Miracle-dz/UniFAR-FTUs huggingface dataset:
Can Cosmological Simulations Reproduce the Spectroscopically Confirmed Galaxies Seen at z ≥ 10?

Recent photometric detections of extreme (z > 10) redshift galaxies from the JWST have been shown to be in strong tension with existing simulation models for galaxy formation and in the most acute case, in tension with ΛCDM itself. These results, however, all rest on the confirmation of these distances by spectroscopy. Recently, the JADES survey has detected the most distant galaxies with spectroscopically confirmed redshifts, with four galaxies found with redshifts between z = 10.38 and z = 13.2. In this Letter, we compare simulation predictions from four large cosmological volumes and two zoom-in protoclusters with the JADES observations to determine whether these spectroscopically confirmed galaxy detections are in tension with existing models for galaxy formation or with ΛCDM more broadly. We find that existing models for cosmological galaxy formation can generally reproduce the observations for JADES in terms of galaxy stellar masses, star formation rates, and the number density of galaxies at z > 10.
Towards Long-term Autonomy: A Perspective from Robot Learning

In the future, service robots are expected to be able to operate autonomously for long periods of time without human intervention. Many work striving for this goal have been emerging with the development of robotics, both hardware and software. Today we believe that an important underpinning of long-term robot autonomy is the ability of robots to learn on site and on-the-fly, especially when they are deployed in changing environments or need to traverse different environments. In this paper, we examine the problem of long-term autonomy from the perspective of robot learning, especially in an online way, and discuss in tandem its premise"data"and the subsequent"deployment".
Saliency-Augmented Memory Completion for Continual Learning

Continual Learning is considered a key step toward next-generation Artificial Intelligence. Among various methods, replay-based approaches that maintain and replay a small episodic memory of previous samples are one of the most successful strategies against catastrophic forgetting. However, since forgetting is inevitable given bounded memory and unbounded tasks, how to forget is a problem continual learning must address. Therefore, beyond simply avoiding catastrophic forgetting, an under-explored issue is how to reasonably forget while ensuring the merits of human memory, including 1. storage efficiency, 2. generalizability, and 3. some interpretability. To achieve these simultaneously, our paper proposes a new saliency-augmented memory completion framework for continual learning, inspired by recent discoveries in memory completion separation in cognitive neuroscience. Specifically, we innovatively propose to store the part of the image most important to the tasks in episodic memory by saliency map extraction and memory encoding. When learning new tasks, previous data from memory are inpainted by an adaptive data generation module, which is inspired by how humans complete episodic memory. The module's parameters are shared across all tasks and it can be jointly trained with a continual learning classifier as bilevel optimization. Extensive experiments on several continual learning and image classification benchmarks demonstrate the proposed method's effectiveness and efficiency.
A Survey of Face Recognition

Recent years witnessed the breakthrough of face recognition with deep convolutional neural networks. Dozens of papers in the field of FR are published every year. Some of them were applied in the industrial community and played an important role in human life such as device unlock, mobile payment, and so on. This paper provides an introduction to face recognition, including its history, pipeline, algorithms based on conventional manually designed features or deep learning, mainstream training, evaluation datasets, and related applications. We have analyzed and compared state-of-theart works as many as possible, and also carefully designed a set of experiments to find the effect of backbone size and data distribution. This survey is a material of the tutorial named The Practical Face Recognition Technology in the Industrial World in the FG2023.
Mitigating heating of degenerate fermions in a ring-dimple atomic trap

We report on the impact of the extended geometry of a ring-dimple trap on particle loss heating of a degenerate Fermi gas. When the Fermi level is slightly greater than the depth of the dimple and a non-degenerate"halo"is present, the overall heating rate is reduced relative to the case of a bare ring. We find that the experimentally measured heating rates for the overfilled dimple are in good agreement with a model of the hole-induced heating caused by background gas collisions. This suppression of the heating rate can be helpful for experimental studies of fermionic superfluids in the weak pairing limit, where achieving and maintaining low temperatures over long time scales is essential.
Stationary solitons in F=1 spin-orbit coupled Bose-Einstein condensates

