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CambPlants

A networking organisation for plants-related research and impact
 

Wed 20 Jul 16:00: One health, many challenges no silver bullet.

http://talks.cam.ac.uk/show/rss/29729 - Mon, 11/07/2022 - 11:34
One health, many challenges no silver bullet.

The University of Minnesota (UMN) is a land grant higher education institution that is globally recognized for its work on policy and public health. The Center for Animal Health and Food Safety (CAHFS) at the UMN College of Veterinary Medicine is an OIE collaborating center for capacity building and an FAO reference center for veterinary public health. The objective of the presentation will be to provide an overview of relevant research, educational, and outreach activities on applied epidemiology, veterinary public health, and food animal population medicine at CAHFS and other relevant groups at the UMN CVM .

Bio: Andres M. Perez (DVM, PhD) is a veterinary epidemiologist, originally from Argentina. He is a Professor, Endowed Chair of Global Animal Health and Food Security; and Director of the Center for Animal Health and Food Safety (CAHFS) at the College of Veterinary Medicine, University of Minnesota (UMN). He is an Honorary Professor at the Universidad Complutense de Madrid, Spain, a Collaborating scientist/advisor to the USDA /ARS Foreign Animal Disease Research Unit at the Plum Island Animal Disease Research Center, and has been awarded an American Veterinary Epidemiology Society (AVES) Honorary Diploma for 2021. He has led educational, outreach, and research activities in >40 countries, supervised, co-supervised, or mentored >50 graduate students in the field of animal health and food safety, and published >230 manuscripts in peer-reviewed journals. He also serves as Chief editor of the Frontiers in Veterinary Science journal.

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Wed 03 Aug 16:30: Statistics Clinic Summer 2022 II The clinic takes place in MR5 at the Centre for Mathematical Sciences.

http://talks.cam.ac.uk/show/rss/49845 - Mon, 11/07/2022 - 10:37
Statistics Clinic Summer 2022 II

If you would like to participate, please fill in the following form. The deadline for signing up for a session is 12pm on Monday the 1st of August. Subject to availability of members of the Statistics Clinic team, we will confirm your in-person or remote appointment.

This event is open only to members of the University of Cambridge (and affiliated institutes). Please be aware that we are unable to offer consultations outside clinic hours.

The clinic takes place in MR5 at the Centre for Mathematical Sciences.

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Thu 14 Jul 14:00: BSU Seminar: "Challenges in risk prediction using routinely collected health data" This will be a free online seminar. To register, click here: https://us02web.zoom.us/meeting/register/tZEkdOupqDspHtK1D30gXlBykdbIvLV8DMdH

http://talks.cam.ac.uk/show/rss/49845 - Mon, 11/07/2022 - 09:39
BSU Seminar: "Challenges in risk prediction using routinely collected health data"

Identifying who is at highest risk of severe consequences of COVID -19, such as hospitalisation or death, is an important component of any policy response. One of the best sources of information on who experiences these events is linked electronic health record data. Using these data to identify high-risk patients, however, raises a number of methodological challenges. This talk discusses the use of linked primary care data within the OpenSAFELY platform to predict risk of COVID -19 mortality and the challenges of doing so.

This will be a free online seminar. To register, click here: https://us02web.zoom.us/meeting/register/tZEkdOupqDspHtK1D30gXlBykdbIvLV8DMdH

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Thu 21 Jul 15:00: cISP: A Speed-of-Light Internet Service Provider

http://talks.cam.ac.uk/show/rss/49845 - Mon, 11/07/2022 - 09:20
cISP: A Speed-of-Light Internet Service Provider

Low latency is a requirement for a variety of interactive network applications. The Internet, however, is not optimized for latency. We thus explore the design of wide-area networks that move data at nearly the speed of light in vacuum. Our cISP design augments the Internet’s fiber with free-space microwave wireless connectivity over paths very close to great-circle paths. cISP addresses the fundamental challenge of simultaneously providing ultra-low latency while accounting for numerous practical factors ranging from transmission tower availability to packet queuing, achieving mean latencies within 5% of that achievable using great-circle paths at the speed of light. Further, using experiments conducted on a nearly-speed-of-light algorithmic trading network, together with an analysis of trading data at its end points, we show that microwave networks are reliably faster than fiber networks even in inclement weather. We provide estimates showing the economic value of such networks would substantially exceed their expense. Finally, we will discuss how this work can influence future directions using a combination of multiple communication channels—low latency and high bandwidth—such as with 5g wireless.

