490 research outputs found
Aqueous ozonation of furans:Kinetics and transformation mechanisms leading to the formation of α,β-unsaturated dicarbonyl compounds
Despite the widespread occurrence of furan moieties in synthetic and natural compounds, their fate in aqueous ozonation has not been investigated in detail. Reaction rate constants of seven commonly used furans with ozone were measured and ranged from kO3 = 8.5 × 104 to 3.2 × 106 M−1 s−1, depending on the type and position of furan ring substituents. Transformation product analysis of the reaction of furans with ozone focusing on the formation of toxic organic electrophiles using a novel amino acid reactivity assay revealed the formation of α,β-unsaturated dicarbonyl compounds, 2-butene-1,4-dial (BDA) and its substituted analogues (BDA-Rs). Their formation can be attributed to ozone attack at the reactive α-C position leading to furan ring opening. The molar yields of α,β-unsaturated dicarbonyl compounds varied with the applied ozone concentration reaching maximum values of 7% for 2-furoic acid. The identified α,β-unsaturated dicarbonyls are well-known toxicophores that are also formed by enzymatic oxidation of furans in the human body. In addition to providing data on kinetics, transformation product analysis and proposed reaction mechanisms for the ozonation of furans, this study raises concern about the presence of α,β-unsaturated dicarbonyl compounds in water treatment and the resulting effects on human and environmental health.</p
Oculomotoric Biometric Identification under the Influence of Alcohol and Fatigue
Patterns of micro- and macro-movements of the eyes are highly individual and can serve as a biometric characteristic. It is also known that both alcohol inebriation and fatigue can reduce saccadic velocity and accuracy. This prompts the question of whether changes of gaze patterns caused by alcohol consumption and fatigue impact the accuracy of oculomotoric biometric identification. We collect an eye tracking data set from 66 participants in sober, fatigued and alcohol-intoxicated states. We find that after enrollment in a rested and sober state, identity verification based on a deep neural embedding of gaze sequences is significantly less accurate when probe sequences are taken in either an inebriated or a fatigued state. Moreover, we find that fatigue and intoxication appear to randomize gaze patterns: when the model is fine-tuned for invariance with respect to inebriation and fatigue, and even when it is trained exclusively on inebriated training person, the model still performs significantly better for sober than for sleep-deprived or intoxicated subjects
Macrophage-Derived Biomarkers of Idiopathic Pulmonary Fibrosis
Idiopathic pulmonary fibrosis (IPF) is a severe, rapidly progressive diffuse lung disease. Several pathogenetic mechanisms have been hypothesized on the basis of the fibrotic lung damage occurring in this disease, and a potential profibrotic role of activated alveolar macrophages and their mediators in the pathogenesis of IPF was recently documented. This paper focuses on recent literature on potential biomarkers of IPF derived from activated alveolar macrophages. Biomarker discovery and clinical application are a recent topic of interest in the field of interstitial lung diseases (ILDs). Cytokines, CC-chemokines, and other macrophage-produced mediators are the most promising prognostic biomarkers. Many molecules have been proposed in the literature as potential biomarker of IPF; however, a rigorous validation is needed to confirm their clinical utility
Pre-Trained Language Models Augmented with Synthetic Scanpaths for Natural Language Understanding
Human gaze data offer cognitive information that reflects natural language
comprehension. Indeed, augmenting language models with human scanpaths has
proven beneficial for a range of NLP tasks, including language understanding.
However, the applicability of this approach is hampered because the abundance
of text corpora is contrasted by a scarcity of gaze data. Although models for
the generation of human-like scanpaths during reading have been developed, the
potential of synthetic gaze data across NLP tasks remains largely unexplored.
We develop a model that integrates synthetic scanpath generation with a
scanpath-augmented language model, eliminating the need for human gaze data.
