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"Free college" programs are widespread in American higher education. They are discussed as addressing college access, affordability, inequality, and skills shortages. Many are last-dollar tuition guarantees restricted to use at single community colleges. Using student-level data spanning the transition to college, we investigate how two similar local community college tuition guarantees in Pennsylvania affected college-going outcomes. The Morgan Success Scholarship has large impacts on community college attendance and associate degree attainment. The program diverts students away from four-year colleges, though much of this effect is temporary. Meanwhile, we find little evidence that the Community College of Philadelphia's 50th Anniversary Scholars program has any impact on college-going behavior. We suggest reasons for divergent findings and offer suggestions for practice.

This article focuses on covariance estimation for multi-study data. Popular approaches employ factor-analytic terms with shared and study-specific loadings that decompose the variance into (i) a shared low-rank component, (ii) study-specific low-rank components, and (iii) a diagonal term capturing idiosyncratic variability. Our proposed methodology estimates the latent factors via spectral decompositions and infers the factor loadings via surrogate regression tasks, avoiding identifiability and computational issues of existing alternatives. Reliably inferring shared vs study-specific components requires novel developments that are of independent interest. The approximation error decreases as the sample size and the data dimension diverge, formalizing a blessing of dimensionality. Conditionally on the factors, loadings and residual error variances are inferred via conjugate normal-inverse gamma priors. The conditional posterior distribution of factor loadings has a simple product form across outcomes, facilitating parallelization. We show favorable asymptotic properties, including central limit theorems for point estimators and posterior contraction, and excellent empirical performance in simulations. The methods are applied to integrate three studies on gene associations among immune cells., Comment: Main 36 pages (including bibliography). With Appendix, 80 Pages and 3 figures

Moire patterns in van der Waals bilayer materials complicate the analysis of atomic-resolution images, hindering the atomic-scale insight typically attainable with scanning transmission electron microscopy. Here, we report a method to detect the positions and identity of atoms in each of the individual layers that compose bilayer heterostructures. We developed a deep learning model, Gomb-Net, which can distinguish atomic species in each individual layer, effectively deconvoluting the moire pattern to enable layer-specific mapping of strain and dopant distributions, unlike other methods which struggle with moire-induced complexity. Using this approach, we explored Se atom substitutional sites in a twisted fractional Janus WS2-WS2(1-x)Se2x heterostructure and found that layer specific implantation sites are unaffected by the moire pattern's local energetic or electronic modulation. This advancement enables atom-identification within material regimes where it was not possible before, opening new insights into previously inaccessible material physics.

Statistical inference on biodiversity has a rich history going back to RA Fisher. An influential ecological theory suggests the existence of a fundamental biodiversity number, denoted $\alpha$, which coincides with the precision parameter of a Dirichlet process (DP). In this paper, motivated by this theory, we develop Bayesian nonparametric methods for statistical inference on biodiversity, building on the literature on Gibbs-type priors. We argue that $\sigma$-diversity is the most natural extension of the fundamental biodiversity number and discuss strategies for its estimation. Furthermore, we develop novel theory and methods starting with an Aldous-Pitman (AP) process, which serves as the building block for any Gibbs-type prior with a square-root growth rate. We propose a modeling framework that accommodates the hierarchical structure of Linnean taxonomy, offering a more refined approach to quantifying biodiversity. The analysis of a large and comprehensive dataset on Amazon tree flora provides a motivating application.

We present a new framework for the fast solution of inhomogeneous elliptic boundary value problems in domains with smooth boundaries. High-order solvers based on adaptive box codes or the fast Fourier transform can efficiently treat the volumetric inhomogeneity, but require care to be taken near the boundary to ensure that the volume data is globally smooth. We avoid function extension or cut-cell quadratures near the boundary by dividing the domain into two regions: a bulk region away from the boundary that is efficiently treated with a truncated free-space box code, and a variable-width boundary-conforming strip region that is treated with a spectral collocation method and accompanying fast direct solver. Particular solutions in each region are then combined with Laplace layer potentials to yield the global solution. The resulting solver has an optimal computational complexity of $O(N)$ for an adaptive discretization with $N$ degrees of freedom. With an efficient two-dimensional (2D) implementation we demonstrate adaptive resolution of volumetric data, boundary data, and geometric features across a wide range of length scales, to typically 10-digit accuracy. The cost of all boundary corrections remains small relative to that of the bulk box code. The extension to 3D is expected to be straightforward in many cases because the strip ``thickens'' an existing boundary quadrature.

