Miracle-dz/UniFAR-FTUs huggingface dataset:
Explicit Solutions of Fuzzy Time Fractional Diffusion Equations in Double Parametric Form of Fuzzy Number

Fuzzy fractional diffusion equations are used to model certain physical phenomena. In this paper, two explicit finite difference schemes, that is the forward time centre space (FTCS) and Saulev’s scheme methods are considered for solving fuzzy time fractional diffusion equation. The time fractional derivative is defined using the Caputo formula. The fuzziness is represented using convex normalized triangular fuzzy numbers based on a double parametric form of fuzzy number. A numerical example is presented to illustrate the feasibility of the proposed methods.
A Model for Small Basket Equities Financing

Small basket equities financing is the bread and butter of hedge fund financing businesses. The collapse of Archegos Capital Management and the losses of its lending banks incurred in early 2021 are timely reminders of how challenging it is for banks to price and risk manage hedge fund transactions. This article develops an equity portfolio financing haircut model that gives credit analysts flexibility when assessing borrower credit quality given hedge funds’ operational opacity and lack of market traded credit products. Recent historical data are used to estimate the Black-Karasinski credit model and double-exponential jump-diffusion asset price model per stock. Computational results for a basket of four Archegos holding stocks show sensible haircuts and haircut diversification effect. A bank opting to trade at less favorable haircut levels could price in higher economic capital charge to compensate and use that to incentivize its funding unit.
Performance of MRI Brain Tumor Classification Using Neural Network Classifiers

In Medical Image processing Brain tumor classification plays vital role to diagnosis system for Magnetic Resonance Imaging analysis. As traditional approaches performs low classification accuracy due to its inadequate functionalities to handle the medical image datasets having multiple classes. In this paper the work presents an effective algorithm to classify a tumor in brain MRI images using intensity-based statistical features and deep neural network. Data, within the region of interest (ROI), features extraction based on gray-level co-occurrence matrix (GLCM) and BT segmentation based on Discrete Wavelet Transform (DWT). These filters are combined in this algorithm as directional transformation methods for exploiting all information in all directions of the MRI input image. MRI Features are extracted based on the first and second order statistics from both domains. Furthermore this work incorporates two types of neural network classifiers: Stacked Sparse Auto encoder (SSA) and Softmax Classifier (SMC). BRATS dataset consist of 6643 MRI images and classified into three tumor classes such as: Pituitary, Glioma, and Meningioma. We have evaluated the classification accuracy with traditional classifiers such as support vector machine, K Nearest Neighbor Classifier and Logistic regression: The performance of the proposed algorithm is validated using the experimental results in terms of accuracy, specificity, and sensitivity compared to the existing algorithms. The highest accuracy obtained is 98.8 % by the proposed classifier on BRATS dataset respectively.
Well-Aligned Channels Derived from Wood for Electron and Mass Transportation in Aqueous Flow Batteries

Nature has provided scientists and engineers with tremendous inspirations and resources to design new materials, architectures, and devices. Inspired by the transportation of water and ions in trees, for the first time, a natural wood-derived freestanding electrode with well-aligned channels was designed as an alternative for the pricey electrode in aqueous vanadium redox flow battery (VRFB). The microscale vertically well-aligned channels (the largest channels size are 60 µm) with pit pores on the channel walls work as efficient mass transfer pathways in VRFB. Meanwhile, the designed freestanding wood carbon (WC) electrode is chemically stable, electrically conductive, and has a large specific surface area of 542.28 m<sup>2</sup>g<sup>-1</sup> for fast charge transfer and electrochemical reaction. Furthermore, to improve the wettability and electrochemical catalytic activity, carboxylic groups were introduced on WC via nitric acid (HNO<sup>3</sup>) treatment (N-WC). Negatively charged carboxylic groups (with a zeta potential of -50.6 mV for N-WC suspension) provide extra driving force for the adsorption of vanadium cations towards the electrode surface, and therefore further enhance the mass transfer. The discharge capacity retention for N-WC electrode is 83.58 % after 100 cycles at 100 mA cm<sup>-2</sup>, whereas, the corresponding capacity retention is 57.21 % for WC electrode. Moreover, we found that the N-WC electrode has a predominant effect on the positive side of VRFB with the associated half-cell presents voltage efficiency of 92.10 % and energy efficiency of 88.23 % at current density of 40 mA cm-2. Notably, the discharge capacity for the positive half-cell has an excellent cycle stability of 90.19 % retention after 100 cycles at 100 mA cm<sup>-2</sup>. This work opens a new strategy to apply Nature derived structure and materials for mass and charge transfer in energy storage areas.
Microstructural Characterization, Functional Behavior and Deformation Mechanism of Ni>sub>54Mn 25Ga 21-xDy x High Temperature Shape Memory Alloys with Low Thermal Hysteresis

