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Earlier sources: Networks
This hardware thread follows networks through original research, public projects, and source reports. Read the evidence below to see which developments are represented in SYMTRI’s archive.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗23 classified signals in the fourteen-day window. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- OpenAlex · Published · First archived
Source Intensity Errors and Data Correction Schemes in Downstream Continuous-Variable Quantum Key Distribution Access Networks ↗
Downstream multi-user continuous variable quantum key distribution (CV–QKD) based on passive optical networks represents a promising solution for large-scale quantum-secure access. However, global source errors can introduce biased parameter estimation and potential security vulnerabilities. To address this issue without additional hardware overhead, two data postprocessing correction schemes adapted to different deployment conditions are proposed. For scenarios with known intensity fluctuation statistics, a low-intensity pulse data tagging strategy is employed to construct conservative securi…
- OpenAlex · Published · First archived
AoI-aware coexistence scheduling for industrial wireless sensor networks under uncontrollable external networks via deep reinforcement learning ↗
Industrial wireless sensor networks (IWSNs) are increasingly deployed in shared-spectrum environments, where coexistence with external wireless networks is often unavoidable. Existing coexistence scheduling methods usually rely on joint coordination frameworks that assume all coexisting networks are observable and manageable. In practice, however, some external networks are independently operated and uncontrollable, while their internal access rules and future transmission behaviors are unavailable to the target IWSN. This paper investigates an age-of-information (AoI)-aware coexistence schedu…
- OpenAlex · Published · First archived
Asynchronous intermittent control strategy for stability of stochastic delayed complex networks with deception attacks based on Dupire’s functional Itô formula ↗
This paper investigates the mean-square exponential stability of stochastic delayed complex networks under asynchronous intermittent control with deception attacks (AICDA). Because different nodes have different dynamics, each node requires its own independent intermittent control. Meanwhile, sensor–controller and controller–actuator channels may suffer from deception attacks. The asynchrony makes traditional Halanay inequality methods inapplicable. To overcome this difficulty, we introduce an auxiliary timer for each node, which increases during working intervals and decreases during resting…
- OpenAlex · Published · First archived
Contributions to Integrity and Collaboration in Dynamic Sensor Networks ↗
Abstract Reliable and safe navigation of autonomous systems is an active field of research, particularly in the context of complex urban environments. A key challenge is navigation integrity, defined as the trust in the correctness of the estimated system state; the transfer of this concept from aviation to collaborative multi-agent systems remains an open research challenge. Addressing this gap, this paper presents the methodological developments and key findings of the DFG-funded i.c.sens research project, which focused on navigation integrity in dynamic collaborative sensor networks. To acc…
- OpenAlex · Published · First archived
Learning to Route On-Chip: A Survey of Machine Learning-Based Routing in Networks-on-Chip and Its Hardware Overheads ↗
Network-on-Chip (NoC) fabrics are the dominant interconnect for many-core processors, yet widely used routing methods—dimension-order routing and turn-model-based adaptive variants—assume mostly local, low-variance congestion. Modern workloads generate dense and bursty traffic that breaks these assumptions, motivating data-driven routing. Following a PRISMA-ScR protocol across five databases (2015–2026), this survey reviews machine-learning-based NoC routing across a 48-study evidence base, where path selection is posed as a Markov Decision Process and addressed with reinforcement learning and…
- OpenAlex · Published · First archived
Testing Robustness of Temporal Transportation Networks via Interval Separators ↗
This paper addresses the problem of identifying time interval separators in temporal networks. We introduce d-MinIntSep, a new variant of the temporal separator problem, which models failures as time intervals assigned to vertices and aims to block all temporal paths between a source and a target that can be completed within a given deadline d. We prove that the d-MinIntSep problem is NP-hard and hard to approximate within a logarithmic function of the size of the vertex set, assuming P ≠ NP, and we propose an Integer Linear Programming (ILP) formulation to compute minimum interval separators.…
- OpenAlex · Published · First archived
Simulating and sampling from quantum circuits with 2D tensor networks ↗
Classical simulations of quantum circuits play a vital role in the development of quantum computers and for taking the temperature of the field. Here, we classically simulate various physically-motivated circuits using 2D tensor network ansätze for the many-body wavefunction which match the geometry of the underlying quantum processor. We then employ a generalized version of the boundary Matrix Product State contraction algorithm to controllably generate samples from the resultant tensor network states. Our approach allows us to systematically converge both the quality of the final state and t…
- OpenAlex · Published · First archived
Evolving robust memristive spiking neural networks via metaheuristic algorithms for noise-resilient neuromorphic inference ↗
Abstract Neuromorphic systems hold the promise of ultra-low-power event-conditions intelligence, but noise and non-idealities of hardware of the memristive devices limit their practical use. Nonetheless, it is not understood how to attain an inference which is both noise-robust and accurate as well as energy-efficient across realistic multi-source perturbations. In this case we develop a metaheuristic evolutionary learning system which substantially alleviates this well-known robustness–efficiency trade-off in memristive spiking neural networks. The optimization of spiking networks is done wit…
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
How to assess evidence about Networks
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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