1140793 results (page 75 of 45632)
-
Collaborative Contextual Bayesian Optimization
Discovering optimal designs through sequential data collection is essential in many real-world applications. While Bayesian Optimization (BO) has achieved remarkable success in this setting, growing attention has recently turned to context-specific optimal design, formalized as Contextual Bayesian Optimization (CBO). Unlike BO, CBO is inherently more challenging as it must approximate an entire ma…
-
Safety-Certified CRT Sparse FFT: $Ω(k^2)$ Lower Bound and $O(N \log N)$ Worst-Case
Computing Fourier transforms of k-sparse signals, where only k of N frequencies are non-zero, is fundamental in compressed sensing, radar, and medical imaging. While the Fast Fourier Transform (FFT) evaluates all N frequencies in $O(N \log N)$ time, sufficiently sparse signals should admit sub-linear complexity in N. Existing sparse FFT algorithms using Chinese Remainder Theorem (CRT) reconstructi…
-
Predicting Redshift in Seyfert Galaxies Using Machine Learning
Photometric redshift estimation is a key requirement for modern large-area surveys, where spectroscopic measurements are observationally prohibitive. Seyfert II galaxies provide a particularly challenging test case due to the combined effects of nuclear activity, host-galaxy emission, and dust attenuation. In this work, we develop a machine learning approach for photometric redshift estimation usi…
-
Ternary Memristive Logic: Hardware for Reasoning Realized via Domain Algebra
Memristive crossbars store numerical weights needing aggregation and decoding; a single junction means nothing alone. This paper presents a fundamentally different use: each junction stores a complete, domain-scoped logical assertion (holds/negated/undefined). Ternary resistance states encode these values directly. We establish a structure-preserving mapping from a domain algebra to crossbar topol…
-
Gradient-Based Program Synthesis with Neurally Interpreted Languages
A central challenge in program induction has long been the trade-off between symbolic and neural approaches. Symbolic methods offer compositional generalisation and data efficiency, yet their scalability is constrained by formalisms such as domain-specific languages (DSLs), which are labour-intensive to create and may not transfer to new domains. In contrast, neural networks flexibly learn from da…
-
Task-Adaptive Admittance Control for Human-Quadrotor Cooperative Load Transportation with Dynamic Cable-Length Regulation
The collaboration between humans and robots is critical in many robotic applications, especially in those requiring physical human-robot interaction (pHRI). Previous research in pHRI has largely focused on robotic manipulators, employing impedance or admittance control to maintain operational safety. Conversely, research in human-quadrotor cooperative load transportation (CLT) is still in its infa…
-
Cosmic Ray Electron Evolution in Supernova Remnants: Log-Parabola Distribution
The shock fronts of supernova remnants (SNRs) are believed to be significant sites of acceleration of cosmic ray particles. Previous researchers have shown that a particle distribution similar to a log-parabola can be generated when particles have an energy-dependent escape. We explore the acceleration of electrons at SNR shock fronts, and show that modeling this energy-dependent particle escape m…
-
Option Pricing on Noisy Intermediate-Scale Quantum Computers: A Quantum Neural Network Approach
In a global derivatives market with notional values in the hundreds of trillions of dollars, the accuracy and efficiency of pricing models are of fundamental importance, with direct implications for risk management, capital allocation, and regulatory compliance. In this work, we employ the Black-Scholes-Merton (BSM) framework not as an end in itself, but as a controlled benchmark environment in wh…
-
Harmful Intent as a Geometrically Recoverable Feature of LLM Residual Streams
Harmful intent is geometrically recoverable from large language model residual streams: as a linear direction in most layers, and as angular deviation in layers where projection methods fail. Across 12 models spanning four architectural families (Qwen2.5, Qwen3.5, Llama-3.2, Gemma-3) and three alignment variants (base, instruction-tuned, abliterated), under single-turn, English evaluation, we char…
-
Thrust Regulation Through Wing Linkage Modulation on the Aerobat Platform: Piezoelectric Slip-Stick Actuated Regulator Development
Aerobat is a bat-inspired flapping-wing robot with a wing gait generate by the computational structure, a planar linkage of carbon fiber links driven by a single motor. This design minimizes weight but couples both wings to a shared input motor, eliminating independent thrust control and preventing asymmetric maneuvers. This thesis investigates thrust regulation by modifying the effective length o…
-
Van der Waals Gravity Theory
In this study, we propose an extension of general relativity inspired by the van der Waals equation of state, incorporating non-ideal thermodynamic effects into the gravitational sector. Our approach is based on the thermodynamic interpretation of gravity introduced by Jacobson, in which the field equations arise from the Clausius relation. Within this framework, we obtain modified gravitational f…
-
Less Is More: Cognitive Load and the Single-Prompt Ceiling in LLM Mathematical Reasoning
We present a systematic empirical study of prompt engineering for formal mathematical reasoning in the context of the SAIR Equational Theories Stage 1 competition. The task requires deciding whether one equational law implies another over all magmas -- a problem that is undecidable in general but decidable for FALSE via finite model search. Over five weeks, we designed, tested, and analyzed more t…
-
Kazhdan-Lusztig Basis and Optimization
We describe a conjectural approach to obtaining canonical bases of the Hecke algebra at $q=1$ via continuous quadratic optimization. We focus on Specht modules $S^λ$ and proper cones inside $S^λ$ that are invariant under the action of $1+s$ for all simple reflections $s\in S$. We show that there are unique minimal and maximal cones invariant under all $1+s$. For hook shapes, two-column shapes, and…
