United States

A molecular and cellular understanding of how SARS-CoV-2 variably infects and causes severe COVID-19 remains a bottleneck in developing interventions to end the pandemic. We sought to use deep learning (DL) to study the biology of SARS-CoV-2 infection and COVID-19 severity by identifying transcriptomic patterns and cell types associated with SARS-CoV-2 infection and COVID-19 severity. To do this, we developed a new approach to generating self-supervised edge features. We propose a model that builds on Graph Attention Networks (GAT), creates edge features using self-supervised learning, and ingests these edge features via a Set Transformer. This model achieves significant improvements in predicting the disease state of individual cells, given their transcriptome. We apply our model to single-cell RNA sequencing datasets of SARS-CoV-2 infected lung organoids and bronchoalveolar lavage fluid samples of patients with COVID-19, achieving state-of-the-art performance on both datasets with our model. We then borrow from the field of explainable AI (XAI) to identify the features (genes) and cell types that discriminate bystander vs. infected cells across time and moderate vs. severe COVID-19 disease. To the best of our knowledge, this represents the first application of DL to identifying the molecular and cellular determinants of SARS-CoV-2 infection and COVID-19 severity using single-cell omics data.

AAAI 2021

Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-Supervised Edge Features and Graph Neural Networks

[![](https://assets.underline.io/uploads/markdown_image/1/image/261902fdf187a88bfa74da226ff77186.png)](https://drive.google.com/file/d/1gUViQJJHCSgM38OhwpDoTdN89evCsZ-7/view?usp=sharing)
[![](https://assets.underline.io/uploads/markdown_image/1/image/1605292c232e6f185c7f87ee38e8502a.png)](https://drive.google.com/file/d/1kPkznVX_6cx-n846z_pUtyVDZql9KImc/view?usp=sharing)


### **Welcome Message**

Welcome to the inaugural **Dutch Lung Congress.**  We have all missed our opportunities to network and interact with our colleagues in the last twelve months or more.  While our desire was to meet face to face once again, when it became evident that this would not be possible until much later in 2021 (or even into 2022) the hard working committee organizing the Dutch Lung Congress found, what we hope will be, an excellent alternative to facilitate our annual meeting.

The Dutch Lung Congress aims to bring together everyone interested in research related to pulmonary diseases in the Netherlands.  We hope this event will provide opportunities for multidisciplinary interactions to enhance our approaches for addressing these challenging diseases.  Each session will include input from patients, clinical and basic and/or translational scientists discussing the latest information around the central themes chosen by the organizing committee.  One advantage of holding our congress as a virtual event this year is that we will have the opportunity to hear from world leading experts from all over the globe.  We hope you enjoy the programme the organizing committee have put together and will engage in dynamic discussions with all the speakers.

One highlight of our congress will be the Young Investigator Session.  Remember to join us for this exciting session when we will hear from the presenters who submitted the top scoring abstracts.  This promises to be a session full of exciting science that will display the strengths that constitute the future talent in our field.

When we are not listening to these great presentations we can all meet in Gathertown.  Within this interactive platform you will be able to meet colleagues and friends for networking. Enjoy a private chat over a virtual coffee, visit the booths of our sponsors and other parties, chat with the Young Investigator Board members or take some timeout in the relaxation spaces.  While in Gathertown take the opportunity to visit all the diverse posters on display. Chat live with the presenters or leave them a question on the discussion board for them to engage with you.  Make sure you check for any messages each time you enter Gathertown to make the most of your networking opportunities.

We look forward to seeing you during the DLC and the great scientific (and other) discussions we are anticipating during our 2021 meeting.

