arXiv research

A framework identifies worst-case decision points in safety-critical scenarios, improving risk assessment by 10 hours.

It is proved that the curve graph $C^1(Σ)$ of a surface $Σ_{g,n}$ has a local pathology that had not been identified as such: there are vertices $α,β$ in $C^1(Σ)$ such that $β$ is a dead end of every geodesic joining $α$ to $β$. It also has double dead-ends. Every dead end has depth 1.

Study on curvature in finitely generated groups, showing positive curvature in specific cases.

We introduce the notion of connection thickness of spheres in a Cayley graph, related to dead-ends and their retreat depth. It was well-known that connection thickness is bounded for finitely presented one-ended groups. We compute that for natural generating sets of lamplighter groups on a line or on a tree, connection…

PoPCoin aims to create a more equitable cryptocurrency.

Paper explores using EEG for better speaker identification, even in noisy environments.

In the present paper we study interval identification systems of order three. We prove that the Rauzy induction preserves symmetry: for any symmetric interval identification system of order three after finitely many iterations of the Rauzy induction we always obtain a symmetric system. We also provide an example of sym…

Simplified identification methods for causal inference with arbitrary interventional distributions.

Cyclic coordinate descent identifies models in finite time and converges linearly.

Study on identifying and inferring nonlinear dynamics on unknown networks.

The paper describes the K-theory of $C^*$-algebras of locally finite graphs.

New findings on complexity limits in fixed budget bandit identification.

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…

Bayesian methods reduce variance in subspace identification for small data sets.

Driver identification has emerged as a vital research field, where both practitioners and researchers investigate the potential of driver identification to enable a personalized driving experience. Within recent years, a selection of studies have reported that individuals could be perfectly identified based on their dr…

A tutorial on non-asymptotic system identification methods.

A new algorithm identifies one of several nearly optimal arms in linear bandits.

Online algorithm identifies PDEs from noisy data snapshots.

A distributed system identification method for LTI systems using reverse experience replay.

dynoGP uses deep Gaussian processes for dynamic system identification.

Proposes SPCA to incorporate structural constraints in model identification.

ADSGD method speeds up model identification in sparse optimization.

This paper tackles Bayesian system identification with probabilistic numerical methods.

Study measures uncertainty in MST identification across different correlation networks.

Optimal best-arm identification in linear bandits reduces sampling budget.

Equation discovery methods enable modelers to combine domain-specific knowledge and system identification to construct models most suitable for a selected modeling task. The method described and evaluated in this paper can be used as a nonlinear system identification method for gray-box modeling. It consists of two int…

Objective: Patient notes in electronic health records (EHRs) may contain critical information for medical investigations. However, the vast majority of medical investigators can only access de-identified notes, in order to protect the confidentiality of patients. In the United States, the Health Insurance Portability a…

Patient notes contain a wealth of information of potentially great interest to medical investigators. However, to protect patients' privacy, Protected Health Information (PHI) must be removed from the patient notes before they can be legally released, a process known as patient note de-identification. The main objectiv…

Logical neural networks solve mazes by filling dead ends, but not all methods generalize well.

This research improves deep neural networks for parameter identification and prediction in stochastic Volterra integral equations.

Modeling dynamical systems is important in many disciplines, e.g., control, robotics, or neurotechnology. Commonly the state of these systems is not directly observed, but only available through noisy and potentially high-dimensional observations. In these cases, system identification, i.e., finding the measurement map…

Given a set of points that sample a shape, the Rips complex of the data points is often used in machine-learning to provide an approximation of the shape easily-computed. It has been proved recently that the Rips complex captures the homotopy type of the shape assuming the vertices of the complex meet some mild samplin…

Efficiently identifies best policies in tabular MDPs with reduced computational cost.

Semi-parametric framework for nonlinear system identification

A new Bayesian approach to linear system identification has been proposed in a series of recent papers. The main idea is to frame linear system identification as predictor estimation in an infinite dimensional space, with the aid of regularization/Bayesian techniques. This approach guarantees the identification of stab…

Consistent partial identification of causal effects proved for neural models.

New methods identify causal effects without needing complete proxy variables.

De-identification of electronic health records (EHR) is a vital step towards advancing health informatics research and maximising the use of available data. It is a two-step process where step one is the identification of protected health information (PHI), and step two is replacing such PHI with surrogates. Despite th…

Unified method for learning from selectively labeled data.

Deep SSMs use neural networks to identify complex systems.

Bayesian neural networks with nonparametric noise models for system identification.

Most users of online services have unique behavioral or usage patterns. These behavioral patterns can be exploited to identify and track users by using only the observed patterns in the behavior. We study the task of identifying users from statistics of their behavioral patterns. Specifically, we focus on the setting i…

Paper introduces a conformer-based system for streaming language identification in long-form speech.

New method identifies diffusion sources on networks with statistical confidence.

Data augmentation is usually used by supervised learning approaches for offline writer identification, but such approaches require extra training data and potentially lead to overfitting errors. In this study, a semi-supervised feature learning pipeline was proposed to improve the performance of writer identification b…

Study identifies and validates a method for system identification of Markov jump linear systems.

New algorithm identifies optimal subtrees in fixed-budget tree search.

Risk is part of the fabric of every business; surprisingly, there is little work on establishing best practices for systematic, repeatable risk identification, arguably the first step of any risk management process. In this paper, we present a proposal that constitutes a more holistic risk management approach, a method…