Miracle-dz/UniFAR-FTUs huggingface dataset:
Agent-Based modeling in Medical Research. Example in Health Economics

This chapter presents the main lines of agent based modeling in the field of medical research. The general diagram consists of a cohort of patients (virtual or real) whose evolution is observed by means of so-called evolution models. Scenarios can then be explored by varying the parameters of the different models. This chapter presents techniques for virtual patient generation and examples of execution models. The advantages and disadvantages of these models are discussed as well as the pitfalls to be avoided. Finally, an application to the medico-economic study of the impact of the penetration rate of generic versions of treatments on the costs associated with HIV treatment is presented.
LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation

Combinatorial Optimisation problems arise in several application domains and are often formulated in terms of graphs. Many of these problems are NP-hard, but exact solutions are not always needed. Several heuristics have been developed to provide near-optimal solutions; however, they do not typically scale well with the size of the graph. We propose a low-complexity approach for identifying a (possibly much smaller) subgraph of the original graph where the heuristics can be run in reasonable time and with a high likelihood of finding a global near-optimal solution. The core component of our approach is LeNSE, a reinforcement learning algorithm that learns how to navigate the space of possible subgraphs using an Euclidean subgraph embedding as its map. To solve CO problems, LeNSE is provided with a discriminative embedding trained using any existing heuristics using only on a small portion of the original graph. When tested on three problems (vertex cover, max-cut and influence maximisation) using real graphs with up to $10$ million edges, LeNSE identifies small subgraphs yielding solutions comparable to those found by running the heuristics on the entire graph, but at a fraction of the total run time.
A Novel Weighted Ensemble Learning Based Agent for the Werewolf Game

Abstract —Werewolf is a popular party game throughout the world, and research on its signiﬁcance has progressed in recent years. The Werewolf game is based on conversation, and in order to win, participants must use all of their cognitive abilities. This communication game requires the playing agents to be very sophisticated to win. In this research, we generated a sophisticated agent to play the Werewolf game using a complex weighted ensemble learning approach. This research work aimed to estimate what other agents/players think of us in the game. The agent was developed by aggregating strategies of different participants in the AI Wolf competition and thereby learning from them using machine learning. Moreover, the agent created was able to perform much better than other competitors using very basic strategies to show the approach’s effectiveness in the Werewolf game. The machine learning technique used here is not restricted to the Werewolf game but may be extended to any game that requires communication and action depending on other participants
Sample Complexity of Learning Heuristic Functions for Greedy-Best-First and A* Search

Greedy best-first search (GBFS) and A* search (A*) are popular algorithms for path-finding on large graphs. Both use so-called heuristic functions, which estimate how close a vertex is to the goal. While heuristic functions have been handcrafted using domain knowledge, recent studies demonstrate that learning heuristic functions from data is effective in many applications. Motivated by this emerging approach, we study the sample complexity of learning heuristic functions for GBFS and A*. We build on a recent framework called \textit{data-driven algorithm design} and evaluate the \textit{pseudo-dimension} of a class of utility functions that measure the performance of parameterized algorithms. Assuming that a vertex set of size $n$ is fixed, we present $\mathrm{O}(n\lg n)$ and $\mathrm{O}(n^2\lg n)$ upper bounds on the pseudo-dimensions for GBFS and A*, respectively, parameterized by heuristic function values. The upper bound for A* can be improved to $\mathrm{O}(n^2\lg d)$ if every vertex has a degree of at most $d$ and to $\mathrm{O}(n \lg n)$ if edge weights are integers bounded by $\mathrm{poly}(n)$. We also give $\Omega(n)$ lower bounds for GBFS and A*, which imply that our bounds for GBFS and A* under the integer-weight condition are tight up to a $\lg n$ factor. Finally, we discuss a case where the performance of A* is measured by the suboptimality and show that we can sometimes obtain a better guarantee by combining a parameter-dependent worst-case bound with a sample complexity bound.
Random Coordinate Descent for Resource Allocation in Open Multi-Agent Systems

We propose a method for analyzing the distributed random coordinate descent algorithm for solving separable resource allocation problems in the context of an open multiagent system, where agents can be replaced during the process. In particular, we characterize the evolution of the distance to the minimizer in expectation by following a time-varying optimization approach which builds on two components. First, we establish the linear convergence of the algorithm in closed systems, in terms of the estimate towards the minimizer, for general graphs and appropriate step-size. Second, we estimate the change of the optimal solution after a replacement, in order to evaluate its effect on the distance between the current estimate and the minimizer. From these two elements, we derive stability conditions in open systems and establish the linear convergence of the algorithm towards a steady-state expected error. Our results enable to characterize the trade-off between speed of convergence and robustness to agent replacements, under the assumptions that local functions are smooth, strongly convex, and have their minimizers located in a given ball. The approach proposed in this paper can moreover be extended to other algorithms guaranteeing linear convergence in closed system.
Frontrunning Block Attack in PoA Clique: A Case Study

