This study investigates the small-signal stability of centralized energy storage systems (CESSs) using grid-following (GFL) and grid-forming (GFM) controls, particularly focusing on bidirectional power flow and multiple energy storage systems (ESSs). To address the issue of complex dynamics in CESSs when comprehensive GFL and GFM control loops are considered, high-order dynamics are simplified using the virtual damping method by focusing on the dominant oscillation mode. Damping analysis verifies that CESSs using a single-type control (either GFL or GFM) have dynamic superimposition characteristics. Specifically, as ESS number increases, the damping of GFM-CESSs improves but that of GFL-CESSs decreases. The damping sensitivity shows that the damping of GFM-CESSs is more sensitive to bidirectional power flow and all control loops, whereas that of GFL-CESSs is more sensitive to d-axis control loop. Consequently, GFM-CESSs are preferred for large-scale integration but are limited in scenarios with significant power reversal. If GFL and GFM controls are hybridized in CESSs, the ratio of GFM-CESSs should be constrained to avoid instability from modal resonance between GFL-CESSs and GFM-CESSs. This highlights that implementing GFM-CESSs necessitates considering scenario limitations rather than pursuing maximal integration under hybrid integration conditions. The conclusions are validated through modal analysis and time-domain simulations.

Tool-using LLM agents are shifting the unit of computation from explicit human-issued commands to model-driven tasks with stateful consequences. Yet today's agent runtimes still expose tools as isolated RPCs. This interface gives runtimes a convenient integration point, but it lacks a task-scoped execution boundary for commit, rollback, recovery, and audit across multi-step agent workflows. We argue that this mismatch calls for a runtime containment boundary rather than another per-call guardrail. This paper introduces Cordon, a transactional runtime system for staging and validating irreversible agent effects before commit. A semantic transaction is a task-level execution boundary that binds tool intents and runtime-tracked result lineage to reversible local state, staged external effects, delegated authority, and audit metadata. Cordon implements this abstraction with a transaction manager that tracks derived result objects, executes reversible mutations in shadow state, stages outward-facing actions in an effect outbox, and records recovery metadata. The runtime then validates the composed execution flow before it commits state or releases external effects. Our evaluation across adversarial and benign workflows shows that Cordon exposes cross-step violations missed by existing defenses. It also reduces irreversible-effect failures while preserving benign task completion with modest approval and latency overhead.

It has been well known that inverter-based resources (IBRs) can lead to converter-driven stability issues under weak grid connections. However, as the number of IBRs increases, instabilities can also occur even under strong grid connections. A practical case is presented to demonstrate this conclusion, using large-scale energy storage systems (ESSs) as an example. In this study, the ESSs induce oscillations with a frequency of 150 Hz in the d-q coordinates while providing both capacitive and inductive reactive power support (achieved by ESS functional control loops) to the connected power system. Theoretical analysis reveals that under strong grid connections, the dynamic interactions among power conversion systems (PCSs) of ESSs can be superimposed and intensified as the ESS scale extends, which reduces oscillation damping and leads to system instability. This indicates that ESS functional control loops also have potential instability risks when providing supports to power systems, which should be carefully examined. Finally, major impact factors are identified to mitigate the oscillations, and the conclusions are validated based on the SIMULINK platform. This paper provides valuable practical insights into system instabilities even under strong grid conditions, emphasizing the importance of functional control design and careful planning of the scale for IBR-dominated systems.

Massive controlled DC resources (CDCRs), such as battery energy storage systems, are connected to AC power systems through bidirectional inverters for power balance requirements. This study investigates converter-driven stability (CDS) issues in the sub-synchronous frequency range caused by large-scale bidirectional inverter-based stations (IBSs). The impacts of the AC and DC connections of IBSs on subsynchronous oscillations (SSOs) are compared by examining three factors: the number of CDCRs, power flow direction, and control parameters of the inverters. For AC connections, IBSs may induce instability as the number of CDCRs increases, regardless of the power flow direction. To maintain stability, the maximum power amplitude of the IBS is calculated. It is found that switching to DC connections can reduce these instability risks if the DC line resistance is much less than the AC line reactance. Moreover, the method of tuning control parameters is demonstrated to be more effective in improving power-related critical stability under DC connections. Therefore, The DC-IBS is preferred for high-voltage transmission. Finally, the conclusions are validated in power systems connected with both AC- and DC-IBSs under various network topologies and system scales.

