arXiv subjects

Tao Jiang

Publications and source records attributed to Tao Jiang.

At least 271 records · Page 15Linked to original sources

arXiv2019

Turan numbers of bipartite subdivisions

Given a graph $H$, the Turán number $ex(n,H)$ is the largest number of edges in an $H$-free graph on $n$ vertices. We make progress on a recent conjecture of Conlon, Janzer, and Lee on the Turán numbers of bipartite graphs, which in turn yields further progress on a conjecture of Erdős and Simonovits. Let $s,t,k\geq 2$ be integers. Let $K_{s,t}^k$ denote the graph obtained from the complete bipartite graph $K_{s,t}$ by replacing each edge $uv$ in it with a path of length $k$ between $u$ and $v$ such that the $st$ replacing paths are internally disjoint. It follows from a general theorem of Bukh and Conlon that $ex(n,K_{s,t}^k)=Ω(n^{1+\frac{1}{k}-\frac{1}{sk}})$. Conlon, Janzer, and Lee recently conjectured that for any integers $s,t,k\geq 2$, $ex(n,K_{s,t}^k)=O(n^{1+\frac{1}{k}-\frac{1}{sk}})$. Among many other things, they settled the $k=2$ case of their conjecture. As the main result of this paper, we prove their conjecture for $k=3,4$. Our main results also yield infinitely many new so-called Turán exponents: rationals $r\in (1,2)$ for which there exists a bipartite graph $H$ with $ex(n, H)=Θ(n^r)$, adding to the lists recently obtained by Jiang, Ma, Yepremyan, by Kang, Kim, Liu, and by Conlon, Janzer, Lee. Our method builds on an extension of the Conlon-Janzer-Lee method. We also note that the extended method also gives a weaker version of the Conlon-Janzer-Lee conjecture for all $k\geq 2$.

math.CO↗

arXiv2019

Training Multi-Task Adversarial Network for Extracting Noise-Robust Speaker Embedding

Under noisy environments, to achieve the robust performance of speaker recognition is still a challenging task. Motivated by the promising performance of multi-task training in a variety of image processing tasks, we explore the potential of multi-task adversarial training for learning a noise-robust speaker embedding. In this paper we present a novel framework which consists of three components: an encoder that extracts noise-robust speaker embedding; a classifier that classifies the speakers; a discriminator that discriminates the noise type of the speaker embedding. Besides, we propose a training strategy using the training accuracy as an indicator to stabilize the multi-class adversarial optimization process. We conduct our experiments on the English and Mandarin corpus and the experimental results demonstrate that our proposed multi-task adversarial training method could greatly outperform the other methods without adversarial training in noisy environments. Furthermore, experiments indicate that our method is also able to improve the speaker verification performance the clean condition.

cs.SD↗

arXiv2019

Over-the-Air Computation via Intelligent Reflecting Surfaces

Over-the-air computation (AirComp) becomes a promising approach for fast wireless data aggregation via exploiting the superposition property in a multiple access channel. To further overcome the unfavorable signal propagation conditions for AirComp, in this paper, we propose an intelligent reflecting surface (IRS) aided AirComp system to build controllable wireless environments, thereby boosting the received signal power significantly. This is achieved by smartly tuning the phase shifts for the incoming electromagnetic waves at IRS, resulting in reconfigurable signal propagations. Unfortunately, it turns out that the joint design problem for AirComp transceivers and IRS phase shifts becomes a highly intractable nonconvex bi-quadratic programming problem, for which a novel alternating difference-of-convex (DC) programming algorithm is developed. This is achieved by providing a novel DC function representation for the rank-one constraint in the low-rank matrix optimization problem via matrix lifting. Simulation results demonstrate the algorithmic advantages and admirable performance of the proposed approaches compared with the state-of-art solutions.

cs.IT↗

arXiv2019

KPI/KQI-Driven Coordinated Multi-Point in 5G: Measurements, Field Trials, and Technical Solutions

The fifth generation (5G) systems are expected to be able to support massive number of wireless devices and intense demands for high data rates while maintaining low latency. Coordinated multipoint (CoMP) is advocated by recent advances and is envisioned to continue its adoption in 5G to meet these requirements by alleviating inter-cell interference and improving spectral efficiency. The higher requirements in 5G have raised the stakes on developing a new CoMP architecture. To understand the merits and limitations of CoMP in 5G, this article systematically investigates evaluation criteria including key performance indicators (KPIs) and key quality indicators (KQIs) in 5G, conducts empirical measurements and field tests, and then proposes a KPI/KQI-driven CoMP architecture that fulfills KPI requirements and provides KQI guarantee for each user.

