Papers

CXL-enabled memory expands server memory capacity, but introduces a page-placement problem: the operating system must decide which pages should reside in DRAM and which should reside on slower CXL memory. Existing systems make this tradeoff in one of two ways. Userspace controllers support flexible policies, but expose placement decisions to scheduler jitter and kernel-userspace crossing overhead. Kernel-space systems avoid this latency, but rely on fixed heuristics that must generalize across workloads. We present xTier, a kernel-resident learned memory-tiering system. xTier attaches eBPF programs to PEBS events and uses a compact quantized MLP to score sampled pages inside the kernel at microsecond-scale latency. Rather than reacting to every candidate, xTier converges to a low-churn placement for the current workload phase, reduces sampling cost after convergence, and returns to a higher sampling cadence when the workload shifts. We evaluate xTier on six memory-bound workloads at DRAM:CXL ratios from 1:5 to 1:25. The advantage grows as the DRAM budget tightens. At 1:15 and beyond, xTier is the fastest system in 14 of 18 configurations. Where it is not fastest, it trails the best baseline by 3.9% on average. It reaches this performance while moving 13% fewer pages in geometric mean, and 22% fewer at the tighter ratios. When a workload changes phase, xTier rebuilds its hot set in DRAM faster and more completely than any baseline.

Secondary use of educational records is increasingly mediated by platforms that share a differentially private synthetic version of a dataset and validate specific findings against the real data on request. The synthetic version is evaluated by comparing summary statistics of each variable, yet reported confirmation rates suggest that such comparisons do not predict which findings survive. We propose a structural check: the number of connected components of a weekly proximity graph over learners, tracked across a term. Across four annual cohorts of lower-secondary study-habit logs, the synthetic versions reproduced the level of this quantity and the shape of the weekly partition, but its variation across the term was between 2.6 and 4.9 times smaller than in the real data at a common working point, without exception, and those changes fell in different weeks: the synthetic cohorts single out the term's examination weeks and the real cohorts do not. We also show that a routine rule for setting the graph threshold makes naive comparisons between two datasets invalid, and illustrate this with an error of our own. The real curves are also distinguishable from marginal-preserving surrogates of themselves in all four cohorts, where three of the four synthetic ones are not, a comparison that needs no real data; these differences trace to what the generator was given.

We present GaussPDE, a framework that injects physically structured partial differential equation (PDE) dynamics into pretrained 3D Gaussian scenes without mesh extraction, voxelization, or retraining. Our key observation is that PDE rendering requires not only accurate appearance, but also a reliable discrete computational domain. We therefore first introduce camera-aware regularization during 3DGS reconstruction to suppress camera-near floaters and oversized primitives that would create unstable graph topology. We then construct an active Gaussian graph using covariance-aware distances and opacity, appearance, and boundary-aware conductance, enabling mass-weighted graph Laplacian PDE evolution directly over Gaussian primitives. The evolving scalar PDE state is coupled back to rendering by modifying the direct-current spherical harmonic color coefficients while preserving geometry, opacity, and view-dependent rendering behavior. Experiments on real and synthetic scenes show that GaussPDE produces stable, controllable, and spatially coherent dynamic visualizations, with reduced cross-boundary leakage compared with baselines.

Agentic workflows shift software development from prompting AI agents for individual tasks to defining recurring work that agents execute automatically. GitHub Agentic Workflows (gh-aw) enables this approach through Markdown files that combine natural-language instructions with configuration and compile into executable GitHub Actions workflows. Unlike conventional workflows that primarily prescribe scripted operations, these files delegate tasks requiring interpretation to AI agents. They also couple agent instructions with execution triggers, making those instructions operational specifications for repeated repository activities. However, how developers structure and maintain these specifications, and which execution requirements and safeguards they express, remains insufficiently understood. In this paper, we examine the structure, evolution, and instruction content of gh-aw Markdown files to inform how practitioners define and maintain agent-run work. We analyze 1,248 files from 276 repositories, 20,841 commit-file events, and 288 resolved instruction-label sets from a sample of 294 files. Our results show that workflow instructions extend beyond short prompts, with a median of 556.5 words and code blocks in 62.1% of files. Among files with at least 120 days of observed activity, 78.2% still receive updates in month 4, while size-normalized churn decreases after the first month. Tasks, outputs, constraints, and process instructions each appear in over 93% of labeled workflows, yet only 9.4% explicitly address prompt-injection defense. LLM classification achieves an F1 score of 0.818 and Cohen's Kappa of 0.715 against the resolved human labels. These findings suggest that developers should account for the evolution of workflows copied or referenced across repositories and consider adding prompt-injection defenses, resource budgets, and evidencecredibility checks where applicable.

