Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Nonhomogeneous Grey Model with Fractional-Order Accumulation and Its Application

e nonhomogeneous grey model has been seen as an effective method for forecasting time series with approximate nonhomogeneous index law, which has been widely used in diverse disciplines on account of its high prediction precision. However, there remains room for improvements. For this, this study presents an improved nonhomogeneous grey model by incorporating the dynamic integral mean value theorem and fractional accumulation simultaneously. In order to promote the efficacy of the optimised model, we apply the whale optimization algorithm (WOA) to ascertain its optimal parameter. In particular, two examples are conducted to validate the superiority of the proposed model in contrast with other benchmarks, and the experimental results show that the mean absolute percentage error of the proposed approach is 808692% and 6.0706%, respectively, indicating the proposed approach performs better than other competing models.
Crystal structure of synthetic makatite Na2Si4O8(OH)2 · 4H2O

The crystal structure of synthetic makatite, Na2Si408(0H)2 • 4 H 2 0 , [monoclinic, a = 7.3881(5) A, b = 18.094(3)Ä, c = 9.5234(5)Ä, β = 90.64(1)°, Pljc, ζ = 4] was solved by direct methods, (MULTAN), and Fourier syntheses and refined to R = 0.051 for 834 independent reflexions. The makatite structure is built up of corrugated [Si204(0H)j; ~ layers containing rings of six tetrahedra. The layers are connected by octahedral [Na(H20)4]JJ rods and sodium atoms coordinating five oxygens which form distorted trigonal bipyramids. Hydrogen atoms positions have not been determined. Introduction As a part of a general study of zeolites with ion exchange properties for the treatment of nuclear power station wastes we have synthesized a large number of different zeolites. Exclusion of the aluminium component from the N a 2 0 —A1203 —Si02 —H20 system led to the preparation of a number of hydrous layer silicates, of which some are known as minerals, e.g. magadiite (NazO · 14SiOz · 9 H 2 0 ) (McAtee, House, and Eugster, 1968), kenyaite (Na 20 · 22SiOz · 10H 20) (Eugster, 1967) and makatite (Hay, 1968), and some as purely synthetic products, e.g. sodium polysilicate (Na 2 0 • 8 Si02 · 9 H 2 0 ) previously reported by McCulloh (1952) and Iler (1964). Many of these layer silicates have interesting ion exchange properties and this present structure determination was undertaken in order to understand the mechanism of the ion exchange. The details of the syntheses will be published elsewhere but in general most of the preparations yielded crystals to small for single crystal X-ray work. However, in the case of makatite, the introduction * Permanent adress: School of Chemistry, University of Western Australia, Nedlands, 6009. W. Australia 204 Η. Annehed et al.: The crystal structure of makatite of the complexing agent triethanolamine (Charnell, 1971) resulted in the growth of larger, more suitable crystals. Recently Dent Glasser et al. (1980) have proposed by help of electron microscopy methods a new chain type structure for makatite with a repeat distance of 3.4 A in the fibre direction which by our investigation is shown not to be the case.
BiRep: A reputation scheme to mitigate effects of black-hole nodes in Vehicular Delay-Tolerant Networks

—Delay-Tolerant Networks (DTNs) are networks that enable communication in disruptive scenarios where issues like sparse and intermittent connectivity, long and variable delays, high latency, high error rates or no end-to-end connectivity exist. Vehicular Delay-Tolerant Networks (VDTNs) are DTNs where the nodes are vehicles. VDTNs emerged and became a popular research topic due to the important applications that can be realized with them, such as notification of traffic conditions and road accident warnings. For VDTNs to work efficiently, a grade of cooperation between nodes is necessary in order to deliver the messages to their destination. However, nodes might misbehave and not transmit messages. Nodes misbehavior may significantly impact the network performance, being an important problem to study in the context of VDTNs. One of the most studied attacks is the black-hole attack where a node drops forwarded messages in which it is not the destination. Various solutions have been proposed to diminish the effects of black-hole nodes but not one yet that was proven fail-proof, and tested for various scenarios. In this sense, this paper focuses on creating a reputation scheme that identifies and punishes black-hole nodes in the network. The reputation scheme, denominated BiRep, is tested Prophet protocol and for seven different network scenarios. Simulation results show very good performance for Prophet in all scenarios, comparable or better to other reputation schemes comprised in this study. Most importantly, they show a significant increase in delivery ratio of messages.
The Third-Party Funding in Arbitration: A Challenge in Times of Crisis

