Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical Monitoring of Clinical Trials: A Unified Approach

To some extent, the book follows the same path as early research on queueing theory, which was focused on obtaining exact expressions for performance measures. Novel additions include a few references on applications throughout the text, two detailed chapters on computational methods, and another chapter on simulation. Compared with other books on the subject, the book’s price is reasonable, but is certainly not its strongest point. The authors have adopted a few unusual features in its presentation. The presentation does not follow the typical technique of analyzing each queueing model to its fullest extent. Rather, the authors prefer to emphasize the applicability of a method (such as the formation of difference-differential equations for the steady-state distribution of queue length) by presenting various queueing models in which this technique can be used. Inevitably, this tactic breaks the continuity of the presentation, as one needs to revisit the same models, but it better demonstrates the various paths that one can follow when tackling a problem. This gives the book a rather pleasant flavor; rather than being presented with a recipe book where one can find all of the necessary formulas for each model, the reader is challenged to understand the various ways in which these results are derived. Another unusual characteristic concerns the material presented; for example, the chapter on Markovian queues includes a section on optimization. Although the material that is covered in such atypical sections is elementary, it nonetheless helps give the reader a more holistic view of queueing theory and its function. My main concern regarding this book is its appropriateness for self-study. A lot of relevant material is left to the reader as exercises, while typically only one example (usually the M/M/1 queue) is presented to the reader. For example, standard models, such as the M/M/∞ queue, or the moments of the busy period in an M/G/1 queue are left to the reader. On the other hand, the authors consider it worthwhile to elaborate on the transient behavior of the M/M/1 queue; small parts are left as an exercise and there is no appendix on Bessel functions, while some of their most trivial properties are used without any further comments. At some points, one gets the distinct impression that a graduate student would need the careful guidance of an instructor to get through the material. An instructor wishing to adopt this book as a course text would need to carefully select which sections should be presented, and in which order. One also may need to refer to other sources and probably would need to design some exercises (perhaps those that help the student master the material, rather than prove something not discussed in the text). On the other hand, an instructor certainly would benefit from adopting a few of the ideas in this book, such as spending some time on optimization, various computational methods, or simulation, and stressing the applicability of a method before overwhelming the student with the derivation of all conceivable performance measures of a model.
The SenSE Toolkit: A System for Visualization and Explanation of Semantic Shift

Lexical Semantic Change (LSC) detection, also known as Semantic Shift, is the process of identifying and characterizing variations in language usage across different scenarios such as time and domain. It allows us to track the evolution of word senses, as well as to understand the difference between the languages used by distinct communities. LSC detection is often done by applying a distance measure over vectors of two aligned word embedding matrices. In this paper, we present SenSE, an interactive semantic shift exploration toolkit that provides visualization and explanation of lexical semantic change for an input pair of text sources. Our system focuses on showing how the different alignment strategies may affect the output of an LSC model as well as on explaining semantic change based on the neighbors of a chosen target word, while also extracting examples of sentences where these semantic deviations appear. The system runs as a web application (available at http://sense. mgruppi.me ), allowing the audience to interact by configuring the alignment strategies while visualizing the results in a web browser.
Postverbal constituents in Turkish and the linear correspondence axiom

Kayne (1994) argues that specifier-head-complement is the universal order and that the traditional SOV languages are derived by the leftward movement of various constituents in a head-initial structure. The universality of this claim is called into question by the fact that postverbal quantifiers take scope over preverbal quantifiers in Turkish. This article shows that head-final structures derive the correct scope readings in scrambled clauses in a straightforward and consistent manner, whereas head-initial structures require more complicated and sometimes contradictory assumptions. This suggests that although some SOV languages may be derived as Kayne suggests, others, like Turkish, must truly be head-final
Application of Dynamic Programming Method to Marketing Decisions Based on Customer Database

