Miracle-dz/UniFAR-FTUs huggingface dataset:
Photochemistry and organic complexation of iron: Interactions in the Southern Ocean

Het spoor-metaal ijzer (Fe) speelt in grote delen van de oceaan een belangrijke rol in de limitatie van algengroei. De chemie van Fe is daarom ondermeer van belang voor het koolstofdioxide budget in de Zuidelijke IJszee. De fotoreductie van ijzer is essentieel in de transformatie van colloidaal en organisch gebonden ijzer naar een reactievere Fe vorm die beter beschikbaar is voor opname door algen. Iedere lente ontstaat boven Antarctica een gat in de ozonlaag resulterend in een verhoogde intensiteit ultaviolet-B licht (UVB: 280-315 nm) die het aard- en zeeoppervlak bereikt. Onderzocht werd of deze hogere intensiteit aan UVB de fotoreductie van Fe(III) bevordert, resulterend in hogere concentraties biologisch beschikbaar Fe. Daarnaast werd onderzocht hoe de aanwezigheid van Fe-bindende organische moleculen (liganden) en twee typische Antarctische algen van invloed zijn op de fotoreductie van Fe. Natuurlijke organische liganden, zoals die voorkomen in de Westerschelde en het Marsdiep, werden onderzocht op hun gevoeligheid voor afbraak door ultraviolet licht. Zie: Samenvatting
Decoding of the Walking States and Step Rates from Cortical Electrocorticogram Signals

Brain-computer interfaces (BCIs) have shown promising results in restoring motor function to individuals with spinal cord injury. These systems have traditionally focused on the restoration of upper extremity function; however, the lower extremities have received relatively little attention. Early feasibility studies used noninvasive electroencephalogram (EEG)-based BCIs to restore walking function to people with paraplegia. However, the limited spatiotemporal resolution of EEG signals restricted the application of these BCIs to elementary gait tasks, such as the initiation and termination of walking. To restore more complex gait functions, BCIs must accurately decode additional degrees of freedom from brain signals. In this study, we used subdurally recorded electrocorticogram (ECoG) signals from able-bodied subjects to design a decoder capable of predicting the walking state and step rate information. We recorded ECoG signals from the motor cortices of two individuals as they walked on a treadmill at different speeds. Our offline analysis demonstrated that the state information could be decoded from>16 minutes of ECoG data with an unprecedented accuracy of 99.8%. Additionally, using a Bayesian filter approach, we achieved an average correlation coefficient between the decoded and true step rates of 0.934. When combined, these decoders may yield decoding accuracies sufficient to safely operate present-day walking prostheses.
Local equilibrium charge and spin currents in two-dimensional topological systems

We study the equilibrium and nonequilibrium electronic transport properties of multiprobe topological systems using a combination of the Landauer-Büttiker approach and nonequilibrium Green’s functions techniques. We obtain general expressions for both nonequilibrium and equilibrium local electronic currents that, by suitable projections, allow one to compute charge, spin, valley, and orbital currents. We show that external magnetic fields give rise to equilibrium charge currents in mesoscopic system and study the latter in the quantum Hall regime. Likewise, a spin-orbit interaction leads to local equilibrium spin currents, that we analyze in the quantum spin Hall regime. We show that an accurate theoretical assessment of the equilibrium currents is quite challenging and propose a two-measurement protocol that facilitates a comparison between experiment and theory.
Effect of the Instrument Slit Function on Upwelling Radiance from a Wavelength Dependent Surface Reflectance

The Radiance Enhancement (RE) method was introduced for ef-ﬁcient detection of clouds from the space. Recently, we have also re-ported that due to high reﬂectance of combustion-originated smokes, this approach can also be generalized for detection of the forest ﬁres by retrieving and analyzing datasets collected from a space orbiting micro-spectrometer operating in the near infrared spectral range. In our previous publication, we have performed a comparison of observed and synthetic radiance spectra by developing a method for computation of surface reﬂectance consisting of diﬀerent canopies by weighted sum based on their areal coverage. However, this approach should be justiﬁed by a method based on corresponding proportions of the upwelling radiance. The results of computations we performed in this study reveal a good match between areal coverage of canopies and the corresponding proportions of the upwelling radiance due to eﬀect of the instrument slit function.
Synthetic aperture interference light (SAIL) microscopy for high-throughput label-free imaging.

