Miracle-dz/UniFAR-FTUs huggingface dataset:
RobustGCNs: Robust Norm Graph Convolutional Networks in the Presence of Node Missing Data and Large Noises

Graph Convolutional Networks (GCNs) have been widely studied for attribute graph data representation and learning. In many applications, graph node attribute/feature may contain various kinds of noises, such as gross corruption, outliers and missing values. Existing graph convolutions (GCs) generally focus on feature propagation on structured graph which i) fail to address the graph data with missing values and ii) often perform susceptibility to the large feature errors/noises and outliers. To address this issue, in this paper, we propose to incorporate robust norm feature learning mechanism into graph convolution and present Robust Graph Convolutions (RGCs) for graph data in the presence of feature noises and missing values. Our RGCs is proposed based on the interpretation of GCs from a propagation function aspect of 'data reconstruction on graph'. Based on it, we then derive our RGCs by exploiting robust norm based propagation functions into GCs. Finally, we incorporate the derived RGCs into an end-to-end network architecture and propose a kind of RobustGCNs for graph data learning. Experimental results on several noisy datasets demonstrate the effectiveness and robustness of the proposed RobustGCNs.
Examples of Fano manifolds with non‐pseudoeffective tangent bundle

Let X$X$ be a Fano manifold. While the properties of the anticanonical divisor −KX$-K_X$ and its multiples have been studied by many authors, the positivity of the tangent bundle TX$T_X$ is much more elusive. We give a complete characterisation of the pseudoeffectivity of TX$T_X$ for del Pezzo surfaces, hypersurfaces in the projective space and del Pezzo threefolds.
Spin-orbit-coupling-assisted roton softening and superstripes in a Rydberg-dressed Bose-Einstein condensate

Rotons can exist in ultracold atomic gases either with long-range interactions or with spin-orbitcoupled dispersions. We find that two different kinds of rotons coexist in a joint system combining long-range interactions and spin-orbit coupling. One roton originates from spin-orbit coupling and two others come from long-range interactions. Their softening can be controlled separately. The interesting new phenomenon which we find is that spin-orbit-coupled roton can push down the energy of one long-range-interaction roton. The spin-orbit coupling accelerates the softening of this roton. The post phase of spin-orbit-coupling-assisted roton softening and instability is identified as a superstripe.
One model does not fit all: a multi-scale analysis of eighty-four cryptocurrencies

This letter expands the studies of the informational efficiency in the cryptocurrency market. Most studies have focused on Bitcoin, the foremost known cryptocurrency, and a few more coins. However, this market is more diverse, with cryptocurrencies entering and leaving the market on a weekly basis. This letter fills an important gap in the literature, by studying the informational efficiency using a multi-scaling methodology, which represents a new approach. We compute the generalized Hurst exponent of eighty-four cryptoassets daily returns. The multi-scaling methodology used in this paper find compelling evidence that cryptocurrencies have different degree of long range dependence, and --more importantly -- follow different stochastic processes. Some of them follow traditional monofractal models consistent with fractional Brownian motion, while others exhibit complex multifractal dynamics.
Modal descent

Abstract Any modality in homotopy type theory gives rise to an orthogonal factorization system of which the left class is stable under pullbacks. We show that there is a second orthogonal factorization system associated with any modality, of which the left class is the class of ○-equivalences and the right class is the class of ○-étale maps. This factorization system is called the modal reflective factorization system of a modality, and we give a precise characterization of the orthogonal factorization systems that arise as the modal reflective factorization system of a modality. In the special case of the n-truncation, the modal reflective factorization system has a simple description: we show that the n-étale maps are the maps that are right orthogonal to the map $${\rm{1}} \to {\rm{ }}{{\rm{S}}^{n + 1}}$$ . We use the ○-étale maps to prove a modal descent theorem: a map with modal fibers into ○X is the same thing as a ○-étale map into a type X. We conclude with an application to real-cohesive homotopy type theory and remark how ○-étale maps relate to the formally etale maps from algebraic geometry.
A Survey on Growth and Nutritional Status of Children with Cerebral Palsy in the Northwest Iran

