i-Razor: A Differentiable Neural Input Razor for Feature Selection and Dimension Search in DNN-Based Recommender Systems

Input features play a crucial role in DNN-based recommender systems with thousands of categorical and continuous fields from users, items, contexts, and interactions. Noisy features and inappropriate embedding dimension assignments can deteriorate the performance of recommender systems and introduce unnecessary complexity in model training and online serving. Optimizing the input configuration of DNN models, including feature selection and embedding dimension assignment, has become one of the essential topics in feature engineering. However, in existing industrial practices, feature selection and dimension search are optimized sequentially, i.e., feature selection is performed first, followed by dimension search to determine the optimal dimension size for each selected feature. Such a sequential optimization mechanism increases training costs and risks generating suboptimal input configurations. To address this problem, we propose a differentiable neural input razor (i-Razor) that enables joint optimization of feature selection and dimension search. Concretely, we introduce an end-to-end differentiable model to learn the relative importance of different embedding regions of each feature. Furthermore, a flexible pruning algorithm is proposed to achieve feature filtering and dimension derivation simultaneously. Extensive experiments on two large-scale public datasets in the Click-Through-Rate (CTR) prediction task demonstrate the efficacy and superiority of i-Razor in balancing model complexity and performance.Comment: Accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE

Chern insulators and high Curie temperature Dirac half-metal in two-dimensional metal-organic frameworks

Two-dimensional (2D) magnetic materials with nontrivial topological states have drawn considerable attention recently. Among them, 2D metal-organic frameworks (MOFs) are standing out due to their advantages, such as the easy synthesis in practice and less sensitivity to oxidation that are distinctly different from inorganic materials. By means of density-functional theory calculations, we systematically investigate the electronic and topological properties of a class of 2D MOFs X(C21H15N3) (X = transition metal element from 3d to 5d). Excitingly, we find that X(C21H15N3) (X = Ti, Zr, Ag, Au) are Chern insulators with sizable band gaps (~7.1 meV). By studying a four-band effective model, it is revealed that the Chern insulator phase in X(C21H15N3) (X = Ti, Zr, Ag, Au) is caused cooperatively by the band inversion of the p orbitals of the C21H15N3 molecule and the intrinsic ferromagnetism of X(C21H15N3). Additionally, Mn(C21H15N3) is a Dirac half-metal ferromagnet with a high Curie temperature up to 156 K. Our work demonstrates that 2D MOFs X(C21H15N3) are good platforms for realizing Quantum anomalous Hall effect and designing novel spintronic devices based on half-metals with high-speed and long-distance spin transport.Comment: 16 pages, 4 figure

Room-temperature antiferromagnetic CrSe monolayer with tunable metal-insulator transition in ferroelectric heterostructures

Recently, there has been a rapidly growing interest in two-dimensional (2D) transition metal chalcogenide monolayers (MLs) due to their unique magnetic and electronic properties. By using an evolutionary algorithm and first-principles calculations, we report the discovery of a previously unexplored, chemically, energetically, and thermodynamically stable 2D antiferromagnetic (AFM) CrSe ML with a N\'eel temperature higher than room temperature. Remarkably, we predict an electric field-controllable metal-insulator transition (MIT) in a van der Waals (vdW) heterostructure comprised of CrSe ML and ferroelectric Sc2CO2. This tunable transition in CrSe/Sc2CO2 heterostructure is attributed to the change in the band alignment between CrSe and Sc2CO2 caused by the ferroelectric polarization reversal in Sc2CO2. Our findings suggest that 2D AFM CrSe ML has important potential applications in AFM spintronics, particularly in the gate voltage conducting channel.Comment: 13 Pages, 4 Figure

Ethyl 7-(4-bromo­phen­yl)-5-trifluoro­methyl-4,7-dihydro­tetra­zolo[1,5-a]pyrimidine-6-carboxyl­ate

In the title compound, C14H11BrF3N5O2, the pyrimidine ring adopts a flattened envelope conformation with sp 3-hybridized carbon as the flap [deviation = 0.177 (3) Å]. The dihedral angle between tetra­zole and bromo­phenyl rings is 84.3 (1)°. In the crystal, mol­ecules are linked into centrosymmetric dimers by pairs of N—H⋯N hydrogen bonds

Electrically tunable Gilbert damping in van der Waals heterostructures of two-dimensional ferromagnetic metals and ferroelectrics

