Search

Using individual-level data from Gallup World Poll and the Levada Center, we provide an in-depth analysis of how Russia's invasion of Ukraine has affected sentiments in the Russian population. Our results show that after the invasion, a larger share of Russians expressed support for President Putin, optimism about the future, anti-West attitudes, and lower migration aspirations. The 2022 mobilization and the 2023 Wagner rebellion had only short-lived and no effects on sentiments, respectively. Regions with low pre-war support for Putin displayed stronger rally effects, higher casualty rates, and increased incomes, suggesting a recruitment strategy that maximizes political support. Taken together, our results suggest strong public support for the war, except among Russians abroad who became more critical of Putin in line with global views.

The CAGE-2 challenge is considered a standard benchmark to compare methods for autonomous cyber defense. Current state-of-the-art methods evaluated against this benchmark are based on model-free (offline) reinforcement learning, which does not provide provably optimal defender strategies. We address this limitation and present a formal (causal) model of CAGE-2 together with a method that produces a provably optimal defender strategy, which we call Causal Partially Observable Monte-Carlo Planning (C-POMCP). It has two key properties. First, it incorporates the causal structure of the target system, i.e., the causal relationships among the system variables. This structure allows for a significant reduction of the search space of defender strategies. Second, it is an online method that updates the defender strategy at each time step via tree search. Evaluations against the CAGE-2 benchmark show that C-POMCP achieves state-of-the-art performance with respect to effectiveness and is two orders of magnitude more efficient in computing time than the closest competitor method., Comment: This work has been submitted to the IEEE for possible publication

We formulate intrusion tolerance for a system with service replicas as a two-level optimal control problem. On the local level node controllers perform intrusion recovery, and on the global level a system controller manages the replication factor. The local and global control problems can be formulated as classical problems in operations research, namely, the machine replacement problem and the inventory replenishment problem. Based on this formulation, we design TOLERANCE, a novel control architecture for intrusion-tolerant systems. We prove that the optimal control strategies on both levels have threshold structure and design efficient algorithms for computing them. We implement and evaluate TOLERANCE in an emulation environment where we run 10 types of network intrusions. The results show that TOLERANCE can improve service availability and reduce operational cost compared with state-of-the-art intrusion-tolerant systems., Comment: Preprint to appear in the conference proceedings of the 54th IEEE/IFIP Dependable Systems and Networks Conference (DSN'24)

Asymmetric information stochastic games (AISGs) arise in many complex socio-technical systems, such as cyber-physical systems and IT infrastructures. Existing computational methods for AISGs are primarily offline and can not adapt to equilibrium deviations. Further, current methods are limited to particular information structures to avoid belief hierarchies. Considering these limitations, we propose conjectural online learning (COL), an online learning method under generic information structures in AISGs. COL uses a forecaster-actor-critic (FAC) architecture, where subjective forecasts are used to conjecture the opponents' strategies within a lookahead horizon, and Bayesian learning is used to calibrate the conjectures. To adapt strategies to nonstationary environments based on information feedback, COL uses online rollout with cost function approximation (actor-critic). We prove that the conjectures produced by COL are asymptotically consistent with the information feedback in the sense of a relaxed Bayesian consistency. We also prove that the empirical strategy profile induced by COL converges to the Berk-Nash equilibrium, a solution concept characterizing rationality under subjectivity. Experimental results from an intrusion response use case demonstrate COL's {faster convergence} over state-of-the-art reinforcement learning methods against nonstationary attacks., Comment: Accepted to the 63rd IEEE Conference on Decision and Control, Special Session on Networks, Games and Learning

We study automated security response for an IT infrastructure and formulate the interaction between an attacker and a defender as a partially observed, non-stationary game. We relax the standard assumption that the game model is correctly specified and consider that each player has a probabilistic conjecture about the model, which may be misspecified in the sense that the true model has probability 0. This formulation allows us to capture uncertainty and misconception about the infrastructure and the intents of the players. To learn effective game strategies online, we design Conjectural Online Learning (COL), a novel method where a player iteratively adapts its conjecture using Bayesian learning and updates its strategy through rollout. We prove that the conjectures converge to best fits, and we provide a bound on the performance improvement that rollout enables with a conjectured model. To characterize the steady state of the game, we propose a variant of the Berk-Nash equilibrium. We present COL through an advanced persistent threat use case. Testbed evaluations show that COL produces effective security strategies that adapt to a changing environment. We also find that COL enables faster convergence than current reinforcement learning techniques., Comment: This work has been submitted to the IEEE for possible publication

