Miracle-dz/UniFAR-FTUs huggingface dataset:
Free-streaming and coupled dark radiation isocurvature perturbations: constraints and application to the Hubble tension

Dark radiation (DR) appears as a new physics candidate in various scenarios beyond the Standard Model. While it is often assumed that perturbations in DR are adiabatic, they can easily have an isocurvature component if more than one field was present during inflation, and whose decay products did not all thermalize with each other. By implementing the appropriate isocurvature initial conditions (IC), we derive the constraints on both uncorrelated and correlated DR density isocurvature perturbations from the full Planck 2018 data alone, and also in combination with other cosmological data sets. Our study on free-streaming DR (FDR) updates and generalizes the existing bound on neutrino density isocurvature perturbations by including a varying number of relativistic degrees of freedom, and for coupled DR (CDR) isocurvature, we derive the first bound. We also show that for CDR qualitatively new physical effects arise compared to FDR. One such effect is that for isocurvature IC, FDR gives rise to larger CMB anisotropies compared to CDR — contrary to the adiabatic case. More generally, we find that a blue-tilt of DR isocurvature spectrum is preferred. This gives rise to a larger value of the Hubble constant H 0 compared to the standard ΛCDM+Δ N eff cosmology with adiabatic spectra and relaxes the H 0 tension.
Parametric Scattering Networks

The wavelet scattering transform creates geometric in-variants and deformation stability. In multiple signal do-mains, it has been shown to yield more discriminative rep-resentations compared to other non-learned representations and to outperform learned representations in certain tasks, particularly on limited labeled data and highly structured signals. The wavelet filters used in the scattering trans-form are typically selected to create a tight frame via a pa-rameterized mother wavelet. In this work, we investigate whether this standard wavelet filterbank construction is op-timal. Focusing on Morlet wavelets, we propose to learn the scales, orientations, and aspect ratios of the filters to produce problem-specific parameterizations of the scattering transform. We show that our learned versions of the scattering transform yield significant performance gains in small-sample classification settings over the standard scat-tering transform. Moreover, our empirical results suggest that traditional filterbank constructions may not always be necessary for scattering transforms to extract effective rep-resentations.
Semi-prorepresentability of formal moduli problems and equivariant structures

We generalize the notion of semi-universality in the classical deformation problems to the context of derived deformation theories. A criterion for a formal moduli problem to be semi-prorepresentable is produced. This can be seen as an analogue of Schlessinger's conditions for a functor of Artinian rings to have a semi-universal element. We also give a sufficient condition for a semi-prorepresentable formal moduli problem to admit a $G$-equivariant structure in a sense specified below, where $G$ is a linearly reductive group. Finally, by making use of these criteria, we derive many classical results including the existence of ($G$-equivariant) formal semi-universal deformations of algebraic schemes and that of complex compact manifolds.
ReSSL: Relational Self-Supervised Learning with Weak Augmentation

Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations. However, most of methods mainly focus on the instance level information (\ie, the different augmented images of the same instance should have the same feature or cluster into the same class), but there is a lack of attention on the relationships between different instances. In this paper, we introduced a novel SSL paradigm, which we term as relational self-supervised learning (ReSSL) framework that learns representations by modeling the relationship between different instances. Specifically, our proposed method employs sharpened distribution of pairwise similarities among different instances as \textit{relation} metric, which is thus utilized to match the feature embeddings of different augmentations. Moreover, to boost the performance, we argue that weak augmentations matter to represent a more reliable relation, and leverage momentum strategy for practical efficiency. Experimental results show that our proposed ReSSL significantly outperforms the previous state-of-the-art algorithms in terms of both performance and training efficiency. Code is available at \url{https://github.com/KyleZheng1997/ReSSL}.
Hot Subdwarf Atmospheric Parameters, Kinematics, and Origins Based on 1587 Hot Subdwarf Stars Observed in Gaia DR2 and LAMOST DR7

Based on the Gaia DR2 catalog of hot subdwarf star candidates, we identified 1587 hot subdwarf stars with spectra in LAMOST DR7. We present atmospheric parameters for these stars by fitting the LAMOST spectra with Tlusty/Synspec non-LTE synthetic spectra. Combining LAMOST radial velocities and Gaia Early Data Release 3 (EDR3) parallaxes and proper motions, we also present the Galactic space positions, velocity vectors, orbital parameters and the Galactic population memberships of the stars. With our He classification scheme, we identify four groups of He rich hot subdwarf stars in the T eff − log g and T eff − log(nHe/nH) diagrams. We find two extreme He-rich groups (eHe-1 and eHe-2) for stars with log(nHe/nH) ≥ 0 and two intermediate He-rich groups (iHe-1 and iHe-2) for stars with −1 ≤ log(nHe/nH) < 0. We also find that over half of the stars in Group eHe-1 are thick-disk stars, while over half of the stars in Group eHe-2 correspond to thin-disk stars. The disk population fractions of Group iHe-1 are between those of Group eHe-1 and eHe-2. Almost all of the stars in Group iHe-2 belong to the thin disk. These differences indicate that the four groups probably have very different origins. Comparisons between hot subdwarf stars in the halo and in the Galactic globular cluster ω Cen show that only He-deficient stars with −2.2 ≤ log(nHe/nH) < −1 have similar fractions. Hot subdwarfs with log(nHe/nH) ≥ 0 in ω Cen have no counterparts in the thick-disk and halo populations, but they appear in the thin disk.
The radio loudness of SDSS quasars from the LOFAR Two-metre Sky Survey: ubiquitous jet activity and constraints on star formation

