Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the open cluster census. II. An all-sky cluster catalogue with Gaia DR3

Data from the Gaia satellite are revolutionising our understanding of the Milky Way. With every new data release, there is a need to update the census of open clusters. We aim to conduct a blind, all-sky search for open clusters using 729 million sources from Gaia DR3 down to magnitude $G\sim20$, creating a homogeneous catalogue of clusters including many new objects. We used the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm to recover clusters. We validated our clusters using a statistical density test and a Bayesian convolutional neural network for colour-magnitude diagram classification. We inferred basic astrometric parameters, ages, extinctions, and distances for the clusters in the catalogue. We recovered 7167 clusters, 2387 of which are candidate new objects and 4782 of which crossmatch to objects in the literature, including 134 globular clusters. A more stringent cut of our catalogue contains 4105 highly reliable clusters, 739 of which are new. Owing to the scope of our methodology, we are able to tentatively suggest that many of the clusters we are unable to detect may not be real, including 1152 clusters from the Milky Way Star Cluster (MWSC) catalogue that should have been detectable in Gaia data. Our cluster membership lists include many new members and often include tidal tails. Our catalogue's distribution traces the galactic warp, the spiral arm structure, and the dust distribution of the Milky Way. While much of the content of our catalogue contains bound open and globular clusters, as many as a few thousand of our clusters are more compatible with unbound moving groups, which we will classify in an upcoming work. We have conducted the largest search for open clusters to date, producing a single homogeneous star cluster catalogue which we make available with this paper.
Sequential Cauchy Combination Test for Multiple Testing Problems with Financial Applications

We introduce a simple tool to control for false discoveries and identify individual signals in scenarios involving many tests, dependent test statistics, and potentially sparse signals. The tool applies the Cauchy combination test recursively on a sequence of expanding subsets of $p$-values and is referred to as the sequential Cauchy combination test. While the original Cauchy combination test aims to make a global statement about a set of null hypotheses by summing transformed $p$-values, our sequential version determines which $p$-values trigger the rejection of the global null. The sequential test achieves strong familywise error rate control, exhibits less conservatism compared to existing controlling procedures when dealing with dependent test statistics, and provides a power boost. As illustrations, we revisit two well-known large-scale multiple testing problems in finance for which the test statistics have either serial dependence or cross-sectional dependence, namely monitoring drift bursts in asset prices and searching for assets with a nonzero alpha. In both applications, the sequential Cauchy combination test proves to be a preferable alternative. It overcomes many of the drawbacks inherent to inequality-based controlling procedures, extreme value approaches, resampling and screening methods, and it improves the power in simulations, leading to distinct empirical outcomes.
PointGame: Geometrically and Adaptively Masked Autoencoder on Point Clouds

Self-supervised learning is attracting large attention in point cloud understanding. However, exploring discriminative and transferable features still remains challenging due to their nature of irregularity. We propose a geometrically and adaptively masked autoencoder on point clouds for self-supervised learning, termed PointGame. PointGame contains two core components: GATE and EAT. GATE stands for the geometrical and adaptive token embedding module; it not only absorbs the conventional wisdom of geometric descriptors that capture the surface shape effectively, but also exploits adaptive saliency to focus on the salient part of a point cloud. EAT stands for the external attention-based transformer encoder with linear computational complexity, which increases the efficiency of the whole pipeline. Unlike cutting-edge unsupervised learning models, PointGame leverages geometric descriptors to perceive surface shapes and adaptively mines discriminative features from training data. PointGame showcases clear advantages over its competitors on various downstream tasks under both global and local fine-tuning strategies. The code and pretrained models will be publicly available.
‘Crackle’: an annoying component of jet noise

The paper describes an investigation of a subjectively distinguishable element of high speed jet noise known as ‘crackle’. ‘Crackle’ cannot be characterized by the normal spectral description of noise. It is shown to be due to intense spasmodic short-duration compressive elements of the wave form. These elements have low energy spread over a wide frequency range. The crackling of a large jet engine is caused by groups of sharp compressions in association with gradual expansions. The groups occur at random and persist for some 10−1s, each group containing about 10 compressions, typically of strength 5 × 10−3 atmos at a distance of 50 m. The skewness of the amplitude probability distribution of the recorded sound quantifies crackle, though the recording process probably changes the skewness level. Skewness values in excess of unity have been measured; noises with skewness less than 0·3 seem to be crackle free. Crackle is uninfluenced by the jet scale, but varies strongly with jet velocity and angular position. The jet temperature does not affect crackle, neither does combustion. Supersonic jets crackle strongly whether or not they are ideally expanded through convergent-divergent nozzles. Crackle is formed (we think) because of local shock formation due to nonlinear wave steepening at the source and not from long-term nonlinear propagation. Such long-term effects are important in flight, where they are additive. Some jet noise suppressors inhibit crackle.
Laplacian Segmentation Networks: Improved Epistemic Uncertainty from Spatial Aleatoric Uncertainty

