Miracle-dz/UniFAR-FTUs huggingface dataset:
Miocene uplift of the NE Greenland margin linked to plate tectonics: Seismic evidence from the Greenland Fracture Zone, NE Atlantic

Tectonic models predict that following breakup, rift margins undergo only decaying thermal subsidence during their postrift evolution. However, postbreakup stratigraphy beneath the NE Atlantic shelves shows evidence of regional‐scale unconformities, commonly cited as outer margin responses to inner margin episodic uplift, including the formation of coastal mountains. The origin of these events remains enigmatic. We present a seismic reflection study from the Greenland Fracture Zone‐East Greenland Ridge (GFZ‐EGR) and the NE Greenland shelf. We document a regional intra‐Miocene seismic unconformity (IMU), which marks the termination of synrift deposition in the deep‐sea basins and onset of (i) thermomechanical coupling across the GFZ, (ii) basin compression, and (iii) contourite deposition, north of the EGR. The onset of coupling across the GFZ is constrained by results of 2‐D flexural backstripping. We explain the thermomechanical coupling and the deposition of contourites by the formation of a continuous plate boundary along the Mohns and Knipovich ridges, leading to an accelerated widening of the Fram Strait. We demonstrate that the IMU event is linked to onset of uplift and massive shelf progradation on the NE Greenland margin. Given an estimated middle to late Miocene (~15–10 Ma) age of the IMU, we speculate that the event is synchronous with uplift of the east and west Greenland margins. The correlation between margin uplift and plate motion changes further indicates that the uplift was triggered by plate tectonic forces, induced perhaps by a change in the Iceland plume (a hot pulse) and/or by changes in intraplate stresses related to global tectonics.
Space- and Ground-Based Geophysical Data Tracking of Magma Migration in Shallow Feeding System of Mount Etna Volcano

After a month-long increase in activity at the summit craters, on 24 December 2018, the Etna volcano experienced a short-lived lateral effusive event followed by a rapid resumption of low-level explosive and degassing activity at the summit vents. By combining space (Moderate Resolution Imaging Spectroradiometer; MODIS and SENTINEL-2 images) and ground-based geophysical data, we track, in near real-time, the thermal, seismic and infrasonic changes associated with Etna’s activity during the September–December 2018 period. Satellite thermal data reveal that the fissural eruption was preceded by a persistent increase of summit activity, as reflected by overflow episodes in New SouthEast Crater (NSE) sector. This behavior is supported by infrasonic data, which recorded a constant increase both in the occurrence and in the energy of the strombolian activity at the same crater sectors mapped by satellite. The explosive activity trend is poorly constrained by the seismic tremor, which shows instead a sudden increase only since the 08:24 GMT on the 24 December 2018, almost concurrently with the end of the infrasonic detections occurred at 06:00 GMT. The arrays detected the resumption of infrasonic activity at 11:13 GMT of 24 December, when tremors almost reached the maximum amplitude. Infrasound indicates that the explosive activity was shifting from the summit crater along the flank of the Etna volcano, reflecting, with the seismic tremor, the intrusion of a gas-rich magma batch along a ~2.0 km long dyke, which reached the surface generating an intense explosive phase. The dyke propagation lasted for almost 3 h, during which magma migrated from the central conduit system to the lateral vent, at a mean speed of 0.15–0.20 m s−1. Based on MODIS and SENTINEL 2 images, we estimated that the summit outflows erupted a volume of lava of 1.4 Mm3 (±0.5 Mm3), and that the lateral effusive episode erupted a minimum volume of 0.85 Mm3 (±0.3 Mm3). The results presented here outline the support of satellite data on tracking the evolution of volcanic activity and the importance to integrate satellite with ground-based geophysical data in improving assessments of volcanic hazard during eruptive crises.
On Knowledge distillation from complex networks for response prediction

Recent advances in Question Answering have lead to the development of very complex models which compute rich representations for query and documents by capturing all pairwise interactions between query and document words. This makes these models expensive in space and time, and in practice one has to restrict the length of the documents that can be fed to these models. Such models have also been recently employed for the task of predicting dialog responses from available background documents (e.g., Holl-E dataset). However, here the documents are longer, thereby rendering these complex models infeasible except in select restricted settings. In order to overcome this, we use standard simple models which do not capture all pairwise interactions, but learn to emulate certain characteristics of a complex teacher network. Specifically, we first investigate the conicity of representations learned by a complex model and observe that it is significantly lower than that of simpler models. Based on this insight, we modify the simple architecture to mimic this characteristic. We go further by using knowledge distillation approaches, where the simple model acts as a student and learns to match the output from the complex teacher network. We experiment with the Holl-E dialog data set and show that by mimicking characteristics and matching outputs from a teacher, even a simple network can give improved performance.
Molecular Basis of Resistance to Selected Antimicrobial Agents in the Emerging Zoonotic Pathogen Streptococcus suis

