Miracle-dz/UniFAR-FTUs huggingface dataset:
Quasi-universal Behavior of the Threshold Mass in Unequal-mass, Spinning Binary Neutron Star Mergers

The lifetime of the remnant produced by the merger of two neutron stars can provide a wealth of information on the equation of state of nuclear matter and on the processes leading to the electromagnetic counterpart. Hence, it is essential to determine when this lifetime is the shortest, corresponding to when the remnant has a mass equal to the threshold mass, M th, to prompt collapse to a black hole. We report on the results of more than 360 simulations of merging neutron-star binaries covering 40 different configurations differing in mass ratio and spin of the primary. Using this data, we have derived a quasi-universal relation for M th and expressed its dependence on the mass ratio and spin of the binary. The new expression recovers the results of Koeppel et al. for equal-mass, irrotational binaries and reveals that M th can increase (decrease) by 5% (10%) for binaries that have spins aligned (antialigned) with the orbital angular momentum and provides evidence for a nonmonotonic dependence of M th on the mass asymmetry in the system. Finally, we extend to unequal masses and spinning binaries the lower limits that can be set on the stellar radii once a neutron star binary is detected, illustrating how the merger of an unequal-mass, rapidly spinning binary can significantly constrain the allowed values of the stellar radii.
Dialog-based Automation of Decision Making in Processes

The use of chatbots has spread, generating great interest in the industry for the possibility of automating tasks within the execution of their processes. The implementation of chatbots, however simple, is a complex endeavor that involves many low-level details, which makes it a time-consuming and error-prone task. In this paper we aim at facilitating the development of decision-support chatbots that guide users or help knowledge workers to make decisions based on interactions between different process participants, aiming at decreasing the workload of human workers, for example, in healthcare to identify the first symptoms of a disease. Our work concerns a methodology to systematically build decision-support chatbots, semi-automatically, from existing DMN models. Chatbots are designed to leverage natural language understanding platforms, such as Dialogflow or LUIS. We implemented Dialogflow chatbot prototypes based on our methodology and performed a pilot test that revealed insights into the usability and appeal of the chatbots developed.
IMPLEMENTATION OF EARLY WARNING SYSTEM IN NURSING WARD: A NARRATIVE REVIEW

DOI: https://doi.org/10.36720/nhjk.v10i1. 275 Abstract • Hospital service is a process that involves all elements in the hospital including nurses and inpatient rooms or nursing wards. Different inpatient conditions will be treated in separated wards, by the same token patients with unstable conditions are admitted in intensive care units, this procedure aims to reduce the mortality incidence due to sudden cardiac arrest, therefore early detection of patients’ clinical deterioration using the early warning score system performed by the nurse in the nursing wards is required. This review aims to describe the importance of the early warning system in the nursing wards. The data was obtained from international journal providers s from data base search based on Scopus, Ebscohot: CINHL, Proquest, Springerlink, Google scholar, and Midline. We used the keyword "Early Warning System" or "Early Warning Score" with a search range in 2016-2021. The search results in 127 articles. Language selection was performed as all journals found were in English. Here, we will review 10 articles. Implementation of early warning score in nursing ward is expected to improve the quality of service to patients and reduce the mortality rate. Early detection clinical emergency or known as the Early Warning Score System (EWSS) is the application of a scoring system for early detection of patient's condition before a worsening situation occurs. The implementation of this scoring system is necessary due to the high rate of deterioration of patient conditions that requiring immediate management to prevent profound deterioration and its subsequent adverse effect. • •
Time-Correlated Window-Carrier-Phase-Aided GNSS Positioning Using Factor Graph Optimization for Urban Positioning

This article proposes an improved global navigation satellite system (GNSS) positioning method that explores the time correlation between consecutive epochs of the code and carrier-phase measurements, which significantly increases the robustness against outlier measurements. Instead of relying on the time difference carrier phase which only considers two neighboring epochs using an extended Kalman filter estimator, this article proposed to employ the carrier-phase measurements inside a window, the so-called window carrier phase (WCP), to constrain the states inside a factor graph. A left null space matrix is employed to eliminate the shared unknown ambiguity variables and, therefore, correlate the associated states inside the WCP. Then, the pseudorange, Doppler, and the constructed WCP measurements are integrated simultaneously using factor graph optimization to estimate the state of the GNSS receiver. We evaluated the performance of the proposed method in two typical urban canyons in Hong Kong, achieving the mean positioning errors of 1.76 and 2.96 m, respectively, using the automobile-level GNSS receiver. Meanwhile, the effectiveness of the proposed method is further evaluated using a low-cost smartphone-level GNSS receiver, and similar improvement is also obtained when compared with several existing GNSS positioning methods.
The Rapid ASKAP Continuum Survey Paper II: First Stokes I Source Catalogue Data Release

