Who is in the Epstein files? The list of some of the world's most rich and powerful people with ties to late sex offender Jeffrey Epstein has lengthened with the latest US government release of millions of new files from its investigation into the disgraced financier. The 30 January drop of new material - dubbed the Epstein files - included three million pages, 180,000 images, 2,000 videos, and a number of household names like Richard Branson, Bill Gates and Elon Musk. There is no suggestion that appearing in the documents implies any wrongdoing, and many people who have featured in previous releases have denied any wrongdoing in relation to Epstein. The release came weeks after the deadline set by the Epstein Files Transparency Act which was signed into law by US President Donald Trump in November and required a full release of all Epstein-related documents.

Neural Radiance Fields (NeRF) have achieved remarkable results in novel view synthesis, typically using sRGB images for supervision. However, little attention has been paid to the color space in which the network is learning the radiance field representation. Inspired by the BiIlluminant Dichromatic Reflection (BIDR) model, which suggests that a logarithmic transformation simplifies the separation of illumination and reflectance, we hypothesize that log RGB space enables NeRF to learn a more compact and effective representation of scene appearance. To test this, we captured approximately 30 videos using a GoPro camera, ensuring linear data recovery through inverse encoding. We trained NeRF models under various color space interpretations linear, sRGB, GPLog, and log RGB by converting each network output to a common color space before rendering and loss computation, enforcing representation learning in different color spaces. Quantitative and qualitative evaluations demonstrate that using a log RGB color space consistently improves rendering quality, exhibits greater robustness across scenes, and performs particularly well in low light conditions while using the same bit-depth input images. Further analysis across different network sizes and NeRF variants confirms the generalization and stability of the log space advantage.

This work proposes a wavelet-based physics-informed quantum neural network framework to efficiently address multiscale partial differential equations that involve sharp gradients, stiffness, rapid local variations, and highly oscillatory behavior. Traditional physics-informed neural networks (PINNs) have demonstrated substantial potential in solving differential equations, and their quantum counterparts, quantum-PINNs, exhibit enhanced representational capacity with fewer trainable parameters. However, both approaches face notable challenges in accurately solving the multiscale features. Furthermore, their reliance on automatic differentiation for constructing loss functions introduces considerable computational overhead, resulting in longer training times. To overcome these challenges, we developed a wavelet-accelerated physics-informed quantum neural network that eliminates the need for automatic differentiation, significantly reducing computational complexity. The proposed framework incorporates the multiresolution property of wavelets within the quantum neural network architecture, thereby enhancing the network's ability to effectively capture both local and global features of multiscale problems. Numerical experiments demonstrate that our proposed method achieves superior accuracy while requiring less than five percent of the trainable parameters compared to classical wavelet-based PINNs, resulting in faster convergence. Moreover, it offers three to five times speed-up compared to existing quantum PINNs, highlighting the potential of the proposed approach for solving challenging multiscale and oscillatory problems efficiently.

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We establish a fundamental connection between score-based diffusion models and non-equilibrium thermodynamics by deriving performance limits based on entropy rates. Our main theoretical contribution is a lower bound on the negative log-likelihood of the data that relates model performance to entropy rates of diffusion processes. We numerically validate this bound on a synthetic dataset and investigate its tightness. By building a bridge to entropy rates - system, intrinsic, and exchange entropy - we provide new insights into the thermodynamic operation of these models, drawing parallels to Maxwell's demon and implications for thermodynamic computing hardware. Our framework connects generative modeling performance to fundamental physical principles through stochastic thermodynamics.

Plus: ICE deploys secretive phone surveillance tech, officials warn of Chinese surveillance tools in US highway infrastructure, and more. Right-wing internet personality and Turning Point USA cofounder Charlie Kirk was shot and killed on Wednesday during a speaking engagement at Utah Valley University in Orem, Utah. After a chaotic 24-hour manhunt, the FBI named 22-year-old Utah resident Tyler Robinson as a suspect in the murder. As polarization and political violence continues to increase in the US, a new platform from the Public Service Alliance is offering tools like data-removal services and threat monitoring to public servants who increasingly need to defend themselves and their data. Meanwhile, new research this week warned that the number of US investors putting money into invasive commercial spyware rose significantly in 2024.

