Characterizations and Quantifications of Macroscopic Quantumness and Its Implementations using Optical Fields

We present a review and discussions on characterizations and quantifications of macroscopic quantum states as well as their implementations and applications in optical systems. We compare and criticize different measures proposed to define and quantify macroscopic quantum superpositions and extend such comparisons to several types of optical quantum states actively considered for experimental implementations within recent research topics.Comment: 13 pages, 2 figures, references added, review article to be published in the Special Issue of Optics Communications on Macroscopic Quantumness: Theory and Applications in Optical Science

Characterization of fiber-optic light delivery and light-induced temperature changes in a rodent brain for precise optogenetic neuromodulation

Understanding light intensity and temperature increase is of considerable importance in designing or performing in vivo optogenetic experiments. Our study describes the optimal light power at target depth in the rodent brain that would maximize activation of light-gated ion channels while minimizing temperature increase. Monte Carlo (MC) simulations of light delivery were used to provide a guideline for suitable light power at a target depth. In addition, MC simulations with the Pennes bio-heat model using data obtained from measurements with a temperature-measuring cannula having 12.3 mV/°C of thermoelectric sensitivity enabled us to predict tissue heating of 0.116 °C/mW on average at target depth of 563 μm and specifically, a maximum mean plateau temperature increase of 0.25 °C/mW at 100 μm depth for 473 nm light. Our study will help to improve the design and performance of optogenetic experiments while avoiding potential over-and underillumination. © 2016 Optical Society of America.1

Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report

The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20-50 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Unwinding the threads of anthropocentrism through interspecies relation : Nina Katchadourian’s Mended Spiderweb & GIFT/GIFT

In this thesis, I read Nina Katchadourian’s Mended Spiderweb and GIFT/GIFT as the contact zone defined by Donna J. Haraway. Starting with understanding the contact zone within the critical animal studies discourse, I discuss how the interspecies interactions at the contact zone enhance the entangled encounters at the site and bring about changes in understanding the world as a naturalcultural one through becoming-with with more-than-human figures on earth. Furthermore, I argue that playful creativity and the act of playing are elementary in formulating the complexities of the contact zone. By bringing in Donald W. Winnicott’s object-relations theory and Marcel Mauss’s gift theory, I examine GIFT/GIFT as the case study of contradictory, subverted gift-giving and gift-receiving practices across species. Then, I focus on how the notions of touch and silence function to emphasize the connection and disconnection between the human and arachnid artists in GIFT/GIFT. In the closing section, I scrutinize the reiteration occurring in the contact zone of Mended Spiderweb and GIFT/GIFT to extend it to other works by Katchadourian.Art Histor

First Person Action Recognition via Two-stream ConvNet with Long-term Fusion Pooling

First person action recognition is an active research area with increasingly popular wearable devices. Action classification for first person video (FPV) is more challenging than conventional action classification due to strong egocentric motions, frequent changes of viewpoints, and diverse global motion patterns. To tackle these challenges, we introduce a two-stream convolutional neural network that improves action recognition via long-term fusion pooling operators. The proposed method effectively captures the temporal structure of actions by leveraging a series of frame-wise features of both appearance and motion in actions. Our experiments validate the effect of the feature pooling operators, and show that the proposed method achieves state-of-the-art performance on standard action datasets. (c) 2018 Elsevier B.V. All rights reserved.11Nsciescopu

Relational Embedding for Few-Shot Classification

We propose to address the problem of few-shot classification by meta-learning "what to observe" and "where to attend" in a relational perspective. Our method leverages relational patterns within and between images via self-correlational representation (SCR) and cross-correlational attention (CCA). Within each image, the SCR module transforms a base feature map into a self-correlation tensor and learns to extract structural patterns from the tensor. Between the images, the CCA module computes cross-correlation between two image representations and learns to produce co-attention between them. Our Relational Embedding Network (RENet) combines the two relational modules to learn relational embedding in an end-to-end manner. In experimental evaluation, it achieves consistent improvements over state-of-the-art methods on four widely used few-shot classification benchmarks of miniImageNet, tieredImageNet, CUB-200-2011, and CIFAR-FS.1

IntegralAction: Pose-driven Feature Integration for Robust Human Action Recognition in Videos

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Learning Self-Similarity in Space and Time as Generalized Motion for Video Action Recognition

Spatio-temporal convolution often fails to learn motion dynamics in videos and thus an effective motion representation is required for video understanding in the wild. In this paper, we propose a rich and robust motion representation based on spatio-temporal self-similarity (STSS). Given a sequence of frames, STSS represents each local region as similarities to its neighbors in space and time. By converting appearance features into relational values, it enables the learner to better recognize structural patterns in space and time. We leverage the whole volume of STSS and let our model learn to extract an effective motion representation from it. The proposed neural block, dubbed SELFY, can be easily inserted into neural architectures and trained end-to-end without additional supervision. With a sufficient volume of the neighborhood in space and time, it effectively captures long-term interaction and fast motion in the video, leading to robust action recognition. Our experimental analysis demonstrates its superiority over previous methods for motion modeling as well as its complementarity to spatio-temporal features from direct convolution. On the standard action recognition benchmarks, Something-Something-V1 & V2, Diving-48, and FineGym, the proposed method achieves the state-of-the-art results.1