Generative AI (GenAI) will have significant impact on content creation platforms. In this paper, we study the dynamic competition between a GenAI and a human contributor. Unlike the human, the GenAI's content only improves when more contents are created by the human over time; however, GenAI has the advantage of generating content at a lower cost. We study the algorithmic problem in this dynamic competition model about how the human contributor can maximize her utility when competing against the GenAI for content generation over a set of topics. In time-sensitive content domains (e.g., news or pop music creation) where contents' value diminishes over time, we show that there is no polynomial time algorithm for finding the human's optimal (dynamic) strategy, unless the randomized exponential time hypothesis is false. Fortunately, we are able to design a polynomial time algorithm that naturally cycles between myopically optimizing over a short time window and pausing and provably guarantees an approximation ratio of 1/2 . We then turn to time-insensitive content domains where contents do not lose their value (e.g., contents on history facts). Interestingly, we show that this setting permits a polynomial time algorithm that maximizes the human's utility in the long run. Finally, we conduct simulations that demonstrate the advantage of our algorithms in comparison to a collection of baselines.

We propose PaintTeR, our Paintings TextRank algorithm for extracting art-related text spans from passages on paintings. PaintTeR combines a lexicon of painting words curated automatically through distant supervision with random walks on a large-scale word co-occurrence graph for ranking passage spans for artistic characteristics. The spans extracted with PaintTeR are used in state-of-the-art Question Generation and Reading Comprehension models for designing an interactive aid that enables gallery and museum visitors focus on the artistic elements of paintings. We provide experiments on two datasets of expert-written passages on paintings to showcase the effectiveness of PaintTeR. Evaluations by both gallery experts as well as crowdworkers indicate that our proposed algorithm can be used to select relevant and interesting art-centered questions. To the best of our knowledge, ours is the first work to effectively fine-tune question generation models using minimal supervision for a low-resource, specialized context such as gallery visits.

Priority queues are abstract data structures where each element is associated with a priority, and the highest priority element is always retrieved first from the queue. The data structure is widely used within databases, including the last stage of a merge-sort, forecasting read-ahead I/O to stream data for the merge-sort, and replacement selection sort. Typical software implementations use a balanced binary tree-based structure, providing O(log N) time for both enqueue and dequeue operations. To improve the performance, we propose several scalable and high-speed FPGA-based implementations of a priority queue. Our insight is that the above listed applications primarily use priority queues through “replace” operations, which remove the highest priority element and place a new element into the queue. Thus, our designs are customized for this operation, allowing for a simple and scalable architecture. We implement three priority queue designs, including use of a register-based array, register-based tree, and BRAM-based tree, which have different benefits and trade-offs of throughput, frequency, and maximum size. More importantly, all designs achieve O(1) time between replace operations. To incorporate the best aspects of our designs, we propose a Hybrid Priority Queue (H-PQ), which combines a register-based array with multiple BRAM-based trees. This design provides, on average, very fast access times to the top items in the queue (through the register-based array), while scaling to large priority queue sizes (through the BRAM-based trees). In our evaluations, we find that H-PQ achieves 4.3x speedup and 21.5x energy efficiency, compared with the Xeon CPU implementations.

MOTIVATION: Microarray data analysis is often applied to characterize disease populations by identifying individual genes linked to the disease. In recent years, efforts have shifted to focus on sets of genes known to perform related biological functions (i.e. in the same pathways). Evaluating gene sets reduces the need to correct for false positives in multiple hypothesis testing. However, pathways are often large, and genes in the same pathway that do not contribute to the disease can cause a method to miss the pathway. In addition, large pathways may not give much insight to the cause of the disease. Moreover, when such a method is applied independently to two datasets of the same disease phenotypes, the two resulting lists of significant pathways often have low agreement. RESULTS: We present a powerful method, PFSNet, that identifies smaller parts of pathways (which we call subnetworks), and show that significant subnetworks (and the genes therein) discovered by PFSNet are up to 51% (64%) more consistent across independent datasets of the same disease phenotypes, even for datasets based on different platforms, than previously published methods. We further show that those methods which initially declared some large pathways to be insignificant would declare subnetworks detected by PFSNet in those large pathways to be significant, if they were given those subnetworks as input instead of the entire large pathways. AVAILABILITY: http://compbio.ddns.comp.nus.edu.sg:8080/pfsnet/

BACKGROUND: The metabolic network is an aggregation of enzyme catalyzed reactions that converts one compound to another. Paths in a metabolic network are a sequence of enzymes that describe how a chemical compound of interest can be produced in a biological system. As the number of such paths is quite large, many methods have been developed to score paths so that the k-shortest paths represent the set of paths that are biologically meaningful or efficient. However, these approaches do not consider whether the sequence of enzymes can be manufactured in the same pathway/species/localization. As a result, a predicted sequence might consist of groups of enzymes that operate in distinct pathway/species/localization and may not truly reflect the events occurring within cell. RESULTS: We propose a path weighting method CMPF (Class-switching Minimized Pathfinder) to search for routes in a metabolic network which minimizes pathway switching. In biological terms, a pathway is a series of chemical reactions which define a specific function (e.g. glycolysis). We conjecture that routes that cross many pathways are inefficient since different pathways define different metabolic functions. In addition, native routes are also well characterized within pathways, suggesting that reasonable paths should not involve too many pathway switches. Our method can be generalized when reactions participate in a class set (e.g., pathways, species or cellular localization) so that the paths predicted have minimal class crossings. CONCLUSIONS: We show that our method generates k-paths that involve the least number of class switching. In addition, we also show that native paths are recoverable and alternative paths deviates less from native paths compared to other methods. This suggests that paths ranked by our method could be a way to predict paths that are likely to occur in biological systems.