Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial Machine Learning Threat Analysis in Open Radio Access Networks

—The Open Radio Access Network (O-RAN) is a new, open , adaptive , and intelligent RAN architecture. Motivated by the success of artiﬁcial intelligence in other domains, O-RAN strives to leverage machine learning (ML) to automatically and efﬁciently manage network resources in diverse use cases such as trafﬁc steering, quality of experience prediction, and anomaly detection. Unfortunately, ML-based systems are not free of vulnerabilities; speciﬁcally, they suffer from a special type of logical vulnerabilities that stem from the inherent limitations of the learning algorithms. To exploit these vulnerabilities, an adversary can utilize an attack technique referred to as adversarial machine learning (AML). These special type of attacks has already been demonstrated in recent researches. In this paper, we present a systematic AML threat analysis for the O-RAN. We start by reviewing relevant ML use cases and analyzing the different ML workﬂow deployment scenarios in O-RAN. Then, we deﬁne the threat model, identifying potential adversaries, enumerating their adversarial capabilities, and analyzing their main goals. Finally, we explore the various AML threats in the O-RAN and review a large number of attacks that can be performed to materialize these threats and demonstrate an AML attack on a trafﬁc steering model.
Visualizing Privacy-Utility Trade-Offs in Differentially Private Data Releases

Abstract Organizations often collect private data and release aggregate statistics for the public’s benefit. If no steps toward preserving privacy are taken, adversaries may use released statistics to deduce unauthorized information about the individuals described in the private dataset. Differentially private algorithms address this challenge by slightly perturbing underlying statistics with noise, thereby mathematically limiting the amount of information that may be deduced from each data release. Properly calibrating these algorithms—and in turn the disclosure risk for people described in the dataset—requires a data curator to choose a value for a privacy budget parameter, ɛ. However, there is little formal guidance for choosing ɛ, a task that requires reasoning about the probabilistic privacy–utility tradeoff. Furthermore, choosing ɛ in the context of statistical inference requires reasoning about accuracy trade-offs in the presence of both measurement error and differential privacy (DP) noise. We present Visualizing Privacy (ViP), an interactive interface that visualizes relationships between ɛ, accuracy, and disclosure risk to support setting and splitting ɛ among queries. As a user adjusts ɛ, ViP dynamically updates visualizations depicting expected accuracy and risk. ViP also has an inference setting, allowing a user to reason about the impact of DP noise on statistical inferences. Finally, we present results of a study where 16 research practitioners with little to no DP background completed a set of tasks related to setting ɛ using both ViP and a control. We find that ViP helps participants more correctly answer questions related to judging the probability of where a DP-noised release is likely to fall and comparing between DP-noised and non-private confidence intervals.
Numerical study on viscous fingering using electric fields in a Hele-Shaw cell

We investigate the nonlinear dynamics of a moving interface in a Hele-Shaw cell subject to an in-plane applied electric field. We develop a spectrally accurate boundary integral method where a coupled integral equation system is formulated. Although the stiffness due to the high order spatial derivatives can be removed, the long-time simulation is still expensive since the evolving velocity of the interface drops dramatically as the interface expands. We remove this physically imposed stiffness by employing a rescaling scheme, which accelerates the slow dynamics and reduces the computational cost. Our nonlinear results reveal that positive currents restrain finger ramification and promote overall stabilization of patterns. On the other hand, negative currents make the interface more unstable and lead to the formation of thin tail structures connecting the fingers and a small inner region. When no flux is injected, and a negative current is utilized, the interface tends to approach the origin and break up into several drops. We investigate the temporal evolution of the smallest distance between the interface and the origin and find that it obeys an algebraic law $\displaystyle (t_*-t)^b$, where $t_*$ is the estimated pinch-off time.
Integrated Sensing and Communication With mmWave Massive MIMO: A Compressed Sampling Perspective

Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for realizing future wireless systems. In this paper, we propose an ISAC processing framework relying on millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Specifically, we provide a compressed sampling (CS) perspective to facilitate ISAC processing, which can not only recover the high-dimensional channel state information or/and radar imaging information, but also significantly reduce pilot overhead. First, an energy-efficient widely spaced array (WSA) architecture is tailored for the radar receiver, which enhances the angular resolution of radar sensing at the cost of angular ambiguity. Then, we propose an ISAC frame structure for time-varying ISAC systems considering different timescales. The pilot waveforms are judiciously designed by taking into account both CS theories and hardware constraints induced by hybrid beamforming (HBF) architecture. Next, we design the dedicated dictionary for WSA that serves as a building block for formulating the ISAC processing as sparse signal recovery problems. The orthogonal matching pursuit with support refinement (OMP-SR) algorithm is proposed to effectively solve the problems in the existence of the angular ambiguity. We also provide a framework for estimating the Doppler frequencies during payload data transmission to guarantee communication performances. Simulation results demonstrate the good performances of both communications and radar sensing under the proposed ISAC framework.
Pulse Shaping for MC via Particle Size

