Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative distribution of Syrian and great spotted woodpeckers in different landscapes of Poland

Abstract. Species often express some differences in habitat choice which enable their co-occurrence in sympatry and this phenomenon is particularly noticeable for related taxa and during range shifts. Here, co-distribution of two sibling woodpeckers (Syrian woodpecker Dendrocopos syriacus and great spotted woodpecker Dendrocopos major) in different types of urban, rural and riparian landscapes of Poland is presented. The Syrian woodpecker reached high densities in urban (city) landscape, but it was less common in town, rural and riparian areas, and everywhere it mostly occupied orchards and poplar and willow hedges. In contrast, the great spotted woodpecker was the dominant species in most areas, except city and everywhere was associated with forests or larger wooded areas (like parks). The Syrian woodpecker was dependent on the presence of softwood trees (poplars, willows), but it needed generally less than 40 % of wooded area in its territories, whereas the second species needed more than 70 %. The data presented here, together with information available from other areas in Central Europe, suggest that protection of Syrian woodpecker (species annexed in the European Union Bird Directive) should not be restricted to only rural landscapes but also needs to include its urban populations. Protection of this species should be focused on preservation of softwood tree hedges and woodlots and traditional orchards.
Criniferous entire maps with absorbing Cantor bouquets

<p style='text-indent:20px;'>It is known that, for many transcendental entire functions in the Eremenko-Lyubich class <inline-formula><tex-math id="M1">\begin{document}$ \mathcal{B} $\end{document}</tex-math></inline-formula>, every escaping point can eventually be connected to infinity by a curve of escaping points. When this is the case, we say that the functions are <i>criniferous</i>. In this paper, we extend this result to a new class of maps in <inline-formula><tex-math id="M2">\begin{document}$ \mathcal{B} $\end{document}</tex-math></inline-formula>. Furthermore, we show that if a map belongs to this class, then its Julia set contains a <i>Cantor bouquet</i>; in other words, it is a subset of <inline-formula><tex-math id="M3">\begin{document}$ \mathbb{C} $\end{document}</tex-math></inline-formula> ambiently homeomorphic to a straight brush. </p>
Effect of different inoculum levels, plant age and multiplication of Meloidogyne incognita on growth of blackgram (Vigna mango L)

The root knot nematode (Meloidogyne incognita) caused significant reduction in plant height, root length fresh and dry weights of shoots and roots. Root galls and final nematode population increased with the increasing levels of nematode inoculum. Highest level of nematode inoculum indicated inhibitory and damaging potential on plant growth parameters on blackgram. Two second stage larvae of M. incognita were found to the damaging level on blackgram (Cv TU 98-14). The root knot nematode continues to penetrate at all the stages of plant growth. However, seven days old plants were observed to be most susceptible to root knot. The treatments receiving the nematode inoculation prior to fungus resulted in higher reduction and plant growth than the other treatments. When nematode inoculation was done seven days prior to fungal inoculation, it showed maximum synergistic effect followed by treatment where both the pathogens were inoculated simultaneously. Presence of nematodes not only predisposed the host but also shortened the incubation period for disease expression.
Anomaly detection with variational quantum generative adversarial networks

Generative adversarial networks (GANs) are a machine learning framework comprising a generative model for sampling from a target distribution and a discriminative model for evaluating the proximity of a sample to the target distribution. GANs exhibit strong performance in imaging or anomaly detection. However, they suffer from training instabilities, and sampling efficiency may be limited by the classical sampling procedure. We introduce variational quantum–classical Wasserstein GANs (WGANs) to address these issues and embed this model in a classical machine learning framework for anomaly detection. Classical WGANs improve training stability by using a cost function better suited for gradient descent. Our model replaces the generator of WGANs with a hybrid quantum–classical neural net and leaves the classical discriminative model unchanged. This way, high-dimensional classical data only enters the classical model and need not be prepared in a quantum circuit. We demonstrate the effectiveness of this method on a credit card fraud dataset. For this dataset our method shows performance on par with classical methods in terms of the F 1 score. We analyze the influence of the circuit ansatz, layer width and depth, neural net architecture parameter initialization strategy, and sampling noise on convergence and performance.
Scalable Shared-Memory Hypergraph Partitioning

