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Recent advancements in Large Language Models (LLMs) are transforming biology, computer science, and many other research fields, as well as impacting everyday life. While transformer-based technologies are currently being deployed in biology, no available agentic system has been developed to tackle bioinformatics workflows. We present a prototype Bioinformatics Retrieval Augmented Data (BRAD) digital assistant. BRAD is a chatbot and agentic system that integrates a suite of tools to handle bioinformatics tasks, from code execution to online search. We demonstrate its capabilities through (1) improved question-and-answering with retrieval augmented generation (RAG), (2) the ability to run complex software pipelines, and (3) the ability to organize and distribute tasks in agentic workflows. We use BRAD for automation, performing tasks ranging from gene enrichment and searching the archive to automatic code generation for running biomarker identification pipelines. BRAD is a step toward autonomous, self-driving labs for digital biology., Comment: 49 pages, 3 tables, 12 figures

Behaviours of hydrogen, such as fluidity and metallicity, are crucial for our understanding of planetary interiors and the emerging field of high-temperature superconducting hydrides. These behaviours were discovered in complex phase diagrams of hydrogen and hydrides, however, the transition mechanism of behaviours driven by temperature, pressure and chemical compression remain unclear, particularly in the processes of metallization. Until now, a comprehensive theoretical framework to quantify atomization and metallization of hydrogen in phase diagram of hydrides has been lacking. In this study, we address this gap by combining molecular dynamics and electronic structure analysis to propose a theoretical framework, which clarify the content and properties of atomic hydrogen under various temperature and pressure conditions and chemical compression exerted by non-hydrogen elements in hydrides. Applying this framework to the superconducting hydride LaBeH8, we identify three general hydrogen orderings within its phase diagram: molecular, sublattice and warm hydrogens. During the phase transition from molecule to sublattice, hydrogen exhibits different properties from three general hydrogen orderings, such as fast superionicity, metallicity and unusual atomic content response to temperature. These abnormal behaviours were defined as the critical metallization of hydrogen, which not only suggests a potential synthesis route for the metastable phase but also provides valuable insights into the complex synthetic products of superconducting hydrides.

The SNO+ detector operated initially as a water Cherenkov detector. The implementation of a sealed covergas system midway through water data taking resulted in a significant reduction in the activity of $^{222}$Rn daughters in the detector and allowed the lowest background to the solar electron scattering signal above 5 MeV achieved to date. This paper reports an updated SNO+ water phase $^8$B solar neutrino analysis with a total livetime of 282.4 days and an analysis threshold of 3.5 MeV. The $^8$B solar neutrino flux is found to be $\left(2.32^{+0.18}_{-0.17}\text{(stat.)}^{+0.07}_{-0.05}\text{(syst.)}\right)\times10^{6}$ cm$^{-2}$s$^{-1}$ assuming no neutrino oscillations, or $\left(5.36^{+0.41}_{-0.39}\text{(stat.)}^{+0.17}_{-0.16}\text{(syst.)} \right)\times10^{6}$ cm$^{-2}$s$^{-1}$ assuming standard neutrino oscillation parameters, in good agreement with both previous measurements and Standard Solar Model Calculations. The electron recoil spectrum is presented above 3.5 MeV.

Data driven methods have transformed the prospects of the computational chemical sciences, with machine learned interatomic potentials (MLIPs) speeding up calculations by several orders of magnitude. I reflect on theory driven, as opposed to data driven, discovery based on ab initio random structure searching (AIRSS), and then introduce two methods which exploit machine learning acceleration. I show how long high throughput anneals, between direct structural relaxation, enabled by ephemeral data derived potentials (EDDPs), can be incorporated into AIRSS to bias the sampling of challenging systems towards low energy configurations. Hot AIRSS (hot-AIRSS) preserves the parallel advantage of random search, while allowing much more complex systems to be tackled. This is demonstrated through searches for complex boron structures in large unit cells. I then show how low energy carbon structures can be directly generated from a single, experimentally determined, diamond structure. An extension to the generation of random sensible structures, candidates are stochastically generated and then optimised to minimise the difference between the EDDP environment vector and that of the reference diamond structure. The distance-based cost function is captured in an actively learned EDDP. Graphite, small nanotubes and caged, fullerene-like, structures emerge from searches using this potential, along with a rich variety of tetrahedral framework structures. Using the same approach, the pyrope, Mg$_3$Al$_2$(SiO$_4$)$_3$, garnet structure is recovered from a low energy AIRSS structure generated in a smaller unit cell with a different chemical composition. The relationship of this approach to modern diffusion model based generative methods is discussed., Comment: 13 pages, 9 figures, contributed to Data-driven discovery in the chemical sciences Faraday Discussion

