Miracle-dz/UniFAR-FTUs huggingface dataset:
Cohomological Hall algebras and perverse coherent sheaves on toric Calabi–Yau $3$-folds

To a smooth local toric Calabi-Yau 3-fold $X$ we associate the Heisenberg double of the (equivariant spherical) Cohomological Hall algebra in the sense of Kontsevich and Soibelman. This Heisenberg double is a generalization of the notion of the Cartan doubled Yangian defined earlier by Finkelberg and others. We extend this "$3d$ Calabi-Yau perspective" on the Lie theory furthermore by associating a root system to certain families of $X$. By general reasons, the COHA acts on the cohomology of the moduli spaces of certain perverse coherent systems on $X$ via "raising operators". We conjecture that the Heisenberg double acts on the same cohomology via not only by raising operators but also by "lowering operators". We also conjecture that this action factors through the shifted Yangian of the above-mentioned root system. We add toric divisors to the story and explain the shifts in the shifted Yangian in terms of the intersection numbers with the divisors. We check the conjectures in several examples.
On the robustness of effectiveness estimation of nonpharmaceutical interventions against COVID-19 transmission

There remains much uncertainty about the relative effectiveness of different nonpharmaceutical interventions (NPIs) against COVID-19 transmission. Several studies attempt to infer NPI effectiveness with cross-country, data-driven modelling, by linking from NPI implementation dates to the observed timeline of cases and deaths in a country. These models make many assumptions. Previous work sometimes tests the sensitivity to variations in explicit epidemiological model parameters, but rarely analyses the sensitivity to the assumptions that are made by the choice the of model structure (structural sensitivity analysis). Such analysis would ensure that the inferences made are consistent under plausible alternative assumptions. Without it, NPI effectiveness estimates cannot be used to guide policy. We investigate four model structures similar to a recent state-of-the-art Bayesian hierarchical model. We find that the models differ considerably in the robustness of their NPI effectiveness estimates to changes in epidemiological parameters and the data. Considering only the models that have good robustness, we find that results and policy-relevant conclusions are remarkably consistent across the structurally different models. We further investigate the common assumptions that the effect of an NPI is independent of the country, the time, and other active NPIs. We mathematically show how to interpret effectiveness estimates when these assumptions are violated.
Recursive Rules with Aggregation: A Simple Unified Semantics

Complex reasoning problems are most clearly and easily specified using logical rules, especially recursive rules with aggregation such as counts and sums for practical applications. Unfortunately, the meaning of such rules has been a significant challenge, leading to many different conflicting semantics. This paper describes a unified semantics for recursive rules with aggregation, extending the unified founded semantics and constraint semantics for recursive rules with negation. The key idea is to support simple expression of the different assumptions underlying different semantics, and orthogonally interpret aggregation operations straightforwardly using their simple usual meaning.
The influence of spermine and hydrogen peroxide on programmed cell death in tuberculate rhizoids of Marchantia polymorpha

