Miracle-dz/UniFAR-FTUs huggingface dataset:
Concentration and local smoothness of the averaging process

We consider the averaging process on the discrete $d$-dimensional torus. On this graph, the process is known to converge to equilibrium on diffusive timescales, not exhibiting cutoff. In this work, we refine this picture in two ways. Firstly, we prove a concentration phenomenon of the averaging process around its mean, occurring on a shorter timescale than the one of its relaxation to equilibrium. Secondly, we establish sharp gradient estimates, which capture its fast local smoothness property. This is the first setting in which these two features of the averaging process -- concentration and local smoothness -- can be quantified. These features carry useful information on a number of large scale properties of the averaging process. As an illustration of this fact, we determine the limit profile of its distance to equilibrium and derive a quantitative hydrodynamic limit for it. Finally, we discuss their implications on cutoff for the binomial splitting process, the particle analogue of the averaging process.
IDD-AW: A Benchmark for Safe and Robust Segmentation of Drive Scenes in Unstructured Traffic and Adverse Weather

Large-scale deployment of fully autonomous vehicles requires a very high degree of robustness to unstructured traffic, and weather conditions, and should prevent unsafe mispredictions. While there are several datasets and benchmarks focusing on segmentation for drive scenes, they are not specifically focused on safety and robustness issues. We introduce the IDD-AW dataset, which provides 5000 pairs of high-quality images with pixel-level annotations, captured under rain, fog, low light, and snow in unstructured driving conditions. As compared to other adverse weather datasets, we provide i. ) more annotated images, ii. ) paired Near-Infrared (NIR) image for each frame, iii. ) larger label set with a 4-level label hierarchy to capture unstructured traffic conditions. We benchmark state-of-the-art models for semantic segmentation in IDD-AW. We also propose a new metric called ''Safe mean Intersection over Union (Safe mIoU)'' for hierarchical datasets which penalizes dangerous mispredictions that are not captured in the traditional definition of mean Intersection over Union (mIoU). The results show that IDD-AW is one of the most challenging datasets to date for these tasks. The dataset and code will be available here: http://iddaw.github. io.
Examples of cosmological spacetimes without CMC Cauchy surfaces

CMC (constant mean curvature) Cauchy surfaces play an important role in mathematical relativity as finding solutions to the vacuum Einstein constraint equations is made much simpler by assuming CMC initial data. However, in [2] Bartnik constructed a cosmological spacetime without a CMC Cauchy surface whose spatial topology is the connected sum of two three-dimensional tori. Similarly, in [9], Chru\'sciel, Isenberg, and Pollack constructed a vacuum cosmological spacetime without CMC Cauchy surfaces whose spatial topology was also the connected sum of two tori. In this article, we enlarge the known number of spatial topologies for cosmological spacetimes without CMC Cauchy surfaces by generalizing Bartnik's construction. Specifically, we show that there are cosmological spacetimes without CMC Cauchy surfaces whose spatial topologies are the connected sum of any compact oriented Euclidean or hyperbolic three-manifold with any another compact oriented Euclidean or hyperbolic three-manifold. We work with the Tolman-Bondi class of metrics and prove gluing results for variable marginal conditions, which allows for smooth gluing of Schwarzschild to FLRW models.
Solid-that-Flows Picture of Glass-Forming Liquids

This perspective article reviews arguments that glass-forming liquids are different from those of standard liquid-state theory, which typically have a viscosity in the mPa·s range and relaxation times on the order of picoseconds. These numbers grow dramatically and become 1012 – 1015 times larger for liquids cooled toward the glass transition. This translates into a qualitative difference, and below the “solidity length” which is roughly one micron at the glass transition, a glass-forming liquid behaves much like a solid. Recent numerical evidence for the solidity of ultraviscous liquids is reviewed, and experimental consequences are discussed in relation to dynamic heterogeneity, frequency-dependent linear-response functions, and the temperature dependence of the average relaxation time.
Spectral Distortion Signatures of Step-like Inflationary Potential

