Miracle-dz/UniFAR-FTUs huggingface dataset:
Test Results for a High Power Thermal Management System

In response to the identified needs of emerging high power spacecraft applications, a multiple evaporator hybrid loop heat pipe (H‐LHP) was developed and tested as part of a Dual Use Science and Technology (DUS&T) program co‐sponsored by ATK and AFRL/PRP. During the course of the DUS&T program, a two‐kilowatt system with three evaporators was developed and tested to identify viable system architectures and characterize system performance capabilities as a function of heat load profiles and spatial distribution of the evaporators. Following the successful development of the two‐kilowatt system, a 10‐kilowatt system with six evaporators was fabricated and tested. Tests were performed with the system operating in a totally passive mode, where applying a small amount of power to a sweepage evaporator provides the auxiliary flow through the primary evaporators, and as a self‐regulating, capillary‐controlled mechanically pumped system. This paper will provide a description of the 10‐kilowatt multi‐evaporator system and present the results of the passive and mechanically pump test programs.
The Voluntary Adoption of Internationally Recognized Accounting Standards and Firm Internal Performance Evaluation

A large body of research is devoted to understanding the causes and consequences of firms' adoption of international accounting standards. Thus far, researchers' attention has focused almost exclusively on the informational benefits of the adoption. We extend the existing literature by offering a different, stewardship perspective. We hypothesize that the voluntary adoption of international accounting standards is associated with changes in the firm internal performance evaluation process; in particular, it is associated with increases in the sensitivities of CEO turnover and employee layoffs to accounting earnings. Our results are consistent with these predictions.
Do Termination Provisions Truncate the Takeover Bidding Process

We provide new evidence on termination provisions and the takeover bidding process. Our central contribution is a novel database from SEC documents that accurately measures the incidence of termination provisions and the depth of competition in takeover deals. We show that biased data in prior research produced incorrect conclusions on the relation between termination provisions and judicial decisions, bidder toeholds and deal size. Our comprehensive data also show that termination provisions are positively related to takeover competition. Our evidence is consistent with the information/commitment hypothesis in which termination provisions do not truncate bidding but instead culminate the takeover process.
Dysnatremia in hospitalized elderly patients

Dysnatremia is the most common electrolyte disorder in hospitalized patients; it encompasses hyponatremic and hypernatremic conditions. It is a common finding at Intensive Care Unit (ICU) admission [1-3]. Up to one third of critically ill patients have dysnatremia at ICU admission [2]. Another one-third of critically ill patients will develop an ICU-acquired dysnatremia during ICU stay [4,5]. Prevalence of dysnatremia at ICU admission, however, varies greatly according to the chosen definition [1,2,6,7]. Hyponatremia is a pathologic condition defined as a serum sodium < 135 mmol/L. It is the most common electrolyte disorder in hospitalized patients. Up to 40% of the overall population of hospitalized patients has a hyponatremia at admission [8]. Presence of severe hyponatremia on hospital admission has been demonstrated to be independently associated with an increased risk for ICU admission and poor prognosis [9]. Hyponatremia may be due to chronic organ dysfunctions (that is heart failure or liver dysfunction) but also to diuretic use, syndrome of inappropriate antidiuretic hormone (ADH) secretion, adrenal insufficiency, and cerebral or renal salt wasting syndromes [10,11]. Hypernatremia is defined as a serum sodium > 145 mmol/L. It is generally related to water losses with increase of plasma osmolality. Serum sodium is the main cation of extracellular fluid and plays a key role in serum osmolarity. It is less frequent than hyponatremia [10] with a reported in-hospital prevalence of up to 7.7% [2]. Since thirst and free access to water are the most important mechanisms that prevent hypernatremia, critically ill patients and older patients are at high risk for this disorder [4,11]. Only 2.5% patients have been found to develop moderate to severe hypernatremia in the general in-hospital population of patients. Higher prevalence has been Abstract Background: Dysnatremia is the most common elect roly te disorder in hospi tal ized pat ients ; i t encompasses hyponatremic and hypernat remic condi t ions . Dysnatremia is also a common f inding at In tensive Care Uni t admission. Abnormal serum sodium concentrat ions are known to adversely af fect physiologic funct ion and an increasing evidence suggests that dysnatremia may be associated wi th adverse outcome.
Effects of Chicken Manure and Mineral Fertilizer on Some Nutritive Parameters and Lead Accumulation in Two Vigna Species Grown in Lead Contaminated Soil

