Miracle-dz/UniFAR-FTUs huggingface dataset:
Antibiotics and antibiotic resistance from animal manures to soil: a review

The overuse of veterinary antibiotics in animal production and the subsequent land applications of manures contribute to the elevated antibiotic resistance in the soil environment. To minimize the risk of antibiotic resistance, it is important to understand the fate of antibiotics and the spread of antibiotic resistance genes (ARGs) from animal production systems to soil. In this paper, we review recent studies on veterinary antibiotic use, the concentrations of antibiotics and the abundance and diversity of AGRs in animal manures and in soil that receives manures or manure composts. The mechanisms of ARG dissemination in the environment are also discussed. Although we focus on China where around 3 billion tons of animal manures are produced and more than 84 000 tons of antibiotics are consumed annually in animal production industries, the problem is worldwide. Approximately 58% of the veterinary antibiotics consumed are excreted into the environment, more than half of which end up in the soil. The abundance of ARGs in manures can reach up to 10−1 of the 16S rRNA genes. Applications of manures or manure composts can enrich soil ARGs in at least three ways: (i) by the direct introduction of manure‐derived ARGs, (ii) by elevating the intrinsic soil ARGs and (iii) by imposing a selection of ARGs with the antibiotics in the manures. We also discuss the need for more stringent regulations on the use of veterinary antibiotics and future research directions on the mechanisms of antibiotic resistance and resistance management.
The fate of Landau levels under $\delta$-interactions

We consider the self-adjoint Landau Hamiltonian $H_0$ in $L^2(\mathbb{R}^2)$ whose spectrum consists of infinitely degenerate eigenvalues $\Lambda_q$, $q \in \mathbb{Z}_+$, and the perturbed operator $H_\upsilon = H_0 + \upsilon\delta_\Gamma$, where $\Gamma \subset \mathbb{R}^2$ is a regular Jordan $C^{1,1}$-curve, and $\upsilon \in L^p(\Gamma;\mathbb{R})$, $p>1$, has a constant sign. We investigate ${\rm Ker}(H_\upsilon -\Lambda_q)$, $q \in \mathbb{Z}_+$, and show that generically $$0 \leq {\rm dim \, Ker}(H_\upsilon -\Lambda_q) - {\rm dim \, Ker}(T_q(\upsilon \delta_\Gamma))<\infty,$$ where $T_q(\upsilon \delta_\Gamma) = p_q (\upsilon \delta_\Gamma)p_q$, is an operator of Berezin-Toeplitz type, acting in $p_q L^2(\mathbb{R}^2)$, and $p_q$ is the orthogonal projection on ${\rm Ker}\,(H_0 -\Lambda_q)$. If $\upsilon \neq 0$ and $q = 0$, we prove that ${\rm Ker}\,(T_0(\upsilon \delta_\Gamma)) = \{0\}$. If $q \geq 1$, and $\Gamma = \mathcal{C}_r$ is a circle of radius $r$, we show that ${\rm dim \, Ker} (T_q(\delta_{\mathcal{C}_r})) \leq q$, and the set of $r \in (0,\infty)$ for which ${\rm dim \, Ker}(T_q(\delta_{\mathcal{C}_r})) \geq 1$, is infinite and discrete.
A Characterization of Individualization-Refinement Trees

Individualization-Refinement (IR) algorithms form the standard method and currently the only practical method for symmetry computations of graphs and combinatorial objects in general. Through backtracking, on each graph an IR-algorithm implicitly creates an IR-tree whose order is the determining factor of the running time of the algorithm. We give a precise and constructive characterization which trees are IR-trees. This characterization is applicable both when the tree is regarded as an uncolored object but also when regarded as a colored object where vertex colors stem from a node invariant. We also provide a construction that given a tree produces a corresponding graph whenever possible. This provides a constructive proof that our necessary conditions are also sufficient for the characterization.
Improving Streaming Transformer Based ASR Under a Framework of Self-supervised Learning

