Miracle-dz/UniFAR-FTUs huggingface dataset:
Morphological and biochemical diversity in Rosa species

The Rosa L. is one of the widely grown plants in the world and its flowers and fruits has been used in different parts of the world for centuries. Wild grown Rosa plants is one of the most important element of the natural landscape and used as rootstock for roses for centuries as well. In this study the main flower, shrub and fruit characteristics of a large number of Rosa canina L. and Rosa dumalis Bechst. genotypes naturally found in Ardahan province of Turkey has been studied. All plants found very health conditions and indicated their strong defense mechanism of the genera to harsh abiotic and biotic conditions. The majority of genotypes had attractive flowers. The genotypes exhibited fruit mass between 2.81 g to 4.60 g. SSC (soluble solid content), vitamin C, total phenolic, total flavonoid, total carotenoid, and total anthocyanin content of the genotypes ranged from 15.8–20.6%, 502–714 mg per 100 g FW (fresh weight), 405–507 mg gallic acid equivalent per 100 g FW, 1.02–2.00 mg per g FW, 8.40–13.30 mg per g FW and 4.35–6.98 mg cyanidin-3-glucoside equivalent per liter, respectively. Antioxidant activity was determined between 22.3–30.9 mg ascorbic acid equivalent per g fresh weight. Chlorogenic acid was the major phenolic acids in fruits of Rosa ecotypes. Our results indicated promising perspectives for usage of R. canina and R. dumalis fresh fruits studied with considerable levels of bioactive compounds.
Highly Sensitive and Stable Encapsulated Ionic Polymer–Metal Composite Sensor Under the Optimal Water Content

Sensitivity and stability are crucial for sensors in practical applications. For ionic polymer–metal composite (IPMC) sensors, these properties are closely related to inner water molecules, due to the unique composite structure. In this article, a technique for encapsulating IPMC sensors under the optimal water content is proposed for the first time. First, by means of gravimetric analysis, the amount of water removed from the saturated IPMC sensor by evaporation was measured to acquire the optimal water content. Second, IPMC sensors were encapsulated by polyolefin (POF) shrink film under the optimal water content (denoted as IPMC-OE). The results manifest that the sensing property obviously increased after quantitative removal of water molecules (i.e., the ratios of water lost range from 1.8 to 3.6). The sensitivity of IPMC-OE is 4–6 times higher than that of the naked IPMC sensor not encapsulated by a polymer film (denoted as IPMC-N). Besides, the stability of IPMC-OE has also been evidently enhanced. The proposed method is a simple, low-cost, and facile strategy. The IPMC-OE sensors maintain high signal strength and stable output signal after up to 19000 deformation cycles. Moreover, apart from underwater detection of biomimetic robotic fish and UUV, the monitoring of wrist pulse, breathing, and finger bending has also been accomplished in the air by IPMC-OE sensors. It is also proved that sensing in media ranging from water to air and a physical quantity ranging from displacement to water flow velocity can be realized. The article guides the performance optimization and practical application of IPMC sensors.
RECONNECTION OUTFLOWS AND CURRENT SHEET OBSERVED WITH HINODE/XRT IN THE 2008 APRIL 9 “CARTWHEEL CME” FLARE

Supra-arcade downflows (SADs) have been observed with Yohkoh/SXT (soft X-rays (SXR)), TRACE (extreme ultraviolet (EUV)), SOHO/LASCO (white light), SOHO/SUMER (EUV spectra), and Hinode/XRT (SXR). Characteristics such as low emissivity and trajectories, which slow as they reach the top of the arcade, are consistent with post-reconnection magnetic flux tubes retracting from a reconnection site high in the corona until they reach a lower-energy magnetic configuration. Viewed from a perpendicular angle, SADs should appear as shrinking loops rather than downflowing voids. We present X-ray Telescope (XRT) observations of supra-arcade downflowing loops (SADLs) following a coronal mass ejection (CME) on 2008 April 9 and show that their speeds and decelerations are consistent with those determined for SADs. We also present evidence for a possible current sheet observed during this flare that extends between the flare arcade and the CME. Additionally, we show a correlation between reconnection outflows observed with XRT and outgoing flows observed with LASCO.
Semi-Overcomplete Convolutional Auto-Encoder Embedding as Shape Priors for Deep Vessel Segmentation

