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Two groups of continuing education students were tested using three of Viktor Lowenfeld's tests of visual-haptic perception. The scores from the three tests were added to yield a score which identified an individual's location on Lowenfeld's visual-haptic continuum. The investigation also uncovered scoring problems with two of the tests which had not been previously reported in the literature. It was concluded that the Picture a Building Test as well as the Test of Visual and Haptical Word Association should not be used in their original form as a vehicle for ascertaining an individual's perceptual aptitude. Raw data tables are included. (Author)

Get A Skill, Get A Job, Get Ahead (G3) is a state-funded last-dollar scholarship program for students enrolling in eligible associate degree, certificate, and noncredit occupational training programs in the Virginia Community College System (VCCS) in five high-demand fields: early childhood education, healthcare, information technology, public safety, and skilled trades (construction and manufacturing). Launched in the 2021-22 academic year, the G3 initiative aims to increase enrollment, persistence, completion, and labor market success for low-to-middle-income learners. G3 has evolved over the past two years along with VCCS priorities to include a specific focus on boosting college enrollment and success of adult, Black and Hispanic, and low-income learners. In this brief, the authors highlight early insights from a study of G3 implementation and outcomes. Early analysis by VCCS shows that colleges are making progress in increasing enrollment in the five high-demand fields listed above. The authors draw on published enrollment and outcome data from VCCS and interview data collected from eight of the 23 community colleges implementing the G3 program to examine college-level G3 outreach, recruitment, and enrollment strategies and the process for awarding G3 aid.

Building on previous works to construct a homogeneous sample of brown dwarfs in binary systems, we investigate microlensing events detected by the Korea Microlensing Telescope Network (KMTNet) survey during the 2022 and 2023 seasons. Given the difficulty in distinguishing brown-dwarf events from those produced by binary lenses with nearly equal-mass components, we analyze all lensing events detected during the seasons that exhibit anomalies characteristic of binary-lens systems. Using the same criteria consistently applied in previous studies, we identify six additional brown dwarf candidates through the analysis of lensing events KMT-2022-BLG-0412, KMT-2022-BLG-2286, KMT-2023-BLG-0201, KMT-2023-BLG-0601, KMT-2023-BLG-1684, and KMT-2023-BLG-1743. An examination of the mass posteriors shows that the median mass of the lens companions ranges from 0.02 $M_\odot$ to 0.05 $M_\odot$, indicating that these companions fall within the brown-dwarf mass range. The mass of the primary lenses ranges from 0.11 $M_\odot$ to 0.68 $M_\odot$, indicating that they are low-mass stars with substantially lower masses compared to the Sun., Comment: 13 pages, 17 figures, 12 tables

Second-order Raman scattering has been extensively studied in carbon-based nanomaterials, \emph{e.g.} nanotube and graphene, because it activates normally forbidden Raman modes that are sensitive to crystal disorder, such as defects, dopants, strain, etc. The sp$^2$-hybridized carbon systems are, however, the exception among most nanomaterials, where first-order Raman processes usually dominate. Here we report the identification of four second-order Raman modes, named $D_1$, $D_1'$, $D_2$ and $D_2'$, in exfoliated black phosphorus (P(black)), an elemental direct-gap semiconductor exhibiting strong mechanical and electronic anisotropies. Located in close proximity to the $A^1_g$ and $A^2_g$ modes, these new modes dominate at an excitation wavelength of 633 nm. Their evolutions as a function of sample thickness, excitation wavelength, and defect density indicate that they are defect-activated and involve high-momentum phonons in a doubly-resonant Raman process. \emph{Ab initio} simulations of a monolayer reveal that the $D'$ and $D$ modes occur through intravalley scatterings with split contributions in the armchair and zigzag directions, respectively. The high sensitivity of these $D$ modes to disorder helps explaining several discrepancies found in the literature., Comment: 18 pages, 5 figures, with supplementary file

This article focuses on estimating relative transfer functions (RTFs) for beamforming applications. While traditional methods assume that spectra are uncorrelated, this assumption is often violated in practical scenarios due to natural phenomena such as the Doppler effect, artificial manipulations like time-domain windowing, or the non-stationary nature of the signals, as observed in speech. To address this, we propose an RTF estimation technique that leverages spectral and spatial correlations through subspace analysis. To overcome the challenge of estimating second-order spectral statistics for real data, we employ a phase-adjusted estimator originally proposed in the context of engine fault detection. Additionally, we derive Cram\'er--Rao bounds (CRBs) for the RTF estimation task, providing theoretical insights into the achievable estimation accuracy. The bounds show that channel estimation can be performed more accurately if the noise or the target presents spectral correlations. Experiments on real and synthetic data show that our technique outperforms the narrowband maximum-likelihood estimator when the target exhibits spectral correlations. Although the accuracy of the proposed algorithm is generally close to the bound, there is some room for improvement, especially when noise signals with high spectral correlation are present. While the applications of channel estimation are diverse, we demonstrate the method in the context of array processing for speech., Comment: Under review at IEEE/ACM Transactions on Audio, Speech, and Language Processing

