Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporal Distribution of Ectomycorrhizal Fungi and Pollen as a Seasonal Nutrient Source in a Boreal Forest, Canada

Seasonal distribution of ectomycorrhizal associations in various types of forest in a boreal forest in Manitoba. Canada was investigated. Alsohe relationship between ectomycorrhizal growth and pine pollen nutrients was examined. In four different forest stands, ectomycorrhizas tended to be lower in the spring than in the summer and fall samples. In addition. a mature jack pine (Pinus banksiana) stand showed higher mycorrhizal activities than a young jack pine stand. Growth of Suillus brevipes hyphae wa ts stimulated by additions of pollen representing mean pollen deposition in Mistik Creek study area after 30 and 70 days of growth with dextrose availability. This result suggests that the peak ectomycorrhizal activity is followed by pollen deposition in the study region and therefore, addition of pine and spruce pollen in early or middle of June in the boreal forest can be an important seasonal nutrient source for ectomycorrhizal growth.
Estimation et validation de modèles GARCH asymétriques en puissance multivariés à corrélations conditionnelles.

Cette these presente quelques contributions a la modelisation des series financieres, notamment dans le developpement d’extensions de modeles ainsi que le developpement d’outils utiles a la validation de ceux-ci. Tous les resultats sont illustres par des donnees simulees et mis en œuvre sur des donnees reelles. L’ensemble des resultats est base sur la procedure d’estimation du Quasi- Maximum de Vraisemblance et repose sur les tests Portmanteau en ce qui concerne la validation des modeles.Dans le cadre univarie, nous proposons une extension des tests Portmanteau pour le modele GARCH asymetrique en puissance, dans le cas ou la puissance est inconnue et doit etre estimee simultanement aux autres parametres. Une extension de ce modele est realisee dans le cas multivarie, en considerant la correlation conditionnelle constante au cours du temps.Dans ce cadre, nous avons developpe la procedure d’estimation du modele CCC-APGARCH dans les cas ou la puissance est connue et inconnue. Les proprietes asymptotiques de l’estimateur sont etablies dans les deux cas et sont utilisees pour etendre le test Portmanteau a ce modele.
Query-level Early Exit for Additive Learning-to-Rank Ensembles

Search engine ranking pipelines are commonly based on large ensembles of machine-learned decision trees. The tight constraints on query response time recently motivated researchers to investigate algorithms to make faster the traversal of the additive ensemble or to early terminate the evaluation of documents that are unlikely to be ranked among the top-k. In this paper, we investigate the novel problem of query-level early exiting, aimed at deciding the profitability of early stopping the traversal of the ranking ensemble for all the candidate documents to be scored for a query, by simply returning a ranking based on the additive scores computed by a limited portion of the ensemble. Besides the obvious advantage on query latency and throughput, we address the possible positive impact on ranking effectiveness. To this end, we study the actual contribution of incremental portions of the tree ensemble to the ranking of the top-k documents scored for a given query. Our main finding is that queries exhibit different behaviors as scores are accumulated during the traversal of the ensemble and that query-level early stopping can remarkably improve ranking quality. We present a reproducible and comprehensive experimental evaluation, conducted on two public datasets, showing that query-level early exiting achieves an overall gain of up to 7.5% in terms of NDCG@10 with a speedup of the scoring process of up to 2.2x.
Efficacy of feeding frequency on growth and survival of Labeo rohita (Ham.) fingerlings under intensive rearing.

Effect of feeding frequency on growth and survival of Labeo rohita (rohu), during fingerlings rearing was studied in 280 L glass aquaria. Feeding frequency one, two and three times daily as three treatments were evaluated against a control without feeding in triplicate glass aquaria in a trial of 60 days. Single breed fingerlings ranges from Mean length 6.8±0.3 cm, Mean weight 4.18±0.02 g were stocked at the density of 25/aquarium. Survival rate were recorded as 100 %, Similarly, the growth in terms of length and the feed conversion ratio did not differ significantly (P > 0.05) among treatments and were in the ranges of 7.92 – 13.6 cm and 1.39 1.54 respectively. The study suggests that feeding frequency of three times daily is sufficient for under intensive rearing culture of Labeo rohita fingerlings.
Analysis and Control of Continuous Microaerobic Ethanol Production by Yeast