In this paper, we propose a frontrunning block attack against the Clique-based Proof of Authority (PoA) algorithms. Our attack can frontrun blocks from honest in-turn sealers by breaking the leader rotation’s proper order. By falsifying the priority parameters (both difficulty and delay time), a malicious non-in-turn sealer can always successfully occupy the leader position and produce advantageous blocks that may contain profitable transactions. As a typical instance, we apply our attack to a mature Clique-based project, HPB (with the market cap $10,128,116, as of Jan 2022). Experimental results demonstrate the effectiveness and feasibility. Then, we further propose fixes by checking sealer’s identity. Our investigation and suggestion have been submitted to its official team. We believe this work can act as, at least, a warning case for Clique variants to avoid repeating such design mistakes.
Impact of Retardation in the Holstein-Hubbard Model: a two-site Calculation

Eliashberg theory provides a theoretical framework for understanding the phenomenon of superconductivity when pairing between two electrons is mediated by phonons, and retardation effects are fully accounted for. However, when a direct Coulomb interaction between two electrons is also present, this interaction is only partially accounted for. In this work we use a well-defined Hamiltonian, the Hubbard-Holstein model, to examine this competition more rigorously, using exact diagonalization on a two-site system. We find that the direct electron-electron repulsion between two electrons has a significantly more harmful effect on pairing than suggested through the standard treatment of this interaction.
Balancing Exploration and Exploitation for Solving Large-scale Multiobjective Optimization via Attention Mechanism

Large-scale multiobjective optimization problems (LSMOPs) refer to optimization problems with multiple con-flicting optimization objectives and hundreds or even thousands of decision variables. A key point in solving LSMOPs is how to balance exploration and exploitation so that the algorithm can search in a huge decision space efficiently. Large-scale multi-objective evolutionary algorithms consider the balance between exploration and exploitation from the individual's perspective. However, these algorithms ignore the significance of tackling this issue from the perspective of decision variables, which makes the algorithm lack the ability to search from different dimensions and limits the performance of the algorithm. In this paper, we propose a large-scale multiobjective optimization algorithm based on the attention mechanism, called (LMOAM). The attention mechanism will assign a unique weight to each decision variable, and LMOAM will use this weight to strike a balance between exploration and exploitation from the decision variable level. Nine different sets of LSMOP benchmarks are conducted to verify the algorithm proposed in this paper, and the experimental results validate the effectiveness of our design.
The thermodynamics of isolated horizon in higher dimensional loop quantum gravity

The statistical mechanical calculation of the thermodynamical properties of higher-dimensional non-rotating isolated horizons is studied in the loop quantum gravity framework. By employing the Hawking temperature and horizon mass of isolated horizons as physical input, the microcanonical ensemble associated with the system are well established. As a result, the black hole entropy and other thermodynamical quantities can be computed and consistent with well known Hawking’s semiclassical analysis. Moreover, the value of the Immirzi parameter of higher-dimensional loop quantum gravity is also obtained.
Detector requirements for model-independent measurements of ultrahigh energy neutrino cross sections

The ultrahigh energy range of neutrino physics (above $\sim 10^{7} \, \mathrm{GeV}$), as yet devoid of detections, is an open landscape with challenges to be met and discoveries to be made. Neutrino-nucleon cross sections in that range - with center-of-momentum energies $\sqrt{s} \gtrsim 4 \, \mathrm{TeV}$ - are powerful probes of unexplored phenomena. We present a simple and accurate model-independent framework to evaluate how well these cross sections can be measured for an unknown flux and generic detectors. We also demonstrate how to characterize and compare detector sensitivity. We show that cross sections can be measured to $\simeq ^{+65}_{-30}$% precision over $\sqrt{s} \simeq$ 4-140 TeV ($E_\nu = 10^7$-$10^{10}$ GeV) with modest energy and angular resolution and $\simeq 10$ events per energy decade. Many allowed novel-physics models (extra dimensions, leptoquarks, etc.) produce much larger effects. In the distant future, with $\simeq 100$ events at the highest energies, the precision would be $\simeq 15\%$, probing even QCD saturation effects.
A new matrix representation of the Maxwell equations based on the Riemann-Silberstein-Weber vector for a linear inhomogeneous medium

