Search

Artificial engineering often relies on scarce materials, possessing ecological, social, and political risks. In contrast, nature achieves advanced functionalities using only abundant elements and water. Despite this simple insight, incorporating nature-inspired devices made from readily available resources into industrial applications remains a formidable challenge. Here, we present a sustainable 'blue battery'-a water-based energy storage device that mimics natural processes and reduces dependence on scarce materials. This battery harnesses the distinctive electrical properties of water confined within nanometer-scale pores, enabling construction from abundant, non-toxic materials using scalable nanofabrication techniques. It operates without unwanted side reactions, maintains nearly 100% efficiency after 60,000 charge-discharge cycles, and supports a voltage window of up to 1.65 V while achieving competitive power and energy density. The core component, a van der Waals clay nanostructure, uses water as the sole electrolyte with enhanced activity due to interactions with its 2D surface. This water-driven technology offers a sustainable platform for energy systems, with potential applications from small-scale electronics to global energy infrastructure-even in extreme environments like Mars. Our work advances nature-inspired energy solutions, opening new possibilities for the sustainable engineering of blue devices., Comment: Main text: 17 pages, 4 figures, 50 references Suplementary: 29 pages, 26 figures, 3 tables, 3 references

Scaling laws in language modeling traditionally quantify training loss as a function of dataset size and model parameters, providing compute-optimal estimates but often neglecting the impact of data quality on model generalization. In this paper, we extend the conventional understanding of scaling law by offering a microscopic view of data quality within the original formulation -- effective training tokens -- which we posit to be a critical determinant of performance for parameter-constrained language models. Specifically, we formulate the proposed term of effective training tokens to be a combination of two readily-computed indicators of text: (i) text diversity and (ii) syntheticity as measured by a teacher model. We pretrained over $200$ models of 25M to 1.5B parameters on a diverse set of sampled, synthetic data, and estimated the constants that relate text quality, model size, training tokens, and eight reasoning task accuracy scores. We demonstrated the estimated constants yield +0.83 Pearson correlation with true accuracies, and analyzed it in scenarios involving widely-used data techniques such as data sampling and synthesis which aim to improve data quality., Comment: Accepted to EMNLP 2024 Industry Track, 18 pages, 9 figures, 4 tables

We show that placing a radioactive source such as ${}^{51}$Cr near a liquid xenon detector may allow to detect the contribution induced by the anapole moment to neutrino-electron scattering in the Standard Model. Although the anapole moment of neutrinos induces a scattering rate with the same spectral shape as the neutral and charged current contributions, exposures of $\sim$ 10 tonne $\times$ year at XENONnT or XLZD may be enough to accumulate sufficient statistics. We also discuss a simple model where the anapole moment of neutrinos is shifted with respect to the SM expectation, further demonstrating how a potential measurement of the anapole moment of neutrinos would allow to constrain new physics., Comment: Clarified helicity structure of the BSM lagrangian in the supplementary material

Distilling conversational skills into Small Language Models (SLMs) with approximately 1 billion parameters presents significant challenges. Firstly, SLMs have limited capacity in their model parameters to learn extensive knowledge compared to larger models. Secondly, high-quality conversational datasets are often scarce, small, and domain-specific. Addressing these challenges, we introduce a novel data distillation framework named CoDi (short for Conversational Distillation, pronounced "Cody"), allowing us to synthesize large-scale, assistant-style datasets in a steerable and diverse manner. Specifically, while our framework is task agnostic at its core, we explore and evaluate the potential of CoDi on the task of conversational grounded reasoning for question answering. This is a typical on-device scenario for specialist SLMs, allowing for open-domain model responses, without requiring the model to "memorize" world knowledge in its limited weights. Our evaluations show that SLMs trained with CoDi-synthesized data achieve performance comparable to models trained on human-annotated data in standard metrics. Additionally, when using our framework to generate larger datasets from web data, our models surpass larger, instruction-tuned models in zero-shot conversational grounded reasoning tasks., Comment: 13 pages

This is the report from the 2023 Particle Physics Project Prioritization Panel (P5) approved by High Energy Physics Advisory Panel (HEPAP) on December 8, 2023. The final version was made public on May 8, 2024 and submitted to DOE SC and NSF MPS., Comment: 2-page spread version. The online version is available at https://www.usparticlephysics.org/2023-p5-report/ and the graphics at https://usparticlephysics.org/media-assets-library

