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A networking organisation for plants-related research and impact
 

Wed 07 Jun 17:00: Untangling genome assembly graphs with graph neural networks

http://talks.cam.ac.uk/show/rss/49845 - Fri, 02/06/2023 - 22:10
Untangling genome assembly graphs with graph neural networks

With the emergence of PacBio HiFi and ultra-long ONT reads, the efforts to assemble genomes of various species in a de novo manner have significantly increased. This is manifested in projects including the T2T consortium project, the Human Pangenome Project and the Vertebrate Genome Project, which strive to assemble a large number of genomes with contemporary tools and data. However, even with all the recent advances in sequencing technologies, manual curation of the assembly genomes is still necessary. At the same time, most de novo assembly tools rely on graph-simplification heuristics, which have remained largely unchanged in recent years. Moreover, heuristics parameters have been hand-crafted using several genomes for which a high-quality reference was available during the time of development.

We implemented an entirely novel approach for resolving assembly graphs into genomes, one based on graph neural networks. We evaluated our method on different types of reads and with initial assembly graphs produced in a different way, comparing it against state-of-the-art de novo assemblers used in the field. Moreover, the preliminary results indicate the modularity and the adaptability of our approach, which should generalize better with every new genome assembly released, requiring minimal adjustments to the existing pipeline.

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Wed 07 Jun 12:00: The role of climate change in altering the risk of vector-borne disease

http://talks.cam.ac.uk/show/rss/29729 - Fri, 02/06/2023 - 11:43
The role of climate change in altering the risk of vector-borne disease

As climate change accelerates, vectors of communicable diseases (e.g. tiger mosquitoes transmitting dengue) may be found in greater numbers in European climates, where there are currently relatively few recorded incidents of autochthonous transmission. We are thus faced with a new array of risks and challenges to public health prevention and treatment strategies. These risks are not to be ignored, with prior documented cases of malaria outbreaks in Europe during the medieval warming period. In this talk, we will discuss these data-based and methodological challenges, and the time-scales over which risk prevention strategies can reasonably be implemented.

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Wed 14 Jun 15:00: RNA Controls DNA in the Human Cell Nucleus

http://talks.cam.ac.uk/show/rss/29729 - Thu, 01/06/2023 - 10:22
RNA Controls DNA in the Human Cell Nucleus

Transcription factors, histone methyltransferases, DNA methyltransferases, CTCF , and RNA pol II – which must necessarily bind chromatin – also bind RNA . In many cases, the RNA and DNA binding sites overlap enough that their binding is mutually antagonistic. We have determined the cryo-EM structure of Polycomb Repressive Complex 2 (PRC2) bound to RNA , which gives unexpected insights regarding the mechanism of RNA inhibition. Furthermore, we find that PRC2 and other DNA - and RNA -binding proteins can be directly transferred or “handed off” between nucleic acid ligands without a free protein intermediate. Such direct transfer may be necessary for these proteins to find their cognate binding sites in cells and may be a prerequisite for biological condensates to facilitate recruitment of proteins to chromatin.

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Thu 01 Jun 15:00: SPECIAL SEMINAR - Drying, Diffusion and Interdiffusion in Multicomponent and Multilayer Films

http://talks.cam.ac.uk/show/rss/29538 - Wed, 31/05/2023 - 10:31
SPECIAL SEMINAR - Drying, Diffusion and Interdiffusion in Multicomponent and Multilayer Films

