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arXiv2026

Cell size heterogeneity controls crystallization of the developing fruit fly wing

A fundamental question in biology is to understand how patterns and shapes emerge from the collective interplay of large numbers of cells. Cells forming two-dimensional epithelial tissues behave as active materials that undergo remodeling and spontaneous shape changes. Focusing on the fly wing as a model system, we find that the cellular packing in the wing epithelium transitions from a disordered packing to an ordered, crystalline packing. While previous studies propose a role of tissue shear flow in establishing the ordered cell packing in the fly wing, we reveal a role of cell size heterogeneity. Indeed, we find that even if tissue shear have been inhibited, cell packings in the fruit fly wing epithelium transition from disordered to an ordered packing. We propose that the transition is controlled by the cell size heterogeneity, which is quantified by the cell size polydispersity. To explore the role of cell size polydispersity in controlling cellular packings, we implement polydispersity in a vertex model of epithelial tissues. Through numerical simulations of this model, we show that there is a critical value of cell size polydispersity above which cellular packings are disordered and below which they form a crystalline packing. By analyzing experimental data, we find that cell size polydispersity decreases during fly wing development. The observed dynamics of tissue crystallisation is consistent with the slow ordering kinetics we observe in the vertex model. Therefore, although tissue shear does not control the transition, it significantly enhances the rate of tissue-scale ordering by facilitating alignment of locally ordered crystallites. Our results identify cell size heterogeneity as a control parameter, in both the vertex model and the fruit fly wing epithelium, controlling the transition between ordered and disordered cellular packings.

physics.bio-ph↗

arXiv2026

Electronic band structure reconstruction in Ni intercalated ZrTe$_2$

The filling of the large van der Waals gap in Transition Metal Dichalcogenides (TMDs) often leads to lattice and electronic instabilities, which prelude the onset of a rich phenomenology. Motivated by previous results from macroscopic measurements, we investigate the electronic structure of the TMDs ZrTe$_2$ and Ni intercalated ZrTe$_2$ (Ni$_x$ZrTe$_2$, $x\approx 0.05$) employing angle-resolved photoemission spectroscopy (ARPES). We readily identify in Ni$_x$ZrTe$_2$ two flat bands, most likely associated with localized Ni-derived 3$d$-states, at about $\approx-0.7$ eV and $\approx-1.2$ eV in binding energy. More significantly, at low $T$, we identify an electronic structure reconstruction in Ni$_x$ZrTe$_2$, which halves the electronic periodicity along the $k_{z}$ direction. This is reminiscent of a commensurate band folding with wave-vector $q=(0,0,π)$. A mean-field model for a charge density wave (CDW) transition driven by interband hole-hole interactions is solved with inputs from the experimentally determined electronic band structure. Comparison between theory and experiments unveils a CDW transition in Ni$_x$ZrTe$_2$, for which the gap opens above the Fermi level. Our findings invite further investigation into the structural properties of intercalated ZrTe$_2$.

cond-mat.str-el↗

arXiv2026

Driven square lattice of quantum dots in a magnetic field coupled to a cylindrical FIR-photon cavity

We present a comprehensive computational study of driven quantum dot arrays in a square lattice configuration, subject to an external magnetic field and coupled to a cylindrical far-infrared photon cavity. The driving is introduced through a harmonic modulation of the full electron-photon interaction, therefore including both paramagnetic and diamagnetic contributions. The electron-electron Coulomb interactions are treated within density functional theory, while the electron-photon coupling is modeled using a many-body configuration interaction approach at each iteration of the density functional. By exploiting the unique properties of the cylindrical TE$_{011}$ cavity mode, we demonstrate selective enhancement of diamagnetic two-photon transitions. Our results reveal that the effectiveness of harmonic modulation of the electron-photon interaction is strongly dependent on both the driving frequency and the electron occupation number per dot. When the driving frequency approaches twice the cavity photon frequency, the system exhibits resonant behavior characterized by efficient photon pumping, occupation of higher-order photon replicas, and activation of collective radial Coulomb breathing modes. These findings establish a controllable mechanism for manipulating photon states in coupled quantum dot-cavity systems and provide insights into the interplay among harmonic modulation, photonic excitations, magnetic confinement, and many-body electron correlations in dimensionally reduced nanostructures.

