Miracle-dz/UniFAR-FTUs huggingface dataset:
Hair of grazing cattle provides an integrated measure of the effects of site conditions and interannual weather variability on δ13C of temperate humid grassland

The carbon isotope composition (δ13C) of C3 ecosystems is sensitive to water availability, and provides important information for the assessment of terrestrial carbon (C) sink/source activity. Here, we report the effects of plant available soil water (PAW) on community 13C signatures of temperate humid grassland. The 5‐year study was conducted on pastures exhibiting a large range of PAW capacity that were located on two site types: peat and mineral soils. The data set included the centennial drought year 2003, and data from wet years (2000 and 2002). Seasonal variation of PAW was modeled using PAW capacity of each pasture, precipitation inputs and evapotranspiration estimates. Community 13C signatures were derived from the δ13C of vegetation and segments of tail switch hair of cattle grown while grazing pastures. Hair 13C signatures provided an assimilation‐weighted 13C signal that integrated both spatial (paddock‐scale) and temporal (grazing season) variation of 13C signatures on a pasture. The δ13C of hair and vegetation increased with decreasing modeled PAW in the same way on mineral and peat soils. But, at a given PAW, the δ13C of hair was 2.6‰ less negative than that of vegetation, reflecting the diet‐hair isotopic shift. Furthermore, the δ13C of hair and vegetation on peat soil pastures was 0.5‰ more negative than on pastures situated on mineral soil. This may have resulted from a ∼10 ppm CO2 enrichment of canopy air derived from ongoing peat mineralization. Community‐scale season‐mean 13C discrimination (Δ) exhibited a saturation‐type response towards season‐mean modeled PAW (r2=0.78), and ranged between 19.8‰ on soils with low PAW capacity during the drought year of 2003, and 21.4‰ on soils with high PAW capacity in a wet year. This indicated relatively small variation in season‐mean assimilation‐weighted pi/pa (0.68–0.75) between contrasting sites and years. However, this range is similar to that reported in other studies, which encompass the range from subtropical arid to humid temperate grassland. Furthermore, the tight relationship between season‐mean Δ and modeled mean PAW suggests that PAW may be used as proxy for Δ.
Graph Degree Heterogeneity Facilitates Random Walker Meetings

Various graph algorithms have been developed with multiple random walks, the movement of several independent random walkers on a graph. Designing an efficient graph algorithm based on multiple random walks requires investigating multiple random walks theoretically to attain a deep understanding of their characteristics. The first meeting time is one of the important metrics for multiple random walks. The first meeting time on a graph is defined by the time it takes for multiple random walkers to meet at the same node in a graph. This time is closely related to the rendezvous problem, a fundamental problem in computer science. The first meeting time of multiple random walks has been analyzed previously, but many of these analyses have focused on regular graphs. In this paper, we analyze the first meeting time of multiple random walks in arbitrary graphs and clarify the effects of graph structures on expected values. First, we derive the spectral formula of the expected first meeting time on the basis of spectral graph theory. Then, we examine the principal component of the expected first meeting time using the derived spectral formula. The clarified principal component reveals that (a)the expected first meeting time is almost dominated by $n/(1+d_{\rm std}^2/d_{\rm avg}^2)$ and (b)the expected first meeting time is independent of the starting nodes of random walkers, where $n$ is the number of nodes of the graph. $d_{\rm avg}$ and $d_{\rm std}$ are the average and the standard deviation of weighted node degrees, respectively. The characteristic(a) is useful for understanding the effect of the graph structure on the first meeting time. According to the revealed effect of graph structures, the variance of the coefficient $d_{\rm std}/d_{\rm avg}$(degree heterogeneity) for weighted degrees facilitates the meeting of random walkers.
The generalized Cattaneo (telegrapher's) equation and corresponding random walks

