Miracle-dz/UniFAR-FTUs huggingface dataset:
Security and Privacy Issues for Urban Smart Traffic Infrastructure

In recent times, the research works relating to smart traffic infrastructure have gained serious attention. As a result, research has been carried out in multiple directions to ensure that such infrastructure can improve upon our existing (mostly) human-controlled traffic infrastructure, without violating the safety margins. For this reason, cyber security issues of such infrastructure are of paramount interest. Keeping this in mind, we conduct a review of existing models, their vulnerabilities and how such vulnerabilities can be handled. Our work covers a vast area from the domain of security, starting from the theoretical notions of cryptography to the real-life adaptation of them. At the same time, we also consider the security issues that may arise due to the usage of artificial intelligence/machine learning in the infrastructure. We believe that our work will help future researchers to gain a comprehensive yet concise look at cyber security for smart traffic infrastructure.
Fluence to Effective Dose Conversion Coefficients for Negatively and Positively Charged Pions

Fluence to effective dose conversion coefficients have been computed for both negatively and positively charged pions using the Monte Carlo transport code FLUKA. Calculations have been carried out for various geometrical conditions of irradiation of an anthropomorphic computational model. The medium between the source and the phantom was assumed to be a vacuum. In accordance with the rule recommended by ICRP in Paragraph (A14) of ICRP Publication 60, the radiation weighting factors have been approximated by the effective quality factors at 10 mm depth in the ICRU sphere. The application of this rule in the high energy region remains indeed a completely open question. The calculated results are given for 12 incident energies from I MeV to 10 TeV. Comparisons with the values of operational quantities are discussed. The calculated organ doses are also given.
Zero-Shot Multi-Label Topic Inference with Sentence Encoders

Sentence encoders have indeed been shown to achieve superior performances for many downstream text-mining tasks and, thus, claimed to be fairly general. Inspired by this, we performed a detailed study on how to leverage these sentence encoders for the"zero-shot topic inference"task, where the topics are defined/provided by the users in real-time. Extensive experiments on seven different datasets demonstrate that Sentence-BERT demonstrates superior generality compared to other encoders, while Universal Sentence Encoder can be preferred when efficiency is a top priority.
Teacher Network Calibration Improves Cross-Quality Knowledge Distillation

We investigate cross-quality knowledge distillation (CQKD), a knowledge distillation method where knowledge from a teacher network trained with full-resolution images is transferred to a student network that takes as input low-resolution images. As image size is a deciding factor for the computational load of computer vision applications, CQKD notably reduces the requirements by only using the student network at inference time. Our experimental results show that CQKD outperforms supervised learning in large-scale image classification problems. We also highlight the importance of calibrating neural networks: we show that with higher temperature smoothing of the teacher's output distribution, the student distribution exhibits a higher entropy, which leads to both, a lower calibration error and a higher network accuracy.
Thorny Roses: Investigating the Dual Use Dilemma in Natural Language Processing

Dual use, the intentional, harmful reuse of technology and scientific artefacts, is a problem yet to be well-defined within the context of Natural Language Processing (NLP). However, as NLP technologies continue to advance and become increasingly widespread in society, their inner workings have become increasingly opaque. Therefore, understanding dual use concerns and potential ways of limiting them is critical to minimising the potential harms of research and development. In this paper, we conduct a survey of NLP researchers and practitioners to understand the depth and their perspective of the problem as well as to assess existing available support. Based on the results of our survey, we offer a definition of dual use that is tailored to the needs of the NLP community. The survey revealed that a majority of researchers are concerned about the potential dual use of their research but only take limited action toward it. In light of the survey results, we discuss the current state and potential means for mitigating dual use in NLP and propose a checklist that can be integrated into existing conference ethics-frameworks, e.g., the ACL ethics checklist.
Atomistic Wear Mechanisms in Diamond: Effects of Surface Orientation, Stress, and Interaction with Adsorbed Molecules

Despite its unrivaled hardness, diamond can be severely worn during the interaction with others, even softer materials. In this work, we calculate from first-principles the energy and forces necessary to induce the atomistic wear of diamond and compare them for different surface orientations and passivation by oxygen, hydrogen, and water fragments. The primary mechanism of wear is identified as the detachment of the carbon chains. This is particularly true for oxidized diamond and diamonds interacting with silica. A very interesting result concerns the role of stress, which reveals that compressive stresses can highly favor wear, making it even energetically favorable.
Employing Deep Ensemble Learning for Improving the Security of Computer Networks Against Adversarial Attacks