On behalf of the DLC organizing Committee
<br>**Janette Burgess
<br>Chair, DLC Organizing Committee**
![](https://assets.underline.io/uploads/markdown_image/1/image/f461fb275d9667fb2a76a30813388e90.png)



### Special Session: Publieksmiddag
![](https://assets.underline.io/uploads/markdown_image/1/image/77e768dd500e6205175e5cafba59abdc.png)






![](https://assets.underline.io/uploads/markdown_image/1/image/f461fb275d9667fb2a76a30813388e90.png)
**More about the committee team:**:
[Barbro N. Melgert, PhD] (https://drive.google.com/file/d/1q_IIH3JvTivumCUzzsZGX7BgD41HxQmk/view?usp=sharing)
// [Bas Holverda, PhD] (https://drive.google.com/file/d/1QKXHmTTQjgs4OZVuM8doXU055S-fu_uU/view?usp=sharing)
// [Frits Franssen, PhD] (https://drive.google.com/file/d/1d7HRUJaqGuRFzkPN3SVgkbBfqFkDkWq2/view?usp=sharing)
// [Janette Burgess, PhD] (https://drive.google.com/file/d/1VHFlrEN6uCQGeeW3Lj4I8aTw7HsB0GFr/view?usp=sharing)
// [Michel van den Heuvel, PhD] (https://drive.google.com/file/d/1JeOEP8MHxiK_4lONr8UM2HaxdxpkAmA2/view?usp=sharing)
// [Patrick Bhairosing] (https://drive.google.com/file/d/1xI8ORq-g1bbVcrlRDviZ9NhAwanc4dFl/view?usp=sharing)
// [Gimano Amatngalim, PhD] (https://drive.google.com/file/d/1fyVDDKARLNjRk_JZk8_GyhGMjmp6DWHp/view?usp=sharing)
// [Peter Kunst, PhD] (https://drive.google.com/file/d/1QhtB4nQezpAjbfUAcBnINc-Bm3Shy2Lf/view?usp=sharing)
// [Dr. Michel van den Heuvel, Prof., MD, PhD] (https://drive.google.com/file/d/1JBdNEBXZCEikEZhJ6MoD5DhQfXMz3bP6/view?usp=sharing)
![](https://assets.underline.io/uploads/markdown_image/1/image/f461fb275d9667fb2a76a30813388e90.png)

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Dutch Lung Congress

technical paper

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

In many societal resource allocation domains, machine learning methods are increasingly used to either score or rank agents in order to decide which ones should receive either resources (e.g., homeless services) or scrutiny (e.g., child welfare investigations) from social services agencies. An agency's scoring function typically operates on a feature vector that contains a combination of self-reported features and information available to the agency about individuals or households.
    This can create incentives for agents to misrepresent their self-reported features in order to receive resources or avoid scrutiny, but agencies may be able to selectively audit agents to verify the veracity of their reports.
    
    We study the problem of optimal auditing of agents in such settings. When decisions are made using a threshold on an agent's score, the optimal audit policy has a surprisingly simple structure, uniformly auditing all agents who could benefit from lying.
    While this policy can, in general be hard to compute because of the difficulty of identifying the set of agents who could benefit from lying given a complete set of reported types, we also present sufficient conditions under which it is tractable.
    We show that the scarce resource setting is more difficult, and exhibit an approximately optimal audit policy in this case.
    In addition, we show that in either setting verifying whether it is possible to incentivize exact truthfulness is hard even to approximate.
    However, we also exhibit sufficient conditions for solving this problem optimally, and for obtaining good approximations.

Incentivizing Truthfulness through Audits in Strategic Classification

There are many realistic applications of activity recognition where the set of potential activity descriptions is combinatorially large. This makes end-to-end supervised training of a recognition system impractical as no training set is practically able to encompass the entire label set. In this paper, we present an approach to fine-grained recognition that models activities as compositions of dynamic action signatures.  This compositional approach allows us to reframe fine-grained recognition as zero-shot activity recognition,  where a detector is composed "on the fly" from simple first-principles state machines supported by deep-learned components.  We evaluate our method on the Olympic Sports and UCF101 datasets, where our model establishes a new state of the art under multiple experimental paradigms. We also extend this method to form a unique framework for zero-shot joint segmentation and classification of activities in video and demonstrate the first results in zero- shot decoding of complex action sequences on a widely-used surgical dataset. Lastly, we show that we can use  off-the-shelf object detectors to recognize activities in completely de-novo settings with no additional training.