LiDAR sensors are widely deployed in autonomous systems for 3D perception and safety-critical decision-making. We identify a previously unexplored attack surface in which dormant malware embedded in the LiDAR sensing pipeline remains inactive during normal operation and can be externally triggered after deployment, without requiring access to sensor hardware or networking at attack time. To operationalize this threat, we design malware capable of low-level point-cloud manipulation and embed it into LiDAR firmware. This malware was developed in a closed research test environment with vendor technical support, rather than by exploiting an inherent production supply-chain vulnerability. To selectively trigger attack activation, we design and implement an optical trigger that remotely activates the malware by delivering a modulated signal into the sensing environment. Once triggered, the malware performs real-time point cloud manipulation, and we demonstrate false object injection and real object suppression on static and mobile victim platforms. Our evaluation first establishes attack feasibility, including static operation at 300~ft and recorded drive-by runs reaching 35~mph. We then illustrate quantitatively that injected person-like artifacts can remain semantically detectable by a state-of-the-art 3D object detector. Finally, we demonstrate multiple modes of safety-critical impact on a deployed tactical autonomous vehicle. Together, these results highlight the need for stronger integrity guarantees throughout the LiDAR sensor development and deployment pipeline.

Snowpark enables data engineering and AI/ML workloads in Snowflake by executing user-defined functions in secure sandboxes. Many of these workloads require external connectivity to access cloud APIs, external databases, or feature stores, creating a dependability challenge: how to provide transparent network access while preserving strict multi-tenant isolation and resource fairness. This paper presents Secure Network Access in Snowpark (SNAS), a production architecture for secure external communication from sandboxed workloads. SNAS combines Extended Berkeley Packet Filter (eBPF) packet filtering, Generic Network Virtualization Encapsulation (GENEVE) overlay networks, and distributed egress proxies for policy-driven egress control with low overhead. We describe the design, deployment, and measured production behavior of SNAS, including an eBPF-based bandwidth limiter using the Earliest Departure Time (EDT) algorithm, dual-tier policy enforcement, and safeguards for connection limiting and port exhaustion. SNAS is deployed across all Snowflake regions and supports large-scale production workloads including petabyte-scale data transfer and latency-sensitive external integrations.

Projector photometric compensation corrects color distortions introduced by surface texture, reflection, and ambient lighting. Existing deep learning-based methods usually require professional scene-specific data collection and lack consideration for perceptual quality. To address this limitation, we present a diffusion-based photometric compensation method that reconstructs compensation images under photometric and content-aware guidance. Specifically, we first model the photometric distortions introduced during projection as environment-dependent additive noise, thereby reformulating the photometric compensation problem as a denoising task with physical constraints. Next, we introduce a diffusion model, which generates compensation images by following an additive trajectory to iteratively remove the noise. Finally, to accurately estimate the noise at each timestep, by analyzing the factors that contribute to distortions in the physical process of projection and capturing, we design a noise estimation network that incorporates features of both photometry-aware and content conditions. Experiments show that our method achieves superior visual performance in unknown scenarios, thereby exhibiting significant practical advantages over prior methods.

