Miracle-dz/UniFAR-FTUs huggingface dataset:
Profiling News Discourse Structure Using Explicit Subtopic Structures Guided Critics

We present an actor-critic framework to induce subtopical structures in a news article for news discourse proﬁling. The model uses multiple critics that act according to known subtopic structures while the actor aims to outperform them. The content structures constitute sentences that represent latent subtopic bound-aries. Then, we introduce a hierarchical neural network that uses the identiﬁed subtopic boundary sentences to model multi-level interaction between sentences, subtopics, and the document. Experimental results and analyses on the NewsDiscourse corpus show that the actor model learns to effectively segment a document into subtopics and improves the performance of the hierarchical model on the news discourse proﬁling task 1 . runs with random seeds. In addition, we report standard deviation for both macro and micro F1 scores. Statistical signiﬁcance tests show that both the macro and micro F1 scores for RL-IP/TT model are signiﬁcantly better compared to the hierarchical, self-critic, TextTiling and joint-IP models with p < 0.05 on paired t test (Dietterich, 1998). Similarly, the macro F1 scores for RL-TT and RL-IP models are signiﬁcantly better compared to the hierarchical, TextTiling and Joint-IP models with p < 0.05.
Comparison of the influence of two types of cold surge on haze dispersion in eastern China

Abstract. Cold surge (CS) is considered a favorable weather process to improve air quality and is widely recognized. However, there is no detailed study on the differences in the dispersion ability of different types of CSs in relation to haze days in eastern China (HDEC). This paper uses the hierarchical clustering algorithm to classify the cool-season (November to February of the following year) CSs across eastern China into blocking CSs and wave-train CSs and compares their influences on the number of HDEC from 1980 to 2017. Results show that the wave-train CSs can significantly improve the visibility in eastern China and generally improve air quality for about 2 d longer than the blocking CSs, which indicates that the blocking CSs have a weaker ability to dissipate HDEC compared with the wave-train CSs. The CSs affect the HDEC by changing meteorological elements like thermal inversion potential, horizontal surface wind, sea level pressure (SLP), and surface air temperature (SAT). A period of 4 d after the outbreak of CSs, the variations of thermal inversion potential and horizontal surface wind of two types of CSs tend to be consistent. However, the negative SAT anomalies and the positive SLP anomalies caused by the blocking CSs lasted shorter than those caused by the wave-train CSs, forming favorable conditions for the rapid growth of HDEC. Furthermore, results show that in recent years, especially after the 1990s, the frequency of wave-train CSs has decreased significantly, while the frequency of blocking CSs has slightly increased, indicating that the overall ability of CSs to dissipate HDEC has weakened in general. This work may provide reference for the future formulation of haze control policies in East Asia.
Sketching Animations of Organic Shapes

Motivations and context Our understanding of many natural phenomena, such as biological ones – from blood cells circulating in vessels to bacteria evolving in their environment and interacting with each other, can be highly enhanced by scientific illustrations. Contrary to captured data, these illustrations aim at conveying the main structure and deformation of the typical shapes and behaviours involved, while proposing a simplified representation, which is essential for structuring our thoughts. Among them, animated illustrations convey the temporal aspect of motion and deformations, which would be very hard to grasp using static images only. They are usually designed by talented scientific illustrators and digital artists, but their generation typically requires a tedious cycle of trials and errors, while scientists explain their visions to these artists, and the later manipulate their preferred specialized tools to create contents, until the results get closer and eventually converge to the visions the scientists wanted to convey.
Desert pumpkinseed: diet composition and breadth in a Moroccan river

