Miracle-dz/UniFAR-FTUs huggingface dataset:
From earth and ocean: investigating the importance of cross‐ecosystem resource linkages to a mobile estuarine consumer

Externally derived resources often contribute to the structuring of ecological communities. Estuaries are one of the most productive ecosystems in the world and provide an ideal system to test how communities may be shaped by resource subsidies because they occur at the intersection of marine, freshwater and terrestrial habitats. Here we tested the effects of both terrestrial- and salmon-derived subsidies, in addition to other factors such as habitat area, on the diet (inferred from stable isotopes), abundance and size of a mobile estuarine consumer, the Dungeness crab (Metacarcinus magister). Crab trap surveys encompassed 19 watersheds over two seasons in the central coast of British Columbia, Canada, which spanned natural gradients in estuary size, watershed size, riparian tree composition, and Pacific salmon spawning density. Stable isotope ratios of crab tissue confirmed the predictions that estuarine nutrient regimes can be strongly affected by upstream watershed size, salmon density, and the dominance of nitrogen-fixing red alder (Alnus rubra). There were more crabs in larger estuaries and the largest crabs were found in estuaries below the largest watersheds. The proportional contributions of terrestrial- and salmon-derived subsidies to the diet of Dungeness crabs increased with watershed size and salmon density, respectively. These results confirmed that resource subsidies can constitute large proportions of the Dungeness crab's diet, that crab abundance is determined by habitat size, but that crab size is affected by the magnitude of terrestrial resource influx.
Fluctuation theorem and extended thermodynamics of turbulence

Turbulent flows are out-of-equilibrium because the energy supply at large scales and its dissipation by viscosity at small scales create a net transfer of energy among all scales. This energy cascade is modelled by approximating the spectral energy balance with a nonlinear Fokker–Planck equation consistent with accepted phenomenological theories of turbulence. The steady-state contributions of the drift and diffusion in the corresponding Langevin equation, combined with the killing term associated with the dissipation, induce a stochastic energy transfer across wavenumbers. The fluctuation theorem is shown to describe the scale-wise statistics of forward and backward energy transfer and their connection to irreversibility and entropy production. The ensuing turbulence entropy is used to formulate an extended turbulence thermodynamics.
Comprehensive model and performance optimization of phase-only spatial light modulators

Several spurious effects are known to degrade the performance of phase-only spatial light modulators. We introduce a comprehensive model that takes into account the major ones: curvature of the back panel, pixel crosstalk and the internal Fabry–Perot cavity. To estimate the model parameters with high accuracy, we generate blazed grating patterns and acquire the intensity response curves of the first and second diffraction orders. The quantitative model is used to generate compensating holograms, which can produce optical modes with high fidelity.
Krill (Euphausiacea) demography and variability in abundance and distribution

Parmi les euphausiaces se trouvent de nombreuses especes representees par des quantites enormes d'individus et jouant un role considerable dans divers ecosystemes ainsi que pour l'exploitation humaine. Des progres remarquables ont ete realises sur les aspects quantitatifs de la biologie du krill. Ces connaissances detaillees des parametres demographiques du krill sont necessaires pour repondre aux questions sur l'importance des euphausiaces pour le flux d'energie dans certains ecosystemes. Cette information sert aussi a estimer l'importance des effets climatiques saisonniers ou a long terme sur la variation des populations d'euphausiaces, car la variabilite ne doit pas etre consideree simplement comme un bruit. Diverses hypotheses ont ete formulees pour expliquer en quoi les grands changements ecologiques au niveau des saisons ou de la latitude affectent les strategies de survie des especes dans des conditions defavorables et le maintien d'un niveau de population stable. Cette etude analyse: (1) certains aspects de la distribution spatiale par rapport aux changements saisonniers et interannuels; (2) les progres realises pour definir des populations genetiquement distinctes; (3) les resultats de la variabilite a long terme de l'abondance et de la biomasse du krill pour diverses especes occupant differentes regions geographiques.
Viscosity solution of a Delta Greek nonlinear Black-Scholes equation

