Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification and Quantitation Studies of Migrants from BPA Alternative Food-Contact Metal Can Coatings

Bisphenol A (BPA)-based epoxy resins have wide applications as food-contact materials such as metal can coatings. However, negative consumer perceptions toward BPA have driven the food packaging industry to develop other alternatives. In this study, four different metal cans and their lids manufactured with different BPA-replacement food-contact coatings are subjected to migration testing in order to identify migratory chemical species from the coatings. Migration tests are conducted using food simulants and conditions of use corresponding to the intended applications and regulatory guidance from the U.S. Food and Drug Administration. Extracts are analyzed by gas chromatography mass spectrometry (GC-MS) and high resolution GC-MS. The migratory compounds identified include short chain cyclic polyester migrants from polyester-based coatings and bisphenol-type migrants including tetramethyl bisphenol F (TMBPF), tetramethyl bisphenol F diglycidyl ether (TMBPF DGE), bisphenol F (BPF), bisphenol C (BPC), and other related monomers or oligomers. The concentration of the migrants is estimated using an internal standard, and validated trimethylsilyl (TMS) derivatization GC-MS methods are developed to specifically quantify TMBPF, BPF, BPC, and BPA in the coatings. The results will aid the safety evaluation of new food-contact material coating technology based on TMBPF chemistry and will provide an important reference for the industry in identifying and quantifying non-BPA coating-borne migrants.
The electrostatic graph algorithm: a physics-defined method for converting a time-series into a weighted complex network

This paper proposes a new method for converting a time-series into a weighted graph (complex network), which builds on the electrostatic conceptualization originating from physics. The proposed method conceptualizes a time-series as a series of stationary, electrically charged particles, on which Coulomb-like forces can be computed. This allows generating electrostatic-like graphs associated to time-series that, additionally to the existing transformations, can be also weighted and sometimes disconnected. Within this context, the paper examines the structural relevance between five different types of time-series and their associated graphs generated by the proposed algorithm and the visibility graph, which is currently the most established algorithm in the literature. The analysis compares the source time-series with the network-based node-series generated by network measures that are arranged into the node-ordering of the source time-series, in terms of linearity, chaotic behaviour, stationarity, periodicity, and cyclical structure. It is shown that the proposed electrostatic graph algorithm produces graphs that are more relevant to the structure of the source time-series by introducing a transformation that converts the time-series to graphs. This is more natural rather than algebraic, in comparison with existing physics-defined methods. The overall approach also suggests a methodological framework for evaluating the structural relevance between the source time-series and their associated graphs produced by any possible transformation.
Explainable AI for Software Engineering

The success of software engineering projects largely depends on complex decision-making. For example, which tasks should a developer do first, who should perform this task, is the software of high quality, is a software system reliable and resilient enough to deploy, etc. However, erroneous decision-making for these complex questions is costly in terms of money and reputation. Thus, Artificial Intelligence/Machine Learning (AI/ML) techniques have been widely used in software engineering for developing software analytics tools and techniques to improve decision-making, developer productivity, and software quality. However, the predictions of such AI/ML models for software engineering are still not practical (i.e., coarse-grained), not explainable, and not actionable. These concerns often hinder the adoption of AI/ML models in software engineering practices. In addition, many recent studies still focus on improving the accuracy, while a few of them focus on improving explainability. Are we moving in the right direction? How can we better improve the SE community (both research and education)? In this tutorial, we first provide a concise yet essential introduction to the most important aspects of Explainable AI and a hands-on tutorial of Explainable AI tools and techniques. Then, we introduce the fundamental knowledge of defect prediction (an example application of AI for Software Engineering). Finally, we demonstrate three successful case studies on how Explainable AI techniques can be used to address the aforementioned challenges by making the predictions of software defect prediction models more practical, explainable, and actionable. The materials are available at https://xai4se.github. io.
PLASMA CHLORIDE AND SODIUM, AND CHLORIDE SPACE IN THE EUROPEAN EEL, ANGUILLA ANGUILLA L.

1. New intra-vascular cannulation techniques are described, and also an extra-corporal blood circuit containing an artificial heart and a counting cell. This makes possible a continuous study of the radioactivity of the blood. 2. Plasma chloride concentration varies greatly in fresh-water eels despite good sodium regulation. 3. The fresh-water to sea-water adaptation of eels is frequently accompanied by a temporary hypermineralization of the internal medium. This necessitates a high degree of cellular euryhalinity. 4. The sea-water-adapted eel maintains strict homeostasis of its plasma chloride and sodium. 5. The chloride distribution space decreases by 10% when eels are transferred from fresh water to sea water. The internal distribution of chloride is also modified and its fluxes between the ion compartments of the body are considerably increased.
What Makes a Star Teacher? A Hierarchical BERT Model for Evaluating Teacher's Performance in Online Education

