Papers

Molecular communication (MC) is a communication paradigm in which information is conveyed through the release, propagation, and reception of molecules. Many envisioned healthcare applications of MC are expected to operate inside the human body, where the cardiovascular system (CVS) may serve as the physical propagation environment and molecular transport is governed by diffusion and blood flow. Although blood flow is inherently pulsatile, most analytical MC channel models assume steady flow. In this paper, we develop a time-variant analytical model for dispersive MC channels with pulsatile flow. We derive the straight-duct response as a Normal distribution with time-variant mean and variance, capturing the combined effects of diffusion and pulsatile flow, and extend it to closed-loop channels through a wrapped-Normal representation. The model is validated against three-dimensional (3D) particle-based simulations (PBSs) for synthetic and physiologically motivated velocity waveforms. We further derive a closed-form first-order approximation for the first-arrival peak time and introduce the nondimensional indicator $S_{\mathrm{Rx}}$ for assessing the applicability of the reduced one-dimensional (1D) model. Our results show that pulsatility has the strongest influence when molecular transport is predominantly advective and the temporal flow variations are not averaged out, whereas stronger diffusion and faster pulsations reduce its impact on the received signal. Moreover, a PBS-based parameter sweep supports $S_{\mathrm{Rx}} \ge 2$ as a practical criterion for the applicability of the proposed analytical model. Finally, relating the analytical assumptions to representative blood-vessel classes shows that model applicability depends not only on the transport conditions but also on physiological properties such as vessel rigidity, geometric uniformity, and blood rheology.

Modern artificial intelligence is predominantly executed on computing architectures optimized for dense linear algebra. While this has enabled the success of contemporary neural networks and motivated compute-in-memory (CIM) architectures, a growing class of artificial intelligence (AI) workloads depends on associative retrieval, identifying stored information by content or similarity rather than by explicit memory addresses. Such operations are central to transformer attention, tree-based inference, genomic search, and other retrieval-intensive applications, yet remain inefficiently supported by conventional memory systems. In this Perspective, we argue that content-addressable memories (CAMs) provide a complementary hardware primitive for associative processing in AI. We review their ability to perform massively parallel in-memory matching, discuss how emerging memory technologies can improve density and energy efficiency, and identify hierarchical search, application-specific architectures, hardware-aware learning, and heterogeneous integration with CIM as key directions for scalable associative computing. Together, these developments suggest that associative retrieval should complement linear algebra as a foundational computing primitive for future AI systems.

Which execution details must a quantum computation retain, and to what precision, to support a declared comparison? We define a sufficient record by the largest Hellinger distance between outcome laws that share it. Our main result is a circuit-derived certificate for continuous recording precision: independent channel-mixture probabilities enter through the affinity of latent noise flags, while coherent rotation differences enter through conditional quantum fidelity. Their joint composition bounds whole record cells without simulating their outcome distributions. For four-qubit QAOA at depth one, a 13-bit comparison record guarantees distance below 0.05 throughout a declared continuous noise box, uniformly over programmed angles when comparisons hold the logical task fixed. A construction with observable noise flags attains the general bound. Exact tensorization of Hellinger affinity connects recording precision to the length of a future measurement transcript. On finite context classes, separation witnesses certify minimum-cost field retention; a four-field record is the unique minimum for 28 archived QAOA instances. Fresh six-qubit processor measurements establish different minimum label counts at one common tolerance: two labels are necessary and sufficient for raw laws, while one suffices after a fixed decoder for a synthetic image-segmentation task. These hardware conclusions are conditional on stationary independent shots and remain distinct from the analytic channel certificate. Shared task definitions and evidential archives are retained separately. The framework specifies which execution distinctions matter, how precisely to record them, and what evidence supports the resulting agreement guarantee.

Distributed quantum circuit simulation enables the execution of large-scale quantum algorithms by partitioning qubits across multiple computational nodes. However, inefficient qubit placement frequently leads to excessive inter-node communication, severely limiting performance and scalability. This work introduces a communication-aware qubit layout optimization framework that systematically minimizes data exchange among nodes while dynamically selecting the optimal number of computational resources. Integrated into the QuCSLO library, our approach supports dynamic node scaling, OpenQASM parsing, and seamless integration with the QuEST simulator. Extensive evaluations on both synthetic benchmarks and real-world QASMBench circuits demonstrate that optimized layouts significantly reduce communication cuts and yield runtime speedups of over 2x compared to identity layouts, particularly in highly entangled circuits. Our results underscore the critical impact of strategic qubit placement on distributed simulation efficiency and provide a practical, resource-scalable solution for quantum workloads.