Background: Cerebralpalsy(CP)istheleadingcauseof childhooddisability. ChildrenwithCPareatriskforgrowthandnutritional disorders. The current study aimed at evaluating the growth and nutritional status of children with CP and assessing the relationship between the studied variables, clinical presentation, disease severity, and functional level in these patients in Northwest Iran. Methods: In the current cross sectional, descriptive study, 99 outpatient children with CP referring to the rehabilitation clinic of children’sMedicalcenterinTabriz,Iran,werestudiedfora12-monthperiodfromJune2015toJune2016. Demographicvariables,CP type, anthropometric measures, and GMFCS (gross motor function classiﬁcation system) were recorded. The patients’ weight and height were measured, and then, weight-for-age and height-for-age percentiles were calculated based on the percentile charts for all patients. Dietary intake of the children was determined by the 24-hour dietary recalls of 3 nonconsecutive days (2 weekdays and 1 weekend day) by asking their parents as reliable reporters. Results: Sixty-four patients had inadequate calorie intake, while 47 and 42 patients had abnormal weight and height percentiles. QuadriplegicanddiplegictypesCP,comparedwiththehemiplegictype(P=0.003andP=0.043),andGMFCSIIItoV,comparedwith the classes I and II (P = 0.001), had signiﬁcantly less normal weight percentiles, but there were no signiﬁcant diﬀerences between normal and abnormal height percentiles, based on CP type and GMFCS level. Conclusions: Abnormal growth was signiﬁcantly common in children with quadriplegic and diplegic CP and high GMFCS level. About 64% of the children had inadequate calorie intake. Nutritional management should be arranged for the undernourished children to promote their nutritional status and improve their growth and functional capacities.
Equity Factors: To Short or Not to Short, That Is the Question

What is the best market-neutral implementation of classical equity factors? Should one use the specific predictability of the short leg to build a zero beta long–short portfolio, in spite of the specific costs associated to shorting, or is it preferable to ban the shorts and hedge the long leg with—say—an index future? The authors revisit this question by focusing on the relative predictability of the two legs, the issue of diversification, and various sources of costs. Their conclusion is that, using the same factors, a long–short implementation leads to superior risk-adjusted returns than its hedged long-only counterpart, at least when assets under management are not too large. Key Findings ▪ A toy model gives key insights on the long hedged investment versus long–short investment dilemma. ▪ The authors provide an in-depth analysis and revisiting of “When Equity Factors Drop Their Shorts” by Blitz, Baltussen, and van Vliet (2020). ▪ Realistic implementations of long hedged factor investing and long–short factor investing lead to different conclusions than those of Blitz, Baltussen, and van Vliet (2020).
On the limitations of single-step drift and minorization in Markov chain convergence analysis

Over the last three decades, there has been a considerable effort within the applied probability community to develop techniques for bounding the convergence rates of general state space Markov chains. Most of these results assume the existence of drift and minorization (d\&m) conditions. It has often been observed that convergence rate bounds based on single-step d\&m tend to be overly conservative, especially in high-dimensional situations. This article builds a framework for studying this phenomenon. It is shown that any convergence rate bound based on a set of d\&m conditions cannot do better than a certain unknown optimal bound. Strategies are designed to put bounds on the optimal bound itself, and this allows one to quantify the extent to which a d\&m-based convergence rate bound can be sharp. The new theory is applied to several examples, including a Gaussian autoregressive process (whose true convergence rate is known), and a Metropolis adjusted Langevin algorithm. The results strongly suggest that convergence rate bounds based on single-step d\&m conditions are quite inadequate in high-dimensional settings.
Fast Convergence and Asymptotic Preserving of the General Synthetic Iterative Scheme

Recently the general synthetic iteration scheme (GSIS) is proposed to find the steady-state solution of the Boltzmann equation~\cite{SuArXiv2019}, where various numerical simulations have shown that (i) the steady-state solution can be found within dozens of iterations at any Knudsen number $K$, and (ii) the solution is accurate even when the spatial cell size in the bulk region is much larger than the molecular mean free path, i.e. Navier-Stokes solutions are recovered at coarse grids. The first property indicates that the error decay rate between two consecutive iterations decreases to zero with $K$, while the second one implies that the GSIS is asymptotically preserving the Navier-Stokes limit. This paper is dedicated to the rigorous proof of both properties.
DP-Net: Dynamic Programming Guided Deep Neural Network Compression

In this work, we propose an effective scheme (called DP-Net) for compressing the deep neural networks (DNNs). It includes a novel dynamic programming (DP) based algorithm to obtain the optimal solution of weight quantization and an optimization process to train a clustering-friendly DNN. Experiments showed that the DP-Net allows larger compression than the state-of-the-art counterparts while preserving accuracy. The largest 77X compression ratio on Wide ResNet is achieved by combining DP-Net with other compression techniques. Furthermore, the DP-Net is extended for compressing a robust DNN model with negligible accuracy loss. At last, a custom accelerator is designed on FPGA to speed up the inference computation with DP-Net.
Navigation Systems May Deteriorate Stability in Traffic Networks

