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The density ratio of two probability distributions is one of the fundamental tools in mathematical and computational statistics and machine learning, and it has a variety of known applications. Therefore, density ratio estimation from finite samples is a very important task, but it is known to be unstable when the distributions are distant from each other. One approach to address this problem is density ratio estimation using incremental mixtures of the two distributions. We geometrically reinterpret existing methods for density ratio estimation based on incremental mixtures. We show that these methods can be regarded as iterating on the Riemannian manifold along a particular curve between the two probability distributions. Making use of the geometry of the manifold, we propose to consider incremental density ratio estimation along generalized geodesics on this manifold. To achieve such a method requires Monte Carlo sampling along geodesics via transformations of the two distributions. We show how to implement an iterative algorithm to sample along these geodesics and show how changing the distances along the geodesic affect the variance and accuracy of the estimation of the density ratio. Our experiments demonstrate that the proposed approach outperforms the existing approaches using incremental mixtures that do not take the geometry of the

The Confidential Consortium Framework (CCF) is an open-source platform for developing trustworthy and reliable cloud applications. CCF powers Microsoft's Azure Confidential Ledger service and as such it is vital to build confidence in the correctness of CCF's design and implementation. This paper reports our experiences applying smart casual verification to validate the correctness of CCF's novel distributed protocols, focusing on its unique distributed consensus protocol and its custom client consistency model. We use the term smart casual verification to describe our hybrid approach, which combines the rigor of formal specification and model checking with the pragmatism of automated testing, in our case binding the formal specification in TLA+ to the C++ implementation. While traditional formal methods approaches require substantial buy-in and are often one-off efforts by domain experts, we have integrated our smart casual verification approach into CCF's continuous integration pipeline, allowing contributors to continuously validate CCF as it evolves. We describe the challenges we faced in applying smart casual verification to a complex existing codebase and how we overcame them to find subtle bugs in the design and implementation before they could impact production.

We explain the algorithm employed in SHADOW4 to generate bending magnet sources. We provide an overview of the fundamental equations used to calculate the spectral and angular distributions of synchrotron emission from bending magnets. We outline the procedures for ray sampling. We present examples for intensity and polarization of the bending magnet at the old ESRF1 storage ring, and phase spaces for the bending magnet of the upcoming ALSU ring. Finally, we discuss the calculation of the effective source size and compare it with ray tracing., Comment: 6 pages, 9 figures

Various methods have emerged for conducting mediation analyses with multiple correlated mediators, each with distinct strengths and limitations. However, a comparative evaluation of these methods is lacking, providing the motivation for this paper. This study examines six mediation analysis methods for multiple correlated mediators that provide insights to the contributors for health disparities. We assessed the performance of each method in identifying joint or path-specific mediation effects in the context of binary outcome variables varying mediator types and levels of residual correlation between mediators. Through comprehensive simulations, the performance of six methods in estimating joint and/or path-specific mediation effects was assessed rigorously using a variety of metrics including bias, mean squared error, coverage and width of the 95$\%$ confidence intervals. Subsequently, these methods were applied to the REasons for Geographic And Racial Differences in Stroke (REGARDS) study, where differing conclusions were obtained depending on the mediation method employed. This evaluation provides valuable guidance for researchers grappling with complex multi-mediator scenarios, enabling them to select an optimal mediation method for their research question and dataset.

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated., Comment: Prepared for submission to JINST

Within the field of optimal transport (OT), the choice of ground cost is crucial to ensuring that the optimality of a transport map corresponds to usefulness in real-world applications. It is therefore desirable to use known information to tailor cost functions and hence learn OT maps which are adapted to the problem at hand. By considering a class of neural ground costs whose Monge maps have a known form, we construct a differentiable Monge map estimator which can be optimized to be consistent with known information about an OT map. In doing so, we simultaneously learn both an OT map estimator and a corresponding adapted cost function. Through suitable choices of loss function, our method provides a general approach for incorporating prior information about the Monge map itself when learning adapted OT maps and cost functions.

SBND is the near detector of the Short-Baseline Neutrino program at Fermilab. Its location near to the Booster Neutrino Beam source and relatively large mass will allow the study of neutrino interactions on argon with unprecedented statistics. This paper describes the expected performance of the SBND photon detection system, using a simulated sample of beam neutrinos and cosmogenic particles. Its design is a dual readout concept combining a system of 120 photomultiplier tubes, used for triggering, with a system of 192 X-ARAPUCA devices, located behind the anode wire planes. Furthermore, covering the cathode plane with highly-reflective panels coated with a wavelength-shifting compound recovers part of the light emitted towards the cathode, where no optical detectors exist. We show how this new design provides a high light yield and a more uniform detection efficiency, an excellent timing resolution and an independent 3D-position reconstruction using only the scintillation light. Finally, the whole reconstruction chain is applied to recover the temporal structure of the beam spill, which is resolved with a resolution on the order of nanoseconds., Comment: 21 pages, 17 figures

Conversion of non-native accented speech to native (American) English has a wide range of applications such as improving intelligibility of non-native speech. Previous work on this domain has used phonetic posteriograms as the target speech representation to train an acoustic model which is then used to extract a compact representation of input speech for accent conversion. In this work, we introduce the idea of using an effective articulatory speech representation, extracted from an acoustic-to-articulatory speech inversion system, to improve the acoustic model used in accent conversion. The idea to incorporate articulatory representations originates from their ability to well characterize accents in speech. To incorporate articulatory representations with conventional phonetic posteriograms, a multi-task learning based acoustic model is proposed. Objective and subjective evaluations show that the use of articulatory representations can improve the effectiveness of accent conversion., Comment: Accepted at INTERSPEECH 2024

Many cognitive models, including those for predicting the time of future events, can be mapped onto a particular form of neural representation in which activity across a population of neurons is restricted to manifolds that specify the Laplace transform of functions of continuous variables. These populations coding Laplace transform are associated with another population that inverts the transform, approximating the original function. This paper presents a neural circuit that uses continuous attractor dynamics to represent the Laplace transform of a delta function evolving in time. One population places an edge at any location along a 1-D array of neurons; another population places a bump at a location corresponding to the edge. Together these two populations can estimate a Laplace transform of delta functions in time along with an approximate inverse transform. Building the circuit so the edge moves at an appropriate speed enables the network to represent events as a function of log time. Choosing the connections appropriately within the edge network make the network states map onto Laplace transform with exponential change as a function of time. In this paper we model a learned temporal association in which one stimulus predicts another at some fixed delay $T$. Shortly after $t=0$ the first stimulus recedes into the past. The Laplace Neural Manifold representing the past maintains the Laplace transform $\exp(-st)$. Another Laplace Neural Manifold represents the predicted future. At $t=0$, the second stimulus is represented a time $T$ in the future. At each moment between 0 and $T$, firing over the Laplace transform predicting the future changes as $\exp[-s(T-t)]$. Despite exponential growth in firing, the circuit is robust to noise, making it a practical means to implement Laplace Neural Manifolds in populations of neurons for a variety of cognitive models., Comment: 32 pages, 9 figures

We study approximations to the Moreau envelope -- and infimal convolutions more broadly -- based on Laplace's method, a classical tool in analysis which ties certain integrals to suprema of their integrands. We believe the connection between Laplace's method and infimal convolutions is generally deserving of more attention in the study of optimization and partial differential equations, since it bears numerous potentially important applications, from proximal-type algorithms to solving Halmiton-Jacobi equations.

