Dilated convolutions model long-distance genomic dependencies effectively.
problem Detecting regulatory elements from raw DNA with long-distance dependencies.
method Developed and used a novel dataset for dilated convolutional neural networks.
result Dilated convolutions are effective at modeling regulatory elements in the human genome.
This paper uses SPk languages to explore LDD characteristics and multi-element dependencies.
problem Understanding and modeling Long Distance Dependencies (LDDs) with multi-element interactions.
method Generated datasets with various properties using Strictly k-Piecewise languages, analyzed using mutual information.
result The number of interacting elements in a dependency is a key characteristic of LDDs.
Analyzes dependencies in sequential datasets to improve deep neural architectures.
problem Improving deep recurrent neural architectures by understanding long distance dependencies.
method Detailed analysis of dependency decay curves in various datasets, testing factors affecting decay, generating synthesized datasets.
result Factors influencing dependency decay curves (number of unique symbols, dataset size, interacting symbols, distance between symbols) can inform optimal hyper-parameters.
This paper explores how recurrent neural networks handle long-distance dependencies using synthetic data.
problem Challenges in modeling long-distance dependencies in sequential data.
method Used synthetic data with subregular languages to control the degree of long-distance dependencies.
result Recurrent neural networks perform differently based on the presence and degree of long-distance dependencies.
HighwayGraph models long-distance node relations in GNNs with improved performance.
problem Limited-layer information propagation in GNNs hinders long-distance node relation modeling.
method Proposes two solutions: implicit and explicit modeling of long-distance node relations using shallow GNN architectures and a self-training framework.
result HighwayGraph achieves consistent and significant improvements over four GNNs on three benchmark datasets.
Physics: Similar long-distance properties can mask vastly different short-distance metrics.
problem Classifying homogeneous metrics on group manifolds by long-distance properties.
method Apply universality concept to geometry, focusing on metrics on Lie groups.
result Many metrics on low-dimensional Lie groups have similar long-distance properties despite differing short-distance properties.
Machine learning aids in discovering and optimizing quantum protocols for long-distance communication.
problem Designing and optimizing quantum communication protocols for long distances.
method Projective simulation, a learning agent combining reinforcement learning and decision making.
result Machine learning identifies and improves quantum protocols like teleportation and entanglement purification.
Improves retrieval accuracy for hierarchical documents, especially for distant matches.
problem Limited expressive power of dual encoder models in hierarchical retrieval.
method Proves feasibility of DEs for HR, introduces pretrain-finetune recipe to improve long-distance retrieval.
result Pretrain-finetune boosts recall on long-distance pairs from 19% to 76%.
Paper explores using memory models to improve idiomatic language translation.
problem Translation of idiomatic language poses challenges to machine translation systems.
method Uses memory augmented models to enhance neural machine translation of idioms.
result Improves translation of idiomatic language by bridging long-distance dependencies.
Paper shows how LSTM can remember long sequences by attending to persisted information.
problem LSTMs struggle with long sequences due to fading information and bias towards recent data.
method The paper introduces a mechanism that allows LSTMs to attend to information in memory based on how long it was persisted by the gating mechanism.
result The method improves LSTM's ability to process long sequences by retrieving information proportionally to its persistence in memory.
Improved model learns text and hierarchical relations for commonsense knowledge.
problem Predicting hierarchical relations and non-hierarchical knowledge in text data.
method Jointly learns ordering relations and non-hierarchical knowledge from text data. Exploits partial order structure for long-distance triplet constraints.
result Both free text and augmented training constraints improve model performance over baselines.
Fragmented exchanges arise due to speed advantages in high-activity regions.
problem Fragmentation of distributed securities exchanges due to speed advantages in high-activity regions.
method Economic model and Monte Carlo simulations of a decentralized exchange with two miner clusters.
result Speed advantage increases with infrastructure asymmetry between regions.
Develops a combinatorial semi-bandit method for electric vehicle charging station selection.
problem Long-distance navigation for BEVs with unknown charging station availability and performance.
method Combinatorial semi-bandit framework, pre-processing road network, Bayesian modeling, Thompson Sampling, BayesUCB, Epsilon-greedy.
result Demonstrates improved navigation performance on long-distance BEV charging station selection.
Improved signal processing for long-distance optical signals.
problem Compensating walk-off effect in long-distance optical signals.
method Sub-banded DSP architecture with deep learning for walk-off compensation.
result 2.8 dB SNR improvement over linear equalization.
Paper proposes neural approach for Chinese named entity recognition.
problem Challenges in Chinese named entity recognition due to context-dependency and lack of word delimiters.
method Introduces a CNN-LSTM-CRF neural architecture and a unified framework for joint training with word segmentation.
result Improves Chinese named entity recognition performance, especially with limited training data.
