Events: Student project presentation, Arkitektur, Bygg- och miljöteknik, Data- och informationsteknik, Energi och miljö, Kemi- och bioteknik, Matematiska vetenskaper, Material- och tillverkningsteknik, Mikroteknologi och nanovetenskap, Produkt- och produktionsutveckling, Rymd- och geovetenskap, Signaler och system, Sjöfart och marin teknik, Teknikens ekonomi och organisation, Fysik, Tillämpad IT, Tillämpad mekanik, Space, Earth and Environment, Architecture and Civil Engineering, Bioteknik, Electrical Engineering, Industrial and Materials Science, Informations- och kommunikationsteknik, Mechanics and Maritime Sciences events at Chalmers University of TechnologyFri, 15 Jan 2021 10:47:36 +0100 thesis presentation<p>Online</p><p>​John Harrysson: An Avida simulation of the Control Problem in AI</p>​<br />Supervisor: Torbjörn Lundh thesis presentation, Amanda Lundberg<p>Online</p><p>​Regional differences in pesticide use and footprints of Brazilian soybeans</p>​<span style="background-color:initial">Amanda Lundberg presents HIS/HER/THEIR master’s thesis work “Regional differences in pesticide </span><span style="background-color:initial">use and footprints of Brazilian soybeans”</span><span style="background-color:initial">”, at the Department of Space, Earth and Environment.</span><div><br /></div> <div>Supervisor: Christel Cederberg, Physical Resource Theory</div> <div><br /></div> <div><a href="">Zoomlänk till presentationen</a>. Lösenord: 813873</div> <div><br /></div> thesis presentation, Kevin Jaquier<p>Online</p><p>Dynamics of Affective Information in English Novels...</p>​<span style="background-color:initial">Kevin Jaquier </span><span style="background-color:initial">presents his master’s thesis work “</span><span style="background-color:initial">Dynamics of Affective Information in English Novels: </span><span style="background-color:initial">Using Information Theory to measure interaction patterns between fictional characters&quot;, carried out at the Department of Space, Earth and Environment.</span><div><span style="background-color:initial"><br /></span></div> <div><span style="background-color:initial">Contact supervisor <a href="">Kristian Lindgren</a> for zoom-link to the presentation.</span></div> <div><span style="background-color:initial"><br /></span></div> Gertz, MPBME, and Albin Lindmark, MPSYS<p>Web seminar</p><p>​ Knowledge distillation for face recognition on lightweight neural networks</p><div>​</div> <div><a href="">Join the seminar via Zoom.</a></div> <div>Password: 214931</div> <div><br /></div> <div>Examiner: Marija Furdek, Dept of Electrical Engineering</div> <div>Supervisors: Kenneth Jonsson, Smart Eye, Ahmet Oguz Kislal, Dept of Electrical Engineering</div> <div>Opponent: Jacob Larsson</div> <div><br /></div> <h2 class="chalmersElement-H2">Abstract</h2> <div><br /></div> <div>Face recognition is a common bio-metric used in everyday commercial products and is also widely used in safety and surveillance. Accuracy is critical when face recognition is used for authentication purposes. Implementation of accurate face recognition using CNN models is limited to deployment in high-end complex systems due to the computational complexity. Viable implementation of accurate face recognition in mobile devices demands less computationally expensive methods, such as smaller models. This thesis investigates the potential of knowledge distillation (KD), a machine learning technique used to improve a small models performance by transferring knowledge from a large model to the smaller one. KD was implemented on CNNs trained for the task of face-identification and -verification using low resolution near infrared images. Both identification and verification models trained with KD achieved a higher accuracy than the reference models trained with standard procedures. Methods using a staged training procedure or hints comparing features of the models was shown to further improve KD and is useful when there is a large discrepancy between model sizes. Training using KD was proven to increase learning, thus making it possible to increase the accuracy of small face recognition networks.<br /></div> Lexell, MPPAS<p>Online via Zoom</p><p>​ Title of thesis: &quot;A top-down model for layered holographic strange metals&quot; Follow the presentation online Passcode: 936589​</p><h2 class="chalmersElement-H2">​Abstract:</h2> <div>The AdS/CFT correspondence is a potentially powerful tool in describing condensed matter systems for which our current theoretical understanding is lacking. This is because it can be used to map a strongly coupled field theory to a weakly coupled gravitational theory. In this thesis I describe some aspects of the AdS/CFT correspondence and look at a top-down model. This model is built upon a large number of D3 and D5 branes with a D7 probe brane. </div> <div> </div> <div>The goal is to see whether this model can be used to describe aspects of the strange metal phase that is found in layered high-temperature superconductors. Starting from a weakly coupled string theory setting I derive the temperature-dependence of a DC current in different regimes, as well as dispersion relations for electromagnetic fluctuations. There is a possibility of getting resistivity that matches the linear in T dependence for the resistivity in strange metals. This is done by adjusting the number of D5 branes depending on the temperature and the charge density, which is obtained holographically. With a particular choice of boundary conditions, plasmonic dispersion relations are found, as required. This is the first layered top-down model with this behaviour for the current available in its parameter-space. While it has not produced correct predictions without an adjustment of the parameter-space, the freedoms granted from the said parameter-space makes it possible that this model could describe other layered systems that lacks quasi-particles, and not just strange metals. ​</div> thesis presentation<p>Online</p><p>​Louise Leonard: Tackling Missing Values in Mass Spectrometry-based Proteomics Data</p>Supervisor: Erik Kristiansson