While Renewable Energy Communities (RECs) and Collective Self-Consumption (CSC) schemes have emerged as promising tools to accelerate renewable energy adoption and support the net-zero transition, their full potential can only be realised when complemented by demand-side flexibility that aligns consumption with renewable generation. Water storage heaters can function as distributed thermal storage, absorbing excess renewable energy at the community level. This work quantifies both the benefits of water storage heaters flexibility for energy consumers in a CSC community in France (such as energy bill reduction, increase of self-consumption), and the challenges related to the implementation and user acceptance. At the first stage, an annual simulation analysis is performed on a community of 41 households and a large solar PV plant, comparing a scenario without a CSC community, a scenario with a standard CSC community, and a scenario with a CSC community with flexibility from water storage heaters, which showed that an average benefit of 70euro/year per household can be achieved due to flexibility and an increase of 6% and 22% of solar PV community self-consumption and self-production respectively. In the second stage, we present the results of the real-world deployment in the community, analysing its technical performance and user reception, and examine the main factors shaping user engagement and satisfaction.

This paper examines RDF validation practices and challenges to understand stakeholder applications, their needs, and identify areas for improvement in technologies and methodologies, thereby guiding future research and standardization efforts. A community survey was conducted, targeting a diverse group of RDF validation technology users across academia and industry. The survey collected data on current practices, tool usage, perceived benefits, limitations, and desired enhancements to gain a broad overview of the validation landscape. Our analysis shows that while RDF validation is widely adopted and valued for enhancing data quality, significant challenges remain. In particular, users report a need for better documentation, improved tool support, enhanced performance, and greater language expressiveness to handle complex large-scale validation tasks effectively. This work provides crucial insights into the RDF validation landscape, highlighting current practices and key areas for development. It offers a foundation for researchers, developers, and standardization bodies to address current limitations and advance validation technologies, ultimately improving data quality and usability in knowledge graphs.

Where people look during shared activity carries coordination cues that speech and gesture cannot replace, but these patterns remain invisible to participants. XR headsets make gaze available as real-time input, yet few systems feed it back visually. We frame our work using the Attention-Aware Pipeline (Capture, Record, Revisualize), whose feedback loop means the systems visual response alters what users attend to next, triggering further responses. This generates design tensions whose form depends on each stages configuration. We trace the pipeline through three systems casting attention as a mirror (reflecting gaze history), a medium (sharing it across collaborators), and a mediator (intervening through diminished reality). Each encountered a tension the loop predicted, motivating the next. A formative eye-tracking study of four musicians surfaced attentional tunneling and near-total disconnection, confirming the need for intervention. We present these tensions and a next step: testing whether subtractive intervention reduces tunneling for a single sight-reader.

Visual attention is central to ensemble coordination, yet how musicians allocate gaze during naturalistic rehearsal remains poorly understood. We present a pilot study using mobile eye tracking to examine gaze behaviour in a four-member band across three songs, each practiced twice. Musicians wore Pupil Labs Neon eye trackers, and YOLOv8-assisted scene annotations mapped fixations to ensemble members and objects in view. Analyzing fixation matrices, transition matrices, temporal scarf plots, and dwell-transition correlations, we uncover a hub-and-spoke attention topology: the session leader was the dominant gaze target for all members, while the learning guitarist concentrated up to 97% of interpersonal dwell on this single reference. Between attempts, gaze transitions decreased by up to 65% on average for unfamiliar material (up to 82% for individual participants) as scanning stabilized. Scarf plots reveal how teaching breakdowns fragment attention and uninterrupted runs consolidate it. Post-session participant reflections align with the quantitative patterns, and we discuss implications for gaze-aware tools in ensemble pedagogy.

We answer a question posed by Poggiolesi concerning a syntactic decidability proof for GL in the tree-hypersequent system CSGL, and resolve a challenge identified by Maggesi and Perini Brogi, who sought a PSPACE proof-search algorithm for GL in expressive sequent-based formalisms. We work with a notational variant of CSGL formulated in terms of (labeled) tree sequents. Our answer is complexity-optimal: we present a proof-search algorithm that decides the (in)validity of formulae and runs in PSPACE, matching the known PSPACE-completeness of GL. To achieve this, we introduce a "linearization method," which constructs only a single branch of a derivation and of a tree sequent at a time, avoiding the exponential blowup typical of naive proof-search in sequent formalisms. We show how to systematically combine fragments of tree sequents generated during proof-search to extract finite counter-models, which serves as a theoretical device for establishing the correctness of the algorithm when proof-search fails. Finally, we show that every valid formula admits a proof consisting solely of line sequents, which correspond to linear nested sequents. This establishes a connection between depth-first proof-search and linear nested sequent calculi. Our results not only answer the aforementioned questions, but also provide new insights into proof-search and correctness arguments in tree sequent systems for modal logics.

