Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the host range for genetic transfer of magnetic organelle biosynthesis

Magnetosomes produced by magnetotactic bacteria have great potential for application in biotechnology and medicine due to their unique physicochemical properties and high biocompatibility. Recent studies uncovered the genetic determinants for magnetosome formation and inspired ideas to transfer the pathway into hitherto non-magnetic organisms. However, previous efforts to genetically magnetize other species have been successful in only a few bacterial recipients, revealing significant challenges in this approach. Here, by systematic examination of 25 proteobacterial hosts, we generated 7 novel magnetosome-producing strains. We further characterized the recombinant magnetosomes produced by these strains and delineate a set of auxiliary factors linked to magnetosome formation. Our findings will have significant implications for generation of magnetized life cells and the potential to facilitate the production of biogenic magnetic nanoparticles for a wide range of biomedical applications.
Formulations and Direct Numerical Simulation Methods for Effects of Coriolis Force, Latitude, and Stably Stratified Ambient on a Large-Scale Thermal Plume

A thermal plume with (1) the Coriolis force depending on the latitude of the earth, (2) turbulence, (3) varying density and properties, (4) hydrostatic pressure of air column weight placed above a heated square, and (5) stably stratified ambient is first elucidated. The earth model is employed as an analytical model, time-dependent three-dimensional governing equations are formulated, and a direct numerical simulation (DNS) methodology is obtained. Then, the plume without the Coriolis force is computed, the grid dependence is discussed, and the computed results are compared with previous results. As a result, the formulations, numerical methods, and computed results are validated. At the North Pole, a laminar plume with the Coriolis force rises spirally with the earth's rotation in an anticlockwise direction. However, the turbulent plume with the Coriolis has a thin column rotating very quickly and spirally in the clockwise direction. The rotations and heights of laminar and turbulent plumes are suppressed by a stably stratified ambient and/or a decrease of latitude. With increasing Rayleigh number, the rotation direction in the plume is varied to clockwise in the turbulent state from anticlockwise in the laminar state. Whether the thermal flow is turbulent or not is determined by the slopes of power spectrum density (PSD) of time-series data of computed temperature, and a sufficient number of grids and time steps exist in the Kolmogorov microscale.
Expanding the coverage of spatial proteomics: a machine learning approach

Multiplexed protein imaging methods provide valuable information about complex tissue structure and cellular heterogeneity. However, the number of markers that can be measured in the same tissue sample is currently limited. In this paper, we present an efficient method to choose a minimal predictive subset of markers that for the first time allows the prediction of full images for a much larger set of markers. We demonstrate that our approach also outperforms previous methods for predicting cell-level marker composition. Most importantly, we demonstrate that our approach can be used to select a marker set that enables prediction of a much larger set that could not be measured concurrently.
MEG frequency tagging reveals a grid-like code during covert attentional movements

Grid cells in the entorhinal cortex map regular environments with a 60° periodicity, encoding body- and eye-movements’ direction during spatial exploration. Recent evidence in monkeys suggests that grid cells firing is elicited by spatial attention even without eye movements. We investigated whether movements of covert attention can elicit grid-like responses in humans by concurrently recording MEG and eye-tracker. To obtain a measure of grid-like signal non-invasively, we developed a new paradigm based on frequency tagging. While keeping central fixation, participants were presented visually with sequences of linearly-spaced trajectories (15° or 30° in different conditions) formed by static lines or moving dots in separate sessions. Trajectories appeared sequentially on the screen at a fixed rate (6 Hz), allowing different spatial periodicities (e.g., 90°, 60°, 45°) to have corresponding temporal periodicities (e.g., 1, 1.5, 2 Hz), thus resulting in distinct spectral responses in the MEG signal. Analyses of inter-trial coherence evidenced a higher steady-state response for the frequency corresponding to 60° periodicity compared to control periodicities. This effect was localized in medial-temporal sources and not in control regions. Moreover, in a control experiment using a recurrent sequence of letters featuring the same temporal periodicity but lacking spatial structure, the 60° effect did not emerge, suggesting its dependency on spatial movements of attention. We report the first evidence, in humans, that grid-like signals in the medial-temporal lobe can be elicited by covert attentional movements. Moreover, we propose a new neuroimaging paradigm based on frequency tagging to study grid-like activity non-invasively.
Near-Surface Turbulence in the Presence of Breaking Waves

