Dynamic quantum circuits combine mid-circuit measurement with classical feed-forward, enabling circuit constructions with reduced entangling-gate depth. Here, we investigate their use in Quantum Imaginary Time Evolution (QITE), where circuit depth and parameter growth limit practical implementations of ground-state preparation. For dense classical optimization Hamiltonians, we introduce a reduced-parameter QITE ansatz that restricts entanglement generation via a small set of control qubits, enabling each QITE layer to be implemented with constant two-qubit gate depth using fan-out-based dynamic circuits. In noiseless simulations of exact cover and set partitioning instances, the reduced ansatz yields a higher success probability than standard QITE approaches. We implement unitary, dynamic fan-out, and semi-classical adaptive variants on IBM superconducting hardware. The semi-classical variant performs favorably to the unitary implementation, while the fully dynamic construction exposes the trade-offs between entangling-depth reduction and measurement and feed-forward overhead associated to dynamic circuit implementations. Using a fidelity threshold of 0.5 relative to the noiseless QITE ansatz, we show that dynamic fan-out based QITE would outperform unitary implementations on current devices when the measurement and two-qubit gate errors are reduced by 65% and the feedback latency is halved.

Due to the conceptual simplicity, the linear filtering framework, notably the autoregressive (AR) process, has a long history in simulating clutter sequences with specified probability density functions (PDFs) and autocorrelation functions (ACFs). However, linear filtering inevitably distorts the input distribution, which may lead to inaccurate PDF reproduction or restrict applicability to very simple ACFs. To address these challenges, this study proposes a series-based analytic continuation strategy that revitalizes AR process clutter simulation by accurately precomputing the input pre-distortion required to compensate for AR filtering. First, the moments and cumulants of the AR input are derived based on the input-output relationship of the AR process, facilitating the moment and cumulant expansions of the Laplace transform (LT) and the logarithmic LT around zero, respectively. Second, both series expansions are analytically continued via the Padé approximation (PA) to recover the LT over the full complex plane. Notably, the PA-based continuation of the moment expansion, a conventional choice, can be highly inaccurate when the LT exhibits strong oscillations. By contrast, given the logarithmic LT generally has a simpler structure, the continuation of the cumulant expansion provides a more stable and accurate alternative. Third, the LT recovered from the cumulant expansion facilitates fast simulation of the AR input non-Gaussian white sequence via a random variable transformation method, thereby enabling an efficient AR process. Finally, simulations demonstrate that the proposed strategy enables accurate and fast simulation of non-Gaussian correlated clutter sequences.

This paper analyses how firms' skill development strategies affect their propensity to introduce innovation. We develop an adjustment-cost framework that links human capital theory and institutionalist and evolutionary approaches, considering innovation as an activity that entails costs in labour adjustment arising either from the training activities of workers or the recruitment of skilled employees. Using a two-wave panel of Italian manufacturing firms observed in 2017-2018 and 2019-2020, we analyse firms' adoption of total, product, process, and circular innovation as a function of internal training practices and of external skills acquisition. Overall, the empirical analysis confirms the expected positive relationship between training and innovation, while also revealing important nuances in the workforce upskilling strategies required for different types of innovation. Moreover, while training activities and skills development are essential across all forms of innovation, our findings indicate that internal training is particularly effective in supporting the implementation of circular innovations. By contrast, external recruitment appears to be consistently necessary whenever innovations are introduced, regardless of their type.

Classical tensor network and hybrid quantum-classical algorithms are promising candidates for the investigation of real-time properties of lattice gauge theories. We develop here a novel framework which enforces gauge symmetry via a quantum-link virtual rishon representation applied at intermediate steps. Crucially, the gauge and matter degrees of freedom are dynamical variables encoded in terms of qubits, enabling analysis of gauge theories in $d+1$ spacetime dimensions. We benchmark this framework in a U(1) gauge theory with and without matter fields. For $d = 1$, the multi-flavor Schwinger model with $1\leq N_f\leq3$ flavors is analyzed for arbitrary boundary conditions and nonzero topological angle, capturing signatures of the underlying Wess-Zumino-Witten conformal field theory. For $d = 2$, we extract the confining string tension in close agreement with continuum expectations. These results establish the virtual rishon framework as a scalable and robust approach for the simulation of lattice gauge theories using both classical tensor networks as well as near-term quantum hardware.

