Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparing Attack Models for IT Systems: Lockheed Martin’s Cyber Kill Chain, MITRE ATT&CK Framework and Diamond Model

Cyberattacks are a serious challenge for any IT system, which motivates cybersecurity professionals and developers to constantly explore various attack models to analyse the cyberattack lifecycle used by attackers to mount their attacks. Several attack models have been proposed and successfully utilised to analyse the various types of cyberattacks and their mitigations. All the models offer different characteristics, advantages and disadvantages. Therefore, it is important to examine the most popular attack models to understand their characteristics and suitability for modelling specific types of attacks. This paper will examine characteristics, advantages and disadvantages of the three most popular attack models: the Lockheed Martin’s Cyber Kill Chain, the MITRE ATT&CK Framework and the Diamond Model. Finally, it will provide a comparative study of these three attack models to present a relative summary for selecting the most suitable attack model for a specific application.
World Journal of Surgery and Surgical Research

Purpose: Oncoplastic Breast Conserving Surgery (OBCS) has been tested in oncological terms demonstrating that it is as effective as Standard Lumpectomy (SL) nowadays; Patients-Reported Outcomes (PRO) have become a relevant way to assess breast cancer treatment. The aim of this work was to compare OBCS with SL using a PRO measurement, BREAST-Q Breast Conserving Therapy (BCT) module. Methods: One hundred and sixty-five patients with early primary breast cancer treated with BCT were questioned using BREAST-Q Version 2.0 BCT Module, postoperative scales, in Spanish, after radiotherapy treatment was administered. The patients filled in the questionnaire in a paper-and-pencil format at our breast unit. The sample size was estimated to observe differences of 7 points in the satisfaction with breast domain (postoperative scale) between both approaches. This difference is slightly higher than 10% of the median of satisfaction with breast domain reported in published studies. An analysis was done to compare statistics. Results: SL was used in 108 patients and OBCS in 57. Patients treated with OCBS had a larger radiological lesion than patients treated with SL (median 20 mm vs. 15 mm) corresponding with a higher pathological tumor (17 mm vs. 13 mm). The time from the radiotherapy end date to when the questionnaire was filled in was longer in those patients treated with OBCS (mean 8 vs. 15.5 months). No significant statistical differences were found in the BREAST-Q postoperative domain scores between both approaches. Conclusion: The quality of life and the satisfaction with the oncoplastic breast conserving approach or the standard lumpectomy evaluated by the BREAST-Q were similar in our breast unit.
Distributionally Robust Optimization via Haar Wavelet Ambiguity Sets

This paper introduces a spectral parameterization of ambiguity sets to hedge against distributional uncertainty in stochastic optimization problems. We build an ambiguity set of probability densities around a histogram estimator, which is constructed by independent samples from the unknown distribution. The densities in the ambiguity set are determined by bounding the distance between the coefficients of their Haar wavelet expansion and the expansion of the histogram estimator. This representation facilitates the computation of expectations, leading to tractable minimax problems that are linear in the parameters of the ambiguity set, and enables the inclusion of additional constraints that can capture valuable prior information about the unknown distribution.
An Interval Observer for Continuous-Time Persidskii Systems

The paper deals with design of interval observers for a class of generalized Persidskii systems. The conditions of stability for the suggested nonlinear interval observer are formulated using linear matrix inequalities. The nonnegativity of this class of models is investigated for nonlinearities satisfying the incremental passivity conditions. The efficiency of the proposed observer is demonstrated on a Lotka-Volterra model.
Consensus Error Performance of Linear Multi-Agent Systems

In this paper, we investigate the consensus problem of linear time-invariant systems with respect to stochastic noises and deterministic disturbances. We consider first-order dynamic agents interconnected by a directed communication graph with time delay, and we seek to determine the best achievable performance that measures the error in achieving consensus under the influence of noises and disturbances. Our results consist of analytical expressions of the minimal error achievable, measured under both ℋ2 and ℋ∞ criteria. The expressions show explicitly how agent and network dynamics may interact to cast fundamentally dispersion effects on the consensus among the agents.
Wavelet analysis of refinement equations