Agentic kernel optimization automates manual GPU kernel tuning via iterative generation, validation, and profiling with reasoning LLMs, casting the optimization task as feedback-guided search. However, our workload characterization reveals three system-level inefficiencies that limit search efficiency: (1) long generation latency due to LLM reasoning, (2) insufficient profiling feedback, and (3) underutilized validation/profiling resources. Our key insight is that the ongoing reasoning generation exposes a window for producing additional candidate kernels before it completes, allowing the system to terminate reasoning early once a satisfactory kernel appears. We present SpecGen, an agentic kernel optimization system with \emph{speculative generation}. First, SpecGen forks non-reasoning generations at well-chosen trigger points in the reasoning trace to yield kernels, increasing the candidate kernel count per iteration. These kernels are validated and profiled in parallel with the ongoing reasoning, increasing profiling feedback, and keeping resources busy during generation. When a kernel meets the termination criterion, SpecGen terminates the reasoning generation early to reduce the generation latency. Second, SpecGen dynamically reallocates validation and profiling GPU pools based on the arrival rate and prioritizes requests to reduce profiling feedback latency under bursty speculative generation load. Furthermore, SpecGen utilizes spare memory of the validation/profiling GPUs as remote KV cache storage to eliminate prefix recomputation of speculative generations under limited memory budget. Experiments with two reasoning LLMs on H200 show that SpecGen reduces end-to-end time over three baseline systems, while producing more profiling feedback, increasing resource utilization, and improving kernel speedup under a fixed time and token budget.

AI standardized patients are becoming a setting for professional training in clinical consultation. This paper presents MedEasy, a multi-agent system that organizes virtual-patient practice through patient dialogue, clinical actions, decision submission, documentation, and feedback. We first conducted a formative study with 12 clinical-year medical students through interviews and three co-design workshops. The findings informed a staged workflow, structured case records, action-contingent findings, and trajectory-based review. We then conducted an evaluative user study with a separate cohort of 12 clinical-year medical students, with each participant completing two counterbalanced cases. Learners interpreted MedEasy as a connected consultation environment. They used patient responses, examination findings, available actions, and feedback together to judge whether the represented case remained coherent. They valued repeatable practice and recorded review, while questioning missing actions and feedback criteria. The paper contributes design implications for AI-supported professional training systems that use case-specific standards to connect situated practice.

We propose a direct data-driven method for controller synthesis of infinite networks composed of unknown linear time-invariant subsystems. Using a single set of noise-corrupted input-state trajectories collected from each subsystem, and provided that certain linear matrix inequalities hold, each subsystem is rendered exponentially input-to-state stable (eISS) by locally constructing an eISS control Lyapunov function together with an exponentially input-to-state stabilizing feedback controller. We then compose these local components under a compositional small-gain condition in infinite-dimensional spaces to obtain a global control Lyapunov function and an associated stabilizing controller, ensuring uniform global exponential stability of the infinite network. The approach is validated on a physical case study with unknown dynamics.

Reliable, low-latency multi-hop data delivery in vehicular networks is increasingly demanded, yet remains challenging due to frequent route failures caused by high mobility and intermittent blockage. While redundancy-based routing enhances robustness by forwarding packets over multiple paths, over-replication intensifies contention and introduces additional delay, highlighting the need to carefully managing redundancy--reliability trade-off. However, conventional deterministic multi-path replication typically duplicates packets to an integer number of branches, making the redundancy level hard to tune and adapt to time-varying network dynamics in vehicular networks. To this end, Redundancy-Controlled Stochastic (RATIO) routing is proposed in this paper. For each active flow, RATIO constructs a weighted reduced directed acyclic graph (DAG) as the routing structure, where edge weights specify per-link forwarding probabilities. At fork nodes, the aggregate outgoing forwarding probability is allowed to exceed one and a modulo-based stochastic forwarding rule is employed to guarantee feasible forwarding, thereby enabling continuously controllable redundancy. An idealized RATIO design is formulated as a load-minimizing optimization subject to per-flow timely-reliability and link-capacity constraints, but the problem is generally intractable under time-varying wireless dynamics. Accordingly, a practical heuristic, termed H-RATIO, is developed. H-RATIO constructs a compact reduced DAG by taking the union of candidate paths and optimizes forwarding probabilities via local scoring and replication-adjustment iterations. Extensive trace-driven SUMO/ns-3 co-simulations demonstrate that RATIO/H-RATIO consistently achieves the highest timely PDR compared to baselines, while providing substantially better delivery efficiency, especially under high-load scenarios.

