Miracle-dz/UniFAR-FTUs huggingface dataset:
Collaborative Perception in Autonomous Driving: Methods, Datasets, and Challenges

Collaborative perception is essential to address occlusion and sensor failure issues in autonomous driving. In recent years, theoretical and experimental investigations of novel works for collaborative perception have increased tremendously. So far, however, few reviews have focused on systematical collaboration modules and large-scale collaborative perception datasets. This article reviews recent achievements in this field to bridge this gap and motivate future research. We start with a brief overview of collaboration schemes. After that, we systematically summarize the collaborative perception methods for ideal scenarios and real-world issues. The former focuses on collaboration modules and efficiency, and the latter is devoted to addressing the problems in actual application. Furthermore, we present large-scale public datasets and summarize quantitative results on these benchmarks. Finally, we highlight gaps and overlooked challenges between current academic research and real-world applications.
Dynamic SIR/SEIR-like models comprising a time-dependent transmission rate: Hamiltonian Monte Carlo approach with applications to COVID-19 (preprint)

A study of changes in the transmission of a disease, in particular, a new disease like COVID-19, requires very flexible models which can capture, among others, the effects of non-pharmacological and pharmacological measures, changes in population behaviour and random events. In this work, we give priority to data-driven approaches and choose to avoid a priori and ad-hoc methods. We introduce a generalised family of epidemiologically informed mechanistic models, guided by Ordinary Differential Equations and embedded in a probabilistic model. The mechanistic models SIKR and SEMIKR with K Infectious and M Exposed sub-compartments (resulting in non-exponential infectious and exposed periods) are enriched with a time-dependent transmission rate, parametrized using Bayesian P-splines. This enables an extensive flexibility in the transmission dynamics, with no ad-hoc intervention, while maintaining good differentiability properties. Our probabilistic model relies on the solutions of the mechanistic model and benefits from access to the information about under-reporting of new infected cases, a crucial property when studying diseases with a large fraction of asymptomatic infections. Such a model can be efficiently differentiated, which facilitates the use of Hamiltonian Monte Carlo for sampling from the posterior distribution of the model parameters. The features and advantages of the proposed approach are demonstrated through comparison with state-of-the-art methods using a synthetic dataset. Furthermore, we successfully apply our methodology to the study of the transmission dynamics of COVID-19 in the Basque Country (Spain) for almost a year, from mid February 2020 to the end of January 2021.
Mechanisms in neurodegenerative disorders and role of non-pharmacological interventions in improving neurodegeneration and its clinical correlates: A review

Mild cognitive impairment (MCI) leading to dementia results in a constellation of psychiatric disorders including depression, mood disorders, schizophrenia and others. With increasing age, mild cognitive impairment leads to increased disability-adjusted life-years and healthcare burden. A huge number of drug trials for the treatment of MCI associated with Alzheimer's disease have undergone failure leading to the development of drugs that could avert the progression of the disease. However, some novel non-drug-based therapies like ultrasound ablation of amyloid plaques have influenced researchers to explore the non-pharmacological modalities for the treatment of mild cognitive impairment. To compensate for neurodegenerative loss resulting in coexisting psychiatric disorders, neurofeedback therapy has also been proven to improve behavioural outcomes by inducing neuroplasticity. The aim of the current review is to highlight the pathophysiological aspects of mild cognitive impairment leading to dementia that could be addressed with no pharmacological interventions and to understand the mechanisms behind the effects of these interventions.
Stuck in the Permissions With You: Developer & End-User Perspectives on App Permissions & Their Privacy Ramifications

