Miracle-dz/UniFAR-FTUs huggingface dataset:
RFMNet: Robust Deep Functional Maps for unsupervised non-rigid shape correspondence

In traditional deep functional maps for non-rigid shape correspondence, estimating a functional map including high-frequency information requires enough linearly independent features via the least square method, which is prone to be violated in practice, especially at an early stage of training, or costly post-processing, e.g. ZoomOut. In this paper, we introduce a novel strategy to compute the functional map in the deep functional map framework, which jointly considers training stability and more geometric shape features than previous works. We directly ﬁrst produce a pointwise map by resorting to optimal transport and then convert it to an initial functional map. Such a mechanism mitigates the requirements for the descriptor and avoids the training instabilities resulting from the least square solver. Beneﬁtting from the novel strategy, we successfully integrate a state-of-the-art geometric regularization for further optimizing the functional map, which substantially ﬁlters the initial functional map. We show our novel computing functional map module brings more stable training even under encoding the functional map with high-frequency information and faster convergence speed. Considering the point-wise and functional maps, an unsupervised loss is presented for penalizing the correspondence distortion of Delta functions between shapes. To catch discretization-resistant and orientation-aware shape features with our network, we utilize DiffusionNet as a feature extractor. Experimental results demonstrate our apparent superiority in correspondence quality and generalization across various shape discretizations and different datasets compared to the state-of-the-art learning meth-ods.
Concentration waves in dilute bubble/liquid mixtures

In this paper we consider a uniform gas bubble-liquid mixture rising under buoyancy. When the gas volume flux is decreased, while keeping bubble size constant, a smooth transition is formed between the region of lower concentration by volume and the region of initial concentration. This transition travels through the mixture as a permanent wave. We start by discussing the mechanisms which make possible such a permanent wave. The first is its tendency to steepen at the low concentration side. At the root of this is the decrease of the uniform rise velocity, under buoyancy, with increasing concentration. Associated with the motion of the bubbles is the liquid impulse. It is shown that this increases with increasing concentration, producing a reactive force on the bubbles which counteracts buoyancy and reduces the force available to overcome friction. In the transition a balance between these two effects occurs. The internal structure following from this balance is analysed in detail and it is shown that under certain conditions all its properties can be derived from knowledge of the average rise velocities of bubbles in uniform mixtures as a function of concentration. Measurements on these are reported subsequently, followed by a discussion of our experiments on transitions of the kind mentioned in which velocity, thickness etc. of the waves have been measured. The data are compared with the results of the analysis. Order-of-magnitude agreement is found but there are differences as well, requiring further research.
Tractable Data Enriched Distributionally Robust Chance-Constrained Conservation Voltage Reduction

This paper proposes a tractable distributionally robust chance-constrained conservation voltage reduction (DRCC-CVR) method with enriched data-based ambiguity set in unbalanced three-phase distribution systems. The increasing penetration of distributed renewable generation not only brings clean power but also challenges the voltage regulation and energy-saving performance of CVR by introducing high uncertainties to distribution systems. In most cases, the conventional robust optimization methods for CVR only provide conservative solutions. To better consider the impacts of load and PV generation uncertainties on CVR implementation in distribution systems and provide less conservative solutions, this paper develops a data-based DRCC-CVR model with tractable reformulation and data enrichment method. Even though the uncertainties of load and photovoltaic (PV) can be captured by data, the availability of smart meters (SMs) and micro-phasor measurement units (PMUs) is restricted by cost budget. The limited data access may hinder the performance of the proposed DRCC-CVR. Thus, we further present a data enrichment method to statistically recover the high-resolution load and PV generation data from low-resolution data with Gaussian Process Regression (GPR) and Markov Chain (MC) models, which can be used to construct a data-based moment ambiguity set of uncertainty distributions for the proposed DRCC-CVR. Finally, the nonlinear power flow and voltage dependant load models and DRCC with moment-based ambiguity set are reformulated to be computationally tractable and tested on a real distribution feeder in Midwest U.S. to validate the effectiveness and robustness of the proposed method.
Decídetexto México: Recruitment and baseline characteristics of Mexican individuals who smoke in a cessation study

The objective of this study was to evaluate the effectiveness of digital and traditional methods and strategies in the recruitment of Mexican individuals who smoke into a cessation study. Recruitment method refers in general to either digital or traditional recruitment. Recruitment strategies refer to the particular recruitment type utilized within each recruitment method. Traditional recruitment strategies included radio interviews, word of mouth, newspaper advertisement, posters/banners placed in primary healthcare clinics, and medical referrals. Digital recruitment strategies involved emails and study advertisements through social media (i.e., Facebook, Instagram and Twitter) and website. In a 4-month period, 100 Mexican individuals who smoke were successfully enrolled into a smoking cessation study. The majority of participants were enrolled via traditional recruitment strategies (86%) compared to the digital recruitment strategies (14%). Individuals screened in the digital method were more likely to be eligible to participate in the study, compared to the traditional method. Similarly, in comparison to the traditional method, individuals in the digital method were more likely to enroll in the study. However, these differences were not statistically significant. Both traditional and digital strategies made important contributions to the overall recruitment effort.
Affine structure from line correspondences with uncalibrated affine cameras