Facial movements convey subtle and important information that is critical for human social communication. Optical methods for face capture are difficult or impossible to use when the face is occluded by head-mounted devices (HMDs), such as VR headsets. Even with a clear line of sight, such methods raise privacy concerns and require head-mounted capture rigs that offset cameras and lighting from the face. We show that high-density surface electromyography (HD-sEMG) provides a viable non-optical alternative that addresses these challenges. We measured 64 EMG channels, using two textile EMG grids, with 32 from the forehead (typically occluded by an HMD) and 32 from the side of the face. EMG data were digitized at 2048 Hz and filtered. Facial movements were simultaneously recorded and used to estimate 478 3D facial landmarks using MediaPipe's Face Landmarker. A major challenge in such multimodal recordings is synchronizing EMG and video data, which have different sampling frequencies and independent clocks. We developed a novel synchronization method using analog audio bursts that is capable of sub-millisecond synchronization. We also developed a staged fitting method that fits a recent high-resolution parametric head model (GNM), with 253 identity blendshapes and 383 expression blendshapes, to the MediaPipe landmarks as participants performed different facial expressions. We trained a deep neural network comprising per-grid spatial encoders followed by a dilated temporal convolutional network (TCN) to predict blendshape parameters from HD-sEMG signals at 100 Hz. Once trained, the network can predict expression blendshapes solely from HD-sEMG recordings. The output can be rendered using standard real-time blendshape animation methods. We demonstrate the methods using recordings from 25 participants, and direct expression transfer to a variety of human faces and non-human characters.

CXL-SSDs promise memory-semantic access to NAND-scale capacity through a small in-device DRAM cache, but scarce hardware makes emulation central to CXL-SSD research. Cylon, a state-of-the-art emulator, runs workloads in a VM, intercepting DRAM misses to inject NAND latency. We show this VM machinery adds over 3$\mu s$ per miss, exceeding high-performance NAND read latency, while repeated VM exits inflate tail miss latencies several-fold. We present CXDVirt, a kernel-module-based CXL-SSD emulator that removes the VM entirely: the device is exposed through host devdax, hits run natively via the MMU, and a custom page-fault path models NAND latency on misses. CXDVirt preserves CXL-SSDs' bimodal latency profile while reducing per-miss latency 1.4-1.6$\times$ (4.1$\times$ at P99.9); with eight threads its miss latency only doubles, against 8-9$\times$ for Cylon behind QEMU's global lock. CXDVirt stays within 5% of remote DRAM without NAND traffic, where Cylon is 3.1-3.6$\times$ slower, and enables configurable eviction and prefetch policy studies.

Superconducting circuits have emerged as one of the most promising and scalable platforms for quantum com- puting, with substantial industrial adoption driving qubit counts steadily upward. Yet the layout of the corresponding chips is still primarily performed manually, consuming multiple days or weeks of expert effort, even for moderately sized designs. Existing automation approaches sidestep this bottleneck by simplifying the underlying problem and relaxing physical constraints. Therefore, they fall short of fabrication-ready results. To overcome this scalability wall without such compromises, we first introduce a formal abstraction that translates physical device characteristics into a rigorous geometric problem formulation. This abstraction bridges physical realization and design automation. Building upon this abstraction, we then propose a three-stage design automation flow comprising geometry-aware global partitioning, ILP-based port assignment, and a hierarchical routing pipeline. Evaluations conducted by our interdisciplinary team of design au- tomation researchers and superconducting hardware experts con- firm that the resulting flow produces complete, manufacturing- ready layouts that comply with the physical design rules at the push of a button. That is, it operates fully automatically within seconds to a few minutes, and solves complex instances up to 25x faster than the state of the art. All methods are released as the fully open-source tool mqt-scpd as part of the Munich Quantum Toolkit, providing the first readily usable, end- to-end design flow for planar superconducting quantum chips that explicitly addresses the underlying physical characteristics and requirements.

