Miracle-dz/UniFAR-FTUs huggingface dataset:
A rare cause of stroke in young children: minor head trauma associated with mineralising lenticulostriate angiopathy in three patients

ABSTRACT Acute basal ganglia infarct following minor head trauma in association with mineralisation of lenticulostriate arteries is an increasingly recognised entity in childhood stroke. Three cases with a classical history and phenotypical features of mineralising angiopathy are described. Case 1 was a 2-year-old girl who presented with acute onset hemiparesis with a same-side upper motor neuron (UMN)-type facial palsy following minor head trauma. Case 2 was a 14-month-old boy who presented with a left side hemiparesis and a left UMN-type facial nerve palsy following a minor fall. Case 3 was an 8-month-old boy who, following a fall from his cot, had a sudden-onset hemiparesis on the right side and deviation of the angle of the mouth to the left. In brain computed tomography, all three cases demonstrated characteristic basal ganglia calcification of the mineralising angiopathy. Magnetic resonance imaging of the brain demonstrated features supportive of acute infarcts in the lentiform nucleus, caudate nucleus and putamen. Two of the patients had iron deficiency anaemia with haemoglobin of 7.0 g/dL and 7.8 g/dL, respectively. On follow-up, Case 1 had mild residual weakness and the other two made a complete recovery. None of the patients had a recurrence of stroke. Basal ganglia stroke with mineralising angiopathy should be considered in toddlers presenting with sudden-onset focal neurological deficits preceded by minor head trauma. Abbreviations: ADC: apparent diffusion coefficient; CT: computed tomography; DWI: diffusion-weighted imaging; Hb: haemoglobin; IDA: iron deficiency anaemia; MRI: magnetic resonance imaging; SLV: sonographic lenticulostriate vasculopathy; SWI: susceptibility weighted imaging; UMN: upper motor neuron.
Molecular reactivity profiling upon immunotherapy with a 300 IR sublingual house dust mite tablet reveals marked humoral changes towards major allergens

Molecular antibody reactivity profiles have not yet been studied in depth in patients treated by sublingual house dust mite (HDM) tablet immunotherapy. Humoral immune responses to a large panel of HDM mite allergens were studied using allergen microarray technology in a subset of clinically defined high and low responder patients from a double‐blind placebo‐controlled allergen‐specific immunotherapy (AIT) trial using sublingual 300 IR HDM tablets.
Methodologies for assessment of earthquake casualty

In this paper the existing methods for assessing seismic casualty are reviewed and classified into two categories. One is to assess life loss in terms of moneatary form, the other is to estimate human casualty solely. After a systematical and detail analysis,a comparison between these methods is conducted. The comparison study focused to assessing human casualty in some real earthquakes, such as the Tangshan earthquake, Lijiang earthquake and Baotou earthquake by using different methods characterized by using "collapes ratio"as an essential factor. And the estimated results were compared with the observed casualty. Finally, a casualty estimation method is proposed.
Diffraction of an off-axis vector-beam by a tilted aperture

Manifestations of orbital angular momentum induced effects in the diffraction of a radially polarized vector beam by an off-axis tilted aperture are studied both experimentally and theoretically. Experiments were carried out to extract the degree of circular polarization, which was shown to be proportional to the on-axis component of the spin angular momentum density. We report a clear separation of the regions of dominance of the right and left circular polarizations associated with positive and negative topological charges respectively, which is reminiscent of the standard vortex-induced transverse shift, albeit in the diffraction scenario. The experimental results are supported by model simulations and the agreement is quite satisfactory. The results are useful to appreciate the orbit-orbit related effects due to unavoidable misalignment problems (especially for vortex beams).
Hidden interplay of current-induced spin and orbital torques in bulk Fe$_3$GeTe$_2$

