Small social systems can better align objectives and reduce complexity.
problem Complexity and misalignment in large social systems.
method Four-pronged approach: Uncertainty Principle, Objectives, Smaller Organizations, Redirecting Outcomes.
result Smaller organizations can mitigate disruptive effects of corruption and foster development.
Product Kanerva Machines dynamically combine smaller models for better memory organization.
problem Limited organization in the Kanerva Machine.
method Introducing Product Kanerva Machines that dynamically combine multiple smaller Kanerva Machines.
result Product Kanerva Machines can discover spatial tunings that approximately factorize simple images by object.
The kind of realized mission inflows the sensitivity to risk. Among other factors, the risk results from decision about liquid assets investment level and liquid assets financing. The higher the risk exposure, the higher the level of liquid assets. If the specific risk exposure is smaller, the more aggressive could be …
New method identifies self-organized states in stock market declines.
problem Understanding the structure of financial time series.
method Identifying stock declines, finding a critical level, interpreting as phase transition.
result Evidence of self-organization in stock market indices, not in random series.
aweSOM accelerates SOM clustering for large datasets.
problem Scalability issues in existing SOM implementations for large, multidimensional data.
method CPU/GPU-accelerated Self-organizing Maps (SOM) with ensemble stacking.
result 10-100x speed up and improved memory efficiency for large datasets.
Proposes a federated learning approach for industrial asset failure prediction.
problem Lack of data and privacy concerns in industrial prognostics.
method Two-stage federated learning: dimension reduction and parameter estimation.
result Validated the approach using simulated and real data.
SOCP uses SOM to find groups and local calibration buffers for better regional coverage.
problem Heterogeneous regional coverage gaps in conformal prediction.
method Self-Organizing Map (SOM) for group discovery; local calibration buffers at BMU or fixed grid.
result Reduces regional coverage gaps on 7/8 benchmarks by 7.1%.
Enhances solar cell efficiency prediction using deep neural networks.
problem Predicting HOMO values for organic solar cells from limited experimental data.
method Ensemble deep neural network (SINet) using SMILES and InChI molecular representations.
result Significant performance improvement from transfer learning and dual molecular representations.
Study characterizes community structure in Japanese production network.
problem Characterize community structure in a large-scale production network.
method Directed network analysis of one million Japanese firms.
result Large fraction of firms have local interactions, and community strengths are heterogeneous.
Framework allows organizations to collaborate on learning tasks securely.
problem Limited collaboration due to security constraints.
method Assisted Learning framework for supervised learning tasks.
result Near-oracle learning performance achieved without revealing sensitive information.
Neural networks predict organic reactions from examples.
problem Predicting organic chemistry reactions efficiently.
method Used neural networks with a new reaction fingerprinting method.
result System predicts likely products from reagents and reactants.
Graphs predict reaction conditions for organic chemistry.
problem Predicting specific reaction conditions in organic chemistry.
method Graph Neural Networks (GNNs) for modeling reaction graphs.
result GNNs can identify specific graph features affecting reaction conditions.
RIVCoin stabilizes cryptocurrency portfolios through a DAO and redistributes income.
problem Stabilizing cryptocurrency value and aligning incentives for all users.
method Decentralized DAO, diversified reserves, and income redistribution.
result Aligns incentives for wealthier users to stabilize smaller users' risk.
RMT reveals self-regularization in neural networks, including traditional and heavy-tailed forms.
problem Understanding and quantifying self-regularization in neural networks.
method Application of Random Matrix Theory to analyze weight matrices of various neural network models.
result Identification of 5+1 phases of training in neural networks, including traditional and heavy-tailed self-regularization.
The paper organizes complex healthcare data using deep nets.
problem Handling missing data and irrelevant features in high-dimensional datasets.
method Defining neighborhoods, generating new features, and locally z-scored re-organization.
result Smooth intrinsic low-dimensional organization of hospitals, aligned with quality metrics.
Deterministic SOM improves cloud type classification from satellite data.
problem Randomness in SOM leads to inconsistent results.
method Proposes a deterministic approach to SOM.
result Demonstrates effective and efficient organization of satellite cloud data.
