Miracle-dz/UniFAR-FTUs huggingface dataset:
Are Faint Supernovae Responsible for Carbon-enhanced Metal-poor Stars?

Mixing and fallback models in faint supernova models are supposed to reproduce the abundance patterns of observed carbon-enhanced metal-poor (CEMP) stars in the Galactic halo. A fine-tuning of the model parameters for individual stars is required to reproduce the observed ratios of carbon to iron. We focus on extremely metal-poor stars formed out of the ejecta from the mixing and fallback models using a chemical evolution model. Our chemical evolution models take into account the contribution of individual stars to chemical enrichment in host halos, together with their evolution in the context of the hierarchical clustering. Parameterized models of mixing and fallback models for Population III faint supernovae are implemented in the chemical evolution models with merger trees to reproduce the observed CEMP stars. A variety of choices for model parameters on star formation and metal pollution by faint supernovae are unable to reproduce the observed stars with and , which are the majority of CEMP stars among the lowest-metallicity stars. Only possible solution is to form stars from small ejecta mass, which produces an inconsistent metallicity distribution function. We conclude that not all the CEMP stars are explicable by the mixing and fallback models. We also tested the contribution of binary mass transfers from AGB stars that are also supposed to reproduce the abundances of known CEMP stars. This model reasonably reproduces the distribution of carbon and iron abundances simultaneously only if we assume that long-period binaries are favored at .
Discrimination-Aware Network Pruning for Deep Model Compression

We study network pruning which aims to remove redundant channels/kernels and hence speed up the inference of deep networks. Existing pruning methods either train from scratch with sparsity constraints or minimize the reconstruction error between the feature maps of the pre-trained models and the compressed ones. Both strategies suffer from some limitations: the former kind is computationally expensive and difficult to converge, while the latter kind optimizes the reconstruction error but ignores the discriminative power of channels. In this paper, we propose a simple-yet-effective method called discrimination-aware channel pruning (DCP) to choose the channels that actually contribute to the discriminative power. To this end, we first introduce additional discrimination-aware losses into the network to increase the discriminative power of the intermediate layers. Next, we select the most discriminative channels for each layer by considering the discrimination-aware loss and the reconstruction error, simultaneously. We then formulate channel pruning as a sparsity-inducing optimization problem with a convex objective and propose a greedy algorithm to solve the resultant problem. Note that a channel (3D tensor) often consists of a set of kernels (each with a 2D matrix). Besides the redundancy in channels, some kernels in a channel may also be redundant and fail to contribute to the discriminative power of the network, resulting in kernel level redundancy. To solve this issue, we propose a discrimination-aware kernel pruning (DKP) method to further compress deep networks by removing redundant kernels. To avoid manually determining the pruning rate for each layer, we propose two adaptive stopping conditions to automatically determine the number of selected channels/kernels. The proposed adaptive stopping conditions tend to yield more efficient models with better performance in practice. Extensive experiments on both image classification and face recognition demonstrate the effectiveness of our methods. For example, on ILSVRC-12, the resultant ResNet-50 model with 30 percent reduction of channels even outperforms the baseline model by 0.36 percent in terms of Top-1 accuracy. We also deploy the pruned models on a smartphone (equipped with a Qualcomm Snapdragon 845 processor). The pruned MobileNetV1 and MobileNetV2 achieve 1.93× and 1.42× inference acceleration on the mobile device, respectively, with negligible performance degradation. The source code and the pre-trained models are available at https://github.com/SCUT-AILab/DCP.
Understanding galaxy formation and evolution through an all-sky submillimetre spectroscopic survey

