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Parameterizing finger rolling and finger-object contacts in a differentiable manner is important for formulating dexterous manipulation as a trajectory optimization problem. In contrast to previous methods which often assume simplified geometries of the robot and object or do not explicitly model finger rolling, we propose a method to further extend the capabilities of dexterous manipulation by accounting for non-trivial geometries of both the robot and the object. By integrating the object's Signed Distance Field (SDF) with a sampling method, our method estimates contact and rolling-related variables and includes those in a trajectory optimization framework. This formulation naturally allows for the emergence of finger-rolling behaviors, enabling the robot to locally adjust the contact points. Our method is tested in a peg alignment task and a screwdriver turning task, where it outperforms the baselines in terms of achieving desired object configurations and avoiding dropping the object. We also successfully apply our method to a real-world screwdriver turning task, demonstrating its robustness to the sim2real gap.

In recent years, computational improvements have allowed for more nuanced, data-driven and geographically explicit agent-based simulations. So far, simulations have struggled to adequately represent the attributes that motivate the actions of the agents. In fact, existing population synthesis frameworks generate agent profiles limited to socio-demographic attributes. In this paper, we introduce a novel value-enriched population synthesis framework that integrates a motivational layer with the traditional individual and household socio-demographic layers. Our research highlights the significance of extending the profile of agents in synthetic populations by incorporating data on values, ideologies, opinions and vital priorities, which motivate the agents' behaviour. This motivational layer can help us develop a more nuanced decision-making mechanism for the agents in social simulation settings. Our methodology integrates microdata and macrodata within different Bayesian network structures. This contribution allows to generate synthetic populations with integrated value systems that preserve the inherent socio-demographic distributions of the real population in any specific region.

Higher-order interactions underlie complex phenomena in systems such as biological and artificial neural networks, but their study is challenging due to the lack of tractable standard models. By leveraging the maximum entropy principle in curved statistical manifolds, here we introduce curved neural networks as a class of prototypical models for studying higher-order phenomena. Through exact mean-field descriptions, we show that these curved neural networks implement a self-regulating annealing process that can accelerate memory retrieval, leading to explosive order-disorder phase transitions with multi-stability and hysteresis effects. Moreover, by analytically exploring their memory capacity using the replica trick near ferromagnetic and spin-glass phase boundaries, we demonstrate that these networks enhance memory capacity over the classical associative-memory networks. Overall, the proposed framework provides parsimonious models amenable to analytical study, revealing novel higher-order phenomena in complex network systems.

We analyze a long-lived hyper-massive neutron star merger remnant (post-merger lifetime $>250$ ms) that has been obtained via large eddy simulations with a gradient subgrid-scale model. We find a clear helicoidal magnetic field structure that is governed by the toroidal component of the magnetic field. Although no jet emerges during the simulation time, we observe at late times a significant increase of the poloidal component of the magnetic field at all scales. We also compare with the results of several binary neutron star simulations with moderate resolution of $120$~m, that are evolved up to $50$~ms after the merger, which differ in terms of the initial topology and strength of the magnetic field. We find that the best choice is an isotropic small-scale magnetic field distribution that mimics the turbulent state that generically develops during the merger. This initial configuration reaches a closer agreement with our high-resolution simulation results than the purely dipolar large-scale fields that are commonly employed in these type of simulations. This provides a recipe to perform such simulations avoiding the computationally expensive grids required to faithfully capture the amplification of the magnetic field by Kelvin-Helmholtz instabilities., Comment: 13 pages, 11 figures

Transformer-based models have demonstrated exceptional performance across diverse domains, becoming the state-of-the-art solution for addressing sequential machine learning problems. Even though we have a general understanding of the fundamental components in the transformer architecture, little is known about how they operate or what are their expected dynamics. Recently, there has been an increasing interest in exploring the relationship between attention mechanisms and Hopfield networks, promising to shed light on the statistical physics of transformer networks. However, to date, the dynamical regimes of transformer-like models have not been studied in depth. In this paper, we address this gap by using methods for the study of asymmetric Hopfield networks in nonequilibrium regimes --namely path integral methods over generating functionals, yielding dynamics governed by concurrent mean-field variables. Assuming 1-bit tokens and weights, we derive analytical approximations for the behavior of large self-attention neural networks coupled to a softmax output, which become exact in the large limit size. Our findings reveal nontrivial dynamical phenomena, including nonequilibrium phase transitions associated with chaotic bifurcations, even for very simple configurations with a few encoded features and a very short context window. Finally, we discuss the potential of our analytic approach to improve our understanding of the inner workings of transformer models, potentially reducing computational training costs and enhancing model interpretability.