Microwave sensing is a critical enabler for all-weather perception, yet its resolution is fundamentally capped by the diffraction limit of the physical antenna aperture. While vortex electromagnetic (EM) waves offer a route to bypass this barrier, practical deployment is constrained by the trade-off between bandwidth, mode purity, and hardware complexity. Here, we propose a microwave photonic architecture enabled by a chip-scale dissipative Kerr soliton (DKS) microcomb that resolves these constraints. The microcomb provides a grid of over 270 optical lines with linewidths below 30 kHz, which are modulated and optically processed to synthesize vortex waves covering 8 GHz (18-26 GHz) with 15 programmable orbital angular momentum (OAM) modes. In contrast to conventional parallel-laser systems, our approach reduces phase error and improves OAM mode purity, while condensing the multi-wavelength source onto a monolithic chip. We demonstrate superior forward-looking imaging performance, clearly resolving both point targets and complex scenes. This work establishes a scalable framework bridging integrated soliton physics with broadband microwave processing, paving the way for next-generation compact smart sensors.

For $p\in (-\infty,0)\cup(0,1)$ and a convex body $K\subset\mathbb{R}^n$ with the origin in its interior, we construct the family of $p$-affine dual curvature measures $\mathcal{I}_p(K,\cdot)$ with respect to $K$. The affine-invariant measure $\widetilde{C}_{n-1}^{\mathrm{a}}(K, \cdot)$ given in the paper [9] is the limit case of $\mathcal{I}_p(K,\cdot)$ as $p\rightarrow 1^-$. The classical cone-volume measure is the limit case of the affine measures $2|p|\mathcal{I}_p(K,\cdot)/(n^2V(\mathrm{I}_pK))$ when $p\rightarrow 0$ and $V(K)=2$, where $\mathrm{I}_pK$ denotes the $L_p$ intersection body of $K$. The Minkowski problems for the $p$-affine dual curvature measures are proposed and studied. Specifically, we give a sufficient condition for the existence of a solution to the even Minkowski problem for $p$-affine dual curvature measure. Moreover, a necessary condition is given when $p\in (0,1)$. The smooth case of this Minkowski problem is equivalent to solving a new type of partial differential equations with respect to $p$-cosine transforms.

As a work in progress, results from a chemical and physical analysis of molecular cores in early evolutionary stages concerning star formation are presented. Using archival data from the Atacama Large Millimeter Array (ALMA), a sample of 37 sources was investigated, from which spectra in the frequency range 330--350 GHz were extracted towards the central positions of the molecular cores. Transitions of HC$_3$N, H$^{13}$CN, and HN$^{13}$C were analysed using Gaussian fits, obtaining peak intensities, fluxes, and line widths. The column densities of each molecule and their abundances were estimated. The behaviour of these abundances with the temperature of the region was studied, observing positive correlations for H$^{13}$CN and HN$^{13}$C, and none for HC$_3$N. This study contributes to the characterisation of the initial conditions of the interstellar medium in early phases of stellar evolution.

With an increasingly diverse portfolio of quantum backends, the adoption of standardized interfaces has become a key prerequisite for scalable access and interoperability within quantum software stacks. The Quantum Device Management Interface (QDMI) addresses this challenge and is emerging as one of the de facto standards for hardware abstraction, enabling the unified management not only of individual Quantum Processing Units (QPUs) but also of complete full-stack cloud services. This paper presents a case study demonstrating the integration of QDMI with Amazon Braket, a quantum computing cloud service that provides a single access point to a wide range of hardware technologies. By treating the cloud service itself as a unified device, the proposed implementation enables management of the complete task lifecycle - ranging from authentication and circuit submission to result retrieval - across Braket's heterogeneous set of simulators and hardware backends. We detail the engineering insights gained from this integration and present a hands-on example workflow, ultimately paving the way for integrated access to cloud-hosted quantum resources from QDMI-enabled software stacks.