ABSTRACT Characterization of 227 Streptococcus suis strains isolated from pigs during 2010 to 2013 showed high levels of resistance to clindamycin (95.6%), tilmicosin (94.7%), tylosin (93.8%), oxytetracycline (89.4%), chlortetracycline (86.8%), tiamulin (72.7%), neomycin (70.0%), enrofloxacin (56.4%), penicillin (56.4%), ceftiofur (55.9%), and gentamicin (55.1%). Resistance to tetracyclines, macrolides, aminoglycosides, and fluoroquinolone was attributed to the tet gene, erm(B), erm(C), mph(C), and mef(A) and/or mef(E) genes, aph(3′)-IIIa and aac(6′)-Ie-aph(2″)-Ia genes, and single point mutations in the quinolone resistance-determining region of ParC and GyrA, respectively.
On the Importance of Distinguishing Word Meaning Representations: A Case Study on Reverse Dictionary Mapping

Meaning conflation deficiency is one of the main limiting factors of word representations which, given their widespread use at the core of many NLP systems, can lead to inaccurate semantic understanding of the input text and inevitably hamper the performance. Sense representations target this problem. However, their potential impact has rarely been investigated in downstream NLP applications. Through a set of experiments on a state-of-the-art reverse dictionary system based on neural networks, we show that a simple adjustment aimed at addressing the meaning conflation deficiency can lead to substantial improvements.
Explorative Study on Mapping Surface Facies of Selected Glaciers from Chandra Basin, Himalaya Using WorldView-2 Data

Mapping of surface glacier facies has been a part of several glaciological applications. The study of glacier facies in the Himalayas has gained momentum in the last decade owing to the implications imposed by these facies on the melt characteristics of the glaciers. Some of the most commonly reported surface facies in the Himalayas are snow, ice, ice mixed debris, and debris. The precision of the techniques used to extract glacier facies is of high importance, as the result of many cryospheric studies and economic reforms rely on it. An assessment of a customized semi-automated protocol against conventional and advanced mapping algorithms for mapping glacier surface facies is presented in this study. Customized spectral index ratios (SIRs) are developed for effective extraction of surface facies using thresholding in an object-based environment. This method was then tested on conventional and advanced classification algorithms for an evaluation of the mapping accuracy for five glaciers located in the Himalayas, using very high-resolution WorldView-2 imagery. The results indicate that the object-based image analysis (OBIA) based semi-automated SIR approach achieved a higher average overall accuracy of 87.33% (κ = 0.85) than the pixel-based image analysis (PBIA) approach. Among the conventional methods, the Maximum Likelihood performed the best, with an overall accuracy of 78.71% (κ = 0.75). The Constrained Energy Minimization, with an overall accuracy of 68.76% (κ = 0.63), was the best performer of the advanced algorithms. The advanced methods greatly underperformed in this study. The proposed SIRs show a promise in the mapping of minor features such as crevasses and in the discrimination between ice-mixed debris and debris. We have efficiently mapped surface glacier facies independently of short-wave infrared bands (SWIR). There is a scope for the transferability of the proposed SIRs and their performance in varying scenarios.
Institutioning and Community Radio. A comparative perspective

In recent years, designers have pointed to the role of institutioning, the way in which design processes and institutions are mutually shaped, constrained and enabled. This paper seeks to expand this discussion to the field of grassroots communities, a concept that enlightens the intersection between geographic communities and communities of interests/practice. The research draws on empirical work exploring the different experiences in four distinct socio-cultural and institutional contexts of Uganda, Ireland, Portugal and Romania to investigate how institutioning relate to the design of a new form of community radio based on an innovative technology. It also explores what are the practices that designers and grassroots communities use to manage and navigate potential constraints of institutioning, and offers comparative insight into how institutioning influences the design outcome. Using the concept of institutioning, we will show how, in this interaction, the grassroots communities in the making overcome the "space vs interest" dichotomy, and how institutions as well as communities play a role in shaping - and are potentially shaped by - the design process.
Pooled Contextualized Embeddings for Named Entity Recognition

Contextual string embeddings are a recent type of contextualized word embedding that were shown to yield state-of-the-art results when utilized in a range of sequence labeling tasks. They are based on character-level language models which treat text as distributions over characters and are capable of generating embeddings for any string of characters within any textual context. However, such purely character-based approaches struggle to produce meaningful embeddings if a rare string is used in a underspecified context. To address this drawback, we propose a method in which we dynamically aggregate contextualized embeddings of each unique string that we encounter. We then use a pooling operation to distill a ”global” word representation from all contextualized instances. We evaluate these ”pooled contextualized embeddings” on common named entity recognition (NER) tasks such as CoNLL-03 and WNUT and show that our approach significantly improves the state-of-the-art for NER. We make all code and pre-trained models available to the research community for use and reproduction.
Characterizing and Detecting Duplicate Logging Code Smells

