Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence Diagram Bandits: Variational Thompson Sampling for Structured Bandit Problems

We propose a novel framework for structured bandits, which we call an influence diagram bandit. Our framework captures complex statistical dependencies between actions, latent variables, and observations; and thus unifies and extends many existing models, such as combinatorial semi-bandits, cascading bandits, and low-rank bandits. We develop novel online learning algorithms that learn to act efficiently in our models. The key idea is to track a structured posterior distribution of model parameters, either exactly or approximately. To act, we sample model parameters from their posterior and then use the structure of the influence diagram to find the most optimistic action under the sampled parameters. We empirically evaluate our algorithms in three structured bandit problems, and show that they perform as well as or better than problem-specific state-of-the-art baselines.
Protective effects of red wine polyphenols and grape-seed proanthocyanidin extract on acetaminophen-induced liver injury

The present study was designed to examine the potential protective effects of red wine polyphenols (RWPs) and grape seed proanthocyanidin extract (GSPE) against acetaminophen-induced hepatotoxicity. Silymarin was used as a standard reference hepatoprotective agent. A single dose of acetaminophen (800 mg/kg), injected intraperitoneally to male rats, caused a significant increase in serum ALT, AST, alkaline phosphatase (ALP), bilirubin, total cholesterol (TC), triglycerides(TG), tumor necrosis factor alpha (TNF-α), and liver contents of thiobarbituric acid reactive substances (TBARS) measured as malondialdehyde (MDA) and nitric oxide (NO) with significant decrease in serum albumin, HDL cholesterol, reduced glutathione (GSH) and hepatic activities of catalase (CAT), superoxide dismutase (SOD) and caspase-3 in liver tissue as compared with the control group. On the other hand, administration of each of GSPE (100 mg/kg/day, p.o.), RWPs (40 mg/kg/day, p.o.) and silymarin (100 mg/kg/day, p.o.) for 15 consecutive days significantly ameliorated the liver injury which confirmed by the histopathological examination. It was concluded that RWPs and GSPE showed protective effects against acute acetaminophen hepatotoxicity where RWPs were more effective than GSPE; most probably through their antioxidant, anti-inflammatory and anti-apoptotic effects.
Protoplast isolation and culture from carob (Ceratonia siliqua) hypocotyls: ability of regenerated protoplasts to produce mannose-containing polysaccharides

The aim of this study was to isolate protoplasts from carob (Ceratonia siliqua L.) embryonic tissues with the ability to regenerate cell walls, divide and synthesize galactomannan, a valuable polysaccharide for industry. Protoplasts isolated from carob hypocotyl hooks regenerated cell walls within 24 h. The first divisions of the regenerated cells were observed after 2 days of culture. The highest percentage that successfully divided was achieved when the seedlings were grown under diffuse light, the hypocotyl hooks were plasmolysed for 1 h before incubation in the protoplast isolation solution and the protoplasts were cultured under diffuse light. After 9 days of culture, cell clusters, consisting of eight cells, had been produced, which underwent further mitotic divisions and which were expected to lead to callus formation. Polysaccharide and oligosaccharide synthesis during protoplast regeneration was studied by radiolabelling with exogenous D-[U- 14 C]glucose, D-[U- 14 C]mannose or D-[2- 3 H]mannose, which gave rise to uniform, moderately specific and highly specific labelling, respectively. As revealed by the radioactivity distribution in cell wall monosaccharides, the regenerants deposited new wall polymers that differed markedly from those being synthesized by the hypocotyls from which the protoplasts had been isolated. The regenerants deposited large amounts of callose and smaller amounts of galactose-, arabinose- and mannose-containing polymers. The latter included glucuronomannan, as demonstrated by a new method involving partial acid hydrolysis followed by p-glucuronidase (EC 3.2.1.31) digestion. The regenerating protoplasts also released soluble extracellular carbohydrates: polysaccharides which appeared to be mainly acidic arabinogalactans, and oligosaccharides which were mainly neutral and contained glucose, galactose and mannose. We conclude that regenerating carob protoplasts are a useful system for studying carbohydrate secretion, including mannose-rich poly- and oligosaccharides.
Controle de Alternaria solani em tomateiro por extratos de Curcuma longa e curcumina: I. avaliação in vitro

