arXiv subjects

Wen Chen

Publications and source records attributed to Wen Chen.

At least 361 records · Page 20Linked to original sources

arXiv2021

IRS-Aided WPCNs: A New Optimization Framework for Dynamic IRS Beamforming

In this paper, we propose a new dynamic IRS beamforming framework to boost the sum throughput of an intelligent reflecting surface (IRS) aided wireless powered communication network (WPCN). Specifically, the IRS phase-shift vectors across time and resource allocation are jointly optimized to enhance the efficiencies of both downlink wireless power transfer (DL WPT) and uplink wireless information transmission (UL WIT) between a hybrid access point (HAP) and multiple wirelessly powered devices. To this end, we first study three special cases of the dynamic IRS beamforming,namely user-adaptive IRS beamforming, UL-adaptive IRS beamforming, and static IRS beamforming,by characterizing their optimal performance relationships and proposing corresponding algorithms. Interestingly, it is rigorously proved that the latter two cases achieve the same throughput, thus helping halve the number of IRS phase shifts to be optimized and signalling overhead practically required for UL-adaptive IRS beamforming. Then, we propose a general optimization framework for dynamic IRS beamforming, which is applicable for any given number of IRS phase-shift vectors available. Despite of the non-convexity of the general problem with highly coupled optimization variables, we propose two algorithms to solve it and particularly, the low-complexity algorithm exploits the intrinsic structure of the optimal solution as well as the solutions to the cases with user-adaptive and static IRS beamforming. Simulation results validate our theoretical findings, illustrate the practical significance of IRS with dynamic beamforming for spectral and energy efficient WPCNs, and demonstrate the effectiveness of our proposed designs over various benchmark schemes.

cs.IT↗

arXiv2021

Hybrid Supervision Learning for Pathology Whole Slide Image Classification

Weak supervision learning on classification labels has demonstrated high performance in various tasks, while a few pixel-level fine annotations are also affordable. Naturally a question comes to us that whether the combination of pixel-level (e.g., segmentation) and image level (e.g., classification) annotation can introduce further improvement. However in computational pathology this is a difficult task for this reason: High resolution of whole slide images makes it difficult to do end-to-end classification model training, which is challenging to research of weak or hybrid supervision learning in the past. To handle this problem, we propose a hybrid supervision learning framework for this kind of high resolution images with sufficient image-level coarse annotations and a few pixel-level fine labels. This framework, when applied in training patch model, can carefully make use of coarse image-level labels to refine generated pixel-level pseudo labels. Complete strategy is proposed to suppress pixel-level false positives and false negatives. A large hybrid annotated dataset is used to evaluate the effectiveness of hybrid supervision learning. By extracting pixel-level pseudo labels in initially image-level labeled samples, we achieve 5.2% higher specificity than purely training on existing labels while retaining 100% sensitivity, in the task of image-level classification to be positive or negative.

cs.CV↗

arXiv2021

Four-lepton decays of neutral vector mesons

We investigate the rare electromagnetic decays of neutral vector mesons (exemplified by $J/ψ$, $Υ$, $ρ^0$, $ω,ϕ$, \ldots) into four charged leptons ($2(e^+e^-)$, $2(μ^+μ^-)$, $2(τ^+τ^-)$, $e^+e^-μ^+μ^-$, $e^+e^-τ^+τ^-$, \ldots) at lowest order in QED. In contrast to the case of vector meson decay into a single pair of leptons, the lepton mass must be retained in these four-lepton decay channels to cutoff the mass singularity. Owing to more pronounced collinear enhancement, the branching fractions of vector mesons decays into $ 2(e^+e^-)$ are considerably greater than those for decays into $2(μ^+μ^-)$. The decay channels $J/ψ(Υ)\to 2(e^+e^-), e^+e^-μ^+μ^-$ are predicted to have branching fractions of order $10^{-5}$, which appear to have bright observation prospect at {\tt BESIII}, {\tt Belle 2} and {\tt LHC} experiments.

