Miracle-dz/UniFAR-FTUs huggingface dataset:
Barriers to Providing VTE Chemoprophylaxis to Hospitalized Patients: A Nursing-Focused Qualitative Evaluation.

BACKGROUND Venous thromboembolism (VTE) is a serious medical condition that results in preventable morbidity and mortality. OBJECTIVES The objective of this study was to identify nursing-related barriers to administration of VTE chemoprophylaxis to hospitalized patients. DESIGN This was a qualitative study including nurses from five inpatient units at one hospital. METHODS Observations were conducted on five units to gain insight into the process for administering chemoprophylaxis. Focus group interviews were conducted with nurses and were audio-recorded, transcribed verbatim, and analyzed using the Theoretical Domains Framework to identify barriers to providing VTE chemoprophylaxis. RESULTS We conducted 14 focus group interviews with nurses from five inpatient units to assess nurses' perceptions of barriers to administration of VTE chemoprophylaxis. The barriers identified included nurses' misconceptions that ambulating patients did not require chemoprophylaxis, nurses' uncertainty when counseling patients on the importance of chemoprophylaxis, and a lack of comparative data for nurses regarding their specific refusal rates. CONCLUSIONS Multiple factors act as barriers to patients receiving VTE chemoprophylaxis. These barriers are often modifiable targets for quality improvement. There is a need to focus on behavior changes that will remove or minimize barriers and equip nurses to ensure administration of VTE chemoprophylaxis by engaging patients in their care.
Design and Integration of a Deserializer Module for a SerDes Mixed Signal System on Chip

This document presents a mixed signal System on Chip design developed at ITESO as a test vehicle for manufacturing at 180 nm node through MOSIS manufacturing services. The ITESO TV1 is a serializer and deserializer based on the PCIe protocol. The current report includes details on the design for the deserializer digital module for the SerDes. The testing done at RTL and gate level abstractions to the Deserializer are presented. The report finishes by discussing the VLSI technique used for the system integration of analog and digital modules in the SerDes. This research effort is a first attempt at ITESO to generate a mixed signal complex system for manufacturing.
Calibration sources for the LEGEND-200 experiment

In the search for a monochromatic peak as the signature of neutrinoless double beta decay an excellent energy resolution and an ultra-low background around the Q-value of the decay are essential. The LEGEND-200 experiment performs such a search with high-purity germanium detectors enriched in 76Ge immersed in liquid argon. To determine and monitor the stability of the energy scale and resolution of the germanium diodes, custom-made, low-neutron emission 228Th sources are regularly deployed in the vicinity of the crystals. Here we describe the production process of the 17 sources available for installation in the experiment, the measurements of their alpha- and gamma- activities, as well as the determination of the neutron emission rates with a low-background LiI(Eu) detector operated deep underground. With a flux of ( 4.27 ± 0.60stat ± 0.92syst) × 10-4 n / (kBq·s), approximately one order of magnitude below that of commercial sources, the neutron-induced background rate, mainly from the activation of 76Ge, is negligible compared to other background sources in LEGEND-200.
Geometry from local flatness in Lorentzian spin foam theories

Local flatness is a property shared by all the spin foam models. It ensures that the theory's fundamental building blocks are flat by requiring locally trivial parallel transport. In the context of simplicial Lorentzian spin foam theory, we show that local flatness is the main responsible for the emergence of geometry independently of the details of the spin foam model. We discuss the asymptotic analysis of the EPRL spin foam amplitudes in the large quantum number regime, highlighting the interplay with local flatness.
Research on real-time outlier detection over big data streams

ABSTRACT Nowadays technological advances have promoted big data streams common in many applications, including mobile internet applications, internet of things, and industry production process. Outliers should be detected from big data streams in many cases. However, the special characteristics of big data streams, such as transiency, uncertainty, multidimensionality, dynamic distribution, and dynamic relationship make outlier detection more challenging. This paper discusses the key issues, major challenges, and existing most frequently used methods for detecting outliers over big data streams, and then summarizes the directions for further investigation. This research can provide novel theoretical support and technical guidance for outlier detection over big data streams.
Money in the ancient economy: A survey of recent research