This work examines self-mixing in active nematics, a class of fluids in which mobile topological defects drive chaotic flows in a system comprised of biological filaments and molecular motors. We present experiments that demonstrate how geometrical confinement can influence the braiding dynamics of the defects. Notably, we show that confinement in cardioid-shaped wells leads to realization of the golden braid, a maximally efficient mixing state of exactly three defects with no defect creation or annihilation. We characterize the golden braid state using different measures of topological entropy and the Lyapunov exponent. In particular, topological entropy measured from the stretching rate of material lines agrees well with an analytical computation from braid theory. Increasing the size of the confining cardioid produces a transition from the golden braid, to the fully chaotic active turbulent state.

We study automated intrusion response for an IT infrastructure and formulate the interaction between an attacker and a defender as a partially observed stochastic game. To solve the game we follow an approach where attack and defense strategies co-evolve through reinforcement learning and self-play toward an equilibrium. Solutions proposed in previous work prove the feasibility of this approach for small infrastructures but do not scale to realistic scenarios due to the exponential growth in computational complexity with the infrastructure size. We address this problem by introducing a method that recursively decomposes the game into subgames which can be solved in parallel. Applying optimal stopping theory we show that the best response strategies in these subgames exhibit threshold structures, which allows us to compute them efficiently. To solve the decomposed game we introduce an algorithm called Decompositional Fictitious Self-Play (DFSP), which learns Nash equilibria through stochastic approximation. We evaluate the learned strategies in an emulation environment where real intrusions and response actions can be executed. The results show that the learned strategies approximate an equilibrium and that DFSP significantly outperforms a state-of-the-art algorithm for a realistic infrastructure configuration., Comment: A shortened version of this paper will appear in the conference proceedings of GameSec 2023

We consider the problem of optimising an expensive-to-evaluate grey-box objective function, within a finite budget, where known side-information exists in the form of the causal structure between the design variables. Standard black-box optimisation ignores the causal structure, often making it inefficient and expensive. The few existing methods that consider the causal structure are myopic and do not fully accommodate the observation-intervention trade-off that emerges when estimating causal effects. In this paper, we show that the observation-intervention trade-off can be formulated as a non-myopic optimal stopping problem which permits an efficient solution. We give theoretical results detailing the structure of the optimal stopping times and demonstrate the generality of our approach by showing that it can be integrated with existing causal Bayesian optimisation algorithms. Experimental results show that our formulation can enhance existing algorithms on real and synthetic benchmarks.

To leverage the full potential of quantum error-correcting stabilizer codes it is crucial to have an efficient and accurate decoder. Accurate, maximum likelihood, decoders are computationally very expensive whereas decoders based on more efficient algorithms give sub-optimal performance. In addition, the accuracy will depend on the quality of models and estimates of error rates for idling qubits, gates, measurements, and resets, and will typically assume symmetric error channels. In this work, instead, we explore a model-free, data-driven, approach to decoding, using a graph neural network (GNN). The decoding problem is formulated as a graph classification task in which a set of stabilizer measurements is mapped to an annotated detector graph for which the neural network predicts the most likely logical error class. We show that the GNN-based decoder can outperform a matching decoder for circuit level noise on the surface code given only simulated experimental data, even if the matching decoder is given full information of the underlying error model. Although training is computationally demanding, inference is fast and scales approximately linearly with the space-time volume of the code. We also find that we can use large, but more limited, datasets of real experimental data [Google Quantum AI, Nature {\bf 614}, 676 (2023)] for the repetition code, giving decoding accuracies that are on par with minimum weight perfect matching. The results show that a purely data-driven approach to decoding may be a viable future option for practical quantum error correction, which is competitive in terms of speed, accuracy, and versatility., Comment: 15 pages, 12 figures

We study automated intrusion response and formulate the interaction between an attacker and a defender as an optimal stopping game where attack and defense strategies evolve through reinforcement learning and self-play. The game-theoretic modeling enables us to find defender strategies that are effective against a dynamic attacker, i.e. an attacker that adapts its strategy in response to the defender strategy. Further, the optimal stopping formulation allows us to prove that optimal strategies have threshold properties. To obtain near-optimal defender strategies, we develop Threshold Fictitious Self-Play (T-FP), a fictitious self-play algorithm that learns Nash equilibria through stochastic approximation. We show that T-FP outperforms a state-of-the-art algorithm for our use case. The experimental part of this investigation includes two systems: a simulation system where defender strategies are incrementally learned and an emulation system where statistics are collected that drive simulation runs and where learned strategies are evaluated. We argue that this approach can produce effective defender strategies for a practical IT infrastructure., Comment: arXiv admin note: substantial text overlap with arXiv:2205.14694