External funding is crucial for early-stage ventures, particularly technology startups that require significant R&D investment. Business angels offer a critical source of funding, but their decision-making is often subjective and resource-intensive for both investor and entrepreneur. Much research has investigated this investment process to find the critical factors angels consider. One such tool, the Critical Factor Assessment (CFA), deployed more than 20,000 times by the Canadian Innovation Centre, has been evaluated post-decision and found to be significantly more accurate than investors' own decisions. However, a single CFA analysis requires three trained individuals and several days, limiting its adoption. This study builds on previous work validating the CFA to investigate whether the constraints inhibiting its adoption can be overcome using a trained AI model. In this research, we prompted multiple large language models (LLMs) to assign the eight CFA factors to a dataset of 600 transcribed, unstructured startup pitches seeking business angel funding with known investment outcomes. We then trained and evaluated machine learning classification models using the LLM-generated CFA scores as input features. Our best-performing model demonstrated high predictive accuracy (85.0% for predicting BA deal/no-deal outcomes) and exhibited significant correlation (Spearman's r = 0.896, p-value < 0.001) with conventional human-graded evaluations. The integration of AI-based feature extraction with a structured and validated decision-making framework yielded a scalable, reliable, and less-biased model for evaluating startup pitches, removing the constraints that previously limited adoption.

KEYWORDS Physics-informed neural network, Capacitive sensor, Simulation, Surrogate model, Maxwell's equations ABSTRACT Maxwell's equations are the fundamental equations for understanding electric and magnetic field interactions and play a crucial role in designing and optimizing sensor systems like capacitive touch sensors, which are widely prevalent in automotive switches and smartphones. This paper introduces a novel approach using a Physics-Informed Neural Network (PINN) based surrogate model to accelerate the design process. The PINN model solves the governing electrostatic equations describing the interaction between a finger and a capacitive sensor. Inputs include spatial coordinates from a 3D domain encompassing the finger, sensor, and PCB, along with finger distances. The learned model thus serves as a surrogate sensor model on which inference can be carried out in seconds for different experimental setups without the need to run simulations. Efficacy results evaluated on unseen test cases demonstrate the significant potential of PINNs in accelerating the development and design optimization of capacitive touch sensors.

Feedback control of open quantum systems is of fundamental importance for practical applications in various contexts, ranging from quantum computation to quantum error correction and quantum metrology. Its use in the context of thermodynamics further enables the study of the interplay between information and energy. However, deriving optimal feedback control strategies is highly challenging, as it involves the optimal control of open quantum systems, the stochastic nature of quantum measurement, and the inclusion of policies that maximize a long-term time- and trajectory-averaged goal. In this work, we employ a reinforcement learning approach to automate and capture the role of a quantum Maxwell's demon: the agent takes the literal role of discovering optimal feedback control strategies in qubit-based systems that maximize a trade-off between measurement-powered cooling and measurement efficiency. Considering weak or projective quantum measurements, we explore different regimes based on the ordering between the thermalization, the measurement, and the unitary feedback timescales, finding different and highly non-intuitive, yet interpretable, strategies. In the thermalization-dominated regime, we find strategies with elaborate finite-time thermalization protocols conditioned on measurement outcomes. In the measurement-dominated regime, we find that optimal strategies involve adaptively measuring different qubit observables reflecting the acquired information, and repeating multiple weak measurements until the quantum state is "sufficiently pure", leading to random walks in state space. Finally, we study the case when all timescales are comparable, finding new feedback control strategies that considerably outperform more intuitive ones. We discuss a two-qubit example where we explore the role of entanglement and conclude discussing the scaling of our results to quantum many-body systems.

Geometric Algebra Networks, also known as Clifford Algebra Networks, leverage the mathematical framework of Geometric Algebra to represent and manipulate data. Geometric Algebra is a powerful, high-dimensional algebraic system that extends traditional linear algebra, enabling the compact and intuitive representation of geometric transformations, rotations, and reflections [2, 3, 4]. In GA networks, data and operations are expressed in terms of multivectors, which can capture complex geometric relationships more naturally than traditional tensor or matrix representations. Early proposals for neural networks working in Geometric Algebra (GA) can be found in the literature from the end of the last century [5, 6, 7]. However, it is only in the past few years that the need for an effective and intuitive approach to geometrical problems in learning has sparked renewed interest in the field. Today, several architectures in GA exist, capable of handling convolutions and Fourier transforms [1, 8], performing rotations and rigid body motions [9, 10], and preserving end-to-end equivariance [11, 12, 13]. In recent years, the use of machine learning, particularly neural networks, to solve partial differential equations (PDEs) has gained significant traction [14, 15, 16, 17]. State-of-the-art approaches include physics-informed neural networks (PINNs) [18, 19, 20], Fourier neural operators [21, 22], and deep Ritz methods [23, 24].