In this work we aim to bridge the divide between autonomous embodied systems and causal reasoning. Autonomous embodied systems have come to increasingly interact with humans, and in many cases may pose risks to the physical or mental well-being of those they interact with. Meanwhile causal models, despite their inherent transparency and ability to offer contrastive explanations, have found limited usage within such systems. As such, we first identify the challenges that have limited the integration of structural causal models within autonomous embodied systems. We then introduce a number of theoretical extensions to the structural causal model formalism in order to tackle these challenges. This augments these models to possess greater levels of modularisation and encapsulation, as well presenting a constant space temporal causal model representation. While not an extension itself, we also prove through the extensions we have introduced that dynamically mutable sets can be captured within structural causal models while maintaining a form of causal stationarity. Finally we introduce two case study architectures demonstrating the application of these extensions along with a discussion of where these extensions could be utilised in future work., Comment: 33 Pages = 7 Pages (Main Content) + 2 Pages (References) + 24 Pages (Appendix), 17 Figures = 2 Figures (Main Content) + 15 (Appendix), Oxford Robotics Institute Preprint; Significantly updated based upon feedback

With the advent of Large Language Models (LLMs) possessing increasingly impressive capabilities, a number of Large Vision-Language Models (LVLMs) have been proposed to augment LLMs with visual inputs. Such models condition generated text on both an input image and a text prompt, enabling a variety of use cases such as visual question answering and multimodal chat. While prior studies have examined the social biases contained in text generated by LLMs, this topic has been relatively unexplored in LVLMs. Examining social biases in LVLMs is particularly challenging due to the confounding contributions of bias induced by information contained across the text and visual modalities. To address this challenging problem, we conduct a large-scale study of text generated by different LVLMs under counterfactual changes to input images. Specifically, we present LVLMs with identical open-ended text prompts while conditioning on images from different counterfactual sets, where each set contains images which are largely identical in their depiction of a common subject (e.g., a doctor), but vary only in terms of intersectional social attributes (e.g., race and gender). We comprehensively evaluate the text produced by different models under this counterfactual generation setting at scale, producing over 57 million responses from popular LVLMs. Our multi-dimensional analysis reveals that social attributes such as race, gender, and physical characteristics depicted in input images can significantly influence the generation of toxic content, competency-associated words, harmful stereotypes, and numerical ratings of depicted individuals. We additionally explore the relationship between social bias in LVLMs and their corresponding LLMs, as well as inference-time strategies to mitigate bias.

The distribution of stellar obliquities provides critical insight into the formation and evolution pathways of exoplanets. In the past decade, it was found that hot stars hosting hot Jupiters are more likely to have high obliquities than cool stars, but it is not clear whether this trend exists only for hot Jupiters or holds for other types of planets. In this work, we extend the study of the obliquities of hot (6250-7000\,K) stars with transiting super-Earth and sub-Neptune-sized planets. We constrain the obliquity distribution based on measurements of the stars' projected rotation velocities. Our sample consists of 170 TESS and \textit{Kepler} planet-hosting stars and 180 control stars chosen to have indistinguishable spectroscopic characteristics. In our analysis, we find evidence suggesting that the planet hosts have a systematically higher $\langle \sin i \rangle$ compared to the control sample. This result implies that the planet hosts tend to have lower obliquities. However, the observed difference in $\langle \sin i \rangle$ is not significant enough to confirm spin-orbit alignment, as it is 3.8$\sigma$ away from perfect alignment. We also find evidence that within the planet-hosting stars there is a trend of higher obliquity (lower $\langle \sin i\rangle$) for the hotter stars ($\teff > 6250$ K) than for the cooler stars in the sample. This suggests that hot stars hosting smaller planets exhibit a broader obliquity distribution($\langle \sin i\rangle = 0.79 \pm 0.053$) than cooler planet-hosting stars, indicating that high obliquities are not exclusive to hot Jupiters and instead are more broadly tied to hot stars., Comment: Accepted for publication in ApJL

The SNO+ collaboration reports its first spectral analysis of long-baseline reactor antineutrino oscillation using 114 tonne-years of data. Fitting the neutrino oscillation probability to the observed energy spectrum yields constraints on the neutrino mass-squared difference $\Delta m^2_{21}$. In the ranges allowed by previous measurements, the best-fit $\Delta m^2_{21}$ is (8.85$^{+1.10}_{-1.33}$) $\times$ 10$^{-5}$ eV$^2$. This measurement is continuing in the next phases of SNO+ and is expected to surpass the present global precision on $\Delta m^2_{21}$ with about three years of data.

We propose a method for depth estimation under different illumination conditions, i.e., day and night time. As photometry is uninformative in regions under low-illumination, we tackle the problem through a multi-sensor fusion approach, where we take as input an additional synchronized sparse point cloud (i.e., from a LiDAR) projected onto the image plane as a sparse depth map, along with a camera image. The crux of our method lies in the use of the abundantly available synthetic data to first approximate the 3D scene structure by learning a mapping from sparse to (coarse) dense depth maps along with their predictive uncertainty - we term this, SpaDe. In poorly illuminated regions where photometric intensities do not afford the inference of local shape, the coarse approximation of scene depth serves as a prior; the uncertainty map is then used with the image to guide refinement through an uncertainty-driven residual learning (URL) scheme. The resulting depth completion network leverages complementary strengths from both modalities - depth is sparse but insensitive to illumination and in metric scale, and image is dense but sensitive with scale ambiguity. SpaDe can be used in a plug-and-play fashion, which allows for 25% improvement when augmented onto existing methods to preprocess sparse depth. We demonstrate URL on the nuScenes dataset where we improve over all baselines by an average 11.65% in all-day scenarios, 11.23% when tested specifically for daytime, and 13.12% for nighttime scenes., Comment: 8 pages, 4 figures