Film drying processes of multicomponent and multilayer polymeric films play and important role in many applications and processes, such as surface coatings, membranes, adhesives, optical films, battery electrodes, catalyst coated layer for PEM FC and WE, printed multilayer structures and additive manufacturing. After liquid coating, drying, diffusion, interdiffusion, migration have impact on microstructure, distribution and functionality of the final product. Mass transport and diffusion are often a crucial factor. Despite the huge practical relevance, multimaterial drying, mass transport and inter)diffusion is experimentally and by modeling not sufficiently well investigated, often not proper or too complicated described and understood. In practice there are almost no experimental data to describe this properly. One main reason is the lack of experiments in multicomponent systems for experimentally validated theoretical descriptions of multicomponent diffusion. Inverse Micro Raman Spectroscopy (IMRS) offers in-situ measurement of solvent and polymer distribution in thin films during drying with a high spatial and quantitative resolution of all components. Experiments are performed in situ in thin films while applying well-defined boundary conditions and measuring local concentrations of components at different positions within the sample. The influence of different multicomponent material systems will be discussed in different studies. Numerical new model-based description of the multi-component mass transport will be presented. Models were validated for different polymer and solvent systems. Influence of solvent content and molar mass of polymers on the interdiffusion in multilayer systems was studied. It could be shown that interdiffusion lengths strongly correlates with solvent content and polymer mass. The model was verified for different material systems.Investigation on different material systems and the influence of glass transition were performed at drying of thin Nanofilms towards high Deborah numbers with in situ QCM measurements. The accuracy of the drying curve measurements was below 0,1 nm thickness changes during in situ drying. Relaxation kinetic effects dominates diffusion kinetic effects, this can be shown by changing the Deborah number in the different experiments towardsNano-Thin films.

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Fri 09 Jun 12:00: Investigating Reasons for Disagreement in Natural Language Inference

http://talks.cam.ac.uk/show/rss/49845 - Tue, 30/05/2023 - 17:03
Investigating Reasons for Disagreement in Natural Language Inference

Abstract:

Current practices of operationalizing annotations in crowdsourced datasets for natural language understanding (NLU) too often assume one single label per item. In this talk, I argue that NLU should investigate disagreement in annotations – human label variation (Plank 2022) – to fully capture human interpretations of language. I investigate how human label variation in natural language inference (NLI) arises, focusing on linguistic phenomena present in the sentences that lead to different interpretations. I also explore two modeling approaches for detecting items with potential disagreement (a 4-way classification with a Complicated label in addition to the three standard NLI labels, and a multilabel classification approach), and evaluate whether these approaches recall the possible interpretations in the data.

Bio:

Marie-Catherine de Marneffe obtained her PhD from Stanford University in 2012. She is an associate professor in Linguistics at The Ohio State University. She also got appointed as a FNRS Research Associate at UCLouvain in 2022. Her research focuses on developing computational linguistic methods that capture what is conveyed by language beyond the literal meaning of the words. In particular she works on “veridicality”: how do people interpret events they read about in the news—do they think such events really happen, did not happen, or are just a possibility? Primarily she wants to ground meanings in corpus data and show how such meanings can drive pragmatic inference. She has also contributed to defining the Stanford Dependencies and the Universal Dependencies representations. Her research has been funded by Google Inc. and the NSF .

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Tue 30 May 14:00: Computational Neuroscience Journal Club

http://talks.cam.ac.uk/show/rss/49845 - Tue, 30/05/2023 - 13:35
Computational Neuroscience Journal Club

Please join us for our fortnightly Computational Neuroscience journal club on Tuesday 30th May at 2pm UK time in the CBL seminar room, or online on zoom. The title is ‘Dendritic computations and backpropagation’, presented by Will Greedy from the University of Bristol.

https://eng-cam.zoom.us/j/84204498431?pwd=Um1oU284b1YxWThObGw4ZU9XZitWdz09 Meeting ID: 842 0449 8431 Passcode: 684140

Summary: The error-backpropagation (backprop) algorithm remains the most common solution to the credit assignment problem in artificial neural networks. In neuroscience, it is unclear whether the brain could adopt a similar strategy to correctly modify its synapses. Recent models have attempted to bridge this gap while being consistent with a range of experimental observations.

In this journal club, I will:

1. Introduce the general field of backprop in dendritic networks.

2. Go over Error-encoding Dendritic Networks (EDNs; Sacramento et al. 2018 NeurIPS). In this paper, it was first introduced the idea of using cell-types and distal dendrites to jointly encode error signals.

