Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey of Simple Geometric Primitives Detection Methods for Captured 3D Data

The amount of captured 3D data is continuously increasing, with the democratization of consumer depth cameras, the development of modern multi‐view stereo capture setups and the rise of single‐view 3D capture based on machine learning. The analysis and representation of this ever growing volume of 3D data, often corrupted with acquisition noise and reconstruction artefacts, is a serious challenge at the frontier between computer graphics and computer vision. To that end, segmentation and optimization are crucial analysis components of the shape abstraction process, which can themselves be greatly simplified when performed on lightened geometric formats. In this survey, we review the algorithms which extract simple geometric primitives from raw dense 3D data. After giving an introduction to these techniques, from the acquisition modality to the underlying theoretical concepts, we propose an application‐oriented characterization, designed to help select an appropriate method based on one's application needs and compare recent approaches. We conclude by giving hints for how to evaluate these methods and a set of research challenges to be explored.
A Queueing Analysis of a Security-Check System with Two Types of Inspection Channels

This paper studies a security-check system at the high-speed railway stations with two types of inspection channels, including Normal channels and Green channels. Here, general passengers go through the strict inspections in the Normal channels with unlimited waiting space, whereas special passengers are offered a fast inspection in the Green channels with a finite capacity. To investigate the stationary behavior of the studied security-check system, a queueing model is developed through deriving the two-dimensional quasi-birth-and-death (QBD) process. In addition, a matrix-geometric solution is presented in the paper to determine the steady-state probability and evaluate the system performance of interest, such as the average queue length and average waiting time. A sensitivity analysis is also conducted in the numerical experiments to demonstrate the managerial impact of varying the finite capacity in Green channels on the overall system performance. Keyword—security check; queueing system; green channels; finite capacity; matrix geometric method
Physical and Microwave Radiative Properties of Precipitating Clouds. Part II: A Parametric 1D Rain-Cloud Model for Use in Microwave Radiative Transfer Simulations

Abstract A highly simplified, yet meteorologically realistic and flexible, parametric model is described for generating hydrometeor profiles and other environmental properties relevant to microwave radiative transfer calculations in quasi-stratiform rain clouds. With this model, it is possible, via 19 adjustable parameters, to vary cloud and environmental properties, including hydrometeor size distributions and densities, in a continuous, yet self-consistent, fashion and to assess the impact of these changes on computed multichannel microwave brightness radiances. It is also possible to utilize gradient descent methods to find plausible combinations of cloud properties that explain observed multichannel microwave radiances in rain clouds. Potential applications of the above model include 1) gaining insight into effective microphysical properties, for microwave radiative transfer purposes, of actual precipitating cloud systems and 2) accurate extrapolation of observed microwave multichannel radiances in ra.
A FIRST APPROACH FOR RAPID COPYING OF FREE-FORM SURFACES IN 5-AXIS MACHINING

The paper deals with the various steps of free-form copying from surface acquisition to machining using 5 « positioned » axes. The originality of the proposed approach is to be free from surface reconstruction. The object surface acquisition is performed using an optical digitizing system in order to obtain a representative image of the real surface. Relative positions and orientations sensor/surface are chosen to guarantee the completeness of the digitized points as regards the whole object surface. The next step concerns the pre-processing of the points. Indeed, the data are discrete, non homogeneous, dense and noisy. Cleaning, filtering and densification are essential steps. A voxel representation of the data is carried out with the objective to recreate " pseudo-continuity ". Therefore, the calculation of local characteristics such as local normal to the discrete data is thus possible. The last step is the choice of optimal part setups for the machining using 5 positioned axes. To initialize the automatic research of the best setup , each relative orientation sensor/surface may define one setup. For each setup , the trajectory of the ball-en cutter tool is calculated in 3 axes, using the offset inverse method. The feasibility of the whole approach is illustrated through an example
Accretion Disk Parameters Determined from the Great 2015 Flare of OJ 287

In the binary black hole model of OJ 287, the secondary black hole orbits a much more massive primary, and impacts on the primary accretion disk at predictable times. We update the parameters of the disk, the viscosity, α, and the mass accretion rate, . We find α = 0.26 ± 0.1 and in Eddington units. The former value is consistent with Coroniti, and the latter with Marscher & Jorstad. Predictions are made for the 2019 July 30 superflare in OJ 287. We expect that it will take place simultaneously at the Spitzer infrared channels, as well as in the optical, and that therefore the timing of the flare in optical can be accurately determined from Spitzer observations. We also discuss in detail the light curve of the 2015 flare, and find that the radiating volume has regions where bremsstrahlung dominates, as well as regions that radiate primarily in synchrotron radiation. The former region produces the unpolarized first flare, while the latter region gives rise to a highly polarized second flare.
Online Supervised Learning for Traffic Load Prediction in Framed-ALOHA Networks