Classical shadow tomography has emerged as a powerful framework for predicting properties of quantum many-body systems with favorable sample complexity. Standard theoretical guarantees, however, rely on the assumption that experimental rounds are independent and identically distributed (i.i.d.). This idealization is often violated in practice, where parameter drift, environmental noise, and active feedback generate history-dependent sequences of states or channels. To address this, we introduce a robust classical shadow protocol based on a truncated mean estimator. We prove that its sample complexity for predicting properties of the time-averaged state or channel matches the standard i.i.d. scaling governed by the shadow norm, even when experimental rounds depend arbitrarily on the past. Our results establish the robustness of the shadow formalism beyond the i.i.d. regime.

Snapshot HDR imaging is essential to capture the full dynamic range of a scene in a single exposure, making it essential for video and dynamic environments where motion prevents the use of multi-exposure techniques or complex hardware set-ups. This work presents a snapshot HDR imaging sensor that is based on spatially varying apertures, implemented by combining two differently sized prototype pixels. The different light integration areas physically extend the dynamic range towards the lower end, compared to a standard high resolution sensor. A non-regular pixel arrangement is suggested, to mitigate aliasing and overcome a loss in spatial resolution that is associated with increased light integration area of the larger prototype pixel. Subsequent reconstruction in the Fourier domain, where natural images can be sparsely represented allows to recover the image with high detail. The image acquisition approach with the proposed non-regular HDR sensor is simulated and analysed with special emphasis on the spatial resolution. The results suggest the snapshot HDR sensor layout to be an effective way to acquire images with high dynamic range and free from aliasing artefacts.

Magnonic crystals (MCs) are emerging spintronic metamaterials capable of manipulating transmission properties of magnons, the quanta of spin waves. Due to the complex relationship between lattice geometry and magnonic band dispersion, it remains challenging to establish general design strategies for optimizing targeted properties in MCs. In this study, we demonstrated an inverse-design framework for two-dimensional MCs to explore unconventional lattice structures with large magnonic band gaps. We employed genetic algorithms to enable global exploration of structures with a complete band gap as the objective property, and used frequency-domain micromagnetic simulations for computationally efficient band gap evaluation. Our established inverse-design method successfully discovered several previously unreported designs of MCs, whose performance was validated using time-domain micromagnetic simulations. Furthermore, we observed that the design landscape becomes increasingly non-convex at high-order bands, suggesting the existence of multiple design solutions. The overall inverse-design framework is expected to be broadly applicable to experimentally accessible material systems and device dimensions, facilitating the formulation of design rules for MCs.

We introduce the semantically-defined constructive master-modality logics $\sf CK^*$ and $\sf WK^*$, extending the basic constructive modal logic $\sf CK$ and the Wijesekera-style logic $\sf WK$ obtained by impossing infallibility. Using translations between our logics and fragments of $\sf PDL$, we show that both $\sf CK^*$ and $\sf WK^*$ are EXPTIME-complete and admit an exponential-size finite model property. In particular, for their diamond-free fragment, also studied by Afshari et al. and Celoni, we establish EXPTIME-completeness, thereby settling the conjecture of Afshari et al. As an application, we embed $\sf CS4$ and $\sf WS4$ into the master-modality logics, showing that their validity problems are in EXPTIME.