Inference network approximates structured argmax inference for structured prediction.
problem Efficient inference for complex structured prediction problems.
method Trains a neural network to approximate structured argmax inference, using large-margin training criteria.
result Significant speed-ups and improved accuracy compared to gradient descent methods.
BlockEcho method improves imputation of block-wise missing data.
problem Block-wise missing data reduces interpolation capability and predictive power.
method Integrates Matrix Factorization (MF) within Generative Adversarial Networks (GAN) to retain long-distance inter-element relationships.
result Superior performance on public datasets across three domains, especially at higher missing rates.
Proposes a novel framework for multi-label text classification.
problem Lack of coherent consideration of non-consecutive and long-distance semantics and hierarchical relations among labels.
method Hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework.
result Significantly improves multi-label text classification performance.
Develops a hybrid model for text summarization.
problem Summarizing long text sequences concisely.
method Extends sequence encoders with a graph component to handle long-distance relationships in text.
result Hybrid models outperform pure sequence or graph models on summarization tasks.
pLSTM tackles long-range language modeling and computer vision tasks with parallelizable linear source transition mark networks.
problem Challenges of existing recurrent architectures in handling sequences and multi-dimensional data.
method Introduces pLSTM, a parallelizable linear source transition mark network for linear graphs and DAGs, addressing vanishing/exploding activation/gradient issues.
result pLSTM outperforms Transformers in long-range tasks like arrow-pointing extrapolation and image size extrapolation.
Sharp upper bounds found for solutions of a specific equation on Riemannian manifolds.
problem Finding upper bounds for solutions of a specific equation on Riemannian manifolds.
method Proved sharp upper estimates of weak subsolutions to the Leibenson equation on Riemannian manifolds with non-negative Ricci curvature.
result Improved and proved a conjecture about upper bounds for solutions of the Leibenson equation.
New model for natural language learning using tensor networks.
problem Machine learning of systems with long distance correlations like natural languages.
method Directed acyclic graph decorated by multi-linear tensor maps.
result Explicit algebro-geometric analysis of parameter moduli space for tree graphs.
A new method detects financial fraud using graph transformers.
problem Detecting fraudulent transactions in financial data.
method Spatial-Temporal-Aware Graph Transformer (STA-GT) integrating GNNs and transformers.
result STA-GT outperforms general GNN models on financial fraud detection.
Geography effect is investigated for the Chinese stock market including the Shanghai and Shenzhen stock markets, based on the daily data of individual stocks. The Shanghai city and the Guangdong province can be identified in the stock geographical sector. By investigating a geographical correlation on a geographical pa…
Cloned HMMs efficiently learn variable order sequences without local minima issues.
problem Learning long-term temporal structure in sequences.
method Constrained HMMs with a sparsity structure that maps hidden states deterministically to emissions.
result Cloned HMMs can model temporal dependencies at arbitrarily long distances and recognize contexts with 'holes'.
The study uses heat flow to analyze properties of Laplace eigenfunctions on manifolds and domains.
problem Analyzing mass concentration and nodal domains of Laplace eigenfunctions.
method Heat diffusion technique to study eigenfunctions and their nodal sets.
result Discovers new insights into the decay and behavior of Laplace eigenfunctions.
Graph rewiring method alleviates over-squashing in GNNs.
problem Over-squashing and bottlenecks in graph neural networks.
method Curvature-based graph rewiring method.
result Negatively curved edges cause over-squashing in GNNs.
OTT services are replacing traditional telecom services, affecting revenue streams.
problem OTT services are reducing traditional telecom revenue streams.
method Quantitative analysis of mobile user trends and revenue changes.
result OTT services are becoming more prevalent and impacting telecom revenue.
DIFNET tackles the suspended animation problem in deep graph neural networks.
problem Deep graph neural networks suffer from the suspended animation problem.
method DIFNET uses neural gates and graph residual learning for node hidden state modeling, and includes an attention mechanism for node neighborhood information diffusion.
result DIFNET effectively addresses the suspended animation problem and improves learning performance.
New model optimizes oil product distribution via pipelines.
problem Optimizing oil product distribution via pipelines.
method Discrete-time mixed integer linear programming model.
result Significant reductions in pipeline operational cost.
Study finds telemetric data not effective for predicting truck accident risk.
problem Estimating the risk of truck accidents using telemetric data.
method Used machine learning approaches: Random Forests and Convolutional Neural Networks.
result Neither approach successfully predicted truck accident risk.
A graph abstraction speeds up reinforcement learning in complex environments.
problem Learning hierarchical reinforcement learning tasks in complex environments.
method Jointly trains a latent pivotal state model and a curiosity-driven policy. Uses a world graph to guide high-level and low-level agents.
result Significant performance and efficiency improvements over baseline methods.
