Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimising grapevine summer stress responses and hormonal balance by applying kaolin in two Portuguese Demarcated Regions

In Mediterranean-like climate areas, field-grown grapevines are typically exposed to severe environmental conditions during the summer season, which can negatively impact the sustainability of viticulture. Despite the short-term mitigation strategies available nowadays to cope with climate change, little is known regarding their effectiveness in different demarcated winegrowing regions with differing climate features. Hence, we applied a kaolin suspension (5 %) to Touriga-Franca (TF) and Touriga-Nacional (TN) grapevine varieties located in two Portuguese demarcated regions (Alentejo and Douro) with different mesoclimates to study its effect on the physiological performance, hormonal balance and ABA-related grapevine leaf gene expression during the 2017 and 2018 growing seasons. Data show that 2017 was warmer than 2018 due to the occurrence of two heatwaves in both locations, highlighting the protective effect of kaolin application under severe environmental conditions. In the first study year, at midday, kaolin enhanced water use efficiency (23 % in Douro and 13 % in Alentejo), carbon assimilation rates (PN; 72 % in Douro and 25 % in Alentejo), and the soluble sugar content of grapevine leaves, while decreasing the accumulation of plant growth regulators (ABA, IAA, and SA) during the ripening stage. The results show an up-regulation of ABA biosynthesis-related genes (VvNCED) in TF treated vines from the Douro vineyard mainly in 2017, suggesting an increased stress response under severe summer conditions. Additionally, kaolin triggered the expression of ABA-responsive genes (VvHVA22a and VvSnRK2.6) mainly in TF, indicating different varietal responses to kaolin application under fluctuating periods of summer stress.
Pneumothorax Following COVID-19: Based on Autopsy Findings

Background More and more studies showed pneumothorax is a complication of the 2019 novel coronavirus disease (COVID-19). But no autopsy findings of pneumothorax in COVID-19 decedent were reported, and direct relations between pneumothorax and lung pathology in these decedents were not discussed so far.Methods A 62-year-old man with COVID-19 presenting with persistent hypoxemia and suddenly dead, who was treated by mechanical ventilation in the intensive care unit (ICU) for 5 days. A systemic autopsy examination of COVID-19 decedent, including histopathology study, was conducted and the medical record, chest computerized tomography (CT) image were reviewed by forensic pathologists and clinicians. Results Severe pneumothorax, diffuse alveolar damage and airway obstruction were observed. Pneumothorax should be one of the causes of death.Conclusion Pneumothorax, due to SARS-CoV-2 infection, is a fatal complication of COVID-19. Regular examination of chest CT or X-ray and airway management are important to clinical treatment.
Study on very-high-cycle fatigue property of 2A60 aluminum alloy

During the rotation, aero-engine impeller blades are subjected to the alternating load of high frequency, low stress and small strain, so it is effortless for the impeller blades to enter the stage of very-high-cycle fatigue (VHCF). The research on the VHCF performance of 2A60 aluminum alloy is of great engineering significance to verify the reliability of the impeller in service. In this paper, the VHCF performance of 2A60 aluminum alloy is studied by fatigue life curve and fracture morphology. The results show that the fatigue properties of 2A60 aluminum alloy under different cooling conditions are different, and the VHCF properties under ethanol cooling conditions are better than those under water cooling conditions. However, under either water-cooled or ethanol-cooled conditions, the fatigue fracture mode of 2A60 aluminum alloy is single, showing a surface crack initiation mode, and the S-N curve shows a continuous downward trend without the intermediate platform. The VHCF crack originated from the stress concentration on the sample surface and the cleavage fracture occurred in the grains near the crack source in the early stage of crack initiation. The VHCF process of the sample is also the relaxation process of its residual stress.
Fragmenting complex network based on non-backtracking matrix

Identifying the influential nodes to fragment a network is of significant importance in hindering the spread of epidemics and maximizing the influence of advertisements. Here, we address the problem by minimizing the largest eigenvalue of the non-backtracking matrices of networks, based on which a novel method is proposed to identify the optimal influential nodes. Interestingly, the proposed method could find some particular nodes to fragment complex networks. Experiments in real networks demonstrate the effectiveness of the proposed method.
Biological aspects of the two-spotted spider mite on strawberry plants under silicon application

