Miracle-dz/UniFAR-FTUs huggingface dataset:
A critical review of univariate non-parametric estimation of first derivatives

This paper gives new guidance for selection of univariate non-parametric derivative estimators with non-iid errors in finite samples. It is shown via an extensive set of Monte Carlo simulations that the generalized criterion of Charnigo et al. (A generalized criterion for derivative estimation. Technometrics. 2011;53:238–253.) with spline smoothing performs the best in situations with minimal noise. For increased noise, generalized cross-validation of Craven and Wahba (Smoothing noisy data with spline functions. Numer Math. 1978;31:377–403) with P spline smoothing and the improved Akaike Information Criterion of Hurvich et al. (Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. J R Stat Soc Ser B. 1998;60:271–293.) with P spline smoothing are preferred. In the class of kernel smoothing and local regression methods, the local-cubic estimator of Henderson et al. (Gradient-based smoothing parameter selection for nonparametric regression estimation. J Econom. 2015;184:233–241.) generally outperforms its competitors. An internal meta-analysis separately favours the generalized method and P spline smoothing. The empirical example given provides support for use of the local-cubic estimator.
Detection of two power-law tails in the probability distribution functions of massive GMCs

We report the novel detection of complex high-column density tails in the probability distribution functions (PDFs) for three high-mass star-forming regions (CepOB3, MonR2, NGC6334), obtained from dust emission observed with Herschel. The low column density range can be fit with a lognormal distribution. A first power-law tail starts above an extinction (Av) of ~6-14. It has a slope of alpha=1.3-2 for the rho~r^-alpha profile for an equivalent density distribution (spherical or cylindrical geometry), and is thus consistent with free-fall gravitational collapse. Above Av~40, 60, and 140, we detect an excess that can be fitted by a flatter power law tail with alpha>2. It correlates with the central regions of the cloud (ridges/hubs) of size ~1 pc and densities above 10^4 cm^-3. This excess may be caused by physical processes that slow down collapse and reduce the flow of mass towards higher densities. Possible are: 1. rotation, which introduces an angular momentum barrier, 2. increasing optical depth and weaker cooling, 3. magnetic fields, 4. geometrical effects, and 5. protostellar feedback. The excess/second power-law tail is closely linked to high-mass star-formation though it does not imply a universal column density threshold for the formation of (high-mass) stars.
THE MID-INFRARED TULLY–FISHER RELATION: CALIBRATION OF THE TYPE Ia SUPERNOVA SCALE AND H0

This Letter builds on a calibration of the Type Ia supernova (SNIa) absolute distance scale begun with a core of distances based on the correlation between galaxy rotation rates and optical IC-band photometry. This new work extends the calibration through the use of mid-infrared photometry acquired at 3.6 μm with the Spitzer Space Telescope. The great virtue of the satellite observations is the constancy of the photometry at a level better than 1% across the sky. The new calibration is based on 39 individual galaxies and 8 clusters that have been the sites of well-observed SNIa. The new 3.6 μm calibration is not yet as extensively based as the IC-band calibration but is already sufficient to justify a preliminary report. Distances based on the mid-infrared photometry are 2% greater in the mean than reported at the IC band. This difference is only marginally significant. The IC-band result is confirmed with only a small adjustment. Incorporating a 1% decrease in the Large Magellanic Cloud distance, the present study indicates H0 = 75.2 ± 3.0 km s−1 Mpc−1.
PROPERTIES OF A CORONAL SHOCK WAVE AS A DRIVER OF EARLY SEP ACCELERATION

