arXiv research

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

New algorithm SFHC achieves near-optimal costs with predictions for non-convex optimization.

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…

Bayesian method infers local rules for collective animal movement.

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

E2GC optimizes energy efficiency in DNNs by balancing computational and data movement costs.

Computer-aided assessment of physical rehabilitation entails evaluation of patient performance in completing prescribed rehabilitation exercises, based on processing movement data captured with a sensory system. Despite the essential role of rehabilitation assessment toward improved patient outcomes and reduced healthc…

Sparse RSP routing improves graph exploration and classification.

SCaLE tackles dynamic regret in noisy bandit feedback with switching costs.

Study liquidity provision in decentralized exchanges considering risk aversion and replication costs.

GPT-4 improves stock price prediction from microblogging sentiments.

Based on a recent theorem due to the authors, it is shown how the extreme tail dependence between an asset and a factor or index or between two assets can be easily calibrated. Portfolios constructed with stocks with minimal tail dependence with the market exhibit a remarkable degree of decorrelation with the market at…

We devise an optimal allocation strategy for the execution of a predefined number of stocks in a given time frame using the technique of discrete-time Stochastic Control Theory for a defined market model. This market structure allows an instant execution of the market orders and has been analyzed based on the assumptio…

Framework uses optimal transport for neural architecture search.

In this work, a new approach for Sun tracking systems is presented. Due to the current system limitations regarding costs and operational problems, a new approach based on low cost, computer vision open hardware and deep learning has been developed. The preliminary tests carried out successfully in Plataforma solar de …

The study uses LSTM and random forests to forecast stock price movements for intraday trading.

Investment strategies derived from commodity futures curves exploit dynamics in price movements.

Paper reduces movement primitive dimensionality in parameter space.

This paper identifies a negative profit effect in limit order fills.

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…

SmartDeal reduces energy and storage costs for deep neural networks.

New algorithm achieves logarithmic regret for adversarial online control.

Taureau uses Twitter sentiment analysis to predict stock market movement.

In informationally efficient financial markets, option prices and this implied volatility should immediately be adjusted to new information that arrives along with a jump in underlying's return, whereas gradual changes in implied volatility would indicate market inefficiency. Using minute-by-minute data on S&P 500 inde…

Non-negative Matrix Factorization (NMF) is a key kernel for unsupervised dimension reduction used in a wide range of applications, including topic modeling, recommender systems and bioinformatics. Due to the compute-intensive nature of applications that must perform repeated NMF, several parallel implementations have b…

The study models market price movement based on investors' expectations.

Neural nets predict user attention from mouse movements.

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

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…

Enhances hedging strategies using deep neural networks.

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…

Study identifies key trades predicting market 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…

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

Optimizing data movement significantly improves transformer training efficiency.

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

Study identifies personality traits from dance movements in music.

Adaptive robust strategy improves online portfolio selection by managing market trends and costs.

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

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 introduces a new investment strategy model using lazy factor and probability weights.

We propose a prediction model based on the minority game in which traders continuously evaluate a complete set of trading strategies with different memory lengths using the strategies' past performance. Based on the chosen trading strategy they determine their prediction of the movement for the following time period of…

Generically learns movement control policies from exploration data.

We employ stochastic dynamic microsimulations to analyse and forecast the pension cost dependency ratio for England and Wales from 1991 to 2061, evaluating the impact of the ongoing state pension reforms and changes in international migration patterns under different Brexit scenarios. To fully account for the recently …

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