Potential applications at elevated temperatures require shape memory alloys (SMAs) to possess high transformation temperature, excellent shape recovery ability and thermal/functional stability. The present study investigated the influences of Dy addition on the microstructural characterization, functional behavior and possible deformation mechanisms in high temperature Ni<sub>54</sub>Mn<sub>25</sub>Ga<sub>21</sub> SMAs. The results indicated that adequate Dy addition contributed to a desirable tradeoff among compressive strength, ductility and functional performance. Ni<sub>54</sub>Mn<sub>25</sub>Ga<sub>20.9</sub>Dy<sub>0.1</sub> alloy showed the highest shape memory recoverable strain due to the improvement of mechanical properties and formation of small Dy-rich precipitates. Simultaneously, an obvious pseudoelasticity was observed over a wide temperature range with a maximum recoverable strain of 3.74% at 320°C in this alloy. All the Dy-containing alloys exhibited high thermal cyclic stability and quite low thermal hysteresis. However, it was revealed that a complicated and abnormal relationship between thermal hysteresis and the middle eigenvalue <i>λ<sub>2</sub></i> existed for the investigated alloys with a lattice transition from cubic to tetragonal. Calculated results based on energy minimization theory revealed that both the transformation strain and critical stress for inducing martensitic transformation varied with crystallographic orientations. The detwinning of pre-existing (112) compound twins and formation of (112) deformation twins were the dominant deformation mechanisms of martensite, which were highly compatible with the macroscopical recoverable strain. The present study provided valuable insights for developing high-performance multifunctional shape memory alloys for high temperature applications.
Malliavin-Stein method for the multivariate compound Hawkes process

In this paper, we provide upper bounds on the d2 distance between a large class of functionals of a multivariate compound Hawkes process and a given Gaussian vector. This is proven using Malliavin’s calculus defined on an underlying Poisson embedding. The upper bound is then used to infer the speed of convergence of Central Limit Theorems for the multivariate compound Hawkes process with exponential kernels as the observation time T goes to infinity.
Effect of extraction method and extraction solvent on recovery of phenolic compounds from olive leaves in Kemalpaşa-İzmir (Turkey): Oleuropein recovery as a case example

ABSTRACT Oleuropein, especially in its leaves, has an important role in durability of olive tree with its protective effect against diseases and pests. In this study, oleuropein has been extracted from olive leaves by using different extraction methods. Effects of extraction solvent and the extraction method that were obtained from Kemalpaşa-İzmir, Turkey, were studied. Results showed that the proposed method and the solvent contained more amount of extracts than existing extraction methods. Efficient extraction without degradation was achieved in 2 h. It has also been found that modified superheated liquid extraction in 50:52:25 ethanol:isopropanol:water composition gave the highest recovery.
Quantifying and Generalizing the CAP Theorem

In distributed applications, Brewer’s CAP theorem tells us that when networks become partitioned, there is a tradeoff between consistency and availability. Consistency is agreement on the values of shared variables across a system, and availability is the ability to respond to reads and writes accessing those shared variables. We quantify these concepts, giving numerical values to inconsistency and unavailability. Recognizing that network partitioning is not an all-or-nothing proposition, we replace the P in CAP with L, a numerical measure of apparent latency, and derive the CAL theorem, an algebraic relation between inconsistency, unavailability, and apparent latency. This relation shows that if latency becomes unbounded (e.g., the network becomes partitioned), then one of inconsistency and unavailability must also become unbounded, and hence the CAP theorem is a special case of the CAL theorem. We describe two distributed coordination mechanisms, which we have implemented as an extension of the Lingua Franca coordination language, that support arbitrary tradeoffs between consistency and availability as apparent latency varies. With centralized coordination, inconsistency remains bounded by a chosen numerical value at the cost that unavailability becomes unbounded under network partitioning. With decentralized coordination, unavailability remains bounded by a chosen numerical quantity at the cost that inconsistency becomes unbounded under network partitioning. Our centralized coordination mechanism is an extension of techniques that have historically been used for distributed simulation, an application where consistency is paramount. Our decentralized coordination mechanism is an extension of techniques that have been used in distributed databases when availability is paramount.
Internal Friction Measures to Study Precipitates Formation in EP and N-Doped Bulk Nb for SRF Applications

