Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on Bayesian Deep Learning

A comprehensive artificial intelligence system needs to not only perceive the environment with different “senses” (e.g., seeing and hearing) but also infer the world’s conditional (or even causal) relations and corresponding uncertainty. The past decade has seen major advances in many perception tasks, such as visual object recognition and speech recognition, using deep learning models. For higher-level inference, however, probabilistic graphical models with their Bayesian nature are still more powerful and flexible. In recent years, Bayesian deep learning has emerged as a unified probabilistic framework to tightly integrate deep learning and Bayesian models.1 In this general framework, the perception of text or images using deep learning can boost the performance of higher-level inference and, in turn, the feedback from the inference process is able to enhance the perception of text or images. This survey provides a comprehensive introduction to Bayesian deep learning and reviews its recent applications on recommender systems, topic models, control, and so on. We also discuss the relationship and differences between Bayesian deep learning and other related topics, such as Bayesian treatment of neural networks.
Hedysarum kalatense sp. nov. (Fabaceae) from Iran

Hedysarum kalatense Dehshiri sp. nov. (Fabaceae) is described and illustrated from Khorassan, Iran. It belongs to sect. Crinifera and is distinct among Iranian species. This species appears to be closely related to H. macranthum (Freyn & Sint.) B. Fedtschenko. The diagnostic characters, as well as a full description and a detailed illustration are provided. Hedysarum kalatense is evaluated as ‘Vulnerable’ (VU) according to IUCN red list criteria.
An Epizootic of Plague in Gunnison's Prairie Dogs (Cynomys Gunnisoni) in South‐Central Colorado

In June 1964 and epizootic of plague began in Gunnison's prairie dogs (Cynomys gunnisoni) in Cochetopa Park, Saguache County, Colordo. Seven colonies of prairie dogs were known in the area at that time. By the end of the summer, 2 of these colonies were exterminated and several others were greatly decimated. A residual phase of the epizootic continued through the summer of 1965 and terminated in 1966 leaving only a few prairie dogs in 2 colonies. No evidence was obtained to indicate that the populations of other mammals living in the area wer adversely affected by plague. The mammalian reservoir of plague in this area remains unknown, but the data suggest that small mammals such as the deer mouse (Peromyscus maniculatus) and the golden—mantled ground squirrel (Citellus lateralis) may be involved. The spread of infection from prairie dog to prairie dog is via flea vectors, especially Opisocrostis tuberculatus, O. labis, and Oropsylla idahoensis. The vector responsible for getting the initial infection into.
Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach

Structural equation models (SEMs) are widely used in sciences, ranging from economics to psychology, to uncover causal relationships underlying a complex system under consideration and estimate structural parameters of interest. We study estimation in a class of generalized SEMs where the object of interest is defined as the solution to a linear operator equation. We formulate the linear operator equation as a min-max game, where both players are parameterized by neural networks (NNs), and learn the parameters of these neural networks using the stochastic gradient descent. We consider both 2-layer and multi-layer NNs with ReLU activation functions and prove global convergence in an overparametrized regime, where the number of neurons is diverging. The results are established using techniques from online learning and local linearization of NNs, and improve in several aspects the current state-of-the-art. For the first time we provide a tractable estimation procedure for SEMs based on NNs with provable convergence and without the need for sample splitting.
Comment on “Tidal Love numbers of neutron and self-bound quark stars”

We comment on the paper of S. Postnikov et al. in Phys. Rev. D 82, 024016 (2010) and give a modified formula that needs to be taken into account when calculating the tidal Love number of neutron stars in case a first order phase-transition occurs at non-zero pressure. We show that the error made when using the original formula tends to zero as $p \rightarrow 0$ and we estimate the maximum relative error to be $\sim 5\%$ if the density discontinuity is at larger densities.
Chemical Immobilization of Bornean Leopard Cats (Prionailurus bengalensis borneoensis) with Tiletamine and Zolazepam under Field Conditions in Borneo

