Study uses open data to improve traffic emissions estimation.
problem Estimating accurate link level traffic emissions.
method Data-driven framework integrating MOVES, GPS, OSM, and satellite imagery.
result Neural network reduces RMSE by over 50% for key pollutants.
The abstract discusses open data resources for studying and controlling the spread of COVID-19.
problem Understanding and controlling the spread of COVID-19.
method Identification and description of open data resources and data-driven methodologies.
result Identification of variables and open data resources for analyzing COVID-19.
FinGPT is an open-source financial LLM for democratizing financial data.
problem Accessing high-quality financial data for LLMs.
method Data-centric approach with automatic data curation and low-rank adaptation.
result FinGPT provides accessible and transparent financial data for FinLLMs.
The study connects periodic surface homeomorphisms to contact structures using rational open books.
problem Understanding the properties of contact structures associated with periodic surface homeomorphisms.
method Associate rational open books to marked data sets, study contact structures, and prove Stein fillability conditions.
result A class of data sets gives rise to Stein fillable contact structures under certain combinatorial conditions.
Novel categories are commonly defined as those unobserved during training but present during testing. However, partially labelled training datasets can contain unlabelled training samples that belong to novel categories, meaning these can be present in training and testing. This research is the first to generalise betw…
End-to-end PGL framework tackles open-set domain shift.
problem Real-world domain shift with unknown additional classes.
method Episodic training in graph neural network with adversarial learning.
result Guarantees tighter upper bound of target error.
Study predicts customer data sharing in Open Banking and explains key factors.
problem Predicting and explaining customer data sharing in Open Banking environments.
method Hybrid data balancing strategy with ADASYN and NEARMISS, XGBoost models, SHAP, CART.
result 91.39% accuracy for inflow and 91.53% for outflow predictions, revealing influential features.
A new method trains deep neural networks for open set domain adaptation without negative open set difference.
problem Training deep neural networks for open set domain adaptation without negative open set difference.
method Proposes a new upper bound of target-domain risk, including source-domain risk, ε-open set difference (Δε), distributional discrepancy, and constant. Uses gradient descent for source-domain risk and Δε, and adversarial training for distributional discrepancy. Trains DNNs via minimizing the new upper bound. result Shows state-of-the-art performance on benchmark datasets.
The reproducibility of scientific research has become a point of critical concern. We argue that openness and transparency are critical for reproducibility, and we outline an ecosystem for open and transparent science that has emerged within the human neuroimaging community. We discuss the range of open data sharing re…
This paper introduces a new type of open book decomposition for a contact three-manifold with a specified characteristic foliation Fξ on its boundary. These \textit{foliated open books} offer a finer tool for studying contact manifolds with convex boundary than existing models, as the boundary foliation ca…
Machine learning detects type Ia supernovae from photometric data.
problem Detecting type Ia supernovae accurately from photometric data.
method Machine learning approach using only real observation data.
result Good results on real data from the Open Supernovae Catalog.
OpenViewer tackles multi-view learning challenges with interpretability and generalization.
problem Lack of interpretability and insufficient generalization in multi-view learning models.
method OpenViewer introduces a Pseudo-Unknown Sample Generation Mechanism, Expression-Enhanced Deep Unfolding Network, and Perception-Augmented Open-Set Training Regime.
result OpenViewer effectively addresses openness challenges and enhances recognition performance for both known and unknown samples.
Study uses Open Banking data to estimate customer value, showing potential 21% increase.
problem Limited CLV estimation using single-entity data.
method Introduces PCLV framework using Open Banking data for comprehensive customer value estimation.
result Open Banking data can estimate PCLV per competitor, showing a 21.06% increase over Actual CLV.
Paper develops a model for verifying facts in tables without pre-retrieved evidence.
problem Verification of factual claims in structured data, especially in open-domain settings.
method Joint reranking-and-verification model that fuses evidence documents.
result Model achieves comparable performance to closed-domain state-of-the-art on TabFact dataset.