Out of distribution (OOD) medical images are frequently encountered, e.g. because of site- or scanner differences, or image corruption. OOD images come with a risk of incorrect image segmentation, potentially negatively affecting downstream diagnoses or treatment. To ensure robustness to such incorrect segmentations, we propose Laplacian Segmentation Networks (LSN) that jointly model epistemic (model) and aleatoric (data) uncertainty in image segmentation. We capture data uncertainty with a spatially correlated logit distribution. For model uncertainty, we propose the first Laplace approximation of the weight posterior that scales to large neural networks with skip connections that have high-dimensional outputs. Empirically, we demonstrate that modelling spatial pixel correlation allows the Laplacian Segmentation Network to successfully assign high epistemic uncertainty to out-of-distribution objects appearing within images.
$T_{3}$Pb$_{2}Ch_{2}$ ($T$=Pd,Pt and $Ch$=S,Se) with transition metal kagome net: Dynamical properties, phonon nodal line, phonon surface states, and chiral phonons

Shandite with Ni$_{3}$Pb$_{2}$S$_{2}$ chemical formula and R$\bar{3}$m symmetry, contains the kagome sublattice formed by the transition metal atoms. Recent experimental results confirmed the possibility of successfully synthesizing Pd$_{3}$Pb$_{2}Ch_{2}$ ($Ch$=S,Se) with the same structure. In this paper, we theoretically investigate the dynamical properties of such compounds. Furthermore, we study the possibility of realizing Pt$_{3}$Pb$_{2}Ch_{2}$ with the shandite structure. We show that the Pd$_{3}$Pb$_{2}${\it Ch}$_{2}$ and Pt$_{3}$Pb$_{2}$S$_{2}$ are stable with R$\bar{3}$m symmetry. In the case of Pt$_{3}$Pb$_{2}$S$_{2}$, there is a soft mode, which is the source of the structural phase transition from R$\bar{3}$m to R$\bar{3}$c symmetry, related to the distortion within the kagome sublattice. We discuss realized phonon nodal lines in the bulk phonon dispersions in upper frequency modes. We show that the shandite structure can host the phonon surface states, with strong dependence by the surface kind. Additionally, chiral phonons with circular motion of the Pb atoms around the equilibrium position are realized.
Pyramid Multi-branch Fusion DCNN with Multi-Head Self-Attention for Mandarin Speech Recognition

As one of the major branches of automatic speech recognition, attention-based models greatly improves the feature representation ability of the model. In particular, the multi-head mechanism is employed in the attention, hoping to learn speech features of more aspects in different attention subspaces. For speech recognition of complex languages, on the one hand, a small head size will lead to an obvious shortage of learnable aspects. On the other hand, we need to reduce the dimension of each subspace to keep the size of the overall feature space unchanged when we increase the number of heads, which will significantly weaken the ability to represent the feature of each subspace. Therefore, this paper explores how to use a small attention subspace to represent complete speech features while ensuring many heads. In this work we propose a novel neural network architecture, namely, pyramid multi-branch fusion DCNN with multi-head self-attention. The proposed architecture is inspired by Dilated Convolution Neural Networks (DCNN), it uses multiple branches with DCNN to extract the feature of the input speech under different receptive fields. To reduce the number of parameters, every two branches are merged until all the branches are merged into one. Thus, its shape is like a pyramid rotated 90 degrees. We demonstrate that on Aishell-1, a widely used Mandarin speech dataset, our model achieves a character error rate (CER) of 6.45% on the test sets.
Controlled Lagrangians and Stabilization of Euler--Poincar\'e Equations with Symmetry Breaking Nonholonomic Constraints

We extend the method of Controlled Lagrangians to nonholonomic Euler--Poincar\'e equations with advected parameters, specifically to those mechanical systems on Lie groups whose symmetry is broken not only by a potential force but also by nonholonomic constraints. We introduce advected-parameter-dependent quasivelocities in order to systematically eliminate the Lagrange multipliers in the nonholonomic Euler--Poincar\'e equations. The quasivelocities facilitate the method of Controlled Lagrangians for these systems, and lead to matching conditions that are similar to those by Bloch, Leonard, and Marsden for the standard Euler--Poincar\'e equation. Our motivating example is what we call the pendulum skate, a simple model of a figure skater developed by Gzenda and Putkaradze. We show that the upright spinning of the pendulum skate is stable under certain conditions, whereas the upright sliding equilibrium is always unstable. Using the matching condition, we derive a control law to stabilize the sliding equilibrium.
Self-supervised Learning with Speech Modulation Dropout

