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Patrick Huber

Publications and source records attributed to Patrick Huber.

At least 37 records · Page 2Linked to original sources
arXiv2025

CONFLUX: A Standardized Framework to Calculate Reactor Antineutrino Flux

Nuclear fission reactors are abundant sources of antineutrinos. The flux and spectrum of antineutrinos emitted by a reactor can indicate its activity and composition, suggesting potential applications of neutrino measurements beyond fundamental scientific studies that may be valuable to society. The utility of reactor antineutrinos for applications and fundamental science is dependent on the availability of precise predictions of these emissions. For example, in the last decade, disagreements between reactor antineutrino measurements and models have inspired revision of reactor antineutrino calculations and standard nuclear databases as well as searches for new fundamental particles not predicted by the Standard Model of particle physics. Past predictions and descriptions of the methods used to generate them are documented to varying degrees in the literature, with different modeling teams incorporating a range of methods, input data, and assumptions. The resulting difficulty in accessing or reproducing past models and reconciling results from differing approaches complicates the future study and application of reactor antineutrinos. The CONFLUX (Calculation Of Neutrino FLUX) software framework is a neutrino prediction tool built with the goal of simplifying, standardizing, and democratizing the process of reactor antineutrino flux calculations. CONFLUX include three primary methods for calculating the antineutrino emissions of nuclear reactors or individual beta decays that incorporate common nuclear data and beta decay theory. The software is prepackaged with the current nuclear database. It includes the capability to predict time-dependent neutrino model, adjust decay information entries, and propagate uncertainties. This paper describes the software structure, details the methods used for flux and spectrum calculations, and talks about potential use cases.

physics.ins-det↗
arXiv2025

CoSMoEs: Compact Sparse Mixture of Experts

Sparse Mixture of Expert (MoE) models are popular foundational architectures at large scale, however, under-explored at smaller sizes. Here, we show how to enable Compact Sparse Mixture of Experts (CoSMoEs) for on-device inference. Specifically, we tackle the three main on-device dimensions: Quality, Memory and Latency. Along the quality axis, we show that in a fair evaluation (removing confounding factors) MoE architectures outperform FLOP-aligned dense models at on-device scale. We introduce weight-decomposed experts, further improving the MoE model performance. Regarding model memory and latency, we significantly improve model offloading efficiency and, in turn, reduce model inference latency.

cs.LG↗
arXiv2024

First-principles Spin and Optical Properties of Vacancy Clusters in Lithium Fluoride

Vacancy-cluster color centers in lithium fluoride have been studied in detail both theoretically and experimentally for over a century, giving rise to various applications in solid-state lasers, broadband photonic devices, and radiation dosimeters. These color centers are also attractive candidate platforms for applications in quantum information science, due to their spin properties and strong coupling to the crystal lattice, which allows their properties to be easily tuned. Here we present hybrid functional calculations of common vacancy defects in lithium fluoride, including their energetic, spin, and optical properties. We show that for a wide range of hybrid functional parameters tuned to match the experimental band gap, certain defects have little variation in their predicted optical properties. We further demonstrate that the parameters needed to satisfy the generalized Koopman's theorem and correctly position defect levels within the gap, can vary dramatically, even for different charge states of the same defect. Our work establishes the accuracy of the computationally lightweight hybrid-functional approach for predicting the optical and energetic properties of color centers in polar materials.

cond-mat.mtrl-sci↗
arXiv2024

Maximum entropy mediated liquid-to-solid nucleation and transition

Molecular Dynamics (MD) simulations are a powerful tool for studying matter at the atomic scale. However, to simulate solids, an initial atomic structure is crucial for the successful execution of MD simulations, but can be difficult to prepare due to insufficient atomistic information. At the same time Wide Angle X-ray Scattering (WAXS) measurements can determine the Radial Distribution Function (RDF) of atomic structures. However, the interpretation of RDFs is often challenging. Here we present an algorithm that can bias MD simulations with RDFs by combining the information of the MD atomic interaction potential and the RDF under the principle of maximum relative entropy. We show that this algorithm can be used to adjust the RDF of one liquid model, e.g., the TIP3P water model, to reproduce the RDF and improve the Angular Distribution Function (ADF) of another model, such as the TIP4P/2005 water model. In addition, we demonstrate that the algorithm can initiate crystallization in liquid systems, leading to both stable and metastable crystalline states defined by the RDF, e.g., crystallization of water to ice and liquid TiO2 to rutile or anatase. Finally, we discuss how this method can be useful for improving interaction models, studying crystallization processes, interpreting measured RDFs, or training machine learned potentials.

physics.comp-ph↗
arXiv2024

Scaling Parameter-Constrained Language Models with Quality Data

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.

cs.CL↗
arXiv2024

Deformation Dynamics of Nanopores upon Water Imbibition

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.

cond-mat.soft↗
arXiv2024

CoDi: Conversational Distillation for Grounded Question Answering

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.

cs.CL↗
arXiv2024

Large Language Models as Zero-shot Dialogue State Tracker through Function Calling

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

cs.CL↗
arXiv2024

Mineral Detection of Neutrinos and Dark Matter 2024. Proceedings

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.

astro-ph.CO↗
arXiv2024

Small But Funny: A Feedback-Driven Approach to Humor Distillation

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.

cs.CL↗
arXiv2024

Probing the Sterile Neutrino Dipole Portal with SN1987A and Low-Energy Supernovae

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.

hep-ph↗
arXiv2024

Straight versus Spongy -- Effect of Tortuosity on Polymer Imbibition into Nanoporous Matrices Assessed by Segmentation-Free Analysis of 3D Sample Reconstructions

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.

cond-mat.mtrl-sci↗
arXiv2024

How do ionic superdiscs self-assemble in nanopores?

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.

cond-mat.soft↗
arXiv2024

Wafer-scale fabrication of mesoporous silicon functionalized with electrically conductive polymers

The fabrication of hybrid materials consisting of nanoporous hosts with conductive polymers is a challenging task, since the extreme spatial confinement often conflicts with the stringent physico-chemical requirements for polymerization of organic constituents. Here, several low-threshold and scalable synthesis routes for such hybrids are presented. First, the electrochemical synthesis of composites based on mesoporous silicon (pore size of 7 nm) and the polymers PANI, PPy and PEDOT is discussed and validated by scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX). Polymer filling degrees of 74% are achieved. Second, the production of PEDOT/pSi hybrids, based on the solid-state polymerization (SSP) of DBEDOT to PEDOT is shown. The resulting amorphous structure of the nanopore-embedded PEDOT is investigated via in-situ synchrotron-based X-ray scattering. In addition, a twofold increase in the electrical conductivity of the hybrid compared to the porous silicon host is shown, making this system particularly promising for thermoelectric applications.

cond-mat.mtrl-sci↗
arXiv2023

A Mott-Schottky Analysis of Mesoporous Silicon in Aqueous Electrolyte by Electrochemical Impedance Spectroscopy

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.

cond-mat.mtrl-sci↗
arXiv2023

Report of the 2021 U.S. Community Study on the Future of Particle Physics (Snowmass 2021) Summary Chapter

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.

hep-ex↗
arXiv2023

Polymeric Liquids in Nanoporous Photonic Structures: From Precursor Film Spreading to Imbibition Dynamics at the Nanoscale

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.

cond-mat.soft↗