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Abstract Recent work indicates that low-dimensional dynamics of neural and behavioral data are often preserved across days and subjects. However, extracting these preserved dynamics remains challenging: high-dimensional neural population activity and the recorded neuron populations vary across recording sessions. While existing modeling tools can improve alignment between neural and behavioral data, they often operate on a per-subject basis or discretize behavior into categories, disrupting its natural continuity and failing to capture the underlying dynamics. We introduceContrastiveAlignedNeuralDYnamics (CANDY), an end-to-end framework that aligns neural and behavioral data using rank-based contrastive learning, adapted for continuous behavioral variables, to project neural activity from different sessions onto a shared low-dimensional embedding space. CANDY fits a shared linear dynamical system to the aligned embeddings, enabling an interpretable model of the conserved temporal structure in the latent space. We validate CANDY on synthetic and real-world datasets spanning multiple species, behaviors, and recording modalities. Our results show that CANDY is able to learn aligned latent embeddings and preserved dynamics across neural recording sessions and subjects, and it achieves improved cross-session behavior decoding performance. We further show that the latent linear dynamical system generalizes to new sessions and subjects, achieving comparable or even superior behavior decoding performance to models trained from scratch. These advances enable robust cross-session behavioral decoding and offer a path towards identifying shared neural dynamics that underlie behavior across individuals and recording conditions. The code and two-photon imaging data of striatal neural activity that we acquired here are available athttps://github.com/schnitzer-lab/CANDY-public.git. 

This paper details a poster presented in the National Science Foundation (NSF) Grantees Poster Session for the 2022 ASEE Annual Conference. The study, aptly titled, aims to examine the ‘Long-Term Effect of Involvement in Humanitarian Engineering Projects on Student Professional Formation and Views of Diversity, Equity, and Inclusion (DEI).’ As part of the larger study, this poster details the results from alumni (n=19) of the Lipscomb University engineering program collected through an open-ended questionnaire. The research team performed an inductive coding analysis of the qualitative data to understand the connections between humanitarian engineering projects, professional formation, and views of DEI. Quantitative results as well as data from other participant groups, including current students and non-alumni engineering professionals, will be presented elsewhere. Emergent codes showed that participants found both outward and inward value in serving others. Outward value reflected a better quality of life for the person benefiting from service while inward value provided personal satisfaction, learning, or growth for the participant. This inward value was also evident with respect to views of DEI where participants mentioned learning or growing from past events. Two participants directly mentioned a connection between their experiences with humanitarian engineering projects and their views of DEI. Additionally, the codes connected to existing literature in engineering education as well as theories like empathy, identity development, and emotional intelligence. These results are promising for this study and will be expanded upon through interviews where these connections will be examined at a deeper level. 

Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from video data. Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this tool by extracting interpretable behavioral features from videos of three different head-fixed mouse preparations, as well as a freely moving mouse in an open field arena, and show how these interpretable features can facilitate downstream behavioral and neural analyses. We also show how the behavioral features produced by our model improve the precision and interpretation of these downstream analyses compared to using the outputs of either fully supervised or fully unsupervised methods alone. 

A<sc>bstract</sc> Thepp→W^±(→μ^±ν_μ)Xcross-sections are measured at a proton-proton centre-of-mass energy$$ \sqrt{s}=5.02 $$ $\sqrt{s}=5.02$TeV using a dataset corresponding to an integrated luminosity of 100 pb⁻¹recorded by the LHCb experiment. Considering muons in the pseudorapidity range 2.2< η <4.4, the cross-sections are measured differentially in twelve intervals of muon transverse momentum between 28< p_T<52 GeV. Integrated overp_T, the measured cross-sections are$$ {\displaystyle \begin{array}{c}{\sigma}_{W^{+}\to {\mu}^{+}{\nu}_{\mu }}=300.9\pm 2.4\pm 3.8\pm 6.0\ \textrm{pb},\\ {}{\sigma}_{W^{-}\to {\mu}^{-}{\overline{\nu}}_{\mu }}=236.9\pm 2.1\pm 2.7\pm 4.7\ \textrm{pb},\end{array}} $$ $\begin{array}{c}{\sigma }_{W^{+}\to {\mu }^{+}{\nu }_{\mu }}=300.9\pm 2.4\pm 3.8\pm 6.0\mathrm{pb},\\ {\sigma }_{W^{-}\to {\mu }^{-}{\overline{\nu }}_{\mu }}=236.9\pm 2.1\pm 2.7\pm 4.7\mathrm{pb},\end{array}$ where the first uncertainties are statistical, the second are systematic, and the third are associated with the luminosity calibration. These integrated results are consistent with theoretical predictions. This analysis introduces a new method to determine theW-boson mass using the measured differential cross-sections corrected for detector effects. The measurement is performed on this statistically limited dataset as a proof of principle and yields$$ {m}_W=80369\pm 130\pm 33\ \textrm{MeV}, $$ $m_W=80369\pm 130\pm 33\mathrm{MeV},$ where the first uncertainty is experimental and the second is theoretical. 

The first dedicated $Z$-boson mass measurement at the LHC with $Z\to {\mu }^{+}{\mu }^{-}$decays is reported. The dataset uses proton-proton collisions at a center-of-mass energy of 13 TeV, recorded in 2016 by the LHCb experiment, and corresponds to an integrated luminosity of $1.7{\mathrm{fb}}^{-1}$. A template fit to the ${\mu }^{+}{\mu }^{-}$mass distribution yields the following result for the $Z$-boson mass: $m_Z=91,185.7\pm 8.3\pm 3.9\mathrm{MeV}$, where the first uncertainty is statistical and the second systematic. This result is consistent with previous measurements and predictions from global electroweak fits. 

The substructure of jets in quantum chromodynamics (QCD) has garnered significant attention with the advent of infrared- and collinear-safe clustering algorithms and observables. A key question emerging from these studies is how in-jet emissions at soft and hard energy scales, across collinear and wide angles relative to the emitter, differ with the mass of the emitting parton. The Lund jet plane (LJP) is a perturbatively well-defined substructure observable that maps the radiation pattern of jets onto a plane, visually distinguishing emissions with different kinematic properties. Comparing LJP for jets containing hadrons of low versus high mass enables the testing of QCD splitting functions from first-principles calculations across both soft and hard regimes and at different radiation angles. This article presents the first measurement of the LJP for light-quark-enriched and beauty-initiated jets at a center-of-mass energy of 13 TeV at LHCb. This marks the first direct observation of the dead-cone effect in beauty-quark jets, measured in the collinear region of the LJP.