Miracle-dz/UniFAR-FTUs huggingface dataset:
Continuous Covering on Networks: Strong Mixed Integer Programming Formulations

Covering problems are well-studied in the domain of Operations Research, and, more specifically, in Location Science. When the location space is a network, the most frequent assumption is to consider the candidate facility locations, the points to be covered, or both, to be discrete sets. In this work, we study the set-covering location problem when both candidate locations and demand points are continuous sets on a network. This variant has received little attention, and the scarce existing approaches have focused on particular cases, such as tree networks and integer covering radius. Here we study the general problem and present a Mixed Integer Linear Programming formulation (MILP) for networks with edges’ lengths no greater than the covering radius. The model does not lose generality, as any edge not satisfying this condition can be partitioned into subedges of appropriate lengths without changing the problem. We propose a preprocessing algorithm to reduce the size of the MILP, and devise tight big- M constants and valid inequalities to strengthen our formulations. Moreover, a second MILP is proposed, which admits edges’ lengths greater than the covering radius. As opposed to existing formulations of the problem (including the first MILP proposed herein), the number of variables and constraints of this second model does not depend on the lengths of the network’s edges. This second model represents a scalable approach that particularly suits real-world networks, whose edges are usually greater than the covering radius. Our computational experiments show the strengths and limitations of our exact approach on both real-world and random networks. Our formulations are also tested against an existing exact method.
WEMAC: Women and Emotion Multi-modal Affective Computing dataset

Among the seventeen Sustainable Development Goals (SDGs) proposed within the 2030 Agenda and adopted by all the United Nations member states, the Fifth SDG is a call for action to turn Gender Equality into a fundamental human right and an essential foundation for a better world. It includes the eradication of all types of violence against women. Within this context, the UC3M4Safety research team aims to develop Bindi. This is a cyber-physical system which includes embedded Artificial Intelligence algorithms, for user real-time monitoring towards the detection of affective states, with the ultimate goal of achieving the early detection of risk situations for women. On this basis, we make use of wearable affective computing including smart sensors, data encryption for secure and accurate collection of presumed crime evidence, as well as the remote connection to protecting agents. Towards the development of such system, the recordings of different laboratory and into-the-wild datasets are in process. These are contained within the UC3M4Safety Database. Thus, this paper presents and details the first release of WEMAC, a novel multi-modal dataset, which comprises a laboratory-based experiment for 47 women volunteers that were exposed to validated audio-visual stimuli to induce real emotions by using a virtual reality headset while physiological, speech signals and self-reports were acquired and collected. We believe this dataset will serve and assist research on multi-modal affective computing using physiological and speech information.
Self-Repelling Elastic Manifolds with Low Dimensional Range

We consider self-repelling elastic manifolds with a domain $[-N,N]^d \cap \mathbb{Z}^d$, that take values in $\mathbb{R}^D$. Our main result states that when the dimension of the domain is $d=2$ and the dimension of the range is $D=1$, the effective radius $R_N$ of the manifold is approximately $N^{4/3}$. This verifies the conjecture of Kantor, Kardar and Nelson [7]. Our results for the case where $d \geq 3$ and $D<d$ give a lower bound on $R_N$ of order $N^{\frac{1}{D} \left(d-\frac{2(d-D)}{D+2} \right)}$ and an upper bound proportional to $N^{\frac{d}{2}+\frac{d-D}{D+2}}$. These results imply that self-repelling elastic manifolds with a low dimensional range undergo a significantly stronger stretching than in the case where $d=D$, which was studied by the authors in [10].
The Right Spin: Learning Object Motion from Rotation-Compensated Flow Fields

A good understanding of geometrical concepts as well as a broad familiarity with objects lead to excellent human perception of moving objects. The human ability to detect and segment moving objects works in the presence of multiple objects, complex background geometry, motion of the observer and even camouflage. How we perceive moving objects so reliably is a longstanding research question in computer vision and borrows findings from related areas such as psychology, cognitive science and physics. One approach to the problem is to teach a deep network to model all of these effects. This is in contrast with the strategy used by human vision, where cognitive processes and body design are tightly coupled and each is responsible for certain aspects of correctly identifying moving objects. Similarly, from the computer vision perspective there is evidence that classical, geometry-based techniques are better suited to the “motion-based” parts of the problem, while deep networks are more suitable for modeling appearance. In this work, we argue that the coupling of camera rotation and camera translation can create complex motion fields that are difficult for a deep network to untangle directly. We present a novel probabilistic model to estimate the camera’s rotation given the motion field. We then rectify the flow field to obtain a rotation-compensated motion field for subsequent segmentation. This strategy of first estimating camera motion, and then allowing a network to learn the remaining parts of the problem, yields improved results on the widely used DAVIS benchmark as well as the more recent motion segmentation data set MoCA (Moving Camouflaged Animals).
The XP Stabiliser Formalism: a Generalisation of the Pauli Stabiliser Formalism with Arbitrary Phases

