226 research outputs found
Scallop: A Language for Neurosymbolic Programming
We present Scallop, a language which combines the benefits of deep learning
and logical reasoning. Scallop enables users to write a wide range of
neurosymbolic applications and train them in a data- and compute-efficient
manner. It achieves these goals through three key features: 1) a flexible
symbolic representation that is based on the relational data model; 2) a
declarative logic programming language that is based on Datalog and supports
recursion, aggregation, and negation; and 3) a framework for automatic and
efficient differentiable reasoning that is based on the theory of provenance
semirings. We evaluate Scallop on a suite of eight neurosymbolic applications
from the literature. Our evaluation demonstrates that Scallop is capable of
expressing algorithmic reasoning in diverse and challenging AI tasks, provides
a succinct interface for machine learning programmers to integrate logical
domain knowledge, and yields solutions that are comparable or superior to
state-of-the-art models in terms of accuracy. Furthermore, Scallop's solutions
outperform these models in aspects such as runtime and data efficiency,
interpretability, and generalizability
A Unified Distributed Method for Constrained Networked Optimization via Saddle-Point Dynamics
This paper develops a unified distributed method for solving two classes of
constrained networked optimization problems, i.e., optimal consensus problem
and resource allocation problem with non-identical set constraints. We first
transform these two constrained networked optimization problems into a unified
saddle-point problem framework with set constraints. Subsequently, two
projection-based primal-dual algorithms via Optimistic Gradient Descent Ascent
(OGDA) method and Extra-gradient (EG) method are developed for solving
constrained saddle-point problems. It is shown that the developed algorithms
achieve exact convergence to a saddle point with an ergodic convergence rate
for general convex-concave functions. Based on the proposed
primal-dual algorithms via saddle-point dynamics, we develop unified
distributed algorithm design and convergence analysis for these two networked
optimization problems. Finally, two numerical examples are presented to
demonstrate the theoretical results
DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion
A reasonable and balanced diet is essential for maintaining good health. With
the advancements in deep learning, automated nutrition estimation method based
on food images offers a promising solution for monitoring daily nutritional
intake and promoting dietary health. While monocular image-based nutrition
estimation is convenient, efficient, and economical, the challenge of limited
accuracy remains a significant concern. To tackle this issue, we proposed
DPF-Nutrition, an end-to-end nutrition estimation method using monocular
images. In DPF-Nutrition, we introduced a depth prediction module to generate
depth maps, thereby improving the accuracy of food portion estimation.
Additionally, we designed an RGB-D fusion module that combined monocular images
with the predicted depth information, resulting in better performance for
nutrition estimation. To the best of our knowledge, this was the pioneering
effort that integrated depth prediction and RGB-D fusion techniques in food
nutrition estimation. Comprehensive experiments performed on Nutrition5k
evaluated the effectiveness and efficiency of DPF-Nutrition
Research Progress of Breast Tissue Marker Clips and Their Application in Neoadjuvant Therapy for Breast Cancer
Currently, breast cancer being of rapidly increasing incidence rates and as the most commonly diagnosed malignant tumor in breast surgery, has attracted much attention. Neoadjuvant therapy (NAT) has been proved to be beneficial for reducing tumor size and breast-conserving surgery. As a new type of metal localization marker, breast tissue marker clips can be used to precisely locate tumor tissue and improve cure rates. This review focuses on the marker clips and their significance in the diagnosis and treatment of neoadjuvant therapy for breast cancer, hoping to provide more clinical bases for research and promote this technology
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