Miracle-dz/UniFAR-FTUs huggingface dataset:
3D reconstruction from spherical images: A review of techniques, applications, and prospects

3D reconstruction plays an increasingly important role in modern photogrammetric systems. Conventional satellite or aerial-based remote sensing (RS) platforms can provide the necessary data sources for the 3D reconstruction of large-scale landforms and cities. Even with low-altitude UAVs (Unmanned Aerial Vehicles), 3D reconstruction in complicated situations, such as urban canyons and indoor scenes, is challenging due to frequent tracking failures between camera frames and high data collection costs. Recently, spherical images have been extensively used due to the capability of recording surrounding environments from one camera exposure. In contrast to perspective images with limited FOV (Field of View), spherical images can cover the whole scene with full horizontal and vertical FOV and facilitate camera tracking and data acquisition in these complex scenes. With the rapid evolution and extensive use of professional and consumer-grade spherical cameras, spherical images show great potential for the 3D modeling of urban and indoor scenes. Classical 3D reconstruction pipelines, however, cannot be directly used for spherical images. Besides, there exist few software packages that are designed for the 3D reconstruction of spherical images. As a result, this research provides a thorough survey of the state-of-the-art for 3D reconstruction of spherical images in terms of data acquisition, feature detection and matching, image orientation, and dense matching as well as presenting promising applications and discussing potential prospects. We anticipate that this study offers insightful clues to direct future research.
Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples

Recently, Diffusion Models (DMs) boost a wave in AI for Art yet raise new copyright concerns, where infringers benefit from using unauthorized paintings to train DMs to generate novel paintings in a similar style. To address these emerging copyright violations, in this paper, we are the first to explore and propose to utilize adversarial examples for DMs to protect human-created artworks. Specifically, we first build a theoretical framework to define and evaluate the adversarial examples for DMs. Then, based on this framework, we design a novel algorithm, named AdvDM, which exploits a Monte-Carlo estimation of adversarial examples for DMs by optimizing upon different latent variables sampled from the reverse process of DMs. Extensive experiments show that the generated adversarial examples can effectively hinder DMs from extracting their features. Therefore, our method can be a powerful tool for human artists to protect their copyright against infringers equipped with DM-based AI-for-Art applications. The code of our method is available on GitHub: https://github.com/mist-project/mist.git.
Beating binary powering for polynomial matrices

The Nth power of a polynomial matrix of fixed size and degree can be computed by binary powering as fast as multiplying two polynomials of linear degree in N. When Fast Fourier Transform (FFT) is available, the resulting complexity is softly linear in N, i.e. linear in N with extra logarithmic factors. We show that it is possible to beat binary powering, by an algorithm whose complexity is purely linear in N, even in absence of FFT. The key result making this improvement possible is that the entries of the Nth power of a polynomial matrix satisfy linear differential equations with polynomial coefficients whose orders and degrees are independent of N. Similar algorithms are proposed for two related problems: computing the Nth term of a C-finite sequence of polynomials, and modular exponentiation to the power N for bivariate polynomials.
The 2-Set-enriched Grothendieck construction and the lax normal conical 2-limits

We study in detail an extended version of the Grothendieck construction, that corresponds to a 2-categorical generalization of the construction of the category of elements. After refining the universal property of the lax comma object to suit the lax 3-categorical ambient in which we need to work, we describe how we can think of this extended Grothendieck construction as the archetypal 3-dimensional classification process, in the sense of a would-be 3-dimensional elementary topos theory. We show a new, more intuitive and elementary proof of the reduction of the weighted 2-limits to essentially conical ones, and use a generalized version of the latter to propose an original definition of pointwise Kan extension in the weakly enriched context that hosts the studied Grothendieck construction. We then present a pointwise Kan extension result, and we conclude by proving that a pointwise Kan extension as defined here is always a weak one as well, after showing a lax but not too lax generalization of the parametrized Yoneda lemma.
Global and Preference-based Optimization with Mixed Variables using Piecewise Affine Surrogates

