Method creates resilient classifiers by focusing on highly predictive features.
problem Adversarial attacks exploit weakly correlated features learned during training.
method Resilient feature engineering to prioritize highly predictive features.
result Serial and Parallel Composition Resilience theorems support the design of resilient classifiers.
Paper evaluates ML's resilience in detecting ransomware.
problem Resilience of ML algorithms for ransomware detection.
method Used GAN to generate dynamic features to test ML models.
result ML models degrade in performance when faced with GAN-generated samples.
SI learning is effective for unbalanced data, showing resilience to object dependencies.
problem Learning from unbalanced data in multi-instance learning.
method Analysis of SI learning objective with rich classifier families, focusing on unbalanced data.
result Unbalanced data improves resilience of SI method to object dependencies, especially in neural networks.
Paper builds ML classifier to detect crypto-ransomware.
problem Detecting crypto-ransomware with high accuracy and low false positives.
method Behavior-based detection using input/output activities and file-content entropy. Deep-learning classifier with adversarial research and Integrated Gradient method for explanation.
result Deep-learning classifier achieves high accuracy and low false positive rate in detecting crypto-ransomware.
One of the most defining features of the global financial network is its inherent complex and intertwined structure. From the perspective of systemic risk it is important to understand the influence of this network structure on default contagion. Using sparse random graphs to model the financial network, asymptotic met…
Paper introduces a new index to measure financial and workplace resilience of firms.
problem Corporate resilience and its types in turbulent markets.
method Quantitative analysis of earnings expectations and implied discount rates.
result Evidence of workplace resilience amplification by financial status in the COVID-19 era.
Class imbalance is an intrinsic characteristic of multi-label data. Most of the labels in multi-label data sets are associated with a small number of training examples, much smaller compared to the size of the data set. Class imbalance poses a key challenge that plagues most multi-label learning methods. Ensemble of Cl…
Measures financial resilience using BSDEs and their properties.
problem Measuring financial resilience in dynamic risk environments.
method Developed stochastic calculus for BSDEs with jumps, revealing resilience rate as expectation of generator.
result Resilience rate can be represented as expectation of BSDE generator, revealing properties of dynamic risk measures.
Python tool assesses European agricultural production resilience.
problem Estimating resilience of European agricultural production systems.
method Python software to compute annual production resilience indicator.
result Demonstrates resilience of European agricultural production.
This paper measures financial market resilience in China and identifies key uncertainties.
problem Measuring financial market resilience in China.
method Quantitative analysis of total financial market and sub-markets, Diebold-Yilmaz connectedness approach.
result Financial market resilience in China is event-driven and influenced by geopolitical risks, economic and trade policy uncertainty, and U.S.-China tensions.
Study proposes a new resilience metric for stock market performance analysis.
problem Quantifying resilience cycles in stock market performance.
method Systems-oriented approach with Robustness Range and Elasticity Threshold.
result New metric quantifies non-linear resilience cycles in stock markets.
The resilience of low-degree Rademacher chaos is studied, providing probabilistic lower bounds.
problem Understanding how much a Rademacher chaos can withstand adversarial sign-flips without significant probability changes.
method Probabilistic lower-bound guarantees for the resilience of Rademacher chaos of arbitrary degree.
result Probabilistic lower-bound guarantees for the resilience of Rademacher chaos of arbitrary degree, especially meaningful for constant degree.
Boosting is known to be sensitive to label noise. We studied two approaches to improve AdaBoost's robustness against labelling errors. One is to employ a label-noise robust classifier as a base learner, while the other is to modify the AdaBoost algorithm to be more robust. Empirical evaluation shows that a committee of…
New model shows negative resilience can improve trading efficiency.
problem Optimal trade execution in limit order books with negative resilience.
method Stochastic order book model with negative resilience.
result Negative resilience can lead to more efficient trading.
Proposes resilience metrics for large blackout costs with logarithmic resilience.
problem Large variations in blackout costs make estimating risk impractical.
method Uses mean of log of large blackout costs, tail slope index, and frequency.
result Solves problems of heavy tail and large variations in blackout costs.
Study enhances classifier robustness against noisy labels.
problem Impact of label noise on model performance in real-world scenarios.
method Integrates adversarial machine learning and importance reweighting techniques with CNN.
result Improved model resilience against noisy data.
