arXiv research

Optimizing data movement significantly improves transformer training efficiency.

Generically learns movement control policies from exploration data.

New algorithm reduces dynamic regret in time-varying movement costs.

Study identifies key trades predicting market movements.

Movement primitives are an important policy class for real-world robotics. However, the high dimensionality of their parametrization makes the policy optimization expensive both in terms of samples and computation. Enabling an efficient representation of movement primitives facilitates the application of machine learni…

A large body of animation research focuses on optimization of movement control, either as action sequences or policy parameters. However, as closed-form expressions of the objective functions are often not available, our understanding of the optimization problems is limited. Building on recent work on analyzing neural …

We study online optimization in a setting where an online learner seeks to optimize a per-round hitting cost, which may be non-convex, while incurring a movement cost when changing actions between rounds. We ask: \textit{under what general conditions is it possible for an online learner to leverage predictions of futur…

To define oscillatory movements of securities market, we put in the non-local extension of Ito- equation for wavelet-images of random processes. It is proposed an algorithm of creation of evolutionary equation and a model of prediction of the most probable price movement path. It is carried out experimental validation …

NoTMF forecasts sparse urban road movement speeds with nonstationary temporal matrix factorization.

Study finds weak solutions for complex map flows with optimal lifespan.

We study online convex optimization in a setting where the learner seeks to minimize the sum of a per-round hitting cost and a movement cost which is incurred when changing decisions between rounds. We prove a new lower bound on the competitive ratio of any online algorithm in the setting where the costs are $m$-strong…

New algorithm reduces costs in wind energy systems by minimizing decision changes.

Machine learning struggles to predict binary options movements due to randomness.

Infants' spontaneous and voluntary movements mirror developmental integrity of brain networks since they require coordinated activation of multiple sites in the central nervous system. Accordingly, early detection of infants with atypical motor development holds promise for recognizing those infants who are at risk for…

We recast the Calabi flow in DeGiorgi's language of minimizing movements. We establish the long time existence of minimizing movements for K-energy with arbitrary initial condition. Furthermore we establish some a priori regularity of these solutions, and that sufficiently regular minimizing movements are smooth soluti…

Framework uses optimal transport for neural architecture search.

Taureau uses Twitter sentiment analysis to predict stock market movement.

ChatGPT predicts stock market movements based on Bloomberg headlines, showing a positive correlation over short to medium terms.

In this paper, the problem of head movement prediction for virtual reality videos is studied. In the considered model, a deep learning network is introduced to leverage position data as well as video frame content to predict future head movement. For optimizing data input into this neural network, data sample rate, red…

Neural nets predict user attention from mouse movements.

Paper proves minimizing movements match smooth droplet flow in 3D.

Model predicts option movements using residual transactions for better market timing.

Study uses FinBERT for financial sentiment analysis to predict stock movement.

MPANF improves naive forecast by incorporating directional information.

We study involuntary micro-movements of the eye for biometric identification. While prior studies extract lower-frequency macro-movements from the output of video-based eye-tracking systems and engineer explicit features of these macro-movements, we develop a deep convolutional architecture that processes the raw eye-t…

This study develops an online predictive optimization framework for dynamically operating a transit service in an area of crowd movements. The proposed framework integrates demand prediction and supply optimization to periodically redesign the service routes based on recently observed demand. To predict demand for the …

Neuroscientific studies of drawing-like movements usually analyze neural representation of either geometric (eg. direction, shape) or temporal (eg. speed) features of trajectories rather than trajectory's representation as a whole. This work is about empirically supported mathematical ideas behind splitting and merging…

Risk hedging can reduce operational costs by adjusting prices and production levels in response to asset price movements.

The study introduces a new stickiness parameter for stock prices using a non-linear model.

The rise of non-linear and interactive media such as video games has increased the need for automatic movement animation generation. In this survey, we review and analyze different aspects of building automatic movement generation systems using machine learning techniques and motion capture data. We cover topics such a…

Bayesian method infers local rules for collective animal movement.

Study on pseudo-Riemannian Bertrand manifolds finds no closed movement systems.

Study identifies personality traits from dance movements in music.

Mid-price movement prediction based on limit order book (LOB) data is a challenging task due to the complexity and dynamics of the LOB. So far, there have been very limited attempts for extracting relevant features based on LOB data. In this paper, we address this problem by designing a new set of handcrafted features …

Rehabilitation training is the primary intervention to improve motor recovery after stroke, but a tool to measure functional training does not currently exist. To bridge this gap, we previously developed an approach to classify functional movement primitives using wearable sensors and a machine learning (ML) algorithm.…

Study on droplet flow on uneven surfaces, proving existence and properties.

Paper uses deep Q-network to correct eye movement artifacts in OCT volumes.

The paper tracks patient recovery using graphs of joint movement data.

Framework analyzes stock price co-movement with fundamentals using big data.

DeepFolio uses neural networks to predict stock price movements from LOB data.

Alternative proof of weak solutions to mean curvature flow using minimizing movements.

Wrist movements can reveal digits, posing security risks.

The paper presents a step forward into the development of the theory of meaning. Stock and financial markets are examined from communication-theoretical perspective on the dynamics of information and meaning. This study focuses on the link between the dynamics of investors' expectations and market price movement. The m…

Enhances stock movement prediction using Higher Order Transformers for multimodal time-series data.

Diversification of an investment into independently fluctuating assets reduces its risk. In reality, movement of assets are are mutually correlated and therefore knowledge of cross--correlations among asset price movements are of great importance. Our results support the possibility that the problem of finding an inves…

This paper predicts weekly stock market movements using machine learning and introduces a new benchmark.

Paper uses CNN to predict stock price movement as an image classification problem.

Proposes a THGNN for dynamic financial time series prediction.