Abstract The paper focuses on modeling longitudinal customer behavior and develops a dynamic programming (DP) to show how customer transaction database may be used to guide marketing decisions such as pricing and the design of customer reward programs. Dynamic programming is not as a tool to marketing decisions making in this research but rather as a description of consumer behavior. The results show that the method provides a means for evaluating the effectiveness of marketing strategy, for example, customer reward programs. Moreover, the findings from the model estimation indicate that reward program can actually increase the customer’s purchase level and stimulate the repeat purchase behavior.
LES OF THE ATMOSPHERIC BOUNDARY LAYER OVER HETEROGENEOUS SURFACES USING A DYNAMIC LAGRANGIAN SGS MODEL

A pressure gauge in which the displacement tip of the Bourdon tube is sealed and secured to a bracket of an amplifier movement by a selected solder composition affording a failure response to an encountered overpressure at a pressure value lower than the burst pressure of the Bourdon tube. Overpressure causes a fracture in the solder to be incurred enabling release of the overpressure while a throttle plug situated in the inlet path to the Bourdon tube severely restricts continuing gas flow to the fracture site. As a consequence, released flow from the solder fracture will be at a pressure insufficient to potentially cause explosion of the gauge case/lens to occur.
SpecNFS: A Challenge Dataset Towards Extracting Formal Models from Natural Language Specifications

Can NLP assist in building formal models for verifying complex systems? We study this challenge in the context of parsing Network File System (NFS) specifications. We define a semantic-dependency problem over SpecIR, a representation language we introduce to model sentences appearing in NFS specification documents (RFCs) as IF-THEN statements, and present an annotated dataset of 1,198 sentences. We develop and evaluate semantic-dependency parsing systems for this problem. Evaluations show that even when using a state-of-the-art language model, there is significant room for improvement, with the best models achieving an F1 score of only 60.5 and 33.3 in the named-entity-recognition and dependency-link-prediction sub-tasks, respectively. We also release additional unlabeled data and other domain-related texts. Experiments show that these additional resources increase the F1 measure when used for simple domain-adaption and transfer-learning-based approaches, suggesting fruitful directions for further research
Ab initio study of the elastic and electronic properties of tetragonal Th2NiC2

This work reports on the elastic and electronic properties of the newly discovered superconductor Th2NiC2 (A .Machado, et al., Supercond. Sci. Technol. 25 (2012) 045010) as obtained within ab initio calculations. We found that Th2NiC2 is mechanically stable and it will behave as a ductile material exhibiting enhanced elastic anisotropy in shear and a rather low hardness Our data reveal that for Th2NiC2 the Fermi level is located in a deep DOS minimum and the experimentally observed increase in TC in the sequence Th2NiC2 -> Th1.8Sc0. 2NiC2 may be explained by the growth of N(EF). We also speculate that (i) an increase in the hole concentration will promote exchange splitting of Ni 3d bands, therefore the hole-doped Th2NiC2 should have a certain concentration border, where a phase transition from the superconducting to the magnetic state will be expected, and (ii) an increase in N(EF) (and, probably, in TC) for Th2NiC2-based materials may be also achieved by an alternative way: by electron doping - for example, by partial substitution of V for Th or Cu for Ni, as well as by partial substitution of N for C with the formation of Th-Ni carbonitrides like Th2NiC2-xNx.
Reducing parameter estimation bias for data with missing values using simulation extrapolation

xiii CHAPTER 1. OVERVIEW . 1 1.1 Missing Data Analysis . 2 1.1.1 Categories of missing-data mechanism . 2 1.1.2 Strategies for analyzing missing data . 5 1.2 The SIMEX Method for Measurement Error Problems . 6 1.2.1 The expectation of θSIMEX . 7 1.2.2 The variance of θSIMEX . 9 1.2. 3 An example estimate e . 12 1.2. 4 Extensions and applications . 13 1.3 The SIMEX method for missing data . 16 CHAPTER 2. METHOD . 18 2.1 Notation and Model . 18 2.1.1 The response model . 18 2.1.2 The missing-data mechanism . 19 2.1.3 The simulation model . 20 2.2 The SIMEX Algorithm . 22
Diachronic Parsing of Pre-Standard Irish

