Miracle-dz/UniFAR-FTUs huggingface dataset:
The Use of Microbiological Soil Properties as Indicators of Soil Status in Forestry

Copyright: © 2015 Lucas-Borja ME. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Natural ecosystems provide the basic conditions without which humanity could not survive. Good and services provided by ecosystems include for example provision of food, fibre and fuel, purification of water and air, cultural and aesthetic benefits, stabilization and moderation of the Earth’s climate, generation and renewal of soil fertility, including nutrient cycling or maintenance of genetic resources as key inputs to crop varieties and livestock breeds, medicines, and other products. However, the ability of natural ecosystems to continue performing these services is seriously threatened since plant species diversity or soil are being seriously deteriorated, and in some cases destroyed. While loss of species has always occurred as a natural phenomenon, the pace of extinction has accelerated dramatically as a result of human activity. Ecosystems are being fragmented or eliminated, and innumerable species are in decline or already extinct. At the same time, various studies worldwide have shown that soils do not support intensive annual plant cultivation without fertilizer applications and even these may not maintain sustainability. Inappropriate silvicultural operations or the use of land for intensive agricultural purposes is one of the main causes of soil degradation, and there is therefore worldwide interest in quantifying the loss of soil quality generated by incorrect agricultural operations or forest management practises. This can only happen if people have the right information, skills, and organizations for understanding and dealing with soil and plant diversity issues.
Portal Hypertensive Gastric Polyps

Portal hypertension is associated with an increased number of gastric polyps, hence the term portal hypertension-associated gastric polyps. Histopathological examination reveals mucosal hyperplasia and vascular proliferation. Formation may be due to mucosal injury that is vascular in nature. Their malignant potential has not been fully elucidated; however, lesions can grow to a large size. Patients with portal hypertension are at increased risk of post polypectomy bleeding and other complications of polypectomy. Surveillance is likely to be safer than multiple polypectomies, therefore in patients with portal hypertension it is essential that gastric polyps are photographed and biopsied during routine surveillance gastroscopies.
NOTAS ACERCA DE LA DISTRIBUCIÓN DE PAPILIONIDAE (LEPIDOPTERA: PAPILIONOIDEA) EN EL NORTE DE LA ORINOQUIA COLOMBIANA

Fifty six specimens of Papilionidae butterflies were non-systematically collected and are documented from the northern part of the Colombian Orinoquia. Specifically, specimens were collected in Arauca, Casanare, and Norte de Santander departments in an altitudinal gradient between 120 and 494 m.a.s.l. The specimens belong to three genera, seven species and seven subspecies being the most abundant Battus polydamas polydamas and Parides eurimedes arriphuswith fifteen and twelve individuals respectively. The seven species identified correspond to 11.5 % of the 61species reported to Colombia and 29.2% of the 24 species of this family registered for the whole Colombian Orinoco region. At regional scale, the species richness of Papilionidae in Arauca equals t reported in a locality of Meta department (seven sp.) and lesser than the department of Casanare (nine sp.). For four subspecies, the collection localities imply the enlargement of their previously reported geographical distribution, at regional spatial scale.
Village-Level Production and Use of Biocontrol Agents and Biofertilizers

CONTENTS 45.1 Challenges Faced 648 45.2 An Integrated Initiative to Support the Production and Use of Biological Agro-Inputs 649 45.2. 1 The Biovillage Model 650 45.2. 2 Village-Level Production of Biocontrol Agents and Biofertilizers 650 45.3 Discussion 652 Acknowledgments 653 References 653
Structural and dynamical description of the enzymatic reaction of a phosphohexomutase

Enzymes are known to adopt various conformations at different points along their catalytic cycles. Here, we present a comprehensive analysis of 15 isomorphous, high resolution crystal structures of the enzyme phosphoglucomutase from the bacterium Xanthomonas citri. The protein was captured in distinct states critical to function, including enzyme-substrate, enzyme-product, and enzyme-intermediate complexes. Key residues in ligand recognition and regions undergoing conformational change are identified and correlated with the various steps of the catalytic reaction. In addition, we use principal component analysis to examine various subsets of these structures with two goals: (1) identifying sites of conformational heterogeneity through a comparison of room temperature and cryogenic structures of the apo-enzyme and (2) a priori clustering of the enzyme-ligand complexes into functionally related groups, showing sensitivity of this method to structural features difficult to detect by traditional methods. This study captures, in a single system, the structural basis of diverse substrate recognition, the subtle impact of covalent modification, and the role of ligand-induced conformational change in this representative enzyme of the α-D-phosphohexomutase superfamily.
Investigation of Seismic Imaging Capabilities for Small, Shallow Carbonate-Hosted Massive Sulphide Deposits

