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The determination of the nuclear spectral function from the measured cross section of the electron-nucleus scattering process $e + A \to e^\prime + p + (A-1)$is discussed, and illustrated for the case of a carbon target. The theoretical model based on the local density approximation, previously employed to derive the spectral function from a combination of accurate theoretical calculations and experimental data, has been developed further by including additional information obtained from measurements performed with high missing energy resolution. The implications for the analysis of $\gamma$-ray emission associated with nuclear deexcitation are considered.

We consider a generic higher order mass aggregation term for interactions between particles exhibiting oscillatory clumping and disaggregation behavior in the F ring of Saturn, using a novel predator-prey model that relates the mean mass aggregate (prey) and the square of the relative dispersion velocity (predator) of the interacting particles. The resulting cyclic dynamic behavior is demonstrated through time series plots, phase portraits and their stroboscopic phase maps. Employing an eigenvalue stability analysis of the Jacobian of the system, we find out that there are two distinct regimes depending on the exponent and the amplitude of the higher order interactions of the nonlinear mass term. In particular, the system exhibits a limit cycle oscillatory stable behavior for a range of values of these parameters and a non-cyclic behavior for another range, separated by a curve across which phase transitions would occur between the two regimes. This shows that the observed clumping dynamics in Saturn's F ring, corresponding to a limit cycle stability regime, can be systematically maintained in presence of physical higher order mass aggregation terms in the introduced model., Comment: 11 pages, 6 figures

Gymnasium is an open-source library providing an API for reinforcement learning environments. Its main contribution is a central abstraction for wide interoperability between benchmark environments and training algorithms. Gymnasium comes with various built-in environments and utilities to simplify researchers' work along with being supported by most training libraries. This paper outlines the main design decisions for Gymnasium, its key features, and the differences to alternative APIs., Comment: 6 pages, 1 figure, preprint

Over the last decade, there has been increasing interest in autonomous driving systems. Reinforcement Learning (RL) shows great promise for training autonomous driving controllers, being able to directly optimize a combination of criteria such as efficiency comfort, and stability. However, RL- based controllers typically offer no safety guarantees, making their readiness for real deployment questionable. In this paper, we propose SECRM-2D (the Safe, Efficient and Comfortable RL- based driving Model with Lane-Changing), an RL autonomous driving controller (both longitudinal and lateral) that balances optimization of efficiency and comfort and follows a fixed route, while being subject to hard analytic safety constraints. The aforementioned safety constraints are derived from the criterion that the follower vehicle must have sufficient headway to be able to avoid a crash if the leader vehicle brakes suddenly. We evaluate SECRM-2D against several learning and non-learning baselines in simulated test scenarios, including freeway driving, exiting, merging, and emergency braking. Our results confirm that representative previously-published RL AV controllers may crash in both training and testing, even if they are optimizing a safety objective. By contrast, our controller SECRM-2D is successful in avoiding crashes during both training and testing, improves over the baselines in measures of efficiency and comfort, and is more faithful in following the prescribed route. In addition, we achieve a good theoretical understanding of the longitudinal steady-state of a collection of SECRM-2D vehicles.

We present a scalable and efficient framework for the inference of spatially-varying parameters of continuum materials from image observations of their deformations. Our goal is the nondestructive identification of arbitrary damage, defects, anomalies and inclusions without knowledge of their morphology or strength. Since these effects cannot be directly observed, we pose their identification as an inverse problem. Our approach builds on integrated digital image correlation (IDIC, Besnard Hild, Roux, 2006), which poses the image registration and material inference as a monolithic inverse problem, thereby enforcing physical consistency of the image registration using the governing PDE. Existing work on IDIC has focused on low-dimensional parameterizations of materials. In order to accommodate the inference of heterogeneous material propertes that are formally infinite dimensional, we present $\infty$-IDIC, a general formulation of the PDE-constrained coupled image registration and inversion posed directly in the function space setting. This leads to several mathematical and algorithmic challenges arising from the ill-posedness and high dimensionality of the inverse problem. To address ill-posedness, we consider various regularization schemes, namely $H^1$ and total variation for the inference of smooth and sharp features, respectively. To address the computational costs associated with the discretized problem, we use an efficient inexact-Newton CG framework for solving the regularized inverse problem. In numerical experiments, we demonstrate the ability of $\infty$-IDIC to characterize complex, spatially varying Lam\'e parameter fields of linear elastic and hyperelastic materials. Our method exhibits (i) the ability to recover fine-scale and sharp material features, (ii) mesh-independent convergence performance and hyperparameter selection, (iii) robustness to observational noise.

In the current work we revisit the pair-potential recently proposed by Wang et al. (Phys. Chem. Chem. Phys. 10624, 22, 2020) as a well defined finite-range alternative to the widely used Lennard-Jones interaction model. The advantage of their proposed potential is that it not only goes smoothly to zero at the cutoff distance, hence eliminating inconsistencies caused by different treatments of the truncation, but with changing the range of the potential, it is capable of describing soft matter-like behaviour as well as traditional "Lennard-Jones-like" properties. We used the nested sampling method to perform an unbiased sampling of the potential energy surface, and mapped the pressure-temperature phase diagram of a range of truncation distances. We found that the interplay between the location of the energy minimum and interaction range has a complex and strong effect on both the structural and thermodynamic properties of the condensed phases. We discuss the appearance of the liquid-vapour co-existence line and critical point at longer interaction ranges, as well as the relatively small changes in the melting line. We present the ground state diagram, demonstrating that different close-packed polytypic phases appear to be global minima for the model at different pressure and cutoff values, similar to what had been shown in case of the Lennard-Jones model. Finally, we compare the lowest energy structure of some of the N < 60 clusters to that of the known minima of Lennard-Jones and Morse potential, using different cutoff values, revealing a behaviour closely resembling that of the Morse clusters., Comment: 9 pages, 8 figures

Starting from the isomorphism between the AdS Carroll and Poincar\'e algebras, we map the three-dimensional asymptotically flat solutions of Poincar\'e gravity into an AdS Carroll spacetime. We show the mapped solutions satisfy the field equations of the Chern-Simons formulation of AdS Carroll gravity and exhibit a Carroll geometry structure. Despite the presence of a negative cosmological constant in the mapped spacetime, the algebra of the canonical generators of the asymptotic symmetries is given by the $\mathfrak{bms}_3$ algebra., Comment: 15 pages