Software logs are widely used by developers to assist in various tasks. Despite the importance of logs, prior studies show that there is no industrial standard on how to write logging statements. Recent research on logs often only considers the appropriateness of a log as an individual item (e.g., one single logging statement); while logs are typically analyzed in tandem. In this paper, we focus on studying duplicate logging statements, which are logging statements that have the same static text message. Such duplications in the text message are potential indications of logging code smells, which may affect developers' understanding of the dynamic view of the system. We manually studied over 3K duplicate logging statements and their surrounding code in four large-scale open source systems and uncovered five patterns of duplicate logging code smells. For each instance of the problematic code smell, we contact developers in order to verify our manual study result. We integrated our manual study result and developers' feedback into our automated static analysis tool, DLFinder, which automatically detects problematic duplicate logging code smells. We evaluated DLFinder on the manually studied systems and two additional systems. In total, combining the results of DLFinder and our manual analysis, DLFinder is able to detect over 85% of the instances which were reported to developers and then fixed.
THE GENETIC ARCHITECTURE OF WING SIZE DIVERGENCE AT VARYING SPATIAL SCALES ALONG A BODY SIZE CLINE IN DROSOPHILA MELANOGASTER

Latitudinal clines in quantitative traits are common, but surprisingly little is known about the genetic bases of these divergences and how they vary within and between clines. Here, we use line‐cross analysis to investigate the genetic architecture of wing size divergences at varying spatial scales along a body size cline in Drosophila melanogaster. Our results revealed that divergences in wing size along the cline were due to strong additive effects. Significant nonadditive genetic effects, including epistasis and maternal effects, were also detected, but they were relatively minor in comparison to the additive effects and none were common to all crosses. There was no evidence of increased epistasis in crosses between more geographically distant populations and, unlike in previous studies, we found no significant dominance effects on wing size in any cross. Our results suggest there is little variation in the genetic control of wing size along the length of the Australian cline. They also highlight marked inconsistencies in the magnitude of dominance effects across studies, which may reflect different opportunities for mutation accumulation while lines are in laboratory culture.
Description-Based Zero-shot Fine-Grained Entity Typing

Fine-grained Entity typing (FGET) is the task of assigning a fine-grained type from a hierarchy to entity mentions in the text. As the taxonomy of types evolves continuously, it is desirable for an entity typing system to be able to recognize novel types without additional training. This work proposes a zero-shot entity typing approach that utilizes the type description available from Wikipedia to build a distributed semantic representation of the types. During training, our system learns to align the entity mentions and their corresponding type representations on the known types. At test time, any new type can be incorporated into the system given its Wikipedia descriptions. We evaluate our approach on FIGER, a public benchmark entity tying dataset. Because the existing test set of FIGER covers only a small portion of the fine-grained types, we create a new test set by manually annotating a portion of the noisy training data. Our experiments demonstrate the effectiveness of the proposed method in recognizing novel types that are not present in the training data.
A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization

This paper provides a simple procedure to fit generative networks to target distributions, with the goal of a small Wasserstein distance (or other optimal transport costs). The approach is based on two principles: (a) if the source randomness of the network is a continuous distribution (the "semi-discrete" setting), then the Wasserstein distance is realized by a deterministic optimal transport mapping; (b) given an optimal transport mapping between a generator network and a target distribution, the Wasserstein distance may be decreased via a regression between the generated data and the mapped target points. The procedure here therefore alternates these two steps, forming an optimal transport and regressing against it, gradually adjusting the generator network towards the target distribution. Mathematically, this approach is shown to minimize the Wasserstein distance to both the empirical target distribution, and also its underlying population counterpart. Empirically, good performance is demonstrated on the training and testing sets of the MNIST and Thin-8 data. The paper closes with a discussion of the unsuitability of the Wasserstein distance for certain tasks, as has been identified in prior work [Arora et al., 2017, Huang et al., 2017].
The World in My Mind: Visual Dialog with Adversarial Multi-modal Feature Encoding

Visual Dialog is a multi-modal task that requires a model to participate in a multi-turn human dialog grounded on an image, and generate correct, human-like responses. In this paper, we propose a novel Adversarial Multi-modal Feature Encoding (AMFE) framework for effective and robust auxiliary training of visual dialog systems. AMFE can force the language-encoding part of a model to generate hidden states in a distribution closely related to the distribution of real-world images, resulting in language features containing general knowledge from both modalities by nature, which can help generate both more correct and more general responses with reasonably low time cost. Experimental results show that AMFE can steadily bring performance gains to different models on different scales of data. Our method outperforms both the supervised learning baselines and other fine-tuning methods, achieving state-of-the-art results on most metrics of VisDial v0.5/v0.9 generative tasks.
A CNN based method for automatic mass detection and classification in mammograms

