Miracle-dz/UniFAR-FTUs huggingface dataset:
Subspace Least Square Approach for Drift Removal with Application to Herschel Data

We present a method to estimate and remove the baseline drift affecting the data produced by the two infrared imaging photometers onboard the ESA Herschel satellite, namely PACS and SPIRE. The method exploits a Least Square approach which is performed after that the signal component has been removed from the data by means of a projection into an appropriate subspace. We present the method, discuss its performance and implementation details and show that it is perfectly suited for Herschel data, for which it can ideally guarantee a complete drift removal.
Critical Information Infrastructure Objects: Operator Support Systems

Currently, one of the significant elements of control systems for critical infrastructure objects (transport, oil, and gas industry objects, energy objects) is the use of operator support systems. The paper discusses the problems of the operator support system (OSS) with their assignment to groups in terms of system organization, tracking signals coming from the control system, and execution time. The relationship between the functional parts of the OSS and the system’s external environment had shown. The connection between the OSS, the process control system, and other diagnostic systems had considered. The use of operator support systems makes it possible to increase the safety and efficiency of the operation of a critical infrastructure object and reduce the number of incidents and emergencies due to the human factor.
The Establishment of a High Standard Skin Bank in Taiwan as a Burn Care Pioneer

Introduction: The establishment of skin banks is especially important in providing crucial cadaveric skin for burn treatments. Although the 2015 Formosa Fun Coast Explosion Disaster resulted in tragic casualties, it raised public awareness of the importance of having a well-functioning skin bank and led to the initiation of a skin bank development project, as well as the establishment of the first high standard skin bank in Taiwan. Methods: We standardized the size of our procured skin to avoid broken or disrupted skin graft, fit properly on a Zimmer Dermacarrier® for mesh expansion, and to be convenient for storage. We also integrated gamma radiation with the glycerol stored skin method for our skin preservation which effectively eradicates all contamination. Results: From 2016 to 2020, 453,100 cm2 of skin were procured from 62 donors and stored in the NTUH skin bank; 254,000 cm2 of which were utilized in the treatment of 75 patients. Between 2016 and 2017, 141 microbiological examinations were administered with six were positive. Since the introduction of gamma radiation, no contamination has been observed hereafter in our examinations. Conclusions: We report the success of establishing a high standard and well-functioning skin bank at NTUH that supply sufficient skin allografts in Taiwan. It is the first interhospital supply of charitable skin allografts across Taiwan. Being humanitarian, our skin bank is passing great love forward.
Generic inference of inflation models by non-Gaussianity and primordial power spectrum reconstruction

We present a generic inference method for inflation models from observational data by the usage of higher-order statistics of the curvature perturbation on uniform density hypersurfaces. This method is based on the calculation of the posterior for the primordial non-Gaussianity parameters fNL and gNL, which in general depend on specific parameters of inflation and reheating models, and enables to discriminate among the still viable inflation models. To keep analyticity as far as possible to dispense with numerically expensive sampling techniques a saddle-point approximation is introduced, whose precision is validated for a numerical toy example. The mathematical formulation is done in a generic way so that the approach remains applicable to cosmic microwave background data as well as to large scale structure data. Additionally, we review a few currently interesting inflation models and present numerical toy examples thereof in two and three dimensions to demonstrate the efficiency of the higher-order statistics method. A second quantity of interest is the primordial power spectrum. Here, we present two Bayesian methods to infer it from observational data, the so called critical filter and an extension thereof with smoothness prior, both allowing for a non-parametric spectrum reconstruction. These methods are able to reconstruct the spectra of the observed perturbations and the primordial ones of curvature perturbation even in case of non-Gaussianity and partial sky coverage. We argue that observables like T- and B-modes permit to measure both spectra. This also allows to infer the level of non-Gaussianity generated since inflation.
Spinning Dust Emission from Wobbling Grains: Important Physical Effects and Implications

We review the major progress on the modeling of electric dipole emission from rapidly spinning tiny dust grains, including polycyclic aromatic hydrocarbons (PAHs). We begin by summarizing the original model of spinning dust proposed by Draine and Lazarian and recent theoretical results improving the Draine and Lazarian model. The paper is focused on important physical effects that were disregarded in earlier studies for the sake of simplicity and recently accounted for by us, including grain wobbling due to internal relaxation, impulsive excitation by single-ion collisions, the triaxiality of grain shape, charge fluctuations, and the turbulent nature of astrophysical environments. Implications of the spinning dust for constraining the physical properties of ultrasmall dust grains and environmental conditions are discussed. We discuss the alignment of tiny dust grains and the possibility of polarized spinning dust emission. Suggestions for constraining the alignment of tiny grains and polarization of spinning dust are also discussed.
On the area of accretion curtains from fast aperiodic time variability of the intermediate polar EX Hya

