Papers

The orbits of exceptional CR-quadrics are studied. It is proved that the orbit of the exceptional (4,4)-quadric constructed in [1] is isolated. Using the algebraization procedure described in [2], it is shown that the space of orbits of exceptional quadrics of type (28,28) has positive dimension.

In a question recorded in 1978, Vasconcelos asked whether the integral closure of a one-dimensional coherent local domain in its fraction field is Prüfer. We construct such a domain whose integral closure is not Prüfer, giving a negative answer.

Artistic style classification is usually studied on complete artworks, where models can exploit global composition, spatial organisation, and iconographic structure. In archaeological settings, however, artworks often survive only as fragmented remains, forcing recognition from incomplete, irregular, and context-limited visual evidence. We study fresco-fragment style classification using a progressive transformer-based framework. Starting from a ViT-B/16 baseline, we introduce foreground-guided masking to suppress background-only tokens, inpainting-based geometric regularisation to align irregular fragment supports with the ViT patch grid, and a supervised contrastive objective that operates on predictive distributions through a Kullback-Leibler similarity and consistently improves every branch. We combine the branches with a deliberately simple learnable logit ensemble. Experiments on CLEOPATRA and POMPAAF show that fragment-aware modelling improves over the standard ViT baseline, with the ensemble increasing accuracy from 0.604 to 0.656 and macro-F1 from 0.596 to 0.648 on CLEOPATRA, and outperforming the best single branch in four of six fragmentation settings on POMPAAF. We additionally evaluate a more complex graph-fusion variant and find that it matches the simple ensemble on POMPAAF while offering only a small, dataset-specific gain on CLEOPATRA, which does not justify its added complexity. Beyond these empirical gains, our contribution is twofold: a distribution-level contrastive objective that consistently sharpens single-branch recognition, and an interpretability analysis that verifies the models exploit genuine painted evidence, while quantifying that the inpainting-based branch draws part of its attribution from the synthesised surround.

Motivated by the $2.6\sigma$ high-energy recoil event recently reported by the LUX-ZEPLIN (LZ) Collaboration, we consider an endothermic $Z^\prime$-portal Majorana dark matter framework and discuss the complementarity between the LZ event and LHC searches for a $Z^\prime$ resonance. As a concrete realization, we consider a gauged U(1) ${B-L}$ extension of the Standard Model. The phenomenology of the framework is essentially controlled by two free parameters: the U(1) ${B-L}$ gauge coupling $g_{BL}$ and the $Z^\prime$ boson mass $m_{Z^\prime}$. For a fixed $m_{Z^\prime}$, the observed dark matter abundance requires the dark matter mass to be near the $Z^\prime$ resonance, $m_{\rm DM} \sim m_{Z^\prime}/2$, and sets a lower bound on $g_{BL}$. Complementarily, LHC searches for a $Z^\prime$ resonance set an upper bound on $g_{BL}$. The parameter space allowed by the dark matter abundance and LHC constraints can account for the recent LZ event. The synergy between future $Z^\prime$ resonance searches at the High-Luminosity LHC and the LZ experiment may provide a test of this framework.

Leveraging a sample of 3457 galaxies drawn from DESI DR1 and cross-matched with archival GALEX UV photometry, we construct new dust attenuation curves and measure how the shape of these curves vary as a function of galaxy properties. Our curves are built by comparing the median flux densities of galaxies that have been divided into bins of nebular opacity, measured via the Balmer optical depth (i.e. the H$\alpha$/H$\beta$ ratio). When no further cuts on galaxy properties are applied, we find an attenuation curve very similar to previous work at low redshifts. We then test how the attenuation curves vary as a function of galaxy properties; stellar mass, specific star formation rate, metallicity, and ionization parameter. We find that the curve steepens significantly at higher specific star formation rate. In our data, there is not a clear link between the ionization parameter, mass, or metallicity and the attenuation curve slope.