Tuning the Gilbert damping of ferromagnetic (FM) metals via a nonvolatile way is of importance to exploit and design next-generation novel spintronic devices. Through systematical first-principles calculations, we study the magnetic properties of the van der Waals heterostructure of two-dimensional FM metal CrTe2 and ferroelectric (FE) In2Te3 monolayers. The ferromagnetism of CrTe2 is maintained in CrTe2/In2Te3 and its magnetic easy axis can be switched from in-plane to out-of-plane by reversing the FE polarization of In2Te3. Excitingly, we find that the Gilbert damping of CrTe2 is tunable when the FE polarization of In2Te3 is reversed from upward to downward. By analyzing the k-dependent contributions to the Gilbert damping, we unravel that such tunability results from the changed intersections between the bands of CrTe2 and Fermi level on the reversal of the FE polarizations of In2Te3 in CrTe2/In2Te3. Our work provides an appealing way to electrically tailor Gilbert dampings of two-dimensional FM metals by contacting them with ferroelectrics.Comment: 4 Figures, accepted by Applied Physics Letter

5′-Amino-2-oxo-2′,3′-dihydro­spiro­[indoline-3,7′-thieno[3,2-b]pyran]-6′-carbonitrile 1′,1′-dioxide

In the title compound, C15H11N3O4S, the dihedral angle between the mean planes of the dihydro­indol-2-one (r.m.s. deviation = 0.015 Å) and dihydro­thieno[3,2-b]pyran (r.m.s. deviation = 0.011 Å) ring systems is 89.53 (3)°. The crytal packing is consolidated by inter­molecular N—H⋯O and N—H⋯N hydrogen bonds, which link the mol­ecules into a two-dimensional network into sheets lying parallel to (100)

Bs0→η(′)η(′)B_s^0 \to \eta^{(\prime)} \eta^{(\prime)} decays in the pQCD approach

We calculate the CP averaged branching ratios and CP-violating asymmetries for $B_s^0 \to \eta \eta, \eta \eta^\prime$ and $\eta^\prime \eta^\prime$ decays in the perturbative QCD (pQCD) approach here. The pQCD predictions for the CP-averaged branching ratios are Br(B_s^0 \to \eta \eta) = \left (14.2^{+18.0}_{-7.5}) \times 10^{-6}, Br(B_s^0 \to \eta \eta^\prime)= \left (12.4 ^{+18.2}_{-7.0}) \times 10^{-6}, and Br(B_s^0 \to \eta^{\prime} \eta^{\prime}) = \left (9.2^{+15.3}_{-4.9}) \times 10^{-6}, which agree well with those obtained by employing the QCD factorization approach and also be consistent with available experimental upper limits. The gluonic contributions are small in size: less than 7% for $B_s \to \eta \eta$ and $\eta \eta^\prime$ decays, and around 18% for $B_s \to \eta' \eta'$ decay. The CP-violating asymmetries for three decays are very small: less than 3% in magnitude.Comment: 11 pages, 1 ps figure, Revte

SPM: Structured Pretraining and Matching Architectures for Relevance Modeling in Meituan Search

In e-commerce search, relevance between query and documents is an essential requirement for satisfying user experience. Different from traditional e-commerce platforms that offer products, users search on life service platforms such as Meituan mainly for product providers, which usually have abundant structured information, e.g. name, address, category, thousands of products. Modeling search relevance with these rich structured contents is challenging due to the following issues: (1) there is language distribution discrepancy among different fields of structured document, making it difficult to directly adopt off-the-shelf pretrained language model based methods like BERT. (2) different fields usually have different importance and their length vary greatly, making it difficult to extract document information helpful for relevance matching. To tackle these issues, in this paper we propose a novel two-stage pretraining and matching architecture for relevance matching with rich structured documents. At pretraining stage, we propose an effective pretraining method that employs both query and multiple fields of document as inputs, including an effective information compression method for lengthy fields. At relevance matching stage, a novel matching method is proposed by leveraging domain knowledge in search query to generate more effective document representations for relevance scoring. Extensive offline experiments and online A/B tests on millions of users verify that the proposed architectures effectively improve the performance of relevance modeling. The model has already been deployed online, serving the search traffic of Meituan for over a year.Comment: Accepted by CIKM '2

5-Amino-7-(3-chloro­phen­yl)-3,7-di­hydro-2H-thieno[3,2-b]pyran-6-carbo­nitrile 1,1-dioxide

The title compound, C14H11ClN2O3S, with fused thiophene and pyran rings, was synthesized via the condensation of dihydro­thio­phen-3(2H)-one 1,1-dioxide and 2-(3-chloro­benz­yl­idene)malononitrile catalysed by triethyl­amine in ethanol. The thio­phene ring adopts an envelope conformation and the pyran ring is planar (r.m.s. deviation = 0.0067 Å). The dihedral angle between the pyran and phenyl rings is 80.8 (1)°. The crystal packing is stabilized by inter­molecular N—H⋯N and N—H⋯O hydrogen bonds in which the cyano N and sulphone O atoms, respectively, acting as acceptors