The discovery of plant secondary compounds with antimicrobial activity is very promising. Turmeric, Curcuma longa, has compounds in its rhizomes with fungicidal activity. The objective of this study was to evaluate the in vitro fungitoxic activity of turmeric extracts and curcumin against Alternaria solani. Four different concentrations (0, 1, 5, 10 and 20%) of aqueous extracts of turmeric rhizomes (sterilized by autoclave) and four curcumin solutions (0, 50, 100, 200 and 400 mg/L) were incorporated into potato dextrose agar medium in order to evaluate fungal mycelial growth and sporulation. To evaluate the effect of using an autoclave, 10% and 15% turmeric extracts were sterilized by filtration. The effects of autoclaved and non-autoclaved turmeric extracts and curcumin on in vitro spore germination were tested. The concentrations of 10 and 15% of non-autoclaved turmeric extracts inhibited the mycelial growth by 38.2% and 23.2% respectively and the fungal sporulation by 71.7% and 87% respectively. When turmeric extracts were autoclaved, neither mycelial growth nor spore germination was inhibited and the effect on sporulation was reduced, suggesting the presence of thermolabile antimicrobial compounds. The non-autoclaved 5% extract inhibited spore germination by up to 15%. At the highest concentration, the curcumin solution inhibited mycelial growth by 29.5%. Neither in vitro sporulation nor spore germination was affected by curcumin. These results show the fungitoxic potential of turmeric and curcumin against A. solani.
Words as Art Materials: Generating Paintings with Sequential GANs

Converting text descriptions into images using Generative Adversarial Networks has become a popular research area. Visually appealing images have been generated successfully in recent years. Inspired by these studies, we investigated the generation of artistic images on a large variance dataset. This dataset includes images with variations, for example, in shape, color, and content. These variations in images provide originality which is an important factor for artistic essence. One major characteristic of our work is that we used keywords as image descriptions, instead of sentences. As the network architecture, we proposed a sequential Generative Adversarial Network model. The first stage of this sequential model processes the word vectors and creates a base image whereas the next stages focus on creating high-resolution artistic-style images without working on word vectors. To deal with the unstable nature of GANs, we proposed a mixture of techniques like Wasserstein loss, spectral normalization, and minibatch discrimination. Ultimately, we were able to generate painting images, which have a variety of styles. We evaluated our results by using the Frechet Inception Distance score and conducted a user study with 186 participants.
Monocular Vision based Crowdsourced 3D Traffic Sign Positioning with Unknown Camera Intrinsics and Distortion Coefficients

Autonomous vehicles and driver assistance systems utilize maps of 3D semantic landmarks for improved decision making. However, scaling the mapping process as well as regularly updating such maps come with a huge cost. Crowdsourced mapping of these landmarks such as traffic sign positions provides an appealing alternative. The state-of-the-art approaches to crowdsourced mapping use ground truth camera parameters, which may not always be known or may change over time. In this work, we demonstrate an approach to computing 3D traffic sign positions without knowing the camera focal lengths, principal point, and distortion coefficients a priori. We validate our proposed approach on a public dataset of traffic signs in KITTI. Using only a monocular color camera and GPS, we achieve an average single journey relative and absolute positioning accuracy of 0.26 m and 1.38 m, respectively.
Quantum enhanced agents

The concept of an embodied intelligent agent is a key concept in modern AI and robotics. Physically, an agent---like a Turing machine---is an open system embedded in an environment which it interacts with through sensors and actuators. It contains a learning algorithm that correlates the sensor and actuator results by learning features about its environment. The sensor-actuator system is similar to a measurement based control system. Quantum mechanics enables new measurement and control protocols capable of exceeding what can be achieved classically. We demonstrate how quantum optical sensors and actuators can dramatically improve an agent's ability to learn in a thermal environment. Furthermore we use the Jarzynski equality to show that learning maximises the exchange in free energy $\Delta F$ between the agent's sensor and actuator when considered as a stochastic feedback cycle.
Evaluation of the chromogenic Cica-β-Test for detecting extended-spectrum, AmpC and metallo-β-lactamases

