Addressing Data Misalignment in Image-LiDAR Fusion on Point Cloud Segmentation

With the advent of advanced multi-sensor fusion models, there has been a notable enhancement in the performance of perception tasks within in terms of autonomous driving. Despite these advancements, the challenges persist, particularly in the fusion of data from cameras and LiDAR sensors. A critial concern is the accurate alignment of data from these disparate sensors. Our observations indicate that the projected positions of LiDAR points often misalign on the corresponding image. Furthermore, fusion models appear to struggle in accurately segmenting these misaligned points. In this paper, we would like to address this problem carefully, with a specific focus on the nuScenes dataset and the SOTA of fusion models 2DPASS, and providing the possible solutions or potential improvements.Comment: arXiv admin note: text overlap with arXiv:2309.1175

Improving Textless Spoken Language Understanding with Discrete Units as Intermediate Target

Spoken Language Understanding (SLU) is a task that aims to extract semantic information from spoken utterances. Previous research has made progress in end-to-end SLU by using paired speech-text data, such as pre-trained Automatic Speech Recognition (ASR) models or paired text as intermediate targets. However, acquiring paired transcripts is expensive and impractical for unwritten languages. On the other hand, Textless SLU extracts semantic information from speech without utilizing paired transcripts. However, the absence of intermediate targets and training guidance for textless SLU often results in suboptimal performance. In this work, inspired by the content-disentangled discrete units from self-supervised speech models, we proposed to use discrete units as intermediate guidance to improve textless SLU performance. Our method surpasses the baseline method on five SLU benchmark corpora. Additionally, we find that unit guidance facilitates few-shot learning and enhances the model's ability to handle noise.Comment: Accepted by interspeech 202

POSTURAL STABILITY PERFORMANCE BETWEEN SEDENTARY AND ACTIVE SUBJECTS WITH THE BIODEX STABILITY SYSTEM

INTRODUCTION: Postural stability (PS) has been defined as the ability to maintain an upright posture within the base of support (Lee and Lin, 2007) and is considered to be an important indicator of musculoskeletal health and physical performance. This study examined the PS performance between sedentary and active subjects using the Biodex Balance System (BBS) with well intraclass correlation coefficient (Hinman, 2000)

Automatic Diagnosis of Late-Life Depression by 3D Convolutional Neural Networks and Cross-Sample Entropy Analysis From Resting-State fMRI

Resting-state fMRI has been widely used in investigating the pathophysiology of late-life depression (LLD). Unlike the conventional linear approach, cross-sample entropy (CSE) analysis shows the nonlinear property in fMRI signals between brain regions. Moreover, recent advances in deep learning, such as convolutional neural networks (CNNs), provide a timely application for understanding LLD. Accurate and prompt diagnosis is essential in LLD; hence, this study aimed to combine CNN and CSE analysis to discriminate LLD patients and non-depressed comparison older adults based on brain resting-state fMRI signals. Seventy-seven older adults, including 49 patients and 28 comparison older adults, were included for fMRI scans. Three-dimensional CSEs with volumes corresponding to 90 seed regions of interest of each participant were developed and fed into models for disease classification and depression severity prediction. We obtained a diagnostic accuracy \u3e 85% in the superior frontal gyrus (left dorsolateral and right orbital parts), left insula, and right middle occipital gyrus. With a mean root-mean-square error (RMSE) of 2.41, three separate models were required to predict depressive symptoms in the severe, moderate, and mild depression groups. The CSE volumes in the left inferior parietal lobule, left parahippocampal gyrus, and left postcentral gyrus performed best in each respective model. Combined complexity analysis and deep learning algorithms can classify patients with LLD from comparison older adults and predict symptom severity based on fMRI data. Such application can be utilized in precision medicine for disease detection and symptom monitoring in LLD

SARS-related Virus Predating SARS Outbreak, Hong Kong

Using immunofluorescence and neutralization assays, we detected antibodies to human severe acute respiratory syndrome–associated coronavirus (SARS-CoV) and/or animal SARS-CoV–like virus in 17 (1.8%) of 938 adults recruited in 2001. This finding suggests that a small proportion of healthy persons in Hong Kong had been exposed to SARS-related viruses at least 2 years before the recent SARS outbreak

Bromidotricarbon­yl[2-phenyl-5-(pyridin-2-yl-κN)-1,3,4-oxadiazole-κN 4]rhenium(I) dichloro­methane monosolvate

In the title rhenium(I) complex, [ReBr(C13H9N3O)(CO)3]·CH2Cl2, the dichloro­methane solvent mol­ecule is disordered over two positions with an occupancy ratio of 0.81 (15):0.19 (15). The ReI atom is coordinated by two N atoms from a 2-phenyl-5-(pyridin-2-yl-κN)-1,3,4-oxadiazole (L) ligand, three C atoms from three carbonyl groups and a Br atom in a distorted octa­hedral geometry. The three rings in L are almost coplanar (a mean plane fitted through all non-H atoms of this ligand has an r.m.s. deviation of 0.063 Å), and the carbonyl ligands are coordinated in a fac arrangement

Recommendation for a contouring method and atlas of organs at risk in nasopharyngeal carcinoma patients receiving intensity-modulated radiotherapy

Background and purpose To recommend contouring methods and atlas of organs at risk (OARs) for nasopharyngeal carcinoma (NPC) patients receiving intensity-modulated radiotherapy, in order to help reach a consensus on interpretations of OARs delineation. Methods and materials Two to four contouring methods for the middle ear, inner ear, temporal lobe, parotid gland and spinal cord were identified via systematic literature review; their volumes and dosimetric parameters were compared in 41 patients. Areas under the receiver operating characteristic curves for temporal lobe contouring were compared in 21 patients with unilateral temporal lobe necrosis (TLN). Results Various contouring methods for the temporal lobe, middle ear, inner ear, parotid gland and spinal cord lead to different volumes and dosimetric parameters (P < 0.05). For TLN, D1 of PRV was the most relevant dosimetric parameter and 64 Gy was the critical point. We suggest contouring for the temporal lobe, middle ear, inner ear, parotid gland and spinal cord. A CT-MRI fusion atlas comprising 33 OARs was developed. Conclusions Different dosimetric parameters may hinder the dosimetric research. The present recommendation and atlas, may help reach a consensus on subjective interpretation of OARs delineation to reduce inter-institutional differences in NPC patients. © 2013 Elsevier Ireland Ltd. All rights reserved.published_or_final_versio

Robotic arm gripper using force sensor for crop picking mechanism

A dynamic gripper with qualities that resemble the human hand as closely as possible is sought after in the field of robotics. The idea of a robotic arm has been used invarious cutting-edge technology fields, including agriculture,to assist people or farmers in carrying out regular tasks, such as gathering fruit, etc.The robot arm's end effector is one of the essential parts of the robot that we can configure based on their tasks, such as a spraying adaptor for fertilization function or a gripper for the picking mechanism. Since fruits have a delicate and fragile surfaces, it is vital to have a gripper with a smooth contact surface that can apply the right amount of force to pick the fruits without causing any bruising that can degrade the crop's quality. Hence, this paper proposes a robotic arm gripper design for the crop-picking mechanism using a force sensor as the main component of the Arduino Uno embedded system. There liability result for the chili obtained is around 95% showing that this design is promising for designing an adaptive robotic arm gripper