The widely invasive North American pumpkinseed sunfish, Lepomis gibbosus, is currently established in desert rivers in Morocco. The success of pumpkinseed in novel ecosystems has been associated with its generalist diet, but this trait remains unevaluated in arid regions. Desert rivers are harsh environments with limited water and prey availability which may adversely constrain the diet of fish. Here we studied the diet of pumpkinseed across 4 sites in the Draa River, embracing a 450 m elevational gradient covering from extremely dry lowlands to relatively humid highlands. We described pumpkinseed diet through the analysis of stomach contents of 82 individuals, collected in the fall of 2013. Pumpkinseed diet was dominated by Chironomidae in dry lowlands, while Ephemeroptera, Heteroptera, Trichoptera and Odonata were relevant prey in more humid highlands. Population diet breadth expanded with elevation, but individual specialization in diet was low among all sites. Our results highlight considerable changes in diet composition and breadth with aridity, suggesting that feeding plasticity and use of exclusive, locally abundant prey rather than generalized feeding may be associated with the success of pumpkinseed in most arid areas in desert rivers.
Impact of the 2020 China Diabetes Society Guideline on the Prevalence of Diabetes Mellitus and Eligibility for Antidiabetic Treatment in China

Purpose This study aimed to estimate the impact of the 2020 China Diabetes Society’s (CDS) guideline on the prevalence of diabetes mellitus and eligibility for antidiabetic treatment in China. Material and Methods Baseline data from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2012) were used to estimate the prevalence of diabetes mellitus and compare the recommendations for antidiabetic medication and intensification of therapy between the 2017 and 2020 CDS guidelines. Results According to the 2017 CDS guideline, the prevalence of diabetes mellitus was 12.56% among Chinese adults who were ≥45 years of age. However, according to the 2020 CDS guideline, 0.65% (0.35%, 1.20%), or 3.54 (2.50, 4.57) million Chinese adults who were ≥45 years would additionally be diagnosed with diabetes mellitus. Among Chinese adults not taking antidiabetic medications, 1.06% (0.87%, 1.28%), or 5.37 (4.36, 6.38) million Chinese adults with diabetes mellitus were recommended to start antidiabetic medication according to the 2017 CDS guideline, while 1.27% (1.01%, 1.58%), or 6.44 (5.29, 7.60) million Chinese adults with diabetes would be recommended to initiate antidiabetic medication according to the 2020 CDS guideline. Among Chinese adults taking antidiabetic medication, 51.59% (44.19%, 58.93%), or 18.35 (15.58, 21.12) million Chinese adults with diabetes received antidiabetic treatment but had a hemoglobin A1c (HbA1c) level higher than that mentioned in the 2017 and 2020 CDS guidelines. Conclusion The addition of HbA1c in the 2020 CDS guideline will result in a modest increase in the number of Chinese adults who are diagnosed with diabetes and diabetes patients recommended for antidiabetic medication; however, the 2020 CDS guideline does not affect the number of diabetes patients eligible for intensification of treatment.
Improving SCOBY starter using co-culture of tapai and bakery yeast

Abstract. Urbahillah A, Jayus J, Nurhayati N. 2021. Improving SCOBY starter using co-culture of tapai and bakery yeast. Biodiversitas 22: 4617-4624. Kombucha is a beverage fermented by a symbiotic bacteria and yeast known as SCOBY (Symbiotic Culture of Bacteria and Yeast). Bacteria and yeast contribute to the formation of organic acids, aroma, taste, and flavor of kombucha. The commercial yeasts used in Indonesian are baker’s yeast and tapai yeast. This study was conducted to develop SCOBYco-culture with tapai yeast and baker’s yeast and evaluate its activity. The ingredients for the kombucha were cascara, water, and sugar, which were fermented with three formula starter, i.e. original SCOBY 10% w/v (SN), co-culture SCOBY 10% w/v with 0.1% w/v of baker’s yeast (SNR), and co-culture SCOBY 10% w/v with tapai yeast 0.1% w/v (SNT). The starter activity were determined based on the OD (Optical Density) value. Yeast screening was carried out on the dominant starter population. Furthermore, morphologically yeast was identified based on colony type, color, and shape of cell. Then yeast was identified by their fermentation profile using API 20C Aux Kit. Isolate A showed white colony with convex elevation and the cell was round-shaped. Colony of isolate B and isolate C were creamy in color and oval cell shaped. The API results revealed that the first isolate was identified as Candida famata, second isolate was as Candida krusei, and the third isolate was as Candida magnoliae. Three types of fungi were found from SCOBY, namely Mucor sp. , Trichoderma sp. , and Fusarium sp. Mucor sp. has non-septate hyphae, and round black spores. Trichoderma sp. has septate hyphae, greenish-white spores, and the conidia have the shape of globose to ellipsoidal Fusarium sp. has a mold with septate hyphae, yellowish-white colonies, and the conidia have the shape of obovoid. Bacteria, yeast, and mold present in the medium form a powerful symbiosis for produce metabolite.
The fragile triangle: Price stability, bank regulation and central bank reserves