Coronal mass ejectmons (CMEs) are thought to drive collisionless shocks in the solar corona, which in turn have been shown to be capable of accelerating solar energetic particles (SEPs) in minutes. It has been notoriously difficult to extract information about energetic particle spectra in the corona, owing to a lack of in situ measurements. It is possible, however, to combine remote observations with data-driven models in order to deduce coronal shock properties relevant to the local acceleration of SEPs and their heliospheric connectivity to near-Earth space. We present such novel analysis applied to the 2011 May 11 CME event on the western solar limb, focusing on the evolution of the eruption-driven, dome-like shock wave observed by the Atmospheric Imaging Assembly (AIA) EUV telescopes on board the Solar Dynamics Observatory spacecraft. We analyze the shock evolution and estimate its strength using emission measure modeling. We apply a new method combining a geometric model of the shock front with a potential field source surface model to estimate time-dependent field-to-shock angles and heliospheric connectivity during shock passage in the low corona. We find that the shock was weak, with an initial speed of ∼450 km s−1. It was initially mostly quasi-parallel, but a significant portion of it turned quasi-perpendicular later in the event. There was good magnetic connectivity to near-Earth space toward the end of the event as observed by the AIA instrument. The methods used in this analysis hold a significant potential for early characterization of coronal shock waves and forecasting of SEP spectra based on remote observations.
Development of Object's Structured Information Field with Specific Properties for its Semantic Model Building

Implementation of applied artificial intelligence systems requires novel approaches to the formalization and modeling of domain knowledge. Collecting data about information objects, which represent components of intelligent systems, calls for creation of reliable information field of the object with predetermined restrictions. These restrictions affect the structure of the field and ensure its filling from the open Web environment. The paper proposes means and methods of pre-processing the data field (classification, clustering, associative relationships) and integration of accumulated resources. Also, we suggest a method of step-by-step development of the structure of the information field, which begins with the definition of the information object of research around which the field will be built. The problem of the reliability of information resources from which the information field is filled is solved by analytical methods and the method of semantic analysis of metadata. It's provided interpretation, recognition, and selection of reliable data relevant to the issue being solved. The issue of accuracy of data recognition and interpretation of accumulated data is solved on the basis of methods for calculating semantic proximity and semantic distance between semantic models. The practical implementation of the obtained results created a multi-agent e-commerce system based on the ontological approach.
A Lesson from the Pandemic: Utilizing Digital Tools To Support Student Engagement during Instructional Assistant-Led Sessions

Student instructional assistants (IAs) are an integral part of most students’ college experience in higher education. When properly trained, IAs can improve students’ grades, engagement with course content, persistence, and retention. ABSTRACT Student instructional assistants (IAs) are an integral part of most students’ college experience in higher education. When properly trained, IAs can improve students’ grades, engagement with course content, persistence, and retention. Recently, the COVID-19 pandemic forced the transition of nearly all instructional practices online. At the University of Alabama at Birmingham, IAs, including Biology Learning Assistants (BLAs), began hosting their instructional sessions virtually, outside of class time. The goals of these sessions were to reinforce fundamental concepts using active learning strategies and to address student questions by building a supportive learning community. In this article, we summarize the training and guidance we provided to the BLAs regarding how best to adapt digital educational tools to engage students during their virtual sessions. We recommend that institutions of higher education recognize the expansion of digital educational tools as an opportunity to increase the technological literacy and competence of their IAs to best serve their student body in this increasingly digital age of education.
Using Machine Learning for Precision Prognostics in Head and Neck Cancer Images

Radiomics is a new and emerging field that leverages advancements in machine learning and computational capabilities to provide prognostic and diagnostic outcomes. Using standard medical imaging techniques used to assess the location and spread of the tumor, radiomics is used to compute a wealth of information using sub-visual features from existing images to identify cancer biomarkers. This paper explores the various applications of radiomics, specifically in head and neck cancer imaging. We provide a detailed radiomics workflow for medical image data. We identify the various applications and highlight relevant research works in these applications. We address the challenges faced in the field and highlight future work in this area. This work is helpful to those researchers interested in interdisciplinary research focused on image processing in the Head and Neck anatomy.
Complete x-ray sample of the high-latitude (Vertical BarbVertical Bar>20/sup 0/) sky from HEAO 1 A-2: Log N-log S and luminosity functions