The second "Mineral Detection of Neutrinos and Dark Matter" (MDvDM'24) meeting was held January 8-11, 2024 in Arlington, VA, USA, hosted by Virginia Tech's Center for Neutrino Physics. This document collects contributions from this workshop, providing an overview of activities in the field. MDvDM'24 was the second topical workshop dedicated to the emerging field of mineral detection of neutrinos and dark matter, following a meeting hosted by IFPU in Trieste, Italy in October 2022. Mineral detectors have been proposed for a wide variety of applications, including searching for dark matter, measuring various fluxes of astrophysical neutrinos over gigayear timescales, monitoring nuclear reactors, and nuclear disarmament protocols; both as paleo-detectors using natural minerals that could have recorded the traces of nuclear recoils for timescales as long as a billion years and as detectors recording nuclear recoil events on laboratory timescales using natural or artificial minerals. Contributions to this proceedings discuss the vast physics potential, the progress in experimental studies, and the numerous challenges lying ahead on the path towards mineral detection. These include a better understanding of the formation and annealing of recoil defects in crystals; identifying the best classes of minerals and, for paleo-detectors, understanding their geology; modeling and control of the relevant backgrounds; developing, combining, and scaling up imaging and data analysis techniques; and many others. During the last years, MDvDM has grown rapidly and gained attention. Small-scale experimental efforts focused on establishing various microscopic readout techniques are underway at institutions in North America, Europe and Asia. We are looking ahead to an exciting future full of challenges to overcome, surprises to be encountered, and discoveries lying ahead of us., Comment: Summary and proceedings of the MDvDM'24 conference, Jan 8-11 2024

A novel two-qubit entangling gate for RF-controlled trapped-ion quantum processors is proposed theoretically and demonstrated experimentally. The speed of this gate is an order of magnitude higher than that of previously demonstrated two-qubit entangling gates in static magnetic field gradients. At the same time, the phase-modulated field driving the gate, dynamically decouples the qubits from amplitude and frequency noise, increasing the qubits' coherence time by two orders of magnitude. The gate requires only a single continuous RF field per qubit, making it well suited for scaling a quantum processor to large numbers of qubits. Implementing this entangling gate, we generate the Bell states $|\Phi^+\rangle$ and $|\Psi^+\rangle$ in $\leq 313$ $\mathrm{\mathrm{\mu}}$s with fidelities up to $98^{+2}_{-3}$ % in a static magnetic gradient of only 19.09 T/m. At higher magnetic field gradients, the entangling gate speed can be further improved to match that of laser-based counterparts.

The emergence of Large Language Models (LLMs) has brought to light promising language generation capabilities, particularly in performing tasks like complex reasoning and creative writing. Consequently, distillation through imitation of teacher responses has emerged as a popular technique to transfer knowledge from LLMs to more accessible, Small Language Models (SLMs). While this works well for simpler tasks, there is a substantial performance gap on tasks requiring intricate language comprehension and creativity, such as humor generation. We hypothesize that this gap may stem from the fact that creative tasks might be hard to learn by imitation alone and explore whether an approach, involving supplementary guidance from the teacher, could yield higher performance. To address this, we study the effect of assigning a dual role to the LLM - as a "teacher" generating data, as well as a "critic" evaluating the student's performance. Our experiments on humor generation reveal that the incorporation of feedback significantly narrows the performance gap between SLMs and their larger counterparts compared to merely relying on imitation. As a result, our research highlights the potential of using feedback as an additional dimension to data when transferring complex language abilities via distillation.

