Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Volatile Analog Control and Reconfiguration of a Vortex Nano-Oscillator Frequency

Magnetic tunnel junctions are nanoscale devices which have recently attracted interested in the context of frequency multiplexed spintronic neural networks, due to their interesting dynamical properties, which are defined during the fabrication process, and depend on the material parameters and geometry. This paper proposes an approach to extending the functionality of a standard magnetic tunnel junction (MTJ) by introducing an additional ferromagnet/antiferromagnet (FM/AFM) storage layer (SL) vertically integrated with the standard Vortex MTJ stack into the nanopillar. The magnetostatic field created by this storage layer acts on the free layer and can be used to change its static and dynamic properties. To tune the magnitude and direction of this magnetostatic field, magnetic reconfiguration is carried out through a thermally assisted switching mechanism using a voltage pulse that heats the AFM layer in the SL above the Neel temperature in the presence of an external field. It is experimentally shown that using an MTJ based on a 600 nm diameter nanopillar with a vortex in the free layer, reconfiguration of the SL allows to continuously change the core precession frequency in the 15 MHz range. The reconfigurable analogue storage layer locally affects both the static and dynamic properties of the MTJ free layer, demonstrating vertical 3D integration of additional functionalities into a single MTJ nanopillar.
Zero-entropy encoders and simultaneous decoders in identification via quantum channels

Motivated by deterministic identification via (classical) channels, where the encoder is not allowed to use randomization, we revisit the problem of identification via quantum channels but now with the additional restriction that the message encoding must use pure quantum states, rather than general mixed states. Together with the previously considered distinction between simultaneous and general decoders, this suggests a two-dimensional spectrum of different identification capacities, whose behaviour could a priori be very different. We demonstrate two new results as our main findings: first, we show that all four combinations (pure/mixed encoder, simultaneous/general decoder) have a double-exponentially growing code size, and that indeed the corresponding identification capacities are lower bounded by the classical transmission capacity for a general quantum channel, which is given by the Holevo-Schumacher-Westmoreland Theorem. Secondly, we show that the simultaneous identification capacity of a quantum channel equals the simultaneous identification capacity with pure state encodings, thus leaving three linearly ordered identification capacities. By considering some simple examples, we finally show that these three are all different: general identification capacity can be larger than pure-state-encoded identification capacity, which in turn can be larger than pure-state-encoded simultaneous identification capacity.
High-precision and low-noise dielectric tensor tomography using a micro-electromechanical system mirror

Dielectric tensor tomography is an imaging technique for mapping three-dimensional distributions of dielectric properties in transparent materials. This work introduces an enhanced illumination strategy employing a micro-electromechanical system mirror to achieve high precision and reduced noise in imaging. This illumination approach allows for precise manipulation of light, significantly improving the accuracy of angle control and minimizing diffraction noise compared to traditional beam steering approaches. Our experiments have successfully reconstructed the dielectric properties of liquid crystal droplets, which are known for their anisotropic structures, while demonstrating a notable reduction in background noise of the imag-es. Additionally, the technique has been applied to more complex samples, revealing its capability to achieve a high signal-to-noise ratio. This development represents a significant step forward in the field of birefringence imaging, offering a powerful tool for detailed study of materials with anisotropic properties.
Detecting Adversarial Spectrum Attacks via Distance to Decision Boundary Statistics

Machine learning has been adopted for efficient cooperative spectrum sensing. However, it incurs an additional security risk due to attacks leveraging adversarial machine learning to create malicious spectrum sensing values to deceive the fusion center, called adversarial spectrum attacks. In this paper, we propose an efficient framework for detecting adversarial spectrum attacks. Our design leverages the concept of the distance to the decision boundary (DDB) observed at the fusion center and compares the training and testing DDB distributions to identify adversarial spectrum attacks. We create a computationally efficient way to compute the DDB for machine learning based spectrum sensing systems. Experimental results based on realistic spectrum data show that our method, under typical settings, achieves a high detection rate of up to 99\% and maintains a low false alarm rate of less than 1\%. In addition, our method to compute the DDB based on spectrum data achieves 54\%--64\% improvements in computational efficiency over existing distance calculation methods. The proposed DDB-based detection framework offers a practical and efficient solution for identifying malicious sensing values created by adversarial spectrum attacks.
Multi-Query Focused Disaster Summarization via Instruction-Based Prompting

