We present radiance field propagation (RFP), a novel approach to segmenting objects in 3D during reconstruction given only unlabeled multi-view images of a scene. RFP is derived from emerging neural radiance field-based techniques, which jointly encodes semantics with appearance and geometry. The core of our method is a novel propagation strategy for individual objects' radiance fields with a bidirectional photometric loss, enabling an unsupervised partitioning of a scene into salient or meaningful regions corresponding to different object instances. To better handle complex scenes with multiple objects and occlusions, we further propose an iterative expectation-maximization algorithm to refine object masks. To the best of our knowledge, RFP is the first unsupervised approach for tackling 3D scene object segmentation for neural radiance field (NeRF) without any supervision, annotations, or other cues such as 3D bounding boxes and prior knowledge of object class. Experiments demonstrate that RFP achieves feasible segmentation results that are more accurate than previous unsupervised image/scene segmentation approaches, and are comparable to existing supervised NeRF-based methods. The segmented object representations enable individual 3D object editing operations. Codes and datasets will be made publicly available.

We present radiance field propagation (RFP), a novel approach to segmenting objects in 3D during reconstruction given only unlabeled multi-view images of a scene. RFP is derived from emerging neural radiance field-based techniques, which jointly encodes semantics with appearance and geometry. The core of our method is a novel propagation strategy for individual objects' radiance fields with a bidirectional photometric loss, enabling an unsupervised partitioning of a scene into salient or meaningful regions corresponding to different object instances. To better handle complex scenes with multiple objects and occlusions, we further propose an iterative expectation-maximization algorithm to refine object masks. To the best of our knowledge, RFP is the first unsupervised approach for tackling 3D scene object segmentation for neural radiance field (NeRF) without any supervision, annotations, or other cues such as 3D bounding boxes and prior knowledge of object class.

In many modern astronomical facilities, multi-object telescopes are crucial instruments. Most of these telescopes have thousands of robotic fiber positioners(RFPs) installed on their focal plane, sharing an overlapping workspace. Collisions between RFPs during their movement can result in some targets becoming unreachable and cause structural damage. Therefore, it is necessary to reasonably assess and evaluate the collision probability of the RFPs. In this study, we propose a mathematical models of collision probability and validate its results using Monte Carlo simulations. In addition, a new collision calculation method is proposed for faster calculation(nearly 0.15% of original time). Simulation experiments have verified that our method can evaluate the collision probability between RFPs with both equal and unequal arm lengths. Additionally, we found that adopting a target distribution based on a Poisson distribution can reduce the collision probability by approximately 2.6% on average.

Wining and dining, wooing clients with creative offers, and cashing big bonuses provide the glamor to sales work. Drafting answers to hundreds of dull questions posed by a prospective customer's request for proposals? Mercifully for workers, after months of speculation about ChatGPT-style AI taking over white-collar work, the corporate chore of responding to RFPs is one of the first that generative AI is disrupting. In April, communications software maker Twilio introduced RFP Genie, a generative AI tool that digests an RFP, scours thousands of internal files for relevant information, and uses OpenAI's GPT-4 to generate a suitable response. The company's sales staff simply copy and paste the text over into a formal document and make a few adjustments.

Two main families of node feature augmentation schemes have been explored for enhancing GNNs: random features and spectral positional encoding. Surprisingly, however, there is still no clear understanding of the relation between these two augmentation schemes. Here we propose a novel family of positional encoding schemes which draws a link between the above two approaches and improves over both. The new approach, named Random Feature Propagation (RFP), is inspired by the power iteration method and its generalizations. It concatenates several intermediate steps of an iterative algorithm for computing the dominant eigenvectors of a propagation matrix, starting from random node features. Notably, these propagation steps are based on graph-dependent propagation operators that can be either predefined or learned. We explore the theoretical and empirical benefits of RFP. First, we provide theoretical justifications for using random features, for incorporating early propagation steps, and for using multiple random initializations. Then, we empirically demonstrate that RFP significantly outperforms both spectral PE and random features in multiple node classification and graph classification benchmarks.

In this paper, we establish novel data-dependent upper bounds on the generalization error through the lens of a "variable-size compressibility" framework that we introduce newly here. In this framework, the generalization error of an algorithm is linked to a variable-size 'compression rate' of its input data. This is shown to yield bounds that depend on the empirical measure of the given input data at hand, rather than its unknown distribution. Our new generalization bounds that we establish are tail bounds, tail bounds on the expectation, and in-expectations bounds. Moreover, it is shown that our framework also allows to derive general bounds on any function of the input data and output hypothesis random variables. In particular, these general bounds are shown to subsume and possibly improve over several existing PAC-Bayes and data-dependent intrinsic dimension-based bounds that are recovered as special cases, thus unveiling a unifying character of our approach. For instance, a new data-dependent intrinsic dimension based bounds is established, which connects the generalization error to the optimization trajectories and reveals various interesting connections with rate-distortion dimension of process, R\'enyi information dimension of process, and metric mean dimension.

Parents in Fairfax, Va., protest two controversial books being displayed in the holiday reading section of school libraries. Fairfax County Public Schools (FCPS) is seeking a social media monitoring service that will track hate speech as well as purported harassment and threats against employees, students, or racial groups – resurfacing concerns about First Amendment rights and school safety. A request for proposal (RFP), which closed last week, showed the Virginia school district offering $200,000 to "detect help deter any negative actions or consequences coming from social media which may be directed to racial groups or any student or teacher within FCPS." Under technical and functional requirements, the school district lists "Automatically classify aliases, usernames, emails websites, etc."; "Visually identify relationships and connections between persons"; "Save search queries and set alerts for active listening"; and "False positive reduction with embedded violence language classifier and metadata optimization technology." LINCOLNIA, VIRGINIA - AUGUST 23: Masked students arrive to the first day of class at Glasgow Middle School in Lincolnia, Virginia, on Monday, August 23, 2021, the first day back to school for the Fairfax County school district.

We introduce an emerging AI-based approach and prototype system for assisting team formation when researchers respond to calls for proposals from funding agencies. This is an instance of the general problem of building teams when demand opportunities come periodically and potential members may vary over time. The novelties of our approach are that we: (a) extract technical skills needed about researchers and calls from multiple data sources and normalize them using Natural Language Processing (NLP) techniques, (b) build a prototype solution based on matching and teaming based on constraints, (c) describe initial feedback about system from researchers at a University to deploy, and (d) create and publish a dataset that others can use.

TRB's National Cooperative Highway Research Program (NCHRP) has issued a request for proposals to advance the use of artificial intelligence, machine learning and Big Data and unconventional data and to assess their effectiveness to support safe system and modal priority decision-making as well as performance tracking. Proposals are due September 13, 2021 at 5:00 PM Eastern.

In February 2021, Facebook launched a request for proposals (RFP) on sample-efficient sequential Bayesian decision-making. View RFP In a Q&A about the RFP, Core Data Science researchers said they are keen to learn more about all the great research that is going on in the area of Bayesian optimization. Eytan Bakshy and Max Balandat, members of the team behind the RFP, also spoke about sharing a number of really interesting real-world use cases that they hope can help inspire additional applied research and increase interest and research activity into sample-efficient sequential Bayesian decision-making. The team reviewed 89 high-quality proposals and are pleased to announce the two winning proposals below, as well as the 10 finalists. Thank you to everyone who took the time to submit a proposal, and congratulations to the winners.