Miracle-dz/UniFAR-FTUs huggingface dataset:
LION: laser interferometer on the moon

Gravitational wave astronomy has now left its infancy and has become an important tool for probing the most violent phenomena in our Universe. The LIGO/Virgo-KAGRA collaboration operates ground based detectors which cover the frequency band from 10 Hz to the kHz regime. Meanwhile, the pulsar timing array and the soon to launch LISA mission will cover frequencies below 0.1 Hz, leaving a gap in detectable gravitational wave frequencies. Here we show how a laser interferometer on the moon (LION) gravitational wave detector would be sensitive to frequencies from sub Hz to kHz. We find that the sensitivity curve is such that LION can measure compact binaries with masses between 10 and 100M ⊙ at cosmological distances, with redshifts as high as z = 100 and beyond, depending on the spin and the mass ratio of the binaries. LION can detect binaries of compact objects with higher-masses, with very large signal-to-noise ratios (SNRs), help us to understand how supermassive black holes got their colossal masses on the cosmological landscape, and it can observe in detail intermediate-mass ratio inspirals at distances as large as at least 100 Gpc. Compact binaries that never reach the LIGO/Virgo sensitivity band can spend significant amounts of time in the LION band, while sources present in the LISA band can be picked up by the detector and observed until their final merger. Since LION covers the deci-Hertz regime with such large SNRs, it truly achieves the dream of multi messenger astronomy.
YouNiverse: Large-Scale Channel and Video Metadata from English-Speaking YouTube

YouTube plays a key role in entertaining and informing people around the globe. However, studying the platform is difficult due to the lack of randomly sampled data and of systematic ways to query the platform's colossal catalog. In this paper, we present YouNiverse, a large collection of channel and video metadata from English-language YouTube. YouNiverse comprises metadata from over 136k channels and 72.9M videos published between May 2005 and October 2019, as well as channel-level time-series data with weekly subscriber and view counts. Leveraging channel ranks from socialblade.com, an online service that provides information about YouTube, we are able to assess and enhance the representativeness of the sample of channels. Additionally, the dataset also contains a table specifying which videos a set of 449M anonymous users commented on. YouNiverse, publicly available at https://zenodo.org/record/4650046, will empower the community to do research with and about YouTube.
Learning Compositional Radiance Fields of Dynamic Human Heads

Photorealistic rendering of dynamic humans is an important capability for telepresence systems, virtual shopping, special effects in movies, and interactive experiences such as games. Recently, neural rendering methods have been developed to create high-fidelity models of humans and objects. Some of these methods do not produce results with high-enough fidelity for driveable human models (Neural Volumes) whereas others have extremely long rendering times (NeRF). We propose a novel compositional 3D representation that combines the best of previous methods to produce both higher-resolution and faster results. Our representation bridges the gap between discrete and continuous volumetric representations by combining a coarse 3D-structure-aware grid of animation codes with a continuous learned scene function that maps every position and its corresponding local animation code to a view-dependent emitted radiance and local volume density. Differentiable volume rendering is employed to compute photo-realistic novel views of the human head and upper body as well as to train our novel representation end-to-end using only 2D supervision. In addition, we show that the learned dynamic radiance field can be used to synthesize novel unseen expressions based on a global animation code. Our approach achieves state-of-the-art results for synthesizing novel views of dynamic human heads and the upper body. See our project page1 for more results.
END-TO-END ASR AND AUDIO SEGMENTATION WITH NON-AUTOREGRESSIVE INSERTION-BASED MODEL