The HEAO 2 experiment A-2 has performed a complete x-ray survey of the 8.2 sr of the sky at Vertical BargVertical Bar by 20/sup 0/ down to a limiting sensitivity of approx.3. 1 x 10/sup hyphen11/ ergs cm/sup -2/ s/sup -1/ in the 2-10 keV band. Of the 85 detected sources (excluding the LMC and SMC sources), have been identified with galactic objects, 61 have been identified with extragalactic objects, and 7 remain unidentified. The log N-log S relation for the nongalactic objects is well fitted by the Euclidean relationship. We have used the x-ray spectra of these objects to construct the log N-log S relation in physical units. The complete sample of identified sources have been used to construct x-ray luminosity functions, using the absolute maximum likelihood method, for clusters of galaxies and active galactic nuclei.
Lean Six Sigma approach to the study of the LOS of patients who undergo laparoscopic cholecystectomy: a bicentric study

Laparoscopic cholecystectomy (LC) is considered the standard procedure for the treatment of gallstone. Compared to traditional surgery with the introduction of this procedure reduces the recovery time for a patient. According to literature, the postoperative LOS after LC is generally between 3 and 5 days. Length of stay (LOS) is a crucial piece of the patient experience and has proven to be an important indicator of health care efficiency. In the work the clinical data of the patients of the University Hospital "San Giovanni di Dio e Ruggi d'Aragona" of Salerno and the patients of the A.O. R.N. "Antonio Cardarelli" of Naples (Italy) were used, comparing two years (2016-2017) to demonstrate that the introduction of corrective actions have led to a reduction in the postoperative LOS. A Lean Six Sigma approach was used, with the implementation of the DMAIC cycle (Define, Measure, Analyze, Improve, Control), thanks to which it was possible to demonstrate significant results in the reduction of post-operative hospitalizations.
Experimental observation of double-peak structure of coincidence spectra in ultrafast spontaneous parametric down-conversion

The requirement on the symmetry of the biphoton wave function results in a double peak structure of the coincidence spectra of Spontaneous Parametric Down Conversion (SPDC) [1]. In this work we report the experimental observation of this effect by careful tailoring parameters of the SPDC crystal, the ultra-fast pump laser, and the measurement setup. The results are shown to be relevant to quantum state engineering of ultra-fast polarization-, or frequency-entangled mixed states.
Vibrotactile Motion Guidance for Stroke Rehabilitation: A Comparative Study

Stroke is one of the most common causes of death globally and a reason for severe impairments. Many stroke survivors report a loss of muscle strength and, thus, need to regain motor control of their upper limbs with rehabilitation. In some cases, patients may compensate for muscle weakness with harmful compensatory movements using other muscles. We envision that VR-based training can provide multimodal feedback during sensorimotor training to avoid compensatory movements. However, feedback may be hampered by changes in patients’ somatosensory system, resulting in both weakened and intensified tactile perceptions. We explored the differences in perception of vibration metaphors for motion guidance between healthy participants and stroke patients and assessed the efficiency of multimodal feedback for the correction of arm trajectory. Multimodal stimuli for trajectory correction benefited the patients but there were also differences in their tactile perception. These patient-specific findings call for the involvement of patients in the design process of haptic rehabilitation devices, following the recommendations of patient-centric healthcare.
The relationship among health and wellbeing program attributes, personal lifestyle, and purchase intention on podcast

The purpose of this study is to explore the relationship among health and wellbeing program attributes, personal lifestyle, and purchase intention on podcast. A total of 300 questionnaires are distributed, 278 valid questionnaires are collected, and the valid response rate is 92.6%. The results indicated that personal lifestyle is significantly and positively related to purchase intention on Podcast, and different program attributes moderated the relationship between personal lifestyle and purchase intention. The results also indicated that online audiences who are active in learning, seeking new knowledge, and have high-quality-concern personalities, have higher purchase intention on the health and wellbeing Podcast programs. Based on our empirical results, this study suggests that an online marketing manager might design an interactive activity with the online audiences who are actively learning, seeking new knowledge and high-quality-concern, to ask the Podcast audience for feedback interactively, to enhance their attention and interest in this kind health and wellbeing programs, which could truly increase their purchase intention of the health and well-being program on podcast.
A compact entanglement distillery

