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This appendix volume supplements report NCEE 2025-005r, describing the effects of two experimental Pell Grant expansions: the first allowed income-eligible students with a bachelor's degree to obtain this form of federal financial aid for short-term occupational training programs and the second allowed income-eligible students to obtain this aid for very short-term programs lasting as little as eight weeks. The report updated earlier results from a rigorous evaluation of the experiments conducted by the U.S. Department of Education's (ED's) Institute of Education Sciences, adding new information about the experiments' effects on employment and earnings outcomes. This appendix volume similarly updates the evaluation's previous published appendix to detail the steps taken to assess these effects. The volume first describes the Pell Grant experiments as overseen by ED's Office of Federal Student Aid, including information about the schools offering the experimental Pell Grants. The next sections document how the study was conducted, including the research questions asked and the approach used to answer the research questions, additional details supporting the key findings of the study, and additional analyses to assess the sensitivity of the key findings.

Pell Grants are the cornerstone of federal financial aid for students with low income who are enrolled in postsecondary education. Currently, these grants are available only to those who seek an initial undergraduate degree or credential requiring at least a typical semester of instruction. Because these rules may restrict access to programs providing skills needed for new or better jobs, in 2011 the U.S. Department of Education (ED) began pilots of two experimental expansions to Pell Grant eligibility. The first experiment allowed income-eligible students with a bachelor's degree to obtain Pell Grants for short-term occupational training programs. The second experiment allowed income-eligible students to obtain Pell Grants for very short-term programs lasting as little as eight weeks. This report updates earlier results from a rigorous evaluation of the experiments conducted by ED's Institute of Education Sciences (IES), adding new information about the experiments' impacts on labor market success. This fuller picture could help Congress as it considers legislation to make Pell Grants for short-term occupational training permanent policy.

Mastery learning -- the process by which students must demonstrate proficiency with a single topic before moving on -- is well recognized as one of the best ways to learn, yet many teachers struggle or remain unsure about how to implement it into a classroom setting. This study leverages two field experiments to test the efficacy of a program designed to encourage greater mastery learning through technology and proactive continuous teacher support. Focusing on elementary and middle school mathematics, teachers receive weekly coaching in how to use Computer Assisted Learning (CAL) for students to follow a customized roadmap of incremental progress. Results indicate significant intent-to-treat effects on math performance of 0.12-0.22 standard deviations. Further analysis shows that these gains are concentrated among students in classrooms with at least an average of 35 minutes of practice per week. Teachers able to achieve high-dosage practice have a high degree of initial buy-in, a clear implementation strategy for when practice occurs, and a willingness to closely monitor progress and follow-up with struggling students. [The Wilson Sheehan Lab for Economic Opportunities (LEO) at Notre Dame provided additional support for this research.]

Novel materials drive progress across applications from energy storage to electronics. Automated characterization of material structures with machine learning methods offers a promising strategy for accelerating this key step in material design. In this work, we introduce an autoregressive language model that performs crystal structure prediction (CSP) from powder diffraction data. The presented model, deCIFer, generates crystal structures in the widely used Crystallographic Information File (CIF) format and can be conditioned on powder X-ray diffraction (PXRD) data. Unlike earlier works that primarily rely on high-level descriptors like composition, deCIFer performs CSP from diffraction data. We train deCIFer on nearly 2.3M unique crystal structures and validate on diverse sets of PXRD patterns for characterizing challenging inorganic crystal systems. Qualitative and quantitative assessments using the residual weighted profile and Wasserstein distance show that deCIFer produces structures that more accurately match the target diffraction data when conditioned, compared to the unconditioned case. Notably, deCIFer can achieve a 94% match rate on unseen data. deCIFer bridges experimental diffraction data with computational CSP, lending itself as a powerful tool for crystal structure characterization and accelerating materials discovery., Comment: 24 pages, 17 figures, 6 tables. v2: Figure 8 revision

Beta-delayed proton emission in the lower half of the sd-shell will involve deformed nuclei. We derive the normalized matrix element connecting emission of one particle from an initial rotational nuclear state to another final rotating state, and we extract selection rules involving the $K$ quantum number. The initial state is approximated as having a core identical to the final nuclear state. The formalism is then directly applicable to $\beta^+$-delayed proton decays of even-$Z$, odd-$N$ nuclei or $\beta^-$-delayed neutron decays of odd-$Z$, even $N$ nuclei. These beta-decay results are compared to the outcomes of possible transfer reactions. As an example the beta-delayed proton emission of $^{21}$Mg is considered, where new quantum numbers can be assigned to several states in $^{21}$Na., Comment: 12 pages, 4 figures

This paper reviews pioneering works in microphone array processing and multichannel speech enhancement, highlighting historical achievements, technological evolution, commercialization aspects, and key challenges. It provides valuable insights into the progression and future direction of these areas. The paper examines foundational developments in microphone array design and optimization, showcasing innovations that improved sound acquisition and enhanced speech intelligibility in noisy and reverberant environments. It then introduces recent advancements and cutting-edge research in the field, particularly the integration of deep learning techniques such as all-neural beamformers. The paper also explores critical applications, discussing their evolution and current state-of-the-art technologies that significantly impact user experience. Finally, the paper outlines future research directions, identifying challenges and potential solutions that could drive further innovation in these fields. By providing a comprehensive overview and forward-looking perspective, this paper aims to inspire ongoing research and contribute to the sustained growth and development of microphone arrays and multichannel speech enhancement., Comment: accepted by ICASSP 2025

