Given a combinatorial optimization problem taking an input, can we learn a strategy to solve it from the examples of input-solution pairs without knowing its objective function? In this paper, we consider such a setting and study the misinformation prevention problem. Given the examples of attacker-protector pairs, our goal is to learn a strategy to compute protectors against future attackers, without the need of knowing the underlying diffusion model. To this end, we design a structured prediction framework, where the main idea is to parameterize the scoring function using random features constructed through distance functions on randomly sampled subgraphs, which leads to a kernelized scoring function with weights learnable via the large margin method. Evidenced by experiments, our method can produce near-optimal protectors without using any information of the diffusion model, and it outperforms other possible graph-based and learning-based methods by an evident margin.

Algorithmic recommender systems such as Spotify and Netflix affect not only consumer behavior but also . Producers seek to create content that will be shown by the recommendation algorithm, which can impact both the diversity and quality of their content. In this work, we investigate the resulting supply-side equilibria in personalized content recommender systems. We model the decisions of producers as choosing content vectors and users as having preferences, which contrasts with classical low-dimensional models. Multi-dimensionality and heterogeneity creates the potential for, where different producers create different types of content at equilibrium. Using a duality argument, we derive necessary and sufficient conditions for whether specialization occurs. Then, we characterize the distribution of content at equilibrium in concrete settings with two populations of users. Lastly, we show that specialization can enable producers to achieve, which means that specialization can reduce the competitiveness of the marketplace. At a conceptual level, our analysis of supply-side competition takes a step towards elucidating how personalized recommendations shape the marketplace of digital goods.

The mysteries of Christmas might that parcel be game or console-shaped? The mysteries of Christmas might that parcel be game or console-shaped? 'I plugged in Zelda and everything changed': developers share their fondest Christmas gaming memories From a family showdown on Guitar Hero III to the winter levels in Diddy Kong Racing, the designers of some of today's top titles recall the gifts and moments that lit up their childhoods T here is a viral video that tends to get passed around at this time of year. It's an old home movie showing a boy and a girl on Christmas morning eagerly unwrapping a present that turns out to be an N64 console - the boy is, to put it mildly, extremely pleased. It's a scene a lot of us who play games will recognise: the excitement and anticipation provided by that big console-sized parcel, or the little DVD-shaped package that could be the latest Super Mario adventure.

Text-conditioned image editing has emerged as a powerful tool for editing images.However, in many situations, language can be ambiguous and ineffective in describing specific image edits.When faced with such challenges, visual prompts can be a more informative and intuitive way to convey ideas.We present a method for image editing via visual prompting.Given pairs of example that represent the before and after images of an edit, our goal is to learn a text-based editing direction that can be used to perform the same edit on new images.We leverage the rich, pretrained editing capabilities of text-to-image diffusion models by inverting visual prompts into editing instructions.Our results show that with just one example pair, we can achieve competitive results compared to state-of-the-art text-conditioned image editing frameworks.

Social recommendation has shown promising improvements over traditional systems since it leverages social correlation data as an additional input. Most existing work assumes that all data are available to the recommendation platform. However, in practice, user-item interaction data (e.g.,rating) and user-user social data are usually generated by different platforms, and both of which contain sensitive information. Therefore, How to perform secure and efficient social recommendation across different platforms, where the data are highly-sparse in nature remains an important challenge. In this work, we bring secure computation techniques into social recommendation, and propose S3Rec, a sparsity-aware secure cross-platform social recommendation framework. As a result, our model can not only improve the recommendation performance of the rating platform by incorporating the sparse social data on the social platform, but also protect data privacy of both platforms. Moreover, to further improve model training efficiency, we propose two secure sparse matrix multiplication protocols based on homomorphic encryption and private information retrieval. Our experiments on two benchmark datasets demonstrate the effectiveness of S3Rec.

Humans have the remarkable ability to recognize and acquire novel visual concepts in a zero-shot manner. Given a high-level, symbolic description of a novel concept in terms of previously learned visual concepts and their relations, humans can recognize novel concepts without seeing any examples. Moreover, they can acquire new concepts by parsing and communicating symbolic structures using learned visual concepts and relations. Endowing these capabilities in machines is pivotal in improving their generalization capability at inference time. In this work, we introduce Zero-shot Concept Recognition and Acquisition (ZeroC), a neuro-symbolic architecture that can recognize and acquire novel concepts in a zero-shot way.

Stranger Things: What could happen next as the show's finale looms? Spoiler warning: This contains some details about what has happened in the show so far, but does not reveal anything about the final four episodes. A Christmas feast may be around the corner, or perhaps another chocolate (no strawberry creams, thanks), but for fans of Stranger Things, another gift is waiting to be consumed. The grand finale of Netflix's hugely popular sci-fi fantasy horror series, which also showcases some questionable 80s fashion choices, is looming. Fans last saw the inhabitants of Hawkins in a perilous place as season five opened, with Demogorgons running rampant, along with the monstrous Vecna.

We consider two important aspects in understanding and editing images: modeling regular, program-like texture or patterns in 2D planes, and 3D posing of these planes in the scene. Unlike prior work on image-based program synthesis, which assumes the image contains a single visible 2D plane, we present Box Program Induction (BPI), which infers a program-like scene representation that simultaneously models repeated structure on multiple 2D planes, the 3D position and orientation of the planes, and camera parameters, all from a single image. Our model assumes a box prior, i.e., that the image captures either an inner view or an outer view of a box in 3D. It uses neural networks to infer visual cues such as vanishing points, wireframe lines to guide a search-based algorithm to find the program that best explains the image. Such a holistic, structured scene representation enables 3D-aware interactive image editing operations such as inpainting missing pixels, changing camera parameters, and extrapolate the image contents.

A research group led by Kyoto University has developed a Christian bot to help broaden access to Christianity in Japan. A research group from Kyoto University has developed a Protestant catechism bot, which recites passages from the Bible, as "a starting point for future Christian AI creation." The project, announced last week, is the latest in a series of collaborations between professor Seiji Kumagai of the Institute for the Future of Human Society, who led the project, and Toshikazu Furuya, CEO of Teraverse, which has previously focused on Buddhist artificial intelligence products and tools. Initially, its use will be limited to "believers under clergy guidance or by the general public within church settings," said Kumagai, but "subsequently, discussions with clergy will explore how to expand the reach to Christian believers and further to non-Christian believers," he said. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

Structured distributions, i.e. distributions over combinatorial spaces, are commonly used to learn latent probabilistic representations from observed data. However, scaling these models is bottlenecked by the high computational and memory complexity with respect to the size of the latent representations. Common models such as Hidden Markov Models (HMMs) and Probabilistic Context-Free Grammars (PCFGs) require time and space quadratic and cubic in the number of hidden states respectively. This work demonstrates a simple approach to reduce the computational and memory complexity of a large class of structured models. We show that by viewing the central inference step as a matrix-vector product and using a low-rank constraint, we can trade off model expressivity and speed via the rank. Experiments with neural parameterized structured models for language modeling, polyphonic music modeling, unsupervised grammar induction, and video modeling show that our approach matches the accuracy of standard models at large state spaces while providing practical speedups.