Miracle-dz/UniFAR-FTUs huggingface dataset:
Short-term probabilistic photovoltaic power forecast based on deep convolutional long short-term memory network and kernel density estimation

Short-term probabilistic photovoltaic power forecast based on deep convolutional long short-term memory network and kernel density estimation Mingliang Bai, Xinyu Zhao, Zhenhua Long, Jinfu Liu*, Daren Yu 1. Department of Control Science and Engineering, Harbin Institute of Technology, 150001, Harbin, Heilongjiang, China 2. School of Energy Science and Engineering, Harbin Institute of Technology, 150001, Harbin, Heilongjiang, China Abstract: Solar energy is a clean and renewable energy. Photovoltaic (PV) power is an important way to utilize solar energy. Accurate PV power forecast is crucial to the largescale application of PV power and the stability of electricity grid. This paper proposes a novel method for short-term photovoltaic power forecast using deep convolutional long short-term memory (ConvLSTM) network and kernel density estimation (KDE). In the proposed method, ConvLSTM is used to forecast the future photovoltaic power and KDE is used for estimating the joint probabilistic density function and giving the probabilistic confidence interval. Experiments in an actual photovoltaic power station verify the effectiveness of the proposed method. Comparison experiments with convolutional neural network (CNN) and long short-term memory network (LSTM) shows that ConvLSTM can combine the advantages of both CNN and LSTM and significantly outperform CNN and LSTM in terms of forecast accuracy. Through further comparison with other five conventional methods including multilayer perceptron (MLP), support vector regression (SVR), extreme learning machine (ELM), classification and regression tree (CART) and gradient boosting decision tree (GBDT), ConvLSTM can significantly improve the forecast accuracy by more than 20% for most of the five methods and the superiorities of ConvLSTM are further verified.
Random Neural Networks in the Infinite Width Limit as Gaussian Processes

This article gives a new proof that fully connected neural networks with random weights and biases converge to Gaussian processes in the regime where the input dimension, output dimension, and depth are kept fixed, while the hidden layer widths tend to infinity. Unlike prior work, convergence is shown assuming only moment conditions for the distribution of weights and for quite general non-linearities.
Can Transformers Jump Around Right in Natural Language? Assessing Performance Transfer from SCAN

Despite their failure to solve the compositional SCAN dataset, seq2seq architectures still achieve astonishing success on more practical tasks. This observation pushes us to question the usefulness of SCAN-style compositional generalization in realistic NLP tasks. In this work, we study the benefit that such compositionality brings about to several machine translation tasks. We present several focused modifications of Transformer that greatly improve generalization capabilities on SCAN and select one that remains on par with a vanilla Transformer on a standard machine translation (MT) task. Next, we study its performance in low-resource settings and on a newly introduced distribution-shifted English-French translation task. Overall, we find that improvements of a SCAN-capable model do not directly transfer to the resource-rich MT setup. In contrast, in the low-resource setup, general modifications lead to an improvement of up to 13.1% BLEU score w.r. t. a vanilla Transformer. Similarly, an improvement of 14% in an accuracy-based metric is achieved in the introduced compositional English-French translation task. This provides experimental evidence that the compositional generalization assessed in SCAN is particularly useful in resource-starved and domain-shifted scenarios.
Nondetection of Water-ice Grains in the Coma of Comet 46P/Wirtanen and Implications for Hyperactivity

