Miracle-dz/UniFAR-FTUs huggingface dataset:
A Case Report on Premature Twins: Primary Congenital Glaucoma or Large Cupping Disks Mimicking Primary Congenital Glaucoma?

Large cupping of the optic disk in a baby or a child can be indicative of primary congenital glaucoma. Primary congenital glaucoma is often refractory to treatment, and lifelong management and follow-up are necessary; therefore, diagnosis requires careful consideration. In this study, we describe the case of twins in whom primary congenital glaucoma was initially suspected due to large cupping of the optic disks. Twin babies (a boy and a girl weighing 455 g and 592 g respectively), born prematurely (at 28 gestational weeks), were referred to our hospital eight months after birth because large cupping of the optic disks was detected during follow-up for retinopathy of prematurity. According to color fundus photographs, the cup/disk ratios in both eyes of both babies ranged from 0.75 to 0.86. However, the axial length ranged from 18.57 to 19.91 mm, the anterior chamber depth ranged from 2.68 to 2.93 mm, and the horizontal diameters of the corneas, which were clear, ranged from 10 to 10.5 mm. The intraocular pressures (IOPs), as measured by a rebound tonometer, were 15.3-19.7 mmHg. Glaucoma was strongly suspected due to the large cupping of the optics disks; however, other ocular biometric tests demonstrated that the eyes were normal. After eight months of follow-up without any medication or intervention, the IOPs stabilized between 6-22.1 mmHg, the refractive errors were between -2.5 and 0 diopters, and we found no apparent enlargement of the optic disks. In addition, we investigated both parents’ optic disks to evaluate the genetic factors and found that they had relatively large C/D ratios (0.68-0.79). These premature twins exhibited glaucoma-like optic disks with large cupping, but no solid glaucomatous changes were observed with ocular biometry and IOP testing. We concluded, therefore, that the early birth and lower birth weights may have been associated with the large cupping of the patients’ optic disks. To differentiate between normal physiological cupping and primary congenital glaucoma, ocular morphological examination of the eye, IOP measurements, and investigation of the parents’ optic disks were useful.
HeMI: Multi-view Embedding in Heterogeneous Graphs

Many real-world graphs involve different types of nodes and relations between nodes, being heterogeneous by nature. The representation learning of heterogeneous graphs (HGs) embeds the rich structure and semantics of such graphs into a low-dimensional space and facilitates various data mining tasks, such as node classification, node clustering, and link prediction. In this paper, we propose a self-supervised method that learns HG representations by relying on knowledge exchange and discovery among different HG structural semantics (meta-paths). Specifically, by maximizing the mutual information of meta-path representations, we promote meta-path information fusion and consensus, and ensure that globally shared semantics are encoded. By extensive experiments on node classification, node clustering, and link prediction tasks, we show that the proposed self-supervision both outperforms and improves competing methods by 1% and up to 10% for all tasks.
The ELITR ECA Corpus

We present the ELITR ECA corpus, a multilingual corpus derived from publications of the European Court of Auditors. We use automatic translation together with Bleualign to identify parallel sentence pairs in all 506 translation directions. The result is a corpus comprising 264k document pairs and 41.9M sentence pairs.
Novel Approaches for the Analysis of Phytosteryl/-stanyl Fatty Acid Esters and Phytosterol Oxidation Products in Enriched Foods

For the analysis of intact individual phytosteryl and/or phytostanyl fatty acid esters in enriched foods, UHPLC-APCI-MS-based methodologies were established. For phytosterol oxidation products resulting thereof, on-line LC-GC was implemented as novel analytical platform. The developed methods were employed to investigate the effects of heating processes on the decreases of intact esters and the formation of oxidation products in enriched margarines.
PointManifoldCut: Point-wise Augmentation in the Manifold for Point Clouds

Mixed-based point cloud augmentation is a popular solution to the problem of limited availability of large-scale public datasets. But the mismatch between mixed points and corresponding semantic labels hinders the further application in point-wise tasks such as part segmentation. This paper proposes a point cloud augmentation approach, PointManifoldCut(PMC), which replaces the neural network embedded points, rather than the Euclidean space coordinates. This approach takes the advantage that points at the higher levels of the neural network are already trained to embed its neighbors relations and mixing these representation will not mingle the relation between itself and its label. We set up a spatial transform module after PointManifoldCut operation to align the new instances in the embedded space. The effects of different hidden layers and methods of replacing points are also discussed in this paper. The experiments show that our proposed approach can enhance the performance of point cloud classification as well as segmentation networks, and brings them additional robustness to attacks and geometric transformations. The code of this paper is available at: https://github.com/fun0515/PointManifoldCut.
Automatic Symmetry Discovery with Lie Algebra Convolutional Network

