Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Role of Supervision in Unsupervised Constituency Parsing

We analyze several recent unsupervised constituency parsing models, which are tuned with respect to the parsing $F_1$ score on the Wall Street Journal (WSJ) development set (1,700 sentences). We introduce strong baselines for them, by training an existing supervised parsing model (Kitaev and Klein, 2018) on the same labeled examples they access. When training on the 1,700 examples, or even when using only 50 examples for training and 5 for development, such a few-shot parsing approach can outperform all the unsupervised parsing methods by a significant margin. Few-shot parsing can be further improved by a simple data augmentation method and self-training. This suggests that, in order to arrive at fair conclusions, we should carefully consider the amount of labeled data used for model development. We propose two protocols for future work on unsupervised parsing: (i) use fully unsupervised criteria for hyperparameter tuning and model selection; (ii) use as few labeled examples as possible for model development, and compare to few-shot parsing trained on the same labeled examples.
BERT Knows Punta Cana Is Not Just Beautiful, It’s Gorgeous: Ranking Scalar Adjectives with Contextualised Representations

Adjectives like pretty, beautiful and gorgeous describe positive properties of the nouns they modify but with different intensity. These differences are important for natural language understanding and reasoning. We propose a novel BERT-based approach to intensity detection for scalar adjectives. We model intensity by vectors directly derived from contextualised representations and show they can successfully rank scalar adjectives. We evaluate our models both intrinsically, on gold standard datasets, and on an Indirect Question Answering task. Our results demonstrate that BERT encodes rich knowledge about the semantics of scalar adjectives, and is able to provide better quality intensity rankings than static embeddings and previous models with access to dedicated resources.
Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate

Recent works (e.g., (Li and Arora, 2020)) suggest that the use of popular normalization schemes (including Batch Normalization) in today's deep learning can move it far from a traditional optimization viewpoint, e.g., use of exponentially increasing learning rates. The current paper highlights other ways in which behavior of normalized nets departs from traditional viewpoints, and then initiates a formal framework for studying their mathematics via suitable adaptation of the conventional framework namely, modeling SGD-induced training trajectory via a suitable stochastic differential equation (SDE) with a noise term that captures gradient noise. This yields: (a) A new ' intrinsic learning rate' parameter that is the product of the normal learning rate and weight decay factor. Analysis of the SDE shows how the effective speed of learning varies and equilibrates over time under the control of intrinsic LR. (b) A challenge -- via theory and experiments -- to popular belief that good generalization requires large learning rates at the start of training. (c) New experiments, backed by mathematical intuition, suggesting the number of steps to equilibrium (in function space) scales as the inverse of the intrinsic learning rate, as opposed to the exponential time convergence bound implied by SDE analysis. We name it the Fast Equilibrium Conjecture and suggest it holds the key to why Batch Normalization is effective.
Safety Aware Reinforcement Learning (SARL)

As reinforcement learning agents become increasingly integrated into complex, real-world environments, designing for safety becomes a critical consideration. We specifically focus on researching scenarios where agents can cause undesired side effects while executing a policy on a primary task. Since one can define multiple tasks for a given environment dynamics, there are two important challenges. First, we need to abstract the concept of safety that applies broadly to that environment independent of the specific task being executed. Second, we need a mechanism for the abstracted notion of safety to modulate the actions of agents executing different policies to minimize their side-effects. In this work, we propose Safety Aware Reinforcement Learning (SARL) - a framework where a virtual safe agent modulates the actions of a main reward-based agent to minimize side effects. The safe agent learns a task-independent notion of safety for a given environment. The main agent is then trained with a regularization loss given by the distance between the native action probabilities of the two agents. Since the safe agent effectively abstracts a task-independent notion of safety via its action probabilities, it can be ported to modulate multiple policies solving different tasks within the given environment without further training. We contrast this with solutions that rely on task-specific regularization metrics and test our framework on the SafeLife Suite, based on Conway's Game of Life, comprising a number of complex tasks in dynamic environments. We show that our solution is able to match the performance of solutions that rely on task-specific side-effect penalties on both the primary and safety objectives while additionally providing the benefit of generalizability and portability.
IMPROVEMENT OF SEED GERMINATION TRAITS IN CANOLA (BRASSICA NAPUS L.) AS AFFECTED BY SALINE AND DROUGHT STRESSES

