arXiv:2601.18987v1 Announce Type: new Abstract: Determining whether a program terminates is a central problem in computer science. Turing’s foundational result established the Halting Problem as undecidable, showing that no algorithm can universally determine termination for all programs and inputs. Consequently, automatic verification tools approximate termination, sometimes failing to prove or disprove; these tools rely on problem-specific architectures and abstractions, and are usually tied to particular programming languages. Recent success and progress in large language models (LLMs) raises the […]

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arXiv:2602.18530v1 Announce Type: new Abstract: This study compares multiple deep learning architectures for the automated, image-based classification of five locally cultivated black table olive varieties in Turkey: Gemlik, Ayvalik, Uslu, Erkence, and Celebi. Using a dataset of 2500 images, ten architectures – MobileNetV2, EfficientNetB0, EfficientNetV2-S, ResNet50, ResNet101, DenseNet121, InceptionV3, ConvNeXt-Tiny, ViT-B16, and Swin-T – were trained using transfer learning. Model performance was evaluated using accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), Cohen’s Kappa, ROC-AUC, number of parameters, […]

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arXiv:2602.12604v1 Announce Type: cross Abstract: Differential Privacy (DP) provides a rigorous framework for deriving privacy-preserving estimators by injecting calibrated noise to mask individual contributions while preserving population-level insights. Its central challenge lies in the privacy-utility trade-off: calibrating noise levels to ensure robust protection without compromising statistical performance. Standard DP methods struggle with a particular class of two-stage problems prevalent in individualized treatment rules (ITRs) and causal inference. In these settings, data-dependent weights are first computed to satisfy distributional […]

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The Motion Planning course is going faster than I expected. I completed 2 weeks within 5 days. Thats good I guess, since it means I might get to the Capstone project before I take a vacation to India. Heres the stuff from this week: Graphcore and the Intelligent Processing Unit (IPU) Graphcore aims to disrupt the world of ML-focussed computing devices. In an interesting blog post, they visualize neuron connections in different CNN architectures, and talk about how […]

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arXiv:2305.10413v5 Announce Type: replace Abstract: Signatures are iterated path integrals of continuous and discrete-time processes, and their universal nonlinearity linearizes the problem of feature selection in time series data analysis. This paper studies the consistency of signature using Lasso regression, both theoretically and numerically. We establish conditions under which the Lasso regression is consistent both asymptotically and in finite sample. Furthermore, we show that the Lasso regression is more consistent with the It^o signature for time series and […]

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arXiv:2603.25750v1 Announce Type: new Abstract: As the paradigm of AI shifts from text-based LLMs to Speech Language Models (SLMs), there is a growing demand for full-duplex systems capable of real-time, natural human-computer interaction. However, the development of such models is constrained by the scarcity of high-quality, multi-speaker conversational data, as existing large-scale resources are predominantly single-speaker or limited in volume. Addressing the complex dynamics of natural dialogue, such as overlapping and back-channeling remains a challenge, with standard processing […]

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We study a structured bi-level optimization problem where the upper-level objective is a smooth function and the lower-level problem is policy optimization in a Markov decision process (MDP). The upper-level decision variable parameterizes the reward of the lower-level MDP, and the upper-level objective depends on the optimal induced policy. Existing methods for bi-level optimization and RL often require second-order information, impose strong regularization at the lower level, or inefficiently use samples through nested-loop procedures. In this work, we […]

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This paper addresses the limitations of traditional protocol anomaly detection methods in handling dynamic state changes and unstructured behaviors. A deep protocol anomaly detection algorithm based on status code sequence modeling is proposed. The method uses the status codes returned during protocol communication as the core input. A state embedding layer is employed to transform discrete status codes into continuous vector representations. A gated recurrent unit (GRU) is then used to capture temporal dependencies and behavior patterns within […]

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arXiv:2603.28913v1 Announce Type: new Abstract: Polarity detection becomes substantially more challenging under domain shift, particularly in heterogeneous, long-form narratives with complex discourse structure, such as Holocaust oral histories. This paper presents a corpus-scale diagnostic study of off-the-shelf sentiment classifiers on long-form Holocaust oral histories, using three pretrained transformer-based polarity classifiers on a corpus of 107,305 utterances and 579,013 sentences. After assembling model outputs, we introduce an agreement-based stability taxonomy (ABC) to stratify inter-model output stability. We report pairwise […]

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