Spiking Neural Networks (SNNs) provide an attractive framework for energy-efficient inference due to their event-driven computation and biologically inspired dynamics. However, efficient hardware realization of SNNs remains challenging because neuronal computations incur significant power and area costs, and uncontrolled approximate arithmetic can destabilize recurrent state updates when precision is not properly managed. To address these challenges, this paper presents ReSCom, a reconfigurable SNN accelerator that leverages stochastic computing to reduce hardware complexity while maintaining stable inference. The proposed architecture employs stochastic arithmetic for multiplication operations in neuron dynamics, while preserving exact fixed-point addition/subtraction operations. This stochastic strategy enables runtime trade-offs between accuracy, latency, and energy consumption. A unified reconfigurable neuron design supports Integrate-and-Fire (IF), Leaky Integrate-and-Fire (LIF), and Synaptic neuron models within a single hardware framework. Experimental results for MNIST inference on a Xilinx Artix-7 FPGA show that ReSCom achieves $92.80\%$ classification accuracy while consuming just $0.05~\mathrm{mJ}$ of operational energy per image at $100~\mathrm{MHz}$, outperforming the energy efficiency of recent state-of-the-art implementations. Furthermore, managing the stochastic bit-stream length allows explicit, dynamic control over accuracy-latency-energy trade-offs to meet target application constraints.

Industrial e-commerce search serves hundreds of millions of items through a multi-branch retrieval stage fused by hand-tuned merging without joint optimization. Generative retrieval (GR) raises the prospect of collapsing this stage into a single model, yet unification is gated by more than retrieval quality: the inverted-index branch converts below the platform average yet persists because it is almost the only branch where operations can inject a new term within hours without any model update; a one-model substitute must preserve this real-time editability. Existing GR methods structurally lack it: closed-codebook methods fix each slot to a quantized embedding at training, while open-vocabulary methods leave new-term routing to model generalization. We present OneRetrieval, a one-model GR framework built on Keyword-Aligned Encoding (KAE), which ties each identifier position to an interpretable attribute word, pairing competitive recall quality with the editability of the inverted index -- to our knowledge the first editable generative retrieval method. An information-theoretic merging organizes 18 attribute categories into six codebook groups with non-uniform capacity; reserved slots in each codebook can be bound to new words after deployment without retraining; and a four-stage fine-tuning pipeline secures quality and editability jointly. On five million real-traffic requests, OneRetrieval matches the deep recall of the strongest generative baseline, with an intervention hit rate over an order of magnitude above closed-codebook encodings. Online, replacing the inverted-index branch significantly lifts order volume; extending to nearly the entire stage holds conversion while improving CTR. The system is deployed at Kuaishou, serving hundreds of millions of PVs daily.

Futures-based implementations of out-of-order choreographies can substantially improve latency and throughput, but their actual behavior depends on resources such as communication delay, computation time, failures, and recovery. Existing formal models such as Ozone's O3 describe which executions are possible, but do not directly explain how likely those executions are or how long they take. In this work we present AsInst, a probabilistic, resource-aware language for modeling the semantics of asynchronous choreographies with out-of-order execution. AsInst programs are interpreted as temporal Bayesian networks that model both the values produced at runtime and the times at which they become available. We prove that this central semantics correctly captures a corresponding futures-style network semantics. We also show that AsInst can encode Ozone-style select-and-merge conditionals, and we use case studies to model communication-failure recovery and analyze runtime performance.

Frequency Modulated Continuous Wave (FMCW) radar systems traditionally rely on Fourier-based methods, such as the Fast Fourier Transform (FFT), to estimate target range and velocity. While computationally efficient, these approaches require storing and processing large blocks of data, which can become a bottleneck in memory-constrained or low-latency applications. In this work, we propose a neuromorphic-inspired signal processing method based on adaptive resonate-and-fire (ARF) neurons formulated as a discrete-time dynamical system. Each neuron dynamically adjusts its internal frequency to match dominant frequency components of the input radar signal, enabling direct estimation of target ranges and velocities without computing the full frequency spectrum. The proposed model operates in a sample-by-sample fashion, resulting in memory requirements that scale with the number of tracked targets rather than the signal length. A feedback mechanism is also introduced to enable multiple neurons to lock on distinct frequency components in multi-target cases. Results on simulated and experimental data demonstrate that the method can successfully track multiple targets. Compared to conventional FFT-based approaches, the proposed method offers reduced memory usage proportional only to the number of tracked targets, making it suitable for resource-constrained and edge-based radar applications.

