Controller synthesis in power electronics-based systems depends predominantly on the mathematical model of the system, which is a limitation when the actual system is complex and the mathematical model cannot capture all its dynamics. Model-free control addresses this limitation by using an ad-hoc simple model which is compensated by high-rate evaluation of dynamics in terms of their derivatives. However, application of the model-free control strategy to power electronics-based multi-time scale dynamical systems is challenging because of the derivative action needed to implement such control. Grid-connected power converters are examples of such systems, yet experimental validation has not been adequately addressed in the literature. This letter presents the validation of such control including the hardware implementation level. An intelligent proportional-integral (iPI) controller is synthesized and validated on a 16 kW experimental test bench. This proves the benefits of the approach in control of grid-connected power converters, among which their participation in the secondary voltage control.

The coordinated attack problem models the challenge of coordinating a joint action within a bounded time by communicating over unreliable links. It was the first distributed computing problem proven unsolvable. Its analysis also revealed the importance of common knowledge, a central concept in epistemic logic. However, the randomized version of coordinated attack, which is solvable, has not, to the best of our knowledge, been studied through the lens of probabilistic epistemic logic, where processes generate randomness by flipping coins. We present an epistemic logic framework for studying randomized algorithms that execute for a bounded number of rounds. The framework applies to coordinated attack, approximate agreement, and consensus, and supports dynamic graph models: synchronous systems in which reliable processes execute a bounded number of rounds while an adversary determines which messages are lost. Our approach combines techniques from the logical characterization of dynamic networks and task solvability with ideas from probabilistic dynamic epistemic logic. It is inspired by the operational model of Varghese and Lynch for randomized coordinated attack. More broadly, the resulting notion of probabilistic epistemic task solvability provides a foundation for the epistemic study of randomized distributed computation. Using this framework, we analyze the Varghese-Lynch algorithm from a knowledge-theoretic perspective, providing a formal treatment of the algorithm and its lower bound. As a byproduct, we strengthen the lower bound and show it is tight. The proof relies on indistinguishability arguments, demonstrating that reasoning about knowledge remains essential in the probabilistic setting. We also formalize the notion of information level introduced by Varghese and Lynch, showing that it corresponds to a specific epistemic formula.

Biological plausibility is a key concept in neuromorphic computing and spiking neural networks, yet it remains inconsistently defined and difficult to quantify. In this work, we present an open-source framework for the automated assessment of biological plausibility in spiking neuron models. Our method builds on the idea of evaluating a model's ability to replicate canonical neuronal firing patterns observed in biological systems, following the classification proposed by Izhikevich. By encoding these patterns into objective functions and optimizing model parameters accordingly, our framework enables empirical assessment without requiring prior analytical modeling. Treating neuron models as black boxes, it provides a practical and flexible means of characterizing their dynamic capabilities. We demonstrate the effectiveness of the framework on several established models and a previously unexplored custom model. Implemented in Python and compatible with PyTorch and the Norse library, the framework is tailored for machine learning contexts. It is intended as a starting point for systematic research into the relationship between biological plausibility and network-level performance metrics such as accuracy, energy efficiency, robustness, and adaptability.

Coarse-grained thermal simulation tends to underestimate localized thermal issues, potentially missing critical hotspots. Accurate analysis, therefore, demands fine-grained information, which dramatically increases grid resolution and thus computational workload. Fortunately, the coefficient matrices are often sparse with regular sparsity patterns, offering optimization opportunities. However, existing general-purpose matrix solvers on GPUs rarely exploit these domain-specific properties, thereby encountering bottlenecks in data storage, memory access, parallelism, computational efficiency, and hardware utilization. Therefore, we propose CUTh-Solver, a co-designed GPU-accelerated Preconditioned Conjugate Gradient (PCG)-based sparse solver framework for Symmetric Positive Definite (SPD) systems arising from high-resolution steady-state and transient 3D IC thermal simulation. For data storage, CUTh-Solver condenses the Diagonal (DIA) storage format to remove redundancy. To optimize the memory access, CUTh-Solver employs diagonal-wise SpMV to achieve coalesced memory access. We further observe a critical conflict between parallelism and preconditioning quality and thus adopt a high-parallelism preconditioning strategy. To improve computational efficiency and hardware utilization, we employ an adaptive fine-grained mixed-precision strategy that leverages diverse floating-point units to avoid resource contention, enhancing throughput without compromising numerical stability. Experimental results show that CUTh-Solver achieves up to 25.8x speedup over GPU-accelerated COMSOL Multiphysics 6.4 and over 3x speedup over NVIDIA's native general-purpose libraries (AmgX, cuSPARSE, cuDSS). Ablation studies validate the individual contribution of each optimization. The code is available at: https://github.com/Chenghan-Wang/CUTh-Solver