Cross-border electricity exchanges are crucial for operating and planning highly renewable power systems. Many studies reduce spatial granularity to keep the models tractable and prescribe cross-border exchanges exogenously, often by reusing historical import/export time series. Such assumptions become inconsistent as renewable penetration changes the magnitude and timing of flows. This paper proposes a machine-learning (ML) surrogate framework that maps available nodal time series data (e.g., hourly demand and renewable generation) to synthetic, interconnector-level flow time series. The goal is to provide consistent flow profiles that are used as fixed boundary conditions in reduced power system optimization models (PSOMs). To improve downstream feasibility when surrogate flows are imposed in optimization, we further introduce a custom loss for the neural-network surrogate that penalizes physically impossible flow patterns. We demonstrate the framework on a pan-European single-node per country DC optimal power flow setting using the open-source LEGO PSOM with ENTSO-E TYNDP 2024 National Trends assumptions for 2030. We assess two model classes: k-nearest neighbors (KNN) and feedforward neural networks (SQU), using both full and reduced feature sets. The SQU models generalize more robustly than KNN to unseen climate years and substantially improve upon scaled historical benchmarks in terms of predictive accuracy. When imposed as fixed boundary flows in single-node PSOMs, the ML-generated profiles produce outcomes that closely match the results of the full European simulation, while delivering substantial runtime reductions (up to ~500x). These results indicate that ML-based flow surrogates can provide decision-relevant interconnector flows for tractable reduced studies in high-renewable systems.

Data analytics increasingly runs on distributed lakehouse systems, where platform operators must optimise monetary, resource, and environmental costs. Query Performance Prediction (QPP) helps to balance these costs and supports workload management techniques, such as adaptive resource scaling and low-carbon scheduling. However, runtimes in lakehouses can vary substantially, and the impact of runtime variance on QPP accuracy and workload orchestration has not previously been systematically studied for lakehouse systems. This paper addresses this gap by investigating the runtime variance observed for distributed lakehouse analytical queries and its impact on QPP. First, we quantify the run-to-run variance using Kubernetes deployments across three public clouds and one private cloud, spanning multiple database scales and three analytical benchmarks. Our results demonstrate that repeated executions of the same query can vary in runtime by nearly twofold. Second, we conduct a factor analysis study assessing key sources of this runtime variance such as data locality, co-tenant load, and caching effects. Third, we examine how variance influences state-of-the-art QPP models, revealing that addressing key sources of variance can reduce prediction error up to 80%. Finally, we demonstrate the downstream implications for low-carbon scheduling as an example of a workload management technique that relies on performance prediction, showing that accounting for runtime variance can lead to a significant reduction in carbon costs.

A new form of Maximal Extractable Value (MEV), termed speculative MEV, has emerged across Layer-2 blockchains. Unlike Ethereum mainnet, many Layer-2 systems lack a public mempool, forcing extraction strategies to become probabilistic: searchers emit multiple identical transactions hoping to capture an opportunity first. This generates substantial transaction spam, increasing fees and wasting block space. We investigate speculative Oracle Extractable Value (OEV), a form of MEV associated with liquidating undercollateralized loans via speculative backrunning of oracle price updates. We propose a methodology for detecting speculative liquidations in the wild and apply it across Arbitrum, Base, and Optimism. On October 10, 2025, we identify 64 speculative liquidators on Aave (57% of all detected liquidators) and 831 successful speculative liquidations (39% of all successful liquidations across the three chains). We further examine whether latency differences in oracle price feed updates across blockchains can be exploited for cross-chain OEV. Specifically, we ask whether a searcher can observe oracle updates on one chain and frontrun liquidation opportunities on another. We systematically analyze Chainlink Decentralized Oracle Network (DON) configurations (deviation thresholds, heartbeat intervals, and submitted price observations) across Arbitrum, Base, Ethereum, and Optimism. Our dataset comprises 63 Chainlink feeds, 12,009 price updates, and over 100,000 oracle observations linked to 2,986 Aave liquidations. We show that independent DONs consume largely identical off-chain price data nearly simultaneously yet publish updates at different times, creating statistically predictable cross-chain exploitation windows. We demonstrate that Chainlink updates on Optimism can predict subsequent updates on Arbitrum and Base, enabling speculative cross-chain OEV extraction.