Production customer-support systems often require LLMs to support multiple skills, such as intent classification, question answering, summarization, or tool-use decisions. A central deployment question is whether these skills should be handled by separate task-specialist models or by a single model trained through multi-task training, sequential updates, or model merging. We study this question using thirteen models spanning five families (Qwen3, Qwen3.5, Gemma-3, Llama-3.1, and Mistral) from 0.6B to 32B parameters across eight customer-support datasets, spanning four public and four proprietary datasets with approximately 74.5k training and 8.7k evaluation samples. Under a fixed training protocol, we train more than 200 checkpoints. Our experiments reveal that multi-task full fine-tuning is the strongest operational default at every model size we test. Specialist models are strong on their target tasks but often degrade sharply off-task, making reliable routing important. Sequential Low-Rank Adaptation (LoRA) preserves earlier skills better than sequential full fine-tuning, while merging a specialist with its base model improves off-task robustness with limited same-task loss for larger models. We conclude with practical guidelines for selecting fine-tuning strategies in real-world settings.

We give algorithms to explicitly compute local Arthur packets and their intersections for even orthogonal groups $G_n$ over $p$-adic fields using extended multi-segments. In addition, we provide an algorithm which determines whether a representation of $G_n$ lies in a local Arthur packet. As applications, these results are used to identify distinguished parameters, prove the closure ordering conjecture, the enhanced Shahidi conjecture, the weak local Arthur packet conjecture, and the good parity unitary dual conjecture for $G_n$.

Self-supervised learning (SSL) for speech and audio has largely progressed along separate tracks: speech models emphasise time-domain prediction, whereas audio representation learning has focused on time-frequency patterns. This separation creates a compatibility gap, limiting cross-domain generalization. In this work, we introduce JASPER, Joint Audio and Speech Pre-trained Encoder Representations, a framework that augments speech-pretrained models with time-frequency objectives. Specifically, JASPER performs masked prediction of temporal and spectral targets over long audio segments, enabling spectro-temporal representation learning of speech and audio signals. The proposed method consistently outperforms multiple baselines and existing speech/audio encoders on diverse speech, audio, and music tasks, demonstrating the effectiveness of unified spectro-temporal modeling.

We show that the minimum volume among normal stable surface pairs with nonzero reduced boundary containing a zero-dimensional non-klt center is $\frac{1}{42}$, and show that the surface pair attaining this minimum is unique up to isomorphism. As an application, we show that $\frac{1}{42}$ is an infinite accumulation point of the volume set of normal stable surfaces.

This paper investigates the anti-localization uplink communication in a satellite-terrestrial system, where a ground transmitter Alice communicates with a legitimate satellite receiver Bob in the presence of multiple cooperative adversarial satellites attempting to localize Alice with the time difference of arrival (TDOA) technique. Specifically, we propose a cooperative jamming-based scheme for such anti-localization communication,in which Alice exploits the superposition coding with power allocation to simultaneously transmit information/jamming signals for communication with Bob and for confusing signal detection/TDOA measurement at adversarial satellites, while Bob employs the combining vector technique to enhance the desired information signal and also suppress the jamming. We define a localization error probability (LEP) metric to jointly depict both the impacts of signal detection and TDOA measurement on localization performance, and then develop a theoretical framework for the LEP modeling under the proposed scheme. We further explore the joint optimal design of jamming coding and power for LEP maximization, subject to the constraints of AliceBob communication reliability and Alice's transmit power. An effective sample average approximation method is also provided to tackle this non-convex optimization problem. Finally, extensive numerical results are illustrated to validate our theoretical models and demonstrate how the cooperative jamming helps to provide an anti-localization guarantee while ensuring communication reliability

Enterprise data agents must preserve organization specific semantics, not just translate questions into queries. We present ChinaUnicom DataAgent (UniDataAgent), an ontology grounded system for reusable question-to-report analysis that separates semantic acquisition from online execution. Ontology Acquisition and Validation stage (OAV) builds versioned enterprise ontologies from metadata, business knowledge, and supporting materials through expert authored business skills, constrained generation, question verification, and selected expert review. Question-to-Report Execution (QRE) stage retrieves semantic contracts for each question, coordinates skills and data tools, validates results, and produces evidence linked reports. Across 27 enterprise tables and roughly thousands of metric types, ontology construction took a few hours instead of about one week manually. It took just a few minutes to generate the reports, instead of several working days. Ontology grounding achieved 95.0% strict accuracy on real business questions, versus 72.5% for document RAG, especially on structured and compositional tasks. The system has already been deployed to generate cost savings and has the potential to be replicated in other enterprises.