We present results of a study of the fast timing variability of the magnetic cataclysmic variable (mCV) EX Hya. It was previously shown that one may expect the rapid flux variability of mCVs to be smeared out at timescales shorter than the cooling time of hot plasma in the post shock region of the accretion curtain near the WD surface. Estimates of the cooling time and the mass accretion rate, thus provide us with a tool to measure the density of the post-shock plasma and the cross-sectional area of the accretion funnel at the WD surface. We have probed the high frequencies in the aperiodic noise of one of the brightest mCV EX Hya with the help of optical telescopes, namely SALT and the SAAO 1.9m telescope. We place upper limits on the plasma cooling timescale $\tau<$0.3 sec, on the fractional area of the accretion curtain footprint $f<1.6\times10^{-4}$, and a lower limit on the specific mass accretion rate $\dot{M}/A \gtrsim $3 g/sec/cm$^{-2}$. We show that measurements of accretion column footprints via eclipse mapping highly overestimate their areas. We deduce a value of $\Delta r/r \lesssim 10^{-3}$ as an upper limit to the penetration depth of the accretion disc plasma at the boundary of the magnetosphere.
Using Neural Networks to Guide Data-Driven Operational Decisions

We propose to use Deep Neural Networks to solve data-driven stochastic optimization problems. Given the historical data of the observed covariate, taken decision, and the realized cost in past periods, we train a neural network to predict the objective value as a function of the decision and the covariate. Once trained, for a given covariate, we optimize the neural network over the decision variable using gradient-based methods because the gradient and the Hessian matrix can be analytically computed. We characterize the performance of our methodology based on the generalization bound of the neural network. We show strong performance on two signature problems in operations management, the newsvendor problem and the assortment pricing problem.
Symbolic Methods and Tools for Discrete Event Dynamic Systems

The interest in Discrete Event Dynamic Systems (DEDS) has increased during the last years, due to the lack of methods and tools that are capable of handling the complexity of problems and tasks present in industry today. In this thesis we will consider a framework based on relations over finite domains. The framework is used for modeling, analysis, and synthesis of DEDS.Binary Decision Diagrams (BDDs) are used to represent relations, as well as the operations for modeling, analysis and synthesis of DEDS. To utilized the structure of integers and arithmetic operation, Integer Decision Diagrams (IDDs) are developed and implemented. Polynomials over finite fields are another type of representation that is used for the relational framework. Here Grobner bases, and Integrated Monomial Diagrams (IMDs) are the tools that are used. IDDs and IMDs are both developed, by the author, to represent integer structures and arithmetic operations efficiently.With tools for efficient relational representation, it possible to improve scalability of DEDS computations, as shown in this thesis by the modeling and analysis of the landing gear controller of the Swedish fighter aircraft JAS 39 Gripen. A relational model, represented by a BDD, is automatically generated from a 1200 lines Pascal implementation, which contains 105 binary variables of which 26 are state variables. Function specifications expressed with temporal algebra, are verified using tools for dynamic analysis, which we also use to compute a polynomial representing the set of all reachable states in the model. The landing gear controller serves as a benchmark test of BDDs and IDDs. The IDDs reduced the computation time by 50%. To explore the ability and applicability of using a polynomial relational representation when doing synthesis, we use a tank system containing actuators (pump and valves) and sensors (the tank level and measurable disturbances). We propose a synthesis method that uses actuator priority, weighting of states, and Grobner bases to compute explicit control laws for the actuators, fulfilling the control objectives even if one of the actuators (the pump) is defective.Modeling aspects are emphasized further, by comparing the polynomial approach which we have used, with Boolean expressions and established DEDS approaches in the community of automatic control like Ramadge-Wonham, Petri nets, and COCOLOG. We discuss how to handle transformation between signals and events for DEDS and how to modularize DEDS to gain complexity advantages. Model description languages are discussed and desirable features are stated, using the experiences achieved from the modeling of the tank system and the landing gear controller.
News from KM3NeT

KM3NeT is a future research infrastructure in the Mediterranean Sea, hosting a multi-cubic-kilometre neutrino telescope and nodes for Earth and Sea sciences. In this report we shortly summarise the genesis of the KM3NeT project and present key elements of its technical design. The physics objectives of the KM3NeT neutrino telescope and some selected sensitivity estimates are discussed. Finally, some first results from prototype operations and the next steps towards implementation – in particular the first construction phase in 2014/15 – are described.
Selection, Generalization, and Theories of Cause in Case-Oriented Physics Education Research