In this work, we analyze a power-law inflationary potential enhanced with a step that can introduce features in the primordial power spectrum. We focus on the computation of the Spectral Distortions (SD) induced by these features obtained from the inflationary dynamics. In this scenario, we explore the potential of upcoming experimental missions like PIXIE to detect the SD of the model within a power of $n = 2/3$, a power that agrees with recent tensor-to-scalar ratio constraints. The model offers insights into models with cosmological phases and different scalar field dynamics. Introducing a step in the inflaton potential leads to distinct features in the primordial power spectrum, such as oscillations and localized enhancements/suppressions at specific scales. We analyze the impact of three primary parameters$-\beta$, $\delta$, and $\phi_{\text{step}}-$on the amplitude and characteristics of the SD. The $\phi_{\rm step}$ places the onset of the oscillations in the primordial power spectrum. The $\beta$ parameter significantly influences the magnitude of the $\mu$-SD, with its increase leading to larger SD and vice versa. Similarly, the $\delta$ parameter affects the smoothness of the step in the potential, with larger values resulting in smaller SD. Our findings indicate a distinct parameter space defined by $0.02<\delta/{\rm M_{pl}} \lesssim 0.026$, $0.10 \lesssim \beta<0.23$, and $ 7.53 \lesssim \phi_{\rm step}/{\rm M_{pl}} \lesssim 7.55$, which produces SD potentially detectable by PIXIE. This region also corresponds to the maximum observed values of $\mu$ and $y$ SD, which in special cases are an order of magnitude larger than the expected for $\Lambda$CDM. However, we also identify parameter ranges where $\mu$ and $y$ SD may not be detectable due to the limitations of current observational technology.
Large Language Models as Automated Aligners for benchmarking Vision-Language Models

With the advancements in Large Language Models (LLMs), Vision-Language Models (VLMs) have reached a new level of sophistication, showing notable competence in executing intricate cognition and reasoning tasks. However, existing evaluation benchmarks, primarily relying on rigid, hand-crafted datasets to measure task-specific performance, face significant limitations in assessing the alignment of these increasingly anthropomorphic models with human intelligence. In this work, we address the limitations via Auto-Bench, which delves into exploring LLMs as proficient aligners, measuring the alignment between VLMs and human intelligence and value through automatic data curation and assessment. Specifically, for data curation, Auto-Bench utilizes LLMs (e.g., GPT-4) to automatically generate a vast set of question-answer-reasoning triplets via prompting on visual symbolic representations (e.g., captions, object locations, instance relationships, and etc.). The curated data closely matches human intent, owing to the extensive world knowledge embedded in LLMs. Through this pipeline, a total of 28.5K human-verified and 3,504K unfiltered question-answer-reasoning triplets have been curated, covering 4 primary abilities and 16 sub-abilities. We subsequently engage LLMs like GPT-3.5 to serve as judges, implementing the quantitative and qualitative automated assessments to facilitate a comprehensive evaluation of VLMs. Our validation results reveal that LLMs are proficient in both evaluation data curation and model assessment, achieving an average agreement rate of 85%. We envision Auto-Bench as a flexible, scalable, and comprehensive benchmark for evaluating the evolving sophisticated VLMs.
Learning to Solve Inverse Problems for Perceptual Sound Matching