An eight dimensional matrix representation of the Maxwell equations for a linear inhomogeneous medium has been obtained earlier based on the Riemann-Silberstein-Weber vector starting from the equations satisﬁed by it. A new eight dimensional matrix representation, related to the ealier one by a similarity transformation, is derived starting ab initio from the Maxwell equations. The new representation has a more advantageous structure compared to the earlier one from the point of view of applications. In the case of a homogeneous medium the new matrix representation reduces to a block diagonal form with four two dimensional Pauli matrix blocks along the diagonal. This matrix representation of the Maxwell equations should be suitable for studying the propagation of electromagnetic waves in a linear inhomogeneous medium, particularly adopting techniques used for studying time evolution equations in quantum mechanics.
HyBNN and FedHyBNN: (Federated) Hybrid Binary Neural Networks

Binary Neural Networks (BNNs), neural networks with weights and activations constrained to -1(0) and +1, are an alternative to deep neural networks which offer faster training, lower memory consumption and lightweight models, ideal for use in resource constrained devices while being able to utilize the architecture of their deep neural network counterpart. However, the input binarization step used in BNNs causes a severe accuracy loss. In this paper, we introduce a novel hybrid neural network architecture, Hybrid Binary Neural Network (HyBNN), consisting of a task-independent, general, full-precision variational autoencoder with a binary latent space and a task specific binary neural network that is able to greatly limit the accuracy loss due to input binarization by using the full precision variational autoencoder as a feature extractor. We use it to combine the state-of-the-art accuracy of deep neural networks with the much faster training time, quicker test-time inference and power efficiency of binary neural networks. We show that our proposed system is able to very significantly outperform a vanilla binary neural network with input binarization. We also introduce FedHyBNN, a highly communication efficient federated counterpart to HyBNN and demonstrate that it is able to reach the same accuracy as its non-federated equivalent. We make our source code, experimental parameters and models available at: https://anonymous.4open. science/r/HyBNN.
Cross Reconstruction Transformer for Self-Supervised Time Series Representation Learning

—Unsupervised/self-supervised representation learning in time series is critical since labeled samples are usually scarce in real-world scenarios. Existing approaches mainly leverage the contrastive learning framework, which automatically learns to understand the similar and dissimilar data pairs. Never-theless, they are restricted to the prior knowledge of constructing pairs, cumbersome sampling policy, and unstable performances when encountering sampling bias. Also, few works have focused on effectively modeling across temporal-spectral relations to extend the capacity of representations. In this paper, we aim at learning representations for time series from a new perspective and propose Cross Reconstruction Transformer (CRT) to solve the aforementioned problems in a uniﬁed way. CRT achieves time series representation learning through a cross-domain dropping-reconstruction task. Speciﬁcally, we transform time series into the frequency domain and randomly drop certain parts in both time and frequency domains. Dropping can maximally preserve the global context compared to cropping and masking. Then a transformer architecture is utilized to adequately capture the cross-domain correlations between temporal and spectral information through reconstructing data in both domains, which is called Dropped Temporal-Spectral Modeling. To discriminate the representations in global latent space, we propose Instance Discrimination Constraint to reduce the mutual information be-tween different time series and sharpen the decision boundaries. Additionally, we propose a speciﬁed curriculum learning strategy to optimize the CRT, which progressively increases the dropping ratio in the training process. We conduct extensive experiments to evaluate the effectiveness of the proposed method on multiple real-world datasets. Results show that CRT consistently achieves the best performance over existing methods by 2% ∼ 9%.
Complex behavior from intrinsic motivation to occupy action-state path space

Most theories of behavior posit that agents tend to maximize some form of reward or utility. However, animals very often move with curiosity and seem to be motivated in a reward-free manner. Here we abandon the idea of reward maximization, and propose that the goal of behavior is maximizing occupancy of future paths of actions and states. According to this maximum occupancy principle, rewards are the means to occupy path space, not the goal per se; goal-directedness simply emerges as rational ways of searching for resources so that movement, understood amply, never ends. We find that action-state path entropy is the only measure consistent with additivity and other intuitive properties of expected future action-state path occupancy. We provide analytical expressions that relate the optimal policy and state-value function, and prove convergence of our value iteration algorithm. Using discrete and continuous state tasks, including a high--dimensional controller, we show that complex behaviors such as `dancing', hide-and-seek and a basic form of altruistic behavior naturally result from the intrinsic motivation to occupy path space. All in all, we present a theory of behavior that generates both variability and goal-directedness in the absence of reward maximization.
Conformal Prediction with Temporal Quantile Adjustments