End-to-end models are suitable for realizing practical automatic speech recognition (ASR) systems run on a device with limited computing capability. This is because the models are composed of a single neural network hence many techniques like quantization and compression to make the model footprint lower are easily applied. One important issue when realizing such an ASR system is the segmentation of input audio. The ideal scenario is to integrate audio segmentation and ASR into a single model achieving low latency and high accuracy. In addition, reducing the real-time factor of decoding is important to achieve low latency. Non-autoregressive Transformer is a promising approach since it can decode an N - length token sequence with less than N iterations. We propose a method to realize audio segmentation and non-autoregressive decoding using a single model. It is an insertion-based model that jointly trains with connectionist temporal classiﬁcation (CTC) loss. The CTC part is used for segmentation. Then, the insertion-based non-autoregressive decoding generates a hypothesis considering the full context inside the segment. It can decode an N -length token sequence with approximately log 2 ( N ) iterations. Experimental re-sults on unsegmented evaluation sets of a Japanese dataset show the method achieves better accuracy than baseline CTC.
New results from the DANSS experiment

We present new preliminary DANSS results based on 3.5 million Inverse Beta Decay (IBD) events collected at 3 distances (10.9 m, 11.9 m, and 12.9 m) from the detector center to the reactor core center. The detector position is changed typically 3 times a week. Therefore many systematic uncertainties are canceled out. A new analysis that uses information about relative IBD counting rates in addition to changes in positron energy spectra shapes is employed. The excluded area covers a very interesting range of the sterile neutrino parameters up to $\sin^2 2\theta_{ee} < 0.008$ in the most sensitive region. No statistically significant evidence for sterile neutrinos is observed. The significance of the best-fit point in the 4$\nu$ case is 1.5$\sigma$
Modeling and simulating the spatial spread of an epidemic through multiscale kinetic transport equations

In this paper, we propose a novel space-dependent multiscale model for the spread of infectious diseases in a two-dimensional spatial context on realistic geographical scenarios. The model couples a system of kinetic transport equations describing a population of commuters moving on a large scale (extra-urban) with a system of diffusion equations characterizing the non-commuting population acting over a small scale (urban). The modeling approach permits to avoid unrealistic effects of traditional diffusion models in epidemiology, like infinite propagation speed on large scales and mass migration dynamics. A construction based on the transport formalism of kinetic theory allows to give a clear model interpretation to the interactions between infected and susceptible in compartmental space-dependent models. In addition, in a suitable scaling limit, our approach permits to couple the two populations through a consistent diffusion model acting at the urban scale. A discretization of the system based on finite volumes on unstructured grids, combined with an asymptotic preserving method in time, shows that the model is able to describe correctly the main features of the spatial expansion of an epidemic. An application to the initial spread of COVID-19 is finally presented.
Deep Open Intent Classification with Adaptive Decision Boundary

Open intent classification is a challenging task in dialogue systems. On the one hand, it should ensure the quality of known intent identification. On the other hand, it needs to detect the open (unknown) intent without prior knowledge. Current models are limited in finding the appropriate decision boundary to balance the performances of both known intents and the open intent. In this paper, we propose a post-processing method to learn the adaptive decision boundary (ADB) for open intent classification. We first utilize the labeled known intent samples to pre-train the model. Then, we automatically learn the adaptive spherical decision boundary for each known class with the aid of well-trained features. Specifically, we propose a new loss function to balance both the empirical risk and the open space risk. Our method does not need open intent samples and is free from modifying the model architecture. Moreover, our approach is surprisingly insensitive with less labeled data and fewer known intents. Extensive experiments on three benchmark datasets show that our method yields significant improvements compared with the state-of-the-art methods.
Fortuitous partners of antiferromagnetic and Mott states in spin-orbit-coupled Sr2IrO4: A study of Sr2Ir1-xMxO4 (M=Fe or Co)