Abstract The Rapid ASKAP Continuum Survey (RACS) is the first large sky survey using the Australian Square Kilometre Array Pathfinder (ASKAP), covering the sky south of $+41^\circ$ declination. With ASKAP’s large, instantaneous field of view, ${\sim}31\,\mathrm{deg}^2$ , RACS observed the entire sky at a central frequency of 887.5 MHz using 903 individual pointings with 15 minute observations. This has resulted in the deepest radio survey of the full Southern sky to date at these frequencies. In this paper, we present the first Stokes I catalogue derived from the RACS survey. This catalogue was assembled from 799 tiles that could be convolved to a common resolution of $25^{\prime\prime}$ , covering a large contiguous region in the declination range $\delta=-80^{\circ}$ to $+30^\circ$ . The catalogue provides an important tool for both the preparation of future ASKAP surveys and for scientific research. It consists of $\sim$ 2.1 million sources and excludes the $|b|<5^{\circ}$ region around the Galactic plane. This provides a first extragalactic catalogue with ASKAP covering the majority of the sky ( $\delta<+30^{\circ}$ ). We describe the methods to obtain this catalogue from the initial RACS observations and discuss the verification of the data, to highlight its quality. Using simulations, we find this catalogue detects 95% of point sources at an integrated flux density of $\sim$ 5 mJy. Assuming a typical sky source distribution model, this suggests an overall 95% point source completeness at an integrated flux density $\sim$ 3 mJy. The catalogue will be available through the CSIRO ASKAP Science Data Archive (CASDA).
Big Data Analytics in Data Mining – A Review

The Age of Big Data are already running because the traditional data analytics may not be able to handle such large quantities of data. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle the extract value and knowledge from these data sets. Furthermore, decision makers need to be able to gain valuable insights from such varied and rapidly changing data, ranging from daily transactions to customer interactions and social network data. Such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper presents brief introduction of data analytics, architecture of big data, knowledge discovery and big data algorithms.
Leveraging Electronic Word of Mouth on TikTok: Somethinc Skin Care Product Innovation to Increase Consumer Purchase Intention

Social media platforms continually assisting the industry as a media promoter for its customer. This study aims to determine how the influence of electronic word of mouth on TikTok on purchase intention of Somethinc skincare products through brand image. This study uses a quantitative approach with data collected using a survey method. The research instrument used a questionnaire distributed via Google Form. The number of samples in this study were 100 respondents who were TikTok users, knew about, and had never bought Somethinc skincare products. The analysis technique used in this research is simple linear regression. The results of this study indicate that the four hypotheses of this study are accepted. It was concluded that there was an influence between eWOM on TikTok on purchase intention of Somethinc skincare products, the influence between e-WOM on TikTok on Somethinc’s brand image, the influence between Somethinc brand image on purchase intention of Somethinc skincare product, and influence between Electronic word of mouth on TikTok. towards purchase intention of Somethinc skincare products through brand image.
Balanced Pairs on Triangulated Categories

Let [Formula: see text] be a triangulated category. We first introduce the notion of balanced pairs in [Formula: see text], and then establish the bijective correspondence between balanced pairs and proper classes [Formula: see text] with enough [Formula: see text]-projectives and [Formula: see text]-injectives. Assume that [Formula: see text] is the proper class induced by a balanced pair [Formula: see text]. We prove that [Formula: see text] is an extriangulated category. Moreover, it is proved that [Formula: see text] is a triangulated category if and only if [Formula: see text], and that [Formula: see text] is an exact category if and only if [Formula: see text]. As an application, we produce a large variety of examples of extriangulated categories which are neither exact nor triangulated.
GNSS Outlier Mitigation via Graduated Non-Convexity Factor Graph Optimization

Accurate and globally referenced global navigation satellite system (GNSS) based vehicular positioning can be achieved in outlier-free open areas. However, the performance of GNSS can be significantly degraded by outlier measurements, such as multipath effects and non-line-of-sight (NLOS) receptions arising from signal reflections of buildings. Inspired by the advantage of batch historical data in resisting outlier measurements, in this paper, we propose a graduated non-convexity factor graph optimization (FGO-GNC) to improve the GNSS positioning performance, where the impact of GNSS outliers is mitigated by estimating the optimal weightings of GNSS measurements. Different from the existing local solutions, the proposed FGO-GNC employs the non-convex Geman McClure (GM) function to globally estimate the weightings of GNSS measurements via a coarse-to-fine relaxation. The effectiveness of the proposed method is verified through several challenging datasets collected in urban canyons of Hong Kong using automobile level and low-cost smartphone level GNSS receivers.
Sensing and Classification Using Massive MIMO: A Tensor Decomposition-Based Approach

Wireless-based activity sensing has gained significant attention due to its wide range of applications. We investigate radio-based multi-class classification of human activities using massive multiple-input multiple-output (MIMO) channel measurements in line-of-sight and non line-of-sight scenarios. We propose a tensor decomposition-based algorithm to extract features by exploiting the complex correlation characteristics across time, frequency, and space from channel tensors formed from the measurements, followed by a neural network that learns the relationship between the input features and output target labels. Through evaluations of real measurement data, it is demonstrated that the classification accuracy using a massive MIMO array achieves significantly better results compared to the state-of-the-art even for a smaller experimental data set.
Big data, big problems: Responding to"Are we there yet?"

