circular design of planar linkage mechanisms using available standard parts. Unlike most mechanism design methods, which assume the ability to create custom parts and infinite part availability, ReLink prioritizes the reuse of discrete, standardized components, thus minimizing the need for…

SIMulator: SIM Tracing on a (Pico-)Budget
Gabriel K. Gegenhuber, Philipp \'E. Frenzel, Adrian Dabrowski
https://arxiv.org/abs/2506.20800 https://

SIMulator: SIM Tracing on a (Pico-)Budget
SIM tracing -- the ability to inspect, modify, and relay communication between a SIM card and modem -- has become a significant technique in cellular network research. It enables essential security- and development-related applications such as fuzzing communication interfaces, extracting session keys, monitoring hidden SIM activity (e.g., proactive SIM commands or over-the-air updates), and facilitating scalable, distributed measurement platforms through SIM reuse. Traditionally, achieving thes…

NeMo: A Neuron-Level Modularizing-While-Training Approach for Decomposing DNN Models
Xiaohan Bi, Binhang Qi, Hailong Sun, Xiang Gao, Yue Yu, Xiaojun Liang
https://arxiv.org/abs/2508.11348

NeMo: A Neuron-Level Modularizing-While-Training Approach for Decomposing DNN Models
With the growing incorporation of deep neural network (DNN) models into modern software systems, the prohibitive construction costs have become a significant challenge. Model reuse has been widely applied to reduce training costs, but indiscriminately reusing entire models may incur significant inference overhead. Consequently, DNN modularization has gained attention, enabling module reuse by decomposing DNN models. The emerging modularizing-while-training (MwT) paradigm, which incorporates mod…

Learn from previous runs to improve future automation and save plans in a gallery for reuse.
🔀 Parallel execution
Run multiple tasks simultaneously with status indicators for efficient workflow management.
🌐 Web automation
Browse websites, fill forms, navigate deep sites not indexed by search engines, with real-time browser view.
💻 Code execution
Generate and execute code alongside web browsing for comprehensive task automation capabilities.
🔗

🔋 Reusing EV batteries for energy storage can offer greater carbon savings than direct recycling
https://techxplore.com/news/2025-07-reusing-ev-batteries-energy-storage.html

Reusing EV batteries for energy storage can offer greater carbon savings than direct recycling
When electric vehicle (EV) batteries reach the end of their service life, they can be recycled to recover valuable raw materials for the production of new batteries. Alternatively, retired EV batteries can be repurposed for use as stationary energy storage systems, helping to integrate renewable energy into the power grid, manage peak loads, and enhance energy security.

The 4th Dimension for Scaling Model Size
Ruike Zhu, Hanwen Zhang, Tianyu Shi, Chi Wang, Tianyi Zhou, Zengyi Qin
https://arxiv.org/abs/2506.18233 https://…

The 4th Dimension for Scaling Model Size
Scaling the size of large language models typically involves three dimensions: depth, width, and the number of parameters. In this work, we explore a fourth dimension, virtual logical depth (VLD), which increases the effective algorithmic depth without changing the overall parameter count by reusing parameters within the model. Although parameter reuse is not a new concept, its potential and characteristics in model scaling have not been thoroughly studied. Through carefully designed controlled…

SpeedLLM: An FPGA Co-design of Large Language Model Inference Accelerator
Peipei Wang, Wu Guan, Liping Liang, Zhijun Wang, Hanqing Luo, Zhibin Zhang
https://arxiv.org/abs/2507.14139

SpeedLLM: An FPGA Co-design of Large Language Model Inference Accelerator
This paper introduces SpeedLLM, a neural network accelerator designed on the Xilinx Alevo U280 platform and optimized for the Tinyllama framework to enhance edge computing performance. Key innovations include data stream parallelism, a memory reuse strategy, and Llama2 operator fusion, which collectively reduce latency and energy consumption. SpeedLLM's data pipeline architecture optimizes the read-compute-write cycle, while the memory strategy minimizes FPGA resource demands. The operator fusi…

Influence of Cell Position on the Capacity of Retired Batteries: Experimental and Statistical Studies
Marwan Hassini, Colette Mintsa-Eya, Eduardo Redondo-Iglesias, Pascal Venet
https://arxiv.org/abs/2507.14020

Influence of Cell Position on the Capacity of Retired Batteries: Experimental and Statistical Studies
Understanding how batteries perform after automotive use is crucial to determining their potential for reuse. This article presents experimental results aimed at advancing knowledge of retired battery performance. Three modules extracted from electric vehicles were tested. Their performance was assessed, and the results were analyzed statistically using analysis of variance (ANOVA). The 36 retired cells exhibited a high level of performance, albeit with significant variation. On average, the ce…

State of Abdominal CT Datasets: A Critical Review of Bias, Clinical Relevance, and Real-world Applicability
Saeide Danaei, Zahra Dehghanian, Elahe Meftah, Nariman Naderi, Seyed Amir Ahmad Safavi-Naini, Faeze Khorasanizade, Hamid R. Rabiee
https://arxiv.org/abs/2508.13626