Low crystal symmetry of magnetic van der Waals materials naturally promotes spin-orbital complexity unachievable in common magnetic materials used for spin-orbit torque switching. Here, using first-principles methods, we demonstrate that an interplay of spin and orbital degrees of freedom has a profound impact on spin-orbit torques in a prototype van der Waals ferromagnet: Fe$_3$GeTe$_2$ (FGT). While we show that bulk FGT hosts strong"hidden"current-induced torques harvested by each of its layers, we uncover that their origin alternates between the conventional spin flux torque and the so-called orbital torque as the magnetization direction is varied. A drastic difference in the behavior of the two types of torques results in a non-trivial evolution of switching properties with doping. Our findings promote the design of non-equilibrium orbital properties as the guiding mechanism for crafting the properties of spin-orbit torques in layered van der Waals materials.
Refining magnetic interactions from the magnetic field dependence of spin-wave excitations in magnetoelectric LiFePO$_4$

L. Peedu, V. Kocsis, D. Szaller, B. Forrai, S. Bordács, I. Kézsmárki, 5 J. Viirok, U. Nagel, B. Bernáth, D. L. Kamenskyi, A. Miyata, O. Portugall, Y. Tokunaga, 8 Y. Tokura, 9 Y. Taguchi, and T. Rõõm National Institute of Chemical Physics and Biophysics, Akadeemia tee 23, 12618 Tallinn, Estonia RIKEN Center for Emergent Matter Science (CEMS), Wako, Saitama 351-0198, Japan Institute of Solid State Physics, TU Wien, 1040 Vienna, Austria Department of Physics, Institute of Physics, Budapest University of Technology and Economics, Műegyetem rkp. 3. , H-1111 Budapest, Hungary Experimental Physics 5, Center for Electronic Correlations and Magnetism, Institute of Physics, University of Augsburg, 86159 Augsburg, Germany High Field Magnet Laboratory (HFML-EMFL), Radboud University, Toernooiveld 7, 6525 ED Nijmegen, The Netherlands Laboratoire National des Champs Magnétiques Intenses (LNCMI-EMFL), CNRS-UGA-UT3-INSA,143 Avenue de Rangueil, 31400 Toulouse, France Department of Advanced Materials Science, University of Tokyo, Kashiwa 277-8561, Japan Department of Applied Physics, University of Tokyo, Hongo, Tokyo 113-8656, Japan (Dated: April 28, 2022)
Quantum Compressive Sensing: Mathematical Machinery, Quantum Algorithms, and Quantum Circuitry

Compressive sensing is a sensing protocol that facilitates the reconstruction of large signals from relatively few measurements by exploiting known structures of signals of interest, typically manifested as signal sparsity. Compressive sensing’s vast repertoire of applications in areas such as communications and image reconstruction stems from the traditional approach of utilizing non-linear optimization to exploit the sparsity assumption by selecting the lowest-weight (i.e., maximum sparsity) signal consistent with all acquired measurements. Recent efforts in the literature consider instead a data-driven approach, training tensor networks to learn the structure of signals of interest. The trained tensor network is updated to “project” its state onto one consistent with the measurements taken, and is then sampled site by site to “guess” the original signal. In this paper, we take advantage of this computing protocol by formulating an alternative “quantum” protocol, in which the state of the tensor network is a quantum state over a set of entangled qubits. Accordingly, we present the associated algorithms and quantum circuits required to implement the training, projection, and sampling steps on a quantum computer. We supplement our theoretical results by simulating the proposed circuits with a small, qualitative model of LIDAR imaging of earth forests. Our results indicate that a quantum, data-driven approach to compressive sensing may have significant promise as quantum technology continues to make new leaps.
A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D Shape Matching

We present a scalable combinatorial algorithm for globally optimizing over the space of geometrically consistent mappings between 3D shapes. We use the mathematically elegant formalism proposed by Windheuser et al. [66] where 3D shape matching was formulated as an integer linear program over the space of orientation-preserving diffeomorphisms. Until now, the resulting formulation had limited practical applicability due to its complicated constraint structure and its large size. We propose a novel primal heuristic coupled with a Lagrange dual problem that is several orders of magnitudes faster compared to previous solvers. This allows us to handle shapes with substantially more triangles than previously solvable. We demonstrate compelling results on diverse datasets, and, even showcase that we can address the challenging setting of matching two partial shapes without availability of complete shapes. Our code is publicly available at http://github.com/paulOnoah/sm-comb.
On subspace designs

