The origin of life on Earth via the spontaneous emergence of a protocell prior to Darwinian evolution remains a fundamental open question in physics and chemistry. Here, we develop a conceptual framework based on information theory and algorithmic complexity. Using estimates grounded in modern computational models, we evaluate the difficulty of assembling structured biological information under plausible prebiotic conditions. Our results highlight the formidable entropic and informational barriers to forming a viable protocell within the available window of Earth's early history. While the idea of Earth being terraformed by advanced extraterrestrials might violate Occam's razor from within mainstream science, directed panspermia -- originally proposed by Francis Crick and Leslie Orgel -- remains a speculative but logically open alternative. Ultimately, uncovering physical principles for life's spontaneous emergence remains a grand challenge for biological physics.

Deep reinforcement learning (DRL) has shown significant promise for uncovering sophisticated control policies that interact in complex environments, such as stabilizing a tokamak fusion reactor or minimizing the drag force on an object in a fluid flow. However, DRL requires an abundance of training examples and may become prohibitively expensive for many applications. In addition, the reliance on deep neural networks often results in an uninterpretable, black-box policy that may be too computationally expensive to use with certain embedded systems. Recent advances in sparse dictionary learning, such as the sparse identification of nonlinear dynamics (SINDy), have shown promise for creating efficient and interpretable data-driven models in the low-data regime. In this work we introduce SINDy-RL, a unifying framework for combining SINDy and DRL to create efficient, interpretable, and trustworthy representations of the dynamics model, reward function, and control policy. We demonstrate the effectiveness of our approaches on benchmark control environments and flow control problems, including gust mitigation on a 3D NACA 0012 airfoil at $Re=1000$. SINDy-RL achieves comparable performance to modern DRL algorithms using significantly fewer interactions in the environment and results in an interpretable control policy orders of magnitude smaller than a DRL policy.

Superfluid ${}^4$He is an ideal candidate for the direct detection of light dark matter via nuclear recoils thanks to its low nuclear mass and the possibility to reach a low detection energy threshold by exploiting the generated quasiparticles. The design of future detectors based on this target, such as the DELight experiment, requires a proper understanding of the formation and partitioning of the signal for different energy depositions from various sources. This work presents an overview of the physical processes involved in the energy deposition of recoiling electrons and ions, and describes a Monte Carlo approach to the partitioning of the signal into different channels. Despite an overall good agreement with existing literature, differences in the region of interest for light dark matter searches below 200 eV are observed.

Sign language is a natural and visual form of language that uses movements and expressions to convey meaning, serving as a crucial means of communication for individuals who are deaf or hard-of-hearing (DHH). However, the number of people proficient in sign language remains limited, highlighting the need for technological advancements to bridge communication gaps and foster interactions with minorities. Based on recent advancements in embodied humanoid robots, we propose SignBot, a novel framework for human-robot sign language interaction. SignBot integrates a cerebellum-inspired motion control component and a cerebral-oriented module for comprehension and interaction. Specifically, SignBot consists of: 1) Motion Retargeting, which converts human sign language datasets into robot-compatible kinematics; 2) Motion Control, which leverages a learning-based paradigm to develop a robust humanoid control policy for tracking sign language gestures; and 3) Generative Interaction, which incorporates translator, responser, and generator of sign language, thereby enabling natural and effective communication between robots and humans. Simulation and real-world experimental results demonstrate that SignBot can effectively facilitate human-robot interaction and perform sign language motions with diverse robots and datasets. SignBot represents a significant advancement in automatic sign language interaction on embodied humanoid robot platforms, providing a promising solution to improve communication accessibility for the DHH community.

