Miracle-dz/UniFAR-FTUs huggingface dataset:
A Dive into WhatsApp's End-to-End Encryption

—We live in a generation where the world around us is witnessing technological revolutions every single day, and as a result of this, everything around us is getting digitized with the touch of technology. In order to keep up the pace of this technological revolution and help reaching this progress its zenith, one of the most important aspects that needs to be taken care of is security. One of the biggest boons of technology in the recent times has been the invention of smartphones. As smartphones started becoming more popular, affordable and easily accessible, hundreds of free messaging applications were launched, but WhatsApp emerged as the ultimate winner in the race. This paper describes one of the most important and popular features of WhatsApp, the End-to-End (E2E) encryption system, which sets it apart from most other messaging applications and is one of the reasons which helped it become so popular.
Multiple State Analysis, a Multidimentional Approach to Multiple Time-to-Event Data and Life Course Health Trajectories: Application to Patients with Myocardial Infarction

Life course epidemiology of chronic diseases has been dominated so far by the environmental approach. Whether it focuses on early life exposures and events or later lifestyle behaviors, this approach assumes that previous life experiences interact at each stage of life and shape subsequent health trajectories. Inspired by the analysis of social trajectories, focusing on transitions between multiple states, in multiple dimensions of the social experience, we propose in this paper a novel empirical approach to multiple time-to-event health data, denoted as multiple state analysis. Alike the so-called state sequence analysis, the aim of multiple state analysis is to create typologies of the main life course trajectories. This approach is illustrated by the analysis of records from a south London general practice electronic health record from which multiple long term conditions associated with myocardial infarction were considered. Among expected results such as the recurrent role of hypertension, multiple state analysis shows that different patterns of long term conditions including physical and mental health conditions, are associated with the onset timing of myocardial infarction but also with socio-demographics such as sex and ethnicity.
Effects of Initial Density Fluctuations on Cumulants in Au + Au Collisions at sNN = 7.7 GeV

Within the ultrarelativistic quantum molecular dynamics (UrQMD) model, the effect of initial density fluctuations on cumulants of the net-proton multiplicity distribution in Au + Au Collisions at sNN = 7.7 GeV was investigated by varying the minimum distance dmin between two nucleons in the initialization. It was found that the initial density fluctuations increased with the decrease of dmin from 1.6 fm to 1.0 fm, and the influence of dmin on the magnitude of the net-proton number fluctuation in a narrow pseudorapidity window (Δη≤ 4) was negligible even if it indeed affected the density evolution during the collision. At a broad pseudorapidity window (Δη≥ 4), the cumulant ratios were enlarged when the initial density fluctuations were increased with the smaller value of dmin, and this enhancement was comparable to that observed in the presence of the nuclear mean-field potential. Moreover, the enhanced cumulants were more evident in collisions with a larger impact parameter. The present work demonstrates that the fingerprint of the initial density fluctuations on the cumulants in a broad pseudorapidity window is clearly visible, while it is not obvious as the pseudorapidity window becomes narrow.
Bootstrapping Multi-View Representations for Fake News Detection

Previous researches on multimedia fake news detection include a series of complex feature extraction and fusion networks to gather useful information from the news. However, how cross-modal consistency relates to the fidelity of news and how features from different modalities affect the decision-making are still open questions. This paper presents a novel scheme of Bootstrapping Multi-view Representations (BMR) for fake news detection. Given a multi-modal news, we extract representations respectively from the views of the text, the image pattern and the image semantics. Improved Multi-gate Mixture-of-Expert networks (iMMoE) are proposed for feature refinement and fusion. Representations from each view are separately used to coarsely predict the fidelity of the whole news, and the multimodal representations are able to predict the cross-modal consistency. With the prediction scores, we reweigh each view of the representations and bootstrap them for fake news detection. Extensive experiments conducted on typical fake news detection datasets prove that BMR outperforms state-of-the-art schemes.
Continuous phase transition induced by non-Hermiticity in the quantum contact process model

Non-Hermitian (NH) quantum system recently have attracted a lots of attentions theoretically and experimentally. However, the results based on the single-particle picture may not apply to understand the property of NH many-body system. How the property of quantum many-body system especially the phase transition will be affected by the non-Hermiticity remains unclear. Here we study NH quantum contact process (QCP) model, whose effective Hamiltonian is derived from Lindbladian master equation. We show that there is a continuous phase transition induced by the non-Hermiticity in QCP. We also determine the critical exponents β of order parameter, γ of susceptibility and study the correlation and entanglement near phase transition point. We observe that the order parameter and susceptibility display infinitely singularity even for finite size system, since non-Hermiticity endow many-body system with different singular behavior from classical phase transition. Moreover our results show that the phase transition have no counterpart in Hermitian case and belongs to completely different universality class.
This is what a pandemic looks like: Visual framing of COVID-19 on search engines