We examine the distribution of radio emission from ∼42 000 quasars from the Sloan Digital Sky Survey, as measured in the LOFAR Two-metre Sky Survey (LoTSS). We present a model of the radio luminosity distribution of the quasars that assumes that every quasar displays a superposition of two sources of radio emission: active galactic nuclei (jets) and star formation. Our two-component model provides an excellent match to the observed radio flux density distributions across a wide range of redshifts and quasar optical luminosities; this suggests that the jet-launching mechanism operates in all quasars but with different powering efficiency. The wide distribution of jet powers allows for a smooth transition between the ‘radio-quiet’ and ‘radio-loud’ quasar regimes, without need for any explicit bimodality. The best-fitting model parameters indicate that the star formation rate of quasar host galaxies correlates strongly with quasar luminosity and also increases with redshift at least out to z ∼ 2. For a model where star formation rate scales as $L_{\rm bol}^{\alpha } (1+z)^{\beta }$, we find α = 0.47 ± 0.01 and β = 1.61 ± 0.05, in agreement with far-infrared studies. Quasars contribute ≈0.15 per cent of the cosmic star formation rate density at z = 0.5, rising to 0.4 per cent by z ∼ 2. The typical radio jet power is seen to increase with both increasing optical luminosity and black hole mass independently, but does not vary with redshift, suggesting intrinsic properties govern the production of the radio jets. We discuss the implications of these results for the triggering of quasar activity and the launching of jets.
Record-based transmuted generalized linear exponential distribution with increasing, decreasing and bathtub shaped failure rates

The linear exponential distribution is a generalization of the exponential and Rayleigh distributions. This distribution is one of the best models to fit data with increasing failure rate (IFR). But it does not provide a reasonable fit for modeling data with decreasing failure rate (DFR) and bathtub shaped failure rate (BTFR). To overcome this drawback, we propose a new record-based transmuted generalized linear exponential (RTGLE) distribution by using the technique of Balakrishnan and He (2021). The family of RTGLE distributions is more flexible to fit the data sets with IFR, DFR, and BTFR, and also generalizes several well-known models as well as some new record-based transmuted models. This paper aims to study the statistical properties of RTGLE distribution, like, the shape of the probability density function and hazard function, quantile function and its applications, moments and its generating function, order and record statistics, Renyi entropy. The maximum likelihood estimators, least squares and weighted least squares estimators, Anderson-Darling estimators, Cramer-von Mises estimators of the unknown parameters are constructed and their biases and mean squared errors are reported via Monte Carlo simulation study. Finally, the real data set based on failure time illustrates the goodness of fit and applicability of the proposed distribution; hence, suitable recommendations are forwarded.
Spatiotemporal Analysis of Waves in Compressively Driven Magnetohydrodynamics Turbulence

Using direct numerical simulations (DNSs), the interaction between linear waves and turbulence under the compressible magnetohydrodynamic (CMHD) approach was studied. A set of DNSs in three dimensions for a spatial resolution of 1283 and 2563 were performed. A parametric study was carried out varying the sonic Mach number, the mean-magnetic field, and the compressibility amplitude of the forcing. Spatiotemporal spectra of the magnetic energy were built and analyzed, allowing for direct identification of all wave modes in a CMHD turbulent system and quantification of the amount of energy in each mode as a function of the wavenumber. Thus, linear waves were detected, that is Alfvén waves and fast and slow magnetosonic waves. Furthermore, different responses of the plasma were found according to whether the Mach number or the mean-magnetic field was varied. On the other hand, making use of spatiotemporal spectra and two different integration methods, we accurately quantified the amount of energy present in each of the normal modes. Finally, although the presence of linear waves was observed, in all the cases studied the system was mainly dominated by the nonlinear dynamics of the plasma.
Inverse cascade anomalies in fourth-order Leith models

