Miracle-dz/UniFAR-FTUs huggingface dataset:
Organic acid profile of chestnut (Castanea sativa Mill.) as affected by hot air convective drying

ABSTRACT The objective of the present work was to evaluate the effect of hot air convective drying on the organic acid profile of chestnut. An extractive method, with internal standard, followed by HPLC-UV was validated, allowing the quantification of malic, ascorbic, citric, and fumaric acids with good precision and accuracy. Fresh chestnuts presented differences between varieties. The thermal process caused equivalent losses of ascorbic acid on both varieties, but higher losses of malic acid in Longal. Furthermore, fumaric acid contents increased on both varieties. Therefore, hot air convective drying affected the organic acid composition of chestnut slices, and this effect was varietal dependent.
Oral desensitization in egg acute food protein-induced enterocolitis syndrome.

OBJECTIVE Strict avoidance of trigger food is the primary management of food protein-induced enterocolitis syndrome (FPIES). No published data are available on active induction of tolerance with oral desensitization (OD) in FPIES. CASE REPORT We carried out an OD in a 9 and a half years old boy with persistent acute egg FPIES. OD was performed with increasing doses of raw egg every week, starting with an initial dose of 0.2 ml. The boy presented mild and transient gastrointestinal adverse reactions when the 4 ml dose was reached. He could tolerate a whole raw egg in less than 14 months. CONCLUSIONS Even though randomized controlled clinical trials on patients including various phenotypes of FPIES are needed, our experience is encouraging about the possible efficacy and safety of OD in this food allergy.
Influence of wild, local and cultivated tobacco varieties on the oviposition preference and offspring performance of Spodoptera litura

Abstract The influences of different plants on herbivores have recently attracted research interest; however, little is known regarding the effects of wild, local and cultivated varieties of the same plant from the same origin on herbivores. This study aimed to examine the effects of different tobacco varieties from the same origin on the oviposition preference and offspring performance of Spodoptera litura. We selected two wild (‘Bishan wild tobacco’ and ‘Badan wild tobacco’), two local (‘Liangqiao sun-cured tobacco’ and ‘Shuangguan sun-cured tobacco’) and two cultivated (‘Xiangyan No. 5’ and ‘Cunsanpi’) tobacco varieties from Hunan Province, China. We found that female S. litura varied in oviposition preferences across the tobacco varieties. They preferred to lay eggs on the cultivated varieties, followed by the local varieties, with the wild varieties being the least preferred. Furthermore, different tobacco varieties significantly influenced the life history parameters of S. litura. Survival rate, pupal weight, emergence rate and adult dry weight decreased in the following order: cultivated varieties > local varieties > wild varieties. Conversely, the pupal stage and development period decreased in the following order: wild varieties > local varieties > cultivated varieties. Therefore, we conclude that wild tobacco varieties have higher resistance to S. litura than cultivated and local varieties, reflecting the evolutionary advantages of wild tobacco varieties.
Linking of concepts measured by SATIS-Stroke and the PM-Scale to the international classification of functioning, disability and health

ABSTRACT Background Rules have been developed to simplify the understanding of researchers/clinicians and standardize the linking process between concepts contained in the items on assessment measures and the International Classification of Functioning, Disability, and Health (ICF). Objective Link the concepts of the SATISPART Stroke (SATIS-Stroke) and Participation Measurement Scale (PM-Scale) to ICF codes. Methods Linking was performed by two evaluators with experience in the ICF taxonomy who applied the ten standard linking rules specifically developed and updated for this purpose. The level of agreement between the evaluators was determined using the Kappa coefficient with 95% confidence intervals. Results The SATIS-Stroke and PM-Scale address the nine domains of the ICF “Activities and Participation” component. Adequate agreement was found between the researchers during the linking process, mainly between the 1º and 3º evaluators. Identified 41 ICF codes were found among the SATIS-Stroke items and 24 ICF codes were found among the items of the PM-Scale. Conclusion SATIS-Stroke addresses more issues regarding the domains of Learning and Applying Knowledge, Mobility, Self-Care, Domestic Life, and Major Life Areas, whereas the PM-Scale addresses more issues regarding the General Tasks and Demands, Communication, and Community, Social and Civic Life. Both instruments equally address issues related to Interpersonal Interactions and Relationships.
Activity concentrations and radiological hazard assessments of 226Ra, 232Th, and 40K in soil samples of oil-producing areas of South Africa