Study reveals a log-periodic structure in ETF sizes and finds large ETFs outperform small ones.
problem Understanding the size distribution and performance of ETFs.
method Detailed statistical analyses of ETF size distribution and performance metrics.
result Large ETFs outperform small ones, with a log-periodic structure in size distribution.
Paper learns data-driven organ matching rules from observational data.
problem Tackles organ transplantation compatibility using observational data.
method Representation learning to cluster donors and apply recipient transformations.
result Model outperforms human experts in predicting transplant outcomes.
This paper proposes a new algorithm for multiple sparse regression in high dimensions, where the task is to estimate the support and values of several (typically related) sparse vectors from a few noisy linear measurements. Our algorithm is a "forward-backward" greedy procedure that -- uniquely -- operates on two disti…
A simple model economy with locally interacting producers and consumers is introduced. When driven by extremal dynamics, the model self-organizes {\em not} to an attractor state, but to an asymptote, on which the economy has a constant rate of deflation, is critical, and exhibits avalanches of activity with power-law d…
BLOB combines organic and bandit signals for better user interest estimation.
problem Combining organic and bandit signals for improved user interest estimation.
method Bayesian Latent Organic Bandit (BLOB) model using variational auto-encoders and local re-parametrization.
result BLOB outperforms organic and bandit-based methods in both organic and bandit-rich environments.
New method segments brain tumors and organs-at-risk for radiation therapy.
problem Automating segmentation of brain tumors and organs-at-risk in radiation therapy planning.
method Combines contrast-adaptive generative model and spatial regularization model.
result Tumor segmentation accuracy comparable to state-of-the-art methods.
Study uses multifractal detrended cross-correlation to detect Forex arbitrage opportunities.
problem Detecting arbitrage opportunities in Forex markets.
method Multifractal detrended cross-correlation analysis applied to Forex time series.
result Strong cross-correlations found between exchange rates involved in triangular relations, including AUD and NZD.
Researchers organize and analyze a large public safety imagery dataset.
problem Efficiently organizing and analyzing a vast public safety imagery dataset.
method Hierarchical organization approach using Lincoln Laboratory Supercomputing Cluster for compute and storage.
result Successfully organized and evaluated the dataset with large-scale imagery inference across terabytes of data.
The paper identifies collaborations in codebases using commit activity and language usage.
problem Identifying organic team interactions and collaborations in large codebases.
method Embedding and clustering commit activity, language usage, and code identifier topics.
result Restores engineering organization and reveals hidden collaborations.
Model market shows self-organized behavior with price adjustments.
problem Understanding collective behavior in market economics.
method Simple model of market economics with extremal dynamics.
result Market self-organizes through price adjustments in critical states.
The paper explores how organisms and machines learn from their environment using machine learning, information theory, and thermodynamics.
problem How organisms and machines adapt to their environment.
method Machine learning, information theory, and thermodynamics approaches to understanding and modeling adaptation.
result Unified view of adaptation in organisms and machines based on principles of machine learning, information theory, and thermodynamics.
New modeling approach for self-organizing complex systems.
problem Identifying general principles of self-organizing complex systems.
method Self-deploying system structure and activities for goal-driven agents.
result Self-organization emerges from rational activity algorithm based on goals dependency network.
Gradient-free deep learning for large datasets.
problem Training deep neural networks on large-scale datasets is resource-intensive and requires specialized techniques.
method Recursive Local Representation Alignment (RLRA) for gradient-free training.
result RLRA achieves comparable performance to backprop while converging faster and being parallelizable.
BERT learns molecular substructures for chemistry problems.
problem Predicting chemical properties and synthesizing molecules.
method Transformer-based BERT model on molecule string representations, attention visualization.
result BERT learns to represent functional groups and atoms for various chemical properties.
A game environment simulates competition among many agents for resources.
problem Understanding large-scale multiagent interactions and resource competition.
method Developed a persistent, massively multiplayer AI environment.
result Population size affects the development of skillful behaviors and niche differentiation.
Study characterizes bladder motion using dynamic MRI and statistical analysis.
problem Limited volume coverage in dynamic MRI sequences hinders 3D shape reconstruction.
method 3D dense velocity measurements, LDDMM framework, statistical characterization, mean curvature changes, surface deformation analysis.
result Stable shape descriptor for characterizing bladder surface dynamics.
