Miracle-dz/UniFAR-FTUs huggingface dataset:
On the interplay between mobility and hospitalization capacity during the COVID-19 pandemic: The SEIRHUD model

Measures to reduce the impact of COVID19 require a mix of logistic, political and social capacity. Depending on the country, different capacities to increase of hospitalization or to properly apply lockdowns are observed. In order to better understand the impact of these measures we have developed a compartmental model which, on the one hand allows to calibrate the reduction of movement of people within and among different areas, and on the other hand it incorporates a hospitalization dynamics that differentiates the available kinds of treatment that infected people can receive. By bounding the hospitalization capacity, we are able to study in detail the interplay between mobility and hospitalization capacity.
Reducing Class Collapse in Metric Learning with Easy Positive Sampling

Metric learning seeks perceptual embeddings where visually similar instances are close and dissimilar instances are apart, but learn representation can be sub-optimal when the distribution of intra-class samples is diverse and distinct sub-clusters are present. We theoretically prove and empirically show that under reasonable noise assumptions, prevalent embedding losses in metric learning, e.g., triplet loss, tend to project all samples of a class with various modes onto a single point in the embedding space, resulting in class collapse that usually renders the space ill-sorted for classification or retrieval. To address this problem, we propose a simple modification to the embedding losses such that each sample selects its nearest same-class counterpart in a batch as the positive element in the tuple. This allows for the presence of multiple sub-clusters within each class. The adaptation can be integrated into a wide range of metric learning losses. Our method demonstrates clear benefits on various fine-grained image retrieval datasets over a variety of existing losses; qualitative retrieval results show that samples with similar visual patterns are indeed closer in the embedding space.
How to become large quickly: quantitative genetics of growth and foraging in a flush feeding lepidopteran larva

Rapid larval growth in insects may be selected for by rapid ephemeral phenological changes in food resources modifying the structure of phenotypic and genetic (co)variation in and among individual traits. We studied the relative effects of three processes which can modify expression of additive genetic and nongenetic variation in traits. First, natural selection tends to erode genetic variation in fitness‐related traits. Second, there may be high variance even in traits closely coupled with fitness, if these traits are themselves products of variable lower level traits. Third, traits may be canalized by developmental processes which reduce phenotypic variation. Moreover, we investigated the phenotypic and genetic role played by the underlying traits in attaining simultaneously both large size and short development time.
Approximating Lipschitz continuous functions with GroupSort neural networks

Recent advances in adversarial attacks and Wasserstein GANs have advocated for use of neural networks with restricted Lipschitz constants. Motivated by these observations, we study the recently introduced GroupSort neural networks, with constraints on the weights, and make a theoretical step towards a better understanding of their expressive power. We show in particular how these networks can represent any Lipschitz continuous piecewise linear functions. We also prove that they are well-suited for approximating Lipschitz continuous functions and exhibit upper bounds on both the depth and size. To conclude, the efficiency of GroupSort networks compared with more standard ReLU networks is illustrated in a set of synthetic experiments.
Maximum Entropy Model Rollouts: Fast Model Based Policy Optimization without Compounding Errors

Model usage is the central challenge of model-based reinforcement learning. Although dynamics model based on deep neural networks provide good generalization for single step prediction, such ability is over exploited when it is used to predict long horizon trajectories due to compounding errors. In this work, we propose a Dyna-style model-based reinforcement learning algorithm, which we called Maximum Entropy Model Rollouts (MEMR). To eliminate the compounding errors, we only use our model to generate single-step rollouts. Furthermore, we propose to generate \emph{diverse} model rollouts by non-uniform sampling of the environment states such that the entropy of the model rollouts is maximized. We mathematically derived the maximum entropy sampling criteria for one data case under Gaussian prior. To accomplish this criteria, we propose to utilize a prioritized experience replay. Our preliminary experiments in challenging locomotion benchmarks show that our approach achieves the same sample efficiency of the best model-based algorithms, matches the asymptotic performance of the best model-free algorithms, and significantly reduces the computation requirements of other model-based methods.
Calicioid Lichens and Fungi in the Muota Valley, Central Switzerland: High Species Diversity in a Small Area

