Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic diversity within and among races and vegetative compatibility groups of Fusarium oxysporum f.sp. lycopersici as determined by isozyme analysis.

A worldwide collection of 111 isolates of Fusarium oxysporum f. sp. lyropersici from tomato was examined for isozyme polymorphisms. Of 15 enzymes assayed, eight and seven revealed single and multiple bands, respectively, for a total of 30 putative loci. Twenty-two of the 30 loci exhibited complete homogeneity among all isolates surveyed. Eight loci were variable and revealed two to five putative alleles per locus. Thirty-four electrophoretic phenotypes (EPs) were identified. EPs 1 and 2, with 49 and 13 members, respectively, included 53% of all the isolates []
TOAN: Target-Oriented Alignment Network for Fine-Grained Image Categorization With Few Labeled Samples

In this paper, we study the fine-grained categorization problem under the few-shot setting, i.e., each fine-grained class only contains a few labeled examples, termed Fine-Grained Few-Shot classification (FGFS). The core predicament in FGFS is the high intra-class variance yet low inter-class fluctuations in the dataset. In traditional fine-grained classification, the high intra-class variance can be somewhat relieved by conducting the supervised training on the abundant labeled samples. However, with few labeled examples, it is hard for the FGFS model to learn a robust class representation with the significantly higher intra-class variance. Moreover, the inter- and intra-class variance are closely related. The significant intra-class variance in FGFS often aggravates the low inter-class variance issue. To address the above challenges, we propose a Target-Oriented Alignment Network (TOAN) to tackle the FGFS problem from both intra- and inter-class perspective. To reduce the intra-class variance, we propose a target-oriented matching mechanism to reformulate the spatial features of each support image to match the query ones in the embedding space. To enhance the inter-class discrimination, we devise discriminative fine-grained features by integrating local compositional concept representations with the global second-order pooling. We conducted extensive experiments on four public datasets for fine-grained categorization, and the results show the proposed TOAN obtains the state-of-the-art.
The influence of continued heavy grazing and of promiscuous burning on spring-fall ranges in Utah.

Since approximately eighty-five per cent of the gross area of about fiftytwo million acres of land in Utah is now utilized for grazing purposes 1 the production of livestock is a very important phase of agriculture in the state. Because of a dry climate, a short growing season, rough topography or insufficient water for irrigation, but little more than twice the present area of about two and one-half million acres of farm land in Utah will ever be cultivated under present known methods of agriculture. Hence the economic welfare of the state depends to a great extent upon keeping the grazing lands fully productive. The aim of the present study has been to determine the changes which have taken place in the character of the vegetation and the grazing capacity on the foothill area or the so-called spring-fall range type of Utah as the result of grazing, fire or other humanly induce(l factors, during the four score years that the region has been settled. This study is part of a research project recently undertaken by the Intermountain Forest and Range Experiment Station of the Forest Service, to develop more suitable methods of range management for this important class of grazing land. Range livestock are either grazed on the semi-desert lands or fed on farms and ranches during the winter and, for the most part, are grazed in the high mountains during the summer. From approximately the middle of April until the latter part of June each year, after the animals cease to do well on the sem-ni-desert range or in the feed lot and before the vegetation on the high mountain range has made sufficient growth to be grazed, the cattle and sheep are turned on to the lower mountain slopes and adjacent foothills. They are returned to this range again for a period of a month to six weeks in the fall. Climatic conditions as influenced by topography limit the area of spring-fall range. The available area has been reduced by farming and that left has been heavily overgrazed, and in places, further damaged by fire until there now exists a serious shortage of range for spring and fall grazing. The topography of Utah is characterized by a series of parallel mountain ranges and high, narrow jl~ateaus, with interjacent valleys, which extend in a general north and south direction from the north middle to the central part of the state and then sweep in a broad curve to the southwest corner, with the
Dihydrocapsaicin Oxidation by Capsicum annuum (var. annuum) Peroxidase

Oxidation of dihydrocapsaian (8-methyl-N-vanillyl-6-nonanamide) by peroxidase (EC 1.11.1.7) from Capsicum annuum var. annuum fruits yielded one absorbent oxidation product with e 262 =4.7 10 3 M -1 cm -1 . Dependence of oxidation rate on dihydrocapsaicin and H 2 O 2 concentrations revealed Michaelis-Menten type kinetics with inhibition at high substrate concentrations and optimal pH near 6.0. Dihydrocapsaicin was oxidized by pepper peroxidase, and participation of peroxidase in capsaicinoid metabolism of pepper fruits should be taken into account
ExoMol line lists – XXXVIII. High-temperature molecular line list of silicon dioxide (SiO2)