We present a composite machine learning framework to estimate posterior probability distributions of bulge-to-total light ratio, half-light radius, and flux for Active Galactic Nucleus (AGN) host galaxies within $z<1.4$ and $m<23$ in the Hyper Supreme-Cam Wide survey. We divide the data into five redshift bins: low ($0

Multi-plane light converters (MPLCs) - also known as linear diffractive neural networks - are an emerging optical technology, capable of converting an orthogonal set of optical fields into any other orthogonal set via a unitary transformation. MPLC design is a non-linear problem typically solved by optimising a digital model of the optical system. However, inherently high levels of design complexity mean that even a minor mismatch between this digital model and the physically realised MPLC leads to a severe reduction in real-world performance. Here we address this challenge by creating a self-configuring free-space MPLC. Despite the large number of parameters to be optimised (typically tens of thousands or more), our proof-of-principle device converges in minutes using a method in which light only needs to be transmitted in one direction through the MPLC. Two innovations make this possible. Firstly, we devise an in-situ optimisation algorithm combining wavefront shaping with the principles of wavefront matching that would conventionally be used to inverse-design MPLCs offline in simulation. Secondly, we introduce a new MPLC platform incorporating a microelectromechanical system (MEMS) phase-only light modulator - allowing rapid MPLC switching at up to kiloHertz rates. Our scheme automatically accounts for the physical characteristics of all system components and absorbs any unknown misalignments and aberrations into the final design. We demonstrate self-configured MPLCs capable of mapping random orthogonal speckle input fields to well-defined Laguerre-Gaussian and Hermite-Gaussian output modes, as well as universal mode sorters. Our work paves the way towards large-scale ultra-high-fidelity fast-switching MPLCs and diffractive neural networks, which promises to unlock new applications in areas ranging from optical communications to optical computing and imaging., Comment: 14 pages, 7 figures, 1 table

We report measurements of anisotropic triple-$q$ charge density wave (CDW) fluctuations in the transition metal dichalcogenide 1$T$-TiSe$_2$ over a large volume of reciprocal space with X-ray diffuse scattering. Above the transition temperature, $T_{\text{CDW}}$, the out-of-plane diffuse scattering is characterized by rod-like structures which indicate that the CDW fluctuations in neighboring layers are largely decoupled. In addition, the in-plane diffuse scattering is marked by ellipses which reveal that the in-plane fluctuations are anisotropic. Our analysis of the diffuse scattering line shapes and orientations suggests that the three charge density wave components contain independent phase fluctuations. At $T_{\text{CDW}}$, long range coherence is established in both the in-plane and out-of-plane directions, consistent with the large observed value of the CDW gap compared to $T_{\text{CDW}}$, and the predicted presence of a hierarchy of energy scales.

We investigate non-reciprocal scattering within the modes of a microwave frequency comb. Adjusting the pump frequencies, amplitudes, and phases of a Josephson parametric oscillator, we control constructive interference for the $m \longrightarrow \ell$ scattering processes, while concurrently achieving destructive interference for the inverse process $\ell \longrightarrow m$. We outline the methodology for realizing non-reciprocity in the context of two-mode isolation and a three-mode circulation, which we extend to multiple modes. We find good agreement between the experiments and a linearized theoretical model. Nonreciprocal scattering expands the toolset for parametric control, with the potential to engineer novel quantum correlations., Comment: 9 pages, 5 figures