In the past few years, Convolutional Neural Networks (CNN) have demonstrated promising performance in various real-world cybersecurity applications, such as network and multimedia security. However, the underlying fragility of CNN structures poses major security problems, making them inappropriate for use in security-oriented applications, including computer networks. Protecting these architectures from adversarial attacks necessitates using security-wise architectures that are challenging to attack. In this study, we present a novel architecture based on an ensemble classifier that combines the enhanced security of 1-Class classification (known as 1C) with the high performance of conventional 2-Class classification (known as 2C) in the absence of attacks. Our architecture is referred to as the 1.5-Class (cmb-classifier) classifier and is constructed using a final dense classifier, one 2C classifier (i.e., CNNs), and two parallel 1C classifiers (i.e., auto-encoders). In our experiments, we evaluated the robustness of our proposed architecture by considering eight possible adversarial attacks in various scenarios. We performed these attacks on the 2C and cmb-classifier architectures separately. The experimental results of our study showed that the Attack Success Rate (ASR) of the I-FGSM attack against a 2C classifier trained with the N-BaIoT dataset is 0.9900. In contrast, the ASR is 0.0000 for the cmb-classifier.
Limits to the observation of Unruh radiation via first-quantized hydrogenlike atoms

We consider ionized hydrogen-like atoms accelerated by an external electric field to detect Unruh radiation. By applying quantum field theory in the Rindler spacetime, we show that the first-quantized description for hydrogen-like atoms cannot always be adopted. This is due to the frame-dependent definition of particles as positive and negative frequency field modes. We show how to suppress such a frame-dependent effect by constraining the atomic ionization and the electric field. We identify the physical regimes with nonvanishing atomic excitation probability due to the Unruh electromagnetic background. We recognize the observational limits for the Unruh effect via first-quantized atomic detectors, which appear to be compatible with current technology. Notably, the non-relativistic energy spectrum of the atom cannot induce coupling with the thermal radiation, even when special relativistic and general relativistic corrections are considered. On the contrary, the coupling with the Unruh radiation arises because of relativistic hyperfine splitting.
Classification of Topological Phase in 2D Photonic Continuous Media Using Electromagnetic Parameters

Refractive index is a fundamental electromagnetic (EM) parameter that can describe photonic continuous media (PCM) traditionally as either transparency or opacity. Recently, topological theory offers a new set of phases to characterize PCM as either trivial or nontrivial, by using topological invariant which are not direct to EM parameters. As all of optical properties in PCM should be related to EM parameters, we formulate a topological index based on EM parameters and establish its phase map in this work. The map can analytically describe the deterministic condition for a topologically nontrivial phase. Our findings indicate that the topology of 2D bi-anisotropic PCM is determined by the sign of the topological index. Another EM parameter of pseudo surface impedance is also introduced for the opaque regions of PCM, showing that the topological opacity has a full range of impedance values ranging from negative to positive, while the trivial case only has either negative or positive impedance. The simulation results show that an interface between two opacities with differing index signs can support robustly optical propagation of topological edge states. As the index only depends on EM parameters, it will pave an insightful way to further understand the intrinsic properties of photonic topology.
More Is Less: When Do Recommenders Underperform for Data-rich Users?

Users of recommender systems tend to differ in their level of interaction with these algorithms, which may affect the quality of recommendations they receive and lead to undesirable performance disparity. In this paper we investigate under what conditions the performance for data-rich and data-poor users diverges for a collection of popular evaluation metrics applied to ten benchmark datasets. We find that Precision is consistently higher for data-rich users across all the datasets; Mean Average Precision is comparable across user groups but its variance is large; Recall yields a counter-intuitive result where the algorithm performs better for data-poor than for data-rich users, which bias is further exacerbated when negative item sampling is employed during evaluation. The final observation suggests that as users interact more with recommender systems, the quality of recommendations they receive degrades (when measured by Recall). Our insights clearly show the importance of an evaluation protocol and its influence on the reported results when studying recommender systems.
On Learning Time Series Summary DAGs: A Frequency Domain Approach