T. Sandev Research Center for Computer Science and Information Technologies, Macedonian Academy of Sciences and Arts, Bul. Krste Misirkov 2, 1000 Skopje, Macedonia Institute of Physics & Astronomy, University of Potsdam, D-14776 Potsdam-Golm, Germany and Institute of Physics, Faculty of Natural Sciences and Mathematics, Ss Cyril and Methodius University, Arhimedova 3, 1000 Skopje, Macedonia (Dated: June 11, 2020)
The Impact of Non-stationarity on Generalisation in Deep Reinforcement Learning

Non-stationarity arises in Reinforcement Learning (RL) even in stationary environments. Most RL algorithms collect new data throughout training, using a non-stationary behaviour policy. Furthermore, training targets in RL can change even with a fixed state distribution when the policy, critic, or bootstrap values are updated. We study these types of non-stationarity in supervised learning settings as well as in RL, finding that they can lead to worse generalisation performance when using deep neural network function approximators. Consequently, to improve generalisation of deep RL agents, we propose Iterated Relearning (ITER). ITER augments standard RL training by repeated knowledge transfer of the current policy into a freshly initialised network, which thereby experiences less non-stationarity during training. Experimentally, we show that ITER improves performance on the challenging generalisation benchmarks ProcGen and Multiroom.
MeshWalker

Most attempts to represent 3D shapes for deep learning have focused on volumetric grids, multi-view images and point clouds. In this paper we look at the most popular representation of 3D shapes in computer graphics---a triangular mesh---and ask how it can be utilized within deep learning. The few attempts to answer this question propose to adapt convolutions & pooling to suit Convolutional Neural Networks (CNNs). This paper proposes a very different approach, termed MeshWalker to learn the shape directly from a given mesh. The key idea is to represent the mesh by random walks along the surface, which "explore" the mesh's geometry and topology. Each walk is organized as a list of vertices, which in some manner imposes regularity on the mesh. The walk is fed into a Recurrent Neural Network (RNN) that "remembers" the history of the walk. We show that our approach achieves state-of-the-art results for two fundamental shape analysis tasks: shape classification and semantic segmentation. Furthermore, even a very small number of examples suffices for learning. This is highly important, since large datasets of meshes are difficult to acquire.
Smooth Proxy-Anchor Loss for Noisy Metric Learning

Many industrial applications use Metric Learning as a way to circumvent scalability issues when designing systems with a high number of classes. Because of this, this field of research is attracting a lot of interest from the academic and non-academic communities. Such industrial applications require large-scale datasets, which are usually generated with web data and, as a result, often contain a high number of noisy labels. While Metric Learning systems are sensitive to noisy labels, this is usually not tackled in the literature, that relies on manually annotated datasets. In this work, we propose a Metric Learning method that is able to overcome the presence of noisy labels using our novel Smooth Proxy-Anchor Loss. We also present an architecture that uses the aforementioned loss with a two-phase learning procedure. First, we train a confidence module that computes sample class confidences. Second, these confidences are used to weight the influence of each sample for the training of the embeddings. This results in a system that is able to provide robust sample embeddings. We compare the performance of the described method with current state-of-the-art Metric Learning losses (proxy-based and pair-based), when trained with a dataset containing noisy labels. The results showcase an improvement of 2.63 and 3.29 in Recall@1 with respect to MultiSimilarity and Proxy-Anchor Loss respectively, proving that our method outperforms the state-of-the-art of Metric Learning in noisy labeling conditions.
Dynamic Interleaving of Content and Structure for Robust Indexing of Semi-Structured Hierarchical Data

We propose a robust index for semi-structured hierarchical data that supports content-and-structure (CAS) queries specified by path and value predicates. At the heart of our approach is a novel dynamic interleaving scheme that merges the path and value dimensions of composite keys in a balanced way. We store these keys in our trie-based Robust Content-And-Structure index, which efficiently supports a wide range of CAS queries, including queries with wildcards and descendant axes. Additionally, we show important properties of our scheme, such as robustness against varying selectivities, and demonstrate improvements of up to two orders of magnitude over existing approaches in our experimental evaluation.
Ultralight DM bosons with an axion-like potential: scale-dependent constraints revisited

