3,145 research outputs found
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)
decays in the pQCD approach
We calculate the CP averaged branching ratios and CP-violating asymmetries
for and
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 and
decays, and around 18% for 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
Preparation of shape memory polymers or self-healing composites based on cyclodextrin-based inclusion
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
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