Understanding the dynamics of real world complex networks is crucial for assessing their predictability, resilience, and improving ecosystem management, especially in the context of climate change. The relationship between stability and complexity in ecological networks is still debated in the literature. In this modeling study, we investigate whether a complex marine trophic network, characterized by multiple trophic interactions and environmental constraints, exhibits predominantly stable, periodic or chaotic dynamics. We incorporate the microbial loop into a trophic network model, which includes one to three primary producers, one or two consumers, and up to three trophic levels of predators. The microbial loop is a key process in which bacteria recycle detritus from higher trophic levels into nutrients available for the growth of primary producers, ensuring mass conservation within the system. We perform numerical simulations to investigate the dynamic behavior of the network, exploring several configurations by turning off predator prey links between species and varying the high dimensional parameter space. Our results show that (i) longer trophic chains and (ii) a higher number of consumers increase system chaoticity, whereas (iii) omnivorous interactions promote stability. Notably, many of the configurations exhibit high percentages of chaotic behavior. Feedback loop analysis suggests that the balance between negative and positive interactions plays a key role in the convergence of the system toward a steady state. This study shows that interactions and feedback, rather than complexity, are key drivers of stability, pointing to the absence of a clear stability complexity relationship and instead highlighting a stability interaction dependence. Chaotic dynamics may also play an important role, with potential implications for predictability and ecosystem management.

High-dimensional multicomponent systems, including immune and epigenetic repertoires, must selectively retain rare, beneficial components while purging a massive influx of suboptimal variants. We demonstrate that critical tuning of component control parameters through competition naturally implements proofreading in these systems. Competition for shared inputs pins the system to the marginal stability threshold of the most persistent species. This grants dominant species extended lifetimes, concentrating the population into dominant components while forcing less-stable variants into rapid drift-driven turnover. When aggregate drive exceeds a characteristic scale, this pinning fails, producing a non-selective state where component lifetimes scale as a universal power law with aggregate drive. Applying this framework to biological memory, we identify the hallmarks of this effect in plasma cell accumulation dynamics and propose that de-pinning transitions may represent failure points across biological domains, including cancer, immunodeficiencies, and the aberrant activation of harmful genomic elements during ageing.

Trap crops reduce damage to a cash (main) crop by attracting pests away from it. Yet this protection is weakened when pests disperse back into the cash crop. In this paper, we focus on the importance of preventing this backflow, showing that effective trap cropping depends jointly on how strongly pests are attracted to trap plants and how rarely they leave them. Together with the proportion of the field devoted to trap plants, these processes determine both the efficacy and feasibility of trap cropping at commercial scales. We formalise this relationship using a simple yield-maximisation framework, in which growers weigh pest suppression benefits against the land sacrificed to trap plants. The model shows that when dispersal from trap plants equals that from the cash crop, optimal trap coverage can exceed 20 to 30 percent of the landscape, levels rarely acceptable to growers. However, reducing pest dispersal off trap plants to just one-quarter of cash crop dispersal lowers the optimal required trap area to approximately 5 percent, transforming trap cropping from impractical to feasible. Understanding these relationships can guide trap-cropping design, from plant choice to targeted interventions that reduce pest movement, to minimise damage, maximise yield, and make trap cropping a reliable component of sustainable pest management.

Computational simulation provides a powerful toolkit for in silico experimentation. However, while the field has developed best practices for the design and implementation of such models, there remains ambiguity in discussions about how to understand and/or interpret their results due to their inherent ability to overwhelm traditional frequentist statistics by simply increasing the number of trials simulated. This fails the discipline in two ways: first, it leaves the community unsure of what constitutes a best practice for uniform understanding, and second, it potentially overburdens computational studies that burn clock cycles solely to ensure "enough runs to satisfy peers" without any theoretical underpinning for a definition of "enough". We propose a simple and straightforward standard for when to stop simulating additional trials, the Ω test, designed to be analogous to the function of traditional frequentist P-tests. Community adoption of a reasonable and uniform standard will permit more efficient computational experimentation and clearly communication/interpretation of the findings discovered in this way.