We show that training a multi-headed self-attention-based deep network to predict deleted, information-dense 2-8 Hz speech modulations over a 1.5-second section of a speech utterance is an effective way to make machines learn to extract speech modulations using time-domain contextual information. Our work exhibits that, once trained on large volumes of unlabelled data, the outputs of the self-attention layers vary in time with a modulation peak at 4 Hz. These pre-trained layers can be used to initialize parts of an Automatic Speech Recognition system to reduce its reliance on labeled speech data greatly.
Coronal X-Ray Emission from Nearby, Low-mass, Exoplanet Host Stars Observed by the MUSCLES and Mega-MUSCLES HST Treasury Survey Projects

The high-energy X-ray and ultraviolet (UV) radiation fields of exoplanet host stars play a crucial role in controlling the atmospheric conditions and the potential habitability of exoplanets. Major surveys of the X-ray/UV emissions from late-type (K and M spectral types) exoplanet hosts have been conducted by the Measurements of the Ultraviolet Spectral Characteristics of Low-mass Exoplanetary systems (MUSCLES) and Mega-MUSCLES Hubble Space Telescope Treasury programs. These samples primarily consist of relatively old, “inactive,” low-mass stars. In this paper we present results from X-ray observations of the coronal emission from these stars obtained using the Chandra X-ray Observatory, the XMM-Newton Observatory, and the Neil Gehrels Swift Observatory. The stars effectively sample the coronal activity of low-mass stars over a wide range of masses and ages. The vast majority (21 of 23) of the stars are detected and their X-ray luminosities measured. Short-term flaring variability is detected for most of the fully convective (M ≤ 0.35 M ⊙) stars but not for the more massive M dwarfs during these observations. Despite this difference, the mean X-ray luminosities for these two sets of M dwarfs are similar, with more massive (0.35 M ⊙ ≤ M ≤ 0.6 M ⊙) M dwarfs at ∼5 × 1026 erg s−1 compared to ∼2 × 1026 erg s−1 for fully convective stars older than 1 Gyr. Younger, fully convective M dwarfs have X-ray luminosities between 3 and 6 × 1027 erg s−1. The coronal X-ray spectra have been characterized and provide important information that is vital for the modeling of the stellar EUV spectra.
Paraconsistent Transition Systems

Often in Software Engineering, a modeling formalism has to support scenarios of inconsistency in which several requirements either reinforce or contradict each other. Paraconsistent transition systems are proposed in this paper as one such formalism: states evolve through two accessibility relations capturing weighted evidence of a transition or its absence, respectively. Their weights come from a specific residuated lattice. A category of these systems, and the corresponding algebra, is defined as providing a formal setting to model different application scenarios. One of them, dealing with the effect of quantum decoherence in quantum programs, is used for illustration purposes.
Predicting the Initial Conditions of the Universe using Deep Learning

Finding the initial conditions that led to the current state of the universe is challenging because it involves searching over a vast input space of initial conditions, along with modeling their evolution via tools such as N-body simulations which are computationally expensive. Deep learning has emerged as an alternate modeling tool that can learn the mapping between the linear input of an N-body simulation and the ﬁnal nonlinear displacements at redshift zero, which can signiﬁ-cantly accelerate the forward modeling. However, this does not help reduce the search space for initial conditions. In this paper, we demonstrate for the ﬁrst time that a deep learning model can be trained for the reverse mapping. We train a V-Net based convolutional neural network, which outputs the linear displacement of an N-body system, given the current time nonlinear displacement and the cosmological parameters of the system. We demonstrate that this neural network accurately recovers the initial linear displacement ﬁeld over a wide range of scales ( < 1 - 2% error up to nearly k = 1 Mpc − 1 h ), despite the ill-deﬁned nature of the inverse problem at smaller scales. Speciﬁcally, smaller scales are dominated by nonlinear effects which makes the backward dynamics much more susceptible to numerical and computational errors leading to highly divergent backward trajectories and a one-to-many backward mapping. The re-sults of our method motivate that neural network based models can act as good approximators of the initial linear states and their predictions can serve as good starting points for sampling-based methods to infer the initial states of the universe.
FUSE Observations of the SMC 16 Day Wolf-Rayet Binary Sanduleak 1 (WO4+O4): Atmospheric Eclipses and Colliding Stellar Winds