cond-mat.mes-hall↗

arXiv2026

Flexoelectricity-driven softening of bend elasticity leads to spontaneous chiral symmetry breaking in a polar fluid

The origin of the recently observed spontaneous chiral symmetry breaking in polar fluids composed of achiral molecules is an unsolved problem, raising fundamental questions about how heliconical structures emerge in such systems. Here, we investigate the pretransitional fluctuations leading to the formation of the spontaneously chiral twist-bend ferroelectric nematic phase using dielectric spectroscopy, light scattering, and small-angle X-ray scattering. We observe simultaneous softening of the bend elastic constant and the emergence of a collective dielectric mode on approaching the transition. By developing a theoretical model, we show that these phenomena are signatures of a flexoelectricity-driven transition arising from the coupling between electric polarization and bend deformation.

cond-mat.soft↗

arXiv2026

Traceable Quantum Secret Sharing

Quantum secret sharing protects distributed quantum information against unauthorized reconstruction, yet existing schemes provide no intrinsic accountability when shares are leaked or forged. This missing capability is particularly relevant in realistic distributed settings, where compromised participants may either expose valid shares or maliciously interfere with reconstruction. Here we establish a framework for traceable quantum secret sharing under these two threat models. We introduce leakage-verifiable and traceable quantum secret sharing (LVT-QSS), which attributes leaked shares through an explicit leakage verification mechanism, and malicious-traceable and accountable quantum secret sharing (MTA-QSS), which identifies participants who submit forged shares during reconstruction. We formulate security definitions for both settings and prove that the proposed protocols satisfy the corresponding guarantees against the relevant adversaries. Numerical simulations under per-share depolarizing noise further quantify the robustness of the proposed schemes and identify verification as the dominant noise sensitive stage. These results incorporate accountability into quantum secret sharing and provide a foundation for traceable distributed quantum cryptography.

quant-ph↗

arXiv2026

Transmitter-device-independent quantum key distribution

Transmitter-side device dependence is a longstanding yet implicit problem in quantum key distribution (QKD), in contrast to the thoroughly addressed measurement-side case. Quantum steering, which intrinsically distinguishes the roles of sender and receiver in entanglement certification, offers a natural route to lifting trust assumptions on the transmitter. Existing one-sided device-independent (1sDI) protocols that exploit quantum steering as the security resource typically operate in a tripartite architecture, whereas adapting them to a transmitter-receiver architecture can introduce a signaling loophole. Here we conceptually formalize the transmitter-device-independence on the basis of faithful quantum steering and propose a practical transmitter-device-independent (TDI) QKD protocol within the 1sDI trust model. The TDI scheme operationalizes the implicit no-signaling condition by enforcing a causal isolation between the input information and the transmitted quantum states, thereby eliminating the associated transmitter-side device vulnerabilities. In a proof-of-principle experiment, we validate the feasibility of the TDI protocol, achieving a key rate in the asymptotic limit of 410~bps over 27~km spool fiber. By delivering TDI security while retaining strong loss tolerance, our approach helps bridge the gap between security and practicality for real-world QKD deployments.

quant-ph↗

arXiv2026

Hidden chirality and half-vortex formation in exciton-polariton condensates

We show that a radially symmetric, nonresonantly pumped spinor exciton-polariton condensate can acquire chirality without a rotating drive, chiral geometry, or pump orbital angular momentum. Spin relaxation in a two-reservoir system shifts the reservoir-induced blueshift relative to the gain profile, creating an effective non-Hermitian chiral potential. A reduced angular-mode theory reveals tunable exceptional points and non-reciprocal coupling between counter-rotating modes. Full driven-dissipative Gross-Pitaevskii simulations show that this hidden chirality enables all-optical formation of spin-selective half-vortices.

