The paper surveys open problems and questions related to different aspects of integrable systems with finitely many degrees of freedom. Many of the open problems were suggested by the participants of the conference "Finite-dimensional Integrable Systems, FDIS 2017" held at CRM, Barcelona in July 2017.
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48 results for “Open Systems”
New learning methods for open systems with variable agents.
problem Learning in open systems with dynamic agent arrivals and departures.
method Formulated a unified open-system bandit problem with general dynamics, introducing new concepts like pre-training degree and stability.
result Certified global-UCB learning methodologies with provable guarantees, revealing dependencies between entry uncertainty, stability, and agent patterns.
Information Entropy of the Financial Market: Modelling Random Processes Using Open Quantum Systemsq-fin.MF
The financial market entropy is modeled using open quantum systems.
problem Understanding entropy in financial market dynamics.
method Using Open Quantum Systems to model entropy gain in financial markets.
result Interesting non-classical results generated by relaxing assumptions.
Survey of open problems linking integrable systems and Nijenhuis geometry.
problem Interplay between integrable systems and Nijenhuis geometry.
method Discussion of open problems and questions.
result Challenges and simple questions in the field.
Many systems of interest in science and engineering are made up of interacting subsystems. These subsystems, in turn, could be made up of collections of smaller interacting subsystems and so on. In a series of papers David Spivak with collaborators formalized these kinds of structures (systems of systems) as algebras o…
Paper introduces a specialized text classification system for French Open Banking transactions.
problem Classifying specialized banking text data with high accuracy and efficiency.
method Data collection, labeling, preprocessing, modeling, and evaluation stages with language-specific techniques.
result Enhanced performance and efficiency compared to generic approaches.
Fatal accidents are a major issue hindering the wide acceptance of safety-critical systems using machine-learning and deep-learning models, such as automated-driving vehicles. Quality assurance frameworks are required for such machine learning systems, but there are no widely accepted and established quality-assurance …
Model financial markets using open quantum systems to understand market imperfections.
problem Understanding market imperfections through imperfect trading mechanisms.
method Using open quantum systems to represent financial markets, characterizing orbits, and analyzing reduced density matrices.
result Non-classical modes of time evolution can incorporate factors like illiquid trades and imperfect trading mechanisms.
Wealth Condensation in Pareto Macro-Economiescond-mat.stat-mech
We discuss a Pareto macro-economy (a) in a closed system with fixed total wealth and (b) in an open system with average mean wealth and compare our results to a similar analysis in a super-open system (c) with unbounded wealth. Wealth condensation takes place in the social phase for closed and open economies, while it …
In recent years, an active field of research has developed around automated machine learning (AutoML). Unfortunately, comparing different AutoML systems is hard and often done incorrectly. We introduce an open, ongoing, and extensible benchmark framework which follows best practices and avoids common mistakes. The fram…
Paper bridges quantum and classical mechanics for open systems.
problem Quantum open systems with bi-Lindblad structure.
method Develops a bridge between bi-Hamiltonian structures and GKSL formalism, introducing contact-compatible Lindblad generators.
result Provides a mathematical mechanism for semiclassical limit of quantum open systems.
Mathematical model predicts international trade and global economy dynamics.
problem Understanding complex international trade and economy interactions.
method Developed a mathematical model for non-equilibrium processes in open systems.
result Predicted model accurately reflects international trade and economy.
The formal structure of geometrical thermodynamics is reviewed with particular emphasis on the geometry of equilibria submanifolds. On these submanifolds thermodynamic metrics are defined as the Hessian of thermodynamic potentials. Links between geometry and thermodynamics are explored for single and multiple component…
The VC-dimension of a set system is a way to capture its complexity and has been a key parameter studied extensively in machine learning and geometry communities. In this paper, we resolve two longstanding open problems on bounding the VC-dimension of two fundamental set systems: -fold unions/intersections of half-s…
Introducing ReQuEST: an Open Platform for Reproducible and Quality-Efficient Systems-ML Tournamentsstat.ML
Co-designing efficient machine learning based systems across the whole hardware/software stack to trade off speed, accuracy, energy and costs is becoming extremely complex and time consuming. Researchers often struggle to evaluate and compare different published works across rapidly evolving software frameworks, hetero…
This paper reviews low voltage load forecasting methods and applications.
problem Reliable forecasting for low voltage networks is needed for decarbonization.
method Comprehensive survey of current approaches, challenges, and trends.
result Established an open list of low voltage datasets for further research.
