TF-ICON: Diffusion-Based Training-Free Cross-Domain Image Composition

Text-driven diffusion models have exhibited impressive generative capabilities, enabling various image editing tasks. In this paper, we propose TF-ICON, a novel Training-Free Image COmpositioN framework that harnesses the power of text-driven diffusion models for cross-domain image-guided composition. This task aims to seamlessly integrate user-provided objects into a specific visual context. Current diffusion-based methods often involve costly instance-based optimization or finetuning of pretrained models on customized datasets, which can potentially undermine their rich prior. In contrast, TF-ICON can leverage off-the-shelf diffusion models to perform cross-domain image-guided composition without requiring additional training, finetuning, or optimization. Moreover, we introduce the exceptional prompt, which contains no information, to facilitate text-driven diffusion models in accurately inverting real images into latent representations, forming the basis for compositing. Our experiments show that equipping Stable Diffusion with the exceptional prompt outperforms state-of-the-art inversion methods on various datasets (CelebA-HQ, COCO, and ImageNet), and that TF-ICON surpasses prior baselines in versatile visual domains. Code is available at https://github.com/Shilin-LU/TF-ICONComment: Accepted by ICCV202

Learning Human Kinematics by Modeling Temporal Correlations between Joints for Video-based Human Pose Estimation

Estimating human poses from videos is critical in human-computer interaction. By precisely estimating human poses, the robot can provide an appropriate response to the human. Most existing approaches use the optical flow, RNNs, or CNNs to extract temporal features from videos. Despite the positive results of these attempts, most of them only straightforwardly integrate features along the temporal dimension, ignoring temporal correlations between joints. In contrast to previous methods, we propose a plug-and-play kinematics modeling module (KMM) based on the domain-cross attention mechanism to model the temporal correlation between joints across different frames explicitly. Specifically, the proposed KMM models the temporal correlation between any two joints by calculating their temporal similarity. In this way, KMM can learn the motion cues of each joint. Using the motion cues (temporal domain) and historical positions of joints (spatial domain), KMM can infer the initial positions of joints in the current frame in advance. In addition, we present a kinematics modeling network (KIMNet) based on the KMM for obtaining the final positions of joints by combining pose features and initial positions of joints. By explicitly modeling temporal correlations between joints, KIMNet can infer the occluded joints at present according to all joints at the previous moment. Furthermore, the KMM is achieved through an attention mechanism, which allows it to maintain the high resolution of features. Therefore, it can transfer rich historical pose information to the current frame, which provides effective pose information for locating occluded joints. Our approach achieves state-of-the-art results on two standard video-based pose estimation benchmarks. Moreover, the proposed KIMNet shows some robustness to the occlusion, demonstrating the effectiveness of the proposed method

MACE: Mass Concept Erasure in Diffusion Models

The rapid expansion of large-scale text-to-image diffusion models has raised growing concerns regarding their potential misuse in creating harmful or misleading content. In this paper, we introduce MACE, a finetuning framework for the task of mass concept erasure. This task aims to prevent models from generating images that embody unwanted concepts when prompted. Existing concept erasure methods are typically restricted to handling fewer than five concepts simultaneously and struggle to find a balance between erasing concept synonyms (generality) and maintaining unrelated concepts (specificity). In contrast, MACE differs by successfully scaling the erasure scope up to 100 concepts and by achieving an effective balance between generality and specificity. This is achieved by leveraging closed-form cross-attention refinement along with LoRA finetuning, collectively eliminating the information of undesirable concepts. Furthermore, MACE integrates multiple LoRAs without mutual interference. We conduct extensive evaluations of MACE against prior methods across four different tasks: object erasure, celebrity erasure, explicit content erasure, and artistic style erasure. Our results reveal that MACE surpasses prior methods in all evaluated tasks. Code is available at https://github.com/Shilin-LU/MACE.Comment: Accepted by CVPR 202

Long-Term Outcomes of Three-Dimensional High-Dose-Rate Brachytherapy for Locally Recurrent Early T-Stage Nasopharyngeal Carcinoma

