7,372 research outputs found
Recommended from our members
Autonomic arousal and attentional orienting to visual threat are predicted by awareness
The rapid detection and evaluation of threat is of fundamental importance for survival. Theories suggest that this evolutionary pressure has driven functional adaptations in a specialized visual pathway that evaluates threat independently of conscious awareness. This is supported by evidence that threat-relevant stimuli rendered invisible by backward masking can induce physiological fear responses and modulate spatial attention. The validity of these findings has since been questioned by research using stringent, objective measures of awareness. Here, we use a modified continuous flash suppression paradigm to ask whether threatening images induce adaptive changes in autonomic arousal, attention, or perception when presented outside of awareness. In trials where stimuli broke suppression to become visible, threatening stimuli induced a significantly larger skin conductance response than nonthreatening stimuli and attracted spatial attention over scrambled images. However, these effects were eliminated in trials where observers were unaware of the stimuli. In addition, concurrent behavioral data provided no evidence that threatening images gained prioritized access to awareness. Taken together, our data suggest that the evaluation and spatial detection of visual threat are predicted by awareness
Recommended from our members
Fearful faces have a sensory advantage in the competition for awareness
Only a subset of visual signals give rise to a conscious percept. Threat signals, such as fearful faces, are particularly salient to human vision. Research suggests that fearful faces are evaluated without awareness and preferentially promoted to conscious perception. This agrees with evolutionary theories that posit a dedicated pathway specialized in processing threat-relevant signals. We propose an alternative explanation for this "fear advantage." Using psychophysical data from continuous flash suppression (CFS) and masking experiments, we demonstrate that awareness of facial expressions is predicted by effective contrast: the relationship between their Fourier spectrum and the contrast sensitivity function. Fearful faces have higher effective contrast than neutral expressions and this, not threat content, predicts their enhanced access to awareness. Importantly, our findings do not support the existence of a specialized mechanism that promotes threatening stimuli to awareness. Rather, our data suggest that evolutionary or learned adaptations have molded the fearful expression to exploit our general-purpose sensory mechanisms
Multilingual Training and Cross-lingual Adaptation on CTC-based Acoustic Model
Multilingual models for Automatic Speech Recognition (ASR) are attractive as
they have been shown to benefit from more training data, and better lend
themselves to adaptation to under-resourced languages. However, initialisation
from monolingual context-dependent models leads to an explosion of
context-dependent states. Connectionist Temporal Classification (CTC) is a
potential solution to this as it performs well with monophone labels.
We investigate multilingual CTC in the context of adaptation and
regularisation techniques that have been shown to be beneficial in more
conventional contexts. The multilingual model is trained to model a universal
International Phonetic Alphabet (IPA)-based phone set using the CTC loss
function. Learning Hidden Unit Contribution (LHUC) is investigated to perform
language adaptive training. In addition, dropout during cross-lingual
adaptation is also studied and tested in order to mitigate the overfitting
problem.
Experiments show that the performance of the universal phoneme-based CTC
system can be improved by applying LHUC and it is extensible to new phonemes
during cross-lingual adaptation. Updating all the parameters shows consistent
improvement on limited data. Applying dropout during adaptation can further
improve the system and achieve competitive performance with Deep Neural Network
/ Hidden Markov Model (DNN/HMM) systems on limited data
Activation and regioselectivity of five-membered cyclic thionocarbamates to nucleophilic attack
The cyclic thionocarbamate of alaninol undergoes nucleophilic attack by sulfur nucleophiles at 5-C to give 1-thiopropyl-2-amine derivatives when derivatised on nitrogen with a Boc group. Iodide under microwave conditions causes a rearrangement to the isomeric thiazolidinone, while "hard" nucleophiles react at the thione group to yield a variety of product types by subsequent C–N or C–O cleavage. X-ray crystallography studies showed that the N-Boc group reduces delocalisation of electron density from nitrogen into the thione group, and thus promotes activation of the ring to nucleophilic attack
Superior sperm competitors sire higher-quality young
The evolution of polyandry remains controversial. This is because, unlike males, in many cases multiple mating by females does not increase fecundity and inevitably involves some costs. As a result, a large number of indirect benefit models have been proposed to explain polyandry. One of these, the good sperm hypothesis, posits that high-quality males are better sperm competitors and sire higher-quality offspring. Hence, by mating multiply, females produce offspring of superior quality. Despite being potentially widely applicable across species, this idea has received little attention. In a laboratory experiment with yellow dung flies ( Scathophaga stercoraria ) we found that males that were more successful in sperm competition also had offspring that developed faster. There was no relationship between paternal success in sperm competition and the ability of offspring to survive post-emergence starvation. Since faster development times are likely to be advantageous in this species, our data provide some support for polyandry evolving as a means of producing higher-quality offspring via sperm competition
Ad Hoc Microphone Array Calibration: Euclidean Distance Matrix Completion Algorithm and Theoretical Guarantees
This paper addresses the problem of ad hoc microphone array calibration where
only partial information about the distances between microphones is available.