Robustness analysis plays a central role in the verification and design of computational and hybrid systems, particularly when system behaviour depends continuously on parameters subject to perturbation. Existing domain-theoretic frameworks provide a principled foundation for reasoning about such perturbations via monotone maps on lattices of closed sets. However, these frameworks face significant limitations when the underlying state space is not locally compact, as is the case for the infinite-dimensional spaces that arise in analysis, machine learning, and control theory (e.g., $\ell_p$ and $L_p$ spaces). In these settings, the lattice of closed subsets fails to be continuous, and classical compactifications either sacrifice precision or lack computable structure. We propose Gromov's horofunction compactification as a new tool for robustness analysis over a class of separable metric spaces of practical importance, including separable reflexive Banach spaces. Given a metric space $\mathbb{S}$, we show that its horofunction extension yields a compact metric space together with a Lipschitz embedding, which enables robust approximations of monotone maps via Scott-continuous maps on the compactified domain. For separable spaces, the horofunction compactification is metrizable, which provides a path toward effective domain-theoretic constructions.

Temporary obstacles that may block a robot's planned route create a sequential navigation problem: a robot must decide whether to wait for a blockage to clear, reroute, or acquire more information about the obstacle before acting. We formulate graph navigation among temporary obstacles as a partially observable semi-Markov decision process and introduce SPARROW, a belief-space planner built on Partially Observable Monte Carlo Planning (POMCP). SPARROW searches over traversal, observation, and finite-duration waiting actions while maintaining a particle belief over latent obstacle classes and clearance times. Class-conditioned survival models are learned online from both clearance observations and right-censored encounters where the robot reroutes before clearance is observed. A generative model simulates obstacle arrivals and clearances as each action unfolds, so the planner can account for blockages that may occur along alternative routes. We further introduce a value-of-learning criterion that trades the immediate cost of collecting labelled survival data against its expected reduction in future navigation regret. Across two simulation graphs and multiple obstacle-class settings, SPARROW reduces mean time-to-goal by 12-26% relative to OSCAR, a recent survival-based method for the same problem. On a physical mobile robot, SPARROW reduces mean time-to-goal by 20.5% relative to OSCAR while selectively observing, waiting, and rerouting as environment conditions change.

We prove that every germ of an abelian relation of a codimension-one web on a complex projective manifold is defined over a Liouvillian extension of the function field.

We show that the logotropic dark fluid, proposed as a single-fluid unification of dark matter and dark energy, is exactly dissipative unimodular gravity with pressureless matter and the logarithmic energy diffusion function $Q(a)=3A\ln a+Q_{c}$. The correspondence is exact at the level of the homogeneous background, rather than asymptotic, and introduces no parameters beyond those already present in either description. It provides a dynamical reinterpretation of the logotropic temperature $A$, whose origin was left open in the original proposal: up to the expansion rate, $A$ is the rate at which energy is diffused from matter into the geometric sector, $\dot Q=3AH$. The adiabatic sound speed of the effective fluid is $c_{s}^{2}=A/\rho$, where $\rho$ is the unimodular matter energy density, and reaches the speed of light at $a_{s}=a_{M}/2^{1/3}$; the matter energy density then vanishes and changes sign at $a_{M}$, which is precisely the scale factor at which the model is known to become phantom. Since $\rho$ coincides with the enthalpy density $\epsilon+P$ of the logotropic fluid, the phantom branch is the branch on which the matter sector carries negative energy density and on which the apparent horizon entropy decreases. Finally, we show that a one-parameter saturating diffusion law removes these late-time pathologies while preserving the low-redshift phenomenology.

