A compact butterfly-style silicon photonic-electronic neural chip for hardware-efficient deep learning

The optical neural network (ONN) is a promising hardware platform for next-generation neurocomputing due to its high parallelism, low latency, and low energy consumption. Previous ONN architectures are mainly designed for general matrix multiplication (GEMM), leading to unnecessarily large area cost and high control complexity. Here, we move beyond classical GEMM-based ONNs and propose an optical subspace neural network (OSNN) architecture, which trades the universality of weight representation for lower optical component usage, area cost, and energy consumption. We devise a butterfly-style photonic-electronic neural chip to implement our OSNN with up to 7x fewer trainable optical components compared to GEMM-based ONNs. Additionally, a hardware-aware training framework is provided to minimize the required device programming precision, lessen the chip area, and boost the noise robustness. We experimentally demonstrate the utility of our neural chip in practical image recognition tasks, showing that a measured accuracy of 94.16% can be achieved in hand-written digit recognition tasks with 3-bit weight programming precision.Comment: 17 pages,5 figure

Understanding Segment Anything Model: SAM is Biased Towards Texture Rather than Shape

In contrast to the human vision that mainly depends on the shape for recognizing the objects, deep image recognition models are widely known to be biased toward texture. Recently, Meta research team has released the first foundation model for image segmentation, termed segment anything model (SAM), which has attracted significant attention. In this work, we understand SAM from the perspective of texture \textit{v.s.} shape. Different from label-oriented recognition tasks, the SAM is trained to predict a mask for covering the object shape based on a promt. With this said, it seems self-evident that the SAM is biased towards shape. In this work, however, we reveal an interesting finding: the SAM is strongly biased towards texture-like dense features rather than shape. This intriguing finding is supported by a novel setup where we disentangle texture and shape cues and design texture-shape cue conflict for mask prediction

One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era

OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is demonstrated to be one small step for generative AI (GAI), but one giant leap for artificial general intelligence (AGI). Since its official release in November 2022, ChatGPT has quickly attracted numerous users with extensive media coverage. Such unprecedented attention has also motivated numerous researchers to investigate ChatGPT from various aspects. According to Google scholar, there are more than 500 articles with ChatGPT in their titles or mentioning it in their abstracts. Considering this, a review is urgently needed, and our work fills this gap. Overall, this work is the first to survey ChatGPT with a comprehensive review of its underlying technology, applications, and challenges. Moreover, we present an outlook on how ChatGPT might evolve to realize general-purpose AIGC (a.k.a. AI-generated content), which will be a significant milestone for the development of AGI.Comment: A Survey on ChatGPT and GPT-4, 29 pages. Feedback is appreciated ([email protected]

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Soil Moisture, Nutrients, and Plant Growths under Various Irrigation and Fertilization Regimes during the Crop Replacement Period in an Alley Intercropping System on the Loess Plateau of China

The uneven rainfall distribution, limited soil nutrients, and continuous cropping effect in the Loess Plateau restrict the sustainable development of fruit tree/crop (soybean and maize) intercropping systems. A two-year field experiment was conducted to investigate the effects of different water and fertilizer regimes on the soil nutrients and growth of intercropping systems during the crop replacement period. The experiment involved three irrigation levels (0% (I0), 50% (I1), and 80% (I2) of field capacity), two irrigation methods (drip irrigation (DI) and flood irrigation (FI)), and three fertilizer application rates (0 (F0), 375 (F1), and 750 (F2) kg/hm2). The results showed that in 2020 and 2021, the soil water contents increased with increasing irrigation and fertilization. The soil ammonium nitrogen, nitrate nitrogen, and soil organic matter contents in 2021 were 21.0%–68.4% higher than those in 2020. Increasing the fertilizer application rate improved the photosynthesis rate and transpiration rate of apples and maize in 2020 but had a reverse U-shape effect on soybeans in 2021. Irrigation and fertilization increased soybean and maize yields by 2.9%–30.5% compared with the I0F0 treatment. The maize root indicators generally showed an increasing trend followed by a decreasing trend with increasing fertilizer application in 2020, while soybean exhibited an opposite pattern in 2021. The FI1F1 and DI2F2 treatments yielded the optimal economic benefit in 2020 and 2021, respectively. Therefore, from an economic standpoint, FI and DI would have been recommended in 2020 and 2021, respectively. Factor analysis suggested that the DI2F2 treatments had the highest comprehensive benefits over the two years studied. Therefore, we recommend using DI combined with 80% field capacity irrigation and 750 kg/hm2 fertilization to maximize the comprehensive benefits of intercropping systems during the crop replacement period