Miracle-dz/UniFAR-FTUs huggingface dataset:
NEAR-SURFACE EFFECTS AT THE ANTIFERROMAGNETIC PHASE TRANSITION IN URANIUM PHOSPHIDE

A careful investigation of the resonant x-ray magnetic-scattering response in uranium monophosphide reveals the crucial role played by magnetization density correlations in the discontinuous paramagnetic to antiferromagnetic phase transition in this material. The magnetization density correlations, which are measured on the short time scale typical of the resonant x-ray-scattering technique, appear highly anisotropic in their inferred spatial distribution. Such correlated regions exist in the near-surface regime probed by the low-energy x-ray beam (3.278 keV) and exhibit strongly temperature-dependent behavior at and above the bulk phase transition. For specular reflections this leads to well defined two length scale diffraction profiles in the vicinity of the phase transition and, when taken with the off-specular data, to a phenomenological model which suggests that the extended length scale of near-surface antiferromagnetism may arise from a localized surface distortion. This type of model may be more generally useful both in the two length scale problem and in other systems where defects or fluctuations may, in a similar manner, drive pseudo-long-range order. {copyright} {ital 1997} {ital The American Physical Society}
Learning List-Level Domain-Invariant Representations for Ranking

Domain adaptation aims to transfer the knowledge learned on (data-rich) source domains to (low-resource) target domains, and a popular method is invariant representation learning, which matches and aligns the data distributions on the feature space. Although this method is studied extensively and applied on classification and regression problems, its adoption on ranking problems is sporadic, and the few existing implementations lack theoretical justifications. This paper revisits invariant representation learning for ranking. Upon reviewing prior work, we found that they implement what we call item-level alignment, which aligns the distributions of the items being ranked from all lists in aggregate but ignores their list structure. However, the list structure should be leveraged, because it is intrinsic to ranking problems where the data and the metrics are defined and computed on lists, not the items by themselves. To close this discrepancy, we propose list-level alignment -- learning domain-invariant representations at the higher level of lists. The benefits are twofold: it leads to the first domain adaptation generalization bound for ranking, in turn providing theoretical support for the proposed method, and it achieves better empirical transfer performance for unsupervised domain adaptation on ranking tasks, including passage reranking.
MultiInstruct: Improving Multi-Modal Zero-Shot Learning via Instruction Tuning

Instruction tuning, a new learning paradigm that fine-tunes pre-trained language models on tasks specified through instructions, has shown promising zero-shot performance on various natural language processing tasks. However, it has yet to be explored for vision and multimodal tasks. In this work, we introduce MultiInstruct, the first multimodal instruction tuning benchmark dataset that consists of 62 diverse multimodal tasks in a unified seq-to-seq format covering 10 broad categories. The tasks are derived from 21 existing open-source datasets and each task is equipped with 5 expert-written instructions. We take OFA as the base pre-trained model for multimodal instruction tuning, and to further improve its zero-shot performance, we explore multiple transfer learning strategies to leverage the large-scale Natural Instructions dataset. Experimental results demonstrate strong zero-shot performance on various unseen multimodal tasks and the benefit of transfer learning from a text-only instruction dataset. We also design a new evaluation metric – Sensitivity, to evaluate how sensitive the model is to the variety of instructions. Our results indicate that fine-tuning the model on a diverse set of tasks and instructions leads to a reduced sensitivity to variations in instructions for each task.
Measurement of the cosmogenic neutron yield in Super-Kamiokande with gadolinium loaded water

Cosmic-ray muons that enter the Super-Kamiokande detector cause hadronic showers due to spallation in water, producing neutrons and radioactive isotopes. Those are a major background source for studies of MeV-scale neutrinos and searches for rare events. Since 2020, gadolinium was introduced in the ultra-pure water in the Super-Kamiokande detector to improve the detection efficiency of neutrons. In this study, the cosmogenic neutron yield was measured using data acquired during the period after the gadolinium loading. The yield was found to be $(2.76 \pm 0.02\,\mathrm{(stat.) \pm 0.19\,\mathrm{(syst.)}}) \times 10^{-4}\,\mu^{-1} \mathrm{g^{-1} cm^{2}}$ at 259 GeV of average muon energy at the Super-Kamiokande detector.
Invertible and noninvertible symbolic dynamics and their C*-algebras