The nature of money and its capacity to stimulate economic growth has become one of the pivotal issues in the debate on the ancient economy. The relative abundance of numismatic evidence, moreover, and the problems of this evidence as data for economic history have raised important methodological issues which have implications for economic historians more generally. 25 years ago any aspect of the ancient economy would have started from or against the argument of Moses Finley's „The Ancient Economy" (2nd ed. 1985). Yet while research has become more specialised, regional analyses more complex and a wider geographical context is taken into consideration, scholars have become increasingly reluctant to offer a single model of the ancient economy and the role of money within it.1 This survey therefore offers an introduction to current debates as they have developed over the last quarter of a century. Its chronological frame will be roughly from the archaic Greek period to the 4 th century AD, and it will concentrate on broad monetary developments rather than specific coinages.2 In „The Ancient Economy" Finley has little to say about money; yet his negative argument is instructive as it still offers a focus for understanding the directions recent research has taken. First, Finley observed that all money was coin (or coin accepted as bullion), all monetary exchange cash exchange, and there were no such things as fiduciary money (money not backed by coinage or bullion) or negotiable instruments such as bank-notes and cheques (Finley [1985], 166—9, 174). Credit was not used as a means of increasing the volume of money, but for instant needs when cash in hand was scarce, which frequendy occurred due to slow circulation and the degree to which coins were bound up in hoards. The purely moral problematisation of lending at interest illustrated the limited perspective from which credit was viewed (53-4 , 115-6 ; 141-3)/ Secondly, according to Finley, money did not convey complex market information and was not used as an accounting unit guiding rational economic choice. The famous example of Columella (the owner of several large estates in Italy and author of a comprehensive agrarian manual in the 1st century AD) suggested that even on large estates accounting
PhaseAug: A Differentiable Augmentation for Speech Synthesis to Simulate One-to-Many Mapping

Previous generative adversarial network (GAN)-based neural vocoders are trained to reconstruct the exact ground truth wave-form from the paired mel-spectrogram and do not consider the one-to-many relationship of speech synthesis. This conventional training causes overfitting for both the discriminators and the generator, leading to the periodicity artifacts in the generated audio signal. In this work, we present PhaseAug, the first differentiable augmentation for speech synthesis that rotates the phase of each frequency bin to simulate one-to-many mapping. With our proposed method, we outperform baselines without any architecture modification. Code and audio samples will be available at https://github.com/mindslab-ai/phaseaug.
Understanding Cross-modal Interactions in V&L Models that Generate Scene Descriptions

Image captioning models tend to describe images in an object-centric way, emphasising visible objects. But image descriptions can also abstract away from objects and describe the type of scene depicted. In this paper, we explore the potential of a state of the art Vision and Language model, VinVL, to caption images at the scene level using (1) a novel dataset which pairs images with both object-centric and scene descriptions. Through (2) an in-depth analysis of the effect of the fine-tuning, we show (3) that a small amount of curated data suffices to generate scene descriptions without losing the capability to identify object-level concepts in the scene; the model acquires a more holistic view of the image compared to when object-centric descriptions are generated. We discuss the parallels between these results and insights from computational and cognitive science research on scene perception.
MASS TRANSFER CONTROLLED REACTIONS IN PACKED BEDS AT LOW REYNOLDS NUMBERS - eScholarship

I HECE! VED U\WRENCf; BERK~UEY LABORhTORY I LBL-85096, . 2. FEB 2619'lQ LlBRARY Al'm DOCUMENTS SECTION I (i t? I r ,(II '. .--\-j . v\ MASS TRANSFER CONTROLLED REACTIONS IN PACKED BEDS AT LOW REYNOLDS NUMBERS Peter S. Fedkiw (Ph. D. thesis) . December 1978 Prepared for the U. S. Department of Energy under Contract W-7405-ENG-. 48 TWO-WEEK LOAN COpy This is a Library Circulating Copy which may be borrDwed for two weeks. For a personal retention copy, call Tech. Info. Diuision, Ext. 6782
Mineral transformations

