Miracle-dz/UniFAR-FTUs huggingface dataset:
Naturalistic affective behaviors decoded from spectro-spatial features of multi-day human intracranial recordings

Task-based studies have uncovered distributed neural networks that support emotions, but little is known about how these networks produce affective behaviors in non-laboratory, ecological settings. We obtained continuous intracranial electroencephalography (iEEG) recordings from the emotion network in 11 patients with epilepsy during multi-day hospitalizations. We coded naturalistic affective behaviors (spontaneous expressions of positive or negative affect) from 116 hours of time-locked video recordings obtained over multiple days from subjects’ hospital rooms and utilized data driven classifiers to determine whether we could decode naturalistic affective behaviors from the neural data. Results indicated that binary within-subject random forest models could decode positive and negative affective behaviors from affectless behaviors (behaviors lacking valence) with up to 93% accuracy. Across the emotion network, positive and negative affective behaviors were associated with increased high frequency activity and decreased lower frequency activity. The anterior insula, amygdala, hippocampus, and anterior cingulate cortex (ACC) made strong contributions to affective behaviors in general. In a subset of subjects, three-state decoders distinguished among the positive, negative, and affectless behaviors using the spectro-spatial features from the emotion network. This study demonstrates that multi-day, highly resolved iEEG recordings in cortical and deep brain structures can reveal the circuit-level physiology of affective behaviors. By measuring behavior in an ecologically valid setting, our findings provide novel insights into the spatially distributed dynamics of local neural populations underlying naturalistic affective behaviors. Significance Previous neuroimaging and neurophysiological studies have identified a distributed network that supports emotions, but much remains unknown about how this network produces affective behaviors in ecological settings. We used intracranial electroencephalography recordings from the emotion network to decode naturalistic affective behaviors—spontaneous expressions of positive and negative affect that occurred during multi-day hospital stays—from neural data in patients with epilepsy. Our results complement prior neuroscientific studies of emotion and offer novel insights into the spectral and spatial dynamics of the emotion network that characterize naturalistic affective behaviors. The present study suggests intracranial electroencephalography can uncover new details about emotion network physiology and help to expand current neuroanatomical frameworks of human emotions.
Conception: Multilingually-Enhanced, Human-Readable Concept Vector Representations

To date, the most successful word, word sense, and concept modelling techniques have used large corpora and knowledge resources to produce dense vector representations that capture semantic similarities in a relatively low-dimensional space. Most current approaches, however, suffer from a monolingual bias, with their strength depending on the amount of data available across languages. In this paper we address this issue and propose Conception, a novel technique for building language-independent vector representations of concepts which places multilinguality at its core while retaining explicit relationships between concepts. Our approach results in high-coverage representations that outperform the state of the art in multilingual and cross-lingual Semantic Word Similarity and Word Sense Disambiguation, proving particularly robust on low-resource languages. Conception – its software and the complete set of representations – is available at https://github.com/SapienzaNLP/conception.
Flight of the PEGASUS? Comparing Transformers on Few-Shot and Zero-Shot Multi-document Abstractive Summarization

Recent work has shown that pre-trained Transformers obtain remarkable performance on many natural language processing tasks including automatic summarization. However, most work has focused on (relatively) data-rich single-document summarization settings. In this paper, we explore highly-abstractive multi-document summarization where the summary is explicitly conditioned on a user-given topic statement or question. We compare the summarization quality produced by three state-of-the-art transformer-based models: BART, T5, and PEGASUS. We report the performance on four challenging summarization datasets: three from the general domain and one from consumer health in both zero-shot and few-shot learning settings. While prior work has shown significant differences in performance for these models on standard summarization tasks, our results indicate that with as few as 10 labeled examples there is no statistically significant difference in summary quality, suggesting the need for more abstractive benchmark collections when determining state-of-the-art.
Universal Dependencies for Manx Gaelic

Manx Gaelic is one of the three Q-Celtic languages, along with Irish and Scottish Gaelic. We present a new dependency treebank for Manx consisting of 291 sentences and about 6000 tokens, annotated according to the Universal Dependency (UD) guidelines. To the best of our knowledge, this is the first annotated corpus of any kind for Manx. Our annotations generally follow the conventions established by the existing UD treebanks for Irish and Scottish Gaelic, although we highlight some areas where the grammar of Manx diverges, requiring new analyses. We use 10fold cross validation to evaluate the accuracy of dependency parsers trained on the corpus, and compare these results with delexicalized models transferred from Irish and Scottish Gaelic.
Large Scale Author Obfuscation Using Siamese Variational Auto-Encoder: The SiamAO System

