5,829 research outputs found
The impact of cell site re-homing on the performance of umts core networks
Mobile operators currently prefer optimizing their radio networks via
re-homing or cutting over the cell sites in 2G or 3G networks. The core
network, as the parental part of radio network, is inevitably impacted by the
re-homing in radio domain. This paper introduces the cell site re-homing in
radio network and analyzes its impact on the performance of GSM/UMTS core
network. The possible re-homing models are created and analyzed for core
networks. The paper concludes that appropriate re-homing in radio domain, using
correct algorithms, not only optimizes the radio network but also helps improve
the QoS of the core network and saves the carriers' OPEX and CAPEX on their
core networks.Comment: 14 Pages, IJNGN Journa
WACO: Word-Aligned Contrastive Learning for Speech Translation
End-to-end Speech Translation (E2E ST) aims to directly translate source
speech into target text. Existing ST methods perform poorly when only extremely
small speech-text data are available for training. We observe that an ST
model's performance closely correlates with its embedding similarity between
speech and source transcript. In this paper, we propose Word-Aligned
COntrastive learning (WACO), a simple and effective method for extremely
low-resource speech-to-text translation. Our key idea is bridging word-level
representations for both speech and text modalities via contrastive learning.
We evaluate WACO and other methods on the MuST-C dataset, a widely used ST
benchmark, and on a low-resource direction Maltese-English from IWSLT 2023. Our
experiments demonstrate that WACO outperforms the best baseline by 9+ BLEU
points with only 1-hour parallel ST data. Code is available at
https://github.com/owaski/WACO.Comment: ACL 2023 Poste
Reinforcement Learning in Computing and Network Convergence Orchestration
As computing power is becoming the core productivity of the digital economy
era, the concept of Computing and Network Convergence (CNC), under which
network and computing resources can be dynamically scheduled and allocated
according to users' needs, has been proposed and attracted wide attention.
Based on the tasks' properties, the network orchestration plane needs to
flexibly deploy tasks to appropriate computing nodes and arrange paths to the
computing nodes. This is a orchestration problem that involves resource
scheduling and path arrangement. Since CNC is relatively new, in this paper, we
review some researches and applications on CNC. Then, we design a CNC
orchestration method using reinforcement learning (RL), which is the first
attempt, that can flexibly allocate and schedule computing resources and
network resources. Which aims at high profit and low latency. Meanwhile, we use
multi-factors to determine the optimization objective so that the orchestration
strategy is optimized in terms of total performance from different aspects,
such as cost, profit, latency and system overload in our experiment. The
experiments shows that the proposed RL-based method can achieve higher profit
and lower latency than the greedy method, random selection and
balanced-resource method. We demonstrate RL is suitable for CNC orchestration.
This paper enlightens the RL application on CNC orchestration
Deciphering ion concentration polarization-based electrokinetic molecular concentration at the micro-nanofluidic interface: theoretical limits and scaling laws
The electrokinetic molecular concentration (EMC) effect at the micro-nanofluidic interface, which enables million-fold preconcentration of biomolecules, is one of the most compelling yet least understood nanofluidic phenomena. Despite the tremendous interests in EMC and the substantial efforts devoted, the detailed mechanism of EMC remains an enigma so far owing to its high complexity, which gives rise to the significant scientific controversies outstanding for over a decade and leaves the precise engineering of EMC devices infeasible. We report a series of experimental and theoretical new findings that decipher the mechanism of EMC. We demonstrate the first elucidation of two separate operating regimes of EMC, and establish the first theoretical model that analytically yet concisely describes the system. We further unveil the dramatically different scaling behaviors of EMC in the two regimes, thereby clarifying the long-lasting controversies. We believe this work represents important progress towards the scientific understanding of EMC and related nano-electrokinetic systems, and would enable the rational design and optimization of EMC devices for a variety of applications.National Institutes of Health (U.S.) (Grant No. U19AI109755)National Science Council (China) (Grant No. 11372229)National Science Council (China) (Grant No. 21576130)National Science Council (China) (Grant No. 21490584
UrbanFM: Inferring Fine-Grained Urban Flows
Urban flow monitoring systems play important roles in smart city efforts
around the world. However, the ubiquitous deployment of monitoring devices,
such as CCTVs, induces a long-lasting and enormous cost for maintenance and
operation. This suggests the need for a technology that can reduce the number
of deployed devices, while preventing the degeneration of data accuracy and
granularity. In this paper, we aim to infer the real-time and fine-grained
crowd flows throughout a city based on coarse-grained observations. This task
is challenging due to two reasons: the spatial correlations between coarse- and
fine-grained urban flows, and the complexities of external impacts. To tackle
these issues, we develop a method entitled UrbanFM based on deep neural
networks. Our model consists of two major parts: 1) an inference network to
generate fine-grained flow distributions from coarse-grained inputs by using a
feature extraction module and a novel distributional upsampling module; 2) a
general fusion subnet to further boost the performance by considering the
influences of different external factors. Extensive experiments on two
real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness
and efficiency of our method compared to seven baselines, demonstrating the
state-of-the-art performance of our approach on the fine-grained urban flow
inference problem
Complex phase diagram and supercritical matter
Supercritical region is often described as uniform with no definite
transitions. The distinct behaviors of the matter therein, e.g., as liquid-like
and gas-like, however, indicate their should-be different belongings. Here, we
provide a mathematical description of these phenomena by revisiting the
Lee-Yang (LY) theory and using a complex phase diagram, e.g. a 4-D one with
complex and . Beyond the critical point, the 2-D phase diagram with real
and , i.e. the physical plane, is free of LY zeros and hence no
criticality emerges. But off-plane zeros in this 4-D scenario still come into
play by inducing critical anomalies for different physical properties. This is
evidenced by the correlation between the Widom lines and LY edges in van der
Waals model and water. The present distinct criteria to distinguish the
supercritical matter manifest the high-dimensional feature of the phase
diagram: e.g. when the LY zeros of complex or are projected onto the
physical plane, a boundary defined by isobaric heat capacity or adiabatic
compression coefficient emanates. These results demonstrate the incipient
phase transition nature of the supercritical matter
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