58 research outputs found
Cold-start Sequential Recommendation via Meta Learner
This paper explores meta-learning in sequential recommendation to alleviate
the item cold-start problem. Sequential recommendation aims to capture user's
dynamic preferences based on historical behavior sequences and acts as a key
component of most online recommendation scenarios. However, most previous
methods have trouble recommending cold-start items, which are prevalent in
those scenarios. As there is generally no side information in the setting of
sequential recommendation task, previous cold-start methods could not be
applied when only user-item interactions are available. Thus, we propose a
Meta-learning-based Cold-Start Sequential Recommendation Framework, namely
Mecos, to mitigate the item cold-start problem in sequential recommendation.
This task is non-trivial as it targets at an important problem in a novel and
challenging context. Mecos effectively extracts user preference from limited
interactions and learns to match the target cold-start item with the potential
user. Besides, our framework can be painlessly integrated with neural
network-based models. Extensive experiments conducted on three real-world
datasets verify the superiority of Mecos, with the average improvement up to
99%, 91%, and 70% in HR@10 over state-of-the-art baseline methods.Comment: Accepted at AAAI 202
Exploring & Exploiting High-Order Graph Structure for Sparse Knowledge Graph Completion
Sparse knowledge graph (KG) scenarios pose a challenge for previous Knowledge
Graph Completion (KGC) methods, that is, the completion performance decreases
rapidly with the increase of graph sparsity. This problem is also exacerbated
because of the widespread existence of sparse KGs in practical applications. To
alleviate this challenge, we present a novel framework, LR-GCN, that is able to
automatically capture valuable long-range dependency among entities to
supplement insufficient structure features and distill logical reasoning
knowledge for sparse KGC. The proposed approach comprises two main components:
a GNN-based predictor and a reasoning path distiller. The reasoning path
distiller explores high-order graph structures such as reasoning paths and
encodes them as rich-semantic edges, explicitly compositing long-range
dependencies into the predictor. This step also plays an essential role in
densifying KGs, effectively alleviating the sparse issue. Furthermore, the path
distiller further distills logical reasoning knowledge from these mined
reasoning paths into the predictor. These two components are jointly optimized
using a well-designed variational EM algorithm. Extensive experiments and
analyses on four sparse benchmarks demonstrate the effectiveness of our
proposed method.Comment: 12 pages, 5 figure
One-shot ultraspectral imaging with reconfigurable metasurfaces
One-shot spectral imaging that can obtain spectral information from thousands
of different points in space at one time has always been difficult to achieve.
Its realization makes it possible to get spatial real-time dynamic spectral
information, which is extremely important for both fundamental scientific
research and various practical applications. In this study, a one-shot
ultraspectral imaging device fitting thousands of micro-spectrometers (6336
pixels) on a chip no larger than 0.5 cm, is proposed and demonstrated.
Exotic light modulation is achieved by using a unique reconfigurable
metasurface supercell with 158400 metasurface units, which enables 6336
micro-spectrometers with dynamic image-adaptive performances to simultaneously
guarantee the density of spectral pixels and the quality of spectral
reconstruction. Additionally, by constructing a new algorithm based on
compressive sensing, the snapshot device can reconstruct ultraspectral imaging
information (/~0.001) covering a broad (300-nm-wide)
visible spectrum with an ultra-high center-wavelength accuracy of 0.04-nm
standard deviation and spectral resolution of 0.8 nm. This scheme of
reconfigurable metasurfaces makes the device can be directly extended to almost
any commercial camera with different spectral bands to seamlessly switch the
information between image and spectral image, and will open up a new space for
the application of spectral analysis combining with image recognition and
intellisense
Comparing the economic value of lithium-ion battery technologies in the nine wholesale electricity markets in North America
Lithium-ion batteries are becoming critical flexibility assets in future electric power systems. Batteries can arbitrage price differences in wholesale electricity markets to make a profit while at the same time reducing total system operating costs and improving renewable energy integration. However, lithium-ion batteries have a limited lifetime due to capacity degradation, and one battery pack can only make a limited profit before reaching its end-of-life. In this paper, we screen the profit potential of Lithium iron phosphate (LFP), nickel manganese cobalt (NMC), and lithium nickel cobalt aluminum oxides (NCA) batteries in all nine wholesale electricity markets in North America. We apply a systematic dynamic valuation framework that finds the highest revenue potential for the considered lithium-ion battery project subjecting to its degradation mechanism, while the degradation model used in the valuation is derived based on real lab test data over varying cycle conditions. The study found that battery valuation depends largely on battery technology and storage duration and varies across operational locations. Moreover, the study revealed that calendar life has a greater impact on battery valuation than cycle life for an 8-years calendar life scenario while cycle life shows greater impact for a 15-year calendar life scenario for all battery technologies. This impact is more pronounced in LFP than in NMC and NCA. The study recommends battery operators consider strategies that would maximize a longer cycle life or calendar life usage of a battery as this would accumulate higher profits over its lifetime
EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models
Large Language Models (LLMs) usually suffer from knowledge cutoff or fallacy
issues, which means they are unaware of unseen events or generate text with
incorrect facts owing to the outdated/noisy data. To this end, many knowledge
editing approaches for LLMs have emerged -- aiming to subtly inject/edit
updated knowledge or adjust undesired behavior while minimizing the impact on
unrelated inputs. Nevertheless, due to significant differences among various
knowledge editing methods and the variations in task setups, there is no
standard implementation framework available for the community, which hinders
practitioners to apply knowledge editing to applications. To address these
issues, we propose EasyEdit, an easy-to-use knowledge editing framework for
LLMs. It supports various cutting-edge knowledge editing approaches and can be
readily apply to many well-known LLMs such as T5, GPT-J, LlaMA, etc.