We outline how bank regulation and central bank reserves affect bank money creation, and how an examination of this link can contribute to the understanding of inflation risks. We suggest that as the tightening of bank capital regulation has come to an end and as banks maintain large central bank reserves, inflation risks are generally higher than currently assumed. While paying interest on reserves may be the easy solution, renouncing such payments, slowly reversing asset purchases, and setting temporary reserve requirements would make central bank reserves scarce again — with better monitoring incentives for banks and stronger mitigation of inflation risks.
Using random forests to understand unrecognized progression to late-stage CKD, a case-control study

Abstract Background and objectives Patients with undiagnosed CKD are at increased risk of suboptimal dialysis initiation and therefore reduced access to home dialysis and transplantation as well as poor outcomes. Improved understanding of how patients remain undiagnosed is important to determine better intervention strategies. Design, setting, participants, and measurements A retrospective, matched, case-control analysis of 1,535,053 patients was performed to identify factors differentiating 4 patient types: unrecognized late-stage CKD, recognized late-stage CKD, early-stage CKD and a control group without CKD. The sample included patients with commercial insurance, Medicare Advantage, and Medicare fee-for service coverage. Patient demographics, comorbidities, health care utilization, and prescription use were analyzed using random forests to determine the most salient features discriminating the types. Models were built using all four types, as well as pairwise for each type versus the unrecognized late-stage type. Results Area under the curve for the three pairwise models (unrecognized late-stage vs recognized late-stage; unrecognized late-stage vs early-stage; unrecognized late-stage vs no CKD) were 82%, 68% and 82%. Conclusions The lower performance of the unrecognized late-stage vs early-stage model indicates a greater similarity of these two patient groups. The unrecognized late-stage CKD group is not simply avoiding or unable to get care in a manner distinguishable from the early-stage group. New outreach for CKD to undiagnosed or undetected, insured patients should look more closely at patient sets that are like diagnosed early-stage CKD patients.
Anxiety Status and Coping Strategies in Association with Sociodemographic Factors, Dietary and Lifestyle Habits in Greece

Aim: The aim of this study was to investigate the relationship between nutritional habits, lifestyle, anxiety, and coping strategies. Background: Anxiety is an underestimated and often undiagnosed subclinical disorder that burdens the general public of modern societies and increases illness suscentibility. Methods: The study group consisted of 693 individuals living in Peloponnese, Greece. A standardized questionnaire that consists of the dietary habits and lifestyle questionnaire, the trait Anxiety STAI-X-2 questionnaire and the brief-COPE questionnaire, was used. Principal components analysis identified the factors from the questionnaires, and stepwise multivariate regression analysis investigated their relationships. Results: Weekly consumption of fruits, tomatoes, salads and lettuce, together with Εmotional/Ιnstrumental support, Denial/Behavioural disengagement, substance use and self-blame, was the most important predictors of anxiety scores. Positive reframing/Humour and Acceptance/Planning are also associated with the Positive STAI factor and decreased anxiety scores. Conclusion: Healthy nutritional habits, comprised of consumption of salads and fruits, together with adaptive coping strategies, such as Positive reframing/Humour and Active problem solving, may provide the most profound improvement in the anxiety levels of a healthy population in Peloponnese, Greece.
IoT Based Patient Monitoring and Future Health Estimation System using NodeMCU

