Method reveals soccer teams' passing strategies from ball trajectories.
problem Inferring soccer teams' tactics from ball movement data.
method Dynamic Time Warping analysis of player and ball trajectories.
result Insights into passing strategies and individual player capabilities.
Diameters of ball intersections decrease as centers move apart.
problem Behavior of intersections of moving balls in Riemannian manifolds.
method Continuous decrease of intersection diameter as centers move apart.
result Diameter of intersections decreases continuously.
New theorem shows shapes close to balls, flow converges to balls in 2D and 3D.
problem Understanding the asymptotic behavior of volume-preserving mean curvature flow.
method Proved a new quantitative Alexandrov theorem and used it to show flow convergence.
result Weak solutions of volume-preserving mean curvature flow converge to disjoint balls in R^2 and R^3.
Taureau uses Twitter sentiment analysis to predict stock market movement.
problem Predicting stock market movement using public opinion on Twitter.
method Obtained historical tweets, filtered and labeled, generated word embeddings, assessed sentiment scores, correlated with stock price movement, designed and evaluated predictive model.
result Taureau can predict stock price movement from lagged sentiment scores.
The paper studies the geometry of eye movements and cycles.
problem Understanding the visual stability and eye movement patterns.
method Develops differential geometry of saccades and saccadic cycles, characterizing them as geodesic segments and polygons.
result Provides necessary and sufficient conditions for a system of lines to be axes of rotation for saccades in a saccadic cycle.
The paper supports task classification from eye movements, achieving 95.4% accuracy.
problem Decoding the observer's task from eye movements.
method Exploratory analysis, feature projection, feature elimination, SVM and Ada Boosting classifier training.
result Achieved 95.4% accuracy in task classification.
New benchmark for EEG-eye movement reconstruction from functional data.
problem Reconstructing eye movements from EEG data.
method Functional neural networks and open challenges for evaluation.
result Baseline results for consumer-grade and research-grade hardware.
Study shows past market trends reduce or increase correlations between futures contracts.
problem Estimating and managing risk in non-stationary futures markets.
method Applied Principal Regression Analysis (PRA) to quantify past market movements' effect on correlations.
result Past up or down 10-day trends reduce or increase instantaneous correlations, respectively.
Paper reduces movement primitive dimensionality in parameter space.
problem High dimensionality of movement primitives makes policy optimization expensive.
method Investigates dimensionality reduction in parameter space, identifying principal movements.
result Dimensionality reduction in parameter space is more effective than in configuration space.
A new method reduces data movement in neural network training.
problem Large data movement during neural network training.
method Streaming batch principal component analysis for low-rank updates.
result Effective training of convolutional neural networks with low overhead.
Enhances LLMs for predicting stock movements by considering news dissemination and context.
problem Lack of consideration for news dissemination and insufficient contextual data in LLMs for stock price prediction.
method Clusters news for reach assessment, enriches prompts with specific data and instructions, fine-tunes an LLM using the dataset.
result Improves prediction accuracy by 8% compared to existing methods.
Study uses Wavelet Analysis and VARMA to forecast metal prices and detect dynamic co-movement.
problem Understanding dynamic co-movement among metal prices and interactions.
method Utilized Wavelet Analysis for co-movement detection and VARMA for forecasting.
result Improved forecasting power via VARMA model with reduced errors.
ChatGPT struggles in predicting stock movements, underperforming traditional methods.
problem Predicting stock market movements using ChatGPT.
method Zero-shot analysis of ChatGPT's multimodal stock prediction capabilities.
result ChatGPT underperforms traditional methods and state-of-the-art models in predicting stock movements.
Study uses FinBERT for financial sentiment analysis to predict stock movement.
problem Predicting stock movement with greater accuracy.
method Integrates sentiment analysis with FinBERT and LSTM networks.
result FinBERT enhances model's ability to predict market fluctuations.
Study identifies key trades predicting market movements.
problem Predicting future market price movements.
method Optimized neural network predictor to identify influential trades.
result Trades with specific characteristics significantly impact future price predictions.
The study analyzes co-movements in Indian stock market sectors using network techniques.
problem Understanding co-movements among Indian stock sectors.
method Mesoscopic network analysis, recurrence analysis, partial correlations, multidimensional scaling, minimum spanning tree.
result Minimum spanning tree effectively separates technologically related sectors.
