Miracle-dz/UniFAR-FTUs huggingface dataset:
A Complete HCN Survey of the Perseus Molecular Cloud

We present a survey of the Perseus molecular cloud in the J = 1 → 0 transition of HCN, a widely used tracer of dense molecular gas. The survey was conducted with the CfA 1.2 m telescope, which at 89 GHz has a beamwidth of 11′ and a spectral resolution of 0.85 km s−1. A total of 8.1 deg2 was surveyed on a uniform 10′ grid to a sensitivity of 14 mK per channel. We compared the survey with similar surveys of CO and dust in order to study and calibrate the HCN line as a dense-gas tracer. We find the HCN emission to extend over a considerable fraction of the cloud. We show that the HCN intensity remains linear with H2 column density well into the regime where the CO line saturates. We use radiative-transfer modeling to show that this likely results from subthermal excitation of HCN in a cloud where the column and volume densities of H2 are positively correlated. To match our HCN observations the model requires an exponential decrease in HCN abundance with increasing extinction, consistent with HCN depletion onto grains. The modeling also reveals that the mean volume density of H2 in the HCN-emitting regions is ∼104 cm−3, well below the HCN critical density. For the first time, we obtain a direct measurement of the ratio of dense-gas mass to HCN luminosity for an entire nearby molecular cloud: α(HCN) = 92 M ⊙/(K km s−1 pc2).
Complexity Analysis of a Countable-armed Bandit Problem

We consider a stochastic multi-armed bandit (MAB) problem motivated by ``large'' action spaces, and endowed with a population of arms containing exactly $K$ arm-types, each characterized by a distinct mean reward. The decision maker is oblivious to the statistical properties of reward distributions as well as the population-level distribution of different arm-types, and is precluded also from observing the type of an arm after play. We study the classical problem of minimizing the expected cumulative regret over a horizon of play $n$, and propose algorithms that achieve a rate-optimal finite-time instance-dependent regret of $\mathcal{O}\left( \log n \right)$. We also show that the instance-independent (minimax) regret is $\tilde{\mathcal{O}}\left( \sqrt{n} \right)$ when $K=2$. While the order of regret and complexity of the problem suggests a great degree of similarity to the classical MAB problem, properties of the performance bounds and salient aspects of algorithm design are quite distinct from the latter, as are the key primitives that determine complexity along with the analysis tools needed to study them.
Galaxy three-point correlation function in modified gravity

The next generation of galaxy surveys will provide highly accurate measurements of the large-scale structure of the Universe, allowing for more stringent tests of gravity on cosmological scales. Higher order statistics are a valuable tool to study the non-Gaussianities in the matter field and to break degeneracies between modified gravity and other physical or nuisance parameters. However, understanding from first principles the behaviour of these correlations is essential to characterise deviations from General Relativity (GR), and the purpose of this work. This work uses contemporary ideas of Standard Perturbation Theory on biased tracers to characterize the three point correlation function (3PCF) at tree level for Modified Gravity models with a scale-dependent gravitational strength, and applies the theory to two specific models ($f(R)$ and DGP) that are representative for Chameleon and Vainshtein screening mechanisms. Additionally, we use a multipole decomposition, which apart from speeding up the algorithm to extract the signal from data, also helps to visualize and characterize GR deviations.
Training Semantic Segmentation on Heterogeneous Datasets

