Scholarly Works, Computer Science

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Research articles, presentations, and other scholarship

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The need to implement FAIR principles in biomolecular simulations
Amaro, Rommie E.; Aqvist, Johan; Bahar, Ivet; Battistini, Federica; Bellaiche, Adam; Beltran, Daniel; Biggin, Philip C.; Bonomi, Massimiliano; Bowman, Gregory R.; Bryce, Richard A.; Bussi, Giovanni; Carloni, Paolo; Case, David A.; Cavalli, Andrea; Chang, Chia-En A.; Cheatham, Thomas E.; Cheung, Margaret S.; Chipot, Christophe; Chong, Lillian T.; Choudhary, Preeti; Cisneros, G. Andres; Clementi, Cecilia; Collepardo-Guevara, Rosana; Coveney, Peter; Covino, Roberto; Crawford, T. Daniel; Dal Peraro, Matteo; de Groot, Bert L.; Delemotte, Lucie; De Vivo, Marco; Essex, Jonathan W.; Fraternali, Franca; Gao, Jiali; Gelpi, Josep Ll; Gervasio, Francesco L.; Gonzalez-Nilo, Fernando D.; Grubmuller, Helmut; Guenza, Marina G.; Guzman, Horacio V.; Harris, Sarah; Head-Gordon, Teresa; Hernandez, Rigoberto; Hospital, Adam; Huang, Niu; Huang, Xuhui; Hummer, Gerhard; Iglesias-Fernandez, Javier; Jensen, Jan H.; Jha, Shantenu; Jiao, Wanting; Jorgensen, William L.; Kamerlin, Shina CL L.; Khalid, Syma; Laughton, Charles; Levitt, Michael; Limongelli, Vittorio; Lindahl, Erik; Lindorff-Larsen, Kresten; Loverde, Sharon; Lundborg, Magnus; Luo, Yun L.; Luque, F. Javier; Lynch, Charlotte I.; MacKerell, Alexander D.; Magistrato, Alessandra; Marrink, Siewert J.; Martin, Hugh; McCammon, J. Andrew; Merz, Kenneth; Moliner, Vicent; Mulholland, Adrian J.; Murad, Sohail; Naganathan, Athi N.; Nangia, Shikha; Noe, Frank; Noy, Agnes; Olah, Julianna; O'Mara, Megan L.; Ondrechen, Mary Jo; Onuchic, Jose N.; Onufriev, Alexey V.; Osuna, Silvia; Palermo, Giulia; Panchenko, Anna R.; Pantano, Sergio; Parish, Carol; Parrinello, Michele; Perez, Alberto; Perez-Acle, Tomas; Perilla, Juan R.; Pettitt, B. Montgomery; Pietropaolo, Adriana; Piquemal, Jean-Philip; Poma, Adolfo B.; Praprotnik, Matej; Ramos, Maria J.; Ren, Pengyu; Reuter, Nathalie; Roitberg, Adrian; Rosta, Edina; Rovira, Carme; Roux, Benoit; Rothlisberger, Ursula; Sanbonmatsu, Karissa Y.; Schlick, Tamar; Shaytan, Alexey K.; Simmerling, Carlos; Smith, Jeremy C.; Sugita, Yuji; Swiderek, Katarzyna; Taiji, Makoto; Tao, Peng; Tieleman, D. Peter; Tikhonova, Irina G.; Tirado-Rives, Julian; Tunon, Inaki; van der Kamp, Marc W.; van der Spoel, David; Velankar, Sameer; Voth, Gregory A.; Wade, Rebecca; Warshel, Ariel; Welborn, Valerie Vaissier; Wetmore, Stacey D.; Wheeler, Travis J.; Wong, Chung F.; Yang, Lee-Wei; Zacharias, Martin; Orozco, Modesto (Nature Portfolio, 2025-04)
In the Big Data era, a change of paradigm in the use of molecular dynamics is required. Trajectories should be stored under FAIR (findable, accessible, interoperable and reusable) requirements to favor its reuse by the community under an open science paradigm.