Stocks of more resilient firms outperformed during the pandemic, reflecting disaster risk.
problem The impact of social distancing on firms' operations and stock performance.
method Cross-sectional analysis of firms' resilience and stock performance, controlling for risk factors.
result Stocks of more resilient firms are expected to yield significantly lower returns than less resilient ones, reflecting disaster risk.
ResiliNet improves distributed neural network inference resilience.
problem Physical node failures in distributed neural networks cause performance drops.
method Skip hyperconnection and failout technique.
result ResiliNet provides inference resiliency for distributed neural networks.
Paper benchmarks DRL policies' resilience to state transitions.
problem Measuring DRL policies' resilience to state perturbations.
method Disentangled representation learning and RL-based techniques.
result Demonstrated feasibility of resilience benchmarking in DQN, A2C, and PPO2.
New method quantifies resilience of electric distribution systems from historical data.
problem Large blackouts caused by extreme winds have significant costs and impacts.
method Formulate large event risk from utility outage data, quantify resilience improvements through investments.
result Investments in wind hardening and faster restoration can reduce the probability of large cost events.
New framework for resilient bi-criteria optimization under noisy feedback.
problem Bi-criteria combinatorial optimization with noisy function evaluations.
method Introducing (α,β,δ,extttN)-resilience and developing a black-box framework. result Achieves sublinear regret and constraint violation for bi-criteria bandit problems.
Paper tackles Byzantine resilience in distributed multi-task learning.
problem Resilience of distributed algorithms in the presence of Byzantine agents.
method Online weight assignment rule based on accumulated loss and filtering.
result Aggregation with proposed weight assignment rule improves expected regret.
This paper uses robust optimization to analyze supply chain resilience.
problem Supply chain resilience analysis of multi-modal logistics networks.
method Robust optimization with budget-of-uncertainty.
result Interactive effects of network size, disruption scale, and degree on resilience.
Small stocks drive market crashes by suppressing resilience.
problem The single-security price limit exacerbates market panic during crashes.
method Simplified dynamic model on networks of investors and stocks, empirical verification.
result Unexpected linear association between price limit and critical market confidence.
Improved deep neural network generalization through noise resilience.
problem Understanding and predicting generalization error of deep neural networks.
method Noise resilience measures to predict generalization error.
result Secured 5th position in the PGDL competition at NeurIPS 2020.
CyBeR-0 optimizes federated learning with Byzantine resilience and reduced communication costs.
problem Byzantine attacks and communication inefficiency in federated learning.
method Transformed robust aggregation for zero-order optimization under client heterogeneity.
result CyBeR-0 achieves stable performance with minimal communication costs and reduced memory usage.
A methodology for resilience analysis of Capsule Networks under approximation errors.
problem Resilience of Capsule Networks under approximation errors.
method Modeling and analyzing approximation errors in Capsule Networks' inference.
result Capsule Networks are more resilient to errors during dynamic routing than other stages.
Firms' collaboration networks can decline but remain resilient.
problem Resilience of firms' collaboration networks during decline.
method Analysis of 21,500 R&D collaborations over 25 years, simulating drop-out cascades.
result Firms' collaboration networks can adapt to mitigate decline and recover.
This paper evaluates how well outlier detection methods work with samples of data.
problem Resilience of outlier detection methods to sampling.
method Estimating resilience to sampling of outlier detection methods and their ensembles.
result Outlier detection methods are not equally resilient to sampling, and careful selection of sampling scheme and method is necessary.
Automated design of resilient, efficient DNNs for hardware.
problem Designing reliable and efficient DNNs for hardware.
method Evolutionary optimization technique for DNN architecture design.
result Strong correlation between predicted and actual error resilience.
We present a large-scale study of commonality in liquidity and resilience across assets in an ultra high-frequency (millisecond-timestamped) Limit Order Book (LOB) dataset from a pan-European electronic equity trading facility. We first show that extant work in quantifying liquidity commonality through the degree of ex…
Study reveals clusters of resilient and vulnerable Spanish agri-food firms post-Ukraine-Russia war.
problem Financial resilience of agri-food companies in Spain during the Ukraine-Russia conflict.
method Cluster analysis using centred log-ratios for compositional data of financial ratios.
result Increase in resilient firms by 2023, highlighting sectoral adaptation to economic challenges.
Mitigates faults in DNNs by clipping activation values, improving their resilience.
problem Fault tolerance of DNNs in safety-critical applications.
method Clipping activation functions to reduce impact of faulty weights.
result Significant improvement in classification accuracy (68.92%) for fault mitigation.
