Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated Sensing and Communications: Recent Advances and Ten Open Challenges

It is anticipated that integrated sensing and communications (ISAC) would be one of the key enablers of next-generation wireless networks (such as beyond 5G (B5G) and 6G) for supporting a variety of emerging applications. In this paper, we provide a comprehensive review of the recent advances in ISAC systems, with a particular focus on their foundations, system design, networking aspects and ISAC applications. Furthermore, we discuss the corresponding open questions of the above that emerged in each issue. Hence, we commence with the information theory of sensing and communications (S$\&$C), followed by the information-theoretic limits of ISAC systems by shedding light on the fundamental performance metrics. Next, we discuss their clock synchronization and phase offset problems, the associated Pareto-optimal signaling strategies, as well as the associated super-resolution ISAC system design. Moreover, we envision that ISAC ushers in a paradigm shift for the future cellular networks relying on network sensing, transforming the classic cellular architecture, cross-layer resource management methods, and transmission protocols. In ISAC applications, we further highlight the security and privacy issues of wireless sensing. Finally, we close by studying the recent advances in a representative ISAC use case, namely the multi-object multi-task (MOMT) recognition problem using wireless signals.
Unsupervised anomaly detection algorithms on real-world data: how many do we need?

In this study we evaluate 32 unsupervised anomaly detection algorithms on 52 real-world multivariate tabular datasets, performing the largest comparison of unsupervised anomaly detection algorithms to date. On this collection of datasets, the $k$-thNN (distance to the $k$-nearest neighbor) algorithm significantly outperforms the most other algorithms. Visualizing and then clustering the relative performance of the considered algorithms on all datasets, we identify two clear clusters: one with ``local'' datasets, and another with ``global'' datasets. ``Local'' anomalies occupy a region with low density when compared to nearby samples, while ``global'' occupy an overall low density region in the feature space. On the local datasets the $k$NN ($k$-nearest neighbor) algorithm comes out on top. On the global datasets, the EIF (extended isolation forest) algorithm performs the best. Also taking into consideration the algorithms' computational complexity, a toolbox with these three unsupervised anomaly detection algorithms suffices for finding anomalies in this representative collection of multivariate datasets. By providing access to code and datasets, our study can be easily reproduced and extended with more algorithms and/or datasets.
Directional sensitivity of the NEWSdm experiment to cosmic ray boosted dark matter

We present a study of a directional search for Dark Matter boosted forward when scattered by cosmic-ray nuclei, using a module of the NEWSdm experiment. The boosted Dark Matter flux at the edge of the Earth's atmosphere is expected to be pointing to the Galactic Center, with a flux 15 to 20 times larger than in the transverse direction. The module of the NEWSdm experiment consists of a 10 kg stack of Nano Imaging Trackers, i.e. newly developed nuclear emulsions with AgBr crystal sizes down to a few tens of nanometers. The module is installed on an equatorial telescope. The relatively long recoil tracks induced by boosted Dark Matter, combined with the nanometric granularity of the emulsion, result in an extremely low background. This makes an installation at the INFN Gran Sasso laboratory, both on the surface and underground, viable. A comparison between the two locations is made. The angular distribution of nuclear recoils induced by boosted Dark Matter in the emulsion films at the surface laboratory is expected to show an excess with a factor of 3.5 in the direction of the Galactic Center. This excess allows for a Dark Matter search with directional sensitivity. The surface laboratory configuration prevents the deterioration of the signal in the rock overburden and it emerges as the most powerful approach for a directional observation of boosted Dark Matter with high sensitivity. We show that, with this approach, a 10 kg module of the NEWSdm experiment exposed for one year at the Gran Sasso surface laboratory can probe Dark Matter masses between 1 keV/c2 and 1 GeV/c2 and cross-section values down to 10-30 cm2 with a directional sensitive search.
Threat Perception Modulation by Capturing Emotion, Motor and Empathetic System Responses: A Systematic Review

The fight or flight phenomena is of evolutionary origin and responsible for the type of defensive behaviours enacted, when in the face of threat. This review attempts to draw the link between fear and aggression as behavioural motivations for fight or flight defensive behaviours. Hence, this review intends to examine whether fight or flight behavioural responses are the result of fear and aggression. Furthermore, this review investigates whether human biological motion captures the affective states associated with the fight or flight phenomenon. This review also aims to investigate how threat informed emotion and motor systems have the potential to result in empathetic appraisal modulation. This is of interest to this systematic review, as empathetic modulation is crucial to prosocial drive, which has the potential to increase the inclination of alleviating the perceived threat of another. Hence, this review investigates the role of affective computing in capturing the potential outcome of empathy from threat perception. To gain a comprehensive understanding of the affective states and biological motion evoked from threat scenarios, affective computing methods used to capture these behavioural responses are discussed. A systematic review using Google Scholar and Web of Science was conducted as of 2023, and findings were supplemented by bibliographies of key articles. A total of 22 studies were analysed from initial web searches to explore the topics of empathy, threat perception, fight or flight, fear, aggression, and human motion. Relationships between affective states (fear, aggression) and corresponding motor defensive behaviours (fight or flight) were examined within threat scenarios, and whether existing affective computing methods are succinct in capturing these responses, identifying the varying consensus in the literature, challenges, and limitations of existing research.
Robustified Learning for Online Optimization with Memory Costs

