Miracle-dz/UniFAR-FTUs huggingface dataset:
A Decade of Archaeological Research on Carriacou, Grenadine Islands, West Indies

Abstract Archaeological research on Carriacou in the southern Grenadines has demonstrated that despite its relatively small size, the island was inhabited prehistorically for at least a thousand years (ca. AD 400-1400) and has an unusually large number of sites. Over the past decade we have conducted research at two major coastal village sites— Grand Bay and Sabazan—that have revealed an archaeologically rich heritage, but one that is also extremely threatened as a result of looting, sand mining, and tidal erosion. Here we provide a brief summary of research from seven field seasons on Carriacou with a focus on results from the 2014 project in what has become one of the longest continuously running field programs in the region. Results demonstrate that Carriacou was: 1) settled slightly later in time (ca. AD 400) compared to nearby islands with higher elevations; 2) engaged in widespread exchange with other islands and possibly mainland South America as evidenced by the discovery of translocated animals, non-local pottery, and ritual objects; and 3) heavily focused on the exploitation of marine resources for subsistence.
Ultrastructure and histochemistry of the fat body of Anthonomus grandis (Coleoptera: Curculionidae)

Abstract Metabolic demands such as growth and reproduction of insects relate directly to the fat body. This organ is responsible for the biosynthesis and storage of nutrients, and participates in the neutralization and detoxification of xenobiotics. Studies show a relationship between its ability to accumulate nutrients and activities, such as diapause and reproduction in the beetle Anthonomus grandis, an important pest of cotton plants. However, there are no detailed studies on the structure and histochemistry of the fat body in this insect. We describe the ultrastructure and histochemistry of the fat body in A. grandis and discuss some of its characteristic features. The fat body is composed of a single cell type, the trophocyte, which is a voluminous cell with a spherical or bilobed nucleus, and cytoplasm containing several lipid droplets, glycogen granules (electron-dense), and protein granules. These trophocytes have few areas of contact with each other and form lobes, enclosed by connective tissue. A high metabolic activity of the fat body is deduced by the intense histochemical staining for protein and polysaccharide compounds, demonstrating that specific processes, such as the synthesis and secretion of vitellogenin, could be a target for more specific studies on methods to control this insect.
Karyology of seven species of bats(Mammalia:Chiroptera)from Guangdong,China

Karyotypes and chromosomal data were presented for 7 species of bats that represent 4 families collected during field studies in Guangdong,China.The species investigated here were Cynopterus sphinx(2n=34,FN=58),Megaderma lyra(2n=54,FN=104),Hipposideros pomona(2n=32,FN=60),H.larvatus(2n=32,FN=60),Myotis davidii(2n=46,FN=52),Scotophilus heathi(2n=36,FN=54),Miniopterus australis(2n=46,FN=50). Hipposideros pomona and Myotis davidii were karyotyped for the first time.In addition,karyotypes of Cynopterus sphinx,Megaderma lyra,Hipposideros larvatus,Scotophilus heathi,and Miniopterus australis had not been previously reported in China.
Alexandronectes zealandiensis gen. et sp. nov., A New Aristonectine Plesiosaur from the Lower Maastrichtian of New Zealand

ABSTRACT A fragmentary plesiosaur skull from lower Maastrichtian levels of the Conway Formation, New Zealand, is redescribed. Originally regarded as pertaining to two separate individuals, we argue that they represent a single individual belonging to a new aristonectine elasmosaurid, Alexandronectes zealandiensis gen. et sp. nov. This new taxon has common morphologies with other aristonectines such as expansion of the pterygoids extending posteriorly beyond the occipital condyle (as observed in Ar. quiriquinensis and probably in Kaiwhekea katiki) and the presence of an ‘A’-shaped squamosal arch in dorsal view. Otherwise, it is distinguished from these latter species by having different paraoccipital processes, a different mandibular glenoid, and an adult skull comparatively smaller than K. katiki and Aristonectes spp. The new taxon is a morphologically intermediate form between the dorsoventrally high skull of K. katiki and the mediolaterally expanded skulls of Aristonectes spp. The studied specimen is the second genus and species and the third report of an aristonectine recovered from lower Maastrichtian beds of New Zealand, emphasizing the diversity of this group in New Zealand and also indicating that aristonectines could include smaller species than those already known.
Joint action of Hall and ambipolar effects in 3D magneto-convection simulations of the quiet Sun