This paper investigates a multi-modal and human-centric framework for low-cost road roughness assessment. The evaluation was based on three complementary data sources: smartphone-based International Roughness Index (IRI) estimates from two independent smartphone-based applications; in-vehicle GNSS-IMU Receiver (Global Navigation Satellite System Receiver with Inertial Measurement Unit) measurements, and passenger Present Serviceability Ratings (PSR). Data were collected over 1700 km across Austria, Hungary, and Romania under real traffic conditions. Inter-application agreement was evaluated using correlation analysis, Intraclass Correlation Coefficient (ICC), and Bland-Altman methods. While the two smartphone applications show strong correlation, systematic bias limits their interchangeability. A significant inverse relationship between IRI and PSR confirms perceptual sensitivity to roughness, and positive correlations between IRI and vertical acceleration validate the physical linkage between pavement irregularities and vehicle dynamics. The results demonstrate the challenges of integrating consumer-grade sensing and perception-based evaluation for road roughness monitoring as an alternative to high-cost specialized survey equipment.

Sequence-based deep learning offers a scalable alternative to structure-based scoring for protein-ligand binding affinity prediction. However, progress is hard to interpret when architectural priors are evaluated on canonical PDBbind-style splits that leak similarity classes across folds. We present HonestAffinity, a compact 1D-input predictor to isolate two priors under a leak-aware protocol: frozen ESM-2 (650M) protein embeddings and a learned binary pocket-position marker. We evaluate a multi-scale convolutional/Transformer template in three variants: HonestAffinity-Pocket, HonestAffinity-NoPocket, and HonestAffinity-Pocket-NoESM. All three train on 11,513 LP-PDBBind complexes in ~3 GPU-hours. We benchmark against five baselines on the LP-PDBBind 3-tier no-leak hold-out, CASF-2016, and a CASF-2016 non-train subset. Our central finding is a split-conditioned reversal rather than a uniformly best prior: HonestAffinity-Pocket achieves the best mean Pearson R on validation and CASF-2016 splits, whereas HonestAffinity-Pocket-NoESM achieves the best mean Pearson R on every strict LP no-leak tier (test_cl1-cl3). Both the pocket marker and ESM-2 input improve performance on familiar splits but reduce Pearson R on strict no-leak tiers. We argue models should report paired canonical and leak-proof ablations, and that deployment-regime-matched variants better describe these reversals than a single default. Code and scripts are linked in the footnote; checkpoints will be released upon acceptance.

Large language models (LLMs) are increasingly used for health-related decision support. Yet most evaluations treat diagnosis as a single-shot task with complete information provided upfront, often as a multiple-choice selection. This diverges from clinical practice, where diagnosis is interactive and open-ended, involving sequential hypothesis refinement through targeted questioning. We address this gap. We build MeDxBench, a large-scale benchmark of 4,421 clinical cases across 20 specialties. We further propose MeDxAgent, a multi-agent consultation system for interactive diagnosis, and systematically study its prompt-, flow- and agent-level design choices. MeDxAgent achieves a 10.3% accuracy gain over the baseline on MeDxBench, closing 52.3% of the gap to a full-information oracle. We find that specific design choices: collecting demographics first, passing summarized dialogue for diagnosis, and feeding candidate diagnoses for targeted questioning, improve accuracy, mirroring how physicians reason, though their effect emerges fully only in combination. Code and dataset will be released upon publication.

