Miracle-dz/UniFAR-FTUs huggingface dataset:
Malware Finances and Operations: a Data-Driven Study of the Value Chain for Infections and Compromised Access

We investigate the criminal market dynamics of infostealer malware and publish three evidence datasets on malware infections and trade. We justify the value chain between illicit enterprises using the datasets, compare the prices and added value, and use the value chain to identify the most effective countermeasures. We begin by examining infostealer malware victim logs shared by actors on hacking forums, and extract victim information and mask sensitive data to protect privacy. We find access to these same victims for sale at Genesis Market. This technically sophisticated marketplace provides its own browser to access victim’s online accounts. We collect a second dataset and discover that 91% of prices fall between 1–20 US dollars, with a median of 5 US dollars. Database Market sells access to compromised online accounts. We produce yet another dataset, finding 91% of prices fall between 1–30 US dollars, with a median of 7 US dollars.
Divide-and-rule policy in the Naming Game

The Naming Game is a classic model for studying the emergence and evolution of language in a population. In this paper, we consider the Naming Game with multiple committed opinions and investigate the dynamics of the game on a complete graph with an arbitrary large population. The homogeneous mixing condition enables us to use mean-field theory to analyze the opinion evolution of the system. However, when the number of opinions increases, the number of variables describing the system grows exponentially. We focus on a special scenario where the largest group of committed agents competes with a motley of committed groups, each of which is significantly smaller than the largest one, while the majority of uncommitted agents initially hold one unique opinion. We choose this scenario for two reasons. The first is that it arose many times in different societies, while the second is that its complexity can be reduced by merging all agents of small committed groups into a single committed group. We show that the phase transition occurs when the group of the largest committed fraction dominates the system, and the threshold for the size of the dominant group at which this transition occurs depends on the size of the committed group of the unified category. Further, we derive the general formula for the multi-opinion evolution using a recursive approach. Finally, we use agent-based simulations to reveal the opinion evolution in the random graphs. Our results provide insights into the conditions under which the dominant opinion emerges in a population and the factors that influence this process.
Space Weather Effects on Critical Infrastructure

Gas pipelines, transmission lines, overhead wires, transformers, GNSS navigation, and telecommunication systems are part of critical infrastructure. Industry, transportation, service operations, farming, and everyday life highly depend on this infrastructure. However, these systems are very sensitive to solar activity. Therefore, all activities above are vulnerable and defenseless against the catastrophic changes in Earth's cosmic environment. The Solar System is dominated by the influence of our star. A small fraction of the energy produced in the core of our star turns into a magnetic field and emits the constant high-velocity flow, the solar wind. Solar magnetic activity produces radiation and ejects matter from the upper atmosphere of our star. The magnetic field of the solar wind interacts with the planetary magnetic fields and atmospheres. These phenomena, called Space Weather have a serious influence on the radiation environment of Earth where telecommunication, Global Navigation Satellite System, meteorological, and other purpose satellites are located. The conductivity and transparency of the higher partly ionized atmospheric layer, the ionosphere also depend on solar radiation and activity. This fact makes the navigation and communication systems dependent on solar activity. Finally, the solar magnetic activity creates magnetic variations in the terrestrial magnetic field and induces currents in gas pipelines, transmission lines, overhead wires, and transformers. In this short briefing, we introduce the solar activity phenomena, and their influence on our planet's cosmic neighborhood and provide a detailed description of the Space Weather effects on critical infrastructure. We describe the Hungarian national and global space weather forecast centers and capabilities. Finally, we share some guidelines on how to prepare for extreme space weather events.
The JSI-KneExo: Active, Quasi-Passive, Pneumatic, Portable Knee Exo with Bidirectional Energy Flow for Air Recovery in Sit-Stand Tasks

