The Gravity Spy project aims to uncover the origins of glitches, transient bursts of noise that hamper analysis of gravitational-wave data. By using both the work of citizen-science volunteers and machine-learning algorithms, the Gravity Spy project enables reliable classification of glitches. Citizen science and machine learning are intrinsically coupled within the Gravity Spy framework, with machine-learning classifications providing a rapid first-pass classification of the dataset and enabling tiered volunteer training, and volunteer-based classifications verifying the machine classifications, bolstering the machine-learning training set and identifying new morphological classes of glitches. These classifications are now routinely used in studies characterizing the performance of the LIGO gravitational-wave detectors. Providing the volunteers with a training framework that teaches them to classify a wide range of glitches, as well as additional tools to aid their investigations of interesting glitches, empowers them to make discoveries of new classes of glitches. This demonstrates that, when giving suitable support, volunteers can go beyond simple classification tasks to identify new features in data at a level comparable to domain experts. The Gravity Spy project is now providing volunteers with more complicated data that includes auxiliary monitors of the detector to identify the root cause of glitches.

The intersection of LLMs (Large Language Models) and UAV (Unoccupied Aerial Vehicles) technology represents a promising field of research with the potential to enhance UAV capabilities significantly. This study explores the application of LLMs in UAV control, focusing on the opportunities for integrating advanced natural language processing into autonomous aerial systems. By enabling UAVs to interpret and respond to natural language commands, LLMs simplify the UAV control and usage, making them accessible to a broader user base and facilitating more intuitive human-machine interactions. The paper discusses several key areas where LLMs can impact UAV technology, including autonomous decision-making, dynamic mission planning, enhanced situational awareness, and improved safety protocols. Through a comprehensive review of current developments and potential future directions, this study aims to highlight how LLMs can transform UAV operations, making them more adaptable, responsive, and efficient in complex environments. A template development framework for integrating LLMs in UAV control is also described. Proof of Concept results that integrate existing LLM models and popular robotic simulation platforms are demonstrated. The findings suggest that while there are substantial technical and ethical challenges to address, integrating LLMs into UAV control holds promising implications for advancing autonomous aerial systems.

Traffic light and sign recognition are key for Autonomous Vehicles (AVs) because perception mistakes directly influence navigation and safety. In addition to digital adversarial attacks, models are vulnerable to existing perturbations (glare, rain, dirt, or graffiti), which could lead to dangerous misclassifications. The current work lacks consideration of temporal continuity, multistatic field-of-view (FoV) sensing, and robustness to both digital and natural degradation. This study proposes a dual FoV, sequence-preserving robustness framework for traffic lights and signs in the USA based on a multi-source dataset built on aiMotive, Udacity, Waymo, and self-recorded videos from the region of Texas. Mid and long-term sequences of RGB images are temporally aligned for four operational design domains (ODDs): highway, night, rainy, and urban. Over a series of experiments on a real-life application of anomaly detection, this study outlines a unified three-layer defense stack framework that incorporates feature squeezing, defensive distillation, and entropy-based anomaly detection, as well as sequence-wise temporal voting for further enhancement. The evaluation measures included accuracy, attack success rate (ASR), risk-weighted misclassification severity, and confidence stability. Physical transferability was confirmed using probes for recapture. The results showed that the Unified Defense Stack achieved 79.8mAP and reduced the ASR to 18.2%, which is superior to YOLOv8, YOLOv9, and BEVFormer, while reducing the high-risk misclassification to 32%.

Exclusive photoproduction of $K^+ \Lambda$ final states off a proton target has been an important component in the search for missing nucleon resonances and our understanding of the production of final states containing strange quarks. Polarization observables have been instrumental in this effort. The current work is an extension of previously published CLAS results on the beam-recoil transferred polarization observables $C_x$ and $C_z$. We extend the kinematic range up to invariant mass $W=3.33$~GeV from the previous limit of $W=2.5$~GeV with significantly improved statistical precision in the region of overlap. These data will provide for tighter constraints on the reaction models used to unravel the spectrum of nucleon resonances and their properties by not only improving the statistical precision of the data within the resonance region, but also by constraining $t$-channel processes that dominate at higher $W$ but extend into the resonance region.