One of the major goals of mineral science is attaining an understanding of the atomic-scale mechanisms and dynamics of minerals that control their structural transformations as a function of pressure, temperature or chemical composition in the natural environment. Examples of research programmes that sail under this heading include those devoted to observing and modelling the role of phase transformations on controlling mineral microstructures, ordering, elasticity, transport, premelting and exsolution. The geological relevance and intrinsic importance (as being representative of specific properties or thermodynamic/ kinetic behaviour) of mineral transformations has long been appreciated. It prompted the recent initiation of a network on Mineral Transformations (http://www.esc.cam.ac.uk/mintrans/) under the European Union TMR Programme. In response to this development, a special session on Mineral Transformations was held at the EUG congress in Strasbourg last year.
Parallel-Connected Battery Current Imbalance Dynamics

In this work, we derive analytical expressions governing state-of-charge and current imbalance dynamics for two parallel-connected batteries. The model, based on equivalent circuits and an affine open circuit voltage relation, describes the evolution of state-of-charge and current imbalance over the course of a complete charge and discharge cycle. Using this framework, we identify the conditions under which an aged battery will experience a higher current magnitude and state-of-charge deviation towards the end of a charge or discharge cycle. This work enables a quantitative understanding of how mismatches in battery capacities and resistances influence imbalance dynamics in parallel-connected battery systems, helping to pave a path forward for battery degradation modeling in heterogeneous battery systems.
Differentiable Quantum Programming with Unbounded Loops

The emergence of variational quantum applications has led to the development of automatic differentiation techniques in quantum computing. Existing work has formulated differentiable quantum programming with bounded loops, providing a framework for scalable gradient calculation by quantum means for training quantum variational applications. However, promising parameterized quantum applications, e.g., quantum walk and unitary implementation, cannot be trained in the existing framework due to the natural involvement of unbounded loops. To fill in the gap, we provide the first differentiable quantum programming framework with unbounded loops, including a newly designed differentiation rule, code transformation, and their correctness proof. Technically, we introduce a randomized estimator for derivatives to deal with the infinite sum in the differentiation of unbounded loops, whose applicability in classical and probabilistic programming is also discussed. We implement our framework with Python and Q#, and demonstrate a reasonable sample efficiency. Through extensive case studies, we showcase an exciting application of our framework in automatically identifying close-to-optimal parameters for several parameterized quantum applications.
Credit Market Imperfections and Business Cycle Dynamics: A Nonlinear Approach

Linear Vector Autoregression (VAR) models provide a useful starting point for analysing multivariate relationships between economic variables. They are frequently used for empirical macroeconomic modelling, policy analysis and forecasting. However, linear VAR systems fail to capture non-linear dynamics such as regime switching and asymmetric responses to shocks, suggested by the recent theoretical developments in macroeconomic research. In addition, an increasing body of empirical evidence suggests that the linear conditional expectations implied by standard VAR models do not always accord with the observed facts. For example, a significant number of empirical studies document asymmetries in the effects of monetary policy on output growth. This paper employs a more general nonlinear VAR methodology to re-examine previous findings that credit market conditions contribute to economic fluctuations as a propagator of shocks. Unlike linear projections it allows for nonlinear dynamics and asymmetric effects of shocks. We estimate a threshold vector autoregression (TVAR), in which the system’s dynamics change back and forth between credit constrained and unconstrained regimes. Using generalised impulse response functions (GIRF) generated from the estimated nonlinear model, we examine the real effects of monetary policy. We find evidence of asymmetry in the effects of monetary policy in the credit constrained and unconstrained regimes as well as different output effects of monetary contractions and expansions.
Clinical Features and Correlates of Excessive Daytime Sleepiness in Parkinson's Disease