A scalar field ϕ endowed with a trigonometric potential has been proposed to play the role of Dark Matter. A deep study of the cosmological evolution of linear perturbations, and its comparison to the Cold Dark Matter (CDM) and Fuzzy Dark Matter (FDM) cases (scalar field with quadratic potential), reveals an enhancement in the amplitude of the mass power spectrum for large wave numbers due to the non-linearity of the axion-like potential. For the first time, we study the scale-dependence on physical quantities such as the growth factor Dk, the velocity growth factor fk, and fk σ8. We found that for z<10, all these quantities recover the CDM evolution, whereas for high redshift there is a clear distinction between each model (FDM case, and axion-like potential) depending on the wavenumber k and on the decay parameter of the axion-like potential as well. A semi-analytical Halo Mass Function is also revisited, finding a suppression of the number of low mass halos, as in the FDM case, but with a small increment in the amplitude of the variance and halo mass function due to the non-linearity of the axion-like potential. Finally, we present constraints on the axion mass and the axion decay parameter by using data of the Planck Collaboration 2018 and Lyman-α forest.
Precise Robot Localization in Architectural 3D Plans

This paper presents a localization system for mobile robots enabling precise localization in inaccurate building models. The approach leverages local referencing to counteract inherent deviations between as-planned and as-built data for locally accurate registration. We further fuse a novel image-based robust outlier detector with LiDAR data to reject a wide range of outlier measurements from clutter, dynamic objects, and sensor failures. We evaluate the proposed approach on a mobile robot in a challenging real world building construction site. It consistently outperforms the traditional ICP-based alingment, reducing localization error by at least 30%.
VAEs in the Presence of Missing Data

Real world datasets often contain entries with missing elements e.g. in a medical dataset, a patient is unlikely to have taken all possible diagnostic tests. Variational Autoencoders (VAEs) are popular generative models often used for unsupervised learning. Despite their widespread use it is unclear how best to apply VAEs to datasets with missing data. We develop a novel latent variable model of a corruption process which generates missing data, and derive a corresponding tractable evidence lower bound (ELBO). Our model is straightforward to implement, can handle both missing completely at random (MCAR) and missing not at random (MNAR) data, scales to high dimensional inputs and gives both the VAE encoder and decoder principled access to indicator variables for whether a data element is missing or not. On the MNIST and SVHN datasets we demonstrate improved marginal log-likelihood of observed data and better missing data imputation, compared to existing approaches.
Rendering Natural Camera Bokeh Effect with Deep Learning

Bokeh is an important artistic effect used to highlight the main object of interest on the photo by blurring all out-of-focus areas. While DSLR and system camera lenses can render this effect naturally, mobile cameras are unable to produce shallow depth-of-field photos due to a very small aperture diameter of their optics. Unlike the current solutions simulating bokeh by applying Gaussian blur to image background, in this paper we propose to learn a realistic shallow focus technique directly from the photos produced by DSLR cameras. For this, we present a large-scale bokeh dataset consisting of 5K shallow / wide depth-of-field image pairs captured using the Canon 7D DSLR with 50mm f/1.8 lenses. We use these images to train a deep learning model to reproduce a natural bokeh effect based on a single narrow-aperture image. The experimental results show that the proposed approach is able to render a plausible non-uniform bokeh even in case of complex input data with multiple objects. The dataset, pre-trained models and codes used in this paper are available on the project website: https://people.ee. ethz.ch/˜ihnatova/pynet-bokeh.html
Optimal Bounds between $f$-Divergences and Integral Probability Metrics

