Implementing Default and Autoepistemic Logics via the Logic of GK

The logic of knowledge and justified assumptions, also known as logic of grounded knowledge (GK), was proposed by Lin and Shoham as a general logic for nonmonotonic reasoning. To date, it has been used to embed in it default logic (propositional case), autoepistemic logic, Turner's logic of universal causation, and general logic programming under stable model semantics. Besides showing the generality of GK as a logic for nonmonotonic reasoning, these embeddings shed light on the relationships among these other logics. In this paper, for the first time, we show how the logic of GK can be embedded into disjunctive logic programming in a polynomial but non-modular translation with new variables. The result can then be used to compute the extension/expansion semantics of default logic, autoepistemic logic and Turner's logic of universal causation by disjunctive ASP solvers such as claspD(-2), DLV, GNT and cmodels.Comment: Proceedings of the 15th International Workshop on Non-Monotonic Reasoning (NMR 2014

Differential metabolic responses of clam Ruditapes philippinarum to Vibrio anguillarum and Vibrio splendidus challenges

Clam Ruditapes philippinarum is one of the important marine aquaculture species in North China. However, pathogens can often cause diseases and lead to massive mortalities and economic losses of clam. In this work, we compared the metabolic responses induced by Vibrio anguillarum and Vibrio splendidus challenges towards hepatopancreas of clam using NMR-based metabolomics. Metabolic responses suggested that both V anguillarum and V splendidus induced disturbances in energy metabolism and osmotic regulation, oxidative and immune stresses with different mechanisms, as indicated by correspondingly differential metabolic biomarkers (e.g., amino acids, ATP, glucose, glycogen, taurine, betaine, choline and hypotaurine) and altered mRNA expression levels of related genes including ATP synthase, ATPase, glutathione peroxidase, heat shock protein 90, defensin and lysozyme. However, V. anguillarum caused more severe oxidative and immune stresses in clam hepatopancreas than V splendidus. Our results indicated that metabolomics could be used to elucidate the biological effects of pathogens to the marine clam R. philippinarum. (C) 2013 Elsevier Ltd. All rights reserved.Clam Ruditapes philippinarum is one of the important marine aquaculture species in North China. However, pathogens can often cause diseases and lead to massive mortalities and economic losses of clam. In this work, we compared the metabolic responses induced by Vibrio anguillarum and Vibrio splendidus challenges towards hepatopancreas of clam using NMR-based metabolomics. Metabolic responses suggested that both V anguillarum and V splendidus induced disturbances in energy metabolism and osmotic regulation, oxidative and immune stresses with different mechanisms, as indicated by correspondingly differential metabolic biomarkers (e.g., amino acids, ATP, glucose, glycogen, taurine, betaine, choline and hypotaurine) and altered mRNA expression levels of related genes including ATP synthase, ATPase, glutathione peroxidase, heat shock protein 90, defensin and lysozyme. However, V. anguillarum caused more severe oxidative and immune stresses in clam hepatopancreas than V splendidus. Our results indicated that metabolomics could be used to elucidate the biological effects of pathogens to the marine clam R. philippinarum. (C) 2013 Elsevier Ltd. All rights reserved

Computing Loops With at Most One External Support Rule

If a loop has no external support rules, then its loop formula is equivalent to a set of unit clauses; and if it has exactly one external support rule, then its loop formula is equivalent to a set of binary clauses. In this paper, we consider how to compute these loops and their loop formulas in a normal logic program, and use them to derive consequences of a logic program. We show that an iterative procedure based on unit propagation, the program completion and the loop formulas of loops with no external support rules can compute the same consequences as the “Expand ” operator in smodels, which is known to compute the well-founded model when the given normal logic program has no constraints. We also show that using the loop formulas of loops with at most one external support rule, the same procedure can compute more consequences, and these extra consequences can help ASP solvers such as cmodels to find answer sets of certain logic programs

PSAT1 prompted cell proliferation and inhibited cell apoptosis in multiple myeloma through regulating PI3K/AKT pathway