In many crop species, seed germination and early seedling growth are the most sensitive stages to stress. Salinity and drought may delay the onset, reduce the rate and increase the dispersion of germination events, leading to reductions in plant growth and final crop yield. The adverse effects of salt stress can be alleviated by various measures, including seed priming (pre-sowing seed treatment). The general purpose of seed priming is to partially hydrate the seed to a point where germination processes are begun but not completed. Most priming treatments involve imbibing seed with restricted amounts of water to allow sufficient hydration and advancement of metabolic processes but preventing germination or loss of desiccation tolerance. The objective of this study was to determine factors responsible for delayed germination and early seedling growth due to salt toxicity or osmotic effect and to optimize the best priming treatment for these stress conditions. In this experiment, treated seeds (control, KNO3 and hydropriming) of canola (Brassica napus L. cv. Okapi) were evaluated at germination and seedling growth stages for tolerance to salt (NaCl) and drought conditions (PEG-6000) at the same water potentials of 0.0, −3, −6, −9 and −12 bar. Electrical conductivity (EC) values of the NaCl solutions were 0, 3, 6, 9 and 12 dS.m, respectively. Results showed that germination delayed in both solutions, being variable with different priming treatments. Germination, and root and shoot length were higher, but mean germination time and unusual germination percentage were lower in NaCl than in PEG at the same water potential. Seeds were germinated at all concentrations of NaCl, but no seed germination was observed at −12 MPa of PEG treatments. NaCl had less inhibiting effect on the seedling growth than the germination. It was concluded that inhibition of germination at the same water potential of NaCl and PEG resulted from osmotic effect rather than salt toxicity. Hydro priming increased germination and seedling growth under salt and drought stresses.
Semantically Driven Sentence Fusion: Modeling and Evaluation

Sentence fusion is the task of joining related sentences into coherent text. Current training and evaluation schemes for this task are based on single reference ground-truths and do not account for valid fusion variants. We show that this hinders models from robustly capturing the semantic relationship between input sentences. To alleviate this, we present an approach in which ground-truth solutions are automatically expanded into multiple references via curated equivalence classes of connective phrases. We apply this method to a large-scale dataset and use the augmented dataset for both model training and evaluation. To improve the learning of semantic representation using multiple references, we enrich the model with auxiliary discourse classification tasks under a multi-tasking framework. Our experiments highlight the improvements of our approach over state-of-the-art models.
Investigating Representations of Verb Bias in Neural Language Models

Languages typically provide more than one grammatical construction to express certain types of messages. A speaker's choice of construction is known to depend on multiple factors, including the choice of main verb -- a phenomenon known as \emph{verb bias}. Here we introduce DAIS, a large benchmark dataset containing 50K human judgments for 5K distinct sentence pairs in the English dative alternation. This dataset includes 200 unique verbs and systematically varies the definiteness and length of arguments. We use this dataset, as well as an existing corpus of naturally occurring data, to evaluate how well recent neural language models capture human preferences. Results show that larger models perform better than smaller models, and transformer architectures (e.g. GPT-2) tend to out-perform recurrent architectures (e.g. LSTMs) even under comparable parameter and training settings. Additional analyses of internal feature representations suggest that transformers may better integrate specific lexical information with grammatical constructions.
The White Dwarf Binary Pathways Survey. V. The Gaia White Dwarf Plus AFGK Binary Sample and the Identification of 23 Close Binaries