Predicting the current backlog, or traffic load, in framed-ALOHA networks enables the optimization of resource allocation, e.g., of the frame size. However, this prediction is made difficult by the lack of information about the cardinality of collisions and by possibly complex packet generation statistics. Assuming no prior information about the traffic model, apart from a bound on its temporal memory, this letter develops an online learning-based adaptive traffic load prediction method that is based on recurrent neural networks (RNN) and specifically on the long short-term memory (LSTM) architecture. In order to enable online training in the absence of feedback on the exact cardinality of collisions, the proposed strategy leverages a novel approximate labeling technique that is inspired by the method of moments (MOM) estimators. Numerical results show that the proposed online predictor considerably outperforms conventional methods and is able to adapt changing traffic statistics.
Privacy Preserving Multi Keyword Ranked Search Over Encrypted Cloud data

Cloud computing is modern technology as a new computing model in number of business domains. Large numbers of large scale departments are startingto shift the data on to the cloud environment. With the advantage of storage as a service many enterprises are moving their valuable data to the cloud, since it costs less, easily scalable and can be accessed from anywhere any time. Improved dynamic multi-keyword ranking search scheme with top key via encrypted cloud data that simultaneously supports dynamic update operations as deleting and inserting documents. Greedy depth first search algorithm is provided for efficiency multi keywords on place and index structure. Cryptography is one of the establishing trust models. Searchable security is a cryptographic method to provide security. In number of researchers have been working on developing security and efficient searchable encryption methods. We take new effective cryptographic techniques based on data structures like CRSA and B-Tree to enhance the level of security. We propose new multi-keyword search query over encrypted cloud data in retrieving top k scored documents.The vector space model and TFIDF model are used to construct index and query generation. This paper focuses on multi keyword search based on ranking over an encrypted cloud data (MRSE). The search uses the feature of similarity and inner product similarity matching. We propose to support the top-k Multi-full-text search security and performance analysis show that the proposed scheme guarantees a high safety and practicality and dynamic update operations, such as deleting and inserting docum
OPTIMIZATION OF PARAMETERS AFFECTING THE PERFORMANCE OF CNC MACHINE

CNC machine tool is broadly utilized by manufacturing engineers and production personnel to rapidly and successfully set up manufacturing forms for new products. This study talks about an investigation concerning the utilization of Taguchi Parameter Design approach for Parametric Study of CNC processing activity for Surface Roughness and Material Removal Rate as a response variable. The Taguchi parameter plan strategy is a efficient experimental technique in which a response variable can be examine, utilizing less experimental run than factorial design strategy. The control parameters for this operation included: spindle speed, feed rate and depth of cut.
Novel mechanism for weak magnetization with high Curie temperature observed in H-adsorption on graphene

To elucidate the physics associated with the magnetism observed in nominally nonmagnetic materials containing only sp-electrons, we have developed an extreme model to simulate the adsorption of H (in a straight-line form) on graphene. Our first principles calculations for the model result in a ferromagnetic ground state at a high temperature with a magnetic moment of one Bohr magneton per H atom. The removal of p z-orbitals from sublattice B of graphene introduces p z-vacancies. The p z-vacancy-induced states are created not because of the variations in interatomic interactions but because of the p z-orbital imbalance between two sublattices (A and B) of the conjugated p z-orbital network. Therefore, some critical requirements should be satisfied to create these states (denoted as ) to avoid further imbalances and to minimally affect the conjugated p z-orbital network. The requirements for the creation of can be given as follows: (1) consists of p z-orbitals of only the atoms in sublattice A, (2) the spatial wavefunction of is antisymmetric, and (3) in principle, extends over the entire crystal without decaying, unless other p z-vacancies are encountered. Both the origin of spin polarization and the magnetic ordering of the model can be attributed to the aforementioned requirements.
Localization with signal-based signature distance

Node localization plays a key and basic role for wireless sensor network. Existing range-free localization approaches suffer from precision limit of positioning, while range-based solutions may obtain good accuracy but pay high costs for ranging hardware. Instead of directly mapping received signal strength values into physical distances, a novel localization scheme with signal-based signature distance estimation is proposed for wireless sensor network. More specifically, we first quantify the near-far relationship between neighbor nodes through received signal strength comparison, and then the distance between non-neighbor nodes is calculated by an improved shortest path algorithm. Based on the obtained node distance, a relative map can be constructed by multidimensional scaling technology. Eventually, the locations of nodes can be acquired by virtue of Procrustes analysis method. For purpose of verifying the effectiveness of our design, several simulations are made in a sensor network with 200 randomly deployed nodes, and two experiments are implemented in real outdoor environments: a linear network and a two-dimensional grid network with 25 nodes. It is remarkable from results that the proposed scheme performs better in positioning and reduces localization errors by as much as 37% compared with state-of-the-art methods.
Medical Image Registration Using a New Information Discrepancy Measure

