New Limits on Heavier Electroweakinos and their LHC Signatures

We investigate the heavier electroweakino sectors in several versions of the MSSM, which has not been explored so far in the light of the LHC data, and obtain new bounds using the ATLAS Run I constraints in the $3l + {E\!\!\!\!/_T}$ channel. We also venture beyond the trilepton events and predict several novel multilepton + ${E\!\!\!\!/_T}$ signatures of these electroweakinos which may show up before the next shutdown of the LHC.Comment: 12 pages;3 tables and some texts are added;numerical results remain unchange

Multilepton signals of heavier electroweakinos at the LHC

As sequel to a recent paper we examine the phenomenology of the full electroweakino sector of the pMSSM without invoking the adhoc but often employed assumption that the heavier ones are decoupled. We identify several generic models which illustrate the importance of the heavier electroweakinos and constrain them with the LHC $3l$ + ${E\!\!\!\!/_T}$ data. The constraints are usually stronger than that for decoupled heavier electroweakinos indicating that the LHC data is already sensitive to their presence. We also take into account the constraints from the observed dark matter relic density of the universe and precisely measured anomalous magnetic moment of the muon. Using the allowed parameter space thus obtained, we show that in addition to the conventional $3l$ + ${E\!\!\!\!/_T}$ signatures novel multilepton ($ml$) + ${E\!\!\!\!/_T}$ final states with $m > 3$, which are not viable in models with lighter electroweakinos only, can be observed before the next long shut down of the LHC.Comment: 40 pages, 5 figure

Generalized theory for node disruption in finite-size complex networks

International audienceAfter a failure or attack the structure of a complex network changes due to node removal. Here, we show that the degree distribution of the distorted network, under any node disturbances, can be easily computed through a simple formula. Based on this expression, we derive a general condition for the stability of noncorrelated finite complex networks under any arbitrary attack. We apply this formalism to derive an expression for the percolation threshold fc under a general attack of the form fk∼kγ, where fk stands for the probability of a node of degree k of being removed during the attack. We show that fc of a finite network of size N exhibits an additive correction which scales as N−1 with respect to the classical result for infinite networks

On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning

The creation and destruction of agents in cooperative multi-agent reinforcement learning (MARL) is a critically under-explored area of research. Current MARL algorithms often assume that the number of agents within a group remains fixed throughout an experiment. However, in many practical problems, an agent may terminate before their teammates. This early termination issue presents a challenge: the terminated agent must learn from the group's success or failure which occurs beyond its own existence. We refer to propagating value from rewards earned by remaining teammates to terminated agents as the Posthumous Credit Assignment problem. Current MARL methods handle this problem by placing these agents in an absorbing state until the entire group of agents reaches a termination condition. Although absorbing states enable existing algorithms and APIs to handle terminated agents without modification, practical training efficiency and resource use problems exist. In this work, we first demonstrate that sample complexity increases with the quantity of absorbing states in a toy supervised learning task for a fully connected network, while attention is more robust to variable size input. Then, we present a novel architecture for an existing state-of-the-art MARL algorithm which uses attention instead of a fully connected layer with absorbing states. Finally, we demonstrate that this novel architecture significantly outperforms the standard architecture on tasks in which agents are created or destroyed within episodes as well as standard multi-agent coordination tasks.Comment: RL in Games Workshop AAAI 202

Technology Readiness Levels for Machine Learning Systems

The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, where mission critical measures and robustness are ingrained in the development process. Drawing on experience in both spacecraft engineering and ML (from research through product across domain areas), we have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" (MLTRL) framework defines a principled process to ensure robust, reliable, and responsible systems while being streamlined for ML workflows, including key distinctions from traditional software engineering. Even more, MLTRL defines a lingua franca for people across teams and organizations to work collaboratively on artificial intelligence and machine learning technologies. Here we describe the framework and elucidate it with several real world use-cases of developing ML methods from basic research through productization and deployment, in areas such as medical diagnostics, consumer computer vision, satellite imagery, and particle physics

Measuring Robustness of Superpeer Topologies

In this paper, we propose an analytical framework based on percolation theory to assess the robustness of superpeer topologies in face of user churns and/or attacks targeted towards important nodes. It is observed in practice that in spite of churn of peers, superpeer networks show exceptional robustness and do not disintegrate into disconnected components. With the help of the analytical framework developed, we formally measure its stability against user churn and validate the general observation. The effect of intentional attacks upon the superpeer networks is also investigated. Our analysis shows that fraction of superpeers in the network and their connectivity have profound impact upon the stability of the network. The results obtained from the theoretical analysis are validated through simulation. The simulation results and theoretical predictions match with high degree of precision