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The core challenge of de novo protein design lies in creating proteins with specific functions or properties, guided by certain conditions. Current models explore to generate protein using structural and evolutionary guidance, which only provide indirect conditions concerning functions and properties. However, textual annotations of proteins, especially the annotations for protein domains, which directly describe the protein's high-level functionalities, properties, and their correlation with target amino acid sequences, remain unexplored in the context of protein design tasks. In this paper, we propose Protein-Annotation Alignment Generation (PAAG), a multi-modality protein design framework that integrates the textual annotations extracted from protein database for controllable generation in sequence space. Specifically, within a multi-level alignment module, PAAG can explicitly generate proteins containing specific domains conditioned on the corresponding domain annotations, and can even design novel proteins with flexible combinations of different kinds of annotations. Our experimental results underscore the superiority of the aligned protein representations from PAAG over 7 prediction tasks. Furthermore, PAAG demonstrates a nearly sixfold increase in generation success rate (24.7% vs 4.7% in zinc finger, and 54.3% vs 8.7% in the immunoglobulin domain) in comparison to the existing model.

Molecule-and-text cross-modal representation learning has emerged as a promising direction for enhancing the quality of molecular representation, thereby improving performance in various scientific fields, including drug discovery and materials science. Existing studies adopt a global alignment approach to learn the knowledge from different modalities. These global alignment approaches fail to capture fine-grained information, such as molecular fragments and their corresponding textual description, which is crucial for downstream tasks. Furthermore, it is incapable to model such information using a similar global alignment strategy due to data scarcity of paired local part annotated data from existing datasets. In this paper, we propose Atomas, a multi-modal molecular representation learning framework to jointly learn representations from SMILES string and text. We design a Hierarchical Adaptive Alignment model to concurrently learn the fine-grained fragment correspondence between two modalities and align these representations of fragments in three levels. Additionally, Atomas's end-to-end training framework incorporates the tasks of understanding and generating molecule, thereby supporting a wider range of downstream tasks. In the retrieval task, Atomas exhibits robust generalization ability and outperforms the baseline by 30.8% of recall@1 on average. In the generation task, Atomas achieves state-of-the-art results in both molecule captioning task and molecule generation task. Moreover, the visualization of the Hierarchical Adaptive Alignment model further confirms the chemical significance of our approach. Our codes can be found at https://anonymous.4open.science/r/Atomas-03C3.

RENiX2 compounds, where RE = rare-earth element and X = p-block element, have been highly regarded for cryogenic magnetocaloric applications. Depending on the elements, they can crystallize in CeNiSi2-type, NdNiGa2-type, or MgCuAl2-type crystal structures, showing different types of magnetic ordering and thus affect their magnetic properties. Regarding the magnetocaloric effect, MgCuAl2-type aluminides show larger values than those of the CeNiSi2-type silicides and the NdNiGa2-type gallides due to the favored ferromagnetic ground state. However, RENiGa2 gallides can crystallize in either NdNiGa2- or MgCuAl2-type structures depending on the RE element. In this work, we select heavy RE (HRE) elements for exploring the microstructure, magnetic ordering and magnetocaloric performance of HRENiGa2 (HRE = Dy, Ho or Er) gallides. They all crystallize in the desired MgCuAl2-type crystal structure which undergoes a second-order transition from ferro- to para-magnetic state with increasing temperature. The maximum isothermal entropy change (∣∆Sisomax∣) values are 6.2, 10.4, and 11.4 J kg−1 K−1 (0–5 T) for DyNiGa2, HoNiGa2, and ErNiGa2, respectively, which are comparable to many recently reported cryogenic magnetocaloric materials. Particularly, the excellent magnetocaloric properties of HoNiGa2 and ErNiGa2 compounds, including their composite, fall in the temperature range that enables them for the in-demand hydrogen liquefaction systems.

RENiX2 compounds, where RE = rare-earth element and X = p-block element, have been highly regarded for cryogenic magnetocaloric applications. Depending on the elements, they can crystallize in CeNiSi2-type, NdNiGa2-type, or MgCuAl2-type crystal structures, showing different types of magnetic ordering and thus affect their magnetic properties. Regarding the magnetocaloric effect, MgCuAl2-type aluminides show larger values than those of the CeNiSi2-type silicides and the NdNiGa2-type gallides due to the favored ferromagnetic ground state. However, RENiGa2 gallides can crystallize in either NdNiGa2- or MgCuAl2-type structures depending on the RE element. In this work, we select heavy RE (HRE) elements for exploring the microstructure, magnetic ordering and magnetocaloric performance of HRENiGa2 (HRE = Dy, Ho or Er) gallides. They all crystallize in the desired MgCuAl2-type crystal structure which undergoes a second-order transition from ferro- to para-magnetic state with increasing temperature. The maximum isothermal entropy change (∣∆Sisomax∣) values are 6.2, 10.4, and 11.4 J kg−1 K−1 (0–5 T) for DyNiGa2, HoNiGa2, and ErNiGa2, respectively, which are comparable to many recently reported cryogenic magnetocaloric materials. Particularly, the excellent magnetocaloric properties of HoNiGa2 and ErNiGa2 compounds, including their composite, fall in the temperature range that enables them for the in-demand hydrogen liquefaction systems.