We study causal discovery where each node is a random function. Previous studies on this topic rely on structural assumptions, e.g., linearity or non-linearity, and distributional assumptions, e.g., Gaussianity or non-Gaussianity. In contrast, we make use of covariance operators to avoid these assumptions. Under functional additive noise models, we propose a new sufficient condition to identify a valid topological ordering based on comparing norms of conditional covariance operators. Taking advantage of this identifiability condition, we develop a new mixed regression model that subsumes linear and non-linear models. Together with variable selection, our procedure yields an estimation of the causal directed acyclic graph (DAG) for functional variables. In theory, we develop the least-squares-type theory of this regression model, and derive asymptotic consistency of order determination, sparse regression, as well as identifying the DAG. Computational algorithms based on discrete observations are provided. Applied to simulated data, our approach performs satisfactorily among existing approaches. A real data example of brain effective connectivity is also presented.

Consider the compressed shift $T=P_{K_\theta}M_z|_{K_\theta}$ on $K_\theta=H^2\ominus\theta H^2$ associated with the atomic singular inner function $\theta(z)=\exp(-a(\zeta+z)/(\zeta-z))$, $a>0$ and $|\zeta|=1$. Let $k_0=P_{K_\theta}1$ and define real powers $T^s$ using a holomorphic logarithm near the singleton spectrum of $T$. For any temporal multisequence $\mathsf S=(s_k)_{k\in I}\subset[0,\infty)$ with uniformly bounded numbers of samples in unit intervals, we prove that $\{T^{s_k}k_0:k\in I\}$ is a frame if and only if $\mathsf S$ is Kummer-thick: the frequencies $\omega(s_k)=\sqrt{8s_k+4}$, weighted by $1/\omega(s_k)$, have uniformly positive total mass in every sufficiently distant interval of some fixed length. Equivalently, the occupied unit cells fill a fixed positive proportion of every window $[N-C\sqrt N,N+C\sqrt N]$ for some $C>0$ and all sufficiently large $N$. The proof combines a Volterra transmutation into Bessel waves with the Fourier–Bessel Logvinenko–Sereda theorem.

We report a new source of systematic bias in the Bayesian Templated Background Search (TBS) for the astrophysical stochastic gravitational-wave background. This bias arises from the statistical uncertainty in nested sampling evidence estimates at the segment level and persists even when the likelihood model accurately characterizes the data. Using an analytically tractable model and a frequency-domain mock data analysis with an astrophysically motivated binary black hole population, we show that this uncertainty coherently inflates the inferred duty cycle by up to an order of magnitude when millions of segments are accumulated. We propose a simple and efficient correction and demonstrate its effectiveness across all tested configurations. We also find that a search prior restricted to high optimal signal-to-noise ratio (SNR) misattributes intermediate-strength signals, while extending the prior to support weaker signals amplifies the evidence uncertainty. Both the correction and careful search prior design are essential for robust inference as TBS analyses scale to upcoming observing runs.

Dyer conjectured that the join of two biclosed sets of positive roots can be described by increasing Bruhat paths whose reflection labels belong to their union. We give a type-uniform proof of this conjecture for all finite Coxeter groups. More generally, for a $2$-coclosed set $C$ of positive roots contained in an inversion set, we show that its $2$-closure is an inversion set $I(w)$ and that the elements reachable from the identity using reflections labeled by roots in $C$ form exactly the set $[e,w]_B w^{-1}$. The proof combines Dyer's closure criteria with a root-selection argument.

