Miracle-dz/UniFAR-FTUs huggingface dataset:
Gated Attention Coding for Training High-performance and Efficient Spiking Neural Networks

Spiking neural networks (SNNs) are emerging as an energy-efficient alternative to traditional artificial neural networks (ANNs) due to their unique spike-based event-driven nature. Coding is crucial in SNNs as it converts external input stimuli into spatio-temporal feature sequences. However, most existing deep SNNs rely on direct coding that generates powerless spike representation and lacks the temporal dynamics inherent in human vision. Hence, we introduce Gated Attention Coding (GAC), a plug-and-play module that leverages the multi-dimensional gated attention unit to efficiently encode inputs into powerful representations before feeding them into the SNN architecture. GAC functions as a preprocessing layer that does not disrupt the spike-driven nature of the SNN, making it amenable to efficient neuromorphic hardware implementation with minimal modifications. Through an observer model theoretical analysis, we demonstrate GAC's attention mechanism improves temporal dynamics and coding efficiency. Experiments on CIFAR10/100 and ImageNet datasets demonstrate that GAC achieves state-of-the-art accuracy with remarkable efficiency. Notably, we improve top-1 accuracy by 3.10% on CIFAR100 with only 6-time steps and 1.07% on ImageNet while reducing energy usage to 66.9% of the previous works. To our best knowledge, it is the first time to explore the attention-based dynamic coding scheme in deep SNNs, with exceptional effectiveness and efficiency on large-scale datasets. Code is available at https://github.com/bollossom/GAC.
LA TRANSICIÓN MEDITERRÁNEO-EUROSIBERIANAEN NAVARRA: CARACTERIZACIÓNDE LOS TOMILLARES Y ALIAGARES SUBMEDITERRÁNEOS(THYMELAEO­ APHYLLANTHETUM MONSPELIENSIS)

Structure, flora, ecology and dynamics of submediterranean Thymus vulgaris garrigues ( Thymelaeo-Aphyllanthetum monspeliensis ) are analysed and compared with two groups of calcicolous scrubs, based upon 391 releves from Navarre (NE Spain). Those submediterranean Thymus vulgaris garrigues are more diverse, have higher proportion of hemicryptophytes than cushion heaths of Genista occidentalis ( Genistion occidentalis ) and show wide ecological differences, as they thrive at lower altitudes, on less inclined slopes with preference for southward exposures. They are distinguished from the more xerophillous somontano-aragones Thymus vulgaris garrigues ( Aphyllanthion ) by their higher frequency of Atlantic and Eurosiberian taxa, lower proportion of nano-phanerophytes and affinity for more humid climates. There is a gradual change in the flora and structure of those three scrub groups related with the Eurosiberian-mediterranean climatic transition. Thymelaeo-Aphyllanthetum floristic composition has higher frequency of character taxa of Rosmarinetea and Rosmarinetalia than those of Festuco-Ononidetea striatae and Ononidetalia striatae , and this feature together with its structure justify the inclusion of this community in Aphyllanthion alliance, pointing out their relationship with somontano-aragones Thymus vulgaris garrigues.
On a generalization of Ulrich modules and its applications

We study a modified version of the classical Ulrich modules, which we call $c$-Ulrich. Unlike the traditional setting, $c$-Ulrich modules always exist. We prove that these modules retain many of the essential properties and applications observed in the literature. Additionally, we reveal their significance as obstructions to Cohen-Macaulay properties of tensor products. Leveraging this insight, we show the utility of these modules in testing the finiteness of homological dimensions across various scenarios.
Large Deformation Dynamic Analysis Method for Partially Saturated Elasto-Viscoplastic Soils

In most of the geotechnical problems there are several complexities which make the computational analysis of earth structures complicated. First, geomaterials usually fall into the field of multiphase porous materials. Second, the constitutive behavior of soil is nonlinear and rate-dependent. Third, most of earth structures must be designed and constructed to resist the effects of large ground motions. Finally, they can experience large deformation during large ground motions. The main objective of this study is to develop a method to concern all the four mentioned challenges in computational geomechanics. In this study, a cyclic elasto-viscoplastic model and its application is presented to simulate the behavior of fully saturated and partially saturated soil specimens under cyclic triaxial loading conditions. In the constitutive model, the skeleton stress and suction are adopted as the basic stress variables. The collapse behavior, which occurs with a decrease in suction, is considered with the shrinkage of the overconsolidation boundary surface, the static yield function, and the viscoplastic potential surface. In this research, the finite element formulation suitable for large deformation dynamic analysis is extended to the case of multiphase materials. Base on the formulation, a new computer program entitled “COMVI3D-DY011” is developed for the large deformation dynamic analysis of partially saturated elasto-viscoplastic soils. Using “COMVI3D-DY011”, the dynamic strain localization phenomenon in saturated and partially saturated clay is studied. In addition, mesh-size dependency is analyzed to provide stable and convergent solutions. Also, the effects of the initial suction, loading rate, and confining pressure on shear band development are studied and discussed. By comparing the saturated and partially saturated cases, it is seen that in partially saturated clay the strain localizes more prominently, and more brittleness, i.e. strain softening, is observed due to the collapse of suction force caused by shearing. In this dissertation, the potential failure modes affecting embankments as a consequence of earthquake shaking is, also, presented and discussed. Furthermore, the seismic behavior of a partially saturated embankment on non-liquefiable soft clay is numerically studied in two cases with different ground water levels. As the results of this simulation, the subsidence of the crest and lateral stretching is observed in the embankment. i
Performance Analysis for Resource Constrained Decentralized Federated Learning Over Wireless Networks