Current blockchain research and analytics tend to prioritize observable on-chain transactions, obscuring the processes through which cryptocurrencies are created, publicised, retained, and disposed of. In response, this paper considers distributed ledger technologies from records management principles in ISO 15489-1:2016. Setting off by specifying the parallels -- that is transactions as "records", crypto-asset units as "information assets", and blockchains as "aggregations" -- we introduce a seven-stage lifecycle for blockchain data. We apply the framework to Bitcoin, a fungible token, and a non-fungible token. On this basis, we argue that blockchain systems are not merely transactional infrastructures but record management systems with distinctive characteristics. We discuss how the on-chain/off-chain boundary and privacy-enhancing technologies can complicate lifecycle visibility, with particular relevance for crypto-crime research and investigation. As a meta-level framework, the lifecycle perspective enables positioning existing research, decomposing legal, regulatory, technological, and operational challenges by stage, and informing lifecycle-aware approaches to blockchain governance, analytics, and regulation.

Small Office/Home Office (SOHO) devices are widely popular, yet often attacked due to security vulnerabilities in their firmware, affecting thousands of devices. These security vulnerabilities often stem from outdated Linux kernel versions included in SOHO device firmware. Naturally, prior work audited the extent and impact of this issue by simple Linux version extraction and version number based vulnerability mapping. However, it is unclear how many of these anticipated vulnerabilities actually exist in the heavily customized SOHO kernels and if there are any barriers towards updating Linux kernels in SOHO firmwares. To address this gap, we uncover actual kernel-related vulnerabilities found in 306 SOHO devices using a high-precision template-based CVE detection mechanism on GPL source releases of more than 900 firmwares from these devices. Next, as a first, we traced the supply chain of these vulnerable SOHO devices at scale and identify kernel lock-in as a significant security issue -- SOHO vendors are effectively locked to specific (often older) kernel versions due to the system-on-chip (SoC) SDKs they use. This kernel lock-in produces a vulnerability debt that is inherited along the supply chain from SoC vendor to firmware creators (ODM/OEM) to router/IP-camera vendor and ultimately borne by end users. All five SoC vendors in our dataset had used SDKs with Linux kernels that had reached EoL more than a year before their usage in a SOHO device. Finally, we explore the mitigation-potential of individual, regulatory and community governance by analyzing social media posts, regulations and community efforts. Our results show that regulation compliance is insufficient and only SoC vendors who engage with communities for kernel upgradation offered a viable path towards mitigation. The data and code for this work is available at https://doi.org/10.5281/zenodo.20433799

As data center energy demand approaches grid-level constraints, optimizing conventional server infrastructure is essential for sustainable growth. The long-standing assumption that "cooler is better", i.e., lower CPU temperatures reduce power, does not fully hold for modern low-voltage CPUs, where inverse temperature dependence (ITD) drives higher supply voltages at lower temperatures. This creates a non-monotonic performance-per-watt curve where efficiency peaks at an intermediate thermal point. In this paper, for the first time, we empirically characterize ITD on production Intel Xeon CPUs and demonstrate that efficiency-optimal temperatures are CPU part-specific, and frequently higher than typical data center operating conditions. Measurements from commercial cloud data center platforms (Amazon, Equinix) reveal that approximately half of modern high-power CPUs operate about 10°C below their efficiency-optimal thermal point. By implementing ITD-aware thermal grouping of CPUs and inlet temperature adjustments, data center operators can optimize facility-level cooling and overall sustainability. Our case study shows that this approach can reduce total data center energy by 4-13% without sacrificing performance or reliability.