Quantitative phase imaging (QPI) is a valuable label-free modality that has gained significant interest due to its wide potentials, from basic biology to clinical applications. Most existing QPI systems measure microscopic objects via interferometry or nonlinear iterative phase reconstructions from intensity measurements. However, all imaging systems compromise spatial resolution for the field of view and vice versa, i.e., suffer from a limited space bandwidth product. Current solutions to this problem involve computational phase retrieval algorithms, which are time-consuming and often suffer from convergence problems. In this article, we presented synthetic aperture interference light (SAIL) microscopy as a solution for high-resolution, wide field of view QPI. The proposed approach employs low-coherence interferometry to directly measure the optical phase delay under different illumination angles and produces large space-bandwidth product label-free imaging. We validate the performance of SAIL on standard samples and illustrate the biomedical applications on various specimens: pathology slides, entire insects, and dynamic live cells in large cultures. The reconstructed images have a synthetic numeric aperture of 0.45 and a field of view of 2.6 × 2.6 mm2. Due to its direct measurement of the phase information, SAIL microscopy does not require long computational time, eliminates data redundancy, and always converges.
Metal‐Catalyzed Direct Difluoromethylation Reactions

Owing to the excellent performance of fluorinated compounds in the areas of pharmaceuticals, agrochemicals, and materials chemistry, organic chemists have made great efforts towards the selective incorporation of fluorine or fluorinated moieties into organic molecules through nucleophilic, electrophilic, radical, and metal-catalyzed pathways. Impressive progress in fluorination and perfluoroalkylation (especially trifluoromethylation) reactions has been made over the past few decades. However, methods for incorporating lightly fluorinated groups (such as −CF2H) are still underdeveloped, in spite of their important applications in pharmaceuticals and agrochemicals. This Focus Review summarizes recent developments in metal-catalyzed direct difluoromethylation reactions.
Combining the Projective Consciousness Model and Virtual Humans to assess ToM capacity in Virtual Reality: a proof-of-concept

Relating explicit psychological mechanisms and observable behaviours is a central aim of psychological and behavioural science. We implemented the principles of the Projective Consciousness Model into artificial agents embodied as virtual humans, as a proof-of-concept for a methodological framework aimed at simulating behaviours and assessing underlying psychological parameters, in the context of experiments in virtual reality. We focus on simulating the role of Theory of Mind (ToM) in the choice of strategic behaviours of approach and avoidance to optimise the satisfaction of agents’ preferences. We designed an experiment in a virtual environment that could be used with real humans, allowing us to classify behaviours as a function of order of ToM, up to the second order. We show that our agents demonstrate expected behaviours with consistent parameters of ToM in this experiment. We also show that the agents can be used to estimate correctly each other order of ToM. A similar approach could be used with real humans in virtual reality experiments not only to enable human participants to interact with parametric, virtual humans as stimuli, but also as a mean of inference to derive model-based psychological assessments of the participants.
Gaseous nebulae and massive stars in the giant H I ring in Leo

