Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of electric locomotive traction of the passenger vehicle Ride quality in longitudinal train dynamics in the context of Indian railways

Rail transport is one of the major modes of transportation in India. The dynamic performance of a railway vehicle, in terms of serviceability and safety, was evaluated on the basis of specific performance indices such as ride quality and comfort. This paper is an attempt to analyse the longitudinal dynamic model of the passenger train for the attainment of better vehicle ride quality and comfort. The modelling has been done in two phases: in the first phase, a mathematical model was analysed which was further used for the evaluation of the longitudinal dynamic forces that appear on the buffer, draw gear and fastening devices during the braking process. The second phase includes simulation of longitudinal train dynamics considering the effects of forces like rolling resistance, brake force, coupler force and the gravitational force acting externally on a vehicle for better ride quality and comfort in comparison to the traction effect on WAP-5 and WAP-7 Indian locomotives. The Sperling ride index and ISO 2631 were used respectively to calculate the ride quality and comfort using filtered RMS accelerations. Simulation results revealed that WAP-5 was better at an initial speed of 30 km.h-1 and 45 km.h-1 , whereas, the WAP-7 gave better ride quality and comfort than WAP-5 at and above the speed of 60 km.h-1 .
Wideband Spectrum Compressed Blind Sensing without Reconstruction Based on Higher-order Moment

To improve the detection performance and detection speed of wideband spectrum sensing algorithms with little priori knowledge, a scheme of cooperative wideband compressed blind sensing without reconstruction based on higher-order moment (CWCBS-HOM for short) is proposed. The test statistic is extracted from third-order moment of the compressed samples. The proposed method is blind in the sense since it need neither signal reconstruction, nor the prior knowledge of the primary user (PU) signal. Theoretical analysis and simulation results show that the proposed method can enhance the spectrum sensing capability with low computational complexity and high speed. Keywords-Wideband spectrum sensing; Compressed sensing; Higher order moment; Blind sensing
Pitch and flight path controller design for F-16 aircraft using combination of LQR and EA techniques

In a linear regulator problem with a quadratic cost function, there are two weighting matrices which determine the feedback gain matrix, and thereby the closed loop eigenvalues and eigenvectors of the system. To find the weighting matrices leading to a desired eigenstructure, trial and error is a common approach. In spite of its simplicity, trial and error is too time-consuming and does not guarantee achieving expected results. In this regard, this paper presents a step-by-step algorithm for calculation of state and control weighting matrices in such a way that the pre-specified eigenstructure is generated with acceptable accuracy. Accordingly, once the designer chooses preferred eigenvalues and eigenvectors, the feedback gain matrix will be available uniquely. In design procedure, tracking of flight path and pitch attitude commands for an F-16 aircraft will be provided. To achieve this aim completely, the feedforward gain matrix necessary for getting a zero steady state error is calculated based on the mathematical model of the aircraft as well as the pseudoinverse matrix concept. Various simulations demonstrate that the resulted controller has good performance, gives the desired eigenstructure satisfactorily and also shows the expected robustness of a linear quadratic regulator.
Non‐Isometric Shape Matching via Functional Maps on Landmark‐Adapted Bases

We propose a principled approach for non‐isometric landmark‐preserving non‐rigid shape matching. Our method is based on the functional map framework, but rather than promoting isometries we focus on near‐conformal maps that preserve landmarks exactly. We achieve this, first, by introducing a novel landmark‐adapted basis using an intrinsic Dirichlet‐Steklov eigenproblem. Second, we establish the functional decomposition of conformal maps expressed in this basis. Finally, we formulate a conformally‐invariant energy that promotes high‐quality landmark‐preserving maps, and show how it can be optimized via a variant of the recently proposed ZoomOut method that we extend to our setting. Our method is descriptor‐free, efficient and robust to significant mesh variability. We evaluate our approach on a range of benchmark datasets and demonstrate state‐of‐the‐art performance on non‐isometric benchmarks and near state‐of‐the‐art performance on isometric ones.
Deep Learning-based Schemes for Singularly Perturbed Convection-Diffusion Problems

