Peer Revenue Publish or profit

Papers

Fresh from arXiv, typeset properly. Highlighted words trade on the market.

Computer Science Emerging Technologies

cs.ET Watch Emerging Technologies

Monday 24 August

Emerging Technologies

A unified dynamical modeling framework for cruise control and adaptive cruise control

Mingfeng Shang and Shian Wang

Adaptive cruise control (ACC) vehicles are the first generation of automated vehicles. While fully automated vehicles are expected to benefit traffic flow, field experiments have shown that commercially available ACC vehicles may instead degrade it by reducing string stability and roadway throughput. To mitigate these effects, existing studies adjust the ACC control algorithm or introduce additional control inputs; however, few have examined the transition between the cruise control (CC) and ACC modes without modifying the ACC control algorithm itself, leaving the impacts of ACC vehicles incompletely understood. To address this gap, we propose a unified dynamical model of CC and ACC that interpolates continuously between the two modes through a sigmoid weighting function, and improve traffic flow by designing the mode switching. Based on this new model, we conduct an equilibrium and string stability analysis of the platoon, revealing the trade-off among safety, throughput, and string stability. The optimal switching threshold is designed under throughput-priority and safety-priority criteria, and compared against the threshold adopted by commercially available ACC vehicles. Numerical experiments show that, with a properly designed switching threshold, the throughput increases by up to 58.6% and the average speed variation, a measure of speed oscillations, decreases by up to 39.7% relative to the commercial baseline. We conclude that the excessively large switching threshold of commercially available ACC vehicles is a likely cause of their negative impact on traffic flow, and that this impact can be mitigated by properly reducing the threshold toward a safer and more string-stable regime.
Read the full abstract

Emerging Technologies · Cross-listed from cs.AR

A Process-Aware Hybrid Si/IGO Monolithic-3D 6T SRAM with BEOL Pass-Gates for the 2nm Node

Po-Chuan Wang, Dongwon Jang, Eknath Sarkar et al. (+5 more)

We propose a monolithic-3D (M3D) 6T SRAM at the 2nm node, integrating BEOL IGO pass-gates (PGs) with an all-silicon nanosheet latch, buried power rails (BPRs), and Ru interconnects. TCAD calibrated to a state-of-the-art double-gate IGO transistor with a tri-layer HfO$_2$/ZrO$_2$/HfO$_2$ (HZH) gate stack is combined with virtual fabrication and 3D parasitic extraction to realize the first process-aware layout of this topology. A novel neighbor-cell shared source/drain (S/D) bitline (BL) design enlarges the IGO contact area to mitigate contact resistance and restore PG drive without area penalty. The resulting cell achieves a 25% footprint reduction vs the high-performance (HP) 122 Si baseline while maintaining robust static noise margin (SNM) over a wide supply voltage range. At the 128$\times$256 subarray-level, it reduces write delay by 42.2% and EDP by 9.7% compared to the high-density (HD) 111 Si baseline, owing to reduced cell parasitics and wordline (WL) loading from the smaller footprint.
Read the full abstract

All authors Po-Chuan Wang, Dongwon Jang, Eknath Sarkar, Alexei Svizhenko, Md. Nahid Haque Shazon, Piyush Kumar, Suman Datta, Azad Naeemi

Sunday 23 August

Emerging Technologies · Cross-listed from cs.AR

Precision-Aware Variable Bit Processing Elements for Hardware-Efficient Systolic Array Designs

Dantu Nandini Devi and Madhav Rao

Systolic arrays (SAs) have emerged as prominent hardware accelerators for matrix operations in deep learning, while floating point number formats enable precision control across computational domains. This research investigates approximate computing techniques for floating point (FP) multipliers in Weight Stationary Systolic Arrays, focusing on IEEE 754 (FP32), TensorFloat-32 (TF32), and Brain Floating point (BF16) formats. By integrating partial product matrix (PPM) column truncation with positive and negative compressors in the FP multiplier architecture, we optimize the trade-off between computational efficiency and accuracy. NSGA-II optimization algorithm was employed to explore the vast design space for evolving FP multiplier designs, towards achieving substantial hardware improvements while maintaining acceptable output quality. Substantial hardware benefits were observed in the FP multiplier designs across various applications, while preserving output quality. The FP approximated Processing Elements designed in the SA was found to offer comparable CNN accuracy for models trained on MNIST, F-MNIST, and CIFAR-10 dataset. The FP approximated SA designs that fall in the top 10 CNN performance offered substantial hardware gains in the range of 66% to 92% footprint savings, 60% to 93% of power benefits with 21% to 54% improvement in the delay when compared with the corresponding exact implementations mentioned in the literature for running the model trained on CIFAR-10 dataset. The TF32 and BF16 approximated SA designs also achieved substantial gains while maintaining comparable CNN accuracy. Our findings confirm that targeted approximation in FP multiplier design significantly improves the efficiency of hardware accelerators for error-tolerant applications, establishing an effective approach to hardware resource optimization in contemporary computing architectures.
Read the full abstract

Emerging Technologies · Cross-listed from cs.NI

STAR-GS: Truthful and Visibility-Aware Resource Scheduling for Ground Station as a Service

Zhiying Wang, Xiaojian Wang, Huayue Gu et al. (+2 more)