cond-mat.mes-hall↗

arXiv2026

Eigenstate condensation in quantum systems with finite-dimensional Hilbert spaces

Random quantum states drawn from the Haar ensemble with a constraint on the energy expectation value $E_{\mathrm{av}} = \langle ψ| H | ψ\rangle$ display \textit{eigenstate condensation}: for $E_{\mathrm{av}}$ below a critical value $E_c$, they develop macroscopic overlap with the ground state. We study eigenstate condensation in systems with finite-dimensional Hilbert spaces. These systems display three phases: a ground-state phase, in which energy-constrained random states have macroscopic overlap with the ground state; a high-temperature phase, in which they have exponentially small overlap with each energy eigenstate; and an anti-ground-state phase, in which they have macroscopic overlap with the most highly excited state. In local spin systems the ground-state and anti-ground-state phases approach the middle of the spectrum as $1/[\text{system size}]$, but -- because the condensation phase transitions have exponential, rather than polynomial, finite-size scaling -- the crossover becomes exponentially sharp in system size and the high-temperature phase is best understood as an extended phase.

quant-ph↗

arXiv2026

Realization of a Synthetic Hall Torus with a Topological Charge Pump in a Spinor Bose-Einstein Condensate

We report the first experimental realization of a synthetic Hall torus using a spinor Bose-Einstein condensate confined in a ring-shaped trap with in situ imaging. By cyclically coupling three hyperfine spin states via Raman and microwave fields, we impose a periodic boundary condition in the synthetic dimension, which, together with a real-space ring trap, realizes a toroidal geometry with synthetic magnetic flux penetrating the surface. This flux induces azimuthal density modulations whose periodicity is uniquely determined by the quantized toroidal flux, a hallmark of the Hall torus. We then implement a topological charge pump by adiabatically varying an axial flux through the torus, inducing quantized azimuthal transport of the atoms. We further investigate the onset of these modulations as the system transitions from cylindrical to toroidal topology. Our results establish a versatile platform for exploring quantum Hall physics and topological phenomena in synthetic curved spaces.

cond-mat.quant-gas↗

arXiv2026

Power- and Fragmentation-aware Online Scheduling for GPU Datacenters

The rise of Artificial Intelligence and Large Language Models is driving increased GPU usage in data centers for complex training and inference tasks, impacting operational costs, energy demands, and the environmental footprint of large-scale computing infrastructures. This work addresses the online scheduling problem in GPU datacenters, which involves scheduling tasks without knowledge of their future arrivals. We focus on two objectives: minimizing GPU fragmentation and reducing power consumption. GPU fragmentation occurs when partial GPU allocations hinder the efficient use of remaining resources, especially as the datacenter nears full capacity. A recent scheduling policy, Fragmentation Gradient Descent (FGD), leverages a fragmentation metric to address this issue. Reducing power consumption is also crucial due to the significant power demands of GPUs. To this end, we propose PWR, a novel scheduling policy to minimize power usage by selecting power-efficient GPU and CPU combinations. This involves a simplified model for measuring power consumption integrated into a Kubernetes score plugin. Through an extensive experimental evaluation in a simulated cluster, we show how PWR, when combined with FGD, achieves a balanced trade-off between reducing power consumption and minimizing GPU fragmentation.