Liquid films coating vertical cylinders can form annular liquid collars which translate downwards under gravity. We investigate the dynamics of a thin viscoplastic liquid film coating the interior or exterior of a vertical cylindrical tube, quantifying how the yield stress modifies both the Rayleigh-Plateau instability leading to collar formation and the translation of collars down the tube. We use thin-film theory to derive an evolution equation for the layer thickness, which we solve numerically to determine the nonlinear dynamics of an initially flat layer. A flat layer is unstable to small disturbances in the free-surface height when gravity is sufficiently strong to make the fluid yield. We use matched asymptotics to derive a model describing the quasi-steady translation of a slender liquid collar when the Bond number is small. The structure of the asymptotic solution for a viscoplastic collar shares some features with the Newtonian version, but there are several novel asymptotic regions that emerge at the two ends of the collar. The global force balance, which determines the collar's speed, is modified by a leading-order contribution from viscous drag in the collar when the liquid is viscoplastic. We use the asymptotic model to describe slow changes in collar volume when the film thicknesses ahead of, and behind, the collar are unequal. When the film thickness ahead of the collar is less than a critical value that we determine, viscoplastic collars adjust their volume and reach a steadily-translating state. This contrasts with the Newtonian problem, where the only state in which steady translation occurs is unstable to small changes in the film thickness., Comment: 50 pages, 9 figures

Photonic computing has emerged as a promising platform for accelerating computational tasks with high degrees of parallelism, such as image processing and neural network. We present meta-DFT (discrete Fourier transform), a single layer metasurface device, designed to perform optical complex-to-complex DFT with O(N) time complexity. One critical challenge in free-space analog optical computing is to control the measurement error. Our scheme addresses this issue by focusing light on spatially separated focal points and reconstructing the complex phase, which enable error correction. We systematically evaluate the device's performance using input vectors with random complex amplitudes and phases, to demonstrate its robust accuracy. Our findings pave the way towards advancement of metasurface-based computation, offering a robust framework that is readily extensible to an arbitrary complex-valued matrix-vector multiplication (MVM)., Comment: 17 pages, 4 figures

Observations of molecular lines are a key tool to determine the main physical properties of prestellar cores. However, not all the information is retained in the observational process or easily interpretable, especially when a larger number of physical properties and spectral features are involved. We present a methodology to link the information in the synthetic spectra with the actual information in the simulated models (i.e., their physical properties), in particular, to determine where the information resides in the spectra. We employ a 1D gravitational collapse model with advanced thermochemistry, from which we generate synthetic spectra. We then use neural network emulations and the SHapley Additive exPlanations (SHAP), a machine learning technique, to connect the models' properties to the specific spectral features. Thanks to interpretable machine learning, we find several correlations between synthetic lines and some of the key model parameters, such as the cosmic-ray ionization radial profile, the central density, or the abundance of various species, suggesting that most of the information is retained in the observational process. Our procedure can be generalized to similar scenarios to quantify the amount of information lost in the real observations. We also point out the limitations for future applicability., Comment: Accepted A&A

Prisoners of Nazi concentration camps created paintings as a means to express their daily life experiences and feelings. Several thousand such paintings exist, but a quantitative analysis of them has not been carried out. We created an extensive dataset of 1,939 Holocaust prisoner artworks, and we employed an object detection framework that found 19,377 objects within these artworks. To support the quantitative and qualitative analysis of the art collection and its objects, we have developed an intuitive and interactive dashboard to promote a deeper engagement with these visual testimonies. The dashboard features various visual interfaces, e.g., a word cloud showing the detected objects and a map of artwork origins, and options for filtering. We presented the interface to domain experts, whose feedback highlights the dashboard's intuitiveness and potential for both quantitative and qualitative analysis while also providing relevant suggestions for improvement. Our project demonstrates the benefit of digital methods such as machine learning and visual analytics for Holocaust remembrance and educational purposes., Comment: 7 pages, 6 figures, Prague Visual History and Digital Humanities Conference (PRAVIDCO) 2025

The four nucleons in $^8$Li outside the $\alpha$-particle ($\alpha=^4$He) can be divided into pairs of one neutron ($n$) and 3 nucleons in the triton ($t=^3$H), or 2 in the deuteron ($d=^2$H) and two neutrons in a dineutron ($^2n$). The corresponding three-body structures, $\alpha$+$t$+$n$ or $\alpha$+$d$+$^2n$, are suggested to describe the bulk part of the low-energy ($<10$~MeV) states of $^8$Li. Several breakup thresholds influence the structures and possible decays. We calculate the three-body structures of the various $J^{\pi}$ states, where different clustering appear, e.g. $^7$Li*+$n$, $^6$Li*$+^2n$, $^6$He*$+d$. The experimental $^8$Li spectrum can be reproduced with fine tuning by a three-body potential parameter. Three unobserved $0^+$ and an excited 2$^+$ states are found. All states appear as bound states or resonances. The lowest or highest energies have cluster structures, $\alpha$+$t$+$n$ or $\alpha$+$d$+$^2n$, respectively. We give calculated energy and width (if possible), geometry, and partial wave decomposition for all states., Comment: To be published in Physical Review C