Data centers increasingly host mutually distrustful users on shared infrastructure. A powerful tool to safeguard such users are digital signatures. Digital signatures have revolutionized Internet-scale applications, but current signatures are too slow for the growing genre of microsecond-scale systems in modern data centers. We propose DSig, the first digital signature system to achieve single-digit microsecond latency to sign, transmit, and verify signatures in data center systems. DSig is based on the observation that, in many data center applications, the signer of a message knows most of the time who will verify its signature. We introduce a new hybrid signature scheme that combines cheap single-use hash-based signatures verified in the foreground with traditional signatures pre-verified in the background. Compared to prior state-of-the-art signatures, DSig reduces signing time from 18.9 to 0.7 us and verification time from 35.6 to 5.1 us, while keeping signature transmission time below 2.5 us. Moreover, DSig achieves 2.5x higher signing throughput and 6.9x higher verification throughput than the state of the art. We use DSig to (a) bring auditability to two key-value stores (HERD and Redis) and a financial trading system (based on Liquibook) for 86% lower added latency than the state of the art, and (b) replace signatures in BFT broadcast and BFT replication, reducing their latency by 73% and 69%, respectively, Comment: To appear in the proceedings of OSDI '24. Authors listed in alphabetical order

In this work, we study the magnetocaloric effect applied to a magnetic working substance corresponding to a square lattice of spins with $Q$ possible orientations known as the ``$Q$-state clock model" where for $Q\geq 5$, the systems present the famous Berezinskii-Kosterlitz-Thouless phase (BKT). Thermodynamic quantities are obtained in exact form for a small lattice size of $L \times L$ with $L=3$ and by the mean-field approximation and Monte Carlo simulations for $Q$ pairs between 2 and 8 with $L = 3, 8, 16, 32$ with free boundary conditions, and magnetic fields varying between $B = 0$ and $1$ in natural units of the system. By obtaining the entropy, it is possible to quantify the caloric effect through an isothermal process in which the external magnetic field on the spin system is varied. In particular, we find the values of $Q$ that maximize the effect depending on the lattice size and the magnetic phase transitions related to maximizing the caloric phenomena. These indicate that in a small lattice (up to $\sim 7\times 7$), when $Q\geq 5$, the transition that maximizes the effect is related to ferromagnetic to BKT type. In contrast, transitioning from BKT to paramagnetic type increases the system's caloric response when we work with a larger lattice size.

ZrSiS has been identified as an exemplary Dirac nodal-line semimetal, in which the Dirac band crossings extend along a closed loop in momentum space. Recently, the topology of the Fermi surface of ZrSiS was uncovered in great detail by quantum oscillation studies. For a magnetic field along the tetragonal $c$ axis, a rich frequency spectrum was observed stemming from the principal electron and hole pockets, and multiple magnetic breakdown orbits. In this work we use uniaxial strain as a tuning parameter for the Fermi surface and the low energy excitations. We measure the magnetoresistance of a single crystal under tensile (up to 0.34 %) and compressive (up to -0.28 %) strain exerted along the $a$ axis and in magnetic fields up to 30 T. We observe a systematic weakening of the peak structure in the Shubnikov-de Haas frequency spectrum upon changing from compressive to tensile strain. This effect may be explained by a decrease in the effective quantum mobility upon decreasing the $c/a$ ratio, which is corroborated by a concurrent increase in the Dingle temperature., Comment: 18 pages, 11 figures, to be published in Physical Review B