Irish underwent a major spelling standardization in the 1940’s and 1950’s, and as a result it can be challenging to apply language technologies designed for the modern language to older, “pre-standard” texts. Lemmatization, tagging, and parsing of these pre-standard texts play an important role in a number of applications, including the lexicographical work on Foclóir Stairiúil na Gaeilge, a historical dictionary of Irish covering the period from 1600 to the present. We have two main goals in this paper. First, we introduce a small benchmark corpus containing just over 3800 words, annotated according to the Universal Dependencies guidelines and covering a range of dialects and time periods since 1600. Second, we establish baselines for lemmatization, tagging, and dependency parsing on this corpus by experimenting with a variety of machine learning approaches.
Estimation of multidimensional homeomorphisms for object recognition in noisy environments

In this paper, we consider the general problem of object recognition based on a set of known templates, where the available observations are noisy. While the set of templates is known, the tremendous set of possible transformations and deformations between the template and the observed signature, makes any detection and recognition problem ill-defined unless this variability is taken into account. We propose a method that reduces the high dimensional problem of evaluating the orbit created by applying the set of all possible transformations in the group to a template, into a problem of analyzing a function in a low dimensional Euclidian space. In this setting, the problem of estimating the parametric model of the deformation is transformed using a set on nonlinear operators into a set of equations which is solved by a linear least squares solution in the low dimensional Euclidian space. For the case where the signal to noise ratio is high, and the nonlinear operators are polynomial compositions, a maximum-likelihood estimator is derived, as well.
Ultrasensitive Detection of Majorana Fermions via Spin-based Optomechanics with Carbon Nanotubes

We propose a novel optical method to detect the existence of Majorana fermions at the ends of the semiconductor nanowire via the coupling to an electron spin trapped on a carbon nanotube resonator under the control of a strong pump field and a weak probe field. The coupling strength of Majorana fermion to the spin in the carbon nanotube and the decay rate of the Majorana fermion can be easily measured from the probe absorption spectrum via manipulating the spin-mechanical coupling in the suspended\ carbon nanotube. The scheme proposed here will open a good perspective for its applications in all-optical controlled Majorana fermion-based quantum computation and quantum information processing.
Mid-latitude neutral wind responses to sub-auroral polarization streams

Abstract. We investigate the response of the mid-latitude thermospheric neutral winds to a sub-auroral polarization stream (SAPS) event. Using red line (F region) airglow data from two Fabry–Pérot interferometers (FPIs), and F-region ionospheric flow velocities from four Super Dual Auroral Radar Network (SuperDARN) radars, the drivers behind changes seen in the neutral winds are explored within the context of the larger SAPS structure. Different, although strong, neutral wind responses to the SAPS are seen at the two FPI sites, even though they are relatively close geographically. We attribute the wind differences to the varying balance of pressure gradient, ion drag, and Coriolis forces, which ultimately depend on proximity to the SAPS. At the FPI site equatorward of the SAPS, pressure gradient and Coriolis forces drive the winds equatorward and then westward. At the FPI site co-located with the SAPS, the ion drag is strong and results in the winds surging westward before turning eastward when becoming influenced by dawnside sunward plasma convection drifts.
An improved representation of aerosol mixing state for air quality–weather interactions

