Miracle-dz/UniFAR-FTUs huggingface dataset:
On Daphnia (Ctenodaphnia) similis claus, 1876 and other interesting anomopods (crustacea, branchiopoda) from Apulia (Southern Italy)

It Nella presente nota sono riportati i risultati dello studio dei campioni di anomopodi (Branchiopoda) raccolti nell’ambito di indagini faunistiche condotte dal Laboratorio di Zoogeografia e Faunistica dell’Universita di Lecce. Sono stati rinvenuti alcuni taxa di interesse da un punto vista tassonomico e faunistico: il rinvenimento di Daphnia (Ctenodaphnia) similis costituisce il primo reperto di questo dafnide in Italia; Pleuroxus letourneuxi , gia conosciuto per la Sicilia e la Sardegna, e stato rinvenuto per la prima volta in Italia peninsulare. Viene brevemente discusso il rinvenimento di alcuni taxa poco comuni e dall’incerta posizione sistematica: Daphnia (Ctenodaphnia) bolivari, Macrothrix groenlandica e Ceriodaphnia quadrangula var. hamata . Sulla base dei campioni studiati la Penisola Salentina ed il promontorio del Gargano sembrano ospitare una ricca fauna ad anomopodi che merita di essere studiata piu estesamente. En This paper refers about the anomopods collected in the frame of a faunistic survey carried out in Apulia by the “Laboratorio di Zoogeografia e Faunistica” of the University of Salento. Some interesting taxa were found. Among these, Daphnia (Ctenodaphnia) similis (Daphniidae) was recorded for the first time in Italy, and Pleuroxus letourneuxi (Chydoridae) in Peninsular Italy. The presence of uncommon taxa with an unclear taxonomical status as Daphnia (Ctenodaphnia) bolivari, Macrothrix groenlandica , and Ceriodaphnia quadrangula var. hamata is briefly discussed. On the basis of samples studied, Salentine Peninsula and Gargano Promontory show a high anomopod species richness and appear to be worth of further, more punctual, studies.
Callus induction from Ipomoea aquatica Forsk. leaf and its antioxidant activity

Callus from the leaves of Ipomoea aquatica Forsk. was initiated on Murashige and Skoog’s basal media supplemented with various combinations of auxins 2,4-dichlorophenoxyacetic acid (2,4-D), -naphthalene acetic acid (NAA), indole-3-acetic acid (IAA) and indole butyric acid (IBA) with kinetin/6-benzyl aminopurine (BA). Callus production was observed in all the media but with varied mass. Highest percentage of callus response was obtained in combination of NAA (1.5 mg L-1) with kinetin (0.5 mg L-1). The friable callus was white in NAA supplemented and brown in 2,4-D and kinetin supplemented media. Three distinct phases viz., lag, exponential or liner and log phase were observed in the growth of callus. Antioxidant activities were analyzed by DPPH, TBARS and metal chelating method. Hyper antioxidant activity was observed in 1-month-old callus produced by NAA in combination with kinetin. The EC50 value of callus extract was 38 ± 3.05 and 54 ± 3.60 by DPPH and TBARS methods, respectively as against 58 ± 2.6 and 64 ±1.2 in in vivo plant material, however, no metal chelating activity was found in both.
Interference of Annual Weeds in Seedling Alfalfa (Medicago sativa)