Extracting accurate results from neutrino oscillation and cross section experiments requires accurate simulation of the neutrino-nucleus interaction. The rescattering of outgoing hadrons (final state interactions) by the rest of the nucleus is an important component of these interactions. We present a new measurement of proton transparency (defined as the fraction of outgoing protons that emerge without significant rescattering) using electron-nucleus scattering data recorded by the CLAS detector at Jefferson Laboratory on helium, carbon, and iron targets. This analysis by the Electrons for Neutrinos ($e4\nu$) collaboration uses a new data-driven method to extract the transparency. It defines transparency as the ratio of electron-scattering events with a detected proton to quasi-elastic electron-scattering events where a proton should have been knocked out. Our results are consistent with previous measurements that determined the transparency from the ratio of measured events to theoretically predicted events. We find that the GENIE event generator, which is widely used by oscillation experiments to simulate neutrino-nucleus interactions, needs to better describe both the nuclear ground state and proton rescattering in order to reproduce our measured transparency ratios, especially at lower proton momenta.

Measurements of SIDIS multiplicities for $\pi^+$ and $\pi^-$ from proton and deuteron targets are reported on a grid of hadron kinematic variables $z$, $P_{T}$, and $\phi^{*}$ for leptonic kinematic variables in the range $0.3

Charged lepton flavor violation is forbidden in the Standard Model but possible in several new physics scenarios. In many of these models, the radiative decays $\tau^{\pm}\rightarrow\ell^{\pm}\gamma$ ($\ell=e,\mu$) are predicted to have a sizeable probability, making them particularly interesting channels to search at various experiments. An updated search via $\tau^{\pm}\rightarrow\ell^{\pm}\gamma$ using full data of the Belle experiment, corresponding to an integrated luminosity of 988 fb$^{-1}$, is reported for charged lepton flavor violation. No significant excess over background predictions from the Standard Model is observed, and the upper limits on the branching fractions, $\mathcal{B}(\tau^{\pm}\rightarrow \mu^{\pm}\gamma)$ $\leq$ $4.2\times10^{-8}$ and $\mathcal{B}(\tau^{\pm}\rightarrow e^{\pm}\gamma)$ $\leq$ $5.6\times10^{-8}$, are set at 90\% confidence level.

This paper presents a simulation workflow for generating synthetic LiDAR datasets to support autonomous vehicle perception, robotics research, and sensor security analysis. Leveraging the CoppeliaSim simulation environment and its Python API, we integrate time-of-flight LiDAR, image sensors, and two dimensional scanners onto a simulated vehicle platform operating within an urban scenario. The workflow automates data capture, storage, and annotation across multiple formats (PCD, PLY, CSV), producing synchronized multimodal datasets with ground truth pose information. We validate the pipeline by generating large-scale point clouds and corresponding RGB and depth imagery. The study examines potential security vulnerabilities in LiDAR data, such as adversarial point injection and spoofing attacks, and demonstrates how synthetic datasets can facilitate the evaluation of defense strategies. Finally, limitations related to environmental realism, sensor noise modeling, and computational scalability are discussed, and future research directions, such as incorporating weather effects, real-world terrain models, and advanced scanner configurations, are proposed. The workflow provides a versatile, reproducible framework for generating high-fidelity synthetic LiDAR datasets to advance perception research and strengthen sensor security in autonomous systems. Documentation and examples accompany this framework; samples of animated cloud returns and image sensor data can be found at this Link.

Precise proton and neutron form factor measurements at Jefferson Lab, using spin observables, have recently made a significant contribution to the unraveling of the internal structure of the nucleon. Accurate experimental measurements of the nucleon form factors are a test-bed for understanding how the nucleon's static properties and dynamical behavior emerge from QCD, the theory of the strong interactions between quarks. There has been enormous theoretical progress, since the publication of the Jefferson Lab proton form factor ratio data, aiming at reevaluating the picture of the nucleon. We will review the experimental and theoretical developments in this field and discuss the outlook for the future.