Deep learning-based numerical schemes such as Physically Informed Neural Networks (PINNs) have recently emerged as an alternative to classical numerical schemes for solving Partial Differential Equations (PDEs). They are very appealing at first sight because implementing vanilla versions of PINNs based on strong residual forms is easy, and neural networks offer very high approximation capabilities. However, when the PDE solutions are low regular, an expert insight is required to build deep learning formulations that do not incur in variational crimes. Optimization solvers are also significantly challenged, and can potentially spoil the final quality of the approximated solution due to the convergence to bad local minima, and bad generalization capabilities. In this paper, we present an exhaustive numerical study of the merits and limitations of these schemes when solutions exhibit low-regularity, and compare performance with respect to more benign cases when solutions are very smooth. As a support for our study, we consider singularly perturbed convection-diffusion problems where the regularity of solutions typically degrades as certain multiscale parameters go to zero.
Does soccer players’ sprint follow the Fujimura-Sugihara model?: validation using soccer tracking data

In soccer game analysis, the widespread availability of play-by-play and tracking data has made it possible to test mathematical models that have been discussed mainly theoretically. One of the essential models in soccer game analysis is a motion model that predicts the arrival point of a player in t s. Although many space evaluation and pass prediction methods rely on motion models, the validity of each has not been fully clariﬁed. This study focuses on the motion model proposed by Fujimura and Sugihara (Fujimura-Sugihara model) under sprint conditions based on the equation of motion. A previous study indicated that the Fujimura-Sugihara model is ineﬀective for soccer games because it generates a circular arrival region. In this study, we quantitatively examined the validity and limitations of the Fujimura-Sugihara model using soccer tracking data. We show that the boundary of the player’s arrival region is circular rather than elliptical. Furthermore, we show that the initial speed dependence of the arrival region satisﬁes the solution of the Fujimura-Sugihara model. We also proposed a method to estimate kinetic parameters directly from tracking data and discussed the limitations of the Fujimura-Sugihara model for soccer games.
GeV Signatures of Short Gamma-Ray Bursts in Active Galactic Nuclei

The joint detection of gravitational waves and the gamma-ray counterpart of a binary neutron star merger event, GW170817, unambiguously validates the connection between short gamma-ray bursts and compact binary object (CBO) mergers. We focus on a special scenario where short gamma-ray bursts produced by CBO mergers are embedded in disks of active galactic nuclei (AGNs), and we investigate the γ-ray emission produced in the internal dissipation region via synchrotron, synchrotron self-Compton, and external inverse Compton (EIC) processes. In this scenario, isotropic thermal photons from the AGN disks contribute to the EIC component. We show that a low-density cavity can be formed in the migration traps, leading to the embedded mergers producing successful GRB jets. We find that the EIC component would dominate the GeV emission for typical CBO mergers with an isotropic-equivalent luminosity of L j,iso = 1048.5 erg s−1 that are located close to the central supermassive black hole. Considering a long-lasting jet of duration T dur ∼ 102–103 s, we find that the future Cherenkov Telescope Array (CTA) will be able to detect its 25–100 GeV emission out to a redshift z = 1.0. In the optimistic case, it is possible to detect the on-axis extended emission simultaneously with GWs within one decade using MAGIC, H.E. S.S. , VERITAS, CTA, and LHAASO-WCDA. Early diagnosis of prompt emissions with Fermi-GBM and HAWC can provide valuable directional information for the follow-up observations.
CODEC: Complex Document and Entity Collection

