Miracle-dz/UniFAR-FTUs huggingface dataset:
Traditional use , antibacterial activity and antifungal activity of crude extract of Aloe excelsa

The fleshy leaves and roots of most species within the Aloe family are used in many traditional treatments (Mabberley, 1987). Traditional healers and indigenous people utilize mainly the leaf sap of this genus widely for the treatment of wounds, burns, rashes, itches, cracked lips and cracked skin (Cera et al., 1980). Antimicrobial activities on the crude extract of Aloe exclesa was carried out in attempts to validate the use by traditional healers in the use of there latex and gel exudates for various medicinal ailments.
CO emission in distant galaxies on and above the main sequence

We present the detection of multiple carbon monoxide CO line transitions with ALMA in a few tens of infrared-selected galaxies on and above the main sequence atz = 1.1−1.7. We reliably detected the emission of CO (5 − 4), CO (2 − 1), and CO (7 − 6)+[C I](3P2 − 3P1) in 50, 33, and 13 galaxies, respectively, and we complemented this information with available CO (4 − 3) and [C I](3P1 − 3P0) fluxes for part of the sample, and by modeling of the optical-to-millimeter spectral energy distribution. We retrieve a quasi-linear relation betweenLIRand CO (5 − 4) or CO (7 − 6) for main-sequence galaxies and starbursts, corroborating the hypothesis that these transitions can be used as star formation rate (SFR) tracers. We find the CO excitation to steadily increase as a function of the star formation efficiency, the mean intensity of the radiation field warming the dust (⟨U⟩), the surface density of SFR (ΣSFR), and, less distinctly, with the distance from the main sequence (ΔMS). This adds to the tentative evidence for higher excitation of the CO+[C I] spectral line energy distribution (SLED) of starburst galaxies relative to that for main-sequence objects, where the dust opacities play a minor role in shaping the high-JCO transitions in our sample. However, the distinction between the average SLED of upper main-sequence and starburst galaxies is blurred, driven by a wide variety of intrinsic shapes. Large velocity gradient radiative transfer modeling demonstrates the existence of a highly excited component that elevates the CO SLED of high-redshift main-sequence and starbursting galaxies above the typical values observed in the disk of the Milky Way. This excited component is dense and it encloses ∼50% of the total molecular gas mass in main-sequence objects. We interpret the observed trends involving the CO excitation as to be mainly determined by a combination of large SFRs and compact sizes, as a large ΣSFRis naturally connected with enhanced dense molecular gas fractions and higher dust and gas temperatures, due to increasing ultraviolet radiation fields, cosmic ray rates, as well as dust and gas coupling. We release the full data compilation and the ancillary information to the community.
Response of a Gibberellin-deficient Potato Mutant to Induction and Growth Regulators as a Working Model for Tuber Initiation

To explore further the role endogenous gibberellins play in the formation of the potato tuber, a gibberellin-deficient dwarf of Solanum tuberosum subsp. andigena (PI 281036) and its normal sibling were used in this study. Because gibberellins are known to be elevated and to affect tuber formation negatively when potato plants are grown under long day conditions, test plants were first grown in a growth chamber under conditions that did not favor tuberization (noninducing conditions). The stem apices of dwarfs received weekly applications of a 100-ppm gibberellic acid (GA 3 ) solution to achieve growth similar to that of normal plants. When the requisite height was achieved, five dwarfs and five normal sibs were treated to foliar runoff with a 100-ppm GA 3 solution, and a like number of these plants received a control spray of distilled water. These plants were then placed in a growth chamber adjusted to inducing conditions for tuber initiation. An additional five dwarfs and five normal sibs were sprayed with 600 ppm paclobutrazol (PB), a gibberellin inhibitor, and a similar number of plants received a distilled water control spray. This group of plants was returned to the noninducing chamber. After 1 week, plants were removed from their respective growth chambers and divided into two-node apical, medial, and basal leaf-bud cuttings. Basal buds of the cuttings were buried in moist potting mix in a mist chamber with a 16-hour photoperiod. Rhizome and tuberization responses were evaluated after 3 weeks. The experiment was repeated and results combined for statistical treatment. Orthogonal contrasts revealed that apical cuttings from normal donor plants produced rhizomes only under noninducing conditions or when treated with GA 3 . No rhizomes formed on apical cuttings from normal plants growing under inducing conditions (favoring tuberization) or noninduced plants receiving PB (a gibberellin inhibitor). For apical dwarf tissues, there were no effects of treatments on rhizome production, except for the PB treatment, which resulted in shorter rhizomes. Tuberization was observed in apical tissues of induced and noninduced dwarfs, which lack the ability to synthesize gibberellin, but only in induced cuttings of normal sibs. Noninduced dwarf cuttings tuberized as well as those from normal plants receiving the antigibberellin treatment. Tuber weights from induced apical cuttings of dwarfs and normal sibs were not significantly different. These results support the significant role played by gibberellins in tuber formation.
Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks

We propose a versatile framework based on random search, Sparse-RS, for score-based sparse targeted and untargeted attacks in the black-box setting. Sparse-RS does not rely on substitute models and achieves state-of-the-art success rate and query efficiency for multiple sparse attack models: L0-bounded perturbations, adversarial patches, and adversarial frames. The L0-version of untargeted Sparse-RS outperforms all black-box and even all white-box attacks for different models on MNIST, CIFAR-10, and ImageNet. Moreover, our untargeted Sparse-RS achieves very high success rates even for the challenging settings of 20x20 adversarial patches and 2-pixel wide adversarial frames for 224x224 images. Finally, we show that Sparse-RS can be applied to generate targeted universal adversarial patches where it significantly outperforms the existing approaches. Our code is available at https://github.com/fra31/sparse-rs.
Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks

Data poisoning and backdoor attacks manipulate training data in order to cause models to fail during inference. A recent survey of industry practitioners found that data poisoning is the number one concern among threats ranging from model stealing to adversarial attacks. However, we find that the impressive performance evaluations from data poisoning attacks are, in large part, artifacts of inconsistent experimental design. Moreover, we find that existing poisoning methods have been tested in contrived scenarios, and they fail in realistic settings. In order to promote fair comparison in future work, we develop unified benchmarks for data poisoning and backdoor attacks.
Deep Polynomial Neural Networks

Deep convolutional neural networks (DCNNs) are currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. The success of DCNNs can be attributed to the careful selection of their building blocks (e.g., residual blocks, rectifiers, sophisticated normalization schemes, to mention but a few). In this paper, we propose <inline-formula><tex-math notation="LaTeX">$\Pi$</tex-math><alternatives><mml:math><mml:mi>Π</mml:mi></mml:math><inline-graphic xlink:href="chrysos-ieq1-3058891.gif"/></alternatives></inline-formula>-Nets, a new class of function approximators based on polynomial expansions. <inline-formula><tex-math notation="LaTeX">$\Pi$</tex-math><alternatives><mml:math><mml:mi>Π</mml:mi></mml:math><inline-graphic xlink:href="chrysos-ieq2-3058891.gif"/></alternatives></inline-formula>-Nets are polynomial neural networks, i.e., the output is a high-order polynomial of the input. The unknown parameters, which are naturally represented by high-order tensors, are estimated through a collective tensor factorization with factors sharing. We introduce three tensor decompositions that significantly reduce the number of parameters and show how they can be efficiently implemented by hierarchical neural networks. We empirically demonstrate that <inline-formula><tex-math notation="LaTeX">$\Pi$</tex-math><alternatives><mml:math><mml:mi>Π</mml:mi></mml:math><inline-graphic xlink:href="chrysos-ieq3-3058891.gif"/></alternatives></inline-formula>-Nets are very expressive and they even produce good results without the use of non-linear activation functions in a large battery of tasks and signals, i.e., images, graphs, and audio. When used in conjunction with activation functions, <inline-formula><tex-math notation="LaTeX">$\Pi$</tex-math><alternatives><mml:math><mml:mi>Π</mml:mi></mml:math><inline-graphic xlink:href="chrysos-ieq4-3058891.gif"/></alternatives></inline-formula>-Nets produce state-of-the-art results in three challenging tasks, i.e., image generation, face verification and 3D mesh representation learning. The source code is available at <uri>https://github.com/grigorisg9gr/polynomial_nets</uri>.
Post-hoc Calibration of Neural Networks

