Tootfinder

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…

ASDFormer: A Transformer with Mixtures of Pooling-Classifier Experts for Robust Autism Diagnosis and Biomarker Discovery
Mohammad Izadi, Mehran Safayani
https://arxiv.org/abs/2508.14005

ASDFormer: A Transformer with Mixtures of Pooling-Classifier Experts for Robust Autism Diagnosis and Biomarker Discovery
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition marked by disruptions in brain connectivity. Functional MRI (fMRI) offers a non-invasive window into large-scale neural dynamics by measuring blood-oxygen-level-dependent (BOLD) signals across the brain. These signals can be modeled as interactions among Regions of Interest (ROIs), which are grouped into functional communities based on their underlying roles in brain function. Emerging evidence suggests that connectivity p…

Context Pooling: Query-specific Graph Pooling for Generic Inductive Link Prediction in Knowledge Graphs
Zhixiang Su, Di Wang, Chunyan Miao
https://arxiv.org/abs/2507.07595

Context Pooling: Query-specific Graph Pooling for Generic Inductive Link Prediction in Knowledge Graphs
Recent investigations on the effectiveness of Graph Neural Network (GNN)-based models for link prediction in Knowledge Graphs (KGs) show that vanilla aggregation does not significantly impact the model performance. In this paper, we introduce a novel method, named Context Pooling, to enhance GNN-based models' efficacy for link predictions in KGs. To our best of knowledge, Context Pooling is the first methodology that applies graph pooling in KGs. Additionally, Context Pooling is first-of-its-ki…

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

Synthesizing Evidence: Data-Pooling as a Tool for Treatment Selection in Online Experiments
Zhenkang Peng (Philip), Chengzhang Li (Philip), Ying Rong (Philip), Renyu (Philip), Zhang
https://arxiv.org/abs/2508.10331

Synthesizing Evidence: Data-Pooling as a Tool for Treatment Selection in Online Experiments
Randomized experiments are the gold standard for causal inference but face significant challenges in business applications, including limited traffic allocation, the need for heterogeneous treatment effect estimation, and the complexity of managing overlapping experiments. These factors lead to high variability in treatment effect estimates, making data-driven policy roll out difficult. To address these issues, we introduce the data pooling treatment roll-out (DTR) framework, which enhances pol…

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

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…

A Nonparametric Approach to Augmenting a Bayesian VAR with Nonlinear Factors
Todd Clark, Florian Huber, Gary Koop
https://arxiv.org/abs/2508.13972 https://…

A Nonparametric Approach to Augmenting a Bayesian VAR with Nonlinear Factors
This paper proposes a Vector Autoregression augmented with nonlinear factors that are modeled nonparametrically using regression trees. There are four main advantages of our model. First, modeling potential nonlinearities nonparametrically lessens the risk of mis-specification. Second, the use of factor methods ensures that departures from linearity are modeled parsimoniously. In particular, they exhibit functional pooling where a small number of nonlinear factors are used to model common nonli…

Is Exchangeability better than I.I.D to handle Data Distribution Shifts while Pooling Data for Data-scarce Medical image segmentation?
Ayush Roy, Samin Enam, Jun Xia, Vishnu Suresh Lokhande, Won Hwa Kim
https://arxiv.org/abs/2507.19575

Is Exchangeability better than I.I.D to handle Data Distribution Shifts while Pooling Data for Data-scarce Medical image segmentation?
Data scarcity is a major challenge in medical imaging, particularly for deep learning models. While data pooling (combining datasets from multiple sources) and data addition (adding more data from a new dataset) have been shown to enhance model performance, they are not without complications. Specifically, increasing the size of the training dataset through pooling or addition can induce distributional shifts, negatively affecting downstream model performance, a phenomenon known as the "Data Ad…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[6/6]:
- ASDFormer: A Transformer with Mixtures of Pooling-Classifier Experts for Robust Autism Diagnosis ...
Mohammad Izadi, Mehran Safayani

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…

Ensemble Average Analysis of Non-Adaptive Group Testing with Sparse Pooling Graphs
Emna Ben Yacoub, Gianluigi Liva, Enrico Paolini, Marco Chiani
https://arxiv.org/abs/2507.20281

Ensemble Average Analysis of Non-Adaptive Group Testing with Sparse Pooling Graphs
A combinatorial analysis of the false alarm (FA) and misdetection (MD) probabilities of non-adaptive group testing with sparse pooling graphs is developed. The analysis targets the combinatorial orthogonal matching pursuit and definite defective detection algorithms in the noiseless, non-quantitative setting. The approach follows an ensemble average perspective, where average FA/MD probabilities are computed for pooling graph ensembles with prescribed degree distributions. The accuracy of the a…