Main focus of this study is the investigation of thermodynamics phenomena responsible for the High Field Q Slope (HFQS) in SRF cavities by Internal Friction (IF) measurement. Mechanical spectroscopy is, indeed, a well-established technique to study precipitate formations in BCC materials and several works on the effects of impurities as N and O on the Snoek peak have been published so far and will be taken as reference to explain the mechanisms behind the observed dissipation effects. Internal Friction measurements were performed in Belgium at IMCE on Nb rectangular shape samples with different RRR values prepared at Fermilab by using Electro Polishing (EP), N-doping and heat treatments in order to reproduce the same conditions during the standard treatments applied on bulk Nb SRF cavities. From IF spectra, the H trapping mechanism by interstitial atoms (N and O and/or vacancies, depending on the purity level, RRR) can be easily recognized leading to results that perfectly corroborate previous findings on Q-disease, HFQS and RRR phenomena.
MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks

We report a workflow and the output of a natural language processing (NLP)-based procedure to mine the extant metal-organic framework (MOF) literature describing structurally characterized MOFs and their solvent removal and thermal stabilities. We obtain over 2,000 solvent removal stability measures from text mining and 3,000 thermal decomposition temperatures from thermogravimetric analysis data. We assess the validity of our NLP methods and the accuracy of our extracted data by comparing to a hand-labeled subset. Machine learning (ML, i.e. artificial neural network) models trained on this data using graph- and pore-geometry-based representations enable prediction of stability on new MOFs with quantified uncertainty. Our web interface, MOFSimplify, provides users access to our curated data and enables them to harness that data for predictions on new MOFs. MOFSimplify also encourages community feedback on existing data and on ML model predictions for community-based active learning for improved MOF stability models.
STRUCTURAL HIGHNESS NOTIONS

Abstract We introduce several highness notions on degrees related to the problem of computing isomorphisms between structures, provided that isomorphisms exist. We consider variants along axes of uniformity, inclusion of negative information, and several other problems related to computing isomorphisms. These other problems include Scott analysis (in the form of back-and-forth relations), jump hierarchies, and computing descending sequences in linear orders.
Branched SL(r,C)-opers

Branched projective structures were introduced by Mandelbaum [22], [23], and opers were introduced by Beilinson and Drinfeld [2], [3]. We define the branched analog of SL(r,C)–opers and investigate their properties. For the usual SL(r,C)–opers, the underlying holomorphic vector bundle is actually determined uniquely up to tensoring with a holomorphic line bundle of order r. For the branched SL(r,C)–opers, the underlying holomorphic vector bundle depends more intricately on the oper. While the holomorphic connection for a branched SL(r,C)–oper is nonsingular, given a branched SL(r,C)–oper, we associate to it a certain holomorphic vector bundle equipped with a logarithmic connection. This holomorphic vector bundle in question supporting a logarithmic connection does not depend on the branched oper. We characterize the branched SL(r,C)–opers in terms of the logarithmic connections on this fixed holomorphic vector bundle.
A Quantitative Model for Optical Coherence Tomography

Optical coherence tomography (OCT) is a widely used imaging technique in the micrometer regime, which gained accelerating interest in medical imaging in the last twenty years. In up-to-date OCT literature, certain simplifying assumptions are made for the reconstructions, but for many applications, a more realistic description of the OCT imaging process is of interest. In mathematical models, for example, the incident angle of light onto the sample is usually neglected or a plane wave description for the light–sample interaction in OCT is used, which ignores almost completely the occurring effects within an OCT measurement process. In this article, we make a first step to a quantitative model by considering the measured intensity as a combination of back-scattered Gaussian beams affected by the system. In contrast to the standard plane wave simplification, the presented model includes system relevant parameters, such as the position of the focus and the spot size of the incident laser beam, which allow a precise prediction of the OCT data. The accuracy of the proposed model—after calibration of all necessary system parameters—is illustrated by simulations and validated by a comparison with experimental data obtained from a 1300 nm swept-source OCT system.
SketchHairSalon

Recent deep generative models allow real-time generation of hair images from sketch inputs. Existing solutions often require a user-provided binary mask to specify a target hair shape. This not only costs users extra labor but also fails to capture complicated hair boundaries. Those solutions usually encode hair structures via orientation maps, which, however, are not very effective to encode complex structures. We observe that colored hair sketches already implicitly define target hair shapes as well as hair appearance and are more flexible to depict hair structures than orientation maps. Based on these observations, we present SketchHairSalon, a two-stage framework for generating realistic hair images directly from freehand sketches depicting desired hair structure and appearance. At the first stage, we train a network to predict a hair matte from an input hair sketch, with an optional set of non-hair strokes. At the second stage, another network is trained to synthesize the structure and appearance of hair images from the input sketch and the generated matte. To make the networks in the two stages aware of long-term dependency of strokes, we apply self-attention modules to them. To train these networks, we present a new dataset containing thousands of annotated hair sketch-image pairs and corresponding hair mattes. Two efficient methods for sketch completion are proposed to automatically complete repetitive braided parts and hair strokes, respectively, thus reducing the workload of users. Based on the trained networks and the two sketch completion strategies, we build an intuitive interface to allow even novice users to design visually pleasing hair images exhibiting various hair structures and appearance via freehand sketches. The qualitative and quantitative evaluations show the advantages of the proposed system over the existing or alternative solutions.
Direction-Assisted Beam Management in Full Duplex Millimeter Wave Massive MIMO Systems