This paper introduces a new macroscopic perspective for simulating transportation networks. The idea is to look at the network as connected nodes. Each node sends an information package to its neighbors. Basically, the information package contains a state change that a specific node experienced, and it might affect the traffic network state in the future. Different types of information can be counted in transportation network. Each information type has different characteristics. It propagates through the network and interacts with other IPs and nodes. As a result, the model enables implementing and analyzing dynamic and inconvenient control strategies. This paper focus on flow dynamics and demand routing information under complex environment. The flow dynamics flows the LWR theory along the links. The demand routing follows a node equilibrium model. The node model takes into account the users preferences and choices under dynamic control agents such as dynamic tolling and sends the needed information for other facilities to operate. A DNL model is developed based on the IPM. Several case studies demonstrate the use IPM and its potential to provide a reliable and results for real world complicated transportation applications.

The Cox Proportional Hazards (CPH) model has long been the preferred survival model for its explainability. However, to increase its predictive power beyond its linear log-risk, it was extended to utilize deep neural networks sacrificing its explainability. In this work, we explore the potential of self-explaining neural networks (SENN) for survival analysis. we propose a new locally explainable Cox proportional hazards model, named CoxSE, by estimating a locally-linear log-hazard function using the SENN. We also propose a modification to the Neural additive (NAM) models hybrid with SENN, named CoxSENAM, which enables the control of the stability and consistency of the generated explanations. Several experiments using synthetic and real datasets have been performed comparing with a NAM-based model, DeepSurv model explained with SHAP, and a linear CPH model. The results show that, unlike the NAM-based model, the SENN-based model can provide more stable and consistent explanations while maintaining the same expressiveness power of the black-box model. The results also show that, due to their structural design, NAM-based models demonstrated better robustness to non-informative features. Among these models, the hybrid model exhibited the best robustness.

We present an efficient and scalable algorithm for performing matrix-vector multiplications ("matvecs") for block Toeplitz matrices. Such matrices, which are shift-invariant with respect to their blocks, arise in the context of solving inverse problems governed by autonomous systems, and time-invariant systems in particular. In this article, we consider inverse problems that are solved for inferring unknown parameters from observational data of a linear time-invariant dynamical system given in the form of partial differential equations (PDEs). Matrix-free Newton-conjugate-gradient methods are often the gold standard for solving these inverse problems, but they require numerous actions of the Hessian on a vector. Matrix-free adjoint-based Hessian matvecs require solution of a pair of linearized forward/adjoint PDE solves per Hessian action, which may be prohibitive for large-scale inverse problems, especially when efficient low-rank approximations of the Hessian are not readily available, such as for hyperbolic PDE operators. Time invariance of the forward PDE problem leads to a block Toeplitz structure of the discretized parameter-to-observable (p2o) map defining the mapping from inputs (parameters) to outputs (observables) of the PDEs. This block Toeplitz structure enables us to exploit two key properties: (1) compact storage of the p2o map and its adjoint; and (2) efficient fast Fourier transform (FFT)-based Hessian matvecs. The proposed algorithm is mapped onto large multi-GPU clusters and achieves more than 80 percent of peak bandwidth on an NVIDIA A100 GPU. Excellent weak scaling is shown for up to 48 A100 GPUs. For the targeted problems, the implementation executes Hessian matvecs within fractions of a second, orders of magnitude faster than can be achieved by the conventional matrix-free Hessian matvecs via forward/adjoint PDE solves.

Given a set of $I \subseteq \mathbb{N}$, consider the sequences $\{d_n(I)\},\{p_n(I)\}$ where for any $n$, $d_n(I)$ and $p_n(I)$ respectively count the number of permutations in the symmetric group $\mathfrak{S}_n$ whose descent set (respectively peak set) is $I \cap [n-1]$. We investigate the growth rates $\text{gr} \ d_n(I) = \lim_{n \to \infty} \left(d_n(I)/n!\right)^{1/n}$ and $\text{gr} \ p_n(I) = \lim_{n \to \infty} \left(p_n(I)/n!\right)^{1/n}$ over all $I \subseteq \mathbb{N}$. Our main contributions are two-fold. Firstly, we prove that the numbers $\text{gr} \ d_n(I)$ over all $I \subseteq \mathbb{N}$ are exactly the interval $\left[0,2/\pi\right]$. To do so, we construct an algorithm that explicitly builds $I$ for any desired limit $L$ in the interval. Secondly, we prove the numbers $\text{gr} \ p_n(I)$ for periodic sets $I \subseteq \mathbb{N}$ form a dense set in $\left[0,1/\sqrt[3]{3}\right]$. We do this by explicitly finding, for any prescribed limit $L$ in the interval, a set $I$ whose corresponding growth rate is arbitrarily close to $L$., Comment: 18 pages

Extended teleparallel gravity, characterized by $F(Q)$ function where $Q$ is the non-metricity scalar, is one of the most promising approaches to general relativity. In this paper, we reexamine a specific dynamical dark energy model, which is indistinguishable from the $\Lambda$CDM model at present time and exhibits a special event in the future, within $F(Q)$ gravity. To constrain the free parameters of the model, we perform a Markov Chain Monte Carlo (MCMC) analysis, using the last data from Pantheon$^{+}$ and the latest measurements of the H(z) parameter combined. On the basis of this analysis, we have find that our dynamical dark energy model, in the context of F(Q) gravity, lies in the quintessence regime rather than in the phantom regime as in the case of general relativity. Furthermore, this behaviour affects the future expansion of the Universe as it becomes decelerating at $1\sigma$ confidence level for $z<-0.5$ and showing a bounce at $z_{\text{B}}\approx -0.835$. Finally, we have support our conclusion with a cosmographic analysis., Comment: 15 pages, 9 figures