The partial ionization of the solar plasma causes several nonideal effects such as the ambipolar diffusion, the Hall effect, and the Biermann battery effect. Here we report on the first three-dimensional realistic simulations of solar local dynamo where all three effects were taken into account. The simulations started with a snapshot of already saturated battery-seeded dynamo, where two new series were developed: one with solely ambipolar diffusion and another one also taking into account the Hall term in the generalized Ohm’s law. The simulations were then run for about 4 h of solar time to reach the stationary regime and improve the statistics. In parallel, a purely MHD dynamo simulation was also run for the same amount of time. The simulations are compared in a statistical way. We consider the average properties of simulation dynamics, the generation and dissipation of compressible and incompressible waves, and the magnetic Poynting flux. The results show that, with the inclusion of the ambipolar diffusion, the amplitudes of the incompressible perturbations related to Alfvén waves are reduced, and the Poynting flux is absorbed, with a frequency dependence. The Hall effect causes the opposite action: significant excess of incompressible perturbations is generated and an excess of the Poynting flux is observed in the chromospheric layers. The model with ambipolar diffusion shows, on average, sharper current sheets and slightly more abundant fast magneto-acoustic shocks in the chromosphere. The model with the Hall effect has higher temperatures at the lower chromosphere and stronger and more vertical magnetic field concentrations all over the chromosphere. The study of high-frequency waves reveals that significant power of incompressible perturbations is associated with areas with intense and more vertical magnetic fields and larger temperatures. This behavior explains the large Poynting fluxes in the simulations with the Hall effect and provides confirmation as to the role of Alfvén waves in chromospheric heating in internetwork regions, under the action of both Hall and ambipolar effects. We find a positive correlation between the magnitude of the ambipolar heating and the temperature increase at the same location after a characteristic time of 102 s.
Quantum limited superresolution of extended sources in one and two dimensions

We calculate the quantum Fisher information (QFI) for estimating, using a circular imaging aperture, the length of a uniformly bright incoherent line source with a fixed mid-point and the radius of a uniformly bright incoherent disk shaped source with a fixed center. Prolate spheroidal wavefunctions (PSWFs) on a centered line segment and its generalized version on a centered disk furnish the respective bases for computing the eigenstates and eigenvalues of the one-photon density operator, from which we subsequently calculate QFI with respect to the spatial parameters of the two sources. Zernike polynomials provide a good set into which to project the full source wavefront, and such classical wavefront projection data can realize quantum limited estimation error bound in each case. We subsequently generalize our approach to analyze sources of arbitrary brightness distributions and shapes using a certain class of Bessel Fourier functions that are closely related to the PSWFs. We illustrate the general approach by computing QFI for estimating the lengths of the principal axes of a uniformly bright, centered elliptical disk.
Influence of microbial culture in combination with micronutrient in improving the groundnut productivity under alluvial soil of India

A field experiment was carried out to study the effect of cobalt, Rhizobium and phosphobacterium inoculations on growth, yield and nutrient uptake of summer groundnut in an alluvial soil at three levels of cobalt viz., zero, 0.21 kg and 0.42 kg ha-1 with four levels of inoculations viz., uninoculation, inoculation with Rhizobium, inoculation with phosphobacterium and inoculation with both Rhizobium and phosphobacterium. Results indicated that combined application of Rhizobium and phosphobacterium inoculation promoted higher dry matter production, pod yield, oil content and nutrient uptake as compared to application of either of the inoculations. Cobalt @ 0.21 kg ha-1 proved to be better to other doses of cobalt. The percent increase in pod yield of different treatment combinations over untreated control varied from, 2.4% with no inoculation + cobalt @ 0.42 kg ha-1 to 35.6% with Rhizobium + phosphobacterium + cobalt @ 0.21 kg ha-1. The uptake of N, P and K by groundnut was significantly higher in the treatments receiving both inoculation and cobalt at 0.21 kg ha-1 than sole application of either inoculation or cobalt.
Memory-based Jitter: Improving Visual Recognition on Long-tailed Data with Diversity In Memory

This paper considers deep visual recognition on long-tailed data. To make our method general, we tackle two applied scenarios, i.e. , deep classification and deep metric learning. Under the long-tailed data distribution, the most classes (i.e., tail classes) only occupy relatively few samples and are prone to lack of within-class diversity. A radical solution is to augment the tail classes with higher diversity. To this end, we introduce a simple and reliable method named Memory-based Jitter (MBJ). We observe that during training, the deep model constantly changes its parameters after every iteration, yielding the phenomenon of weight jitters. Consequentially, given a same image as the input, two historical editions of the model generate two different features in the deeply-embedded space, resulting in feature jitters. Using a memory bank, we collect these (model or feature) jitters across multiple training iterations and get the so-called Memory-based Jitter. The accumulated jitters enhance the within-class diversity for the tail classes and consequentially improves long-tailed visual recognition. With slight modifications, MBJ is applicable for two fundamental visual recognition tasks, i.e., deep image classification and deep metric learning (on long-tailed data). Extensive experiments on five long-tailed classification benchmarks and two deep metric learning benchmarks demonstrate significant improvement. Moreover, the achieved performance are on par with the state of the art on both tasks.
Scientific Cooperation between the United States and Central and Eastern Europe and the Newly Independent States