Abstract We illustrate the extraordinary discovery potential for extragalactic astrophysics of a far-infrared/submillimetre (far-IR/submm) all-sky spectroscopic survey with a 3-m-class space telescope. Spectroscopy provides a three-dimensional view of the Universe and allows us to take full advantage of the sensitivity of present-day instrumentation, close to fundamental limits, overcoming the spatial confusion that affects broadband far-IR/submm surveys. A space telescope of the 3-m class (which has already been described in recent papers) will detect emission lines powered by star formation in galaxies out to $z\,{\simeq}\,8$ . It will specifically provide measurements of spectroscopic redshifts, star-formation rates (SFRs), dust masses, and metal content for millions of galaxies at the peak epoch of cosmic star formation and of hundreds of them at the epoch of reionisation. Many of these star-forming galaxies will be strongly lensed; the brightness amplification and stretching of their sizes will make it possible to investigate (by means of follow-up observations with high-resolution instruments like ALMA, JWST, and SKA) their internal structure and dynamics on the scales of giant molecular clouds (40–100 pc). This will provide direct information on the physics driving the evolution of star-forming galaxies. Furthermore, the arcmin resolution of the telescope at submm wavelengths is ideal for detecting the cores of galaxy proto-clusters, out to the epoch of reionisation. Due to the integrated emission of member galaxies, such objects (as well as strongly lensed sources) will dominate at the highest apparent far-IR luminosities. Tens of millions of these galaxy-clusters-in-formation will be detected at $z \simeq 2 - 3$ –3, with a tail extending out to $z\,{\simeq}\,7$ , and thousands of detections at $6\,{<}\,z\,{<}\,7$ . Their study will allow us to track the growth of the most massive halos well beyond what is possible with classical cluster surveys (mostly limited to $z\,\lesssim\, 1.5 - 2$ –2), tracing the history of star formation in dense environments and teaching us how star formation and galaxy-cluster formation are related across all epochs. The obscured cosmic SFR density of the Universe will thereby be constrained. Such a survey will overcome the current lack of spectroscopic redshifts of dusty star-forming galaxies and galaxy proto-clusters, representing a quantum leap in far-IR/submm extragalactic astrophysics.
Human-robot co-manipulation of extended objects: Data-driven models and control from analysis of human-human dyads

Human teams are able to easily perform collaborative manipulation tasks. However, for a robot and human to simultaneously manipulate an extended object is a difficult task using existing methods from the literature. Our approach in this paper is to use data from human-human dyad experiments to determine motion intent which we use for a physical human-robot co-manipulation task. We first present and analyze data from human-human dyads performing co-manipulation tasks. We show that our human-human dyad data has interesting trends including that interaction forces are non-negligible compared to the force required to accelerate an object and that the beginning of a lateral movement is characterized by distinct torque triggers from the leader of the dyad. We also examine different metrics to quantify performance of different dyads. We also develop a deep neural network based on motion data from human-human trials to predict human intent based on past motion. We then show how force and motion data can be used as a basis for robot control in a human-robot dyad. Finally, we compare the performance of two controllers for human-robot co-manipulation to human-human dyad performance.
Ultra-fast prompt gamma detection in single proton counting regime for range monitoring in particle therapy

In order to fully exploit the ballistic potential of particle therapy, we propose an online range monitoring concept based on time-of-flight (TOF)-resolved prompt gamma (PG) detection in a single proton counting regime. In a proof of principle experiment, different types of monolithic scintillating gamma detectors are read in time coincidence with a diamond-based beam hodoscope, in order to build TOF spectra of PG generated in a target presenting an air cavity of variable thickness. Since the measurement was carried out at low beam currents (< 1 proton/bunch) it was possible to reach excellent coincidence time resolutions, of the order of 100 ps (σ). Our goal is to detect possible deviations of the proton range with respect to treatment planning within a few intense irradiation spots at the beginning of the session and then carry on the treatment at standard beam currents. The measurements were limited to 10 mm proton range shift. A Monte Carlo simulation study reproducing the experiment has shown that a 3 mm shift can be detected at 2σ by a single detector of ∼1.4 × 10−3 absolute detection efficiency within a single irradiation spot (∼108 protons) and an optimised experimental set-up.
Local correlations in dual-unitary kicked chains

