386 research outputs found
Community-Based Learning: A Foundation for Meaningful Educational Reform
Many of today\u27s leaders in education, business, and community development are coming to realize, even more than in the past, that schools alone cannot prepare our youth for productive adulthood. These leaders are ready to try new approaches that link learning activities in classrooms with a full range of learning experiences available in our communities
Why It Takes So Long to Connect to a WiFi Access Point
Today's WiFi networks deliver a large fraction of traffic. However, the
performance and quality of WiFi networks are still far from satisfactory. Among
many popular quality metrics (throughput, latency), the probability of
successfully connecting to WiFi APs and the time cost of the WiFi connection
set-up process are the two of the most critical metrics that affect WiFi users'
experience. To understand the WiFi connection set-up process in real-world
settings, we carry out measurement studies on million mobile users from
representative cities associating with million APs in billion WiFi
sessions, collected from a mobile "WiFi Manager" App that tops the Android/iOS
App market. To the best of our knowledge, we are the first to do such large
scale study on: how large the WiFi connection set-up time cost is, what factors
affect the WiFi connection set-up process, and what can be done to reduce the
WiFi connection set-up time cost. Based on the measurement analysis, we develop
a machine learning based AP selection strategy that can significantly improve
WiFi connection set-up performance, against the conventional strategy purely
based on signal strength, by reducing the connection set-up failures from
to and reducing time costs of the connection set-up
processes by more than times.Comment: 11pages, conferenc
Stacking-dependent electronic structure of trilayer graphene resolved by nanospot angle-resolved photoemission spectroscopy
The crystallographic stacking order in multilayer graphene plays an important
role in determining its electronic structure. In trilayer graphene,
rhombohedral stacking (ABC) is particularly intriguing, exhibiting a flat band
with an electric-field tunable band gap. Such electronic structure is distinct
from simple hexagonal stacking (AAA) or typical Bernal stacking (ABA), and is
promising for nanoscale electronics, optoelectronics applications. So far clean
experimental electronic spectra on the first two stackings are missing because
the samples are usually too small in size (um or nm scale) to be resolved by
conventional angle-resolved photoemission spectroscopy (ARPES). Here by using
ARPES with nanospot beam size (NanoARPES), we provide direct experimental
evidence for the coexistence of three different stackings of trilayer graphene
and reveal their distinctive electronic structures directly. By fitting the
experimental data, we provide important experimental band parameters for
describing the electronic structure of trilayer graphene with different
stackings
4-(4-Fluoroanilino)-N-(4-fluorophenyl)-3-nitrobenzamide
In the title compound, C19H13F2N3O3, the anilinobenzamide unit is essentially planar, with a maximum deviation of 0.036 (3) Å. The nitro group and the benzene ring form dihedral angles of 9.6 (5)and 62.20 (8)°, respectively, with the anilinobenzamide unit. An intramolecular N—H⋯O interaction occurs. In the crystal, molecules are linked by weak intermolecular C—H⋯O, N—H⋯O and C—H⋯F hydrogen bonds, which stabilize the structure
DiffusionInst: Diffusion Model for Instance Segmentation
Diffusion frameworks have achieved comparable performance with previous
state-of-the-art image generation models. Researchers are curious about its
variants in discriminative tasks because of its powerful noise-to-image
denoising pipeline. This paper proposes DiffusionInst, a novel framework that
represents instances as instance-aware filters and formulates instance
segmentation as a noise-to-filter denoising process. The model is trained to
reverse the noisy groundtruth without any inductive bias from RPN. During
inference, it takes a randomly generated filter as input and outputs mask in
one-step or multi-step denoising. Extensive experimental results on COCO and
LVIS show that DiffusionInst achieves competitive performance compared to
existing instance segmentation models with various backbones, such as ResNet
and Swin Transformers. We hope our work could serve as a strong baseline, which
could inspire designing more efficient diffusion frameworks for challenging
discriminative tasks. Our code is available in
https://github.com/chenhaoxing/DiffusionInst
Backpropagation Path Search On Adversarial Transferability
Deep neural networks are vulnerable to adversarial examples, dictating the
imperativeness to test the model's robustness before deployment. Transfer-based
attackers craft adversarial examples against surrogate models and transfer them
to victim models deployed in the black-box situation. To enhance the
adversarial transferability, structure-based attackers adjust the
backpropagation path to avoid the attack from overfitting the surrogate model.
However, existing structure-based attackers fail to explore the convolution
module in CNNs and modify the backpropagation graph heuristically, leading to
limited effectiveness. In this paper, we propose backPropagation pAth Search
(PAS), solving the aforementioned two problems. We first propose SkipConv to
adjust the backpropagation path of convolution by structural
reparameterization. To overcome the drawback of heuristically designed
backpropagation paths, we further construct a DAG-based search space, utilize
one-step approximation for path evaluation and employ Bayesian Optimization to
search for the optimal path. We conduct comprehensive experiments in a wide
range of transfer settings, showing that PAS improves the attack success rate
by a huge margin for both normally trained and defense models.Comment: Accepted by ICCV202
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