Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Carvotacetone Derivative from the Aerial Part of Sphaeranthus africanus

Sphaeranthus africanus L. is native in Vietnam. Little is known about α‐glucosidase inhibition of Sphaeranthus africanus and its isolated compounds. A bioactive‐guided isolation was applied to the Vietnamese Sphaeranthus africanus to find α‐glucosidase inhibitory components. Eight compounds were detected and structurally elucidated. They are 3‐angeloyloxy‐5‐[2′′,3′′‐epoxy‐2′′‐methylbutanoyloxy]‐7‐hydroxycarvotacetone, 3‐angeloyloxy‐5‐[3′′‐chloro‐2′′‐hydroxy‐2′′‐methylbutanoyloxy]‐7‐hydroxycarvotacetone, 3‐angeloyloxy‐5‐[2′′R,3′′R‐dihydroxy‐2′′‐methyl‐butanoyloxy]‐7‐hydroxycarvotacetone, 3‐angeloyloxy‐5‐[2′′S,3′′R‐dihydroxy‐2′′‐methylbutanoyloxy]‐7‐hydroxycarvotacetone, 3‐angeloyloxy‐5‐[2′′S,3′′S‐dihydroxy‐2′′‐methylbutanoyloxy]‐7‐hydroxycarvotacetone, 5‐angeloyloxy‐7‐hydroxy‐3‐tigloyloxycarvotacetone, 3‐O‐methylquercetin, and chrysosplenol D. Their chemical structures were elucidated by extensive 1D and 2D NMR analysis and high‐resolution mass spectroscopy as well as comparisons in literature. 3‐Angeloyloxy‐5‐[2′′S,3′′S‐dihydroxy‐2′′‐methylbutanoyloxy]‐7‐hydroxycarvotacetone is a new compound. Isolated compounds were evaluated for the α‐glucosidase inhibition. Isolated compounds showed moderate activity with IC50 values ranging from 128.9–274.3 μM while others are weak. A molecular docking study was conducted, indicating that isolated compounds are potent α‐glucosidase inhibitory compounds.
Growth-fragmentation equations in biology

We consider a linear integro-differential equation which arises to describe both aggregationfragmentation processes and cell division. We prove the existence of a solution (λ,U , φ) to the related eigenproblem, where λ denotes its first eigenvalue. Our study concerns a non-constant transport term that can vanish at x = 0. Non lower-bounded transport terms bring difficulties to find a priori estimates. We use weighted-norms to solve this problem.
Multi-Agent Deep Reinforcement Learning For Persistent Monitoring With Sensing, Communication, and Localization Constraints

Determining multi-robot motion policies for persistently monitoring a region with limited sensing, communication, and localization constraints in non-GPS environments is a challenging problem. To take the localization constraints into account, in this paper, we consider a heterogeneous robotic system consisting of two types of agents: anchor agents with accurate localization capability and auxiliary agents with low localization accuracy. To localize itself, the auxiliary agents must be within the communication range of an {anchor}, directly or indirectly. The robotic team's objective is to minimize environmental uncertainty through persistent monitoring. We propose a multi-agent deep reinforcement learning (MARL) based architecture with graph convolution called Graph Localized Proximal Policy Optimization (GALOPP), which incorporates the limited sensor field-of-view, communication, and localization constraints of the agents along with persistent monitoring objectives to determine motion policies for each agent. We evaluate the performance of GALOPP on open maps with obstacles having a different number of anchor and auxiliary agents. We further study (i) the effect of communication range, obstacle density, and sensing range on the performance and (ii) compare the performance of GALOPP with non-RL baselines, namely, greedy search, random search, and random search with communication constraint. For its generalization capability, we also evaluated GALOPP in two different environments -- 2-room and 4-room. The results show that GALOPP learns the policies and monitors the area well. As a proof-of-concept, we perform hardware experiments to demonstrate the performance of GALOPP.
Higher Berry curvature from matrix product states

The higher Berry curvature was introduced by Kapustin and Spodyneiko as an extension of the Berry curvature in quantum mechanical systems with finite degrees of freedom to quantum many-body systems in finite spatial dimensions. In this paper, we propose an alternative formulation of the higher Berry curvature using translationally invariant matrix product states. They are the ground states of a set of gapped Hamiltonians which are evolved adiabatically through a discretized parameter space. Because matrix product states transform under a projective representation, evaluating the Berry curvature on a closed loop through parameter space is not sufficient to fix all the gauge degrees of freedom. To obtain a gauge-invariant real quantity, the higher-dimensional Berry curvature is evaluated on small tetrahedra in parameter space. Our numerical calculations confirm that the higher Berry curvature varies continuously throughout an adiabatic evolution and becomes quantized over a closed 3-dimensional parameter space.
Bunch management for profitable production in winter developing banana bunches

