Deterministic Version Research Articles

From MLWE to RLWE: A Differential Fault Attack on Randomized & Deterministic Dilithium

The post-quantum digital signature scheme CRYSTALS-Dilithium has been recently selected by the NIST for standardization. Implementing CRYSTALSDilithium, and other post-quantum cryptography schemes, on embedded devices raises a new set of challenges, including ones related to performance in terms of speed and memory requirements, but also related to side-channel and fault injection attacks security. In this work, we investigated the latter and describe a differential fault attack on the randomized and deterministic versions of CRYSTALS-Dilithium. Notably, the attack requires a few instructions skips and is able to reduce the MLWE problem that Dilithium is based on to a smaller RLWE problem which can be practically solved with lattice reduction techniques. Accordingly, we demonstrated key recoveries using hints extracted on the secret keys from the same faulted signatures using the LWE with side-information framework introduced by Dachman-Soled et al. at CRYPTO’20. As a final contribution, we proposed algorithmic countermeasures against this attack and in particular showed that the second one can be parameterized to only induce a negligible overhead over the signature generation.

An efficient quasi-Monte Carlo method with forced fixed detection for photon scatter simulation in CT.

Detected scattered photons can cause cupping and streak artifacts, significantly degrading the quality of CT images. For fast and accurate estimation of scatter intensities resulting from photon interactions with a phantom, we first transform the path probability of photons interacting with the phantom into a high-dimensional integral. Secondly, we develope a new efficient algorithm called gQMCFFD, which combines graphics processing unit(GPU)-based quasi-Monte Carlo (QMC) with forced fixed detection to approximate this integral. QMC uses low discrepancy sequences for simulation and is deterministic versions of Monte Carlo. Numerical experiments show that the results are in excellent agreement and the efficiency improvement factors are 4 ∼ 46 times in all simulations by gQMCFFD with comparison to GPU-based Monte Carlo methods. And by combining gQMCFFD with sparse matrix method, the simulation time is reduced to 2 seconds in a single projection angle and the relative difference is 3.53%.

Estimating the instantaneous reproduction number ([formula omitted]) by using particle filter

BackgroundMonitoring the transmission of coronavirus disease 2019 (COVID-19) requires accurate estimation of the effective reproduction number (Rt). However, existing methods for calculating Rt may yield biased estimates if important real-world factors, such as delays in confirmation, pre-symptomatic transmissions, or imperfect data observation, are not considered. MethodTo include real-world factors, we expanded the susceptible-exposed-infectious-recovered (SEIR) model by incorporating pre-symptomatic (P) and asymptomatic (A) states, creating the SEPIAR model. By utilizing both stochastic and deterministic versions of the model, and incorporating predetermined time series of Rt, we generated simulated datasets that simulate real-world challenges in estimating Rt. We then compared the performance of our proposed particle filtering method for estimating Rt with the existing EpiEstim approach based on renewal equations. ResultsThe particle filtering method accurately estimated Rt even in the presence of data with delays, pre-symptomatic transmission, and imperfect observation. When evaluating via the root mean square error (RMSE) metric, the performance of the particle filtering method was better in general and was comparable to the EpiEstim approach if perfectly deconvolved infection time series were provided, and substantially better when Rt exhibited short-term fluctuations and the data was right truncated. ConclusionsThe SEPIAR model, in conjunction with the particle filtering method, offers a reliable tool for predicting the transmission trend of COVID-19 and assessing the impact of intervention strategies. This approach enables enhanced monitoring of COVID-19 transmission and can inform public health policies aimed at controlling the spread of the disease.

Optimization problems with evidential linear objective

We investigate a general optimization problem with a linear objective in which the coefficients are uncertain and the uncertainty is represented by a belief function. We consider five common criteria to compare solutions in this setting: generalized Hurwicz, strong dominance, weak dominance, maximality and E-admissibility. We provide characterizations for the non-dominated solutions with respect to these criteria when the focal sets of the belief function are Cartesian products of compact sets. These characterizations correspond to established concepts in optimization. They make it possible to find non-dominated solutions by solving known variants of the deterministic version of the optimization problem or even, in some cases, simply by solving the deterministic version.