Sr2IrO4 is an archetypal spin-orbit-coupled Mott insulator with an antiferromagnetic state below 240 K. Here we report results of our study on single crystals of Sr2Ir1-xFexO4 (0<x<0.32) and Sr2Ir1-xCoxO4 (0<x<0.22). Fe doping retains the antiferromagnetic state but simultaneously precipitates an emergent metallic state whereas Co doping causes a rapid collapse of both the antiferromagnetic and Mott states, giving rise to a confined metallic state featuring a pronounced linearity of the basal-plane resistivity up to 700 K. The results indicate tetravalent Fe4+(3d4) ions in the intermediate spin state with S=1 and Co4+(3d5) ions in the high spin state with S=5/2 substituting for Ir4+(5d5) ions in Sr2IrO4, respectively. The effective magnetic moment closely tracks the N\'eel temperature as doping increases, suggesting that the spin state of the dopant predominately determines the magnetic properties in doped Sr2IrO4. Furthermore, all relevant properties including charge-carrier density (e.g., 1028/m3), Sommerfeld coefficient (e.g., 19 mJ/mole K2) and Wilson ratio (e.g., 2.6), consistently demonstrates a metallic state that is both robust and highly correlated in the two systems, arising from the percolation of bound states and the weakening of structural distortions. This study strongly suggests that the antiferromagnetic and Mott states merely coexist in a fortuitous manner in Sr2IrO4.
Concentration for nodal component count of Gaussian Laplace eigenfunctions

We study nodal component count of the following Gaussian Laplace eigenfunctions: monochromatic random waves (MRW) on R, arithmetic random waves (ARW) on T and random spherical harmonics (RSH) on S. Exponential concentration for nodal component count of RSH on S and ARW on T were established in [22] and [23] respectively. We prove exponential concentration for nodal component count in the following three cases: MRW on growing Euclidean balls in R; RSH and ARW on geodesic balls, in S and T respectively, whose radius is slightly larger than the wavelength scale.
Recent Star Formation in a Massive Slowly Quenched Lensed Quiescent Galaxy at z = 1.88

In this Letter, we reconstruct the formation pathway of MRG-S0851, a massive, logM*/M⊙=11.02±0.04 , strongly lensed, red galaxy at z = 1.883 ± 0.001. While the global photometry and spatially resolved outskirts of MRG-S0851 imply an early formation scenario with a slowly decreasing or constant star formation history, a joint fit of 2D grism spectroscopy and photometry reveals a more complex scenario: MRG-S0851 is likely to be experiencing a centrally concentrated rejuvenation in the inner ∼1 kpc in the last ∼100 Myr of evolution. We estimate 0.5 ± 0.1% of the total stellar mass is formed in this phase. Rejuvenation episodes are suggested to be infrequent for massive galaxies at z ∼ 2, but as our analyses indicate, more examples of complex star formation histories may yet be hidden within existing data. By adding an FUV color criterion to the standard U–V/V–J diagnostic—thereby heightening our sensitivity to recent star formation—we show that we can select populations of galaxies with similar spectral energy distributions to that of MRG-S0851, but note that deep follow-up spectroscopic observations and/or spatially resolved analyses are necessary to robustly confirm the rejuvenation of these candidates. Using our criteria with MRG-S0851 as a prototype, we estimate that ∼1% of massive quiescent galaxies at 1 < z < 2 are potentially rejuvenating.
Formation of anion fragments from gas-phase glycine by low energy (0-15 eV) electron impact

We have measured the formation of anion fragments in gas phase glycine (H2NCH2COOH) via dissociative electron attachment (DEA) reactions in the 0–15 eV electron energy range, using a monochromatic electron beam and mass spectrometric detection of the negative ions. By far the most intense product observed is the closed shell glycine anion (H2NCH2COO)− which appears from a low-energy resonance with a peak located at 1.4 eV and a cross section in the range 10−16 cm2. The corresponding precursor ion can be characterized by electron attachment into the empty π* orbital of the −COOH group as recently assigned from electron transmission experiments and ab initio self-consistent field calculations [Aflatooni, Hitt, Gallup, and Burrow, J. Chem. Phys. 115, 6489 (2001)]. This precursor state is also observed to decompose (with much lower intensity) yielding a negative ion fragment with 58 amu, which is attributed to anions of the stoichiometric composition H2C2O2− or H4C2NO−. A further prominent DEA peak is observe.
Schr\"{o}dinger's problem with cats: measurements and states in the Groupoid Picture of Quantum Mechanics

