A novel clustering method for micro-panel data improves efficiency and accuracy.
problem Insufficient clustering methods for micro-panel data.
method Feature-based two-step approach for identifying homogeneous clusters.
result The CluMP method outperforms existing methods in clustering micro-panel data.
Study investigates micro-event detection on FLOSS version releases from Stack Overflow.
problem Detecting micro-events in FLOSS version release events from textual messages.
method Developed pipelines using LDA topic modeling, hSBM topics, and sentiment analysis; optimized feature spaces with RFECV; evaluated models with statistical analysis.
result Found characteristic changes in topics or sentiment features before or after FLOSS version releases.
Paper analyzes churn behavior in mobile games at micro and macro levels.
problem Understanding churn behavior in mobile games, especially at micro and macro levels.
method Developed a semi-supervised and inductive embedding model for micro-level churn prediction and constructed a relationship graph for macro-level churn ranking.
result Accurate micro-level churn prediction and macro-level churn ranking were achieved using novel techniques.
The paper gives picture of enrichment to economic and financial system analysis using agent-based models as a form of advanced study for financial economic data post-statistical-data analysis and micro-simulation analysis. Theoretical exploration is carried out by using comparisons of some usual financial economy syste…
Generative adversarial networks reconstruct oolitic limestone micro-structures.
problem Stochastic image reconstruction of oolitic limestone micro-structures.
method Generative adversarial neural networks (GANs) for unsupervised learning.
result GANs accurately reconstruct oolitic limestone micro-structures.
We present a domain-general account of causation that applies to settings in which macro-level causal relations between two systems are of interest, but the relevant causal features are poorly understood and have to be aggregated from vast arrays of micro-measurements. Our approach generalizes that of Chalupka et al. (…
New framework analyzes pre-stock jump trading behaviors using multivariate time series analysis.
problem Understanding micro-trading behaviors before stock price jumps.
method Multivariate time series analysis considering temporal information.
result Identifies highly informative attributes for predicting price jumps.
Paper develops a credit scoring system for micro-loans, addressing interpretability and data quality challenges.
problem Developing a credit scoring system for micro-loans with interpretability and data quality concerns.
method Introduces semi-supervised algorithm to aid model development and evaluates its performance.
result Semi-supervised algorithm aids in model development and demonstrates improved performance.
Paper generates synthetic radar signatures for motion classification.
problem Lack of large training datasets for radar-based human activity recognition.
method Adversarial learning for synthetic data generation, kinematic sifting for consistency.
result 93% overall accuracy achieved on diverse aspect angles.
Paper explores multimodal methods for detecting urban micro-events.
problem Detecting urban micro-events in cities with limited geographical coverage.
method Exploring multimodal fusion methods including early, late, hybrid fusion and representation learning.
result Multimodal approach yields higher performance than unimodal alternatives.
Study uses multimodal machine learning to predict ICD-10 codes.
problem Improving accuracy and interpretability of ICD-10 code predictions.
method Developed separate models for text and tabular data, integrated using an ensemble method.
result Best-performing model achieved micro-F1 of 0.7633 and micro-AUC of 0.9541.
Study shows household inequality accounts for 30% of total global income inequality.
problem Intra-household inequality is often overlooked in studies of income inequality.
method Used LIS micro data to analyze inequality trends in 1973-2013 across multiple countries.
result At least 30% of total global income inequality is due to intra-household inequality.
Study detects spoofing in high-frequency trading using micro-structural analysis.
problem Challenges in detecting spoofing due to complex electronic platforms and high-frequency trading.
method Micro-structural study in a simplified setting, optimization of spoofing strategy, monitoring with Wasserstein distance.
result Optimal spoofing strategy and its impact on market imbalance quantified.
Noise affects 1.7%-4.5% of SPY ETF options market volume.
problem Systematic noise in equity options pricing.
method Analysis of SPY ETF options market data.
result Approximately 1.7%-4.5% of SPY ETF options market volume is due to systematic noise.
A new model for defective media using two scales.
problem Modeling defects in media with two scales.
method Generalization of Riemann-Cartan manifolds and fibre bundle theory, constructing a first-order placement map.
result Emergent behaviors like dislocations and disclinations arise from the interaction of macroscopic and microscopic scales.
In this paper we introduce a micro-clustering strategy for Functional Boxplots. The aim is to summarize a set of streaming time series splitted in non overlapping windows. It is a two step strategy which performs at first, an on-line summarization by means of functional data structures, named Functional Boxplot micro-c…
New method uses small perturbations to improve representation learning from few labels.
problem Stability issues and label scarcity in representation learning.
method Introduces small-perturbation ideology on representation probability distribution models.
result Proposed models show better performance in clustering compared to baseline methods.