Perceptual sound matching (PSM) aims to find the input parameters to a synthesizer so as to best imitate an audio target. Deep learning for PSM optimizes a neural network to analyze and reconstruct prerecorded samples. In this context, our article addresses the problem of designing a suitable loss function when the training set is generated by a differentiable synthesizer. Our main contribution is perceptual-neural-physical loss (PNP), which aims at addressing a tradeoff between perceptual relevance and computational efficiency. The key idea behind PNP is to linearize the effect of synthesis parameters upon auditory features in the vicinity of each training sample. The linearization procedure is massively paralellizable, can be precomputed, and offers a 100-fold speedup during gradient descent compared to differentiable digital signal processing (DDSP). We demonstrate PNP on two datasets of nonstationary sounds: an AM/FM arpeggiator and a physical model of rectangular membranes. We show that PNP is able to accelerate DDSP with joint time-frequency scattering transform (JTFS) as auditory feature, while preserving its perceptual fidelity. Additionally, we evaluate the impact of other design choices in PSM: parameter rescaling, pretraining, auditory representation, and gradient clipping. We report state-of-the-art results on both datasets and find that PNP-accelerated JTFS has greater influence on PSM performance than any other design choice.
The reggeon model with the pomeron and odderon: renormalization group approach

The Regge-Gribov model of the pomeron and odderon in the non-trivial transverse space is studied by the renormalization group technique. The single loop approximation is adopted. Five real fixed points are found and the high-energy behaviour of the propagators is correspondingly calculated. As without odderon, the asymptotic is modulated by logarithms of energy in certain rational powers. Movement of coupling constants away from the fixed points is investigated both analytically (close to the fixed points) and numerically (far away). In the former case attraction occurs only in restricted domains of initial coupling constants. More generally in one third of the cases the coupling constants instead grow large indicating the breakdown of the single loop approximation.
Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study

Mobile health (mHealth) technologies aim to improve distal outcomes, such as clinical conditions, by optimizing proximal outcomes through just-in-time adaptive interventions. Contextual bandits provide a suitable framework for customizing such interventions according to individual time-varying contexts, intending to maximize cumulative proximal outcomes. However, unique challenges such as modeling count outcomes within bandit frameworks have hindered the widespread application of contextual bandits to mHealth studies. The current work addresses this challenge by leveraging count data models into online decision-making approaches. Specifically, we combine four common offline count data models (Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regressions) with Thompson sampling, a popular contextual bandit algorithm. The proposed algorithms are motivated by and evaluated on a real dataset from the Drink Less trial, where they are shown to improve user engagement with the mHealth system. The proposed methods are further evaluated on simulated data, achieving improvement in maximizing cumulative proximal outcomes over existing algorithms. Theoretical results on regret bounds are also derived. A user-friendly R package countts that implements the proposed methods for assessing contextual bandit algorithms is made publicly available at https://cran.r-project.org/web/packages/countts.
Einstein-Gauss-Bonnet gravity: Phase transitions and micro-structure in an FLRW background

For an FLRW model, thermodynamic phase transitions are investigated in the Einstein-Gauss-Bonnet gravity framework. Using the work density, the equation of state is derived, and the criticality conditions are employed to determine the critical points where possible phase transitions occur. The appearance of phase transitions strongly depends upon the space-time dimension $n$. In this concern, for $n=5$, there is a first-order phase transition, a liquid-gas van der Waals-like phase transition, where the Gibbs free energy presents a swallowtail behavior. On the other hand, for $n=6$, the system does not exhibit a first-order van der Waals-like phase transition. In such a case, the Gibbs free energy presents a cusp with stable and unstable branches. For the present study, the mentioned phenomena are present for an expanding cosmology, where the matter distribution filling the Universe corresponds to a speculative matter distribution with an equation of state parameter greater than one. Interestingly, there are no phase transitions for dimensions higher than $n=6$, nor for expanding or contracting cosmological scenarios. To gain more insights into the system, the micro-structure is analyzed using thermodynamic geometry to quantify the normalized scalar curvature. This invariant shows that a repulsive interaction dominates the phase transition region, while the region where the system has a gas-like behavior attractive interaction prevails. Besides, the critical exponents have been obtained, coinciding their numerical values with the universal ones.
Smoothed particle magnetohydrodynamics

