Alginate hydrogels are widely researched in phar-maceutical applications for their abilities to encapsulate and dis-perse therapeutics in response to stimuli. While effective, their utility can be greatly improved once converted into artificial cell soft-microrobots, allowing them to actively navigate through complex in vivo environments and facilitate targeted drug deliv-ery. In this study, artificial cells were fabricated by crosslinking alginate with magnetic nanoparticles and then deployed within mucus solutions to characterize their propulsion capabilities. The goal of this study was to understand how variations in simplified gastrointestinal fluid, artificial cell properties, and magnetic field characteristics could affect surface locomotion. A comparison between automatic feedback control and manual “open-loop” operation was also quantitatively explored. Under feedback control, individual artificial cells were navigated with automatically generated waypoints and a PID controller. Simu-lations were used to verify controller performance and accuracy. User operation was carried out using an Xbox controller, where the joystick could directly change navigation direction. We conclude in this study that the surface navigation of artificial cells is highly predictable within mucus concentrations and that both feedback and open-loop control are equally successful in navigation.

Infants possess a remarkable ability to rapidly learn and process visual inputs. As an infant's mobility increases, so does the variety and dynamics of their visual inputs. Is this change in the properties of the visual inputs beneficial or even critical for the proper development of the visual system? To address this question, we used video recordings from infants wearing head-mounted cameras to train a variety of self-supervised learning models. Critically, we separated the infant data by age group and evaluated the importance of training with a curriculum aligned with developmental order. We found that initiating learning with the data from the youngest age group provided the strongest learning signal and led to the best learning outcomes in terms of downstream task performance. We then showed that the benefits of the data from the youngest age group are due to the slowness and simplicity of the visual experience. The results provide strong empirical evidence for the importance of the properties of the early infant experience and developmental progression in training. More broadly, our approach and findings take a noteworthy step towards reverse engineering the learning mechanisms in newborn brains using image-computable models from artificial intelligence.

One desiderata of topic modeling is to produce interpretable topics. Given a cluster of document-tokens comprising a topic, we can order the topic by counting each word. It is natural to think that each topic could easily be labeled by looking at the words with the highest word count. However, this is not always the case. A human evaluator can often have difficulty identifying a single label that accurately describes the topic as many top words seem unrelated. This paper aims to improve interpretability in topic modeling by providing a novel, outperforming interpretable topic model Our approach combines two previously established subdomains in topic modeling: nonparametric and weakly-supervised topic models. Given a nonparametric topic model, we can include weakly-supervised input using novel modifications to the nonparametric generative model. These modifications lay the groundwork for a compelling setting—one in which most corpora, without any previous supervised or weakly-supervised input, can discover interpretable topics. This setting also presents various challenging sub-problems of which we provide resolutions. Combining nonparametric topic models with weakly-supervised topic models leads to an exciting discovery—a complete, self-contained and outperforming topic model for interpretability.

Biologists synthesize research articles into coherent models—ideally, causal models, which predict how systems will respond to interventions. But it is challenging to derive causal models from articles alone, without primary data. To enable causal discovery using only literature, we built software for annotating empirical results in free text and computing valid explanations, expressed as causal graphs. This paper presents our meta-analytic pipeline: with the “research map” schema, we annotate results in literature, which we convert into logical constraints on causal structure; with these constraints, we find consistent causal graphs using a state-of-the-art, causal discovery algorithm based on answer set programming. Because these causal graphs show which relations are underdetermined, biologists can use this pipeline to select their next experiment. To demonstrate this approach, we annotated neuroscience articles and applied a “degrees-of-freedom” analysis for concisely visualizing features of the causal graphs that remain consistent with the evidence—a model space that is often too large for a machine to compute quickly, or for a researcher to examine exhaustively.

Topic modeling has increasingly attracted interests from researchers. Common methods of topic modeling usually produce a collection of unlabeled topics where each topic is depicted by a distribution of words. Associating semantic meaning with these word distributions is not always straightforward. Traditionally, this task is left to human interpretation. Manually labeling the topics is unfortunately not always easy, as topics generated by unsupervised learning methods do not necessarily align well with our prior knowledge in the subject domains. Currently, two approaches to solve this issue exist. The first is a post-processing procedure that assigns each topic with a label from the prior knowledge base that is semantically closest to the word distribution of the topic. The second is a supervised topic modeling approach that restricts the topics to a predefined set whose word distributions are provided beforehand. Neither approach is ideal, as the former may produce labels that do not accurately describe the word distributions, and the latter lacks the ability to detect unknown topics that are crucial to enrich our knowledge base. Our goal in this paper is to introduce a semisupervised Latent Dirichlet allocation (LDA) model, Source-LDA, which incorporates prior knowledge to guide the topic modeling process to improve both the quality of the resulting topics and of the topic labeling. We accomplish this by integrating existing labeled knowledge sources representing known potential topics into a probabilistic topic model. These knowledge sources are translated into a distribution and used to set the hyperparameters of the Dirichlet generated distribution over words. This approach ensures that the topic inference process is consistent with existing knowledge, and simultaneously, allows for discovery of new topics. The results show improved topic generation and increased accuracy in topic labeling when compared to those obtained using various labeling approaches based off LDA.

In a sponsored search market, the problem of measuring the intensity of competition among advertisers is increasingly gaining prominence today. Usually, search providers want to monitor the advertiser communities that share common bidding keywords, so that they can intervene when competition slackens. However, to the best of our knowledge, not much research has been conducted in identifying advertiser communities and understanding competition within these communities. In this paper we introduce a novel approach to detect competitive communities in a weighted bi-partite network formed by advertisers and their bidding keywords. The proposed approach is based on an advertiser vertex metric called intensity score, which takes the following two factors into consideration: the competitors that bid on the same keywords, and the advertisers' consumption proportion within the community. Evidence shows that when market competition rises, the revenue for a search provider also increases. Our community detection algorithm Max-Intensity is designed to detect communities which have the maximum intensity score. In this paper, we conduct experiments and validate the performance of Max-Intensity on sponsored search advertising data. Compared to baseline methods, the communities detected by our algorithm have low Herfindahl-Hirschman index (HHI) and comprehensive concentration index (CCI), which demonstrates that the communities given by Max-Intensity can capture the structure of the competitive communities.