Large language models (LLMs) are increasingly prevalent in conversational systems due to their advanced understanding and generative capabilities in general contexts. However, their effectiveness in task-oriented dialogues (TOD), which requires not only response generation but also effective dialogue state tracking (DST) within specific tasks and domains, remains less satisfying. In this work, we propose a novel approach FnCTOD for solving DST with LLMs through function calling. This method improves zero-shot DST, allowing adaptation to diverse domains without extensive data collection or model tuning. Our experimental results demonstrate that our approach achieves exceptional performance with both modestly sized open-source and also proprietary LLMs: with in-context prompting it enables various 7B or 13B parameter models to surpass the previous state-of-the-art (SOTA) achieved by ChatGPT, and improves ChatGPT's performance beating the SOTA by 5.6% average joint goal accuracy (JGA). Individual model results for GPT-3.5 and GPT-4 are boosted by 4.8% and 14%, respectively. We also show that by fine-tuning on a small collection of diverse task-oriented dialogues, we can equip modestly sized models, specifically a 13B parameter LLaMA2-Chat model, with function-calling capabilities and DST performance comparable to ChatGPT while maintaining their chat capabilities. We have made the code publicly available at https://github.com/facebookresearch/FnCTOD, Comment: ACL 2024 Main. Code available at: https://github.com/facebookresearch/FnCTOD

BSM electromagnetic properties of neutrinos may lead to copious production of sterile neutrinos in the hot and dense core of a core-collapse supernova. In this work, we focus on the active-sterile transition magnetic moment portal for heavy sterile neutrinos. Firstly, we revisit the SN1987A cooling bounds for dipole portal using the integrated luminosity method, which yields more reliable results (especially in the trapping regime) compared to the previously explored via emissivity loss, aka the Raffelt criterion. Secondly, we obtain strong bounds on the dipole coupling strength reaching as low as $10^{-11} \text{ GeV}^{-1}$ from energy deposition, i.e., constrained from the observation of explosion energies of underluminous Type IIP supernovae. In addition, we find that sterile neutrino production from Primakoff upscattering off of proton dominates over scattering off of electron for low sterile neutrino masses., Comment: 14 pages, 6 figures

We comparatively analyzed imbibition of polystyrene (PS) into two complementary pore models having pore diameters of about 380 nm and hydroxyl-terminated inorganic-oxidic pore walls, controlled porous glass (CPG) and self-ordered porous alumina (AAO), by X-ray computed tomography and EDX spectroscopy. CPG contains continuous spongy-tortuous pore systems. AAO containing arrays of isolated straight cylindrical pores is a reference pore model with a tortuosity close to 1. Comparative evaluation of the spatiotemporal imbibition front evolution yields important information on the pore morphology of a probed tortuous matrix like CPG and on the imbibition mechanism. To this end, pixel brightness dispersions in tomographic 3D reconstructions and 2D EDX maps of infiltrated AAO and CPG samples were condensed into 1D brightness dispersion profiles normal to the membrane surfaces. Their statistical analysis yielded positions and widths of the imbibition fronts without segmentation or determination of pore positions. The retardation of the imbibition front movement with respect to AAO reference samples may be used as a descriptor for the tortuosity of a tested porous matrix. The velocity of the imbibition front movements in CPG equaled two-thirds of the velocity of the imbibition front movements in AAO. Moreover, the dynamics of the imbibition front broadening discloses whether porous matrices are dominated by cylindrical neck-like pore segments or by nodes. Independent single-meniscus movements in cylindrical AAO pores result in faster imbibition front broadening than in CPG, in which a morphology dominated by nodes results in slower cooperative imbibition front movements involving several menisci.

Discotic ionic liquid crystals (DILCs) consist of self-assembled superdiscs of cations and anions that spontaneously stack in linear columns with high one-dimensional ionic and electronic charge mobility, making them prominent model systems for functional soft matter. Unfortunately, a homogeneous alignment of DILCs on the macroscale is often not achievable, which significantly limits their applicability. Infiltration into nanoporous solid scaffolds can in principle overcome this drawback. However, due to the extreme experimental challenges to scrutinise liquid crystalline order in extreme spatial confinement, little is known about the structures of DILCs in nanopores. Here, we present temperature-dependent high-resolution optical birefringence measurement and 3D reciprocal space mapping based on synchrotron-based X-ray scattering to investigate the thermotropic phase behaviour of dopamine-based ionic liquid crystals confined in cylindrical channels of 180~nm diameter in macroscopic anodic aluminum oxide (AAO) membranes. As a function of the membranes' hydrophilicity and thus the molecular anchoring to the pore walls (edge-on or face-on) and the variation of the hydrophilic-hydrophobic balance between the aromatic cores and the alkyl side chain motifs of the superdiscs by tailored chemical synthesis, we find a particularly rich phase behaviour, which is not present in the bulk state. It is governed by a complex interplay of liquid crystalline elastic energies (bending and splay deformations), polar interactions and pure geometric confinement, and includes textural transitions between radial and axial alignment of the columns with respect to the long nanochannel axis., Comment: 19 pages, 9 figures, 1 ancillary file

