Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Projected Newton-like Method for Precision Matrix Estimation under Total Positivity

We study the problem of estimating precision matrices in Gaussian distributions that are multivariate totally positive of order two ($\mathrm{MTP}_2$). The precision matrix in such a distribution is an M-matrix. This problem can be formulated as a sign-constrained log-determinant program. Current algorithms are designed using the block coordinate descent method or the proximal point algorithm, which becomes computationally challenging in high-dimensional cases due to the requirement to solve numerous nonnegative quadratic programs or large-scale linear systems. To address this issue, we propose a novel algorithm based on the two-metric projection method, incorporating a carefully designed search direction and variable partitioning scheme. Our algorithm substantially reduces computational complexity, and its theoretical convergence is established. Experimental results on synthetic and real-world datasets demonstrate that our proposed algorithm provides a significant improvement in computational efficiency compared to the state-of-the-art methods.
A Two Martian Years Survey of Water Ice Clouds on Mars With ACS Onboard TGO

The middle infrared (MIR) channel of the atmospheric chemistry suite (ACS) instrument onboard the ExoMars Trace Gas Orbiter ESA‐Roscosmos mission has performed Solar occultation measurements of the Martian atmosphere in the 2.3–4.2 μm spectral range since March 2018, which now covers two Martian years (MY). We use the methodology previously developed for the study of the MY 34 global dust storm (GDS) (Stcherbinine et al., 2020, https://doi.org/10.1029/2019je006300) to monitor the properties (effective radii, extinction, and altitude) of the Martian water ice clouds over the first two Martian years covered by ACS‐MIR. The observations encompass the period Ls = 163° in MY 34 to Ls = 181° in MY 36. We determine that the typical altitude of the clouds varies by 20–40 km between the summer and winter, with a maximum extension up to 80 km during summer in the midlatitudes. Similarly, we also note that for a limited temporal range, the altitude of the clouds also varies by 20–40 km between the polar regions and the midlatitudes. We also compare observations acquired during the MY 34 GDS to observations from the same period in MY 35, using the latter as a reference to characterize the effects of this GDS on the clouds' properties. In addition, we compare our retrievals with the predictions of the Mars planetary climate model, which shows a reasonable agreement overall for the altitude of the clouds, although the model usually predicts lower altitudes for the top of the clouds.
How good are deep models in understanding the generated images?

My goal in this paper is twofold: to study how well deep models can understand the images generated by DALL-E 2 and Midjourney, and to quantitatively evaluate these generative models. Two sets of generated images are collected for object recognition and visual question answering (VQA) tasks. On object recognition, the best model, out of 10 state-of-the-art object recognition models, achieves about 60\% and 80\% top-1 and top-5 accuracy, respectively. These numbers are much lower than the best accuracy on the ImageNet dataset (91\% and 99\%). On VQA, the OFA model scores 77.3\% on answering 241 binary questions across 50 images. This model scores 94.7\% on the binary VQA-v2 dataset. Humans are able to recognize the generated images and answer questions on them easily. We conclude that a) deep models struggle to understand the generated content, and may do better after fine-tuning, and b) there is a large distribution shift between the generated images and the real photographs. The distribution shift appears to be category-dependent. Data is available at: https://drive.google. com/file/d/1n2nCiaXtYJRRF2R73-LNE3zggeU_HeH0/view? usp=sharing.
Solving Royal Game of Ur Using Reinforcement Learning

Reinforcement Learning has recently surfaced as a very powerful tool to solve complex problems in the domain of board games, wherein an agent is generally required to learn complex strategies and moves based on its own experiences and rewards received. While RL has outper- formed existing state-of-the-art methods used for playing simple video games and popular board games, it is yet to demonstrate its capability on ancient games. Here, we solve one such problem, where we train our agents using different methods namely Monte Carlo, Q-learning and Expected Sarsa to learn optimal policy to play the strategic Royal Game of Ur. The state space for our game is complex and large, but our agents show promising results at playing the game and learning important strategic moves. Although it is hard to conclude that when trained with limited resources which algorithm performs better overall, but Expected Sarsa shows promising results when it comes to fastest learning.
Transductive Decoupled Variational Inference for Few-Shot Classification

The versatility to learn from a handful of samples is the hallmark of human intelligence. Few-shot learning is an endeavour to transcend this capability down to machines. Inspired by the promise and power of probabilistic deep learning, we propose a novel variational inference network for few-shot classification (coined as TRIDENT) to decouple the representation of an image into semantic and label latent variables, and simultaneously infer them in an intertwined fashion. To induce task-awareness, as part of the inference mechanics of TRIDENT, we exploit information across both query and support images of a few-shot task using a novel built-in attention-based transductive feature extraction module (we call AttFEX). Our extensive experimental results corroborate the efficacy of TRIDENT and demonstrate that, using the simplest of backbones, it sets a new state-of-the-art in the most commonly adopted datasets miniImageNet and tieredImageNet (offering up to 4% and 5% improvements, respectively), as well as for the recent challenging cross-domain miniImagenet -->CUB scenario offering a significant margin (up to 20% improvement) beyond the best existing cross-domain baselines. Code and experimentation can be found in our GitHub repository: https://github.com/anujinho/trident
Parameter Sensitivity of Deep-Feature based Evaluation Metrics for Audio Textures

