Miracle-dz/UniFAR-FTUs huggingface dataset:
Rethinking Classification and Localization for Cascade R-CNN

We extend the state-of-the-art Cascade R-CNN with a simple feature sharing mechanism. Our approach focuses on the performance increases on high IoU but decreases on low IoU thresholds--a key problem this detector suffers from. Feature sharing is extremely helpful, our results show that given this mechanism embedded into all stages, we can easily narrow the gap between the last stage and preceding stages on low IoU thresholds without resorting to the commonly used testing ensemble but the network itself. We also observe obvious improvements on all IoU thresholds benefited from feature sharing, and the resulting cascade structure can easily match or exceed its counterparts, only with negligible extra parameters introduced. To push the envelope, we demonstrate 43.2 AP on COCO object detection without any bells and whistles including testing ensemble, surpassing previous Cascade R-CNN by a large margin. Our framework is easy to implement and we hope it can serve as a general and strong baseline for future research.
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks

Abstract Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing. By warm-starting from the publicly released checkpoints, NLP practitioners have pushed the state-of-the-art on multiple benchmarks while saving significant amounts of compute time. So far the focus has been mainly on the Natural Language Understanding tasks. In this paper, we demonstrate the efficacy of pre-trained checkpoints for Sequence Generation. We developed a Transformer-based sequence-to-sequence model that is compatible with publicly available pre-trained BERT, GPT-2, and RoBERTa checkpoints and conducted an extensive empirical study on the utility of initializing our model, both encoder and decoder, with these checkpoints. Our models result in new state-of-the-art results on Machine Translation, Text Summarization, Sentence Splitting, and Sentence Fusion.
High-Energy Multimessenger Transient Astrophysics

The recent discoveries of high-energy cosmic neutrinos and gravitational waves from astrophysical objects have led to a new era of multimessenger astrophysics. In particular, electromagnetic follow-up observations triggered by these cosmic signals have proved to be highly successful and have brought about new opportunities in time-domain astronomy. We review high-energy particle production in various classes of astrophysical transient phenomena related to black holes and neutron stars, and discuss how high-energy emission can be used to reveal the underlying physics of neutrino and gravitational-wave sources.
Structure of Trees with Respect to Nodal Vertex Sets

Let $T$ be a tree with a given adjacency eigenvalue $\lambda$. In this paper, by using the $\lambda$-minimal trees, we determine the structure of trees with a given multiplicity of the eigenvalue $\lambda$. Furthermore, we consider the relationship between the structure of trees and the eigensystem of a given Laplacian eigenvalue.
Bandit Convex Optimization in Non-stationary Environments

Bandit Convex Optimization (BCO) is a fundamental framework for modeling sequential decision-making with partial information, where the only feedback available to the player is the one-point or two-point function values. In this paper, we investigate BCO in non-stationary environments and choose the \emph{dynamic regret} as the performance measure, which is defined as the difference between the cumulative loss incurred by the algorithm and that of any feasible comparator sequence. Let $T$ be the time horizon and $P_T$ be the path-length of the comparator sequence that reflects the non-stationarity of environments. We propose a novel algorithm that achieves $O(T^{3/4}(1+P_T)^{1/2})$ and $O(T^{1/2}(1+P_T)^{1/2})$ dynamic regret respectively for the one-point and two-point feedback models. The latter result is optimal, matching the $\Omega(T^{1/2}(1+P_T)^{1/2})$ lower bound established in this paper. Notably, our algorithm is more adaptive to non-stationary environments since it does not require prior knowledge of the path-length $P_T$ ahead of time, which is generally unknown.
The Kerr-Cat Qubit: Stabilization, Readout, and Gates

Quantum superpositions of macroscopically distinct classical states, so-called Schr\"{o}dinger cat states, are a resource for quantum metrology, quantum communication, and quantum computation. In particular, the superpositions of two opposite-phase coherent states in an oscillator encode a qubit protected against phase-flip errors. However, several challenges have to be overcome in order for this concept to become a practical way to encode and manipulate error-protected quantum information. The protection must be maintained by stabilizing these highly excited states and, at the same time, the system has to be compatible with fast gates on the encoded qubit and a quantum non-demolition readout of the encoded information. Here, we experimentally demonstrate a novel method for the generation and stabilization of Schr\"{o}dinger cat states based on the interplay between Kerr nonlinearity and single-mode squeezing in a superconducting microwave resonator. We show an increase in transverse relaxation time of the stabilized, error-protected qubit over the single-photon Fock-state encoding by more than one order of magnitude. We perform all single-qubit gate operations on time-scales more than sixty times faster than the shortest coherence time and demonstrate single-shot readout of the protected qubit under stabilization. Our results showcase the combination of fast quantum control with the robustness against errors intrinsic to stabilized macroscopic states and open up the possibility of using these states as resources in quantum information processing.
Hierarchical Multi-Label Dialog Act Recognition on Spanish Data

Os actos de diálogo revelam a intenção por trás das palavras pronunciadas. Por isso, o seu reconhecimento automático é importante para um sistema de diálogo que tenta entender o seu interlocutor. O estudo apresentado neste artigo aborda essa tarefa no corpus DIHANA, cujo esquema de anotação de actos de diálogo em três níveis coloca problemas que não foram explorados em estudos recentes. Além do problema hierárquico, os dois níveis inferiores colocam problemas de classificação multi-etiqueta. Além disso, cada nível da hierarquia refere-se a um aspecto diferente relativo à intenção do orador, tanto em termos da estrutura do diálogo, como da tarefa. Por outro lado, uma vez que os diálogos são em espanhol, este corpus permite-nos avaliar se as melhores abordagens para dados em inglês generalizam para uma língua diferente. Mais especificamente, comparamos o desempenho de diferentes abordagens de representação de segmentos, com foco tanto em sequências como em padrões de palavras, e avaliamos a importância do histórico do diálogo e das relações entre os múltiplos níveis da hierarquia. No que diz respeito ao problema de classificação de etiqueta única colocado pelo nível superior, mostramos que as conclusões obtidas a partir de dados em inglês se mantêm em dados em espanhol. Para além disso, mostramos que as abordagens podem ser adaptadas para cenários multi-etiqueta. Por fim, combinando hierarquicamente os melhores classificadores para cada nível, obtemos os melhores resultados reportados para este corpus.
The Two-Dimensional Infinite Heisenberg Classical Square Lattice: Exact Theory and Experimental Results

We rigorously examine 2d-infinite square lattices composed of classical spins isotropically coupled between first-nearest neighbors. Each local exchange Hamiltonian is expanded on the basis of its eigenfunctions played by spherical harmonics Yinf{l,m}. The corresponding eigenvalues are modified Bessel functions of the first kind. In the thermodynamic limit a numerical study allows one to select the higher-degree term of the characteristic polynomial associated with the zero-field partition function Zinf{N}(0). A very simple exact closed-form expression is derived, thus permitting to express the free energy F and the specific heat Cinf{V}, for any temperature. We report a thermal study of the basic term appearing in the higher-degree term of Zinf{N}(0). We show that it appears crossovers between two consecutive terms. Coming from high temperatures where the l= 0-term is dominant, near the critical temperature Tinf{c}= 0 K, eigenvalues showing increasing l-values are more and more selected. We derive an exact expression for the spin-spin correlations, the correlation length ksi and the susceptibility khi. Near Tinf{c}= 0 K we obtain a diagram of magnetic phases. We derive the same expressions for xsi, F, Cinf{V} and khi as the corresponding ones derived through a renormalization process. We show that, near 0 K, the lattice is composed of quasi rigid quasi independent Kadanoff blocks of length ksi and magnetic moment M(T), the unit cell moment, so that khi.kinf{B}T=ksi^2.M(T)^2. Finally we compare experimental susceptibilities to the theoretical expression of khi, for two types of 2d-compounds (showing or not organic ligands inside and between sheets of Mn2+ ions). We obtain a remarkable good agreement between the J-values of the exchange energy derived from the fits and the corresponding ones previously measured as well as a value of the Lande factor close to the theoretical one.
Reference Models for Lithospheric Geoneutrino Signal

Debate continues on the amount and distribution of radioactive heat producing elements (i.e., U, Th, and K) in the Earth, with estimates for mantle heat production varying by an order of magnitude. Constraints on the bulk‐silicate Earth's (BSE) radiogenic power also places constraints on overall BSE composition. Geoneutrino detection is a direct measure of the Earth's decay rate of Th and U. The geoneutrino signal has contributions from the local ( ∼ 40%) and global ( ∼ 35%) continental lithosphere and the underlying inaccessible mantle ( ∼ 25%). Geophysical models are combined with geochemical data sets to predict the geoneutrino signal at current and future geoneutrino detectors. We propagated uncertainties, both chemical and physical, through Monte Carlo methods. Estimated total signal uncertainties are on the order of ∼ 20%, proportionally with geophysical and geochemical inputs contributing ∼ 30% and ∼ 70%, respectively. We find that estimated signals, calculated using CRUST2.0, CRUST1.0, and LITHO1.0, are within physical uncertainty of each other, suggesting that the choice of underlying geophysical model will not change results significantly, but will shift the central value by up to ∼ 15%. Similarly, we see no significant difference between calculated layer abundances and bulk crustal heat production when using these geophysical models. The bulk crustal heat production is calculated as 7 ± 2 TW, which includes an increase of 1 TW in uncertainty relative to previous studies. Combination of our predicted lithospheric signal with measured signals yield an estimated BSE heat production of 21.5 ± 10.4 TW. Future improvements, including uncertainty attribution and near‐field modeling, are discussed.
Blind Deblurring Using GANs

Deblurring is the task of restoring a blurred image to a sharp one, retrieving the information lost due to the blur. In blind deblurring we have no information regarding the blur kernel. As deblurring can be considered as an image to image translation task, deep learning based solutions, including the ones which use GAN (Generative Adversarial Network), have been proven effective for deblurring. Most of them have an encoder-decoder structure. Our objective is to try different GAN structures and improve its performance through various modifications to the existing structure for supervised deblurring. In supervised deblurring we have pairs of blurred and their corresponding sharp images, while in the unsupervised case we have a set of blurred and sharp images but their is no correspondence between them. Modifications to the structures is done to improve the global perception of the model. As blur is non-uniform in nature, for deblurring we require global information of the entire image, whereas convolution used in CNN is able to provide only local perception. Deep models can be used to improve global perception but due to large number of parameters it becomes difficult for it to converge and inference time increases, to solve this we propose the use of attention module (non-local block) which was previously used in language translation and other image to image translation tasks in deblurring. Use of residual connection also improves the performance of deblurring as features from the lower layers are added to the upper layers of the model. It has been found that classical losses like L1, L2, and perceptual loss also help in training of GANs when added together with adversarial loss. We also concatenate edge information of the image to observe its effects on deblurring. We also use feedback modules to retain long term dependencies
Shortcomings of Shapiro delay-based tests of the equivalence principle on cosmological scales

The ``Shapiro delay'' experienced by an astronomical messenger traveling through a gravitational field has been used to place constraints on possible deviations from the equivalence principle. The standard Shapiro delay used to obtain these constraints is not itself an observable in general relativity, but is rather obtained by comparing with a fiducial Euclidean distance. There is not a mapping between the constraints obtained in this manner and alternative theories that exhibit equivalence principle violations. However, even assuming that the comparison with the fiducial Euclidean distance is carried out in a way that is useful for some class of alternative theories, we show that the standard calculation of these constraints cannot be applied on cosmological scales, as is often done. Specifically, we find that the Shapiro delay computed in the standard way (taking the Newtonian potential to vanish at infinity) diverges as one includes many remote sources. We use an infinite homogeneous lattice model to illustrate this divergence, and also show how the divergence can be cured by using Fermi coordinates associated with an observer. With this correction, one finds that the Shapiro delay is no longer monotonic with the number of sources. Thus, one cannot compute a conservative lower bound on the Shapiro delay using a subset of the sources of the gravitational field without further assumptions and/or observational input. As an illustration, we compute the Shapiro delay by applying the Fermi coordinate expression to two catalogs of galaxy clusters, illustrating the dependence of the result on the completeness of the catalogue and the mass estimates.
Chiral susceptibility in the Nambu–Jona-Lasinio model: A Wigner function approach

We estimate here chiral susceptibility at finite temperature within the framework of the Nambu-Jona-Lasinio model (NJL) using the Wigner function approach. We also estimate it in the presence of chiral chemical potential ($\mu_5$) as well as a non vanishing magnetic field ($B$). We use medium separation regularization scheme (MSS) to calculate the chiral condensate and corresponding susceptibility. It is observed that for a fixed value of chiral chemical potential ($\mu_5$), transition temperature increases with the magnetic field. While for the fixed value of the magnetic field, transition temperature decreases with chiral chemical potential. For a strong magnetic field, we observe non degeneracy in susceptibility for up and down type quarks.
FAKIR : An algorithm for estimating the pose and elementary anatomy of archaeological statues

The digitization of archaeological artefacts has become an essential part of cultural heritage research be it for purposes of preservation or restoration. Statues, in particular, have been at the center of many projects. In this paper, we introduce a way to improve the understanding of acquired statues by registering a simple and pliable anatomical model to the raw point set data. Our method performs a Forward And bacKward Iterative Registration (FAKIR) which proceeds joint by joint, needing only a few iterations to converge. Furthermore, we introduce a simple detail-preserving skinning approach working directly on the point cloud, without needing a mesh. By combining FAKIR with our skinning method we are able to detect the pose and the elementary anatomy of a sculpture and modify it, paving the way for pose-independent style comparison and statue restoration by combination of parts belonging to statues with different poses.
Observational evidence for a local underdensity in the Universe and its effect on the measurement of the Hubble constant

For precision cosmological studies it is important to know the local properties of the reference point from which we observe the Universe. Particularly for the determination of the Hubble constant with low-redshift distance indicators, the values observed depend on the average matter density within the distance range covered. In this study we used the spatial distribution of galaxy clusters to map the matter density distribution in the local Universe. The study is based on our CLASSIX galaxy cluster survey, which is highly complete and well characterised, where galaxy clusters are detected by their X-ray emission. In total, 1653 galaxy clusters outside the “zone of avoidance” fulfil the selection criteria and are involved in this study. We find a local underdensity in the cluster distribution of about 30–60% which extends about 85 Mpc to the north and ∼170 Mpc to the south. We study the density distribution as a function of redshift in detail in several regions in the sky. For three regions for which the galaxy density distribution has previously been studied, we find good agreement between the density distribution of clusters and galaxies. Correcting for the bias in the cluster distribution we infer an underdensity in the matter distribution of about −30 ± 15% (−20 ± 10%) in a region with a radius of about 100 (∼140) Mpc. Calculating the probability of finding such an underdensity through structure formation theory in a ΛCDM universe with concordance cosmological parameters, we find a probability characterised by σ-values of 1.3 − 3.7. This indicates low probabilities, but with values of around 10% at the lower uncertainty limit, the existence of an underdensity cannot be ruled out. Inside this underdensity, the observed Hubble parameter will be larger by about 5.5 +2.1−2.8%, which explains part of the discrepancy between the locally measured value of H0 compared to the value of the Hubble parameter inferred from the Planck observations of cosmic microwave background anisotropies. If distance indicators outside the local underdensity are included, as in many modern analyses, this effect is diluted.
Thermodynamic geometry of the quark-meson model

We study the thermodynamic geometry of the Quark-Meson model, focusing on the curvature, $R$, around the chiral crossover at finite temperature and baryon chemical potential. We find a peculiar behavior of $R$ in the crossover region, in which the sign changes and a local maximum develops; in particular, the height of the peak of $R$ in the crossover region becomes large in proximity of the critical endpoint and diverges at the critical endpoint. The appearance of a pronounced peak of $R$ close to the critical endpoint supports the idea that $R$ grows with the correlation volume around the phase transition. We also analyze the mixed fluctuations of energy and baryon number, $\langle\Delta U\Delta N\rangle$, which grow up substantially in proximity of the critical endpoint: in the language of thermodynamic geometry these fluctuations are responsible for the vanishing of the determinant of the metric, which results in thermodynamic instability and are thus related to the appearance of the second order phase transition at the critical endpoint.
Electrical resistivity imaging of the architecture of substream sediments

The modeling of fluvial systems is constrained by a lack of spatial information about the continuity and structure of streambed sediments. There are few methods for noninvasive characterization of streambeds. Invasive methods using wells and cores fail to provide detailed spatial information on the prevailing architecture and its continuity. Geophysical techniques play a pivotal role in providing spatial information on subsurface properties and processes across many other environments, and we have applied the use of one of those techniques to streambeds. We demonstrate, through two examples, how electrical resistivity imaging can be utilized for characterization of subchannel architecture. In the first example, electrodes installed in riparian boreholes and on the streambed are used for imaging, under the river bed, the thickness and continuity of a highly permeable alluvial gravel layer overlying chalk. In the second example, electrical resistivity images, determined from data collected using electrodes installed on the river bed, provide a constrained estimate of the sediment volume behind a log jam, vital to modeling biogeochemical exchange, which had eluded measurement using conventional drilling methods owing to the boulder content of the stream. The two examples show that noninvasive electrical resistivity imaging is possible in complex stream environments and provides valuable information about the subsurface architecture beneath the stream channels.
Modulation of early visual processing alleviates capacity limits in solving multiple tasks

In daily life situations, we have to perform multiple tasks given a visual stimulus, which requires task-relevant information to be transmitted through our visual system. When it is not possible to transmit all the possibly relevant information to higher layers, due to a bottleneck, task-based modulation of early visual processing might be necessary. In this work, we report how the effectiveness of modulating the early processing stage of an artificial neural network depends on the information bottleneck faced by the network. The bottleneck is quantified by the number of tasks the network has to perform and the neural capacity of the later stage of the network. The effectiveness is gauged by the performance on multiple object detection tasks, where the network is trained with a recent multi-task optimization scheme. By associating neural modulations with task-based switching of the state of the network and characterizing when such switching is helpful in early processing, our results provide a functional perspective towards understanding why task-based modulation of early neural processes might be observed in the primate visual cortex
Dilated Point Convolutions: On the Receptive Field of Point Convolutions

In this work, we propose Dilated Point Convolutions (DPC) which drastically increase the receptive field of convolutions on 3D point clouds. As we show in our experiments, the size of the receptive field is directly related to the performance of dense tasks such as semantic segmentation. We look at different network architectures and mechanisms to increase the receptive field size of point convolutions and propose in particular dilated point convolutions. Importantly, our dilation mechanism can easily be integrated into all existing methods using nearest-neighbor-based point convolutions. To evaluate the resulting network architectures, we visualize the receptive field and report competitive scores on the task of 3D semantic segmentation on the S3DIS and ScanNet datasets.