Close white dwarf binaries consisting of a white dwarf and an A-, F-, G-, or K-type main-sequence star, henceforth close WD+AFGK binaries, are ideal systems to understand the nature of type Ia supernovae progenitors and to test binary evolution models. In this work we identify 775 WD+AFGK candidates from TGAS (The Tycho-Gaia Astrometric Solution) and Gaia Data Release 2 (DR2), a well-defined sample of stars with available parallaxes, and we measure radial velocities (RVs) for 275 of them with the aim of identifying close binaries. The RVs have been measured from high-resolution spectra obtained at the Xinglong 2.16 m Telescope and the San Pedro Mártir 2.12 m Telescope and/or from available LAMOST DR6 (low-resolution) and RAVE DR5 (medium-resolution) spectra. We identify 23 WD+AFGK systems displaying more than 3σ RV variation among 151 systems for which the measured values are obtained from different nights. Our WD+AFGK binary sample contains both AFGK dwarfs and giants, with a giant fraction ∼43%. The close binary fractions we determine for the WD+AFGK dwarf and giant samples are ≃24% and ≃15%, respectively. We also determine the stellar parameters (i.e., effective temperature, surface gravity, metallicity, mass, and radius) of the AFGK companions with available high-resolution spectra. The stellar parameter distributions of the AFGK companions that are members of close and wide binary candidates do not show statistically significant differences.
Semantic Evaluation for Text-to-SQL with Distilled Test Suite

We propose test suite accuracy to approximate semantic accuracy for Text-to-SQL models. Our method distills a small test suite of databases that achieves high code coverage for the gold query from a large number of randomly generated databases. At evaluation time, it computes the denotation accuracy of the predicted queries on the distilled test suite, hence calculating a tight upper-bound for semantic accuracy efficiently. We use our proposed method to evaluate 21 models submitted to the Spider leader board and manually verify that our method is always correct on 100 examples. In contrast, the current Spider metric leads to a 2.5% false negative rate on average and 8.1% in the worst case, indicating that test suite accuracy is needed. Our implementation, along with distilled test suites for eleven Text-to-SQL datasets, is publicly available.
Meta-Learning of Compositional Task Distributions in Humans and Machines

Modern machine learning systems struggle with sample efficiency and are usually trained with enormous amounts of data for each task. This is in sharp contrast with humans, who often learn with very little data. In recent years, meta-learning, in which one trains on a family of tasks (i.e. a task distribution), has emerged as an approach to improving the sample complexity of machine learning systems and to closing the gap between human and machine learning. However, in this paper, we argue that current meta-learning approaches still differ significantly from human learning. We argue that humans learn over tasks by constructing compositional generative models and using these to generalize, whereas current meta-learning methods are biased toward the use of simpler statistical patterns. To highlight this difference, we construct a new meta-reinforcement learning task with a compositional task distribution. We also introduce a novel approach to constructing a "null task distribution" with the same statistical complexity as the compositional distribution but without explicit compositionality. We train a standard meta-learning agent, a recurrent network trained with model-free reinforcement learning, and compare it with human performance across the two task distributions. We find that humans do better in the compositional task distribution whereas the agent does better in the non-compositional null task distribution -- despite comparable statistical complexity. This work highlights a particular difference between human learning and current meta-learning models, introduces a task that displays this difference, and paves the way for future work on human-like meta-learning.
NCP-VAE: Variational Autoencoders with Noise Contrastive Priors

Variational autoencoders (VAEs) are one of the powerful likelihood-based generative models with applications in various domains. However, they struggle to generate high-quality images, especially when samples are obtained from the prior without any tempering. One explanation for VAEs' poor generative quality is the prior hole problem: the prior distribution fails to match the aggregate approximate posterior. Due to this mismatch, there exist areas in the latent space with high density under the prior that do not correspond to any encoded image. Samples from those areas are decoded to corrupted images. To tackle this issue, we propose an energy-based prior defined by the product of a base prior distribution and a reweighting factor, designed to bring the base closer to the aggregate posterior. We train the reweighting factor by noise contrastive estimation, and we generalize it to hierarchical VAEs with many latent variable groups. Our experiments confirm that the proposed noise contrastive priors improve the generative performance of state-of-the-art VAEs by a large margin on the MNIST, CIFAR-10, CelebA 64, and CelebA HQ 256 datasets.
Synthetic line and continuum observations of simulated turbulent clouds: the apparent widths of filaments

