MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation

Recent advances in text-to-image generation with diffusion models present transformative capabilities in image quality. However, user controllability of the generated image, and fast adaptation to new tasks still remains an open challenge, currently mostly addressed by costly and long re-training and fine-tuning or ad-hoc adaptations to specific image generation tasks. In this work, we present MultiDiffusion, a unified framework that enables versatile and controllable image generation, using a pre-trained text-to-image diffusion model, without any further training or finetuning. At the center of our approach is a new generation process, based on an optimization task that binds together multiple diffusion generation processes with a shared set of parameters or constraints. We show that MultiDiffusion can be readily applied to generate high quality and diverse images that adhere to user-provided controls, such as desired aspect ratio (e.g., panorama), and spatial guiding signals, ranging from tight segmentation masks to bounding boxes. Project webpage: https://multidiffusion.github.i

TokenFlow: Consistent Diffusion Features for Consistent Video Editing

The generative AI revolution has recently expanded to videos. Nevertheless, current state-of-the-art video models are still lagging behind image models in terms of visual quality and user control over the generated content. In this work, we present a framework that harnesses the power of a text-to-image diffusion model for the task of text-driven video editing. Specifically, given a source video and a target text-prompt, our method generates a high-quality video that adheres to the target text, while preserving the spatial layout and motion of the input video. Our method is based on a key observation that consistency in the edited video can be obtained by enforcing consistency in the diffusion feature space. We achieve this by explicitly propagating diffusion features based on inter-frame correspondences, readily available in the model. Thus, our framework does not require any training or fine-tuning, and can work in conjunction with any off-the-shelf text-to-image editing method. We demonstrate state-of-the-art editing results on a variety of real-world videos. Webpage: https://diffusion-tokenflow.github.io

Evolution of Protein-Mediated Biomineralization in Scleractinian Corals

While recent strides have been made in understanding the biological process by which stony corals calcify, much remains to be revealed, including the ubiquity across taxa of specific biomolecules involved. Several proteins associated with this process have been identified through proteomic profiling of the skeletal organic matrix (SOM) extracted from three scleractinian species. However, the evolutionary history of this putative “biomineralization toolkit,” including the appearance of these proteins’ throughout metazoan evolution, remains to be resolved. Here we used a phylogenetic approach to examine the evolution of the known scleractinians’ SOM proteins across the Metazoa. Our analysis reveals an evolutionary process dominated by the co-option of genes that originated before the cnidarian diversification. Each one of the three species appears to express a unique set of the more ancient genes, representing the independent co-option of SOM proteins, as well as a substantial proportion of proteins that evolved independently. In addition, in some instances, the different species expressed multiple orthologous proteins sharing the same evolutionary history. Furthermore, the non-random clustering of multiple SOM proteins within scleractinian-specific branches suggests the conservation of protein function between distinct species for what we posit is part of the scleractinian “core biomineralization toolkit.” This “core set” contains proteins that are likely fundamental to the scleractinian biomineralization mechanism. From this analysis, we infer that the scleractinians’ ability to calcify was achieved primarily through multiple lineage-specific protein expansions, which resulted in a new functional role that was not present in the parent gene

Text2LIVE: Text-Driven Layered Image and Video Editing

We present a method for zero-shot, text-driven appearance manipulation in natural images and videos. Given an input image or video and a target text prompt, our goal is to edit the appearance of existing objects (e.g., object's texture) or augment the scene with visual effects (e.g., smoke, fire) in a semantically meaningful manner. We train a generator using an internal dataset of training examples, extracted from a single input (image or video and target text prompt), while leveraging an external pre-trained CLIP model to establish our losses. Rather than directly generating the edited output, our key idea is to generate an edit layer (color+opacity) that is composited over the original input. This allows us to constrain the generation process and maintain high fidelity to the original input via novel text-driven losses that are applied directly to the edit layer. Our method neither relies on a pre-trained generator nor requires user-provided edit masks. We demonstrate localized, semantic edits on high-resolution natural images and videos across a variety of objects and scenes.Comment: Project page: https://text2live.github.i

Different skeletal protein toolkits achieve similar structure and performance in the tropical coral Stylophora pistillata and the temperate Oculina patagonica

Stony corals (order: Scleractinia) differ in growth form and structure. While stony corals have gained the ability to form their aragonite skeleton once in their evolution, the suite of proteins involved in skeletogenesis is different for different coral species. This led to the conclusion that the organic portion of their skeleton can undergo rapid evolutionary changes by independently evolving new biomineralization-related proteins. Here, we used liquid chromatography-tandem mass spectrometry to sequence skeletogenic proteins extracted from the encrusting temperate coral Oculina patagonica. We compare it to the previously published skeletal proteome of the branching subtropical corals Stylophora pistillata as both are regarded as highly resilient to environmental changes. We further characterized the skeletal organic matrix (OM) composition of both taxa and tested their effects on the mineral formation using a series of overgrowth experiments on calcite seeds. We found that each species utilizes a different set of proteins containing different amino acid compositions and achieve a different morphology modification capacity on calcite overgrowth. Our results further support the hypothesis that the different coral taxa utilize a species-specific protein set comprised of independent gene co-option to construct their own unique organic matrix framework. While the protein set differs between species, the specific predicted roles of the whole set appear to underline similar functional roles. They include assisting in forming the extracellular matrix, nucleation of the mineral and cell signaling. Nevertheless, the different composition might be the reason for the varying organization of the mineral growth in the presence of a particular skeletal OM, ultimately forming their distinct morphologies