Nanoporosity in silicon leads to completely new functionalities of this mainstream semiconductor. In recent years, it has been shown that filling the pores with aqueous electrolytes in addition opens a particularly wide field for modifying and achieving active control of these functionalities, e.g., for electrochemo-mechanical actuation and tunable photonics, or for the design of on-chip supercapacitors. However, a mechanistic understanding of these new features has been hampered by the lack of a detailed characterization of the electrochemical behavior of mesoporous silicon in aqueous electrolytes. Here, the capacitive, potential-controlled charging of the electrical double layer in a mesoporous silicon electrode (pore diameter $7\,\mathrm{nm}$) imbibed with perchloric acid solution is studied by electrochemical impedance spectroscopy. Thorough measurements with detailed explanations of the observed phenomena lead to a comprehensive understanding of the capacitive properties of porous silicon. An analysis based on the Mott-Schottky equation allows general conclusions to be drawn about the state of the band structure within the pore walls. Essential parameters such as the flat band potential, the doping density and the width of the space charge region can be determined. A comparison with bulk silicon shows that the flat band potential in particular is significantly altered by the introduction of nanopores, as it shifts from $1.4\pm0.1\,\mathrm{V}$ to $1.9\pm0.2\,\mathrm{V}$. Overall, this study provides a unique insight into the electrochemical processes, especially the electrical double layer charging, of nanoporous semiconductor electrodes., Comment: 5 pages, 3 figures

Polymers are known to wet nanopores with high surface energy through an atomically thin precursor film followed by slower capillary filling. We present here light interference spectroscopy using a nanoporous membrane-based chip that allows us to observe the dynamics of these phenomena in situ with sub-nanometer spatial and milli- to microsecond temporal resolution. The device consists of a mesoporous silicon film (average pore size 6 nm) with an integrated photonic crystal, which permits to simultaneously measure the phase shift of the thin-film interference and the resonance of the photonic crystal upon imbibition. For a styrene dimer, we find a flat fluid front without a precursor film, while the pentamer forms an expanding molecular thin film moving in front of the menisci of the capillary filling. These different behaviors are attributed to a significantly faster pore-surface diffusion compared to the imbibition dynamics for the pentamer and vice versa for the dimer. In addition, both oligomers exhibit anomalously slow imbibition dynamics, which could be explained by apparent viscosities of six and eleven times the bulk value, respectively. However, a more consistent description of the dynamics is achieved by a constriction model that emphasizes the increasing importance of local undulations in the pore radius with the molecular size and includes a sub-nanometer hydrodynamic dead, immobile zone at the pore wall, but otherwise uses bulk fluid parameters. Overall, our study illustrates that interferometric, opto-fluidic experiments with nanoporous media allow for a remarkably detailed exploration of the nano-rheology of polymeric liquids., Comment: 9 pages, 5 figures

Capillarity-driven transport in nanoporous solids is widespread in nature and crucial for modern liquid-infused engineering materials. During imbibition, curved menisci driven by high negative Laplace pressures exert an enormous contractile load on the porous matrix. Due to the challenge of simultaneously monitoring imbibition and deformation with high spatial resolution, the resulting coupling of solid elasticity to liquid capillarity has remained largely unexplored. Here, we study water imbibition in mesoporous silica using optical imaging, gravimetry, and high-resolution dilatometry. In contrast to an expected Laplace pressure-induced contraction, we find a square-root-of-time expansion and an additional abrupt length increase when the menisci reach the top surface. The final expansion is absent when we stop the imbibition front inside the porous medium in a dynamic imbibition-evaporation equilibrium, as is typical for transpiration-driven hydraulic transport in plants, especially in trees. These peculiar deformation behaviors are validated by single-nanopore molecular dynamics simulations and described by a continuum model that highlights the importance of expansive surface stresses at the pore walls (Bangham effect) and the buildup or release of contractile Laplace pressures as menisci collectively advance, arrest, or disappear. Our model suggests that these observations apply to any imbibition process in nanopores, regardless of the liquid/solid combination, and that the Laplace contribution upon imbibition is precisely half that of vapor sorption, due to the linear pressure drop associated with viscous flow. Thus, simple deformation measurements can be used to quantify surface stresses and Laplace pressures or transport in a wide variety of natural and artificial porous media., Comment: 11 pages, 4 figures. Supplementary information and 3 movies are stored as ancillary files