We investigate the loss of spatial coherence of one-dimensional bosonic gases in optical lattices illuminated by a near-resonant excitation laser. Because the atoms recoil in a random direction after each spontaneous emission, the atomic momentum distribution progressively broadens. Equivalently, the spatial correlation function (the Fourier-conjugate quantity of the momentum distribution) progressively narrows down as more photons are scattered. Here we measure the correlation function of the matter field for fixed distances corresponding to nearest-neighbor (n-n) and next-nearest-neighbor (n-n-n) sites of the optical lattice as a function of time, hereafter called n-n and n-n-n correlators. For strongly interacting lattice gases, we find that the n-n correlator $C_1$ decays as a power-law at long times, $C_1\propto 1/t^{\alpha}$, in stark contrast with the exponential decay expected for independent particles. The power-law decay reflects a non-trivial dissipative many-body dynamics, where interactions change drastically the interplay between fluorescence destroying spatial coherence, and coherent tunnelling between neighboring sites restoring spatial coherence at short distances. The observed decay exponent $\alpha \approx 0.54(6) $ is in good agreement with the prediction $\alpha=1/2$ from a dissipative Bose-Hubbard model accounting for the fluorescence-induced decoherence. Furthermore, we find that the n-n correlator $C_1$ controls the n-n-n correlator $C_2$ through the relation $C_2 \approx C_1^2$, also in accordance with the dissipative Bose-Hubbard model., Comment: published in the special issue of Comptes Rendus Physique dedicated to Jean Dalibard's CNRS Gold Medal. Details of calculations, included as an appendix in the published article, are available in the ancillary file

In recent years, the supply and demand of electricity has significantly increased. As a result, the interconnecting grid infrastructure has required (and will continue to require) further expansion, while allowing for rapid resolution of unforeseen failures. Energy grid operators strive for networks that satisfy different levels of security requirements. In the case of N-1 security for medium voltage networks, the goal is to ensure the continued provision of electricity in the event of a single-link failure. However, the process of determining if networks are N-1 secure is known to scale polynomially in the network size. This poses restrictions if we increase our demand of the network. In that case, more computationally hard cases will occur in practice and the computation time also increases significantly. In this work, we explore the potential of quantum computers to provide a more scalable solution. In particular, we consider gate-based quantum computing, quantum annealing, and photonic quantum computing.

The most metal-poor stars provide valuable insights into the early chemical enrichment history of a system, carrying the chemical imprints of the first generations of supernovae. The most metal-poor region of the Sagittarius dwarf galaxy remains inadequately observed and characterised. To date, only $\sim4$ stars with [Fe/H]~$<-2.0$ have been chemically analysed with high-resolution spectroscopy. In this study, we present the most extensive chemical abundance analysis of 12 low-metallicity stars with metallicities down to [Fe/H]~$=-3.26$ and located in the main body of Sagittarius. These targets, selected from the Pristine Inner Galaxy Survey, were observed using the MIKE high-resolution spectrograph at the {\it Magellan-Clay} telescope, which allowed us to measure up to 17 chemical species. The chemical composition of these stars reflects the imprint of a variety of type~II supernovae (SNe~II). A combination of low- to intermediate-mass high-energy SNe and hypernovae ($\sim10-70\msun$) is required to account for the abundance patterns of the lighter elements up to the Fe-peak. The trend of the heavy elements suggests the involvement of compact binary merger events and fast-rotating (up to $\sim300\kms$) intermediate-mass to massive metal-poor stars ($\sim25-120\msun$) that are the sources of rapid and slow processes, respectively. Additionally, asymptotic giant branch stars contribute to a wide dispersion of [Ba/Mg] and [Ba/Eu]. The absence of an $\alpha-$knee in our data indicates that type Ia supernovae did not contribute in the very metal-poor region ([Fe/H]~$\leq-2.0$). However, they might have started to pollute the interstellar medium at [Fe/H]~$>-2.0$, given the relatively low [Co/Fe] in this metallicity region., Comment: Accepted for publication in A&A. New plot on [Eu/Mg]. Some refs updated