3. Next, I will introduce Burstprop (Payeur et al. Nature Neuro 2021), which proposes that there are two types of signals in the brain. Single-spike events for inference and bursts for learning. This then suggests the need for specialised short-term synaptic plasticity with which to decode these signals.

4. Both EDNs and burstprop are either unable to effectively backpropagate error signals across multiple layers or require a multi-phase learning process, neither of which are reminiscent of learning in the brain. Next, I will introduce our recent model, Bursting Cortico-Cortical Networks (BurstCCN; Greedy et al. Neurips 2022), which solves these issues by integrating known properties of cortical networks namely bursting activity, short-term plasticity (STP) and dendrite-targeting interneurons.

Overall, these results suggest that cortical features across sub-cellular, cellular, microcircuit, and systems levels jointly underlie single-phase efficient deep learning in the brain.

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Wed 31 May 11:00: On choosing the mass matrix for Hamiltonian Monte Carlo Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.

http://talks.cam.ac.uk/show/rss/49845 - Mon, 29/05/2023 - 20:17
On choosing the mass matrix for Hamiltonian Monte Carlo

In this talk will first go through the basics of Hamiltonian Monte Carlo (HMC), and then discuss some old and some recent developments in the field, with a particular focus on the role of the covariance matrix of the momentum distribution.

Potential reading: Neal, R. M. (2012). Mcmc Using Hamiltonian Dynamics. arXiv:1206.1901. http://arxiv.org/abs/1206.1901v1. Girolami, M., & Calderhead, B. (2011). Riemann manifold langevin and hamiltonian monte carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73(2), 123–214. Betancourt, M. J., & Girolami, M. (2013). Hamiltonian monte carlo for hierarchical models. arXiv:1312.0906. http://arxiv.org/abs/1312.0906v1. Langmore, I., Dikovsky, M., Geraedts, S., Norgaard, P., & Behren, R. V. (2019). A condition number for hamiltonian monte carlo. arXiv:1905.09813. http://arxiv.org/abs/1905.09813v3.

Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.

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Mon 29 May 18:00: MaRF: Representing Mars as Neural Radiance Fields

http://talks.cam.ac.uk/show/rss/49845 - Mon, 29/05/2023 - 16:41
MaRF: Representing Mars as Neural Radiance Fields

MaRF is a novel framework able to synthesize the Martian environment using several collections of images from rover cameras. The idea is to generate a 3D scene of Mars’ surface to address key challenges in planetary surface exploration such as: planetary geology, simulated navigation and shape analysis. Although there exist different methods to enable a 3D reconstruction of Mars’ surface, they rely on classical computer graphics techniques that incur high amounts of computational resources during the reconstruction process, and have limitations with generalizing reconstructions to unseen scenes and adapting to new images coming from rover cameras. The proposed framework solves the aforementioned limitations by exploiting Neural Radiance Fields (NeRFs), a method that synthesize complex scenes by optimizing a continuous volumetric scene function using a sparse set of images. To speed up the learning process, we replaced the sparse set of rover images with their neural graphics primitives (NGPs), a set of vectors of fixed length that are learned to preserve the information of the original images in a significantly smaller size. In the experimental section, we demonstrate the environments created from actual Mars datasets captured by Curiosity rover, Perseverance rover and Ingenuity helicopter, all of which are available on the Planetary Data System (PDS).

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Mon 29 May 16:30: Unveiling Bounded Confidence Dynamics in Sheaf Neural Networks

http://talks.cam.ac.uk/show/rss/49845 - Mon, 29/05/2023 - 15:00
Unveiling Bounded Confidence Dynamics in Sheaf Neural Networks

The study of opinion dynamics is an intriguing and challenging field that has attracted researchers from various disciplines. Opinion dynamics models aim to capture the intricate and dynamic nature of social interactions that shape the formation and evolution of opinions in human societies, and they have been proven valuable in investigating a wide range of phenomena, including political polarization, rumor propagation and emergence of consensus. Recently, there has been a growing interest in employing computational tools to model opinion dynamics, and within this realm, sheaf theory has emerged as a powerful mathematical framework. Sheaf theory enables the study of complex systems with both local and global interactions, treating opinions as mathematical entities associated with network nodes.