Standard evaluation metrics such as the Inception score and Fr\'echet Audio Distance provide a general audio quality distance metric between the synthesized audio and reference clean audio. However, the sensitivity of these metrics to variations in the statistical parameters that define an audio texture is not well studied. In this work, we provide a systematic study of the sensitivity of some of the existing audio quality evaluation metrics to parameter variations in audio textures. Furthermore, we also study three more potentially parameter-sensitive metrics for audio texture synthesis, (a) a Gram matrix based distance, (b) an Accumulated Gram metric using a summarized version of the Gram matrices, and (c) a cochlear-model based statistical features metric. These metrics use deep features that summarize the statistics of any given audio texture, thus being inherently sensitive to variations in the statistical parameters that define an audio texture. We study and evaluate the sensitivity of existing standard metrics as well as Gram matrix and cochlear-model based metrics to control-parameter variations in audio textures across a wide range of texture and parameter types, and validate with subjective evaluation. We find that each of the metrics is sensitive to different sets of texture-parameter types. This is the first step towards investigating objective metrics for assessing parameter sensitivity in audio textures.
Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey

Deep learning technologies have demonstrated remarkable effectiveness in a wide range of tasks, and deep learning holds the potential to advance a multitude of applications, including in edge computing, where deep models are deployed on edge devices to enable instant data processing and response. A key challenge is that while the application of deep models often incurs substantial memory and computational costs, edge devices typically offer only very limited storage and computational capabilities that may vary substantially across devices. These characteristics make it difficult to build deep learning solutions that unleash the potential of edge devices while complying with their constraints. A promising approach to addressing this challenge is to automate the design of effective deep learning models that are lightweight, require only a little storage, and incur only low computational overheads. This survey offers comprehensive coverage of studies of design automation techniques for deep learning models targeting edge computing. It offers an overview and comparison of key metrics that are used commonly to quantify the proficiency of models in terms of effectiveness, lightness, and computational costs. The survey then proceeds to cover three categories of the state-of-the-art of deep model design automation techniques: automated neural architecture search, automated model compression, and joint automated design and compression. Finally, the survey covers open issues and directions for future research.
Vox-Surf: Voxel-Based Implicit Surface Representation

Virtual content creation and interaction play an important role in modern 3D applications. Recovering detailed 3D models from real scenes can significantly expand the scope of its applications and has been studied for decades in the computer vision and computer graphics community. In this work, we propose Vox-Surf, a voxel-based implicit surface representation. Our Vox-Surf divides the space into finite sparse voxels, where each voxel is a basic geometry unit that stores geometry and appearance information on its corner vertices. Due to the sparsity inherited from the voxel representation, Vox-Surf is suitable for almost any scene and can be easily trained end-to-end from multiple view images. We utilize a progressive training process to gradually cull out empty voxels and keep only valid voxels for further optimization, which greatly reduces the number of sample points and improves inference speed. Experiments show that our Vox-Surf representation can learn fine surface details and accurate colors with less memory and faster rendering than previous methods. The resulting fine voxels can also be considered as the bounding volumes for collision detection, which is useful in 3D interactions. We also show the potential application of Vox-Surf in scene editing and augmented reality. The source code is publicly available at https://github.com/zju3dv/Vox-Surf.
Locations and Morphologies of Jellyfish Galaxies in A2744 and A370

We present a study of the orbits, environments, and morphologies of 13 ram-pressure stripped galaxies in the massive, intermediate redshift (z ∼ 0.3−0.4) galaxy clusters A2744 and A370, using MUSE integral-field spectroscopy and Hubble Space Telescope imaging from the Frontier Fields Program. We compare different measures of the locations and morphologies of the stripped sample with a sample of six post-starburst galaxies identified within the same clusters, as well as the general cluster population. We calculate the phase-space locations of all cluster galaxies and carry out a substructure analysis, finding that the ram-pressure stripped galaxies in A370 are not associated with any substructures, but are likely isolated infalling galaxies. In contrast, the ram-pressure stripped galaxies in A2744 are strictly located within a high-velocity substructure, moving through a region of dense X-ray emitting gas. We conclude that their ram-pressure interactions are likely to be the direct result of the merger between two components of the cluster. Finally, we study the morphologies of the stripped and post-starburst galaxies, using numerical measures to quantify the level of visual disturbances. We explore any morphological deviations of these galaxies from the cluster population, particularly the weaker cases that have been confirmed via the presence of ionized gas tails to be undergoing ram-pressure stripping, but are not strongly visually disturbed in the broadband data. We find that the stripped sample galaxies are generally divergent from the general cluster sample, with post-starburst galaxies being intermediary in morphology between stripped galaxies and red passive cluster members.
Local electron correlation effects on the fermiology of the weak itinerant ferromagnet ZrZn2