An experiment on bunch management technology of Cavendish banana (Musa AAA) cv. Giant Governor in winter season developing bunches with different bunch covers, viz. transparent, blue, yellow, red and black polythene (0.08 mm thick), and jute bags and dried banana leaves was conducted at the Horticultural Research Station, Mondouri, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, Nadia, West Bengal during 1996–97 and 1997–98. Bagging bunches with transparent and blue polythene covers increased the size and weight of fingers. The use of transparent and blue polythene covers enhanced fruit yield by 20 and 16% respectively over the control (uncovered bunches). The highest harvest index obtained under tansparent polythene cover indicated the significance of bunch cover in larger partitioning of nutrients to the developing bunches. Shooting-harvest internal was also reduced by 15 days with transparent polythene cover.
Is end-to-end learning enough for fitness activity recognition?

End-to-end learning has taken hold of many computer vision tasks, in particular, related to still images, with task-specific optimization yielding very strong performance. Nevertheless, human-centric action recognition is still largely dominated by hand-crafted pipelines, and only individual components are replaced by neural networks that typically operate on individual frames. As a testbed to study the relevance of such pipelines, we present a new fully annotated video dataset of fitness activities. Any recognition capabilities in this domain are almost exclusively a function of human poses and their temporal dynamics, so pose-based solutions should perform well. We show that, with this labelled data, end-to-end learning on raw pixels can compete with state-of-the-art action recognition pipelines based on pose estimation. We also show that end-to-end learning can support temporally fine-grained tasks such as real-time repetition counting.
Comprehensive Evaluation of Food based on the Fuzzy Mathematics

--Nowadays, the evaluation of all kinds of food depends on the senses. So it is important to seek a suitable method to the comprehensive evaluation of food efficiently. The article uses the method of fuzzy mathematics to choose potato pork as evaluation objects. With quality, color, taste, aroma and shape, etc, to evaluate the elements. Through the calculation to determine the weights of various factors and evaluate the four potatoes shredded meat. Through the calculation of fuzzy matrix, we sort for four shredded meat potatoes. It is thus obvious that the fuzzy mathematics in all kinds of food of the comprehensive evaluation has good maneuverability and reliability. Keywords-Fuzzy mathematics; The fuzzy matrix; Food; Evaluation
Accelerating genetic optimization of nonlinear model predictive control by learning optimal search space size

Nonlinear model predictive control (NMPC) solves a multivariate optimization problem to estimate the system's optimal control inputs in each control cycle. Such optimization is made more difficult by several factors, such as nonlinearities inherited in the system, highly coupled inputs, and various constraints related to the system's physical limitations. These factors make the optimization to be non-convex and hard to solve traditionally. Genetic algorithm (GA) is typically used extensively to tackle such optimization in several application domains because it does not involve differential calculation or gradient evaluation in its solution estimation. However, the size of the search space in which the GA searches for the optimal control inputs is crucial for the applicability of the GA with systems that require fast response. This paper proposes an approach to accelerate the genetic optimization of NMPC by learning optimal search space size. The proposed approach trains a multivariate regression model to adaptively predict the best smallest search space in every control cycle. The estimated best smallest size of search space is fed to the GA to allow for searching the optimal control inputs within this search space. The proposed approach not only reduces the GA's computational time but also improves the chance of obtaining the optimal control inputs in each cycle. The proposed approach was evaluated on two nonlinear systems and compared with two other genetic-based NMPC approaches implemented on the GPU of a Nvidia Jetson TX2 embedded platform in a processor-in-the-loop (PIL) fashion. The results show that the proposed approach provides a 39-53\% reduction in computational time. Additionally, it increases the convergence percentage to the optimal control inputs within the cycle's time by 48-56\%, resulting in a significant performance enhancement. The source code is available on GitHub.
On The Stability of Approximate Message Passing with Independent Measurement Ensembles

Approximate message passing (AMP) is a scalable, iterative approach to signal recovery. For structured random measurement ensembles, including independent and identically distributed (i.i.d.) Gaussian and rotationally-invariant matrices, the performance of AMP can be characterized by a scalar recursion called state evolution (SE). The pseudo-Lipschitz (polynomial) smoothness is conventionally assumed. In this work, we extend the SE for AMP to a new class of measurement matrices with independent (not necessarily identically distributed) entries. We also extend it to a general class of functions, called controlled functions which are not constrained by the polynomial smoothness; unlike the pseudo-Lipschitz function that has polynomial smoothness, the controlled function grows exponentially. The lack of structure in the assumed measurement ensembles is addressed by leveraging Lindeberg-Feller. The lack of smoothness of the assumed controlled function is addressed by a proposed conditioning technique leveraging the empirical statistics of the AMP instances. The resultants grant the use of the SE to a broader class of measurement ensembles and a new class of functions.
Numerical and Experimental Investigation of A Three-Axis Free Rotation Wind Tunnel Model

