Cultivating a Social Neighbourhood

As urbanisation continues throughout the world, issues of sustainability have been raised. Discussing ecological sustainability is becoming increasingly common, where green space in urban planning, has been given a particular function. At the same time, this sustainability goes beyond the environment and nature, since it also relates to social interaction and trust between people. The city of Malmö, Sweden's third largest city, has in recent years been a hot topic in media and society overall,where two discourses arise; one presenting Malmö as a city of crime and immigration, and another emphasising the city's cultural diversity and sustainability. Through garden projects, green space could serve as a tool for supporting cultural diversity and social sustainability. This bachelor thesis will investigate the theoretical reasoning behind green space and its affects on the social neighbourhood, contextualised in an urban garden project in the city of Malmö. The thesis is based on participant observations and interviews conducted at Odlingsnätverket Seved (“The Cultivation- Network Seved”) during April and May 2015, as well as a literature review that sets the theoretical framework. The results of the field study seems to validate the findings argued in previous literature to a large extent, where there seems to be a positive relation between green space and social interaction

Non-Convex Rank/Sparsity Regularization and Local Minima

This paper considers the problem of recovering either a low rank matrix or a sparse vector from observations of linear combinations of the vector or matrix elements. Recent methods replace the non-convex regularization with $\ell_1$ or nuclear norm relaxations. It is well known that this approach can be guaranteed to recover a near optimal solutions if a so called restricted isometry property (RIP) holds. On the other hand it is also known to perform soft thresholding which results in a shrinking bias which can degrade the solution. In this paper we study an alternative non-convex regularization term. This formulation does not penalize elements that are larger than a certain threshold making it much less prone to small solutions. Our main theoretical results show that if a RIP holds then the stationary points are often well separated, in the sense that their differences must be of high cardinality/rank. Thus, with a suitable initial solution the approach is unlikely to fall into a bad local minima. Our numerical tests show that the approach is likely to converge to a better solution than standard $\ell_1$/nuclear-norm relaxation even when starting from trivial initializations. In many cases our results can also be used to verify global optimality of our method

Practical Robust Two-View Translation Estimation

Outliers pose a problem in all real structure from motion systems. Due to the use of automatic matching methods one has to expect that a (sometimes very large) portion of the detected correspondences can be incorrect. In this paper we propose a method that estimates the relative translation between two cameras and simultaneously maximizes the number of inlier correspondences. Traditionally, outlier removal tasks have been addressed using RANSAC approaches. However, these are random in nature and offer no guarantees of finding a good solution. If the amount of mismatches is large, the approach becomes costly because of the need to evaluate a large number of random samples. In contrast, our approach is based on the branch and bound methodology which guarantees that an optimal solution will be found. While most optimal methods trade speed for optimality, the proposed algorithm has competitive running times on problem sizes well beyond what is common in practice. Experiments on both real and synthetic data show that the method outperforms state-of-the-art alternatives, including RANSAC, in terms of solution quality. In addition, the approach is shown to be faster than RANSAC in settings with a large amount of outliers

DACS: Domain Adaptation via Cross-domain Mixed Sampling

Semantic segmentation models based on convolutional neural networks have recently displayed remarkable performance for a multitude of applications. However, these models typically do not generalize well when applied on new domains, especially when going from synthetic to real data. In this paper we address the problem of unsupervised domain adaptation (UDA), which attempts to train on labelled data from one domain (source domain), and simultaneously learn from unlabelled data in the domain of interest (target domain). Existing methods have seen success by training on pseudo-labels for these unlabelled images. Multiple techniques have been proposed to mitigate low-quality pseudo-labels arising from the domain shift, with varying degrees of success. We propose DACS: Domain Adaptation via Cross-domain mixed Sampling, which mixes images from the two domains along with the corresponding labels and pseudo-labels. These mixed samples are then trained on, in addition to the labelled data itself. We demonstrate the effectiveness of our solution by achieving state-of-the-art results for GTA5 to Cityscapes, a common synthetic-to-real semantic segmentation benchmark for UDA.Comment: This paper has been accepted to WACV202

Digital implementation of a wavelet-based event detector for cardiac pacemakers

This paper presents a digital hardware implementation of a novel wavelet-based event detector suitable for the next generation of cardiac pacemakers. Significant power savings are achieved by introducing a second operation mode that shuts down 2/3 of the hardware for long time periods when the pacemaker patient is not exposed to noise, while not degrading performance. Due to a 0.13-mu m CMOS technology and the low clock frequency of 1 kHz, leakage power becomes the dominating power source. By introducing sleep transistors in the power-supply rails, leakage power of the hardware being shut off is reduced by 97%. Power estimation on RTL-level shows that the overall power consumption is reduced by 67% with a dual operation mode. Under these conditions, the detector is expected to operate in the sub-mu W region. Detection performance is evaluated by means of databases containing electrograms to which five types of exogenic and endogenic interferences are added. The results show that reliable detection is obtained at moderate and low signal to noise-ratios (SNRs). Average detection performance in terms of detected events and false alarms for 25-dB SNR is P-D = 0.98 and P-FA = 0.014, respectively

ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning

The state of the art in semantic segmentation is steadily increasing in performance, resulting in more precise and reliable segmentations in many different applications. However, progress is limited by the cost of generating labels for training, which sometimes requires hours of manual labor for a single image. Because of this, semi-supervised methods have been applied to this task, with varying degrees of success. A key challenge is that common augmentations used in semi-supervised classification are less effective for semantic segmentation. We propose a novel data augmentation mechanism called ClassMix, which generates augmentations by mixing unlabelled samples, by leveraging on the network's predictions for respecting object boundaries. We evaluate this augmentation technique on two common semi-supervised semantic segmentation benchmarks, showing that it attains state-of-the-art results. Lastly, we also provide extensive ablation studies comparing different design decisions and training regimes.Comment: This paper has been accepted to WACV202

Interference cancellation detectors in a hardware implementation perspective

To combat interference between users in a DS/CDMA system, several multiuser detection schemes have been proposed. This paper presents a prestudy for a custom DSP implementation of a multi-user detector scheme based on non-decision directed interference cancellation. Two architectural implementation methods for asynchronous detection are suggested and mutually compared. Each of the architectures is shown to have its particular advantages and therefore, a design combining the methods described in this paper is worth future studies

The value of eye-tracking technology in the analysis and interpretations of skeletal remains: A pilot study

This initial study is the first to use eye-trackers as a tool in order to study gaze pattern strategies and decision making processes involved in the assessment of skeletal remains. Three experienced participants were asked to wear eye-tracking glasses (Tobii Pro Glasses 2) when estimating sex and age-at-death of one set of skeletal remains from a known archeological sample. The study assessed participants' fixation points (the features of the skeleton focused on), fixation duration (the total time spent on each assessment and feature) as well as visit count and duration (the total number of visits and the duration of visits to particular areas). The preliminary results of this study identified differences in gaze “strategies” with regards to fixation points, visit duration, and visit counts between the participants. The data generated provide a starting point for assessing how such technologies could be used in order to more fully understand the decision making processes involved in forensic anthropological interpretations and their role in forensic reconstructions

Distribution Network Fault Prediction Utilising Protection Relay Disturbance Recordings And Machine Learning

As society becomes increasingly reliant on electricity, the reliability requirements for electricity supply continue to rise. In response, transmission/distribution system operators (T/DSOs) must improve their networks and operational practices to reduce the number of interruptions and enhance their fault localization, isolation, and supply restoration processes to minimize fault duration. This paper proposes a machine learning based fault prediction method that aims to predict incipient faults, allowing T/DSOs to take action before the fault occurs and prevent customer outages

Illikviditetsrabatter- En estimering av illikviditetsrabatter på den svenska aktiemarknaden

Abstract Title: Illiquidity discounts – An estimation of illiquidity discounts on the Swedish stock market Seminar date: 4-5/6 2015 Course: FEKH89, bachelor thesis in finance, 15 ECTS Authors: Viktor Gårdemyr, Jacob Källholm, Emelie Storckenfeldt och Nicholas Thurow Advisor: Sara Lundqvist Keywords: illiquidity discount, estimation, Swedish markets, industry, bid ask spread Purpose: The purpose of this thesis is to estimate illiquidity discounts through the bid-ask spread on the Swedish stock markets, as well as investigate how the discount varies between industries. Method: The thesis is subdivided in a qualitative part, where we have conducted interviews and researched previous studies and theses to gain knowledge into ways of estimating the illiquidity discount. The quantitative part is a statistical estimation of the illiquidity discount with data processing done in SPSS, Excel and Numbers. Theory: Practitioners in real business life use in earlier studies as well as the estimation process through the bid ask-spead. The theory presented comes from textbooks, studies and theses and describes what illiquidity is, what affects its size as well as how it is possible to estimate the illiquidity discount. Empirical data: Quantitative data has been fetched from various databases online and been processed in SPSS, Excel and Numbers with regards to market, industry, spread and stock price. Qualitative data comes from research of previous material within the subject as well as from conducting interviews. Results: We have been able to prove a statistical significant difference in the relative spead, and thereby the illiquidity discount, on the different markets, but unable to prove a statistical significant difference between different industries