Paper develops a model to predict kidney transplant success.
problem Mismatched deceased donor-recipient kidney leads to post-transplant death.
method Data analysis of 584 imported kidneys from 12 transplant centers.
result Predicting model reduces mortality rate due to kidney mismatch.
We present a detailed analysis of the self-organization phenomenon in which the stylized facts originate from finite size effects with respect to the number of agents considered and disappear in the limit of an infinite population. By introducing the possibility that agents can enter or leave the market depending on th…
Neural model predicts organic reactions with high accuracy.
problem Predicting outcomes of complex organic chemistry reactions.
method Sequence-to-sequence model trained end-to-end, data-driven.
result Achieved 80.1% top-1 accuracy on a smaller dataset.
The paper develops new data organization methods for non-regular data.
problem Organizing high-dimensional data with arbitrary feature order and non-regular observations.
method Data-driven tree transforms and metrics based on iterative refinement.
result Improved clustering of tumor samples from multiple gene expression cohorts.
In self-organizing networks, topology and dynamics coevolve in a continuous feedback, without exogenous driving. The World Trade Network (WTN) is one of the few empirically well documented examples of self-organizing networks: its topology strongly depends on the GDP of world countries, which in turn depends on the str…
Study tackles workplace injury prediction and prevention.
problem Rare and imbalanced data makes injury risk prediction challenging.
method Ensemble resampling, transfer learning, and variable analysis.
result Improved injury risk prediction and prevention techniques.
New molecular descriptors improve machine learning for small and large molecules.
problem Improving machine learning models for molecular properties.
method Developed constant-size molecular descriptors combining connectivity counts and encoded distances.
result Models using these descriptors perform comparably to or better than state-of-the-art models.
New method detects organized eCommerce fraud by clustering orders.
problem Detecting coordinated fraud by groups of fraudsters.
method Scalable categorical clustering using agglomerative clustering and sampling.
result Groups 35-45% of fraudulent orders together, detects 26.2% of fraud with low false alarms.
Machine learning predicts band gaps for large organic crystals.
problem Predicting band gaps for complex organic crystal structures.
method Released a dataset of 12,500 crystal structures and their band gaps. Trained two state-of-the-art models to achieve a mean absolute error of 0.388 eV.
result Trained models predict band gaps with 13% error for an average gap of 3.05 eV.
This study investigates self-organizing dynamics in a stochastic exponential DAM model using Temporal Complexity.
problem Understanding self-organizing behavior in artificial neural systems.
method Investigation of a stochastic exponential DAM model through Temporal Complexity analysis.
result The model exhibits regimes of complex intermittency with nontrivial temporal correlations and scale-free behavior.
Unified view of SOMs and SNE from a common framework.
problem Comparing and understanding SOMs and SNE.
method Unified mathematical framework, quantitative comparison on datasets.
result SOMs and SNE can be derived from a common framework.
Model shows how imitation and randomness stabilize financial markets.
problem Stabilizing financial markets with self-organized criticality.
method Simple order book mechanism with self-organized criticality dynamics.
result Imitation and randomness stabilize market fluctuations.
Automated discovery of diverse self-organized patterns in complex systems.
problem Automated identification of interesting spatially localized patterns in self-organizing systems.
method Intrinsically motivated machine learning algorithms (POP-IMGEPs) combined with deep auto-encoders and CPPN primitives.
result Efficiency and effectiveness of the proposed method in discovering diverse patterns compared to baselines.
New trading model uses self-organization for real-world complexity.
problem Traditional trading models are too analytical for real-world conditions.
method Embraces self-organization, complex systems theory, and event-based time.
result Example Delta Engine algorithm demonstrates effectiveness.
The self-organizing methods were used for the investigation of financial market. As an example we consider data time-series of Dow Jones index for the years 2002-2003 (R. Mantegna, cond-mat/9802256). In order to reveal new structures in stock market behavior of the companies drawing up Dow Jones index we apply SOM (Sel…
Graphs represent gene segment organization, revealing complex interrelationships in a scrambled genome.
problem Understanding gene segment organization and interrelationships in a scrambled genome.
method Directed graphs representing gene segments and their relationships, with graph properties mapped to higher-dimensional space for analysis.
result Emerging star-like structures indicate complex interrelationships, including segments from multiple genes interleaving or overlapping.