Guruswami and Xing introduced subspace designs in 2013 to give the first construction of positive rate rank metric codes list-decodable beyond half the distance. In this paper we provide bounds involving the parameters of a subspace design, showing they are tight via explicit constructions. We point out a connection with sum-rank metric codes, dealing with optimal codes and minimal codes with respect to this metric. Applications to two-intersection sets with respect to hyperplanes, two-weight codes, cutting blocking sets and lossless dimension expanders are also provided.
Evaluation and Learning in Two-Player Symmetric Games via Best and Better Responses

Artificial intelligence and robotic competitions are accompanied by a class of game paradigms in which each player privately commits a strategy to a game system which simulates the game using the collected joint strategy and then returns payoffs to players. This paper considers the strategy commitment for two-player symmetric games in which the players' strategy spaces are identical and their payoffs are symmetric. First, we introduce two digraph-based metrics at a meta-level for strategy evaluation in two-agent reinforcement learning, grounded on sink equilibrium. The metrics rank the strategies of a single player and determine the set of strategies which are preferred for the private commitment. Then, in order to find the preferred strategies under the metrics, we propose two variants of the classical learning algorithm self-play, called strictly best-response and weakly better-response self-plays. By modeling learning processes as walks over joint-strategy response digraphs, we prove that the learnt strategies by two variants are preferred under two metrics, respectively. The preferred strategies under both two metrics are identified and adjacency matrices induced by one metric and one variant are connected. Finally, simulations are provided to illustrate the results.
When performance is not enough—A multidisciplinary view on clinical decision support

Scientific publications about the application of machine learning models in healthcare often focus on improving performance metrics. However, beyond often short-lived improvements, many additional aspects need to be taken into consideration to make sustainable progress. What does it take to implement a clinical decision support system, what makes it usable for the domain experts, and what brings it eventually into practical usage? So far, there has been little research to answer these questions. This work presents a multidisciplinary view of machine learning in medical decision support systems and covers information technology, medical, as well as ethical aspects. The target audience is computer scientists, who plan to do research in a clinical context. The paper starts from a relatively straightforward risk prediction system in the subspecialty nephrology that was evaluated on historic patient data both intrinsically and based on a reader study with medical doctors. Although the results were quite promising, the focus of this article is not on the model itself or potential performance improvements. Instead, we want to let other researchers participate in the lessons we have learned and the insights we have gained when implementing and evaluating our system in a clinical setting within a highly interdisciplinary pilot project in the cooperation of computer scientists, medical doctors, ethicists, and legal experts.
Dropout Inference with Non-Uniform Weight Scaling

Dropout as regularization has been used extensively to prevent overfitting for training neural networks. During training, units and their connections are randomly dropped, which could be considered as sampling many different submodels from the original model. At test time, weight scaling and Monte Carlo approximation are two widely applied approaches to approximate the outputs. Both approaches work well practically when all submodels are low-bias complex learners. However, in this work, we demonstrate scenarios where some submodels behave closer to high-bias models and a non-uniform weight scaling is a better approximation for inference.
FlowGNN: A Dataflow Architecture for Universal Graph Neural Network Inference via Multi-Queue Streaming

Graph neural networks (GNNs) have recently exploded in popularity thanks to their broad applicability to graph-related problems such as quantum chemistry, drug discovery, and high energy physics. However, meeting demand for novel GNN models and fast inference simultaneously is challenging because of the gap between developing efﬁcient accelerators and the rapid creation of new GNN models. Prior art focuses on the acceleration of speciﬁc classes of GNNs, such as Graph Convolutional Network (GCN), but lacks the generality to support a wide range of existing or new GNN models. Meanwhile, most work rely on graph pre-processing to exploit data locality, making them unsuitable for real-time applications. To address these limitations, in this work, we propose a generic dataﬂow architecture for GNN acceleration, named FlowGNN , which can ﬂexibly support the majority of message-passing GNNs. The contributions are three-fold. First, we propose a novel and scalable dataﬂow architecture, which ﬂexibly supports a wide range of GNN models with message-passing mechanism. The architecture features a con-ﬁgurable dataﬂow optimized for simultaneous computation of node embedding, edge embedding, and message passing, which is generally applicable to all models. We also propose a rich library of model-speciﬁc components. Second, we deliver ultra-fast real-time GNN inference without any graph pre-processing, making it agnostic to dynamically changing graph structures. Third, we verify our architecture on the Xilinx Alveo U50 FPGA board and measure the on-board end-to-end performance. We achieve a speed-up of up to 51–254 × against CPU (6226R) and 1.3–
False vacuum Skyrmions revisited