State of Abdominal CT Datasets: A Critical Review of Bias, Clinical Relevance, and Real-world Applicability
This systematic review critically evaluates publicly available abdominal CT datasets and their suitability for artificial intelligence (AI) applications in clinical settings. We examined 46 publicly available abdominal CT datasets (50,256 studies). Across all 46 datasets, we found substantial redundancy (59.1\% case reuse) and a Western/geographic skew (75.3\% from North America and Europe). A bias assessment was performed on the 19 datasets with >=100 cases; within this subset, the most preval…

Filtering for Creativity: Adaptive Prompting for Multilingual Riddle Generation in LLMs
Duy Le, Kent Ziti, Evan Girard-Sun, Sean O'Brien, Vasu Sharma, Kevin Zhu
https://arxiv.org/abs/2508.18709

Filtering for Creativity: Adaptive Prompting for Multilingual Riddle Generation in LLMs
Multilingual riddle generation challenges large language models (LLMs) to balance cultural fluency with creative abstraction. Standard prompting strategies -- zero-shot, few-shot, chain-of-thought -- tend to reuse memorized riddles or perform shallow paraphrasing. We introduce Adaptive Originality Filtering (AOF), a prompting framework that filters redundant generations using cosine-based similarity rejection, while enforcing lexical novelty and cross-lingual fidelity. Evaluated across three LL…

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…

Toward Scalable Quantum Compilation for Modular Architecture: Qubit Mapping and Reuse via Deep Reinforcement Learning
Sokea Sang, Leanghok Hour, Youngsun Han
https://arxiv.org/abs/2506.09323

Toward Scalable Quantum Compilation for Modular Architecture: Qubit Mapping and Reuse via Deep Reinforcement Learning
Modular quantum architectures have emerged as a promising approach for scaling quantum computing systems by connecting multiple Quantum Processing Units (QPUs). However, this approach introduces significant challenges due to costly inter-core operations between chips and quantum state transfers, which contribute to noise and quantum decoherence. This paper presents QARMA, a novel Qubit mapping using Attention-based deep Reinforcement learning (DRL) for Modular quantum Architectures, along with …

What Makes the Best Decomposition? Investigating Binary Decomposition Under FCG Variance
Ang Jia, He Jiang, Zhilei Ren, Xiaochen Li, Ming Fan, Ting Liu
https://arxiv.org/abs/2506.19425

What Makes the Best Decomposition? Investigating Binary Decomposition Under FCG Variance
Binary decomposition, which decomposes binary files into modules, plays a critical role in binary reuse detection. Existing binary decomposition works either apply anchor-based methods by extending anchor functions to generate modules, or apply clustering-based methods by using clustering algorithms to group binary functions, which all rely on that reused code shares similar function call relationships. However, we find that function call graphs (FCGs) vary a lot when using different compilatio…

Money in Motion: Micro-Velocity and Usage of Ethereums Liquid Staking Tokens
Benjamin Kraner, Luca Pennella, Nicol\`o Vallarano, Claudio J. Tessone
https://arxiv.org/abs/2508.15391

Money in Motion: Micro-Velocity and Usage of Ethereums Liquid Staking Tokens
We introduce a micro-velocity framework for analysing the on-chain circulation of Lidos liquid-staking tokens, stETH, and its wrapped ERC-20 form, wstETH. By reconstructing full transfer and share-based accounting histories, we compute address-level velocities and decompose them into behavioural components. Despite their growing importance, the micro-level monetary dynamics of LSTs remain largely unexplored. Our data reveal persistently high velocity for both tokens, reflecting intensive reuse …

Life Cycle Analysis for Emissions of Scientific Computing Centres
Wim Vanderbauwhede, Mattias Wadenstein
https://arxiv.org/abs/2506.14365 https://

Life Cycle Analysis for Emissions of Scientific Computing Centres
We propose a dedicated model to assist with the life cycle analysis of emissions of scientific computing centres. The model takes into account both the embodied carbon and emissions from use, as well as other factors such as data centre power usage efficiency, data centre expansion, hardware replacement, increase in energy efficiency of next-generation hardware, reduction in carbon intensity of the electricity supply and potential for heat reuse. If differs from existing models in its detailed …

A case study: the savings potential thanks to FAIR data in one Materials Science PhD project
Michael Seitz, Nick Garabedian, Ilia Bagov, Christian Greiner
https://arxiv.org/abs/2506.12043

A case study: the savings potential thanks to FAIR data in one Materials Science PhD project
The FAIR (Findable, Accessible, Interoperable, and Reusable) data principles have gained significant attention as a means to enhance data sharing, collaboration, and reuse across various domains. Here, we explore the potential benefits of implementing FAIR data practices within engineering projects, with a monetary focus in the German context, but by considering aspects which are relatively universal. By examining the FAIR-data aspect of a Materials Science and Engineering PhD project, it becom…

PyPitfall: Dependency Chaos and Software Supply Chain Vulnerabilities in Python
Jacob Mahon, Chenxi Hou, Zhihao Yao
https://arxiv.org/abs/2507.18075 https://

