Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss

Reducing bit-widths of activations and weights of deep networks makes it efficient to compute and store them in memory, which is crucial in their deployments to resource-limited devices, such as mobile phones. However, decreasing bit-widths with quantization generally yields drastically degraded accuracy. To tackle this problem, we propose to learn to quantize activations and weights via a trainable quantizer that transforms and discretizes them. Specifically, we parameterize the quantization intervals and obtain their optimal values by directly minimizing the task loss of the network. This quantization-interval-learning (QIL) allows the quantized networks to maintain the accuracy of the full-precision (32-bit) networks with bit-width as low as 4-bit and minimize the accuracy degeneration with further bit-width reduction (i.e., 3 and 2-bit). Moreover, our quantizer can be trained on a heterogeneous dataset, and thus can be used to quantize pretrained networks without access to their training data. We demonstrate the effectiveness of our trainable quantizer on ImageNet dataset with various network architectures such as ResNet-18, -34 and AlexNet, on which it outperforms existing methods to achieve the state-of-the-art accuracy

Development of HER2-Targeting-Ligand-Modified Albumin Nanoparticles Based on the SpyTag/SpyCatcher System for Photothermal Therapy

The successful development of targeted nanoparticle (NP)-based therapeutics depends on the effective conjugation of targeting ligands to the NP. However, conventional methods based on chemical reactive groups such as N-hydroxysuccinimide, 1-ethyl-3-(3-dimethylaminopropyi) carbodiimide, and maleimide have several limitations, including low binding efficiency, complex reaction methods, long reaction times, and reduced activity of the targeting ligand. In this study, we developed a novel method for conjugating targeting ligands to albumin NPs using the recently developed bacterial superglue the SpyTag/SpyCatcher (ST/SC) ligation system. This method involves a rapid one-step conjugation process with almost 100% efficiency. Albumin NPs conjugated to human epidermal growth factor receptor 2 (HER2) affibody molecules using the ST/SC system showed strong binding to HER2-overexpressing cells. In addition, NPs encapsulated with indocyanine green accumulated in cells overexpressing HER2 and exhibited superior photothermal treatment effects. Thus, surface functionalization of NPs using the ST/SC reaction may be used to develop new nanosystems that exhibit improved therapeutic benefits