Abstract GRONER, U. (2010). Calicioid lichens and fungi in the Muota Valley, central Switzerland: high species diversity in a small area. Candollea 65: 377–391. In English, English and French abstracts. An inventory of the calicioid lichens and fungi of the Bödmeren Forest region in the Muota Valley, central Switzerland, produced no less than 51 lichenized and non-lichenized taxa on bark and wood. This mainly subalpine spruce forest in the Prealps hosts the most diverse calicioid flora in Switzerland today and a similarly rich flora from a comparably small area is unknown from neighbouring countries. Calicium denigratum (Vain.) Tibell and Chaenothecopsis oregana Rikkinen are new to Switzerland. Calicium adaequatum Nyl. , Chaenothecopsis tasmanica Tibell and Chaenothecopsis vainioana (Nádv.) Tibell are also reported for the first time from this region. Chaenotheca chrysocephala (Ach.) Th. Fr. , Calicium viride Pers. , Chaenotheca trichialis (Ach.) Th. Fr. and Microcalicium disseminatum (Ach.) Vain. are the most frequent species, the majority of the other taxa are uncommon or rare. The diversity of the calicioid (and other) species is due to several favourable conditions, such as the presence of old-growth forest stands and the occurrence of standing dead trees. More than 40% of the recorded lichenized calicioid taxa are threatened; the state of the poorly known non-lichenized calicioids is undetermined as they were not evaluated in the “Red List”.
Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges

A variety of methods have been applied to the architectural configuration and learning or training of artificial deep neural networks (DNN). These methods play a crucial role in the success or failure of the DNN for most problems and applications. Evolutionary algorithms (EAs) are gaining momentum as a computationally feasible method for the automated optimization of DNNs. Neuroevolution is a term, which describes these processes of automated configuration and training of DNNs using EAs. While many works exist in the literature, no comprehensive surveys currently exist focusing exclusively on the strengths and limitations of using neuroevolution approaches in DNNs. Absence of such surveys can lead to a disjointed and fragmented field preventing DNNs researchers potentially adopting neuroevolutionary methods in their own research, resulting in lost opportunities for wider application within real-world deep learning problems. This article presents a comprehensive survey, discussion, and evaluation of the state-of-the-art in using EAs for architectural configuration and training of DNNs. This article highlights the most pertinent current issues and challenges in neuroevolution and identifies multiple promising future research directions. Impact Statement—The concept of deep learning originated from the study of artificial neural networks (ANNs). ANNs have achieved extraordinary results in a variety of diverse application areas. Numerous methods have been applied to the architectural configuration and learning or training of artificial DNN and these methods play a crucial role in the success or failure of the DNN for most problems and applications. Recently, EAs have been gaining momentum as a computationally feasible method (called neuroevolution) for the automated configuration and learning or training of DNNs. This article reviews over 170 recent scientific papers describing how major EAs paradigms are being applied by researchers to the configuration and optimization of multiple DNNs. By articulating a clear understanding of the context, state-of-the-art, and feasibility of Neuroevolution, researchers in AI, EAs, and DNN will benefit from this article. The impact of this article comes from contributing toward enhancing research capacity, knowledge, and skills for researchers currently working in neuroevolution and actively engaging those considering becoming involved in this area.
Leaf and fruiting phenology and gas exchange of Mangabeira in response to irrigation

The mangabeira (Hancornia speciosa Gomez) is a native species of Brazil occurring in the savanna (Cerrado) and Atlantic Coastal forests. It is a promising species for large-scale horticulture due to the high-protein properties of its fruits, but extensive cultivation has not yet been accomplished. Research is still needed to determine cultivation techniques that will optimize productivity. As irrigation is considered to be the most important agronomic technique to enhance productivity, the goal of this investigation was to evaluate the effects of irrigation on leaf gas exchange (net photosynthesis rate and transpiration rate) and foliar and fruiting phenology. Our results suggest that irrigation acts by increasing the growth and fruit production of mangabeira due to its effects on phenology. Gas exchange of adult plants was only marginally affected by irrigation; however, irrigated plants retained leaves longer than non-irrigated plants during the dry season, which increased the C gain over an annual period. The increase in C gain presumably led to irrigated plants having a higher relative frequency of fruited plants than the non-irrigated. A significant positive correlation between net photosynthesis rate per unit dry mass and specific leaf area was found for irrigated plants, which suggests that irrigation can promote the development of new leaf cells with no direct contribution to
Modeling Long-horizon Tasks as Sequential Interaction Landscapes

Complex object manipulation tasks often span over long sequences of operations. Task planning over long-time horizons is a challenging and open problem in robotics, and its complexity grows exponentially with an increasing number of subtasks. In this paper we present a deep learning network that learns dependencies and transitions across subtasks solely from a set of demonstration videos. We represent each subtask as an action symbol (e.g. move cup), and show that these symbols can be learned and predicted directly from image observations. Learning from demonstrations and visual observations are two main pillars of our approach. The former makes the learning tractable as it provides the network with information about the most frequent transitions and relevant dependency between subtasks (instead of exploring all possible combination), while the latter allows the network to continuously monitor the task progress and thus to interactively adapt to changes in the environment. We evaluate our framework on two long horizon tasks: (1) block stacking of puzzle pieces being executed by humans, and (2) a robot manipulation task involving pick and place of objects and sliding a cabinet door on a 7-DoF robot arm. We show that complex plans can be carried out when executing the robotic task and the robot can interactively adapt to changes in the environment and recover from failure cases.
The restoration of coastal shingle vegetation: effects of substrate composition on the establishment of seedlings

