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DTSTAMP:20241216T144715
DTSTART;TZID=America/Detroit:20250121T160000
DTEND;TZID=America/Detroit:20250121T170000
SUMMARY:Lecture / Discussion:Charting the molecular landscape of cells and tissues with cryo-correlative light and electron microscopy (cryo-CLEM)
DESCRIPTION:Herman Fung\, Ph.D.\nAssistant Professor\, Cell & Developmental Biology\nResearch Assistant Professor\, Life Sciences Institute\nUniversity of Michigan
UID:130050-21865183@events.umich.edu
URL:https://events.umich.edu/event/130050
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Basic Science,Undergraduate Students,seminar,Science,Research,Rackham,Public Health,Postdoctoral Research Fellows,Medicine,Life Science,Lecture,Interdisciplinary,In Person,Graduate Students,AEM Featured,Biointerfaces,Biology,biomedical,biomedical engineering,Biosciences,Ecology,Education,Engineering,Free,Graduate School,human genetics
LOCATION:Taubman Biomedical Science Research Building - ABC Seminar Rooms
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20250120T111918
DTSTART;TZID=America/Detroit:20250121T160000
DTEND;TZID=America/Detroit:20250121T170000
SUMMARY:Workshop / Seminar:CM-AMO Seminar | Double Feature
DESCRIPTION:Shuaifeng Li \nU-M Physics\n\nTraining All-Mechanical Neural Networks for Task Learning Through in Situ Backpropagation\n\nRecent advances unveiled physical neural networks as promising machine learning platforms\, offering faster and more energy-efficient information processing. Compared with extensively-studied optical neural networks\, the development of mechanical neural networks remains nascent and faces significant challenges\, including heavy computational demands and learning with approximate gradients. Here\, we introduce the mechanical analogue of in situ backpropagation to enable highly efficient training of mechanical neural networks. We theoretically prove that the exact gradient can be obtained locally\, enabling learning through the immediate vicinity\, and we experimentally demonstrate this backpropagation to obtain gradient with high precision. With the gradient information\, we showcase the successful training of networks in simulations for behavior learning and machine learning tasks\, achieving high accuracy in experiments of regression and classification. Our findings\, which integrate the theory for training mechanical neural networks and experimental and numerical validations\, pave the way for mechanical machine learning hardware and autonomous self-learning material systems.\n\nShriya Sinha/Zecheng You \nU-M Physics\n\nEmerging Conduction Pathways in Semiconducting Bismuth-Antimony Alloys\n\nBi_{1-x}Sb_x alloys with ~0.07 < x < 0.22 have long been reported as narrow bandgap semiconductors with inverted bands. In addition to hosting topologically protected surface states\, the material has certain topological indices allowing it to host conduction pathways via extended defects such as dislocations. At temperatures above 150 K\, transport is dominated by thermally activated electrons and holes. The carrier density and their mobilities are determined by performing a two-channel analysis of the longitudinal and transverse magneto-conductivity. A 40 meV band gap is extracted by analyzing the temperature dependence of the carrier densities. ARPES measurements were conducted to explore the bulk bandgap focused on the L-point. We extract electron mobility from a sharp peak of magneto-conductance. From room temperature to cryogenic temperatures\, electron mobility increases by more than two orders of magnitude and approaches a remarkably high value of 750\,000 cm^2/Vs below 10 K. Because of the high mobility of electrons\, we were able to suppress the contribution of the bulk electron by 5-6 orders of magnitude by applying a large magnetic field and discover this presence of a new magnetic-field-independent conducting path of unknown origin.
UID:131382-21868320@events.umich.edu
URL:https://events.umich.edu/event/131382
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Physics,Science
LOCATION:West Hall - 340
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20250121T100030
DTSTART;TZID=America/Detroit:20250121T160000
DTEND;TZID=America/Detroit:20250121T170000
SUMMARY:Social / Informal Gathering:German Convo on the Go
DESCRIPTION:Members of the U-M community can walk and talk in German with Mary Gell (magell@umich.edu)\, German language instructor. Meet at Burton Tower\,  'rain or shine'\, for a 1-hour walk. If the temperature is dangerously low\, this event will meet in room 3110 Modern Languages Building. Please contact Mary if you have questions. Note that the group leaves at 4pm sharp.
UID:131291-21868109@events.umich.edu
URL:https://events.umich.edu/event/131291
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:German,German Studies,Germanic Languages And Literatures
LOCATION:Burton Memorial Tower
CONTACT:
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