Miracle-dz/UniFAR-FTUs huggingface dataset:
On Schubert varieties of complexity one

Let $B$ be a Borel subgroup of $\mathrm{GL}_n(\mathbb{C})$ and $\mathbb{T}$ a maximal torus contained in $B$. Then $\mathbb{T}$ acts on $\mathrm{GL}_{n}(\mathbb{C})/B$ and every Schubert variety is $\mathbb{T}$-invariant. We say that a Schubert variety is of complexity $k$ if a maximal $\mathbb{T}$-orbit in $X_w$ has codimension $k$. In this paper, we discuss topology, geometry, and combinatorics related to Schubert varieties of complexity one.
Composite Estimation for Quantile Regression Kink Models with Longitudinal Data

Kink model is developed to analyze the data where the regression function is twostage linear but intersects at an unknown threshold. In quantile regression with longitudinal data, previous work assumed that the unknown threshold parameters or kink points are heterogeneous across different quantiles. However, the location where kink effect happens tend to be the same across different quantiles, especially in a region of neighboring quantile levels. Ignoring such homogeneity information may lead to efficiency loss for estimation. In view of this, we propose a composite estimator for the common kink point by absorbing information from multiple quantiles. In addition, we also develop a sup-likelihood-ratio test to check the kink effect at a given quantile level. A test-inversion confidence interval for the common kink point is also developed based on the quantile rank score test. The simulation study shows that the proposed composite kink estimator is more competitive with the least square estimator and the single quantile estimator. We illustrate the practical value of this work through the analysis of a body mass index and blood pressure data set.
Effect of auxin type on growth in vitro of Kiwifruit axillary buds.

Kiwifruit Cultures were initiated from single-node explants on Murashige and Skoog medium containing 2 mg.l-1 BA. In shoot proliferation stage, 20 culture media containing MS supplemented with 3 concentrations of BA (0, 1, 2 mg.l-1 ) individually and along with IAA (0, 0.1, 0.2 mg.l-1 ) were compared. Maximum shoot proliferation from axillary buds was achived on media supplemented with 2 mg.l-1 BA alone. Formation of axillary shoots was influenced by concentration of cytokinin. In the rooting experiment, the shoots were cultured on MS medium containing BA (0, 1, 2, 3 mg.l-1) with various concentrations of IBA or IAA (0, 1, 2, 3 mg.l-1). Regenerated shoots produced prominent roots when transferred to MS medium supplemented with IBA (3.0 mg.l-1). The effect of IAA on rooting was not significant. Approximately 80% of the rooted microcuttings were established successfully as container-grown plants.
Large singular solutions for conformal $Q$-curvature equations on $\mathbb{S}^n$.

In this paper, we study the existence of positive functions $K \in C^1(\mathbb{S}^n)$ such that the conformal $Q$-curvature equation \begin{equation}\label{001} P_m (v) =K v^{\frac{n+2m}{n-2m}}~~~~~~ {on} ~ \mathbb{S}^n \{equation} has a singular positive solution $v$ whose singular set is a single point, where $m$ is an integer satisfying $1 \leq m 2m$, does not exist a singular positive solution whose singular set is a single point.
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching

Data Poisoning attacks involve an attacker modifying training data to maliciouslycontrol a model trained on this data. Previous poisoning attacks against deep neural networks have been limited in scope and success, working only in simplified settings or being prohibitively expensive for large datasets. In this work, we focus on a particularly malicious poisoning attack that is both "from scratch" and"clean label", meaning we analyze an attack that successfully works against new, randomly initialized models, and is nearly imperceptible to humans, all while perturbing only a small fraction of the training data. The central mechanism of this attack is matching the gradient direction of malicious examples. We analyze why this works, supplement with practical considerations. and show its threat to real-world practitioners, finding that it is the first poisoning method to cause targeted misclassification in modern deep networks trained from scratch on a full-sized, poisoned ImageNet dataset. Finally we demonstrate the limitations of existing defensive strategies against such an attack, concluding that data poisoning is a credible threat, even for large-scale deep learning systems.
A Comprehensive Analysis of Information Leakage in Deep Transfer Learning