1. Shingle beach vegetation at Sizewell, Suffolk, UK, was extensively damaged by the construction Qf a power station. We investigated the feasibility of restoring the vegetation by directly sowing seeds of species important in the existing plant communities, in field and glasshouse experiments. The species sown were: Crambe maritima, Eryngium maritimum, Glaucium flavum, Lathyrus japonicus and Rumex crispus. 2. The field experiment examined the effects of proximity to the sea, composition of the beach substrate, and its amendment with organic matter on seedling emergence and establishment. There was little emergence of seedlings during the first season and most occurred in the following spring, between February and April; G. flavum emergence was nearly confined to this period. A low percentage of the viable seeds of all five species had emerged even after two seasons. Greatest emergence was in the monocarpic G. flavum, whereas the relatively long-lived perennials showed lower emergence and slower growth. Only G. flavum produced reproductive plants, in the second year. 3. Seedling emergence was greater in organic matter treated plots for all species and most emerged better from sandy plots than shingle dominated plots. Although G. flavum emergence was greater in sandy plots, seedling survivorship and growth were much greater in shingle plots; mortality showed evidence of density dependence. 4. A glasshouse experiment examined the effects of sowing density and substrate texture on the survival of G. flarum. On shingle, survival was negatively density-dependent, but the much lower survival on sand was density-independent. Consequently, the higher mortality on sandy field plots was probably directly associated with substrate effects. 5. Direct sowing of seeds on the beach cannot be recommended as a general technique for the restoration of shingle beach vegetation, except for annual or monocarpic species. Amendment with organic matter provided little benefit that could not have been achieved more simply by the addition of beach sand to the coarsest shingle substrates.
Effects of Bot Fly Parasitism on Movements of Peromyscus leucopus

Abstract Factors that affect movement of small mammals include vegetation structure, resource distribution, interspecific interactions and parasitism. The objective of this study was to assess the effect of bot fly (Cuterebra fontinella) parasitism on movement distance [estimated by mean squared distance (MSD)] of white-footed deermice (Peromyscus leucopus), using a repeated measures approach. Although there was no significant effect of infestation on MSD, there was a nonsignificant trend towards increased movement during infestation for females. In addition, there was no difference in MSD between individuals with single infestations and those harboring several larvae. These results are unexpected given the large size of the parasite relative to the host. Studies of fine-scale temporal movements within a single night could shed further light on the effects of infestation on movement.
Applying Deep-Learning-Based Computer Vision to Wireless Communications: Methodologies, Opportunities, and Challenges

Deep learning (DL) has seen great success in the computer vision (CV) field, and related techniques have been used in security, healthcare, remote sensing, and many other areas. As a parallel development, visual data has become universal in daily life, easily generated by ubiquitous low-cost cameras. Therefore, exploring DL-based CV may yield useful information about objects, such as their number, locations, distribution, motion, etc. Intuitively, DL-based CV can also facilitate and improve the designs of wireless communications, especially in dynamic network scenarios. However, so far, such work is rare in the literature. The primary purpose of this article, then, is to introduce ideas about applying DL-based CV in wireless communications to bring some novel degrees of freedom to both theoretical research and engineering applications. To illustrate how DL-based CV can be applied in wireless communications, an example of using a DL-based CV with a millimeter-wave (mmWave) system is given to realize optimal mmWave multiple-input and multiple-output (MIMO) beamforming in mobile scenarios. In this example, we propose a framework to predict future beam indices from previously observed beam indices and images of street views using ResNet, 3-dimensional ResNext, and a long short-term memory network. The experimental results show that our frameworks achieve much higher accuracy than the baseline method, and that visual data can significantly improve the performance of the MIMO beamforming system. Finally, we discuss the opportunities and challenges of applying DL-based CV in wireless communications.
Majorana-induced DC Shapiro steps in topological Josephson junctions

The demonstration of the non-Abelian properties of Majorana bound states (MBS) is a crucial step toward topological quantum computing. We theoretically investigate how Majorana fusion rules manifest themselves in the current-voltage characteristics of a topological Josephson junction. The junction is built on U-shaped quantum spin Hall edges and hosts a Majorana qubit formed by four MBS. Owing to Majorana fusion rules, inter- and intra-edge couplings among adjacent MBS provide two orthogonal components in the rotation axis of the Majorana qubit. We show that the interplay of the dynamics of the superconductor phase difference and the Majorana qubit governs the Josephson effect. Strikingly, we identify sequential jumps of the voltage across the junction with increasing DC current bias without external AC driving. Its role is replaced by the intrinsic Rabi oscillations of the Majorana qubit. This phenomenon, DC Shapiro steps, is a manifestation of the non-trivial fusion rules of MBS.
SAL++: Sign Agnostic Learning with Derivatives

