Understanding the limitations of self-supervised learning for tabular anomaly detection

While self-supervised learning has improved anomaly detection in computer vision and natural language processing, it is unclear whether tabular data can benefit from it. This paper explores the limitations of self-supervision for tabular anomaly detection. We conduct several experiments spanning various pretext tasks on 26 benchmark datasets to understand why this is the case. Our results confirm representations derived from self-supervision do not improve tabular anomaly detection performance compared to using the raw representations of the data. We show this is due to neural networks introducing irrelevant features, which reduces the effectiveness of anomaly detectors. However, we demonstrate that using a subspace of the neural network’s representation can recover performance

Warning: Humans Cannot Reliably Detect Speech Deepfakes

Speech deepfakes are artificial voices generated by machine learning models. Previous literature has highlighted deepfakes as one of the biggest security threats arising from progress in artificial intelligence due to their potential for misuse. However, studies investigating human detection capabilities are limited. We presented genuine and deepfake audio to n = 529 individuals and asked them to identify the deepfakes. We ran our experiments in English and Mandarin to understand if language affects detection performance and decision-making rationale. We found that detection capability is unreliable. Listeners only correctly spotted the deepfakes 73% of the time, and there was no difference in detectability between the two languages. Increasing listener awareness by providing examples of speech deepfakes only improves results slightly. As speech synthesis algorithms improve and become more realistic, we can expect the detection task to become harder. The difficulty of detecting speech deepfakes confirms their potential for misuse and signals that defenses against this threat are needed

Warning: Humans cannot reliably detect speech deepfakes

Speech deepfakes are artificial voices generated by machine learning models. Previous literature has highlighted deepfakes as one of the biggest security threats arising from progress in artificial intelligence due to their potential for misuse. However, studies investigating human detection capabilities are limited. We presented genuine and deepfake audio to n = 529 individuals and asked them to identify the deepfakes. We ran our experiments in English and Mandarin to understand if language affects detection performance and decision-making rationale. We found that detection capability is unreliable. Listeners only correctly spotted the deepfakes 73% of the time, and there was no difference in detectability between the two languages. Increasing listener awareness by providing examples of speech deepfakes only improves results slightly. As speech synthesis algorithms improve and become more realistic, we can expect the detection task to become harder. The difficulty of detecting speech deepfakes confirms their potential for misuse and signals that defenses against this threat are needed

Utilizing Service Learning in the Analytical Chemistry Classroom

Service learning has been incorporated into the Analytical Chemistry Laboratory to give students a real world sampling experience including both soil and water, alongside professionals in their fields. Analysis of the soil and water includes metals, suspended solids, phosphorus and nitrogen containing compounds requiring knowledge of several different instrumental and wet chemical techniques. Most educational experiences do not afford students the chance to see the real world applications of their classroom knowledge, but with the service learning aspects this deficiency has been resolved. In the soil experience, students provide homeowners from the Highland Park and South Wedge neighbors with lead analysis of their soil as well as written reports of those levels and information on removing or working with lead contaminated soil. For the water project, students are providing baseline analysis of nutrients and metals found in Buckland Creek for the Department of Environmental Services, Division of Pure Waters, which studies the effects of industrial expansion and human activity on water quality in Rochester. The analytical chemistry students further their experience in an advanced analytical chemistry course the following year by performing further analysis on the soil and water, but on a more independent level. They use their previous learned skills to gather water after rainfall and perform analysis back in the laboratory with no structured guidance. The class is also expanding to include a plant biology section, where students will test the affects on growth and safety of plants grown in leaded soil. This experiment will allow students to provide proof to homeowners as to which plants are healthy to eat and which can be used for phytoremediation. In addition to feeling like active contributors to the community, the students and homeowners have been interviewed and photographed for an article detailing lead contamination issues

How to Stay Curious while avoiding Noisy TVs using Aleatoric Uncertainty Estimation

When extrinsic rewards are sparse, artificial agents struggle to explore an environment. Curiosity, implemented as an intrinsic reward for prediction errors, can improve exploration but it is known to fail when faced with action-dependent noise sources (‘noisy TVs’). In an attempt to make exploring agents robust to noisy TVs, we present a simple solution: aleatoric mapping agents (AMAs). AMAs are a novel form of curiosity that explicitly ascertain which state transitions of the environment are unpredictable, even if those dynamics are induced by the actions of the agent. This is achieved by generating separate forward predictions for the mean and aleatoric uncertainty of future states, with the aim of reducing intrinsic rewards for those transitions that are unpredictable. We demonstrate that in a range of environments AMAs are able to circumvent actiondependent stochastic traps that immobilise conventional curiosity driven agents. Furthermore, we demonstrate empirically that other common exploration approaches—previously thought to be immune to agent-induced randomness—can be trapped by stochastic dynamics. Code to reproduce our experiments is provided

Verb preference effects in the sentence comprehension of fluent aphasic individuals