The Platonic Representation Hypothesis suggests that increasingly capable models converge toward shared representations. Recent work narrows this claim to shared local neighborhood relationships, finding that capacity-dependent trends in several global similarity measures largely disappear after calibration. We challenge this interpretation by showing that prior local-global comparisons confound structural scale (local versus global) with what is compared: relational structure, defined by which samples are related, versus metric geometry, characterized by quantitative relations such as distances, similarities, or correlations. To disentangle these factors, we construct a controlled $2\times2$ framework that evaluates both relational structure and metric geometry at local and global scales. We introduce $H_0$ skeleton overlap as a global counterpart to mutual $k$-nearest neighbors, together with matched distance-aware variants. Across vision-language models, relational structure exhibits robust representational convergence at both scales after calibration, whereas increasingly stringent distance agreement substantially weakens alignment and progressively flattens the capacity-dependent trend. We further extend the analysis beyond ambient Euclidean geometry by evaluating distance agreement under a Riemannian metric approximation and recover the same structure-geometry pattern. The pattern is also reproduced in video-text representations. Together, these results show that relational convergence extends beyond local neighborhoods to global spanning structure, whereas metric geometry exhibits substantially weaker convergence.

This paper investigates a self-consistent effective-one-body (EOB) theory developed to describe the dynamics of spinning binary systems. We systematically derive, using field-theoretic principles, all expressions and quantities entering Hamilton's equations, which govern the dynamical evolution of the system. Based on the effective rotating metric up to fifth post-Minkowskian (PM) order recently obtained by us, we construct the Hamiltonian and the stress-energy tensor. We then derive a decoupled, separable Teukolsky-like equation with a source term for the Newman–Penrose Weyl scalar $\psi_4^B$ and present its formal solution. Finally, using this solution, we obtain the energy flux, radiation-reaction force, and gravitational waveforms for the “plus” and “cross” polarizations generated by spinning binaries. The EOB theory applies to systems with arbitrary mass ratio, which filling the gap between comparable mass ratios and extreme mass ratios.

The Schrödinger bridge owes its computational power to a single structural fact: by Girsanov's theorem the controlled problem is a Kullback–Leibler (KL) projection onto a fixed reference measure, solvable by alternating projections. This letter shows that the fact does not survive risk sensitivity. When the expected path cost is replaced by the entropic risk measure and both endpoint marginals are kept as hard constraints, the resulting fixed-point bridge value $J_\theta$ (the soft-problem value at the multiplier that enforces the terminal constraint) admits no representation as a constrained KL minimum against any fixed path-space reference with a regular endpoint law (a class strictly larger than the uniformly elliptic diffusion references: no Markov property is required), even allowing an additive normalisation depending on the initial marginal. Moreover, no single reference generates the one-parameter family in the risk parameter. The obstruction is computed in closed form: the Gaussian bridge value violates, by exactly $\theta/2$, a heat equation that any Gaussian smoothing of a fixed endpoint density must obey. In place of the projection, the theory rests on a terminal-multiplier fixed point and an asymmetric factorisation penalising the score energy of the backward factor.

Edge AI model deployment is a multi-stage engineering process involving model conversion, operator compatibility handling, runtime integration, and precision verification. While prior work has demonstrated agent-based automation for Qualcomm AI Runtime, the broader edge inference runtime ecosystem, including Intel OpenVINO, Rockchip RKNN, NVIDIA TensorRT, and ONNX Runtime, presents distinct toolchains and optimization strategies. This paper extends AIPC (AI Porting Conversion, an LLM agent-driven methodology for AI model deployment automation previously demonstrated on Qualcomm AI Runtime) to multi-runtime scenarios, proposing an LLM agent-driven approach for automated single-model-to-single-runtime deployment across heterogeneous inference backends, such as Intel OpenVINO, Rockchip RKNN, NVIDIA TensorRT, and ONNX Runtime. We decompose the edge AI deployment into standardized, verifiable stages, and inject runtime-specific domain knowledge into the agent execution flow through agent skills, auxiliary scripts, and staged verification loops. Using representative vision models, we demonstrate that agent-based deployment can complete the conversion from a PyTorch model to its executable inference, targeting OpenVINO for x86/NPU, RKNN for RK3588, TensorRT for NVIDIA GPU, and ONNX Runtime for Qualcomm NPU with a focus on FP16 precision deployment feasibility verification. The contributions of this paper primarily lie in providing multi-runtime deployment engineering practice experience, toolchain mapping analysis, a layout-adaptation and inference-replacement layer that removes manual transpose insertion from the agent's repair burden, and an empirical characterization of agent deviation behavior under structured knowledge injection, rather than large-scale systematic benchmarking or cross-runtime operator repair strategy comparison.