The frequency bands between 7 and 24 GHz, also known as upper midband or Frequency Range (FR) 3, are being considered as an enabler of 6th Generation (6G) mobile networks. This portion of the spectrum exhibits different propagation characteristics compared to frequencies above 24 GHz, while also offering the potential to provide larger bandwidth allocations for mobile systems than those available in the sub-6 GHz range. 6G technology and spectrum policy, however, will need to guarantee coexistence with the incumbents that already use these frequency bands, which include a variety of services, from radiolocation to satellite-based communications, remote sensing, and radioastronomy. In this paper, we consider the challenge of coexistence between 6G terrestrial systems and satellite incumbents in different portions of the FR3 bands. Using a large-scale 3D model of a terrestrial deployment in the city of Boston and an open-source ray tracing solution, we evaluate the level of Radio Frequency Interference (RFI) that tens of terrestrial Next Generation Node Bs (gNBs) generate toward satellites at different elevation angles. Our model, based on realistic obstruction, clutter, diffraction, and reflections, shows that sidelobes and Non-Line-of-Sight (NLoS) paths can significantly contribute to RFI. Besides directionality, the spatial distribution of gNBs also plays a key role in defining the RFI levels, suggesting that a careful design and operation of terrestrial deployments can create coexistence opportunities.

This paper investigates the potential of a Digital Twin (DT) for freight routing across inland waterway networks under uncertain water-level conditions. Existing approaches insufficiently account for increasing climate-induced volatility in water levels, which often result in higher operational costs and emissions due to the need for more expensive transport alternatives, such as road transport. These existing methods often rely on reactive countermeasures to remain resilient. To address these limitations, six interviews with experts in domains related to inland shipping were conducted to identify three common contingency scenarios and appropriate operational responses. These scenarios were subsequently incorporated into a time-sliced simulation environment in which predictive decision-making, enabled by a DT environment, was compared against reactive approaches. The results demonstrate that predictive modeling substantially reduces operational costs and modal shifts at prediction accuracies between 70% and 100%, despite extreme conditions. In addition, the predictive model achieves an average 28.3% reduction in fuel-related costs by reducing the total distance ships travel. The simulation outcomes were evaluated together with domain experts to assess the practical relevance and applicability of the proposed DT-enabled approach.

Several recent contributions have analyzed and illustrated the effectiveness of operator filtering, both in terms of regularization and compression, when handling dense matrices arising from the discretization of integral operators, e.g. the single-layer operator. Previous works have introduced different filtering strategies, ranging from Laplacian-based filters to analytically derived ones, with the goal of improving the computational efficiency of iterative and direct solvers for integral equations in the two-dimensional space, like the 2D Electric Field Integral Equation (EFIE). In this work, we propose a filtering strategy based on the spectral truncation of the kernels of integral operators associated with the 3D EFIE. The approach relies on an appropriate spectral representation of the Green's function obtained via the spherical Hankel transform, which provides an analytical foundation for the proposed approach. Finally, we provide semi-analytical and numerical evidence of the impact of this filtering technique on the spectral properties of continuous integral operators and of their discretization through boundary elements, both for the static and dynamic cases.

We present a communication-aware task decomposition framework for multi-agent systems with collaborative relative configuration objectives specified in Signal Temporal Logic (STL), allowing for dynamic task reallocation under time-varying communication networks. Building on our prior work, the framework supports the direct use of existing feedback controllers for reactive task satisfaction. We address two key challenges: disjunctive STL specifications and time-varying communication networks. Disjunctive specifications are handled through a graph transition system that captures the alternative task sequences induced by logical OR operators. To address time-varying connectivity, we introduce a redistribution mechanism that transfers tasks from disconnected agents to connected ones as the network evolves while preserving decentralized execution. Simulations and experiments on a swarm of Crazyflie drones demonstrate scalability in the number of agents, communication connectivity, and specification complexity.

