Miracle-dz/UniFAR-FTUs huggingface dataset:
High-dimensional Asymptotic Theory of Bayesian Multiple Testing Procedures Under General Dependent Setup and Possible Misspecification

In this article, we investigate the asymptotic properties of Bayesian multiple testing procedures under general dependent setup, when the sample size and the number of hypotheses both tend to infinity. Specifically, we investigate strong consistency of the procedures and asymptotic properties of different versions of false discovery and false non-discovery rates under the high dimensional setup. We particularly focus on a novel Bayesian non-marginal multiple testing procedure and its associated error rates in this regard. Our results show that the asymptotic convergence rates of the error rates are directly associated with the Kullback-Leibler divergence from the true model, and the results hold even when the postulated class of models is misspecified. For illustration of our high-dimensional asymptotic theory, we consider a Bayesian variable selection problem in a time-varying covariate selection framework, with autoregressive response variables. We particularly focus on the setup where the number of hypotheses increases at a faster rate compared to the sample size, which is the so-called ultra-high dimensional situation.
A Parameterized Approximation Scheme for Min $k$-Cut

In the Min $k$-cut problem, input is an edge weighted graph $G$ and an integer $k$, and the task is to partition the vertex set into $k$ non-empty sets, such that the total weight of the edges with endpoints in different parts is minimized. When $k$ is part of the input, the problem is NP-complete and hard to approximate within any factor less than 2. Recently, the problem has received significant attention from the perspective of parameterized approximation. Gupta et al. [SODA 2018] initiated the study of FPT-approximation for the Min $k$-Cut problem and gave an 1.9997-approximation algorithm running in time $2^{\mathcal{O}(k^{6})}n^{\mathcal{O}(1)}$. Later, the same set of authors [FOCS 2018] designed an ($1+\epsilon$)-approximation algorithm that runs in time $(k/\epsilon)^{\mathcal{O}(k)}n^{k+\mathcal{O}(1)}$, and a 1.81-approximation algorithm running in time $2^{\mathcal{O}(k^{2})}n^{\mathcal{O}(1)}$. More, recently, Kawarabayashi and Lin [SODA 2020] gave a $(5/3+\epsilon)$-approximation for Min $k$-Cut running in time $2^{\mathcal{O}(k^{2}\log k)}n^{\mathcal{O}(1)}$. In this paper we give a parameterized approximation algorithm with best possible approximation guarantee, and best possible running time dependence on said guarantee (up to Exponential Time Hypothesis (ETH) and constants in the exponent). In particular, for every $\epsilon > 0$, the algorithm obtains a ($1+\epsilon$)-approximate solution in time $(k/\epsilon)^{\mathcal{O}(k)}n^{\mathcal{O}(1)}$. The main ingredients of our algorithm are: a simple sparsification procedure, a new polynomial time algorithm for decomposing a graph into highly connected parts, and a new exact algorithm with running time $s^{\mathcal{O}(k)}n^{\mathcal{O}(1)}$ on unweighted (multi-) graphs. Here, $s$ denotes the number of edges in a minimum $k$-cut. The latter two are of independent interest.
The effect of mechanical processing of wheaten straw on intake and rumen parameters following grain feeding in sheep

While the effect of roughage chop length on decreasing the risk of acidosis in cattle is well established, there is limited information on the effect of roughage processing on intake and rumen parameters in sheep. Intake and rumen parameters were assessed in 10-month-old crossbred wethers (n = 24, 48.4 kg ± 5.3) offered wheaten straw as finely chaffed (6 mm), coarsely chaffed (15–20 mm) or unprocessed (40 mm) lengths, along with barley grain. Animals were offered 300 g/head of barley grain on the first day of grain introduction, 450 g/head.day for Days 2–3, 550 g/head.day for Days 4–6, 650 g/head.day for Days 7–9 and 750 g/head on Day 10. Rumen fluid was collected 3 h post-feeding from each animal the day before grain introduction and on Days 1–3 of grain introduction. During Days 1–3 of grain introduction, roughage intake within 3 h of feed being offered was greater (P < 0.05) in sheep offered finely chaffed (0.20 kg ± 0.03) than those offered either coarsely chaffed (0.09 kg ± 0.03) or unprocessed (0.08 kg ± 0.03) straw. Rumen pH did not differ between treatments, but increased grain feeding significantly (P < 0.001) reduced pH. Sheep offered finely chaffed straw had a greater (P < 0.05) daily straw intake (0.59 kg ± 0.06) than sheep offered coarsely chaffed straw (0.37 kg ± 0.06), while sheep offered unprocessed straw had an intermediate intake (0.49 kg ± 0.06); sheep consumed all grain offered. Shorter straw particle lengths may have a role in reducing the risk of acidosis during grain introduction.
Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations

The idea of using multi-task learning approaches to address the joint extraction of entity and relation is motivated by the relatedness between the entity recognition task and the relation classification task. Existing methods using multi-task learning techniques to address the problem learn interactions among the two tasks through a shared network, where the shared information is passed into the task-specific networks for prediction. However, such an approach hinders the model from learning explicit interactions between the two tasks to improve the performance on the individual tasks. As a solution, we design a multi-task learning model which we refer to as recurrent interaction network which allows the learning of interactions dynamically, to effectively model task-specific features for classification. Empirical studies on two real-world datasets confirm the superiority of the proposed model.
Selecting Backtranslated Data from Multiple Sources for Improved Neural Machine Translation

Machine translation (MT) has benefited from using synthetic training data originating from translating monolingual corpora, a technique known as backtranslation. Combining backtranslated data from different sources has led to better results than when using such data in isolation. In this work we analyse the impact that data translated with rule-based, phrase-based statistical and neural MT systems has on new MT systems. We use a real-world low-resource use-case (Basque-to-Spanish in the clinical domain) as well as a high-resource language pair (German-to-English) to test different scenarios with backtranslation and employ data selection to optimise the synthetic corpora. We exploit different data selection strategies in order to reduce the amount of data used, while at the same time maintaining high-quality MT systems. We further tune the data selection method by taking into account the quality of the MT systems used for backtranslation and lexical diversity of the resulting corpora. Our experiments show that incorporating backtranslated data from different sources can be beneficial, and that availing of data selection can yield improved performance.
Draba streptobrachia (Brassicaceae), a new species from Colorado

Draba streptobrachia Price is an alpine species endemic to Colorado, U.S.A. It was treated byHitchcock (1941) asDraba spectabilis var.dasycarpa (Schulz) C. L. Hitchc. , but is distinct fromD. spectabilis Greene in morphology, habitat, and chromosome number (g = ca 32 inD. streptobrachia, versusg = 10 and 16 inD. spectabilis) and therefore has been raised to the rank of species. Pollen fertility inD. streptobrachia is at or near zero and anthers consistently fail to dehisce, but fertile seeds are still produced, suggesting that the taxon reproduces by agamospermy.
Effects of surfactant solubility on the hydrodynamics of a viscous drop in a dc electric field

The physico-chemistry of surfactants (amphiphilic surface active agents) is often used to control the dynamics of viscous drops and bubbles. Surfactant sorption kinetics has been shown to play a critical role in the deformation of drops in extensional and shear flows, yet to the best of our knowledge these kinetics effects on a viscous drop in an electric fieldhave not been accounted for. In this paper we numerically investigate the effects of sorption kinetics on a surfactant-covered viscous drop in an electric field. Over a range of electric conductivity and permittivity ratios between the interior and exterior fluids, we focus on the dependence of deformation and flow on the transfer parameter $J$, and Biot number $\text{Bi}$ that characterize the extent of surfactant exchange between the drop surface and the bulk. Our findings suggest solubility affects the electrohydrodynamics of a viscous drop in distinct ways as we identify parameter regions where (1) surfactant solubility may alter both the drop deformation and circulation of fluid around a drop, and (2) surfactant solubility affects mainly the flow and not the deformation.
An Efficient Integration of Disentangled Attended Expression and Identity FeaturesFor Facial Expression Transfer andSynthesis

In this paper, we present an Attention-based Identity Preserving Generative Adversarial Network (AIP-GAN) to overcome the identity leakage problem from a source image to a generated face image, an issue that is encountered in a cross-subject facial expression transfer and synthesis process. Our key insight is that the identity preserving network should be able to disentangle and compose shape, appearance, and expression information for efficient facial expression transfer and synthesis. Specifically, the expression encoder of our AIP-GAN disentangles the expression information from the input source image by predicting its facial landmarks using our supervised spatial and channel-wise attention module. Similarly, the disentangled expression-agnostic identity features are extracted from the input target image by inferring its combined intrinsic-shape and appearance image employing our self-supervised spatial and channel-wise attention mod-ule. To leverage the expression and identity information encoded by the intermediate layers of both of our encoders, we combine these features with the features learned by the intermediate layers of our decoder using a cross-encoder bilinear pooling operation. Experimental results show the promising performance of our AIP-GAN based technique.
Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society

With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first global infodemic. Fighting this infodemic is ranked second in the list of the most important focus areas of the World Health Organization, with dangers ranging from promoting fake cures, rumors, and conspiracy theories to spreading xenophobia and panic. Addressing the issue requires solving a number of challenging problems such as identifying messages containing claims, determining their check-worthiness and factuality, and their potential to do harm as well as the nature of that harm, to mention just a few. Thus, here we design, annotate, and release to the research community a new dataset for fine-grained disinformation analysis that (i)focuses on COVID-19, (ii) combines the perspectives and the interests of journalists, fact-checkers, social media platforms, policy makers, and society as a whole, and (iii) covers both English and Arabic. Finally, we show strong evaluation results using state-of-the-art Transformers, thus confirming the practical utility of the annotation schema and of the dataset.
Bipartite Flat-Graph Network for Nested Named Entity Recognition