We show that for dual-unitary kicked chains, built upon a pair of complex Hadamard matrices, correlators of strictly local, traceless operators vanish identically for sufficiently long chains. On the other hand, operators supported at pairs of adjacent chain sites, generically, exhibit nontrivial correlations along the light cone edges. In agreement with Bertini et. al. [Phys. Rev. Lett. 123, 210601 (2019)], they can be expressed through the expectation values of a transfer matrix $T$. Furthermore, we identify a remarkable family of dual-unitary models where an explicit information on the spectrum of $T$ is available. For this class of models we provide a closed analytical formula for the corresponding two-point correlators. This result, in turn, allows an evaluation of local correlators in the vicinity of the dual-unitary regime which is exemplified on the kicked Ising spin chain.
Asymptotic shear and the intrinsic conformal geometry of null-infinity

In this article we propose a new geometrization of the radiative phase space of asymptotically flat space-times: we show that the geometry induced on null-infinity by the presence of gravitational waves can be understood to be a generalisation of the tractor calculus of conformal manifolds adapted to the case of degenerate conformal metrics. It follows that the whole formalism is, by construction, manifestly conformally invariant. We first show that a choice of asymptotic shear amounts to a choice of linear differential operator of order two on the bundle of scales of null-infinity. We refer to these operators as Poincare operators. We then show that Poincare operators are in one-to-one correspondence with a particular class of tractor connections which we call "null-normal" (they generalise the normal tractor connection of conformal geometry). The tractor curvature encodes the presence of gravitational waves and the non-uniqueness of flat null-normal tractor connections correspond to the "degeneracy of gravity vacuum" that has been extensively discussed in the literature. This work thus brings back the investigation of the radiative phase space of gravity to the study of (Cartan) connections and associated bundles. This allows, in particular, to proliferate invariants of the phase space.
Few-shot Learning with Multi-scale Self-supervision

Learning concepts from the limited number of datapoints is a challenging task usually addressed by the so-called one- or few-shot learning. Recently, an application of second-order pooling in few-shot learning demonstrated its superior performance due to the aggregation step handling varying image resolutions without the need of modifying CNNs to fit to specific image sizes, yet capturing highly descriptive co-occurrences. However, using a single resolution per image (even if the resolution varies across a dataset) is suboptimal as the importance of image contents varies across the coarse-to-fine levels depending on the object and its class label e. g. , generic objects and scenes rely on their global appearance while fine-grained objects rely more on their localized texture patterns. Multi-scale representations are popular in image deblurring, super-resolution and image recognition but they have not been investigated in few-shot learning due to its relational nature complicating the use of standard techniques. In this paper, we propose a novel multi-scale relation network based on the properties of second-order pooling to estimate image relations in few-shot setting. To optimize the model, we leverage a scale selector to re-weight scale-wise representations based on their second-order features. Furthermore, we propose to a apply self-supervised scale prediction. Specifically, we leverage an extra discriminator to predict the scale labels and the scale discrepancy between pairs of images. Our model achieves state-of-the-art results on standard few-shot learning datasets.
Compressed Super-Resolution I: Maximal Rank Sum-of-Squares