Automatic summarization of mass-emergency events plays a critical role in disaster management. The second edition of CrisisFACTS aims to advance disaster summarization based on multi-stream fact-finding with a focus on web sources such as Twitter, Reddit, Facebook, and Webnews. Here, participants are asked to develop systems that can extract key facts from several disaster-related events, which ultimately serve as a summary. This paper describes our method to tackle this challenging task. We follow previous work and propose to use a combination of retrieval, reranking, and an embarrassingly simple instruction-following summarization. The two-stage retrieval pipeline relies on BM25 and MonoT5, while the summarizer module is based on the open-source Large Language Model (LLM) LLaMA-13b. For summarization, we explore a Question Answering (QA)-motivated prompting approach and find the evidence useful for extracting query-relevant facts. The automatic metrics and human evaluation show strong results but also highlight the gap between open-source and proprietary systems.
FedSiKD: Clients Similarity and Knowledge Distillation: Addressing Non-i.i.d. and Constraints in Federated Learning

In recent years, federated learning (FL) has emerged as a promising technique for training machine learning models in a decentralized manner while also preserving data privacy. The non-independent and identically distributed (non-i.i.d.) nature of client data, coupled with constraints on client or edge devices, presents significant challenges in FL. Furthermore, learning across a high number of communication rounds can be risky and potentially unsafe for model exploitation. Traditional FL approaches may suffer from these challenges. Therefore, we introduce FedSiKD, which incorporates knowledge distillation (KD) within a similarity-based federated learning framework. As clients join the system, they securely share relevant statistics about their data distribution, promoting intra-cluster homogeneity. This enhances optimization efficiency and accelerates the learning process, effectively transferring knowledge between teacher and student models and addressing device constraints. FedSiKD outperforms state-of-the-art algorithms by achieving higher accuracy, exceeding by 25\% and 18\% for highly skewed data at $\alpha = {0.1,0.5}$ on the HAR and MNIST datasets, respectively. Its faster convergence is illustrated by a 17\% and 20\% increase in accuracy within the first five rounds on the HAR and MNIST datasets, respectively, highlighting its early-stage learning proficiency. Code is publicly available and hosted on GitHub (https://github.com/SimuEnv/FedSiKD)
Disambiguated Node Classification with Graph Neural Networks

Graph Neural Networks (GNNs) have demonstrated significant success in learning from graph-structured data across various domains. Despite their great successful, one critical challenge is often overlooked by existing works, i.e., the learning of message propagation that can generalize effectively to underrepresented graph regions. These minority regions often exhibit irregular homophily/heterophily patterns and diverse neighborhood class distributions, resulting in ambiguity. In this work, we investigate the ambiguity problem within GNNs, its impact on representation learning, and the development of richer supervision signals to fight against this problem. We conduct a fine-grained evaluation of GNN, analyzing the existence of ambiguity in different graph regions and its relation with node positions. To disambiguate node embeddings, we propose a novel method, {\method}, which exploits additional optimization guidance to enhance representation learning, particularly for nodes in ambiguous regions. {\method} identifies ambiguous nodes based on temporal inconsistency of predictions and introduces a disambiguation regularization by employing contrastive learning in a topology-aware manner. {\method} promotes discriminativity of node representations and can alleviating semantic mixing caused by message propagation, effectively addressing the ambiguity problem. Empirical results validate the efficiency of {\method} and highlight its potential to improve GNN performance in underrepresented graph regions.
Unity is Strength: Enhancing Precision in Reentrancy Vulnerability Detection of Smart Contract Analysis Tools

Reentrancy is one of the most notorious vulnerabilities in smart contracts, resulting in significant digital asset losses. However, many previous works indicate that current Reentrancy detection tools suffer from high false positive rates. Even worse, recent years have witnessed the emergence of new Reentrancy attack patterns fueled by intricate and diverse vulnerability exploit mechanisms. Unfortunately, current tools face a significant limitation in their capacity to adapt and detect these evolving Reentrancy patterns. Consequently, ensuring precise and highly extensible Reentrancy vulnerability detection remains critical challenges for existing tools. To address this issue, we propose a tool named ReEP, designed to reduce the false positives for Reentrancy vulnerability detection. Additionally, ReEP can integrate multiple tools, expanding its capacity for vulnerability detection. It evaluates results from existing tools to verify vulnerability likelihood and reduce false positives. ReEP also offers excellent extensibility, enabling the integration of different detection tools to enhance precision and cover different vulnerability attack patterns. We perform ReEP to eight existing state-of-the-art Reentrancy detection tools. The average precision of these eight tools increased from the original 0.5% to 73% without sacrificing recall. Furthermore, ReEP exhibits robust extensibility. By integrating multiple tools, the precision further improved to a maximum of 83.6%. These results demonstrate that ReEP effectively unites the strengths of existing works, enhances the precision of Reentrancy vulnerability detection tools.
Exploring the Relationship: Transformative Adaptive Activation Functions in Comparison to Other Activation Functions

Neural networks are the state-of-the-art approach for many tasks and the activation function is one of the main building blocks that allow such performance. Recently, a novel transformative adaptive activation function (TAAF) allowing for any vertical and horizontal translation and scaling was proposed. This work sets the TAAF into the context of other activation functions. It shows that the TAAFs generalize over 50 existing activation functions and utilize similar concepts as over 70 other activation functions, underscoring the versatility of TAAFs. This comprehensive exploration positions TAAFs as a promising and adaptable addition to neural networks.
Global existence and long time behavior of solutions to some Oldroyd type models in hybrid Besov spaces