Enhancing Digital Libraries as Communication Tools: LLMs for Automated Subject Classification of Electronic Theses and Dissertations
Klair, Hajra (ACM, 2025-10-24)
Digital libraries are vital communication platforms that facilitate discoverability, collaboration, and strategic engagement among academics, administrators, funding agencies, and policymakers. Central to their effectiveness is accurate subject classification of Electronic Theses and Dissertations (ETDs), which enables clear information sharing and supports scholarly communication. However, author-supplied categories are frequently inconsistent or incorrect, often requiring manual review and complicating search and reporting. This study examines how Large Language Models (LLMs) can automate ETD subject classification, comparing prompt-based and fine-tuned approaches using over 9,200 records from Virginia Tech. Both methods are evaluated against established machine learning baselines, such as Support Vector Machines and multinomial Naive Bayes. Results indicate LLMs perform competitivSely in applied fields, but show systematic biases in more abstract or interdisciplinary categories—highlighting both their promise and the need for thoughtful communication system design in digital repositories.
Evaluating Human-LLM Alignment in ETD Subject Classification
Klair, Hajra; German, Fausto; Banerjee, Bipasha; Ingram, William A. (Springer, 2025-09-27)
Author-assigned subject labels in Electronic Theses and Dissertations (ETDs) are often inconsistent, overly broad, or misaligned with the research focus. This hampers discovery, aggregation, and analysis, especially for interdisciplinary research. LLMs offer a scalable alternative for automated classification, but their labeling rationale is opaque and introduces systematic biases. This study compares subject labels generated by LLMs with human-assigned labels for over 9,000 ETDs across 21 academic categories to assess the disagreement. We evaluate multiple prompt-based and fine-tuned LLM configurations and analyze areas of agreement and disagreement to identify patterns of misclassification. LLMs achieve competitive performance overall but frequently misclassify theoretical or interdisciplinary texts, often due to overweighting lexical cues and disregarding context. We show such errors are not random but reflect structured semantic divergences from human interpretation. These findings suggest a need for hybrid frameworks that combine LLM scalability with human contextual judgment to improve subject labeling in academic repositories.
Identifying and forecasting importation and asymptomatic spreaders of multi-drug resistant organisms in hospital settings
Cui, Jiaming; Heavey, Jack; Klein, Eili; Madden, Gregory R.; Sifri, Costi D.; Vullikanti, Anil; Prakash, B. Aditya (Nature Portfolio, 2025-03-07)
Healthcare-associated infections (HAIs) from multi-drug resistant organisms (MDROs) pose a significant challenge for healthcare systems. Patients can arrive at hospitals already infected ("importation”) or acquire infections during their stay ("nosocomial infection”). Many cases, often asymptomatic, complicate rapid identification due to testing limitations and delays. Although recent advancements in mathematical modeling and machine learning have aimed to identify at-risk patients, these methods face challenges: transmission models often overlook valuable electronic health record (EHR) data, while machine learning approaches typically lack mechanistic insights into underlying processes. To address these issues, we propose NeurABM, a novel framework that integrates neural networks and agent-based models (ABM) to leverage the strengths of both methods. NeurABM simultaneously learns a neural network for patient-level importation predictions and an ABM for infection identification. Our findings show that NeurABM significantly outperforms existing methods, marking a breakthrough in accurately identifying importation cases and forecasting future nosocomial infections in clinical practice.
