Miracle-dz/UniFAR-FTUs huggingface dataset:
Ontogenetical and experimental studies of the floral apex of Portulaca grandiflora. 1. Histology of transformation of the shoot apex into the floral apex

The vegetative apex was a low dome consisting of two layers of tunica surmounting a very small corpus. Foliar primordia originated as periclines in the flanks of T2. The transition apex became first a steep cone and then a hemisphere. All floral primordia—the two bracts, the two sepals, the several whorls of petals, the several whorls of stamens, and the carpels—originated in the manner of leaves, as periclines in T2 on the flanks of the apex. All appendages, including carpels, were therefore lateral. In the early transition, the apex had a brief stage in which there were three tunica layers, but the inner one was lost with the onset of the sepals. The bracts and the first sepal continued the normal positions of primordia for the vegetative phyllotaxy of 3/8, but with the second sepal, this phyllotaxy was lost, and petals, stamens, and carpels were produced in whorls. While leaves, bracts, sepals, and petals were produced in acropetal sequence, stamens were produced in basipetal sequence, and carpels appe.
Adaptive Cascade Submodular Maximization

In this paper, we propose and study the cascade submodular maximization problem under the adaptive setting. The input of our problem is a set of items, each item is in a particular state (i.e., the marginal contribution of an item) which is drawn from a known probability distribution. However, we can not know its actual state before selecting it. As compared with existing studies on stochastic submodular maximization, one unique setting of our problem is that each item is associated with a continuation probability which represents the probability that one can continue to select the next item after selecting the current one. Intuitively, this term captures the externality of one item to all its subsequent items in terms of the opportunity of being selected. Therefore, the actual set of items that can be selected by a policy depends on the specific ordering it adopts to select items, this makes our problem fundamentally different from classical submodular set optimization problems. Our objective is to identify the best sequence of selecting items so as to maximize the expected utility of the selected items. We propose a class of stochastic utility functions, \emph{adaptive cascade submodular functions}, and show that the objective functions in many practical application domains satisfy adaptive cascade submodularity. Then we develop a $\frac{1-1/e}{8}$ approximation algorithm to the adaptive cascade submodular maximization problem.
Learning Combined Set Covering and Traveling Salesman Problem

The Traveling Salesman Problem is one of the most intensively studied combinatorial optimization problems due both to its range of real-world applications and its computational complexity. When combined with the Set Covering Problem, it raises even more issues related to tractability and scalability. We study a combined Set Covering and Traveling Salesman problem and provide a mixed integer programming formulation to solve the problem. Motivated by applications where the optimal policy needs to be updated on a regular basis and repetitively solving this via MIP can be computationally expensive, we propose a machine learning approach to effectively deal with this problem by providing an opportunity to learn from historical optimal solutions that are derived from the MIP formulation. We also present a case study using the vaccine distribution chain of the World Health Organization, and provide numerical results with data derived from four countries in sub-Saharan Africa.
Suppression of a dominant G protein β‐subunit mutation in yeast by Gα protein expression

SCG1/GPA1, STE4, and STE18 encode the α, β and λ components of the G protein involved in mating pheromone signal transduction in Saccharomyces cerevisiae. Responses, including G1 arrest and expression of genes such as FUS1, are activated by βλ, which is negatively controlled by α(GDP), We previously demonstrated that overexpression of Scg1 suppresses responses to α factor and that expression of certain hybrids between Scg1 and mammalian Gα proteins has the same effect and also suppresses growth arrest in an scg1‐null mutant. Effects were attributed to sequestration of βλ. We now show that effects on growth rate, morphology and FUS1 expression are consistent with this model. The STE4HPL allele causes dominant activation of the response pathway, and is presumed to encode a β subunit insensitive to control by α(GDP). Scg1 overexpression suppresses the growth arrest due to STE4HPL; normal α‐factor responses and fertility are restored. A model based on sequestration of βγ reconciles this result with the apparent paradox that the same level of Scg1 overexpression inhibits responses and mating in wild‐type cells. A Gαi hybrid also restores growth and allows inefficient mating in the STEHPL strain.
Índices faunísticos para a entomofauna coletada em plantios de Eucalyptus spp.