Context. Planets with radii of between 2-4 RE closely orbiting solar-type stars are of significant importance for studying the transition from rocky to giant planets. Aims. Our goal is to determine the mass of a transiting planet around the very bright F6 star HD 73344 . This star exhibits high activity and has a rotation period that is close to the orbital period of the planet. Methods. The transiting planet, initially a K2 candidate, is confirmed through TESS observations . We refined its parameters and rule out a false positive with Spitzer observations. We analyzed high-precision RV data from the SOPHIE and HIRES spectrographs. We conducted separate and joint analyses using the PASTIS software. We used a novel observing strategy, targeting the star at high cadence for two consecutive nights with SOPHIE to understand the short-term stellar variability. We modeled stellar noise with two Gaussian processes. Results. High-cadence RV observations provide better constraints on stellar variability and precise orbital parameters for the transiting planet. The derived mean density suggests a sub-Neptune-type composition, but uncertainties in the planet's mass prevent a detailed characterization. In addition, we find a periodic signal in the RV data that we attribute to the signature of a nontransiting exoplanet, without totally excluding the possibility of a nonplanetary origin. Dynamical analyses confirm the stability of the two-planet system and provide constraints on the inclination of the candidate planet; these findings favor a near-coplanar system. Conclusions. While the transiting planet orbits the bright star at a short period, stellar activity prevented us from precise mass measurements. Long-term RV tracking of this planet could improve this measurement, as well as our understanding of the activity of the host star., Comment: Accepted for publication in Astronomy and Astrophysics

Timely status updating is the premise of emerging interaction-based applications in the Internet of Things (IoT). Using redundant devices to update the status of interest is a promising method to improve the timeliness of information. However, parallel status updating leads to out-of-order arrivals at the monitor, significantly challenging timeliness analysis. This work studies the Age of Information (AoI) of a multi-queue status update system where multiple devices monitor the same physical process. Specifically, two systems are considered: the Basic System, which only has type-1 devices that are ad hoc devices located close to the source, and the Hybrid System, which contains additional type-2 devices that are infrastructure-based devices located in fixed points compared to the Basic System. Using the Stochastic Hybrid Systems (SHS) framework, a mathematical model that combines discrete and continuous dynamics, we derive the expressions of the average AoI of the considered two systems in closed form. Numerical results verify the accuracy of the analysis. It is shown that when the number and parameters of the type-1 devices/type-2 devices are fixed, the logarithm of average AoI will linearly decrease with the logarithm of the total arrival rate of type-2 devices or that of the number of type-1 devices under specific condition. It has also been demonstrated that the proposed systems can significantly outperform the FCFS M/M/N status update system.

The Neural Network Field Theory correspondence (NNFT) is a mapping from neural network (NN) architectures into the space of statistical field theories (SFTs). The Bayesian renormalization group (BRG) is an information-theoretic coarse graining scheme that generalizes the principles of the Exact Renormalization Group (ERG) to arbitrarily parameterized probability distributions, including those of NNs. In BRG, coarse graining is performed in parameter space with respect to an information-theoretic distinguishability scale set by the Fisher information metric. In this paper, we unify NNFT and BRG to form a powerful new framework for exploring the space of NNs and SFTs, which we coin BRG-NNFT. With BRG-NNFT, NN training dynamics can be interpreted as inducing a flow in the space of SFTs from the information-theoretic `IR' $\rightarrow$ `UV'. Conversely, applying an information-shell coarse graining to the trained network's parameters induces a flow in the space of SFTs from the information-theoretic `UV' $\rightarrow$ `IR'. When the information-theoretic cutoff scale coincides with a standard momentum scale, BRG is equivalent to ERG. We demonstrate the BRG-NNFT correspondence on two analytically tractable examples. First, we construct BRG flows for trained, infinite-width NNs, of arbitrary depth, with generic activation functions. As a special case, we then restrict to architectures with a single infinitely-wide layer, scalar outputs, and generalized cos-net activations. In this case, we show that BRG coarse-graining corresponds exactly to the momentum-shell ERG flow of a free scalar SFT. Our analytic results are corroborated by a numerical experiment in which an ensemble of asymptotically wide NNs are trained and subsequently renormalized using an information-shell BRG scheme., Comment: 46 pages, 10 figures, 2 tables

Quantum walks have frequently envisioned the behavior of a quantum state traversing a classically defined, generally finite, graph structure. While this approach has already generated significant results, it imposes a strong assumption: all nodes where the walker is not positioned are quiescent. This paper will examine some mathematical structures that underlie state diffusion on arbitrary graphs, that is the circulation of states within a graph. We will seek to frame the multi-walker problem as a finite quantum cellular automaton. Every vertex holds a walker at all times. The walkers will never collide and at each time step their positions update non-deterministically by a quantum swap of walkers at opposite ends of a randomly chosen edge. The update is accomplished by a unitary transformation of the position of a walker to a superposition of all such possible swaps and then performing a quantum measurement on the superposition of possible swaps. This behavior generates strong entanglement between vertex states which provides a path toward developing local actions producing diffusion throughout the graph without depending on the specific structure of the graph through blind computation., Comment: 14 pages, 2 figures

The process of calibrating computer models of natural phenomena is essential for applications in the physical sciences, where plenty of domain knowledge can be embedded into simulations and then calibrated against real observations. Current machine learning approaches, however, mostly rely on rerunning simulations over a fixed set of designs available in the observed data, potentially neglecting informative correlations across the design space and requiring a large amount of simulations. Instead, we consider the calibration process from the perspective of Bayesian adaptive experimental design and propose a data-efficient algorithm to run maximally informative simulations within a batch-sequential process. At each round, the algorithm jointly estimates the parameters of the posterior distribution and optimal designs by maximising a variational lower bound of the expected information gain. The simulator is modelled as a sample from a Gaussian process, which allows us to correlate simulations and observed data with the unknown calibration parameters. We show the benefits of our method when compared to related approaches across synthetic and real-data problems., Comment: Preprint, currently under review

We consider coarsening dynamics associated with a Burgers--Cahn--Hilliard system modeling a two-phase flow in one space dimension. Our emphasis is on the effect that coupling between the phase and fluid dynamics has on coarsening rates, and on the mechanisms driving this effect. We start with a detailed examination of coarsening dynamics for the uncoupled Cahn--Hilliard equation, comparing numerically generated rates with two analytic methods, and then we consider how these dynamics are affected by appropriate coupling with a viscous Burgers equation. In order to keep the analysis as self-contained as possible, we establish the global well-posedness of the system under consideration., Comment: 55 pages, 14 figures