Abstract Observations with a three-axis pulse-to-pulse coherent acoustic Doppler profiler and acoustic resonators reveal the turbulence and bubble field beneath breaking waves in the open ocean at wind speeds up to 14 m s−1. About 55%–80% of velocity wavenumber spectra, calculated with Hilbert spectral analysis based on empirical mode decomposition, are consistent with an inertial subrange. Time series of turbulent kinetic energy dissipation at approximately 1 m beneath the free surface and 1-Hz sampling rate are obtained. High turbulence levels with dissipation rates more than four orders larger than the background dissipation are linked to wave breaking. Initial dissipation levels beneath breaking waves yield the Hinze scale of the maximum bubble size aH ≅ 2 × 10−3 m. Turbulence induced by discrete breaking events was observed to decay as e ∝ tn, where n = −4.3 is close to the theoretical value for isotropic turbulence (−17/4). In the crest region above the mean waterline, dissipation increases as e(z) .
Viscous effects on motion and heating of electrons in inductively coupled plasma reactors

A transport model is developed for nonlocal effects on motion and heating of electrons in inductively coupled plasma reactors. The model is based on the electron momentum equation derived from the Boltzmann equation, retaining anisotropic stress components which in fact are viscous stresses. The resulting model consists of transport equations for the magnitude of electron velocity oscillation and terms representing energy dissipation due to viscous stresses in the electron energy equation. In this model, electrical current is obtained in a nonlocal manner due to viscous effects, instead of Ohm's law or the electron momentum equation without viscous effects, while nonlocal heating of electrons is represented by the viscous dissipation. Computational results obtained by two-dimensional numerical simulations show that nonlocal determination of electrical current indeed is important, and viscous dissipation becomes an important electron heating mechanism at low pressures. It is suspected that viscous dissipation in inductively coupled plasma reactors in fact represents stochastic heating of electrons, and this possibility is exploited by discussing physical similarities between stochastic heating and energy dissipation due to the stress tensor.
Quantitative Annotations of T-Cell Repertoire Specificity

The specificity of a T-cell receptor (TCR) repertoire determines personalized immune capacity. Existing methods have modelled the qualitative aspects of TCR specificity, while the quantitative aspects remained unaddressed. We developed a package, TCRanno, to quantify the specificity of TCR repertoires. Applying TCRanno to 4,195 TCR repertoires revealed quantitative changes in repertoire specificity upon infections, autoimmunity and cancers. Specifically, TCRanno found cytomegalovirus-specific TCRs in seronegative healthy individuals, supporting the possibility of abortive infections. TCRanno discovered age-accumulated fraction of SARS-CoV2-specific TCRs in pre-pandemic samples, which may explain the aggressive symptoms and age-related severity of COVID-19. TCRanno also identified the encounter of Hepatitis B antigens as a potential trigger of systemic lupus erythematosus. TCRanno annotations showed capability in distinguishing TCR repertoires of healthy and cancers including melanoma, lung and breast cancers. TCRanno may also facilitate single-cell TCRseq+gene expression data analyses by isolating T-cells with the specificity of interest.
Unlinked background selection reduces neutral diversity more than linked background selection

Background selection describes the reduction in neutral diversity caused by selection against deleterious alleles at other loci. It is typically assumed that the purging of deleterious alleles affects linked neutral variants, and indeed simulations typically only treat a genomic window. However, background selection at unlinked loci also depresses neutral diversity. We find that for a realistically high genome-wide deleterious mutation rate, the effects of unlinked background selection exceed those of linked background selection. This can be derived from previous approximate analytical approximations of the effects of both kinds of background selection, and we confirm it with simulations that treat multi-locus complexities in a human-like genome. Background selection reduces neutral genetic diversity by a factor that is independent of census population size. Outside of genic regions, the strength of background selection increases with the mean selection coefficient, contradicting the linked theory but in agreement with the unlinked theory. Neutral diversity within genic regions is fairly independent of the strength of selection.
Opinion-leading media as indicators of a democracy at risk: The press and the rise of National Socialism between 1927 and 1932

The rise of National Socialism in the late 1920s and 1930s happened within democratic societies with well-developed media systems. This gives rise to whether an increasingly right-wing press climate aided the emergence of National Socialism. Based on a comparative analysis of post-election reporting in German and Austrian newspapers, this paper argues that changes in the spectrum of opinion-leading media and in the relations both between them and with the opinion-following media can serve as indicators of a democracy at risk. Utilizing sociometric theory, it is hypothesized that in times of severe democratic crisis, the gap between elite-dominated, opinion-leading media and mass newspapers widens; reciprocal orientation among the opinion-leading media is lessened; and, finally, the composition of this group changes in favour of radical political forces. There is empirical evidence for all assumptions, reflecting a growing significance of extreme right-wing positions in the political information environment.
Multi-Label Intent Detection via Contrastive Task Specialization of Sentence Encoders