Over the past 50 years, the scientific structure of the satellite countries of Central and Eastern Europe (CEE) and the Newly Independent States (NIS) followed the communist pattern of centralized control (Glenday, 1993). Policy decisions and research priorities were made at the top with little input from scientists or stakeholders. Because of the economic importance of the agriculture sector, agricultural research was often subject to a greater degree of centralized planning than theoretical fields of basic science. Agricultural research in CEE and the NIS was conducted within three main structures: the academies of science; research institutes affiliated with the ministries of agriculture; and, to a lesser degree, within agricultural universities. Academies of science were honorary organizations comprised of eminent scholars. The academies wielded influence and power within the scientific community because they managed large budgets. Much of the theoretically based agricultural research came under the purview of academy scientists. The agricultural research institutes were designed to serve the needs of large state farms and cooperatives through the improvement of production agriculture. The research focus of these institutes was in applied areas such as plant and animal breeding, engineering and machinery design, aquaculture, and forestry. With some exceptions, the role of the agricultural universities was vocational education. Students attending universities were not adequately trained in research methods and seldom participated in active research programs. Interaction between universities, research institutes, and academies was minimal. Duplication of research was common, and multi-disciplinary projects were rare. Though the current structure of agricultural science in CEE and the NIS continues to reflect that of the past, there have been some changes in recent years (Dragavtsev and Alexanian, 1993; Glenday, 1993; Jeffrey, 1994). First and foremost, funding for agricultural science at research institutes has been reduced dramatically. In the past, large communities—often up to 500 people—worked in field stations and agricultural research institutes. All of these people were paid by the
Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and Resources

Machine Reading Comprehension (MRC) aims to extract answers to questions given a passage. It has been widely studied recently, especially in open domains. However, few efforts have been made on closed-domain MRC, mainly due to the lack of large-scale training data. In this paper, we introduce a multi-target MRC task for the medical domain, whose goal is to predict answers to medical questions and the corresponding support sentences from medical information sources simultaneously, in order to ensure the high reliability of medical knowledge serving. A high-quality dataset is manually constructed for the purpose, named Multi-task Chinese Medical MRC dataset (CMedMRC), with detailed analysis conducted. We further propose the Chinese medical BERT model for the task (CMedBERT), which fuses medical knowledge into pre-trained language models by the dynamic fusion mechanism of heterogeneous features and the multi-task learning strategy. Experiments show that CMedBERT consistently outperforms strong baselines by fusing context-aware and knowledge-aware token representations.
Quantitative Survey of the State of the Art in Sign Language Recognition

This work presents a meta study covering around 300 published sign language recognition papers with over 400 experimental results. It includes most papers between the start of the field in 1983 and 2020. Additionally, it covers a fine-grained analysis on over 25 studies that have compared their recognition approaches on RWTH-PHOENIX-Weather 2014, the standard benchmark task of the field. Research in the domain of sign language recognition has progressed significantly in the last decade, reaching a point where the task attracts much more attention than ever before. This study compiles the state of the art in a concise way to help advance the field and reveal open questions. Moreover, all of this meta study's source data is made public, easing future work with it and further expansion. The analyzed papers have been manually labeled with a set of categories. The data reveals many insights, such as, among others, shifts in the field from intrusive to non-intrusive capturing, from local to global features and the lack of non-manual parameters included in medium and larger vocabulary recognition systems. Surprisingly, RWTH-PHOENIX-Weather with a vocabulary of 1080 signs represents the only resource for large vocabulary continuous sign language recognition benchmarking world wide.
An Ensemble of Simple Convolutional Neural Network Models for MNIST Digit Recognition

We report that a very high accuracy on the MNIST test set can be achieved by using simple convolutional neural network (CNN) models. We use three different models with 3x3, 5x5, and 7x7 kernel size in the convolution layers. Each model consists of a set of convolution layers followed by a single fully connected layer. Every convolution layer uses batch normalization and ReLU activation, and pooling is not used. Rotation and translation is used to augment training data, which is frequently used in most image classification tasks. A majority voting using the three models independently trained on the training data set can achieve up to 99.87% accuracy on the test set, which is one of the state-of-the-art results. A two-layer ensemble, a heterogeneous ensemble of three homogeneous ensemble networks, can achieve up to 99.91% test accuracy. The results can be reproduced by using the code at: this https URL
Evidence for localized onset of episodic mass loss in Mira