PyPitfall: Dependency Chaos and Software Supply Chain Vulnerabilities in Python
Python software development heavily relies on third-party packages. Direct and transitive dependencies create a labyrinth of software supply chains. While it is convenient to reuse code, vulnerabilities within these dependency chains can propagate through dependencies, potentially affecting down-stream packages and applications. PyPI, the official Python package repository, hosts many packages and lacks a comprehensive analysis of the prevalence of vulnerable dependencies. This paper introduces…

Enforcing Speech Content Privacy in Environmental Sound Recordings using Segment-wise Waveform Reversal
Modan Tailleur, Mathieu Lagrange, Pierre Aumond, Vincent Tourre
https://arxiv.org/abs/2507.08412

Enforcing Speech Content Privacy in Environmental Sound Recordings using Segment-wise Waveform Reversal
Environmental sound recordings often contain intelligible speech, raising privacy concerns that limit analysis, sharing and reuse of data. In this paper, we introduce a method that renders speech unintelligible while preserving both the integrity of the acoustic scene, and the overall audio quality. Our approach involves reversing waveform segments to distort speech content. This process is enhanced through a voice activity detection and speech separation pipeline, which allows for more precise…

Understanding and Improving Data Repurposing
J. Parsons, R. Lukyanenko, B. Greenwood, C. Cooper
https://arxiv.org/abs/2506.09073 https://

Understanding and Improving Data Repurposing
We live in an age of unprecedented opportunities to use existing data for tasks not anticipated when those data were collected, resulting in widespread data repurposing. This commentary defines and maps the scope of data repurposing to highlight its importance for organizations and society and the need to study data repurposing as a frontier of data management. We explain how repurposing differs from original data use and data reuse and then develop a framework for data repurposing consisting o…

ReFrame: Layer Caching for Accelerated Inference in Real-Time Rendering
Lufei Liu, Tor M. Aamodt
https://arxiv.org/abs/2506.13814 https://

ReFrame: Layer Caching for Accelerated Inference in Real-Time Rendering
Graphics rendering applications increasingly leverage neural networks in tasks such as denoising, supersampling, and frame extrapolation to improve image quality while maintaining frame rates. The temporal coherence inherent in these tasks presents an opportunity to reuse intermediate results from previous frames and avoid redundant computations. Recent work has shown that caching intermediate features to be reused in subsequent inferences is an effective method to reduce latency in diffusion m…

A Framework for FAIR and CLEAR Ecological Data and Knowledge: Semantic Units for Synthesis and Causal Modelling
Lars Vogt, Birgitta K\"onig-Ries, Tim Alamenciak, Joshua I. Brian, Carlos Alberto Arnillas, Lotte Korell, Robert Fr\"uhst\"uckl, Tina Heger
https://arxiv.org/abs/2508.08959…

A Framework for FAIR and CLEAR Ecological Data and Knowledge: Semantic Units for Synthesis and Causal Modelling
Ecological research increasingly relies on integrating heterogeneous datasets and knowledge to explain and predict complex phenomena. Yet, differences in data types, terminology, and documentation often hinder interoperability, reuse, and causal understanding. We present the Semantic Units Framework, a novel, domain-agnostic semantic modelling approach applied here to ecological data and knowledge in compliance with the FAIR (Findable, Accessible, Interoperable, Reusable) and CLEAR (Cognitively…

ONLINE WORKSHOP> Navigating Indra’s Net: Digital Approaches to Text Reuse-based Inter-textuality in Pre-Modern East Asian Texts
https://ift.tt/ajGgrIY
When did aliens become a problem? The Mediterranean Association for Marine Biology and Oceanology in…
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ACS: An interactive framework for conformal selection
Yu Gui, Ying Jin, Yash Nair, Zhimei Ren
https://arxiv.org/abs/2507.15825 https://

ACS: An interactive framework for conformal selection
This paper presents adaptive conformal selection (ACS), an interactive framework for model-free selection with guaranteed error control. Building on conformal selection (Jin and Candès, 2023b), ACS generalizes the approach to support human-in-the-loop adaptive data analysis. Under the ACS framework, we can partially reuse the data to boost the selection power, make decisions on the fly while exploring the data, and incorporate new information or preferences as they arise. The key to ACS is a c…

Semantic Caching for Improving Web Affordability
Hafsa Akbar, Danish Athar, Muhammad Ayain Fida Rana, Chaudhary Hammad Javed, Zartash Afzal Uzmi, Ihsan Ayyub Qazi, Zafar Ayyub Qazi
https://arxiv.org/abs/2506.20420

Semantic Caching for Improving Web Affordability
The rapid growth of web content has led to increasingly large webpages, posing significant challenges for Internet affordability, especially in developing countries where data costs remain prohibitively high. We propose semantic caching using Large Language Models (LLMs) to improve web affordability by enabling reuse of semantically similar images within webpages. Analyzing 50 leading news and media websites, encompassing 4,264 images and over 40,000 image pairs, we demonstrate potential for si…

ASDR: Exploiting Adaptive Sampling and Data Reuse for CIM-based Instant Neural Rendering
Fangxin Liu, Haomin Li, Bowen Zhu, Zongwu Wang, Zhuoran Song, Habing Guan, Li Jiang
https://arxiv.org/abs/2508.02304