Filamentary structures are ubiquitous in observations of real molecular clouds, and also in simulations of turbulent, self-gravitating gas. However, making comparisons between observations and simulations is complicated by the difficulty of estimating volume-densities observationally. Here, we have post-processed hydrodynamical simulations of a turbulent isothermal molecular cloud, using a full time-dependent chemical network. We have then run radiative transfer models to obtain synthetic line and continuum intensities that can be compared directly with those observed. We find that filaments have a characteristic width of ∼ 0.1 pc, both on maps of their true surface density, and on maps of their 850 μm dust-continuum emission, in agreement with previous work. On maps of line emission from CO isotopologues, the apparent widths of filaments are typically several times larger because the line intensities are poorly correlated with the surface density. On maps of line emission from dense-gas tracers such as N2H+ and HCN, the apparent widths of filaments are ≲0.1pc⁠. Thus, current observations of molecular-line emission are compatible with the universal 0.1 pc filament width inferred from Herschel observations, provided proper account is taken of abundance, optical-depth, and excitation considerations. We find evidence for ∼0.4 km s−1 radial velocity differences across filaments. These radial velocity differences might be a useful indicator of the mechanism by which a filament has formed or is forming, for example the turbulent cloud scenario modelled here, as against other mechanisms such as cloud-cloud collisions.
Evaluation of Cotton Seed Cake or Soybean Meal as Home-Made Concentrate Mixture for Dairy Cows from Farmers’ Perspective

Eight lactating dairy cows were divided into two groups i.e. Cotton Seed Cake (CSC) Group and Soybean Meal (SBM) Group of four animals in each. Randomly, animals of the CSC and SBM groups were individually offered daily 4 kg CMM+CSC (concentrate mixture containing maize grain 70 and CSC 30 parts by weight) and CMM+SBM (containing maize grain 70 and SBM 30 parts by weight) respectively along with limited (5 kg) fresh green fodder and ad lib. jowar straw for 90 days. To make both the rations isonitrogenous, extra 1.2 kg CSC was offered to the animals of the CSC group. The dry matter (DM) intake (kg/day) per 100 kg body weight (BW) was similar between the groups (2.76 vs 2.78). The roughage: concentrate ratio in CSC group (47.89: 53.11) was lower than the SBM group (57.62: 42.39). The milk yield (5.36 vs 5.33 lit/day; 5.52 vs 5.50 kg/day) and 4% fat corrected milk (FCM) yield (5.77 vs 5.76 kg/day) of the animals of the CSC group was similar (P>0.05) to the animals of SBM group. Further, there were no differences (P>0.05) on the milk compositions between the animals of the groups. The cost () of the feed of CSC group (130.48/day) was higher than the SBM group (120.94/day). The cost of milk was similar in both the groups based on the 4% FCM yield. There was higher net loss of 9.24 per animal per day due to feeding of mixture of maize grain and CSC as home-made concentrate mixture on dairy cows. It was concluded that exclusive feeding of mixture of maize grain and cotton seed cake as home-made concentrate mixture to dairy animals was not a profitable venture in comparison to of mixture of maize grain and soybean meal.
Adaptation of beans (Phaseolus vulgaris L.) to low phosphorus availability