Intrusion detection systems (IDS) trained on wired-network benchmarks degrade sharply in real-world unmanned aerial vehicle (UAV) swarms, where mobility, fluctuating link quality, and decentralized routing reshape traffic distributions. Existing UAV-specific datasets also do not systematically vary these conditions, leaving no way to train or test an IDS against the very shift that defeats it. We present UAV-CAS, a large-scale labeled flow dataset for UAV-network intrusion detection, generated by a Containernet digital twin that is systematically calibrated against AERPAW testbed measurements. We have a four-layer calibration pipeline spanning altitude-dependent path loss, mission-specific mobility, the link-level performance chain, and end-to-end trace fidelity. UAV-CAS comprises 99,492 flows drawn from 1,024 configurations that span five attack families (DoS, DDoS, blackhole, wormhole, replay) and nine collaborative attack compositions. A diversity analysis shows that high-rate attacks separate from benign traffic up to an order of magnitude more strongly than in any prior benchmark, while stealth attacks deliberately blend with benign traffic. Across ten baseline IDS, binary attack detection saturates above $0.98$, confirming the dataset is learnable, whereas full attack-class identification remains hard -- per-class $F_1$ ranges from near zero to $0.82$ and falls into the single digits for stealth attacks. We release the dataset, simulator, and calibration data to support reproducible UAV intrusion-detection research.

This paper presents the unified Markov binary (UMB) format, an efficient, extensible, and well-supported explicit-state file format for representing a wide range of probabilistic systems. UMB addresses the problem that, while probabilistic model checking tools often support common high-level modelling languages, there is no effective mechanism for exchanging low-level model representations. In practice, textual, tool-specific formats are used, hampering interoperability and resulting in large overheads in writing and reading model files. UMB provides a clean, unified, and efficient solution, based on a general underlying mathematical model, and encoded using a small set of bit-level primitive data structures. The format has already been adopted by prominent tools and comes with a convenient Python library for reading, manipulating, creating, and validating models, plus infrastructure for cross-tool installation and continuous validation. We report on both the efficiency of the file format and the new practical use cases that it facilitates.

This work introduces ARES, a platform and open pilot dataset for auditing adaptive social engineering risks in LLM-mediated social decision-making through controlled social games. ARES supports human--human, human--AI, and AI--AI settings, combining configurable game templates, role-conditioned LLM agents, psychology-informed participant profiling, structured interaction trees, and synchronised behavioural and biometric acquisition, filtering, and deep-learning-based feature extraction. The pilot dataset was collected from 15 participants interacting with a role-conditioned GPT-5.4 agent in two concatenated games: an adapted Prisoner's Dilemma and an Ultimatum Game. It comprises 340 GB of raw and processed multimodal data across six streams: interaction logs, video, screen recordings, gaze logs, smartwatch signals, and game/questionnaire metadata. These data include interaction paths, written justifications, psychological profiles, subjective feedback, perceived counterpart identity, game outcomes, and derived behavioural, facial, and gaze features. Alongside the dataset, we provide descriptive analyses to characterise the pilot release. Rigorous risk evaluation is essential for the deployment of secure AI systems, as it enables the identification and mitigation of vulnerabilities, ensures the protection of sensitive data, and supports compliance with evolving regulatory and ethical standards in society.

Access to evidence-based psychotherapy remains limited worldwide, with long waitlists even in high-income regions. Recent advances in large language models (LLMs) offer potential for scalable mental health support when designed with clinical oversight and safety mechanisms. We present Mind Companion, an LLM-based embodied conversational agent integrating multi-layered psychological analysis with process-based therapy principles. The system performs real-time analysis of client statements across fact extraction, psychological flexibility process detection, emotion recognition, and safety monitoring. Analysis results are stored for supervising clinicians to inform therapeutic planning. Response generation incorporates retrieval-augmented generation from evidence-based therapeutic literature and context-aware prompting. Responses are delivered through an embodied avatar with synchronized speech synthesis and animation. We evaluated three LLM configurations (GPT-4.1-mini, GPT-5.2, Claude Sonnet 4.5) against therapist responses from real therapy sessions using automated LLM-judge assessment and expert evaluation with 11 professional psychotherapists. GPT-5.2 achieved higher ratings than human therapist responses across understanding, interpersonal effectiveness, collaboration, and therapeutic alignment in both evaluations, demonstrating the feasibility of LLM-based conversational agents as tools to complement clinical care.

