Miracle-dz/UniFAR-FTUs huggingface dataset:
Sparse 3D Reconstruction via Object-Centric Ray Sampling

We propose a novel method for 3D object reconstruction from a sparse set of views captured from a 360-degree calibrated camera rig. We represent the object surface through a hybrid model that uses both an MLP-based neural representation and a triangle mesh. A key contribution in our work is a novel object-centric sampling scheme of the neural representation, where rays are shared among all views. This efficiently concentrates and reduces the number of samples used to update the neural model at each iteration. This sampling scheme relies on the mesh representation to ensure also that samples are well-distributed along its normals. The rendering is then performed efficiently by a differentiable renderer. We demonstrate that this sampling scheme results in a more effective training of the neural representation, does not require the additional supervision of segmentation masks, yields state of the art 3D reconstructions, and works with sparse views on the Google's Scanned Objects, Tank and Temples and MVMC Car datasets. Code available at: https://github.com/llukmancerkezi/ROSTER
Enhancing the Stretchability of Two-Dimensional Materials through Kirigami: A Molecular Dynamics Study on Tungsten Disulfide

In recent years, the 'kirigami' technique has gained significant attention for creating meta-structures and meta-materials with exceptional characteristics, such as unprecedented stretchability. These properties, not typically inherent in the original materials or structures, present new opportunities for applications in stretchable electronics and photovoltaics. However, despite its scientific and practical significance, the application of kirigami patterning on a monolayer of tungsten disulfide (WS2), a van der Waals material with exceptional mechanical, electronic, and optical properties, has remained unexplored. This study utilizes molecular dynamics (MD) simulations to investigate the mechanical properties of monolayer WS2 with rectangular kirigami cuts. We find that, under tensile loading, the WS2 based kirigami structure exhibits a notable increase in tensile strain and a decrease in strength, thus demonstrating the effectiveness of the kirigami cutting technique in enhancing the stretchability of monolayer WS2. Additionally, increasing the overlap ratio enhances the stretchability of the structure, allowing for tailored high strength or high strain requirements. Furthermore, our observations reveal that increasing the density of cuts and reducing the length-to-width ratio of the kirigami nanosheet further improve the fracture strain, thereby enhancing the overall stretchability of the proposed kirigami patterned structure of WS2.
SEAL: A Framework for Systematic Evaluation of Real-World Super-Resolution

Real-world Super-Resolution (Real-SR) methods focus on dealing with diverse real-world images and have attracted increasing attention in recent years. The key idea is to use a complex and high-order degradation model to mimic real-world degradations. Although they have achieved impressive results in various scenarios, they are faced with the obstacle of evaluation. Currently, these methods are only assessed by their average performance on a small set of degradation cases randomly selected from a large space, which fails to provide a comprehensive understanding of their overall performance and often yields inconsistent and potentially misleading results. To overcome the limitation in evaluation, we propose SEAL, a framework for systematic evaluation of real-SR. In particular, we cluster the extensive degradation space to create a set of representative degradation cases, which serves as a comprehensive test set. Next, we propose a coarse-to-fine evaluation protocol to measure the distributed and relative performance of real-SR methods on the test set. The protocol incorporates two new metrics: acceptance rate (AR) and relative performance ratio (RPR), derived from acceptance and excellence lines. Under SEAL, we benchmark existing real-SR methods, obtain new observations and insights into their performance, and develop a new strong baseline. We consider SEAL as the first step towards creating a comprehensive real-SR evaluation platform, which can promote the development of real-SR. The source code is available at https://github.com/XPixelGroup/SEAL
Development of a self-consistent thermodynamically optimized database along with phase transition experiments in Ni-Mn-Ga system for magnetocaloric applications

Magnetocaloric materials have received significant attention of research community as they can minimize the use of harmful gases (CFCs, HFCs) and render eco-friendly refrigeration. Heusler alloys (Ni2MnGa) are known for their magnetocaloric effects, which make them useful as energy efficient and eco-friendly refrigerating materials. Magnetocaloric properties significantly depend on the composition of these alloys. Ni-Mn-Ga is one of the interesting Heusler systems, which exhibits magnetocaloric properties. In the present study, we performed the thermodynamic optimization of two sub binaries of the Ni-Mn-Ga system: Mn-Ga and Ni-Ga, using CALPHAD approach. A Modified Quasichemical Model (MQM) was used to describe the thermodynamic properties of the liquid solutions in both the binaries. Both the binaries were combined with Mn-Ni to develop a self-consistent thermodynamic database for Ni-Mn-Ga. In order to resolve the existing experimental discrepancies in the Mn-Ga and Ni-Ga system, few alloy compositions were prepared and analyzed using differential thermal analysis. Finally, the developed thermodynamic database was used to calculate the ternary isothermal section of the Ni-Mn-Ga (Heusler alloy) system at 1073 K with a proposed phase region for magnetocaloric applications.
STEP - Towards Structured Scene-Text Spotting