Since the model's error gradient can be propagated throughout all parts of the
model, the scanpath generator can be fine-tuned to downstream tasks. We find
that the proposed model not only outperforms the underlying language model, but
achieves a performance that is comparable to a language model augmented with
real human gaze data. Our code is publicly available.Comment: Pre-print for EMNLP 202
Fairness in Oculomotoric Biometric Identification
Gaze patterns are known to be highly individual, and therefore eye movements can serve as a biometric characteristic. We explore aspects of the fairness of biometric identification based on gaze patterns. We find that while oculomotoric identification does not favor any particular gender and does not significantly favor by age range, it is unfair with respect to ethnicity. Moreover, fairness concerning ethnicity cannot be achieved by balancing the training data for the best-performing model
Selection of XAI Methods Matters: Evaluation of Feature Attribution Methods for Oculomotoric Biometric Identification
Substantial advances in oculomotoric biometric identification have been made due to deep neural networks processing non-aggregated time series data that replace methods processing theoretically motivated engineered features. However, interpretability of deep neural networks is not trivial and needs to be thoroughly investigated for future eye tracking applications. Especially in medical or legal applications explanations can be required to be provided alongside predictions. In this work, we apply several attribution methods to a state of the art model for eye movement-based biometric identification. To asses the quality of the generated attributions, this work is focused on the quantitative evaluation of a range of established metrics. We find that Layer-wise Relevance Propagation generates the least complex attributions, while DeepLIFT attributions are the most faithful. Due to the absence of a correlation between attributions of these two methods we advocate to consider both methods for their potentially complementary attributions
Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Reading
Eye movements during reading offer insights into both the reader's cognitive
processes and the characteristics of the text that is being read. Hence, the
analysis of scanpaths in reading have attracted increasing attention across
fields, ranging from cognitive science over linguistics to computer science. In
particular, eye-tracking-while-reading data has been argued to bear the
potential to make machine-learning-based language models exhibit a more
human-like linguistic behavior. However, one of the main challenges in modeling
human scanpaths in reading is their dual-sequence nature: the words are ordered
following the grammatical rules of the language, whereas the fixations are
chronologically ordered. As humans do not strictly read from left-to-right, but
rather skip or refixate words and regress to previous words, the alignment of
the linguistic and the temporal sequence is non-trivial. In this paper, we
develop Eyettention, the first dual-sequence model that simultaneously
processes the sequence of words and the chronological sequence of fixations.
The alignment of the two sequences is achieved by a cross-sequence attention
mechanism. We show that Eyettention outperforms state-of-the-art models in
predicting scanpaths. We provide an extensive within- and across-data set
evaluation on different languages. An ablation study and qualitative analysis
support an in-depth understanding of the model's behavior
Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models
Recent work in XAI for eye tracking data has evaluated the suitability of
feature attribution methods to explain the output of deep neural sequence
models for the task of oculomotric biometric identification. These methods
provide saliency maps to highlight important input features of a specific eye
gaze sequence. However, to date, its localization analysis has been lacking a
quantitative approach across entire datasets. In this work, we employ
established gaze event detection algorithms for fixations and saccades and
quantitatively evaluate the impact of these events by determining their concept
influence. Input features that belong to saccades are shown to be substantially
more important than features that belong to fixations. By dissecting saccade
events into sub-events, we are able to show that gaze samples that are close to
the saccadic peak velocity are most influential. We further investigate the
effect of event properties like saccadic amplitude or fixational dispersion on
the resulting concept influence.Comment: Preprint for ETRA '23: 2023 Symposium on Eye Tracking Research and
Application
ScanDL: A Diffusion Model for Generating Synthetic Scanpaths on Texts
Eye movements in reading play a crucial role in psycholinguistic research
studying the cognitive mechanisms underlying human language processing. More
recently, the tight coupling between eye movements and cognition has also been
leveraged for language-related machine learning tasks such as the
interpretability, enhancement, and pre-training of language models, as well as
the inference of reader- and text-specific properties. However, scarcity of eye
movement data and its unavailability at application time poses a major
challenge for this line of research. Initially, this problem was tackled by
resorting to cognitive models for synthesizing eye movement data. However, for
the sole purpose of generating human-like scanpaths, purely data-driven
machine-learning-based methods have proven to be more suitable. Following
recent advances in adapting diffusion processes to discrete data, we propose
ScanDL, a novel discrete sequence-to-sequence diffusion model that generates
synthetic scanpaths on texts. By leveraging pre-trained word representations
and jointly embedding both the stimulus text and the fixation sequence, our
model captures multi-modal interactions between the two inputs. We evaluate
ScanDL within- and across-dataset and demonstrate that it significantly
outperforms state-of-the-art scanpath generation methods. Finally, we provide
an extensive psycholinguistic analysis that underlines the model's ability to
exhibit human-like reading behavior. Our implementation is made available at
https://github.com/DiLi-Lab/ScanDL.Comment: EMNLP 202
- …