In molecular communication (MC), combining different types of particles at the transmitter is a degree of freedom which can be utilized to improve performance. In this paper, we address the problem of pulse shaping to simplify time synchronization requirements by exploiting and combining the received signal characteristics of particles of different sizes. In particular, we optimize the mixture of particles of different sizes used for transmission in order to support a prescribed detection time period for on-off keying, guaranteeing on average 1) a sufficiently large received signal if a binary one is transmitted, and 2) a low enough received signal if a binary zero is transmitted even in the presence of inter-symbol interference. For illustration, we consider an optimization problem based on a free space diffusion channel model. It is shown that there is a tradeoff between the maximum feasible detection duration and the peak detection value for different particle sizes from the smallest particle size enabling the largest detection duration to the largest particle size minimizing the peak detection value at the expense of a limited detection duration.
Measuring Changes in Disparity Gaps: An Application to Health Insurance

It is now widely recognized that achieving health equity for members of racial and ethnic minority groups requires isolating and intervening on the manipulable factors that underlie health inequities (Bailey et al., 2017). The key empirical challenge—identifying the sources of differences across groups (typically means)—echoes work in other contexts outside of health—e.g., the gender wage gap (Blau and Kahn, 2017); or the racial wealth gap (Derenoncourt et al., 2021). One approach in this literature is the Kitagawa-OaxacaBlinder approach (Kitagawa, 1955; Oaxaca, 1973; Blinder, 1973), which measures how much mean differences across groups in relevant covariates can explain the gaps. While the Kitagawa-Oaxaca-Blinder (KOB) approach has been used to decompose mean differences between groups in observational settings (Fortin, Lemieux and Firpo, 2011) or as a tool for estimating treatment effects (Kline, 2011; Słoczyński, 2015), in this paper we show that KOB-style decompositions can also be used to decompose sources of treatment effect heterogeneity in policies that reduce disparities, with a focus on identifying sources of treatment effect heterogeneity between groups. Our approach is closely related to work on decomposing sources of treatment effect variation (Heckman, Smith and Clements, 1997; Djebbari and Smith, 2008; Ding, Feller and Miratrix, 2019; Currie, Voorheis and Walker, 2020), but also to a more recent literature on how to estimate heterogeneity in causal effects (e.g., Athey and Imbens, 2016; Wager and Athey,
UWC: Unit-wise Calibration Towards Rapid Network Compression

This paper introduces a post-training quantization~(PTQ) method achieving highly efficient Convolutional Neural Network~ (CNN) quantization with high performance. Previous PTQ methods usually reduce compression error via performing layer-by-layer parameters calibration. However, with lower representational ability of extremely compressed parameters (e.g., the bit-width goes less than 4), it is hard to eliminate all the layer-wise errors. This work addresses this issue via proposing a unit-wise feature reconstruction algorithm based on an observation of second order Taylor series expansion of the unit-wise error. It indicates that leveraging the interaction between adjacent layers' parameters could compensate layer-wise errors better. In this paper, we define several adjacent layers as a Basic-Unit, and present a unit-wise post-training algorithm which can minimize quantization error. This method achieves near-original accuracy on ImageNet and COCO when quantizing FP32 models to INT4 and INT3.
Language-Model-Based Paired Variational Autoencoders for Robotic Language Learning

Human infants learn the language while interacting with their environment in which their caregivers may describe the objects and actions they perform. Similar to human infants, artificial agents can learn the language while interacting with their environment. In this work, first, we present a neural model that bidirectionally binds robot actions and their language descriptions in a simple object manipulation scenario. Building on our previous paired variational autoencoders (PVAEs) model, we demonstrate the superiority of the variational autoencoder over standard autoencoders by experimenting with cubes of different colors, and by enabling the production of alternative vocabularies. Additional experiments show that the model’s channel-separated visual feature extraction module can cope with objects of different shapes. Next, we introduce PVAE-BERT, which equips the model with a pretrained large-scale language model, i.e., bidirectional encoder representations from transformers (BERTs), enabling the model to go beyond comprehending only the predefined descriptions that the network has been trained on; the recognition of action descriptions generalizes to unconstrained natural language as the model becomes capable of understanding unlimited variations of the same descriptions. Our experiments suggest that using a pretrained language model as the language encoder allows our approach to scale up for real-world scenarios with instructions from human users.
Concise Logarithmic Loss Function for Robust Training of Anomaly Detection Model

