13 accepted papers
Instruction tuning improves the LLMs performance but depends on high-quality training data. Recently, LLMs have been used to synthesize data, enhancing training with seeds like question-answer (QA) pairs. However, this synthesis often results in instruction examples similar to the seeds, lacking div…
Resting-state functional connectivity networks (rs-FCNs) have been most frequently used for brain network analysis in neuroscience. However, a body of evidence indicates that task-state FCNs (ts-FCNs) are better associated with individual differences in behavior than rs-FCN. Until now there have bee…
Functional connectivity networks (FCNs), as graph-structured data derived from functional magnetic resonance imaging (fMRI), are essential for understanding how brain functions coordinate with behavior and cognition. However, the utility of these FCNs is often limited by the brain atlas, since the p…
Patients with glioblastoma (GBM) have a poor survival rate. In order to facilitate early interventions and personalized therapeutic treatment, there is a pressing need for employing routine non-invasive MRI for preoperative GBM survival prediction. In this paper, we investigate to what extent region…
Recently, functional connectivity network (FCN) analysis via graph convolutional networks (GCNs) has greatly boosted diagnostic performance of brain diseases on a population graph for subject classification. However, most existing methods only focus on FCNs based on a single brain atlas (ignoring co…
We propose a novel method, LoLep, which regresses Locally-Learned planes from a single RGB image to represent scenes accurately, thus generating better novel views. Without the depth information, regressing appropriate plane locations is a challenging problem. To solve this issue, we pre-partition t…
Communication between robots and the server is a major problem for cloud robotic systems. In this paper, we address the problem caused by data loss during such communications and propose an efficient buffering algorithm, called AFR, to solve the problem. We model the problem into an optimization pro…
We present a novel self-supervised algorithm named MotionHint for monocular visual odometry (VO) that takes motion constraints into account. A key aspect of our approach is to use an appropriate motion model that can help existing self-supervised monocular VO (SSM-VO) algorithms to overcome issues r…
ROS is popular in robotic-software development, and thus detecting bugs in ROS programs is important for modern robots. Fuzzing is a promising technique of runtime testing. But existing fuzzing approaches are limited in testing ROS programs, due to neglecting ROS properties, such as multi-dimensiona…
The data loss caused by unreliable network seriously impacts the results of remote visual SLAM systems. From our experiment, a loss of less than 1 second of data can cause a visual SLAM algorithm to lose tracking. We present a novel buffering method, ORBBuf, to reduce the impact of data loss on remo…
Inter-process communication (IPC) is one of the core functions of modern robotics middleware. We propose an efficient IPC technique called TZC (Towards Zero-Copy). As a core component of TZC, we design a novel algorithm called partial serialization. Our formulation can generate messages that can be…
Functional magnetic resonance imaging (fMRI) is a powerful tool to analyze brain development and neuronal activity. Identifying discriminative brain regions between various groups within a population has generated great interest in recent years. In this work, we consider the problem of estimating mu…
Schizophrenia (SZ) is a complex disease caused by a lot genetic variants, epigenetic and brain region abnormalities. In this study, we adopted a joint nonnegative matrix factorization method to integrate three datasets including single nucleotide polymorphism (SNP), brain activity measured by functi…