en u ur Phosphorus availability in native soils is seldom adequate optimal plant growth. Of the macronutrients, K, Ca, Mg, and S are uncommon in the earth’s crust and in fresh water (Epstein, 1972) abundant in the atmosphere and is present in most soil solutio millimolar concentrations. In contrast, P is present in soil solution fresh water in only micromolar concentrations (Rendig and Tay 1989), in large part because P is commonly bound to many constituents that make it unavailable or only sparingly availab plants (Sample et al., 1980). An additional problem is that the P c in most terrestrial ecosystems is open-ended and tends toward tion, unlike the N cycle, in which atmospheric pools provide contin inputs to soil pools (Stevenson, 1986). Phosphorus availabili generally low in volcanic soils (Andosols) with high allophane c tent, weathered mineral soils (Oxisols, Ultisols, some Alfisols) c acterized by Fe and Al oxide chemistry, and coniferous forest (Spodosols). Taken together, these soils support most of the e vegetation and much of its human population. Low P availability is a primary constraint to agricultural product ity in many low-input systems in developing countries and can significant cost of production in high-input systems, especially wh soil chemical conditions “fix” applied fertilizer into less availab forms over time so that continual high inputs are needed. In high-v crops in the United States, the costs of P fertilizer are typically a s percentage of overall production costs, but in the aggregate, P fe ers cost U.S. farmers close to 10 billion dollars per year (Netzer, 1 Over the next century or two, the economics of P fertilization wil changed by the exhaustion of high-grade ore deposits (Cath 1980). In this sense also, P differs from N in that the Haber pro continually replenishes a vast pool of atmospheric N that ca converted to fertilizer, whereas high-grade P ore deposits are of lim size and distribution and represent an essentially nonrenewab source. Perhaps the most important economic and environm concern over P fertilizer in the United States pertains to its role pollutant in surface waters from agricultural runoff. Understanding the nature of plant adaptation to limited P avail ity has relevance to the low-input agriculture that still predomin over much of the globe, and to high-input systems, where mo efficient crops and cropping systems would require less fertilizer cause less environmental pollution. The focus of this article wi recent approaches taken to understand the adaptation of bean to availability.
On the Branching Bias of Syntax Extracted from Pre-trained Language Models

Many efforts have been devoted to extracting constituency trees from pre-trained language models, often proceeding in two stages: feature definition and parsing. However, this kind of methods may suffer from the branching bias issue, which will inflate the performances on languages with the same branch it biases to. In this work, we propose quantitatively measuring the branching bias by comparing the performance gap on a language and its reversed language, which is agnostic to both language models and extracting methods. Furthermore, we analyze the impacts of three factors on the branching bias, namely feature definitions, parsing algorithms, and language models. Experiments show that several existing works exhibit branching biases, and some implementations of these three factors can introduce the branching bias.
SupMMD: A Sentence Importance Model for Extractive Summarisation using Maximum Mean Discrepancy

Most work on multi-document summarization has focused on generic summarization of information present in each individual document set. However, the under-explored setting of update summarization, where the goal is to identify the new information present in each set, is of equal practical interest (e.g., presenting readers with updates on an evolving news topic). In this work, we present SupMMD, a novel technique for generic and update summarization based on the maximum mean discrepancy from kernel two-sample testing. SupMMD combines both supervised learning for salience and unsupervised learning for coverage and diversity. Further, we adapt multiple kernel learning to make use of similarity across multiple information sources (e.g., text features and knowledge based concepts). We show the efficacy of SupMMD in both generic and update summarization tasks by meeting or exceeding the current state-of-the-art on the DUC-2004 and TAC-2009 datasets.
The Autonomous Racing Software Stack of the KIT19d

Formula Student Driverless challenges engineering students to develop autonomous single-seater race cars in a quest to bring about more graduates who are well-prepared to solve the real world problems associated with autonomous driving. In this paper, we present the software stack of KA-RaceIng's entry to the 2019 competitions. We cover the essential modules of the system, including perception, localization, mapping, motion planning, and control. Furthermore, development methods are outlined and an overview of the system architecture is given. We conclude by presenting selected runtime measurements, data logs, and competition results to provide an insight into the performance of the final prototype.