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Machine Learning Methods for Device Identification Using Wireless Fingerprinting

Industrial Internet of Things (IoT) systems increasingly rely on wireless communication standards. Such device identification methods based on wireless fingerprinting gained increased attention lately as an additional cyber-security mechanism for critical IoT infrastructures. The proposed solution is currently being deployed in a real-world industrial IoT environment as part of H2020 project COLLABS. Industrial Internet of Things (IoT) systems increasingly rely on wireless communication standards. In a common industrial scenario, indoor wireless IoT devices communicate with access points to deliver data collected from industrial sensors, robots and factory machines. Due to static or quasi-static locations of IoT devices and access points, historical observations of IoT device channel conditions provide a possibility to precisely identify the device without observing its traditional identifiers (e.g., MAC or IP address). Such device identification methods based on wireless fingerprinting gained increased attention lately as an additional cyber-security mechanism for critical IoT infrastructures. In this paper, we perform a systematic study of a large class of machine learning algorithms for device identification using wireless fingerprints for the most popular cellular and Wi-Fi IoT technologies. We design, implement, deploy, collect relevant data sets, train and test a multitude of machine learning algorithms, as a part of the complete end-to-end solution design for device identification via wireless fingerprinting. The proposed solution is currently being deployed in a real-world industrial IoT environment as part of H2020 project COLLABS.

Authors: Srđan Šobot, Vukan Ninković, Dejan Vukobratović, Milan Pavlović, Miloš Radovanović.

A Posterior Sampling Framework for Interactive Decision Making

In specific, GEC captures the hardness of exploration by comparing the error of predicting the performance of the updated policy with the in-sample training error evaluated on the historical data. We show that RL problems with low GEC form a remarkably rich class, which subsumes low Bellman eluder dimension problems, bilinear class, low witness rank problems, PO-bilinear class, and generalized regular PSR, where generalized regular PSR, a new tractable PSR class identified by us, includes nearly all known tractable POMDPs. We prove that the proposed algorithm is sample efficient by establishing a sublinear regret upper bound in terms of GEC. In summary, we provide a new and unified understanding of both fully observable and partially observable RL.

We study sample efficient reinforcement learning (RL) under the general framework of interactive decision making, which includes Markov decision process (MDP), partially observable Markov decision process (POMDP), and predictive state representation (PSR) as special cases. Toward finding the minimum assumption that empowers sample efficient learning, we propose a novel complexity measure, generalized eluder coefficient (GEC), which characterizes the fundamental tradeoff between exploration and exploitation in online interactive decision making. In specific, GEC captures the hardness of exploration by comparing the error of predicting the performance of the updated policy with the in-sample training error evaluated on the historical data. We show that RL problems with low GEC form a remarkably rich class, which subsumes low Bellman eluder dimension problems, bilinear class, low witness rank problems, PO-bilinear class, and generalized regular PSR, where generalized regular PSR, a new tractable PSR class identified by us, includes nearly all known tractable POMDPs. Furthermore, in terms of algorithm design, we propose a generic posterior sampling algorithm, which can be implemented in both model-free and model-based fashion, under both fully observable and partially observable settings. The proposed algorithm modifies the standard posterior sampling algorithm in two aspects: (i) we use an optimistic prior distribution that biases towards hypotheses with higher values and (ii) a loglikelihood function is set to be the empirical loss evaluated on the historical data, where the choice of loss function supports both model-free and model-based learning. We prove that the proposed algorithm is sample efficient by establishing a sublinear regret upper bound in terms of GEC. In summary, we provide a new and unified understanding of both fully observable and partially observable RL.

Authors: Han Zhong, Wei Xiong, Sirui Zheng, Liwei Wang, Zhaoran Wang, Zhuoran Yang, Tong Zhang.