hep-ph↗

arXiv2021

Beamforming Design and Power Allocation for Transmissive RMS-based Transmitter Architectures

This letter investigates a downlink multiple input single output (MISO) system based on transmissive reconfigurable metasurface (RMS) transmitter. Specifically, a transmitter design based on a transmissive RMS equipped with a feed antenna is first proposed. Then, in order to maximize the achievable sum-rate of the system, the beamforming design and power allocation are jointly optimized. Since the optimization variables are coupled, this formulated optimization problem is non-convex, so it is difficult to solve it directly. To solve this problem, we propose an alternating optimization (AO) technique based on difference-of-convex (DC) programming and successive convex approximation (SCA). Simulation results verify that the proposed algorithm can achieve convergence and improve the achievable sum-rate of the system.

cs.IT↗

arXiv2021

IRS-aided Wireless Powered MEC Systems: TDMA or NOMA for Computation Offloading?

An intelligent reflecting surface (IRS)-aided wireless powered mobile edge computing (WP-MEC) system is conceived, where each device's computational task can be divided into two parts for local computing and offloading to mobile edge computing (MEC) servers, respectively. Both time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) schemes are considered for uplink (UL) offloading. Given the capability of IRSs in intelligently reconfiguring wireless channels over time, it is fundamentally unknown which multiple access scheme is superior for MEC UL offloading. To answer this question, we first investigate the impact of three different dynamic IRS beamforming (DIBF) schemes on the computation rate of both offloading schemes, based on the flexibility for the IRS in adjusting its beamforming (BF) vector in each transmission frame. Under the DIBF framework, computation rate maximization problems are formulated for both the NOMA and TDMA schemes, respectively, by jointly optimizing the IRS passive BF and the resource allocation. We rigorously prove that offloading adopting TDMA can achieve the same computation rate as that of NOMA, when all the devices share the same IRS BF vector during the UL offloading. By contrast, offloading exploiting TDMA outperforms NOMA, when the IRS BF vector can be flexibly adapted for UL offloading. Despite the non-convexity of the computation rate maximization problems for each DIBF scheme associated with highly coupled optimization variables, we conceive computationally efficient algorithms by invoking alternating optimization. Our numerical results demonstrate the significant performance gains achieved by the proposed designs over various benchmark schemes.

cs.IT↗

arXiv2021

Rare decays of the positronium ion and molecule, $\text{Ps}^{-}\to e^{-}γ$ and $\text{Ps}_{2}\to e^{+}e^{-}γ,~γγ,~e^{+}e^{-}$

Decay rates of the positronium molecule $\text{Ps}_{2}$ into two photons and into an electron-positron pair are determined. Previous studies find that these rates are very different, \[ Γ\left(\text{Ps}_{2}\to e^{+}e^{-}\right)/Γ\left(\text{Ps}_{2}\toγγ\right)\simeq250\text{ (previous studies)}. \] This is puzzling since both processes have two body final states and are of the same order in the fine structure constant. We propose a simple calculational method and test it with the well-established decay of the positronium ion into an electron and a photon. We then employ it to correct predictions for both these $\text{Ps}_{2}$ decays. We find that previous studies overestimated the $e^{+}e^{-}$ and underestimated the $γγ$ channel by factors of about 5.44 and 3.93 respectively. Our results give $Γ\left(\text{Ps}_2\to e^+e^-\right)/Γ\left(\text{Ps}_2 \to γγ\right) \simeq 11.7$.

hep-ph↗

arXiv2021

Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design

Owing to the low communication costs and privacy-promoting capabilities, Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients. However, with the distributed architecture, low quality models could be uploaded to the aggregator server by unreliable clients, leading to a degradation or even a collapse of training. In this paper, we model these unreliable behaviors of clients and propose a defensive mechanism to mitigate such a security risk. Specifically, we first investigate the impact on the models caused by unreliable clients by deriving a convergence upper bound on the loss function based on the gradient descent updates. Our theoretical bounds reveal that with a fixed amount of total computational resources, there exists an optimal number of local training iterations in terms of convergence performance. We further design a novel defensive mechanism, named deep neural network based secure aggregation (DeepSA). Our experimental results validate our theoretical analysis. In addition, the effectiveness of DeepSA is verified by comparing with other state-of-the-art defensive mechanisms.