A eucaliptocultura tornou-se uma importante atividade economica no Brasil. No entanto, com o aumento das areas com plantios de Eucalyptus sp. os problemas entomologicos tendem a aumentar nas mesmas proporcoes, pois cultivos com este genero propiciam condicoes para adaptacao de pragas, tornando-se necessario monitoramento constante atraves de levantamentos populacionais de insetos. Assim, o objetivo deste trabalho e o levantamento populacional da entomofauna por meio de armadilhas luminosas em plantios de Eucalyptus spp. O estudo foi conduzido em tres talhoes de Eucalyptus , pertencentes as especies: E. dunni, E. grandis e E. grandis x E. urophylla (clone hibrido), com tres anos de idade, localizados na fazenda Taquari, municipio de Sao Francisco de Assis, Rio Grande do Sul. Para a coleta da entomofauna utilizou-se armadilhas luminosas, uma em cada especie avaliada, com coletas realizadas mensalmente, de agosto de 2008 a julho de 2009. Os insetos coletados foram analisados atraves dos indices de frequencia, abundância, constância e diversidade. Durante o levantamento, foram coletados 3623 individuos distribuidos em oito ordens (Blatodea, Coleoptera, Dermaptera, Hemiptera, Hymenoptera, Lepidoptera, Mantodea e Odonata) e 34 familias. As ordens que apresentaram maior numero de insetos coletados foram: Coleoptera, Lepidoptera e Hemiptera com 51, 15 e 10% dos individuos coletados, respectivamente. As ordens Coleoptera e Lepidoptera apresentaram as familias com indices mais significativos, destacando-se as familias Elateridae, Ptilodactylidae, Scarabeidae e Staphylinidae da ordem coleoptera, e Arctiidae e Noctuidae da ordem Lepidoptera. Considerando os resultados obtidos neste levantamento, conclui-se que a area em estudo apresenta grupos importantes, alguns com potencial de tornarem-se pragas, e outros considerados como instrumento em programas de manejo integrado de insetos-praga em plantios de Eucalyptus , contribuindo para a reducao do uso de quimicos em seu controle.
EpiMob: Interactive Visual Analytics of CitywideHuman Mobility Restrictions for Epidemic Control

The outbreak of coronavirus disease (COVID-19) has swept across more than 180 countries and territories since late January 2020. As a worldwide emergency response, governments have implemented various measures and policies, such as self-quarantine, travel restrictions, work from home, and regional lockdown, to control the spread of the epidemic. These countermeasures seek to restrict human mobility because COVID-19 is a highly contagious disease that is spread by human-to-human transmission. Medical experts and policymakers have expressed the urgency to effectively evaluate the outcome of human restriction policies with the aid of big data and information technology. Thus, based on big human mobility data and city POI data, an interactive visual analytics system called Epidemic Mobility (EpiMob) was designed in this study. The system interactively simulates the changes in human mobility and infection status in response to the implementation of a certain restriction policy or a combination of policies (e.g., regional lockdown, telecommuting, screening). Users can conveniently designate the spatial and temporal ranges for different mobility restriction policies. Then, the results reflecting the infection situation under different policies are dynamically displayed and can be flexibly compared and analyzed in depth. Multiple case studies consisting of interviews with domain experts were conducted in the largest metropolitan area of Japan (i.e., Greater Tokyo Area) to demonstrate that the system can provide insight into the effects of different human mobility restriction policies for epidemic control, through measurements and comparisons.
Multi-Armed Bandits with Local Differential Privacy

