Searcharxiv⌕ Search

arXiv subjects

Tao Jiang

Publications and source records attributed to Tao Jiang.

At least 163 records · Page 9Linked to original sources
arXiv2023

Active Sensing for Localization with Reconfigurable Intelligent Surface

This paper addresses an uplink localization problem in which the base station (BS) aims to locate a remote user with the aid of reconfigurable intelligent surface (RIS). This paper proposes a strategy in which the user transmits pilots over multiple time frames, and the BS adaptively adjusts the RIS reflection coefficients based on the observations already received so far in order to produce an accurate estimate of the user location at the end. This is a challenging active sensing problem for which finding an optimal solution involves a search through a complicated functional space whose dimension increases with the number of measurements. In this paper, we show that the long short-term memory (LSTM) network can be used to exploit the latent temporal correlation between measurements to automatically construct scalable information vectors (called hidden state) based on the measurements. Subsequently, the state vector can be mapped to the RIS configuration for the next time frame in a codebook-free fashion via a deep neural network (DNN). After all the measurements have been received, a final DNN can be used to map the LSTM cell state to the estimated user equipment (UE) position. Numerical result shows that the proposed active RIS design results in lower localization error as compared to existing active and nonactive methods. The proposed solution produces interpretable results and is generalizable to early stopping in the sequence of sensing stages.

eess.SP↗
arXiv2023

Unidirectional guided-wave-driven metasurfaces for arbitrary wavefront control

Metasurfaces, composed of subwavelength electromagnetic microstructures, known as meta-atoms, are capable of reshaping the wavefronts of incident beams in desired manners, making them great candidates for revolutionizing conventional optics. However, the requirement for external light excitation and the resonant nature of meta-atoms make it difficult to fully integrate metasurfaces on-chip or to control wavefronts at deep-subwavelength scales. Here, we introduce the concept and design of a new class of metasurfaces, driven by unidirectional guided waves, and being capable of arbitrary wavefront control based on the unique dispersion properties of unidirectional guided waves rather than resonant meta-atoms. Upon experimentally demonstrating the feasibility and practicality of the unidirectional nature of our designs in the microwave regime, we numerically validate this new principle through the design of several microwave meta-devices using metal-air-gyromagnetic unidirectional surface magnetoplamons, agilely converting unidirectional guided modes into the wavefronts of 3D Bessel beams, focused waves, and controllable vortex beams. We also numerically demonstrate sub-diffraction focusing, which is currently beyond the capability of conventional metasurfaces. Furthermore, we directly show how these concepts can be transferred to the terahertz regime, and discuss their feasibility in the optical domain, too. Based on this nonresonant (that is, broadband) mechanism and on standard plasmonic platforms, our metasurfaces can be integrated on-chip, enabling the manipulation of electromagnetic waves on deep subwavelength scales and over wide frequency ranges, thereby opening up new opportunities for applications in communications, remote sensing, displays, and so forth.

physics.optics↗
arXiv2023

Rainbow subdivisions of cliques

We show that for every integer $m \ge 2$ and large $n$, every properly edge-coloured graph on $n$ vertices with at least $n (\log n)^{53}$ edges contains a rainbow subdivision of $K_m$. This is sharp up to a polylogarithmic factor. Our proof method exploits the connection between the mixing time of random walks and expansion in graphs.

math.CO↗
arXiv2023

Ab-initio Simulations of Coherent Phonon-Induced Pumping of Carriers in Zirconium Pentatelluride

Laser-driven coherent phonons can act as modulated strain fields and modify the adiabatic ground state topology of quantum materials. Here we use time-dependent first-principles and effective model calculations to simulate the effect of the coherent phonon induced by strong terahertz electric field on electronic carriers in the topological insulator ZrTe$_5$. We show that a coherent $A_\text{1g}$ Raman mode modulation can effectively pump carriers across the band gap, even though the phonon energy is about an order of magnitude smaller than the equilibrium band gap. We reveal the microscopic mechanism of this effect which occurs via Landau-Zener-Stückelberg tunneling of Bloch electrons in a narrow region in the Brillouin zone center where the transient energy gap closes when the system switches from strong to weak topological insulator. The quantum dynamics simulation results are in excellent agreement with recent pump-probe experiments in ZrTe$_5$ at low temperature.