The United States Food and Drug Administration's (FDA's) Premarket Notification 510(K) pathway allows manufacturers to gain approval for a medical device by demonstrating its substantial equivalence to another legally marketed device. However, the inherent ambiguity of this regulatory procedure has led to high recall rates for many devices cleared through this pathway. This trend has raised significant concerns regarding the efficacy of the FDA's current approach, prompting a reassessment of the 510(K) regulatory framework. In this paper, we develop a combined human-algorithm approach to assist the FDA in improving its 510(k) medical device clearance process by reducing the risk of potential recalls and the workload imposed on the FDA. We first develop machine learning methods to estimate the risk of recall of 510(k) medical devices based on the information available at the time of submission. We then propose a data-driven clearance policy that recommends acceptance, rejection, or deferral to FDA's committees for in-depth evaluation. We conduct an empirical study using a unique large-scale dataset of over 31,000 medical devices and 12,000 national and international manufacturers from over 65 countries that we assembled based on data sources from the FDA and Centers for Medicare and Medicaid Service (CMS). A conservative evaluation of our proposed policy based on this data shows a 38.9% improvement in the recall rate and a 43.0% reduction in the FDA's workload. Our analyses also indicate that implementing our policy could result in significant annual cost-savings ranging between \$2.4 billion and \$2.7 billion, which highlights the value of using a holistic and data-driven approach to improve the FDA's current 510(K) medical device evaluation pathway.

Natural Language Processing (NLP) systems are increasingly taking the form of multi-stage pipelines involving multiple distinct language models (LMs) and prompting strategies. Here we address the question of how to fine-tune such systems to improve their performance. We cast this as a problem of optimizing the underlying LM weights and the prompting strategies together, and consider a challenging but highly realistic scenario in which we have no gold labels for any intermediate stages in the pipeline. To address this challenge, we evaluate approximate optimization strategies in which we bootstrap training labels for all pipeline stages and use these to optimize the pipeline's prompts and fine-tune its weights alternatingly. In experiments with multi-hop QA, mathematical reasoning, and feature-based classification, we find that simple approaches for optimizing the prompts and weights together outperform directly optimizing weights alone and prompts alone by up to 65% and 5%, respectively, on average across LMs and tasks. We will release our new optimizers in DSPy at http://dspy.ai

We prove that a bounded domain in $\mathbb{C}^n$ admitting a complete K\"ahler metric with negatively pinched holomorphic bisectional curvature near the boundary, admits a complete K\"ahler metric with negatively pinched holomorphic bisectional curvature everywhere. As a consequence we prove that strictly pseudoconvex bounded domains with $C^2$ boundary and bounded domains with squeezing function tending to 1 at every point of the boundary, admit a complete K\"ahler metric with negatively pinched holomorphic bisectional curvature everywhere.

The repeating fast radio burst source FRB 20220912A was remarkably active in the weeks after its discovery. Here we report 696 bursts detected with the Nan\c{c}ay Radio Telescope (NRT) as part of the Extragalactic Coherent Light from Astrophysical Transients (\'ECLAT) monitoring campaign. We present 68 observations, conducted from October 2022 to April 2023, with a total duration of 61 hours and an event rate peaking at $75^{+10}_{-9}$ bursts per hour above a fluence threshold of 0.59 Jy ms in the $1.2-1.7$-GHz band. Most bursts in the sample occur towards the bottom of the observing band. They follow a bimodal wait-time distribution, with peaks at 33.4 ms and 67.0 s. We find a roughly constant dispersion measure (DM) over time ($\delta$DM $\lesssim$ 2 pc cm$^{-3}$) when taking into account `sad-trombone' drift, with a mean drift rate of $-8.8 $MHz ms$^{-1}$. Nonetheless, we confirm small $\sim0.3$ pc cm$^{-3}$ DM variations using microshot structure, while finding that microstructure is rare in our sample -- despite the 16 $\mu$s time resolution of the data. The cumulative spectral energy distribution shows more high-energy bursts ($E_\nu \gtrsim 10^{31}$ erg/Hz) than would be expected from a simple power-law distribution. The burst rate per observation appears Poissonian, but the full set of observations is better modelled by a Weibull distribution, showing clustering. We discuss the various observational similarities that FRB 20220912A shares with other (hyper)active repeaters, which as a group are beginning to show a common set of phenomenological traits that provide multiple useful dimensions for their quantitative comparison and modelling., Comment: 18 pages, 20 figures

We prove a global well-posedness result for the 2D Muskat problem with surface tension. Given any regular enough initial data which is small in some critical space but possibly large in Lipschitz, we prove that the associated Cauchy problem has a unique global solution. Our result allows for the slope of the interface between the two fluids to be arbitrarily large., Comment: 91 pages

Using a representative translational dimer model, high throughput calculations are implemented for fast screening of a total of 74 diacenaphtho-extended heterocycle (DAH) derivatives as hole transport layer (HTL) materials in perovskite solar cells (PVSCs). Different electronic properties, including band structures, band gaps, and band edges compared to methylammonium and formamidinium lead iodide perovskites, along with reorganization energies, electronic couplings, and hole mobilities are calculated in order to decipher the effects of different parameters, including the polarity, steric and pi-conjugation, as well as the presence of explicit hydrogen bond interactions on the computed carrier mobilities of the studied materials. Full crystal structure predictions and hole mobility calculations of the top candidates resulted in some mobilities exceeding 10 cm2/V.s, further validating the employed translational dimer model as a robust approach for inverse design and fast high throughput screening of new HTL organic semiconductors with superior properties. The studied models and simulations performed in this work are instructive in designing next-generation HTL materials for higher-performance PVSCs.

Top-down Bird's Eye View (BEV) maps are a popular representation for ground robot navigation due to their richness and flexibility for downstream tasks. While recent methods have shown promise for predicting BEV maps from First-Person View (FPV) images, their generalizability is limited to small regions captured by current autonomous vehicle-based datasets. In this context, we show that a more scalable approach towards generalizable map prediction can be enabled by using two large-scale crowd-sourced mapping platforms, Mapillary for FPV images and OpenStreetMap for BEV semantic maps. We introduce Map It Anywhere (MIA), a data engine that enables seamless curation and modeling of labeled map prediction data from existing open-source map platforms. Using our MIA data engine, we display the ease of automatically collecting a dataset of 1.2 million pairs of FPV images & BEV maps encompassing diverse geographies, landscapes, environmental factors, camera models & capture scenarios. We further train a simple camera model-agnostic model on this data for BEV map prediction. Extensive evaluations using established benchmarks and our dataset show that the data curated by MIA enables effective pretraining for generalizable BEV map prediction, with zero-shot performance far exceeding baselines trained on existing datasets by 35%. Our analysis highlights the promise of using large-scale public maps for developing & testing generalizable BEV perception, paving the way for more robust autonomous navigation.