This investigation examined sentence processing of fluent aphasic subjects with varying severity levels. Subjects performed a cross-modal lexical decision task for transitive and intransitive verbs in preferred and non-preferred frameworks. Verb processing was measured by reaction times during on-line sentence comprehension. Reaction times to the cross-modal lexical decision (CMLD) task indicated that the subjects with aphasia were insensitive to preference information associated with the processing of verbs in sentences. Severity level did not alter the pattern observed regarding verb type and preferences

Genetic structure and admixture in sheep from terminal breeds in the United States

Selection for performance in diverse production settings has resulted in variation across sheep breeds worldwide. Although sheep are an important species to the United States, the current genetic relationship among many terminal sire breeds is not well characterized. Suffolk, Hampshire, Shropshire and Oxford (terminal) and Rambouillet (dual purpose) sheep (n = 248) sampled from different flocks were genotyped using the Applied Biosystems Axiom Ovine Genotyping Array (50K), and additional Shropshire sheep (n = 26) using the Illumina Ovine SNP50 BeadChip. Relationships were investigated by calculating observed heterozygosity, inbreeding coefficients, eigenvalues, pairwise Wright’s FST estimates and an identity by state matrix. The mean observed heterozygosity for each breed ranged from 0.30 to 0.35 and was consistent with data reported in other US and Australian sheep. Suffolk from two different regions of the United States (Midwest and West) clustered separately in eigenvalue plots and the rectangular cladogram. Further, divergence was detected between Suffolk from different regions with Wright’s FST estimate. Shropshire animals showed the greatest divergence from other terminal breeds in this study. Admixture between breeds was examined using ADMIXTURE, and based on cross-validation estimates, the best fit number of populations (clusters) was K = 6. The greatest admixture was observed within Hampshire, Suffolk, and Shropshire breeds. When plotting eigenvalues, US terminal breeds clustered separately in comparison with sheep from other locations of the world. Understanding the genetic relationships between terminal sire breeds in sheep will inform us about the potential applicability of markers derived in one breed to other breeds based on relatedness

Failure modes of protection layers produced by atomic layer deposition of amorphous TiO₂ on GaAs anodes

Amorphous titanium dioxide (a-TiO₂) films formed by atomic layer deposition can serve as protective coatings for semiconducting photoanodes in water-splitting cells using strongly alkaline aqueous electrolytes. Herein, we experimentally examine the mechanisms of failure for p⁺-GaAs anodes coated with a-TiO₂ films (GaAs/a-TiO₂). Galvanic displacement of exposed GaAs by Au allowed imaging of pinholes in the a-TiO₂ coatings, and enabled collection of quantitative and statistical data associated with pinhole defects. A combination of imaging, electrochemical measurements, and quantitative analyses of corrosion products indicated that extrinsic pinholes were present in the a-TiO₂ films before electrochemical operation. During electrochemical operation these pinholes led to pitting corrosion of the underlying GaAs substrate. The dominant source of pinholes was the presence of atmospheric particulate matter on the GaAs surface during deposition of the a-TiO₂ layer. The pinhole density decreased substantially when the thickness of the a-TiO₂ coating increased beyond 45 nm, and approached zero when the thickness of the film exceeded 112 nm. The density of pinholes in films thinner than 45 nm decreased when the a-TiO₂ coating was deposited in an environmentally controlled cleanroom. Pinhole-free GaAs/a-TiO₂ devices were also tested via chronoamperometry to quantify the rate of pinhole formation during electrochemistry. The time-to-failure increased with thickness, suggesting that the failure mechanism may involve diffusion or migration through the film. However, other mechanisms may also contribute to the degradation of thicker films (>112 nm). Nevertheless, as previously hypothesized, extrinsic pinhole defects formed during deposition and testing control the short-term protective performance of the a-TiO₂ film for GaAs anodes evolving O₂ from water

The Highest-Copy Repeats are Methylated in the Small Genome of the Early Divergent Vascular Plant Selaginella moellendorffii

Background The lycophyte Selaginella moellendorffii is a vascular plant that diverged from the fern/seed plant lineage at least 400 million years ago. Although genomic information for S. moellendorffii is starting to be produced, little is known about basic aspects of its molecular biology. In order to provide the first glimpse to the epigenetic landscape of this early divergent vascular plant, we used the methylation filtration technique. Methylation filtration genomic libraries select unmethylated DNA clones due to the presence of the methylation-dependent restriction endonuclease McrBC in the bacterial host. Results We conducted a characterization of the DNA methylation patterns of the S. moellendorffii genome by sequencing a set of S. moellendorffii shotgun genomic clones, along with a set of methylation filtered clones. Chloroplast DNA, which is typically unmethylated, was enriched in the filtered library relative to the shotgun library, showing that there is DNA methylation in the extremely small S. moellendorffii genome. The filtered library also showed enrichment in expressed and gene-like sequences, while the highest-copy repeats were largely under-represented in this library. These results show that genes and repeats are differentially methylated in the S.moellendorffii genome, as occurs in other plants studied. Conclusion Our results shed light on the genome methylation pattern in a member of a relatively unexplored plant lineage. The DNA methylation data reported here will help understanding the involvement of this epigenetic mark in fundamental biological processes, as well as the evolutionary aspects of epigenetics in land plants