Learning Temporal Transformations From Time-Lapse Videos

Based on life-long observations of physical, chemical, and biologic phenomena in the natural world, humans can often easily picture in their minds what an object will look like in the future. But, what about computers? In this paper, we learn computational models of object transformations from time-lapse videos. In particular, we explore the use of generative models to create depictions of objects at future times. These models explore several different prediction tasks: generating a future state given a single depiction of an object, generating a future state given two depictions of an object at different times, and generating future states recursively in a recurrent framework. We provide both qualitative and quantitative evaluations of the generated results, and also conduct a human evaluation to compare variations of our models.Comment: ECCV201

The learning technologies of the future: technologies that learn?

Higher Education Institutions (HEIs) operate in a borderless and complex environment, abundant in potentially useful information. The Creating Academic Learning Futures (CALF) research project, carried out in partnership by the University of Leicester and University College Falmouth in the UK, involves the development of approaches and tools for structuring and filtering information, in order to facilitate institutional decision-making in participative and creative ways. One of the aims of the CALF project is to involve students in creating and exploring a variety of plausible ‘alternative futures’ for learning and teaching technologies in higher education. This paper discusses some of the issues that are emerging in the course of the research process and presents ideas for the future, grounded in and emergent from ‘student voices’ from the CALF research project. Students expected the technologies of the near future to enable them to become co-creators in their own education processes. The future scenarios imagined the rise of learning technologies which instead of becoming outdated with use, become more valuable as more user-generated content is invested, technologies which are truly learning in that they learn about their users and constantly morph/adapt to their users’ needs. Finally, increasing virtualisation was a recurrent theme across most student-generated scenarios. The paper concludes with a discussion of some of the strengths and limitations of using technologies for involving students in creative activities for generating future scenarios for higher education. The technologies used by the project enabled collaborative creative thinking across a broader spectrum of possibilities about the relationship between the present and the future of higher education

Zero-Shot Recommendations with Pre-Trained Large Language Models for Multimodal Nudging

We present a method for zero-shot recommendation of multimodal non-stationary content that leverages recent advancements in the field of generative AI. We propose rendering inputs of different modalities as textual descriptions and to utilize pre-trained LLMs to obtain their numerical representations by computing semantic embeddings. Once unified representations of all content items are obtained, the recommendation can be performed by computing an appropriate similarity metric between them without any additional learning. We demonstrate our approach on a synthetic multimodal nudging environment, where the inputs consist of tabular, textual, and visual data

The institutional character of computerized information systems

We examine how important social and technical choices become part of the history of a computer-based information system (CB/SJ and embedded in the social structure which supports its development and use. These elements of a CBIS can be organized in specific ways to enhance its usability and performance. Paradoxically, they can also constrain future implementations and post-implementations.We argue that CBIS developed from complex, interdependent social and technical choices should be conceptualized in terms of their institutional characteristics, as well as their information-processing characteristics. The social system which supports the development and operation of a CBIS is one major element whose institutional characteristics can effectively support routine activities while impeding substantial innovation. Characterizing CBIS as institutions is important for several reasons: (1) the usability of CBIS is more critical than the abstract information-processing capabilities of the underlying technology; (2) CBIS that are well-used and have stable social structures are more difficult to replace than those with less developed social structures and fewer participants; (3) CBIS vary from one social setting to another according to the ways in which they are organized and embedded in organized social systems. These ideas are illustrated with the case study of a failed attempt to convert a complex inventory control system in a medium-sized manufacturing firm

User Multi-Interest Modeling for Behavioral Cognition

Representation modeling based on user behavior sequences is an important direction in user cognition. In this study, we propose a novel framework called Multi-Interest User Representation Model. Specifically, the model consists of two sub-models. The first sub-module is used to encode user behaviors in any period into a super-high dimensional sparse vector. Then, we design a self-supervised network to map vectors in the first module to low-dimensional dense user representations by contrastive learning. With the help of a novel attention module which can learn multi-interests of user, the second sub-module achieves almost lossless dimensionality reduction. Experiments on several benchmark datasets show that our approach works well and outperforms state-of-the-art unsupervised representation methods in different downstream tasks.Comment: during peer revie

Proprioception, Non-Law, and BioLegal History

This Article explores several advantages of incorporating into law various insights from behavioral biology about how and why the brain works as it does. In particular, the Article explores the ways in which those insights can help illuminate the deep structure of human legal systems. That effort is termed biolegal history