Tootfinder

En Route Path-planning for Partially Occupied Vehicles in Ride-pooling Systems
Pengbo Zhu, Giancarlo Ferrari-Trecate, Nikolas Geroliminis
https://arxiv.org/abs/2506.04968

En Route Path-planning for Partially Occupied Vehicles in Ride-pooling Systems
Ride-pooling services, such as UberPool and Lyft Shared Saver, enable a single vehicle to serve multiple customers within one shared trip. Efficient path-planning algorithms are crucial for improving the performance of such systems. For partially occupied vehicles with available capacity, we introduce a novel routing algorithm designed to maximize the likelihood of picking up additional passengers while serving the current passengers to their destination. Unlike traditional methods that group p…

An unnoticed leak
Pooling above the ceiling
Now a waterfall.
#dailyhaikuprompt - waterfall
#haiku
#poem

This https://arxiv.org/abs/2212.02658 has been replaced.
link: https://scholar.google.com/scholar?q=a

Pooling information in likelihood-free inference
Likelihood-free inference (LFI) methods, such as approximate Bayesian computation, have become commonplace for conducting inference in complex models. Many approaches are based on summary statistics or discrepancies derived from synthetic data. However, determining which summary statistics or discrepancies to use for constructing the posterior remains a challenging question, both practically and theoretically. Instead of relying on a single vector of summaries for inference, we propose a new po…

This https://arxiv.org/abs/2506.00891 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Uneven Event Modeling for Partially Relevant Video Retrieval
Given a text query, partially relevant video retrieval (PRVR) aims to retrieve untrimmed videos containing relevant moments, wherein event modeling is crucial for partitioning the video into smaller temporal events that partially correspond to the text. Previous methods typically segment videos into a fixed number of equal-length clips, resulting in ambiguous event boundaries. Additionally, they rely on mean pooling to compute event representations, inevitably introducing undesired misalignment…

Explainable speech emotion recognition through attentive pooling: insights from attention-based temporal localization
Tahitoa Leygue (DIASI), Astrid Sabourin (DIASI), Christian Bolzmacher (DIASI), Sylvain Bouchigny (DIASI), Margarita Anastassova (DIASI), Quoc-Cuong Pham (DIASI)
https://arxiv.org/abs/2506.15754

Explainable speech emotion recognition through attentive pooling: insights from attention-based temporal localization
State-of-the-art transformer models for Speech Emotion Recognition (SER) rely on temporal feature aggregation, yet advanced pooling methods remain underexplored. We systematically benchmark pooling strategies, including Multi-Query Multi-Head Attentive Statistics Pooling, which achieves a 3.5 percentage point macro F1 gain over average pooling. Attention analysis shows 15 percent of frames capture 80 percent of emotion cues, revealing a localized pattern of emotional information. Analysis of hi…

Temperature Sensitivity of Residential Energy Demand on the Global Scale: A Bayesian Partial Pooling Model
Peer Lasse Hinrichsen, Katrin Rehdanz, Richard S. J. Tol
https://arxiv.org/abs/2506.22768

Temperature Sensitivity of Residential Energy Demand on the Global Scale: A Bayesian Partial Pooling Model
This paper contributes to the limited literature on the temperature sensitivity of residential energy demand on a global scale. Using a Bayesian Partial Pooling model, we estimate country-specific intercepts and slopes, focusing on non-linear temperature response functions. The results, based on data for up to 126 countries spanning from 1978 to 2023, indicate a higher demand for residential electricity and natural gas at temperatures below -5 degrees Celsius and a higher demand for electricity…

Robust Noise Attenuation via Adaptive Pooling of Transformer Outputs
Greyson Brothers
https://arxiv.org/abs/2506.09215 https://arxiv.…

Robust Noise Attenuation via Adaptive Pooling of Transformer Outputs
We investigate the design of pooling methods used to summarize the outputs of transformer embedding models, primarily motivated by reinforcement learning and vision applications. This work considers problems where a subset of the input vectors contains requisite information for a downstream task (signal) while the rest are distractors (noise). By framing pooling as vector quantization with the goal of minimizing signal loss, we demonstrate that the standard methods used to aggregate transformer…

Vortex Detection from Quantum Data
Chelsea A. Williams, Annie E. Paine, Antonio A. Gentile, Daniel Berger, Oleksandr Kyriienko
https://arxiv.org/abs/2506.23976

Vortex Detection from Quantum Data
Quantum solutions to differential equations represent quantum data -- states that contain relevant information about the system's behavior, yet are difficult to analyze. We propose a toolbox for reading out information from such data, where customized quantum circuits enable efficient extraction of flow properties. We concentrate on the process referred to as quantum vortex detection (QVD), where specialized operators are developed for pooling relevant features related to vorticity. Specificall…

MedRegion-CT: Region-Focused Multimodal LLM for Comprehensive 3D CT Report Generation
Sunggu Kyung, Jinyoung Seo, Hyunseok Lim, Dongyeong Kim, Hyungbin Park, Jimin Sung, Jihyun Kim, Wooyoung Jo, Yoojin Nam, Namkug Kim
https://arxiv.org/abs/2506.23102