In contrast to equilibrium systems, inertia can profoundly impact the phase behavior of active systems. This has been made particularly evident in recent years, with motility-induced phase separation (MIPS) exhibiting several intriguing dependencies on translational inertia. Here we report extensive simulations characterizing the phase behavior of inertial active matter and develop a mechanical theory for the complete phase diagram without appealing to equilibrium notions. Our theory qualitatively captures all aspects of liquid-gas coexistence, including the critical value of inertia above which MIPS ceases. Notably, our findings highlight that particle softness, and not inertia, is responsible for the MIPS reentrance effect at the center of a proposed active refrigeration cycle.

The theoretical design of donor chromophores based on triphenylamine and 2-(1,1-dicyanomethylene)rhodanine (\textbf{DCRD-DCRD-2}) is proposed through structural adaptation with several molecular bridges derived from thiophene that can be used as new organic materials for organic solar cells (OSC). The optoelectronics properties and geometries of the \textbf{DCRD-DCRD-2} organic molecules are characterized using the B3LYP and CAM-B3LYP functional, with the basis set 6-31G(d,p). Consequently, the UV-Visible results revealed that a good relationship was found between the experimental values and the calculated using the DFT and TD-DFT level of theory. The study involved the prediction of photo-physical descriptors such as frontier molecular orbitals, ionization potential, electron affinity, molecular electrostatic potential, reorganization energy, open circuit voltage ($V_{oc}$), fill factor (FF), and short-circuit current ($J_{sc}$) in the ground state geometry, using the B3LYP/6-31G(d,p) basis set. Structural tailoring with various molecular bridges resulted in a narrowing of the energy gap (2.130--1.96eV) with broader absorption spectra (525.55--417.69 nm). An effective charge transfer toward the lowest unoccupied molecular orbitals (LUMO) from the highest occupied molecular orbitals (HOMO) was studied, which played a crucial role in conducting materials. \textbf{DCRD-2} exhibited $\lambda_{max}$ at $417.69$~nm in EtOH (ethanol) solvent with the lowest band gap (1.96 eV) and the lowest excitation energy of 2.968 eV. The highest mobility of holes and electrons is determined in all the designed molecules due to their low reorganization energy values that validated preferable photovoltaic properties in the \textbf{DCRD-1} molecular system., Comment: 11 pages, 5 figures

In this work, we present a novel approach for general object segmentation from a monocular image, eliminating the need for manually labeled training data and enabling rapid, straightforward training and adaptation with minimal data. Our model initially learns from LiDAR during the training process, which is subsequently removed from the system, allowing it to function solely on monocular imagery. This study leverages the concept of the Stixel-World to recognize a medium level representation of its surroundings. Our network directly predicts a 2D multi-layer Stixel-World and is capable of recognizing and locating multiple, superimposed objects within an image. Due to the scarcity of comparable works, we have divided the capabilities into modules and present a free space detection in our experiments section. Furthermore, we introduce an improved method for generating Stixels from LiDAR data, which we use as ground truth for our network.

This paper we present our vision and ongoing work for a novel dataset designed to advance research into the interoperability of intelligent vehicles and infrastructure, specifically aimed at enhancing cooperative perception and interaction in the realm of public transportation. Unlike conventional datasets centered on ego-vehicle data, this approach encompasses both a stationary sensor tower and a moving vehicle, each equipped with cameras, LiDARs, and GNSS, while the vehicle additionally includes an inertial navigation system. Our setup features comprehensive calibration and time synchronization, ensuring seamless and accurate sensor data fusion crucial for studying complex, dynamic scenes. Emphasizing public transportation, the dataset targets to include scenes like bus station maneuvers and driving on dedicated bus lanes, reflecting the specifics of small public buses. We introduce the open-source ".4mse" file format for the new dataset, accompanied by a research kit. This kit provides tools such as ego-motion compensation or LiDAR-to-camera projection enabling advanced research on intelligent vehicle-infrastructure integration. Our approach does not include annotations; however, we plan to implement automatically generated labels sourced from state-of-the-art public repositories. Several aspects are still up for discussion, and timely feedback from the community would be greatly appreciated. A sneak preview on one data frame will be available at a Google Colab Notebook. Moreover, we will use the related GitHub Repository to collect remarks and suggestions.

For a graph $G$, let Conf$(G,n)$ denote the classical configuration space of $n$ labelled points in $G$. Abrams introduced a cubical complex, denoted here by DConf$(G,n)$, sitting inside Conf$(G,n)$ as a strong deformation retract provided $G$ is suitably subdivided. Using discrete Morse Theory techniques, we extend Abrams' result to the realm of configurations having no $k$-fold collisions., Comment: 23 pages, 4 figures

Vision Transformers (ViTs) have achieved significant advancement in computer vision tasks due to their powerful modeling capacity. However, their performance notably degrades when trained with insufficient data due to lack of inherent inductive biases. Distilling knowledge and inductive biases from a Convolutional Neural Network (CNN) teacher has emerged as an effective strategy for enhancing the generalization of ViTs on limited datasets. Previous approaches to Knowledge Distillation (KD) have pursued two primary paths: some focused solely on distilling the logit distribution from CNN teacher to ViT student, neglecting the rich semantic information present in intermediate features due to the structural differences between them. Others integrated feature distillation along with logit distillation, yet this introduced alignment operations that limits the amount of knowledge transferred due to mismatched architectures and increased the computational overhead. To this end, this paper presents Hybrid Data-efficient Knowledge Distillation (HDKD) paradigm which employs a CNN teacher and a hybrid student. The choice of hybrid student serves two main aspects. First, it leverages the strengths of both convolutions and transformers while sharing the convolutional structure with the teacher model. Second, this shared structure enables the direct application of feature distillation without any information loss or additional computational overhead. Additionally, we propose an efficient light-weight convolutional block named Mobile Channel-Spatial Attention (MBCSA), which serves as the primary convolutional block in both teacher and student models. Extensive experiments on two medical public datasets showcase the superiority of HDKD over other state-of-the-art models and its computational efficiency. Source code at: https://github.com/omarsherif200/HDKD

The task of writing rap is challenging and involves producing complex rhyming schemes, yet meaningful lyrics. In this work, we propose Raply, a fine-tuned GPT-2 model capable of producing meaningful rhyming text in the style of rap. In addition to its rhyming capabilities, the model is able to generate less offensive content. It was achieved through the fine-tuning the model on a new dataset Mitislurs, a profanity-mitigated corpus. We evaluate the output of the model on two criteria: 1) rhyming based on the rhyme density metric; 2) profanity content, using the list of profanities for the English language. To our knowledge, this is the first attempt at profanity mitigation for rap lyrics generation.