In this paper, we deal with some Oldroyd type models, which describe incompressible viscoelastic fluids. There are 3 parameters in these models: the viscous coefficient of fluid $\nu_{1}$, the viscous coefficient of the elastic part of the stress tensor $\nu_{2}$, and the damping coefficient of the elastic part of the stress tensor $\alpha$. In this paper, we assume at least one of the parameters is zero: $(\nu_{1}, \nu_{2},\alpha)=(+,0,+), (+,0,0), (0,+,+), (0,+,0), (0,0,+)$ and prove the global existence of unique solutions to all these 5 cases in the framework of hybrid Besov spaces. We also derive decay rates of the solutions except for the case $(\nu_{1}, \nu_{2},\alpha)=(+,0,0)$. To the best of our knowledge, decay rates in this paper are the first results in this framework, and can improve some previous works.
Towards Realistic Landmark-Guided Facial Video Inpainting Based on GANs

Facial video inpainting plays a crucial role in a wide range of applications, including but not limited to the removal of obstructions in video conferencing and telemedicine, enhancement of facial expression analysis, privacy protection, integration of graphical overlays, and virtual makeup. This domain presents serious challenges due to the intricate nature of facial features and the inherent human familiarity with faces, heightening the need for accurate and persuasive completions. In addressing challenges specifically related to occlusion removal in this context, our focus is on the progressive task of generating complete images from facial data covered by masks, ensuring both spatial and temporal coherence. Our study introduces a network designed for expression-based video inpainting, employing generative adversarial networks (GANs) to handle static and moving occlusions across all frames. By utilizing facial landmarks and an occlusion-free reference image, our model maintains the user's identity consistently across frames. We further enhance emotional preservation through a customized facial expression recognition (FER) loss function, ensuring detailed inpainted outputs. Our proposed framework exhibits proficiency in eliminating occlusions from facial videos in an adaptive form, whether appearing static or dynamic on the frames, while providing realistic and coherent results.
Anomalous long-distance RKKY interaction in one-dimensional quasiperiodic chains

Long-range quantum entanglement and its controllability is a key mechanism in quantum information science and condensed matter physics. However, most quantum systems are governed by short-ranged interactions, so entanglement decays rapidly with distance. In this paper, using mediated itinerant electrons and quasiperiodicity, we discuss a novel way to induce the strong interaction between distant magnetic moments. When the localized magnetic moments coexist with itinerant electrons, the exchange mechanism between the moments is well understood by the so-called Ruderman-Kittel-Kasuya-Yoshida (RKKY) interaction. In contrast to the conventional RKKY interaction, which shows a power-law decay, it turns out that the critical nature of the quasiperiodicity of the itinerant electrons realizes the long-distance interaction. We exemplify the Fibonacci tilings and several metallic mean tiling cases. We show that, rather than the uniform decaying, the RKKY interaction becomes significant between the moments in non-local region. It realizes a strong coupling between the widely separated moments, whose distance can be controlled by the quasi-periodicity of the systems. Moreover, it shows the fractal structure from the self-similarity of the critical wave functions. Finally, we discuss a possible non-local manipulation of the magnetic moments due to such anomalous RKKY coupling in quasicrystals. Our work provides the quasiperiodicity mediated RKKY interaction as a new way to induce a controllable long-distance interaction and its long-range quantum entanglement.
Critical speeding-up in dynamical percolation

We study the autocorrelation time of the size of the cluster at the origin in discrete-time dynamical percolation. We focus on binary trees and high-dimensional tori, and show in both cases that this autocorrelation time is linear in the volume in the subcritical regime, but strictly sublinear in the volume at criticality. This establishes rigorously that the cluster size at the origin in these models exhibits critical speeding-up. The proofs involve controlling relevant Fourier coefficients. In the case of binary trees, these Fourier coefficients are studied explicitly, while for high-dimensional tori we employ a randomised algorithm argument introduced by Schramm and Steif in the context of noise sensitivity.
Enhancing Hemodynamic Parameter Estimations: Nonlinear Blood Behavior in 4D Flow MRI

Hemodynamic parameters have been estimated assuming a Newtonian constant viscosity, even when blood exhibits shear-thinning behavior. This article investigates the influence of blood rheology and hematocrit percentage on estimating Wall Shear Stress (WSS) and Energy Loss ($E_L$) at different time instants of the cardiac cycle, as well as the Oscillatory Shear Index (OSI). We specifically focus on a hematocrit-dependent power-law non-Newtonian model, considering a wide range of hematocrit values. The rheological parameters are obtained from experimentally fitted data reported previously. This study contributes to understanding the impact of blood rheology on hemodynamic parameter estimations using both in-silico and in-vivo aortic 4D Flow magnetic resonance images. Across all cases, we systematically compared WSS, $E_L$, and OSI parameters using Newtonian and power-law models, highlighting the crucial role of blood rheology in accurately assessing cardiovascular diseases.
Who is driving the conversation? Analysing the nodality of British MPs and journalists on Twitter