The rapid growth of Low Earth Orbit satellite constellations has created increasing demand for efficient and scalable downlink services. Ground Station as a Service (GSaaS) provides an on-demand access model for satellite operators, but commercial GSaaS providers must schedule limited ground-station bandwidth among multiple satellites with heterogeneous data demands, overlapping visibility windows, strict deadlines, and strategic bidding behaviors. This paper studies GSaaS resource scheduling from a ground-station-centric perspective, where the provider jointly determines task admission, ground-station assignment, bandwidth allocation, and payments. Under satellite orbital dynamics, bandwidth constraints, and downlink task deadlines, maximizing the provider's revenue is NP-hard. To address this challenge, we propose STAR-GS, a truthful and feasibility-aware scheduling mechanism that combines bid-aware admission control, best-fit ground-station assignment, Earliest Deadline First (EDF)-based bandwidth scheduling, and critical-payment pricing. By integrating auction theory with schedulability analysis, STAR-GS incentivizes task owners to truthfully report their private valuations while ensuring that admitted tasks can be feasibly completed before their deadlines. Simulations using Ansys Systems Tool Kit (STK) show that STAR-GS consistently achieves higher revenue than heuristic baselines, obtains near-MILP performance with substantially lower runtime, and scales smoothly to workloads containing up to 900 tasks.
Read the full abstract

All authors Zhiying Wang, Xiaojian Wang, Huayue Gu, Zhishan Guo, Ruozhou Yu

Emerging Technologies · Cross-listed from cs.CR

Lessons from the Hardware Hacking Competitions: Verification Techniques, Findings, and Insights

Sudipta Paria, Aritra Dasgupta, Raghul Saravanan et al. (+3 more)

Hardware hacking competitions have emerged as practical platforms for evaluating security weaknesses in complex System-on-Chip (SoC) designs while promoting security-aware verification and tool development. This paper presents a systematic study of SoC security verification through open-box hardware hacking competitions, focusing on practical vulnerability analysis strategies, observed findings, and lessons for security-aware verification. We present a multi-strategy vulnerability analysis methodology, combining simulation-based verification, formal verification, lint analysis, Large Language Model (LLM)-assisted bug detection, and coverage-guided hybrid fuzzing. Representative vulnerability findings are analyzed to illustrate how different techniques expose complementary classes of security flaws, and we derive practical lessons for pre-silicon security verification. Finally, we discuss how competition benchmarks can support the reproducible evaluation of emerging hardware security techniques and guide future security-aware EDA research.
Read the full abstract

All authors Sudipta Paria, Aritra Dasgupta, Raghul Saravanan, Jayanth Thangellamudi, Sai Manoj P D, Swarup Bhunia

Emerging Technologies · Cross-listed from cs.SE

Towards Actionable Visualization: Ten Years Later, What Generative AI Changes and What It Cannot

Leonel Merino, Mohammad Ghafari and Oscar Nierstrasz

Ten years ago, we surveyed software visualization research under the assumption that the challenge for adoption was matching developers' needs with techniques. Generative AI might have made that assumption obsolete by driving the cost of producing a visualization on demand toward zero. We argue that this mirrors a broader inversion already underway in software engineering: as AI devalues the production of artifacts, it elevates the human work of perceiving and directing them. Looking back at our 2016 research, we found that domains we flagged as neglected, such as rationale, are exactly the ones AI now makes tractable, and a tool-sustainability problem we diagnosed then is one generative AI may worsen before it helps solve. One finding shifted outright: the share of studies delivered through immersive environments grew roughly fifteen-fold, though it remains a small minority of the field. We argue that future research should focus on helping people understand large and complex software systems, including the reasoning processes generated by AI agents alongside the code itself. Humans have always been accountable for what they trust, but AI may lead them to trust without enough scrutiny. We confront the risks that follow, from the reliability of AI-generated visualization itself to the temptation of competing with machines on their own terrain, and conclude that actionable visualization is no longer a distant destination but a standard the field can now meet.
Read the full abstract

Saturday 22 August

Emerging Technologies · Cross-listed from cs.HC

A Hybrid Gaze-Motor Imagery BCI Framework for Effective Decision Communication

Gowtham Reddy N, KongFatt Wong-Lin and Yogesh Kumar Meena

Non-invasive brain-computer interfaces (BCIs) and eye-tracking technologies offer promising communication pathways; however, motor imagery (MI)-based BCIs often suffer from low discriminability and high inter-subject variability. To mitigate these issues, this study investigates the impact of visual fixation on neural response stability in both standalone MI and hybrid MI-eye tracking systems. We then propose a novel asynchronous hybrid paradigm that streamlines user intent by utilising eye-tracking for direct selection, followed by MI-based confirmation, significantly reducing the operational steps required by conventional systems. The paradigm was evaluated with 15 healthy participants using a 16-channel EEG system. Results show that MI-related information is predominantly localised within motor cortex regions, with limited-channel configurations (SVM: 0.58) achieving performance comparable to full-montage setups (SVM: 0.54). The hybrid MI paradigm further outperforms conventional MI, achieving up to 100% accuracy with greater robustness across all channel configurations. Our findings indicate that visual fixation enhances neural response stability, while integrating eye-tracking with MI enables the development of reliable, scalable multi-command BCI systems suitable for real-world applications.
Read the full abstract

Emerging Technologies · Cross-listed from cs.AI

Can You Say This for Me? Speaking Up by Proxy in Co-Located Discussion

Yue Shen, Rehema Abulikemu, Ryan P. McMahan et al. (+1 more)

Equal participation in co-located discussion is important for effective collaboration, yet people often hold back when they anticipate negative interpersonal or professional consequences, especially when raising a point requires voicing it themselves. We present SecondVoice, a mixed-reality system that enables people to speak up through an embodied virtual proxy. By separating what is said from who says it, SecondVoice brings hesitant points into the live spoken discussion without putting the speaker on the spot. Using a private overlay, users specify their intent through a structured specification process rather than composing a full utterance. The system reformulates the input and voices it into the conversation through the proxy. We characterize a design space of participation channels under social risk. In a preliminary within-subject study (N = 16), we compare the complete SecondVoice system with an anonymous text-board channel across two group discussion tasks. Half of participants reported using SecondVoice for a point they did not say aloud, compared with 18.8% for the text board. Proxy-delivered points entered the spoken floor and were followed by multi-turn group engagement, which we did not observe after text-board posts. Participants described the channel as situationally valuable but identified tradeoffs around timing, ownership, and trust in reformulation.
Read the full abstract

All authors Yue Shen, Rehema Abulikemu, Ryan P. McMahan, Yan Chen

Emerging Technologies · Cross-listed from quant-ph

How Far Can You Do Nothing On a Quantum Computer?