Co-circulation of Dengue and Zika viruses: A modelling approach applied to epidemics data

Since the 1980s, the prevalence of diseases caused by arboviruses, in particular, those transmitted by Aedes Aegypti mosquitoes have increased in the Americas. The diseases transmitted by this mosquito include Dengue, Zika, and Chikungunya, which are endemic diseases in the region between the tropics. It is relevant to investigate the effect of virus co-circulation in the transmission of these diseases because there are evidences of both cross-protection and cross-enhancement among the viruses causing Zika and Dengue. In order to understand that effect, in this work we formulate an stochastic model, based on a previous deterministic model for co-circulation of Dengue serotypes. We applied both the deterministic and stochastic versions of the model to epidemics data of Dengue and Zika in Brazilian cities during 2015–2016. We use the deterministic model to calculate the basic reproductive number associated to the co-circulation of both diseases, and we show a non-trivial epidemic behavior associated to the entrance of Zika disease in 2015 in Brazil. Based on the stochastic model we evaluated the importance of the population size and the initial number of cases for the subsequent development of both diseases. Using actual data of simultaneous epidemics of dengue and Zika from Brazilian cities, we mainly find regions of the parameters compatible to cross protection of the population against the infections.

A decision support framework for optimal vaccine distribution across a multi-tier cold chain network

In this paper, we present a decision support framework for optimizing multiple aspects of vaccine distribution across a multi-tier cold chain network. We propose two multi-period optimization formulations within this framework: first to minimize inventory, ordering, transportation, personnel and shortage costs associated with a single vaccine; the second for the case when multiple vaccines with differing efficacies and costs are available for the same disease. We use the case of the Indian state of Bihar and COVID-19 vaccines to illustrate the implementation of the framework. We present computational experiments including what-if scenario and sensitivity analyses to demonstrate: (a) the organization of the model outputs; (b) how the models can be used to assess the impact of cold chain point storage capacities, transportation vehicle capacities, and manufacturer capacities on the optimal vaccine distribution pattern; and (c) the impact of vaccine efficacies and associated costs such as ordering and transportation costs on the vaccine selection decision informed by the model. We then demonstrate how robust optimization versions of the single vaccine model, with box and budgeted uncertainty sets, outperform the deterministic version under multiple levels of uncertainty in key model parameters. Finally, we also consider the computational expense of the framework for realistic problem instances, and suggest multiple preprocessing techniques to reduce computational runtimes. Our study presents public health authorities and other stakeholders with a vaccine distribution and capacity planning tool for multi-tier cold chain networks.

Energy reduction of stochastic time-constrained robot stations

This paper looks at the problem of reducing the energy use of robot movements in a robot station with stochastic execution times, while keeping the productivity of the station. The problem is formulated as a stochastic optimization problem, that constrains the makespan of the station to meet a deadline with a high probability. The energy use of the station is a function of the execution times of the robot operations, and the goal is to reduce this energy use by finding the optimal execution times and operation order. A theoretical motivation to why the stochastic variables in the problem, under some conditions, can be approximated as independent and normally distributed is presented, together with a derivation of the max function of stochastic variables. This allows the stochastic optimization problem to be approximated with a deterministic version, that can be solved with a commercial solver. The accuracy of the deterministic approximation is evaluated on multiple numerical examples, which show that the method successfully reduces the energy use, while the deadlines of the stations are met with high probabilities.

Squeeziness for non-deterministic systems

Failed Error Propagation greatly reduces the effectiveness of Software Testing by masking faults present in the code. This situation happens when the System Under Test executes a faulty statement, the state of the system is affected by this fault, but the expected output is observed. Therefore, it is a must to assess its impact in the testing process. Squeeziness has been shown to be a useful measure to assess the likelihood of fault masking in deterministic systems. The main goal of this paper is to define a new Squeeziness notion that can be used in a scenario where we may have non-deterministic behaviours. The new notion should be a conservative extension of the previous one. In addition, it would be necessary to evaluate whether the new notion appropriately estimates the likelihood that a component of a system introduces Failed Error Propagation. We defined our black-box scenario where non-deterministic behaviours might appear. Next, we presented a new Squeeziness notion that can be used in this scenario. Finally, we carried out different experiments to evaluate the usefulness of our proposal as an appropriate estimation of the likelihood of Failed Error Propagation. We found a high correlation between our new Squeeziness notion and the likelihood of Failed Error Propagation in non-deterministic systems. We also found that the extra computation time with respect to the deterministic version of Squeeziness was negligible. Our new Squeeziness notion is a good measure to estimate the likelihood of Failed Error Propagation being introduced by a component of a system (potentially) showing non-deterministic behaviours. Since it is a conservative extension of the original notion and the extra computation time needed to compute it, with respect to the time needed to compute the former notion, is very small, we conclude that the new notion can be safely used to assess the likelihood of fault masking in deterministic systems.