: The Internet is a revolution in information technology. Internet of Thing plays important role in health care application.In our system, body temperature sensor , heart rate monitor sensor, ECG sensor and pulse sensor are used for collecting the vital sign of a patient. The collected information is get transmitted via NodeMCU over the internet. For the transmission and reception of a data MQTT IoT protocol is used. As a result, the doctor can examine his patient from anywhere and anytime. The system allows doctors ,nurses as well as patient easily to use the computer for checking and to save in the database. The doctor or patient can see the analyzed data on IoT web page or in Android App (Blynk App).
Algebraic Geometry and Geometric Modeling 13w5062

We describe sets of minimal generators of the defin ing ideal of the Rees Algebra associated to the ideal of three bivariate homogeneous polynomials parametr izing a proper rational curve in projective plane, having a minimal syzygy of degree 2. 3.3 Multivariate Roots Gershon Elber (Technion) Multivariate (Geometric) Constraints Solving using Subdi vision based Solvers Abstract: We explore a subdivision based paradigm to solve a s t of (piecewise) rational constraints represented by (piecewise) rational multivariate spline functi ons. While the basic approach is robust, it can also be slow. We examine and survey several recently presented sche mes to alleviate these computational costs. The addition of inequality filters, single solution isolation t echniques, orthogonalization and domain reduction, and the minimization of the memory explosion that results fr om the exponential dependency on the number of variables will be discussed. With this machinery, we will demonstrate that this type of so lvers can be successfully used to solve a large variety of (geometric) problems above the (piecewise ) rationals domain. This set of problems includes point-curve and curve-curve bi-tangents, convex hulls and kernels of planar curves and 3-space surfaces, ray-traps (bouncing billiard balls) between planar curves , the 10th Apollonius problem (a circle tangent to given three circles) and its generalization, bounding circ les and spheres, bisectors and Voronoi regions, mold design, visibility and accessibility, sweeps and envelope s, and self-intersection computation and trimming in offset approximations of curves and surfaces. Minho Kim (University of Seoul) GPU Isosurface Raycasting of Volume Datasets Based On Box-S plines Abstract: I will discuss the techniques for GPU isosurface v olume raycasting on the BCC and FCC datasets reconstructed by box-splines. The six-direction cubic box -spline and the seven-direction quartic box-spline are used for reconstruction of FCC and BCC datasets, respect ively. For real-time isosurface raycasting, the evaluation procedures should be optimized for graphics ardware. Specifically, conditional branches and lookup tables, which are natural choices for implementa tion, need to be avoided. Other techniques for performance improvements and fast and accurate normal comp utation are also discussed. The results show both performance and quality improvements compared to prev ious compatible methods. Ileana Streinu (Smith College) Algebraic equations with real roots arising in Origami desi gn Abstract: In mid 1990’s, Robert Lang proposed a beautiful an d very original method for designing origami shapes with an underlying metric tree structure and impleme nted it in a freely available software called TreeMaker. Lang’s method takes as input the metric tree and a polygonal region (the “piece of paper”). It starts with a non-linear optimization phase which—when succ essful—produces a decomposition into special
A Comparative Study for Methodologies and Algorithms Used In Colon Cancer Diagnoses and Detection

Colon cancer is also referred to as colorectal cancer, a kind of cancer that starts with colon damage to the large intestine in the last section of the digestive tract. Elderly people typically suffer from colon cancer, but this may occur at any age.It normally starts as little, noncancerous (benign) mass of cells named polyps that structure within the colon. After a period of time these polyps can turn into advanced malignant tumors that attack the human body and some of these polyps can become colon cancers. So far, no concrete causes have been identified and the complete cancer treatment is very difficult to be detected by doctors in the medical field. Colon cancer often has no symptoms in early stage so detecting it at this stage is curable but colorectal cancer diagnosis in the final stages (stage IV), gives it the opportunity to spread to different pieces of the body, difficult to treat successfully, and the person's chances of survival are much lower. False diagnosis of colorectal cancer which mean wrong treatment for patients with long-term infections and they are suffering from colon cancer this causing the death for these patients. Also, the cancer treatment needs more time and a lot of money. This paper provides a comparative study for methodologies and algorithms used in colon cancer diagnoses and detection this can help for proposing a prediction for risk levels of colon cancer disease using CNN algorithm of the deep learning (Convolutional Neural Networks Algorithm).
The Relationship between Social Support and Depression in University students: The Meaning in Life as Mediation