Efficiency in multiwinner voting is most naturally captured by Pareto-optimality (PO), yet this notion is computationally and structurally difficult to handle. We therefore study fractional Pareto-optimality (fPO), under which a committee may not be dominated even by a fractional committee, i.e., any convex combination of committees. fPO turns out to be a natural refinement of PO as it retains exactly those Pareto-optimal committees whose efficiency is robust under uniform cloning of candidates. Furthermore, fPO committees are guaranteed to exist and have strong structural properties. We present a characterization of fPO in terms of weighted utilitarian welfare maximization, which yields a polynomial-time algorithm for verifying fPO and shows that the set of fPO committees satisfies committee monotonicity and is connected under single-candidate swaps. Analyzing welfarist rules through the lens of fPO, we further uncover an incompatibility between fPO and equality-oriented objectives. Most notably, we show that proportional approval voting (PAV) violates fPO in the approval setting. We close by pinpointing preference domains, including various one-dimensional ones, on which PO and fPO collapse into one notion.

As custom hardware accelerators become increasingly central to machine learning workloads, efficient data transfer is critical for maximizing accelerator performance on linear algebra kernels. AXI4MLIR, an extension of the Multi-Level Intermediate Representation (MLIR) compiler framework for automated generation of host-accelerator driver code, incurs significant runtime overhead due to non-zero-copy CPU-accelerator data movement. During transfers from the host to the accelerator, data is copied from heap-allocated memory buffers into contiguous Direct Memory Access (DMA)-mapped buffers. This work identifies this copy as a redundant staging operation and eliminates it through zero-copy data movement. The optimization extends accel, an MLIR dialect introduced by AXI4MLIR, and implements lowering support that allocates buffers directly within DMA-mapped memory, thereby omitting the staging copy. We evaluate the proposed scheme using a configurable matrix-matrix multiplication accelerator and show that the zero-copy optimization reduces main memory data movement by up to 2x, increasing overall accelerator utilization.

Generative AI applications such as personal AI agents, image generators, and chat assistants offer advanced capabilities to improve user experience. Behind the scenes, Large Language Models (LLMs) that power these services require a massive amount of computation and are usually deployed in the cloud, available as APIs, meaning that a user's request has to be sent to a Cloud Inference Service (CIS) for processing. However, the strong capabilities of LLM also mean that user's requests now contain much more personal sensitive or enterprise confidential information, demanding equally strong protection in CIS. While early industry efforts such as Apple Private Cloud Compute (PCC) and Google Private AI Compute have emerged to show the potential of secure CIS, they are not adoptable for deployment by others due to their reliance on proprietary hardware and closed ecosystem. In addition, they all suffer from their own design glitches that can undermine the ambitious goal of bringing in true privacy protection to end users. In this paper, we present our analysis of the fundamental requirements of building a secure yet open CIS. We then present OpenPCC, a Confidential CIS framework that does not rely on proprietary hardware but instead uses commercially available TEEs. We implement an open-source prototype and characterize it end-to-end on a Llama-3 8B vLLM workload, separating OpenPCC's own cost from the underlying TEE hardware. Our analysis and evaluation demonstrated the feasibility and security of the system.

While fuzzing effectively catches crashes, its shallow oracles often miss semantic drifts and optimization-related errors in data-intensive scalable computing (DISC) frameworks. Property-based testing (PBT) addresses this limitation by checking general semantic invariants across diverse workloads and inputs, rather than relying on specific expected outputs. However, systematically operationalizing PBT for DISC systems remains difficult because it requires both reusable property definitions and effective instantiation into valid workloads and data. We present DiscPBT, a property-based testing engine for Apache Spark. DiscPBT introduces eight reusable meta-properties for DISC semantic testing, spanning equivalence rewriting, data decomposition, computation decomposition, and operator-local semantic relations. To operationalize these meta-properties, DiscPBT provides reusable generators for synthesizing valid workload skeletons and input data, together with an instantiation framework that realizes each meta-property in schema-compatible contexts through compatible operators, expressions, and UDFs. Our evaluation on PySpark shows that DiscPBT achieves 1.2$\times$ higher branch coverage and 1153$\times$ greater plan diversity than CometFuzz. Across 66 concrete properties, DiscPBT reveals cross-version semantic drift as well as subtle corner-case pitfalls involving NaN and empty inputs, that are not captured by crash-based fuzzing alone. These results demonstrate the value of systematic PBT for uncovering semantic issues in DISC frameworks.