Next-token prediction has enabled highly fluent autoregressive language models, but it represents global structure only indirectly through sequential factorization. In contrast, high-fidelity autoencoders have become a standard primitive in image generation, enabling generative models to operate over continuous latent spaces; text lacks a comparably faithful continuous representation. We propose LLMAE, a method for repurposing a pretrained decoder-only language model as a continuous text autoencoder by exposing the activations of an intermediate layer as a fixed-length latent bottleneck. LLMAE achieves this interface with structured attention masks and LoRA adaptation, leveraging the generative prior of the original LLM. Across two backbones (270M Gemma 3 and 0.5B Qwen2.5), LLMAE reconstructs sequences up to 1024 tokens with high accuracy, reproducing up to 97% of documents verbatim. We demonstrate downstream utility by training a lightweight diffusion model that generates detailed image captions directly in the frozen LLMAE latent space. By mapping text into a fixed-length continuous latent space, our approach provides an effective substrate for downstream adaptation while benefiting from the fluency of the original LLM. Code and models are available at https://github.com/arkanath/LLMAE.

A robot returning a block to its origin tray may encounter two task-consistent pasts that reconverge to the same current input but warrant different actions. Yet memory-policy evaluations often rely on task success or action change under memory perturbation, neither of which establishes that memory guides the decision. We propose the Counterfactual Memory Audit (CMA), an evaluation protocol that crosses two histories at a verified-identical present, queries a frozen policy under common randomness, and evaluates each saved action under both pasts. This separates memory sensitivity, warranted choice, matched-world physical value, and per-pair reliability. On Mem-0, every audited Put Back pair changes action, but only $20/64$ pairs are fully reliable; at a later Swap decision, all paired actions change while both memories select the same branch. Native interventions further show closed-loop influence: replacing the history bank redirects behavior toward the replaced content, while restoring a 4096-byte protected anchor recovers $38.9$ points of Swap success lost to injected bank faults. On a dual-arm physical platform, memory changes saved actions, yet five of nine completed Put Back manipulations reach the wrong target. These results show that a robot can remember and react without reliably using memory to choose the behavior its past warrants. CMA provides a decision-level audit for distinguishing these cases.

As embodied agents take on increasingly social and relational roles in VR, visual realism and embodiment alone may be insufficient; users must also perceive these agents as emotionally attuned, supportive, and humanlike. Prior work suggests that verbal attunement and nonverbal mimicry can each improve users' social evaluations of embodied agents. However, behavioral mimicry has largely been studied outside of real-time, conversational AI interactions, leaving limited understanding of how users respond when an agent simultaneously generates contextually responsive dialogue and adapts its nonverbal behavior during an immersive conversation. To address this gap, we developed an embodied AI counselor that combines conversational AI with real-time facial-expression and posture mimicry, while producing either verbally attuned or neutral responses. We evaluated the system in a 2 X 2 within-subjects study with 20 participants, manipulating verbal attunement and behavioral mimicry. Results showed that verbal attunement was the most reliable driver of perceived empathy. Behavioral mimicry showed a marginal relationship with perceived humanness, while greater mimicry exposure showed preliminary, exploratory positive associations with empathy, positivity, and humanness, particularly among female participants. Together, these findings show that multimodal synchrony is not a simple additive strategy for designing empathic conversational agents in VR and underscore the need to consider how verbal and nonverbal behaviors are combined during real-time interaction.

National Basketball Association (NBA) teams have increasingly detailed metrics, but better predictions do not necessarily improve decisions. This critical integrative review draws on prior reviews, citation tracing, and topic searches across seven research streams: on-court action, player value, role, lineup synergy, availability, draft and development, and contracts and roster construction. An observation-state-action-decision-evaluation chain organizes the synthesis. Six decision-readiness gates guide our assessment: point-in-time validity, uncertainty, context portability, action feasibility, opportunity-set observability, and evaluation, with requirements matched to each claim. The reviewed literature is strongest in measuring and predicting individual components of a decision. Evidence is less developed at interfaces that combine components, transfer them across settings, and compare feasible actions. We outline a proposed deployment workflow, a reporting contract, and a research agenda covering player transport, role substitution, roster fragility, legal action generation, and asset valuation. The 2023 collective bargaining agreement and forthcoming 3-2-1 Draft Lottery illustrate how institutional changes generate research questions. Models should inform evaluable comparisons of feasible choices. While its effect on organizational decision quality remains an empirical question, the framework provides a diagnostic and reporting structure for matching decision claims to evidence requirements.