We consider the classical static soliton solutions of the Skyrme model with false vacuum potential. We make use of fully three-dimensional relaxation calculations to construct global energy minimizers in the sectors of topological degrees from $Q=1$ to $Q=6$. These solutions may be metastable, they contain a domain of true vacuum inside the core. Further, we explore small regions of negative topological charge density which appear for the Skyrmions of degrees $Q=3,5,6$.
UTILIZAÇÃO DE CASCA DE ARROZ E ESTERCO BOVINO COMO SUBSTRA TO PARA A MULTIPLICAÇÃO DE Eisenia fetida Savigny (1826) Rice husk and cattle manure used as substrate for the Eisenia fetida Savigny (1826) multiplication

RESUMOA utilizacao de minhocas, principalmente para a alimentacao animal, vem sendo adotada e estudada, pois esses organismosapresentam em sua constituicao vitaminas e aminoacidos. Entre os materiais possiveis de serem utilizados na minhocultura, atualmenteincentiva-se o aproveitamento de residuos agroindustriais de alta disponibilidade, como a casca de arroz no Rio Grande do Sul.Objetivou-se, neste trabalho, avaliar a eficiencia de substratos a base de casca de arroz e esterco bovino na multiplicacao e producaode biomassa de minhocas (Eisenia fetida). A populacao de minhocas foi avaliada quanto ao numero de cocons, ao indice de multiplicacao(populacao final/populacao inicial) e a biomassa de minhocas frescas e secas. A avaliacao foi realizada aos 60 dias apos a instalacao doexperimento, com quatro repeticoes. A analise estatistica dos resultados mostrou que a inclusao de casca de arroz favoreceu odesenvolvimento e a taxa de reproducao das minhocas. A maior biomassa de minhocas foi obtida nos tratamentos: 50% casca de arrozinteira e 50% esterco bovino, esterco bovino mais casca de arroz inteira tratada com hidroxido de amonio a 20 e 40%, e esterco bovinomais casca de arroz moida tratada com hidroxido de sodio a 10 e 40%. Conclui-se que a adicao de casca de arroz ao esterco bovinofavorece a multiplicacao e o desenvolvimento de E. fetida.Termos para indexacao: Minhocas, multiplicacao, substrato, Eisenia fetida.ABSTRACTThe use of earthworms for animal feeding has been adopted and studied, because these organisms are rich in vitaminsand essential amino acids. Among the organic materials that could be used in the earthworm breeding, highly available agroindustrialresidues such as rice husk are being currently used in Rio Grande do Sul. The aim of this work was to evaluate substrate basedon rice husk and cattle manure on the multiplication and biomass of earthworms (Eisenia fetida). In this work, the populationof earthworms, the cocoon numbers, the multiplication index, as well as fresh and dry biomass of earthworms were evaluated.The evaluation was performed 60 days after the installation of the experiment and each treatment had four replications. Thestatistical analysis of the results showed that the inclusion of rice husk favored the development and rate of earthwormreproduction. The highest earthworm biomass was obtained in the treatments 50% rice husk and 50% cattle manure, cattlemanure plus rice husk with ammonium hydroxide at 20 and 40%, and cattle bovine plus ground rice husk with sodiumhydroxide at 10 and 40%. It was concluded that the addition of rice husk to the cattle manure was efficient in the multiplicationand development of E. fetida.Index terms: Earthworms, multiplication, substrate, Eisenia fetida.
PRE-NAS: predictor-assisted evolutionary neural architecture search