Finite-gap solutions approximate jets of initial data for certain BKM systems.
problem Approximating jets of initial data for specific PDE systems.
method Using finite-reduction map to finite-gap solutions of Stäckel systems.
result Full jet-surjectivity for KdV and Kaup--Boussinesq, partial for Camassa--Holm.
Survey of AI in finance covering models, strategies, and knowledge systems.
problem Challenges in applying AI to financial markets, especially in high-frequency trading.
method Systematic analysis of financial AI across predictive models, decision frameworks, and knowledge augmentation systems.
result Critical trade-offs and gaps between theoretical advances and practical implementation in financial AI.
CoinTossX is a low-latency, open-source matching engine for financial trading.
problem Efficiently matching orders in financial markets with low latency and high throughput.
method Developed in Java, orders submitted via UDP SBE, low-latency message transport (Aeron Media Driver). Separates order generation and matching.
result Demonstrated low-latency, high-throughput performance in various deployment scenarios.
The notion of an open collar is generalized to that of a pseudo-collar. Important properties and examples are discussed. The main result gives conditions which guarantee the existence of a pseudo-collar structure on the end of an open n-manifold (n > 6). This paper may be viewed as a generalization of Siebenmann's famo…
A large body of recent work has investigated the phenomenon of evasion attacks using adversarial examples for deep learning systems, where the addition of norm-bounded perturbations to the test inputs leads to incorrect output classification. Previous work has investigated this phenomenon in closed-world systems where …
Study finds neural dialog models struggle with conversational tasks.
problem Insufficient understanding of dialog by neural models.
method Analysis of internal representations and evaluation of model performance.
result Neural dialog models lack key conversational skills like answering questions and inferring contradiction.
Open-source Vizier optimizes complex systems for Google and beyond.
problem Optimizing large-scale systems with multiple objectives and constraints.
method Distributed, fault-tolerant, flexible API for blackbox optimization.
result OSS Vizier supports a wide range of optimization problems and is available as open-source.
Model for open, decentralized network with task load balancing.
problem Complex computational tasks in open, decentralized networks.
method Incentive-based load balancing using economic mechanisms.
result Optimized resource allocation and enhanced system resilience.
Survey on deep learning for malware classification, including unknown threats.
problem Classifying and recognizing unknown malware variants.
method Review of deep learning techniques and OSR solutions.
result Deep learning can effectively classify known malware and recognize unknown threats.
A reinforcement learning approach prepares quantum squeezed states in open spin systems.
problem Generating non-classical states in open quantum systems with dissipation and dephasing.
method Reinforcement learning to determine optimal control pulses for spin-squeezing.
result Optimal control sequences enhance collective spin squeezing and entanglement.
TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resourcescs.AI
The recent advances in computer-assisted learning systems and the availability of open educational resources today promise a pathway to providing cost-efficient, high-quality education to large masses of learners. One of the most ambitious use cases of computer-assisted learning is to build a lifelong learning recommen…
We introduce a criterion that a given bihamiltonian structure allows a local coordinate system where both brackets have constant coefficients. This criterion is applied to the bihamiltonian open Toda lattice in a generic point, which is shown to be locally isomorphic to a Kronecker odd-dimensional pair of brackets with…
New method for evaluating LLMs reduces bias in open-ended evaluations.
problem Bias in Elo-based ratings of LLMs due to data redundancies.
method Proposes evaluation as a 3-player game and introduces novel solution concepts.
result Novel method leads to more robust and intuitive ratings.
CK simplifies ML model deployment and reproducibility with open APIs and DevOps.
problem Making ML models reproducible and deployable across different environments.
method Decompose complex systems into reusable sub-components with unified APIs and DevOps principles.
result Automatically co-design and optimize ML models for speed, accuracy, energy, and size.
New invariants measure entanglement of open curves in 3D space.
problem Characterizing the complexity of open curves in 3-space.
method Introducing linkoids and virtual knots, and new invariants.
result Strong invariants of linkoids independent of virtual closure.
