Photonic quadrupole topological insulator using orbital-induced synthetic flux.

The rich physical properties of multiatomic crystals are determined, to a significant extent, by the underlying geometry and connectivity of atomic orbitals. The mixing of orbitals with distinct parity representations, such as s and p orbitals, has been shown to be useful for generating systems that require alternating phase patterns, as with the sign of couplings within a lattice. Here we show that by breaking the symmetries of such mixed-orbital lattices, it is possible to generate synthetic magnetic flux threading the lattice. We use this insight to experimentally demonstrate quadrupole topological insulators in two-dimensional photonic lattices, leveraging both s and p orbital-type modes. We confirm the nontrivial quadrupole topology by observing the presence of protected zero-dimensional states, which are spatially confined to the corners, and by confirming that these states sit at mid-gap. Our approach is also applicable to a broader range of time-reversal-invariant synthetic materials that do not allow for tailored connectivity, and in which synthetic fluxes are essential.

Self-Induced Dirac Boundary State and Digitization in a Nonlinear Resonator Chain.

The low-energy excitations in many condensed matter and metamaterial systems can be well described by the Dirac equation. The mass term associated with these collective excitations, also known as the Dirac mass, can take any value and is directly responsible for determining whether the resultant band structure exhibits a band gap or a Dirac point with linear dispersion. Manipulation of this Dirac mass has inspired new methods of band structure engineering and electron confinement. Notably, it has been shown that a massless state necessarily localizes at any domain wall that divides regions with Dirac masses of different signs. These localized states are known as Jackiw-Rebbi-type Dirac boundary modes and their tunability and localization features have valuable technological potential. In this study, we experimentally demonstrate that nonlinearity within a 1D Dirac material can result in a self-induced domain boundary for the Dirac mass. Our experiments are performed in a dimerized magnetomechanical metamaterial that allows complete control of both the magnitude and sign of the local material nonlinearity, as well as the sign of the Dirac mass. We find that the massless bound state that emerges at the self-induced domain boundary acts similarly to a dopant site within an insulator, causing the material to exhibit a dramatic binary switch in its conductivity when driven above an excitation threshold.

Twitter discourse on nicotine as potential prophylactic or therapeutic for COVID-19.

BACKGROUND: An unproven "nicotine hypothesis" that indicates nicotine's therapeutic potential for COVID-19 has been proposed in recent literature. This study is about Twitter posts that misinterpret this hypothesis to make baseless claims about benefits of smoking and vaping in the context of COVID-19. We quantify the presence of such misinformation and characterize the tweeters who post such messages. METHODS: Twitter premium API was used to download tweets (n = 17,533) that match terms indicating (a) nicotine or vaping themes, (b) a prophylactic or therapeutic effect, and (c) COVID-19 (January-July 2020) as a conjunctive query. A constraint on the length of the span of text containing the terms in the tweets allowed us to focus on those that convey the therapeutic intent. We hand-annotated these filtered tweets and built a classifier that identifies tweets that extrapolate the nicotine hypothesis to smoking/vaping with a positive predictive value of 85%. We analyzed the frequently used terms in author bios, top Web links, and hashtags of such tweets. RESULTS: 21% of our filtered COVID-19 tweets indicate a vaping or smoking-based prevention/treatment narrative. Qualitative analyses show a variety of ways therapeutic claims are being made and tweeter bios reveal pre-existing notions of positive stances toward vaping. CONCLUSION: The social media landscape is a double-edged sword in tobacco communication. Although it increases information reach, consumers can also be subject to confirmation bias when exposed to inadvertent or deliberate framing of scientific discourse that may border on misinformation. This calls for circumspection and additional planning in countering such narratives as the COVID-19 pandemic continues to ravage our world. Our results also serve as a cautionary tale in how social media can be leveraged to spread misleading information about tobacco products in the wake of pandemics.

Joint Learning for Biomedical NER and Entity Normalization: Encoding Schemes, Counterfactual Examples, and Zero-Shot Evaluation.