Optimization problems involving mixed variables, i.e., variables of numerical and categorical nature, can be challenging to solve, especially in the presence of complex constraints. Moreover, when the objective function is the result of a complicated simulation or experiment, it may be expensive to evaluate. This paper proposes a novel surrogate-based global optimization algorithm to solve linearly constrained mixed-variable problems up to medium-large size (around 100 variables after encoding and 20 constraints) based on constructing a piecewise affine surrogate of the objective function over feasible samples. We introduce two types of exploration functions to efficiently search the feasible domain via mixed-integer linear programming solvers. We also provide a preference-based version of the algorithm, which can be used when only pairwise comparisons between samples can be acquired while the underlying objective function to minimize remains unquantified. The two algorithms are tested on mixed-variable benchmark problems with and without constraints. The results show that, within a small number of acquisitions, the proposed algorithms can often achieve better or comparable results than other existing methods.
Aperiodicity in Quantum Wang Tilings

By reformulating the Wang tiles formalism with tensors, we propose a natural generalization to the probabilistic and quantum setting. In this new framework, we introduce notions of tilings and periodicity directly extending their classical counterparts. In the one dimensional case, we recover the decidability of the generalized domino problem by linking it to the trace characterization of nilpotent matrices. In the two-dimensional case, we provide extension of weak and strong aperiodicity respectively and show the equivalence of those generalized notions, extending the well known equivalence in the classical case. We also exhibit a quantum tile set being aperiodic while its underlying classical tile set is not, proving that quantum interference can suppress periodic patterns.
Neonatal Face and Facial Landmark Detection from Video Recordings

This paper explores automated face and facial landmark detection of neonates, which is an important first step in many video-based neonatal health applications, such as vital sign estimation, pain assessment, sleep-wake classification, and jaundice detection. Utilising three publicly available datasets of neonates in the clinical environment, 366 images (258 subjects) and 89 (66 subjects) were annotated for training and testing, respectively. Transfer learning was applied to two YOLO-based models, with input training images augmented with random horizontal flipping, photo-metric colour distortion, translation and scaling during each training epoch. Additionally, the re-orientation of input images and fusion of trained deep learning models was explored. Our proposed model based on YOLOv7Face outperformed existing methods with a mean average precision of 84.8% for face detection, and a normalised mean error of 0.072 for facial landmark detection. Overall, this will assist in the development of fully automated neonatal health assessment algorithms.Clinical relevance— Accurate face and facial landmark detection provides an automated and non-contact option to assist in video-based neonatal health applications.
A method for evaluation of aerodynamic lift and drag based on statistical mechanics

Despite intensive applications of Navier–Stokes equations in computational-fluid-dynamics (CFD) to understand aerodynamics, fundamental questions remain open since the statistical nature of discrete air molecules with random thermal motion is not considered in CFD. Here, we introduce an approach based on statistical mechanics, termed as “volume-element” method, for numerical evaluation of aerodynamic lift and drag. Pressure and friction as a function of angle of attack have been obtained for canonical flat-plate airfoils, and the method is applicable to convex-shape airfoils directly and viable for concave-shape airfoils if combined with Monte Carlo simulations. This approach opens a door not only for aerodynamic applications, but also for further applications in Boson or Fermi gases.
The Consequences of Modifying Fundamental Cosmological Theories