cs.LG↗

arXiv2021

Neutral bots probe political bias on social media

Social media platforms attempting to curb abuse and misinformation have been accused of political bias. We deploy neutral social bots who start following different news sources on Twitter, and track them to probe distinct biases emerging from platform mechanisms versus user interactions. We find no strong or consistent evidence of political bias in the news feed. Despite this, the news and information to which U.S. Twitter users are exposed depend strongly on the political leaning of their early connections. The interactions of conservative accounts are skewed toward the right, whereas liberal accounts are exposed to moderate content shifting their experience toward the political center. Partisan accounts, especially conservative ones, tend to receive more followers and follow more automated accounts. Conservative accounts also find themselves in denser communities and are exposed to more low-credibility content.

cs.SI↗

arXiv2021

Continuous-Wave Frequency Upconversion with a Molecular Optomechanical Nanocavity

Frequency upconversion is a cornerstone of electromagnetic signal processing, analysis and detection. It is used to transfer energy and information from one frequency domain to another where transmission, modulation or detection is technically easier or more efficient. Optomechanical transduction is emerging as a flexible approach to coherent frequency upconversion; it has been successfully demonstrated for conversion from radio- and microwaves (kHz to GHz) to optical fields. Nevertheless, optomechanical transduction of multi-THz and mid-infrared signals remains an open challenge. Here, we utilize molecular cavity optomechanics to demonstrate upconversion of sub-microwatt continuous-wave signals at $\sim$32~THz into the visible domain at ambient conditions. The device consists in a plasmonic nanocavity hosting a small number of molecules. The incoming field resonantly drives a collective molecular vibration, which imprints an optomechanical modulation on a visible pump laser and results in Stokes and anti-Stokes upconverted Raman sidebands with sub-natural linewidth, indicating a coherent process. The nanocavity offers 13 orders of magnitude enhancement of upconversion efficiency per molecule compared to free space, with a measured phonon-to-photon internal conversion efficiency larger than $10^{-4}$ per milliwatt of pump power. Our results establish a flexible paradigm for optomechanical frequency conversion using molecular oscillators coupled to plasmonic nanocavities, whose vibrational and electromagnetic properties can be tailored at will using chemical engineering and nanofabrication.

physics.optics↗

arXiv2021

Federated Learning Over Wireless Channels: Dynamic Resource Allocation and Task Scheduling

With the development of federated learning (FL), mobile devices (MDs) are able to train their local models with private data and sends them to a central server for aggregation, thereby preventing sensitive raw data leakage. In this paper, we aim to improve the training performance of FL systems in the context of wireless channels and stochastic energy arrivals of MDs. To this purpose, we dynamically optimize MDs' transmission power and training task scheduling. We first model this dynamic programming problem as a constrained Markov decision process (CMDP). Due to high dimensions rooted from our CMDP problem, we propose online stochastic learning methods to simplify the CMDP and design online algorithms to obtain an efficient policy for all MDs. Since there are long-term constraints in our CMDP, we utilize Lagrange multipliers approach to tackle this issue. Furthermore, we prove the convergence of the proposed online stochastic learning algorithm. Numerical results indicate that the proposed algorithms can achieve better performance than the benchmark algorithms.

cs.DC↗

arXiv2021

Joint Beamforming Design and Power Splitting Optimization in IRS-Assisted SWIPT NOMA Networks