Computing is conventionally implemented by hardware engineered for information processing. Here we investigate infrastructure-native computing: the use of a physical system built for another primary function as a fixed computational operator. In time-domain simulations of an IEEE 14-bus electrical network, Kirchhoff's current law and Ohm's law relate voltage-reference perturbations applied at distributed controllable nodes interfaced by power electronics converters to current responses through a topology-dependent transformation. A trained digital encoder and decoder exploit this transformation for image classification, reaching 91.5% accuracy on MNIST and 82.25% on Fashion-MNIST. The modeled operator is represented by 933 surrogate parameters, compared with 12,340 task-trained parameters for an accuracy-matched fully connected core transformation. Current superposition further supports concurrent spatial sharing of the operator and sequential temporal reuse, with per-stream accuracies above 85% and 93%, respectively, in surrogate-model evaluations. Evaluations on CIFAR-10 and repeated 10-class tasks sampled from a Butterflies-and-Moths dataset show that the incremental utility of the physical operator depends on the representation supplied by upstream digital feature extraction. These results provide a simulation-based proof of concept for infrastructure-native computing with electrical networks and identify topology, accessible control channels, and input representation as determinants of its computational utility.

The COVID-19 pandemic emphasised the importance of remote emotional communication and highlighted a gap in existing technologies that lack haptic channels. While video calls maintain visual and auditory presence, they cannot convey the emotional depth of physical contact, especially hugging, a universally recognised form of intimacy and support. To explore this challenge, we developed the Hugging Suit, a pneumatically-actuated wearable system that enables users to simulate and receive remote hugs in real time. Unlike prior haptic systems that focus solely on tactile sensation, our approach integrates both technical and experiential considerations. The system integrates a programmable Air Actuator Matrix (AAM), a portable high-pressure pneumatic unit, and a fabric-based pressure sensor layer, enabling precise, wearable haptic feedback while remaining lightweight and mobile. Guided by a Research through Design (RtD) methodology, we iteratively refined the prototype to improve tactile resolution, overall usability, and user comfort, while introducing modular components to support flexible and scalable haptic configurations. In parallel, we explored how specific contextual factors, such as lighting, privacy, and the visibility of the remote partner, might shape the emotional and perceptual experience of receiving a remote hug. Preliminary feedback suggests that low-lit, private spaces, especially when users could also see their remote partner via video, improved emotional engagement and comfort during mediated haptic experiences. Our low-cost, maker-space-friendly approach contributes to affective haptics applications in long-distance relationships, emotional regulation, and remote therapeutic support.

Computing-in-Memory using DRAM (CIMD) has demonstrated substantial energy and throughput gains for memory-bound workloads consisting of bulk-bitwise operations, by performing computation directly within DRAM subarrays. Realizing CIMD, however, requires a redesign of the memory controller and careful mapping of operands onto the memory arrays. Presently, accurate FPGA-based testbeds exist, but they are costly and labor-intensive, while open-source simulators are largely trace-based and cannot execute full application runs. The only full-application CIMD simulator available, included with MIMDRAM, is built on an outdated version of gem5 and its toolchains. We present gem5-CIMD, a full-application CIMD simulation framework built on the latest version of gem5. We extend the simulated instruction set from four bitwise operations to sixteen CIM instructions spanning arithmetic, relational, conditional, and utility operations across a range of data types and bitwidths (4 to 64 bits). A complete memory-management stack comprising multi-level huge-page support, a CIM-aware allocator, and a subarray-aware address-mapping scheme ensures that CIM operands are co-located in the same DRAM subarray. In addition, gem5-CIMD is aware of the DRAM refresh interval and faithfully models and schedules the mandatory refresh operations when needed. We provide a CIM standard library intended as a compiler target and evaluate gem5-CIMD on multiple case studies including an end-to-end KNN workload. The simulator sources, including the evaluated workloads, are publicly available.

Small language models (SLMs) are attractive as local agent controllers because they reduce remote inference, latency, and deployment footprint, yet structured tool errors can cause an agent step to fail. Existing routers typically select a model once per query. However, agents expose sequential decision points whose difficulty dynamically changes based on intermediate observations. We propose STEPGATE, an uncertainty-aware handoff framework that scores each local SLM action and selectively escalates challenging steps to a stronger model. On a 52-task held-out single-step BFCL-derived test split, the Qwen2.5-1.5B/7B pair attains 82.7% task success with 30.8% escalation, versus 67.3% local-only and 75.4% random escalation (which uses 33.8% escalation). In a separate multi-turn evaluation, STEPGATE achieves 69.0% trajectory success and 84.0% action success using only 30.0% cloud actions, compared with 48.0%/70.5% local-only, 60.0%/78.2% random escalation, and 57.0%/77.1% query-level routing (strong-only achieves 82.0% trajectory success at 100% cloud actions). These results suggest that step-level escalation recovers a large share of the performance gap to the stronger Qwen2.5-7B backend at a matched cloud-action rate while transmitting fewer tokens remotely. However, our evaluation is limited to one model family, a single stronger backend, and scripted tasks. Furthermore, the test sets are small, multi-turn comparisons rely on paired intervals and statistical tests, and our risk tiers serve as research annotations rather than formal safety guarantees.

