arXiv subjects

Wei Yu

Publications and source records attributed to Wei Yu.

At least 109 records · Page 6Linked to original sources

arXiv2023

Transport of intense ion beams in plasmas: collimation and energy-loss reduction

We compare the transport properties of a well-characterized hydrogen plasma for low and high current ion beams. The energy-loss of low current beams can be well understood, within the framework of current stopping power models. However, for high current proton beams, significant energy-loss reduction and collimation is observed in the experiment. We have developed a new particle-in-cell code, which includes both collective electromagnetic effects and collisional interactions. Our simulations indicate that resistive magnetic fields, induced by the transport of an intense proton beam, act to collimate the proton beam and simultaneously deplete the local plasma density along the beam path. This in turn causes the energy-loss reduction detected in the experiment.

physics.plasm-ph↗

arXiv2023

Scaling Law Analysis for Covariance Based Activity Detection in Cooperative Multi-Cell Massive MIMO

This paper studies the covariance based activity detection problem in a multi-cell massive multiple-input multiple-output (MIMO) system, where the active devices transmit their signature sequences to multiple base stations (BSs), and the BSs cooperatively detect the active devices based on the received signals. The scaling law of covariance based activity detection in the single-cell scenario has been thoroughly analyzed in the literature. This paper aims to analyze the scaling law of covariance based activity detection in the multi-cell massive MIMO system. In particular, this paper shows a quadratic scaling law in the multi-cell system under the assumption that the exponent in the classical path-loss model is greater than 2, which demonstrates that in the multi-cell MIMO system the maximum number of active devices that can be correctly detected in each cell increases quadratically with the length of the signature sequence and decreases logarithmically with the number of cells (as the number of antennas tends to infinity). This paper also characterizes the distribution of the estimation error in the multi-cell scenario.

cs.IT↗

arXiv2023

Fast transitions of X-ray variability in the black hole transient GX 339--4: comparison with MAXI J1820+070 and MAXI J1348-630

Fast transitions between different types of power density spectra (PDS) happening over timescales of several tens of seconds are rare phenomena in black hole X-ray binaries. In this paper, we report a broadband spectral-timing analysis of the fast transitions observed in the 2021 outburst of GX 339-4 using NICER and HXMT observations. We observe transitions between band-limited noise-dominated PDS and type-B quasi-periodic oscillations (QPOs), and their rapid appearance or disappearance. We also make a detailed comparison between the fast transitions in GX 339-4 with those seen in MAXI J1820+070 and MAXI J1348--630. By comparing the spectra of the periods with and without type-B QPOs, we find that the spectral ratios above 10 keV are nearly constant or slightly decreasing, and the values are different between sources. Below 10 keV, the flux change of the Comptonization component is inversely proportional to the flux change of the thermal component, suggesting that the appearance of type-B QPOs is associated with a redistribution of the accretion power between the disc and the Comptonizing emission region. The spectral ratios between the periods with type-B QPO and those with broadband noise are significantly different from that with type-B QPO and without type-B QPO, where the ratios (type-B QPO/broadband noise) show a maximum at around 4 keV and then decrease gradually towards high energies. Finally, we discuss the possible change of the geometry of the inner accretion flow and/or jet during the transitions.

astro-ph.HE↗

arXiv2023

Uncertainty Injection: A Deep Learning Method for Robust Optimization

This paper proposes a paradigm of uncertainty injection for training deep learning model to solve robust optimization problems. The majority of existing studies on deep learning focus on the model learning capability, while assuming the quality and accuracy of the inputs data can be guaranteed. However, in realistic applications of deep learning for solving optimization problems, the accuracy of inputs, which are the problem parameters in this case, plays a large role. This is because, in many situations, it is often costly or sometime impossible to obtain the problem parameters accurately, and correspondingly, it is highly desirable to develop learning algorithms that can account for the uncertainties in the input and produce solutions that are robust against these uncertainties. This paper presents a novel uncertainty injection scheme for training machine learning models that are capable of implicitly accounting for the uncertainties and producing statistically robust solutions. We further identify the wireless communications as an application field where uncertainties are prevalent in problem parameters such as the channel coefficients. We show the effectiveness of the proposed training scheme in two applications: the robust power loading for multiuser multiple-input-multiple-output (MIMO) downlink transmissions; and the robust power control for device-to-device (D2D) networks.