This paper investigates the problem of regret minimization for multi-armed bandit (MAB) problems with local differential privacy (LDP) guarantee. In stochastic bandit systems, the rewards may refer to the users' activities, which may involve private information and the users may not want the agent to know. However, in many cases, the agent needs to know these activities to provide better services such as recommendations and news feeds. To handle this dilemma, we adopt differential privacy and study the regret upper and lower bounds for MAB algorithms with a given LDP guarantee. In this paper, we prove a lower bound and propose algorithms whose regret upper bounds match the lower bound up to constant factors. Numerical experiments also confirm our conclusions.
Full Duplex Integrated Access and Backhaul for 5G NR: Analyses and Prototype Measurements

Researchers for the third-generation partnership project (3GPP) have been exploring — as a cost-effective alternative to wired backhaul — integrated access and backhaul (IAB) frameworks for 5G new radio (NR). A promising solution for this framework is the integration of full duplex (FD) technologies to enhance the spectral efficiency and efficiently utilize network resources. This approach, which we refer to as FD IAB, involves a significant technical challenge — self-interference (SI) in the IAB framework. In fact, this challenge casts doubt over the performance and feasibility of FD IAB. In this article, we introduce the FD IAB framework and its enabling technologies and also evaluate the framework's link-level SI reduction and system-level downlink throughput performance. Thereafter, we validate the attenuation level in the SI channel with antenna separation and high directional gain through 28 GHz hardware prototype measurements. Our numerical evaluations and hardware prototype measurements confirm that FD IAB represents a promising framework for 5G NR.
Discrete self-similarity in the formation of satellites for viscous cavity break up

The breakup of a jet of a viscous fluid with viscosity $\mu _{1}$ immersed into another viscous fluid with viscosity $\mu _{2}$ is considered in the limit when the viscosity ratio $\lambda =\mu _{1}/\mu _{2}$ is close to zero. We show that, in this limit, a transition from ordinary continuous selfsimilarity to discrete selfsimilarity takes place as $\lambda $decreases. The result being that instead of a single point breakup, the rupture of the inner jet occurs through the appearance of an infinite sequence of filaments of decreasing size that will eventually produce infinite sequences of bubbles of the inner fluid inside the outer fluid. The transition can be understood as the result of a Hopf bifurcation in the system of equations modelling the physical problem.
Text Recognition - Real World Data and Where to Find Them

We present a method for exploiting weakly annotated images to improve text extraction pipelines. The approach uses an arbitrary end-to-end text recognition system to obtain text region proposals and their, possibly erroneous, transcriptions. The method includes matching of imprecise transcriptions to weak annotations and an edit distance guided neighbourhood search. It produces nearly error-free, localised instances of scene text, which we treat as “pseudo ground truth” (PGT). The method is applied to two weakly-annotated datasets. Training with the extracted PGT consistently improves the accuracy of a state of the art recognition model, by 3.7% on average, across different benchmark datasets (image domains) and 24.5% on one of the weakly annotated datasets11Acknowledgements. The authors were supported by Czech Technical University student grant SGS20/171/0HK3/3TJ13, the MEYS VVV project CZ.02. 1.01/0.010. 0J16 019/0000765 Research Center for Informatics, the Spanish Research project TIN2017-89779-P and the CERCA Programme / Generalitat de Catalunya.
MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activity

Here we present our Python toolbox “MR. Estimator” to reliably estimate the intrinsic timescale from electrophysiologal recordings of heavily subsampled systems. Originally intended for the analysis of time series from neuronal spiking activity, our toolbox is applicable to a wide range of systems where subsampling—the difficulty to observe the whole system in full detail—limits our capability to record. Applications range from epidemic spreading to any system that can be represented by an autoregressive process. In the context of neuroscience, the intrinsic timescale can be thought of as the duration over which any perturbation reverberates within the network; it has been used as a key observable to investigate a functional hierarchy across the primate cortex and serves as a measure of working memory. It is also a proxy for the distance to criticality and quantifies a system’s dynamic working point.
Are Ensemble Classifiers Powerful Enough for the Detection and Diagnosis of Intermediate-Severity Faults?

