1,974 research outputs found
A Self-Correcting Sequential Recommender
Sequential recommendations aim to capture users' preferences from their
historical interactions so as to predict the next item that they will interact
with. Sequential recommendation methods usually assume that all items in a
user's historical interactions reflect her/his preferences and transition
patterns between items. However, real-world interaction data is imperfect in
that (i) users might erroneously click on items, i.e., so-called misclicks on
irrelevant items, and (ii) users might miss items, i.e., unexposed relevant
items due to inaccurate recommendations. To tackle the two issues listed above,
we propose STEAM, a Self-correcTing sEquentiAl recoMmender. STEAM first
corrects an input item sequence by adjusting the misclicked and/or missed
items. It then uses the corrected item sequence to train a recommender and make
the next item prediction.We design an item-wise corrector that can adaptively
select one type of operation for each item in the sequence. The operation types
are 'keep', 'delete' and 'insert.' In order to train the item-wise corrector
without requiring additional labeling, we design two self-supervised learning
mechanisms: (i) deletion correction (i.e., deleting randomly inserted items),
and (ii) insertion correction (i.e., predicting randomly deleted items). We
integrate the corrector with the recommender by sharing the encoder and by
training them jointly. We conduct extensive experiments on three real-world
datasets and the experimental results demonstrate that STEAM outperforms
state-of-the-art sequential recommendation baselines. Our in-depth analyses
confirm that STEAM benefits from learning to correct the raw item sequences
Modeling the clustering in citation networks
For the study of citation networks, a challenging problem is modeling the
high clustering. Existing studies indicate that the promising way to model the
high clustering is a copying strategy, i.e., a paper copies the references of
its neighbour as its own references. However, the line of models highly
underestimates the number of abundant triangles observed in real citation
networks and thus cannot well model the high clustering. In this paper, we
point out that the failure of existing models lies in that they do not capture
the connecting patterns among existing papers. By leveraging the knowledge
indicated by such connecting patterns, we further propose a new model for the
high clustering in citation networks. Experiments on two real world citation
networks, respectively from a special research area and a multidisciplinary
research area, demonstrate that our model can reproduce not only the power-law
degree distribution as traditional models but also the number of triangles, the
high clustering coefficient and the size distribution of co-citation clusters
as observed in these real networks
2-Methylsulfanyl-4-(3-pyridyl)pyrimidine
In the title compound, C10H9N3S, the dihedral angle between the aromatic rings is 8.09 (14)°. In the crystal, a C—H⋯N interaction links the molecules, forming chains
Looking Through the Glass: Neural Surface Reconstruction Against High Specular Reflections
Neural implicit methods have achieved high-quality 3D object surfaces under
slight specular highlights. However, high specular reflections (HSR) often
appear in front of target objects when we capture them through glasses. The
complex ambiguity in these scenes violates the multi-view consistency, then
makes it challenging for recent methods to reconstruct target objects
correctly. To remedy this issue, we present a novel surface reconstruction
framework, NeuS-HSR, based on implicit neural rendering. In NeuS-HSR, the
object surface is parameterized as an implicit signed distance function (SDF).
To reduce the interference of HSR, we propose decomposing the rendered image
into two appearances: the target object and the auxiliary plane. We design a
novel auxiliary plane module by combining physical assumptions and neural
networks to generate the auxiliary plane appearance. Extensive experiments on
synthetic and real-world datasets demonstrate that NeuS-HSR outperforms
state-of-the-art approaches for accurate and robust target surface
reconstruction against HSR. Code is available at
https://github.com/JiaxiongQ/NeuS-HSR.Comment: 17 pages, 20 figure
Plasmon-enhanced Stimulated Raman Scattering Microscopy with Single-molecule Detection Sensitivity
Stimulated Raman scattering (SRS) microscopy allows for high-speed label-free
chemical imaging of biomedical systems. The imaging sensitivity of SRS
microscopy is limited to ~10 mM for endogenous biomolecules. Electronic
pre-resonant SRS allows detection of sub-micromolar chromophores. However,
label-free SRS detection of single biomolecules having extremely small Raman
cross-sections (~10-30 cm2 sr-1) remains unreachable. Here, we demonstrate
plasmon-enhanced stimulated Raman scattering (PESRS) microscopy with
single-molecule detection sensitivity. Incorporating pico-Joule laser
excitation, background subtraction, and a denoising algorithm, we obtained
robust single-pixel SRS spectra exhibiting the statistics of single-molecule
events. Single-molecule detection was verified by using two isotopologues of
adenine. We further demonstrated the capability of applying PESRS for
biological applications and utilized PESRS to map adenine released from
bacteria due to starvation stress. PESRS microscopy holds the promise for
ultrasensitive detection of molecular events in chemical and biomedical
systems
Bridgeness: A Local Index on Edge Significance in Maintaining Global Connectivity
Edges in a network can be divided into two kinds according to their different
roles: some enhance the locality like the ones inside a cluster while others
contribute to the global connectivity like the ones connecting two clusters. A
recent study by Onnela et al uncovered the weak ties effects in mobile
communication. In this article, we provide complementary results on document
networks, that is, the edges connecting less similar nodes in content are more
significant in maintaining the global connectivity. We propose an index named
bridgeness to quantify the edge significance in maintaining connectivity, which
only depends on local information of network topology. We compare the
bridgeness with content similarity and some other structural indices according
to an edge percolation process. Experimental results on document networks show
that the bridgeness outperforms content similarity in characterizing the edge
significance. Furthermore, extensive numerical results on disparate networks
indicate that the bridgeness is also better than some well-known indices on
edge significance, including the Jaccard coefficient, degree product and
betweenness centrality.Comment: 10 pages, 4 figures, 1 tabl
Editorial: Community series in the role of angiogenesis and immune response in tumor microenvironment of solid tumor: volume II
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