The rapid proliferation of unauthorized unmanned aerial vehicles (UAVs) has created a growing need for robust, jamming-resistant counter-UAV systems for perimeter defense. This paper presents SCOUT (Spatial Computation for Optimized UAV Tracking), a ROS-integrated onboard perception and control framework for real-time aerial defense against incoming UAVs. SCOUT performs visual detection, target association, track filtering, and control command generation directly onboard the defender UAV, without relying on external sensing infrastructure or ground-station computation. To provide stable control inputs, the perception pipeline combines TensorRT-accelerated drone detection with ByteTrack-based association and a lightweight track-retention state machine. The state machine rejects abrupt target jumps and maintains short-term target continuity during temporary detection degradation, reducing unstable control responses caused by false detections or target switching. We evaluate the proposed architecture through an integrated hardware deployment executing a planar “goalkeeping” interception strategy. In this setting, the defender UAV tracks the incoming target and adjusts its motion to maintain a blocking configuration near the protected boundary. Real-world flight results show that SCOUT maintains valid target detections for 92.2% of frames while operating at real-time onboard detection rates, demonstrating the feasibility of visual tracking and closed-loop control for UAV perimeter defense. A video demonstration of the end-to-end perimeter defense operation is available online. https://figshare.com/s/497befc033091fc0b84f

We study the imaginary-time evolution of a domain-wall state constrained to return to itself in a staggered free-fermion chain. The resulting space-time profile separates into frozen, liquid, and gas phases divided by sharp boundaries known as arctic curves. The spectral gap produces an incompressible half-filled gas phase bounded by an inner arctic curve, in addition to the outer frozen-liquid boundary. We determine both arctic curves, the thermodynamic return amplitude, and the complete equal-time correlation kernel. Correlations decay algebraically in the liquid regions and exponentially in the gas, while both arctic boundaries arise as caustics of free quasiparticle trajectories. The return amplitude and correlation kernel are respectively controlled by the determinant and inverse of the same block-Toeplitz operator. We obtain an exact matrix Wiener-Hopf factorization of this operator by reducing the problem to a scalar Riemann-Hilbert problem on an elliptic spectral curve. The factorization also yields the exact thermodynamic return amplitude: its logarithm consists of a quadratic term with explicit gap dependence and a bounded periodic theta-function correction. The resulting frozen-liquid-gas structure is a continuous-time free-fermion counterpart of that found in doubly periodic dimer models.

We develop a variational formulation of the Ornstein-Uhlenbeck evolution equation based on a Gaussian divergence structure, in which the drift term is absorbed into the first variation of a weighted Dirichlet energy rather than treated as a lower-order perturbation. For the linear equation, the distributional, energy weak, and variational notions of solution coincide and determine the same unique solution. The same framework yields a variational formulation of the associated parabolic obstacle problem, for which we prove existence and then uniqueness under a standard additional regularity assumption on the obstacle. The same mechanism applies to nonlinear Gaussian gradient flows associated with convex integrands of standard $p$-growth. Finally, we extend the Gaussian-divergence formulation to the force-free kinetic Fokker-Planck equation with periodic position variables, establishing the same equivalence for the linear equation.

Engineering emergent ferromagnetism at correlated-oxide interfaces offers a powerful route to creating collective states that do not exist in the parent materials. Here, we show that interfacial valence reconstruction in NdNiO$_3$/CaMnO$_3$ superlattices generates dual-sublattice ferromagnetism involving both Mn and Ni. Depth-resolved standing-wave X-ray photoelectron spectroscopy reveals enhanced Mn$^{3+}$ character on the CaMnO$_3$ side of the interface and enhanced Ni$^{2+}$ character on the NdNiO$_3$ side, establishing the configurations required for Mn$^{4+}$-O-Mn$^{3+}$ double exchange and Ni$^{2+}$-O-Mn$^{4+}$ superexchange, respectively. At low temperature, element-specific XMCD reveals ferromagnetic responses from both sublattices, with the Ni response concentrated predominantly in the Ni$^{2+}$-derived spectral component that SW-XPS independently shows to be enhanced at the interface. Together, these results show that cooperative double exchange within CaMnO$_3$ and superexchange across the interface couple the Mn and Ni sublattices, providing a general strategy for engineering interfacial ferromagnetism in correlated oxides.