New method improves OSSL by learning from all unlabeled data.
problem Handling open-set semi-supervised learning with unknown classes.
method Self-supervision and energy-based score for all unlabeled data.
result State-of-the-art results on benchmark problems.
We present a large scale data set, OpenEDS: Open Eye Dataset, of eye-images captured using a virtual-reality (VR) head mounted display mounted with two synchronized eyefacing cameras at a frame rate of 200 Hz under controlled illumination. This dataset is compiled from video capture of the eye-region collected from 152…
This paper proposes a method to use deep neural networks as end-to-end open-set classifiers. It is based on intra-class data splitting. In open-set recognition, only samples from a limited number of known classes are available for training. During inference, an open-set classifier must reject samples from unknown class…
Fine-tuned open-source LLMs match or exceed closed-source models in social science research.
problem Limited scalability and high costs of large LLMs in social science research.
method Fine-tuning open-source models for specific tasks, exploring training set size effects, proposing hybrid workflow.
result Small, fine-tuned open-source LLMs achieve equal or superior performance to commercial alternatives.
OpenHAIV integrates OOD detection and incremental learning for open-world models.
problem Challenges in open-world recognition, especially in model knowledge updates and OOD detection.
method Unified pipeline combining OOD detection, new class discovery, and incremental fine-tuning.
result Models can autonomously acquire and update knowledge in open-world environments.
Motivated by the Moore-Segal axioms for an open-closed topological field theory, we consider planar open string topological field theories. We rigorously define a category 2Thick whose objects and morphisms can be thought of as open strings and diffeomorphism classes of planar open string worldsheets. Just as the categ…
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.
Proposes overnight volatility model for better market dynamics.
problem Lack of high-frequency data during close-to-open period.
method Itô diffusion model with weighted least squares estimation.
result Developed and validated overnight volatility model.
SAGDA generates synthetic agricultural datasets to improve ML in African farming.
problem Data scarcity in African agriculture limits machine learning innovations.
method SAGDA is an open-source Python toolkit that generates, augments, and validates synthetic agricultural datasets.
result SAGDA enhances ML applications in agriculture, such as yield prediction and fertilizer recommendation.
Modern machine learning systems such as image classifiers rely heavily on large scale data sets for training. Such data sets are costly to create, thus in practice a small number of freely available, open source data sets are widely used. We suggest that examining the geo-diversity of open data sets is critical before …
This work bridges continual learning, active learning, and open set recognition in deep neural networks.
problem Protecting previously acquired representations from catastrophic forgetting in deep neural networks.
method Surveying the literature and proposing a consolidated view to integrate open set recognition and active learning principles.
result Joint improvement in alleviating catastrophic forgetting, querying data, selecting task orders, and robust open world application.
OwMatch improves open-world semi-supervised learning by self-labeling and consistency.
problem Misclassification of unseen classes in open-world semi-supervised learning.
method Conditional self-labeling and open-world hierarchical thresholding.
result OwMatch enhances performance across known and unknown classes.
New method for estimating class proportions in open-set label shift data.
problem Estimating class proportions and distributions when test data includes novel classes.
method Semiparametric density ratio model framework with maximum empirical likelihood estimators and confidence intervals.
result Improved estimation accuracy and classification performance compared to existing methods.
Introduces data ethics for mathematicians, covering background, open data, and privacy.
problem Ensuring ethical use of data in mathematical research and education.
method Overview of data ethics concepts, societal context, and resources.
result Highlights the importance of ethical considerations in data use.
Open dataset and pipeline for realistic OPE research.
problem Lack of realistic and reproducible OPE experimental studies.
method Public logged bandit dataset and Python software.
result Enables experimental comparisons of OPE estimators.
Data stream clustering tackles real-time data processing challenges.
problem Real-time processing of data streams with less prior information.
method Review of data stream clustering algorithms and their characteristics.
result Comparison and analysis of data stream clustering algorithms.
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.
