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DiffFont Diffusion Model for Robust OneShot Font Generation ; Font generation is a difficult and timeconsuming task, especially in those languages using ideograms that have complicated structures with a large number of characters, such as Chinese. To solve this problem, fewshot font generation and even oneshot font ge... |
HumanLiff Layerwise 3D Human Generation with Diffusion Model ; 3D human generation from 2D images has achieved remarkable progress through the synergistic utilization of neural rendering and generative models. Existing 3D human generative models mainly generate a clothed 3D human as an undetectable 3D model in a singl... |
Democracy versus Dictatorship in SelfOrganized Models of Financial Markets ; Models to mimic the transmission of information in financial markets are introduced. As an attempt to generate the demand process, we distinguish between dictatorship associations, where groups of agents rely on one of them to make decision, ... |
On asymptotic models in Banach spaces ; A well known application of Ramsey's Theorem to Banach Space Theory is the notion of a spreading model e'i of a normalized basic sequence xi in a Banach space X. We show how to generalize the construction to define a new creature ei, which we call an asymptotic model of X. Every... |
On Variational MicroMacro Models and their Application to Polycrystals ; Some variational micromacro models are briefly reviewed it is shown how, starting from the Taylor model and passing through the relaxed Taylor model, a consistent intermediate between Taylor's upper bound and the lower bound Sachs or rather stati... |
Multispecies reactiondiffusion models admitting shock solutions ; A method for classifying nspecies reactiondiffusion models, admitting shock solutions is presented. The most general onedimensional twospecies reactiondiffusion model with nearest neighbor interactions admitting uniform product measures as the stationar... |
Modelling Word Burstiness in Natural Language A Generalised Polya Process for Document Language Models in Information Retrieval ; We introduce a generalised multivariate Polya process for document language modelling. The framework outlined here generalises a number of statistical language models used in information re... |
U1T3R Extension of Standard Model A SubGeV Dark Matter Model ; We present a model based on a U1T3R extension of the Standard Model. The model addresses the mass hierarchy between the third generation and the first two generation fermions. U1T3R is spontaneously broken at sim 110 GeV. The model contains a subGeV dark m... |
Let the Models Respond Interpreting Language Model Detoxification Through the Lens of Prompt Dependence ; Due to language models' propensity to generate toxic or hateful responses, several techniques were developed to align model generations with users' preferences. Despite the effectiveness of such methods in improvi... |
BAGM A Backdoor Attack for Manipulating TexttoImage Generative Models ; The rise in popularity of texttoimage generative artificial intelligence AI has attracted widespread public interest. We demonstrate that this technology can be attacked to generate content that subtly manipulates its users. We propose a Backdoor ... |
Fractal growth of tumors and other cellular populations Linking the mechanistic to the phenomenological modeling and vice versa ; In this paper we study and extend the mechanistic mean field theory of growth of cellular populations proposed by Mombach et al in Mombach J. C. M. et al., Europhysics Letter, 59 2002 923 M... |
Polite Dialogue Generation Without Parallel Data ; Stylistic dialogue response generation, with valuable applications in personalitybased conversational agents, is a challenging task because the response needs to be fluent, contextuallyrelevant, as well as paralinguistically accurate. Moreover, parallel datasets for r... |
Latent Topic Conversational Models ; Latent variable models have been a preferred choice in conversational modeling compared to sequencetosequence seq2seq models which tend to generate generic and repetitive responses. Despite so, training latent variable models remains to be difficult. In this paper, we propose Laten... |
Maximum entropy models capture melodic styles ; We introduce a Maximum Entropy model able to capture the statistics of melodies in music. The model can be used to generate new melodies that emulate the style of the musical corpus which was used to train it. Instead of using the nbody interactions of n1order Markov mod... |
Socratic Learning Augmenting Generative Models to Incorporate Latent Subsets in Training Data ; A challenge in training discriminative models like neural networks is obtaining enough labeled training data. Recent approaches use generative models to combine weak supervision sources, like userdefined heuristics or knowl... |
SHAPED SharedPrivate EncoderDecoder for Text Style Adaptation ; Supervised training of abstractive language generation models results in learning conditional probabilities over language sequences based on the supervised training signal. When the training signal contains a variety of writing styles, such models may end... |
Least Angle Regression in Tangent Space and LASSO for Generalized Linear Models ; This study proposes sparse estimation methods for the generalized linear models, which run one of least angle regression LARS and least absolute shrinkage and selection operator LASSO in the tangent space of the manifold of the statistic... |