cond-mat.mtrl-sci↗
arXiv2023

Chirality manipulation of ultrafast phase switchings in a correlated CDW-Weyl semimetal

A recently emerging concept for quantum phase discovery is the controlled gapping of linear band crossings in topological semimetals. For example, achieving topological superconducting and charge-density-wave (CDW) gapping could introduce Majorana zero modes and axion electrodynamics, respectively. Light engineering of correlation gaps in topological materials provides a new avenue of achieving exotic topological phases inaccessible by conventional tuning methods such as doping and straining. Here we demonstrate a light control of correlation gaps and ultrafast phase switchings in a model CDW and polaron insulator (TaSe$_4$)$_2$I recently predicted to be an axion insulator. Our ultrafast terahertz photocurrent spectroscopy reveals a two-step, non-thermal melting of polarons and electronic CDW gap via studying the fluence dependence of a {\em longitudinal} circular photogalvanic current. The helicity-dependent photocurrent observed along the propagation of light reveals continuous ultrafast switchings from the polaronic state, to the CDW (axion) phase, and finally to a hidden Weyl phase as the pump fluence increases. Other distinguishing features corroborating with the light-induced switchings include: mode-selective coupling of coherent phonons to polaron and CDW modulation, and the emergence of a {\em non-thermal} chiral photocurrent above pump threshold of CDW-related phonons. The ultrafast chirality control of correlated topological states revealed here is important to realize axion electrodynamics and quantum computing.

cond-mat.str-el↗
arXiv2023

Triple Correlations-Guided Label Supplementation for Unbiased Video Scene Graph Generation

Video-based scene graph generation (VidSGG) is an approach that aims to represent video content in a dynamic graph by identifying visual entities and their relationships. Due to the inherently biased distribution and missing annotations in the training data, current VidSGG methods have been found to perform poorly on less-represented predicates. In this paper, we propose an explicit solution to address this under-explored issue by supplementing missing predicates that should be appear in the ground-truth annotations. Dubbed Trico, our method seeks to supplement the missing predicates by exploring three complementary spatio-temporal correlations. Guided by these correlations, the missing labels can be effectively supplemented thus achieving an unbiased predicate predictions. We validate the effectiveness of Trico on the most widely used VidSGG datasets, i.e., VidVRD and VidOR. Extensive experiments demonstrate the state-of-the-art performance achieved by Trico, particularly on those tail predicates.

cs.CV↗
arXiv2023

RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose

Recent studies on 2D pose estimation have achieved excellent performance on public benchmarks, yet its application in the industrial community still suffers from heavy model parameters and high latency. In order to bridge this gap, we empirically explore key factors in pose estimation including paradigm, model architecture, training strategy, and deployment, and present a high-performance real-time multi-person pose estimation framework, RTMPose, based on MMPose. Our RTMPose-m achieves 75.8% AP on COCO with 90+ FPS on an Intel i7-11700 CPU and 430+ FPS on an NVIDIA GTX 1660 Ti GPU, and RTMPose-l achieves 67.0% AP on COCO-WholeBody with 130+ FPS. To further evaluate RTMPose's capability in critical real-time applications, we also report the performance after deploying on the mobile device. Our RTMPose-s achieves 72.2% AP on COCO with 70+ FPS on a Snapdragon 865 chip, outperforming existing open-source libraries. Code and models are released at https://github.com/open-mmlab/mmpose/tree/1.x/projects/rtmpose.