CODEC is a document and entity ranking benchmark that focuses on complex research topics. We target essay-style information needs of social science researchers, i.e. "How has the UK's Open Banking Regulation benefited Challenger Banks". CODEC includes 42 topics developed by researchers and a new focused web corpus with semantic annotations including entity links. This resource includes expert judgments on 17,509 documents and entities (416.9 per topic) from diverse automatic and interactive manual runs. The manual runs include 387 query reformulations, providing data for query performance prediction and automatic rewriting evaluation. CODEC includes analysis of state-of-the-art systems, including dense retrieval and neural re-ranking. The results show the topics are challenging with headroom for document and entity ranking improvement. Query expansion with entity information shows significant gains on document ranking, demonstrating the resource's value for evaluating and improving entity-oriented search. We also show that the manual query reformulations significantly improve document ranking and entity ranking performance. Overall, CODEC provides challenging research topics to support the development and evaluation of entity-centric search methods.
Weakly nonlinear analysis of pattern formation in active suspensions

Abstract We consider the Saintillan–Shelley kinetic model of active rod-like particles in Stokes flow (Saintillan & Shelley, Phys. Rev. Lett., vol. 100, issue 17, 2008a, 178103; Saintillan & Shelley, Phys. Fluids, vol. 20, issue 12, 2008b, 123304), for which the uniform isotropic suspension of pusher particles is known to be unstable in certain settings. Through weakly nonlinear analysis accompanied by numerical simulations, we determine exactly how the isotropic steady state loses stability in different parameter regimes. We study each of the various types of bifurcations admitted by the system, including both subcritical and supercritical Hopf and pitchfork bifurcations. Elucidating this system's behaviour near these bifurcations provides a theoretical means of comparing this model with other physical systems that transition to turbulence, and makes predictions about the nature of bifurcations in active suspensions that can be explored experimentally.
Multi-Tree Guided Efficient Robot Motion Planning

Motion Planning is necessary for robots to complete different tasks. Rapidly-exploring Random Tree (RRT) and its variants have been widely used in robot motion planning due to their fast search in state space. However, they perform not well in many complex environments since the motion planning needs to simultaneously consider the geometry constraints and differential constraints. In this article, we propose a novel robot motion planning algorithm that utilizes multi-tree to guide the exploration and exploitation. The proposed algorithm maintains more than two trees to search the state space at first. Each tree will explore the local environment. The tree starts from the root will gradually collect information from other trees and grow towards the goal state. This simultaneous exploration and exploitation method can quickly find a feasible trajectory. We compare the proposed algorithm with other popular motion planning algorithms. The experiment results demonstrate that our algorithm achieves the best performance on different evaluation metrics.
An implementation of Hasselmann’s paradigm for stochastic climate modelling based on stochastic Lie transport

A generic approach to stochastic climate modelling is developed for the example of an idealised Atmosphere-Ocean model that rests upon Hasselmann’s paradigm for stochastic climate models. Namely, stochasticity is incorporated into the fast moving atmospheric component of an idealised coupled model by means of stochastic Lie transport, while the slow moving ocean model remains deterministic. More specifically the stochastic model stochastic advection by Lie transport (SALT) is constructed by introducing stochastic transport into the material loop in Kelvin’s circulation theorem. The resulting stochastic model preserves circulation, as does the underlying deterministic climate model. A variant of SALT called Lagrangian-averaged (LA)-SALT is introduced in this paper. In LA-SALT, we replace the drift velocity of the stochastic vector field by its expected value. The remarkable property of LA-SALT is that the evolution of its higher moments are governed by deterministic equations. Our modelling approach is substantiated by establishing local existence results, first, for the deterministic climate model that couples compressible atmospheric equations to incompressible ocean equation, and second, for the two stochastic SALT and LA-SALT models.
WG-VITON: Wearing-Guide Virtual Try-On for Top and Bottom Clothes

Studies of virtual try-on (VITON) have been shown their effectiveness in utilizing the generative neural network for virtually exploring fashion products, and some of recent researches of VITON attempted to synthesize human image wearing given multiple types of garments (e.g., top and bottom clothes). However, when replacing the top and bottom clothes of the target human, numerous wearing styles are possible with a certain combination of the clothes. In this paper, we address the problem of variation in wearing style when simultaneously replacing the top and bottom clothes of the model. We introduce Wearing-Guide VITON (i.e., WG-VITON) which utilizes an additional input binary mask to control the wearing styles of the generated image. Our experiments show that WG-VITON effectively generates an image of the model wearing given top and bottom clothes, and create complicated wearing styles such as partly tucking in the top to the bottom
Turing machine interaction problem

