Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of antagonistic fungi and their antifungal activities against aconite root rot pathogens

ABSTRACT Root rot is one of the main diseases affecting Aconitum carmichaelii Debx. during cultivation, seriously limiting yields of this herb. Currently, there is no effective control measure for aconite root rot. The antifungal activities of antagonistic strains against aconite root rot pathogens (Fusarium proliferatum, Fusarium solani, and Fusarium oxysporum) were investigated in this study. Three antagonistic strains, JKT7, JKT28 and JKT39, were screened and identified as Trichoderma asperellum, Trichoderma hamatum and Trichoderma virens, respectively. Dual culture tests showed that the inhibition rates of the three Trichoderma strains on the pathogens were all approximately 70%. The volatile metabolites had inhibitory effects on the mycelial growth of pathogens, while the nonvolatile metabolites in the culture filtrates did not show significant inhibitory effects. The volatile components analyzed by GC‒MS were mainly ketones, esters, and alcohols. These results indicate that these strains of Trichoderma and their secondary metabolites have antimicrobial activities against the pathogens of aconite root rot. This study could provide a scientific basis for the biocontrol of aconite root rot.
Phenotypic and genomic analysis of the hypervirulent ST22 methicillin-resistant Staphylococcus aureus in China

ABSTRACT ST22 MRSA (methicillin-resistant Staphylococcus aureus) strains are only sporadically reported in China. Through the phylogenetic reconstruction of 30 ST22 strains from China and 480 ST22 strains from global sources, we found that the global ST22 strains can be divided into three clades (I, II, and III). The China ST22 strains were found primarily in clade II (IIb and IIc) and also in clade III, indicating that the China ST22-MRSA clones have different origins. The China subclade IIb strains (SCCmec Vb-t309) may evolve from the native ST22 MSSA clone, while the China IIc strains may have spread from other countries. Subclade IIc (SCCmecIVa-t309) strains exhibited particularly strong lethality and invasiveness in Galleria mellonella infection and mouse skin abscess models in comparison to USA300 and other dominant China HA-MRSA (ST5 and ST239) or CA-MRSA (ST59) strains. This study described the emergence of a highly virulent ST22 MRSA subclade and improved our insight into the molecular epidemiology of ST22 strains in China. IMPORTANCE ST22 is a successful hospital-associated MRSA lineage which first appeared in the United Kingdom as EMRSA-15. At present, ST22 MRSA clones are spreading rapidly around the world and even replaced other dominant clones in some regions. We placed the Chinese ST22 in the worldwide phylogeny of ST22, demonstrating a distinctive molecular epidemiology and to our knowledge, this is the first time that a novel clade of ST22 has been found in China. Among the 15 ST22 MRSA strains belonging to the novel clade, 14 ST22 SCCmecIVa strains from different regions carried both pvl and tst and displayed significantly higher in vitro and in vivo virulence in comparison to other clade/subclade ST22 strains as well as other common China HA-MRSA or CA-MRSA strains. The further spread of this subclade of strains could pose a serious threat to the health system in China and other regions. ST22 is a successful hospital-associated MRSA lineage which first appeared in the United Kingdom as EMRSA-15. At present, ST22 MRSA clones are spreading rapidly around the world and even replaced other dominant clones in some regions. We placed the Chinese ST22 in the worldwide phylogeny of ST22, demonstrating a distinctive molecular epidemiology and to our knowledge, this is the first time that a novel clade of ST22 has been found in China. Among the 15 ST22 MRSA strains belonging to the novel clade, 14 ST22 SCCmecIVa strains from different regions carried both pvl and tst and displayed significantly higher in vitro and in vivo virulence in comparison to other clade/subclade ST22 strains as well as other common China HA-MRSA or CA-MRSA strains. The further spread of this subclade of strains could pose a serious threat to the health system in China and other regions.
Ultrasensitive NGS MRD assessment in Ph+ ALL: Prognostic impact and correlation with RT‐PCR for BCR::ABL1