Effect of chicken manure and mineral fertilizer on Vigna radiata and Vigna unguiculata plants grown in lead contaminated soil were examined. Plants were grown in lead polluted soil, which is amended with different concentrations of chicken manure and mineral fertilizer (NPK). Accumulation of lead in the leaves of both Vigna species was reduced when chicken manure was added. Application of mineral fertilizer increased the accumulation of lead in Vigna radiata, but caused a decrease in Vigna unguiculata. Application of both fertilizers improved the chlorophyll, protein and carbohydrate contents of both species. Results are discussed in relation to the significance of organic and inorganic manure with accumulation of heavy metals such as lead particularly in contaminated soils and their consequences on protein and chlorophyll synthesis.
Phase separated reconstruction patterns on strained FCC (111) metal surfaces

In this paper, we use the newly developed shear-induced reconstruction computer simulation method to explore various phase separated reconstruction patterns on strained Au(111), Pt(111), Ni(111) and alloy Ag/Pt(111) systems via molecular dynamic computer simulations. We find that specific surface reconstruction patterns are not confined to specific metallic systems; instead the results show that almost any reconstruction pattern could arise in almost any of the model systems dependent on the substrate to reconstruction layer lattice constant mismatch, and the substrate to reconstruction layer interaction strength.
Study on Photo Degradation Kinetics of Methylene Blue by Nanometer TiO_2

The kinetics of photocatalytic oxidation reaction for methylene blue solution was studied by using immobilized nanometer TiO2 photocatalyst under the irradiation of high pressure mercury lamp.The results indicated that the reaction was that of first-order and the velocity of reaction was coincident with multiphase catalytic kinetic equation:Langmuir-Hinshelwood equation.The reaction velocity constant and adsorption equilibrium constant were 0.2544 mgL-1min-1 and 0.4474 mg-1L respectively.
Application of cationic modification agent to dyeing of cotton with natural dyes

In view of the low dye-uptake and poor color fastness of natural dyes on cotton fabrics,a quaternary ammonium salt based cationic modification agent was synthesized and applied to the cotton.The modification mechanics was studied and the changes of cotton fiber structure before and after modification were analyzed by FTIR and XRD.The modification processes were optimized by single factor tests.The results indicated that the modification agent improves the dyeing performance of natural dyes on cotton fabrics.With 15 ~ 21 g/L of modification agent,4.5~5 g/L of sodium hydroxide at 60~70℃for 50~60 minutes,theK/Svalue of the modified cotton increased obviously.The washing fastness of the modified cotton was rated Grade 4 and the crocking fastness,Grade 3.
Gas exchange rates between air and sea

Gas exchange rate studies carried out in the laboratory suggest that the stagnant film model is adequate to relate the transfer coefficients of most gases between the atmosphere and sea to an accuracy of ±15%. Estimates of the average film thickness prevailing for the world ocean based on the distribution of natural radiocarbon, bomb-produced radiocarbon, and radon are in good agreement. Radon data from the BOMEX area and from station PAPA lend support to Kanwisher's suggestion that gas exchange rates should vary in proportion to the square of the wind velocity. These observations permit a number of generalizations regarding the potential of the ocean as a source and sink for trace gases to be made. They also permit the more complicated situation for carbon dioxide to be assessed. DOI: 10.1111/j.2153-3490.1974. tb01948.x
Study on refine process of phosphatide from soybean lecithin

The method of refine process of phosphatide from soybean lecithin was introduced.Research on the influences of the acetone quantity,the extraction temperature,time of removing oil,quantity of lively white soil and the temperature of discoloring are discussed.The best experiment condition was obtained.The yield of the refined phosphatide is 97% or so.
Effects of Cd Polluted Soil on the Modular Growth and Physiological Characteristics of Pennisetum hydridum