Advanced traffic navigation systems, which provide routing recommendations to drivers based on real-time congestion information, are nowadays widely adopted by roadway transportation users. Yet, the emerging effects on the traffic dynamics originating from the widespread adoption of these tools have remained largely unexplored until now. In this paper, we propose a dynamic model where drivers imitate the path preferences of previous drivers, and we study the properties of its equilibrium points. Our model is a dynamic generalization of the classical traffic assignment framework, and extends it by accounting for dynamics both in the path decision process and in the network's traffic flows. We show that when travelers learn shortest paths by imitating other travelers, the overall traffic system benefits from this mechanism and transfers the maximum admissible amount of traffic demand. On the other hand, we demonstrate that when the travel delay functions are not sufficiently steep or the rates at which drivers imitate previous travelers are not adequately chosen, the trajectories of the traffic system may fail to converge to an equilibrium point, thus failing asymptotic stability. Illustrative numerical simulations combined with empirical data from highway sensors illustrate our findings.
Anisotropic separate universe simulations

The long-wavelength coherent overdensity and tidal force, which are not direct observables for a finite-volume survey, affect time evolution of cosmic structure formation and therefore clustering observables through the mode coupling. In this paper, we develop an ‘anisotropic’ separate universe (SU) simulation technique to simulate large-scale structure formation taking into account the effect of large-scale tidal force into the anisotropic expansion of local background. We modify the treepmN-body simulation code to implement the anisotropic SU simulations, and then study the ‘response’ function of matter power spectrum that describes how the matter power spectrum responds to the large-scale tidal effect as a function of wavenumber and redshift for a given global cosmology. We test and validate the SU simulation results from the comparison with the perturbation theory predictions and the results from high-resolution particle-mesh simulations. We find that the response function displays characteristic scale dependencies over the range of scales down to non-linear scales, up to k ≃ 6 h Mpc−1.
GeoGraph: Learning graph-based multi-view object detection with geometric cues end-to-end

In this paper we propose an end-to-end learnable approach that detects static urban objects from multiple views, re-identifies instances, and finally assigns a geographic position per object. Our method relies on a Graph Neural Network (GNN) to, detect all objects and output their geographic positions given images and approximate camera poses as input. Our GNN simultaneously models relative pose and image evidence, and is further able to deal with an arbitrary number of input views. Our method is robust to occlusion, with similar appearance of neighboring objects, and severe changes in viewpoints by jointly reasoning about visual image appearance and relative pose. Experimental evaluation on two challenging, large-scale datasets and comparison with state-of-the-art methods show significant and systematic improvements both in accuracy and efficiency, with 2-6% gain in detection and re-ID average precision as well as 8x reduction of training time.
Self-Supervised 2D Image to 3D Shape Translation with Disentangled Representations

We present a framework to translate between 2D image views and 3D object shapes. Recent progress in deep learning enabled us to learn structure-aware representations from a scene. However, the existing literature assumes that pairs of images and 3D shapes are available for training in full supervision. In this paper, we propose SIST, a Self-supervised Image to Shape Translation framework that fulfills three tasks: (i) reconstructing the 3D shape from a single image; (ii) learning disentangled representations for shape, appearance and viewpoint; and (iii) generating a realistic RGB image from these independent factors. In contrast to the existing approaches, our method does not require image-shape pairs for training. Instead, it uses unpaired image and shape datasets from the same object class and jointly trains image generator and shape reconstruction networks. Our translation method achieves promising results, comparable in quantitative and qualitative terms to the state-of-the-art achieved by fully-supervised methods1.
A Boothby-Wang theorem for Besse contact forms

We prove a Boothby-Wang type theorem for Besse Reeb flows that are not necessarily Zoll, i.e. Reeb flows all of whose orbits are periodic, but possibly with different periods. More precisely, we characterize contact manifolds whose Reeb flows are Besse as principal circle-orbibundles over integral symplectic orbifolds satisfying some cohomological condition. As a corollary of this and of a result by Cristofaro-Gardiner and Mazzucchelli we obtain a complete classification of closed Besse contact 3-manifolds up to strict contactomorphism.
Improving Calibration in Mixup-trained Deep Neural Networks through Confidence-Based Loss Functions

Deep Neural Networks (DNN) represent the state of the art in many tasks. However, due to their overparameterization, their generalization capabilities are in doubt and are still under study. Consequently, DNN can overfit and assign overconfident predictions, as they tend to learn highly oscillating decision thresholds. This has been shown to affect the calibration of the confidences assigned to unseen data. Data Augmentation (DA) strategies have been proposed to overcome some of these limitations. One of the most popular is Mixup, which has shown a great ability to improve the accuracy of these models. Recent work has provided evidence that Mixup also improves the uncertainty quantification and calibration of DNN. In this work, we argue and provide empirical evidence that, due to its fundamentals, Mixup does not necessarily improve calibration. Based on our observations we propose a new loss function that improves the calibration, and also sometimes the accuracy. Our loss is inspired by Bayes decision theory and introduces a new training framework for designing losses for probabilistic modelling. We provide state-of-the-art accuracy with consistent improvements in calibration performance.
Attention U-Net Based Adversarial Architectures for Chest X-ray Lung Segmentation information