The halo model offers a framework for investigating galaxy clustering, and for understanding the growth of galaxies and the distribution of galaxies of different types. Here, we use the halo model to study the small-scale clustering and halo occupation distribution (HOD) of the unWISE-Blue galaxy sample, an infrared-selected sample of $\sim$100 million galaxies across the entire extragalactic sky at $z\sim 0.5$ $-$ similar redshifts to the Baryon Oscillation Spectroscopic Survey (BOSS) CMASS sample. Although the photometric unWISE galaxies cannot be easily split in redshift, we use their cross-correlation with the BOSS CMASS sample to tomographically probe the HOD of the unWISE galaxies at $0.45 < z < 0.75$. To do so, we develop a new method for applying the halo model to cross-correlations between a photometric sample and a spectroscopic sample in narrow redshift bins, incorporating halo exclusion, post-Limber corrections, and redshift-space distortions. We reveal strong evolution in the CMASS HOD, and modest evolution in the unWISE-Blue HOD. For unWISE-Blue, we find that the average bias and mean halo mass drop from $b = 1.6$ and $\log_{10}(M_{\mathrm{h}}/M_{\odot}) \sim 13.4$ at $z \sim 0.5$ to $b = 1.4$ and $\log_{10}(M_{\mathrm{h}}/M_{\odot}) \sim 13.1$ at $z \sim 0.7$, and that the satellite fraction drops modestly from $\sim$20% to $\sim$10% in the same range. These results are useful for creating mock samples of the unWISE-Blue galaxies. Furthermore, the techniques developed to obtain these results are applicable to other tomographic cross-correlations between photometric samples and narrowly-binned spectroscopic samples, such as clustering redshifts., Comment: 19 pages, 10 figures, to be submitted to OJAp

The Keck Planet Imager and Characterizer (KPIC) is a series of upgrades for the Keck II Adaptive Optics (AO) system and the NIRSPEC spectrograph to enable diffraction limited, high resolution (R>30000) spectroscopy of exoplanets and low mass companions in the K and L bands. Phase I consisted of single mode fiber injection/extraction units (FIU/FEU) used in conjunction with a H band pyramid wavefront sensor. The use of single mode fibers provides a gain in stellar rejection, a substantial reduction in sky background, and an extremely stable line spread function in the spectrograph. Phase II, deployed and commissioned in 2022, brought a 1000 actuator deformable mirror, beam shaping optics, a vortex mask, and other upgrades to the FIU/FEU. An additional service mission in 2024 extended operations down to y band, delivered an atmospheric dispersion corrector, and provided access to two laser frequency combs. KPIC phase II brings higher planet throughput, lower stellar leakage and many new observing modes which extend its ability to characterize exoplanets at high spectral resolution, building on the success of phase I. In this paper we present a description of the final phase II version of KPIC, along with results of system level laboratory testing and characterization showing the instrument's phase II throughput, stability, repeatability, and other key performance metrics prior to delivery and during installation at Keck. We outlined the capabilities of the various observing modes enabled by the new modules as well as efforts to compensate for static aberrations and non common path errors at Keck, which were issues that plagued phase I. Finally, we show results from commissioning., Comment: 36 pages, 16 figures

Trajectory similarity is fundamental to many spatio-temporal data mining applications. Recent studies propose deep learning models to approximate conventional trajectory similarity measures, exploiting their fast inference time once trained. Although efficient inference has been reported, challenges remain in similarity approximation accuracy due to difficulties in trajectory granularity modeling and in exploiting similarity signals in the training data. To fill this gap, we propose TSMini, a highly effective trajectory similarity model with a sub-view modeling mechanism capable of learning multi-granularity trajectory patterns and a k nearest neighbor-based loss that guides TSMini to learn not only absolute similarity values between trajectories but also their relative similarity ranks. Together, these two innovations enable highly accurate trajectory similarity approximation. Experiments show that TSMini can outperform the state-of-the-art models by 22% in accuracy on average when learning trajectory similarity measures.