Recently self-supervised learning has emerged as an effective approach to improve the performance of automatic speech recognition (ASR). Under such a framework, the neural network is usually pre-trained with massive unlabeled data and then fine-tuned with limited labeled data. However, the non-streaming architecture like bidirectional transformer is usually adopted by the neural network to achieve competitive results, which can not be used in streaming scenarios. In this paper, we mainly focus on improving the performance of streaming transformer under the self-supervised learning framework. Specifically, we propose a novel two-stage training method during fine-tuning, which combines knowledge distilling and self-training. The proposed training method achieves 16.3% relative word error rate (WER) reduction on Librispeech noisy test set. Finally, by only using the 100h clean subset of Librispeech as the labeled data and the rest (860h) as the unlabeled data, our streaming transformer based model obtains competitive WERs 3.5/8.7 on Librispeech clean/noisy test sets.
Fusion with Hierarchical Graphs for Multimodal Emotion Recognition

Automatic emotion recognition (AER) based on enriched multimodal inputs, including text, speech, and visual clues, is crucial in the development of emotionally intelligent machines. Although complex modality relationships have been proven effective for AER, they are still largely underexplored because previous works predominantly relied on various fusion mechanisms with simply concatenated features to learn mul-timodal representations for emotion classification. This paper proposes a novel hierarchical fusion graph convolutional net-work (HFGCN) model that learns more informative multimodal representations by considering the modality dependencies during the feature fusion procedure. Specifically, the proposed model fuses multimodality inputs using a two-stage graph construction approach and encodes the modality dependencies into the con-versation representation. We verified the interpretable capabilities of the proposed method by projecting the emotional states to a 2D valence-arousal (VA) subspace. Extensive experiments showed the effectiveness of our proposed model for more accurate AER, which yielded state-of-the-art results on two public datasets, IEMOCAP and MELD.
Incorporating Residual and Normalization Layers into Analysis of Masked Language Models

Transformer architecture has become ubiquitous in the natural language processing field. To interpret the Transformer-based models, their attention patterns have been extensively analyzed. However, the Transformer architecture is not only composed of the multi-head attention; other components can also contribute to Transformers’ progressive performance. In this study, we extended the scope of the analysis of Transformers from solely the attention patterns to the whole attention block, i.e., multi-head attention, residual connection, and layer normalization. Our analysis of Transformer-based masked language models shows that the token-to-token interaction performed via attention has less impact on the intermediate representations than previously assumed. These results provide new intuitive explanations of existing reports; for example, discarding the learned attention patterns tends not to adversely affect the performance. The codes of our experiments are publicly available.
In Search of Infall Motion in Molecular Clumps. III. HCO+ (1-0) and H13CO+ (1-0) Mapping Observations toward Confirmed Infall Sources

The study of infall motion helps us to understand the initial stages of star formation. In this paper, we use the IRAM 30 m telescope to make mapping observations of 24 infall sources confirmed in previous work. The lines we use to track gas infall motions are HCO+ (1-0) and H13CO+ (1-0). All 24 sources show HCO+ emissions, while 18 sources show H13CO+ emissions. The HCO+ integrated intensity maps of 17 sources show clear clumpy structures; for the H13CO+ line, 15 sources show clumpy structures. We estimated the column density of HCO+ and H13CO+ using the RADEX radiation transfer code, and the obtained [HCO+]/[H2] and [H13CO+]/[HCO+] of these sources are about 10−11–10−7 and 10−3–1, respectively. Based on the asymmetry of the line profile of the HCO+, we distinguish these sources: 19 sources show blue asymmetric profiles, and the other sources show red profiles or symmetric peak profiles. For eight sources that have double-peaked blue line profiles and signal-to-noise ratios greater than 10, the RATRAN model is used to fit their HCO+ (1-0) lines, and to estimate their infall parameters. The mean V in of these sources is 0.3–1.3 km s−1, and the Ṁin is about 10−3–10−4 M ⊙ yr−1, which is consistent with the results of intermediate or massive star formation in previous studies. The V in estimated from the Myers model is 0.1–1.6 km s−1, and the Ṁin is within 10−3–10−5 M ⊙ yr−1. In addition, some identified infall sources show other star-forming activities, such as outflows and maser emissions. Especially for those sources with a double-peaked blue asymmetric profile, most of them have both infall and outflow evidence.
ARCH: Efficient Adversarial Regularized Training with Caching