Silicon dioxide (SiO2) is expected to occur in the atmospheres of hot rocky super-Earth exoplanets but a lack of spectroscopic data is hampering its possible detection. Here, we present the first, comprehensive molecular line list for SiO2. The line list, named OYT3, covers the wavenumber range 0 – 6000 cm−1 (wavelengths λ > 1.67 μm) and is suitable for temperatures up to T = 3000 K. Almost 33 billion transitions involving 5.69 million rotation–vibration states with rotational excitation up to J = 255 have been computed using robust first-principles methodologies. The OYT3 line list is available from the ExoMol data base at www.exomol. com.
Language Representation Models for Fine-Grained Sentiment Classification

Sentiment classification is a quickly advancing field of study with applications in almost any field. While various models and datasets have shown high accuracy inthe task of binary classification, the task of fine-grained sentiment classification is still an area with room for significant improvement. Analyzing the SST-5 dataset,previous work by Munikar et al. (2019) showed that the embedding tool BERT allowed a simple model to achieve state-of-the-art accuracy. Since that paper, several BERT alternatives have been published, with three primary ones being AlBERT (Lan et al., 2019), DistilBERT (Sanh et al. 2019), and RoBERTa (Liu etal. 2019). While these models report some improvement over BERT on the popular benchmarks GLUE, SQuAD, and RACE, they have not been applied to the fine-grained classification task. In this paper, we examine whether the improvements hold true when applied to a novel task, by replicating the BERT model from Munikar et al., and swapping the embedding layer to the alternative models. Over the experiments, we found that AlBERT suffers significantly more accuracy loss than reported on other tasks, DistilBERT has accuracy loss similar to their reported loss on other tasks while being the fastest model to train, and RoBERTa reaches anew state-of-the-art accuracy for prediction on the SST-5 root level (60.2%).
Tunability of multiple ultraflat bands and effect of spin-orbit coupling in twisted bilayer transition metal dichalcogenides

Ultraflat bands that have been theoretically and experimentally detected in a bunch of van der Waals stacked materials show some peculiar properties, for instance, highly localized electronic states and enhanced electron-electron interactions. In this Rapid Communication, using an accurate tight-binding model, we study the formation and evolution of ultraflat bands in transition metal dichalcogenides (TMDCs) under low rotation angles. We find that, unlike in twisted bilayer graphene, ultraflat bands exist in TMDCs for almost any small twist angles and their wave function becomes more localized when the rotation angle decreases. Lattice relaxation, pressure, and local deformation can tune the width of the flat bands, as well as their localization. Furthermore, we investigate the effect of spin-orbit coupling on the flat bands and discover spin/orbital/valley locking at the minimum of the conduction band at the $K$ point of the Brillouin zone. Ultraflat bands found in TMDCs with a range of rotation angle below ${7}^{\ensuremath{\circ}}$ may provide an ideal platform to study strongly correlated states.
Microscopic approach to the macrodynamics of matter with broken symmetries

A unified set of hydrodynamic equations describing condensed phases of matter with broken continuous symmetries is derived using a generalization of the statistical-mechanical approach based on the local equilibrium distribution. The dissipativeless and dissipative parts of the current densities and the entropy production are systematically deduced in this approach by expanding in powers of the gradients of the macrofields. Green–Kubo formulas are obtained for all the linear transport coefficients. The consequences of microreversibility and spatial symmetries are investigated, leading to the prediction of cross effects resulting from Onsager–Casimir reciprocal relations. Crystalline solids and liquid crystals are potential examples of application. The approach is clarifying the links between the microscopic Hamiltonian dynamics and the thermodynamic and transport properties at the macroscale.
Demonstrating High-precision Photometry with a CubeSat: ASTERIA Observations of 55 Cancri e

Arcsecond Space Telescope Enabling Research In Astrophysics (ASTERIA) is a 6U CubeSat space telescope (10 cm × 20 cm × 30 cm, 10 kg). ASTERIA’s primary mission objective was demonstrating two key technologies for reducing systematic noise in photometric observations: high-precision pointing control and high-stability thermal control. ASTERIA demonstrated 0. ″5 rms pointing stability and ±10 mK thermal control of its camera payload during its primary mission, a significant improvement in pointing and thermal performance compared to other spacecraft in ASTERIA’s size and mass class. ASTERIA launched in 2017 August and deployed from the International Space Station in 2017 November. During the prime mission (2017 November–2018 February) and the first extended mission that followed (2018 March–2018 May), ASTERIA conducted opportunistic science observations, which included the collection of photometric data on 55 Cancri, a nearby exoplanetary system with a super-Earth transiting planet. The 55 Cancri data were reduced using a custom pipeline to correct complementary metal-oxide semiconductor (CMOS) detector column-dependent gain variations. A Markov Chain Monte Carlo approach was used to simultaneously detrend the photometry using a simple baseline model and fit a transit model. ASTERIA made a marginal detection of the known transiting exoplanet 55 Cancri e (∼2 ), measuring a transit depth of 374 ± 170 ppm. This is the first detection of an exoplanet transit by a CubeSat. The successful detection of super-Earth 55 Cancri e demonstrates that small, inexpensive spacecraft can deliver high-precision photometric measurements.
A Deep Dive into the Distribution Function: Understanding Phase Space Dynamics with Continuum Vlasov-Maxwell Simulations