Federated learning (FL) can lead to significant communication overhead and reliance on a central server. To address these challenges, decentralized federated learning (DFL) has been proposed as a more resilient framework. DFL involves parameter exchange between devices through a wireless network. This study analyzes the performance of resource-constrained DFL using different communication schemes (digital and analog) over wireless networks to optimize communication efficiency. Specifically, we provide convergence bounds for both digital and analog transmission approaches, enabling analysis of the model performance trained on DFL. Furthermore, for digital transmission, we investigate and analyze resource allocation between computation and communication and convergence rates, obtaining its communication complexity and the minimum probability of correction communication required for convergence guarantee. For analog transmission, we discuss the impact of channel fading and noise on the model performance and the maximum errors accumulation with convergence guarantee over fading channels. Finally, we conduct numerical simulations to evaluate the performance and convergence rate of convolutional neural networks (CNNs) and Vision Transformer (ViT) trained in the DFL framework on fashion-MNIST and CIFAR-10 datasets. Our simulation results validate our analysis and discussion, revealing how to improve performance by optimizing system parameters under different communication conditions.
On Darmon's program for the Generalized Fermat equation, II

We obtain additional Diophantine applications of the methods surrounding Darmon's program for the generalized Fermat equation developed in the first part of this series of papers. As a first application, we use a multi-Frey approach combining two Frey elliptic curves over totally real fields, a Frey hyperelliptic curve over $\mathbb{Q}$ due to Kraus, and ideas from the Darmon program to give a complete resolution of the generalized Fermat equation $$x^7 + y^7 = 3 z^n$$ for all integers $n \ge 2$. Moreover, we explain how the use of higher dimensional Frey abelian varieties allows a more efficient proof of this result due to additional structures that they afford, compared to using only Frey elliptic curves. As a second application, we use some of these additional structures that Frey abelian varieties possess to show that a full resolution of the generalized Fermat equation $x^7 + y^7 = z^n$ depends only on the Cartan case of Darmon's big image conjecture. In the process, we solve the previous equation for solutions $(a,b,c)$ such that $a$ and $b$ satisfy certain $2$ or $7$-adic conditions and all $n \ge 2$.
Qibolab: an open-source hybrid quantum operating system

We present Qibolab, an open-source software library for quantum hardware control integrated with the Qibo quantum computing middleware framework. Qibolab provides the software layer required to automatically execute circuit-based algorithms on custom self-hosted quantum hardware platforms. We introduce a set of objects designed to provide programmatic access to quantum control through pulses-oriented drivers for instruments, transpilers and optimization algorithms. Qibolab enables experimentalists and developers to delegate all complex aspects of hardware implementation to the library so they can standardize the deployment of quantum computing algorithms in a extensible hardware-agnostic way, using superconducting qubits as the first officially supported quantum technology. We first describe the status of all components of the library, then we show examples of control setup for superconducting qubits platforms. Finally, we present successful application results related to circuit-based algorithms.
Towards visual localization, mapping and moving objects tracking by a mobile robot: a geometric and probabilistic approach

In this thesis we give new means for a machine to understand complex and dynamic visual scenes in real time. In particular, we solve the problem of simultaneously reconstructing a certain representation of the world's geometry, the observer's trajectory, and the moving objects' structures and trajectories, with the aid of vision exteroceptive sensors. We proceeded by dividing the problem into three main steps: First, we give a solution to the Simultaneous Localization And Mapping problem (SLAM) for monocular vision that is able to adequately perform in the most ill-conditioned situations: those where the observer approaches the scene in straight line. Second, we incorporate full 3D instantaneous observability by duplicating vision hardware with monocular algorithms. This permits us to avoid some of the inherent drawbacks of classic stereo systems, notably their limited range of 3D observability and the necessity of frequent mechanical calibration. Third, we add detection and tracking of moving objects by making use of this full 3D observability, whose necessity we judge almost inevitable. We choose a sparse, punctual representation of both the world and the moving objects in order to alleviate the computational payload of the image processing algorithms, which are required to extract the necessary geometrical information out of the images. This alleviation is additionally supported by active feature detection and search mechanisms which focus the attention to those image regions with the highest interest. This focusing is achieved by an extensive exploitation of the current knowledge available on the system (all the mapped information), something that we finally highlight to be the ultimate key to success.
Modular System Synthesis