Function of Degree of Disagreement (FDOD), a new measure of information discrepancy, was proposed originally to quantify the discrepancy of multiple sequences. On the one hand, this function has been successfully used in many other fields recently. On the other hand, the Kullback-Leibler divergence (KLD)measure has made great success in multimodality image registration. Comparing these two measures, we find that the FDOD has some peculiar mathematical properties superior to the KLD measure. Motivated by these facts, in this contribution, we introduce the FDOD function to solve the (3-D) multimodality medical image registration problem. Furthermore, we propose a normalized version of the FDOD function which will be more suitable to image registration. Finally, we carried out many experiments to validate our methods. Our results illustrate that the proposed registration methods based on the FDOD function and the normalized FDOD function are feasible and competitive, and compared with the methods based on mutual information and normalized mutual information, the proposed normalized FDOD function performs best in most cases, obtaining subvoxel registration accuracy with higher speed and higher success rate.
A Tag-based Recommendation Algorithm Integrating Short-term and Long-term Interests of Users

In the collaborative tagging system, tags of users contain rich information on personalized preference, and time stamps of users show their interest changes. Users’ interest has the varied timeliness, but the existing recommendation algorithm fusing time information only emphasizes the short-term interest of users but fails to dig into the long-term stable interest of users and thus presents the low recommendation precision. In this paper, we propose a tag-based recommendation algorithm integrating short-term and long-term interests of users. The algorithm first builds the short-term and long-term interest characteristics of users based on tags according to the use frequency, time decay, life cycle and volatility; then it maps the characteristics to the resources tagged by the user to form a user-resource pseudo-scoring matrix. Finally, it calculates the set of the nearest neighbors of user with the matrix to give recommendations. The verification on the delicious data set shows that the algorithm improves recommendation precision and diversity compared with other classical algorithms.
Inverting Spectrogram Measurements via Aliased Wigner Distribution Deconvolution and Angular Synchronization

We propose a two-step approach for reconstructing a signal $\textbf x\in \mathbb{C}^d$ from subsampled discrete short-time Fourier transform magnitude (spectogram) measurements: first, we use an aliased Wigner distribution deconvolution approach to solve for a portion of the rank-one matrix $\widehat{\textbf{x}}\widehat{\textbf{x}}^{*}. $ Secondly, we use angular synchronization to solve for $\widehat{\textbf{x}}$ (and then for $\textbf{x}$ by Fourier inversion). Using this method, we produce two new efficient phase retrieval algorithms that perform well numerically in comparison to standard approaches and also prove two theorems; one which guarantees the recovery of discrete, bandlimited signals $\textbf{x}\in \mathbb{C}^{d}$ from fewer than $d$ short-time Fourier transform magnitude measurements and another which establishes a new class of deterministic coded diffraction pattern measurements which are guaranteed to allow efficient and noise robust recovery.
Elemental and molecular abundances in comet 67P/Churyumov-Gerasimenko

Comets are considered to be some of the most pristine and unprocessed Solar system objects accessible to in situ exploration. Investigating their molecular and elemental composition takes us on a journey back to the early period of our Solar system and possibly even further. In this work, we deduce the bulk abundances of the major volatile species in comet 67P/Churyumov-Gerasimenko, the target of the European Space Agency’s (ESA) Rosetta mission. The basis are measurements obtained with the ROSINA instrument suite on board the Rosetta orbiter during a suitable period of high outgassing near perihelion. The results are combined with both gas and dust composition measurements published in the literature. This provides an integrated inventory of the major elements present in the nucleus of 67P/Churyumov-Gerasimenko. Similar to comet 1P/Halley, which was visited by ESA’s Giotto spacecraft in 1986, comet 67P/Churyumov-Gerasimenko also shows near-solar abundances of oxygen and carbon, whereas hydrogen and nitrogen are depleted compared to solar. Still, the degree of devolatilization is lower than that of inner Solar system objects, including meteorites and the Earth. This supports the idea that comets are amongst the most pristine objects in our Solar system.
Biophysically grounded mean-field models of neural populations under electrical stimulation