Context. Chemical abundances in the Leo ring, the largest H I cloud in the local Universe, have recently been determined to be close to or above Solar values. This is a finding that is incompatible with prior claims that the ring was primordial in origin. The gas, pre-enriched in a galactic disk and tidally stripped, did not manage to efficiently form stars in the intergalactic space. Aims. Using Hα emission and a multi-wavelengths analysis of its extremely faint optical counterpart, we investigate the process of star formation and the slow build-up of a stellar population that could form diffuse and metal-rich dwarf galaxies in the Leo ring. Methods. We mapped nebular lines in three dense H I clumps and complemented the data with archival stellar continuum observations from the Galaxy Evolution Explorer and the Hubble Space Telescope, as well as with population synthesis models. Results. We detect a sparse population of stars in the main body of the ring, with individual young stars as massive as O7-types powering some H II regions. The average star-formation rate density in the ring is on order of 10−5 M⊙ yr−1 kpc−2 and proceeds with local bursts a few hundred parsecs in size, where loose stellar associations of 500−1000 M⊙ occasionally host massive outliers. The far ultraviolet-to-Hα emission ratio in nebular regions implies recent stellar bursts, from 2 to 7 Myr ago. The relation between the local H I gas density and the star-formation rate in the ring is similar to what is found in dwarfs and outer disks with gas depletion times as long as 100 Gyr. We find a candidate planetary nebula in a compact and faint Hα region with [OIII]/Hα line enhancement, which is consistent with the estimated mean stellar surface brightness of the ring. The presence of a 1 kpc partial ring that is emitting weak Hα lines around the brightest and youngest H II region suggests that local shocks might triggers future star-forming sites.
An Alignment-Agnostic Model for Chinese Text Error Correction

This paper investigates how to correct Chinese text errors with types of mistaken, missing and redundant characters, which is common for Chinese native speakers. Most existing models based on detect-correct framework can correct mistaken characters errors, but they cannot deal with missing or redundant characters. The reason is that lengths of sentences before and after correction are not the same, leading to the inconsistence between model inputs and outputs. Although the Seq2Seq-based or sequence tagging methods provide solutions to the problem and achieved relatively good results on English context, but they do not perform well in Chinese context according to our experimental results. In our work, we propose a novel detect-correct framework which is alignment-agnostic, meaning that it can handle both text aligned and non-aligned occasions, and it can also serve as a cold start model when there are no annotated data provided. Experimental results on three datasets demonstrate that our method is effective and achieves the best performance among existing published models.
Generic vanishing and holomorphic 1-forms

The goal of this article is three-fold; first we establish an interesting link between some sets related to cohomologies of complex local systems and the stratification of the Albanese morphism of smooth irregular complex projective varieties. This gives a different interpretation of a result due to Green and Lazarsfeld. This observation originated out of our interest in studying zeros of holomorphic 1-forms. Secondly, we show that the set of 1-forms that admit codimension 1 zeros are in fact a linear subvariety of H(X,ΩX). We ask the same question for all codimensions, in particular for the set of all 1-forms that admit zeros. Thirdly, in a somewhat different direction we deduce some geometric consequences. The key ingredient in the third part is a result of Kashiwara which relates the singular support produced by Albanese morphism to 1-forms that admit zeros. We show that when a smooth projective variety with simple Albanese admits a C∞-fibre bundle structure over the circle, its Albanese morphism is cohomologically trivial, i.e. the decomposition theorem behaves like that of a smooth morphism. Such a C∞-fibre bundle structure is conjecturally equivalent to the existence of holomorphic 1-forms without zeros.
Tourist route optimization in the context of Covid-19 pandemic

The paper presents an innovative method for tourist route planning inside a destination. The necessity of reorganizing the tourist routes within a destination comes as an immediate response to the Covid-19 crisis. The implementation of the method inside tourist destinations can bring an important advantage in transforming a destination into a safer one in times of Covid-19 and post-Covid-19. The existing trend of shortening the tourist stay length has been accelerated while the epidemic became a pandemic. Moreover, the wariness for future pandemics has brought into spotlight the issue of overcrowded attractions inside a destination at certain moments. The method presented in this paper proposes a backtracking algorithm, more precisely an adaptation of the travelling salesman problem. The method presented is aimed to facilitate the navigation inside a destination and to revive certain less-visited sightseeing spots inside a destination while facilitating conformation with the social distancing measures imposed for Covid-19 control.
XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation

Machine learning has brought striking advances in multilingual natural language processing capabilities over the past year. For example, the latest techniques have improved the state-of-the-art performance on the XTREME multilingual benchmark by more than 13 points. While a sizeable gap to human-level performance remains, improvements have been easier to achieve in some tasks than in others. This paper analyzes the current state of cross-lingual transfer learning and summarizes some lessons learned. In order to catalyze meaningful progress, we extend XTREME to XTREME-R, which consists of an improved set of ten natural language understanding tasks, including challenging language-agnostic retrieval tasks, and covers 50 typologically diverse languages. In addition, we provide a massively multilingual diagnostic suite and fine-grained multi-dataset evaluation capabilities through an interactive public leaderboard to gain a better understanding of such models.
On the Assessment of Benchmark Suites for Algorithm Comparison

Benchmark suites, i.e. a collection of benchmark functions, are widely used in the comparison of black-box optimization algorithms. Over the years, research has identified many desired qualities for benchmark suites, such as diverse topology, different difficulties, scalability, representativeness of real-world problems among others. However, while the topology characteristics have been subjected to previous studies, there is no study that has statistically evaluated the difficulty level of benchmark functions, how well they discriminate optimization algorithms and how suitable is a benchmark suite for algorithm comparison. In this paper, we propose the use of an item response theory (IRT) model, the Bayesian two-parameter logistic model for multiple attempts, to statistically evaluate these aspects with respect to the empirical success rate of algorithms. With this model, we can assess the difficulty level of each benchmark, how well they discriminate different algorithms, the ability score of an algorithm, and how much information the benchmark suite adds in the estimation of the ability scores. We demonstrate the use of this model in two well-known benchmark suites, the Black-Box Optimization Benchmark (BBOB) for continuous optimization and the Pseudo Boolean Optimization (PBO) for discrete optimization. We found that most benchmark functions of BBOB suite have high difficulty levels (compared to the optimization algorithms) and low discrimination. For the PBO, most functions have good discrimination parameters but are often considered too easy. We discuss potential uses of IRT in benchmarking, including its use to improve the design of benchmark suites, to measure multiple aspects of the algorithms, and to design adaptive suites.
Tracking entities in technical procedures - a new dataset and baselines

We introduce TechTrack, a new dataset for tracking entities in technical procedures. The dataset, prepared by annotating open domain articles from WikiHow, consists of 1351 procedures, e.g.,"How to connect a printer", identifies more than 1200 unique entities with an average of 4.7 entities per procedure. We evaluate the performance of state-of-the-art models on the entity-tracking task and find that they are well below the human annotation performance. We describe how TechTrack can be used to take forward the research on understanding procedures from temporal texts.
Regularizing Models via Pointwise Mutual Information for Named Entity Recognition

In Named Entity Recognition (NER), pre-trained language models have been overestimated by focusing on dataset biases to solve current benchmark datasets. However, these biases hinder generalizability which is necessary to address real-world situations such as weak name regularity and plenty of unseen mentions. To alleviate the use of dataset biases and make the models fully exploit data, we propose a debiasing method that our bias-only model can be replaced with a Pointwise Mutual Information (PMI) to enhance generalization ability while outperforming an in-domain performance. Our approach enables to debias highly correlated word and labels in the benchmark datasets; reﬂect informative statistics via subword frequency; alleviates a class imbalance between positive and negative examples. For long-named and complex-structure entities, our method can predict these entities through debiasing on conjunction or special characters. Extensive experiments on both general and biomedical domains demonstrate the e ﬀ ectiveness and generalization capabilities of the PMI.
Coarse- and fine-scale geometric information content of Multiclass Classification and implied Data-driven Intelligence