An exploratory assessment of the Bering-Chukchi-Beaufort Seas stock of bowhead whales using a stochastic population dynamics model

The stochastic population dynamics model used by Aboriginal Whaling Management Procedure developers is revised to correct weaknesses related to uncertainty parameterisation and replacement yield estimation. Two variants of this model, along with the standard deterministic version, are used to assess the Bering-Chukchi-Beaufort Seas stock of bowhead whales. The variants differ with respect to the magnitude and complexity of the stochastic variation they introduce into natural mortality and birth/calf survival processes. An allowable catch statistic, E (Q0), is defined for appropriate use with stochastic model assessments. Using the same assessment methods, likelihood and priors as IWC (1999a), 5th percentiles of E (Q0) were found to be 117, 106 and 91 for the deterministic, simpler stochastic and extreme stochastic models, respectively. Bayes factor results show that there is no evidence suggesting that either stochastic model should be favoured over any simpler alternative, and the deterministic model yielded the best fit overall. The E (Q0) estimates confirm and strengthen past IWC Scientific Committee inference that under current bowhead subsistence hunting levels the stock should continue to increase towards stabilisation above its maximum sustainable yield level.

Stochastic SEIR epidemic models with virus mutation and logistic growth of susceptible populations

This article introduces a kind of stochastic SEIR models containing virus mutation and logistic growth of susceptible populations, whose deterministic versions have a positively invariant set and globally asymptotically stable equilibrium points. For these stochastic SEIR epidemic models, we prove they have a unique global positive solution, and also obtain sufficient conditions respectively for survival and extinction of the infectious disease. Eventually, we validate our theoretical findings using numerical simulations.

Deterministic quantum search with adjustable parameters: Implementations and applications

Grover's algorithm provides a quadratic speedup over classical algorithms to search for marked elements in an unstructured database. The original algorithm is probabilistic, and there are several schemes to achieve the deterministic version, by using the generalized Grover's iteration G(α,β):=Sr(β)So(α). However, in all the existing schemes the value range of α and β is limited; for instance, in the three early schemes they are determined by the proportion of marked states M/N. In this paper, we break through this limitation by presenting a search framework with adjustable parameters, which allows α or β to be arbitrarily given. The significance of the framework lies not only in the expansion of mathematical form, but also in its application value, as we present two disparate problems which we are able to solve deterministically using the proposed framework, whereas previous schemes are ineffective.

Solving the stochastic team orienteering problem: comparing simheuristics with the sample average approximation method

AbstractThe team orienteering problem (TOP) is an NP‐hard optimization problem with an increasing number of potential applications in smart cities, humanitarian logistics, wildfire surveillance, etc. In the TOP, a fixed fleet of vehicles is employed to obtain rewards by visiting nodes in a network. All vehicles share common origin and destination locations. Since each vehicle has a limitation in time or traveling distance, not all nodes in the network can be visited. Hence, the goal is focused on the maximization of the collected reward, taking into account the aforementioned constraints. Most of the existing literature on the TOP focuses on its deterministic version, where rewards and travel times are assumed to be predefined values. This paper focuses on a more realistic TOP version, where travel times are modeled as random variables, which introduces reliability issues in the solutions due to the route‐length constraint. In order to deal with these complexities, we propose a simheuristic algorithm that hybridizes biased‐randomized heuristics with a variable neighborhood search and MCS. To test the quality of the solutions generated by the proposed simheuristic approach, we employ the well‐known sample average approximation (SAA) method, as well as a combination model that hybridizes the metaheuristic used in the simheuristic approach with the SAA algorithm. The results show that our proposed simheuristic outperforms the SAA and the hybrid model both on the objective function values and computational time.

A stochastic optimization algorithm for the supply vessel planning problem under uncertain demand and uncertain weather conditions

The Supply Vessel Planning Problem (SVPP) with stochastic demands and uncertain weather conditions is a transportation problem occurring in offshore oil and gas logistics. A fleet of supply vessels based at an onshore depot delivers supplies to a set of offshore oil platforms in a weekly sailing schedule. However, the schedules are frequently disrupted due to adverse weather conditions and uncertain demand for cargo from the oil platforms. The two sources of uncertainty are generally addressed in separate, most often through the use of two-phased methods, where simulation is combined with an optimization algorithm. The most common approach to incorporate robustness in the constructed schedules is to use a subjective penalized cost for non-robust voyages, with explicit modelling of recourse actions. In contrast, this paper proposes a two-stage stochastic programming algorithm accounting for both uncertain demand and uncertain weather conditions, allowing for the incorporation of the cost of recourse in the objective function. The cost of each solution is approximated through the use of discrete event simulation within a genetic algorithm. For the tested problem instances, the potential benefit from solving the stochastic program over solving the corresponding deterministic version leads to average relative annual cost savings of approximately 12%.

