Miracle-dz/UniFAR-FTUs huggingface dataset:
Requirements for Gravitational Collapse in Planetesimal Formation—The Impact of Scales Set by Kelvin–Helmholtz and Nonlinear Streaming Instability

The formation of planetesimals is a challenging problem in planet formation theory. A prominent scenario for overcoming dust growth barriers is the gravitational collapse of locally over-dense regions, shown to robustly produce ∼100 km–sized objects. Still, the conditions under which planetesimal formation occurs remain unclear. For collapse to proceed, the self-gravity of an over-density must overcome stellar tidal disruption on large scales and turbulent diffusion on small scales. Here, we relate the scales of streaming and Kelvin–Helmholtz instability (KHI), which both regulate particle densities on the scales of gravitational collapse, directly to planetesimal formation. We support our analytic findings by performing 3D hydrodynamical simulations of streaming and KHI and planetesimal formation. We find that the vertical extent of the particle mid-plane layer and the radial width of streaming instability filaments are set by the same characteristic length scale, thus governing the strength of turbulent diffusion on the scales of planetesimal formation. We present and successfully test a collapse criterion, 0.1Q β ϵ−1Z−1 ≲ 1, and show that even for solar metallicities, planetesimals can form in dead zones of sufficiently massive disks. For a given gas Toomre parameter Q, pressure gradient β, metallicity Z, and local particle enhancement ϵ, the collapse criterion also provides a range of unstable scales, instituting a promising path for studying initial planetesimal mass distributions. Streaming instability is not required for planetesimal collapse but, by increasing ϵ, can evolve a system to instability.
Minimizing relative entropy of path measures under marginal constraints

We study generalizations of the Schrodinger problem in statistical mechanics in two directions: when the density is constrained at more than two times, and when the joint law of the initial and final positions for the particles is prescribed. This is done in agreement with the so-called Brodinger problem recently introduced to regularize Brenier's variational model for incompressible fluids. We recover generalizations of the standard factorization result for the Radon-Nikodym derivative of the solution $P$ with respect to the reference measure $R$: this density can be written in terms of an additive functional on the set of constrained times. The specificity of this work is that we place ourselves in the case when $R$ is Markov (or reciprocal), and that we use Markovian methods rather than classical convex analysis arguments. In this setting, it appears that a natural assumption to be made on the reference measure $R$ is of irreducibility type.
Multimodal Story Generation on Plural Images

Traditionally, text generation models take in a sequence of text as input, and iteratively generate the next most probable word using pre-trained parameters. In this work, we propose the architecture to use images instead of text as the input of the text generation model, called StoryGen. In the architecture, we design a Relational Text Data Generator algorithm that relates different features from multiple images. The output samples from the model demonstrate the ability to generate meaningful paragraphs of text containing the extracted features from the input images.
Sub‐Gaussian Matrices on Sets: Optimal Tail Dependence and Applications

Random linear mappings are widely used in modern signal processing, compressed sensing, and machine learning. These mappings may be used to embed the data into a significantly lower dimension while at the same time preserving useful information. This is done by approximately preserving the distances between data points, which are assumed to belong to ℝn . Thus, the performance of these mappings is usually captured by how close they are to an isometry on the data. Gaussian linear mappings have been the object of much study, while the sub‐Gaussian settings is not yet fully understood. In the latter case, the performance depends on the sub‐Gaussian norm of the rows. In many applications, e.g., compressed sensing, this norm may be large, or even growing with dimension, and thus it is important to characterize this dependence.
Constraining neutrino masses with weak-lensing multiscale peak counts

Massive neutrinos influence the background evolution of the Universe as well as the growth of structure. Being able to model this effect and constrain the sum of their masses is one of the key challenges in modern cosmology. Weak gravitational lensing by the large-scale structure has proven to be a powerful tool to achieve this goal, and its importance to precision cosmology is borne out in the scientific results of galaxy surveys such as the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS), the Kilo-Degree Survey (KiDS), the Dark Energy Survey (DES) and Hyper SuprimeCam (HSC). Weak-lensing cosmological constraints will also soon reach higher levels of precision with next-generation surveys like LSST, WFIRST and Euclid. In this context, we use the MassiveNus simulations to derive constraints on the sum of neutrino masses $M_{\nu}$, the present-day total matter density ${\Omega}_m$, and the primordial power spectrum normalization $A_s$ in a tomographic setting. Along with the lensing power spectrum, we use peak counts as higher-order statistics. We find that the combination of the two statistics does not add any relevant information over considering just the peaks alone. We also compare two different filtering techniques on noisy maps and show that a starlet (wavelet) filter produces tighter constraints than Gaussian filtering by 50%, 25%, and 38% on $M_{\nu}$, ${\Omega}_m$, and $A_s$, respectively. For the starlet case, we further find a minimum resolution that allows us to obtain constraints comparable to what is achieved with the full wavelet decomposition, and we show that the information contained in the coarse map cannot be neglected.
The Indian Chefs Process

