Interpretable End-to-End Driving Model for Implicit Scene Understanding

Driving scene understanding is to obtain comprehensive scene information through the sensor data and provide a basis for downstream tasks, which is indispensable for the safety of self-driving vehicles. Specific perception tasks, such as object detection and scene graph generation, are commonly used. However, the results of these tasks are only equivalent to the characterization of sampling from high-dimensional scene features, which are not sufficient to represent the scenario. In addition, the goal of perception tasks is inconsistent with human driving that just focuses on what may affect the ego-trajectory. Therefore, we propose an end-to-end Interpretable Implicit Driving Scene Understanding (II-DSU) model to extract implicit high-dimensional scene features as scene understanding results guided by a planning module and to validate the plausibility of scene understanding using auxiliary perception tasks for visualization. Experimental results on CARLA benchmarks show that our approach achieves the new state-of-the-art and is able to obtain scene features that embody richer scene information relevant to driving, enabling superior performance of the downstream planning.Comment: Accepted by 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023

An Embeddable Strain Sensor with 30 Nano-Strain Resolution based on Optical Interferometry

A cost-effective, robust and embeddable optical interferometric strain sensor with nanoscale strain resolution is presented in this paper. The sensor consists of an optical fiber, a quartz rod with one end coated with a thin gold layer, and two metal shells employed to transfer the strain and orient and protect the optical fiber and the quartz rod. The optical fiber endface, combining with the gold-coated surface, forms an extrinsic Fabry—Perot interferometer. The sensor was firstly calibrated, and the result showed that our prototype sensor could provide a measurement resolution of 30 nano-strain (nε) and a sensitivity of 10.01 µε/ µm over a range of 1000 µε. After calibration of the sensor, the shrinkage strain of a cubic brick of mortar in real time during the drying process was monitored. The strain sensor was compared with a commercial linear variable displacement transducer, and the comparison results in four weeks demonstrated that our sensor had much higher measurement resolution and gained more detailed and useful information. Due to the advantages of the extremely simple, robust and cost-effective configuration, it is believed that the sensor is significantly beneficial to practical applications, especially for structural health monitoring

sPortfolio: Stratified Visual Analysis of Stock Portfolios

Quantitative Investment, built on the solid foundation of robust financial theories, is at the center stage in investment industry today. The essence of quantitative investment is the multi-factor model, which explains the relationship between the risk and return of equities. However, the multi-factor model generates enormous quantities of factor data, through which even experienced portfolio managers find it difficult to navigate. This has led to portfolio analysis and factor research being limited by a lack of intuitive visual analytics tools. Previous portfolio visualization systems have mainly focused on the relationship between the portfolio return and stock holdings, which is insufficient for making actionable insights or understanding market trends. In this paper, we present sPortfolio, which, to the best of our knowledge, is the first visualization that attempts to explore the factor investment area. In particular, sPortfolio provides a holistic overview of the factor data and aims to facilitate the analysis at three different levels: a Risk-Factor level, for a general market situation analysis; a Multiple-Portfolio level, for understanding the portfolio strategies; and a Single-Portfolio level, for investigating detailed operations. The system's effectiveness and usability are demonstrated through three case studies. The system has passed its pilot study and is soon to be deployed in industry

Harmonizing across datasets to improve the transferability of drug combination prediction

Combination treatment has multiple advantages over traditional monotherapy in clinics, thus becoming a target of interest for many high-throughput screening (HTS) studies, which enables the development of machine learning models predicting the response of new drug combinations. However, most existing models have been tested only within a single study, and these models cannot generalize across different datasets due to significantly variable experimental settings. Here, we thoroughly assessed the transferability issue of single-study-derived models on new datasets. More importantly, we propose a method to overcome the experimental variability by harmonizing dose-response curves of different studies. Our method improves the prediction performance of machine learning models by 184% and 1367% compared to the baseline models in intra-study and inter-study predictions, respectively, and shows consistent improvement in multiple cross-validation settings. Our study addresses the crucial question of the transferability in drug combination predictions, which is fundamental for such models to be extrapolated to new drug combination discovery and clinical applications that are de facto different datasets.A machine learning-based method improves the transferability of drug combination predictions across datasets from studies with variable experimental settings, such as the number of doses and dose ranges tested.Peer reviewe

An Optical Interferometric Triaxial Displacement Sensor for Structural Health Monitoring: Characterization of Sliding and Debonding for a Delamination Process

This paper presents an extrinsic Fabry-Perot interferometer-based optical fiber sensor (EFPI) for measuring three-dimensional (3D) displacements, including interfacial sliding and debonding during delamination. The idea employs three spatially arranged EFPIs as the sensing elements. In our sensor, the three EFPIs are formed by three endfaces of three optical fibers and their corresponding inclined mirrors. Two coincident roof-like metallic structures are used to support the three fibers and the three mirrors, respectively. Our sensor was calibrated and then used to monitor interfacial sliding and debonding between a long square brick of mortar and its support structure (i.e., a steel base plate) during the drying/curing process. This robust and easy-to-manufacture triaxial EFPI-based 3D displacement sensor has great potential in structural health monitoring, the construction industry, oil well monitoring, and geotechnology