The current need of improving performance in terms of control and aerodynamic efficiency of ammunitions leads to the necessity of performing accurate flying geometry characterizations. Therefore, new investigation methods are developed in order to increase the aerodynamic knowledge. Free flight measurements experiments are the most common way to obtain dynamic aerodynamic coefficients. However, they do not always allow neither easy nor perfect measurements conditions. Currently ISL develops a stereovision method based wind-tunnel measurements methodology for investigation of a 3-axis free rotation model. This methods has been applied to the DREV-ISL reference model in order to compare coefficients obtained by this method with numerical results.
Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension

Recently, there has been remarkable progress in reinforcement learning (RL) with general function approximation. However, all these works only provide regret or sample complexity guarantees. It is still an open question if one can achieve stronger performance guarantees, i.e., the uniform probably approximate correctness (Uniform-PAC) guarantee that can imply both a sub-linear regret bound and a polynomial sample complexity for any target learning accuracy. We study this problem by proposing algorithms for both nonlinear bandits and model-based episodic RL using the general function class with a bounded eluder dimension. The key idea of the proposed algorithms is to assign each action to different levels according to its width with respect to the confidence set. The achieved uniform-PAC sample complexity is tight in the sense that it matches the state-of-the-art regret bounds or sample complexity guarantees when reduced to the linear case. To the best of our knowledge, this is the first work for uniform-PAC guarantees on bandit and RL that goes beyond linear cases.
Portfolio Optimization Rules beyond the Mean-Variance Approach

In this paper, we revisit the relationship between investors' utility functions and portfolio allocation rules. We derive portfolio allocation rules for asymmetric Laplace distributed $ALD(\mu,\sigma,\kappa)$ returns and compare them with the mean-variance approach, which is based on Gaussian returns. We reveal that in the limit of small $\frac{\mu}{\sigma}$, the Markowitz contribution is accompanied by a skewness term. We also obtain the allocation rules when the expected return is a random normal variable in an average and worst-case scenarios, which allows us to take into account uncertainty of the predicted returns. An optimal worst-case scenario solution smoothly approximates between equal weights and minimum variance portfolio, presenting an attractive convex alternative to the risk parity portfolio. We address the issue of handling singular covariance matrices by imposing conditional independence structure on the precision matrix directly. Finally, utilizing a microscopic portfolio model with random drift and analytical expression for the expected utility function with log-normal distributed cross-sectional returns, we demonstrate the influence of model parameters on portfolio construction. This comprehensive approach enhances allocation weight stability, mitigates instabilities associated with the mean-variance approach, and can prove valuable for both short-term traders and long-term investors.
Skeleton Graph-Based Ultrasound-CT Non-Rigid Registration

Autonomous ultrasound (US) scanning has attracted increased attention, and it has been seen as a potential solution to overcome the limitations of conventional US examinations, such as inter-operator variations. However, it is still challenging to autonomously and accurately transfer a planned scan trajectory on a generic atlas to the current setup for different patients, particularly for thorax applications with limited acoustic windows. To address this challenge, we proposed a skeleton graph-based non-rigid registration to adapt patient-specific properties using subcutaneous bone surface features rather than the skin surface. To this end, the self-organization mapping is successively used twice to unify the input point cloud and extract the key points, respectively. Afterward, the minimal spanning tree is employed to generate a tree graph to connect all extracted key points. To appropriately characterize the rib cartilage outline to match the source and target point cloud, the path extracted from the tree graph is optimized by maximally maintaining continuity throughout each rib. To validate the proposed approach, we manually extract the US cartilage point cloud from one volunteer and seven CT cartilage point clouds from different patients. The results demonstrate that the proposed graph-based registration is more effective and robust in adapting to the inter-patient variations than the ICP (distance error mean <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> SD: <inline-formula><tex-math notation="LaTeX">$5.0\pm \text{1.9}\,\text{mm}$</tex-math></inline-formula> vs <inline-formula><tex-math notation="LaTeX">$8.6\pm \text{6.7}\,\text{mm}$</tex-math></inline-formula> on seven CTs).
One-step quantum search algorithms based on smooth operators

The discovery of derivatives and integrals was a tremendous leap in scientific knowledge and completely revolutionized many fields, including mathematics, physics, and engineering. The existence of higher-order derivatives means better approximation and, thus, more accurate modeling of any physical phenomenon. Here we use smooth operators that are infinitely differentiable to construct two quantum search algorithms and connect these seemingly different areas. Along with smooth functions, permutation operators and the roots of unity are exploited to create quantum circuits to perform a quantum search. We validate our models through quantum simulators and test them on IBM's quantum hardware. Furthermore, we investigate the effect of noise and error propagation and demonstrate that our approach is more robust to noise compared to iterative methods like Grover's algorithm.
Tracking an Underwater Target with Unknown Measurement Noise Statistics Using Variational Bayesian Filters