10CACHE: Heterogeneous Resource-Aware Tensor Caching and Migration for LLM Training
Afroz, Sabiha; Khan, Redwan Ibne Seraj; Albahar, Hadeel; Han, Jingoo; Butt, Ali R. (ACM, 2025-11-19)
Training large language models (LLMs) in the cloud faces growing memory bottlenecks due to the limited capacity and high cost of GPUs. While GPU memory offloading to CPU and NVMe has made large-scale training more feasible, existing approaches suffer from high tensor migration latency and suboptimal device memory utilization, ultimately increasing training time and cloud costs. To address these challenges, we present 10Cache, a resource-aware tensor caching and migration system that accelerates LLM training by intelligently coordinating memory usage across GPU, CPU, and NVMe tiers. 10Cache profiles tensor execution order to construct prefetch policies, allocates memory buffers in pinned memory based on tensor size distributions, and reuses memory buffers to minimize allocation overhead. Designed for cloud-scale deployments, 10Cache improves memory efficiency and reduces reliance on high-end GPUs. Across diverse LLM workloads, it achieves up to 2× speedup in training time, improves GPU cache hit rate by up to 86.6×, and increases CPU/GPU memory utilization by up to 2.15× and 1.33×, respectively, compared to state-of-the-art offloading methods. These results demonstrate that 10Cache is a practical and scalable solution for optimizing LLM training throughput and resource efficiency in cloud environments.
Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters
Dongare, Shruti; Khan, Redwan Ibne Seraj; Albahar, Hadeel; Zhao, Nannan; Meléndez-Maita, Diego; Butt, Ali R. (ACM, 2025-11-19)
Modern cloud platforms increasingly host large-scale deep learning (DL) workloads, demanding high-throughput, low-latency GPU scheduling. However, the growing heterogeneity of GPU clusters and limited visibility into application characteristics pose major challenges for existing schedulers, which often rely on offline profiling or application-specific assumptions. We present RLTune, an application-agnostic reinforcement learning (RL)–based scheduling framework that dynamically prioritizes and allocates DL jobs on heterogeneous GPU clusters. RLTune integrates RL–driven prioritization with MILP–based job-to-node mapping to optimize systemwide objectives such as job completion time (JCT), queueing delay, and resource utilization. Trained on large-scale production traces from Microsoft Philly, Helios, and Alibaba, RLTune improves GPU utilization by up to 20%, reduces queueing delay by up to 81%, and shortens JCT by as much as 70%. Unlike prior approaches, RLTune generalizes across diverse workloads without requiring per-job profiling, making it practical for cloud providers to deploy at scale for more efficient, fair, and sustainable DLworkload management.
iMIA: Assessing Mission Risk in Uncertain, Interdependent AI Systems
Yoon, Han Jun; Thukkaraju, Ashrith; Cho, Jin-Hee; Matsumoto, Shou; Ferrari, Jair; Costa, Paulo; Lee, Donghwan; Ahn, Myung Kil (ACM, 2026)
Mission Impact Assessment (MIA) is critical for enhancing system effectiveness and ensuring mission success. This paper presents iMIA, an interdependent MIA framework that models relationships among mission components and enables probabilistic reasoning under uncertainty. Designed for AI-driven mission systems operating in dynamic, low-data, or poorly observable environments, iMIA addresses the limitations of traditional methods that often rely on overly confident assumptions about adversary behavior. While conventional hypergame theory (HGT) captures perceptual uncertainty from asymmetric or inaccurate views, it overlooks epistemic uncertainty arising from limited knowledge. To bridge this gap, we introduce a hybrid SL-based HGT model (SLHG), integrating Subjective Logic (SL) to represent epistemic uncertainty and HGT to account for misperceptions. This integration supports informed decision-making under both uncertain strategy beliefs and divergent environmental views. iMIA evaluates mission impact using multidimensional system quality metrics, security, trust, resilience, and agility, across diverse attacker-defender interactions. It identifies critical nodes influencing mission outcomes and quantifies performance gains from asset capacity reinforcement and asset vulnerability mitigation. Applied to a vehicle-assisted AI-based mission system, iMIA with SLHG improves performance by 16% in ASR, 20% in MTBF, 11% in TSA, and 14% in P_ACC. Designed for incremental development, iMIA supports continuous feedback and iterative refinement. Our results show that feedback-driven adjustments improve overall system performance by up to 18% in the accuracy performance.