We construct a renormalization-group improved Schwarzschild-like black hole geometry using the exact new scheme running for the Newton coupling. The scale identification is implemented via a standard interpolating proper-distance function that smoothly connects the ultraviolet and infrared regimes. We present the resulting coordinate-dependent coupling and the improved metric function, analyzing its asymptotic expansions. The large-distance limit is shown to recover the classical Schwarzschild solution, while the short-distance behavior exhibits a regular de Sitter-like core, demonstrating the regularization of the central singularity. We also analyze the thermodynamic properties of the solution, showing that quantum corrections significantly modify the small-radius behavior, leading to a remnant configuration and a nontrivial phase structure. Finally, we perform a topological classification of the thermodynamic phase space and demonstrate that asymptotically safe effects shift the critical point while preserving the global topological number of the Schwarzschild solution.

Spectrally-efficient communication systems rely on the use of multi-level modulation formats. At the receiver side, a demodulator is often used to extract soft information about the transmitted bits. Such a demodulator is typically implemented in the digital domain. However, analog implementations of such demodulators are also possible. In this paper, we design and simulate an analog 8-ary pulse-amplitude modulation (8-PAM) demapper in IHP SG13G2 SiGe BiCMOS technology. We generalize and improve a design available in the literature for 4-PAM. A fully MOSFET-based 8-PAM design is proposed. Our simulations and design are completely based on open-source IC design tools. Our results show an energy efficiency of 0.33 pJ/bit for a data rate of 1Gbit/s.

We numerically investigate the nonlinear dynamics of a two-dimensional exciton-polariton quantum fluid coherently driven by two counter-propagating laser beams. Using an exciton-photon coupled driven-dissipative Gross-Pitaevskii framework, we identify four distinct regimes-linear, solitonic, turbulent, and superfluid-emerging from the interplay between pump strength, laser detuning, and injected momentum, which together control the balance between kinetic and interaction energies in the quantum fluid. The different regimes are characterized through real-space and momentum-space observables, as well as through the temporal first-order coherence function. We show that turbulence occupies a well-defined and extended region of parameter space, marked by spontaneous vortex nucleation, and a pronounced reduction of temporal coherence, providing a clear signature of nonstationary dynamics. By constructing quantitative phase diagrams, we delineate the transitions between the various regimes and identify multiple pathways connecting solitonic, turbulent, and superfluid behaviors. Finally, we demonstrate that the turbulent regime persists over experimentally realistic parameter ranges compatible with state-of-the-art GaAs-based micro-cavity platforms, establishing counter-propagating polariton flows as a robust and versatile setting for the study of driven-dissipative quantum turbulence in two dimensions.

We present preliminary results of an extensive research project aimed at describing the physical and chemical conditions of hot molecular cores (HMCs). Using millimeter continuum and spectroscopic data extracted from the Atacama Large Millimeter Array (ALMA) archive, we have estimated rotational temperatures ($\rm T_{rot}$) and column densities of $\rm{CH_{3}CN}$, $\rm{CH_{3}CCH}$, and A-- and E--$\rm CH_{3}OH$ for a sample of molecular cores. We present a thermal characterization of these cores, revealing the existence of temperature gradients within them. These cores are, in turn, embedded in large molecular clouds. Additionally, we estimated molecular abundances that were evaluated as tracers of the chemical evolution of these cores. Finally, in a pilot study aimed to link observations with simulations, some of the obtained molecular abundances are compared with predictions from the Nautilus code.

We develop a first-principles framework for evaluating the fundamental length scales of superconductivity, namely the coherence length $ξ_0$ and the magnetic penetration depth $λ_\mathrm{L}$, within superconducting density functional theory (SCDFT). By incorporating finite-momentum Cooper pairs, we formulate a microscopic scheme that enables a consistent and parameter-free determination of $ξ_0$, $λ_\mathrm{L}$, and the superconducting transition temperature $T_\mathrm{c}$ on the same theoretical footing. Applying the method to representative elemental superconductors, the A15 compound V$_3$Si, and H$_3$S under high pressure, we obtain results in good agreement with available experimental data. Furthermore, the unified access to $ξ_0$ and $λ_\mathrm{L}$ allows us to construct the Uemura plot entirely from first principles, demonstrating that conventional elemental superconductors systematically exhibit small $T_\mathrm{c}$/$T_\mathrm{F}$, while higher-$T_\mathrm{c}$ systems are characterized by the simultaneous realization of strong pairing and large phase stiffness. Our results establish a predictive first-principles route to superconducting length scales and provide a microscopic interpretation of empirical correlations in superconductivity.