RENiX2 compounds, where RE = rare-earth element and X = p-block element, have been highly regarded for cryogenic magnetocaloric applications. Depending on the elements, they can crystallize in CeNiSi2-type, NdNiGa2-type, or MgCuAl2-type crystal structures, showing different types of magnetic ordering and thus affect their magnetic properties. Regarding the magnetocaloric effect, MgCuAl2-type aluminides show larger values than those of the CeNiSi2-type silicides and the NdNiGa2-type gallides due to the favored ferromagnetic ground state. However, RENiGa2 gallides can crystallize in either NdNiGa2- or MgCuAl2-type structures depending on the RE element. In this work, we select heavy RE (HRE) elements for exploring the microstructure, magnetic ordering and magnetocaloric performance of HRENiGa2 (HRE = Dy, Ho or Er) gallides. They all crystallize in the desired MgCuAl2-type crystal structure which undergoes a second-order transition from ferro- to para-magnetic state with increasing temperature. The maximum isothermal entropy change (∣∆Sisomax∣) values are 6.2, 10.4, and 11.4 J kg−1 K−1 (0–5 T) for DyNiGa2, HoNiGa2, and ErNiGa2, respectively, which are comparable to many recently reported cryogenic magnetocaloric materials. Particularly, the excellent magnetocaloric properties of HoNiGa2 and ErNiGa2 compounds, including their composite, fall in the temperature range that enables them for the in-demand hydrogen liquefaction systems.

The latticework structure known as the cosmic web provides a valuable insight into the assembly history of large-scale structures. Despite the variety of methods to identify the cosmic web structures, they mostly rely on the assumption that galaxies are embedded in a Euclidean geometric space. Here we present a novel cosmic web identifier called SCONCE (Spherical and CONic Cosmic wEb finder) that inherently considers the 2D (RA,DEC) spherical or the 3D (RA,DEC,$z$) conic geometry. The proposed algorithms in SCONCE generalize the well-known subspace constrained mean shift (SCMS) method and primarily address the predominant filament detection problem. They are intrinsic to the spherical/conic geometry and invariant to data rotations. We further test the efficacy of our method with an artificial cross-shaped filament example and apply it to the SDSS galaxy catalogue, revealing that the 2D spherical version of our algorithms is robust even in regions of high declination. Finally, using N-body simulations from Illustris, we show that the 3D conic version of our algorithms is more robust in detecting filaments than the standard SCMS method under the redshift distortions caused by the peculiar velocities of halos. Our cosmic web finder is packaged in python as SCONCE-SCMS and has been made publicly available., Comment: 20 pages, 9 figures, 2 tables

The set of local modes and the ridge lines estimated from a dataset are important summary characteristics of the data-generating distribution. In this work, we consider estimating the local modes and ridges from point cloud data in a product space with two or more Euclidean/directional metric spaces. Specifically, we generalize the well-known (subspace constrained) mean shift algorithm to the product space setting and illuminate some pitfalls in such generalization. We derive the algorithmic convergence of the proposed method, provide practical guidelines on the implementation, and demonstrate its effectiveness on both simulated and real datasets., Comment: 51 pages, 10 figures

Directional data consist of observations distributed on a (hyper)sphere, and appear in many applied fields, such as astronomy, ecology, and environmental science. This paper studies both statistical and computational problems of kernel smoothing for directional data. We generalize the classical mean shift algorithm to directional data, which allows us to identify local modes of the directional kernel density estimator (KDE). The statistical convergence rates of the directional KDE and its derivatives are derived, and the problem of mode estimation is examined. We also prove the ascending property of the directional mean shift algorithm and investigate a general problem of gradient ascent on the unit hypersphere. To demonstrate the applicability of the algorithm, we evaluate it as a mode clustering method on both simulated and real-world data sets., Comment: 92 pages, 11 figures. Accepted to the Journal of Machine Learning Research

Dose reduction in computed tomography (CT) is essential for decreasing radiation risk in clinical applications. Iterative reconstruction is one of the most promising ways to compensate for the increased noise due to reduction of photon flux. Rather than most existing prior-driven algorithms that benefit from manually designed prior functions or supervised learning schemes, in this work we integrate the data-consistency as a conditional term into the iterative generative model for low-dose CT. At the stage of prior learning, the gradient of data density is directly learned from normal-dose CT images as a prior. Then at the iterative reconstruction stage, the stochastic gradient descent is employed to update the trained prior with annealed and conditional schemes. The distance between the reconstructed image and the manifold is minimized along with data fidelity during reconstruction. Experimental comparisons demonstrated the noise reduction and detail preservation abilities of the proposed method.