While existing literature encompasses exoskeleton-assisted sit-stand tasks, the integration of energy recovery mechanisms remains unexplored. To push these boundaries further, this study introduces a portable pneumatic knee exoskeleton that operates in both quasi-passive and active modes, where active mode is utilized for aiding in standing up (power generation), thus the energy flows from the exoskeleton to the user, and quasi-passive mode for aiding in sitting down (power absorption), where the device absorbs and can store energy in the form of compressed air, leading to energy savings in active mode. The absorbed energy can be stored and later reused without compromising exoskeleton transparency in the meantime. In active mode, a small air pump inflates the pneumatic artificial muscle (PAM), which stores the compressed air, that can then be released into a pneumatic cylinder to generate torque. All electronic and pneumatic components are integrated into the system, and the exoskeleton weighs 3.9 kg with a maximum torque of 20 Nm at the knee joint. The paper describes the mechatronic design, mathematical model and includes a pilot study with an able-bodied subject performing sit-to-stand tasks. The results show that the exoskeleton can recover energy while assisting the subject and reducing muscle activity. Furthermore, results underscore air regeneration's impact on energy-saving in portable pneumatic exoskeletons.
X-Ray Properties of Optically Variable Low-mass AGN Candidates

We present an X-ray analysis of 14 nearby (z < 0.044) active galactic nuclei (AGNs) in low-mass galaxies (M * ≲ 5 × 109 M ⊙) selected based on their optical variability. Comparing and contrasting different AGN selection techniques in low-mass galaxies is essential for obtaining an accurate estimate of the active fraction in this regime. We use both new and archival observations from the Chandra X-ray Observatory to search for X-ray point sources consistent with AGNs. Four objects have detected nuclear X-ray emission with luminosities ranging from L 0.5−7 ≈ 3 × 1040 to 9 × 1042 erg s−1 with two more marginal detections. All of the detected galaxies have luminosities exceeding those anticipated from X-ray binaries, and all sources are nuclear, suggesting the X-ray emission in most sources is due to an AGN. These observations demonstrate the success of variability at identifying AGNs in low-mass galaxies. We also explore emission-line diagnostics and discuss the differences in the results of these methods for AGN selection, in particular regarding low-mass and low-metallicity systems.
LoRa-Based Over-the-Air Computing for Sat-IoT

Satellite Internet of Things (Sat-IoT) is a novel framework in which satellites integrate sensing, communication and computing capabilities to carry out task-oriented communications. In this paper we propose to use the Long Range (LoRa) modulation for the purpose of estimation in a Sat-IoT scenario. Then we realize that the collisions generated by LoRa can be harnessed in an Over-the-Air Computing (AirComp) framework. Specifically, we propose to use LoRa for Type-based Multiple Access (TBMA), a semantic-aware scheme in which communication resources are assigned to different parameters, not users. Our experimental results show that LoRa-TBMA is suitable as a massive access scheme, provides large gains in terms of mean squared error (MSE) and saves scarce satellite communication resources (i.e., power, latency and bandwidth) with respect to orthogonal multiple access schemes. We also analyze the satellite scenarios that could take advantage of the LoRa-TBMA scheme. In summary, that angular modulations, which are very useful in satellite communications, can also benefit from AirComp.
Extreme data compression for Bayesian model comparison

We develop extreme data compression for use in Bayesian model comparison via the MOPED algorithm, as well as more general score compression. We find that Bayes Factors from data compressed with the MOPED algorithm are identical to those from their uncompressed datasets when the models are linear and the errors Gaussian. In other nonlinear cases, whether nested or not, we find negligible differences in the Bayes Factors, and show this explicitly for the Pantheon-SH0ES supernova dataset. We also investigate the sampling properties of the Bayesian Evidence as a frequentist statistic, and find that extreme data compression reduces the sampling variance of the Evidence, but has no impact on the sampling distribution of Bayes Factors. Since model comparison can be a very computationally-intensive task, MOPED extreme data compression may present significant advantages in computational time.
Disentangled variational auto-encoder enhanced by counterfactual data for debiasing recommendation