This paper describes a way to improve the scalability of program synthesis by exploiting modutarity: larger programs are synthesized from smaller programs. The key issue is to make each “larger-created-from-smaller” synthesis sub-problem be of a similar nature, so that the kind of synthesis sub-problem that needs to be solved–and the size of each search space–has roughly the same character at each level. This work holds promise for creating program-synthesis tools that have far greater capabilities than currently available tools, and opens new avenues for synthesis research: how synthesis tools should support modular system design, and how synthesis applications can best exploit such capabilities.
Neural radiance fields in the industrial and robotics domain: applications, research opportunities and use cases

The proliferation of technologies, such as extended reality (XR), has increased the demand for high-quality three-dimensional (3D) graphical representations. Industrial 3D applications encompass computer-aided design (CAD), finite element analysis (FEA), scanning, and robotics. However, current methods employed for industrial 3D representations suffer from high implementation costs and reliance on manual human input for accurate 3D modeling. To address these challenges, neural radiance fields (NeRFs) have emerged as a promising approach for learning 3D scene representations based on provided training 2D images. Despite a growing interest in NeRFs, their potential applications in various industrial subdomains are still unexplored. In this paper, we deliver a comprehensive examination of NeRF industrial applications while also providing direction for future research endeavors. We also present a series of proof-of-concept experiments that demonstrate the potential of NeRFs in the industrial domain. These experiments include NeRF-based video compression techniques and using NeRFs for 3D motion estimation in the context of collision avoidance. In the video compression experiment, our results show compression savings up to 48\% and 74\% for resolutions of 1920x1080 and 300x168, respectively. The motion estimation experiment used a 3D animation of a robotic arm to train Dynamic-NeRF (D-NeRF) and achieved an average peak signal-to-noise ratio (PSNR) of disparity map with the value of 23 dB and an structural similarity index measure (SSIM) 0.97.
Ensemble of expanded ensembles: A generalized ensemble approach with enhanced flexibility and parallelizability

Over the past decade, alchemical free energy methods like Hamiltonian replica exchange (HREX) and expanded ensemble (EXE) have gained popularity for the computation of solvation free energies and binding free energies. These methods connect the end states of interest via nonphysical pathways defined by states with different modified Hamiltonians. However, there exist systems where traversing all alchemical intermediate states is challenging, even if alchemical biases (e.g., in EXE) or coordinate exchanges (e.g., in HREX) are applied. This issue is exacerbated when the state space is multidimensional, which can require extensive communications between hundreds of cores that current parallelization schemes do not fully support. To address this challenge, we present the method of ensemble of expanded ensembles (EEXE), which integrates the principles of EXE and HREX. Specifically, the EEXE method periodically exchanges coordinates of EXE replicas sampling different ranges of states and allows combining weights across replicas. With the solvation free energy calculation of anthracene, we show that the EEXE method achieves accuracy akin to the EXE and HREX methods in free energy calculations, while offering higher flexibility in parameter specification. Additionally, its parallelizability opens the door to wider applications, such as estimating free energy profiles of serial mutations. Importantly, extensions to the EEXE approach can be done asynchronously, allowing looser communications between larger numbers of loosely coupled processors, such as when using cloud computing, than methods such as replica exchange. They also allow adaptive changes to the parameters of ensembles in response to data collected. All algorithms for the EEXE method are available in the Python package ensemble_md, which offers an interface for EEXE simulation management without modifying the source code in GROMACS.
A Brief Wellbeing Training Session Delivered by a Humanoid Social Robot: A Pilot Randomized Controlled Trial

Mental health and psychological distress are rising in adults, showing the importance of wellbeing promotion, support, and technique practice that is effective and accessible. Interactive social robots have been tested to deliver health programs but have not been explored to deliver wellbeing technique training in detail. A pilot randomised controlled trial was conducted to explore the feasibility of an autonomous humanoid social robot to deliver a brief mindful breathing technique to promote information around wellbeing. It contained two conditions: brief technique training (‘Technique’) and control designed to represent a simple wait-list activity to represent a relationship-building discussion (‘Simple Rapport’). This trial also explored willingness to discuss health-related topics with a robot. Recruitment uptake rate through convenience sampling was high (53%). A total of 230 participants took part (mean age = 29 years) with 71% being higher education students. There were moderate ratings of technique enjoyment, perceived usefulness, and likelihood to repeat the technique again. Interaction effects were found across measures with scores varying across gender and distress levels. Males with high distress and females with low distress who received the simple rapport activity reported greater comfort to discuss non-health topics than males with low distress and females with high distress. This trial marks a notable step towards the design and deployment of an autonomous wellbeing intervention to investigate the impact of a brief robot-delivered mindfulness training program for a sub-clinical population.
Regularization of Kriging interpolation on irregularly spaced data

