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.
Machine learning improves accuracy of running gait event detection from tibial acceleration.
Gait patterns reveal emotions, offering a non-invasive method for automated recognition.
In quadruped gait learning, policy search methods that scale high dimensional continuous action spaces are commonly used. In most approaches, it is necessary to introduce prior knowledge on the gaits to limit the highly non-convex search space of the policies. In this work, we propose a new approach to encode the symme…
Compared to other biometrics, gait is difficult to conceal and has the advantage of being unobtrusive. Inertial sensors, such as accelerometers and gyroscopes, are often used to capture gait dynamics. These inertial sensors are commonly integrated into smartphones and are widely used by the average person, which makes …
Phone sensors could be useful in assessing changes in gait that occur with alcohol consumption. This study determined (1) feasibility of collecting gait-related data during drinking occasions in the natural environment, and (2) how gait-related features measured by phone sensors relate to estimated blood alcohol concen…
Study investigates XAI methods in clinical gait analysis.
Paper predicts TUG score from gait characteristics using machine learning.
Gait is a person's natural walking style and a complex biological process that is unique to each person. Recently, the channel state information (CSI) of WiFi devices have been exploited to capture human gait biometrics for user identification. However, the performance of existing CSI-based gait identification systems …
Machine learning (ML) techniques such as (deep) artificial neural networks (DNN) are solving very successfully a plethora of tasks and provide new predictive models for complex physical, chemical, biological and social systems. However, in most cases this comes with the disadvantage of acting as a black box, rarely pro…
Ability to quantify and predict progression of a disease is fundamental for selecting an appropriate treatment. Many clinical metrics cannot be acquired frequently either because of their cost (e.g. MRI, gait analysis) or because they are inconvenient or harmful to a patient (e.g. biopsy, x-ray). In such scenarios, in …
Study uses GMM-UBM and i-vectors to assess Parkinson's patients via speech, handwriting, and gait.
GAIT-prop derives a biologically plausible learning rule from backpropagation.
This study compares different data preprocessing methods for gait analysis.
The design of gaits for robot locomotion can be a daunting process which requires significant expert knowledge and engineering. This process is even more challenging for robots that do not have an accurate physical model, such as compliant or micro-scale robots. Data-driven gait optimization provides an automated alter…
The fact that every human has a distinctive walking style has prompted a proposal to use gait recognition as an identification criterion. Using end-to-end learning, I investigated whether the center-of-pressure trajectory is sufficiently unique to identify a person with a high certainty. Thirty-six adults walked on a t…
Pre-trained model from healthy ADLs improves gait pattern classification for Parkinson's disease.
Wi-Fi signals-based person identification attracts increasing attention in the booming Internet-of-Things era mainly due to its pervasiveness and passiveness. Most previous work applies gaits extracted from WiFi distortions caused by the person walking to achieve the identification. However, to extract useful gait, a p…
Freezing of gait (FoG) is a common gait disability in Parkinson's disease, that usually appears in its advanced stage. Freeze episodes are associated with falls, injuries, and psychological consequences, negatively affecting the patients' quality of life. For detecting FoG episodes automatically, a highly accurate dete…
Machine learning classifies Parkinson's Disease stages from walker sensors data.
PerCDL learns personalized dictionaries for physiological signals combining global and local structures.
Bayesian model predicts circular data with fast Gibbs sampling.
In this paper, we introduce a new gait segmentation method based on accelerometer data and develop a new distance function between two time series, showing novel and effectiveness in simultaneously identifying user and adversary. Comparing with the normally used Neural Network methods, our approaches use geometric feat…
New system uses wearable bio-signals for easy authentication.
Model uses smartphone data to assess MS trajectories.
Deep reinforcement learning (deep RL) holds the promise of automating the acquisition of complex controllers that can map sensory inputs directly to low-level actions. In the domain of robotic locomotion, deep RL could enable learning locomotion skills with minimal engineering and without an explicit model of the robot…
Study shows not all ML models are uniquely identifiable from data.
ES-VAE models skeletal pose trajectories by removing nuisance factors.
The Poincaré map is widely used to study the qualitative behavior of dynamical systems. For instance, it can be used to describe the existence of periodic solutions. The Poincaré map for dynamical systems with impulse effects was introduced in the last decade and mainly employed to study the existence of limit cycles (…
We advocate the use of a notion of entropy that reflects the relative abundances of the symbols in an alphabet, as well as the similarities between them. This concept was originally introduced in theoretical ecology to study the diversity of ecosystems. Based on this notion of entropy, we introduce geometry-aware count…
We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies which can perform vision-based object reorientation on a physical Shadow Dexterous Hand. The training is performed in a simulated environment in which we randomize many of the physical properties of the system like friction coefficients…
Develops a neural model to predict event occurrence and timing.
Paper proposes a new trading strategy using corporate event detection from news articles.
Events are happening in real-world and real-time, which can be planned and organized occasions involving multiple people and objects. Social media platforms publish a lot of text messages containing public events with comprehensive topics. However, mining social events is challenging due to the heterogeneous event elem…
ProxiModel extracts high-quality news events from news corpora.
AUC is unreliable in rare event settings but stable with moderate numbers of events.
Non-spanning identification of scheduled event risk in option pricing.
New approach predicts event probabilities for better event detection.
This paper examines linear embeddings for high-dimensional Bayesian optimization, identifying and addressing issues to improve performance.
Study examines HTE estimation from time-to-event data with competing events.
CAUSE learns Granger causality from event sequences, outperforming existing methods.
New method detects events with keywords, adapting to new types.
REST framework predicts stock trends by considering stock-specific and related-stock events.
GANs improve event generation in physics experiments.
Considering event structure information has proven helpful in text-based stock movement prediction. However, existing works mainly adopt the coarse-grained events, which loses the specific semantic information of diverse event types. In this work, we propose to incorporate the fine-grained events in stock movement pred…
New deep learning method handles rare and imbalanced events in time series.
Combining multiple collider events improves machine learning performance.
The study uses financial events to predict stock market movements.
Proposes a model for predicting events from event streams.