Existing equivariant neural networks require prior knowledge of the symmetry group and discretization for continuous groups. We propose to work with Lie algebras (infinitesimal generators) instead of Lie groups. Our model, the Lie algebra convolutional network (L-conv) can automatically discover symmetries and does not require discretization of the group. We show that L-conv can serve as a building block to construct any group equivariant feedforward architecture. Both CNNs and Graph Convolutional Networks can be expressed as L-conv with appropriate groups. We discover direct connections between L-conv and physics: (1) group invariant loss generalizes field theory (2) Euler-Lagrange equation measures the robustness, and (3) equivariance leads to conservation laws and Noether current.These connections open up new avenues for designing more general equivariant networks and applying them to important problems in physical sciences
Linear-Time Contact and Friction Dynamics in Maximal Coordinates using Variational Integrators

Simulation of contact and friction dynamics is an important basis for control- and learning-based algorithms. However, the numerical difficulties of contact interactions pose a challenge for robust and efficient simulators. A maximal-coordinate representation of the dynamics enables efficient solving algorithms, but current methods in maximal coordinates require constraint stabilization schemes. Therefore, we propose an interior-point algorithm for the numerically robust treatment of rigid-body dynamics with contact interactions in maximal coordinates. Additionally, we discretize the dynamics with a variational integrator to prevent constraint drift. Our algorithm achieves linear-time complexity both in the number of contact points and the number of bodies, which is shown theoretically and demonstrated with an implementation. Furthermore, we simulate two robotic systems to highlight the applicability of the proposed algorithm.
Efficient Domain Adaptation of Language Models via Adaptive Tokenization

Contextual embedding-based language models trained on large data sets, such as BERT and RoBERTa, provide strong performance across a wide range of tasks and are ubiquitous in modern NLP. It has been observed that fine-tuning these models on tasks involving data from domains different from that on which they were pretrained can lead to suboptimal performance. Recent work has explored approaches to adapt pretrained language models to new domains by incorporating additional pretraining on domain-specific corpora and task data. We propose an alternative approach for transferring pretrained language models to new domains by adapting their tokenizers. We show that domain-specific subword sequences can be determined efficiently directly from divergences in the conditional token distributions of the base and domain-specific corpora. In datasets from four disparate domains, we find adaptive tokenization on a pretrained RoBERTa model provides greater than 85% of the performance benefits of domain specific pretraining. Our approach produces smaller models and less training and inference time than other approaches using tokenizer augmentation. Although using adaptive tokenization incurs a 6% increase in model parameters (due to the introduction of 10k new domain-specific tokens), our approach, using 64 CPUs, is >72x faster than further pretraining the language model on domain-specific corpora on 8 TPUs.
Shape-adaptive Hysteresis Compensation for Tendon-driven Continuum Manipulators

Tendon-driven continuum manipulators (TDCM) are commonly used in minimally invasive surgical systems due to their long, thin, flexible structure that is compliant in narrow or tortuous environments. There exist many researches for precise tip control of the articulating section. However, these models do not account for the proximal shaft shape of TDCM, affecting the tip controls in practical settings. In this paper, we propose a gradient-based shift detection method based on motor current that can easily find the offset of task space models (i.e., hysteresis). We analyze our proposed methods with multiple Intra-cardiac Echocardiography catheters, which are typical commercial example of TDCM. Our results show that the errors from varied proximal shape are considerably reduced, and the accuracy of the tip manipulation is improved when changing external environmental structures.
How Much do Lyrics Matter? Analysing Lyrical Simplicity Preferences for Individuals At Risk of Depression

Music affects and in some cases reflects one's emotional state. Key to this influence is lyrics and their meaning in conjunction with the acoustic properties of the track. Recent work has focused on analysing these acoustic properties and showing that individuals prone to depression primarily consume low valence and low energy music. However, no studies yet have explored lyrical content preferences in relation to online music consumption of such individuals. In the current study, we examine lyrical simplicity, measured as the Compressibility and Absolute Information Content of the text, associated with preferences of individuals at risk for depression. Using the six-month listening history of 541 Last.fm users, we compare lyrical simplicity trends for users grouped as being at risk (At-Risk) of depression from those that are not (No-Risk). Our findings reveal that At-Risk individuals prefer songs with greater information content (lower Compressibility) on average, especially for songs characterised as Sad. Furthermore, we found that At-Risk individuals also have greater variability of Absolute Information Content across their listening history. We discuss the results in light of existing socio-psychological lab-based research on music habits associated with depression and their relevance to naturally occurring online music listening behaviour.
Spline-PINN: Approaching PDEs without Data using Fast, Physics-Informed Hermite-Spline CNNs