Аннотация. The article introduces some ideas for solving special cases of the following problem, proposed in a somewhat generalized form by Marcus Hutter in 2000. Given two Turing machines A and C, it is required to build a Turing machine B, such that after interacting of A and B on a shared tape for a fixed number of iterations, the machine C outputs 1 on the communication protocol of A and B. Details in the introduction.
Collaborative Product Design And Realization In Mechanical Engineering Technology Curricula

Mechanical and manufacturing engineers are working in a global environment which requires new skills. New term developed over the last decade for them: the “knowledge workers”. Those engineers need a new skill set to work successfully on collaborative projects involving people located not only at different companies, but also potentially on different continents. This new skill set can be developed using secure web sites for storing product related data, virtual chat rooms which would enable students to share presentations and applications. Those tools could enable faster collaboration on those projects. They could save that information as knowledge for their future capstone projects and alike. Working around the clock has recently become the goal of every company. Collaboration needs to be facilitated by digital means such as web portals, voice over internet protocol, and video and audio conferencing. Mechanical Engineering Technology students should be able to adapt quickly into this new working environment after their graduation. The student projects should involve working on web-based collaborative projects. Midwest Coalition for Comprehensive Design Education, National Science Foundation founded project, various projects were developed in the web based collaborative environment.
KeypointNeRF: Generalizing Image-based Volumetric Avatars using Relative Spatial Encoding of Keypoints

Image-based volumetric humans using pixel-aligned features promise generalization to unseen poses and identities. Prior work leverages global spatial encodings and multi-view geometric consistency to reduce spatial ambiguity. However, global encodings often suffer from overfitting to the distribution of the training data, and it is difficult to learn multi-view consistent reconstruction from sparse views. In this work, we investigate common issues with existing spatial encodings and propose a simple yet highly effective approach to modeling high-fidelity volumetric humans from sparse views. One of the key ideas is to encode relative spatial 3D information via sparse 3D keypoints. This approach is robust to the sparsity of viewpoints and cross-dataset domain gap. Our approach outperforms state-of-the-art methods for head reconstruction. On human body reconstruction for unseen subjects, we also achieve performance comparable to prior work that uses a parametric human body model and temporal feature aggregation. Our experiments show that a majority of errors in prior work stem from an inappropriate choice of spatial encoding and thus we suggest a new direction for high-fidelity image-based human modeling. https://markomih.github. io/KeypointNeRF
Explaining the effectiveness of fear extinction through latent-cause inference

Acquiring fear responses to predictors of aversive outcomes is crucial for survival. At the same time, it is important to be able to modify such associations when they are maladaptive, for instance in treating anxiety and trauma-related disorders. Standard extinction procedures can reduce fear temporarily, but with sufficient delay or with reminders of the aversive experience, fear often returns. The latent-cause inference framework explains the return of fear by presuming that animals learn a rich model of the environment, in which the standard extinction procedure triggers the inference of a new latent cause, preventing the extinguishing of the original aversive associations. This computational framework had previously inspired an alternative extinction paradigm -- gradual extinction -- which indeed was shown to be more effective in reducing fear. However, the original framework was not sufficient to explain the pattern of results seen in the experiments. Here, we propose a formal model to explain the effectiveness of gradual extinction, in contrast to the ineffectiveness of standard extinction and a gradual reverse control procedure. We demonstrate through quantitative simulation that our model can explain qualitative behavioral differences across different extinction procedures as seen in the empirical study. We verify the necessity of several key assumptions added to the latent-cause framework, which suggest potential general principles of animal learning and provide novel predictions for future experiments.
Manufacturing Optimization through Intelligent Techniques (Manufacturing Engineering and Materials Processing)