Plants respond to biotic and abiotic stresses through complex and dynamic mechanisms that integrate physical, chemical, and biological cues. Here, we present a multi-physics platform designed to systematically investigate these responses across scales. The platform combines a six-axis micromanipulator with interchangeable probes to deliver precise mechanical, electrostatic, optical, and chemical stimuli. Using this system, we explore calcium signaling in Arabidopsis thaliana, thigmonastic motion in Mimosa pudica, and chemical exchange via microinjection in Rosmarinus officinalis L. and Ocimum basilicum. Our findings highlight stimulus-specific and spatially dependent responses: mechanical and electrostatic stimuli elicit distinct calcium signaling patterns, while repeated electrostatic stimulation exhibited evidence of response fatigue. Thigmonastic responses in Mimosa pudica depend on the location of perturbation, highlighting the intricate bi-directional calcium signaling. Microinjection experiments successfully demonstrate targeted chemical perturbations in glandular trichomes, opening avenues for biochemical studies. This open-source platform provides a versatile tool for dissecting plant stress responses, bridging the gap between fundamental research and applied technologies in agriculture and bioengineering. By enabling precise, scalable, and reproducible studies of plant-environment interactions, this work offers new insights into the mechanisms underlying plant resilience and adaptability.

In the pursuit of identifying rare two-particle events within the GADGET II Time Projection Chamber (TPC), this paper presents a comprehensive approach for leveraging Convolutional Neural Networks (CNNs) and various data processing methods. To address the inherent complexities of 3D TPC track reconstructions, the data is expressed in 2D projections and 1D quantities. This approach capitalizes on the diverse data modalities of the TPC, allowing for the efficient representation of the distinct features of the 3D events, with no loss in topology uniqueness. Additionally, it leverages the computational efficiency of 2D CNNs and benefits from the extensive availability of pre-trained models. Given the scarcity of real training data for the rare events of interest, simulated events are used to train the models to detect real events. To account for potential distribution shifts when predominantly depending on simulations, significant perturbations are embedded within the simulations. This produces a broad parameter space that works to account for potential physics parameter and detector response variations and uncertainties. These parameter-varied simulations are used to train sensitive 2D CNN object detectors. When combined with 1D histogram peak detection algorithms, this multi-modal detection framework is highly adept at identifying rare, two-particle events in data taken during experiment 21072 at the Facility for Rare Isotope Beams (FRIB), demonstrating a 100% recall for events of interest. We present the methods and outcomes of our investigation and discuss the potential future applications of these techniques.

The a posteriori speech presence probability (SPP) is the fundamental component of noise power spectral density (PSD) estimation, which can contribute to speech enhancement and speech recognition systems. Most existing SPP estimators can estimate SPP accurately from the background noise. Nevertheless, numerous challenges persist, including the difficulty of accurately estimating SPP from non-stationary noise with statistics-based methods and the high latency associated with deep learning-based approaches. This paper presents an improved SPP estimation approach based on deep learning to achieve higher SPP estimation accuracy, especially in non-stationary noise conditions. To promote the information extraction performance of the DNN, the global information of the observed signal and the local information of the decoupled frequency bins from the observed signal are connected as hybrid global-local information. The global information is extracted by one encoder. Then, one decoder and two fully connected layers are used to estimate SPP from the information of residual connection. To evaluate the performance of our proposed SPP estimator, the noise PSD estimation and speech enhancement tasks are performed. In contrast to existing minimum mean-square error (MMSE)-based noise PSD estimation approaches, the noise PSD is estimated by the sub-optimal MMSE based on the current frame SPP estimate without smoothing. Directed by the noise PSD estimate, a standard speech enhancement framework, the log spectral amplitude estimator, is employed to extract clean speech from the observed signal. From the experimental results, we can confirm that our proposed SPP estimator can achieve high noise PSD estimation accuracy and speech enhancement performance while requiring low model complexity.

Background Advanced adaptive randomised clinical trials are increasingly used. Compared to their conventional counterparts, their flexibility may make them more efficient, increase the probability of obtaining conclusive results without larger samples than necessary, and increase the probability that individual participants are allocated to more promising interventions. However, limited guidance is available on designing and evaluating the performance of advanced adaptive trials. Methods We summarise the methodological considerations and provide practical guidance on the entire workflow of planning and evaluating advanced adaptive trials using adaptive stopping, adaptive arm dropping, and response-adaptive randomisation within a Bayesian statistical framework. Results This comprehensive practical guide covers the key methodological decisions for advanced adaptive trials and their specification and evaluation using statistical simulation. These considerations include interventions and common control use; outcome type and generation; analysis timing and outcome-data lag; allocation rules; analysis model; adaptation rules for stopping and arm dropping; clinical scenarios assessed; performance metrics; calibration; sensitivity analyses; and reporting. The considerations are covered in the context of realistic examples, along with simulation code using the adaptr R package. Conclusions This practical guide will help clinical trialists, methodologists, and biostatisticians design and evaluate advanced adaptive trials., Comment: 63 pages (30 without appendices), 5 figures (3 without appendices), 3 tables

We analyze the content of the anonymous online discussion forum Economics Job Market Rumors (EJMR) and document its evolving interactions with external information sources. We focus on three key aspects: the prevalence and impact of links to external domains, the surge in discussions driven by Twitter posts since 2018, and the categorization of individuals whose tweets are most frequently discussed on EJMR. Using data on linked domains, we show how these trends reflect broader changes in the economics profession's digital footprint. Our analysis sheds light on EJMR's informational role but also raises questions about inclusivity and professional ethics in economics., Comment: submitted for AEA P&P

Traditional microperforated panels (MPPs) and metamaterial-based sound absorbers rely on local resonances or multi-resonator designs, which limit their bandwidth, angular applicability, and ease of fabrication. Leveraging the reciprocity theorem and cavity resonances, we introduce a new class of robust MPP absorbers, termed meta-MPPs, capable of achieving ultrabroadband near-total sound absorption across a range of 0.37 to 10 kHz. These absorbers demonstrate average performance exceeding that of traditional MPPs by over 100%, approaching the theoretical causality limit. Notably, their absorption performance can be tuned between angularly asymmetric and omnidirectional modes and remains highly robust to variations in MPP parameters and geometrical configurations. Validated through simulations and experiments, our findings present a simpler, more robust, and highly adaptable solution for noise control.