Adversarial regularization can improve model generalization in many natural language processing tasks. However, conventional approaches are computationally expensive since they need to generate a perturbation for each sample in each epoch. We propose a new adversarial regularization method ARCH (adver-sarial regularization with caching), where perturbations are generated and cached once every several epochs. As caching all the perturbations imposes memory usage concerns, we adopt a K-nearest neighbors-based strategy to tackle this issue. The strategy only requires caching a small amount of perturbations, without introducing additional training time. We evaluate our proposed method on a set of neural machine translation and natural language understanding tasks. We observe that ARCH signiﬁcantly eases the computational burden (saves up to 70% of computational time in comparison with conventional approaches). More surprisingly, by reducing the variance of stochastic gradients, ARCH produces a notably better (in most of the tasks) or comparable model generalization. Our code is publicly available.
Cross-lingual Transfer of Monolingual Models

Recent studies in cross-lingual learning using multilingual models have cast doubt on the previous hypothesis that shared vocabulary and joint pre-training are the keys to cross-lingual generalization. We introduce a method for transferring monolingual models to other languages through continuous pre-training and study the effects of such transfer from four different languages to English. Our experimental results on GLUE show that the transferred models outperform an English model trained from scratch, independently of the source language. After probing the model representations, we find that model knowledge from the source language enhances the learning of syntactic and semantic knowledge in English.
Distract Your Attention: Multi-Head Cross Attention Network for Facial Expression Recognition

This paper presents a novel facial expression recognition network, called Distract your Attention Network (DAN). Our method is based on two key observations in biological visual perception. Firstly, multiple facial expression classes share inherently similar underlying facial appearance, and their differences could be subtle. Secondly, facial expressions simultaneously exhibit themselves through multiple facial regions, and for recognition, a holistic approach by encoding high-order interactions among local features is required. To address these issues, this work proposes DAN with three key components: Feature Clustering Network (FCN), Multi-head Attention Network (MAN), and Attention Fusion Network (AFN). Specifically, FCN extracts robust features by adopting a large-margin learning objective to maximize class separability. In addition, MAN instantiates a number of attention heads to simultaneously attend to multiple facial areas and build attention maps on these regions. Further, AFN distracts these attentions to multiple locations before fusing the feature maps to a comprehensive one. Extensive experiments on three public datasets (including AffectNet, RAF-DB, and SFEW 2.0) verified that the proposed method consistently achieves state-of-the-art facial expression recognition performance. The DAN code is publicly available.
Non-uniqueness in law of three-dimensional magnetohydrodynamics system forced by random noise

We prove non-uniqueness in law of the three-dimensional magnetohydrodynamics system that is forced by random noise of an additive and a linear multiplicative type and has viscous and magnetic diffusion, both of which are weaker than a full Laplacian. We apply convex integration to both equations of velocity and magnetic fields in order to obtain the non-uniqueness in law in the class of probabilistically strong solutions.
Data Hunches: Incorporating Personal Knowledge into Visualizations

The trouble with data is that it frequently provides only an imperfect representation of a phenomenon of interest. Experts who are familiar with their datasets will often make implicit, mental corrections when analyzing a dataset, or will be cautious not to be overly confident about their findings if caveats are present. However, personal knowledge about the caveats of a dataset is typically not incorporated in a structured way, which is problematic if others who lack that knowledge interpret the data. In this work, we define such analysts' knowledge about datasets as data hunches. We differentiate data hunches from uncertainty and discuss types of hunches. We then explore ways of recording data hunches, and, based on a prototypical design, develop recommendations for designing visualizations that support data hunches. We conclude by discussing various challenges associated with data hunches, including the potential for harm and challenges for trust and privacy. We envision that data hunches will empower analysts to externalize their knowledge, facilitate collaboration and communication, and support the ability to learn from others' data hunches.
Lyapunov exponents for truncated unitary and Ginibre matrices