ABSTRACT The specific activity of natural radionuclide in soil samples of the oil-producing areas of South Africa was measured using a High Purity Germanium detector. The activity of226Ra, 232Th, and 40K in the soil range from 16.5 ± 1.3 to 64.9 ± 3.1, 16.8 ± 1.5 to 88.6 ± 1.2, 135.2 ± 17.5 to 604.8 ± 13.4 Bqkg-1 for Mossel Bay; 14.3 ± 1.2 to 48.9 ± 8.2, 22.3 ± 1.4 to 45.1 ± 3.2, 237.7 ± 10.9 to 486.5 ± 40.1 Bqkg-1 for Cape Town; 10.5 ± 1.1 to 25.8 ± 3.2, 13.1 ± 1.9 to 44.3 ± 5.2, 140.2 ± 10.9 to 229.8 ± 12.8 Bqkg-1 for Nelson Mandela Bay and 5.6 ± 2.2 to 13.1 ± 2.9, 4.5 ± 2.5 to 14.1 ± 2.7, 62.7 ± 22.6 to 126.5 ± 21.2 Bqkg-1 for Msunduzi. Most soil samples’ radiological hazards were within the world average.
GenCo: Generative Co-training on Data-Limited Image Generation

Training effective Generative Adversarial Networks (GANs) requires large amounts of training data, without which the trained models are usually sub-optimal with discriminator over-ﬁtting. Several prior studies address this issue by expanding the distribution of the limited training data via massive and hand-crafted data augmentation. We handle data-limited image generation from a very different perspective. Speciﬁcally, we design GenCo, a Generative Co-training network that mitigates the discriminator over-ﬁtting issue by introducing multiple complementary discriminators that provide diverse supervision from multiple distinctive views in training. We instantiate the idea of GenCo in two ways. The ﬁrst way is Weight-Discrepancy Co-training (WeCo) which co-trains multiple distinctive discriminators by diversifying their parameters. The second way is Data-Discrepancy Co-training (DaCo) which achieves co-training by feeding discriminators with different views of the input images ( e.g. , different frequency components of the input images). Extensive experiments over multiple benchmarks show that GenCo achieves superior generation with limited training data. In addition, GenCo also complements the augmentation approach with consistent and clear performance gains when combined.
Welsch Based Multiview Disparity Estimation

In this work, we explore disparity estimation from a high number of views. We experimentally identify occlusions as a key challenge for disparity estimation for applications with high numbers of views. In particular, occlusions can actually result in a degradation in accuracy as more views are added to a dataset. We propose the use of a Welsch loss function for the data term in a global variational framework for disparity estimation. We also propose a disciplined warping strategy and a progressive inclusion of views strategy that can reduce the need for coarse to fine strategies that discard high spatial frequency components from the early iterations. Experimental results demonstrate that the proposed approach produces superior and/or more robust estimates than other conventional variational approaches.
Disarranged Zone Learning (DZL): An unsupervised and dynamic automatic stenosis recognition methodology based on coronary angiography

We proposed a novel unsupervised methodology named Disarranged Zone Learning (DZL) to automatically recognize stenosis in coronary angiography. The methodology firstly disarranges the frames in a video, secondly it generates an effective zone and lastly trains an encoder-decoder GRU model to learn the capability to recover disarranged frames. The breakthrough of our study is to discover and validate the Sequence Intensity (Recover Difficulty) is a measure of Coronary Artery Stenosis Status. Hence, the prediction accuracy of DZL is used as an approximator of coronary stenosis indicator. DZL is an unsupervised methodology and no label engineering effort is needed, the sub GRU model in DZL works as a self-supervised approach. So DZL could theoretically utilize infinitely huge amounts of coronary angiographies to learn and improve performance without laborious data labeling. There is no data preprocessing precondition to run DZL as it dynamically utilizes the whole video, hence it is easy to be implemented and generalized to overcome the data heterogeneity of coronary angiography. The overall average precision score achieves 0.93, AUC achieves 0.8 for this pure methodology. The highest segmented average precision score is 0.98 and the highest segmented AUC is 0.87 for coronary occlusion indicator. Finally, we developed a software demo to implement DZL methodology.
A Aelf-supervised Tibetan-chinese Vocabulary Alignment Method Based On Adversarial Learning