Following long-standing predictions associated with hydrogen, high-temperature superconductivity has recently been observed in several hydride-based materials. Nevertheless, these high-$T_c$ phases only exist at extremely high pressures, and achieving high transition temperatures at ambient pressure remains a major challenge. Recent predictions of the complex hydride Mg$_{2}$IrH$_{6}$ may help overcome this challenge with calculations of high-$T_c$ superconductivity (65 K$~<~T_c~<~$ 170 K) in a material that is stable at atmospheric pressure. In this work, the synthesis of Mg$_{2}$IrH$_{6}$ was targeted over a broad range of $P$-$T$ conditions, and the resulting products were characterized using X-ray diffraction (XRD) and vibrational spectroscopy, in concert with first-principles calculations. The results indicate that the charge-balanced complex hydride Mg$_{2}$IrH$_{5}$ is more stable over all conditions tested up to ca 28 GPa. The resulting hydride is isostructural with the predicted superconducting Mg$_{2}$IrH$_{6}$ phase except for a single hydrogen vacancy, which shows a favorable replacement barrier upon insertion of hydrogen into the lattice. Bulk Mg$_{2}$IrH$_{5}$ is readily accessible at mild $P$-$T$ conditions and may thus represent a convenient platform to access superconducting Mg$_{2}$IrH$_{6}$ via non-equilibrium processing methods., Comment: 18 pages, 17 Figures

Fundamental physical constants govern key effects in high-energy particle physics and astrophysics, including the stability of particles, nuclear reactions, formation and evolution of stars, synthesis of heavy nuclei and emergence of stable molecular structures. Here, we show that fundamental constants also set an upper bound for the frequency of phonons in condensed matter phases, or how rapidly an atom can vibrate. This bound is in agreement with \textit{ab initio} simulations of atomic hydrogen and high-temperature hydride superconductors, and implies an upper limit to the superconducting transition temperature $T_c$ in condensed matter. Fundamental constants set this limit to the order of 10$^2-10^3$ K. This range is consistent with our calculations of $T_c$ from optimal Eliashberg functions. As a corollary, we observe that the very existence of the current research of finding $T_{\mathrm{c}}$ at and above $300$ K is due to the observed values of fundamental constants. We finally discuss how fundamental constants affect the observability and operation of other effects and phenomena including phase transitions.

The SNO+ collaboration reports its first spectral analysis of long-baseline reactor antineutrino oscillation using 114 tonne-years of data. Fitting the neutrino oscillation probability to the observed energy spectrum yields constraints on the neutrino mass-squared difference $\Delta m^2_{21}$. In the ranges allowed by previous measurements, the best-fit $\Delta m^2_{21}$ is (8.85$^{+1.10}_{-1.33}$) $\times$ 10$^{-5}$ eV$^2$. This measurement is continuing in the next phases of SNO+ and is expected to surpass the present global precision on $\Delta m^2_{21}$ with about three years of data.

Biomarkers enable objective monitoring of a given cell or state in a biological system and are widely used in research, biomanufacturing, and clinical practice. However, identifying appropriate biomarkers that are both robustly measurable and capture a state accurately remains challenging. We present a framework for biomarker identification based upon observability guided sensor selection. Our methods, Dynamic Sensor Selection (DSS) and Structure-Guided Sensor Selection (SGSS), utilize temporal models and experimental data, offering a template for applying observability theory to data from biological systems. Unlike conventional methods that assume well-known, fixed dynamics, DSS adaptively select biomarkers or sensors that maximize observability while accounting for the time-varying nature of biological systems. Additionally, SGSS incorporates structural information and diverse data to identify sensors which are resilient against inaccuracies in our model of the underlying system. We validate our approaches by performing estimation on high dimensional systems derived from temporal gene expression data from partial observations. Our algorithms reliably identify known biomarkers and uncover new ones within our datasets. Additionally, integrating chromosome conformation and gene expression data addresses noise and uncertainty, enhancing the reliability of our biomarker selection approach for the genome.