Service placement in the cloud-edge continuum requires assigning application components to heterogeneous resources under multiple constraints, including latency, locality, and policy requirements. Existing approaches rely on optimisation models or heuristics that require explicit modelling, while neural methods lack transparency and formal guarantees. This work proposes a neuro-symbolic alternative based on a Prolog skill, a reusable interface for schema-constrained fact generation and querying, for constraint-aware placement. The skill enables a language model to structure placement intent into symbolic facts, rules, and queries, while delegating validation and reasoning to Prolog. This design bridges high-level intent and formal constraint evaluation, enabling inspectable and policy-aware placement decisions in cloud-edge environments.

Industrial Augmented Reality (AR) has progressed from laboratory demonstrations to operational pilots across design, training, assembly, maintenance and quality assurance, yet broad, dependable deployment in manufacturing remains the exception. We synthesise existing evidence into a full-stack deployment framework structured along six distinct but coupled decision axes: (i) value and benefits, (ii) technical and integration constraints, (iii) human factors and safety, (iv) organisational and economic considerations, (v) data, security and privacy, and (vi) governance, ethics and long-term risk. Within each axis we separate (a)benefits, (b)failure modes and (c)design considerations, and cross-link them through a deployment checklist that engineering managers and vendors can apply when scoping projects. The contribution is conceptual and practice-oriented: a synthesis grounded in the literature and public deployment reports. We mark where the evidence base is mature (e.g. assembly task time, training efficacy), emerging (e.g. cognitive workload trade-offs, cobot safety zones), or speculative (e.g. metaverse-scale governance), and identify open questions whose resolution conditions the transition from demos to dependable infrastructure.

We present a longitudinal study of approximately 1.52 million malicious domains observed on VirusTotal (VT) between January and May 2026. Domains were selected on the basis of detection by at least five independent VT scanning engines and a first-seen date within the study window. We group the dataset into compromised domains and attacker created domains, which account for approximately 89.3% of the dataset. Combining WHOIS registration records and passive DNS (PDNS) data with the VT dataset, we characterise attacker behaviour across eight dimensions: temporal distribution, compromisedvs.attack classification, domain age at first detection, registrar and TLD preferences, DNS query volume as a damage proxy, hosting infrastructure concentration (IP and ASN level), bulk registration patterns, and brand impersonation. Key findings include: the majority of attacker created domains are short lived registrations used within weeks of creation; a small number of registrars and TLDs account for most abuse; Cloudflare infrastructure is heavily exploited for domain fronting; bulk registration events involving thousands of domains from a single registrar on a single day are widespread; and several global brands, particularly WhatsApp and Google, are heavily impersonated. We share the annotated dataset in the GitHub repo https://github.com/mufimash/malicious_domains for further research.

Network control theory can be used to model intrinsic and extrinsic strategies to steer neural dynamics. Standard approaches are node-centric, structural, and focused on achieving desired instantaneous states. Here, we develop an edge-centric approach which incorporates both structure and function to achieve extended patterns of neural dynamics characterized by desired synchronization states. Our method, Quantifying Underutilized Influential Edges for Targeted Synchronization (QUIET), is an edge-centric framework that integrates structural controllability of individual white matter connections and mutual information between pairwise functional timeseries to identify energy-efficient synchronization pathways. QUIET identifies quiet highways, edges that are structurally influential but functionally underutilized, to optimize regional synchronization. We validated QUIET across 75 synthetic configurations, where QUIET-ranked edge sets significantly outperformed random selection in 93% of cases (p<0.01). The framework, tested on Human Connectome Project participants, revealed that the control energy required for synchronization of the salience network correlates with fluid intelligence. QUIET, applied to healthy adults undergoing dexmedetomidine-induced unresponsiveness, showed that the frontoparietal and default-mode networks exhibited the largest control energy required for synchronization in both awake and sedated states. QUIET is released as a stand-alone software to be used to study theoretically-defined synchronization pathways, which in turn could inform testable hypotheses in perturbative studies.

