520 research outputs found
Remove-Win: a Design Framework for Conflict-free Replicated Data Collections
Internet-scale distributed systems often replicate data within and across
data centers to provide low latency and high availability despite node and
network failures. Replicas are required to accept updates without coordination
with each other, and the updates are then propagated asynchronously. This
brings the issue of conflict resolution among concurrent updates, which is
often challenging and error-prone. The Conflict-free Replicated Data Type
(CRDT) framework provides a principled approach to address this challenge.
This work focuses on a special type of CRDT, namely the Conflict-free
Replicated Data Collection (CRDC), e.g. list and queue. The CRDC can have
complex and compound data items, which are organized in structures of rich
semantics. Complex CRDCs can greatly ease the development of upper-layer
applications, but also makes the conflict resolution notoriously difficult.
This explains why existing CRDC designs are tricky, and hard to be generalized
to other data types. A design framework is in great need to guide the
systematic design of new CRDCs.
To address the challenges above, we propose the Remove-Win Design Framework.
The remove-win strategy for conflict resolution is simple but powerful. The
remove operation just wipes out the data item, no matter how complex the value
is. The user of the CRDC only needs to specify conflict resolution for
non-remove operations. This resolution is destructed to three basic cases and
are left as open terms in the CRDC design skeleton. Stubs containing
user-specified conflict resolution logics are plugged into the skeleton to
obtain concrete CRDC designs. We demonstrate the effectiveness of our design
framework via a case study of designing a conflict-free replicated priority
queue. Performance measurements also show the efficiency of the design derived
from our design framework.Comment: revised after submissio
Towards -finiteness: -deformed open string amplitude
Revisiting the Coon amplitude, a deformation of the Veneziano amplitude with
a logarithmic generalization of linear Regge trajectories, we scrutinize its
potential origins in a worldsheet theory by proposing a definition of its
-deformation through the integral representation of the -beta function.
By utilizing -deformed commutation relations and vertex operators, we derive
the Coon amplitude within the framework of the dual resonance model. We extend
this to the open-string context by -deforming the Lie algebra
, resulting in a well-defined -deformed open superstring
amplitude. We further demonstrate that the -prefactor in the Coon amplitude
arises naturally from the property of the -integral. Furthermore, we find
that two different types of -prefactors, corresponding to different
representations of the same scattering amplitude, are essentially the same by
leveraging the properties of -numbers. Our findings indicate that the
-deformed string amplitude defines a continuous family of amplitudes,
illustrating how string amplitudes with a finite uniquely flow
to the amplitudes of scalar scattering in field theory at energy scale
as changes from to . This happens without the requirement
of an expansion, presenting a fresh perspective on the
connection between string and field theories
Function annotation of hepatic retinoid x receptor α based on genome-wide DNA binding and transcriptome profiling.
BackgroundRetinoid x receptor α (RXRα) is abundantly expressed in the liver and is essential for the function of other nuclear receptors. Using chromatin immunoprecipitation sequencing and mRNA profiling data generated from wild type and RXRα-null mouse livers, the current study identifies the bona-fide hepatic RXRα targets and biological pathways. In addition, based on binding and motif analysis, the molecular mechanism by which RXRα regulates hepatic genes is elucidated in a high-throughput manner.Principal findingsClose to 80% of hepatic expressed genes were bound by RXRα, while 16% were expressed in an RXRα-dependent manner. Motif analysis predicted direct repeat with a spacer of one nucleotide as the most prevalent RXRα binding site. Many of the 500 strongest binding motifs overlapped with the binding motif of specific protein 1. Biological functional analysis of RXRα-dependent genes revealed that hepatic RXRα deficiency mainly resulted in up-regulation of steroid and cholesterol biosynthesis-related genes and down-regulation of translation- as well as anti-apoptosis-related genes. Furthermore, RXRα bound to many genes that encode nuclear receptors and their cofactors suggesting the central role of RXRα in regulating nuclear receptor-mediated pathways.ConclusionsThis study establishes the relationship between RXRα DNA binding and hepatic gene expression. RXRα binds extensively to the mouse genome. However, DNA binding does not necessarily affect the basal mRNA level. In addition to metabolism, RXRα dictates the expression of genes that regulate RNA processing, translation, and protein folding illustrating the novel roles of hepatic RXRα in post-transcriptional regulation
Super-NeRF: View-consistent Detail Generation for NeRF super-resolution
The neural radiance field (NeRF) achieved remarkable success in modeling 3D
scenes and synthesizing high-fidelity novel views. However, existing NeRF-based
methods focus more on the make full use of the image resolution to generate
novel views, but less considering the generation of details under the limited
input resolution. In analogy to the extensive usage of image super-resolution,
NeRF super-resolution is an effective way to generate the high-resolution
implicit representation of 3D scenes and holds great potential applications. Up
to now, such an important topic is still under-explored. In this paper, we
propose a NeRF super-resolution method, named Super-NeRF, to generate
high-resolution NeRF from only low-resolution inputs. Given multi-view
low-resolution images, Super-NeRF constructs a consistency-controlling
super-resolution module to generate view-consistent high-resolution details for
NeRF. Specifically, an optimizable latent code is introduced for each
low-resolution input image to control the 2D super-resolution images to
converge to the view-consistent output. The latent codes of each low-resolution
image are optimized synergistically with the target Super-NeRF representation
to fully utilize the view consistency constraint inherent in NeRF construction.