Substantial efforts have been devoted to making deep learning objectives, representations, and architectures interpretable, with the goal of improving the safety, robustness, and generalisation of learning systems in diverse real-world applications. The recently proposed maximal coding rate reduction ($\mathrm{MCR}^{2}$) offers a promising information-theoretic framework for learning structured, discriminative representations of class-wise submanifolds and has inspired interpretable white-box architectures. However, we observe that $\mathrm{MCR}^{2}$ can completely fail under distribution shift, motivating our study of its out-of-distribution (OOD) generalisation limits. We establish two limitations of $\mathrm{MCR}^{2}$ for OOD generalisation. First, the $\mathrm{MCR}^{2}$ objective alone can admit complete prediction failure: a representation based entirely on unstable environmental features can achieve the global coding optimum yet fail completely after correlation reversal, despite an available perfectly stable feature. This exact-optimum example includes test inputs that cannot occur during training. Even when every possible test input can also occur during training, coding quality can be arbitrarily close to optimal while prediction error is arbitrarily close to 100%. Second, directly incorporating the invariance principle underlying widely successful invariant risk minimisation (IRM) and risk extrapolation (REx) does not eliminate this failure. The failing representation admits the same optimal coding operator across training environments, showing that shared coding optimality does not ensure stable prediction. Reliable OOD guarantees for $\mathrm{MCR}^{2}$ therefore require additional new assumptions or learning principles that establish stable predictive relationships across environments.

Spinning frequency-modulated continuous-wave (FMCW) radars have been gaining popularity in autonomous vehicle perception on account of their robustness to adverse weather conditions and 360{\deg} field of view. Recently, scanning radars have also been shown capable of generating per-azimuth Doppler velocity. In this paper, we investigate whether these Doppler velocity measurements improve spinning radar vehicle detection and tracking performance. For detection, we estimate the ego motion and use it to undo the Doppler range distortion of the radar image before passing it to a network. For tracking, we propose a new way to estimate a per-vehicle velocity and use it as a prior for the tracker's motion model. Since Doppler-enabled spinning radar data is not available in any dataset with ground-truth dynamic object labels, our first contribution is an automatic labelling pipeline that uses an ensemble of fine-tuned off-the-shelf lidar detectors to label all 643 km of the Boreas Road Trip dataset. We then transfer detections to radar, and use over 250 km of vehicle-dense sequences as ground-truth training data. By training and evaluating two state-of-the-art detectors, we show that Doppler undistortion can improve detection accuracy by up to $2.37$ points on mean average precision. Furthermore, we show that the Doppler velocity prior can improve tracking accuracy by $13.68$ points on multi-object tracking accuracy (MOTA) versus the zero-velocity initialization baseline, while achieving $99.7\%$ of the MOTA obtained using ground-truth velocities as the prior.

Latent variable generative models are commonly fit using simple priors over latent variables, but draws from these priors often fail to produce realistic data. This failure is due to a mismatch between the prior and the aggregated posterior, the distribution of latent variables induced by the fitted model and the data. This mismatch is often viewed as evidence that the prior is misspecified and should be replaced. Alternatively, in modern generative models, a two-stage strategy is increasingly used where first, the model is fit, and second, the aggregated posterior is estimated (van den Oord et al.,2017; Rombach et al., 2022.). Synthetic data are then obtained by sampling from this aggregated posterior instead of the prior. To check such procedures, we introduce the aggregated posterior predictive check (APPC). Theoretically, we establish sufficient conditions under which the APPC is asymptotically calibrated. For probabilistic principal component analysis, we show that the APPC can remain calibrated under a misspecified latent prior when pervasive factors permit recovery of the signal space. Experiments with variational autoencoders show that aggregated posterior sampling improves generation for heavy-tailed and clustered data relative to Gaussian prior sampling while performing comparably to models with more flexible latent priors.