cs.DC↗

arXiv2026

ICASSP 2026 Radar Acoustic Speech Enhancement (RASE) Challenge

This paper presents the ICASSP 2026 Radar Acoustic Speech Enhancement (RASE) Grand Challenge, the first community challenge dedicated to benchmarking speech enhancement from millimeter-wave (mmWave) radar vibrometry. The challenge targets realistic through-glass sensing scenarios where microphones are ineffective or impractical. We introduce a curated dataset of paired radar and clean speech recordings, standardized preprocessing, a baseline system, and a secure Codabench-based evaluation framework with unified objective metrics. The RASE Challenge establishes the first public benchmark for radar-based speech enhancement and aims to foster reproducible research in this emerging field.

eess.AS↗

arXiv2026

Dual-Adaptive Z-Score PPO: Dynamic Clipping Using Action Confidence and Normalized Advantage Magnitude

Proximal Policy Optimization (PPO) remains a popular on-policy reinforcement learning algorithm owing to its stability and ease of implementation. Its clipping threshold, however, is fixed, so every training sample receives the same trust-region width regardless of how reliable the corresponding policy update is. This work examines adaptive-clipping variants of PPO in which the constant clipping parameter is replaced by sample-dependent thresholds computed from policy and return statistics. The central method, Dual-Adaptive Z-Score PPO (PPO-DAZ3), sets the clipping interval for each sample from two signals: action confidence, given by the probability of the selected action under the current policy, and update confidence, given by the normalized advantage magnitude. The rest of the standard PPO training pipeline is left unchanged. Evaluation on three classic control benchmarks (LunarLander-v3, CartPole-v1, and Acrobot-v1) indicates that adaptive clipping improves learning in some settings but not all. PPO-DAZ3 performs best on LunarLander-v3, the smooth advantage-based variant PPO-AZ3S leads on Acrobot-v1, and standard PPO stays competitive on CartPole-v1, suggesting that adaptive clipping is most beneficial on harder control tasks.

cs.LG↗

arXiv2026

Do Language Models Trust Their Own Justifications? A Study on Functional Consistency

Large Language Models (LLMs) have been widely adopted in text classification tasks, where they not only output class predictions but also generate explanations that highlight the tokens deemed most relevant for reaching the predicted label. Yet it remains unclear whether these highlighted elements are behaviorally consistent with the model's own predictions under targeted interventions, which we refer to as functional auto-consistency. While much of the literature evaluates the textual plausibility of such explanations, few studies assess their functional consistency with the model's actual behavior. In this work, we propose an experimental framework based on the principle of auto-consistency: if a model identifies certain tokens as decisive, then isolating, removing, or semantically inverting them should produce systematic and interpretable changes in its predictions. We operationalize this evaluation through sufficiency, comprehensiveness, and counterfactuality metrics, and conduct experiments on IMDB and Steam reviews across both closed-source (GPT-4o-mini) and open-source LLMs (Gemma3, Granite8B, DeepSeek). Results show that GPT-4o-mini follows the expected progression across all metrics, Gemma3 and Granite8B maintain coherence under sufficiency but lose consistency under more demanding interventions, while DeepSeek variants display structural deviations, either failing to preserve sufficiency or overreacting under comprehensiveness and counterfactuality. These findings show that explanation reliability varies across LLM families and scales, with smaller models displaying contradictions and larger ones exhibiting over-sensitivity. By combining sufficiency, comprehensiveness, and counterfactuality, our approach provides a systematic methodology for assessing the functional consistency of LLM self-explanations.

cs.AI↗

arXiv2026

Clifford Kolmogorov-Arnold Networks

We introduce Clifford Kolmogorov-Arnold Network (ClKAN), a flexible and efficient architecture for function approximation in arbitrary Clifford Algebra spaces. We propose the use of Randomized Quasi-Monte Carlo grid generation as a solution to the exponential scaling associated with higher-dimensional algebras. Our ClKAN also introduces new batch normalization strategies to deal with variable domain input. ClKAN finds application in scientific discovery and engineering, and is validated in synthetic and physics-inspired tasks.

cs.LG↗

arXiv2026

Sam-Correction: Fully Adaptive Label Noise Reduction for Medical Image Segmentation

