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.
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.
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.
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.
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…
Framework for managing cyber risks in networks.
problem Managing systemic cyber risks in digital networks.
method Three components: acceptable configurations, risk mitigation interventions, and cost function.
result Effective decision-making for network resilience.
Study optimal stock purchases under fluctuating market resilience.
problem Optimal stock purchases over time with varying market resilience.
method Model market resilience and liquidity variations, solve optimization problem.
result Unique optimal execution strategy found.
Develops a framework to assess infrastructure reliability under natural and malicious events.
problem Assessing reliability and costs of infrastructure under various hazards.
method Coupling mechanical reliability analyses with economical reliability analyses using probabilistic considerations.
result Indicators of probable cost of failure for infrastructure, aiding safety investments.
Asynchronous distributed machine learning solutions have proven very effective so far, but always assuming perfectly functioning workers. In practice, some of the workers can however exhibit Byzantine behavior, caused by hardware failures, software bugs, corrupt data, or even malicious attacks. We introduce \emph{Karda…
Bayesian model uses mobile data to assess business resilience after hurricanes.
problem Evaluating economic impact of extreme shocks on businesses.
method Bayesian structural time series model with mobile phone data.
result Estimates business resilience after hurricanes, revealing key characteristics.
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…
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.
This work reveals symmetries in quantum circuits and develops a noise-aware optimization method.
problem Understanding and optimizing the cost landscape of parametrized quantum circuits.
method Analytical proof of symmetries and their resilience to noise, followed by the development of SYMH optimization method.
result Symmetries in PQCs lead to degeneracy in the cost landscape and can be exploited to improve optimization under noise.
This paper introduces resilient constrained learning to adapt learning constraints while solving machine learning tasks.
problem Adapting machine learning solutions to satisfy multiple requirements beyond accuracy.
method Adapts learning constraints during training by balancing performance gains against a user-defined cost of relaxation.
result Shows conditions and introduces a practical algorithm to compute the balance, with approximation and generalization guarantees.
Financial markets can be seen as complex systems that are constantly evolving and sensitive to external disturbance, such as systemic risks and economic instabilities. Analysis of resilient market performance, therefore, becomes useful for investors. From a systems perspective, this paper proposes a novel function-base…
This paper develops a stochastic learning-optimization model for resilient automotive supply chains.
problem Supply chain disruptions and volatile demand pose challenges to the UK automotive industry.
method Integrates Bayesian inference with inventory optimization for a two-echelon system subject to stochastic demand and disruptions.
result The integrated approach achieves significant cost reductions and improved resilience during disruptions.
Could a gradient aggregation rule (GAR) for distributed machine learning be both robust and fast? This paper answers by the affirmative through multi-Bulyan. Given n workers, f of which are arbitrary malicious (Byzantine) and m=n−f are not, we prove that multi-Bulyan can ensure a strong form of Byzantine resilien…
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.
Proposes a method for private aggregation in heterogeneous federated learning.
problem Ensuring resilience to Byzantine clients and maintaining client data privacy in federated learning with heterogeneous data.
method Careful co-design of verifiable secret sharing, secure aggregation, and private information retrieval scheme.
result Achieves information-theoretic privacy guarantees and Byzantine resilience under data heterogeneity.
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.
We consider a broker who has to place a large order which consumes a sizable part of average daily trading volume. The broker's aim is thus to minimize execution costs he incurs from the adverse impact of his trades on market prices. By contrast to the previous literature, see, e.g., Obizhaeva and Wang (2005), Predoiu,…
We consider optimal execution strategies for block market orders placed in a limit order book (LOB). We build on the resilience model proposed by Obizhaeva and Wang (2005) but allow for a general shape of the LOB defined via a given density function. Thus, we can allow for empirically observed LOB shapes and obtain a n…
We provide a methodology, resilient feature engineering, for creating adversarially resilient classifiers. According to existing work, adversarial attacks identify weakly correlated or non-predictive features learned by the classifier during training and design the adversarial noise to utilize these features. Therefore…
The control and sensing of large-scale systems results in combinatorial problems not only for sensor and actuator placement but also for scheduling or observability/controllability. Such combinatorial constraints in system design and implementation can be captured using a structure known as matroids. In particular, the…
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.
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.