Study shows news from various topics impacts Nifty 50 index.
problem Lack of analysis on news impact on Nifty 50 index.
method Analyzed Nifty 50 index movement with sentiments from diverse news topics.
result Sentiment scores from different topics significantly impact Nifty 50 index.
MPANF improves naive forecast by incorporating directional information.
problem Challenging to surpass naive forecast in financial time series.
method Combines naive forecast with movement prediction and accuracy.
result MPANF generally outperforms common benchmarks.
Study predicts cryptocurrency price movements using Twitter sentiment analysis.
problem Predicting short-term price movements of cryptocurrencies.
method Conditional examination of return and excess return rates following tweet publication.
result Statistically significant increases in return rates within the first three minutes after tweet publication.
This study evaluates LLMs for sentiment analysis in stock price prediction.
problem Improving stock price prediction accuracy using LLMs for news sentiment analysis.
method Compared 3 LLMs (DeBERTa, RoBERTa, FinBERT) for sentiment-driven stock prediction.
result DeBERTa outperforms other models with 75% accuracy, and ensemble model increases accuracy to 80%.
Study improves stock movement prediction using multimodal data.
problem Inaccurate stock movement prediction due to incomplete multimodal data integration.
method Introduces MSGCA framework for robust multimodal fusion.
result MSGCA framework outperforms existing methods by 21.7% on multimodal datasets.
Mapper and Ball Mapper tools for complex data analysis.
problem Exploring and visualizing high-dimensional data and scalar functions.
method Combining Mapper and Ball Mapper, adding new features for encoding structure and symmetries.
result A new hybrid algorithm, Mapper on Ball Mapper, for comparing high-dimensional data descriptors.
Study identifies personality traits from dance movements in music.
problem Predicting individual differences from music-induced movement.
method Identified Big Five personality traits and EQ/SQ scores from dance movements.
result Successfully explored unseen space for personality and EQ/SQ.
Enhances stock movement prediction using Higher Order Transformers for multimodal time-series data.
problem Predicting stock movements in financial markets with complex dynamics.
method Introduced Higher Order Transformers, extending self-attention and transformer architecture to capture complex market dynamics. Employed low-rank tensor decomposition and kernel attention to manage computational complexity. Integrated technical and fundamental analysis from historical prices and tweets.
result Demonstrated effectiveness of the method on the Stocknet dataset, improving stock movement prediction.
Paper uses ML to predict stock price movements from order book data.
problem Forecasting stock price movements in financial markets.
method Combines handcrafted and ML features for three classifiers, evaluated on two setups.
result Machine Learning shows promise for this task, suggesting future research.
Study analyzes market co-movements in critical mineral investments using change point detection and cross-sectional analysis.
problem Market dynamics in critical mineral investments during significant global events.
method Combines change-point detection (PELT algorithm) with cross-sectional analysis on ESG-ranked ETFs.
result Investors herded during market downturns and shifted to anti-herding after positive news and geopolitical shocks.
Stochastic heavy ball method achieves linear convergence for general loss minimization.
problem Minimizing generalization error in machine learning models.
method SGD steps with heavy ball momentum, focusing on expected loss, not finite-sum minimization.
result Established the first linear convergence result for the stochastic heavy ball method.
GPT-4 improves stock price prediction from microblogging sentiments.
problem Improving stock price prediction using sentiment analysis of microblogs.
method Developed a novel method for contextual sentiment analysis using GPT-4, fine-tuning prompts for better accuracy.
result GPT-4 outperformed BERT in predicting stock price movements, achieving a peak accuracy of 71.47%.
Hybrid model predicts stock prices using ML, DL, and NLP.
problem Improving prediction accuracy of stock price movement.
method Machine learning, deep learning, natural language processing, sentiment analysis.
result LSTM model outperforms traditional machine learning models.
Study uses wearable sensors to predict infant motor development status.
problem Early detection of infant motor development status in high-risk infants.
method Machine learning classification algorithms using day-long inertial movement data from wearable sensors.
result Machine learning can predict infant motor development status from day-long movement data.
Noise affects 1.7%-4.5% of SPY ETF options market volume.
problem Systematic noise in equity options pricing.
method Analysis of SPY ETF options market data.
result Approximately 1.7%-4.5% of SPY ETF options market volume is due to systematic noise.