In this paper, a class of nonlinear option pricing models involving transaction costs is considered. The diffusion coefficient of the nonlinear parabolic equation for the price $V$ is assumed to be a linear function of the option's underlying asset price and the Gamma Greek $V_{xx}$. The main aim of this work is to study the governing PDE of the Delta Greek. The existence of viscosity solutions is proved using the vanishing viscosity method. Regularizing the equation by adding a small perturbation to the initial problem, a sequence of approximate solutions $u^{\varepsilon}$ is constructed and then the method of weak limits is applied to prove the convergence of the sequence to the viscosity solution of the Delta equation. The approximate problems constructed are shown to have good regularity, which allows the use of efficient and robust numerical methods.
A globally stable practically implementable PI passivity‐based controller for switched power converters

In this article, we propose a PI passivity‐based controller, applicable to a large class of switched power converters, that ensures global state regulation to a desired equilibrium point. A solution to this problem requires full state‐feedback, which makes it practically unfeasible. To overcome this limitation we construct a state observer that is implementable with measurements that are available in practical applications. The observer reconstructs the state in finite‐time, ensuring global convergence of the PI. An adaptive version of the observer, where some parameters of the converter are estimated, is also proposed. The excitation requirement for the observer is very weak and is satisfied in normal operation of the converters. Realistic simulation results illustrate the excellent performance and robustness vis‐à‐vis noise and parameter uncertainty of the proposed output‐feedback PI.
Accelerated Learning with Robustness to Adversarial Regressors

High order iterative momentum-based parameter update algorithms have seen widespread applications in training machine learning models. Recently, connections with variational approaches and continuous dynamics have led to the derivation of new classes of high order learning algorithms with accelerated learning guarantees. Such methods however, have only considered the case of static regressors. There is a significant need in continual/lifelong learning applications for parameter update algorithms which can be proven stable in the presence of adversarial time-varying regressors. In such settings, the learning algorithm must continually adapt to changes in the regressors. In this paper, we propose a new discrete time algorithm which: 1) provides stability and asymptotic convergence guarantees in the presence of adversarial regressors by leveraging insights from adaptive control theory and 2) provides non-asymptotic accelerated learning guarantees leveraging insights from convex optimization. In particular, our algorithm reaches an $\epsilon$ sub-optimal point in at most $\tilde{\mathcal{O}}(1/\sqrt{\epsilon})$ iterations when regressors are constant - matching lower bounds due to Nesterov of $\Omega(1/\sqrt{\epsilon})$, up to a $\log(1/\epsilon)$ factor and provides guaranteed bounds for stability when regressors are time-varying. We provide representative numerical experiments for a variant of Nesterov's provably hard convex optimization problem with time-varying regressors, as well as the problem of recovering an image in the presence of a time-varying blur using streaming data.
Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints

Text generation from a knowledge base aims to translate knowledge triples to natural language descriptions. Most existing methods ignore the faithfulness between a generated text description and the original table, leading to generated information that goes beyond the content of the table. In this paper, for the first time, we propose a novel Transformer-based generation framework to achieve the goal. The core techniques in our method to enforce faithfulness include a new table-text optimal-transport matching loss and a table-text embedding similarity loss based on the Transformer model. Furthermore, to evaluate faithfulness, we propose a new automatic metric specialized to the table-to-text generation problem. We also provide detailed analysis on each component of our model in our experiments. Automatic and human evaluations show that our framework can significantly outperform state-of-the-art by a large margin.
Water Stress Enhances Expression of an α-Amylase Gene in Barley Leaves