Hyperactive comets have high water production rates, with inferred sublimation areas of order the surface area of the nucleus. Comets 46P/Wirtanen and 103P/Hartley 2 are two examples of this cometary class. Based on observations of comet Hartley 2 by the Deep Impact spacecraft, hyperactivity appears to be caused by the ejection of water-ice grains and/or water-ice-rich chunks of nucleus into the coma. These materials increase the sublimating surface area and yield high water production rates. The historic close approach of comet Wirtanen to Earth in 2018 afforded an opportunity to test Hartley 2–style hyperactivity in a second Jupiter-family comet. We present high spatial resolution, near-infrared spectroscopy of the inner coma of Wirtanen. No evidence for the 1.5 or 2.0 μm water-ice absorption bands is found in six 0.8–2.5 μm spectra taken around perihelion and closest approach to Earth. In addition, the strong 3.0 μm water-ice absorption band is absent in a 2.0–5.3 μm spectrum taken near perihelion. Using spectroscopic and sublimation lifetime models, we set constraints on the physical properties of the ice grains in the coma, assuming they are responsible for the comet’s hyperactivity. We rule out pure water-ice grains of any size, given their long lifetime. Instead, the hyperactivity of the nucleus and lack of water-ice absorption features in our spectra can be explained either by icy grains on the order of 1 μm in size with a small amount of low-albedo dust (greater than 0.5% by volume) or by large chunks containing significant amounts of water ice.
When and How to Fool Explainable Models (and Humans) with Adversarial Examples

Reliable deployment of machine learning models such as neural networks continues to be challenging due to several limitations. Some of the main shortcomings are the lack of interpretability and the lack of robustness against adversarial examples or out-of-distribution inputs. In this exploratory review, we explore the possibilities and limits of adversarial attacks for explainable machine learning models. First, we extend the notion of adversarial examples to fit in explainable machine learning scenarios, in which the inputs, the output classifications and the explanations of the model's decisions are assessed by humans. Next, we propose a comprehensive framework to study whether (and how) adversarial examples can be generated for explainable models under human assessment, introducing and illustrating novel attack paradigms. In particular, our framework considers a wide range of relevant yet often ignored factors such as the type of problem, the user expertise or the objective of the explanations, in order to identify the attack strategies that should be adopted in each scenario to successfully deceive the model (and the human). The intention of these contributions is to serve as a basis for a more rigorous and realistic study of adversarial examples in the field of explainable machine learning.
A Cusp in the Symmetry Energy, PREX-II and Quasifermions in the Core of Massive Neutron Stars

The "dilemma" posed by the recent announcement of the PREX-II measurement of the neutron skin of $^{208}$Pb that suggests a stiff symmetry energy near nuclear matter density $n_0$ and its impact on the EoS of massive compact stars raise the issue as to whether the EoS determined at $n_0$ necessarily gives a stringent constraint at high densities relevant to massive compact stars, the currently widely accepted "lore" in astro-nuclear field. We present the argument that the "cusp" structure in the symmetry energy at $n_{1/2}\gtrsim 2 n_0$ predicted by a topology change in dense matter encoding hadron-quark continuity could provide a strong obstruction to the validity of the "lore. " The topology change predicts an EoS that is soft below and stiffened above $n\gtrsim n_{1/2}$, involving no phase transitions, and yields the macrophysical properties of neutron stars more or less consistent with the overall observations and the maximum mass $ 2.0\lesssim M/ M_\odot\lesssim 2.2$ as well as the GW data. Furthermore it describes the interior core of the massive stars constituted of baryon-charge-fractionalized quasifermions, that are neither baryonic nor quarkonic, with the "pseudo-conformal" sound speed $v^2_{pcs}/c^2\approx 1/3$ with a nonzero trace of energy-momentum tensor converging from below at $n_{1/2}$.
TANNER’S LAW FOR TRAVELING WAVES IN THE PARTIAL WETTING REGIME

= 0 in {h > 0} with partialwetting boundary conditions and inhomogeneous mobility of the form m(h) = h + λh, where h ≥ 0 is the film height, λ > 0 is the slip length, y > 0 denotes the lateral variable, and n ∈ (0, 3) is the mobility exponent parameterizing the nonlinear slip condition. The partialwetting regime implies the boundary condition ∂yh = const. > 0 at the triple junction ∂{h > 0} (nonzero microscopic contact angle). Existence and uniqueness of traveling-wave solutions to this problem under the constraint ∂ yh → 0 as h → ∞ have been proved in previous work by Chiricotto and Giacomelli in [Commun. Appl. Ind. Math., 2(2):e–388, 16, 2011]. We are interested in the asymptotics as h ↓ 0 and h → ∞. By reformulating the problem as h ↓ 0 as a dynamical system for the error between the solution and the microscopic contact angle, values for n are found for which linear as well as nonlinear resonances occur. These resonances lead to a different asymptotic behavior of the solution as h ↓ 0 depending on n.
Finding Important Edges in Networks through Local Information

