1588-P: Therapy Trends in Initial 6 Months of the First Large-Scale Longitudinal Nationwide Study on Management and Real-World Outcomes of Diabetes in India (LANDMARC)

During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments. This poses the need to count on new generations of students, researchers and practitioners with a solid background in ML applied to networks. During 2020, the International Telecommunication Union (ITU) has organized the "ITU AI/ML in 5G challenge", an open global competition that has introduced to a broad audience some of the current main challenges in ML for networks. This large-scale initiative has gathered 23 different challenges proposed by network operators, equipment manufacturers and academia, and has attracted a total of 1300+ participants from 60+ countries. This paper narrates our experience organizing one of the proposed challenges: the "Graph Neural Networking Challenge 2020". We describe the problem presented to participants, the tools and resources provided, some organization aspects and participation statistics, an outline of the top-3 awarded solutions, and a summary with some lessons learned during all this journey. As a result, this challenge leaves a curated set of educational resources openly available to anyone interested in the topic.

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A Heuristic Algorithm for Solving a Large-scale Real-world Territory Design Problem

Omega ◽

10.1016/j.omega.2021.102442 ◽

2021 ◽

pp. 102442

Author(s):

Lin Zhou ◽

Lu Zhen ◽

Roberto Baldacci ◽

Marco Boschetti ◽

Ying Dai ◽

...

Keyword(s):

Heuristic Algorithm ◽

Real World ◽

Design Problem ◽

Large Scale ◽

Territory Design

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An information theoretic approach to link prediction in multiplex networks

Scientific Reports ◽

10.1038/s41598-021-92427-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Seyed Hossein Jafari ◽

Amir Mahdi Abdolhosseini-Qomi ◽

Masoud Asadpour ◽

Maseud Rahgozar ◽

Naser Yazdani

Keyword(s):

Real World ◽

Link Prediction ◽

Large Scale ◽

Similarity Measures ◽

Prediction Method ◽

General Purpose ◽

Fast Method ◽

Theoretic Approach ◽

Multiplex Networks ◽

Wide Range

AbstractThe entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.

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Monoclonal Antibodies Targeting CGRP: From Clinical Studies to Real-World Evidence—What Do We Know So Far?

Pharmaceuticals ◽

10.3390/ph14070700 ◽

2021 ◽

Vol 14 (7) ◽

pp. 700

Author(s):

Theodoros Mavridis ◽

Christina I. Deligianni ◽

Georgios Karagiorgis ◽

Ariadne Daponte ◽

Marianthi Breza ◽

...

Keyword(s):

Monoclonal Antibodies ◽

Real World ◽

Large Scale ◽

Randomized Clinical Trials ◽

Clinical Investigation ◽

Phase Iii ◽

Real World Evidence ◽

Symptomatic Management ◽

Repurposed Drugs ◽

Cephalic Pain

Now more than ever is the time of monoclonal antibody use in neurology. In headaches, disease-specific and mechanism-based treatments existed only for symptomatic management of migraines (i.e., triptans), while the standard prophylactic anti-migraine treatments consist of non-specific and repurposed drugs that share limited safety profiles and high risk for interactions with other medications, resulting in rundown adherence rates. Recent advances in headache science have increased our understanding of the role of calcitonin gene relate peptide (CGRP) and pituitary adenylate cyclase-activating polypeptide (PACAP) pathways in cephalic pain neurotransmission and peripheral or central sensitization, leading to the development of monoclonal antibodies (mAbs) or small molecules targeting these neuropeptides or their receptors. Large scale randomized clinical trials confirmed that inhibition of the CGRP system attenuates migraine, while the PACAP mediated nociception is still under scientific and clinical investigation. In this review, we provide the latest clinical evidence for the use of anti-CGRP in migraine prevention with emphasis on efficacy and safety outcomes from Phase III and real-world studies.

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Extrinsic Camera Calibration with Line-Laser Projection

Sensors ◽

10.3390/s21041091 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1091

Author(s):

Izaak Van Crombrugge ◽

Rudi Penne ◽

Steve Vanlanduit

Keyword(s):

Camera Calibration ◽

Real World ◽

Large Scale ◽

State Of The Art ◽

Bundle Adjustment ◽

Field Of View ◽

Extrinsic Calibration ◽

Practical Procedure ◽

Partial Overlap

Knowledge of precise camera poses is vital for multi-camera setups. Camera intrinsics can be obtained for each camera separately in lab conditions. For fixed multi-camera setups, the extrinsic calibration can only be done in situ. Usually, some markers are used, like checkerboards, requiring some level of overlap between cameras. In this work, we propose a method for cases with little or no overlap. Laser lines are projected on a plane (e.g., floor or wall) using a laser line projector. The pose of the plane and cameras is then optimized using bundle adjustment to match the lines seen by the cameras. To find the extrinsic calibration, only a partial overlap between the laser lines and the field of view of the cameras is needed. Real-world experiments were conducted both with and without overlapping fields of view, resulting in rotation errors below 0.5°. We show that the accuracy is comparable to other state-of-the-art methods while offering a more practical procedure. The method can also be used in large-scale applications and can be fully automated.

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Model checking driven static analysis for the real world: designing and tuning large scale bug detection

Innovations in Systems and Software Engineering ◽

10.1007/s11334-012-0192-5 ◽

2012 ◽

Vol 9 (1) ◽

pp. 45-56 ◽

Cited By ~ 5

Author(s):

Ansgar Fehnker ◽

Ralf Huuck

Keyword(s):

Model Checking ◽

Static Analysis ◽

Real World ◽

Large Scale ◽

Bug Detection ◽

The Real

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Exploiting Proximity-Based Mobile Apps for Large-Scale Location Privacy Probing

Security and Communication Networks ◽

10.1155/2018/3182402 ◽

2018 ◽

Vol 2018 ◽

pp. 1-22 ◽

Cited By ~ 1

Author(s):

Shuang Zhao ◽

Xiapu Luo ◽

Xiaobo Ma ◽

Bo Bai ◽

Yankang Zhao ◽

...

Keyword(s):

Social Networks ◽

Case Studies ◽

Real World ◽

Emission Reduction ◽

Large Scale ◽

Location Privacy ◽

Mobile Apps ◽

Physical World ◽

Typical Type ◽

Location Spoofing

Proximity-based apps have been changing the way people interact with each other in the physical world. To help people extend their social networks, proximity-based nearby-stranger (NS) apps that encourage people to make friends with nearby strangers have gained popularity recently. As another typical type of proximity-based apps, some ridesharing (RS) apps allowing drivers to search nearby passengers and get their ridesharing requests also become popular due to their contribution to economy and emission reduction. In this paper, we concentrate on the location privacy of proximity-based mobile apps. By analyzing the communication mechanism, we find that many apps of this type are vulnerable to large-scale location spoofing attack (LLSA). We accordingly propose three approaches to performing LLSA. To evaluate the threat of LLSA posed to proximity-based mobile apps, we perform real-world case studies against an NS app named Weibo and an RS app called Didi. The results show that our approaches can effectively and automatically collect a huge volume of users’ locations or travel records, thereby demonstrating the severity of LLSA. We apply the LLSA approaches against nine popular proximity-based apps with millions of installations to evaluate the defense strength. We finally suggest possible countermeasures for the proposed attacks.

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