Paper tackles curve pattern identification from fragmented cultural heritage objects.
problem Identify full design of curve patterns from fragmented cultural heritage objects.
method Two-stage matching algorithm combining template matching and CNN re-ranking.
result Proposed algorithm outperforms traditional methods in identifying curve patterns from fragmented objects.
Paper improves deep point cloud compression techniques.
problem Efficiently compressing 3D point cloud data for various applications.
method Integrates scale hyperprior model, deeper transforms, focal loss, and optimal thresholding.
result Achieves significant BD-PSNR gains over existing methods.
This paper intends to present the opportunities emerging for the national economy, out of the financial crisis. In particular the management of those, which arise from the commercial real estate owned property sector, defined by the author as crisis heritage management. On one hand, as real estate property prices are s…
We outline a framework which generalizes Felix Klein's Erlanger Programm which he announced in 1872 after exchanging ideas with Sophus Lie.
The paper uses WeChat data to map urban cultural resource needs.
problem Optimizing cultural resource allocation in cities.
method Data-driven framework using WeChat user check-ins and temporal LDA model.
result Identifies urban regions lacking cultural resources.
Enhances content moderation with culturally-aware models.
problem Global content moderation policies miss local cultural nuances.
method Fine-tuning encoder-decoder models on media-diet data.
result Improved accuracy in local violation detection and cultural alignment.
Breiman's two cultures reconciled through blending statistical thinking.
problem Tension between parametric statistical and machine learning approaches.
method Establishing a link between parametric statistical and machine learning frameworks.
result Integrated statistical thinking can bridge the gap between two cultures.
Examines algorithmic modeling across three cultures.
problem Tackles algorithmic modeling in different cultural contexts.
method Uses parametric regressions, interpretable algorithms, and complex algorithms.
result Extension of Leo Breiman's thesis to include cultural differences.
Study shows how national culture influences investors' tendency to hold or sell stocks.
problem Investors' tendency to hold or sell stocks varies internationally.
method Examined brokerage data from 83 countries, analyzed cultural dimensions and age/gender.
result National culture, specifically long-term orientation and indulgence, influences the disposition effect.
Surveying isoparametric hypersurfaces in spheres, focusing on Élie Cartan's techniques.
problem Classifying isoparametric hypersurfaces in spheres.
method Historical survey and focus on Élie Cartan's techniques.
result Attention to Élie Cartan's techniques in the case of four principal curvatures.
Social interactions and personal tastes shape our consumption behavior of cultural products. In this study, we present a computational model of a cultural market and we aim to analyze the behavior of the consumer population as an emergent phenomena. Our results suggest that the final market shares of cultural products …
Breiman discusses two statistical cultures, advocating for more research on 'before' and 'after' the black box.
problem Statistical modeling lacks exploration of processes before and after the 'black box'.
method Analyzes Breiman's visual metaphor of two statistical cultures.
result Promotes the importance of studying the 'before' and 'after' of data transformations.
Breiman (2001) proposed to statisticians awareness of two cultures: 1. Parametric modeling culture, pioneered by R.A.Fisher and Jerzy Neyman; 2. Algorithmic predictive culture, pioneered by machine learning research. Parzen (2001), as a part of discussing Breiman (2001), proposed that researchers be aware of many cultu…
New method shows cultural transmission improves language compositionality.
problem How cultural transmission affects language emergence and compositionality.
method Introduced cultural evolutionary dynamics into language emergence by periodically replacing agents in a population.
result Languages emerge with better compositional generalization due to implicit cultural transmission.
Study investigates impact of labeler socio-cultural background on affect detection model performance.
problem Impact of labeler socio-cultural background on affect detection model performance.
method Investigates the impact of labeler socio-cultural background on affect detection model performance.
result Differences in labeler background impact the performance of affect detection models.
Study finds conformity bias drives music sampling traditions.
problem How frequency-based bias drives cultural diversity in music sampling.
method Agent-based simulations in approximate Bayesian computation framework.
result Sampling patterns at population-level consistent with conformity bias.
Commentary on Rashomon Effect complicating model selection.
problem Many models equally predict data; hard to draw conclusions.
method Connections to recent ML literature exploring implications.
result Grasping Rashomon Effect can foster collaboration.
