Miracle-dz/UniFAR-FTUs huggingface dataset:
Teaching Machine Comprehension with Compositional Explanations

Advances in machine reading comprehension (MRC) rely heavily on the collection of large scale human-annotated examples in the form of (question, paragraph, answer) triples. In contrast, humans are typically able to generalize with only a few examples, relying on deeper underlying world knowledge, linguistic sophistication, and/or simply superior deductive powers. In this paper, we focus on “teaching” machines reading comprehension, using a small number of semi-structured explanations that explicitly inform machines why answer spans are correct. We extract structured variables and rules from explanations and compose neural module teachers that annotate instances for training downstream MRC models. We use learnable neural modules and soft logic to handle linguistic variation and overcome sparse coverage; the modules are jointly optimized with the MRC model to improve final performance. On the SQuAD dataset, our proposed method achieves 70.14% F1 score with supervision from 26 explanations, comparable to plain supervised learning using 1,100 labeled instances, yielding a 12x speed up.
The environment of Lake Victoria (East Africa): current status and historical changes.

Beginning in the mid-1980s Lake Victoria experienced severe eutrophication and it was suggested that deteriorating water quality might lead to a collapse of its fisheries. A series of lake-wide surveys carried out 1999–2001 and 2005–2009 revealed that the temperature of the lake had risen by > 1 °C since 1927, with more rapid warming of the deeper waters reducing the thermal gradient in the water column and thus weakening stratification and the extent and severity of deoxygenation. The chlorophyll a concentrations in open water decreased since the 1980s, while Secchi disc visibility increased, indicating a reduced severity of algal blooms. Chlorophyll a was higher and Secchi disc visibility lower in inshore waters but there has been no deterioration in these areas since the 1980s. The conductivity remained unchanged, although it was about 50% greater in the semi-enclosed Nyanza Gulf than in the open lake. The water quality of the lake has therefore improved considerably despite the fact than concentrations of plant nutrients have not decreased and the reasons why this may be the case are discussed. (© 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
A Model-Driven Deep Neural Network for Single Image Rain Removal

Deep learning (DL) methods have achieved state-of-the-art performance in the task of single image rain removal. Most of current DL architectures, however, are still lack of sufficient interpretability and not fully integrated with physical structures inside general rain streaks. To this issue, in this paper, we propose a model-driven deep neural network for the task, with fully interpretable network structures. Specifically, based on the convolutional dictionary learning mechanism for representing rain, we propose a novel single image deraining model and utilize the proximal gradient descent technique to design an iterative algorithm only containing simple operators for solving the model. Such a simple implementation scheme facilitates us to unfold it into a new deep network architecture, called rain convolutional dictionary network (RCDNet), with almost every network module one-to-one corresponding to each operation involved in the algorithm. By end-to-end training the proposed RCDNet, all the rain kernels and proximal operators can be automatically extracted, faithfully characterizing the features of both rain and clean background layers, and thus naturally lead to its better deraining performance, especially in real scenarios. Comprehensive experiments substantiate the superiority of the proposed network, especially its well generality to diverse testing scenarios and good interpretability for all its modules, as compared with state-of-the-arts both visually and quantitatively.
Rethinking Self-Attention: Towards Interpretability in Neural Parsing

Attention mechanisms have improved the performance of NLP tasks while allowing models to remain explainable. Self-attention is currently widely used, however interpretability is difficult due to the numerous attention distributions. Recent work has shown that model representations can benefit from label-specific information, while facilitating interpretation of predictions. We introduce the Label Attention Layer: a new form of self-attention where attention heads represent labels. We test our novel layer by running constituency and dependency parsing experiments and show our new model obtains new state-of-the-art results for both tasks on both the Penn Treebank (PTB) and Chinese Treebank. Additionally, our model requires fewer self-attention layers compared to existing work. Finally, we find that the Label Attention heads learn relations between syntactic categories and show pathways to analyze errors.
Coarsening in (nearly) mass-conserving two-component reaction diffusion systems