Recent applications of the Full Duplex (FD) technology focus on enabling simultaneous control communication and data transmission to reduce the control information exchange overhead, which impacts end-to-end latency and spectral efficiency. In this paper, we present a simultaneous direction estimation and data transmission scheme for millimeter Wave (mmWave) massive Multiple-Input Multiple-Output (MIMO) systems, enabled by a recent FD MIMO technology with reduced hardware complexity Self-Interference (SI) cancellation. We apply the proposed framework in the mmWave analog beam management problem, considering a base station equipped with a large transmit antenna array realizing downlink analog beamforming and few digitally controlled receive antenna elements used for uplink Direction-of-Arrival (DoA) estimation. A joint optimization framework for designing the DoA-assisted analog beamformer and the analog as well as digital SI cancellation is presented with the objective to maximize the achievable downlink rate. Our simulation results showcase that the proposed scheme outperforms its conventional half-duplex counterpart, yielding reduced DoA estimation error and superior downlink data rate.
Metro Stations as Crowd-shipping Catalysts: An Empirical and Computational Study

Crowd-shipping is a promising shared mobility service that involves the delivery of goods using non-professional shippers. This service is mainly intended to reduce congestion and pollution in city centers but, as some authors observe, in most crowd-shipping initiatives the crowd rely on private motorized vehicles and hence the environmental benefits could be small, if not negative. Conversely, a crowd-shipping service relying on public transport should maximize the environmental benefits. Motivated by this observation, in this study we assess the potentials of crowd-shipping based on metro commuters in the city of Brescia, Italy. Our contribution is twofold. First, we analyze the results of a survey conducted among metro users to assess their willingness to act as crowd-shippers. The main result is that most young commuters and retirees are willing to be crowd-shippers even for a null reward. Second, we assess the potential economic impact of using metro-based crowd-shipping coupled with a traditional home delivery service. To this end, we formulate a variant of the VRP model where the customers closest to the metro stations may be served either by a conventional vehicle or by a crowd-shipper. The model is implemented using Python with Gurobi solver. A computational study based on the Brescia case is performed to get insights on the economic advantages that a metro-based crowd delivery option may have for a retailing company.
Different Faces of Confinement

In this review, we provide a short outlook of some of the current most popular pictures and promising approaches to non-perturbative physics and confinement in gauge theories. A qualitative and by no means exhaustive discussion presented here covers such key topics as the phases of QCD matter, the order parameters for confinement, the central vortex and monopole pictures of the QCD vacuum structure, fundamental properties of the string tension, confinement realisations in gauge-Higgs and Yang–Mills theories, magnetic order/disorder phase transition, among others.
Does External Knowledge Help Explainable Natural Language Inference? Automatic Evaluation vs. Human Ratings

Natural language inference (NLI) requires models to learn and apply commonsense knowledge. These reasoning abilities are particularly important for explainable NLI systems that generate a natural language explanation in addition to their label prediction. The integration of external knowledge has been shown to improve NLI systems, here we investigate whether it can also improve their explanation capabilities. For this, we investigate different sources of external knowledge and evaluate the performance of our models on in-domain data as well as on special transfer datasets that are designed to assess fine-grained reasoning capabilities. We find that different sources of knowledge have a different effect on reasoning abilities, for example, implicit knowledge stored in language models can hinder reasoning on numbers and negations. Finally, we conduct the largest and most fine-grained explainable NLI crowdsourcing study to date. It reveals that even large differences in automatic performance scores do neither reflect in human ratings of label, explanation, commonsense nor grammar correctness.
Dark matter transient annihilations in the early Universe

The cosmological evolution can modify the dark matter (DM) properties in the early Universe to be vastly different from the properties today. Therefore, the relation between the relic abundance and the DM constraints today needs to be revisited. We propose novel \textit{transient} annihilations of DM which helps to alleviate the pressure from DM null detection results. As a concrete example, we consider the vector portal DM and focus on the mass evolution of the dark photon. When the Universe cools down, the gauge boson mass can increase monotonically and go across several important thresholds; opening new transient annihilation channels in the early Universe. Those channels are either forbidden or weakened at the late Universe which helps to evade the indirect searches. In particular, the transient resonant channel can survive direct detection (DD) without tuning the DM to be half of the dark photon mass and can be soon tested by future DD or collider experiments. A feature of the scenario is the existence of a light dark scalar.