Deep learning reveals how cells pull, buckle, and navigate fibrous environments
Padhi, Abinash; Daw, Arka; Agashe, Atharva; Sawhney, Medha; Talukder, Maahi M.; Pour, Mehran M. H.; Jafari, Mohammad; Genin, Guy M.; Alisafaei, Farid; Kale, Sohan; Karpatne, Anuj; Nain, Amrinder S. (National Academy of Sciences, 2025-11-25)
Cells in tissues navigate fibrous environments fundamentally differently than they do on flat substrates, but the establishment of cell forces in physiological fibrous settings remains poorly understood. Although factors such as the stiffness of the extracellular matrix (ECM) are known to drive behaviors, including cell motility on flat nonfibrous substrates, the interplay between fiber architecture and stiffness in fibrous ECM is not known. Here, we find that in fibrous environments, the directionality of mechanical forces overrides ECM stiffness as the primary regulator of contractility in migrating cells. Using an approach combining phase microscopy with deep learning to map forces in real time, termed deep learning-enabled live-cell fiber-force microscopy (DLFM), we reveal that when cells transition between anisotropic and isotropic stress fields, their contractility significantly drops despite encountering stiffer ECM, contrary to the behavior of cells on flat nonfibrous substrates. Unlike the peripheral adhesions observed on flat nonfibrous substrates, cells in fibrous matrices form force-generating adhesions throughout their body, stabilized by out-of-plane mechanical components unique to fiber geometry. Cells exhibit distinct force signatures during migration, division, and differentiation, with temporal signatures that predict stem cell fate. These findings, enabled by combining deep learning and the mechanics of cells and fibers, explain long-standing paradoxical behavior of cells navigating deformable fibrous environments, how they can pull and tug at them, and identify tension anisotropy as a master regulator of cell behavior, with implications for cancer invasion, tissue engineering, and regenerative medicine.
3D-Printed Wearable Biosensors for Livestock Health Monitoring
Ali, Md Azahar; Howell, Brittany R.; Zhang, Liqing (IEEE, 2025-07)
Livestock health monitoring stands as a linchpin in ensuring both the welfare of animals and the optimization of productivity. As we navigate toward meeting current and future food crises, the role of biosensors in this context cannot be overstated. Such biosensors serve as indispensable tools, offering real-time insights into the health status of livestock, thereby enabling early detection of diseases and prompt intervention. In addressing the challenges and potential of biosensors for livestock sensing, it is clear that while biosensors have seen extensive use in human health monitoring, their application in livestock is crucial for ensuring animal well-being and productivity, vital in meeting global food demands. To maximize effectiveness, there is a need for advanced manufacturing to develop customized, user-friendly, and cost-effective sensors. By harnessing the synergistic potential of electrochemical biosensors and advanced manufacturing, this review discusses the challenges that currently impede the widespread adoption of wearable electrochemical biosensors, advanced manufacturing techniques, and artificial intelligence in livestock sensing. This strategic approach not only bolsters animal welfare and productivity but also fortifies agricultural resilience in the face of evolving global food demands. This review highlights recent advancements in biosensors for livestock monitoring.
Understanding Tradeoffs of Replicated Data Library Integration Strategies in Multilingual Environments
Mondal, Provakar; Tilevich, Eli (ACM, 2025-12-15)
Modern distributed systems replicate data across multiple execution sites by means of special-purpose replicated data libraries (RDLs), which provide read-write data access and synchronization. Programming languages often need to be mixed across replica sites to meet business requirements and resource constraints. Because RDLs are typically written in a single language, integrating them in multilingual environments requires special-purpose code, whose characteristics are poorly understood. We aim to bridge this knowledge gap by reviewing two key strategies for integrating RDLs in multilingual environments: (1) foreign-function interface (FFI) and (2) common data format (CDF). Our preliminary results indicate performance and implementation tradeoffs: CDF offers latency and memory consumption advantages, while incurring an additional implementation burden. With modern distributed systems utilizing multiple languages, our findings can inform the design of RDLs for multilingual replicated data systems.