Growing energy crops in heavy metals-contaminated soils is a good approach to both environmental and bio-energy issues.In this experiment,a pot experiment was conducted to examine the effects of Cd on the growth of Pennisetumhydridum with adding different concentrations of cadmium of 0 mg·kg-1,1 mg·kg-1,5 mg·kg-1and 10 mg·kg-1.The results showed that the growth of P.hydridum was not significantly influenced by low Cd,but adding 10 mg·kg-1Cd treatment had a significant inhibitory effects on the plant height and aboveground biomass after six months of growth.There were significant negative linear relationships between soil Cd concentrations and plant growth pa-rameters including plant height,fresh leaf biomass,and stem biomass with coefficients(R2)of 0.438 3,0.403 1 and 0.618 5 respectively.The maximum photochemical efficiency(Fv/Fm)and activities of antioxidant enzymes(CAT,POD and SOD)were stimulated by Cd at low,but inhibited at high Cd concentrations.Concentrations of adding Cd in the aboveground parts were 0.39,2.65,10.83 and 11.77 mg·kg-1and 0.49,3.66,16.04 and 25.82 mg·kg-1in underground parts for the treatments 0,1,5,and 10 mg·kg-1respectively.Hence,P.hydridum could be used to reclaim Cd contaminated soil.
CuInxGa1–xS2 Nanocrystals with Tunable Composition and Band Gap Synthesized via a Phosphine-Free and Scalable Procedure

We report a phosphine-free colloidal synthesis of CuInxGa1–xS2 (CIGS) nanocrystals (NCs) by heating a mixture of metal salts, elemental sulfur, octadecene, and oleylamine. In contrast with the more commonly used hot injection, this procedure is highly suitable for large-scale NC production, which we tested by performing a gram-scale synthesis. The composition of the CIGS NCs could be tuned by varying the In and Ga precursor ratios, and the samples showed a composition-dependent band gap energy. The average particle size was scaled from 13 to 19 nm by increasing the reaction temperature from 230 to 270 °C. Two concomitant growth mechanisms took place: in one, covellite (CuS) NCs nucleated already at room temperature and then incorporated increasing amounts of In and Ga until they evolved into chalcopyrite CIGS NCs. In the second mechanism, CIGS NCs directly nucleated at intermediate temperatures. They were smaller than the NCs formed by the first mechanism, but richer in In and Ga. In the final sample, obt.
The Gaussian equivalence of generative models for learning with shallow neural networks

Understanding the impact of data structure on the computational tractability of learning is a key challenge for the theory of neural networks. Many theoretical works do not explicitly model training data, or assume that inputs are drawn component-wise independently from some simple probability distribution. Here, we go beyond this simple paradigm by studying the performance of neural networks trained on data drawn from pre-trained generative models. This is possible due to a Gaussian equivalence stating that the key metrics of interest, such as the training and test errors, can be fully captured by an appropriately chosen Gaussian model. We provide three strands of rigorous, analytical and numerical evidence corroborating this equivalence. First, we establish rigorous conditions for the Gaussian equivalence to hold in the case of single-layer generative models, as well as deterministic rates for convergence in distribution. Second, we leverage this equivalence to derive a closed set of equations describing the generalisation performance of two widely studied machine learning problems: two-layer neural networks trained using one-pass stochastic gradient descent, and full-batch pre-learned features or kernel methods. Finally, we perform experiments demonstrating how our theory applies to deep, pre-trained generative models. These results open a viable path to the theoretical study of machine learning models with realistic data.
Unit Test Case Generation with Transformers

