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Flexoelectricity is universal in all dielectrics, effective at high temperatures, and a promising transduction technique for nanoelectromechanical systems (NEMS). However, as flexoelectricity is still in its early stages, many aspects require further investigation. Understanding how flexoelectricity depends on material parameters like crystallographic phase and how temperature might affect it, is important for selecting and optimizing the right material for technological applications. This work studies the influence of high-temperature annealing (and the consequent crystallization) in the flexoelectricity of hafnium oxide (HfO2), a material with significant technological relevance. We measure the flexoelectric coefficient for amorphous (not annealed) and annealed (slightly crystalline) phases of HfO2, with samples annealed in nitrogen or oxygen atmospheres. Our results indicate that the amorphous phase of HfO2 exhibits the highest flexoelectric coefficient (105 $\pm$ 10 pC/m), while annealed samples show a significant decrease, with the lowest value in nitrogen-annealed samples (26 $\pm$ 4 pC/m). Samples annealed in an oxygen atmosphere improve flexoelectric properties (54 $\pm$ 6 pC/m) compared to those annealed in nitrogen. Using cross-sectional imaging, X-ray diffraction, resonance frequency characterization, and relative permittivity measurements, we find that annealing promotes crystallization into the tetragonal phase and increases internal stress within the HfO2 layer, while most other parameters remain constant. We attribute the differences in flexoelectricity from the annealed samples to the quantity of oxygen vacancies in hafnium oxide. These oxygen vacancies in hafnium oxide seem to negatively affect the flexoelectric coefficient. This finding can be applied to optimize materials to enhance their flexoelectric properties.

Memristors offer significant advantages as in-memory computing devices due to their non-volatility, low power consumption, and history-dependent conductivity. These attributes are particularly valuable in the realm of neuromorphic circuits for neural networks, which currently face limitations imposed by the Von Neumann architecture and high energy demands. This study evaluates the feasibility of using memristors for in-memory processing by constructing and training three digital convolutional neural networks with the datasets MNIST, CIFAR10 and CIFAR100. Subsequent conversion of these networks into memristive systems was performed using Memtorch. The simulations, conducted under ideal conditions, revealed minimal precision losses of nearly 1% during inference. Additionally, the study analyzed the impact of tile size and memristor-specific non-idealities on performance, highlighting the practical implications of integrating memristors in neuromorphic computing systems. This exploration into memristive neural network applications underscores the potential of Memtorch in advancing neuromorphic architectures.

Flexoelectricity, inherent in all materials, offers a promising alternative to piezoelectricity for nanoscale actuation and sensing. However, its widespread application faces significant challenges: differentiating flexoelectric effects from those of piezoelectricity and other phenomena, verifying its universality across all material structures and thicknesses, and establishing a comprehensive database of flexoelectric coefficients across different materials. This work introduces a groundbreaking methodology that accurately isolates flexoelectricity from piezoelectric, electrostrictive and electrostatic effects, with a detection threshold extending below 1 fC/m. The robustness of this method is demonstrated through its application to amorphous hafnium oxide (HfO$_2$), successfully measuring a flexoelectric coefficient of 105 $\pm$ 10 pC/m. This measurement signifies the first measurement of flexoelectricity in hafnia, as well as in any amorphous material. Additionally, the study compiles a list of published flexoelectric coefficients, revealing an important insight. The relationship between the flexoelectric coefficient and the material's relative permittivity is better approximated by a quadratic proportionality. This challenges the traditional linear assumption proposed in Kogan's work and opens new avenues for future research in flexoelectric materials.

HoNiSi$_{3}$ is an intermetallic compound characterized by two successive antiferromagnetic transitions at $T_{N1} = 6.3$ K and $T_{N2} = 10.4$ K. Here, its zero-field microscopic magnetic structure is inferred from resonant x-ray magnetic diffraction experiments on a single crystalline sample that complement previous bulk magnetic susceptibility data. For $T < T_{N2}$, the primitive magnetic unit cell matches the chemical cell. The magnetic structure features ferromagnetic {\it ac} planes stacked in an antiferromagnetic $\uparrow \downarrow \uparrow \downarrow$ pattern. For $T_{N1} < T < T_{N2}$, the ordered magnetic moment points along $\vec{a}$, and for $T < T_{N1}$ a component along $\vec{c}$ also orders. A symmetry analysis indicates that the magnetic structure for $T

Despite the many recent achievements in developing and deploying social robotics, there are still many underexplored environments and applications for which systematic evaluation of such systems by end-users is necessary. While several robotic platforms have been used in gerontological healthcare, the question of whether or not a social interactive robot with multi-modal conversational capabilities will be useful and accepted in real-life facilities is yet to be answered. This paper is an attempt to partially answer this question, via two waves of experiments with patients and companions in a day-care gerontological facility in Paris with a full-sized humanoid robot endowed with social and conversational interaction capabilities. The software architecture, developed during the H2020 SPRING project, together with the experimental protocol, allowed us to evaluate the acceptability (AES) and usability (SUS) with more than 60 end-users. Overall, the users are receptive to this technology, especially when the robot perception and action skills are robust to environmental clutter and flexible to handle a plethora of different interactions.