Recommender system always suffers from various recommendation biases, seriously hindering its development. In this light, a series of debias methods have been proposed in the recommender system, especially for two most common biases, i.e., popularity bias and amplified subjective bias. However, existing debias methods usually concentrate on correcting a single bias. Such single-functionality debiases neglect the bias-coupling issue in which the recommended items are collectively attributed to multiple biases. Besides, previous work cannot tackle the lacking supervised signals brought by sparse data, yet which has become a commonplace in the recommender system. In this work, we introduce a disentangled debias variational auto-encoder framework (DB-VAE) to address the single-functionality issue as well as a counterfactual data enhancement method to mitigate the adverse effect due to the data sparsity. In specific, DB-VAE first extracts two types of extreme items only affected by a single bias based on the collier theory, which are, respectively, employed to learn the latent representation of corresponding biases, thereby realizing the bias decoupling. In this way, the exact unbiased user representation can be learned by these decoupled bias representations. Furthermore, the data generation module employs Pearl’s framework to produce massive counterfactual data to help fully train the model, making up the lacking supervised signals due to the sparse data. Extensive experiments on three real-world data sets demonstrate the effectiveness of our proposed model. Specifically, our model outperforms the best baseline by 19.5% in terms of Recall@20 and 9.5% in terms of NDCG@100 in the best scenario. Besides, the counterfactual data can further improve DB-VAE, especially on the data set with low sparsity.
Spectral Distribution of Twisted Laplacian on Typical Hyperbolic Surfaces of High Genus

We investigate the spectral distribution of the twisted Laplacian associated with uniform square-integrable bounded harmonic 1-form on typical hyperbolic surfaces of high genus. First, we estimate the spectral distribution by the supremum norm of the corresponding harmonic form. Subsequently, we show that the square-integrable bounded harmonic form exhibits a small supremum norm for typical hyperbolic surfaces of high genus. Based on these findings, we prove a uniform Weyl law for the distribution of real parts of the spectrum on typical hyperbolic surfaces.
UnitSpeech: Speaker-adaptive Speech Synthesis with Untranscribed Data

We propose UnitSpeech, a speaker-adaptive speech synthesis method that fine-tunes a diffusion-based text-to-speech (TTS) model using minimal untranscribed data. To achieve this, we use the self-supervised unit representation as a pseudo transcript and integrate the unit encoder into the pre-trained TTS model. We train the unit encoder to provide speech content to the diffusion-based decoder and then fine-tune the decoder for speaker adaptation to the reference speaker using a single $<$unit, speech$>$ pair. UnitSpeech performs speech synthesis tasks such as TTS and voice conversion (VC) in a personalized manner without requiring model re-training for each task. UnitSpeech achieves comparable and superior results on personalized TTS and any-to-any VC tasks compared to previous baselines. Our model also shows widespread adaptive performance on real-world data and other tasks that use a unit sequence as input.
New Perspectives on Sensitivity and Identifiability Analysis using the Unscented Kalman Filter

Detailed dynamical systems' models used in the life sciences may include hundreds of state variables and many input parameters, often with physical meaning. Therefore, efficient and unique input parameter identification, from experimental data, is an essential but challenging task for this class of model. To clarify our understating of the process (which within a clinical context amounts to a personalisation), we utilise the computational methods of Unscented Kalman filtration (UKF), sensitivity and orthogonality analysis. We have applied these three techniques to a test-bench model of a single ventricle, coupled, via Ohmic valves, to a Compliance-Resistor-Compliance (CRC) Windkessel electrical analogue model of the systemic circulation, chosen in view of its relative simplicity, interpretability and prior art. Utilising an efficient, novel and real-time implementation of the UKF (Code available at https://github.com/H-Sax/CMSB-2023), we show how, counter-intuitively, input parameters are efficiently recovered from experimental data \emph{even if they are not sensitive parameters in the currently accepted sense}. This result (i) exposes potential limitations in the standard interpretation of what it means for an input parameter to be designated identifiable and (ii) suggests that the concepts of sensitivity and identifiability may have a weaker relationship than commonly thought - at least in the presence of an appropriate data set. We rationalise these observations. Practically, we present results which show the UKF to be an efficient method for assigning personalised input parameters from experimental data in real-time, which enhances the clinical significance of our approach.
Stochastic Gradient Bayesian Optimal Experimental Designs for Simulation-based Inference

