One of the key goals of brain-inspired computing is to develop methods that draw inspiration from computational neuroscience and cognitive science to build effective adaptive and efficient agents that are capable of intelligently interacting with their environment. Notably, brain-inspired computational research seeks to develop intelligent systems that are capable of circumventing the current limitations of modern-day approaches [1, 2], such as deep neural networks trained by the popular backpropagation of errors (or backprop)[3]. This goal is complementary to (and, to an extent, even a precursor to some elements of) the domain of neurorobotics [4, 5], which focuses on designing robotic devices that contain control systems based on or are inspired by principles of animal/human nervous systems and/or brain structures guided by the key premise that (neural) models are embodied in a body and an environment. While the gap between neurorobotics and many brain-inspired approaches largely is largely divided between focus on real-world hardware (the former) or software simulation (the latter), one pathway to bridging this gap might lie in developing powerful brain-inspired approaches that scale up to and operate robustly on problems that may ultimately be tackled by embodied robotic systems as well as using higher-quality, more realistic simulation platforms (as we do in this work). It is along this path that this work takes a step forward by developing a neurobiologically-grounded neural circuit that is used to craft a complete agent that can tackle extremely sparse reward learning control problems (tested on a more realistic, higher quality robotic system simulator), a problem that many robotic systems must ultimately face, much as humans and animals do in the real world. To build such building neural blocks and an agent system, we start from two neurocognitive theoretical foundations, predictive processing (or coding) and planning-as-inference. With respect to predictive coding, which views the brain as a type of hierarchical, pattern-creation engine [6] that engages in continual self-correction [7], we implement a fundamental circuit where each of its levels/regions are implemented by clusters of neurons that attempt to predict the state of other neural clusters/regions and adjust their synapses based on how different their predictions were from observed signals. This allows us to sidestep many of the key issues central to backprop, such as the vanishing/exploding gradient problems [8], the requirement for a long, unstable credit assignment feedback pathway [9], forward and backward locking problems [10], and the need for differentiability [11, 9]. On the other hand, motivated by planningarXiv:2209.09174v1

Despite achieving accuracies higher than 90% on recognizing upper-limb movements through sEMG (surface Electromyography) signal with the state of art classifiers in the laboratory environment, there are still issues to be addressed for a myo-controlled prosthesis achieve similar performance in real environment conditions. Thereby, the main goal of this review is to expose the latest researches in terms of strategies in each block of the system, giving a global view of the current state of academic research. A systematic review was conducted, and the retrieved papers were organized according to the system step related to the proposed method. Then, for each stage of the upper limb motion recognition system, the works were described and compared in terms of strategy, methodology and issue addressed. An additional section was destined for the description of works related to signal contamination that is often neglected in reviews focused on sEMG based motion classifiers. Therefore, this section is the main contribution of this paper. Deep learning methods are a current trend for classification stage, providing strategies based on time-series and transfer learning to address the issues related to limb position, temporal/inter-subject variation, and electrode displacement. Despite the promising strategies presented for contaminant detection, identification, and removal, there are still some factors to be considered, such as the occurrence of simultaneous contaminants.

Tom Mustill was kayaking with his friend Charlotte in Monterey Bay, California, when an animal three times the size of the largest Tyrannosaurus Rex hurtled from the water and crashed down on their tiny craft. As the flying humpback whale fell upon them and their kayak was sucked beneath the waves, Mustill assumed he would die. Miraculously he and Charlotte found themselves gasping for breath, clinging to their capsized kayak. How had they survived a smash with a creature three times the weight of a double-decker bus? What happened next was almost as weird.

Artificial Intelligence Software Market is projected to grow to Multimillion by 2026 from USD million in 2021, at a Impressive CAGR during the forecast period. Google, Baidu, IBM, Microsoft, SAP, Intel, Salesforce, Brighterion, KITT.AI, IFlyTek, Megvii Technology, Albert Technologies, H2O.ai, Brainasoft, Yseop, Ipsoft, NanoRep(LogMeIn), Ada Support, Astute Solutions, IDEAL.com, This report focuses on the Artificial Intelligence Software in global market, especially in North America, Europe and Asia-Pacific, South America, Middle East and Africa. This report categorizes the market based on manufacturers, regions, type and application. The worldwide market for Artificial Intelligence Software is expected to grow at a CAGR of roughly xx% over the next five years, will reach xx million US$ in 2024, from xx million US$ in 2017.

Transductive Few-Shot learning has gained increased attention nowadays considering the cost of data annotations along with the increased accuracy provided by unlabelled samples in the domain of few shot. Especially in Few-Shot Classification (FSC), recent works explore the feature distributions aiming at maximizing likelihoods or posteriors with respect to the unknown parameters. Following this vein, and considering the parallel between FSC and clustering, we seek for better taking into account the uncertainty in estimation due to lack of data, as well as better statistical properties of the clusters associated with each class. Therefore in this paper we propose a new clustering method based on Variational Bayesian inference, further improved by Adaptive Dimension Reduction based on Probabilistic Linear Discriminant Analysis. Our proposed method significantly improves accuracy in the realistic unbalanced transductive setting on various Few-Shot benchmarks when applied to features used in previous studies, with a gain of up to $6\%$ in accuracy. In addition, when applied to balanced setting, we obtain very competitive results without making use of the class-balance artefact which is disputable for practical use cases. We also provide the performance of our method on a high performing pretrained backbone, with the reported results further surpassing the current state-of-the-art accuracy, suggesting the genericity of the proposed method.

