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Abstract Optical spectrometers are essential tools for analysing light‒matter interactions, but conventional spectrometers can be complicated and bulky. Recently, efforts have been made to develop miniaturized spectrometers. However, it is challenging to overcome the trade-off between miniaturizing size and retaining performance. Here, we present a complementary metal oxide semiconductor image sensor-based miniature computational spectrometer using a plasmonic nanoparticles-in-cavity microfilter array. Size-controlled silver nanoparticles are directly printed into cavity-length-varying Fabry‒Pérot microcavities, which leverage strong coupling between the localized surface plasmon resonance of the silver nanoparticles and the Fabry‒Pérot microcavity to regulate the transmission spectra and realize large-scale arrayed spectrum-disparate microfilters. Supported by a machine learning-based training process, the miniature computational spectrometer uses artificial intelligence and was demonstrated to measure visible-light spectra at subnanometre resolution. The high scalability of the technological approaches shown here may facilitate the development of high-performance miniature optical spectrometers for extensive applications. 

This paper introduces the concept of Language- Guided World Models (LWMs)—probabilistic models that can simulate environments by read- ing texts. Agents equipped with these models provide humans with more extensive and effi- cient control, allowing them to simultaneously alter agent behaviors in multiple tasks via nat- ural verbal communication. In this work, we take initial steps in developing robust LWMs that can generalize to compositionally novel language descriptions. We design a challenging world modeling benchmark based on the game of MESSENGER (Hanjie et al., 2021), featuring evaluation settings that require varying degrees of compositional generalization. Our exper- iments reveal the lack of generalizability of the state-of-the-art Transformer model, as it of- fers marginal improvements in simulation qual- ity over a no-text baseline. We devise a more robust model by fusing the Transformer with the EMMA attention mechanism (Hanjie et al., 2021). Our model substantially outperforms the Transformer and approaches the perfor- mance of a model with an oracle semantic pars- ing and grounding capability. To demonstrate the practicality of this model in improving AI safety and transparency, we simulate a scenario in which the model enables an agent to present plans to a human before execution, and to re- vise plans based on their language feedback. 

This paper presents a non-resonant vibration energy harvester (VEH) optimized for 0.5-1.0 Hz at 0.2g acceleration, typically associated with human motion in daily activities. Different amounts of water-based and oil-based ferrofluids as liquid bearings have been studied in an experimental setup with a precisely controllable spacing between top and bottom coil plates where the magnet array and ferrofluid bearings reside. The sub-miniature VEH (1.4cc and 3.3gram) steadily generates voltages between 0.5-1.0 Hz and is measured to produce an open-circuit voltage of Vrms = 19.5 - 31.9 mV (or 0.33-0.89 μW into a match load) from 0.2g sub-Hz applied acceleration. The highest figure of merit (FOM) of the VEH at 0.2g at 1.0 Hz is 15.5 μW/cc/g2. 

Sensing plays a pivotal role in robotic manipulation, dictating the accuracy and versatility with which objects are handled. Vision-based sensing methods often suffer from fabrication complexity and low durability, while approaches that rely on direct measurements on the gripper often have limited resolution and are difficult to scale. Here we present a robotic gripper that is made of two cubic lattices that are sensorized using air channels. the fabrication process. The lattices are printed using a 3D printer, simplifying the fabrication process. The flexibility of this approach offers significant control over sensor and lattice design, while the pressure-based internal sensing provides measurements with minimal disruption to the grasping surface. With only 12 sensors, 6 per lattice, this gripper can estimate an object's weight and location and offer new insights into grasp parameters like friction coefficients and grasp force. 

To use robots within early childhood education requires the preparation of early childhood teachers to use and teach block-based programming. We used a hierarchical linear model approach to address our research question: How can study cohort, cognitive challenge types, and motivational challenge types be used to predict lesson plan quality? Positive motivational challenge predictors were task value of programming, task value of teaching, mastery goals of programming, belonging in teaching, and autonomy in robotics. Negative motivational challenge predictors were mastery goals of teaching, belonging in robotics, self-efficacy in teaching, autonomy in programming, and autonomy in teaching. Positive cognitive challenge predictors were technical issues, problem solving - higher-order skills, and lesson design - other issues.