Education has a significant impact on both society and personal life. With the development of technology, online education has been growing rapidly over the past decade. While there are several online education studies on student behavior analysis, the course concept mining, and course recommendations (Feng, Tang, and Liu 2019; Pan et al. 2017), there is little research on evaluating teachers' performance in online education. In this paper, we conduct a systematic study to understand and effectively predict teachers' performance using the subtitles of 1,085 online courses. Our model-free analysis shows that teachers' verbal cues (e.g., question strategy, emotional appealing, and hedging) and their course structure design are both significantly correlated with teachers' performance evaluation. Based on these insights, we then propose a hierarchical course BERT model to predict teachers' performance in online education. Our proposed model can capture the hierarchical structure within each course as well as the deep semantic features extracted from the course content. Experiment results show that our proposed method achieves significant gain over several state-of-the-art methods. Our study provides a significant social impact in helping teachers improve their teaching style and enhance their instructional material design for more effective online teaching in the future.
Stellar Population Inference with Prospector

Inference of the physical properties of stellar populations from observed photometry and spectroscopy is a key goal in the study of galaxy evolution. In recent years, the quality and quantity of the available data have increased, and there have been corresponding efforts to increase the realism of the stellar population models used to interpret these observations. Describing the observed galaxy spectral energy distributions in detail now requires physical models with a large number of highly correlated parameters. These models do not fit easily on grids and necessitate a full exploration of the available parameter space. We present Prospector, a flexible code for inferring stellar population parameters from photometry and spectroscopy spanning UV through IR wavelengths. This code is based on forward modeling the data and Monte Carlo sampling the posterior parameter distribution, enabling complex models and exploration of moderate dimensional parameter spaces. We describe the key ingredients of the code and discuss the general philosophy driving the design of these ingredients. We demonstrate some capabilities of the code on several data sets, including mock and real data.
Gastroprotective Effects of Inulae Flos on HCl/Ethanol-Induced Gastric Ulcers in Rats

Inulae Flos, the flower of Inula britannica L. , is used as a dietary supplement, beverage, and medicine in East Asia. In this study, we evaluated the gastroprotective effects of Inulae Flos extract (IFE) against gastric mucosal lesions induced by hydrochloric acid (HCl)/ethanol in rats and explored its potential mechanisms by measuring antioxidant enzyme activity, mucus secretion, and prostaglandin E2 (PGE2) levels. Pretreatment with IFE at doses of 100 and 300 mg/kg significantly inhibited gastric lesions in HCl/ethanol-treated rats. IFE increased the activities of superoxide dismutase and catalase and the levels of glutathione and PGE2 in gastric tissues. The administration of IFE also significantly increased the gastric wall mucus contents in HCl/ethanol-induced gastric lesions. These findings suggest that IFE has gastroprotective effects against HCl/ethanol-induced gastric lesions and exerts these effects through increased antioxidant levels and gastric mucus secretion. Inulae Flos may be a promising agent for the prevention and treatment of gastritis and gastric ulcers.
A comparison of CrossFit and concurrent training on myonectin, insulin resistance and physical performance in healthy young women

Abstract Objective To compare the effect of CrossFit and concurrent aerobic + resistance training (CT) on circulating myonectin levels, insulin resistance index, and physical performance in young women. Materials and methods Thirty healthy women were randomly assigned to CrossFit (n = 16) and CT (n = 14) groups, exercising three sessions per week for eight weeks. Serum myonectin levels, insulin resistance index, body composition, and performance variables were evaluated before and after the intervention period. Results Neither the CrossFit nor the CT group improved in myonectin levels and insulin resistance index. While, fat percentage, muscle mass, upper-limb strength, VO2max, HR at rest, lower body mean power output, and upper body peak power output improved more after CrossFit than the CT. Conclusion It seems that CrossFit exercises by targeting multiple fitness components in one exercise session may allow participants to perform more activity in less time.
A Study on the Autoregressive and non-Autoregressive Multi-label Learning

Extreme classification tasks are multi-label tasks with an extremely large number of labels (tags). These tasks are hard because the label space is usually (i) very large, e.g. thousands or millions of labels, (ii) very sparse, i.e. very few labels apply to each input document, and (iii) highly correlated, meaning that the existence of one label changes the likelihood of predicting all other labels. In this work, we propose a self-attention based variational encoder-model to extract the label-label and label-feature dependencies jointly and to predict labels for a given input. In more detail, we propose a non-autoregressive latent variable model and compare it to a strong autoregressive baseline that predicts a label based on all previously generated labels. Our model can therefore be used to predict all labels in parallel while still including both label-label and label-feature dependencies through latent variables, and compares favourably to the autoregressive baseline. We apply our models to four standard extreme classification natural language data sets, and one news videos dataset for automated label detection from a lexicon of semantic concepts. Experimental results show that although the autoregressive models, where use a given order of the labels for chain-order label prediction, work great for the small scale labels or the prediction of the highly ranked label, but our non-autoregressive model surpasses them by around 2% to 6% when we need to predict more labels, or the dataset has a larger number of the labels.
Deep learning collocation method for solid mechanics: Linear elasticity, hyperelasticity, and plasticity as examples

