This study proposes a new approach to quantum state recovery following measurement. Specifically, we introduce a special operation that transfers the probability amplitude of the quantum state into its orthogonal complement. This operation is followed by a measurement performed on this orthogonal subspace, enabling the undisturbed original quantum state to be regained. Remarkably, this recovery is achieved without dependence of the post-measurement operation on the measurement outcome, thus allowing the recovery without historical dependence. This constitutes a highly nontrivial phenomenon. From the operational perspective, as the no-cloning theorem forbids perfect and probabilistic cloning of arbitrary quantum states, and traditional post-measurement reversal methods typically rely on operations contingent on the measurement outcomes, it questions fundamental assumptions regarding the necessity of historic dependence. From an informational perspective, since this recovery method erases the information about the measurement outcome, it's intriguing that the information can be erased without accessing the measurement outcome. These results imply the operational and informational non-triviality formulated in a direct-sum Hilbert space framework.

We present a theorem concerning the invariance of cross-correlation peak positions, which provides a foundation for a new method for time difference estimation that is potentially faster than the conventional fast Fourier transform (FFT) approach for real/complex sequences. This theoretical result shows that the peak position of the cross-correlation function between two shifted discrete-time signals remains unchanged under arbitrary monotonic transformations of the input signals. By exploiting this property, we design an efficient estimation algorithm based on the cross-correlation function between signals quantized into low-bit integers. The proposed method requires only integer arithmetic instead of real-valued operations, and further computational efficiency can be achieved through number-theoretic algorithms. Numerical experiments demonstrate that the proposed method achieves a shorter processing time than conventional FFT-based approaches.

We propose a quantum-classical hybrid method for solving large-scale mixed-integer quadratic problems (MIQP). Although extended Benders decomposition is effective for MIQP, its master problem which handles the integer and quadratic variables often becomes a computational bottleneck. To address this challenge, we integrate the D-Wave CQM solver into the decomposition framework to solve the master problem directly. Our results show that this hybrid approach efficiently yields near-optimal solutions and, for certain problem instances, achieves exponential speedups over the leading commercial classical solver. These findings highlight a promising computational strategy for tackling complex mixed-integer optimization problems.

We present NusaCrowd, a collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have brought together 137 datasets and 118 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their value is demonstrated through multiple experiments. NusaCrowd's data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and the local languages of Indonesia. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and the local languages of Indonesia. Our work strives to advance natural language processing (NLP) research for languages that are under-represented despite being widely spoken.

Galaxy clusters are expected to be dark matter (DM) reservoirs and storage rooms for the cosmic-ray protons (CRp) that accumulate along the cluster's formation history. Accordingly, they are excellent targets to search for signals of DM annihilation and decay at gamma-ray energies and are predicted to be sources of large-scale gamma-ray emission due to hadronic interactions in the intracluster medium. We estimate the sensitivity of the Cherenkov Telescope Array (CTA) to detect diffuse gamma-ray emission from the Perseus galaxy cluster. We perform a detailed spatial and spectral modelling of the expected signal for the DM and the CRp components. For each, we compute the expected CTA sensitivity. The observing strategy of Perseus is also discussed. In the absence of a diffuse signal (non-detection), CTA should constrain the CRp to thermal energy ratio within the radius $R_{500}$ down to about $X_{500}<3\times 10^{-3}$, for a spatial CRp distribution that follows the thermal gas and a CRp spectral index $\alpha_{\rm CRp}=2.3$. Under the optimistic assumption of a pure hadronic origin of the Perseus radio mini-halo and depending on the assumed magnetic field profile, CTA should measure $\alpha_{\rm CRp}$ down to about $\Delta\alpha_{\rm CRp}\simeq 0.1$ and the CRp spatial distribution with 10% precision. Regarding DM, CTA should improve the current ground-based gamma-ray DM limits from clusters observations on the velocity-averaged annihilation cross-section by a factor of up to $\sim 5$, depending on the modelling of DM halo substructure. In the case of decay of DM particles, CTA will explore a new region of the parameter space, reaching models with \tau_{\chi}&gt;10^{27}s for DM masses above 1 TeV. These constraints will provide unprecedented sensitivity to the physics of both CRp acceleration and transport at cluster scale and to TeV DM particle models, especially in the decay scenario.

Deep learning has revolutionized computer vision, yet a major gap persists between complex, data-hungry deep learning models and the practical demands of state-of-the-art scientific measurements. To fundamentally bridge this gap, we propose deep prior-based denoising, a robust deep learning model that requires no training data. We demonstrate its effectiveness by removing grid artifacts in angle-resolved photoemission spectroscopy (ARPES), a long-standing and critical data analysis challenge in materials science. Our results demonstrate that deep prior-based denoising yields clearer ARPES images in a fraction of the time required by conventional, experiment-based denoising methods. This ultra-efficient approach to ARPES will enable high-speed, high-resolution three-dimensional band structure mapping in momentum space, thereby dramatically accelerating our understanding of microscopic electronic structures of materials. Beyond ARPES, deep prior-based denoising represents a versatile tool that could become a new standard in any advanced scientific measurement fields where data acquisition is limited.

