Tootfinder

Quantum Stochastic Walks for Portfolio Optimization: Theory and Implementation on Financial Networks
Yen Jui Chang, Wei-Ting Wang, Yun-Yuan Wang, Chen-Yu Liu, Kuan-Cheng Chen, Ching-Ray Chang
https://arxiv.org/abs/2507.03963

Quantum Stochastic Walks for Portfolio Optimization: Theory and Implementation on Financial Networks
Financial markets are noisy yet contain a latent graph-theoretic structure that can be exploited for superior risk-adjusted returns. We propose a quantum stochastic walk (QSW) optimizer that embeds assets in a weighted graph: nodes represent securities while edges encode the return-covariance kernel. Portfolio weights are derived from the walk's stationary distribution. Three empirical studies support the approach. (i) For the top 100 S\&P 500 constituents over 2016-2024, six scenario portfolio…

Finding easy regions for short-read variant calling from pangenome data
Heng Li
https://arxiv.org/abs/2507.03718 https://arxiv.org/pd…

Finding easy regions for short-read variant calling from pangenome data
Background: While benchmarks on short-read variant calling suggest low error rate below 0.5%, they are only applicable to predefined confident regions. For a human sample without such regions, the error rate could be 10 times higher. Although multiple sets of easy regions have been identified to alleviate the issue, they fail to consider non-reference samples or are biased towards existing short-read data or aligners. Results: Here, using hundreds of high-quality human assemblies, we derived …

Gas Pressure Driven Screening Forces and Pebble Aggregation: A Pathway for Growth in Planet Formation
Mukesh Kumar Vyas
https://arxiv.org/abs/2507.02570 ht…

Gas Pressure Driven Screening Forces and Pebble Aggregation: A Pathway for Growth in Planet Formation
The formation of planetesimals from cm-sized pebbles in protoplanetary disks faces significant barriers, including fragmentation and radial drift. We identify a previously unaccounted screening force, arising from mutual shielding of thermal gas particles between pebbles when their separation falls below the gas mean free path. This force facilitates pebble binding, overcoming key growth barriers under turbulent disk conditions. Unlike conventional mechanisms, screening forces operate independe…

Exploring Societal Concerns and Perceptions of AI: A Thematic Analysis through the Lens of Problem-Seeking
Naomi Omeonga wa Kayembe
https://arxiv.org/abs/2505.23930

Exploring Societal Concerns and Perceptions of AI: A Thematic Analysis through the Lens of Problem-Seeking
This study introduces a novel conceptual framework distinguishing problem-seeking from problem-solving to clarify the unique features of human intelligence in contrast to AI. Problem-seeking refers to the embodied, emotionally grounded process by which humans identify and set goals, while problem-solving denotes the execution of strategies aimed at achieving such predefined objectives. The framework emphasizes that while AI excels at efficiency and optimization, it lacks the orientation derived…

Leveraging AM and FM Rhythm Spectrograms for Dementia Classification and Assessment
Parismita Gogoi, Vishwanath Pratap Singh, Seema Khadirnaikar, Soma Siddhartha, Sishir Kalita, Jagabandhu Mishra, Md Sahidullah, Priyankoo Sarmah, S. R. M. Prasanna
https://arxiv.org/abs/2506.00861

Leveraging AM and FM Rhythm Spectrograms for Dementia Classification and Assessment
This study explores the potential of Rhythm Formant Analysis (RFA) to capture long-term temporal modulations in dementia speech. Specifically, we introduce RFA-derived rhythm spectrograms as novel features for dementia classification and regression tasks. We propose two methodologies: (1) handcrafted features derived from rhythm spectrograms, and (2) a data-driven fusion approach, integrating proposed RFA-derived rhythm spectrograms with vision transformer (ViT) for acoustic representations alo…

Changing disc compositions via internal photoevaporation II: M dwarf systems
Julia Lena Lienert, Bertram Bitsch, Thomas Henning
https://arxiv.org/abs/2505.21470

Changing disc compositions via internal photoevaporation II: M dwarf systems
The chemical evolution of the inner regions of protoplanetary discs is a complex process. Several factors influence it, one being the inward drift and evaporation of volatile-rich pebbles. During the disc's evolution, its inner part is first enriched with evaporating water-ice, resulting in a low C/O ratio. Afterwards, C-rich gas from the outer disc is transported inwards. Consequently, the C/O ratio of the inner disc increases again after 2 Myr. Previously, we studied how internal photoevapora…

Uncertainty-Aware Strategies: A Model-Agnostic Framework for Robust Financial Optimization through Subsampling
Hans Buehler, Blanka Horvath, Yannick Limmer, Thorsten Schmidt
https://arxiv.org/abs/2506.07299

Uncertainty-Aware Strategies: A Model-Agnostic Framework for Robust Financial Optimization through Subsampling
This paper addresses the challenge of model uncertainty in quantitative finance, where decisions in portfolio allocation, derivative pricing, and risk management rely on estimating stochastic models from limited data. In practice, the unavailability of the true probability measure forces reliance on an empirical approximation, and even small misestimations can lead to significant deviations in decision quality. Building on the framework of Klibanoff et al. (2005), we enhance the conventional ob…

Machine Learning Enhanced Multi-Factor Quantitative Trading: A Cross-Sectional Portfolio Optimization Approach with Bias Correction
Yimin Du
https://arxiv.org/abs/2507.07107

Machine Learning Enhanced Multi-Factor Quantitative Trading: A Cross-Sectional Portfolio Optimization Approach with Bias Correction
This paper presents a comprehensive machine learning framework for quantitative trading that achieves superior risk-adjusted returns through systematic factor engineering, real-time computation optimization, and cross-sectional portfolio construction. Our approach integrates multi-factor alpha discovery with bias correction techniques, leveraging PyTorch-accelerated factor computation and advanced portfolio optimization. The system processes 500-1000 factors derived from open-source alpha101 ex…