Science & Mathematics

The integration of dynamic legged locomotion and aerial mobility offers versatility that is deterred by challenges in payload distribution, actuation, and the contrasting requirements of both domains. In this paper, we present the design and operation of a multimodal quadrupedal aerial-legged robot. The presence of legged aerial systems also allows for thruster-assisted legged locomotion, which we make possible with a unified control framework using Control Contraction Metrics (CCM). We search for separate domain-specific contraction metrics for walking and flight independently offline and unify them using an online energy-based Control Lyapunov Function Quadratic Program (CLF QP). This enables optimal, cross-domain control allocation, which we show using simulations. We also show experimental results that validate the mechanical viability of our hardware and the paradigm of thruster-assisted trotting. The stability of said experiments motivates the deployment of the control framework for multimodal operations.

Vehicle-to-Everything (V2X) communications rely on elliptic-curve signatures that a quantum adversary breaks and on pseudonym certificates whose anonymity rests on rotation rate, not cryptography. This work presents qDGSP, a post-quantum V2X infrastructure: a hash-based dynamic group signature scheme (DGSP, over SPHINCS+ and WOTS+) distributed between a central controller and roadside verifiers. Group keys derive deterministically from quantum key distribution (QKD) seeds served over the ETSI GS QKD 014 interface, so no group-signature secret crosses the classical channel. qDGSP-EA (Ephemeral Association) binds an ephemeral ML-DSA-65 key to one group signature per 5G cell: messages remain unlinkable across cells, linkable within one. On a testbed with emulated 5G links, median end-to-end latency stayed within the 100ms ETSI budget for every variant. The qDGSP-EA intra-cell DENM matched the NIST ML-DSA-65 baseline on the wire, and under load EA mode raised delivery from roughly 78% to 93%.

Sparse regression methods for discovering governing partial differential equations (PDEs) from data are sensitive to observation noise and are not constrained to reproduce the data under forward integration. We address both limitations by performing weak-form discovery in the spatial discretization in which the PDE is solved, learned by differentiating through multistep rollouts anchored at the observed states. The new method, Weak-form Anchored Rollout for PDEs (WARP), produces a machine-learned PDE solver. In this work, we focus on systems amenable to a spectral Galerkin solver. Across eight benchmark fluid and plasma systems, including high-dimensional turbulent simulations, WARP produces numerically stable rollouts from new random initial conditions and substantially improves coefficient recovery, rollout accuracy, and statistical measures compared with a single-step weak-form SINDy baseline.

IP geolocation supports many Internet services and operations, including but not limited to content delivery, fraud detection, content localization, and regulatory compliance. However, assessing the reliability and accuracy of IP geolocation providers is challenging and only partially explored. In this paper, we present a longitudinal study evaluating the consistency between RIPE Atlas latency measurements and location estimates from four popular geoproviders as a proxy for geolocation quality. Analyzing six months of data we observe mostly stable consistency levels through time, with fluctuations largely related to changes to the mix of targets being measured by RIPE Atlas each day. We also observe that consistency levels can differ greatly between geoproviders, with IPinfo leading DB-IP (the next best) by 5.4 percentage points for IPv4 and 11.5 percentage points for IPv6. Finally, the analysis reveals distinct degradation patterns across providers for different subsets of IP addresses. For example, for IPv4 router addresses, MaxMind consistency is 10.7 percentage points worse than in the general case while IPinfo is only 1.6 percentage points worse; for IPv6, the pattern flips and routers are geolocated more consistently than non-routers by all providers.

Gradient descent with momentum can be viewed as a particle moving with friction in the loss landscape. In deep learning the learning rate is usually large, so training is a discrete dynamical system: in many full-batch experiments the sharpness grows to the optimizer's stability limit, the edge of stability (EoS), and training continues there. Physics-inspired optimizers change this motion; Velocity-Regularized Adam (VRAdam), for example, shrinks the step at high speed. We study kinetic maps, in which the step is the gradient of a kinetic energy of the momentum, and ask what they do beyond the EoS. (i) In a reduced model, a kinetic map creates a symmetric period-two orbit beyond the edge, strictly linearly stable if and only if the map's elasticity (the log-log slope of step length versus momentum) lies between 0 and 1; on it, training sits at an effective edge of stability. VRAdam's damper fails above twice the critical learning rate, where its kinetic energy is not convex; the Legendre dual of VRAdam's own Lagrangian is convex and passes at every learning rate. (ii) Because the sharpness rises gradually, the loss of stability is delayed, as in Neishtadt's theory of slowly swept bifurcations. In a network with a controlled sweep, the dynamics match this theory without fitted parameters, including the memory of where the sweep started and its erasure by noise. (iii) With momentum, reaching the oscillation also requires elasticity at large momentum: maps that bound the step have competing period-four and chaotic attractors already at beta1 = 0.9, while the cubic-Legendre map reached the oscillation from every sampled start up to beta1 = 0.99. Experiments on an exact quadratic problem, a full-batch MLP and ResNet-32 support the theory; on ResNet-18, much of the apparent benefit of the maps is reproduced by matching the effective learning rate.

