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In-beam $\gamma$-ray spectroscopy was used to study excited states of the neutron-deficient nucleus $^{37}$K populated in fast-beam inelastic-scattering and proton-removal reactions at high-momentum loss. New $\gamma$-ray transitions and $\gamma\gamma$ coincidence relationships were established using the $\gamma$-ray tracking array GRETINA. The extension of the level scheme up to the first $(13/2^-)$ state highlights the potential of this recently demonstrated population pathway for studies of isospin symmetry involving mirror-energy differences. The nature of the newly identified states is discussed in comparison to shell-model calculations with the FSU cross-shell effective interaction. The calculated occupation numbers of individual orbitals are shown to offer a consistent explanation of the measured mirror-energy differences between $^{37}$K and $^{37}$Ar., Comment: 8 pages, 6 figures

Passive acoustic monitoring (PAM) data is often weakly labelled, audited at the scale of detection presence or absence on timescales of minutes to hours. Moreover, this data exhibits great variability from one deployment to the next, due to differences in ambient noise and the signals across sources and geographies. This study proposes a two-step solution to leverage weakly annotated data for training Deep Learning (DL) detection models. Our case study involves binary classification of the presence/absence of sperm whale (\textit{Physeter macrocephalus}) click trains in 4-minute-long recordings from a dataset comprising diverse sources and deployment conditions to maximise generalisability. We tested methods for extracting acoustic features from lengthy audio segments and integrated Temporal Convolutional Networks (TCNs) trained on the extracted features for sequence classification. For feature extraction, we introduced a new approach using Variational AutoEncoders (VAEs) to extract information from both waveforms and spectrograms, which eliminates the necessity for manual threshold setting or time-consuming strong labelling. For classification, TCNs were trained separately on sequences of either VAE embeddings or handpicked acoustic features extracted from the waveform and spectrogram representations using classical methods, to compare the efficacy of the two approaches. The TCN demonstrated robust classification capabilities on a validation set, achieving accuracies exceeding 85\% when applied to 4-minute acoustic recordings. Notably, TCNs trained on handpicked acoustic features exhibited greater variability in performance across recordings from diverse deployment conditions, whereas those trained on VAEs showed a more consistent performance, highlighting the robust transferability of VAEs for feature extraction across different deployment conditions.

Climate change is negatively impacting the world's biodiversity. To build automated systems to monitor these negative biodiversity impacts, large-scale, volunteer-collected datasets like iNaturalist are built from community-identified, natural imagery. However, such volunteer-based data are opportunistic and lack a structured sampling strategy, resulting in geographic, temporal, observation quality, and socioeconomic, biases that stymie uptake of these models for downstream biodiversity monitoring tasks. Here we introduce DivShift North American West Coast (DivShift-NAWC), a curated dataset of almost 8 million iNaturalist plant images across the western coast of North America, for exploring the effects of these biases on deep learning model performance. We compare model performance across four known biases and observe that they indeed confound model performance. We suggest practical strategies for curating datasets to train deep learning models for monitoring climate change's impacts on the world's biodiversity., Comment: Accepted to NeurIPS 2024 Workshop on Tackling Climate Change with Machine Learning

Equipping graph neural networks with a convolution operation defined in terms of a cellular sheaf offers advantages for learning expressive representations of heterophilic graph data. The most flexible approach to constructing the sheaf is to learn it as part of the network as a function of the node features. However, this leaves the network potentially overly sensitive to the learned sheaf. As a counter-measure, we propose a variational approach to learning cellular sheaves within sheaf neural networks, yielding an architecture we refer to as a Bayesian sheaf neural network. As part of this work, we define a novel family of reparameterizable probability distributions on the rotation group $SO(n)$ using the Cayley transform. We evaluate the Bayesian sheaf neural network on several graph datasets, and show that our Bayesian sheaf models outperform deterministic sheaf models when training data is limited, and are less sensitive to the choice of hyperparameters., Comment: 19 pages, 2 figures

Quantum error correction (QEC) will be essential to achieve the accuracy needed for quantum computers to realise their full potential. The field has seen promising progress with demonstrations of early QEC and real-time decoded experiments. As quantum computers advance towards demonstrating a universal fault-tolerant logical gate set, implementing scalable and low-latency real-time decoding will be crucial to prevent the backlog problem, avoiding an exponential slowdown and maintaining a fast logical clock rate. Here, we demonstrate low-latency feedback with a scalable FPGA decoder integrated into the control system of a superconducting quantum processor. We perform an 8-qubit stability experiment with up to $25$ decoding rounds and a mean decoding time per round below $1$ ${\mu}s$, showing that we avoid the backlog problem even on superconducting hardware with the strictest speed requirements. We observe logical error suppression as the number of decoding rounds is increased. We also implement and time a fast-feedback experiment demonstrating a decoding response time of $9.6$ ${\mu}s$ for a total of $9$ measurement rounds. The decoder throughput and latency developed in this work, combined with continued device improvements, unlock the next generation of experiments that go beyond purely keeping logical qubits alive and into demonstrating building blocks of fault-tolerant computation, such as lattice surgery and magic state teleportation., Comment: 11 pages, 4 figures, Supplementary Information