Nitay Mayo, Tal Mor and Aryeh Lev Zabokritskiy

We present a route-resolved comparative assessment of Rigetti's Cepheus-1-108Q and IBM Heron-r2 processors using the established do-nothing state-transfer protocol. Rather than proposing a new protocol, we use this deterministic, low-complexity task as a high-resolution spatial probe. For each evaluated initial qubit, we report two complementary quantities: the largest tested radius within which every evaluated shortest route satisfies the operational success rule, and the longest successful route identified within the evaluated route family. To address the question "How far can you do-nothing on a quantum computer?", we prepare a quantum state, route it outward using SWAP gates, apply the inverse preparation at the destination, and route it back for measurement. We compare the measured probability of returning $0$ with the empirical cutoff $2/3$. For this circuit ordering, that comparison does not certify above-classical unknown-state transfer. While this trivial state-transfer protocol serves as the most intuitive baseline, actively preserving a quantum state across a physical lattice proves to be a non-trivial task that exposes the information to cumulative relaxation, dephasing, and environmental cross-talk. In the highlighted IBM QPU case, we identify an isotropic radius of 10 and a successful path of swap distance 27, whereas the highlighted Rigetti Cepheus case exhibits an isotropic radius of 1 but selected above-threshold routes reaching swap distance 8. These results reveal a sharp distinction between uniform spatial reliability and best-route performance. The presented quantities are empirical and conditional on the evaluated route families, finite-shot decision rule, calibration state, and execution time; they are not architecture-wide constants.
Read the full abstract

Friday 21 August

Emerging Technologies · Cross-listed from cs.IR

Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence

Yuyuan Feng, Zhishang Xiang, Chaobin Yang et al. (+32 more)

LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms including Prompt Engineering to elicit model capabilities, Context Engineering to manage information access, Harness Engineering to organize external tools and resources, and Loop Engineering to support continual reflection and self-improvement. Yet as tasks grow more complex, individual intelligence faces a fundamental limit: many tasks require heterogeneous expertise, interdependent subtasks, parallel execution, independent verification, and persistent state, exceeding any single agent's organizational capacity. Augmenting one agent's capabilities or context cannot resolve this architectural mismatch; intelligence must instead be distributed across specialized agents and organized at the system level. We call this System Intelligence: an agent system's ability to organize and coordinate multiple intelligent components into a coherent, adaptive whole pursuing a shared objective. Achieving it requires more than adding agents; it demands explicit structures to organize work, coordinate heterogeneous agents, and maintain evolving execution states. We introduce Graph Engineering, an emerging paradigm for next-generation agent systems. Unlike prior paradigms that mainly optimize individual interactions or agent-level behavior, Graph Engineering constructs explicit, dynamic, evolving graph structures representing tasks, agents, and system states. These abstractions provide a unified foundation for organizing complex objectives, orchestrating heterogeneous agents, modeling system dynamics, and enabling scalable agent evolution. We systematically review the principles, methodologies, and applications of Graph Engineering for LLM agents. Related papers, open-source data, and projects are collected at https://github.com/DEEP-JLU/Awesome-Graph-Engineering.
Read the full abstract

All authors Yuyuan Feng, Zhishang Xiang, Chaobin Yang, Qichao Ma, Zerui Chen, Yujing Zhang, Ke Huang, Chuanjie Wu, Zhaoxu Liu, Yili Wang, Xin He, Jiapu Wang, Zijin Hong, Hao Chen, Yuanchen Bei, Kun Wang, Shengyuan Chen, Ningyu Zhang, Enyan Dai, Linhao Luo, Qingyi Pan, Qi Wang, Wenqi Fan, Guangjing Wang, Na Zou, Yangqiu Song, Xin Wang, Zechao Li, Xia Hu, Qing Li, Xiao Huang, Zhihong Zhang, Jinsong Su, Qinggang Zhang, Yi Chang

Emerging Technologies · Cross-listed from cs.CR

The Claws in Plain Sight: Unauthorized Context Disclosure through LLM Agent Tool Calls

Ben Dong, Zhonghao Guo, Tianyi Lu et al. (+1 more)

LLM agents routinely construct tool-call arguments from user profiles, conversation history, retrieved documents, and prior tool results. However, legitimate access to contextual information does not imply authorization to transmit that information for every purpose or destination. We present Claw in Plain Sight, an authority- pressure attack in which task-adjacent content frames protected attributes as operationally or procedurally required, causing a model to include them in otherwise valid generated arguments. We evaluate Claw in Plain Sight using a controlled synthetic benchmark that crosses six pressure levels with four privacy-policy levels across five DeepSeek and Claude model configurations, producing 120 calls. Across the complete pressure-policy matrix, session-level disclosure rates range from 20.8% to 75.0% among the tested models. Stronger privacy instructions reduce aggregate disclosure but do not eliminate it consistently across models, showing that prompt-level policies do not provide a portable enforcement boundary. Our experiments use only synthetic profiles and capture proposed arguments locally; they measure policy-violating generation at the context-to-argument boundary, not completed network exfiltration or leakage from deployed users. These findings motivate purpose- and destination-aware inspection of generated tool arguments before execution.
Read the full abstract