Current human motion synthesis frameworks rely on global action descriptions, creating a modality gap that limits both motion understanding and generation capabilities. A single coarse description, such as run, fails to capture details such as variations in speed, limb positioning, and kinematic dynamics, leading to ambiguities between text and motion modalities. To address this challenge, we introduce KinMo, a unified framework built on a hierarchical describable motion representation that extends beyond global actions by incorporating kinematic group movements and their interactions. We design an automated annotation pipeline to generate high-quality, fine-grained descriptions for this decomposition, resulting in the KinMo dataset and offering a scalable and cost-efficient solution for dataset enrichment. To leverage these structured descriptions, we propose Hierarchical Text-Motion Alignment that progressively integrates additional motion details, thereby improving semantic motion understanding. Furthermore, we introduce a coarse-to-fine motion generation procedure to leverage enhanced spatial understanding to improve motion synthesis. Experimental results show that KinMo significantly improves motion understanding, demonstrated by enhanced text-motion retrieval performance and enabling more fine-grained motion generation and editing capabilities. Project Page: this https URL

To deepen our understanding of Quantum Gravity and its connections with black holes and cosmology, building a common language and exchanging ideas across different approaches is crucial. The Nordita Program "Quantum Gravity: from gravitational effective field theories to ultraviolet complete approaches" created a platform for extensive discussions, aimed at pinpointing both common grounds and sources of disagreements, with the hope of generating ideas and driving progress in the field. This contribution summarizes the twelve topical discussions held during the program and collects individual thoughts of speakers and panelists on the future of the field in light of these discussions.

We propose an O(100)m Atom Interferometer (AI) experiment -- AICE -- to be installed against a wall of the PX46 access shaft to the LHC. This experiment would probe unexplored ranges of the possible couplings of bosonic ultralight dark matter (ULDM) to atomic constituents and undertake a pioneering search for gravitational waves (GWs) at frequencies intermediate between those to which existing and planned experiments are sensitive, among other fundamental physics studies. A conceptual feasibility study showed that this AI experiment could be isolated from the LHC by installing a shielding wall in the TX46 gallery, and surveyed issues related to the proximity of the LHC machine, finding no technical obstacles. A detailed technical implementation study has shown that the preparatory civil-engineering work, installation of bespoke radiation shielding, deployment of access-control systems and safety alarms, and installation of an elevator platform could be carried out during LS3, allowing installation and operation of the AICE detector to proceed during Run 4 without impacting HL-LHC operation. These studies have established that PX46 is a uniquely promising location for an AI experiment. We foresee that, if the CERN management encourages this Letter of Intent, a significant fraction of the Terrestrial Very Long Baseline Atom Interferometer (TVLBAI) Proto-Collaboration may wish to contribute to AICE.

Conductive cooling of the solar corona at a magnetic null is examined. An initial equilibrium is set up, balancing thermal conduction and a constant, spatially uniform coronal heating. The heating is then turned off and the subsequent conductive cooling calculated. An equation for the cooling is obtained using the method of separation of variables and it is shown that the equations for the equilibrium between conduction and heating, and the time-dependent cooling are mathematically identical with a simple change of variables. Thus the properties of the cooling phase are automatically determined by the equilibrium state. For a two-dimensional null, the characteristic cooling timescale increases over that in a straight field by a factor of between 2 and 5, with a scaling determined by the ratio of the {\it{average}} and base areas of a flux element. There is {\it{no explicit dependence}} on the very large areas that can arise near the null.

Co-speech gesture generation is crucial for creating lifelike avatars and enhancing human-computer interactions by synchronizing gestures with speech. Despite recent advancements, existing methods struggle with accurately identifying the rhythmic or semantic triggers from audio for generating contextualized gesture patterns and achieving pixel-level realism. To address these challenges, we introduce Contextual Gesture, a framework that improves co-speech gesture video generation through three innovative components: (1) a chronological speech-gesture alignment that temporally connects two modalities, (2) a contextualized gesture tokenization that incorporate speech context into motion pattern representation through distillation, and (3) a structure-aware refinement module that employs edge connection to link gesture keypoints to improve video generation. Our extensive experiments demonstrate that Contextual Gesture not only produces realistic and speech-aligned gesture videos but also supports long-sequence generation and video gesture editing applications, shown in Fig.1.

