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Sensor degradation poses a significant challenge in autonomous driving. During heavy rainfall, interference from raindrops can adversely affect the quality of LiDAR point clouds, resulting in, for instance, inaccurate point measurements. This, in turn, can potentially lead to safety concerns if autonomous driving systems are not weather-aware, i.e., if they are unable to discern such changes. In t
Robots are more capable of achieving manipulation tasks for everyday activities than before. However, the safety of manipulation skills that robots employ is still an open problem. Considering all possible failures during skill learning increases the complexity of the process and restrains learning an optimal policy. Nonetheless, safety-focused modularity in the acquisition of skills has not been
In this work, we present a simple yet effective framework to address the domain translation problem between different sensor modalities with unique data formats. By relying only on the semantics of the scene, our modular generative framework can, for the first time, synthesize a panoramic color image from a given full 3D LiDAR point cloud.The framework starts with semantic segmentation of the poin
A cell’s ability to sense and respond to the mechanical properties of the extracellular matrix (ECM) is essential for maintaining tissue homeostasis, and its disruption contributes to diseases such as fibrosis, cardiovascular disorders, and cancer. Effective mechanical coupling between the plasma membrane, the underlying filamentous actin (F-actin) cytoskeleton, and integrin-based adhesion complex
Effective environmental policies for the tropics depend on accurate, representative scientific data. However, there is strong evidence from particular disciplines and regions that existing research is patchily distributed. Here, we show that poor representation of sampling and citation in some biomes and across key environmental gradients from all disciplines for the entire tropics may lead to fla
Background: The aim was to evaluate whether an artificial intelligence (AI)-based tool for the automated quantification of the total metabolic tumour volume (tMTV) in patients with Hodgkin lymphoma (HL) could support nuclear medicine specialists in lesion segmentation and thereby enhance inter-observer agreement. Methods: Forty-eight consecutive patients who underwent staging with [18F]FDG PET/CT
In this paper, we present a new approach for facial anonymization in images and videos, abbreviated as FIVA. Our proposed method is able to maintain the same face anonymization consistently over frames with our suggested identity-tracking and guarantees a strong difference from the original face. FIVA allows for 0 true positives for a false acceptance rate of 0.001. Our work considers the importan
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
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
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
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
A tight correlation between the baryonic and observed acceleration of galaxies has been reported over a wide mass range (108 < M bar/M⊙ < 1011 M⊙); this is known as the radial acceleration relation (RAR). This has been interpreted as evidence that dark matter is actually a manifestation of a modified, weak-field gravity theory. In this work, we studied the radially resolved RAR of 12 nearby dwarf
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
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.
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
Denna avhandling undersöker hur subtila former av medborgerligt motstånd tar form i Hongkongs pågående autokratisering. I en kontext präglad av vilande sociala rörelser, ökande mediecensur och digital övervakning visar studien att uttryck som inte aktivt söker uppmärksamhet ändå kan spela en viktig roll i medborgerligt engagemang. Med utgångspunkt i Hongkong efter införandet av den nationella säkThis thesis challenges the assumption that expressions that do not actively seek attention is less significant in the research of civic engagement, by foregrounding the arts of subtle resistance in contexts of social movement abeyance, heightened media censorship and digital surveillance. Drawing upon the empirical case of Hong Kong, where a National Security Law was enacted in 2020 to discourage
Swedish architectural modernism is intimately tied to thedevelopment of the welfare state, where the roles and work ofarchitects largely followed governmental building policies. Thepolitical and economical crisis of the late 1960s and 1970swas particularly devastating for Swedish architecture as theshortcomings of the welfare state were partly blamed on thearchitecture profession. This paper is a
Spindle cell lipomas (SCL) and pleomorphic lipomas (PL) are today considered a single tumor entity (SCLPL). Atypical SCLPL, in contrast, represents a recently recognized related but distinct entity. Here we explored the correlation between genomic features and morphological aspects of SCLPL and atypical SCLPL and the role of the RB1 gene in tumor development. Seventy-one samples from 68 patients w
