131 research outputs found
Self-Supervised Sketch-to-Image Synthesis
Imagining a colored realistic image from an arbitrarily drawn sketch is one
of the human capabilities that we eager machines to mimic. Unlike previous
methods that either requires the sketch-image pairs or utilize low-quantity
detected edges as sketches, we study the exemplar-based sketch-to-image (s2i)
synthesis task in a self-supervised learning manner, eliminating the necessity
of the paired sketch data. To this end, we first propose an unsupervised method
to efficiently synthesize line-sketches for general RGB-only datasets. With the
synthetic paired-data, we then present a self-supervised Auto-Encoder (AE) to
decouple the content/style features from sketches and RGB-images, and
synthesize images that are both content-faithful to the sketches and
style-consistent to the RGB-images. While prior works employ either the
cycle-consistence loss or dedicated attentional modules to enforce the
content/style fidelity, we show AE's superior performance with pure
self-supervisions. To further improve the synthesis quality in high resolution,
we also leverage an adversarial network to refine the details of synthetic
images. Extensive experiments on 1024*1024 resolution demonstrate a new
state-of-art-art performance of the proposed model on CelebA-HQ and Wiki-Art
datasets. Moreover, with the proposed sketch generator, the model shows a
promising performance on style mixing and style transfer, which require
synthesized images to be both style-consistent and semantically meaningful. Our
code is available on
https://github.com/odegeasslbc/Self-Supervised-Sketch-to-Image-Synthesis-PyTorch,
and please visit https://create.playform.io/my-projects?mode=sketch for an
online demo of our model.Comment: AAAI-202
BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning
An ever increasing number of configuration parameters are provided to system
users. But many users have used one configuration setting across different
workloads, leaving untapped the performance potential of systems. A good
configuration setting can greatly improve the performance of a deployed system
under certain workloads. But with tens or hundreds of parameters, it becomes a
highly costly task to decide which configuration setting leads to the best
performance. While such task requires the strong expertise in both the system
and the application, users commonly lack such expertise.
To help users tap the performance potential of systems, we present
BestConfig, a system for automatically finding a best configuration setting
within a resource limit for a deployed system under a given application
workload. BestConfig is designed with an extensible architecture to automate
the configuration tuning for general systems. To tune system configurations
within a resource limit, we propose the divide-and-diverge sampling method and
the recursive bound-and-search algorithm. BestConfig can improve the throughput
of Tomcat by 75%, that of Cassandra by 63%, that of MySQL by 430%, and reduce
the running time of Hive join job by about 50% and that of Spark join job by
about 80%, solely by configuration adjustment
Methyl 2-(5-fluoro-1H-indol-3-yl)-2-oxoacetate
The indolyl portion of the title molecule, C11H8FNO3, is flat, the five- and six-membered rings making a dihedral angle of 0.815 (6)°. Intermolecular N—H⋯O hydrogen bonds link adjacent molecules into a linear chain. Slipped π–π stacking interactions between two neighboring indole groups further consolidate the molecules into a three-dimensional supramolecular architecture [centroid–centroid distances = 3.555 (10) and 3.569 (10) Å]
TIME: Text and Image Mutual-Translation Adversarial Networks
Focusing on text-to-image (T2I) generation, we propose Text and Image
Mutual-Translation Adversarial Networks (TIME), a lightweight but effective
model that jointly learns a T2I generator G and an image captioning
discriminator D under the Generative Adversarial Network framework. While
previous methods tackle the T2I problem as a uni-directional task and use
pre-trained language models to enforce the image--text consistency, TIME
requires neither extra modules nor pre-training. We show that the performance
of G can be boosted substantially by training it jointly with D as a language
model. Specifically, we adopt Transformers to model the cross-modal connections
between the image features and word embeddings, and design an annealing
conditional hinge loss that dynamically balances the adversarial learning. In
our experiments, TIME achieves state-of-the-art (SOTA) performance on the CUB
and MS-COCO dataset (Inception Score of 4.91 and Fr\'echet Inception Distance
of 14.3 on CUB), and shows promising performance on MS-COCO on image captioning
and downstream vision-language tasks.Comment: AAAI-202
SHAPFUZZ: Efficient Fuzzing via Shapley-Guided Byte Selection
Mutation-based fuzzing is popular and effective in discovering unseen code
and exposing bugs. However, only a few studies have concentrated on quantifying
the importance of input bytes, which refers to the degree to which a byte
contributes to the discovery of new code. They often focus on obtaining the
relationship between input bytes and path constraints, ignoring the fact that
not all constraint-related bytes can discover new code. In this paper, we
conduct Shapely analysis to understand the effect of byte positions on fuzzing
performance, and find that some byte positions contribute more than others and
this property often holds across seeds. Based on this observation, we propose a
novel fuzzing solution, ShapFuzz, to guide byte selection and mutation.
Specifically, ShapFuzz updates Shapley values (importance) of bytes when each
input is tested during fuzzing with a low overhead, and utilizes contextual
multi-armed bandit to trade off between mutating high Shapley value bytes and
low-frequently chosen bytes. We implement a prototype of this solution based on
AFL++, i.e., ShapFuzz. We evaluate ShapFuzz against ten state-of-the-art
fuzzers, including five byte schedule-reinforced fuzzers and five commonly used
fuzzers. Compared with byte schedule-reinforced fuzzers, ShapFuzz discovers
more edges and exposes more bugs than the best baseline on three different sets
of initial seeds. Compared with commonly used fuzzers, ShapFuzz exposes 20 more
bugs than the best comparison fuzzer, and discovers 6 more CVEs than the best
baseline on MAGMA. Furthermore, ShapFuzz discovers 11 new bugs on the latest
versions of programs, and 3 of them are confirmed by vendors
Automated turnkey microcomb for low-noise microwave synthesis
Microresonator-based optical frequency comb (microcomb) has the potential to
revolutionize the accuracy of frequency synthesizer in radar and communication
applications. However, fundamental limit exists for low noise microcomb
generation, especially in low size, weight, power and cost (SWaP-C) package.
Here we resolve this limit, by the demonstration of an automated turnkey
microcomb, operating close to its low quantum-limited phase noise, within a
compact setup size of 85 mm * 90 mm * 25 mm. High quality factor fiber
Fabry-Perot resonator (FFPR), with Q up to 4.0 * 10^9, is the key for both low
quantum noise and pump noise limit, in the diode-pump case in a self-injection
locking scheme. Low phase noise of -80 and -105 dBc/Hz at 100 Hz, -106 and -125
dBc/Hz at 1 kHz, -133 and -148 dBc/Hz at 10 kHz is achieved at 10.1 GHz and 1.7
GHz repetition frequencies, respectively. With the simultaneous automated
turnkey, low-noise and direct-diode-pump capability, our microcomb is ready to
be used as a low-noise frequency synthesizer with low SWaP-C and thus field
deployability
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