Under any Multiclass Classification (MCC) setting defined by a collection of labeled point-cloud specified by a feature-set, we extract only stochastic partial orderings from all possible triplets of point-cloud without explicitly measuring the three cloud-to-cloud distances. We demonstrate that such a collective of partial ordering can efficiently compute a label embedding tree geometry on the Label-space. This tree in turn gives rise to a predictive graph, or a network with precisely weighted linkages. Such two multiscale geometries are taken as the coarse scale information content of MCC. They indeed jointly shed lights on explainable knowledge on why and how labeling comes about and facilitates error-free prediction with potential multiple candidate labels supported by data. For revealing within-label heterogeneity, we further undergo labeling naturally found clusters within each point-cloud, and likewise derive multiscale geometry as its fine-scale information content contained in data. This fine-scale endeavor shows that our computational proposal is indeed scalable to a MCC setting having a large label-space. Overall the computed multiscale collective of data-driven patterns and knowledge will serve as a basis for constructing visible and explainable subject matter intelligence regarding the system of interest.
Enhanced LSTM-based Service Decomposition for Mobile Augmented Reality

Undoubtedly, Mobile Augmented Reality (MAR) applications for 5G and Beyond wireless networks are witnessing a notable attention recently. However, they require significant computational and storage resources at the end device and/or the network via Edge Cloud (EC) support. In this work, a MAR service is considered under the lenses of microservices where MAR service components can be decomposed and anchored at different locations ranging from the end device to different ECs in order to optimize the overall service and network efficiency. To this end, we propose a mobility aware MAR service decomposition using a Long Short Term Memory (LSTM) deep neural network to provide efficient pro-active decision making in real-time. More specifically, the LSTM deep neural network is trained with optimal solutions derived from a mathematical programming formulation in an offline manner. Then, decision making at the inference stage is used to optimize service decomposition of MAR services. A wide set of numerical investigations reveal that the mobility aware LSTM deep neural network manage to outperform recently proposed schemes in terms of both decision making quality as well as computational time.
Simultaneous Multi-Pivot Neural Machine Translation

Parallel corpora are indispensable for training neural machine translation (NMT) models, and parallel corpora for most language pairs do not exist or are scarce. In such cases, pivot language NMT can be helpful where a pivot language is used such that there exist parallel corpora between the source and pivot and pivot and target languages. Naturally, the quality of pivot language translation is more inferior to what could be achieved with a direct parallel corpus of a reasonable size for that pair. In a real-time simultaneous translation setting, the quality of pivot language translation deteriorates even further given that the model has to output translations the moment a few source words become available. To solve this issue, we propose multi-pivot translation and apply it to a simultaneous translation setting involving pivot languages. Our approach involves simultaneously translating a source language into multiple pivots, which are then simultaneously translated together into the target language by leveraging multi-source NMT. Our experiments in a low-resource setting using the N-way parallel UN corpus for Arabic to English NMT via French and Spanish as pivots reveals that in a simultaneous pivot NMT setting, using two pivot languages can lead to an improvement of up to 5.8 BLEU.
A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation*

Recently, significant progress has been made on semantic segmentation. However, the success of supervised semantic segmentation typically relies on a large amount of labeled data, which is time-consuming and costly to obtain. Inspired by the success of semi-supervised learning methods for image classification, here we propose a simple yet effective semi-supervised learning framework for semantic segmentation. We demonstrate that the devil is in the details: a set of simple designs and training techniques can collectively improve the performance of semi-supervised semantic segmentation significantly. Previous works [3], [25] fail to effectively employ strong augmentation in pseudo-label learning, as the large distribution disparity caused by strong augmentation harms the batch nor-malization statistics. We design a new batch normalization, namely distribution-specific batch normalization (DSBN) to address this problem and show the importance of strong augmentation for semantic segmentation. Moreover, we design a self-correction loss, which is effective in terms of noise resistance. We conduct a series of ablation studies to show the effectiveness of each component. Our method achieves state-of-the-art results in the semi-supervised settings on the Cityscapes and Pascal VOC datasets.