Interpolation models are critical for a wide range of applications, from numerical optimization to artificial intelligence. The reliability of the provided interpolated value is of utmost importance, and it is crucial to avoid the insurgence of spurious noise. Noise sources can be prevented using proper countermeasures when the training set is designed, but the data sparsity is inevitable in some cases. A typical example is represented by the application of an optimization algorithm: the area where the minimum or maximum of the objective function is assumed to be present is where new data is abundantly added, but other areas of design variable space are significantly neglected. In these cases, a regularization of the interpolation model becomes absolutely crucial. In this paper we are presenting an approach for the regularization of an interpolator based on the control of its kernel function via the condition number of the self-correlation matrix.
Quantum thermodynamics: Inside-outside perspective

We introduce an energy-resolved variant of quantum thermodynamics for open systems strongly coupled to their baths. The approach generalizes the Landauer-Buttiker inside-outside duality method [Phys. Rev. Lett. 120, 107701 (2018)] to interacting systems subjected to arbitrary external driving. It is consistent with the underlying dynamical quantum transport description and is capable of overcoming limitations of the only other consistent approach [New J. Phys. 12, 013013 (2010)]. We illustrate viability of the generalized inside-outside method with numerical simulations for generic junction models.
Robustified ANNs Reveal Wormholes Between Human Category Percepts

The visual object category reports of artificial neural networks (ANNs) are notoriously sensitive to tiny, adversarial image perturbations. Because human category reports (aka human percepts) are thought to be insensitive to those same small-norm perturbations -- and locally stable in general -- this argues that ANNs are incomplete scientific models of human visual perception. Consistent with this, we show that when small-norm image perturbations are generated by standard ANN models, human object category percepts are indeed highly stable. However, in this very same"human-presumed-stable"regime, we find that robustified ANNs reliably discover low-norm image perturbations that strongly disrupt human percepts. These previously undetectable human perceptual disruptions are massive in amplitude, approaching the same level of sensitivity seen in robustified ANNs. Further, we show that robustified ANNs support precise perceptual state interventions: they guide the construction of low-norm image perturbations that strongly alter human category percepts toward specific prescribed percepts. These observations suggest that for arbitrary starting points in image space, there exists a set of nearby"wormholes", each leading the subject from their current category perceptual state into a semantically very different state. Moreover, contemporary ANN models of biological visual processing are now accurate enough to consistently guide us to those portals.
Parameterized Matroid-Constrained Maximum Coverage

In this paper, we introduce the concept of Density-Balanced Subset in a matroid, in which independent sets can be sampled so as to guarantee that (i) each element has the same probability to be sampled, and (ii) those events are negatively correlated. These Density-Balanced Subsets are subsets in the ground set of a matroid in which the traditional notion of uniform random sampling can be extended. We then provide an application of this concept to the Matroid-Constrained Maximum Coverage problem. In this problem, given a matroid $\mathcal{M} = (V, \mathcal{I})$ of rank $k$ on a ground set $V$ and a coverage function $f$ on $V$, the goal is to find an independent set $S \in \mathcal{I}$ maximizing $f(S)$. This problem is an important special case of the much-studied submodular function maximization problem subject to a matroid constraint; this is also a generalization of the maximum $k$-cover problem in a graph. In this paper, assuming that the coverage function has a bounded frequency $\mu$ (i.e., any element of the underlying universe of the coverage function appears in at most $\mu$ sets), we design a procedure, parameterized by some integer $\rho$, to extract in polynomial time an approximate kernel of size $\rho \cdot k$ that is guaranteed to contain a $1 - (\mu - 1)/\rho$ approximation of the optimal solution. This procedure can then be used to get a Fixed-Parameter Tractable Approximation Scheme (FPT-AS) providing a $1 - \varepsilon$ approximation in time $(\mu/\varepsilon)^{O(k)} \cdot |V|^{O(1)}$. This generalizes and improves the results of [Manurangsi, 2019] and [Huang and Sellier, 2022], providing the first FPT-AS working on an arbitrary matroid. Moreover, because of its simplicity, the kernel construction can be performed in the streaming setting.