We propose an extension to the Pauli stabiliser formalism that includes fractional 2π/N rotations around the Z axis for some integer N. The resulting generalised stabiliser formalism – denoted the XP stabiliser formalism – allows for a wider range of states and codespaces to be represented. We describe the states which arise in the formalism, and demonstrate an equivalence between XP stabiliser states and 'weighted hypergraph states' – a generalisation of both hypergraph and weighted graph states. Given an arbitrary set of XP operators, we present algorithms for determining the codespace and logical operators for an XP code. Finally, we consider whether measurements of XP operators on XP codes can be classically simulated.
Reasonable Space for the 𝜆 -Calculus, Logarithmically

Can the 𝜆 -calculus be considered a reasonable computational model? Can we use it for measuring the time and space consumption of algorithms? While the literature contains positive answers about time, much less is known about space. This paper presents a new reasonable space cost model for the 𝜆 -calculus, based on a variant over the Krivine abstract machine. For the ﬁrst time, this cost model is able to accommodate logarithmic space. Moreover, we study the time behavior of our machine and show how to trans-port our results to the call-by-value 𝜆 -calculus.
Rectifying homographies for stereo vision: analytical solution for minimal distortion

Stereo rectification is the determination of two image transformations (or homographies) that map corresponding points on the two images, projections of the same point in the 3D space, onto the same horizontal line in the transformed images. Rectification is used to simplify the subsequent stereo correspondence problem and speeding up the matching process. Rectifying transformations, in general, introduce perspective distortion on the obtained images, which shall be minimised to improve the accuracy of the following algorithm dealing with the stereo correspondence problem. The search for the optimal transformations is usually carried out relying on numerical optimisation. This work proposes a closed-form solution for the rectifying homographies that minimise perspective distortion. The experimental comparison confirms its capability to solve the convergence issues of the previous formulation. Its Python implementation is provided.
Overleaf Example

Tensegrity robots, composed of rigid rods and flexible cables, are difficult to accurately model and control given the presence of complex dynamics and high number of DoFs. Differentiable physics engines have been recently proposed as a data-driven approach for model identification of such complex robotic systems. These engines are often executed at a high-frequency to achieve accurate simulation. Ground truth trajectories for training differentiable engines, however, are not typically available at such high frequencies due to limitations of real-world sensors. The present work focuses on this frequency mismatch, which impacts the modeling accuracy. We proposed a recurrent structure for a differentiable physics engine of tensegrity robots, which can be trained effectively even with low-frequency trajectories. To train this new recurrent engine in a robust way, this work introduces relative to prior work: (i) a new implicit integration scheme, (ii) a progressive training pipeline, and (iii) a differentiable collision checker. A model of NASA’s icosahedron SUPERballBot on MuJoCo is used as the ground truth system to collect training data. Simulated experiments show that once the recurrent differentiable engine has been trained given the low-frequency trajectories from MuJoCo, it is able to match the behavior of MuJoCo’s system. The criterion for success is whether a locomotion strategy learned using the differentiable engine can be transferred back to the ground-truth system and result in a similar motion. Notably, the amount of ground truth data needed to train the differentiable engine, such that the policy is transferable to the ground truth system, is 1% of the data needed to train the policy directly on the ground-truth system.
Multi-Channel Man-in-the-Middle Attacks Against Protected Wi-Fi Networks: A State of the Art Review

Multi-Channel Man-in-the-Middle (MitM) attacks are special MitM attacks capable of manipulating encrypted wireless frames between two legitimate endpoints. Since its inception in 2014, attackers have been targeting Wi-Fi networks to perform different attacks, such as cipher downgrades, denial of service, key reinstallation attacks (KRACK) in 2017, and recently FragAttacks in 2021, which widely impacted millions of Wi-Fi devices, especially IoT devices. To the best of our knowledge, there are no studies in the literature that holistically review the different types of Multi-Channel MitM enabled attacks and analyze their potential impact. To this end, we evaluate the capabilities of Multi-Channel MitM and review every reported attack in the state of the art. We examine practical issues that hamper the total adoption of protection mechanisms, i.e., security patches and Protected Management Frames (PMF), and review available defense mechanisms in confronting the Multi-Channel MitM enabled attacks in the IoT context. Finally, we highlight the potential research problems and identify future research lines in this field.
se-Shweshwe Inspired Fashion Generation