Recently, deep learning-based algorithms are widely adopted due to the advantage of being able to establish anomaly detection models without or with minimal domain knowledge of the task. Instead, to train the artificial neural network more stable, it should be better to define the appropriate neural network structure or the loss function. For the training anomaly detection model, the mean squared error (MSE) function is adopted widely. On the other hand, the novel loss function, logarithmic mean squared error (LMSE), is proposed in this paper to train the neural network more stable. This study covers a variety of comparisons from mathematical comparisons, visualization in the differential domain for backpropagation, loss convergence in the training process, and anomaly detection performance. In an overall view, LMSE is superior to the existing MSE function in terms of strongness of loss convergence, anomaly detection performance. The LMSE function is expected to be applicable for training not only the anomaly detection model but also the general generative neural network.
A Critical Analysis of Image-based Camera Pose Estimation Techniques

Camera, and associated with its objects within the field of view, localization could benefit many computer vision fields, such as autonomous driving, robot navigation, and augmented reality (AR). In this survey, we first introduce specific application areas and the evaluation metrics for camera localization pose according to different sub-tasks (learning-based 2D-2D task, feature-based 2D-3D task, and 3D-3D task). Then, we review common methods for structure-based camera pose estimation approaches, absolute pose regression and relative pose regression approaches by critically modelling the methods to inspire further improvements in their algorithms such as loss functions, neural network structures. Furthermore, we summarise what are the popular datasets used for camera localization and compare the quantitative and qualitative results of these methods with detailed performance metrics. Finally, we discuss future research possibilities and applications.
Characterizing Sensor Leaks in Android Apps

While extremely valuable to achieve advanced functions, mobile phone sensors can be abused by attackers to implement malicious activities in Android apps, as experimentally demonstrated by many state-of-the-art studies. There is hence a strong need to regulate the usage of mobile sensors so as to keep them from being exploited by malicious attackers. However, despite the fact that various efforts have been put in achieving this, i.e., detecting privacy leaks in Android apps, we have not yet found approaches to automatically detect sensor leaks in Android apps. To fill the gap, we designed and implemented a novel prototype tool, Seeker, that extends the famous FlowDroid tool to detect sensor-based data leaks in Android apps. Seeker conducts sensor-focused static taint analyses directly on the Android apps' bytecode and reports not only sensor-triggered privacy leaks but also the sensor types involved in the leaks. Experimental results using over 40,000 real-world Android apps show that Seeker is effective in detecting sensor leaks in Android apps, and malicious apps are more interested in leaking sensor data than benign apps.
Deep convolutional neural network for shape optimization using level-set approach

This article presents a reduced-order modeling methodology via deep convolutional neural networks (CNNs) for shape optimization applications. The CNN provides a nonlinear mapping between the shapes and their associated attributes while conserving the equivariance of these attributes to the shape translations. To implicitly represent complex shapes via a CNN-applicable Cartesian structured grid, a level-set method is employed. The CNN-based reduced-order model (ROM) is constructed in a completely data-driven manner thus well suited for non-intrusive applications. We demonstrate our ROM-based shape optimization framework on a gradient-based three-dimensional shape optimization problem to minimize the induced drag of a wing in low-fidelity potential flow. We show a good agreement between ROM-based optimal aerodynamic coefficients and their counterparts obtained via a potential flow solver. The predicted behavior of the optimized shape is consistent with theoretical predictions. We also present the learning mechanism of the deep CNN model in a physically interpretable manner. The CNN-ROM-based shape optimization algorithm exhibits significant computational efficiency compared to the full-order model-based online optimization applications. The proposed algorithm promises to develop a tractable DL-ROM-driven framework for shape optimization and adaptive morphing structures.
Mode attraction in Floquet systems with memory: Application to magnonics

Level attraction is a type of mode hybridization in open systems where instead of forming a hybridization gap, the energy spectrum of two modes coalesce in a region bounded by exceptional points. We demonstrate that this phenomenon can be realized in a Floquet system with memory, which appears in describing linear excitations in a nonlinear driven system with a limit cycle. Linear response of the system in this state is different from its response near thermodynamic equilibrium. We develop a general formalism and provide an example in the context of cavity magnonics, where we show that magnetic excitations in systems driven far from the equilibrium may show level attraction with cavity photons. Our approach works equally well for quantum and semiclassical magnetic dynamics. The theory is formulated so that it can be used in combination with micromagnetic simulations to explore a wide range of experimentally interesting systems.
Spin-orbit coupled superconductivity with spin-singlet nonunitary pairing