Bio: Brighten Godfrey is a professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign, and a technical director at VMware. He co-founded and served as CTO of network verification pioneer Veriflow through its 2019 acquisition by VMware. He received his Ph.D. at UC Berkeley in 2009. His research interests lie in the design of networked systems and algorithms. He is a winner of the ACM SIGCOMM Rising Star Award, the Sloan Research Fellowship, and the National Science Foundation CAREER Award, and has chaired several conferences including ACM HotNets 2014, the Symposium on SDN Research 2016, and ACM SIGCOMM 2022 .

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Thu 18 Aug 15:00: Achieving Consistent Low Latency for Wireless Real-Time Communications with the Shortest Control Loop

http://talks.cam.ac.uk/show/rss/49845 - Sun, 10/07/2022 - 08:21
Achieving Consistent Low Latency for Wireless Real-Time Communications with the Shortest Control Loop

Real-time communication (RTC) applications like video conferencing or cloud gaming require consistent low latency to provide a seamless interactive experience. However, wireless networks including WiFi and cellular, albeit providing a satisfactory median latency, drastically degrade at the tail due to frequent and substantial wireless bandwidth fluctuations. We observe that the control loop for the sending rate of RTC applications is inflated when congestion happens at the wireless access point (AP), resulting in untimely rate adaption to wireless dynamics. Existing solutions, however, suffer from the inflated control loop and fail to quickly adapt to bandwidth fluctuations. In this paper, we propose Zhuge, a pure wireless AP based solution that reduces the control loop of RTC applications by separating congestion feedback from congested queues. We design a Fortune Teller to precisely estimate per-packet wireless latency upon its arrival at the wireless AP. To make Zhuge deployable at scale, we also design a Feedback Updater that translates the estimated latency to comprehensible feedback messages for various protocols and immediately delivers them back to senders for rate adaption. Trace-driven and real-world evaluation shows that Zhuge reduces the ratio of large tail latency and RTC performance degradation by 17% to 95%.

Speaker Bio: Zili is a 3rd-year PhD student in Tsinghua University. His current research interest focuses on real-time video communications. He has published several papers in SIGCOMM / NSDI and received the Microsoft Research Asia PhD Fellowship, Gold Medal of SIGCOMM 2018 Student Research Competition, and two best paper awards.

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Thu 18 Aug 15:00: Achieving Consistent Low Latency for Wireless Real-Time Communications with the Shortest Control Loop

http://talks.cam.ac.uk/show/rss/49845 - Sun, 10/07/2022 - 08:20
Achieving Consistent Low Latency for Wireless Real-Time Communications with the Shortest Control Loop

Real-time communication (RTC) applications like video conferencing or cloud gaming require consistent low latency to provide a seamless interactive experience. However, wireless networks including WiFi and cellular, albeit providing a satisfactory median latency, drastically degrade at the tail due to frequent and substantial wireless bandwidth fluctuations. We observe that the control loop for the sending rate of RTC applications is inflated when congestion happens at the wireless access point (AP), resulting in untimely rate adaption to wireless dynamics. Existing solutions, however, suffer from the inflated control loop and fail to quickly adapt to bandwidth fluctuations. In this paper, we propose Zhuge, a pure wireless AP based solution that reduces the control loop of RTC applications by separating congestion feedback from congested queues. We design a Fortune Teller to precisely estimate per-packet wireless latency upon its arrival at the wireless AP. To make Zhuge deployable at scale, we also design a Feedback Updater that translates the estimated latency to comprehensible feedback messages for various protocols and immediately delivers them back to senders for rate adaption. Trace-driven and real-world evaluation shows that Zhuge reduces the ratio of large tail latency and RTC performance degradation by 17% to 95%.

