2021

Challenges and new directions of preventing mental illness (in the offspring of parents with depression)

Challenges and new directions of preventing mental illness (in the offspring of parents with depression)

Speaker: Dr. Johanna Löchner

Time: 11:00, 07 May 2021  

Location: Online

 

Abstract

Depression is one of the most common disorders worldwide, causing great personal burden and costs at the societal level. Children of parents suffering from depression are one of the largest risk groups for mental illness. However, there is a lack of research and services for prevention, especially for this high-risk group. The few prevention programs that do exist and have been evaluated generally show small to moderate effects that diminish over time. Therefore, it is questionable how to conduct and implement effective prevention programs for this high-risk group.
A parallel randomized controlled trial evaluated the effectiveness of the German version of the Family Group Cognitive Behavioral Intervention (FGCB): Families with i) a depressed parent and ii) a healthy child aged 8-17 years (mean age = 11.63 years; 53% female) were randomly assigned (block-wise; stratified by child age and parental depression) to the 12-session intervention (EG; n = 50) or no intervention (CG; usual care; n=50). We hypothesized that the CG children would show greater increases in self-reported symptoms of depression and internalizing and externalizing disorder over time than the EG. In addition, potential mechanisms of change were examined (e.g., emotion regulation, attributional style, knowledge of depression, and parenting style). We found significant intervention effects on self-reported internalizing and externalizing symptoms but not on depressive symptoms or parent-reported psychopathology. Although uptake of the intervention was high, parents and children reported being stressed by the number of hours and content invested. Thus, a key question is how to balance behavior change with personal investment. Digital offers for measurement and psychotherapeutic interventions could be a bridge here - also in prevention.

 

Biography

Dr. Johanna Löchner is a clinical psychologist and licensed psychotherapist and is currently the head of the Early Intervention Group at the German Youth Institute (DJI) focusing on research of prevention programs for families with children aged 0-3 funded by the family ministry. Before this, she was employed as a post-doctoral research fellow at the department of Psychology and Psychotherapy at LMU (2018-2020) and the Clinic for Child and Adolescent Psychiatry (LMU) (2014-2020). She finalized her dissertation about risk factors and prevention in the children of parents with depression in 2018. Her research focused on the transmission of mental disorders, the prevention of depression in children of mentally ill parents and adolescents and young adults, face-to-face and with digital solutions.
Psychotherapeutic work: Since 2012 she has been working as a psychotherapist in the university hospitals of LMU and TU Munich and in the AVM outpatient center with adults and children with different psychiatric disorders (Cognitive Behavioral Therapy, license psychotherapy/Approbation 2019).

 

 

 

Learning-Based Quality of Service Prediction in Cellular Vehicle Communication

Guest Speaker: Josef Schmid

Time: 19. April 2021, 14:00

Location: Online

 

Abstract

At the moment nearly all automotive manufactures as well as a lot of newcomers like Google and Tesla are working in the area of automated driving. Since today’s automated driving solution are based on onboard sensor technologies like radar, laser or camera system, they are limited in the observing range of about 250 m in front of the vehicle. In case of need for transfer of the driving task from automated mode to the driver, drivers will need some time to react. Therefore the vehicle needs an extended observing area which can be achieved by communication. A common approach for such a communication is to use a mobile network connection. But due to temporary lacks of radio coverage the mobile networks link e.g. LTE or 5G are not as stable as needed. To improve the quality of service of the mobile network it is a key objective to analyse the behaviour of the mobile network at certain driving scenarios. The presentation introduces a method on how to record, collect and analyse such a communication. In addition two different approaches to predict the state of the mobile connection are shown. The first is geo based solution using connectivity maps, the second using different machine learning regression models to achieve this goal.

 

Short biography

Josef Schmid received his B. Eng. in 2014 and his M. Sc. in Applied Research in Engineering Sciences in March 2016 at the OTH Amberg-Weiden. During his master studies, he started working as research associate at the Faculty of Electrical, Information and Media at the OTH Amberg-Weiden. Since Mai 2016 his research focus is on mobile network based vehicle communication for cooperative highly automated driving. His main research interests are vehicle to X communication (V2X) as well as quality of service for mobile network and machine learning methods.