Deep learning and the collocation method are merged and used to solve partial differential equations describing structures' deformation. We have considered different types of materials: linear elasticity, hyperelasticity (neo-Hookean) with large deformation, and von Mises plasticity with isotropic and kinematic hardening. The performance of this deep collocation method (DCM) depends on the architecture of the neural network and the corresponding hyperparameters. The presented DCM is meshfree and avoids any spatial discretization, which is usually needed for the finite element method (FEM). We show that the DCM can capture the response qualitatively and quantitatively, without the need for any data generation using other numerical methods such as the FEM. Data generation usually is the main bottleneck in most data-driven models. The deep learning model is trained to learn the model's parameters yielding accurate approximate solutions. Once the model is properly trained, solutions can be obtained almost instantly at any point in the domain, given its spatial coordinates. Therefore, the deep collocation method is potentially a promising standalone technique to solve partial differential equations involved in the deformation of materials and structural systems as well as other physical phenomena.
How Degeneration of Cells Surrounding Motoneurons Contributes to Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal neurological disorder characterized by the progressive degeneration of upper and lower motoneurons. Despite motoneuron death being recognized as the cardinal event of the disease, the loss of glial cells and interneurons in the brain and spinal cord accompanies and even precedes motoneuron elimination. In this review, we provide striking evidence that the degeneration of astrocytes and oligodendrocytes, in addition to inhibitory and modulatory interneurons, disrupt the functionally coherent environment of motoneurons. We discuss the extent to which the degeneration of glial cells and interneurons also contributes to the decline of the motor system. This pathogenic cellular network therefore represents a novel strategic field of therapeutic investigation.
Current Immunotherapy Approaches in Non-Hodgkin Lymphomas

Non-Hodgkin lymphomas (NHLs) are lymphoid malignancies of B- or T-cell origin. Despite great advances in treatment options and significant improvement of survival parameters, a large part of NHL patients either present with a chemotherapy-refractory disease or experience lymphoma relapse. Chemotherapy-based salvage therapy of relapsed/refractory NHL is, however, capable of re-inducing long-term remissions only in a minority of patients. Immunotherapy-based approaches, including bispecific antibodies, immune checkpoint inhibitors and genetically engineered T-cells carrying chimeric antigen receptors, single-agent or in combination with therapeutic monoclonal antibodies, immunomodulatory agents, chemotherapy or targeted agents demonstrated unprecedented clinical activity in heavily-pretreated patients with NHL, including chemotherapy-refractory cases with complex karyotype changes and other adverse prognostic factors. In this review, we recapitulate currently used immunotherapy modalities in NHL and discuss future perspectives of combinatorial immunotherapy strategies, including patient-tailored approaches.
Quantum computational advantage using photons

A light approach to quantum advantage Quantum computational advantage or supremacy is a long-anticipated milestone toward practical quantum computers. Recent work claimed to have reached this point, but subsequent work managed to speed up the classical simulation and pointed toward a sample size–dependent loophole. Quantum computational advantage, rather than being a one-shot experimental proof, will be the result of a long-term competition between quantum devices and classical simulation. Zhong et al. sent 50 indistinguishable single-mode squeezed states into a 100-mode ultralow-loss interferometer and sampled the output using 100 high-efficiency single-photon detectors. By obtaining up to 76-photon coincidence, yielding a state space dimension of about 1030, they measured a sampling rate that is about 1014-fold faster than using state-of-the-art classical simulation strategies and supercomputers. Science, this issue p. 1460 Quantum computational advantage is demonstrated using boson sampling with photons. Quantum computers promise to perform certain tasks that are believed to be intractable to classical computers. Boson sampling is such a task and is considered a strong candidate to demonstrate the quantum computational advantage. We performed Gaussian boson sampling by sending 50 indistinguishable single-mode squeezed states into a 100-mode ultralow-loss interferometer with full connectivity and random matrix—the whole optical setup is phase-locked—and sampling the output using 100 high-efficiency single-photon detectors. The obtained samples were validated against plausible hypotheses exploiting thermal states, distinguishable photons, and uniform distribution. The photonic quantum computer, Jiuzhang, generates up to 76 output photon clicks, which yields an output state-space dimension of 1030 and a sampling rate that is faster than using the state-of-the-art simulation strategy and supercomputers by a factor of ~1014.
Robust Cooperative Optimal Sliding-Mode Control for High-Order Nonlinear Systems: Directed Topologies