We argue that Content-based filtering (CBF) and Graph-based methods (GB) complement one another in Academic Search recommendations. The scientific literature can be viewed as a conversation between authors and the audience. CBF uses abstracts to infer authors' positions, and GB uses citations to infer responses from the audience. In this paper, we describe nine differences between CBF and GB, as well as synergistic opportunities for hybrid combinations. Two embeddings will be used to illustrate these opportunities: (1) Specter, a CBF method based on BERT-like deepnet encodings of abstracts, and (2) ProNE, a GB method based on spectral clustering of more than 200M papers and 2B citations from Semantic Scholar.

Homogeneous nucleation, a textbook transition path for phase transitions, is typically understood on thermodynamic grounds through the prism of classical nucleation theory. However, recent studies have suggested the applicability of classical nucleation theory to systems far from equilibrium. In this Article, we formulate a purely mechanical perspective of homogeneous nucleation and growth, elucidating the criteria for the properties of a critical nucleus without appealing to equilibrium notions. Applying this theory to active fluids undergoing motility-induced phase separation, we find that nucleation proceeds in a qualitatively similar fashion to equilibrium systems, with concepts such as the Gibbs-Thomson effect and nucleation barriers remaining valid. We further demonstrate that the recovery of such concepts allows us to extend classical theories of nucleation rates and coarsening dynamics to active systems upon using the mechanically-derived definitions of the nucleation barrier and surface tensions.Three distinct surface tensions -- the mechanical, capillary, and Ostwald tensions -- play a central role in our theory. While these three surface tensions are identical in equilibrium, our work highlights the distinctive role of each tension in the stability of active interfaces and the nucleation and growth of motility-induced phases., Comment: SI included. Comments welcome!

We present a high-significance cross-correlation of CMB lensing maps from the Atacama Cosmology Telescope (ACT) Data Release 6 (DR6) with spectroscopically calibrated luminous red galaxies (LRGs) from the Dark Energy Spectroscopic Instrument (DESI). We detect this cross-correlation at a significance of 38$\sigma$; combining our measurement with the Planck Public Release 4 (PR4) lensing map, we detect the cross-correlation at 50$\sigma$. Fitting this jointly with the galaxy auto-correlation power spectrum to break the galaxy bias degeneracy with $\sigma_8$, we perform a tomographic analysis in four LRG redshift bins spanning $0.4 \le z \le 1.0$ to constrain the amplitude of matter density fluctuations through the parameter combination $S_8^\times = \sigma_8 \left(\Omega_m / 0.3\right)^{0.4}$. Prior to unblinding, we confirm with extragalactic simulations that foreground biases are negligible and carry out a comprehensive suite of null and consistency tests. Using a hybrid effective field theory (HEFT) model that allows scales as small as $k_{\rm max}=0.6$ $h/{\rm Mpc}$, we obtain a 3.3% constraint on $S_8^\times = \sigma_8 \left(\Omega_m / 0.3\right)^{0.4} = 0.792^{+0.024}_{-0.028}$ from ACT data, as well as constraints on $S_8^\times(z)$ that probe structure formation over cosmic time. Our result is consistent with the early-universe extrapolation from primary CMB anisotropies measured by Planck PR4 within 1.2$\sigma$. Jointly fitting ACT and Planck lensing cross-correlations we obtain a 2.7% constraint of $S_8^\times = 0.776^{+0.019}_{-0.021}$, which is consistent with the Planck early-universe extrapolation within 2.1$\sigma$, with the lowest redshift bin showing the largest difference in mean. The latter may motivate further CMB lensing tomography analyses at $z<0.6$ to assess the impact of potential systematics or the consistency of the $\Lambda$CDM model over cosmic time., Comment: Prepared for submission to JCAP (47 pages, 13 figures)

Background: Machine learning (ML) model composition is a popular technique to mitigate shortcomings of a single ML model and to design more effective ML-enabled systems. While ensemble learning, i.e., forwarding the same request to several models and fusing their predictions, has been studied extensively for accuracy, we have insufficient knowledge about how to design energy-efficient ensembles. Objective: We therefore analyzed three types of design decisions for ensemble learning regarding a potential trade-off between accuracy and energy consumption: a) ensemble size, i.e., the number of models in the ensemble, b) fusion methods (majority voting vs. a meta-model), and c) partitioning methods (whole-dataset vs. subset-based training). Methods: By combining four popular ML algorithms for classification in different ensembles, we conducted a full factorial experiment with 11 ensembles x 4 datasets x 2 fusion methods x 2 partitioning methods (176 combinations). For each combination, we measured accuracy (F1-score) and energy consumption in J (for both training and inference). Results: While a larger ensemble size significantly increased energy consumption (size 2 ensembles consumed 37.49% less energy than size 3 ensembles, which in turn consumed 26.96% less energy than the size 4 ensembles), it did not significantly increase accuracy. Furthermore, majority voting outperformed meta-model fusion both in terms of accuracy (Cohen's d of 0.38) and energy consumption (Cohen's d of 0.92). Lastly, subset-based training led to significantly lower energy consumption (Cohen's d of 0.91), while training on the whole dataset did not increase accuracy significantly. Conclusions: From a Green AI perspective, we recommend designing ensembles of small size (2 or maximum 3 models), using subset-based training, majority voting, and energy-efficient ML algorithms like decision trees, Naive Bayes, or KNN., Comment: Currently under review at a journal