In transportation, communication, social and other real complex networks, some critical edges act a pivotal part in controlling the flow of information and maintaining the integrity of the structure. Due to the importance of critical edges in theoretical studies and practical applications, the identification of critical edges gradually become a hot topic in current researches. Considering the overlap of communities in the neighborhood of edges, a novel and effective metric named subgraph overlap (SO) is proposed to quantifying the significance of edges. The experimental results show that SO outperforms all benchmarks in identifying critical edges which are crucial in maintaining the integrity of the structure and functions of networks.
Active elastocapillarity in soft solids with negative surface tension

Active solids consume energy to allow for actuation, shape change, and wave propagation not possible in equilibrium. Whereas active interfaces have been realized across many experimental systems, control of three-dimensional (3D) bulk materials remains a challenge. Here, we develop continuum theory and microscopic simulations that describe a 3D soft solid whose boundary experiences active surface stresses. The competition between active boundary and elastic bulk yields a broad range of previously unexplored phenomena, which are demonstrations of so-called active elastocapillarity. In contrast to thin shells and vesicles, we discover that bulk 3D elasticity controls snap-through transitions between different anisotropic shapes. These transitions meet at a critical point, allowing a universal classification via Landau theory. In addition, the active surface modifies elastic wave propagation to allow zero, or even negative, group velocities. These phenomena offer robust principles for programming shape change and functionality into active solids, from robotic metamaterials down to shape-shifting nanoparticles.
Affective Visualization in Virtual Reality: An Integrative Review

A cluster of research in Affective Computing suggests that it is possible to infer some characteristics of users’ affective states by analyzing their electrophysiological activity in real-time. However, it is not clear how to use the information extracted from electrophysiological signals to create visual representations of the affective states of Virtual Reality (VR) users. Visualization of users’ affective states in VR can lead to biofeedback therapies for mental health care. Understanding how to visualize affective states in VR requires an interdisciplinary approach that integrates psychology, electrophysiology, and audio-visual design. Therefore, this review aims to integrate previous studies from these fields to understand how to develop virtual environments that can automatically create visual representations of users’ affective states. The manuscript addresses this challenge in four sections: First, theories related to emotion and affect are summarized. Second, evidence suggesting that visual and sound cues tend to be associated with affective states are discussed. Third, some of the available methods for assessing affect are described. The fourth and final section contains five practical considerations for the development of virtual reality environments for affect visualization.
The Sitting Closer to Friends than Enemies Problem in Trees

A metric space $\mathcal{T}$ is a \emph{real tree} if for any pair of points $x, y \in \mathcal{T}$ all topological embeddings $\sigma$ of the segment $[0,1]$ into $\mathcal{T}$, such that $\sigma (0)=x$ and $\sigma (1)=y$, have the same image (which is then a geodesic segment from $x$ to $y$). A \emph{signed graph} is a graph where each edge has a positive or negative sign. The \emph{Sitting Closer to Friends than Enemies} problem in trees has a signed graph $S$ as an input. The purpose is to determine if there exists an injective mapping (called \emph{valid distance drawing}) from $V(S)$ to the points of a real tree such that, for every $u \in V(S)$, for every positive neighbor $v$ of $u$, and negative neighbor $w$ of $u$, the distance between $v$ and $u$ is smaller than the distance between $w$ and $u$. In this work, we show that a complete signed graph has a valid distance drawing in a real tree if and only if its subgraph composed of all (and only) its positive edges has an intersection representation by unit balls in a real tree. Besides, as an instrumental result, we show that a graph has an intersection representation by unit balls in a real tree if and only if it has an intersection representation by proper balls, and if and only if it has an intersection representation by arbitrary balls in a real tree.
Angiotensin II receptor I auto-antibodies following SARS-CoV-2 infection