cs.LG↗

arXiv2023

Learning cross space mapping via DNN using large scale click-through logs

The gap between low-level visual signals and high-level semantics has been progressively bridged by continuous development of deep neural network (DNN). With recent progress of DNN, almost all image classification tasks have achieved new records of accuracy. To extend the ability of DNN to image retrieval tasks, we proposed a unified DNN model for image-query similarity calculation by simultaneously modeling image and query in one network. The unified DNN is named the cross space mapping (CSM) model, which contains two parts, a convolutional part and a query-embedding part. The image and query are mapped to a common vector space via these two parts respectively, and image-query similarity is naturally defined as an inner product of their mappings in the space. To ensure good generalization ability of the DNN, we learn weights of the DNN from a large number of click-through logs which consists of 23 million clicked image-query pairs between 1 million images and 11.7 million queries. Both the qualitative results and quantitative results on an image retrieval evaluation task with 1000 queries demonstrate the superiority of the proposed method.

cs.CV↗

arXiv2023

Channel Estimation for Reconfigurable Intelligent Surface Aided Multi-User mmWave MIMO Systems

Channel acquisition is one of the main challenges for the deployment of reconfigurable intelligent surface (RIS) aided communication systems. This is because an RIS has a large number of reflective elements, which are passive devices with no active transmitting/receiving abilities. In this paper, we study the channel estimation problem for the RIS aided multi-user millimeter-wave (mmWave) multi-input multi-output (MIMO) system. Specifically, we propose a novel channel estimation protocol for the above system to estimate the cascaded channels, which are the products of the channels from the base station (BS) to the RIS and from the RIS to the users. Further, since the cascaded channels are typically sparse, this allows us to formulate the channel estimation problem as a sparse recovery problem using compressive sensing (CS) techniques, thereby allowing the channels to be estimated with less training overhead. Moreover, the sparse channel matrices of the cascaded channels of all users have a common block sparsity structure due to the common channel between the BS and the RIS. To take advantage of the common sparsity pattern, we propose a two-step multi-user joint channel estimation procedure. In the first step, we make use of the common column-block sparsity and project the received signals onto the common column subspace. In the second step, we make use of the row-block sparsity of the projected signals and propose a multi-user joint sparse matrix recovery algorithm that takes into account the common channel between the BS and the RIS.

eess.SP↗

arXiv2022

See, Plan, Predict: Language-guided Cognitive Planning with Video Prediction

Cognitive planning is the structural decomposition of complex tasks into a sequence of future behaviors. In the computational setting, performing cognitive planning entails grounding plans and concepts in one or more modalities in order to leverage them for low level control. Since real-world tasks are often described in natural language, we devise a cognitive planning algorithm via language-guided video prediction. Current video prediction models do not support conditioning on natural language instructions. Therefore, we propose a new video prediction architecture which leverages the power of pre-trained transformers.The network is endowed with the ability to ground concepts based on natural language input with generalization to unseen objects. We demonstrate the effectiveness of this approach on a new simulation dataset, where each task is defined by a high-level action described in natural language. Our experiments compare our method again stone video generation baseline without planning or action grounding and showcase significant improvements. Our ablation studies highlight an improved generalization to unseen objects that natural language embeddings offer to concept grounding ability, as well as the importance of planning towards visual "imagination" of a task.

