Inflicted Viewing: Examining Moral Masochism, Empathy, and the Frustration of Trauma Cinema

The contemporary turn of psychoanalytic film analysis has opened a new mode of understanding cinematic language. However, rejecting classical psychoanalysis would be premature. This thesis will place the two in conjunction, specifically through Sigmund Freud’s conceptualization of moral masochism and Wilfred Bion’s theory of thinking. Through four films: Una, The Tale, The Tribe, and Son of Saul I explore the affective nature of films that depict trauma and why one would gravitate towards such upsetting material. The spectator who seeks to be frustrated is not looking to harm oneself but to process this frustration in order to expand their emotional experience

The Effect of Social Anxiety and Approachability on Motivation in Online Classrooms

A Research Methods Project supervised by Dr. Hilary Stebbins (Spring 2021)

Ignite: Not Just Surviving PBIS, But Succeeding: 1st Year Success Toolkit (Group 2)

This presentation will target schools that are in the installing phase or planning phase. However, the presentation could also benefit schools that may not have had a successful implementation year due to a variety of reasons. The presentation will focus on 7 critical components (tools) Stockbridge Elementary School utilized during Year 1 of implementing the PBIS framework. By using these tools SES reduced the number of office referrals by 33%. The 7 success tools are: Community Buy-In, Strategic Introduction to Student Body, Planned PBIS Leadership Meetings, PBIS Student Ambassadors, Constant Reflection of Practices / Procedures, Effective and Consistent Acknowledgement System, and Data-Driven Decision Making. During the session participants will be actively engaged in the practices that allowed SES to have a successful 1st year of implementation. Presenters will give examples of how each tool assisted with ensuring the PBIS framework was implemented with fidelity. The presentation will also allow participants to reflect on their practices and build a toolkit that will allow their school to successfully implement PBIS. In addition to hearing from teachers, the presentation will include feedback from the school’s principal. As we all know, PBIS is a very intricate framework that must be implemented with fidelity. The presentation will highlight the importance of using the TFI and how each success tool is linked to a particular item on the TFI. The presentation will focus on the 15 items in Tier I

Ritualizing Madness: Case Files as Sites of Enforced Performativity, 1894-1950

In this article, I argue that case files kept by doctors, nurses, and attendants in Canadian Asylums, act as sites of performative madness enforced by the observer. In applying Foucauldian and performance theories, I look at the production of knowledge, and influence of power, which allow for the encoding of madness as a ritualized behaviour that is repeatable outside of the individual being recorded as mad. To illustrate this point, I use several case files from the Brockville Asylum to highlight how certain physical characteristics and behaviours were pathologized to support the medical argument that the inmate in question was in fact mad and belonged in the asylum. I suggest that one is not born mad, but they become mad through enforced ideas of madness, which enforced by the observer’s categorization of a physical characteristic or behaviour as mad

Fast Trainable Projection for Robust Fine-Tuning

Robust fine-tuning aims to achieve competitive in-distribution (ID) performance while maintaining the out-of-distribution (OOD) robustness of a pre-trained model when transferring it to a downstream task. Recently, projected gradient descent has been successfully used in robust fine-tuning by constraining the deviation from the initialization of the fine-tuned model explicitly through projection. However, algorithmically, two limitations prevent this method from being adopted more widely, scalability and efficiency. In this paper, we propose a new projection-based fine-tuning algorithm, Fast Trainable Projection (FTP) for computationally efficient learning of per-layer projection constraints, resulting in an average $35\%$ speedup on our benchmarks compared to prior works. FTP can be combined with existing optimizers such as AdamW, and be used in a plug-and-play fashion. Finally, we show that FTP is a special instance of hyper-optimizers that tune the hyper-parameters of optimizers in a learnable manner through nested differentiation. Empirically, we show superior robustness on OOD datasets, including domain shifts and natural corruptions, across four different vision tasks with five different pre-trained models. Additionally, we demonstrate that FTP is broadly applicable and beneficial to other learning scenarios such as low-label and continual learning settings thanks to its easy adaptability. The code will be available at https://github.com/GT-RIPL/FTP.git.Comment: Accepted to NeurIPS 202

Learning Task Requirements and Agent Capabilities for Multi-agent Task Allocation

This paper presents a learning framework to estimate an agent capability and task requirement model for multi-agent task allocation. With a set of team configurations and the corresponding task performances as the training data, linear task constraints can be learned to be embedded in many existing optimization-based task allocation frameworks. Comprehensive computational evaluations are conducted to test the scalability and prediction accuracy of the learning framework with a limited number of team configurations and performance pairs. A ROS and Gazebo-based simulation environment is developed to validate the proposed requirements learning and task allocation framework in practical multi-agent exploration and manipulation tasks. Results show that the learning process for scenarios with 40 tasks and 6 types of agents uses around 12 seconds, ending up with prediction errors in the range of 0.5-2%.Comment: The video and open-source code are at https://brg.engin.umich.edu/publications/learn-multiagent-taskreq