Generative Machine Learning for Detector Response Modeling with a Conditional Normalizing Flow

In this paper, we explore the potential of generative machine learning models as an alternative to the computationally expensive Monte Carlo (MC) simulations commonly used by the Large Hadron Collider (LHC) experiments. Our objective is to develop a generative model capable of efficiently simulating detector responses for specific particle observables, focusing on the correlations between detector responses of different particles in the same event and accommodating asymmetric detector responses. We present a conditional normalizing flow model (CNF) based on a chain of Masked Autoregressive Flows, which effectively incorporates conditional variables and models high-dimensional density distributions. We assess the performance of the \cnf model using a simulated sample of Higgs boson decaying to diphoton events at the LHC. We create reconstruction-level observables using a smearing technique. We show that conditional normalizing flows can accurately model complex detector responses and their correlation. This method can potentially reduce the computational burden associated with generating large numbers of simulated events while ensuring that the generated events meet the requirements for data analyses.Comment: 16 pages, 6 figure

Parton Labeling without Matching: Unveiling Emergent Labelling Capabilities in Regression Models

Parton labeling methods are widely used when reconstructing collider events with top quarks or other massive particles. State-of-the-art techniques are based on machine learning and require training data with events that have been matched using simulations with truth information. In nature, there is no unique matching between partons and final state objects due to the properties of the strong force and due to acceptance effects. We propose a new approach to parton labeling that circumvents these challenges by recycling regression models. The final state objects that are most relevant for a regression model to predict the properties of a particular top quark are assigned to said parent particle without having any parton-matched training data. This approach is demonstrated using simulated events with top quarks and outperforms the widely-used $\chi^2$ method.Comment: 6 pages, 4 figure

Src kinase up-regulates the ERK cascade through inactivation of protein phosphatase 2A following cerebral ischemia

<p>Abstract</p> <p>Background</p> <p>The regulation of protein phosphorylation requires a balance in the activity of protein kinases and protein phosphatases. Our previous data indicates that Src can increase ERK activity through Raf kinase in response to ischemic stimuli. This study examined the molecular mechanisms by which Src activates ERK cascade through protein phosphatases following cerebral ischemia.</p> <p>Results</p> <p>Ischemia-induced Src activation is followed by phosphorylation of PP2A at Tyr307 leading to its inhibition in the rat hippocampus. SU6656, a Src inhibitor, up-regulates PP2A activity, resulting in a significant decreased activity in ERK and its targets, CREB and ERα. In addition, the PP2A inhibitor, cantharidin, led to an up-regulation of ERK activity and was able to counteract Src inhibition during ischemia.</p> <p>Conclusion</p> <p>Src induces up-regulation of ERK activity and its target transcription factors, CREB and ERα, through attenuation of PP2A activity. Therefore, activation of ERK is the result of a crosstalk between two pathways, Raf-dependent positive regulators and PP2A-dependent negative regulators.</p

Structure-Preservation Model Aggregation for Two-Stage Inverters Based Large-Scale Photovoltaic System

With the increasing penetration level of large-scale photovoltaic (PV) generator connected to the grid, an accurate simulation model is required for the dynamic analysis of the PV system. However, the detailed electromagnetic simulation of the large-scale system is complex and the dynamic response capability is estimated with obstacle caused by large computational burdens. Therefore, a precise dynamic aggregated model is indispensable for the displacement of the large-scale PV system. The structure-preservation based aggregated model with comprehensive equivalent parameters for large-scale PV system is proposed in this paper. A complete two-stage PV system model is established to analyze the dynamics of the system. Then, the aggregation method is obtained by comparing the dynamic equations of the detailed model with the aggregated model, which is based on the energy relationship in the PV system. Furthermore, four different case studies are considered including the aggregation of identical and different ten parallel-connected PV units both under the same irradiance condition, and the aggregation of different ten parallel-connected PV units under different irradiance and weak grid scenarios, where the aggregation models are obtained through the proposed equivalent modeling method. Finally, the effectiveness of the proposed aggregation method is verified by the simulation results from PSCAD/EMTDC platform, and the consistency between the aggregated model and the detailed model is confirmed under different disturbances of irradiance variation, and continuous symmetric and asymmetric grid faults.Published versio

Dendrimer-entrapped gold nanoparticles as potential CT contrast agents for blood pool imaging

The purpose of this study was to evaluate dendrimer-entrapped gold nanoparticles [Au DENPs] as a molecular imaging [MI] probe for computed tomography [CT]. Au DENPs were prepared by complexing AuCl4- ions with amine-terminated generation 5 poly(amidoamine) [G5.NH2] dendrimers. Resulting particles were sized using transmission electron microscopy. Serial dilutions (0.001 to 0.1 M) of either Au DENPs or iohexol were scanned by CT in vitro. Based on these results, Au DENPs were injected into mice, either subcutaneously (10 μL, 0.007 to 0.02 M) or intravenously (300 μL, 0.2 M), after which the mice were imaged by micro-CT or a standard mammography unit. Au DENPs prepared using G5.NH2 dendrimers as templates are quite uniform and have a size range of 2 to 4 nm. At Au concentrations above 0.01 M, the CT value of Au DENPs was higher than that of iohexol. A 10-μL subcutaneous dose of Au DENPs with [Au] ≥ 0.009 M could be detected by micro-CT. The vascular system could be imaged 5 and 20 min after injection of Au DENPs into the tail vein, and the urinary system could be imaged after 60 min. At comparable time points, the vascular system could not be imaged using iohexol, and the urinary system was imaged only indistinctly. Findings from this study suggested that Au DENPs prepared using G5.NH2 dendrimers as templates have good X-ray attenuation and a substantial circulation time. As their abundant surface amine groups have the ability to bind to a range of biological molecules, Au DENPs have the potential to be a useful MI probe for CT

Genome-wide and molecular evolution analysis of the subtilase gene family in

Background Vitis vinifera (grape) is one of the most economically significant fruit crops in the world. The availability of the recently released grape genome sequence offers an opportunity to identify and analyze some important gene families in this species. Subtilases are a group of subtilisin-like serine proteases that are involved in many biological processes in plants. However, no comprehensive study incorporating phylogeny, chromosomal location and gene duplication, gene organization, functional divergence, selective pressure and expression profiling has been reported so far for the grape. Results In the present study, a comprehensive analysis of the subtilase gene family in V. vinifera was performed. Eighty subtilase genes were identified. Phylogenetic analyses indicated that these subtilase genes comprised eight groups. The gene organization is considerably conserved among the groups. Distribution of the subtilase genes is non-random across the chromosomes. A high proportion of these genes are preferentially clustered, indicating that tandem duplications may have contributed significantly to the expansion of the subtilase gene family. Analyses of divergence and adaptive evolution show that while purifying selection may have been the main force driving the evolution of grape subtilases, some of the critical sites responsible for the divergence may have been under positive selection. Further analyses of real-time PCR data suggested that many subtilase genes might be important in the stress response and functional development of plants. Conclusions Tandem duplications as well as purifying and positive selections have contributed to the functional divergence of subtilase genes in V. vinifera. The data may contribute to a better understanding of the grape subtilase gene family