Automated GMA using accelerometers detects abnormal infant movements with human-level accuracy.
problem Undetected perinatal stroke leads to lifelong disability.
method Wearable accelerometers and Discriminative Pattern Discovery (DPD) for automated GMA.
result Automated method correctly identifies abnormal movements with human-level accuracy.
Quantifies nearly spherical subsets in complex ball geometry.
problem Isoperimetric inequality for nearly spherical domains in Bergman ball.
method Proves a quantitative isoperimetric inequality for nearly spherical subsets of Bergman ball.
result First result on isoperimetric phenomenon in Bergman ball.
Paper improves Heavy-ball method convergence in convex settings.
problem Convergence analysis of Heavy-ball method in convex optimization.
method Improved convergence complexity results for Heavy-ball method with constant step size.
result First non-ergodic O(1/k) rate result for coercive objective functions.
Research uses Sutte Indicator to predict stock market movements.
problem Predicting stock market movements accurately.
method Applied Sutte Indicator alongside SMA and MACD for comparison.
result Sutte Indicator shows better reliability in predicting stock movements.
A taxonomy of large financial crashes proposed in the literature locates the burst of speculative bubbles due to endogenous causes in the framework of extreme stock market crashes, defined as falls of market prices that are outlier with respect to the bulk of drawdown price movement distribution. This paper goes on dee…
New method for evolving surfaces using generalized power mean curvature flow.
problem Evolve surfaces with volume penalization replaced by a generalized term.
method Generalized minimizing movement scheme converging to geometric evolution equation.
result Minimizing movements coincide with smooth classical solutions and preserve mean convexity.
Automatic detection of reflective thinking in math problem solving using body movement data.
problem Detecting reflective thinking in children's mathematical problem solving.
method WeDraw-1 Movement Dataset, Long Short-Term Memory neural networks, end-to-end detection.
result Average F1 scores of 0.73 for automatic detection and 0.79 for end-to-end detection of reflective thinking.
We introduce a new graphical model for tracking radio-tagged animals and learning their movement patterns. The model provides a principled way to combine radio telemetry data with an arbitrary set of userdefined, spatial features. We describe an efficient stochastic gradient algorithm for fitting model parameters to da…
New tool helps analyze complex financial data.
problem Difficulty in comprehending high-dimensional financial data.
method Topological Data Analysis Ball Mapper algorithm.
result Shows new way to see detail in financial data.
Robot learns to juggle two balls from 56 minutes of experience.
problem Learning high-acceleration tasks in the real-world with binary rewards.
method Designs a learning system that incorporates safety and sample efficiency.
result High-speed manipulator learns to juggle for up to 33 minutes.
Sentiment analysis from news and social media predicts forex market movements.
problem Forecasting forex market movements using sentiment analysis.
method Lexicon-based analysis and Naive Bayes machine learning.
result Sentiment analysis is effective in predicting forex market movements.
Predicts short-term futures contract direction using neural networks and order flow data.
problem Challenges in predicting short-term directional movement of futures contracts.
method Engineering features from technical analysis, order flow, and order-book data; training a Tabnet neural network.
result Achieved an accuracy of 0.601 in predicting directional change on the Silver Futures Contract.
Complex domains covering manifolds are biholomorphic to balls.
problem Covering compact manifolds by bounded domains with smooth boundaries.
method Using biholomorphic mappings and properties of C1,1 boundaries. result Bounded domains with smooth boundaries covering compact manifolds are biholomorphic to the unit ball.
Existence and instability of biharmonic maps from balls to spheres.
problem Existence and stability of biharmonic maps between balls and spheres.
method Existence proof and instability analysis using bienergy.
result Existence of two proper biharmonic maps and instability in low dimensions.
In this paper we investigate predictability of electricity prices in the Canadian provinces of Alberta and Ontario, as well as in the US Mid-C market. Using scale-dependent detrended fluctuation analysis, spectral analysis, and the probability distribution analysis we show that the studied markets exhibit strongly anti…
The paper defines the time function of stock prices using a mathematical model.
problem Understanding the movement and predictability of stock prices over time.
method Empirical evidence and mathematical modeling of white noise.
result Derives auto-correlation function, displacement formula, and power spectral density of stock price movement.
Study earnings calls to predict stock price movements, finding them more predictive than traditional data.
problem Improving investment decisions by analyzing earnings calls for stock price predictions.
method Graph Neural Network based approach to process and analyze earnings call transcripts.
result Earnings call transcripts are more predictive of stock price movements than traditional hard data.