In today's high-choice media environment, search engines play an integral role in informing individuals and societies about the latest events. The importance of search algorithms is even higher at the time of crisis, when users search for information to understand the causes and the consequences of the current situation and decide on their course of action. In our paper, we conduct a comparative audit of how different search engines prioritize visual information related to COVID-19 and what consequences it has for the representation of the pandemic. Using a virtual agent-based audit approach, we examine image search results for the term "coronavirus" in English, Russian and Chinese on five major search engines: Google, Yandex, Bing, Yahoo, and DuckDuckGo. Specifically, we focus on how image search results relate to generic news frames (e.g., the attribution of responsibility, human interest, and economics) used in relation to COVID-19 and how their visual composition varies between the search engines.
Evaluation of Misfit Relaxation in α-Ga2O3 Epitaxial Growth on α-Al2O3 Substrate

Corundum-structured α-Ga2O3 epitaxial thin films were grown on c-plane α-Al2O3 (sapphire) substrates by a mist chemical vapor deposition method. To reveal the defect structures, the α-Ga2O3 film was observed by high-resolution transmission electron microscopy (TEM). We found that the α-Ga2O3 thin film was in-plane compressive stressed from the α-Al2O3 substrate. Although misfit dislocations were periodically generated at the α-Ga2O3/α-Al2O3 interface owing to the large lattice mismatches between α-Ga2O3 and α-Al2O3, 3.54% (c-axis) and 4.81% (a-axis), most of the misfit dislocations did not thread through the layer. An extra-half plane was {2̄110} consisting only of Ga. Screw dislocations were not confirmed, i.e., the density was under 107 cm-2. The threading dislocation density was 7 ×1010 cm-2.
Privacy Attacks Against Biometric Models with Fewer Samples: Incorporating the Output of Multiple Models

Authentication systems are vulnerable to model inversion attacks where an adversary is able to approximate the inverse of a target machine learning model. Biometric models are a prime candidate for this type of attack. This is because inverting a biometric model allows the attacker to produce a realistic biometric input to spoof biometric authentication systems. One of the main constraints in conducting a successful model inversion attack is the amount of training data required. In this work, we focus on iris and facial biometric systems and propose a new technique that drastically reduces the amount of training data necessary. By leveraging the output of multiple models, we are able to conduct model inversion attacks with 1 / 10 th the training set size of Ahmad and Fuller (IJCB 2020) for iris data and 1 / 1000 th the training set size of Mai et al. (Pattern Analysis and Machine Intelligence 2019) for facial data. We denote our new attack technique as structured random with alignment loss . Our attacks are black-box, requiring no knowledge on the weights of the target neural network, only the dimension and values of the output vector. To show the versatility of the alignment loss, we apply our attack framework to the task of membership inference (Shokri et al., IEEE S&P 2017) on biometric data. For the iris, membership inference attack against classiﬁcation networks improves from 52% to 62% accuracy.
Affective Role of the Future Autonomous Vehicle Interior

Recent advancements in autonomous technology allow for new opportunities in vehicle interior design. Such a shift in in-vehicle activity suggests vehicle interior spaces should provide an adequate manner by considering users’ affective desires. Therefore, this study aims to investigate the affective role of future vehicle interiors. Thirty-one participants in ten focus groups were interviewed about challenges they face regarding their current vehicle interior and expectations they have for future vehicles. Results from content analyses revealed the affective role of future vehicle interiors. Advanced exclusiveness and advanced convenience were two primary aspects identified. The identified affective roles of each aspect are a total of eight visceral levels, four visceral levels each, including focused, stimulating, amused, pleasant, safe, comfortable, accommodated, and organized. We expect the results from this study to lead to the development of affective vehicle interiors by providing the fundamental knowledge for developing conceptual direction and evaluating its impact on user experiences.
Understandable Robots

Finally, the work will include an investigation of the contextual form of explanations. In this study, we will include a time-bounded scenario in which the different levels of understanding will be tested to enable us to evaluate suitable and comprehensible explanations. For this we have proposed different levels of understanding (LOU). A user study will be designed to compare different LOU for different contexts of interaction. A user study simultating a hospital environment will be investigated.
A Spatial-channel-temporal-fused Attention for Spiking Neural Networks