Bounded confidence, in the context of opinion dynamics, refers to a model where individuals are willing to adjust their opinions only if others’ opinions are sufficiently similar to their own. By incorporating bounded confidence into sheaf theory, it becomes possible to model and comprehend the emergence of opinion clusters, polarization, and the convergence or divergence of opinions within intricate social networks. An intriguing question arises: how does the integration of bounded confidence dynamics into a Sheaf Neural Network affect its expressiveness, signal diffusion, and ultimately its performance? Furthermore, what unique properties does it offer?

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Thu 01 Jun 16:00: Towards Human-Centered Explanations of AI Predictions

http://talks.cam.ac.uk/show/rss/49845 - Mon, 29/05/2023 - 11:45
Towards Human-Centered Explanations of AI Predictions

Explanations of AI predictions are considered crucial for human-AI interactions. I argue that successful human-AI interactions require two steps: AI explanation and human interpretation. Therefore, effective explanations necessitates the understanding of human interpretation. In this talk, I will present our work to address this challenge through human-centered evaluation and generation of explanations. First, I will discuss the distinction between emulation and discovery tasks, which shapes human interpretation. In emulation tasks, humans provide groundtruth labels and the goal of AI is to emulate human intelligence. While it may seem intuitive that humans can provide valid explanations in this case, I argue that humans may not be able to provide “good” explanations. Caution is thus required to use human explanations for evaluation or as supervision signals despite the growing efforts in building datasets of human explanations. In contrast, in discovery tasks, humans may not necessarily know the groundtruth label. Human-subject experiments show that explanations fail to improve human decisions, namely, human+AI rarely outperforms AI alone. I will highlight the importance of identifying human strengths and AI strengths, and introduce decision-focused summarization. Finally, I will discuss recent work on leveraging explanations to improve AI models.

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Mon 29 May 17:00: Unveiling Bounded Confidence Dynamics in Sheaf Neural Networks

http://talks.cam.ac.uk/show/rss/49845 - Sun, 28/05/2023 - 21:07
Unveiling Bounded Confidence Dynamics in Sheaf Neural Networks

The study of opinion dynamics is an intriguing and challenging field that has attracted researchers from various disciplines. Opinion dynamics models aim to capture the intricate and dynamic nature of social interactions that shape the formation and evolution of opinions in human societies, and they have been proven valuable in investigating a wide range of phenomena, including political polarization, rumor propagation and emergence of consensus. Recently, there has been a growing interest in employing computational tools to model opinion dynamics, and within this realm, sheaf theory has emerged as a powerful mathematical framework. Sheaf theory enables the study of complex systems with both local and global interactions, treating opinions as mathematical entities associated with network nodes.

Bounded confidence, in the context of opinion dynamics, refers to a model where individuals are willing to adjust their opinions only if others’ opinions are sufficiently similar to their own. By incorporating bounded confidence into sheaf theory, it becomes possible to model and comprehend the emergence of opinion clusters, polarization, and the convergence or divergence of opinions within intricate social networks. An intriguing question arises: how does the integration of bounded confidence dynamics into a Sheaf Neural Network affect its expressiveness, signal diffusion, and ultimately its performance? Furthermore, what unique properties does it offer?