This paper surveys the recent advances in the interactions between symbolic dynamics and C*-algebras. We explain how conjugacies and orbit equivalences of both two-sided (invertible) and one-sided (noninvertible) symbolic systems may be encoded into C*-algebras, and how the dynamical systems can be recovered from structure-preserving *-isomorphisms of C*-algebras. We have included many illustrative examples as well as open problems.
Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning

As of 2022, greenhouse gases (GHG) emissions reporting and auditing are not yet compulsory for all companies, and methodologies of measurement and estimation are not unified. We propose a machine learning-based model to estimate scope 1 and scope 2 GHG emissions of companies not reporting them yet. Our model, designed to be transparent and completely adapted to this use case, is able to estimate emissions for a large universe of companies. It shows good out-of-sample global performances as well as good out-of-sample granular performances when evaluating it by sectors, countries, or revenue buckets. We also compare the model results to those of other providers and find our estimates to be more accurate. Explainability tools based on Shapley values allow the constructed model to be fully interpretable, the user being able to understand which factors split explains the GHG emissions for each particular company.
Fundamentals of Physical Acoustics

Detailed Development of the Acoustical Wave Equation. Reflection and Transmission of Normally Incident Plane Waves of Arbitrary Waveform. Normal Incidence Continued: Steady-State Analysis. Transmission Phenomena: Oblique Incidence. Normal Modes in Cartesian Coordinates: Strings, Membranes, Rooms, and Rectangular Waveguides. Horns. Propagation in Stratified Media. Propagation in Dissipative Fluids: Absorption and Dispersion. Spherical Waves. Cylindrical Waves. Waveguides. Radiation from a Baffled Piston. Diffraction. Arrays. Appendices. Index.
Random linear estimation with rotationally-invariant designs: Asymptotics at high temperature

We study estimation in the linear model y = Aβ⋆+ ϵ, in a Bayesian setting where β⋆ has an entrywise i.i. d. prior and the design A is rotationally-invariant in law. In the large system limit as dimension and sample size increase proportionally, a set of related conjectures have been postulated for the asymptotic mutual information, Bayes-optimal mean squared error, and TAP mean-field equations that characterize the Bayes posterior mean of β⋆. In this work, we prove these conjectures for a general class of signal priors and for arbitrary rotationally-invariant designs A, under a "high-temperature" condition that restricts the range of eigenvalues of A⊤A. Our proof uses a conditional second-moment method argument, where we condition on the iterates of a version of the Vector AMP algorithm for solving the TAP mean-field equations.
Computer says "No": The Case Against Empathetic Conversational AI

Emotions are an integral part of human cognition and they guide not only our understanding of the world but also our actions within it. As such, whether we soothe or flame an emotion is not inconsequential. Recent work in conversational AI has focused on responding empathetically to users, validating and soothing their emotions without a real basis. This AI-aided emotional regulation can have negative consequences for users and society, tending towards a one-noted happiness defined as only the absence of"negative"emotions. We argue that we must carefully consider whether and how to respond to users' emotions.
High-energy Neutrinos from Gamma-Ray-faint Accretion-powered Hypernebulae

Hypernebulae are inflated by accretion-powered winds accompanying hyper-Eddington mass transfer from an evolved post-main-sequence star onto a black hole or neutron star companion. The ions accelerated at the termination shock—where the collimated fast disk winds and/or jet collide with the slower, wide-angled wind-fed shell—can generate high-energy neutrinos via hadronic proton–proton reactions, and photohadronic (p γ) interactions with the disk thermal and Comptonized nonthermal background photons. It has been suggested that some fast radio bursts (FRBs) may be powered by such short-lived jetted hyper-accreting engines. Although neutrino emission associated with the millisecond duration bursts themselves is challenging to detect, the persistent radio counterparts of some FRB sources—if associated with hypernebulae—could contribute to the high-energy neutrino diffuse background flux. If the hypernebula birth rate follows that of stellar-merger transients and common envelope events, we find that their volume-integrated neutrino emission—depending on the population-averaged mass-transfer rates—could explain up to ∼25% of the high-energy diffuse neutrino flux observed by the IceCube Observatory and the Baikal Gigaton Volume Detector Telescope. The time-averaged neutrino spectrum from hypernebula—depending on the population parameters—can also reproduce the observed diffuse neutrino spectrum. The neutrino emission could in some cases furthermore extend to >100 PeV, detectable by future ultra-high-energy neutrino observatories. The large optical depth through the nebula to Breit–Wheeler (γ γ) interaction attenuates the escape of GeV–PeV gamma rays coproduced with the neutrinos, rendering these gamma-ray-faint neutrino sources, consistent with the Fermi observations of the isotropic gamma-ray background.
Seven definitions of bipartite bound entanglement