Intermediate-Severity (IS) faults present milder symptoms compared to severe faults, and are more difficult to detect and diagnose due to their close resemblance to normal operating conditions. The lack of IS fault examples in the training data can pose severe risks to Fault Detection and Diagnosis (FDD) methods that are built upon Machine Learning (ML) techniques, because these faults can be easily mistaken as normal operating conditions. Ensemble models are widely applied in ML and are considered promising methods for detecting out-of-distribution (OOD) data. We identify common pitfalls in these models through extensive experiments with several popular ensemble models on two real-world datasets. Then, we discuss how to design more effective ensemble models for detecting and diagnosing IS faults.
Response of common bean genotypes to inoculation with rhizobia and effects of P and N on biological nitrogen fixation

The study on the response of common bean genotypes to rhizobia inoculation and effects of P and N on biological nitrogen fixation was carried out in field and Screen house experiments. Field experiments arranged in a split-split plot design were conducted to (a) Screen 16 common bean genotypes for capacity to fix nitrogen when inoculated with Rhizobium inoculants and (b) Assess genotype by environment interaction (G x E) on biological nitrogen fixation. The locations were the main-plot, two rhizobia inoculants Biostacked, Nitrosua and control as sub-plot, and 16 common bean genotypes as sub-sub plot factor. A Screen house experiment was carried out in factorial arrangement with rhizobia inoculant at three levels as one factor, and two levels of Nitrogen and Phosphorous (with and without) as second and third factor respectively and three common bean genotypes namely Kablanketi, Rojo and G51105-A as fourth factor. This resulted to 36 treatment combinations replicated three times. The results from the field experiments revealed high significant difference (P < 0.05) among genotypes and genotypes x environment interaction. Inoculation had significant effect in measured variables such as nodule number and dry weight per plant, seed percent nitrogen content, shoot ureides concentration. Biostacked inoculant was found to be superior compared to Nitrosua. Genotypes which showed promising results in nodulation included Lyamungu 85 closely followed by Pesa, Zawadi, Seliani 97, Rojo, Kablanketi and Carioka, indicating good adaptability for nodulation trait across the locations. Screen house experiment revealed that inoculation using either rhizobia in combination with application of Phosphorous increased nodule number, nodule dry weight, root dry weight, shoot
Constraint-Based Learning for Continuous-Time Bayesian Networks

Dynamic Bayesian networks have been well explored in the literature as discrete-time models; however, their continuous-time extensions have seen comparatively little attention. In this paper, we propose the first constraint-based algorithm for learning the structure of continuous-time Bayesian networks. We discuss the different statistical tests and the underlying hypotheses used by our proposal to establish conditional independence. Finally, we validate its performance using synthetic data, and discuss its strengths and limitations. We find that score-based is more accurate in learning networks with binary variables, while our constraint-based approach is more accurate with variables assuming more than two values. However, more experiments are needed for confirmation.
Exploring Heterogeneous Information Networks via Pre-Training

To explore heterogeneous information networks (HINs), network representation learning (NRL) is proposed, which represents a network in a low-dimension space. Recently, graph neural networks (GNNs) have drawn a lot of attention which are very expressive for mining a HIN, while they suffer from low efficiency issue. In this paper, we propose a pre-training and fine-tuning framework PF-HIN to capture the features of a HIN. Unlike traditional GNNs that have to train the whole model for each downstream task, PF-HIN only needs to fine-tune the model using the pre-trained parameters and minimal extra task-specific parameters, thus improving the model efficiency and effectiveness. Specifically, in pre-training phase, we first use a ranking-based BFS strategy to form the input node sequence. Then inspired by BERT, we adopt deep bi-directional transformer encoders to train the model, which is a variant of GNN aggregator that is more powerful than traditional deep neural networks like CNN and LSTM. The model is pre-trained based on two tasks, i.e., masked node modeling (MNM) and adjacent node prediction (ANP). Additionally, we leverage factorized embedding parameterization and cross-layer parameter sharing to reduce the parameters. In fine-tuning stage, we choose four benchmark downstream tasks, i.e., link prediction, similarity search, node classification and node clustering. We use node sequence pairs as input for link prediction and similarity search, and a single node sequence as input for node classification and clustering. The experimental results of the above tasks on four real-world datasets verify the advancement of PF-HIN, as it outperforms state-of-the-art alternatives consistently and significantly.
Relaxed Conformal Prediction Cascades for Efficient Inference Over Many Labels