cs.AI↗

arXiv2022

Role of Deep Learning in Wireless Communications

Traditional communication system design has always been based on the paradigm of first establishing a mathematical model of the communication channel, then designing and optimizing the system according to the model. The advent of modern machine learning techniques, specifically deep neural networks, has opened up opportunities for data-driven system design and optimization. This article draws examples from the optimization of reconfigurable intelligent surface, distributed channel estimation and feedback for multiuser beamforming, and active sensing for millimeter wave (mmWave) initial alignment to illustrate that a data-driven design that bypasses explicit channel modelling can often discover excellent solutions to communication system design and optimization problems that are otherwise computationally difficult to solve. We show that by performing an end-to-end training of a deep neural network using a large number of channel samples, a machine learning based approach can potentially provide significant system-level improvements as compared to the traditional model-based approach for solving optimization problems. The key to the successful applications of machine learning techniques is in choosing the appropriate neural network architecture to match the underlying problem structure.

cs.IT↗

arXiv2022

High energy Millihertz quasi-periodic oscillations in 1A 0535+262 with Insight-HXMT challenge current models

We studied the millihertz quasi-periodic oscillation (mHz QPO) in the 2020 outburst of the Be/X-ray binary 1A 0535+262 using Insight-HXMT data over a broad energy band. The mHz QPO is detected in the 27-120 keV energy band. The QPO centroid frequency is correlated with the source flux, and evolves in the 35-95 mHz range during the outburst. The QPO is most significant in the 50-65 keV band, with a significance of ~ 8 sigma, but is hardly detectable (<2 sigma) in the lowest (1-27 keV) and highest (>120 keV) energy bands. Notably, the detection of mHz QPO above 80 keV is the highest energy at which mHz QPOs have been detected so far. The fractional rms of the mHz QPO first increases and then decreases with energy, reaching the maximum amplitude at 50-65 keV. In addition, at the peak of the outburst, the mHz QPO shows a double-peak structure, with the difference between the two peaks being constant at ~0.02 Hz, twice the spin frequency of the neutron star in this system. We discuss different scenarios explaining the generation of the mHz QPO, including the beat frequency model, the Keplerian frequency model, the model of two jets in opposite directions, and the precession of the neutron star, but find that none of them can explain the origin of the QPO well. We conclude that the variability of non-thermal radiation may account for the mHz QPO, but further theoretical studies are needed to reveal the physical mechanism.

astro-ph.HE↗

arXiv2022

An Insight-HXMT view of the mHz quasi-regular modulation phenomenon in the black hole X-ray binary 4U 1630-47

Here we report the spectral-timing results of the black hole X-ray binary 4U 1630-47 during its 2021 outburst using observations from the Hard X-ray Modulation Telescope. Type-C quasi-periodic oscillations (QPOs) in 1.6--4.2 Hz and quasi-regular modulation (QRM) near 60 mHz are detected during the outburst. The mHz QRM has a fractional rms of 10%--16% in the 8--35 keV energy band with a Q factor (frequency/width) of 2--4. Benefiting from the broad energy band of hxmt, we study the energy dependence of the 60 mHz QRM in 1--100 keV for the first time. We find that the fractional rms of the mHz QRM increases with photon energy, while the time lags of the mHz QRM are soft and decrease with photon energy. Fast recurrence of the mHz QRM, in a timescale of less than one hour, has been observed during the outburst. During this period, the corresponding energy spectra moderately change when the source transitions from the QRM state to the non-QRM state. The QRM phenomena also shows a dependence with the accretion rate. We suggest that the QRM could be caused by an unknown accretion instability aroused from the corona.

astro-ph.HE↗

arXiv2022

Scheduling Versus Contention for Massive Random Access in Massive MIMO Systems

Massive machine-type communications protocols have typically been designed under the assumption that coordination between users requires significant communication overhead and is thus impractical. Recent progress in efficient activity detection and collision-free scheduling, however, indicates that the cost of coordination can be much less than the naive scheme for scheduling. This work considers a scenario in which a massive number of devices with sporadic traffic seek to access a massive multiple-input multiple-output (MIMO) base-station (BS) and explores an approach in which device activity detection is followed by a single common feedback broadcast message, which is used both to schedule the active users to different transmission slots and to assign orthogonal pilots to the users for channel estimation. The proposed coordinated communication scheme is compared to two prevalent contention-based schemes: coded pilot access, which is based on the principle of coded slotted ALOHA, and an approximate message passing scheme for joint user activity detection and channel estimation. Numerical results indicate that scheduled massive access provides significant gains in the number of successful transmissions per slot and in sum rate, due to the reduced interference, at only a small cost of feedback.