Online optimization with memory costs has many real-world applications, where sequential actions are made without knowing the future input. Nonetheless, the memory cost couples the actions over time, adding substantial challenges. Conventionally, this problem has been approached by various expert-designed online algorithms with the goal of achieving bounded worst-case competitive ratios, but the resulting average performance is often unsatisfactory. On the other hand, emerging machine learning (ML) based optimizers can improve the average performance, but suffer from the lack of worst-case performance robustness. In this paper, we propose a novel expert-robustified learning (ERL) approach, achieving both good average performance and robustness. More concretely, for robustness, ERL introduces a novel projection operator that robustifies ML actions by utilizing an expert online algorithm; for average performance, ERL trains the ML optimizer based on a recurrent architecture by explicitly considering downstream expert robustification. We prove that, for any λ ≥ 1, ERL can achieve λ-competitive against the expert algorithm and λ•C-competitive against the optimal offline algorithm (where C is the expert’s competitive ratio). Additionally, we extend our analysis to a novel setting of multi-step memory costs. Finally, our analysis is supported by empirical experiments for an energy scheduling application.
Importance Weighted Expectation-Maximization for Protein Sequence Design

Designing protein sequences with desired biological function is crucial in biology and chemistry. Recent machine learning methods use a surrogate sequence-function model to replace the expensive wet-lab validation. How can we efficiently generate diverse and novel protein sequences with high fitness? In this paper, we propose IsEM-Pro, an approach to generate protein sequences towards a given fitness criterion. At its core, IsEM-Pro is a latent generative model, augmented by combinatorial structure features from a separately learned Markov random fields (MRFs). We develop an Monte Carlo Expectation-Maximization method (MCEM) to learn the model. During inference, sampling from its latent space enhances diversity while its MRFs features guide the exploration in high fitness regions. Experiments on eight protein sequence design tasks show that our IsEM-Pro outperforms the previous best methods by at least 55% on average fitness score and generates more diverse and novel protein sequences.
Quantitative Schauder estimates for hypoelliptic equations

We derive Schauder estimates using ideas from Campanato's approach for a general class of local hypoelliptic operators and non-local kinetic equations. The method covers equations in divergence and non-divergence form. In particular our results are applicable to the inhomogeneous Landau and Boltzmann equation without cut-off. The paper is self-contained.
Perpendicular Magnetic Anisotropy of an Ultrathin Fe Layer Grown on NiO(001)

The magnetic anisotropy and magnetic interactions at the interface between Fe and NiO(001) were investigated. Depending on the growth conditions of the NiO(001) layers and the post-annealing temperature, the preferential magnetization direction of the ultrathin Fe layer grown on a NiO(001) layer changed from in-plane to a direction perpendicular to the film plane. The lattice constant of the NiO(001) layers parallel to the growth direction increased with O$_2$ flow rate, while that parallel to the in-plane were locked onto the MgO(001) substrate regardless of the growth conditions of the NiO layers. Moreover, perpendicular magnetization was observed only when the NiO layer was grown with O$_2$ flow rates higher than 2.0 sccm corresponding to oxygen-rich NiO. X-ray magnetic circular dichroism measurements revealed an enhancement in anisotropic orbital magnetic moments similar to the origin of perpendicular magnetic anisotropy at the Fe/MgO(001) interface. The interfacial magnetic anisotropy energies were 0.93 and 1.02 mJ/m$^2$ at room temperature and at 100 K, respectively, indicating less temperature dependence. In contrast, the coercivity $H_c$ exhibited a significant temperature dependence. Although no signature of exchange bias or unidirectional loop shift was observed, $H_c$ was strongly dependent on the NiO layer thickness, indicating that the exchange interaction at the interface between the ferromagnetic and antiferromagnetic layers was not negligible, despite the NiO(001) being a spin-compensated surface.
Asymmetric spoof-fluid-spoof acoustic waveguide and its application as a 
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 sensor