 

Student presentations, February

Audiovisual Data-Driven Android App for Emotion Recognition (Master’s thesis)

Speaker: Qiang Chang

Time: 10:00, 9th February 2021

Location: Online

 

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Implementierung einer Android Applikation für die Klassifikation von Schnarchdaten mittels neuronaler Netze (Bachelor’s thesis)

Speaker: Igor Tkatschenko

Time: 10:00, 9th February 2021

Location: Online

 

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Seminar presentations, 10.02.2021, online

Digital Health

09:30 Marius Pleyer
09:45 Stefan Crummenauer
10:00 Fabian Brain
10:15 Qiang Chang
10:30 Benjamin Jin
10:45 Bernhard Scherer
11:00 Frederic Schulz

 

Computational Intelligence

11:30 Michael Ihrler
11:45 Francois Lux
12:00 Daniel Schubert
12:15 Reinhard Seidl
12:30 Lena Holland
12:45 Sarah Sporck

 

Thesis Presentations, January

Deep Learning Annotation Optimisation for Emotion Recognition (Master’s thesis)

Speaker: Lea Schumann

Time: 11:00, 15th January 2021

Location: Online

 

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Author-centric Machine Reviewing of Papers for Deep Learning Utilising Natural Language Feature (Master’s thesis)

Speaker: Philip Müller

Time: 11:00, 15th January 2021

Location: Online

 

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Automated Detection and Classification of Airborne Pollen Grains Using Deep Learning (Master’s thesis)

Speaker: Jakob Schäfer

Time: 11:00, 15th January 2021

Location: Online

 

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Towards End-to-End Intrusion Detection Utilising Convolutional Recurrent Neural Networks (Bachelor’s thesis)

Speaker: Tobias Hallmen

Time: 15. Januar 2021, 11:00

Location: Online

 

​​​​​​​Learning with known operators reduces maximum error bounds

Guest Speaker: Prof. Andreas Maier  

Time: 12. Januar 2021, 14:00

Location: Online

 

Abstract

We describe an approach for incorporating prior knowledge into machine learning algorithms. We aim at applications in physics and signal processing in which we know that certain operations must be embedded into the algorithm. Any operation that allows computation of a gradient or sub-gradient towards its inputs is suited for our framework. We derive a maximal error bound for deep nets that demonstrates that inclusion of prior knowledge results in its reduction. Furthermore, we show experimentally that known operators reduce the number of free parameters. We apply this approach to various tasks ranging from computed tomography image reconstruction over vessel segmentation to the derivation of previously unknown imaging algorithms. As such, the concept is widely applicable for many researchers in physics, imaging and signal processing. We assume that our analysis will support further investigation of known operators in other fields of physics, imaging and signal processing.

 

Article: https://www.nature.com/articles/s42256-019-0077-5

 

Biography

Prof. Dr. Andreas Maier was born on 26th of November 1980 in Erlangen. He studied Computer Science, graduated in 2005, and received his PhD in 2009. From 2005 to 2009 he was working at the Pattern Recognition Lab at the Computer Science Department of the University of Erlangen-Nuremberg. His major research subject was medical signal processing in speech data. In this period, he developed the first online speech intelligibility assessment tool - PEAKS - that has been used to analyze over 4.000 patient and control subjects so far.
From 2009 to 2010, he started working on flat-panel C-arm CT as post-doctoral fellow at the Radiological Sciences Laboratory in the Department of Radiology at the Stanford University. From 2011 to 2012 he joined Siemens Healthcare as innovation project manager and was responsible for reconstruction topics in the Angiography and X-ray business unit. 


In 2012, he returned the University of Erlangen-Nuremberg as head of the Medical Reconstruction Group at the Pattern Recognition lab. In 2015 he became professor and head of the Pattern Recognition Lab. Since 2016, he is member of the steering committee of the European Time Machine Consortium. In 2018, he was awarded an ERC Synergy Grant "4D nanoscope".  Current research interests focuses on medical imaging, image and audio processing, digital humanities, and interpretable machine learning and the use of known operators

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