LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields

We introduce a new task, novel view synthesis for LiDAR sensors. While traditional model-based LiDAR simulators with style-transfer neural networks can be applied to render novel views, they fall short of producing accurate and realistic LiDAR patterns because the renderers rely on explicit 3D reconstruction and exploit game engines, that ignore important attributes of LiDAR points. We address this challenge by formulating, to the best of our knowledge, the first differentiable end-to-end LiDAR rendering framework, LiDAR-NeRF, leveraging a neural radiance field (NeRF) to facilitate the joint learning of geometry and the attributes of 3D points. However, simply employing NeRF cannot achieve satisfactory results, as it only focuses on learning individual pixels while ignoring local information, especially at low texture areas, resulting in poor geometry. To this end, we have taken steps to address this issue by introducing a structural regularization method to preserve local structural details. To evaluate the effectiveness of our approach, we establish an object-centric multi-view LiDAR dataset, dubbed NeRF-MVL. It contains observations of objects from 9 categories seen from 360-degree viewpoints captured with multiple LiDAR sensors. Our extensive experiments on the scene-level KITTI-360 dataset, and on our object-level NeRF-MVL show that our LiDAR-NeRF surpasses the model-based algorithms significantly.Comment: This paper introduces a new task of novel LiDAR view synthesis, and proposes a differentiable framework called LiDAR-NeRF with a structural regularization, as well as an object-centric multi-view LiDAR dataset called NeRF-MV

High Sensitivity Refractometer Based on Reflective Smf-Small Diameter No Core Fiber Structure

A high sensitivity refractive index sensor based on a single mode-small diameter no core fiber structure is proposed. In this structure, a small diameter no core fiber (SDNCF) used as a sensor probe, was fusion spliced to the end face of a traditional single mode fiber (SMF) and the end face of the SDNCF was coated with a thin film of gold to provide reflective light. The influence of SDNCF diameter and length on the refractive index sensitivity of the sensor has been investigated by both simulations and experiments, where results show that the diameter of SDNCF has significant influence. However, SDNCF length has limited influence on the sensitivity. Experimental results show that a sensitivity of 327 nm/RIU (refractive index unit) has been achieved for refractive indices ranging from 1.33 to 1.38, which agrees well with the simulated results with a sensitivity of 349.5 nm/RIU at refractive indices ranging from 1.33 to 1.38

The East-Asian VLBI Network

The East-Asian VLBI Network (EAVN) is the international VLBI facility in East Asia and is conducted in collaboration with China, Japan, and Korea. The EAVN consists of VLBI arrays operated in each East Asian country, containing 21 radio telescopes and three correlators. The EAVN will be mainly operated at 6.7 (C-band), 8 (X-band), 22 (K-band), and 43 GHz (Q-band), although the EAVN has an ability to conduct observations at 1.6 - 129 GHz. We have conducted fringe test observations eight times to date at 8 and 22 GHz and fringes have been successfully detected at both frequencies. We have also conducted science commissioning observations of 6.7 GHz methanol masers in massive star-forming regions. The EAVN will be operational from the second half of 2017, providing complementary results with the FAST on AGNs, massive star-forming regions, and evolved stars with high angular resolution at cm- to mm-wavelengths.Comment: 6 pages, 3 figures, 2 tables. To appear in the proceedings of "Frontiers in Radio Astronomy and FAST Early Sciences Symposium 2015" ed. Lei Qian (ASP Conf. Ser.

Amino Acid Sensor Kinase Gcn2 Is Required for Conidiation, Secondary Metabolism, and Cell Wall Integrity in the Taxol-Producer Pestalotiopsis microspora

The canonical Gcn2/Cpc1 kinase in fungi coordinates the expression of target genes in response to amino acid starvation. To investigate its possible role in secondary metabolism, we characterized a gcn2 homolog in the taxol-producing fungus Pestalotiopsis microspora. Deletion of the gene led to severe physiological defects under amino acid starvation, suggesting a conserved function of gcn2 in amino acid sensing. The mutant strain Δgcn2 displayed retardation in vegetative growth. It generated dramatically fewer conidia, suggesting a connection between amino acid metabolism and conidiation in this fungus. Importantly, disruption of the gene altered the production of secondary metabolites by HPLC profiling. For instance, under amino acid starvation, the deletion strain Δgcn2 barely produced secondary metabolites including the known natural product pestalotiollide B. Even more, we showed that gcn2 played critical roles in the tolerance to several stress conditions. Δgcn2 exhibited a hypersensitivity to Calcofluor white and Congo red, implying a role of Gcn2 in maintaining the integrity of the cell wall. This study suggests that Gcn2 kinase is an important global regulator in the growth and development of filamentous fungi and will provide knowledge for the manipulation of secondary metabolism in P. microspora

Enhancing Event Sequence Modeling with Contrastive Relational Inference

Neural temporal point processes(TPPs) have shown promise for modeling continuous-time event sequences. However, capturing the interactions between events is challenging yet critical for performing inference tasks like forecasting on event sequence data. Existing TPP models have focused on parameterizing the conditional distribution of future events but struggle to model event interactions. In this paper, we propose a novel approach that leverages Neural Relational Inference (NRI) to learn a relation graph that infers interactions while simultaneously learning the dynamics patterns from observational data. Our approach, the Contrastive Relational Inference-based Hawkes Process (CRIHP), reasons about event interactions under a variational inference framework. It utilizes intensity-based learning to search for prototype paths to contrast relationship constraints. Extensive experiments on three real-world datasets demonstrate the effectiveness of our model in capturing event interactions for event sequence modeling tasks.Comment: 6 pages, 2 figure

Leveraging Large Language Models for Pre-trained Recommender Systems

Recent advancements in recommendation systems have shifted towards more comprehensive and personalized recommendations by utilizing large language models (LLM). However, effectively integrating LLM's commonsense knowledge and reasoning abilities into recommendation systems remains a challenging problem. In this paper, we propose RecSysLLM, a novel pre-trained recommendation model based on LLMs. RecSysLLM retains LLM reasoning and knowledge while integrating recommendation domain knowledge through unique designs of data, training, and inference. This allows RecSysLLM to leverage LLMs' capabilities for recommendation tasks in an efficient, unified framework. We demonstrate the effectiveness of RecSysLLM on benchmarks and real-world scenarios. RecSysLLM provides a promising approach to developing unified recommendation systems by fully exploiting the power of pre-trained language models.Comment: 13 pages, 4 figure