While attention-based architectures, such as Conformers, excel in speech enhancement, they face challenges such as scalability with respect to input sequence length. In contrast, the recently proposed Extended Long Short-Term Memory (xLSTM) architecture offers linear scalability. However, xLSTM-based models remain unexplored for speech enhancement. This paper introduces xLSTM-SENet, the first xLSTM-based single-channel speech enhancement system. A comparative analysis reveals that xLSTM-and notably, even LSTM-can match or outperform state-of-the-art Mamba- and Conformer-based systems across various model sizes in speech enhancement on the VoiceBank+Demand dataset. Through ablation studies, we identify key architectural design choices such as exponential gating and bidirectionality contributing to its effectiveness. Our best xLSTM-based model, xLSTM-SENet2, outperforms state-of-the-art Mamba- and Conformer-based systems on the Voicebank+DEMAND dataset.

The divisor theory of the complete graph $K_n$ is in many ways similar to that of a plane curve of degree $n$. We compute the splitting types of all divisors on the complete graph $K_n$. We see that the possible splitting types of divisors on $K_n$ exactly match the possible splitting types of line bundles on a smooth plane curve of degree $n$. This generalizes the earlier result of Cori and Le Borgne computing the ranks of all divisors on $K_n$, and the earlier work of Cools and Panizzut analyzing the possible ranks of divisors of fixed degree on $K_n$.

Over the past several years, there have been many studies demonstrating the ability of neural networks and deep learning methods to identify phase transitions in many physical systems, notably in classical statistical physics systems. One often finds that the prediction of deep learning methods trained on many ensembles below and above the critical temperature $T_{\mathrm{c}}$ behave analogously to an order parameter, and this analogy has been successfully used to locate $T_{\mathrm{c}}$ and estimate universal critical exponents. In this work, we pay particular attention to the ability of a convolutional neural network to capture these critical parameters for the 2-$d$ Ising model, when the network is trained on configurations at $T=0$ and $T=\infty$ only. We apply histogram reweighting to the neural network prediction and compare its capabilities when trained more conventionally at multiple temperatures. We find that the network trained on two temperatures is still able to identify $T_{\mathrm{c}}$ and $\nu$, while the extraction of $\gamma$ becomes more challenging.

We present optical photometric and spectroscopic observations of the peculiar Type Ia supernova ASASSN-20jq/SN 2020qxp. It is a low-luminosity object with a peak absolute magnitude of $M_B=-17.1\pm0.5$ mag. Despite its low luminosity, its post-peak light-curve decline rate ($\Delta m_{15}(B)=1.35\pm0.09$ mag) and color-stretch parameter (sBV>0.82) are similar to normal SNe Ia, making it an outlier in the luminosity-width and luminosity-color-stretch relations. Early light curves suggest a "bump" during the first 1.4 days of explosion. ASASSN-20jq synthesized a low radioactive $^{56}$Ni mass of $0.09\pm0.01M_\odot$. Near-maximum light spectra reveal strong Si II absorption lines, indicating a cooler photosphere than normal SNe Ia, but lack Ti II absorption lines. Unusually strong O I $\lambda$7773 and Ca II near-infrared triplet absorption features are present. Nebular spectra show a strong, narrow forbidden [Ca II] $\lambda\lambda$7291,7324 doublet emission, rarely seen in SNe Ia except in some Type Iax events. Marginal detection of [O I] $\lambda\lambda$6300,6364 doublet emission, which is extremely rare, is observed. Both [Ca II] and [O I] lines are redshifted by $\sim2000$ km/s. A strong [Fe II] $\lambda$7155 emission line with a tilted-top profile, identical to the [Fe II] $\lambda$16433 profile, is also observed. These asymmetric [Fe II] profiles and redshifted [Ca II] and [O I] emissions suggest a high central density white dwarf progenitor undergoing an off-center delayed-detonation explosion mechanism, producing roughly equal amounts of $^{56}$Ni in deflagration and detonation phases. This distinguishes ASASSN-20jq from normal and subluminous SNe Ia. ASASSN-20jq's light curve and spectra do not align with any single SNe Ia subclass but show similarities to 2002es-like objects. Thus, we add it as an extreme candidate within the heterogeneous parameter space of 2002es-like SNe Ia., Comment: 27 pages, 20 figures, 5 tables, submitted to A&A