Hypergraph partitioning is an important preprocessing step for optimizing data placement and minimizing communication volumes in high-performance computing applications. To cope with ever growing problem sizes, it has become increasingly important to develop fast parallel partitioning algorithms whose solution quality is competitive with existing sequential algorithms. To this end, we present Mt-KaHyPar, the first shared-memory multilevel hypergraph partitioner with parallel implementations of many techniques used by the sequential, high-quality partitioning systems: a parallel coarsening algorithm that uses parallel community detection as guidance, initial partitioning via parallel recursive bipartitioning with work-stealing, a scalable label propagation refinement algorithm, and the first fully-parallel direct $k$-way formulation of the classical FM algorithm. Experiments performed on a large benchmark set of instances from various application domains demonstrate the scalability and effectiveness of our approach. With 64 cores, we observe self-relative speedups of up to 51 and a harmonic mean speedup of 23.5. In terms of solution quality, we outperform the distributed hypergraph partitioner Zoltan on 95% of the instances while also being a factor of 2.1 faster. With just four cores,Mt-KaHyPar is also slightly faster than the fastest sequential multilevel partitioner PaToH while producing better solutions on 83% of all instances. The sequential high-quality partitioner KaHyPar still finds better solutions than our parallel approach, especially when using max-flow-based refinement. This, however, comes at the cost of considerably longer running times.
Epidemiology and Genetics of Preserved Ratio Impaired SpiroMetry (PRISm): An Analysis of UK Biobank

Rationale Preserved ratio impaired spirometry (PRISm) is defined as Forced Expiratory Volume in one second <80% predicted with a FEV1/Forced Vital Capacity of >0.70. It may be a precursor of COPD and has been associated with increased respiratory symptoms and comorbidities. Current understanding of PRISm is based on small selective cohorts and genetic determinants remain unknown. Objectives Identify the prevalence, risk factors, co-morbidity, and genetics of PRISm in a large cohort to improve power and generalizability. Methods The UKBiobank, provides a large cohort of genotyped individuals. Regression analysis determined risk factors for and associated co-morbidities of PRISm. Genome wide association studies, conditional, and gene-based analyses were performed with a PheWAS and literature search conducted on the results. Measurements and Main Results PRISm has a similar prevalence to airflow obstruction (8% vs 7.8%) and is associated with increased cardiovascular co-morbidity, OR of 1.26 (95%CI 1.20-1.31) after adjusting for BMI, smoking status, age, diabetes, and sex. Genetic analysis discovered four associated single nucleotide polymorphisms and eight genes. Two SNPs have not been described with respiratory disease before. Other genetic loci identified are known to increase risk of asthma and COPD, and influence response to inhaled steroids. Conclusions PRISm affects 8% of adults of whom ~40% report cardiovascular comorbidity. Detailed analysis reveals that PRISm has shared genetic associations with asthma and COPD. These findings highlight the need for research into the potential therapeutic strategies and screening for individuals with PRISm, in particular whether this may reduce transition to COPD and reduce cardiovascular disease.
Experiences of Women Who Gave Birth in US Hospitals During the COVID-19 Pandemic