We introduce and investigate non-wellfounded and cyclic linear nested sequent calculi, and, as a case study, develop such systems for linear temporal logic (LTL). The paper addresses two central problems, which we call 'cycle recognition' and 'unraveling.' Cycle recognition concerns identifying cycles in non-wellfounded proofs in order to extract corresponding cyclic proofs, while unraveling studies the converse transformation, from cyclic proofs to non-wellfounded ones. Although these processes are well understood for Gentzen sequents, they have received little attention for more expressive sequent formalisms and become more challenging in the linear nested sequent setting. To address cycle recognition, we show the completeness of non-wellfounded proofs relative to a particular normal form exhibiting a property we call 'saturation recurrence,' which enables the systematic extraction of cyclic proofs. To address unraveling, we introduce a specialized procedure that shifts rule applications forward along linear nested sequents, allowing non-wellfounded proofs to be reconstructed from cyclic ones. Overall, our work provides new proof-theoretic techniques for cycle recognition and unraveling in expressive multisequent formalisms.

The integration of Generative AI (GenAI) and Agentic AI into software development is reconfiguring software engineering from an activity centered on human authorship of code into a discipline centered on directing, verifying, and governing autonomous and semi-autonomous systems. Drawing on a curated, multi-source evidence base of recent peer-reviewed and archival studies -- including large-scale empirical observations of autonomous coding agents contributing hundreds of thousands of pull requests to open-source repositories -- this paper synthesizes how the locus of engineering work is shifting from individual coding productivity toward human--AI collaboration, agent orchestration, verification and validation, governance, and socio-technical systems thinking. We adopt a structured interpretive synthesis to characterize three coexisting paradigms: Traditional, Generative AI-Enabled, and Agentic AI-Enabled software engineering. We map which traditional activities are being automated, which are being augmented, and which are newly emerging, and we trace plausible role trajectories over the next decade. The paper's principal contribution is an original, theory-driven competency framework that organizes the capabilities required of future engineers into five interacting categories -- % technical, cognitive, socio-technical, governance, and organizational -- % operationalized through a competency matrix and a transformation framework linking paradigm shifts to capability demands. We derive nine empirically testable propositions and articulate implications for theory, industry workforce transformation, university curricula, and organizational leadership. We argue that, as code becomes abundant, the durable value of the software engineer increasingly resides in intent specification, critical judgment, and accountable oversight rather than in the sheer volume of code produced.

Smart contract vulnerabilities in Decentralized Finance (DeFi) protocols resulted in over 1.49 billion USD in confirmed losses in 2024 alone, across 192 incidents [1]. As LLM-based vulnerability detection emerges as a promising approach to address these threats, the quality of evaluation datasets has become a critical bottleneck. Existing datasets suffer from three fundamental problems: they are built on outdated Solidity versions (e.g., v0.4) that no longer reflect modern DeFi contracts [5][6][7]; they rely on automated or LLM-generated annotations that introduce hallucination-driven label noise [9][10]; and they apply coarse single-layer labeling that fails to capture the semantic complexity of real-world business logic vulnerabilities [6][7][11][12]. We present Bastet, an expert-labeled DeFi smart contract vulnerability dataset that addresses all three problems through real-world audit findings (2021-2024), human expert annotation with discussion-based consensus, and a two-layer taxonomy of 46 Tags and 77 Subtags. Bastet comprises 4,402 findings collected from 394 Code4rena competitive audit reports spanning April 2021 to November 2024, of which 849 findings are fully annotated by white-hat security researchers from the DeFiHackLabs community. All annotations are produced through a two-annotator consensus workflow, ensuring label accuracy grounded in real-world vulnerability root causes.

While AI and machine learning for cyber attack prediction have advanced, a critical gap persists between theoretical research and practical operational deployment. Building on Ankalaki et al. (2025), this paper provides a comprehensive analysis of 150+ benchmark datasets and 200+ studies to identify and prioritize five implementation hurdles: (1) temporal dataset obsolescence, (2) narrow attack scope, (3) real-time model interpretability, (4) inadequate adversarial robustness, and (5) privacy/ethical concerns. We introduce a novel gap-prioritization framework that evaluates these limitations based on detection impact, implementation cost, and remediation time. Our analysis identifies dataset obsolescence and adversarial robustness as the highest-priority gaps, while highlighting model interpretability as the most cost-effective path for resource-constrained environments. To bridge the research-practice divide, we provide a practical implementation roadmap and a dataset quality assessment framework that classifies 45 benchmarks into production-ready, research-only, and unusable categories. This work translates academic findings into actionable decision-support tools for robust, production-oriented AI-driven cyber defense.