—We explore semantic segmentation beyond the con- ventional, single-dataset homogeneous training and bring forward the problem of Heterogeneous Training of Semantic Seg- mentation (HTSS). HTSS involves simultaneous training on multiple heterogeneous datasets, i.e . datasets with conﬂicting label spaces and different (weak) annotation types from the perspective of semantic segmentation. The HTSS formulation exposes deep networks to a larger and previously unexplored aggregation of information that can potentially enhance semantic segmentation in three directions: i) performance: increased segmentation metrics on seen datasets, ii) generalization: improved segmentation metrics on unseen datasets, and iii) knowledgeability: increased number of recognizable semantic concepts. To research these beneﬁts of HTSS, we propose a uniﬁed framework, that in-corporates heterogeneous datasets in a single-network training pipeline following the established FCN standard. Our framework ﬁrst curates heterogeneous datasets to bring them in a common format and then trains a single-backbone FCN on all of them simultaneously. To achieve this, it transforms weak annotations, which are incompatible with semantic segmentation, to per-pixel labels, and hierarchizes their label spaces into a universal taxonomy. The trained HTSS models demonstrate performance and generalization gains over a wide range of datasets and extend the inference label space entailing hundreds of semantic classes.
$H^1$ and BMO spaces for exponentially decreasing measures on homogeneous trees

We consider a family of measures on a 𝑞 -homogeneous tree that decrease exponentially with respect to the distance from the origin. Such measures are doubling with respect to the Gromov distance. We deﬁne atomic Hardy and BMO spaces for that measures, and we prove interpolation results regarding such spaces. As a consequence we have boundedness results for integral operators involving Hardy, BMO, and 𝐿 𝑝 spaces.
Additive Schwarz methods for fourth-order variational inequalities

Fourth-order variational inequalities are encountered in various scientific and engineering disciplines, including elliptic optimal control problems and plate obstacle problems. In this paper, we consider additive Schwarz methods for solving fourth-order variational inequalities. Based on a unified framework of various finite element methods for fourth-order variational inequalities, we develop one- and two-level additive Schwarz methods. We prove that the two-level method is scalable in the sense that the convergence rate of the method depends on $H/h$ and $H/\delta$ only, where $h$ and $H$ are the typical diameters of an element and a subdomain, respectively, and $\delta$ measures the overlap among the subdomains. This proof relies on a new nonlinear positivity-preserving coarse interpolation operator, the construction of which was previously unknown. To the best of our knowledge, this analysis represents the first investigation into the scalability of the two-level additive Schwarz method for fourth-order variational inequalities. Our theoretical results are verified by numerical experiments.
Particle-in-cell simulations of Alfvén wave parametric decay in a low-beta plasma

We study the parametric decay instability of parallel-propagating Alfvén waves in a low-beta plasma using one-dimensional fully kinetic simulations. We focus for the first time on the conversion of the energy stored in the initial Alfvén wave into particle internal energy, and on its partition between particle species. We show that compressible fluctuations generated by the decay of the pump wave into a secondary ion-acoustic mode and a reflected Alfvén wave contribute to the gain of internal energy via two distinct mechanisms. First, the ion-acoustic mode leads nonlinearly to proton trapping and proton phase-space mixing, in agreement with previous work based on hybrid simulations. Second, during the nonlinear stage, a compressible front of the fast type develops at the steepened edge of the backward Alfvén wave leading to a field-aligned proton beam propagating backwards at the Alfvén speed. We find that parametric decay heats preferentially protons, which gain approximately 50 % of the pump wave energy in the form of internal energy. However, we find that electrons are also energized and that they contribute to the total energy balance by gaining 10 % of the pump wave energy. By investigating energy partition and particle heating during parametric decay, our results contribute to the determination of the role of compressible and kinetic effects in wave-driven models of the solar wind.
Understanding the Spectral Bias of Coordinate Based MLPs Via Training Dynamics

Spectral bias is an important observation of neural network training, stating that the network will learn a low frequency representation of the target function before converging to higher frequency components. This property is interesting due to its link to good generalization in over-parameterized networks. However, in low dimensional settings, a severe spectral bias occurs that obstructs convergence to high frequency components entirely. In order to overcome this limitation, one can encode the inputs using a high frequency sinusoidal encoding. Previous works attempted to explain this phenomenon using Neural Tangent Kernel (NTK) and Fourier analysis. However, NTK does not capture real network dynamics, and Fourier analysis only offers a global perspective on the network properties that induce this bias. In this paper, we provide a novel approach towards understanding spectral bias by directly studying ReLU MLP training dynamics. Specifically, we focus on the connection between the computations of ReLU networks (activation regions), and the speed of gradient descent convergence. We study these dynamics in relation to the spatial information of the signal to understand how they influence spectral bias. We then use this formulation to study the severity of spectral bias in low dimensional settings, and how positional encoding overcomes this.
OVER TOTALLY