The quality of the teacher label has a dramatic impact on the outcome of the machine learning process. Medical image segmentation often suffers from a noisy ground truth (GT) mask due to difficulties in consistent annotation. Although a recent study has proposed a promising method to curate the noisy GT mask, this requires additional well-curated GT masks that are not always accessible without the aid of multiple medical experts. As an alternative, we propose SAM-Correction, which utilizes a foundation model to correct the noisy GT mask in a fully adaptive manner instead of manually preparing well-curated masks. In our approach, we progressively mitigate label noise via the segment anything model (SAM), where prompts are given from another segmentation network that we train concurrently. Experiments with various types of label noise derived from actual human annotations demonstrate that SAM-C achieves precise noise reduction and high-performing segmentation models trained thereby. Our method paves the way for robust model training even when clean labels are entirely unavailable.

cs.CV↗

arXiv2026

Automated Fetal Magnetocardiography Reconstruction Using a Biophysical Forward Model

Objective: Fetal magnetocardiography (fMCG) enables early detection of cardiac disease by non-invasively measuring fetal electrophysiological activity. However, current signal estimation methods for fMCG require complex, labor-intensive human processing because they rely on blind source separation or template-based projection techniques. Methods: We propose an automated signal reconstruction approach that recovers fetal and maternal cardiac activity from fMCG measurements without manual annotation. Our approach leverages the relationship between measurements and cardiac signals via a biophysically grounded forward model that represents cardiac sources as time-varying magnetic dipoles. This enables reconstruction of physiologically interpretable three-dimensional vectorcardiographic signals along with their spatial position. Results: Validation on synthetic data with known ground truth confirms accurate recovery of cardiac source parameters. On real fMCG recordings, the reconstruction shows strong agreement with established reference methods. Conclusion: The proposed forward-model approach automatically reconstructs fetal and maternal cardiac activity from fMCG recordings and produces signals in an interpretable vectorcardiographic source domain. Significance: Automated, interpretable fMCG reconstruction could make fetal cardiac assessment more reliable and support potential translation into prenatal care.

eess.SP↗

arXiv2026

Privacy-Preserving Fully Distributed Gaussian Process Regression

Although distributed Gaussian process regression (GPR) enables multiple agents to jointly learn a model of the target function, its collaborative nature poses a risk of private data leakage. To address this, we propose a privacy-preserving fully distributed GPR protocol based on secure multi-party computation, which hides each agent's individual contribution from semi-honest coalitions of bounded size, beyond what is implied by the aggregated value. Building upon a secure distributed average consensus algorithm, it guarantees that each agent's local model practically converges to the same global model obtained by the standard distributed GPR. Formal privacy guarantees are established within the simulation based security paradigm. The protocol is further extended to privacy-preserving optimization of kernel hyperparameters, which is critical yet often overlooked in the literature. Experimental results demonstrate the effectiveness and practical applicability of the proposed method.

eess.SY↗

arXiv2026

Interactive image diagnosis using test-time backpropagation from the auxiliary task

Multi-task deep learning has been developed to ensure explainability in computer-aided diagnosis (CAD) systems. These systems simultaneously predict interpretable characteristics of findings in auxiliary tasks to help radiologists assess the reliability of the diagnostic prediction. However, the CAD's opinion would not be referred to when the auxiliary task prediction deviates from the radiologist's expectation. To further leverage CAD's knowledge in the above situation, we proposed a multi-task gradient assist (MGA) method that brings interactivity to multi-task models without additional training. MGA allows radiologists to give feedback to the CAD and ask for additional diagnostic suggestions. Using two datasets of mammography and CT, we show that MGA successfully improves diagnostic accuracy upon feedback on various auxiliary tasks. Compared to an existing method using forward propagation only, MGA performs more robustly and can be combined to further improve accuracy. Our method paves a promising way for integrating artificial intelligence into image diagnosis.

cs.CV↗