The fields of time series and graphical models emerged and advanced separately. Previous work on the structure learning of continuous and real-valued time series utilizes the time domain, with a focus on either structural autoregressive models or linear (non-)Gaussian Bayesian Networks. In contrast, we propose a novel frequency domain approach to identify a topological ordering and learn the structure of both real and complex-valued multivariate time series. In particular, we define a class of complex-valued Structural Causal Models (cSCM) at each frequency of the Fourier transform of the time series. Assuming that the time series is generated from the transfer function model, we show that the topological ordering and corresponding summary directed acyclic graph can be uniquely identified from cSCM. The performance of our algorithm is investigated using simulation experiments and real datasets.
Climatologies of Various OH Lines From About 90,000 X‐Shooter Spectra

The nocturnal mesopause region of the Earth's atmosphere radiates chemiluminescent emission from various roto‐vibrational bands of hydroxyl (OH), which is therefore a good tracer of the chemistry and dynamics at the emission altitudes. Intensity variations can, for example, be caused by the general circulation, gravity waves, tides, planetary waves, and the solar activity. While the basic OH response to the different dynamical influences has been studied quite frequently, detailed comparisons of the various individual lines are still rare. Such studies can improve our understanding of the OH‐related variations as each line shows a different emission profile. We have therefore used about 90,000 spectra of the X‐shooter spectrograph of the Very Large Telescope at Cerro Paranal in Chile in order to study 10 years of variations of 298 OH lines. The analysis focuses on climatologies of intensity, solar cycle effect (SCE), and residual variability (especially with respect to time scales of hours and about 2 days) for day of year and local time. For a better understanding of the resulting variability patterns and the line‐specific differences, we applied decomposition techniques, studied the variability depending on time scale, and calculated correlations. As a result, the mixing of thermalized and nonthermalized OH level populations clearly influences the amplitude of the variations. Moreover, the local times of the variability features shift depending on the effective line emission height, which can mainly be explained by the propagation of the migrating diurnal tide. This behavior also contributes to remarkable differences in the effective SCE.
Set-Valued State Estimation for Nonlinear Systems Using Hybrid Zonotopes

This paper proposes a method for set-valued state estimation of nonlinear, discrete-time systems. This is achieved by combining graphs of functions representing system dynamics and measurements with the hybrid zonotope set representation that can efficiently represent nonconvex and disjoint sets. Tight over-approximations of complex nonlinear functions are efficiently produced by leveraging special ordered sets and neural networks, which enable computation of set-valued state estimates that grow linearly in memory complexity with time. A numerical example demonstrates significant reduction of conservatism in the set-valued state estimates using the proposed method as compared to an idealized convex approach.
Implicit Bayes Adaptation: A Collaborative Transport Approach

The power and flexibility of Optimal Transport (OT) have pervaded a wide spectrum of problems, including recent Machine Learning challenges such as unsupervised domain adaptation. Its essence of quantitatively relating two probability distributions by some optimal metric, has been creatively exploited and shown to hold promise for many real-world data challenges. In a related theme in the present work, we posit that domain adaptation robustness is rooted in the intrinsic (latent) representations of the respective data, which are inherently lying in a non-linear submanifold embedded in a higher dimensional Euclidean space. We account for the geometric properties by refining the l2 Euclidean metric to better reflect the geodesic distance between two distinct representations. We integrate a metric correction term as well as a prior cluster structure in the source data of the OT-driven adaptation. We show that this is tantamount to an implicit Bayesian framework, which we demonstrate to be viable for a more robust and better-performing approach to domain adaptation. Substantiating experiments are also included for validation purposes.
Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects.
Convex NMPC reformulations for a special class of nonlinear multi-input systems with application to rank-one bilinear networks

We show that a special class of (nonconvex) NMPC problems admits an exact solution by reformulating them as a finite number of convex subproblems, extending previous results to the multi-input case. Our approach is applicable to a special class of input-affine discrete-time systems, which includes a class of bilinear rank-one systems that is considered useful in modeling certain controlled networks. We illustrate our results with two numerical examples, including the aforementioned rank-one bilinear network.
Formation energy of intrinsic defects in silicon from the Galitskii-Migdal formula