Spiking neural networks (SNNs) mimic brain computational strategies, and exhibit substantial capabilities in spatiotemporal information processing. As an essential factor for human perception, visual attention refers to the dynamic process for selecting salient regions in biological vision systems. Although visual attention mechanisms have achieved great success in computer vision applications, they are rarely introduced into SNNs. Inspired by experimental observations on predictive attentional remapping, we propose a new spatial-channel-temporal-fused attention (SCTFA) module that can guide SNNs to efficiently capture underlying target regions by utilizing accumulated historical spatial-channel information in the present study. Through a systematic evaluation on three event stream datasets (DVS Gesture, SL-Animals-DVS, and MNIST-DVS), we demonstrate that the SNN with the SCTFA module (SCTFA-SNN) not only significantly outperforms the baseline SNN (BL-SNN) and two other SNN models with degenerated attention modules, but also achieves competitive accuracy with the existing state-of-the-art (SOTA) methods. Additionally, our detailed analysis shows that the proposed SCTFA-SNN model has strong robustness to noise and outstanding stability when faced with incomplete data, while maintaining acceptable complexity and efficiency. Overall, these findings indicate that incorporating appropriate cognitive mechanisms of the brain may provide a promising approach to elevate the capabilities of SNNs.
UPIR: Toward the Design of Unified Parallel Intermediate Representation for Parallel Programming Models

The complexity of heterogeneous computing architectures, as well as the demand for productive and portable parallel application development, have driven the evolution of parallel programming models to become more comprehensive and complex than before. Enhancing the conventional compilation technologies and software infrastructure to be parallelism-aware has become one of the main goals of recent compiler development. In this work, we propose the design of unified parallel intermediate representation (UPIR) for multiple parallel programming models and for enabling unified compiler transformation for the models. UPIR specifies three commonly used parallelism patterns (SPMD, data and task parallelism), data attributes and explicit data movement and memory management, and synchronization operations used in parallel programming. We demonstrate UPIR via a prototype implementation in the ROSE compiler for unifying IR for both OpenMP and OpenACC and in both C/C++ and Fortran, for unifying the transformation that lowers both OpenMP and OpenACC code to LLVM runtime, and for exporting UPIR to LLVM MLIR dialect. The fully extended paper of this abstract can be found from https://arxiv.org/abs/2209.10643.
Stability of equilibrium positions of mechanical systems with switched force fields

Mechanical system with switched dissipative and potential forces is considered. It is assumed that dissipative forces are linear, whereas potential forces are nonlinear and homogeneous. The conditions of the existence of a common Lyapunov function for corresponding family of subsystems are investigated. The fulfillment of these conditions provides asymptotic stability of the equilibrium position of the switched system for any admissible switching law.
Tachyon driven quantum cosmology in string theory

Recently an effective action of the SDp-brane decaying process in string theory has been proposed. This effective description involves the Tachyon driven matter coupled to bosonic ten-dimensional Type II supergravity. Here the Hamiltonian formulation of this system is given. Exact solutions for the corresponding quantum theory by solving the Wheeler-deWitt equation in the late-time limit of the rolling tachyon are found. The energy spectrum and the probability densities for several values of p are shown and their possible interpretation is discussed. In the process the effects of electromagnetic fields are also incorporated and it is shown that in this case the interpretation of tachyon regarded as 'matter clock' is modified.
Variance estimation for the average treatment effects on the treated and on the controls

Common causal estimands include the average treatment effect, the average treatment effect of the treated, and the average treatment effect on the controls. Using augmented inverse probability weighting methods, parametric models are judiciously leveraged to yield doubly robust estimators, that is, estimators that are consistent when at least one the parametric models is correctly specified. Three sources of uncertainty are associated when we evaluate these estimators and their variances, that is, when we estimate the treatment and outcome regression models as well as the desired treatment effect. In this article, we propose methods to calculate the variance of the normalized, doubly robust average treatment effect of the treated and average treatment effect on the controls estimators and investigate their finite sample properties. We consider both the asymptotic sandwich variance estimation, the standard bootstrap as well as two wild bootstrap methods. For the asymptotic approximations, we incorporate the aforementioned uncertainties via estimating equations. Moreover, unlike the standard bootstrap procedures, the proposed wild bootstrap methods use perturbations of the influence functions of the estimators through independently distributed random variables. We conduct an extensive simulation study where we vary the heterogeneity of the treatment effect as well as the proportion of participants assigned to the active treatment group. We illustrate the methods using an observational study of critical ill patients on the use of right heart catherization.
On the Heritage of Crypto Assets - Tales From the Crypt Protocol

We discuss some issues to the inheritance of crypto assets. We propose a distributed, privacy preserving, protocol to establish a consensus on the death of the owner of crypto assets: the Tales From the Crypt Protocol. Until the actual death of the owner no link can be made between public information and the corresponding crypto assets. This protocol is generic and could be incorparated into any arbitrary crypto platform.
Faithiful Embeddings for EL++ Knowledge Bases