Next-generation accelerator concepts which hinge on the precise shaping of beam distributions, demand equally precise diagnostic methods capable of reconstructing beam distributions within 6-dimensional position-momentum spaces. However, the characterization of intricate features within 6-dimensional beam distributions using conventional diagnostic techniques necessitates hundreds of measurements, using many hours of valuable beam time. Novel phase space reconstruction techniques are needed to substantially reduce the number of measurements required to reconstruct detailed, high-dimensional beam features in order to resolve complex beam phenomena, and as feedback in precision beam shaping applications. In this study, we present a novel approach to reconstructing detailed 6-dimensional phase space distributions from experimental measurements using generative machine learning and differentiable beam dynamics simulations. We demonstrate that for a collection of synthetic beam distribution test cases that this approach can be used to resolve 6-dimensional phase space distributions using basic beam manipulations and as few as 20 2-dimensional measurements of the beam profile, without the need for prior data collection or model training. We also demonstrate an application of the reconstruction method in an experimental setting at the Argonne Wakefield Accelerator, where it is able to reconstruct the beam distribution and accurately predict previously unseen measurements 75x faster than previous methods.

In the realm of Artificial Intelligence (AI), the need for privacy and security in data processing has become paramount. As AI applications continue to expand, the collection and handling of sensitive data raise concerns about individual privacy protection. Federated Learning (FL) emerges as a promising solution to address these challenges by enabling decentralized model training on local devices, thus preserving data privacy. This paper introduces FLEX: a FLEXible Federated Learning Framework designed to provide maximum flexibility in FL research experiments. By offering customizable features for data distribution, privacy parameters, and communication strategies, FLEX empowers researchers to innovate and develop novel FL techniques. The framework also includes libraries for specific FL implementations including: (1) anomalies, (2) blockchain, (3) adversarial attacks and defences, (4) natural language processing and (5) decision trees, enhancing its versatility and applicability in various domains. Overall, FLEX represents a significant advancement in FL research, facilitating the development of robust and efficient FL applications., Comment: Submitted to Information Fusion

Resistive Random Access Memories (RRAMs) are being studied by the industry and academia because it is widely accepted that they are promising candidates for the next generation of high density nonvolatile memories. Taking into account the stochastic nature of mechanisms behind resistive switching, a new technique based on the use of functional data analysis has been developed to accurately model resistive memory device characteristics. Functional principal component analysis (FPCA) based on Karhunen-Loeve expansion is applied to obtain an orthogonal decomposition of the reset process in terms of uncorrelated scalar random variables. Then, the device current has been accurately described making use of just one variable presenting a modeling approach that can be very attractive from the circuit simulation viewpoint. The new method allows a comprehensive description of the stochastic variability of these devices by introducing a probability distribution that allows the simulation of the main parameter that is employed for the model implementation. A rigorous description of the mathematical theory behind the technique is given and its application for a broad set of experimental measurements is explained.

A functional linear discriminant analysis approach to classify a set of kinematic data (human movement curves of individuals performing different physical activities) is performed. Kinematic data, usually collected in linear acceleration or angular rotation format, can be identified with functions in a continuous domain (time, percentage of gait cycle, etc.). Since kinematic curves are measured in the same sample of individuals performing different activities, they are a clear example of functional data with repeated measures. On the other hand, the sample curves are observed with noise. Then, a roughness penalty might be necessary in order to provide a smooth estimation of the discriminant functions, which would make them more interpretable. Moreover, because of the infinite dimension of functional data, a reduction dimension technique should be considered. To solve these problems, we propose a multi-class approach for penalized functional partial least squares (FPLS) regression. Then linear discriminant analysis (LDA) will be performed on the estimated FPLS components. This methodology is motivated by two case studies. The first study considers the linear acceleration recorded every two seconds in 30 subjects, related to three different activities (walking, climbing stairs and down stairs). The second study works with the triaxial angular rotation, for each joint, in 51 children when they completed a cycle walking under three conditions (walking, carrying a backpack and pulling a trolley). A simulation study is also developed for comparing the performance of the proposed functional LDA with respect to the corresponding multivariate and non-penalized approaches.