We present a simple $\mathcal{O}\left( n^2 \frac{ \log N }{ \log \log N } + N \right)$ enumeration algorithm for solving a problem from mathematical and computational music analysis where, given a strictly increasing integer sequence, $S$, with $n$ entries and maximum value $N$, the task is to enumerate all $m$ $\textit{inclusion-maximal arithmetic progressions (IMAPs)}$ in this sequence. An IMAP is a subsequence, $S' \subseteq S$ with $k>2$ integers, in which (i) the difference between any two consecutive integers is the same number, $d$ (i.e., $S'$ is an $\textit{arithmetic progression}$), (ii) $S'$ cannot be further extended to the left or to the right with any additional integers from $S$ while still remaining an arithmetic progression (i.e., $S'$ is a $\textit{maximal}$ arithmetic progression), and (iii) there is no other maximal arithmetic progression, $S'' \subseteq S$, which $\textit{properly}$ contains $S'$ (i.e., $S'$ is an $\textit{inclusion-maximal}$ arithmetic progression). We further provide proofs for the expected number of IMAPs in random integer sequences, $S$, and a bound on their order of growth. Finally, we provide empirical experiments comparing both (a) the practical running time performance of the proposed algorithm against that of a previously known algorithm which has higher time complexity $\mathcal{O}(N^{2+o(1)}n)$, and (b) the actual enumerated number of IMAPs to that of their mathematically expected number. Notably, the proposed algorithm demonstrates a significant improvement in running time over the previously known algorithm, and in immediate practical applications, will allow for more efficient analysis of large and rhythmically complex musical pieces.

Deep Neural Networks increasingly employ low-precision quantization to reduce computational requirements. While FPGAs are well suited for workloads with heterogeneous precisions, their dedicated digital signal processing (DSP) slices only feature fixed-width datapaths that are significantly underutilized by low-bitwidth arithmetic. While previous approaches have already introduced the packing of multiple values onto the same wide DSP datapath, they either only support specific fixed bitwidths or are wasteful regarding the use of additional support logic external to the DSP. This paper proposes an efficient method to dynamically pack multiple (un-)signed inputs with arbitrary bitwidths into a wide multiplier path by leveraging the DSP's internal pre-adder. Building on this, we present two distinct architectures, one optimized for matrix-vector multiplications and the other for convolutions. Our implementations are integrated into AMD's FINN framework. With these optimizations, we reduce the LUT utilization by 21% and increase the FPS/DSP by 36% for the UltraNet model compared to the FINN reference.

Adopting a single measure can improve the usability, modularity and accountability of software: a commitment to explicit meaning. This entails constructing and agreeing upon a representation of the behavior of the software, as observed in the domain of application. The phenomena comprising this behavior become a vocabulary that grounds all discourse about the software, among all stakeholders, and for all artifacts and activities. These phenomena are individuals; actions they participate in; and facts that result from actions. They can be organized, by partitioning the set of actions, into concepts, offering larger units of meaning. Examples of exploiting meaning are given in three areas: designing for usability (by aligning user and designer on a single shared meaning); generating modular code with LLMs (by mapping units of meaning to units of code, achieving not only modularity but also legibility); and making agents accountable (by having them adhere to a code of conduct that defines their intended behavior).