Machine-learned diagnosis models have shown promise as medical aides but are trained under a closed-set assumption, i.e. that models will only encounter conditions on which they have been trained. However, it is practically infeasible to obtain sufficient training data for every human condition, and once deployed such …
We provide complete source code for building a fundamental industry classification based on publically available and freely downloadable data. We compare various fundamental industry classifications by running a horserace of short-horizon trading signals (alphas) utilizing open source heterotic risk models (https://ssr…
Paper develops a new method for open-set and imbalanced classification with valid prediction sets.
problem Tackles open-set and imbalanced classification with new prediction methods.
method Develops a new family of conformal p-values and a selective sample splitting algorithm.
result Valid prediction sets with valid coverage in open-set scenarios and informative predictions under extreme class imbalance.
The classification of textual data often yields important information. Most classifiers work in a closed world setting where the classifier is trained on a known corpus, and then it is tested on unseen examples that belong to one of the classes seen during training. Despite the usefulness of this design, often there is…
Deep learning applied to biological data mining.
problem Mining complex biological data from diverse sources.
method Artificial neural networks, deep learning architectures.
result Deep learning techniques improve pattern recognition in biological data.
Open-FinLLMs tackle financial tasks with multimodal capabilities.
problem Financial LLMs lack multimodal capabilities and real-world applicability.
method Developed Open-FinLLMs, an open-source multimodal financial LLM suite.
result Open-FinLLMs outperform advanced financial and general LLMs in diverse tasks.
An RNN-Survival model predicts optimal email send times based on recipient behavior.
problem Predicting optimal send times for emails to maximize open rates.
method Recurrent Neural Network (RNN) in a survival model framework.
result The RNN-Survival model outperforms traditional survival analysis in predicting times-to-open.
Scoping review and benchmarking of synthetic EHR data generation methods.
problem Creating realistic synthetic electronic health records for research and training.
method Conducted a scoping review and benchmarked seven methods on open-source EHR datasets.
result GAN-based methods excel in fidelity and utility, while rule-based methods excel in privacy protection.
SMART is an open source web application designed to help data scientists and research teams efficiently build labeled training data sets for supervised machine learning tasks. SMART provides users with an intuitive interface for creating labeled data sets, supports active learning to help reduce the required amount of …
Email messages have been an important mode of communication, not only for work, but also for social interactions and marketing. When messages have time sensitive information, it becomes relevant for the sender to know what is the expected time within which the email will be read by the recipient. In this paper we use a…
FedOS tackles challenges in federated learning by using open-set learning.
problem Challenges in federated learning due to data locality and privacy constraints.
method Introduces open-set learning to stabilize training in federated learning.
result Demonstrates improved model performance through open-set learning.
FSD50K provides an open dataset of over 51k audio clips for sound event recognition.
problem Small and domain-specific sound event recognition datasets.
method Creation of an open dataset with over 51k audio clips manually labeled using 200 classes.
result FSD50K is a new open benchmark for sound event recognition research.
FinRobot opens-source AI for financial tasks, breaking down complex problems.
problem Barriers to AI adoption in finance due to proprietary data and specialized knowledge.
method Develops open-source AI agent platform with four layers: Financial AI Agents, LLM Algorithms, LLMOps/DataOps, and Foundation Models.
result FinRobot democratizes AI access for financial analysis.
ProSub uses angles in feature space to classify data as in- or out-of-distribution.
problem Open-set semi-supervised learning with unknown classes.
method Probabilistic approach based on angles in feature space, estimating conditional distributions of scores.
result ProSub achieves state-of-the-art performance on benchmark problems.
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection an…
FinGPT democratizes financial data for LLMs, enabling innovation.
problem Limited financial text datasets and disparities between general and financial text data.
method Automates collection and curation of real-time financial data from diverse Internet sources, fine-tuning with RLSP and LoRA.
result Democratizes access to financial data for LLMs, enabling innovation.
New benchmark for EEG-eye movement reconstruction from functional data.
problem Reconstructing eye movements from EEG data.
method Functional neural networks and open challenges for evaluation.
result Baseline results for consumer-grade and research-grade hardware.