The amylases of the second leaves of barley seedlings ( Hordeum vulgare L. cv Betzes) were resolved into eight isozymes by isoelectric focusing, seven of which were β-amylase and the other, α-amylase. The α-amylase had the same isoelectric point as one of the gibberellin-induced α-amylase isozymes in the aleurone layer. This and other enzyme characteristics indicated that the leaf isozyme corresponded to the type A aleurone α-amylase (low pI group). Crossing experiments indicated that leaf and type A aleurone isozymes resulted from expression of the same genes. In unwatered seedlings, leaf α-amylase increased as leaf water potential decreased and ABA increased. Water stress had no effect on β-amylase. α-Amylase occurred uniformly along the length of the leaf but β-amylase was concentrated in the basal half of the leaf. Cell fractionation studies indicated that none of the leaf α-amylase occurred inside chloroplasts. Leaf radiolabeling experiments followed by extraction of α-amylase by affinity chromatography and immunoprecipitation showed that increase of α-amylase activity involved synthesis of the enzyme. However, water stress caused no major change in total protein synthesis. Hybridization of a radiolabeled α-amylase-related cDNA clone to size fractionated RNA showed that water-stressed leaves contained much more α-amylase mRNA than unstressed plants. The results of these and other studies indicate that regulation of gene expression may be a component in water-stress induced metabolic changes.
Open Graph Benchmark: Datasets for Machine Learning on Graphs

We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research. OGB datasets are large-scale (up to 100+ million nodes and 1+ billion edges), encompass multiple important graph ML tasks, and cover a diverse range of domains, ranging from social and information networks to biological networks, molecular graphs, source code ASTs, and knowledge graphs. For each dataset, we provide a unified evaluation protocol using meaningful application-specific data splits and evaluation metrics. In addition to building the datasets, we also perform extensive benchmark experiments for each dataset. Our experiments suggest that OGB datasets present significant challenges of scalability to large-scale graphs and out-of-distribution generalization under realistic data splits, indicating fruitful opportunities for future research. Finally, OGB provides an automated end-to-end graph ML pipeline that simplifies and standardizes the process of graph data loading, experimental setup, and model evaluation. OGB will be regularly updated and welcomes inputs from the community. OGB datasets as well as data loaders, evaluation scripts, baseline code, and leaderboards are publicly available at this https URL .
Improving Truthfulness of Headline Generation

Most studies on abstractive summarization report ROUGE scores between system and reference summaries. However, we have a concern about the truthfulness of generated summaries: whether all facts of a generated summary are mentioned in the source text. This paper explores improving the truthfulness in headline generation on two popular datasets. Analyzing headlines generated by the state-of-the-art encoder-decoder model, we show that the model sometimes generates untruthful headlines. We conjecture that one of the reasons lies in untruthful supervision data used for training the model. In order to quantify the truthfulness of article-headline pairs, we consider the textual entailment of whether an article entails its headline. After confirming quite a few untruthful instances in the datasets, this study hypothesizes that removing untruthful instances from the supervision data may remedy the problem of the untruthful behaviors of the model. Building a binary classifier that predicts an entailment relation between an article and its headline, we filter out untruthful instances from the supervision data. Experimental results demonstrate that the headline generation model trained on filtered supervision data shows no clear difference in ROUGE scores but remarkable improvements in automatic and manual evaluations of the generated headlines.
From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers

Massively multilingual transformers pretrained with language modeling objectives (e.g., mBERT, XLM-R) have become a de facto default transfer paradigm for zero-shot cross-lingual transfer in NLP, offering unmatched transfer performance. Current downstream evaluations, however, verify their efficacy predominantly in transfer settings involving languages with sufficient amounts of pretraining data, and with lexically and typologically close languages. In this work, we analyze their limitations and show that cross-lingual transfer via massively multilingual transformers, much like transfer via cross-lingual word embeddings, is substantially less effective in resource-lean scenarios and for distant languages. Our experiments, encompassing three lower-level tasks (POS tagging, dependency parsing, NER), as well as two high-level semantic tasks (NLI, QA), empirically correlate transfer performance with linguistic similarity between the source and target languages, but also with the size of pretraining corpora of target languages. We also demonstrate a surprising effectiveness of inexpensive few-shot transfer (i.e., fine-tuning on a few target-language instances after fine-tuning in the source) across the board. This suggests that additional research efforts should be invested to reach beyond the limiting zero-shot conditions.
Analyzing course programmes using complex networks.