AVEC 2019 challenges AI in detecting depression and cross-cultural emotions.
problem Detecting depression and cross-cultural emotions from audiovisual data.
method Comparison of machine learning methods under standardized conditions.
result Baseline system performance on state-of-mind, depression, and cross-cultural tasks.
Town hall discusses AI's impact on statistics, culture, and training.
problem Adapting statistics to AI advancements and infrastructure.
method Open panel discussion and audience Q&A.
result Candid perspectives on evolving statistical practices.
Turing test relevance questioned in age of AI.
problem Relevance of Turing test in AI age.
method Examining cultural and technological context.
result Turing test may not fully capture AI capabilities.
Weak supervision challenges black-box models, suggesting fusion of modeling cultures.
problem Challenges of strong supervision in achieving accurate predictions.
method Integrating data modeling into algorithmic modeling for weak supervision.
result Integration of data modeling culture improves model stability and accuracy.
Study introduces KorFinMTEB for Korean financial texts, revealing model limitations.
problem Limited evaluation benchmarks for low-resource domains, especially Korean.
method Developed KorFinMTEB, a tailored benchmark for Korean financial texts.
result Models perform better on translated benchmarks than on domain-specific ones.
A hybrid scheme uses GAs and DL for tile panel reconstruction.
problem Reconstructing Portuguese tile panels with real-world effects.
method Enhanced GA-based puzzle solver with novel DLCM.
result 82% accuracy for tile reconstruction compared to 3.5% for best known method.
Deep CNN model predicts neuronal cell health from images.
problem Predicting the biological activity of chemical compounds on neuronal cells.
method Deep convolutional neural network (CNN) with residual connections.
result Achieved 99.6% accuracy in distinguishing treated from untreated cells.
Study examines active travel in Chicago communities, revealing mixed perceptions.
problem Transport disadvantage and lack of active mobility in underserved communities.
method Focus groups, qualitative discourse analysis, quantitative text-mining (topic modeling, sentiment analysis).
result Residents view active travel as both necessity and symbol of privilege, influenced by local culture.
The study finds solar terms significantly impact China's stock market returns and volatility.
problem Investigating the effect of solar terms on China's stock market.
method Regression framework, analyzing multiple solar terms and their impact on return and volatility.
result Solar terms 1, 3, and 4 cause significant positive returns, while 8, 11, and 14 bring high volatility.
The presence of bacteria or fungi in the bloodstream of patients is abnormal and can lead to life-threatening conditions. A computational model based on a bidirectional long short-term memory artificial neural network, is explored to assist doctors in the intensive care unit to predict whether examination of blood cult…
We introduce MosAIc, an interactive web app that allows users to find pairs of semantically related artworks that span different cultures, media, and millennia. To create this application, we introduce Conditional Image Retrieval (CIR) which combines visual similarity search with user supplied filters or "conditions". …
Recently, mobile operators in many developing economies have launched "Mobile Money" platforms that deliver basic financial services over the mobile phone network. While many believe that these services can improve the lives of the poor, a consistent difficulty has been identifying individuals most likely to benefit fr…
The paper argues for a social-economic approach to AI development.
problem The current AI development overlooks social and cultural aspects of intelligence.
method A blend of economic and social concepts with computational and inferential concepts in algorithm design.
result A social-economic approach is necessary for proper AI development.
Optimized biopharmaceutical seed train design reduces variability and saves time.
problem Designing robust biotechnological processes with cell cultures.
method Coupling uncertainty-based upstream simulation and Bayes optimization using Gaussian processes.
result Optimized seed train design results in lower cell density variability and reduced process duration.
Introduces MPR to measure and optimize representation across intersectional groups in retrieval.
problem Harmful stereotypes, cultural erasure, and social disparities in image search and retrieval.
method Develops MPR metric, practical estimation methods, theoretical guarantees, and optimization algorithms.
result Optimizing MPR yields more proportional representation across multiple intersectional groups, often with minimal retrieval accuracy compromise.