There is a well-known fact in Poisson geometry that reduction commutes with integration (of the associated integrable Lie algebroid). This is also valid for other types of geometries given by 2-dimensional closed forms. In this manuscript we extend this type of result to the particular case of cosymplectic groupoids, obtaining in particular the reduction of a central extension defined by a 2-IM form and a 1-IM form., Comment: 9 pages

We study M-Theory solutions with $G$-flux on the Fermat sextic Calabi-Yau fourfold, focussing on the relationship between the number of stabilized complex structure moduli and the tadpole contribution of the flux. We use two alternative approaches to define the fluxes: algebraic cycles and (appropriately quantized) Griffiths residues. In both cases, we collect evidence for the non-existence of solutions which stabilize all moduli and stay within the tadpole bound, Comment: v2: typos corrected and references added

We solve the problem of determining under which conditions the monodromy of a vanishing cycle generates the whole homology of a regular fiber for a polynomial $f(x,y)=g(x)+h(y)$ where $h$ and $g$ are polynomials with real coefficients having real critical points and satisfying $deg(g)\cdot deg(h)\leq 2500$ and $\gcd(deg(g),deg(h))\leq 2$. As an application, under the same assumptions we solve the tangential-center problem which is known to be linked to the weak 16th Hilbert problem., Comment: 11 pages. Final version accepted for publication in Proceedings of the American Mathematical Society

Today, conversational systems are expected to handle conversations in multi-party settings, especially within Socially Assistive Robots (SARs). However, practical usability remains difficult as there are additional challenges to overcome, such as speaker recognition, addressee recognition, and complex turn-taking. In this paper, we present our work on a multi-party conversational system, which invites two users to play a trivia quiz game. The system detects users' agreement or disagreement on a final answer and responds accordingly. Our evaluation includes both performance and user assessment results, with a focus on detecting user agreement. Our annotated transcripts and the code for the proposed system have been released open-source on GitHub., Comment: Proceedings of the workshop on advancing GROup UNderstanding and robots aDaptive behaviour (GROUND), 2023

Conversational agents participating in multi-party interactions face significant challenges in dialogue state tracking, since the identity of the speaker adds significant contextual meaning. It is common to utilise diarisation models to identify the speaker. However, it is not clear if these are accurate enough to correctly identify specific conversational events such as agreement or disagreement during a real-time interaction. This study uses a cooperative quiz, where the conversational agent acts as quiz-show host, to determine whether diarisation or a frequency-and-proximity-based method is more accurate at determining agreement, and whether this translates to feelings of engagement from the players. Experimental results show that our procedural system was more engaging to players, and was more accurate at detecting agreement, reaching an average accuracy of 0.44 compared to 0.28 for the diarised system., Comment: Proceedings of the workshop on advancing GROup UNderstanding and robots aDaptive behaviour (GROUND), 2023

This paper evaluates the extent to which current Large Language Models (LLMs) can capture task-oriented multi-party conversations (MPCs). We have recorded and transcribed 29 MPCs between patients, their companions, and a social robot in a hospital. We then annotated this corpus for multi-party goal-tracking and intent-slot recognition. People share goals, answer each other's goals, and provide other people's goals in MPCs - none of which occur in dyadic interactions. To understand user goals in MPCs, we compared three methods in zero-shot and few-shot settings: we fine-tuned T5, created pre-training tasks to train DialogLM using LED, and employed prompt engineering techniques with GPT-3.5-turbo, to determine which approach can complete this novel task with limited data. GPT-3.5-turbo significantly outperformed the others in a few-shot setting. The `reasoning' style prompt, when given 7% of the corpus as example annotated conversations, was the best performing method. It correctly annotated 62.32% of the goal tracking MPCs, and 69.57% of the intent-slot recognition MPCs. A `story' style prompt increased model hallucination, which could be detrimental if deployed in safety-critical settings. We conclude that multi-party conversations still challenge state-of-the-art LLMs., Comment: Accepted and will appear in the Proceedings of SIGdial 2023