All authors Ben Dong, Zhonghao Guo, Tianyi Lu, Qian Wang

Thursday 20 August

Emerging Technologies · Cross-listed from cs.AI

Enhanced Artificial Neural Networks Using QHAdamW in Air Quality Forecasting

Mary Joy Daniel Vinas

The study employed an Artificial Neural Network in combination with the optimized Adaptive Moment Estimation (Adam) algorithm, currently the only AQI forecasting model available in the Philippines. The modified QHAdamW - Quasi-Hyperbolic Momentum (QHAdam) and Adam with decoupled weight decay (AdamW) were both extensions of the Adam optimizer, and both offer unique advantages for training ANN. The proposed QHAdamW optimizer addresses the issues on convergence, generalization, and forecasting performance of Adam. Hyperparameter tuning results revealed that 0.01 and 0.001 were the most effective optimal values for the generalization performance of QHAdamW. The comparative analysis results using seven evaluation metrics revealed that the error value range is lower, and the regression coefficient, having a value approximately equal to 1, improved the model accuracy performance. Likewise, the model converges to a satisfactory level of performance with the convergence performance results of lower loss values as obtained from training and validation losses. Based on data from a real-time air quality tracking station in Manila, a feed-forward neural network is used to predict the AQI of PM2.5 and PM10 separately. This model can be used to forecast Particulate Matter (PM), to help the Department of Environment and Natural Resources-Environmental Monitoring Bureau (DENR-EMB) implement a comprehensive air quality management.
Read the full abstract

Emerging Technologies · Cross-listed from quant-ph

Granthi: Higher-Order Quantum Programming via Unitary Wiring

Samson Abramsky and Radha Jagadeesan

Many mainstream quantum programming languages confine higher-order structure to a classical host while restricting the quantum layer to first-order operations on qubits. This paper presents Granthi, a purely unitary higher-order quantum programming language built on three design commitments: quantum programs are first-class values that may be passed, returned, and coherently composed; additive structure is tag-preserving routing rather than observational branching, so control may remain in superposition; and programmer-facing finite label types with staged reversible-operation bindings provide domain-level control spaces without exposing tag management. These bindings are eliminated by elaboration before Source typing. Granthi deterministically normalizes each Source program to a canonical wiring form. Every well-typed Source program, including a term of function type, has a unitary boundary interpretation. Under backend correctness (BC), the reference compiler produces a unitary circuit realizing that interpretation. Granthi's currently supported executable fragment is implemented end-to-end: an OCaml DSL elaborates surface programs through a higher-order Core IR to executable quantum circuits via pytket. The language directly supports the pure-unitary quantum switch for explicitly supplied operations; closed instances compile to static circuits. It also supports interference on control-flow history and structured finite control, all within the purely unitary fragment
Read the full abstract

Emerging Technologies · Cross-listed from cs.RO

Planning-Oriented End-to-End Autonomous Driving: Architectures, Evaluation, and Emerging Paradigms

Yanchen Guan, Xingcheng Liu, Bin Rao et al. (+7 more)

End-to-end autonomous driving has evolved from camera-to-control regression toward planning-oriented systems that use structured representations, trajectory-level outputs, and increasingly realistic evaluation protocols. This survey reviews this transition across behavior cloning, conditional imitation learning, privileged distillation, BEV and vectorized planning, unified perception-prediction-planning architectures, world-model-based planners, and vision-language-action systems. We argue that the key distinction in modern end-to-end driving is not whether intermediate representations are used, but whether they are learned, supervised, and evaluated to support safe, feasible, and route-compliant planning. To organize the literature, we synthesize existing methods along four axes: input representation, planning output, supervision signal, and evaluation protocol. We further examine the benchmark shift from open-loop trajectory matching to closed-loop simulation, non-reactive real-log evaluation, long-tail testing, and human-preference-aware metrics. Our analysis highlights that architectural progress is difficult to interpret without benchmark-consistent evaluation, and that displacement-based open-loop metrics alone provide limited evidence for safe and human-aligned driving. We conclude with open challenges in uncertainty-aware planning, learner-expert mismatch, runtime safety assurance, language-action grounding, world-model validation, and reproducible benchmarking.
Read the full abstract

All authors Yanchen Guan, Xingcheng Liu, Bin Rao, Chengyue Wang, Guofa Li, Yunjian Li, Lishengsa Yue, Zhiyong Cui, Chengzhong Xu, Zhenning Li

Emerging Technologies · Cross-listed from cs.SE

Evaluating Automated Testing on an Open-Source Web Application Using Cypress

Quoc-Binh Nguyen, Truc-Ly Phan Nguyen, Ngoc Hong Tran et al. (+1 more)

End-to-end automated testing is increasingly used in web software development to ensure system quality and shorten response times during development. However, the true effectiveness of automated testing depends on many factors including execution time, stability of test results, and maintainability of the test suite as the application continues to evolve. In this paper, we evaluate the effectiveness of end-to-end automated testing using the Cypress framework for an open-source web application. We deployed the experiment with 27 test cases. The test's effectiveness is measured by execution speed, reliability, and maintainability. The experimental results show that the Cypress-based end-to-end test suite has short and stable execution times. It suits frequent runs during software development. The majority of test cases achieved consistent results across multiple runs, while flakiness only occurred in a few tests which involve complex interactive functions. Furthermore, the study highlights the impact of element locator strategies and Page Object Model (POM) architecture on test suite maintainability, demonstrating that resilient data-cy attributes significantly reduce maintenance overhead when UI changes occur.
Read the full abstract