Animesh Datta,1, ∗ Lijian Zhang,1 Joshua Nunn,1 Nathan K. Langford,1 Alvaro Feito,2 Martin B. Plenio,3, 4 and Ian A. Walmsley1 Clarendon Laboratory, Department of Physics, University of Oxford, OX1 3PU, United Kingdom Vestas Technology Research & Development, Venture Quays, East Cowes, PO32 6EZ, United Kingdom Institut für Theoretische Physik, Albert-Einstein-Allee 11, Universität Ulm, D-89069 Ulm, Germany QOLS, Blackett Laboratory, Imperial College London, Prince Consort Rd., SW7 2BW, United Kingdom (Dated: January 31, 2013)
Supplier Selection during the COVID-19 Pandemic Situation by Applying Fuzzy TOPSIS: A Case Study

Abstract During the COVID-19 pandemic situation, many factories, companies, and organizations faced difficulties supplying raw materials. Multi-criteria decision-making techniques (MCDM) could be a proper solution that helps managers to make an appropriate and fast decision in supplier selection in such unusual situation. The goal of my research is not only to employ the technique for order of preference by similarity to ideal solution (TOPSIS) in a fuzzy environment but also to use a new criterion, namely “number of employees”, for evaluating suppliers during the COVID-19 pandemic situation. Applying fuzzy logic during unstable conditions helps decision makers to make a logical and more precise decision. In this study, five criteria, that is, quality, delivery, price, number of employees, and lead time, are considered to compare and select an appropriate supplier. The results show that “number of employees” is an essential criterion in supplier selection during abnormal conditions like the COVID-19 pandemic situation.
Simulation Model of a Fuzzy Cyber Attack Detection System

The method of applying a simulation model of a fuzzy cyberattack detection system is considered. The functional diagram of the simulation model is given. The block diagram of the simulation model is considered and the purpose of its elements is described. The main steps of using a simulation model for conducting an experimental study of evaluating the effectiveness of models and methods for detecting cyber attacks based on the theory of fuzzy sets and fuzzy inference are described. The procedure for generating initial data is given, the classes of cyberattacks to be detected are defined, the vectors of cyberattack features are identified, the parameters of the studied traffic are described, the types of membership functions are defined to formalize expert knowledge and represent it in the knowledge base in the form of fuzzy production rules. The issue of parametric adaptation of membership functions to clarify the subjective judgments of experts are considered. To implement the possibility of detecting polymorphic cyberattacks, the procedure for determining the required number of the most important features for each known class of cyberattacks, represented by fuzzy sets and linguistic variables that characterize them quite fully, is described. A comparative analysis of the results of modeling the process of detecting cyber attacks based on the proposed approach with existing methods for detecting cyber attacks was carried out, based on the theory of fuzzy sets and fuzzy logic, artificial immune systems and neural networks in terms of accuracy.
KIDE4Assistant: an Ontology-Driven Dialogue System Adaptation for Assistance in Maintenance Procedures

Developing systems that facilitate natural interaction between humans and industrial systems by using current state-of-the art technologies, such as Deep Learning techniques, requires large amounts of training data, usually scarce in industrial scenarios, and the resulting systems are difficult to adapt to other settings. This paper illustrates an ontology-driven adaptation of KIDE4I, a generic semantic-based task-oriented dialogue system, to set up with a limited amount of effort KIDE4Assistant, a dialogue system for assistance in maintenance procedures through natural interactions. This adaptation approach bridges the gaps related to data availability and system reuse by exploiting expert knowledge together with existing ontologies and resources to obtain a fully-functional task-oriented dialogue system. Furthermore, the evaluation of KIDE4Assistant, performed through a user experimentation, shows the promising results of this approach.
Steady-state entanglement of a Bose-Einstein condensate and a nanomechanical resonator