Abstract Evidence for naturally occurring programmed cell death in Marchantia polymorpha tuberculate rhizoids as a function of age is described. Membrane degradation and DNA fragmentation, two major events that occur during programmed cell death, are detected and quantified using Trypan Blue staining and the TUNEL Assay. Membrane permeability and DNA fragmentation both increase as the tuberculate rhizoids aged. Spermine suppressed this response; hydrogen peroxide accelerated it. This research has advanced the study of PCD by demonstrating that nutrient recycling of the thallus and tuberculate rhizoids of Marchantia polymorpha are interrelated.
Learning Task-oriented Disentangled Representations for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) aims to address the domain-shift problem between a labeled source domain and an unlabeled target domain. Many efforts have been made to address the mismatch between the distributions of training and testing data, but unfortunately, they ignore the task-oriented information across domains and are inflexible to perform well in complicated open-set scenarios. Many efforts have been made to eliminate the mismatch between the distributions of training and testing data by learning domain-invariant representations. However, the learned representations are usually not task-oriented, i.e., being class-discriminative and domain-transferable simultaneously. This drawback limits the flexibility of UDA in complicated open-set tasks where no labels are shared between domains. In this paper, we break the concept of task-orientation into task-relevance and task-irrelevance, and propose a dynamic task-oriented disentangling network (DTDN) to learn disentangled representations in an end-to-end fashion for UDA. The dynamic disentangling network effectively disentangles data representations into two components: the task-relevant ones embedding critical information associated with the task across domains, and the task-irrelevant ones with the remaining non-transferable or disturbing information. These two components are regularized by a group of task-specific objective functions across domains. Such regularization explicitly encourages disentangling and avoids the use of generative models or decoders. Experiments in complicated, open-set scenarios (retrieval tasks) and empirical benchmarks (classification tasks) demonstrate that the proposed method captures rich disentangled information and achieves superior performance.
Optical Anisotropy in Tellurene and its Janus Allotropes -- A first principle Study

Here, we present first principle study of structural, electronic, and optical properties of pristine and janus tellurene allotropes using density functional theory (DFT). The $\alpha$, $\beta$, $\delta$, and $\eta$ allotrope of pristine tellurene exhibit indirect band gap while $\gamma$ and $\sigma$ allotropes are metallic. The bandgap shows tunability in janus tellurene compared to pristine tellurene. A metallic to semiconductor transition occurs in janus $\gamma$, $\sigma$ and its allotropes. Dirac cone-like interesting feature has been observed for pristine $\sigma$ tellurene which remains preserved with the opening in energy gap for janus allotrope. In optical properties, imaginary part dielectric function and electron energy loss spectra show a blue shift in janus tellurene as compared to pristine form. Static dielectric constant is tunable in janus tellurene. All allotropes of tellurene are optically active in UV-Vis region with optical anisotropy in all three directions. We believe that our findings will provide useful pointers in the experimental fabrication of devices based on janus tellurene.
Multi-Armed Bandits for Minesweeper: Profiting From Exploration–Exploitation Synergy

A popular computer puzzle, the game of Minesweeper requires its human players to have a mix of both luck and strategy to succeed. Analyzing these aspects more formally, in our research, we assessed the feasibility of a novel methodology based on reinforcement learning as an adequate approach to tackle the problem presented by this game. For this purpose, we employed multi-armed bandit algorithms which were carefully adapted in order to enable their use to define autonomous computational players, targeting to make the best use of some game peculiarities. After experimental evaluation, results showed that this approach was indeed successful, especially in smaller game boards, such as the standard beginner level. Despite this fact, the main contribution of this work is a detailed examination of Minesweeper from a learning perspective, which led to various original insights which are thoroughly discussed.
Effect of Copper on Antioxidant Enzyme Activities and Mineral Nutrition of White Lupin Plants Grown in Nutrient Solution

ABSTRACT We analyzed the effect of different copper (Cu) concentrations (0.10, 0.15, 0.20 and 0.35 mM) and time (1 day to 9 days) on several growth and biochemical parameters of roots and shoots of white lupin plants (Lupinus albus cv Estoril) grown in nutrient solution. A significant decrease in leaf fresh weight and leaf area was detected. Copper accumulated in the roots, and an impairment of nutrient translocation was only observed after six days at the highest Cu concentrations applied. A transient increase in the activity of polyphenoloxidase (EC 1.10.3.1) enforces a role for lignification as a defense strategy under enhanced Cu levels. The activities of several antioxidative enzymes were enhanced after Cu application. Our results indicate that Lupinus albus cv ‘Estoril’ is a rather resistant plant that can cope with moderate concentrations of copper, mostly by controlling up to a certain point, the uptake of excessive amounts of this metal.
Knowledge and adherence visits to PMTCT option B+ services among HIV positive pregnant women in Ilala Municipal Council