We show that coarsening in two-component mass-conserving reaction-diffusion systems is driven by self-amplifying mass transport between neighboring high-density domains. We derive a general coarsening criterion and provide a geometric argument, independent of the specific reaction kinetics, to show that coarsening is generically uninterrupted, confirming a long-standing hypothesis in the field. The theory is then generalized to study interrupted coarsening and anti-coarsening (domain splitting) for systems with weakly broken mass conservation, providing a path to analyze length scale selection in pattern formation far from equilibrium.
An unknottedness result for noncompact self shrinkers

In this article we extend an unknottedness theorem for compact self shrinkers to the mean curvature flow to shrinkers with finite topology and one asymptotically conical end, which conjecturally comprises the entire set of self shrinkers with finite topology and one end. A partial result for asymptotically cylindrical such shrinkers is also given. The mean curvature flow itself is used in the argument presented.
On the triple correlations of fractional parts of 
$n^2\alpha $

Abstract For fixed $\alpha \in [0,1]$ , consider the set $S_{\alpha ,N}$ of dilated squares $\alpha , 4\alpha , 9\alpha , \dots , N^2\alpha \, $ modulo $1$ . Rudnick and Sarnak conjectured that, for Lebesgue, almost all such $\alpha $ the gap-distribution of $S_{\alpha ,N}$ is consistent with the Poisson model (in the limit as N tends to infinity). In this paper, we prove a new estimate for the triple correlations associated with this problem, establishing an asymptotic expression for the third moment of the number of elements of $S_{\alpha ,N}$ in a random interval of length $L/N$ , provided that $L> N^{1/4+\varepsilon }$ . The threshold of $\tfrac {1}{4}$ is substantially smaller than the threshold of $\tfrac {1}{2}$ (which is the threshold that would be given by a naïve discrepancy estimate). Unlike the theory of pair correlations, rather little is known about triple correlations of the dilations $(\alpha a_n \, \text {mod } 1)_{n=1}^{\infty } $ for a nonlacunary sequence $(a_n)_{n=1}^{\infty } $ of increasing integers. This is partially due to the fact that the second moment of the triple correlation function is difficult to control, and thus standard techniques involving variance bounds are not applicable. We circumvent this impasse by using an argument inspired by works of Rudnick, Sarnak, and Zaharescu, and Heath-Brown, which connects the triple correlation function to some modular counting problems. In Appendix B, we comment on the relationship between discrepancy and correlation functions, answering a question of Steinerberger.
Detritivores in Kenyan highland streams: more evidence for the paucity of shredders in the tropics?

1. The relationship between coarse particulate organic matter (CPOM) standing stock and benthic macroinvertebrate assemblages in Kenyan highland streams was determined by sampling seven sites on three rivers (2000-2700 m a.s.l.). Taxa recorded were allocated to functional feeding groups using published literature, mouthpart analysis and examination of gut contents. Patterns were compared with five structurally similar streams in three areas of Europe (south-west France, south-east England, north-east England). 2. Number of individuals and proportion of detritivores in Kenyan streams were equivalent to, or greater than, those in European sites. Shredders were, however, almost completely absent from Kenyan sites, despite high standing stocks of CPOM. Shredders were abundant in all European sites. 3. The phenomenon of low shredder abundance has been observed in other tropical streams in south-east Asia and Central and South America but, in contrast to these regions, the African rivers studied were devoid of shrimps or fish which may occupy the shredding niche elsewhere. 4. These preliminary data suggest that shredder-mediated detritus processing, which is a key functional component of streams in the North Temperate Zone, does not operate in East African streams. There are three possible reasons for this. The first is that tropical African rivers are functionally different to those in temperate regions. This could be because of enhanced microbial activity replacing shredder activity at high temperatures. Alternatively, it could be a result of low palatability of detrital inputs from dominant riparian trees in the region. The second and third are methodological: that our allocation to functional feeding groups is incorrect, and that our sampling methods missed a potentially key shredding taxon - the freshwater crab Potamonautes sp.
Positivity of direct images with a Poincaré type twist