Effective demand response in smart microgrids requires prosumers to cooperate voluntarily under strategic self-interest, a coordination problem structurally equivalent to a repeated Prisoner's Dilemma on a social network. This paper presents a multi-agent simulation in which a Large Language Model (LLM) Influence Compiler issues structured demand-response directives to a population of heterogeneous prosumer agents, each governed by a hybrid decision architecture combining game-theoretic base probability (derived from payoff history, neighbour imitation, and exploitation memory) with LLM narrative evaluation of incoming coordination signals. The hybrid architecture resolves a key methodological challenge: LLMs aligned via Reinforcement Learning from Human Feedback (RLHF) exhibit strong cooperation bias when used as direct decision-makers, producing flat dynamics regardless of grid conditions. By separating strategic reasoning from grounded narrative evaluation, the model generates realistic prosumer behaviour across six personality archetypes, with baseline cooperation near 50% and clear differentiation under influence. Compiled structured directives achieve 33.3% demand-curtailment cooperation versus 27.0% for unstructured messaging and 28.0% for a no-intervention baseline ($Δ_\mathrm{comp} = +0.063$), with the advantage preserved across both grounded and idealized agent substrates ($Δ= +0.083$) and across all resistance levels ($R = 0.1$ to $0.7$). Hub-targeted dissemination via high-centrality network nodes outperforms peripheral or random targeting, confirming that grid topology provides mechanistic amplification independent of message content. These results suggest that structured LLM compilation, grounded agent reasoning, and network-aware targeting are complementary design principles for scalable, interpretable demand-response coordination in smart-city energy systems.

Designing stabilizing control policies for nonlinear systems while optimizing complex objectives remains a formidable challenge. Neural networks (NNs), despite their expressive power, can be highly sensitive to small input perturbations and can easily destabilize the closed-loop system. Existing approaches often impose explicit constraints on the controller's parameters to ensure stability, but this typically leads to additional computational overhead. To address this issue, we leverage recently proposed structured state-space models (SSMs) to parametrize discrete-time control policies for nonlinear systems. Our key contribution is a new free parametrization of linear time-invariant (LTI) systems with a prescribed L2 gain. We use this result to construct the L2-Recurrent Unit (L2RU), an SSM layer that enforces the desired L2 bound by design. The resulting architecture can be used to guarantee closed-loop stability via the small-gain theorem or the so-called performance-boosting framework, independently of the controller's optimization parameters, thereby enabling fully unconstrained optimization of general nonlinear objectives. Furthermore, the structure induced by the proposed parametrization enables the efficient processing of long input sequences, as it is highly parallelizable through algorithms such as parallel scan. We demonstrate the effectiveness of this approach on a formation-control task for mobile robots, where the L2RU-based controller ensures collision and obstacle avoidance while maintaining stability and performance.

The Internet Quality Barometer (IQB) framework was designed to transform raw Internet measurement data into actionable insights about Internet quality. Specifically, the framework maps raw speed test measurements to network requirements (e.g., throughput, latency), maps these requirements to representative Internet use cases (such as video streaming or web browsing), and finally aggregates performance across use cases into a single IQB score. The IQB score is a composite index ranging from 0 to 1, intended to capture overall Internet quality in a way that is both interpretable and comparable across locations. We implemented the IQB framework in practice by developing an open-source IQB library and a prototype web application. These tools enabled us to compute IQB scores at scale, including global estimates aggregated at the level of countries, regions, and cities. In this report we conduct a preliminary sensitivity analysis of the IQB framework, investigating how different parameter choices affect the resulting IQB scores, identifying which parameters the framework is most sensitive to, and highlighting cases that may lead to outliers or potentially misleading results.