In this work, we examine alternatives to three fundamental cosmological theories: extended Press-Schechter merger theory, general relativity, and single-field inflation, and derive their observational consequences. The extended Press-Schechter merger rate for dark matter haloes is mathematically inconsistent and double-valued, and yet it has been widely applied in cosmology. One such application is the merger rate of supermassive black holes, and we show that the two predictions for this rate from extended Press-Schechter merger theory are nearly equal. We then compare the supermassive-black-hole merger rate derived from the extended Press-Schechter merger formalism to the rate derived from an alternate theory, in which halo merger rates are obtained by inverting the coagulation equation. Next, we show how two modifications to general relativity may be tested inside the Solar System. First we consider f(R) gravity, which was proposed to explain late-time cosmic acceleration. We find that several forms of f(R) gravity are inconsistent with observations, and we establish a set of criteria that determines whether or not a given form of f(R) gravity is ruled out by Solar System gravitational tests. Second, we study Chern-Simons gravity: a parity-violating theory inspired by string theory. We find that Chern-Simons gravity predicts orbital precessions that are different from those predicted by general relativity, and we use the motion of satellites to constrain the Chern-Simons coupling parameter. Finally, we consider an alternative to single-field inflation; in the curvaton scenario, the inflaton does not generate all of the primordial perturbations. Using this theory, we propose an origin for the hemispherical power asymmetry that has been observed in the cosmic microwave background on large angular scales. While this asymmetry cannot be produced by a superhorizon fluctuation in the inflaton field, it may be generated by a superhorizon fluctuation in the curvaton field. A superhorizon fluctuation would also induce large-scale anisotropies in the cosmic microwave background; we analyze this effect and prove that our model is consistent with observations. We also show how the power asymmetry may be suppressed on smaller scales if the curvaton creates isocurvature perturbations when it decays.
Data Augmentation for Robust Character Detection in Fantasy Novels

Named Entity Recognition (NER) is a low-level task often used as a foundation for solving higher level NLP problems. In the context of character detection in novels, NER false negatives can be an issue as they possibly imply missing certain characters or relationships completely. In this article, we demonstrate that applying a straightforward data augmentation technique allows training a model achieving higher recall, at the cost of a certain amount of precision regarding ambiguous entities. We show that this decrease in precision can be mitigated by giving the model more local context, which resolves some of the ambiguities.
On Sampling with Approximate Transport Maps

Transport maps can ease the sampling of distributions with non-trivial geometries by transforming them into distributions that are easier to handle. The potential of this approach has risen with the development of Normalizing Flows (NF) which are maps parameterized with deep neural networks trained to push a reference distribution towards a target. NF-enhanced samplers recently proposed blend (Markov chain) Monte Carlo methods with either (i) proposal draws from the flow or (ii) a flow-based reparametrization. In both cases, the quality of the learned transport conditions performance. The present work clarifies for the first time the relative strengths and weaknesses of these two approaches. Our study concludes that multimodal targets can be reliably handled with flow-based proposals up to moderately high dimensions. In contrast, methods relying on reparametrization struggle with multimodality but are more robust otherwise in high-dimensional settings and under poor training. To further illustrate the influence of target-proposal adequacy, we also derive a new quantitative bound for the mixing time of the Independent Metropolis-Hastings sampler.
Self-Supervised Node Representation Learning via Node-to-Neighbourhood Alignment

Self-supervised node representation learning aims to learn node representations from unlabelled graphs that rival the supervised counterparts. The key towards learning informative node representations lies in how to effectively gain contextual information from the graph structure. In this work, we present simple-yet-effective self-supervised node representation learning via aligning the hidden representations of nodes and their neighbourhood. Our first idea achieves such node-to-neighbourhood alignment by directly maximizing the mutual information between their representations, which, we prove theoretically, plays the role of graph smoothing. Our framework is optimized via a surrogate contrastive loss and a Topology-Aware Positive Sampling (TAPS) strategy is proposed to sample positives by considering the structural dependencies between nodes, which enables offline positive selection. Considering the excessive memory overheads of contrastive learning, we further propose a negative-free solution, where the main contribution is a Graph Signal Decorrelation (GSD) constraint to avoid representation collapse and over-smoothing. The GSD constraint unifies some of the existing constraints and can be used to derive new implementations to combat representation collapse. By applying our methods on top of simple MLP-based node representation encoders, we learn node representations that achieve promising node classification performance on a set of graph-structured datasets from small- to large-scale.
RelightableHands: Efficient Neural Relighting of Articulated Hand Models