ABSTRACT Silicon is an inducer of plant resistance to arthropod pests, being a promising strategy for integrated management. The aim of this study was to evaluate the effect of silicon on biological, reproductive and population aspects of parental and F1 generations of the two-spotted spider mite on strawberry plants. Potassium silicate, nanosilica and water were applied to the plants. Two-spotted spider mite females were confined to strawberry leaf disks for oviposition and, after hatching, larvae were observed until the emergence of adults. Once adults had been obtained, couples were formed in order to evaluate pre-oviposition, oviposition, longevity and fertility, with an estimated net reproduction rate, intrinsic rate of increase, finite rate of increase and the necessary time for doubling the generation. Silicon prolonged the duration of some immature stages of the mites in parental and F1 generations, did not affect the duration of the whole biological cycle, though. The periods of pre-oviposition, oviposition and longevity of the parental generation and the longevity and oviposition of F1 generation of the two-spotted spider mite were negatively affected by potassium silicate and nanosilica. The population parameters of the parental generation of the mites indicated that nanosilica is able to lead to a long-run decrease of this pest population.
An Empirical Study on the factors affecting the Student’s Willingness to Use of Mobile Social E-commerce Based on UTAUT 2

With the rapid development of e-commerce, mobile social e-commerce has been accepted by consumers. College students are the main components of netizens. This study integrates the variables of perceived trust and perceived risk based on the UTAUT2 model, and constructs an influencing factors research model on student users’ willingness to use mobile social e-commerce. Moreover, an empirical analysis is conducted aiming at the sample data obtained by users in colleges and universities. SPSS24.0 is used to accomplish the analysis of the descriptive statistical, AMOS24.0 is used to conduct the structural equation model verification. In the study, it is found that the willingness for college student to use mobile social e-commerce is positively affected by perceived trust and negative influence by perceived risk. The hedonic motive affects college student’s willingness to use mobile social e-commerce through influencing the perceived trust and the performance expectation.
Characteristics of Formation and Decay of Interfacial Charge near the Interface between PE and EVA

現 在 、 さ ま ざ まな 研 究 機 関 でパ ル ス静 電 応 力(Pulsed Electroacoustic;PEA)法1)や レーザ 誘 起 圧 力 パ ルス(LaserInduced Pressure Pulse; LIPP)法2)な どの 非 破 壊 的 な測 定 法 を用 い て 、 絶 縁 体 内 の 電荷 分 布 の 測 定 が 盛 ん に行 わ れ て い る 。 しか し、LDPEやXLPEの よ う な単 体 の 試 料 につ い ての 研 究 が 多 く、 異 種 の 絶縁 材 料 を張 り合 わせ た試 料 につ い て の 報告 は あ ま り多 くは ない 。 3). 4)一方 、実 際 の電 力 ケ ーブ ル には 、 異 種 絶 縁 材 料 間や ケ ー ブ ル の接 続 部 分 な ど多 数 の 界 面(絶 縁 体-電 極 、 絶 縁 体-絶 縁 体)が 存 在 し、 そ の界 面 に蓄 積 す る界 面 電 荷 に よる影 響 が 指摘 され て い る 。 したが って 、 異 種 絶縁 材 料 間の 界 面 付 近 に お け る 界 面 電荷 の挙 動 を解 明す る こ とは 、 新 規 ケ ー ブ ル 等 の 設 計 ・開 発 に とっ て 大 変 有 意 義 で あ る と 考 え られ る 。 そ こ で 、 我 々 はLIPP法 を用 い て 、電 力 ケ ー ブ ル用 絶 縁 材 料 と して 用 い られ て い る、 低 密 度 ポ リエ チ レ ン(LDPE)と エ チ レ ン酢 酸 ビ ニ ル 共 重 合 体(EVA)の ラ ミ ネー トシ ー ト に 直 流 高 電 圧 を 印加 した と き、LDPEとEVAの 異 種 絶縁 材 料 間 の 界 面 の み に 着 目 して 、 そ の 付 近 に 蓄積 す る 界面 電荷 の 蓄 積 ・減 衰 の 過 程 に つい て研 究 して きた5),6),7),8),9)。 本論 文 で は、 直 流 高 電 圧 印加 後 、 短 絡 した 後 の 界 面 電 荷 の 減 衰 過 程 に つ い て述 べ る。
Association of Urinary Sodium Excretion and Left Ventricular Hypertrophy in Patients With Type 2 Diabetes: A Cross-Section Study