Nine wild Bornean leopard cats were anesthetized using a combination of tiletamine and zolazepam (Zoletil©) after being captured in humanely-designed live traps in Sabah, Malaysian Borneo, for the purpose of fitting radio-collars. For five leopard cats (group 1) a single dose of 6.92±1.06 mg/kg of Zoletil© was administered. The mean induction time from the initial Zoletil© dose was 7.9 ± 1.77 minutes, and the mean anesthesia time was 47.2 ± 25.1. For 4 leopard cats (group 2) after an initial mean dose of 6.92±1.06 mg/kg of Zoletil©, it was necessary to administer a second dose (or booster) of Zoletil© (mean dose 2.6±0.33 mg/kg) or ketamine (mean dose 3.5± 0.05mg / kg) to achieve complete immobilization. There were differences between the periods of anesthesia resulting from these boosters, which were 43.5 ± 2.1 minutes for ketamine and 89.5 ± 6.36 minutes for Zoletil©. We conclude that an initial dose of Zoletil© of 6.92 mg/kg can produce an adequate plane of anaesthesia without needing additional or booster injections of anaesthetic; if a booster is required, the use of ketamine in preference to Zoletil© has the benefit of shorter release times (245 minutes for ketamine booster compared to 350 minutes for Zoletil© booster) whilst providing adequate anesthetic times (mean 43.5 minutes for ketamine booster).
Sliced Iterative Generator

We introduce the Sliced Iterative Generator (SIG), an iterative generative model that is a Normalizing Flow (NF), but shares the advantages of Generative Adversarial Networks (GANs). The model is based on iterative Optimal Transport of a series of 1D slices through the data space, matching on each slice the probability distribution function (PDF) of the samples to the data. To improve the efficiency, the directions of the orthogonal slices are chosen to maximize the PDF difference between the generated samples and the data using Wasserstein distance at each iteration. A patch based approach is adopted to model the images in a hierarchical way, enabling the model to scale well to high dimensions. Unlike GANs, SIG has a NF structure and allows efficient likelihood evaluations that can be used in downstream tasks. We show that SIG is capable of generating realistic, high dimensional samples of images, achieving state of the art FID scores on MNIST and Fashion MNIST without any dimensionality reduction. It also has good Out of Distribution detection properties using the likelihood. To the best of our knowledge, SIG is the first iterative (greedy) deep learning algorithm that is competitive with the state of the art non-iterative generators in high dimensions. While SIG has a deep neural network architecture, the approach deviates significantly from the current deep learning paradigm, as it does not use concepts such as mini-batching, stochastic gradient descent, gradient back-propagation through deep layers, or non-convex loss function optimization. SIG is very insensitive to hyper-parameter tuning, making it a useful generator tool for ML experts and non-experts alike.
Predation on Artificial Nests in Upland Habitats Adjacent to Freshwater Marshes

Abstract Predation patterns on artificial waterfowl and passerine nests were studied in upland habitats adjacent to freshwater marshes located in urban, agricultural and natural habitats, near Ottawa, Ontario in 1989 and 1990. Nest predation on waterfowl nests did not change during the breeding season in urban and in agricultural habitats but it increased significantly in natural habitats. There were no significant differences over time of year in predation on passerine nests in any of the three types of habitats. Nest camouflage was not a factor in preventing nest destruction. Nest predation was higher on passerine nests than on waterfowl nests in urban and natural habitats but was similar in agricultural habitats. Losses of waterfowl nests were similar among habitats in both years. Passerine nests were more frequently destroyed in natural habitats than in urban and agricultural habitats. Small predators such as the blue jay (Cyanocitta cristata) and red squirrel (Tamiasciurus hudsonicus), more commonly observed in urban and natural habitats compared to agricultural habitats, would most likely explain differences in nest predation among habitats as well as differences between nest types.
Tree Encroachment in Forest Openings: a Case Study From Buffalo Mountain, Virginia