Transfer learning is widely used for transferring knowledge from a source domain to the target domain where the labeled data is scarce. Recently, deep transfer learning has achieved remarkable progress in various applications. However, the source and target datasets usually belong to two different organizations in many real-world scenarios, potential privacy issues in deep transfer learning are posed. In this study, to thoroughly analyze the potential privacy leakage in deep transfer learning, we first divide previous methods into three categories. Based on that, we demonstrate specific threats that lead to unintentional privacy leakage in each category. Additionally, we also provide some solutions to prevent these threats. To the best of our knowledge, our study is the first to provide a thorough analysis of the information leakage issues in deep transfer learning methods and provide potential solutions to the issue. Extensive experiments on two public datasets and an industry dataset are conducted to show the privacy leakage under different deep transfer learning settings and defense solution effectiveness.
About Graph Degeneracy, Representation Learning and Scalability

Graphs or networks are a very convenient way to represent data with lots of interaction. Recently, Machine Learning on Graph data has gained a lot of traction. In particular, vertex classification and missing edge detection have very interesting applications, ranging from drug discovery to recommender systems. To achieve such tasks, tremendous work has been accomplished to learn embedding of nodes and edges into finite-dimension vector spaces. This task is called Graph Representation Learning. However, Graph Representation Learning techniques often display prohibitive time and memory complexities, preventing their use in real-time with business size graphs. In this paper, we address this issue by leveraging a degeneracy property of Graphs - the K-Core Decomposition. We present two techniques taking advantage of this decomposition to reduce the time and memory consumption of walk-based Graph Representation Learning algorithms. We evaluate the performances, expressed in terms of quality of embedding and computational resources, of the proposed techniques on several academic datasets. Our code is available at this https URL
A Review of Wind Speed and Wind Power Forecasting Techniques

Forecasting a particular variable can depend upon temporal or spatial scale. Temporal variations that indicate variations with time, reflect the stochasticity present in the variable. Spatial variation usually are dominant in climatology and meteorology. Temporal scale for a variable can be modeled in terms of time-series. A time series is a successively ordered sequence of numerical data points, and can be taken on any variable changing with time. Wind speed forecasting applications lie majorly in the area of electricity market clearing, economic load dispatch and scheduling, and sometimes to provide ancillary support. Thus, a proper classification based on the prediction horizon i.e. the duration of prediction becomes important for various transmission system operators.
Standing on the Shoulders of Giants: New Mass and Distance Estimates for Betelgeuse through Combined Evolutionary, Asteroseismic, and Hydrodynamic Simulations with MESA

We conduct a rigorous examination of the nearby red supergiant Betelgeuse by drawing on the synthesis of new observational data and three different modeling techniques. Our observational results include the release of new, processed photometric measurements collected with the space-based Solar Mass Ejection Imager instrument prior to Betelgeuse’s recent, unprecedented dimming event. We detect the first radial overtone in the photometric data and report a period of 185 ± 13.5 days. Our theoretical predictions include self-consistent results from multi-timescale evolutionary, oscillatory, and hydrodynamic simulations conducted with the Modules for Experiments in Stellar Astrophysics software suite. Significant outcomes of our modeling efforts include a precise prediction for the star’s radius: . In concert with additional constraints, this allows us to derive a new, independent distance estimate of pc and a parallax of mas, in good agreement with Hipparcos but less so with recent radio measurements. Seismic results from both perturbed hydrostatic and evolving hydrodynamic simulations constrain the period and driving mechanisms of Betelgeuse’s dominant periodicities in new ways. Our analyses converge to the conclusion that Betelgeuse’s ≈400 day period is the result of pulsation in the fundamental mode, driven by the κ-mechanism. Grid-based hydrodynamic modeling reveals that the behavior of the oscillating envelope is mass-dependent, and likewise suggests that the nonlinear pulsation excitation time could serve as a mass constraint. Our results place α Orionis definitively in the early core helium-burning phase of the red supergiant branch. We report a present-day mass of 16.5–19 M⊙—slightly lower than typical literature values.
Advance Warning Methodologies for COVID-19 Using Chest X-Ray Images

Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019. As a result, accurate and reliable advance warning system for the early diagnosis of COVID-19 has now become a priority. The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage. In this study, our first aim is to evaluate the ability of recent state-of-the-art Machine Learning techniques for the early detection of COVID-19 from chest X-ray images. Both compact classifiers and deep learning approaches are considered in this study. Furthermore, we propose a recent compact classifier, Convolutional Support Estimator Network (CSEN) approach for this purpose since it is well-suited for a scarce-data classification task. Finally, this study introduces a new benchmark dataset called Early-QaTa-COV19, which consists of 1065 early-stage COVID-19 pneumonia samples (very limited or no infection signs) labeled by the medical doctors and 12544 samples for control (normal) class. A detailed set of experiments shows that the CSEN achieves the top (over 97%) sensitivity with over 95.5% specificity. Moreover, DenseNet-121 network produces the leading performance among other deep networks with 95% sensitivity and 99.74% specificity.
Phylogenetic analysis of HA and NA genes and clinical characteristics of the co-occurring mutations in HA and NA of influenza A(H1N1)pdm09 viruses during 2015-2017 in Beijing, China

Background: Influenza A(H1N1)pdm09 viruses have undergone rapid evolution, and in recent years the complementary and antagonistic effects of HA and NA have gathered more attentions; however, the effects of co-occurring mutations in HA and NA on the patients’ clinical characteristics are still poorly understood. In this study, we analyzed molecular epidemiology and evolution of A(H1N1) pdm09, explored co-occurring mutations of HA and NA, and investigated effect of co-occurring mutations on patients’ clinical features.Methods: A(H1N1)pdm09 was confirmed by reverse transcription-polymerase chain reaction. HA and NA genes were sequenced and phylogenetically analyzed. Clinical characteristics of the co-occurring mutations were analyzed statistically.Results: By analyzing the HA and NA gene sequences of 33 A(H1N1)pdm09 viruses during the 2015-2017 influenza season, we found that all the viruses shared high similarities to each other and the HA genes of these viruses exclusively belonged to subclade 6B.1A. Several unreported substitutions in HA and NA proteins were observed, furthermore, co-occurring mutations of HA-V169T, A278S, E508G, D518E and NA-V67I were detected in 30.3% (10/33) A(H1N1)pdm09 virus strains when comparing with vaccine strains A/California/07/2009 and A/Michigan/45/2015 (H1N1). Sore throat was significantly associated with co-occurring mutations in HA and NA of A(H1N1)pdm09 (χ2, P < 0.05). Conclusions: Co-occurring mutations in HA and NA were detected in A(H1N1)pdm09 isolated during 2015-2017 in Beijing. Symptomatically, sore throat was associated with co-occurring mutations in HA and NA of A(H1N1)pdm09. Therefore, studying the effect and mechanism of co-occurring mutations in HA and NA on patients’ clinical features is of note needed.
Energy–Climate–Economy–Population Nexus: An Empirical Analysis in Kenya, Senegal, and Eswatini