We present the first neural relighting approach for rendering high-fidelity personalized hands that can be animated in real-time under novel illumination. Our approach adopts a teacher-student framework, where the teacher learns appearance under a single point light from images captured in a light-stage, allowing us to synthesize hands in arbitrary illuminations but with heavy compute. Using images rendered by the teacher model as training data, an efficient student model directly predicts appearance under natural illuminations in real-time. To achieve generalization, we condition the student model with physics-inspired illumination features such as visibility, diffuse shading, and specular reflections computed on a coarse proxy geometry, maintaining a small computational overhead. Our key insight is that these features have strong correlation with subsequent global light transport effects, which proves sufficient as conditioning data for the neural relighting network. Moreover, in contrast to bottleneck illumination conditioning, these features are spatially aligned based on underlying geometry, leading to better generalization to unseen illuminations and poses. In our experiments, we demonstrate the efficacy of our illumination feature representations, outperforming baseline approaches. We also show that our approach can photorealistically relight two interacting hands at real-time speeds. https://sh8.io/#/relightable_hands
Existence of a small cover over a 15-colorable simple 4-polytope

The chromatic number for properly colouring the facets of a combinatorial simple $n$-polytope $P^n$ that is the orbit space of a quasitoric manifold satisfies the inequality $n\leq P^n\leq 2^n-1$. The inequality is sharp for $n=2$ but not for $n=3$ due to the Four Color theorem. In this note, we construct a simple 4-polytope admitting a characteristic map whose chromatic number equals $15$ and deduce that the predicted upper bound is attained for $n=4$. Analogues results are verified for the case of oriented small covers in dimensions $4$ and $5$.
Shared Information-Based Safe And Efficient Behavior Planning For Connected Autonomous Vehicles

The recent advancements in wireless technology enable connected autonomous vehicles (CAVs) to gather data via vehicle-to-vehicle (V2V) communication, such as processed LIDAR and camera data from other vehicles. In this work, we design an integrated information sharing and safe multi-agent reinforcement learning (MARL) framework for CAVs, to take advantage of the extra information when making decisions to improve traffic efficiency and safety. We first use weight pruned convolutional neural networks (CNN) to process the raw image and point cloud LIDAR data locally at each autonomous vehicle, and share CNN-output data with neighboring CAVs. We then design a safe actor-critic algorithm that utilizes both a vehicle's local observation and the information received via V2V communication to explore an efficient behavior planning policy with safety guarantees. Using the CARLA simulator for experiments, we show that our approach improves the CAV system's efficiency in terms of average velocity and comfort under different CAV ratios and different traffic densities. We also show that our approach avoids the execution of unsafe actions and always maintains a safe distance from other vehicles. We construct an obstacle-at-corner scenario to show that the shared vision can help CAVs to observe obstacles earlier and take action to avoid traffic jams.
Formation and emergent dynamics of spatially organized microbial systems

Spatial organization is the norm rather than the exception in the microbial world. While the study of microbial physiology has been dominated by studies in well-mixed cultures, there is now increasing interest in understanding the role of spatial organization in microbial physiology, coexistence and evolution. Where studied, spatial organization has been shown to influence all three of these aspects. In this mini review and perspective article, we emphasize that the dynamics within spatially organized microbial systems (SOMS) are governed by feedbacks between local physico-chemical conditions, cell physiology and movement, and evolution. These feedbacks can give rise to emergent dynamics, which need to be studied through a combination of spatio-temporal measurements and mathematical models. We highlight the initial formation of SOMS and their emergent dynamics as two open areas of investigation for future studies. These studies will benefit from the development of model systems that can mimic natural ones in terms of species composition and spatial structure.
Global stability, sensitivity and passive control of low-Reynolds-number flows around NACA 4412 swept wings