Background: Brachytherapy (BT) is one of the techniques available for retreatment of patients with locally recurrent nasopharyng eal carcinoma (rNPC). In this study, we evaluated the treatment outcome and late toxicities of three-dimensional high-dose-rate brachytherapy (3D-HDR-BT) for patients with locally rNPC.Materials and Methods: This is a retrospective study involving 36 patients with histologically confirmed rNPC from 2004 to 2011. Of the 36 patients, 17 underwent combined-modality treatment (CMT) consisting of external beam radiotherapy (EBRT) followed by 3D-HDR-BT, while the other 19 underwent 3D-HDR-BT alone. The median dose of EBRT for the CMT group was 60 (range, 50–66) Gy, with an additional median dose of BT of 16 (range, 9–20) Gy. The median dose for the 3D-HDR-BT group was 32 (range, 20–36) Gy. The measured treatment outcomes were the 5- and 10-year locoregional recurrence-free survival (LRFS), disease-free survival (DFS), overall survival (OS), and late toxicities.Results: The median age at recurrence was 44.5 years. The median follow-up period was 70 (range, 6–142) months. The 5-year LRFS, DFS, and OS for the entire patient group were 75.4, 55.6, and 74.3%, respectively, while the 10-year LRFS, DFS, and OS for the entire patient group were 75.4, 44.2, and 53.7%, respectively. The 10-year LRFS in the CMT group was higher than that in the 3D-HDR-BT-alone group (93.8 vs. 58.8%, HR: 7.595, 95%CI: 1.233–61.826, p = 0.025). No grade 4 late radiotherapy-induced toxicities were observed.Conclusions: 3D-HDR-BT achieves favorable clinical outcomes with mild late toxicity in patients with locally rNPC

A novel solar-assisted ground-source heat pump (SAGSHP) with seasonal heat-storage and heat cascade utilization: field test and performance analysis

To maintain the energy quality with high temperature and reduce the energy loss of seasonal heat-storage in solar-assisted ground-source heat pumps (SAGSHPs), a novel SAGSHP system with the heat-cascading of borehole heat-exchangers was designed and its field-test was conducted in this paper. The borehole heat-exchangers were divided into two regions: the core region and the peripheral region. The core region can maintain a high temperature (e.g. 45 ℃), which is much higher than in previous studies, and the heat from this region can be used directly, without the operation of a heat pump. The field-test was conducted in a community in the province Shandong, China. The results indicate that a sufficient soil-temperature gradient (the temperature is high in the core but low at the periphery) can be created and maintained. The monthly averaged borehole-wall-temperature difference between the borehole heat-exchangers (BHEs) at the core and the periphery can be as high as 30.1 ℃. This means that both cascaded heat-storage and heat-utilization can be realized. In addition, an average performance of CCOP=5.15 and SCOP=4.66 can be achieved. Compared with previous studies, despite the lower CCOP, a higher SCOP can be attained, thanks to heat cascade storage and -utilization. The novel approach described in this paper represents a viable alternative for space heating in North China

Exploring the multifaceted potential of (R)-ketamine beyond antidepressant applications

(R, S)- and (S)-ketamine have made significant progress in the treatment of treatment-resistant depression (TRD) and have become a research focus in recent years. However, they both have risks of psychomimetic effects, dissociative effects, and abuse liability, which limit their clinical use. Recent preclinical and clinical studies have shown that (R)-ketamine has a more efficient and lasting antidepressant effect with fewer side effects compared to (R, S)- and (S)-ketamine. However, a recent small-sample randomized controlled trial found that although (R)-ketamine has a lower incidence of adverse reactions in adult TRD treatment, its antidepressant efficacy is not superior to the placebo group, indicating its antidepressant advantage still needs further verification and clarification. Moreover, an increasing body of research suggests that (R)-ketamine might also have significant applications in the prevention and treatment of medical fields or diseases such as cognitive disorders, perioperative anesthesia, ischemic stroke, Parkinson’s disease, multiple sclerosis, osteoporosis, substance use disorders, inflammatory diseases, COVID-19, and organophosphate poisoning. This article briefly reviews the mechanism of action and research on antidepressants related to (R)-ketamine, fully revealing its application potential and development prospects, and providing some references and assistance for subsequent expanded research

Architectures for Multinode Superconducting Quantum Computers

Many proposals to scale quantum technology rely on modular or distributed designs where individual quantum processors, called nodes, are linked together to form one large multinode quantum computer (MNQC). One scalable method to construct an MNQC is using superconducting quantum systems with optical interconnects. However, a limiting factor of these machines will be internode gates, which may be two to three orders of magnitude noisier and slower than local operations. Surmounting the limitations of internode gates will require a range of techniques, including improvements in entanglement generation, the use of entanglement distillation, and optimized software and compilers, and it remains unclear how improvements to these components interact to affect overall system performance, what performance from each is required, or even how to quantify the performance of each. In this paper, we employ a `co-design' inspired approach to quantify overall MNQC performance in terms of hardware models of internode links, entanglement distillation, and local architecture. In the case of superconducting MNQCs with microwave-to-optical links, we uncover a tradeoff between entanglement generation and distillation that threatens to degrade performance. We show how to navigate this tradeoff, lay out how compilers should optimize between local and internode gates, and discuss when noisy quantum links have an advantage over purely classical links. Using these results, we introduce a roadmap for the realization of early MNQCs which illustrates potential improvements to the hardware and software of MNQCs and outlines criteria for evaluating the landscape, from progress in entanglement generation and quantum memory to dedicated algorithms such as distributed quantum phase estimation. While we focus on superconducting devices with optical interconnects, our approach is general across MNQC implementations.Comment: 23 pages, white pape