Modern large language model (LLM) serving clusters distribute inference requests across multiple worker processes on different GPUs, but failures are prevalent at scale. When a worker fails, the cluster simultaneously loses the failed worker's GPU-resident key-value (KV) caches and serving capacity, leaving surviving workers to absorb the redirected traffic while re-running interrupted requests from scratch. Existing fault-tolerant systems either restart interrupted requests from scratch or restore KV caches from checkpoints stored on a fixed neighboring worker, but both approaches route recovery work without considering current cluster load and leave the recovering worker idle during model reload. We present LUMEN, a fault-tolerant LLM serving system that treats recovery as a load-aware coordination problem across three decision points: checkpoint placement before failures, interrupted-request distribution at failure time, and serving capacity restoration during model reload. We evaluate LUMEN using both prototype experiments and large-scale simulations and demonstrate significant improvements in serving and recovery times.

Consumer mixed reality (MR) headsets seamlessly blend virtual content into physical environments with sufficient fidelity that users may be unable to distinguish virtual objects from physical ones. We identify this virtual-physical discrimination vulnerability as an exploitable security primitive. Through speculative design workshops with 12 experts from cybersecurity and MR/HCI, we develop a taxonomy of virtual-physical confusion attacks and implement four proof-of-concept attacks on Apple Vision Pro, evaluating them with 26 participants in realistic MR tasks. All four attacks altered user behavior, with success rates ranging from 85% to 100%, producing misdirected interactions, misjudged object identities, biased purchasing decisions, and altered navigation paths. Notably, the most successful attacks were also the hardest to detect according to participants' subjective ratings. Even participants who recognized virtual content still complied behaviorally, and no participant attributed anomalous events to adversarial causes. We propose platform-level provenance, interaction gating, and user education as countermeasures.

Network traffic classification enables website fingerprinting, intrusion detection, and Quality of Service management. However, developing methods that capture stable and generalizable traffic patterns under realistic deployment conditions remains challenging. We introduce FlowCLIP, a contrastive pretraining framework for domain name prediction from encrypted traffic using only side-channel features: packet inter-arrival times, packet sizes, and packet directions. FlowCLIP uses raw domain names as textual supervision by aligning traffic flow representations with domain name representations through a CLIP-style contrastive objective. The pretrained traffic encoder is then frozen and evaluated through linear probing on canonicalized domain name labels. We evaluate FlowCLIP on a large-scale QUIC traffic dataset using a time-based protocol, where models are trained on Week 1 traffic and evaluated on traffic from Weeks 2-4. FlowCLIP outperforms competitive machine learning baselines across later evaluation weeks, suggesting that raw domain names provide a textual supervision signal for learning transferable encrypted traffic representations.

A-mode ultrasound (US) is a promising sensing modality for Virtual Reality (VR) interaction, as it enables the mapping of muscular activity into control commands while retaining the benefits of wearable sensing. However, existing approaches still face limitations in terms of wearability and interaction complexity, often relying on external hardware such as cameras. In this work, we propose a fully wearable multimodal interface for real-time VR-interaction, based on concurrent US and inertial (accelerometry) sensing from the forearm and upper arm. The system is built on the WULPUS platform and integrates an end-to-end software framework for real-time acquisition, visualization, and communication with a Unity-based VR environment. A multimodal learning pipeline is introduced for concurrent hand pose and forearm position estimation in 2D space. The interface is evaluated through offline and online experiments with five subjects, during the execution of three functional tasks: cylinder grasping (gross motor) and relocation, marble pinching (fine motor) and relocation, and liquid pouring. For offline experiments, we collect 5 acquisition sessions across multiple days, achieving an average inter-session accuracy across subjects of 80$\pm$6\% for hand pose estimation and 77$\pm$7\% for forearm position estimation. Online validation with minimal fine-tuning (5 min) demonstrates success rates of 92.0$\pm$16.0\%, 88.0$\pm$9.8\%, and 96.0$\pm$8.0\% for the three tasks, respectively. With a power consumption of only 19.9~mW, our system enables more than 2.5 days of continuous use on a small 350 mAh LiPo battery without the need for recharge, enabling truly wearable, multimodal, and functionally meaningful VR interaction.