cs.CV↗
arXiv2023

Carbon emissions and sustainability of launching 5G mobile networks in China

Since 2021, China has deployed more than 2.1 million 5G base stations to increase the network capacity and provide ubiquitous digital connectivity for mobile terminals. However, the launch of 5G networks also exacerbates the misalignment between cellular traffic and energy consumption, which reduces carbon efficiency - the amount of network traffic that can be delivered for each unit of carbon emission. In this study, we develop a large-scale data-driven framework to estimate the carbon emissions induced by mobile networks. We show that the decline in carbon efficiency leads to a carbon efficiency trap, estimated to cause additional carbon emissions of 23.82 +- 1.07 megatons in China. To mitigate the misalignment and improve energy efficiency, we propose DeepEnergy, an energy-saving method leveraging collaborative deep reinforcement learning and graph neural networks. DeepEnergy models complex collaboration among cells, making it possible to effectively coordinate the working state of tens of thousands of cells, which could help over 71% of Chinese provinces avoid carbon efficiency traps. In addition, applying DeepEnergy is estimated to reduce 20.90 +- 0.98 megatons of carbon emissions at the national level in 2023. We further assess the effects of adopting renewable energy and discover that the mobile network could accomplish more than 50% of its net-zero goal by integrating DeepEnergy and solar energy systems. Our study provides insight into carbon emission mitigation in 5G network infrastructure launching in China and overworld, paving the way towards achieving sustainable development goals and future net-zero mobile networks.

eess.SY↗
arXiv2023

Active Sensing for Two-Sided Beam Alignment and Reflection Design Using Ping-Pong Pilots

Beam alignment is an important task for millimeter-wave (mmWave) communication, because constructing aligned narrow beams both at the transmitter (Tx) and the receiver (Rx) is crucial in terms of compensating the significant path loss in very high-frequency bands. However, beam alignment is also a highly nontrivial task because large antenna arrays typically have a limited number of radio-frequency chains, allowing only low-dimensional measurements of the high-dimensional channel. This paper considers a two-sided beam alignment problem based on an alternating ping-pong pilot scheme between Tx and Rx over multiple rounds without explicit feedback. We propose a deep active sensing framework in which two long short-term memory (LSTM) based neural networks are employed to learn the adaptive sensing strategies (i.e., measurement vectors) and to produce the final aligned beamformers at both sides. In the proposed ping-pong protocol, the Tx and the Rx alternately send pilots so that both sides can leverage local observations to sequentially design their respective sensing and data transmission beamformers. The proposed strategy can be extended to scenarios with a reconfigurable intelligent surface (RIS) for designing, in addition, the reflection coefficients at the RIS for both sensing and communications. Numerical experiments demonstrate significant and interpretable performance improvement. The proposed strategy works well even for the challenging multipath channel environments.

cs.IT↗
arXiv2023

Robust Multi-agent Communication via Multi-view Message Certification

Many multi-agent scenarios require message sharing among agents to promote coordination, hastening the robustness of multi-agent communication when policies are deployed in a message perturbation environment. Major relevant works tackle this issue under specific assumptions, like a limited number of message channels would sustain perturbations, limiting the efficiency in complex scenarios. In this paper, we take a further step addressing this issue by learning a robust multi-agent communication policy via multi-view message certification, dubbed CroMAC. Agents trained under CroMAC can obtain guaranteed lower bounds on state-action values to identify and choose the optimal action under a worst-case deviation when the received messages are perturbed. Concretely, we first model multi-agent communication as a multi-view problem, where every message stands for a view of the state. Then we extract a certificated joint message representation by a multi-view variational autoencoder (MVAE) that uses a product-of-experts inference network. For the optimization phase, we do perturbations in the latent space of the state for a certificate guarantee. Then the learned joint message representation is used to approximate the certificated state representation during training. Extensive experiments in several cooperative multi-agent benchmarks validate the effectiveness of the proposed CroMAC.

cs.MA↗
arXiv2023

Taking A Closer Look at Visual Relation: Unbiased Video Scene Graph Generation with Decoupled Label Learning