Wave-based processors promise fast, energy-efficient Fourier layers for neural operators. They are usually validated on randomly sampled devices, but using them requires knowing how large their error can become under fabrication and alignment variation. In a stylised numerical case study, a hybrid Fourier neural operator runs its four spectral layers on simulated coherent 4f processors with 32 toleranced knobs, whose half-widths are representative rather than calibrated. For 120 models (four tasks, six training methods, five seeds), we compared the worst of N random in-spec devices with a searched one. On a deterministic simulator with one frozen draw of the random static errors, the searched device's held-out error was 1.08-3.10 times the maximum over 200 Monte Carlo devices and 1.06-2.71 times that over 1000. With 20 fresh static draws, it still exceeded the maximum over 200 random devices in 116 of 120 models. Under uniform sampling, the probability of drawing such a device is at most 0.37% per model (two-sided 95% Clopper-Pearson), which says nothing about how large its error is. The gap persisted with uniform or Sobol' sampling at the search's budget, shared knobs, a second crosstalk model, box scales of 0.25-2 and a pixel-level device model. Models trained only with random static errors reached 3.7-39.9 times their nominal error on searched devices, and fine-tuning on random and gradient-searched devices gave the lowest searched error of the six in all 20 task-seed pairs. For two heat-exchanger quantities, a search targeted at each exceeded the worst of 1000 random devices in all 39 models, and hence the Wilks 95/95 limit (worst of 59). For the mean pressure of 11 models, no random device exceeded a 1% error threshold, but the searched device did. Random testing estimates how often errors exceed a threshold; worst-device search gives a lower bound on how large they can be.

Neuromorphic hardware combines memory (synapses) and computation (neurons) into the same silicon substrate to avoid the von-Neumann bottleneck. This way, neuromorphic computing aims to make computational neuroscience simulations and AI processing faster and more energy-efficient. The common crossbar architecture integrates a synapse matrix (analog, mixed-signal or digital) with a neuron array into a neurosynaptic core, whereas multiple cores are interconnected via a dedicated spike routing network. One of such neuromorphic architecture is the BrainScaleS wafer-scale system which allows the realization of very flexible network architecture, supporting both dense and sparse connectivity by highly configurable neurosynaptic cores. Yet, when network models from computational neuroscience are mapped to the BrainScaleS wafer, synapse loss can occur which means that for some model synapses no hardware synapse is available due to the restricted connectivity of the hardware. In this work we analyze the synapse loss when mapping uniform random networks to BrainScaleS both theoretically and empirically. We first develop a methodology to estimate maximum-sized networks without synapse loss based on probability distributions and the hardware's synapse routing architecture. Next, we adopt the methodology to predict the synapse loss for denser or larger network models. Then, we compare the predictions with results from actual runs of the mapping software: The results show the capabilities and limitations of the BrainScaleS system itself and spot shortcomings of the current mapping algorithms. The developed methodology can be helpful in multiple ways: to find the optimal settings when mapping a given network model to BrainScaleS, to act as a benchmark for the mapping software, and as tool for design space exploration for new hardware.

The Minimum Vertex Cover (MVC) problem is a fundamental NP-hard combinatorial optimization problem with applications in network analysis and resource allocation. Grover's algorithm provides a quadratic reduction in query complexity for unstructured search, but existing Grover-based MVC formulations can incur substantial quantum resource overhead due to costly vertex-counting circuits and complex oracle constructions. We develop and evaluate several encoding and oracle-design strategies for reducing the qubit count, circuit depth, and gate complexity of Grover-based MVC search. First, we construct a Dicke-Parallel formulation that restricts the search to fixed-cardinality subsets, eliminating explicit vertex counting, together with a parallel edge-verification oracle that reduces feasibility-checking overhead. We then develop an Edge-Counting formulation that replaces per-edge auxiliary storage with a logarithmic-size counting register, substantially reducing ancillary-qubit requirements. Finally, we propose an Edge-Centric encoding that represents endpoint selections directly and derives vertex-selection states through incident-edge Boolean operations, enabling more depth-efficient oracle construction. Our resource analysis reveals complementary trade-offs among qubit width, circuit depth, gate count, and Grover iteration count. Edge-Counting is particularly attractive under tight qubit constraints, while Edge-Centric can reduce both circuit depth and Grover iteration count when the graph has a moderate edge count and sufficient representation multiplicity. Dicke-Parallel provides a more robust choice when such multiplicity is limited or graph density makes the edge-based search space large. These results provide practical guidance for selecting Grover-based MVC formulations according to hardware constraints and graph structure.

