Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data

Machine-learning algorithms have gained popularity in recent years in the field of ecological modeling due to their promising results in predictive performance of classification problems. While the application of such algorithms has been highly simplified in the last years due to their well-documented integration in commonly used statistical programming languages such as R, there are several practical challenges in the field of ecological modeling related to unbiased performance estimation, optimization of algorithms using hyperparameter tuning and spatial autocorrelation. We address these issues in the comparison of several widely used machine-learning algorithms such as Boosted Regression Trees (BRT), k-Nearest Neighbor (WKNN), Random Forest (RF) and Support Vector Machine (SVM) to traditional parametric algorithms such as logistic regression (GLM) and semi-parametric ones like generalized additive models (GAM). Different nested cross-validation methods including hyperparameter tuning methods are used to evaluate model performances with the aim to receive bias-reduced performance estimates. As a case study the spatial distribution of forest disease Diplodia sapinea in the Basque Country in Spain is investigated using common environmental variables such as temperature, precipitation, soil or lithology as predictors. Results show that GAM and RF (mean AUROC estimates 0.708 and 0.699) outperform all other methods in predictive accuracy. The effect of hyperparameter tuning saturates at around 50 iterations for this data set. The AUROC differences between the bias-reduced (spatial cross-validation) and overoptimistic (non-spatial cross-validation) performance estimates of the GAM and RF are 0.167 (24%) and 0.213 (30%), respectively. It is recommended to also use spatial partitioning for cross-validation hyperparameter tuning of spatial data

Two-dimensional electron gas formation in undoped In[0.75]Ga[0.25]As/In[0.75]Al[0.25]As quantum wells

We report on the achievement of a two-dimensional electron gas in completely undoped In[0.75]Al[0.25]As/In[0.75]Ga[0.25]As metamorphic quantum wells. Using these structures we were able to reduce the carrier density, with respect to reported values in similar modulation-doped structures. We found experimentally that the electronic charge in the quantum well is likely due to a deep-level donor state in the In[0.75]Al[0.25]As barrier band gap, whose energy lies within the In[0.75]Ga[0.25]As/In[0.75]Al[0.25]As conduction band discontinuity. This result is further confirmed through a Poisson-Schroedinger simulation of the two-dimensional electron gas structure.Comment: 17 pages, 6 figures, to be published in J. Vac. Sci. Technol.

VERICA - Verification of Combined Attacks

Physical attacks, including passive Side-Channel Analysis and active Fault Injection Analysis, are considered among the most powerful threats against physical cryptographic implementations. These attacks are well known and research provides many specialized countermeasures to protect cryptographic implementations against them. Still, only a limited number of combined countermeasures, i.e., countermeasures that protect implementations against multiple attacks simultaneously, were proposed in the past. Due to increasing complexity and reciprocal effects, design of efficient and reliable combined countermeasures requires longstanding expertise in hardware design and security. With the help of formal security specifications and adversary models, automated verification can streamline development cycles, increase quality, and facilitate development of robust cryptographic implementations. In this work, we revise and refine formal security notions for combined protection mechanisms and specifically embed them in the context of hardware implementations. Based on this, we present the first automated verification framework that can verify physical security properties of hardware circuits with respect to combined physical attacks. To this end, we conduct several case studies to demonstrate the capabilities and advantages of our framework, analyzing secure building blocks (gadgets), S-boxes build from Toffoli gates, and the ParTI scheme. For the first time, we reveal security flaws in analyzed structures due to reciprocal effects, highlighting the importance of continuously integrating security verification into modern design and development cycles

CINI MINIS: Domain Isolation for Fault and Combined Security

Observation and manipulation of physical characteristics are well-known and powerful threats to cryptographic devices. While countermeasures against passive side-channel and active fault-injection attacks are well understood individually, combined attacks, i.e., the combination of fault injection and side-channel analysis, is a mostly unexplored area. Naturally, the complexity of analysis and secure construction increases with the sophistication of the adversary, making the combined scenario especially challenging. To tackle complexity, the side-channel community has converged on the construction of small building blocks, which maintain security properties even when composed. In this regard, Probe-Isolating Non-Interference (PINI) is a widely used notion for secure composition in the presence of side-channel attacks due to its efficiency and elegance. In this work, we transfer the core ideas behind PINI to the context of fault and combined security and, from that, construct the first trivially composable gadgets in the presence of a combined adversary

Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data

Machine-learning algorithms have gained popularity in recent years in the field of ecological modeling due to their promising results in predictive performance of classification problems. While the application of such algorithms has been highly simplified in the last years due to their well-documented integration in commonly used statistical programming languages such as R, there are several practical challenges in the field of ecological modeling related to unbiased performance estimation, optimization of algorithms using hyperparameter tuning and spatial autocorrelation. We address these issues in the comparison of several widely used machine-learning algorithms such as Boosted Regression Trees (BRT), kNearest Neighbor (WKNN), Random Forest (RF) and Support Vector Machine (SVM) to traditional parametric algorithms such as logistic regression (GLM) and semi-parametric ones like Generalized Additive Models (GAM). Different nested cross-validation methods including hyperparameter tuning methods are used to evaluate model performances with the aim to receive bias-reduced performance estimates. As a case study the spatial distribution of forest disease (Diplodia sapinea) in the Basque Country in Spain is investigated using common environmental variables such as temperature, precipitation, soil or lithology as predictors. Results show that GAM and Random Forest (RF) (mean AUROC estimates 0.708 and 0.699) outperform all other methods in predictive accuracy. The effect of hyperparameter tuning saturates at around 50 iterations for this data set. The AUROC differences between the bias-reduced (spatial cross-validation) and overoptimistic (non-spatial cross-validation) performance estimates of the GAM and RF are 0.167 (24%) and 0.213 (30%), respectively. It is recommended to also use spatial partitioning for cross-validation hyperparameter tuning of spatial data. The models developed in this study enhance the detection of Diplodia sapinea in the Basque Country compared to previous studies

The intensity correlation function of "blinking" quantum systems

Explicit expressions are determined for the photon correlation function of ``blinking'' quantum systems, i.e. systems with different types of fluorescent periods. These expressions can be used for a fit to experimental data and for obtaining system parameters therefrom. For two dipole-dipole interacting $V$ systems the dependence on the dipole coupling constant is explicitly given and shown to be particularly pronounced if the strong driving is reduced. We propose to use this for an experimental verification of the dipole-dipole interaction.Comment: 12 pages, 5 figures, uses iopams.st