Background: In 2013, Tanzania adopted the provision of lifelong antiretroviral therapy (Option B+) to HIV positive pregnant women, throughout the pregnancy and breastfeeding period and continues taking ART for the rest of their lives. However, in 2014, about 38.5% women were lost in adhering to PMTCT option B+ services at national level. More specially, in Ilala municipality, data from district health information system (DHIS2) shows that the rate of lost to follow up on PMTCT option B+ ranges from 36 to 49 percent, which has resulted into high transmission of HIV infection from mother to child. Against this observation a study was initiated to find out the relationship between knowledge and adherence to PMTCT option B+ services. Objectives: The study was undertaken to assess the quality of knowledge and adherence visit to PMTCT option B+ services among HIV positive pregnant women in selected health centers and hospitals in Ilala Municipality. Specifically it focused on determining quality of knowledge delivered to HIV pregnant women during counseling for PMTCT option B+ services in Ilala Municipality, to determine level of knowledge on services among the HIV positive women, to determine adherence visits to PMTCT option B+ services and to explore socio cultural beliefs affecting adherence to visits to PMTCT option B+ services. Methodology: A cross sectional study design. The sample of the study was 383 respondents composed of 378 HIV pregnant women and 6 HCPs. The sample was selected purposively. Data collection was done through questionnaire, interview and documentary reviews. Data analysis was done through SPSS and the findings were presented in the form of frequency tables and percentages. Results: The findings indicated that, 90.8% of the respondents were well informed in area of PMTCT Option B+ services and other related sexually transmitted diseases. The quality of knowledge on PMTCT Option B+ services among HIV positive pregnant women was also very satisfactory 79.9%. On the adherence to PMTCT Option B+ services, 77.7% of the HIV pregnant women were adhering to PMTCT Option B+ service provision. Counseling services
Modern Hopfield Networks and Attention for Immune Repertoire Classification

A central mechanism in machine learning is to identify, store, and recognize patterns. How to learn, access, and retrieve such patterns is crucial in Hopfield networks and the more recent transformer architectures. We show that the attention mechanism of transformer architectures is actually the update rule of modern Hop-field networks that can store exponentially many patterns. We exploit this high storage capacity of modern Hopfield networks to solve a challenging multiple instance learning (MIL) problem in computational biology: immune repertoire classification. Accurate and interpretable machine learning methods solving this problem could pave the way towards new vaccines and therapies, which is currently a very relevant research topic intensified by the COVID-19 crisis. Immune repertoire classification based on the vast number of immunosequences of an individual is a MIL problem with an unprecedentedly massive number of instances, two orders of magnitude larger than currently considered problems, and with an extremely low witness rate. In this work, we present our novel method DeepRC that integrates transformer-like attention, or equivalently modern Hopfield networks, into deep learning architectures for massive MIL such as immune repertoire classification. We demonstrate that DeepRC outperforms all other methods with respect to predictive performance on large-scale experiments, including simulated and real-world virus infection data, and enables the extraction of sequence motifs that are connected to a given disease class. Source code and datasets: https://github.com/ml-jku/DeepRC
SPÉCIFICITÉ DES SUBSTANCES NON VOLATILES DES ALLIUM RESPONSABLES DE LA PONTE DE LA TEIGNE DU POIREAU, ACROLEPIOPSIS ASSECTELLA (LEPIDOPTERA)

La ponte de la teigne du poireau est examinée sur papiers filtres imprégnés d'extraits méthanoliques de différents végétaux. La technique par femelle fécondée isolée est décrite. Elle permet d'obtenir environ 80% des pontes observées sur feuille de poireau. Les autres végétaux utilisés sont essentiellement des Crucifères qui contiennent comme les Allium des substances soufrées et des Liliacées. Seules les espèces appartenant au genre Allium stimulent la ponte. Des substances chimiques spécifiques de ces seules plantes sont donc nécessaires au déclenchement de la ponte de la teigne du poireau. Un essai de fractionnement de l'extrait méthanolique pourrait indiquer que des substances inhibitrices de la ponte seraient également présentes dans les Allium.
Analysis of gene expression in the Leishmania life cycle : Leishmania (Viannia) braziliensis and Leishmania (Viannia) peruviana model