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Sun 28 May 17:00: Unveiling Bounded Confidence Dynamics in Sheaf Neural Networks

http://talks.cam.ac.uk/show/rss/49845 - Sun, 28/05/2023 - 18:57
Unveiling Bounded Confidence Dynamics in Sheaf Neural Networks

The study of opinion dynamics is an intriguing and challenging field that has attracted researchers from various disciplines. Opinion dynamics models aim to capture the intricate and dynamic nature of social interactions that shape the formation and evolution of opinions in human societies, and they have been proven valuable in investigating a wide range of phenomena, including political polarization, rumor propagation and emergence of consensus. Recently, there has been a growing interest in employing computational tools to model opinion dynamics, and within this realm, sheaf theory has emerged as a powerful mathematical framework. Sheaf theory enables the study of complex systems with both local and global interactions, treating opinions as mathematical entities associated with network nodes.

Bounded confidence, in the context of opinion dynamics, refers to a model where individuals are willing to adjust their opinions only if others’ opinions are sufficiently similar to their own. By incorporating bounded confidence into sheaf theory, it becomes possible to model and comprehend the emergence of opinion clusters, polarization, and the convergence or divergence of opinions within intricate social networks. An intriguing question arises: how does the integration of bounded confidence dynamics into a Sheaf Neural Network affect its expressiveness, signal diffusion, and ultimately its performance? Furthermore, what unique properties does it offer?

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Thu 15 Jun 15:00: Monitoring and Improving QoE for the home network via large scale CPE deployment. https://cl-cam-ac-uk.zoom.us/j/97216272378?pwd=M2diTFhMTnppckJtNWhFVTBKK0REZz09

http://talks.cam.ac.uk/show/rss/49845 - Sat, 27/05/2023 - 16:16
Monitoring and Improving QoE for the home network via large scale CPE deployment.

The demand on the home network has seen an explosion since the COVID -19 pandemic. The home router has a unique vantage point that can be exploited to monitor and improve the quality of experience (QoE) for people using the Internet. Realising this involves an eclectic mix of systems and networking knowledge.

Netduma is a small UK tech startup that is partnered with Netgear and multiple Tier 1 ISPs to deliver these solutions. In this talk we’ll briefly cover some of the key technologies including dynamic QoS, low-latency congestion control, self-learning DPI , behavioural flow classification and passive QoE measurements to help with home network diagnostics.

Large scale deployments via customer premise equipment (CPE) for Tier 1 providers is challenging because of the limited resources and the requirement to use restricted hardware acceleration for data-plane QoS. We briefly touch on these challenges.

https://cl-cam-ac-uk.zoom.us/j/97216272378?pwd=M2diTFhMTnppckJtNWhFVTBKK0REZz09

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Wed 07 Jun 16:30: Statistics Clinic Easter 2023 IV The clinic takes place in MR14 at the Centre for Mathematical Sciences.

http://talks.cam.ac.uk/show/rss/49845 - Fri, 26/05/2023 - 13:26
Statistics Clinic Easter 2023 IV

This free 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.

If you would like to participate, please sign up as we will not be able to offer a consultation otherwise. Please sign up through the following form: https://forms.gle/x7jvqZo6VigwGZm97. Sign-up is possible from Jun 1 midday until Jun 5 midday or until we reach full capacity, whichever is earlier. If you successfully signed up, we will confirm your appointment by Jun 7 midday.

The clinic takes place in MR14 at the Centre for Mathematical Sciences.

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Thu 01 Jun 11:30: Buckling of a fast drying drop of particle suspension

http://talks.cam.ac.uk/show/rss/29538 - Fri, 26/05/2023 - 11:34
Buckling of a fast drying drop of particle suspension

Fast evaporation of particle-suspension drops results in complex morphologies of the final dried granules. Understanding the morphological transformations is important to industrial processes such as spray drying where droplets of particulate suspensions are dried at a fast rate to produce granules of thermally sensitive materials. The transformation of an initial spherical shell to complex morphologies of the final dried granule has been attributed to the buckling of particle-packed shells. Here, we demonstrate a universal scaling law for buckling that depends on the particle size, hardness, particle packing and size of drying drop. The critical transition for buckling is set by a dimensionless number that measures the competition between the compressive stress generated by capillary forces and the elastic strength of the packing. The same dimensionless number is also responsible for cracking of drying colloidal films, suggesting a universality in the mechanical behaviour of particle packings saturated with a solvent. These results should enable design of hierarchically structured, buckle-free granules with varying porosity, surface composition and internal structure.