Internal deployment of agentic AI systems for coding and research creates a sociotechnical control problem that extends beyond model behaviour. We treat internal-deployment Loss of Control as the inability to reliably constrain, audit, reverse, or halt AI-mediated changes to code, infrastructure, evaluation, or deployment processes in time to prevent serious organisational or societal harms. We ask whether established systems-safety methods can identify risks that model-level evaluations may miss. Using a generic frontier-lab coding-agent scenario reconstructed from public materials, we apply STECA, STPA, and FRAM. The analyses surface complementary findings: published frameworks can leave governance responsibilities and feedback loops externally unverifiable; delays in monitoring and intervention can make otherwise valid control actions ineffective; and routine operational variability can gradually erode the calibration and independence of safeguards. We argue that frontier-AI risk management should pair model-focused evaluations with systems-level hazard analysis and operational assurance that tracks whether controls remain effective over time.

This paper proposes a novel adaptive control framework that embeds nonlinear opinion dynamics within the dynamical sensorimotor layers of an automated vehicle governed by second-order nonholonomic bicycle kinematics. The framework enables an ego vehicle to perform adaptive decision-making and achieve safe motion control under interaction uncertainty with non-cooperative neighboring agents. We consider a representative case study in which an ego vehicle autonomously attempts to merge into a lane occupied by human-driven or automated vehicles whose intentions are unknown. Within the proposed framework, the ego vehicle adaptively selects and executes merging versus non-merging behaviors in response to changing environmental conditions. Formal safety guarantees, as well as equilibrium and stability analyses of the closed-loop system, are provided. Numerical simulations further demonstrate the effectiveness of the proposed approach.

Wireless network automation has progressed from rule-based self-organising networks (SON) to data-driven optimisation, yet existing systems remain fundamentally disembodied, optimising performance indicators without perceiving the physical environment that governs radio propagation. We propose the embodied intelligent empowered base station (eBS), a paradigm that adopts a Vision-Language-Action (VLA) pipeline to transform base stations into autonomous agents capable of situated perception, causal physical reasoning, and physics-aware action generation. The eBS employs a two-tier asynchronous architecture: a Semantic Planner powered by a frontier Vision-Language Model (VLM) generates structured action directives on human timescales, whilst a Tactical Controller executes real-time adaptation. Case studies demonstrate that a single VLA pipeline, without task-specific training, can perform zero-shot material reasoning, generalise across viewpoints, and predict dynamic events before signal degradation occurs, illustrating a paradigm shift from traditional rule-following network automation to embodied intelligence empowered future wireless networks.

As organizations move toward post-quantum cryptography, they face the major challenge of updating cryptographic algorithms across large, complex software portfolios. However, most cryptographic APIs in use today were designed around specific algorithms. These APIs expect explicit use of specific algorithms, provide little or no support for policy-based algorithm selection, and offer no straightforward way to migrate existing keys to newer algorithms. This makes the transition to post-quantum cryptography challenging. The companion assessment framework identifies the barriers to cryptographic agility and explains why algorithm transition is largely a software engineering problem. To address the limitations of current cryptographic APIs, we identify the principles necessary to design a cryptographically agile API. The design principles are derived from five fundamental architectural characteristics (Abstraction, Stability, Temporal Flexibility, Separation, and Extensibility). We also show how the design principles can be implemented using several examples of Protocol Buffers API design patterns. In particular, we present an intent vocabulary that is based on scopes which allows for decoupling key creation from algorithm identities. It also supports transparent substitutions of algorithms in the applicable scope. Cryptographic governance is enabled by an abstract policy API that does not prescribe the policy format. Keys are represented by stable identifiers and support key evolution operations (rotation, transformation, migration), facilitating migration between algorithms and providers while tracking both the original key identity and its evolution history. With this approach, updating cryptography becomes an operational process without the need to rewrite application code.