This paper introduces the Indian Chefs Process (ICP), a Bayesian nonparametric prior on the joint space of infinite directed acyclic graphs (DAGs) and orders that generalizes Indian Buffet Processes. As our construction shows, the proposed distribution relies on a latent Beta Process controlling both the orders and outgoing connection probabilities of the nodes, and yields a probability distribution on sparse infinite graphs. The main advantage of the ICP over previously proposed Bayesian nonparametric priors for DAG structures is its greater flexibility. To the best of our knowledge, the ICP is the first Bayesian nonparametric model supporting every possible DAG. We demonstrate the usefulness of the ICP on learning the structure of deep generative sigmoid networks as well as convolutional neural networks.
Active Inference: Demystified and Compared

Active inference is a first principle account of how autonomous agents operate in dynamic, nonstationary environments. This problem is also considered in reinforcement learning, but limited work exists on comparing the two approaches on the same discrete-state environments. In this letter, we provide (1) an accessible overview of the discrete-state formulation of active inference, highlighting natural behaviors in active inference that are generally engineered in reinforcement learning, and (2) an explicit discrete-state comparison between active inference and reinforcement learning on an OpenAI gym baseline. We begin by providing a condensed overview of the active inference literature, in particular viewing the various natural behaviors of active inference agents through the lens of reinforcement learning. We show that by operating in a pure belief-based setting, active inference agents can carry out epistemic exploration—and account for uncertainty about their environment—in a Bayes-optimal fashion. Furthermore, we show that the reliance on an explicit reward signal in reinforcement learning is removed in active inference, where reward can simply be treated as another observation we have a preference over; even in the total absence of rewards, agent behaviors are learned through preference learning. We make these properties explicit by showing two scenarios in which active inference agents can infer behaviors in reward-free environments compared to both Q-learning and Bayesian model-based reinforcement learning agents and by placing zero prior preferences over rewards and learning the prior preferences over the observations corresponding to reward. We conclude by noting that this formalism can be applied to more complex settings (e.g., robotic arm movement, Atari games) if appropriate generative models can be formulated. In short, we aim to demystify the behavior of active inference agents by presenting an accessible discrete state-space and time formulation and demonstrate these behaviors in a OpenAI gym environment, alongside reinforcement learning agents.
Compact Recurrent Neural Networks for Acoustic Event Detection on Low-Energy Low-Complexity Platforms

Outdoor acoustic event detection is an exciting research field but challenged by the need for complex algorithms and deep learning techniques, typically requiring many computational, memory, and energy resources. These challenges discourage IoT implementations, where an efficient use of resources is required. However, current embedded technologies and microcontrollers have increased their capabilities without penalizing energy efficiency. This paper addresses the application of sound event detection at the very edge, by optimizing deep learning techniques on resource-constrained embedded platforms for the IoT. The contribution is two-fold: firstly, a two-stage student-teacher approach is presented to make state-of-the-art neural networks for sound event detection fit on current microcontrollers; secondly, we test our approach on an ARM Cortex M4, particularly focusing on issues related to 8-bits quantization. Our embedded implementation can achieve 68% accuracy in recognition on Urbansound8k, not far from state-of-the-art performance, with an inference time of 125 ms for each second of the audio stream, and power consumption of 5.5 mW in just 34.3 kB of RAM.
Environment-Aware Reconfigurable Noise Suppression

The paper proposes an efficient, robust, and reconfigurable technique to suppress various types of noises for any sampling rate. The theoretical analyses, subjective and objective test results show that the proposed noise suppression (NS) solution significantly enhances the speech transmission index (STI), speech intelligibility (SI), signal-to-noise ratio (SNR), and subjective listening experience. The STI and SI consists of 5 levels, i.e., bad, poor, fair, good, and excellent. The most common noisy condition is of SNR ranging from -5 to 8 dB. For the input SNR between -5 and 2.5 dB, the proposed NS improves the STI and SI from "fair" to "good". For the input SNR between 2.5 to 8 dB, the STI and SI are improved from "good" to "excellent" by the proposed NS. The proposed NS can be adopted in both capture and playback paths for voice over internet protocol, voice-trigger, and automatic speech recognition applications.
A new approach for estimating healthy body weights.