A neural network's layers can be treated as time steps of a state-space model, turning Bayesian training into a smoothing problem: a forward pass propagates Gaussian moments through the network, and a backward Rauch–Tung–Striebel pass updates the weight posteriors in closed form. Such methods learn from each observation in a single pass, in an uncertainty-aware manner, and without gradient-based iterations or replay, which makes them well suited for online adaptation and data-efficient learning. Existing smoothing-based methods, however, are restricted to diagonal covariances across activations, discarding correlations between neurons. We overcome this limitation via a cross-covariance identity that enables full-covariance propagation through a network's nonlinear activations. We derive a one-step-per-layer smoother that approximates as Gaussian only each layer's affine output, and that applies both to deterministic systems with noisy observations and to stochastic systems described by output statistics. We demonstrate this method in non-stationary classification, online dynamics learning, and policy adaptation of a vision-language-action model, and find that it is generally more accurate than other smoothing-based methods.

Variational data assimilation computes a Bayesian update through optimization on a fixed posterior geometry, whose conditioning is determined by the prior covariance and the observation operator. In strongly anisotropic, weakly observed, or highly informative regimes, this geometry can become severely ill conditioned, leading to substantial sensitivity to solver design and preconditioning. This work investigates an alternative computational route based on flow-based transport, using the ensemble score filter as a training-free realization. Rather than repeatedly optimizing over the full posterior geometry, the flow-based update transports the predictive distribution through a sequence of intermediate distributions whose covariance and curvature are progressively regularized before reaching the target posterior. In the linear-Gaussian setting, we characterize this mechanism through the evolution of the diffused posterior covariance and the associated time-dependent curvature, revealing a continuation from a near-isotropic reference geometry to the ill-conditioned posterior geometry targeted by variational assimilation. Numerical stress tests show that this geometric regularization makes the flow-based method substantially less sensitive to severe ill-conditioning than the variational method, while a heterogeneous $10^4$-dimensional Lorenz-96 benchmark demonstrates improved robustness and lower delivered computational cost. These results identify posterior-geometry regularization as a key mechanism distinguishing transport-based from optimization-based data assimilation.

LLMs can generate programs for ARC-AGI-2 tasks, but the provided compute only allows a small number of attempts to generate, debug and validate solutions. Genetic algorithms can search and test many more programs, but random search rarely starts in a useful neighborhood of the solution space. We combine the two methods through a compact domain specific language (DSL). First, a quantized Qwen3.5-4B LLM generates an initial set of programs for each ARCAGI-2 task. Then, we use those programs to seed an initial population of starting programs, and use genetic algorithms to evolve these programs towards a solution to the given task. The DSL is designed such that every mutated program remains valid and can be executed. The initial programs proposed by the LLM solve 2 (3.3%) of the first 60 tasks of the ARC-2 public evaluation set. The genetic algorithm solves an additional 4, giving 6 correct test outputs in total (10.0%). If we try using evolving solutions without this LLM seeding, we do not arrive at any solutions at all. The results show that genetic search can improve programs generated by LLMs and produce additional correct solutions.

This work revisits a fundamental question in active learning: how powerful is the ability to synthesize arbitrary queries? Compared to pool-based active learning, where the learner only selects queries from a given unlabeled pool, it has been observed that this seemingly mild change in query ability may dramatically alter the difficulty of statistical learning. In particular some hypothesis classes, such as two dimensional halfspaces, permit only polynomial error decay in the number of samples when the learner must draw its labels from the pool, yet become exponentially learnable once synthesized queries are allowed. This striking gap suggests that membership query synthesis induces a fundamentally different mode of learning, one that is not adequately captured by existing active learning theory and calls for new analytical tools to characterize its sample complexity for general hypothesis classes. Motivated by this phenomenon, we develop several sufficient conditions, present intriguing examples, and propose a conjectural perspective toward understanding which hypothesis classes admit efficient learning through synthesized queries.