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
Covert Identification over Binary-Input Memoryless Channels

This paper considers the covert identification problem in which a sender aims to reliably convey an identification (ID) message to a set of receivers via a binary-input memoryless channel (BMC), and simultaneously to guarantee that the communication is covert with respect to a warden who monitors the communication via another independent BMC. We prove a square-root law for the covert identification problem. This states that an ID message of size \exp(\exp(\Theta(\sqrt{n}))) can be transmitted over n channel uses. We then characterize the exact pre-constant in the \Theta(.) notation. This constant is referred to as the covert identification capacity. We show that it equals the recently developed covert capacity in the standard covert communication problem, and somewhat surprisingly, the covert identification capacity can be achieved without any shared key between the sender and receivers. The achievability proof relies on a random coding argument with pulse-position modulation (PPM), coupled with a second stage which performs code refinements. The converse proof relies on an expurgation argument as well as results for channel resolvability with stringent input constraints.
Sequential Design of Multi-Fidelity Computer Experiments: Maximizing the Rate of Stepwise Uncertainty Reduction

Abstract This article deals with the sequential design of experiments for (deterministic or stochastic) multi-fidelity numerical simulators, that is, simulators that offer control over the accuracy of simulation of the physical phenomenon or system under study. Accurate simulations usually entail a high computational effort, while coarse simulations are obtained at a lower cost. The cost can be measured, for example, by the run time of the simulator or the financial cost of the computing resources. In this setting, simulation results obtained at several levels of fidelity can be combined in order to estimate quantities of interest (the optimal value of the output, the probability that the output exceeds a given threshold, etc.) in an efficient manner. We propose a new Bayesian sequential strategy called maximal rate of stepwise uncertainty reduction (MR-SUR), that selects additional simulations to be performed by maximizing the ratio between the expected reduction of uncertainty and the cost of simulation. This generic strategy unifies several existing methods, and provides a principled approach to develop new ones. We assess its performance on several examples, including a computationally intensive problem of fire safety analysis where the quantity of interest is the probability of exceeding a tenability threshold during a building fire.
A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges

Question Answering (QA) over Knowledge Base (KB) aims to automatically answer natural language questions via well-structured relation information between entities stored in knowledge bases. In order to make KBQA more applicable in actual scenarios, researchers have shifted their attention from simple questions to complex questions, which require more KB triples and constraint inference. In this paper, we introduce the recent advances in complex QA. Besides traditional methods relying on templates and rules, the research is categorized into a taxonomy that contains two main branches, namely Information Retrieval-based and Neural Semantic Parsing-based. After describing the methods of these branches, we analyze directions for future research and introduce the models proposed by the Alime team.
Fungal Endophytes of Grasses

The tenn endophyte (Greek: endo == within + phyte = plant) has been defined as an organism contained or growing (entirely) within the substrate plant, whether parasitically or not ( 100, 106). Using this tenn in its broadest sense, the subject of this chapter could include all fungi that spend all or nearly all of their life cycles in the host grasses. We do not discuss such endophytic fungi as the smuts and vesicular-arbuscular mycorrhizas that infect grass. Instead, our interests lie primarily with a specific group of c1avicipitaceous fungi that belong or are related to fungi in the tribe Balansiae (5, 24). These fungi either are true endophytes that never produce external fructifications on the plant or else may produce external mycelium andlor spores that affect flower and seed production. We focus specifically on those grass-endophyte complexes that may also cause maladies of grazing animals. Using the above criteria, two recent events involving grass-endophyte associations have important implications for the livestock industry. In 1977 Bacon et al (9) reported the close association of an endophyte (Sphacelia typhina) in infected tall fescue (Festuca arundinacea) and the incidence of
Tighter risk certificates for neural networks