This paper introduces an energy-adaptive noise scheduling and whitening strategy for transform-domain diffusion models. Existing spectral diffusion methods account for the non-uniform statistics of transform coefficients through coefficient scaling, normalization, or frequency prioritization, while the forward diffusion noise schedule remains largely independent of the underlying spectral-energy distribution. We investigate whether the temporal evolution of the forward diffusion process should also follow the spectral organization of natural images. The proposed formulation combines global spectral whitening with energy-conditioned noise allocation that jointly modulates the injected noise according to the energy of individual transform coefficients and an image-dependent energy path over diffusion time. The resulting forward process preserves Gaussian transitions with closed-form marginals and remains compatible with standard DDPM and DDIM procedures without modifying the diffusion architecture. Experiments on CIFAR-10 demonstrate the contribution of the proposed energy-conditioned noise schedule and spectral whitening, reducing Fréchet Inception Distance from 142.48 for a compact DCTdiff U-Net variant to 100.45.

Variational autoencoders are powerful representation learning models that map complex data into low-dimensional latent spaces, enabling the discovery of interpretable and disentangled factors. Such representations can facilitate the interpretation and controllable generation of data describing complex scientific systems. Understanding how these factors are organized and encoded in latent space is therefore important for developing reliable representation learning models. Recently, quantum variational autoencoders (QVAEs) have been proposed as quantum representation models, demonstrating informative latent representations and improved latent-space occupancy through quantum regularization. However, it remains unclear whether and how QVAEs can learn disentangled and interpretable latent factors. A key challenge in investigating quantum latent factors is that a small number of qubits spans an exponentially large Hilbert space, making the notion of an individual quantum latent dimension nontrivial. Here, we investigate what constitutes an individual quantum latent dimension and whether it can encode a distinct factor. We develop theoretical insights into quantum latent dimensions and support them with empirical studies on representative synthetic problems, including MNIST variants. Across three datasets, we demonstrate that QVAEs can discover factorized and semantically interpretable latent representations, with individual qubits functioning as meaningful latent factors. These results establish a foundation for understanding quantum latent spaces and their potential for structured and interpretable representation learning.

Despite the promise and potential of quantum computing, most of the current day developments of quantum computing have several limitations for relevant practical use. As a result, hybrid classical-quantum computing has emerged as a viable solution. This paper discusses the software aspects of hybrid classical-quantum computing solutions to understand and examine how hybrid computing solutions can be expected to work in practice. This paper examines existing software solutions (cloud applications, middleware frameworks, and API gateways) that can enable classical and quantum computing to work together. For practitioners the paper provides a curated list of algorithms, full stack libraries, simulators, and cloud service providers that are likely to be central to the development of any hybrid computing solutions. We conclude by identifying an agenda for future research on classical-quantum hybrid software engineering, and some near-term priorities for development of functional, robust hybrid software systems. The paper argues that due to the deep dependency of quantum execution on classical pre- and post-processing, hybrid computing may represent a near-permanent outcome of classical-quantum computing rather than a transitional workaround.

Underwater Wireless Optical Communications (UWOC) provide essential high data rates for Autonomous Underwater Vehicles (AUVs), but reliable connectivity is critically affected by transmitter-receiver misalignment. This work addresses the beam pointing problem for a moving AUV subject to unknown ocean currents through a Deep Reinforcement Learning (DRL) framework. We develop a comprehensive 3D UWOC channel model incorporating depth-dependent attenuation, turbulence, and a discrete-ray method to accurately quantify geometric and misalignment losses. The resulting agent jointly optimizes beam steering and divergence angles, learning a policy that prioritizes continuous link maintenance. Evaluated using real oceanographic data, the framework's performance is assessed via the excess outage metric, which isolates outages occurring exclusively due to pointing errors. Relative to perfect alignment between nodes, the proposed method bounds this metric to 8.5% under ocean current-free tracking conditions and limits it to 16.2% when subjected to current-induced drift. The findings obtained in this work confirms that DRL-enabled beam control is capable of adaptive tracking and mitigating link outages in dynamic underwater environments.

