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Drivers of basement flooding: Rainfall intensity and infrastructure impacts in Trelleborg, Sweden

Study regionTrelleborg is a mid-sized coastal municipality in southern Sweden that has experienced repeated pluvial flooding in recent decades. The city comprises areas served by both combined and separate sewer systems and is increasingly exposed to intense short-duration rainfall events. This study uses 17 years (2006–2023) of property-level flood reports, in situ rainfall observations from mult

Dynamic Category Queries Transformer for Generalized Few-shot Semantic Segmentation

Few-shot segmentation (FSS) tackles data scarcity using multiple priors, but its simplicity limits handling base and novel classes with limited data access. Generalized few-shot semantic segmentation (GFSS) enhances model performance for base classes with abundant data, while novel classes have limited data access, improving generalization with scarce data. Building on the design of query-based se

From Weak to Strong Sound Event Labels using Adaptive Change-Point Detection and Active Learning

We propose an adaptive change point detection method (A-CPD) for machine guided weak label annotation of audio recording segments. The goal is to maximize the amount of information gained about the temporal activations of the target sounds. For each unlabeled audio recording, we use a prediction model to derive a probability curve used to guide annotation. The prediction model is initially pre-tra

Coherence Estimation Tracks Auditory Attention in Listeners with Hearing Impairment

Coherence estimation between speech envelope and electroencephalography (EEG) is a proven method in neural speech tracking. This paper proposes an improved coherence estimation algorithm which utilises phase sensitive multitaper cross-spectral estimation. Estimated EEG coherence differences between attended and ignored speech envelopes for a hearing impaired (HI) population are evaluated and compa

Estimation of Impulse Responses for a Moving Source Using Optimal Transport Regularization

The estimation of impulse responses (IRs) is fundamental to various audio applications, including active noise control, telecommunication, and sound zone control. Despite its long history, estimating impulse responses remains challenging when dealing with short signals and with signals having poor spectral excitation. However, in many applications the source is moving such that one has access to s

Impulse Response Interpolation Using Optimal Transport

The spatial impulse response (IR) interpolation problem is of general interest, e.g. for imaging of subsurface structures based on seismic waves, rendering of audio and radar IRs, as well as for numerous spatial audio applications. A commonly used model represents the occurring reflections as equivalent source positions, often being determined using a sparse re-construction framework employing spa

Room Impulse Response Estimation using Optimal Transport : Simulation-Informed Inference

The ability to accurately estimate room impulse responses (RIRs) is integral to many applications of spatial audio processing. Regrettably, estimating the RIR using ambient signals, such as speech or music, remains a challenging problem due to, e.g., low signal-to-noise ratios, finite sample lengths, and poor spectral excitation. Commonly, in order to improve the conditioning of the estimation pro

Sound Field Estimation Using Deep Kernel Learning Regularized by the Wave Equation

In this work, we introduce a spatio-temporal kernel for Gaussian process (GP) regression-based sound field estimation. Notably, GPs have the attractive property that the sound field is a linear function of the measurements, allowing the field to be estimated efficiently from distributed microphone measurements. However, to ensure analytical tractability, most existing kernels for sound field estim

Description and evaluation of a secondary organic aerosol and new particle formation scheme within TM5-MP v1.2

We have implemented and evaluated a secondary organic aerosol scheme within the chemistry transport model TM5-MP in this work. In earlier versions of TM5-MP the secondary organic aerosol (SOA) was emitted as Aitken-sized particle mass emulating the condensation. In the current scheme we simulate the formation of secondary organic aerosol from oxidation of isoprene and monoterpenes by ozone and hyd

Trilateration Using Motion Models

In this paper, we present a framework for doing localization from distance measurements, given an estimate of the local motion. We show how we can register the local motion of a receiver, to a global coordinate system, using trilateration of given distance measurements from the receivers to senders in known positions. We describe how many different motion models can be formulated within the same t

Smooth pursuit detection in binocular eye-tracking data with automatic video-based performance evaluation

An increasing number of researchers record binocular eye-tracking signals from participants viewing moving stimuli, but the majority of event-detection algorithms are monocular and do not consider smooth pursuit movements. The purposes of the present study are to develop an algorithm that discriminates between fixations and smooth pursuit movements in binocular eye-tracking signals and to evaluate

Modeling the role of highly oxidized multifunctional organic molecules for the growth of new particles over the boreal forest region

In this study, the processes behind observed new particle formation (NPF) events and subsequent organic-dominated particle growth at the Pallas Atmosphere-Ecosystem Supersite in Northern Finland are explored with the one-dimensional column trajectory model ADCHEM. The modeled sub-micron particle mass is up to ∼75 % composed of SOA formed from highly oxidized multifunctional organic molecules (HOMs

On Distributed Knowledge Bases for Small-Batch Assembly

This paper presents ongoing research involving design and evaluation of different architectures for providing knowledge-based solutions in industrial robotized automation systems. The conclusions are that distributed, cloud-based approaches offer many possibilities, in particular for knowledge exchange and reuse, and facilitate new business models for industrial solutions. However, there are many

Image Segmentation and Labeling Using Free-Form Semantic Annotation

In this paper we investigate the problem of segmenting images using the information in text annotations. In contrast to the general image understanding problem, this type of annotation guided segmentation is less ill-posed in the sense that for the output there is higher consensus among human annotations. In the paper we present a system based on a combined visual and semantic pipeline. In the vis

Improving the Detection of Relations Between Objects in an Image Using Textual Semantics

In this article, we describe a system that classifies relations between entities extracted from an image. We started from the idea that we could utilize lexical and semantic information from text associated with the image, such as captions or surrounding text, rather than just the geometric and visual characteristics of the entities found in the image. We collected a corpus of images from Wikipedi

On force control for assembly and deburring of castings

Traditional industrial robots have problems interacting with an uncalibrated, ill-defined environment where part geometry and position may vary. Active force control technology has therefore been suggested as a solution to add the extra sensory dimension needed to handle manufacturing tasks like assembly and deburring. The technology is proposed to give increased flexibility compared to other solu