Motivated by the Sustainable Development Goals (SDGs) and its impact by 2030, this study examines the relationship between energy consumption (SDG 7), climate (SDG 13), economic growth and population in Kenya, Senegal and Eswatini. We employ a Kernel Regularized Least Squares (KRLS) machine learning technique and econometric methods such as Dynamic Ordinary Least Squares (DOLS), Fully Modified Ordinary Least Squares (FMOLS) regression, the Mean-Group (MG) and Pooled Mean-Group (PMG) estimation models. The econometric techniques confirm the Environmental Kuznets Curve (EKC) hypothesis between income level and CO 2 emissions while the machine learning method confirms the scale effect hypothesis. We find that while CO 2 emissions, population and income level spur energy demand and utilization, economic development is driven by energy use and population dynamics. This demonstrates that income, population growth, energy and CO 2 emissions are inseparable, but require a collective participative decision in the achievement of the SDGs.
SHORT REVIEW: PROBING MECHANICAL PROPERTIES OF INDIVIDUAL MOLECULES WITH ATOMIC FORCE SPECTROSCOPY

ABTRACT In this short review I will first concisely describe the principles of single-molecule force spectroscopy (SMFS) for measuring the mechanical properties of individual polymeric molecules, as implemented on an Atomic Force Microscope (AFM) platform. Next, I will review a selected number of the most striking, in my opinion, discoveries and observations accumulated in this field of research that now spans over 25 years of dynamic growth. This selection will be limited to biomolecular systems such as DNA and polysaccharides (sugars) that for the last two decades were an important part of my own research. The mechanical properties of single protein molecules are described by the author or other researchers in numerous original or review papers that can be found in the world literature.
ON TH E STRONG CONVERGENCE OF MODIFIED ISHIKAWA ITERATES WITH ERRORS FOR ASYMPTOTICALLY NONEXPANSIVE MAPPINGS

Abst\^iact; Let $C$ be a nonempty bounded closed convex subset of a uniform ly convex Banach space and let $T:Carrow C$ be a completely continuous asymptotically nonexpansive mapping with sequence $\{k_{n}’\geq 1\}$ su ch that $\lim_{narrow\infty}k_{n}=1$ . We prove that modified Mann and modified Ishikawa iterative schemes with errors converge strongly to a fixed point of $T$ without assuming $\sum_{n=1}^{\infty}(k_{n}^{r}-1)$ $<\infty$ for some $r\geq 1$ .
Linking Ethical Leadership to Work Engagement: The Effects of Person–Group Fit and Person–Job Fit

The study of ethical leadership involving corporate leaders has gained importance in recent years. This research investigated the link between ethical leadership and performance using data from the medical center in Taiwan. Drawing on person–environment fit theory, we examined the mediating roles of person– group fit and person–job fit in the relationship between ethical leadership and work engagement. A convenience sampling was used to collect 447 valid questionnaires, and structural equation modeling was used for analysis and testing. Results revealed that ethical leadership is positively related to work engagement. Further, P–G fit and P–J fit mediate the relationship between ethical leadership and work engagement. Finally, implications for theory and practice are discussed. Key-Words: ethical leadership, person–group fit, person–job fit, work engagement Received: May 17, 2020. Revised: July 1, 2020. Accepted: July 8, 2020. Published: July 9, 2020.
Factors Influencing Behavioural Intention towards Adoption of Digital Banking Services in Malaysia

Digital banking will be a disruptive digital transformation to Malaysians as the country is widely regarded as cash-based society. It is unknown on the factors that could influence behavioural intention towards adoption of digital banking services among Malaysian. This research aims to investigate the relationship of core constructs such as trialability, compatibility, observability, perceived usefulness, and perceived ease of use with consumer? s intention to adopt digital banking services. This will be the first study in Malaysia to understand the public perception regarding digital banking service based on technology acceptance model (TAM) and innovation diffusion theory (IDT). The results obtained in this study demonstrated that perceived ease of use, compatibility and observability are important explanatory variables to influence the behavioural intention to adopt digital banking services. The study showed some practical implications that may be useful for policy makers, bankers and fintech players to design or participate in policy that can motivate adoption rate of digital banking services among Malaysian.
Biological Activity of Diterpenoids Isolated from Anatolian Lamiaceae Plants