We develop Temporal Quantile Adjustment (TQA), a general method to construct efficient and valid prediction intervals (PIs) for regression on cross-sectional time series data. Such data is common in many domains, including econometrics and healthcare. A canonical example in healthcare is predicting patient outcomes using physiological time-series data, where a population of patients composes a cross-section. Reliable PI estimators in this setting must address two distinct notions of coverage: cross-sectional coverage across a cross-sectional slice, and longitudinal coverage along the temporal dimension for each time series. Recent works have explored adapting Conformal Prediction (CP) to obtain PIs in the time series context. However, none handles both notions of coverage simultaneously. CP methods typically query a pre-specified quantile from the distribution of nonconformity scores on a calibration set. TQA adjusts the quantile to query in CP at each time $t$, accounting for both cross-sectional and longitudinal coverage in a theoretically-grounded manner. The post-hoc nature of TQA facilitates its use as a general wrapper around any time series regression model. We validate TQA's performance through extensive experimentation: TQA generally obtains efficient PIs and improves longitudinal coverage while preserving cross-sectional coverage.
Learning Progress Driven Multi-Agent Curriculum

Curriculum reinforcement learning (CRL) aims to speed up learning by gradually increasing the difficulty of a task, usually quantified by the achievable expected return. Inspired by the success of CRL in single-agent settings, a few works have attempted to apply CRL to multi-agent reinforcement learning (MARL) using the number of agents to control task difficulty. However, existing works typically use manually defined curricula such as a linear scheme. In this paper, we first apply state-of-the-art single-agent self-paced CRL to sparse reward MARL. Although with satisfying performance, we identify two potential flaws of the curriculum generated by existing reward-based CRL methods: (1) tasks with high returns may not provide informative learning signals and (2) the exacerbated credit assignment difficulty in tasks where more agents yield higher returns. Thereby, we further propose self-paced MARL (SPMARL) to prioritize tasks based on \textit{learning progress} instead of the episode return. Our method not only outperforms baselines in three challenging sparse-reward benchmarks but also converges faster than self-paced CRL.
Descartes: Generating Short Descriptions of Wikipedia Articles

Wikipedia is one of the richest knowledge sources on the Web today. In order to facilitate navigating, searching, and maintaining its content, Wikipedia’s guidelines state that all articles should be annotated with a so-called short description indicating the article’s topic (e.g., the short description of beer is “Alcoholic drink made from fermented cereal grains”). Nonetheless, a large fraction of articles (ranging from 10.2% in Dutch to 99.7% in Kazakh) have no short description yet, with detrimental effects for millions of Wikipedia users. Motivated by this problem, we introduce the novel task of automatically generating short descriptions for Wikipedia articles and propose Descartes, a multilingual model for tackling it. Descartes integrates three sources of information to generate an article description in a target language: the text of the article in all its language versions, the already-existing descriptions (if any) of the article in other languages, and semantic type information obtained from a knowledge graph. We evaluate a Descartes model trained for handling 25 languages simultaneously, showing that it beats baselines (including a strong translation-based baseline) and performs on par with monolingual models tailored for specific languages. A human evaluation on three languages further shows that the quality of Descartes’s descriptions is largely indistinguishable from that of human-written descriptions; e.g., 91.3% of our English descriptions (vs. 92.1% of human-written descriptions) pass the bar for inclusion in Wikipedia, suggesting that Descartes is ready for production, with the potential to support human editors in filling a major gap in today’s Wikipedia across languages.
Pre-hijacked accounts: An Empirical Study of Security Failures in User Account Creation on the Web

The ubiquity of user accounts in websites and online services makes account hijacking a serious security concern. Although previous research has studied various techniques through which an attacker can gain access to a victim's account, relatively little attention has been directed towards the process of account creation. The current trend towards federated authentication (e.g., Single Sign-On) adds an additional layer of complexity because many services now support both the classic approach in which the user directly sets a password, and the federated approach in which the user authenticates via an identity provider. Inspired by previous work on preemptive account hijacking [Ghasemisharif et al., USENIX SEC 2018], we show that there exists a whole class of account pre-hijacking attacks. The distinctive feature of these attacks is that the attacker performs some action before the victim creates an account, which makes it trivial for the attacker to gain access after the victim has created/recovered the account. Assuming a realistic attacker who knows only the victim's email address, we identify and discuss five different types of account pre-hijacking attacks. To ascertain the prevalence of such vulnerabilities in the wild, we analyzed 75 popular services and found that at least 35 of these were vulnerable to one or more account pre-hijacking attacks. Whilst some of these may be noticed by attentive users, others were completely undetectable from the victim's perspective. Finally, we investigated the root cause of these vulnerabilities and present a set of security requirements to prevent such vulnerabilities arising in future.