Abstract. We implement a detailed representation of aerosol mixing state in the Global Environmental Multiscale – Modelling Air quality and CHemistry (GEM-MACH) air quality and weather forecast model. Our mixing-state representation includes three categories: one for more hygroscopic aerosol, one for less hygroscopic aerosol with a high black carbon (BC) mass fraction, and one for less hygroscopic aerosol with a low BC mass fraction. The more detailed representation allows us to better resolve two different aspects of aerosol mixing state: differences in hygroscopicity due to aerosol composition and the amount of absorption enhancement of BC due to non-absorbing coatings. Notably, this three-category representation allows us to account for BC thickly coated with primary organic matter, which enhances the absorption of the BC but has a low hygroscopicity. We compare the results of the three-category representation (1L2B, (one hydrophilic, two hydrophobic)) with a simulation that uses two categories, split by hygroscopicity (HYGRO), and a simulation using the original size-resolved internally mixed assumption (SRIM). We perform a case study that is focused on North America during July 2016, when there were intense wildfires over northwestern North America. We find that the more detailed representation of the aerosol hygroscopicity in both 1L2B and HYGRO decreases wet deposition, which increases aerosol concentrations, particularly of less hygroscopic species. The concentration of PM2.5 increases by 23 % on average. We show that these increased aerosol concentrations increase cloud droplet number concentrations and cloud reflectivity in the model, decreasing surface temperatures. Using two categories based on hygroscopicity yields only a modest benefit in resolving the coating thickness on black carbon, however. The 1L2B representation resolves BC with thinner coatings than the HYGRO simulation, resulting in absorption aerosol optical depths that are 3 % less on average, with greater differences over strong anthropogenic source regions. We did not find strong subsequent effects of this decreased absorption on meteorology.
A Decision Metric for the Use of a Deep Reinforcement Learning Policy

Uncertainty estimation techniques such as those found in Osband et al. (2018) and Burda et al. (2019) have been shown to be useful for efficient exploration during training. This paper demonstrates that such uncertainty estimation techniques can also be used as part of a time-series based methodology for out-of-distribution (OOD) detection for an off-line model-free deep reinforcement learning policy. In particular, this paper defines a “decision metric” that can be utilized for determining when another decision-making process should be used in place of the deep reinforcement learning policy.
Small but strong: Pivotal roles and potential applications of snoRNAs in hematopoietic malignancies

Small nucleolar RNAs (snoRNAs) belong to a family of noncoding RNAs that are 60-300 nucleotides in length, and they are classified into two classes according to their structure and function: C/D box snoRNAs, playing an essential role in 2’-O-methylation modification on ribosomal RNA; H/ACA box snoRNAs, involved in the pseudouridylation of rRNA. SnoRNAs with unclear functions, no predictable targets, and unusual subcellular locations are called orphan snoRNAs. Recent studies have revealed abnormal expression and demonstrated the pivotal roles of snoRNAs and their host genes in various types of hematological malignancies. This review discusses recent discoveries concerning snoRNAs in a variety of hematological malignancies, including multiple myeloma, lymphoma and leukemia, and sheds light on the application of snoRNAs as diagnostic and prognostic markers as well as therapeutic targets of hematological malignancies in the future.
Bibliometric analysis of research trends of physical activity intervention for autism spectrum disorders

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social impairment, restricted interests, and repetitive stereotyped behaviors. At present, its pathogenesis has not been fully understood. Various methods are used for clinical treatment and intervention, among which physical activity (PA) intervention also has an obvious effect. This study has used bibliometric methods and visual analysis methods to analyze 885 studies of PA intervention in ASD from 2003 to 2022 in the Web of Science (WoS) database in order to provide theoretical support for the follow-up research on the effect of PA with ASD. The main findings of this study are as follows. First, the literature on PA interventions in ASD research showed a growing trend. The leading institution in this field is the University of Delaware, forming a core group of authors represented by authors such as Sean Healy and Carol Curtin et al. Second, the research focus of this research area mainly includes PA interventions for children and adolescents with ASD. PA can improve symptoms such as stereotyped behaviors and motor function in patients with ASD as well as can reduce childhood obesity rates and improve quality of life. Third, skill, youth, prevalence, and meta-analysis systematic reviews were found. It is the long-term concern and focus of researchers. In conclusion, the current research is only a short-term analysis, and it is not possible to verify the long-term effect; thus, future data analysis should evaluate and explore the long-term effects of PA interventions on ASD including cohort and longitudinal study types focused on the rehabilitation of patients with ASD. Moreover, testing the sustainability of benefits for children with ASD and constructing a multidimensional exercise integrated intervention model are the main directions for future research in this field.
Determination of an RT-qPCR viral load cutoff point for the etiologic diagnosis of rotavirus A diarrhea in neonate dairy calves