This paper considers a bearings-only tracking problem using noisy measurements of unknown noise statistics from a passive sensor. It is assumed that the process and measurement noise follows the Gaussian distribution where the measurement noise has an unknown non-zero mean and unknown covariance. Here an adaptive nonlinear filtering technique is proposed where the joint distribution of the measurement noise mean and its covariance are considered to be following normal inverse Wishart distribution (NIW). Using the variational Bayesian (VB) method the estimation technique is derived with optimized tuning parameters i.e, the confidence parameter and the initial degree of freedom of the measurement noise mean and the covariance, respectively. The proposed filtering technique is compared with the adaptive filtering techniques based on maximum likelihood and maximum aposteriori in terms of root mean square error in position and velocity, bias norm, average normalized estimation error squared, percentage of track loss, and relative execution time. Both adaptive filtering techniques are implemented using the traditional Gaussian approximate filters and are applied to a bearings-only tracking problem illustrated with moderately nonlinear and highly nonlinear scenarios to track a target following a nearly straight line path. Two cases are considered for each scenario, one when the measurement noise covariance is static and another when the measurement noise covariance is varying linearly with the distance between the target and the ownship. In this work, the proposed adaptive filters using the VB approach are found to be superior to their corresponding adaptive filters based on the maximum aposteriori and the maximum likelihood at the expense of higher computation cost.
Dirac quantum criticality and the web of dualities

We grow the web of dualities for conformal field theories (CFTs) in 2+1 spacetime dimensions to include quantum critical transitions of Dirac fermions. Our construction uses the seed duality, equating a free Dirac fermion with a complex boson coupled to a Chern-Simons gauge field, to express various Gross-Neveu-Yukawa (GNY) critical points of N fermions in terms of the same number of complex bosons and partner gauge fields. In some instances, monopole operators appear explicitly in the dual action, such as for the superconducting transition of a single Dirac fermion, which is a CFT with emergent supersymmetry. We match phase diagrams, symmetries and their anomalies. By further gauging certain symmetries, we derive offspring dualities. For example, starting with the duality for the regular GNY transition of 2 Dirac fermions, we arrive at the duality between a Neel-to-VBS deconfined quantum critical point and a gauged-Gross-Neveu transition. Finally, we use a large-N expansion for monopole operators, and compare with known scaling dimensions in the simplest single-flavour GNY transition.
Beqi: Revitalize the Senegalese Wolof Language with a Robust Spelling Corrector

The progress of Natural Language Processing (NLP), although fast in recent years, is not at the same pace for all languages. African languages in particular are still behind and lack automatic processing tools. Some of these tools are very important for the development of these languages but also have an important role in many NLP applications. This is particularly the case for automatic spell checkers. Several approaches have been studied to address this task and the one modeling spelling correction as a translation task from misspelled (noisy) text to well-spelled (correct) text shows promising results. However, this approach requires a parallel corpus of noisy data on the one hand and correct data on the other hand, whereas Wolof is a low-resource language and does not have such a corpus. In this paper, we present a way to address the constraint related to the lack of data by generating synthetic data and we present sequence-to-sequence models using Deep Learning for spelling correction in Wolof. We evaluated these models in three different scenarios depending on the subwording method applied to the data and showed that the latter had a significant impact on the performance of the models, which opens the way for future research in Wolof spelling correction.
Distilling Knowledge for Short-to-Long Term Trajectory Prediction

Long-term trajectory forecasting is an important and challenging problem in the fields of computer vision, machine learning, and robotics. One fundamental difficulty stands in the evolution of the trajectory that becomes more and more uncertain and unpredictable as the time horizon grows, subsequently increasing the complexity of the problem. To overcome this issue, in this paper, we propose Di-Long, a new method that employs the distillation of a short-term trajectory model forecaster that guides a student network for long-term trajectory prediction during the training process. Given a total sequence length that comprehends the allowed observation for the student network and the complementary target sequence, we let the student and the teacher solve two different related tasks defined over the same full trajectory: the student observes a short sequence and predicts a long trajectory, whereas the teacher observes a longer sequence and predicts the remaining short target trajectory. The teacher's task is less uncertain, and we use its accurate predictions to guide the student through our knowledge distillation framework, reducing long-term future uncertainty. Our experiments show that our proposed Di-Long method is effective for long-term forecasting and achieves state-of-the-art performance on the Intersection Drone Dataset (inD) and the Stanford Drone Dataset (SDD).