This paper presents a linear algorithm for recovering 3D affine shape and motion from line correspondences with uncalibrated affine cameras. The algorithm requires a minimum of seven line correspondences over three views. The key idea is the introduction of a one-dimensional projective camera. This converts 3D affine reconstruction of "line directions" into 2D projective reconstruction of "points". In addition, a line-based factorization method is also proposed to handle redundant views. Experimental results both on simulated and real image sequences validate the robustness and the accuracy of the algorithm.
Plasma Equilibrium in a Magnetic Field with Stochastic Regions

The nature of plasma equilibrium in a magnetic field with stochastic regions is examined. It is shown that the magnetic differential equation that determines the equilibrium Pfirsch-Schluter currents can be cast in a form similar to various nonlinear equations for a turbulent plasma, allowing application of the mathematical methods of statistical turbulence theory. An analytically tractable model, previously studied in the context of resonance-broadening theory, is applied with particular attention paid to the periodicity constraints required in toroidal configurations. It is shown that even a very weak radial diffusion of the magnetic field lines can have a significant effect on the equilibrium in the neighborhood of the rational surfaces, strongly modifying the near-resonant Pfirsch-Schluter currents. Implications for the numerical calculation of 3D equilibria are discussed
Learning Bayesian Network Model Structure from Data

Abstract : In this thesis I address the important problem of the determination of the structure of directed statistical models, with the widely used class of Bayesian network models as a concrete vehicle of my ideas. The structure of a Bayesian network represents a set of conditional independence relations that hold in the domain. Learning the structure of the Bayesian network model that represents a domain can reveal insights into its underlying causal structure. Moreover, it can also be used for prediction of quantities that are dif cult, expensive, or unethical to measure such as the probability of lung cancer for example based on other quantities that are easier to obtain. The contributions of this thesis include (a) an algorithm for determining the structure of a Bayesian network model from statistical independence statements; (b) a statistical independence test for continuous variables; and nally (c) a practical application of structure learning to a decision support problem, where a model learned from the database most importantly its structure is used in lieu of the database to yield fast approximate answers to count queries, surpassing in certain aspects other state-of-the-art approaches to the same problem.
Experimental study of the behaviour of mini-charge underwater explosion bubbles near different boundaries

This work experimentally studies the behaviour of underwater explosion bubbles near different boundaries. The results are compared with theoretical and experimental data on cavitation bubbles. Although explosion and cavitation bubbles behave similarly on a macroscopic level, there are still some differences, most of which are from the explosive nature of the explosion bubble. The relationship between bubble migration and the Kelvin impulse, surface inertia m* and surface stiffness k* is investigated. We found that none of them comprehensively predicts the migration of both cavitation and explosion bubbles when boundary elasticity is considered. This elasticity should be considered as a relative value with respect to bubble size. On the other hand, the phase between local vibration of boundaries and the pulsation of bubbles could be a useful predictive index of bubble migration. When using research results developed for cavitation bubbles in relation to explosion bubbles, the material presented here may be useful for pointing out their similarities and differences.
Spectroscopy and photometry of IP Peg in the near-infrared

Spectroscopie entre 7600 et 8300 A de cette nova naine, utilisee pour calculer une courbe de vitesse radiale de l'etoile secondaire. On calcule egalement les masses possibles des composantes. Photometrie centree autour de 9300 A, montrant l'existence d'une large variation ellipsoidale dans la luminosite de l'etoile secondaire
Efficient Algorithms for Max-Margin Structured Classification

We present a general and e! cien t optimization methodology for max-margin structured classification tasks. The e! ciency of the method relies on the interplay of several techniques: formulation of the structured support vector machine or maxmargin Markov problem as an optimization problem; marginalization of the dual of the optimization; partial decomposition via a gradient formulation; and finally tight coupling of a maximum likelihood inference algorithm into the optimization algorithm, as opposed to using inference as a working set maintenance mechanism only. The tight coupling also allows fast approximate inference to be used e"ectiv ely in the learning. The generality of the method follows from the fact that changing the output structure in essence only changes the inference algorithm, that is, the method can to a large extent be used in a ‘plug and play’ fashion.
Rapid distortion theory for homogeneous compressed turbulence with application to modelling