Glucose fermentation by Saccharomyces cerevisiae, and xylose fermentation by Candida utilis and Pichia stipitis, were investigated under microaerobic conditions. A dynamic experimental method named oxygen programmed fermentation (OPF), and a method for microaerobic RQ control in continuous culture have been developed. An OPF allows a relatively fast identification of the range of interesting oxygen uptake rates, and gives information on the dynamics of the culture in response to a slow washout of oxygen. Results obtained with this method were in good agreement with the characteristics of the three yeasts. In situ NAD(P)H fluorescence measurements during OPF:s with S. cerevisiae indicated, that the intracellular redox level increased with decreasing oxygen concentration up to a certain level, at which glycerol was produced as a redox sink. Different glycerol-3-phospate dehydrogenase deletion mutants of S. cerevisiae responded remarkably similar in OPF experiments. Under microaerobic conditions, the . DELTA.gpd1 / . DELTA.gpd2 double mutant produced somewhat more ethanol and only traces of glycerol. In microaerobic, nitrogen-limited chemostat cultures of S. cerevisiae, the ethanol yield was up to 8 % higher compared to anaerobic, carbon-limited conditions, depending on the dilution rate. Apart from NADH reoxidation, the major effect of the oxygen was to increase the magnitude of the nitrogen limitation. The respiratory qotient (RQ) was controlled in continuous cultures of S. cerevisiae over a range of setpoints of 6 to 80, by changing the inlet gas composition with a PID controller with gain scheduling. The ethanol yield reached a flat maximum at RQ values between 12.5 and 50. The decrease in the glycerol yield at lower RQ was accompanied by an increase in the biomass yield. Metabolic flux analysis indicated that the disappearance of glycerol coincided with the cyclic operation of the TCA cycle reactions. This means, that glucose is not catabolized oxidatively until there is sufficient oxygen to allow for redox balancing of the produced NADH. A combination of anabolic limitation, and a controlled oxygen addition, should therefore have a large potential for increasing the ethanol yield.
PreCNet: Next Frame Video Prediction Based on Predictive Coding

Predictive coding, currently a highly influential theory in neuroscience, has not been widely adopted in machine learning yet. In this work, we transform the seminal model of Rao and Ballard (1999) into a modern deep learning framework while remaining maximally faithful to the original schema. The resulting network we propose (PreCNet) is tested on a widely used next-frame video prediction benchmark, which consists of images from an urban environment recorded from a car-mounted camera, and achieves state-of-the-art performance. Performance on all measures (MSE, PSNR, and SSIM) was further improved when a larger training set (2M images from BDD100k) pointed to the limitations of the KITTI training set. This work demonstrates that an architecture carefully based on a neuroscience model, without being explicitly tailored to the task at hand, can exhibit exceptional performance.
Arithmetic level raising on triple product of Shimura curves and Gross-Schoen diagonal cycles II: Bipartite Euler system

In this article, we study the Gross-Schoen diagonal cycle on the triple product of Shimura curves at a place of good reduction. We prove the unramified arithmetic level raising theorem for triple product of Shimura curves and we deduce from it the second reciprocity law which relates the image of the diagonal cycle under the Abel-Jacobi map to certain period integral of Gross-Kudla type. Along with the first reciprocity law we proved in a previous work, we show that the Gross-Schoen diagonal cycles form a Bipartite Euler system for the symmetric cube motive of modular forms. As an application we provide some evidence for the rank $1$ case of the Bloch-Kato conjecture for the symmetric cube motive of a modular form.
Manifestation of strong correlations in transport in ultraclean SiGe/Si/SiGe quantum wells

We observe that in a strongly interacting two-dimensional electron system in ultra-clean SiGe/Si/SiGe quantum wells, the resistivity on the metallic side near the metal-insulator transition increases with decreasing temperature, reaches a maximum at some temperature, and then decreases by more than one order of magnitude. We scale the resistivity data in line with expectations for the transport of strongly correlated Fermi systems and find a nearly perfect agreement with theory over a wide range of electron densities.
Analysis of the spectrum of a $2\times 2$ operator matrix. Discrete spectrum asymptotics

We consider a $2 \times 2$ operator matrix ${\mathcal A}_\mu,$ $\mu>0$ related with the lattice systems describing two identical bosons and one particle, another nature in interactions, without conservation of the number of particles. We obtain an analogue of the Faddeev equation and its symmetric version for the eigenfunctions of ${\mathcal A}_\mu$. We describe the new branches of the essential spectrum of ${\mathcal A}_\mu$ via the spectrum of a family of generalized Friedrichs models. It is established that the essential spectrum of ${\mathcal A}_\mu$ consists the union of at most three bounded closed intervals and their location is studied. For the critical value $\mu_0$ of the coupling constant $\mu$ we establish the existence of infinitely many eigenvalues, which are located in the both sides of the essential spectrum of ${\mathcal A}_\mu$. In this case, an asymptotic formula for the discrete spectrum of ${\mathcal A}_\mu$ is found.
The Role of Context in Neural Pitch Accent Detection in English