Despite the recent ubiquity of large language models and their high zero-shot prompted performance across a wide range of tasks, it is still not known how well they perform on tasks which require processing of potentially idiomatic language. In particular, how well do such models perform in comparison to encoder-only models fine-tuned specifically for idiomaticity tasks? In this work, we attempt to answer this question by looking at the performance of a range of LLMs (both local and software-as-a-service models) on three idiomaticity datasets: SemEval 2022 Task 2a, FLUTE, and MAGPIE. Overall, we find that whilst these models do give competitive performance, they do not match the results of fine-tuned task-specific models, even at the largest scales (e.g. for GPT-4). Nevertheless, we do see consistent performance improvements across model scale. Additionally, we investigate prompting approaches to improve performance, and discuss the practicalities of using LLMs for these tasks., Comment: Presented at the MWE-UD Workshop at LREC-COLING 2024

This paper contributes to the existing literature on hedging American options with Deep Reinforcement Learning (DRL). The study first investigates hyperparameter impact on hedging performance, considering learning rates, training episodes, neural network architectures, training steps, and transaction cost penalty functions. Results highlight the importance of avoiding certain combinations, such as high learning rates with a high number of training episodes or low learning rates with few training episodes and emphasize the significance of utilizing moderate values for optimal outcomes. Additionally, the paper warns against excessive training steps to prevent instability and demonstrates the superiority of a quadratic transaction cost penalty function over a linear version. This study then expands upon the work of Pickard et al. (2024), who utilize a Chebyshev interpolation option pricing method to train DRL agents with market calibrated stochastic volatility models. While the results of Pickard et al. (2024) showed that these DRL agents achieve satisfactory performance on empirical asset paths, this study introduces a novel approach where new agents at weekly intervals to newly calibrated stochastic volatility models. Results show DRL agents re-trained using weekly market data surpass the performance of those trained solely on the sale date. Furthermore, the paper demonstrates that both single-train and weekly-train DRL agents outperform the Black-Scholes Delta method at transaction costs of 1% and 3%. This practical relevance suggests that practitioners can leverage readily available market data to train DRL agents for effective hedging of options in their portfolios.

Compositionality in language models presents a problem when processing idiomatic expressions, as their meaning often cannot be directly derived from their individual parts. Although fine-tuning and other optimization strategies can be used to improve representations of idiomatic expressions, this depends on the availability of relevant data. We present the Noun Compound Synonym Substitution in Books - NCSSB - datasets, which are created by substitution of synonyms of potentially idiomatic English noun compounds in public domain book texts. We explore the trade-off between data quantity and quality when training models for idiomaticity detection, in conjunction with contextual information obtained locally (from the surrounding sentences) or externally (through language resources). Performance on an idiomaticity detection task indicates that dataset quality is a stronger factor for context-enriched models, but that quantity also plays a role in models without context inclusion strategies., Comment: 14 pages, 10 figures. Presented at the Joint Workshop on Multiword Expressions and Universal Dependencies (MWE-UD 2024) https://multiword.org/mweud2024/

This article leverages deep reinforcement learning (DRL) to hedge American put options, utilizing the deep deterministic policy gradient (DDPG) method. The agents are first trained and tested with Geometric Brownian Motion (GBM) asset paths and demonstrate superior performance over traditional strategies like the Black-Scholes (BS) Delta, particularly in the presence of transaction costs. To assess the real-world applicability of DRL hedging, a second round of experiments uses a market calibrated stochastic volatility model to train DRL agents. Specifically, 80 put options across 8 symbols are collected, stochastic volatility model coefficients are calibrated for each symbol, and a DRL agent is trained for each of the 80 options by simulating paths of the respective calibrated model. Not only do DRL agents outperform the BS Delta method when testing is conducted using the same calibrated stochastic volatility model data from training, but DRL agents achieves better results when hedging the true asset path that occurred between the option sale date and the maturity. As such, not only does this study present the first DRL agents tailored for American put option hedging, but results on both simulated and empirical market testing data also suggest the optimality of DRL agents over the BS Delta method in real-world scenarios. Finally, note that this study employs a model-agnostic Chebyshev interpolation method to provide DRL agents with option prices at each time step when a stochastic volatility model is used, thereby providing a general framework for an easy extension to more complex underlying asset processes.