Deep generative models are routinely used in generating samples from complex, high-dimensional distributions. Despite their apparent successes, their statistical properties are not well understood. A common assumption is that with enough training data and sufficiently large neural networks, deep generative model samples will have arbitrarily small errors in sampling from any continuous target distribution. We set up a unifying framework that debunks this belief. We demonstrate that broad classes of deep generative models, including variational autoencoders and generative adversarial networks, are not universal generators. Under the predominant case of Gaussian latent variables, these models can only generate concentrated samples that exhibit light tails. Using tools from concentration of measure and convex geometry, we give analogous results for more general log-concave and strongly log-concave latent variable distributions. We extend our results to diffusion models via a reduction argument. We use the Gromov--Levy inequality to give similar guarantees when the latent variables lie on manifolds with positive Ricci curvature. These results shed light on the limited capacity of common deep generative models to handle heavy tails. We illustrate the empirical relevance of our work with simulations and financial data.

Promise programs are discussed as a policy movement that began with the 2005 launch of the Kalamazoo Promise. Since then, programs bearing the Promise label or sharing similar features have spread across the higher educational landscape, appearing in most states and across postsecondary sectors. Simultaneously, scholarly literature discussing these programs has burgeoned. And yet, scholars and others are unable to formulate a clear conception of what a Promise program is and what if anything sets such a program apart from other scholarship programs (e.g., state need-based grants). In this paper, we examine how scholars have discussed these programs over time. We begin with the initial theorization of the Kalamazoo Promise as a case and observe its use as a prototype in the formulation of a general model once "Promise program" was established as a category. We follow how the spread and transformation of "Promise programs" was reflected in repeated partial reconceptualization. We find three competing conceptual models emerging in sequence: 1) a thick, place-based causal model derived as a generalization of the Kalamazoo Promise, 2) a thin empirical model crafted in the aftermath of the launch of the Tennessee Promise, and 3) a partially acknowledged minimal or symbolic model advanced haltingly in response to critiques of last-dollar community college state programs. Scholarly conceptualization is largely reactive to empirical program diffusion and transformation, though scholarly idealization may have played a role in this diffusion itself.

"Free college" (sometimes called Promise) programs are common in U.S. higher education. Reviewing 88 studies of 25 state and local programs, I provide a nuanced picture of the mechanisms through which these programs may work and their likely effects on students, communities, and colleges. Some commonly-claimed mechanisms for these effects--e.g., improving secondary school environments or impacting residential decisions--lack empirical support or are implausible for most existing programs. Programs are consistently found to shift college-bound students to colleges where they can use more scholarship dollars, increase enrollment at eligible colleges, and (for generous local programs only) increase community school district enrollment. Less consistently, programs boost college participation and thereby degree attainment, but evidence for direct effects on college performance, persistence or completion net of enrollment is weak. There is insufficient or inconsistent evidence for program effects on secondary school performance and graduation, post-college income and debt, community population or property values, and inequality reduction according to gender, race, or socioeconomic status.

Over time, the distribution of medical image data drifts due to factors such as shifts in patient demographics, acquisition devices, and disease manifestations. While human radiologists can adjust their expertise to accommodate such variations, deep learning models cannot. In fact, such models are highly susceptible to even slight variations in image characteristics. Consequently, manufacturers must conduct regular updates to ensure that they remain safe and effective. Performing such updates in the United States and European Union required, until recently, obtaining re-approval. Given the time and financial burdens associated with these processes, updates were infrequent, and obsolete systems remained in operation for too long. During 2024, several regulatory developments promised to streamline the safe rollout of model updates: The European Artificial Intelligence Act came into effect last August, and the Food and Drug Administration (FDA) issued final marketing submission recommendations for a Predetermined Change Control Plan (PCCP) in December. We provide an overview of these developments and outline the key building blocks necessary for successfully deploying dynamic systems. At the heart of these regulations - and as prerequisites for manufacturers to conduct model updates without re-approval - are clear descriptions of data collection and re-training processes, coupled with robust real-world quality monitoring mechanisms.