Reverse transcription polymerase chain reaction (RT‐PCR) for BCR::ABL1 is the most common and widely accepted method of measurable residual disease (MRD) assessment in Philadelphia chromosome‐positive acute lymphoblastic leukemia (Ph+ ALL); however, RT‐PCR may not be an optimal measure of MRD in many cases of Ph+ ALL. We evaluated the clinical impact of a highly sensitive next‐generation sequencing (NGS) MRD assay (sensitivity of 10−6) and its correlation with RT‐PCR for BCR::ABL1 in patients with Ph+ ALL. Overall, 32% of patients had a discordance between MRD assessment by RT‐PCR and NGS, and 31% of patients who achieved NGS MRD negativity were PCR+ at the same timepoint. Among eight patients with long‐term detectable BCR::ABL1 by PCR, six were PCR+/NGS−. These patients generally had stable PCR levels that persisted despite therapeutic interventions, and none subsequently relapsed; in contrast, patients who were PCR+/NGS+ had more variable PCR values that responded to therapeutic intervention. In a separate cohort of prospectively collected clinical samples, 11 of 65 patients (17%) with Ph+ ALL who achieved NGS MRD negativity had detectable BCR::ABL1 by PCR, and none of these patients relapsed. Relapse‐free survival and overall survival were similar in patients who were PCR+/NGS− and PCR−/NGS−, suggesting that PCR for BCR::ABL1 did not provide additional prognostic information in patients who achieved NGS MRD negativity. NGS‐based assessment of MRD is prognostic in Ph+ ALL and identifies patients with low‐level detectable BCR::ABL1 who are unlikely to relapse nor to benefit from therapeutic interventions.
Ultrasonography-based diagnosis of hemorrhage syndrome in adipose tissues in the crest of the neck of heavy horse breeds

Hemorrhage syndrome in adipose tissues in the crest of the neck (HSCN), specifically in hemorrhagic adipose tissues on the longitudinally sectioned surface of the neck fat at the dorsal nuchal ligament, is prevalent in heavy horse breeds. Herein, we aimed to establish an ultrasonographic method to successfully diagnose HSCN in heavy horse breeds and assess its efficacy. Horses with homogeneous echogenicity images were included in the control group, whereas those with linear high-echogenicity images were classified as having HSCN. Horses with confirmed linear high-echogenicity images exhibited pathological features and significantly higher percentages of adipose tissue with hemorrhage than those observed in horses with homogeneous echogenicity images (P<0.01). Our results suggest the effectiveness of ultrasonography in identifying and diagnosing HSCN.
Online Control of Adaptive Large Neighborhood Search using Deep Reinforcement Learning

The Adaptive Large Neighborhood Search (ALNS) algorithm has shown considerable success in solving complex combinatorial optimization problems (COPs). ALNS selects various heuristics adaptively during the search process, leveraging their strengths to find good solutions for optimization problems. However, the effectiveness of ALNS depends on the proper configuration of its selection and acceptance parameters. To address this limitation, we propose a Deep Reinforcement Learning (DRL) approach that selects heuristics, adjusts parameters, and controls the acceptance criteria during the search process. The proposed method aims to learn, based on the state of the search, how to configure the next iteration of the ALNS to obtain good solutions to the underlying optimization problem. We evaluate the proposed method on a time-dependent orienteering problem with stochastic weights and time windows, used in an IJCAI competition. The results show that our approach outperforms vanilla ALNS and ALNS tuned with Bayesian Optimization. In addition, it obtained better solutions than two state-of-the-art DRL approaches, which are the winning methods of the competition, with much fewer observations required for training. The implementation of our approach will be made publicly available.
Satellite-to-Ground Continuous Variable Quantum Key Distribution: The Gaussian and Discrete Modulated Protocols in Low Earth Orbit