ASDR: Exploiting Adaptive Sampling and Data Reuse for CIM-based Instant Neural Rendering
Neural Radiance Fields (NeRF) offer significant promise for generating photorealistic images and videos. However, existing mainstream neural rendering models often fall short in meeting the demands for immediacy and power efficiency in practical applications. Specifically, these models frequently exhibit irregular access patterns and substantial computational overhead, leading to undesirable inference latency and high power consumption. Computing-in-memory (CIM), an emerging computational parad…

Continuous-time multifarious systems -- Part II: non-reciprocal multifarious self-organization
Jakob Metson, Saeed Osat, Ramin Golestanian
https://arxiv.org/abs/2506.07649

Continuous-time multifarious systems -- Part II: non-reciprocal multifarious self-organization
In the context of self-assembly, where complex structures can be assembled from smaller units, it is desirable to devise strategies towards disassembly and reassembly processes that reuse the constituent parts. A non-reciprocal multifarious self-organization strategy has been recently introduced, and shown to have the capacity to exhibit this complex property. In this work, we study the model using continuous-time Gillespie simulations, and compare the results against discrete-time Monte Carlo …

Fact-Checking at Scale: Multimodal AI for Authenticity and Context Verification in Online Media
Van-Hoang Phan, Tung-Duong Le-Duc, Long-Khanh Pham, Anh-Thu Le, Quynh-Huong Dinh-Nguyen, Dang-Quan Vo, Hoang-Quoc Nguyen-Son, Anh-Duy Tran, Dang Vu, Minh-Son Dao
https://arxiv.org/abs/2508.08592

Fact-Checking at Scale: Multimodal AI for Authenticity and Context Verification in Online Media
The proliferation of multimedia content on social media platforms has dramatically transformed how information is consumed and disseminated. While this shift enables real-time coverage of global events, it also facilitates the rapid spread of misinformation and disinformation, especially during crises such as wars, natural disasters, or elections. The rise of synthetic media and the reuse of authentic content in misleading contexts have intensified the need for robust multimedia verification to…

"Reusable cups take center stage as venues ditch single-use plastic"
#Reusables #Environment

Reusable cups take center stage as venues ditch single-use plastic
Music venues, arenas and festivals across the United States are swapping single-use plastic cups for washable, reusable ones in a growing shift away from recycling toward full-scale reuse.Anna Phillips reports for The Washington Post.In short:About 200 U.S. venues now use r.World’s reusable drinkwar...

VulCoCo: A Simple Yet Effective Method for Detecting Vulnerable Code Clones
Tan Bui, Yan Naing Tun, Thanh Phuc Nguyen, Yindu Su, Ferdian Thung, Yikun Li, Han Wei Ang, Yide Yin, Frank Liauw, Lwin Khin Shar, Eng Lieh Ouh, Ting Zhang, David Lo
https://arxiv.org/abs/2507.16661

VulCoCo: A Simple Yet Effective Method for Detecting Vulnerable Code Clones
Code reuse is common in modern software development, but it can also spread vulnerabilities when developers unknowingly copy risky code. The code fragments that preserve the logic of known vulnerabilities are known as vulnerable code clones (VCCs). Detecting those VCCs is a critical but challenging task. Existing VCC detection tools often rely on syntactic similarity or produce coarse vulnerability predictions without clear explanations, limiting their practical utility. In this paper, we propo…

EGNInfoLeaker: Unveiling the Risks of Public Key Reuse and User Identity Leakage in Blockchain
Chenyu Li, Xueping Liang, Xiaorui Gong, Xiu Zhang
https://arxiv.org/abs/2507.01635

EGNInfoLeaker: Unveiling the Risks of Public Key Reuse and User Identity Leakage in Blockchain
While Ethereum's discovery protocols (Discv4/ Discv5) incorporate robust cryptographic designs to protect user privacy, real-world deployment reveals critical vulnerabilities when users deviate from security guidelines. In this paper, we design a system called EGNInfoLeaker. Our study is the first work that uncovers widespread public key reuse across Ethereum's peer-to-peer networks - a practice that fundamentally undermines the protocol's privacy guarantees. Through systematic analysis of 300 …

Method-Based Reasoning for Large Language Models: Extraction, Reuse, and Continuous Improvement
Hong Su
https://arxiv.org/abs/2508.04289 https://arxiv.org/…

Method-Based Reasoning for Large Language Models: Extraction, Reuse, and Continuous Improvement
Large language models (LLMs) have shown impressive capabilities across a wide range of language tasks. However, their reasoning process is primarily guided by statistical patterns in training data, which limits their ability to handle novel problems and perform consistent logical reasoning. In this paper, we propose a method-based model that enhances LLMs with explicit, reusable procedures extracted from training content, generated responses, and user interactions. Each method is represented as…

Self-Organizing Agent Network for LLM-based Workflow Automation
Yiming Xiong, Jian Wang, Bing Li, Yuhan Zhu, Yuqi Zhao
https://arxiv.org/abs/2508.13732 https://