The LP-update policy for weakly coupled Markov decision processes

We consider a scaling model with $N$ statistically identical arms. The definition of non-degeneracy extends the same notion for the classical two-action restless bandits. By using this property, we also provide a more efficient implementation of the LP-update policy. We illustrate the performance of our policy in a generalized applicant screening problem. In this work we propose a novel policy called the LP-update policy for finite horizon weakly coupled Markov decision processes. The latter can be seen as multi-constraint multi-action bandits, and generalizes the classical restless bandit problems (that are single-constraint two-action bandits), widely studied in the literature. We consider a scaling model with $N$ statistically identical arms. We show that our LP-update policy becomes asymptotically optimal at rate $O(1/\sqrt{N})$ for any problem. This rate can be improved to $O(1/N)$ if the problem is non-degenerate, and even to $e^{-\Omega(N)}$ if in addition the problem admits a perfect rounding. The definition of non-degeneracy extends the same notion for the classical two-action restless bandits. By using this property, we also provide a more efficient implementation of the LP-update policy. We illustrate the performance of our policy in a generalized applicant screening problem.

Authors: Nicolas Gast, Bruno Gaujal, Chen Yan.

An Efficient Approach with Dynamic Multi-Swarm of UAVs for Forest Firefighting

The multi-swarm cooperative information-driven search uses a multi-level search comprising cooperative information-driven exploration and exploitation for quick/accurate detection of fire location. The search level is selected based on the thermal sensor information about the potential fire area. The performance of MSCIDC has been compared with different multi-UAV methods using a simulated environment of pine forest. The performance clearly shows that MSCIDC mitigates fire much faster than the multi-UAV methods.

In this paper, the Multi-Swarm Cooperative Information-driven search and Divide and Conquer mitigation control (MSCIDC) approach is proposed for faster detection and mitigation of forest fire by reducing the loss of biodiversity, nutrients, soil moisture, and other intangible benefits. A swarm is a cooperative group of Unmanned Aerial Vehicles (UAVs) that fly together to search and quench the fire effectively. The multi-swarm cooperative information-driven search uses a multi-level search comprising cooperative information-driven exploration and exploitation for quick/accurate detection of fire location. The search level is selected based on the thermal sensor information about the potential fire area. The dynamicity of swarms, aided by global regulative repulsion and merging between swarms, reduces the detection and mitigation time compared to the existing methods. The local attraction among the members of the swarm helps the non-detector members to reach the fire location faster, and divide-and-conquer mitigation control ensures a non-overlapping fire sector allocation for all members quenching the fire. The performance of MSCIDC has been compared with different multi-UAV methods using a simulated environment of pine forest. The performance clearly shows that MSCIDC mitigates fire much faster than the multi-UAV methods. The Monte-Carlo simulation results indicate that the proposed method reduces the average forest area burnt by $65\%$ and mission time by $60\%$ compared to the best result case of the multi-UAV approaches, guaranteeing a faster and successful mission.

Authors: Josy John, K. Harikumar, J. Senthilnath, Suresh Sundaram.

Quantifying the `end of history' through a Bayesian Markov-chain approach

Political regimes have been changing throughout human history. This view has been challenged by recent developments that seem to indicate the rise of defective democracies across the globe. There has been no attempt to quantify the expected EoH with a statistical approach. Furthermore, we find that, under our model, the fraction of autocracies in the world is expected to increase for the next half-century before it declines. Quantifying the expected EoH allows us to challenge common beliefs about the nature of political equilibria. Political regimes have been changing throughout human history. After the apparent triumph of liberal democracies at the end of the twentieth century, Francis Fukuyama and others have been arguing that humankind is approaching an `end of history' (EoH) in the form of a universality of liberal democracies. This view has been challenged by recent developments that seem to indicate the rise of defective democracies across the globe. There has been no attempt to quantify the expected EoH with a statistical approach. In this study, we model the transition between political regimes as a Markov process and -- using a Bayesian inference approach -- we estimate the transition probabilities between political regimes from time-series data describing the evolution of political regimes from 1800--2018. We then compute the steady state for this Markov process which represents a mathematical abstraction of the EoH and predicts that approximately 46 % of countries will be full democracies. Furthermore, we find that, under our model, the fraction of autocracies in the world is expected to increase for the next half-century before it declines. Using random-walk theory, we then estimate survival curves of different types of regimes and estimate characteristic lifetimes of democracies and autocracies of 244 years and 69 years, respectively. Quantifying the expected EoH allows us to challenge common beliefs about the nature of political equilibria. Specifically, we find no statistical evidence that the EoH constitutes a fixed, complete omnipresence of democratic regimes.