Distributed Software-Defined Networking (SDN) clusters replicate flow state asynchronously between a master node and its backups, leaving a window during which two backup nodes can each commit a contradictory rule, the master can serialize both into the data plane, and the kernel datapath can latch onto an action that no node believes authoritative. Existing SDN fuzzers miss this fault: they confine their oracle to the control plane, target a single controller, or do not steer concurrency to provoke replication races. We present GapFuzz, a stateful concurrency fuzzer for distributed SDN clusters. GapFuzz injects pairs of contradictory Northbound requests on two non-master nodes with controlled inter-injection delay $Δt$, and reconstructs the global cross-plane state by querying every replica and the kernel-datapath action through ovs-appctl ofproto/trace. A two-phase timing search detects whether a divergence exists, then doubles and bisects on $Δt$ to bound the injection-time window; a lifetime probe labels each verdict transient or persistent and assigns it to one of four cross-plane state classes derived from the ONOS 2.7 source. On a three-node ONOS 2.7 cluster, GapFuzz produces a divergent verdict in 81.7% of attempts ($N=50$, Wilson 95% CI $[77.3, 85.4]$%); every divergence sits between the cluster's authoritative state and the kernel datapath. Phase 2 separates a 5 ms race window for one template from a doubling-cap regime ($Δt_{\max}=10.24$ s) for six others, and 99.4% of divergences persist past 30 s. Replacing the kernel-datapath probe with the OpenFlow user-space probe used by prior fuzzers drops detection by 26.6 percentage points overall and by 46.5 points after excluding canonicalization-forced verdicts.

Advanced AI systems for code analysis, binary analysis, fuzzing orchestration, and penetration-test planningmay significantly increase the rate at which latent vulnerabilities are discovered. While improved discovery can benefit defenders, it can also overload remediation pipelines and accelerate adversarial weaponization. This paper develops a queueing and network-theoretic model of AI-accelerated vulnerability discovery in interconnected systems. We represent an enterprise as a weighted dependency graph with replenishing vulnerability pools, finite remediation capacity, triage degradation, exploit window compression, and dynamic compromise propagation. We derive stability conditions for vulnerability backlogs, formulate a dynamic coupling between unresolved backlog and cascade risk, and evaluate mitigation strategies through simulation. Results indicate that when actionable discovery arrivals exceed remediation throughput, backlogs grow rapidly and systemic risk increases nonlinearly. In hub-dominated topologies, segmentation can reduce propagated compromise more effectively than remediation speed alone, while the strongest defense combines remediation automation with reduced network coupling.

In this paper, we present a companion app for an autonomous vehicle aimed at user groups who would normally require an accompanying person to drive them. Two aspects of a companion app are presented in this paper: First, the possibility for a trusted person to track the ride of the person in need of support and second, to put the settings of the vehicle for persons in need of support in the hands of a trusted person. In addition, this article describes the requirements and addressed values and discusses the safety-relevant aspects of such a companion app. We also discuss and identify the values that influence passengers and trusted persons using the companion app. Overall, a companion app can provide new perspectives and opportunities for people in need of support, allowing them to take advantage of the features offered by autonomous vehicles. It enables trusted individuals to configure the vehicle according to the passengers needs. Also such an app can be a mechanism to involve trusted persons in the options given by the vehicle and give them the possibility to adapt the vehicle to the needs of the person in need of support.

This paper develops a cyclic labelled proof-theoretic framework for process logic -- an extension of dynamic logic in which formulas specify properties of execution traces rather than only final states. The main difficulty is that first-order process logic must reason about concrete computations while preserving temporal information along regular-program traces. Existing compositional calculi cover important fragments, but do not provide a complete treatment of full first-order process logic over regular programs. We address this difficulty by enriching process-logic formulas with labels that explicitly record trace and update information during derivations. Based on this construction, we define cyclic labelled proof systems for propositional and first-order process logic, respectively denoted by G3PPL and G3FOPL. We prove the soundness by using the cyclic conditions to obtain an infinite descent in a well-founded multiset ordering, and prove the completeness by showing that the labelled systems can derive the established proof rules of process logic and first-order dynamic logic. The result is a uniform framework for process logic in which for the first time, trace-based program properties and first-order computations can be handled within the same proof structure.