Deploying task-oriented dialog ToD systems for new domains and tasks requires natural language understanding models that are 1) resource-efficient and work under low-data regimes; 2) adaptable, efficient, and quick-to-train; 3) expressive and can handle complex ToD scenarios with multiple user intents in a single utterance. Motivated by these requirements, we introduce a novel framework for multi-label intent detection (mID): MultI-ConvFiT (Multi-Label Intent Detection via Contrastive Conversational Fine-Tuning). While previous work on efficient single-label intent detection learns a classifier on top of a fixed sentence encoder (SE), we propose to 1) transform general-purpose SEs into task-specialized SEs via contrastive fine-tuning on annotated multi-label data, 2) where task specialization knowledge can be stored into lightweight adapter modules without updating the original parameters of the input SE, and then 3) we build improved mID classifiers stacked on top of fixed specialized SEs. Our main results indicate that MultI-ConvFiT yields effective mID models, with large gains over non-specialized SEs reported across a spectrum of different mID datasets, both in low-data and high-data regimes.
Improving Low-Resource Languages in Pre-Trained Multilingual Language Models

Pre-trained multilingual language models are the foundation of many NLP approaches, including cross-lingual transfer solutions. However, languages with small available monolingual corpora are often not well-supported by these models leading to poor performance. We propose an unsupervised approach to improve the cross-lingual representations of low-resource languages by bootstrapping word translation pairs from monolingual corpora and using them to improve language alignment in pre-trained language models. We perform experiments on nine languages, using contextual word retrieval and zero-shot named entity recognition to measure both intrinsic cross-lingual word representation quality and downstream task performance, showing improvements on both tasks. Our results show that it is possible to improve pre-trained multilingual language models by relying only on non-parallel resources.
Plasmon excitations in cylindrical wires by external charged particles

We study the interaction of external charged particles with the bulk and surface modes of cylindrical wires using classical and quantum mechanical formulations. First, the dielectric function approach is used to calculatethe main induced effects and to derive the average number of excited plasmons in a semiclassical way. Second, the surface and bulk plasmon fields are quantized and the corresponding processes of plasmon excitations are described in detail. The correspondence between the classical and quantum pictures is shown. General scaling properties of the interaction terms are presented, and various illustrative examples are given.
FinSim4-ESG Shared Task: Learning Semantic Similarities for the Financial Domain. Extended edition to ESG insights

This paper describes FinSim4-ESG 1 shared task organized in the 4th FinNLP workshopwhich is held in conjunction with the IJCAI-ECAI-2022 confer- enceThis year, the FinSim4 is extended to the Environment, Social and Government (ESG) insights and proposes two subtasks, one for ESG Taxonomy Enrichment and the other for Sustainable Sentence Prediction. Among the 28 teams registered to the shared task, a total of 8 teams submitted their systems results and 6 teams also submitted a paper to describe their method. The winner of each subtask shows good performance results of 0.85% and 0.95% in terms of accuracy, respectively.
Bernice: A Multilingual Pre-trained Encoder for Twitter

The language of Twitter differs significantly from that of other domains commonly included in large language model training. While tweets are typically multilingual and contain informal language, including emoji and hashtags, most pre-trained language models for Twitter are either monolingual, adapted from other domains rather than trained exclusively on Twitter, or are trained on a limited amount of in-domain Twitter data.We introduce Bernice, the first multilingual RoBERTa language model trained from scratch on 2.5 billion tweets with a custom tweet-focused tokenizer. We evaluate on a variety of monolingual and multilingual Twitter benchmarks, finding that our model consistently exceeds or matches the performance of a variety of models adapted to social media data as well as strong multilingual baselines, despite being trained on less data overall.We posit that it is more efficient compute- and data-wise to train completely on in-domain data with a specialized domain-specific tokenizer.
TUB at WANLP22 Shared Task: Using Semantic Similarity for Propaganda Detection in Arabic

Propaganda and the spreading of fake news through social media have become a serious problem in recent years. In this paper we present our approach for the shared task on propaganda detection in Arabic in which the goal is to identify propaganda techniques in the Arabic social media text. We propose a semantic similarity detection model to compare text in the test set with the sentences in the train set to find the most similar instances. The label of the target text is obtained from the most similar texts in the train set. The proposed model obtained the micro F1 score of 0.494 on the text data set.
Emoji Sentiment Roles for Sentiment Analysis: A Case Study in Arabic Texts

Emoji (digital pictograms) are crucial features for textual sentiment analysis. However, analysing the sentiment roles of emoji is very complex. This is due to its dependency on different factors, such as textual context, cultural perspective, interlocutor’s personal traits, interlocutors’ relationships or a platforms’ functional features. This work introduces an approach to analysing the sentiment effects of emoji as textual features. Using an Arabic dataset as a benchmark, our results confirm the borrowed argument that each emoji has three different norms of sentiment role (negative, neutral or positive). Therefore, an emoji can play different sentiment roles depending upon the context. It can behave as an emphasizer, an indicator, a mitigator, a reverser or a trigger of either negative or positive sentiment within a text. In addition, an emoji may have a neutral effect (i.e., no effect) on the sentiment of the text.
AVA-TMP: A Human-in-the-Loop Multi-layer Dynamic Topic Modeling Pipeline