Effects of variable temperature and moisture conditions on respiration and nonstructural carbohydrate dynamics of tree roots

In warming climates, soil water content (SWC) may act as an important factor in determining belowground carbon dynamics in boreal forests. Here, we estimated the respiration and nonstructural carbohydrate (NSC) concentrations of tree roots in a mature Scots pine (Pinus sylvestris L.) stand in southern Finland during two growing seasons with contrasting weather conditions. Root respiration was estimated with four different methods: 1) incubating excised roots, 2) partitioning forest floor respirations with root exclusion, or 3) based on temperature response functions and 4) modelling with the whole-tree carbon model 'CASSIA'. In addition, we conducted a drought experiment in a greenhouse to determine the effect of reduced soil-water availability on respiration by incubating soil and roots of Scots pine saplings. We observed that the respiration of incubated roots of Scots pine saplings and soil decreased with drying after excluding the effect of temperature on respiration (RRES), soil being more sensitive to drought than roots. Similarly, RRES of incubated roots in the field was significantly decreased by lowered SWC, whereas respiration of the entire root system estimated with other methods was clearly higher in dryer and warmer than moister and cooler year. Nevertheless, incubated roots excavated from the topsoil are most affected by drying soil, which might not reflect the response of the entire root system. RRES of incubated roots was negatively associated with root fructose and glucose concentrations. At the same time, root fructose, glucose and sucrose concentrations were negatively associated with SWC due to their role in osmoregulation. Thereby it seems that RRES does not directly follow the changes in NSCs despite the apparent correlation. Our study highlights the responsive nature of root carbon dynamics in varying weather events that should be taken into account in estimating and modelling the impacts of warming climate.Peer reviewe

Integrated Bioinformatic Analysis Reveals TXNRD1 as a Novel Biomarker and Potential Therapeutic Target in Idiopathic Pulmonary Arterial Hypertension

Idiopathic pulmonary arterial hypertension (IPAH) is a life-threatening cardiopulmonary disease lacking specific diagnostic markers and targeted therapy, and its mechanism of development remains to be elucidated. The present study aimed to explore novel diagnostic biomarkers and therapeutic targets in IPAH by integrated bioinformatics analysis. Four eligible datasets (GSE117261, GSE15197, GSE53408, GSE48149) was firstly downloaded from GEO database and subsequently integrated by Robust rank aggregation (RRA) method to screen robust differentially expressed genes (DEGs). Then functional annotation of robust DEGs was performed by GO and KEGG enrichment analysis. The protein-protein interaction (PPI) network was constructed followed by using MCODE and CytoHubba plug-in to identify hub genes. Finally, 10 hub genes were screened including ENO1, TALDO1, TXNRD1, SHMT2, IDH1, TKT, PGD, CXCL10, CXCL9, and CCL5. The GSE113439 dataset was used as a validation cohort to appraise these hub genes and TXNRD1 was selected for verification at the protein level. The experiment results confirmed that serum TXNRD1 concentration was lower in IPAH patients and the level of TXNRD1 had great predictive efficiency (AUC:0.795) as well as presents negative correlation with mean pulmonary arterial pressure (mPAP) and pulmonary vascular resistance (PVR). Consistently, the expression of TXNRD1 was proved to be inhibited in animal and cellular model of PAH. In addition, GSEA analysis was performed to explore the functions of TXNRD1 and the results revealed that TXNRD1 was closely correlated with mTOR signaling pathway, MYC targets, and unfolded protein response. Finally, knockdown of TXNRD1 was shown to exacerbate proliferative disorder, migration and apoptosis resistance in PASMCs. In conclusion, our study demonstrates that TXNRD1 is a promising candidate biomarker for diagnosis of IPAH and plays an important role in PAH pathogenesis, although further research is necessary

Evaluation and Analysis of Hallucination in Large Vision-Language Models

Large Vision-Language Models (LVLMs) have recently achieved remarkable success. However, LVLMs are still plagued by the hallucination problem, which limits the practicality in many scenarios. Hallucination refers to the information of LVLMs' responses that does not exist in the visual input, which poses potential risks of substantial consequences. There has been limited work studying hallucination evaluation in LVLMs. In this paper, we propose Hallucination Evaluation based on Large Language Models (HaELM), an LLM-based hallucination evaluation framework. HaELM achieves an approximate 95% performance comparable to ChatGPT and has additional advantages including low cost, reproducibility, privacy preservation and local deployment. Leveraging the HaELM, we evaluate the hallucination in current LVLMs. Furthermore, we analyze the factors contributing to hallucination in LVLMs and offer helpful suggestions to mitigate the hallucination problem. Our training data and human annotation hallucination data will be made public soon.Comment: 11 pages, 5 figure