The Besov regularity of a compactly supported refinement equation solution is determined by the spectral radius of a linear operator acting on $\ell ^p (\mathbb{Z})$. The proof of this is obtained by using a wavelet basis. Exact criteria for Holder and Sobolev regularity follow immediately. Continuity, differentiability, and integrability can also be characterized.The results are applied to examples from the theory of orthonormal and biorthogonal wavelets and subdivision schemes for curve design.
Equivalence between different stability definitions for 2D linear discrete Roesser models *

In this paper, we review different stability definitions that have been used in the literature concerning 2D Roesser models: structural stability, asymptotic stability, and exponential stability. We clarify the relations between all these definitions: when the considered Roesser model is linear, structural stability and exponential stability are equivalent and both of them imply asymptotic stability. However asymptotic stability does not imply exponential or structural stability. The proof is based on previous results obtained for Fornasini-Marchesini models and requires careful switching between both models.
Requirement Analysis of Lidar Sensor Setups for Self-Localization in Automated Valet Parking

In the use case of automated valet parking (AVP), localization must be accomplished based on environmental sensors due to the lack of satellite visibility. Previous research has presented suitable methods, which, on the other hand, generally apply a 360° lidar setup. In order to achieve a series introduction of AVP, it is necessary from an economic point of view to achieve self-localization with scaled-down sensor setups that correspond to the current state of series production. Addressing this challenge, this work investigates to what extent a 360° lidar reference sensor setup can be reduced in order to achieve sufficiently robust self-localization in parking garage environments. The investigations are carried out in a real environment in two parking garages in conjunction with a test vehicle equipped with a 360° lidar reference setup. On the basis of a definition of different sensor arrangements, a parameter variation is carried out. As evaluation basis for the requirement analysis, a self-localization based on a modified iterative closest point (ICP) algorithm introduced in the scope of the work is applied.
Pion Production for Neutrino Factory‐challenges

One of the key issues in the design of a Neutrino Factory target station is the determination of the optimum kinetic energy of the proton beam due to the large uncertainties in simulations of protons impinging on nuclear targets. In this paper we have developed a procedure to correct GEANT4 simulations for the HARP data, and we have determined the yield of muons expected at the front‐end of a Neutrino Factory as a function of target material (Be, C, Al, Ta and Pb) and energy (3‐12 GeV). The maximum muon yield is found between 5 and 8 GeV for high Z targets and 3 GeV for low Z targets.
Uplink-Downlink Joint Antenna Optimization in Cellular Systems with Sample-Efficient Learning

In this paper, we jointly optimize the capacity and coverage of both uplink and downlink transmissions by tuning the downtilt angle, vertical half-power beamwidth (HPBW), and horizontal HPBW of each cell's antenna array across a heterogeneous cellular network. We formulate an optimization problem and propose a novel sample-efficient algorithm to solve this non-convex problem. We evaluate our framework on a state-of-the-art cellular system-level simulator developed by AT&T Labs by comparing it with the 3GPP baseline. Example results tuned to optimize uplink coverage and downlink rate indicate that jointly optimizing the uplink and downlink directions improves uplink median and 5% outage SINR by (i) 1.6 dB and 4.5 dB, respectively, compared to downlink only-optimization and by (ii) 6.7 dB and 14.6 dB compared to the 3GPP baseline. Simultaneously, we can increase downlink median and outage SINR by comparable amounts compared to uplink-only optimization, but with larger gains in median SINR and downlink sum-rate. Our results indicate that there are significant gains to be harvested from site-specific data-driven base station parameter optimization, and they can be achieved in a scalable and automated fashion.
A Global and Semi-Global Controller for SIQR Epidemic Model by A Control Lyapunov Function via Logarithmic Sum