In this paper we consider aspects of geometric observability for hypergraphs, extending our earlier work from the uniform to the nonuniform case. Hypergraphs, a generalization of graphs, allow hyperedges to connect multiple nodes and unambiguously represent multi-way relationships which are ubiquitous in many real-world networks including those that arise in biology. We consider polynomial dynamical systems with linear outputs defined according to hypergraph structure, and we propose methods to evaluate local, weak observability., Comment: Accepted to IFAC LHMNC 2024, 6 pages, 2 figures. arXiv admin note: text overlap with arXiv:2304.04883

We investigate the pressure-temperature ($p$-$T$) phase diagram of elemental lithium (Li) up to multiterapascal (TPa) pressures using ab-initio random structure search (AIRSS) and density functional theory (DFT). At zero temperature, beyond the high-pressure $Fd\bar{3}m$ diamond structure predicted in previous studies, we find eleven solid-state phase transitions to structures of greatly varying complexity, in addition to two structures that we calculate will become stable with sufficient temperature. The full $p$-$T$ dependence of the phase boundaries are computed within the vibrational harmonic approximation, and the solid-liquid melting line is calculated using ab-initio molecular dynamics simulations. Notably, between 39.1 TPa and 55.7 TPa, Li adopts an elaborate monoclinic structure with 46 atoms in the primitive unit cell, and between 71.9 TPa and $103$ TPa, an incommensurate host-guest phase of the Ba-IV type. We find that Li, hitherto predicted to be an electride at TPa pressures, abruptly loses its electride character above 16 TPa, reverting back to normal metallic behaviour with a corresponding rise in the Fermi-level electronic density of states (eDOS) and broadening of the electronic bands.

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is a 26-ton water Cherenkov neutrino detector installed on the Booster Neutrino Beam (BNB) at Fermilab. Its main physics goals are to perform a measurement of the neutron yield from neutrino-nucleus interactions, as well as a measurement of the charged-current cross section of muon neutrinos. An equally important focus is placed on the research and development of new detector technologies and target media. Specifically water-based liquid scintillator (WbLS) is of interest as a novel detector medium, as it allows for the simultaneous detection of scintillation and Cherenkov light. This paper presents the deployment of a 366L WbLS vessel in ANNIE in March 2023 and the subsequent detection of both Cherenkov light and scintillation from the WbLS. This proof-of-concept allows for the future development of reconstruction and particle identification algorithms in ANNIE, as well as dedicated analyses, such as the search for neutral current events and the hadronic scintillation component within the WbLS volume., Comment: 19 pages, 16 figures

This document explores structural controllability of polynomial dynamical systems or polysystems. We extend Lin's concept of structural controllability for linear systems, offering hypergraph-theoretic methods to rapidly assess strong controllability. Our main result establishes that a polysytem is structurally controllable when its hypergraph representation contains no hyperedge dilation and no inaccessible vertices. We propose two efficient, scalable algorithms to validate these conditions., Comment: 12 pages, 3 algorithms

A key challenge in materials discovery is to find high-temperature superconductors. Hydrogen and hydride materials have long been considered promising materials displaying conventional phonon-mediated superconductivity. However, the high pressures required to stabilize these materials have restricted their application. Here, we present results from high-throughput computation, considering a wide range of high-symmetry ternary hydrides from across the periodic table at ambient pressure. This large composition space is then reduced by considering thermodynamic, dynamic, and magnetic stability, before direct estimations of the superconducting critical temperature. This approach has revealed a metastable ambient-pressure hydride superconductor, Mg$_2$IrH$_6$, with a predicted critical temperature of 160 K, comparable to the highest temperature superconducting cuprates. We propose a synthesis route via a structurally related insulator, Mg$_2$IrH$_7$, which is thermodynamically stable above 15 GPa and discuss the potential challenges in doing so., Comment: 30 pages, 30 figures, including SM