We study the problem of $(1+\varepsilon)$-approximating the number of four-cycles in graphs given as arbitrary order edge streams. We propose two new algorithms based on sampling induced subgraphs. Our first contribution is a two-pass algorithm that uses $\widetilde{O}(κm / \sqrt{T})$ space, where $m$ is the number of edges, $T$ is the number of four-cycles, and $κ$ is the graph's degeneracy. This algorithm improves upon existing theoretical bounds and is provably optimal for constant-degeneracy graphs, matching the known $Ω(m/\sqrt{T})$ lower bound up to lower-order factors. Our second contribution is a one-pass algorithm that remains accurate when four-cycles are not highly concentrated around individual nodes, edges, or wedges; this structural property is common in sparse social and collaboration networks. We evaluate both algorithms on a variety of real-world graph streams. The two-pass algorithm consistently outperforms state-of-the-art methods, using substantially less space to achieve a desired accuracy. The one-pass algorithm is competitive when four-cycles are evenly distributed, matching our theoretical analysis. Unlike several recent works, our algorithms perform well even on non-bipartite graphs such as social networks.

Evolutionary algorithms have been used for a wide range of multi-objective combinatorial optimization problems. Despite practical success, theoretical results on the runtime of evolutionary algorithms for multi-objective combinatorial problems are rather limited. One classical problem that has been investigated is the multi-objective minimum spanning tree problem for which runtime bounds have been obtained to compute all extremal corner points of the Pareto front. With this paper, we provide some more detailed insights into the structure of the Pareto front when the edge weights take on a small number of distinct values. Based on these insights, we derive new runtime results for evolutionary multi-objective algorithms and complement our theoretical results with experimental investigations.

Generative Retrieval (GR) is an emerging Information Retrieval (IR) paradigm that is motivated by increasingly capable language models. In GR, a model directly generates identifiers for relevant documents. While these systems offer unique advantages, they also introduce distinct failure mechanisms. We explore these failure modes in three contributions: (1) We present a taxonomy of GR failure modes based on GR literature. (2) We empirically investigate failure in a subset of GR: ngram-based methods, more specifically, SEAL and MINDER. Our analysis reveals common issues, such as ambiguous docids, low identifier diversity, and the disproportionate impact of specific identifiers. (3) We introduce a new web-based tool that helps the IR community analyze generated ngrams and their respective contribution to the final ranking, providing an intuitive interface to identify where such GR methods go wrong.

In this paper we tackle the problem of adding varying delay to packets for link emulation. Naive approaches either add more delay than desired or cause packet reordering, both of which are undesirable. We develop adaptive delay correlation, which adds positively correlated delays to packets efficiently. It takes a mean delay and standard deviation (jitter) as input, as well as a half-life period to control the delay dynamics. We investigate the accuracy and dynamics of the resulting packet delays with and without bandwidth limitation. As a result we give a recommendation for the configuration of the half-life period. We implement adaptive delay correlation in a DPDK-based packet delayer and spacer (DPDS), investigate its performance on hardware, and compare it with the widely used link emulator NetEm and the recently developed DPDK-based emulator MoonEm. DPDS outperforms both of them with a zero-loss throughput of 95 Gbit/s for constant delay and, with spacing enabled, 85 Gbit/s for varying delay with 3 ms jitter. Further, DPDS supports packet reordering with zero-loss throughputs of 73 Gbit/s and 58 Gbit/s for constant and varying delay, respectively, as well as policing and two packet loss models.