Neural architecture search (NAS) aims to automate architecture engineering in neural networks. This often requires a high computational overhead to evaluate a number of candidate networks from the set of all possible networks in the search space during the search. Prediction of the networks' performance can alleviate this high computational overhead by mitigating the need for evaluating every candidate network. Developing such a predictor typically requires a large number of evaluated architectures which may be difficult to obtain. We address this challenge by proposing a novel evolutionary-based NAS strategy, Predictor-assisted E-NAS (PRE-NAS), which can perform well even with an extremely small number of evaluated architectures. PRE-NAS leverages new evolutionary search strategies and integrates high-fidelity weight inheritance over generations. Unlike one-shot strategies, which may suffer from bias in the evaluation due to weight sharing, offspring candidates in PRE-NAS are topologically homogeneous, which circumvents bias and leads to more accurate predictions. Extensive experiments on NAS-Bench-201 and DARTS search spaces show that PRE-NAS can outperform state-of-the-art NAS methods. With only a single GPU searching for 0.6 days, competitive architecture can be found by PRE-NAS which achieves 2.40% and 24% test error rates on CIFAR-10 and ImageNet respectively.
Conformer and Blind Noisy Students for Improved Image Quality Assessment

Generative models for image restoration, enhancement, and generation have significantly improved the quality of the generated images. Surprisingly, these models produce more pleasant images to the human eye than other methods, yet, they may get a lower perceptual quality score using traditional perceptual quality metrics such as PSNR or SSIM. Therefore, it is necessary to develop a quantitative metric to reflect the performance of new algorithms, which should be well-aligned with the person’s mean opinion score (MOS). Learning-based approaches for perceptual image quality assessment (IQA) usually require both the distorted and reference image for measuring the perceptual quality accurately. However, commonly only the distorted or generated image is available. In this work, we explore the performance of transformer-based full-reference IQA models. We also propose a method for IQA based on semi-supervised knowledge distillation from full-reference teacher models into blind student models using noisy pseudo-labeled data.Our approaches achieved competitive results on the NTIRE 2022 Perceptual Image Quality Assessment Challenge: our full-reference model was ranked 4th, and our blind noisy student was ranked 3rd among 70 participants, each in their respective track. https://github.com/burchim/IQA-Conformer-BNS.
Modeling the Adoption of Good Tech: Extending TAM3 to Incorporate the Phenomenon of Warm-Glow

: In this paper we extend the third evolution of the Technology Acceptance Model (TAM3) to incorporate warm-glow in order to understand the role this phenomenon plays on consumer adoption of ‘good tech’. Warm-glow is the feeling of satisfaction, pleasure, or both, which is experi-enced by individuals after they do something ‘good’ for their fellow human. Two constructs, perceived extrinsic warm-glow (PEWG) and perceived intrinsic warm-glow (PIWG), were incorporated into the TAM3 model to measure the two dimensions of user-experienced warm-glow, form-ing what we refer to as the TAM3+WG model. An experimental approach was taken to evaluate the suitability of the proposed model (i.e., TAM3+WG). A vignette was used to describe a hypothetical internet search engine solution designed to evoke in participants warm-glow. Our TAM3+WG model was found to be superior to the TAM3 model within our context. Furthermore, the PIWG and PEWG constructs were found to be unique within the original TAM3 model. Our findings indicate that the factors that have the greatest influence on consumer decisions are (in decreasing order) PU, PIWG, SN, and PEWG. In other words, both extrinsic and intrinsic warm-glow play a prominent role in user decisions as to whether or not to adopt technology.
Masked Spectrogram Prediction for Self-Supervised Audio Pre-Training

Transformer-based models attain excellent results and generalize well when trained on sufficient amounts of data. However, constrained by the limited data available in the audio domain, most transformer-based models for audio tasks are finetuned from pre-trained models in other domains (e.g. image), which has a notable gap with the audio domain. Other methods explore the self-supervised learning approaches directly in the audio domain but currently do not perform well in the downstream tasks. In this paper, we present a novel self-supervised learning method for transformer-based audio models, called masked spectrogram prediction (MaskSpec), to learn powerful audio representations from unlabeled audio data (AudioSet used in this paper). Our method masks random patches of the input spectrogram and reconstructs the masked regions with an encoder-decoder architecture. Experimental results demonstrate MaskSpec reaches the performance of 0.471 (mAP) on AudioSet, 0.854 (mAP) on Open-MIC2018, 0.982 (accuracy) on ESC-50, 0.976 (accuracy) on SCV2, and 0.823 (accuracy) on DCASE2019 Task1A. The source code and pre-trained models have been released.1