DASZL: Dynamic Action Signatures for Zero-Shot Learning

Previous work for text summarization in scientific domain mainly focused on the content of the input document, but seldom considering its citation network.
However, scientific papers are full of uncommon domain-specific terms, making it almost impossible for the model to understand its true meaning without the help of the relevant research community.
In this paper, we redefine the task of scientific papers summarization by utilizing their citation graph and propose a citation graph-based summarization model CGSum which can incorporate the information of both the source paper and its references.
In addition,  we construct a novel scientific papers summarization dataset Semantic Scholar Network (SSN) which contains 141K research papers in different domains and 661K citation relationships. The entire dataset constitutes a large connected citation graph.
Extensive experiments show that our model can achieve competitive performance when compared with the pretrained models even with a simple architecture.
The results also indicates the citation graph is crucial to better understand the content of papers and generate high-quality summaries.

Enhancing Scientific Papers Summarization with Citation Graph

A radiograph visualizes the internal anatomy of a patient through the use of X-ray, which projects 3D information onto a 2D plane. Hence, radiograph analysis naturally requires physicians to relate their prior knowledge about 3D human anatomy to 2D radiographs. Synthesizing novel radiographic views in a small range can assist physicians in interpreting anatomy more reliably; however, radiograph view synthesis is heavily ill-posed, lacking in paired data, and lacking in differentiable operations to leverage learning-based approaches. To address these problems, we use Computed Tomography (CT) for radiograph simulation and design a differentiable projection algorithm, which enables us to achieve geometrically consistent transformations between the radiography and CT domains. Our method, XraySyn, can synthesize novel views on real radiographs through a combination of realistic simulation and finetuning on real radiographs. To the best of our knowledge, this is the first work on radiograph view synthesis. We show that by gaining an understanding of radiography in 3D space, our method can be applied to radiograph bone extraction and suppression without requiring groundtruth bone labels.

XraySyn: Realistic View Synthesis from a Single Radiograph through CT Prior

The exploitation of extra state information has been an active research area in multi-agent reinforcement learning (MARL). QMIX represents the joint action-value using a non-negative function approximator and achieves the best performance on the StarCraft II micromanagement testbed, a common MARL benchmark. However, our experiments demonstrate that, in some cases, QMIX performs sub-optimally with the A2C framework, a training paradigm that promotes algorithm training efficiency. To obtain a reasonable trade-off between training efficiency and algorithm performance, we extend value-decomposition to actor-critic methods that are compatible with A2C and propose a novel actor-critic framework, value-decomposition actor-critic (VDAC). We evaluate VDAC on the StarCraft II micromanagement task and demonstrate that the proposed framework improves median performance over other actor-critic methods. Furthermore, we use a set of ablation experiments to identify the key factors that contribute to the performance of VDAC.

Value-Decomposition Multi-Agent Actor-Critics

We study a social choice setting of manipulation in elections and extend the usual model in two major ways: first, instead of considering a single manipulating agent, in our setting there are several, possibly competing ones; second, instead of evaluating an election after the first manipulative action, we allow several back-and-forth rounds to take place. We show that in certain situations, such as in elections with only a few candidates, optimal strategies for each of the manipulating agents can be computed efficiently. Our algorithmic results rely on formulating the problem of finding an optimal strategy as sentences of Presburger arithmetic that are short and only involve small coefficients, which we show is fixed-parameter tractable -- indeed, one of our contributions is a general result regarding fixed-parameter tractability of Presburger arithmetic that might be useful in other settings. Following our general theorem, we design quite general algorithms; in particular, we describe how to design efficient algorithms for various settings, including settings in which we model diffusion of opinions in a social network, complex budgeting schemes available to the manipulating agents, and various realistic restrictions on adversary actions.