Abstract Arbitration as a way of resolving disputes between companies is essentially linked to the advantages of arbitration, especially with the speed and neutrality of arbitration, as well as the confidentiality, the possibility of choosing arbitrators with precise technical knowledge in the area of litigation, among others. The parties choose arbitration as a means of resolving disputes, relating to interests of an equity nature, bearing in mind that for some legislators the emphasis is on the availability of rights, arising from the contractual relationship that unites them. The payment of costs is a sine qua non condition for the constitution of the arbitral tribunal. The parties must proceed with the payment of taxes and fees, respectively to the arbitration center they have chosen and the arbitrators they have chosen. Considering that the economic situation of the companies may fluctuate, either during the execution of the main contract, or when the dispute arises, the constitution of the arbitral tribunal and during the procedural iter, the possibility of financing the arbitration was outlined. Third-Party Funding is a figure that involves a third-party, unrelated to the litigation, who will defray the expenses due by one of the parties to the arbitration. It will have as a counterpart the participation in the eventual financial result achieved through the success of the arbitration. As a methodology, in addition to analyzing the state of the art, we will indicate real cases and the reasons for the growth of this instrument, without forgetting the ethical issues involved.
Frequency Regulation of the Bulk Power System for Distributed Energy Resources

Abstrac : Due to their intermittent features and poorer inertial response, distributed energy resources (DERs) such as photovoltaic (PV) systems and fuel cell systems have increased the complexity of the power system. This reorganization of the power system has a significant impact on the system's transient responsiveness, resulting in inter-area oscillations, less synchronised coupling, and power swings. Furthermore, the idea of being dispersed and generating electricity from many points in the electrical system exacerbates the transitory impact of DERs by introducing difficulties like reverse power flows. This technique proposed the effects of changing power system characteristics that are impeding large-scale DER integration. In addition, by introducing virtual inertia to inverter-based DERs in the electricity system, a method to enhance the system's inertial responsiveness is addressed. The suggested control increased the stability margin and allowed the system to follow its rated frequency. By increasing the rate of change of frequency, the injected synchronized active power to the system avoids the protective relays from tripping. The suggested system functioning is tested experimentally using the MATLAB 2015 simulation system on a sample power grid that includes generation, transmission, and distribution with solar pv and fuel cell system integration.
Injectable and Biodegradable Supramolecular Hydrogels by Inclusion Complexation between Poly(organophosphazenes) and α-Cyclodextrin

Biodegradable poly(organophosphazenes) containing side chains of various oligo(ethylene glycol) methyl ethers (mPEGs) and glycine ethyl ester units were synthesized and characterized. Novel supramolecular-structured hydrogel systems based on the inclusion complex between the mPEG grafted polyphosphazenes and α-cyclodextrin were prepared in aqueous media. The gelation time depended on the length of the mPEG side chains, the molar ratio between mPEG repeat units and α-cyclodextrin, and the concentration of the polymeric gel precursors. The rheological measurements of the supramolecular hydrogels indicate a fast gelation process and flowable character under a large strain. The hydrogel systems demonstrate unique structure-related reversible gel–sol transition properties at a certain temperature due to the reversible supramolecular assembly. The formation of a channel-type inclusion complex induced gelation mechanism was studied by DSC, TGA, 13C CP/MAS NMR, and X-ray diffraction techniques. The strong potenti.
Knowledge of the RS about the Contextual Factors Partially Observable UnobservableFully Observable Everything Known about Context Partial and Static Context Knowledge Latent Knowledge of Context Context Relevance Is Dynamic Partial and Dynamic Context Knowledge Nothing Is Known about Context Static 

content-based and collaborative filtering, tend to use fairly simple user models. For example, user-based collaborative filtering generally models the user as a vector of item ratings. As additional observations are made about users’ preferences, the user models are extended, and the full collection of user preferences is used to generate recommendations or make predictions. This approach, therefore, ignores the notion of “situated actions” (Suchman 1987), the fact that users interact with the system within a particular “context” and that preferences for items within one context may be different from those in another context. In many application domains, a context-independent representation may lose predictive power because potentially useful information from multiple contexts is aggregated. For example, when a user is buying books, the preferences the user expresses in one context, such as “books for my children,” may be of no predictive value when the user seeks recommendations in a different context, such as “work-related books. ” The ideal contextaware recommendation system would, therefore, be able reliably to label each user action with an appropriate context and effectively tailor the system output to the user in that given context. The concept of “context” has been studied extensively in several areas of computing and other disciplines. For example, Bazire and Brezillon (2005) examine and compare some 150 different definitions of context from a number of different fields and conclude that the multifaceted nature of the concept makes it difficult to find a unifying definition: “Is context a frame for a given object? Is it the set of elements that have any influence on the object? Is it possible to define context a priori or just state Articles
Mental Age Compatibility: Quantification through the Convolution of Probability Distributions

