N-Hy­droxy­pyridine-4-carboxamide

The title compound, C6H6N2O2, is approximately planar with an r.m.s. deviation for the non-H atoms of 0.052 Å. In the crystal, a two-dimensional array in the bc plane is stabilized by O—H⋯N and N—H⋯O hydrogen bonds

3-Hy­droxy-N′-[(E)-2-thienyl­methyl­idene]-2-naphtho­hydrazide

The asymmetric unit of the title compound, C16H12N2O2S, contains three independent mol­ecules. Intra­molecular N—H⋯O hydrogen bonds in the three mol­ecules lead to very similar conformations: the thio­pene ring and naphthalene ring system in the three mol­ecules form dihedral angles of 10.3 (2), 9.1 (2) and 9.3 (3)°. In the crystal structure, inter­molecular O—H⋯O hydrogen bonds link the mol­ecules into chains propagating in [031]

3-Hy­droxy-N′-[(E)-3-pyridyl­methyl­idene]-2-naphtho­hydrazide

The title compound, C17H13N3O2, displays an E configuration about the C=N bond. The mean planes of the pyridine and benzene rings make a dihedral angle of 31.2 (2)°. An intra­molecular O—H⋯O hydrogen bond is observed. In the crystal, inter­molecular N—H⋯N hydrogen bonding links the mol­ecules into a chain along [101]

ReConTab: Regularized Contrastive Representation Learning for Tabular Data

Representation learning stands as one of the critical machine learning techniques across various domains. Through the acquisition of high-quality features, pre-trained embeddings significantly reduce input space redundancy, benefiting downstream pattern recognition tasks such as classification, regression, or detection. Nonetheless, in the domain of tabular data, feature engineering and selection still heavily rely on manual intervention, leading to time-consuming processes and necessitating domain expertise. In response to this challenge, we introduce ReConTab, a deep automatic representation learning framework with regularized contrastive learning. Agnostic to any type of modeling task, ReConTab constructs an asymmetric autoencoder based on the same raw features from model inputs, producing low-dimensional representative embeddings. Specifically, regularization techniques are applied for raw feature selection. Meanwhile, ReConTab leverages contrastive learning to distill the most pertinent information for downstream tasks. Experiments conducted on extensive real-world datasets substantiate the framework's capacity to yield substantial and robust performance improvements. Furthermore, we empirically demonstrate that pre-trained embeddings can seamlessly integrate as easily adaptable features, enhancing the performance of various traditional methods such as XGBoost and Random Forest

Field emission from in situ-grown vertically aligned SnO2 nanowire arrays

Vertically aligned SnO2 nanowire arrays have been in situ fabricated on a silicon substrate via thermal evaporation method in the presence of a Pt catalyst. The field emission properties of the SnO2 nanowire arrays have been investigated. Low turn-on fields of 1.6 to 2.8 V/μm were obtained at anode-cathode separations of 100 to 200 μm. The current density fluctuation was lower than 5% during a 120-min stability test measured at a fixed applied electric field of 5 V/μm. The favorable field-emission performance indicates that the fabricated SnO2 nanowire arrays are promising candidates as field emitters

Allicin reverses diabetes-induced dysfunction of human coronary artery endothelial cells

Cardiovascular disease (CVD) is the leading cause of death in the United States, and is the major source of morbidity and mortality associated with diabetes mellitus. Because the incidence of diabetes continues to increase, reducing the risk of CVD in diabetes will continue to be a major focus of cardiovascular research. An early manifestation of diabetes-induced CVD is dysfunction of the vascular endothelium, as indicated by depressed production of NO. Our findings now demonstrate depressed activity of endothelial nitric oxide synthase (eNOS) in diabetes, and suggest that treating human coronary artery endothelial cells with allicin, the major bioactive organosulfur component in garlic extract, can restore NO production in these cells. Coronary artery endothelial cells (Lonza) were obtained from control (HCAEC) or diabetic (DHCAEC) donors, and NO production was measured by fluorescence microscopy via 4,5-diaminofluorescein diacetate. On average, NO production was depressed by 12.9% in DHCAEC compared to controls. Treating these cells for 20 minutes with 4 μM allicin restored NO production by 32.9%. Further, immunoblot studies revealed that diabetes decreased expression of eNOS protein by 20.3%; however, allicin was able to reverse this effect of diabetes. On average, eNOS expression was increased by 26% by overnight exposure to 5 μM allicin. Taken together, these data indicate that allicin improves endothelium-dependent NO production in diabetes by enhancing the expression and/or activity of eNOS in human coronary artery endothelial cells. These studies further suggest that this improved endothelial function likely contributes to the established health benefits of garlic consumption (e.g., lowering blood pressure), and also suggests a natural means of reducing the devastating consequences of diabetes on CVD. Future experiments are needed to identify the mechanism of allicin action on eNOS and in vascular endothelial cells

Harnessing the Spatial-Temporal Attention of Diffusion Models for High-Fidelity Text-to-Image Synthesis

Diffusion-based models have achieved state-of-the-art performance on text-to-image synthesis tasks. However, one critical limitation of these models is the low fidelity of generated images with respect to the text description, such as missing objects, mismatched attributes, and mislocated objects. One key reason for such inconsistencies is the inaccurate cross-attention to text in both the spatial dimension, which controls at what pixel region an object should appear, and the temporal dimension, which controls how different levels of details are added through the denoising steps. In this paper, we propose a new text-to-image algorithm that adds explicit control over spatial-temporal cross-attention in diffusion models. We first utilize a layout predictor to predict the pixel regions for objects mentioned in the text. We then impose spatial attention control by combining the attention over the entire text description and that over the local description of the particular object in the corresponding pixel region of that object. The temporal attention control is further added by allowing the combination weights to change at each denoising step, and the combination weights are optimized to ensure high fidelity between the image and the text. Experiments show that our method generates images with higher fidelity compared to diffusion-model-based baselines without fine-tuning the diffusion model. Our code is publicly available at https://github.com/UCSB-NLP-Chang/Diffusion-SpaceTime-Attn.Comment: 20 pages, 16 figure

Octa­kis(2-chloro­benz­yl)di-μ2-hydroxido-di-μ3-oxido-bis­(2-phenyl­acetato)tetra­tin(IV)

The asymmetric unit of the title compound, [Sn4(C7H6Cl)8(C8H7O2)2O2(OH)2], comprises one-half of the centrosymmetric tin(IV) complex. μ3-Oxide and μ2-hydroxide bridges link the four five-coordinate SnIV atoms to generate three fused four-membered Sn—O—Sn—O rings in a ladder-like structure. The two endocyclic Sn atoms each bind to two μ3-oxide anions and a μ2-hydroxide ligand, together with two 2-chloro­benzyl groups. The exocyclic Sn atoms each carry a monodentate phenyl­acetate ligand, two 2-chloro­benzyl groups, and μ3-oxide and μ2-hydroxide ligands. Both types of Sn atoms adopt a distorted trigonal–bipyramidal coordination geometry. The mol­ecular conformation is stabilized by intra­molecular O—H⋯O inter­actions involving the μ2-hydroxide ligands and the C=O group of the phenyl­acetate ligand