Background: It has been well documented that left ventricular hypertrophy (LVH) is highly associated with incidence of cardiovascular disease (CVD). Evidence indicated that high sodium intake has been observed related with LVH in general population. However, information is not available regarding the association between urinary sodium excretion and LVH in patients with type 2 diabetes (T2DM). This study aimed to explore the association between urinary sodium excretion and LVH in patients with T2DM.Methods: A total of 1356 patients with T2DM were recruited. Urinary sodium was measured from 24-hour urine samples of inpatients and morning fasting urine samples of outpatients. LVH was assessed by echocardiography. The associations between urinary sodium excretion and the risk factors for cardiovascular events, LVH and left ventricular mass index (LVMI) were examined using linear regression analysis, logistic regression and restricted cubic splines.Results: Urinary sodium excretion levels was positively associated with cardiometabolic risk factors, including systolic blood pressure (P<0.001), body mass index (P<0.001), waist circumference (P<0.001) and LVMI (P<0.001). In multivariable logistic regression analyses, increased urinary sodium excretion were significantly associated with increased risks of LVH [OR (95% CI), 1.47 (1.02-2.10); P=0.037] and CVD [OR (95% CI), 2.08 (1.20-3.61); P=0.009], after adjusted for demographics, lifestyle risk factors and cardiovascular risk factors. Multivariable-adjusted restricted cubic spline analyses of the association between urinary sodium excretion and LVMI showed a significant association (P=0.002) and provided no evidence of a nonlinear association (P=0.135). Conclusions: This study indicated that high urinary sodium excretion was independently associated with increased risk of LVH and CVD in patients with T2DM, suggesting that control of sodium intake is valuable in the prevention of diabetic cardiovascular complications.
Chinese Implicit Sentiment Analysis based on Hybrid Neural Networks

Implicit sentiment analysis is an important part of sentiment computing, especially sentiment analysis based on deep learning has become a research hotspot in recent years. In this paper, convolutional neural networks are used to extract text features, combined with long-term and short-term memory network (LSTM) structure to extract context information, and add attention mechanism to the network to construct a new hybrid neural network model to achieve implicit emotion for text analysis. The hybrid neural network model extracts more meaningful sentence semantic and structural hidden features from the word-level and sentence-level hierarchies, respectively, and pays attention to the features with a large emotional contribution rate through the attention mechanism. The classification accuracy rate of the model on the public implicit sentiment dataset has reached 77%. The research of implicit sentiment analysis can improve the effect of text sentiment analysis more comprehensively, and further promote the application of text sentiment analysis in the fields of knowledge embedding, text representation learning, user modeling and natural language.
2-D Study of Near-field Generated by Surface Plasmon Resonance of Short Axis of Gold Nano-rods on SiO2 and CeO2/SiO2 Substrates for Optimisation of Water Splitting Reaction

Distinguishing near-field intensity distribution and plasmon resonance peak wavelength of surface plasmon resonance of gold nano-rods (AuNRs) can provide information for the optimisation of localised surface plasmon and gap plasmon resonances of gold nano-rods. This work, shows the influence of how refractive indexes of the surrounding medium and adjacent surface, as well as, AuNRs pair inter-particle gap contributes to the generation of surface plasmon resonances. The simulation model presented consists of AuNRs pair situated on silicon dioxide (SiO2) and cerium dioxide/silicon dioxide (CeO2/SiO2) substrates with air and water as the surrounding media. The results show high near-field intensities at AuNRs/CeO2 interfaces with CeO2 refractive index (n = 2.38), and the near-field intensities contributed by the gap resonance is minimal between the AuNRs of inter-particle gap of 10 nm, however, the near-field intensities become significant near to the AuNRs/CeO2 interfaces because of reduced near-field interference. The simulation set-up provides the conditions for water splitting in thermochemical redox reaction of CeO2/CeO2-x resulting in the production of hydrogen. AuNRs pair with inter-particle gap of 5 nm situated on 10 nm thick CeO2 of CeO2/SiO2 substrate shows the most favorable conditions for water splitting.
Byzantine-Robust and Privacy-Preserving Framework for FedML