To improve the resilience of distributed training to worst-case, or Byzantine node failures, several recent approaches have replaced gradient averaging with robust aggregation methods. Such techniques can have high computational costs, often quadratic in the number of compute nodes, and only have limited robustness gua…
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.
The study shows that limited liability can make banks more stable by choosing less risky assets.
problem How limited liability affects bank stability and risk management.
method Dynamic portfolio approach with continuous time models, including and excluding limited liability, and using the KMV model to measure resiliency.
result Inclusion of limited liability leads to a bank choosing less risky assets, increasing its resilience.
Develops a two-layer model to design mortgage assistance products.
problem Designing effective mortgage assistance products to improve household resilience.
method Two-layer approach: simulation and optimization.
result Shows how the approach can design and evaluate mortgage assistance products.
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.
Applications in machine learning, optimization, and control require the sequential selection of a few system elements, such as sensors, data, or actuators, to optimize the system performance across multiple time steps. However, in failure-prone and adversarial environments, sensors get attacked, data get deleted, and a…
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.
Paper develops a robust federated recommendation system against poisoning attacks.
problem Low-cost poisoning attacks degrade federated recommendation systems' performance.
method Develops a robust learning strategy using gradients to filter out Byzantine clients.
result Empirically validated robust learning strategy on four datasets.
This paper optimizes cybersecurity resource allocation in networks with heterogeneous attacker and defender valuations.
problem Optimizing cybersecurity resource allocation in networks with heterogeneous attacker and defender valuations.
method Combining strategic behavior of players with contagion dynamics, a method is extended to determine optimal resource allocation based on simple network metrics weighted by risk profiles.
result The asymmetry between attacker and defender valuations drives optimal attack and defense strategies, shaping system resilience.
Incorporating sparsity priors in learning tasks can give rise to simple, and interpretable models for complex high dimensional data. Sparse models have found widespread use in structure discovery, recovering data from corruptions, and a variety of large scale unsupervised and supervised learning problems. Assuming the …
Model predicts time evolution of supply chain networks under varying costs.
problem Regulating downstream relationships for sustainable SMEs.
method Time varying SCN model based on Lagrangian mechanics, incorporating EDES cost kernels.
result Model predicts bankruptcy and break-even states under different cost scenarios.
Applications of safety, security, and rescue in robotics, such as multi-robot target tracking, involve the execution of information acquisition tasks by teams of mobile robots. However, in failure-prone or adversarial environments, robots get attacked, their communication channels get jammed, and their sensors may fail…
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.
LiuBei is a resilient ML algorithm that tolerates Byzantine workers and servers without trusting any component.
problem Byzantine failures in distributed ML solutions.
method Byzantine-resilient ML algorithm that aggregates gradients and replicates parameter servers, using a filtering mechanism and scatter/gather protocol.
result LiuBei achieves Byzantine resilience to both servers and workers and guarantees convergence, with an accuracy loss of around 5% and a 24% convergence overhead.
Applying deep neural networks (DNNs) in mobile and safety-critical systems, such as autonomous vehicles, demands a reliable and efficient execution on hardware. Optimized dedicated hardware accelerators are being developed to achieve this. However, the design of efficient and reliable hardware has become increasingly d…
While machine learning is going through an era of celebrated success, concerns have been raised about the vulnerability of its backbone: stochastic gradient descent (SGD). Recent approaches have been proposed to ensure the robustness of distributed SGD against adversarial (Byzantine) workers sending poisoned gradients …
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.
Decentralized ranking consensus via gossip for robust and scalable systems.
problem Achieving reliable and resilient consensus on collective rankings in a decentralized setting.
method Random gossip communication for decentralized computation of global rankings.
result Robust and scalable consensus on collective rankings achieved through decentralized, local interactions.
The theory of multilayer networks is in its early stages, and its development provides vital methods for understanding complex systems. Multilayer networks, in their multiplex form, have been introduced within the last three years to analysing the structure of financial systems, and existing studies have modelled and e…
PARyOpt is a python based implementation of the Bayesian optimization routine designed for remote and asynchronous function evaluations. Bayesian optimization is especially attractive for computational optimization due to its low cost function footprint as well as the ability to account for uncertainties in data. A key…
Optimal trade execution in a fluctuating market with stochastic liquidity.
problem Minimizing costs in a market with unpredictable liquidity.
method Developed a recursion to find the least costly trade execution strategy.
result Explicit recursion characterizes the least costly trade execution.