Multi-Party Campaigning

The constraint satisfaction problem (CSP) has important applications in computer science and AI. In particular, infinite-domain CSPs have been intensively used in subareas of AI such as spatio-temporal reasoning. Since constraint satisfaction is a computationally hard problem, much work has been devoted to  identifying restricted problems that are efficiently solvable.  One way of doing this is to restrict the interactions of variables and constraints, and a highly successful approach is to bound the treewidth of the underlying primal graph. Bodirsky & Dalmau [J. Comput. System. Sci., 79(1), 2013] and Huang et al. [Artif. Intell., 195, 2013] proved that CSP(Î“) can be solved in n^(f(w)) time (where n is the size of the instance, w is the treewidth of the primal graph and f is a computable function)  for certain classes of constraint languages Î“. We improve this bound to f(w)n^(O(1)), where the function f only depends on the language Î“, for CSPs whose basic relations have the patchwork property.  Hence, such problems are fixed-parameter tractable and our algorithm is asymptotically faster than the previous ones. Additionally, our approach is not restricted to binary constraints, so it is applicable to a strictly  larger class of problems than that of Huang et al. However, there exist natural problems that are covered by Bodirsky & Dalmau's algorithm but not by ours.

Solving Infinite-Domain CSPs Using the Patchwork Property

The disjunctive temporal problem (DTP) is an expressive temporal formalism that extends Dechter et al.'s simple temporal problem.  The DTP is well studied in the literature and has many important applications. It is known that deciding satisfiability of DTPs is NP-hard and that, in many cases, single-exponential algorithms (running in O(c^n) time) do not exist under the Exponential-Time Hypothesis. The computational hardness makes it worthwhile to identify restricted problems that are efficiently solvable.  One way of doing this is to restrict the interactions of variables and constraints. We show that instances of DTP of any arity with integers bounded by poly(n) can be solved in n^{f(w)} time, where n denotes the problem size, w is the treewidth of the incidence graph and f is a computable function; in other words, this problem is in the complexity class XP  and it can be solved in polynomial time whenever w is fixed. We complement this result by showing that binary DTPs that only involve the integers 0 and 1 are not fixed-parameter tractable with respect to treewidth,  i.e. they do not admit a f(w)poly(n)$ time algorithm for any computable function f, under standard complexity assumptions. For instances with unbounded integers,  we show that even binary DTPs parameterized by treewidth  cannot be in XP, unless P = NP.

Disjunctive Temporal Problems under Structural Restrictions

We study a class of reinforcement learning tasks in which the agent receives its reward for complex, temporally-extended behaviors sparsely. For such tasks, the problem is how to augment the state-space so as to make the reward function Markovian in an efficient way. While some existing solutions assume that the reward function is explicitly provided to the learning algorithm (e.g., in the form of a reward machine), the others learn the reward function from the interactions with the environment, assuming no prior knowledge provided by the user. In this paper, we generalize both approaches and enable the user to give advice to the agent, representing the userâ€™s best knowledge about the reward function, potentially fragmented, partial, or even incorrect. We formalize advice as a set of DFAs and present a reinforcement learning algorithm that takes advantage of such advice, with optimal con- vergence guarantee. The experiments show that using well- chosen advice can reduce the number of training steps needed for convergence to optimal policy, and can decrease the computation time to learn the reward function by up to two orders of magnitude.

Advice-Guided Reinforcement Learning in a Non-Markovian Environment

Machine teaching is an algorithmic framework for teaching a
target hypothesis via a sequence of examples or demonstrations.
We investigate machine teaching for temporal logic
formulasâ€”a novel and expressive hypothesis class amenable
to time-related task specifications. In the context of teaching
temporal logic formulas, an exhaustive search even for a myopic
solution takes exponential time (with respect to the time
span of the task). We propose an efficient approach for teaching
parametric linear temporal logic formulas. Concretely,
we derive a necessary condition for the minimal time length
of a demonstration to eliminate a set of hypotheses. Utilizing
this condition, we propose an efficient myopic teaching algorithm
by solving a sequence of integer programming problems.
We further show that, under two notions of teaching
complexity, the proposed algorithm has near-optimal performance.
We evaluate our algorithm extensively under different
classes of learners (i.e., learners with different preferences
over hypotheses) and interaction protocols (e.g., non-adaptive
and adaptive). Our results demonstrate the effectiveness of
the proposed algorithm in teaching temporal logic formulas;
in particular, we show that there are significant gains of
teaching efficacy when the teacher adapts to feedback of the
learner, or adapts to a (non-myopic) oracle.

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