We study a novel class of evolution variational inequalities (EVI) driven by bifunctions $\mathsf{b}:\mathrm{D} \times \mathrm{D} \to \mathbb{R}$ defined on a subset $\mathrm{D}$ of a metric space $(\mathrm{X},\mathrm{d})$ and satisfying a natural dissipativity condition $$\mathsf{b}(x,y)+\mathsf{b}(y,x)\le \eta \mathrm{d}^2(x,y).$$ We provide general conditions for existence, stability, regularity, approximation and asymptotic behavior of solutions, by proposing a metric framework that generalizes the classical structure of evolutions driven by monotone operators in Hilbert spaces. A motivating application is the setting of minimax and multispecies coupled gradient flows, in which each of $N$ species evolves in the direction of steepest descent of its own energy functional and the joint system is not a gradient flow of any single energy. To construct solutions, we introduce a variational movement scheme (VMS), a time discretization scheme in which each update is a saddle point of a metrically penalized bifunction, generalizing the classical minimizing movement/ JKO scheme for single species gradient descent. In the coupled multispecies case, the VMS reduces to a Nash equilibrium problem. We prove existence of discrete solutions to the VMS via a general approach which combines dissipativity of $\mathsf{b}$ with a new notion of convexity along suitable $\textit{barycentric interpolations}$ (which is inspired by convexity along generalized geodesics, a crucial notion for gradient flows). Together, these conditions provide a zeroth-order notion of game-theoretic monotonicity applicable to general metric spaces without linear structure.

Symbolic regression aims to recover closed-form equations from observations, providing interpretable models for scientific discovery. Existing approaches struggle to combine flexible structural search with efficient inference. Search-based methods can refine expression structure but often rely on costly combinatorial optimization with random initialization. Pretrained neural models generate formulas almost instantly, but their predictions often contain symbolic errors. We introduce MOSAIC-SR, which uses a pretrained Transformer to propose multiple initial sketches. These sketches initialize searches in several promising regions, avoiding random starts in the vast expression space. Each search jointly recovers structure and constants through scale-aware constant optimization and local symbolic repair. We evaluate MOSAIC-SR on the SRSD-Feynman dataset with and without dummy variables and on six additional benchmarks. MOSAIC-SR obtains the highest symbolic solution rate on every dataset while ranking among the top two methods in predictive accuracy. This advantage persists in the presence of irrelevant dummy inputs. The results show that learned priors can focus search on promising equation structures, and that numerical optimization and symbolic repair are important for recovery.

HR 8799 is the benchmark young multi-planet system, hosting four directly imaged giant planets orbiting between a warm inner belt and a cold outer debris ring. However, the few-au region has remained observationally inaccessible, lying beyond the reach of conventional high-contrast imaging and challenging for radial-velocity techniques due to stellar pulsations. Here we combine Gaia absolute astrometry and Hipparcos-Gaia proper-motion anomalies with radial velocities and high-contrast imaging to reveal an additional close-in substellar companion. The allowed solutions span approx. 0.3-6 au, with posteriors peaking at approx. 2-3 au and with masses around 10-14 Jupiter masses. Dynamical modelling shows that such a companion with a low eccentricity can coexist with the long-lived resonant chain of the four outer planets, and sculpt the inner edge of the warm debris belt, linking the planet architecture to the debris-disk morphology. Our results close a long-standing detection gap in HR 8799 and demonstrate the power of Gaia astrometry to uncover massive planets at intermediate separations that may remain undetectable with radial velocities or direct imaging alone.

