EMI: Exploration with Mutual Information

Reinforcement learning algorithms struggle when the reward signal is very sparse. In these cases, naive random exploration methods essentially rely on a random walk to stumble onto a rewarding state. Recent works utilize intrinsic motivation to guide the exploration via generative models, predictive forward models, or discriminative modeling of novelty. We propose EMI, which is an exploration method that constructs embedding representation of states and actions that does not rely on generative decoding of the full observation but extracts predictive signals that can be used to guide exploration based on forward prediction in the representation space. Our experiments show competitive results on challenging locomotion tasks with continuous control and on image-based exploration tasks with discrete actions on Atari. The source code is available at https://github.com/snu-mllab/EMI .Comment: Accepted and to appear at ICML 201

Improvement in carrier mobility of metal oxide thin-film transistor by a microstructure modification

Metal oxide thin-film transistors (TFTs) have been rapidly penetrating as an emerging backplane technology for the next generation high pixel density, large-size liquid crystal displays and organic light-emitting diodes panels because of their intriguing properties such as their high field-effect mobility, low subthreshold gate swing, good uniformity, low temperature processing capability, and transparency to visible light.[1-3] However, the typical field-effect mobility of IGZO TFTs in the practical production line is ~10 cm2/Vs, which is still not enough to drive the high-end flat panel displays with the ultra-high-definition, large size ( 60 inch) and high frame rate ( 240 Hz). One of ways to improve the mobility of electron carriers in metal oxide semiconductor would involve the lattice ordering, which leads to the substantial reduction in the carrier scattering with the semiconductor. Approach that seeks to utilize the crystallization of metal oxide semiconductor has yet to be attempted despite the potential scientific and engineering implication. In this presentation, we explored the metal-induced crystallization of amorphous zinc thin oxide (a-ZTO) and indium gallium zinc oxide (a-IGZO) semiconductor at a low temperature. The fabricated crystalline ZTO TFTs exhibited a high field-effect mobility of 33.5 cm2/Vs, subthreshold gate swing of 0.40 V/decade, and ION/OFF ratio of \u3e 5 107. The method in this study is expected to be applied to any type of metal oxide semiconductor. Acknowledgment This study was supported by the National Research Foundation of Korea (NRF) grant funded the Korean government (NRF-2015R1A2A2A01003848) and the industrial strategic technology development program funded by MKE/KEIT (10051403). References 1. K. Nomura et al., Nature 432, 488 (2004). 2. T. Kamyia et al., Sci. Technol. Adv. Mater. 11, 044305 (2010). 3. J. Y. Kwon and J. K. Jeong, Semicond. Sci. Technol. 30, 024002 (2015

Efficient Latency-Aware CNN Depth Compression via Two-Stage Dynamic Programming

Recent works on neural network pruning advocate that reducing the depth of the network is more effective in reducing run-time memory usage and accelerating inference latency than reducing the width of the network through channel pruning. In this regard, some recent works propose depth compression algorithms that merge convolution layers. However, the existing algorithms have a constricted search space and rely on human-engineered heuristics. In this paper, we propose a novel depth compression algorithm which targets general convolution operations. We propose a subset selection problem that replaces inefficient activation layers with identity functions and optimally merges consecutive convolution operations into shallow equivalent convolution operations for efficient end-to-end inference latency. Since the proposed subset selection problem is NP-hard, we formulate a surrogate optimization problem that can be solved exactly via two-stage dynamic programming within a few seconds. We evaluate our methods and baselines by TensorRT for a fair inference latency comparison. Our method outperforms the baseline method with higher accuracy and faster inference speed in MobileNetV2 on the ImageNet dataset. Specifically, we achieve $1.41\times$ speed-up with $0.11$\%p accuracy gain in MobileNetV2-1.0 on the ImageNet.Comment: ICML 2023; Codes at https://github.com/snu-mllab/Efficient-CNN-Depth-Compressio

Is neighborhood poverty harmful to every child? Neighborhood poverty, family poverty, and behavioral problems among young children

This longitudinal study investigates the association between neighborhood poverty and behavioral problems among young children. This study also examines whether social environments mediate the relationship between neighborhood poverty and behavioral problems. We used data from the third and fourth waves of the Fragile Families and Child Wellbeing study to assess behavioral problems separately for children who experienced no family poverty, moved out of family poverty, moved into family poverty, and experienced long‐term family poverty. Regression models assessed the effect of neighborhood poverty on behavioral problem outcomes among children aged 5 years, after controlling for sociodemographic characteristics and earlier behavioral problems. Results showed an association between neighborhood poverty and lower social cohesion and safety, which lead to greater externalizing problems among children with long‐term family poverty living in high‐poverty neighborhoods compared with those in low‐poverty neighborhoods. Policies and community resources need to be allocated to improve neighborhood social environments, particularly for poor children in high‐poverty neighborhoods.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148233/1/jcop22140.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148233/2/jcop22140_am.pd

Effects of language background on executive function: Transfer across task and modality

The relation between linguistic experience and cognitive function has been of great interest, but recent investigations of this question have produced widely disparate results, ranging from proposals for a “bilingual advantage,” to a “bilingual disadvantage,” to claims of no difference at all as a function of language. There are many possible sources for this lack of consensus, including the heterogeneity of bilingual populations, and the choice of different tasks and implementations across labs. We propose that another reason for this inconsistency is the task demands of transferring from linguistic experience to laboratory tasks can differ greatly as the task is modified. In this study, we show that task modality (visual, audio, and orthographic) can yield different patterns of performance between monolingual and multilingual participants. The very same task can show similarities or differences in performance, as a function of modality. In turn, this may be explained by the distance of transfer – how close (or far) the laboratory task is to the day to day lived experience of language usage. We suggest that embodiment may provide a useful framework for thinking about task transfer by helping to define the processes of linguistic production and comprehension in ways that are easily connected to task manipulations

Coexistence Issues concerning 4G and mmWave 5G Antennas for Mobile Terminals

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Millimeter wave antenna issues in metal frame mobile devices

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