Bradley et al. (arXiv:2106.05818v2), as part of an analysis of the performance of large-but-biased surveys during the COVID-19 pandemic, argue that the data defect correlation provides a useful tool to quantify the effects of sampling bias on survey results. We examine their analyses of results from the COVID-19 Trends and Impact Survey (CTIS) and show that, despite their claims, CTIS in fact performs well for its intended goals. Our examination reveals several limitations in the data defect correlation framework, including that it is only applicable for a single goal (population point estimation) and that it does not admit the possibility of measurement error. Through examples, we show that these limitations seriously affect the applicability of the framework for analyzing CTIS results. Through our own alternative analyses, we arrive at different conclusions, and we argue for a more expansive view of survey quality that accounts for the intended uses of the data and all sources of error, in line with the Total Survey Error framework that have been widely studied and implemented by survey methodologists.
Detection of mitochondrial coupling factor 6 in placental tissues from preeclamptic pregnancies and its influence on biological behavior of trophoblast cells

Increased levels of mitochondrial coupling factor 6 (CF6) are present in the peripheral blood of patients with preeclamptic pregnancies, and are particularly evident in cases of early-onset or severe preeclampsia. The present study examined the location and expression levels of CF6 in the placental tissue and its effect on the biological behavior of trophoblast cells. Placental tissue microarrays, including placental villous cytotrophoblast and extravillous cytotrophoblast microarrays, were used to detect the location and relative expression levels of CF6 in the placenta using immunohistochemistry. It was found that CF6 was expressed in both the normal and preeclamptic placenta, but its levels were higher in the preeclamptic tissues. In addition, the effects of the hypoxic environment on the biological behaviors of trophoblast cells were investigated in the JAR and JEG-3 cell lines. Following induction of hypoxia, the expression levels of CF6 were increased. Moreover, exogenous addition of human recombinant CF6 attenuated cell invasion, but exerted no effect on cell proliferation. At the molecular level, the expression levels of MMP-2 were decreased and were accompanied with a reduction in cell invasion following addition of exogenous CF6. In conclusion, the increased expression levels of CF6 and its effects in reducing the invasive abilities of trophoblast cells may be involved in the pathogenesis of severe preeclampsia.
Total Quality Management and Organizational Performance of Deposit Taking Micro Finance Institutions in Nyandarua County, Kenya

In Kenya, micro finance institutions have continued to grow though most of them continue to face serious financial difficulties and poor performance. The micro finance institutions have barely been able to grow the customer base and have faced stiff competition from other players in the financial sector. The firms have experienced internal inefficiencies in customer focus as the financial sector transforms powered by innovations with a change customer demand. While studies indicate that total quality management is important for businesses, there still exists a dearth of information on its effect on the organizational performance of deposit taking microfinance institutions in Nyandarua County. The study adopted a descriptive survey research design. The target population comprised 86 managers of 11 micro finance institutions in Nyandarua County, Kenya. A census approach was adopted. Primary data was collected through semi structured questionnaire and administered through the drop and pick method. Secondary data was extracted from the managements and financial reports and corporate handbooks. The study findings indicate that all the four Total Quality Managements practices (management commitment, customer focus, employee engagement and continuous improvement) affected the performance of micro finance institutions in Nyandarua County. Continuous improvement had a significant effect on the micro finance institutions performance. However, the effect of management commitment, customer focus and employee engagement on micro finance institution performance were lowly rated. The study thus concluded that among the Total Quality Management practices under considerations, continuous improvement had the most significant effect on the performance of the micro finance institutions in Nyandarua County. The study recommended that the micro finance institutions should re-evaluate the policies for quality improvements to ensure that management is fully supported to influence desired total quality. Clear guidelines on quality goals achievement should be fully disseminated to the managers to ensure that the right strategies are adopted and implemented.
Deep Face Video Inpainting via UV Mapping