cs.IT↗

arXiv2022

Deep Learning for Channel Sensing and Hybrid Precoding in TDD Massive MIMO OFDM Systems

This paper proposes a deep learning approach to channel sensing and downlink hybrid beamforming for massive multiple-input multiple-output systems operating in the time division duplex mode and employing either single-carrier or multicarrier transmission. The conventional precoding design involves a two-step process of first estimating the high-dimensional channel, then designing the precoders based on such estimate. This two-step process is, however, not necessarily optimal. This paper shows that by using a learning approach to design the analog sensing and the hybrid downlink precoders directly from the received pilots without the intermediate high-dimensional channel estimation, the overall system performance can be significantly improved. Training a neural network to design the analog and digital precoders simultaneously is, however, difficult. Further, such an approach is not generalizable to systems with different number of users. In this paper, we develop a simplified and generalizable approach that learns the uplink sensing matrix and downlink analog precoder using a deep neural network that decomposes on a per-user basis, then designs the digital precoder based on the estimated low-dimensional equivalent channel. Numerical comparisons show that the proposed methodology results in significantly less training overhead and leads to an architecture that generalizes to various system settings.

cs.IT↗

arXiv2022

Orbital hybridization and electrostatic interaction in a double molecule transistor

Understanding the intermolecular interactions and utilize these interactions to effectively control the transport behavior of single molecule is the key step from single molecule device to molecular circuits1-6. Although many single molecule detection techniques are used to detect the molecular interaction at single-molecule level1,4,5,7,8, probing and tuning the intermolecular interaction all by electrical approaches has not been demonstrated. In this work, we successful assemble a double molecule transistor incorporating two manganese phthalocyanine molecules, on which we probe and tune the interaction in situ by implementing electrical manipulation on molecular orbitals using gate voltage. Orbital levels of the two molecules couple to each other and couple to the universal gate differently. Electrostatic interaction is observed when single electron changing in one molecule alters the transport behavior of the other, providing the information about the dynamic process of electron sequent tunneling through a molecule. Orbital hybridization is found when two orbital levels are put into degeneracy under non-equilibrium condition, making the tunneling electrons no longer localized to a specific molecule but shared by two molecules, offering a new mechanism to control charge transfer between non-covalent molecules. Current work offer a forelook into working principles of functional electrical unit based on single molecules.

cond-mat.mes-hall↗

arXiv2022

Learning Based User Scheduling in Reconfigurable Intelligent Surface Assisted Multiuser Downlink

Reconfigurable intelligent surface (RIS) is capable of intelligently manipulating the phases of the incident electromagnetic wave to improve the wireless propagation environment between the base-station (BS) and the users. This paper addresses the joint user scheduling, RIS configuration, and BS beamforming problem in an RIS-assisted downlink network with limited pilot overhead. We show that graph neural networks (GNN) with permutation invariant and equivariant properties can be used to appropriately schedule users and to design RIS configurations to achieve high overall throughput while accounting for fairness among the users. As compared to the conventional methodology of first estimating the channels then optimizing the user schedule, RIS configuration and the beamformers, this paper shows that an optimized user schedule can be obtained directly from a very short set of pilots using a GNN, then the RIS configuration can be optimized using a second GNN, and finally the BS beamformers can be designed based on the overall effective channel. Numerical results show that the proposed approach can utilize the received pilots more efficiently than the conventional channel estimation based approach, and can generalize to systems with an arbitrary number of users.

