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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
Partially Premixed Combustion has shown to be a promising internal combustion concept in terms of engine efficiency and emission levels, however, since the combustion process is partly kinetically controlled, unacceptably high pressure-rise rates could occur at various operating points. A remedy to this problem is the use of a pilot-fuel injection. This paper characterizes double injection effects
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
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
This work analyzes the nonlinear effects in the track and hold circuit applied in high-speed ADCs or RF sampling receiver (RX) front-ends. Non-ideal effects inside the main sampling NMOS switch are studied. Parasitic varactor and sampling on-resistance modulation effects are analyzed through frequency domain Volterra series and the EKV MOS transistor model. Polynomial curve fitting is applied show
Stochastic sampling of light transport paths is key to Monte Carlo forward rendering, and previous studies have led to mature techniques capable of drawing high-contribution light paths in complex scenes. These sampling techniques have also been applied to differentiable rendering.In this paper, we demonstrate that path sampling techniques developed for forward rendering can become inefficient for
Aerosol particles, small solid or liquid particles suspended in a gas, are found everywhere in the atmosphere. Air pollution from such particles, mainly soot from combustion-derived activity is a leading cause of premature deaths in the world and have a large effect on the climate. Aerosol particles can interact with climate-related processes in the atmosphere either directly by absorbing or refle
In recent years the number of connected devices and the demand for high data-rates have been significantly increased. This enormous growth is more pronounced by the introduction of the Internet of things (IoT) in which several devices are interconnected to exchange data for various applications like smart homes and smart cities. Moreover, new applications such as eHealth, autonomous vehicles, and c
The notion of Cloud RAN is taking a prominent role in narrative for the next generation wireless infrastructure. It is also seen as a mean to industrial communication systems. In order to provide reliable wireless connectivity for industrial deployments, by conventional means, the cloud infrastructure needs to be reliable and incur little latency, which however, is contradictory to the stochastic
We address the problem of insufficient quality of robot simulators to produce precise sensor readings for joint positions, velocities and torques. Realistic simulations of sensor readings are particularly important for real time robot control laws and for data intensive Reinforcement Learning of robot movements in simulation. We systematically construct two architectures based on Long Short-Term M
We establish an exactly tight relation between reversiblepebblings of graphs and Nullstellensatz refutations of pebbling formulas,showing that a graph G can be reversibly pebbled in time t and space s if and only if there is a Nullstellensatz refutation of the pebbling formulaover G in size t + 1 and degree s (independently of the field in whichthe Nullstellensatz refutation is made). We use this
Systems mixing Boolean logic and arithmetic have been a long-standing challenge for verification tools such as SAT-based bit-vector solvers. Though SAT solvers can be highly efficient for Boolean reasoning, they scale poorly once multiplication is involved. Algebraic methods using Gröbner basis reduction have recently been used to efficiently verify multiplier circuits in isolation, but generally
