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SUMMARY:Judith L. Bronstein (University of Arizona)\, "Why Cooperate with Another Species? The Puzzles of Mutualism"
DESCRIPTION:Abstract:\nThe classic view of nature is one of a deathly struggle for existence. Yet\, throughout nature\, organisms cooperate with each other. Mutualisms – mutually beneficial interactions between species – are more than fascinating natural history stories: they are central to the diversity and the diversification of life on Earth. Charles Darwin\, well aware of mutualisms\, mused that if species could be shown to act exclusively for the good of others\, “it would annihilate my theory”. The very young field of mutualism research attempts in part to address Darwin’s challenge. I will first briefly discuss the relationship between within-species cooperation and mutualism. I will then introduce two underlying concepts that are helping to guide our growing understanding: mutualism not only confers benefits but also exacts costs on the participants; and the immediate interests of mutualists commonly conflict. Then\, I will review some of my group’s recent findings that help address two of the most vexing puzzles mutualism poses: if mutualisms are beneficial\, why isn’t the world covered with them; and if mutualisms are costly\, then why doesn’t everyone cheat their partners? Our understanding of mutualism has exploded in recent years\, but this new focus has come at the cost of exploring connections between and mutualism and other forms of interaction – a situation I am working to mend during my Wiko fellowship. I will conclude by highlighting the interfaces that excite me the most.\n\nThis talk will take place in person at SCIoI. \n  \nPhoto by Joseph Sharp on Unsplash \n 
URL:https://www.scienceofintelligence.de/event/thursday-morning-talk-judith-l-bronstein-university-of-arizona-why-cooperate-with-another-species-the-puzzles-of-mutualism/
CATEGORIES:Thursday Morning Talk
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DTSTAMP:20260409T104946
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LAST-MODIFIED:20240813T102507Z
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SUMMARY:Dr. Arlena Jung\, "Time Management & Resilience"
DESCRIPTION:Abstract: \nIn this talk\, Dr. Jung will focus on the three key principles of good time management: defining priorities\, managing expectations and developing routines that work. Following the lecture\, the participants have the opportunity to discuss their time management challenges in an individual coaching session. \nDefining Priorities: Dealing with high performance expectations in wide array of areas ranging from research to writing\, presenting\, networking and teaching is a key challenge for early-stage researches. In order to deal effectively with the in part conflicting expectations PhD students and early stage researchers need both the mindset and the self-confidence to define priorities. This means developing short and middle term goals that are both compatible with one’s own long-term goals and the expectations one’s “relevant other’s”. Without clear goals defining priorities and quality criteria become impossible tasks. That participants learn to understand the use of time management tools using the power of the 4 Zs to define SMART goals\, and integrating a “definition of done” into work packages\, milestones and at times even individual tasks. We also address the emotional challenge of dealing with in part conflicting goals\, roles and expectations. Together we discuss how ambiguity tolerance and strategic thinking can be used as key strengths in dealing with the multifaceted challenges but also opportunities of this career phase.  \nManaging Expectations: Complex interdependencies are an inherent part of the qualification phase of early stage research. Without the ability to manage expectations. PhD students have a very limited ability to actually turn their priorities into actionable plans. In this section of the lecture the participants are acquainted with key stakeholder-management tools such as the stakeholder-matrix and the systemic portrait. We\, however\, also discuss key communication skills needed to manage expectations effectively such as “7 shades of no”\, turning “yes” into a deliberate decision and creating solution  oriented dialogues. \nDeveloping routines that work: In order to use the limited resources available as effectively as possible early stage researchers need to learn to develop routines that work. This means figuring out what time management tools fit nicely both with their individual needs and their operational and conceptual tasks. In the last section of the lecture we present time management tools that help PhD students structure their working days and weeks ranging from the pomodoro and the ivy lee method to stimulus-response regulation practices and self-monitoring and self-evaluation methods.\n \n  \nThis talk will take place in person at SCIoI. \nPhoto by freestocks on Unsplash \n 
URL:https://www.scienceofintelligence.de/event/thursday-morning-talk-dr-arlena-jung-time-management-resilience/
CATEGORIES:Thursday Morning Talk
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SUMMARY:Marah Halawa (Science of Intelligence)\, "Contrastive Learning Approaches for Computer Vision Applications"
DESCRIPTION:Abstract: \nThe recent success in Computer Vision has been mostly attributed to improved results using deep learning models trained on large labeled datasets. Many of these datasets have been labeled by humans. The labeling process\, however\, can be time-consuming\, and in many applications\, it may require expertise that could be costly to acquire. In order to address this requirement\, more research focus and effort have shifted toward unsupervised learning algorithms\, in order to utilize the ever-increasing quantities of unlabeled data. Self-supervised learning (SSL)\, in particular\, is a set of algorithms that specifically aim to learn rich data representations from unlabeled samples\, and it achieves comparable results to fully supervised methods on common benchmarks for image classification and segmentation. The idea behind SSL methods is to learn broad features from the signals that exist in unlabeled data. In other words\, to acquire more general information and knowledge\, and store them as neural network features that will be useful as prior knowledge for subsequent downstream supervised tasks (classification\, segmentation\, regression\, ..etc.). \nThere are two types of SSL methods. First\, self-prediction methods\, which predict some omitted parts (in purpose) of the data using the other existing part of the data\, such as jigsaw puzzle solving. Second\, contrastive learning methods\, which utilize similarities and dissimilarities\, or simply relations\, amongst data samples to form a classification problem\, such as SimCLR (simple contrastive learning of representations). Contrastive learning methods have proven effective as representation learners in applications of natural image classification. Nevertheless\, extending such algorithms to multiple application domains comes with challenges\, and we identify certain limitations in these approaches. Therefore\, in this talk\, we will focus on contrastive learning methods and how to apply them in several computer vision applications. We also discuss the challenges and limitations we identified and how to address them in project 29. \nThis talk will take place in person at SCIoI. \n 
URL:https://www.scienceofintelligence.de/event/thursday-morning-talk-marah-halawa-contrastive-learning-approaches-for-computer-vision-applications/
CATEGORIES:Thursday Morning Talk
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