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The Noise of Unheard Sounds : Donald D. Hoffman, Chetan Prakash, and Nick Bostrom on the Subjectivity of Being

This article examines the emergence of idealism in evolutionary cognitive science by analyzing the conscious realism of the cognitive theorist Donald D. Hoffman and the mathematician Chetan Prakash. Perception is for them not about veridical experience, but rather a mechanism for survival. It helps us survive but does not give us access to reality as such. By relating Hoffman and Prakash's conscio

Ironies of Programming Automation: Exploring the Experience of Code Synthesis via Large Language Models

The widespread availability of large language models (LLMs) has presented the opportunity for novice programmers to make use of them for the purpose of understanding and synthesising code. In this paper, we discuss a small pilot study intended to explore the user experience of doing so in a limited way, and the attitudes of a group of novice programmers towards this style of programming. We also d

Quality Requirements for Code : On the Untapped Potential in Maintainability Specifications

Quality requirements are critical for successful software engineering, with maintainability being a key internal quality. Despite significant attention in software metrics research, maintainability has attracted surprisingly little focus in the Requirements Engineering (RE) community. This position paper proposes a synergistic approach, combining code-oriented research with RE expertise, to create

Property Probes: Live Exploration of Source Code Analysis

Source code analysis is ubiquitous in the development of software tools, for example in compilers to detect compile-time errors and possible optimizations, inIDEs to provide interactive coding assistance, and in stand-alone analysis tools todetect bugs. There are several techniques that have been developed to help theanalysis developer, including one called Reference Attribute Grammars (RAGs).With

SMIRK : A machine learning-based pedestrian automatic emergency braking system with a complete safety case

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

Optimization of Anomaly Detection in a Microservice System Through Continuous Feedback from Development

Monitoring a microservice system may bring a lot of benefits to development teams such as early detection of run-time errors and various performance anomalies. In this study, we explore deep learning (DL) solutions for detection of anomalous system's behavior based on collected monitoring data that consists of applications' and systems' performance metrics. The study is conducted in a collaboratio

Learning Skill-based Industrial Robot Tasks with User Priors

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

Generalizing Behavior Trees and Motion-Generator (BTMG) Policy Representation for Robotic Tasks Over Scenario Parameters

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

Pose estimation from RGB images of highly symmetric objects using a novel multi-pose loss and differential rendering

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

Deployment Strategies for Large Intelligent Surfaces

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

Industrial Robotics

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

Experimental Validation of Single Base Station 5G mm Wave Positioning : Initial Findings

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,

Recent Increased Loading of Carbonaceous Pollution from Biomass Burning in the Baltic Sea

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.

Sparse Codes on Graphs with Convolutional Code Constraints

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

The importance of time of day for magnetic body alignment in songbirds

Spontaneous magnetic alignment is the simplest known directional response to the geomagnetic field that animals perform. Magnetic alignment is not a goal directed response and its relevance in the context of orientation and navigation has received little attention. Migratory songbirds, long-standing model organisms for studying magnetosensation, have recently been reported to align their body with

Antipsychotic Use During Pregnancy and Risk for Gestational Diabetes : A National Register-Based Cohort Study in Sweden

Objective: We aimed to study whether antipsychotic use during pregnancy is associated with gestational diabetes. Methods: This was a Swedish national register‐based cohort study on the Medical Birth Register and the Prescribed Drug Register including all 1,307,487 singleton births between July 2006 and December 2017. Antipsychotics were divided into first-generation antipsychotics (n = 728), high-

Scaling massive MIMO with imperfect transceivers

The number of users and the information transmitted over wireless networks have been growing constantly during the last decades. Nowadays, the pace of this growth is extremely sharp because of the new applications which heavily rely on wireless networks to meet users' demands. Wireless networks infrastructures are constantly developing to meet these demands. Massive multiple-input multiple-output

Alexander Paulsson

Alexander Paulsson | April 2024 | Urban Arena Skip to main content This site uses cookies to enhance the user experience. By continuing to use the site you agree that cookies are used according to our Cookie Policy (on the website of LTH) . Essential cookies These cookies are necessary for the website to function and cannot be turned off in our systems. These cookies do not store any personally id

https://www.urban.lu.se/urban-arena-interview-sessions/alexander-paulsson-april-2024/ - 2025-12-11