Deep neural network approximates flow averages for rough walls in multiscale simulations.
problem Approximating flow averages in rough-wall Stokes flow simulations.
method Fourier neural operator for local averages, parameterized by local wall geometry.
result Stable and accurate HMM solution with reduced micro problem solving cost.
Improved sparse-view CT images with deep learning sinogram interpolation.
problem Sparse-view CT images quality improvement with limited projection data.
method Combination of U-Net and residual learning for sinogram interpolation.
result Significantly improved CT image quality (RMSE and SSIM metrics) over standard methods.
Introduces a new RL formalism focusing on event desirability.
problem Limited consideration of performance distribution in RL.
method Introduces micro-objective reinforcement learning.
result New formalism allows for prior knowledge and event desirability.
Paper presents a novel neural network method for topic detection in micro-blogs.
problem Challenging topic detection without known topic count.
method Unsupervised neural sentence embedding model with attention mechanism.
result Improved clustering algorithm (RADBSCAN) discovers topics based on dataset character.
A novel method captures both micro- and macro-dynamics in temporal networks.
problem Capturing both micro- and macro-dynamics in temporal networks.
method Temporal Attention Point Process for micro-dynamics and a dynamics equation for macro-dynamics.
result Significantly outperforms state-of-the-arts in temporal tendency-related tasks.
mSAM improves generalization by making models flatter.
problem Over-parameterized models can have varying generalization performance.
method mSAM modifies loss function to favor flatter minima through adversarial perturbations.
result mSAM achieves flatter minima than SAM and SGD, improving generalization.
Aggregated variables can mask causal effects, turning unconfounded into confounded relations.
problem Aggregated variables can mask causal effects, leading to paradoxical confounding.
method Analysis of how aggregated variables can change the definition of causality and the feasibility of causal relations.
result Macro causal relations are defined by micro states, not just aggregated variables.
A new car following model reduces platoon level traffic errors.
problem Enhance traffic micro-simulation accuracy in platoon level traffic.
method Unidirectional interconnected LSTM model structure and scheduled sampling technique.
result The proposed model reduces errors by almost 40% compared to traditional models.
Cyclic Data Parallelism reduces memory usage and balances gradient communications.
problem Training large deep learning models requires efficient parallelism to scale.
method Cyclic Data Parallelism shifts micro-batches from simultaneous to sequential execution, balancing memory and gradient communications.
result Cyclic Data Parallelism reduces total memory usage and balances gradient communications.
This paper is trying to unveil general statistical characteristic of financial; time series data that is subjected to several financial time series data present in Indonesia, e.g. individual index such as stock price of PT. TELKOM, stock price of PT HM SAMPOERNA, and compiled stock price index (Jakarta Stock Exchange I…
Entropy helps explain disorder in both macro and micro systems.
problem Connecting macro and micro systems with entropy analysis.
method Analyzing entropy from both macroscopic and microscopic perspectives.
result Entropy measures disorder in both macroscopic and microscopic systems.
Midicoth compresses online probability estimates by correcting prior smoothing biases.
problem Compression inefficiency due to prior smoothing in adaptive models.
method Micro-diffusion denoising applied in a bitwise tree hierarchy.
result Significant compression improvement with reliable calibration.
ChronoMID uses neural networks to classify bone disease in mice from micro-CT scans.
problem Classifying bone disease in mice from micro-CT scans.
method ChronoMID applies cross-modal convolutional neural networks to incorporate temporal information from timestamps and difference images.
result The top-performing model achieved 99.54% accuracy, significantly outperforming a baseline CNN.
Chicle tackles elastic machine learning training by avoiding micro-tasks.
problem Elasticity and load balancing in distributed machine learning training.
method Chicle is a new elastic distributed training framework that exploits machine learning algorithms to implement elasticity and load balancing without micro-tasks.
result Chicle achieves performance competitive with state-of-the-art rigid frameworks while enabling elastic execution and dynamic load balancing.
In this paper, making use of recent statistical physics techniques and models, we address the specific role of randomness in financial markets, both at the micro and the macro level. In particular, we review some recent results obtained about the effectiveness of random strategies of investment, compared with some of t…
Useful alpha returns vanished in modern stock markets.
problem The inefficiency of modern stock markets in generating useful alpha.
method Analysis of 200 published long-short anomaly equity portfolios over different time periods and stock selection criteria.
result Even modest allowances for luck or transaction costs eliminated published academic anomalies.