Deterministic and stochastic phase-field modeling of anisotropic brittle fracture

We investigate variational phase-field formulations of anisotropic brittle fracture to model zigzag crack patterns in cubic materials. Our objective is twofold: (i) to analytically derive and numerically test the fundamental behavioral aspects predicted by the two main available fourth-order models, and to guide the calibration of their unknown parameters; (ii) motivated by the pronounced non-uniqueness of the phase-field solution in the anisotropic case and uncertainties in the material model, to transition from a deterministic to a stochastic model by introducing a material-related random field in the anisotropic phase-field energy functional. We employ Monte Carlo, randomized quasi-Monte Carlo and stochastic spectral methods to estimate statistical moments of the phase-field variable. The two fourth-order phase-field models are shown to predict a very different response both in their deterministic and stochastic versions. For either modeling choice, we believe that the stochastic approach, which captures several possible zigzag crack paths, holds significant promise to enable meaningful predictions of anisotropic fracture with phase-field models.

An ILP model for stochastic placement of [formula omitted]PMUs with limited voltage and current channels in a reconfigurable distribution network

Micro phasor measurement units (μPMUs) play a major role in the evolution of distribution networks toward the smart grids by providing precise and synchronized measurements. In this study, an integer linear programming (ILP) model for stochastic optimal μPMU placement (SOMPP) problem is formulated in order that the network would remain observable following a network reconfiguration. Furthermore, the current measurement channels are considered limited and it is also extended to voltage measurement channels, in order to be employed in cases that measurement channels are insufficient (e.g. three-phase networks). The uncertain quantities including the variability of load demands and the unreliability of lines are characterize by proper probability distribution functions (PDFs) instead of constant values, and employed in a Monte Carlo simulation (MCS) process. The most probable scenarios are obtained by a scenario reduction method, and the most probable topologies within each scenario are identified by multiple execution of reconfiguration for the reduced scenarios using the genetic algorithm (GA). Then the SOMPP model is solved for each reduced scenario using the concept of equivalent network that is also introduced in present work. A formulation for the deterministic form of the problem is also developed in order to compare the results of the SOMPP model with the deterministic one, by which the observability of all possible topologies is guaranteed with a limited number of channels. Furthermore, using the concept of voltage channels limit, the formulation of the SOMPP model is extended to include the unbalanced networks. The simulations are performed on 33-bus and 240-bus distribution networks. The numerical results revealed the relative superiority of the SOMPP model over the deterministic version and previous works in terms of decreasing the required number of μPMUs. Moreover, the effectiveness of the model in handling the random variables with unknown PDFs is also determined. The success of the reduced scenarios in representing the most general scenarios is also demonstrated by evaluating a large number of scenarios. The validation of the concept of equivalent network is also examined for multiple simulation cases.

A Formulation for the Stochastic Multi-Mode Resource-Constrained Project Scheduling Problem Solved with a Multi-Start Iterated Local Search Metaheuristic

This research introduces a stochastic version of the multi-mode resource-constrained project scheduling problem (MRCPSP) and its mathematical model. In addition, an efficient multi-start iterated local search (MS-ILS) algorithm, capable of solving the deterministic MRCPSP, is adapted to deal with the proposed stochastic version of the problem. For its deterministic version, the MRCPSP is an NP-hard optimization problem that has been widely studied. The problem deals with a trade-off between the amount of resources that each project activity requires and its duration. In the case of the proposed stochastic formulation, the execution times of the activities are uncertain. Benchmark instances of projects with 10, 20, 30, and 50 activities from well-known public libraries were adapted to create test instances. The adapted algorithm proved to be capable and efficient for solving the proposed stochastic problem.

A Multi criteria Decision Making based nonparametric method of fragments to disaggregate daily precipitation

