Phase segregation, the process by which the components of a binary mixture spontaneously separate, is a key process in the evolution and design of many chemical, mechanical, and biological systems. In this work, we present a data-driven approach for the learning, modeling, and prediction of phase segregation. A direct …
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36 results for “segregation”
New approach detects racial segregation patterns in machine learning systems.
problem Challenges of fairness in machine learning systems due to racial identity.
method Unsupervised learning to detect patterns of segregation.
result Mitigates root cause of social disparities without reifying race.
New model values equity-linked securities with guaranteed return.
problem Valuation of equity-linked securities with guaranteed return.
method Replicate security price as sum of guaranteed amount and Asian style option price on basket.
result Analytical formulas derived for security price and hedge ratios.
Bayesian MS-VAR model for pricing equity-linked life insurance products.
problem Pricing and hedging equity-linked life insurance products on maximum of several assets.
method Introduces Bayesian Markov-Switching Vector Autoregressive (MS-VAR) process to model economic variables and insured's lifetime.
result Obtains net single premiums and hedging formulas for equity-linked life insurance products.
New method infers centromere locations in yeast using Hi-C data.
problem Difficulty in inferring centromere locations in yeast.
method Simulation-based inference using Hi-C data and simulated contact maps.
result Infers stochastic locations of all centromeres in budding yeast.
Loyal buyer-seller relationships can arise by design, e.g. when a seller tailors a product to a specific market niche to accomplish the best possible returns, and buyers respond to the dedicated efforts the seller makes to meet their needs. We ask whether it is possible, instead, for loyalty to arise spontaneously, and…
Study explores how wealth dynamics change with preferential interactions in kinetic exchange models.
problem Investigate how preferential interactions affect wealth dynamics and distributions in kinetic exchange models.
method Conducted Monte Carlo simulations to explore two types of preferential interactions: one with random selection and another with wealth difference constraint.
result Emergence of quasi-oligarchic societies and segregation into economic classes observed in preferential interactions.
By incorporating market impact and asymmetric sensitivity into the evolutionary minority game, we study the coevolutionary dynamics of stock prices and investment strategies in financial markets. Both the stock price movement and the investors' global behavior are found to be closely related to the phase region they fa…
We developed a perturbation model for affine gravity theories.
problem Cosmological perturbations in theories without metric.
method Segregated perturbations into symmetric and antisymmetric components, decomposing into irreducible elements.
result Fully addressed gauge freedom in affine gravity theories.
Model shows partial compliance can lead to less fair outcomes than expected.
problem How partial compliance affects fairness in competitive markets.
method Simple model of employment market, simulation to explore effects.
result Partial compliance can lead to less fair outcomes than expected.
Cash collateral is perfect in that it provides simultaneous counterparty credit risk protection and derivatives funding. Securities are imperfect collateral, because of collateral segregation or differences in CSA haircuts and repo haircuts. Moreover, the collateral rate term structure is not observable in the repo mar…
Bayesian optimization has recently attracted the attention of the automatic machine learning community for its excellent results in hyperparameter tuning. BO is characterized by the sample efficiency with which it can optimize expensive black-box functions. The efficiency is achieved in a similar fashion to the learnin…
Proposes a novel SAM operator for separate item and relational memories.
problem Limited memory interactions in neural networks.
method Introduces a Self-attentive Associative Memory (SAM) operator to separate item and relational memories.
result Achieves competitive results in various tasks, including geometry, graph, reinforcement learning, and question answering.
Study on optimal fees in hedge funds with first-loss compensation.
problem Determining the best fee structure for hedge funds with first-loss compensation.
method Solved the manager's non-concave utility maximization problem, calculated Pareto optimal first-loss schemes, and maximized a decision criterion on this set.
result Traditional fees are not Pareto optimal, and the preferred first-loss coverage guarantee varies with investor and market factors.
Crisis Propagation in a Heterogeneous Self-Reflexive DSGE Modelcond-mat.stat-mech
Model shows how confidence feedback can lead to different crisis outcomes.
problem Characterizing the impact of economic recessions on different social strata.
method A self-reflexive DSGE model with heterogeneous households, varying parameters to analyze crisis typologies.
result Crisis propagation can be confined to high or low income households, depending on social network structure and income inequality.
Dynamic Function Market Makerq-fin.GN
DFMM automates market making with adaptive pricing and risk management.
problem Challenges in decentralised automated market making (AMMs).
method Data aggregator, order routing, rebalancing, arbitrageurs, protective buffers, algorithmic accounting.
result DFMM optimises inventory risk and ensures market stability.
Novel CNN array for sign language recognition using wearable IMUs.
problem Efficiently recognizing sign language from wearable IMU signals.
method Two-dimensional Convolutional Neural Network array architecture for Indian sign language recognition.
result Peak classification accuracies of 94.20% for general sentences and 95.00% for interrogative sentences achieved.
Tuning into Climate Risks: Extracting Innovation from Television News for Clean Energy Firmsq-fin.ST
Study uses TV news to measure climate risks affecting clean energy firms.
problem Understanding how climate risks impact clean energy firms' financial stability.
method Developed climate risk measures from TV news coverage and analyzed their effects on clean energy firms' risks.
result Increased TV news coverage of climate risks correlates with higher systematic risk and lower idiosyncratic risk for clean energy firms.