This paper addresses the problem of face video inpainting. Existing video inpainting methods target primarily at natural scenes with repetitive patterns. They do not make use of any prior knowledge of the face to help retrieve correspondences for the corrupted face. They therefore only achieve sub-optimal results, particularly for faces under large pose and expression variations where face components appear very differently across frames. In this paper, we propose a two-stage deep learning method for face video inpainting. We employ 3DMM as our 3D face prior to transform a face between the image space and the UV (texture) space. In Stage I, we perform face inpainting in the UV space. This helps to largely remove the influence of face poses and expressions and makes the learning task much easier with well aligned face features. We introduce a frame-wise attention module to fully exploit correspondences in neighboring frames to assist the inpainting task. In Stage II, we transform the inpainted face regions back to the image space and perform face video refinement that inpaints any background regions not covered in Stage I and also refines the inpainted face regions. Extensive experiments have been carried out which show our method can significantly outperform methods based merely on 2D information, especially for faces under large pose and expression variations. Project page: https://ywq.github. io/FVIP.
TrouSPI-Net: Spatio-temporal attention on parallel atrous convolutions and U-GRUs for skeletal pedestrian crossing prediction

Understanding the behaviors and intentions of pedestrians is still one of the main challenges for vehicle autonomy, as accurate predictions of their intentions can guarantee their safety and driving comfort of vehicles. In this paper, we address pedestrian crossing prediction in urban traffic environments by linking the dynamics of a pedestrian's skeleton to a binary crossing intention. We introduce TrouSPI-Net: a context-free, lightweight, multi-branch predictor. TrouSPI-Net extracts spatio-temporal features for different time resolutions by encoding pseudo-images sequences of skeletal joints' positions and processes them with parallel attention modules and atrous convolutions. The proposed approach is then enhanced by processing features such as relative distances of skeletal joints, bounding box positions, or ego-vehicle speed with U-GRUs. Using the newly proposed evaluation procedures for two large public naturalistic data sets for studying pedestrian behavior in traffic: JAAD and PIE, we evaluate TrouSPI-Net and analyze its performance. Experimental results show that TrouSPI-Net achieved 76% F1 score on JAAD and 80% F1 score on PIE, therefore outperforming current state-of-the-art while being lightweight and context-free.
DILBERT: Customized Pre-Training for Domain Adaptation with Category Shift, with an Application to Aspect Extraction

The rise of pre-trained language models has yielded substantial progress in the vast majority of Natural Language Processing (NLP) tasks. However, a generic approach towards the pre-training procedure can naturally be sub-optimal in some cases. Particularly, fine-tuning a pre-trained language model on a source domain and then applying it to a different target domain, results in a sharp performance decline of the eventual classifier for many source-target domain pairs. Moreover, in some NLP tasks, the output categories substantially differ between domains, making adaptation even more challenging. This, for example, happens in the task of aspect extraction, where the aspects of interest of reviews of, e.g., restaurants or electronic devices may be very different. This paper presents a new fine-tuning scheme for BERT, which aims to address the above challenges. We name this scheme DILBERT: Domain Invariant Learning with BERT, and customize it for aspect extraction in the unsupervised domain adaptation setting. DILBERT harnesses the categorical information of both the source and the target domains to guide the pre-training process towards a more domain and category invariant representation, thus closing the gap between the domains. We show that DILBERT yields substantial improvements over state-of-the-art baselines while using a fraction of the unlabeled data, particularly in more challenging domain adaptation setups.
Re‐evaluation of pectin (E 440i) and amidated pectin (E 440ii) as food additives

Abstract Following a request from the European Commission, the EFSA Panel on Food Additives and Nutrient sources added to Food (ANS) was asked to deliver a scientific opinion on the re‐evaluation of pectin (E 440i) and amidated pectin (E 440ii) as food additives. An acceptable daily intake (ADI) ‘not specified’ was allocated by the Scientific Committee for Food (SCF) for E 440i and E 440ii. Pectin and amidated pectin would not be absorbed intact, but extensively fermented by intestinal microbiota in animals and humans; products formed from pectins in the gastrointestinal tract are similar to manufactured pectin‐derived acidic oligosaccharides (pAOS). There is no indication of genotoxicity for pectin and amidated pectin, although the available data were limited. No adverse effects were reported in a chronic toxicity study in rats at levels up to 5,000 mg pectin/kg bw per day, the highest dose tested. No treatment‐related effects were observed in a dietary one‐generation reproductive toxicity study with pAOS in rats at up to 6,200 mg/kg body weight (bw) per day, the highest dose tested. The Panel did not consider E 440i and E 440ii as having allergenic potential. A dose of 36 g/day (equivalent to 515 mg/kg bw per day) for 6 weeks in humans was without adverse effects. Exposure to pectins from their use as food additives ranged up to 442 mg/kg bw per day for toddlers at the 95th percentile (brand‐loyal scenario). The Panel concluded that there is no safety concern for the use of pectin (E 440i) and amidated pectin (E 440ii) as food additives for the general population and that there is no need for a numerical ADI.