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Mechanoluminescent materials, which emit light in response to mechanical stimuli, have recently been explored as promising candidates for photonic skins, remote optogenetics, and stress sensing. All mechanoluminescent materials reported thus far are bulk solids with microns-sized grains, and their light emission is only produced when fractured or deformed in bulk form. In contrast, mechanoluminescence has never been observed in liquids and colloidal solutions, thus limiting its biological application in living organisms. Here we report the synthesis of mechanoluminescent fluids via a suppressed dissolution approach. We demonstrate that this approach yields stable colloidal solutions comprising mechanoluminescent nanocrystals with bright emission in the range of 470-610 nm and diameters down to 20 nm. These colloidal solutions can be recharged and discharged repeatedly under photoexcitation and hydrodynamically focused ultrasound, respectively, thus yielding rechargeable mechanoluminescent fluids that can store photon energy in a reversible manner. This rechargeable fluid can facilitate a systemically delivered light source gated by tissue-penetrant ultrasound for biological applications that require light in the tissue, such as optogenetic stimulation in the brain. 

Claim verification is challenging because it requires first to find textual evidence and then apply claim-evidence entailment to verify a claim. Previous works evaluate the entailment step based on the retrieved evidence, whereas we hypothesize that the entailment prediction can provide useful signals for evidence retrieval, in the sense that if a sentence supports or refutes a claim, the sentence must be relevant. We propose a novel model that uses the entailment score to express the relevancy. Our experiments verify that leveraging entailment prediction improves ranking multiple pieces of evidence. 

We propose an unsupervised solution to the Authorship Verification task that utilizes pre-trained deep language models to compute a new metric called DV-Distance. The proposed metric is a measure of the difference between the two authors comparing against pre-trained language models. Our design addresses the problem of non-comparability in authorship verification, frequently encountered in small or cross-domain corpora. To the best of our knowledge, this paper is the first one to introduce a method designed with non-comparability in mind from the ground up, rather than indirectly. It is also one of the first to use Deep Language Models in this setting. The approach is intuitive, and it is easy to understand and interpret through visualization. Experiments on four datasets show our methods matching or surpassing current state-of-the-art and strong baselines in most tasks. 

Predicting users’ opinions in their response to social events has important real-world applications, many of which political and social impacts. Existing approaches derive a population’s opinion on a going event from large scores of user generated content. In certain scenarios, we may not be able to acquire such content and thus cannot infer an unbiased opinion on those emerging events. To address this problem, we propose to explore opinion on unseen articles based on one’s fingerprinting: the prior reading and commenting history. This work presents a focused study on modeling and leveraging fingerprinting techniques to predict a user’s future opinion. We introduce a recurrent neural network based model that integrates fingerprinting. We collect a large dataset that consists of event-comment pairs from six news websites. We evaluate the proposed model on this dataset. The results show substantial performance gains demonstrating the effectiveness of our approach. 

We extend evidence-aware claim verification to the context of positive-unlabeled (PU) learning. Existing works assume the truth and the falsity of the claims are known for training and form the task as a supervised learning problem. However, this assumption underestimates the difficulty of collecting false claims; we argue that claim verification is more challenging in the absence of negative labels. We consider a more practical setting, where only a comparatively small number of true claims are labeled and more claims remain unlabeled. Thus, we formulate the claim verification task as a PU learning problem. We decouple learning representation of claim-evidence pair from PU learning and adopt a pre-trained universal language model to encode claim-evidence pairs. We further propose to use the generative adversarial network (GAN) to capture the latent alignment between encoded claim-evidence pair and the truthfulness. We leverage the verification as part of the GAN by extending previous GAN based PU learning. We show that the proposed model achieves the best performance with a small amount of labeled data and is robust to the truthfulness prior estimation. We conduct a thorough analysis of the model selection. The proposed approach performs the best under two practical scenarios: (i) the unlabeled data is more than the labeled data; (ii) and the unlabeled positive data is more than the unlabeled negative data.