arXiv:2601.16242v1 Announce Type: new Abstract: This paper presents a novel and scalable screw-theoretic multibody synthesis framework for PDE-based dynamic modeling of serial robotic manipulators with an arbitrary number of flexible links in three-dimensional space. The proposed approach systematically constructs screw-theoretic PDE models for individual flexible links and rigorously enforces holonomic joint constraints through interaction forces. The dynamics of each link are formulated using a set of dual screws expressed in body-fixed coordinates: one describing the motion of the […]

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Traditional time-series analysis methods, such as Fourier and wavelet transforms, excel at identifying frequency components and their temporal localization. While powerful for spectral analysis, these methods do not explicitly capture the global geometric structure of state transitions or the emergence of cyclic (non-conservative) dynamics within the signal. In this paper, we propose a novel geometric framework that encodes the local complexity dynamics of a time series as a simplicial complex. Using a sliding Hann window, we map the […]

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Dario Amodei envisions a “compressed 21st century” where powerful AI achieves one hundred years of progress in one decade. This radical upside includes curing most diseases, doubling human lifespans, and eliminating global poverty. By accelerating science and supporting democracy, AI offers an inspiring, humanitarian future worth fighting for.

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arXiv:2604.13039v1 Announce Type: new Abstract: The decomposition of datasets is a useful mechanism in the processing of large datasets and it is required in many cases. In formal concept analysis (FCA), the dataset is interpreted as a context and the notion of independent context is relevant in the decomposition of a context. In this paper, we have introduced a formal definition of independent context within the multi-adjoint concept lattice framework, which can be translated to other fuzzy approaches. […]

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arXiv:2512.13228v2 Announce Type: replace-cross Abstract: Semi-supervised classification leverages both labeled and unlabeled data to improve predictive performance, but existing software support remains fragmented across methods, learning settings, and data modalities. We introduce ModSSC, an open source Python framework for inductive and transductive semi-supervised classification designed to support reproducible and controlled experimentation. ModSSC provides a modular and extensible software architecture centered on reusable semi-supervised learning components, stable abstractions, and fully declarative experiment specification. Experiments are defined through configuration files, […]

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Machine unlearning enables models to selectively forget training data, driven by privacy regulations such as GDPR and CCPA. However, its fairness implications remain underexplored: when a model forgets a demographic group, does it neutralize that concept or redistribute it to correlated groups, potentially amplifying bias? We investigate this bias redistribution phenomenon on CelebA using CLIP models (ViT/B-32, ViT-L/14, ViT-B/16) under a zero-shot classification setting across intersectional groups defined by age and gender. We evaluate three unlearning methods, Prompt […]

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As we move into 2026, the demand for fast, reliable, and real-time financial data continues to grow. Developers, analysts, and financial institutions increasingly rely on accurate market insights to build smarter tools and make informed decisions. Financial APIs are now transforming how market data is accessed, analyzed, and applied across global markets. Whether you are building a next-generation personal finance application or powering a high-frequency trading system, Stock Market Data APIs and Forex Data APIs play a critical […]

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This work presents the largest curation of Southern Resident Killer Whale (SRKW) acoustic data to date, also containing other marine mammals in their environment. We systematically search all available public archival hydrophone data within the SRKW habitat (over 30 years of audio data). The search consists of a weakly-supervised, positive-unlabelled, active learning strategy to identify all instances of marine mammals. The resulting transformer-based detectors outperform state-of-the-art detectors on the DEEPAL, DCLDE-2026, and two newly introduced expert-annotated datasets in […]

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