Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinicopathologic Study of Ewing ’ s Sarcoma in 82 Sudanese Patients Diagnosed at Histo Centre / Khartoum ( 2013-2018 )

Ewing’s sarcoma family of tumors is group of aggressive malignant neoplasms. They are the second most common childhood bone tumours after osteosarcoma accounting for 5-10% of paediatric bone tumours [1], in Sudan there is no data registry for Ewing’s sarcoma and more studies and documentation are required in this field. A common feature shared among these group of sarcomas is the presence of chimeric transcript, resulting from fusion of the EWS gene with genes that encode for structurally related transcription factors, usually FLI1 or ERG2 [2]. They show a morphologic features of undifferentiated small round blue cell tumour with varying degree of neuroendocrine differentiation. Ewing’s sarcoma family of tumours are in general called ES/ PNET and include: Ewing sarcoma of bone, extra-osseous Ewing’s sarcoma and peripheral primitive neuroendocrine tumour which can be osseous or extra-osseous, and called Askin tumour when it ABSTRACT
Pre-training for Spoken Language Understanding with Joint Textual and Phonetic Representation Learning

In the traditional cascading architecture for spoken language understanding (SLU), it has been observed that automatic speech recognition errors could be detrimental to the performance of natural language understanding. End-to-end (E2E) SLU models have been proposed to directly map speech input to desired semantic frame with a single model, hence mitigating ASR error propagation. Recently, pre-training technologies have been explored for these E2E models. In this paper, we propose a novel joint textual-phonetic pre-training approach for learning spoken language representations, aiming at exploring the full potentials of phonetic information to improve SLU robustness to ASR errors. We explore phoneme labels as high-level speech features, and design and compare pre-training tasks based on conditional masked language model objectives and inter-sentence relation objectives. We also investigate the efficacy of combining textual and phonetic information during fine-tuning. Experimental results on spoken language understanding benchmarks, Fluent Speech Commands and SNIPS, show that the proposed approach significantly outperforms strong baseline models and improves robustness of spoken language understanding to ASR errors.
Multipolar Fermi-surface deformation in a Rydberg-dressed Fermi gas with long-range anisotropic interactions

We study theoretically the deformation of the Fermi surface (FS) of a three-dimensional gas of Rydberg-dressed $^6$Li atoms. The laser dressing to high-lying Rydberg $D$ states results in angle-dependent soft-core-shaped interactions whose anisotropy is described by multiple spherical harmonics. We show that this can drastically modify the shape of the FS and that its deformation depends on the interplay between the Fermi momentum $k_F$ and the reciprocal momentum $\bar{k}$ corresponding to the characteristic soft-core radius of the dressing-induced potential. When $k_F<\bar{k}$, the dressed interaction stretches a spherical FS into an ellipsoid. When $k_F\gtrsim \bar{k}$, complex deformations are encountered which exhibit multipolar characteristics. We analyze the formation of Cooper pairs around the deformed FS and show that they occupy large orbital angular momentum states ($p$, $f$, and $h$ wave) coherently. Our study demonstrates that Rydberg dressing to high angular momentum states may pave a route toward the investigation of unconventional Fermi gases and multiwave superconductivity.
Comparison of active region upflow and core properties using simultaneous spectroscopic observations from IRIS and Hinode