Retrieval-Augmented Generation (RAG) has become a common approach for improving the factuality of Large Language Models (LLMs), yet its reliability remains highly sensitive to how external evidence is retrieved and used. Semantically equivalent queries with different syntactic forms may lead to different retrieval results, while irrelevant or misleading documents can further induce hallucinated answers. Existing multi-path reasoning methods improve robustness by sampling multiple candidate answers and applying voting- or confidence-based selection, but they still face two limitations: diversity is often injected through uncontrollable decoding randomness, and answer evaluation is usually confined to a single query-induced evidence view. To address these limitations, we propose a Cross-Query Consistency Hypothesis: correct answers tend to maintain high confidence across semantically equivalent but syntactically diverse queries, whereas noise-induced hallucinations exhibit unstable confidence under such query variations. Based on this hypothesis, we introduce CQC-RAG, a framework that co-designs query-level diversity injection with cross-query consistency evaluation. CQC-RAG rewrites the original question into diverse but meaning-preserving queries, reranks a shared document pool to construct query-conditioned reasoning contexts, applies an evidence-grounded protocol to extract answer-evidence pairs and selects answers according to their confidence stability across these contexts. This design enables self-evaluation without external supervision and does not rely on expanded retrieval coverage. Experiments on four open-domain question answering benchmarks show that CQC-RAG outperforms the strongest previous multi-query baseline by +4.76 pp EM on TriviaQA and +9.12 pp EM on MuSiQue, validating the effectiveness of cross-query consistency for filtering noise-induced hallucinations.

The impending post-quantum transition to new cryptography will require complete replacement of algorithms within all software. The cryptographic APIs used today make this transition challenging because they were not designed with agility as a concern. There is no method for systematically assessing cryptographic agility as an overall ability. In addition to this, the term itself refers to multiple independent capabilities. Specifically, it includes replacing algorithms, selecting by policy, and substituting implementations. This lack of structured decomposition limits both the evaluation of systems and the development of cryptographically agile APIs. We introduce a component-based assessment framework that characterizes application-level cryptographic agility along seven orthogonal dimensions: three coupling dimensions that measure what the application code knows about algorithms and providers, a cross-cutting decoupling mechanism, a governance authority dimension, and two agility enablers that measure actual migration capability. The framework is non-linear and captures non-hierarchical profiles: a system may achieve high operation decoupling yet low creation decoupling, or strong versioning without externalized configuration. We evaluate six representative APIs (PKCS#11, OpenSSL~3.0, JCA, Google Tink, AWS KMS, and HashiCorp Vault Transit) against the framework, revealing three pervasive and independent gaps: no system supports intent-based key creation, none provides policy-driven algorithm selection (as distinct from access control), and none offers dedicated/first-class operations for algorithm transformation of existing keys. These gaps are individually sufficient to prevent agile migration, explaining why the post-quantum transition remains a software engineering problem despite decades of API progress.

We consider stochastic model predictive control (MPC) for constrained linear systems subject to multiplicative binary input uncertainty, motivated by applications such as networked control with packet losses and intermittent actuation. A common approach in this setting replaces the stochastic dynamics with their expectation, yielding tractable formulations that admit standard terminal ingredients and stability guarantees in expectation. We show that such formulations can exhibit structural properties that differ fundamentally from those of deterministic MPC and may be misleading as indicators of realized closed-loop behaviour. In particular, the expected value function is not necessarily monotonic in the prediction horizon, and value function-based inner approximations of the region of attraction may deteriorate as the horizon increases. Furthermore, we establish a probabilistic comparison with certainty-equivalent (optimistic) MPC, showing that the latter can ensure a strictly positive probability of recursive feasibility in situations where stochastic MPC certifies feasibility but fails with probability one. These results highlight inherent limitations of expectation-based stochastic MPC for systems with multiplicative binary uncertainty and motivate a re-examination of how stochasticity is incorporated into constrained predictive control design for such systems.