Case-oriented physics education research - which seeks to refine and develop theory by linking that theory to cases - incorporates distinct practices for selecting data for analysis, generalizing results, and making causal claims. Unanswered questions about these practices may constrain researchers more familiar with the recurrence-oriented research paradigm - which seeks to inform instructional predictions by discerning reproducible, representative patterns and relationships - from participating in or critically engaging with case-oriented research. We use results from interviews with physics education researchers, a synthesis of the literature on research methodologies, and published examples of case-oriented and recurrence-oriented research to answer "hard-hitting questions" that researchers may pose. In doing so, we aim to substantiate our position that both case-oriented and recurrence- oriented PER are rigorous but that the rigor is of a different nature in each paradigm.
Network Toxicology Guided Mechanism Study on the Association between Thyroid Function and Exposures to Polychlorinated Biphenyls Mixture

Polychlorinated biphenyls (PCBs) are persistent and highly toxic pollutants, which can accumulate in organisms and produce toxic effects, especially damaging the function of thyroid hormones. So far, the molecular mechanism of PCBs mixture and their metabolites interfering with thyroid hormones has not been studied thoroughly except for individual compounds. In this study, PubMed, Web of Science, and STITCH databases were used to search PCBs and their corresponding target proteins. The intersection of PCBs and thyroid hormone dysfunction target proteins was obtained from GeneCards. The “compounds-targets-pathways” network was constructed by Cytoscape software. And KEGG and Go analyses were performed for key targets. Finally, molecular docking was used to verify the binding effect. Four major active components, five key targets, and 10 kernel pathways were successfully screened by constructing the network. Functional enrichment analysis showed that the interference was mediated by cancer, proteoglycans, PI3K-Akt, thyroid hormone, and FoxO signaling pathways. The molecular docking results showed that the binding energies were less than -5 kcal·mol−1. PCBs and their metabolites may act on the key targets of MAPK3, MAPK1, RXRA, PIK3R1, and TP53. The toxic effect of sulfated and methyl sulfone PCBs is greater. The method of screening targets based on the simultaneous action of multiple PCBs can provide a reference for other research. The targets were not found in previous metabolite toxicity studies. It also provides a bridge for the toxic effects and experimental research of PCBs and their metabolites in the future.
Mood and Wellbeing

The two main subjectivist accounts of wellbeing, hedonism and desire-satisfactionism, focus on pleasure and desire (respectively) as the subjective states relevant to evaluating the goodness of a life. In this paper, I argue that another type of subjective state, mood, is much more central to wellbeing. After a general characterization of some central features of mood (§1), I argue that the folk concept of happiness construes it as preponderance of good mood (§2). I then leverage this connection between mood and happiness to argue that having certain mood patterns in one’s life is sufficient for having a good life (§3), and explore their potential necessity for a good life (§4). I close with discussion of the role that mood patterns might play within the three leading philosophical theories of wellbeing: hedonism (§5), desire-satisfactionism (§6), and objective-list theory (§7).
Use of Correlation Matrix to Assess the Stirring Performance of a Reverberation Chamber: a Comparative Study

The use of correlation matrices to evaluate the number of uncorrelated stirrer positions of reverberation chamber has widespread applications in electromagnetic compatibility. We present a comparative study of recent techniques based on multivariate correlation functions aimed at relating space-frequency inhomogeneities/anisotropies to the reduction of uncorrelated positions. Full wave finite-difference time domain simulations of an actual reverberation chamber are performed through an in-house parallel code. The efficiency of this code enables for capturing extensive inhomogeneous/anisotropic spatial volumes (frequency ranges). The concept of threshold crossing is revised under the light of random field sampling, which is important to the performance of arbitrary reverberation chambers.
Towards AI-Enabled Hardware Security: Challenges and Opportunities

Recent developments in Artificial Intelligence (AI) and Machine Learning (ML), driven by a substantial increase in the size of data in emerging computing systems, have led into successful applications of such intelligent techniques in various disciplines including security. Traditionally, integrity of data has been protected with various security protocols at the software level with the underlying hardware assumed to be secure. This assumption however is no longer true with an increasing number of attacks reported on the hardware. The emergence of new security threats (e.g., malware, side-channel attacks, etc.) requires patching/updating the software-based solutions that needs a vast amount of memory and hardware resources. Therefore, the security should be delegated to the underlying hardware, building a bottom-up solution for securing computing devices rather than treating it as an afterthought. This paper highlights the growing role of AI/ML techniques in hardware and architecture security field and provides insightful discussions on pressing challenges, opportunities, and future directions of designing accurate and efficient machine learning-based attacks and defense mechanisms in response to emerging hardware security vulnerabilities in modern computer systems and next generation of cryptosystems.
Text Classification of Conversational Implicatures Based on Lexical Features

