Between Evidence and Facts: An Argumentative Perspective of Legal Evidence

In this paper, we will present an argumentative view of legal evidence. In an argumentation-based litigation game, the only purpose of the suitor (S) or the respondent (R) is to maximize their own legal rights while the purpose of the trier (T) is to maintain judicial fairness and justice. Different selections of evidence and different orders of presenting evidence will lead to different case-facts and even adjudicative results, the purpose of litigation is to reconcile a balance among the three parties - S, R, and T

Study on the growth mechanism of the internal oxide layer in 9% Ni cryogenic steel

The oxidation behavior of the Ni-rich layer in the internal oxide layer (IOL) in 9% Ni cryogenic steel is investigated at 1,150°C for 0–240 min in the air atmosphere. The morphology and phase composition of the Ni—rich layer are analyzed with energy dispersive spectroscopy, scanning electron microscopy, metallographic microscopy, and X—ray diffraction. The results show that the Ni—rich layer mainly consists of gray Fe3O4/FeO and white Ni–Fe particles, with a small amount of black Fe2SiO4. The morphologies of Ni–Fe particles undergo the following changes with isothermal oxidation time: dot—like → strip—like → net-like; at the same time, layered Ni–Fe particles were formed at about 1/3 of the thickness of the Ni—rich layer. Compared with the dot-like Ni–Fe particle, the net-like and layered Ni–Fe particles provide a fast path for the diffusion of O in the Ni—rich layer. However, the experimental steel still has a much lower oxidation rate because of the hindrance of Ni–Fe particles on the out-diffusion of Fe. During the oxidation process, the Kirkendall effect induces pores/cavities in the IOL, which weakens the stability of the IOL. In the end, the spalling phenomenon of the layered Ni–Fe particle occurs at 1,150°C for 180 min

Detecting extreme-mass-ratio inspirals for space-borne detectors with deep learning

One of the primary objectives for space-borne gravitational wave detectors is the detection of extreme-mass-ratio inspirals (EMRIs). This undertaking poses a substantial challenge because of the complex and long EMRI signals, further complicated by their inherently faint signal. In this research, we introduce a 2-layer Convolutional Neural Network (CNN) approach to detect EMRI signals for space-borne detectors. Our method employs the Q-transform for data preprocessing, effectively preserving EMRI signal characteristics while minimizing data size. By harnessing the robust capabilities of CNNs, we can reliably distinguish EMRI signals from noise, particularly when the signal-to-noise~(SNR) ratio reaches 50, a benchmark considered a ``golden'' EMRI. At the meantime, we incorporate time-delay interferometry (TDI) to ensure practical utility. We assess our model's performance using a 0.5-year dataset, achieving a true positive rate~(TPR) of 94.2\% at a 1\% false positive rate~(FPR) across various signal-to-noise ratio form 50-100, with 91\% TPR and 1\% FPR at an SNR of 50. This study underscores the promise of incorporating deep learning methods to advance EMRI data analysis, potentially leading to rapid EMRI signal detection.Comment: 12 pages, 8 figures, 2 table

The detection, extraction and parameter estimation of extreme-mass-ratio inspirals with deep learning

One of the primary goals of space-borne gravitational wave detectors is to detect and analyze extreme-mass-ratio inspirals (EMRIs). This endeavor presents a significant challenge due to the complex and lengthy EMRI signals, further compounded by their inherently faint nature. In this letter, we introduce a 2-layer Convolutional Neural Network (CNN) approach to detect EMRI signals for space-borne detectors, achieving a true positive rate (TPR) of 96.9 % at a 1 % false positive rate (FPR) for signal-to-noise ratio (SNR) from 50 to 100. Especially, the key intrinsic parameters of EMRIs such as mass and spin of the supermassive black hole (SMBH) and the initial eccentricity of the orbit can be inferred directly by employing a VGG network. The mass and spin of the SMBH can be determined at 99 % and 92 % respectively. This will greatly reduce the parameter spaces and computing cost for the following Bayesian parameter estimation. Our model also has a low dependency on the accuracy of the waveform model. This study underscores the potential of deep learning methods in EMRI data analysis, enabling the rapid detection of EMRI signals and efficient parameter estimation .Comment: 6 pages, 5 figure