Materials literature contains detailed experimental knowledge, but procedures, chemical entities and measurements remain difficult to aggregate because they are reported in heterogeneous forms and depend on process-specific context. We present SciKGExtract, a schema-guided framework that combines large-language-model extraction with chemical normalization and agent-based evaluation and refinement before knowledge-graph integration. We evaluate the framework on 176 atomic-layer-deposition papers describing zinc oxide (ZnO) and indium–gallium–zinc oxide (IGZO), together with an expert-annotated full-schema subset. PubChem normalization improves exact-match extraction F1 for every tested model. For ZnO, the best F1 increases from 0.591 for direct normalized extraction to 0.805 with agentic refinement, whereas the best IGZO result is 0.344, revealing the greater difficulty of multicomponent supercycle processes. Evaluation against a deeply nested schema containing 65 experimental properties and 155 quantitative measurement nodes further exposes errors in process segmentation and numerical assignment. These results show that chemical canonicalization and targeted agentic verification provide complementary controls for converting complex materials literature into reusable, machine-actionable experimental knowledge.

Living neuronal networks transform inputs through recurrent cellular and population dynamics, yet it is unknown which network architecture supports which computation. Neurons-on-a-chip turn this question into a design problem because microchannels guide axonal growth and set the network architecture. We introduce IC$^3$, an Integrated Characterisation of Communication-Driven Computation, which characterizes network state through neuronal dynamics, functional communication, and structural support. We implemented nine architectures in silico as conductance-based spiking networks and tested each on frequency decoding, temporal-order discrimination, and fading memory. Predominantly feedforward circuits decoded best. Sequential Chain and Microchannel Diode had the lowest IC$^3$ and recruited a third of reachable neurons, yet achieved the two highest scores on both classification tasks. Across architectures, higher IC$^3$ went with lower classification scores. We term this new direction neurotopomorphic computing, in which the physical organisation of neuronal connectivity is engineered as part of the computing substrate.

Agentic AI is emerging as a promising paradigm for automating complex cybersecurity decisions, yet its use in enforcing zero trust introduces significant challenges in safety, reliability, and policy compliance. This paper presents Agentic AI based zero trust architecture (Agentic-ZTA) that operationalizes the NIST SP 800-207 ZTA architecture control loop through coordinated multi- agent decision pipeline. In the proposed framework, policy knowledge is embedded into a retrieval-augmented generation pipeline and retrieved at inference time as top-k relevant policies. Access requests are intercepted by the Policy Enforcement Point (PEP), enriched with contextual metadata. The request context is routed to a policy engine agent which invokes domain-specialized core agents first followed by supporting agents, if further evaluation needed. AI agents reason over access context, policy constraints and determine trust. The retrieved policies are embedded into agent prompt during inference time and agentic trust scores are aggregated and evaluated by a trust-algorithm, producing the final access decision for enforcement under continuous verification. We implement Agentic-ZTA in a testbed and evaluate it on representative access-control use cases scenarios. Our Agentic-ZTA framework achieves 95.0% accuracy, 93.9% precision, and 96.3% recall, and demonstrate the feasibility of enforcing zero trust using AI agents.

Molecular communication (MC) uses molecules as information carriers among engineered cells and artificial devices in the Internet of Bio-Nano Things. MC receivers commonly employ ligand receptors, whose discrimination between the two received concentration levels of binary signaling depends on their position relative to the dissociation constant. Changes in the transmitter-receiver distance, ligand degradation, and interference can shift both levels below or above the dynamic range of fixed receptors, impairing detection. Previous work restored performance by externally tuning the dissociation constant to the geometric mean of the estimated received levels. In this paper, inspired by receptor adaptation in living cells, we investigate a receiver whose intracellular antithetic integral feedback network senses the receptor occupancy and autonomously tunes the dissociation constant through a modulator molecule. For constant received levels and equiprobable bits, we prove that, without knowing these levels, the network sets the dissociation constant at equilibrium to their geometric mean, which minimizes the bit error probability (BEP) for any fixed receptor cooperativity. We derive the gain, bandwidth, noise, and stability boundary of this feedback loop. The random bits averaged by the network induce self-noise, which sets an error floor independent of the network molecule count and, balanced against the channel-tracking error, yields a loop-speed design rule. For received levels varying fivefold over a coherence time of 24 loop time constants, adaptation reduces the BEP 61-fold for non-cooperative and 410-fold for cooperative receptors, compared with the best non-adaptive receiver. This benefit grows with the coherence time and peaks at moderate variation depths.