BACKGROUND Extended-spectrum, metallo- and AmpC beta-lactamases usually are sought subsequently to susceptibility testing, meaning that producers are not identified until 72 h after a clinical specimen is taken. Chromogenic tests might usefully shorten this delay, and we investigated the Cica-beta-Test for this purpose. METHODS Reference and clinical strains with known beta-lactamases, or controls, were grown with a cefpodoxime disc to promote conservation of resistance. The cultures were then tested with nitrocefin and with the Cica-beta-Test, which examines for hydrolysis of the chromogenic oxyimino-cephalosporin HMRZ-86 with and without specific inhibitors of extended-spectrum, metallo- and AmpC beta-lactamases. RESULTS were scored, as colour changes from yellow to red, with the tester blinded to the strain identity and the mechanism(s) present. Results Proportions of extended-spectrum, metallo- and AmpC beta-lactamase producers correctly identified by the Cica-beta-Test were 85%, 77% and 72%, respectively. Such performance should be achievable if testing colonies from a primary culture plate, 24 h after a specimen was taken. Greater precision, albeit at more delay, would be achieved if results were read in conjunction with antibiogram data available 48 h after the specimen was taken. Limitations were frequent confusion of Klebsiella oxytoca hyperproducing K1 enzyme with AmpC hyperproducers, and that isolates with NMC-A or KPC carbapenemases were wrongly inferred to have AmpC enzymes. CONCLUSIONS The Cica-beta-Test has the potential to provide useful therapeutic guidance, identifying isolates with potent beta-lactamases and informing early therapy; it will also help to monitor beta-lactamase epidemiology among multiresistant strains.
Multi-Granularity Modularized Network for Abstract Visual Reasoning

Abstract visual reasoning connects mental abilities to the physical world, which is a crucial factor in cognitive development. Most toddlers display sensitivity to this skill, but it is not easy for machines. Aimed at it, we focus on the Raven Progressive Matrices Test, designed to measure cognitive reasoning. Recent work designed some black-boxes to solve it in an end-to-end fashion, but they are incredibly complicated and difficult to explain. Inspired by cognitive studies, we propose a Multi-Granularity Modularized Network (MMoN) to bridge the gap between the processing of raw sensory information and symbolic reasoning. Specifically, it learns modularized reasoning functions to model the semantic rule from the visual grounding in a neuro-symbolic and semi-supervision way. To comprehensively evaluate MMoN, our experiments are conducted on the dataset of both seen and unseen reasoning rules. The result shows that MMoN is well suited for abstract visual reasoning and also explainable on the generalization test.
VisImages: A Large-scale, High-quality Image Corpus in Visualization Publications

Images in visualization publications contain rich information, such as novel visual designs, model details, and experiment results. Constructing such an image corpus can contribute to the community in many aspects, including literature analysis from the perspective of visual representations, empirical studies on visual memorability, and machine learning research for chart detection. This study presents VisImages, a high-quality and large-scale image corpus collected from visualization publications. VisImages contain fruitful and diverse annotations for each image, including captions, types of visual representations, and bounding boxes. First, we algorithmically extract the images associated with captions and manually correct the errors. Second, to categorize visualizations in publications, we extend and iteratively refine the existing taxonomy through a multi-round pilot study. Third, guided by this taxonomy, we invite senior visualization practitioners to annotate visual representations that appear in each image. In this process, we borrow techniques such as "gold standards" and majority voting for quality control. Finally, we recruit the crowd to draw bounding boxes for visual representations in the images. The resulting corpus contains 35,096 annotated visualizations from 12,267 images with 12,057 captions in 1397 papers from VAST and InfoVis. We demonstrate the usefulness of VisImages through the following four use cases: 1) analysis of color usage in VAST and InfoVis papers across years, 2) discussion of the researcher preference on visualization types, 3) spatial distribution analysis of visualizations in visual analytic systems, and 4) training visualization detection models.
POLLEN13K: A Large Scale Microscope Pollen Grain Image Dataset