This paper develops a robust parametric framework for jump detection in discretely observed CKLS-type jump-diffusion processes with high-frequency asymptotics, based on the minimum density power divergence estimator (MDPDE). The methodology exploits the intrinsic asymptotic scale separation between diffusion increments, which decay at rate $\sqrt{Δ_n}$, and jump increments, which remain of non-vanishing stochastic magnitude. Using robust MDPDE-based estimators of the drift and diffusion coefficients, we construct standardized residuals whose extremal behavior provides a principled basis for statistical discrimination between continuous and discontinuous components. We establish that, over diffusion intervals, the maximum of the normalized residuals converges to the Gumbel extreme-value distribution, yielding an explicit and asymptotically valid detection threshold. Building on this result, we prove classification consistency of the proposed robust detection procedure: the probability of correctly identifying all jump and diffusion increments converges to one under proper asymptotics. The MDPDE-based normalization attenuates the influence of atypical increments and stabilizes the detection boundary in the presence of discontinuities. Simulation results confirm that robustness improves finite-sample stability and reduces spurious detections without compromising asymptotic validity. The proposed methodology provides a theoretically rigorous and practically resilient robust approach to jump identification in high-frequency stochastic systems.

We study the classical Election problem in anonymous net- works, where solutions can rely on the use of random bits, which may be either shared or unshared among nodes. We provide a complete char- acterization of the conditions under which a randomized Election algo- rithm exists, for arbitrary structural knowledge. Our analysis considers both Las Vegas and Monte Carlo randomized algorithms, under the as- sumptions of shared and unshared randomness. In our setting, random sources are considered shared if the output bits are identical across spe- cific subsets of nodes. The algorithms and impossibility proofs are extensions of those of [5] for the deterministic setting. Our results are a complete generalization of those from [8]. Moreover, as applications, we consider many specific knowledge: no knowledge, a bound on the size, a bound on the number of nodes sharing a source, the size, or the full topology of the network. For each of them, we show how the general characterizations apply, showing they actually correspond to classes of structural knowledge. We also de- scribe also how randomized Election algorithms from the literature fits in this landscape. We therefore provide a comprehensive picture illustrating how knowledge influences the computability of the Election problem in arbitrary anonymous graphs with shared randomness.

This work addresses the problem of constrained motion control of the uncrewed surface vessels. The constraints are imposed on states/inputs of the vehicles due to the physical limitations, mission requirements, and safety considerations. We develop a nonlinear feedback controller utilizing log-type Barrier Lyapunov Functions to enforce static and dynamic motion constraints. The proposed scheme uniquely addresses asymmetric constraints on position and heading alongside symmetric constraints on surge, sway, and yaw rates. Additionally, a smooth input saturation model is incorporated in the design to guarantee stability even under actuator bounds, which, if unaccounted for, can lead to severe performance degradation and poor tracking. Rigorous Lyapunov stability analysis shows that the closed-loop system remains stable and that all state variables remain within their prescribed bounds at all times, provided the initial conditions also lie within those bounds. Numerical simulations demonstrate the effectiveness of the proposed strategies for surface vessels without violating the motion and actuator constraints.

Altermagnetism has recently emerged as a new class of spin compensated magnetic materials that exhibit momentum dependent spin splitting despite having zero net magnetization. The origin of these electronic signatures lies in symmetry operations that connect opposite spin sublattices while allowing spin splitting in momentum space. While most candidate materials identified so far belong to inorganic crystals with fixed lattice symmetries, the realization of altermagnetism ultimately requires platforms in which magnetic symmetry can be deliberately engineered. In this Perspective, we discuss how metal-organic frameworks (MOFs) provide a unique chemical platform to address this challenge. We first place altermagnetism in the broader context of magnetic and electronically active metal-organic networks, highlighting how reticular chemistry enables precise control over lattice geometry, dimensionality and electronic structure. We then discuss how these features position framework materials as promising candidates for realizing altermagnetism and highlight the key challenges that must be addressed to translate theoretical proposals into experimentally accessible systems. Finally, we critically assess current experimental challenges and outline emerging directions for realizing and controlling altermagnetism in coordination framework materials, which emerge as a versatile and powerful platform for exploring new paradigms in spintronics.