We study pressure acoustic propagation in asymmetric spoof-fluid-spoof acoustic waveguides and its potential application in acoustic gas sensors. First, a stable and efficient analytical method is established for fast calculation of the dispersion curves based on spectral expansion and enforcement of continuity between segments at suitable collocation points. The analysis is validated by a commercial finite element software. The geometric design of the waveguide is then optimized for the emergence of a nearly-flat dispersion curve associated with vertical geometric asymmetry. The waveguide is fabricated using 3D printing technology and the measurement results corroborate the numerical simulations. Based on the nearly-flat dispersion curve supported by this waveguide, a CO$_2$ sensor is proposed allowing to relate the phase difference measured between two points in the waveguide to the composition of the gas in the waveguide. The proposed sensor is experimentally validated in a controlled environment and the measurement results match the computational predictions well. The sensor is robust with respect to noise and signal-recording duration due to fast phase measurements and shows high sensitivity to gas concentration due to reliance on the second, nearly-flat, dispersion curve. In addition, the sensor is label-free and low-cost, while exhibiting rapid response, low-maintenance requirements and potential for measurements in a wide range of CO$_2$ concentrations without saturation issues.
Object-Centric Voxelization of Dynamic Scenes via Inverse Neural Rendering

Understanding the compositional dynamics of the world in unsupervised 3D scenarios is challenging. Existing approaches either fail to make effective use of time cues or ignore the multi-view consistency of scene decomposition. In this paper, we propose DynaVol, an inverse neural rendering framework that provides a pilot study for learning time-varying volumetric representations for dynamic scenes with multiple entities (like objects). It has two main contributions. First, it maintains a time-dependent 3D grid, which dynamically and flexibly binds the spatial locations to different entities, thus encouraging the separation of information at a representational level. Second, our approach jointly learns grid-level local dynamics, object-level global dynamics, and the compositional neural radiance fields in an end-to-end architecture, thereby enhancing the spatiotemporal consistency of object-centric scene voxelization. We present a two-stage training scheme for DynaVol and validate its effectiveness on various benchmarks with multiple objects, diverse dynamics, and real-world shapes and textures. We present visualization at https://sites.google. com/view/dynavol-visual.
Online fault compensation control based on policy iteration algorithm for a class of affine non-linear systems with actuator failures

In this study, a novel online fault compensation control scheme based on policy iteration (PI) algorithm is developed for a class of affine non-linear systems with actuator failures. The control scheme consists of a PI algorithm and a fault compensator. For fault-free dynamic models, the PI algorithm is developed to solve the Hamilton–Jacobi–Bellman equation by constructing a critic neural network, and then the approximate optimal control policy can be derived directly. Alternatively, the actuator failure is reconstructed adaptively to achieve online fault compensation without the fault detection and isolation mechanism. The closed-loop system is proved to be asymptotically stable via Lyapunov's direct method. Two numerical simulation examples are given to demonstrate the effectiveness of the proposed fault compensation control scheme.
Wegner model on a tree graph: U(1) symmetry breaking and a non-standard phase of disordered electronic matter

Assuming the self-consistent theory of localization due to Abou-Chacra et al., we solve the N=1 Wegner model in the regime of strong disorder and high dimension. In the process, we uncover a non-standard electronic phase with spontaneously broken U(1) symmetry -- the missing field-theory basis underlying phenomena associated with fractal eigenstates and singular continuous spectra.
General Distance Balancing for Quantum Locally Testable Codes

In this paper, we prove a lower bound on the soundness of quantum locally testable codes under the distance balancing construction of Evra et al. arXiv:2004.07935 [quant-ph]. Our technical contribution is that the new soundness of the quantum code is at least the old soundness divided by the classical code length (up to a constant factor). This allows us to use any classical code with independent checks when distance balancing, where previously only the repetition code had been considered for qLTCs. By using a good classical LDPC code, we are able to grow the dimension of the hypersphere product codes arXiv:1608.05089 [quant-ph] and the hemicubic codes arXiv:1911.03069 [quant-ph] while maintaining their distance and locality, but at the expense of soundness. From this, and also by distance balancing a chain complex of Cross et al. arXiv:2209.11405 [cs.IT], we obtain quantum locally testable codes of new parameters.
Massless Rarita-Schwinger equations: Half and three halves spin solution

Counting the degrees of freedom of the massless Rarita-Schwinger theory is revisited using Behrends-Fronsdal projectors. The identification of the gauge invariant part of the vector-spinor is thus straightforward, consisting of spins 1/2 and 3/2. The validity of this statement is supported by the explicit solution found in the standard gamma-traceless gauge. Since the obtained systems are deterministic -free of arbitrary functions of time- we argue that the often-invoked residual gauge symmetry lacks fundamental grounding and should not be used to enforce new external constraints. The result is verified by the total Hamiltonian dynamics. We conclude that eliminating the spin-1/2 mode via the extended Hamiltonian dynamics would be acceptable if the Dirac conjecture was assumed; however, this framework does not accurately describe the original Lagrangian system.
Examining European Press Coverage of the Covid-19 No-Vax Movement: An NLP Framework