Tibetan is a low-resource language. In order to alleviate the shortage of parallel corpus between Tibetan and Chinese, this paper uses two monolingual corpora and a small number of seed dictionaries to learn the semi-supervised method with seed dictionaries and self-supervised adversarial training method through the similarity calculation of word clusters in different embedded spaces and puts forward an improved self-supervised adversarial learning method of Tibetan and Chinese monolingual data alignment only. The experimental results are as follows. First, the experimental results of Tibetan syllables Chinese characters are not good, which reflects the weak semantic correlation between Tibetan syllables and Chinese characters; second, the seed dictionary of semi-supervised method made before 10 predicted word accuracy of 66.5 (Tibetan - Chinese) and 74.8 (Chinese - Tibetan) results, to improve the self-supervision methods in both language directions have reached 53.5 accuracy.
Quantum anomalous Hall effect in perfectly compensated collinear antiferromagnetic thin films

We show that the quantum anomalous Hall effect almost always occurs in magnetic topological insulator thin films whenever the top and bottom surface layer magnetizations are parallel, independent of the interior layer magnetization configuration. Using this criteria we identify structures that have a quantum anomalous Hall effect even though they have collinear magnetic structures with no net magnetization, and discuss strategies for realizing these interesting magnetic states experimentally.
Non-Linear Gaussian Smoothing With Taylor Moment Expansion

This letter is concerned with solvingcontinuous-discrete Gaussian smoothing problems by using the Taylor moment expansion (TME) scheme. In the proposed smoothing method, we apply the TME method to approximate the transition density of the stochastic differential equation in the dynamic model. Furthermore, we derive a theoretical error bound (in the mean square sense) of the TME smoothing estimates showing that the smoother is stable under weak assumptions. Numerical experiments show that the proposed smoother outperforms a number of baseline smoothers.
Time-dependent lepto-hadronic modeling of the emission from blazar jets with SOPRANO: the case of TXS 0506+056, 3HSP J095507.9+355101 and 3C 279

The observation of a very-high-energy neutrino by IceCube (IceCube-170922A) and its association with the flaring blazar TXS 0506+056 provided the first multimessenger observations of blazar jets, demonstrating the important role of protons in their dynamics and emission. In this paper, we present SOPRANO, a new conservative implicit kinetic code which follows the time evolution of the isotropic distribution functions of protons, neutrons and the secondaries produced in photo-pion and photo-pair interactions, alongside with the evolution of photon and electron/positron distribution functions. SOPRANO is designed to study leptonic and hadronic processes in relativistic sources such as blazars and gamma-ray bursts. Here, we use SOPRANO to model the broadband spectrum of TXS 0506+056 and 3HSP J095507.9+355101, which are associated with neutrino events, and of the extreme flaring blazar 3C 279. The SEDs are interpreted within the guise of both a hadronic and a hybrid model. We discuss the implications of our assumptions in terms of jet power and neutrino flux.
TopiOCQA: Open-domain Conversational Question Answering with Topic Switching

In a conversational question answering scenario, a questioner seeks to extract information about a topic through a series of interdependent questions and answers. As the conversation progresses, they may switch to related topics, a phenomenon commonly observed in information-seeking search sessions. However, current datasets for conversational question answering are limiting in two ways: 1) they do not contain topic switches; and 2) they assume the reference text for the conversation is given, that is, the setting is not open-domain. We introduce TopiOCQA (pronounced Tapioca), an open-domain conversational dataset with topic switches based on Wikipedia. TopiOCQA contains 3,920 conversations with information-seeking questions and free-form answers. On average, a conversation in our dataset spans 13 question-answer turns and involves four topics (documents). TopiOCQA poses a challenging test-bed for models, where efficient retrieval is required on multiple turns of the same conversation, in conjunction with constructing valid responses using conversational history. We evaluate several baselines, by combining state-of-the-art document retrieval methods with neural reader models. Our best model achieves F1 of 55.8, falling short of human performance by 14.2 points, indicating the difficulty of our dataset. Our dataset and code are available at https://mcgill-nlp.github. io/topiocqa.
Spiked Covariance Estimation from Modulo-Reduced Measurements

