Contributions in Merlin

In the realm of industrial anomaly detection, machine and deep learning models face a critical vulnerability to adversarial attacks. In this context, existing attack methodologies primarily target continuous features, often in the context of images, making them unsuitable for the categorical or discrete features prevalent in industrial systems. To fortify the cybersecurity of industrial environments, this paper introduces a groundbreaking adversarial attack approach tailored to the unique demands of these settings. Our novel technique enables the creation of targeted adversarial samples that are valid within the framework of supervised cyberattack detection models in industrial scenarios, preserving the consistency of discrete values and correcting cases where an adversarial sample transitions into a normal one. Our approach leverages the SHAP interpretability method to identify the most salient features for each sample. Subsequently, the Projected Gradient Descent technique is employed to perturb continuous features, ensuring adversarial sample generation. To handle categorical features for a specific adversarial sample, our method scrutinizes the closest sample within the normal training dataset and replicates its categorical feature values. Additionally, Decision Trees trained within a Random Forest are utilized to ensure that the resulting adversarial samples maintain the essential abnormal behavior required for detection. The validation of our proposal was conducted using the WADI dataset obtained from a water distribution plant, providing a realistic industrial context. During validation, we assessed the mean error and the total number of adversarial samples generated by our approach, comparing it with the original Projected Gradient Descent method and the Carlini & Wagner attack across various parameter configurations. Remarkably, our proposal consistently achieved the best trade-off between mean error and the number of generated adversarial samples, showcasing its superiority in safeguarding industrial systems.

We present a novel analysis of a competitive dynamic present on Ethereum known as "waiting games", where validators can use their distinct monopoly position in their assigned slots to delay block proposals in order to optimize returns through Maximal Extractable Value (MEV) payments, a type of incentive outside the Proof-of-Stake incentive scheme. However, this strategy risks block exclusion due to missed slots or potential orphaning. Our analysis reveals evidence that, although there are substantial incentives to undertaking the risks, validators are not capitalizing on waiting games, leaving potential profits unrealized. Moreover, we present an agent-based model to test the eventual consensus disruption caused by waiting games under different settings, arguing that such disruption only occurs with significant delay strategies. Ultimately, this research provides in-depth insights into Ethereum's waiting games, illuminating the trade-offs and potential profit opportunities for validators in this evolving blockchain landscape.

Return event studies generally involve several firms but there are also cases when only one firm is involved. This makes the relevant testing problems, abnormal return and cumulative abnormal return, more difficult since one cannot exploit the multitude of firms (by using a relevant central limit theorem, say) to design hypothesis tests. We propose a permutation test which is of nonparametric nature and more generally valid than the tests that have previously been proposed in the literature in this context. We address the question of the power of the test via a brief simulation study and also illustrate the method with two applications to real data.

We analyze how individual investors respond to inflation. We introduce a unique dataset containing information on local inflation and security portfolios of more than 2,000 clients of a German bank between 1920 and 1924, covering the German hyperinflation. We find that individual investors buy less (sell more) stocks when facing higher local inflation. This effect is more pronounced for less sophisticated investors. Moreover, we document a positive relation between local inflation and forgone returns following stock sales. Our findings are consistent with individual investors suffering from money illusion. Alternative explanations such as consumption needs are unlikely to drive our results.

