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.
New algorithm detects tensor dependence structure alterations efficiently.
Bayesian model detects altered neural circuits in MCI patients.
A new mathematical approach detects frequency-based alterations in brain networks.
Paper presents neural network-based change-point detection methods.
Radiomics identifies subtle cardiac changes in hypertension.
In an ego-network, an individual (ego) organizes its friends (alters) in different groups (social circles). This social network can be efficiently analyzed after learning representations of the ego and its alters in a low-dimensional, real vector space. These representations are then easily exploited via statistical mo…
New method alters Seifert surfaces without compression.
The influence of human judgement is ubiquitous in datasets used across the analytics industry, yet humans are known to be sub-optimal decision makers prone to various biases. Analysing biased datasets then leads to biased outcomes of the analysis. Bias by protected characteristics (e.g. race) is of particular interest …
Enhances social spam detection using multi-level dependency of relational sequences.
Machine learning identifies types of alterations in historical manuscripts.
Alzheimer's disease is the most common dementia leading to an irreversible neurodegenerative process. To date, subject revealed advanced brain structural alterations when the diagnosis is established. Therefore, an earlier diagnosis of this dementia is crucial although it is a challenging task. Recently, many studies h…
The paper explores how model complexity affects OOD detection performance.
Currently there is no validated objective measure of pain. Recent neuroimaging studies have explored the feasibility of using functional near-infrared spectroscopy (fNIRS) to measure alterations in brain function in evoked and ongoing pain. In this study, we applied multi-task machine learning methods to derive a pract…
Detecting a change point is a crucial task in statistics that has been recently extended to the quantum realm. A source state generator that emits a series of single photons in a default state suffers an alteration at some point and starts to emit photons in a mutated state. The problem consists in identifying the poin…
During the directional drilling, a bit may sometimes go to a nonproductive rock layer due to the gap about 20m between the bit and high-fidelity rock type sensors. The only way to detect the lithotype changes in time is the usage of Measurements While Drilling (MWD) data. However, there are no general mathematical mode…
Study uses machine learning to detect depression in Twitter users.
Online anomaly detection of time-series data is an important and challenging task in machine learning. Gaussian processes (GPs) are powerful and flexible models for modeling time-series data. However, the high time complexity of GPs limits their applications in online anomaly detection. Attributed to some internal or e…
Machine learning monitors detect motor overheating, adapting to concept drift.
Study examines how digital image alterations affect AI classification models.
In this paper, we generalize the Almgren-Chriss's market impact model to a more realistic and flexible framework and employ it to derive and analyze some aspects of optimal liquidation problem in a security market. We illustrate how a trader's liquidation strategy alters when multiple venues and extra information are b…
New method predicts AD progression using MEG brain networks.
The backbone of most proteins forms an open curve. To study their entanglement, a common strategy consists in searching for the presence of knots in their backbones using topological invariants. However, this approach requires to close the curve into a loop, which alters the geometry of curve. Knoto-ID allows evaluatin…
Paper tackles anomaly detection and RCA in dynamical systems using ICODE Networks.
A novel method using saliency maps detects adversarial attacks effectively.
Artificial intelligence (AI) comes with great opportunities but can also pose significant risks. Automatically generated explanations for decisions can increase transparency and foster trust, especially for systems based on automated predictions by AI models. However, given, e.g., economic incentives to create dishones…
Purpose - Functional bowel diseases, including irritable bowel syndrome, chronic constipation, and chronic diarrhea, are some of the most common diseases seen in clinical practice. Many patients describe a range of triggers for altered bowel consistency and symptoms. However, characterization of the relationship betwee…
Paper proposes robust method to detect risk heterogeneity across ethnic groups.
The paper analyzes real-time methods to detect rapidly varying liquidity in markets.
New study on time series anomaly detection shows overlapping inference improves performance.
Graph neural networks help assess how global changes affect plant-pollinator networks.
SVMs improve forest fire detection accuracy on challenging datasets.
A hierarchical Bayesian classifier is trained at pixel scale with spectral data from the CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) imagery. Its utility in detecting rare phases is demonstrated with new geologic discoveries near the Mars-2020 rover landing site. Akaganeite is found in sediments on the…
Topological surgery in dimension is intrinsically connected with the classification of -manifolds and with patterns of natural phenomena. In this expository paper, we present two different approaches for understanding and visualizing the process of -dimensional surgery. In the first approach, we view the proc…
CDLEEDS detects local changes in evolving data streams for accurate feature attributions.
Empirical investor networks (EIN) proposed by \cite{Ozsoylev-Walden-Yavuz-Bildik-2014-RFS} are assumed to capture the information spreading path among investors. Here, we perform a comparative analysis between the EIN and the cellphone communication networks (CN) to test whether EIN is an information exchanging network…
New framework for robust uncertainty quantification in strategic settings.
The possibility that price dynamics is affected by its distance from a moving average has been recently introduced as new statistical tool. The purpose is to identify the tendency of the price dynamics to be attractive or repulsive with respect to its own moving average. We consider a number of tests for various models…
There is substantial interest in the use of machine learning (ML) based techniques throughout the electronic computer-aided design (CAD) flow, particularly those based on deep learning. However, while deep learning methods have surpassed state-of-the-art performance in several applications, they have exhibited intrinsi…
This paper studies the 28 time series of Libor rates, classified in seven maturities and four currencies), during the last 14 years. The analysis was performed using a novel technique in financial economics: the Complexity-Entropy Causality Plane. This planar representation allows the discrimination of different stocha…
Framework identifies brain connectivity alterations for MDD patients using limited rs-fMRI data.
The exposition has been significantly altered, hopefully improved.
In the present work, we study the decompositions of codimension-one transitions that alter the singular set the of stable maps of into the topological behaviour of the singular set and the singularities in the branch set that involves cuspidal curves and swallowtails that alter the singular set. W…
This paper extends adversarial attacks to produce desired class probability distributions.
Trojans can be inserted into deep neural networks without altering training data.
'Big' high-dimensional data are commonly analyzed in low-dimensions, after performing a dimensionality-reduction step that inherently distorts the data structure. For the same purpose, clustering methods are also often used. These methods also introduce a bias, either by starting from the assumption of a particular geo…
New monitoring method detects ML risk models' performance changes in medical interventions.
The emergence of an ageing population is a significant public health concern. This has led to an increase in the number of people living with progressive neurodegenerative disorders like dementia. Consequently, the strain this is places on health and social care services means providing 24-hour monitoring is not sustai…
Data taggants verify dataset ownership without harming models or requiring model internals.