All authors Quoc-Binh Nguyen, Truc-Ly Phan Nguyen, Ngoc Hong Tran, Dung Hai Dinh

Wednesday 19 August

Emerging Technologies · Cross-listed from cs.AI

Agentic AI for Safety-critical Multi-drone Systems: Challenges and Opportunities

Timothy Merritt, Alejandro Jarabo-Peñas, Juan Bravo-Arrabal et al. (+2 more)

Multi-drone systems are increasingly positioned for safety-critical missions such as search and rescue (SAR) and critical infrastructure monitoring. Yet, real-world adoption remains constrained not only by autonomy performance, but by the difficulty of integrating agentic behavior into professional work: operators must understand, trust, and govern automation under uncertainty, time pressure, and accountability. This position paper synthesizes the ambitions and lessons from two ongoing efforts: NAMUR, which explores LLM-supported robot control in SAR and firefighting contexts, and PERSIST, which explores persistent drone operations for monitoring and security at critical infrastructure sites. We argue that agentic AI should be approached as a socio-technical design problem, where interfaces, oversight mechanisms, and evaluation practices are as critical as algorithms. We outline a human-centered, participatory, and iterative research approach aimed at uncovering stakeholder needs, shaping agent capabilities through successive prototypes, and producing transferable proof-of-concept systems and evaluation strategies for other safety-critical contexts.
Read the full abstract

All authors Timothy Merritt, Alejandro Jarabo-Peñas, Juan Bravo-Arrabal, Maria-Theresa Bahodi, Anders Lyhne Christensen

Emerging Technologies · Cross-listed from cs.LG

Enhancing Distance-Based Graph Autoencoders with Structural Penalties for Dynamic Graph Embedding

Aleksandar Tomčić, Miloš Savić and Miloš Radovanović

Graph autoencoders (GAEs) are widely used for learning representations of dynamic graphs. However, their optimisation objectives typically do not take structural heterogeneity across nodes into account. We propose three distance-based GAE variants that incorporate structural penalties into the reconstruction loss. All variants share a two-layer Graph Convolutional Network encoder and a Euclidean-distance decoder trained with distance-based reconstruction objectives. We extend sparsity-corrected loss with two node-level regularization terms: (i) a hub penalty based on degree centrality, and (ii) a penalty based on Natural Community Local Intrinsic Dimensionality (NC-LID). The paper is motivated by prior evidence linking high NC-LID to reduced embedding quality. The proposed methods are designed to emphasize reconstruction errors for structurally ambiguous nodes. Experiments on multiple dynamic graph data sets show that incorporating NC-LID-based regularization consistently improves reconstruction performance over the baseline without structural regularization and the method using hub-aware regularization. These findings highlight NC-LID as a useful structural signal for enhancing distance-based graph autoencoders in dynamic settings.
Read the full abstract

Emerging Technologies · Cross-listed from cs.NE

Biological-Hybrid Intelligence: A Conceptual Framework for Distributed Biological–Artificial Computation

Michael Taynnan Barros, Sergio Lopez Bernal and Reinhold Scherer

Biological and artificial systems offer complementary forms of adaptation, learning, and computation, with advances in in-vitro neurotechnology increasingly enabling bidirectional coupling between them. As these systems become more tightly integrated, a key architectural question is how task-relevant computation should be distributed across both substrates. Yet existing biohybrid solutions optimise the biological substrate, the AI model, or their interface without explicitly addressing how such computation is allocated, reassigned, and evaluated. We introduce Biological-Hybrid Intelligence (BHI), a conceptual framework for distributing computation across adaptive biological and artificial substrates coupled through a bioelectronic interface and coordinated by an orchestrator. BHI treats both substrates as computational entities whose computational responsibilities may change during operation. BHI requires reciprocal co-adaptation and differs from systems that merely decode biological activity, stimulate a living substrate, or adapt a single component. BHI further defines three operating modes: adversarial, collaborative, and codependent, distinguished by whether the substrates compete, divide computational labour, or become mutually necessary for task performance. BHI provides a common basis for comparing computational frameworks, defining benchmarks for latency, viability, interface bandwidth, learning efficiency, and reproducibility. It also highlights governance considerations arising from reciprocal stimulation, adaptation, and data exchange. More broadly, BHI invites computer scientists to consider biological substrates as active computational resources and to ask not only how a task should be computed, but where its computation should reside. BHI therefore reframes biological-artificial integration as a system-level problem of computational allocation, coordination, and control.
Read the full abstract

Emerging Technologies · Cross-listed from cs.HC

Reducing Technician Search Burden: A Multimodal RAG for Cessna 172 Maintenance Manual

Seongjun Ha, Md Rashedul Islam, Gaurav Nanda et al. (+1 more)

Proper use of the aircraft maintenance manual is essential for correct maintenance, providing procedures, diagrams, cautions, and specifications. However, technicians often avoid consulting it because it is difficult to navigate and time-consuming under strict schedules. Retrieval augmented generation (RAG) models have recently been introduced in aircraft maintenance, yet existing models focus solely on textual retrieval. This research therefore targeted the Cessna 172 Maintenance Manual (C172-MM), widely used in general aviation, and developed a multimodal manual retriever (MMR) capable of retrieving multimodal manual pages. Retrieval performance was evaluated using synthetic queries covering procedures, diagrams, caution/safety information, and specifications; the MMR achieved 93.37% recall@5. Beyond retrieval, a multimodal RAG (MRAG) pipeline was examined, in which retrieved pages were input to a vision-language model that generated responses to the synthetic queries, achieving 87.20% semantic similarity to ground-truth answers. Three practical feasibilities were also assessed: inference time, operational cost, and interpretability. Average retrieval time for five pages was 11.93 seconds and response generation took 4.95 seconds, at $0.0091 per query, while interpretability was validated through heatmap visualizations. These results indicate that the MRAG pipeline for the C172-MM can reduce the time technicians spend searching manuals and retrieving multimodal information.
Read the full abstract