Proceedings of the 6th CIRP-Sponsored International Conference on Digital Enterprise TechnologyOptimization of Manufacturing ProcessesMeta-heuristic Optimization TechniquesEvolutionary Computing in Advanced ManufacturingOptimizing Big Data Management and Industrial Systems with Intelligent TechniquesConfigurable Intelligent Optimization AlgorithmComputer-Based Analysis of the Stochastic Stability of Mechanical Structures Driven by White and Colored NoiseHandbook of Machining with Grinding WheelsDynamics in LogisticsComplex System Modelling and Control Through Intelligent Soft ComputationsHandbook of Lapping and PolishingIntelligent ManufacturingCoordinate Measuring Machines and SystemsAdvances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production SystemsOptimization of Manufacturing Systems Using the Internet of ThingsArtificial Intelligence Techniques for Networked Manufacturing Enterprises ManagementEnabling Manufacturing Competitiveness and Economic SustainabilityNature-Inspired Optimization in Advanced Manufacturing Processes and SystemsManufacturing Processes & Materials, 5th EditionData-Driven Optimization of Manufacturing ProcessesProcess Planning Optimization in Reconfigurable Manufacturing SystemsAdvanced Engineering Optimization Through Intelligent TechniquesOptimizing Big Data Management and Industrial Systems With Intelligent TechniquesIntelligent Optimization Modelling in Energy ForecastingProceedings of International Conference on Intelligent Manufacturing and AutomationManufacturing Optimization through Intelligent Techniques (2006)Supply Chain Optimization, Design, and Management: Advances and Intelligent MethodsAdvanced Engineering Optimization Through Intelligent TechniquesHot Deformation and Processing of Aluminum AlloysApplications of Artificial Intelligence Techniques in Industry 4.0Futuristic Trends in Intelligent ManufacturingComputational Methods for Optimizing Manufacturing Technology: Models and TechniquesKnowledge-Based Intelligent Techniques in IndustryMetalworking FluidsManufacturing Intelligence for Industrial Engineering: Methods for System Self-Organization, Learning, and AdaptationIntelligent Optimisation TechniquesConfigurable Intelligent Optimization AlgorithmIntelligent Decisionmaking Models for Production and Retail OperationsHandbook of Research on Artificial Intelligence Techniques and AlgorithmsProduct Design for Manufacture and Assembly
A lipid-structured mathematical model of atherosclerosis with macrophage proliferation

We extend the lipid-structured model for atherosclerotic plaque development of Ford et al. (2019) to account for macrophage proliferation. Proliferation is modelled as a non-local decrease in the lipid structural variable that is similar to the treatment of cell division in size-structured models (e.g. Efendiev et al. (2018)). Steady state analysis indicates that proliferation assists in reducing eventual necrotic core size and acts to spread the lipid load of the macrophage population amongst the cells. The relative contribution of plaque macrophages by proliferation and recruitment from the bloodstream is also examined. The model suggests that a more proliferative plaque differs from an equivalent (same lipid content and cell count) recruitment-dominant plaque only in the way lipid is distributed amongst the macrophages.
From Trade-only to Zero-Value NFTs: The Asset Proxy NFT Paradigm in Web3

Many implementations of smart contracts available in NFT marketplaces today allow the modification of NFT token attributes, without any specific mechanism to control the consistency with off-chain metadata. We believe this is a weakness in overall design of NFTs today. We propose a computation model called the Asset Proxy NFT that guarantees the consistency between the NFT token (on-chain) and its corresponding asset metadata (off-chain). In general, the proposed model can be applied to any type of NFT that requires immutability or controlled mutability of metadata. A second contribution of this paper is the notion of the NFT design patterns which recognizes that a coherent framework for dealing with hybrid assets is required, and that for specific hybrid-asset deployments, suitable technological components must be utilized under the framework.