Speaker Bio: Zili is a 3rd-year PhD student in Tsinghua University. His current research interest focuses on real-time video communications. He has published several papers in SIGCOMM / NSDI and received the Microsoft Research Asia PhD Fellowship, Gold Medal of SIGCOMM 2018 Student Research Competition, and two best paper awards.

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Wed 13 Jul 16:00: Introducing JUNE

http://talks.cam.ac.uk/show/rss/29729 - Fri, 08/07/2022 - 15:08
Introducing JUNE

In this talk I introduce the JUNE model for the simulation of infectious diseases transmitted by human interactions such as Covid-19. Examples for its applications include the spread of COVID -19 in England and in Cox’s Bazaar, one of the largest refugee camps in the world.

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Wed 13 Jul 12:00: An Introduction to Data and Commercialisation

http://talks.cam.ac.uk/show/rss/49845 - Fri, 08/07/2022 - 12:55
An Introduction to Data and Commercialisation

Hosted by Cambridge Enterprise and Cambridge Centre for Data-Driven Discovery (C2D3).

There are two parts to this webinar:

Part 1: ‘Future Proofing your Data Project’

Building a foundation for dissemination and impact into your research project can feel overwhelming when you’re dealing in data. We look at some practical steps you can take to manage data when you have commercialisation in mind, and talk about when and how to find support from across the University.

Managing data with future commercialisation in mind Basics of ‘protecting data’ and IP and an introduction to commercial licensing Using third party and open source materials Do’s and don’ts with health and personal data *When things go wrong!

Part 2: ‘Creating Impact through Data Commercialisation’

We’ll introduce some case studies of data commercialisation from Cambridge and beyond. Data commercialisation uses diverse models including consultancy, licensing and company creation. How can you devise a pathway to maximise your impact and create financial sustainability for your project beyond grant funding?

Speakers will include:

Dr Terry Parlett, Dr Emma Salgård Cunha and Dr Sian Fogden

Webinar details:

Date: Wednesday 13th July 2022 Time: 12:00 – 13:30 Platform: Microsoft Teams

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Tue 12 Jul 14:00: Claim-Dissector: An Interpretable Fact-Checking System with Joint Re-ranking and Veracity Prediction

http://talks.cam.ac.uk/show/rss/49845 - Wed, 06/07/2022 - 13:50
Claim-Dissector: An Interpretable Fact-Checking System with Joint Re-ranking and Veracity Prediction

Abstract:

We present Claim-Dissector: a novel latent variable model for fact-checking and fact-analysis, which given a claim and a set of retrieved provenances allows learning jointly (i) what are the provenances relevant to this claim (ii) what is the veracity of this claim. We show that our system achieves state-of-the-art results on FEVER comparable to two-stage systems often used in traditional fact-checking pipelines, while using significantly less parameters and computation. Our analysis shows that proposed approach further allows to learn not just which provenances are relevant, but also which provenances lead to supporting and which toward denying the claim, without direct supervision. This not only adds interpretability, but also allows to detect claims with conflicting evidence automatically. Furthermore, we study whether our model can learn fine-grained relevance cues while using coarse-grained supervision. We show that our model can achieve competitive sentence-recall while using only paragraph-level relevance supervision. Finally, traversing towards the finest granularity of relevance, we show that our framework is capable of achieving strong token-level interpretability. To do this, we present a new benchmark focusing on token-level interpretability ― humans annotate tokens in relevant provenances they considered essential when making their judgement. Then we measure how similar are these annotations to tokens our model is focusing on. Our code, dataset and demo will be released online.

Bio:

Martin Fajčík (read as Fay-Cheek) is a PhD candidate in Natural Language Processing from Knowledge Technology Research Group active at FIT -BUT in Brno, Czech Republic, advised by prof. Pavel Smrž (ž is read like j in french “Jean”). From 2021, he also works as a research assistant in IDIAP research institute based in Martigny, Switzerland. His PhD work is focusing on open-domain knowledge processing, mainly in question answering and fact-checking. He enjoys a good hikes and an informal discussions over tea.