In the pursuit of understanding the population of stellar remnants within the Milky Way, we analyze the sample of $\sim 950$ microlensing events observed by the Spitzer Space Telescope between 2014 and 2019. In this study we focus on a sub-sample of nine microlensing events, selected based on their long timescales, small microlensing parallaxes and joint observations by the Gaia mission, to increase the probability that the chosen lenses are massive and the mass is measurable. Among the selected events we identify lensing black holes and neutron star candidates, with potential confirmation through forthcoming release of the Gaia time-series astrometry in 2026. Utilizing Bayesian analysis and Galactic models, along with the Gaia Data Release 3 proper motion data, four good candidates for dark remnants were identified: OGLE-2016-BLG-0293, OGLE-2018-BLG-0483, OGLE-2018-BLG-0662, and OGLE-2015-BLG-0149, with lens masses of $2.98^{+1.75}_{-1.28}~M_{\odot}$, $4.65^{+3.12}_{-2.08}~M_{\odot}$, $3.15^{+0.66}_{-0.64}~M_{\odot}$ and $1.4^{+0.75}_{-0.55}~M_{\odot}$, respectively. Notably, the first two candidates are expected to exhibit astrometric microlensing signals detectable by Gaia, offering the prospect of validating the lens masses. The methodologies developed in this work will be applied to the full Spitzer microlensing sample, populating and analyzing the time-scale ($t_{\rm E}$) vs. parallax ($\pi_{\rm E}$) diagram to derive constraints on the population of lenses in general and massive remnants in particular., Comment: submitted to ApJ

Many of the most intriguing features, including spirals and cavities, in the current disc observations are found in binary systems like GG Tau, HD 142527 or HD 100453. Such features are evidence of the dynamic interaction between binary stars and the viscous disc. Understanding these dynamic interactions and how they result in the structure and growth of asymmetric circumbinary discs is a difficult problem, for which there is no complete analytical solution, that predicts the shape of the observed disc accurately. We use numeric simulation to evolve circumbinary discs with varying disc viscosities and investigate the size and shape of the inner cavities in such protoplanetary discs. We have simulated over 140 locally isothermal 2D grid-based disc models for > 3e4 binary orbits each and mapped out the parameter space relevant for protoplanetary discs. With this, it becomes possible to create parametrised profiles for individual discs to compare to observation and find limits to their binary eccentricity or internal viscosity from the simulation data. In the long-term simulations larger cavity sizes than previously considered are possible within the parameter space (< 6 binary separations). As an example, we find that the eccentricity of the disc around HD 142527 suggests the impact of the binary dynamics on the disc. However, even considering the larger cavity sizes, the large size of the cavity in HD 142527 remains unexplained by the simulations considering the most recent orbital constraints., Comment: 13 pages, 14 figures (appendix: 2 pages, 3 figures); accepted by mnras

Elastic wave manipulation using large arrays of resonators is driving the need for advanced simulation and optimization methods. To address this we introduce and explore a robust framework for wave control: Quasi-normal modes (QNMs). Specifically we consider the problem for thin elastic plates, where the Green's function formalism is well known and readily exploited to solve multiple scattering problems. By studying the associated nonlinear eigenvalue problem we derive a dispersive QNM expansion, providing a reduced-order model for efficient forced response computations which reveals physical insight into the resonant mode excitation. Furthermore, we derive eigenvalue sensitivities with respect to resonator parameters and apply a gradient-based optimization to design quasi-bound states in the continuum and position eigenfrequencies precisely in the complex plane. Scattering simulations validate our approach in structures such as graded line arrays and quasi-crystals. Drawing on QNM concepts from electromagnetism we demonstrate significant advances in elastic metamaterials, highlighting their potential for tailored wave manipulation.

Due to the space radiation environment at L2, ESA's Euclid mission will be subject to a large amount of highly energetic particles over its lifetime. These particles can cause damage to the detectors by creating defects in the silicon lattice. These defects degrade the returned image in several ways, one example being a degradation of the Charge Transfer Efficiency, which appears as readout trails in the image data. This can be problematic for the Euclid VIS instrument, which aims to measure the shapes of galaxies to a very high degree of accuracy. Using a special clocking technique called trap pumping, the single defects in the CCDs can be detected and characterised. Being the first instrument in space with this capability, it will provide novel insights into the creation and evolution of radiation-induced defects and give input to the radiation damage correction of the scientific data. We present the status of the radiation damage of the Euclid VIS CCDs and how it has evolved over the first year in space., Comment: Submitted to the Proceedings for the SPIE Space Telescopes and Instrumentation 2024: Optical, Infrared, and Millimeter Wave, paper no. 13092-24, 10 pages, 6 figures