The Gaussian modulated continuous variable quantum key distribution (GM-CVQKD) protocol is known to maximise the mutual information between two parties during quantum key distribution (QKD). An alternative modulation scheme is the discrete modulated CVQKD (DM-CVQKD) protocol. In this paper, we study the Phase Shift Keying (M-PSK) and Quadrature Amplitude Modulation (M QAM) DM-CVQKD protocols along with the GM-CVQKD protocol over a satellite-to-ground link in the low SNR regime. We use a satellite-to-ground link model which takes into account geometric losses, scintillation, and scattering losses from the link distance, atmospheric turbulence, and atmospheric aerosols, respectively. In addition, recent multidimensional (MD) and multilevel coding and multistage decoding (MLC-MSD) reconciliation method models in combination with multiedge-type low-density parity-check (MET-LDPC) code models have been used to determine the reconciliation efficiency. The results show that GM-CVQKD outperforms DM-CVQKD. In addition, GM-CVQKD with MD reconciliation outperforms GM-CVQKD with MLC-MSD reconciliation in the finite size limit by producing positive secret key rates at larger link distances and lower elevation angles.
Exact solutions for optimal execution of portfolios transactions and the Riccati equation

We propose two methods to obtain exact solutions for the Almgren-Chriss model about optimal execution of portfolio transactions. In the first method we rewrite the Almgren-Chriss equation and find two exact solutions. In the second method, employing a general reparametrized time, we show that the Almgren-Chriss equation can be reduced to some known equations which can be exactly solved in different cases.For this last case we obtain a quantity conserved. In addition, we show that in both methods the Almgren-Chriss equation is equivalent to a Riccati equation.
Multiple-level Green Noise Mask Design for Practical Fourier Phase Retrieval

Phase retrieval, a long-established challenge for recovering a complex-valued signal from its Fourier intensity measurements, has attracted significant interest because of its far-flung applications in optical imaging. To enhance accuracy, researchers introduce extra constraints to the measuring procedure by including a random aperture mask in the optical path that randomly modulates the light projected on the target object and gives the coded diffraction patterns (CDP). It is known that random masks are non-bandlimited and can lead to considerable high-frequency components in the Fourier intensity measurements. These high-frequency components can be beyond the Nyquist frequency of the optical system and are thus ignored by the phase retrieval optimization algorithms, resulting in degraded reconstruction performances. Recently, our team developed a binary green noise masking scheme that can significantly reduce the high-frequency components in the measurement. However, the scheme cannot be extended to generate multiple-level aperture masks. This paper proposes a two-stage optimization algorithm to generate multi-level random masks named $\textit{OptMask}$ that can also significantly reduce high-frequency components in the measurements but achieve higher accuracy than the binary masking scheme. Extensive experiments on a practical optical platform were conducted. The results demonstrate the superiority and practicality of the proposed $\textit{OptMask}$ over the existing masking schemes for CDP phase retrieval.
Quantum-Enhanced Metrology in Cavity Magnomechanics

Magnons, as fundamental quasiparticles emerged in elementary spin excitations, hold a big promise for innovating quantum technologies in information coding and processing. Here we discover subtle roles of entanglement in a metrological scheme based on an experimentally feasible cavity magnomechanical system, where the magnons are responsible for sensing a weak magnetic field whereas the cavity field carries out a precision measurement of the weak field. By establishing exact relations between the Fisher information and entanglement, we show that for the weak coupling case the measurement precision can reach the Heisenberg limit, whereas quantum criticality enables us to enhance measurement precision for the strong coupling case. In particular, we also find that the entanglement between magnons and photons is of crucial importance during the dynamical encoding process, but the presence of such an entanglement in the measurement process dramatically reduces the final measurement precision.
Linear-Sized Sparsifiers via Near-Linear Time Discrepancy Theory