The extraction of blood vessels has recently experienced a widespread interest in medical image analysis. Automatic vessel segmentation is highly desirable to guide clinicians in computer-assisted diagnosis, therapy or surgical planning. Despite a good ability to extract large anatomical structures, the capacity of U-Net inspired architectures to automatically delineate vascular systems remains a major issue, especially given the scarcity of existing datasets. In this paper, we present a novel approach that integrates into deep segmentation shape priors from a Semi-Overcomplete Convolutional Auto-Encoder (S-OCAE) embedding. Compared to standard Convolutional Auto-Encoders (CAE), it exploits an over-complete branch that projects data onto higher dimensions to better characterize tiny structures. Experiments on retinal and liver vessel extraction, respectively performed on publicly-available DRIVE and 3D-IRCADb datasets, highlight the effectiveness of our method compared to U-Net trained without and with shape priors from a traditional CAE.
Weak approximation of second-order BSDEs.

We study the weak approximation of the second-order backward SDEs (2BSDEs), when the continuous driving martingales are approximated by discrete time martingales. We establish a convergence result for a class of 2BSDEs, using both robustness properties of BSDEs, as proved in Briand, Delyon and Memin [Stochastic Process. Appl. 97 (2002) 229–253], and tightness of solutions to discrete time BSDEs. In particular, when the approximating martingales are given by some particular controlled Markov chains, we obtain several concrete numerical schemes for 2BSDEs, which we illustrate on specific examples.
Categorical Quantum Mechanics II: Classical-Quantum Interaction

This is the second part of a three-part overview, in which we derive the category-theoretic backbone of quantum theory from a process ontology, treating quantum theory as a theory of systems, processes and their interactions. In this part we focus on classical-quantum interaction. Classical and quantum systems are treated as distinct types, of which the respective behavioural properties are specified in terms of processes and their compositions. In particular, classicality is witnessed by 'spiders' which fuse together whenever they connect. We define mixedness and show that pure processes are extremal in the space of all processes, and we define entanglement and show that quantum theory indeed exhibits entanglement. We discuss the classification of tripartite qubit entanglement and show that both the GHZ-state and the W-state come from spider-like families of processes, which differ only in how they behave when they are connected by two or more wires. We define measurements and provide fully-comprehensive descriptions of several quantum protocols involving classical data flow. Finally, we give a notion of 'genuine quantumness', from which special processes called 'phase spiders' arise, and get a first glimpse of quantum non-locality.
Optimization of Tumor Targeting Gold Nanoparticles for Glioblastoma Applications

Glioblastoma brain tumors represent an aggressive form of gliomas that is hallmarked by being extremely invasive and aggressive due to intra and inter-tumoral heterogeneity. This complex tumor microenvironment makes even the newer advancements in glioblastoma treatment less effective long term. In developing newer treatment technologies against glioblastoma, one should tailor the treatment to the tumor microenvironment, thus allowing for a more robust and sustained anti-glioblastoma effect. Here, we present a novel gold nanoparticle therapy explicitly designed for bioactivity against glioblastoma representing U87MG cell lines. We employ standard conjugation techniques to create oligonucleotide-coated gold nanoparticles exhibiting strong anti-glioblastoma behavior and optimize their design to maximize bioactivity against glioblastoma. Resulting nanotherapies are therapy specific and show upwards of 75% inhibition in metabolic and proliferative activity with stark effects on cellular morphology. Ultimately, these gold nanotherapies are a good base for designing more multi-targeted approaches to fighting against glioblastoma.
The $\lambda$-invariant measures of subcritical Bienaymé–Galton–Watson processes

