Experimental Test of Tracking the King Problem

In quantum theory, the retrodiction problem is not as clear as its classical counterpart because of the uncertainty principle of quantum mechanics. In classical physics, the measurement outcomes of the present state can be used directly for predicting the future events and inferring the past events which is known as retrodiction. However, as a probabilistic theory, quantum-mechanical retrodiction is a nontrivial problem that has been investigated for a long time, of which the Mean King Problem is one of the most extensively studied issues. Here, we present the first experimental test of a variant of the Mean King Problem, which has a more stringent regulation and is termed "Tracking the King". We demonstrate that Alice, by harnessing the shared entanglement and controlled-not gate, can successfully retrodict the choice of King's measurement without knowing any measurement outcome. Our results also provide a counterintuitive quantum communication to deliver information hidden in the choice of measurement.Comment: 16 pages, 5 figures, 2 table

Excellent Absorption of LaCoxO3 Over Full Solar Spectrum and Direct Photothermal Energy Storage of Ca(OH)2–LaCoxO3

Abstract: Photothermal conversion is a vital way for solar energy applications. The strong absorption of near Infrared light is essential for excellent photothermal performance. In this study, we demonstrated that nano LaCoxO3 is able to harvest light intensely across the full solar spectrum with high photothermal temperature. A core-shell-like structure of LaCoxO3-coated Ca(OH)2 particles was fabricated and shows excellent photothermal conversion, high kinetics of dehydration and remarkable cycle stability of heat storage and release. The photothermal dehydration-conversion of Ca(OH)2 increases 8.4-fold. Results demonstrate the multifunctionality of LaCoxO3, intensifying light harvesting, high photothermal conversion, good stability, considerable strength, and porous framework favouring the performance of photothermal storage and release cycles. LaCoxO3–Ca(OH)2 composite can simultaneously harvest light and store thermal energy

μ-Biphenyl-3,3′,4,4′-tetra­carboxyl­ato-κ2 O 3:O 3′-bis­[triaqua­(2,2′-bipyridyl-κ2 N,N′)nickel(II)] hexa­hydrate

The asymmetric unit of the title complex, [Ni2(C16H6O8)(C10H8N2)2(H2O)6]·6H2O, contains one NiII atom, one 2,2′-bipyridine ligand, three coordinated water mol­ecules, one-half of a fully deprotonated biphenyl-3,3′,4,4′-tetra­carboxyl­ate anion and three lattice water mol­ecules. The NiII atom displays a distorted NiN2O4 octa­hedral coordination formed by one carboxyl­ate O atom, three water O atoms and two N atoms of the chelating ligand. The complete biphenyl-3,3′,4,4′-tetra­carboxyl­ate ligand displays inversion symmetry and links two symmetry-related NiII atoms into a binuclear complex. Neighbouring complex mol­ecules are linked through O—H⋯O hydrogen bonds into a three-dimensional structure. Additional O—H⋯O hydrogen bonds between the lattice water mol­ecules help to consolidate the crystal packing

Offspring sex ratio shifts of the solitary parasitoid wasp, Trichopria drosophilae (Hymenoptera: Diapriidae), under local mate competition

Localmate competition (LMC) models predict a female-biased offspring sex ratio when a single foundress oviposits alone in a patch and an increasing proportion of sons with increasing foundress number. We tested whether the solitary pupal parasitoid, Trichopria drosophilae (Hymenoptera: Diapriidae), adjusted offspring sex ratio with foundress number when parasitizing Drosophila melanogaster pupae. Mean number of female offspring was higher than that of males, with a male proportion of 26 ± 16% when only one foundress oviposited. However, male proportion reached 58 ± 26%, 48 ± 22%, and 51 ± 19% in three-, five and seven-foundress cohorts. That the male proportion of offspring increased with foundress number is consistent with LMC models

Equivariant Energy-Guided SDE for Inverse Molecular Design

Inverse molecular design is critical in material science and drug discovery, where the generated molecules should satisfy certain desirable properties. In this paper, we propose equivariant energy-guided stochastic differential equations (EEGSDE), a flexible framework for controllable 3D molecule generation under the guidance of an energy function in diffusion models. Formally, we show that EEGSDE naturally exploits the geometric symmetry in 3D molecular conformation, as long as the energy function is invariant to orthogonal transformations. Empirically, under the guidance of designed energy functions, EEGSDE significantly improves the baseline on QM9, in inverse molecular design targeted to quantum properties and molecular structures. Furthermore, EEGSDE is able to generate molecules with multiple target properties by combining the corresponding energy functions linearly

Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics

Traditional data-driven deep learning models often struggle with high training costs, error accumulation, and poor generalizability in complex physical processes. Physics-informed deep learning (PiDL) addresses these challenges by incorporating physical principles into the model. Most PiDL approaches regularize training by embedding governing equations into the loss function, yet this depends heavily on extensive hyperparameter tuning to weigh each loss term. To this end, we propose to leverage physics prior knowledge by ``baking'' the discretized governing equations into the neural network architecture via the connection between the partial differential equations (PDE) operators and network structures, resulting in a PDE-preserved neural network (PPNN). This method, embedding discretized PDEs through convolutional residual networks in a multi-resolution setting, largely improves the generalizability and long-term prediction accuracy, outperforming conventional black-box models. The effectiveness and merit of the proposed methods have been demonstrated across various spatiotemporal dynamical systems governed by spatiotemporal PDEs, including reaction-diffusion, Burgers', and Navier-Stokes equations.Comment: 51 pages, 27 figure

Faithful completion of images of scenic landmarks using internet images

Abstract—Previous works on image completion typically aim to produce visually plausible results rather than factually correct ones. In this paper, we propose an approach to faithfully complete the missing regions of an image. We assume that the input image is taken at a well-known landmark, so similar images taken at the same location can be easily found on the Internet. We first download thousands of images from the Internet using a text label provided by the user. Next, we apply two-step filtering to reduce them to a small set of candidate images for use as source images for completion. For each candidate image, a co-matching algorithm is used to find correspondences of both points and lines between the candidate image and the input image. These are used to find an optimal warp relating the two images. A completion result is obtained by blending the warped candidate image into the missing region of the input image. The completion results are ranked according to combination score, which considers both warping and blending energy, and the highest ranked ones are shown to the user. Experiments and results demonstrate that our method can faithfully complete images