In this paper we present the results of a FUSE monitoring campaign of the SMC WO4+O4 V Wolf-Rayet binary Sanduleak 1. Our 18 spectra obtained during a little more than one orbital cycle in 2000 October combined with four archival spectra show variability in the S VI, C III, C IV, and O VI P Cygni profiles, which we attribute to emission from the shock cone resulting from the collision between the two strong winds and to atmospheric eclipses of the O star continuum light by the W-R wind. All the lines vary in concert indicating that the cooling is such that even lines such as the OVI λλ1032, 1038 doublet form in the linear part of the cone. We have also applied both a simple geometrical model and profile fits, including emission from the normal wind, extra emission from the shock cone, and the atmospheric eclipse. Adopting an orbital inclination of ~40°, we deduce a total cone opening angle of ~80° and a streaming velocity for the gas along the shock cone of ~3000 km s-1. The luminosity ratio required to fit our spectra is L O/L W-R = 3.5, and the stellar radii are 3.5 and 12 R ☉, respectively, for the W-R and O stars. We also present radiative driving models for this binary system having two massive stars with strong winds and discuss radiative inhibition and braking effects. In particular, we address the coupling of the O star radiation with the W-R star wind. Finally, we present a PICA hydrodynamic colliding-wind model for Sand 1. We find an opening angle for the shock cone similar to that deduced from the line-profile fitting, but significantly longer cooling lengths along the shock cone. However, the model reveals some cold gas that is stripped off the O4 surface and mixed with the hotter WO4 material, thereby accelerating its cooling. This could very well explain why shorter cooling lengths are inferred from the profile fits.
An Empirical Analysis of the Shift and Scale Parameters in BatchNorm

Batch Normalization (BatchNorm) is a technique that improves the training of deep neural networks, especially Convolutional Neural Networks (CNN). It has been empirically demonstrated that BatchNorm increases performance, stability, and accuracy, although the reasons for such improvements are unclear. BatchNorm includes a normalization step as well as trainable shift and scale parameters. In this paper, we empirically examine the relative contribution to the success of BatchNorm of the normalization step, as compared to the re-parameterization via shifting and scaling. To conduct our experiments, we implement two new optimizers in PyTorch, namely, a version of BatchNorm that we refer to as AffineLayer, which includes the re-parameterization step without normalization, and a version with just the normalization step, that we call BatchNorm-minus. We compare the performance of our AffineLayer and BatchNorm-minus implementations to standard BatchNorm, and we also compare these to the case where no batch normalization is used. We experiment with four ResNet architectures (ResNet18, ResNet34, ResNet50, and ResNet101) over a standard image dataset and multiple batch sizes. Among other findings, we provide empirical evidence that the success of BatchNorm may derive primarily from improved weight initialization.
The power and limitations of learning quantum dynamics incoherently

Quantum process learning is emerging as an important tool to study quantum systems. While studied extensively in coherent frameworks, where the target and model system can share quantum information, less attention has been paid to whether the dynamics of quantum systems can be learned without the system and target directly interacting. Such incoherent frameworks are practically appealing since they open up methods of transpiling quantum processes between the different physical platforms without the need for technically challenging hybrid entanglement schemes. Here we provide bounds on the sample complexity of learning unitary processes incoherently by analyzing the number of measurements that are required to emulate well-established coherent learning strategies. We prove that if arbitrary measurements are allowed, then any efficiently representable unitary can be efficiently learned within the incoherent framework; however, when restricted to shallow-depth measurements only low-entangling unitaries can be learned. We demonstrate our incoherent learning algorithm for low entangling unitaries by successfully learning a 16-qubit unitary on \texttt{ibmq\_kolkata}, and further demonstrate the scalabilty of our proposed algorithm through extensive numerical experiments.
Decentralized Adversarial Training over Graphs

The vulnerability of machine learning models to adversarial attacks has been attracting considerable attention in recent years. Most existing studies focus on the behavior of stand-alone single-agent learners. In comparison, this work studies adversarial training over graphs, where individual agents are subjected to perturbations of varied strength levels across space. It is expected that interactions by linked agents, and the heterogeneity of the attack models that are possible over the graph, can help enhance robustness in view of the coordination power of the group. Using a min-max formulation of diffusion learning, we develop a decentralized adversarial training framework for multi-agent systems. We analyze the convergence properties of the proposed scheme for both convex and non-convex environments, and illustrate the enhanced robustness to adversarial attacks.
Fairness-guided Few-shot Prompting for Large Language Models

Large language models have demonstrated surprising ability to perform in-context learning, i.e., these models can be directly applied to solve numerous downstream tasks by conditioning on a prompt constructed by a few input-output examples. However, prior research has shown that in-context learning can suffer from high instability due to variations in training examples, example order, and prompt formats. Therefore, the construction of an appropriate prompt is essential for improving the performance of in-context learning. In this paper, we revisit this problem from the view of predictive bias. Specifically, we introduce a metric to evaluate the predictive bias of a fixed prompt against labels or a given attributes. Then we empirically show that prompts with higher bias always lead to unsatisfactory predictive quality. Based on this observation, we propose a novel search strategy based on the greedy search to identify the near-optimal prompt for improving the performance of in-context learning. We perform comprehensive experiments with state-of-the-art mainstream models such as GPT-3 on various downstream tasks. Our results indicate that our method can enhance the model's in-context learning performance in an effective and interpretable manner.