ABSTRACT Following the guiding hypothesis in NLP, similar word frequency vectors may have similar implicatures, but some scholars are more inclined conversational implicatures cannot be obtained only through lexical features. To judge which view is more reasonable and explore the reasons for the divergence between them, whether conversational implicatures can be obtained only through lexical features is verified empirically. Main work of this paper includes: First, based on 600 corpora in the annotated dataset, the values of 20 lexical features of each corpus are obtained by automatic calculation. Second, meta-transformer of logistic regression for selecting features is adopted for feature selection and ranking. Third, after determining the features, the text is classified by the binomial logistic regression with the type of implicatures as labels. Fourth, results are tested for significance to identify relationships between variables. Experiments show that there is a statistical dependence between lexical features and conversational implicatures, and the text classification of implicatures can be performed only based on lexical features. In addition, the results of text classification will not be different due to the difference in context utterance or the type of implicature, and the text classification of implicatures only based on “response utterance” is more efficient.
HealthLies: Dataset and Machine Learning Models for Detecting Fake Health News

Current datasets and models focusing on health fake news identification are few and far between and primarily based on COVID-19. In this paper, we introduce a new health news-specific dataset called HealthLies, which includes 11,001 facts and myths about diseases such as COVID-19, Cancer, Polio, Zika, HIV/AIDS, SARS, and Ebola collected from a wide range of sources. We train several machine learning models, including KNN, SVM, Logistic Regression, Naive Bayes, an MLP Classifier, and a deep learning model based on the state-of-the-art Natural Language Processing (NLP) BERT model, which we name BERT-HealthLies. We find that BERT-HealthLies typically achieves the highest accuracy across models, though other models may be preferable in some real-time applications due to their orders of magnitude faster prediction and training times. In addition, ensembling BERT-HealthLies with the other models performs up to 12% better than BERT-HealthLies alone when identifying fake news related to a new disease for which we do not yet have training data.
COMPARING ULTRAVIOLET- AND INFRARED-SELECTED STARBURST GALAXIES IN DUST OBSCURATION AND LUMINOSITY

We present samples of starburst galaxies that represent the extremes discovered with infrared and ultraviolet observations, including 25 Markarian galaxies, 23 ultraviolet-luminous galaxies discovered with GALEX, and the 50 starburst galaxies having the largest infrared/ultraviolet ratios. These sources have z < 0.5 and cover a luminosity range of ∼104. Comparisons between infrared luminosities determined with the 7.7 μm polycyclic aromatic hydrocarbon feature and ultraviolet luminosities from the stellar continuum at 153 nm are used to determine obscuration in starbursts and dependence of this obscuration on infrared or ultraviolet luminosity. A strong selection effect arises for the ultraviolet-selected samples: the brightest sources appear bright because they have the least obscuration. Obscuration correction for the ultraviolet-selected Markarian+GALEX sample has the form log[UV(intrinsic)/UV(observed)] = 0.07(±0.04)M(UV) + 2.09 ± 0.69 but for the full infrared-selected Spitzer sample is log[UV(intrinsic)/UV(observed)] = 0.17(±0.02)M(UV) + 4.55 ± 0.4. The relation of total bolometric luminosity Lir to M(UV) is also determined for infrared-selected and ultraviolet-selected samples. For ultraviolet-selected galaxies, log Lir = −(0.33 ± 0.04)M(UV) + 4.52 ± 0.69. For the full infrared-selected sample, log Lir = −(0.23 ± 0.02)M(UV) + 6.99 ± 0.41, all for Lir in L☉ and M(UV) the AB magnitude at rest frame 153 nm. These results imply that obscuration corrections by factors of 2–3 determined from reddening of the ultraviolet continuum for Lyman break galaxies with z>2 are insufficient, and should be at least a factor of 10 for M(UV) ∼ −17, with decreasing correction for more luminous sources.
The analysis of vaccination and the prediction model based on the seasonal-ARIMA model

Since the outbreak of the novel Coronavirus in 2019, every country around the world has invested many resources in vaccine research and development. The goal of this paper is to investigate the current vaccination status around the world. And using the time series model to have a general grasp of the future vaccination trend. We used the Seasonal-ARIMA Model to predict the vaccination status in the next two years based on the collected data. Seasonal-ARIMA model, as a very commonly used model in time series forecasting, has a relatively good performance in forecasting. The data in this article comes from the OWID organization (our world in data), which is a non-profit organization based in the United Kingdom focusing on large global issues. And the data from this organization has high credibility. We have a prediction of future vaccination by writing this article, which can serve as a reference for the relevant departments to formulate vaccine production plans.