Author obfuscation is the task of masking the author of a piece of text, with applications in privacy. Recent advances in deep neural networks have boosted author identification performance making author obfuscation more challenging. Existing approaches to author obfuscation are largely heuristic. Obfuscation can, however, be thought of as the construction of adversarial examples to attack author identification, suggesting that the deep learning architectures used for adversarial attacks could have application here. Current architectures are proposed to construct adversarial examples against classification-based models, which in author identification would exclude the high-performing similarity-based models employed when facing large number of authorial classes. In this paper, we propose the first deep learning architecture for constructing adversarial examples against similarity-based learners, and explore its application to author obfuscation. We analyse the output from both success in obfuscation and language acceptability, as well as comparing the performance with some common baselines, and showing promising results in finding a balance between safety and soundness of the perturbed texts.
Incremental Neural Lexical Coherence Modeling

Pretrained language models, neural models pretrained on massive amounts of data, have established the state of the art in a range of NLP tasks. They are based on a modern machine-learning technique, the Transformer which relates all items simultaneously to capture semantic relations in sequences. However, it differs from what humans do. Humans read sentences one-by-one, incrementally. Can neural models benefit by interpreting texts incrementally as humans do? We investigate this question in coherence modeling. We propose a coherence model which interprets sentences incrementally to capture lexical relations between them. We compare the state of the art in each task, simple neural models relying on a pretrained language model, and our model in two downstream tasks. Our findings suggest that interpreting texts incrementally as humans could be useful to design more advanced models.
Preliminary Hardware Benchmarking of a Group of Round 2 NIST Lightweight AEAD Candidates

Abstract. In this report, we analyze the hardware implementations of 10 candidates for Round 2 of the NIST lightweight cryptography standardization process. These candidates are Ascon, DryGASCON, Elephant, Gimli, PHOTON-Beetle, Pyjamask, Romulus, Subterranean, TinyJAMBU and Xoodyak. Specifically, we study the implementations of these algorithms when synthesized using the TSMC 65nm and FDSOI 28nm technologies and Synopsys Design Compiler, targeting various performance trade-offs and different use-cases. We show how different candidates stack-up against such trade-offs. We base our benchmarking parameters and metrics on real-world use-cases, such as high-speed applications, lightweight communication protocols and internet payloads.
TemporalTeller at SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection with Temporal Referencing

This paper describes our TemporalTeller system for SemEval Task 1: Unsupervised Lexical Semantic Change Detection. We develop a unified framework for the common semantic change detection pipelines including preprocessing, learning word embeddings, calculating vector distances and determining threshold. We also propose Gamma Quantile Threshold to distinguish between changed and stable words. Based on our system, we conduct a comprehensive comparison among BERT, Skip-gram, Temporal Referencing and alignment-based methods. Evaluation results show that Skip-gram with Temporal Referencing achieves the best performance of 66.5% classification accuracy and 51.8% Spearman’s Ranking Correlation.
Modality Enriched Neural Network for Metaphor Detection

Metaphor as a cognitive mechanism in human’s conceptual system manifests itself an effective way for language communication. Although being intuitively sensible for human, metaphor detection is still a challenging task due to the subtle ontological differences between metaphorical and non-metaphorical expressions. This work proposes a modality enriched deep learning model for tackling this unsolved issue. It provides a new perspective for understanding metaphor as a modality shift, as in ‘sweet voice’. It also attempts to enhance metaphor detection by combining deep learning with effective linguistic insight. Extending the work at Wan et al. (2020), we concatenate word sensorimotor scores (Lynott et al., 2019) with word vectors as the input of attention-based Bi-LSTM using a benchmark dataset–the VUA corpus. The experimental results show great F1 improvement (above 0.5%) of the proposed model over other methods in record, demonstrating the usefulness of leveraging modality norms for metaphor detection.
Controllable Abstractive Sentence Summarization with Guiding Entities

Entities are the major proportion and build up the topic of text summaries. Although existing text summarization models can produce promising results of automatic metrics, for example, ROUGE, it is difficult to guarantee that an entity is contained in generated summaries. In this paper, we propose a controllable abstractive sentence summarization model which generates summaries with guiding entities. Instead of generating summaries from left to right, we start with a selected entity, generate the left part first, then the right part of a complete summary. Compared to previous entity-based text summarization models, our method can ensure that entities appear in final output summaries rather than generating the complete sentence with implicit entity and article representations. Our model can also generate more novel entities with them incorporated into outputs directly. To evaluate the informativeness of the proposed model, we develop a fine-grained informativeness metrics in the relevance, extraness and omission perspectives. We conduct experiments in two widely-used sentence summarization datasets and experimental results show that our model outperforms the state-of-the-art methods in both automatic evaluation scores and informativeness metrics.
Multitask Easy-First Dependency Parsing: Exploiting Complementarities of Different Dependency Representations