Target-Speaker Voice Activity Detection (TS-VAD) is the task of detecting the presence of speech from a known target-speaker in an audio frame. Recently, deep neural network-based models have shown good performance in this task. However, training these models requires extensive labelled data, which is costly and time-consuming to obtain, particularly if generalization to unseen environments is crucial. To mitigate this, we propose a causal, Self-Supervised Learning (SSL) pretraining framework, called Denoising Autoregressive Predictive Coding (DN-APC), to enhance TS-VAD performance in noisy conditions. We also explore various speaker conditioning methods and evaluate their performance under different noisy conditions. Our experiments show that DN-APC improves performance in noisy conditions, with a general improvement of approx. 2% in both seen and unseen noise. Additionally, we find that FiLM conditioning provides the best overall performance. Representation analysis via tSNE plots reveals robust initial representations of speech and non-speech from pretraining. This underscores the effectiveness of SSL pretraining in improving the robustness and performance of TS-VAD models in noisy environments., Comment: Submitted to IEEE/ACM Transactions on Audio, Speech, and Language Processing for possible publication. 12 pages, 4 figures, 5 tables

Accurate assessment of forest biodiversity is crucial for ecosystem management and conservation. While traditional field surveys provide high-quality assessments, they are labor-intensive and spatially limited. This study investigates whether deep learning-based fusion of close-range sensing data from 2D orthophotos (12.5 cm resolution) and 3D airborne laser scanning (ALS) point clouds (8 points/m^2) can enhance biodiversity assessment. We introduce the BioVista dataset, comprising 44.378 paired samples of orthophotos and ALS point clouds from temperate forests in Denmark, designed to explore multi-modal fusion approaches for biodiversity potential classification. Using deep neural networks (ResNet for orthophotos and PointVector for ALS point clouds), we investigate each data modality's ability to assess forest biodiversity potential, achieving mean accuracies of 69.4% and 72.8%, respectively. We explore two fusion approaches: a confidence-based ensemble method and a feature-level concatenation strategy, with the latter achieving a mean accuracy of 75.5%. Our results demonstrate that spectral information from orthophotos and structural information from ALS point clouds effectively complement each other in forest biodiversity assessment.

Photofragment translational spectroscopy has been used to characterize the photodissociation of CH$_3$I and CF$_3$I adsorbed on thin films of a variety of aromatic molecules, initiated by near-UV light. Thin films (nominally 10 monolayers) of benzene, five substituted benzenes and two naphthalenes have been employed to study systematic changes in the photochemical activity. Illumination of these systems with 248nm light is found to result in a dissociation process for the CH$_3$I and CF$_3$I mediated by initial absorption in the aromatic thin film, followed by electronic energy transfer (EET) to the dissociating species. The effective cross sections for dissociation are found to be substantially increased via this mechanism, by amounts that differ depending on the aromatic molecule thin film used, and is connected to the aromatic photabsorption profile. Distinctive translational energy distributions for the CH$_3$ and CF$_3$ photofragments are found to vary systematically for the different aromatic molecule thin film used, and are related to the aromatic molecule excited states. The CH$_3$ and CF$_3$ photofragment kinetic energy distributions found for the aromatic thin films suggest that the dissociation occurs via EET to the $^3Q_1$ excited state of CH$_3$I and CF$_3$I., Comment: arXiv admin note: text overlap with arXiv:2403.12277

This project explores the design and development of a transition edge sensor (TES) spectrometer for resonant soft X- ray scattering (RSXS) measurements developed in collaboration between Argonne National Laboratory (ANL) and the National Institute of Standards and Technology (NIST). Soft X-ray scattering is a powerful technique for studying the electronic and magnetic properties of materials on a microscopic level. However, the lack of high-performance soft X-ray spectrometers has limited the potential of this technique. TES spectrometers have the potential to overcome these limitations due to their high energy resolution, high efficiency, and broad energy range. This project aims to optimize the design of a TES spectrometer for RSXS measurements and more generally soft X-ray spectroscopy at the Advanced Photon Source (APS) 29-ID, leading to improved understanding of advanced materials. We will present a detailed description of the instrument design and implementation. The spectrometer consists of a large array of approximately 250 high-speed and high-resolution pixels. The pixels have saturation energies of approximately 1 keV, sub-ms pulse duration and energy resolution of approximately 1 eV. The array is read out using microwave multiplexing chips with MHz bandwidth per channel, enabling efficient data throughput. To facilitate measurement of samples in situ under ultra-high vacuum conditions at the beamline, the spectrometer is integrated with an approximately 1 m long snout.

The advancement of machine learning and the availability of large-scale reaction datasets have accelerated the development of data-driven models for computer-aided synthesis planning (CASP) in the past decade. Here, we detail the newest version of ASKCOS, an open source software suite for synthesis planning that makes available several research advances in a freely available, practical tool. Four one-step retrosynthesis models form the basis of both interactive planning and automatic planning modes. Retrosynthetic planning is complemented by other modules for feasibility assessment and pathway evaluation, including reaction condition recommendation, reaction outcome prediction, and auxiliary capabilities such as solubility prediction and quantum mechanical descriptor prediction. ASKCOS has assisted hundreds of medicinal, synthetic, and process chemists in their day-to-day tasks, complementing expert decision making. It is our belief that CASP tools like ASKCOS are an important part of modern chemistry research, and that they offer ever-increasing utility and accessibility.