Postsecondary education and middle-skills occupational training are viewed as important paths to higher-paying jobs and careers. Lifelong learning pathways geared toward working learners aged 25 and older also seem essential for career advancement and professional growth in the current job market. However, many learners face financial and other barriers to accessing and completing occupational training. Social Finance, Inc., a national nonprofit that creates impact-first investments, launched the UP Fund, a $50 million fund that aims to improve economic mobility by expanding access to job training programs to underserved learners and learners from low-income backgrounds. The UP Fund enables learners to enroll in short-term, sector-based occupational training programs with access to career and supportive services without paying up-front tuition costs. Learners enter into "income share agreements" (ISAs) that are intended to repay tuition costs. This is achieved by learners committing to repay a fixed percentage or amount of their income over a set term and up to a capped amount. However, this repayment obligation is contingent upon the learner's earnings meeting a minimum income threshold. The UP Fund's Career Impact Bond (CIB) model focuses on providing ISAs to learners from low-income backgrounds who might not otherwise have access to the training programs. The CIB model also emphasizes the importance of comprehensive support services to help learners achieve career success. MDRC launched a multisite, multiyear study of the UP Fund's CIB model in 2022. The study includes four training providers that enroll learners financed by the UP Fund across multiple industries. This brief provides an overview of the study, details of the UP Fund's CIB model, and early implementation findings. Initial results suggest that individuals who enrolled in a training program supported by the UP Fund learned helpful skills to find jobs in their chosen careers.

Government agencies collect vast amounts of administrative data in their day-to-day activities, primarily for program operations. But the information is less often used as a research tool or fully harnessed for its evidence-building potential. This brief is the fourth in a series of publications from MDRC about the Temporary Assistance for Needy Families (TANF) Data Innovation (TDI) project, initiated by the Administration for Children and Families (ACF) in 2017. It describes TDI's efforts to transform federal TANF and employment data into an integrated resource for evidence-informed program management and policymaking. Such efforts are rarely an easy lift. Creating the data resource required deliberate federal investments and the resolution of many technical, legal, and security issues by the TDI team, which was led by MDRC in collaboration with Actionable Intelligence for Social Policy, Chapin Hall at the University of Chicago, and the Coleridge Initiative. This brief describes how the team tackled this challenging project. Although the work was focused on the TANF program, the lessons learned may be useful for members of federal and state agencies, researchers, and advocates of evidence-informed policy making interested in unlocking the potential of administrative data. [Additional creators of this report are the Actionable Insights for Social Policy at the University of Pennsylvania and Coleridge Initiative.]

Monitoring the Future (MTF) is designed to give such attention to substance use among the nation's youth and adults. It is an investigator-initiated study that originated with, and is conducted by, a team of research professors at the University of Michigan's Institute for Social Research. Since its onset in 1975, MTF has been funded continuously by the National Institute on Drug Abuse--one of the National Institutes of Health--under a series of peer reviewed, competitive research grants. This 2023 survey is the 49th consecutive national survey of 12th grade students and the 33rd national survey of 8th and 10th grade students (who were added to the study in 1991). MTF contains ongoing national surveys of both adolescents and adults in the United States. It provides the nation with a vital window into the important but often hidden problem behaviors of use of illegal drugs, alcohol, tobacco, and psychotherapeutic drugs used without a doctor's orders. Two of the major topics included in the present monograph are: (1) the prevalence and frequency of use of a great many substances, both licit and illicit, among U.S. secondary school students in 8th, 10th, and 12th grades; and (2) historical trends in use by students in those grades. Distinctions are made among important demographic subgroups in these populations based on gender, college plans, region of the country, population density, parent education, and race/ethnicity. MTF has demonstrated that key attitudes and beliefs about drug use are important determinants of usage trends, in particular the amount of risk to the user perceived to be associated with the various drugs and disapproval of using them; thus, those measures also are tracked over time, as are students' perceptions of certain relevant aspects of the social environment--in particular, perceived availability of each drug, peer norms about their use, and use by friends. Data on grade of first use, noncontinuation of use, trends in use in lower grades (based on retrospective reports), and intensity of use are also reported. [For "Monitoring the Future National Survey Results on Drug Use, 1975-2022: Secondary School Students," see ED627366.]

Numerical models of complex systems like the Earth system are expensive to run and involve many uncertain and typically hand-tuned parameters. In the context of anthropogenic climate change, there is particular concern that specific tipping elements, like the Atlantic Meridional Overturning Circulation, might be overly stable in models due to imperfect parameter choices. However, estimates of the critical forcing thresholds are highly uncertain because the parameter spaces can practically not be explored. Here, we introduce a method for efficient, systematic, and objective calibration of process-based models. Our method drives the system toward parameter configurations where it loses or gains stability, and scales much more efficiently than a brute force approach. We successfully apply the method to a simple bistable model and a conceptual but physically plausible model of the global ocean circulation, demonstrating that our method can help find hidden tipping points, and can calibrate complex models under user-defined constraints.