Complex oxides offer a wide range of functional properties, and recent advances in fabrication of freestanding membranes of these oxides are adding new mechanical degrees of freedom to this already rich functional ecosystem. Here, we demonstrate photoactuation in freestanding thin film resonators of ferroelectric Barium Titanate (BaTiO3) and paraelectric Strontium Titanate (SrTiO3). The free-standing films, transferred onto perforated supports, act as nano-drums, oscillating at their natural resonance frequency when illuminated by a frequency-modulated laser. The light-induced deflections in the ferroelectric BaTiO3 membranes are two orders of magnitude larger than in the paraelectric SrTiO3 ones. Time-resolved X-ray micro-diffraction under illumination and temperature-dependent and holographic interferometry provide combined evidence for the photostrictive strain in BaTiO3 originating from partial screening of ferroelectric polarization by photo-excited carriers, which decreases the tetragonality of the unit cell. These findings showcase the potential of photostrictive freestanding ferroelectric films as wireless actuators operated by light.

Particle tracking has several important applications for solute transport studies in aquifer systems. Travel time distribution at observation points, particle coordinates in time and streamlines are some practical results providing information of expected transport patterns and interaction with boundary conditions. However, flow model complexity and simultaneous displacement of multiple particle groups leads to rapid increase of computational requirements. MODPATH is a particle tracking engine for MODFLOW models and source code displays potential for parallel processing of particles. This article addresses the implementation of this feature with the OpenMP library. Two synthetic aquifer applications are employed for performance tests on a desktop computer with increasing number of particles. Speed up analysis shows that dynamic thread scheduling is preferable for highly heterogeneous flows, providing processing adaptivity to the presence of slow particles. In simulations writing particles position in time, thread exclusive output files lead to higher speed up factors. Results show that above a threshold number of particles, simulation runtimes become independent of flow model grid complexity and are controlled by the large number of particles, then parallel processing reduces simulation runtimes for the particle tracking model MODPATH.

The Artificial Intelligence (AI) for Human-Robot Interaction (HRI) Symposium has been a successful venue of discussion and collaboration on AI theory and methods aimed at HRI since 2014. This year, after a review of the achievements of the AI-HRI community over the last decade in 2021, we are focusing on a visionary theme: exploring the future of AI-HRI. Accordingly, we added a Blue Sky Ideas track to foster a forward-thinking discussion on future research at the intersection of AI and HRI. As always, we appreciate all contributions related to any topic on AI/HRI and welcome new researchers who wish to take part in this growing community. With the success of past symposia, AI-HRI impacts a variety of communities and problems, and has pioneered the discussions in recent trends and interests. This year's AI-HRI Fall Symposium aims to bring together researchers and practitioners from around the globe, representing a number of university, government, and industry laboratories. In doing so, we hope to accelerate research in the field, support technology transition and user adoption, and determine future directions for our group and our research.

We consider the rational map $F$ defined by the quotient of products of lines in general position and we study the monodromy problem and tangential center-focus problem for the fibration associated with $F$. Thus, we study the submodule of the 1-homology group of a regular fiber of $F$ generated by the orbit of the monodromy action on a vanishing cycle. Moreover, we characterize the meromorphic 1-forms $\omega$ in $\mathbb{P}^2$ such that the Abelian integral $\int_{\delta_t}\omega$ vanishes on a family of cycles $\delta_t$ around a center singularity., Comment: 17 pages, 1 figure

The geographical variability, frequency content, and vertical structure of near-surface oceanic kinetic energy (KE) are important for air-sea interaction, marine ecosystems, operational oceanography, pollutant tracking, and interpreting remotely sensed velocity measurements. Here, KE in high-resolution global simulations (HYbrid Coordinate Ocean Model; HYCOM, and Massachusetts Institute of Technology general circulation model; MITgcm), at the sea surface (0 m) and 15 m, are respectively compared with KE from undrogued and drogued surface drifters. Global maps and zonal averages are computed for low-frequency ($<$ 0.5 cpd), near-inertial, diurnal, and semi-diurnal bands. Both models exhibit low-frequency equatorial KE that is low relative to drifter values. HYCOM near-inertial KE is higher than in MITgcm, and closer to drifter values, probably due to more frequently updated atmospheric forcing. HYCOM semi-diurnal KE is lower than in MITgcm, and closer to drifter values, likely due to inclusion of a parameterized topographic internal wave drag. A concurrent tidal harmonic analysis in the diurnal band demonstrates that much of the diurnal flow is non-tidal. We compute a simple proxy of near-surface vertical structure, the ratio of 0 m KE to 0 m KE plus 15 m KE in model outputs, and undrogued KE to undrogued KE plus drogued KE in drifter observations. Over most latitudes and frequency bands, model ratios track the drifter ratios to within error bars. Values of this ratio demonstrate significant vertical structure in all frequency bands except the semidiurnal band. Latitudinal dependence in the ratio is greatest in diurnal and low-frequency bands., Comment: revised for AGU JGR: Oceans

