Re-Evaluating LiDAR Scene Flow for Autonomous Driving

Current methods for self-supervised LiDAR scene flow estimation work poorly on real data. A variety of flaws in common evaluation protocols have caused leading approaches to focus on problems that do not exist in real data. We analyze a suite of recent works and find that despite their focus on deep learning, the main challenges of the LiDAR scene flow problem -- removing the dominant rigid motion and robustly estimating the simple motions that remain -- can be more effectively solved with classical techniques such as ICP motion compensation and enforcing piecewise rigid assumptions. We combine these steps with a test-time optimization method to form a state-of-the-art system that does not require any training data. Because our final approach is dataless, it can be applied on different datasets with diverse LiDAR rigs without retraining. Our proposed approach outperforms all existing methods on Argoverse 2.0, halves the error rate on NuScenes, and even rivals the performance of supervised networks on Waymo and lidarKITTI

ZeroFlow: Scalable Scene Flow via Distillation

Scene flow estimation is the task of describing the 3D motion field between temporally successive point clouds. State-of-the-art methods use strong priors and test-time optimization techniques, but require on the order of tens of seconds to process full-size point clouds, making them unusable as computer vision primitives for real-time applications such as open world object detection. Feedforward methods are considerably faster, running on the order of tens to hundreds of milliseconds for full-size point clouds, but require expensive human supervision. To address both limitations, we propose Scene Flow via Distillation, a simple, scalable distillation framework that uses a label-free optimization method to produce pseudo-labels to supervise a feedforward model. Our instantiation of this framework, ZeroFlow, achieves state-of-the-art performance on the Argoverse 2 Self-Supervised Scene Flow Challenge while using zero human labels by simply training on large-scale, diverse unlabeled data. At test-time, ZeroFlow is over 1000x faster than label-free state-of-the-art optimization-based methods on full-size point clouds (34 FPS vs 0.028 FPS) and over 1000x cheaper to train on unlabeled data compared to the cost of human annotation (\$394 vs ~\$750,000). To facilitate further research, we will release our code, trained model weights, and high quality pseudo-labels for the Argoverse 2 and Waymo Open datasets.Comment: 9 pages, 4 pages of citations, 6 pages of Supplemental. Project page with data releases is at http://vedder.io/zeroflow.htm

With good intentions: complexity in unsolicited informal support for Aboriginal and Torres Strait Islander peoples. A qualitative study

<p>Abstract</p> <p>Background</p> <p>Understanding people's social lived experiences of chronic illness is fundamental to improving health service delivery and health outcomes, particularly in relation to self-management activity. In explorations of social lived experiences this paper uncovers the ways in which Aboriginal and Torres Strait Islander people with chronic illness experience informal unsolicited support from peers and family members.</p> <p>Methods</p> <p>Nineteen Aboriginal and Torres Islander participants were interviewed in the Serious and Continuing Illness Policy and Practice Study (SCIPPS). Participants were people with Type 2 diabetes (N = 17), chronic obstructive pulmonary disease (N = 3) and/or chronic heart failure (N = 11) and family carers (N = 3). Participants were asked to describe their experience of having or caring for someone with chronic illness. Content and thematic analysis of in-depth semi-structured interviews was undertaken, assisted by QSR Nvivo8 software.</p> <p>Results</p> <p>Participants reported receiving several forms of unsolicited support, including encouragement, practical suggestions for managing, nagging, growling, and surveillance. Additionally, participants had engaged in 'yarning', creating a 'yarn' space, the function of which was distinguished as another important form of unsolicited support. The implications of recognising these various support forms are discussed in relation to responses to unsolicited support as well as the needs of family carers in providing effective informal support.</p> <p>Conclusions</p> <p>Certain locations of responsibility are anxiety producing. Family carers must be supported in appropriate education so that they can provide both solicited and unsolicited support in effective ways. Such educational support would have the added benefit of helping to reduce carer anxieties about caring roles and responsibilities. Mainstream health services would benefit from fostering environments that encourage informal interactions that facilitate learning and support in a relaxed atmosphere.</p

Akt-dependent metabolic reprogramming regulates tumor cell histone acetylation

Histone acetylation plays important roles in gene regulation, DNA replication, and the response to DNA damage, and it is frequently deregulated in tumors. We postulated that tumor cell histone acetylation levels are determined in part by changes in acetyl coenzyme A (acetyl-CoA) availability mediated by oncogenic metabolic reprogramming. Here, we demonstrate that acetyl-CoAis dynamically regulated by glucose availability in cancer cells and that the ratio of acetyl-CoA: coenzyme A within the nucleus modulates global histone acetylation levels. In vivo, expression of oncogenic Kras or Akt stimulates histone acetylation changes that precede tumor development. Furthermore, we show that Akt's effects on histone acetylation are mediated through the metabolic enzyme ATP-citrate lyase and that pAkt(Ser473) levels correlate significantly with histone acetylation marks in human gliomas and prostate tumors. The data implicate acetyl-CoA metabolism as a key determinant of histone acetylation levels in cancer cells