We present a procedure for calculating the heating of, and the infrared emission from, dust in a homogeneous spherical shell surrounded by a spherically symmetric source of radiation. The results are applicable to newly formed dust either in supernova ejecta or in the circumstellar medium that has been swept up by the expanding shock wave. They can also be applied to the heating and IR emission from dust in clumps or clouds embedded in a homogeneous radiation field.

In this paper, we extend the standard Attention in transformer by exploiting the consensus discrepancy from a distributed optimization perspective, referred to as AttentionX. It is noted that %the popular distributed optimization algorithm \cite{Boyd11ADMM} and the primal-dual method of multipliers (PDMM) \cite{Zhang16PDMM} is designed to iteratively solve a broad class of distributed optimization problems over a pear-to-pear (P2P) network, where neighbouring nodes gradually reach consensus as specified by predefined linear edge-constraints in the optimization process. In particular, at each iteration of PDMM, each node in a network first performs information-gathering from neighbours and then performs local information-fusion. From a high-level point of view, the $KQ$-softmax-based weighted summation of $V$-representations in Attention corresponds information-gathering from neighbours while the feature-processing via the feed-forward network (FFN) in transformer corresponds to local information fusion. PDMM exploits the Lagrangian multipliers to capture the historical consensus discrepancy in the form of residual errors of the linear edge-constraints, which plays a crucial role for the algorithm to converge. Inspired by PDMM, we propose AttentionX to incorporate the consensus discrepancy in the output update-expression of the standard Attention. The consensus discrepancy in AttentionX refers to the difference between the weighted summation of $V$-representations and scaled $V$-representions themselves. Experiments on ViT and nanoGPT show promising performance.

We present the results of the Large Binocular Telescope Satellites Of Nearby Galaxies Survey (LBT-SONG) ``Far Sample,'' including survey completeness estimates. We find 10 satellite candidates in the inner virial regions of 13 star-forming galaxies outside the Local Group. The hosts are at distances between $\sim 5-11$ Mpc and have stellar masses in the little explored range of $\sim 5 \times 10^8 - 5\times 10^{10}~\text{M}_{\odot}$. Among the 10 satellite candidates, 3 are new discoveries in this survey. In this paper, we characterize the properties of 8 low-mass satellite candidates, including the 3 new discoveries but excluding 2 well-studied massive satellites. Of the 8 low-mass dwarfs, optical colors from the LBT imaging and measurements in the ultraviolet with GALEX suggest that 2 show signs of active star formation, and 6 are likely quenched (although some may still have H\textsc{i} gas reservoirs). Notably, we report the discovery of an ultrafaint dwarf candidate, NGC 672 dwD, with $\text{M}_{\text{V}} = -6.6$ and an estimated stellar mass of $5.6 \times 10^4 ~\text{M}_{\odot}$ if its association with the host is confirmed. It is spatially coincident with a weak detection of H\textsc{i}, with $\text{M}_{\text{HI}}/\text{M}_{\text{*}} \sim 1$. If confirmed, it would be the least luminous known ultrafaint satellite to be so gas-rich. The prevalence of quenched satellites in our sample suggests there are environmental effects at work in lower mass hosts that are similar to those at play in Milky Way-size hosts, although the preponderance of H\textsc{i} detections is at odds with the paucity of H\textsc{i} detections in Milky Way satellites. By robustly measuring our survey completeness function, we are able to compare our observational results to predictions from theory, finding good agreement with the Cold Dark Matter galaxy evolution paradigm., Comment: 18 pages, 9 figures

This paper studies asynchronous stochastic approximation (SA) algorithms and their application to reinforcement learning in semi-Markov decision processes (SMDPs) with an average-reward criterion. We first extend Borkar and Meyn's stability proof method to accommodate more general noise conditions, leading to broader convergence guarantees for asynchronous SA algorithms. Leveraging these results, we establish the convergence of an asynchronous SA analogue of Schweitzer's classical relative value iteration algorithm, RVI Q-learning, for finite-space, weakly communicating SMDPs. Furthermore, to fully utilize the SA results in this application, we introduce new monotonicity conditions for estimating the optimal reward rate in RVI Q-learning. These conditions substantially expand the previously considered algorithmic framework, and we address them with novel proof arguments in the stability and convergence analysis of RVI Q-learning., Comment: The materials in this paper extend the authors' results from 2023, reported in arXiv:2408.16262 and arXiv:2312.15091. This paper incorporates and subsumes the results of arXiv:2312.15091 and serves as Part II of arXiv:2408.16262