Abstract We consider a holomorphic family $f:\mathscr {X} \to S$ of compact complex manifolds and a line bundle $\mathscr {L}\to \mathscr {X}$. Given that $\mathscr {L}^{-1}$ carries a singular hermitian metric that has Poincaré type singularities along a relative snc divisor $\mathscr {D}$, the direct image $f_*(K_{\mathscr {X}/S}\otimes \mathscr {D} \otimes \mathscr {L})$ carries a smooth hermitian metric. If $\mathscr {L}$ is relatively positive, we give an explicit formula for its curvature. The result applies to families of log-canonically polarized pairs. Moreover, we show that it improves the general positivity result of Berndtsson-Păun in a special situation of a big line bundle.
GenericsKB: A Knowledge Base of Generic Statements

We present a new resource for the NLP community, namely a large (3.5M+ sentence) knowledge base of *generic statements*, e.g., "Trees remove carbon dioxide from the atmosphere", collected from multiple corpora. This is the first large resource to contain *naturally occurring* generic sentences, as opposed to extracted or crowdsourced triples, and thus is rich in high-quality, general, semantically complete statements. All GenericsKB sentences are annotated with their topical term, surrounding context (sentences), and a (learned) confidence. We also release GenericsKB-Best (1M+ sentences), containing the best-quality generics in GenericsKB augmented with selected, synthesized generics from WordNet and ConceptNet. In tests on two existing datasets requiring multihop reasoning (OBQA and QASC), we find using GenericsKB can result in higher scores and better explanations than using a much larger corpus. This demonstrates that GenericsKB can be a useful resource for NLP applications, as well as providing data for linguistic studies of generics and their semantics. GenericsKB is available at this https URL.
Adapt or Perish: Evolutionary Rescue in a Gradually Deteriorating Environment

We investigate the evolutionary rescue of a microbial population in a gradually deteriorating environment, through a combination of analytical calculations and stochastic simulations. We consider a population destined for extinction in the absence of mutants, which can survive only if mutants sufficiently adapted to the new environment arise and fix. We show that mutants that appear later during the environment deterioration have a higher probability to fix. The rescue probability of the population increases with a sigmoidal shape when the product of the carrying capacity and of the mutation probability increases. Furthermore, we find that rescue becomes more likely for smaller population sizes and/or mutation probabilities if the environment degradation is slower, which illustrates the key impact of the rapidity of environment degradation on the fate of a population. We also show that our main conclusions are robust across various types of adaptive mutants, including specialist and generalist ones, as well as mutants modeling antimicrobial resistance evolution. We further express the average time of appearance of the mutants that do rescue the population and the average extinction time of those that do not. Our methods can be applied to other situations with continuously variable fitnesses and population sizes, and our analytical predictions are valid in the weak-to-moderate mutation regime.
SEEK: Segmented Embedding of Knowledge Graphs

In recent years, knowledge graph embedding becomes a pretty hot research topic of artificial intelligence and plays increasingly vital roles in various downstream applications, such as recommendation and question answering. However, existing methods for knowledge graph embedding can not make a proper trade-off between the model complexity and the model expressiveness, which makes them still far from satisfactory. To mitigate this problem, we propose a lightweight modeling framework that can achieve highly competitive relational expressiveness without increasing the model complexity. Our framework focuses on the design of scoring functions and highlights two critical characteristics: 1) facilitating sufficient feature interactions; 2) preserving both symmetry and antisymmetry properties of relations. It is noteworthy that owing to the general and elegant design of scoring functions, our framework can incorporate many famous existing methods as special cases. Moreover, extensive experiments on public benchmarks demonstrate the efficiency and effectiveness of our framework. Source codes and data can be found at https://github.com/Wentao-Xu/SEEK.
How Does Selective Mechanism Improve Self-Attention Networks?