During our spectroscopic survey of central stars of faint planetary nebulae (PNe), we found that the nucleus of Abell 57 exhibits strong nebular emission lines. Using synthetic narrow-band images, we show that the emission arises from an unresolved compact emission knot (CEK) coinciding with the hot (90,000 K) central star. Thus Abell 57 belongs to the rare class of "EGB 6-type" PNe, characterized by dense emission cores. Photometric data show that the nucleus exhibits a near-IR excess, due to a dusty companion body with the luminosity of an M0 dwarf but a temperature of ~1800 K. Emission-line analysis reveals that the CEK is remarkably dense (electron density ~1.6x10**7 cm**-3), and has a radius of only ~4.5 AU. The CEK suffers considerably more reddening than the central star, which itself is more reddened than the surrounding PN. These puzzles may suggest an interaction between the knot and central star; however, Hubble Space Telescope imaging of EGB 6 itself shows that its CEK lies more than ~125 AU from the PN nucleus. We discuss a scenario in which a portion of the AGB wind that created the PN was captured into a dust cloud around a distant stellar companion; this cloud has survived to the present epoch, and has an atmosphere photoionized by radiation from the hot central star. However, in this picture EGB 6-type nuclei should be relatively common, yet they are actually extremely rare; thus they may arise from a different transitory phenomenon. We suggest future observations of Abell 57 that may help unravel its mysteries., Comment: Accepted by Astrophysical Journal

We show that the statement ``In every separable pseudometric space there is a maximal non-strictly \delta-separated set.'' implies the axiom of choice for countable families of sets. This gives answers to a question of Dybowski and G\'{o}rka in [M. Dybowski and P. G\'{o}rka, The axiom of choice in metric measure spaces and maximal \delta-separated sets, Archive for Mathematical Logic 62, 735-749, 2023.]. We also prove several related results.

Studying the relative orientations of the orbits of exoplanets and wide-orbiting binary companions (semimajor axis greater than 100 AU) can shed light on how planets form and evolve in binary systems. Previous observations by multiple groups discovered a possible alignment between the orbits of visual binaries and the exoplanets that reside in them. In this study, using data from \textit{Gaia} DR3 and TESS, we confirm the existence of an alignment between the orbits of small planets $(R<6 R_\oplus)$ and binary systems with semimajor axes below 700 AU ($p=10^{-6}$). However, we find no statistical evidence for alignment between planet and binary orbits for binary semimajor axes greater than 700 AU, and no evidence for alignment of large, closely-orbiting planets (mostly hot Jupiters) and binaries at any separation. The lack of orbital alignment between our large planet sample and their binary companions appears significantly different from our small planet sample, even taking into account selection effects. Therefore, we conclude that any alignment between wide-binaries and our sample of large planets (predominantly hot Jupiters) is probably not as strong as what we observe for small planets in binaries with semimajor axes less than 700 AU. The difference in the alignment distribution of hot Jupiters and smaller planets may be attributed to the unique evolutionary mechanisms occuring in systems that form hot Jupiters, including potentially destabilizing secular resonances that onset as the protoplanetary disk dissipates and high-eccentricity migration occurring after the disk is gone., Comment: 16 pages, 10 figures, submitted to AJ. Email corresponding author for data files

NASA's Juno mission provided exquisite measurements of Jupiter's gravity field that together with the Galileo entry probe atmospheric measurements constrains the interior structure of the giant planet. Inferring its interior structure range remains a challenging inverse problem requiring a computationally intensive search of combinations of various planetary properties, such as the cloud-level temperature, composition, and core features, requiring the computation of ~10^9 interior models. We propose an efficient deep neural network (DNN) model to generate high-precision wide-ranged interior models based on the very accurate but computationally demanding concentric MacLaurin spheroid (CMS) method. We trained a sharing-based DNN with a large set of CMS results for a four-layer interior model of Jupiter, including a dilute core, to accurately predict the gravity moments and mass, given a combination of interior features. We evaluated the performance of the trained DNN (NeuralCMS) to inspect its predictive limitations. NeuralCMS shows very good performance in predicting the gravity moments, with errors comparable with the uncertainty due to differential rotation, and a very accurate mass prediction. This allowed us to perform a broad parameter space search by computing only ~10^4 actual CMS interior models, resulting in a large sample of plausible interior structures, and reducing the computation time by a factor of 10^5. Moreover, we used a DNN explainability algorithm to analyze the impact of the parameters setting the interior model on the predicted observables, providing information on their nonlinear relation., Comment: 8 pages, 6 figures, 4 tables, accepted for publication in A&A

Developing low-power, high-sensitivity photodetectors for the terahertz (THz) band that operate at room temperature is an important challenge in optoelectronics. In this study, we introduce a photo-thermal-electric (PTE) effect detector based on quasi-free standing bilayer graphene (BLG) on a silicon carbide (SiC) substrate, designed for the THz frequency range. Our detector's performance hinges on a quasi-optical coupling scheme, which integrates an aspherical silicon lens, to optimize impedance matching between the THz antenna and the graphene p-n junction. At room temperature, we achieved a noise equivalent power (NEP) of less than 300 $pW/\sqrt{Hz}$. Through an impedance matching analysis, we coupled a planar antenna with a graphene p-n junction, inserted in parallel to the nano-gap of the antenna, via two coupling capacitors. By adjusting the capacitors and the antenna arm length, we tailored the antenna's maximum infrared power absorption to specific frequencies. The sensitivity, spectral properties, and scalability of our material make it an ideal candidate for future development of far-infrared detectors operating at room temperature., Comment: 21 pages, 4 figures

Early identification of head and neck cancer (HNC) patients who would experience significant anatomical change during radiotherapy (RT) is important to optimize patient clinical benefit and treatment resources. This study aims to assess the feasibility of using a vision-transformer (ViT) based neural network to predict RT-induced anatomic change in HNC patients. We retrospectively included 121 HNC patients treated with definitive RT/CRT. We collected the planning CT (pCT), planned dose, CBCTs acquired at the initial treatment (CBCT01) and fraction 21 (CBCT21), and primary tumor volume (GTVp) and involved nodal volume (GTVn) delineated on both pCT and CBCTs for model construction and evaluation. A UNet-style ViT network was designed to learn spatial correspondence and contextual information from embedded CT, dose, CBCT01, GTVp, and GTVn image patches. The model estimated the deformation vector field between CBCT01 and CBCT21 as the prediction of anatomic change, and deformed CBCT01 was used as the prediction of CBCT21. We also generated binary masks of GTVp, GTVn, and patient body for volumetric change evaluation. The predicted image from the proposed method yielded the best similarity to the real image (CBCT21) over pCT, CBCT01, and predicted CBCTs from other comparison models. The average MSE and SSIM between the normalized predicted CBCT to CBCT21 are 0.009 and 0.933, while the average dice coefficient between body mask, GTVp mask, and GTVn mask are 0.972, 0.792, and 0.821 respectively. The proposed method showed promising performance for predicting radiotherapy-induced anatomic change, which has the potential to assist in the decision-making of HNC Adaptive RT.