Deep latent variable models (DLVMs) underpin many modern analysis pipelines, for example in single-cell genomics, yet their prior distributions have received markedly little attention. The VampPrior approximates the optimal prior (the aggregate posterior) and often attains high model evidence. Here, we identify two key limitations of the VampPrior: it ties prior and posterior uncertainty to a single amortized covariance and clusters poorly due to its fixed uniform mixture weights. We unleash the VampPrior by decoupling its prior and posterior covariances, resolving the conflict of generating high prior and low posterior entropy with the same shared parameters and substantially improving model evidence. We then replace the uniform mixture with a Dirichlet process. When combined, our proposals provide higher model evidence than the VampPrior and robust non-parametric clustering. Augmenting single cell Variational Inference (scVI) with our prior improves single-cell integration and clustering across six atlases while significantly raising model evidence. Our approach changes only the prior and thus can be applied to any DLVM with a continuous latent space.

Vision-language models (VLMs) can infer sensitive information such as geolocation from subtle visual cues, creating privacy risks when responses disclose more detail than the visual context warrants. We study visual-contextual privacy in geolocation disclosure, where a VLM should reveal location information only at the granularity appropriate to the visual context depicted in the image. We first show that this contextually appropriate granularity is recoverable from pre-generation hidden states, even when generated responses are misaligned, with disclosure levels exhibiting an ordered structure along a common hidden-state direction. Motivated by this, we propose an inference-time route-and-steer framework: an ordinal granularity router maps visual-contextual privacy representations to a disclosure route, and a disclosure-level-conditioned steering module applies route-specific hidden-state interventions during decoding. Across four VLM backbones, our method improves visual-context-aware disclosure alignment, better balances over- and under-disclosure, and incurs only modest changes on general capability benchmarks. It further transfers to another dataset with a distinct data distribution and privacy-risk taxonomy.

AI governance agreements require methods to detect large model training runs on a monitored computing cluster, without trusting the operator. We develop a method to detect synchronous model training in facility-level power by Viterbi tracking of the wandering iteration cadence. On simulated datacentre power, when the operator makes no attempt to hide, the Viterbi tracker detects at least $96\%$ of training runs against a declared inference workload, at a $1\%$ false-alarm rate and within $0.04$ of the Neyman--Pearson optimum, given a meter that samples at $2$ Hz or faster. Of eight adversarial strategies an operator might use to hide, only extreme variation of the work per iteration evades the tracker. Its strongest form hides training in up to $73\%$ of five-minute observations, but over a day at least $87\%$ of such runs are still caught, at a $0.1\%$ false-alarm rate per run. This adversarial strategy costs no throughput, but in small-scale training the operator needs $93$--$267\%$ more compute to reach the same loss. A modest passive meter is sufficient to make hidden training both detectable and expensive, which makes power metering a promising part of compute verification that does not depend on trusting the operator.

Rust is increasingly used in Linux to improve memory safety, but its guarantees do not prevent logical races: individually synchronized operations can still interleave in ways that violate higher-level invariants. Detecting such bugs typically requires manually written specifications and carefully controlled execution environments. We present XRust, a technique for detecting logical races in Linux Rust drivers without manually specified invariants. Using Logical Happens-Before (LHB) analysis, XRust identifies shared-state accesses relevant to logical ordering, automatically constructs operation-pair harnesses, and uses serial differential oracles to detect concurrent executions inconsistent with either serial order. XRust also uses lightweight userspace abstractions to execute driver code under reproducible schedules. Applied to Linux 7.3-rc2, XRust identifies 17 shared objects across 102 Rust files from ten drivers, constructs 1,654 executable harnesses, and discovers two previously unknown logical races. The rnull block-driver race has been confirmed by Linux maintainers, with a fix accepted into the Linux block subsystem tree.

Coding agents can answer analytical questions over a data lake by discovering, cleaning, and joining the relevant data. However, they discard this work after each question and redo it from scratch for the next one. Tool evolution, which automatically builds and refines reusable tools across tasks, is a natural fit for this repetition. Since a ground-truth label for a data-lake question requires an expert to solve it first, we consider the unsupervised setting. We propose TEAL (Tool Evolution for Analytics over data Lakes), which evolves a library of tools and usage skills for a given Worker agent without labels. Two agents propose tools from complementary evidence, an Explorer from the data lake itself and a Critic from Worker trajectories. A proposed change is kept only if the Worker adopts the tool on a rerun without extra steps and any changed answer is supported by trajectory evidence. We also introduce TEAL-Q, which generates synthetic questions for tool evolution when real ones are unavailable. We evaluate on KramaBench and LakeQA with four Worker LLMs under step budgets, which cap the number of steps the Worker may take per question. With a tight budget of 10 or 15 steps, TEAL improves every Worker on both benchmarks, lifting Sonnet-5 at 15 steps by 9.3 points on KramaBench and 21.7 points on LakeQA. A tighter budget also yields better feedback for evolution. For example, Sonnet-5's LakeQA library evolved at 20 steps and deployed unchanged at 30 steps scores 68.3, above both the baseline (59.6) and a library evolved directly at 30 steps (64.2). Additionally, a library evolved on synthetic questions performs comparably to one evolved on real questions. These results show that useful tools can be evolved without labels, and hopefully inspire future work on what to evolve and how to obtain feedback in unsupervised agent evolution.