The offset and gain of voltage-to-time converters (VTCs) in time-domain circuits vary with process, supply, and temperature. This work presents a VTC whose nonvolatile input-referred offset trim is the programmed multilevel state of the ferroelectric field-effect transistor (FeFET) that converts the input. Fabricated in 28-nm CMOS, the 6.07-$\mu$m$^2$ VTC consists of a current-starved inverter with the FeFET and a parallel NMOS leaker in its tail, a capacitor bank, and a following inverter. The programmed state shifts the transfer curve onto the input range, leaving the gain to the capacitor bank and the leaker bias. Measured at 10~MS/s, centered states weaken the second harmonic from $-$12.5~dB in the low-threshold state to $-$32.2~dB and lower the static integral nonlinearity (INL) at 5~bit from 2.42 to 0.92~LSB. Post-layout simulations indicate that the proposed VTC can operate at 500~MS/s, drawing 2.0~$\mu$W from a 0.9-V supply, with nearly unchanged distortion and gain up to 700~MS/s. Across simulated process corners and $\pm$10\,% supply variation, the input-referred offset is calibrated to within 10~mV and the gain to within 4\,% of nominal by adjusting the programmed state and leaker bias.

Ising machines have emerged as promising hardware accelerators for intractable optimization and sampling problems, yet their practical impact increasingly hinges on the co-design of algorithms and hardware, where algorithmic demands shape new architectures and new hardware capabilities inspire entirely new algorithms. In this Review, we survey probabilistic algorithms designed for portability across diverse Ising platforms, advocating a top-down perspective that prioritizes principled methods with provable guarantees. We cover foundational methods such as simulated annealing and parallel tempering, including two-dimensional extensions that natively encode hard constraints, and examine approaches that expand the scale of solvable problems from cluster mean-field methods to variational samplers. We highlight the Probabilistic Approximate Optimization Algorithm (PAOA), a classical analog of QAOA that emerged directly from probabilistic hardware development, and explore how generative AI and Ising machines might reinforce each other: learned models propose global moves to accelerate optimization, while probabilistic techniques improve inference in large language models. Much as quantum computing has seen algorithms co-evolve with hardware, probabilistic and Ising computing stand at a similar inflection point. We outline a co-design framework for accelerating the capabilities and adoption of next-generation Ising machines.

In Fifth-Generation (5G) wireless networks, Quality of Service (QoS) flow management supports diverse applications with heterogeneous QoS requirements, including latency-sensitive and high-throughput services. 5G architectures adopt a flow based QoS framework, where QoS flows are mapped to Data Radio Bearers (DRBs) to achieve the desired radio resource allocation. Existing mapping strategies typically rely on static one-to-one QoS-to-DRB mappings per PDU session, leading to inefficient spectrum utilization or inadequate support for delay sensitive traffic. This work presents a hybrid QoS-aware DRB mapping mechanism based on standardized 5G QoS Identifier (5QI) characteristics. The proposed framework incorporates multiple QoS flows within a single PDU session, supporting both many-to-one and one-to-one mapping strategies. The hybrid framework improves radio resource utilization and reduced bearer overhead by enabling flexible DRB utilization, where compatible non critical flows share DRBs while delay-critical flows are assigned dedicated DRBs to ensure strict QoS guarantees. The proposed hybrid QoS-aware DRB mapping is implemented using the open-source OpenAirInterface (OAI) mobile stack platform to demonstrate the integration of the proposed features within the existing 5G framework.

The human menstrual cycle is regulated by complex hormonal feedback mechanisms which are essential for reproductive health. Existing mathematical models either rely on simplified phenomenological assumptions or achieve physiological accuracy at the cost of high mathematical complexity and computational costs. This paper presents a novel mechanistic model which balances biological realism with mathematical simplicity. The proposed framework describes the dynamics of the four central cycle-driving hormones, namely follicle stimulating hormone, luteinizing hormone, estradiol, and progesterone, using a system of ordinary differential equations. Mechanistic detail is incorporated through representations of feedback mechanisms, follicular development, luteinization, vascularization of the corpus luteum, and aspects of hormone synthesis mechanisms. Despite its comparatively low complexity, the model reproduces characteristic trends of hormone activity and concentration dynamics reported in experimental data. Due to its computational efficiency and modularity, the model is well suited as a basis for extended in-silico experiments, including applications in pharmacology and personalized medicine. Our results demonstrate that targeted incorporation of biological mechanisms enables accurate yet tractable modeling of endocrine system dynamics.

