629 research outputs found
miRDB: An online database for prediction of functional microRNA targets
MicroRNAs (miRNAs) are small noncoding RNAs that act as master regulators in many biological processes. miRNAs function mainly by downregulating the expression of their gene targets. Thus, accurate prediction of miRNA targets is critical for characterization of miRNA functions. To this end, we have developed an online database, miRDB, for miRNA target prediction and functional annotations. Recently, we have performed major updates for miRDB. Specifically, by employing an improved algorithm for miRNA target prediction, we now present updated transcriptome-wide target prediction data in miRDB, including 3.5 million predicted targets regulated by 7000 miRNAs in five species. Further, we have implemented the new prediction algorithm into a web server, allowing custom target prediction with user-provided sequences. Another new database feature is the prediction of cell-specific miRNA targets. miRDB now hosts the expression profiles of over 1000 cell lines and presents target prediction data that are tailored for specific cell models. At last, a new web query interface has been added to miRDB for prediction of miRNA functions by integrative analysis of target prediction and Gene Ontology data. All data in miRDB are freely accessible at http://mirdb.org
Mitigating Motion Blur for Robust 3D Baseball Player Pose Modeling for Pitch Analysis
Using videos to analyze pitchers in baseball can play a vital role in
strategizing and injury prevention. Computer vision-based pose analysis offers
a time-efficient and cost-effective approach. However, the use of accessible
broadcast videos, with a 30fps framerate, often results in partial body motion
blur during fast actions, limiting the performance of existing pose keypoint
estimation models. Previous works have primarily relied on fixed backgrounds,
assuming minimal motion differences between frames, or utilized multiview data
to address this problem. To this end, we propose a synthetic data augmentation
pipeline to enhance the model's capability to deal with the pitcher's blurry
actions. In addition, we leverage in-the-wild videos to make our model robust
under different real-world conditions and camera positions. By carefully
optimizing the augmentation parameters, we observed a notable reduction in the
loss by 54.2% and 36.2% on the test dataset for 2D and 3D pose estimation
respectively. By applying our approach to existing state-of-the-art pose
estimators, we demonstrate an average improvement of 29.2%. The findings
highlight the effectiveness of our method in mitigating the challenges posed by
motion blur, thereby enhancing the overall quality of pose estimation.Comment: Accepted in the 6th International Workshop on Multimedia Content
Analysis in Sports (MMSports'23) @ ACM Multimedi
Reducing Tarski to Unique Tarski (In the Black-Box Model)
We study the problem of finding a Tarski fixed point over the k-dimensional grid [n]^k. We give a black-box reduction from the Tarski problem to the same problem with an additional promise that the input function has a unique fixed point. It implies that the Tarski problem and the unique Tarski problem have exactly the same query complexity. Our reduction is based on a novel notion of partial-information functions which we use to fool algorithms for the unique Tarski problem as if they were working on a monotone function with a unique fixed point
The Model Inversion Eavesdropping Attack in Semantic Communication Systems
In recent years, semantic communication has been a popular research topic for
its superiority in communication efficiency. As semantic communication relies
on deep learning to extract meaning from raw messages, it is vulnerable to
attacks targeting deep learning models. In this paper, we introduce the model
inversion eavesdropping attack (MIEA) to reveal the risk of privacy leaks in
the semantic communication system. In MIEA, the attacker first eavesdrops the
signal being transmitted by the semantic communication system and then performs
model inversion attack to reconstruct the raw message, where both the white-box
and black-box settings are considered. Evaluation results show that MIEA can
successfully reconstruct the raw message with good quality under different
channel conditions. We then propose a defense method based on random
permutation and substitution to defend against MIEA in order to achieve secure
semantic communication. Our experimental results demonstrate the effectiveness
of the proposed defense method in preventing MIEA.Comment: Accepted by 2023 IEEE Global Communications Conference (GLOBECOM
Large deviations and fluctuation theorems for cycle currents defined in the loop-erased and spanning tree manners: a comparative study
The cycle current is a crucial quantity in stochastic thermodynamics. The
absolute and net cycle currents of a Markovian system can be defined in the
loop-erased (LE) or the spanning tree (ST) manner. Here we make a comparative
study between the large deviations and fluctuation theorems for the LE and ST
currents, i.e. cycle currents defined in the LE and ST manners. First, we
derive the exact joint distribution and the large deviation rate function for
the LE currents of a system with a cyclic topology and also obtain the rate
function for the ST currents of a general system. The relationship between the
rate functions for the LE and ST currents is clarified. Furthermore, we examine
various types of fluctuation theorems satisfied by the LE and ST currents and
clarify their ranges of applicability. We show that both the absolute and net
LE currents satisfy the strong form of all types of fluctuation theorems. In
contrast, the absolute ST currents do not satisfy fluctuation theorems, while
the net ST currents only satisfy the weak form of fluctuation theorems under
the periodic boundary condition
ShapeShift: Superquadric-based Object Pose Estimation for Robotic Grasping
Object pose estimation is a critical task in robotics for precise object
manipulation. However, current techniques heavily rely on a reference 3D
object, limiting their generalizability and making it expensive to expand to
new object categories. Direct pose predictions also provide limited information
for robotic grasping without referencing the 3D model. Keypoint-based methods
offer intrinsic descriptiveness without relying on an exact 3D model, but they
may lack consistency and accuracy. To address these challenges, this paper
proposes ShapeShift, a superquadric-based framework for object pose estimation
that predicts the object's pose relative to a primitive shape which is fitted
to the object. The proposed framework offers intrinsic descriptiveness and the
ability to generalize to arbitrary geometric shapes beyond the training set
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