We compute the equivariant K-theory of torus fixed points of Cherkis bow varieties of affine type A. We deduce formulas for the generating series of the Euler numbers of these varieties and observe their modularity in certain cases. We also obtain refined formulas on the motivic level for a class of bow varieties strictly containing Nakajima quiver varieties. These series hence generalise results of Nakajima-Yoshioka. As a special case, we obtain formulas for certain Zastava spaces. We define a parabolic analogue of Nekrasov's partition function and find an equation relating it to the classical partition function., Comment: 32 pages. Comments are welcome

In many real-world applications, the frequency distribution of class labels for training data can exhibit a long-tailed distribution, which challenges traditional approaches of training deep neural networks that require heavy amounts of balanced data. Gathering and labeling data to balance out the class label distribution can be both costly and time-consuming. Many existing solutions that enable ensemble learning, re-balancing strategies, or fine-tuning applied to deep neural networks are limited by the inert problem of few class samples across a subset of classes. Recently, vision-language models like CLIP have been observed as effective solutions to zero-shot or few-shot learning by grasping a similarity between vision and language features for image and text pairs. Considering that large pre-trained vision-language models may contain valuable side textual information for minor classes, we propose to leverage text supervision to tackle the challenge of long-tailed learning. Concretely, we propose a novel text-guided mixup technique that takes advantage of the semantic relations between classes recognized by the pre-trained text encoder to help alleviate the long-tailed problem. Our empirical study on benchmark long-tailed tasks demonstrates the effectiveness of our proposal with a theoretical guarantee. Our code is available at https://github.com/rsamf/text-guided-mixup., Comment: Accepted by BMVC'24, code is available at https://github.com/rsamf/text-guided-mixup

Scaling up robot learning requires large and diverse datasets, and how to efficiently reuse collected data and transfer policies to new embodiments remains an open question. Emerging research such as the Open-X Embodiment (OXE) project has shown promise in leveraging skills by combining datasets including different robots. However, imbalances in the distribution of robot types and camera angles in many datasets make policies prone to overfit. To mitigate this issue, we propose RoVi-Aug, which leverages state-of-the-art image-to-image generative models to augment robot data by synthesizing demonstrations with different robots and camera views. Through extensive physical experiments, we show that, by training on robot- and viewpoint-augmented data, RoVi-Aug can zero-shot deploy on an unseen robot with significantly different camera angles. Compared to test-time adaptation algorithms such as Mirage, RoVi-Aug requires no extra processing at test time, does not assume known camera angles, and allows policy fine-tuning. Moreover, by co-training on both the original and augmented robot datasets, RoVi-Aug can learn multi-robot and multi-task policies, enabling more efficient transfer between robots and skills and improving success rates by up to 30%., Comment: CoRL 2024 (Oral)

The recent surge in Generative Artificial Intelligence (AI) has introduced exciting possibilities for computational chemistry. Generative AI methods have made significant progress in sampling molecular structures across chemical species, developing force fields, and speeding up simulations. This Perspective offers a structured overview, beginning with the fundamental theoretical concepts in both Generative AI and computational chemistry. It then covers widely used Generative AI methods, including autoencoders, generative adversarial networks, reinforcement learning, flow models and language models, and highlights their selected applications in diverse areas including force field development, and protein/RNA structure prediction. A key focus is on the challenges these methods face before they become truly predictive, particularly in predicting emergent chemical phenomena. We believe that the ultimate goal of a simulation method or theory is to predict phenomena not seen before, and that Generative AI should be subject to these same standards before it is deemed useful for chemistry. We suggest that to overcome these challenges, future AI models need to integrate core chemical principles, especially from statistical mechanics.

The decay rate of Riesz capacity as the exponent increases to the dimension of the set is shown to yield Hausdorff measure. The result applies to strongly rectifiable sets, and so in particular to submanifolds of Euclidean space. For strictly self-similar fractals, a one-sided decay estimate is found. Along the way, a purely measure theoretic proof is given for subadditivity of the reciprocal of Riesz energy., Comment: 16 pages, 2 figures

The data that underlies automated methods in computer vision and machine learning, such as image retrieval and fine-grained recognition, often comes from crowdsourcing. In contexts that rely on the intrinsic motivation of users, we seek to understand how the application design affects a user's willingness to contribute and the quantity and quality of the data they capture. In this project, we designed three versions of a camera-based mobile crowdsourcing application, which varied in the amount of labeling effort requested of the user and conducted a user study to evaluate the trade-off between the level of user-contributed information requested and the quantity and quality of labeled images collected. The results suggest that higher levels of user labeling do not lead to reduced contribution. Users collected and annotated the most images using the application version with the highest requested level of labeling with no decrease in user satisfaction. In preliminary experiments, the additional labeled data supported increased performance on an image retrieval task.