Motivational factors such as self-efficacy and how favorably students feel toward practice play a crucial role in shaping learning, particularly in technology-supported environments. Yet, educational interventions often overlook how these factors interact with practice format. This paper examines the influence of self-efficacy and favorability on learning outcomes across two common practice formats: paper-based and system-based tutoring practice. Using a counterbalanced within-subject design with matched problem sets, we isolate the effect of practice format while modeling motivational differences. Results indicate that students with lower baseline self-efficacy achieved greater learning gains regardless of practice format. Among students with lower baseline self-efficacy, greater favorability toward the tutor was associated with greater learning gains during tutor practice, whereas the pattern differed in paper-based practice. Intelligent Tutoring System (ITS)-based practice did not significantly improve post-training self-efficacy relative to paper-based methods. These findings underscore the potential value of tailoring practice format to students' motivational profiles, as the benefits of tutor- and paper-based practice varied with baseline self-efficacy and favorability. They lay the groundwork for future research on how instructional formats can be aligned more effectively with learners' motivational needs.

As enterprises deploy RAG-based systems to provide grounded responses to user queries, reranking has become a critical component for the final filtering step that separates relevant from distracting or irrelevant documents. Existing rerankers often rely on heuristic thresholds to achieve optimal filtering. Moreover, for relevance scoring, state-of-the-art methods use a language model's logit signals, which are designed for next-token prediction, not for assessing relevance. To address these limitations, we identify a principled signal for relevance: the representational shift (RS) induced in a query's internal state when conditioned on a document. We observe that the alignment between (a) RS induced by a candidate document and (b) RS induced by an oracle document-set provides a robust indicator of relevance. Building on this insight, we introduce a lightweight training framework that learns projections mapping RS to calibrated relevance scores. Our training objectives naturally filter irrelevant content at a zero threshold, reducing dependence on heuristic tuning. Across diverse retrieval datasets, our method delivers gains over SOTA rerankers.

With the rapid advancement of Deep Research Agents in knowledge-intensive domains such as finance, establishing reliable and domain-aligned evaluation standards remains a critical challenge. Existing benchmarks focus on either closed-ended question answering or open-ended report evaluation, failing to jointly capture retrieval-reasoning accuracy and end-to-end research quality required in real-world workflows. We introduce ICBCBench, a consortium-driven benchmark for financial deep research, developed in collaboration with domain experts from a broad range of financial institutions and academia, involving over 50 experts across more than 40 organizations. It adopts a dual-track paradigm integrating objective tasks with verifiable answers and subjective long-form report evaluation, enabling complementary assessment of retrieval-reasoning accuracy and end-to-end report quality in terms of expert alignment, citation consistency, and source quality. Experiments on state-of-the-art DRAs and large language models reveal substantial gaps in complex reasoning, factual grounding, and report quality, highlighting the challenges of achieving industry-level performance. Our dataset and evaluation framework are available at https://github.com/DeepFin-Intelligence/ICBCBench.

Cloud-hosted transformer and large language model (LLM) inference creates a direct confidentiality problem: user prompts may contain sensitive code, business data, personal information, or regulated documents, yet remote serving exposes intermediate state to the cloud software stack and accelerator runtime. Fully homomorphic encryption (FHE) keeps accelerator-side execution ciphertext-only, but end-to-end LLM inference remains expensive because linear layers are interleaved with non-linear, cache-state, and refresh-sensitive operators. CPU trusted execution environments (TEEs) can execute those operators natively, but a CPU TEE alone does not define how an untrusted accelerator should participate. We present Bifrost, a hybrid TEE-FHE serving architecture in which secrets are provisioned only to an attested CPU TEE, while the accelerator, device memory, driver/runtime stack, and host software remain outside the trusted computing base. Bifrost uses FHE as a secure delegation mechanism for projection and feed-forward linear layers on accelerator-backed CKKS, while non-linear operators, attention-side control logic, KV-state transitions, and decrypt-then-encrypt refresh execute inside the CPU TEE. Bifrost+ further applies a prefill/decode split: prompt-side KV state is built inside the CPU TEE, and only decode-side state enters the hybrid ciphertext path. In an estimator-style comparison matching Euston's methodology, Bifrost reduces projected latency by 9.25x on GPT-2 (1.5B) and 9.91x on LLaMA 3 (8B). In direct CKKS/FHE deployments, Bifrost+ reduces TTFT by 14.6-45.8x on GPT-2 (124M) and 15.3-53.4x on Qwen3 (0.6B). The systems lesson is selective encrypted execution: use FHE only where ciphertext-only accelerator delegation is required, and keep non-linear, refresh, and prompt-side work inside the CPU TEE.