Toward Thorough and Practical Integration Testing of Replicated Data Systems
Mondal, Provakar (ACM, 2025-12-15)
Highly available applications rely on replicated data, but complex event interleavings between application logic and replicated data libraries (RDLs) often cause subtle integration bugs. Detecting such bugs is challenging due to the inherent nondeterminism of distributed execution, as certain bugs can only manifest under specific interleavings. Correctness testing, therefore, requires replaying all possible interleavings—a challenging task due to the combinatorial explosion of the interleaving space. My doctoral dissertation addresses this challenge with ER-𝜋, a middleware framework that exercises all possible interleavings between the application code and RDL; it also eliminates redundant and impossible interleavings via novel pruning techniques. Initial results show that ER-𝜋 successfully reproduces 12 real-world bugs across multiple opensource RDLs while significantly reducing the interleaving search space. Our ongoing work extends this foundation with interleaving prioritization, ranking interleavings execution by their likelihood of exposing faults—particularly those introduced by recent code changes, thus accelerating bug discovery. This research supports developers responsible for ensuring the correctness and reliability of replicated data systems.
RailEstate: An Interactive System for Metro Linked Property Trends
Chang, Chen-Wei; Cheng, Yu-Chieh; Tsai, Yun-En; Chen, Fanglan; Lu, Chang-Tien (ACM, 2025-11-03)
Access to metro systems plays a critical role in shaping urban housing markets by enhancing neighborhood accessibility and driving property demand.We present RailEstate, a novel web-based system that integrates spatial analytics, natural language interfaces, and interactive forecasting to analyze how proximity to metro stations influences residential property prices in the Washington metropolitan area. Unlike static mapping tools or generic listing platforms, RailEstate combines 25 years of historical housing data with transit infrastructure to support low-latency geospatial queries, time-series visualizations, and predictive modeling. Users can interactively explore ZIP-code-level price patterns, investigate longterm trends, and forecast future housing values around any metro station. A key innovation is our natural language chatbot, which translates plain-English questions (e.g., “What is the highest price in Falls Church in the year 2000?”) into executable SQL over a spatial database. This unified and interactive platform empowers urban planners, investors, and residents to derive actionable insights from metro-linked housing data—without requiring technical expertise. A demonstration video of the system is available at https://www.youtube.com/watch?v=ZLiz8S1UXsc.
Sparsity-aware Kernel Selection for Edge-Connected Jaccard Similarity in Graph Datasets
Gondhalekar, Atharva; Sathre, Paul; Chaudhury, Nabayan; Feng, Wu-chun (ACM, 2025-09-08)
Performance of graph algorithms often depends both on the underlying hardware architecture and on structural properties of the input graph. Optimizations that deliver high performance on one class of graphs, such as hypersparse graphs with low average degree, can degrade performance on other classes, for example denser graphs with high average degree. In this work, we investigate sparsityaware GPU kernel selection for computing the Jaccard similarity index, a measure of neighborhood overlap in graph datasets. In our kernel selection approach, we use the vertex-centric Jaccard similarity implementation from the cuGraph library as the baseline and include both vertex- and edge-centric variants of this kernel, with set-intersection algorithms varying between two-pointer linear search, binary search, and adaptive dynamic search. We use 80 real-world graphs in our evaluation with variation in average degree, maximum degree, Gini index, and average intersection cost. A random forest classifier, trained on a subset of these graphs on an NVIDIA A100 GPU, achieves 88.8% inference accuracy in predicting the fastest kernel. Kernels selected by the classifier achieve a 4.37× mean speedup over the vertex-centric cuGraph baseline from NVIDIA.