Direct-to-Satellite IoT (DtS-IoT) has the potential to support multiple verticals, including agriculture, industry, smart cities, and environmental disaster prevention. This work introduces two novel DtS-IoT schemes using power domain NonOrthogonal Multiple Access (NOMA) in the uplink with either fixed (FTP) or controlled (CTP) transmit power. We consider that the IoT devices use LoRa technology to transmit data packets to the satellite in orbit, equipped with a Successive Interference Cancellation (SIC)-enabled gateway. We also assume the IoT devices are empowered with a predictor of the satellite orbit. Using real geographic location and trajectory data, we evaluate the performance of the average number of successfully decoded transmissions, goodput (bytes/lap), and energy consumption (bytes/Joule) as a function of the number of network devices. Numerical results show the trade-off between goodput and energy efficiency for both proposed schemes. Comparing FTP and CTP with regular ALOHA for 100 (600) devices, we find goodput improvements of 65% (29%) and 52% (101%), respectively. Notably, CTP effectively leverages transmission opportunities as the network size increases, outperforming the other strategies. Moreover, CTP shows the best performance in energy efficiency compared to FTP and ALOHA., Comment: 9 pages, 8 figures

Timescales of neural activity are diverse across and within brain areas, and experimental observations suggest that neural timescales reflect information in dynamic environments. However, these observations do not specify how neural timescales are shaped, nor whether particular timescales are necessary for neural computations and brain function. Here, we take a complementary perspective and synthesize three directions where computational methods can distill the broad set of empirical observations into quantitative and testable theories: We review (i) how data analysis methods allow us to capture different timescales of neural dynamics across different recording modalities, (ii) how computational models provide a mechanistic explanation for the emergence of diverse timescales, and (iii) how task-optimized models in machine learning uncover the functional relevance of neural timescales. This integrative computational approach, combined with empirical findings, would provide a more holistic understanding of how neural timescales capture the relationship between brain structure, dynamics, and behavior., Comment: 18 pages, 4 figures, 2 boxes

Bird's Eye View (BEV) map prediction is essential for downstream autonomous driving tasks like trajectory prediction. In the past, this was accomplished through the use of a sophisticated sensor configuration that captured a surround view from multiple cameras. However, in large-scale production, cost efficiency is an optimization goal, so that using fewer cameras becomes more relevant. But the consequence of fewer input images correlates with a performance drop. This raises the problem of developing a BEV perception model that provides a sufficient performance on a low-cost sensor setup. Although, primarily relevant for inference time on production cars, this cost restriction is less problematic on a test vehicle during training. Therefore, the objective of our approach is to reduce the aforementioned performance drop as much as possible using a modern multi-camera surround view model reduced for single-camera inference. The approach includes three features, a modern masking technique, a cyclic Learning Rate (LR) schedule, and a feature reconstruction loss for supervising the transition from six-camera inputs to one-camera input during training. Our method outperforms versions trained strictly with one camera or strictly with six-camera surround view for single-camera inference resulting in reduced hallucination and better quality of the BEV map., Comment: This Paper has been accepted to the 27th IEEE International Conference on Intelligent Transportation Systems (ITSC 2024)

Fully harvesting the gain of multiple-input and multiple-output (MIMO) requires accurate channel information. However, conventional channel acquisition methods mainly rely on pilot training signals, resulting in significant training overheads (time, energy, spectrum). Digital twin-aided communications have been proposed in [1] to reduce or eliminate this overhead by approximating the real world with a digital replica. However, how to implement a digital twin-aided communication system brings new challenges. In particular, how to model the 3D environment and the associated EM properties, as well as how to update the environment dynamics in a coherent manner. To address these challenges, motivated by the latest advancements in computer vision, 3D reconstruction and neural radiance field, we propose an end-to-end deep learning framework for future generation wireless systems that can reconstruct the 3D EM field covered by a wireless access point, based on widely available crowd-sourced world-locked wireless samples between the access point and the devices. This visionary framework is grounded in classical EM theory and employs deep learning models to learn the EM properties and interaction behaviors of the objects in the environment. Simulation results demonstrate that the proposed learnable digital twin can implicitly learn the EM properties of the objects, accurately predict wireless channels, and generalize to changes in the environment, highlighting the prospect of this novel direction for future generation wireless platforms.

In the presence of unmeasured heterogeneity, the hazard ratio for exposure has a complex causal interpretation. To address this, accelerated failure time (AFT) models, which assess the effect on the survival time ratio scale, are often suggested as a better alternative. AFT models also allow for straightforward confounder adjustment. In this work, we formalize the causal interpretation of the acceleration factor in AFT models using structural causal models and data under independent censoring. We prove that the acceleration factor is a valid causal effect measure, even in the presence of frailty and treatment effect heterogeneity. Through simulations, we show that the acceleration factor better captures the causal effect than the hazard ratio when both AFT and proportional hazards models apply. Additionally, we extend the interpretation to systems with time-dependent acceleration factors, revealing the challenge of distinguishing between a time-varying homogeneous effect and unmeasured heterogeneity. While the causal interpretation of acceleration factors is promising, we caution practitioners about potential challenges in estimating these factors in the presence of effect heterogeneity.