We study automated intrusion prevention using reinforcement learning. Following a novel approach, we formulate the interaction between an attacker and a defender as an optimal stopping game and let attack and defense strategies evolve through reinforcement learning and self-play. The game-theoretic perspective allows us to find defender strategies that are effective against dynamic attackers. The optimal stopping formulation gives us insight into the structure of optimal strategies, which we show to have threshold properties. To obtain the optimal defender strategies, we introduce T-FP, a fictitious self-play algorithm that learns Nash equilibria through stochastic approximation. We show that T-FP outperforms a state-of-the-art algorithm for our use case. Our overall method for learning and evaluating strategies includes two systems: a simulation system where defender strategies are incrementally learned and an emulation system where statistics are produced that drive simulation runs and where learned strategies are evaluated. We conclude that this approach can produce effective defender strategies for a practical IT infrastructure.

We present a system for interactive examination of learned security policies. It allows a user to traverse episodes of Markov decision processes in a controlled manner and to track the actions triggered by security policies. Similar to a software debugger, a user can continue or or halt an episode at any time step and inspect parameters and probability distributions of interest. The system enables insight into the structure of a given policy and in the behavior of a policy in edge cases. We demonstrate the system with a network intrusion use case. We examine the evolution of an IT infrastructure's state and the actions prescribed by security policies while an attack occurs. The policies for the demonstration have been obtained through a reinforcement learning approach that includes a simulation system where policies are incrementally learned and an emulation system that produces statistics that drive the simulation runs., Comment: Preprint, original submission to NOMS22 Demo track. Copyright IEEE, may be transferred without notice. arXiv admin note: text overlap with arXiv:2111.00289

Explanation methods and their evaluation have become a significant issue in explainable artificial intelligence (XAI) due to the recent surge of opaque AI models in decision support systems (DSS). Since the most accurate AI models are opaque with low transparency and comprehensibility, explanations are essential for bias detection and control of uncertainty. There are a plethora of criteria to choose from when evaluating explanation method quality. However, since existing criteria focus on evaluating single explanation methods, it is not obvious how to compare the quality of different methods. This lack of consensus creates a critical shortage of rigour in the field, although little is written about comparative evaluations of explanation methods. In this paper, we have conducted a semi-systematic meta-survey over fifteen literature surveys covering the evaluation of explainability to identify existing criteria usable for comparative evaluations of explanation methods. The main contribution in the paper is the suggestion to use appropriate trust as a criterion to measure the outcome of the subjective evaluation criteria and consequently make comparative evaluations possible. We also present a model of explanation quality aspects. In the model, criteria with similar definitions are grouped and related to three identified aspects of quality; model, explanation, and user. We also notice four commonly accepted criteria (groups) in the literature, covering all aspects of explanation quality: Performance, appropriate trust, explanation satisfaction, and fidelity. We suggest the model be used as a chart for comparative evaluations to create more generalisable research in explanation quality., Comment: 15 pages, 4 figures, 2 tables, conference article

Novel understanding of the recent nanomagnet tailoring experiments and the possibility to further unveil the mechanisms by which the magnetic interactions arise in an atom by atom fashion covers importance as the demand for spin qubit and quantum state detection architectures increases. Here, we address the spin states of a molecular trimer comprising three localized spin moments embedded in a metallic tunnel junction and show that the pair spin interactions can be engineered through the electronic structure of the molecular trimer. We show that bias and gate voltages induce either a completely ferromagnetic state of the localized moments or a spin frustrated state with different stabilities, and that switching between these states is possible on demand by electrical control. The role of quantum coherence in the molecular trimer is discussed with regards to the spin ordering as well as the interplay among electronic interference and induced dephasing by the metallic leads. This work sets foundations for more robust all electrically controlled spin architectures usable in quantum engineering systems and serves as a test bench for exploring unresolved questions in magnetic ordering and symmetry.