Are We on the Same Page? Examining Developer Perception Alignment in Open Source Code Reviews
Alebachew, Yoseph Berhanu; Ko, Minhyuk; Brown, Chris (ACM, 2025-06-17)
Code reviews are a critical aspect of open-source software (OSS) development, ensuring quality and fostering collaboration. This study examines perceptions, challenges, and biases in OSS code review processes, focusing on the perspectives of Contributors and Maintainers. Through surveys (𝑛 = 289), interviews (𝑛 = 23), and repository analysis (𝑛 = 81), we identify key areas of alignment and disparity. While both groups share common objectives, differences emerge in priorities, e.g, with Maintainers emphasizing alignment with project goals while Contributors overestimated the value of novelty. Bias, particularly familiarity bias, disproportionately affects underrepresented groups, discouraging participation and limiting community growth. Misinterpretation of approach differences as bias further complicates reviews. Our findings underscore the need for improved documentation, better tools, and automated solutions to address delays and enhance inclusivity. This work provides actionable strategies to promote fairness and sustain the long-term innovation of OSS ecosystems.
Investigating Seamless Transitions Between Immersive Computational Notebooks and Embodied Data Interactions
In, Sungwon; Krokos, Eric; Whitley, Kirsten; North, Christopher L.; Yang, Yalong (ACM, 2025-11-12)
A growing interest in Immersive Analytics (IA) has led to the extension of computational notebooks (e.g., Jupyter Notebook) into an immersive environment to enhance analytical workflows. However, existing solutions rely on the WIMP (windows, icons, menus, pointer) metaphor, which remains impractical for complex data exploration. Although embodied interaction offers a more intuitive alternative, immersive computational notebooks and embodied data exploration systems are implemented as standalone tools. This separation requires analysts to invest considerable effort to transition from one environment to an entirely different one during analytical workflows. To address this, we introduce ICoN, a prototype that facilitates a seamless transition between computational notebooks and embodied data explorations within a unified, fully immersive environment. Our findings reveal that unification improves transition efficiency and intuitiveness during analytical workflows, highlighting its potential for seamless data analysis.
The Importance of Cueing While Visually Searching a 360 Degree Environment for Multiple Targets in the Presence of Distractors
Kelley, Brendan; McMahan, Ryan P.; Wickens, Christopher; Clegg, Benjamin; Ortega, Francisco (ACM, 2025-11-12)
Visually searching for objects is an everyday task. In many contexts, people must visually search for multiple objects at the same time while avoiding distractor objects, such as triage during a mass casualty incident. While many prior augmented reality (AR) and virtual reality (VR) studies have investigated cues to aid in visual search tasks, few have investigated cues in contexts involving multiple targets and distractors with a full 360° effective field of regard (EFOR). Individually, multiple targets, distractors, and a full 360° EFOR each add complexity to visual search; when combined, they compound the difficulty even further. In this paper, we present such a study that compares three common types of visual cues (2D Wedge, 3D Arrow, and Gaze Line) to a baseline condition with no cueing for a 360° visual search task. Our results reinforce the importance of providing some type of cue, with the Gaze Line design being particularly beneficial. We discuss the potential implications of these findings for designing cues specifically for such complex visual search tasks.
Towards an Embodied Composition Framework for Organizing Immersive Computational Notebooks
In, Sungwon; Krokos, Eric; Whitley, Kirsten; North, Christopher L.; Yang, Yalong (ACM, 2025-11-12)
As immersive technologies evolve, immersive computational notebooks offer new opportunities for interacting with code, data, and outputs. However, scaling these environments remains a challenge, particularly when analysts manually arrange large numbers of cells to maintain both execution logic and visual coherence. To address this, we introduce an embodied composition framework, facilitating organizational processes in the context of immersive computational notebooks. To evaluate the effectiveness of the embodied composition framework, we conducted a controlled user study comparing manual and embodied composition frameworks in an organizational process. The results show that embodied composition frameworks significantly reduced user effort and decreased completion time. However, the design of the triggering mechanism requires further refinement. Our findings highlight the potential of embodied composition frameworks to enhance the scalability of the organizational process in immersive computational notebooks.