Abstract A novel system for detection and classification of masses in breast mammography is introduced. The system integrates a breast segmentation module together with a modified region-based convolutional network to obtain detection and classification of masses according to BI-RADS score. While most of the previous work on mass identification in breast mammography has focused on classification, this study proposes to solve both the detection and the classification problems. The method is evaluated on a large multi-centre clinical data-set and compared to ground truth annotated by expert radiologists. Preliminary experimental results show the high accuracy and efficiency obtained by the suggested network structure. As the volume and complexity of data in health care continues to accelerate generalising such an approach may have a profound impact on patient care in many applications.
Lossless or Quantized Boosting with Integer Arithmetic

In supervised learning, efficiency often starts with the choice of a good loss: support vector machines popularised Hinge loss, Adaboost popularised the exponential loss, etc. Recent trends in machine learning have highlighted the necessity for training routines to meet tight requirements on communication, bandwidth, energy, operations, encoding, among others. Fitting the often decades-old state of the art training routines into these new constraints does not go without pain and uncertainty or reduction in the original guarantees. Our paper starts with the design of a new strictly proper canonical, twice differentiable loss called the Q-loss. Importantly, its mirror update over (arbitrary) rational inputs uses only integer arithmetics – more precisely, the sole use of +,−, /,×, |. |. We build a learning algorithm which is able, under mild assumptions, to achieve a lossless boosting-compliant training. We give conditions for a quantization of its main memory footprint, weights, to be done while keeping the whole algorithm boosting-compliant. Experiments display that the algorithm can achieve a fast convergence during the early boosting rounds compared to AdaBoost, even with a weight storage that can be 30+ times smaller. Lastly, we show that the Bayes risk of the Q-loss can be used as node splitting criterion for decision trees and guarantees optimal boosting convergence.
Online Learning with Sleeping Experts and Feedback Graphs

We consider the scenario of online learning with sleeping experts, where not all experts are available at each round, and analyze the general framework of learning with feedback graphs, where the loss observations associated with each expert are characterized by a graph. A critical assumption in this framework is that the loss observations and the set of sleeping experts at each round are independent. We first extend the classical sleeping experts algorithm of Kleinberg et al. (2008) to the feedback graphs scenario, and prove matching upper and lower bounds for the sleeping regret of the resulting algorithm under the independence assumption. Our main contribution is then to relax this assumption, present a more general notion of sleeping regret, and derive a general algorithm with strong theoretical guarantees. We apply this new framework to the important scenario of online learning with abstention, where a learner can elect to abstain from making a prediction at the price of a certain cost. We empirically validate our algorithm against multiple online abstention algorithms on several real-world datasets, showing substantial performance improvements.
Learning to Describe Unknown Phrases with Local and Global Contexts

When reading a text, it is common to become stuck on unfamiliar words and phrases, such as polysemous words with novel senses, rarely used idioms, internet slang, or emerging entities. If we humans cannot figure out the meaning of those expressions from the immediate local context, we consult dictionaries for definitions or search documents or the web to find other global context to help in interpretation. Can machines help us do this work? Which type of context is more important for machines to solve the problem? To answer these questions, we undertake a task of describing a given phrase in natural language based on its local and global contexts. To solve this task, we propose a neural description model that consists of two context encoders and a description decoder. In contrast to the existing methods for non-standard English explanation [Ni+ 2017] and definition generation [Noraset+ 2017; Gadetsky+ 2018], our model appropriately takes important clues from both local and global contexts. Experimental results on three existing datasets (including WordNet, Oxford and Urban Dictionaries) and a dataset newly created from Wikipedia demonstrate the effectiveness of our method over previous work.
The transition module: a method for preventing overfitting in convolutional neural networks

Abstract Digital pathology has advanced substantially over the last decade with the adoption of slide scanners in pathology labs. The use of digital slides to analyse diseases at the microscopic level is both cost-effective and efficient. Identifying complex tumour patterns in digital slides is a challenging problem but holds significant importance for tumour burden assessment, grading and many other pathological assessments in cancer research. The use of convolutional neural networks (CNNs) to analyse such complex images has been well adopted in digital pathology. However, in recent years, the architecture of CNNs has altered with the introduction of inception modules which have shown great promise for classification tasks. In this paper, we propose a modified ‘transition’ module which encourages generalisation in a deep learning framework with few training samples. In the transition module, filters of varying sizes are used to encourage class-specific filters at multiple spatial resolutions followed by global average pooling. We demonstrate the performance of the transition module in AlexNet and ZFNet, for classifying breast tumours in two independent data-sets of scanned histology sections; the inclusion of the transition module in these CNNs improved performance.