This paper proposes a novel network framework of intelligent reflecting surface (IRS)-assisted simultaneous wireless information and power transfer (SWIPT) non-orthogonal multiple access (NOMA) networks, where IRS is used to enhance the NOMA performance and the wireless power transfer (WPT) efficiency of SWIPT. We formulate a problem of minimizing base station (BS) transmit power by jointly optimizing successive interference cancellation (SIC) decoding order, BS transmit beamforming vector, power splitting (PS) ratio and IRS phase shift while taking into account the quality-of-service (QoS) requirement and energy harvested threshold of each user. The formulated problem is non-convex optimization problem, which is difficult to solve it directly. Hence, a two-stage algorithm is proposed to solve the above-mentioned problem by applying semidefinite relaxation (SDR), Gaussian randomization and successive convex approximation (SCA). Specifically, after determining SIC decoding order by designing IRS phase shift in the first stage, we alternately optimize BS transmit beamforming vector, PS ratio, and IRS phase shift to minimize the BS transmit power. Numerical results validate the effectiveness of our proposed optimization algorithm in reducing BS transmit power compared to other baseline algorithms. Meanwhile, compared with non-IRS-assisted network, the IRS-assisted SWIPT NOMA network can decrease BS transmit power by 51.13\%.

cs.IT↗

arXiv2021

Intrinsic Luminescence Blinking from Plasmonic Nanojunctions

Plasmonic nanojunctions, consisting of adjacent metal structures with nanometre gaps, can support localised plasmon resonances that boost light matter interactions and concentrate electromagnetic fields at the nanoscale. In this regime, the optical response of the system is governed by poorly understood dynamical phenomena at the frontier between the bulk, molecular and atomic scales. Here, we report ubiquitous spectral fluctuations in the intrinsic light emission from photo-excited gold nanojunctions, which we attribute to the light-induced formation of domain boundaries and quantum-confined emitters inside the noble metal. Our data suggest that photoexcited carriers and gold adatom - molecule interactions play key roles in triggering luminescence blinking. Surprisingly, this internal restructuring of the metal has no measurable impact on the Raman signal and scattering spectrum of the plasmonic cavity. Our findings demonstrate that metal luminescence offers a valuable proxy to investigate atomic fluctuations in plasmonic cavities, complementary to other optical and electrical techniques.

physics.optics↗

arXiv2021

Reduction of Feynman Integrals in the Parametric Representation III: Integrals with Cuts

Phase space cuts are implemented by inserting Heaviside theta functions in the integrands of momentum-space Feynman integrals. By directly parametrizing theta functions and constructing integration-by-parts (IBP) identities in the parametric representation, we provide a systematic method to reduce integrals with cuts. Since the IBP method is available, it becomes possible to evaluate integrals with cuts by constructing and solving differential equations.

hep-ph↗

arXiv2021

Reduction of Feynman Integrals in the Parametric Representation II: Reduction of Tensor Integrals

In a recent paper by the author (Chen in JHEP 02:115, 2020), the reduction of Feynman integrals in the parametric representation was considered. Tensor integrals were directly parametrized by using a generator method. The resulting parametric integrals were reduced by constructing and solving parametric integration-by-parts (IBP) identities. In this paper, we furthermore show that polynomial equations for the operators that generate tensor integrals can be derived. Based on these equations, two methods to reduce tensor integrals are developed. In the first method, by introducing some auxiliary parameters, tensor integrals are parametrized without shifting the spacetime dimension. The resulting parametric integrals can be reduced by using the standard IBP method. In the second method, tensor integrals are (partially) reduced by using the technique of Gröbner basis combined with the application of symbolic rules. The unreduced integrals can further be reduced by solving parametric IBP identities.