Calibration of neural networks is a critical aspect to consider when incorporating machine learning models in real-world decision-making systems where the confidence of decisions are equally important as the decisions themselves. In recent years, there is a surge of research on neural network calibration and the majority of the works can be categorized into post-hoc calibration methods, defined as methods that learn an additional function to calibrate an already trained base network. In this work, we intend to understand the post-hoc calibration methods from a theoretical point of view. Especially, it is known that minimizing Negative Log-Likelihood (NLL) will lead to a calibrated network on the training set if the global optimum is attained (Bishop, 1994). Nevertheless, it is not clear learning an additional function in a post-hoc manner would lead to calibration in the theoretical sense. To this end, we prove that even though the base network ($f$) does not lead to the global optimum of NLL, by adding additional layers ($g$) and minimizing NLL by optimizing the parameters of $g$ one can obtain a calibrated network $g \circ f$. This not only provides a less stringent condition to obtain a calibrated network but also provides a theoretical justification of post-hoc calibration methods. Our experiments on various image classification benchmarks confirm the theory.
Numerical simulation of solar photospheric jet-like phenomena caused by magnetic reconnection

Jet phenomena with a bright loop in their footpoint, called anemone jets, have been observed in the solar corona and chromosphere. These jets are formed as a consequence of magnetic reconnection, and from the scale universality of magnetohydrodynamics (MHD), it can be expected that anemone jets exist even in the solar photosphere. However, it is not necessarily apparent that jets can be generated as a result of magnetic reconnection in the photosphere, where the magnetic energy is not dominant. Furthermore, MHD waves generated from photospheric jets could contribute to chromospheric heating and spicule formation; however, this hypothesis has not yet been thoroughly investigated. In this study, we perform three-dimensional MHD simulation including gravity with the solar photospheric parameter to investigate anemone jets in the solar photosphere. In the simulation, jet-like structures were induced by magnetic reconnection in the solar photosphere. We determined that these jet-like structures were caused by slow shocks formed by the reconnection and were propagated approximately in the direction of the background magnetic field. We also suggested that MHD waves from the jet-like structures could influence local atmospheric heating and spicule formation.
Dark dust and single-cloud sightlines in the ISM

The precise characteristics of clouds and the nature of dust in the diffuse interstellar medium can only be extracted by inspecting the rare cases of single-cloud sightlines. In our nomenclature such objects are identified by interstellar lines, such as K I, that show at a resolving power of λ∕Δλ ~ 75 000 one dominating Doppler component that accounts for more than half of the observed column density. We searched for such sightlines using high-resolution spectroscopy towards reddened OB stars for which far-UV extinction curves are known. We compiled a sample of 186 spectra, 100 of which were obtained specifically for this project with UVES. In our sample we identified 65 single-cloud sightlines, about half of which were previously unknown. We used the CH/CH+ line ratio of our targets to establish whether the sightlines are dominated by warm or cold clouds. We found that CN is detected in all cold (CH/CH+ > 1) clouds, but is frequently absent in warm clouds. We inspected the WISE (3−22 μm) observed emission morphology around our sightlines and excluded a circumstellar nature for the observed dust extinction. We found that most sightlines are dominated by cold clouds that are located far away from the heating source. For 132 stars, we derived the spectral type and the associated spectral type-luminosity distance. We also applied the interstellar Ca II distance scale, and compared these two distance estimates with Gaia parallaxes. These distance estimates scatter by ~40%. By comparing spectral type-luminosity distances with those of Gaia, we detected a hidden dust component that amounts to a few mag of extinction for eight sightlines. This dark dust is populated by ≳ 1 μm large grains and predominately appears in the field of the cold interstellar medium.
Seasonal Influence of Limnological Variables on Plankton Dynamics of a Small, Shallow, Tropical African Reservoir