Federated learning has emerged as a popular paradigm for collaboratively training a model from data distributed among a set of clients. This learning setting presents, among others, two unique challenges: how to protect privacy of the clients' data during training, and how to ensure integrity of the trained model. We propose a two-pronged solution that aims to address both challenges under a single framework. First, we propose to create secure enclaves using a trusted execution environment (TEE) within the server. Each client can then encrypt their gradients and send them to verifiable enclaves. The gradients are decrypted within the enclave without the fear of privacy breaches. However, robustness check computations in a TEE are computationally prohibitive. Hence, in the second step, we perform a novel gradient encoding that enables TEEs to encode the gradients and then offloading Byzantine check computations to accelerators such as GPUs. Our proposed approach provides theoretical bounds on information leakage and offers a significant speed-up over the baseline in empirical evaluation.
(ASNA) An Attention-based Siamese-Difference Neural Network with Surrogate Ranking Loss function for Perceptual Image Quality Assessment

Recently, deep convolutional neural networks (DCNN) that leverage the adversarial training framework for image restoration and enhancement have significantly improved the processed images’ sharpness. Surprisingly, although these DCNNs produced crispier images than other methods visually, they may get a lower quality score when popular measures are employed for evaluating them. Therefore it is necessary to develop a quantitative metric to reflect their performances, which is well-aligned with the perceived quality of an image. Famous quantitative metrics such as Peak signal-to-noise ratio (PSNR), The structural similarity index measure (SSIM), and Perceptual Index (PI) are not well-correlated with the mean opinion score (MOS) for an image, especially for the neural networks trained with adversarial loss functions. This paper has proposed a convolutional neural network using an extension architecture of the traditional Siamese network so-called Siamese-Difference neural network. We have equipped this architecture with the spatial and channel-wise attention mechanism to increase our method’s performance. Finally, we employed an auxiliary loss function to train our model. The suggested additional cost function surrogates ranking loss to increase Spearman’s rank correlation coefficient while it is differentiable concerning the neural network parameters. Our method achieved superior performance in NTIRE 2021 Perceptual Image Quality Assessment Challenge. The implementations of our proposed method are publicly available.1 2
Hilbert curve vs Hilbert space: exploiting fractal 2D covering to increase tensor network efficiency

We present a novel mapping for studying 2D many-body quantum systems by solving an effective, one-dimensional long-range model in place of the original two-dimensional short-range one. In particular, we address the problem of choosing an efficient mapping from the 2D lattice to a 1D chain that optimally preserves the locality of interactions within the TN structure. By using Matrix Product States (MPS) and Tree Tensor Network (TTN) algorithms, we compute the ground state of the 2D quantum Ising model in transverse field with lattice size up to 64×64, comparing the results obtained from different mappings based on two space-filling curves, the snake curve and the Hilbert curve. We show that the locality-preserving properties of the Hilbert curve leads to a clear improvement of numerical precision, especially for large sizes, and turns out to provide the best performances for the simulation of 2D lattice systems via 1D TN structures.
Toroidal magnetic fields in self-gravitating disks around black holes

We investigate stationary models of magnetized, self-gravitating disks around black holes. The disks are assumed to rotate according to a recently introduced Keplerian rotation law. We consider different prescriptions of the toroidal magnetic field. Similarly to the purely hydrodynamical case (i.e., with no magnetic field), we observe a bifurcation in the parameter space of solutions. There are usually two branches of solutions: a branch corresponding to relatively light disks and a branch for which the disk can be more massive than the black hole. The existence of this latter branch can be explained by geometric properties of the spacetime. We investigate the influence of the magnetic field in the disk on these effects.
Faster Monotone Min-Plus Product, Range Mode, and Single Source Replacement Paths