Functional principal component analysis based on Karhunen Loeve expansion allows to describe the stochastic evolution of the main characteristics associated to multiple systems and devices. Identifying the probability distribution of the principal component scores is fundamental to characterize the whole process. The aim of this work is to consider a family of statistical distributions that could be accurately adjusted to a previous transformation. Then, a new class of distributions, the linear-phase-type, is introduced to model the principal components. This class is studied in detail in order to prove, through the KL expansion, that certain linear transformations of the process at each time point are phase-type distributed. This way, the one-dimensional distributions of the process are in the same linear-phase-type class. Finally, an application to model the reset process associated with resistive memories is developed and explained.

The homogeneity problem for testing if more than two different samples come from the same population is considered for the case of functional data. The methodological results are motivated by the study of homogeneity of electronic devices fabricated by different materials and active layer thicknesses. In the case of normality distribution of the stochastic processes associated with each sample, this problem is known as Functional ANOVA problem and is reduced to test the equality of the mean group functions (FANOVA). The problem is that the current/voltage curves associated with Resistive Random Access Memories (RRAM) are not generated by a Gaussian process so that a different approach is necessary for testing homogeneity. To solve this problem two different parametric and nonparametric approaches based on basis expansion of the sample curves are proposed. The first consists of testing multivariate homogeneity tests on a vector of basis coefficients of the sample curves. The second is based on dimension reduction by using functional principal component analysis of the sample curves (FPCA) and testing multivariate homogeneity on a vector of principal components scores. Different approximation numerical techniques are employed to adapt the experimental data for the statistical study. An extensive simulation study is developed for analyzing the performance of both approaches in the parametric and non-parametric cases. Finally, the proposed methodologies are applied on three samples of experimental reset curves measured in three different RRAM technologies.

In the last decades, there has been a deceleration in the rates of poverty reduction, suggesting that traditional redistributive approaches to poverty mitigation could be losing effectiveness, and alternative insights to advance the number one UN Sustainable Development Goal are required. The criminalization of poor people has been denounced by several NGOs, and an increasing number of voices suggest that discrimination against the poor (a phenomenon known as \emph{aporophobia}) could be an impediment to mitigating poverty. In this paper, we present the novel Aporophobia Agent-Based Model (AABM) to provide evidence of the correlation between aporophobia and poverty computationally. We present our use case built with real-world demographic data and poverty-mitigation public policies (either enforced or under parliamentary discussion) for the city of Barcelona. We classify policies as discriminatory or non-discriminatory against the poor, with the support of specialized NGOs, and we observe the results in the AABM in terms of the impact on wealth inequality. The simulation provides evidence of the relationship between aporophobia and the increase of wealth inequality levels, paving the way for a new generation of poverty reduction policies that act on discrimination and tackle poverty as a societal problem (not only a problem of the poor).

Beams with cross-plane coupling or extreme asymmetries between the two transverse phase spaces are often encountered in particle accelerators. Flat beams with large transverse-emittance ratios are critical for future linear colliders. Similarly, magnetized beams with significant cross-plane coupling are expected to enhance the performance of electron cooling in hadron beams. Preparing these beams requires precise control and characterization of the four-dimensional transverse phase space. In this study, we employ generative phase space reconstruction (GPSR) techniques to rapidly characterize magnetized and flat-beam phase-space distributions using a conventional quadrupole-scan method. The reconstruction technique is experimentally demonstrated on an electron beam produced at the Argonne Wakefield Accelerator and successfully benchmarked against conventional diagnostics techniques. Specifically, we show that predicted beam parameters from the reconstructed phase-space distributions (e.g. as magnetization and flat beam emittances) are in excellent agreement with those measured from the conventional diagnostic methods.

Individuals with Autism Spectrum Disorder (ASD) often experience challenges in health, communication, and sensory processing; therefore, early diagnosis is necessary for proper treatment and care. In this work, we consider the problem of detecting or classifying ASD children to aid medical professionals in early diagnosis. We develop a deep learning model that analyzes video clips of children reacting to sensory stimuli, with the intent of capturing key differences in reactions and behavior between ASD and non-ASD participants. Unlike many recent studies in ASD classification with MRI data, which require expensive specialized equipment, our method utilizes a powerful but relatively affordable GPU, a standard computer setup, and a video camera for inference. Results show that our model effectively generalizes and understands key differences in the distinct movements of the children. It is noteworthy that our model exhibits successful classification performance despite the limited amount of data for a deep learning problem and limited temporal information available for learning, even with the motion artifacts., Comment: Poster Abstract. Accepted into 2024 IEEE Green Technologies Conference