Named entity recognition (NER) and normalization (EN) form an indispensable first step to many biomedical natural language processing applications. In biomedical information science, recognizing entities (e.g., genes, diseases, or drugs) and normalizing them to concepts in standard terminologies or thesauri (e.g., Entrez, ICD-10, or RxNorm) is crucial for identifying more informative relations among them that drive disease etiology, progression, and treatment. In this effort we pursue two high level strategies to improve biomedical ER and EN. The first is to decouple standard entity encoding tags (e.g., "B-Drug" for the beginning of a drug) into type tags (e.g., "Drug") and positional tags (e.g., "B"). A second strategy is to use additional counterfactual training examples to handle the issue of models learning spurious correlations between surrounding context and normalized concepts in training data. We conduct elaborate experiments using the MedMentions dataset, the largest dataset of its kind for ER and EN in biomedicine. We find that our first strategy performs better in entity normalization when compared with the standard coding scheme. The second data augmentation strategy uniformly improves performance in span detection, typing, and normalization. The gains from counterfactual examples are more prominent when evaluating in zero-shot settings, for concepts that have never been encountered during training.

Improved biomedical word embeddings in the transformer era.

BACKGROUND: Recent natural language processing (NLP) research is dominated by neural network methods that employ word embeddings as basic building blocks. Pre-training with neural methods that capture local and global distributional properties (e.g., skip-gram, GLoVE) using free text corpora is often used to embed both words and concepts. Pre-trained embeddings are typically leveraged in downstream tasks using various neural architectures that are designed to optimize task-specific objectives that might further tune such embeddings. OBJECTIVE: Despite advances in contextualized language model based embeddings, static word embeddings still form an essential starting point in BioNLP research and applications. They are useful in low resource settings and in lexical semantics studies. Our main goal is to build improved biomedical word embeddings and make them publicly available for downstream applications. METHODS: We jointly learn word and concept embeddings by first using the skip-gram method and further fine-tuning them with correlational information manifesting in co-occurring Medical Subject Heading (MeSH) concepts in biomedical citations. This fine-tuning is accomplished with the transformer-based BERT architecture in the two-sentence input mode with a classification objective that captures MeSH pair co-occurrence. We conduct evaluations of these tuned static embeddings using multiple datasets for word relatedness developed by previous efforts. RESULTS: Both in qualitative and quantitative evaluations we demonstrate that our methods produce improved biomedical embeddings in comparison with other static embedding efforts. Without selectively culling concepts and terms (as was pursued by previous efforts), we believe we offer the most exhaustive evaluation of biomedical embeddings to date with clear performance improvements across the board. CONCLUSION: We repurposed a transformer architecture (typically used to generate dynamic embeddings) to improve static biomedical word embeddings using concept correlations. We provide our code and embeddings for public use for downstream applications and research endeavors: https://github.com/bionlproc/BERT-CRel-Embeddings.

Twitter Discourse on Nicotine as Potential Prophylactic or Therapeutic for COVID-19.

BACKGROUND: An unproven "nicotine hypothesis" that indicates nicotine's therapeutic potential for COVID-19 has been proposed in recent literature. This study is about Twitter posts that misinterpret this hypothesis to make baseless claims about benefits of smoking and vaping in the context of COVID-19. We quantify the presence of such misinformation and characterize the tweeters who post such messages. METHODS: Twitter premium API was used to download tweets (n = 17,533) that match terms indicating (a) nicotine or vaping themes, (b) a prophylactic or therapeutic effect, and (c) COVID-19 (January-July 2020) as a conjunctive query. A constraint on the length of the span of text containing the terms in the tweets allowed us to focus on those that convey the therapeutic intent. We hand-annotated these filtered tweets and built a classifier that identifies tweets that extrapolate the nicotine hypothesis to smoking/vaping with a positive predictive value of 85%. We analyzed the frequently used terms in author bios, top Web links, and hashtags of such tweets. RESULTS: 21% of our filtered COVID-19 tweets indicate a vaping or smoking-based prevention/treatment narrative. Qualitative analyses show a variety of ways therapeutic claims are being made and tweeter bios reveal pre-existing notions of positive stances toward vaping. CONCLUSION: The social media landscape is a double-edged sword in tobacco communication. Although it increases information reach, consumers can also be subject to confirmation bias when exposed to inadvertent or deliberate framing of scientific discourse that may border on misinformation. This calls for circumspection and additional planning in countering such narratives as the COVID-19 pandemic continues to ravage our world. Our results also serve as a cautionary tale in how social media can be leveraged to spread misleading information about tobacco products in the wake of pandemics.

Literature Retrieval for Precision Medicine with Neural Matching and Faceted Summarization.