Let $\mu(t) = \sum_{\tau\in S} \alpha_\tau \delta(t-\tau)$ denote an $|S|$-atomic measure defined on $[0,1]$, satisfying $\min_{\tau\neq \tau'}|\tau - \tau'|\geq |S|\cdot n^{-1}$. Let $\eta(\theta) = \sum_{\tau\in S} a_\tau D_n(\theta - \tau) + b_\tau D'_n(\theta - \tau)$, denote the polynomial obtained from the Dirichlet kernel $D_n(\theta) = \frac{1}{n+1}\sum_{|k|\leq n} e^{2\pi i k \theta}$ and its derivative by solving the system $\left\{\eta(\tau) = 1, \eta'(\tau) = 0,\; \forall \tau \in S\right\}$. We provide evidence that for sufficiently large $n$, $\Delta> |S|^2 n^{-1}$, the non negative polynomial $1 - |\eta(\theta)|^2$ which vanishes at the atoms $\tau \in S$, and is bounded by $1$ everywhere else on the $[0,1]$ interval, can be written as a sum-of-squares with associated Gram matrix of rank $n-|S|$. Unlike previous work, our approach does not rely on the Fejer-Riesz Theorem, which prevents developing intuition on the Gram matrix, but requires instead a lower bound on the singular values of a (truncated) large ($O(1e10)$) matrix. Despite the memory requirements which currently prevent dealing with such a matrix efficiently, we show how such lower bounds can be derived through Power iterations and convolutions with special functions for sizes up to $O(1e7)$. We also provide numerical simulations suggesting that the spectrum remains approximately constant with the truncation size as soon as this size is larger than $100$.
Gaia RR Lyrae Stars in Nearby Ultra-faint Dwarf Satellite Galaxies

We search for RR Lyrae stars (RRLs) in 27 nearby (<100 kpc) ultra-faint dwarf satellite galaxies using the Gaia DR2 catalog of RRLs. Based on proper motions, magnitudes, and location on the sky, we associate 47 Gaia RRLs with 14 different satellites. Distances based on RRLs are provided for those galaxies. We have identified RRLs for the first time in the Tucana II dwarf galaxy, and find additional members in Ursa Major II, Coma Berenices, Hydrus I, Bootes I, and Bootes III. In addition we have identified candidate extra-tidal RRLs in six galaxies, which suggests they may be undergoing tidal disruption. We found 10 galaxies have no RRLs either in Gaia or in the literature. However, given the known completeness of Gaia DR2 we cannot conclude these galaxies indeed lack variable stars of this type.
Throughput Maximization of Mixed FSO/RF UAV-Aided Mobile Relaying With a Buffer

In this paper, we investigate a mobile relaying system assisted by an unmanned aerial vehicle (UAV) with a finite size of the buffer. Under the buffer size limit and delay constraints at the UAV relay, we consider a dual-hop mixed free-space optical/radio frequency (FSO/RF) relaying system (i.e., the source-to-relay and relay-to-destination links employ FSO and RF links, respectively). Taking an imbalance in the transmission rate between RF and FSO links into consideration, we address the trajectory design of the UAV relay node to obtain the maximum data throughput at the ground user terminal. Specifically, we classify two relaying transmission schemes according to the delay requirements, i.e., i) delay-limited transmission and ii) delay-tolerant transmission. Accordingly, we propose an iterative algorithm to effectively obtain the locally optimal solution to our throughput optimization problems and further present the complexity analysis of this algorithm. Through this algorithm, we present the resulting trajectories over the atmospheric condition, the buffer size, and the delay requirement. In addition, we show the optimum buffer size and the throughput-delay tradeoff for a given system. The numerical results validate that the proposed buffer-aided and delay-considered mobile relaying scheme obtains 223.33% throughput gain compared to the conventional static relaying scheme.
The Binary–Host Connection: Astrophysics of Gravitational-Wave Binaries from Host Galaxy Properties

Gravitational waves from the merger of binary neutron stars (BNSs) are accompanied by electromagnetic counterparts, making it possible to identify the associated host galaxy. In this work, we explore how properties of the hosts relate to the astrophysical processes leading to the mergers. It is thought that the BNS merger rate within a galaxy at a given epoch depends primarily on the galaxy’s star formation history, as well as the underlying merger time-delay distribution of the binary systems. The stellar history of a galaxy, meanwhile, depends on the cosmological evolution of the galaxy through time, and is tied to the growth of structure in the universe. We study the hosts of BNS mergers in the context of structure formation by populating the UniverseMachine simulations with gravitational wave (GW) events, based on a simple time-delay model. We find that different time-delay distributions predict different properties of the associated host galaxies, including the distributions of stellar mass, star formation rate, halo mass, and local and large-scale clustering of hosts. Moreover, BNSs merging today with short delay times occur preferentially in hosts with high star formation rates, while those with long delay times live in dense regions within massive halos that have low star formation. We show that with events from current GW detector networks, it is possible to make preliminary distinctions between formation channels which trace stellar mass, halo mass, or star formation rate. We also find that strategies to follow-up GW events with electromagnetic telescopes can be significantly optimized using the clustering properties of their hosts.
Design of Capacity-Approaching Low-Density Parity-Check Codes using Recurrent Neural Networks

