FAIR: A Causal Framework for Accurately Inferring Judgments Reversals

Artificial intelligence researchers have made significant advances in legal intelligence in recent years. However, the existing studies have not focused on the important value embedded in judgments reversals, which limits the improvement of the efficiency of legal intelligence. In this paper, we propose a causal Framework for Accurately Inferring case Reversals (FAIR), which models the problem of judgments reversals based on real Chinese judgments. We mine the causes of judgments reversals by causal inference methods and inject the obtained causal relationships into the neural network as a priori knowledge. And then, our framework is validated on a challenging dataset as a legal judgment prediction task. The experimental results show that our framework can tap the most critical factors in judgments reversal, and the obtained causal relationships can effectively improve the neural network's performance. In addition, we discuss the generalization ability of large language models for legal intelligence tasks using ChatGPT as an example. Our experiment has found that the generalization ability of large language models still has defects, and mining causal relationships can effectively improve the accuracy and explain ability of model predictions

Morphing and Sampling Network for Dense Point Cloud Completion

3D point cloud completion, the task of inferring the complete geometric shape from a partial point cloud, has been attracting attention in the community. For acquiring high-fidelity dense point clouds and avoiding uneven distribution, blurred details, or structural loss of existing methods' results, we propose a novel approach to complete the partial point cloud in two stages. Specifically, in the first stage, the approach predicts a complete but coarse-grained point cloud with a collection of parametric surface elements. Then, in the second stage, it merges the coarse-grained prediction with the input point cloud by a novel sampling algorithm. Our method utilizes a joint loss function to guide the distribution of the points. Extensive experiments verify the effectiveness of our method and demonstrate that it outperforms the existing methods in both the Earth Mover's Distance (EMD) and the Chamfer Distance (CD).Comment: 8pages, 7 figures, AAAI202

Regression modeling based on improved genetic algorithm

Regresijski model je dobro uhodana metoda u analizi podataka s primjenom u raznim područjima. Izbor nezavisnih varijabli i matematički transformiranih u regresijski model, često predstavlja izazovan problem. Nedavno je nekoliko znanstvenika primijenilo evolucijski proračun za rješenje tog problema, ali rezultat nije učinkovit onoliko koliko smo željeli. Ukrižena (crossover) operacija u GA redizajnirana je primjenom Latin hypercube uzorkovanja, a zatim, kombinacijom dvaju uobičajeno korištenih statističkih kriterija (AIC, BIC), dajemo poboljšani genetički algoritam za rješavanje problema izbora statističkog modela. Predloženim se algoritmom može prevladati jaka ovisnost o putanji i osloniti na iskustvo stečeno primjenom klasičnih pristupa. Usporedba rezultata simulacije u rješavanju problema odabira statističkog modela s ovim poboljšanim GA, tradicionalnog genetičkog algoritma i klasičnog algoritma za odabir modela pokazuje da je novi GA superiorniji u rješavanju kvalitete, brzine konvergencije i drugih različitih pokazatelja.Regression model is a well-established method in data analysis with applications in various fields. The selection of independent variables and mathematically transformed in a regression model is often a challenging problem. Recently, some scholars have used evolutionary computation to solve this problem, but the result is not effective as we desired. The crossover operation in GA is redesigned by using Latin hypercube sampling, then combining two commonly used statistical criteria (AIC, BIC) we are presenting an improved genetic algorithm based for solving statistical model selection problem. The proposed algorithm can overcome strong path-dependence and rely on experience of classical approaches. Comparison of simulation results in solving statistical model selection problem with this improved GA, traditional genetic algorithm and classical algorithm for model selection show that the new GA has superiority in solution of quality, convergence rate and other various indices

Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model

We report Zero123++, an image-conditioned diffusion model for generating 3D-consistent multi-view images from a single input view. To take full advantage of pretrained 2D generative priors, we develop various conditioning and training schemes to minimize the effort of finetuning from off-the-shelf image diffusion models such as Stable Diffusion. Zero123++ excels in producing high-quality, consistent multi-view images from a single image, overcoming common issues like texture degradation and geometric misalignment. Furthermore, we showcase the feasibility of training a ControlNet on Zero123++ for enhanced control over the generation process. The code is available at https://github.com/SUDO-AI-3D/zero123plus