Summary In this study synthetic seismic data was generated in an effort to better understand the behavior of the seismic wave field for a shallow carbonate-hosted Pb-Zn massive sulphide orebody. Shallow (<400m) seismic imaging of orebodies presents many processing and detectability challenges; most of the reflected/scattered energy sits too close to surface and p-waves to be easily discerned and secondly their finite size and heterogeneity of the geology often present effects how strong the seismic response is. Despite these challenges, the high density contrast of the orebody with the surrounding carbonate rocks make it a viable target for reflection seismic imaging. In order to study the full wave field response associated with this geologic setting it is necessary to have modeling capabilities that can simulate the elastic wave field for heterogeneous media. In this research we present a forward modeling study that utilizes a 2D finite difference elastic wave code (Bohlen 2002) to generate realistic synthetic high resolution seismic data for a carbonate-hosted shallow massive sulphide lens. In addition to impedance contrast and depth of a target, the size of the deposit and seismic source frequency play an important role in the scattering response and detectability. For orebodies with dimensions similar to the incident seismic wavelength, as is the one modeled here, the response is characterized by scattering of the seismic energy as oppose to specular reflections (Wu, 1999). Using a seismic source frequency centered at 75Hz and a shallow (175m) Pb-Zn orebody lens with a lateral dimension of 100m and maximum thickness of 20m, synthetic seismic data addresses concerns in acquisition and processing procedures for imaging such a deposit.
PHANGS-ML: dissecting multiphase gas and dust in nearby galaxies using machine learning

The PHANGS survey uses ALMA, HST, VLT, and JWST to obtain an unprecedented high-resolution view of nearby galaxies, covering millions of spatially independent regions. The high dimensionality of such a diverse multi-wavelength dataset makes it challenging to identify new trends, particularly when they connect observables from different wavelengths. Here we use unsupervised machine learning algorithms to mine this information-rich dataset to identify novel patterns. We focus on three of the PHANGS-JWST galaxies, for which we extract properties pertaining to their stellar populations; warm ionized and cold molecular gas; and Polycyclic Aromatic Hydrocarbons (PAHs), as measured over 150 pc-scale regions. We show that we can divide the regions into groups with distinct multiphase gas and PAH properties. In the process, we identify previously-unknown galaxy-wide correlations between PAH band and optical line ratios and use our identified groups to interpret them. The correlations we measure can be naturally explained in a scenario where the PAHs and the ionized gas are exposed to different parts of the same radiation field that varies spatially across the galaxies. This scenario has several implications for nearby galaxies: (i) The uniform PAH ionized fraction on 150 pc scales suggests significant self-regulation in the ISM, (ii) the PAH 11.3/7.7 \mic~ band ratio may be used to constrain the shape of the non-ionizing far-ultraviolet to optical part of the radiation field, and (iii) the varying radiation field affects line ratios that are commonly used as PAH size diagnostics. Neglecting this effect leads to incorrect or biased PAH sizes.
A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Low-Rank MDPs

Offline reinforcement learning (RL) aims to learn a policy that maximizes the expected cumulative reward using a pre-collected dataset. Offline RL with low-rank MDPs or general function approximation has been widely studied recently, but existing algorithms with sample complexity $O(\epsilon^{-2})$ for finding an $\epsilon$-optimal policy either require a uniform data coverage assumptions or are computationally inefficient. In this paper, we propose a primal dual algorithm for offline RL with low-rank MDPs in the discounted infinite-horizon setting. Our algorithm is the first computationally efficient algorithm in this setting that achieves sample complexity of $O(\epsilon^{-2})$ with partial data coverage assumption. This improves upon a recent work that requires $O(\epsilon^{-4})$ samples. Moreover, our algorithm extends the previous work to the offline constrained RL setting by supporting constraints on additional reward signals.
Continuous Multidimensional Scaling

Multidimensional scaling (MDS) is the act of embedding proximity information about a set of $n$ objects in $d$-dimensional Euclidean space. As originally conceived by the psychometric community, MDS was concerned with embedding a fixed set of proximities associated with a fixed set of objects. Modern concerns, e.g., that arise in developing asymptotic theories for statistical inference on random graphs, more typically involve studying the limiting behavior of a sequence of proximities associated with an increasing set of objects. Standard results from the theory of point-to-set maps imply that, if $n$ is fixed and a sequence of proximities converges, then the limit of the embedded structures is the embedded structure of the limiting proximities. But what if $n$ increases? It then becomes necessary to reformulate MDS so that the entire sequence of embedding problems can be viewed as a sequence of optimization problems in a fixed space. We present such a reformulation and derive some consequences.
Towards Deterministic End-to-end Latency for Medical AI Systems in NVIDIA Holoscan