Optimizes biomanufacturing processes with a new digital twin calibration method.
problem Lack of interpretability and sample efficiency in traditional DoE methods.
method Developed a computational approach to calibrate Bio-SoS digital twin model.
result Guides sample-efficient and interpretable DoEs by quantifying sub-model parameter estimation errors.
Spin networks are at the core of quantum gravity. Our aim is to plug the mathematical community at large into the procedures turn to create a finite quantum theory of general relativity. For this, because of the different cultural backgraund, we would like to change the tack: to relate discrete (combinatorial) objects …
Shifu2 discovers advisor-advisee relationships in collaboration networks.
problem Discovering hidden advisor-advisee relationships in scientific collaboration networks.
method Network Representation Learning (NRL) model, considering both network structure and node/edge semantics.
result Improved stability and effectiveness compared to state-of-the-art methods.
Breiman's data analysis dichotomy is outdated, offering a third approach: mechanistic models.
problem Data analysis dichotomy between data modelers and algorithmic modelers.
method Interpolating between simple interpretable models and flexible function approximations using mechanistic models.
result Flexible, interpretable, and scientifically-informed hybrids can provide accurate and robust predictions.
Barcelona evaluates major events for economic and social impact.
problem Evaluating the economic and social impact of major events in Barcelona.
method Analyzes the economic and social dimensions of Barcelona's major events from 1888 to 2004.
result Develops a rational argument for communicating the economic benefits of major events.
CLARITY compares dissimilar datasets, identifying structural and relationship inconsistencies.
problem Integrating qualitatively different datasets from various disciplines.
method Non-parametric approach decomposing similarities into structural and relationship components.
result Identifies and interprets inconsistencies between datasets.
Leo Breiman's Rashomon Effect and Occam Dilemma are re-evaluated in the context of modern machine learning.
problem The tradeoff between model complexity and accuracy in machine learning.
method Modern perspective on Breiman's arguments using current computational capabilities.
result Algorithmic models can be accurate without being complex, nullifying the Occam Dilemma.
A new hierarchy quantifies agency in systems based on information processing.
problem Lack of a measurable, universal definition for agency in intelligent systems.
method Developed a bottom-up framework based on information processing hierarchy.
result Identified three orders of information processing (I, II, III) as necessary for agency.
Efron et al. (2001) proposed empirical Bayes formulation of the frequentist Benjamini and Hochbergs False Discovery Rate method (Benjamini and Hochberg,1995). This article attempts to unify the `two cultures' using concepts of comparison density and distribution function. We have also shown how almost all of the existi…
Knowing and modelling the migration phenomena and especially the social and economic consequences have a theoretical and practical importance, being related to their consequences for development, economic progress (or as appropriate, regression), environmental influences etc. One of the causes of migration, especially …
How do we assign value to economic transactions? To answer this question, we must consider whether the value of objects is inherent, is a product of social interaction, or involves other mechanisms. Economic theory predicts that there is an optimal price for any market transaction, and can be observed during auctions o…
MAntRA combines machine learning and Bayesian methods for time-dependent reliability analysis of unknown systems.
problem Time-dependent reliability analysis of systems with unknown governing physics.
method Combines machine learning, Bayesian statistics, and stochastic integration to discover and analyze SDEs from data.
result Demonstrates the effectiveness of MAntRA on three numerical examples, indicating its potential for in-situ and heritage structure analysis.
A database of objects discovered in houses in the Roman city of Pompeii provides a unique view of ordinary life in an ancient city. Experts have used this collection to study the structure of Roman households, exploring the distribution and variability of tasks in architectural spaces, but such approaches are necessari…
AI helps complete ancient tablets with missing parts.
problem Fragmented ancient tablets lack information.
method Used recurrent neural networks to model ancient language.
result Automatic completion of ancient texts possible.
Deep learning identifies 30 common bacterial pathogens from Raman spectra.
problem Rapid and accurate identification of pathogenic bacteria.
method State-of-the-art deep learning applied to large Raman spectroscopy dataset.
result 99.0% accuracy in identifying 30 common bacterial pathogens.
New method controls bias in training data for fair outcomes.
problem Ensuring equal treatment between different groups in machine learning.
method Contrastive information estimation to control mutual information between representations and protected attributes.
result Our method provides strong theoretical guarantees on the parity of any downstream algorithm.