Discrepancy theory provides powerful tools for producing higher-quality objects which"beat the union bound"in fundamental settings throughout combinatorics and computer science. However, this quality has often come at the price of more expensive algorithms. We introduce a new framework for bridging this gap, by allowing for the efficient implementation of discrepancy-theoretic primitives. Our framework repeatedly solves regularized optimization problems to low accuracy to approximate the partial coloring method of [Rot17], and simplifies and generalizes recent work of [JSS23] on fast algorithms for Spencer's theorem. In particular, our framework only requires that the discrepancy body of interest has exponentially large Gaussian measure and is expressible as a sublevel set of a symmetric, convex function. We combine this framework with new tools for proving Gaussian measure lower bounds to give improved algorithms for a variety of sparsification and coloring problems. As a first application, we use our framework to obtain an $\widetilde{O}(m \cdot \epsilon^{-3.5})$ time algorithm for constructing an $\epsilon$-approximate spectral sparsifier of an $m$-edge graph, matching the sparsity of [BSS14] up to constant factors and improving upon the $\widetilde{O}(m \cdot \epsilon^{-6.5})$ runtime of [LeeS17]. We further give a state-of-the-art algorithm for constructing graph ultrasparsifiers and an almost-linear time algorithm for constructing linear-sized degree-preserving sparsifiers via discrepancy theory; in the latter case, such sparsifiers were not known to exist previously. We generalize these results to their analogs in sparsifying isotropic sums of positive semidefinite matrices. Finally, to demonstrate the versatility of our technique, we obtain a nearly-input-sparsity time constructive algorithm for Spencer's theorem (where we recover a recent result of [JSS23]).
On the connections between optimization algorithms, Lyapunov functions, and differential equations: theory and insights

We revisit the general framework introduced by Fazylab et al. (SIAM J. Optim. 28, 2018) to construct Lyapunov functions for optimization algorithms in discrete and continuous time. For smooth, strongly convex objective functions, we relax the requirements necessary for such a construction. As a result we are able to prove for Polyak's ordinary differential equations and for a two-parameter family of Nesterov algorithms rates of convergence that improve on those available in the literature. We analyse the interpretation of Nesterov algorithms as discretizations of the Polyak equation. We show that the algorithms are instances of Additive Runge-Kutta integrators and discuss the reasons why most discretizations of the differential equation do not result in optimization algorithms with acceleration. We also introduce a modification of Polyak's equation and study its convergence properties. Finally we extend the general framework to the stochastic scenario and consider an application to random algorithms with acceleration for overparameterized models; again we are able to prove convergence rates that improve on those in the literature.
Neural network predictions of inclusive electron-nucleus cross sections

We investigate whether a neural network approach can reproduce and predict the electron-nucleus cross sections in the kinematical domain of present and future accelerator-based neutrino oscillation experiments. For this purpose, we consider the large amount of data available to the community via the web-page ``Quasielastic Electron Nucleus scattering archive'', and use a residual, fully connected feedforward neural network. We illustrate the training performances of the neural network by comparing its results with experimental data for the electron double-differential cross section on carbon. The agreement between predictions and data is remarkable from quasielastic to deep-inelastic scattering. To test the predicting power of the neural network we consider the numerous kinematical conditions for which experimental cross sections on calcium are available. Furthermore, we show the predictions of the electron scattering cross sections on oxygen, argon, and titanium: nuclei of particular interest in the context of present and future accelerator-based neutrino oscillation program. The agreement between these predictions and the data is comparable to the one of other theoretical models commonly used to calculate electron and neutrino cross sections, such as SuSAv2 and GiBUU. Results obtained with GENIE, a Monte Carlo event generator, are also discussed for comparison. The good performances obtained with our neural network suggest that neural networks could be exploited for theoretical and experimental investigations of electron- and neutrino-nucleus scattering.
Identifying World Events in Dynamic International Relations Data Using a Latent Space Model