Compressible rapid distortion theory is used to examine pressure fluctuations and the pressure–dilatation correlation in a field of turbulence subjected to rapid homogeneous compression. It is shown how a one-dimensional compression produces large solenoidal pressure fluctuations. As the dimensionality of the compression increases, the magnitude of these fluctuations decreases — it vanishes for a spherically symmetric compression. By contrast the dilatational, or acoustic, pressure fluctuations depend mainly on the net volumetric compression, and are relatively insensitive to the dimensionality of the compression. These same comments apply to the pressure–dilatation correlation. The pressure–dilatation correlation appears in the compressible turbulent kinetic energy equation and is significant in rapidly evolving flows; Reynolds stress closure models require that it be represented. The continuity equation provides a relation between pressure dilatation and the rate of change of pressure fluctuation variance. This relation is the basis for our RDT analysis. That analysis leads to a proposal for modelling the rapid contribution to pressure dilatation.
On MISO Broadcast Channel With Alternating CSIT and Secrecy Constraints

We study the problem of secure transmission over a two-user Gaussian multi-input single-output (MISO) broadcast channel under the assumption that the state of the channel to each receiver is conveyed either perfectly (P) or with delay (D) to the transmitter. Denoting by S1 and S2 the channel state information at the transmitter (CSIT) of user 1 and user 2, respectively, the overall CSIT can then alternate between four possible states, i.e., (S1,S2)∈ {P,D} 2 . We denote by λS1S2 the fraction of time during which the state S1S2 occurs and focus on the symmetric case such that λS1S2 =λS2S1. Under these assumptions, we first consider the Gaussian MISO wiretap channel and characterize the secure degrees of freedom (SDoF). Next, we generalize this model to the two-user Gaussian MISO broadcast channel and establish bounds on the SDoF region. The analysis reveals that alternating CSIT allows synergistic gains in terms of SDoF; and shows that, by opposition to encoding separately over different states, joint encoding across the states enables strictly better secure rates.
Numerical simulation of grid-generated turbulent particulate flow by three-dimensional Reynolds stress

A three-dimensional (3D) Reynolds stress turbulence model based on 3D Reynolds-averaged Navier-Stokes equations has been elaborated for grid-generated turbulence in particulate downward flow arranged in the channel domain of the square cross section. The model presented considers both the enhancement and attenuation of turbulence by means of the additional terms of the transport equations of the normal Reynolds stress components. It allows us to carry out calculations covering the long distance of the channel length without using algebraic assumptions for various components of the Reynolds stress. The results obtained show the effects of particles and mesh size of the turbulence generating grids on turbulence modification. In particular, the presence of solid particles at the initial period of turbulence decay results in the pronounced enhancement of turbulence that diminishes appreciably downwards in the area of typical channel turbulent flow. As the results show, the character of modification of all three normal components of the Reynolds stress taking place at the initial period of turbulence decay are uniform almost all over the channel cross sections. The increase in the grid mesh size slows down the rate of the turbulence enhancement which is caused by particles.
A 145-GHz Interferometer for Measuring the Anisotropy of the Cosmic Microwave Background

This thesis presents the design, construction, testing, and preliminary data analysis of MINT, the Millimeter INTerferometer. MINT is a 145-GHz, four-element interferometer designed to measure the anisotropy of the Cosmic Microwave Background (CMB) at spherical harmonics of ` = 800 to 1900. In this region of `-space, the CMB angular power spectrum should exhibit an exponential damping due to two effects related to the finite thickness of the last-scattering surface: photon diffusion and lineof-sight projection. Measurements in this region have already been made at 31 GHz by the Cosmic Background Imager (CBI). MINT’s goal is to complement CBI by extending these results to a higher frequency that is much less prone to extragalactic point-source contamination. MINT’s mission is also complementary to that of the Microwave Anisotropy Probe (MAP) satellite. MINT observed the CMB in November and December, 2001, from an altitude of 17,000 feet in the Atacama Desert of northern Chile. We describe the performance of the instrument during the observing campaign. Based on radiometric hot/cold-load tests, the SIS-mixer-based receivers are found to have an average receiver noise temperature (double sideband) of 39 K in a 2-GHz IF bandwidth. The typical atmosphere contribution is 5 K. We assess the phase stability, gain stability, pointing accuracy, and overall sensitivity of the interferometer via observations of Mars and Jupiter, and find that the instrument is sufficiently stable to allow an ultimate experimental sensitivity at the few-μK level needed for detection of the CMB anisotropy.
Local error estimates for radial basis function interpolation of scattered data

Introducing a suitable variational formulation for the local error of scattered data intepolation by radial basis functions φ(r), the error can be bounded by a term depending on the Fourier transform of the interpolated function f and a certain «Kriging function», which allows a formulation as an integral involving the Fourier transform of φ. The explicit construction of locally well-behaving admissible coefficient vectors makes the Kriging function bounded by some power of the local density h of data points
Automated Discovery of Inductive Theorems