Much of software development revolves around understanding the relationship between software changes and their effects. If we could learn and predict those relationships, such predictions could benefit several areas of software engineering. While recent advances in artificial intelligence have shown great promise in software engineering tasks, predicting the semantics of code without executing it remains a big challenge. In this paper, we present Neural Change Prediction, a novel and fundamental technique to learn and predict associations between software changes and their dynamic effects on program behavior. Specifically, for a given program and test inputs, we automatically apply numerous mutations to the code and observe how these changes alter the program's output. From these (changes to software, changes in behavior)-pairs, we create models that: (1) for a desired change in behavior, predict where and how the code should be changed (feature localization, software evolution, and software repair); and (2) for a given code change, predict how this code change affects the output (effect prediction). We have conducted a detailed case study on CSS configuration files and an evaluation on Python programs to demonstrate the generality and wide applicability of Neural Change Prediction. While Neural Change Prediction requires numerous mutations (and thus numerous executions of the program under test), Neural Change Prediction is fully automatic and does not require any prior knowledge of the code or its semantics, making it applicable to any software artifact that can be executed and whose output can be observed.

We identify two constructions from different mathematical traditions. In linear logic and realisability, logical types are generated rather than fixed in advance: one begins with a universe of realisers equipped with execution, uses orthogonality to test their interactions, and takes types to be the biorthogonally closed subsets. In enriched Isbell duality, a quantitative relation induces an adjunction whose fixed points form a category, its nucleus. These constructions proceed by different means; we show that, in the present setting, they produce the same objects. The shared datum is minimal: an associative product, called execution, and a real-valued measurement, with no compatibility assumed between them. The failure of the measurement to be additive is at once the relation defining orthogonality and the quantitative relation whose Isbell nucleus we form, and the types cut out by orthogonality are exactly the fixed points of the associated adjunction. The identification pays off in both directions. The most natural product of types fails to be associative; repairing this failure forces a different notion of type, sensitive to both sides of a composite, on which the induced product is associative and, when execution has units, carries two residuals. What emerges is a noncommutative Lambek calculus, derived directly from execution and orthogonality rather than imposed. In the reverse direction, each such type, read on the categorical side, generates a quantitative relation of its own, and with it a derived adjunction and a further generation of types; these derived types are again types of the original situation, computed by the residuals of the Lambek calculus. We also prove a coherence theorem for the threefold arrangements of this construction and, in the finite-dimensional case, give explicit formulas for the product.

Maintenance organizations in manufacturing try to avoid downtime and unnecessary purchasing by reusing existing assets, but the main obstacle is not a lack of parts but a lack of actionable visibility across sites and partners. Inventories are distributed, described with inconsistent naming conventions, and contain duplicates and partially specified references, so the right part often exists somewhere but remains effectively undiscoverable. The paper proposes PhRAG, a hybrid Retrieval-Augmented Generation for Pooling this fragmented landscape into a Virtual Stock Pool (VSPool) that can be structured and searched as a single resource. Unstructured, heterogeneous spare part descriptions are structured via Named Entity Recognition (NER) into a shared virtual pool dataset and indexed to support robust retrieval even when users express needs in natural language rather than exact technical specifications. The proposed modular pipeline leverages the multitasking nature of generative language models to cover two dimensions that make industrial parts pooling challenging: (i) unstructured technical specifications from diverse data sources (e.g. new partners, catalogs, marketplace listings) are handled through an offline extraction and (ii) request variability at runtime (references, partial references, specifications, price/condition constraints) is handled through a hybrid RAG-based search engine capable of retrieving relevant components and justifying results. The framework demonstrates the potential of generative approaches compared with traditional NER approaches in the presence of data scarcity for technical specifications extraction and overcomes the opacity of standard information retrieval systems by generating justifications for retrieved components. The project's open-source code can be found at https://github.com/roccofelici/vspool.