We study the impact of an early dark energy component (EDE) present during big bang nucleosynthesis (BBN) on the elemental abundances of deuterium D/H, and helium $Y_p$, as well as the effective relativistic degrees of freedom $N_{\rm eff}$. We consider a simple model of EDE that is constant up to a critical time. After this critical time, the EDE transitions into either a radiation component that interacts with the electromagnetic plasma, a dark radiation component that is uncoupled from the plasma, or kination that is also uncoupled. We use measured values of the abundances and $N_{\rm eff}$ as determined by CMB observations to establish limits on the input parameters of this EDE model. In addition, we explore how those parameters are correlated with BBN inputs; the baryon to photon ratio $\eta_b$, neutron lifetime $\tau_n$, and number of neutrinos $N_\nu$. Finally, we study whether this setup can alleviate the tension introduced by recent measurements of the primordial helium abundance., Comment: 13 pages, 8 figures

We present NEBULA, the first latent 3D generative model for scalable generation of large molecular libraries around a seed compound of interest. Such libraries are crucial for scientific discovery, but it remains challenging to generate large numbers of high quality samples efficiently. 3D-voxel-based methods have recently shown great promise for generating high quality samples de novo from random noise (Pinheiro et al., 2023). However, sampling in 3D-voxel space is computationally expensive and use in library generation is prohibitively slow. Here, we instead perform neural empirical Bayes sampling (Saremi & Hyvarinen, 2019) in the learned latent space of a vector-quantized variational autoencoder. NEBULA generates large molecular libraries nearly an order of magnitude faster than existing methods without sacrificing sample quality. Moreover, NEBULA generalizes better to unseen drug-like molecules, as demonstrated on two public datasets and multiple recently released drugs. We expect the approach herein to be highly enabling for machine learning-based drug discovery. The code is available at https://github.com/prescient-design/nebula

Atomic short-range order (SRO) in direct-bandgap GeSn for infrared photonics has recently attracted attention due to its notable impact on band structures. However, the SRO in GeSn thin films grown by different methods have hardly been compared. This paper compares SRO in GeSn thin films of similar compositions grown by molecular beam epitaxy (MBE) and chemical vapor deposition (CVD) using atom probe tomography. An $\sim$15% stronger preference for Sn-Sn 1$^{st}$ nearest neighbor (1NN) is observed in MBE GeSn than CVD GeSn for both thin film and quantum well samples with Sn composition ranging from 7 to 20%. Interestingly, samples grown by different deposition tools under the same method (either MBE or CVD) showed remarkable consistency in Sn-Sn 1NN SRO, while MBE vs. CVD showed clear differences. Supported by theoretical modeling, we consider that this difference in SRO originates from the impact of surface termination, where MBE surfaces are exposed to ultrahigh vacuum while CVD surfaces are terminated by H to a good extent. This finding not only suggests engineering surface termination or surfactants during the growth as a potential approach to control SRO in GeSn, but also provides insight into the underlying reasons for very different growth temperature between MBE and CVD that directly impact the strain relaxation behavior.

This work reports an acoustic solidly mounted resonator (SMR) at 18.64 GHz, among the highest operating frequencies reported. The device is built in scandium aluminum nitride (ScAlN) on top of silicon dioxide (SiO2) and tantalum pentoxide (Ta2O5) Bragg reflectors on silicon (Si) wafer. The stack is analyzed with X-ray reflectivity (XRR) and high-resolution X-ray diffraction (HRXRD). The resonator shows a coupling coefficient (k2) of 2.0%, high series quality factor (Qs) of 156, shunt quality factor (Qp) of 142, and maximum Bode quality factor (Qmax) of 210. The third-order harmonics at 59.64 GHz is also observed with k2 around 0.6% and Q around 40. Upon further development, the reported acoustic resonator platform can enable various front-end signal-processing functions, e.g., filters and oscillators, at future frequency range 3 (FR3) bands., Comment: 5 pages, 9 figures, 5 tables

Using cohomological methods, we identify both trivial and nontrivial contributions to the conformal anomaly in the presence of vectorial torsion in $d=2,4$ dimensions. In both cases, our analysis considers two scenarios: one in which the torsion vector transforms in an affine way, i.e., it is a gauge potential for Weyl transformations, and the other in which it is invariant under the Weyl group. An important outcome for the former case in both $d=2,4$ is the presence of anomalies of a "mixed" nature in relation to the classification of Deser and Schwimmer. For invariant torsion in $d=4$, we also find a new type of anomaly which we dub $\Psi$-anomaly. Taking these results into account, we integrate the different anomalies to obtain renormalized anomalous effective actions. Thereafter, we recast such actions in the covariant nonlocal and local forms, the latter being easier to work with. Along the way, we pause to comment on the physical usefulness of these effective actions, in particular to obtain renormalized energy-momentum tensors and thermodynamics of $2d$ black holes., Comment: 38 pages

The D-band, spanning 110 GHz to 170 GHz, has emerged as a relevant frequency range for future mobile communications and radar sensing applications, particularly in the context of 6G technologies. This demonstration presents a high-bandwidth, real-time integrated sensing and communication (ISAC) platform operating in the upper D-band at 160 GHz. The platform comprises a software-defined intermediate frequency transceiver and a D-band radio frequency module. Its flexible software design allows for rapid integration of signal processing algorithms. Highlighting its potential for advanced research in ISAC systems, the platforms's efficacy is showcased through a live demonstration with an algorithm implementing human target tracking and activity classification., Comment: This work has been submitted to the IEEE for possible publication