For each $n\in\mathbb{N}$, let $[n]\phi$ mean "the sentence $\phi$ is true in all $\Sigma_{n+1}$-correct transitive sets." Assuming G\"odel's axiom $V = L$, we prove the following graded variant of Solovay's completeness theorem: the set of formulas valid under this interpretation is precisely the set of theorems of the linear provability logic GLP.3. We also show that this result is not provable in ZFC, so the hypothesis V = L cannot be removed. As part of the proof, we derive (in ZFC) the following purely modal-logical results which are of independent interest: the logic GLP.3 coincides with the logic of closed substitutions of GLP, and is the maximal non-degenerate, normal extension of GLP., Comment: 20 pages, 4 figures

We propose a first-principles theoretical approach for the description of the aging of the linear viscoelastic properties of a colloidal liquid after a sudden quench into a dynamically arrested (glass or gel) state. Specifically, we couple a general expression for the time-evolving shear-stress relaxation function $\eta(\tau;t)$ (whose $\tau$-integral is the instantaneous viscosity $\eta(t)$), written in terms of the non-equilibrium structure factor $S(k;t)$ and intermediate scattering function $F(k,\tau;t)$, with the equations that determine $S(k;t)$ and $F(k,\tau;t)$, provided by the non-equilibrium self-consistent generalized Langevin equation (NE-SCGLE) theory. In this manner, we obtain a closed theoretical scheme that directly connects inter-particle forces with experimentally accessible rheological properties of non-equilibrium amorphous states of matter. The predictive capability of the resulting theoretical formalism is illustrated here with its concrete application to the Weeks-Chandler-Andersen (WCA) model of a soft-sphere fluid.

Faced with novel coronavirus outbreak, the most hard-hit countries adopted a lockdown strategy to contrast the spread of virus. Many studies have already documented that the COVID-19 control actions have resulted in improved air quality locally and around the world. Following these lines of research, we focus on air quality changes in the urban territory of Chieti-Pescara (Central Italy), identified as an area of criticality in terms of air pollution. Concentrations of NO2, PM10, PM2.5 and benzene are used to evaluate air pollution changes in this Region. Data were measured by several monitoring stations over two specific periods: from 1st February to 10 th March 2020 (before lockdown period) and from 11st March 2020 to 18 th April 2020 (during lockdown period). The impact of lockdown on air quality is assessed through functional data analysis. Our work makes an important contribution to the analysis of variance for functional data (FANOVA). Specifically, a novel approach based on multivariate functional principal component analysis is introduced to tackle the multivariate FANOVA problem for independent measures, which is reduced to test multivariate homogeneity on the vectors of the most explicative principal components scores. Results of the present study suggest that the level of each pollutant changed during the confinement. Additionally, the differences in the mean functions of all pollutants according to the location and type of monitoring stations (background vs traffic), are ascribable to the PM10 and benzene concentrations for pre-lockdown and during-lockdown tenure, respectively. FANOVA has proven to be beneficial to monitoring the evolution of air quality in both periods of time. This can help environmental protection agencies in drawing a more holistic picture of air quality status in the area of interest.

The functional logit regression model was proposed by Escabias et al. (2004) with the objective of modeling a scalar binary response variable from a functional predictor. The model estimation proposed in that case was performed in a subspace of L2(T) of squared integrable functions of finite dimension, generated by a finite set of basis functions. For that estimation it was assumed that the curves of the functional predictor and the functional parameter of the model belong to the same finite subspace. The estimation so obtained was affected by high multicollinearity problems and the solution given to these problems was based on different functional principal component analysis. The logitFD package introduced here provides a toolbox for the fit of these models by implementing the different proposed solutions and by generalizing the model proposed in 2004 to the case of several functional and non-functional predictors. The performance of the functions is illustrated by using data sets of functional data included in the fda.usc package from R-CRAN.