In this note, we show that the Lyapunov exponents of mixed products of random truncated Haar unitary and complex Ginibre matrices are asymptotically given by equally spaced `picket-fence' statistics. We discuss how these statistics should originate from the connection between random matrix products and multiplicative Brownian motion on $\operatorname{GL}_n(\mathbb{C})$, analogous to the connection between discrete random walks and ordinary Brownian motion. Our methods are based on contour integral formulas for products of classical matrix ensembles from integrable probability.
Solving Occlusion in Terrain Mapping with Neural Networks

Accurate and complete terrain maps enhance the awareness of autonomous robots and enable safe and optimal path planning. Rocks and topography often create occlusions and lead to missing elevation information in Digital Elevation Maps (DEMs). Currently, mostly traditional inpainting techniques based on diffusion or patch-matching are used by autonomous mobile robots to ﬁll-in incomplete DEMs. These methods cannot leverage the high-level terrain characteristics and the geometric constraints of line of sight we humans use intuitively to predict occluded areas. We propose to use neural networks to reconstruct the occluded areas in DEMs. We introduce a self-supervised learning approach capable of training on real-world data without a need for ground-truth information. We accomplish this by adding artiﬁcial occlusion to the incomplete elevation maps constructed on a real robot by performing ray casting. We evaluate our self-supervised learning approach on several real-world datasets which were recorded during autonomous exploration of both structured and unstructured terrain with a legged robot, and additionally in a planetary scenario on Lunar analogue terrain. We state a signiﬁcant improvement compared to the Telea and Navier-Stokes baseline.
Embedding Convolutions for Short Text Extreme Classification with Millions of Labels

Automatic annotation of short-text data to a 001 large number of target labels, referred to as 002 Short Text Extreme Classification, has found 003 numerous applications including prediction of 004 related searches and product recommendation 005 tasks. In this paper, we propose a convolutional 006 architecture I NCEPTION XML which is light-007 weight, yet powerful, and robust to the inherent 008 lack of word-order in short-text queries encoun-009 tered in search and recommendation tasks. We 010 demonstrate the efficacy of applying convolu-011 tions by recasting the operation along the em-012 bedding dimension instead of the word dimen-013 sion as done in conventional usage of CNNs for 014 text classification. Towards scaling our model 015 to problems with millions of labels, we also pro-016 pose I NCEPTION XML+ framework. This ad-017 dresses the shortcomings of the dynamic hard-018 negative mining framework in the recently pro-019 posed L IGHT XML by improving the alignment 020 between the label-shortlister and extreme clas-021 sifier. I NCEPTION XML+ is not only smaller 022 than state-of-the-art deep extreme classifier, 023 A STEC , in terms of model size but also sig-024 nificantly outperforms it on popular benchmark 025 datasets. For reproducibility, the code is made 026 available with this submission. 027
S3LAM: Structured Scene SLAM

We propose a new SLAM system that uses the semantic segmentation of objects and structures in the scene. Semantic information is relevant as it contains high level information which may make SLAM more accurate and robust. Our contribution is twofold: i) A new SLAM system based on ORB-SLAM2 that creates a semantic map made of clusters of points corresponding to objects instances and structures in the scene. ii) A modification of the classical Bundle Adjustment formulation to constrain each cluster using geometrical priors, which improves both camera localization and reconstruction and enables a better understanding of the scene. We evaluate our approach on sequences from several public datasets and show that it improves camera pose estimation with respect to state of the art.
The Eddington ratio-dependent ‘changing look’ events in NGC 2992

We present an analysis of historical multiwavelength emission of the `Changing Look' (CL) Active Galactic Nucleu (AGN) in NGC 2992, covering epochs ranging from 1978 to 2021, as well as new X-ray and optical spectra. The galaxy presents multiple Seyfert type transitions from Type 2 to intermediate-type, losing and regaining its Hα broad emission lines (BEL) recurrently. In X-rays, the source shows intrinsic variability with the absorption corrected luminosity varying by a factor of ∼ 40. We rule-out tidal disruption events or variable obscuration as causes of the type transitions, and show that the presence and the flux of th Hα BEL is directly correlated with the 2–10 keV X-ray luminosity (L2−10): the component disappears at L2−10 ≤ 2.6 × 1042 $\rm erg\, cm^{-2}\, s^{-1}$; this luminosity value translates into an Eddington ratio (λEdd) of ∼ 1 per cent. The λEdd in which the BEL transitions occur is the same as the critical value at which a state transition between a radiatively inefficient accretion flow and a thin accretion disk is expected, such similarity suggests that the AGN is operating at the threshold mass accretion rate between the two accretion modes. We find a correlation between the narrow Fe Kα flux and λEdd, and an anticorrelation between full-width at half maximum of Hα BEL and λEdd, in agreement with theoretical predictions. Two possible scenarios for type transitions are compatible with our results: either the dimming of the AGN continuum, which reduces the supply of ionizing photons available to excite the gas in the Broad Line Region (BLR), or the fading of the BLR structure itself occurs as the low accretion rate is not able to sustain the required cloud flow rate in a disc-wind BLR model.
Proximal causal inference for complex longitudinal studies