A $\lambda$-invariant measure of a sub-Markov chain is a left eigenvector of its transition matrix of eigenvalue $\lambda$. In this article, we give an explicit integral representation of the $\lambda$-invariant measures of subcritical Bienaym\'e--Galton--Watson processes killed upon extinction, i.e.\ upon hitting the origin. In particular, this characterizes all quasi-stationary distributions of these processes. Our formula extends the Kesten--Spitzer formula for the (1-)invariant measures of such a process and can be interpreted as the identification of its minimal $\lambda$-Martin entrance boundary for all $\lambda$. In the particular case of quasi-stationary distributions, we also present an equivalent characterization in terms of semi-stable subordinators. Unlike Kesten and Spitzer's arguments, our proofs are elementary and do not rely on Martin boundary theory.
Analytic subordination theory of operator-valued free additive convolution and the solution of a general random matrix problem

We develop an analytic theory of operator-valued additive free convolution in terms of subordination functions. In contrast to earlier investigations our functions are not just given by power series expansions, but are defined as Frechet analytic functions in all of the operator upper half plane. Furthermore, we do not have to assume that our state is tracial. Combining this new analytic theory of operator-valued free convolution with Anderson's selfadjoint version of the linearization trick we are able to provide a solution to the following general random matrix problem: How can we calculate the asymptotic eigenvalue distribution of a polynomial evaluated in independent random matrices with known asymptotic eigenvalue distributions?
Errata: "Competing contact processes on homogeneous networks with tunable clusterization"

We investigate two homogeneous networks: the Watts–Strogatz network with mean degree ⟨k⟩ = 4 and the Erdos–Renyi network with ⟨k⟩ = 10. In both kinds of networks, the clustering coefficient C is a tunable control parameter. The network is an area of two competing contact processes, where nodes can be in two states, S or D. A node S becomes D with probability 1 if at least two its mutually linked neighbors are D. A node D becomes S with a given probability p if at least one of its neighbors is S. The competition between the processes is described by a phase diagram, where the critical probability pc depends on the clustering coefficient C. For p > pc the rate of state S increases in time, seemingly to dominate in the whole system. Below pc, the majority of nodes is in the D-state. The numerical results indicate that for the Watts–Strogatz network the D-process is activated at the finite value of the clustering coefficient C, close to 0.3. On the contrary, for the Erdos–Renyi network the transition is observed at the whole investigated range of C.
Polygenic Risk Score and Risk Factors for Preeclampsia and Gestational Hypertension

Preeclampsia and gestational hypertensive disorders (GHD) are common complications of pregnancy that adversely affect maternal and offspring health, often with long-term consequences. High BMI, advanced age, and pre-existing conditions are known risk factors for GHD. Yet, assessing a woman’s risk of GHD based on only these characteristics needs to be reevaluated in order to identify at-risk women, facilitate early diagnosis, and implement lifestyle recommendations. This study demonstrates that a risk score developed with machine learning from the case-control genetics dataset can be used as an early screening test for GHD. We further confirm BMI as a risk factor for GHD and investigate a relationship between GHD and genetically constructed anthropometric measures and biomarkers. Our results show that polygenic risk score can be used as an early screening tool that, together with other known risk factors and medical history, would assist in identifying women at higher risk of GHD before its onset to enable stratification of patients into low-risk and high-risk groups for monitoring and preventative programs to mitigate the risks.
The trace formula and the existence of PEL type Abelian varieties modulo p

We show, using the trace formula, that any Newton stratum of a Shimura variety of PEL-type of types (A) and (C) is non-empty at the primes of good reduction. Furthermore we prove conditionally the non-emptiness for Shimura data associated to odd Spin groups. Our results are conditional on Rapoport-Langlands conjecture and Arthur's conjectures on the discrete spectrum. Both these results have been announced by Arthur and Kisin in significant cases.
Orientation-to-alignment conversion and spin squeezing