This article is concerned with the robust cooperative optimal control of nonlinear multiagent systems (MASs) with external disturbances and modeling uncertainties. Using the super-twisting algorithm, a continuous sliding-mode control protocol is presented for high-order nonlinear MASs with multiple inputs. The sliding-mode dynamics is modeled by the Takagi–Sugeno fuzzy approach, and the nominal control protocol that guarantees the robust optimization of the cost function is designed. Directed topologies are allowed using the presented protocol, and many assumptions about topologies are removed. Finally, three numerical examples are reported to demonstrate the effectiveness and improved performance of the presented protocol.
Rules for Strand Invasion by Chemically Modified Oligonucleotides

Recognition of sequences within duplex DNA is a general strategy for probing DNA function and for disrupting gene expression. Peptide nucleic acids (PNAs) and conjugates between DNA oligonucleotides and cationic peptides possess superior potential for strand invasion at complementary sequences. To elucidate the rules underlying this phenomenon we examined hybridization to sequences throughout plasmid pUC19. We discovered that oligonucleotide−peptide conjugates and PNAs fall into three classes based on their hybridization efficiencies: (i) those complementary to inverted repeats within AT-rich region hybridize with highest efficiency; (ii) those complementary to areas adjacent to inverted repeats or near AT-rich regions hybridize with moderate efficiency; and (iii) those complementary to other regions do not detectably hybridize, with the exception of PNAs that have been modified to incorporate additional positive charge. Hybridization of oligonucleotide−peptide conjugates and PNAs was stringently depende.
Relational Learning for Skill Preconditions

To determine if a skill can be executed in any given environment, a robot needs to learn the preconditions for the skill. As robots begin to operate in dynamic and unstructured environments, precondition models will need to generalize to variable number of objects with different shapes and sizes. In this work, we focus on learning precondition models for manipulation skills in unconstrained environments. Our work is motivated by the intuition that many complex manipulation tasks, with multiple objects, can be simplified by focusing on less complex pairwise object relations. We propose an object-relation model that learns continuous representations for these pairwise object relations. Our object-relation model is trained completely in simulation, and once learned, is used by a separate precondition model to predict skill preconditions for real world tasks. We evaluate our precondition model on $3$ different manipulation tasks: sweeping, cutting, and unstacking. We show that our approach leads to significant improvements in predicting preconditions for all 3 tasks, across objects of different shapes and sizes.
Teaching the Machine to Explain Itself using Domain Knowledge

Machine Learning (ML) has been increasingly used to aid humans to make better and faster decisions. However, non-technical humans-in-the-loop struggle to comprehend the rationale behind model predictions, hindering trust in algorithmic decision-making systems. Considerable research work on AI explainability attempts to win back trust in AI systems by developing explanation methods but there is still no major breakthrough. At the same time, popular explanation methods (e.g., LIME, and SHAP) produce explanations that are very hard to understand for non-data scientist persona. To address this, we present JOEL, a neural network-based framework to jointly learn a decision-making task and associated explanations that convey domain knowledge. JOEL is tailored to human-in-the-loop domain experts that lack deep technical ML knowledge, providing high-level insights about the model's predictions that very much resemble the experts' own reasoning. Moreover, we collect the domain feedback from a pool of certified experts and use it to ameliorate the model (human teaching), hence promoting seamless and better suited explanations. Lastly, we resort to semantic mappings between legacy expert systems and domain taxonomies to automatically annotate a bootstrap training set, overcoming the absence of concept-based human annotations. We validate JOEL empirically on a real-world fraud detection dataset. We show that JOEL can generalize the explanations from the bootstrap dataset. Furthermore, obtained results indicate that human teaching can further improve the explanations prediction quality by approximately $13.57\%$.
Unsupervised Alternating Optimization for Blind Hyperspectral Imagery Super-resolution

Despite the great success of deep model on Hyperspectral imagery (HSI) super-resolution(SR) for simulated data, most of them function unsatisfactory when applied to the real data, especially for unsupervised HSI SR methods. One of the main reason comes from the fact that the predefined degeneration models (e.g. blur in spatial domain) utilized by most HSI SR methods often exist great discrepancy with the real one, which results in these deep models overfit and ultimately degrade their performance on real data. To well mitigate such a problem, we explore the unsupervised blind HSI SR method. Specifically, we investigate how to effectively obtain the degeneration models in spatial and spectral domain, respectively, and makes them can well compatible with the fusion based SR reconstruction model. To this end, we first propose an alternating optimization based deep framework to estimate the degeneration models and reconstruct the latent image, with which the degeneration models estimation and HSI reconstruction can mutually promotes each other. Then, a meta-learning based mechanism is further proposed to pre-train the network, which can effectively improve the speed and generalization ability adapting to different complex degeneration. Experiments on three benchmark HSI SR datasets report an excellent superiority of the proposed method on handling blind HSI fusion problem over other competing methods.