173 research outputs found
Video Games and Social Relation
This article will be discussing how multi-player games encourage social interactions. It will over multiple games as examples to illustrate the argument. The article has two focus: game mechanics and player psychology
Determinacy Race Detector for Promises (Artifact)
Much of the past work on dynamic data-race and determinacy-race detection algorithms for task parallelism has focused on structured parallelism with fork-join constructs and, more recently, with future constructs. This paper addresses the problem of dynamic detection of data-races and determinacy-races in task-parallel programs with promises, which are more general than fork-join constructs and futures.
We have introduced a dynamic data race detector, DRDP, to help examine task-parallelism programs with promises. DRDP is designed for the HCLIB parallel programming model and capable of pinpointing data races in a HCLIB program. In this artifact, we provide the race detector implementation and all benchmarks to help reproduce the reported results in the paper
Characterization and Correction of the Scattering Background Produced by Dust on the Objective Lens of the Lijiang 10-cm Coronagraph
Scattered light from the objective lens, directly exposed to the intense
sunlight, is a dominant source of stray light in internally occulted
coronagraphs. The variable stray light, such as the scatter from dust on the
objective lens, can produce varying scattering backgrounds in coronal images,
significantly impacting image quality and data analysis. Using data acquired by
the Lijiang 10-cm Coronagraph, the quantitative relationship between the
distribution of dust on the objective lens and the resulting scattering
backgrounds background is analyzed. Two empirical models for the scattering
background are derived, and used to correct the raw coronal data. The second
model, which depends on three parameters and performs better, shows that the
scattering-background distribution varies with angle, weakens with increasing
height, and enhances with increasing dust level on the objective lens.
Moreover, we find that the dust on the center of the objective lens can
contribute more significantly to the scattering background than on the edge.
This study not only quantitatively confirms the significant impact of the stray
light produced by dust on the objective lens of the coronagraph, but also
corrects the coronal data with this stray light for the first time. Correcting
for dust-scattered light is crucial for the high-precision calibration of
ground-based coronagraph data, enabling a more accurate analysis of coronal
structures. Furthermore, our model is envisioned to support the provision of
reliable observational data for future routine coronal magnetic-field
measurements using ground-based coronagraphs.Comment: 18 pages, 14 figrue
Functional study of a novel missense single-nucleotide variant of NUP107 in two daughters of Mexican origin with premature ovarian insufficiency.
BackgroundHypergonadotropic hypogonadism (HH) is a genetically heterogeneous disorder that usually presents with amenorrhea, atrophic ovaries, and low estrogen. Most cases of HH are idiopathic and nonsyndromic. Nucleoporin 107 (NUP107), a protein involved in transport between cytoplasm and nucleus with putative roles in meiosis/mitosis progression, was recently implicated as a cause of HH. We identified a NUP107 genetic variant in a nonconsanguineous family with two sisters affected with primary amenorrhea and HH, and generated a mouse model that carried the human variant.MethodsWe performed a high-resolution X-chromosome microarray and whole exome sequencing on parents and two sisters with HH to identify pathogenic variants. We generated a mouse model of candidate NUP107 variant using CRISPR/Cas9.ResultsWhole exome sequencing identified a novel and rare missense variant in the NUP107 gene (c.1063C>T, p.R355C) in both sisters with HH. In order to determine functional significance of this variant, we used CRISPR/Cas9 to introduce the human variant into the mouse genome. Mice with the homolog of the R355C variant, as well as the nine base pairs deletion in Nup107 had female subfertility.ConclusionsOur findings indicate that NUP107 R355C variant falls in the category of variant of unknown significance as the cause of HH and infertility
Dynamic Determinacy Race Detection for Task-Parallel Programs with Promises
Much of the past work on dynamic data-race and determinacy-race detection algorithms for task parallelism has focused on structured parallelism with fork-join constructs and, more recently, with future constructs. This paper addresses the problem of dynamic detection of data-races and determinacy-races in task-parallel programs with promises, which are more general than fork-join constructs and futures. The motivation for our work is twofold. First, promises have now become a mainstream synchronization construct, with their inclusion in multiple languages, including C++, JavaScript, and Java. Second, past work on dynamic data-race and determinacy-race detection for task-parallel programs does not apply to programs with promises, thereby identifying a vital need for this work.