Among the Scalar-Tensor modified theories of gravity, DHOST models could play a special role for dark energy while being consistent with current observations, notably those constraining the speed of gravitational waves. Schwarzschild-de Sitter black holes were shown to be exact solutions of a particular subclass of quadratic DHOST theories, while carrying a nontrivial scalar profile that linearly evolves in time and hence potentially providing exciting new phenomenological windows to explore this model. We investigate the physical perturbations about such black holes and find that the odd-parity tensor perturbations behave in a way indistinguishable to GR. On the other hand, the effective metric for the (even-parity) scalar perturbations is singular, indicating that those exact black hole solutions are infinitely strongly coupled and cannot be trusted within the regime of validity of the DHOST effective field theory. We show how this strong coupling result is generalizable to a whole class of solutions with arbitrary manifolds both for DHOST and Horndeski.

We provide a convergence analysis of deep feature instrumental variable (DFIV) regression (Xu et al., 2021), a nonparametric approach to IV regression using data-adaptive features learned by deep neural networks in two stages. We prove that the DFIV algorithm achieves the minimax optimal learning rate when the target structural function lies in a Besov space. This is shown under standard nonparametric IV assumptions, and an additional smoothness assumption on the regularity of the conditional distribution of the covariate given the instrument, which controls the difficulty of Stage 1. We further demonstrate that DFIV, as a data-adaptive algorithm, is superior to fixed-feature (kernel or sieve) IV methods in two ways. First, when the target function possesses low spatial homogeneity (i.e., it has both smooth and spiky/discontinuous regions), DFIV still achieves the optimal rate, while fixed-feature methods are shown to be strictly suboptimal. Second, comparing with kernel-based two-stage regression estimators, DFIV is provably more data efficient in the Stage 1 samples.

Large Language Models (LLMs) are one of the most promising developments in the field of artificial intelligence, and the software engineering community has readily noticed their potential role in the software development life-cycle. Developers routinely ask LLMs to generate code snippets, increasing productivity but also potentially introducing ownership, privacy, correctness, and security issues. Previous work highlighted how code generated by mainstream commercial LLMs is often not safe, containing vulnerabilities, bugs, and code smells. In this paper, we present a framework that leverages testing and static analysis to assess the quality, and guide the self-improvement, of code generated by general-purpose, open-source LLMs. First, we ask LLMs to generate C code to solve a number of programming tasks. Then we employ ground-truth tests to assess the (in)correctness of the generated code, and a static analysis tool to detect potential safety vulnerabilities. Next, we assess the models ability to evaluate the generated code, by asking them to detect errors and vulnerabilities. Finally, we test the models ability to fix the generated code, providing the reports produced during the static analysis and incorrectness evaluation phases as feedback. Our results show that models often produce incorrect code, and that the generated code can include safety issues. Moreover, they perform very poorly at detecting either issue. On the positive side, we observe a substantial ability to fix flawed code when provided with information about failed tests or potential vulnerabilities, indicating a promising avenue for improving the safety of LLM-based code generation tools.

Large Multimodal Models (LMMs) often face a modality representation gap during pretraining: while language embeddings remain stable, visual representations are highly sensitive to contextual noise (e.g., background clutter). To address this issue, we introduce a visual comprehension stage, which we call ViCToR (Visual Comprehension via Token Reconstruction), a novel pretraining framework for LMMs. ViCToR employs a learnable visual token pool and utilizes the Hungarian matching algorithm to select semantically relevant tokens from this pool for visual token replacement. Furthermore, by integrating a visual token reconstruction loss with dense semantic supervision, ViCToR can learn tokens which retain high visual detail, thereby enhancing the large language model's (LLM's) understanding of visual information. After pretraining on 3 million publicly accessible images and captions, ViCToR achieves state-of-the-art results, improving over LLaVA-NeXT-8B by 10.4%, 3.2%, and 7.2% on the MMStar, SEED$^I$, and RealWorldQA benchmarks, respectively. Code is available at this https URL.