In this paper, we propose a novel bipartite flat-graph network (BiFlaG) for nested named entity recognition (NER), which contains two subgraph modules: a flat NER module for outermost entities and a graph module for all the entities located in inner layers. Bidirectional LSTM (BiLSTM) and graph convolutional network (GCN) are adopted to jointly learn flat entities and their inner dependencies. Different from previous models, which only consider the unidirectional delivery of information from innermost layers to outer ones (or outside-to-inside), our model effectively captures the bidirectional interaction between them. We first use the entities recognized by the flat NER module to construct an entity graph, which is fed to the next graph module. The richer representation learned from graph module carries the dependencies of inner entities and can be exploited to improve outermost entity predictions. Experimental results on three standard nested NER datasets demonstrate that our BiFlaG outperforms previous state-of-the-art models.
Low Resource Multi-Task Sequence Tagging - Revisiting Dynamic Conditional Random Fields

We compare different models for low resource multi-task sequence tagging that leverage dependencies between label sequences for different tasks. Our analysis is aimed at datasets where each example has labels for multiple tasks. Current approaches use either a separate model for each task or standard multi-task learning to learn shared feature representations. However, these approaches ignore correlations between label sequences, which can provide important information in settings with small training datasets. To analyze which scenarios can profit from modeling dependencies between labels in different tasks, we revisit dynamic conditional random fields (CRFs) and combine them with deep neural networks. We compare single-task, multi-task and dynamic CRF setups for three diverse datasets at both sentence and document levels in English and German low resource scenarios. We show that including silver labels from pretrained part-of-speech taggers as auxiliary tasks can improve performance on downstream tasks. We find that especially in low-resource scenarios, the explicit modeling of inter-dependencies between task predictions outperforms single-task as well as standard multi-task models.
Accuracy of hybrid functionals with non-self-consistent Kohn-Sham orbitals for predicting the properties of semiconductors.

Accurately modeling the electronic structure of materials is a persistent challenge to high-throughput screening. A promising means of balancing accuracy against computational cost are non-self-consistent calculations with hybrid density-functional theory, where the electronic band energies are evaluated using a hybrid functional from orbitals obtained with a less demanding (semi-)local functional. We have quantified the performance of this technique for predicting the physical properties of sixteen tetrahedral semiconductors with bandgaps from 0.2-5.5 eV. Provided the base functional predicts a non-metallic electronic structure, bandgaps within 5 % of the PBE0 and HSE06 gaps can be obtained with an order of magnitude reduction in computing time. The positions of the valence and conduction band extrema and the Fermi level are well reproduced, further enabling calculation of the band dispersion, density of states, and dielectric properties using Fermi's Golden Rule. While the error in the non-self-consistent total energies is ~50 meV atom-1, the energy-volume curves are reproduced accurately enough to obtain the equilibrium volume and bulk modulus with minimal error. We also test the dielectric-dependent scPBE0 functional and obtain bandgaps and dielectric constants to within 2.5 % of the self-consistent results, which amount to a significant improvement over self-consistent PBE0 for a similar computational cost. We identify cases where the non-self-consistent approach is expected to perform poorly, and demonstrate that partial self-consistency provides a practical and efficient workaround. Finally, we perform proof-of-concept calculations on CoO and NiO to demonstrate the applicability of the technique to strongly-correlated open-shell transition-metal oxides.
Representations of Syntax [MASK] Useful: Effects of Constituency and Dependency Structure in Recursive LSTMs

Sequence-based neural networks show significant sensitivity to syntactic structure, but they still perform less well on syntactic tasks than tree-based networks. Such tree-based networks can be provided with a constituency parse, a dependency parse, or both. We evaluate which of these two representational schemes more effectively introduces biases for syntactic structure that increase performance on the subject-verb agreement prediction task. We find that a constituency-based network generalizes more robustly than a dependency-based one, and that combining the two types of structure does not yield further improvement. Finally, we show that the syntactic robustness of sequential models can be substantially improved by fine-tuning on a small amount of constructed data, suggesting that data augmentation is a viable alternative to explicit constituency structure for imparting the syntactic biases that sequential models are lacking.
An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing

Knowledge graph (KG) schemas can vary greatly from one domain to another. Therefore supervised approaches to graph-to-text generation and text-to-graph knowledge extraction (semantic parsing) will always suffer from a shortage of domain-specific parallel graph-text data, while adapting a model trained on a different domain is often impossible due to little or no overlap in entities and relations. This situation calls for an approach that (1) does not need large amounts of annotated data and (2) is easy to adapt to new KG schemas. To this end, we present the first approach to fully unsupervised text generation from KGs and KG generation from text. Inspired by recent work on unsupervised machine translation, we serialize a KG as a sequence of facts and frame both tasks as sequence translation. By means of a shared sequence encoder and decoder, our model learns to map both graphs and texts into a joint semantic space and thus generalizes over different surface representations with the same meaning. We evaluate our approach on WebNLG v2.1 and a new benchmark leveraging scene graphs from Visual Genome. Our system outperforms strong baselines for both text$\leftrightarrow$graph tasks without any manual adaptation from one dataset to the other. In additional experiments, we investigate the impact of using different unsupervised objectives.
Rethinking Neuromorphic Hardware Design - A Complexity Engineering Approach

As the compute demands for machine learning and artificial intelligence applications continue to grow, neuromorphic hardware has been touted as a potential solution. New emerging devices like memristors, atomic switches, etc have shown tremendous potential to replace CMOS-based circuits but have been hindered by multiple challenges with respect to device variability, stochastic behavior and scalability. In this paper we will introduce a Description Design framework to analyze past successes in computing, understand current problems and identify solutions moving forward. Engineering systems with these emerging devices might require the modification of both the type of descriptions of learning that we will design for, and the design methodologies we employ in order to realize these new descriptions. We will explore ideas from complexity engineering and analyze the advantages and challenges they offer over traditional approaches to neuromorphic design with novel computing fabrics. A reservoir computing example is used to understand the specific changes that would accompany in moving towards a complexity engineering approach. The time is ideal for a significant reboot of our design methodologies and success will represent a radical shift in how neuromorphic hardware is designed and pave the way for a new paradigm.
Anatoxin‐a and neurotoxic cyanobacteria in German lakes and reservoirs

During a screening program conducted from 1995 to 1997 in Germany, 80 water bodies were analyzed, some regularly, some sporadically, for content of the neurotoxin anatoxin‐a. Analysis for the majority of the samples was differentiated between anatoxin‐a contained in the cells and that dissolved in the water. A total of 554 extracts (from 393 samples, many with analysis of seston as well as filtrate for differentiation of intra‐ and extracellular toxin content) were extracted with CH2Cl2 and derivatized with pentafluorobenzylbromide for GC‐ECD analysis with a detection limit of 2.5×10−4 μg L1. Only 22% of all analyzed samples contained anatoxin‐a, and the highest concentration found was 13.1 μg L−1 (the sum of the toxin in the cells and in the water). If anatoxin‐a was detected, it was usually found in the seston fraction, but only in some of the corresponding filtrates (with one exception, where it was detected only in the filtrate). In these cases, concentrations were often even higher than in the seston. The earliest occurrence of anatoxin‐a during the year was in May and the latest in November. Usually Anabaena and/or Aphanizomenon were present when anatoxin‐a was found. ©1999 John Wiley & Sons, Inc. Environ Toxicol 14: 117–125, 1999
The Effect of Lubricant Viscosity on Nucleation for Water Condensation on Lubricant-Infused Surfaces

Lubricant-infused surfaces (LIS) can promote stable dropwise condensation and improve heat transfer rates due to a low nucleation energy barrier and high droplet mobility. Yet nucleation dynamics on a non-uniform lubricant film on LIS during condensation are not well understood. Previous work demonstrated a dynamic re-distribution of the lubricant film during condensation, leading to oil-rich and oil-poor regions. In this work, we study water vapor condensation with nitrogen as carrier gas on LIS infused with Krytox oils of different viscosity within a custom-built chamber at various gas temperatures. We combine high-speed imaging and optical microscopy to determine the apparent (droplet size ~ 1 {\mu}m) nucleation rate and density on LIS. We show that nucleation predominantly occurs in oil-poor regions where the oil-vapor interfacial temperature is lower than in oil-rich regions. This small temperature difference is enough to cause a significantly lower nucleation free-energy barrier in the oil-poor regions, leading to enhanced nucleation. The relative area ratio of oil-poor regions to total surface area, i.e., preferred area for nucleation, can be tailored between 11% and 19% by changing the lubricant viscosity from 1627 to 73 cP. We statistically analyze the influence of lubricant viscosity on the new emerging droplets on LIS at a wide range of gas-substrate temperature differences. The results show that the nucleation rate density dramatically increases with this temperature difference. More importantly, a strong dependence of nucleation rate density on lubricant viscosity is discovered, which means that higher heat transfer rates can potentially be achieved by lowering lubricant viscosity. We attribute the enhanced nucleation primarily to enhanced droplet mobility and more frequent surface refreshing at lower lubricant viscosity.