Efficient high-performance decoding of topological stabilizer codes has the potential to crucially improve the balance between logical failure rates and the number and individual error rates of the constituent qubits. High-threshold maximum-likelihood decoders require an explicit error model for Pauli errors to decode a specific syndrome, whereas lower-threshold heuristic approaches such as minimum weight matching are "error agnostic". Here we consider an intermediate approach, formulating a decoder that depends on the bias, i.e., the relative probability of phase-flip to bit-flip errors, but is agnostic to error rate. Our decoder is based on counting the number and effective weight of the most likely error chains in each equivalence class of a given syndrome. We use Metropolis-based Monte Carlo sampling to explore the space of error chains and find unique chains, that are efficiently identified using a hash table. Using the error-rate invariance the decoder can sample chains effectively at an error rate which is higher than the physical error rate and without the need for "thermalization" between chains in different equivalence classes. Applied to the surface code and the XZZX code, the decoder matches maximum-likelihood decoders for moderate code sizes or low error rates. We anticipate that, because of the compressed information content per syndrome, it can be taken full advantage of in combination with machine-learning methods to extrapolate Monte Carlo-generated data., Comment: 15 pages, 9 figures; V2 Added analysis of low error-rate performance

We study automated intrusion prevention using reinforcement learning. Following a novel approach, we formulate the problem of intrusion prevention as an (optimal) multiple stopping problem. This formulation gives us insight into the structure of optimal policies, which we show to have threshold properties. For most practical cases, it is not feasible to obtain an optimal defender policy using dynamic programming. We therefore develop a reinforcement learning approach to approximate an optimal threshold policy. We introduce T-SPSA, an efficient reinforcement learning algorithm that learns threshold policies through stochastic approximation. We show that T-SPSA outperforms state-of-the-art algorithms for our use case. Our overall method for learning and validating policies includes two systems: a simulation system where defender policies are incrementally learned and an emulation system where statistics are produced that drive simulation runs and where learned policies are evaluated. We show that this approach can produce effective defender policies for a practical IT infrastructure., Comment: Preprint; Submitted to IEEE for review. major revision 1/4 2022. arXiv admin note: substantial text overlap with arXiv:2106.07160

We study automated intrusion prevention using reinforcement learning. In a novel approach, we formulate the problem of intrusion prevention as an optimal stopping problem. This formulation allows us insight into the structure of the optimal policies, which turn out to be threshold based. Since the computation of the optimal defender policy using dynamic programming is not feasible for practical cases, we approximate the optimal policy through reinforcement learning in a simulation environment. To define the dynamics of the simulation, we emulate the target infrastructure and collect measurements. Our evaluations show that the learned policies are close to optimal and that they indeed can be expressed using thresholds., Comment: Fix typos

We present a method to automatically find security strategies for the use case of intrusion prevention. Following this method, we model the interaction between an attacker and a defender as a Markov game and let attack and defense strategies evolve through reinforcement learning and self-play without human intervention. Using a simple infrastructure configuration, we demonstrate that effective security strategies can emerge from self-play. This shows that self-play, which has been applied in other domains with great success, can be effective in the context of network security. Inspection of the converged policies show that the emerged policies reflect common-sense knowledge and are similar to strategies of humans. Moreover, we address known challenges of reinforcement learning in this domain and present an approach that uses function approximation, an opponent pool, and an autoregressive policy representation. Through evaluations we show that our method is superior to two baseline methods but that policy convergence in self-play remains a challenge.

We present optical characterization, calibration, and performance tests of the Mesospheric Airglow/Aerosol Tomography Spectroscopy (MATS) satellite, which for the first time for a satellite applies a linear-astigmatism-free confocal off-axis reflective optical design. Mechanical tolerances of the telescope were investigated using Monte-Carlo methods and single-element perturbations. The sensitivity analysis results indicate that tilt errors of the tertiary mirror and a surface RMS error of the secondary mirror mainly degrade optical performance. From the Monte-Carlo simulation, the tolerance limits were calculated to $\pm$0.5 mm, $\pm$1 mm, and $\pm$0.15$^\circ$ for decenter, despace, and tilt, respectively. We performed characterization measurements and optical tests with the flight model of the satellite. Multi-channel relative pointing, total optical system throughput, and distortion of each channel were characterized for end-users. Optical performance was evaluated by measuring modulation transfer function (MTF) and point spread function (PSF). The final MTF performance is 0.25 MTF at 20 lp/mm for the ultraviolet channel (304.5 nm), and 0.25 - 0.54 MTF at 10 lp/mm for infrared channels. The salient fact of the PSF measurement of this system is that there is no noticeable linear astigmatism detected over wide field of view (5.67$^\circ$ $\times$ 0.91$^\circ$). All things considered, the design method showed great advantages in wide field of view observations with satellite-level optical performance., Comment: 21 pages, 11 figures