Recently, increasing efforts are put into learning continual representations for symbolic knowledge bases (KBs). However, these approaches either only embed the data-level knowledge (ABox) or suffer from inherent limitations when dealing with concept-level knowledge (TBox), i.e., they cannot faithfully model the logical structure present in the KBs. We present BoxEL, a geometric KB embedding approach that allows for better capturing the logical structure (i.e., ABox and TBox axioms) in the description logic EL ++ . BoxEL models concepts in a KB as axis-parallel boxes that are suitable for modeling concept intersection, entities as points inside boxes, and relations between concepts/entities as afﬁne transformations . We show theoretical guarantees ( soundness ) of BoxEL for preserving logical structure. Namely, the learned model of BoxEL embedding with loss 0 is a (logical) model of the KB. Experimental results on (plausible) subsumption reasonings and a real-world application–protein-protein prediction show that BoxEL outperforms traditional knowledge graph embedding methods as well as state-of-the-art EL ++ embedding approaches.
Neural Generalized Ordinary Differential Equations with Layer-varying Parameters

Deep residual networks (ResNets) have shown state-of-the-art performance in various real-world applications. Recently, the ResNets model was reparameterized and interpreted as solutions to a continuous ordinary differential equation or Neural-ODE model. In this study, we propose a neural generalized ordinary differential equation (Neural-GODE) model with layer-varying parameters to further extend the Neural-ODE to approximate the discrete ResNets. Specifically, we use nonparametric B-spline functions to parameterize the Neural-GODE so that the trade-off between the model complexity and computational efficiency can be easily balanced. It is demonstrated that ResNets and Neural-ODE models are special cases of the proposed Neural-GODE model. Based on two benchmark datasets, MNIST and CIFAR-10, we show that the layer-varying Neural-GODE is more flexible and general than the standard Neural-ODE. Furthermore, the Neural-GODE enjoys the computational and memory benefits while performing comparably to ResNets in prediction accuracy.
Sharply o-minimal structures and sharp cellular decomposition

Sharply o-minimal structures (denoted # o-minimal) are a strict subclass of the o-minimal structures, aimed at capturing some ﬁner features of structures arising from algebraic geometry and Hodge theory. Sharp o-minimality associates to each deﬁnable set a pair of integers known as format and degree , similar to the ambient dimension and degree in the algebraic case; gives bounds on the growth of these quantities under the logical operations; and allows one to control the geometric complexity of a set in terms of its format and degree. These axioms have signiﬁcant implications on arithmetic properties of deﬁnable sets – for example, # o-minimality was recently used by the authors to settle Wilkie’s conjecture on rational points in R exp -deﬁnable sets. In this paper we develop some basic theory of sharply o-minimal structures. We introduce the notions of reduction and equivalence on the class of # o-minimal structures. We give three variants of the deﬁnition of # o-minimality, of increasing strength, and show that they all agree up to reduction. We also consider the problem of “sharp cell decomposition”, i.e. cell decomposition with good control on the number of the cells and their formats and degrees. We show that every # o-minimal structure can be reduced to one admitting sharp cell decomposition, and use this to prove bounds on the Betti numbers of deﬁnable sets in terms of format and degree.
Challenges in Visual Anomaly Detection for Mobile Robots

We consider the task of detecting anomalies for autonomous mobile robots based on vision. We categorize relevant types of visual anomalies and discuss how they can be detected by unsupervised deep learning methods. We propose a novel dataset built specifically for this task, on which we test a state-of-the-art approach; we finally discuss deployment in a real scenario.
Flare quasi-periodic pulsation associated with recurrent jets

Quasi-periodic pulsations (QPPs), which carry time features and plasma characteristics of flare emissions, are frequently observed in light curves of solar/stellar flares. In this study, we investigated non-stationary QPPs associated with recurrent jets during an M1.2 flare on 2022 July 14. A quasi-period of ∼ 45 ± 10 s, determined by the wavelet transform technique, is simultaneously identified at wavelengths of soft/hard X-ray and microwave emissions, which are recorded by the Gravitational Wave High-Energy Electromagnetic Counterpart All-sky Monitor, Fermi and the Nobeyama Radio Polarimeters, respectively. A group of recurrent jets with an intermittent cadence of about 45 ± 10 s are found in the Atmospheric Imaging Assembly (AIA) image series at 304 Å, but they are 180 s earlier than the flare QPP. All observational facts suggest that the flare QPPs could be excited by recurrent jets, and they should be associated with non-thermal electrons that are periodically accelerated by a repeated energy release process, such as repetitive magnetic reconnection. Moreover, the same quasi-period is discovered at double footpoints connected by a hot flare loop in AIA 94 Å, and the phase speed is measured to be ∼1,420 km s−1. Based on the differential emission measure, the average temperatures, number densities, and magnetic field strengths at the loop top and footpoint are estimated to be ∼7.7/6.7 MK, ∼7.5/3.6 × 1010 cm−3, and ∼143/99 G, respectively. Our measurements indicate that the 45-s QPP is probably modulated by the kink-mode wave of the flare loop.