Pancreatic lymphoepithelial cyst: a case report

Pancreatic lymphoepithelialcyst （PLEC） is a rare benign pancreatic cyst， which has been rarely reported in clinical practice. The incidence of PLEC is low and specific imaging and clinical manifestations are lacking. It is difficult to make differential diagnosis between PLEC and pancreatic cancer， which is likely to be misdiagnosed. In this article， a 63-year-old male patient who was suspected with PLEC due to a pancreatic mass detected during physical examination was reported. Upon admission， endoscopic ultrasound-guided fine-needle aspiration （EUS-FNA） promoted the diagnosis of benign lesions. Subsequently， surgical resection was performed and he was pathologically diagnosed with PLEC.This case prompts that EUS-FNA can be used as a preferential treatment for pancreatic cystic lesions

Reversible Watermarking in Deep Convolutional Neural Networks for Integrity Authentication

Deep convolutional neural networks have made outstanding contributions in many fields such as computer vision in the past few years and many researchers published well-trained network for downloading. But recent studies have shown serious concerns about integrity due to model-reuse attacks and backdoor attacks. In order to protect these open-source networks, many algorithms have been proposed such as watermarking. However, these existing algorithms modify the contents of the network permanently and are not suitable for integrity authentication. In this paper, we propose a reversible watermarking algorithm for integrity authentication. Specifically, we present the reversible watermarking problem of deep convolutional neural networks and utilize the pruning theory of model compression technology to construct a host sequence used for embedding watermarking information by histogram shift. As shown in the experiments, the influence of embedding reversible watermarking on the classification performance is less than 0.5% and the parameters of the model can be fully recovered after extracting the watermarking. At the same time, the integrity of the model can be verified by applying the reversible watermarking: if the model is modified illegally, the authentication information generated by original model will be absolutely different from the extracted watermarking information.Comment: Accepted to ACM MM 202

Reinforcement Learning Experience Reuse with Policy Residual Representation

Experience reuse is key to sample-efficient reinforcement learning. One of the critical issues is how the experience is represented and stored. Previously, the experience can be stored in the forms of features, individual models, and the average model, each lying at a different granularity. However, new tasks may require experience across multiple granularities. In this paper, we propose the policy residual representation (PRR) network, which can extract and store multiple levels of experience. PRR network is trained on a set of tasks with a multi-level architecture, where a module in each level corresponds to a subset of the tasks. Therefore, the PRR network represents the experience in a spectrum-like way. When training on a new task, PRR can provide different levels of experience for accelerating the learning. We experiment with the PRR network on a set of grid world navigation tasks, locomotion tasks, and fighting tasks in a video game. The results show that the PRR network leads to better reuse of experience and thus outperforms some state-of-the-art approaches.Comment: Conference version appears in IJCAI 201

Inter-Particle Electronic and Ionic Modifications of the Ternary Ni-Co-Mn Oxide for Efficient and Stable Lithium Storage

A combined electronic and ionic interparticular modification strategy is designed for the improvement of lithium storage in the layer structured ternary Ni-Co-Mn oxide (LiNi0.6Co0.2Mn0.2O2) in the form of spherical particles. In this design, a thin layer of the ion conducting polypropylene carbonate is applied to wrap the individual oxide particles for three purposes: (1) prevention of direct stacking and packing between oxide particles that will otherwise impede or block ions from accessing all the surface of the oxide particles, (2) provision of additional ionic pathways between the oxide particles, and (3) stabilization of the oxide particles during lithium storage and release. The design includes also the use of nitrogen doped carbon nanotubes for electronic connection between the polymer coated individual spheres of the layered nickel-rich LiNi0.6Co0.2Mn0.2O2. According to the physicochemical and electrochemical characterizations, and laboratory battery tests, it can be concluded that the LiNi0.6Co0.2Mn0.2O2 composite has a unique porous structure that is assembled by the polymer coated ternary oxide microspheres and the nitrogen-doped carbon nanotube networks. Significant improvements are achieved in both the ionic and electronic conductivities (double or more increase), and in discharge specific capacity (201.3 mAh·g−1 at 0.1 C, improved by 13.28% compared to the non-modified LiNi0.6Co0.2Mn0.2O2), rate performance and cycling stability (94.40% in capacity retention after 300 cycles at 1.0 C)

Action Quality Assessment with Temporal Parsing Transformer

Action Quality Assessment(AQA) is important for action understanding and resolving the task poses unique challenges due to subtle visual differences. Existing state-of-the-art methods typically rely on the holistic video representations for score regression or ranking, which limits the generalization to capture fine-grained intra-class variation. To overcome the above limitation, we propose a temporal parsing transformer to decompose the holistic feature into temporal part-level representations. Specifically, we utilize a set of learnable queries to represent the atomic temporal patterns for a specific action. Our decoding process converts the frame representations to a fixed number of temporally ordered part representations. To obtain the quality score, we adopt the state-of-the-art contrastive regression based on the part representations. Since existing AQA datasets do not provide temporal part-level labels or partitions, we propose two novel loss functions on the cross attention responses of the decoder: a ranking loss to ensure the learnable queries to satisfy the temporal order in cross attention and a sparsity loss to encourage the part representations to be more discriminative. Extensive experiments show that our proposed method outperforms prior work on three public AQA benchmarks by a considerable margin.Comment: accepted by ECCV 202

Learning to Coordinate with Anyone

In open multi-agent environments, the agents may encounter unexpected teammates. Classical multi-agent learning approaches train agents that can only coordinate with seen teammates. Recent studies attempted to generate diverse teammates to enhance the generalizable coordination ability, but were restricted by pre-defined teammates. In this work, our aim is to train agents with strong coordination ability by generating teammates that fully cover the teammate policy space, so that agents can coordinate with any teammates. Since the teammate policy space is too huge to be enumerated, we find only dissimilar teammates that are incompatible with controllable agents, which highly reduces the number of teammates that need to be trained with. However, it is hard to determine the number of such incompatible teammates beforehand. We therefore introduce a continual multi-agent learning process, in which the agent learns to coordinate with different teammates until no more incompatible teammates can be found. The above idea is implemented in the proposed Macop (Multi-agent compatible policy learning) algorithm. We conduct experiments in 8 scenarios from 4 environments that have distinct coordination patterns. Experiments show that Macop generates training teammates with much lower compatibility than previous methods. As a result, in all scenarios Macop achieves the best overall coordination ability while never significantly worse than the baselines, showing strong generalization ability