164 research outputs found
LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee's Advertisement Recommendation
Graph Neural Network (GNN) is the trending solution for item retrieval in
recommendation problems. Most recent reports, however, focus heavily on new
model architectures. This may bring some gaps when applying GNN in the
industrial setup, where, besides the model, constructing the graph and handling
data sparsity also play critical roles in the overall success of the project.
In this work, we report how GNN is applied for large-scale e-commerce item
retrieval at Shopee. We introduce our simple yet novel and impactful techniques
in graph construction, modeling, and handling data skewness. Specifically, we
construct high-quality item graphs by combining strong-signal user behaviors
with high-precision collaborative filtering (CF) algorithm. We then develop a
new GNN architecture named LightSAGE to produce high-quality items' embeddings
for vector search. Finally, we design multiple strategies to handle cold-start
and long-tail items, which are critical in an advertisement (ads) system. Our
models bring improvement in offline evaluations, online A/B tests, and are
deployed to the main traffic of Shopee's Recommendation Advertisement system
Optical excitation and detection of neuronal activity
Optogenetics has emerged as an exciting tool for manipulating neural
activity, which in turn, can modulate behavior in live organisms. However,
detecting the response to the optical stimulation requires electrophysiology
with physical contact or fluorescent imaging at target locations, which is
often limited by photobleaching and phototoxicity. In this paper, we show that
phase imaging can report the intracellular transport induced by optogenetic
stimulation. We developed a multimodal instrument that can both stimulate cells
with high spatial resolution and detect optical pathlength changes with
nanometer scale sensitivity. We found that optical pathlength fluctuations
following stimulation are consistent with active organelle transport.
Furthermore, the results indicate a broadening in the transport velocity
distribution, which is significantly higher in stimulated cells compared to
optogenetically inactive cells. It is likely that this label-free, contactless
measurement of optogenetic response will provide an enabling approach to
neuroscience.Comment: 20 pages, 5 figure
Effect of Inclination Angle on the Response of Double-row Retaining Piles: Experimental and Numerical Investigation
The excavation depth of foundation pits has been increasing along with the continuous development of underground space and high-rise buildings. As a result, traditional double-row vertical piles cannot meet the ground settlement and deflection requirements. This study proposed a double-row pile optimization method to extend the suitability of double-row retaining piles to greater excavation depth. The optimization model was established by adjusting the inclination angle of the front and rear piles. Physical scale model tests were performed to analyze the effect of the inclination angle on the pile head displacements and bending moments during excavations and step loadings using three schemes, namely, traditional double-row piles with vertical piles, double-row contiguous retaining piles with batter pile in the front row, and double-row contiguous retaining piles with batter pile in both rows. Numerical simulations were also conducted to verify the effectiveness of the inclination angle adjustment in optimizing the double-row piles. Results indicate that the increase in the displacement and bending moment of the double-row contiguous retaining batter piles is not evident during excavation and step loading when compared with those of the double-row vertical piles and the double-row contiguous retaining piles with batter pile in the front row. Thus, double-row contiguous retaining batter piles can be used in deep foundation pits. The tilt angle is also excessively small to reduce the lateral displacement of the foundation pit, and the optimal tilt angle is 8° – 16°. Although the embedment depth can influence the deformation of the double-row contiguous retaining batter piles significantly, a critical embedment depth may be reached. The findings of this study can provide references for the optimization of double-row piles in foundation pits
sEMG-Based Continuous Estimation of Finger Kinematics via Large-Scale Temporal Convolutional Network
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PHF8 and REST/NRSF co-occupy gene promoters to regulate proximal gene expression
Chromatin regulators play an important role in the development of human diseases. In this study, we focused on Plant Homeo Domain Finger protein 8 (PHF8), a chromatin regulator that has attracted special concern recently. PHF8 is a histone lysine demethylase ubiquitously expressed in nuclei. Mutations of PHF8 are associated with X-linked mental retardation. It usually functions as a transcriptional co-activator by associating with H3K4me3 and RNA polymerase II. We found that PHF8 may associate with another regulator, REST/NRSF, predominately at promoter regions via studying several published PHF8 chromatin immunoprecipitation-sequencing (ChIP-Seq) datasets. Our analysis suggested that PHF8 not only activates but may also repress gene expression
Regulation of high-fat diet-induced microglial metabolism by transient receptor potential vanilloid type 1
Objective·Transcriptomic and lipidomic analysis techniques were used to investigate the role of transient receptor potential vanilloid type 1 (TRPV1) channel activation in the regulation of high-fat diet-induced microglial metabolism.Methods·Eight-week-old C57BL/6J mice (WT) and Trpv1-/- (KO) mice were used as experimental animals, and fed high-fat diet (HFD) for 3 days, 7 days, and 8 weeks to induce modelling (WT and KO groups, n = 3; WT-HFD and KO-HFD groups, n = 4). TRPV1 channel expression and cellular localisation were measured by immunofluorescence in the brains of mice in the WT-HFD and KO-HFD group. RNA sequencing and liquid chromatography-mass spectrometry were performed to determine the brain phenotype of mice in the WT-HFD and KO-HFD groups.Results·The expression level of Trpv1 mRNA in microglia was significantly increased in mice in the WT-HFD group compared to mice in the WT group. The expression levels of genes related to brain lipid metabolism, mitochondrial function, glucose transfer, and glycolysis were down-regulated in the KO-HFD group of mice compared with the WT-HFD group of mice. Lipidomic analysis showed that although lipids accumulated in the brain tissue of mice in the KO-HFD group, Trpv1 knockdown attenuated HFD-induced microglia activation, and in addition the TRPV1 agonist capsaicin attenuated palmitate-induced depolarisation of mitochondrial membrane potential in vitro.Conclusion·Together, these findings suggest that TRPV1 regulates lipid and glucose metabolism in microglia via fuel availability driven by a mitochondrial mechanism
{\pi}-{\pi} Interaction-facilitated formation of interwoven trimeric cage-catenanes with topological chirality
Catenanes as interlocked molecules with a nonplanar graph have gained
increasing attention for their unique features such as topological chirality.