We consider solitary wave excitations above the ground state of $F=1$ spin-orbit coupled Bose-Einstein condensates (SOBECs). The low energy properties of SOBECs in any of the three branches of the single particle dispersion relation can be described by suitable scalar nonlinear Schr\"odinger (NLS) equations which we obtain using multiple-scale expansions. This enables us to examine a variety of different configurations, such as dark solitary waves associated with higher energy branches, as well as dark and bright structures in the lowest branch. The lowest branch can also exhibit a ``superstripe'' phase that supports solitary waves. In all cases, we provide explicit expressions for the NLS coefficients, and confirm their validity with full numerical simulations of the SOBEC system including a harmonic confining potential.
ESVIO: Event-Based Stereo Visual Inertial Odometry

Event cameras that asynchronously output low-latency event streams provide great opportunities for state estimation under challenging situations. Despite event-based visual odometry having been extensively studied in recent years, most of them are based on the monocular, while few research on stereo event vision. In this letter, we present ESVIO, the first event-based stereo visual-inertial odometry, which leverages the complementary advantages of event streams, standard images, and inertial measurements. Our proposed pipeline includes the ESIO (purely event-based) and ESVIO (event with image-aided), which achieves spatial and temporal associations between consecutive stereo event streams. A well-design back-end tightly-coupled fused the multi-sensor measurement to obtain robust state estimation. We validate that both ESIO and ESVIO have superior performance compared with other image-based and event-based baseline methods on public and self-collected datasets. Furthermore, we use our pipeline to perform onboard quadrotor flights under low-light environments. Autonomous driving data sequences and real-world large-scale experiments are also conducted to demonstrate long-term effectiveness. We highlight that this work is a real-time, accurate system that is aimed at robust state estimation under challenging environments.
Hyperspherical Loss-Aware Ternary Quantization

Most of the existing works use projection functions for ternary quantization in discrete space. Scaling factors and thresholds are used in some cases to improve the model ac- curacy. However, the gradients used for optimization are inaccurate and result in a notable accuracy gap between the full precision and ternary models. To get more accurate gradients, some works gradually increase the discrete portion of the full precision weights in the forward propagation pass, e.g., using temperature-based Sigmoid function. Instead of directly per- forming ternary quantization in discrete space, we push full precision weights close to ternary ones through regularization term prior to ternary quantization. In addition, inspired by the temperature-based method, we introduce a re-scaling factor to obtain more accurate gradients by simulating the deriva-tives of Sigmoid function. The experimental results show that our method can signiﬁcantly improve the accuracy of ternary quantization in both image classiﬁcation and object detection tasks.
Kidney and Kidney Tumour Segmentation in CT Images

Automatic segmentation of kidney and kidney tumour in Computed Tomography (CT) images is essential, as it uses less time as compared to the current gold standard of manual segmentation. However, many hospitals are still reliant on manual study and segmentation of CT images by medical practitioners because of its higher accuracy. Thus, this study focuses on the development of an approach for automatic kidney and kidney tumour segmentation in contrast-enhanced CT images. A method based on Convolutional Neural Network (CNN) was proposed, where a 3D U-Net segmentation model was developed and trained to delineate the kidney and kidney tumour from CT scans. Each CT image was pre-processed before inputting to the CNN, and the effect of down-sampled and patch-wise input images on the model performance was analysed. The proposed method was evaluated on the publicly available 2021 Kidney and Kidney Tumour Segmentation Challenge (KiTS21) dataset. The method with the best performing model recorded an average training Dice score of 0.6129, with the kidney and kidney tumour Dice scores of 0.7923 and 0.4344, respectively. For testing, the model obtained a kidney Dice score of 0.8034, and a kidney tumour Dice score of 0.4713, with an average Dice score of 0.6374.
Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks

Nowadays, fake news easily propagates through online social networks and becomes a grand threat to individuals and society. Assessing the authenticity of news is challenging due to its elaborately fabricated contents, making it difficult to obtain large-scale annotations for fake news data. Due to such data scarcity issues, detecting fake news tends to fail and overfit in the supervised setting. Recently, graph neural networks (GNNs) have been adopted to leverage the richer relational information among both labeled and unlabeled instances. Despite their promising results, they are inherently focused on pairwise relations between news, which can limit the expressive power for capturing fake news that spreads in a group-level. For example, detecting fake news can be more effective when we better understand relations between news pieces shared among susceptible users. To address those issues, we propose to leverage a hypergraph to represent group-wise interaction among news, while focusing on important news relations with its dual-level attention mechanism. Experiments based on two benchmark datasets show that our approach yields remarkable performance and maintains the high performance even with a small subset of labeled news data.
Generalized characters of the generalized symmetric group

We prove that $(\mathbb{Z}_k \wr \mathcal{S}_n \times \mathbb{Z}_k \wr \mathcal{S}_{n-1}, \text{diag} (\mathbb{Z}_k \wr \mathcal{S}_{n-1}) )$ is a symmetric Gelfand pair, where $\mathbb{Z}_k \wr \mathcal{S}_n$ is the wreath product of the cyclic group $\mathbb{Z}_k$ with the symmetric group $\mathcal{S}_n. $ The proof is based on the study of the $\mathbb{Z}_k \wr \mathcal{S}_{n-1}$-conjugacy classes of $\mathbb{Z}_k \wr \mathcal{S}_n. $ We define the generalized characters of $\mathbb{Z}_k \wr \mathcal{S}_n$ using the zonal spherical functions of $(\mathbb{Z}_k \wr \mathcal{S}_n \times \mathbb{Z}_k \wr \mathcal{S}_{n-1}, \text{diag} (\mathbb{Z}_k \wr \mathcal{S}_{n-1}) ). $ We show that these generalized characters have properties similar to usual characters. A Murnaghan-Nakayama rule for the generalized characters of the hyperoctahedral group is presented. The generalized characters of the symmetric group were first studied by Strahov in [7].
Microwave absorption of nanoscale CoNi powders

Ferromagnetic resonance (FMR) and microwave magnetoabsorption can be expected to be powerful tools for characterization of magnetic nanoparticles. In the present work, we have studied the FMR and magnetic field dependence of nonresonant microwave losses in CoNi nanoparticles of about 45, 100, and 200nm caliper sizes prepared by the polyol technique. Contrary to our expectation, FMR lines, although wide, show that the particles are not truly spherical. Subsequent scanning and transmission electron microscopy investigations have confirmed this finding. Further, as before, all the powders are found to have large zero field absorption which reduces on application of a direct current (dc) field parallel to the microwave magnetic field but is relatively insensitive to a dc field if the powder is located in the microwave electric field, hence defining a magnetoimpedance for the system.
On global in time self-similar solutions of Smoluchowski equation with multiplicative kernel

We study the similarity solutions (SS) of Smoluchowski coagulation equation with multiplicative kernel $K(x,y)=(xy)^{s}$ for $s<\frac {1}{2}$. When $s<0$ , the SS consists of three regions with distinct asymptotic behaviours. The appropriate matching yields a global description of the solution consisting of a Gamma distribution tail, an intermediate region described by a lognormal distribution and a region of very fast decay of the solutions to zero near the origin. When $s\in \left ( 0,\frac {1}{2}\right ) $, the SS is unbounded at the origin. It also presents three regions: a Gamma distribution tail, an intermediate region of power-like (or Pareto distribution) decay and the region close to the origin where a singularity occurs. Finally, full numerical simulations of Smoluchowski equation serve to verify our theoretical results and show the convergence of solutions to the selfsimilar regime.
Rethinking Dense Cells for Integrated Sensing and Communications: A Stochastic Geometric View