: Optimal body weight standards have most often been based on the relationship of relative weight to all cause mortality. This report proposes a strategy based on a more direct measure of adiposity, subscapular skinfolds and cardiovascular disease risk factors, rather than mortality. This approach provides a means for determining standards that are consistent with optimum cardiovascular health without the lengthy follow-up required for mortality studies. The report utilizes data on 2447 non-smoking men and women aged 20-59 years. Seven cardiovascular disease risk factors were significantly related to subscapular skinfold thickness in both sexes in an unfavourable direction. The optimal subscapular skinfolds based on these risk factors for 20-39 year olds were determined to be below 12 mm for men and 15 mm for women. Men and women who had subscapular skinfolds at or below the optimal level had a mean body mass index of 22.6 kg/m2 and 21.1 kg/m2 for men and women, respectively. The probability of being above the optimum adiposity rises rapidly across body mass index levels above 20 kg/m2 and plateaus at above 0.90 in both men and women with body mass index above 24 kg/m2. Thus, screening for above optimal adiposity is necessary only in individuals with body mass index at or below 24 kg/m2.
A New Arc-Routing Algorithm Applied to Winter Road Maintenance

This paper studies large scale instances of a fairly general arc-routing problem as well as incorporate practical constraints in particular coming from the scheduling problem of the winter road maintenance (e.g. different priorities for and methods of road maintenance). We develop a new algorithm based on a bin-packing heuristic which is well-scalable and able to solve road networks on thousands of crossroads and road segments in few minutes. Since it is impossible to find an optimal solution for such a large instances to compare it with a result of our algorithm, we also develop techniques to compute lower bounds which are based on Integer Linear Programming and Lazy Constraints.
FOREST unbiased Galactic plane imaging survey with the Nobeyama 45 m telescope (FUGIN). VI. Dense gas and mini-starbursts in the W 43 giant molecular cloud complex

We performed new large-scale 12CO, 13CO, and C18O J = 1–0 observations of the W 43 giant molecular cloud complex in the tangential direction of the Scutum arm (l ∼30°) as a part of the FUGIN project. The low-density gas traced by 12CO is distributed over 150 pc × 100 pc (l × b), and has a large velocity dispersion (20–30 km s−1). However, the dense gas traced by C18O is localized in the W 43 Main, G30.5, and W 43 South (G29.96−0.02) high-mass star-forming regions in the W 43 giant molecular cloud (GMC) complex, which have clumpy structures. We found at least two clouds with a velocity difference of ∼10–20 km s−1, both of which are likely to be physically associated with these high-mass star-forming regions based on the results of high 13CO J = 3–2 to J = 1–0 intensity ratio and morphological correspondence with the infrared dust emission. The velocity separation of these clouds in W 43 Main, G30.5, and W 43 South is too large for each cloud to be gravitationally bound. We also revealed that the dense gas in the W 43 GMC has a high local column density, while “the current SFE” (star formation efficiency) of the entire GMC is low ($\sim\! \! 4\%$) compared with the W 51 and M 17 GMC. We argue that the supersonic cloud–cloud collision hypothesis can explain the origin of the local mini-starbursts and dense gas formation in the W 43 GMC complex.
Topological photonic Tamm states and the Su-Schrieffer-Heeger model

In this paper we study the formation of topological Tamm states at the interface between a semi-infinite one-dimensional photonic-crystal and a metal. We show that when the system is topologically non-trivial there is a single Tamm state in each of the band-gaps, whereas if it is topologically trivial the band-gaps host no Tamm states. We connect the disappearance of the Tamm states with a topological transition from a topologically non-trivial system to a topologically trivial one. This topological transition is driven by the modification of the dielectric functions in the unit cell. Our interpretation is further supported by an exact mapping between the solutions of Maxwell's equations and the existence of a tight-binding representation of those solutions. We show that the tight-binding representation of the 1D photonic crystal, based on Maxwell's equations, corresponds to a Su-Schrieffer-Heeger-type model (SSH-model) for each set of pairs of bands. Expanding this representation near the band edge we show that the system can be described by a Dirac-like Hamiltonian. It allows one to characterize the topology associated with the solution of Maxwell's equations via the winding number. In addition, for the infinite system, we provide an analytical expression for the photonic bands from which the band-gaps can be computed.
Using the Marked Power Spectrum to Detect the Signature of Neutrinos in Large-Scale Structure.