To date, the majority of research in this field has been focusing on catenanes
comprising monocyclic rings. Due to the lack of rational synthetic strategy,
catenanes of cage-like monomers are hardly accessible. Here we report on the
construction of an interwoven trimeric catenane that is composed of achiral
organic cages, which exhibits topological chirality. Our rational design begins
with a pure mathematical analysis, revealing that the formation probability of
the interwoven trimeric catenane surpasses that of its chain-like analogue by
20%; while driven by efficient template effect provided by strong {\pi}-{\pi}
stacking of aromatic panels, the interwoven structure emerges as the dominant
species, almost ruling out the formation of the chain-like isomer. Its
topological chirality is unambiguously unravelled by chiral-HPLC, CD
spectroscopy and X-ray diffraction. Our probability analysis-aided rational
design strategy would pave a new venue for the efficient synthesis of
topologically sophisticated structures in one pot
ReCo: Region-Controlled Text-to-Image Generation
Recently, large-scale text-to-image (T2I) models have shown impressive
performance in generating high-fidelity images, but with limited
controllability, e.g., precisely specifying the content in a specific region
with a free-form text description. In this paper, we propose an effective
technique for such regional control in T2I generation. We augment T2I models'
inputs with an extra set of position tokens, which represent the quantized
spatial coordinates. Each region is specified by four position tokens to
represent the top-left and bottom-right corners, followed by an open-ended
natural language regional description. Then, we fine-tune a pre-trained T2I
model with such new input interface. Our model, dubbed as ReCo
(Region-Controlled T2I), enables the region control for arbitrary objects
described by open-ended regional texts rather than by object labels from a
constrained category set. Empirically, ReCo achieves better image quality than
the T2I model strengthened by positional words (FID: 8.82->7.36, SceneFID:
15.54->6.51 on COCO), together with objects being more accurately placed,
amounting to a 20.40% region classification accuracy improvement on COCO.
Furthermore, we demonstrate that ReCo can better control the object count,
spatial relationship, and region attributes such as color/size, with the
free-form regional description. Human evaluation on PaintSkill shows that ReCo
is +19.28% and +17.21% more accurate in generating images with correct object
count and spatial relationship than the T2I model
TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs
Artificial Intelligence (AI) has made incredible progress recently. On the
one hand, advanced foundation models like ChatGPT can offer powerful
conversation, in-context learning and code generation abilities on a broad
range of open-domain tasks. They can also generate high-level solution outlines
for domain-specific tasks based on the common sense knowledge they have
acquired. However, they still face difficulties with some specialized tasks
because they lack enough domain-specific data during pre-training or they often
have errors in their neural network computations on those tasks that need
accurate executions. On the other hand, there are also many existing models and
systems (symbolic-based or neural-based) that can do some domain-specific tasks
very well. However, due to the different implementation or working mechanisms,
they are not easily accessible or compatible with foundation models. Therefore,
there is a clear and pressing need for a mechanism that can leverage foundation
models to propose task solution outlines and then automatically match some of
the sub-tasks in the outlines to the off-the-shelf models and systems with
special functionalities to complete them. Inspired by this, we introduce
TaskMatrix.AI as a new AI ecosystem that connects foundation models with
millions of APIs for task completion. Unlike most previous work that aimed to
improve a single AI model, TaskMatrix.AI focuses more on using existing
foundation models (as a brain-like central system) and APIs of other AI models
and systems (as sub-task solvers) to achieve diversified tasks in both digital
and physical domains. As a position paper, we will present our vision of how to
build such an ecosystem, explain each key component, and use study cases to
illustrate both the feasibility of this vision and the main challenges we need
to address next
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