Endogenous Stochastic Arbitrage Bubbles and the BlackScholes model ; This paper develops a model that incorporates the presence of stochastic arbitrage explicitly in the BlackScholes equation. Here, the arbitrage is generated by a stochastic bubble, which generalizes the deterministic arbitrage model obtained in the l... |
Imagebased model parameter optimization using ModelAssisted Generative Adversarial Networks ; We propose and demonstrate the use of a modelassisted generative adversarial network GAN to produce fake images that accurately match true images through the variation of the parameters of the model that describes the feature... |
Relatively complicated Using models to teach general relativity at different levels ; This review presents an overview of various kinds of models physical, abstract, mathematical, visual that can be used to present the concepts and applications of Einstein's general theory of relativity at the level of undergraduate... |
Learning NonConvergent NonPersistent ShortRun MCMC Toward EnergyBased Model ; This paper studies a curious phenomenon in learning energybased model EBM using MCMC. In each learning iteration, we generate synthesized examples by running a nonconvergent, nonmixing, and nonpersistent shortrun MCMC toward the current mode... |
General Ftheory models with tuned operatornameSU3 times operatornameSU2 times operatornameU1 mathbbZ6 symmetry ; We construct a general form for an Ftheory Weierstrass model over a general base giving a 6D or 4D supergravity theory with gauge group operatornameSU3 times operatornameSU2 times operatornameU1 mathbbZ6 ... |
A Systematic Assessment of Syntactic Generalization in Neural Language Models ; While stateoftheart neural network models continue to achieve lower perplexity scores on language modeling benchmarks, it remains unknown whether optimizing for broadcoverage predictive performance leads to humanlike syntactic knowledge. F... |
A Multiattribute Controllable Generative Model for Histopathology Image Synthesis ; Generative models have been applied in the medical imaging domain for various image recognition and synthesis tasks. However, a more controllable and interpretable image synthesis model is still lacking yet necessary for important appl... |
Image SuperResolution With Deep Variational Autoencoders ; Image superresolution SR techniques are used to generate a highresolution image from a lowresolution image. Until now, deep generative models such as autoregressive models and Generative Adversarial Networks GANs have proven to be effective at modelling highre... |
Timeseries Transformer Generative Adversarial Networks ; Many realworld tasks are plagued by limitations on data in some instances very little data is available and in others, data is protected by privacy enforcing regulations e.g. GDPR. We consider limitations posed specifically on timeseries data and present a model... |
Text Generation with TextEditing Models ; Textediting models have recently become a prominent alternative to seq2seq models for monolingual textgeneration tasks such as grammatical error correction, simplification, and style transfer. These tasks share a common trait they exhibit a large amount of textual overlap bet... |
Your Autoregressive Generative Model Can be Better If You Treat It as an EnergyBased One ; Autoregressive generative models are commonly used, especially for those tasks involving sequential data. They have, however, been plagued by a slew of inherent flaws due to the intrinsic characteristics of chainstyle conditiona... |
Cold Diffusion Inverting Arbitrary Image Transforms Without Noise ; Standard diffusion models involve an image transform adding Gaussian noise and an image restoration operator that inverts this degradation. We observe that the generative behavior of diffusion models is not strongly dependent on the choice of image ... |
Audiovisual speech enhancement with a deep Kalman filter generative model ; Deep latent variable generative models based on variational autoencoder VAE have shown promising performance for audiovisual speech enhancement AVSE. The underlying idea is to learn a VAEbased audiovisual prior distribution for clean speech da... |
The Benefits of Bad Advice Autocontrastive Decoding across Model Layers ; Applying language models to natural language processing tasks typically relies on the representations in the final model layer, as intermediate hidden layer representations are presumed to be less informative. In this work, we argue that due to ... |
Assessing the efficacy of large language models in generating accurate teacher responses ; Tack et al., 2023 organized the shared task hosted by the 18th Workshop on Innovative Use of NLP for Building Educational Applications on generation of teacher language in educational dialogues. Following the structure of the sh... |
Learning Evaluation Models from Large Language Models for Sequence Generation ; Large language models achieve stateoftheart performance on sequence generation evaluation, but typically have a large number of parameters. This is a computational challenge as presented by applying their evaluation capability at scale. To... |