Self-Organizing Agent Network for LLM-based Workflow Automation
Recent multi-agent frameworks built upon large language models (LLMs) have demonstrated remarkable capabilities in complex task planning. However, in real-world enterprise environments, business workflows are typically composed through modularization and reuse of numerous subprocesses, resulting in intricate workflows characterized by lengthy and deeply nested execution paths. Such complexity poses significant challenges for LLM-driven orchestration, as extended reasoning chains and state-space…

Prompt-Guided Latent Diffusion with Predictive Class Conditioning for 3D Prostate MRI Generation
Emerson P. Grabke, Masoom A. Haider, Babak Taati
https://arxiv.org/abs/2506.10230 …

Prompt-Guided Latent Diffusion with Predictive Class Conditioning for 3D Prostate MRI Generation
Latent diffusion models (LDM) could alleviate data scarcity challenges affecting machine learning development for medical imaging. However, medical LDM training typically relies on performance- or scientific accessibility-limiting strategies including a reliance on short-prompt text encoders, the reuse of non-medical LDMs, or a requirement for fine-tuning with large data volumes. We propose a Class-Conditioned Efficient Large Language model Adapter (CCELLA) to address these limitations. CCELLA …

KVFlow: Efficient Prefix Caching for Accelerating LLM-Based Multi-Agent Workflows
Zaifeng Pan, Ajjkumar Patel, Zhengding Hu, Yipeng Shen, Yue Guan, Wan-Lu Li, Lianhui Qin, Yida Wang, Yufei Ding
https://arxiv.org/abs/2507.07400

KVFlow: Efficient Prefix Caching for Accelerating LLM-Based Multi-Agent Workflows
Large language model (LLM) based agentic workflows have become a popular paradigm for coordinating multiple specialized agents to solve complex tasks. To improve serving efficiency, existing LLM systems employ prefix caching to reuse key-value (KV) tensors corresponding to agents' fixed prompts, thereby avoiding redundant computation across repeated invocations. However, current systems typically evict KV caches using a Least Recently Used (LRU) policy, which fails to anticipate future agent us…

FlexCAST: Enabling Flexible Scientific Data Analyses
Benjamin Nachman, Dennis Noll
https://arxiv.org/abs/2507.11528 https://arxiv.org…

FlexCAST: Enabling Flexible Scientific Data Analyses
The development of scientific data analyses is a resource-intensive process that often yields results with untapped potential for reuse and reinterpretation. In many cases, a developed analysis can be used to measure more than it was designed for, by changing its input data or parametrization. Existing reinterpretation frameworks, such as RECAST, enable analysis reinterpretation by preserving the analysis implementation to allow for changes of particular parts of the input data. We introduce Fl…

AnnoGram: An Annotative Grammar of Graphics Extension
Md Dilshadur Rahman, Md Rahat-uz- Zaman, Andrew McNutt, Paul Rosen
https://arxiv.org/abs/2507.04236 h…

AnnoGram: An Annotative Grammar of Graphics Extension
Annotations are central to effective data communication, yet most visualization tools treat them as secondary constructs -- manually defined, difficult to reuse, and loosely coupled to the underlying visualization grammar. We propose a declarative extension to Wilkinson's Grammar of Graphics that reifies annotations as first-class design elements, enabling structured specification of annotation targets, types, and positioning strategies. To demonstrate the utility of our approach, we develop a …

This https://arxiv.org/abs/2504.05307 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Toward Total Recall: Enhancing FAIRness through AI-Driven Metadata Standardization
Scientific metadata often suffer from incompleteness, inconsistency, and formatting errors, which hinder effective discovery and reuse of the associated datasets. We present a method that combines GPT-4 with structured metadata templates from the CEDAR knowledge base to automatically standardize metadata and to ensure compliance with established standards. A CEDAR template specifies the expected fields of a metadata submission and their permissible values. Our standardization process involves u…

A first look at License Variants in the PyPI Ecosystem
Weiwei Xu, Hengzhi Ye, Kai Gao, Minghui Zhou
https://arxiv.org/abs/2507.14594 https://

A first look at License Variants in the PyPI Ecosystem
Open-source licenses establish the legal foundation for software reuse, yet license variants, including both modified standard licenses and custom-created alternatives, introduce significant compliance complexities. Despite their prevalence and potential impact, these variants are poorly understood in modern software systems, and existing tools do not account for their existence, leading to significant challenges in both effectiveness and efficiency of license analysis. To fill this knowledge g…

Linguistic Ordered Weighted Averaging based deep learning pooling for fault diagnosis in a wastewater treatment plant
Alicia Beneyto-Rodriguez, Gregorio I. Sainz-Palmero, Marta Galende-Hern\'andez, Mar\'ia J. Fuente
https://arxiv.org/abs/2506.08676

Linguistic Ordered Weighted Averaging based deep learning pooling for fault diagnosis in a wastewater treatment plant
Nowadays, water reuse is a serious challenge to help address water shortages. Here, the wastewater treatment plants (WWTP) play a key role, and its proper operation is mandatory. So, fault diagnosis is a key activity for these plants. Their high complexity and large-scale require of smart methodologies for that fault diagnosis and safety operation. All these large-scale and complex industrial processes are monitored, allowing the data collection about the plant operation, so data driven approac…

