A paperLink opens in a new window co-authored by Arpan MukhopadhyayLink opens in a new window has received the Best Paper Award at ACM Mobihoc 2024Link opens in a new window. Mobihoc is a premier international conference on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing. The other authors in the paper are Samira Ghanbarian (uWaterloo), Ravi R. Mazumdar (uWaterloo), and Fabrice Guillemin (Orange Labs, France).

The paper addresses the problem of optimally allocating processors to parallelisable tasks having arbitrary concave speed-up functions. In general, determining the optimal number of processors to allocate to each task in an online fashion is a hard problem since allocating too many processors to one job will make those processors unavailable to other jobs whereas allocating too few processors will result in a small speed-up for the job. The paper proposes a simple randomised algorithm for determining the optimal number of processors to allocate to each job without requiring preemption (or repacking). It shows that the proposed algorithm is asymptotically optimal as the number of processors becomes large (which is often the case in modern clouds) and is also robust to variations in the job size distribution. This is the first time such an algorithm has been found in the literature.

Neha Rino, a PhD student in the Theory and Foundations group in the Department of Computer Science and a member of the Cyber Security group at WMG, has won an Oded Maler award at FORMATS 2024.

The Oded Maler award is a distinction presented for the best paper of the International Conference on Formal Modeling and Analysis of Timed Systems (FORMATS). This year's edition of the conference was held in September in Calgary, Canada, jointly with QEST (International Conference on Quantitative Evaluation of SysTems) as a common research forum dedicated to quantitative modelling, analysis, and verification.

Neha's paper, "Efficiently Computable Distance-Based Robustness for a Practical Fragment of STL", is co-authored with Mohammed Foughali and Eugene Asarin, both from Université Paris Cité and IRIF in Paris, France, where Neha completed the Master's degree (ENS Paris-Saclay) prior to joining Warwick.

Neha's paper contributes to the research framework of quantitative monitoring, which is the analysis of individual executions of systems which yields numerical output (real numbers), rather than binary yes/no. The paper formulates and solves, by an efficient algorithm, a new problem of this kind: computing a real number that characterises to which extent the given execution of a real-time system satisfies its specification expressed in Signal Temporal Logic (STL).

Eight papers authored by Computer Science researchers from Warwick have been accepted for publication at the 38th Conference on Neural Information Processing Systems, the leading international venue for machine learning research, which will be held on 10-15 December 2024 in Vancouver, British Columbia, Canada:

• Generating Origin-Destination Matrices in Neural Spatial Interaction Models, by Ioannis Zachos, Mark Girolami, and Theodoros Damoulas
• Interventionally Consistent Surrogates for Complex Simulation Models, by Joel Dyer, Nicholas Bishop, Yorgos Felekis, Fabio Massimo Zennaro, Ani Calinescu, Theodoros Damoulas, and Michael Wooldridge
• Learning the Expected Core of Strictly Convex Stochastic Cooperative Games, by Phuong Nam Tran, The Anh Ta, Shuqing Shi, Debmalya Mandal, Yali Du, and Long Tran-Thanh
• Physics-Informed Variational State-Space Gaussian Processes, by Oliver Hamelijnck, Arno Solin, and Theodoros Damoulas
• SARAD: Spatial Association-Aware Anomaly Detection and Diagnosis for Multivariate Time Series, by Zhihao Dai, Ligang He, Shuanghua Yang, and Matthew Leeke
• Symmetric Linear Bandits with Hidden Symmetry, by Phuong Nam Tran, The Anh Ta, Debmalya Mandal, and Long Tran-Thanh
• The Effectiveness of Surprisingly Popular Voting with Partial Preferences, by Hadi Hosseini, Debmalya Mandal, and Amrit Puhan
• What makes unlearning hard and what to do about it, by Kairan Zhao, Meghdad Kurmanji, George-Octavian Bărbulescu, Eleni Triantafillou, and Peter Triantafillou