In collisionless and weakly collisional plasmas, the particle distribution function is a rich tapestry of the underlying physics. However, actually leveraging the particle distribution function to understand the dynamics of a weakly collisional plasma is challenging. The equation system of relevance, the Vlasov-Maxwell-Fokker-Planck (VM-FP) system of equations, is difficult to numerically integrate, and traditional methods such as the particle-in-cell method introduce counting noise into the distribution function. In this thesis, we present a new algorithm for the discretization of VM-FP system of equations for the study of plasmas in the kinetic regime. Using the discontinuous Galerkin (DG) finite element method for the spatial discretization and a third order strong-stability preserving Runge-Kutta for the time discretization, we obtain an accurate solution for the plasma's distribution function in space and time. We both prove the numerical method retains key physical properties of the VM-FP system, such as the conservation of energy and the second law of thermodynamics, and demonstrate these properties numerically. These results are contextualized in the history of the DG method. We discuss the importance of the algorithm being alias-free, a necessary condition for deriving stable DG schemes of kinetic equations so as to retain the implicit conservation relations embedded in the particle distribution function, and the computational favorable implementation using a modal, orthonormal basis in comparison to traditional DG methods applied in computational fluid dynamics. Finally, we demonstrate how the high fidelity representation of the distribution function, combined with novel diagnostics, permits detailed analysis of the energization mechanisms in fundamental plasma processes such as collisionless shocks.
Light mesons within the basis light-front quantization framework

We study the light-unflavored mesons as relativistic bound states in the nonperturbative Hamiltonian formalism of the basis light-front quantization (BLFQ) approach. The dynamics for the valence quarks of these mesons is specified by an effective Hamiltonian containing the one-gluon exchange interaction and the confining potentials both introduced in our previous work on heavy quarkonia, supplemented additionally by a pseudoscalar contact interaction. We diagonalize this Hamiltonian in our basis function representation to obtain the mass spectrum and the light-front wave functions (LFWFs). Based on these LFWFs, we then study the structure of these mesons by computing the electromagnetic form factors, the decay constants, the parton distribution amplitudes (PDAs), and the parton distribution functions (PDFs). Our results are comparable to those from experiments and other theoretical models.
HAWC J2227+610 and Its Association with G106.3+2.7, a New Potential Galactic PeVatron

We present the detection of very-high-energy gamma-ray emission above 100 TeV from HAWC J2227+610 with the High-Altitude Water Cherenov Gamma-Ray Observatory (HAWC) observatory. Combining our observations with previously published results by the Very Energetic Radiation Imaging Telescope Array System (VERTIAS), we interpret the gamma-ray emission from HAWC J2227+610 as emission from protons with a lower limit in their cutoff energy of 800 TeV. The most likely source of the protons is the associated supernova remnant G106.3+2.7, making it a good candidate for a Galactic PeVatron. However, a purely leptonic origin of the observed emission cannot be excluded at this time.
Measurement-induced entanglement transitions in many-body localized systems

The resilience of quantum entanglement to a classicality-inducing environment is tied to fundamental aspects of quantum many-body systems. The dynamics of entanglement has recently been studied in the context of measurement-induced entanglement transitions, where the steady-state entanglement collapses from a volume-law to an area-law at a critical measurement probability $p_{c}$. Interestingly, there is a distinction in the value of $p_{c}$ depending on how well the underlying unitary dynamics scramble quantum information. For strongly chaotic systems, $p_{c} > 0$, whereas for weakly chaotic systems, such as integrable models, $p_{c} = 0$. In this work, we investigate these measurement-induced entanglement transitions in a system where the underlying unitary dynamics are many-body localized (MBL). We demonstrate that the emergent integrability in an MBL system implies a qualitative difference in the nature of the measurement-induced transition depending on the measurement basis, with $p_{c} > 0$ when the measurement basis is scrambled and $p_{c} = 0$ when it is not. This feature is not found in Haar-random circuit models, where all local operators are scrambled in time. When the transition occurs at $p_{c} > 0$, we use finite-size scaling to obtain the critical exponent $\nu = 1.3(2)$, close to the value for 2+0D percolation. We also find a dynamical critical exponent of $z = 0.98(4)$ and logarithmic scaling of the Renyi entropies at criticality, suggesting an underlying conformal symmetry at the critical point. This work further demonstrates how the nature of the measurement-induced entanglement transition depends on the scrambling nature of the underlying unitary dynamics. This leads to further questions on the control and simulation of entangled quantum states by measurements in open quantum systems.
On the Mass Distribution of the Intracluster Light in Galaxy Groups and Clusters