Natural spoken interaction requires more than streaming ASR, language generation, and speech synthesis: a system must react to overlap without canceling on every acknowledgment, prepare a response before a turn is certain, and ensure canceled audio cannot enter conversation history. We present Voice-Light, a full-duplex cascaded voice agent that combines immediate acoustic onset, a causal adapter sharing a streaming ASR encoder, reversible playback control, and private speculative response generation. Structured tool calls execute concurrently with audible bridge speech, while browser acknowledgments make rendered audio authoritative for durable history. Locked evaluation on 1,673 real-conversation silence candidates found that an earlier learned completion checkpoint preserved a 2.70% false-cutoff rate but reached only 12.53% end-of-turn recall, compared with 95.60% for a Silero timing policy. The deployed system therefore retains a hybrid controller rather than claiming a learned-policy replacement. Across three unscripted operator-run microphone sessions, 36 measured response turns had a 758 ms median from final VAD endpoint to first server audio; 21 turns were below 800 ms. These sessions are an instrumented case study, not a controlled user evaluation. We release the synthetic data, model artifacts, evaluation code and summaries, source code, and deployment configuration supporting the result.

Fiber lasers and amplifiers offer attractive alternatives to conventional solid-state systems. However, generation of high-energy ultrashort laser pulses in fibers faces challenges due to the complex interplay of multiple nonlinear effects arising due to pulse confinement within a small fiber core and also limitations imposed by the gain bandwidth of the available active fibers. The discovery of self-similar amplification and gain-managed nonlinear amplification (GMNA) pulse propagation regimes in fibers with normal dispersion suggests that these challenges can be turned into an advantage. Here we show that pulses generated in the GMNA regime are, in fact, the realization of the idealized similariton-type pulses in realistic fibers with limited gain bandwidth. Our analytical and numerical results show how one should shape the fiber gain as a function of propagation length to achieve constant peak power similariton-like pulses with steadily increasing energy, the pulse bandwidth exceeding the gain bandwidth, and the nearly linear frequency chirp allowing for efficient pulse compression to its Fourier limit. Absent Raman nonlinearities, these pulses can reach $\mu$J level energies in standard single-mode fibers, representing a tenfold increase in pulse energy compared to the best currently available nonlinear amplifiers. Our results have significant implications for the fundamental understanding of nonlinear wave dynamics and for the advancement of fiber laser technology, supporting the reliable generation of high-energy pulses for practical use in areas such as micromachining, metrology, and bioimaging.

Objective: The aim of this study was to enhance patient comfort and diagnostic accuracy in urodynamic studies (UDS) by developing a novel framework that uses a single vesical pressure (Pves) channel combined with a surface abdominal electromyography (AEMG) electrode array to estimate detrusor pressure (Pdet) without the need for an abdominal catheter. The proposed method was designed to operate in real-time, and to be compatible with current UDS clinical hardware. Methods: 40 UDS studies were performed with AEMG measured simultaneously with both Pves and Pabd. A predefined, provocative abdominal maneuver was used before each study to ensure accurate coupling between the AEMG signal and associated abdominal pressures seen in Pves. Criteria were set to determine if coupling was sufficient for Pdet estimation from Pves and AEMG. Post-hoc signal processing was used to further refine and evaluate estimated Pdet using automated, non-causal event detection. An event-based scoring metric was used to determine how accurately this approach estimated Pdet by calculating the percentage of signal contained in the appropriate channel for that event (Pdet vs abdominal pressure). Results: The results showed that 30 of the 40 cases indicated a "Go" status after the provocative maneuver, demonstrating feasibility of this method in a majority of UDS studies. The scoring metric, along with correlation analysis, confirmed accuracy of estimated Pdet with scores ranging from 85% to 95% with median at 93%. Significance: This innovative approach has the potential to revolutionize urodynamic studies by providing a more comfortable and accurate diagnostic tool for assessing bladder function. This is particularly beneficial for vulnerable populations, such as the elderly and children, reducing the need for multiple catheters and improving the overall patient experience during UDS.