Automated Unit Test Case generation has been the focus of extensive literature within the research community. Existing approaches are usually guided by the test coverage criteria, generating synthetic test cases that are often difficult to read or understand for developers. In this paper we propose AthenaTest, an approach that aims at generating unit test cases by learning from real-world, developer-written test cases. Our approach relies on a state-of-the-art sequence-to-sequence transformer model which is able to write useful test cases for a given method under test (i.e., focal method). We also introduce methods2test - the largest publicly available supervised parallel corpus of unit test case methods and corresponding focal methods in Java, which comprises 630k test cases mined from 70k open-source repositories hosted on GitHub. We use this dataset to train a transformer model to translate focal methods into the corresponding test cases. We evaluate the ability of our model in generating test cases using natural language processing as well as code-specific criteria. First, we assess the quality of the translation compared to the target test case, then we analyze properties of the test case such as syntactic correctness and number and variety of testing APIs (e.g., asserts). We execute the test cases, collect test coverage information, and compare them with test cases generated by EvoSuite and GPT-3. Finally, we survey professional developers on their preference in terms of readability, understandability, and testing effectiveness of the generated test cases.
Hybrid Machine Learning for Scanning Near-Field Optical Spectroscopy

The underlying physics behind an experimental observation often lacks a simple analytical description. This is especially the case for scanning probe microscopy techniques, where the interaction between the probe and the sample is nontrivial. Realistic modeling to include the details of the probe is always exponentially more difficult than its “spherical cow” counterparts. On the other hand, a well-trained artificial neural network based on real data can grasp the hidden correlation between the signal and sample properties. In this work, we show that, via a combination of model calculation and experimental data acquisition, a physics-infused hybrid neural network can predict the tip-sample interaction in the widely used scattering-type scanning near-field optical microscope. This hybrid network provides a long-sought solution for accurate extraction of material properties from tip-specific raw data. The methodology can be extended to other scanning probe microscopy techniques as well as other data-oriented physical problems in general.
Context-Preserving Text Simplification

We present a context-preserving text simplification (TS) approach that recursively splits and rephrases complex English sentences into a semantic hierarchy of simplified sentences. Using a set of linguistically principled transformation patterns, input sentences are converted into a hierarchical representation in the form of core sentences and accompanying contexts that are linked via rhetorical relations. Hence, as opposed to previously proposed sentence splitting approaches, which commonly do not take into account discourse-level aspects, our TS approach preserves the semantic relationship of the decomposed constituents in the output. A comparative analysis with the annotations contained in the RST-DT shows that we are able to capture the contextual hierarchy between the split sentences with a precision of 89% and reach an average precision of 69% for the classification of the rhetorical relations that hold between them.
DDR-Net: Dividing and Downsampling Mixed Network for Diffeomorphic Image Registration

Deep diffeomorphic registration faces significant challenges for high-dimensional images, especially in terms of memory limits. Existing approaches either downsample original images, or approximate underlying transformations, or reduce model size. The information loss during the approximation or insufficient model capacity is a hindrance to the registration accuracy for high-dimensional images, e.g., 3D medical volumes. In this paper, we propose a Dividing and Downsampling mixed Registration network (DDR-Net), a general architecture that preserves most of the image information at multiple scales. DDR-Net leverages the global context via downsampling the input and utilizes the local details from divided chunks of the input images. This design reduces the network input size and its memory cost; meanwhile, by fusing global and local information, DDR-Net obtains both coarse-level and fine-level alignments in the final deformation fields. We evaluate DDR-Net on three public datasets, i.e., OASIS, IBSR18, and 3DIRCADB-01, and the experimental results demonstrate our approach outperforms existing approaches.
QCD analysis of pion fragmentation functions in the xFitter framework

We present the first open-source analysis of fragmentation functions (FFs) of charged pions (entitled IPM x FITTER ) performed at next-to-leading order (NLO) and next-to-next-to-leading order (NNLO) accuracy in perturbative QCD using the x FITTER framework. This study incorporates a comprehensive and up-to-date set of pion production data from single-inclusive annihilation (SIA) processes, together with the most recent measurements of inclusive cross-sections of single pion by the BELLE collaboration. The determination of pion FFs along with their theoretical uncertainties is performed in the zero-mass variable-flavor number scheme (ZM-VFNS). We also present comparisons of our FFs set with recent fits from the literature. The resulting NLO and NNLO pion FFs provide valuable insights for applications in present and future high-energy analysis of pion final state processes.
GMAC: A Distributional Perspective on Actor-Critic Framework