We build on the empirical finding that a human being's mental age is normally distributed around the chronological age. This opposes the frequent societal assumption"mental = chronological"which is known to be false in general but entertained for simplicity due to lack of methodology; hence disregarding that, f.e. , people of different chronological ages can be much closer in their mental ages. As a quantitative approach on a scientific basis, we set up a general formula for the probability that two individuals of given ages are mentally within a certain range of years and investigate its implications i.a. by critically analyzing popular assumptions on age and computing statistical expectations within populations.
Identifying Periodic Variable Stars and Eclipsing Binary Systems with Long-term Las Cumbres Observatory Photometric Monitoring of ZTF J0139+5245

We present the results of our search for variable stars using the long-term Las Cumbres Observatory (LCO) monitoring of white dwarf ZTF J0139+5245 with the two 1.0 m telescope nodes located at McDonald Observatory using the Sinistro imaging instrument. In this search, we find 38 variable sources, of which 27 are newly discovered or newly classified (71%) based on comparisons with previously published catalogs, thereby increasing the number of detections in the field of view under consideration by a factor of ≈2.5. We find that the improved photometric precision per exposure due to longer exposure time for LCO images combined with the greater time sampling of LCO photometry enables us to increase the total number of detections in this field of view. Each LCO image covers a field of view of 26′ × 26′ and observes a region close to the Galactic plane (b = −9.°4) abundant in stars with an average stellar density of ≈8 arcmin−2. We perform aperture photometry and Fourier analysis on over 2000 stars across 1560 LCO images spanning 537 days to find 28 candidate BY Draconis variables, three candidate eclipsing binaries of type EA, and seven candidate eclipsing binaries of type EW. In assigning preliminary classifications to our detections, we demonstrate the applicability of the Gaia color–magnitude diagram as a powerful classification tool for variable-star studies.
uiCA: accurate throughput prediction of basic blocks on recent intel microarchitectures

Performance models that statically predict the steady-state throughput of basic blocks on particular microarchitectures, such as IACA, Ithemal, llvm-mca, OSACA, or CQA, can guide optimizing compilers and aid manual software optimization. However, their utility heavily depends on the accuracy of their predictions. The average error of existing models compared to measurements on the actual hardware has been shown to lie between 9% and 36%. But how good is this? To answer this question, we propose an extremely simple analytical throughput model that may serve as a baseline. Surprisingly, this model is already competitive with the state of the art, indicating that there is significant potential for improvement. To explore this potential, we develop a simulation-based throughput predictor. To this end, we propose a detailed parametric pipeline model that supports all Intel Core microarchitecture generations released between 2011 and 2021. We evaluate our predictor on an improved version of the BHive benchmark suite and show that its predictions are usually within 1% of measurement results, improving upon prior models by roughly an order of magnitude. The experimental evaluation also demonstrates that several microarchitectural details considered to be rather insignificant in previous work, are in fact essential for accurate prediction. Our throughput predictor is available as open source.
Untangling the Complexity in the Galactic Centre: a way to understand the origin of the gamma-ray emission from the inner Galaxy

The origin of the high-energy gamma-ray emission from the Milky Way center is still unclear and debated because of the impact of systematics afflicting the measurements from current experiments. Several theories and phenomenological models attempt to explain the intricate panorama. The presence of a PeVatron in the Central Molecular Zone or in its vicinity, the contribution of the hard-component of the diffuse gamma-ray emission, and dark matter annihilation scenario are among the most promising mechanisms for describing the observed excess. The development of increasingly precise models able to reproduce the measured gamma-ray emission is the challenge for the scientific community in view of the next generation telescopes. A detailed treatment of phenomenological models for the dubbed Cosmic Rays Sea (CR-sea) characterized by different configurations is scrutinized in comparison with the observed spectrum in the inner Galaxy, using DRAGON and GAMMASKY codes.
Studies of Gamma Ray Shower Reconstruction Using Deep Learning

The Cosmic Multiperspective Event Tracker (CoMET) R&D project aims to optimize the techniques for the detection of soft-spectrum sources through very-high-energy gamma-ray observations using particle detectors (called ALTO detectors), and atmospheric Cherenkov light collectors (called CLiC detectors). The accurate reconstruction of the energies and maximum depths of gamma-ray events using a surface array only, is an especially challenging problem at low energies, and the focus of the project. In this contribution, we leverage Convolutional Neural Networks (CNNs) using the ALTO detectors only, to try to improve reconstruction performance at lower energies ( < 1 TeV ) as compared to the SEMLA analysis procedure, which is a more traditional method using manually derived features.
Individual Fundamental Diagrams from Single-File Movement Experiments