Context. The origin of the slow solar wind is still an open issue. It has been suggested that upflows at the edge of active regions are a possible source of the plasma outflow and therefore contribute to the slow solar wind. Aims. We investigate the origin and morphology of the upflow regions and compare the upflow region and the active region core properties. Methods. We studied how the plasma properties of flux, Doppler velocity, and non-thermal velocity change throughout the solar atmosphere, from the chromosphere via the transition region to the corona in the upflow region and the core of an active region. We studied limb-to-limb observations of the active region (NOAA 12687) obtained from 14 to 25 November 2017. We analysed spectroscopic data simultaneously obtained from IRIS and Hinode/EIS in the six emission lines Mg II 2796. 4Å, C II 1335. 71Å, Si IV 1393. 76Å, Fe XII 195. 12Å, Fe XIII 202. 04Å, and Fe XIV 270. 52Å and 274. 20Å. We studied the mutual relationships between the plasma properties for each emission line, and we compared the plasma properties between the neighbouring formation temperature lines. To find the most characteristic spectra, we classified the spectra in each wavelength using the machine learning technique k-means. Results. We find that in the upflow region the Doppler velocities of the coronal lines are strongly correlated, but the transition region and coronal lines show no correlation. However, their fluxes are strongly correlated. The upflow region has a lower density and lower temperature than the active region core. In the upflow region, the Doppler velocity and non-thermal velocity show a strong correlation in the coronal lines, but the correlation is not seen in the active region core. At the boundary between the upflow region and the active region core, the upflow region shows an increase in the coronal non-thermal velocity, the emission obtained from the DEM, and the domination of the redshifted regions in the chromosphere. Conclusions. The obtained results suggest that at least three parallel mechanisms generate the plasma upflow: (1) The reconnection between closed loops and open magnetic field lines in the lower corona or upper chromosphere; (2) the reconnection between the chromospheric small-scale loops and open magnetic field; and (3) the expansion of the magnetic field lines that allows the chromospheric plasma to escape to the solar corona.
An analytic hybrid halo + perturbation theory model for small-scale correlators: baryons, halos, and galaxies

We update Halo Zeldovich Perturbation Theory (HZPT, [1]), an analytic model for the two-point statistics of dark matter, to describe halo and galaxy clustering, and galaxy-matter cross-correlation on nonlinear scales. The model correcting Zeldovich has an analytic Fourier transform, and therefore is valid in both configuration space and Fourier space. The model is accurate at the 2%-level or less for P_mm (k< 1 h/Mpc), P_hm (k< 1 h/Mpc), P_hh (k< 2 h/Mpc), Pgm (k< 1 h/Mpc), Pgg (k< 1 h/Mpc), ξmm (r> 1 Mpc/h), ξhm (r> 2 Mpc/h), ξhh (r> 2 Mpc/h), ξgm (r> 1 Mpc/h), ξgg (r> 2 Mpc/h), for LRG-like mock galaxies. We show that the HZPT model for matter correlators can account for the effects of a wide range of baryonic feedback models and provide two extended dark matter models which are of 1% (3%) accuracy for k < 10 (8) h/Mpc. We explicitly model the non-perturbative features of halo exclusion for the halo-halo and galaxy-galaxy correlators, as well as the presence of satellites for galaxy-matter and galaxy-galaxy correlation functions. We perform density estimation using N-body simulations and a wide range of HOD galaxy mocks to obtain correlations of model parameters with the cosmological parameters Ωm and σ8. HZPT can provide a fast, interpretable, and analytic model for combined-probe analyses of redshift surveys using scales well into the non-linear regime.
The SAMI Galaxy Survey: stellar population and structural trends across the Fundamental Plane

We study the Fundamental Plane (FP) for a volume- and luminosity-limited sample of 560 early-type galaxies from the SAMI survey. Using r-band sizes and luminosities from new multi-Gaussian expansion photometric measurements, and treating luminosity as the dependent variable, the FP has coefficients a = 1.294 ± 0.039, b = 0.912 ± 0.025, and zero-point c = 7.067 ± 0.078. We leverage the high signal-to-noise ratio of SAMI integral field spectroscopy, to determine how structural and stellar population observables affect the scatter about the FP. The FP residuals correlate most strongly (8σ significance) with luminosity-weighted simple stellar population (SSP) age. In contrast, the structural observables surface mass density, rotation-to-dispersion ratio, Sérsic index, and projected shape all show little or no significant correlation. We connect the FP residuals to the empirical relation between age (or stellar mass-to-light ratio Υ⋆ ) and surface mass density, the best predictor of SSP age amongst parameters based on FP observables. We show that the FP residuals (anti)correlate with the residuals of the relation between surface density and Υ⋆ . This correlation implies that part of the FP scatter is due to the broad age and Υ⋆ distribution at any given surface mass density. Using virial mass and Υ⋆, we construct a simulated FP and compare it to the observed FP. We find that, while the empirical relations between observed stellar population relations and FP observables are responsible for most (75 per cent) of the FP scatter, on their own they do not explain the observed tilt of the FP away from the virial plane.
Fault-tolerant resource estimate for quantum chemical simulations: Case study on Li-ion battery electrolyte molecules