Current video-based scene graph generation (VidSGG) methods have been found to perform poorly on predicting predicates that are less represented due to the inherent biased distribution in the training data. In this paper, we take a closer look at the predicates and identify that most visual relations (e.g. sit_above) involve both actional pattern (sit) and spatial pattern (above), while the distribution bias is much less severe at the pattern level. Based on this insight, we propose a decoupled label learning (DLL) paradigm to address the intractable visual relation prediction from the pattern-level perspective. Specifically, DLL decouples the predicate labels and adopts separate classifiers to learn actional and spatial patterns respectively. The patterns are then combined and mapped back to the predicate. Moreover, we propose a knowledge-level label decoupling method to transfer non-target knowledge from head predicates to tail predicates within the same pattern to calibrate the distribution of tail classes. We validate the effectiveness of DLL on the commonly used VidSGG benchmark, i.e. VidVRD. Extensive experiments demonstrate that the DLL offers a remarkably simple but highly effective solution to the long-tailed problem, achieving the state-of-the-art VidSGG performance.

cs.CV↗
arXiv2023

Thickness dependence of superconductivity in FeSe films

The films of FeSe on substrates have attracted attention because of their unusually high-temperature (Tc) superconducting properties whose origins continue to be debated. To disentangle the competing effects of the substrate and interlayer and intralayer processes, we present here results of density functional theory (DFT)-based analysis of the electronic structure of unsupported FeSe films consisting of 1 to 5 layers (1L-5L). Furthermore, by solving the Bardeen-Schrieffer-Cooper (BCS) equation with spin-wave exchange attraction derived from the Hubbard model, we find the superconducting critical temperature Tc for 1L-5L and bulk FeSe systems in reasonable agreement with experimental data. Our results point to the importance of correlation effects in superconducting properties of single- and multi-layer FeSe films, independently of the role of substrate.

cond-mat.supr-con↗
arXiv2023

Negligible obstructions and Turán exponents

We show that for every rational number $r \in (1,2)$ of the form $2 - a/b$, where $a, b \in \mathbb{N}^+$ satisfy $\lfloor b/a \rfloor^3 \le a \le b / (\lfloor b/a \rfloor +1) + 1$, there exists a graph $F_r$ such that the Turán number $\operatorname{ex}(n, F_r) = Θ(n^r)$. Our result in particular generates infinitely many new Turán exponents. As a byproduct, we formulate a framework that is taking shape in recent work on the Bukh--Conlon conjecture.

math.CO↗
arXiv2023

Re-embedding data to strengthen recovery guarantees of clustering

We propose a clustering method that involves chaining four known techniques into a pipeline yielding an algorithm with stronger recovery guarantees than any of the four components separately. Given $n$ points in $\mathbb R^d$, the first component of our pipeline, which we call leapfrog distances, is reminiscent of density-based clustering, yielding an $n\times n$ distance matrix. The leapfrog distances are then translated to new embeddings using multidimensional scaling and spectral methods, two other known techniques, yielding new embeddings of the $n$ points in $\mathbb R^{d'}$, where $d'$ satisfies $d'\ll d$ in general. Finally, sum-of-norms (SON) clustering is applied to the re-embedded points. Although the fourth step (SON clustering) can in principle be replaced by any other clustering method, our focus is on provable guarantees of recovery of underlying structure. Therefore, we establish that the re-embedding improves recovery SON clustering, since SON clustering is a well-studied method that already has provable guarantees.