We explore whether survival model performance in underrepresented high- and low-risk subgroups - regions of the prognostic spectrum where clinical decisions are most consequential - can be improved through targeted restructuring of the training dataset. Rather than modifying model architecture, we propose a novel risk-stratified sampling method that addresses imbalances in prognostic subgroup density to support more reliable learning in underrepresented tail strata. We introduce a novel methodology that partitions patients by baseline prognostic risk and applies matching within each stratum to equalize representation across the risk distribution. We implement this framework on a cohort of 1,799 patients with resected colorectal liver metastases (CRLM), including 1,197 who received adjuvant chemotherapy and 602 who did not. All models used in this study are Cox proportional hazards models trained on the same set of selected variables. Model performance is assessed via Harrell's C index, time-dependent AUC, and Integrated Calibration Index (ICI), with internal validation using Efron's bias-corrected bootstrapping. External validation is conducted on two independent CRLM datasets. Cox models trained on risk-balanced cohorts showed consistent improvements in internal validation compared to models trained on the full dataset while noticeably enhancing stratified C-index values in underrepresented high- and low-risk strata of the external cohorts. Our findings suggest that survival model performance in observational oncology cohorts can be meaningfully improved through targeted rebalancing of the training data across prognostic risk strata. This approach offers a practical and model-agnostic complement to existing methods, especially in applications where predictive reliability across the full risk continuum is critical to downstream clinical decisions.

We investigate the capabilities of various stages of the SKA to perform world-leading weak gravitational lensing surveys. We outline a way forward to develop the tools needed for pursuing weak lensing in the radio band. We identify the key analysis challenges and the key pathfinder experiments that will allow us to address them in the run up to the SKA. We identify and summarize the unique and potentially very powerful aspects of radio weak lensing surveys, facilitated by the SKA, that can solve major challenges in the field of weak lensing. These include the use of polarization and rotational velocity information to control intrinsic alignments, and the new area of weak lensing using intensity mapping experiments. We show how the SKA lensing surveys will both complement and enhance corresponding efforts in the optical wavebands through cross-correlation techniques and by way of extending the reach of weak lensing to high redshift.

Faraday tomography is a new method of the study of cosmic magnetic fields enabled by broadband low-frequency radio observations. By Faraday tomography, it is possible to obtain the Faraday dispersion function which contains information on the line-of-sight distributions of magnetic fields, thermal electron density, and cosmic-ray electron density by measuring the polarization spectrum from a source of synchrotron radiation over a wide band. Furthermore, by combining it with 2-dimensional imaging, Faraday tomography allows us to explore the 3-dimensional structure of polarization sources. The application of Faraday tomography has been active in the last 20 years, when broadband observation has become technically feasible. However, the Faraday dispersion function is mathematically the Fourier transform of the polarization spectrum, and since the observable band is finite, it is impossible to obtain a complete Faraday dispersion function by performing Fourier transform. In addition, the Faraday dispersion function does not directly reflect the distribution of magnetic field, thermal-electron density, and cosmic-ray electron density in the physical space, and its physical interpretation is not straightforward. Despite these two difficult problems, Faraday tomography is attracting much attention because it has great potential as a new method for studying cosmic magnetic fields and magnetized plasmas. In particular, the next-generation radio telescope SKA (Square Kilometre Array) is capable of polarization observation with unprecedented sensitivity and broad bands, and the application of Faraday tomography is expected to make dramatic progress in the field of cosmic magnetic fields. In this review, we explain the basics of Faraday tomography with simple and instructive examples. Then representative algorithms to realize Faraday tomography are introduced and finally some applications are shown.

Bent band structures have been empirically described in ferroelectric materials to explain the functioning of recently developed ferroelectric tunneling junction and photovoltaic devices. This report presents experimental evidence for ferroelectric band bending, which was observed in the depth profiles of atomic orbitals of angle-resolved hard x-ray photoemission spectra of ferroelectric BaTiO3 thin films. The ferroelectric bent band structure is separated into three depth regions; the shallowest and deepest regions are slightly modulated by the screening effect at surface and interface, respectively, and the intermediate region exhibits the pure ferroelectric effect. In the pure ferroelectric bent band structure, we found that the binding energy of outer shell electrons shows a larger shift than that of inner shell electrons, and that the difference in energy shift is correlated with the atomic configuration of the soft phonon mode. These findings could lead to a simple understanding of the origin of electric polarization.