Shuffling or repartitioning data streams is an essential operation of state-of-the-art stream processing frameworks to support stateful workloads in a large-scale, distributed setting. In today's cloud deployments, however, shuffling can become a major cost driver due to substantial network traffic across multiple availability zones (AZs) as well as an operational burden when operating a high-throughput, strongly consistent messaging backbone at scale. We present BlobShuffle, a novel approach to cost-effective shuffling for stream processing systems that leverages cloud object storage as an intermediate exchange layer. Instead of sending all shuffled records directly, BlobShuffle groups records into batches, stores these batches in cloud object storage, and forwards only compact notifications. Downstream operators use these notifications to retrieve the relevant batches and extract the corresponding records. BlobShuffle balances cost efficiency and latency through configurable batching and a distributed caching mechanism. BlobShuffle is implemented as an add-on for Kafka Streams that requires only minimal code changes to existing applications, leaves Kafka and the underlying infrastructure unmodified, and preserves Kafka Streams' consistency and correctness guarantees. In a large-scale experimental evaluation on a Kubernetes-based AWS deployment, we show that BlobShuffle can reduce shuffling costs by more than 40x compared to native Kafka Streams shuffling while keeping the 95th percentile shuffle latency below 2 seconds. Moreover, it scales to processing more than 2 GiB/s without encountering a scalability limit in our experiments, indicating that BlobShuffle can economically support shuffle-intensive workloads at large scale.

This study examined emerging and established topics in drone research, focusing on citation impact and knowledge flows across China, the United States, the EU, Ukraine, and Russia between 2020 and 2025 using OpenAlex bibliographic data. The findings revealed that drone-related science is characterised by growing geopolitical asymmetries in scientific production, citation concentration, and international knowledge exchange. In particular, China increasingly dominated scientific production, fractional authorship contribution, and domestic citation circulation. In contrast, the United States and EU countries maintained comparatively more internationally distributed citation structures. However, China-affiliated publications became increasingly integrated into global citation networks, particularly through growing citation exchange with the United States and European countries. Notably, the interpretation of authorship and citation patterns was complicated by the high proportion of publications with unidentified affiliations, which reached 50% in 2025 within weak-signal topics. These findings underscore the importance of developing comprehensive national Research Organisation Registries (RORs). Although China demonstrated a citation advantage, this was partly driven by high internal domestic citation concentration rather than exclusively by global integration. Moreover, China still imported proportionally more knowledge from the EU-14 and the United States than it exported, with this asymmetry increasing over time. EU-14 countries maintained the strongest citation impact in weak-signal topics, suggesting a more prominent role in shaping emerging research directions. At the same time, China-affiliated publications cited the United States more frequently than the EU-14 in both strong- and weak-signal topics, with this pattern being particularly pronounced in weak-signal areas.

Image-based Retrieval-Augmented Generation (IRAG) conditions a frozen generator on reference images retrieved from an external database, supporting both text-to-image (T2I) and question answering (Q&A) tasks. Because these databases are opaque and web-scraped, copyright holders need ways to audit whether specific images appear in them. While prior work employs membership inference attacks (MIAs) to audit uni-modal, text-based RAG, they fail to transfer to IRAG due to two key challenges. First, cross-modal retrieval: text-RAG MIAs force retrieval of the target passage by injecting its content into the query, which is unavailable in IRAG since images cannot be embedded into text queries; even accurate image captions fail to bridge the modality gap. Second, discriminative signal extraction: text-RAG MIAs extract membership signals by prompting the generator to answer multiple questions over the target passage, whereas T2I generators in IRAG produce images rather than follow Q&A commands. To fill this gap, we introduce the first MIA tailored to IRAG, ImageAuditor, which decomposes each attack query into a retrieval segment and an extraction segment, enabling dedicated optimization for each challenge. For retrieval, we propose Reward-Guided Policy Optimization (RGPO), which updates a stochastic policy from reward-ranked candidates to navigate the cross-modal embedding landscape and admits finite-sample optimality guarantees to balance exploration and exploitation. For extraction, we analyze the distribution of the MIA score to guide the co-design of the prompting strategy and scoring rule, and derive task-specific instantiations for T2I and Q&A tasks. We aggregate signals across queries via K-means clustering for reliable membership decisions. Across various IRAG systems, ImageAuditor exceeds 80% AUROC with only four queries per audited image and remains robust across diverse settings.