Abstract The stability and sensitivity of two- and three-dimensional global modes developing on steady spanwise-homogeneous laminar separated flows around NACA 4412 swept wings are numerically investigated for different Reynolds numbers ${\textit {Re}}$ and angles of attack $\alpha$. The wake dynamics is driven by the two-dimensional von Kármán mode whose emergence threshold in the $\alpha \unicode{x2013}{\textit {Re}}$ plane is computed with that of the three-dimensional centrifugal mode. At the critical Reynolds number, the Strouhal number, the streamwise wavenumber of the von Kármán mode and the spanwise wavenumber of the leading three-dimensional centrifugal mode scale as a power law of $\alpha$. The introduction of a sweep angle attenuates the growth of all unstable modes and entails a Doppler effect in the leading modes’ dynamics and a shift towards non-zero frequencies of the three-dimensional centrifugal modes. These are found to be non-dispersive as opposed to the von Kármán modes. The sensitivity of the leading global modes is investigated in the vicinity of the critical conditions through adjoint-based methods. The growth-rate sensitivity map displays a region on the suction side of the wing, wherein a streamwise-oriented force has a net stabilising effect, comparable to what could have been obtained inside the recirculation bubble. In agreement with the predictions of the sensitivity analysis, a spanwise-homogeneous force suppresses the Hopf bifurcation and stabilises the entire branch of von Kármán modes. In the limit of small amplitudes, passive control via spanwise-wavy forcing produces a stabilising effect similar to that of a spanwise-homogeneous control and is more effective than localised spherical forces.
Une exploration numérique des performances de l'échantillonage compressé

Cet article explore numeriquement l'efficacite de la minimisation $\lun$ pour la restauration de signaux parcimonieux depuis des mesures compressibles, dans le cas sans bruit. Nous proposons un algorithme glouton qui calcule des vecteurs parcimonieux difficile a retrouver par minimisation $\lun$. Cet algorithme est inspire par des criteres topologiques d'identifiabilite $\lun$. Nous evaluons numeriquement l'analyse theorique sans avoir a utiliser un echantillonnage de Monte-Carlo, qui tend a evider les cas pathologiques. Ceci permet de mettre a l'epreuve les criteres d'identifiabilite exploitant des projections de polytopes et des proprietes d'isometrie restreinte.
Levocarnitine regulates the growth of angiotensin II-induced myocardial fibrosis cells via TIMP-1

Abstract This study aimed to explore the effects of tissue inhibitor of metalloproteinases‐1 (TIMP‐1) on levocarnitine (LC)-mediated regulation of angiotensin II (AngII)-induced myocardial fibrosis (MF) and its underlying mechanisms. H9C2 cells were treated with AngII for 24 h to induce fibrosis. The cells were then treated with LC or transfected with TIMP‐1-OE plasmid/si‑TIMP‐1. Cell apoptosis, viability, migration, and related gene expression were analyzed. AngII treatment significantly upregulated Axl, α-SMA, and MMP3 expression (P < 0.05) and downregulated STAT4 and TIMP1 expression (P < 0.05) relative to the control levels. After transfection, cells with TIMP-1 overexpression/knockdown were successfully established. Compared with that of the control, AngII significantly inhibited cell viability and cell migration while promoting cell apoptosis (P < 0.05). LC and TIMP-1-OE transfection further suppressed cell viability and migration induced by Ang II and upregulated apoptosis, whereas si-TIMP-1 had the opposite effect. Furthermore, LC and TIMP-1-OE transfection downregulated Axl, AT1R, α-SMA, collagen III, Bcl-2, and MMP3 expression caused by AngII and upregulated caspase 3, p53, and STAT4 expression, whereas si-TIMP-1 had the opposite effect. TIMP-1 is therefore a potential therapeutic target for delaying MF progression.