Providing a small set of promising candidates in place of a single prediction is well-suited for many open-ended classification tasks. Conformal Prediction (CP) is a technique for creating classifiers that produce a valid set of predictions that contains the true answer with arbitrarily high probability. In practice, however, standard CP can suffer from both low predictive and computational efficiency during inference---i.e., the predicted set is both unusably large, and costly to obtain. This is particularly pervasive in the considered setting, where the correct answer is not unique and the number of total possible answers is high. In this work, we develop two simple and complementary techniques for improving both types of efficiencies. First, we relax CP validity to arbitrary criterions of success---allowing our framework to make more efficient predictions while remaining "equivalently correct. " Second, we amortize cost by conformalizing prediction cascades, in which we aggressively prune implausible labels early on by using progressively stronger classifiers---while still guaranteeing marginal coverage. We demonstrate the empirical effectiveness of our approach for multiple applications in natural language processing and computational chemistry for drug discovery.
Streaming Verification for Graph Problems: Optimal Tradeoffs and Nonlinear Sketches

We study graph computations in an enhanced data streaming setting, where a space-bounded client reading the edge stream of a massive graph may delegate some of its work to a cloud service. We seek algorithms that allow the client to verify a purported proof sent by the cloud service that the work done in the cloud is correct. A line of work starting with Chakrabarti et al. (ICALP 2009) has provided such algorithms, which we call schemes, for several statistical and graph-theoretic problems, many of which exhibit a tradeoff between the length of the proof and the space used by the streaming verifier. This work designs new schemes for a number of basic graph problems---including triangle counting, maximum matching, topological sorting, and single-source shortest paths---where past work had either failed to obtain smooth tradeoffs between these two key complexity measures or only obtained suboptimal tradeoffs. Our key innovation is having the verifier compute certain nonlinear sketches of the input stream, leading to either new or improved tradeoffs. In many cases, our schemes in fact provide optimal tradeoffs up to logarithmic factors. Specifically, for most graph problems that we study, it is known that the product of the verifier's space cost $v$ and the proof length $h$ must be at least $\Omega(n^2)$ for $n$-vertex graphs. However, matching upper bounds are only known for a handful of settings of $h$ and $v$ on the curve $h \cdot v=\tilde{\Theta}(n^2)$. For example, for counting triangles and maximum matching, schemes with costs lying on this curve are only known for $(h=\tilde{O}(n^2), v=\tilde{O}(1))$, $(h=\tilde{O}(n), v=\tilde{O}(n))$, and the trivial $(h=\tilde{O}(1), v=\tilde{O}(n^2))$. A major message of this work is that by exploiting nonlinear sketches, a significant ``portion'' of costs on the tradeoff curve $h \cdot v = n^2$ can be achieved.
Benchmarking in Optimization: Best Practice and Open Issues

This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world. Promoting best practice in benchmarking is its main goal. The article discusses eight essential topics in benchmarking: clearly stated goals, well-specified problems, suitable algorithms, adequate performance measures, thoughtful analysis, effective and efficient designs, comprehensible presentations, and guaranteed reproducibility. The final goal is to provide well-accepted guidelines (rules) that might be useful for authors and reviewers. As benchmarking in optimization is an active and evolving field of research this manuscript is meant to co-evolve over time by means of periodic updates.