In this paper we present a parsing model for projective dependency trees which takes advantage of the existence of complementary dependency annotations which is the case in Arabic, with the availability of CATiB and UD treebanks. Our system performs syntactic parsing according to both annotation types jointly as a sequence of arc-creating operations, and partially created trees for one annotation are also available to the other as features for the score function. This method gives error reduction of 9.9% on CATiB and 6.1% on UD compared to a strong baseline, and ablation tests show that the main contribution of this reduction is given by sharing tree representation between tasks, and not simply sharing BiLSTM layers as is often performed in NLP multitask systems.
Bacterial community associated with the dry-rot fungus Serpula lacrymans is dominated by Firmicutes and Proteobacteria

The dry-rot fungus Serpula lacrymans causes enormous structural damage by decaying construction timber thereby resulting in tremendous financial loss. Dry-rot fungi decompose cellulose and hemicellulose and, if the wood remains wet, are often accompanied by a succession of bacteria and other fungi. Bacterial-fungal interactions have considerable impact on all interaction partners ranging from antagonistic to beneficial relationships. However, little is known about possible interaction partners of S. lacrymans. Here we show that S. lacrymans has many co-existing, mainly Gram-positive bacteria. By investigating differences in the bacterial community associated with fruiting bodies, mycelia and rhizomorphs, we provide evidence of preferential colonization of S. lacrymans tissues by certain bacterial phyla. Bacteria isolated from fruiting bodies and mycelia were dominated by Firmicutes, while bacteria isolated from rhizomorphs were dominated by Proteobacteria. Actinobacteria and Bacteroidetes were found in lower abundances. In situ fluorescence hybridization (FISH) analysis revealed that bacteria were not present biofilm-like, but occurred as independent cells, sometimes also attached to fungal spores. In co-culture, single bacterial isolates caused growth inhibition of S. lacrymans and vice versa. Additionally, certain bacteria induced pigment production in the fungus. Our results provide first insights for a better understanding of the holobiont S. lacrymans and give hints that bacteria are able to influence the behavior, e.g. growth and secondary metabolite production, of S. lacrymans in culture. Importance Serpula lacrymans is a very effective dry-rot causing fungus, specialized in degradation of coniferous timber in houses. The initial colonization is favored by water damage, and after establishment, the fungus starts to destruct cellulose and hemicellulose. It is among the most feared wood-rotting fungi in the built environment as the remediation of S. lacrymans damaged buildings is expensive and tedious. After improper renovation, the possibility of a recolonization by S. lacrymans is likely. As bacteria influence fungal establishment on wood, the need to investigate the bacterial community associated with S. lacrymans is apparent. The significance of our research is in identifying and characterizing bacteria associated with S. lacrymans. This will allow the assessment of their influence on fungal life style, leading to a broader understanding of the properties that make S. lacrymans so extraordinarily aggressive at decaying wood compared to other indoor wood destroyers.
ManyEnt: A Dataset for Few-shot Entity Typing

We introduce ManyEnt, a benchmark for entity typing models in few-shot scenarios. ManyEnt offers a rich typeset, with a fine-grain variant featuring 256 entity types and a coarse-grain one with 53 entity types. Both versions have been derived from the Wikidata knowledge graph in a semi-automatic fashion. We also report results for two baselines using BERT, reaching up to 70.68% accuracy (10-way 1-shot).
GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation

Data collection for the knowledge graph-to-text generation is expensive. As a result, research on unsupervised models has emerged as an active field recently. However, most unsupervised models have to use non-parallel versions of existing small supervised datasets, which largely constrain their potential. In this paper, we propose a large-scale, general-domain dataset, GenWiki. Our unsupervised dataset has 1.3M text and graph examples, respectively. With a human-annotated test set, we provide this new benchmark dataset for future research on unsupervised text generation from knowledge graphs.
Type VI secretion system killing by commensal Neisseria is influenced by the spatial dynamics of bacteria

Type VI Secretion Systems (T6SS) are widespread in bacteria and can dictate the development and organisation of polymicrobial ecosystems by mediating contact dependent killing. In Neisseria species, including Neisseria cinerea a commensal of the human respiratory tract, interbacterial contacts are mediated by Type four pili (Tfp) which promote formation of aggregates and govern the spatial dynamics of growing Neisseria microcolonies. Here we show that N. cinerea expresses a plasmid-encoded T6SS that is active and can limit growth of related pathogens. We explored the impact of Tfp expression on N. cinerea T6SS-dependent killing and show that expression of Tfp by prey strains enhances their susceptibility to T6SS, by keeping them in close proximity of T6SS-wielding attacker strains. Our findings have important implications for understanding how spatial constraints during contact-dependent antagonism can shape the evolution of microbial communities.
An Empirical Study on Multi-Task Learning for Text Style Transfer and Paraphrase Generation