This paper makes multiple contributions. First, we introduce a featherweight programming language that captures the semantics of task-parallel programs with promises and provides a basis for formally defining determinacy using our semantics. This definition subsumes functional determinacy (same output for same input) and structural determinacy (same computation graph for same input). The main theoretical result shows that the absence of data races is sufficient to guarantee determinacy with both properties. We are unaware of any prior work that established this result for task-parallel programs with promises. Next, we introduce a new Dynamic Race Detector for Promises that we call DRDP. DRDP is the first known race detection algorithm that executes a task-parallel program sequentially without requiring the serial-projection property; this is a critical requirement since programs with promises do not satisfy the serial-projection property in general. Finally, the paper includes experimental results obtained from an implementation of DRDP. The results show that, with some important optimizations introduced in our work, the space and time overheads of DRDP are comparable to those of more restrictive race detection algorithms from past work. To the best of our knowledge, DRDP is the first determinacy race detector for task-parallel programs with promises
Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces
Continual graph learning routinely finds its role in a variety of real-world
applications where the graph data with different tasks come sequentially.
Despite the success of prior works, it still faces great challenges. On the one
hand, existing methods work with the zero-curvature Euclidean space, and
largely ignore the fact that curvature varies over the coming graph sequence.
On the other hand, continual learners in the literature rely on abundant
labels, but labeling graph in practice is particularly hard especially for the
continuously emerging graphs on-the-fly. To address the aforementioned
challenges, we propose to explore a challenging yet practical problem, the
self-supervised continual graph learning in adaptive Riemannian spaces. In this
paper, we propose a novel self-supervised Riemannian Graph Continual Learner
(RieGrace). In RieGrace, we first design an Adaptive Riemannian GCN (AdaRGCN),
a unified GCN coupled with a neural curvature adapter, so that Riemannian space
is shaped by the learnt curvature adaptive to each graph. Then, we present a
Label-free Lorentz Distillation approach, in which we create teacher-student
AdaRGCN for the graph sequence. The student successively performs
intra-distillation from itself and inter-distillation from the teacher so as to
consolidate knowledge without catastrophic forgetting. In particular, we
propose a theoretically grounded Generalized Lorentz Projection for the
contrastive distillation in Riemannian space. Extensive experiments on the
benchmark datasets show the superiority of RieGrace, and additionally, we
investigate on how curvature changes over the graph sequence.Comment: Accepted by AAAI 2023 (Main Track), 9 pages, 4 figure
Deep Safe Multi-Task Learning
In recent years, Multi-Task Learning (MTL) has attracted much attention due
to its good performance in many applications. However, many existing MTL models
cannot guarantee that their performance is no worse than their single-task
counterparts on each task. Though some works have empirically observed this
phenomenon, little work aims to handle the resulting problem. In this paper, we
formally define this phenomenon as negative sharing and define safe multi-task
learning where no negative sharing occurs. To achieve safe multi-task learning,
we propose a Deep Safe Multi-Task Learning (DSMTL) model with two learning
strategies: individual learning and joint learning. We theoretically study the
safeness of both learning strategies in the DSMTL model to show that the
proposed methods can achieve some versions of safe multi-task learning.
Moreover, to improve the scalability of the DSMTL model, we propose an
extension, which automatically learns a compact architecture and empirically
achieves safe multi-task learning. Extensive experiments on benchmark datasets
verify the safeness of the proposed methods
Integrated Sensing and Communication for Network-Assisted Full-Duplex Cell-Free Distributed Massive MIMO Systems
In this paper, we combine the network-assisted full-duplex (NAFD) technology
and distributed radar sensing to implement integrated sensing and communication
(ISAC). The ISAC system features both uplink and downlink remote radio units
(RRUs) equipped with communication and sensing capabilities. We evaluate the
communication and sensing performance of the system using the sum communication
rates and the Cramer-Rao lower bound (CRLB), respectively. We compare the
performance of the proposed scheme with other ISAC schemes, the result shows
that the proposed scheme can provide more stable sensing and better
communication performance. Furthermore, we propose two power allocation
algorithms to optimize the communication and sensing performance jointly. One
algorithm is based on the deep Q-network (DQN) and the other one is based on
the non-dominated sorting genetic algorithm II (NSGA-II). The proposed
algorithms provide more feasible solutions and achieve better system
performance than the equal power allocation algorithm.Comment: 14 pages, 7 figures,submit to China Communication February 28, 2023,
date of major revision July 09, 202
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