Consider the rank-1 spiked model: $\bf{X}=\sqrt{\nu}\xi \bf{u}+ \bf{Z}$, where $\nu$ is the spike intensity, $\bf{u}\in\mathbb{S}^{k-1}$ is an unknown direction and $\xi\sim \mathcal{N}(0,1),\bf{Z}\sim \mathcal{N}(\bf{0},\bf{I})$. Motivated by recent advances in analog-to-digital conversion, we study the problem of recovering $\bf{u}\in \mathbb{S}^{k-1}$ from $n$ i.i. d. modulo-reduced measurements $\bf{Y}=[\bf{X}]\mod \Delta$, focusing on the high-dimensional regime ($k\gg 1$). We develop and analyze an algorithm that, for most directions $\bf{u}$ and $\nu=\mathrm{poly}(k)$, estimates $\bf{u}$ to high accuracy using $n=\mathrm{poly}(k)$ measurements, provided that $\Delta\gtrsim \sqrt{\log k}$. Up to constants, our algorithm accurately estimates $\bf{u}$ at the smallest possible $\Delta$ that allows (in an information-theoretic sense) to recover $\bf{X}$ from $\bf{Y}$. A key step in our analysis involves estimating the probability that a line segment of length $\approx\sqrt{\nu}$ in a random direction $\bf{u}$ passes near a point in the lattice $\Delta \mathbb{Z}^k$. Numerical experiments show that the developed algorithm performs well even in a non-asymptotic setting.
Chiral Transport of Hot Carriers in Graphene in the Quantum Hall Regime

Photocurrent (PC) measurements can reveal the relaxation dynamics of photoexcited hot carriers beyond the linear response of conventional transport experiments, a regime important for carrier multiplication. Here, we study the relaxation of carriers in graphene in the quantum Hall regime by accurately measuring the PC signal and modeling the data using optical Bloch equations. Our results lead to a unified understanding of the relaxation processes in graphene over different magnetic field strength regimes, which is governed by the interplay of Coulomb interactions and interactions with acoustic and optical phonons. Our data provide clear indications of a sizable carrier multiplication. Moreover, the oscillation pattern and the saturation behavior of PC are manifestations of not only the chiral transport properties of carriers in the quantum Hall regime but also the chirality change at the Dirac point, a characteristic feature of a relativistic quantum Hall effect.
Adversarial Examples Generation for Reducing Implicit Gender Bias in Pre-trained Models

Over the last few years, Contextualized Pre-trained Neural Language Models, such as BERT, GPT, have shown significant gains in various NLP tasks. To enhance the robustness of existing pre-trained models, one way is adversarial examples generation and evaluation for conducting data augmentation or adversarial learning. In the meanwhile, gender bias embedded in the models seems to be a serious problem in practical applications. Many researches have covered the gender bias produced by word-level information(e.g. gender-stereotypical occupations), while few researchers have investigated the sentence-level cases and implicit cases. In this paper, we proposed a method to automatically generate implicit gender bias samples at sentence-level and a metric to measure gender bias. Samples generated by our method will be evaluated in terms of accuracy. The metric will be used to guide the generation of examples from Pre-trained models. Therefore, those examples could be used to impose attacks on Pre-trained Models. Finally, we discussed the evaluation efficacy of our generated examples on reducing gender bias for future research.
Enhanced microscopic dynamics in mucus gels under a mechanical load in the linear viscoelastic regime