This paper presents an empirical study regarding training probabilistic neural networks using training objectives derived from PAC-Bayes bounds. In the context of probabilistic neural networks, the output of training is a probability distribution over network weights. We present two training objectives, used here for the first time in connection with training neural networks. These two training objectives are derived from tight PAC-Bayes bounds. We also re-implement a previously used training objective based on a classical PAC-Bayes bound, to compare the properties of the predictors learned using the different training objectives. We compute risk certificates that are valid on any unseen examples for the learnt predictors. We further experiment with different types of priors on the weights (both data-free and data-dependent priors) and neural network architectures. Our experiments on MNIST and CIFAR-10 show that our training methods produce competitive test set errors and non-vacuous risk bounds with much tighter values than previous results in the literature, showing promise not only to guide the learning algorithm through bounding the risk but also for model selection. These observations suggest that the methods studied here might be good candidates for self-certified learning, in the sense of certifying the risk on any unseen data without the need for data-splitting protocols.
Receptive-Field Regularized CNNs for Music Classification and Tagging

Convolutional Neural Networks (CNNs) have been successfully used in various Music Information Retrieval (MIR) tasks, both as end-to-end models and as feature extractors for more complex systems. However, the MIR field is still dominated by the classical VGG-based CNN architecture variants, often in combination with more complex modules such as attention, and/or techniques such as pre-training on large datasets. Deeper models such as ResNet -- which surpassed VGG by a large margin in other domains -- are rarely used in MIR. One of the main reasons for this, as we will show, is the lack of generalization of deeper CNNs in the music domain. In this paper, we present a principled way to make deep architectures like ResNet competitive for music-related tasks, based on well-designed regularization strategies. In particular, we analyze the recently introduced Receptive-Field Regularization and Shake-Shake, and show that they significantly improve the generalization of deep CNNs on music-related tasks, and that the resulting deep CNNs can outperform current more complex models such as CNNs augmented with pre-training and attention. We demonstrate this on two different MIR tasks and two corresponding datasets, thus offering our deep regularized CNNs as a new baseline for these datasets, which can also be used as a feature-extracting module in future, more complex approaches.
Prediction of the voluntary intake of low quality roughages by sheep from chemical composition and ruminal degradation characteristics

Six cereal straws and two traditional hays were used to study the relationship between voluntary dry matter (DM) intake and chemical composition and ruminal degradation characteristics. The voluntary DM intake was measured during 60 days using Barbarin adult ewes given food individually in four groups of six animals. The forages were offered to the ewes twice daily and were supplemented with 10 g DM of soya-bean meal per kg M(0.75) per day and 30 g per ewe per day of a commercial mixture of minerals and vitamins. The chemical composition and the leaf/stem (LIS) ratio of forages were determined. Their ruminal degradation characteristics were measured by using the nylon bag technique according to the exponenfini model Dg = a + b (1 - e(-ct)). Crude protein contents of straws ranged from 22 to 35 g/kg DM and that of hays from 35 to 57 g/kg DM. The corresponding values of L/S ratio varied from 0 . 68 to 0 . 88. The voltintauy DM intake was highly variable and the hays were ingested at greater amounts than the straws, although their degradable (b) and their rate of digestion (c) were not significantly different (P > 0 . 05). Significant relationships were established between voluntary DM intake and the neutral-detergent fibre (R(2) = -0 . 72; P < 0 . 001), acid-detergent fibre (R(2) = -0 . 75; P < 0 . 001) and a values (R(2) = 0 . 83; P < 0 . 001). The precision of the prediction was significantly improved using ruminal degradation characteristics (S-yx = 4 . 42)compared with the chemical composition (S-yx = 5 . 60).