Image inpainting is currently a hot topic within the field of computer vision. It offers a viable solution for various applications, including photographic restoration, video editing, and medical imaging. Deep learning advancements, notably convolutional neural networks (CNNs) and generative adversarial networks (GANs), have significantly enhanced the inpainting task with an improved capability to fill missing or damaged regions in an image or video through the incorporation of contextually appropriate details. These advancements have improved other aspects, including efficiency, information preservation, and achieving both realistic textures and structures. Recently, visual transformers have been exploited and offer some improvements to image or video inpainting. The advent of transformer-based architectures, which were initially designed for natural language processing, has also been integrated into computer vision tasks. These methods utilize self-attention mechanisms that excel in capturing long-range dependencies within data; therefore, they are particularly effective for tasks requiring a comprehensive understanding of the global context of an image or video. In this paper, we provide a comprehensive review of the current image or video inpainting approaches, with a specific focus on transformer-based techniques, with the goal to highlight the significant improvements and provide a guideline for new researchers in the field of image or video inpainting using visual transformers. We categorized the transformer-based techniques by their architectural configurations, types of damage, and performance metrics. Furthermore, we present an organized synthesis of the current challenges, and suggest directions for future research in the field of image or video inpainting., Comment: The paper have been submitted to Artificial Intelligence Review journal

The gravitational lensing signal from the Cosmic Microwave Background is highly valuable to constrain the growth of the structures in the Universe in a clean and robust manner over a wide range of redshifts. One of the theoretical systematics for lensing reconstruction is the impact of the lensing field non-Gaussianities on its estimators. Non-linear matter clustering and post-Born lensing corrections are known to bias standard quadratic estimators to some extent, most significantly so in temperature. In this work, we explore the impact of non-Gaussian deflections on Maximum a Posteriori lensing estimators, which, in contrast to quadratic estimators, are able to provide optimal measurements of the lensing field. We show that these naturally reduce the induced non- Gaussian bias and lead to unbiased cosmological constraints in $\Lambda$CDM at CMB-S4 noise levels without the need for explicit modelling. We also test the impact of assuming a non-Gaussian prior for the reconstruction; this mitigates the effect further slightly, but generally has little impact on the quality of the reconstruction. This shows that higher-order statistics of the lensing deflections are not expected to present a major challenge for optimal CMB lensing reconstruction in the foreseeable future., Comment: 18 figures

Understanding the mechanisms behind opinion formation is crucial for gaining insight into the processes that shape political beliefs, cultural attitudes, consumer choices, and social movements. This work aims to explore a nuanced model that captures the intricacies of real-world opinion dynamics by synthesizing principles from cognitive science and employing social network analysis. The proposed model is a hybrid continuous-discrete extension of the well-known Naming Game opinion model. The added latent continuous layer of opinion strength follows cognitive processes in the human brain, akin to memory imprints. The discrete layer allows for the conversion of intrinsic continuous opinion into discrete form, which often occurs when we publicly verbalize our opinions. We evaluated our model using real data as ground truth and demonstrated that the proposed mechanism outperforms the classic Naming Game model in many cases, reflecting that our model is closer to the real process of opinion formation.

Electric power systems are undergoing a major transformation as they integrate intermittent renewable energy sources, and batteries to smooth out variations in renewable energy production. As privately-owned batteries grow from their role as marginal "price-takers" to significant players in the market, a natural question arises: How do batteries operate in electricity markets, and how does the strategic behavior of decentralized batteries distort decisions compared to centralized batteries? We propose an analytically tractable model that captures salient features of the highly complex electricity market. We derive in closed form the resulting battery behavior and generation cost in three operating regimes: (i) no battery, (ii) centralized battery, and (ii) decentralized profit-maximizing battery. We establish that a decentralized battery distorts its discharge decisions in three ways. First, there is quantity withholding, i.e., discharging less than centrally optimal. Second, there is a shift in participation from day-ahead to real-time, i.e., postponing some of its discharge from day-ahead to real-time. Third, there is reduction in real-time responsiveness, or discharging less in response to smoothing real-time demand than centrally optimal. We quantify each of the three forms of distortions in terms of market fundamentals. To illustrate our results, we calibrate our model to Los Angeles and Houston and show that the loss from incentive misalignment could be consequential.

GRBs produced by the collapse of massive stars are usually found near the most prominent star-forming regions of star-forming galaxies. GRB 171205A happened in the outskirts of a spiral galaxy, a peculiar location in an atypical GRB host. In this paper we present a highly-resolved study of the molecular gas of this host, with CO(1-0) observations from ALMA. We compare with GMRT atomic HI observations, and with data at other wavelengths to provide a broad-band view of the galaxy. The ALMA observations have a spatial resolution of 0.2" and a spectral resolution of 10 km/s, observed when the afterglow had a flux density of ~53 mJy. This allowed a molecular study both in emission and absorption. The HI observations allowed to study the host galaxy and its extended environment. The CO emission shows an undisturbed spiral structure with a central bar, and no significant emission at the location of the GRB. Our CO spectrum does not reveal any CO absorption, with a column density limit of < 10^15 cm^-2. This argues against the progenitor forming in a massive molecular cloud. The molecular gas traces the galaxy arms with higher concentration in the regions dominated by dust. The HI gas does not follow the stellar light or the molecular gas and is concentrated in two blobs, with no emission towards the centre of the galaxy, and is slightly displaced towards the southwest of the galaxy, where the GRB exploded. Within the extended neighbourhood of the host galaxy, we identify another prominent HI source at the same redshift, at a projected distance of 188 kpc. Our observations show that the progenitor of this GRB is not associated to a massive molecular cloud, but more likely related to low-metallicity atomic gas. The distortion in the HI gas field is indicator of an odd environment that could have triggered star formation and could be linked to a past interaction with the companion galaxy., Comment: 13 pages, 10 figures, 8 tables, A&A submitted after 1st referee review

Repairing a large-scale buggy program using current automated program repair (APR) approaches can be a time-consuming operation that requires significant computational resources. We describe a program repair framework that effectively handles large-scale buggy programs of industrial complexity. The framework exploits program reduction in the form of program slicing to eliminate parts of the code irrelevant to the bug being repaired without adversely affecting the capability of the repair system in producing correct patches. Observation-based slicing is a recently introduced, language-independent slicing technique that shows a good effectiveness in a wide range of applications. In this work, we show how ORBS can be effectively integrated with APR to improve all aspects of the repair process including the fault localization step, patch generation step, and patch validation step. The presented repair framework indeed enhances the capability of APR by reducing the execution cost of a test suite and the search cost for the appropriate faulty statement corresponding to the bug being repair. Our empirical results on the widely used Defects4J dataset reveal that a substantial improvement in performance can be obtained without any degradation in repair quality.