eess.SP↗

arXiv2022

Modular Action Concept Grounding in Semantic Video Prediction

Recent works in video prediction have mainly focused on passive forecasting and low-level action-conditional prediction, which sidesteps the learning of interaction between agents and objects. We introduce the task of semantic action-conditional video prediction, which uses semantic action labels to describe those interactions and can be regarded as an inverse problem of action recognition. The challenge of this new task primarily lies in how to effectively inform the model of semantic action information. Inspired by the idea of Mixture of Experts, we embody each abstract label by a structured combination of various visual concept learners and propose a novel video prediction model, Modular Action Concept Network (MAC). Our method is evaluated on two newly designed synthetic datasets, CLEVR-Building-Blocks and Sapien-Kitchen, and one real-world dataset called Tower-Creation. Extensive experiments demonstrate that MAC can correctly condition on given instructions and generate corresponding future frames without need of bounding boxes. We further show that the trained model can make out-of-distribution generalization, be quickly adapted to new object categories and exploit its learnt features for object detection, showing the progression towards higher-level cognitive abilities. More visualizations can be found at http://www.pair.toronto.edu/mac/.

cs.CV↗

arXiv2022

The accretion flow geometry of MAXI J1820+070 through broadband noise research with Insight-HXMT

Here we present a detailed study of the broadband noise in the power density spectra of the black hole X-ray binary MAXI J1820+070 during the hard state of its 2018 outburst, using the Hard X-ray Modulation Telescope (Insight-HXMT) observations. The broadband noise shows two main humps, which might separately correspond to variability from a variable disk and two Comptonization regions. We fitted the two humps with multiple Lorentzian functions and studied the energy-dependent properties of each component up to 100--150 keV and their evolution with spectral changes. The lowest frequency component is considered as the sub-harmonic of QPO component and shows different energy dependence compared with other broadband noise components. We found that although the fractional rms of all the broadband noise components mainly decrease with energy, their rms spectra are different in shape. Above $\sim$ 20--30 keV, the characteristic frequencies of these components increase sharply with energy, meaning that the high-energy component is more variable on short timescales. Our results suggest that the hot inner flow in MAXI J1820+070 is likely to be inhomogeneous. We propose a geometry with a truncated accretion disk, two Comptonization regions.

astro-ph.HE↗

arXiv2022

Quasi-periodic oscillations of the X-ray burst from the magnetar SGR J1935+2154 and associated with the fast radio burst FRB 200428

The origin(s) and mechanism(s) of fast radio bursts (FRBs), which are short radio pulses from cosmological distances, have remained a major puzzle since their discovery. We report a strong Quasi-Periodic Oscillation(QPO) of 40 Hz in the X-ray burst from the magnetar SGR J1935+2154 and associated with FRB 200428, significantly detected with the Hard X-ray Modulation Telescope (Insight-HXMT) and also hinted by the Konus-Wind data. QPOs from magnetar bursts have only been rarely detected; our 3.4 sigma (p-value is 2.9e-4) detection of the QPO reported here reveals the strongest QPO signal observed from magnetars (except in some very rare giant flares), making this X-ray burst unique among magnetar bursts. The two X-ray spikes coinciding with the two FRB pulses are also among the peaks of the QPO. Our results suggest that at least some FRBs are related to strong oscillation processes of neutron stars. We also show that we may overestimate the significance of the QPO signal and underestimate the errors of QPO parameters if QPO exists only in a fraction of the time series of a X-ray burst which we use to calculate the Leahy-normalized periodogram.

astro-ph.HE↗

arXiv2022

Energy Efficient HARQ for Ultrareliability via Novel Outage Probability Bound and Geometric Programming

Hybrid automatic repeat 1 request (HARQ) is a key enabler for ultrareliable communications. This paper optimizes transmit power for the initial transmission and the subsequent retransmissions of HARQ with either incremental redundancy or Chase combining, aiming to minimize the expected energy consumption given the target outage probability and the target latency. The main challenge is due to the fact that the outage probability is a complicated function of the power variables which are nested in successive convolutions. The existing works mostly use a classic upper bound to approximate the outage probability by assuming unbounded transmit power, then convert the original problem to a geometric programming (GP) problem. In contrast, we propose a novel and much tighter upper bound by taking the practical power limit into consideration. The new bound and the resulting new GP method are further extended to a broader group of channel models with various fading, multiple antennas, and multiple receivers. As shown in simulations, the GP method based on the new bound significantly outperforms the existing strategies that either fix transmit power or optimize power by the classic bounding technique.

cs.IT↗