694 research outputs found
OysterNet: Enhanced Oyster Detection Using Simulation
Oysters play a pivotal role in the bay living ecosystem and are considered
the living filters for the ocean. In recent years, oyster reefs have undergone
major devastation caused by commercial over-harvesting, requiring preservation
to maintain ecological balance. The foundation of this preservation is to
estimate the oyster density which requires accurate oyster detection. However,
systems for accurate oyster detection require large datasets obtaining which is
an expensive and labor-intensive task in underwater environments. To this end,
we present a novel method to mathematically model oysters and render images of
oysters in simulation to boost the detection performance with minimal real
data. Utilizing our synthetic data along with real data for oyster detection,
we obtain up to 35.1% boost in performance as compared to using only real data
with our OysterNet network. We also improve the state-of-the-art by 12.7%. This
shows that using underlying geometrical properties of objects can help to
enhance recognition task accuracy on limited datasets successfully and we hope
more researchers adopt such a strategy for hard-to-obtain datasets
Whale Detection Enhancement through Synthetic Satellite Images
With a number of marine populations in rapid decline, collecting and
analyzing data about marine populations has become increasingly important to
develop effective conservation policies for a wide range of marine animals,
including whales. Modern computer vision algorithms allow us to detect whales
in images in a wide range of domains, further speeding up and enhancing the
monitoring process. However, these algorithms heavily rely on large training
datasets, which are challenging and time-consuming to collect particularly in
marine or aquatic environments. Recent advances in AI however have made it
possible to synthetically create datasets for training machine learning
algorithms, thus enabling new solutions that were not possible before. In this
work, we present a solution - SeaDroneSim2 benchmark suite, which addresses
this challenge by generating aerial, and satellite synthetic image datasets to
improve the detection of whales and reduce the effort required for training
data collection. We show that we can achieve a 15% performance boost on whale
detection compared to using the real data alone for training, by augmenting a
10% real data. We open source both the code of the simulation platform
SeaDroneSim2 and the dataset generated through it
MFI2-AS1 enhances the survival of esophageal cancer cell via regulation of miR-331-3p/SOX4
Purpose: To investigate the specific role of melanotransferrin antisense RNA (MFI2-AS1) in esophageal cancer (EC) progression. Methods: The differential expression of MFI2-AS1 in EC tissues and cells was determined using quantitative reverse transcription–polymerase chain reaction (qRT-PCR). Silencing MFI2-AS1 was performed by transfection with specific short hairpin RNAs targeting MFI2-AS1. The 3-(4,5- dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay (MTT) and flow cytometry (FC) were used to assess cell viability and apoptosis of EC cells, respectively. The sponging microRNA (miRNA) of MFI2-AS1 was validated using luciferase activity and RNA immunoprecipitation assays while the downstream target gene of the sponging miRNA was evaluated by luciferase activity assay. Results: MFI2-AS1 was significantly enhanced in EC tissues (p < 0.01) and indicated a poor prognosis in EC patients. Knockdown of MFI2-AS1 in EC cells decreased cell viability and promoted cell apoptosis of EC cells. Functionally, MFI2-AS1 targeted miR-331-3p, and sex-determining region on Ychromosome-related high-mobility-group box4 (SOX4) was identified as a target gene of miR-331-3p. Ectopic expression of SOX4 counteracted the suppressive effect of MFI2-AS1 knockdown on EC cell viability and stimulative effect on EC cell apoptosis. Conclusion: The pro-oncogenic effect of MFI2-AS1 on EC progression occurs via the regulation of the miR-331-3p/SOX4 axis, providing a new potential therapeutic target for EC
Topic Shift Detection in Chinese Dialogues: Corpus and Benchmark
Dialogue topic shift detection is to detect whether an ongoing topic has
shifted or should shift in a dialogue, which can be divided into two
categories, i.e., response-known task and response-unknown task. Currently,
only a few investigated the latter, because it is still a challenge to predict
the topic shift without the response information. In this paper, we first
annotate a Chinese Natural Topic Dialogue (CNTD) corpus consisting of 1308
dialogues to fill the gap in the Chinese natural conversation topic corpus. And
then we focus on the response-unknown task and propose a teacher-student
framework based on hierarchical contrastive learning to predict the topic shift
without the response. Specifically, the response at high-level teacher-student
is introduced to build the contrastive learning between the response and the
context, while the label contrastive learning is constructed at low-level
student. The experimental results on our Chinese CNTD and English TIAGE show
the effectiveness of our proposed model
GENET: Unleashing the Power of Side Information for Recommendation via Hypergraph Pre-training
Recommendation with side information has drawn significant research interest
due to its potential to mitigate user feedback sparsity. However, existing
models struggle with generalization across diverse domains and types of side
information. In particular, three challenges have not been addressed, and they
are (1) the diverse formats of side information, including text sequences. (2)
The diverse semantics of side information that describes items and users from
multi-level in a context different from recommendation systems. (3) The diverse
correlations in side information to measure similarity over multiple objects
beyond pairwise relations. In this paper, we introduce GENET (Generalized
hypErgraph pretraiNing on sidE informaTion), which pre-trains user and item
representations on feedback-irrelevant side information and fine-tunes the
representations on user feedback data. GENET leverages pre-training as a means
to prevent side information from overshadowing critical ID features and
feedback signals. It employs a hypergraph framework to accommodate various
types of diverse side information. During pre-training, GENET integrates tasks
for hyperlink prediction and self-supervised contrast to capture fine-grained
semantics at both local and global levels. Additionally, it introduces a unique
strategy to enhance pre-training robustness by perturbing positive samples
while maintaining high-order relations. Extensive experiments demonstrate that
GENET exhibits strong generalization capabilities, outperforming the SOTA
method by up to 38% in TOP-N recommendation and Sequential recommendation tasks
on various datasets with different side information
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