The purpose of this study was to describe the experiences of women who gave birth in a US hospital during the COVID-19 pandemic. Women who gave birth between March and July 2020 completed a survey on the experience of giving birth during a pandemic. Of this, 885 women were consented and participated in the study; 22.5% of women reported hypertension, 33.8% reported anxiety, 18.6% reported depression, and 1.13% reported testing positive for COVID-19. Of this, 61% of women reported inadequate support for childbirth, and 20.5% reported that they did not feel safe giving birth in the hospital. Women who tested positive for COVID-19 were more likely to be of Asian race, have a cesarean delivery, not have a birth partner present, and discontinue breastfeeding before 6 weeks. Pandemic-related changes to maternity care practices may have impacted birthing women’s perceptions of safety and support in the hospital environment and affected symptoms of stress. Health care policy and maternity care practices should promote feelings of safety and control and overall experience for women giving birth in the hospital during a pandemic.
CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters

Due to the compelling improvements brought by BERT, many recent representation models adopted the Transformer architecture as their main building block, consequently inheriting the wordpiece tokenization system despite it not being intrinsically linked to the notion of Transformers. While this system is thought to achieve a good balance between the flexibility of characters and the efficiency of full words, using predefined wordpiece vocabularies from the general domain is not always suitable, especially when building models for specialized domains (e.g., the medical domain). Moreover, adopting a wordpiece tokenization shifts the focus from the word level to the subword level, making the models conceptually more complex and arguably less convenient in practice. For these reasons, we propose CharacterBERT, a new variant of BERT that drops the wordpiece system altogether and uses a Character-CNN module instead to represent entire words by consulting their characters. We show that this new model improves the performance of BERT on a variety of medical domain tasks while at the same time producing robust, word-level, and open-vocabulary representations.
Empowering Rural Livelihoods through Farmers' Field School on Vegetable Production in Aceh Province-Indonesia

This paper evaluates immediate impacts of farmers’ field school (FFS) in Indonesian farmers’ communities on five livelihoods assets: physical, financial, human, social, and natural capitals. The impacts of FFS were documented at least a year after completion of FFS. A before and after method of impact assessment framework was applied to this study. The results indicate that FFS has very favourably increased vegetable farming capability and knowledge base of the FFS participants. Farmers reported very positive impacts of FFS on all of the five categories of livelihood assets. In summary, FFS has successfully delivered improved knowledge and skill on vegetable production in general. Besides, the FFS has strengthened the group formation and social capitals related to vegetable farming.
On the vibrational free energy of hydrated proteins

When the hydration shell of a protein is filled with at least 0.6 gram of water per gram of protein, a significant anti-correlation between the vibrational free energy and the potential energy of energy-minimized conformers is observed. This means that low potential energy, well-hydrated, protein conformers tend to be more rigid than high-energy ones. On the other hand, in the case of CASP target 624, when its hydration shell is filled, a significant energy gap is observed between the crystal structure and the best conformers proposed during the prediction experiment, strongly suggesting that including explicit water molecules may help identifying unlikely conformers among good-looking ones.
FLAG: Adversarial Data Augmentation for Graph Neural Networks

Data augmentation helps neural networks generalize better, but it remains an open question how to effectively augment graph data to enhance the performance of GNNs (Graph Neural Networks). While most existing graph regularizers focus on augmenting graph topological structures by adding/removing edges, we offer a novel direction to augment in the input node feature space for better performance. We propose a simple but effective solution, FLAG (Free Large-scale Adversarial Augmentation on Graphs), which iteratively augments node features with gradient-based adversarial perturbations during training, and boosts performance at test time. Empirically, FLAG can be easily implemented with a dozen lines of code and is flexible enough to function with any GNN backbone, on a wide variety of large-scale datasets, and in both transductive and inductive settings. Without modifying a model's architecture or training setup, FLAG yields a consistent and salient performance boost across both node and graph classification tasks. Using FLAG, we reach state-of-the-art performance on the large-scale ogbg-molpcba, ogbg-ppa, and ogbg-code datasets.
AutoBSS: An Efficient Algorithm for Block Stacking Style Search