This paper proposes a feedback controller of isolation, contact regulation, and vaccination that can be simultaneously applied to mitigate the spread of infectious diseases among humans. A particular function that sums up logarithmic functions of all state variables is proved to be a control Lyapunov function and guarantee input-to-state stability of the controlled system with respect to perturbation of susceptible inflows on an arbitrarily large domain of the state variables. The single simultaneous controller is shown to be effective in all two situations which are separated by a bifurcation point.
Achievable Rate of Multiuser Scheduling in RIS-based Massive MIMO Systems

In this paper, the achievable sum rate of multiuser scheduling in reconfigurable intelligent surface (RIS)-based mas-sive multiple-input multiple-output systems is investigated. Using asymptotic analysis under the generic condition of large numbers of base station antennas, RISs, and users, the RIS-based sum rate is proven to follow a Gaussian distribution. In addition, based on the characteristics of Gaussian distribution, the conditions for the occurrence of the channel hardening phenomenon and achievable scheduling gain are derived as a function of the number of RISs and users. Numerical results show that the derived RIS-based sum rate and the Monte Carlo simulation results are in close agreement as well as that the proposed achievable sum rate constitutes a meaningful bound to verify the performance of various multiuser scheduling algorithms.
Color compensation for multi-view video coding based on diversity of cameras

A novel color compensation method for multi-view video coding (MVC) is proposed, which efficiently exploits the inter-view dependencies between views with the existence of color mismatch caused by the diversity of cameras. A color compensation model is developed in RGB channels and then extended to YCbCr channels for practical use. A modified inter-view reference picture is constructed based on the color compensation model, which is more similar to the coding picture than the original inter-view reference picture. Moreover, the color compensation factors can be derived in both encoder and decoder, therefore no additional data need to be transmitted to the decoder. The experimental results show that the proposed method improves the coding efficiency of MVC and maintains good subjective quality.
SMOOTH QUANTILE PROCESSES FROM RIGHT CENSORED DATA AND CONSTRUCTION OF SIMULTANEOUS CONFIDENCE BANDS

The smooth nonparametric estimator of a quantile function Q(p) is defined as the solution of , where is the distribution function corresponding to a kernel estimator of a density function. The asymptotic properties of the smooth quantile process, , based on randomly right censored lifetime data are studied. The bootstrap approaches to approximate the distributions of the smooth quantile processes are investigated and are used to construct simultaneous confidence bands for quantile functions. Data-based selection of the bandwidth required for computing is also investigated using bootstrap methods. A Monte Carlo simulation is carried out to assess small sample performance of the proposed confidence bands. An application to construct confidence bands for the quantile function of the time between a manuscript's submission and its first review is provided using a JASA data set. The developed results can be applied to construct simultaneous confidence bands for the difference of two quantile functions and to check whether there is a location shift or scale change for two distributions under study.
Probabilistic Edge Multicast Routing for the XRP Network

The XRP Ledger relies on a trusted set of validator nodes to advance the ledger history. Nodes use flood-based broad-casting to disseminate messages. Flooding offers strong message delivery guarantees at the cost of high network utilisation caused by duplicate messages. In this paper, we present pemcast, an application layer algorithm for efficient one-to-many message routing. The algorithm leverages limited topology awareness and application layer multi-casting to deliver messages in the network. The evaluation shows that compared to flooding and gossiping algorithms, pemcast can maintain similar reliability whilst generating significantly less redundant traffic.
Anomaly Traffic Detection with Federated Learning toward Network-based Malware Detection in IoT