One of the most basic graph problems, All-Pairs Shortest Paths (APSP) is known to be solvable in $n^{3-o(1)}$ time, and it is widely open whether it has an $O(n^{3-\epsilon})$ time algorithm for $\epsilon>0$. To better understand APSP, one often strives to obtain subcubic time algorithms for structured instances of APSP and problems equivalent to it, such as the Min-Plus matrix product. A natural structured version of Min-Plus product is Monotone Min-Plus product which has been studied in the context of the Batch Range Mode [SODA'20] and Dynamic Range Mode [ICALP'20] problems. This paper improves the known algorithms for Monotone Min-Plus Product and for Batch and Dynamic Range Mode, and establishes a connection between Monotone Min-Plus Product and the Single Source Replacement Paths (SSRP) problem on an $n$-vertex graph with potentially negative edge weights in $\{-M, \ldots, M\}$. SSRP with positive integer edge weights bounded by $M$ can be solved in $\tilde{O}(Mn^\omega)$ time, whereas the prior fastest algorithm for graphs with possibly negative weights [FOCS'12] runs in $O(M^{0.7519} n^{2.5286})$ time, the current best running time for directed APSP with small integer weights. Using Monotone Min-Plus Product, we obtain an improved $O(M^{0.8043} n^{2.4957})$ time SSRP algorithm, showing that SSRP with constant negative integer weights is likely easier than directed unweighted APSP, a problem that is believed to require $n^{2.5-o(1)}$ time. Complementing our algorithm for SSRP, we give a reduction from the Bounded-Difference Min-Plus Product problem studied by Bringmann et al. [FOCS'16] to negative weight SSRP. This reduction shows that it might be difficult to obtain an $\tilde{O}(M n^{\omega})$ time algorithm for SSRP with negative weight edges, thus separating the problem from SSRP with only positive weight edges.
A model for cooperative scientific research inspired by the ant colony algorithm

Modern scientific research has become largely a cooperative activity in the Internet age. We build a simulation model to understand the population-level creativity based on the heuristic ant colony algorithm. Each researcher has two heuristic parameters characterizing the goodness of his own judgments and his trust on literature. We study how the distributions of contributor heuristic parameters change with the research problem scale, stage of the research problem, and computing power available. We also identify situations where path dependence and hasty research due to the pressure on productivity can significantly impede the long-term advancement of scientific research. Our work provides some preliminary understanding and guidance for the dynamical process of cooperative scientific research in various disciplines.
Distributed Optimization with Projection-free Dynamics

We consider continuous-time dynamics for distributed optimization with set constraints in the paper. To handle the computational complexity of projection-based dynamics due to solving a general quadratic optimization subproblem with projection, we propose a distributed projection-free dynamics by employing the Frank-Wolfe method, also known as the conditional gradient algorithm. The process searches a feasible descent direction by solving an alternative linear optimization instead of a quadratic one. To make the approach applicable over weight-balanced digraphs, we design a dynamics for the consensus of local decision variables and another dynamics of auxiliary variables to track the global gradient. Then we prove the convergence of the dynamical systems to the optimal solution, and provide detailed numerical comparisons with both projection-based dynamics and other distributed projection-free algorithms. Also, we derive the distributed discrete-time scheme following the instructive ideas of the proposed dynamics and provide its accordingly convergence rate.
MAFER: a Multi-resolution Approach to Facial Expression Recognition

Emotions play a central role in the social life of every human being, and their study, which represents a multidisciplinary subject, embraces a great variety of research fields. Especially concerning the latter, the analysis of facial expressions represents a very active research area due to its relevance to human-computer interaction applications. In such a context, Facial Expression Recognition (FER) is the task of recognizing expressions on human faces. Typically, face images are acquired by cameras that have, by nature, different characteristics, such as the output resolution. It has been already shown in the literature that Deep Learning models applied to face recognition experience a degradation in their performance when tested against multi-resolution scenarios. Since the FER task involves analyzing face images that can be acquired with heterogeneous sources, thus involving images with different quality, it is plausible to expect that resolution plays an important role in such a case too. Stemming from such a hypothesis, we prove the benefits of multi-resolution training for models tasked with recognizing facial expressions. Hence, we propose a two-step learning procedure, named MAFER, to train DCNNs to empower them to generate robust predictions across a wide range of resolutions. A relevant feature of MAFER is that it is task-agnostic, i.e., it can be used complementarily to other objective-related techniques. To assess the effectiveness of the proposed approach, we performed an extensive experimental campaign on publicly available datasets: \fer{}, \raf{}, and \oulu{}. For a multi-resolution context, we observe that with our approach, learning models improve upon the current SotA while reporting comparable results in fix-resolution contexts. Finally, we analyze the performance of our models and observe the higher discrimination power of deep features generated from them.