Physico-chemical properties and plankton species composition, diversity and abundance of a small, shallow tropical African reservoir (Oyun reservoir, Offa, Nigeria) were investigated for two years between January 2002 and December 2003, to determine the effect of water chemistry on the seasonal assemblage of the plankton. Twenty five genera of phytoplankton in four classes and three groups of zooplankton were found from three chosen stations in the reservoir. The phytoplankton was dominated by Bacillariophyceae constituting 75.3% with 8 genera, followed by Chlorophyceae 12.22% also with 8 genera while Cyanobacteria and Desmidiaceae made up 11.12% and 0.73% with 6 and 3 genera respectively. The zooplankton comprised of Rotifera with 8 genera, Cladocera with 3 genera and Copepoda also with 3 genera. Rotifera dominated the zooplankton constituting 71.02%, Cladocera 16.45% and Copepoda 12.53%. The presence of high amount of nutrients like nitrate, phosphate, sulphate and silica were responsible for the heterogenesity of the phytoplankton. This is reflected in the positive correlation (P<0.05) of the nutrients with the phytoplankton classes. Both phytoplankton and zooplankton were highly abundant in the rains and the variations were along the reservoir continuum. Fluctuations in the density of the plankton were as a result of seasonal concentration of nutrients, grazing pressure and the reservoir hydrology. Other factors such as temperature, food availability, reservoir morphometry, genus reproductive strategies and overall physico-chemistry of the water were found to strongly influence the plankton assemblage. Based on the physico-chemistry of the reservoir, the water quality is excellent; the phytoplankton assemblage diverse, zooplankton composition moderate, the fish production would be high. These conditions were as a result of strategies employed for the management of the reservoir which include the use of watershed best management practices (BMPs) to control eutrophication and sedimentation and establishment of important priorities through legislations for water usage by space and time. Other measures that will prevent oligotrophy, hypereutrophy, excessive bloom of the phytoplankton, establishment and proliferation of toxic cyanobacteria, run-off of organic wastes and high dissolved salts were recommended for a better plankton assemblage and productivity of the reservoir.
Hall Cascade with Fractional Magnetic Helicity in Neutron Star Crusts

The ohmic decay of magnetic fields in the crusts of neutron stars is generally believed to be governed by Hall drift, which leads to what is known as a Hall cascade. Here we show that helical and fractionally helical magnetic fields undergo strong inverse cascading like in magnetohydrodynamics (MHD), but the magnetic energy decays more slowly with time t: instead of ∝t−2/3 in MHD. Even for a nonhelical magnetic field there is a certain degree of inverse cascading for sufficiently strong magnetic fields. The inertial range scaling with wavenumber k is compatible with earlier findings for the forced Hall cascade, i.e., proportional to k−7/3, but in the decaying cases, the subinertial range spectrum steepens to a novel k5 slope instead of the k4 slope in MHD. The energy of the large-scale magnetic field can increase quadratically in time through inverse cascading. For helical fields, the energy dissipation is found to be inversely proportional to the large-scale magnetic field and proportional to the fifth power of the rms magnetic field. For neutron star conditions with an rms magnetic field of a few times , the large-scale magnetic field might only be , while still producing magnetic dissipation of for thousands of years, which could manifest itself through X-ray emission. Finally, it is shown that the conclusions from local unstratified models agree rather well with those from stratified models with boundaries.
An Improved Semi-Supervised VAE for Learning Disentangled Representations

Learning interpretable and disentangled representations is a crucial yet challenging task in representation learning. In this work, we focus on semi-supervised disentanglement learning and extend work by Locatello et al. (2019) by introducing another source of supervision that we denote as label replacement. Specifically, during training, we replace the inferred representation associated with a data point with its ground-truth representation whenever it is available. Our extension is theoretically inspired by our proposed general framework of semi-supervised disentanglement learning in the context of VAEs which naturally motivates the supervised terms commonly used in existing semi-supervised VAEs (but not for disentanglement learning). Extensive experiments on synthetic and real datasets demonstrate both quantitatively and qualitatively the ability of our extension to significantly and consistently improve disentanglement with very limited supervision.
Magneto‐Mechanical Metamaterials with Widely Tunable Mechanical Properties and Acoustic Bandgaps