hep-ph↗

arXiv2021

A compact core-jet structure in the changing-look Seyfert NGC 2617

The nearby face-on spiral galaxy NGC 2617 underwent an unambiguous 'inside-out' multi-wavelength outburst in Spring 2013, and a dramatic Seyfert type change probably between 2010 and 2012, with the emergence of broad optical emission lines. To search for the jet activity associated with this variable accretion activity, we carried out multi-resolution and multi-wavelength radio observations. Using the very long baseline interferometric (VLBI) observations with the European VLBI Network (EVN) at 1.7 and 5.0 GHz, we find that NGC 2617 shows a partially synchrotron self-absorbed compact radio core with a significant core shift, and an optically thin steep-spectrum jet extending towards the north up to about two parsecs in projection. We also observed NGC 2617 with the electronic Multi-Element Remotely Linked Interferometer Network (e-MERLIN) at 1.5 and 5.5 GHz, and revisited the archival data of the Very Large Array (VLA) and the Very Long Baseline Array (VLBA). The radio core had a stable flux density of about 1.4 mJy at 5.0 GHz between 2013 June and 2014 January, in agreement with the expectation of a supermassive black hole in the low accretion rate state. The northern jet component is unlikely to be associated with the 'inside-out' outburst of 2013. Moreover, we report that most optically selected changing-look AGN at z<0.83 are sub-mJy radio sources in the existing VLA surveys at 1.4 GHz, and it is unlikely that they are more active than normal AGN at radio frequencies.

astro-ph.HE↗

arXiv2020

Mapping Diffuse Emission in Lyman UV band

The CAFE (Census of warm-hot intergalactic medium, Accretion, and Feedback Explorer) and LyRIC (Lyman UV Radiation from Interstellar medium and Circum-galactic medium) have been proposed to the space agencies in China respectively. CAFE was first proposed in 2015 as a joint scientific CAS-ESA small space mission. LyRIC was proposed in 2019 as an independent external payload operating on the Chinese Space Station. Both missions are dedicated to mapping the Lyman UV emission (ionized oxygen (O VI) resonance lines at 103.2 and 103.8 nm, and Lyman series) for the diffuse sources in our Galaxy and the circum-galactic mediums of the nearby galaxies. We present the primary science objectives, mission concepts, the enabling technologies, as well as the current status.

astro-ph.IM↗

arXiv2020

The hyperluminous, dust-obscured quasar W2246-0526 at z=4.6: detection of parsec-scale radio activity

WISE J224607.56$-$052634.9 (W2246-0526) is a hyperluminous ($L_{\rm bol}\approx 1.7\times 10^{14}~L_\odot$), dust-obscured and radio-quiet quasar at redshift $z=4.6$. It plays a key role in probing the transition stage between dusty starbursts and unobscured quasars in the co-evolution of galaxies and supermassive black holes (SMBHs). To search for the evidence of the jet activity launched by the SMBH in W2246-0526, we performed very long baseline interferometry (VLBI) observations of its radio counterpart with the European VLBI Network (EVN) plus the enhanced Multi Element Remotely Linked Interferometer Network (e-MERLIN) at 1.66 GHz and the Very Long Baseline Array (VLBA) at 1.44 and 1.66 GHz. The deep EVN plus e-MERLIN observations detect a compact (size $\leq32$ pc) sub-mJy component contributing about ten percent of its total flux density, which spatially coincides with the peak of dust continuum and [C II] emissions. Together with its relatively high brightness temperature ($\geq8\times10^{6}$ K), we interpret the component as a consequence of non-thermal radio activity powered by the central SMBH, which likely originates from a stationary jet base. The resolved-out radio emission possibly come from a diffuse jet, quasar-driven winds, or both, while the contribution by star formation activity is negligible. Moreover, we propose an updated geometry structure of its multi-wavelength active nucleus and shed light on the radio quasar selection bias towards the blazars at $z>4$.

astro-ph.GA↗

arXiv2020

Joint Transceiver Design Based on Dictionary Learning Algorithm for SCMA

With the explosively increasing demands on the network capacity, throughput and number of connected wireless devices, massive connectivity is an urgent problem for the next generation wireless communications. In this paper, we propose a grant-free access protocol for massive connectivity that utilizes a large number of antennas in a base station (BS) and is expected to be widely deployed in cellular networks. The scheme consists of a sparse structure in sparse code multiple access (SCMA) and receiver processing based on dictionary learning (DL). A large number of devices can transmit data without any scheduling process. Unlike existing schemes, whose signal schedulings require a lot of overhead, the scheduling overhead required by the proposed scheme is negligible, which is attractive for resource utilization and transmission power efficiency. The numerical results show that the proposed scheme has promising performance in massive connectivity scenario of cellular networks.

eess.SP↗