Stoichiometry determination of chalcogenide superlattices by means of X-ray diffraction and its limits

In this paper we explore the potential of stoichiometry determination for chalcogenide superlattices, promising candidates for next-generation phase-change memory, via X-ray diffraction. To this end, a set of epitaxial GeTe/Sb2Te3 superlattice samples with varying layer thicknesses is sputter-deposited. Kinematical scattering theory is employed to link the average composition with the diffraction features. The observed lattice constants of the superlattice reference unit cell follow Vegard's law, enabling a straight-forward and non-destructive stoichiometry determination.Comment: physica status solidi (RRL) - Rapid Research Letters (2019

Learning not to learn: Nature versus nurture in silico

Animals are equipped with a rich innate repertoire of sensory, behavioral and motor skills, which allows them to interact with the world immediately after birth. At the same time, many behaviors are highly adaptive and can be tailored to specific environments by means of learning. In this work, we use mathematical analysis and the framework of meta-learning (or 'learning to learn') to answer when it is beneficial to learn such an adaptive strategy and when to hard-code a heuristic behavior. We find that the interplay of ecological uncertainty, task complexity and the agents' lifetime has crucial effects on the meta-learned amortized Bayesian inference performed by an agent. There exist two regimes: One in which meta-learning yields a learning algorithm that implements task-dependent information-integration and a second regime in which meta-learning imprints a heuristic or 'hard-coded' behavior. Further analysis reveals that non-adaptive behaviors are not only optimal for aspects of the environment that are stable across individuals, but also in situations where an adaptation to the environment would in fact be highly beneficial, but could not be done quickly enough to be exploited within the remaining lifetime. Hard-coded behaviors should hence not only be those that always work, but also those that are too complex to be learned within a reasonable time frame

Lottery Tickets in Evolutionary Optimization: On Sparse Backpropagation-Free Trainability

Is the lottery ticket phenomenon an idiosyncrasy of gradient-based training or does it generalize to evolutionary optimization? In this paper we establish the existence of highly sparse trainable initializations for evolution strategies (ES) and characterize qualitative differences compared to gradient descent (GD)-based sparse training. We introduce a novel signal-to-noise iterative pruning procedure, which incorporates loss curvature information into the network pruning step. This can enable the discovery of even sparser trainable network initializations when using black-box evolution as compared to GD-based optimization. Furthermore, we find that these initializations encode an inductive bias, which transfers across different ES, related tasks and even to GD-based training. Finally, we compare the local optima resulting from the different optimization paradigms and sparsity levels. In contrast to GD, ES explore diverse and flat local optima and do not preserve linear mode connectivity across sparsity levels and independent runs. The results highlight qualitative differences between evolution and gradient-based learning dynamics, which can be uncovered by the study of iterative pruning procedures.Comment: 13 pages, 11 figures, International Conference on Machine Learning (ICML) 202

Disaggregation by State Inference A Probabilistic Framework For Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM), the problem of disaggregating whole home power measurements into single-appliance measurements, has received increasing attention from the academic community because of its energy saving potentials, however the majority of NILM approaches are either variants of event-based or event-less disaggregation. Event-based approaches are able to capture much information about the transient behavior of appliances but suffer from error-propagation problems whereas event-less approaches are lessprone to error-propagation problems but can only incorporate transient information to a small degree. On top of that inference techniques for event-less approaches are either computationally expensive, do not allow to trade off computational time for approximation accuracy or are prone to local minima. This work will contribute three-fold: first an automated way to infer ground truth from single appliance readings is introduced, second an augmentation for event-less approaches is introduced that allows to capture side-channel as well as transient information of change-points, third an inference technique is presented that allows to control the trade-off between computational expense and accuracy. Ultimately, this work will try to put the NILM problem into a probabilistic framework that allows for closing feedback loops between the different stages of event-based NILM approaches, effectively bridging event-less and event-based approaches. The performance of the inference technique is evaluated on a synthetic data set and compared to state-of-the-art approaches. Then the hypothesis that incorporating transient information increases the disaggregation performance is tested on a real-life data set

On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning

The lottery ticket hypothesis questions the role of overparameterization in supervised deep learning. But how is the performance of winning lottery tickets affected by the distributional shift inherent to reinforcement learning problems? In this work, we address this question by comparing sparse agents who have to address the non-stationarity of the exploration-exploitation problem with supervised agents trained to imitate an expert. We show that feed-forward networks trained with behavioural cloning compared to reinforcement learning can be pruned to higher levels of sparsity without performance degradation. This suggests that in order to solve the RL-specific distributional shift agents require more degrees of freedom. Using a set of carefully designed baseline conditions, we find that the majority of the lottery ticket effect in both learning paradigms can be attributed to the identified mask rather than the weight initialization. The input layer mask selectively prunes entire input dimensions that turn out to be irrelevant for the task at hand. At a moderate level of sparsity the mask identified by iterative magnitude pruning yields minimal task-relevant representations, i.e., an interpretable inductive bias. Finally, we propose a simple initialization rescaling which promotes the robust identification of sparse task representations in low-dimensional control tasks.Comment: 20 pages, 15 figure

Modeling Appropriate Language in Argumentation

Online discussion moderators must make ad-hoc decisions about whether the contributions of discussion participants are appropriate or should be removed to maintain civility. Existing research on offensive language and the resulting tools cover only one aspect among many involved in such decisions. The question of what is considered appropriate in a controversial discussion has not yet been systematically addressed. In this paper, we operationalize appropriate language in argumentation for the first time. In particular, we model appropriateness through the absence of flaws, grounded in research on argument quality assessment, especially in aspects from rhetoric. From these, we derive a new taxonomy of 14 dimensions that determine inappropriate language in online discussions. Building on three argument quality corpora, we then create a corpus of 2191 arguments annotated for the 14 dimensions. Empirical analyses support that the taxonomy covers the concept of appropriateness comprehensively, showing several plausible correlations with argument quality dimensions. Moreover, results of baseline approaches to assessing appropriateness suggest that all dimensions can be modeled computationally on the corpus