Fashion is one of the ways in which we show ourselves to the world. It is a reflection of our personal decisions and one of the ways in which people distinguish and represent themselves. In this paper, we focus on the fashion design process and expand computer vision for fashion beyond its current focus on western fashion. We discuss the history of Southern African se-Shweshwe fabric fashion, the collection of a se-Shweshwe dataset, and the application of sketch-to-design image generation for affordable fashion-design. The application to fashion raises both technical questions of training with small amounts of data, and also important questions for computer vision beyond fairness, in particular ethical considerations on creating and employing fashion datasets, and how computer vision supports cultural representation and might avoid algorithmic cultural appropriation.
Automorphisms and representations of quasi Laurent polynomial algebras

We study automorphisms and representations of quasi polynomial algebras (QPAs) and quasi Laurent polynomial algebras (QLPAs). For any QLPA defined by an arbitrary skew symmetric integral matrix, we explicitly describe its automorphism groups at generic q and at roots of unity. Any QLPA is isomorphic to the tensor product of copies of the QLPA of degree 2 at different powers of q and the centre, thus the study of representations of QPAs and QLPAs largely reduces to that of Lq(2) andAq(2), the QLPA and QPA of degree 2. We study a category of Aq(2)-modules which have finite covers by submodules with natural local finiteness properties and satisfy some condition under localisation, determining its blocks, classifying the simple objects and providing two explicitly constructions for the simples. One construction produces the simpleAq(2)-modules from Lq(2)-modules via monomorphisms composed of the natural embedding of Aq(2) in Lq(2) and automorphisms of Lq(2), and the other explores a class of holonomic Dq-modules for the algebra Dq of q-differential operators.
GA-DRL: Genetic Algorithm-Based Function Optimizer in Deep Reinforcement Learning for Robotic Manipulation Tasks

—Reinforcement learning (RL) enables agents to make a decision based on a reward function. However, in the process of learning, the choice of values for learning algorithm parameters can signiﬁcantly impact the overall learning process. In this paper, we proposed a Genetic Algorithm-based Deep Deterministic Policy Gradient and Hindsight Experience Replay method (called GA-DRL) to ﬁnd near-optimal values of learning parameters. We used the proposed GA-DRL method on fetch-reach, slide, push, pick and place, and door opening in robotic manipulation tasks. With some modiﬁcations, our proposed GA-DRL method was also applied to the aubo-reach environment. Our experimental evaluation shows that our method leads to signiﬁcantly better performance, faster than the original algorithm. Also, we provide evidence that GA-DRL performs better than the existing methods.
Atmospheric Aerosol Clearing by Femtosecond Filaments

Atmospheric aerosols, such as water droplets in fog, interfere with laser propagation through scattering and absorption. Femtosecond optical ﬁlaments are shown to clear foggy regions, improving the transmission of subsequent pulses. However, the detailed fog-clearing mechanism had yet to be determined. Here, we directly measure and simulate the dynamics of water droplets with a radius of about 5 μ m, typical of fog, under the inﬂuence of optical and acoustic interactions that are characteristic of femtosecond ﬁlaments. We ﬁnd that, for ﬁlaments generated by the collapse of collimated near-infrared femtosecond pulses, the main droplet-clearing mechanism is optical shattering by laser light. For such ﬁlaments, the single-cycle acoustic wave launched by ﬁlament-energy deposition in air leaves droplets intact and drives negligible transverse displacement, and therefore, negligible fog clearing. Only for tightly focused nonﬁlamentary pulses, where local energy deposition greatly exceeds that of a ﬁlament, do acoustic waves signiﬁcantly displace aerosols.
Fast Bayesian estimation of brain activation with cortical surface and subcortical fMRI data using EM

Analysis of brain imaging scans is critical to understanding the way the human brain functions, which can be leveraged to treat injuries and conditions that affect the quality of life for a significant portion of the human population. In particular, functional magnetic resonance imaging (fMRI) scans give detailed data on a living subject at high spatial and temporal resolutions. Due to the high cost involved in the collection of these scans, robust methods of analysis are of critical importance in order to produce meaningful inference. Bayesian methods in particular allow for the inclusion of expected behavior from prior study into an analysis, increasing the power of the results while circumventing problems that arise in classical analyses, including the effects of smoothing results and sensitivity to multiple comparison testing corrections. Recent development of a surface-based spatial Bayesian general linear model for cortical surface fMRI (cs-fMRI) data provides the desired power increase in task fMRI data using stochastic partial differential equation (SPDE) priors. This model relies on the computational efficiencies of the integrated nested Laplace approximation (INLA) to perform powerful analyses that have been validated to outperform classical analyses. In this article, we develop an exact Bayesian analysis method for the GLM, employing an expectation-maximization (EM) algorithm to find maximum a posteriori (MAP) estimates of task-based regressors on cs-fMRI and subcortical fMRI data while using minimal computational resources. Our proposed method is compared to the INLA implementation of the Bayesian GLM, as well as a classical GLM on simulated data. A validation of the method on data from the Human Connectome Project is also provided. Keywords— fMRI studies, expectation maximization, brain activation, SPDE prior, general linear model 1 ar X iv :2 20 3. 00 05 3v 1 [ st at . M E ] 2 8 Fe b 20 22
Enhancing Local Feature Learning for 3D Point Cloud Processing using Unary-Pairwise Attention

