We introduce a novel segmentation-aware joint training framework called generative reinforcement network (GRN) that integrates segmentation loss feedback to optimize both image generation and segmentation performance in a single stage. An image enhancement technique called segmentation-guided enhancement (SGE) is also developed, where the generator produces images tailored specifically for the segmentation model. Two variants of GRN were also developed, including GRN for sample-efficient learning (GRN-SEL) and GRN for semi-supervised learning (GRN-SSL). GRN's performance was evaluated using a dataset of 69 fully annotated 3D ultrasound scans from 29 subjects. The annotations included six anatomical structures: dermis, superficial fat, superficial fascial membrane (SFM), deep fat, deep fascial membrane (DFM), and muscle. Our results show that GRN-SEL with SGE reduces labeling efforts by up to 70% while achieving a 1.98% improvement in the Dice Similarity Coefficient (DSC) compared to models trained on fully labeled datasets. GRN-SEL alone reduces labeling efforts by 60%, GRN-SSL with SGE decreases labeling requirements by 70%, and GRN-SSL alone by 60%, all while maintaining performance comparable to fully supervised models. These findings suggest the effectiveness of the GRN framework in optimizing segmentation performance with significantly less labeled data, offering a scalable and efficient solution for ultrasound image analysis and reducing the burdens associated with data annotation.

Parallelization of non-admissible search algorithms such as GBFS poses a challenge because straightforward parallelization can result in search behavior which significantly deviates from sequential search. Previous work proposed PUHF, a parallel search algorithm which is constrained to only expand states that can be expanded by some tie-breaking strategy for GBFS. We show that despite this constraint, the number of states expanded by PUHF is not bounded by a constant multiple of the number of states expanded by sequential GBFS with the worst-case tie-breaking strategy. We propose and experimentally evaluate One Bench At a Time (OBAT), a parallel greedy search which guarantees that the number of states expanded is within a constant factor of the number of states expanded by sequential GBFS with some tie-breaking policy.

Parallelization of Greedy Best First Search (GBFS) has been difficult because straightforward parallelization can result in search behavior which differs significantly from sequential GBFS, exploring states which would not be explored by sequential GBFS with any tie-breaking strategy. Recent work has proposed a class of parallel GBFS algorithms which constrains search to exploration of the Bench Transition System (BTS), which is the set of states that can be expanded by GBFS under some tie-breaking policy. However, enforcing this constraint is costly, as such BTS-constrained algorithms are forced to spend much of the time waiting so that only states which are guaranteed to be in the BTS are expanded. We propose an improvement to parallel search which decouples state generation and state evaluation and significantly improves state evaluation rate, resulting in better search performance.

Recognizing a traffic accident is an essential part of any autonomous driving or road monitoring system. An accident can appear in a wide variety of forms, and understanding what type of accident is taking place may be useful to prevent it from reoccurring. The task of being able to classify a traffic scene as a specific type of accident is the focus of this work. We approach the problem by likening a traffic scene to a graph, where objects such as cars can be represented as nodes, and relative distances and directions between them as edges. This representation of an accident can be referred to as a scene graph, and is used as input for an accident classifier. Better results can be obtained with a classifier that fuses the scene graph input with representations from vision and language. This work introduces a multi-stage, multimodal pipeline to pre-process videos of traffic accidents, encode them as scene graphs, and align this representation with vision and language modalities for accident classification. When trained on 4 classes, our method achieves a balanced accuracy score of 57.77% on an (unbalanced) subset of the popular Detection of Traffic Anomaly (DoTA) benchmark, representing an increase of close to 5 percentage points from the case where scene graph information is not taken into account.

Large Language Models (LLMs) have become pivotal in addressing reasoning tasks across diverse domains, including arithmetic, commonsense, and symbolic reasoning. They utilize prompting techniques such as Exploration-of-Thought, Decomposition, and Refinement to effectively navigate and solve intricate tasks. Despite these advancements, the application of LLMs to Combinatorial Problems (CPs), known for their NP-hardness and critical roles in logistics and resource management remains underexplored. To address this gap, we introduce a novel prompting strategy: Self-Guiding Exploration (SGE), designed to enhance the performance of solving CPs. SGE operates autonomously, generating multiple thought trajectories for each CP task. It then breaks these trajectories down into actionable subtasks, executes them sequentially, and refines the results to ensure optimal outcomes. We present our research as the first to apply LLMs to a broad range of CPs and demonstrate that SGE outperforms existing prompting strategies by over 27.84% in CP optimization performance. Additionally, SGE achieves a 2.46% higher accuracy over the best existing results in other reasoning tasks (arithmetic, commonsense, and symbolic). Our implementation is available online.

This article is from Big Technology, a newsletter by Alex Kantrowitz. For years, Google dominated search with little opposition. The format faced little disruption; it was always just a bunch of blue links. And the company's multibillion-dollar deals with phone-makers to keep Google search as a default cemented its lead. But its comfortable perch is actually, really starting to fade.

Google's Search Generative Experience (SGE), which currently provides generative AI summaries at the top of the search results page for select users, is about to be much more available. Just six months after its debut at I/O 2023, the company announced Wednesday that SGE is expanding to Search Labs users in 120 countries and territories, gaining support for four additional languages and receiving a handful of helpful new features. Unlike its frenetic rollout of the Bard chatbot in March, Google has taken a slightly more measured tone in distributing its AI search assistant. The company began with English language searches in the US in May, expanded to English-language users in India and Japan in August and on to teen users in September. As of Wednesday, users from Brazil to Bhutan can give the feature a try.

Google is opening its AI-powered search experience to teens. In addition, the company's Search Generative Experience (SGE) is adding new context pages to shed light on generated responses and individual web links within answers. The company is opening its search-based AI tool to US teenagers between 13 and 17. Google says it received "particularly positive feedback" from 18- to 24-year-olds who tested SGE, which influenced its decision. SGE has been available as part of Google Search Labs since late May. Google says it has added safeguards to prevent inappropriate or harmful content based on its research with experts in teen development.

Google is bringing one element of its AI-powered "Search Generative Experience" (SGE) to Google Chrome, following in the footsteps of Microsoft and its migration of Bing Chat into Edge and mainstream search experiences. Google calls this "SGE while browsing," but that's an overly complex way of putting it. What Google will be adding to the desktop version of Chrome (as well as the Google app on Android and iOS) is an AI-generated, bulleted summary of longer articles. If this sounds familiar, it should. Amazon said this week that it's adding AI summaries of user reviews on product pages, and Newegg has done the same.

Google is testing a new capability for its generative AI in search that will make it a more veritable rival to Microsoft's AI Copilot in Edge. The tech giant has launched an early experiment for its generative AI-powered Search experience (SGE) that breaks out of Search itself. Called "SGE while browsing," the feature can quickly generate the most salient points of long-form content found on the web. The tech giant positions it as a tool you can use to more easily digest complex topics that might require extensive research. However, the tool will not be able to provide key points for paywalled articles, only for some web pages that you can view free of charge.