3D color lookup tables (LUTs) enable precise color manipulation by mapping input RGB values to specific output RGB values. 3D LUTs are instrumental in various applications, including video editing, in-camera processing, photographic filters, computer graphics, and color processing for displays. While an individual LUT does not incur a high memory overhead, software and devices may need to store dozens to hundreds of LUTs that can take over 100 MB. This work aims to develop a neural network architecture that can encode hundreds of LUTs in a single compact representation. To this end, we propose a model with a memory footprint of less than 0.25 MB that can reconstruct 512 LUTs with only minor color distortion ($\bar{\Delta}E_M$ $\leq$ 2.0) over the entire color gamut. We also show that our network can weight colors to provide further quality gains on natural image colors ($\bar{\Delta}{E}_M$ $\leq$ 1.0). Finally, we show that minor modifications to the network architecture enable a bijective encoding that produces LUTs that are invertible, allowing for reverse color processing. Our code is available at https://github.com/vahidzee/ennelut., Comment: 14 pages, 13 figures; extended version; to appear in AAAI 2025

The degradation of thermal conductivity in advanced nuclear fuels due to the accumulation of fission products and irradiation-induced defects is inevitable, and must be considered as part of safety and efficiency analyses of nuclear reactors. This study examines the thermal conductivity of a zirconium-doped ThO$_2$ crystal, synthesized via the hydrothermal method using a spatial domain thermo-reflectance technique. Zirconium is one of the soluble fission products in oxide fuels that can effectively scatter heat-carrying phonons in the crystalline lattice of fuel. Thus, thermal property measurements of zirconium-doped ThO$_2$ single crystals provide insights into the effects of substitutional zirconium doping, isolated from extrinsic factors such as grain boundary scattering. The experimental results are compared with first-principles calculations of the lattice thermal conductivity of ThO$_2$, employing an iterative solution of the Peierls-Boltzmann transport equation. Additionally, the non-perturbative Greens function methodology is utilized to compute phonon-point defect scattering rates, accounting for local distortions around point defects, including mass difference changes, interatomic force constants, and structural relaxation. The congruence between the predicted results from first-principles calculations and the measured temperature-dependent thermal conductivity validates the computational methodology. Furthermore, the methodologies employed in this study enable systematic investigations of thermal conductivity reduction by fission products, potentially leading to the development of more accurate fuel performance codes.

Reconstructing photorealistic and dynamic portrait avatars from images is essential to many applications including advertising, visual effects, and virtual reality. Depending on the application, avatar reconstruction involves different capture setups and constraints $-$ for example, visual effects studios use camera arrays to capture hundreds of reference images, while content creators may seek to animate a single portrait image downloaded from the internet. As such, there is a large and heterogeneous ecosystem of methods for avatar reconstruction. Techniques based on multi-view stereo or neural rendering achieve the highest quality results, but require hundreds of reference images. Recent generative models produce convincing avatars from a single reference image, but visual fidelity yet lags behind multi-view techniques. Here, we present CAP4D: an approach that uses a morphable multi-view diffusion model to reconstruct photoreal 4D (dynamic 3D) portrait avatars from any number of reference images (i.e., one to 100) and animate and render them in real time. Our approach demonstrates state-of-the-art performance for single-, few-, and multi-image 4D portrait avatar reconstruction, and takes steps to bridge the gap in visual fidelity between single-image and multi-view reconstruction techniques., Comment: 23 pages, 15 figures

Accelerating global biodiversity loss has highlighted the role of complex relationships and shared patterns among species in mediating responses to environmental changes. The structure of ecological communities signals their fragility or robustness more so than individual niches of species. We focus on obtaining community-level insights that characterize underlying patterns in abundances of bird species in Finland. We propose a novel \texttt{barcode} framework for inferring latent binary features underlying samples and species. \texttt{barcode} provides a more nuanced alternative to clustering, while improving current multivariate abundance models. \texttt{barcode} addresses key limitations of popular methods for model-based ordination and expands the class of concurrent ordinations. A key feature is our use of binary latent variables, which admit simple interpretations such as habitat and sampling factors that explain observed variation. In studying 137 bird species using this framework, we find that three of the five leading factors indicate different types of forest habitat, signaling the importance of diverse forest in this community. In contrast, a single factor simultaneously proxies both human intervention and coastal habitats. Supervised species clusters and species-specific geospatial distributions are also inferred., Comment: 33 pages, 8 figures