The Internet of Things (IoT) is increasingly prevalent in various applications, such as healthcare and logistics. One significant service of IoT technologies that is essential for these applications is localization. The goal of this service is to determine the precise position of a specific target. The localization data often needs to be private, accessible only to specific entities, and must maintain authenticity and integrity to ensure trustworthiness. IoT technology has evolved significantly, with Ultra-Wide Band (UWB) technology enhancing localization speed and precision. However, IoT device security remains a concern, as devices can be compromised or act maliciously. Furthermore, localization data is typically stored centrally, which can also be a point of vulnerability. Our approach leverages the features of a permissioned blockchain, specifically Hyperledger Fabric, to address these challenges. Hyperledger Fabric's collection feature ensures data privacy, and its smart contracts (chaincode) enhance trustworthiness. We tested our solution using a network of devices known as CLOVES, demonstrating robust performance characteristics with UWB technology. Additionally, we evaluated our approach through an indoor localization use case.

Countless domains rely on Machine Learning (ML) models, including safety-critical domains, such as autonomous driving, which this paper focuses on. While the black box nature of ML is simply a nuisance in some domains, in safety-critical domains, this makes ML models difficult to trust. To fully utilize ML models in safety-critical domains, it would be beneficial to have a method to improve trust in model robustness and accuracy without human experts checking each decision. This research proposes a method to increase trust in ML models used in safety-critical domains by ensuring the robustness and completeness of the model's training dataset. Because ML models embody what they are trained with, ensuring the completeness of training datasets can help to increase the trust in the training of ML models. To this end, this paper proposes the use of a domain ontology and an image quality characteristic ontology to validate the domain completeness and image quality robustness of a training dataset. This research also presents an experiment as a proof of concept for this method, where ontologies are built for the emergency road vehicle domain.

Diffusion models are state-of-the-art methods in generative modeling when samples from a target probability distribution are available, and can be efficiently sampled, using score matching to estimate score vectors guiding a Langevin process. However, in the setting where samples from the target are not available, e.g. when this target's density is known up to a normalization constant, the score estimation task is challenging. Previous approaches rely on Monte Carlo estimators that are either computationally heavy to implement or sample-inefficient. In this work, we propose a computationally attractive alternative, relying on the so-called dilation path, that yields score vectors that are available in closed-form. This path interpolates between a Dirac and the target distribution using a convolution. We propose a simple implementation of Langevin dynamics guided by the dilation path, using adaptive step-sizes. We illustrate the results of our sampling method on a range of tasks, and shows it performs better than classical alternatives.

We develop a method for training small-scale (under 100M parameter) neural information retrieval models with as few as 10 gold relevance labels. The method depends on generating synthetic queries for documents using a language model (LM), and the key step is that we automatically optimize the LM prompt that is used to generate these queries based on training quality. In experiments with the BIRCO benchmark, we find that models trained with our method outperform RankZephyr and are competitive with RankLLama, both of which are 7B parameter models trained on over 100K labels. These findings point to the power of automatic prompt optimization for synthetic dataset generation.

Language Model Programs, i.e. sophisticated pipelines of modular language model (LM) calls, are increasingly advancing NLP tasks, but they require crafting prompts that are jointly effective for all modules. We study prompt optimization for LM programs, i.e. how to update these prompts to maximize a downstream metric without access to module-level labels or gradients. To make this tractable, we factorize our problem into optimizing the free-form instructions and few-shot demonstrations of every module and introduce several strategies to craft task-grounded instructions and navigate credit assignment across modules. Our strategies include (i) program- and data-aware techniques for proposing effective instructions, (ii) a stochastic mini-batch evaluation function for learning a surrogate model of our objective, and (iii) a meta-optimization procedure in which we refine how LMs construct proposals over time. Using these insights we develop MIPRO, a novel optimizer that outperforms baselines on five of six diverse LM programs using a best-in-class open-source model (Llama-3-8B), by as high as 12.9% accuracy. We will release our new optimizers and benchmark in DSPy at https://github.com/stanfordnlp/dspy, Comment: Krista and Michael contributed equally to this work

We discuss the inclusion of relativistic boost corrections into the CBF effective nuclear Hamiltonian, derived from a realistic model of two- and three-nucleon interactions using the formalism of correlated basis functions and the cluster expansion technique. Different procedures to take into account the effects of boost interactions are compared on the basis of the ability to reproduce the nuclear matter equation of state obtained from accurate many-body calculations. The results of our study show that the repulsive contribution of the boost interaction significantly depends on the underlying model of the non relativistic potential. On the other hand, the dominant relativistic correction turns out to be the corresponding reduction of the strength of repulsive three-nucleon interactions, leading to a significant softening of the equation of state at supranuclear densities.

Nanocrystalline (NC) materials are intrinsically unstable against grain growth. Significant research efforts have been dedicated to suppressing the grain growth by solute segregation, including the pursuit of a special NC structure that minimizes the total free energy and completely eliminates the driving force for grain growth. This fully stabilized state has been predicted theoretically and by simulations but is yet to be confirmed experimentally. To better understand the nature of the full stabilization, we propose a simple two-dimensional model capturing the coupled processes of grain boundary (GB) migration and solute diffusion. Kinetic Monte Carlo simulations based on this model reproduce the fully stabilized polycrystalline state and link it to the condition of zero GB free energy. The simulations demonstrate the emergence of a fully stabilized state by the divergence of capillary wave amplitudes on planar GBs and by fragmentation of a large grain into a stable ensemble of smaller grains. The role of solute diffusion in the full stabilization is examined. Possible extensions of the model are discussed.

Pixelated dark energy is a string theory scenario with a quantum mechanically stable cosmological constant. The number of pixels that make up the universe slowly increases, manifesting as a time-dependent source of dark energy. DESI has recently reported evidence for dynamical dark energy that fits within this framework. In light of this, we perform the first cosmological analysis of the pixelated model. We find that the simplest model where the pixel growth rate is constant is unable to accommodate the data, providing a comparable fit to $\Lambda$CDM; but that models where the pixel growth rate is increasing and of order the Hubble constant today are compatible. Our analysis helps to clarify the features of UV constructions of dark energy necessary to accommodate the data., Comment: 5 pages, 2 figures