Quantization of Prior Probabilities for Hypothesis Testing

Bayesian hypothesis testing is investigated when the prior probabilities of the hypotheses, taken as a random vector, are quantized. Nearest neighbor and centroid conditions are derived using mean Bayes risk error as a distortion measure for quantization. A high-resolution approximation to the distortion-rate function is also obtained. Human decision making in segregated populations is studied assuming Bayesian hypothesis testing with quantized priors

Real-time decoding of question-and-answer speech dialogue using human cortical activity.

Natural communication often occurs in dialogue, differentially engaging auditory and sensorimotor brain regions during listening and speaking. However, previous attempts to decode speech directly from the human brain typically consider listening or speaking tasks in isolation. Here, human participants listened to questions and responded aloud with answers while we used high-density electrocorticography (ECoG) recordings to detect when they heard or said an utterance and to then decode the utterance's identity. Because certain answers were only plausible responses to certain questions, we could dynamically update the prior probabilities of each answer using the decoded question likelihoods as context. We decode produced and perceived utterances with accuracy rates as high as 61% and 76%, respectively (chance is 7% and 20%). Contextual integration of decoded question likelihoods significantly improves answer decoding. These results demonstrate real-time decoding of speech in an interactive, conversational setting, which has important implications for patients who are unable to communicate

Minimum mean bayes risk error quantization of prior probabilities

Bayesian hypothesis testing is investigated when the prior probabili-ties of the hypotheses, taken as a random vector, must be quantized. Nearest neighbor and centroid conditions for quantizer optimality are derived using mean Bayes risk error as a distortion measure. An example of optimal quantization for hypothesis testing is provided. Human decision making is briefly studied assuming quantized prior Bayesian hypothesis testing; this model explains several experimen-tal findings. Index Terms — quantization, categorization, Bayesian hypoth-esis testing, signal detection, Bayes risk erro

Aesthetics of musical timing : Culture and expertise affect preferences for isochrony but not synchrony

Este es un artículo de acceso abierto bajo la licencia CC BY.Expressive communication in the arts often involves deviations from stylistic norms, which can increase the aesthetic evaluation of an artwork or performance. The detection and appreciation of such expressive deviations may be amplified by cultural familiarity and expertise of the observer. One form of expressive communication in music is playing “out of time,” including asynchrony (deviations from synchrony between different instruments) and non-isochrony (deviations from equal spacing between subsequent note onsets or metric units). As previous research has provided somewhat conflicting perspectives on the degree to which deviations from synchrony and isochrony are aesthetically relevant, we aimed to shed new light on this topic by accounting for the effects of listeners' cultural familiarity and expertise. We manipulated (a)synchrony and (non-)isochrony separately in excerpts from three groove-based musical styles (jazz, candombe, and jembe), using timings from real performances. We recruited musician and non-musician participants (N = 176) from three countries (UK, Uruguay, and Mali), selected to vary in their prior experience of hearing and performing these three styles. Participants completed both an aesthetic preference rating task and a perceptual discrimination task for the stimuli. Our results indicate an overall preference toward synchrony in these styles, but culturally contingent, expertise-dependent preferences for deviations from isochrony. This suggests that temporal processing relies on mechanisms that vary in their dependence on low-level and high-level perception, and emphasizes the role of cultural familiarity and expertise in shaping aesthetic preferences

Composite Score for Anomaly Detection in Imbalanced Real-World Industrial Dataset

In recent years, the industrial sector has evolved towards its fourth revolution. The quality control domain is particularly interested in advanced machine learning for computer vision anomaly detection. Nevertheless, several challenges have to be faced, including imbalanced datasets, the image complexity, and the zero-false-negative (ZFN) constraint to guarantee the high-quality requirement. This paper illustrates a use case for an industrial partner, where Printed Circuit Board Assembly (PCBA) images are first reconstructed with a Vector Quantized Generative Adversarial Network (VQGAN) trained on normal products. Then, several multi-level metrics are extracted on a few normal and abnormal images, highlighting anomalies through reconstruction differences. Finally, a classifer is trained to build a composite anomaly score thanks to the metrics extracted. This three-step approach is performed on the public MVTec-AD datasets and on the partner PCBA dataset, where it achieves a regular accuracy of 95.69% and 87.93% under the ZFN constraint