Dynamic network data have become ubiquitous in social network analysis, with new information becoming available that captures when friendships form, when corporate transactions happen and when countries interact with each other. Flexible and interpretable models are needed in order to properly capture the behavior of individuals in such networks. In this paper, we focus on study the underlying latent space that describes the social properties of a dynamic and directed international relations network of countries. We extend the directed additive and multiplicative effects network model to the continuous time setting by treating the time-evolution of model parameters using Gaussian processes. Importantly we incorporate both time-varying covariates and node-level additive random effects that aid in increasing model realism. We demonstrate the usefulness and flexibility of this model on a longitudinal dataset of formal state visits between the world's 18 largest economies. Not only does the model offer high quality predictive accuracy, but the latent parameters naturally map onto world events that are not directly measured in the data.
Towards Automated COVID-19 Presence and Severity Classification

COVID-19 presence classification and severity prediction via (3D) thorax computed tomography scans have become important tasks in recent times. Especially for capacity planning of intensive care units, predicting the future severity of a COVID-19 patient is crucial. The presented approach follows state-of-theart techniques to aid medical professionals in these situations. It comprises an ensemble learning strategy via 5-fold cross-validation that includes transfer learning and combines pre-trained 3D-versions of ResNet34 and DenseNet121 for COVID19 classification and severity prediction respectively. Further, domain-specific preprocessing was applied to optimize model performance. In addition, medical information like the infection-lung-ratio, patient age, and sex were included. The presented model achieves an AUC of 79.0% to predict COVID-19 severity, and 83.7% AUC to classify the presence of an infection, which is comparable with other currently popular methods. This approach is implemented using the AUCMEDI framework and relies on well-known network architectures to ensure robustness and reproducibility.
Neural Boltzmann Machines

Conditional generative models are capable of using contextual information as input to create new imaginative outputs. Conditional Restricted Boltzmann Machines (CRBMs) are one class of conditional generative models that have proven to be especially adept at modeling noisy discrete or continuous data, but the lack of expressivity in CRBMs have limited their widespread adoption. Here we introduce Neural Boltzmann Machines (NBMs) which generalize CRBMs by converting each of the CRBM parameters to their own neural networks that are allowed to be functions of the conditional inputs. NBMs are highly flexible conditional generative models that can be trained via stochastic gradient descent to approximately maximize the log-likelihood of the data. We demonstrate the utility of NBMs especially with normally distributed data which has historically caused problems for Gaussian-Bernoulli CRBMs. Code to reproduce our results can be found at https://github.com/unlearnai/neural-boltzmann-machines.
Monitoring and Adapting ML Models on Mobile Devices

ML models are increasingly being pushed to mobile devices, for low-latency inference and offline operation. However, once the models are deployed, it is hard for ML operators to track their accuracy, which can degrade unpredictably (e.g., due to data drift). We design the first end-to-end system for continuously monitoring and adapting models on mobile devices without requiring feedback from users. Our key observation is that often model degradation is due to a specific root cause, which may affect a large group of devices. Therefore, once the system detects a consistent degradation across a large number of devices, it employs a root cause analysis to determine the origin of the problem and applies a cause-specific adaptation. We evaluate the system on two computer vision datasets, and show it consistently boosts accuracy compared to existing approaches. On a dataset containing photos collected from driving cars, our system improves the accuracy on average by 15%.
Unsupervised Melody-Guided Lyrics Generation

Automatic song writing is a topic of significant practical interest. However, its research is largely hindered by the lack of training data due to copyright concerns and challenged by its creative nature. Most noticeably, prior works often fall short of modeling the cross-modal correlation between melody and lyrics due to limited parallel data, hence generating lyrics that are less singable. Existing works also lack effective mechanisms for content control, a much desired feature for democratizing song creation for people with limited music background. In this work, we propose to generate pleasantly listenable lyrics without training on melody-lyric aligned data. Instead, we design a hierarchical lyric generation framework that disentangles training (based purely on text) from inference (melody-guided text generation). At inference time, we leverage the crucial alignments between melody and lyrics and compile the given melody into constraints to guide the generation process. Evaluation results show that our model can generate high-quality lyrics that are more singable, intelligible, coherent, and in rhyme than strong baselines including those supervised on parallel data.