Separating relevant and irrelevant information is key to any modeling process or scientific inquiry. Theoretical physics offers a powerful tool for achieving this in the form of the renormalization group (RG). Here we demonstrate a practical approach to performing Wilsonian RG in the context of Gaussian Process (GP) Regression. We systematically integrate out the unlearnable modes of the GP kernel, thereby obtaining an RG flow of the GP in which the data sets the IR scale. In simple cases, this results in a universal flow of the ridge parameter, which becomes input-dependent in the richer scenario in which non-Gaussianities are included. In addition to being analytically tractable, this approach goes beyond structural analogies between RG and neural networks by providing a natural connection between RG flow and learnable vs. unlearnable modes. Studying such flows may improve our understanding of feature learning in deep neural networks, and enable us to identify potential universality classes in these models., Comment: 17 pages, 1 figure; rewrote introduction, added references, section IIIA, section IVA, and appendix C

Quantization-aware training comes with a fundamental challenge: the derivative of quantization functions such as rounding are zero almost everywhere and nonexistent elsewhere. Various differentiable approximations of quantization functions have been proposed to address this issue. In this paper, we prove that when the learning rate is sufficiently small, a large class of weight gradient estimators is equivalent with the straight through estimator (STE). Specifically, after swapping in the STE and adjusting both the weight initialization and the learning rate in SGD, the model will train in almost exactly the same way as it did with the original gradient estimator. Moreover, we show that for adaptive learning rate algorithms like Adam, the same result can be seen without any modifications to the weight initialization and learning rate. We experimentally show that these results hold for both a small convolutional model trained on the MNIST dataset and for a ResNet50 model trained on ImageNet.

We present a novel frequency-filtering method for measuring and calculating frequency-filtered photon-correlations. This novel method is a cavity-based system we call the multi-mode array filter, which consists of an array of tunable single-mode cavities that are equally spaced in frequency. By introducing a mode-dependent phase modulation, we produce a near rectangular frequency response, allowing us to increase the filter bandwidth -- and thus the temporal response -- without sacrificing frequency isolation. We model the frequency filtering using a cascaded quantum open systems approach which completely neglects any back-action of the filter onto the source system. This allows us to derive a closed set of operator moment equations for source and filter system operators, thus providing an extremely efficient method to calculate frequency-filtered first- and second-order correlation functions. We demonstrate this novel filtering method by applying it to a resonantly driven two-level atom. We present examples of frequency-filtered power spectra to demonstrate the improved frequency isolation of the multi-mode array filter over the single-mode filter. We then present results for the single-mode and multi-mode-array filtered second-order auto- and cross-correlation functions. These are compared against expressions derived in the secular approximation. The improved frequency isolation of the multi-mode array filter allows us to investigate new regimes of frequency-filtered photon correlations, such as two-photon leapfrog processes, and the effect of vanishing bandwidth on filtered auto-correlation functions., Comment: 21 pages, 15 figures

At present, the Standard Model (SM) agrees with almost all collider data. Yet, three finetuning issues -- the Higgs mass problem, the strong CP problem and the cosmological constant problem -- all call for new physics. The most plausible solutions at present are weak scale SUSY, the PQWW axion and the string landscape. A re-evaluation of EW finetuning in SUSY allows for a higgsino-like LSP and naturalness upper bounds well beyond LHC limits. Rather general arguments from string theory allow for statistical predictions that m_h~ 125 GeV with sparticles beyond present LHC limits. The most lucrative LHC search channel may be for light higgsino pair production. Dark matter turns out to be a SUSY DFSZ axion along with a diminished abundance of higgsino-like WIMPs., Comment: 6 pages with 2 figures; contribution to the 2024 QCD session of the 58th Rencontres de Moriond

Background: Understanding healthy human brain function is crucial to identify and map pathological tissue within it. Whilst previous studies have mapped intracranial EEG (icEEG) from non-epileptogenic brain regions, these maps do not consider the effects of age and sex. Further, most existing work on icEEG has often suffered from a small sample size due to this modality's invasive nature. Here, we substantially increase the subject sample size compared to existing literature, to create a multi-centre, normative map of brain activity which additionally considers the effects of age, sex and recording site. Methods: Using interictal icEEG recordings from n = 513 subjects originating from 15 centres, we constructed a normative map of non-pathological brain activity by regressing age and sex on relative band power in five frequency bands, whilst accounting for the site effect. Results: Recording site significantly impacted normative icEEG maps in all frequency bands, and age was a more influential predictor of band power than sex. The age effect varied by frequency band, but no spatial patterns were observed at the region-specific level. Certainty about regression coefficients was also frequency band specific and moderately impacted by sample size. Conclusion: The concept of a normative map is well-established in neuroscience research and particularly relevant to the icEEG modality, which does not allow healthy control baselines. Our key results regarding the site and age effect guide future work utilising normative maps in icEEG.

Membraneless droplets formed through liquid-liquid phase separation (LLPS) play a crucial role in mRNA storage, enabling organisms to swiftly respond to environmental changes. However, the mechanisms underlying mRNA integration and protection within droplets remain unclear. Here, we unravel the role of bacterial aggresomes as stress granules (SGs) in safeguarding mRNA during stress. We discovered that upon stress onset, mobile mRNA molecules selectively incorporate into individual proteinaceous SGs based on length-dependent enthalpic gain over entropic loss. As stress prolongs, SGs undergo compaction facilitated by stronger non-specific RNA-protein interactions, thereby promoting recruitment of shorter RNA chains. Remarkably, mRNA ribonucleases are repelled from bacterial SGs, due to the influence of protein surface charge. This exclusion mechanism ensures the integrity and preservation of mRNA within SGs during stress conditions, explaining how mRNA can be stored and protected from degradation. Following stress removal, SGs facilitate mRNA translation, thereby enhancing cell fitness in changing environments. These droplets maintain mRNA physiological activity during storage, making them an intriguing new candidate for mRNA therapeutics manufacturing.