Prosody is a rich information source in natural language, serving as a marker for phenomena such as contrast. In order to make this information available to downstream tasks, we need a way to detect prosodic events in speech. We propose a new model for pitch accent detection, inspired by the work of Stehwien et al. (2018), who presented a CNN-based model for this task. Our model makes greater use of context by using full utterances as input and adding an LSTM layer. We find that these innovations lead to an improvement from 87.5% to 88.7% accuracy on pitch accent detection on American English speech in the Boston University Radio News Corpus, a state-of-the-art result. We also find that a simple baseline that just predicts a pitch accent on every content word yields 82.2% accuracy, and we suggest that this is the appropriate baseline for this task. Finally, we conduct ablation tests that show pitch is the most important acoustic feature for this task and this corpus.
Emergent black hole thermodynamics from monodromy

We argue that the equations of motion of quantum field theories in curved backgrounds encode new fundamental black hole thermodynamic relations. We define new entropy variation relations. These `emerge' through the monodromies that capture the infinitesimal changes in the black hole background produced by the field excitations. This raises the possibility of new thermodynamic relations defined as independent sums involving entropies, temperatures and angular velocities defined at every black hole horizon. We present explicit results for the sum of all horizon entropy variations for general rotating black holes, both in asymptotically at and asymptotically anti-de Sitter spacetimes in four and higher dimensions. The expressions are universal, and in most cases add up to zero. We also find that these thermodynamic summation relations apply in theories involving multi-charge black holes.
EER: Enterprise Expert Ranking using Employee Reputation

The emergence of online enterprises spread across continents have given rise to the need for expert identification in this domain. Scenarios that includes the intention of the employer to find tacit expertise and knowledge of an employee that is not documented or self-disclosed has been addressed in this article. The existing reputation based approaches towards expertise ranking in enterprises utilize PageRank, normal distribution, and hidden Markov model for expertise ranking. These models suffer issue of negative referral, collusion, reputation inflation, and dynamism. The authors have however proposed a Bayesian approach utilizing beta probability distribution based reputation model for employee ranking in enterprises. The experimental results reveal improved performance compared to previous techniques in terms of Precision and Mean Average Error (MAE) with almost 7% improvement in precision on average for the three data sets. The proposed technique is able to differentiate categories of interactions in a dynamic context. The results reveal that the technique is independent of the rating pattern and density of data.
A Matter of Framing: The Impact of Linguistic Formalism on Probing Results

Deep pre-trained contextualized encoders like BERT (Delvin et al., 2019) demonstrate remarkable performance on a range of downstream tasks. A recent line of research in probing investigates the linguistic knowledge implicitly learned by these models during pre-training. While most work in probing operates on the task level, linguistic tasks are rarely uniform and can be represented in a variety of formalisms. Any linguistics-based probing study thereby inevitably commits to the formalism used to annotate the underlying data. Can the choice of formalism affect probing results? To investigate, we conduct an in-depth cross-formalism layer probing study in role semantics. We find linguistically meaningful differences in the encoding of semantic role- and proto-role information by BERT depending on the formalism and demonstrate that layer probing can detect subtle differences between the implementations of the same linguistic formalism. Our results suggest that linguistic formalism is an important dimension in probing studies, along with the commonly used cross-task and cross-lingual experimental settings.
Spin-up of a superfluid vortex lattice driven by rough boundaries

We study numerically the formation of a vortex lattice inside a rotating bucket containing superfluid helium, paying attention to an important feature which is practically unavoidable in all experiments: the microscopic roughness of the bucket's surface. We model this using the Gross-Pitaevskii for a weakly-interacting Bose gas, a model which is idealised when applied to superfluid helium but captures the key physics of the vortex dynamics which we are interested in. We find that the vortex lattice arises from the interaction and reconnections of nucleated U-shaped vortex lines, which merge and align along the axis of rotation. We quantify the effects which the surface roughness and remanent vortex lines play in this process.
Structural Features of Fructans from the Root of Cyathula officinalis Kuan

Three fructans (CoPS1, CoPS2 and CoPS3) were isolated rom the root of cyathula officinalis Kuan, a traditional chinese medicine. The structures of the fructans were determined by methylation, reductive-cleavage method combined with GC-MS analysis, and 13~C NMR spectroscopy. These results show that the fructans (CoPS1, CoPS2 and CoPS3) are graminan typefructans, and comprised of (2→1)-and (2→6)-linked β-D-fructofuranosyl backbone residues containing high branches.
Memristors—From In‐Memory Computing, Deep Learning Acceleration, and Spiking Neural Networks to the Future of Neuromorphic and Bio‐Inspired Computing