We study the robustness of the Strong Stackelberg Equilibrium (SSE) in finite leader--follower games when the follower's best-response correspondence is set-valued. While optimistic tie-breaking (in the leader's favor) is commonly adopted, it can hinge on knife-edge indifferences. We formalize a stability notion: an SSE is unstable if, at the leader's committed strategy, the follower has an alternative best response that strictly reduces the leader's payoff. Our main results establish a sharp generic dichotomy. Fixing the follower's utility and sampling the leader's utility from any continuous distribution, with probability one the optimal Stackelberg commitment is unique and is either (i) pure, or (ii) mixed and unstable. When both players' utilities are sampled generically, this strengthens to: with probability one, the unique optimal commitment is either pure and stable or mixed and unstable. These theorems complement the classic generic-value result of von Stengel and Zamir by showing that even when optimistic and pessimistic leader values coincide generically, the strategy-level SSE prediction is generically fragile whenever optimality requires genuine randomization. We further apply this perspective to Stackelberg satisfaction games, disproving a conjecture from prior work via counterexamples and identifying conditions under which it nonetheless holds.

Directed community detection is challenging because edge directions encode asymmetric source-target relations. Most directed modularity and random-walk methods assign one label to each vertex, whereas recent bimodularity-based methods cluster directed edges more freely. We propose TT-SR, a Two-Tier Sender-Receiver framework that lies between these two viewpoints. Each vertex is assigned a sender role and a receiver role, and each directed edge receives the type induced by the sender role of its source and the receiver role of its target. Thus, TT-SR is more expressive than one-label vertex clustering while remaining more interpretable than unrestricted edge clustering. The method generates candidate sender-receiver assignments from count-residual, stationary-flow, degree-corrected, and order-score views. The candidates are refined by local role updates and selected by a two-tier rule: a degree-corrected profile score provides the primary structural criterion, while Bernoulli density and order-flow scores are used only as secondary ranking signals. We justify the main spectral views through sender-receiver modularity relaxations and interpret the degree-corrected score as a likelihood-based residual comparison. Experiments on pathway-type, co-block, and ordered-flow synthetic benchmarks show that TT-SR achieves the strongest or essentially tied strongest edge-community recovery across three scale settings. The gains are most pronounced on degree-corrected co-block and ordered-flow graphs. Real-network diagnostics further indicate that TT-SR aligns well with Email-Eu-core metadata and extracts strong sender-receiverbicommunity summaries on unlabeled directed networks.

We present a fully differentiable, GPU-accelerated finite element framework for forward simulation and inverse characterization of finite-strain anisotropic elastoplastic materials. Built on JAX, the framework exploits modern accelerator architectures by parallelizing the three major computational bottlenecks in nonlinear FEM: elemental weak-form and tangent-stiffness evaluation, global sparse matrix assembly, and sparse linear solution. For a large-scale forward problem with 3 million degrees of freedom, JAX-FEM on a single NVIDIA H100 GPU achieves up to 9.4$\times$ speed-up over a 24-core CPU Abaqus baseline. Automatic differentiation is applied through the constitutive update and solver workflow, providing consistent Jacobians for complex constitutive models without manual derivation and accurate gradients for PDE-constrained inverse analysis. Compared with finite differences, the JAX-AD gradients avoid step-size sensitivity and provide the required sensitivities at substantially lower computational cost. For inverse characterization, we combine information-rich, topology-optimized heterogeneous specimen geometries with full-field displacement data to identify complex constitutive models with many parameters that would otherwise require many conventional experiments to characterize. We demonstrate accurate recovery of anisotropic yield and hardening parameters in progressively challenging settings, including uniform and spatially varying material properties. The resulting AD-based formulation enables efficient optimization in high-dimensional parameter spaces where finite-difference approaches are computationally infeasible. These results establish differentiable, GPU-accelerated FEM as a practical high-throughput engine for simulation, characterization, and optimization workflows in advanced manufacturing.