Agent-based model compares different COVID-19 testing policies and their effectiveness.
problem Understanding how different testing policies reveal the true number of infected cases.
method Developed an agent-based simulation framework in Python to model various testing policies and interventions.
result Contact Tracing consistently captures more positive cases than Random Symptomatic Testing, and LBT performs similarly.
Geometric analysis of normal distributions using Fisher and Killing metrics.
problem Quantifying the difference between Fisher and Killing metrics on the space of normal distributions.
method Riemannian geometry, Fisher information metric, Killing metric, asymptotic geodesics.
result Approximation of Fisher metric by Killing metric for long distances is justified.
A method detects vehicles far from tunnel CCTV using AI.
problem Tunnel CCTV's height limits detection of far-away vehicles.
method Object detection algorithm with inverse perspective transform.
result Deep learning model trained on warped images detects vehicles more accurately.
New curvature measure improves graph neural network performance.
problem Oversmoothing and oversquashing in GNNs due to local edge comparisons.
method Introduces Entropic Curvature, a global transport-based curvature.
result Entropic Curvature unifies oversmoothing and oversquashing as opposite ends of a curvature spectrum.
LSTM neural networks improve fiber nonlinearities in coherent systems.
problem Compensating fiber nonlinearities in digital coherent systems.
method Utilization of Long short-term memory (LSTM) neural networks.
result LSTM neural networks provide superior performance compared to digital back propagation, especially in multi-channel scenarios.
KGCN uses KGs to improve recommender systems by connecting user and item attributes.
problem Collaborative filtering sparsity and cold start issues.
method End-to-end framework that captures inter-item relatedness using KGs and minibatch sampling.
result KGCN outperforms strong recommender baselines on movie, book, and music recommendation datasets.
This study aims to predict vessel stay and delay times at ports to optimize logistics.
problem Uncertainties in maritime logistics, including weather, cargo diversity, and port dynamics, lead to increased costs and inefficiencies.
method Developed predictive analytics to address shortcomings in previous works, using feature analysis and SHAP explanations.
result Predictive analytics can assist in efficient planning and scheduling of port operations, reducing costs and improving logistics.
The paper analyzes how grid cells perform path integration and learns hexagon grid patterns.
problem Understanding how grid cells perform path integration calculations.
method Theoretical analysis of a general representation model of path integration by grid cells, identifying group representation and isotropic scaling conditions.
result The learned model of hexagon grid patterns is capable of accurate long distance path integration.
A new EnKF method for elliptic PDEs reduces dimensionality for accurate state estimation.
problem Elliptic PDEs in fluid flows make traditional EnKF regularization ineffective.
method Low-rank factorization of the Kalman gain based on the Jacobian spectrum.
result Inference can be performed in a low-dimensional subspace of the state space.
Language models fail to execute simple steps, showing gating and binding errors.
problem Procedural hallucinations in language models, failing to execute simple steps.
method Analyzed long-context binding tasks, identifying gating and binding errors.
result Procedural errors are due to gating and binding failures, with recency bias contributing to the latter.
New geometric theory explains nonuniform origami responses.
problem Understanding nonuniform responses in origami sheets.
method Purely geometric continuum theory capturing nonuniform, nonlinear response.
result Three modes govern nonuniform response, varying smoothly across the sheet.
PureTS uses simple linear models to improve long-term time series forecasting.
problem Improving long-term time series forecasting with complex models.
method Developed PureTS with three pure linear layers.
result PureTS achieves state-of-the-art performance in long sequence prediction tasks.
Improved autoencoder boosts sequence learning with less memory.
problem Training recurrent networks with high memory costs.
method Sparse Predictive Autoencoder (bRSM) with recurrent connections and boosting rule.
result Near optimal performance on stochastic sequence learning task.
Optimizes QoS in FSO links over South Africa using ensemble learning.
problem Impact of weather on QoS in FSO links.
method Ensemble learning models (Random Forest, ADaBoost Regression, Stacking Regression, Gradient Boost Regression, Multilayer Neural Network) applied to meteorological data.
result Significant enhancement in QoS with RMSE and R-squared values of 0.0032 and 0.9906 respectively at George.
Modeling time-varying extreme value dependence in European stock markets.
problem Non-stationary extremal dependence between European stock markets.
method Regression model for angular density of bivariate extreme value distribution.
result Evidence of increasing extremal dependence in recent years.
New tail dependence measures for stock indices.
problem Measuring tail dependence between financial variables.
method Introducing a new stochastic order and studying monotone tail dependence measures.
result Advantage of new tail dependence measures over classical ones.