We present a simple but effective attention named the unary-pairwise attention (UPA) for modeling the relationship between 3D point clouds. Our idea is motivated by the analysis that the standard self-attention (SA) that operates globally tends to produce almost the same attention maps for different query positions, revealing difficulties for learning query-independent and query-dependent information jointly. Therefore, we reformulate the SA and propose query-independent (Unary) and query-dependent (Pairwise) components to facilitate the learning of both terms. In contrast to the SA, the UPA ensures query dependence via operating locally. Extensive experiments show that the UPA outperforms the SA consistently on various point cloud understanding tasks including shape classification, part segmentation, and scene segmentation. Moreover, simply equipping the popular PointNet++ method with the UPA even outperforms or is on par with the state-of-the-art attention-based approaches. In addition, the UPA systematically boosts the performance of both standard and modern networks when it is integrated into them as a compositional module.
Tello Leg: The Study of Design Principles and Metrics for Dynamic Humanoid Robots

To be useful tools in real scenarios, humanoid robots must realize tasks dynamically. This means that they must be capable of applying substantial forces and also mitigating impacts that may occur during the motion. Towards creating capable humanoids, this letter presents the leg of the robot Tello and demonstrates how it embodies two new fundamental design concepts for dynamic legged robots. The limbs follow the principles of: (i) Cooperative Actuation (CA), by combining motors in differential configurations to increase the force capability of the limb. We demonstrate that the CA configuration requires half the motor torque to perform a jump in comparison to conventional serial design configurations. And (ii) proximal actuation, by placing heavy motors near the body to reduce the inertia of the limb. To quantify the effect of motor placement on the robot’s dynamics, we introduce a novel metric entitled Centroidal Inertia Isotropy (CII). We show that the design of state-of-the-art dynamic legged robots empirically reduces the range of CII to improve agility and facilitate model-based control. We hope this metric will enable a quantifiable way to design these machines in the future.
Interactive Human-in-the-loop Coordination of Manipulation Skills Learned from Demonstration

Learning from demonstration (LfD) provides a fast, intuitive and efficient framework to program robot skills, which has gained growing interest both in research and industrial applications. Most complex manipulation tasks are long-term and involve a set of skill primitives. Thus it is crucial to have a reliable coordination scheme that selects the correct sequence of skill primitive and the correct parameters for each skill, under various scenarios. Instead of relying on a precise simulator, this work proposes a human-in-the-loop coordination framework for LfD skills that: builds parameterized skill models from kinesthetic demonstrations; constructs a geometric task network (GTN) on-the-fly from human instructions; learns a hierarchical control policy incrementally during execution. This framework can reduce significantly the manual design efforts, while improving the adaptability to new scenes. We show on a 7-DoF robotic manipulator that the proposed approach can teach complex industrial tasks such as bin sorting and assembly in less than 30 minutes.
Modeling the kinematics of globular cluster systems

Globular clusters (GCs) are old massive star clusters that serve as ‘fossils’ of galaxy formation. The advent of Gaia observatory has enabled detailed kinematics studies of the Galactic GCs and revolutionized our understanding of the connections between GC properties and galaxy assembly. However, lack of kinematic measurements of extragalactic GCs limits the sample size of GC systems that we can fully study. In this work, we present a model for GC formation and evolution, which includes positional and kinematic information of individual GCs by assigning them to particles in the Illustris TNG50-1 simulation based on age and location. We calibrate the three adjustable model parameters using observed properties of the Galactic and extragalactic GC systems, including the distributions of position, systemic velocity, velocity dispersion, anisotropy parameter, orbital actions, and metallicities. We also analyse the properties of GCs from different origins. In outer galaxy, ex-situ clusters are more dominant than the clusters formed in-situ. This leads to the GC metallicities decreasing outwards due to the increasing abundance of accreted, metal-poor clusters. We also find the ex-situ GCs to have greater velocity dispersions and orbital actions, in agreement with their accretion origin.
Bargmann Representation of Spin Chains

Spin chain Hamiltonians can be written in terms of complex differential operators using the Bargmann representation of the Jordan-Schwinger map. In this case, the eigenfunctions are expressed as the product of orthonormal monomials of the phase-space coordinates $z_{i}=Q_{i}+i P_{i}$ in the complex plane. Furthermore, the series constructed from each phase-space coordinate converges uniformly in any compact domain of the complex plane. Formulating spin chains with respect to the phase-space coordinates helps in discussing their classical limit and in the calculations of quasi-probability distributions.