2-(2-Furylmethyl­amino­meth­yl)-4-sulfanylphenol

In the title compound, C12H13NO2S, the dihedral angle between the furan and benzene rings is 62.2 (2)° and an intra­molecular O—H⋯N hydrogen bond is formed. In the crystal, mol­ecules are linked by weak inter­molecular N—H⋯S hydrogen bonds

Benzimidazolium 2-(2,4-dichloro­phen­oxy)acetate monohydrate

In the crystal of the title hydrated mol­ecular salt, C7H7N2 +·C8H5Cl2O3·H2O, the components inter­act by way of N—H⋯O and O—H⋯O hydrogen bonds, leading to chains propagating in [100]

Bis(2-cyclo­hexyl­imino­methyl-4,6-disulfanylphenolato)zinc(II)

In the title complex, [Zn(C13H16NOS2)2], the ZnII ion is four-coordinated by two N,O-bidentate Schiff base ligands, resulting in a distorted trans-ZnN2O2 square-planar geometry for the metal ion

Noise suppression of on-chip mechanical resonators by chaotic coherent feedback

We propose a method to decouple the nanomechanical resonator in optomechanical systems from the environmental noise by introducing a chaotic coherent feedback loop. We find that the chaotic controller in the feedback loop can modulate the dynamics of the controlled optomechanical system and induce a broadband response of the mechanical mode. This broadband response of the mechanical mode will cut off the coupling between the mechanical mode and the environment and thus suppress the environmental noise of the mechanical modes. As an application, we use the protected optomechanical system to act as a quantum memory. It's shown that the noise-decoupled optomechanical quantum memory is efficient for storing information transferred from coherent or squeezed light

Bis(2-chloro-1,10-phenanthroline-κ2 N,N′)(thio­cyanato-κN)zinc (2-chloro-1,10-phenanthroline-κ2 N,N′)tris­(thio­cyanato-κN)zincate

The asymmetric unit of the title compound, [Zn(NCS)(C12H7ClN2)2][Zn(NCS)3(C12H7ClN2)], contains two cations and two anions. In the cations, the ZnII ions have distorted trigonal–bipyramidal environments formed by four N atoms from two 2-chloro-1,10-phenanthroline (cphen) ligands and one N atom from a thio­cyanate ligand. The ZnII atoms in the complex anions also have distorted trigonal–bipyramidal environments, formed by two N atoms from a cphen ligand and three N atoms from three thio­cyanato ligands. The crystal packing exhibits π–π inter­actions between the rings of the cphen ligands [shortest centroid–centroid distance = 3.586 (5) Å] and short inter­molecular S⋯Cl [3.395 (5) Å] and S⋯S [3.440 (4) Å] contacts

Disentangled Generative Causal Representation Learning

This paper proposes a Disentangled gEnerative cAusal Representation (DEAR) learning method. Unlike existing disentanglement methods that enforce independence of the latent variables, we consider the general case where the underlying factors of interests can be causally correlated. We show that previous methods with independent priors fail to disentangle causally correlated factors. Motivated by this finding, we propose a new disentangled learning method called DEAR that enables causal controllable generation and causal representation learning. The key ingredient of this new formulation is to use a structural causal model (SCM) as the prior for a bidirectional generative model. The prior is then trained jointly with a generator and an encoder using a suitable GAN loss incorporated with supervision. We provide theoretical justification on the identifiability and asymptotic consistency of the proposed method, which guarantees disentangled causal representation learning under appropriate conditions. We conduct extensive experiments on both synthesized and real data sets to demonstrate the effectiveness of DEAR in causal controllable generation, and the benefits of the learned representations for downstream tasks in terms of sample efficiency and distributional robustness

Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning

Modern deep learning heavily relies on large labeled datasets, which often comse with high costs in terms of both manual labeling and computational resources. To mitigate these challenges, researchers have explored the use of informative subset selection techniques, including coreset selection and active learning. Specifically, coreset selection involves sampling data with both input (\bx) and output (\by), active learning focuses solely on the input data (\bx). In this study, we present a theoretically optimal solution for addressing both coreset selection and active learning within the context of linear softmax regression. Our proposed method, COPS (unCertainty based OPtimal Sub-sampling), is designed to minimize the expected loss of a model trained on subsampled data. Unlike existing approaches that rely on explicit calculations of the inverse covariance matrix, which are not easily applicable to deep learning scenarios, COPS leverages the model's logits to estimate the sampling ratio. This sampling ratio is closely associated with model uncertainty and can be effectively applied to deep learning tasks. Furthermore, we address the challenge of model sensitivity to misspecification by incorporating a down-weighting approach for low-density samples, drawing inspiration from previous works. To assess the effectiveness of our proposed method, we conducted extensive empirical experiments using deep neural networks on benchmark datasets. The results consistently showcase the superior performance of COPS compared to baseline methods, reaffirming its efficacy

{4-[(Diphenyl­phosphino)methyl­amino]pyridinium-κP}bis­(nitrato-κO)silver(I)

In the title mononuclear complex, [Ag(C18H18N2P)(NO3)2], the metal centre is coordinated in a slightly distorted trigonal–planar geometry by the P atom of the phosphine ligand and the O atoms of the two monodentate nitrate anions. In the crystal structure, complex mol­ecules are connected by inter­molecular N—H⋯O hydrogen bonds, forming chains running parallel to the b axis

Graphene Field-Effect Transistor for Terahertz Modulation

The real-world applications of terahertz (THz) technology necessitate versatile adaptive optical components, for example, modulators. In this chapter, we begin with a brief review on different techniques for THz modulation. After that, we introduce the extraordinary features of graphene along with its advantages and disadvantages as channel materials for field effect transistor (FET). We then discuss two types of graphene FET-based THz modulators, one is rigid and another is flexible. The feasibility of the high-quality THz modulators with different graphene FET structures has been successfully demonstrated. It is observed that by tuning the carrier concentration of graphene by electrical gating, the THz modulation can be obtained with relatively large modulation depth, broad width band, and moderate speed. This chapter helps the reader in obtaining guidelines for the proper choice of a specific structure for THz modulator with graphene FET