The Spin Asymmetries of the Nucleon Experiment (SANE) measured two double spin asymmetries using a polarized proton target and polarized electron beam at two beam energies, 4.7 GeV and 5.9 GeV. A large-acceptance open-configuration detector package identified scattered electrons at 40$^{\circ}$ and covered a wide range in Bjorken $x$ (0.3 < x < 0.8). Proportional to an average color Lorentz force, the twist-3 matrix element, $\tilde{d}_2^p$, was extracted from the measured asymmetries at $Q^2$ values ranging from 2.0 to 6.0 GeV$^2$. The data display the opposite sign compared to most quark models, including the lattice QCD result, and an apparently unexpected scale dependence. Furthermore when combined with the neutron data in the same $Q^2$ range the results suggest a flavor independent average color Lorentz force.

A first measurement of the longitudinal beam spin asymmetry ALU in the semi-inclusive electroproduction of pairs of charged pions is reported. ALU is a higher-twist observable and offers the cleanest access to the nucleon twist-3 parton distribution function e(x). Data have been collected in the Hall-B at Jefferson Lab by impinging a 5.498 GeV electron beam on a liquid-hydrogen target, and reconstructing the scattered electron and the pion pair with the CLAS detector. One-dimensional projections of the sin(phiR) moments of ALU are extracted for the kinematic variables of interest in the valence quark region. The understanding of di-hadron production is essential for the interpretation of observables in single hadron production in semi-inclusive DIS, and pioneering measurements of single spin asymmetries in di-hadron production open a new avenue in studies of QCD dynamics.

This White Paper is exploring the potential of intense secondary muon, neutrino, and (hypothetical) light dark matter beams produced in interactions of high-intensity electron beams with beam dumps. Light dark matter searches with the approved Beam Dump eXperiment (BDX) are driving the realization of a new underground vault at Jefferson Lab that could be extended to a Beamdump Facility with minimal additional installations. The paper summarizes contributions and discussions from the International Workshop on Secondary Beams at Jefferson Lab (BDX & Beyond). Several possible muon physics applications and neutrino detector technologies for Jefferson Lab are highlighted. The potential of a secondary neutron beam will be addressed in a future edition.

Inclusive electron scattering cross sections off a hydrogen target at a beam energy of 10.6 GeV have been measured with data collected from the CLAS12 spectrometer at Jefferson Laboratory. These first absolute cross sections from CLAS12 cover a wide kinematic area in invariant mass W of the final state hadrons from the pion threshold up to 2.5 GeV for each bin in virtual photon four-momentum transfer squared $Q^2$ from 2.55 to 10.4~GeV$^2$ owing to the large scattering angle acceptance of the CLAS12 detector. Comparison of the cross sections with the resonant contributions computed from the CLAS results on the nucleon resonance electroexcitation amplitudes has demonstrated a promising opportunity to extend the information on their $Q^2$ evolution up to 10 GeV$^2$. Together these results from CLAS and CLAS12 offer good prospects for probing the nucleon parton distributions at large fractional parton momenta $x$ for $W$ < 2.5 GeV, while covering the range of distances where the transition from the strongly coupled to the perturbative regimes is expected.

Numerical relativity (NR) simulations of binary black hole (BBH) systems provide the most accurate gravitational wave predictions, but at a high computational cost -- especially when the black holes have nearly extremal spins (i.e. spins near the theoretical upper limit) or very unequal masses. Recently, the technique of Reduced Order Modeling (ROM) has enabled the construction of surrogate models trained on an existing set of NR waveforms. Surrogate models enable the rapid computation of the gravitational waves emitted by BBHs. Typically these models are used for interpolation to compute gravitational waveforms for BBHs with mass ratios and spins within the bounds of the training set. Because simulations with nearly extremal spins are so technically challenging, surrogate models almost always rely on training sets with only moderate spins. In this paper, we explore how well surrogate models can extrapolate to nearly extremal spins when the training set only includes moderate spins. For simplicity, we focus on one-dimensional surrogate models trained on NR simulations of BBHs with equal masses and equal, aligned spins. We assess the performance of the surrogate models at higher spin magnitudes by calculating the mismatches between extrapolated surrogate model waveforms and NR waveforms, by calculating the differences between extrapolated and NR measurements of the remnant black-hole mass, and by testing how the surrogate model improves as the training set extends to higher spins. We find that while extrapolation in this one-dimensional case is viable for current detector sensitivities, surrogate models for next-generation detectors should use training sets that extend to nearly extremal spins.