The inclusion of the sensing functionality in the coming generations of cellular networks necessitates a rethink of dense cell deployments. In this paper, we analyze and optimize dense cell topologies for dual-functional radar-communication (DFRC) cellular networks. With the aid of tools from stochastic geometry, we derive new analytical expressions of the potential area spectral efficiencies in (bit/sec/m2) of radar and communication systems. Based on the new formulations of the potential area spectral efficiencies, the energy efficiency (bit/Joule) of DFRC systems is provided in a closed-form formula. Then, an optimization problem to obtain the optimal base station (BS) density that maximizes the network-level energy efficiency is formulated and investigated. In this regard, the mathematical expression of the energy efficiency is shown to be a uni-modal and pseudo-concave function in the density of the BSs. Therefore, the optimal density of the BSs that maximizes the energy efficiency can be obtained. Our analytical and numerical results demonstrate that the inclusion of the sensing functionality clearly differentiates the optimal BS topologies for the DFRC systems against classical communication-only systems.
The Cauchy-Dirichlet problem for parabolic deformed Hermitian-Yang-Mills equation

The purpose of this paper is to investigate the parabolic deformed Hermitian-Yang-Mills equation with hypercritical phase in a smooth domain Ω ⊂ C n \Omega \subset \mathbb {C}^{n} . By using J J -functional, we are able to prove the convergence of solutions. As an application, we give an alternative proof of the Dirichlet problem for deformed Hermitian-Yang-Mills equation.
The number of string C-groups of high rank

If G is a transitive group of degree n having a string C-group of rank r ≥ ( n + 3) / 2, then G is necessarily the symmetric group S n . We prove that if n is large enough, up to isomorphism and duality, the number of string C-groups of rank r for S n (with r ≥ ( n + 3) / 2) is the same as the number of string C-groups of rank r + 1 for S n +1 . This result and the tools used in its proof, in particular the rank and degree extension, imply that if one knows the string C-groups of rank ( n + 3) / 2 for S n with n odd, one can construct from them all string C-groups of rank ( n +3) / 2+ k for S n + k for any positive integer k . The classiﬁcation of the string C-groups of rank r ≥ ( n +3) / 2 for S n is thus reduced to classifying string C-groups of rank r for S 2 r − 3 . A consequence of this result is the complete classiﬁcation of all string C-groups of S n with rank n − κ for κ ∈ { 1 ,. , 6 } , when n ≥ 2 κ + 3, which extends previously known results. The number of string C-groups of rank n − κ , with n ≥ 2 κ +3, of this classiﬁcation gives the following sequence of integers indexed by κ and starting at κ = 1: (1 , 1 , 7 , 9 , 35 , 48) This sequence of integers is new according to the On-Line Encyclopedia of Integer Sequences. It will be available as sequence number A359367.
Federated Learning with Imbalanced and Agglomerated Data Distribution for Medical Image Classiﬁcation

—Federated learning (FL), training deep models from decentralized data without privacy leakage, has drawn great attention recently. Two common issues in FL, namely data heterogeneity from the local perspective and class imbalance from the global perspective have limited FL’s performance. These two coupling problems are under-explored, and existing few studies may not be sufﬁciently realistic to model data distributions in practical sceneries (e.g. medical sceneries). One common observation is that the overall class distribution across clients is imbalanced ( e.g. common vs. rare diseases) and data tend to be agglomerated to those more advanced clients ( i.e. , the data agglomeration effect), which cannot be modeled by existing settings. Inspired by real medical imaging datasets, we identify and formulate a new and more realistic data distribution denoted as L 2 distribution where global class distribution is highly imbalanced and data distributions across clients are imbalanced but forming a certain degree of data agglomeration. To pursue effective FL under this distribution, we propose a novel privacy-preserving framework named FedIIC that calibrates deep models to alleviate bias caused by imbalanced training. To calibrate the feature extractor part, intra-client contrastive learning with a modiﬁed similarity measure and inter-client contrastive learning guided by shared global prototypes are introduced to produce a uniform embedding distribution of all classes across clients. To calibrate the classiﬁcation heads, a softmax cross entropy loss with difﬁculty-aware logit adjustment is constructed to ensure balanced decision boundaries of all classes. Experimental results on publicly-available datasets demonstrate the superior performance of FedIIC in dealing with both the proposed realistic modeling and the existing modeling of the two coupling problems.
INDETERMINACY AND STRONG IDENTIFIABILITY IN GENERATIVE MODELS