Generative models based on invertible transformations provide a physics-aware route to sample equilibrium configurations directly from the Boltzmann distribution, enabling efficient exploration of complex thermodynamic landscapes. Here, we evaluate their applicability in regions where conventional simulations suffer from severe dynamical bottlenecks, focusing on the liquid-gas critical point of a Lennard-Jones fluid. We show that Boltzmann Generators capture essential signatures of critical behavior, retain reliable performance when trained at or near criticality, and extrapolate across neighboring states of the phase diagram. An intriguing observation is that the model's efficiency metric closely traces the underlying phase boundaries, hinting at a connection between generative performance and thermodynamics. However, the approach remains limited by the small system sizes currently accessible, which suppress the large fluctuations that characterize critical phenomena. Our results delineate the current capabilities and boundaries of Boltzmann Generators in challenging regions of phase space, while pointing toward future applications in problems dominated by slow dynamics, such as glass formation and nucleation.

We consider the uniform parallel machines scheduling problem in the context of optimistic bilevel optimization, where two speed options are considered. In this scenario, the leader aims to minimize the weighted number of tardy jobs, while the follower seeks to minimize the total completion time on a set of uniform machines. This problem has practical applications in Industry 4.0. We show that this problem is NP-hard in the strong sense by providing a reduction from the Numerical 3-Dimensional Matching problem and we provide a moderately exponential-time dynamic programming algorithm. The problem is solved by means of a concise MIP formulation and a branch-and-bound algorithm that embeds a column generation approach for the lower bound computation. Computational experiments are presented for instances with up to 80 jobs and 4 machines while larger problems are out of reach for the proposed approaches.

We prove that split reductive BT group schemes over a higher dimensional base are {\em affine}. Our method also gives a new construction of higher BT-group schemes more general than parahoric ones. The new ingredients are an extension of J.-K.Yu's construction in \cite{yu} to higher dimensional bases, Néron-Raynaud dilatations of subgroup schemes on divisors, combined with techniques from \cite{bt2} and the structure theory developed in \cite{bp}.

There are strong arguments favoring the Flavor Democracy hypothesis (or the Democratic Mass Matrix approach) within the Standard Model framework. However, the large mass of the top quark ($m_t >> m_b, m_τ$) poses an obstacle to the functioning of Flavor Democracy in the three SM family scenario. While a fourth Standard Model generation could have provided a natural resolution, this possibility is now almost entirely excluded by precision data on Higgs boson production and decay rates. The Flavor Democracy hypothesis can be elegantly resurrected through the introduction of Vector-Like Leptons (VLLs) and Vector-Like Quarks (VLQs), which naturally accommodate the observed fermion mass hierarchies while remaining consistent with current experimental constraints. Currently, experimental searches for VLLs conducted by the ATLAS and CMS collaborations rely on a highly constrained Restricted Model. This model imposes a mass degeneracy between charged and neutral VLLs within a doublet and assumes the absence of right-handed neutrinos. Consequently, current results are valid only for the restricted model and do not cover a more realistic, general scenario. Therefore, to accurately reflect the physical reality, it is imperative to conduct a comprehensive re-evaluation that incorporates all viable decay channels.