The methodological contribution in this paper is motivated by biomechanical studies where data characterizing human movement are waveform curves representing joint measures such as flexion angles, velocity, acceleration, and so on. In many cases the aim consists of detecting differences in gait patterns when several independent samples of subjects walk or run under different conditions (repeated measures). Classic kinematic studies often analyse discrete summaries of the sample curves discarding important information and providing biased results. As the sample data are obviously curves, a Functional Data Analysis approach is proposed to solve the problem of testing the equality of the mean curves of a functional variable observed on several independent groups under different treatments or time periods. A novel approach for Functional Analysis of Variance (FANOVA) for repeated measures that takes into account the complete curves is introduced. By assuming a basis expansion for each sample curve, two-way FANOVA problem is reduced to Multivariate ANOVA for the multivariate response of basis coefficients. Then, two different approaches for MANOVA with repeated measures are considered. Besides, an extensive simulation study is developed to check their performance. Finally, two applications with gait data are developed.

A powerful time series analysis modeling technique is presented to describe cycle-to-cycle variability in memristors. These devices show variability linked to the inherent stochasticity of device operation and it needs to be accurately modeled to build compact models for circuit simulation and design purposes. A new multivariate approach is proposed for the reset and set voltages that accurately describes the statistical data structure of a resistive switching series. Experimental data were measured from advanced hafnium oxide based devices. The models reproduce the experiments correctly and a comparison of the multivariate and univariate approaches is shown for comparison.

A new statistical approach has been developed to analyze Resistive Random Access Memory (RRAM) variability. The stochastic nature of the physical processes behind the operation of resistive memories makes variability one of the key issues to solve from the industrial viewpoint of these new devices. The statistical features of variability have been usually studied making use of Weibull distribution. However, this probability distribution does not work correctly for some resistive memories, in particular for those based on the Ni/HfO2/Si structure that has been employed in this work. A completely new approach based on phase-type modeling is proposed in this paper to characterize the randomness of resistive memories operation. An in-depth comparison with experimental results shows that the fitted phase-type distribution works better than the Weibull distribution and also helps to understand the physics of the resistive memories.

The task of determining the physical coordinates of a target in indoor environments is still a key factor for many applications including people and robot navigation, user tracking, location-based advertising, augmented reality, gaming, emergency response or ambient assisted living environments. Among the different possibilities for indoor positioning, Acoustic Local Positioning Systems (ALPS) have the potential for centimeter level positioning accuracy with coverage distances up to tens of meters. In addition, acoustic transducers are small, low cost and reliable thanks to the room constrained propagation of these mechanical waves. Waveform design (coding and modulation) is usually incorporated into these systems to facilitate the detection of the transmitted signals at the receiver. The aperiodic correlation properties of the emitted signals have a large impact on how the ALPS cope with common impairment factors such as multipath propagation, multiple access interference, Doppler shifting, near-far effect or ambient noise. This work analyzes three of the most promising families of codes found in the literature for ALPS: Kasami codes, Zadoff-Chu and Orthogonal Chirp signals. The performance of these codes is evaluated in terms of time of arrival accuracy and characterized by means of model simulation under realistic conditions and by means of experimental tests in controlled environments. The results derived from this study can be of interest for other applications based on spreading sequences, such as underwater acoustic systems, ultrasonic imaging or even Code Division Multiple Access (CDMA) communications systems.

This paper proposes a multipath compensation algorithm (MCA) to enhance the performance of an ultrasonic local positioning system under adverse multipath conditions. The proposed algorithm is based on the accurate estimation of the environment impulse response from which the corresponding line of sight for each channel is obtained. Experimental results in two different environments and with different conditions have been conducted in order to evaluate the performance of this proposal. In both environments, results confirm the expected improvements, even under severe multipath conditions where positioning errors have been reduced from 44 to 9 cm for the 95% of the measurements.