While the literature on permissions from the end-user perspective is rich, there is a lack of empirical research on why developers request permissions, their conceptualization of permissions, and how their perspectives compare with end-users’ perspectives. Our study aims to address these gaps using a mixed-methods approach. Through interviews with 19 app developers and a survey of 309 Android and iOS end-users, we found that both groups shared similar concerns about unnecessary permissions breaking trust, damaging the app’s reputation, and potentially allowing access to sensitive data. We also found that developer participants sometimes requested multiple permissions due to confusion about the scope of certain permissions or third-party library requirements. Additionally, most end-user participants believed they were responsible for granting a permission request, and it was their choice to do so, a belief shared by many developer participants. Our findings have implications for improving the permission ecosystem for both developers and end-users.
Product formula for the one-dimensional (k,a)-generalized Fourier kernel

In this paper, a product formula for the one-dimensional -generalized Fourier kernel is given for , a>0 and , extending the special case of [Boubatra MA, Negzaoui S, Sifi M. A new product formula involving Bessel functions. Integral Transforms Spec Funct. 2022;33:247–263. ] when , .
VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter

Vaccine hesitancy has been a common concern, probably since vaccines were created and, with the popularisation of social media, people started to express their concerns about vaccines online alongside those posting pro- and anti-vaccine content. Predictably, since the first mentions of a COVID-19 vaccine, social media users posted about their fears and concerns or about their support and belief into the effectiveness of these rapidly developing vaccines. Identifying and understanding the reasons behind public hesitancy towards COVID-19 vaccines is important for policy markers that need to develop actions to better inform the population with the aim of increasing vaccine take-up. In the case of COVID-19, where the fast development of the vaccines was mirrored closely by growth in anti-vaxx disinformation, automatic means of detecting citizen attitudes towards vaccination became necessary. This is an important computational social sciences task that requires data analysis in order to gain in-depth understanding of the phenomena at hand. Annotated data is also necessary for training data-driven models for more nuanced analysis of attitudes towards vaccination. To this end, we created a new collection of over 3,101 tweets annotated with users' attitudes towards COVID-19 vaccination (stance). Besides, we also develop a domain-specific language model (VaxxBERT) that achieves the best predictive performance (73.0 accuracy and 69.3 F1-score) as compared to a robust set of baselines. To the best of our knowledge, these are the first dataset and model that model vaccine hesitancy as a category distinct from pro- and anti-vaccine stance.
YeLan: Event Camera-Based 3D Human Pose Estimation for Technology-Mediated Dancing in Challenging Environments with Comprehensive Motion-to-Event Simulator

As a beloved sport worldwide, dancing is getting integrated into traditional and virtual reality-based gaming platforms nowadays. It opens up new opportunities in the technology-mediated dancing space. These platforms primarily rely on passive and continuous human pose estimation as an input capture mechanism. Existing solutions are mainly based on RGB or RGB-Depth cameras for dance games.Theformersuffersinlow-lightingconditionsduetothemotionblurandlowsensitivity,whilethelatteristoopower-hungry,has a low frame rate, and has limited working distance. With ultra-low latency, energy efficiency, and wide dynamic range characteristics, the event camera is a promising solution to overcome these shortcomings. We propose YeLan, an event camera-based 3-dimensional human pose estimation(HPE) system that survives low-lighting and dynamic background contents. We collected the world’s first event camera dance dataset and developed a fully customizable motion-to-event physics-aware simulator. YeLan outperforms the baseline models in these challenging conditions and demonstrated robustness against different types of clothing, background motion, viewing angle, occlusion, and lighting fluctuations.
Spinor ﬁelds, CPTM symmetry and smallness of cosmological constant in framework of extended manifold

A model of an extended manifold for the Dirac spinor field is considered. Two Lagrangians related by CPTM (charge-parity-time-mass) symmetry are constructed for a pair of the Dirac spinor fields with each spinor field defined in a separate manifold. An interaction between the matter fields in the manifolds is introduced through gravity. A fermionic effective action of the general system is constructed and a tadpole one-loop spinor diagram and part of the one-loop vacuum diagrams with two external gravitational off-shell fields which contribute to the effective action are calculated. It is demonstrated that among different versions of the second spinor Lagrangian there is a special one for which a cancellation of the mentioned diagrams in the total effective action takes place. As a result, the diagrams do not contribute to the cosmological constant, as well there is a zero contribution of the zero point energies of the spinor fields to the action. The non-zero leading order value of the cosmological constant for each manifold in the framework is proportional to the trace of an momentum-energy tensor of each separated manifold or difference of the tensors of the related manifolds. The result is depending on the chosen model of interaction of gravitational fields with fermions, the different possibilities are discussed. An appearance of the dark matter in the model is shortly discussed as well as further applications of the approach and it's interconnections with other cosmology models.
Matroid Horn functions

