Proton and neutron electromagnetic form factors and uncertainties

We determine the nucleon electromagnetic form factors and their uncertainties from world electron scattering data. The analysis incorporates two-photon exchange corrections, constraints on the low-Q2 and high-Q2 behavior, and additional uncertainties to account for tensions between different data sets and uncertainties in radiative corrections.Comment: 9 pages, 7 figures. Published on Phys. Lett.

New Chinese Facilities for Short-Range Correlation Physics

This article explores the significant advancements in Short-Range Correlation (SRC) research enabled by the latest Chinese nuclear physics facilities- CSR at HIRFL, HIAF, SHINE, and the upcoming EicC. These facilities introduce cutting-edge technologies and methodologies, addressing existing challenges and broadening the scope for SRC studies. By providing detailed insights into the capabilities and expected contributions of each facility, the paper highlights China's emerging role in the global nuclear physics landscape. The collaborative potential, alongside complementary global efforts, positions these facilities to deeply influence our understanding of nuclear matter's fundamental properties and interactions.Comment: 16 pages, 14 figures, to be submitted to EPJA Topical Collection: Short-Range Correlations and the EMC Effec

Mineralization of pentachlorophenol by ferrioxalate-assisted solar photo-fenton process at mild pH

This work reports the use of ferrioxalate complexes to assist solar photo-Fenton treatment of pentachlorophenol (PCP) in aqueous medium at mild pH, which inhibits the precipitation of iron hydroxides and allows working at a low iron dosage. The experimental parameters were optimized by assessing the effect of initial concentrations of H2O2 (0-2.5 mM) and Fe(II) (2-10 mg/L), pH (3.0-9.0) and iron/oxalic acid molar ratios (1:0-1:13.5) on total organic carbon (TOC) removal. Ferrioxalate-assisted solar photo-Fenton achieved 97.5% mineralization in 120 min, clearly outperforming conventional Fenton and solar photo-Fenton. The presence of photosensitive ferrioxalate complexes accounted for the enhancement, as a result of Fe(II) regeneration that accelerated the hydroxyl radical (OH) production. The time course of H2O2 and Fe(II) concentrations was evaluated under different iron/oxalic acid ratios. The five carboxylic acids determined by ion-exclusion HPLC and the eight aromatic by-products identified by GC-MS allowed the proposal of a degradation pathway that included hydroxylation, dechlorination and dimerization steps. Complete chloride ion release was achieved after 90 min of treatment

Unveiling the nucleon tensor charge at Jefferson Lab: A study of the SoLID case

Future experiments at the Jefferson Lab 12 GeV upgrade, in particular, the Solenoidal Large Intensity Device (SoLID), aim at a very precise data set in the region where the partonic structure of the nucleon is dominated by the valence quarks. One of the main goals is to constrain the quark transversity distributions. We apply recent theoretical advances of the global QCD extraction of the transversity distributions to study the impact of future experimental data from the SoLID experiments. Especially, we develop a simple strategy based on the Hessian matrix analysis that allows one to estimate the uncertainties of the transversity quark distributions and their tensor charges extracted from SoLID data simulation. We find that the SoLID measurements with the proton and the effective neutron targets can improve the precision of the u- and d-quark transversity distributions up to one order of magnitude in the range 0.05 < x < 0.6.Comment: 11 pages, 3 figures, published on Physics Letters

ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding

Spoken language understanding (SLU) is a fundamental task in the task-oriented dialogue systems. However, the inevitable errors from automatic speech recognition (ASR) usually impair the understanding performance and lead to error propagation. Although there are some attempts to address this problem through contrastive learning, they (1) treat clean manual transcripts and ASR transcripts equally without discrimination in fine-tuning; (2) neglect the fact that the semantically similar pairs are still pushed away when applying contrastive learning; (3) suffer from the problem of Kullback-Leibler (KL) vanishing. In this paper, we propose Mutual Learning and Large-Margin Contrastive Learning (ML-LMCL), a novel framework for improving ASR robustness in SLU. Specifically, in fine-tuning, we apply mutual learning and train two SLU models on the manual transcripts and the ASR transcripts, respectively, aiming to iteratively share knowledge between these two models. We also introduce a distance polarization regularizer to avoid pushing away the intra-cluster pairs as much as possible. Moreover, we use a cyclical annealing schedule to mitigate KL vanishing issue. Experiments on three datasets show that ML-LMCL outperforms existing models and achieves new state-of-the-art performance

ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance

Most of ranking models are trained only with displayed items (most are hot items), but they are utilized to retrieve items in the entire space which consists of both displayed and non-displayed items (most are long-tail items). Due to the sample selection bias, the long-tail items lack sufficient records to learn good feature representations, i.e. data sparsity and cold start problems. The resultant distribution discrepancy between displayed and non-displayed items would cause poor long-tail performance. To this end, we propose an entire space adaptation model (ESAM) to address this problem from the perspective of domain adaptation (DA). ESAM regards displayed and non-displayed items as source and target domains respectively. Specifically, we design the attribute correlation alignment that considers the correlation between high-level attributes of the item to achieve distribution alignment. Furthermore, we introduce two effective regularization strategies, i.e. \textit{center-wise clustering} and \textit{self-training} to improve DA process. Without requiring any auxiliary information and auxiliary domains, ESAM transfers the knowledge from displayed items to non-displayed items for alleviating the distribution inconsistency. Experiments on two public datasets and a large-scale industrial dataset collected from Taobao demonstrate that ESAM achieves state-of-the-art performance, especially in the long-tail space. Besides, we deploy ESAM to the Taobao search engine, leading to significant improvement on online performance. The code is available at \url{https://github.com/A-bone1/ESAM.git}Comment: Accept by SIGIR-202

A novel NH2-MIL-88B(Fe)-modified ceramic membrane for the integration of electro-Fenton and filtration processes: A case study on naproxen degradation

Process intensification based on innovative coupling between membrane microfiltration and catalytic oxidation technologies has become a promising strategy for water treatment. Here, a surface-nucleated metal-organic framework (MOF) was grown in situ to obtain an NH2-MIL-88B(Fe)-functionalized catalytic ceramic membrane (NH2-MIL-88B(Fe)@CM), whose ability to remove naproxen from water matrices via the so-called electro-Fenton with catalytic ceramic membrane (EFCCM) process was systematically investigated. The physicochemical properties of the NH2-MIL-88B(Fe) and membranes were characterized by XRD, FTIR, XPS and SEM, revealing the formation of a well-defined NH2-MIL-88B(Fe) layer on the porous CM with a thickness of around 13.5 μm, which provides a large amount of active sites for H2O2 activation to generate hydroxyl radical (¿OH). The EFCCM treatment of naproxen in Na2SO4 solution under recirculation batch mode yielded almost complete drug removal in 90 min at 50 mA, whereas the stability and catalyst loss tests gave evidence of good membrane reusability for 5 cycles. The treatment of naproxen in urban wastewater confronted severe membrane fouling, but this was effectively mitigated by combining hot water backwash with EF self-cleaning. Finally, the naproxen degradation routes involving 7 byproducts are proposed. This is an effective approach to the fabrication of CCM, which could be used for wastewater treatment in continuous mode as suggested by the minimal NPX content at the membrane outlet

Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding

Scientific literature understanding tasks have gained significant attention due to their potential to accelerate scientific discovery. Pre-trained language models (LMs) have shown effectiveness in these tasks, especially when tuned via contrastive learning. However, jointly utilizing pre-training data across multiple heterogeneous tasks (e.g., extreme multi-label paper classification, citation prediction, and literature search) remains largely unexplored. To bridge this gap, we propose a multi-task contrastive learning framework, SciMult, with a focus on facilitating common knowledge sharing across different scientific literature understanding tasks while preventing task-specific skills from interfering with each other. To be specific, we explore two techniques -- task-aware specialization and instruction tuning. The former adopts a Mixture-of-Experts Transformer architecture with task-aware sub-layers; the latter prepends task-specific instructions to the input text so as to produce task-aware outputs. Extensive experiments on a comprehensive collection of benchmark datasets verify the effectiveness of our task-aware specialization strategy, where we outperform state-of-the-art scientific pre-trained LMs. Code, datasets, and pre-trained models can be found at https://scimult.github.io/.Comment: 17 pages; Accepted to Findings of EMNLP 2023 (Project Page: https://scimult.github.io/