MedRegion-CT: Region-Focused Multimodal LLM for Comprehensive 3D CT Report Generation
The recent release of RadGenome-Chest CT has significantly advanced CT-based report generation. However, existing methods primarily focus on global features, making it challenging to capture region-specific details, which may cause certain abnormalities to go unnoticed. To address this, we propose MedRegion-CT, a region-focused Multi-Modal Large Language Model (MLLM) framework, featuring three key innovations. First, we introduce Region Representative ($R^2$) Token Pooling, which utilizes a 2D-…

Sky, ITV, and Channel 4 plan to provide streaming ad space in one marketplace, letting advertisers run campaigns simultaneously, to combat Google and Meta (Mark Sweney/The Guardian)
https://www.theguardian.com/media/2025/jun

Sky, ITV, and Channel 4 plan to provide streaming ad space in one marketplace, letting advertisers run campaigns simultaneously, to combat Google and Meta (Mark Sweney/The Guardian)
https://www.theguardian.com/media/2025/jun

Teaching Cars to Drive: Spotlight on Connected and Automated Vehicles
Filippos N. Tzortzoglou, Andreas A. Malikopoulos
https://arxiv.org/abs/2507.01211 htt…

Teaching Cars to Drive: Spotlight on Connected and Automated Vehicles
In recent decades, society has witnessed significant advancements in emerging mobility systems. These systems refer to transportation solutions that incorporate digital technologies, automation, connectivity, and sustainability to create safer, more efficient, and user-centered mobility. Examples include connected and automated vehicles (CAVs), shared mobility services (car-pooling), electric vehicles, and mobility-as-a-service platforms. These innovations have the potential to greatly impact a…

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences
Nicolas Thewes, Philipp Steinhauer, Patrick Trampert, Markus Pauly, Georg Schneider
https://arxiv.org/abs/2506.21921

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences
Anomaly detection is the task of identifying rarely occurring (i.e. anormal or anomalous) samples that differ from almost all other samples in a dataset. As the patterns of anormal samples are usually not known a priori, this task is highly challenging. Consequently, anomaly detection lies between semi- and unsupervised learning. The detection of anomalies in sound data, often called 'ASD' (Anomalous Sound Detection), is a sub-field that deals with the identification of new and yet unknown effe…

Expanding Relevance Judgments for Medical Case-based Retrieval Task with Multimodal LLMs
Catarina Pires, S\'ergio Nunes, Lu\'is Filipe Teixeira
https://arxiv.org/abs/2506.17782

Expanding Relevance Judgments for Medical Case-based Retrieval Task with Multimodal LLMs
Evaluating Information Retrieval (IR) systems relies on high-quality manual relevance judgments (qrels), which are costly and time-consuming to obtain. While pooling reduces the annotation effort, it results in only partially labeled datasets. Large Language Models (LLMs) offer a promising alternative to reducing reliance on manual judgments, particularly in complex domains like medical case-based retrieval, where relevance assessment requires analyzing both textual and visual information. In t…

On the Linear Programming Model for Dynamic Stochastic Matching and Its Application on Pricing
Junlin Chen, Chiwei Yan, Hai Jiang
https://arxiv.org/abs/2506.09924

On the Linear Programming Model for Dynamic Stochastic Matching and Its Application on Pricing
Important pricing problems in centralized matching markets -- such as carpooling and food delivery platforms -- often exhibit a bi-level structure. At the upper level, the platform sets prices for different types of agents (e.g., riders with various origins and destinations, or delivery orders from diverse restaurants to customer locations). The lower level then matches converted agents to minimize operational costs, such as pooling riders with similar itineraries into the same vehicle or conso…

Clustering Tails in High Dimension
Liujun Chen, Marco Oesting, Chen Zhou
https://arxiv.org/abs/2506.19414 https://arxiv.org/pdf/2506.…

Clustering Tails in High Dimension
One potential solution to combat the scarcity of tail observations in extreme value analysis is to integrate information from multiple datasets sharing similar tail properties, for instance, a common extreme value index. In other words, for a multivariate dataset, we intend to group dimensions into clusters first, before applying any pooling techniques. This paper addresses the clustering problem for a high dimensional dataset, according to their extreme value indices. We propose an iterative…

Choquet rating criteria, risk measures, and risk consistency
Nan Guo, Ruodu Wang, Chenxi Xia, Jingping Yang
https://arxiv.org/abs/2506.13435 https://

Choquet rating criteria, risk measures, and risk consistency
Credit ratings are widely used by investors as a screening device. We introduce and study several natural notions of risk consistency that promote prudent investment decisions in the framework of Choquet rating criteria. Three closely related notions of risk consistency are considered: with respect to risk aversion, the asset pooling effect, and the benefit of portfolio diversification. These notions are formulated either under a single probability measure or multiple probability measures. We s…

Linguistic Ordered Weighted Averaging based deep learning pooling for fault diagnosis in a wastewater treatment plant
Alicia Beneyto-Rodriguez, Gregorio I. Sainz-Palmero, Marta Galende-Hern\'andez, Mar\'ia J. Fuente
https://arxiv.org/abs/2506.08676

Linguistic Ordered Weighted Averaging based deep learning pooling for fault diagnosis in a wastewater treatment plant
Nowadays, water reuse is a serious challenge to help address water shortages. Here, the wastewater treatment plants (WWTP) play a key role, and its proper operation is mandatory. So, fault diagnosis is a key activity for these plants. Their high complexity and large-scale require of smart methodologies for that fault diagnosis and safety operation. All these large-scale and complex industrial processes are monitored, allowing the data collection about the plant operation, so data driven approac…