Barnyardgrass [Echinochloa crus-galli (L.) Beauv. #4 ECHCG] and pigweeds (mixture of Amaranthus retroflexus L. # AMARE and A. powellii S. Wats. # AMAPO) seeded separately with alfalfa (Medicago sativa L.) in mid-August suppressed alfalfa severely before frost killed them in October and November. Some alfalfa was killed, and yield of alfalfa forage was reduced in each of three harvests the following year. These weeds did not harm alfalfa seeded in mid-September. Downy brome (Bromus tectorum L. # BROTE) and tumble mustard (Sisymbrium altissimum L. # SSYAL) suppressed alfalfa seeded in August and September. They reduced alfalfa stands and reduced yield of alfalfa forage in each of three harvests the following year. Alfalfa seeded August 27 and allowed to compete with a mixture of these species for various periods was injured most by weeds that emerged with the alfalfa and remained uncontrolled until forage harvest in May. These weeds did not reduce alfalfa yields if removed by 36 days after alfalfa emergence. Thereafter, yield decreased as the period of weed interference increased. Interference was most damaging in early spring, when growth of winter annual weeds was rapid and vigorous. Weeds seeded 65 or more days after alfalfa emergence did not reduce alfalfa yields but sometimes produced enough biomass to reduce the quality of the first-cutting alfalfa hay.
Local energy flux of turbulent flows

We investigate the local energy flux rate $\Pi_\ell(\bf x)$ towards small scales in isotropic turbulent flows using direct numerical simulations and applying different low-pass filters. Two different filters are examined, a sharp Fourier filter and a Gaussian filter. The probability density function (pdf) of the local energy flux is calculated for the different filters and for different filtering scales. It is shown that the local energy flux is a largely fluctuating quantity taking both negative and positive values and this is more pronounced for the sharp filter. The variance, the skewness and the kurtosis of these fluctuations are shown to increase as the filtering scale is decreased. Furthermore we calculate the joint pdf of $\Pi_\ell(\bf x)$ with the local filtered strain rate $S_\ell$ and the enstrophy $\Omega_\ell$. The flux shows a good correlation with the strain but not with the enstrophy. It is shown that its conditional mean value scales like $ _S \propto \ell^2 S_\ell^{3} $ in support to the Smagorinsky eddy viscosity model. Nonetheless strong fluctuations exist around this value that also need to be modeled. We discuss the implications of our results for subgrid scale models, and propose new modelling directions.
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation

We introduce CGA, a conditional VAE architecture, to control, generate, and augment text. CGA is able to generate natural English sentences controlling multiple semantic and syntactic attributes by combining adversarial learning with a context-aware loss and a cyclical word dropout routine. We demonstrate the value of the individual model components in an ablation study. The scalability of our approach is ensured through a single discriminator, independently of the number of attributes. We show high quality, diversity and attribute control in the generated sentences through a series of automatic and human assessments. As the main application of our work, we test the potential of this new NLG model in a data augmentation scenario. In a downstream NLP task, the sentences generated by our CGA model show significant improvements over a strong baseline, and a classification performance often comparable to adding same amount of additional real data.
Erysiphaceae of New Zealand.

In 1974 I began the study of the New Zealand Erysiphaceae to satisfy my curiosity as to whether different species of powdery mildew are more or less indistinguishable in their imperfect state, as is generally believed, or whether, as is the case in most fungi, species which can be distinguished in their perfect state also possess morphological differences in their imperfect state. I also thought it was strange that only few species had been reported on few host plants although climatic conditions were suitable for the development on a wide range of plants. Most of this study was carried out at the Mt Albert Research Centre, Ministry of Agriculture and Fisheries, Auckland, and extensive use was made of the library of the adjoining Plant Diseases Division, Department of Scientific and Industrial Research (D. S. I. R.). Many of my new recordings have been deposited in the herbarium of the Plant Diseases Division, DSIR. At the completion of my study of living collections of powdery mildews, I examined herbarium samples collected throughout New Zealand.
When does data augmentation help generalization in NLP?