Chest X-ray is the most common test among medical imaging modalities. It is applied for detection and differentiation of, among others, lung cancer, tuberculosis, and pneumonia, the last with importance due to the COVID-19 disease. Integrating computer-aided detection methods into the radiologist diagnostic pipeline, greatly reduces the doctors' workload, increasing reliability and quantitative analysis. Here we present a novel deep learning approach for lung segmentation, a basic, but arduous task in the diagnostic pipeline. Our method uses state-of-the-art fully convolutional neural networks in conjunction with an adversarial critic model. It generalized well to CXR images of unseen datasets with different patient profiles, achieving a final DSC of 97.5% on the JSRT dataset.
Td
 to 
1T′
 structural phase transition in the 
WTe2
 Weyl semimetal

Elastic neutron scattering on a single crystal and powder X-ray diffraction measurements were carried out to investigate how the crystal structure evolves as a function of temperature in the Weyl semimetal WTe$_{2}$. A sharp transition from the low-temperature orthorhombic phase (T$_{d}$) to the high-temperature monoclinic phase (1T$^{\prime}$) was observed at ambient pressure in the single crystal near $\sim$565 K. Unlike in MoTe$_{2}$, the solid-solid transition from T$_{d}$ to 1T$^{\prime}$ occurs without the cell doubling of the intermediate T$_{d}^{*}$ phase with AABB (or ABBA) layer stacking. In powders however, the thermal transition from the T$_{d}$ to the 1T$^{\prime}$ phase is broadened and a two phase coexistence was observed until 700K, well above the structural transition.
Caregivers’ Environmental Barriers in Caregiving in Adults with a Terminal Illness

Caring for a patient at the end of their life can be highly demanding and becomes a challenge for caregivers. The participants were recruited using a convenience sampling of ten caregivers who provided the most care to an adult with a terminal illness. All interviews were recorded, transcribed verbatim and analyzed using the six steps of Interpretative Phenomenological Analysis (IPA). The findings showed that the environmental barriers caregivers had recognized were transportation, insufficient material resources, and remote home facilities. By acknowledging these barriers, healthcare professionals can design and implement therapeutic environments that are clinically relevant toward those most in need.Keywords: Environmental barriers; terminal illness; adult; caregivereISSN 2398-4295 © 2017 The Authors. Published for AMER ABRA by e-International Publishing House, Ltd., UK. This is an open-access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.
Multilingual Probing Tasks for Word Representations

Despite an ever growing number of word representation models introduced for a large number of languages, there is a lack of a standardized technique to provide insights into what is captured by these models. Such insights would help the community to get an estimate of the downstream task performance, as well as to design more informed neural architectures, while avoiding extensive experimentation which requires substantial computational resources not all researchers have access to. A recent development in NLP is to use simple classification tasks, also called probing tasks, that test for a single linguistic feature such as part-of-speech. Existing studies mostly focus on exploring the linguistic information encoded by the continuous representations of English text. However, from a typological perspective the morphologically poor English is rather an outlier: the information encoded by the word order and function words in English is often stored on a subword, morphological level in other languages. To address this, we introduce 15 type-level probing tasks such as case marking, possession, word length, morphological tag count and pseudoword identification for 24 languages. We present a reusable methodology for creation and evaluation of such tests in a multilingual setting, which is challenging due to lack of resources, lower quality of tools and differences among languages. We then present experiments on several diverse multilingual word embedding models, in which we relate the probing task performance for a diverse set of languages to a range of five classic NLP tasks: POS-tagging, dependency parsing, semantic role labeling, named entity recognition and natural language inference. We find that a number of probing tests have significantly high positive correlation to the downstream tasks, especially for morphologically rich languages. We show that our tests can be used to explore word embeddings or black-box neural models for linguistic cues in a multilingual setting. We release the probing datasets and the evaluation suite LINSPECTOR with https://github.com/UKPLab/linspector.
Who are the acknowledgees? An analysis of gender and academic status

Acknowledgements found in scholarly papers allow for credit attribution of nonauthor contributors. As such, they are associated with a different kind of recognition than authorship. While several studies have shown that social factors affect authorship and citation practices, few analyses have been performed on acknowledgements. Based on 878,250 acknowledgees mentioned in 291,167 papers published between 2015 and 2017, this study analyzes the gender and academic status of individuals named in the acknowledgements of scientific papers. Our results show that gender disparities generally found in authorship can be extended to acknowledgements, and that women are even more underrepresented in acknowledgements section than in authors’ lists. Our findings also show that women acknowledge proportionally more women than men do. Regarding academic status, our results show that acknowledgees who have already published tend to have a higher position in the academic hierarchy compared with all Web of Science (WoS) authors. Taken together, these findings suggest that acknowledgement practices might be associated with academic status and gender.