This contribution follows a four-year investigation (2022--2026) into the political economy of graphics card miniaturization. It begins from the premise that rethinking our relationship to artificial intelligence and its sociotechnical entanglements requires demystifying and opening the black box of this technical object. Within our algorithmic culture, the graphics card (GPU) enables the massive, parallel processing of large datasets, making possible the training of the models that underpin our intelligent systems. GPU miniaturization is equally crucial: as a key driver of the Internet of Things, this sociotechnical phenomenon enables the inclusion of these cards in increasingly compact and powerful systems while also enabling better management of energy resources. The development of these everyday objects and technologies nevertheless reinforces several major problems. Drawing on both the social sciences and the critical, reflexive, speculative, and fictional methodologies of research-creation, the author developed several investigative fieldwork sites -- among liquid nitrogen overclockers in Taiwan and urban miners in Ghana -- and conducted situated experimentations on some fifty acquired graphics cards. Structured around three themes (dismantle and dissolve, rebuild, remix), this paper demonstrates how research-creation methods constitute full epistemologies for apprehending what seems a priori external, opaque, or inaccessible, and for restoring artificial intelligence to its tangible materialities. In doing so, it contributes to the field of ICT for sustainability by affirming research-creation as a rigorous means of disentangling the material and environmental infrastructures that computational systems both depend on and obscure.

Deciding whether a graph has k-edge-disjoint spanning trees is a well-studied problem. We consider the problem of enumerating all sets of spanning trees with polynomial delay. This work is based on the alternate proof of Tutte and Nash-Williams' characterization of graphs with k edge-disjoint spanning trees by Kaiser [1]. The idea is to maintain a decision tree for all forest-packs and perform a Depth-First Search over it. We build this decision tree inductively by computing each node's children using a variant of Kaiser's technique [1].

AI-powered code generation systems have transformed software development but introduce critical inference-time security vulnerabilities. This research presents a systematic investigation of context-based adversarial attacks, where strategically crafted contextual inputs, including comments, documentation, variable names, bias large language models toward generating exploitable code. Through 2,800 controlled experiments across CodeT5+, CodeLlama, GPT-3.5-Turbo, and GPT-4, we quantify attack effectiveness and defense mechanisms. Results demonstrate that adversarial conditions increase vulnerability generation 10.7x (from 3.5% to 37.4%), with direct instruction attacks achieving 100% success on GPT-3.5-Turbo. Cross-model transferability reaches 60-100%, indicating systemic architectural vulnerabilities rather than model-specific flaws. Our dual-layer defense framework achieves 89.1% detection rate with 0.3% false positives and 520ms latency, demonstrating practical feasibility for real-time deployment in development environments.

In the bi-infinite Post Correspondence Problem ($\Z$PCP), it is asked whether the same bi-infinite word can be constructed correspondingly from a given finite set of pairs of words. In this article, we study its complexity with respect to the arithmetical hierarchy and prove that it is in $\Si^0_2 \setminus (Π^0_1 \cup \Si^0_1)$ and, therefore, at the level 2 of the arithmetical hierarchy. For the proof, we present a sequence of reductions starting from the nonhalting of the Turing machine all the way to $\Z$PCP via infinite PCP, an $s$-shift infinite PCP and $s$-shift $\Z$PCP for all natural numbers $s$. In the process, we prove that the infinite PCP is undecidable for injective morphisms, and that the infinite injective PCP, $s$-shift infinite PCP, $s$-shift $\Z$PCP and the non-termination problem for (deterministic and reversible) semi-Thue systems are all $Π^0_1$-complete.

Software vulnerability detection is increasingly important as modern applications combine multiple programming languages. This paper presents an early comparative evaluation of BERT, RoBERTa, and CodeBERT for binary vulnerability detection across HTML, Python, JavaScript, and PHP using the CVEFixes dataset and language-wise three-fold stratified cross-validation. The results show clear performance differences across languages, indicating that multilingual vulnerability detection requires more language-aware and robust transformer-based modelling strategies.