Understanding and measuring the resilience of food supply networks is a global imperative to tackle increasing food insecurity. However, the complexity of these networks, with their multidimensional interactions and decisions, presents significant challenges. This paper proposes FLEE-GNN, a novel Federated Learning System for Edge-Enhanced Graph Neural Network, designed to overcome these challenges and enhance the analysis of geospatial resilience of multicommodity food flow network, which is one type of spatial networks. FLEE-GNN addresses the limitations of current methodologies, such as entropy-based methods, in terms of generalizability, scalability, and data privacy. It combines the robustness and adaptability of graph neural networks with the privacy-conscious and decentralized aspects of federated learning on food supply network resilience analysis across geographical regions. This paper also discusses FLEE-GNN's innovative data generation techniques, experimental designs, and future directions for improvement. The results show the advancements of this approach to quantifying the resilience of multicommodity food flow networks, contributing to efforts towards ensuring global food security using AI methods. The developed FLEE-GNN has the potential to be applied in other spatial networks with spatially heterogeneous sub-network distributions., Comment: 10 pages, 5 figures

Our research explores the use of natural language processing (NLP) methods to automatically classify entities for the purpose of knowledge graph population and integration with food system ontologies. We have created NLP models that can automatically classify organizations with respect to categories associated with environmental issues as well as Standard Industrial Classification (SIC) codes, which are used by the U.S. government to characterize business activities. As input, the NLP models are provided with text snippets retrieved by the Google search engine for each organization, which serves as a textual description of the organization that is used for learning. Our experimental results show that NLP models can achieve reasonably good performance for these two classification tasks, and they rely on a general framework that could be applied to many other classification problems as well. We believe that NLP models represent a promising approach for automatically harvesting information to populate knowledge graphs and aligning the information with existing ontologies through shared categories and concepts., Comment: Presented at IFOW 2023 Integrated Food Ontology Workshop at the Formal Ontology in Information Systems Conference (FOIS) 2023 in Sherbrooke, Quebec, Canada July 17-20th, 2023

Sterile neutrinos can be produced through mixing with active neutrinos in the hot and dense core of a core-collapse supernova (SN). The standard bounds on the active-sterile mixing ($\sin^2 \theta$) from SN arise from SN1987A energy-loss, requiring $E_{\text{loss}}<10^{52}~{\rm erg}$. In this letter, we discuss a novel bound on sterile neutrino parameter space arising from the energy deposition through its decays inside the SN envelope. Using the observed underluminous SN IIP population, this energy deposition is constrained to be below $\sim 10^{50}~{\rm erg}$. Focusing on sterile neutrino mixing only with the tau neutrino, for heavy sterile masses $m_s$ in the range $100$-$500$ MeV, we find stringent constraints on $\sin^2 \theta_\tau$ reaching two orders of magnitude lower than those from the SN1987A energy-loss argument. Similar bounds will also be applicable to sterile mixing only with muons ($\sin^2 \theta_\mu$)., Comment: 7 pages, 3 figures, 1 table

Discourse processing suffers from data sparsity, especially for dialogues. As a result, we explore approaches to build discourse structures for dialogues, based on attention matrices from Pre-trained Language Models (PLMs). We investigate multiple tasks for fine-tuning and show that the dialogue-tailored Sentence Ordering task performs best. To locate and exploit discourse information in PLMs, we propose an unsupervised and a semi-supervised method. Our proposals achieve encouraging results on the STAC corpus, with F1 scores of 57.2 and 59.3 for unsupervised and semi-supervised methods, respectively. When restricted to projective trees, our scores improved to 63.3 and 68.1.