In this paper, we model Density Evolution (DE) using Recurrent Neural Networks (RNNs) with the aim of designing capacity-approaching Irregular Low-Density Parity-Check (LDPC) codes for binary erasure channels. In particular, we present a method for determining the coefficients of the degree distributions, characterizing the structure of an LDPC code. We refer to our RNN architecture as Neural Density Evolution (NDE) and determine the weights of the RNN that correspond to optimal designs by minimizing a loss function that enforces the properties of asymptotically optimal design, as well as the desired structural characteristics of the code. This renders the LDPC design process highly configurable, as constraints can be added to meet applications' requirements by means of modifying the loss function. In order to train the RNN, we generate data corresponding to the expected channel noise. We analyze the complexity and optimality of NDE theoretically, and compare it with traditional design methods that employ differential evolution. Simulations illustrate that NDE improves upon differential evolution both in terms of asymptotic performance and complexity. Although we focus on asymptotic settings, we evaluate designs found by NDE for finite codeword lengths and observe that performance remains satisfactory across a variety of channels.
Field-Angle-Resolved Magnetic Excitations as a Probe of Hidden-Order Symmetry in 
CeB6

In contrast to magnetic order formed by electrons' dipolar moments, ordering phenomena associated with higher-order multipoles (quadrupoles, octupoles, etc.) are more difficult to characterize because of the limited choice of experimental probes that can distinguish different multipolar moments. The heavy-fermion compound CeB6 and its La-diluted alloys are among the best-studied realizations of the long-range-ordered multipolar phases, often referred to as "hidden order". Previously the hidden order in phase II was identified as primary antiferroquadrupolar (AFQ) and field-induced octupolar (AFO) order. Here we present a combined experimental and theoretical investigation of collective excitations in the phase II of CeB6. Inelastic neutron scattering (INS) in fields up to 16.5 T reveals a new high-energy mode above 14 T in addition to the low-energy magnetic excitations. The experimental dependence of their energy on the magnitude and angle of the applied magnetic field is compared to the results of a multipolar interaction model. The magnetic excitation spectrum in rotating field is calculated within a localized approach using the pseudo-spin presentation for the Gamma8 states. We show that the rotating-field technique at fixed momentum can complement conventional INS measurements of the dispersion at constant field and holds great promise for identifying the symmetry of multipolar order parameters and the details of inter-multipolar interactions that stabilize hidden-order phases.
Local and global rigidity for isometric actions of simple Lie groups on pseudo-Riemannian manifolds

Let $M$ be a finite volume analytic pseudo-Riemannian manifold that admits an isometric $G$-action with a dense orbit, where $G$ is a connected non-compact simple Lie group. For low-dimensional $M$, i.e. $\dim(M) < 2\dim(G)$, when the normal bundle to the $G$-orbits is non-integrable and for suitable conditions, we prove that $M$ has a $G$-invariant metric which is locally isometric to a Lie group with a bi-invariant metric (local rigidity theorem). The latter does not require $M$ to be complete as in previous works. We also prove a general result showing that $M$ is, up to a finite covering, of the form $H/\Gamma$ ($\Gamma$ a lattice in the group $H$) when we assume that $M$ is complete (global rigidity theorem). For both the local and the global rigidity theorems we provide cases that imply the rigidity of $G$-actions for $G$ given by $\mathrm{SO}_0(p,q)$, $G_{2(2)}$ or a non-compact simple Lie group of type $F_4$ over $\mathbb{R}$. We also survey the techniques and results related to this work.