We analyse the Cartesian perfectly matched layer (PML) approximation of the two-dimensional Helmholtz equation $-c^2\Delta u-\omega^2 u=f$, in which the propagation speed $c$ is variable inside the region of interest and equal to a constant $c_\infty$ outside a disc $B_R$. Existing analyses of the Cartesian PML assume a homogeneous, or piecewise homogeneous, background. We assume of $c$ only a two-sided bound, so that discontinuous speeds – layered media and compact inclusions – are covered. This is possible because the speed enters the PML sesquilinear form only through its zeroth order coefficient, the principal part being built from the stretching profiles alone. Well-posedness of the physical problem at this regularity follows from a Lippmann-Schwinger equation, uniqueness from Rellich's lemma and unique continuation. A Morawetz multiplier yields a sharp non-trapping condition on $c$, namely $\nabla c(x)\cdot(x-x_0)<c(x)$ for some $x_0$, which reduces in the radial case to the Herglotz condition $\frac{d}{d\rho}(\rho/c(\rho))>0$; its failure produces a trapped bicharacteristic. The variable speed is invisible to the essential spectrum: after factoring out $c^2$, the two operators differ by a compactly supported multiplication, so the spectral analysis of the homogeneous Cartesian layer applies verbatim. This yields well-posedness of the truncated problem for every $\omega>0$, with a stability constant independent of the layer width, and convergence to the physical solution in $H^1$ at a rate exponential in $\omega\gamma_M\delta_M$, where $\gamma_M$ and $\delta_M$ are the absorption strength and the layer thickness. Numerical experiments with a low-velocity inclusion confirm the exponential decay in both $\delta$ and $\omega$.

Physiological emotion recognition using wearable sensors has important applications in mental health monitoring, affective computing, and human-computer interaction. However, existing studies typically evaluate a single model, sensing configuration, or dataset, limiting our understanding of how these factors influence recognition performance. We present a comparative study of temporal deep learning architectures for physiological emotion recognition using two multimodal wearable datasets: WESAD and EmoWear. Bidirectional long short-term memory (LSTM), temporal convolutional network (TCN), and Transformer models are evaluated under wrist-only, chest-only, and multimodal sensing configurations using participant-independent leave-one-subject-out cross-validation (LOSO-CV). We also investigate soft-voting ensembles, sensor ablation, sampling frequency, and gradient-based saliency. The Transformer achieved the highest multimodal accuracy on WESAD (99.02% +/- 0.51%), whereas the LSTM achieved the best multimodal accuracy on EmoWear for both arousal (91.80% +/- 1.06%) and valence (89.96% +/- 0.36%). These results show that relative architecture performance depends on dataset characteristics rather than one architecture being uniformly superior. Multimodal sensing consistently outperformed wrist-only and chest-only configurations across both datasets. Sampling-frequency analysis showed that 4 Hz provides a practical operating point, with performance comparable to higher frequencies at substantially lower training cost. These findings provide guidance for selecting architectures, sensing modalities, and sampling frequencies for wearable physiological emotion recognition.

We show that Landau-Zener tunneling in a small-gap insulator produces a singular DC current–voltage relation, and correspondingly, strong high-harmonic generation in AC transport. Remarkably, high-harmonic responses decrease subexponentially with harmonic order, which is parametrically slower than in ordinary conductors and PN diodes. We further derive a scaling law for the current amplitude at frequency $n\omega$ produced by the applied electric field $E$ at fundamental frequency $\omega$. Our work offers a promising route to THz generation based on frequency multiplication in solids.

As generative AI is increasingly used as a source of personal financial guidance, understanding how people appraise such advice is important for supporting appropriate reliance. We conducted a randomized vignette experiment with 285 U.S. adults across eight financial decisions, independently varying three advice styles—AI, expert, and online community—and displayed source labels while holding the underlying recommendation consistent. Advice style most strongly shaped message and safety appraisals, Expert labels selectively increased perceived source knowledge, and decision context primarily shaped risk and safety appraisals. These appraisals were associated with downstream judgments, with models explaining 69.2% of overall quality, 75.9% of trust, and 82.9% of intended reliance. Expert-style advice also remained most preferred when shown without source labels. Our findings have implications for understanding financial advice evaluation, distinguishing the roles of advice style and source labels, and designing financial AI that supports grounded evaluation rather than simply maximizing trust.