Many techniques for automated inference of inductive invariants for distributed protocols have been developed over the past several years, but their performance can still be unpredictable and their failure modes opaque for large-scale verification tasks. In this paper, we present inductive proof slicing, a new automated, compositional technique for inductive invariant inference that scales effectively to large distributed protocol verification tasks. Our technique is built on a core, novel data structure, the inductive proof graph, which explicitly represents the lemma and action dependencies of an inductive invariant and is built incrementally during the inference procedure, backwards from a target safety property. We present an invariant inference algorithm that integrates localized syntax-guided lemma synthesis routines at nodes of this graph, which are accelerated by computation of localized grammar and state variable slices. Additionally, in the case of failure to produce a complete inductive invariant, maintenance of this proof graph structure allows failures to be localized to small sub-components of this graph, enabling fine-grained failure diagnosis and repair by a user. We evaluate our technique on several complex distributed and concurrent protocols, including a large scale specification of the Raft consensus protocol, which is beyond the capabilities of modern distributed protocol verification tools, and also demonstrate how its interpretability features allow effective diagnosis and repair in cases of initial failure.

The Flory-Huggins theory describes the phase separation of solutions containing polymers. Although it finds widespread application from polymer physics to materials science to biology, the concentrations that coexist in separate phases at equilibrium have not been determined analytically, and numerical techniques are required that restrict the theory's ease of application. In this work, we derive an implicit analytical solution to the Flory-Huggins theory of one polymer in a solvent by applying a procedure that we call the implicit substitution method. While the solutions are implicit and in the form of composite variables, they can be mapped explicitly to a phase diagram in composition space. We apply the same formalism to multicomponent polymeric systems, where we find analytical solutions for polydisperse mixtures of polymers of one type. Finally, while complete analytical solutions are not possible for arbitrary mixtures, we propose computationally efficient strategies to map out coexistence curves for systems with many components of different polymer types.

Burning plasma performance, transport, and the effect of hydrogen isotope on confinement has been predicted for ITER baseline scenario (IBS) conditions using nonlinear gyrokinetic profile predictions. Accelerated by surrogate modeling [P. Rodriguez-Fernandez NF 2022], high fidelity, nonlinear gyrokinetic simulations performed with the CGYRO code [J. Candy JCP 2016], were used to predict profiles of Ti, Te, and ne while including the effects of alpha heating, auxiliary power, collisional energy exchange, and radiation losses. Predicted profiles and resulting energy confinement are found to produce fusion power and gain that are approximately consistent with mission goals (Pfusion = 500MW at Q=10) for the baseline scenario and exhibit energy confinement that is within 1 sigma of the H-mode energy confinement scaling. The power of the surrogate modeling technique is demonstrated through the prediction of alternative ITER scenarios with reduced computational cost. These scenarios include conditions with maximized fusion gain and an investigation of potential Resonant Magnetic Perturbation effects on performance with a minimal number of gyrokinetic profile iterations required. These predictions highlight the stiff ITG nature of the core turbulence predicted in the ITER baseline and demonstrate that Q>17 conditions may be accessible by reducing auxiliary input power while operating in IBS conditions. Prediction of full kinetic profiles allowed for the projection of hydrogen isotope effects around ITER baseline conditions. The gyrokinetic fuel ion species was varied from H, D, and 50/50 D-T and kinetic profiles were predicted. Results indicate that a weak or negligible isotope effect will be observed to arise from core turbulence in ITER baseline scenario conditions. The resulting energy confinement, turbulence, and density peaking, and the implications for ITER operations will be discussed.

People with Parkinson's Disease (PD) can slow the progression of their symptoms with physical therapy. However, clinicians lack insight into patients' motor function during daily life, preventing them from tailoring treatment protocols to patient needs. This paper introduces PD-Insighter, a system for comprehensive analysis of a person's daily movements for clinical review and decision-making. PD-Insighter provides an overview dashboard for discovering motor patterns and identifying critical deficits during activities of daily living and an immersive replay for closely studying the patient's body movements with environmental context. Developed using an iterative design study methodology in consultation with clinicians, we found that PD-Insighter's ability to aggregate and display data with respect to time, actions, and local environment enabled clinicians to assess a person's overall functioning during daily life outside the clinic. PD-Insighter's design offers future guidance for generalized multiperspective body motion analytics, which may significantly improve clinical decision-making and slow the functional decline of PD and other medical conditions., Comment: 18 pages, 11 figures, Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '24), May 11--16, 2024, Honolulu, HI, USA

We present the latest generation of MobileNets, known as MobileNetV4 (MNv4), featuring universally efficient architecture designs for mobile devices. At its core, we introduce the Universal Inverted Bottleneck (UIB) search block, a unified and flexible structure that merges Inverted Bottleneck (IB), ConvNext, Feed Forward Network (FFN), and a novel Extra Depthwise (ExtraDW) variant. Alongside UIB, we present Mobile MQA, an attention block tailored for mobile accelerators, delivering a significant 39% speedup. An optimized neural architecture search (NAS) recipe is also introduced which improves MNv4 search effectiveness. The integration of UIB, Mobile MQA and the refined NAS recipe results in a new suite of MNv4 models that are mostly Pareto optimal across mobile CPUs, DSPs, GPUs, as well as specialized accelerators like Apple Neural Engine and Google Pixel EdgeTPU - a characteristic not found in any other models tested. Finally, to further boost accuracy, we introduce a novel distillation technique. Enhanced by this technique, our MNv4-Hybrid-Large model delivers 87% ImageNet-1K accuracy, with a Pixel 8 EdgeTPU runtime of just 3.8ms.

SPHEREx, the Spectro-Photometer for the History of the Universe, Epoch of Reionization, and ices Explorer, is a NASA MIDEX mission planned for launch in 2024. SPHEREx will carry out the first all-sky spectral survey at wavelengths between 0.75 micron and 5 micron with spectral resolving power ~40 between 0.75 and 3.8 micron and ~120 between 3.8 and 5 micron At the end of its two-year mission, SPHEREx will provide 0.75-to-5 micron spectra of each 6.2"x6.2" pixel on the sky - 14 billion spectra in all. This paper updates an earlier description of SPHEREx presenting changes made during the mission's Preliminary Design Phase, including a discussion of instrument integration and test and a summary of the data processing, analysis, and distribution plans.

