Miracle-dz/UniFAR-FTUs huggingface dataset:
GPR-Net: Multi-view Layout Estimation via a Geometry-aware Panorama Registration Network

We present a room layout estimation framework that jointly learns wide baseline panorama registration and layout estimation given a pair of 360° panoramas. To effectively tackle the wide baseline registration problem, we introduce a novel end-to-end supervised Geometry-aware Panorama Registration Network or GPR-Net that exploits the layout geometry and computes fine-grained correspondences on the layout boundary, instead of the global pixel-space. GPR-Net consists of two main parts. The geometry transformer learns a set of 1D horizon features sampled on the panorama. These 1D feature maps encode geometric cues describing the ceiling-wall and floor-wall layout boundaries, and the correspondence and co-visibility between layout boundaries. These learned geometric cues are further used for direct regression of relative pose (translation and rotation) with a pose transformer. The final layout is then obtained by registering the two layouts using the estimated pose and taking the union of the two individual layouts derived from the estimated layout boundary maps. Experimental results indicate that our method achieves state-of-the-art performance in both panorama registration and layout estimation on a large-scale indoor panorama dataset ZInD [3]. Our code is available online1.
Local intraspecific aggregation in phytoplankton model communities: spatial scales of occurrence and implications for coexistence

The coexistence of multiple phytoplankton species despite their reliance on similar resources is often explained with mean-field models assuming mixed populations. In reality, observations of phytoplankton indicate spatial aggregation at all scales, including at the scale of a few individuals. Local spatial aggregation can hinder competitive exclusion since individuals then interact mostly with other individuals of their own species, rather than competitors from different species. To evaluate how microscale spatial aggregation might explain phytoplankton diversity maintenance, an individual-based, multispecies representation of cells in a hydrodynamic environment is required. We formulate a three-dimensional and multispecies individual-based model of phytoplankton population dynamics at the Kolmogorov scale. The model is studied through both simulations and the derivation of spatial moment equations, in connection with point process theory. The spatial moment equations show a good match between theory and simulations. We parameterized the model based on phytoplankters' ecological and physical characteristics, for both large and small phytoplankton. Defining a zone of potential interactions as the overlap between nutrient depletion volumes, we show that local species composition-within the range of possible interactions-depends on the size class of phytoplankton. In large phytoplankton, individuals are surrounded by cells from other species, while in small phytoplankton, individuals remain in mostly monospecific clusters. Spatial structure therefore favours intra- over inter-specific interactions for small phytoplankton, which likely contributes to coexistence mechanisms. Other factors behind diversity maintenance must be examined for large phytoplankton.
Improving Data Quality with Training Dynamics of Gradient Boosting Decision Trees

Real world datasets contain incorrectly labeled instances that hamper the performance of the model and, in particular, the ability to generalize out of distribution. Also, each example might have different contribution towards learning. This motivates studies to better understanding of the role of data instances with respect to their contribution in good metrics in models. In this paper we propose a method based on metrics computed from training dynamics of Gradient Boosting Decision Trees (GBDTs) to assess the behavior of each training example. We focus on datasets containing mostly tabular or structured data, for which the use of Decision Trees ensembles are still the state-of-the-art in terms of performance. Our methods achieved the best results overall when compared with confident learning, direct heuristics and a robust boosting algorithm. We show results on detecting noisy labels in order clean datasets, improving models' metrics in synthetic and real public datasets, as well as on a industry case in which we deployed a model based on the proposed solution.
lemon: LEns MOdelling with Neural networks – I. Automated modelling of strong gravitational lenses with Bayesian Neural Networks

The unprecedented number of gravitational lenses expected from new-generation facilities such as the ESA Euclid telescope and the Vera Rubin Observatory makes it crucial to rethink our classical approach to lens-modelling. In this paper, we present lemon (Lens Modelling with Neural networks): a new machine-learning algorithm able to analyse hundreds of thousands of gravitational lenses in a reasonable amount of time. The algorithm is based on a Bayesian Neural Network: a new generation of neural networks able to associate a reliable confidence interval to each predicted parameter. We train the algorithm to predict the three main parameters of the Singular Isothermal Ellipsoid model (the Einstein radius and the two components of the ellipticity) by employing two simulated datasets built to resemble the imaging capabilities of the Hubble Space Telescope and the forthcoming Euclid satellite. In this work, we assess the accuracy of the algorithm and the reliability of the estimated uncertainties by applying the network to several simulated datasets of 104 images each. We obtain accuracies comparable to previous studies present in the current literature and an average modelling time of just ∼0.5s per lens. Finally, we apply the lemon algorithm to a pilot dataset of real lenses observed with HST during the SLACS program, obtaining unbiased estimates of their SIE parameters. The code is publicly available on GitHub (https://github.com/fab-gentile/LEMON).
Wireless Powering Efficiency of Deep-Body Implantable Devices

The wireless power transfer (WPT) efficiency to implanted bioelectronic devices is constrained by several frequency-dependent physical mechanisms. Recent works have developed several mathematical formulations to understand these mechanisms and predict the optimal operating conditions. However, the existing approaches rely on simplified body models, which are unable to capture important aspects of WPT. Therefore, this article proposes the efficiency analysis approach in anatomical models that can provide insightful information on achieving the optimum operation conditions. First, this approach is validated with a theoretical spherical wave expansion (SWE) analysis, and the results for a simplified spherical model and a human pectoral model are compared. The results show that although a magnetic receiver outperforms an electric one for near-field operation and both the sources could be equally use in the far-field range, it is in the mid-field that the maximum efficiency is achieved with an optimum frequency between 1 and 5 GHz depending on the implantation depth. The receiver orientation is another factor that affects the efficiency, with a maximum difference between the best and worst case scenarios around five times for the electric source and over 13 times for the magnetic one. This approach is used to analyze the case of a deep-implanted pacemaker wirelessly powered by an on-body transmitter and subjected to stochastic misalignments. We evaluate the efficiency and exposure, and we demonstrate how a buffered transmitter can be tailored to achieve maximum powering efficiency. Finally, design guidelines that lead to optimal implantable WPT systems are established from the results obtained with the proposed approach.
Inferring Implicit Relations in Complex Questions with Language Models

A prominent challenge for modern language understanding systems is the ability to answer implicit reasoning questions, where the required reasoning steps for answering the question are not mentioned in the text explicitly. In this work, we investigate why current models struggle with implicit reasoning question answering (QA) tasks, by decoupling inference of reasoning steps from their execution. We define a new task of implicit relation inference and construct a benchmark, IMPLICITRELATIONS, where given a question, a model should output a list of concept-relation pairs, where the relations describe the implicit reasoning steps required for answering the question. Using IMPLICITRELATIONS, we evaluate models from the GPT-3 family and find that, while these models struggle on the implicit reasoning QA task, they often succeed at inferring implicit relations. This suggests that the challenge in implicit reasoning questions does not stem from the need to plan a reasoning strategy alone, but to do it while also retrieving and reasoning over relevant information.
Learning Multi-Objective Curricula for Robotic Policy Learning

Various automatic curriculum learning (ACL) methods have been proposed to improve the sample efficiency and final performance of deep reinforcement learning (DRL). They are designed to control how a DRL agent collects data, which is inspired by how humans gradually adapt their learning processes to their capabilities. For example, ACL can be used for subgoal generation, reward shaping, environment generation, or initial state generation. However, prior work only considers curriculum learning following one of the aforementioned predefined paradigms. It is unclear which of these paradigms are complementary, and how the combination of them can be learned from interactions with the environment. Therefore, in this paper, we propose a unified automatic curriculum learning framework to create multi-objective but coherent curricula that are generated by a set of parametric curriculum modules. Each curriculum module is instantiated as a neural network and is responsible for generating a particular curriculum. In order to coordinate those potentially conflicting modules in unified parameter space, we propose a multi-task hyper-net learning framework that uses a single hyper-net to parameterize all those curriculum modules. In addition to existing hand-designed curricula paradigms, we further design a flexible memory mechanism to learn an abstract curriculum, which may otherwise be difficult to design manually. We evaluate our method on a series of robotic manipulation tasks and demonstrate its superiority over other state-of-the-art ACL methods in terms of sample efficiency and final performance.
Chikungunya Vaccine Candidates: Current Landscape and Future Prospects

Abstract Chikungunya virus (CHIKV) is an alphavirus that has spread globally in the last twenty years. Although mortality is rather low, infection can result in debilitating arthralgia that can persist for years. Unfortunately, no treatments or preventive vaccines are currently licensed against CHIKV infections. However, a large range of promising preclinical and clinical vaccine candidates have been developed during recent years. This review will give an introduction into the biology of CHIKV and the immune responses that are induced by infection, and will summarize CHIKV vaccine development.
Early Assessment of Atherosclerotic Lesions and Vulnerable Plaques in vivo by Targeting Apoptotic Macrophages with AV Nanobubbles

Background The early detection of atherosclerotic lesions is particularly important for risk prediction of acute cardiovascular events. Macrophages apoptosis was significantly associated with the degree of AS lesions and especially contributed to plaque vulnerability. In this research, we mainly sought to explore the feasibility of a home-made AV-nanobubbles (NBAV) for visualization of apoptotic macrophages and assessment of atherosclerosis (AS) lesions by contrast-enhanced ultrasound (CEUS) imaging. Methods NBAV were prepared by “Optimized Thin-Film Hydration” and “Biotin-Avidin-Biotin” methods. Then, the characterization and echogenicity of NBAV were measured and analyzed in vitro. The targeting ability of NBAV to ox-LDL–induced apoptotic macrophages was observed by laser scanning confocal microscope. The ApoE−/− mice mode fed with high fat diet were observed by high-frequency ultrasound, microanatomy and oil red O staining. CEUS imaging in vivo was performed on AS plaques with NBAV and NBCtrl injection through the tail vein in turn in ApoE−/− mice. After CEUS imaging, the plaques were confirmed and analyzed by histopathological and immunological assessment. Results The prepared NBAV had a nano-scale size distribution with a low PDI and a negative zeta potential. Moreover, NBAV showed an excellent stability and exhibited a significantly echogenic signal than saline in vitro. In addition, we found that NBAV could target apoptotic macrophages induced by ox-LDL. Compared with NBCtrl, CEUS imaging of NBAV showed strong and sustained echo enhancement in plaque area of aortic arch in vivo. Further research showed that NBAV sensitive plaques presented more significant pathological changes with several vulnerable plaque features and abundant TUNEL-positive area. Conclusion NBAV displayed a sensitive indicator to evaluate apoptotic macrophages, indicating a promising CEUS molecular probe for AS lesions and vulnerable plaques identification.
Dietary influences on nutrient partitioning and anatomical body composition of growing pigs; modelling and experimental approaches

The aim of this paper is review of modelling in growing and fattening pigs and the results of studies that have been conducted in this field. The authors present the types of models, the history of growth modelling, the basic principles underlying their development, the factors effecting modelling accuracy, and the areas where such models can be applied. It is concluded, on the basis of available data, that mathematical models enable a safer, more predictable, and less erratic, production as a result of which the economics of meat production can be substantially improved. Models make possible a determination of the nutrient requirements of the animals and predicting of the performance of fattening pigs. It is very important to further improve modelling accuracy, for which it is necessary to obtain a more thorough knowledge of the physiological and biochemical processes taking place in the animal body. Another possible challenge in modelling may be to provide an accurate estimate of the quality and quantity of meat at slaughter time.
Replicated Echelle Diagrams in Asteroseismology: A Tool for Studying Mixed Modes and Avoided Crossings

In oscillating stars, mixed modes have p-mode character in the envelope and g-mode character in the core. They are observed in subgiants and red giants, in which the large density gradient outside the core effectively divides the star into two coupled cavities. This leads to the phenomenon of mode bumping, in which mode frequencies are shifted from their regular spacing as they undergo avoided crossings. This short contribution introduces a new diagram that involves extending the traditional echelle diagram by replication. This new diagram should prove a useful way of displaying mixed modes and helping to identify missing modes that may lie below the detection threshold.
A generalization of generalized beta distributions

This paper introduces the ``compound confluent hypergeometric'' (CCH) distribution. The CCH unifies and generalizes three recently introduced generalizations of the beta distribution: the Gauss hypergeometric (GH) distribution of Armero and Bayarri (1994), the generalized beta (GB) distribution of McDonald and Xu (1995), and the confluent hypergeometric (CH) distribution of Gordy (forthcoming). Unlike the beta, GB and GH, the CCH allows for conditioning on explanatory variables in a natural and convenient way. The CCH family is conjugate for gamma distributed signals, and so may also prove useful in Bayesian analysis. Application of the CCH is demonstrated with two measures of household liquid assets. In each case, the CCH yields a statistically significant improvement in fit over the more restrictive alternatives.
Advances in structure and function of auxin response factor in plants.

Auxin is a crucial phytohormone that has various effects on the regulators of plant growth and development. Auxin signal transduction is mainly controlled by two gene families: auxin response factor (ARF) and auxin/indole-3-acetic acid (Aux/IAA). ARFs are plant-specific transcription factors that bind directly to auxin response elements in the promoters of auxin-responsive genes. ARF proteins contain three conserved regions: a conserved N-terminal B3 DNA-binding domain, a variable intermediate middle region domain that functions in activation or repression, and a C-terminal domain including the Phox and Bem1p region for dimerization, similar to the III and IV elements of Aux/IAA, which facilitate protein-protein interaction through homodimerization of ARF proteins or heterodimerization of ARF and Aux/IAA proteins. In the two decades following the identification of the first ARF, 23 ARF members have been identified and characterized in Arabidopsis. Using whole-genome sequencing, 22, 25, 23, 25, and 36 ARF genes have been identified in tomato, rice, wheat, sorghum, and maize, respectively, in addition to which the related biofunctions of some ARFs have been reported. ARFs play crucial roles in regulating the growth and development of roots, leaves, flowers, fruits, seeds, responses to biotic and abiotic stresses, and phytohormone signal crosstalk. In this review, we summarize the research progress on the structures and functions of ARFs in Arabidopsis, tomato, and cereal crops, to provide clues for future basic research on phytohormone signaling and the molecular design breeding of crops. This article is protected by copyright. All rights reserved.
Cerebellar Gray Matter Volume and its Role in Executive Function, and Attention: Sex Differences by Age in Adolescents