Improving Generative Modelbased Unfolding with Schrodinger Bridges ; Machine learningbased unfolding has enabled unbinned and highdimensional differential cross section measurements. Two main approaches have emerged in this research area one based on discriminative models and one based on generative models. The main a... |
Compatibility of the expansive nondecelerative universe model with the Newton gravitational theory and the general theory of relativity ; Applying the Vaidya metrics in the model of Expansive Nondecelerative Universe ENU leads to compatibility of the ENU model both with the classic Newton gravitational theory and the ... |
Inhomogeneous Cosmological Models with Flat Slices Generated from the Einsteinde Sitter Universe ; A family of cosmological models is considered which in a certain synchronized system of reference possess flat slices t const. They are generated from the Einsteinde Sitter universe by a suitable transformation. Under p... |
Generalized XYZ Model Associated to Sklyanin Algebra ; The free energy of a lattice model, which is a generalization of the Heisenberg XYZ model with the higher spin representation of the Sklyanin algebra, is calculated by the generalized Bethe Ansatz of Takhtajan and Faddeev. Talk given at the XXI Differential Geomet... |
A Generalization of the Submodel of Nonlinear CP1 Models ; We generalize the submodel of nonlinear CP1 models. The generalized models include higher order derivatives. For the systems of higher order equations, we construct a Backlundlike transformation of solutions and an infinite number of conserved currents by usin... |
The multihistory approach to the timetravel paradoxes of General Relativity mathematical analysis of a toy model ; With a mathematical eye to Matt Visser's multihistory approach to the timetravelparadoxes of General Relativity, a non relativistic toy model is analyzed in order of characterizing the conditions in which... |
Generalized selfdual ChernSimons vortices ; We search for vortices in a generalized Abelian ChernSimons model with a nonstandard kinetic term. We illustrate our results, plotting and comparing several features of the vortex solution of the generalized model with those of the vortex solution found in the standard Chern... |
Sufficient FTP Schedulability Test for the NonCyclic Generalized Multiframe Task Model ; Our goal is to provide a sufficient schedulability test ideally polynomial for the scheduling of NonCyclic Generalized Multiframe Task Model using FixedTaskPriority schedulers. We report two first results i we present and prove co... |
Fourth Generations with an Inert Doublet Higgs ; We explore an extension of the fourth generation model with multiHiggs doublets and three fermion singlets. The Standard Model neutrinos acquire mass radiatively at one loop level while the fourth generation neutrinos acquire a heavy treelevel mass. The model also conta... |
Singlefield attractors ; I describe a simple class of alphaattractors, generalizing the singlefield GL model of inflation in supergravity. The new class of models is defined for 0alpha lesssim 1, providing a good match to the present cosmological data. I also present a generalized version of these models which can des... |
On generalized ARCH model with stationary liquidity ; We study a generalized ARCH model with liquidity given by a general stationary process. We provide minimal assumptions that ensure the existence and uniqueness of the stationary solution. In addition, we provide consistent estimators for the model parameters by usi... |
Iterative Descent Method for Generalized Leontief Model ; In this paper we consider generalized Leontief model. We show that under certain condition the generalized Leontief model is solvable by iterative descent method based on infeasible interior point algorithm. We prove the convergence of the method from strictly ... |
Frequency vs. Association for Constraint Selection in UsageBased Construction Grammar ; A usagebased Construction Grammar CxG posits that slotconstraints generalize from common exemplar constructions. But what is the best model of constraint generalization This paper evaluates competing frequencybased and associationb... |
Adversarial Attack with Pattern Replacement ; We propose a generative model for adversarial attack. The model generates subtle but predictive patterns from the input. To perform an attack, it replaces the patterns of the input with those generated based on examples from some other class. We demonstrate our model by at... |
Multitransition solutions for a generalized FrenkelKontorova model ; We study a generalized FrenkelKontorova model. Using minimal and Birkhoff solutions as building blocks, we construct a lot of homoclinic solutions and heteroclinic solutions for this generalized FrenkelKontorova model under gap conditions. These new ... |
Elementary functions solutions to the Bachelier model generated by Lie point symmetries ; Under the recent negative interest rate situation, the Bachelier model has been attracting attention and adopted for evaluating the price of interest rate options. In this paper we find the Lie point symmetries of the Bachelier p... |
A timesymmetric generalization of quantum mechanics ; I propose a timesymmetric generalization of quantum mechanics that is inspired by scattering theory. The model postulates two interacting quantum states, one traveling forward in time and one backward in time. The interaction is modeled by a unitary scattering oper... |