The topic of this paper is neural multi-task training for text style transfer. We present an efficient method for neutral-to-style transformation using the transformer framework. We demonstrate how to prepare a robust model utilizing large paraphrases corpora together with a small parallel style transfer corpus. We study how much style transfer data is needed for a model on the example of two transformations: neutral-to-cute on internal corpus and modern-to-antique on publicly available Bible corpora. Additionally, we propose a synthetic measure for the automatic evaluation of style transfer models. We hope our research is a step towards replacing common but limited rule-based style transfer systems by more flexible machine learning models for both public and commercial usage.
Precise oculocentric mapping of TMS-evoked phosphenes

Transcranial Magnetic Stimulation (TMS) – evoked phosphenes are oculocentric; their perceived location depends upon eye position. We investigated the accuracy and precision of TMS-evoked phosphene oculocentric mapping and demonstrate that perceived phosphene locations map veridically to eye position, although there are considerable individual differences in the reliability of this mapping. Our results emphasize the need to carefully control eye movements when carrying out phosphene localization studies and suggest that individual differences in the reliability of the reported position of individual phosphenes must be considered.
Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer

Emotion recognition in textual conversations (ERTC) plays an important role in a wide range of applications, such as opinion mining, recommender systems, and so on. ERTC, however, is a challenging task. For one thing, speakers often rely on the context and commonsense knowledge to express emotions; for another, most utterances contain neutral emotion in conversations, as a result, the confusion between a few non-neutral utterances and much more neutral ones restrains the emotion recognition performance. In this paper, we propose a novel Knowledge Aware Incremental Transformer with Multi-task Learning (KAITML) to address these challenges. Firstly, we devise a dual-level graph attention mechanism to leverage commonsense knowledge, which augments the semantic information of the utterance. Then we apply the Incremental Transformer to encode multi-turn contextual utterances. Moreover, we are the first to introduce multi-task learning to alleviate the aforementioned confusion and thus further improve the emotion recognition performance. Extensive experimental results show that our KAITML model outperforms the state-of-the-art models across five benchmark datasets.
KS@LTH at SemEval-2020 Task 12: Fine-tuning Multi- and Monolingual Transformer Models for Offensive Language Detection

This paper describes the KS@LTH system for SemEval-2020 Task 12 OffensEval2: Multilingual Offensive Language Identification in Social Media. We compare mono- and multilingual models based on fine-tuning pre-trained transformer models for offensive language identification in Arabic, Greek, English and Turkish. For Danish, we explore the possibility of fine-tuning a model pre-trained on a similar language, Swedish, and additionally also cross-lingual training together with English.
The Financial Document Causality Detection Shared Task (FinCausal 2020)

We present the FinCausal 2020 Shared Task on Causality Detection in Financial Documents and the associated FinCausal dataset, and discuss the participating systems and results. Two sub-tasks are proposed: a binary classification task (Task 1) and a relation extraction task (Task 2). A total of 16 teams submitted runs across the two Tasks and 13 of them contributed with a system description paper. This workshop is associated to the Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (FNP-FNS 2020), held at The 28th International Conference on Computational Linguistics (COLING’2020), Barcelona, Spain on September 12, 2020.
Jack-of-all-trades paradigm meets long-term data: generalist herbivores are more widespread and locally less abundant

Some of the most common ecological interactions are between plants and herbivorous insects, and these relationships are central to the study of ecological specialization. We address established assumptions about the positive association between local abundance and dietary specialization using a 17-year dataset on the caterpillars of Ecuador. Our long-term data include standardized plot-based samples as well as general, regional collections, allowing for investigations across spatial scales and using different indices of abundance for 1917 morphospecies of Lepidoptera from 33 families. We find that specialists are locally more abundant than generalists, consistent with a key component of the “jack of all trades, master of none” hypothesis, which has otherwise received poor to mixed support from previous studies that have mostly involved fewer species and shorter time series. Generalists achieve greater prevalence across the landscape, and we find some evidence for geographic variation in the abundance-diet breadth relationship, in particular among elevational bands. Interspecific variation in abundance also had a negative relationship with diet breadth, with specialists having more variable abundances across species. The fact that more specialized species can be both rare and common highlights the ecological complexity of specialization.