Vehicular applications such as cooperative driving, teleoperation, and real-time perception increasingly rely on low-latency wireless communication. In this context, ITS-G5, based on IEEE 802.11p, represents a key technology for enabling direct vehicle-to-vehicle and vehicle-to-infrastructure communication. Despite its relevance, experimental studies focusing on the performance of UDP-based traffic over IEEE 802.11p under realistic conditions remain limited. This paper presents an experimental evaluation of UDP transmission over an IEEE 802.11p ITS-G5 testbed composed of Raspberry Pi-based onboard units and commercial roadside units. The analysis investigates the impact of different modulation and coding schemes (MCS). It also evaluates two network-layer configurations (IPv4 unicast and IPv6 multicast) and the use of CAKE for active queue management. In addition to synthetic traffic generated with iPerf, the evaluation includes real-time video streaming using MPEG-TS over UDP to emulate latency-sensitive vehicular applications. Results show that the modulation scheme is the dominant factor influencing latency at low traffic loads, while the choice of transmission mode and IP version becomes increasingly significant under congested conditions. Higher-order modulations significantly reduce latency and variability, whereas IPv6 multicast exhibits greater delay dispersion than IPv4 unicast. Furthermore, active queue management does not seem to improve delay predictability. These findings provide practical insights for configuring ITS-G5 networks supporting latency-sensitive vehicular services.

As Generative AI (GenAI) disrupts higher education, institutions increasingly require students to declare AI use. However, generic, binary declarations (e.g., "I used GenAI") fail to capture the nuanced application of these tools in different academic tasks. Establishing transparency is key to protecting academic integrity, promoting AI literacy, and shifting the focus from policing to professional practice. In response, this paper contributes a design artefact and an accompanying position: a framework of two task-specific declaration structures, one for writing-focused activities and one for coding assessments, developed for a Computer Science department on the basis of an existing taxonomy of GenAI usage, together with an argument that task-specific disclosure is needed to move beyond binary declarations. By categorising AI usage across specific cognitive and developmental stages, such as structural planning vs. Textual Content Generation, or code improvement vs. code generation, the framework encourages students to reflect on their own learning process and clarifies the boundary between acceptable assistance and academic misconduct. We propose this domain-specific approach as a foundation for fostering more honest assessment in Computer Science and other disciplines, aiming to better prepare students for professional environments where documenting GenAI workflows might be an essential job requirement.

We study the $(1 + 1)$-EA in dynamic linear environments, where in every generation selection is performed with respect to a freshly sampled linear function with positive weights. We consider the Dynamic Binary Value problem, where each generation uses a uniformly random permutation of $1,2,4,\dots,2^{n-1}$, and a Uniform weight variant, where the weights are drawn independently from $\mathrm{Unif}(0,1)$. Both of them have recently been integrated into the IOHprofiler platform and empirically studied. For both models we prove a sharp threshold in the mutation parameter $χ$ for mutation rate $χ/n$. Below the threshold, the expected optimisation time is $\mathcal{O}(n\log n)$, whereas above it the runtime becomes $2^{Ω(n)}$. For the Dynamic Binary Value problem in the exponential regime, we also quantify at what distance from the optimum the optimisation process stagnates. We show that there is a second threshold: a distance that is efficiently reached, but reaching any smaller distance takes exponential time. This quantifies and proves previous empirical findings.

Line-commutated converter high-voltage direct current (LCC-HVDC) has proven to be a reliable technology for bulk power transmission over long distances. However, the growing penetration of converter interfaced generation (CIG) is resulting in weaker AC grids, rendering the operation of LCC-HVDC systems vulnerable and posing a serious challenge to their stability. Grid-forming (GFM) controlled voltage source converter (VSC) have been shown to provide stabilizing impact in weak grid conditions. However, the impact of GFM controlled VSCs (GFM-VSC) on stability of LCC-HVDC in weak grid conditions has not been studied in depth in the literature. In this paper, a simplified model of LCC-HVDC is proposed and validated. Then a small-signal state-space model of a system consisting of aforementioned LCC-HVDC, a GFM-VSC and an infinite grid is developed to study the interactions between different components. The small-signal stability analysis shows the stabilizing effect of the GFM-VSC on the stability of the LCC-HVDC link in weak grid condition. Furthermore, the study on the sizing of the GFM power converter reveals that even a modest share of the capacity of the GFM power converter relative to the total nominal apparent power (sum of nominal power of LCC-HVDC and the nominal apparent power of GFM-VSC) is sufficient to ensure the stability of the system, in the test system analyzed in this study. This work just focuses in small-signal stability, but it is important to highlight that other stability phenomena should also be taken into account when selecting the final size of the GFM-VSC.