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…

This https://arxiv.org/abs/2505.19111 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Remote Sensing Image Classification with Decoupled Knowledge Distillation
To address the challenges posed by the large number of parameters in existing remote sensing image classification models, which hinder deployment on resource-constrained devices, this paper proposes a lightweight classification method based on knowledge distillation. Specifically, G-GhostNet is adopted as the backbone network, leveraging feature reuse to reduce redundant parameters and significantly improve inference efficiency. In addition, a decoupled knowledge distillation strategy is employ…

Sub- {\mu} W Battery-Less and Oscillator-Less Wi-Fi Backscattering Transmitter Reusing RF Signal for Harvesting, Communications, and Motion Detection
Marco Privitera, Andrea Ballo, Karim Ali Ahmed, Alfio Dario Grasso, Massimo Alioto
https://arxiv.org/abs/2508.05479

Sub- μ W Battery-Less and Oscillator-Less Wi-Fi Backscattering Transmitter Reusing RF Signal for Harvesting, Communications, and Motion Detection
In this paper, a sub-uW power 802.11b backscattering transmitter is presented to enable reuse of the same incident wave for three purposes: RF harvesting, backscattering communications and position/motion sensing. The removal of the battery and any off-chip motion sensor (e.g., MEMS) enables unprecedented level of miniaturization and ubiquity, unrestricted device lifespan, low fabrication and maintenance cost. The uW power wall for WiFi transmitters is broken for the first time via local oscill…

Methods for Reducing Ancilla-Overhead in Block Encodings
Francisca Vasconcelos, Andr\'as Gily\'en
https://arxiv.org/abs/2507.07900 https://

Methods for Reducing Ancilla-Overhead in Block Encodings
Block encodings are a fundamental primitive in quantum algorithms, but can often have large ancilla overhead. In this work, we introduce novel techniques for reducing this overhead in two distinct ways. In Part I, we prove the existence of a "space-time tradeoff" by deriving an algorithm that, for any block encoding, approximately uncomputes all but one of its ancilla (freeing up those ancillae for reuse in later parts of a quantum algorithm). In Part II, we evaluate the minimum number of ancil…

Reuse or Generate? Accelerating Code Editing via Edit-Oriented Speculative Decoding
Peiding Wang, Li Zhang, Fang Liu, Yinghao Zhu, Wang Xu, Lin Shi, Xiaoli Lian, Minxiao Li, Bo Shen, An Fu
https://arxiv.org/abs/2506.02780

Reuse or Generate? Accelerating Code Editing via Edit-Oriented Speculative Decoding
Large Language Models (LLMs) have demonstrated remarkable capabilities in code editing, substantially enhancing software development productivity. However, the inherent complexity of code editing tasks forces existing approaches to rely on LLMs' autoregressive end-to-end generation, where decoding speed plays a critical role in efficiency. While inference acceleration techniques like speculative decoding are applied to improve the decoding efficiency, these methods fail to account for the uniqu…

Predictive control of wastewater treatment plants as energy-autonomous water resource recovery facilities
Otacilio B. L. Neto, Michela Mulas, Iiro Harjunkoski, Francesco Corona
https://arxiv.org/abs/2506.10490

Predictive control of wastewater treatment plants as energy-autonomous water resource recovery facilities
This work proposes an automatic control solution for the operation of conventional wastewater treatment plants (WWTPs) as energy-autonomous water resource recovery facilities. We first conceptualize a classification of the quality of treated water for three resource recovery applications (environmental, industrial, and agricultural water reuse). We then present an output-feedback model predictive controller (Output MPC) that operates a plant to produce water of specific quality class, while als…

DRIVE-T: A Methodology for Discriminative and Representative Data Viz Item Selection for Literacy Construct and Assessment
Angela Locoro, Silvia Golia, Davide Falessi
https://arxiv.org/abs/2508.04160

DRIVE-T: A Methodology for Discriminative and Representative Data Viz Item Selection for Literacy Construct and Assessment
The underspecification of progressive levels of difficulty in measurement constructs design and assessment tests for data visualization literacy may hinder the expressivity of measurements in both test design and test reuse. To mitigate this problem, this paper proposes DRIVE-T (Discriminating and Representative Items for Validating Expressive Tests), a methodology designed to drive the construction and evaluation of assessment items. Given a data vizualization, DRIVE-T supports the identificat…

STI-SNN: A 0.14 GOPS/W/PE Single-Timestep Inference FPGA-based SNN Accelerator with Algorithm and Hardware Co-Design
Kainan Wang, Chengyi Yang, Chengting Yu, Yee Sin Ang, Bo Wang, Aili Wang
https://arxiv.org/abs/2506.08842

STI-SNN: A 0.14 GOPS/W/PE Single-Timestep Inference FPGA-based SNN Accelerator with Algorithm and Hardware Co-Design
Brain-inspired Spiking Neural Networks (SNNs) have attracted attention for their event-driven characteristics and high energy efficiency. However, the temporal dependency and irregularity of spikes present significant challenges for hardware parallel processing and data reuse, leading to some existing accelerators falling short in processing latency and energy efficiency. To overcome these challenges, we introduce the STI-SNN accelerator, designed for resource-constrained applications with high…