Smoothed particle magnetohydrodynamics has reached a level of maturity that enables the study of a wide range of astrophysical problems. In this review, the numerical details of the modern SPMHD method are described. The three fundamental components of SPMHD are methods to evolve the magnetic field in time, calculate accelerations from the magnetic field, and maintain the divergence-free constraint on the magnetic field (no monopoles). The connection between these three requirements in SPMHD will be highlighted throughout. The focus of this review is on the methods that work well in practice, with discussion on why they work well and other approaches do not. Numerical instabilities will be discussed, as well as strategies to overcome them. The inclusion of non-ideal MHD effects will be presented. A prospective outlook on possible avenues for further improvements will be discussed.
Imaging genetics of language network functional connectivity reveals links with language-related abilities, dyslexia and handedness

Language is supported by a distributed network of brain regions with a particular contribution from the left hemisphere. A multi-level understanding of this network requires studying its genetic architecture. We used resting-state imaging data from 29,681 participants (UK Biobank) to measure connectivity between 18 left-hemisphere regions involved in multimodal sentence-level processing, as well as their right-hemisphere homotopes, and interhemispheric connections. Multivariate genome-wide association analysis of this total network, based on genetic variants with population frequencies > 1%, identified 14 genomic loci, of which three were also associated with asymmetry of intrahemispheric connectivity. Polygenic dispositions to lower language-related abilities, dyslexia and left-handedness were associated with generally reduced leftward asymmetry of functional connectivity. Exome-wide association analysis based on rare, protein-altering variants (frequencies ≤ 1%) suggested 7 additional genes. These findings shed new light on genetic contributions to language network organization and related behavioural traits.
Individual cell fate and population dynamics revealed by a mathematical model linking telomere length and replicative senescence

Progressive shortening of telomeres ultimately causes replicative senescence and is linked with aging and tumor suppression. Studying the intricate link between telomere shortening and senescence at the molecular level and its population-scale effects over time is challenging with current approaches but crucial for understanding behavior at the organ or tissue level. In this study, we developed a mathematical model for telomere shortening and the onset of replicative senescence using data from Saccharomyces cerevisiae without telomerase. Our model tracks individual cell states, their telomere length dynamics, and lifespan over time, revealing selection forces within a population. We discovered that both cell genealogy and global telomere length distribution are key to determine the population proliferation capacity. We also discovered that cell growth defects unrelated to telomeres also affect subsequent proliferation and may act as confounding variables in replicative senescence assays. Overall, while there is a deterministic limit for the shortest telomere length, the stochastic occurrence of non-terminal arrests drive cells into a totally different regime, which may promote genome instability and senescence escape. Our results offer a comprehensive framework for investigating the implications of telomere length on human diseases. One-sentence Summary Key determinants of population proliferation capacity in the context of telomere shortening.
Epigenetic clocks, sex markers, and age-class diagnostics in three harvested large mammals

The development of epigenetic clocks, or the DNA methylation-based inference of age, is an emerging tool for ageing in free ranging populations. In this study, we developed epigenetic clocks for three species of large mammals that are the focus of extensive management throughout their range: white-tailed deer, black bear, and mountain goat. We quantified differential DNA methylation patterns at over 30,000 cytosine-guanine sites (CpGs) from tissue samples (N=141) of all three species. We used a penalized regression model (elastic net) to build highly explanatory (black bear r = 0.95; white-tailed deer r = 0.99; mountain goat r = 0.97) and robust (black bear Median Absolute Error or MAE = 1.33; white-tailed deer MAE = 0.29; mountain goat MAE = 0.61) models of age (clocks). We also characterized individual CpG sites within each species that demonstrated clear differences in methylation levels between age classes and sex, which can be used to develop a suite of accessible diagnostic markers. Our results demonstrate promising tools for the large-scale estimation of age in wild animals, which have the potential to contribute to wildlife monitoring by providing easily obtainable representations of age structure in managed populations.
Adaptive gene loss in the common bean pan-genome during range expansion and domestication