The introduction of AI and ML technologies into medical devices has revolutionized healthcare diagnostics and treatments. Medical device manufacturers are keen to maximize the advantages afforded by AI and ML by consolidating multiple applications onto a single platform. However, concurrent execution of several AI applications, each with its own visualization components, leads to unpredictable end-to-end latency, primarily due to GPU resource contentions. To mitigate this, manufacturers typically deploy separate workstations for distinct AI applications, thereby increasing financial, energy, and maintenance costs. This paper addresses these challenges within the context of NVIDIA's Holoscan platform, a real-time AI system for streaming sensor data and images. We propose a system design optimized for heterogeneous GPU workloads, encompassing both compute and graphics tasks. Our design leverages CUDA MPS for spatial partitioning of compute workloads and isolates compute and graphics processing onto separate GPUs. We demonstrate significant performance improvements across various end-to-end latency determinism metrics through empirical evaluation with real-world Holoscan medical device applications. For instance, the proposed design reduces maximum latency by 21-30% and improves latency distribution flatness by 17-25% for up to five concurrent endoscopy tool tracking AI applications, compared to a single-GPU baseline. Against a default multi-GPU setup, our optimizations decrease maximum latency by 35% for up to six concurrent applications by improving GPU utilization by 42%. This paper provides clear design insights for AI applications in the edge-computing domain including medical systems, where performance predictability of concurrent and heterogeneous GPU workloads is a critical requirement.
Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective

How would randomly shuffling feature vectors among nodes from the same class affect graph neural networks (GNNs)? The feature shuffle, intuitively, perturbs the dependence between graph topology and features (A-X dependence) for GNNs to learn from. Surprisingly, we observe a consistent and significant improvement in GNN performance following the feature shuffle. Having overlooked the impact of A-X dependence on GNNs, the prior literature does not provide a satisfactory understanding of the phenomenon. Thus, we raise two research questions. First, how should A-X dependence be measured, while controlling for potential confounds? Second, how does A-X dependence affect GNNs? In response, we (i) propose a principled measure for A-X dependence, (ii) design a random graph model that controls A-X dependence, (iii) establish a theory on how A-X dependence relates to graph convolution, and (iv) present empirical analysis on real-world graphs that aligns with the theory. We conclude that A-X dependence mediates the effect of graph convolution, such that smaller dependence improves GNN-based node classification.
Multimode characterization of an optical beam deflection setup

Optical beam deflection is a popular method to measure the deformation of micro-mechanical devices. As it measures mostly a local slope, its sensitivity depends on the location and size of the optical spot. We present a method to evaluate precisely these parameters, using the relative amplitude of the thermal noise induced vibrations. With a case study of a micro-cantilever, we demonstrate the accuracy of the approach, as well as its ability to fully characterize the sensitivity of the detector, and the parameters (mass, stiffness) of the resonator.
Stability under dwell time constraints: Discretization revisited

We decide the stability and compute the Lyapunov exponent of continuous-time linear switching systems with a guaranteed dwell time. The main result asserts that the discretization method with step size~$h$ approximates the Lyapunov exponent with the precision~$C\,h^2$, where~$C$ is a constant. Let us stress that without the dwell time assumption, the approximation rate is known to be linear in~$h$. Moreover, for every system, the constant~$C$ can be explicitly evaluated. In turn, the discretized system can be treated by computing the Markovian joint spectral radius of a certain system on a graph. This gives the value of the Lyapunov exponent with a high accuracy. The method is efficient for dimensions up to, approximately, ten; for positive systems, the dimensions can be much higher, up to several hundreds.
Motile bacteria crossing liquid-liquid interfaces

Real-life bacteria often swim in complex fluids, but our understanding of the interactions between bacteria and complex surroundings is still evolving. In this work, rod-like $\textit{Bacillus subtilis}$ swims in a quasi-2D environment with aqueous liquid-liquid interfaces, i.e., the isotropic-nematic coexistence phase of an aqueous chromonic liquid crystal. Focusing on the bacteria motion near and at the liquid-liquid interfaces, we collect and quantify bacterial trajectories from the isotropic to the nematic phase. Despite its small magnitude, the interfacial tension of the order of 10 $\mathrm{\mu N/m}$ at the isotropic-nematic interface justifies our observations that bacteria swimming more perpendicular to the interface have a higher probability of crossing the interface. Our force-balance model, considering the interfacial tension, further predicts how the length and speed of the bacteria affect their crossing behaviors.
Stein Boltzmann Sampling: A Variational Approach for Global Optimization