We analyze a family of fourth-order non-linear diffusion models corresponding to local approximations of four-wave kinetic equations of weak wave turbulence. We focus on a class of parameters for which a dual cascade behavior is expected with an infrared finite-time singularity associated to inverse transfer of waveaction. This case is relevant for wave turbulence arising in the nonlinear Schrödinger model and for the gravitational waves in the Einstein’s vacuum field model. We show that inverse transfer is not described by a scaling of the constant-flux solution but has an anomalous scaling. We compute the anomalous exponents and analyze their origin using the theory of dynamical systems.
Study of the parent-of-origin effect in monogenic diseases with variable age of onset. Application on ATTRv

In genetic diseases with variable age of onset, an accurate estimation of the survival function for the mutation carriers and also modifying factors effects estimations are important for the management of asymptomatic gene carriers across life. Among the modifying factors, the gender of the parent transmitting the mutation (i.e. the parent-of-origin effect) has been shown to have a significant effect on survival curve estimation on transthyretin familial amyloid polyneuropathy (ATTRv) families. However, as most genotypes are unknown, the parent-of-origin must be calculated through a probability estimated from the pedigree. We propose in this article to extend the method providing mutation carrier survival estimates in order to estimate the parent-of-origin effect. The method is both validated on simulated data and applied to familly samples with ATTRv.
Scale Symmetry and Friction

Dynamical similarities are non-standard symmetries found in a wide range of physical systems that identify solutions related by a change of scale. In this paper, we will show through a series of examples how this symmetry extends to the space of couplings, as measured through observations of a system. This can be exploited to focus on observations that can be used to distinguish between different theories and identify those which give rise to identical physical evolutions. These can be reduced into a description that makes no reference to scale. The resultant systems can be derived from Herglotz’s principle and generally exhibit friction. Here, we will demonstrate this through three example systems: the Kepler problem, the N-body system and Friedmann–Lemaître–Robertson–Walker cosmology.
Combinatorial Gap Theorem and Reductions between Promise CSPs

A value of a CSP instance is typically defined as a fraction of constraints that can be simultaneously met. We propose an alternative definition of a value of an instance and show that, for purely combinatorial reasons, a value of an unsolvable instance is bounded away from one; we call this fact a gap theorem. We show that the gap theorem implies NP-hardness of a gap version of the Layered Label Cover Problem. The same result can be derived from the PCP Theorem, but a full, self-contained proof of our reduction is quite short and the result can still provide PCP-free NP-hardness proofs for numerous problems. The simplicity of our reasoning also suggests that weaker versions of Unique-Games-type conjectures, e.g., the d-to-1 conjecture, might be accessible and serve as an intermediate step for proving these conjectures in their full strength. As the second, main application we provide a sufficient condition under which a fixed template Promise Constraint Satisfaction Problem (PCSP) reduces to another PCSP. The correctness of the reduction hinges on the gap theorem, but the reduction itself is very simple. As a consequence, we obtain that every CSP can be canonically reduced to most of the known NP-hard PCSPs, such as the approximate hypergraph coloring problem.
Evaluating Probabilistic Inference in Deep Learning: Beyond Marginal Predictions

A fundamental challenge for any intelligent system is prediction: given some inputs X 1 , . , X τ can you predict outcomes Y 1 , . , Y τ . The KL divergence d KL provides a natural measure of prediction quality, but the majority of deep learning research looks only at the marginal predictions per input X t . In this technical report we propose a scoring rule d τ KL , parameterized by τ ∈ N that evaluates the joint predictions at τ inputs simultaneously. We show that the commonly-used τ = 1 can be insuﬃcient to drive good decisions in many settings of interest. We also show that, as τ grows, performing well according to d τ KL recovers universal guarantees for any possible decision. Finally, we provide problem-dependent guidance on the scale of τ for which our score provides suﬃcient guarantees for good performance.
A New Design Framework for Heterogeneous Uncoded Storage Elastic Computing

Elasticity is one important feature in modern cloud computing systems and can result in computation failure or significantly increase computing time. Such elasticity means that virtual machines over the cloud can be preempted under a short notice (e.g., hours or minutes) if a high-priority job appears; on the other hand, new virtual machines may become available over time to compensate the computing resources. Coded Storage Elastic Computing (CSEC) introduced by Yang et al. in 2018 is an effective and efficient approach to overcome the elasticity and it costs relatively less storage and computation load. However, one of the limitations of the CSEC is that it may only be applied to certain types of computations (e.g., linear) and may be challenging to be applied to more involved computations because the coded data storage and approximation are often needed. Hence, it may be preferred to use uncoded storage by directly copying data into the virtual machines. In addition, based on our own measurement, virtual machines on Amazon EC2 clusters often have heterogeneous computation speed even if they have exactly the same configurations (e.g., CPU, RAM, I/O cost). In this paper, we introduce a new optimization framework on Uncoded Storage Elastic Computing (USEC) systems with heterogeneous computing speed to minimize the overall computation time. Under this framework, we propose optimal solutions of USEC systems with or without straggler tolerance using different storage placements. Our proposed algorithms are evaluated using power iteration applications on Amazon EC2.
The design and performance of CUBES — a CubeSat X-ray detector