Ordinal regression based on data relationship

Ordinal regression is a supervised learning problem which aims to classify instances into ordinal categories. It is different from multi-class classification because there is an ordinal relationship between the categories. Moreover, it is different from metric regression because the target values to be predicted are discrete and the distances between different categories are not defined. Ordinal regression is an active research area because of numerous governmental, commercial and scientific applications, such as quality assessment, disease grading, credit rating, and risk stratification. The main challenge of ordinal regression is to model the ordinal information carried by the labels. Traditionally, there are two angles to tackle the ordinal regression problem: from metric regression perspective and classification perspective. However, most of existing works under both above categories are pointwise methods, in which the relationship between pairs or lists of data points is not explored sufficiently. Furthermore, learning models, especially deep neural network based models, on small datasets is challenging, but many real-world ordinal regression problems are in fact small data problems. The aim of this research is to propose ordinal regression algorithms by exploring data relationship and give consideration to suitability for small datasets and scalability for large datasets. This thesis proposes four approaches for ordinal regression problems based on data relationship. The first approach is a pairwise ordinal regression approach for small datasets. In the training phase, the labeled instances are paired up to train a binary classifier, and the relationship between two data points in each pair is represented by a pairwise kernel. In the testing phase, a decoder algorithm is developed to recover the ordinal information from the binary outputs. By pairing up the training points, the size of the training dataset is squared, which alleviates the lack of training points in small datasets. A proof is presented that if the pairwise kernel fulfills certain properties, the time complexity solving the QP problem can be reduced from O(n^4) to O(n^2) without any loss of accuracy, where n is the number of training points. Motivated by the study of the pairwise relationship, the second approach extends the data relationship representation from pairs to triplets based on deep neural networks. The intuition is to predict rank labels by answering the question: “Is the rank of an input greater than k −1 and smaller than k + 1?”. Therefore, the proposed approach transforms the ordinal regression problem to binary classification problems answering above question and uses triplets with instances from different categories to train deep neural networks such that high-level features describing their ordinal relationship can be extracted automatically. In the testing phase, triplets are formed by a testing instance and other instances with known ranks. A decoder is designed to estimate the rank of the testing instance based on the outputs of the network. Because of the data argumentation by permutation, deep learning can work for ordinal regression even on small datasets. The third proposed approach is a constrained deep neural network for ordinal regression, which aims to automatically extract high-level features for representing intraclass information and interclass ordinal relationship simultaneously. A constrained optimization formulation is proposed for the ordinal regression problem which minimizes the negative loglikelihood for multiple categories constrained by the order between instances. Mathematically, it is equivalent to an unconstrained formulation with a pairwise regularizer. An implementation based on a convolutional neural network framework is proposed to solve the problem such that high-level features can be extracted automatically, and the optimal solution can be learned through the traditional back-propagation method. The proposed pairwise constraints as a regularizer make the algorithm work even on small datasets, and a proposed efficient implementation makes it be scalable for large datasets. Furthermore, an ordinal network architecture is proposed for ordinal regression. The proposed approach embeds the ordinal relationship into the edges between nodes of the same layers in the neural network. Existing deep learning based ordinal regression approaches are implemented by traditional architectures for classification, in which no edges exist between nodes of the same layers. The proposed architecture performs as a latent function mapping the instances to a real line, and the target categories are the intervals on this line which are decided by multiple boundaries. Most significant benefit is that the ordinal network is able to predict the rank labels directly by the outputs of the network without explicit predictions of the multiple boundaries. This research breaks the limits of traditional ordinal regression approaches, and shows the effective and efficient performance of the proposed approaches comparing with the state-of-the-art ordinal regression approaches.Doctor of Philosoph

Chaos in attitude dynamics of spacecraft

Attitude dynamics is the theoretical basis of attitude control of spacecrafts in aerospace engineering. With the development of nonlinear dynamics, chaos in spacecraft attitude dynamics has drawn great attention since the 1990's. The problem of the predictability and controllability of the chaotic attitude motion of a spacecraft has a practical significance in astronautic science. This book aims to summarize basic concepts, main approaches, and recent progress in this area. It focuses on the research work of the author and other Chinese scientists in this field, providing new methods and viewpoints in the investigation of spacecraft attitude motion, as well as new mathematical models, with definite engineering backgrounds, for further analysis. Professor Yanzhu Liu was the Director of the Institute of Engineering Mechanics, Shanghai Jiao Tong University, China. Dr. Liqun Chen is a Professor at the Department of Mechanics, Shanghai University, China