An entangled state is bound entangled if one cannot combine any number of copies of the state to a maximally entangled state by using only local operations and classical communication. If one formalizes this notion of bound entanglement, one arrives immediately at four different definitions. In addition, at least three more definitions are commonly used in the literature, in particular in the very first paper on bound entanglement. Here we review critical distillation protocols and we examine how different results from quantum information theory interact in order to prove that all seven definitions are eventually equivalent. Our self-contained analysis unifies and extends previous results scattered in the literature and reveals details of the structure of bound entanglement.
Perching on Moving Inclined Surfaces using Uncertainty Tolerant Planner and Thrust Regulation

Quadrotors with the ability to perch on moving inclined surfaces can save energy and extend their travel distance by leveraging ground vehicles. Achieving dynamic perching places high demands on the performance of trajectory planning and terminal state accuracy in SE(3). However, in the perching process, uncertainties in target surface prediction, tracking control and external disturbances may cause trajectory planning failure or lead to unacceptable terminal errors. To address these challenges, we first propose a trajectory planner that considers adaptation to uncertainties in target prediction and tracking control. To facilitate this work, the reachable set of quadrotors' states is first analyzed. The states whose reachable sets possess the largest coverage probability for uncertainty targets, are defined as optimal waypoints. Subsequently, an approach to seek local optimal waypoints for static and moving uncertainty targets is proposed. A real-time trajectory planner based on optimized waypoints is developed accordingly. Secondly, thrust regulation is also implemented in the terminal attitude tracking stage to handle external disturbances. When a quadrotor's attitude is commanded to align with target surfaces, the thrust is optimized to minimize terminal errors. This makes the terminal position and velocity be controlled in closed-loop manner. Therefore, the resistance to disturbances and terminal accuracy is improved. Extensive simulation experiments demonstrate that our methods can improve the accuracy of terminal states under uncertainties. The success rate is approximately increased by $50\%$ compared to the two-end planner without thrust regulation. Perching on the rear window of a car is also achieved using our proposed heterogeneous cooperation system outdoors. This validates the feasibility and practicality of our methods.
Numerically inverting a class of singular Fourier transforms: theory and application to mountain waves

Many partial differential equations of physics are solved using Fourier transforms. Even when solutions to the transformed equations can be found analytically it is rare for their inverse transforms to be known in terms of simple functions. Instead, asymptotic and/or numerical approaches are commonly used to approximate the inverse transforms. A numerical technique is here developed to invert a one–dimensional Fourier transform with singularities on the real axis of the complex plane that are at worst simple poles, subject to the condition that the inverse transform vanishes as its transform variable becomes infinite in one direction. By way of demonstration, it is used to compute the respective solutions of Long and Wurtele for trapped and evanescent lee waves driven by flow over gently sloping hills.
An Audio-Visual Speech Separation Model Inspired by Cortico-Thalamo-Cortical Circuits

Audio-visual approaches involving visual inputs have laid the foundation for recent progress in speech separation. However, the optimization of the concurrent usage of auditory and visual inputs is still an active research area. Inspired by the cortico-thalamo-cortical circuit, in which the sensory processing mechanisms of different modalities modulate one another via the non-lemniscal sensory thalamus, we propose a novel cortico-thalamo-cortical neural network (CTCNet) for audio-visual speech separation (AVSS). First, the CTCNet learns hierarchical auditory and visual representations in a bottom-up manner in separate auditory and visual subnetworks, mimicking the functions of the auditory and visual cortical areas. Then, inspired by the large number of connections between cortical regions and the thalamus, the model fuses the auditory and visual information in a thalamic subnetwork through top-down connections. Finally, the model transmits this fused information back to the auditory and visual subnetworks, and the above process is repeated several times. The results of experiments on three speech separation benchmark datasets show that CTCNet remarkably outperforms existing AVSS methods with considerably fewer parameters. These results suggest that mimicking the anatomical connectome of the mammalian brain has great potential for advancing the development of deep neural networks. Project repo is https://github.com/JusperLee/CTCNet.
Forward- or Reverse-Mode Automatic Differentiation: What's the Difference?

