Representations over diagrams of abelian categories I: Global structure and homological objects

Representations over diagrams of abelian categories unify quite a few notions appearing widely in literature such as representations of categories, presheaves of modules over categories, representations of species, etc. In this series of papers we study them systematically, characterizing special homological objects in representation category and constructing various structures (such as model structures and Wandhuasen category strcutres) on it. In the first paper we investigate the Grothendieck structure of the representation category, describe important functors and adjunction relations between them, and characterize special homological objects. These results lay a foundation for our future works.Comment: We reorganize the work in the original version as a series of papers, and this is the first on

A flat model structure on functor categories

In this paper we construct a flat model structure on the category $_{{\mathcal{Q}},A}{\rm Mod}$ of additive functors from a preadditive category ${\mathcal{Q}}$ satisfying certain conditions to the module category $_{A}{\rm Mod}$, whose homotopy category is the ${\mathcal{Q}}$-shaped derived category introduced by Holm and Jorgensen.Comment: 11 pages. Any comments are very welcom

Hierarchical Structure with Highly Ordered Macroporous-Mesoporous Metal-Organic Frameworks as Dual Function for CO2 Fixation.

As a major greenhouse gas, the continuous increase of carbon dioxide (CO2) in the atmosphere has caused serious environmental problems, although CO2 is also an abundant, inexpensive, and nontoxic carbon source. Here, we use metal-organic framework (MOF) with highly ordered hierarchical structure as adsorbent and catalyst for chemical fixation of CO2 at atmospheric pressure, and the CO2 can be converted to the formate in excellent yields. Meanwhile, we have successfully integrated highly ordered macroporous and mesoporous structures into MOFs, and the macro-, meso-, and microporous structures have all been presented in one framework. Based on the unique hierarchical pores, high surface area (592 m2/g), and high CO2 adsorption capacity (49.51 cm3/g), the ordered macroporous-mesoporous MOFs possess high activity for chemical fixation of CO2 (yield of 77%). These results provide a promising route of chemical CO2 fixation through MOF materials

Haptic interaction with a virtual 3D model: A multimodal interactive system for 3D solar system

Haptic interaction has become more and more important in interactive technology. In current human-computer interaction, haptic interaction has been considered as an important additional interactive method. The benefits of haptic interaction mainly include high efficiency, accuracy and naturalness. In this thesis, a multimodal interactive system was implemented based on a large volume 3D model of the solar system. This multimodal interactive system included two subsystems which separately used traditional computer interactive devices, a mouse and a keyboard, as well as a new haptic interaction device. These two interactive subsystems contained many relevant interactive functions for the user to interact with the model of the solar system and the models of celestial bodies inside it. In addition, the interactive methods for a large volume 3D model were studied in this research. Finally, a user study was employed to demonstrate the benefits of haptic interaction in a multimodal interactive system, and also the methods for improving current haptic technology had been discussed. To sum up, the work of the thesis includes a theoretical discussion, the implementation of a multimodal interactive system and a user study, which focuses on the research for haptic interaction in the field of human-computer interaction. Asiasanat: Haptic interaction, virtual 3D model, multimodal interactive system, human-computer interactio

Towards Deep Network Steganography: From Networks to Networks

With the widespread applications of the deep neural network (DNN), how to covertly transmit the DNN models in public channels brings us the attention, especially for those trained for secret-learning tasks. In this paper, we propose deep network steganography for the covert communication of DNN models. Unlike the existing steganography schemes which focus on the subtle modification of the cover data to accommodate the secrets, our scheme is learning task oriented, where the learning task of the secret DNN model (termed as secret-learning task) is disguised into another ordinary learning task conducted in a stego DNN model (termed as stego-learning task). To this end, we propose a gradient-based filter insertion scheme to insert interference filters into the important positions in the secret DNN model to form a stego DNN model. These positions are then embedded into the stego DNN model using a key by side information hiding. Finally, we activate the interference filters by a partial optimization strategy, such that the generated stego DNN model works on the stego-learning task. We conduct the experiments on both the intra-task steganography and inter-task steganography (i.e., the secret and stego-learning tasks belong to the same and different categories), both of which demonstrate the effectiveness of our proposed method for covert communication of DNN models.Comment: 8 pages. arXiv admin note: text overlap with arXiv:2302.1452

Object-oriented backdoor attack against image captioning

Backdoor attack against image classification task has been widely studied and proven to be successful, while there exist little research on the backdoor attack against vision-language models. In this paper, we explore backdoor attack towards image captioning models by poisoning training data. Assuming the attacker has total access to the training dataset, and cannot intervene in model construction or training process. Specifically, a portion of benign training samples is randomly selected to be poisoned. Afterwards, considering that the captions are usually unfolded around objects in an image, we design an object-oriented method to craft poisons, which aims to modify pixel values by a slight range with the modification number proportional to the scale of the current detected object region. After training with the poisoned data, the attacked model behaves normally on benign images, but for poisoned images, the model will generate some sentences irrelevant to the given image. The attack controls the model behavior on specific test images without sacrificing the generation performance on benign test images. Our method proves the weakness of image captioning models to backdoor attack and we hope this work can raise the awareness of defending against backdoor attack in the image captioning field

Purified and Unified Steganographic Network

Steganography is the art of hiding secret data into the cover media for covert communication. In recent years, more and more deep neural network (DNN)-based steganographic schemes are proposed to train steganographic networks for secret embedding and recovery, which are shown to be promising. Compared with the handcrafted steganographic tools, steganographic networks tend to be large in size. It raises concerns on how to imperceptibly and effectively transmit these networks to the sender and receiver to facilitate the covert communication. To address this issue, we propose in this paper a Purified and Unified Steganographic Network (PUSNet). It performs an ordinary machine learning task in a purified network, which could be triggered into steganographic networks for secret embedding or recovery using different keys. We formulate the construction of the PUSNet into a sparse weight filling problem to flexibly switch between the purified and steganographic networks. We further instantiate our PUSNet as an image denoising network with two steganographic networks concealed for secret image embedding and recovery. Comprehensive experiments demonstrate that our PUSNet achieves good performance on secret image embedding, secret image recovery, and image denoising in a single architecture. It is also shown to be capable of imperceptibly carrying the steganographic networks in a purified network. Code is available at \url{https://github.com/albblgb/PUSNet}Comment: 8 pages, 9 figures, Accepted at CVPR202