A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Innovative ball bearing converts rotary to reciprocating motion.
New method studies moving points on curves using rotating frames.
It has been suggested in 1999 that a certain volume growth condition for geodesically complete Riemannian manifolds might imply that the manifold is stochastically complete. This is motivated by a large class of examples and by a known analogous criterion for recurrence of Brownian motion. We show that the suggested im…
Linking human whole-body motion and natural language is of great interest for the generation of semantic representations of observed human behaviors as well as for the generation of robot behaviors based on natural language input. While there has been a large body of research in this area, most approaches that exist to…
In this paper we study symmetries, Newtonoid vector fields, conservation laws, Noether's Theorem and its converse, in the framework of the -symplectic formalism, using the Frölicher-Nijenhuis formalism on the space of -velocities of the configuration manifold. For the case , it is well known that Cartan sy…
A new method learns object representations from motion in slot representations.
Paper introduces a method for generating interlocutor-aware facial gestures in dyadic settings.
We develop a conservation law for constant mean curvature (CMC) surfaces introduced by Korevaar, Kusner and Solomon, and provide a converse, so as to characterize CMC surfaces by a conservation law. We work with `twizzler' construction, which applies a screw-motion to some base curve. We show that, excluding cylinders,…
On any timelike surface with zero mean curvature in the four-dimensional Minkowski space we introduce special geometric (canonical) parameters and prove that the Gauss curvature and the normal curvature of the surface satisfy a system of two natural partial differential equations. Conversely, any two solutions to this …
Integrable nets described with curvature relations to pseudospherical surfaces.
Deep learning models price convertible bonds with complex reset and call features.
Extends stochastic modified equations for optimization algorithms.
Study examines cash conversion cycle in manufacturing firms, finding negative relationships with profitability and size.
FasterVoiceGrad speeds up VC by 6-7x with novel distillation.
We propose a flexible framework that deals with both singer conversion and singers vocal technique conversion. The proposed model is trained on non-parallel corpora, accommodates many-to-many conversion, and leverages recent advances of variational autoencoders. It employs separate encoders to learn disentangled latent…
Conversion prediction plays an important role in online advertising since Cost-Per-Action (CPA) has become one of the primary campaign performance objectives in the industry. Unlike click prediction, conversions have different types in nature, and each type may be associated with different decisive factors. In this pap…
FedConPE improves conversational recommender systems efficiency and privacy.
Contextual bandit algorithms provide principled online learning solutions to balance the exploitation-exploration trade-off in various applications such as recommender systems. However, the learning speed of the traditional contextual bandit algorithms is often slow due to the need for extensive exploration. This poses…
The goal of online display advertising is to entice users to "convert" (i.e., take a pre-defined action such as making a purchase) after clicking on the ad. An important measure of the value of an ad is the probability of conversion. The focus of this paper is the development of a computationally efficient, accurate, a…
CycleGAN-VC3 improves CycleGAN-VCs for mel-spectrogram conversion.
A new algorithm for conversational recommendation systems using dueling bandits in GLMs.
The paper extracts structured data from physician-patient conversations, reducing clerical burden.
Schedule-free SGD is optimal for nonconvex optimization problems.
Improved autoencoder for F0-consistent voice conversion.
Improved conversion rate prediction in online advertising using self-supervised pre-training.
A fast voice conversion method using diffusion models.
It is known that the so-called rotation minimizing (RM) frames allow for a simple and elegant characterization of geodesic spherical curves in Euclidean, hyperbolic, and spherical spaces through a certain linear equation involving the coefficients that dictate the RM frame motion (da Silva, da Silva in Mediterr J Math …
Proposes a new voice conversion model that preserves pitch patterns.
We present a voice conversion solution using recurrent sequence to sequence modeling for DNNs. Our solution takes advantage of recent advances in attention based modeling in the fields of Neural Machine Translation (NMT), Text-to-Speech (TTS) and Automatic Speech Recognition (ASR). The problem consists of converting be…
The paper provides a converse to linking theorems for graphs in 3-space and higher dimensions.
Long-term human motion can be represented as a series of motion modes---motion sequences that capture short-term temporal dynamics---with transitions between them. We leverage this structure and present a novel Motion Transformation Variational Auto-Encoders (MT-VAE) for learning motion sequence generation. Our model j…
This paper proposes a voice conversion (VC) method using sequence-to-sequence (seq2seq or S2S) learning, which flexibly converts not only the voice characteristics but also the pitch contour and duration of input speech. The proposed method, called ConvS2S-VC, has three key features. First, it uses a model with a fully…
Introduces Motion Programs for better video analysis of human motion.
Extracting relevant information from medical conversations and providing it to doctors and patients might help in addressing doctor burnout and patient forgetfulness. In this paper, we focus on extracting the Medication Regimen (dosage and frequency for medications) discussed in a medical conversation. We frame the pro…
We address the problem of speech act recognition (SAR) in asynchronous conversations (forums, emails). Unlike synchronous conversations (e.g., meetings, phone), asynchronous domains lack large labeled datasets to train an effective SAR model. In this paper, we propose methods to effectively leverage abundant unlabeled …
This paper explores how LLMs can improve pipeline-based conversational agents.
A new framework converts EEG signals between subjects and tasks.
Unified approach for conversational recommendation by integrating attributes and items.
Neuro-symbolic system tackles conversational AI's need for natural, broad-ranging dialogue.
Estimates conversion probabilities from click sequences with privacy constraints.
Study finds neural dialog models struggle with conversational tasks.
Deep learning improves conversational recommender systems.
We describe a procedure naturally associating relativistic Klein-Gordon equations in static curved spacetimes to non-relativistic quantum motion on curved spaces in the presence of a potential. Our procedure is particularly attractive in application to (typically, superintegrable) problems whose energy spectrum is give…
In this work we explored building automatic speech recognition models for transcribing doctor patient conversation. We collected a large scale dataset of clinical conversations ( hr), designed the task to represent the real word scenario, and explored several alignment approaches to iteratively improve data qua…
Operator fields in the bundle of Dirac spinors and their conversion to spatial fields are considered. Some commutator equations are studied with the use of the conversion technique.
In this paper we propose a multi-state model for the evaluation of the conversion option contract. The multi-state model is based on age-indexed semi-Markov chains that are able to reproduce many important aspects that influence the valuation of the option such as the duration problem, the time non-homogeneity and the …
Prospective display advertising poses a great challenge for large advertising platforms as the strongest predictive signals of users are not eligible to be used in the conversion prediction systems. To that end efforts are made to collect as much information as possible about each user from various data sources and to …
Study shows different trajectory prediction models generalize better under OoD conditions.