BACKGROUND: Coronavirus disease 2019 (COVID-19) is associated with endothelial activation and coagulopathy, which may be related to pre-existing or infection-induced pro-thrombotic autoantibodies such as those targeting angiotensin II type I receptor (AT1R-Ab). METHODS: We compared prevalence and levels of AT1R-Ab in COVID-19 cases with mild or severe disease to age and sex matched negative controls. RESULTS: There were no significant differences between cases and controls. However, there were trends toward a higher proportion with AT1R-Ab positivity among severe cases versus controls (32% vs. 11%, p=0.1) and higher levels in those with mild COVID-19 compared to controls (median 9.5U/mL vs. 5.9U/mL, p=0.06). CONCLUSIONS: These findings suggest that AT1R-Ab are not consistently associated with COVID-19 but do not exclude a contribution to endothelial pathology in a subset of people.
THERMAL PERFORMANCE OF CELLULOSE ACETATE/TEA POLYPHENOL NANOFIBERS

In this paper, the cellulose acetate/tea polyphenol nanofibers are manufactured by electrostatic spinning technique. The surface morphology and thermal property of obtained nanofibers are characterized via scanning electron microscope, thermal gravity analysis, and differential scanning calorimetry. Different concentrations of cellulose acetate/dimethyl formamide solutions are prepared before sonicated tea polyphenol powder was added. The diameter of cellulose acetate/tea polyphenol nanofibers increases with the increase of the cellulose acetate component.
Organizational strategies to optimize women's safety during labor and birth: A scoping review.

BACKGROUND Safety is a priority for organizations that provide maternity care, however, preventable harm and errors in maternity care remain. Maternity care is considered a high risk and high litigation area of health care. To mitigate risk and litigation, organizations have implemented strategies to optimize women's safety. Our objectives were to identify the strategies implemented by organizations to optimize women's safety during labor and birth, and to consider how the concept of safety is operationalized to measure and evaluate outcomes of these strategies. METHOD This scoping review was conducted using the Joanna Briggs Institute Scoping Review Methodology. Published peer-reviewed literature indexed in CINAHL, Medline, and Embase, databases from 2010 to 2020, were reviewed for inclusion. Fifty studies were included. Data were extracted and thematically analyzed. RESULTS Three categories of organizational strategies were identified to optimize women's safety during labor and birth: clinical governance, models of care, and staff education. Clinical governance programs (n = 30 studies), specifically implementing checklists and audits, models of care, such as midwifery led-care (n = 11 studies), and staff training programs (n = 9 studies), were predominately for the management of obstetric emergencies. Outcome measures included morbidity and mortality for woman and newborns. Three studies discussed women's perceptions of safety during labor and birth as an outcome measure. CONCLUSIONS Organizations utilize a range of strategies to optimize women's safety during labor and birth. The main outcome measure used to evaluate strategies was focused on clinical outcomes for the mother and newborn.
Tuning Thermoelectric Properties of Type I Clathrate K8–xBaxAl8+xSi38–x through Barium Substitution

The thermal stability and thermoelectric properties of type I clathrate K8Al8Si38 up to 873 K are reported. K8Al8Si38 possesses a high absolute Seebeck coefficient value and high electrical resistivity in the temperature range of 323 to 873 K, which is consistent with previously reported low temperature thermoelectric properties. Samples with Ba partial substitution at the K guest atom sites were synthesized from metal hydride precursors. The samples with the nominal chemical formula of K8–xBaxAl8+xSi38–x (x = 1, 1.5, 2) possess type I clathrate structure (cubic, Pm3n), confirmed by X-ray diffraction. The guest atom site occupancies and thermal motions were investigated with Rietveld refinement of synchrotron powder X-ray diffraction. Transport properties of Ba-containing samples were characterized from 2 to 300 K. The K–Ba alloy phases showed low thermal conductivity and improved electrical conductivity compared to K8Al8Si38. Electrical resistivity and Seebeck coefficients were measured over the tempera.
Immunoinformatics based design and prediction of proteome-wide killer cell epitopes of Leishmania donovani: Potential application in vaccine development