In recent decades, privacy-enhancing technologies (PETs) have been recognized as a means of meeting regulatory and user privacy requirements in software systems that process personal data. Despite substantial research efforts, support from regulators, contributions by large technology companies such as Google and Microsoft, and growing interest among software practitioners, the practical adoption of PETs remains limited. Existing research consistently identifies recurring challenges to PETs adoption in SE, such as technical complexity and insufficient training. Despite ongoing research efforts, these challenges largely remain unresolved in practice. In this industrial challenge paper, we apply a practical, requirements engineering (RE)-driven perspective to examine challenges to PET adoption across multiple stakeholder groups (PET developers, integrators, and adopters) as well as across different disciplinary perspectives (engineering, law, and business). We argue that RE can facilitate the adoption of PETs by systematically addressing each of the complementary engineering, business, and legal viewpoints on privacy. Neglecting challenges in any of these viewpoints (e.g., the impact of PETs on software architecture, their business implications, and their contribution to regulatory compliance) can increase the impediments or even lead to implementation failure. In practice, explicit specification of these viewpoints within RE can enable meaningful coordination among stakeholders to more effectively realize the benefits of PETs in software engineering.

Text-to-image diffusion models are increasingly deployed at the network edge to serve heterogeneous workloads with diverse quality and latency requirements. However, existing deployment strategies choose either large edge-side models with high fidelity but high latency or lightweight device-side models that offer speed at the cost of semantic coherence. Moreover, these approaches rarely split the denoising workload between models of different sizes across edge servers and user devices. To bridge this gap, we propose RISE, a method for edge-device diffusion model services that combines relay inference with online scheduling. Driven by the finding that the latent intensity exhibits minimal deviation after a model handoff, RISE uses a training-free relay mechanism that exploits the shared latent space within a model family: the large model on the edge handles the early denoising steps that shape semantic structure, then passes the intermediate latent to a small device-side model for detail refinement. To deploy this mechanism as a practical service, a contextual bandit scheduler selects the best relay configuration based on prompt complexity, user preferences, network quality and real-time node loads. Experiments on two benchmarks show that RISE's relay mechanism achieves up to 2.1$\times$ speedup while preserving full-model quality, and its context-aware scheduler effectively balances quality and latency under mixed workloads.

Rust's type system prevents many classes of memory errors, yet its standard library relies heavily on unsafe code whose correctness is validated through testing, including dynamic checks under Miri, but lacks static verification. We present what is, to the best of our knowledge, the largest verification campaign reported for a software library: an open, crowdsourced effort that integrates complementary verification tools into the continuous integration of a verification repository forked from the Rust standard library. We analyze the campaign's effectiveness, discuss the practical value of machine-checked proofs for a subset of undefined behaviors (e.g., out-of-bounds access, null and dangling pointer dereferences, and use of uninitialized memory), and frame the remaining obstacles as open challenges for the formal-methods community.

AI systems are increasingly deployed in real-world settings where their behavior is shaped by dynamic environments, evolving data distributions, and complex interactions with users and infrastructure. Traditional machine learning evaluation focuses on benchmarks and operates within sandboxed environments, providing only a limited view of the true system behavior in the wild. We argue for the development of principled auditing frameworks that monitor deployed AI systems throughout their lifecycle. We further propose framing auditing as a statistical problem of monitoring constraint violations under uncertainty, where desired properties (e.g., fairness and safety) are treated as risk-controlled constraints that must be continuously evaluated as systems evolve through iterative feedback. This perspective highlights the need for uncertainty-aware monitoring methods, socio-technical specifications of audit criteria, and auditing infrastructures that enable ongoing oversight of AI systems in the wild.