Hypergraph Horn functions were introduced as a subclass of Horn functions that can be represented by a collection of circular implication rules. These functions possess distinguished structural and computational properties. In particular, their characterizations in terms of implicate-duality and the closure operator provide extensions of matroid duality and the Mac Lane-Steinitz exchange property of matroid closure, respectively. In the present paper, we introduce a subclass of hypergraph Horn functions that we call matroid Horn functions. We provide multiple characterizations of matroid Horn functions in terms of their canonical and complete CNF representations. We also study the Boolean minimization problem for this class, where the goal is to find a minimum size representation of a matroid Horn function given by a CNF representation. While there are various ways to measure the size of a CNF, we focus on the number of circuits and circuit clauses. We determine the size of an optimal representation for binary matroids, and give lower and upper bounds in the uniform case. For uniform matroids, we show a strong connection between our problem and Tur\'an systems that might be of independent combinatorial interest.
Spectral extremal graphs without intersecting triangles as a minor

: Let F s be the friendship graph obtained from s triangles by sharing a common vertex. For ﬁxed s ≥ 2 and suﬃciently large n , the F s -free graphs of order n which attain the maximal spectral radius was ﬁrstly characterized by Cioab˘a, Feng, Tait and Zhang [Electron. J. Combin. 27 (4) (2020)], and later uniquely determined by Zhai, Liu and Xue [Electron. J. Combin. 29 (3) (2022)]. Recently, the spectral extremal problems was widely studied for graphs containing no H as a minor. For instance, Tait [J. Combin. Theory Ser. A 166 (2019)], Zhai and Lin [J. Combin. Theory Ser. B 157 (2022)] solved the case H = K r and H = K s,t , respectively. Motivated by these results, we consider the spectral extremal problems in the case H = F s . We shall prove that K s ∨ I n − s is the unique graph that attain the maximal spectral radius over all n -vertex F s -minor-free graphs. Moreover, let Q t be the graph obtained from t copies of the cycle of length 4 by sharing a common vertex. We also determine the unique Q t -minor-free graph attaining the maximal spectral radius. Namely, K t ∨ M n − t , where M n − t is a graph obtained from an independent set of order n − t by embedding a matching consisting of ⌊ n − t 2 ⌋ edges.
Thin-film PMUTs: A Review of 40 Years of Research

A concise review on the thin-film PMUTs, which has been one of the rather important research topics amongst micro-ultrasound experts is being reported. It has been rigorously surveyed, scrutinized, and perceived that the work in this direction began nearly 44 years ago, with the primitive development of functional piezoelectric thin-film material, and now there are already three major companies commercializing them on a bulk scale. This fascinating fact illustrates the enormous contribution made by more than 70 different centers, research institutes, and agencies spread across 4 different continents to develop the vast know-how of these devices’ design, make, and function. This review covers such important contributions in a short but comprehensive fashion and in particular, intends to educate the readers about – the global scenario of PMUTs, the principles governing their design, the ways following their make, all non-conventional useful PMUT designs, and lastly, category wise applications. Crucial comparison charts in terms of thin-film piezoelectric material used in PMUTs, and in terms of targeted applications are also depicted and discussed, which is believed to enlighten any MEMS designer planning to begin working with PMUTs. Moreover, each relevant section is provided with a crisp future forecast, from the author’s past knowledge and expertise in this very field of research along with the elements of a careful literature survey. In short, this review can be considered a one-stop time-efficient guide for whoever is interested in knowing about these small devices.
The SwaNNFlight System: On-the-Fly Sim-to-Real Adaptation via Anchored Learning