The Fermi surface topology plays an important role in the macroscopic properties of metals. It can be particularly sensitive to electron correlation, which appears to be especially significant for the weak itinerant ferromagnet ZrZn2. Here, we look at the differences in the predicted Fermi surface sheets of this metallic compound in its paramagnetic phase for both density functional theory (DFT) and the combination of DFT with dynamical mean field theory (DFT + DMFT). The theoretical spectral functions evaluated at the Fermi level were used along with calculations of the electron–positron momentum density (also known as the two-photon momentum density) in k-space to provide insights into the origin of certain features of the Fermi surface topology. We compare this two photon momentum density to that extracted from the positron annihilation experimental data (2004 Phys. Rev. Lett. 92 107003). The DFT + DMFT densities are in better agreement with the experiment than the DFT, particularly with regard to the flat bands around the L and W high symmetry points. The experimental neck around L, which relates to a van Hove singularity, is present in DFT + DMFT but not in the DFT. We find that these flat bands, and as such the Fermi surface topology, are sensitive to the many body electron correlation description, and show that the positron annihilation technique is able to probe this. This description is significant for the observed behavior such as the Lifshiftz transition around the quantum critical point.
Distance-Aware Occlusion Detection With Focused Attention

For humans, understanding the relationships between objects using visual signals is intuitive. For artificial intelligence, however, this task remains challenging. Researchers have made significant progress studying semantic relationship detection, such as human-object interaction detection and visual relationship detection. We take the study of visual relationships a step further from semantic to geometric. In specific, we predict relative occlusion and relative distance relationships. However, detecting these relationships from a single image is challenging. Enforcing focused attention to task-specific regions plays a critical role in successfully detecting these relationships. In this work, (1) we propose a novel three-decoder architecture as the infrastructure for focused attention; 2) we use the generalized intersection box prediction task to effectively guide our model to focus on occlusion-specific regions; 3) our model achieves a new state-of-the-art performance on distance-aware relationship detection. Specifically, our model increases the distance F1-score from 33.8% to 38.6% and boosts the occlusion F1-score from 34.4% to 41.2%. Our code is publicly available.
The Effect of Course Time Compression on Academic Performance in Construction Surveying

ABSTRACT The goal of this study was to explore the differences in academic performance between students who took the full 16-week fall-spring semester courses and students that took the compressed 10-day mini-mester course in construction surveying. There were no instructional design differences between semester courses with respect to instructor, contact hours, instructional design, delivery method, materials, activities, applications, quizzes, exams or grading. This study found that students taking the compressed mini-mester course had significantly better achievement on the laboratory activities and the reading quizzes than that of the students taking the full-semester courses. However, the students taking the full-semester courses significantly exhibit better achievement on the two course exams than did the students taking the compressed mini-mester course. No significant difference was found on total course points. Student undergraduate Grade Point Average (GPA), Scholastic Aptitude Test (SAT) and Test of Logical Thinking (TOLT) scores were also found to be good predictors of course achievement.
Higher-order finite element methods for the nonlinear Helmholtz equation

In this work, we analyze the finite element method with arbitrary but fixed polynomial degree for the nonlinear Helmholtz equation with impedance boundary conditions. We show well-posedness and error estimates of the finite element solution under a resolution condition between the wave number k, the mesh size h and the polynomial degree p of the form “$$k(kh)^{p+1}$$ k ( k h ) p + 1 sufficiently small” and a so-called smallness of the data assumption. For the latter, we prove that the logarithmic dependence in h from the case $$p=1$$ p = 1 in Wu and Zou (SIAM J Numer Anal 56(3):1338–1359, 2018) can be removed for $$p\ge 2$$ p ≥ 2 . We show convergence of two different fixed-point iteration schemes. Numerical experiments illustrate our theoretical results and compare the robustness of the iteration schemes with respect to the size of the nonlinearity and the right-hand side data.
META-CODE: Community Detection via Exploratory Learning in Topologically Unknown Networks