We take advantage of a semianalytic model with a state-of-art implementation of the formation of the intracluster light (ICL) to study the mass distribution of the ICL in galaxy groups and clusters, at different redshifts. We assume the ICL to follow a Navarro–Frenk–White (NFW) profile with a different concentration, linked to that of the dark matter by the relation cICL = γ cDM, where the parameter γ is set to reproduce the observed relation between the stellar mass in the brightest cluster galaxy (BCG) and ICL in the innermost 100 kpc and the halo mass (M*100 − M500) at z = 0. The model is then tested against several theoretical and observational results, from the present time to z ∼ 1.5. Our analysis shows that the fraction of stellar mass in the BCG and ICL within the innermost 100 kpc is an increasing function of redshift, parameter γ, and a decreasing function of the halo mass. The value of γ required to match the observed M*100 − M500 is γ = 3 at z = 0, but there are indications that it might be a function of redshift and halo mass. This result indicates that the distribution of the ICL is more concentrated than the dark matter one, but less concentrated than previously found by other studies. We suggest that a modified version of the NFW is a good description of the distribution of the diffuse light in groups and clusters, which makes the ICL a reliable tracer of the dark matter, in good agreement with recent observational findings.
Demystifying Orthogonal Monte Carlo and Beyond

Orthogonal Monte Carlo (OMC) is a very effective sampling algorithm imposing structural geometric conditions (orthogonality) on samples for variance reduction. Due to its simplicity and superior performance as compared to its Quasi Monte Carlo counterparts, OMC is used in a wide spectrum of challenging machine learning applications ranging from scalable kernel methods to predictive recurrent neural networks, generative models and reinforcement learning. However theoretical understanding of the method remains very limited. In this paper we shed new light on the theoretical principles behind OMC, applying theory of negatively dependent random variables to obtain several new concentration results. We also propose a novel extensions of the method leveraging number theory techniques and particle algorithms, called Near-Orthogonal Monte Carlo (NOMC). We show that NOMC is the first algorithm consistently outperforming OMC in applications ranging from kernel methods to approximating distances in probabilistic metric spaces.
Weak SINDy: Galerkin-Based Data-Driven Model Selection

We present a novel weak formulation and discretization for discovering governing equations from noisy measurement data. This method of learning differential equations from data fits into a new class of algorithms that replace pointwise derivative approximations with linear transformations and variance reduction techniques. Compared to the standard SINDy algorithm presented in [S. L. Brunton, J. L. Proctor, and J. N. Kutz, Proc. Natl. Acad. Sci. USA, 113 (2016), pp. 3932-3937], our so-called weak SINDy (WSINDy) algorithm allows for reliable model identification from data with large noise (often with ratios greater than 0.1) and reduces the error in the recovered coefficients to enable accurate prediction. Moreover, the coefficient error scales linearly with the noise level, leading to high-accuracy recovery in the low-noise regime. Altogether, WSINDy combines the simplicity and efficiency of the SINDy algorithm with the natural noise reduction of integration, as demonstrated in [H. Schaeffer and S. G. McCalla, Phys. Rev. E, 96 (2017), 023302], to arrive at a robust and accurate method of sparse recovery.
Network Fusion for Content Creation with Conditional INNs

Artificial Intelligence for Content Creation has the potential to reduce the amount of manual content creation work significantly. While automation of laborious work is welcome, it is only useful if it allows users to control aspects of the creative process when desired. Furthermore, widespread adoption of semi-automatic content creation depends on low barriers regarding the expertise, computational budget and time required to obtain results and experiment with new techniques. With state-of-the-art approaches relying on task-specific models, multi-GPU setups and weeks of training time, we must find ways to reuse and recombine them to meet these requirements. Instead of designing and training methods for controllable content creation from scratch, we thus present a method to repurpose powerful, existing models for new tasks, even though they have never been designed for them. We formulate this problem as a translation between expert models, which includes common content creation scenarios, such as text-to-image and image-to-image translation, as a special case. As this translation is ambiguous, we learn a generative model of hidden representations of one expert conditioned on hidden representations of the other expert. Working on the level of hidden representations makes optimal use of the computational effort that went into the training of the expert model to produce these efficient, low-dimensional representations. Experiments demonstrate that our approach can translate from BERT, a state-of-the-art expert for text, to BigGAN, a state-of-the-art expert for images, to enable text-to-image generation, which neither of the experts can perform on its own. Additional experiments show the wide applicability of our approach across different conditional image synthesis tasks and improvements over existing methods for image modifications.