Spiking Neural Networks (SNNs) offer a brain-inspired path toward highly efficient computation, but their practical deployment is constrained by the challenge of managing and executing their massive parallelism on physical hardware. This problem mirrors the historical challenge in processor design of moving beyond serial execution, a barrier broken by superscalar architectures that dispatch multiple instructions to parallel functional units. Drawing inspiration from this paradigm, we introduce a hardware-software co-design framework that treats synaptic events as parallelizable micro-operations. We present SupraSNN, a superscalar-inspired architecture that achieves high synapse-level parallelism by physically decoupling synaptic and neuronal computations. Within this architecture, a Multi-Cast Tree routes spike data to multiple parallel Synapse Processing Units serve as the computational pipelines, while a Merge Tree consolidates distributed results for processing by a unified Neuron Unit--deliberately centralizing complex neuron state dynamics to mitigate hardware overhead and resource duplication. The efficacy of this architecture is enabled by a sophisticated partitioning and scheduling framework that first maps the SNN onto hardware respecting memory constraints, then heuristic scheduling determines the synaptic execution order, maximizing throughput and resource utilization. Implementing a feedforward SNN trained on MNIST (93.44% accuracy), SupraSNN achieves 149 $μs$ inference latency and 0.025 mJ per image (0.276 nJ per synapse) on the Xilinx Zynq XC7Z020 FPGA--delivering 47.6% lower latency and 5.6$\times$ better energy efficiency than prior FPGA-based SNN accelerators. Beyond vision tasks, a recurrent SNN on the Spiking Heidelberg Dataset (71.82% accuracy) achieves 1.41 ms latency and 0.77 mJ per sample on XC7Z030.

We study the $(μ+1)$ EA on the Binary Value function BinVal. We show that it needs at most $O(μ\log μ\cdot n \log n)$ function evaluations to find the optimum when $μ= o(n/\log n)$. This substantially improves upon the recent upper bound of $O(μ^5 n \log(n/μ^4))$ by Krejca, Neumann and Witt. Our results hold for several mutation operators including standard bit mutation. In particular, our bound implies that the $(μ+1)$ EA is at most a factor $O(\log μ\cdot \log n)$ slower on BinVal than on OneMax.

This paper presents a measurement-based performance evaluation of two custom Smart Roadside Units (SmartRSUs) featuring different V2X antenna architectures. The first configuration integrates GNSS and communication antennas into an all-in-one rooftop module, whereas the second uses external dual ITS-G5 (IEEE 802.11p) antennas operating at 5.9~GHz and a dedicated GNSS antenna. Both systems are built upon a proprietary On-Board Unit (OBU) platform adapted for infrastructure deployment. The experimental campaign evaluates key V2X communication metrics, including coverage, received signal strength indicator (RSSI), packet loss, and end-to-end latency in both transmission (OBU-to-infrastructure) and reception (infrastructure-to-OBU) directions. To ensure objective validation, a commercial off-the-shelf V2X Roadside Unit is co-located on the same infrastructure and used as a performance benchmark, providing ground-truth reference measurements under identical environmental conditions through a controlled co-located deployment. Results highlight the impact of antenna design and placement on communication reliability and latency, revealing trade-offs between integrated and external antenna configurations in real-world deployment scenarios. The findings provide practical insights for the design and optimization of next-generation SmartRSUs in cooperative intelligent transportation systems (C-ITS).