All authors Seongjun Ha, Md Rashedul Islam, Gaurav Nanda, Damon Lercel

Tuesday 18 August

Emerging Technologies · Cross-listed from cs.NE

Low-Power, Neuromorphic, Acoustic Anomaly Detection for Persistent Machine Monitoring

Steven C. Nesbit, Victor M. Vergara, Michael A. Felix et al. (+4 more)

Persistent acoustic monitoring can detect machine faults without physical contact, but always-on inference is constrained by power, latency, and deployment complexity. We demonstrate autoencoder-based acoustic anomaly detection on an Intel Loihi 2 neuromorphic processor under clean and noisy conditions. Log-mel features are computed off chip; normalization, autoencoder inference, L1 reconstruction scoring, and thresholding run on chip. In a clean, microphone-position-invariant ToyADMOS ToyCar benchmark, the on-chip model achieves 0.9959 AUC and 0.9785 standardized pAUC at maximum false-positive rate 0.1. In the DCASE 2026 Task 2 ToyCar noisy benchmark, the model achieves source AUC 0.7990, target AUC 0.6466, and pAUC 0.6426, exceeding reported baseline metrics. Power profiling on a 16-chip Loihi 2 VPX system shows real-time throughput with 0.0406$\unicode{x2013}$0.0426 mJ dynamic energy per sample, two orders of magnitude lower than both a CPU and GPU. These results support neuromorphic acoustic anomaly detection as a practical candidate for low-power, persistent machine monitoring.
Read the full abstract

All authors Steven C. Nesbit, Victor M. Vergara, Michael A. Felix, Evan T. Kain, Luis R. García Carrillo, Gerd J. Kunde, Andrew T. Sornborger

Emerging Technologies · Cross-listed from cs.CV

Training with synthetic data for drone detection in thermal imagery

Tanel Liiv, Sander Soodla, Nzamba Bignoumba et al. (+2 more)

Ground-to-Air (G2A) drone detection in medium- and long-wave infrared (MWIR/LWIR) imagery is challenging due to reduced texture information, sensor noise, weak thermal contrast, and the scarcity of annotated data. This work investigates a synthetic-first training strategy that combines synthetic scene generation with fine-tuning on real data. We show that synthetic data provides an effective basis for learning initial object representations, while real in-domain thermal imagery is still essential for reliable deployment. Even small amounts of real IR data substantially reduce domain gaps. Our experiments indicate that dataset alignment has a stronger impact on performance than model scale. Finally, our analysis of the dataset suggests that semantic alignment in feature space is the strongest predictor of model performance, while radiometric properties such as entropy and dynamic range also contribute to detection robustness. This work provides a foundation for combining synthetic and real IR data for effective G2A drone detection.
Read the full abstract

All authors Tanel Liiv, Sander Soodla, Nzamba Bignoumba, Alma M. Liezenga, Toomas Pruuden

Emerging Technologies

Adaptive Participation Under Statically Equivalent Incentives in Distributed Demand Response Systems

Xun Shao, Ryoichi Inoue, Shinken Takekawa et al. (+1 more)

Aggregators recruit distributed energy resources with settlement rules and participation payments. Such designs are normally validated at fixed points: zero participation must cease to be an equilibrium, and truthful capability reporting must remain a best reply. We ask whether those checks determine the participation that owners reach once they adapt from the settlements they receive. In a five-unit event with fixed dispatch, payment rule and penalty, we vary only how a scarcity-contingent participation payment decays with the capability others have declared. Of two decay structures that agree on all five static criteria, one reaches full participation from a collapse initialization in 96 of 96 seeds and the other in none, within an 8000-round horizon and with disjoint 95% confidence intervals. The difference lies in the payoffs offered at partial participation, which the static criteria never evaluate; it is a property of experience-based feedback and closes when counterfactual payoffs are supplied. Because those payoffs make each unit's settlement depend on what the others declared, we also ask what survives when the mechanism is distributed. Running the aggregator and the five units as separate processes reproduced the centralized reference at every round, and a deliberately misattributed declaration was detected although every message was delivered.
Read the full abstract

All authors Xun Shao, Ryoichi Inoue, Shinken Takekawa, Go Hasegawa

Emerging Technologies · Cross-listed from cs.CV

Learning latent progression states from spatial heterogeneity in uterine histopathology

Qiming He, Yan Liu, Shuang Ge et al. (+20 more)

Tumor progression is accompanied by changes in architecture, morphology and microenvironmental organization, yet progression-associated heterogeneity is usually compressed into static diagnostic categories in histopathology. Here we present SpaTIE, a uterus-specific computational pathology framework that learns morphology-aware representations and organizes spatial histopathological heterogeneity into progression-associated tumor states. SpaTIE was developed using 10,426 uterine hematoxylin and eosin whole-slide images and evaluated in TCGA-UCEC and TCGA-UCS cohorts. The learned representations formed morphology manifolds, supported diagnostic, molecular and survival-related prediction tasks, and localized attention to informative tumor regions. Beyond supervised prediction, SpaTIE inferred tumor-state axes from cross-sectional morphology without temporal or molecular supervision. These morphology-derived states were spatially coherent and showed associations with clinicopathological variables and survival outcomes, while not simply recapitulating staging or diagnostic labels. Integrative multi-omics analyses linked the inferred states to DNA methylation, somatic copy-number variation, mutation, RNA-seq and RPPA profiles, highlighting molecular programs related to chromatin regulation, copy-number-associated structural variation, receptor tyrosine kinase signaling, cell adhesion, extracellular-matrix remodeling and metabolic adaptation. Progression-guided virtual perturbation further prioritized molecular features coupled to the morphology-derived state organization. Together, these findings suggest that uterine histopathology contains recoverable progression-associated tumor-state information and establish SpaTIE as a framework for connecting spatial morphology with multi-omics-informed tumor-state discovery.
Read the full abstract