Abstract: In recent years, numerous studies have been published dealing with the effect of individual characteristics of pedestrians on the fundamental diagram. These studies compared cumulative data on individuals in a group homogeneous in terms of one human factor such as age but heterogeneous in terms of other factors for instance gender. In order to examine the effect of all determined as well as undetermined human factors, individual fundamental diagrams are introduced and analyzed using multiple linear regression. A single-file school experiment with students of different age, gender, and height is therefore considered. Single individuals appearing in different runs are analyzed to study the effect of human factors such as height, age and gender and all other unknown individual effects such as motivation or attention to the individual velocity. The analysis shows that age and height are strongly correlated and, consequently, age can be ignored. Furthermore, the study shows that gender has a weak effect and other nonmeasurable individual characteristics have a stronger effect than height. In a further step, a mixed model is used as well as the multiple linear model. Here, it is shown that the mixed model that considers all other unknown individual effects of each person as a random factor is preferable to the model where the individual velocity only depends on the variables of headway, height, and all other unknown individual effects as fixed factors.
A Preconditioned Alternating Minimization Framework for Nonconvex and Half Quadratic Optimization

For some typical and widely used non-convex half-quadratic regularization models and the Ambrosio-Tortorelli approximate Mumford-Shah model, based on the Kurdyka-\L ojasiewicz analysis and the recent nonconvex proximal algorithms, we developed an efficient preconditioned framework aiming at the linear subproblems that appeared in the nonlinear alternating minimization procedure. Solving large-scale linear subproblems is always important and challenging for lots of alternating minimization algorithms. By cooperating the efficient and classical preconditioned iterations into the nonlinear and nonconvex optimization, we prove that only one or any finite times preconditioned iterations are needed for the linear subproblems without controlling the error as the usual inexact solvers. The proposed preconditioned framework can provide great flexibility and efficiency for dealing with linear subproblems and guarantee the global convergence of the nonlinear alternating minimization method simultaneously.
A Deep Graph Reinforcement Learning Model for Improving User Experience in Live Video Streaming

In this paper we present a deep graph reinforcement learning model to predict and improve the user experience during a live video streaming event, orchestrated by an agent/tracker. We first formulate the user experience prediction problem as a classification task, accounting for the fact that most of the viewers at the beginning of an event have poor quality of experience due to low-bandwidth connections and limited interactions with the tracker. In our model we consider different factors that influence the quality of user experience and train the proposed model on diverse state-action transitions when viewers interact with the tracker. In addition, provided that past events have various user experience characteristics we follow a gradient boosting strategy to compute a global model that learns from different events. Our experiments with three real-world datasets of live video streaming events demonstrate the superiority of the proposed model against several baseline strategies. Moreover, as the majority of the viewers at the beginning of an event has poor experience, we show that our model can significantly increase the number of viewers with high quality experience by at least 75% over the first streaming minutes. Our evaluation datasets and implementation are publicly available at https://publicresearch.z13. web.core. windows.net
Neural network approximation for superhedging prices

This article examines neural network‐based approximations for the superhedging price process of a contingent claim in a discrete time market model. First we prove that the α‐quantile hedging price converges to the superhedging price at time 0 for α tending to 1, and show that the α‐quantile hedging price can be approximated by a neural network‐based price. This provides a neural network‐based approximation for the superhedging price at time 0 and also the superhedging strategy up to maturity. To obtain the superhedging price process for t>0$t>0$ , by using the Doob decomposition, it is sufficient to determine the process of consumption. We show that it can be approximated by the essential supremum over a set of neural networks. Finally, we present numerical results.
P4COM: In-Network Computation with Programmable Switches

Traditionally, switches only provide forwarding services and have no credits on computation in distributed computing frameworks. The emerging programmable switches make in-network computing (INC) possible, i.e., offloading some computation to the switch data plane. While some proposals have attempted to offload computation onto special hardwares (e.g., NetFPGA), many practical issues have not been addressed. Therefore, we propose P4COM - a user-friendly, memory-efficient, and fault-tolerant framework realizing in-network computation (e.g., MapReduce) with programmable switches. P4COM consists of three modules. First, P4COM automatically translates application logic to switch data plane programs with a lightweight interpreter. Second, P4COM adopts a memory management policy to efficiently utilize the limited switch on-chip memory. Third, P4COM provides a cutting-payload mechanism to handle packet losses. We have built a P4COM prototype with a Barefoot Tofino switch and multiple commodity servers. Through a combination of testbed experiments and large-scale simulations, we show that P4COM is able to achieve line-rate processing at 10Gbps links, and can increase the data shuffling throughput by 2-5 times for the MapReduce-style applications.