Characterizing Movement Patterns of Older Individuals with T2D in Free-Living Environments Using Wearable Accelerometers

(1) Background: Type 2 Diabetes (T2D) is associated with reduced muscle mass, strength, and function, leading to frailty. This study aims to analyze the movement patterns (MPs) of older individuals with T2D across varying levels of physical capacity (PC). (2) Methods: A cross-sectional study was conducted among individuals aged 60 or older with T2D. Participants (n = 103) were equipped with a blinded continuous glucose monitoring (CGM) system and an activity monitoring device for one week. PC tests were performed at the beginning and end of the week, and participants were categorized into three groups: low PC (LPC), medium PC (MPC), and normal PC (NPC). Group differences in MPs and physical activity were analyzed using non-parametric Kruskal-Wallis tests for both categorical and continuous variables. Dunn post-hoc statistical tests were subsequently carried out for pairwise comparisons. For data analysis, we utilized pandas, a Python-based data analysis tool, and conducted the statistical analyses using the scipy.stats package in Python. The significance level was set at p < 0.05. (3) Results: Participants in the LPC group showed lower medio-lateral acceleration and higher vertical and antero-posterior acceleration compared to the NPC group. LPC participants also had higher root mean square values (1.017 m/s2). Moreover, the LPC group spent less time performing in moderate to vigorous physical activity (MVPA) and had fewer daily steps than the MPC and NPC groups. (4) Conclusions: The LPC group exhibited distinct movement patterns and lower activity levels compared to the NPC group. This study is the first to characterize the MPs of older individuals with T2D in their free-living environment. Several accelerometer-derived features were identified that could differentiate between PC groups. This novel approach offers a manpower-free alternative to identify physical deterioration and detect low PC in individuals with T2D based on real free-living physical behavior

Energy Policy as a Socio-Scientific Issue: Argumentation in the Context of Economic, Environmental and Citizenship Education

One goal of environmental civic education is preparing students, both as citizens and as professionals, to use effective arguments in public debates. Such debates include dominantly economic claims, which are multifaceted and rarely taught in schools. A learning unit that applied the pedagogical principles of socio-scientific issues was developed for ‘Israel’s Natural Gas Export Policy’, a real sustainability dilemma. The study aimed to understand how pre- and in-service science teachers craft their arguments, by comparing their written reasoned opinions on the gas export debate, before and after the learning unit. Content analysis was conducted using Grounded Theory on the two groups’ texts in a multiple case study design. Five reasoning rationales were found: ‘Profits and Risks’, ‘Ethics or Ideology’, ‘Pragmatic Objectives’, ‘Evidence Base’ and ‘Stakeholder Motivations’. Each rationale yielded different reasoning strategies, including ‘Costs/Benefits’, ‘the Trade-Off Dilemma’ or ‘Compromise’, ‘Compensatory Benefits’ and ‘Non-Compensatory Costs/Risks’. The findings show that both groups used more argument types in the post-task. The development of ‘Profits and Risks’ strategies, between the pre- and post-texts, shows how the teachers’ arguments became more complex and decisive. These results exemplify how the SSI-focused learning unit enables learners to enhance their critical citizenship thinking, one of the cornerstones of democracy

Patterns of Students’ Utilization of Flexibility in Online Academic Courses and Their Relation to Course Achievement

Online academic courses provide students with flexible learning opportunities by allowing them to make choices regarding diverse aspects of their learning process; hence, such courses support personalized learning. This study aimed to analyze the ways students make use of flexibility in online academic courses based on learning time, place, and access to learning resources, as well as to investigate how this relates to differences in course achievement. The study examined 587 students in four online courses. Educational data mining (EDM) methodology was used to trace students’ behavior in the courses and to compute 34 variables, which describe their use of flexibility. The results show that students developed different patterns of learning time, place, and access to content, which indicates that flexibility was used substantially. Students’ achievements were significantly related to patterns of learning time and access to learning resources. Understanding the different patterns of flexibility usage may support the design of personalized learning and increase collaboration among students with similar characteristics

Types of Participant Behavior in a Massive Open Online Course

In recent years there has been a proliferation of massive open online courses (MOOCs), which provide unprecedented opportunities for lifelong learning. Registrants approach these courses with a variety of motivations for participation. Characterizing the different types of participation in MOOCs is fundamental in order to be able to better evaluate the phenomenon and to support MOOCs developers and instructors in devising courses which are adapted for different learners' needs. Thus, the purpose of this study was to characterize the different types of participant behavior in a MOOC. Using a data mining methodology, 21,889 participants of a MOOC were classified into clusters, based on their activity in the main learning resources of the course: video lectures, discussion forums, and assessments. Thereafter, the participants in each cluster were characterized in regard to demographics, course participation, and course achievement characteristics. Seven types of participant behavior were identified: Tasters (64.8%), Downloaders (8.5%), Disengagers (11.5%), Offline Engagers (3.6%), Online Engagers (7.4%), Moderately Social Engagers (3.7%), and Social Engagers (0.6%). A significant number of 1,020 participants were found to be engaged in the course, but did not achieve a certificate. The types are discussed according to the established research questions. The results provide further evidence regarding the utilization of the flexibility, which is offered in MOOCs, by the participants according to their needs. Furthermore, this study supports the claim that MOOCs' impact should not be evaluated solely based on certification rates but rather based on learning behaviors