Modern vehicles are equipped with numerous in-vehicle components that interact with the external environment through remote communications and services, such as Bluetooth and vehicle-to-infrastructure communication. These components form a network, exchanging information to ensure the proper functioning of the vehicle. However, the presence of false or fabricated information can disrupt the vehicle's performance. Given that these components are interconnected, erroneous data can propagate throughout the network, potentially affecting other components and leading to catastrophic consequences. To address this issue, we propose TrustConnect, a framework designed to assess the trustworthiness of a vehicle's in-vehicle network by evaluating the trust levels of individual components under various network configurations. The proposed framework leverages the interdependency of all the vehicle's components, along with the correlation of their values and their vulnerability to remote injection based on the outside exposure of each component, to determine the reliability of the in-vehicle network. The effectiveness of the proposed framework has been validated through programming simulations conducted across various scenarios using a random distribution of an in-vehicle network graph generated with the Networkx package in Python.

We report measurements of the beam spin asymmetry in Deeply Virtual Compton Scattering (DVCS) at an electron beam energy of 4.8 GeV using the CLAS detector at the Thomas Jefferson National Accelerator Facility. The DVCS beam spin asymmetry has been measured in a wide range of kinematics, 1(GeV/c)$^2$ $

Strange matter is believed to exist in the cores of neutron stars based on simple kinematics. If this is true, then hyperon-nucleon interactions will play a significant part in the neutron star equation of state (EOS). Yet, compared to other elastic scattering processes, there is very little data on $\Lambda$-$N$ scattering. This experiment utilized the CLAS detector to study the $\Lambda p \rightarrow \Lambda p$ elastic scattering cross section in the incident $\Lambda$ momentum range 0.9-2.0 GeV/c. This is the first data on this reaction in several decades. The new cross sections have significantly better accuracy and precision than the existing world data, and the techniques developed here can also be used in future experiments.

We present a new extraction of unpolarized Dihadron Fragmentation Functions, which describe the probability density for an unpolarized parton to fragment into a $\pi^+ \pi^-$ pair. Our analysis is based on data from the BELLE collaboration. We improve on previous determinations in several key aspects: we employ state-of-the-art perturbative QCD calculations up to next-to-next-to-leading order (NNLO); we limit the use of Monte Carlo event generators to estimating the relative contributions of different flavors, a necessary input due to the limited flavor sensitivity of the available data; and, in addition to a traditional fit based on a physics-informed functional form, we explore a Neural Network parametrization. This latter approach paves the way for more robust and flexible determinations of Dihadron Fragmentation Functions using machine learning techniques.

Hyperon recoil polarization measurements for the exclusive electroproduction of $K^+\Lambda$ and $K^+\Sigma^0$ final states from an unpolarized proton target have been carried out using the CLAS12 spectrometer at Jefferson Laboratory. The measurements at beam energies of 6.535~GeV and 7.546~GeV span the range of four-momentum transfer $Q^2$ from 0.3 to 4.5~GeV$^2$ and invariant mass $W$ from 1.6 to 2.4~GeV, while covering the full center-of-mass angular range of the $K^+$. These new $\Lambda$ polarization observables extend the existing data in a similar kinematic range but from a significantly larger dataset. However, they represent the first electroproduction measurements of this observable for the $\Sigma^0$. These data will allow for better exploration of the reaction mechanism in strangeness production, for further understanding of the spectrum and structure of excited nucleon states that couple to $KY$, and for improved insight into the strong interaction in the non-perturbative domain.

Teleseismic, or distant, earthquakes regularly disrupt the operation of ground--based gravitational wave detectors such as Advanced LIGO. Here, we present \emph{EQ mode}, a new global control scheme, consisting of an automated sequence of optimized control filters that reduces and coordinates the motion of the seismic isolation platforms during earthquakes. This, in turn, suppresses the differential motion of the interferometer arms with respect to one another, resulting in a reduction of DARM signal at frequencies below 100\,mHz. Our method greatly improved the interferometers' capability to remain operational during earthquakes, with ground velocities up to 3.9\,$\mu \mbox{m/s}$ rms in the beam direction, setting a new record for both detectors. This sets a milestone in seismic controls of the Advanced LIGO detectors' ability to manage high ground motion induced by earthquakes, opening a path for further robust operation in other extreme environmental conditions.