Objective: To explore the clinical features and correlates of excessive daytime sleepiness (EDS) in a Chinese population of Parkinson's disease (PD) patients. Methods: Patients with clinically established or clinically probable PD were recruited. Clinical and demographic data were collected, and participants were evaluated using standardized assessment protocols. Patients were divided into PD with EDS and PD without EDS groups based on the Epworth sleepiness scale (ESS) scores, with a cutoff score of 10. Clinical manifestations were compared between patients with and without EDS, and correlates of EDS were also studied. In addition, the relationship between EDS and poor nighttime sleep quality was analyzed. Results: A total of 1,221 PD patients were recruited in our study. The mean ESS (min, max) score was 7.6 ± 6.1 (0, 24), and 34.1% of the patients had ESS scores ≥10. No difference was seen in lifestyle (except for alcohol consumption), environmental factors, BMI, levodopa equivalent dose (LED), initial presentation of motor symptoms, motor subtype, and wearing off between patients with and without EDS. The PD with EDS group had a higher proportion of male patients and a higher average patient age. Moreover, the PD with EDS group showed older age at PD onset, lower educational level, and longer disease duration. Patients with EDS had higher scores on the Hoehn-Yahr scale and the Unified Parkinson's Disease Rating Scale (UPDRS) parts I, II, and III score, more severe non-motor symptoms, and poorer quality of sleep and life. Logistic regression analyses demonstrated that EDS was associated with male sex, age, cognitive impairment, PD-related sleep problems, rapid eye movement sleep behavior disorder (RBD), and worse quality of life (QoL). Conclusion: EDS is a general clinical manifestation in PD, and there were significant differences in clinical features between patients with and without EDS. Moreover, our study proved that many factors were associated with EDS, including male sex, age, cognitive impairment, PD-related sleep problems, RBD, and worse QoL. Understanding the clinical characteristics of EDS in PD patients may help identify EDS early, improve QoL, and reduce the occurrence of accidents.
A Dynamic Analysis Tool for Real-time Characteristics Related to Cache Hit-ratio of Real-time Applications

It is important to guarantee the reliability of applications for real-time systems such as avionics. Unfortunately, existing profilers that measure the performance of applications cannot provide the information related to the cache hit-ratio of real-time applications. This paper presents a dynamic analysis tool for analyzing real-time characteristics related to the cache hit-ratio in real-time applications. This tool consists of a monitor module, which traces every function to find periodic functions, and an analyzer module, which analyzes the information of the function executions of the application, such as the thread identifier, caller, callee, number of calls, average time of execution, whether the signal callback or not, average periodic, violation of periodicity, and cache read miss rate in real-time applications
Convolutional neural networks based on augmented training samples for synthetic aperture radar target recognition

Abstract. A synthetic aperture radar (SAR) automatic target recognition (ATR) method based on the convolutional neural networks (CNN) trained by augmented training samples is proposed. To enhance the robustness of CNN to various extended operating conditions (EOCs), the original training images are used to generate the noisy samples at different signal-to-noise ratios (SNRs), multiresolution representations, and partially occluded images. Then, the generated images together with the original ones are used to train a designed CNN for target recognition. The augmented training samples can contrapuntally improve the robustness of the trained CNN to the covered EOCs, i.e., the noise corruption, resolution variance, and partial occlusion. Moreover, the significantly larger training set effectively enhances the representation capability for other conditions, e.g., the standard operating condition (SOC), as well as the stability of the network. Therefore, better performance can be achieved by the proposed method for SAR ATR. For experimental evaluation, extensive experiments are conducted on the Moving and Stationary Target Acquisition and Recognition dataset under SOC and several typical EOCs.
A Method to Judge the Style of Classical Poetry Based on Pre-trained Model

