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 $T$ and $p$. Beyond the critical point, the 2-D phase diagram with real $T$ and $p$, 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 $T$ or $p$ are projected onto the physical plane, a boundary defined by isobaric heat capacity $C_p$ or adiabatic compression coefficient $K_T$ emanates. These results demonstrate the incipient phase transition nature of the supercritical matter