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

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…

Learnable quantum spectral filters for hybrid graph neural networks
Ammar Daskin
https://arxiv.org/abs/2507.05640 https://arxiv.org/p…

Learnable quantum spectral filters for hybrid graph neural networks
In this paper, we describe a parameterized quantum circuit that can be considered as convolutional and pooling layers for graph neural networks. The circuit incorporates the parameterized quantum Fourier circuit where the qubit connections for the controlled gates derived from the Laplacian operator. Specifically, we show that the eigenspace of the Laplacian operator of a graph can be approximated by using QFT based circuit whose connections are determined from the adjacency matrix. For an $N\t…

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…

Multi-modal Adaptive Mixture of Experts for Cold-start Recommendation
Van-Khang Nguyen, Duc-Hoang Pham, Huy-Son Nguyen, Cam-Van Thi Nguyen, Hoang-Quynh Le, Duc-Trong Le
https://arxiv.org/abs/2508.08042

Multi-modal Adaptive Mixture of Experts for Cold-start Recommendation
Recommendation systems have faced significant challenges in cold-start scenarios, where new items with a limited history of interaction need to be effectively recommended to users. Though multimodal data (e.g., images, text, audio, etc.) offer rich information to address this issue, existing approaches often employ simplistic integration methods such as concatenation, average pooling, or fixed weighting schemes, which fail to capture the complex relationships between modalities. Our study propo…

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…

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…

An Overlapping Coalition Game Approach for Collaborative Block Mining and Edge Task Offloading in MEC-assisted Blockchain Networks
Licheng Ye, Zehui Xiong, Lin Gao, Dusit Niyato
https://arxiv.org/abs/2508.06031

An Overlapping Coalition Game Approach for Collaborative Block Mining and Edge Task Offloading in MEC-assisted Blockchain Networks
Mobile edge computing (MEC) is a promising technology that enhances the efficiency of mobile blockchain networks, by enabling miners, often acted by mobile users (MUs) with limited computing resources, to offload resource-intensive mining tasks to nearby edge computing servers. Collaborative block mining can further boost mining efficiency by allowing multiple miners to form coalitions, pooling their computing resources and transaction data together to mine new blocks collaboratively. Therefore…

Exploring Cross-Utterance Speech Contexts for Conformer-Transducer Speech Recognition Systems
Mingyu Cui, Mengzhe Geng, Jiajun Deng, Chengxi Deng, Jiawen Kang, Shujie Hu, Guinan Li, Tianzi Wang, Zhaoqing Li, Xie Chen, Xunying Liu
https://arxiv.org/abs/2508.10456

Exploring Cross-Utterance Speech Contexts for Conformer-Transducer Speech Recognition Systems
This paper investigates four types of cross-utterance speech contexts modeling approaches for streaming and non-streaming Conformer-Transformer (C-T) ASR systems: i) input audio feature concatenation; ii) cross-utterance Encoder embedding concatenation; iii) cross-utterance Encoder embedding pooling projection; or iv) a novel chunk-based approach applied to C-T models for the first time. An efficient batch-training scheme is proposed for contextual C-Ts that uses spliced speech utterances withi…

Resource-Efficient Adaptation of Large Language Models for Text Embeddings via Prompt Engineering and Contrastive Fine-tuning
Benedikt Roth, Stephan Rappensperger, Tianming Qiu, Hamza Imamovi\'c, Julian W\"ormann, Hao Shen
https://arxiv.org/abs/2507.22729

Resource-Efficient Adaptation of Large Language Models for Text Embeddings via Prompt Engineering and Contrastive Fine-tuning
Large Language Models (LLMs) have become a cornerstone in Natural Language Processing (NLP), achieving impressive performance in text generation. Their token-level representations capture rich, human-aligned semantics. However, pooling these vectors into a text embedding discards crucial information. Nevertheless, many non-generative downstream tasks, such as clustering, classification, or retrieval, still depend on accurate and controllable sentence- or document-level embeddings. We explore se…

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-…

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…

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…

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…

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…

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…

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…

CUHK-EE Systems for the vTAD Challenge at NCMMSC 2025
Aemon Yat Fei Chiu, Jingyu Li, Yusheng Tian, Guangyan Zhang, Tan Lee
https://arxiv.org/abs/2507.23266 https://

CUHK-EE Systems for the vTAD Challenge at NCMMSC 2025
This paper presents the Voice Timbre Attribute Detection (vTAD) systems developed by the Digital Signal Processing & Speech Technology Laboratory (DSP&STL) of the Department of Electronic Engineering (EE) at The Chinese University of Hong Kong (CUHK) for the 20th National Conference on Human-Computer Speech Communication (NCMMSC 2025) vTAD Challenge. The proposed systems leverage WavLM-Large embeddings with attentive statistical pooling to extract robust speaker representations, followed by two…

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…