Modern data center applications increasingly require microsecond-scale service time with strict tail latency requirements, which can hardly be realized with existing in-network task schedulers due to their inherent limitations. Specifically, software-based schedulers struggle to balance throughput and latency, while switch-based designs either lack global coordination, rely on packet recirculation heavily, or only offer limited support for large tasks. In light of these restrictions of the state-of-the-arts (SOTAs), we, in this work, propose Rain, an RDMA-assisted in-network scheduler built atop programmable switches that maintains centralized queues while bounding worker-local queues. Rain introduces a bidirectional on-switch queuing mechanism to buffer and match tasks and worker-issued tokens directly in the switch, avoiding worker-side polling and approximating the optimal behavior of join-bounded-shortest-queue without global aggregation. A switch-driven RDMA engine pre-writes arbitrarily large tasks via one-sided WRITE multicasts, keeping only compact metadata on the switch. Slice-aware scheduling further localizes decisions to more homogeneous queues, reducing dispersion-induced head-of-line blocking. Moreover, our study reveals that real-world systems can diverge from theoretical predictions: shallower worker queues do not always improve tail latency. Leveraging this insight, Rain incorporates an adaptive scheduling strategy to optimize worker queue depths and worker-to-slice mappings at runtime. Evaluations with the real-world application RocksDB show that Rain achieves 1.75x higher throughput than the best-performing SOTA while satisfying the same tail latency requirement.

We present a framework to generalize the standard b-ary Gray code to get the k-bonacci ones obtained in [5] as well as many others by using theoretical tools that allow to make calculations on lists. We introduce the notion of Z-Gray product, from which we deduce sequences of lists of finite words avoiding a predefinite list Z of factors and which satisfy a power-associativity property as well a generalizations of the classical flipping digit property.

Large-scale digital transformation programs must simultaneously sustain existing operations and navigate deep unknowns emerging from IT-business-operations interactions -a challenge conventional project governance frameworks inadequately address. Based on a longitudinal case study of a transformation program, we investigate the ''Sirius Days,'' a monthly senior management retreat identified as a critical success factor. We show that this framework constitutes a pilot-organization: an organizational 'dispositif' (or apparatus) that deconstructs established knowledge or assumptions, formulates rigorous conjectures, and tests them in real conditions. It generated five resilience levers -systemic characterization of unknowns, early anomaly discernment, expansion of performance norms, social capital creation through a community of inquiry, and expansion of organizational agility across scales -revealing a model of an organizational 'dispositif' that operationalizes navigating unknowns across cognitive, social, and normative dimensions.

Program construction in imperative languages remains largely based on writing textual code that specifies sequences of instructions operating on program data. This approach requires developers to anticipate the effects of instructions on evolving data states, which increases cognitive load and the likelihood of errors during early and incremental development. This paper presents AlgoTouch, an execution-based system for incremental construction of imperative programs through direct manipulation of program data. Rather than assembling syntactic structures, programs are constructed by executing concrete data transformations that are recorded and incorporated into an internal intermediate representation. AlgoTouch relies on an explicit notional machine that exposes data storage, computation, and control flow, enabling continuous alignment between observed execution and program structure. A central contribution of the system lies in its deterministic synthesis of control structures from execution behavior. Conditional statements are derived from observed comparisons, while iterative behaviors are encapsulated in loop macros that support non-linear and incremental construction. This design enables partial and incomplete programs to be executed, refined, and completed while preserving semantic consistency. AlgoTouch automatically generates correct and readable programs in several mainstream imperative languages, including Python, C, C++, and Java. The system is evaluated through engineering-level validation on a representative set of algorithmic benchmarks, demonstrating correctness, expressiveness, robustness, and language independence. By integrating execution, construction, and code generation within a unified architecture, this work introduces an alternative model for interactive program construction and contributes a new class of execution-centered development systems.

Cloud permission governance implicitly treats permission equivalence as a static relation. We show that for non-human identities (NHIs), equivalence has two irreducible components: structural equivalence, capturing identical permission profiles at a snapshot via graph fibration, and temporal equivalence, capturing recurring permission states via strongly connected components (SCCs) in a fiber transition graph. We call the equivalence classes under temporal equivalence privilege circuits. We formalize a three-layer framework: (1) a spatial quotient of the permission graph via fibration, (2) a lineage partition organizing stable transition compartments, (3) windowed SCC analysis as a temporal quotient within lineages. Empirical evaluation on a large Azure tenant supports the framework. Backtesting demonstrates that early observation of ratchet-type privilege circuits predicts long-term structural stability.