We introduce the structured scene-text spotting task, which requires a scene-text OCR system to spot text in the wild according to a query regular expression. Contrary to generic scene text OCR, structured scene-text spotting seeks to dynamically condition both scene text detection and recognition on user-provided regular expressions. To tackle this task, we propose the Structured TExt sPotter (STEP), a model that exploits the provided text structure to guide the OCR process. STEP is able to deal with regular expressions that contain spaces and it is not bound to detection at the word-level granularity. Our approach enables accurate zero-shot structured text spotting in a wide variety of real-world reading scenarios and is solely trained on publicly available data. To demonstrate the effectiveness of our approach, we introduce a new challenging test dataset that contains several types of out-of-vocabulary structured text, reflecting important reading applications of fields such as prices, dates, serial numbers, license plates etc. We demonstrate that STEP can provide specialised OCR performance on demand in all tested scenarios.
Quantum Character Theory

We develop a $\mathtt{q}$-analogue of the theory of conjugation equivariant $\mathcal D$-modules on a complex reductive group $G$. In particular, we define quantum Hotta-Kashiwara modules and compute their endomorphism algebras. We use the Schur-Weyl functor of the second author, and develop tools from the corresponding double affine Hecke algebra to study this category in the cases $G=GL_N$ and $SL_N$. Our results also have an interpretation in skein theory (explored further in a sequel paper), namely a computation of the $GL_N$ and $SL_N$-skein algebra of the 2-torus.
Fast and accurate electromagnetic modeling of non-insulated and metal-insulated REBCO magnets

REBCO high-temperature superconductors are promising for all-superconducting high-field magnets, including ultra-high field magnets. Non-insulated (NI) and metal-insulated (MI) windings are a good solution for protection against electro-thermal quench. Design and optimization requires numerical modelling of REBCO inserts for high-field magnets. Here, we detail a fast and accurate two-dimensional (2D) cross-sectional model for the electromagnetic response of NI and MI coils, which is based on the Minimum Electro Magnetic Entropy Production (MEMEP). Benchmarking with an $A-V$ formulation method on a double pancake coil shows good agreement. We also analyse a fully superconducting 32 T magnet with a REBCO insert and a low-temperature superconducing (LTS) outsert. In particular, we analyze the current density, the screening curren induced field (SCIF), and the AC loss. We have shown that metal-insulated coils enable transfer of angular current in the radial direction, and hence magnet protection, while keeping the same screening currents and AC loss of insulated coils, even at relatively high ramp rates of 1 A/s. Surprisingly, soldered coils with low resistance between turns present relatively low AC loss for over-current configuration, which might enable higher generated magnetic fields. The numerical method presented here can be applied to optimize high-field magnets regarding SCIF in MI or NI magnets. It also serves as the basis for future electro-thermal modelling and multi-physics modeling that also includes mechanical properties.
Optimal Observation-Intervention Trade-Off in Optimisation Problems with Causal Structure

We consider the problem of optimising an expensive-to-evaluate grey-box objective function, within a finite budget, where known side-information exists in the form of the causal structure between the design variables. Standard black-box optimisation ignores the causal structure, often making it inefficient and expensive. The few existing methods that consider the causal structure are myopic and do not fully accommodate the observation-intervention trade-off that emerges when estimating causal effects. In this paper, we show that the observation-intervention trade-off can be formulated as a non-myopic optimal stopping problem which permits an efficient solution. We give theoretical results detailing the structure of the optimal stopping times and demonstrate the generality of our approach by showing that it can be integrated with existing causal Bayesian optimisation algorithms. Experimental results show that our formulation can enhance existing algorithms on real and synthetic benchmarks.
Evolution of capacity of entanglement and modular entropy in harmonic chains and scalar fields

We examine the temporal evolution of the modular entropy and capacity (in particular, the fluctuation of the entanglement entropy) for systems of time-dependent oscillators coupled by a (time-dependent) parameter. Such models, through the discretization procedure, fit into field theory problems arising from quench phenomena or non-static spacetimes. First, we compare the dynamics of the modular and Renyi entropies and derive the form of the modular capacity for the single time-dependent oscillator as well as chains with bipartite decompositions. In the latter case we analyse distinguished periodicities during the evolution and the role of various boundary conditions. Next, we focus on the dynamics of the capacity (fluctuation) of entanglement. We compare the results obtained with the predictions of quasiparticles models; in particular, we obtain a theoretical value of the initial slope of the capacity for abrupt quenches. We study also continuous protocols with the frequency that vanishes at plus (and minus) infinity, including a model in which the frequency tends to the Dirac delta. All the above issues are discussed with the emphasis on the analytical methods.
Doppelgangers: Learning to Disambiguate Images of Similar Structures