Multimodal image-text contrastive learning has shown that joint representations can be learned across modalities. Here, we show how leveraging multiple views of image data with contrastive learning can improve downstream fine-grained classification performance for species recognition, even when one view is absent. We propose ContRastive Image-remote Sensing Pre-training (CRISP)$\unicode{x2014}$a new pre-training task for ground-level and aerial image representation learning of the natural world$\unicode{x2014}$and introduce Nature Multi-View (NMV), a dataset of natural world imagery including $>3$ million ground-level and aerial image pairs for over 6,000 plant taxa across the ecologically diverse state of California. The NMV dataset and accompanying material are available at hf.co/datasets/andyvhuynh/NatureMultiView., Comment: Accepted to ECCV 2024

The Beryllium Electron capture in Superconducting Tunnel junctions (BeEST) experiment searches for evidence of heavy neutrino mass eigenstates in the nuclear electron capture decay of $^7$Be by precisely measuring the recoil energy of the $^7$Li daughter. In Phase-III, the BeEST experiment has been scaled from a single superconducting tunnel junction (STJ) sensor to a 36-pixel array to increase sensitivity and mitigate gamma-induced backgrounds. Phase-III also uses a new continuous data acquisition system that greatly increases the flexibility for signal processing and data cleaning. We have developed procedures for signal processing and spectral fitting that are sufficiently robust to be automated for large data sets. This article presents the optimized procedures before unblinding the majority of the Phase-III data set to search for physics beyond the standard model.

In this article, we present our recent experiments on fission fragment rocket propulsion, and on an innovative new design for an alpha particle detection system that has been inspired by these rocketry results. Our test platform, which operates within high magnetic fields 3 T over a large cross$\unicode{x2013}$section (approximately 40 cm in diameter), has been used as a test platform to evaluate the containment and thrust within a future fission$\unicode{x2013}$fragment rocket engine (FFRE). This much more efficient nuclear rocket propulsion FFRE design was first proposed in the 1980s with the intent of greatly reducing transit times in long$\unicode{x2013}$duration space travel. Our objective is to enhance the operational efficiency of this nuclear rocket while gaining deeper insights into the behavior of fuel particles and of the fission$\unicode{x2013}$fragment ejecta within strong magnetic fields experimentally. Through a combination of simulations and experimental work, we established a method for the production and detection of alpha particles as a surrogate for fission fragments. To achieve this, we employed Americium$\unicode{x2013}$241 ($^{ 241}$Am) sources, which were situated within a cylindrical vacuum chamber positioned in a 3$\unicode{x2013}$T Siemens MRI superconducting magnet. By simulating, measuring, and analyzing the emitted alpha particle flux, we gained valuable information about the distribution and likelihood of escape of fission fragments in a future FFRE design. This approach could potentially achieve both high specific impulse and power density in advanced nuclear propulsion systems, such as the FFRE. More generally, this work provides a powerful new approach for analyzing ion flux and nuclear particle or nuclear reaction fragments from a wide variety of experimental designs., Comment: 16 pages, 6 figures, and one data table

We show that Iacob-Iyengar's answer to a question of Avromov-Foxby extends from Noetherian to coherent rings. In particular, a coherent ring R is regular if and only if the injective (resp. projective) dimension of each complex X of R-modules agrees with its graded-injective (resp. graded-projective) dimension. The same is shown for the analogous dimensions based on FP-injective R-modules, and on flat R-modules.

We prove that the symmetric function $\Delta'_{e_{k-1}}e_n$ appearing in the Delta Conjecture can be obtained from the symmetric function in the Rational Shuffle Theorem by applying a Schur skewing operator. This generalizes a formula by the first and third authors for the Delta Conjecture at $t=0$, and follows from work of Blasiak, Haiman, Morse, Pun, and Seelinger. Our main result is that we also provide a purely combinatorial proof of this skewing identity, giving a new proof of the Rise Delta Theorem from the Rational Shuffle Theorem.

The region of $N\approx Z\approx 40$ has long been associated with strongly deformed nuclear configurations. The presence of this strong deformation was recently confirmed through lifetime measurements in $N\approx Z$ Sr and Zr nuclei. Theoretically, however, these nuclei present a challenge due to the vast valence space required to incorporate all deformation driving interactions. Recent state-of-the-art predictions indicate a near axial prolate deformation for $N=Z$ and $N=Z+2$ nuclei between $N=Z=36$ and $N=Z=40$. In this work we investigate the shores of this island of deformation through a sub-barrier Coulomb excitation study of the $N=Z+4$ nucleus, \textsuperscript{80}Sr. Extracting a spectroscopic quadrupole moment of $Q_s(2^+_1) = 0.45^{+0.83}_{-0.88}$~eb, we find that \textsuperscript{80}Sr is inconsistent with significant axial prolate deformation. This indicates that the predicted region of strong prolate deformation around $N=Z=40$ is tightly constrained to the quartet of nuclei: \textsuperscript{76,78}Sr and \textsuperscript{78,80}Zr., Comment: Submitted to Physics Letters B