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.
ARIMLE optimizes classifier fusion for brain-computer interface.
A new method improves SSVEP BCI to recognize responses in sub-second time with high accuracy.
Compact-CNN outperforms traditional methods in SSVEP classification.
BrainSurfCNN predicts task contrasts from resting-state fingerprints, improving accuracy over baseline.
Based on the cumulated experience over the past 25 years in the field of Brain-Computer Interface (BCI) we can now envision a new generation of BCI. Such BCIs will not require training; instead they will be smartly initialized using remote massive databases and will adapt to the user fast and effectively in the first m…
Study predicts hearing recovery in MD patients using TEOAE signals.
ASTL uses unlabeled data to improve BCI calibration.
This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate s…
The article explores how organisms and machines learn and recognize the world using Bayesian inference and thermodynamics.
Study uses EEG and ML to predict movie ratings with 72% accuracy.
Riemannian geometry has been applied to Brain Computer Interface (BCI) for brain signals classification yielding promising results. Studying electroencephalographic (EEG) signals from their associated covariance matrices allows a mitigation of common sources of variability (electronic, electrical, biological) by constr…
Improved single-trial P300 classification accuracy using PCA and machine learning.
Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). Despite its efficiency, a potential problem is that using pre-constructed sine-cosine waves as the required reference signals in…
Neurons in cortical circuits exhibit coordinated spiking activity, and can produce correlated synchronous spikes during behavior and cognition. We recently developed a method for estimating the dynamics of correlated ensemble activity by combining a model of simultaneous neuronal interactions (e.g., a spin-glass model)…
Variance-Calibrated Modulation (VCM) addresses the likelihood trap in LLMs by reshaping the probability distribution before truncation.
Brain-computer interfaces (BCIs) have been gaining momentum in making human-computer interaction more natural, especially for people with neuro-muscular disabilities. Among the existing solutions the systems relying on electroencephalograms (EEG) occupy the most prominent place due to their non-invasiveness. However, t…
Brain computer interfaces (BCI) enable direct communication with a computer, using neural activity as the control signal. This neural signal is generally chosen from a variety of well-studied electroencephalogram (EEG) signals. For a given BCI paradigm, feature extractors and classifiers are tailored to the distinct ch…
Breath sounds can identify speakers with high accuracy.
Bayesian optimization has been proposed as a practical and efficient tool through which to tune parameters in many difficult settings. Recently, such techniques have been combined with real-time fMRI to propose a novel framework which turns on its head the conventional functional neuroimaging approach. This closed-loop…
Study uses machine learning to detect pain from brain signals.
Unified approach to characterize and regularize deep neural network local minima.
Finding relevant information from large document collections such as the World Wide Web is a common task in our daily lives. Estimation of a user's interest or search intention is necessary to recommend and retrieve relevant information from these collections. We introduce a brain-information interface used for recomme…
Enhances image memorability and scaryness using deep learning.
New method predicts brain activity using past states for more accurate source estimation.
Paper proposes Imitative Models combining IL and planning for flexible goal achievement.
Differentiable NAS frameworks grow networks wider and deeper, revealing biases in wiring evolution.
New study shows limits to classifying brain activity from randomized EEG trials.
Source imaging based on magnetoencephalography (MEG) and electroencephalography (EEG) allows for the non-invasive analysis of brain activity with high temporal and good spatial resolution. As the bioelectromagnetic inverse problem is ill-posed, constraints are required. For the analysis of evoked brain activity, spatia…
Research shows higher damages may encourage more disclosure in corporate disputes.
Graph Convolutional Networks improve prosthetic sensation interpretation.
A new ICA model identifies shared brain activity patterns across subjects.
Neuro-symbolic system tackles conversational AI's need for natural, broad-ranging dialogue.
This paper characterizes mu-cscK metrics using Perelman's W-entropy.
The object of this contribution is to present the ideas behind the thinking of the French economist Pierre-Joseph Proudhon (1809-1865) in relation to the causes and effects of Stock market speculation. It is based upon the works of this author but particularly on his "Manuel du spéculateur à la Bourse" (Stock Market Sp…
In this paper, we perform a comparative segmentation and clustering analysis of the time series for the ten Dow Jones US economic sector indices between 14 February 2000 and 31 August 2008. From the temporal distributions of clustered segments, we find that the US economy took one and a half years to recover from the m…
Study of negative ads on social media during U.S. midterm elections.
In this article we discuss the distribution of asset price movements by the market potential function. From the principle of free energy minimization we analyze two different kinds of market potentials. We obtain a U-shaped potential when market reversion (i.e. contrarian investors) is dominant. On the other hand, if t…
Develops potential theory for WZW equation in Kähler potentials space.
The paper examines stability of harmonic and symphonic maps with forms and potentials.
Extracts interpretable potential energy from Hamiltonian systems.
The paper examines stability of subelliptic harmonic maps with potential.
The paper describes flat Hessian metrics on surfaces and their potentials.
A hyperKähler potential is a function rho that is a Kähler potential for each complex structure compatible with the hyperKähler structure. Nilpotent orbits in a complex simple Lie algebra are known to carry hyperKähler metrics admitting such potentials. In this paper, we explicitly calculate the hyperKähler potential w…
Investigates how adding a scalar potential affects Dirac-harmonic maps.
Article provides Bernstein gradient estimates for heat equations with potential terms.
In this paper we study potential function of gradient steady Ricci solitons. We prove that infimum of potential function decays linearly; in particular, potential function of rectifiable gradient steady Ricci solitons decays linearly. As a consequence, we show that a gradient steady Ricci soliton with bounded potential…
We consider the geodesic equation for the generalized Kahler potential with only mixed second derivatives bounded. We show that given such two generalized Kahler potentials, there is a unique geodesic segment such that for each point on the geodesic, the generalized Kahler potential has uniformly bounded mixed second d…
We show two results about the Conway potential function which is known as the normalized multivariable Alexander polynomial. We first show that the Conway potential function introduced by Kauffman in "Formal Knot Theory" is indeed a link invariant. Next we show that Kauffman's potential function equals Hartley's potent…