Asynchronous Many-Task (AMT) is a parallel programming model used in High Performance Computing (HPC). An AMT runtime can distribute fine-grained tasks across processing units called workers, through work stealing: when a worker has no tasks left to process, it tries to steal tasks from other workers. Workers are not restricted to a single compute node but can also be distributed across multiple nodes of an HPC cluster. Existing AMT runtimes assume a fully connected network with low, uniform latency and perform global work stealing, selecting another worker at random from all workers in the system. Space Edge Computing (SEC) uses constellations of satellites in Low Earth Orbit (LEO) as distributed compute clusters. Unlike HPC clusters, LEO satellites communicate through inter-satellite links that form a sparse mesh topology. Reaching a distant satellite requires multiple hops, each adding latency. As a step toward adapting AMT to SEC, this paper proposes a neighbor-only work stealing strategy in which workers steal exclusively from directly connected neighbors, avoiding multi-hop communication. An analytical model shows that restricting stealing this way yields a per-attempt latency advantage that grows with constellation size. Preliminary experiments on an HPC cluster with an emulated mesh over uniform low-latency links isolate the effect of victim selection: the neighbor-only strategy performs within ~2.2% of global stealing on both balanced and irregular workloads, indicating that restricting the victim set does not harm load balancing in this setting. Taken together, the experiments suggest that neighbor-only stealing can be on a par with global stealing, and the model suggests that neighbor-only stealing becomes preferable at scale.

Modern deep learning hardware paradigms rely heavily on computationally expensive floating-point arithmetic (FP32, FP16, and FP8), requiring massive thermal and energetic overheads to maintain gradient-based optimization. This paper introduces a non-parametric, training-free computational framework for dual-manifold mapping that operates strictly within an 8-bit signed integer boundary and leverages simple bitwise and accumulation logic. By mapping a Spatial Manifold (N_spatial = 8192 neurons) and a Gabor-pooled Structural Manifold (N_structural = 4096 neurons) through an integer-based transformation matrix (Z-matrix), we eliminate the need for floating-point multipliers. Inference is achieved via cache-friendly pointer offsets and bitwise masks, accumulating directional sign-charges using fixed thresholds (theta_reject = 8.0, theta_cut = 2.0). Learning is executed through a localized, bounded update mechanism restricted strictly within [-127, 127], modulated by stochastic noise injection. Both architectures demonstrate extreme holographic resilience, preserving near-perfect reconstruction via a global scaling factor under 90% truncation sparsity and 20% random node destruction. By reducing core AI inference to 8-bit boundaries and boolean-like execution, this framework outlines a paradigm shift toward neuromorphic edge-computing, directly questioning the long-term necessity of dense, floating-point-centric GPU accelerators.

We introduce a new simple model to study the fitness progress of Evolution Strategies (ES) in generic problems. In this model, we bypass the underlying fitness landscape and assume that the mutation of any individual produces an offspring whose fitness relative to the parent is given by an invariant distribution $Z$, such as a mean-shifted Gaussian. This serves as a prototypical model for the optimisation landscape when an evolution algorithm operates far from the global optimum. This simple model can be used to approximate the optimisation process for problems where it is intractable to model the exact fitness function, including tasks such as hyperparameter tuning in machine learning models. We rigorously analyse the expected growth rate $\mathcal{R}_μ$ of the continuous steady-state $(μ+1)$-ES in this model. Unlike comma-selection strategies, the steady-state $(μ+1)$-ES maintains overlapping generations, introducing complex mathematical dependencies among surviving parents that make it harder to analyse. We give a general technique to analyse the the $(μ+ 1)$-ES by constructing modified processes whose growth rates provably sandwich that of the original process. These modified processes are then easier to analyse but still close enough to the true process to give a tight bound on the expected growth rate. When $Z = \mathcal{N}(-δ, 1)$ and $μ\le e^δ$, we show that $\mathcal{R}_μ = \frac{\log^{1 + o(1)} μ}μ \mathcal{R}_1$.

A skiplist is a fundamental data structure widely used in systems and applications for indexing data stores. In this work, we introduce Foresight, a cache-friendly skiplist optimization. Extending Foresight to concurrent settings introduces significant synchronization challenges that we identify and address. Foresight is a surgical optimization, easy to integrate into a wide variety of skiplist designs. We apply it to one sequential and three concurrent skiplist designs and observe throughput improvements of up to 45% in microbenchmarks. When applied to a skiplist-based index in the DBx1000 in-memory database, Foresight yields end-to-end performance gains of up to 15%.