Cosmological neutrinos have their greatest influence in voids: These are the regions with the highest neutrino to dark matter density ratios. The marked power spectrum can be used to emphasize low-density regions over high-density regions and, therefore, is potentially much more sensitive than the power spectrum to the effects of neutrino masses. Using 22 000 N-body simulations from the Quijote suite, we quantify the information content in the marked power spectrum of the matter field and show that it outperforms the standard power spectrum by setting constraints improved by a factor larger than 2 on all cosmological parameters. The combination of marked and standard power spectra allows us to place a 4. 3σ constraint on the minimum sum of the neutrino masses with a volume equal to 1 (Gpc h^{-1})^{3} and without cosmic microwave background priors. Combinations of different marked power spectra yield a 6σ constraint within the same conditions.
Array Codes for Functional PIR and Batch Codes

A functional PIR array code is a coding scheme which encodes some s information bits into a t × m array such that every linear combination of the s information bits has k mutually disjoint recovering sets. Every recovering set consists of some of the array's columns while it is allowed to read at most ℓ encoded bits from every column in order to receive the requested linear combination of the information bits. Functional batch array codes impose a stronger property where every multiset request of k linear combinations has k mutually disjoint recovering sets. Given the values of s, k, t, ℓ, the goal of this paper is to study the optimal value of the number of columns m such that these codes exist. Several lower bounds are presented as well as explicit constructions for several of these parameters.
Superconductivity and charge density wave under a time-dependent periodic field in the one-dimensional attractive Hubbard model

We investigate the competition between superconductivity (SC) and charge density wave (CDW) under a time-dependent periodic field in the attractive Hubbard model. By employing the time-dependent exact diagonalization method, we show that the driving frequency and amplitude of the external field can control the enhancement of either superconducting pair or CDW correlations, which are degenerate in the ground state of the attractive Hubbard model in the absence of the field. In the strong-coupling limit of the attractive Hubbard interaction, the controllability is characterized by the anisotropic interaction of the effective model. The anisotropy is induced by the external field and lifts the degeneracy of SC and CDW. We find that the enhancement or suppression of the superconducting pair and CDW correlations in the periodically-driven attractive Hubbard model can be well interpreted by the quench dynamics of the effective model derived in the strong-coupling limit.
L-band Calibration of the Green Bank Telescope from 2016–2019

Since 2016, the HI-MaNGA survey has been obtaining neutral hydrogen (which emits a radio 21cm line) measurements for nearby galaxies which are part of a survey known as "MaNGA". This has been done using observations at the Green Bank Radio Telescope (GBT). We use calibration data obtained routinely at the start of observing runs during this entire period, as well as an extra set of calibration data obtained in 2019 to check the standard GBT data reduction calibrations at L-band (1.4 GHz, or 20cm). We find an constant offset of 20% in the calibration relative to the standard values given in the GBT data reduction pipeline for data of this type. This correction will be used to improve measurements for HI-MaNGA.
Three New Fundamental Problems in Physics of the Aftershocks

Recently, the physics of aftershocks has been enriched by three new problems. We will conditionally call them dynamic, inverse, and morphological problems. Dynamic problem is to search for the cumulative effect of a round-the-world seismic echo appearing after the main shock of earthquake. According to the theory, a converging surface seismic wave excited by the main shock returns to the epicenter about 3 hours after the main shock and stimulates the excitation of a strong aftershock. The second problem is an adequate description of the averaged evolution of the aftershock flow. We introduced a new concept about the coefficient of deactivation of the earthquake source, and proposed an equation that describes the evolution of aftershocks. Based on the equation of evolution, we posed and solved the inverse problem of the physics of the earthquake source. The third task is to model the spatio-temporal distribution of aftershocks. Its solution refines our understanding of the structure of the earthquake source.
Indexical Cities: Articulating Personal Models of Urban Preference with Geotagged Data

How to assess the potential of liking a city or a neighborhood before ever having been there. The concept of urban quality has until now pertained to global city ranking, where cities are evaluated under a grid of given parameters, or either to empirical and sociological approaches, often constrained by the amount of available information. Using state of the art machine learning techniques and thousands of geotagged satellite and perspective images from diverse urban cultures, this research characterizes personal preference in urban spaces and predicts a spectrum of unknown likeable places for a specific observer. Unlike most urban perception studies, our intention is not by any means to provide an objective measure of urban quality, but rather to portray personal views of the city or Cities of Indexes.
Natural philosophy versus philosophy of naturalness

I reflect on some of the basic aspects of present day Beyond the Standard Model particle physics, focusing mostly on the issues of naturalness, in particular on the so-called hierarchy problem. To all of us, physics as natural science emerged with Galileo and Newton, and led to centuries of unparalleled success in explaining and often predicting new phenomena of nature. I argue here that the long standing obsession with the hierarchy problem as a guiding principle for the future of our field has had the tragic consequence of deviating high energy physics from its origins as natural philosophy, and turning it into a philosophy of naturalness.