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.
ImmuNeCS uses AI immune system to build neural committees for efficient deep learning model creation.
Computational Intelligence (CI) is a sub-branch of Artificial Intelligence paradigm focusing on the study of adaptive mechanisms to enable or facilitate intelligent behavior in complex and changing environments. There are several paradigms of CI [like artificial neural networks, evolutionary computations, swarm intelli…
Many immunization strategies have been proposed to prevent infectious viruses from spreading through a network. In this study, we propose efficient immunization strategies to prevent a default contagion that might occur in a financial network. An essential difference from the previous studies on immunization strategy i…
Bayesian optimization of antibodies learns from immune system evolution.
New algorithm improves GAN stability and quality.
Study on the probability of immunity and its bounds.
As adversarial attacks pose a serious threat to the security of AI system in practice, such attacks have been extensively studied in the context of computer vision applications. However, few attentions have been paid to the adversarial research on automatic path finding. In this paper, we show dominant adversarial exam…
Feature selection predicts immune state changes in RA mouse model.
Paper uses PCA to analyze Chinese sovereign bonds and discusses bond immunization.
AdvImmune improves certifiable robustness of GNNs against adversarial attacks.
New foundation for Shapley value immune to coalitional manipulations.
In their seminal work Carr and Lee (2008) show how to robustly price and replicate a variety of claims written on the quadratic variation of a risky asset under the assumption that the asset's volatility process is independent of the Brownian motion that drives the asset's price. Additionally, they propose a correlatio…
Estimates vaccine effectiveness and immune correlates in TND studies with missing data.
DeepRC uses Hopfield networks and attention to classify immune repertoires.
Paper uses bond pricing and convexity adjustments to explain herd immunity paradox.
Italy's vaccine coverage fell, leading to political debates and online social media discussions.
Major histocompatibility complex class two (MHC-II) molecules are trans-membrane proteins and key components of the cellular immune system. Upon recognition of foreign peptides expressed on the MHC-II binding groove, helper T cells mount an immune response against invading pathogens. Therefore, mechanistic identificati…
Defense against ASR attacks using dropout uncertainty.
We develop a novel multi-fidelity framework that goes far beyond the classical AR(1) Co-kriging scheme of Kennedy and O'Hagan (2000). Our method can handle general discontinuous cross-correlations among systems with different levels of fidelity. A combination of multi-fidelity Gaussian Processes (AR(1) Co-kriging) and …
Estimates input from output of nonlinear systems using ANN.
New framework tackles deep learning issues like local traps and miscalibration.
Phylogenetic tree inference using deep DNA sequencing is reshaping our understanding of rapidly evolving systems, such as the within-host battle between viruses and the immune system. Densely sampled phylogenetic trees can contain special features, including "sampled ancestors" in which we sequence a genotype along wit…
In this paper we outline initial concepts for an immune inspired algorithm to evaluate price time series data. The proposed solution evolves a short term pool of trackers dynamically through a process of proliferation and mutation, with each member attempting to map to trends in price movements. Successful trackers fee…
Year by year control of normal and emergency conditions of up-to-date power systems becomes an increasingly complicated problem. With the increasing complexity the existing control system of power system conditions which includes operative actions of the dispatcher and work of special automatic devices proves to be ins…
Generative AI reduces IR evaluation costs but introduces errors; this work provides reliable CIs.
Introduces an artificial cyber lab to test and identify cyber resilience measures.
Bayesian ANN method predicts chaotic systems with uncertainty.
Machine learning algorithms can be fooled by small well-designed adversarial perturbations. This is reminiscent of cellular decision-making where ligands (called antagonists) prevent correct signalling, like in early immune recognition. We draw a formal analogy between neural networks used in machine learning and model…
The study examines how model predictions hold up under model extensions.
A new PGA algorithm ensures stable, robust, and noise-immune solutions for non-negative inverse problems.
New framework boosts neural network performance and resilience.
We demonstrate the possibility of classifying causal systems into kinds that share a common structure without first constructing an explicit dynamical model or using prior knowledge of the system dynamics. The algorithmic ability to determine whether arbitrary systems are governed by causal relations of the same form o…
Sepsis, a dysregulated immune system response to infection, is among the leading causes of morbidity, mortality, and cost overruns in the Intensive Care Unit (ICU). Early prediction of sepsis can improve situational awareness amongst clinicians and facilitate timely, protective interventions. While the application of p…
Topologically protected vortex knots and links are proposed and proven.
The paper discusses safety assessment for AI systems, focusing on machine learning models.
A time-dependent SIR model predicts COVID-19 spread and herd immunity.
Antibodies are a critical part of the immune system, having the function of directly neutralising or tagging undesirable objects (the antigens) for future destruction. Being able to predict which amino acids belong to the paratope, the region on the antibody which binds to the antigen, can facilitate antibody design an…
NESYM combines AI and Earth models for new climate insights.
Owing to the advancement of deep learning, artificial systems are now rival to humans in several pattern recognition tasks, such as visual recognition of object categories. However, this is only the case with the tasks for which correct answers exist independent of human perception. There is another type of tasks for w…
Survey on LSTM-based anomaly detection for technical systems.
Paper proposes a DRL-based controller for networked AP systems that reduces communication frequency.
An artificial agent for financial risk and returns' prediction is built with a modular cognitive system comprised of interconnected recurrent neural networks, such that the agent learns to predict the financial returns, and learns to predict the squared deviation around these predicted returns. These two expectations a…
Graphs represent natural and artificial systems; ML can learn from them.
Enhances neural network dynamics to boost computational capacity.
Perceptual capabilities of artificial systems have come a long way since the advent of deep learning. These methods have proven to be effective, however they are not as efficient as their biological counterparts. Visual attention is a set of mechanisms that are employed in biological visual systems to ease computationa…
RNNs trained on head direction task mimic brain's compass and shifter neurons.
A new explainable CBR system predicts financial risks with interpretability and good performance.
The success of modern Artificial Intelligence (AI) technologies depends critically on the ability to learn non-linear functional dependencies from large, high dimensional data sets. Despite recent high-profile successes, empirical evidence indicates that the high predictive performance is often paired with low robustne…