Recommender systems alleviate information overload, yet repeated feedback between recommendations and user interactions can reinforce existing preferences and narrow users' exposure, forming information cocoons. While this phenomenon has been widely studied in traditional sequential recommendation, its impact on generative recommendation remains unclear. By replacing atomic item IDs with Semantic ID (SID) sequences, generative recommenders introduce a different recommendation mechanism whose role in information cocoon formation is not yet understood. To investigate whether generative recommenders deepen information cocoons, we propose \textsc{RecLoop}, a closed-loop simulation framework with LLM-driven user agents. We compare two generative recommenders and two traditional sequential baselines on two Amazon datasets across multiple feedback cycles. In addition to standard exposure-level metrics, we introduce \emph{Code-Space Structural Cocoon}, a model-level metric that measures concentration in the generated SID space. Experimental results show that generative recommenders are generally less prone to exposure-level cocoon formation than traditional baselines, preserving broader exposure diversity and slowing cross-user homogenization. However, feedback loops can still induce concentration within the generated SID space. We further find that cocoon severity depends strongly on tokenization strategy and model scale: collaborative-signal tokenization produces stronger cocoon effects than semantic tokenization, whereas larger models maintain greater code-space diversity and better retain access to niche content. These findings suggest that information cocoons in generative recommendation are shaped not only by recommendation behavior, but also by item tokenization and model capacity. Our code is available at https://github.com/Dregen-Yor/RecLoop.

The total biharmonic distance, which is the sum of the biharmonic distance between every pair of nodes in a network, is a key metric for evaluating network connectivity and robustness. In this paper, we study the problem of minimizing the total biharmonic distance by adding $k$ nonexistent edges for a given graph $G$ and budget $k$. The problem is computationally challenging. We show that the objective function of the problem is monotone but not supermodular. To solve this problem, we propose simple greedy algorithms with cubic time complexity. To mitigate the high time complexity of these greedy algorithms, we apply several techniques, including the projection method, the Laplacian solver, and convex hull approximation. These techniques reduce the time complexity of our proposed algorithms from cubic to nearly linear while providing error guarantees. Finally, extensive experiments on real datasets demonstrate both the efficiency and effectiveness of our proposed algorithms.

We show that existing tools for termination analysis are extremely well suited for LTL model checking of infinite state systems. To this end, we present a framework MoAT which uses the well-known automata-based approach and reduces the LTL model checking problem to fair termination. To prove or disprove fair termination, it then calls the termination tools KoAT and LoAT in the backend. Our experiments show that in this way, MoAT is on par with existing state-of-the-art tools for LTL model checking of infinite state systems.

5G is a fundamental technology for the digitalization of smart manufacturing. Smart manufacturing relies on the use of digital models to optimize industrial processes before implementation on the manufacturing plants. These models should account for the impact of 5G communications to adequately dimension and optimize 5G-based industrial processes. This paper presents the first integration of industrial digital models with a 5G digital model, implemented as an Asset Administration Shell (AAS) of a 5G system. The two models are interconnected using an OPC UA-based interface. We evaluate the impact of the integrated model using a use case where Automated Guided Vehicles (AGVs) transport material from a warehouse to production lines. The AGVs periodically exchange their positions over 5G to avoid potential collisions. If the communications fail, the AGVs stop for safety reasons until a reliable 5G connection can be guaranteed. We demonstrate that, by integrating 5G and industrial digital models, it is possible to account for, and quantify, the impact of 5G communications on the operation and productivity of industrial processes. This result highlights the importance and necessity of integrating 5G into industrial digital models for their joint design and optimization.

Teleoperated driving (ToD) enables the remote driving or control of vehicles. For this purpose, vehicles must transmit video feeds to the ToD control center so that the remote operator is fully aware of the driving conditions and can safely control the vehicle. 5G (and beyond) networks are fundamental for the deployment of ToD as they can provide the low latency, reliable and broadband connection necessary to connect the vehicle and ToD control center. However, it is unclear whether common 5G network architectures and configurations are well-suited to support the simultaneous teleoperation of multiple vehicles with demanding uplink bandwidth, as current networks are mainly configured to support mobile broadband services. This paper demonstrates that MEC or edge-based 5G networks are better suited to support and scale the ToD service than centralized networks, and quantifies the bandwidth required to simultaneously teleoperate multiple vehicles under various 5G network architectures and configurations, including different duplexing modes and TDD frame structures. Finally, the study shows that the configuration of the control channels can help mitigate the impact that the processing time of the video feeds has on the capacity to support and scale the ToD service.