We analyze the curriculum of the early common-years of engineering in our institute using tools of statistical physics of complex networks. Naturally, a course programme is structured in a networked form (temporal dependency and prerequisites). In this approach, each topic within each programme is associated with a node, which in turn is joined by links representing the dependence of a topic for the understanding of another in a different discipline. As a course programme is a time-dependent structure, we propose a simple model to assign links between nodes, taking into account only two ingredients of the teaching-learning process: recursiveness and accumulation of knowledge. Since we already know the programmes, our objective is to verify if the proposed model is able to capture their particularities and to identify implications of different sequencing on the student learning in the early years of engineering degrees. Our model can be used as a systematic tool assisting the construction of a more interdisciplinary curriculum, articulating between disciplines of the undergraduate early-years in exact sciences.
An Imitation Game for Learning Semantic Parsers from User Interaction

Despite the widely successful applications, bootstrapping and fine-tuning semantic parsers are still a tedious process with challenges such as costly data annotation and privacy risks. In this paper, we suggest an alternative, human-in-the-loop methodology for learning semantic parsers directly from users. A semantic parser should be introspective of its uncertainties and prompt for user demonstration when uncertain. In doing so it also gets to imitate the user behavior and continue improving itself autonomously with the hope that eventually it may become as good as the user in interpreting their questions. To combat the sparsity of demonstration, we propose a novel annotation-efficient imitation learning algorithm, which iteratively collects new datasets by mixing demonstrated states and confident predictions and re-trains the semantic parser in a Dataset Aggregation fashion (Ross et al., 2011). We provide a theoretical analysis of its cost bound and also empirically demonstrate its promising performance on the text-to-SQL problem.
An Ensemble Deep Learning-Based Cyber-Attack Detection in Industrial Control System

The integration of communication networks and the Internet of Things (IoT) in Industrial Control Systems (ICSs) increases their vulnerability towards cyber-attacks, causing devastating outcomes. Traditional Intrusion Detection Systems (IDSs), which are mainly developed to support information technology systems, count vastly on predefined models and are trained mostly on specific cyber-attacks. Besides, most IDSs do not consider the imbalanced nature of ICS datasets, thereby suffering from low accuracy and high false-positive when being put to use. In this paper, we propose a deep learning model to construct new balanced representations of the imbalanced datasets. The new representations are fed into an ensemble deep learning attack detection model specifically designed for an ICS environment. The proposed attack detection model leverages Deep Neural Network (DNN) and Decision Tree (DT) classifiers to detect cyber-attacks from the new representations. The performance of the proposed model is evaluated based on 10-fold cross-validation on two real ICS datasets. The results show that the proposed method outperforms conventional classifiers, including Random Forest (RF), DNN, and AdaBoost, as well as recent existing models in the literature. The proposed approach is a generalized technique, which can be implemented in existing ICS infrastructures with minimum effort.
Generalized Entropy Regularization or: There’s Nothing Special about Label Smoothing

Prior work has explored directly regularizing the output distributions of probabilistic models to alleviate peaky (i.e. over-confident) predictions, a common sign of overfitting. This class of techniques, of which label smoothing is one, has a connection to entropy regularization. Despite the consistent success of label smoothing across architectures and data sets in language generation tasks, two problems remain open: (1) there is little understanding of the underlying effects entropy regularizers have on models, and (2) the full space of entropy regularization techniques is largely unexplored. We introduce a parametric family of entropy regularizers, which includes label smoothing as a special case, and use it to gain a better understanding of the relationship between the entropy of a model and its performance on language generation tasks. We also find that variance in model performance can be explained largely by the resulting entropy of the model. Lastly, we find that label smoothing provably does not allow for sparsity in an output distribution, an undesirable property for language generation models, and therefore advise the use of other entropy regularization methods in its place.
Connecting the Dots: Towards Continuous Time Hamiltonian Monte Carlo