CUBES is a X-ray detector payload which will be installed on the KTH 3U CubeSat mission, MIST. The detector comprises cerium-doped Gd3Al2Ga3O12 (GAGG) scintillators read out with silicon photomultipliers through a Citiroc Application-Specific Integrated Circuit. The detector operates in the energy range ∼35–800 keV. The aim of the CUBES mission is to provide experience in the operation of these relatively new technologies in a high-inclination low earth orbit, thereby providing confidence for component selection in more complex satellite missions. The design of the CUBES detector is described, and results from performance characterisation tests carried out on a prototype of CUBES, called Proto-CUBES, are reported. Proto-CUBES was flown on a stratospheric balloon platform from Timmins, Canada, in August 2019. During the ∼12 hour long flight, the performance of Proto-CUBES was studied in the near-space environment. As well as measuring the X-ray counts spectra at different atmospheric depths, a 511 keV line from positron annihilation was observed.
The Limiting Dynamics of SGD: Modified Loss, Phase-Space Oscillations, and Anomalous Diffusion

In this work, we explore the limiting dynamics of deep neural networks trained with stochastic gradient descent (SGD). As observed previously, long after performance has converged, networks continue to move through parameter space by a process of anomalous diffusion in which distance traveled grows as a power law in the number of gradient updates with a nontrivial exponent. We reveal an intricate interaction among the hyperparameters of optimization, the structure in the gradient noise, and the Hessian matrix at the end of training that explains this anomalous diffusion. To build this understanding, we first derive a continuous-time model for SGD with finite learning rates and batch sizes as an underdamped Langevin equation. We study this equation in the setting of linear regression, where we can derive exact, analytic expressions for the phase-space dynamics of the parameters and their instantaneous velocities from initialization to stationarity. Using the Fokker-Planck equation, we show that the key ingredient driving these dynamics is not the original training loss but rather the combination of a modified loss, which implicitly regularizes the velocity, and probability currents that cause oscillations in phase space. We identify qualitative and quantitative predictions of this theory in the dynamics of a ResNet-18 model trained on ImageNet. Through the lens of statistical physics, we uncover a mechanistic origin for the anomalous limiting dynamics of deep neural networks trained with SGD. Understanding the limiting dynamics of SGD, and its dependence on various important hyperparameters like batch size, learning rate, and momentum, can serve as a basis for future work that can turn these insights into algorithmic gains.
Music Tempo Estimation via Neural Networks - A Comparative Analysis

This paper presents a comparative analysis on two artificial neural networks (with different architectures) for the task of tempo estimation. For this purpose, it also proposes the modeling, training and evaluation of a B-RNN (Bidirectional Recurrent Neural Network) model capable of estimating tempo in bpm (beats per minutes) of musical pieces, without using external auxiliary modules. An extensive database (12,550 pieces in total) was curated to conduct a quantitative and qualitative analysis over the experiment. Percussion-only tracks were also included in the dataset. The performance of the B-RNN is compared to that of state-of-the-art models. For further comparison, a state-of-the-art CNN was also retrained with the same datasets used for the B-RNN training. Evaluation results for each model and datasets are presented and discussed, as well as observations and ideas for future research. Tempo estimation was more accurate for the percussion only dataset, suggesting that the estimation can be more accurate for percussion-only tracks, although further experiments (with more of such datasets) should be made to gather stronger evidence.
Targeting chemotherapy of brain tumor using liposome-encapsulated cisplatin. Part 2. Pullulancoated liposomes to target brain tumor.

Liposomes were investigated as a carrier for the delivery of therapeutic agents to target brain tumors. We have developed the coating of liposomes with pullulan derivative and compared with conventional neutral liposome. When pullulan-coated liposome labeled with 14C was intracarotidly administrated into the 9L cells implanted brain tumor rats, brain tumor uptake increased 4.5 times and spleen uptake decrease compared with the neutral liposome. The cytotoxity of cisplatin against 9L-glioma cells was not inhibited by encapsulation of pullulan-coated liposomes. The median survival times were 35.3 days for pullulan-coated liposome-encapsulated cisplatin-treated rats and 20.3 days for untreated rats. The difference was statisticall significant (P<0.05). These findings suggest that pullulan-coated liposome-encapsulated cisplatin could be utilized for targeting the chemotherapy of brain tumors, however in order to increase the targetability of liposome to brain tumors, we must have developed the conjugating of liposome with the specific sensory device.