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Thu 15 Jun 15:00: TBC https://cl-cam-ac-uk.zoom.us/j/97216272378?pwd=M2diTFhMTnppckJtNWhFVTBKK0REZz09

http://talks.cam.ac.uk/show/rss/49845 - Fri, 26/05/2023 - 10:41
TBC

TBC

https://cl-cam-ac-uk.zoom.us/j/97216272378?pwd=M2diTFhMTnppckJtNWhFVTBKK0REZz09

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Wed 31 May 12:00: Improving surveillance and software for epidemic response

http://talks.cam.ac.uk/show/rss/29729 - Fri, 26/05/2023 - 09:42
Improving surveillance and software for epidemic response

COVID -19 spurred a range of new data and analysis efforts, many of which could be valuable for future epidemics and pandemics. This talk will use a draw on a range of examples to discuss opportunities in surveillance and software. In particular, it will cover analysis of testing from arriving travellers to reconstruct international pandemic dynamics, focusing on French Polynesia and Singapore during COVID -19. It will also cover efforts to improve the sustainability and interoperability of outbreaks analysis tools, covering lessons from the real-time response to COVID -19, as well as recent work in the new ‘Epiverse’ initiative.

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Wed 31 May 12:00: Improving surveillance and software for epidemic response

http://talks.cam.ac.uk/show/rss/29729 - Fri, 26/05/2023 - 09:42
Improving surveillance and software for epidemic response

COVID -19 spurred a range of new data and analysis efforts, many of which could be valuable for future epidemics and pandemics. This talk will use a draw on a range of examples to discuss opportunities in surveillance and software. In particular, it will cover analysis of testing from arriving travellers to reconstruct international pandemic dynamics, focusing on French Polynesia and Singapore during COVID -19. It will also cover efforts to improve the sustainability and interoperability of outbreaks analysis tools, covering lessons from the real-time response to COVID -19, as well as recent work in the new ‘Epiverse’ initiative.

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Tue 06 Jun 14:00: BSU Seminar: 'Bayesian analysis of diffusion-driven multi-type epidemic models with application to COVID-19' This will be an online seminar. To register to attend, please click on this link: https://us02web.zoom.us/meeting/register...

http://talks.cam.ac.uk/show/rss/49845 - Thu, 25/05/2023 - 16:08
BSU Seminar: 'Bayesian analysis of diffusion-driven multi-type epidemic models with application to COVID-19'

We consider a flexible Bayesian evidence synthesis approach to model the age-specific transmission dynamics of COVID -19 based on daily age-stratified mortality counts. The temporal evolution of transmission rates in populations containing multiple types of individual is reconstructed via an appropriate dimension-reduction formulation driven by independent diffusion processes assigned to the key epidemiological parameters. A suitably tailored Susceptible-Exposed-Infected -Removed (SEIR) compartmental model is used to capture the latent counts of infections and to account for fluctuations in transmission influenced by phenomena like public health interventions and changes in human behaviour. We analyze the outbreak of COVID -19 in Greece and Austria and validate the proposed model using the estimated counts of cumulative infections from a large-scale seroprevalence survey in England. This is joint work with Nikolaos Demiris (AUEB), Konstantinos Kalogeropoulos (LSE) and Ioannis Ntzoufras (AUEB).

arXiv link: https://aps.arxiv.org/abs/2211.15229 CRAN : https://cran.r-project.org/web/packages/Bernadette/index.html Github repository: https://github.com/bernadette-eu/Bernadette

This will be an online seminar. To register to attend, please click on this link: https://us02web.zoom.us/meeting/register/tZ0ocO6qrTsiH9V3mlmvd_EOYT1sTBOKOmUZ

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