cs.LG↗
arXiv2023

Towards A Unified Conformer Structure: from ASR to ASV Task

Transformer has achieved extraordinary performance in Natural Language Processing and Computer Vision tasks thanks to its powerful self-attention mechanism, and its variant Conformer has become a state-of-the-art architecture in the field of Automatic Speech Recognition (ASR). However, the main-stream architecture for Automatic Speaker Verification (ASV) is convolutional Neural Networks, and there is still much room for research on the Conformer based ASV. In this paper, firstly, we modify the Conformer architecture from ASR to ASV with very minor changes. Length-Scaled Attention (LSA) method and Sharpness-Aware Minimizationis (SAM) are adopted to improve model generalization. Experiments conducted on VoxCeleb and CN-Celeb show that our Conformer based ASV achieves competitive performance compared with the popular ECAPA-TDNN. Secondly, inspired by the transfer learning strategy, ASV Conformer is natural to be initialized from the pretrained ASR model. Via parameter transferring, self-attention mechanism could better focus on the relationship between sequence features, brings about 11% relative improvement in EER on test set of VoxCeleb and CN-Celeb, which reveals the potential of Conformer to unify ASV and ASR task. Finally, we provide a runtime in ASV-Subtools to evaluate its inference speed in production scenario. Our code is released at https://github.com/Snowdar/asv-subtools/tree/master/doc/papers/conformer.md.

eess.AS↗
arXiv2023

Bipartite-ness under smooth conditions

Given a family $\mathcal{F}$ of bipartite graphs, the {\it Zarankiewicz number} $z(m,n,\mathcal{F})$ is the maximum number of edges in an $m$ by $n$ bipartite graph $G$ that does not contain any member of $\mathcal{F}$ as a subgraph (such $G$ is called {\it $\mathcal{F}$-free}). For $1\leq β<α<2$, a family $\mathcal{F}$ of bipartite graphs is $(α,β)$-{\it smooth} if for some $ρ>0$ and every $m\leq n$, $z(m,n,\mathcal{F})=ρm n^{α-1}+O(n^β)$. Motivated by their work on a conjecture of Erdős and Simonovits on compactness and a classic result of Andrásfai, Erdős and Sós, in \cite{AKSV} Allen, Keevash, Sudakov and Verstraëte proved that for any $(α,β)$-smooth family $\mathcal{F}$, there exists $k_0$ such that for all odd $k\geq k_0$ and sufficiently large $n$, any $n$-vertex $\mathcal{F}\cup\{C_k\}$-free graph with minimum degree at least $ρ(\frac{2n}{5}+o(n))^{α-1}$ is bipartite. In this paper, we strengthen their result by showing that for every real $δ>0$, there exists $k_0$ such that for all odd $k\geq k_0$ and sufficiently large $n$, any $n$-vertex $\mathcal{F}\cup\{C_k\}$-free graph with minimum degree at least $δn^{α-1}$ is bipartite. Furthermore, our result holds under a more relaxed notion of smoothness, which include the families $\mathcal{F}$ consisting of the single graph $K_{s,t}$ when $t\gg s$. We also prove an analogous result for $C_{2\ell}$-free graphs for every $\ell\geq 2$, which complements a result of Keevash, Sudakov and Verstraëte in \cite{KSV}.

math.CO↗
arXiv2023

Rainbow Turán number of clique subdivisions

We show that for any integer $t\geq 2$, every properly edge-coloured graph on $n$ vertices with more than $n^{1+o(1)}$ edges contains a rainbow subdivision of $K_t$. Note that this bound on the number of edges is sharp up to the $o(1)$ error term. This is a rainbow analogue of some classical results on clique subdivisions and extends some results on rainbow Turán numbers. Our method relies on the framework introduced by Sudakov and Tomon[2020] which we adapt to find robust expanders in the coloured setting.

math.CO↗
arXiv2022

A linearly convergent Gauss-Newton subgradient method for ill-conditioned problems

We analyze a preconditioned subgradient method for optimizing composite functions $h \circ c$, where $h$ is a locally Lipschitz function and $c$ is a smooth nonlinear mapping. We prove that when $c$ satisfies a constant rank property and $h$ is semismooth and sharp on the image of $c$, the method converges linearly. In contrast to standard subgradient methods, its oracle complexity is invariant under reparameterizations of $c$.

math.OC↗