The molecular Kennicutt-Schmidt (mK-S) Law has been key for understanding star formation (SF) in galaxies across all redshifts. However, recent sub-kpc observations of nearby galaxies reveal deviations from the nearly unity slope (N) obtained with disk-averaged measurements. We study SF and molecular gas (MG) distribution in the early-stage luminous infrared galaxy merger Arp240 (NGC5257-8). Using VLA radio continuum (RC) and ALMA CO(2-1) observations with a uniform grid analysis, we estimate SF rates and MG surface densities ($\Sigma_{\mathrm{SFR}}$ and $\Sigma_{\mathrm{H_2}}$, respectively). In Arp 240, N is sub-linear at 0.52 $\pm$ 0.17. For NGC 5257 and NGC 5258, N is 0.52 $\pm$ 0.16 and 0.75 $\pm$ 0.15, respectively. We identify two SF regimes: high surface brightness (HSB) regions in RC with N $\sim$1, and low surface brightness (LSB) regions with shallow N (ranging 0.15 $\pm$ 0.09 to 0.48 $\pm$ 0.04). Median CO(2-1) linewidth and MG turbulent pressure (P$_{\mathrm{turb}}$) are 25 km s$^{-1}$ and 9 $\times$10$^{5}$ K cm$^{-3}$. No significant correlation was found between $\Sigma_{\mathrm{SFR}}$ and CO(2-1) linewidth. However, $\Sigma_{\mathrm{SFR}}$ correlates with P$_{\mathrm{turb}}$, particularly in HSB regions ($\rho >$0.60). In contrast, SF efficiency moderately anti-correlates with P$_{\mathrm{turb}}$ in LSB regions but shows no correlation in HSB regions. Additionally, we identify regions where peaks in SF and MG are decoupled, yielding a shallow N ($\leq$ 0.28 $\pm$ 0.18). Overall, the range of N reflects distinct physical properties and distribution of both the SF and MG, which can be masked by disk-averaged measurements.

In including random effects to account for dependent observations, the odds ratio interpretation of logistic regression coefficients is changed from population-averaged to subject-specific. This is unappealing in many applications, motivating a rich literature on methods that maintain the marginal logistic regression structure without random effects, such as generalized estimating equations. However, for spatial data, random effect approaches are appealing in providing a full probabilistic characterization of the data that can be used for prediction. We propose a new class of spatial logistic regression models that maintain both population-averaged and subject-specific interpretations through a novel class of bridge processes for spatial random effects. These processes are shown to have appealing computational and theoretical properties, including a scale mixture of normal representation. The new methodology is illustrated with simulations and an analysis of childhood malaria prevalence data in the Gambia.

It has recently been argued that a proposal to nonperturbatively regulate chiral gauge theories on the boundary of a five-dimensional lattice will fail due to the existence of exact $U(1)$ symmetries not present in the target theories. A related observation is that in the presence of gauge fields with nontrivial topology, fermion zeromodes will appear embedded in the extra dimension which do not decouple from the four-dimensional world. Here we show that these problems can be evaded by simulating the theory in the trivial topological sector of the boundary gauge fields. A regulated version of the Standard Model using this approach in the continuum and large volume limits is expected to appear as if it possessed a Peccei-Quinn mechanism for relaxing $\theta_\text{QCD}$ to zero, without there being an axion in the spectrum., Comment: Corrections to references