Inductive mathematical theorems have, as a rule, historically been quite difficult to prove - both for mathematics students a for auto- mated theorem provers. That said, there has been considerable progress over the past several years, within the automated reasoning community, towards proving some of these theorems. However, little work has been done thus far towards automatically discovering them. In this paper we present our methods of discovering (as well as proving) inductive theorems, within an automated system. These methods have been tested over the natural num- bers, with regards to addition and multiplication, as well as to exponents of group elements.
Training of fuzzy inference systems by combining variable structure systems technique and Levenberg-Marquardt algorithm

This paper presents a novel training algorithm for fuzzy inference systems. The algorithm combines the Levenberg-Marquardt algorithm with variable structure systems approach. The combination is performed by expressing the parameter update rule in continuous time and application of sliding control method to the gradient based training procedure. In this paper, a fuzzy inference mechanism that can be trained such that the adjustable parameter values are forced to settle down (parameter stabilization) while minimizing an appropriate cost function (cost optimization), is discussed. In the application example, control of a two degrees of freedom direct drive SCARA robotic manipulator is considered. As the controller, a standard fuzzy system architecture is used and the parameter tuning is performed by the proposed algorithm.
RESIDUAL INVERSE ITERATION FOR THE NONLINEAR EIGENVALUE PROBLEM

For the nonlinear eigenvalue problem $A(\hat \lambda )\hat x = 0$, where $A( \cdot )$ is a matrix-valued operator, residual inverse iteration with shift $\sigma $ is defined by \[ a^{(l + 1)} : = {\text{const. }}(x^{(l)} - A(\sigma )^{ - 1} A(\lambda _{l + 1} )x^{(l)} ),\] where $\lambda _{l + 1} $ is an appropriate approximation of $\hat \lambda $. In the linear case, $A(\lambda ) = A - \lambda I$, this is theoretically equivalent to ordinary inverse iteration, but the residual formulation results in a considerably higher limit accuracy when the residual $A(\lambda _{l + 1} )x^{(l)} = Ax^{(l)} - \lambda _{l + 1} x^{(l)} $ is accumulated in double precision. In the nonlinear case, if $\sigma $ is sufficiently close to $\hat \lambda $, convergence is at least linear with convergence factor proportional to $| {\sigma - \hat \lambda } |$. As with ordinary inverse iteration, the convergence can be accelerated by using variable shifts.
Liquid mixing enhanced by pulse width modulation in a Y-shaped jet configuration

In this paper, mixing between two fluid streams, which are injected into a planar mixing channel via a Y-shaped confluence section at the same volume flow rate, is studied experimentally. The injection of the two fluid streams is controlled by two separate solenoid valves, which are operated with a phase difference of 180°, using pulse width modulation. The experiments are conducted using water at a mean Reynolds number between 83 and 250, a range of pulsation frequencies and two duty cycles (25 and 50%). Both particle-image velocimetry and planar laser-induced fluorescence technique are used to visualize the flow patterns and to quantify the mixing degree in the mixing channel. This experiment shows that the pulsation of each jet produces vortical structures, which promotes mixing via vortex entrainment and vortex breakup, and at the same time the mixing is also greatly enhanced by sequential segmentation produced by a 180° out-of-phase pulsation of the two jets. This mixing enhancement method is effective at a Reynolds number greater than 125 with a mixing degree of 0.9 being achieved. For the Reynolds numbers studied in the present experiments, an optimal frequency exists, which corresponds to a Strouhal number in the range of 0.5–2. Furthermore, at a given mean Reynolds number a lower duty cycle is found to produce a better mixing due to the resultant higher instantaneous Reynolds number in the jet flow. It is also found that pulsation of only one jet can produce a similar mixing effect.
Diffuse fields in open systems and the emergence of the Green’s function (L)

As is now well known, the relation between diffuse field correlations and the Green’s function follows directly from a definition of a diffuse field as an uncorrelated smooth spectral superposition of normal modes. Such a definition is, however, inapplicable in most open structures, the earth in particular. A preferable definition might be that of room acoustics: a diffuse field is an uncorrelated isotropic superposition of plane waves. But that definition is inapplicable to heterogeneous structures, or near boundaries. Here, a definition of a local diffuse field applicable to open heterogeneous systems is proposed. A local diffuse field is taken to be one in steady‐state equilibrium with the field in a homogeneous region having an uncorrelated isotropic superposition of incident plane waves. This definition is applicable to both heterogeneous and open systems, and is shown using a reciprocity argument to lead to the familiar identity between the local Green’s function of the structure and the diffuse fields correlations.