Rotavirus A (RVA) is amongst the most widespread causes of neonatal calf diarrhea. Because subclinical infections are common, the diagnosis of RVA-induced diarrhea cannot rely solely on molecular viral detection. However, RT-qPCR allows for quantification of RVA shedding in feces, which can be correlated with clinical disease. Here, we determine an optimal cutoff of rotaviral load quantified by RT-qPCR to predict RVA causality in diarrheic neonate calves, using RVA antigen-capture ELISA as reference test. Feces from 328 diarrheic (n = 175) and non-diarrheic (n = 153), <30-day-old dairy calves that had been tested by ELISA and tested positive by RT-qPCR were included. Of 82/328 (25.0%) ELISA-positive calves, 53/175 (30.3%) were diarrheic, whereas 124/153 (81.0%) non-diarrheic calves tested negative by ELISA. The median log10 viral load was significantly higher in diarrheic vs. non-diarrheic and ELISA-positive vs. -negative calves, indicating a higher viral load in diarrheic and ELISA-positive calves. A receiver operating characteristic (ROC) analysis was conducted using the viral loads of the 175 diarrheic calves that had tested either positive (n = 53, cases) or negative (n = 122, controls) by ELISA. The optimal log10 viral load cutoff that predicted RVA causality in diarrheic calves was 9.171. A bootstrapping procedure was performed to assess the out-of-bag performance of this cutoff point, resulting in sensitivity = 0.812, specificity = 0.886, area under the curve = 0.922, and positive and negative diagnostic likelihood ratios of 11.184 and 0.142, respectively. The diagnostic accuracy of the cutoff was excellent to outstanding. This information will help in the interpretation of RVA RT-qPCR results in feces of diarrheic calves submitted for laboratory testing.
Alterations in sorting and secretion of hepatic apoA5 induce hypertriglyceridemia due to short-term use of olanzapine

Long-term use of olanzapine, an antipsychotic drug, induces hypertriglyceridemia, resulting in a higher risk of cardiovascular disease. However, the effects and underlying mechanisms of short-term use of olanzapine on circulating triglyceride levels remain poorly understood. Here, the role of apolipoprotein A5 (apoA5), a regulator of triglyceride metabolism, was investigated in olanzapine-induced hypertriglyceridemia. Our multi-center clinical study recruited 36 schizophrenia patients who received short-term (8 weeks) of olanzapine. Besides, female C57BL/6J mice were treated with olanzapine (3 mg/kg/day versus 6 mg/kg/day) for 6 weeks. We demonstrated that short-term use of olanzapine increased plasma triglyceride and decreased plasma apoA5 levels in the patients and mice, with a negative correlation between the two factors. However, no obesity was observed in the patients and mice. Interestingly, olanzapine increased hepatic apoA5 protein in the mice, without significant changes in hepatic Apoa5 mRNA. Consistently, in vitro studies indicated that olanzapine increased medium triglyceride levels and decreased medium apoA5 levels in a dose-dependent manner in human HepG2 cells and primary mouse hepatocytes. Whereas the olanzapine treatment increased hepatic apoA5 protein in vitro, without effects on hepatic APOA5 mRNA. Of note, olanzapine increased the co-localization between apoA5 protein and accumulated lipid droplets in hepatocytes, as opposed to at the hepatocellular plasma membrane, in mouse liver as demonstrated by fluorescence staining. Therefore, our study indicated that short-term use of olanzapine induced hypertriglyceridemia due to defects of sorting and secretion of hepatic apoA5.