Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting the dynamics of 2d objects with a deep residual network   We investigate how a residual network can learn to predict the dynamics of
interacting shapes purely as an image-to-image regression task.
  With a simple 2d physics simulator, we generate short sequences composed of
rectangles put in motion by applying a pulling force at a point picked at
random. The network is trained with a quadratic loss to predict the image of
the resulting configuration, given the image of the starting configuration and
an image indicating the point of grasping.
  Experiments show that the network learns to predict accurately the resulting
image, which implies in particular that (1) it segments rectangles as distinct
components, (2) it infers which one contains the grasping point, (3) it models
properly the dynamic of a single rectangle, including the torque, (4) it
detects and handles collisions to some extent, and (5) it re-synthesizes
properly the entire scene with displaced rectangles.

32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph