Miracle-dz/UniFAR-FTUs huggingface dataset:
Domain-topic models with chained dimensions: modeling the evolution of a major oncology conference (1995-2017)

In this paper we introduce a novel approach for the computational analysis of research activities and their dynamics. Named SASHIMI (Symmetrical And Sequential analysis from Hierarchical Inference of Multidimensional Information), our approach provides a multi-level description of the structure of scientific activities that offers numerous advantages over traditional methods such as topic models or network analyses. Our method generates a dual description of corpora in terms of research domains (collections of documents) and topics (collections of words). It also extends this description to clusters of associated dimensions, such as time. SASHIMI only requires access to the textual content of individual documents, rather than specific metadata such as citations, authors, or keywords as is the case with other science-mapping approaches. We illustrate the analytical power of our method by applying it to the empirical analysis of an original dataset, namely the 1995-2017 collection of abstracts presented at ASCO, the largest annual oncology research conference. We show that SASHIMI is able to detect the presence of significant temporal patterns and to identify the major thematic transformations of oncology that underlie these patterns.
On the Beauville--Bogomolov decomposition in characteristic $p\geq 0$

We prove a variant of the Beauville--Bogomolov decomposition for weakly ordinary, or generally globally $F$-split, varieties $X$ with $K_X \sim 0$, in characteristic $p>0$. We also show that the weakly ordinary assumption in our statement cannot be dropped. Additionally, if the assumption $K_X \sim 0$ is replaced by $-K_X$ being semi-ample, we show the weaker statement that all closed fibers of the Albanese morphism are isomorphic. In the appendix written jointly with Giulio Codogni, we also deduce the singular version of the latter statement, in characteristic zero. Finally, we apply our main theorem to draw consequences to the behavior of rational points and fundamental groups of weakly ordinary $K$-trivial varieties in positive characteristic.
Linear response theory and optical conductivity of Floquet topological insulators

Motivated by the quest for experimentally accessible dynamical probes of Floquet topological insulators, we formulate the linear response theory of a periodically driven system. We illustrate the applications of this formalism by giving general expressions for optical conductivity of Floquet systems, including its homodyne and heterodyne components and beyond. We obtain the Floquet optical conductivity of specific driven models, including two-dimensional Dirac material such as the surface of a topological insulator, graphene, and the Haldane model irradiated with circularly or linearly polarized laser, as well as semiconductor quantum well driven by an ac potential. We obtain approximate analytical expressions and perform numerically exact calculations of the Floquet optical conductivity in different scenarios of the occupation of the Floquet bands, in particular, the diagonal Floquet distribution and the distribution obtained after a quench. We comment on experimental signatures and detection of Floquet topological phases using optical probes.
Cosmological consequences of a scalar field with oscillating equation of state: A possible solution to the fine-tuning and coincidence problems

We propose a new dark energy model for solving the cosmological fine-tuning and coincidence problems. A default assumption is that the fine-tuning problem disappears if we do not interpret dark energy as vacuum energy. The key idea to solving the coincidence problem is that the Universe may have several acceleration phases across the whole cosmic history. The specific example we study is a quintessence model with approximately repeated double exponential potential, which only introduces one Planck scale parameter and three dimensionless parameters of order unity. The cosmological background evolution equations can be recast into a four-dimensional dynamical system and its main properties are discussed in details. Preliminary calculations show that our model is able to explain the observed cosmic late-time acceleration.
On the geometry of co-Hamiltonian diffeomorphisms

This paper studies the geometry of the group of all co-Hamiltonian diffeomorphisms of a compact cosymplectic manifold $(M, \omega, \eta)$. The fix-point theory for co-Hamiltonian diffeomorphisms is studied, and we use Arnold's conjecture to predict the exact minimum number of fix point that such a diffeomorphism must have (this minimum number is at least $1$). It follows that the generating function of any co-Hamiltonian isotopy is a constant function along it orbits. Therefore, we study the co-Hofer norms for co-Hamiltonian isotopies, and establish several co-Hamiltonian analogues of some approximations and reparameterizations results found in the theory of Hamiltonian dynamics, we define two $C^0-$co-Hamiltonian topologies, and use these topologies to define the spaces of co-hameomorphisms. Finnaly, we raise several important questions for future studies.
Bayesian Tensor Network and Optimization Algorithm for Probabilistic Machine Learning

Describing or calculating the conditional probabilities of multiple events is exponentially expensive. In this work, a natural generalization of Bayesian belief network is proposed by incorporating with tensor network, which is dubbed as Bayesian tensor network (BTN), to efficiently describe the conditional probabilities among multiple sets of events. The complexity of BTN that gives the conditional probabilities ofM sets of events scales only polynomially with M . To testify its validity, BTN is implemented to capture the conditional probabilities between images and their classifications, where each feature is mapped to a probability distribution of a set of mutually exclusive events. A rotation optimization method is suggested to update BTN, which avoids gradient vanishing problem and exhibits high efficiency. With a simple tree network structures, BTN exhibits competitive performances on fashion-MNIST dataset. Analogous to the tensor network simulations of quantum systems, the validity of BTN implies an “area law” of fluctuations in image recognition problems.
Modular Parametrization as Polyakov Path Integral - Cases with CM Elliptic Curves as Target Spaces -

For an elliptic curve E over an abelian extension k/K with CM by K of Shimura type, the L-functions of its [k:K] Galois representations are Mellin transforms of Hecke theta functions; a modular parametrization (surjective map) from a modular curve to E pulls back the 1-forms on E to give the Hecke theta functions. This article refines the study of our earlier work and shows that certain class of chiral correlation functions in Type II string theory with [E]_C (E as real analytic manifold) as a target space yield the same Hecke theta functions as objects on the modular curve. The Kahler parameter of the target space [E]_C in string theory plays the role of the index (partially ordered) set in defining the projective/direct limit of modular curves.
Deep learning in medical image registration: a review

This paper presents a review of deep learning (DL)-based medical image registration methods. We summarized the latest developments and applications of DL-based registration methods in the medical field. These methods were classified into seven categories according to their methods, functions and popularity. A detailed review of each category was presented, highlighting important contributions and identifying specific challenges. A short assessment was presented following the detailed review of each category to summarize its achievements and future potential. We provided a comprehensive comparison among DL-based methods for lung and brain registration using benchmark datasets. Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of DL-based medical image registration.
Federated Variance-Reduced Stochastic Gradient Descent With Robustness to Byzantine Attacks

This paper deals with distributed finite-sum optimization for learning over multiple workers in the presence of malicious Byzantine attacks. Most resilient approaches so far combine stochastic gradient descent (SGD) with different robust aggregation rules. However, the sizeable SGD-induced stochastic gradient noise challenges discerning malicious messages sent by the Byzantine attackers from noisy stochastic gradients sent by the ‘honest’ workers. This motivates reducing the variance of stochastic gradients as a means of robustifying SGD. To this end, a novel Byzantine attack resilient distributed (Byrd-) SAGA approach is introduced for federated learning tasks involving multiple workers. Rather than the mean employed by distributed SAGA, the novel Byrd-SAGA relies on the geometric median to aggregate the corrected stochastic gradients sent by the workers. When less than half of the workers are Byzantine attackers, Byrd-SAGA attains provably linear convergence to a neighborhood of the optimal solution, with the asymptotic learning error determined by the number of Byzantine workers. Numerical tests corroborate the robustness to various Byzantine attacks, as well as the merits of Byrd-SAGA over Byzantine attack resilient distributed SGD.
Schr\"odinger Bridge Samplers.

Consider a reference Markov process with initial distribution $\pi_{0}$ and transition kernels $\{M_{t}\}_{t\in[1:T]}$, for some $T\in\mathbb{N}$. Assume that you are given distribution $\pi_{T}$, which is not equal to the marginal distribution of the reference process at time $T$. In this scenario, Schrodinger addressed the problem of identifying the Markov process with initial distribution $\pi_{0}$ and terminal distribution equal to $\pi_{T}$ which is the closest to the reference process in terms of Kullback--Leibler divergence. This special case of the so-called Schrodinger bridge problem can be solved using iterative proportional fitting, also known as the Sinkhorn algorithm. We leverage these ideas to develop novel Monte Carlo schemes, termed Schrodinger bridge samplers, to approximate a target distribution $\pi$ on $\mathbb{R}^{d}$ and to estimate its normalizing constant. This is achieved by iteratively modifying the transition kernels of the reference Markov chain to obtain a process whose marginal distribution at time $T$ becomes closer to $\pi_T = \pi$, via regression-based approximations of the corresponding iterative proportional fitting recursion. We report preliminary experiments and make connections with other problems arising in the optimal transport, optimal control and physics literatures.
Properties of unique information

We study the measure of unique information $UI(T:X\setminus Y)$ defined by Bertschinger et al. (2014) within the framework of information decompositions. We study uniqueness and support of the solutions to the optimization problem underlying the definition of $UI$. We identify sufficient conditions for non-uniqueness of solutions with full support in terms of conditional independence constraints and in terms of the cardinalities of $T$, $X$ and $Y$. Our results are based on a reformulation of the first order conditions on the objective function as rank constraints on a matrix of conditional probabilities. These results help to speed up the computation of $UI(T:X\setminus Y)$, most notably when $T$ is binary. In the case that all variables are binary, we obtain a complete picture of where the optimizing probability distributions lie.
Conditional Correlations and Principal Regression Analysis for Futures

We explore the effect of past market movements on the instantaneous correlations between assets within the futures market. Quantifying this effect is of interest to estimate and manage the risk associated to portfolios of futures in a non-stationary context. We apply and extend a previously reported method called the Principal Regression Analysis (PRA) to a universe of 84 futures contracts between 2009 and 2019. We show that the past up (resp. down) 10 day trends of a novel predictor -- the eigen-factor -- tend to reduce (resp. increase) instantaneous correlations. We then carry out a multifactor PRA on sectorial predictors corresponding to the four futures sectors (indexes, commodities, bonds and currencies), and show that the effect of past market movements on the future variations of the instantaneous correlations can be decomposed into two significant components. The first component is due to the market movements within the index sector, while the second component is due to the market movements within the bonds sector.
Colliding winds in and around the stellar group IRS 13E at the galactic centre

IRS~13E is an enigmatic compact group of massive stars located in projection only 3.6 arcseconds away from Sgr A*. This group has been suggested to be bounded by an intermediate-mass black hole (IMBH). We present a multi-wavelength study of the group and its interplay with the environment. Based on Chandra observations, we find the X-ray spectrum of IRS~13E can be well characterized by an optically thin thermal plasma. The emission peaks between two strongly mass-losing Wolf-Rayet stars of the group. These properties can be reasonably well reproduced by simulated colliding winds of these two stars. However, this scenario under-predicts the X-ray intensity in outer regions. The residual emission likely results from the ram-pressure confinement of the IRS~13E group wind by the ambient medium and is apparently associated with a shell-like warm gas structure seen in Pa-alpha and in ALMA observations. These latter observations also show strongly peaked thermal emission with unusually large velocity spread between the two stars. These results indicate that the group is colliding with the bar of the dense cool gas mini-spiral around Sgr A*. The extended X-ray morphology of IRS~13E and its association with the bar further suggest that the group is physically much farther away than the projected distance from Sgr A*. The presence of an IMBH, while favorable to keep the stars bound together, is not necessary to explain the observed stellar and gas properties of IRS~13E.
Proximal sensing estimation of glyphosate injury on weeds in central Brazil

Abstract. Herbicide injury on weeds is quantified through responses such as mortality, biomass, and visual scores, which are time consuming and/or subjective, making it hard to evaluate injury in experiments and making the site-specific management impracticable in major crops. The objective of our work was to build prediction models of glyphosate injury on weed species of economic importance in the Brazilian Cerrado (savana) through multispectral digital images. Two weed species with different levels of susceptibility to glyphosate, Eleusine indica (L.) Pers. and Brachiaria decumbens L. , were cultivated in pots and subjected to increasing doses of glyphosate, simulating a gradient of injury. Visual scores were given by three trained evaluators during three weeks after the application. Images of the pots were acquired with a digital multispectral camera in the following bands of the electromagnetic spectrum: blue (450 nm), green (560 nm), red (660 nm), and near-infrared (850 nm), with a digital camera at 0.9-m height from the ground. Images were first segmented to separate plant from soil using the hue–saturation–value color space. Vegetation indices were calculated and used as explanatory variables for phytotoxicity in regression models. A field experiment was conducted in order to validate predictions. The injury pattern of glyphosate on E. inidica and B. decumbens was best modeled by an exponential function of the median of hue for plant pixels, which could be reproduced at field scale with prediction errors below 8%. Differences in sensitivity to glyphosate between the two species were detected with the kernel density of hue for plant pixels, with absolute correlation with phytotoxicity above 0.8.
Sparse signal reconstruction via expanded subspace pursuit

Abstract. Due to their low computational complexity, greedy pursuit algorithms are widely used in sparse signal reconstruction. An improved greedy iterative algorithm, called the expanded subspace pursuit, is proposed. By incorporating a simple backtracking technique, the proposed algorithm removes the mismatching atoms to refine the estimated support set effectively. Furthermore, the proposed algorithm can achieve blind sparse reconstruction even without the prior of the sparsity degree. Compared with other greedy algorithms, the proposed algorithm exhibits superior reconstruction accuracy and lower computational complexity. Finally, numerical results are presented to demonstrate the validity of the proposed algorithm.
Detection of bad data images in long-term MODIS land surface temperature image time series using statistical outlier detection methods

Abstract. Land surface temperature (LST) data are available for several years at a high acquisition rate from sensors such as a moderate resolution imaging spectroradiometer (MODIS). Advances in remote sensing technology have exploited this large volume of LST image time series (LITS) data for a clearer understanding of land surface processes. However, remotely sensed LST observations from spaceborne platforms are likely to be influenced by severe weather conditions (e.g., clouds, haze, heavy aerosols, heavy rainfall and flooding, hailstorms, and snowfall). Presence of bad data images in LITS can reduce the credibility of results from LITS data analysis. An algorithm to detect bad data images in a long-term MODIS LITS is proposed. Bad data in MODIS LITS were filtered at various stages. Initially, poor quality pixels were removed from LST maps based on quality control information provided in corresponding quality assurance maps. In further stages, thin cloud-contaminated LST pixels were removed that were left undetected by MODIS official cloud screening procedure. Finally, to remove bad data LST maps, median-based statistical outlier detection method was employed, and outliers were detected in LST pixel time series (LPTS) vectors generated at consistent normalized difference vegetation index (NDVI) pixel (CNP) locations. CNPs were selected by employing Mann–Kendall test statistics on annually aggregated MODIS NDVI time series vectors. Decisions were made to identify bad LST maps based on number of outlier LST values appeared in LPTS vectors at CNP locations for each LST map. The proposed algorithm was applied over parts of Gondwana coalfields situated in Jharkhand and West Bengal states of India.
Semantic segmentation on small datasets of satellite images using convolutional neural networks

Abstract. Semantic segmentation is one of the most popular and challenging applications of deep learning. It refers to the process of dividing a digital image into semantically homogeneous areas with similar properties. We employ the use of deep learning techniques to perform semantic segmentation on high-resolution satellite images representing urban scenes to identify roads, vegetation, and buildings. A SegNet-based neural network with an encoder–decoder architecture is employed. Despite the small size of the dataset, the results are promising. We show that the network is able to accurately distinguish between these groups for different test images, when using a network with four convolutional layers.
Enhancement of terahertz absorption in graphene monolayer with Bragg grating and metallic film

