Scott Sommerfeldt Elected ASA President

Crowds at collegiate basketball games react acoustically to events on the court in many ways, including applauding, chanting, cheering, and making distracting noises. Acoustic features can be extracted from recordings of crowds at basketball games to train machine learning models to classify crowd reactions. Such models may help identify crowd mood, which could help players secure fair contracts, venues refine fan experience, and safety personnel improve emergency response services or to minimize conflict in policing. By exposing the key features in these models, feature selection highlights physical insights about crowd noise, reduces computational costs, and often improves model performance. Feature selection is performed using random forests and least absolute shrinkage and selection operator logistic regression to identify the most useful acoustic features for identifying and classifying crowd reactions. The importance of including short-term feature temporal histories in the feature vector is also evaluated. Features related to specific 1/3-octave band shapes, sound level, and tonality are highly relevant for classifying crowd reactions. Additionally, the inclusion of feature temporal histories can increase classifier accuracies by up to 12%. Interestingly, some features are better predictors of future crowd reactions than current reactions. Reduced feature sets are human-interpretable on a case-by-case basis for the crowd reactions they predict.

Particulate contamination requires dust mitigation techniques to provide low-scatter surfaces on sensitive instrumentation in space. We have shown that poly(olefin sulfone)s photodegrade under spacelike conditions: in vacuum and with UV light exposure. We now demonstrate that photodegradable polymers can reduce dust accumulation on optical surfaces for space applications. This investigation shows that the dissociative degradation of poly(olefin sulfone)s significantly decreased the number of dust particles on a dust-coated surface. These results suggest a powerful way to mitigate the collection of extraterrestrial dust on optical surfaces in space, enabling passive removal of particulate contamination without any direct human intervention.

We present a combined magnetometry, muon spin-relaxation (𝜇⁢SR), and neutron-scattering study of the insulating spin glass Zn_0.5Mn_0.5Te, for which magnetic Mn²⁺ and nonmagnetic Zn2+ ions are randomly distributed on a face-centered cubic lattice. The magnetometry and 𝜇SR results confirm a spin freezing transition around 𝑇𝑓≈23 K, with the spin-fluctuation rate decreasing gradually and somewhat inhomogeneously through the sample volume as the temperature decreases toward 𝑇𝑓. Characteristic spin-correlation times well above 𝑇𝑓 are on the order of 10−10 s, much slower than typically observed in canonical spin glasses but in line with expectations for a cluster spin glass. Using magnetic pair distribution function (mPDF) analysis and reverse Monte Carlo (RMC) modeling of the magnetic diffuse neutron-scattering data, we show that the spin-glass ground state consists of clusters of spins exhibiting short-range-ordered type-III antiferromagnetic correlations with a locally ordered moment of 3.1(1)𝜇_B between nearest-neighbor spins. The type-III correlations decay exponentially as a function of spin separation distance with a correlation length of approximately 5 Å. The diffuse magnetic scattering and corresponding mPDF show no significant changes across 𝑇𝑓, indicating that the dynamically fluctuating short-range spin correlations in the paramagnetic state retain the same basic type-III configuration that characterizes the spin-glass state; the only change apparent from the neutron-scattering data is a gradual reduction of the correlation length and locally ordered moment with increasing temperature. Taken together, these results paint a unique and detailed picture of the local magnetic structure and dynamics in Zn_0.5Mn_0.5Te and provide strong evidence that this material is best described as a cluster spin glass. In addition, this work showcases a statistical method for extracting diffuse scattering signals from neutron powder diffraction data, which we developed to facilitate the mPDF and RMC analysis of the neutron data. This method has the potential to be broadly useful for neutron powder diffraction experiments on a variety of materials with short-range atomic or magnetic order.

Passive radiators are notoriously difficult to characterize because one cannot effectively assess their mechanical parameters with loudspeaker electrical impedance techniques and no motors. This paper discusses the details of passive radiator and dynamic loudspeaker driver parameter measurements through practical experiments conducted with a plane wave tube, the two-microphone transfer function technique, and the two-load method to remove the need for an ideal anechoic termination. A previous theoretical paper demonstrated how normal-incidence transmission losses through these devices in an anechoically terminated tube could yield their mechanical and electrical parameters [Leishman and Anderson, J. Acoust. Soc. Am. 134(1), 223–236 (2013)]. The mechanical parameters follow from an open-circuit transmission loss condition, whereas a driver's electrical parameters follow from an additional closed-circuit condition. This paper presents several experimental results and compares extracted parameters to those derived from electrical impedance measurements and destructive methods. In addition to other parameters, the masses of diaphragm assemblies show favorable agreement. The presented techniques effectively assess passive radiator parameters without employing active driver configurations and then removing their motors, which changes the measured properties. PACS numbers: 43.38.Ja, 43.20.Ye, 43.20.Mv, 43.55.Rg

Protein function emerges from dynamic conformational changes, yet structure prediction methods provide only static snapshots. While AlphaFold3 (AF3) predicts protein structures, the potential for extracting dynamic information from its ensemble predictions has remained underexplored. Here, we demonstrate that AF3 structural ensembles contain substantial dynamic information that correlates remarkably well with molecular dynamics simulations (MD). We developed ChronoSort, a novel algorithm that organizes static structure predictions into temporally coherent trajectories by minimizing structural differences between neighboring frames. Through systematic analysis of four diverse protein targets, we show that root-mean-square fluctuations derived from AF3 ensembles can correlate strongly with those from MD (r = 0.53 to 0.84). Principal component analysis reveals that AF3 predictions capture the same collective motion patterns observed in molecular dynamics trajectories, with eigenvector similarities significantly exceeding random distributions. ChronoSort trajectories exhibit structural evolution profiles comparable to MD. These findings suggest that modern AI-based structure prediction tools encode conformational flexibility information that can be systematically extracted without expensive MD. We provide ChronoSort as open-source software to enable broad community adoption. This work offers a novel approach to extracting functional insights from structure prediction tools in minutes, with significant implications for synthetic biology, protein engineering, drug discovery, and structure–function studies.

We present ExoMiner++, an enhanced deep learning model that builds on the success of ExoMiner to improve transit signal classification in 2-minute TESS data. ExoMiner++ incorporates additional diagnostic inputs, including periodogram, flux trend, difference image, unfolded flux, and spacecraft attitude control data, all of which are crucial for effectively distinguishing transit signals from more challenging sources of false positives (FPs). To further enhance performance, we leverage multisource training by combining high-quality labeled data from the Kepler space telescope with TESS data. This approach mitigates the impact of TESS’s noisier and more ambiguous labels. ExoMiner++ achieves high accuracy across various classification and ranking metrics, significantly narrowing the search space for follow-up investigations to confirm new planets. To serve the exoplanet community, we introduce a new TESS catalog containing ExoMiner++ classifications and confidence scores for each transit signal. Among the 147,568 unlabeled TCEs, ExoMiner++ identifies 7330 as planet candidates (PCs), with the remainder classified as FPs. These 7330 PCs correspond to 1868 existing TESS Objects of Interest (TOIs), 69 Community TESS Objects of Interest (CTOIs), and 50 newly introduced CTOIs. 1797 out of the 2506 TOIs previously labeled as PCs in ExoFOP are classified as PCs by ExoMiner++. This reduction in plausible candidates, combined with the excellent ranking quality of ExoMiner++, allows the follow-up efforts to be focused on the most likely candidates, increasing the overall planet yield.