We study the algebra of observables in a time band on the boundary of anti-de Sitter space in a theory of quantum gravity. Strictly speaking this algebra does not have a commutant because products of operators within the time band give rise to operators outside the time band. However, we show that in a state where the bulk contains a macroscopic observer, it is possible to define a coarse-grained version of this algebra with a non-trivial commutant, and a resolution limited by the observer's characteristics. This algebra acts on a little Hilbert space that describes excitations about the observer's state and time-translated versions of this state. Our construction requires a choice of dressing that determines how elements of the algebra transform under the Hamiltonian. At leading order in gravitational perturbation theory, and with a specific choice of dressing, our construction reduces to the modular crossed-product described previously in the literature. We also prove a theorem showing that this is the only crossed product of a type III$_1$ algebra resulting in an algebra with a trace. This trace can be used to define entropy differences between states in the little Hilbert space that are insensitive to the properties of the observer. We discuss some technical challenges in extending this construction to higher orders in perturbation theory. Lastly, we review the construction of interior operators in the eternal black hole and show that they can be written as elements of a crossed product algebra., Comment: 52 pages

We show that a modified version of Son's Dirac composite fermion theory proposed by Seiberg et al gives a candidate unified description of the gapped and gapless fractional quantum Hall states within a single Landau level. Our main tool is the successive application of three-dimensional dualities to partially filled Landau levels of composite fermions, which imply that this theory has a complicated landscape of gapped vacua and critical points. This construction is the Lagrangian, or effective field theory, analogue of the flux attachment procedure. The critical points exist at even denominator filling and are well-described by a Fermi surface for a weakly coupled composite fermion coupled to an abelian Chern-Simons theory. The gapped states include odd-denominator filling fraction states with an abelian Chern-Simons description which we show matches the one expected for hierarchy states, as well as non-abelian states at even-denominator filling that arise from pair instabilities of the composite fermion's Fermi surface., Comment: 35 pages, 3 figures

Time series forecasting has important applications across diverse domains. EasyTime, the system we demonstrate, facilitates easy use of time-series forecasting methods by researchers and practitioners alike. First, EasyTime enables one-click evaluation, enabling researchers to evaluate new forecasting methods using the suite of diverse time series datasets collected in the preexisting time series forecasting benchmark (TFB). This is achieved by leveraging TFB's flexible and consistent evaluation pipeline. Second, when practitioners must perform forecasting on a new dataset, a nontrivial first step is often to find an appropriate forecasting method. EasyTime provides an Automated Ensemble module that combines the promising forecasting methods to yield superior forecasting accuracy compared to individual methods. Third, EasyTime offers a natural language Q&A module leveraging large language models. Given a question like "Which method is best for long term forecasting on time series with strong seasonality?", EasyTime converts the question into SQL queries on the database of results obtained by TFB and then returns an answer in natural language and charts. By demonstrating EasyTime, we intend to show how it is possible to simplify the use of time series forecasting and to offer better support for the development of new generations of time series forecasting methods., Comment: Accepted by ICDE2025

In cooperative training, humans within a team coordinate on complex tasks, building mental models of their teammates and learning to adapt to teammates' actions in real-time. To reduce the often prohibitive scheduling constraints associated with cooperative training, this article introduces a paradigm for cooperative asynchronous training of human teams in which trainees practice coordination with autonomous teammates rather than humans. We introduce a novel experimental design for evaluating autonomous teammates for use as training partners in cooperative training. We apply the design to a human-subjects experiment where humans are trained with either another human or an autonomous teammate and are evaluated with a new human subject in a new, partially observable, cooperative game developed for this study. Importantly, we employ a method to cluster teammate trajectories from demonstrations performed in the experiment to form a smaller number of training conditions. This results in a simpler experiment design that enabled us to conduct a complex cooperative training human-subjects study in a reasonable amount of time. Through a demonstration of the proposed experimental design, we provide takeaways and design recommendations for future research in the development of cooperative asynchronous training systems utilizing robot surrogates for human teammates., Comment: 19 pages; 6 figures

Graph stream summarization refers to the process of processing a continuous stream of edges that form a rapidly evolving graph. The primary challenges in handling graph streams include the impracticality of fully storing the ever-growing datasets and the complexity of supporting graph queries that involve both topological and temporal information. Recent advancements, such as PGSS and Horae, address these limitations by using domain-based, top-down multi-layer structures in the form of compressed matrices. However, they either suffer from poor query accuracy, incur substantial space overheads, or have low query efficiency. This study proposes a novel item-based, bottom-up hierarchical structure, called HIGGS. Unlike existing approaches, HIGGS leverages its hierarchical structure to localize storage and query processing, thereby confining changes and hash conflicts to small and manageable subtrees, yielding notable performance improvements. HIGGS offers tighter theoretical bounds on query accuracy and space cost. Extensive empirical studies on real graph streams demonstrate that, compared to state-of-the-art methods, HIGGS is capable of notable performance enhancements: it can improve accuracy by over 3 orders of magnitude, reduce space overhead by an average of 30%, increase throughput by more than 5 times, and decrease query latency by nearly 2 orders of magnitude.