Automatic differentiation (AD) has been a topic of interest for researchers in many disciplines, with increased popularity since its application to machine learning and neural networks. Although many researchers appreciate and know how to apply AD, it remains a challenge to truly understand the underlying processes. From an algebraic point of view, however, AD appears surprisingly natural: it originates from the differentiation laws. In this work we use Algebra of Programming techniques to reason about different AD variants, leveraging Haskell to illustrate our observations. Our findings stem from three fundamental algebraic abstractions: (1) the notion of module over a semiring, (2) Nagata's construction of the 'idealization of a module', and (3) Kronecker's delta function, that together allow us to write a single-line abstract definition of AD. From this single-line definition, and by instantiating our algebraic structures in various ways, we derive different AD variants, that have the same extensional behaviour, but different intensional properties, mainly in terms of (asymptotic) computational complexity. We show the different variants equivalent by means of Kronecker isomorphisms, a further elaboration of our Haskell infrastructure which guarantees correctness by construction. With this framework in place, this paper seeks to make AD variants more comprehensible, taking an algebraic perspective on the matter.
Continual Contrastive Finetuning Improves Low-Resource Relation Extraction

Relation extraction (RE), which has relied on structurally annotated corpora for model training, has been particularly challenging in low-resource scenarios and domains. Recent literature has tackled low-resource RE by self-supervised learning, where the solution involves pretraining the entity pair embedding by RE-based objective and finetuning on labeled data by classification-based objective. However, a critical challenge to this approach is the gap in objectives, which prevents the RE model from fully utilizing the knowledge in pretrained representations. In this paper, we aim at bridging the gap and propose to pretrain and finetune the RE model using consistent objectives of contrastive learning. Since in this kind of representation learning paradigm, one relation may easily form multiple clusters in the representation space, we further propose a multi-center contrastive loss that allows one relation to form multiple clusters to better align with pretraining. Experiments on two document-level RE datasets, BioRED and Re-DocRED, demonstrate the effectiveness of our method. Particularly, when using 1% end-task training data, our method outperforms PLM-based RE classifier by 10.5% and 6.1% on the two datasets, respectively.
The Quantitative Finance Aspects of Automated Market Markers in DeFi

Automated Market Makers (AMMs) are a class of smart contracts on Ethereum and other blockchains that ”make markets” autonomously. In other words, AMMs stand ready to trade with other market participants that interact with them, at the conditions determined by the AMM. In this this paper, which relies on the existing and growing corpus of literature available, we review and present the key mathematical and quantitative finance aspects that underpin their operations, including the interesting relationship between AMMs and derivatives pricing and hedging. This paper is a chapter of The AMM Book (theammbook.org) which embeds it into a wider and less technical context, including economics, regulations and a description of the related eco system. 1 ar X iv :2 21 2. 10 97 4v 1 [ qfi n. M F] 2 1 D ec 2 02 2
Detection of x-ray emission from the remnant of the supernova 1006 A. D. [1 to 10 keV]

Observations from OSO-7 identify SN 1006 as a weak X-ray source, with an intensity of 9 x 10/sup -11/ ergs cm/sup -2/ s/sup -1/ in the energy range 1--10 keV. The spectrum can be represented by a power law with energy index 1.3 or by thermal bremsstrahlung at kT = 4 keV. Interpretation of the observations as thermal X-ray emission from an adiabatic blast wave indicates that SN 1006 is about 1.2 kpc distant, and that its initial kinetic energy is of the order of 10/sup 50/ ergs. (auth)
Abnormal methylation caused by folic acid deficiency in neural tube defects

Abstract Neural tube closure disorders, including anencephaly, spina bifida, and encephalocele, cause neural tube defects (NTDs). This congenital disability remained not only a major contributor to the prevalence of stillbirths and neonatal deaths but also a significant cause of lifelong physical disability in surviving infants. NTDs are complex diseases caused by multiple etiologies, levels, and mechanisms. Currently, the pathogenesis of NTDs is considered to be associated with both genetic and environmental factors. Here, we aimed to review the research progress on the etiology and mechanism of NTDs induced by methylation modification caused by folic acid deficiency. Folic acid supplementation in the diet is reported to be beneficial in preventing NTDs. Methylation modification is one of the most important epigenetic modifications crucial for brain neurodevelopment. Disturbances in folic acid metabolism and decreased S-adenosylmethionine levels lead to reduced methyl donors and methylation modification disorders. In this review, we summarized the relationship between NTDs, folic acid metabolism, and related methylation of DNA, imprinted genes, cytoskeletal protein, histone, RNA, and non-coding RNA, so as to clarify the role of folic acid and methylation in NTDs and to better understand the various pathogenesis mechanisms of NTDs and the effective prevention.