A phone call is still one of the primary preferred channels for seniors to express their needs, ask questions, and inform potential problems to their health insurance plans. Alignment Healthis a next-generation, consumer-centric organization that is providing a variety of Medicare Advantage Products for seniors. We combine our proprietary technology platform, AVA, and our high-touch clinical model to provide seniors with care as it should be: high quality, low cost, and accompanied by a vastly improved consumer experience. Our members have the ability to connect with our member services and concierge teams 24/7 for a wide variety of ever-changing reasons through different channels, such as phone, email, and messages. We strive to provide an excellent member experience and ensure our members are getting the help and information they need at every touch —ideally, even before they reach us. This requires ongoing monitoring of reasons for contacting us, ensuring agents are equipped with the right tools and information to serve members, and coming up with proactive strategies to eliminate the need for the call when possible.We developed an NLP-based dynamic call reason tagging and reporting pipeline with an optimized human-in-the-loop approach to enable accurate call reason reporting and monitoring with the ability to see high-level trends as well as drill down into more granular sub-reasons. Our system produces 96.4% precision and 30%-50% better recall in tagging calls with proper reasons. We have also consistently achieved a 60+ Net Promoter Score (NPS) score, which illustrates high consumer satisfaction.
Asymmetries in the location of the Venus and Mars bow shock

An examination of observations of the position of the terminator bow shock at Venus and Mars shows that the terminator bow shock varies with the angle between the local bow shock normal and the upstream magnetic field, {theta}{sub BN}. The part of the shock on the quasi-parallel side is closer to the planet than the part on the quasi-perpendicular side, a result which had been sggested by an earlier computer simulation by Thomas and Winske (1990). This bow shock asymmetry is observed to be larger at Mars than at Venus.
Don’t Stop Fine-Tuning: On Training Regimes for Few-Shot Cross-Lingual Transfer with Multilingual Language Models

A large body of recent work highlights the fallacies of zero-shot cross-lingual transfer (ZS-XLT) with large multilingual language models. Namely, their performance varies substantially for different target languages and is the weakest where needed the most: for low-resource languages distant to the source language. One remedy is few-shot transfer (FS-XLT), where leveraging only a few task-annotated instances in the target language(s) may yield sizable performance gains. However, FS-XLT also succumbs to large variation, as models easily overfit to the small datasets. In this work, we present a systematic study focused on a spectrum of FS-XLT fine-tuning regimes, analyzing key properties such as effectiveness, (in)stability, and modularity. We conduct extensive experiments on both higher-level (NLI, paraphrasing) and lower-level tasks (NER, POS), presenting new FS-XLT strategies that yield both improved and more stable FS-XLT across the board. Our findings challenge established FS-XLT methods: e.g., we propose to replace sequential fine-tuning with joint fine-tuning on source and target language instances, offering consistent gains with different number of shots (including resource-rich scenarios). We also show that further gains can be achieved with multi-stage FS-XLT training in which joint multilingual fine-tuning precedes the bilingual source-target specialization.
Rethinking Positional Encoding in Tree Transformer for Code Representation

Transformers are now widely used in code representation, and several recent works further develop tree Transformers to capture the syntactic structure in source code. Specifically, novel tree positional encodings have been proposed to incorporate inductive bias into Transformer.In this work, we propose a novel tree Transformer encoding node positions based on our new description method for tree structures.Technically, local and global soft bias shown in previous works is both introduced as positional encodings of our Transformer model.Our model finally outperforms strong baselines on code summarization and completion tasks across two languages, demonstrating our model’s effectiveness.Besides, extensive experiments and ablation study shows that combining both local and global paradigms is still helpful in improving model performance. We release our code at https://github.com/AwdHanPeng/TreeTransformer.
Joint Completion and Alignment of Multilingual Knowledge Graphs

Knowledge Graph Completion (KGC) predicts missing facts in an incomplete Knowledge Graph (KG). Multilingual KGs associate entities and relations with surface forms written in different languages. An entity or relation may be associated with distinct IDs in different KGs, necessitating entity alignment (EA) and relation alignment (RA). Many effective algorithms have been proposed for completion and alignment as separate tasks. Here we show that these tasks are synergistic and best solved together. Our multitask approach starts with a state-of-the-art KG embedding scheme, but adds a novel relation representation based on sets of embeddings of (subject, object) entity pairs. This representation leads to a new relation alignment loss term based on a maximal bipartite matching between two sets of embedding vectors. This loss is combined with traditional KGC loss and optionally, losses based on text embeddings of entity (and relation) names. In experiments over KGs in seven languages, we find that our system achieves large improvements in KGC compared to a strong completion model that combines known facts in all languages. It also outperforms strong EA and RA baselines, underscoring the value of joint alignment and completion.