In this paper, we present the Berkeley QUAD (Quasi-direct-drive, Underactuated, Asymmetric Design) Hand, a four-finger anthropomorphic robotic hand with 11 degrees of freedom and 8 degrees of actuation. The design utilizes QDD actuation at the base of each finger, enabling high force transparency for dexterous, adaptive performance. However, the low torque density of these actuators traditionally presents major issues with size, weight, and thermal limits. We overcome this by applying bio-inspired asymmetry, delegating dexterity to the radial fingers through individual QDD actuation, and strength to the ulnar finger through an underactuated, compliantly coupled transmission driven by a larger QDD motor. A novel preloaded, linkage-based transmission permits this ulnar coupling in a way that preserves human-like workspace reachability. Under light loads, the ulnar motor drives the third (middle) finger directly for dexterity while the fourth (ring) finger mirrors its motion. However, under larger loads, the middle finger complies while the motor drives the ring finger further downwards and inwards toward the center of the grasp to apply better closure forces. Hardware evaluations validate this architecture, demonstrating that the hand achieves 29 out of 33 Feix taxonomy grasps and exhibits backdrive forces as low as 50 g for delicate interactions. Additionally, the underactuated fourth finger improves grasp closure and provides the spatial efficiency necessary for larger actuation, yielding up to a 96-fold reduction in heat generation during sustained loading. Webpage: https://benudavis.github.io/berkeley-quadhand/

Cryogenic capacity is a key bottleneck in the scaling of superconducting quantum processors, critically impacted by control wiring. Currently, wiring architectures are often designed following a small set of "best-practice" heuristics, little changed since they emerged for early, small-scale systems, and not rigorously evaluated for wider contexts. Here, we adopt a whole-system numerical modelling approach and present a systematic optimisation for the coaxial wiring design of a superconducting quantum processor. We identify optimised configurations that substantively outperform conventional designs in device temperature, noise burden, qubit capacity and other practical metrics. Importantly, our analysis delivers varied outcomes for different system contexts and operating regimes, illustrating the benefits of a systematic approach when there is no universal solution. Our results evaluate and sometimes challenge conventional wisdom in relation to design factors such as the utility of 0dB attenuators. We also use a wide exploration of cable configurations—directly enabled by our holistic and flexible numerical modelling approach—to develop more rigorously supported general principles for future cryogenic system design, based on a new conceptual framework to describe attenuator cascades that draws on analogies to low-noise amplifier chains. Our approach, design outcomes, and generalised analyses should also be readily applicable to other cryogenic quantum computing platforms. Finally, we support ready adoption of this approach through a web-based graphical tool which streamlines the analysis of the proposed framework under different contexts. Our results illustrate how our approach can help maximise the available computational resources of quantum processors at all system scales.

Oriented ship detection in very high resolution (VHR) remote sensing imagery remains challenging due to elongated hull geometry and dense target distributions in complex port scenes. Existing methods typically address geometric representation and duplicate suppression separately. To jointly tackle these issues, we propose an oriented ship detector with two complementary components. The C3k2_Strip module employs orthogonal strip convolutions to better capture elongated hull structures, while the Class-Aware Direction-Aware Exclusion Loss (CA-DAEL) suppresses redundant predictions using class, direction, and confidence cues. Experiments on HRSC2016 and DIOR-R achieve 78.45% and 53.71% mAP50:95, respectively, with only 2.91M parameters. On HRSC2016, the proposed method improves mAP50:95 by 6.32 percentage points over the YOLOv11-OBB baseline, demonstrating its effectiveness for accurate oriented ship detection.

A high dimensional bi-directionally coupled $N$-loop thermosyphon system in a tandem is modeled by $3N$ Lorenz type of differential equations. The fluid flow in each loop is driven by the heat source (Rayleigh numbers) as well as the moment and heat exchanges between the adjacent loops. The flow becomes chaotic when the Rayleigh numbers are large. A decentralized controller design via proportional local state feedback is employed to stabilize the chaotic flows in each loop. As a result of the stability analysis, we show that there exist lower bounds on the controller gains that guarantee global stability of the system. Under the scenario of unknown parameters, we augment the original system with additional dynamic equations of the feedback gains to adaptively determine the feasible gains that stabilize the system. The analysis also sheds insight on the role of thermal coupling in the stability of the control system, which allows us to extend the results to negative $z$-state coupling coefficient as well as a class of nonlinear $z$-state coupling functions. Numerical simulations further demonstrate the effectiveness of the proposed controller design