Authors: Florian Klimm.

Impact of multiple modes on the evolution of self-interacting axion condensate around rotating black holes

However, the precise evolution of the condensate must be known for the actual detection. Our result indicates that explosive phenomena such as bosenova do not occur in this case. We show that the dissipation process induced by the mode coupling does not occur for small gravitational coupling. Therefore, bosenova might occur in this case.

Ultra-light particles, such as axions, form a macroscopic condensate around a highly spinning black hole by the superradiant instability. Due to its macroscopic nature, the condensate opens the possibility of detecting the axion through gravitational wave observations. However, the precise evolution of the condensate must be known for the actual detection. For future observation, we numerically study the influence of the self-interaction, especially interaction between different modes, on the evolution of the condensate in detail. First, we focus on the case when condensate starts with the smallest possible angular quantum number. For this case, we perform the non-linear calculation and show that the dissipation induced by the mode interaction is strong enough to saturate the superradiant instability, even if the secondary cloud starts with quantum fluctuations. Our result indicates that explosive phenomena such as bosenova do not occur in this case. We also show that the condensate settles to a quasi-stationary state mainly composed of two modes, one with the smallest angular quantum number for which the superradiant instability occurs and the other with the adjacent higher angular quantum number. We also study the case when the condensate starts with the dominance of the higher angular quantum number. We show that the dissipation process induced by the mode coupling does not occur for small gravitational coupling. Therefore, bosenova might occur in this case.

Authors: Hidetoshi Omiya, Takuya Takahashi, Takahiro Tanaka, Hirotaka Yoshino.

Invertible bimodule categories and generalized Schur orthogonality

The Schur orthogonality relations are a cornerstone in the representation theory of groups. We discuss applications to generalized symmetries, including a generalized Wigner-Eckart theorem. The Schur orthogonality relations are a cornerstone in the representation theory of groups. We utilize a generalization to weak Hopf algebras to provide a new, readily verifiable condition on the skeletal data for deciding whether a given bimodule category is invertible and therefore defines a Morita equivalence. As a first application, we provide an algorithm for the construction of the full skeletal data of the invertible bimodule category associated to a given module category, which is obtained in a unitary gauge when the underlying categories are unitary. As a second application, we show that our condition for invertibility is equivalent to the notion of MPO-injectivity, thereby closing an open question concerning tensor network representations of string-net models exhibiting topological order. We discuss applications to generalized symmetries, including a generalized Wigner-Eckart theorem.

Authors: Jacob C. Bridgeman, Laurens Lootens, Frank Verstraete.

Revisiting and Optimising a CNN Colour Constancy Method for Multi-Illuminant Estimation

Generally, it is assumed that the illumination is uniform in the scene. Our approach works well in both the multi and single illuminant cases. The method that we propose outperforms other recent and state of the art methods and has promising visual results.

The aim of colour constancy is to discount the effect of the scene illumination from the image colours and restore the colours of the objects as captured under a 'white' illuminant. For the majority of colour constancy methods, the first step is to estimate the scene illuminant colour. Generally, it is assumed that the illumination is uniform in the scene. However, real world scenes have multiple illuminants, like sunlight and spot lights all together in one scene. We present in this paper a simple yet very effective framework using a deep CNN-based method to estimate and use multiple illuminants for colour constancy. Our approach works well in both the multi and single illuminant cases. The output of the CNN method is a region-wise estimate map of the scene which is smoothed and divided out from the image to perform colour constancy. The method that we propose outperforms other recent and state of the art methods and has promising visual results.

Authors: Ghalia Hemrit, Joseph Meehan.