Neural models often exploit superficial ("weak") features to achieve good performance, rather than deriving the more general ("strong") features that we'd prefer a model to use. Overcoming this tendency is a central challenge in areas such as representation learning and ML fairness. Recent work has proposed using data augmentation--that is, generating training examples on which these weak features fail--as a means of encouraging models to prefer the stronger features. We design a series of toy learning problems to investigate the conditions under which such data augmentation is helpful. We show that augmenting with training examples on which the weak feature fails ("counterexamples") does succeed in preventing the model from relying on the weak feature, but often does not succeed in encouraging the model to use the stronger feature in general. We also find in many cases that the number of counterexamples needed to reach a given error rate is independent of the amount of training data, and that this type of data augmentation becomes less effective as the target strong feature becomes harder to learn.
Distantly-Supervised Neural Relation Extraction with Side Information using BERT

Relation extraction (RE) consists in categorizing the relationship between entities in a sentence. A recent paradigm to develop relation extractors is Distant Supervision (DS), which allows the automatic creation of new datasets by taking an alignment between a text corpus and a Knowledge Base (KB). KBs can sometimes also provide additional information to the RE task. One of the methods that adopt this strategy is the RESIDE model, which proposes a distantly-supervised neural relation extraction using side information from KBs. Considering that this method outperformed state-of-the-art baselines, in this paper, we propose a related approach to RESIDE also using additional side information, but simplifying the sentence encoding with BERT embeddings. Through experiments, we show the effectiveness of the proposed method in Google Distant Supervision and Riedel datasets concerning the BGWA and RESIDE baseline methods. Although Area Under the Curve is decreased because of unbalanced datasets, P@N results have shown that the use of BERT as sentence encoding allows superior performance to baseline methods.
What Happens To BERT Embeddings During Fine-tuning?

While much recent work has examined how linguistic information is encoded in pre-trained sentence representations, comparatively little is understood about how these models change when adapted to solve downstream tasks. Using a suite of analysis techniques—supervised probing, unsupervised similarity analysis, and layer-based ablations—we investigate how fine-tuning affects the representations of the BERT model. We find that while fine-tuning necessarily makes some significant changes, there is no catastrophic forgetting of linguistic phenomena. We instead find that fine-tuning is a conservative process that primarily affects the top layers of BERT, albeit with noteworthy variation across tasks. In particular, dependency parsing reconfigures most of the model, whereas SQuAD and MNLI involve much shallower processing. Finally, we also find that fine-tuning has a weaker effect on representations of out-of-domain sentences, suggesting room for improvement in model generalization.
Image-Chat: Engaging Grounded Conversations

To achieve the long-term goal of machines being able to engage humans in conversation, our models should captivate the interest of their speaking partners. Communication grounded in images, whereby a dialogue is conducted based on a given photo, is a setup naturally appealing to humans (Hu et al., 2014). In this work we study large-scale architectures and datasets for this goal. We test a set of neural architectures using state-of-the-art image and text representations, considering various ways to fuse the components. To test such models, we collect a dataset of grounded human-human conversations, where speakers are asked to play roles given a provided emotional mood or style, as the use of such traits is also a key factor in engagingness (Guo et al., 2019). Our dataset, Image-Chat, consists of 202k dialogues over 202k images using 215 possible style traits. Automatic metrics and human evaluations of engagingness show the efficacy of our approach; in particular, we obtain state-of-the-art performance on the existing IGC task, and our best performing model is almost on par with humans on the Image-Chat test set (preferred 47.7% of the time).
On the Discreteness of Transmission Eigenvalues for the Maxwell Equations

In this paper, we establish the discreteness of transmission eigenvalues for Maxwell's equations. More precisely, we show that the spectrum of the transmission eigenvalue problem is discrete, if the electromagnetic parameters $\eps, \, \mu, \, \heps, \, \hmu$ in the equations characterizing the inhomogeneity and background, are smooth in some neighborhood of the boundary, isotropic on the boundary, and satisfy the conditions $\eps \neq \heps$, $\mu \neq \hmu$, and $\eps/ \mu \neq \heps/ \hmu$ on the boundary. These are quite general assumptions on the coefficients which are easy to check. To our knowledge, our paper is the first to establish discreteness of transmission eigenvalues for Maxwell's equations without assuming any restrictions on the sign combination of the contrasts $\eps-\heps$ and $\mu - \hmu$ near the boundary, and allowing for all the electromagnetic parameters to be inhomogeneous and anisotropic, except for on the boundary where they are isotropic but not necessarily constant as it is often assumed in the literature.
Temporal evolution of oscillating coronal loops