Self-attention networks (SANs) with selective mechanism has produced substantial improvements in various NLP tasks by concentrating on a subset of input words. However, the underlying reasons for their strong performance have not been well explained. In this paper, we bridge the gap by assessing the strengths of selective SANs (SSANs), which are implemented with a flexible and universal Gumbel-Softmax. Experimental results on several representative NLP tasks, including natural language inference, semantic role labelling, and machine translation, show that SSANs consistently outperform the standard SANs. Through well-designed probing experiments, we empirically validate that the improvement of SSANs can be attributed in part to mitigating two commonly-cited weaknesses of SANs: word order encoding and structure modeling. Specifically, the selective mechanism improves SANs by paying more attention to content words that contribute to the meaning of the sentence.
Consistency, convergence and error estimates for a mixed finite element / finite volume scheme to compressible Navier-Stokes equations with general inflow/outflow boundary data

We study convergence of a mixed finite element-finite volume scheme for the compressible Navier–Stokes equations in the isentropic regime under the full range $1<\gamma <\infty $ of the adiabatic coefficients $\gamma $ for the problem with general nonzero inflow–outflow boundary conditions. We propose a modification of Karper’s scheme (2013, A convergent FEM-DG method for the compressible Navier–Stokes equations. Numer. Math., 125, 441–510) in order to accommodate the nonzero boundary data, prove existence of its solutions, establish the stability and uniform estimates, derive a convenient consistency formulation of the balance laws and use it to show the weak convergence of the numerical solutions to a dissipative solution with the Reynolds defect introduced in Abbatiello et al. (2021, Generalized solutions to models of compressible viscous fluids. Discrete Contin. Dyn. Syst., 41, 1--28). If the target system admits a strong solution then the convergence is strong towards the strong solution. Moreover, we establish the convergence rate of the strong convergence in terms of the size of the space discretization $h$ (which is supposed to be comparable with the time step $\varDelta t$). In the case of the nonzero inflow–outflow boundary data all results are new. The latter result is new also for the no-slip boundary conditions and adiabatic coefficients $\gamma $ less than the threshold $3/2$.
Nematodes associated with three indigenous leafy vegetables commonly grown in inland valley ecology in Nigeria

Field study was conducted between January and June 2011 to determine the plant-parasitic and non-parasitic nematodes associated with three indigenous leafy vegetables (Amaranthus viridis, Celosia argentea and Corchorus olitorius) commonly grown in inland valley by farm practical year students’ trainees at the Federal University of Agriculture, Abeokuta. The 1.76-ha land was laid out in randomised complete block design with four replicates. Six soil cores were taken per replicate and bulked. Two sub-samples weighing 250 g per replicate were assayed for nematodes using WhiteHead and Hemming Tray method. Results revealed the presence of five genera of plant-parasitic nematodes, namely Tylenchus, Pratylenchus, Helicotylenchus, Meloidogyne and Rotylenchulus. Populations of Tylenchus differed statistically (p = 0.05) from all others in C. olitorius. The non-parasitic nematodes varied significantly (p = 0.05) among the vegetable types. Galls incited by Meloidogyne spp. were more (p = 0.05) in C. argentea and C. olitorius than A. viridis.
su(2) spin s representations via CP2s sigma models

We establish and analyze a new relationship between the matrix functions describing spin fields of a spin s, where 2s∈Z+ , and CP2s two-dimensional Euclidean sigma models. The spin matrices are constructed from the rank-1 Hermitian projectors of the sigma models or from the anti-Hermitian immersion functions of their soliton surfaces in the su(2s+1) algebra. We provide a geometric interpretation of this construction. For the spin fields which can be represented as linear combinations of the generalized Pauli matrices, we find the dynamics equation satisfied by their coefficients. This equation is identical to the stationary equation of a two-dimensional Heisenberg model. We show that the same holds for matrices congruent to the generalized Pauli matrices through any coordinate-independent unitary linear transformation. These properties allow for new interpretations of the spins as compositions of more elementary objects. They also open up the possibility of future applications of the sigma models to the situations which depend on spin behaviour, including spintronics, spin glasses and quantum computing.
Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward

Sequence-to-sequence models for abstractive summarization have been studied extensively, yet the generated summaries commonly suffer from fabricated content, and are often found to be near-extractive. We argue that, to address these issues, the summarizer should acquire semantic interpretation over input, e.g., via structured representation, to allow the generation of more informative summaries. In this paper, we present ASGARD, a novel framework for Abstractive Summarization with Graph-Augmentation and semantic-driven RewarD. We propose the use of dual encoders—a sequential document encoder and a graph-structured encoder—to maintain the global context and local characteristics of entities, complementing each other. We further design a reward based on a multiple choice cloze test to drive the model to better capture entity interactions. Results show that our models produce significantly higher ROUGE scores than a variant without knowledge graph as input on both New York Times and CNN/Daily Mail datasets. We also obtain better or comparable performance compared to systems that are fine-tuned from large pretrained language models. Human judges further rate our model outputs as more informative and containing fewer unfaithful errors.
Evaluating Explanation Methods for Neural Machine Translation

Recently many efforts have been devoted to interpreting the black-box NMT models, but little progress has been made on metrics to evaluate explanation methods. Word Alignment Error Rate can be used as such a metric that matches human understanding, however, it can not measure explanation methods on those target words that are not aligned to any source word. This paper thereby makes an initial attempt to evaluate explanation methods from an alternative viewpoint. To this end, it proposes a principled metric based on fidelity in regard to the predictive behavior of the NMT model. As the exact computation for this metric is intractable, we employ an efficient approach as its approximation. On six standard translation tasks, we quantitatively evaluate several explanation methods in terms of the proposed metric and we reveal some valuable findings for these explanation methods in our experiments.
The Next Generation Fornax Survey (NGFS): VII. A MUSE view of the nuclear star clusters in Fornax dwarf galaxies

Clues to the formation and evolution of Nuclear Star Clusters (NSCs) lie in their stellar populations. However, these structures are often very faint compared to their host galaxy, and spectroscopic analysis of NSCs is hampered by contamination of light from the rest of the system. With the introduction of wide-field IFU spectrographs, new techniques have been developed to model the light from different components within galaxies, making it possible to cleanly extract the spectra of the NSCs and study their properties with minimal contamination from the light of the rest of the galaxy. This work presents the analysis of the NSCs in a sample of 12 dwarf galaxies in the Fornax Cluster observed with MUSE. Analysis of the stellar populations and star-formation histories reveal that all the NSCs show evidence of multiple episodes of star formation, indicating that they have built up their mass further since their initial formation. The NSCs were found to have systematically lower metallicities than their host galaxies, which is consistent with a scenario for mass-assembly through mergers with infalling globular clusters, while the presence of younger stellar populations and gas emission in the core of two galaxies is indicative of in-situ star formation. We conclude that the NSCs in these dwarf galaxies likely originated as globular clusters that migrated to the core of the galaxy which have built up their mass mainly through mergers with other infalling clusters, with gas-inflow leading to in-situ star formation playing a secondary role.
We Need To Talk About Random Splits

(CITATION) argued for using random splits rather than standard splits in NLP experiments. We argue that random splits, like standard splits, lead to overly optimistic performance estimates. We can also split data in biased or adversarial ways, e.g., training on short sentences and evaluating on long ones. Biased sampling has been used in domain adaptation to simulate real-world drift; this is known as the covariate shift assumption. In NLP, however, even worst-case splits, maximizing bias, often under-estimate the error observed on new samples of in-domain data, i.e., the data that models should minimally generalize to at test time. This invalidates the covariate shift assumption. Instead of using multiple random splits, future benchmarks should ideally include multiple, independent test sets instead; if infeasible, we argue that multiple biased splits leads to more realistic performance estimates than multiple random splits.