The families of $f$-divergences (e.g. the Kullback-Leibler divergence) and Integral Probability Metrics (e.g. total variation distance or maximum mean discrepancies) are widely used to quantify the similarity between probability distributions. In this work, we systematically study the relationship between these two families from the perspective of convex duality. Starting from a tight variational representation of the $f$-divergence, we derive a generalization of the moment-generating function, which we show exactly characterizes the best lower bound of the $f$-divergence as a function of a given IPM. Using this characterization, we obtain new bounds while also recovering in a unified manner well-known results, such as Hoeffding's lemma, Pinsker's inequality and its extension to subgaussian functions, and the Hammersley-Chapman-Robbins bound. The variational representation also allows us to prove new results on topological properties of the divergence which may be of independent interest.
Network visualizations with Pyvis and VisJS

Pyvis is a Python module that enables visualizing and interactively manipulating network graphs in the Jupyter notebook, or as a standalone web application. Pyvis is built on top of the powerful and mature VisJS JavaScript library, which allows for fast and responsive interactions while also abstracting away the low-level JavaScript and HTML. This means that elements of the rendered graph visualization, such as node/edge attributes can be specified within Python and shipped to the JavaScript layer for VisJS to render. This declarative approach makes it easy to quickly explore graph visualizations and investigate data relationships. In addition, Pyvis is highly customizable so that colors, sizes, and hover tooltips can be assigned to the rendered graph. The network graph layout is controlled by a front-end physics engine that is configurable from a Python interface, allowing for the detailed placement of the graph elements. In this paper, we outline use cases for Pyvis with specific examples to highlight key features for any analysis workflow. A brief overview of Pyvis' implementation describes how the Python front-end binding uses simple Pyvis calls.
Hybrid Tree-based Models for Insurance Claims.

Two-part models and Tweedie generalized linear models (GLMs) have been used to model loss costs for short-term insurance contract. For most portfolios of insurance claims, there is typically a large proportion of zero claims that leads to imbalances resulting in inferior prediction accuracy of these traditional approaches. This article proposes the use of tree-based models with a hybrid structure that involves a two-step algorithm as an alternative approach to these traditional models. The first step is the construction of a classification tree to build the probability model for frequency. In the second step, we employ elastic net regression models at each terminal node from the classification tree to build the distribution model for severity. This hybrid structure captures the benefits of tuning hyperparameters at each step of the algorithm; this allows for improved prediction accuracy and tuning can be performed to meet specific business objectives. We examine and compare the predictive performance of such a hybrid tree-based structure in relation to the traditional Tweedie model using both real and synthetic datasets. Our empirical results show that these hybrid tree-based models produce more accurate predictions without the loss of intuitive interpretation.
Susceptibilities to clarithromycin and erythromycin of isolates of Chlamydia pneumoniae from children with pneumonia

We tested in vitro 49 isolates of Chlamydia pneumoniae obtained from 35 children with community-acquired pneumonia against clarithromycin and erythromycin. The children were part of a treatment study comparing the two drugs. Clarithromycin was 2- to 10-fold more active than erythromycin, with a MIC for 90% of strains tested and minimal chlamydiacidal concentration for 90% of strains tested of 0.031 microgram/ml compared with 0.125 microgram/ml for erythromycin. Eight of these children, two of whom were treated with erythromycin and six of whom received clarithromycin, remained culture positive after treatment. We were able to test 21 isolates from these children. All were susceptible to both drugs, and the MICs did not change after therapy.
The Entropic Dynamics of Quantum Scalar Fields Coupled to Gravity