This paper presents EconCSLib, a Lean 4 library and workflow for formalizing research papers in Economics and Computation with language-model assistance. The central design principle is a human-AI-Lean workflow: an LLM writes Lean code, Lean checks formal statements and proofs, and humans (assisted by an LLM) verify the translation boundary from paper claims to formal statements. EconCSLib is organized around research papers, preserving their formal statements and following their proof structure to the extent possible; reusable mathematical statements are elevated into shared EconCS infrastructure. The workflow is designed to be author-facing: researchers can formalize their own papers, inspect the Lean code's translations of paper-facing statements, and contribute reusable components back to the library; this is supported by post-formalization validation reports, paper result dependency graphs, and a review dashboard. The current public repository contains 11 formalized papers and 3 partially formalized papers, along with initial libraries for probability, auctions, matching markets, and graph tools. The library and workflow are available at https://github.com/nikhgarg/EconCSLib, with corresponding project webpage at https://gargnikhil.com/EconCSLib/. To our knowledge, we are also among the first applied math researchers to systematically pursue Lean formalization of one's own publications in the process of building such a community library. We welcome users and contributors to the project.

Remote driving has gained increasing attention as a key enabler for connected and automated vehicles. Yet its practical deployment hinges on wireless networks' ability to guarantee low, predictable latency. In this paper, we present an extensive latency analysis of ITS-G5 and cellular (5G) technologies within the Modena Automotive Smart Area (MASA), a real-world, city-scale testbed equipped with a distributed intelligent transportation infrastructure. By conducting controlled experiments under varying network loads and traffic conditions, we measure network and end-to-end latency components relevant to remote driving, in which the uplink consists of a continuous video stream transmitted from the vehicle to the remote operator, and the downlink conveys control commands back to the car. Measurements conducted under diverse conditions reveal how latency and variability differ across the two technologies and how infrastructure coverage impacts video-stream transmission performance. Based on the observed latency distributions and reliability metrics, we assess the practical feasibility and safety margins of remote driving in mixed network environments. The results provide actionable insights for future teleoperation deployments and motivate hybrid communication strategies that combine the strengths of ITS-G5 and cellular networks.

We study retrospective auditing for dynamic ordered sets maintained by an untrusted party. A passive auditor watches insert, delete, membership, predecessor, successor, min, and max operations, stores five machine words and a flag, and receives a constant-size public tally record per operation. At audit time the maintainer discloses the claimed live vacant intervals. The method represents order semantics by maximal gaps: gaps are born, cited, consumed, and timestamped, while two hidden field accumulators test equality of the birth and consumption ledgers. Honest executions are accepted with probability one. If any answer in a T-operation session is wrong, acceptance occurs with probability at most (4T+1)/p over one secret field element, against computationally unbounded maintainers. We prove that deterministic and visible-coin auditors require linear state, and that removing the timestamp rule permits an exact replay forgery. A leaf-oriented (2,4)-tree implements the maintainer in O(log n) worst-case time per operation with one extra word per element, and its rebalancing events admit an auditable O(m) envelope over m updates. Checkpoint audits compose with additive error.

Future networks will need to make network-wide decisions, including traffic engineering, network slicing, and wireless optimization, under strict latency, energy, and reliability constraints. The computational complexity of these problems increasingly challenges classical optimization methods. This article proposes Q-Backbone (QB), a quantum-enhanced control plane for communication networks in which quantum processing units (QPUs) operate alongside classical computing resources as accelerators for network intelligence. QB is designed as a fourlayer architecture that combines heterogeneous infrastructure, hybrid quantum-classical runtime services, policy-driven task orchestration, and communication-network applications. A central component of QB is the Quantum Invocation Policy (QIP), which dynamically determines when quantum acceleration is beneficial and when classical execution should be preferred. A case study on deadline-aware orchestration of distributed quantum jobs over heterogeneous QPUs shows that QB can improve workload execution under tight deadline constraints, serving up to 25% more jobs than existing quantum-cloud scheduling baselines. Finally, open challenges and opportunities towards the deployment of QB are highlighted and discussed.