UPainting Unified TexttoImage Diffusion Generation with Crossmodal Guidance ; Diffusion generative models have recently greatly improved the power of textconditioned image generation. Existing image generation models mainly include text conditional diffusion model and crossmodal guided diffusion model, which are good ... |
The ExtractiveAbstractive Axis Measuring Content Borrowing in Generative Language Models ; Generative language models produce highly abstractive outputs by design, in contrast to extractive responses in search engines. Given this characteristic of LLMs and the resulting implications for content Licensing Attribution,... |
RenAIssance A Survey into AI TexttoImage Generation in the Era of Large Model ; Texttoimage generation TTI refers to the usage of models that could process text input and generate high fidelity images based on text descriptions. Texttoimage generation using neural networks could be traced back to the emergence of Gene... |
OrthomodularValued Models for Quantum Set Theory ; In 1981, Takeuti introduced quantum set theory by constructing a model of set theory based on quantum logic represented by the lattice of closed linear subspaces of a Hilbert space in a manner analogous to Booleanvalued models of set theory, and showed that appropriat... |
Classification Accuracy Score for Conditional Generative Models ; Deep generative models DGMs of images are now sufficiently mature that they produce nearly photorealistic samples and obtain scores similar to the data distribution on heuristics such as Frechet Inception Distance FID. These results, especially on large... |
The Utility of General Domain Transfer Learning for Medical Language Tasks ; The purpose of this study is to analyze the efficacy of transfer learning techniques and transformerbased models as applied to medical natural language processing NLP tasks, specifically radiological text classification. We used 1,977 labeled... |
How Faithful is your Synthetic Data Samplelevel Metrics for Evaluating and Auditing Generative Models ; Devising domain and modelagnostic evaluation metrics for generative models is an important and as yet unresolved problem. Most existing metrics, which were tailored solely to the image synthesis setup, exhibit a lim... |
Discrepancies in Epidemiological Modeling of Aggregated Heterogeneous Data ; Within epidemiological modeling, the majority of analyses assume a single epidemic process for generating groundtruth data. However, this assumed data generation process can be unrealistic, since data sources for epidemics are often aggregate... |
Regression Transformer Concurrent sequence regression and generation for molecular language modeling ; Despite significant progress of generative models in the natural sciences, their controllability remains challenging. One fundamentally missing aspect of molecular or protein generative models is an inductive bias th... |
Disentangled3D Learning a 3D Generative Model with Disentangled Geometry and Appearance from Monocular Images ; Learning 3D generative models from a dataset of monocular images enables selfsupervised 3D reasoning and controllable synthesis. Stateoftheart 3D generative models are GANs which use neural 3D volumetric rep... |
T2TD Text3D Generation Model based on Prior Knowledge Guidance ; In recent years, 3D models have been utilized in many applications, such as autodriver, 3D reconstruction, VR, and AR. However, the scarcity of 3D model data does not meet its practical demands. Thus, generating highquality 3D models efficiently from tex... |
ToolAlpaca Generalized Tool Learning for Language Models with 3000 Simulated Cases ; Enabling large language models to utilize realworld tools effectively is crucial for achieving embodied intelligence. Existing approaches to tool learning have either primarily relied on extremely large language models, such as GPT4, ... |
Cosmology with a Variable Chaplygin Gas ; We consider a new generalized Chaplygin gas model that includes the original Chaplygin gas model as a special case. In such a model the generalized Chaplygin gas evolves as from dust to quiessence or phantom. We show that the background evolution for the model is equivalent to... |
Approximate NGram Markov Model for Natural Language Generation ; This paper proposes an Approximate ngram Markov Model for bag generation. Directed word association pairs with distances are used to approximate n1gram and ngram training tables. This model has parameters of word association model, and merits of both wor... |
Covariant generalization of the ISGW quark model ; A fairly general Lorentzcovariant quark model of mesons is constructed. It has several versions whose nonrelativistic limit corresponds to the wellknown Isgur, Scora, Grinstein, and Wise model. In the heavyquark limit, the covariant model naturally and automatically p... |
Yukawa Interaction from a SUSY Composite Model ; We present a composite model that is based on nonperturbative effects of N1 supersymmetric SUNC gauge theory with NfNC1 flavors. In this model, we consider NC7, where all matter fields in the supersymmetric standard model, that is, quarks, leptons and Higgs particles ar... |
Some Recent Results from the Generic Supersymmetric Standard Model ; The generic supersymmetric standard model is a model built from a supersymmetrized standard model field spectrum the gauge symmetries only. The popular minimal supersymmetric standard model differs from the generic version in having Rparity imposed b... |
