141 research outputs found
REGULATION OF URBAN CHARACTER: STYLE, COLOUR AND HISTORIC CHARACTER IN A MODERN CHINESE CITY - THE CASE OF HARBIN
Engineering mobility in quasiperiodic lattices with exact mobility edges
We investigate the effect of an additional modulation parameter on
the mobility properties of quasiperiodic lattices described by a generalized
Ganeshan-Pixley-Das Sarma model with two on site modulation parameters. For the
case with bounded quasiperiodic potential, we unveil the existence of
self-duality relation, independent of . By applying Avila's global
theory, we analytically derive Lyapunov exponents in the whole parameter space,
which enables us to determine mobility edges or anomalous mobility edges
exactly. Our analytical results indicate that the mobility edge equation is
described by two curves and their intersection with the spectrum gives the true
mobility edge. Tuning the strength parameter can change the spectrum
of the quasiperiodic lattice, and thus engineers the mobility of quasi-periodic
systems, giving rise to completely extended, partially localized, and
completely localized regions. For the case with unbounded quasiperiodic
potential, we also obtain the analytical expression of the anomalous mobility
edge, which separates localized states from critical states. By increasing the
strength parameter , we find that the critical states can be destroyed
gradually and finally vanishes.Comment: 10 pages,6 figure
Inferring Fluid Dynamics via Inverse Rendering
Humans have a strong intuitive understanding of physical processes such as
fluid falling by just a glimpse of such a scene picture, i.e., quickly derived
from our immersive visual experiences in memory. This work achieves such a
photo-to-fluid-dynamics reconstruction functionality learned from unannotated
videos, without any supervision of ground-truth fluid dynamics. In a nutshell,
a differentiable Euler simulator modeled with a ConvNet-based pressure
projection solver, is integrated with a volumetric renderer, supporting
end-to-end/coherent differentiable dynamic simulation and rendering. By
endowing each sampled point with a fluid volume value, we derive a NeRF-like
differentiable renderer dedicated from fluid data; and thanks to this
volume-augmented representation, fluid dynamics could be inversely inferred
from the error signal between the rendered result and ground-truth video frame
(i.e., inverse rendering). Experiments on our generated Fluid Fall datasets and
DPI Dam Break dataset are conducted to demonstrate both effectiveness and
generalization ability of our method
Research on the Post Occupancy Evaluation of Green Public Building Environmental Performance Combined with Carbon Emissions Accounting
AbstractThe development of green building in China has reached a new stage, needs to turn to the total energy consumption control from the technology control[1]. We should avoid packing technologies in green building projects and regard achieving good environmental performance as the fundamental goal. In this paper, we use the method of post-occupancy evaluation and regard the building environmental performance as the core of the evaluation system, in order to reduce the influence on the accuracy of results from the measures evaluation. We establish the evaluation index system of green public building environmental performance in severe cold and cold regions, including the index of building life-cycle carbon emissions accounting. And we set up the application plan of index and the scoring method, then we put forward a kind of evaluation grade based on environmental performance level, finally proposed the POE System of Green Public Building Environmental Performance in Severe Cold and Cold Regions (POE-GPBEPC)
Skeleton2Humanoid: Animating Simulated Characters for Physically-plausible Motion In-betweening
Human motion synthesis is a long-standing problem with various applications
in digital twins and the Metaverse. However, modern deep learning based motion
synthesis approaches barely consider the physical plausibility of synthesized
motions and consequently they usually produce unrealistic human motions. In
order to solve this problem, we propose a system ``Skeleton2Humanoid'' which
performs physics-oriented motion correction at test time by regularizing
synthesized skeleton motions in a physics simulator. Concretely, our system
consists of three sequential stages: (I) test time motion synthesis network
adaptation, (II) skeleton to humanoid matching and (III) motion imitation based
on reinforcement learning (RL). Stage I introduces a test time adaptation
strategy, which improves the physical plausibility of synthesized human
skeleton motions by optimizing skeleton joint locations. Stage II performs an
analytical inverse kinematics strategy, which converts the optimized human
skeleton motions to humanoid robot motions in a physics simulator, then the
converted humanoid robot motions can be served as reference motions for the RL
policy to imitate. Stage III introduces a curriculum residual force control
policy, which drives the humanoid robot to mimic complex converted reference
motions in accordance with the physical law. We verify our system on a typical
human motion synthesis task, motion-in-betweening. Experiments on the
challenging LaFAN1 dataset show our system can outperform prior methods
significantly in terms of both physical plausibility and accuracy. Code will be
released for research purposes at:
https://github.com/michaelliyunhao/Skeleton2HumanoidComment: Accepted by ACMMM202
A Solution to Co-occurrence Bias: Attributes Disentanglement via Mutual Information Minimization for Pedestrian Attribute Recognition
Recent studies on pedestrian attribute recognition progress with either
explicit or implicit modeling of the co-occurrence among attributes.
Considering that this known a prior is highly variable and unforeseeable
regarding the specific scenarios, we show that current methods can actually
suffer in generalizing such fitted attributes interdependencies onto scenes or
identities off the dataset distribution, resulting in the underlined bias of
attributes co-occurrence. To render models robust in realistic scenes, we
propose the attributes-disentangled feature learning to ensure the recognition
of an attribute not inferring on the existence of others, and which is
sequentially formulated as a problem of mutual information minimization.
Rooting from it, practical strategies are devised to efficiently decouple
attributes, which substantially improve the baseline and establish
state-of-the-art performance on realistic datasets like PETAzs and RAPzs. Code
is released on
https://github.com/SDret/A-Solution-to-Co-occurence-Bias-in-Pedestrian-Attribute-Recognition.Comment: Accepted in IJCAI2
Detecting Silent Data Corruptions in Aerospace-Based Computing Using Program Invariants
Soft error caused by single event upset has been a severe challenge to aerospace-based computing. Silent data corruption (SDC) is one of the results incurred by soft error. SDC occurs when a program generates erroneous output with no indications. SDC is the most insidious type of results and very difficult to detect. To address this problem, we design and implement an invariant-based system called Radish. Invariants describe certain properties of a program; for example, the value of a variable equals a constant. Radish first extracts invariants at key program points and converts invariants into assertions. It then hardens the program by inserting the assertions into the source code. When a soft error occurs, assertions will be found to be false at run time and warn the users of soft error. To increase the coverage of SDC, we further propose an extension of Radish, named Radish_D, which applies software-based instruction duplication mechanism to protect the uncovered code sections. Experiments using architectural fault injections show that Radish achieves high SDC coverage with very low overhead. Furthermore, Radish_D provides higher SDC coverage than that of either Radish or pure instruction duplication
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