The increasing complexity of deep neural networks poses significant barriers to democratizing them to resource-limited edge devices. To address this challenge, split federated learning (SFL) has emerged as a promising solution by of floading the primary training workload to a server via model partitioning while enabling parallel training among edge devices. However, although system optimization substantially influences the performance of SFL under resource-constrained systems, the problem remains largely uncharted. In this paper, we provide a convergence analysis of SFL which quantifies the impact of model splitting (MS) and client-side model aggregation (MA) on the learning performance, serving as a theoretical foundation. Then, we propose AdaptSFL, a novel resource-adaptive SFL framework, to expedite SFL under resource-constrained edge computing systems. Specifically, AdaptSFL adaptively controls client-side MA and MS to balance communication-computing latency and training convergence. Extensive simulations across various datasets validate that our proposed AdaptSFL framework takes considerably less time to achieve a target accuracy than benchmarks, demonstrating the effectiveness of the proposed strategies.

Effective field theories (EFTs) provide a powerful framework to parametrise unknown aspects of possible ultraviolet (UV) physics. For scalar fields in de Sitter space, however, new emergent phenomena can arise when the cut-off scale of the theory lies below the horizon scale $H$, as seen in the stochastic formalism of inflation. In this work, we study EFTs that, at leading order, reproduce the standard quartic theory in de Sitter, but with a variable cut-off identified with the mass of an integrated-out hidden sector. We perform the complete analytic computation for the tree- and loop-level matching between the effective $\lambda\phi^4$ theory and two possible UV realisations. We find that when the cut-off is much larger than the horizon, the theory admits a unitary description, up to exponentially suppressed corrections. In contrast, when the cut-off is lowered below $H$, the system evolves into a mixed state and diffusive effects emerge. Nevertheless, at leading order, the EFT remains local and reproduces the same effective quartic coefficient as in the unitary regime. Furthermore, for the EFT matching at the loop-level, the effective quartic coupling changes sign and becomes negative as the cut-off decreases, in agreement with the result obtained from the stochastic formalism. In general, for cosmological EFTs, our findings highlight the role of non-unitary effects and illustrate their regimes of validity, within and beyond perturbation theory.

A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition time series typically starts with an already pathological scan. This poses problems, as many algorithms are designed to analyze healthy brains and provide no guarantee for images featuring lesions. Examples include, but are not limited to, algorithms for brain anatomy parcellation, tissue segmentation, and brain extraction. To solve this dilemma, we introduce the BraTS inpainting challenge. Here, the participants explore inpainting techniques to synthesize healthy brain scans from lesioned ones. The following manuscript contains the task formulation, dataset, and submission procedure. Later, it will be updated to summarize the findings of the challenge. The challenge is organized as part of the ASNR-BraTS MICCAI challenge.

We present a field-based approach to the analysis of cosmic shear data to infer jointly cosmological parameters and the dark matter distribution. This forward modelling approach samples the cosmological parameters and the initial matter fluctuations, using a physical gravity model to link the primordial fluctuations to the non-linear matter distribution. Cosmological parameters are sampled and updated consistently through the forward model, varying (1) the initial matter power spectrum, (2) the geometry through the distance-redshift relationship, and (3) the growth of structure and light-cone effects. Our approach extracts more information from the data than methods based on two-point statistics. We find that this field-based approach lifts the strong degeneracy between the cosmological matter density, $\Omega_\mathrm{m}$, and the fluctuation amplitude, $\sigma_8$, providing tight constraints on these parameters from weak lensing data alone. In the simulated four-bin tomographic experiment we consider, the field-based likelihood yields marginal uncertainties on $\sigma_8$ and $\Omega_\mathrm{m}$ that are, respectively, a factor of 3 and 5 smaller than those from a two-point power spectrum analysis applied to the same underlying data.