The IceCube Neutrino Observatory at the South Pole has measured the diffuse astrophysical neutrino flux up to ∼PeV energies and is starting to identify first point source candidates. The next generation facility, IceCube-Gen2, aims at extending the accessible energy range to EeV in order to measure the continuation of the astrophysical spectrum, to identify neutrino sources, and to search for a cosmogenic neutrino flux. As part of IceCube-Gen2, a radio array is foreseen that is sensitive to detect Askaryan emission of neutrinos beyond ∼30 PeV. Surface and deep antenna stations have different benefits in terms of effective area, resolution, and the capability to reject backgrounds from cosmic-ray air showers and may be combined to reach the best sensitivity. The optimal detector configuration is still to be identified. This contribution presents the full-array simulation efforts for a combination of deep and surface antennas, and compares different design options with respect to their sensitivity to fulfill the science goals of IceCube-Gen2.

Based on kernel density estimation (KDE) and Kullback-Leibler divergence (KLID), a new data-driven fault diagnosis method is proposed from a statistical perspective. The ensemble empirical mode decomposition (EEMD) together with the Hilbert transform is employed to extract 95 time-and frequency-domain features from raw and processed signals. The distance-based evaluation approach is used to select a subset of fault-sensitive features by removing the irrelevant features. By utilizing the KDE, the statistical distribution of selected features can be readily estimated without assuming any parametric family of distributions; whereas the KLID is able to quantify the discrepancy between two probability distributions of a selected feature before and after adding a testing sample. An integrated Kullback-Leibler divergence, which aggregates the KLID of all the selected features, is introduced to discriminate various fault modes/damage levels. The effectiveness of the proposed method is demonstrated via the case studies of fault diagnosis for bevel gears and rolling element bearings, respectively. The observations from the case studies show that the proposed method outperforms the support vector machine (SVM)-based and neural network-based fault diagnosis methods in terms of classification accuracy. Additionally, the influences of the number of selected features and the training sample size on the classification performance are examined by a set of comparative studies.

Background Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with poor prognosis, and gemcitabine-based chemotherapy remains an effective option for the majority of PDAC patients. Hepatocyte nuclear factor 1α (HNF1A) is a tumor-suppressor in PDAC, but its role in gemcitabine chemoresistance of PDAC has not been clarified. Methods The function of HNF1A in gemcitabine was detected by overexpression and knockdown of HNF1A in vitro and in vitro . The regulatory network between HNF1A and ABCB1 was further demonstrated by luciferase assays, deletion/mutation reporter construct assays and CHIP assays. Findings Here, we found that HNF1A expression is significantly associated with gemcitabine sensitivity in PDAC cell lines. Moreover, we identified that HNF1A overexpression enhanced gemcitabine sensitivity of PDAC both in vitro and in vitro , while inhibition of HNF1A had the opposite effect. Furthermore, by inhibiting and overexpressing HNF1A, we revealed that HNF1A regulates the expression of MDR genes (ABCB1 and ABCC1) in PDAC cells. Mechanistically, we demonstrated that HNF1A regulates ABCB1 expression through binding to its specific promoter region and suppressing its transcription levels. Finally, the survival analyses revealed the clinical value of HNF1A in stratification of gemcitabine sensitive pancreatic cancer patients. Interpretation Our study paved the road for finding novel treatment combinations using conventional cytotoxic agents with functional restoration of the HNF1A protein, individualized treatment through HNF1A staining and improvement of the prognosis of PDAC patients. Fund National Natural Science Foundations of China and National Natural Science Foundation of Guangdong Province.

The field of deep learning has become increasingly important for particle physics experiments, yielding a multitude of advances, predominantly in event classification and reconstruction tasks. Many of these applications have been adopted from other domains. However, data in the field of physics are unique in the context of machine learning, insofar as their generation process and the laws and symmetries they abide by are usually well understood. Most commonly used deep learning architectures fail at utilizing this available information. In contrast, more traditional likelihood-based methods are capable of exploiting domain knowledge, but they are often limited by computational complexity. In this contribution, a hybrid approach is presented that utilizes generative neural networks to approximate the likelihood, which may then be used in a traditional maximum-likelihood setting. Domain knowledge, such as invariances and detector characteristics, can easily be incorporated in this approach. The hybrid approach is illustrated by the example of event reconstruction in IceCube.

Abstract. The IceCube Neutrino Observatory instruments about 1 km3 of deep, glacial ice at the geographic South Pole using 5160 photomultipliers to detect Cherenkov light emitted by charged relativistic particles. A unexpected light propagation effect observed by the experiment is an anisotropic attenuation, which is aligned with the local flow direction of the ice. Birefringent light propagation has been examined as a possible explanation for this effect. The predictions of a first-principles birefringence model developed for this purpose, in particular curved light trajectories resulting from asymmetric diffusion, provide a qualitatively good match to the main features of the data. This in turn allows us to deduce ice crystal properties. Since the wavelength of the detected light is short compared to the crystal size, these crystal properties do not only include the crystal orientation fabric, but also the average crystal size and shape, as a function of depth. By adding small empirical corrections to this first-principles model, a quantitatively accurate description of the optical properties of the IceCube glacial ice is obtained. In this paper, we present the experimental signature of ice optical anisotropy observed in IceCube LED calibration data, the theory and parametrization of the birefringence effect, the fitting procedures of these parameterizations to experimental data as well as the inferred crystal properties.