Multiple Sclerosis (MS) is a heterogeneous autoimmune-mediated disorder affecting the central nervous system, commonly manifesting as fatigue and progressive limb impairment. This can significantly impact quality of life due to weakness or paralysis in the upper and lower limbs. A Brain-Computer Interface (BCI) aims to restore quality of life through control of an external device, such as a wheelchair. However, the limited BCI research in people with MS is insufficient. The current study aims to expand on the current MS-BCI literature by highlighting the feasibility of decoding MS imagined movement. We collected electroencephalography (EEG) data from eight participants with various symptoms of MS and ten neurotypical control participants. Participants made imagined movements of the hands and feet as directed by a go no-go protocol. Binary regularised linear discriminant analysis was used to classify imagined movement at individual time-frequency points. The frequency bands which provided the maximal accuracy, and the associated latency, were compared. In all MS participants, the classification algorithm achieved above 70% accuracy in at least one imagined movement vs. rest classification and most movement vs. movement classifications. There was no significant difference between classification of limbs with weakness or paralysis to neurotypical controls. Both the MS and control groups possessed decodable information within the alpha (7-13 Hz) and beta (16-30 Hz) bands at similar latency. This study is the first to demonstrate the feasibility of decoding imagined movements in people with MS. As an alternative to the P300 response, motor imagery-based control of a BCI may also be combined with existing motor imagery therapy to supplement MS rehabilitation. These promising results merit further long term BCI studies to investigate the effect of MS progression on classification performance., Comment: Word count (main text): 5964 Word count (abstract): 299 Page Count: 21 Number of figures: 6 + 1 table, 3 supplementary

Numerous works have recently integrated 3D camera control into foundational text-to-video models, but the resulting camera control is often imprecise, and video generation quality suffers. In this work, we analyze camera motion from a first principles perspective, uncovering insights that enable precise 3D camera manipulation without compromising synthesis quality. First, we determine that motion induced by camera movements in videos is low-frequency in nature. This motivates us to adjust train and test pose conditioning schedules, accelerating training convergence while improving visual and motion quality. Then, by probing the representations of an unconditional video diffusion transformer, we observe that they implicitly perform camera pose estimation under the hood, and only a sub-portion of their layers contain the camera information. This suggested us to limit the injection of camera conditioning to a subset of the architecture to prevent interference with other video features, leading to 4x reduction of training parameters, improved training speed and 10% higher visual quality. Finally, we complement the typical dataset for camera control learning with a curated dataset of 20K diverse dynamic videos with stationary cameras. This helps the model disambiguate the difference between camera and scene motion, and improves the dynamics of generated pose-conditioned videos. We compound these findings to design the Advanced 3D Camera Control (AC3D) architecture, the new state-of-the-art model for generative video modeling with camera control., Comment: Project Page: https://snap-research.github.io/ac3d/

The role of internal and environmental factors in the star formation activity of galaxies is still a matter of debate, particularly at higher redshifts. Leveraging the most recent release of the COSMOS catalog, COSMOS2020, and density measurements from our previous study we disentangle the impact of environment and stellar mass on the star formation rate (SFR), and specific SFR (sSFR) of a sample of $\sim 210,000$ galaxies within redshift range $0.4< z < 4$ and present our findings in three cosmic epochs: 1) out to $z\sim 1$, the average SFR and sSFR decline at extremely dense environments and high mass end of the distribution which is mostly due to the presence of the massive quiescent population; 2) at $1

Methods for image-to-video generation have achieved impressive, photo-realistic quality. However, adjusting specific elements in generated videos, such as object motion or camera movement, is often a tedious process of trial and error, e.g., involving re-generating videos with different random seeds. Recent techniques address this issue by fine-tuning a pre-trained model to follow conditioning signals, such as bounding boxes or point trajectories. Yet, this fine-tuning procedure can be computationally expensive, and it requires datasets with annotated object motion, which can be difficult to procure. In this work, we introduce SG-I2V, a framework for controllable image-to-video generation that is self-guided$\unicode{x2013}$offering zero-shot control by relying solely on the knowledge present in a pre-trained image-to-video diffusion model without the need for fine-tuning or external knowledge. Our zero-shot method outperforms unsupervised baselines while significantly narrowing down the performance gap with supervised models in terms of visual quality and motion fidelity., Comment: Project page: https://kmcode1.github.io/Projects/SG-I2V/