—Reinforcement Learning (RL) agents trained in simulated environments and then deployed in the real world are often sensitive to the diﬀerences in dynamics presented, com- monly termed the sim-to-real gap. With the goal of minimizing this gap on resource-constrained embedded systems, we train and live-adapt agents on quadrotors built from oﬀ-the-shelf hardware. In achieving this we developed three novel contri- butions. (i) SwaNNFlight, an open-source ﬁrmware enabling wireless data capture and transfer of agents’ observations. Fine-tuning agents with new data, and receiving and swapping onboard NN controllers – all while in ﬂight. We also design SwaNNFlight System (SwaNNFS) allowing new research in training and live-adapting learning agents on similar systems. (ii) Multiplicative value composition, a technique for preserving the importance of each policy optimization criterion, improving training performance and variability in learnt behavior. And (iii) anchor critics to help stabilize the ﬁne-tuning of agents dur- ing sim-to-real transfer, online learning from real data while re-taining behavior optimized in simulation. We train consistently ﬂight-worthy control policies in simulation and deploy them on real quadrotors. We then achieve ‘live’ controller adaptation via over-the-air updates of the onboard control policy from a ground station. Our results indicate that live adaptation unlocks a near-50% reduction in power consumption, attributed to the sim-to-real gap. Finally, we tackle the issues of catastrophic forgetting and controller instability, showing the eﬀectiveness of our novel methods. Project Website: https://github.com/ BU-Cyber-Physical-Systems-Lab/SwaNNFS
Pre-deployment Analysis of Smart Contracts - A Survey

Smart contracts are programs that execute transactions involving independent parties and cryptocurrencies. As programs, smart contracts are susceptible to a wide range of errors and vulnerabilities. Such vulnerabilities can result in significant losses. Furthermore, by design, smart contract transactions are irreversible. This creates a need for methods to ensure the correctness and security of contracts pre-deployment. Recently there has been substantial research into such methods. The sheer volume of this research makes articulating state-of-the-art a substantial undertaking. To address this challenge, we present a systematic review of the literature. A key feature of our presentation is to factor out the relationship between vulnerabilities and methods through properties. Specifically, we enumerate and classify smart contract vulnerabilities and methods by the properties they address. The methods considered include static analysis as well as dynamic analysis methods and machine learning algorithms that analyze smart contracts before deployment. Several patterns about the strengths of different methods emerge through this classification process.
Estimates for exponential functionals of continuous Gaussian processes with emphasis on fractional Brownian motion

Our aim in this article is to provide explicit computable estimates for the cumulative distribution function (c.d.f.) and the $p$-th order moment of the exponential functional of a fractional Brownian motion (fBM) with drift. Using elementary techniques, we prove general upper bounds for the c.d. f. of exponential functionals of continuous Gaussian processes. On the other hand, by applying classical results for extremes of Gaussian processes, we derive general lower bounds. We also find estimates for the $p$-th order moment and the moment-generating function of such functionals. As a consequence, we obtain explicit lower and upper bounds for the c.d. f. and the $p$-th order moment of the exponential functionals of a fBM, and of a series of independent fBMs. In addition, we show the continuity in law of the exponential functional of a fBM with respect to the Hurst parameter.
RedBit: An End-to-End Flexible Framework for Evaluating the Accuracy of Quantized CNNs