Who sets the policy agenda? Much political science research has been dedicated to investigating the influences at work in policy processes. This paper looks at the actors influencing the policy agenda through the lens of "nodality", the capacity to share information and to be at the centre of social and information networks. Nodality has traditionally been seen as a capacity or policy tool relating to government as a set of institutions. But in a digital world, there is greater possibility for individual actors using social media to develop their own nodality and to shape the policy conversation. We analyse information networks on Twitter to quantify the nodality of two sets of actors in the UK political system, Members of Parliament (MPs) and accredited journalists, in conversations on four policy topics: The Russia Ukraine War, the Cost of Living Crisis, Brexit and COVID 19. We perform a Transfer Entropy analysis and a Principle Component Analysis (PCA) using different network centralities while ensuring reproducibility and interpretability. We define and measure two dimensions of nodality; inherent nodality, associated with an actor's institutional position, transferable between topics, and individual nodality, measuring active engagement in a policy conversation on a specific topic. These two dimensions are validated through a linear model showing how the influence on a particular topic exercised by an actor stems from the sum of nodality scores across these two dimensions. We thereby show the relative influence of different types of actor on policy conversations. More generally, we provide a methodology for measuring individual and institutional drivers of actor influence, and understanding where influence relates to individual or institutional characteristics.
Safe Distributed Control of Multi-Robot Systems with Communication Delays

Safe operation of multi-robot systems is critical, especially in communication-degraded environments such as underwater for seabed mapping, underground caves for navigation, and in extraterrestrial missions for assembly and construction. We address safety of networked autonomous systems where the information exchanged between robots incurs communication delays. We formalize a notion of distributed control barrier function (CBF) for multi-robot systems, a safety certificate amenable to a distributed implementation, which provides formal ground to using graph neural networks to learn safe distributed controllers. Further, we observe that learning a distributed controller ignoring delays can severely degrade safety. Our main contribution is a predictor-based framework to train a safe distributed controller under communication delays, where the current state of nearby robots is predicted from received data and age-of-information. Numerical experiments on multi-robot collision avoidance show that our predictor-based approach can significantly improve the safety of a learned distributed controller under communication delays
Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption

This study tackles the challenges of adversarial corruption in model-based reinforcement learning (RL), where the transition dynamics can be corrupted by an adversary. Existing studies on corruption-robust RL mostly focus on the setting of model-free RL, where robust least-square regression is often employed for value function estimation. However, these techniques cannot be directly applied to model-based RL. In this paper, we focus on model-based RL and take the maximum likelihood estimation (MLE) approach to learn transition model. Our work encompasses both online and offline settings. In the online setting, we introduce an algorithm called corruption-robust optimistic MLE (CR-OMLE), which leverages total-variation (TV)-based information ratios as uncertainty weights for MLE. We prove that CR-OMLE achieves a regret of $\tilde{\mathcal{O}}(\sqrt{T} + C)$, where $C$ denotes the cumulative corruption level after $T$ episodes. We also prove a lower bound to show that the additive dependence on $C$ is optimal. We extend our weighting technique to the offline setting, and propose an algorithm named corruption-robust pessimistic MLE (CR-PMLE). Under a uniform coverage condition, CR-PMLE exhibits suboptimality worsened by $\mathcal{O}(C/n)$, nearly matching the lower bound. To the best of our knowledge, this is the first work on corruption-robust model-based RL algorithms with provable guarantees.
Fuzzy clustering of circular time series based on a new dependence measure with applications to wind data

Time series clustering is an essential machine learning task with applications in many disciplines. While the majority of the methods focus on time series taking values on the real line, very few works consider time series defined on the unit circle, although the latter objects frequently arise in many applications. In this paper, the problem of clustering circular time series is addressed. To this aim, a distance between circular series is introduced and used to construct a clustering procedure. The metric relies on a new measure of serial dependence considering circular arcs, thus taking advantage of the directional character inherent to the series range. Since the dynamics of the series may vary over the time, we adopt a fuzzy approach, which enables the procedure to locate each series into several clusters with different membership degrees. The resulting clustering algorithm is able to group series generated from similar stochastic processes, reaching accurate results with series coming from a broad variety of models. An extensive simulation study shows that the proposed method outperforms several alternative techniques, besides being computationally efficient. Two interesting applications involving time series of wind direction in Saudi Arabia highlight the potential of the proposed approach.