Maturation Trajectories of Cortical Resting-State Networks Depend on the Mediating Frequency Bandq-bio.NC
The functional significance of resting state networks and their abnormal manifestations in psychiatric disorders are firmly established, as is the importance of the cortical rhythms in mediating these networks. Resting state networks are known to undergo substantial reorganization from childhood to adulthood, but wheth…
Co-Localization of Audio Sources in Images Using Binaural Features and Locally-Linear Regressioncs.SD
This paper addresses the problem of localizing audio sources using binaural measurements. We propose a supervised formulation that simultaneously localizes multiple sources at different locations. The approach is intrinsically efficient because, contrary to prior work, it relies neither on source separation, nor on mon…
The paper characterizes brain states and transitions using functional MRI data.
problem Characterizing the dynamic reconfiguration of neural systems in brain states.
method Bayesian model-based characterization of latent brain states and posterior predictive discrepancy using the latent block model.
result The model detects transitions between latent brain states and identifies distinctive community patterns in task-fMRI data.
Ventricular Fibrillation (VF), one of the most dangerous arrhythmias, is responsible for sudden cardiac arrests. Thus, various algorithms have been developed to predict VF from Electrocardiogram (ECG), which is a binary classification problem. In the literature, we find a number of algorithms based on signal processing…
Estimates network causal effects considering contagion and latent confounding.
problem Determining if correlations in network studies are due to contagion or latent confounding.
method Segregated graph representation, likelihood ratio tests, network causal effect estimation strategies.
result Proposes methods to estimate network causal effects under full interference scenarios.
Study multi-currency markets with multiple interest rates and collateral.
problem Characterize absence of arbitrage in a multi-currency market.
method Generalize results from Bielecki and Rutkowski (2015) to a multi-currency framework, linking with Piterbarg (2012), Moreni and Pallavicini (2017), and Fujii et al. (2010b). Characterize absence of arbitrage without collateral, then study collateralization schemes under various conventions.
result Complete study of absence of arbitrage and pricing in multi-currency markets with multiple interest rates and collateral.
Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamicsphysics.soc-ph
Study of urban lifestyles from mobility data of 1.2M people in 11 U.S. cities.
problem Lack of interpretability in digital mobility data for understanding urban lifestyles.
method Privacy-enhanced dataset of mobility visitation patterns, latent activity behavior decomposition.
result Detected 12 latent activity behaviors that describe urban lifestyles, not single lifestyles.
The paper uses GIS data to predict urban sprawl.
problem Overgrowth and expansion of low-density areas with car dependency and segregation.
method Data mining algorithms (Apriori, J4.8) adapted for geospatial analysis using ArcGIS.
result Prototype spatial decision support system (SDSS) predicts urban sprawl and estimates impact variables.
The paper tackles interpreting DCM with image data by addressing data isomorphism.
problem Interpreting DCM with image data due to isomorphic information.
method Proposes and benchmarks two methodologies: architectural adjustments and data source mitigation.
result Direct data source mitigation is more effective for maintaining DCM's interpretability.
New model separates object attributes for better perceptual grouping.
problem Perceptual grouping of complex visual scenes.
method Spatial mixture models with learnable priors.
result Outperforms state-of-the-art methods in perceptual grouping.
ProteinNet provides a standardized data set for protein structure prediction.
problem Lack of standardized data sets for protein structure prediction.
method Created high-quality sequence alignments, multiple data splits, and validation sets.
result Facilitates fair assessment of machine learning models for protein structure.
Model forecasts natural gas consumption with Fourier series and feedback.
problem Forecast natural gas consumption for risk minimization.
method Modulated Fourier series with temperature deviations, day-ahead feedback.
result Model outperforms time series methods for long-term projections.
Generates counterfactuals in target domain from source domain observations.
problem Cross-domain learning with domain shifts and lack of parallel datasets.
method Unsupervised, Neural Causal Models, Joint Causal Graphs, Effect-Intrinsic vs Domain-Intrinsic Variables.
result Framework generates counterfactuals that closely match ground truth.
The paper proposes DEA to make graph neural networks fairer in link prediction.
problem Graph neural networks can unfairly prioritize certain social groups in link prediction.
method Drop Edges and Adapt (DEA) fine-tuning strategy with covariance constraints.
result DEA improves fairness and accuracy in link prediction tasks.
Nonlinear Evidence of Investor Heterogeneity: Retail Cash Flows as Drivers of Market Dynamicsq-fin.GN
Study reveals investor heterogeneity in Korean equity market cash flows.
problem Investor heterogeneity and its impact on market dynamics.
method Detrended fluctuation analysis (DFA) on aggregated cash flows.
result Persistence in cash flows varies by investor type, with retail flows showing strong persistence.
MACER accelerates error repair by modularly identifying and applying fixes.
problem Automated compilation error repair for novice programmers.
method Modular segregation of repair process into identification and application, using discriminative learning techniques.
result MACER outperforms existing methods by 20% on popular errors and is competitive on all error types.
This study improves child welfare risk models using clustering methods.
problem Improving predictive risk models for child maltreatment.
method Integration of PCA and K-Means clustering.
result No significant difference in model performance across clusters, but better for younger children.
A novel approach for 3D lung nodule segmentation using adaptive ROI and multi-view residual learning.
problem Inaccurate nodule segmentation due to fixed ROI and redundant structures.
method Two-stage approach: 2D ROI patch-wise investigation with adaptive ROI strategy, followed by 2D and 3D VOI investigation with deep residual U-Net.
result Significantly robust and accurate nodule segmentation compared to previous methods.