We present the development of Linear Astigmatism Free - Three Mirror System (LAF-TMS). This is a prototype of an off-axis telescope that enables very wide field of view (FoV) infrared satellites that can observe Paschen-$\alpha$ emission, zodiacal light, integrated star light, and other infrared sources. It has the entrance pupil diameter of 150 mm, the focal length of 500 mm, and the FoV of 5.5$^\circ$ $\times$ 4.1$^\circ$. LAF-TMS is an obscuration-free off-axis system with minimal out-of-field baffling and no optical support structure diffraction. This optical design is analytically optimized to remove linear astigmatism and to reduce high-order aberrations. Sensitivity analysis and Monte-Carlo simulation reveal that tilt errors are the most sensitive alignment parameters that allow $\sim$1$^\prime$. Optomechanical structure accurately mounts aluminum mirrors, and withstands satellite-level vibration environments. LAF-TMS shows optical performance with 37 $\mu$m FWHM of the point source image satisfying Nyquist sampling requirements for typical 18 $\mu$m pixel Infrared array detectors. The surface figure errors of mirrors and scattered light from the tertiary mirror with 4.9 nm surface micro roughness may affect the measured point spread function (PSF). Optical tests successfully demonstrate constant optical performance over wide FoV, indicating that LAF-TMS suppresses linear astigmatism and high-order aberrations., Comment: 26 pages, 13 figures, will be published in Publications of the Astronomical Society of the Pacific

On-demand sources of entangled photons for the transmission of quantum information in the telecom C-band are required to realize fiber-based quantum networks. So far, non-deterministic sources of quantum states of light were used for long distance entanglement distribution in this lowest loss wavelength range. However, they are fundamentally limited in either efficiency or security due to their Poissonian emission statistics. Here, we show on-demand generation of entangled photon pairs in the telecom C-band by an InAs/GaAs semiconductor quantum dot. Using a robust phonon-assisted excitation scheme we measure a concurrence of $91.4\,\%$ and a fidelity of $95.2\,\%$ to $\Phi^+$. On-demand generation of polarization entangled photons will enable secure quantum communication in fiber-based networks. Furthermore, applying this excitation scheme to several remote quantum dots tuned into resonance will enable first on-demand entanglement distribution over large distances for scalable real-life quantum applications., Comment: 10 pages, 4 figures

With the advent of social media, our online feeds increasingly consist of short, informal, and unstructured text. This textual data can be analyzed for the purpose of improving user recommendations and detecting trends. Instagram is one of the largest social media platforms, containing both text and images. However, most of the prior research on text processing in social media is focused on analyzing Twitter data, and little attention has been paid to text mining of Instagram data. Moreover, many text mining methods rely on annotated training data, which in practice is both difficult and expensive to obtain. In this paper, we present methods for unsupervised mining of fashion attributes from Instagram text, which can enable a new kind of user recommendation in the fashion domain. In this context, we analyze a corpora of Instagram posts from the fashion domain, introduce a system for extracting fashion attributes from Instagram, and train a deep clothing classifier with weak supervision to classify Instagram posts based on the associated text. With our experiments, we confirm that word embeddings are a useful asset for information extraction. Experimental results show that information extraction using word embeddings outperforms a baseline that uses Levenshtein distance. The results also show the benefit of combining weak supervision signals using generative models instead of majority voting. Using weak supervision and generative modeling, an F1 score of 0.61 is achieved on the task of classifying the image contents of Instagram posts based solely on the associated text, which is on level with human performance. Finally, our empirical study provides one of the few available studies on Instagram text and shows that the text is noisy, that the text distribution exhibits the long-tail phenomenon, and that comment sections on Instagram are multi-lingual., Comment: 8 pages, 5 figures. Pre-print for paper to appear in conference proceedings for the Web Intelligence Conference

In this work, we demonstrate reconfigurable frequency manipulation of quantum states of light in the telecom C-band. Triggered single photons are encoded in a superposition state of three channels using sidebands up to 53 GHz created by an off-the-shelf phase modulator. The single photons are emitted by an InAs/GaAs quantum dot grown by metal-organic vapor-phase epitaxy within the transparency window of the backbone fiber optical network. A cross-correlation measurement of the sidebands demonstrates the preservation of the single photon nature; an important prerequisite for future quantum technology applications using the existing telecommunication fiber network., Comment: Samuel Gyger and Katharina D. Zeuner contributed equally