Acoustic local positioning systems (ALPSs) are an interesting alternative for indoor positioning due to certain advantages over other approaches, including their relatively high accuracy, low cost, and room-level signal propagation. Centimeter-level or fine-grained indoor positioning can be an asset for robot navigation, guiding a person to, for instance, a particular piece in a museum or to a specific product in a shop, targeted advertising, or augmented reality. In airborne system applications, acoustic positioning can be based on using opportunistic signals or sounds produced by the person or object to be located (e.g., noise from appliances or the speech from a speaker) or from encoded emission beacons (or anchors) specifically designed for this purpose. This work presents a review of the different challenges that designers of systems based on encoded emission beacons must address in order to achieve suitable performance. At low-level processing, the waveform design (coding and modulation) and the processing of the received signal are key factors to address such drawbacks as multipath propagation, multiple-access interference, nearfar effect, or Doppler shifting. With regards to high-level system design, the issues to be addressed are related to the distribution of beacons, ease of deployment, and calibration and positioning algorithms, including the possible fusion of information. Apart from theoretical discussions, this work also includes the description of an ALPS that was implemented, installed in a large area and tested for mobile robot navigation. In addition to practical interest for real applications, airborne ALPSs can also be used as an excellent platform to test complex algorithms, which can be subsequently adapted for other positioning systems, such as underwater acoustic systems or ultrawideband radiofrequency (UWB RF) systems.

Relevant and fundamental concepts of the statistical mechanical theory of classical liquids are ordinarily introduced in the context of the description of thermodynamic equilibrium states. This makes explicit reference to probability distribution functions of \emph{equilibrium} statistical ensembles (canonical, microcanonical, ...) in the derivation of general and fundamental relations between inter-particle interactions and measurable macroscopic properties of a given system. This includes, for instance, expressing the internal energy and the pressure as functionals of the radial distribution function, or writing transport coefficients (diffusion constant, linear viscosity, ...) in terms of integral relations involving both, static and dynamic auto-correlation functions (density-density, stress-stress, ...). Most commonly, however, matter is not in thermodynamic equilibrium, and this calls for the extension of these relations to out-of-equilibrium conditions with the aim of understanding, for example, the time-dependent transient states during the process of equilibration, or the aging of glass- and gel-forming liquids during the formation of non-equilibrium amorphous solid states. In this work we address this issue from both, a general perspective and an illustrative concrete application focused on the first principles description of rheological and viscoelastic properties of glass- and gel-forming liquids.

Physical activity plays a significant role in the well-being of individuals with Chronic obstructive Pulmonary Disease (COPD). Specifically, it has been directly associated with changes in hospitalization rates for these patients. However, previous investigations have primarily been conducted in a cross-sectional or longitudinal manner and have not considered a continuous perspective. Using the telEPOC program we use telemonitoring data to analyze the impact of physical activity adopting a functional data approach. However, Traditional functional data methods, including functional regression models, typically assume a consistent data domain. However, the data in the telEPOC program exhibits variable domains, presenting a challenge since the majority of functional data methods, are based on the fact that data are observed in the same domain. To address this challenge, we introduce a novel fully functional methodology tailored to variable domain functional data, eliminating the need for data alignment, which can be computationally taxing. Although models designed for variable domain data are relatively scarce and may have inherent limitations in their estimation methods, our approach circumvents these issues. We substantiate the effectiveness of our methodology through a simulation study, comparing our results with those obtained using established methodologies. Finally, we apply our methodology to analyze the impact of physical activity in COPD patients using the telEPOC program's data. Software for our method is available in the form of R code on request at \url{https://github.com/Pavel-Hernadez-Amaro/V.D.F.R.M-new-estimation-approach.git}.

In this work, we study the realization of non-invertible duality symmetries along the toroidal branch of the $c=2$ conformal manifold. A systematic procedure to construct symmetry defects is implemented to show that all Rational Conformal Field Theories along this branch enjoy duality symmetries. Furthermore, we delve into an in-depth analysis of two representative cases of multicritical theories, were the toroidal branch meets various orbifold branches. For these particular examples, the categorical data and the defect Hilbert spaces associated to the duality symmetries are obtained by resorting to modular covariance. Finally, we study the interplay between these novel symmetries and the various exactly marginal and relevant deformations, including some representative examples of Renormalization Group flows where the infrared is constrained by the non-invertible symmetries and their anomalies., Comment: 45 pages + appendices. Minor improvements, matches journal version