Microscopic simulation enables reproducible evaluation in intelligent transportation systems, yet most open SUMO scenarios and toolchains remain road-traffic centric, leaving rail underrepresented despite its importance for public transport and its sensitivity to network-wide disruptions. We present the German Rail Open-Source Scenario (GROSS), an open pipeline that combines OpenStreetMap railway infrastructure with GTFS schedules to generate nation-scale rail scenarios for SUMO (Simulation of Urban MObility). Existing conversions often rely on geometry-only stop-to-track matching and inconsistent platform/track assignments, which can create routing anomalies and unstable simulations dominated by teleportation artefacts. GROSS addresses this with topology-aware stop mapping via a hierarchical station model, followed by station-level routing with validation and targeted repair. Across multiple German regions, GROSS reduces average teleportations per vehicle by a factor of 1.7--76.8$\times$, shortens delays compared to the vanilla SUMO pipeline, and it enables end-to-end generation of a Germany-wide scenario with 35\,925 trips for comparisons with operator-reported delay statistics. While the remaining long delays highlight limitations in available timetable metadata and rail dispatch modeling, GROSS lowers the barrier to building scalable, fully open rail simulations and to studying delay propagation at country scale.

AI agents built on large language models can assist not only legitimate tasks but also relational manipulation. AI agents can be used to help a user maintain a deceptive identity, intensify emotional dependency, isolate a target, or prepare for later extraction. We conceptualise this risk as agentic relationship harm: workflow-level assistance that can exploit recipient vulnerability, persuasive influence, and relational power asymmetry. Existing safety evaluations and generic guardrails often treat harmfulness as a property of isolated outputs, missing role-sensitive interaction patterns. To study this, we introduce a 110-prompt benchmark with balanced attacker- and victim-side cases, a relationship-specific labelling framework, and a lightweight post-generation policy gate for local agent deployments. In our evaluation, the relationship-specific gate outperforms generic safety prompting under automated judging, with no judge-identified harmful-compliance cases on the main benchmark or multi-turn stress test while preserving victim-side protective intervention. These results suggest that relationship harm is a distinct sociotechnical risk surface and that role-sensitive evaluation plus lightweight policy gating offers a practical path beyond generic refusal prompting.

Digitizing complex documents with handwritten content, irregular tables, and heterogeneous layouts remains challenging, as traditional Optical Character Recognition (OCR) systems fail to capture writing nuances, author-specific conventions, and document structure, and recent LLM-based approaches lack mechanisms for precise, scalable correction. We present an interactive document digitization system that integrates layout-aware parsing, OCR, and LLM-based reconstruction with user-driven refinement. The system is informed by a formative study that identifies key challenges and interaction needs in real-world digitization workflows. It supports both direct edits and natural-language instructions, and introduces a layout-aware propagation mechanism that generalizes user corrections across structurally similar regions. This enables not only efficient error correction but also document re-shaping into structured, analyzable representations. We evaluate the system through a within-subjects user study (n=12) on real-world documents. Results show improved correction efficiency and reduced repetitive effort, demonstrating more effective and controllable document digitization procedure.

Virtual commissioning models (VCMs) of discrete manufacturing systems are used to validate automation behavior before physical deployment, but creating and maintaining them remains labor-intensive. Relevant engineering information is distributed across programmable logic controller (PLC) engineering projects, such as Siemens TIA Portal, and kinematic simulation models, such as Siemens NX Mechatronics Concept Designer (NX MCD), where it is stored in incompatible, tool-specific data structures. In practice, IEC 61131-3-based PLC programs and variables are engineered separately from rigid-body and kinematic simulation objects such as parts, joints, sensors, and actuators. As a result, understanding system behavior, generating simulation components, and mapping PLC variables to corresponding simulation objects require cross-domain expertise and remain largely manual. This paper presents a knowledge-graph-grounded multi-agent framework for semi-automated VCM development. A deterministic setup process extracts structured data from Siemens TIA Portal and Siemens NX MCD and transforms both sources into graph-based representations within a shared graph database. The framework uses a hierarchical multi-agent architecture to support three task classes in early-stage VCM development: system understanding, simulation component generation, and cross-domain signal mapping. It provides grounded natural-language access to engineering knowledge, template-guided generation of executable NX Open journal scripts, and ranked mapping suggestions between PLC variables and NX MCD simulation objects. Evaluation on a laboratory-scale discrete manufacturing system shows that the approach reduces manual cross-domain interpretation effort and makes recurring VCM engineering tasks more actionable.