We simulate high-pressure hydrogen in its liquid phase close to molecular dissociation using a machine-learned interatomic potential. The model is trained with density functional theory (DFT) forces and energies, with the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional. We show that an accurate NequIP model, an E(3)-equivariant neural network potential, accurately reproduces the phase transition present in PBE. Moreover, the computational efficiency of this model allows for substantially longer molecular dynamics trajectories, enabling us to perform a finite-size scaling (FSS) analysis to distinguish between a crossover and a true first-order phase transition. We locate the critical point of this transition, the liquid-liquid phase transition (LLPT), at 1200-1300 K and 155-160 GPa, a temperature lower than most previous estimates and close to the melting transition.

The Global Change Analysis Model (GCAM) simulates complex interactions between the coupled Earth and human systems, providing valuable insights into the co-evolution of land, water, and energy sectors under different future scenarios. Understanding the sensitivities and drivers of this multisectoral system can lead to more robust understanding of the different pathways to particular outcomes. The interactions and complexity of the coupled human-Earth systems make GCAM simulations costly to run at scale - a requirement for large ensemble experiments which explore uncertainty in model parameters and outputs. A differentiable emulator with similar predictive power, but greater efficiency, could provide novel scenario discovery and analysis of GCAM and its outputs, requiring fewer runs of GCAM. As a first use case, we train a neural network on an existing large ensemble that explores a range of GCAM inputs related to different relative contributions of energy production sources, with a focus on wind and solar. We complement this existing ensemble with interpolated input values and a wider selection of outputs, predicting 22,528 GCAM outputs across time, sectors, and regions. We report a median $R^2$ score of 0.998 for the emulator's predictions and an $R^2$ score of 0.812 for its input-output sensitivity., Comment: Presented at Tackling Climate Change with Machine Learning, NeurIPS 2024

Recent progresses in magnetoionics offer exciting potentials to leverage its non-linearity, short-term memory, and energy-efficiency to uniquely advance the field of physical reservoir computing. In this work, we experimentally demonstrate the classification of temporal data using a magneto-ionic (MI) heterostructure. The device was specifically engineered to induce non-linear ion migration dynamics, which in turn imparted non-linearity and short-term memory (STM) to the magnetization. These capabilities, key features for enabling reservoir computing, were investigated, and the role of the ion migration mechanism, along with its history-dependent influence on STM, was explained. These attributes were utilized to distinguish between sine and square waveforms within a randomly distributed set of pulses. Additionally, two important performance metrics, short-term memory and parity check capacity (PC), were quantified, yielding promising values of 1.44 and 2, respectively, comparable to those of other state-of-the-art reservoirs. Our work paves the way for exploiting the relaxation dynamics of solid-state magneto-ionic platforms and developing energy-efficient magneto-ionic reservoir computing devices.

Mechanistic interpretability aims to understand the inner workings of large neural networks by identifying circuits, or minimal subgraphs within the model that implement algorithms responsible for performing specific tasks. These circuits are typically discovered and analyzed using a narrowly defined prompt format. However, given the abilities of large language models (LLMs) to generalize across various prompt formats for the same task, it remains unclear how well these circuits generalize. For instance, it is unclear whether the models generalization results from reusing the same circuit components, the components behaving differently, or the use of entirely different components. In this paper, we investigate the generality of the indirect object identification (IOI) circuit in GPT-2 small, which is well-studied and believed to implement a simple, interpretable algorithm. We evaluate its performance on prompt variants that challenge the assumptions of this algorithm. Our findings reveal that the circuit generalizes surprisingly well, reusing all of its components and mechanisms while only adding additional input edges. Notably, the circuit generalizes even to prompt variants where the original algorithm should fail; we discover a mechanism that explains this which we term S2 Hacking. Our findings indicate that circuits within LLMs may be more flexible and general than previously recognized, underscoring the importance of studying circuit generalization to better understand the broader capabilities of these models., Comment: 10 pages, 8 figures

As global life expectancy increases, so does the burden of chronic diseases, yet individuals exhibit considerable variability in the rate at which they age. Identifying biomarkers that distinguish fast from slow ageing is crucial for understanding the biology of ageing, enabling early disease detection, and improving prevention strategies. Using contrastive deep learning, we show that skin biopsy images alone are sufficient to determine an individual's age. We then use visual features in histopathology slides of the skin biopsies to construct a novel biomarker of ageing. By linking with comprehensive health registers in Denmark, we demonstrate that visual features in histopathology slides of skin biopsies predict mortality and the prevalence of chronic age-related diseases. Our work highlights how routinely collected health data can provide additional value when used together with deep learning, by creating a new biomarker for ageing which can be actively used to determine mortality over time., Comment: 20 pages, 5 tables, 5 figures Under review: npj Digital Medicine