The conventional classification of direct band-gap semiconductors relies on point-like extrema in momentum space. Here, we introduce the concept of domain-direct band gaps, where the conduction-band minimum (CBM) and valence-band maximum (VBM) form extended manifolds in the Brillouin zone. We demonstrate this concept through the material realization of an extreme two-dimensional-two-dimensional (2D-2D) domain-direct band gap in twisted diamond. First-principles calculations show that both the CBM and VBM exhibit nearly flat 2D manifolds in the kx-ky plane with minimal energy variation (a few meV), yielding a direct band gap of 3.264 eV. In contrast, strong dispersion along the out-of-plane kz direction induces anisotropic carrier dynamics, with strongly suppressed in-plane Fermi velocities (down to about 10$^1$-10$^3$ m/s in certain directions) and much larger out-of-plane velocities (about 10$^6$ m/s). The nearly flat CBM and VBM manifolds enhance the joint density of states, leading to a pronounced optical absorption peak at the band gap onset. This new type of domain-direct gap, coupled with strong directional anisotropy, opens up opportunities for anisotropic optoelectronic applications. Our results establish domain-direct band gaps as a new class of semiconductors, demonstrating their feasibility in real materials.

The toughness of a graph $G$ is defined as the largest real number $t$ such that for any set $S\subseteq V(G)$ such that $G-S$ is disconnected, $S$ has at least $t$ times more elements than $G-S$ has components (unless $G$ is complete, in which case the toughness is defined to be infinite). A graph is said to be minimally tough if deleting any edge decreases the toughness. It is an open question whether there exists a minimally tough non-complete chordal graph with toughness exceeding $1$. We initiate the study of minimally tough graphs in the larger class of weakly chordal graphs. We obtain complete classifications of minimally tough graphs in the following subclasses of weakly chordal graphs: co-chordal graphs whose complement has diameter at least $3$, net-free co-chordal graphs, complements of forests, $P_4$-free graphs, and complete multipartite graphs. Our approach leads to simple proofs of two results on minimally tough graphs due to Dallard, Fernández, Katona, Milanič, and Varga.

Ferroelectric nematic phase (NF) represents an attractive and foremost field of liquid crystals, combining fluidity with ferroelectricity. NF materials exhibit large polarization values and remarkable non-linear optical properties. We have designed an original molecular structure with halogen substituents in the position of an electron donating group. In a prolonged molecular core, such a modification led to the presence of the ferroelectric nematic phase (NF) below the nematic one. Besides, an application of Cl atom in the molecular core of one of the presented materials has been utilized for the first time for ferroelectric nematogens. We have examined mesogenic behaviour and ferroelectric characteristics of the NF phase. In the NF phase for the cell with antiparallel rubbing, we have detected a textural transformation, which evidences strong polar character of anchoring at the surfaces. The presented results provide valuable insight into the design of ferroelectric nematic liquid crystalline compounds.

Voice timbre attribute detection (vTAD) is the task of determining the relative intensity of timbre attributes between speech utterances. Voice timbre is a crucial yet inherently complex component of speech perception. While deep neural network (DNN) embeddings perform well in speaker modelling, they often act as black-box representations with limited physical interpretability and high computational cost. In this work, a compact acoustic parameter set is investigated for vTAD. The set captures important acoustic measures and their temporal dynamics which are found to be crucial in the task. Despite its simplicity, the acoustic parameter set is competitive, outperforming conventional cepstral features and supervised DNN embeddings, and approaching state-of-the-art self-supervised models. Importantly, the studied set require no trainable parameters, incur negligible computation, and offer explicit interpretability for analysing physical traits behind human timbre perception.

Screened scalar fields such as the symmetron provide a viable description of dark energy yet their laboratory detection remains challenging. We propose an optomechanical scheme to constrain symmetron interactions using two optically levitated nanospheres inside a cavity. The symmetron-mediated interaction induces an effective coupling which leads to a measurable splitting in the optomechanical resonance spectrum. We forecast constraints in the regime $μ\sim 10^{-2}$eV-$10^{-4}$ eV, which shows that this approach can improve existing laboratory bounds by up to several orders of magnitude, demonstrating the sensitivity of optomechanical spectroscopy to screened fifth forces.