Automated medical protocol uses neural networks and decision trees.
problem Improving healthcare delivery through automated decision-making.
method Hybrid model combining neural networks and decision trees.
result Effective early decisions for patient care.
Paper uses weakly-supervised clustering to automatically create network protocol abstractions.
problem Manual definition of abstraction by domain experts is time-consuming.
method Weakly supervised clustering algorithm for automatic abstraction.
result The method successfully matches the reference abstraction with minimal labeled examples.
The paper gives bounds for how long it takes for gossip protocols to spread information in networks.
problem Understanding the diffusion time in asynchronous gossip protocols.
method Provides non-asymptotic bounds for the number of messages needed for consensus in asynchronous gossip protocols.
result Explicit formula and approximation for the number of messages needed for consensus in different types of graphs.
New protocol reduces communication costs for heterogeneous bandits over complex networks.
problem Minimizing group regret in a multi-agent, heterogeneous bandit setting over complex networks.
method Flooding with Absorption (FwA) protocol for heterogeneous bandits over complex networks.
result FwA protocol significantly reduces communication costs compared to flooding while maintaining similar regret performance.
Autoencoders improve wireless protocol classification with fewer parameters and higher accuracy.
problem Classifying wireless protocols with high accuracy and low computational complexity.
method Training FC deep learning networks using multiple denoising autoencoders.
result AE-trained networks achieve higher accuracy than reference FC networks for various SNR values.
Elastic Gossip distributes neural network training using gossip-like protocols.
problem Distributing neural network training across heterogeneous environments.
method Pairwise-communication using Gossip-like protocols, building on Elastic Averaging SGD.
result Elastic Gossip performs better than Gossiping SGD in experiments, but hyper-parameter search may yield better configurations.
The paper analyzes how mutable blockchain protocols affect miner behavior and strategic stability.
problem The mutability of blockchain protocols undermines long-term planning and cooperative equilibria.
method Integrates Austrian capital theory with repeated game theory to examine miner behavior under different institutional conditions.
result Effective time preference increases when protocol rules are mutable, leading to political rent-seeking and undermining strategic coherence.
New benchmark protocol evaluates neural network optimizers for efficiency and data shift sensitivity.
problem Benchmarking neural network optimizers with hyperparameter complexity and data shift sensitivity.
method Proposed a new evaluation protocol combining end-to-end and data-addition training efficiency, using bandit hyperparameter tuning and human study validation.
result No clear winner across all tasks, highlighting the complexity of optimizer performance.
PoEL protocol aims to efficiently create and secure liquidity for blockchain networks.
problem Lack of sustainable liquidity and network security in Proof of Stake blockchains.
method PoEL uses staking rewards to attract risk capital, structuring incentives for capital efficiency and security.
result PoEL protocol enhances blockchain network security and liquidity sustainability.
This paper improves MARL for networked systems through new protocols and discount factors.
problem Improving control in networked systems using multi-agent reinforcement learning.
method Formulated as a spatiotemporal Markov decision process, introduced a spatial discount factor, and proposed NeurComm.
result Appropriate spatial discount factor enhances learning curves of non-communicative MARL algorithms.
The study analyzes how cross-chain interoperability affects decentralized lending protocols' performance.
problem Understudied cross-chain elements in DeFi lending risk management.
method Panel regression fixed effects and OLS models applied to empirical analysis.
result Cross-chain activity impacts protocol performance, with bridge volume being a critical driver.
Novel framework for systemic risk analysis in financial markets.
problem Systemic risk in financial markets.
method Multi-scale network dynamics, transfer entropy networks, agent-based modeling, wavelet decomposition, Model Context Protocol (MCP).
result Multi-scale approach reveals hidden systemic risk patterns.
Efficient decentralized deep learning protocol reduces communication by orders of magnitude.
problem Training deep neural networks from distributed data sources with reduced communication.
method Dynamic model averaging protocol that handles different phases of model training and adapts to concept drifts.
result Reduction in communication by an order of magnitude compared to state-of-the-art approaches.
Secure Aggregation protocols allow a collection of mutually distrust parties, each holding a private value, to collaboratively compute the sum of those values without revealing the values themselves. We consider training a deep neural network in the Federated Learning model, using distributed stochastic gradient descen…
This study aims to improve communication between fragmented blockchain systems in finance.
problem Inefficient and insecure communication in fragmented blockchain systems.
method Analysis of cross-chain interoperability protocols and their properties.
result Comparison and evaluation of cross-chain interoperability protocols.