Future wireless networks must enhance their capacity to sustain deterministic service levels and support emerging time-sensitive services in key verticals. The ability to guarantee bounded latencies heavily depends on efficient radio resource management. Configured Grant (CG) scheduling can reduce latency by pre-allocating resources, but its effectiveness and efficiency decrease under variable traffic patterns. This study presents a novel predictive CG scheduling scheme that pre-allocates resources based on traffic predictions while accounting for prediction inaccuracies. By considering these inaccuracies, the scheme significantly improves the ability to meet bounded latency requirements, which are essential for supporting deterministic service levels. Our evaluations show that the proposed scheme significantly enhances the capacity to support deterministic service levels while improving resource utilization, even in scenarios with variable and mixed traffic flows with diverse requirements.

Due to educational inequality, high-quality lesson plans often mismatch the needs of disparate educational contexts. Teachers typically modify existing lesson plans to fit new contexts, but current tools instead focus on generating content from scratch, creating additional workload. Moreover, a critical gap remains in supporting teachers to quickly adapt to new learning profiles. To bridge these gaps, we present AdaPT, a system leverages LLMs to support transformation of existing lesson plans for cross-regional and differentiated instruction. AdaPT features an interactive interface that allows teachers to input student profiles, offers structured lesson representation, provides explanations for lesson-plan transformations, automatically adapts lesson content for new contexts, and supports iterative, teacher-in-the-loop refinement. We evaluated AdaPT through a user study with 9 teachers and an expert evaluation with 3 specialists. Results show that AdaPT supports workflows of teachers and offers a promising pathway toward promoting educational equity.

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease, often affecting speech due to bulbar dysfunction. In this study, we predict speech impairment in people with ALS (pwALS) using two clinical speech-related scores. We evaluate cross-sectional (across speakers) and personalised (within-speaker) modelling paradigms and analyse the utility of common speech tasks to contribute to the standardisation of speech data collection for pwALS. Experiments on a German-speaking cohort of 66 pwALS show that repetition tasks (/da/-/da/, /da/-/ba/) achieved the best cross-sectional performance (Concordance Correlation Coefficient (CCC) = 0.62) for predicting the Quality of Life in the Dysarthric Speaker questionnaire, while the within-speaker setting reached a CCC of 0.86. This study represents an initial step towards speech impairment prediction in German-speaking pwALS and highlights the potential of automated speech analysis as a supportive tool for speech impairment assessment.

State-of-the-art traffic flow forecasting models, including Graph Convolutional Networks and graph-less MLPs, require centralized GPU training across all sensors, making them impractical for resource-constrained intelligent transportation deployments. We show that much of this complexity is unnecessary. A parametric analysis of the recent graph-less model GLMST reveals that reducing its internal embedding dimension from 64 to 4 degrades MAPE by less than one percentage point, suggesting that the model's effective capacity far exceeds what the task requires. Motivated by this finding, we replace the neural architecture entirely with per-sensor Ridge regression using horizon-aligned periodic features, combined with Recursive Least Squares (RLS) for online adaptation. With only 444 parameters per sensor (80x fewer than GLMST) and test-time online adaptation, our method achieves the best MAPE on three of four PEMS benchmarks, and remains within one percentage point on the fourth. Because each sensor's model is self-contained and involves only elementary linear algebra, the entire pipeline (training, inference, and online adaptation) runs on edge hardware without a GPU. An ESP32 microcontroller (160 MHz, 520 KB SRAM) completes cold-start training in 7.4s and each predict-and-update in under 2ms with zero heap allocation; a single Raspberry Pi 5 core completes cold-start training in 0.21s and each predict-and-update in 0.26ms.

As software systems grow in scale and complexity, debugging and repair remain costly and time-consuming. Large language models (LLMs) have advanced automated program repair (APR), but existing LLM-based APR approaches still largely rely on static or retrieved context, error messages, and coarse-grained validation outcomes. As a result, they underutilize dynamic information for failure understanding and repair, including failure-execution dynamics and patch-validation dynamics. Effectively leveraging such information, however, is challenging: failure-execution traces are large and noisy, raw static-dynamic context is not self-explanatory, and patch-validation dynamics are often reduced to coarse feedback. To address these challenges, we propose \textsc{PracRepair}, a fully automated LLM-based APR framework inspired by human-like debugging practices. \textsc{PracRepair} constructs an on-demand static-dynamic context from buggy programs and failure executions, performs question-driven failure diagnosis to formulate explicit repair hypotheses, and iteratively refines candidate patches using validation diagnostics and trace-level behavioral changes. Experimental results on Defects4J V1.2 and V2.0 show that \textsc{PracRepair} consistently outperforms state-of-the-art baselines. Specifically, under GPT-3.5, \textsc{PracRepair} correctly fixes 139/136 bugs on Defects4J V1.2/V2.0, while under GPT-4o it further improves to 162/171. Moreover, \textsc{PracRepair} generalizes effectively to RWB (Real-World Bugs), achieving the best performance across multiple foundation models.