Objective This research measures the regional GMV (rGMV) of the cerebellum, attention, Executive Function (EF) and we aimed to identify their correlation and sex differences in children and adolescents. Methods Subjects comprised 114 children (male = 62, female = 52, 12.44 ± 2.99 years old) from South Korea. Participants were divided into three groups by age (age 6−9, 10−13, and 14−17). The Stroop Color and Word Test (SCWT), Wisconsin Card Sorting Test (WCST), and Advanced Test of Attention (ATA) were used to estimate executive function. Magnetic resonance imaging (MRI) images were analyzed with Regional Voxel-Based Morphometry Analysis. Results The correlations between cerebellar rGMV and SCWT, WCST, and ATA subcategories showed difference by age and sex. In 6−9 age group, girls showed more overall correlations with cerebellar regions than boys, in WCST Categories Completed and ATA results. In age 10−13 group, more regions of cerebellum corresponded to SCWT subcategories in girls. Nevertheless, more correlation between cerebellar rGMV, WCST subcategories and some ATA subtests were observed in boys in the same age group. In the adolescent group, aged 14−17, boys showed more correlation with cerebellar rGMV, while girls showed little correlation. Conclusion This study highlights that sex-different cerebellum maturation in adolescence might be correlated with EF and attention. These results provides evidence that cerebellum modulates higher cognitive functioning during child development.
Optimization of inulinase production using Jerusalem artichoke (Helianthus tuberosus) as cheap substrate and comparison with pure chicory inulin

Abstract Jerusalem artichoke (JA) is a nutritional vegetable for human diet depending on its natural structure, especially high inulin content and it is the second inulin source for commercial production in the world, after chicory. It was aimed to investigate the inulinase production capability of Galactomyces geotrichum TS61 (GenBank accession: MN749818) using JA as an economical and effective substrate comparing with the pure chicory inulin and to optimize the fermentation using Taguchi design of experiment (DOE) in this study. Besides, the effects of sucrose on inulinase production either combined with JA or in its absence were also studied. Taguchi L16 orthogonal array was employed for optimization. Both of inulinase activities obtained from JA and pure inulin gave the maximum result at the 10th experimental run as 40.21 U/mL and 57.35 U/mL, respectively. The optimum levels were detected for each factor as, 30 g/L JA, 30 g/L sucrose, pH 5.5, and four days for time. The predicted value was found as 41.63 U/mL that was similar to the obtained result as 41.17 U/mL. Finally, inulinase activity was increased approximately 8-folds after optimization. The sucrose-free medium had similar effects with higher concentrations of JA at long incubation time. This is the first investigation about inulinase production by G. geotrichum. NOVELTY IMPACT STATEMENT Low-cost inulinase production was achieved using an economical substrate Sucrose effects were investigated in detail on inulinase production Use of Taguchi DOE supported effective enzyme production
GWAS Atlas: an updated knowledgebase integrating more curated associations in plants and animals

Abstract GWAS Atlas (https://ngdc.cncb.ac.cn/gwas/) is a manually curated resource of genome-wide genotype-to-phenotype associations for a wide range of species. Here, we present an updated implementation of GWAS Atlas by curating and incorporating more high-quality associations, with significant improvements and advances over the previous version. Specifically, the current release of GWAS Atlas incorporates a total of 278,109 curated genotype-to-phenotype associations for 1,444 different traits across 15 species (10 plants and 5 animals) from 830 publications and 3,432 studies. A collection of 6,084 lead SNPs of 439 traits and 486 experiment-validated causal variants of 157 traits are newly added. Moreover, 1,056 trait ontology terms are newly defined, resulting in 1,172 and 431 terms for Plant Phenotype and Trait Ontology and Animal Phenotype and Trait Ontology, respectively. Additionally, it is equipped with four online analysis tools and a submission platform, allowing users to perform data analysis and data submission. Collectively, as a core resource in the National Genomics Data Center, GWAS Atlas provides valuable genotype-to-phenotype associations for a diversity of species and thus plays an important role in agronomic trait study and molecular breeding.