We study the boundary geometry of the Noshiro–Warschawski class $\mathcal{R}$. Using the geometric structure of close-to-convex domains and their relation to Loewner chains, we investigate the boundary behavior of functions in $\mathcal{R}$. In particular, we discuss the relation between spherical length and local connectedness, and show that the boundary of the image domain of a function in $\mathcal{R}$ need not be locally connected. We also revisit the classical fact that $\mathcal{R}$ is not contained in the class $\mathcal{S}^{*}$ of starlike functions and give a simple explicit example of a function in $\mathcal{R}\setminus\mathcal{S}^*$.

In this paper, we give a complete description for the full isometry groups of a class of right invariant Riemannian metrics on the special unitary group $\mathrm{SU}(2^N)$. These metrics have been used by physicists to study Nielsen's geometric approach for complexities in quantum computations.

We present a molecular dynamics (MD) study of Al implantation in 4H-SiC to determine how implantation temperature and dose affect defect evolution and dopant activation during annealing. Simulations use the Gao-Weber potential with a Morse Al-SiC interaction reparameterized to density functional theory diffusion and kick-in/kick-out barriers. At Al concentrations above the saturation limit of ~2e20/cm3, implantation at 900 K promotes interstitial-rich planar clusters already during implantation. During annealing, these clusters trap Al and evolve into faulted interstitial loops, reducing substitutional Al incorporation. In contrast, lower implantation temperatures preserve stronger local disorder that is consumed during epitaxial regrowth, resulting in higher chemical activation within MD-accessible annealing times. Atomistic trajectories show a thermally activated transition to faulted loops once planar clusters reach about 60 interstitials. Rather than nucleating as a single coherent disk, several locally faulted regions form first and subsequently merge. The activation energy is ~1.1 eV for dislocation nucleation and ~2.1 eV for stacking-fault growth. Frank-type loops dominate at high temperature, while transient Shockley partials occur mainly at early stages and below 2000 K. As large planar defects form and dissolve, stable compensating Al-C complexes also emerge. These findings support the experimental hypothesis that secondary defects contribute to the reduced Al activation observed under supersaturation during annealing.

For “ideal anyons,” the Berry phase associated with a closed loop of an anyon around another is robust, i.e., independent of the size and shape of the loop, and directly yields the braid statistics. This is not the case for fractional quantum Hall (FQH) quasiparticles (QPs), which are charged and have finite size. The Berry phase $\Theta$ associated with a loop of a fractionally charged QP around another contains two contributions: $\Theta=\Theta_{\rm AB}+\Theta_{\rm braid}$, where $\Theta_{\rm AB}$, the Aharonov-Bohm phase, is proportional to the enclosed area, and $\Theta_{\rm braid}$ is the order-one contribution from braid statistics. The latter is obtained by determining $\Theta$ for a closed loop twice, once with and once without the other QP inside, and taking the difference. It is known that even small uncertainties in the enclosed areas can produce corrections to $\Theta_{\rm AB}$ larger than $\Theta_{\rm braid}$. We consider how the Berry phase depends on the shape of the QP, which, unlike its charge, is not topological and varies along the path in response to the local potential. Our calculations show that practically imperceptible differences in the shape of the outer QP in the two experiments can produce a non-universal value for the braid statistics, revealing an additional challenge for a precise determination of the braid statistics. We discuss these results in the context of interference experiments. We also note that the fractional phase jumps in these experiments can be understood without assuming QPs with sharply quantized fractional charges at the edges of the FQH system; instead, they directly measure the fractionally quantized vorticity of bulk QPs.