Abstract In eastern forests, openings dominated by grasses, forbs, or shrubs are areas of conservation concern because they typically contain endemic, threatened, and rare plants. Understanding the ecology and mechanisms of tree encroachment would be valuable for conservation managers and would add to a substantial body of literature on forest openings. In this study, we worked in grass-dominated forest openings on Buffalo Mountain, Virginia using a method that combined dendrochronology and belt transects to assess tree encroachment. We discovered both stable ecotones and areas where trees were invading the formerly grass-dominated openings. Both gradual and episodic patterns of tree encroachment were identified; however, successful tree establishment always initiated from the edge of the forest-grass ecotone and progressed towards the center of the opening rather than occurring across the entire forest opening. This spatial pattern of recruitment implies that successional facilitation is necessary for tree encroachment in forested openings at Buffalo Mountain.
Learning to search efficiently for causally near-optimal treatments

Finding an effective medical treatment often requires a search by trial and error. Making this search more efficient by minimizing the number of unnecessary trials could lower both costs and patient suffering. We formalize this problem as learning a policy for finding a near-optimal treatment in a minimum number of trials using a causal inference framework. We give a model-based dynamic programming algorithm which learns from observational data while being robust to unmeasured confounding. To reduce time complexity, we suggest a greedy algorithm which bounds the near-optimality constraint. The methods are evaluated on synthetic and real-world healthcare data and compared to model-free reinforcement learning. We find that our methods compare favorably to the model-free baseline while offering a more transparent trade-off between search time and treatment efficacy.
A generalized de nition of critical thinking

The concept of critical thinking enjoys a near-universal positive connotation. Existing de nitions of critical thinking, however, tend to be rather vague, and, as a consequence, they provide neither an accurate nor a precise understanding of critical thinking. In this paper, I propose to understand critical thinking as a metacognitive skill applicable to the evaluation of truth claims. Critical thinking as a metacognitive skill consists of three components: Minimization of logical fallacies, minimization of cognitive biases, and a probabilistic epistemology. Understood in this manner, critical thinking can improve the quality of our inferences about the world.
A Revision of Neural Tangent Kernel-based Approaches for Neural Networks

Recent theoretical works based on the neural tangent kernel (NTK) have shed light on the optimization and generalization of over-parameterized networks, and partially bridge the gap between their practical success and classical learning theory. Especially, using the NTK-based approach, the following three representative results were obtained: (1) A training error bound was derived to show that networks can fit any finite training sample perfectly by reflecting a tighter characterization of training speed depending on the data complexity. (2) A generalization error bound invariant of network size was derived by using a data-dependent complexity measure (CMD). It follows from this CMD bound that networks can generalize arbitrary smooth functions. (3) A simple and analytic kernel function was derived as indeed equivalent to a fully-trained network. This kernel outperforms its corresponding network and the existing gold standard, Random Forests, in few shot learning. For all of these results to hold, the network scaling factor $\kappa$ should decrease w.r. t. sample size n. In this case of decreasing $\kappa$, however, we prove that the aforementioned results are surprisingly erroneous. It is because the output value of trained network decreases to zero when $\kappa$ decreases w.r. t. n. To solve this problem, we tighten key bounds by essentially removing $\kappa$-affected values. Our tighter analysis resolves the scaling problem and enables the validation of the original NTK-based results.
Investor Demand for Leverage: Evidence from Closed-End Funds

We provide novel evidence supporting investor demand for leverage as a causal driver of the equity closed-end fund discount (CEFD). Closed-end funds (CEFs) utilizing leverage trade at a relative premium, both compared to less-levered funds and themselves in lesslevered periods. We use the introduction of less costly levered exchange-traded funds as a quasi-natural experiment to identify the channel. Following their introduction, the relative premium on levered CEFs decreases by 3%. We reject performance (alpha) and rational demand for risk as likely explanations. Finally, we show that a prominent measure of the CEFD has historically captured levered-fund information, but switches to capturing unlevered-fund information since a change in methodology in 2011.
Multi-Mode OAM Radio Waves: Generation, Angle of Arrival Estimation and Reception With UCAs