Structural and Connectivity Patterns in the Maven Central Software Dependency Network
Daniel Ogenrwot, John Businge, Shaikh Arifuzzaman
https://arxiv.org/abs/2508.13819 https://…

Structural and Connectivity Patterns in the Maven Central Software Dependency Network
Understanding the structural characteristics and connectivity patterns of large-scale software ecosystems is critical for enhancing software reuse, improving ecosystem resilience, and mitigating security risks. In this paper, we investigate the Maven Central ecosystem, one of the largest repositories of Java libraries, by applying network science techniques to its dependency graph. Leveraging the Goblin framework, we extracted a sample consisting of the top 5,000 highly connected artifacts base…

Can NLP Tackle Hate Speech in the Real World? Stakeholder-Informed Feedback and Survey on Counterspeech
Tanvi Dinkar, Aiqi Jiang, Simona Frenda, Poppy Gerrard-Abbott, Nancie Gunson, Gavin Abercrombie, Ioannis Konstas
https://arxiv.org/abs/2508.04638

Can NLP Tackle Hate Speech in the Real World? Stakeholder-Informed Feedback and Survey on Counterspeech
Counterspeech, i.e. the practice of responding to online hate speech, has gained traction in NLP as a promising intervention. While early work emphasised collaboration with non-governmental organisation stakeholders, recent research trends have shifted toward automated pipelines that reuse a small set of legacy datasets, often without input from affected communities. This paper presents a systematic review of 74 NLP studies on counterspeech, analysing the extent to which stakeholder participati…

Deep Learning Based Dynamic Environment Reconstruction for Vehicular ISAC Scenarios
Junzhe Song, Ruisi He, Mi Yang, Zhengyu Zhang, Bingcheng Liu, Jiahui Han, Haoxiang Zhang, Bo Ai
https://arxiv.org/abs/2508.05226

Deep Learning Based Dynamic Environment Reconstruction for Vehicular ISAC Scenarios
Integrated Sensing and Communication (ISAC) technology plays a critical role in future intelligent transportation systems, by enabling vehicles to perceive and reconstruct the surrounding environment through reuse of wireless signals, thereby reducing or even eliminating the need for additional sensors such as LiDAR or radar. However, existing ISAC based reconstruction methods often lack the ability to track dynamic scenes with sufficient accuracy and temporal consistency, limiting the real wor…

Characterising Bugs in Jupyter Platform
Yutian Tang, Hongchen Cao, Yuxi Chen, David Lo
https://arxiv.org/abs/2506.14055 https://arxiv…

Characterising Bugs in Jupyter Platform
As a representative literate programming platform, Jupyter is widely adopted by developers, data analysts, and researchers for replication, data sharing, documentation, interactive data visualization, and more. Understanding the bugs in the Jupyter platform is essential for ensuring its correctness, security, and robustness. Previous studies focused on code reuse, restoration, and repair execution environment for Jupyter notebooks. However, the bugs in Jupyter notebooks' hosting platform Jupyte…

Applying XAI based unsupervised knowledge discovering for Operation modes in a WWTP. A real case: AQUAVALL WWTP
Alicia Beneyto-Rodriguez, Gregorio I. Sainz-Palmero, Marta Galende-Hern\'andez, Mar\'ia J. Fuente, Jos\'e M. Cuenca
https://arxiv.org/abs/2506.05958

Applying XAI based unsupervised knowledge discovering for Operation modes in a WWTP. A real case: AQUAVALL WWTP
Water reuse is a key point when fresh water is a commodity in ever greater demand, but which is also becoming ever more available. Furthermore, the return of clean water to its natural environment is also mandatory. Therefore, wastewater treatment plants (WWTPs) are essential in any policy focused on these serious challenges. WWTPs are complex facilities which need to operate at their best to achieve their goals. Nowadays, they are largely monitored, generating large databases of historical d…

Intrinsic Fingerprint of LLMs: Continue Training is NOT All You Need to Steal A Model!
Do-hyeon Yoon, Minsoo Chun, Thomas Allen, Hans M\"uller, Min Wang, Rajesh Sharma
https://arxiv.org/abs/2507.03014

Intrinsic Fingerprint of LLMs: Continue Training is NOT All You Need to Steal A Model!
Large language models (LLMs) face significant copyright and intellectual property challenges as the cost of training increases and model reuse becomes prevalent. While watermarking techniques have been proposed to protect model ownership, they may not be robust to continue training and development, posing serious threats to model attribution and copyright protection. This work introduces a simple yet effective approach for robust LLM fingerprinting based on intrinsic model characteristics. We d…

HaLert: A Resilient Smart City Architecture for Post-Disaster Based on Wi-Fi HaLow Mesh and SDN
Ana Rita Ortigoso, Gabriel Vieira, Daniel Fuentes, Lu\'is Fraz\~ao, Nuno Costa, Ant\'onio Pereira
https://arxiv.org/abs/2507.07841