We estimate the resources required in the fusion-based quantum computing scheme to simulate electrolyte molecules in Li-ion batteries on a fault-tolerant, photonic quantum computer. We focus on the molecules that can provide practical solutions to industrially relevant problems. Certain fault-tolerant operations require the use of single-qubit"magic states"prepared by dedicated"magic state factories"(MSFs). Producing and consuming magic states in parallel is typically a prohibitively expensive task, resulting in the serial application of fault-tolerant gates. However, for the systems considered, the MSF constitutes a negligible fraction of the total footprint of the quantum computer, allowing for the use of multiple MSFs to produce magic states in parallel. We suggest architectural and algorithmic techniques that can accommodate such a capability. We propose a method to consume multiple magic states simultaneously, which can potentially lead to an order of magnitude reduction in the computational runtime without additional expense in the footprint.
Variation of the core lifetime and fragmentation scale in molecular clouds as an indication of ambipolar diffusion

Ambipolar diffusion likely plays a pivotal role in the formation and evolution of dense cores in weakly ionized molecular clouds. Linear analyses show that the evolutionary times and fragmentation scales are significantly greater than the hydrodynamic (Jeans) values even for clouds with mildly supercritical mass-to-flux ratios. We use values of fragmentation scales and growth times that correspond to typical ionization fractions within a molecular cloud, and apply these in the context of the observed estimated lifetime of prestellar cores and the observed number of such embedded cores forming in a parent clump. By varying a single parameter – the mass-to-flux ratio – over the range of observationally measured densities, we fit the range of estimated prestellar core lifetimes (∼0.1 to a few Myr) identified with Herschel as well as the number of embedded cores formed in a parent clump measured in Perseus with the Submillimeter Array. Our model suggests that the prestellar cores are formed with a transcritical mass-to-flux ratio and higher densities correspond to somewhat higher mass-to-flux ratios, but the normalized mass-to-flux ratio μ remains in the range 1 ≲ μ ≲ 2. Our best-fit model exhibits B ∝ n0.43 for prestellar cores because of the partial flux-freezing caused by ambipolar diffusion.
Lossless Compression with Latent Variable Models

We develop a simple and elegant method for lossless compression using latent variable models, which we call ‘bits back with asymmetric numeral systems’ (BB-ANS). The method involves interleaving encode and decode steps, and achieves an optimal rate when compressing batches of data. We demonstrate it firstly on the MNIST test set, showing that state-of-the-art lossless compression is possible using a small variational autoencoder (VAE) model. We then make use of a novel empirical insight, that fully convolutional generative models, trained on small images, are able to generalize to images of arbitrary size, and extend BB-ANS to hierarchical latent variable models, enabling state-of-the-art lossless compression of full-size colour images from the ImageNet dataset. We describe ‘Craystack’, a modular software framework which we have developed for rapid prototyping of compression using deep generative models.
End-to-end Speech Translation via Cross-modal Progressive Training

End-to-end speech translation models have become a new trend in research due to their potential of reducing error propagation. However, these models still suffer from the challenge of data scarcity. How to effectively use unlabeled or other parallel corpora from machine translation is promising but still an open problem. In this paper, we propose Cross Speech-Text Network (XSTNet), an end-to-end model for speech-to-text translation. XSTNet takes both speech and text as input and outputs both transcription and translation text. The model benefits from its three key design aspects: a self-supervised pre-trained sub-network as the audio encoder, a multi-task training objective to exploit additional parallel bilingual text, and a progressive training procedure. We evaluate the performance of XSTNet and baselines on the MuST-C En-X and LibriSpeech En-Fr datasets. In particular, XSTNet achieves state-of-the-art results on all language directions with an average BLEU of 28.8, outperforming the previous best method by 3.2 BLEU. Code, models, cases, and more detailed analysis are available at https://github.com/ReneeYe/XSTNet.
A novel approach to optimize the regularization and evaluation of dynamical models using a model selection framework