A compact source, G0.02467-0.0727, was detected in ALMA \threemm observations in continuum and very broad line emission. The continuum emission has a spectral index $\alpha\approx3.3$, suggesting that the emission is from dust. The line emission is detected in several transitions of CS, SO, and SO$_2$ and exhibits a line width FWHM $\approx160$ \kms. The line profile appears Gaussian. The emission is weakly spatially resolved, coming from an area on the sky $\lesssim1"$ in diameter ($\lesssim10^4$ AU at the distance of the Galactic Center; GC). The centroid velocity is $v_{LSR}\approx40$-$50$ \kms, which is consistent with a location in the Galactic Center. With multiple SO lines detected, and assuming local thermodynamic equilibrium (LTE) conditions, $T_\mathrm{LTE} = 13$ K, which is colder than seen in typical GC clouds, though we cannot rule out low-density, subthermally excited, warmer gas. Despite the high velocity dispersion, no emission is observed from SiO, suggesting that there are no strong ($\gtrsim10~\mathrm{km~s}^{-1}$) shocks in the molecular gas. There are no detections at other wavelengths, including X-ray, infrared, and radio. We consider several explanations for the Millimeter Ultra-Broad Line Object (MUBLO), including protostellar outflow, explosive outflow, collapsing cloud, evolved star, stellar merger, high-velocity compact cloud, intermediate mass black hole, and background galaxy. Most of these conceptual models are either inconsistent with the data or do not fully explain it. The MUBLO is, at present, an observationally unique object., Comment: Accepted to ApJL

We present optical spectroscopy of the 12th-mag central star of the planetary nebula (PN) Patchick 27 (Pa 27), obtained during a survey of faint PN nuclei (PNNi) with the Low-Resolution Spectrograph (LRS2) of the Hobby-Eberly Telescope. The optical spectrum of Pa 27 is that of a K0 III red giant with rotationally broadened lines. However, the star is detected in the near-ultraviolet (near-UV) with GALEX, showing that a hot binary component is also present. The spectral-energy distribution from the near-UV to the mid-infrared can be fitted with a combination of the K0 III giant and a hot PNN with an effective temperature of about 50,000 K. Photometric observations of Pa 27, both ground-based and from TESS, show a low-amplitude sinusoidal variation with a period of 7.36 days, probably due to starspots on a rotating and magnetically active cool giant. Pa 27 is a new member of the rare class of "Abell 35-type central stars," which are binary PNNi consisting of a spotted late-type star and a hot pre-white dwarf. They are likely the result of a situation where an AGB star ejects its outer layers in a dense wind, part of which is captured by a distant companion, spinning up its rotation by accretion of material and angular momentum. We suggest several useful follow-up observations., Comment: Accepted by Astrophysical Journal

Combining Large Language Models (LLMs) with external specialized tools (LLMs+tools) is a recent paradigm to solve multimodal tasks such as Visual Question Answering (VQA). While this approach was demonstrated to work well when optimized and evaluated for each individual benchmark, in practice it is crucial for the next generation of real-world AI systems to handle a broad range of multimodal problems. Therefore we pose the VQA problem from a unified perspective and evaluate a single system on a varied suite of VQA tasks including counting, spatial reasoning, OCR-based reasoning, visual pointing, external knowledge, and more. In this setting, we demonstrate that naively applying the LLM+tools approach using the combined set of all tools leads to poor results. This motivates us to introduce HAMMR: HierArchical MultiModal React. We start from a multimodal ReAct-based system and make it hierarchical by enabling our HAMMR agents to call upon other specialized agents. This enhances the compositionality of the LLM+tools approach, which we show to be critical for obtaining high accuracy on generic VQA. Concretely, on our generic VQA suite, HAMMR outperforms the naive LLM+tools approach by 19.5%. Additionally, HAMMR achieves state-of-the-art results on this task, outperforming the generic standalone PaLI-X VQA model by 5.0%.

Sidewalk robots are increasingly common across the globe. Yet, their operation on public paths poses challenges for people with mobility disabilities (PwMD) who face barriers to accessibility, such as insufficient curb cuts. We interviewed 15 PwMD to understand how they perceive sidewalk robots. Findings indicated that PwMD feel they have to compete for space on the sidewalk when robots are introduced. We next interviewed eight robotics practitioners to learn about their attitudes towards accessibility. Practitioners described how issues often stem from robotic companies addressing accessibility only after problems arise. Both interview groups underscored the importance of integrating accessibility from the outset. Building on this finding, we held four co-design workshops with PwMD and practitioners in pairs. These convenings brought to bear accessibility needs around robots operating in public spaces and in the public interest. Our study aims to set the stage for a more inclusive future around public service robots.

Quantum steering is the phenomenon whereby one party (Alice) proves entanglement by "steering'' the system of another party (Bob) into distinct ensembles of states, by performing different measurements on her subsystem. Here, we investigate steering in a network scenario involving $n$ parties, who each perform local measurements on part of a global quantum state, that is produced using only two-party entangled states, and mixing with ancillary separable states. We introduce three scenarios which can be straightforwardly implemented in standard quantum optics architecture, which we call random $\frac{n}{2}$-pair entanglement, random pair entanglement and semi-random pair entanglement. We study steerability of the states across two-party marginals which arise in the three scenarios, and derive analytically the necessary and sufficient steering criteria for different sets of measurement settings. Strikingly, using the semi-random pair entanglement construction, one party can steer every one of the $n-1$ other parties, for arbitrarily large $n$, using only two measurements. Finally, exploiting symmetry, we study various small network configurations (three or four parties) in the three scenarios, under different measurements and produced by different two-party entangled states., Comment: 11 pages, 9 figures, Close to published version

In this paper, we introduce a novel mathematical framework for assessing the performance of joint communication and sensing (JCAS) in wireless networks, employing stochastic geometry as an analytical tool. We focus on deriving the meta distribution of the signal-to-interference ratio (SIR) for JCAS networks. This approach enables a fine-grained quantification of individual user or radar performance intrinsic to these networks. Our work involves the modeling of JCAS networks and the derivation of mathematical expressions for the JCAS SIR meta distribution. Through simulations, we validate both our theoretical analysis and illustrate how the JCAS SIR meta distribution varies with the network deployment density.