This paper examines how the European press dealt with the no-vax reactions against the Covid-19 vaccine and the dis- and misinformation associated with this movement. Using a curated dataset of 1786 articles from 19 European newspapers on the anti-vaccine movement over a period of 22 months in 2020-2021, we used Natural Language Processing techniques including topic modeling, sentiment analysis, semantic relationship with word embeddings, political analysis, named entity recognition, and semantic networks, to understand the specific role of the European traditional press in the disinformation ecosystem. The results of this multi-angle analysis demonstrate that the European well-established press actively opposed a variety of hoaxes mainly spread on social media, and was critical of the anti-vax trend, regardless of the political orientation of the newspaper. This confirms the relevance of studying the role of high-quality press in the disinformation ecosystem.
A Simplified Framework for Contrastive Learning for Node Representations

Contrastive learning has recently established itself as a powerful self-supervised learning framework for extracting rich and versatile data representations. Broadly speaking, contrastive learning relies on a data augmentation scheme to generate two versions of the input data and learns low-dimensional representations by maximizing a normalized temperature-scaled cross entropy loss (NT-Xent) to identify augmented samples corresponding to the same original entity. In this paper, we investigate the potential of deploying contrastive learning in combination with Graph Neural Networks for embedding nodes in a graph. Specifically, we show that the quality of the resulting embeddings and training time can be significantly improved by a simple column-wise postprocessing of the embedding matrix, instead of the row-wise postprocessing via multilayer perceptrons (MLPs) that is adopted by the majority of peer methods. This modification yields improvements in downstream classification tasks of up to 1.5% and even beats existing state-of-the-art approaches on 6 out of 8 different benchmarks. We justify our choices of postprocessing by revisiting the"alignment vs. uniformity paradigm", and show that column-wise post-processing improves both"alignment"and"uniformity"of the embeddings.
Identity-driven Three-Player Generative Adversarial Network for Synthetic-based Face Recognition

Many of the commonly used datasets for face recognition development are collected from the internet without proper user consent. Due to the increasing focus on privacy in the social and legal frameworks, the use and distribution of these datasets are being restricted and strongly questioned. These databases, which have a realistically high variability of data per identity, have enabled the success of face recognition models. To build on this success and to align with privacy concerns, synthetic databases, consisting purely of synthetic persons, are increasingly being created and used in the development of face recognition solutions. In this work, we present a three-player generative adversarial network (GAN) framework, namely IDnet, that enables the integration of identity information into the generation process. The third player in our IDnet aims at forcing the generator to learn to generate identity-separable face images. We empirically proved that our IDnet synthetic images are of higher identity discrimination in comparison to the conventional two-player GAN, while maintaining a realistic intra-identity variation. We further studied the identity link between the authentic identities used to train the generator and the generated synthetic identities, showing very low similarities between these identities. We demonstrated the applicability of our IDnet data in training face recognition models by evaluating these models on a wide set of face recognition benchmarks. In comparison to the state-of-the-art works in synthetic-based face recognition, our solution achieved comparable results to a recent rendering-based approach and outperformed all existing GAN-based approaches. The training code and the synthetic face image dataset are publicly available 1.
Tidal Love numbers of a nonexotic compact object with a thin shell

As a possible alternative to black holes, horizonless compact objects have significant implications for gravitational-wave physics. In this work, we utilize the standard linearized theory of general relativity to calculate the quadrupolar tidal Love numbers of a nonexotic compact object with a thin shell proposed by Rosa and Pi\c{c}arra. It is found that both types of tidal Love numbers are positive and increase with the initial radius for almost all values of the compactness parameter. Furthermore, they have an unexpected upper bound and vanish in the most compact configurations. As a result, this model is indeed a suitable mimicker of a black hole. However, we also observed that the speed of sound within the fluid on the shell diverges in the black hole limit.
On the arithmetic of special values of $L$-functions for certain abelian varieties with a rational isogeny

Let $N$ and $p$ be primes $\geq 5$ such that $p \mid \mid N-1$. In this situation, Mazur defined and studied the $p$-Eisenstein quotient $\tilde{J}^{(p)}$ of $J_0(N)$. We prove a kind of modulo $p$ version of the Birch and Swinnerton-Dyer conjecture for the ``$p$-Eisenstein part'' of even quadratic twists of $\tilde{J}^{(p)}$. Our result is the analogue for even quadratic twists of a result of Mazur concerning odd quadratic twists.