To mitigate cyberattacks, detecting anomalies in network traffic is of key importance. In this paper, we propose a model training method for detection of Internet of Things (IoT) anomalous traffic that is robust against the contamination of anomalous samples in the training set. The key idea is to focus on the nature of IoT malware infections (i.e., only a limited number of IoT networks contain infected devices) and employ federated learning (FL) to mitigate the impact of anomalous samples on model training. The simulation evaluation using IoT traffic data obtained from residences and malware traffic data collected from sandbox experiments demonstrates that the proposed method does not cause accuracy degradation even when the anomalous samples are contaminated, in contrast with the detection accuracy of baseline methods, which does degrade.
Spectral and modal parameter estimation from output-only measurements

Abstract A number of approaches that estimate spectra, natural frequencies and dampings from the response to an unknown random input—the autocorrelation, random decrement, maximum entropy and two-stage least squares methods—were compared with traditional frequency response function estimates using both a Monte Carlo simulation and real data. It was found that the Autocorrelation method combined with the Correlation Fit is, arguably, the best method to use if sufficient data are available. All of the methods gave less accurate estimates if the assumption of a ‘white’ random input was invalid.
A Learning-based Distributed Algorithm for Personalized Aggregative Optimization

This paper addresses distributed aggregative optimization, i.e., a recently emerged framework in which agents in a network want to minimize the sum of local objective functions depending both on a local decision variable and on an aggregated version of all the variables (e.g., the mean). We consider a "personalized" set-up in which each local function is given by the sum of a known term and an unknown one capturing the user’s dissatisfaction. We propose a novel data-driven distributed algorithm taking advantage of users’ noisy feedback to learn the parameters of the unknown function concurrently with the optimization steps. We prove an upper bound for the dynamic regret related to (i) the initial conditions, (ii) the temporal variations of the objective functions, and (iii) the learning errors. Moreover, by considering the average dynamic regret, we prove that both initial conditions and learning errors do not affect the asymptotic performance of the algorithm. Finally, a numerical simulation in the context of opinion dynamics validates our findings.
Focused interplanetary transport of approximately 1 MeV solar energetic protons through self-generated Alfven waves

We present a model of the focused transport of approximately 1 MeV solar energetic protons through interplanetary Alfven waves that the protons themselves amplify or damp. It is based on the quasi-linear theory but with a phenomenological pitch angle diffusion coefficient in the 'resonance gap. ' For initial Alfven wave distributions that give mean free paths greater than approximately 0.5 AU for approximately 1 MeV protons in the inner heliosphere, the model predicts greater than roughly an order of magnitude amplification (damping) in the outward (inward) propagating resonant Alfven waves at less than or approximately equal to o.3 AU heliocentric distance. As the strength of proton source is increased, the peak differential proton intensity at approximately 1 MeV at 1 AU increases to a maximum of approximately 250 particles (/(sq cm)(s)(sr)(MeV)) and then decreases slowly. It may be attenuated by a factor of 5 or more relative to the case without wave evolution, provided that the proton source is sufficiently intense that the resulting peak differential intensity of approximately 1 MeV protons at 1 AU exceeds approximately 200 particles (/(sq cm)(s)(sr)(MeV)). Therefore, in large solar proton events, (1) one may have to take into account self-amplified waves in studying solar particle propagation, (2) the number of accelerated protons escaping from a flare or interplanetary shock may have been underestimated in past studies by a significant factor, and (3) accelerated protons escaping from a traveling interplanetary shock at r less than or approximately equal to 0.3 AU should amplify the ambient hydromagnetic waves siginificantly to make the shock an efficient accelerator, even if initially the mean free path is greater than or approximately equal to 1 AU.
Computation Rate Optimization for STAR-RIS-Enhanced Wireless-Powered MEC

The wireless-powered mobile edge computing (MEC) has been considered as a promising approach to enhance the computation capability and prolong the lifetime of user equipments (DEs). To further improve the efficiency of energy transfer and task offloading, in this paper, a novel concept called simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is introduced into the wireless-powered MEC. The total computation rate maximization problem is investigated by jointly optimizing the energy transfer time, UEs' resource allocation, and configuration design of STAR-RIS via a linear search algorithm based on the block coordinate decent (BCD) and successive convex approximation (SCA) techniques. Simulation results illustrate that our proposed algorithm can achieve a higher computation rate than the scheme with conventional RIS and that without local computing.
MWIR HgCdTe avalanche photodiodes