Continuous time Hamiltonian Monte Carlo is introduced, as a powerful alternative to Markov chain Monte Carlo methods for continuous target distributions. The method is constructed in two steps: First Hamiltonian dynamics are chosen as the deterministic dynamics in a continuous time piecewise deterministic Markov process. Under very mild restrictions, such a process will have the desired target distribution as an invariant distribution. Secondly, the numerical implementation of such processes, based on adaptive numerical integration of second order ordinary differential equations is considered. The numerical implementation yields an approximate, yet highly robust algorithm that, unlike conventional Hamiltonian Monte Carlo, enables the exploitation of the complete Hamiltonian trajectories (and hence the title). The proposed algorithm may yield large speedups and improvements in stability relative to relevant benchmarks, while incurring numerical errors that are negligible relative to the overall Monte Carlo errors.
Activity of Blowflies and Associated Insects at Various Heights above the Ground

When one is working with insect traps, baits and repellents, it is important to know the responses at different heights above the ground of the insect being studied. Questions arise as to whether an insect trap for certain pests should be placed near the top or in the center of a tree, whether a flytrap could be set more advantageously on top of a windmill tower than on the ground, or at what level status tests for insects should be made. In April, I930, while a study was being made of the status of blowfly parasites in different localities, interference with the baits by dogs necessitated the placing of the bait containers in trees. It was suggested that the increased height might affect the extent of parasitism. To determine this a test was made at Uvalde, Texas, in which jars containing meat were exposed at various levels, permitting blowflies and their associated insects to breed in the meat. This test was begun in June, 1930, and continued until September; it was repeated during the same months in I93I. The author is indebted to the following specialists for determination of specimens: Diptera, J. M. Aldrich, E. W. Laake, David G. Hall; Hymenoptera, A. B. Gahan; and Coleoptera, E. A. Chapin, H. S. Barber, W. S. Fisher.
On a Remarkable New Nematode, Lappetascaris lutjani gen. et sp. nov. (Anisakidae: Ascaridoidea) from Marine Fishes of Karachi and an Account of Thynnascaris inquies (Linton, 1901) n.comb. and Goezia intermedia n.sp.

1. Three exceedingly interesting parasites of the super-family Ascaridoidea have been discussed in this paper. A new genus Lappetascaris is described from two different kinds of fish and a new subfamily has been established to include this genus. The latter is based on its peculiar head structures and the cuticle. 2. Thynnascaris inquies (Linton, 1901) n.comb, is reported for the first time since its original description, from the type host of a new locality. 3. Goezia intermedia n.sp. is described and a comparative study of the related species has been carried out.
From SPMRL to NMRL: What Did We Learn (and Unlearn) in a Decade of Parsing Morphologically-Rich Languages (MRLs)?

It has been exactly a decade since the first establishment of SPMRL, a research initiative unifying multiple research efforts to address the peculiar challenges of Statistical Parsing for Morphologically-Rich Languages (MRLs). Here we reflect on parsing MRLs in that decade, highlight the solutions and lessons learned for the architectural, modeling and lexical challenges in the pre-neural era, and argue that similar challenges re-emerge in neural architectures for MRLs. We then aim to offer a climax, suggesting that incorporating symbolic ideas proposed in SPMRL terms into nowadays neural architectures has the potential to push NLP for MRLs to a new level. We sketch a strategies for designing Neural Models for MRLs (NMRL), and showcase preliminary support for these strategies via investigating the task of multi-tagging in Hebrew, a morphologically-rich, high-fusion, language.