Quantum annealing (QA) with a transverse field often fails to sample degenerate ground states fairly, limiting applicability to problems requiring diverse optimal solutions. Although Quantum Monte Carlo (QMC) is widely used to simulate QA, its ability to reproduce such unfair ground-state sampling remains unclear because stochastic and coherent quantum dynamics differ fundamentally. We quantitatively evaluate how accurately QMC reproduces the sampling bias in QA by comparing the final ground-state distributions from the QMC master equation and the Schrödinger equation. We find QMC tends to produce uniform ground-state probabilities, unlike QA's biased distribution, and that this uniformity bias strengthens as annealing proceeds. Our analysis reveals that this bias originates from replica alignment -- the dominance of configurations in which all Trotter replicas coincide -- caused by the energetic suppression and entropic reduction of kink configurations (replica mismatches). These findings clarify a fundamental limitation of discrete-time QMC in faithfully simulating QA dynamics, highlighting the importance of replica correlations and transition rules in achieving realistic ground-state sampling.

In this article we study the exterior power structure of the algebraic de Rham cohomology group associated with the Gelfand hypergeometric function and its confluent family. The hypergeometric function F(z) is a function on the Zariski open subset $Z_{r+1}\subset\mat{r+1,N}$, called the generic stratum, defined by an r-dimesional integral on $\Ps^{r}$. For $z\in Z_{r+1}$, the algebraic de Rham cohomology group is associated to the integral. When z belongs to the particular subset of $Z_{r+1}$, called the Veronese image, we show that this cohomology group can be expressed as the exterior power product of the de Rham cohomology group associated with the hypergeometric function defined by 1-dimensional integral.

Faraday tomography through broadband polarimetry can provide crucial information on magnetized astronomical objects, such as quasars, galaxies, or galaxy clusters. However, the limited wavelength coverage of the instruments requires that we solve an ill-posed inverse problem when we want to obtain the Faraday dispersion function (FDF), a tomographic distribution of the magnetoionic media along the line of sight. This paper explores the use of wavelet transforms and the sparsity of the transformed FDFs in the form of wavelet shrinkage (WS) for finding better solutions to the inverse problem. We recently proposed the Constraining and Restoring iterative Algorithm for Faraday Tomography (CRAFT; Cooray et al. 2021), a new flexible algorithm that showed significant improvements over the popular methods such as Rotation Measure Synthesis. In this work, we introduce CRAFT+WS, a new version of CRAFT incorporating the ideas of wavelets and sparsity. CRAFT+WS exhibit significant improvements over the original CRAFT when tested for a complex FDF of realistic Galactic model. Reconstructions of FDFs demonstrate super-resolution in Faraday depth, uncovering previously unseen Faraday complexities in observations. The proposed approach will be necessary for effective cosmic magnetism studies using the Square Kilometre Array and its precursors.

Charged lepton flavor violation is forbidden in the Standard Model but possible in several new physics scenarios. In many of these models, the radiative decays $\tau^{\pm}\rightarrow\ell^{\pm}\gamma$ ($\ell=e,\mu$) are predicted to have a sizeable probability, making them particularly interesting channels to search at various experiments. An updated search via $\tau^{\pm}\rightarrow\ell^{\pm}\gamma$ using full data of the Belle experiment, corresponding to an integrated luminosity of 988 fb$^{-1}$, is reported for charged lepton flavor violation. No significant excess over background predictions from the Standard Model is observed, and the upper limits on the branching fractions, $\mathcal{B}(\tau^{\pm}\rightarrow \mu^{\pm}\gamma)$ $\leq$ $4.2\times10^{-8}$ and $\mathcal{B}(\tau^{\pm}\rightarrow e^{\pm}\gamma)$ $\leq$ $5.6\times10^{-8}$, are set at 90\% confidence level.

The structures and properties of moire patterns in twisted bilayers of two-dimensional (2D) materials are known to depend sensitively on twist angle, yet their dependence on stacking order remains comparatively underexplored. In this study, we use molecular dynamics simulations to systematically investigate the combined effects of stacking order and rotation in MoS2 bilayers. Beginning from five well-established high-symmetry bilayer stackings, we apply twist angles between 1 and 120 to the top layer, revealing a variety of relaxed moire structures. Our results show that the initial stacking significantly influences the moire domain configurations that emerge at a given twist angle. While all five stacking orders are metastable without twist, they form two moire-equivalent classes- AA/AB and AA',A'B,AB', i.e., for a given twist angle, structures within each class relax to the same moire configuration. Specifically, initial AA and AB stackings give rise to triangular ferroelectric domains near 0+/-3, while AA', A'B, and AB' stackings produce triangular ferroelectric domains near 60+/-3. At precisely 60 and 120 twists, the bilayers relax to into pure high-symmetry stackings, highlighting the rotational relationships between these configurations and explaining the shift of 60 in the ferroelectric rotational range. These findings demonstrate the critical role of stacking order in governing the rich moire landscapes accessible in twistronic systems.