We consider the visual disambiguation task of determining whether a pair of visually similar images depict the same or distinct 3D surfaces (e.g., the same or opposite sides of a symmetric building). Illusory image matches, where two images observe distinct but visually similar 3D surfaces, can be challenging for humans to differentiate, and can also lead 3D reconstruction algorithms to produce erroneous results. We propose a learning-based approach to visual disambiguation, formulating it as a binary classification task on image pairs. To that end, we introduce a new dataset for this problem, Doppelgangers, which includes image pairs of similar structures with ground truth labels. We also design a network architecture that takes the spatial distribution of local keypoints and matches as input, allowing for better reasoning about both local and global cues. Our evaluation shows that our method can distinguish illusory matches in difficult cases, and can be integrated into SfM pipelines to produce correct, disambiguated 3D reconstructions. See our project page for our code, datasets, and more results: doppelgangers-3d.github. io.
An Adaptive Spatial-Temporal Local Feature Difference Method For Infrared Small-Moving Target Detection

A significant challenge is correctly detecting tiny moving objects in a sequence of infrared (IR) photographs. we provide a brand-new technique termed spatial-temporal local feature difference (STLFD) to address this problem. In order to successfully increase the contrast between the target and the background, the approach adopted in our work comprises the application of spatial and temporal filters, followed by pixel-level adaptive background suppression (ABS). The proposed method is broken down into three different steps. Initially, the current frame image is used to gather three temporal frame images. Two feature maps are then retrieved from the data using filters from the spatial and temporal domains that were specifically created for this job. After combining the two domains, the output feature is produced. The pixel-level ABS module is further employed to successfully reduce noise interference. Utilizing a threshold allows for the creation of the segmented binary map. The experimental findings presented in this paper reveal that the suggested strategy outperforms current state-of-the-art approaches in the detection of IR tiny-moving object detection.
Near-continuous time Reinforcement Learning for continuous state-action spaces

We consider the Reinforcement Learning problem of controlling an unknown dynamical system to maximise the long-term average reward along a single trajectory. Most of the literature considers system interactions that occur in discrete time and discrete state-action spaces. Although this standpoint is suitable for games, it is often inadequate for mechanical or digital systems in which interactions occur at a high frequency, if not in continuous time, and whose state spaces are large if not inherently continuous. Perhaps the only exception is the Linear Quadratic framework for which results exist both in discrete and continuous time. However, its ability to handle continuous states comes with the drawback of a rigid dynamic and reward structure. This work aims to overcome these shortcomings by modelling interaction times with a Poisson clock of frequency $\varepsilon^{-1}$, which captures arbitrary time scales: from discrete ($\varepsilon=1$) to continuous time ($\varepsilon\downarrow0$). In addition, we consider a generic reward function and model the state dynamics according to a jump process with an arbitrary transition kernel on $\mathbb{R}^d$. We show that the celebrated optimism protocol applies when the sub-tasks (learning and planning) can be performed effectively. We tackle learning within the eluder dimension framework and propose an approximate planning method based on a diffusive limit approximation of the jump process. Overall, our algorithm enjoys a regret of order $\tilde{\mathcal{O}}(\varepsilon^{1/2} T+\sqrt{T})$. As the frequency of interactions blows up, the approximation error $\varepsilon^{1/2} T$ vanishes, showing that $\tilde{\mathcal{O}}(\sqrt{T})$ is attainable in near-continuous time.
DETERMINANTAL MODULES OVER PREPROJECTIVE ALGEBRAS AND OF DYNKIN QUIVERS

In this paper, we study extension groups of modules over a preprojective algebra using the Auslander-Reiten translation of the quiver associated with it. More precisely, based on the recent work given by Aizenbud and Lapid, we give a description of extension groups of a preprojective algebra in terms of the AR translation and nilpotent matrices. As a result, we calculate the extension group of a sort of so-called determinantal modules, which is an analogue of quantum minors in quantum coordinate rings. In particular, we give an equivalent combinatorial condition when the product of two quantum minors (up to q -power rescaling) belongs to the dual canonical basis of quantum coordinate rings in the Dynkin case. More generally, we can check the quasicommuting condition for any two quantum cluster monomials with the seeds of quantum minors.
The Interplay Between COVID-19 and Pediatric Endocrine Disorders. What have we Learned After More than Three Years of the Pandemic?