Paper uses deep reinforcement learning for better control of rocket engines during start-up phases.
problem Lack of optimal control during transient phases of liquid rocket engines.
method Deep reinforcement learning approach for optimal control of a gas-generator engine's continuous start-up phase.
result Deep reinforcement learning controller achieves highest performance and minimal computational effort.
Open set recognition problems exist in many domains. For example in security, new malware classes emerge regularly; therefore malware classification systems need to identify instances from unknown classes in addition to discriminating between known classes. In this paper we present a neural network based representation…
As we enter into the big data age and an avalanche of images have become readily available, recognition systems face the need to move from close, lab settings where the number of classes and training data are fixed, to dynamic scenarios where the number of categories to be recognized grows continuously over time, as we…
This paper presents a study on discriminative artificial neural network classifiers in the context of open-set speaker identification. Both 2-class and multi-class architectures are tested against the conventional Gaussian mixture model based classifier on enrolled speaker sets of different sizes. The performance evalu…
Develops a data-driven fault diagnosis framework for time-series data.
problem Fault diagnosis of dynamic systems using imbalanced and unknown fault classes.
method Kullback-Leibler divergence, data-driven fault classification, open-set classification.
result Framework handles imbalanced datasets, class overlapping, and unknown faults.
This paper addresses the problem of learning the optimal control policy for a nonlinear stochastic dynamical system with continuous state space, continuous action space and unknown dynamics. This class of problems are typically addressed in stochastic adaptive control and reinforcement learning literature using model-b…
Study on estimating unstable open-loop matrices from state trajectories.
problem System identification for stochastic continuous-time dynamics.
method Employing randomized control inputs to estimate unstable open-loop matrix.
result Estimation error decays with trajectory length, signal-to-noise ratio, and excitability.
We show that the property of existence of solution to the Strominger system in dimension six is neither open nor closed under holomorphic deformations of the complex structure. These results are obtained both in the case of positive slope parameter as well as in the case of negative slope parameter in the anomaly cance…
Today, artificial intelligence systems driven by machine learning algorithms can be in a position to take important, and sometimes legally binding, decisions about our everyday lives. In many cases, however, these systems and their actions are neither regulated nor certified. To help counter the potential harm that suc…
In this paper we propose a framework for automated forecasting of energy-related time series using open access data from European Network of Transmission System Operators for Electricity (ENTSO-E). The framework provides forecasts for various European countries using publicly available historical data only. Our solutio…
We list all analytic diffeomorphisms between an open subset of the 4-dimensional projective space and an open subset of the 4-dimensional sphere that take all line segments to arcs of round circles. These are the following: restrictions of the quaternionic Hopf fibrations and projections from a hyperplane to a sphere f…
Information Theory in Open-world Machine Learning Foundations, Frameworks, and Future Directionstat.ML
This paper reviews information theory in open-world machine learning.
problem Lack of a unified theoretical foundation for open-world machine learning.
method Synthesis of information theoretic approaches.
result Established a pathway toward provable and trustworthy open world intelligence.
We show uniqueness of cylindrical blowups for mean curvature flow in all dimension and all codimension. Cylindrical singularities are known to be the most important; they are the most prevalent in any codimension. Mean curvature flow in higher codimension is a nonlinear parabolic system where many of the methods used f…
Combining causality, control, and reinforcement learning for system control.
problem Learning to control dynamical systems using causal, control, and reinforcement learning approaches.
method Combining causal identification, control strategies, and reinforcement learning to control dynamical systems.
result Combining different learning paradigms for effective system control.
Study analyzes portfolio liquidation games influenced by self-exciting order flow.
problem Analyzing portfolio liquidation strategies with market order dynamics.
method Mean-field control problem, novel FBSDE system, sufficient maximum principle.
result Existence and uniqueness of open-loop Nash equilibria proved.
Novel Jones polynomial for open curves in 3D space.
problem Measuring entanglement complexity of open curves in 3-space.
method Defining Jones polynomial for linkoids and extending to collections of open and closed curves.
result Jones polynomial for open curves has real coefficients and is continuous.
Continuous deep learning architectures have recently re-emerged as Neural Ordinary Differential Equations (Neural ODEs). This infinite-depth approach theoretically bridges the gap between deep learning and dynamical systems, offering a novel perspective. However, deciphering the inner working of these models is still a…