We prove that all elliptic curves deﬁned over totally real cubic ﬁelds are modular. This builds on previous work of Freitas, Le Hung and Siksek, who proved modularity of elliptic curves over real quadratic ﬁelds, as well as recent breakthroughs due to Thorne and to Kalyanswamy.
Aging and Alzheimer's Disease Have Dissociable Effects on Medial Temporal Lobe Connectivity

Functional disruption of the medial temporal lobe-dependent networks is thought to underlie episodic memory deficits in aging and Alzheimer's disease. Previous studies revealed that the anterior medial temporal lobe is more vulnerable to pathological and neurodegenerative processes in Alzheimer's disease. In contrast, cognitive and structural imaging literature indicates posterior, as opposed to anterior, medial temporal lobe vulnerability in normal aging. However, the extent to which Alzheimer's and aging-related pathological processes relate to functional disruption of the medial temporal lobe-dependent brain networks is poorly understood. To address this knowledge gap, we examined functional connectivity alterations in the medial temporal lobe and its immediate functional neighborhood -- the Anterior-Temporal and Posterior-Medial brain networks -- in normal agers, individuals with preclinical Alzheimer's disease, and patients with Mild Cognitive Impairment or mild dementia due to Alzheimer's disease. In the Anterior-Temporal network and in the perirhinal cortex, in particular, we observed an inverted 'U-shaped' relationship between functional connectivity and Alzheimer's stage. According to our results, the preclinical phase of Alzheimer's disease is characterized by increased functional connectivity between the perirhinal cortex and other regions of the medial temporal lobe, as well as between the anterior medial temporal lobe and its one-hop neighbors in the Anterior-Temporal system. This effect is no longer present in symptomatic Alzheimer's disease. Instead, patients with symptomatic Alzheimer's disease displayed reduced hippocampal connectivity within the medial temporal lobe as well as hypoconnectivity within the Posterior-Medial system. For normal aging, our results led to three main conclusions: (1) intra-network connectivity of both the Anterior-Temporal and Posterior-Medial networks declines with age; (2) the anterior and posterior segments of the medial temporal lobe become increasingly decoupled from each other with advancing age; and, (3) the posterior subregions of the medial temporal lobe, especially the parahippocampal cortex, are more vulnerable to age-associated loss of function than their anterior counterparts. Together, the current results highlight evolving medial temporal lobe dysfunction in Alzheimer's disease and indicate different neurobiological mechanisms of the medial temporal lobe network disruption in aging vs. Alzheimer's disease.
Magnitude of wasting and its predictors among under-five children in Bakadawula Ari district, Southern Ethiopia

Background: Globally, wasting threatens the lives of 50 million children under-five. In Ethiopia, wasting is not decreasing at the intended rate, but the reason remains unclear. Moreover, Bakadawula Ari district lacks scientific information regarding wasting among children. Objective: This study was conducted to determine the magnitude of wasting and its predictors among under-five children in the district. Methods: A community-based cross-sectional study was conducted from January to March 2022. A multistage sampling technique was used to select 421 children. The data were entered and analyzed by SPSS 26 (Statistical Package for the Social Sciences version 26). Logistic regression analyses were used and presented with crude odds ratio (COR) and adjusted odds ratio (AOR) with their 95% confidence intervals (CI). Results: The prevalence of wasting among children in the study area was 22.6% (95% CI: 18.5-26.8). Fathers with primary education (AOR= 4.48; 95% CI: 1.93-10.39), households with improper solid waste- disposal (AOR= 2.54; 95% CI: 1.11-5.82), not usually sleeping under insecticide-treated bed net (ITN) (AOR=1.79; 95% CI: 1.01-3.19), unacceptable children dietary diversity score (DDS) (AOR= 2.56; 95% CI: 1.28-5.14) and unacceptable household DDS (AOR= 2.26; 95% CI: 1.02-5.00) were predictors of wasting. Conclusions: The prevalence of wasting among children was critically high. Upgrading educational status of fathers, encouraging safe solid waste disposal, ensuring consistent use of ITN, and improving both children and household DDS should be given a due emphasis to reduce wasting in the study area.
Effect of Roughness and Unsteadiness on the Performance of a New Low Pressure Turbine Blade at Low Reynolds Numbers