Orbital angular momentum (OAM) at radio frequency (RF) provides a novel approach of multiplexing a set of orthogonal modes on the same frequency channel to achieve high spectrum efficiencies. However, there are still big challenges in the multi-mode OAM generation, OAM antenna alignment and OAM signal reception. To solve these problems, we propose an overall scheme of the line-of-sight multi-carrier and multi-mode OAM (LoS MCMM-OAM) communication based on uniform circular arrays (UCAs). First, we verify that UCA can generate multi-mode OAM radio beam with both the RF analog synthesis method and the baseband digital synthesis method. Then, for the considered UCA-based LoS MCMM-OAM communication system, a distance and AoA estimation method is proposed based on the two-dimensional ESPRIT (2-D ESPRIT) algorithm. A salient feature of the proposed LoS MCMM-OAM and LoS MCMM-OAM-MIMO systems is that the channel matrices are completely characterized by three parameters, namely, the azimuth angle, the elevation angle and the distance, independent of the numbers of subcarriers and antennas, which significantly reduces the burden by avoiding estimating large channel matrices, as traditional MIMO-OFDM systems. After that, we propose an OAM reception scheme including the beam steering with the estimated AoA and the amplitude detection with the estimated distance. At last, the proposed methods are extended to the LoS MCMM-OAM-MIMO system equipped with uniform concentric circular arrays (UCCAs). Both mathematical analysis and simulation results validate that the proposed OAM reception scheme can eliminate the effect of the misalignment error of a practical OAM channel and approaches the performance of an ideally aligned OAM channel.
Tilted Empirical Risk Minimization

Empirical risk minimization (ERM) is typically designed to perform well on the average loss, which can result in estimators that are sensitive to outliers, generalize poorly, or treat subgroups unfairly. While many methods aim to address these problems individually, in this work, we explore them through a unified framework---tilted empirical risk minimization (TERM). In particular, we show that it is possible to flexibly tune the impact of individual losses through a straightforward extension to ERM using a hyperparameter called the tilt. We provide several interpretations of the resulting framework: We show that TERM can increase or decrease the influence of outliers, respectively, to enable fairness or robustness; has variance-reduction properties that can benefit generalization; and can be viewed as a smooth approximation to a superquantile method. We develop batch and stochastic first-order optimization methods for solving TERM, and show that the problem can be efficiently solved relative to common alternatives. Finally, we demonstrate that TERM can be used for a multitude of applications, such as enforcing fairness between subgroups, mitigating the effect of outliers, and handling class imbalance. TERM is not only competitive with existing solutions tailored to these individual problems, but can also enable entirely new applications, such as simultaneously addressing outliers and promoting fairness.
Better Document-Level Machine Translation with Bayes’ Rule

Abstract We show that Bayes’ rule provides an effective mechanism for creating document translation models that can be learned from only parallel sentences and monolingual documents a compelling benefit because parallel documents are not always available. In our formulation, the posterior probability of a candidate translation is the product of the unconditional (prior) probability of the candidate output document and the “reverse translation probability” of translating the candidate output back into the source language. Our proposed model uses a powerful autoregressive language model as the prior on target language documents, but it assumes that each sentence is translated independently from the target to the source language. Crucially, at test time, when a source document is observed, the document language model prior induces dependencies between the translations of the source sentences in the posterior. The model’s independence assumption not only enables efficient use of available data, but it additionally admits a practical left-to-right beam-search algorithm for carrying out inference. Experiments show that our model benefits from using cross-sentence context in the language model, and it outperforms existing document translation approaches.
Heavy Gauge Bosons in supersymmetric $U(1)'$ models at present and future hadron colliders