Neural network architecture design mostly focuses on the new convolutional operator or special topological structure of network block, little attention is drawn to the configuration of stacking each block, called Block Stacking Style (BSS). Recent studies show that BSS may also have an unneglectable impact on networks, thus we design an efficient algorithm to search it automatically. The proposed method, AutoBSS, is a novel AutoML algorithm based on Bayesian optimization by iteratively refining and clustering Block Stacking Style Code (BSSC), which can find optimal BSS in a few trials without biased evaluation. On ImageNet classification task, ResNet50/MobileNetV2/EfficientNet-B0 with our searched BSS achieve 79.29%/74.5%/77.79%, which outperform the original baselines by a large margin. More importantly, experimental results on model compression, object detection and instance segmentation show the strong generalizability of the proposed AutoBSS, and further verify the unneglectable impact of BSS on neural networks.
Hyperbolic manifolds that fiber algebraically up to dimension 8

We construct some cusped finite-volume hyperbolic $n$-manifolds $M_n$ that fiber algebraically in all the dimensions $5\leq n \leq 8$. That is, there is a surjective homomorphism $\pi_1(M_n) \to \mathbb Z$ with finitely generated kernel. The kernel is also finitely presented in the dimensions $n=7, 8$, and this leads to the first examples of hyperbolic $n$-manifolds $\widetilde M_n$ whose fundamental group is finitely presented but not of finite type. These $n$-manifolds $\widetilde M_n$ have infinitely many cusps of maximal rank and hence infinite Betti number $b_{n-1}$. They cover the finite-volume manifold $M_n$. We obtain these examples by assigning some appropriate colours and states to a family of right-angled hyperbolic polytopes $P_5, \ldots, P_8$, and then applying some arguments of Jankiewicz, Norin, Wise and Bestvina, Brady. We exploit in an essential way the remarkable properties of the Gosset polytopes dual to $P_n$, and the algebra of integral octonions for the crucial dimensions $n=7,8$.
Combining Ensembles and Data Augmentation can Harm your Calibration

Ensemble methods which average over multiple neural network predictions are a simple approach to improve a model's calibration and robustness. Similarly, data augmentation techniques, which encode prior information in the form of invariant feature transformations, are effective for improving calibration and robustness. In this paper, we show a surprising pathology: combining ensembles and data augmentation can harm model calibration. This leads to a trade-off in practice, whereby improved accuracy by combining the two techniques comes at the expense of calibration. On the other hand, selecting only one of the techniques ensures good uncertainty estimates at the expense of accuracy. We investigate this pathology and identify a compounding under-confidence among methods which marginalize over sets of weights and data augmentation techniques which soften labels. Finally, we propose a simple correction, achieving the best of both worlds with significant accuracy and calibration gains over using only ensembles or data augmentation individually. Applying the correction produces new state-of-the art in uncertainty calibration across CIFAR-10, CIFAR-100, and ImageNet.
An Efficient Algorithm For Device Detection And Channel Estimation In Asynchronous IOT Systems

A great amount of endeavour has recently been devoted to the joint device activity detection and channel estimation problem in massive machine-type communications. This paper targets at two practical issues along this line that have not been addressed before: asynchronous transmission from uncoordinated users and efficient algorithms for real-time implementation in systems with a massive number of devices. Specifically, this paper considers a practical system where the preamble sent by each active device is delayed by some unknown number of symbols due to the lack of coordination. We manage to cast the problem of detecting the active devices and estimating their delay and channels into a group LASSO problem. Then, a block coordinate descent algorithm is proposed to solve this problem, where the closed-form solution is available when updating each block of variables with the other blocks of variables being fixed, thanks to the special structure of our interested problem. Our analysis shows that the overall complexity of the proposed algorithm is low, making it suitable for real-time application.
Review of inflammatory bowel disease and COVID-19