Abstract. The absorption enhancement in the graphene monolayer for the terahertz frequency is investigated. This is achieved by placing the graphene monolayer on a dielectric grating backed with a reduced Bragg grating and a metallic mirror. Complete absorption is achieved at resonance and the absorption spectra exhibit an ultranarrow shape, which is attributed to the combined effects of the guided mode resonance with dielectric grating and the photonic bandgap with Bragg grating. Moreover, the designed graphene absorber possesses antenna-like response and thus can be used as a thermal emitter with high directionality. To disclose the physical origins of such absorption effects, the electric-field intensity distributions are investigated. Furthermore, the peak position of the absorption spectra can be tuned by only a change in the Fermi level. Our results may potentially be used for developing the next-generation graphene-based optoelectronic devices.
Epidemiological analysis of 1404 patients with intracranial aneurysm followed in a single Brazilian institution

Background: We sought to evaluate the epidemiology of intracranial aneurysms in relation to location, gender, age, presence of multiple aneurysms, and comorbidities in the Brazilian population. Methods: We performed a prospective analysis of a cohort of 1404 patients diagnosed with intracranial aneurysm admitted to the Hospital das Clinicas of the University of Sao Paulo, a referral hospital for the treatment of cerebrovascular diseases in Brazil. Patients admitted between September 2009 and September 2018 with radiological diagnosis of intracranial aneurysm were included in the study. Results: A total of 2251 aneurysms were diagnosed. Females accounted for 1090 aneurysms (77.6%) and the mean age at diagnosis was 54.9 years (ranging 15–88). The most common location was middle cerebral artery (MCA) with 593 aneurysms (26.3%) followed by anterior cerebral artery (ACA) with 417 aneurysms (18.5%) and internal carotid artery in the posterior communicating segment with 405 aneurysms (18.0%). Males had higher rates of ACA aneurysms (29.7%) while females had higher rates of MCA aneurysms (26.1%). Sorting by size, 492 aneurysms were <5 mm (21.8%), 1524 measured 5–10 mm (67.7%), 119 size 11–24 mm (5.3%), and 116 were >24 mm (5.2%). The occurrence of multiple aneurysms was associated with female gender (P < 0.001) and smoking (P < 0.001), but not with hypertension (P = 0.121). Conclusion: In this population, the occurrence of intracranial aneurysm is related to several factors, including gender, age, smoking, and hypertension. Our study brought to light important characteristics of a large number of Brazilian patients regarding epidemiology, location, size, and multiplicity of intracranial aneurysms.
Determinants of sputum smear nonconversion in smear-positive pulmonary tuberculosis patients in Suriname, 2010 – 2015

ABSTRACT Objective. To identify factors associated with sputum smear nonconversion in patients with pulmonary tuberculosis (PTB) in Suriname. Methods. A case-control study was conducted using routinely-collected surveillance data of PTB cases reported in January 2010 – December 2015 and recorded in the database of the National Tuberculosis Program of Suriname. Cases were smear-positive PTB patients whose sputum results were negative 2 months after treatment initiation. Controls were the smear-positive PTB patients whose sputum results were negative in the same timeframe. Multivariate logistic regression analysis was used to examine associations between potential risk factors and smear conversion. Results. The two age groups ≥ 35 years (35 – 54 years, AOR: 2.7, 95%CI: 1.2 – 6.1; and 55+ years, AOR: 2.5, 95%CI: 1.1 – 5.9) and high bacillary load at baseline (AOR 2.34, 95%CI: 1.2 – 4.8) were significantly associated with delayed smear conversion. Conclusion. The National TB program of Suriname should develop strategies to address patients at higher risk for delayed smear conversion to prevent further spreading and unfavorable treatment outcomes. To better inform decision-making and future studies, the NTP should expand its data collection to include all risk factors for delayed smear conversion.