We construct a matrix consisting of the pairwise distances and propose to
estimate the missing entries based on a novel Euclidean distance matrix
completion algorithm by alternative low-rank matrix completion and projection
onto the Euclidean distance space. This approach confines the recovered matrix
to the EDM cone at each iteration of the matrix completion algorithm. The
theoretical guarantees of the calibration performance are obtained considering
the random and locally structured missing entries as well as the measurement
noise on the known distances. This study elucidates the links between the
calibration error and the number of microphones along with the noise level and
the ratio of missing distances. Thorough experiments on real data recordings
and simulated setups are conducted to demonstrate these theoretical insights. A
significant improvement is achieved by the proposed Euclidean distance matrix
completion algorithm over the state-of-the-art techniques for ad hoc microphone
array calibration.Comment: In Press, available online, August 1, 2014.
http://www.sciencedirect.com/science/article/pii/S0165168414003508, Signal
Processing, 201
Surrogate Gradient Spiking Neural Networks as Encoders for Large Vocabulary Continuous Speech Recognition
Compared to conventional artificial neurons that produce dense and
real-valued responses, biologically-inspired spiking neurons transmit sparse
and binary information, which can also lead to energy-efficient
implementations. Recent research has shown that spiking neural networks can be
trained like standard recurrent neural networks using the surrogate gradient
method. They have shown promising results on speech command recognition tasks.
Using the same technique, we show that they are scalable to large vocabulary
continuous speech recognition, where they are capable of replacing LSTMs in the
encoder with only minor loss of performance. This suggests that they may be
applicable to more involved sequence-to-sequence tasks. Moreover, in contrast
to their recurrent non-spiking counterparts, they show robustness to exploding
gradient problems without the need to use gates
A t-distribution based operator for enhancing out of distribution robustness of neural network classifiers
Neural Network (NN) classifiers can assign extreme probabilities to samples
that have not appeared during training (out-of-distribution samples) resulting
in erroneous and unreliable predictions. One of the causes for this unwanted
behaviour lies in the use of the standard softmax operator which pushes the
posterior probabilities to be either zero or unity hence failing to model
uncertainty. The statistical derivation of the softmax operator relies on the
assumption that the distributions of the latent variables for a given class are
Gaussian with known variance. However, it is possible to use different
assumptions in the same derivation and attain from other families of
distributions as well. This allows derivation of novel operators with more
favourable properties. Here, a novel operator is proposed that is derived using
-distributions which are capable of providing a better description of
uncertainty. It is shown that classifiers that adopt this novel operator can be
more robust to out of distribution samples, often outperforming NNs that use
the standard softmax operator. These enhancements can be reached with minimal
changes to the NN architecture.Comment: 5 pages, 5 figures, to be published in IEEE Signal Processing
Letters, reproducible code https://github.com/idiap/tsoftma
Can ChatGPT Detect Intent? Evaluating Large Language Models for Spoken Language Understanding
Recently, large pretrained language models have demonstrated strong language
understanding capabilities. This is particularly reflected in their zero-shot
and in-context learning abilities on downstream tasks through prompting. To
assess their impact on spoken language understanding (SLU), we evaluate several
such models like ChatGPT and OPT of different sizes on multiple benchmarks. We
verify the emergent ability unique to the largest models as they can reach
intent classification accuracy close to that of supervised models with zero or
few shots on various languages given oracle transcripts. By contrast, the
results for smaller models fitting a single GPU fall far behind. We note that
the error cases often arise from the annotation scheme of the dataset;
responses from ChatGPT are still reasonable. We show, however, that the model
is worse at slot filling, and its performance is sensitive to ASR errors,
suggesting serious challenges for the application of those textual models on
SLU.Comment: 6 pages, 2 figures; Accepted by Interspeech 202
- …