Context. Transverse oscillations of coronal structures are currently intensively studied to explore the associated magnetohydrodynamic wave physics and perform seismology of the local medium. Aims. We make a first attempt to measure the thermodynamic evolution of a sample of coronal loops that undergo decaying kink oscillations in response to an eruption in the corresponding active region. Methods. Using data from the six coronal wavelengths of SDO/AIA, we performed a differential emission measure (DEM) analysis of 15 coronal loops before, during, and after the eruption and oscillation. Results. We find that the emission measure, temperature, and width of the DEM distribution undergo significant variations on timescales relevant for the study of transverse oscillations. There are no clear collective trends of increases or decreases for the parameters we analysed. The strongest variations of the parameters occur during the initial perturbation of the loops, and the influence of background structures may also account for much of this variation. Conclusions. The DEM analysis of oscillating coronal loops in erupting active regions shows evidence of evolution on timescales important for the study of oscillations. Further work is needed to separate the various observational and physical mechanisms that may be responsible for the variations in temperature, DEM distribution width, and total emission measure.
Ethnobotany in the folksongs of Castilla y León (Spain)

En este trabajo se presentan los resultados de analizar 7,012 canciones tradicionales de Castilla y Leon, recopiladas en cancioneros publicados entre los anos 1931-2008. Se han revisado 33 publicaciones. El 18.7% de las canciones mencionan especies vegetales, lo que corresponde a 1316 canciones. En cada cancion se menciona una media de 2.1 plantas. El 59% de las canciones solo menciona una especie vegetal. Se han contabilizado un total de 2756 citas botanicas. El total de especies vegetales mencionadas ascendio a 150, que corresponden a 46 familias botanicas. Las plantas mas mencionadas son: rosa (12.7%), vid (7.1%), clavel (6.3%), trigo (5.1%), olivo (4.1%), laurel (2.8%), manzano (2.6%), pino (2.6%) y naranjo (2.3%). Las familias que presentan mayor numero de citas son: Rosaceae, Poaceae, Vitaceae y Caryophyllaceae. El 55% de las citas vegetales se relacionan con plantas que constituyen cultivos. El 76.1% de las canciones fueron catalogadas en algun genero musical, de las cuales se recogieron 1853 citas vegetales. El mayor numero de citas botanicas se recogen en los cantos de baile, boda, serenata y romance. Los cantos de rogativa citan con mayor frecuencia las Poaceas; y en las canciones de navidad, las Rosaceas. El 33.6% de las citas botanicas aluden al valor simbolico de las plantas. En el 50.7% de los casos, la simbologia hizo alusion a la belleza y el 6.9% a una proposicion amorosa. Las plantas que simbolizan la belleza con mas frecuencia son la rosa y el clavel. La propuesta amorosa se simboliza frecuentemente con rosa y laurel. La clase social alta esta simbolizada por trigo, lino y nogal. La clase social baja vinculada a citas sobre centeno o legumbres
Use of Machine Translation to Obtain Labeled Datasets for Resource-Constrained Languages

The large annotated datasets in NLP are overwhelmingly in English. This is an obstacle to progress for other languages. Unfortunately, obtaining new annotated resources for each task in each language would be prohibitively expensive. At the same time, commercial machine translation systems are now robust. Can we leverage these systems to translate English-language datasets automatically? In this paper, we offer a positive response to this for natural language inference (NLI) in Turkish. We translated two large English NLI datasets into Turkish and had a team of experts validate their quality. As examples of the new issues that these datasets help us address, we assess the value of Turkish-specific embeddings and the importance of morphological parsing for developing robust Turkish NLI models.
The nest, eggs and incubation behaviour of Sickle-winged Guan Chamaepetes goudotii fagani in western Ecuador