In this paper, we introduce a new flow-based method for global optimization of Lipschitz functions, called Stein Boltzmann Sampling (SBS). Our method samples from the Boltzmann distribution that becomes asymptotically uniform over the set of the minimizers of the function to be optimized. Candidate solutions are sampled via the \emph{Stein Variational Gradient Descent} algorithm. We prove the asymptotic convergence of our method, introduce two SBS variants, and provide a detailed comparison with several state-of-the-art global optimization algorithms on various benchmark functions. The design of our method, the theoretical results, and our experiments, suggest that SBS is particularly well-suited to be used as a continuation of efficient global optimization methods as it can produce better solutions while making a good use of the budget.
Ultrafast terahertz field control of the emergent magnetic and electronic interactions at oxide interfaces

Ultrafast electric-field control of emergent electronic and magnetic states at oxide interfaces offers exciting prospects for the development of new generations of energy-efficient devices. Here, we demonstrate that the electronic structure and emergent ferromagnetic interfacial state in epitaxial LaNiO3/CaMnO3 superlattices can be effectively controlled using intense single-cycle THz electric-field pulses. We employ a combination of polarization-dependent X-ray absorption spectroscopy with magnetic circular dichroism and X-ray resonant magnetic reflectivity to measure a detailed magneto-optical profile and thickness of the ferromagnetic interfacial layer. Then, we use time-resolved and temperature-dependent magneto-optical Kerr effect, along with transient optical reflectivity and transmissivity measurements, to disentangle multiple correlated electronic and magnetic processes driven by ultrafast high-field (~1 MV/cm) THz pulses. These processes include an initial sub-picosecond electronic response, consistent with non-equilibrium Joule heating; a rapid (~270 fs) demagnetization of the ferromagnetic interfacial layer, driven by THz-field-induced nonequilibrium spin-polarized currents; and subsequent multi-picosecond dynamics, possibly indicative of a change in the magnetic state of the superlattice due to the transfer of spin angular momentum to the lattice. Our findings shed light on the intricate interplay of electronic and magnetic phenomena in this strongly correlated material system, suggesting a promising avenue for efficient control of two-dimensional ferromagnetic states at oxide interfaces using ultrafast electric-field pulses.
Code as Reward: Empowering Reinforcement Learning with VLMs

Pre-trained Vision-Language Models (VLMs) are able to understand visual concepts, describe and decompose complex tasks into sub-tasks, and provide feedback on task completion. In this paper, we aim to leverage these capabilities to support the training of reinforcement learning (RL) agents. In principle, VLMs are well suited for this purpose, as they can naturally analyze image-based observations and provide feedback (reward) on learning progress. However, inference in VLMs is computationally expensive, so querying them frequently to compute rewards would significantly slowdown the training of an RL agent. To address this challenge, we propose a framework named Code as Reward (VLM-CaR). VLM-CaR produces dense reward functions from VLMs through code generation, thereby significantly reducing the computational burden of querying the VLM directly. We show that the dense rewards generated through our approach are very accurate across a diverse set of discrete and continuous environments, and can be more effective in training RL policies than the original sparse environment rewards.
Matter-wave interferometers with trapped strongly interacting Feshbach molecules

We implement two types of matter wave interferometers using trapped Bose-condensed Feshbach molecules, from weak to strong interactions. In each case, we focus on investigating interaction effects and their implications for the performance. In the Ramsey-type interferometer where the interference between the two motional quantum states in an optical lattice is observed, inter-particle interactions are found to induce energy shifts in the states. Consequently, this results in a reduction of the interferometer frequency and introduces a phase shift during the lattice pulses used for state manipulation. Furthermore, non-uniformity leads to dephasing and collisional losses of condensate contribute to the degradation of contrast. In the Michelson-type interferometer, where matter waves are spatially split and recombined in a waveguide, interference is observed in the presence of significant interaction, however coherence degrades with increasing interaction strength. Notably, coherence is also observed in thermal clouds, indicating the white-color nature of the implemented Michelson interferometer.
Sulphur Supply Increases the Potential of Massai Grass for Cadmium Phytoextraction

Book of the 18 International Conference on Heavy Metals in the Environment, 12 to 15 September 2016, Ghent, Belgium This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. – 349 – 18th International Conference on avy Metals in the Environment 12 to 15 September 2016, Ghent, Belgium ICHMET 2016 Proceedings of the 18 International Confer nce on Heavy Metals in the Environment, 12 to 15 September 2016, Ghent, Belgium