Schrödinger’s famous Gedankenexperiment involving a cat is used as a motivation to discuss the evolution of states of the composition of classical and quantum systems in the groupoid formalism for physical theories introduced recently. It is shown that the notion of classical system, in the sense of Birkhoff and von Neumann, is equivalent, in the case of systems with a countable number of outputs, to a totally disconnected groupoid with Abelian von Neumann algebra. In accordance with Raggio’s theorem, the impossibility of evolving the product state of a composite system made up of a classical and a quantum one into an entangled state by means of a unitary evolution which is internal to the composite system itself is proved in the groupoid formalism.
American cutaneous leishmaniasis associated with degradation of native forest, regardless of economic, social and infrastructure vulnerability

American cutaneous leishmaniasis (ACL) is a notifiable dermatozoonosis with relevant morbidity. The present study aimed to evaluate the epidemiological aspects of cases of ACL reported in Northern Paraná (2007–2016), as well as to georeference and analyse the influence of economic, social and environmental variables. Data from ACL notification forms were obtained and the patients' houses were georeferenced. Descriptive statistics, calculations of disease incidence and proportion of vegetation cover by municipality, spatial analysis, multiple linear regression and vulnerability analysis by census sector (CS) were performed. One thousand four hundred fifty‐one cases of ACL were reported in the 89 municipalities of four regional health (RH). The average incidence of cases per 10,000 inhabitants was as follows: 11.58, 8.79, 4.92 and 4.03 in 18thRH, 15thRH, 16thRH and 17thRH. Peaks of incident cases were observed in 2008, 2012 and 2015. There was a statistically significant difference between the HR when comparing the proportions of the variables gender, age, education level, area of residence, clinical form, diagnostic criteria, response to treatment and drug used in case of failure. It was observed that the lower the proportion of remaining Atlantic Forest, the greater the incidence of ACL in the municipality. With regard to clusters analysis, treatment abandonment clusters were observed in the 15thRH and mucous form clusters were observed in the 15thRH, 16thRH and 17thRH. The vulnerability analysis by CS allowed us to observe a statistically significant difference in all vulnerability indicators: economic and social in 15thRH and 17thRH; home infrastructure in 15thRH and 18thRH, urban infrastructure in 15thRH and 18thRH and compound vulnerability index in 15thRH and 18thRH. However, the most vulnerable areas did not always have the highest number of cases. The data presented demonstrate that preventive and health education measures must be mainly directed to areas of greater degradation of native forest; regardless of the vulnerability situation.
The reactor antineutrino spectrum calculation

New fissile isotopes antineutrino spectra (U, U, Pu and Pu) calculation is presented. On base of summation method the toy model was developed. It was shown that total antineutrino number is conserved in framework of given database on individual fragments yields. The analysis of antineutrino spectrum shape says that any presented antineutrino spectrum should satisfy to the total antineutrino number conservation.
Predicting Decisions in Language Based Persuasion Games