Quasi-periodic eruptions (QPEs) are an extreme X-ray variability phenomenon associated with low-mass supermassive black holes. First discovered in the nucleus of the galaxy GSN 069, they have been so far securely detected in five other galaxies, including RX J1301.9+2747. When detected, the out-of-QPE emission (quiescence) is consistent with the high-energy tail of thermal emission from an accretion disk. We present the X-ray and radio properties of RX J1301.9+2747, both in quiescence and during QPEs. We analyse X-ray data taken during five XMM-Newton observations between 2000 and 2022. The last three observations were taken in coordination with radio observations with the Karl G. Jansky Very Large Array. We also make use of EXOSAT, ROSAT, and Chandra archival observations taken between 1983 and 2009. XMM-Newton detected 34 QPEs of which 8 have significantly lower amplitudes than the others. No correlated radio/X-ray variability was observed during QPEs. In terms of timing properties, the QPEs in RX J1301.9+2747 do not exhibit the striking regularity observed in the discovery source GSN 069. In fact there is no clear repetition pattern between QPEs: the average time separation between their peaks is about four hours, but it can be as short as one, and as long as six hours. The QPE spectral properties of RX J1301.9+2747 as a function of energy are however very similar to those of GSN 069 and of other QPE sources. The quiescent emission of RX J1301.9+2747 is more complex than that of GSN 069, as it requires a soft X-ray excess-like component in addition to the thermal emission from the accretion disk. Its long-term X-ray quiescent flux variations are of low-amplitude and not strictly monotonic, with a general decay over $\sim 22$ years. We discuss our observational results in terms of some of the ideas and models that have been proposed so far for the physical origin of QPEs., Comment: Accepted for publication in Astronomy & Astrophysics. 22 pages and 17 figures in the main text; 11 pages, 5 tables, and 4 figures in the Appendix. Abstract shortened to comply with arXiv requirements

MICADO is a first-generation instrument for the ELT. It will provide diffraction-limited imaging in standard, astrometric, and coronagraphic modes and long-slit spectroscopy at near-infrared wavelengths. The core of the MICADO instrument is its cryostat, which cools the internal optical and mechanical subsystems to 80 K. Following a light ray entering the cryostat through the entrance window, the first mechanism it encounters is the Focal Plane Mechanism. It consists of two independent movable devices mounted in one assembly: the aperture wheel and the focal plane wheel. The primary purpose of the aperture wheel is to rapidly block the light path, which is needed to mitigate persistence on the detectors. The focal plane wheel holds field stops, calibration masks, slits, and coronagraphs. The positioning requirements for the wheel are dominated by the coronographic masks demanding a 15 $\mu$m RMS repeatability. To fulfill this specification and avoid mechanical wear in the drive, a novel magnetically coupled gear system was developed at the Max Planck Institute for Extraterrestrial Physics (MPE). A magnetically coupled worm gear uses magnetic forces to transmit torque from the motor to the driven component without direct mechanical contact. This paper describes the design and performance of the magnetic drive and the first results of the focal plane wheel prototype tests in a cryogenic environment., Comment: Proceeding of SPIE Astronomical Telescopes + Instrumentation 2024

Objective: Gadolinium-based contrast agents (GBCAs) are commonly used in MRI scans of patients with gliomas to enhance brain tumor characterization using T1-weighted (T1W) MRI. However, there is growing concern about GBCA toxicity. This study develops a deep-learning framework to generate T1-postcontrast (T1C) from pre-contrast multiparametric MRI. Approach: We propose the tumor-aware vision transformer (TA-ViT) model that predicts high-quality T1C images. The predicted tumor region is significantly improved (P < .001) by conditioning the transformer layers from predicted segmentation maps through adaptive layer norm zero mechanism. The predicted segmentation maps were generated with the multi-parametric residual (MPR) ViT model and transformed into a latent space to produce compressed, feature-rich representations. The TA-ViT model predicted T1C MRI images of 501 glioma cases. Selected patients were split into training (N=400), validation (N=50), and test (N=51) sets. Main Results: Both qualitative and quantitative results demonstrate that the TA-ViT model performs superior against the benchmark MRP-ViT model. Our method produces synthetic T1C MRI with high soft tissue contrast and more accurately reconstructs both the tumor and whole brain volumes. The synthesized T1C images achieved remarkable improvements in both tumor and healthy tissue regions compared to the MRP-ViT model. For healthy tissue and tumor regions, the results were as follows: NMSE: 8.53 +/- 4.61E-4; PSNR: 31.2 +/- 2.2; NCC: 0.908 +/- .041 and NMSE: 1.22 +/- 1.27E-4, PSNR: 41.3 +/- 4.7, and NCC: 0.879 +/- 0.042, respectively. Significance: The proposed method generates synthetic T1C images that closely resemble real T1C images. Future development and application of this approach may enable contrast-agent-free MRI for brain tumor patients, eliminating the risk of GBCA toxicity and simplifying the MRI scan protocol., Comment: arXiv admin note: text overlap with arXiv:2407.02616