—One of the important topics in the research field of Chinese classical poetry is to analyze the poetic style. By examining the relevant works of previous dynasties, researchers judge a poetic style mostly by their subjective feelings, and refer to the previous evaluations that have become a certain conclusion. Although this judgment method is often effective, there may be some errors. This paper builds the most perfect data set of Chinese classical poetry at present, trains a BART-poem pre -trained model on this data set, and puts forward a generally applicable poetry style judgment method based on this BART-poem model, innovatively introduces in-depth learning into the field of computational stylistics, and provides a new research method for the study of classical poetry. This paper attempts to use this method to solve the problem of poetry style identification in the Tang and Song Dynasties, and takes the poetry schools that are considered to have a relatively clear and consistent poetic style, such as the Hongzheng Qizi and Jiajing Qizi, Jiangxi poetic school and Tongguang poetic school, as the research object, and takes the poems of their representative poets for testing. Experiments show that the judgment results of the tested poetry work made by the model are basically consistent with the conclusions given by critics of previous dynasties, verify some avant-garde judgments of Mr. Qian Zhongshu, and better solve the task of poetry style recognition in the Tang and Song dynasties.
Duality for Neural Networks through Reproducing Kernel Banach Spaces

Reproducing Kernel Hilbert spaces (RKHS) have been a very successful tool in various areas of machine learning. Recently, Barron spaces have been used to prove bounds on the generalisation error for neural networks. Unfortunately, Barron spaces cannot be understood in terms of RKHS due to the strong nonlinear coupling of the weights. This can be solved by using the more general Reproducing Kernel Banach spaces (RKBS). We show that these Barron spaces belong to a class of integral RKBS. This class can also be understood as an infinite union of RKHS spaces. Furthermore, we show that the dual space of such RKBSs, is again an RKBS where the roles of the data and parameters are interchanged, forming an adjoint pair of RKBSs including a reproducing kernel. This allows us to construct the saddle point problem for neural networks, which can be used in the whole field of primal-dual optimisation.
Chemically Peculiar Stars in Integrated Light Stellar Population Models and Local Group Galaxies

Integrated-light models that incorporate common types of chemically peculiar (CP) stars are assembled using synthetic spectra. Selected spectral features encode significant age information for populations with ages $\sim$50 Myr $<$ age $<\sim$2 Gyr. Due to the alleviation of template mismatch, the inclusion of CP star features in model spectra improves the accuracy of recovered stellar population parameters, but we are not able to show that new or unique age information can be extracted from the weak CP features compared to continuum fitting and strong-feature strengths, at least at the present state of the art. An age-extraction routine that recovers 2- and 3-burst age structures is employed to analyze the spectra of local group galaxies. NGC 224 (M31) has a stellar population too old for the types of CP stars we examine. NGC 221 (M32) also shows no CP spectral features. It appears to contain a component at age $\sim$1 Gyr at 1% by mass in addition to its dominant 4.7 Gyr population. Unlike SDSS galaxy spectrum averages, NGC 205 (M110) contains no features due to HgMn stars. This excludes the age range associated with HgMn production, and its near-nuclear spectrum is best fit by a 68$\pm$2 Myr population superimposed on an older population with a 1.85$\pm$0.1 Gyr component. Both NGC 205 and NGC 221 have an ancient component whose mass is not easy to constrain given the overwhelming light-dominance of the younger populations.
REDS: Random ensemble deep spatial prediction

There has been a great deal of recent interest in the development of spatial prediction algorithms for very large datasets and/or prediction domains. These methods have primarily been developed in the spatial statistics community, but there has been growing interest in the machine learning community for such methods, primarily driven by the success of deep Gaussian process regression approaches and deep convolutional neural networks. These methods are often computationally expensive to train and implement and consequently, there has been a resurgence of interest in random projections and deep learning models based on random weights—so called reservoir computing methods. Here, we combine several of these ideas to develop the random ensemble deep spatial (REDS) approach to predict spatial data. The procedure uses random Fourier features as inputs to an extreme learning machine (a deep neural model with random weights), and with calibrated ensembles of outputs from this model based on different random weights, it provides a simple uncertainty quantification. The REDS method is demonstrated on simulated data and on a classic large satellite data set.