Most modern probabilistic generative models, such as the variational au-toencoder (VAE), have certain indeterminacies that are unresolvable even with an inﬁnite amount of data. Different tasks tolerate different indeterminacies, however recent applications have indicated the need for strongly identiﬁable models, in which an observation corresponds to a unique latent code. Progress has been made towards reducing model indeterminacies while maintaining ﬂexibility, and recent work excludes many—but not all—indeterminacies. In this work, we motivate model-identiﬁability in terms of task-identiﬁability, then construct a theoretical framework for analyzing the indeterminacies of latent variable models, which enables their precise characterization in terms of the generator function and prior distribution spaces. We reveal that strong identiﬁability is possible even with highly ﬂexible nonlinear generators, and give two such examples. One is a straightforward modiﬁcation of iVAE (Khe-makhem et al., 2020a); the other uses triangular monotonic maps, leading to novel connections between optimal transport and identiﬁability.
Attentional-Biased Stochastic Gradient Descent

In this paper, we present a simple yet effective provable method (named ABSGD) for addressing the data imbalance or label noise problem in deep learning. Our method is a simple modification to momentum SGD where we assign an individual importance weight to each sample in the mini-batch. The individual-level weight of sampled data is systematically proportional to the exponential of a scaled loss value of the data, where the scaling factor is interpreted as the regularization parameter in the framework of distributionally robust optimization (DRO). Depending on whether the scaling factor is positive or negative, ABSGD is guaranteed to converge to a stationary point of an information-regularized min-max or min-min DRO problem, respectively. Compared with existing class-level weighting schemes, our method can capture the diversity between individual examples within each class. Compared with existing individual-level weighting methods using meta-learning that require three backward propagations for computing mini-batch stochastic gradients, our method is more efficient with only one backward propagation at each iteration as in standard deep learning methods. ABSGD is flexible enough to combine with other robust losses without any additional cost. Our empirical studies on several benchmark datasets demonstrate the effectiveness of the proposed method. \footnote{Code is available at:\url{https://github.com/qiqi-helloworld/ABSGD/}}
The average shapes of flexible mesogenic molecules On the choice of reference frame

Abstract The flexibility of most mesogenic molecules creates a problem when we wish to consider their shape, especially as the anisotropy in this is often invoked to explain liquid-crystalline behaviour. It has been suggested that images constructed from the average positions of atoms in the molecule with superimposed ellipsoids representing the mean square displacements of the atoms could present one solution to the problem. However, the nature of the image necessarily depends on the choice of the reference frame set in the molecule. Here we show how the images change as the atoms used to define the reference frame move along a single heptyl chain attached to a mesogenic core in both the isotropic and nematic phases. The appearance of the images and how they change with phase and reference frame are discussed in general terms. In addition, the possible relationship between the images and the order parameters for axes set in the same reference frame as that used to construct the image is explored. The sig.
SUBNANOSECOND MAGNETIZATION DYNAMICS MEASURED BY THE SECOND-HARMONIC MAGNETO-OPTIC KERR EFFECT

We have measured the in-plane magnetization dynamics of Ni81Fe19 films using the surface- and interface-sensitive second-harmonic magneto-optic Kerr effect. The dynamical magnetization was measured on patterned Ni81Fe19 stripes as a function of an in-plane magnetic field applied parallel to the anisotropy axis. The excitation sources were 100 ps risetime magnetic field impulses and steps. The minimum magnetization switching times were <300 ps, and precessional free-induction decay was observed. The dynamics for both impulse and step excitation are fitted to the Landau–Lifshitz equation, yielding values for the anisotropy field, gyroscopic splitting factor, and damping. The local surface precessional frequency and anisotropy are different from the average bulk values, demonstrating that this technique possesses the necessary sensitivity to detect variations in localized surface and interface dynamics.