Capybara: an Edge-Friendly Distributed Object Store for Diverse Serverless Functions
Chen, Xin; Paidiparthy, Manoj Prabhakar; Qian, Chen; Hu, Liting (ACM, 2025-12-15)
While originally designed for the cloud, the benefits of the serverless paradigm are also vital in Edge/Fog computing environments. This paper presents Capybara, a new scalable, programmable distributed object store for storing and sharing serverless function data objects (state) on edge infrastructures. The key innovations here are (1) achieving scalability and avoiding the significant DRAM cost of indexing metadata servers through a “game-theoretic” DHT-based P2P architecture; (2) providing edge users with a “programmable” handler abstraction to customize data management policies, such as different function image caching policies, warm container “keep-alive” durations, data access control methods, and data replication policies. We implement Capybara prototype on the Pastry DHT, deploy it on 150 Amazon EC2 nodes, and evaluate it by conducting real-world experiments, demonstrating its significant gains in data locality, application-specific customization, and scalability compared to the state-of-the-art.
Cell-specific network-based cell type prediction via graph convolutional network using transcriptomics profiles
Choi, Joung Min; Chae, Heejoon (ACM, 2025)
Identifying cell types is crucial for characterizing biological phenomena in tissues at the single-cell level and understanding intracellular and intercellular interactions. Recent studies have introduced computational tools for cell type prediction using machine learning (ML), tailored for single-cell and spatial transcriptomics datasets. However, these approaches primarily focus on leveraging the gene expression profiles of individual cells, often overlooking the interactions between neighboring cells. Such interactions are vital, as they activate signaling pathways and coordinate gene expression. In this study, we introduce CSNpred, a cell type prediction framework that integrates graph convolutional networks with cellspecific network construction for transcriptomics data. Our model identifies neighboring cells with similar gene expression patterns, particularly those within close spatial proximity (when applicable) and constructs a network for each cell. This approach enables the learning of graph embeddings that account for both the cell’s gene expression and that of its neighbors. CSNpred outperforms the state-of-the-art cell type identification method and widely used ML-based classifiers, demonstrating superior prediction performance across various scenarios. Furthermore, we examined the role of cell-specific network construction in enhancing the classifier’s robustness, further validating its efficacy. CSNpred is publicly available at https://github.com/cbi-bioinfo/CSNpred.
SLED: A Speculative LLM Decoding Framework for Efficient Edge Serving
Li, Xiangchen; Spatharakis, Dimitrios; Ghafouri, Saeid; Fan, Jiakun; Vandierendonck, Hans; John, Deepu; Ji, Bo; Nikolopoulos, Dimitrios (ACM, 2025-12-03)
The growing gap between the increasing complexity of large language models (LLMs) and the limited computational budgets of edge devices poses a key challenge for efficient on-device inference, despite gradual improvements in hardware capabilities. Existing strategies, such as aggressive quantization, pruning, or remote inference, trade accuracy for efficiency or lead to substantial cost burdens. This position paper introduces a new framework that leverages speculative decoding, previously viewed primarily as a decoding acceleration technique for autoregressive generation of LLMs, as a promising approach specifically adapted for edge computing by orchestrating computation across heterogeneous devices. We propose SLED, a framework that allows lightweight edge devices to draft multiple candidate tokens locally using diverse draft models, while a single, shared edge server verifies the tokens utilizing a more precise target model. To further increase the efficiency of verification, the edge server batches the diverse verification requests from devices. This approach supports heterogeneous devices and reduces server-side memory footprint by sharing a single upstream target model across devices. Our initial experiments with Jetson Orin Nano, Raspberry Pi 4B/5, and an edge server equipped with 4 Nvidia A100 GPUs indicate substantial benefits: ×2.2 higher system throughput, ×2.8 higher system capacity, and better cost efficiency, all without sacrificing model accuracy.

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