In recent years, Convolutional Neural Networks (CNNs) have become the standard class of deep neural network for image processing, classification and segmentation tasks. However, the large strides in accuracy obtained by CNNs have been derived from increasing the complexity of network topologies, which incurs sizeable performance and energy penalties in the training and inference of CNNs. Many recent works have validated the effectiveness of parameter quantization, which consists in reducing the bit width of the network's parameters, to enable the attainment of considerable performance and energy efficiency gains without significantly compromising accuracy. However, it is difficult to compare the relative effectiveness of different quantization methods. To address this problem, we introduce RedBit, an open-source framework that provides a transparent, extensible and easy-to-use interface to evaluate the effectiveness of different algorithms and parameter configurations on network accuracy. We use RedBit to perform a comprehensive survey of five state-of-the-art quantization methods applied to the MNIST, CIFAR-10 and ImageNet datasets. We evaluate a total of 2300 individual bit width combinations, independently tuning the width of the network's weight and input activation parameters, from 32 bits down to 1 bit (e.g., 8/8, 2/2, 1/32, 1/1, for weights/activations). Upwards of 20000 hours of computing time in a pool of state-of-the-art GPUs were used to generate all the results in this paper. For 1-bit quantization, the accuracy losses for the MNIST, CIFAR-10 and ImageNet datasets range between [0.26%, 0.79%], [9.74%, 32.96%] and [10.86%, 47.36%] top-1, respectively. We actively encourage the reader to download the source code and experiment with RedBit, and to submit their own observed results to our public repository, available at https://github.com/IT-Coimbra/RedBit.
Applicable Regions of Spherical and Plane Wave Models for Extremely Large-Scale Array Communications

—Extremely large-scale array (XL-array) communications can signiﬁcantly improve the spectral efﬁciency and spatial resolution, and has great potential in next-generation mobile communication networks. A crucial problem in XL-array communications is to determine the boundary of applicable regions of the plane wave model (PWM) and spherical wave model (SWM). In this paper, we propose new PWM/SWM demarcations for XL-arrays from the viewpoint of channel gain and rank. Four sets of results are derived for four different array setups. First, an equi-power line is derived for a point- to-uniform linear array (ULA) scenario, where an inﬂection point is found at ± π 6 central incident angles. Second, an equi-power surface is derived for a point-to-uniform planar array (UPA) scenario, and it is proved that cos 2 ( φ )cos 2 ( ϕ ) = 12 is a dividing curve, where φ and ϕ denote the elevation and azimuth angles, respectively. Third, an accurate and explicit expression of the equi-rank surface is obtained for a ULA-to-ULA scenario. Finally, an approximated expression of the equi-rank surface is obtained for a ULA-to-UPA scenario. With the obtained closed- form expressions, the equi-rank surface for any antenna structure and any angle can be well estimated. Furthermore, the effect of scatterers is also investigated, from which some insights are drawn.
Augmented Lagrangian and Operator-Splitting Methods in Nonlinear Mechanics

1. Some continuous media and their mathematical modeling 2. Variational formulations of the mechanical problems 3. Augmented Lagrangian methods for the solution of variational problems 4. Viscoplasticity and elastoviscoplasticity in small strains 5. Limit load analysis 6. Two-dimensional flow of incompressible viscoplastic fluids 7. Finite elasticity 8. Large displacement calculations of flexible rods References Index.
PUF for the Commons: Enhancing Embedded Security on the OS Level

Security is essential for the Internet of Things (IoT). Cryptographic operations for authentication and encryption commonly rely on random input of high entropy and secure, tamper-resistant identities, which are difficult to obtain on constrained embedded devices. In this paper, we design and analyze a generic integration of physically unclonable functions (PUFs) into the IoT operating system RIOT that supports about 250 platforms. Our approach leverages uninitialized SRAM to act as the digital fingerprint for heterogeneous devices. We ground our design on an extensive study of PUF performance in the wild, which involves SRAM measurements on more than 700 IoT nodes that aged naturally in the real-world. We quantify static SRAM bias, as well as the aging effects of devices and incorporate the results in our system. This work closes a previously identified gap of missing statistically significant sample sizes for testing the unpredictability of PUFs. Our experiments on COTS devices of 64 kB SRAM indicate that secure random seeds derived from the SRAM PUF provide 256 Bits-, and device unique keys provide more than 128 Bits of security. In a practical security assessment we show that SRAM PUFs resist moderate attack scenarios, which greatly improves the security of low-end IoT devices.