We verify the effectiveness of Super-NeRF on synthetic, real-world, and
AI-generated NeRF datasets. Super-NeRF achieves state-of-the-art NeRF
super-resolution performance on high-resolution detail generation and
cross-view consistency
Sequential Keystroke Behavioral Biometrics for Mobile User Identification via Multi-view Deep Learning
With the rapid growth in smartphone usage, more organizations begin to focus
on providing better services for mobile users. User identification can help
these organizations to identify their customers and then cater services that
have been customized for them. Currently, the use of cookies is the most common
form to identify users. However, cookies are not easily transportable (e.g.,
when a user uses a different login account, cookies do not follow the user).
This limitation motivates the need to use behavior biometric for user
identification. In this paper, we propose DEEPSERVICE, a new technique that can
identify mobile users based on user's keystroke information captured by a
special keyboard or web browser. Our evaluation results indicate that
DEEPSERVICE is highly accurate in identifying mobile users (over 93% accuracy).
The technique is also efficient and only takes less than 1 ms to perform
identification.Comment: 2017 Joint European Conference on Machine Learning and Knowledge
Discovery in Database
ImmersiveNeRF: Hybrid Radiance Fields for Unbounded Immersive Light Field Reconstruction
This paper proposes a hybrid radiance field representation for unbounded
immersive light field reconstruction which supports high-quality rendering and
aggressive view extrapolation. The key idea is to first formally separate the
foreground and the background and then adaptively balance learning of them
during the training process. To fulfill this goal, we represent the foreground
and background as two separate radiance fields with two different spatial
mapping strategies. We further propose an adaptive sampling strategy and a
segmentation regularizer for more clear segmentation and robust convergence.
Finally, we contribute a novel immersive light field dataset, named
THUImmersive, with the potential to achieve much larger space 6DoF immersive
rendering effects compared with existing datasets, by capturing multiple
neighboring viewpoints for the same scene, to stimulate the research and AR/VR
applications in the immersive light field domain. Extensive experiments
demonstrate the strong performance of our method for unbounded immersive light
field reconstruction
Finding Causally Different Tests for an Industrial Control System
Industrial control systems (ICSs) are types of cyber-physical systems in
which programs, written in languages such as ladder logic or structured text,
control industrial processes through sensing and actuating. Given the use of
ICSs in critical infrastructure, it is important to test their resilience
against manipulations of sensor/actuator inputs. Unfortunately, existing
methods fail to test them comprehensively, as they typically focus on finding
the simplest-to-craft manipulations for a testing goal, and are also unable to
determine when a test is simply a minor permutation of another, i.e. based on
the same causal events. In this work, we propose a guided fuzzing approach for
finding 'meaningfully different' tests for an ICS via a general formalisation
of sensor/actuator-manipulation strategies. Our algorithm identifies the causal
events in a test, generalises them to an equivalence class, and then updates
the fuzzing strategy so as to find new tests that are causally different from
those already identified. An evaluation of our approach on a real-world water
treatment system shows that it is able to find 106% more causally different
tests than the most comparable fuzzer. While we focus on diversifying the test
suite of an ICS, our formalisation may be useful for other fuzzers that
intercept communication channels.Comment: Accepted by the 45th IEEE/ACM International Conference on Software
Engineering (ICSE 2023
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