Cardiac Magnetic Resonance (CMR) is established as a non-invasive imaging technique for evaluation of heart function, anatomy, and myocardial tissue characterization. Quantitative biomarkers are central for diagnosis and management of heart disease. Deep learning (DL) is playing an ever more important role in extracting these quantitative measures from CMR images. While many researchers have reported promising results in training and evaluating models, model deployment into the imaging workflow is less explored. A new imaging AI framework, the InlineAI, was developed and open-sourced. The main innovation is to enable the model inference inline as a part of imaging computation, instead of as an offline post-processing step and to allow users to plug in their models. We demonstrate the system capability on three applications: long-axis CMR cine landmark detection, short-axis CMR cine analysis of function and anatomy, and quantitative perfusion mapping. The InlineAI allowed models to be deployed into imaging workflow in a streaming manner directly on the scanner. The model was loaded and inference on incoming images were performed while the data acquisition was ongoing, and results were sent back to scanner. Several biomarkers were extracted from model outputs in the demonstrated applications and reported as curves and tabular values. All processes are full automated. the model inference was completed within 6-45s after the end of imaging data acquisition.

Many Hubble type S galaxies have a flat rotation curve, V extending into the outer disk. The surface brightness there is too low to create V from stars of a reasonable mass-to-light ratio. To maintain a stable disk, a massive dark matter halo is usually assumed to dominate in creating V. Gravitational arm amplification is used here to estimate whether V is created by a disk mixture, or by a 100 percent halo. The disk surface density is not estimated from the disk surface brightness and an assumed mass to light ratio. The observed arm pitch angle is presumed to be long term maximally amplified. This permits calculation of the disk surface mass density with radius relative to a 100 percent disk which would fully create V. NGC 7217's tightly wound 4.8 degree arm pitch angle indicates a disk component contributing 43 percent of the observed V for the range over the spiral arms (important but not dominant). It has too low a disk density to create V with the rest presumably arising from a halo. V is proportional to the square root of surface density. By contrast a loose arm galaxy disk can dominate in creating V. For example NGC 3198's arm pitch angle of 30 degrees implies a disk surface density sufficient to account for 88 percent of V which is a dominant fraction. Little dark matter halo is needed for galaxies like this. Observationally, the dark outer disk may be molecular hydrogen. NGC 3198's dominant disk also provides a counter example to the need for any universal (MOND) force law modification over the outer disk spiral arms.

The discovery of planetary systems beyond the solar system has revealed a diversity of architectures, most of which differ significantly from our system. The initial detection of an exoplanet is often followed by subsequent discoveries within the same system as observations continue, measurement precision is improved, or additional techniques are employed. The HD 104067 system is known to consist of a bright K dwarf host star and a giant planet in a $\sim$55 day period eccentric orbit. Here we report the discovery of an additional planet within the HD 104067 system, detected through the combined analysis of radial velocity data from the HIRES and HARPS instruments. The new planet has a mass similar to Uranus and is in an eccentric $\sim$14 day orbit. Our injection-recovery analysis of the radial velocity data exclude Saturn-mass and Jupiter-mass planets out to 3 AU and 8 AU, respectively. We further present TESS observations that reveal a terrestrial planet candidate ($R_p = 1.30\pm0.12$ $R_\oplus$) in a $\sim$2.2~day period orbit. Our dynamical analysis of the three planet model shows that the two outer planets produce significant eccentricity excitation of the inner planet, resulting in tidally induced surface temperatures as high as $\sim$2600 K for an emissivity of unity. The terrestrial planet candidate may therefore be caught in a tidal storm, potentially resulting in its surface radiating at optical wavelengths., Comment: 16 pages, 7 figures, 3 tables, accepted for publication in the Astronomical Journal

The extreme environments of ultra-short-period planets (USPs) make excellent laboratories to study how exoplanets obtain, lose, retain, and/or regain gaseous atmospheres. We present the confirmation and characterization of the USP TOI-1347 b, a $1.8 \pm 0.1$ R$_\oplus$ planet on a 0.85 day orbit that was detected with photometry from the TESS mission. We measured radial velocities of the TOI-1347 system using Keck/HIRES and HARPS-N and found the USP to be unusually massive at $11.1 \pm 1.2$ M$_\oplus$. The measured mass and radius of TOI-1347 b imply an Earth-like bulk composition. A thin H/He envelope (>0.01% by mass) can be ruled out at high confidence. The system is between 1 and 1.8 Gyr old; therefore, intensive photoevaporation should have concluded. We detected a tentative phase curve variation (3$\sigma$) and a secondary eclipse (2$\sigma$) in TESS photometry, which if confirmed could indicate the presence of a high-mean-molecular-weight atmosphere. We recommend additional optical and infrared observations to confirm the presence of an atmosphere and investigate its composition., Comment: 22 pages, 9 figures, accepted for publication in The Astronomical Journal

We present a dedicated transit and radial velocity survey of planets orbiting subgiant stars observed by the TESS Mission. Using $\sim$$16$ nights on Keck/HIRES, we confirm and characterize $12$ new transiting planets -- $\rm TOI-329\,b$, $\rm HD\,39688\,b$ ($\rm TOI-480$), $\rm TOI-603\,b$, $\rm TOI-1199\,b$, $\rm TOI-1294\,b$, $\rm TOI-1439\,b$, $\rm TOI-1605\,b$, $\rm TOI-1828\,b$, $\rm HD\,148193\,b$ ($\rm TOI-1836$), $\rm TOI-1885\,b$, $\rm HD\,83342\,b$ ($\rm TOI-1898$), $\rm TOI-2019\,b$ -- and provide updated properties for 9 previously confirmed TESS subgiant systems ($\rm TOI-197$, $\rm TOI-954$, $\rm TOI-1181$, $\rm TOI-1296$, $\rm TOI-1298$, $\rm TOI-1601$, $\rm TOI-1736$, $\rm TOI-1842$, $\rm TOI-2145$). We also report the discovery of an outer, non-transiting planet, $\rm TOI-1294\,c$ ($P=160.1\pm2.5$ days, $M_{\mathrm{p}}=148.3^{+18.2}_{-16.4} \,M_{\oplus}$), and three additional stars with long-term RV trends. We find that at least $19\pm8\%$ of subgiants in our sample of $21$ stars have outer companions, comparable to main-sequence stars. We perform a homogeneous analysis of the stars and planets in the sample, with median uncertainties of $3\%$, $8\%$ and $15\%$ for planet radii, masses and ages, doubling the number of known planets orbiting subgiant stars with bulk densities measured to better than $10\%$. We observe a dearth of giant planets around evolved stars with short orbital periods, consistent with tidal dissipation theories that predict the rapid inspiral of planets as their host stars leave the main sequence. We note the possible evidence for two distinct classes of hot Jupiter populations, indicating multiple formation channels to explain the observed distributions around evolved stars. Finally, continued RV monitoring of planets in this sample will provide a more comprehensive understanding of demographics for evolved planetary systems., Comment: 22 pages, 9 figures, 9 tables