An unconventional superconductor is distinguished with two types of gap functions: unitary and non-unitary. This core subject has been concentrated on purely spin-triplet or singlet-triplet mixed superconductors. However, the generalization to a purely spin-singlet superconductor has remained primarily of theoretical interest, which requires at least a multi-orbital correlated electronic systems. In this work, we present a possible establishment of both unitary and non-unitary pairings for spin-singlet superconductors with two atomic orbitals. Then we investigate the effects of atomic spin-orbit coupling and find a new spin-orbit-coupled non-unitary superconductor that supports exotic phenomena. Remarkably, there are mainly three features. Firstly, the atomic spin-orbit coupling locks the electron spins to be out-of-plane, which could give birth to the Type II Ising superconductivity with a large in-plane upper critical field compared to the Pauli limit. Secondly, topological chiral or helical Majorana edge state could be realized even in the absence of external magnetic fields or Zeeman fields. In addition, a spin-polarized superconducting state could even be generated by spin-singlet non-unitary pairings with spontaneous time-reversal breaking, which severs as a smoking gun to detect this exotic state by measuring the spin-resolved density of states. Therefore, our work might pave a new avenue to spin-orbit-coupled superconductors with spin-singlet non-unitary pairing symmetries.
Integral means of derivatives of univalent functions in Hardy spaces

for all univalent functions provided that either q ě 2 or 2p 2`p ă q ă 2. This asymptotic was previously known in the cases 0 ă p ď q ă 8 and p 1`p ă q ă p ă 2 ` 2 157 by results due to Pommerenke (1962), Baernstein, Girela and Peláez (2004) and González and Peláez (2009). It is also shown that (†) is satisfied for all close-to-convex functions if 1 ď q ă 8. A counterpart of (†) in the setting of weighted Bergman spaces is also briefly discussed.
Cross-Centroid Ripple Pattern for Facial Expression Recognition

In this paper, we propose a new feature descriptor Cross-Centroid Ripple Pattern (CRIP) for facial expression recognition. CRIP encodes the transitional pattern of a facial expression by incorporating cross-centroid relationship between two ripples located at radius r1 and r2 respectively. These ripples are generated by dividing the local neighborhood region into subregions. Thus, CRIP has ability to preserve macro and micro structural variations in an extensive region, which enables it to deal with side views and spontaneous expressions. Furthermore, gradient information between cross centroid ripples provides strenght to captures prominent edge features in active patches: eyes, nose and mouth, that define the disparities between different facial expressions. Cross centroid information also provides robustness to irregular illumination. Moreover, CRIP utilizes the averaging behavior of pixels at subregions that yields robustness to deal with noisy conditions. The performance of proposed descriptor is evaluated on seven comprehensive expression datasets consisting of challenging conditions such as age, pose, ethnicity and illumination variations. The experimental results show that our descriptor consistently achieved better accuracy rate as compared to existing state-of-art approaches.
VaccinEU: COVID-19 vaccine conversations on Twitter in French, German and Italian

Despite the increasing limitations for unvaccinated people, in many European countries there is still a non-negligible fraction of individuals who refuse to get vaccinated against SARS-CoV-2, undermining governmental efforts to eradicate the virus. We study the role of online social media in influencing individuals' opinion towards getting vaccinated by designing a large-scale collection of Twitter messages in three different languages -- French, German and Italian -- and providing public access to the data collected. Focusing on the European context, our VaccinEU dataset aims to help researchers to better understand the impact of online (mis)information about vaccines and design more accurate communication strategies to maximize vaccination coverage. Data can be fully accessed in a Dataverse repository and a GitHub repository.
Dielectric permittivity, conductivity and breakdown field of hexagonal boron nitride

In view of the extensive use of hexagonal boron nitride (hBN) in 2D material electronics, it becomes important to refine its dielectric characterization in terms of low-field permittivity and high-field strength and conductivity up to the breakdown voltage. The present study aims at filling this gap using DC and RF transport in two Au-hBN-Au capacitor series of variable thickness in the 10–100 nm range, made of large high-pressure, high-temperature (HPHT) crystals and a polymer derivative ceramics (PDC) crystals. We deduce an out-of-plane low field dielectric constant ϵ ∥ = 3.4 ± 0.2 consistent with the theoretical prediction of Ohba et al, that narrows down the generally accepted window ϵ ∥ = 3–4. The DC-current leakage at high-field is found to obey the Frenkel-Pool law for thermally-activated trap-assisted electron transport with a dynamic dielectric constant ϵ ∥ ≃ 3.1 and a trap energy Φ B ≃ 1.3 eV, that is comparable with standard technologically relevant dielectrics.