All authors Qiming He, Yan Liu, Shuang Ge, Fan Yang, Yuxiang Wang, Ieng Man Zhang, Jing Yang, Zihao Jia, Ajin Hu, Yexing Zhang, Zixiu Song, Qiang Huang, Xiaoya Zhao, Zihan Wang, Xianjing Zheng, Yijun Zheng, Liling Lin, Shuxing Liu, Bin Bao, Yue Xie, Tian Guan, Yonghong He, Congrong Liu

Monday 17 August

Emerging Technologies · Cross-listed from cs.AR

RENESIS: Energy-Aware Synthesis of Adiabatic Logic from Irreversible Netlists

Mitchell A. Thornton

We describe Renesis, an automated synthesis tool that accepts an ordinary irreversible netlist and produces a verified, technology-mapped energy-recovery (adiabatic) circuit, using energy rather than area or delay as the optimization criterion. Renesis models the netlist with a vector-space formulation that expresses simulation and justification sweeps as forward and reverse traversals whose cost is linear in the number of circuit components. The traversals populate ledgers with data tags that characterize switching, erasure, and observability information at their natural Rényi orders. The output is a logically reversible circuit mapped to one of eight energy-recovery families, with the associated parameters reported. Reversibility is treated here as a circuit-level requirement rather than a thermodynamic one. When an adiabatic gate erases information the penalty is not $k_B T \ln 2$ but a full non-adiabatic $CV^2$ discharge, which is comparable to the switching energy the circuit style exists to recover. Every synthesis transformation is equivalence-checked, and it must improve one of two reported cost tables, one uncapped and one after a series-realizability bound, while worsening neither before it is accepted. Across a twenty-circuit development set, optional re-synthesis passes improve fourteen circuits. On a held-out set of twenty circuits, fifteen of nineteen are improved, with a best-arm median of $0.91$ of the default energy. A certified optimality-gap program computes the distance between the synthesized circuits and the provable floor of the tool's own search space. A device-level SPICE deck reproduces the tool's per-cycle energy figures on the reference family. The tool, the benchmark netlists, the validation procedure, and the run records behind every reported number are released as open source.
Read the full abstract

Emerging Technologies · Cross-listed from quant-ph

Gate-level Implementation and Resource Analysis of Lackadaisical Quantum Walk Search

Amit Saha, Debanjan Kola, Nishanka Das et al. (+1 more)

Lackadaisical quantum walks (LQW) extend discrete-time quantum walks (DTQW) by introducing weighted self-loops, enabling improved spatial-search performance through controlled localization of the walker. Despite substantial theoretical progress, practical gate-level implementations suitable for quantum hardware remain largely unexplored, limiting evaluation under realistic architectural constraints, noise, and resource requirements. In this work, we present a gate-level implementation framework for lackadaisical quantum walk search. The proposed construction encodes the position and coin spaces into qubit registers, and realizes the walk dynamics through oracle, coin, and flip-flop shift operations. We validate the circuit by reproducing the expected search behavior for single and multiple marked vertices and by analyzing the effect of the self-loop weight on the success probability. We further evaluate the implementation under realistic noisy settings using superconducting hardware's noise models. Logical resource analysis shows that, for grids ranging from $8\times8$ to $64\times64$, the transpiled gate count increases from $3.63\times10^{5}$ to $4.38\times10^{6}$ and the circuit depth from $2.13\times10^{5}$ to $2.56\times10^{6}$. Finally, fault-tolerant resource estimates based on a surface-code model using the Microsoft Quantum Resource Estimator demonstrate the substantial space-time trade-off associated with magic-state production. The results establish a practical circuit-level pathway for implementing the LQW search and provide a basis for evaluating its performance.
Read the full abstract

All authors Amit Saha, Debanjan Kola, Nishanka Das, Amlan Chakrabarti

Emerging Technologies

A Human-LLM Teaming Framework for Privacy Risk Analysis: An Illustration with CBDC-Based Welfare Schemes

Sourya Joyee De and Abdessamad Imine

Central Bank Digital Currency (CBDC)-based welfare schemes may be potentially privacy invasive as they process significant volumes of beneficiary personal data and lead to privacy harms such as surveillance, discrimination and stigmatization. Such welfare delivery schemes involve complex digital ecosystems and large number of stakeholders. Consequently, to examine their privacy risks, privacy risk assessments require extensive information gathering and synthesis, complex reasoning, scenario explorations, contextual evaluation and human judgement. Thus, they present ideal scenarios for human-LLM teaming, where effective integration of complementary human and LLM capabilities can yield an outcome far superior to either human-only or LLM-only assessments. In this paper, we propose a first human-LLM teaming framework for the systematic privacy risk analysis methodology called PRIAM. The framework specifies an iterative collaborative process in which the LLM processes large-scale documentary evidence to produce initial outputs, which are then interpreted and evaluated by human experts who direct their further refinement by the LLM and exercise their judgement to finalize the output. We illustrate the framework on the data characterization activity of PRIAM using a CBDC-based welfare scheme use case. The illustration demonstrates that while LLMs generate the initial data categories and assign initial values to data attributes, human experts evaluate and provide feedback to refine them, distinguishing documented evidence from inferences, identifying information gaps, and flagging unsupported or ambiguous outputs. This framework serves as a foundational contribution towards human-AI teaming for privacy risk assessments.
Read the full abstract