In this study, antibacterial, antifungal, antimycobacterial, cytotoxic, antitumor, cardiovascular, antifeedant, insecticidal, antileishmanial and some other single activities of diterpenoids and norditerpenoids isolated from Turkish Lamiaceae plants, are reviewed. The diterpenoids were isolated from species of Salvia , Sideritis , and Ballota species growing in Anatolia. Fifty abietanes, ten kaurenes, seven pimaranes, six labdanes with their biological activities were reported. While twenty five diterpenoids showed antibacterial activity, eight of which showed activity against fungi. The most cytotoxic one was found to be taxodione (44) isolated from species of Salvia . Antifeedant, insecticidal and insect repellent activity of kaurenes, antimycobacterial activity and cardioactivity of abietanes and norabietanes together with labdanes were also reported.
Investment Cycles

Keynes viewed aggregate investment as an exogenous cause of business cycles driven by “animal spirits”. More recently, it has become common to model investment fluctuations as a rational response to fluctuating incentives, driven by exogenous aggregate technology changes. However, this approach raises a number of questions. Why treat investments in physical capital as endogenous, while treating those in intangible capital as exogenous? Relatedly, why would technology changes exhibit such strong co—movement across diverse sectors of the economy, and why are the short—run, empirical relationships between aggregate investment and standard measures of investment incentives so weak? We address these and other related issues by extending the existing literature on endogenous business cycles to incorporate physical capital. In doing so, we demonstrate the crucial role played by endogenous innovation and incomplete contracting, inherent to the process of creative destruction, in driving investment cycles.
An Overview of Antarctic Sea Ice in the CESM2:

12 We assess Antarctic sea ice climatology and variability in the CESM2, and compare it 13 to that in the CESM1 and (where appropriate) real-world observations. In the CESM2, 14 Antarctic sea ice is thinner and less extensive than in the CESM1, though sea ice area 15 (SIA) is still approximately 1 million km greater in the CESM2 than in observations. 16 Though there is less Antarctic sea ice in the CESM2, the annual cycle of ice growth and 17 melt is more vigorous in the CESM2 than in the CESM1. A new mushy-layer thermo18 dynamics formulation implemented in the latest version of CICE in the CESM2 partially 19 accounts for both greater frazil ice formation in coastal polyñyas and more snow-to-ice 20 conversion near the edge of the ice pack in the new model. Basal melt, coastal ice di21 vergence, and frazil growth are also more substantial in the CESM2 than the CESM1 22 due to a confluence of atmospheric and oceanic processes. In the CESM2, monthly SIA 23 is less variable, annual mean SIA is less persistent, and monthly SIA is less predictable 24 than in the CESM1. Upper ocean temperatures under the seasonal ice pack do confer 25 some interannual predictability to Antarctic September SIA, though the relationship is 26 weaker in the CESM2 than the CESM1. On the other hand, variability in upper ocean 27 temperatures equatorward of the ice edge appear to follow, rather than lead, SIA vari28 ability in both models, suggesting that the state of the open Southern Ocean may be a 29 poor predictor of ice evolution. 30 Plain Language Summary 31 Sea ice is a central part of the Antarctic climate system, and Earth system mod32 els are an indispensable tool for studying the climate of the Antarctic. Advances in mod33 elling are essential for understanding and projecting future changes in the region as the 34 globe warms. Here, we describe Antarctic sea ice climatology in the state-of-the-art Com35 munity Earth System Model, version 2 (CESM2). The CESM2 incorporates several mod36 elling advances which collectively improve representation of Antarctic climate compared 37 to previous model versions. Among these is a ’mushy layer’ treatment of sea ice, where 38 the ice is modelled as a mixture of solid ice and salty water. Modeling sea ice as a mushy 39 layer changes the way that Antarctic sea ice grows in the CESM2, in a manner more closely 40 resembling how Antarctic sea ice has been observed to grow in the real world. Antarc41 tic sea ice area in the CESM2 also more closely matches observed sea ice area, due pri42 marily to differences in atmospheric winds and ocean heating. In conjunction with ob43