Minerals are solid state nuclear track detectors - nuclear recoils in a mineral leave latent damage to the crystal structure. Depending on the mineral and its temperature, the damage features are retained in the material from minutes (in low-melting point materials such as salts at a few hundred degrees C) to timescales much larger than the 4.5 Gyr-age of the Solar System (in refractory materials at room temperature). The damage features from the $O(50)$ MeV fission fragments left by spontaneous fission of $^{238}$U and other heavy unstable isotopes have long been used for fission track dating of geological samples. Laboratory studies have demonstrated the readout of defects caused by nuclear recoils with energies as small as $O(1)$ keV. This whitepaper discusses a wide range of possible applications of minerals as detectors for $E_R \gtrsim O(1)$ keV nuclear recoils: Using natural minerals, one could use the damage features accumulated over $O(10)$ Myr$-O(1)$ Gyr to measure astrophysical neutrino fluxes (from the Sun, supernovae, or cosmic rays interacting with the atmosphere) as well as search for Dark Matter. Using signals accumulated over months to few-years timescales in laboratory-manufactured minerals, one could measure reactor neutrinos or use them as Dark Matter detectors, potentially with directional sensitivity. Research groups in Europe, Asia, and America have started developing microscopy techniques to read out the $O(1) - O(100)$ nm damage features in crystals left by $O(0.1) - O(100)$ keV nuclear recoils. We report on the status and plans of these programs. The research program towards the realization of such detectors is highly interdisciplinary, combining geoscience, material science, applied and fundamental physics with techniques from quantum information and Artificial Intelligence., Comment: 115 pages, many pictures of tracks. Please see the source file for higher resolution versions of some plots. v2: matches the published version

The 2021-22 High-Energy Physics Community Planning Exercise (a.k.a. ``Snowmass 2021'') was organized by the Division of Particles and Fields of the American Physical Society. Snowmass 2021 was a scientific study that provided an opportunity for the entire U.S. particle physics community, along with its international partners, to identify the most important scientific questions in High Energy Physics for the following decade, with an eye to the decade after that, and the experiments, facilities, infrastructure, and R&D needed to pursue them. This Snowmass summary report synthesizes the lessons learned and the main conclusions of the Community Planning Exercise as a whole and presents a community-informed synopsis of U.S. particle physics at the beginning of 2023. This document, along with the Snowmass reports from the various subfields, will provide input to the 2023 Particle Physics Project Prioritization Panel (P5) subpanel of the U.S. High-Energy Physics Advisory Panel (HEPAP), and will help to guide and inform the activity of the U.S. particle physics community during the next decade and beyond., Comment: 75 pages, 3 figures, 2 tables. This is the first chapter and summary of the full report of the Snowmass 2021 Workshop. This version fixes an important omission from Table 2, adds two references that were not available at the time of the original version, fixes a minor few typos, and adds a small amount of material to section 1.1.3

Many biological materials exhibit a multiscale porosity with small, mostly nanoscale pores as well as large, macroscopic capillaries to simultaneously achieve optimized mass transport capabilities and lightweight structures with large inner surfaces. Realizing such a hierarchical porosity in artificial materials necessitates often sophisticated and expensive top-down processing that limits scalability. Here we present an approach that combines self-organized porosity based on metal-assisted chemical etching (MACE) with photolithographically induced macroporosity for the synthesis of single-crystalline silicon with a bimodal pore-size distribution, i.e., hexagonally arranged cylindrical macropores with 1 micrometer diameter separated by walls that are traversed by mesopores 60 nm across. The MACE process is mainly guided by a metal-catalyzed reduction-oxidation reaction, where silver nanoparticles (AgNPs) serve as the catalyst. In this process, the AgNPs act as self-propelled particles that are constantly removing silicon along their trajectories. High-resolution X-ray imaging and electron tomography reveal a resulting large open porosity and inner surface for potential applications in high-performance energy storage, harvesting and conversion or for on-chip sensorics and actuorics. Finally, the hierarchically porous silicon membranes can be transformed structure-conserving by thermal oxidation into hierarchically porous amorphous silica, a material that could be of particular interest for opto-fluidic and (bio-)photonic applications due to its multiscale artificial vascularization., Comment: 15 pages, 7 figures, 2 videos as ancillary files, accepted in the journal "Small" under DOI https://doi.org/10.1002/smll.202206842