Pollen grain classification has a remarkable role in many fields from medicine to biology and agronomy. Indeed, automatic pollen grain classification is an important task for all related applications and areas. This work presents the first large-scale pollen grain image dataset, including more than 13 thousands objects. After an introduction to the problem of pollen grain classification and its motivations, the paper focuses on the employed data acquisition steps, which include aerobiological sampling, microscope image acquisition, object detection, segmentation and labelling. Furthermore, a baseline experimental assessment for the task of pollen classification on the built dataset, together with discussion on the achieved results, is presented.
Analyzing clustering of astrophysical gravitational-wave sources: luminosity-distance space distortions

We present a formulation of observed number density fluctuations of gravitational-wave (GW) sources in a three dimensional space. In GW observations, redshift identification for each GW source is a challenging issue, in particular, for high redshift sources. The use of observed luminosity distance as a distance indicator will be a simple yet optimal way for measuring the clustering signal. We derive the density fluctuations of GW sources estimated from observed luminosity distance and sky position of each source. The density fluctuations are distorted as similar to the so-called redshift space distortions in galaxy surveys but with several differences. We then show the two-point correlation function and multipole power spectrum in the presence of the distortion effect. We find that the line-of-sight derivative of the lensing convergence, which does not appear in the redshift-space distortions, leads to significant distortions in the observed correlation function. In addition, the lensing effect affects higher-order multipole power spectra and its signal-to-noise at high redshifts.
Sparse regression for extreme values

We study the problem of selecting features associated with extreme values in high dimensional linear regression. Normally, in linear modeling problems, the presence of abnormal extreme values or outliers is considered an anomaly which should either be removed from the data or remedied using robust regression methods. In many situations, however, the extreme values in regression modeling are not outliers but rather the signals of interest; consider traces from spiking neurons, volatility in finance, or extreme events in climate science, for example. In this paper, we propose a new method for sparse high-dimensional linear regression for extreme values which is motivated by the Subbotin, or generalized normal distribution. This leads us to utilize an $\ell_p$ norm loss where $p$ is an even integer greater than two; we demonstrate that this loss increases the weight on extreme values. We prove consistency and variable selection consistency for the $\ell_p$ norm regression with a Lasso penalty, which we term the Extreme Lasso. Through simulation studies and real-world data data examples, we show that this method outperforms other methods currently used in the literature for selecting features of interest associated with extreme values in high-dimensional regression.
Evaluation of Effects of Coleopteran-Specific Cry3Bb1 Protein on Earthworms Exposed to Soil Containing Corn Roots or Biomass

Abstract Corn engineered to produce the Cry3Bb1 protein from Bacillus thuringiensis Berliner (Bt) has provided unprecedented control for corn rootworm (Diabrotica virgifera virgifera Le Conte). However, accumulation and persistence of plant-produced Bt protein in soils may occur where Bt crops are repeatedly grown and residues of the crop plants are incorporated into the soil. If Bt protein is released into the soil rhizosphere, it may affect soil organisms. Studies were conducted to determine the effect of Bt or non-Bt corn roots and biomass on the weight and mortality of the earthworm, Lumbricus terrestris L. with different exposure methods and times. Enzyme-linked immunosorbent assay (ELISA) was conducted to determine if the Cry3Bb1 protein was present in the soil from the gut and excreta of earthworms. Results of these studies showed that there were no significant differences in weight and percent mortality of earthworms in soil planted or not planted with Bt and non-Bt corn or in soil containing ground air-dried biomass of Bt and non-Bt plants. Cry3Bb1 protein was detected in the soil from pots and the gut and excreta of earthworms exposed to Bt roots or biomass, whereas it was absent in soil from pot, the gut and excreta of earthworms exposed to roots or biomass of non-Bt corn. There was no significant difference in the amount of the protein recovered from pots or the gut or excreta of earthworms exposed to soils containing Bt roots or biomass for 12 or 33 d.
Efficient Source of Indistinguishable Single-Photons based on Phonon-Assisted Excitation