We study Z' phenomenology at hadron colliders in an U(1)' extended MSSM. We choose a U(1)' model with a secluded sector, where the tension between the electroweak scale and developing a large enough mass for Z' is resolved by incorporating three additional SU(2) singlet fields into the model. We perform a detailed and systematic analysis of the production, followed by decays, including into supersymmetric particles, of a Z' boson with particular emphasis on its possible discovery. We choose three different scenarios consistent with the latest available experimental data and relic density constraints, and concentrate on final signals involving two leptons, four leptons and six leptons. Including possible SM backgrounds, we show the likelihood of observing a Z' boson is not promising for the HL-LHC at 14 TeV, but optimistic for 27 and 100 TeV.
Role of Equity Puts in Trading and Hedging Single-name Credit Risk: Evidence from Price Discovery around Rating Events

This paper provides a unified framework for hedging and trading single-name credit risk in the post-crisis period when liquidity of single-name CDS deteriorates significantly. In a series of event analysis around 420 Moodys full-notch ratings changes from 2002 to 2017, we find investing in issuer’s equity put options mutes downgrade exposure by half, while dynamically combining equity put and the underlying stock produces a (daily) abnormal return of 0.47%. We find the quality of price discovery in option markets is strictly better than stock and bond markets. We find the options premium contains unique information relates to firm-volatility and have predictive power in future bond returns. JEL code: G01, G11, G14, G24
Stock Dividends, Gambling Investors, and Cost of Equity

What are the benefits to a firm of having investors with gambling preference as shareholders? Motivated by studies showing that gambling investors prefer lottery-like stocks and require lower expected returns to take risk, we hypothesize that firms with positively-skewed assets can use stock splits to attract investors with gambling preference to share risk and to lower cost of equity. Indeed, analyzing a sample of Chinese firms that split their stocks through stock dividends and using proprietary trading data to measure retail investors’ gambling preference, we find that, on average, shareholders increase by 54% and retail gambling investors increase by 119% following stock dividends. Furthermore, while firms become more risk-taking, their cost of equity declines substantially, largely due to the increased retail gambling investors’ pricing influence. Thus, stock splits are effective for improving risk-sharing efficiency, and gambling investors contribute to lowering the cost of capital. JEL Classification: G32 and G41
Biofilms: microbial life on surfaces.

A biofilm is a community of microorganisms encased within an exopolysaccharide matrix attached to a surface and causes serious problems for food industry. The extracellular polymeric matrix is an important structural component of biofilms. The matrix, which is composed of polysaccharides, proteins, nucleic acids and water, enables the biofilms to attach to the surfaces. One of the most important functions of the matrix is to protect the bacteria from various stress factors such as UV radiation, extreme pH values, osmotic pressure, dehydration and antibiotics. This study presents a review of the information about formation, structure and prevention of biofilms since they are one of the most important problems in food industry.
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks

For deploying a deep learning model into production, it needs to be both accurate and compact to meet the latency and memory constraints. This usually results in a network that is deep (to ensure performance) and yet thin (to improve computational efficiency). In this paper, we propose an efficient method to train a deep thin network with a theoretic guarantee. Our method is motivated by model compression. It consists of three stages. First, we sufficiently widen the deep thin network and train it until convergence. Then, we use this well-trained deep wide network to warm up (or initialize) the original deep thin network. This is achieved by layerwise imitation, that is, forcing the thin network to mimic the intermediate outputs of the wide network from layer to layer. Finally, we further fine tune this already well-initialized deep thin network. The theoretical guarantee is established by using the neural mean field analysis. It demonstrates the advantage of our layerwise imitation approach over backpropagation. We also conduct large-scale empirical experiments to validate the proposed method. By training with our method, ResNet50 can outperform ResNet101, and BERT Base can be comparable with BERT Large, when ResNet101 and BERT Large are trained under the standard training procedures as in the literature.