Recapitulation of complex transport and action of drugs at tumor microenvironment using tumor-microenvironment-on-chip

Targeted delivery aims to selectively distribute drugs to targeted tumor tissue but not to healthy tissue. This can address many of clinical challenges by maximizing the efficacy but minimizing the toxicity of anti-cancer drugs. However, complex tumor microenvironment poses various barriers hindering the transport of drugs and drug delivery systems. New tumor models that allow for the systematic study of these complex environments are highly desired to provide reliable test beds to develop drug delivery systems for targeted delivery. Recently, research efforts have yielded new in vitro tumor models, the so called tumor-microenvironment-on-chip, that recapitulate certain characteristics of the tumor microenvironment. These new models show benefits over other conventional tumor models, and have the potential to accelerate drug discovery and enable precision medicines. However, further research is warranted to overcome their limitations and to properly interpret the data obtained from these models. In this article, key features of the in vivo tumor microenvironment that are relevant to drug transport processes for targeted delivery was discussed, and the current status and challenges for developing in vitro transport model systems was reviewed

Open-world Semantic Segmentation via Contrasting and Clustering Vision-Language Embedding

To bridge the gap between supervised semantic segmentation and real-world applications that acquires one model to recognize arbitrary new concepts, recent zero-shot segmentation attracts a lot of attention by exploring the relationships between unseen and seen object categories, yet requiring large amounts of densely-annotated data with diverse base classes. In this paper, we propose a new open-world semantic segmentation pipeline that makes the first attempt to learn to segment semantic objects of various open-world categories without any efforts on dense annotations, by purely exploiting the image-caption data that naturally exist on the Internet. Our method, Vision-language-driven Semantic Segmentation (ViL-Seg), employs an image and a text encoder to generate visual and text embeddings for the image-caption data, with two core components that endow its segmentation ability: First, the image encoder is jointly trained with a vision-based contrasting and a cross-modal contrasting, which encourage the visual embeddings to preserve both fine-grained semantics and high-level category information that are crucial for the segmentation task. Furthermore, an online clustering head is devised over the image encoder, which allows to dynamically segment the visual embeddings into distinct semantic groups such that they can be classified by comparing with various text embeddings to complete our segmentation pipeline. Experiments show that without using any data with dense annotations, our method can directly segment objects of arbitrary categories, outperforming zero-shot segmentation methods that require data labeling on three benchmark datasets.Comment: Accepted to ECCV 202

Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving

Aiming towards a holistic understanding of multiple downstream tasks simultaneously, there is a need for extracting features with better transferability. Though many latest self-supervised pre-training methods have achieved impressive performance on various vision tasks under the prevailing pretrain-finetune paradigm, their generalization capacity to multi-task learning scenarios is yet to be explored. In this paper, we extensively investigate the transfer performance of various types of self-supervised methods, e.g., MoCo and SimCLR, on three downstream tasks, including semantic segmentation, drivable area segmentation, and traffic object detection, on the large-scale driving dataset BDD100K. We surprisingly find that their performances are sub-optimal or even lag far behind the single-task baseline, which may be due to the distinctions of training objectives and architectural design lied in the pretrain-finetune paradigm. To overcome this dilemma as well as avoid redesigning the resource-intensive pre-training stage, we propose a simple yet effective pretrain-adapt-finetune paradigm for general multi-task training, where the off-the-shelf pretrained models can be effectively adapted without increasing the training overhead. During the adapt stage, we utilize learnable multi-scale adapters to dynamically adjust the pretrained model weights supervised by multi-task objectives while leaving the pretrained knowledge untouched. Furthermore, we regard the vision-language pre-training model CLIP as a strong complement to the pretrain-adapt-finetune paradigm and propose a novel adapter named LV-Adapter, which incorporates language priors in the multi-task model via task-specific prompting and alignment between visual and textual features.Comment: Accepted at NeurIPS 202

A Kernel-space POF virtual switch

Protocol Oblivious Forwarding (POF) aims at providing a standard southbound interface for sustainable Software Defined Networking (SDN) evolvement. It overcomes the limitations of popular Open Flow protocols (an existing widely-adopted southbound interface), through the enhancement of SDN forwarding plane. This paper pioneers the design and implementation of a Kernel-space POF Virtual Switch (K_POFVS) on Linux platform. K_POFVS can improve the packet processing speed, through fast packet forwarding and the capability of adding/deleting/modifying protocol fields in kernel space. In addition, it is able to enhance flow table matching speed, by separating the mask table (consisting of flow entry masks used to figure out the matching field) and the flow table under a caching mechanism. Furthermore, K_POFVS can achieve efficient communication between the kernel space and the user space, via extending the Netlink communication between them. Experimental results show that K_POFVS can provide much better performance than existing user-space POF virtual switches, in terms of packet forwarding delay, packet processing delay and packet transmission rateThis work is partially supported by the National Program on Key Basic Research Project of China (973 Program) under Grant No. 2012CB315803, the Strategic Priority Research Program of the Chinese Academy of Sciences under grant No. XDA06010306, the National Natural Science Foundation of China under Grant No. 61303241, and the University of Exeter’s Innovation Platform – Link Fund under Award No. LF207