The work is devoted to the formation energy calculations of intrinsic defects in silicon based on the GW method and the Galitskii-Migdal formula. The two methods for calculating the electronic response function are applied. The first one uses direct integration over frequency to determine the response function. The diagonal form of the spectral function is the only assumption within the RPA framework, but the supercell calculations are very time-consuming. Therefore, we propose the method in which the response function is calculated in the plasmon pole approximation, and the GW contribution to the exchange-correlation energy is taken with a certain mixing constant. The value of the constant is found from the correspondence with experimental data. This makes it possible to obtain an accuracy comparable to the first method at significantly lower computational costs. The described method is used to calculate the formation energy of the neutral self-interstitial, vacancy and two divacansy structures in supercells of 214-217 silicon atoms.
Grassroots Systems: Concept, Examples, Implementation and Applications

Informally, a grassroots system is a distributed system that can have multiple instances, independent of each other and of any global resources, that can interoperate once interconnected. Grassroots applications are potentially important as they may allow people to conduct their social, economic, civic, and political lives in the digital realm solely using the networked computing devices they own and operate (e.g., smartphones), free of third-party control, surveillance, manipulation, coercion, or rent seeking (e.g., by global digital platforms such as Facebook or Bitcoin). Here, we formalize the notion of grassroots systems and grassroots implementations; specify an abstract grassroots dissemination protocol; describe and prove an implementation of grassroots dissemination for the model of asynchrony; extend the implementation to mobile (address-changing) devices that communicate via an unreliable network (e.g. smartphones using UDP); and discuss how grassroots dissemination can realize applications that support digital sovereignty -- grassroots social networking and grassroots currencies. The mathematical construction employs distributed multiagent transition systems to define the notions of grassroots protocols and grassroots implementations, to specify grassroots dissemination protocols and their implementation, and to prove their correctness. The implementation uses the blocklace -- a generalization of the blockchain that functions as a fault-tolerant, conflict-free replicated data type.
Therapeutic potential of nitric oxide in vascular aging due to the promotion of angiogenesis

The decrease in angiogenesis that occurs with aging significantly contributes to the higher incidence and mortality of cardiovascular diseases among the elderly. This decline in angiogenesis becomes more pronounced with increasing age and is closely linked to abnormal function and senescence of endothelial cells. Enhancing angiogenesis in aging and targeting senescent endothelial cells have gained considerable attention. Nitric oxide (NO) has been thoroughly investigated for its function in regulating angiogenesis and is an important factor that can counteract endothelial cell senescence. This review summarizes the mechanisms of reduced angiogenesis during aging and therapeutic strategies targeting senescent cells. We also discuss the potential of combining the current approaches with NO in promoting angiogenesis in aging vessels.
Wave Packets in AdS/CFT Correspondence

In this paper, we construct a general bulk wave packet in the AdS/CFT correspondence. This wave packet can be described both in bulk and CFT descriptions. Then, we compute the time evolution of the energy density of this wave packet state on the vacuum in the CFT picture of $AdS_3/CFT_2$. We find that the energy density of the wave packet is localized in two points, which means that the bulk wave packet corresponds to two light-like particle-like objects in the CFT picture. Our result implies that the entanglement wedge reconstruction given in [1] is invalid.
TreeC: a method to generate interpretable energy management systems using a metaheuristic algorithm

Energy management systems (EMS) have classically been implemented based on rule-based control (RBC) and model predictive control (MPC) methods. Recent research are investigating reinforcement learning (RL) as a new promising approach. This paper introduces TreeC, a machine learning method that uses the metaheuristic algorithm covariance matrix adaptation evolution strategy (CMA-ES) to generate an interpretable EMS modeled as a decision tree. This method learns the decision strategy of the EMS based on historical data contrary to RBC and MPC approaches that are typically considered as non adaptive solutions. The decision strategy of the EMS is modeled as a decision tree and is thus interpretable contrary to RL which mainly uses black-box models (e.g. neural networks). The TreeC method is compared to RBC, MPC and RL strategies in two study cases taken from literature: (1) an electric grid case and (2) a household heating case. The results show that TreeC obtains close performances than MPC with perfect forecast in both cases and obtains similar performances to RL in the electrical grid case and outperforms RL in the household heating case. TreeC demonstrates a performant application of machine learning for energy management systems that is also fully interpretable.
Statute-enhanced lexical retrieval of court cases for COLIEE 2022

We discuss our experiments for COLIEE Task 1, a court case retrieval competition using cases from the Federal Court of Canada. During experiments on the training data we observe that passage level retrieval with rank fusion outperforms document level retrieval. By explicitly adding extracted statute information to the queries and documents we can further improve the results. We submit two passage level runs to the competition, which achieve high recall but low precision.