Distributed Maximal Matching and Maximal Independent Set on Hypergraphs

We investigate the distributed complexity of maximal matching and maximal independent set (MIS) in hypergraphs in the LOCAL model. A maximal matching of a hypergraph $H=(V_H,E_H)$ is a maximal disjoint set $M\subseteq E_H$ of hyperedges and an MIS $S\subseteq V_H$ is a maximal set of nodes such that no hyperedge is fully contained in $S$. We show that for maximal matching, this naive algorithm is optimal in the following sense. Any deterministic algorithm for solving the problem requires $\Omega(\min\{\Delta r, \log_{\Delta r} n\})$ rounds, and any randomized one requires $\Omega(\min\{\Delta r, \log_{\Delta r} \log n\})$ rounds. We give two deterministic algorithms for the problem. We investigate the distributed complexity of maximal matching and maximal independent set (MIS) in hypergraphs in the LOCAL model. A maximal matching of a hypergraph $H=(V_H,E_H)$ is a maximal disjoint set $M\subseteq E_H$ of hyperedges and an MIS $S\subseteq V_H$ is a maximal set of nodes such that no hyperedge is fully contained in $S$. Both problems can be solved by a simple sequential greedy algorithm, which can be implemented naively in $O(\Delta r + \log^* n)$ rounds, where $\Delta$ is the maximum degree, $r$ is the rank, and $n$ is the number of nodes. We show that for maximal matching, this naive algorithm is optimal in the following sense. Any deterministic algorithm for solving the problem requires $\Omega(\min\{\Delta r, \log_{\Delta r} n\})$ rounds, and any randomized one requires $\Omega(\min\{\Delta r, \log_{\Delta r} \log n\})$ rounds. Hence, for any algorithm with a complexity of the form $O(f(\Delta, r) + g(n))$, we have $f(\Delta, r) \in \Omega(\Delta r)$ if $g(n)$ is not too large, and in particular if $g(n) = \log^* n$ (which is the optimal asymptotic dependency on $n$ due to Linial's lower bound [FOCS'87]). Our lower bound proof is based on the round elimination framework, and its structure is inspired by a new round elimination fixed point that we give for the $\Delta$-vertex coloring problem in hypergraphs. For the MIS problem on hypergraphs, we show that for $\Delta\ll r$, there are significant improvements over the naive $O(\Delta r + \log^* n)$-round algorithm. We give two deterministic algorithms for the problem. We show that a hypergraph MIS can be computed in $O(\Delta^2\cdot\log r + \Delta\cdot\log r\cdot \log^* r + \log^* n)$ rounds. We further show that at the cost of a worse dependency on $\Delta$, the dependency on $r$ can be removed almost entirely, by giving an algorithm with complexity $\Delta^{O(\Delta)}\cdot\log^* r + O(\log^* n)$.

Authors: Alkida Balliu, Sebastian Brandt, Fabian Kuhn, Dennis Olivetti.

Carbon Monitor Europe, near-real-time daily CO$_2$ emissions for 27 EU countries and the United Kingdom

The data are calculated separately for six sectors: power, industry, ground transportation, domestic aviation, international aviation and residential. Daily CO$_2$ emissions are estimated from a large set of activity data compiled from different sources.

With the urgent need to implement the EU countries pledges and to monitor the effectiveness of Green Deal plan, Monitoring Reporting and Verification tools are needed to track how emissions are changing for all the sectors. Current official inventories only provide annual estimates of national CO$_2$ emissions with a lag of 1+ year which do not capture the variations of emissions due to recent shocks including COVID lockdowns and economic rebounds, war in Ukraine. Here we present a near-real-time country-level dataset of daily fossil fuel and cement emissions from January 2019 through December 2021 for 27 EU countries and UK, which called Carbon Monitor Europe. The data are calculated separately for six sectors: power, industry, ground transportation, domestic aviation, international aviation and residential. Daily CO$_2$ emissions are estimated from a large set of activity data compiled from different sources. The goal of this dataset is to improve the timeliness and temporal resolution of emissions for European countries, to inform the public and decision makers about current emissions changes in Europe.

Authors: Piyu Ke, Zhu Deng, Biqing Zhu, Bo Zheng, Yilong Wang, Olivier Boucher, Simon Ben Arous, Chuanlong Zhou, Xinyu Dou, Taochun Sun, Zhao Li, Feifan Yan, Duo Cui, Yifan Hu, Da Huo, Jean Pierre, Richard Engelen, Steven J. Davis, Philippe Ciais, Zhu Liu.