The relationship between orientation-to-alignment conversion (a form of atomic polarization evolution induced by an electric field) and the phenomenon of spin squeezing is demonstrated. A "stretched" state of an atom or molecule with maximum angular-momentum projection along the quantization axis possesses orientation and is a quantum-mechanical minimum-uncertainty state, where the product of the equal uncertainties of the angular-momentum projections on two orthogonal directions transverse to the quantization axis is the minimum allowed by the uncertainty relation. Application of an electric field for a short time induces orientation-to-alignment conversion and produces a spin-squeezed state, in which the quantum state essentially remains a minimum-uncertainty state, but the uncertainties of the angular-momentum projections on the orthogonal directions are unequal. This property can be visualized using the angular-momentum probability surfaces, where the radius of the surface is given by the probability of measuring the maximum angular-momentum projection in that direction. Brief remarks are also given concerning collective-spin squeezing and quantum nondemolition measurements.
BOO–1137—AN EXTREMELY METAL-POOR STAR IN THE ULTRA-FAINT DWARF SPHEROIDAL GALAXY BOÖTES I

We present high-resolution (R ∼ 40,000), high-signal-to-noise ratio (20–90) spectra of an extremely metal-poor giant star Boo−1137 in the “ultra-faint” dwarf spheroidal galaxy (dSph) Boötes I, absolute magnitude MV ∼ −6.3. We derive an iron abundance of [Fe/H] = –3.7, making this the most metal-poor star as yet identified in an ultra-faint dSph. Our derived effective temperature and gravity are consistent with its identification as a red giant in Boötes I. Abundances for a further 15 elements have also been determined. Comparison of the relative abundances, [X/Fe], with those of the extremely metal-poor red giants of the Galactic halo shows that Boo−1137 is “normal” with respect to C and N, the odd-Z elements Na and Al, the iron-peak elements, and the neutron-capture elements Sr and Ba, in comparison with the bulk of the Milky Way halo population having [Fe/H] ≲−3.0. The α-elements Mg, Si, Ca, and Ti are all higher by Δ[X/Fe] ∼ 0.2 than the average halo values. Monte Carlo analysis indicates that Δ[α/Fe] values this large are expected with a probability ∼0.02. The elemental abundance pattern in Boo–1137 suggests inhomogeneous chemical evolution, consistent with the wide internal spread in iron abundances we previously reported. The similarity of most of the Boo−1137 relative abundances with respect to halo values, and the fact that the α-elements are all offset by a similar small amount from the halo averages, points to the same underlying galaxy-scale stellar initial mass function, but that Boo−1137 likely originated in a star-forming region where the abundances reflect either poor mixing of supernova (SN) ejecta, or poor sampling of the SN progenitor mass range, or both.
Reliability and Validity of the Korean Matrix Sentence-in-Noise Recognition Test for Older Listeners with Normal Hearing and with Hearing Impairment

Purpose: The aim of this study was to examine test-retest reliability and validity of the adaptive Korean matrix sentence-in-noise test for elderly group with normal hearing (ENH) and elderly group with hearing impairment (EHI). Methods: Twenty ENH listeners and 20 EHI listeners based on the World Health Organization (WHO)-proposed hearing-impairment grade participated. For the test-retest reliability of the Korean matrix sentence-in-noise test, each subject was assessed twice in a 2-week interval using three types of noise: speech-shaped noise, International Collegium of Rehabilitative Audiology noise, and International Speech Test Signal. The test-retest agreement was judged by the intraclass correlation coefficient (ICC) and Bland-Altman plots. To differentiate between real change and random measurement error, the standard error of measurement (SEM) and the minimal detectable change (MDC) were also obtained as another reliability index. For the concurrent validity, the results of the Matrix test were compared to the results of the Korean-version Hearing In Noise Test (K-HINT). Results: The test-retest reliability was good to excellent for ENH and EHI groups (ICC, 0.84~0.98). Both groups showed stable reliability, but the ENH group showed smaller SEM and MDC values than the EHI group. No systematic errors were found from the Bland-Altman plots. The correlation between the results of the Matrix test and the K-HINT test was high, revealing good concurrent validity.Conclusion: The Korean matrix sentence-in-noise test appeared to be valid and reliable for the elderly. The Korean matrix sentence-in-noise test can be utilized for clinical measurement of the speech-in-noise ability of elderly individuals.
An analytical model of surface mass densities of cold dark matter haloes - with an application to MACHO microlensing optical depths