We analyze the steady-state entanglement between Bose-Einstein condensate trapped inside an optical cavity with a moving end mirror (nanomechanical resonator) driven by a single mode laser. The quantized laser field mediates the interaction between the Bose-Einstein condensate and nanomechanical resonator. In particular, we study the influence of temperature on the entanglement of the coupled system, and note that the steady-state entanglement is fragile with respect to temperature.
Examining Change in the Frequency of Adaptive Actions as a Mediator of Treatment Outcomes in Internet-Delivered Therapy for Depression and Anxiety

Adaptive actions, including healthy thinking and meaningful activities, have been associated with emotional wellbeing. The Things You Do Questionnaire—21 item (TYDQ-21) has recently been created to measure the frequency of such actions. A study using the TYDQ-21 found that adaptive actions increased across Internet-delivered therapy for symptoms of depression and anxiety, and higher TYDQ-21 scores were associated with lower psychological distress at post-treatment. The current study examined the relationships between adaptive actions and psychological distress among adults (n = 1114) receiving Internet-delivered therapy as part of routine care in Canada, and explored whether adaptive actions mediated reductions in depression and anxiety. As hypothesised, adaptive actions increased alongside reductions in depression and anxiety symptoms from baseline to post-treatment. Treatment effects were consistent when the intervention was provided with regular weekly therapist support or with optional weekly therapist support, and some (but not all) types of adaptive actions had a mediating effect on change in depressive symptoms. The present findings support further work examining adaptive actions as a mechanism of change in psychotherapy, as well as the utility and scalability of Internet-delivered treatments to target and increase adaptive actions with the aim of improving mental health.
Wellbeing Training Based on Contemplative Practices in a Sample of Intensive Care and Homecare Professionals: A Pilot and Feasibility Non-Randomized Clinical Trial

Background: Intensive care unit (ICU) and homecare unit professionals are susceptible to higher levels of stress and burnout than other healthcare professionals, which has an impact on their well-being, and in turn on their patients. In terms of data, there is not much research about the effects of psychological interventions on ICU and homecare professionals. The aim of this study was to investigate the effectiveness of Wellbeing Training based on Contemplative Practices (WTCP) for the increase of psychological functioning in a sample of ICU and homecare professionals. Methods: A pilot and feasibility non-randomized clinical trial was conducted. Participants in the WTCP group (n = 19) attended an at-work 8-session/2 h group WTCP program aimed at directly training four basic skills: (a) sustained positive emotions, (b) recovery from negative emotions, (c) pro-social behavior and generosity, and (d) mind wandering, mindfulness, and “affective stickiness”. Nineteen professionals were allocated in the control group. Results: Results indicated that WTCP had a positive impact on self-compassion, personal accomplishment (burnout), and frequency of negative emotions. Moreover, a thematic analysis of participant interviews (n = 14) was conducted. Conclusions: These preliminary results are promising, though future research is needed to evaluate the effectiveness of WTCP using randomized controlled trial methodologies.
Current delivery and radiation yield in plasma flow switch-driven implosions

Vacuum inductive-store, plasma flow switch-driven implosion experiments have been performed using the Shiva Star capacitor bank (1300 {mu}f, 3 nH, 120 kV, 9.4 MJ). A coaxial plasma gun arrangement is employed to store magnetic energy in the vacuum volume upstream of a dynamic discharge during the 3- to 4-{mu}s rise of current from the capacitor bank. Motion of the discharge off the end of the inner conductor of the gun releases this energy to implode a coaxial cylindrical foil. The implosion loads are 5-cm-radius, 2-cm-long, 200 to 400 {mu}g/cm{sup 2} cylinders of aluminum or aluminized Formvar. With 5 MJ stored initially in the capacitor bank, more than 9 MA are delivered to the implosion load with a rise time of nearly 200 ns. The subsequent implosion results in a radiation output of 0.95 MJ at a power exceeding 5 TW (assuming isotropic emission). Experimental results and related two-dimensional magnetohydrodynamic simulations are discussed. 10 refs., 12 figs.