Certain applications that analyze damping effects require the solution of quadratic eigenvalue problems (QEPs). We use refined isogeometric analysis (rIGA) to solve quadratic eigenproblems. rIGA discretization, while conserving desirable properties of maximum-continuity isogeometric analysis (IGA), reduces the interconnection between degrees of freedom by adding low-continuity basis functions. This connectivity reduction in rIGA's algebraic system results in faster matrix LU factorizations when using multifrontal direct solvers. We compare computational costs of rIGA versus those of IGA when employing Krylov eigensolvers to solve quadratic eigenproblems arising in 2D vector-valued multifield problems. For large problem sizes, the eigencomputation cost is governed by the cost of LU factorization, followed by costs of several matrix-vector and vector-vector multiplications, which correspond to Krylov projections. We minimize the computational cost by introducing C^0 and C^1 separators at specific element interfaces for our rIGA generalizations of the curl-conforming Nedelec and divergence-conforming Raviart-Thomas finite elements. Let p be the polynomial degree of basis functions; the LU factorization is up to O((p-1)^2) times faster when using rIGA compared to IGA in the asymptotic regime. Thus, rIGA theoretically improves the total eigencomputation cost by O((p-1)^2) for sufficiently large problem sizes. Yet, in practical cases of moderate-size eigenproblems, the improvement rate deteriorates as the number of computed eigenvalues increases because of multiple matrix-vector and vector-vector operations. Our numerical tests show that rIGA accelerates the solution of quadratic eigensystems by O(p-1) for moderately sized problems when we seek to compute a reasonable number of eigenvalues.

This paper proposes an optimization framework for sustainable post-disaster building reconstruction. Based on mathematical optimization, it is intended to provide decision-makers with a versatile tool to optimize building designs and to explore the trade-off between costs and environmental impact (represented by embodied energy) of alternative building materials. The mixed-integer nonlinear optimization model includes an analytical building model that considers structural and safety constraints and incorporates regional building codes. Using multi-objective optimization concepts, Pareto-optimal designs are computed that represent the best trade-off designs from which a decision-maker can choose when they take additional criteria into consideration. As a case study, we consider the design of a multi-room one-story masonry building in Nepal. We demonstrate how the framework can be employed to answer a variety of questions, such as the optimal building design and material selection, the sensitivity of the decision to the prices, and the impact of regional safety regulation thresholds.

Mitigating the energy requirements of artificial intelligence requires novel physical substrates for computation. Phononic metamaterials have a vanishingly low power dissipation and hence are a prime candidate for green, always-on computers. However, their use in machine learning applications has not been explored due to the complexity of their design process: Current phononic metamaterials are restricted to simple geometries (e.g. periodic, tapered), and hence do not possess sufficient expressivity to encode machine learning tasks. We design and fabricate a non-periodic phononic metamaterial, directly from data samples, that can distinguish between pairs of spoken words in the presence of a simple readout nonlinearity; hence demonstrating that phononic metamaterials are a viable avenue towards zero-power smart devices., Comment: 13 pages, 11 figures

We propose a superscalar parallel two-stage carrier phase recovery architecture to improve the performance of optical coherent receivers in the presence of Tx I/Q imbalance, Tx I/Q skew, and laser frequency fluctuations.

The Artificial Intelligence (AI) for Human-Robot Interaction (HRI) Symposium has been a successful venue of discussion and collaboration since 2014. During that time, these symposia provided a fertile ground for numerous collaborations and pioneered many discussions revolving trust in HRI, XAI for HRI, service robots, interactive learning, and more. This year, we aim to review the achievements of the AI-HRI community in the last decade, identify the challenges facing ahead, and welcome new researchers who wish to take part in this growing community. Taking this wide perspective, this year there will be no single theme to lead the symposium and we encourage AI-HRI submissions from across disciplines and research interests. Moreover, with the rising interest in AR and VR as part of an interaction and following the difficulties in running physical experiments during the pandemic, this year we specifically encourage researchers to submit works that do not include a physical robot in their evaluation, but promote HRI research in general. In addition, acknowledging that ethics is an inherent part of the human-robot interaction, we encourage submissions of works on ethics for HRI. Over the course of the two-day meeting, we will host a collaborative forum for discussion of current efforts in AI-HRI, with additional talks focused on the topics of ethics in HRI and ubiquitous HRI., Comment: Proceedings of the AI-HRI Symposium at AAAI-FSS 2021