Simulation-based inference (SBI) methods tackle complex scientific models with challenging inverse problems. However, SBI models often face a significant hurdle due to their non-differentiable nature, which hampers the use of gradient-based optimization techniques. Bayesian Optimal Experimental Design (BOED) is a powerful approach that aims to make the most efficient use of experimental resources for improved inferences. While stochastic gradient BOED methods have shown promising results in high-dimensional design problems, they have mostly neglected the integration of BOED with SBI due to the difficult non-differentiable property of many SBI simulators. In this work, we establish a crucial connection between ratio-based SBI inference algorithms and stochastic gradient-based variational inference by leveraging mutual information bounds. This connection allows us to extend BOED to SBI applications, enabling the simultaneous optimization of experimental designs and amortized inference functions. We demonstrate our approach on a simple linear model and offer implementation details for practitioners.
BayesFlow: Amortized Bayesian Workflows With Neural Networks

Modern Bayesian inference involves a mixture of computational techniques for estimating, validating, and drawing conclusions from probabilistic models as part of principled workflows for data analysis. Typical problems in Bayesian workflows are the approximation of intractable posterior distributions for diverse model types and the comparison of competing models of the same process in terms of their complexity and predictive performance. This manuscript introduces the Python library BayesFlow for simulation-based training of established neural network architectures for amortized data compression and inference. Amortized Bayesian inference, as implemented in BayesFlow, enables users to train custom neural networks on model simulations and re-use these networks for any subsequent application of the models. Since the trained networks can perform inference almost instantaneously, the upfront neural network training is quickly amortized.
Analysis of Indian foreign exchange markets: A Multifractal Detrended Fluctuation Analysis (MFDFA) approach

The multifractal spectra of daily foreign exchange rates for US dollar (USD), the British Pound (GBP), the Euro (Euro) and the Japanese Yen (Yen) with respect to the Indian Rupee are analysed for the period 6th January 1999 to 24th July 2018. We observe that the time series of logarithmic returns of all the four exchange rates exhibit features of multifractality. Next, we research the source of the observed multifractality. For this, we transform the return series in two ways: a) We randomly shuffle the original time series of logarithmic returns and b) We apply the process of phase randomisation on the unchanged series. Our results indicate in the case of the US dollar the source of multifractality is mainly the fat tail. For the British Pound and the Euro, we see the long-range correlations between the observations and the thick tails of the probability distribution give rise to the observed multifractal features, while in the case of the Japanese Yen, the origin of the multifractal nature of the return series is mostly due to the broad tail.
Frontiers to the learning of nonparametric hidden Markov models

Hidden Markov models (HMMs) are flexible tools for clustering dependent data coming from unknown populations, allowing nonparametric modelling of the population densities. Identifiability fails when the data is in fact independent, and we study the frontier between learnable and unlearnable two-state nonparametric HMMs. Interesting new phenomena emerge when the cluster distributions are modelled via density functions (the 'emission' densities) belonging to standard smoothness classes compared to the multinomial setting. Notably, in contrast to the multinomial setting previously considered, the identification of a direction separating the two emission densities becomes a critical, and challenging, issue. Surprisingly, it is possible to"borrow strength"from estimators of the smoother density to improve estimation of the other. We conduct precise analysis of minimax rates, showing a transition depending on the relative smoothnesses of the emission densities.
Cosmic-ray-driven enhancement of the C0/CO abundance ratio in W 51 C