Emotion regulation is important for psychological health and can be achieved by implementing various strategies. How one regulates emotions is critical for maximizing psychological health. Few studies, however, tested the psychological correlates of different emotion regulation strategies across multiple cultures. In a preregistered cross-cultural study (N = 3,960, 19 countries), conducted during the COVID-19 pandemic, we assessed associations between the use of seven emotion regulation strategies (situation selection, distraction, rumination, cognitive reappraisal, acceptance, expressive suppression, and emotional support seeking) and four indices of psychological health (life satisfaction, depressive symptoms, perceived stress, and loneliness). Model comparisons based on Bayesian information criteria provided support for cultural differences in 36% of associations, with very strong support for differences in 18% of associations. Strategies that were linked to worse psychological health in individualist countries (e.g., rumination, expressive suppression) were unrelated or linked to better psychological health in collectivist countries. Cultural differences in associations with psychological health were most prominent for expressive suppression and rumination and also found for distraction and acceptance. In addition, we found evidence for cultural similarities in 46% of associations between strategies and psychological health, but none of this evidence was very strong. Cultural similarities were most prominent in associations of psychological health with emotional support seeking. These findings highlight the importance of considering the cultural context to understand how individuals from diverse backgrounds manage unpleasant emotions. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Hippocampal activity linking past experiences and simulations of the future with current goals can play an important role in decision-making. The representation of information within the hippocampus may be especially critical in situations where one needs to overcome past rewarding experiences and exert self-control. Self-control success or failure may depend on how information is represented in the hippocampus and how effectively the representation process can be modified to achieve a specific goal. We test this hypothesis using representational similarity analyses of human (female/male) neuroimaging data during a dietary self-control task in which individuals must overcome taste temptations to choose healthy foods. We find that self-control is indeed associated with the way individuals represent taste information (valance) in the hippocampus and how taste representations there adapt to align with different goals/contexts. Importantly, individuals who were able to shift their hippocampal representations to a larger degree to align with the current motivation were better able to exert self-control when facing a dietary challenge. These results suggest an alternative or complementary neurobiological pathway leading to self-control success and indicate the need to update the classical view of self-control to continue to advance our understanding of its behavioral and neural underpinnings.

Using a novel sample covering 3783 US public firms from 2007 to 2020, we examine how negative media coverage of firm‐level environmental, social, and governance (ESG) practices affects a firm's debt choice. We find that firms with higher ESG reputation risk rely more on public bond than bank loan. The social and governance components, in particular, matter. Moreover, firms that receive more negative news coverage display a higher propensity to issue new bonds as opposed to securing new bank debt. Overall, our study presents empirical evidence on the relation between firm ESG reputation risk and debt financing.

The rapid entry of machine learning approaches in our dailyactivities and high-stakes domains demands transparency andscrutiny of their fairness and reliability. To help gauge ma-chine learning models’ robustness, research typically focuseson the massive datasets used for their deployment,e.g., cre-ating and maintaining documentation to understand theirorigin, process of development, and ethical considerations.However, data collection for AI is still typically a one-offpractice, and oftentimes datasets collected for a certain pur-pose or application are reused for a different problem. Addi-tionally, dataset annotations may not be representative overtime, contain ambiguous or erroneous annotations, or be un-able to generalize across domains. Recent research has shownthese practices might lead to unfair, biased, or inaccurate out-comes. We argue that data collection for AI should be per-formed in a responsible manner where the quality of the datais thoroughly scrutinized and measured through a systematicset of appropriate metrics. In this paper, we propose a Re-sponsible AI (RAI) methodology designed to guide the datacollection with a set of metrics for an iterative in-depth analy-sis of thefactors influencing the quality and reliabilityof thegenerated data. We propose a granular set of measurements toinform on theinternal reliabilityof a dataset and itsexternalstabilityover time. We validate our approach across nine ex-isting datasets and annotation tasks and four input modalities.This approach impacts theassessment of data robustnessusedin real world AI applications, where diversity of users andcontent is eminent. Furthermore, it deals with fairness andaccountability aspects in data collection by providing system-atic and transparent quality analysis for data collections.

Open-set face recognition is a scenario in which biometric systems have incomplete knowledge of all existing subjects. This arduous requirement must dismiss irrelevant faces and focus on subjects of interest only. For this reason, this work introduces a novel method that associates an ensemble of compact neural networks with data augmentation at the feature level and an entropy-based cost function. Deep neural networks pre-trained on large face datasets serve as the preliminary feature extraction module. The neural adapter ensemble consists of binary models trained on original feature representations along with negative synthetic mix-up embeddings, which are adequately handled by the designed open-set loss since they do not belong to any known identity. We carry out experiments on well-known LFW and IJB-C datasets where results show that the approach is capable of boosting closed and open-set identification accuracy.

This paper studies the contribution of analysts to the functioning and failure of the market for Initial Coin Offerings (ICOs). The assessments of freelancing analysts exhibit biases due to reciprocal interactions of analysts with ICO team members. Even favorably rated ICOs tend to fail raising some capital when a greater portion of their ratings reciprocate prior ratings. 90 days after listing on an exchange the market capitalization relative to the initial funds raised is smaller for tokens with more reciprocal ratings. These findings suggest that conflicts of interest help explain the failure of ICOs.