BirdLife International (2004) currently lists the conservation status of Clytoctantes atrogularis as Critical. For the present, however, I suggest that the species be reverted to its prior conservation status of Data Deficient (Stattersfield et al. 1998). Despite ongoing deforestation in the southern part of the range, C. atrogularis may actually benefit somewhat from the opening of forested areas.
Evolutionary Kuramoto dynamics

Biological systems have a variety of time-keeping mechanisms ranging from molecular clocks within cells to a complex interconnected unit across an entire organism. The suprachiasmatic nucleus, comprising interconnected oscillatory neurons, serves as a master-clock in mammals. The ubiquity of such systems indicates an evolutionary benefit that outweighs the cost of establishing and maintaining them, but little is known about the process of evolutionary development. To begin to address this shortfall, we introduce and analyse a new evolutionary game theoretic framework modelling the behaviour and evolution of systems of coupled oscillators. Each oscillator is characterized by a pair of dynamic behavioural dimensions, a phase and a communication strategy, along which evolution occurs. We measure success of mutations by comparing the benefit of synchronization balanced against the cost of connections between the oscillators. Despite the simple set-up, this model exhibits non-trivial behaviours mimicking several different classical games—the Prisoner’s Dilemma, snowdrift games, coordination games—as the landscape of the oscillators changes over time. Across many situations, we find a surprisingly simple characterization of synchronization through connectivity and communication: if the benefit of synchronization is greater than twice the cost, the system will evolve towards complete communication and phase synchronization.
Logic2Text: High-Fidelity Natural Language Generation from Logical Forms

Previous studies on Natural Language Generation (NLG) from structured data have primarily focused on surface-level descriptions of record sequences. However, for complex structured data, e.g., multi-row tables, it is often desirable for an NLG system to describe interesting facts from logical inferences across records. If only provided with the table, it is hard for existing models to produce controllable and high-fidelity logical generations. In this work, we formulate high-fidelity NLG as generation from logical forms in order to obtain controllable and faithful generations. We present a new large-scale dataset, Logic2Text, with 10,753 descriptions involving common logic types paired with the underlying logical forms. The logical forms show diversified graph structure of free schema, which pose great challenges on the model’s ability to understand the semantics. We experiment on (1) Fully-supervised training with the full datasets, and (2) Few-shot setting, provided with hundreds of paired examples; We compare several popular generation models and analyze their performances. We hope our dataset can encourage research towards building an advanced NLG system capable of natural, faithful, and human-like generation. The dataset and code is available at https://github.com/czyssrs/Logic2Text.
Simulated Multiple Reference Training Improves Low-Resource Machine Translation

Many valid translations exist for a given sentence, and yet machine translation (MT) is trained with a single reference translation, exacerbating data sparsity in low-resource settings. We introduce a novel MT training method that approximates the full space of possible translations by: sampling a paraphrase of the reference sentence from a paraphraser and training the MT model to predict the paraphraser's distribution over possible tokens. With an English paraphraser, we demonstrate the effectiveness of our method in low-resource settings, with gains of 1.2 to 7 BLEU.
A Supervised Word Alignment Method Based on Cross-Language Span Prediction Using Multilingual BERT

We present a novel supervised word alignment method based on cross-language span prediction. We first formalize a word alignment problem as a collection of independent predictions from a token in the source sentence to a span in the target sentence. As this is equivalent to a SQuAD v2.0 style question answering task, we then solve this problem by using multilingual BERT, which is fine-tuned on a manually created gold word alignment data. We greatly improved the word alignment accuracy by adding the context of the token to the question. In the experiments using five word alignment datasets among Chinese, Japanese, German, Romanian, French, and English, we show that the proposed method significantly outperformed previous supervised and unsupervised word alignment methods without using any bitexts for pretraining. For example, we achieved an F1 score of 86.7 for the Chinese-English data, which is 13.3 points higher than the previous state-of-the-art supervised methods.