New framework boosts neural network performance and resilience.
problem Susceptibility of compact neural network implementations to system disturbances.
method Realistic crossbar simulations and Mosaics framework to re-use synaptic connections.
result Compact neural networks are noise-immune and perform well under disturbances.
BRIDGE improves decentralized learning resilience against Byzantine failures.
problem Decentralized learning in distributed systems is vulnerable to Byzantine failures.
method Introduces BRIDGE, a scalable Byzantine-resilient decentralized gradient descent algorithm.
result Proves algorithmic and statistical convergence guarantees for BRIDGE.
We show that wealth processes in the block-shaped order book model of Obizhaeva/Wang converge to their counterparts in the reduced-form model proposed by Almgren/Chriss, as the resilience of the order book tends to infinity. As an application of this limit theorem, we explain how to reduce portfolio choice in highly-re…
Study uses machine learning to predict potato clones suitable for processing.
problem Efficiently identifying high-yield, disease-resistant potato varieties.
method Leveraged machine learning algorithms on Russet potato clones data from Oregon.
result Non-linear models like SVM and HGBC outperform traditional linear models in agricultural trials.
This research improves EEG-based MI-BCI systems to be more resilient to emotional arousal.
problem Lack of robustness in EEG-based MI-BCI systems due to emotional arousal.
method Subjects were exposed to VR environments to induce high and low arousal states. Machine learning models were trained on proxy subjects instead of arousal states. MI models were trained for each subject.
result MI-BCI systems are made more resilient to emotional perturbations.
This paper defines resilience in knowledge graph embeddings and surveys existing works.
problem Challenges in knowledge graph embedding models, including noise, missing information, distribution shift, and adversarial attacks.
method Unified definition of resilience, formalization in the context of knowledge graphs, systematic survey of existing works.
result Most existing works focus on robustness, leaving other aspects of resilience unexplored.
Supply chain resilience depends on balancing competition and self-interest.
problem Maintaining resilience in decentralized supply chains under uncertainty and competition.
method Modeling competitive suppliers and retailers with yield uncertainty and congestion, analyzing network formation.
result Decentralized supply chains can form resilient networks through competition and self-interest, contrary to intuition.
The study analyzes performance indices for class-imbalanced data and identifies conditions they must meet.
problem Distortions in performance indices under class imbalance.
method Identified two conditions for performance indices and analyzed four binary and five multi-class indices.
result Recommended appropriate indices for evaluating classifiers in class-imbalanced scenarios.
Study finds ESG investments more resilient than traditional equity indices during market turmoil.
problem Resilience of ESG investments during financial instability.
method Daily returns analysis using MGND and EGARCH-in-mean models.
result ESG investments show higher resilience compared to traditional equity indices during crises.
TensorFI injects faults in TensorFlow programs to assess their reliability.
problem Ensuring reliability of machine learning systems in safety-critical domains.
method TensorFI is a flexible fault injection framework for TensorFlow applications.
result TensorFI evaluates the resilience of 12 ML programs, including autonomous vehicle DNNs.
Ranger improves DNNs' fault resilience without re-computation.
problem Transient faults in DNNs cause errors, reducing reliability.
method Range restriction to transform critical faults to benign faults.
result Significant improvement in error resilience (3x to 50x) with no accuracy loss.
Global catastrophe risk pools increase financial resilience by diversifying risk and including more countries.
problem Low- to middle-income countries rely heavily on foreign aid for recovery from extreme weather events, which is slow and uncertain.
method Developed a method to form global catastrophe risk pools that maximize risk diversification and select countries with low bilateral correlations or low shares in the pool risk.
result Global pooling increases risk diversification, lowers countries' shares in the pool risk, and increases the number of countries benefiting from risk pooling.
Predictive Q-learning algorithm for IoT networks with human operators.
problem Resilient and predictive actions for IoT networks with faulty components.
method Predictive and resilient Q-learning algorithm considering historical data and human operator feedback.
result Optimal scheduling policies avoiding attacked locations and faults.
Cryptos remained resilient after SVB's collapse, contrary to expectations.
problem Impact of SVB collapse on crypto markets.
method Factual summary, sentiment analysis, and market performance examination.
result Cryptocurrencies showed resilience after SVB's collapse.
In order-driven markets, limit-order book (LOB) resiliency is an important microscopic indicator of market quality when the order book is hit by a liquidity shock and plays an essential role in the design of optimal submission strategies of large orders. However, the evolutionary behavior of LOB resilience around liqui…