Orbit superposition models are a non-parametric dynamical modelling technique to determine the mass of a galaxy’s central supermassive black hole (SMBH), its stars, or its dark matter halo. One of the main problems is how to decide which model out of a large pool of trial models based on different assumed mass distributions represents the true structure of an observed galaxy best. We show that the traditional approach to judge models solely by their goodness-of-fit can lead to substantial biases in estimated galaxy properties caused by varying model flexibilities. We demonstrate how the flexibility of the models can be estimated using bootstrap iterations and present a model selection framework that removes these biases by taking the variable flexibility into account in the model evaluation. We extend the model selection approach to optimize the degree of regularization directly from the data. Altogether, this leads to a significant improvement of the constraining power of the modelling technique. We show with simulations that one can reconstruct the mass, anisotropy, and viewing angle of an axisymmetric galaxy with a few per cent accuracy from realistic observational data with fully resolved line-of-sight velocity distributions (LOSVDs). In a first application, we reproduce a photometric estimate of the inclination of the disc galaxy NGC 3368 to within 5° accuracy from kinematic data that cover only a few sphere-of-influence radii around the galaxy’s SMBH. This demonstrates the constraining power that can be achieved with orbit models based on fully resolved LOSVDs and a model selection framework.
Lacon- and Shrub-Decompositions: A New Characterization of First-Order Transductions of Bounded Expansion Classes

The concept of bounded expansion provides a robust way to capture sparse graph classes with interesting algorithmic properties. Most notably, every problem definable in first-order logic can be solved in linear time on bounded expansion graph classes. First-order interpretations and transductions of sparse graph classes lead to more general, dense graph classes that seem to inherit many of the nice algorithmic properties of their sparse counterparts.In this work we introduce lacon- and shrub-decompositions and use them to characterize transductions of bounded expansion graph classes and other graph classes. If one can efficiently compute sparse shrub- or lacon-decompositions of transductions of bounded expansion classes then one can solve every problem definable in first-order logic in linear time on these classes.
Searching to Sparsify Tensor Decomposition for N-ary Relational Data

Tensor, an extension of the vector and matrix to the multi-dimensional case, is a natural way to describe the N-ary relational data. Recently, tensor decomposition methods have been introduced into N-ary relational data and become state-of-the-art on embedding learning. However, the performance of existing tensor decomposition methods is not as good as desired. First, they suffer from the data-sparsity issue since they can only learn from the N-ary relational data with a specific arity, i.e., parts of common N-ary relational data. Besides, they are neither effective nor efficient enough to be trained due to the over-parameterization problem. In this paper, we propose a novel method, i.e., S2S, for effectively and efficiently learning from the N-ary relational data. Specifically, we propose a new tensor decomposition framework, which allows embedding sharing to learn from facts with mixed arity. Since the core tensors may still suffer from the over-parameterization, we propose to reduce parameters by sparsifying the core tensors while retaining their expressive power using neural architecture search (NAS) techniques, which can search for data-dependent architectures. As a result, the proposed S2S not only guarantees to be expressive but also efficiently learns from mixed arity. Finally, empirical results have demonstrated that S2S is efficient to train and achieves state-of-the-art performance. 1
PALI at SemEval-2021 Task 2: Fine-Tune XLM-RoBERTa for Word in Context Disambiguation