This paper reports results obtained on mid-wave IR x equals 0.3 Hg1-xCdxTe avalanche photodiodes (APDs) that utilize a cylindrical 'p-around-n' front side illuminated n+/n-/p geometry. This 'p-around-n' geometry favors electron avalanche gain. These devices are characterized by a uniform, exponential, gain voltage characteristic that is consistent with a hole to electron ionization ratio, k equals (alpha) h/(alpha) e, of zero. At 6 bias and 77 K, gains are typically near 50, and gains of over 100 have been measured at higher biases. Response times have been modeled and measured on these devices. The modeling indicates that the geometry and dimensions of the diode control the diffusion limited device bandwidth. Rise times of less than 0.35 nsec should be possible according to this analysis. TO dat 10 percent to 90 percent rise times as low as 1 nsec have been measured. The gain is approximately noiseless up to gains of over fifty which is consistent with insignificant hole ionization. The noiseless gain behavior reported here is inconsistent with the original theory of McIntyre that predicts an excess noise factor of 2 for the k equals 0 case. The explanation for these results will require application of the modified 'history dependent' theory for excess noise later proposed by McIntyre.
Deep Dense Network-Based Curriculum Reinforcement Learning for High-Speed Overtaking

Reinforcement learning methods have promising applications in autonomous vehicles, such as lane keeping, adaptive cruise control, and overtaking. However, long-time sequential decision making in complicated scenarios, such as continuous high-speed overtaking, remains challenge. One such challenge is that current neural networks in reinforcement learning employ a shallow neural network, which has limitations in feature extractions. To address this, we propose a deep actor–critic network framework using multilayer dense-connection networks to obtain efficient extracted features. To be specific, we employ a deep dense architecture in the framework of soft actor–critic, which has multiple hidden layers serving as feature extractions. To tackle the vanishing or exploding gradient problem, we reuse features and design shortcut connections, as well as feature-concatenating operations between hidden layers. Meanwhile, when learning multitask high-speed overtaking as one whole progression, the agent has to learn driving, lane changing, and avoiding other vehicles at the same time. Therefore, another challenge is that sparse rewards worsen the speed of convergence. We design a task-level sequencing curriculum reinforcement learning method to tackle the reward sparseness. It breaks down a complex task into two successive subtasks: the first aims to drive as fast as possible in a single-vehicle model; and the second aims to overtake other vehicles without any collisions as soon as possible according to the knowledge gained from the first task in a multivehicle model. Finally, we evaluate our method using a racing car simulator with different tracks. In a comparison using the standard actor–critic algorithm in three different multivehicle scenarios, the best median results show that our deep actor–critic network framework reduces overtaking distance and overtaking time by up to 4.9% and 8.7%, respectively.
Epitaxial Regrowth of Ferroelectric Thin Films on Bottom Electrodes

Abstract The practical use of ferroelectric thin films in devices such as FeRAM requires a capacitance-like geometry and then a bottom electrode. Platinum is proposed based on its resistance to oxidation and unit-cell constants fitting quite closely the ones of typical substrates and ferroelectric materials: indeed, high quality, smooth epitaxial Pt layers with various orientations, namely (100), (110) and (111), were reproducibly obtained. Various examples of the epitaxial regrowth on such sublayers of ferroelectrics are shown, in the case of SrBi2Nb2O9 (SBN) and KTa1− x Nb x O3 (KTN). The observed orientations are compared to the ones obtained on bare bulk oxide substrates and oxide sublayers and discussed in terms of interface anchoring (Near Coincidence Site Lattice model), explaining the orientational competition frequently encountered in such metal-oxide heterostructures.