Purpose: To identify the biological function of phosphoserine aminotransferase 1 (PSAT1) in regulating cell proliferation and apoptosis in multiple myeloma (MM).Methods: The mRNA and protein levels of PSAT1 were determined using quantitative real-time polymerase chain reaction (PCR) and western blotting, respectively. Cell proliferation was measured using CCK-8 assay.Results: PSAT1 mRNA and protein expression levels were significantly increased in MM cell lines when compared to control cells. Moreover,  downregulation of PSAT1 inhibited MM cell proliferation and induced cell apoptosis, whereas overexpression of PSAT1 promoted MM cell  proliferation and suppressed cell apoptosis. Further analysis demonstrated that the underlying mechanism was via regulation of PI3K/AKT pathway.Conclusion: The results identified a novel role for PSAT1 in the progression of MM, which may provide a therapeutic and a new anticancer target for the therapy of MM. Keywords: Multiple myeloma, PSAT1, Cell proliferation, PI3K/AKT pathwa

MM-Gaussian: 3D Gaussian-based Multi-modal Fusion for Localization and Reconstruction in Unbounded Scenes

Localization and mapping are critical tasks for various applications such as autonomous vehicles and robotics. The challenges posed by outdoor environments present particular complexities due to their unbounded characteristics. In this work, we present MM-Gaussian, a LiDAR-camera multi-modal fusion system for localization and mapping in unbounded scenes. Our approach is inspired by the recently developed 3D Gaussians, which demonstrate remarkable capabilities in achieving high rendering quality and fast rendering speed. Specifically, our system fully utilizes the geometric structure information provided by solid-state LiDAR to address the problem of inaccurate depth encountered when relying solely on visual solutions in unbounded, outdoor scenarios. Additionally, we utilize 3D Gaussian point clouds, with the assistance of pixel-level gradient descent, to fully exploit the color information in photos, thereby achieving realistic rendering effects. To further bolster the robustness of our system, we designed a relocalization module, which assists in returning to the correct trajectory in the event of a localization failure. Experiments conducted in multiple scenarios demonstrate the effectiveness of our method.Comment: 7 pages, 5 figure

OCC-VO: Dense Mapping via 3D Occupancy-Based Visual Odometry for Autonomous Driving

Visual Odometry (VO) plays a pivotal role in autonomous systems, with a principal challenge being the lack of depth information in camera images. This paper introduces OCC-VO, a novel framework that capitalizes on recent advances in deep learning to transform 2D camera images into 3D semantic occupancy, thereby circumventing the traditional need for concurrent estimation of ego poses and landmark locations. Within this framework, we utilize the TPV-Former to convert surround view cameras' images into 3D semantic occupancy. Addressing the challenges presented by this transformation, we have specifically tailored a pose estimation and mapping algorithm that incorporates Semantic Label Filter, Dynamic Object Filter, and finally, utilizes Voxel PFilter for maintaining a consistent global semantic map. Evaluations on the Occ3D-nuScenes not only showcase a 20.6% improvement in Success Ratio and a 29.6% enhancement in trajectory accuracy against ORB-SLAM3, but also emphasize our ability to construct a comprehensive map. Our implementation is open-sourced and available at: https://github.com/USTCLH/OCC-VO.Comment: 7pages, 3 figure

Multi-Modal 3D Object Detection in Autonomous Driving: a Survey

In the past few years, we have witnessed rapid development of autonomous driving. However, achieving full autonomy remains a daunting task due to the complex and dynamic driving environment. As a result, self-driving cars are equipped with a suite of sensors to conduct robust and accurate environment perception. As the number and type of sensors keep increasing, combining them for better perception is becoming a natural trend. So far, there has been no indepth review that focuses on multi-sensor fusion based perception. To bridge this gap and motivate future research, this survey devotes to review recent fusion-based 3D detection deep learning models that leverage multiple sensor data sources, especially cameras and LiDARs. In this survey, we first introduce the background of popular sensors for autonomous cars, including their common data representations as well as object detection networks developed for each type of sensor data. Next, we discuss some popular datasets for multi-modal 3D object detection, with a special focus on the sensor data included in each dataset. Then we present in-depth reviews of recent multi-modal 3D detection networks by considering the following three aspects of the fusion: fusion location, fusion data representation, and fusion granularity. After a detailed review, we discuss open challenges and point out possible solutions. We hope that our detailed review can help researchers to embark investigations in the area of multi-modal 3D object detection