The discovery of community structures in social networks has gained considerable attention as a fundamental problem for various network analysis tasks. However, due to privacy concerns or access restrictions, the network structure is often unknown, thereby rendering established community detection approaches ineffective without costly data acquisition. To tackle this challenge, we present META-CODE, a novel end-to-end solution for detecting overlapping communities in networks with unknown topology via exploratory learning aided by easy-to-collect node metadata. Specifically, META-CODE consists of three steps: 1) initial network inference, 2) node-level community-affiliation embedding based on graph neural networks (GNNs) trained by our new reconstruction loss, and 3) network exploration via community-affiliation-based node queries, where Steps 2 and 3 are performed iteratively. Experimental results demonstrate that META-CODE exhibits (a) superiority over benchmark methods for overlapping community detection, (b) the effectiveness of our training model, and (c) fast network exploration.
Anisotropic inflation in non-minimal kinetic coupling model

We study anisotropic inflation in non-minimal derivative coupling model where the scalar field non-minimally coupled to the U(1) gauge fields and derivative of the scalar field non-minimally coupled to the Einstein tensor. Within the framework we find power-law anisotropic solutions in this model when both the inflaton potential and the gauge kinetic function are power-law type in the high friction regime. We show the ratio of anisotropy to the expansion rate is nearly constant, small and proportional to the slow-roll parameters of the theory. As a demonstration, we consider numerically calculation of the model to show that the behavior of anisotropy by changing the parameters of the model for quadratic inflationary potential. There is anisotropic attractor solution for a wide range of values of the model parameters. We show both numerically and analytically that there are two phases of inflation, similar to those an anisotropic inflation in minimal coupling model, isotropic and anisotropic phase. We can change the number of e-folds corresponding to each phase of slow roll inflation by changing the gauge coupling constant or non-minimal derivative coupling constant. There are the best agreement with the numerically solutions and analytically solutions in this investigation.
Flat Multi-modal Interaction Transformer for Named Entity Recognition

Multi-modal named entity recognition (MNER) aims at identifying entity spans and recognizing their categories in social media posts with the aid of images. However, in dominant MNER approaches, the interaction of different modalities is usually carried out through the alternation of self-attention and cross-attention or over-reliance on the gating machine, which results in imprecise and biased correspondence between fine-grained semantic units of text and image. To address this issue, we propose a Flat Multi-modal Interaction Transformer (FMIT) for MNER. Specifically, we first utilize noun phrases in sentences and general domain words to obtain visual cues. Then, we transform the fine-grained semantic representation of the vision and text into a unified lattice structure and design a novel relative position encoding to match different modalities in Transformer. Meanwhile, we propose to leverage entity boundary detection as an auxiliary task to alleviate visual bias. Experiments show that our methods achieve the new state-of-the-art performance on two benchmark datasets.
Interaction Modeling with Multiplex Attention

Modeling multi-agent systems requires understanding how agents interact. Such systems are often difficult to model because they can involve a variety of types of interactions that layer together to drive rich social behavioral dynamics. Here we introduce a method for accurately modeling multi-agent systems. We present Interaction Modeling with Multiplex Attention (IMMA), a forward prediction model that uses a multiplex latent graph to represent multiple independent types of interactions and attention to account for relations of different strengths. We also introduce Progressive Layer Training, a training strategy for this architecture. We show that our approach outperforms state-of-the-art models in trajectory forecasting and relation inference, spanning three multi-agent scenarios: social navigation, cooperative task achievement, and team sports. We further demonstrate that our approach can improve zero-shot generalization and allows us to probe how different interactions impact agent behavior.
What can we learn from the electromagnetic properties of hidden-charm molecular pentaquarks with single strangeness?

Inspired by the observation of the $P_{cs}(4459)$ and $P_{\psi s}^{\Lambda}(4338)$, we systematically investigate the magnetic moments, the transition magnetic moments, and the radiative decay behaviors of the $S$-wave isoscalar $\Xi_c^{(\prime)}\bar D^{(*)}$ molecular pentaquark states in this work. Our quantitative investigation shows that their electromagnetic properties can provide important hint to decode the inner structure of these discussed isoscalar $\Xi_c^{(\prime)}\bar D^{*}$ molecular pentaquarks. As the potential research issue, we suggest future experiment to focus on the electromagnetic properties of these isoscalar $\Xi_c^{(\prime)}\bar D^{(*)}$ molecular pentaquarks, which are a typical hidden-charm molecular pentaquark system with single strangeness.
Two-Dimensional Tunable Solid-State Random laser in the Visible

A two-dimensional multimode random laser emitting in the visible frequency range of the optical spectrum is proposed, designed and investigated, in which optical feedback is provided by randomly-distributed air holes embedded in dye-doped polymer ﬁlm. Dependence of lasing threshold on scatterer density and pump spot size has been investigated. Furthermore, peak of the gain curve shows systematic spectral blue shift with increasing scatterer density, as well as pump spot size. Such a 2D random laser provides with a compact on-chip tunable laser source and a unique platform to explore non-Hermitian photonics in the visible.