Entropic dynamics (ED) are a general framework for constructing indeterministic dynamical models based on entropic methods. ED have been used to derive or reconstruct both non-relativistic quantum mechanics and quantum field theory in curved space-time. Here we propose a model for a quantum scalar field propagating in dynamical space-time. The approach rests on a few key ingredients: (1) Rather than modelling the dynamics of the fields, ED models the dynamics of their probabilities. (2) In accordance with the standard entropic methods of inference, the dynamics are dictated by information encoded in constraints. (3) The choice of the physically relevant constraints is dictated by principles of symmetry and invariance. The first of such principle imposes the preservation of a symplectic structure which leads to a Hamiltonian formalism with its attendant Poisson brackets and action principle. The second symmetry principle is foliation invariance, which, following earlier work by Hojman, Kuchař, and Teitelboim, is implemented as a requirement of path independence. The result is a hybrid ED model that approaches quantum field theory in one limit and classical general relativity in another, but is not fully described by either. A particularly significant prediction of this ED model is that the coupling of quantum fields to gravity implies violations of the quantum superposition principle.
Genetic interactions between marine finfish species in European aquaculture and wild conspecifics

The principal species of marine aquaculture in Europe are Atlantic salmon (Salmo salar), sea bass (Dicentrarchus labrax) and sea bream (Sparus auratus). For Atlantic salmon and sea bass, a substantial part of total genetic variation is partitioned at the geographical population level. In the case of sea bream, gene flow across the Azores/Mediterranean scale appears to be extensive and population structuring is not detected. For Atlantic salmon and sea bass, natural population structure is at risk from genetic interaction with escaped aquaculture conspecifics. The locally adaptive features of populations are at risk from interbreeding with non-local aquaculture fish. Wild populations, generally, are at risk from interactions with aquaculture fish that have been subject to artificial selection or domestication. Atlantic salmon is the main European aquaculture species and its population genetics and ecology have been well-studied. A general case regarding genetic interactions can be based on the information available for salmon and extended to cover other species, in the appropriate context. A generalized flow chart for interactions is presented. Salmon escape from aquaculture at all life stages, and some survive to breed among wild salmon. Reproductive fitness in the escaped fish is lower than in native, wild fish because of behavioural deficiencies at spawning. However, as the number of salmon in aquaculture greatly exceeds the number of wild fish, even small fractional rates of escape may result in the local presence of large numbers, and high frequencies, of escaped fish. At present, policy and legislation in relation to minimizing genetic interactions between wild and aquaculture fish is best developed for Atlantic salmon, through the recommendations of the Oslo Agreement developed by the North Atlantic Salmon Conservation Organization and subsequent agreements on their implementation. In future, the potential use of genetically modified fish in aquaculture will make additional policy development necessary. Improved containment is recommended as the key to minimizing the numbers and therefore the effects of escaped fish. Emergency recovery procedures are recommended as a back-up measure in the case of containment failure. Reproductive sterility is recommended as a future key to eliminating the genetic potential of escaped fish. The maintenance of robust populations of wild fish is recommended as a key to minimizing the effects of escaped fish on wild populations.
Toward Building Safer Smart Homes for the People with Disabilities

Situational awareness is a critical foundation for the protection of human life/properties and is challenging to maintain for people with disabilities (i.e., visual impairments and limited mobility). In this paper, we present a dialog enabled end-to-end assistive solution called "SafeAccess" to build a safer smart home by providing situational awareness. The key functions of SafeAccess are: - 1) monitoring homes and identifying incoming persons; 2) helping users in assessing incoming threats (e.g., burglary, robbery, gun violence); and, 3) allowing users to grant safe access to homes for friends/families. In this work, we focus on building a robust model for detecting and recognizing person, generating image descriptions, and designing a prototype for the smart door. To interact with the system, we implemented a dialog enabled smartphone app, especially for creating a personalized profile from face images or videos of friends/families. A Raspberry pi connected to the home monitoring cameras captures the video frames and performs change detection to identify frames with activities. Then, we detect human presence using Faster r-cnn and extract faces using Multi-task Cascaded Convolutional Networks (MTCNN). Subsequently, we match the detected faces using FaceNet/support vector machine (SVM) classifiers. The system notifies users with an MMS containing the name of incoming persons or as "unknown", scene image, facial description, and contextual information. The users can grant access or call emergency services using the SafeAccess app based on the received notification. Our system identifies persons with an F-score 0.97 and recognizes items to generate image description with an average F-score 0.97.