This paper presents a study of the performance of a high-lift profile for low pressure turbines at Reynolds numbers lower than in previous investigations. By following the results of Coull et al. (2008, "Velocity Distributions for Low Pressure Turbines," ASME Paper No. GT2008-50589) on the design of high-lift airfoils, the profile is forward loaded. The separate and combined effects of roughness and wake passing are compared. On a front loaded blade, the effect of incidence becomes more important and the consequences in terms of cascade losses, is evaluated. The experimental investigation was carried out in the high speed wind tunnel of Whittle Laboratory, University of Cambridge. This is a closed-circuit continuous wind tunnel where the Reynolds number and Mach number can be fixed independently. The unsteadiness caused by wake passing in front of the blades is reproduced using a wake generator with rotating bars. The results confirm that the beneficial effect of unsteadiness on losses is present even at the lowest Reynolds number examined (Re 3 = 20,000). This beneficial effect is reduced at positive incidence. With a front loaded airfoil and positive incidence, the transition occurs on the suction side close to the leading edge and this results in higher losses. This has been found valid for the entire Reynolds range investigated (20,000 ≤ Re 3 140,000). Roughening the surface also had a beneficial effect on the losses but this effect vanishes at the lower Reynolds numbers, i.e., (Re 3 ≤ 30,000), where the surface becomes hydraulically smooth. The present study suggests that a blade with as-cast surface roughness has a lower loss than a polished one.
Effects of Dust Aerosols on Warm Cloud Properties over East Asia and the Sahara from Satellite Data

The effects of dust aerosol particles on the properties of clouds over East Asia and the Sahara are studied using moderate resolution imaging spectroradiometer observations from the Terra and Aqua satellites and simulation results of the chemical weather forecast system (CFORS) model. Dust-contaminated clouds are detected using the brightness temperature difference (BTD) method, in which dust or dust-bearing clouds are detected when the BTD between the 11 µm and 12 µm channels in the window region is negative, and by the CFORS model’s dust vertical profile. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation data are also used to assess the above procedures. For the Sahara region, no obvious changes to effective particle radius ( R e ) are attributed to dust aerosols. However, for East Asia, dust aerosols are found to change the cloud microphysical properties, decreasing R e by 12 % and increasing the cloud optical depth by 27 % and the liquid water path by 9 %. R e is found to be negatively correlated with sulfate concentration by CFORS in dust-bearing clouds, but not in dust-free clouds, over East Asia. These findings indicate that mineral dust can act as effective cloud condensation nuclei in environments polluted by water-soluble aerosols such as sulfates. We also estimate the indirect radiative effect of dust aerosols in the East Asia region and determine, by radiative transfer calculation, that the net shortwave radiative flux at the top of the atmosphere decreases and the absorption of shortwave radiation by the atmosphere increases with changes in cloud properties due to dust aerosols.
Use of Mueller polarimetric imaging for the staging of human colon cancer