Electric Dipole Moments in the Generic Supersymmetric Standard Model ; The generic supersymmetric standard model is a model built from a supersymmetrized standard model field spectrum the gauge symmetries only. The popular minimal supersymmetric standard model differs from the generic version in having Rparity imposed... |
No Chaos in BraneWorld Cosmology ; We discuss the asymptotic dynamical evolution of spatially homogeneous braneworld cosmological models close to the initial singularity. We find that generically the cosmological singularity is isotropic in Bianchi type IX braneworld models and consequently these models do not exhibit... |
General Gauge Mediation ; We give a general definition of gauge mediated supersymmetry breaking which encompasses all the known gauge mediation models. In particular, it includes both models with messengers as well as direct mediation models. A formalism for computing the soft terms in the generic model is presented. ... |
On Convergence to SLE6 I Conformal Invariance for Certain Models of the BondTriangular Type ; Following the approach outlined in 26, convergence to SLE6 of the Exploration Processes for the correlated bondtriangular type models studied in 11 is established. This puts the said models in the same universality class as t... |
Statistical Inference for ValuedEdge Networks Generalized Exponential Random Graph Models ; Across the sciences, the statistical analysis of networks is central to the production of knowledge on relational phenomena. Because of their ability to model the structural generation of networks, exponential random graph mode... |
The Structure of Signals Causal Interdependence Models for Games of Incomplete Information ; Traditional economic models typically treat private information, or signals, as generated from some underlying state. Recent work has explicated alternative models, where signals correspond to interpretations of available info... |
On a class of growthmaximal hardcore processes ; Generalizing the wellknown lilypond model we introduce a growthmaximal hardcore model based on a spacetime point process of convex particles. Using a purely deterministic algorithm we prove under fairly general assumptions that the model exists and is uniquely determine... |
An Exponential FR Dark Energy Model ; We present an exponential FR modified gravity model in the Jordan and the Einstein frame. We use a general approach in order to investigate and demonstrate the viability of the model. Apart from the general features that this models has, which actually render it viable at a first ... |
Asymptotics for regression models under loss of identifiability ; This paper discusses the asymptotic behavior of regression models under general conditions. First, we give a general inequality for the difference of the sum of square errors SSE of the estimated regression model and the SSE of the theoretical best regr... |
A Joint Model for Question Answering and Question Generation ; We propose a generative machine comprehension model that learns jointly to ask and answer questions based on documents. The proposed model uses a sequencetosequence framework that encodes the document and generates a question answer given an answer questio... |
Generalized Autoregressive Neural Network Models ; A time series is a sequence of observations taken sequentially in time. The autoregressive integrated moving average is a class of the model more used for times series data. However, this class of model has two critical limitations. It fits well onlyGaussian data with... |
Generalized Additive Model Selection ; We introduce GAMSEL Generalized Additive Model Selection, a penalized likelihood approach for fitting sparse generalized additive models in high dimension. Our method interpolates between null, linear and additive models by allowing the effect of each variable to be estimated as ... |
On soliton solutions of the timediscrete generalized lattice Heisenberg magnet model ; Generalized lattice Heisenberg magnet model is an integrable model exhibiting soliton solutions. The model is physically important for describing the magnon bound states or soliton excitations with arbitrary spin, in magnetic materi... |
Augmented Generator Subtransient Model Using Dynamic Phasor Measurements ; In this article, we present a new model for a synchronous generator based on phasor measurement units PMUs data. The proposed subtransient model allows to estimate the dynamic state variables as well as to calibrate model parameters. The motiva... |
A Relationship Between SIR Model and Generalized Logistic Distribution with Applications to SARS and COVID19 ; This paper shows that the generalized logistic distribution model is derived from the wellknown compartment model, consisting of susceptible, infected and recovered compartments, abbreviated as the SIR model,... |
The Generalization Error of the Minimumnorm Solutions for Overparameterized Neural Networks ; We study the generalization properties of minimumnorm solutions for three overparametrized machine learning models including the random feature model, the twolayer neural network model and the residual network model. We prove... |