Far-infrared (FIR) surveys are critical to probing the co-evolution of black holes and galaxies, since of order half the light from accreting black holes and active star formation is emitted in the rest-frame infrared over $0.5\lesssim z \lesssim 10$. For deep fields with areas of 1 deg$^2$ or less, like the legacy surveys GOODS, COSMOS, and CANDELS, source crowding means that sub-arcsecond resolution is essential. In this paper, we show with a simulation of the FIR sky that observations made with a small telescope (2 m) at low angular resolution preferentially detect the brightest galaxies, and we demonstrate the scientific value of a space mission that would offer sub-arcsecond resolution. We envisage a facility that would provide high-resolution imaging and spectroscopy over the wavelength range $25-400\,\mu m$, and we present predictions for an extragalactic survey covering $0.5\,\hbox{deg}^2$. Such a survey is expected to detect tens of thousands of star-forming galaxies and thousands of Active Galactic Nuclei (AGN), in multiple FIR lines (e.g. [CII], [OI], [CI]) and continuum. At the longest wavelengths (200-400$\,\mu$m), it would probe beyond the reionization epoch, up to $z\sim 7$-8. A combination of spectral resolution, line sensitivity, and broad spectral coverage would allow us to learn about the physical conditions (temperature, density, metallicity) characterizing the interstellar medium of galaxies over the past $\sim 12$ billion years and to investigate galaxy-AGN co-evolution., Comment: 23 pages, 13 figures, 1 table, accepted for publication in ApJ

We describe an experiment demonstrating the generation of three independent square-ladder continuous-variable cluster states with up to 94 qumodes of a microwave frequency comb. This entanglement structure at a large scale is realized by injecting vacuum fluctuations into a Josephson Parametric Amplifier pumped by three coherent signals around twice its resonance frequency, each having a particular well-defined phase relation. We reach up to 1.4 dB of squeezing of the nullifier which verifies the cluster state on the square ladder graph. Our results are consistent with a more familiar measure of two-mode squeezing, where we find up to 5.42 dB for one pump, and up to 1 dB for three pumps., Comment: 5 pages, 4 figures (main article) and 7 pages, 6 figures (suppl. material)

Recently Weyl edge states on manifolds in dimension $d+1$ with a connected $d$-dimensional boundary were proposed as candidates for lattice regularization of chiral gauge theories, for even $d$. The examples considered to date include solid cylinders in any odd dimension, and the 3-ball with boundary $S^2$. Here we consider the general case of a $(d+1)$-dimensional ball for any even $d$ and show that the theory for the edge states on $S^d$ describe a conventional Weyl fermion on a sphere with half-integer momenta. A possible advantage of such theories is that they can be discretized by a square lattice without breaking the underlying discrete hypercubic symmetry.

Feature selection can greatly improve performance and interpretability in machine learning problems. However, existing nonparametric feature selection methods either lack theoretical error control or fail to accurately control errors in practice. Many methods are also slow, especially in high dimensions. In this paper, we introduce a general feature selection method that applies integrated path stability selection to thresholding to control false positives and the false discovery rate. The method also estimates q-values, which are better suited to high-dimensional data than p-values. We focus on two special cases of the general method based on gradient boosting (IPSSGB) and random forests (IPSSRF). Extensive simulations with RNA sequencing data show that IPSSGB and IPSSRF have better error control, detect more true positives, and are faster than existing methods. We also use both methods to detect microRNAs and genes related to ovarian cancer, finding that they make better predictions with fewer features than other methods.

This article focuses on inference in logistic regression for high-dimensional binary outcomes. A popular approach induces dependence across the outcomes by including latent factors in the linear predictor. Bayesian approaches are useful for characterizing uncertainty in inferring the regression coefficients, factors and loadings, while also incorporating hierarchical and shrinkage structure. However, Markov chain Monte Carlo algorithms for posterior computation face challenges in scaling to high-dimensional outcomes. Motivated by applications in ecology, we exploit a blessing of dimensionality to motivate pre-estimation of the latent factors. Conditionally on the factors, the outcomes are modeled via independent logistic regressions. We implement Gaussian approximations in parallel in inferring the posterior on the regression coefficients and loadings, including a simple adjustment to obtain credible intervals with valid frequentist coverage. We show posterior concentration properties and excellent empirical performance in simulations. The methods are applied to insect biodiversity data in Madagascar.