Empirically, we report the knowledge editing results on LlaMA-2 with EasyEdit,
demonstrating that knowledge editing surpasses traditional fine-tuning in terms
of reliability and generalization. We have released the source code on GitHub
at https://github.com/zjunlp/EasyEdit, along with Google Colab tutorials and
comprehensive documentation for beginners to get started. Besides, we present
an online system for real-time knowledge editing, and a demo video at
http://knowlm.zjukg.cn/easyedit.mp4.Comment: The project website is https://github.com/zjunlp/EasyEdi
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
Most recent semantic segmentation methods adopt a fully-convolutional network
(FCN) with an encoder-decoder architecture. The encoder progressively reduces
the spatial resolution and learns more abstract/semantic visual concepts with
larger receptive fields. Since context modeling is critical for segmentation,
the latest efforts have been focused on increasing the receptive field, through
either dilated/atrous convolutions or inserting attention modules. However, the
encoder-decoder based FCN architecture remains unchanged. In this paper, we aim
to provide an alternative perspective by treating semantic segmentation as a
sequence-to-sequence prediction task. Specifically, we deploy a pure
transformer (ie, without convolution and resolution reduction) to encode an
image as a sequence of patches. With the global context modeled in every layer
of the transformer, this encoder can be combined with a simple decoder to
provide a powerful segmentation model, termed SEgmentation TRansformer (SETR).
Extensive experiments show that SETR achieves new state of the art on ADE20K
(50.28% mIoU), Pascal Context (55.83% mIoU) and competitive results on
Cityscapes. Particularly, we achieve the first position in the highly
competitive ADE20K test server leaderboard on the day of submission.Comment: CVPR 2021. Project page at https://fudan-zvg.github.io/SETR
The GATA factor HANABA TARANU promotes runner formation by regulating axillary bud initiation and outgrowth in cultivated strawberry
A runner, as an elongated branch, develops from the axillary bud (AXB) in the leaf axil and is crucial for the clonal propagation of cultivated strawberry (Fragaria x ananassa Duch.). Runner formation occurs in at least two steps: AXB initiation and AXB outgrowth. HANABA TARANU (HAN ) encodes a GATA transcription factor that affects AXB initiation in Arabidopsis and promotes branching in grass species, but the underlying mechanism is largely unknown. Here, the function of a strawberry HAN homolog FaHAN in runner formation was characterized. FaHAN transcripts can be detected in the leaf axils. Overexpression (OE) of FaHAN increased the number of runners, mainly by enhancing AXB outgrowth, in strawberry. The expression of the strawberry homolog of BRANCHED1 , a key inhibitor of AXB outgrowth in many plant species, was significantly downregulated in the AXBs of FaHAN -OE lines, whereas the expression of the strawberry homolog of SHOOT MERISTEMLESS, a marker gene for AXB initiation in Arabidopsis, was upregulated. Moreover, several genes of gibberellin biosynthesis and cytokinin signaling pathways were activated, whereas the auxin response pathway genes were repressed. Further assays indicated that FaHAN could be directly activated by FaNAC2, the overexpression of which in strawberry also increased the number of runners. The silencing of FaNAC2 or FaHAN inhibited AXB initiation and led to a higher proportion of dormant AXBs, confirming their roles in the control of runner formation. Taken together, our results revealed a FaNAC2-FaHAN pathway in the control of runner formation and have provided a means to enhance the vegetative propagation of cultivated strawberry.Peer reviewe
DreamLLM: Synergistic Multimodal Comprehension and Creation
This paper presents DreamLLM, a learning framework that first achieves
versatile Multimodal Large Language Models (MLLMs) empowered with frequently
overlooked synergy between multimodal comprehension and creation. DreamLLM
operates on two fundamental principles. The first focuses on the generative
modeling of both language and image posteriors by direct sampling in the raw
multimodal space. This approach circumvents the limitations and information
loss inherent to external feature extractors like CLIP, and a more thorough
multimodal understanding is obtained. Second, DreamLLM fosters the generation
of raw, interleaved documents, modeling both text and image contents, along
with unstructured layouts. This allows DreamLLM to learn all conditional,
marginal, and joint multimodal distributions effectively. As a result, DreamLLM
is the first MLLM capable of generating free-form interleaved content.
Comprehensive experiments highlight DreamLLM's superior performance as a
zero-shot multimodal generalist, reaping from the enhanced learning synergy.Comment: see project page at https://dreamllm.github.io
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