Linear Models are Most Favorable among Generalized Linear Models ; We establish a nonasymptotic lower bound on the L2 minimax risk for a class of generalized linear models. It is further shown that the minimax risk for the canonical linear model matches this lower bound up to a universal constant. Therefore, the canon... |
Extended Koopman Models ; We introduce two novel generalizations of the Koopman operator method of nonlinear dynamic modeling. Each of these generalizations leads to greatly improved predictive performance without sacrificing a unique trait of Koopman methods the potential for fast, globally optimal control of nonline... |
In and Equivariance for Optimal Designs in Generalized Linear Models The Gamma Model ; We give an overview over the usefulness of the concept of equivariance and invariance in the design of experiments for generalized linear models. In contrast to linear models here pairs of transformations have to be considered which... |
SumProductAttention Networks Leveraging SelfAttention in Probabilistic Circuits ; Probabilistic circuits PCs have become the defacto standard for learning and inference in probabilistic modeling. We introduce SumProductAttention Networks SPAN, a new generative model that integrates probabilistic circuits with Transfor... |
On Johnson's sufficientness postulates for featuressampling models ; In the 1920's, the English philosopher W.E. Johnson introduced a characterization of the symmetric Dirichlet prior distribution in terms of its predictive distribution. This is typically referred to as Johnson's sufficientness postulate, and it has b... |
RITA a Study on Scaling Up Generative Protein Sequence Models ; In this work we introduce RITA a suite of autoregressive generative models for protein sequences, with up to 1.2 billion parameters, trained on over 280 million protein sequences belonging to the UniRef100 database. Such generative models hold the promise... |
Application of a General Family of Bivariate Distributions in Modelling Dependent Competing Risks Data with Associated Model Selection ; In this article, a general family of bivariate distributions is used to model competing risks data with dependent factors. The general structure of competing risks data considered he... |
CLIPDiffusionLM Apply Diffusion Model on Image Captioning ; Image captioning task has been extensively researched by previous work. However, limited experiments focus on generating captions based on nonautoregressive text decoder. Inspired by the recent success of the denoising diffusion model on image synthesis tasks... |
Replacing Language Model for Style Transfer ; We introduce replacing language model RLM, a sequencetosequence language modeling framework for text style transfer. Our method autoregressively replaces each token in the original sentence with a text span in the target style. In contrast, the new span is generated via a ... |
Generative probabilistic matrix model of data with different lowdimensional linear latent structures ; We construct a generative probabilistic matrix model of large data based on mixing of linear latent features distributed following Gaussian and Dirichlet distributions. Key ingredient of our model is that we allow fo... |
Scorebased Generative Modeling Through Backward Stochastic Differential Equations Inversion and Generation ; The proposed BSDEbased diffusion model represents a novel approach to diffusion modeling, which extends the application of stochastic differential equations SDEs in machine learning. Unlike traditional SDEbased... |
Teaching the Pretrained Model to Generate Simple Texts for Text Simplification ; Randomly masking text spans in ordinary texts in the pretraining stage hardly allows models to acquire the ability to generate simple texts. It can hurt the performance of pretrained models on text simplification tasks. In this paper, we ... |
A Rational Model of Dimensionreduced Human Categorization ; Existing models in cognitive science typically assume human categorization as graded generalization behavior in a multidimensional psychological space. However, category representations in these models may suffer from the curse of dimensionality in a natural ... |
Postmodelselection prediction for GLM's ; We give two prediction intervals PI for Generalized Linear Models that take model selection uncertainty into account. The first is a straightforward extension of asymptotic normality results and the second includes an extra optimization that improves nominal coverage for small... |
EzGal A Flexible Interface for Stellar Population Synthesis Models ; We present EzGal, a flexible python program designed to easily generate observable parameters magnitudes, colors, masstolight ratios for any stellar population synthesis SPS model. As has been demonstrated by various authors, the choice of input SPS ... |
Generalrelativistic Model of Magnetically Driven Jet ; The general scheme for the construction of the generalrelativistic model of the magnetically driven jet is suggested. The method is based on the usage of the 31 MHD formalism. It is shown that the critical points of the flow and the explicit radial behavior of the... |
Multilinear generating functions for Charlier polynomials ; Charlier configurations provide a combinatorial model for Charlier polynomials. We use this model to give a combinatorial proof of a multilinear generating function for Charlier polynomials. As special cases of the multilinear generating function, we obtain t... |
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