Advances in robot learning have enabled robots to generate skills for a variety of tasks. Yet, robot learning is typically sample inefficient, struggles to learn from data sources exhibiting varied behaviors, and does not naturally incorporate constraints. These properties are critical for fast, agile tasks such as playing table tennis. Modern techniques for learning from demonstration improve sample efficiency and scale to diverse data, but are rarely evaluated on agile tasks. In the case of reinforcement learning, achieving good performance requires training on high-fidelity simulators. To overcome these limitations, we develop a novel diffusion modeling approach that is offline, constraint-guided, and expressive of diverse agile behaviors. The key to our approach is a kinematic constraint gradient guidance (KCGG) technique that computes gradients through both the forward kinematics of the robot arm and the diffusion model to direct the sampling process. KCGG minimizes the cost of violating constraints while simultaneously keeping the sampled trajectory in-distribution of the training data. We demonstrate the effectiveness of our approach for time-critical robotic tasks by evaluating KCGG in two challenging domains: simulated air hockey and real table tennis. In simulated air hockey, we achieved a 25.4% increase in block rate, while in table tennis, we saw a 17.3% increase in success rate compared to imitation learning baselines.

Gaussian splatting has demonstrated excellent performance for view synthesis and scene reconstruction. The representation achieves photorealistic quality by optimizing the position, scale, color, and opacity of thousands to millions of 2D or 3D Gaussian primitives within a scene. However, since each Gaussian primitive encodes both appearance and geometry, these attributes are strongly coupled--thus, high-fidelity appearance modeling requires a large number of Gaussian primitives, even when the scene geometry is simple (e.g., for a textured planar surface). We propose to texture each 2D Gaussian primitive so that even a single Gaussian can be used to capture appearance details. By employing per-primitive texturing, our appearance representation is agnostic to the topology and complexity of the scene's geometry. We show that our approach, GStex, yields improved visual quality over prior work in texturing Gaussian splats. Furthermore, we demonstrate that our decoupling enables improved novel view synthesis performance compared to 2D Gaussian splatting when reducing the number of Gaussian primitives, and that GStex can be used for scene appearance editing and re-texturing., Comment: Project page: https://lessvrong.com/cs/gstex. Updated Oct. 29 to correct Table 1 numbers. Please see https://github.com/victor-rong/GStex?tab=readme-ov-file#errata for details

Context. Magnetic bright points (MBPs) are one of the smallest manifestations of the magnetic field in the solar atmosphere and are observed to extend from the photosphere up to the chromosphere. As such, they represent an excellent feature to use in searches for types of magnetohydrodynamic (MHD) waves and mode coupling in the solar atmosphere. Aims. In this work, we aim to study wave propagation in the lower solar atmosphere by comparing intensity oscillations in the photosphere with the chromosphere via a search for possible mode coupling, in order to establish the importance of these types of waves in the solar atmosphere, and their contribution to heating the chromosphere. Methods. Observations were conducted in July 2011 with the ROSA and the HARDCam instruments at the Dunn Solar Telescope. We used wavelet analysis to identify traveling MHD waves and derive frequencies in the G-band and H$\alpha$wave bands. We isolated a large sample of MBPs using an automated tracking algorithm throughout our observations. Two dozen of the brightest MBPs were selected from the sample for further study. Results. We find oscillations in the G-band MBPs, with frequencies between 1.5 and 3.6 mHz. Corresponding MBPs in the lower solar chromosphere observed in H$\alpha$ show a frequency range of 1.4 to 4.3 mHz. In about 38\% of the MBPs, the ratio of H$\alpha$ to G-band frequencies was near two. Thus, these oscillations show a form of mode coupling where the transverse waves in the photosphere are converted into longitudinal waves in the chromosphere. Conclusions. From simple estimates we find an energy flux of $\approx$45 $\times 10^{3}$ W m$^{-2}$ and show that the energy flowing through MBPs is enough to heat the chromosphere, and mode coupling is important in helping us understand the types of MHD waves in the lower solar atmosphere and the overall energy budget., Comment: 6 pages, 4 figures, 1 Table; Accepted in Astronomy \& Astrophysics