Learning 3D geometry directly from raw data, such as point clouds, triangle soups, or un-oriented meshes is still a challenging task that feeds many downstream computer vision and graphics applications. In this paper, we introduce SAL++: a method for learning implicit neural representations of shapes directly from such raw data. We build upon the recent sign agnostic learning (SAL) approach and generalize it to include derivative data in a sign agnostic manner. In more detail, given the unsigned distance function to the input raw data, we suggest a novel sign agnostic regression loss, incorporating both pointwise values and gradients of the unsigned distance function. Optimizing this loss leads to a signed implicit function solution, the zero level set of which is a high quality, valid manifold approximation to the input 3D data. We demonstrate the efficacy of SAL++ shape space learning from two challenging datasets: ShapeNet that contains inconsistent orientation and non-manifold meshes, and D-Faust that contains raw 3D scans (triangle soups). On both these datasets, we present state-of-the-art results.
Determining coastal inlet flushing times: a practical expression for use in aquaculture and pollution management

It is often necessary to compare the flushing of coastal inlets in selecting sites for aquaculture or in making recommendations on pollution hazards. We derive an expression for a reference flushing time. in which h is the mean depth of the inlet, (E‐R) is the net evaporation rate, S1 is the salinity of the open ocean, S2 is the (volume) average salinity in the inlet and ΔS = S2‐S1o is easily evaluated from simply obtained data sets, especially in a steady‐state situation, or for an harmonic annual cycle. We show that o is a good estimate of the flushing time for sources within the body of the inlet.
Body size and reproductive traits of Palaemon elegans Rathke, 1837 (Crustacea, Decapoda), a recent colonizer of the Baltic Sea

Body size and reproductive traits of Palaemon elegans Rathke, 1837 (Crustacea, Decapoda), a recent colonizer of the Baltic Sea Palaemon elegans is a species of prawn new (since 2000) to the southern Baltic. The aim of this study was to find out whether there are differences in the sizes of individuals and in the reproductive traits of P. elegans inhabiting different areas of the southern Baltic Sea and to compare the data obtained with existing data for populations from other coastal areas. The present study was carried out in the summers of 2005 and 2007 in three areas differing in their hydrological conditions (primarily in salinity): Puck Bay (PB), the Vistula Delta (VD) and the Vistula Lagoon (VL), Poland. The maximum body lengths of these prawns (females = 60 mm, males = 41 mm), and body masses (1201 mg and 533 mg respectively) found in the southern Baltic were less than those reported from many other parts of the geographical range of this species. The increase in body mass with length was the smallest in the prawns inhabiting VL, and the maximum lengths they grew to were also shorter (females - 52 mm and males - 39 mm) than in PB. The numbers of eggs carried by P. elegans in the southern Baltic were smaller than those found in females in its native regions. Differences were also found in the numbers and sizes of eggs between prawns inhabiting different locations in the southern Baltic. It was concluded that the low salinity of the southern Baltic Sea (even below 7 PSU), while not preventing this prawn from reproducing, caused a shift to the production of fewer but larger eggs. The reproductive strategy that Baltic populations of P. elegans has adopted is one of the factors responsible for its very rapid and large-scale colonization of the southern Baltic Sea.
A Tale of Two Communities: Characterizing Reddit Response to COVID-19 through /r/China_Flu and /r/Coronavirus

The COVID-19 pandemic has deeply impacted people's lives around the globe. During the extended lockdowns caused by the pandemic, online communities are crucial for people to access information and share experiences. In particular, two "new" communities have emerged on Reddit: /r/China_flu and /r/Coronavirus. By studying activities and users in these two communities, we provide a characterization of people's responses to COVID-19 on Reddit. First, we find that user activity peaks around March 17, when the World Health Organization (WHO) announced COVID-19 as a pandemic. Shortly after that, the activity levels of both communities have been declining week by week. We further illustrate the central role of these two communities in the emergence of COVID-related communities. Second, we study the differences between these two communities. /r/Coronavirus is recommended as the official community for COVID-19 on Reddit, while /r/China_flu adopts a loose moderation practice. As a result, we observe that these two communities are gradually growing apart and more extremism is being found in /r/China_flu. Finally, we examine the spillover effect of the COVID-19 pandemic on user activity across the entire Reddit platform. Our results show significant changes in user activities outside COVID-related communities. In subreddits related to finance, food, and countries/cities, user activity is recovering to the pre-pandemic level in late April and May as countries reopen, but subreddits related to travel and sports remain highly impacted and show lower activity levels than the pre-pandemic period. Our work highlights the strength of Reddit as a source for understanding public reactions to COVID-19 and the importance of content moderation on the Internet during a pandemic.