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Wed 06 Jul 16:00: Identifying sources of transmission for zoonotic mosquito-borne viruses

http://talks.cam.ac.uk/show/rss/29729 - Tue, 05/07/2022 - 10:01
Identifying sources of transmission for zoonotic mosquito-borne viruses

Many mosquito-borne pathogens that cause disease in humans, like dengue, are transmitted between humans. However, there are at least 12 mosquito-borne viruses that are transmitted between other animals, for which humans are incidentally infected. These viruses are difficult to control; human vaccination alone cannot eliminate them. Furthermore, because zoonotic mosquito-borne viruses are usually transmitted by multiple host and vector species, they can be present across a range of ecological contexts. In turn, ecological context influences transmission dynamics and risk to humans. The ability to target control within ecological contexts that present sources of onward transmission should increase impact. I will discuss three broad themes of importance for identifying sources of transmission for this group of viruses: heterogeneity, scale, and noise. With respect to these themes, I will highlight current research gaps in the modelling literature and give specific examples from two new projects which aim to address these gaps by integrating empirical studies and modelling for Japanese encephalitis and Rift Valley fever.

  • Speaker: Dr Jennifer Lord, Department of Vector Biology Liverpool School of Tropical Medicine
  • Wednesday 06 July 2022, 16:00-17:00
  • Venue: Zoom.
  • Series: Worms and Bugs; organiser: Dr Ciara Dangerfield.

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Fri 08 Jul 11:00: Synthetics with Digital Humans

http://talks.cam.ac.uk/show/rss/49845 - Mon, 04/07/2022 - 21:36
Synthetics with Digital Humans

Abstract

Nowadays, collecting the right dataset for machine learning is often more challenging than choosing the model. We address this with photorealistic synthetic training data – labelled images of humans made using computer graphics. With synthetics we can generate clean labels without annotation noise or error, produce labels otherwise impossible to annotate by hand, and easily control variation and diversity in our datasets. I will show you how synthetics underpins our work on understanding humans, including how it enables fast and accurate 3D face reconstruction, in the wild.

Bio

Dr. Erroll Wood is a Staff Software Engineer at Google, working on Digital Humans. Previously, he was a member of Microsoft’s Mixed Reality AI Lab, where he worked on hand tracking for HoloLens 2, avatars for Microsoft Mesh, synthetic data for face tracking, and Holoportation. He did his PhD at the University of Cambridge, working on gaze estimation.

Google Calendar for Future Seminars: https://calendar.google.com/calendar/u/0?cid=c2pjcHN0YXM2N3QyMWU3c2FqNjBqNWNiYXNAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ

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Mon 04 Jul 17:00: The Problem of Size Generalization in Graph Neural Networks

http://talks.cam.ac.uk/show/rss/49845 - Sun, 03/07/2022 - 00:21
The Problem of Size Generalization in Graph Neural Networks

In the past few years, graph neural networks (GNNs) have become the de facto model of choice for graph classification and other tasks on graph structured data. While, from the theoretical viewpoint, most GNNs can operate on graphs of any size, it is empirically observed that their classification performance degrades when they are applied on graphs with sizes that differ from those in the training data. In this talk we will give an overview of the current approaches to tackle the issue of poor size-generalization in GNNs, and we will introduce our recent work in this area.

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Mon 11 Jul 14:00: Combining multi-omics and biological knowledge to extract disease mechanisms Please contact Ciara for further details

http://talks.cam.ac.uk/show/rss/49845 - Fri, 01/07/2022 - 17:06
Combining multi-omics and biological knowledge to extract disease mechanisms

Multi-omics technologies, and in particular those with single-cell and spatial resolution, provide unique opportunities to study deregulation of intra- and inter-cellular processes in cancer and other diseases. In this talk I will present recent methods and applications from our group towards this aim, with a focus is on computational approaches that combine data with biological knowledge within statistical and machine learning methods. This combination allows us to increase both the statistical power of our approaches and the mechanistic interpretability of the results. I will also discuss the value to perform perturbation studies, combined with mathematical modeling, to increase our understanding and therapeutic opportunities. Finally, I will show how, using novel microfluidics-based technologies, this approach can also be applied directly to biopsies, allowing to build mechanistic models for individual cancer patients, and use these models to propose new therapies.

Please contact Ciara for further details

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