Machine learning, particularly in the form of deep learning (DL), has driven most of the recent fundamental developments in artificial intelligence (AI). DL is based on computational models that are, to a certain extent, bio‐inspired, as they rely on networks of connected simple computing units operating in parallel. The success of DL is supported by three factors: availability of vast amounts of data, continuous growth in computing power, and algorithmic innovations. The approaching demise of Moore's law, and the consequent expected modest improvements in computing power that can be achieved by scaling, raises the question of whether the progress will be slowed or halted due to hardware limitations. This article reviews the case for a novel beyond‐complementary metal–oxide–semiconductor (CMOS) technology—memristors—as a potential solution for the implementation of power‐efficient in‐memory computing, DL accelerators, and spiking neural networks. Central themes are the reliance on non‐von‐Neumann computing architectures and the need for developing tailored learning and inference algorithms. To argue that lessons from biology can be useful in providing directions for further progress in AI, an example‐based reservoir computing is briefly discussed. At the end, speculation is given on the “big picture” view of future neuromorphic and brain‐inspired computing systems.
Named Entity Recognition without Labelled Data: A Weak Supervision Approach

Named Entity Recognition (NER) performance often degrades rapidly when applied to target domains that differ from the texts observed during training. When in-domain labelled data is available, transfer learning techniques can be used to adapt existing NER models to the target domain. But what should one do when there is no hand-labelled data for the target domain? This paper presents a simple but powerful approach to learn NER models in the absence of labelled data through weak supervision. The approach relies on a broad spectrum of labelling functions to automatically annotate texts from the target domain. These annotations are then merged together using a hidden Markov model which captures the varying accuracies and confusions of the labelling functions. A sequence labelling model can finally be trained on the basis of this unified annotation. We evaluate the approach on two English datasets (CoNLL 2003 and news articles from Reuters and Bloomberg) and demonstrate an improvement of about 7 percentage points in entity-level F1 scores compared to an out-of-domain neural NER model.
Effect of selective consumption on voluntary intake and digestibility of tropical forages.

A review of the literature showed that levels of excess feed in intake and digestibility trials were quite variable. Nineteen forages of the species Stylosanthes guianensis, Desmodium distortum, Centrosema pubescens, Brachiaria mutica and B. decumbens were offered in diverse amounts to study the effect of level of excess feed on intake, and digestibility and digestible crude protein content of the consumed dry matter. Also intake of morphological components was measured. A discussion of statistical methods showed that the effect of level of excess feed on intake was underestimated by direct regression of intake on excess. To arrive at a correct estimate, intake should be related to the amount offered. A number of restrictions were imposed on the regression model for this relationship. For most forages, large effects were found of level of excess feed on intake. The response curves for various forages intersected so that the ranking order of forages varied with the level of excess feed. This behaviour was largely explained by selection between morphological components and stem parts. The pattern of selection was similar to that by grazing animals. It is recommended that intake of tropical forages should be measured not at one but at various levels of excess feed.
Mind Your Inflections! Improving NLP for Non-Standard Englishes with Base-Inflection Encoding

Morphological inflection is a process of word formation where base words are modified to express different grammatical categories such as tense, case, voice, person, or number. World Englishes, such as Colloquial Singapore English (CSE) and African American Vernacular English (AAVE), differ from Standard English dialects in inflection use. Although comprehension by human readers is usually unimpaired by non-standard inflection use, NLP systems are not so robust. We introduce a new Base-Inflection Encoding of English text that is achieved by combining linguistic and statistical techniques. Fine-tuning pre-trained NLP models for downstream tasks under this novel encoding achieves robustness to non-standard inflection use while maintaining performance on Standard English examples. Models using this encoding also generalize better to non-standard dialects without explicit training. We suggest metrics to evaluate tokenizers and extensive model-independent analyses demonstrate the efficacy of the encoding when used together with data-driven subword tokenizers.
On the Baldwin Effect under Coevolution

The potentially beneficial interaction between learning and evolution, the Baldwin effect, has long been established. This paper considers their interaction within a coevolutionary scenario, ie, where the adaptations of one species typically affects the fitness of others. Using the NKCS model, which allows the systematic exploration of the effects of fitness landscape size, ruggedness, and degree of coupling, it is shown how the amount of learning and the relative rate of evolution can alter behaviour.
Spectral theory of Laplace Operators on chemical hypergraphs

Several new spectral properties of the normalized Laplacian defined for chemical hypergraphs are shown. The eigenvalue $1$ and the case of duplicate vertices are discussed; two Courant nodal domain theorems are established; new quantities that bound the eigenvalues are introduced. In particular, the Cheeger constant is generalized and it is shown that the classical Cheeger bounds can be generalized for some classes of hypergraphs; it is shown that a geometric quantity used to study zonotopes bounds the largest eigenvalue from below, and that the notion of coloring number can be generalized and used for proving a Hoffman-like bound. Finally, the spectrum of the unnormalized Laplacian for Cartesian products of hypergraphs is discussed.