Electrical stimulation of neural systems is a key tool for understanding neural dynamics and ultimately for developing clinical treatments. Many applications of electrical stimulation affect large populations of neurons. However, computational models of large networks of spiking neurons are inherently hard to simulate and analyze. We evaluate a reduced mean-field model of excitatory and inhibitory adaptive exponential integrate-and-fire (AdEx) neurons which can be used to efficiently study the effects of electrical stimulation on large neural populations. The rich dynamical properties of this basic cortical model are described in detail and validated using large network simulations. Bifurcation diagrams reflecting the network’s state reveal asynchronous up- and down-states, bistable regimes, and oscillatory regions corresponding to fast excitation-inhibition and slow excitation-adaptation feedback loops. The biophysical parameters of the AdEx neuron can be coupled to an electric field with realistic field strengths which then can be propagated up to the population description. We show how on the edge of bifurcation, direct electrical inputs cause network state transitions, such as turning on and off oscillations of the population rate. Oscillatory input can frequency-entrain and phase-lock endogenous oscillations. Relatively weak electric field strengths on the order of 1 V/m are able to produce these effects, indicating that field effects are strongly amplified in the network. The effects of time-varying external stimulation are well-predicted by the mean-field model, further underpinning the utility of low-dimensional neural mass models.
Forecasting Mobile Traffic with Spatiotemporal correlation using Deep Regression

The concept of mobility prediction represents one of the key enablers for an efficient management of future cellular networks, which tend to be progressively more elaborate and dense due to the aggregation of multiple technologies. In this letter we aim to investigate the problem of cellular traffic prediction over a metropolitan area and propose a deep regression (DR) approach to model its complex spatio-temporal dynamics. DR is instrumental in capturing multi-scale and multi-domain dependences of mobile data by solving an image-to-image regression problem. A parametric relationship between input and expected output is defined and grid search is put in place to isolate and optimize performance. Experimental results confirm that the proposed method achieves a lower prediction error against stateof-the-art algorithms. We validate forecasting performance and stability by using a large public dataset of a European Provider.
Pointwise Convergence of the Schr\"odinger Flow

In this paper we address the question of the pointwise almost everywhere limit of nonlinear Schrodinger flow to the initial data, in both the continuous and the periodic settings. Then we show how, in some cases, certain smoothing effects for the non-homogeneous part of the solution can be used to upgrade to an everywhere convergence to zero of that part and we discuss the sharpness of the results obtained. We also use randomization techniques to prove almost everywhere convergence with much less regularity of the initial data, hence showing how more generic results can be obtained.
A Tensor-Based Approach to Joint Channel Estimation/Data Detection in Flexible Multicarrier MIMO Systems

Filter bank-based multicarrier (FBMC) systems have attracted increasing attention recently in view of their many advantages over the classical cyclic prefix (CP)-based orthogonal frequency division multiplexing (CP-OFDM) modulation. However, their more advanced structure (resulting in, for example, self interference) complicates signal processing tasks at the receiver, including synchronization, channel estimation and equalization. In a multiple-input multiple-output (MIMO) configuration, the multi-antenna interference has also to be taken into account. (Semi-) blind receivers, of increasing interest in (massive) MIMO systems, have been little studied so far for FBMC and mainly for the single-antenna case only. The design of such receivers for flexible MIMO FBMC systems, unifying a number of existing FBMC schemes, is considered in this paper through a tensor-based approach, which is shown to encompass existing joint channel estimation and data detection approaches as special cases, adding to their understanding and paving the way to further developments. Simulation-based results are included, for realistic transmission models, demonstrating the estimation and detection performance gains from the adoption of these receivers over their training only-based counterparts.
On the ultra-compact nature of the neutron star system 1RXS J170854.4−321857: insights from X-ray spectroscopy

The relatively small family of ultra-compact X-ray binary systems is of great interest for many areas of astrophysics. We report on a detailed X-ray spectral study of the persistent neutron star low-mass X-ray binary 1RXS J170854.4−321857. We analysed two XMM–Newton observations obtained in late 2004 and early 2005 when, in agreement with previous studies, the system displayed an X-ray luminosity (0.5–10 keV) of ${\sim} 1\times 10^{36}\, \mathrm{erg~s}^{-1}$. The spectrum can be described by a Comptonized emission component with Γ ∼ 1.9 and a distribution of seed photons with a temperature of ∼0.23 keV. A prominent residual feature is present at soft energies, which is reproduced by the absorption model if overabundances of Ne and Fe are allowed. We discuss how similar observables, which might be attributed to the peculiar (non-solar) composition of the plasma donated by the companion star, are a common feature in confirmed and candidate ultra-compact systems. Although this interpretation is still under debate, we conclude that the detection of these features along with the persistent nature of the source at such low luminosity and the intermediate–long burst that it displayed in the past confirms 1RXS J170854.4−321857 as a solid ultra-compact X-ray binary candidate.