A scalable protocol for federated averaging with privacy and correctness guarantees.
problem Privacy and correctness in federated learning from multiple parties.
method Scalable protocol using correlated and independent Gaussian noise, analyzed for differential privacy and graph topology.
result Nearly matches trusted curator model's utility with minimal communication.
This paper optimizes SMPC for neural network inference, reducing memory and time.
problem Memory and time constraints in secure neural network inference.
method Implemented ABY2.0 protocol, optimized memory usage, and used a helper node.
result MNIST inference reduced from 8.03 GB RAM and 200s to 0.2 GB RAM and 32s.
New CNN method improves brain MR segmentation across scanners and protocols.
problem Degradation of CNN accuracy on images from different scanners and protocols.
method Lifelong multi-domain learning with shared filters and domain-specific batch normalization.
result Significantly closes the gap to benchmark performance.
Adapts CNN for robust medical image segmentation across different scanners and protocols.
problem Performance degradation of CNNs in medical image segmentation due to mismatch between training and test images.
method Designs a segmentation CNN as a concatenation of a shallow normalization CNN and a deep CNN. At test time, adapts the normalization sub-network for each test image using a denoising autoencoder.
result Consistently improves performance on multi-center MRI datasets of brain, heart, and prostate.
We develop a secure aggregation protocol for federated learning that reduces communication and computation costs.
problem Expensive communication and privacy concerns in federated learning.
method Adapting compression-based federated techniques to additive secret sharing.
result Our protocol achieves high accuracy with low communication costs and is more efficient than prior work.
Paper introduces supervised and unsupervised TAM models for binary neurons.
problem Learning and retrieval of structured triplets of patterns in neural networks.
method Extends Hebbian paradigm to supervised and unsupervised protocols, using glassy statistical mechanical techniques.
result Obtained self-consistency equations for critical dataset sizes and retrieval performance.
The paper analyzes risks and revenue dynamics of a liquid restaking protocol in decentralized finance.
problem Interconnected risks and revenue dynamics of a liquid restaking protocol in decentralized finance.
method Empirical analysis using OLS regression, Granger-causality, and random forest feature importance tests.
result Revenue is primarily driven by value locked in the ecosystem, yield of liquid restaking token, and multi-blockchain expansion.
Researchers develop multi-agent systems for quadcopters to collaborate in missions.
problem Enable multiple quadcopters to work together in remote sensing tasks.
method Agent dynamics, network topologies, collective behaviors, agreement protocol, equations of motion for quadcopters.
result Multi-agent systems can successfully collaborate in remote sensing missions.
Hybrid approach for large-scale network synchronization using KF and PTP.
problem Synchronization of large-scale networks in 5G.
method Combines Kalman Filtering and PTP for pairwise synchronization, and Factor Graphs and Belief Propagation for end-to-end synchronization.
result Error in offset estimation remains below 5 ns in simulations.
MPLP learns neural network weights by treating operations as message-passing agents.
problem Training neural networks using gradient-based methods.
method MPLP abstracts neural network operations as message-passing agents, updating internal states and passing messages.
result MPLP outperforms traditional gradient-based methods on simple feed-forward neural networks.
This paper explores how interactive protocols enhance local differential privacy.
problem The power of interactive protocols in local differential privacy.
method Classification of locally private protocols by compositionality and transformation of fully interactive protocols into sequentially interactive ones.
result Fully interactive protocols can solve problems that sequentially interactive protocols cannot without a significant increase in sample complexity.
The paper examines stability of shares in Proof of Stake protocol, identifying different investor behaviors and phase transitions.
problem Stability of shares in Proof of Stake protocol.
method Identification of large, medium, and small investors under various rewarding schemes; dynamical population model analysis.
result Phase transitions and thresholds for stability are characterized; chaotic centralization leads to concentration of shares.
Proposes efficient 4PC framework for privacy-preserving machine learning.
problem Need for privacy-preserving machine learning in healthcare and finance.
method Proposes an actively secure 4PC protocol and a framework for PPML.
result Framework operates efficiently and outperforms existing methods.
LDP-Fed protects privacy in federated learning with neural networks.
problem Privacy protection for high-dimensional, continuous model parameters in federated learning.
method Local Differential Privacy (LDP) for repeated collection of model training parameters, selection and filtering of parameter updates.
result LDP-Fed achieves model accuracy comparable to non-private methods while preserving privacy.
Maker Protocol manages Dai stable coin on Ethereum blockchain.
problem Managing decentralized finance applications on blockchain.
method Analyzes Maker Protocol's components and governance.
result Maker Protocol is a significant decentralized finance application.
New federated learning protocols improve on knowledge distillation's poor performance.
problem Designing a universal API for federated learning without public data.
method Proposed Federated Kernel ridge regression using knowledge distillation.
result Performance of new protocols closely matches theoretical predictions.
