Improving Robustness of TCM-based Robust Steganography with Variable Robustness

Recent study has found out that after multiple times of recompression, the DCT coefficients of JPEG image can form an embedding domain that is robust to recompression, which is called transport channel matching (TCM) method. Because the cost function of the adaptive steganography does not consider the impact of modification on the robustness, the modified DCT coefficients of the stego image after TCM will change after recompression. To reduce the number of changed coefficients after recompression, this paper proposes a robust steganography algorithm which dynamically updates the robustness cost of every DCT coefficient. The robustness cost proposed is calculated by testing whether the modified DCT coefficient can resist recompression in every step of STC embedding process. By adding robustness cost to the distortion cost and using the framework of STC embedding algorithm to embed the message, the stego images have good performance both in robustness and security. The experimental results show that the proposed algorithm can significantly enhance the robustness of stego images, and the embedded messages could be extracted correctly at almost all cases when recompressing with a lower quality factor and recompression process is known to the user of proposed algorithm.Comment: 15 pages, 5 figures, submitted to IWDW 2020: 19th International Workshop on Digital-forensics and Watermarkin

AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation. With a novel attentional generative network, the AttnGAN can synthesize fine-grained details at different subregions of the image by paying attentions to the relevant words in the natural language description. In addition, a deep attentional multimodal similarity model is proposed to compute a fine-grained image-text matching loss for training the generator. The proposed AttnGAN significantly outperforms the previous state of the art, boosting the best reported inception score by 14.14% on the CUB dataset and 170.25% on the more challenging COCO dataset. A detailed analysis is also performed by visualizing the attention layers of the AttnGAN. It for the first time shows that the layered attentional GAN is able to automatically select the condition at the word level for generating different parts of the image

A Link-Based Day-to-Day Traffic Assignment Model

Existing day-to-day traffic assignment models are all built upon path flow variables. This paper demonstrates two essential shortcomings of these path-based models. One is that their application requires a given initial path flow pattern, which is typically unidentifiable, i.e., mathematically nonunique and practically unobservable. In particular, we show that, for the path-based models, different initial path flow patterns constituting the same link flow pattern generally gives different day-to-day link flow evolutions. The other shortcoming of the path-based models is the path-overlapping problem. That is, the path-based models ignore the interdependence among paths and thus can give very unreasonable results for networks with paths overlapping with each other. These two path-based problems exist for most (if not all) deterministic day-to-day dynamics whose fixed points are the classic Wardrop user equilibrium. To avoid the two path-based problems, we propose a day-to-day traffic assignment model that directly deals with link flow variables. Our link-based model captures travelers\u27 cost-minimization behavior in their path finding as well as their inertia. The fixed point of our link-based dynamical system is the classic Wardrop user equilibrium

Bipartite Graph Pre-training for Unsupervised Extractive Summarization with Graph Convolutional Auto-Encoders

Pre-trained sentence representations are crucial for identifying significant sentences in unsupervised document extractive summarization. However, the traditional two-step paradigm of pre-training and sentence-ranking, creates a gap due to differing optimization objectives. To address this issue, we argue that utilizing pre-trained embeddings derived from a process specifically designed to optimize cohensive and distinctive sentence representations helps rank significant sentences. To do so, we propose a novel graph pre-training auto-encoder to obtain sentence embeddings by explicitly modelling intra-sentential distinctive features and inter-sentential cohesive features through sentence-word bipartite graphs. These pre-trained sentence representations are then utilized in a graph-based ranking algorithm for unsupervised summarization. Our method produces predominant performance for unsupervised summarization frameworks by providing summary-worthy sentence representations. It surpasses heavy BERT- or RoBERTa-based sentence representations in downstream tasks.Comment: Accepted by the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023

The Optimal Timing of Antiretroviral Therapy Initiation in HIV-Infected Patients with Cryptococcal Meningitis: A Multicenter Prospective Randomized Controlled Trial

The optimal timing of antiretroviral therapy (ART) initiation in human immunodeficiency virus (HIV)-infected patients with cryptococcal meningitis (HIV/CM) is controversial. We designed a clinical trial to inves-tigate the optimal timing for ART initiation in HIV/CM patients. This will be a multicenter, prospective, and randomized clinical trial. Each enrolled patient will be randomized into either the early ART arm or the deferred ART arm. We will compare the mortality and incident rates of immune reconstitution inflammatory syndrome between the two arms. We hope to elucidate the optimal timing for ART initiation in HIV/CM patients

Characteristics of cadmium accumulation and tolerance in apple plants grown in different soils

Cadmium (Cd) is a nonessential element and highly toxic to apple tree. However, Cd accumulation, translocation and tolerance in apple trees planted in different soils remain unknown. To investigate soil Cd bioavailability, plant Cd accumulation, physiological changes as well as gene expression patterns in apple trees grown in five different soils, ‘Hanfu’ apple seedlings were planted in orchard soils collected from Maliangou village (ML), Desheng village (DS), Xishan village (XS), Kaoshantun village (KS) and Qianertaizi village (QT), and subjected to 500 μM CdCl2 for 70 d. Results showed that soils of ML and XS had higher content of organic matter (OM), clay and silt, and cation exchange capacity (CEC) but lower sand content than the other soils, thereby reduced Cd bioavailability, which could be reflected by lower concentrations and proportions of acid-soluble Cd but higher concentrations and proportions of reducible and oxidizable Cd. The plants grown in soils of ML and XS had relatively lower Cd accumulation levels and bio-concentration factors than those grown in the other soils. Excess Cd reduced plant biomass, root architecture, and chlorophyll content in all plants but to relatively lesser degree in those grown in soils of ML and XS. The plants grown in soils of ML, XS and QT had comparatively lower reactive oxygen species (ROS) content, less membrane lipid peroxidation, and higher antioxidant content and enzyme activity than those grown in soils of DS and KS. Transcript levels of genes regulating Cd uptake, transport and detoxification such as HA11, VHA4, ZIP6, IRT1, NAS1, MT2, MHX, MTP1, ABCC1, HMA4 and PCR2 displayed significant differences in roots of plants grown in different soils. These results indicate that soil types affect Cd accumulation and tolerance in apple plants, and plants grown in soils with higher OM content, CEC, clay and silt content and lower sand content suffer less Cd toxicity