Computing's dominant concepts - innovation, efficiency, resilience, professionalism - often migrate from reflective ideals to instruments that limit behaviour, redirect responsibility, and foreclose critique. We call this drift weaponisation: the discursive repurposing of professional concepts so that they stabilise business-as-usual while making structural alternatives appear unreasonable, illegible, or out of scope. Using collective autoethnography across education, justice, public administration, research management, and computing, we identify recurring mechanisms (simplification, individualisation, binary framing, metric substitution, hero/resilience scripts, organised ignorance). From this synthesis we propose Reframing - peaceful, practice-ready shifts (e.g., From Simplified Slogans to Structural Literacy; From Performative Compliance to Meaningful Outcomes; From Coping to Justice) - each paired with a 'do-now' prompt. The levers restore complexity, surface assumptions, and redirect attention to structural conditions without requiring formal authority. We contribute: (1) a cross-field account of weaponisation as a patterned phenomenon; (2) a portable reframing toolkit grounded in narrative and systems thinking; and (3) implications for computing within limits, including day-to-day practice shifts toward care, sufficiency, and justice.

The CPU-side large language model (LLM) tokenizer is a critical security gap in LLM serving through a confidential computing stack with CPU and GPU trusted execution environments (TEEs). Tokenizers converts the prompts through table-driven lookups, and the resulting memory access patterns are a powerful source of side-channel leakage. Recent work demonstrates end-to-end recovery of user prompts from tokenizer access pattern on production Intel TDX. However, a drop-in use of the popular tree-based Oblivious RAMs (e.g., PathORAM) to prevent access-pattern leakage introduces $\sim$13$\times$ tokenizer slowdown, resulting in 10-58% higher time-to-first-token (TTFT). In this paper, we present OTRO, an efficient, oblivious tokenization path tailored to latency-critical LLM serving. OTRO relies on square-root ORAM for fast single-access lookups, but avoids its prohibitive $O(N\log^2N$) rebuild cost every $\sqrt{N}$ accesses through three key innovations. First, OTRO provides a pool of replicated square-root ORAM instances that utilize the read-only nature of tokenizer table. Second, an epoch-based rotation policy decouples accesses from rebuilds and pads each epoch with dummy accesses to its boundaries, minimizing observable information. Lastly, chunked KV-cache-aware tokenization further overlaps rebuilds with GPU prefill and minimizes the instance count. Implemented as modules in HuggingFace Tokenizers and nano-vLLM, running within a TDX-enabled CVM with an NVIDIA H100 GPU, OTRO limits TTFT overhead to at most 4.5%, keeps tokenizer-induced latency under 10\% of total TTFT, and adds less than 0.5 GB of memory overhead while reducing the tokenizer's observable leakage across various model families and sizes.

This article proposes a novel methodology for the classification of transient and stationary regimes in dynamic systems. Several sensor-based solutions for regime classification in the literature require the setting of several parameters, or are not suitable for scenarios involving multivariate systems that may contain periodic signals. The proposed method introduces a spatial curve representation of the considered system based on its sample mathematical moments. Then, by connecting concepts of stability theory, geometrical properties of spatial curves and stationary stochastic processes, two regime classifiers are designed using the arc length and the curvatures of the proposed curve. Both classifiers are capable of describing and detecting transient regimes, considering behaviors such as: multivariate asymptotically, marginally stability, and cyclostationarity. Furthermore, a quantitative comparison in performance and computation resources of the proposed classifiers against existing classifiers in the literature illustrates that the proposed regime classifier based on the arc length outperforms other techniques in classifying transient regimes for simulated linear, non-linear, and discontinuous multivariate systems under the specified studied conditions.

What happens to permacomputing when electricity grids shift to decentralised green energy, and local communities and municipalities have increased governance over this vital public service? Electricity and computational networks are more than just separate systems that plug together. Shifts to renewable energy generation in the grid are impacting computational systems, and computational demands on electrical power are impacting the electricity grid; one infrastructure limits the other. Permacomputing research tends to focus on 'off-grid' or 'behind-the-meter' energy. This sacrifices some of the social justice benefits that the public electricity grid, managed and regulated for universal service, was designed to provide. Our paper uses empirically-grounded 'speculative fabulation' to identify research opportunities in permacomputing that open up when energy systems are federated across communities. The speculative fabulation takes the form of an interview between the energy manager of a future energy island in the North Sea and a permacomputing podcaster. This allows us to conceive of computing-grid integration in tractable social and ecological terms, and introduce a notion of 'fair trade energy'.