Context. Mass loss from long-period variable stars (LPV) is an important contributor to the evolution of galactic abundances. Dust formation is understood to play an essential role in mass loss. It has, however, proven difficult to develop measurements that strongly constrain the location and timing of dust nucleation and acceleration. Aims. Interferometric imaging has the potential to constrain the geometry and dynamics of mass loss. High angular resolution studies of various types have shown that LPVs have a distinct core-halo structure. These have also shown that LPV images commonly exhibit a non-circular shape. The nature of this shape and its implications are yet to be understood. Methods. Multi-telescope interferometric measurements taken with the Interferometric Optical Telescope Array (IOTA) provide imagery of the LPV Mira in the H-band. This wavelength region is well suited to studying mass loss given the low continuum opacity, which allows for emission to be observed over a very long path in the stellar atmosphere and envelope. Results. The observed visibilities are consistent with a simple core-halo model to represent the central object and the extended molecular layers but, in addition, they demonstrate a substantial asymmetry. An analysis with image reconstruction software shows that the asymmetry is consistent with a localized absorbing patch. The observed opacity is tentatively associated with small dust grains, which will grow substantially during a multi-year ejection process. Spatial information along with a deduced dust content of the cloud, known mass loss rates, and ejection velocities provide evidence for the pulsational pumping of the extended molecular layers. The cloud may be understood as a spatially local zone of enhanced dust formation, very near to the pulsating halo. The observed mass loss could be provided by several such active regions around the star. Conclusions. This result provides an additional clue for better understanding the clumpiness of dust production in the atmosphere of AGB stars. It is compatible with scenarios where the combination of pulsation and convection play a key role in the process of mass loss.
Audio-Visual Waypoints for Navigation

In audio-visual navigation, an agent intelligently travels through a complex, unmapped 3D environment using both sights and sounds to find a sound source (e.g., a phone ringing in another room). Existing models learn to act at a fixed granularity of agent motion and rely on simple recurrent aggregations of the audio observations. We introduce a reinforcement learning approach to audio-visual navigation with two key novel elements 1) audio-visual waypoints that are dynamically set and learned end-to-end within the navigation policy, and 2) an acoustic memory that provides a structured, spatially grounded record of what the agent has heard as it moves. Both new ideas capitalize on the synergy of audio and visual data for revealing the geometry of an unmapped space. We demonstrate our approach on the challenging Replica environments of real-world 3D scenes. Our model improves the state of the art by a substantial margin, and our experiments reveal that learning the links between sights, sounds, and space is essential for audio-visual navigation.
Abstractive Summarization of Spoken and Written Instructions with BERT

Summarization of speech is a difficult problem due to the spontaneity of the flow, disfluencies, and other issues that are not usually encountered in written texts. Our work presents the first application of the BERTSum model to conversational language. We generate abstractive summaries of narrated instructional videos across a wide variety of topics, from gardening and cooking to software configuration and sports. In order to enrich the vocabulary, we use transfer learning and pretrain the model on a few large cross-domain datasets in both written and spoken English. We also do preprocessing of transcripts to restore sentence segmentation and punctuation in the output of an ASR system. The results are evaluated with ROUGE and Content-F1 scoring for the How2 and WikiHow datasets. We engage human judges to score a set of summaries randomly selected from a dataset curated from HowTo100M and YouTube. Based on blind evaluation, we achieve a level of textual fluency and utility close to that of summaries written by human content creators. The model beats current SOTA when applied to WikiHow articles that vary widely in style and topic, while showing no performance regression on the canonical CNN/DailyMail dataset. Due to the high generalizability of the model across different styles and domains, it has great potential to improve accessibility and discoverability of internet content. We envision this integrated as a feature in intelligent virtual assistants, enabling them to summarize both written and spoken instructional content upon request.
Power-type derivatives for rough volatility with jumps

In this paper we propose an efficient pricing-hedging framework for volatility derivatives which simultaneously takes into account path roughness and jumps. Instead of dealing with log-volatility, we directly model the instantaneous variance of a risky asset in terms of a fractional Ornstein-Uhlenbeck process driven by an infinite-activity L\'{e}vy subordinator, which is shown to exhibit roughness under suitable conditions and also eludes the need for an independent Brownian component. This structure renders the characteristic function of forward variance obtainable at least in semi-closed form, subject to a generic integrable kernel. To analyze financial derivatives, primarily swaps and European-style options, on average forward volatility, we introduce a general class of power-type derivatives on the average forward variance, which also provide a way of adjusting the option investor's risk exposure. Pricing formulae are based on numerical inverse Fourier transform and, as illustrated by an empirical study on VIX options, permit stable and efficient model calibration once specified.