Significance Mucus is a biological gel protecting several tissues. Its key properties result from a crucial balance between solid-like and fluid-like behavior, ensured by the nonpermanent nature of the bonds between its macromolecular constituents. Our understanding of the micrometer-scale response of mucus to an applied stress is still rudimentary, although in living organisms, stresses acting on mucus are ubiquitous, from bacterial penetration to coughing and peristalsis. We show that under a modest applied stress, in the mechanical linear regime, the microscopic dynamics of pig gastric mucus transiently accelerate by up to 2 orders of magnitude. A simple model rationalizes this previously unrecognized fluidization mechanism stemming from elastic recoil following bond breaking and generalizes our findings to networks with reversible bonds. Mucus is a biological gel covering the surface of several tissues and ensuring key biological functions, including as a protective barrier against dehydration, pathogen penetration, or gastric acids. Mucus biological functioning requires a finely tuned balance between solid-like and fluid-like mechanical response, ensured by reversible bonds between mucins, the glycoproteins that form the gel. In living organisms, mucus is subject to various kinds of mechanical stresses, e.g., due to osmosis, bacterial penetration, coughing, and gastric peristalsis. However, our knowledge of the effects of stress on mucus is still rudimentary and mostly limited to macroscopic rheological measurements, with no insight into the relevant microscopic mechanisms. Here, we run mechanical tests simultaneously to measurements of the microscopic dynamics of pig gastric mucus. Strikingly, we find that a modest shear stress, within the macroscopic rheological linear regime, dramatically enhances mucus reorganization at the microscopic level, as signaled by a transient acceleration of the microscopic dynamics, by up to 2 orders of magnitude. We rationalize these findings by proposing a simple, yet general, model for the dynamics of physical gels under strain and validate its assumptions through numerical simulations of spring networks. These results shed light on the rearrangement dynamics of mucus at the microscopic scale, with potential implications in phenomena ranging from mucus clearance to bacterial and drug penetration.
Deformation Quantization for Supermanifolds via Gelfand-Kazhdan Descent

We construct a canonical deformation quantization for symplectic supermanifolds. This gives a novel proof of the super-analogue of Fedosov quantization. Our proof uses the formalism of Gelfand-Kazhdan descent, whose foundations we establish in the super-symplectic setting.
Classification of Time-Series Data Using Boosted Decision Trees

Time-series data classification is central to the analysis and control of autonomous systems, such as robots and self-driving cars. Temporal logic-based learning algorithms have been proposed recently as classifiers of such data. However, current frameworks are either inaccurate for real-world applications, such as autonomous driving, or they generate long and complicated formulae that lack interpretability. To address these limitations, we introduce a novel learning method, called Boosted Concise Decision Trees (BCDTs), to generate binary classifiers that are represented as Signal Temporal Logic (STL) formulae. Our algorithm leverages an ensemble of Concise Decision Trees (CDTs) to improve the classification performance, where each CDT is a decision tree that is empowered by a set of techniques to generate simpler formulae and improve interpretability. The effectiveness and classification performance of our algorithm are evaluated on naval surveillance and urban-driving case studies.
Artificial intelligence for Sustainable Energy: A Contextual Topic Modeling and Content Analysis

Parallel to the rising debates over sustainable energy and artificial intelligence solutions, the world is currently discussing the ethics of artificial intelligence and its possible negative effects on society and the environment. In these arguments, sustainable AI is proposed, which aims at advancing the pathway toward sustainability, such as sustainable energy. In this paper, we offered a novel contextual topic modeling combining LDA, BERT, and Clustering. We then combined these computational analyses with content analysis of related scientific publications to identify the main scholarly topics, sub-themes, and cross-topic themes within scientific research on sustainable AI in energy. Our research identified eight dominant topics including sustainable buildings, AI-based DSSs for urban water management, climate artificial intelligence, Agriculture 4, the convergence of AI with IoT, AI-based evaluation of renewable technologies, smart campus and engineering education, and AI-based optimization. We then recommended 14 potential future research strands based on the observed theoretical gaps. Theoretically, this analysis contributes to the existing literature on sustainable AI and sustainable energy, and practically, it intends to act as a general guide for energy engineers and scientists, AI scientists, and social scientists to widen their knowledge of sustainability in AI and energy convergence research.