The first cases of a novel corona virus infection were reported in Wuhan China in December of 2019, followed by the declaration of an international pandemic by the World Health Organization in March 2020. Early reports of the virus, now known as severe acute respiratory syndrome coronavirus 2, and its clinical disease coronavirus disease 2019 (COVID-19), has shown higher rates of morbidity and mortality in the elderly and those with pre-existing medical conditions. Of particular concern is the safety of those with compromised immune systems. Inflammatory Bowel disease (IBD) is itself caused by a disordered immune response, with the most effective medical therapies being immune suppressing or modifying. As such, the risk of COVID-19, virus related outcomes, and appropriate management of IBD patients during the global pandemic is of immediate concern to gastroenterologists worldwide. There has been a rapid accumulation of clinical data and expert opinion on the topic. This review will highlight the latest source information on clinical observation/outcomes of the IBD population and provide a concise summary of the most up to date perspectives on IBD management in the age of COVID-19.
Simulated Chats for Task-oriented Dialog: Learning to Generate Conversations from Instructions

Popular task-oriented dialog data sets such as MultiWOZ (Budzianowski et al. 2018) are created by providing crowd-sourced workers a goal instruction, expressed in natural language, that describes the task to be accomplished. Crowd-sourced workers play the role of a user and an agent to generate dialogs to accomplish tasks involving booking restaurant tables, making train reservations, calling a taxi etc. However, creating large crowd-sourced datasets can be time consuming and expensive. To reduce the cost associated with generating such dialog datasets, recent work has explored methods to automatically create larger datasets from small this http URL this paper, we present a data creation strategy that uses the pre-trained language model, GPT2 (Radford et al. 2018), to simulate the interaction between crowd-sourced workers by creating a user bot and an agent bot. We train the simulators using a smaller percentage of actual crowd-generated conversations and their corresponding goal instructions. We demonstrate that by using the simulated data, we achieve significant improvements in both low-resource setting as well as in over-all task performance. To the best of our knowledge we are the first to present a model for generating entire conversations by simulating the crowd-sourced data collection process
Cyclic Framed Little Disks Algebras, Grothendieck–Verdier Duality And Handlebody Group Representations

We characterize cyclic algebras over the associative and the framed little 2-disks operad in any symmetric monoidal bicategory. The cyclicity is appropriately treated in a coherent way, that is up to coherent isomorphism. When the symmetric monoidal bicategory is specified to be a certain symmetric monoidal bicategory of linear categories subject to finiteness conditions, we prove that cyclic associative and cyclic framed little 2-disks algebras, respectively, are equivalent to pivotal Grothendieck–Verdier categories and ribbon Grothendieck–Verdier categories, a type of category that was introduced by Boyarchenko–Drinfeld based on Barr’s notion of a $\star$-autonomous category. We use these results and Costello’s modular envelope construction to obtain two applications to quantum topology: I) We extract a consistent system of handlebody group representations from any ribbon Grothendieck–Verdier category inside a certain symmetric monoidal bicategory of linear categories and show that this generalizes the handlebody part of Lyubashenko’s mapping class group representations. II) We establish a Grothendieck–Verdier duality for the category extracted from a modular functor by evaluation on the circle (without any assumption on semisimplicity), thereby generalizing results of Tillmann and Bakalov–Kirillov.
What breach? Measuring online awareness of security incidents by studying real-world browsing behavior

Learning about real-world security incidents and data breaches can inform people how their information is vulnerable online and thus encourage safer security behavior. This paper examines 1) how often people read about security incidents online, 2) of those people, whether and to what extent they follow up with an action (e.g., trying to read more about the incident), and 3) what influences the likelihood that they will read about an incident and take some action. Our quantitative study of the real-world internet-browsing behavior of 303 participants finds a low level of awareness. Only 16% of participants visited any web page related to six widely publicized large-scale security incidents; few read about an incident even when it was likely to have affected them. We also found that more severe incidents and articles that constructively spoke about the incident were associated with more action. Our findings highlight two issues: 1) security awareness needs to be increased; and 2) current awareness is so low that expecting users to be aware and take remedial action may not be effective.