Emerging Technologies

Print-Aware Synthesis and Physical Design Methodologies for 3D-Printed Microfluidic Biochips

Yushen Zhang, Tsun-Ming Tseng and Ulf Schlichtmann

Microfluidic devices are widely used in diagnostics, chemical synthesis, and biological analysis, but their development often depends on complex fabrication and design processes. Resin-based three-dimensional (3D) printing has emerged as a promising alternative to conventional microfabrication because it enables low-cost, rapid prototyping of complex multi-layer structures. However, the practical realization of 3D-printed microfluidic biochips remains challenging due to manual and expertise-intensive design workflows, the rigid nature of commonly used printing materials, and fabrication inaccuracies such as over-curing that distort internal features and may block narrow channels. In this paper, we present a cohesive design automation framework for 3D-printed microfluidics that addresses these challenges across both device design and fabrication. The framework combines interactive design tools, automated synthesis methods for functional 3D microfluidic devices, techniques for developing low-cost 3D-printed mixers, and design-for-manufacturing strategies to improve print fidelity on low-cost resin printers.
Read the full abstract

Emerging Technologies · Cross-listed from cs.AI

Process-Constituted Intelligence: A Shared Criterion for Humans and Machines

Michael J. Richardson, Ayeh Alhasan, Cassandra Crone et al. (+5 more)

Intelligence is constituted by process (iterative activity through which output emerges), not in the output itself. Generative AI (GenAI) is trained on traces (textual and visual residues of human cognitive processes), reproducing samples from a distribution of those traces. Its outputs resemble reasoning, problem-solving, and creativity, yet the activity that produces such outputs in humans remains largely absent. Current GenAI is, therefore, weakly equivalent to the cognition it imitates, matching outputs while process stays absent or opaque. The cognitive sciences have long distinguished between weak and strong equivalence. Here, we define strong equivalence across seven process features, assessable against human and machine cognition. Our process-based account addresses a symmetric risk: GenAI tools that outsource a person's generative processes may leave critical capacities unbuilt. We specify design principles for GenAI that instantiate more process and preserve rather than erode human judgment and creativity, and outline process audits that make strong equivalence testable.
Read the full abstract

All authors Michael J. Richardson, Ayeh Alhasan, Cassandra Crone, M. Paula Diaz Monfort, Patrick Nalepka, Mark Dras, Rachel W. Kallen, David M. Kaplan

Emerging Technologies · Cross-listed from cs.LG

A Tree-Structured Approach for Phishing Template and Attacker Attribution Analysis

Unai Agirre, Imanol Jerico, Felipe Castaño et al. (+2 more)

Phishing remains a persistent and evolving cybersecurity threat, with attack volumes reaching record levels. This growth is driven by the industrialization of phishing through widely available phishing kits and reusable templates, which enable cybercriminals to rapidly generate and deploy large numbers of fraudulent webpages. Although surface-level attributes may differ across these websites, their underlying structures often exhibit significant similarities. However, most existing defenses rely on reactive blocklists or supervised classification models that focus on individual phishing instances, limiting their ability to identify structural reuse and detect coordinated phishing campaigns. To address this limitation, this study investigates whether HTML structure can serve as a robust fingerprint for identifying phishing template reuse. We model webpages as Document Object Model (DOM) trees and extract structural features, optionally enriched with HTML tag-based content information. These representations are then clustered using unsupervised learning methods to group structurally similar webpages. Three clustering algorithms are evaluated and compared, while also analyzing how the depth of the extracted DOM-tree affects cluster formation and overall clustering performance. Finally, cluster quality is also evaluated both quantitatively and qualitatively, including a novel level-wise Jaccard Distance Score and manual inspection supported by visualization tools. Results demonstrate that structural representations of webpages can effectively reveal hidden similarities across phishing sites, enabling the detection of emerging and zero-day templates and supporting the analysis of coordinated phishing threats
Read the full abstract

All authors Unai Agirre, Imanol Jerico, Felipe Castaño, Andrea Venturi, Francesco Zola

Sunday 16 August

Emerging Technologies · Cross-listed from cs.AI

PLeDO: Pain Level Detection for Osteoarthritis from EMR Data

Yuhao Chen, Jiahao Cai, Nafiz Sadman et al. (+3 more)

Osteoarthritis (OA) is a progressive chronic joint disease resulting in a breakdown of articular cartilage and bone when damaged joint tissues are not able to normally repair themselves. The aim of this pilot research study is to understand the pain severity for OA from patients' primary care Electronic Medical Records (EMR), both from the structured medical data and the unstructured chart note data using information extraction, natural language processing and machine learning techniques. We propose SPaDe, a Synonym-based Pain level Detection tool to categorize patients into having mild or moderate-to-severe pain to understand diagnosis and treatment methods based on only the pain related expressions in the unstructured chart note. Expressions are subjective, objective, and influenced by cultural background and demography which poses a difficult challenge. Therefore, we improve the model by incorporating the medication information from the structured EMR data and pain scale related information from the chart note to propose an integrated pain level detection tool for OA called PLeDO. With the help of human labeled gold standard data, we demonstrate that both SPaDe and PLeDO can detect mild and moderate-to-severe pain from the EMR data to analyze and potentially improve the quality of care in primary care setting.
Read the full abstract

All authors Yuhao Chen, Jiahao Cai, Nafiz Sadman, Farhana Zulkernine, John Queenan, David Barber