-
Regulating Artificial Intimacy: From Locks and Blocks to Relational Accountability
A series of high-profile tragedies involving companion chatbots has triggered an unusually rapid regulatory response. Several jurisdictions, including Australia, California, and New York, have introduced enforceable regulation, while regulators elsewhere have signaled growing concern about risks posed by companion chatbots, particularly to children. In parallel, leading providers, notably OpenAI, …
-
Prioritizing the Best: Incentivizing Reliable Multimodal Reasoning by Rewarding Beyond Answer Correctness
Reinforcement Learning with Verifiable Rewards (RLVR) improves multimodal reasoning by rewarding verifiable final answers. Yet answer-correct trajectories may still rely on incomplete derivations, weak evidence, or statements that contradict their conclusions. This gap between answer correctness and reasoning validity, which we call reasoning-answer inconsistency, motivates trajectory supervision …
-
AC-SINDy: Compositional Sparse Identification of Nonlinear Dynamics
We present AC-SINDy, a compositional extension of the Sparse Identification of Nonlinear Dynamics (SINDy) framework that replaces explicit feature libraries with a structured representation based on arithmetic circuits. Rather than enumerating candidate basis functions, the proposed approach constructs nonlinear features through compositions of linear functions and multiplicative interactions, yie…
-
Spatiotemporal Link Formation Prediction in Social Learning Networks Using Graph Neural Networks
Social learning networks (SLNs) are graphical representations that capture student interactions within educational settings (e.g., a classroom), with nodes representing students and edges denoting interactions. Accurately predicting future interactions in these networks (i.e., link prediction) is crucial for enabling effective collaborative learning, supporting timely instructional interventions, …
-
HALO: Hybrid Auto-encoded Locomotion with Learned Latent Dynamics, Poincaré Maps, and Regions of Attraction
Reduced-order models are powerful for analyzing and controlling high-dimensional dynamical systems. Yet constructing these models for complex hybrid systems such as legged robots remains challenging. Classical approaches rely on hand-designed template models (e.g., LIP, SLIP), which, though insightful, only approximate the underlying dynamics. In contrast, data-driven methods can extract more accu…
-
Matrix-Free Multigrid with Algebraically Consistent Coarsening on Adaptive Octrees
We present a matrix-free GPU multigrid preconditioner with algebraically consistent coarsening for solving Poisson equations on adaptive octree grids with irregular domains. Within uniform-resolution regions, the coarsening satisfies the Galerkin principle. At T-junctions between refinement levels, we propose a flux-consistent coarse-grid correction that restores cross-level consistency while pres…
-
Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph
Current AI-assisted programming tools are predominantly linear and chat-based, which deviates from the iterative and branching nature of programming itself. Our preliminary study with developers using AI assistants suggested that they often struggle to explore alternatives, manage prompting sequences, and trace changes. Informed by these insights, we created EvoGraph, an IDE plugin that integrates…
-
Formally Verified Patent Analysis via Dependent Type Theory: Machine-Checkable Certificates from a Hybrid AI + Lean 4 Pipeline
We present a formally verified framework for patent analysis as a hybrid AI + Lean 4 pipeline. The DAG-coverage core (Algorithm 1b) is fully machine-verified once bounded match scores are fixed. Freedom-to-operate, claim-construction sensitivity, cross-claim consistency, and doctrine-of-equivalents analyses are formalized at the specification level with kernel-checked candidate certificates. Exist…
-
A Proxy Consistency Loss for Grounded Fusion of Earth Observation and Location Encoders
Supervised learning with Earth observation inputs is often limited by the sparsity of high-quality labeled or in-situ measured data to use as training labels. With the abundance of geographic data products, in many cases there are variables correlated with - but different from - the variable of interest that can be leveraged. We integrate such proxy variables within a geographic prior via a traina…
-
Where Fake Citations Are Made: Tracing Field-Level Hallucination to Specific Neurons in LLMs
LLMs frequently generate fictitious yet convincing citations, often expressing high confidence even when the underlying reference is wrong. We study this failure across 9 models and 108{,}000 generated references, and find that author names fail far more often than other fields across all models and settings. Citation style has no measurable effect, while reasoning-oriented distillation degrades r…
-
LegalBench-BR: A Benchmark for Evaluating Large Language Models on Brazilian Legal Decision Classification
We introduce LegalBench-BR, the first public benchmark for evaluating language models on Brazilian legal text classification. The dataset comprises 3,105 appellate proceedings from the Santa Catarina State Court (TJSC), collected via the DataJud API (CNJ) and annotated across five legal areas through LLM-assisted labeling with heuristic validation. On a class-balanced test set, BERTimbau-LoRA, upd…
-
The General Antiparticle Spectrometer (GAPS) Antarctic Balloon Payload
The General Antiparticle Spectrometer (GAPS) is an Antarctic stratospheric balloon mission designed to provide unmatched sensitivity to low-energy (<0.25 GeV/n) cosmic-ray antiprotons, antideuterons, and antihelium nuclei as signatures of dark matter. The distinctive GAPS particle identification technique relies on measuring the energy loss along the track of an incoming antinucleus as it slows do…