Recipe generation from food images has practical value for intelligent cooking assistance, but traditional one-pass generation often overlooks user-specific safety requirements such as allergies, dietary restrictions, and preparation constraints. We propose PCAR, a Planner-Critic Agentic Remediation framework for trustworthy recipe generation. PCAR separates recipe planning from safety verification: a Planner Agent extracts ingredients and generates recipe drafts conditioned on the user profile, while a Safety Critic Agent audits each draft and provides structured feedback for remediation when violations are detected. This remediation loop enables unsafe recipes to be revised rather than directly returned or discarded. We evaluate PCAR on real food images with 100 benchmark user profiles across four backbone models, including proprietary and locally served open-source models. Results show that PCAR achieves strong safety and generation performance with capable backbone models, while preserving practical recipe quality.

In directed logic (DL), electronically controlled optical elements serve as photonic gates. Such electro-optical elements are of mixed nature: they have two inputs–electronic and optical–but only one, optical, output. This makes cascading such gates without repeated conversion between optical and electronic representations cumbersome. This problem can be largely overcome using the nested cascading scheme proposed by Shamir and Hardy [Opt. Express, 17, 150 (2007)]. Although promising as a solution to the cascading problem, the Shamir-Hardy scheme has so far been proved or implemented only for a few simplest cases. Here, we develop a general rigorous theory of nested cascading, valid for an arbitrary number of gates. We propose a variant of the algorithm which easily extendable to large number of gates, and rigorously prove its validity. Furthermore, we analyze how nested cascading scales with the size of the corresponding Boolean formula. We show that good (linear) scalability is guaranteed in many important cases, while the average scaling with respect to the corresponding Boolean formulas is only moderately polynomial, with an exponent of approximately 3/2. Yet, the worst-case scaling remains exponential with respect to more general Boolean circuits allowing sharing and reuse of intermediate results.

Can programmable radio propagation serve as computational depth rather than only as a communication channel or one-shot analog transform? We introduce the Radio Alternating Operator Ansatz (RAOA), a recurrent computing architecture that alternates an energy-derived problem update with a mixing update over a persistent latent state. Recomputing the problem field after each mix makes repeated passes compositional even when the same learned controls are reused across depth. We evaluate this idea through exact discrete optimization, constrained programmable-propagation simulation, and pretrained-model adaptation. On discrete objectives, repeated execution can improve solution quality without increasing the learned-control count, and the same formulation handles higher-order interactions directly. A passive phase-only free-space model further shows that the required operators can be approximated by programmable propagation while retaining useful downstream behavior despite realization error. When inserted as a zero-initialized residual adapter, RAOA adapts pretrained language models with WikiText performance close to a matched shallow MLP across three model families, while reasoning-task transfer remains model-dependent. Together, these results connect alternating-operator computation, programmable radio propagation, and neural adaptation within one recurrent framework. The RF realization evidence is simulation-based rather than a hardware demonstration.

Formal verification of quantum circuits becomes difficult as circuit size grows because exhaustive reasoning over the Hilbert space is computationally expensive. In previous work [1], we introduced a bit-vector abstraction that separates superposition tracking from basis-state behavior, enabling SMT-based verification of fixed-size error-detection and entanglement circuits built from H, X, and C-NOT gates. This paper extends the framework in five directions. First, we generalise correctness properties to parametrised repetition and concatenated phase-flip codes with up to 101 logical elements. Second, we verify syndrome extraction for CSS codes specified by arbitrary parity-check matrices, including the Steane, [[15,7,3]] Hamming, [[15,1,3]] Reed-Muller, and rotated surface codes with distances up to 101. We consider arbitrary error patterns, detection up to a specified weight, and recovery using a classical decoder, with counterexamples beyond each code's correction or detection capability. Third, we characterize the {H,X,C-NOT} fragment for which the abstraction faithfully represents Hilbert-space semantics. We prove soundness, demonstrate necessity with counterexamples, validate the characterisation against state-vector simulation, and prove the six lemmas from the original work. Fourth, a mutation study using 13 operators identifies equivalent mutants and detects all 622 non-equivalent mutants within the evaluated fault model. Fifth, a direct Z3 implementation verifies GHZ circuits with up to 262,144 qubits across three topologies on a 16GB laptop. Random H+C-NOT networks show verification cost rises sharply with depth when outputs depend on many inputs. The approach does not target general stabilizer circuits, Y-type stabilizers, fault-tolerant protocols, or physical noise; we identify extensions needed to address these cases.