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Memories may integrate elements experienced in different events. For instance, meeting a woman leaving her house, and later meeting another woman entering the same house, may allow us to infer that the two women live together. Such memory representations are thought to rely on integrative encoding mechanisms, allowing us to make inferences about the world and generalize knowledge to entirely new s
ObjectivesThe formation of episodic memories is critically determined by how we visually sample the world over time via sequences of eye movements. Nonetheless, in the neuroscience of human memory, memory encoding has almost exclusively been studied in experimental paradigms where the study material is presented in a single fixed location on the screen, and where eye movements are treated as artif
Episodic memory enables mental time travel, allowing us to relive specific, personally experienced events tied in time and place. This feat of human memory is considered to be dependent on the reinstatement of the cortical patterns that were active at the time of encoding. A growing body of recent literature has provided support for this idea by showing that retrieval success co-varies with the ne
In this paper, we examine variable node (VN) doping to mitigate the error propagation problem in sliding window decoding (SWD) of spatially coupled LDPC (SC-LDPC) codes from the point of view of the encoding process. More specifically, in order to simplify the process of generating an encoded sequence with some number of doped code bits, we propose to employ systematic encoding and to limit doping
The commercial availability of low-cost millimeterwave (mmWave) communication and radar devices is starting to improve the adoption of such technologies in consumer markets, paving the way for large-scale and dense deployments in fifthgeneration (5G)-and-beyond as well as 6G networks. At the same time, pervasive mmWave access will enable device localization and device-free sensing with unprecedent
Cell-free massive multiple-input multiple-output (MIMO) consists of a large set of distributed access points (APs) serving a number of users. The APs can be far from each other, and they can also have a big number of antennas. Thus, decentralized architectures have to be considered so as to reduce the interconnection bandwidth to a central processing unit (CPU) and make the system scalable. On the
SMIRK is a pedestrian automatic emergency braking system that facilitates research on safety-critical systems embedding machine learning components. As a fully transparent driver-assistance system, SMIRK can support future research on trustworthy AI systems, e.g., verification & validation, requirements engineering, and testing. SMIRK is implemented for the simulator ESI Pro-SiVIC with core co
This paper explores an innovation case within a “smart” Swedish mid-sized city that works extensively with digitalization.Over a long period in time, city populations and city tourism have increased, while more urgentchallenges connected to sustainability have emerged along with health-related problems. In parallel the already established and ongoing digitalization of society was fortified in the
Robot skills systems are meant to reduce robot setup time for new manufacturing tasks. Yet, for dexterous, contact-rich tasks, it is often difficult to find the right skill parameters. One strategy is to learn these parameters by allowing the robot system to learn directly on the task. For a learning problem, a robot operator can typically specify the type and range of values of the parameters. Ne
We propose a generalisation of a behaviour tree and motiongenerator based robot arm policy representation for learning and solving tasks such as contact-rich tasks like peg insertion or pushing an object. We use planning to generate skill sequences needed to execute these tasks and rely on reinforcement learning to obtain parameters of the policy. We assume gaussian processes as a suitable method
We propose a pipeline to setup up robot systems for new tasks using symbolic planning, reasoning and reinforcement learning. Furthermore, we
We propose a novel multi-pose loss function to train a neural network for 6D pose estimation, using synthetic data and evaluating it on real images. Our loss is inspired by the VSD (Visible Surface Discrepancy) metric and relies on a differentiable renderer and CAD models. This novel multi-pose approach produces multiple weighted pose estimates to avoid getting stuck in local minima. Our method re
Beyond 5G communication systems must be able to meet the requirements imposed by the ever-increasing demand in capacity, while guaranteeing robustness, reliability, low latency, security, as well as spectral and power efficiencies. Large intelligent surfaces (LIS) as an evolution of massive MIMO have drawn considerable attention among researchers, being already considered as one of the key technol
Much of the technology that makes robots reliable, human friendly, and industrialroboticsadaptable for numerous applications has emerged from manufacturers of industrial robots. With an estimated installation base in 2014 of about 1.5 million units, some 171000 new installations in that year and an annual turnover of the robotics industry estimated to be US$ 32 billion, industrial robots are by fa
5G cellular networks can utilize millimeter wave signals, and support large bandwidths and large antenna arrays, which provide more geometric-based signals and higher delay and angle resolutions. These merits bring new opportunities in positioning the user with limited infrastructure through the use of combined angle and delay information. However, there are many practical challenges to overcome,
Black carbon (BC), spheroidal carbonaceous particles (SCP), and polycyclic aromatic hydrocarbons (PAH) are carbonaceous pollutants affecting the climate, environment, and human health. International regulations limit their emissions, and the present emissions are followed by monitoring programs. However, the monitoring programs have limited spatio-temporal coverage and only span the last decades.
Modern coding theory is based on the foundation of the sparse codes on graphs, such as the low-density parity-check (LDPC) codes, and the turbo-like codes (TCs) with component convolutional codes. The success of the LDPC codes and the TCs lies in their ability to perform low-complexity iterative message passing decoding procedures. The iterative message passing decoders that exchange messages prob
Static analysis tools play a crucial role in software development by detecting bugs and vulnerabilities. However, running these tools separately from the code editing process often causes developers to switch contexts, which can reduce productivity. Previous work has shown how Reference Attribute Grammars (RAGs) can be used for declarative implementation of competitive tooling for intraprocedural
Formal models for the safety validation of autonomous vehicles have become increasingly important. To this end, we present a safety framework for longitudinal automated driving. This framework enables the calculation of minimum safe inter-vehicular distances for arbitrary ego vehicle control policies in a computationally efficient manner. We use this framework to enhance and generalize the Respons
The success in the adoption of autonomous vehicles is dependent on their ability to solve rarely occurring safety-critical corner cases. Vehicular communications (V2X) aim at improving safety and efficiency of autonomous driving by adding the capability of explicit inter-vehicular information exchange. We argue that V2X enables another important function, namely the support of ethical driving deci