The vertical transport of solid material in a stratified medium is fundamental to a number of environmental applications, with implications for the carbon cycle and nutrient transport in marine ecosystems. In this work, we study the diffusion-limited settling of highly porous particles in a density-stratified fluid through a combination of experiment, analysis, and numerical simulation. By delineating and appealing to the diffusion-limited regime wherein buoyancy effects due to mass adaptation dominate hydrodynamic drag, we derive a simple expression for the steady settling velocity of a sphere as a function of the density, size, and diffusivity of the solid, as well as the density gradient of the background fluid. In this regime, smaller particles settle faster, in contrast with most conventional hydrodynamic drag mechanisms. Furthermore, we outline a general mathematical framework for computing the steady settling speed of a body of arbitrary shape in this regime and compute exact results for the case of general ellipsoids. Using hydrogels as a highly porous model system, we validate the predictions with laboratory experiments in linear stratification for a wide range of parameters. Lastly, we show how the predictions can be applied to arbitrary slowly varying background density profiles and demonstrate how a measured particle position over time can be used to reconstruct the background density profile.

Ultrasound is widely employed for clinical intervention and diagnosis, due to its advantages of offering non-invasive, radiation-free, and real-time imaging. However, the accessibility of this dexterous procedure is limited due to the substantial training and expertise required of operators. The robotic ultrasound (RUS) offers a viable solution to address this limitation; nonetheless, achieving human-level proficiency remains challenging. Learning from demonstrations (LfD) methods have been explored in RUS, which learns the policy prior from a dataset of offline demonstrations to encode the mental model of the expert sonographer. However, active engagement of experts, i.e. Coaching, during the training of RUS has not been explored thus far. Coaching is known for enhancing efficiency and performance in human training. This paper proposes a coaching framework for RUS to amplify its performance. The framework combines DRL (self-supervised practice) with sparse expert's feedback through coaching. The DRL employs an off-policy Soft Actor-Critic (SAC) network, with a reward based on image quality rating. The coaching by experts is modeled as a Partially Observable Markov Decision Process (POMDP), which updates the policy parameters based on the correction by the expert. The validation study on phantoms showed that coaching increases the learning rate by $25\%$ and the number of high-quality image acquisition by $74.5\%$., Comment: Accepted in IEEE Transactions on Medical Robotics and Bionics (TMRB) 2024

Strongly driven ion-scale turbulence in tokamak plasmas is shown to be regulated by a new propagating zonal flow mode, the toroidal secondary, which is nonlinearly supported by the turbulence. The mode grows and propagates due to the combined effects of zonal flow shearing and advection by the magnetic drift. Above a threshold in the turbulence level, small-scale toroidal secondary modes become unstable and shear apart turbulent eddies, forcing the turbulence level to remain near the threshold. By including the new zonal flow physics into a theory of turbulence saturation based on the critical balance conjecture, scaling laws for the turbulent heat flux, fluctuation spectra, and zonal flow amplitude are derived and shown to be satisfied in nonlinear gyrokinetic simulations.

The canonical theory for planet formation in circumstellar disks proposes that planets are grown from initially much smaller seeds. The long-considered alternative theory proposes that giant protoplanets can be formed directly from collapsing fragments of vast spiral arms induced by gravitational instability -- if the disk is gravitationally unstable. For this to be possible, the disk must be massive compared to the central star: a disk-to-star mass ratio of 1/10 is widely held as the rough threshold for triggering gravitational instability, inciting significant non-Keplerian dynamics and generating prominent spiral arms. While estimating disk masses has historically been challenging, the motion of the gas can reveal the presence of gravitational instability through its effect on the disk velocity structure. Here we present kinematic evidence of gravitational instability in the disk around AB Aurigae, using deep observations of 13CO and C18O line emission with the Atacama Large Millimeter/submillimeter Array (ALMA). The observed kinematic signals strongly resemble predictions from simulations and analytic modelling. From quantitative comparisons, we infer a disk mass of up to 1/3 the stellar mass enclosed within 1" to 5" on the sky., Comment: Published in Nature. 4 main figures, 9 extended data figures, 1 extended data table. Published version at https://www.nature.com/articles/s41586-024-07877-0. Observational data products available at https://doi.org/10.11570/24.0087 with reduction guide & scripts at https://jjspeedie.github.io/guide.2021.1.00690.S. Simulated data products available at https://doi.org/10.5281/zenodo.11668694