Clustering in a big data setting is an intensively studied problem, with coresets emerging as one of the important paradigms in this line of work. Given a cost function $\text{cost}(P,S)$ mapping input points $P$ and a solution $S$ to an objective value, a coreset is a typically weighted sketch $Ω\subseteq P$ such that $\text{cost}(Ω,S)\approx \text{cost}(P,S)$. In practice, coreset sizes much smaller than those suggested by theoretical guarantees are often found to be sufficient. In this paper, we offer an explanation for this phenomenon. Smaller coreset sizes suffice if we only wish to preserve the costs of \emph{good} solutions, i.e., solutions with low cost. We define and devise \emph{approximation-preserving coresets}, which provide a weaker guarantee than strong coresets, which apply to all solutions, while providing stronger guarantees than weak coresets, which apply only to the optimum solution. We complement this result by showing that even a very small distortion in the approximation factor cannot admit coresets of this size.

Linear probing is one of the simplest and most space-efficient approaches to hash table design, and is widely used in sequential settings due to its compact memory layout. However, designing a concurrent linear-probing hash table with strong liveness guarantees has proved difficult, and only a handful of such algorithms have been proposed, all of which either restrict concurrency or rely on large per-entry metadata, thereby compromising space efficiency. We present a lock-free linear-probing hash table with wait-free lookups that retains the core advantages of sequential linear probing while handling contention gracefully. Our design uses only a small amount of metadata per table entry: a constant number of additional bits when using LL/SC, or a logarithmic number of bits when using CAS. The algorithm is linearizable and lock-free, supports insert, delete, and wait-free lookup operations, and is able to safely reclaim space used by deleted elements without rebuilding the table. We analyze the amortized step complexity of our hash table assuming no concurrent insertions of the same key, and show that each operation has expected amortized step complexity matching that of sequential linear probing, up to the point contention per key.

Transmission failures can lead to cascading failures and system blackout affecting millions of customers if not handled in time, and choosing the best locations to monitor the condition of the transmission system is crucial for power system reliability. In this paper, we propose a new sensitivity factor, the line outage impact factor (LOIF), which is especially useful for power system monitoring and can reveal the impacts of a transmission outage on the power flow of other lines more effectively than existing sensitivity factors, such as the line outage distribution factors (LODF). In this study, we apply the LOIF in transmission line outage detection in three test systems and compare it with LODF using a number of observed transmission line (OTL) selection methods based on these two sensitivity factors. Then we apply a machine learning algorithm to detect the outages of other lines by monitoring the selected OTLs, and the detection accuracy is evaluated using the F1-score. The results show that, in general, with the same number of OTLs, detection using the OTLs selected using LOIF achieved higher F1-scores. The pattern was especially consistent in large-scale systems, showing its potential in real-world applications.

Counts of $k$-cliques are commonly used metrics in subgraph mining. Since graphs often have sensitive data, there also has been a lot of work on $k$-clique counts with differential privacy. However, these metrics have very high global sensitivity, and so more sophisticated techniques have been developed for counting $k$-cliques with privacy. Smooth sensitivity and ladder functions were developed for reducing the noise magnitude for private estimates of these metrics. However, these are computationally very inefficient to estimate. No polynomial time algorithms are known for smooth sensitivity of $k$-cliques for $k>3$, while the time complexity of ladder functions is lower bounded by the time for exact counts, which does not scale very well. In this paper, we develop a new highly scalable algorithm for estimating $k$-clique counts with differential privacy. Our algorithm adapts the ladder function to serve as a smooth upper bound on its local sensitivity, and utilizes the approximation sensitivity framework to calibrate noise with magnitude proportional to an approximation of the bound. This gives us a significant improvement in the running time. Experiments show that our method is several orders of magnitude faster than the ladder function based estimates of $k$-clique counts, while the accuracy is similar. Our algorithm is the first to scale to graphs with millions of edges, and for larger $k$, for which the ladder function algorithm doesn't complete.