HaLert: A Resilient Smart City Architecture for Post-Disaster Based on Wi-Fi HaLow Mesh and SDN
Events such as catastrophes and disasters are, in most cases, unpredictable. Consequently, reusing existing infrastructures to develop alternative communication strategies after disasters is essential to minimise the impact of these events on the population's ability to communicate and promptly receive alerts from authorities. In this context, the emergence of smart cities, characterised by dense and geographically distributed IoT networks, presents significant potential for such reuse. This wo…

CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval
Jiahui Geng, Fengyu Cai, Shaobo Cui, Qing Li, Liangwei Chen, Chenyang Lyu, Haonan Li, Derui Zhu, Walter Pretschner, Heinz Koeppl, Fakhri Karray
https://arxiv.org/abs/2506.11066

CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval
Code retrieval is essential in modern software development, as it boosts code reuse and accelerates debugging. However, current benchmarks primarily emphasize functional relevance while neglecting critical dimensions of software quality. Motivated by this gap, we introduce CoQuIR, the first large-scale, multilingual benchmark specifically designed to evaluate quality-aware code retrieval across four key dimensions: correctness, efficiency, security, and maintainability. CoQuIR provides fine-gra…

On the robustness of Dirichlet-Neumann coupling schemes for fluid-structure-interaction problems with nearly-closed fluid domains
A. Aissa-Berraies, Ferdinando A. Auricchio, Gertjan van Zwieten, E. Harald van Brummelen
https://arxiv.org/abs/2506.04027

On the robustness of Dirichlet-Neumann coupling schemes for fluid-structure-interaction problems with nearly-closed fluid domains
Partitioned methods for fluid-structure interaction (FSI) involve solving the structural and flow problems sequentially. These methods allow for separate settings for the fluid and solid subsystems and thus modularity, enabling reuse of advanced commercial and open-source software. Most partitioned FSI schemes apply a Dirichlet-Neumann (DN) split of the interface conditions. The DN scheme is adequate in a wide range of applications, but it is sensitive to the added-mass effect, and it is suscep…

On the synchronization between Hugging Face pre-trained language models and their upstream GitHub repository
Ajibode Adekunle, Abdul Ali Bangash, Bram Adams, Ahmed E. Hassan
https://arxiv.org/abs/2508.10157

On the synchronization between Hugging Face pre-trained language models and their upstream GitHub repository
Pretrained language models (PTLMs) have advanced natural language processing (NLP), enabling progress in tasks like text generation and translation. Like software package management, PTLMs are trained using code and environment scripts in upstream repositories (e.g., GitHub, GH) and distributed as variants via downstream platforms like Hugging Face (HF). Coordinating development between GH and HF poses challenges such as misaligned release timelines, inconsistent versioning, and limited reuse o…

scikit-package -- software packaging standards and roadmap for sharing reproducible scientific software
S. Lee, C. Myers, A. Yang, T. Zhang, S. J. L. Billinge
https://arxiv.org/abs/2507.03328

scikit-package -- software packaging standards and roadmap for sharing reproducible scientific software
Scientific advancement relies on the ability to share and reproduce results. When data analysis or calculations are carried out using software written by scientists there are special challenges around code versions, quality and code sharing. scikit-package provides a roadmap to facilitate code reuse and sharing with minimal effort through tutorials coupled with automated and centralized reusable workflows. The goal of the project is to provide pedagogical and practical tools for scientists who …

JC-Finder: Detecting Java Clone-based Third-Party Library by Class-level Tree Analysis
Lida Zhao, Chaofan Li, Yueming Wu, Lyuye Zhang, Jiahui Wu, Chengwei Liu, Sen Chen, Yutao Hu, Zhengzi Xu, Yi Liu, Jingquan Ge, Jun Sun, Yang Liu
https://arxiv.org/abs/2508.02397

JC-Finder: Detecting Java Clone-based Third-Party Library by Class-level Tree Analysis
While reusing third-party libraries (TPL) facilitates software development, its chaotic management has brought great threats to software maintenance and the unauthorized use of source code also raises ethical problems such as misconduct on copyrighted code. To identify TPL reuse in projects, Software Composition Analysis (SCA) is employed, and two categories of SCA techniques are used based on how TPLs are introduced: clone-based SCA and package-manager-based SCA (PM-based SCA). Although introd…

Automatic Multi-level Feature Tree Construction for Domain-Specific Reusable Artifacts Management
Dongming Jin, Zhi Jin, Nianyu Li, Kai Yang, Linyu Li, Suijing Guan
https://arxiv.org/abs/2506.03946

Automatic Multi-level Feature Tree Construction for Domain-Specific Reusable Artifacts Management
With the rapid growth of open-source ecosystems (e.g., Linux) and domain-specific software projects (e.g., aerospace), efficient management of reusable artifacts is becoming increasingly crucial for software reuse. The multi-level feature tree enables semantic management based on functionality and supports requirements-driven artifact selection. However, constructing such a tree heavily relies on domain expertise, which is time-consuming and labor-intensive. To address this issue, this paper pr…