Models of acoustic word embeddings (AWEs) learn to map variable-length spoken word segments onto fixed-dimensionality vector representations such that different acoustic exemplars of the same word are projected nearby in the embedding space. In addition to their speech technology applications, AWE models have been shown to predict human performance on a variety of auditory lexical processing tasks. Current AWE models are based on neural networks and trained in a bottom-up approach that integrates acoustic cues to build up a word representation given an acoustic or symbolic supervision signal. Therefore, these models do not leverage or capture high-level lexical knowledge during the learning process. In this paper, we propose a multi-task learning model that incorporates top-down lexical knowledge into the training procedure of AWEs. Our model learns a mapping between the acoustic input and a lexical representation that encodes high-level information such as word semantics in addition to bottom-up form-based supervision. We experiment with three languages and demonstrate that incorporating lexical knowledge improves the embedding space discriminability and encourages the model to better separate lexical categories.

Tensor decompositions are powerful tools for dimensionality reduction and feature interpretation of multidimensional data such as signals. Existing tensor decomposition objectives (e.g., Frobenius norm) are designed for fitting raw data under statistical assumptions, which may not align with downstream classification tasks. In practice, raw input tensors can contain irrelevant information while data augmentation techniques may be used to smooth out class-irrelevant noise in samples. This paper addresses the above challenges by proposing augmented tensor decomposition (ATD), which effectively incorporates data augmentations and self-supervised learning (SSL) to boost downstream classification. To address the non-convexity of the new augmented objective, we develop an iterative method that enables the optimization to follow an alternating least squares (ALS) fashion. We evaluate our proposed ATD on multiple datasets. It can achieve 0.8% - 2.5% accuracy gain over tensor-based baselines. Also, our ATD model shows comparable or better performance (e.g., up to 15% in accuracy) over self-supervised and autoencoder baselines while using less than 5% of learnable parameters of these baseline models

Imaging of facial affects may be used to measure psychophysiological attributes of children through their adulthood, especially for monitoring lifelong conditions like Autism Spectrum Disorder. Deep convolutional neural networks have shown promising results in classifying facial expressions of adults. However, classifier models trained with adult benchmark data are unsuitable for learning child expressions due to discrepancies in psychophysical development. Similarly, models trained with child data perform poorly in adult expression classification. We propose domain adaptation to concurrently align distributions of adult and child expressions in a shared latent space to ensure robust classification of either domain. Furthermore, age variations in facial images are studied in age-invariant face recognition yet remain unleveraged in adult-child expression classification. We take inspiration from multiple fields and propose deep adaptive FACial Expressions fusing BEtaMix SElected Landmark Features (FACE-BE-SELF) for adult-child facial expression classification. For the first time in the literature, a mixture of Beta distributions is used to decompose and select facial features based on correlations with expression, domain, and identity factors. We evaluate FACE-BE-SELF on two pairs of adult-child data sets. Our proposed FACE-BE-SELF approach outperforms adult-child transfer learning and other baseline domain adaptation methods in aligning latent representations of adult and child expressions.

With the advancement of deep learning techniques, an increasing number of methods have been proposed for optic disc and cup (OD/OC) segmentation from the fundus images. Clinically, OD/OC segmentation is often annotated by multiple clinical experts to mitigate the personal bias. However, it is hard to train the automated deep learning models on multiple labels. A common practice to tackle the issue is majority vote, e.g., taking the average of multiple labels. However such a strategy ignores the different expertness of medical experts. Motivated by the observation that OD/OC segmentation is often used for the glaucoma diagnosis clinically, in this paper, we propose a novel strategy to fuse the multi-rater OD/OC segmentation labels via the glaucoma diagnosis performance. Specifically, we assess the expertness of each rater through an attentive glaucoma diagnosis network. For each rater, its contribution for the diagnosis will be reflected as an expertness map. To ensure the expertness maps are general for different glaucoma diagnosis models, we further propose an Expertness Generator (ExpG) to eliminate the high-frequency components in the optimization process. Based on the obtained expertness maps, the multi-rater labels can be fused as a single ground-truth which we dubbed as Diagnosis First Ground-truth (DiagFirstGT). Experimental results show that by using DiagFirstGT as ground-truth, OD/OC segmentation networks will predict the masks with superior glaucoma diagnosis performance.

Scientists have decoded visual images from a dog's brain, offering a first look at how the canine mind reconstructs what it sees. The Journal of Visualized Experiments published the research done at Emory University. The results suggest that dogs are more attuned to actions in their environment rather than to who or what is doing the action. The researchers recorded the fMRI neural data for two awake, unrestrained dogs as they watched videos in three 30-minute sessions, for a total of 90 minutes. They then used a machine-learning algorithm to analyze the patterns in the neural data.