Compound Finance optimizes risk metrics for V3 protocol using Chainrisk simulations.
problem Optimizing systemic risks in Compound V3 protocol.
method Millions of Chainrisk simulations to evaluate VaR and LaR, providing parameter adjustments.
result Optimization framework enhances protocol stability.
This paper examines interest rates and market efficiency in DeFi loanable funds protocols.
problem Equilibrium of supply and demand for loanable funds in DeFi protocols.
method Review of interest rate mechanisms in Compound, Aave, and dYdX; empirical analysis of market efficiency and inter-connectedness.
result Interest rate rules in DeFi protocols do not always equilibrate supply and demand.
Random learning rate improves neural network training without extra cost.
problem Optimizing neural networks with stochastic gradient descent.
method Uniformly-distributed random learning rate in SGD.
result Random learning rate yields better regularization without extra computational cost.
New federated learning protocols resist Byzantine failures and offer privacy guarantees.
problem Resisting Byzantine failures in federated learning.
method Proposes robust federated learning protocols with optimal statistical rates and privacy guarantees.
result Achieves nearly optimal statistical rates and tight rate in terms of all parameters for strongly convex losses.
Improves memory robustness in RNNs for sequential data.
problem Improving memory robustness in RNNs for sequential data processing.
method Utilized various training protocols, datasets, and architectures to analyze hidden state dynamics and propose a regularization technique.
result Manipulating hidden state speeds improves memory robustness over time.
Protocol minimizes disclosure in classification tasks.
problem Ensuring minimal disclosure in classification protocols.
method Developed a protocol for multi-party classification that minimizes non-responsive document disclosure.
result Guarantees minimal disclosure of non-responsive documents.
Paper explores anchoring for vision models, improving generalization and safety.
problem Anchoring can lead to undesirable shortcuts, limiting generalization.
method Introduced a new anchored training protocol with a regularizer to mitigate undesirable shortcuts.
result Significant performance gains in generalization and safety metrics.
This study measures liquidity risks in Aave, a blockchain lending protocol.
problem Liquidity risks in lending protocols, especially in Aave.
method Measurements of liquidity risks using Aave as a case study, focusing on available liquidity and market concentration.
result Liquidity risks in Aave are volatile and affect the protocol negatively, especially for repeat borrowers.
Study quantifies events leading to Terra project failure in 2022.
problem Terra project's fragility and dependency on Anchor protocol.
method Systematic review of social media news, hourly and transaction data analysis, network science techniques.
result Identified trigger events and analyzed dependency structures using network science.
Paper introduces Cycles Protocol to integrate trade credit into market clearing.
problem Liquidity embedded in trade credit outside formal settlement infrastructures.
method Distributed, multilateral clearing mechanism based on double-entry accounting.
result Cycles Protocol maximizes balance sheet compression without redistributing counterparty risk.
Paper presents a risk management framework for blockchain protocols.
problem Blockchain protocol risks affecting DLT and digital assets.
method Developed a comprehensive risk management framework using traditional taxonomy.
result Structured approach to identify, measure, monitor and report blockchain protocol risks.
Study analyzes risk management in Aave and Compound lending protocols, finding v3 better than v2.
problem Risk management in decentralized lending protocols.
method Cross-version and cross-chain analysis using fixed effects model.
result v3 protocols have better risk management, with stronger impact on L2 blockchains.
New protocol benchmarks deep learning methods' data efficiency.
problem Measuring data efficiency of deep learning methods.
method Proposed experimental protocol to benchmark CNNs and HiGSFA.
result HiGSFA outperforms CNNs on smaller datasets.
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.
This paper presents Rudra, a parameter server based distributed computing framework tuned for training large-scale deep neural networks. Using variants of the asynchronous stochastic gradient descent algorithm we study the impact of synchronization protocol, stale gradient updates, minibatch size, learning rates, and n…
Paper explores how to design federated learning protocols that benefit all participants while maintaining privacy.
problem Privacy concerns undermine the accuracy benefits of federated learning in privacy-sensitive domains.
method The paper provides conditions for mutually beneficial federated learning protocols and designs protocols that maximize total utility and accuracy.
result The paper demonstrates that federated learning can be designed to be mutually beneficial, striking a balance between privacy and model accuracy.
Improved privacy-preserving summation protocol with fewer messages.
problem Achieving efficient differential privacy in multi-party summation.
method Combining secure shuffling with Laplace mechanism in the shuffle model.
result Protocol with O(1/ε) error and O(log(n/δ)) messages per party.