Partial Differential Equations (PDEs) are notoriously difficult to solve. In general, closed form solutions are not available and numerical approximation schemes are computationally expensive. In this paper, we propose to approach the solution of PDEs based on a novel technique that combines the advantages of two recently emerging machine learning based approaches. First, physics-informed neural networks (PINNs) learn continuous solutions of PDEs and can be trained with little to no ground truth data. However, PINNs do not generalize well to unseen domains. Second, convolutional neural networks provide fast inference and generalize but either require large amounts of training data or a physics-constrained loss based on finite differences that can lead to inaccuracies and discretization artifacts. We leverage the advantages of both of these approaches by using Hermite spline kernels in order to continuously interpolate a grid-based state representation that can be handled by a CNN. This allows for training without any precomputed training data using a physics-informed loss function only and provides fast, continuous solutions that generalize to unseen domains. We demonstrate the potential of our method at the examples of the incompressible Navier-Stokes equation and the damped wave equation. Our models are able to learn several intriguing phenomena such as Karman vortex streets, the Magnus effect, Doppler effect, interference patterns and wave reflections. Our quantitative assessment and an interactive real-time demo show that we are narrowing the gap in accuracy of unsupervised ML based methods to industrial solvers for computational fluid dynamics (CFD) while being orders of magnitude faster.
Collider search of light dark matter model with dark sector decay

We explore the possibility that the dark matter relic density is not produced by thermal mechanism directly, but by the decay of other heavier dark sector particles which on the other hand can be produced by the thermal freeze-out mechanism. Using a concrete model with a light dark matter from dark sector decay, we study the collider signature of the dark sector particles in association with Higgs production processes. We find that the future lepton colliders can be a better place to probe the signature of this kind of light dark matter model than the hadron collider such as LHC. Meanwhile, it is found that a Higgs factory with center of mass energy 250 GeV has a better potential to resolve the signature of this kind of light dark matter model than the Higgs factory with center of mass energy 350 GeV. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.
RankNAS: Efficient Neural Architecture Search by Pairwise Ranking

This paper addresses the efficiency challenge of Neural Architecture Search (NAS) by formulating the task as a ranking problem. Previous methods require numerous training examples to estimate the accurate performance of architectures, although the actual goal is to find the distinction between “good” and “bad” candidates. Here we do not resort to performance predictors. Instead, we propose a performance ranking method (RankNAS) via pairwise ranking. It enables efficient architecture search using much fewer training examples. Moreover, we develop an architecture selection method to prune the search space and concentrate on more promising candidates. Extensive experiments on machine translation and language modeling tasks show that RankNAS can design high-performance architectures while being orders of magnitude faster than state-of-the-art NAS systems.
Résultats préliminaires de l'emploi de bagasse dans un procédé de remédiation de sols chargés en chlordécone

La pollution par la chlordecone des sols et des eaux aux Antilles represente un danger qu'il est urgent de diminuer. Cette molecule persistante montre des taux d'abattements superieur a 70% suite au traitement de sols par In Situ Chemical Reduction (ISCR), processus combinant une phase biotique et abiotique avec un amendement commercialise sous le nom de Daramend®. Ce procede permet une rehabilitation ecologiquement rationnelle en termes d'espace de traitement utilise, d'energie consommee et de ressources utilisees. Notre travail consiste ici a employer comme matiere organique le residu de la canne a sucre, la bagasse. Produite aux Antilles, la bagasse est riche en carbone, et peut, en melange avec des particules de fer zero valent, constituer la base d'un processus d'ISCR mettant en œuvre des produits locaux. A echelle du laboratoire, nous avons etudie la degradation de la chlordecone dans du sable de Fantainbleau dope. Quatre conditions de traitement ont ete testees : la bagasse seule, le fer zero valent seul (FZV), un melange de fer zero valent et de bagasse, et le Daramend®. Les parametres physico-chimiques (potentiel redox et pH) ainsi que les concentrations de la chlordecone ont ete suivis au cours de deux phases d'anaerobie separes par une phase d'aerobie. Nos premiers resultats montrent entre 16 % a 50% de reduction des concentrations de chlordecone par des traitements de bagasse seule et entre 51% a 71% avec le melange bagasse/FZV. Bien que la degradation du pesticide soit apparemment totale avec le FZV, les pH tres basiques engendres rendent ce traitement difficilement applicable sur le terrain. L'addition de la bagasse au FZV diminue le pH a 7.6 permettant un traitement plus compatible avec le maintien des proprietes du sol et aussi efficace pour degrader la chlordecone.
fairseq Sˆ2: A Scalable and Integrable Speech Synthesis Toolkit