Mechanical metamaterials are architected manmade materials that allow for unique behaviors not observed in nature, making them promising candidates for a wide range of applications. Existing metamaterials lack tunability as their properties can only be changed to a limited extent after the fabrication. Herein, a new magneto‐mechanical metamaterial is presented that allows great tunability through a novel concept of deformation mode branching. The architecture of this new metamaterial employs an asymmetric joint design using hard‐magnetic soft active materials that permits two distinct actuation modes (bending and folding) under opposite‐direction magnetic fields. The subsequent application of mechanical compression leads to the deformation mode branching where the metamaterial architecture transforms into two distinctly different shapes, which exhibit very different deformations and enable great tunability in properties such as mechanical stiffness and acoustic bandgaps. Furthermore, this metamaterial design can be incorporated with magnetic shape memory polymers with global stiffness tunability, which also allows for the global shift of the acoustic behaviors. The combination of magnetic and mechanical actuations, as well as shape memory effects, impart wide tunable properties to a new paradigm of metamaterials.
Simple and Effective VAE Training with Calibrated Decoders

Variational autoencoders (VAEs) provide an effective and simple method for modeling complex distributions. However, training VAEs often requires considerable hyperparameter tuning, and often utilizes a heuristic weight on the prior KL-divergence term. In this work, we study how the performance of VAEs can be improved while not requiring the use of this heuristic hyperparameter, by learning calibrated decoders that accurately model the decoding distribution. While in some sense it may seem obvious that calibrated decoders should perform better than uncalibrated decoders, much of the recent literature that employs VAEs uses uncalibrated Gaussian decoders with constant variance. We observe empirically that the naive way of learning variance in Gaussian decoders does not lead to good results. However, other calibrated decoders, such as discrete decoders or learning shared variance can substantially improve performance. To further improve results, we propose a simple but novel modification to the commonly used Gaussian decoder, which represents the prediction variance non-parametrically. We observe empirically that using the heuristic weight hyperparameter is not necessary with our method. We analyze the performance of various discrete and continuous decoders on a range of datasets and several single-image and sequential VAE models. Project website: this https URL
SoccerDB: A Large-Scale Database for Comprehensive Video Understanding

Soccer videos can serve as a perfect research object for video understanding because soccer games are played under well-defined rules while complex and intriguing enough for researchers to study. In this paper, we propose a new soccer video database named SoccerDB, comprising 171,191 video segments from 346 high-quality soccer games. The database contains 702,096 bounding boxes, 37,709 essential event labels with time boundary, and 17,115 highlight annotations for object detection, action recognition, temporal action localization, and highlight detection tasks. To our knowledge, it is the largest database for comprehensive sports video understanding on various aspects. We further survey a collection of strong baselines on SoccerDB, which have demonstrated state-of-the-art performances on independent tasks. Our evaluation suggests that we can benefit significantly when jointly considering the inner correlations among those tasks. We believe the release of SoccerDB will tremendously advance researches around comprehensive video understanding. Our dataset and code published on https://github.com/newsdata/SoccerDB.
Fast and Flexible Temporal Point Processes with Triangular Maps

Temporal point process (TPP) models combined with recurrent neural networks provide a powerful framework for modeling continuous-time event data. While such models are flexible, they are inherently sequential and therefore cannot benefit from the parallelism of modern hardware. By exploiting the recent developments in the field of normalizing flows, we design TriTPP -- a new class of non-recurrent TPP models, where both sampling and likelihood computation can be done in parallel. TriTPP matches the flexibility of RNN-based methods but permits orders of magnitude faster sampling. This enables us to use the new model for variational inference in continuous-time discrete-state systems. We demonstrate the advantages of the proposed framework on synthetic and real-world datasets.