The common bean (Phaseolus vulgaris L.) is a crucial grain legume crop [1,2] whose life history offers an ideal evolutionary model to identify and study adaptive variants in wild and domestication populations [3]. Here we present the first common bean pan-genome based on five high-quality genomes and whole-genome reads representing 339 genotypes. We found ∼243 Mb of additional sequences containing 7,495 protein-coding genes missing from the reference, constituting 51% of the total presence/absence variations (PAVs). There were more putatively deleterious mutations in PAVs than core genes, probably reflecting the lower effective population size of PAVs as well as fitness advantages due to the purging effect of gene loss. Our results suggest strong pan-genome shrinkage occurred during wild range expansion from Mexico to South America, with more PAV loss per individual in Andean vs Mesoamerican populations. Selection signatures during wild spreading and domestication were also associated with PAV loss involved in important adaptive traits. Our findings provide evidence that partial or complete gene loss was a key adaptive trait leading to localized and genome-wide reductions. This novel result has major implications for the understanding of the process of plant adaptation and claims for a paradigm shift in evolutionary genetics. Moreover, the common bean pan-genome is a valuable resource for food legume research and breeding towards climate change mitigation, and sustainable agriculture.
Impaired oxytocin signaling in the central amygdala in rats with chronic heart failure

Aims Heart failure (HF) patients often suffer from cognitive decline, depression, and mood impairments, but the molecular signals and brain circuits underlying these effects remain elusive. The hypothalamic neuropeptide oxytocin (OT) is critically involved in the regulation of mood, and OTergic signaling in the central amygdala (CeA) is a key mechanism controlling emotional responses including anxiety-like behaviors. Based on this, we used in this study a well-established ischemic rat HF model and aimed to study alterations in the hypothalamus-to-CeA OTergic circuit. Methods and Results To study potential HF-induced changes in the hypothalamus-to-CeA OTertic circuit, we combined patch-clamp electrophysiology, immunohistochemical analysis, RNAScope assessment of OTR mRNA, brain region-specific stereotaxic injections of viral vectors and retrograde tracing, optogenetic stimulation and OT biosensors in the ischemic HF model. We found that most of OTergic innervation of the central amygdala (CeA) originated from the hypothalamic supraoptic nucleus (SON). While no differences in the numbers of SON➜CeA OTertic neurons (or their OT content) was observed between sham and HF rats, we did observe a blunted content and release of OT from axonal terminals within the CeA. Moreover, we report downregulation of neuronal and astrocytic OT receptors, and impaired OTR-driven GABAergic synaptic activity within the CeA microcircuit of rats with HF. Conclusions Our study provides first evidence that HF rats display various perturbations in the hypothalamus-to-amygdala OTergic circuit, and lays the foundation for future translational studies targeting either the OT system or GABAergic amygdala GABA microcircuit to ameliorate depression or mood impairments in rats or patients with chronic HF.
Cancer-associated fibroblasts serve as decoys to suppress NK cell anti-cancer cytotoxicity

Cancer associated fibroblasts (CAFs) are among the most abundant components of the breast tumor microenvironment (TME) and major contributors to immune modulation. CAFs are well-known to regulate the activity of diverse types of immune cells including T cells, macrophages and dendritic cells, however little is known about their interaction with Natural killer (NK) cells, which constitute an important arm of anti-tumor immunity. Here we find, using mouse models of cancer and ex-vivo co-cultures, that CAFs inhibit NK cell cytotoxicity towards cancer cells. We unravel the mechanism by which this suppression occurs, through ligand-receptor engagement between NK cells and CAFs leading to CAF cytolysis, which in turn diminishes the expression of activating receptors on NK cells, promoting cancer escape from NK cell surveillance. Analysis of breast cancer patient samples reveals enrichment of NK cells in CAF-rich regions, and upregulation of NK binding ligands on CAFs which is correlated with poor disease outcome. These results reveal a CAF-mediated immunosuppressive decoy mechanism with implications for treatment of solid tumors.