Information retrieval (IR) for precision medicine (PM) often involves looking for multiple pieces of evidence that characterize a patient case. This typically includes at least the name of a condition and a genetic variation that applies to the patient. Other factors such as demographic attributes, comorbidities, and social determinants may also be pertinent. As such, the retrieval problem is often formulated as ad hoc search but with multiple facets (e.g., disease, mutation) that may need to be incorporated. In this paper, we present a document reranking approach that combines neural query-document matching and text summarization toward such retrieval scenarios. Our architecture builds on the basic BERT model with three specific components for reranking: (a). document-query matching (b). keyword extraction and (c). facet-conditioned abstractive summarization. The outcomes of (b) and (c) are used to essentially transform a candidate document into a concise summary that can be compared with the query at hand to compute a relevance score. Component (a) directly generates a matching score of a candidate document for a query. The full architecture benefits from the complementary potential of document-query matching and the novel document transformation approach based on summarization along PM facets. Evaluations using NIST's TREC-PM track datasets (2017-2019) show that our model achieves state-of-the-art performance. To foster reproducibility, our code is made available here: https://github.com/bionlproc/text-summ-for-doc-retrieval.

Observation of a Charge-2 Photonic Weyl Point in the Infrared.

Weyl points are robust point degeneracies in the band structure of a periodic material, which act as monopoles of Berry curvature. They have been at the forefront of research in three-dimensional topological materials as they are associated with novel behavior both in the bulk and on the surface. Here, we present the experimental observation of a charge-2 photonic Weyl point in a low-index-contrast photonic crystal fabricated by two-photon polymerization. The reflection spectrum obtained via Fourier-transform infrared spectroscopy closely matches simulations and shows two bands with quadratic dispersion around a point degeneracy.

Observation of Photonic Topological Valley Hall Edge States.

We experimentally demonstrate topological edge states arising from the valley-Hall effect in two-dimensional honeycomb photonic lattices with broken inversion symmetry. We break the inversion symmetry by detuning the refractive indices of the two honeycomb sublattices, giving rise to a boron nitridelike band structure. The edge states therefore exist along the domain walls between regions of opposite valley Chern numbers. We probe both the armchair and zigzag domain walls and show that the former become gapped for any detuning, whereas the latter remain ungapped until a cutoff is reached. The valley-Hall effect provides a new mechanism for the realization of time-reversal-invariant photonic topological insulators.

Document Retrieval for Biomedical Question Answering with Neural Sentence Matching.

Document retrieval (DR) forms an important component in end-to-end question-answering (QA) systems where particular answers are sought for well-formed questions. DR in the QA scenario is also useful by itself even without a more involved natural language processing component to extract exact answers from the retrieved documents. This latter step may simply be done by humans like in traditional search engines granted the retrieved documents contain the answer. In this paper, we take advantage of datasets made available through the BioASQ end-to-end QA shared task series and build an effective biomedical DR system that relies on relevant answer snippets in the BioASQ training datasets. At the core of our approach is a question-answer sentence matching neural network that learns a measure of relevance of a sentence to an input question in the form of a matching score. In addition to this matching score feature, we also exploit two auxiliary features for scoring document relevance: the name of the journal in which a document is published and the presence/absence of semantic relations (subject-predicate-object triples) in a candidate answer sentence connecting entities mentioned in the question. We rerank our baseline sequential dependence model scores using these three additional features weighted via adaptive random research and other learning-to-rank methods. Our full system placed 2nd in the final batch of Phase A (DR) of task B (QA) in BioASQ 2018. Our ablation experiments highlight the significance of the neural matching network component in the full system.

Hydrodynamic coupling in polygonal arrays of colloids: experimental and analytical results.

Colloidal particles are trapped harmonically on the vertices of planar regular polygons, using optical tweezers. The particles interact with each other via hydrodynamic coupling, which can be described adequately by Oseen's tensor. Because of the interaction, the dynamics of any individual sphere is complex. Thermal motion results in a spectrum of relaxation times. The configuration of a system of N particles can be decomposed into 2N normal modes. In this work it is shown how to calculate these modes and their relaxation time scale analytically. The mathematical structure of the matrix of interaction leads to general properties for the symmetry of the normal modes and their dynamics, differing between the cases of even and odd N. The theory is compared to experiments performed on a range of rings with 3 ≤ N ≤ 10, varying also the trap stiffness and the distance between particles.