LABS are a fundamental combinatorial optimization problem arising in sequence design and statistical physics, yet the formal complexity of the standard fixed-geometry problem remains unresolved. We study how zero-energy complexity changes when support geometry is included as part of the input and then progressively restricted. For unary standard LABS, we establish membership in $\NP$ and a sparsity/Mahaney consequence: Karp NP-hardness would imply $\Ptime=\NP$. For Pair-Set-LABS and Masked-LABS, zero-energy feasibility is polynomial-time decidable when every pair group or masked row has at most three terms and is NP-complete when four terms are allowed. NP-completeness persists when active supports are restricted to contiguous intervals.

This article presents a Distributed Key Management System (DKMS) that provisions quantum-safe symmetric encryption keys to applications across a network. To overcome the distance and rate limitations inherent to Quantum Key Distribution (QKD), the architecture adopts a hybrid design: it utilizes multi-hop trusted relays for physical key transport, while applying an end-to-end Post-Quantum Cryptography (PQC) layer to ensure the relayed key material remains strictly protected from intermediate nodes. Because QKD transforms key generation into a scarce physical resource, managing unpredictable application demands poses a critical capacity allocation problem. The core contribution of this work is an SDN-driven control plane that globally orchestrates the network. Rather than relying on vulnerable hop-by-hop capacity reservations, a central controller simultaneously computes optimal filling rates and multipath routing for all per-peer key buffers. This global orchestration ensures that no application is starved and that all key buffers synchronize their filling processes. Evaluated on a Kubernetes-based digital twin across five distinct network topologies, the controller successfully filled all key buffers to capacity in under six minutes. Furthermore, under deliberately excessive demand, the network demonstrated graceful degradation, safely rejecting excess requests through controlled rate-limiting rather than experiencing catastrophic failure.

LLMs are increasingly used across research workflows, including research ideation, qualitative analysis, and participant simulation. Within work on participant simulation, prior studies have largely evaluated simulated participants by how realistic or human-like their responses appear, leaving less known about how researchers use and critique these materials when revising interview protocols before fieldwork. We present Rehearsim, an inspectable research probe that treats LLM-simulated participants as materials for methodological rehearsal rather than substitutes for human participants. Rehearsim combines literature-grounded participant construction, interactive interview rehearsal, and traceable thematic review while keeping protocol decisions under researcher control. In a study with 21 qualitative researchers, participants used simulated materials to surface assumptions and gaps, reconsider participant populations, revise target populations, and develop follow-up probes. They also identified risks, including the concealment of weak questions, dismissal of unexpected synthetic details, and interpretive anchoring from generated themes. We derive an inspectable-rehearsal framework emphasizing traceability, contestability, researcher control, and epistemic boundaries.

We propose EMBER, a structure-based framework for TCR--pMHC binding prediction that eliminates the need for a predicted docked complex, and with it the placement error that dominates such predictions. Traditional structure-based predictors utilize the co-folded complex structure of the receptor and the antigen and interpret binding from its contact surface. Consequently, every downstream feature inherits any deviation the structure-prediction tool introduced. Methodologically, EMBER folds the TCR and pMHC independently, encodes them as SE(3)-invariant residue graphs, and compares their learned representations without constructing a shared coordinate frame. A relative pose is therefore not an input of the model, so placement error cannot propagate into the prediction. Computationally, EMBER preprocesses $O(N{+}M)$ monomer folds where a complex-based predictor needs $O(NM)$ operations. Experimentally, under epitope-disjoint evaluation with six retrained baselines, EMBER is first on every metric under all four folding tools, by 0.9 to 14.4% in micro-AUROC, 18.5 to 25.3% in micro-AUPRC and 10.6 to 24.1% in MCC. Its ranking is also up to 85% more stable across folding tools.

Accurate head immobilization is critical for medical procedures such as neuroimaging and intracranial radiation therapy, where even minor motion can compromise precision. Traditional methods, such as rigid stereotactic frames and thermoplastic face masks, are invasive, uncomfortable for patients, and require a time-consuming setup. To overcome these limitations, a novel approach employs a 6-degree-of-freedom (6DoF) robotic head holder that is integrated with real-time optical motion tracking, enabling dynamic compensation for head movements without physical restraint. However, implementing this system requires a detailed biomechanical understanding of skull-cervical spine interactions to define safe and effective motion limits. In this study, we conduct a human volunteer investigation using a 6DoF Stewart platform robot to characterize biokinematic workspace of the head-neck system and establish foundational data for human-robot interaction in precision head stabilization.