We examine spatial variations of the C0$/$CO abundance ratio (XC/CO) in the vicinity of the γ-ray supernova remnant W 51 C, based on [C i] (3P1–3P0), 12CO(1–0), and 13CO(1–0) observations with the ASTE and Nobeyama 45 m telescopes. We find that XC/CO varies in a range of 0.02–0.16 (0.05 in median) inside the molecular clouds of AV > 100 mag, where photodissociation of CO by the interstellar UV is negligible. Furthermore, XC/CO is locally enhanced by a factor of up to four near the W 51 C center, depending on the projected distance from the W 51 C center. In high-AV molecular clouds, XC/CO is determined by the ratio of the cosmic-ray (CR) ionization rate to the H2 density, and we find no clear spatial variation of the H2 density against the projected distance. Hence, the high CR ionization rate may locally enhance XC/CO near the W 51 C center. We also find that the observed spatial extent of the enhanced XC/CO (∼17 pc) is consistent with the diffusion distance of CRs with an energy of 100 MeV. This fact suggests that the low-energy CRs accelerated in W 51 C enhance XC/CO. The CR ionization rate in the XC/CO-enhanced cloud is estimated to be 3 × 10−16 s−1 on the basis of time-dependent photodissociation region simulations of XC/CO, the value of which is 30 times higher than that in the standard Galactic environment. These results demonstrate that [C i] is a powerful probe to investigate the interaction between CRs and the interstellar medium for a wide area in the vicinity of supernova remnants.
Dynamic UAV Swarm Collaboration for Multi-Targets Tracking under Malicious Jamming: Joint Power, Path and Target Association Optimization

In this paper, the multi-target tracking (MTT) with an unmanned aerial vehicle (UAV) swarm is investigated in the presence of jammers, where UAVs in the swarm communicate with each other to exchange information of targets during tracking. The communication between UAVs suffers from severe interference, including inter-UAV interference and jamming, thus leading to a deteriorated quality of MTT. To mitigate the interference and achieve MTT, we formulate a interference minimization problem by jointly optimizing UAV's sub-swarm division, trajectory, and power, subject to the constraint of MTT, collision prevention, flying ability, and UAV energy consumption. Due to the multiple coupling of sub-swarm division, trajectory, and power, the proposed optimization problem is NP-hard. To solve this challenging problem, it is decomposed into three subproblems, i.e., target association, path plan, and power control. First, a cluster-evolutionary target association (CETA) algorithm is proposed, which involves dividing the UAV swarm into the multiple sub-swarms and individually matching these sub-swarms to targets. Second, a jamming-sensitive and singular case tolerance (JSSCT)-artificial potential field (APF) algorithm is proposed to plan trajectory for tracking the targets. Third, we develop a jamming-aware mean field game (JA-MFG) power control scheme, where a novel cost function is established considering the total interference. Finally, to minimize the total interference, a dynamic collaboration approach is designed. Simulation results validate that the proposed dynamic collaboration approach reduces average total interference, tracking steps, and target switching times by 28%, 33%, and 48%, respectively, comparing to existing baselines.
INSTA-BEEER: Explicit Error Estimation and Refinement for Fast and Accurate Unseen Object Instance Segmentation

Efficient and accurate segmentation of unseen objects is crucial for robotic manipulation. However, it remains challenging due to over- or under-segmentation. Although existing refinement methods can enhance the segmentation quality, they fix only minor boundary errors or are not sufficiently fast. In this work, we propose INSTAnce Boundary Explicit Error Estimation and Refinement (INSTA-BEEER), a novel refinement model that allows for adding and deleting instances and sharpening boundaries. Leveraging an error-estimation-then-refinement scheme, the model first estimates the pixel-wise boundary explicit errors: true positive, true negative, false positive, and false negative pixels of the instance boundary in the initial segmentation. It then refines the initial segmentation using these error estimates as guidance. Experiments show that the proposed model significantly enhances segmentation, achieving state-of-the-art performance. Furthermore, with a fast runtime (less than 0.1 s), the model consistently improves performance across various initial segmentation methods, making it highly suitable for practical robotic applications.