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.
Novel method detects spike-and-wave patterns in EEG signals.
Hybrid pipeline detects spike-and-wave discharges in long-term EEG recordings.
Machine learning detects epilepsy development from EEG before seizures.
Binary and multiclass epilepsy detection methods using EEG features.
Novel hybrid bilinear model improves epilepsy diagnosis accuracy.
Deep learning models predict epileptic seizures with high accuracy.
Bidirectional LSTM predicts seizures with 84% accuracy.
Epilepsy is the fourth most common neurological disorder, affecting about 1% of the population at all ages. As many as 60% of people with epilepsy experience focal seizures which originate in a certain brain area and are limited to part of one cerebral hemisphere. In focal epilepsy patients, a precise surgical removal …
Model predicts epileptic seizures by detecting preictal state using wavelet transform and PCA.
New method detects spike-and-wave epileptiform discharges using Kendall's Tau-b.
Neural memory networks improve seizure type classification.
Hippocampal dentate granule cells are among the few neuronal cell types generated throughout adult life in mammals. In the normal brain, new granule cells are generated from progenitors in the subgranular zone and integrate in a typical fashion. During the development of epilepsy, granule cell integration is profoundly…
Novel NDL framework improves spike detection accuracy and channel localization in EEG/MEG data.
Model predicts epileptic seizures with high accuracy using EEG signals.
Study proposes a new early-warning framework for high-dimensional complex systems.
Path signatures help predict seizures from brain activity.
It is shown that absence of arbitrage opportunity in financial markets is a particular case of existence of uncertainty in decision system. Absence of arbitrage opportunity is considered in the sense of the Arrow-Debreu model of financial market with a riskless asset, while uncertainty (or ambiguity) is defined on the …
Epilepsy is the most common neurological disorder and an accurate forecast of seizures would help to overcome the patient's uncertainty and helplessness. In this contribution, we present and discuss a novel methodology for the classification of intracranial electroencephalography (iEEG) for seizure prediction. Contrary…
Machine learning models predict bluebottles' presence on beaches, addressing class imbalance and unreliable absence data.
Paper proposes a k-NN classifier for detecting spike-and-wave seizures in EEG.
In a semimartingale financial market model, it is shown that there is equivalence between absence of arbitrage of the first kind (a weak viability condition) and the existence of a strictly positive process that acts as a local martingale deflator on nonnegative wealth processes.
Study of recurrences in earthquakes, climate, financial time-series, etc. is crucial to better forecast disasters and limit their consequences. However, almost all the previous phenomenological studies involved only a long-ranged autocorrelation function, or disregarded the multi-scaling properties induced by potential…
New model detects gradual changes in processes more accurately.
Data describing historical economic growth are analysed. They demonstrate convincingly that the takeoffs from stagnation to growth, claimed in the Unified Growth Theory, never happened. This theory is again contradicted by data, which were used, but never properly analysed, during its formulation. The absence of the cl…
We provide two examples of spectral analysis techniques of Schroedinger operators applied to geometric Laplacians. In particular we show how to adapt the method of analytic dilation to Laplacians on complete manifolds with corners of codimension 2 finding the absence of singular continuous spectrum for these operators,…
Driven by the multi-level structure of human intracranial electroencephalogram (iEEG) recordings of epileptic seizures, we introduce a new variant of a hierarchical Dirichlet Process---the multi-level clustering hierarchical Dirichlet Process (MLC-HDP)---that simultaneously clusters datasets on multiple levels. Our sei…
Preventing early progression of epilepsy and so the severity of seizures requires an effective diagnosis. Epileptic transients indicate the ability to develop seizures but humans overlook such brief events in an electroencephalogram (EEG) what compromises patient treatment. Traditionally, training of the EEG event dete…
New conditions prevent gaps in optimal control problems.
When a Riemannian manifold is rotationally symmetric, the critical order of the lower bound of radial curvatures for the absence of eigenvalues of the Laplacian is equal to , where stands for the distance to the center point. In this paper, we shall perturb the Riemannian metric around a rota…
We characterize absence of arbitrage with simple trading strategies in a discounted market with a constant bond and several risky assets. We show that if there is a simple arbitrage, then there is a 0-admissible one or an obvious one, that is, a simple arbitrage which promises a minimal riskless gain of ε, if the inves…
DPI quantifies phase differences in 1D and multidimensional signals using Riesz transform.
Develops a method to estimate network difference in high-dimensional time series data.
Study arbitrage theory without numéraire, generalizing NUPBR.
Efficient method classifies locally stationary time series based on second-order characteristics.
Proposes a method for valid inference in GPLSIMs with longitudinal data.
Corners can be identified by a drum's sound spectrum.
Model detects epileptic seizures in EEG with high sensitivity.
Detects outliers in continuous-time event sequences, including unexpected absences and occurrences.
We find a resonance free region polynomially close to the critical line on Conformally compact manifolds with polyhomogeneous metric.
No closed timelike geodesics in Kerr spacetimes, proving absence of closed causal geodesics.
Recent literature on unsupervised learning focused on designing structural priors with the aim of learning meaningful features, but without considering the description length of the representations. In this thesis, first we introduce the metric that evaluates unsupervised models based on their reconstruction …
Method detects critical events in complex systems by learning latent causal structure.
Implantable, closed-loop devices for automated early detection and stimulation of epileptic seizures are promising treatment options for patients with severe epilepsy that cannot be treated with traditional means. Most approaches for early seizure detection in the literature are, however, not optimized for implementati…
The concept of absence of opportunities for free lunches is one of the pillars in the economic theory of financial markets. This natural assumption has proved very fruitful and has lead to great mathematical, as well as economical, insights in Quantitative Finance. Formulating rigorously the exact definition of absence…
Previous literature on unsupervised learning focused on designing structural priors with the aim of learning meaningful features. However, this was done without considering the description length of the learned representations which is a direct and unbiased measure of the model complexity. In this paper, first we intro…
Patients with epilepsy can manifest short, sub-clinical epileptic "bursts" in addition to full-blown clinical seizures. We believe the relationship between these two classes of events---something not previously studied quantitatively---could yield important insights into the nature and intrinsic dynamics of seizures. A…
A concentration graph associated with a random vector is an undirected graph where each vertex corresponds to one random variable in the vector. The absence of an edge between any pair of vertices (or variables) is equivalent to full conditional independence between these two variables given all the other variables. In…
Detects changes in brain signal topology to predict epileptic seizures.