In this paper we show the results of multi-spectral Mueller Imaging applied to the analysis of human colon cancer in a backscattering configuration with diffuse light illumination. The analyzed sample behaves as a pure depolarizer. The depolarization power, for both healthy and cancerous zones, is lower for linearly than for circularly polarized incident light for all used wavelengths and increases with increasing wavelength. Based on their visual staging and polarimetric responses, we chose specific zones which we correlated to the histology of the corresponding cuts. The histological examination shows that we see a multilayer interaction in both healthy and abnormal zones, if the light penetration depth is sufficient. The measured depolarization depends on several factors: the presence or absence of tumor, the microscopic structure of cancer (ratio between cellular density and stroma), its exophytic (budding) or endophytic (penetrating) nature, its thickness, the degree of cancer penetration in deeper layers and the nature of healthy tissue left under abnormal layers. These results demonstrate that multi-spectral Mueller imaging can provide useful contrasts for the quick staging of human colon cancer ex-vivo, with additional information about cancerous zones with different microscopic structures.
Comment on “Low‐energy electron production by relativistic runway electron avalanches in air” by J. R. Dwyer and L. P. Babich

] In their paper Dwyer and Babich [2011, hereinafterDB] purport to demonstrate that the production of copiousnumbers of secondary electrons is not a characteristic of themechanism first proposed by Gurevich et al. [1992]. Unfortunately,severalerrors existinDB’sworkwhichwhencorrected provide evidence for an even larger production oflow energy electrons. Before addressing the latter in somedetail we make the following remarks:[
Frequently hypercyclic operators and vectors

We study frequently hypercyclic operators, a natural new concept in hypercyclicity that was recently introduced by F. Bayart and S. Grivaux. We derive a strengthened version of their Frequent Hypercyclicity Criterion, which allows us to obtain examples of frequently hypercyclic operators in a straightforward way. Moreover, Bayart and Grivaux have noted that the frequent hypercyclicity setting differs from general hypercyclicity in that the set of frequently hypercyclic vectors need not be residual. We show here that, under weak assumptions, this set is only of first category. Motivated by this we study the question of whether one may write every vector in the underlying space as the sum of two frequently hypercyclic vectors. This investigation leads us to the introduction of a new notion, that of Runge transitivity.
Probabilistic Predictability of Stochastic Dynamical Systems: Metric, Optimality and Application

To assess the quality of a probabilistic prediction for stochastic dynamical systems (SDSs), scoring rules assign a numerical score based on the predictive distribution and the measured state. In this paper, we propose an $\epsilon$-logarithm score that generalizes the celebrated logarithm score by considering a neighborhood with radius $\epsilon$. To begin with, we prove that the $\epsilon$-logarithm score is proper (the expected score is optimized when the predictive distribution meets the ground truth) based on discrete approximations. Then, we characterize the probabilistic predictability of an SDS by the optimal expected score and approximate it with an error of scale $\mathcal{O}(\epsilon)$. The approximation quantitatively shows how the system predictability is jointly determined by the neighborhood radius, the differential entropies of process noises, and the system dimension. In addition to the expected score, we also analyze the asymptotic behaviors of the score on individual trajectories. Specifically, we prove that the score on a trajectory will converge to the probabilistic predictability when the process noises are independent and identically distributed. Moreover, the convergence speed against the trajectory length $T$ is of scale $\mathcal{O}(T^{-\frac{1}{2}})$ in the sense of probability. Finally, we apply the predictability analysis to design unpredictable SDSs. Numerical examples are given to elaborate the results.
Human-Scene Network: A Novel Baseline with Self-rectifying Loss for Weakly supervised Video Anomaly Detection

Video anomaly detection in surveillance systems with only video-level labels (i.e. weakly-supervised) is challenging. This is due to, (i) the complex integration of human and scene based anomalies comprising of subtle and sharp spatio-temporal cues in real-world scenarios, (ii) non-optimal optimization between normal and anomaly instances under weak supervision. In this paper, we propose a Human-Scene Network to learn discriminative representations by capturing both subtle and strong cues in a dissociative manner. In addition, a self-rectifying loss is also proposed that dynamically computes the pseudo temporal annotations from video-level labels for optimizing the Human-Scene Network effectively. The proposed Human-Scene Network optimized with self-rectifying loss is validated on three publicly available datasets i.e. UCF-Crime, ShanghaiTech and IITB-Corridor, outperforming recently reported state-of-the-art approaches on five out of the six scenarios considered.