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Abstract Objective. Electrocortical and hemodynamic signals measured by electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) can be used to provide complementary information about neural activity underpinning auditory processing. However, the causal interactions between these signals remain underexplored, partly due to several factors, including the complexity of disentangling cortical and systemic physiological components captured in fNIRS modality. Recent developments in the ability to estimate the influence of non-cortical sources on fNIRS signals provide new opportunities to address these confounds. In this study, we investigated causal interactions between vascular-hemodynamic and electrocortical dynamics through simultaneous recording of fNIRS and EEG during auditory processing.Approach. We employed multimodal multivariate Granger causal analysis to investigate causal interactions between EEG and fNIRS signals. To explore the role of systemic physiology on the obtained causal interactions, a temporally-embedded canonical correlation analysis-general linear model-based preprocessing approach was applied to fNIRS signals to correct for potential systemic physiology confounds.Main results. Our results showed significantly stronger causal interactions originating from fNIRS in the right auditory cortex to frontocentral EEG in the 38–42 Hz frequency range during the auditory task relative to rest before correction. We additionally observed broad connectivity in the direction originating from fNIRS variables to EEG across frequency bands within participants throughout the dataset. These results suggest complex relationships between cortical vascular-hemodynamics, electrocortical oscillations, and systemic physiological components with roles in both task-related and spontaneous neural activity.Significance. This study highlights the complex causal interplay between electrical, cerebral hemodynamic, and systemic physiology components underpinning spontaneous electro-vascular dynamics associated with auditory processing. 

Multi-view learning is a rapidly evolving research area focused on developing diverse learning representations. In neural data analysis, this approach holds immense potential by capturing spatial, temporal, and frequency features. Despite its promise, multi-view application to functional near-infrared spectroscopy (fNIRS) has remained largely unexplored. This study addresses this gap by introducing fNIRSNET, a novel framework that generates and fuses multi-view spatio-temporal representations using convolutional neural networks. It investigates the combined informational strength of oxygenated (HbO2) and deoxygenated (HbR) hemoglobin signals, further extending these capabilities by integrating with electroencephalography (EEG) networks to achieve robust multimodal classification. Experiments involved classifying neural responses to auditory stimuli with nine healthy participants. fNIRS signals were decomposed into HbO2/HbR concentration changes, resulting in Parallel and Merged input types. We evaluated four input types across three data compositions: balanced, subject, and complete datasets. Our fNIRSNET's performance was compared with eight baseline classification models and merged it with four common EEG networks to assess the efficacy of combined features for multimodal classification. Compared to baselines, fNIRSNET using the Merged input type achieved the highest accuracy of 83.22%, 81.18%, and 91.58% for balanced, subject, and complete datasets, respectively. In the complete set, the approach effectively mitigated class imbalance issues, achieving sensitivity of 83.58% and specificity of 95.42%. Multimodal fusion of EEG networks and fNIRSNET outperformed single-modality performance with the highest accuracy of 87.15% on balanced data. Overall, this study introduces an innovative fusion approach for decoding fNIRS data and illustrates its integration with established EEG networks to enhance performance. 

Pion photoproduction in the $\vec{\gamma }\vec{p}\to {\pi }^{0}p$ reaction has been measured in the FROzen Spin Target (FROST) experiment at the Thomas Jefferson National Accelerator Facility. In this experiment, circularly polarized photons with energies up to 3.082 GeV impinged on a transversely polarized frozen-spin target. Final-state protons were detected in the Continuous Electron Beam Accelerator Facility (CEBAF) Large Acceptance Spectrometer. The polarization observables $T$ and $F$ have been extracted for $W$ from 1445 to 2525 MeV, of which the energy range is much broader, and the precision is better than the existing measurements in higher $W$ ranges. The data generally agree with predictions of present partial-wave analyses but also show marked differences for higher $W$ ranges. By incorporating the present data into the databases, the Scattering Analysis Interactive Data (SAID) fits have been improved with relatively small ${\chi }^2$ and significant changes in the parameters of the $\mathrm{\Delta }\left(1910\right)1/2^{+}$ and $N\left(2190\right)7/2^{-}$ have been found with the JüBo model. 