Abstract As an increased body of COVID-19 related research is now available, it becomes apparent that the effects of COVID-19 extend beyond that of the respiratory system. Among others, the endocrine system is particularly vulnerable to perturbation from the COVID-19 infection. The present scoping review summarizes the bidirectional relationship between COVID-19 and endocrine system in children and adolescents, by describing both the possible susceptibility of children and adolescents without endocrinopathies to endocrine disorders following COVID-19 infection, but also the potential susceptibility to COVID-19 infection and severe infection, or the aggravation of endocrine dysfunction in patients with pre-existing endocrine diseases. Data suggest increased obesity and diabetes rates, as well as increased severity and frequency of diabetic ketoacidosis following COVID-19 infection. Conversely, patients with diabetes and obesity may experience a more severe course of COVID-19 infection. However, in the majority of cases, children and adolescents with well-managed and regulated endocrine disorders do not appear to be at increased risk of infection or severe infection from COVID-19. Thus, adhering to the appropriate “sick day management rules”, maintaining adequate supply of medications and supplies, keeping close contact with the therapeutic team and seeking medical help without delay when needed, are the main recommendations for a safe outcome. Additional lessons learnt during the pandemic include the risk for mental health diseases caused by children’s disrupted routine due to COVID-19 related protective measures and the importance of adopting alternative communication options, such as telehealth visits, in order to ensure uninterrupted endocrine care.
Forest raptor nest selection traits in Mediterranean forest (Maamora, North Africa): conservation implications

Summary Forest raptor nest-site selection is mostly influenced by the quality of the habitat, food resources, and competition. Here, we identified common targets of trees selected as breeding sites and differences in selection traits, i.e. prey availability and intra- and interspecific competition, among Booted Eagle, Long-legged Buzzard, Black Kite, and Common Kestrel in a Mediterranean Cork Oak forest (private protected reserve of 25 km2). Using generalised linear mixed models we developed species-specific models describing nesting habitat selection. We tested the overlap in nesting habitat selection among species using environmental principal component analysis. The densities of forest raptor breeding pairs were high (3.1 pairs/km2) and the distance between occupied territories was short, strongly connected with food availability and competition. The results showed that all the species, with the exception of Common Kestrel, selected for nesting areas characterised by higher conspecific distance, highlighting the importance of conspecific competition. Booted Eagle and Black Kite selected areas with a high abundance of rabbits. The height of the nesting tree, the size and distance between surrounding trees, and the scrub cover were significant habitat characteristics for Booted Eagle and Long-legged Buzzard. Indeed, the environmental analyses showed a moderate nest site overlap between Black Kite and both Booted Eagle and Long-legged Buzzard, and a high overlap between Common Kestrel and both Booted Eagle and Long-legged Buzzard. Our study improves knowledge of the habitat requirements for nest selection and the potential competitive interactions between these raptor species in Mediterranean forests, and highlights the need for implementation of habitat management and conservation strategies.
The structure stability of negative symptoms: longitudinal network analysis of the Brief Negative Symptom Scale in people with schizophrenia

Background The structure of negative symptoms of schizophrenia is still a matter of controversy. Although a two-dimensional model (comprising the expressive deficit dimension and the motivation and pleasure dimension) has gained a large consensus, it has been questioned by recent investigations. Aims To investigate the latent structure of negative symptoms and its stability over time in people with schizophrenia using network analysis. Method Negative symptoms were assessed in 612 people with schizophrenia using the Brief Negative Symptom Scale (BNSS) at baseline and at 4-year follow-up. A network invariance analysis was conducted to investigate changes in the network structure and strength of connections between the two time points. Results The network analysis carried out at baseline and follow-up, supported by community detection analysis, indicated that the BNSS's items aggregate to form four or five distinct domains (avolition/asociality, anhedonia, blunted affect and alogia). The network invariance test indicated that the network structure remained unchanged over time (network invariance test score 0.13; P = 0.169), although its overall strength decreased (6.28 at baseline, 5.79 at follow-up; global strength invariance test score 0.48; P = 0.016). Conclusions The results lend support to a four- or five-factor model of negative symptoms and indicate overall stability over time. These data have implications for the study of pathophysiological mechanisms and the development of targeted treatments for negative symptoms.
Influence of moisture absorption on tensile and compressive properties of natural fiber-reinforced thermoplastic composites

ABSTRACT The present research is aimed at examining the changes in tensile and compressive properties of natural fiber-reinforced composites (NFRC) exposed to various humidity conditions; this is due to the fact that moisture content (MC) in fibers is generally assumed to be detrimental to composite performance. It was found that once moisture is exposed, compressive strength and strain of specimens would be higher than those of tensile, whereas modulus of elasticity would be lower. Due to increasing moisture content, although elongation in all tests was enhanced, tensile and compressive strength declined by 15% and modulus of elasticity decreased by 20%. In addition, Poisson’s ratio for both compressive and tensile properties was nearly identical (0.33) in distinct moisture content. A new understanding of moisture’s effects on mechanical behavior is presented in this study by finding an optimum point for exposing moisture to increase compressive and tensile strength.