arXiv:2602.02567v1 Announce Type: new Abstract: Arctic sea ice plays a critical role in regulating Earth’s climate system, significantly influencing polar ecological stability and human activities in coastal regions. Recent advances in artificial intelligence have facilitated the development of skillful pan-Arctic sea ice forecasting systems, where data-driven approaches showcase tremendous potential to outperform conventional physics-based numerical models in terms of accuracy, computational efficiency and forecasting lead times. Despite the latest progress made by deep learning (DL) forecasting models, most […]

Ver mais

arXiv:2602.09239v1 Announce Type: new Abstract: The growing adoption of AI-driven smart home devices has introduced new privacy risks for domestic workers (DWs), who are frequently monitored in employers’ homes while also using smart devices in their own households. We conducted semi-structured interviews with 18 UK-based DWs and performed a human-centered threat modeling analysis of their experiences through the lens of Communication Privacy Management (CPM). Our findings extend existing threat models beyond abstract adversaries and single-household contexts by showing […]

Ver mais

Firm-level financial statement data form multivariate annual time series with strong cross-variable dependencies and temporal dynamics, yet publicly available panels are often short and incomplete, limiting the generalization of predictive models. We present Latent Financial Time-Series Diffusion (LFTD), a structure-aware augmentation framework that synthesizes realistic firm-level financial time series in a compact latent space. LFTD first learns information-preserving representations with a dual encoder: an FT-Transformer that captures within-year interactions across financial variables and a Time Series Transformer (TST) […]

Ver mais

I just want to ask PhDs in AI on this sub, how much does your supervisor support your phd ? In term of research output, how much help do you get from your supervisor? Only ambigious direction (e.g. Active Learning/RL for architecture X)? Or more details idea, like the research gap itself? If you meet a certain problem (e.g. cannot solve X because too hard to solve), do they give you any help, like potential solution direction to […]

Ver mais

Wilson dismantles the absurd claims of religious and political authorities. His core message is a call to intellectual rebellion: since the world is fundamentally chaotic and run by fools, you must trust your own conscience. Question all dogma, laugh at the absurdity, and become the pope of your own personal religion.

Ver mais

Pairwise preference learning is central to machine learning, with recent applications in aligning language models with human preferences. A typical dataset consists of triplets $(x, y^+, y^-)$, where response $y^+$ is preferred over response $y^-$ for context $x$. The Bradley–Terry (BT) model is the predominant approach, modeling preference probabilities as a function of latent score differences. Standard practice assumes data follows this model and learns the latent scores accordingly. However, real data may violate this assumption, and it […]

Ver mais

The traditional Catholic doctrine of creation’s radical contingence—however philosophically defensible—is existentially corrosive in an evolutionary age. Teilhard wants to replace it with a vision where the world genuinely matters to God, where human effort is cosmically significant, and where faith functions as the vital fuel that drives humanity’s ongoing development.

Ver mais

arXiv:2602.15843v1 Announce Type: new Abstract: In “Compress or Route?” (Johnson, 2026), we found that code generation tolerates aggressive prompt compression (r >= 0.6) while chain-of-thought reasoning degrades gradually. That study was limited to HumanEval (164 problems), left the “perplexity paradox” mechanism unvalidated, and provided no adaptive algorithm. This paper addresses all three gaps. First, we validate across six code benchmarks (HumanEval, MBPP, HumanEval+, MultiPL-E) and four reasoning benchmarks (GSM8K, MATH, ARC-Challenge, MMLU-STEM), confirming the compression threshold generalizes across […]

Ver mais

arXiv:2602.17985v1 Announce Type: cross Abstract: Machine learning is at the heart of managing the real-world problems associated with massive data. With the success of neural networks on such large-scale problems, more research in machine learning is being conducted now than ever before. This dissertation focuses on three different projects rooted in mathematical theory for machine learning applications. The first project deals with supervised learning and manifold learning. In theory, one of the main problems in supervised learning is […]

Ver mais