A standard assumption for causal inference about the joint effects of time-varying treatment is that one has measured sufficient covariates to ensure that within covariate strata, subjects are exchangeable across observed treatment values, also known as ‘sequential randomization assumption (SRA)’. SRA is often criticized as it requires one to accurately measure all confounders. Realistically, measured covariates can rarely capture all confounders with certainty. Often covariate measurements are at best proxies of confounders, thus invalidating inferences under SRA. In this paper, we extend the proximal causal inference (PCI) framework of Miao, Geng, et al. (2018. Identifying causal effects with proxy variables of an unmeasured confounder. Biometrika, 105(4), 987–993. https://doi.org/10.1093/biomet/asy038) to the longitudinal setting under a semiparametric marginal structural mean model (MSMM). PCI offers an opportunity to learn about joint causal effects in settings where SRA based on measured time-varying covariates fails, by formally accounting for the covariate measurements as imperfect proxies of underlying confounding mechanisms. We establish nonparametric identification with a pair of time-varying proxies and provide a corresponding characterization of regular and asymptotically linear estimators of the parameter indexing the MSMM, including a rich class of doubly robust estimators, and establish the corresponding semiparametric efficiency bound for the MSMM. Extensive simulation studies and a data application illustrate the finite sample behaviour of proposed methods.
Shimura subvarieties via endomorphisms

We show the existence of two new Shimura subvarieties of A2,A3 generically contained in the Torelli locus. They provide the first examples of Shimura subvarieties obtained by means of Jacobians carrying non-trivial endomorphisms not directly induced by the automorphisms of the curves. We also obtain a new example of a Shimura subvariety of A4 generically contained in the Prym locus.
AnnIE: An Annotation Platform for Constructing Complete Open Information Extraction Benchmark

Open Information Extraction (OIE) is the task of extracting facts from sentences in the form of relations and their corresponding arguments in schema-free manner. Intrinsic performance of OIE systems is difficult to measure due to the incompleteness of existing OIE benchmarks: ground truth extractions do not group all acceptable surface realizations of the same fact that can be extracted from a sentence. To measure performance of OIE systems more realistically, it is necessary to manually annotate complete facts (i.e., clusters of all acceptable surface realizations of the same fact) from input sentences. We propose AnnIE: an interactive annotation platform that facilitates such challenging annotation tasks and supports creation of complete fact-oriented OIE evaluation benchmarks. AnnIE is modular and flexible in order to support different use case scenarios (i.e., benchmarks covering different types of facts) and different languages. We use AnnIE to build two complete OIE benchmarks: one with verb-mediated facts and another with facts encompassing named entities. We evaluate several OIE systems on our complete benchmarks created with AnnIE. We publicly release AnnIE (and all gold datasets generated with it) under non-restrictive license.
Automatically Exposing Problems with Neural Dialog Models

Neural dialog models are known to suffer from problems such as generating unsafe and inconsistent responses. Even though these problems are crucial and prevalent, they are mostly manually identified by model designers through interactions. Recently, some research instructs crowdworkers to goad the bots into triggering such problems. However, humans leverage superficial clues such as hate speech, while leaving systematic problems undercover. In this paper, we propose two methods including reinforcement learning to automatically trigger a dialog model into generating problematic responses. We show the effect of our methods in exposing safety and contradiction issues with state-of-the-art dialog models.