The recent claim of room temperature superconductivity in a copper-doped lead apatite compound, called LK-99, has sparked remarkable interest and controversy. Subsequent experiments have largely failed to reproduce the claimed superconductivity, while theoretical works have identified multiple key features including strong electronic correlation, structural instabilities, and dopability constraints. A puzzling claim of several recent theoretical studies is that both parent and copper-doped lead apatite structures are dynamically unstable at the harmonic level, questioning decades of experimental reports of the parent compound structures and the recently proposed copper-doped structures. In this work, we demonstrate that both parent and copper-doped lead apatite structures are dynamically stable at room temperature. Anharmonic phonon-phonon interactions play a key role in stabilizing some copper-doped phases, while most phases are largely stable even at the harmonic level. We also show that dynamical stability depends on both volume and correlation strength, suggesting controllable ways of exploring the copper-doped lead apatite structural phase diagram. Our results fully reconcile the theoretical description of the structures of both parent and copper-doped lead apatite with experiment., Comment: 4 figures + Supplementary Information, published version

The direction of individual $^8$B solar neutrinos has been reconstructed using the SNO+ liquid scintillator detector. Prompt, directional Cherenkov light was separated from the slower, isotropic scintillation light using time information, and a maximum likelihood method was used to reconstruct the direction of individual scattered electrons. A clear directional signal was observed, correlated with the solar angle. The observation was aided by a period of low primary fluor concentration that resulted in a slower scintillator decay time. This is the first time that event-by-event direction reconstruction in high light-yield liquid scintillator has been demonstrated in a large-scale detector., Comment: 6 pages, 6 figures. Accepted manuscript by PRD

Controlling the formation and stoichiometric content of desired phases of materials has become a central interest for the study of a variety of fields, notably high temperature superconductivity under extreme pressures. The further possibility of accessing metastable states by initiating reactions by x-ray triggered mechanisms over ultra-short timescales is enabled with the development of x-ray free electron lasers (XFEL). Utilizing the exceptionally high brilliance x-ray pulses from the EuXFEL, we report the synthesis of a previously unobserved yttrium hydride under high pressure, along with non-stoichiometric changes in hydrogen content as probed at a repetition rate of 4.5\,MHz using time-resolved x-ray diffraction. Exploiting non-equilibrium pathways we synthesize and characterize a hydride with yttrium cations in an \textit{A}15 structure type at 125\,GPa, predicted using crystal structure searches, with a hydrogen content between 4.0--5.75 hydrogens per cation, that is enthalpically metastable on the convex hull. We demonstrate a tailored approach to changing hydrogen content using changes in x-ray fluence that is not accessible using conventional synthesis methods, and reveals a new paradigm in metastable chemical physics.

Photomultiplier tubes (PMTs) are traditionally an integral part of large underground experiments as they measure the light emission from particle interactions within the enclosed detection media. The BUTTON experiment will utilise around 100 PMTs to measure the response of different media suitable for rare event searches. A subset of low-radioactivity 10-inch Hamamatsu R7081 PMTs were tested, characterised, and compared to manufacture certification. This manuscript describes the laboratory tests and analysis of gain, peak-to-valley ratio and dark rate of the PMTs to give an understanding of the charge response, signal-to-noise ratio and dark noise background as an acceptance test of the suitability of these PMTs for water-based detectors. Following the evaluation of these tests, the PMT performance agreed with the manufacturer specifications. These results are imperative for modeling the PMT response in detector simulations and providing confidence in the performance of the devices once installed in the detector underground., Comment: 11 pages, 11 figures

Machine-learned interatomic potentials are fast becoming an indispensable tool in computational materials science. One approach is the ephemeral data-derived potential (EDDP), which was designed to accelerate atomistic structure prediction. The EDDP is simple and cost-efficient. It relies on training data generated in small unit cells and is fit using a lightweight neural network, leading to smooth interactions which exhibit the robust transferability essential for structure prediction. Here, we present a variety of applications of EDDPs, enabled by recent developments of the open-source EDDP software. New features include interfaces to phonon and molecular dynamics codes, as well as deployment of the ensemble deviation for estimating the confidence in EDDP predictions. Through case studies ranging from elemental carbon and lead to the binary scandium hydride and the ternary zinc cyanide, we demonstrate that EDDPs can be trained to cover wide ranges of pressures and stoichiometries, and used to evaluate phonons, phase diagrams, superionicity, and thermal expansion. These developments complement continued success in accelerated structure prediction., Comment: 22 pages, 15 figures