Permutation approach is suggested as a method to investigate financial time series in micro scales. The method is used to see how high frequency trading in recent years has affected the micro patterns which may be seen in financial time series. Tick to tick exchange rates are considered as examples. It is seen that var…
Paper proposes HIDAM model to improve MSE default risk assessment using heterogeneous information networks.
problem Default risk assessment for MSEs due to lack of credit information and diverse financial activities.
method HIDAM model incorporating heterogeneous information networks with multi-typed nodes and links, extracting interactive information through meta-paths, and using a hierarchical attention mechanism.
result HIDAM model outperforms state-of-the-art competitors on real-world banking data.
μ-cuDNN accelerates deep learning frameworks with micro-batching.
problem Slow performance and memory constraints in GPU kernels.
method Divides layers' mini-batch computation into micro-batches using Dynamic Programming and Integer Linear Programming.
result Achieved speedups of 1.63x for AlexNet and 1.21x for ResNet-18 on P100-SXM2 GPU.
Survey on algorithms for quick robot learning.
problem Efficiently learn robot controllers with limited data.
method Leverage prior knowledge and data-driven models.
result Combining prior knowledge and surrogate models improves learning.
We present new methods to estimate causal effects retrospectively from micro data with the assistance of a machine learning ensemble. This approach overcomes two important limitations in conventional methods like regression modeling or matching: (i) ambiguity about the pertinent retrospective counterfactuals and (ii) p…
Six AI solutions accurately detect growth plate planes in mice bone scans.
problem Manual, time-consuming, and variable bone growth plate detection in micro-CT scans.
method Prepared and annotated a dataset of 3D μCT scans, organized a challenge, and developed six computer vision solutions.
result Achieved mean absolute error of 1.91±0.87 planes from ground truth.
Microdata improves inflation forecasts after major shocks, study finds.
problem Forecasting inflation in a non-stationary environment with microeconomic data.
method Developed a scan test to detect periods of micro forecast outperformance, combined with adaptive machine learning.
result Micro forecasts improve inflation predictions after major shocks, especially after 2020.
Deep convolutional architecture identifies eye movements for biometric faster and more accurately.
problem Biometric identification of eye movements for authentication.
method Developed a deep convolutional architecture to process raw eye-tracking signals.
result Achieved a lower error rate by one order of magnitude and faster identification time by two orders of magnitude.
Study uses network analysis to examine Japanese overseas business networks.
problem Challenges the weak evidence supporting the existence of Japanese conglomerates (HK).
method Large dataset of 20,000 Japanese overseas subsidiaries analyzed using network techniques.
result Rejects Miwa-Ramseyer hypothesis (MRH) for global and regional datasets.
Analyzes how Trump's tweets impact global stock markets.
problem Understanding the financial impact of presidential tweets on stock markets.
method Examined tweets from Donald Trump's presidency, collected from The Guardian and Bloomberg, and analyzed their effect on equity indices.
result Identified tweets that significantly influenced stock market indices.
Deep learning boosts micro-CT image resolution and texture recovery.
problem Compensating for image resolution trade-offs in micro-CT imaging.
method EDSRGAN trained on a diverse dataset of uCT images.
result EDSRGAN outperforms other methods in texture recovery and resolution.
Deep learning predicts Dota 2 deaths with high precision and recall.
problem Predicting key events like hero deaths in fast-paced esports matches.
method Shared weights deep learning network trained on Dota 2 data.
result 0.377 precision and 0.725 recall in predicting deaths within 5 seconds.
System detects financial opportunities in tweets with high precision.
problem Detecting valuable financial insights in micro-blogging data.
method Stacked Machine Learning classification system with NLP features.
result System achieves precision up to 83% in detecting financial opportunities.
L-modules are a combinatorial analogue of constructible sheaves on the reductive Borel-Serre compactification of a locally symmetric space. We define the micro-support of an L-module; it is a set of irreducible modules for the Levi quotients of the parabolic Q-subgroups associated to the strata. We prove a vanishing th…
A machine learning model captures non-Newtonian fluid dynamics from molecular details.
problem Creating accurate non-Newtonian fluid models from molecular data.
method Developed a machine learning framework that maps micro-scale polymer configurations to macro-scale fluid dynamics, preserving molecular fidelity.
result The deep non-Newtonian model (DeePN2) accurately predicts fluid behavior without empirical closures.