Semiconductor quantum dots in cavities are high-performance single-photon sources. Thus far the most efficient sources utilise resonant excitation of an unpolarized quantum emitter coupled to a highly birefringent cavity. However, this demands very high polarization extinction, and challenging experimental operation. Here, we remove these requirements by using off-resonant phonon-assisted excitation of a linear exciton dipole, exploiting the quantum dot's vibrational environment and natural asymmetry. This allows the collection of single photons that are spectrally separated from the excitation laser, and intrinsically present a very high degree of linear polarization up to 0.994 $\pm$ 0.007. This phonon-assisted excitation scheme enables very high single-photon purity and indistinguishability, and only reduces the emitter population by (15 $\pm$ 1) %, as compared to resonant excitation. Overall, we simultaneously demonstrate a polarized first lens brightness of 0.51 $\pm $ 0.01, with a single-photon purity of 0.939 $\pm$ 0.001 and corrected single-photon indistinguishability of 0.915 $\pm$ 0.003.
Model collaboration for the improved assessment of biomass supply, demand, and impacts

Existing assessments of biomass supply and demand and their impacts face various types of limitations and uncertainties, partly due to the type of tools and methods applied (e.g., partial representation of sectors, lack of geographical details, and aggregated representation of technologies involved). Improved collaboration between existing modeling approaches may provide new, more comprehensive insights, especially into issues that involve multiple economic sectors, different temporal and spatial scales, or various impact categories. Model collaboration consists of aligning and harmonizing input data and scenarios, model comparison and/or model linkage. Improved collaboration between existing modeling approaches can help assess (i) the causes of differences and similarities in model output, which is important for interpreting the results for policy‐making and (ii) the linkages, feedbacks, and trade‐offs between different systems and impacts (e.g., economic and natural), which is key to a more comprehensive understanding of the impacts of biomass supply and demand. But, full consistency or integration in assumptions, structure, solution algorithms, dynamics and feedbacks can be difficult to achieve. And, if it is done, it frequently implies a trade‐off in terms of resolution (spatial, temporal, and structural) and/or computation. Three key research areas are selected to illustrate how model collaboration can provide additional ways for tackling some of the shortcomings and uncertainties in the assessment of biomass supply and demand and their impacts. These research areas are livestock production, agricultural residues, and greenhouse gas emissions from land‐use change. Describing how model collaboration might look like in these examples, we show how improved model collaboration can strengthen our ability to project biomass supply, demand, and impacts. This in turn can aid in improving the information for policy‐makers and in taking better‐informed decisions.
Phytochemical profile and antibacterial activity of bark and leaves of Caesalpinia pyramidalis Tul. and Sapium glandulosum (L.) Morong.

This study aimed to analyze the phytochemical profile and antibacterial activity of crude ethanol extracts (CEE) from the bark and leaves of Caesalpinia pyramidalis Tul. andSapium glandulosum (L.) Morong. The phytochemical composition was analyzed by thin layer chromatography (TLC) using eluent and specific revealing. Antibacterial activity was evaluated using the agar diffusion method (well technique) and determining the minimum inhibitory concentration (MIC). The extracts showed anthocyanins, sterols, flavonoids, coumarins, lignans and terpenoids. The results showed that the CEE from the barks of C. pyramidalis showed antibacterial activity against Staphylococcus aureus, Staphylococcuscoagulase-negative and Enterococcus faecalis, while the CEE from the barks and leaves ofS. glandulosum was active against S. aureus and S. coagulase negative. The CEE from the bark C. pyramidalis had MICs of 125 to 500 µg/ml. The antimicrobial activity of the extract of bark of C. pyramidalis indicate that the mechanism of multidrug resistance ofStaphylococcus to current antibiotics does not confer resistance to the compounds present in samples, which provides motivation for the isolation of chemicals responsible for such activities. Key words: Antibacterial, burra-leiteria, catingueira, multidrug resistance, Staphylococcus.