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关键词： Language   Computer science  

摘要： Machine-learning system can process words spoken in 101 languages, spitting out voice-synthesized translations in 36 target languages.

关键词： Machine learning   Brain   Evolution   Computer science  

关键词： Computer science   Machine learning   Scientific community  

摘要： Experimental model’s record-breaking performance on science and maths tests wows researchers.

关键词： Computer science   Quantum information  

摘要： Quantum error mitigation, a data processing technique for recovering the statistics of target processes from their noisy version, is a crucial task for near-term quantum technologies. Most existing methods require prior knowledge of the noise model or the noise parameters. Deep neural networks have the potential to lift this requirement, but current models require training data produced by ideal processes in the absence of noise. Here we build a neural model that achieves quantum error mitigation without any prior knowledge of the noise and without training on noise-free data. To achieve this feature, we introduce a quantum augmentation technique for error mitigation. Our approach applies to quantum circuits and to the dynamics of many-body and continuous-variable quantum systems, accommodating various types of noise models. We demonstrate its effectiveness by testing it both on simulated noisy circuits and on real quantum hardware.

关键词： biological sciences   computer science   natural sciences  

摘要： We hypothesized that the heterogeneous architecture of biological neural networks provides a substrate to regulate the well-known tradeoff between robustness and efficiency, thereby allowing different subpopulations of the same network to optimize for different objectives. To distinguish between subpopulations, we developed a metric based on the mathematical theory of simplicial complexes that captures the complexity of their connectivity by contrasting its higher-order structure to a random control and confirmed its relevance in several openly available connectomes. Using a biologically detailed cortical model and an electron microscopic dataset, we showed that subpopulations with low simplicial complexity exhibit efficient activity. Conversely, subpopulations of high simplicial complexity play a supporting role in boosting the reliability of the network as a whole, softening the robustness-efficiency tradeoff. Crucially, we found that both types of subpopulations can and do coexist within a single connectome in biological neural networks, due to the heterogeneity of their connectivity.

关键词： Applied sciences   Computer science   Signal processing  

摘要： A key property of our environment is the mirror symmetry of many objects, although symmetry is an abstract global property with no definable shape template, making symmetry identification a challenge for standard template-matching algorithms. We therefore ask whether Deep Neural Networks (DNNs) trained on typical natural environmental images develop a selectivity for symmetry similar to that of the human brain. We tested a DNN trained on such typical natural images with object-free random-dot images of 1, 2, and 4 symmetry axes. Symmetry coding was negligible in the earliest DNN layers. The strongest discriminability occurred in the first fully connected layer, FC6, plausibly analogous to the human lateral occipital complex (LOC), matching many structural properties of human symmetry processing. These results support the homology between the feedforward DNN trained on natural images and the global processing of the extended visual hierarchy as it has evolved in the human brain.

关键词： Physics   Biological sciences   Computer science  

摘要： MT+ is pivotal in the dorsal visual stream, encoding tool-use characteristics such as motion speed and direction. Despite its conservation between humans and monkeys, differences in MT+ spatial location and organization may lead to divergent, yet unexplored, connectivity patterns and functional characteristics. Using diffusion tensor imaging, we examined the structural connectivity of MT+ subregions in macaques and humans. We also employed graph-theoretical analyses on the constructed homologous tool-use network to assess their functional roles. Our results revealed location-dependent connectivity in macaques, with MST, MT, and FST predominantly connected to dorsal, middle, and ventral surfaces, respectively. Humans showed similar connectivity across all subregions. Differences in connectivity between MST and FST are more pronounced in macaques. In humans, the entire MT+ region, especially MST, exhibited stronger information transmission capabilities. Our findings suggest that the differences in tool use between humans and macaques may originate earlier than previously thought, particularly within the MT+ region.

关键词： Physics   Optics   Computer science  

摘要： We present a Fourier neural operator (FNO)-based surrogate solver for the efficient optimization of wave- fronts in tunable metasurface controls. Existing methods, including the Gerchberg-Saxton algorithm and the adjoint optimization, are often computationally demanding due to their iterative processes, which require numerical simulations at each step. Our surrogate solver overcomes this limitation by providing highly accurate gradient estimations with respect to changes in tunable meta-atoms without the need for direct simulations. This approach substantially reduces both computational time and cost in wavefront shaping applications. The proposed solver demonstrates a residual of 0.02 when compared to the normalized figure of merit achieved by the optimized structure obtained through the adjoint method, and its inference time is 887.5 times faster than conventional simulation-based methods. This advancement enables ultra-fast wavefront shaping across a range of applications, including optical wavefront shaping, reconfigurable intelligent metasurfaces, and biomedical imaging.

关键词： Computer science   Artificial intelligence   Human-computer interaction  

摘要： Microsaccades, a form of fixational eye movements, help maintain visual stability during stationary observations. This study examines the modulation of microsaccadic rates by various stimulus categories in monkeys and humans during a passive viewing task. Stimulus sets were grouped into four primary categories: human, animal, natural, and man-made. Distinct post-stimulus microsaccade patterns were identified across these categories, enabling successful decoding of the stimulus category with accuracy and recall of up to 85%. We observed that microsaccade rates are independent of pupil size changes. Neural data showed that category classification in the inferior temporal (IT) cortex peaks earlier than changes in microsaccade rates, suggesting feedback from the IT cortex influences eye movements after stimulus discrimination. These results contribute to neurobiological models, enhance human-machine interfaces, optimize experimental visual stimuli, and deepen understanding of microsaccades' role in object decoding.