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Temporal information in episodic memory reflects how experiences are encoded rather than objective elapsed time, giving rise to systematic distortions. These are particularly evident in emotional contexts, consistent with emotion-related biases in attention. The present study investigated whether emotion-driven distortions in remembered temporal distance were explained by encoding-related attentio

Background: Glomerular filtration rate (GFR) is a key indicator of kidney function, typically estimated using creatinine or cystatin C. When cystatin-C-based eGFR is markedly lower than creatinine-based eGFR, this discrepancy may reflect a selective impairment in the filtration of medium-sized molecules, referred to as Shrunken Pore Syndrome (SPS), or more broadly, Selective Glomerular Hypofiltrat

Coronary artery disease is one of the leading causes of mortality worldwide. While early identification and treatment of major cardiovascular risk factors are crucial, recent data suggest the possibility of non-invasively detecting early stages of coronary atherosclerosis and potentially stabilizing or even reversing the burden of atherosclerosis with innovative and existing treatments. Moreover,

The testing and safety cases of assisted and automated driving (AAD) functions require considerations for nonideal environmental conditions, such as adverse and extreme weather. In these extreme conditions, perception sensors (e.g., camera, LiDAR, and RADAR), which build the situational awareness of the vehicle, might produce noisy and degraded data. Therefore, it is key to consider: 1) how to rel

The compression of deep learning models is of fundamental importance in deploying such models to edge devices. The selection of compression parameters can be automated to meet changes in the hardware platform and application. This article introduces a Multi-Objective Hardware-Aware Quantization (MOHAQ) method, which considers hardware performance and inference error as objectives for mixed-precisi

Smart cities and communities (SCC) constitute a new paradigm in urban development. SCC ideate a data-centered society aimed at improving efficiency by automating and optimizing activities and utilities. Information and communication technology along with Internet of Things enables data collection and with the help of artificial intelligence (AI) situation awareness can be obtained to feed the SCC

Recognition of human manipulation actions together with the analysis and execution by a robot is an important issue. Also, perception of spatial relationships between objects is central to understanding the meaning of manipulation actions. Here we would like to merge these two notions and analyze manipulation actions using symbolic spatial relations between objects in the scene. Specifically, we d

This thesis investigates particle sorting in deterministic lateral displacement (DLD) microfluidic devices, with a focus on how particle size and shape influence sorting behavior. A broad range of synthetic and biological particles were studied, including polystyrene particles, fabricated silicon-based structures, and bacterial samples with diverse physical properties.While DLD has traditionally b

We present a novel deep neural network architecture for representing robot experiences in an episodic-like memory that facilitates encoding, recalling, and predicting action experiences. Our proposed unsupervised deep episodic memory model as follows: First, encodes observed actions in a latent vector space and, based on this latent encoding, second, infers most similar episodes previously experie

This paper contributes to semantic representation of human demonstrated actions for robot execution of time constrained tasks. We propose a semantic action encoding method based on interactions between the subject and objects in the scene. Our semantic framework is enriched with a descriptive spatial reasoning method which leads to accurate segmentation and recognition of unique action primitives.

Understanding continuous human actions is a non-trivial but important problem in computer vision. Although there exists a large corpus of work in the recognition of action sequences, most approaches suffer from problems relating to vast variations in motions, action combinations, and scene contexts. In this paper, we introduce a novel method for semantic segmentation and recognition of long and co

We present a novel unsupervised framework, which links continuous visual features and symbolic textual descriptions of manipulation activity videos. First, we extract the semantic representation of visually observed manipulations by applying a bottom-up approach to the continuous image streams. We then employ a rule-based reasoning to link visual and linguistic inputs. The proposed framework allow

Dogs exhibit an exceptional range of morphological diversity as a result of their long-term association with humans. Attempts to identify when dog morphological variation began to expand have been constrained by the limited number of Pleistocene specimens, the fragmentary nature of remains, and difficulties in distinguishing early dogs from wolves on the basis of skeletal morphology. In this study

BACKGROUND: Shiga toxin (Stx) is the main virulence factor of enterohemorrhagic Escherichia coli, a food-borne pathogen that colonizes the intestine causing gastroenteritis and, in severe cases, hemolytic uremic syndrome. Stx was shown to induce ATP release in vivo and in vitro and blockade of purinergic P2X receptors inhibited its cytotoxicity. Here we investigated the intracellular signaling eve

With a significant impact on turbomachinery blade performance, surface curvature distribution becomes one of the essential factors in the design of high-efficiency blades. This study focuses on applying computational fluid dynamics (CFD) to evaluate turbine rotor blade performance. The main aim is to analyze the influence of incidence and geometry shape on the performance of a gas-turbine blade in

In this work, we present a new single-stage method for subject agnostic face swapping and identity transfer, named FaceDancer. We have two major contributions: Adaptive Feature Fusion Attention (AFFA) and Interpreted Feature Similarity Regularization (IFSR). The AFFA module is embedded in the decoder and adaptively learns to fuse attribute features and features conditioned on identity information

This chapter is an exploration of the concept of periphery when applied to regions. Specifically, it unpacks what we mean when we talk about places, specifically regions, as “peripheral”. It does this from the perspective of a sub-discipline of human geography: regional economic geography. In this field, peripheral regions are becoming increasingly interesting to researchers due to their key roles

Regional studies often treat the region as a sub‑national unit, and it spans from neighbourhoods to cross‑border macro‑regions. In Asia, this elasticity is intensified by language. Words that look equivalent on paper rarely carry identical histories, politics, or scales. This short piece reflects on the Chinese notion of quyu (区域) and how it travels between domestic and international contexts, see