The Financial Times 50 (FT50) journal list shapes hiring, promotion, accreditation, and research evaluation across business schools worldwide. Yet journals on the list are typically treated as if they represent a homogeneous tier of excellence. We test this assumption by comparing 53 FT50 and recently removed journals across three distinct impact channels: scholarly influence (field-weighted citations and visibility), policy uptake, and technological reach through patent citations. Using a panel of more than 60,000 publications from 2005 to 2019, we find striking heterogeneity hidden beneath the binary FT50 label. Elite economics journals dominate policy influence, information systems and marketing journals lead technological impact, while many highly cited management journals exhibit limited reach beyond academia. Citation, policy, and patent indicators behave as largely independent dimensions of impact, with a citation-only ranking correlating only moderately with a multidimensional ranking. Nearly half of all journals change quartile once policy and patent indicators are incorporated, demonstrating that assessments based solely on scholarly citations overlook important dimensions of research influence. While the FT50 remains widely used as a binary classification of journal quality, our results reveal a substantial within-list impact spectrum and show that journal rankings are highly sensitive to how impact is defined and measured.

Credibility of a simulation model is an important topic. Several approaches try to quantify the credibility of simulation. However, models are mostly assembled within a simulation architecture. Can the credibility of a simulation architecture be assessed based on the credibility of the models that comprise it? This paper aims to address this issue by providing an overview of the current state of the art in the field of assembly credibility. It will compare sensitivity analysis techniques, qualitative analysis by experts, explainability in AI, and networks. Finally, an assessment of the proposed approaches, based on criteria such as rigor, generalization, and resource requirements, will reveal the strengths and weaknesses of each approach.

We study the problem of data repair, a key task in data cleaning that corrects erroneous entries in raw datasets to improve overall data quality. Although recent data-driven methods, especially those based on large language models (LLMs), achieve remarkable performance, we observe that: (i) they directly repair data in the raw and low-quality context, which may compromise learning signals, and (ii) they directly use uncertain model outputs as repairs, potentially introducing unreliable corrections and compromising repair quality. Motivated by the efficiency of small language models (SLMs) and the capabilities of LLMs, and aiming to address the above limitations, we propose LasRepair, a framework that collaborates Large and small language models for data repair. LasRepair employs an LLM as an instructor, which selects a global repair context to guide the SLM. The SLM acts as a corrector, using the selected context to repair erroneous data more efficiently. Moreover, to further improve context quality, we extend LasRepair to LasRepair+, which formulates data repair as an Expectation-Maximisation (EM) procedure that alternates between an E-step for updating the corrector parameters and an M-step for refining the repair context. Furthermore, to mitigate model uncertainty, we propose LasRepair++, which uses column-calibrated model confidence to down-weight unreliable repaired rows when updating the corrector, thereby enhancing repair quality. Theoretical analysis and empirical evaluation demonstrate the superiority of our methods. We theoretically prove the effectiveness of the EM-style procedure and the confidence-based weighting. Experiments on real-world datasets show that LasRepair++~ achieves an average F1-score improvement of 18.1% over the strongest baseline.

We study exact exponential-time algorithms for Edge Deletion to Cactus. Given a connected graph $G$, the task is to delete a minimum number of edges so that the remaining spanning graph is a connected cactus. Akhtar and Philip (IWOCA 2026) gave an $O^*(3^n)$-time algorithm for the unweighted problem, where $n$ is the number of vertices in the input graph and the $O^*(\cdot)$ notation hides polynomial factors. We improve this bound to $O^*(2^n)$ time and space. More generally, if the deletion costs take at most $q$ distinct nonnegative real values, then the weighted problem can be solved in $O^*(2^n n^{O(q)})$ time and space. Thus every fixed number of distinct costs, and in particular the unweighted case, admits a faster exact algorithm. For nonnegative integer costs of total weight $W$, we obtain an $O^*(2^n(W+1))$ pseudo-polynomial algorithm, while arbitrary nonnegative real costs admit an $O^*(3^n)$ exact algorithm.