This study addresses the issue of scant availability of sub-daily precipitation data by introducing a novel selection methodology named ‘Pattern Mapping’ (PM) within the existing Method of Fragments (MOF) framework for the disaggregation of daily precipitation. PM introduces multiple statistics based criterion in the selection of fragments thus enhancing accuracy of the selection process. The performance of deterministic and stochastic versions of PM-MOF is compared with three widely used disaggregation methods, namely K Nearest Neighbour based MOF (KNN-MOF), Bartlett-Lewis Rectangular Pulse model (using HYETOS software package) and Micro-Canonical Cascade (MCC) using reanalysis data at 14 global locations spanning various climatic zones. It is seen that the PM-MOF method has considerably lower percentage error in replicating the standard statistics (−50.2 to 50.3% for stochastic PM-MOF and −7.4 to 13.2% for deterministic PM-MOF) when compared to existing methods (KNN-MOF (−65.4 to 76.2%), HYETOS (−78.3 to 69.4%) and MCC (−63.1 to 72%)). The comparison of densities using skill score show that skill score of PM-MOF is 0.97 which is higher than other methods (0.94 for KNN-MOF, 0.83 for HYETOS and 0.85 for MCC). Control experiments designed around varying length of training data and stochastic/deterministic mode (of PM-MOF) ascertain the contribution of the novel methodological improvements in the superior performance of PM-MOF. The robustness of the methodology and geography independent performance of PM-MOF makes it a potent candidate for wider application in hydrological and climate studies.

Optimizing the Expected Maximum of Two Linear Functions Defined on a Multivariate Gaussian Distribution

We study stochastic optimization problems with objective function given by the expectation of the maximum of two linear functions defined on the component random variables of a multivariate Gaussian distribution. We consider random variables that are arbitrarily correlated, and we show that the problem is NP-hard even if the space of feasible solutions is unconstrained. We exploit a closed-form expression for the objective function from the literature to construct a cutting-plane algorithm for a highly nonlinear function, which includes the evaluation of the cumulative distribution function and probability density function of a standard normal random variable with decision variables as part of the arguments. To exhibit the model’s applicability, we consider two featured applications. The first is daily fantasy sports, where the algorithm identifies entries with positive returns during the 2018–2019 National Football League season. The second is a special case of makespan minimization for two parallel machines and jobs with uncertain processing times; for the special case where the jobs are uncorrelated, we prove the equivalence between its deterministic and stochastic versions and show that our algorithm can deliver a constant-factor approximation guarantee for the problem. The results of our computational evaluation involving synthetic and real-world data suggest that our discretization and upper bounding techniques lead to significant computational improvements and that the proposed algorithm outperforms suboptimal solutions approaches. History: Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.1259 .

The random two-sector RSS model: On discounted optimal growth without Ramsey-Euler conditions

This paper shows that the introduction of uncertainty in the two-sector model due to Robinson-Solow-Srinivasan (RSS) fully subdues the veritable plethora of the results that have been obtained the theory of deterministic optimal growth. Rather than an “anything goes” theorem that admits optimal cyclical and chaotic trajectories for the discrete-time deterministic version, we present results on the existence, uniqueness, asymptotic stability and comparative-static properties of the steady state measure. We relate the basic intuition of our result to global games, and note that the properties of value and policy functions we identify rely on “supermodularity” and “increasing-differences property” of Veinott-Topkis-Milgrom-Shannon. While of interest in themselves, our results highlight a methodological advance in developing the theory of optimal growth without Ramsey-Euler conditions.

Characterizing spontaneous Ca2+ local transients in OPCs using computational modeling

Spontaneous Ca2+ local transients (SCaLTs) in isolated oligodendrocyte precursor cells are largely regulated by the following fluxes: store-operated Ca2+ entry (SOCE), Na+/Ca2+ exchange, Ca2+ pumping through Ca2+-ATPases, and Ca2+-induced Ca2+-release through ryanodine receptors and inositol-trisphosphate receptors. However, the relative contributions of these fluxes in mediating fast spiking and the slow baseline oscillations seen in SCaLTs remain incompletely understood. Here, we developed a stochastic spatiotemporal computational model to simulate SCaLTs in a homogeneous medium with ionic flow between the extracellular, cytoplasmic, and endoplasmic-reticulum compartments. By simulating the model and plotting both the histograms of SCaLTs obtained experimentally and from the model as well as the standard deviation of inter-SCaLT intervals against inter-SCaLT interval averages of multiple model and experimental realizations, we revealed the following: (1) SCaLTs exhibit very similar characteristics between the two data sets, (2) they are mostly random, (3) they encode information in their frequency, and (4) their slow baseline oscillations could be due to the stochastic slow clustering of inositol-trisphosphate receptors (modeled as an Ornstein-Uhlenbeck noise process). Bifurcation analysis of a deterministic temporal version of the model showed that the contribution of fluxes to SCaLTs depends on the parameter regime and that the combination of excitability, stochasticity, and mixed-mode oscillations are responsible for irregular spiking and doublets in SCaLTs. Additionally, our results demonstrated that blocking each flux reduces SCaLTs’ frequency and that the reverse (forward) mode of Na+/Ca2+ exchange decreases (increases) SCaLTs. Taken together, these results provide a quantitative framework for SCaLT formation in oligodendrocyte precursor cells.