This paper presents fairseq Sˆ2, a fairseq extension for speech synthesis. We implement a number of autoregressive (AR) and non-AR text-to-speech models, and their multi-speaker variants. To enable training speech synthesis models with less curated data, a number of preprocessing tools are built and their importance is shown empirically. To facilitate faster iteration of development and analysis, a suite of automatic metrics is included. Apart from the features added specifically for this extension, fairseq Sˆ2 also benefits from the scalability offered by fairseq and can be easily integrated with other state-of-the-art systems provided in this framework. The code, documentation, and pre-trained models will be made available at https://github.com/pytorch/fairseq/tree/master/examples/speech_synthesis.
Patient-specific solution of the electrocorticography forward problem using biomechanics-based image registration

Invasive intracranial electroencephalography (iEEG) or electrocorticography (ECoG) measures electrical potential directly on the surface of the brain, and, combined with numerical modeling, can be used to inform treatment planning for epilepsy surgery. Accurate solution of the iEEG or ECoG forward problem, which is a crucial prerequisite for solving the inverse problem in epilepsy seizure onset localization, requires accurate representation of the patient’s brain geometry and tissue electrical conductivity after implantation of electrodes. However, implantation of subdural grid electrodes causes the brain to deform, which invalidates preoperatively acquired image data. Moreover, postoperative magnetic resonance imaging (MRI) is incompatible with implanted electrodes and computed tomography (CT) has insufficient range of soft tissue contrast, which precludes both MRI and CT from being used to obtain the deformed postoperative geometry. In this paper, we present a biomechanics-based image warping procedure using preoperative MRI for tissue classification and postoperative CT for locating implanted electrodes to perform non-rigid registration of the preoperative image data to the postoperative configuration. We solve the iEEG forward problem on the predicted postoperative geometry using the finite element method (FEM) which accounts for patient-specific inhomogeneity and anisotropy of tissue conductivity. Results for the simulation of a current source in the brain show large differences in electrical potential (RDM ≥ 0.18 and MAG ≥ 0.84) predicted by the models based on the original images and the deformed images corresponding to the brain geometry deformed by placement of invasive electrodes. Computation of the leadfield matrix (useful for solution of the iEEG inverse problem) also showed significant differences between the different models. The results suggest that significant improvements in source localization accuracy may be realized by the application of the proposed modeling methodology.
NonReciprocal Wave Propagation in Space-Time Modulated Media

We prove the possibility of achieving non-reciprocal wave propagation in space-time modulated media and give an asymptotic analysis of the non-reciprocity property in terms of the amplitude of the time-modulation. Such modulation causes a folding of the band structure of the material, which may induce degenerate points. By breaking time-reversal symmetry, we show that these degeneracies may open into non-symmetric, unidirectional band gaps. Finally we illustrate our results by several numerical simulations.
Sputtered 2D transition metal dichalcogenides: from growth to device applications

Starting from graphene, 2D layered materials family has been recently set up more than 100 different materials with variety of different class of materials such as semiconductors, metals, semimetals, superconductors. Among these materials, 2D semiconductors have found especial importance in the state of the art device applications compared to that of the current conventional devices such as (which material based for example Si based) field effect transistors (FETs) and photodetectors during the last two decades. This high potential in solid state devices is mostly revealed by the transition metal dichalcogenides (TMDCs) semiconductor materials such as MoS2 , WS2 , MoSe2 and WSe2 . Therefore, many different methods and approaches have been developed to grow or obtain so far in order to make use them in solid state devices, which is a great challenge in large area applications. Although there are intensively studied methods such as chemical vapor deposition (CVD), mechanical exfoliation, atomic layer deposition, it is sputtering getting attention day by day due to the simplicity of the growth method together with its reliability, large area growth possibility and repeatability. In this review article, we provide benefits and disadvantages of all the growth methods when growing TMDC materials, then focusing on the sputtering TMDC growth strategies performed. In addition, TMDCs for the FETs and photodetector devices grown by RFMS have been surveyed.
Correction to the article Surface bundles over surfaces with arbitrarily many fiberings

In this paper we give the first example of a surface bundle over a surface with at least three fiberings. In fact, for each n ≥ 3 we construct 4-manifolds E admitting at least n distinct fiberings pi : E → Σgi as a surface bundle over a surface with base and fiber both closed surfaces of negative Euler characteristic. We give examples of surface bundles admitting multiple fiberings for which the monodromy representation has image in the Torelli group, showing the necessity of all of the assumptions made in the main theorem of our recent paper [Sal14]. Our examples show that the number of surface bundle structures that can be realized on a 4-manifold E with Euler characteristic d grows exponentially with d.