This paper presents the PALI team’s winning system for SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation. We fine-tune XLM-RoBERTa model to solve the task of word in context disambiguation, i.e., to determine whether the target word in the two contexts contains the same meaning or not. In implementation, we first specifically design an input tag to emphasize the target word in the contexts. Second, we construct a new vector on the fine-tuned embeddings from XLM-RoBERTa and feed it to a fully-connected network to output the probability of whether the target word in the context has the same meaning or not. The new vector is attained by concatenating the embedding of the [CLS] token and the embeddings of the target word in the contexts. In training, we explore several tricks, such as the Ranger optimizer, data augmentation, and adversarial training, to improve the model prediction. Consequently, we attain the first place in all four cross-lingual tasks.
End-to-end Biomedical Entity Linking with Span-based Dictionary Matching

Disease name recognition and normalization is a fundamental process in biomedical text mining. Recently, neural joint learning of both tasks has been proposed to utilize the mutual benefits. While this approach achieves high performance, disease concepts that do not appear in the training dataset cannot be accurately predicted. This study introduces a novel end-to-end approach that combines span representations with dictionary-matching features to address this problem. Our model handles unseen concepts by referring to a dictionary while maintaining the performance of neural network-based models. Experiments using two major datasaets demonstrate that our model achieved competitive results with strong baselines, especially for unseen concepts during training.
Statistical inference for generative adversarial networks and other minimax problems

This paper studies generative adversarial networks (GANs) from the perspective of statistical inference. A GAN is a popular machine learning method in which the parameters of two neural networks, a generator and a discriminator, are estimated to solve a particular minimax problem. This minimax problem typically has a multitude of solutions and the focus of this paper are the statistical properties of these solutions. We address two key statistical issues for the generator and discriminator network parameters, consistent estimation and confidence sets. We first show that the set of solutions to the sample GAN problem is a (Hausdorff) consistent estimator of the set of solutions to the corresponding population GAN problem. We then devise a computationally intensive procedure to form confidence sets and show that these sets contain the population GAN solutions with the desired coverage probability. Small numerical experiments and a Monte Carlo study illustrate our results and verify our theoretical findings. We also show that our results apply in general minimax problems that may be nonconvex, nonconcave, and have multiple solutions.
Lifting Monocular Events to 3D Human Poses

This paper presents a novel 3D human pose estimation approach using a single stream of asynchronous events as input. Most of the state-of-the-art approaches solve this task with RGB cameras, however struggling when subjects are moving fast. On the other hand, event-based 3D pose estimation benefits from the advantages of event-cameras, especially their efficiency and robustness to appearance changes. Yet, finding human poses in asynchronous events is in general more challenging than standard RGB pose estimation, since little or no events are triggered in static scenes. Here we propose the first learning-based method for 3D human pose from a single stream of events. Our method consists of two steps. First, we process the event-camera stream to predict three orthogonal heatmaps per joint; each heatmap is the projection of of the joint onto one orthogonal plane. Next, we fuse the sets of heatmaps to estimate 3D localisation of the body joints. As a further contribution, we make available a new, challenging dataset for event-based human pose estimation by simulating events from the RGB Human3.6m dataset. Experiments demonstrate that our method achieves solid accuracy, narrowing the performance gap between standard RGB and event-based vision. The code is freely available at https://iit-pavis.github. io/lifting_events_to_3d_hpe.
Towards exploratory landscape analysis for large-scale optimization: a dimensionality reduction framework

Although exploratory landscape analysis (ELA) has shown its effectiveness in various applications, most previous studies focused only on low- and moderate-dimensional problems. Thus, little is known about the scalability of the ELA approach for large-scale optimization. In this context, first, this paper analyzes the computational cost of features in the flacco package. Our results reveal that two important feature classes (ela_level and ela_meta) cannot be applied to large-scale optimization due to their high computational cost. To improve the scalability of the ELA approach, this paper proposes a dimensionality reduction framework that computes features in a reduced lower-dimensional space than the original solution space. We demonstrate that the proposed framework can drastically reduce the computation time of ela_level and ela_meta for large dimensions. In addition, the proposed framework can make the cell-mapping feature classes scalable for large-scale optimization. Our results also show that features computed by the proposed framework are beneficial for predicting the high-level properties of the 24 large-scale BBOB functions.