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Gradient orientations are a common feature used in many computer vision algorithms. It is a good feature when the gradient magnitudes are high, but can be very noisy when the magnitudes are low. This means that some gradient orientations are matched with more confidence than others. By estimating this uncertainty, more weight can be put on the confident matches than those with higher uncertainty.
We analyze reinforcement learning under so-called "dynamic reinforcement." In reinforcement learning, each agent repeatedly interacts with an unknown environment (i.e., other agents), receives a reward, and updates the probabilities of its next action based on its own previous actions and received rewards. Unlike standard reinforcement learning, dynamic reinforcement uses a combination of long-ter
The phenomenon of adopting open source software development practices in a corporate environment is known by many names, one being inner source. The objective of this study is to investigate how an organization consisting of small development teams can benet from adopting inner source and assess the level of applicability. The research has been conducted as a case study at a software development c
Nowadays, the same piece of code should run on different architectures, providing performance guarantees in a variety of environments and situations. To this end, designers often integrate existing systems with ad-hoc adaptive strategies able to tune specific parameters that impact performance or energy—for example, frequency scaling. However, these strategies interfere with one another and unpred
Stochastic optimal power flow can provide the system operator with adequate strategies for controlling the power flow to maintain secure operation under stochastic parameter variations. One limitation of stochastic optimal power flow has been that only steady-state variable limits have been used as security constraints. In many systems voltage stability and small-signal stability also play an impo
Research coordinator Contact details Email: cerina [dot] rydalv [at] mgeo [dot] lu [dot] se Mobile: +46 72 216 86 26Organisation Department of Earth and Environmental Sciences (MGeo) Visiting address: Geocentrum, Sölvegatan 12, Lund Room number: 282 Service point: 16 WebpageCerina Rydälvs profile in Lund University research portalI work as research coordinator at Lund University profile area Natur
https://www.cec.lu.se/cerina-rydalv - 2026-07-08
This study investigated the counterintelligence activities of a criminal organisation – Hells Angels. The evolution of counterintelligence skills can be understood as a learning process in which Hells Angels adapts its counterintelligence activities as authorities adopt new methods and techniques. The study set out to answer the questions of 1) how Hells Angels responded to authorities´ efforts to
This thesis addresses a challenge in continuous high performance liquid chromatography (HPLC): ensuring reliable, timely supply of buffer solutions. As biopharmaceutical production increasingly moves toward continuous operation, buffer management becomes critical, since chromatography requires multiple buffer types with precise compositions for washing, elution, equilibration, and regeneration. In
Agile methods have the recent years gained ground within the software industry by being lean and flexible, and one of the companies having adopted agile methods is the fast‐growing Swedish developer of mobile applications Tactel AB. The company was interested in enhanced knowledge within the agile area, specifically about Scrum, and with one of its development teams – Mobical – in focus, Tactel wa
For several reinforcement learning models in strategic-form games, convergence to action profiles that are not Nash equilibria may occur with positive probability under certain conditions on the payoff function. In this paper, we explore how an alternative reinforcement learning model, where the strategy of each agent is perturbed by a strategy-dependent perturbation (or mutations) function, may e
Abstract in UndeterminedWe develop a discretization and computational procedures for approximation of the action of Fourier integral operators the canonical relations of which are graphs. Such operators appear, for instance, in the formulation of imaging and inverse scattering of seismic reflection data. Our discretization and algorithms are based on a multiscale low-rank expansion of the action o
In synthetic aperture radar one wishes to reconstruct the reflectivity function of a region on the ground from a set of radar measurements taken at several angles. The ground reflectivity is found by interpolating measured samples, which typically lie on a polar grid in frequency space, to an equally spaced rectangular grid in frequency space, then computing an inverse Fourier transform. The class
We present a non-incremental approach to structure from motion. Our solution is based on robustly computing global rotations from relative geometries and feeding these into the known-rotation framework to create an initial solution for bundle adjustment. To increase robustness we present a new method for constructing reliable point tracks from pairwise matches. We show that our method can be seen
In this paper, we study the time-of-arrival (TOA) based self-calibration problem of dual-microphone array for known and unknown rack distance, and also for different combinations of dimension for the affine spaces spanned by the receivers and by the senders. Particularly, we analyze the minimum cases and present minimum solvers for the case of microphones and speakers in 3-D/3-D, in 2-D/3-D, and i
A procedure for acceleration feedback control of the delayed nonlinear teleoperation system is proposed. The acceleration feedback is applied to the master controller, while in the slave side the position error plus nonlinear damping controller is used to guarantee the input-to-state stability (ISS) of the controlled system. The Lyapunov-Krasovskii methodology is applied to analyse the ISS of the
The roof dual bound for quadratic unconstrained binary optimization is the basis for several methods for efficiently computing the solution to many hard combinatorial problems. It works by constructing the tightest possible lower-bounding submodular function, and instead of minimizing the original objective function, the relaxation is minimized. However, for higher-order problems the technique has
Short-term very intensive storms in the years 1980-2007 from rain gauges in Malmo have been analysed to find intensities of long return periods and to investigate trends. Observations from different stations have been pooled into series to which probability functions have been adjusted. Quality control of short-term precipitation records is emphasized. In order to investigate whether high rain int
Many problems in computer vision can be turned into a large-scale boolean optimization problem, which is in general NP-hard. In this paper, we further develop one of the most successful approaches, namely roof duality, for approximately solving such problems for higher-order models. Two new methods that can be applied independently or in combination are investigated. The first one is based on cons
Abstract in UndeterminedIn this paper theoretical results regarding a generalized minimum rank matrix approximation problem in the spectral norm are presented. An alternative solution expression for the generalized matrix approximation problem is obtained. This alternative expression provides a simple characterization of the achievableminimum rank, which is shown to be the same as the optimal obje