Abstract Despite several extensive and exhaustive efforts, search for potential therapy against leishmaniasis has not made much progress. In the present work, we have employed mining strategy to screen Leishmania donovani proteome for identification of promising vaccine candidate. We have screened 21 potential antigenic proteins from 7960 total protein of L. donovani, based on the presence of signal peptide, GPI anchor, antigenicity prediction and substractive proteomic approach. Secondly, we have also performed comprehensive immunogenic epitope prediction from the screened 21 proteins, using IEDB-AR tools. Out of the 21 antigenic proteins, we obtained 11 immunogenic epitopes from 9 proteins. The final results revealed that four predicted epitopes namely; YPAFAALVF, VAVAATVAY, AAAPTEAAL and MYPLVAVVF, have significantly better binding potential with respective alleles and could elicits immune responses. Docking analysis using PATCHDOCK server and molecular dynamic simulation using GROMACS revealed the potential of the sequences as immunogenic epitopes. In silico studies also suggested that the epitopes occupied almost same binding cleft with the respective alleles, when compared with the reference peptides. It is also suggested from the molecular dynamic simulation data that the peptides were intact in the pocket for longer periods of time. Our study was designed to select MHC class I restricted epitopes for the activation of CD8 T cells using immunoinformatics for the prediction of probable vaccine candidate against L. donovani parasites. Communicated by Ramaswamy H. Sarma
Stratification of Microvascular Disease Severity in the Foot Using Spatial Frequency Domain Imaging

Background: Microvascular disease (MVD) describes systemic changes in the small vessels (~100 um diameter) that impair tissue oxygenation and perfusion. MVD is a common but poorly monitored complication of diabetes. Recent studies have demonstrated that MVD: (i) is an independent risk factor for ulceration and amputation and (ii) increases risk of adverse limb outcomes synergistically with PAD. Despite the clinical relevance of MVD, microvascular evaluation is not standard in a vascular assessment. Methods: We evaluated 299 limbs from 153 patients seen clinically for possible lower extremity PAD. The patients were assessed by ankle brachial index (ABI), toe brachial index (TBI), and spatial frequency domain imaging (SFDI). These measurements were evaluated and compared to patient MVD status, defined by clinical diagnoses of (in ascending order of severity) no diabetes; diabetes; diabetes + neuropathy; diabetes + neuropathy + retinopathy. Results: SFDI-derived parameters HbT1 and StO2 were significantly different across the MVD groups (P < .001). A logistic regression model based on HbT1 and StO2 differentiated limbs with severe MVD (diabetes+neuropathy+retinopathy) from the larger group of limbs from patients with only diabetes (P = .001, area under the curve = 0.844). Neither ABI nor TBI significantly differentiated these populations. Conclusions: Standard assessment of PAD using ABI and TBI are inadequate for detecting MVD in at-risk populations. SFDI-defined HbT1 and StO2 are promising tools for evaluating MVD. Prospective studies with wound-based outcomes would be useful to further evaluate the role MVD assessment could play in routine clinical evaluation of patients at risk for lower extremity complications.
Electrospinning Homogeneous Nanofibrous Poly(propylene carbonate)/Gelatin Composite Scaffolds for Tissue Engineering

Nanofibrous poly(propylene carbonate)(PPC)/gelatin composite scaffolds were fabricated by the electrospinning process and characterized for tissue engineering applications. Using trifluoroethanol (TFE) as a solvent and at low mass content of gelatin, nanofibrous scaffold PPC/gelatin membranes with improved wettability and hydrophilicity were produced, despite the fact that PPC is hydrophobic. However, phase separation occurred when the mass content of gelatin was higher than 5%, resulting in a nonuniform fibrous structure and some large splash defects. Inspired by the effect of pH on the behavior of dissolved gelatin, a small amount of acetic acid was added to the PPC/gelatin solution, which enabled the originally turbid solution to become clear immediately. The results showed that the miscibility of PPC and gelatin was enhanced by acetic acid. Using the treated solution, homogeneous PPC/gelatin nanofibers with a smooth surface and a more uniform diameter were obtained, and they performed better in wettab.