Sender-receiver interactions, and specifically persuasion games, are widely researched in economic modeling and artificial intelligence, and serve as a solid foundation for powerful applications. However, in the classic persuasion games setting, the messages sent from the expert to the decision-maker are abstract or well-structured application-specific signals rather than natural (human) language messages, although natural language is a very common communication signal in real-world persuasion setups. This paper addresses the use of natural language in persuasion games, exploring its impact on the decisions made by the players and aiming to construct effective models for the prediction of these decisions. For this purpose, we conduct an online repeated interaction experiment. At each trial of the interaction, an informed expert aims to sell an uninformed decision-maker a vacation in a hotel, by sending her a review that describes the hotel. While the expert is exposed to several scored reviews, the decision-maker observes only the single review sent by the expert, and her payoff in case she chooses to take the hotel is a random draw from the review score distribution available to the expert only. The expert’s payoff, in turn, depends on the number of times the decision-maker chooses the hotel. We also compare the behavioral patterns in this experiment to the equivalent patterns in similar experiments where the communication is based on the numerical values of the reviews rather than the reviews’ text, and observe substantial differences which can be explained through an equilibrium analysis of the game. We consider a number of modeling approaches for our verbal communication setup, differing from each other in the model type (deep neural network (DNN) vs. linear classifier), the type of features used by the model (textual, behavioral or both) and the source of the textual features (DNN-based vs. hand-crafted). Our results demonstrate that given a prefix of the interaction sequence, our models can predict the future decisions of the decision-maker, particularly when a sequential modeling approach and hand-crafted textual features are applied. Further analysis of the hand-crafted textual features allows us to make initial observations about the aspects of text that drive decision making in our setup.
Fractal Analysis of Lung Structure in Chronic Obstructive Pulmonary Disease

Chest CT is often used for localizing and quantitating pathologies associated with chronic obstructive pulmonary disease (COPD). While simple measurements of areas and volumes of emphysema and airway structure are common, these methods do not capture the structural complexity of the COPD lung. Since the concept of fractals has been successfully applied to evaluate complexity of the lung, this review is aimed at describing the fractal properties of airway disease, emphysema, and vascular abnormalities in COPD. An object forms a fractal if it exhibits the property of self-similarity at different length scales of evaluations. This fractal property is governed by power-law functions characterized by the fractal dimension (FD). Power-laws can also manifest in other statistical descriptors of structure such as the size distribution of emphysema clusters characterized by the power-law exponent D. Although D is not the same as FD of emphysematous clusters, it is a useful index to characterize the spatial pattern of disease progression and predict clinical outcomes in patients with COPD. The FD of the airway tree shape and the D of the size distribution of airway branches have been proposed indexes of structural assessment and clinical predictions. Simulations are also useful to understand the mechanism of disease progression. Therefore, the power-law and fractal analysis of the parenchyma and airways, especially when combined with computer simulations, could lead to a better understanding of the structural alterations during the progression of COPD and help identify subjects at a high risk of severe COPD.
The Global Durum Wheat Panel (GDP): An International Platform to Identify and Exchange Beneficial Alleles

Representative, broad and diverse collections are a primary resource to dissect genetic diversity and meet pre-breeding and breeding goals through the identification of beneficial alleles for target traits. From 2,500 tetraploid wheat accessions obtained through an international collaborative effort, a Global Durum wheat Panel (GDP) of 1,011 genotypes was assembled that captured 94–97% of the original diversity. The GDP consists of a wide representation of Triticum turgidum ssp. durum modern germplasm and landraces, along with a selection of emmer and primitive tetraploid wheats to maximize diversity. GDP accessions were genotyped using the wheat iSelect 90K SNP array. Among modern durum accessions, breeding programs from Italy, France and Central Asia provided the highest level of genetic diversity, with only a moderate decrease in genetic diversity observed across nearly 50 years of breeding (1970–2018). Further, the breeding programs from Europe had the largest sets of unique alleles. LD was lower in the landraces (0.4 Mbp) than in modern germplasm (1.8 Mbp) at r2 = 0.5. ADMIXTURE analysis of modern germplasm defined a minimum of 13 distinct genetic clusters (k), which could be traced to the breeding program of origin. Chromosome regions putatively subjected to strong selection pressure were identified from fixation index (Fst) and diversity reduction index (DRI) metrics in pairwise comparisons among decades of release and breeding programs. Clusters of putative selection sweeps (PSW) were identified as co-localized with major loci controlling phenology (Ppd and Vrn), plant height (Rht) and quality (gliadins and glutenins), underlining the role of the corresponding genes as driving elements in modern breeding. Public seed availability and deep genetic characterization of the GDP make this collection a unique and ideal resource to identify and map useful genetic diversity at loci of interest to any breeding program.