This study aims to explore the relationship between social support, meaning in life and depression, and further explore the mediating role of meaning in life between social support and depression. This survey of 287 university students were investigated with the short version of Center for Epidemiologic Studies Depression Scale (CES-D-13), the Social Support Rating Scale (SSRS), and the Chinese Meaning in Life Questionnaire (MLQ). The results showed that: (1)The junior is the most serious period of depressive symptoms(11.33±7.019), and also an important turning point; (2) Non-only-child (10.23±6.713) were significantly higher than the only-child (9.02±6.230) about depressive symptoms (p=0.0368); (3)The depression were significantly negatively correlated with social support (r=-0.35, p<0.01) and the meaning in life(r=0.39, p<0.01), but there was a significant negative correlation between the meaning in life and social support (r=0.28, p<0.01); (4) The meaning in life plays a partial mediating role between social supports and depressive symptoms in university students. This research provides theoretical evidence for the prevention and intervention of university students’ depression from two aspects of social support and meaning in life.
Convolutional neural network-based face recognition using non-subsampled shearlet transform and histogram of local feature descriptors

<span lang="EN-US">Face recognition has been using in a variety of applications like preventing retail crime, unlocking phones, smart advertising, finding missing persons, and protecting law enforcement. However, the ability of face recognition techniques reduces substantially because of changes in pose, illumination, and expressions of the individual. In this paper, a novel face recognition approach based on a non-subsampled shearlet transform (NSST), histogram-based local feature descriptors, and a convolutional neural network (CNN) is proposed. Initially, the Viola-Jones algorithm is used for face detection and then the extracted face region is preprocessed by image resizing operation. Then, NSST decomposes the input image into a low and high-frequency component image. The local feature descriptors such as local phase quantization (LPQ), pyramid of histogram of oriented gradients (PHOG), and the proposed CNN are used for extracting features from the low-frequency component of the NSST decomposition. The extracted features are fused to generate the feature vector and classified using support vector machine (SVM). The efficiency of the suggested method is tested on face databases like Olivetti Research Laboratory (ORL), Yale, and </span><span lang="IN">J</span><span lang="EN-US">apanese female facial expression</span><span lang="IN">(</span><span lang="EN-US">JAFFE</span><span lang="IN">)</span><span lang="EN-US">. The experimental outcomes reveal that the suggested face recognition method outperforms some of the state-of-the-art recognition approaches. </span>
Rapid Detection of Hot-spot by Tensor Decomposition with Application to Weekly Gonorrhea Data

In many bio-surveillance and healthcare applications, data sources are measured from many spatial locations repeatedly over time, say, daily/weekly/monthly. In these applications, we are typically interested in detecting hot-spots, which are defined as some structured outliers that are sparse over the spatial domain but persistent over time. In this paper, we propose a tensor decomposition method to detect when and where the hot-spots occur. Our proposed methods represent the observed raw data as a three-dimensional tensor including a circular time dimension for daily/weekly/monthly patterns, and then decompose the tensor into three components: smooth global trend, local hot-spots, and residuals. A combination of LASSO and fused LASSO is used to estimate the model parameters, and a CUSUM procedure is applied to detect when and where the hot-spots might occur. The usefulness of our proposed methodology is validated through numerical simulation and a real-world dataset in the weekly number of gonorrhea cases from $2006$ to $2018$ for $50$ states in the United States.