The polarization of $\mathrm{\Lambda }$ hyperons is preserved in the angular distribution of their decay products. This property allows one to study the spin structure of the $\mathrm{\Lambda }$ . In semi-inclusive deep inelastic scattering where a high energy lepton interacts with a nucleon target and one or more hadrons and the scattered lepton are detected in the final state, the probability for a struck quark to impart the polarization of the lepton to the $\mathrm{\Lambda }$ may be measured. In particular, in electron-proton scattering this quantity may be related to the longitudinal light quark polarization of the $\mathrm{\Lambda }$ . Currently, limited experimental data cannot discriminate between different models of $\mathrm{\Lambda }$ spin structure. This work reports on the measurement of the longitudinal spin transfer $D_{L{L}^{\prime }}^{\mathrm{\Lambda }}$ to the $\mathrm{\Lambda }$ using data taken by the CLAS12 spectrometer at Jefferson Lab with a 10.6 GeV longitudinally polarized electron beam and an unpolarized hydrogen target. This measurement is the most precise to date, and, in comparison with theory predictions, it offers valuable insight into the relative dominance of current and target fragmentation in $\mathrm{\Lambda }$ production. 

We present measurements of the total and differential cross sections for near-threshold $J/\psi$ photoproduction obtained with the CLAS12 detector at the Thomas Jefferson National Accelerator Facility. The results are based on data collected during the fall 2018 and spring 2019 running periods, using electron beams with energies of 10.6 and 10.2 GeV, respectively, scattered off a liquid-hydrogen target. Near-threshold $J/\psi$ photoproduction offers a unique sensitivity to the strong interaction in the nonperturbative regime of quantum chromodynamics (QCD). The energy dependence of the cross section constrains the underlying $J/\psi$ production mechanisms, including multigluon exchange and potential baryonic excitations. Additionally, the $t$ dependence of the differential cross section can be related to the transverse spatial distribution of gluons in the proton, providing critical input for theoretical descriptions of the gluonic structure of the proton. An interpretation of the results in terms of the gluon content of the proton is presented, providing new experimental constraints on QCD-inspired models of the proton structure and the role of gluonic degrees of freedom in hadronic mass generation. 

Given an input stream of size N , a -heavy hiter is an item that occurs at least N times in S. The problem of finding heavy-hitters is extensively studied in the database literature. We study a real-time heavy-hitters variant in which an element must be reported shortly after we see its T = N - th occurrence (and hence becomes a heavy hitter). We call this the Timely Event Detection (TED) Problem. The TED problem models the needs of many real-world monitoring systems, which demand accurate (i.e., no false negatives) and timely reporting of all events from large, high-speed streams, and with a low reporting threshold (high sensitivity). Like the classic heavy-hitters problem, solving the TED problem without false-positives requires large space ((N ) words). Thus in-RAM heavy-hitters algorithms typically sacrfice accuracy (i.e., allow false positives), sensitivity, or timeliness (i.e., use multiple passes). We show how to adapt heavy-hitters algorithms to exter- nal memory to solve the TED problem on large high-speed streams while guaranteeing accuracy, sensitivity, and timeli- ness. Our data structures are limited only by I/O-bandwidth (not latency) and support a tunable trade-off between report- ing delay and I/O overhead. With a small bounded reporting delay, our algorithms incur only a logarithmic I/O overhead. We implement and validate our data structures empirically using the Firehose streaming benchmark. Multi-threaded ver- sions of our structures can scale to process 11M observations per second before becoming CPU bound. In comparison, a naive adaptation of the standard heavy-hitters algorithm to external memory would be limited by the storage device’s random I/O throughput, i.e., approx 100K observations per second. 

Measurements of beam single-spin asymmetries in semi-inclusive deep-inelastic electron scattering (SIDIS) with positively charged kaons off protons have been performed with 10.6 and 10.2 GeV incident electron beams using the CLAS12 spectrometer at Jefferson Lab. We report an analysis of the electroproduction of positively charged kaons over a large kinematic range of fractional energy, Bjorken $x$ , transverse momentum, and photon virtualities $Q^2$ ranging from 1 ${\mathrm{GeV}}^2$ up to 6 ${\mathrm{GeV}}^2$ . This is the first published multidimensionally binned CLAS12 measurement of a kaon SIDIS single-spin asymmetry in the valence quark regime. The data provide constraints on the structure function ratio $F_{LU}^{sin\varphi }/F_{UU}$ , where $F_{LU}^{sin\varphi }$ is a quantity with a leading twist of twist-3 that can reveal novel aspects of the quark-gluon correlations within the nucleon. The impact of the data on understanding the underlying reaction mechanisms and their kinematic variation is explored using theoretical models for the different contributing twist-3 parton-distribution functions (PDFs) and fragmentation functions (FFs).