In this paper, we devise a distributional framework on actor-critic as a solution to distributional instability, action type restriction, and conflation between samples and statistics. We propose a new method that minimizes the Cram\'er distance with the multi-step Bellman target distribution generated from a novel Sample-Replacement algorithm denoted SR($\lambda$), which learns the correct value distribution under multiple Bellman operations. Parameterizing a value distribution with Gaussian Mixture Model further improves the efficiency and the performance of the method, which we name GMAC. We empirically show that GMAC captures the correct representation of value distributions and improves the performance of a conventional actor-critic method with low computational cost, in both discrete and continuous action spaces using Arcade Learning Environment (ALE) and PyBullet environment.
WSSOD: A New Pipeline for Weakly- and Semi-Supervised Object Detection

The performance of object detection, to a great extent, depends on the availability of large annotated datasets. To alleviate the annotation cost, the research community has explored a number of ways to exploit unlabeled or weakly labeled data. However, such efforts have met with limited success so far. In this work, we revisit the problem with a pragmatic standpoint, trying to explore a new balance between detection performance and annotation cost by jointly exploiting fully and weakly annotated data. Specifically, we propose a weakly- and semi-supervised object detection framework (WSSOD), which involves a two-stage learning procedure. An agent detector is first trained on a joint dataset and then used to predict pseudo bounding boxes on weakly-annotated images. The underlying assumptions in the current as well as common semi-supervised pipelines are also carefully examined under a unified EM formulation. On top of this framework, weakly-supervised loss (WSL), label attention and random pseudo-label sampling (RPS) strategies are introduced to relax these assumptions, bringing additional improvement on the efficacy of the detection pipeline. The proposed framework demonstrates remarkable performance on PASCAL-VOC and MSCOCO benchmark, achieving a high performance comparable to those obtained in fully-supervised settings, with only one third of the annotations.
The New Ephemeris and Light Curve Analysis of V870 Ara by the Ground-Based and TESS Data

Abstract New CCD photometric observations and their investigation of the W UMa-type binary, V870 Ara, are presented. Light curves of the system were taken through BVIfilters from the Congarinni Observatory in Australia. The new ephemeris is calculated based on seven new determined minimum times, together with the TESS data and others compiled from the literature. Photometric solutions determined by the Wilson-Devinney (W-D) code are combined with the Monte Carlo simulation to determine the adjustable parameters’ uncertainties. These solutions suggest that V870 Ara is a contact binary system with a mass ratio of 0.082, a fillout factor of 96±4 percent, and an inclination of 73.60±0.64 degrees. The absolute parameters of V870 Ara were determined by combining the Gaia EDR3 parallax and photometric elements.
Tuning of magnetic structures topology via single-ion anisotropy and magnetic field in frustrated 2D semiconductors

The effects of competing magnetic interactions in stabilizing different spin configurations are drawing a renewed attention in order to both unveil emerging topological spin textures and to highlight microscopic mechanisms leading to their stabilization. The possible key role of the two-site exchange anisotropy in selecting specific helicity and vorticity of skyrmionic lattices has only recently been proposed. In this work we explore the phase diagram of a frustrated localized magnet on a two-dimensional centrosymmetric triangular lattice, focusing on the interplay between the two-ion anisotropy (TIA) and the single-ion anisotropy (SIA). The effects of an external magnetic field applied perpendicularly to the magnetic layer are also investigated. By means of Monte Carlo simulations, we find a profusion of different spin configurations, going from trivial to high-order Q skyrmionic and meronic lattices. In closer detail, we find that a dominant role is played by the two-ion over the single-ion anisotropy in determining the planar spin texture, whereas the strength and sign of SIA, together with the magnitude of the magnetic field, tune the perpendicular spin components, mostly affecting the polarity (and, in turn, the topology) of the spin-texture. Our analysis confirms the crucial role of anisotropic symmetric exchange in systems with dominant short-range interactions, at the same time predicting a rich variety of complex magnetic textures that may arise from a fine tuning of competing anisotropic mechanisms.