Restoring original signals from pile-up using deep learning

Pile-up signals are frequently produced in experimental physics. They create inaccurate physics data with high uncertainties and cause multiple problems. Therefore, the correction of pile-up signals is crucially required. In this study, we implemented a deep learning method to restore the original signals from signals piled up with unwanted signals. We showed that a deep learning model could accurately reconstruct the original signal waveforms from the pile-up waveforms. By substituting the pile-up signals with the original signals predicted by the model, the energy and timing resolution of the data are notably enhanced. The model implementation significantly improved the quality of the particle identification plot and particle tracks. This method is applicable to similar problems, such as separating multiple signals or correcting pile-up signals with other types of noises and backgrounds. © 2023 Elsevier B.V.11Nsciescopu

Noise signal identification in time projection chamber data using deep learning model

Deep learning has been employed in various scientific fields and has provided promising results. In this study, a deep learning classifier was implemented to improve the quality of data obtained from a time projection chamber. Digital waveforms of the detected signals were classified into the following three categories: particles, noises, and particles piled up with noises. A simple 1-dimensional convolutional neural network was developed for the classification. The model demonstrated an excellent performance on the test dataset. Its practical performance was also examined using track images and particle identification plots by comparing the original and clean data without the noise signals. The comparison clearly showed that the deep learning model improved the quality of data. The current study presents an effective application of the deep learning model for the time projection chamber data. © 2023 Elsevier B.V.11Nsciescopu

Differential Approximation of min sat, max sat and Related Problems

We present differential approximation results (both positive and negative) for optimal satisfiability, optimal constraint satisfaction, and some of the most popular restrictive versions of them. As an important corollary, we exhibit an interesting structural difference between the landscapes of approximability classes in standard and differential paradigms.ou

α{\alpha}-cluster structure of 18Ne^{18}\mathrm{Ne}

International audienceIn this paper, we study α clustering in Ne18 and compare it with what is known about clustering in the mirror nucleus O18. The excitation function for O14+α resonant elastic scattering was measured in inverse kinematics using the Texas active target detector system (TexAT). The data cover the excitation-energy range from 8 to 17 MeV. The analysis was performed using a multichannel R-matrix approach. The detailed spectroscopic information is obtained from the R-matrix analysis: excitation energy of the states, spin, and parity as well as partial α and total widths. This information is compared with theoretical models and previous data. Correspondence between the levels in O18 and Ne18 is established. We carried out an extensive shell-model analysis of the O18 and Ne18 mirror pair. The agreement between theory and experiment is very good and especially useful when it comes to understanding the clustering strength distribution. The comparison of the experimental data with theory shows that certain states especially at high excitation energies are significantly more clustered than predicted. This indicates that the structure of these states is collective and is aligned towards the corresponding α reaction channel

Elezanumab, a human anti-RGMa monoclonal antibody, promotes neuroprotection, neuroplasticity, and neurorecovery following a thoracic hemicompression spinal cord injury in non-human primates.

Spinal cord injury (SCI) is a devastating condition characterized by loss of function, secondary to damaged spinal neurons, disrupted axonal connections, and myelin loss. Spontaneous recovery is limited, and there are no approved pharmaceutical treatments to reduce ongoing damage or promote repair. Repulsive guidance molecule A (RGMa) is upregulated following injury to the central nervous system (CNS), where it is believed to induce neuronal apoptosis and inhibit axonal growth and remyelination. We evaluated elezanumab, a human anti-RGMa monoclonal antibody, in a novel, newly characterized non-human primate (NHP) hemicompression model of thoracic SCI. Systemic intravenous (IV) administration of elezanumab over 6 months was well tolerated and associated with significant improvements in locomotor function. Treatment of animals for 16 weeks with a continuous intrathecal infusion of elezanumab below the lesion was not efficacious. IV elezanumab improved microstructural integrity of extralesional tissue as reflected by higher fractional anisotropy and magnetization transfer ratios in treated vs. untreated animals. IV elezanumab also reduced SCI-induced increases in soluble RGMa in cerebrospinal fluid, and membrane bound RGMa rostral and caudal to the lesion. Anterograde tracing of the corticospinal tract (CST) from the contralesional motor cortex following 20 weeks of IV elezanumab revealed a significant increase in the density of CST fibers emerging from the ipsilesional CST into the medial/ventral gray matter. There was a significant sprouting of serotonergic (5-HT) fibers rostral to the injury and in the ventral horn of lower thoracic regions. These data demonstrate that 6 months of intermittent IV administration of elezanumab, beginning within 24 h after a thoracic SCI, promotes neuroprotection and neuroplasticity of key descending pathways involved in locomotion. These findings emphasize the mechanisms leading to improved recovery of neuromotor functions with elezanumab in acute SCI in NHPs