The cold dark matter (CDM) scenario generically predicts the existence of triaxial dark matter haloes which contain notable amounts of substructure. However, analytical halo models with smooth, spherically symmetric density profiles are routine ly adopted in the modelling of light propagation effects through such objects. In this paper, we address the biases introduced by this procedure by comparing the surface mass densities of actual N-body haloes against the widely used analytical model suggested by Navarro, Frenk and White (1996) (NFW). We conduct our analysis in the redshift range of 0.0 - 1.5. In cluster sized haloes, we find that triaxiality can cause sc atter in the surface mass density of the haloes up to �+ = +60% and � = 70%, where the 1-� limits are relative to the analytical NFW model given value. Subhaloes can increase this scatter to �+ = +70% and � = 80%. In galaxy sized haloes, the triaxial scatter can be as high a s �+ = +80% and � = 70%, and with subhaloes the values can change to �+ = +40% and � = 80%. We present an analytical model for the surface mass density scatter as a function of distance to the halo centre, halo redshift and halo mass. The analytical description enables one to investigate the reliability of results obtained with sim plified halo models. Additionally, it provides the means to add simulated surface density scatter to analytical density profiles. As an example, we discuss the impact of our results on the calculation of microlensing optical depths for MACHOs in CDM haloes.
Bilinear Forms with Kloosterman and Gauss Sums

We obtain several estimates for bilinear form with Kloosterman sums. Such results can be interpreted as a measure of cancellations amongst with parameters from short intervals. In particular, for certain ranges of parameters we improve some recent results of Blomer, Fouvry, Kowalski, Michel and Mili\'cevi\'c and also of Fouvry, Kowalski and Michel. In particular, we improve the bound on the error term in the asymptotic formula for mixed moments of $L$-series associated with Hecke eigenforms.
Money Laundering Detection using Machine Learning and Deep Learning

—In recent years, money laundering activities have shown rapid progress and have indeed become the main concern for governments and financial institutions all over the world. As per recent statistics, $800 billion to $2 trillion is the estimated value of money laundered annually, in which $5 billion of the total is obtained from cryptocurrency money laundering. As per the financial action task force (FATF), the criminals may trade illegally obtained fiat money for the cryptocurrency. Accordingly, detecting and preventing illegal transactions becomes a serious threat to governments and it has been indeed challenging. To combat money laundering, especially in cryptocurrency, effective techniques for detecting suspicious transactions must be developed since the current preventive efforts are outdated. In fact, deep learning and machine learning techniques may provide novel methods to detect suspect currency movements. This study investigates the applicability of deep learning and machine learning techniques for anti-money laundering in cryptocurrency. The techniques employed in this study are Deep Neural Network (DNN), random forest (RF), K-Nearest Neighbors(KNN), and Naive Bayes (NB) with the bitcoin elliptic dataset. It was observed that the DNN and random forest classifier have achieved the highest accuracy rate with promising findings in decreasing the false positives as compared to the other classifiers. In particular, the random forest classifier outperforms DNN and achieves an F1-score of 0.99%.
Clustering Wind Turbines for SCADA Data-Based Fault Detection

Condition monitoring systems are commonly employed for incipient fault detection of wind turbines (WTs) to reduce downtime and increase availability. Data from supervisory control and data acquisition (SCADA) systems offer a potential monitoring solution. Multi-turbine approaches, which merge variables recorded from different WTs on the same wind farm, have been developed to improve fault detection performance by reducing the influence of variations in environmental conditions. However, in complex terrain, environmental conditions vary among WTs. Moreover, manual control (e.g., maintenance and curtailment) can also raise false alarms in some WTs. Here, the false alarm characteristics of a wind farm in complex terrain are investigated. A clustering-based multi-turbine fault detection approach is proposed, consisting of three steps: WT clustering, single-turbine modeling, and fault indicator calculation. First, k-medoids clustering with dependent multivariate dynamic time warping is applied for WT clustering. Then, an autoregressive neural network is used to construct a single-turbine model. Finally, residuals between median values of the model output of all WTs in the same cluster and the target WT are used to calculate the anomaly level. Evaluation results for real large-scale SCADA data confirm that the proposed approach raises fewer false alarms without degrading detection performance.