cs.NI↗

arXiv2019

Towards Motion Invariant Authentication for On-Body IoT Devices

As the rapid proliferation of on-body Internet of Things (IoT) devices, their security vulnerabilities have raised serious privacy and safety issues. Traditional efforts to secure these devices against impersonation attacks mainly rely on either dedicated sensors or specified user motions, impeding their wide-scale adoption. This paper transcends these limitations with a general security solution by leveraging ubiquitous wireless chips available in IoT devices. Particularly, representative time and frequency features are first extracted from received signal strengths (RSSs) to characterize radio propagation profiles. Then, an adversarial multi-player network is developed to recognize underlying radio propagation patterns and facilitate on-body device authentication. We prove that at equilibrium, our adversarial model can extract all information about propagation patterns and eliminate any irrelevant information caused by motion variances. We build a prototype of our system using universal software radio peripheral (USRP) devices and conduct extensive experiments with both static and dynamic body motions in typical indoor and outdoor environments. The experimental results show that our system achieves an average authentication accuracy of 90.4%, with a high area under the receiver operating characteristic curve (AUROC) of 0.958 and better generalization performance in comparison with the conventional non-adversarial-based approach.

cs.CR↗

arXiv2019

Attentional Encoder Network for Targeted Sentiment Classification

Targeted sentiment classification aims at determining the sentimental tendency towards specific targets. Most of the previous approaches model context and target words with RNN and attention. However, RNNs are difficult to parallelize and truncated backpropagation through time brings difficulty in remembering long-term patterns. To address this issue, this paper proposes an Attentional Encoder Network (AEN) which eschews recurrence and employs attention based encoders for the modeling between context and target. We raise the label unreliability issue and introduce label smoothing regularization. We also apply pre-trained BERT to this task and obtain new state-of-the-art results. Experiments and analysis demonstrate the effectiveness and lightweight of our model.

cs.CL↗

arXiv2019

Cluster Pairwise Error Probability and Construction of Parity-Check-Concatenated Polar Codes

A successive cancellation list (SCL) decoder with limited list size for polar codes can not be analyzed as a successive cancellation (SC) decoder, nor as a maximum likelihood (ML) decoder, due to the complicated decoding errors caused by path elimination. To address this issue, an analytical tool, named as cluster pairwise error probability (CPEP), is proposed in this paper to measure the competitiveness of the correct path against the error paths in an SCL decoder. It is shown that the sum of CPEPs over error paths could be used as an indicator of the probability of correct path being eliminated from the decoder list. Then, we use CPEP to explain the error performance gain of parity-check-concatenated (PCC) polar code, and apply CPEP as the optimization criterion in the construction of PCC polar codes, aiming to reduce the elimination probability of the correct path in an SCL decoder with limited list size. Simulation results show that the constructed CRC-PCC polar codes outperform their counterparts of CRC-concatenated polar codes over various codeword lengths, code rates and puncturing patterns.

cs.IT↗

arXiv2019

Cache-Aided Massive MIMO: Linear Precoding Design and Performance Analysis

In this paper, we propose a novel joint caching and massive multiple-input multiple-output (MIMO) transmission scheme, referred to as cache-aided massive MIMO, for advanced downlink cellular communications. In addition to reaping the conventional advantages of caching and massive MIMO, the proposed scheme also exploits the side information provided by cached files for interference cancellation at the receivers. This interference cancellation increases the degrees of freedom available for precoding design. In addition, the power freed by the cache-enabled offloading can benefit the transmissions to the users requesting non-cached files. The resulting performance gains are not possible if caching and massive MIMO are designed separately. We analyze the performance of cache-aided massive MIMO for cache-dependent maximum-ratio transmission (MRT), zero-forcing (ZF) precoding, and regularized zero-forcing (RZF) precoding. Lower bounds on the ergodic achievable rates are derived in closed form for MRT and ZF precoding. The ergodic achievable rate of RZF precoding is obtained for the case when the numbers of transmit antennas and users are large but their ratio is fixed. Compared to conventional massive MIMO, the proposed cache-aided massive MIMO scheme achieves a significantly higher ergodic rate especially when the number of users approaches the number of transmit antennas.

eess.SP↗

arXiv2019

New bounds for a hypergraph Bipartite Turán problem

Let $t$ be an integer such that $t\geq 2$. Let $K_{2,t}^{(3)}$ denote the triple system consisting of the $2t$ triples $\{a,x_i,y_i\}$, $\{b,x_i,y_i\}$ for $1 \le i \le t$, where the elements $a, b, x_1, x_2, \ldots, x_t,$ $y_1, y_2, \ldots, y_t$ are all distinct. Let $ex(n,K_{2,t}^{(3)})$ denote the maximum size of a triple system on $n$ elements that does not contain $K_{2,t}^{(3)}$. This function was studied by Mubayi and Verstraëte, where the special case $t=2$ was a problem of Erdős that was studied by various authors. Mubayi and Verstraëte proved that $ex(n,K_{2,t}^{(3)})<t^4\binom{n}{2}$ and that for infinitely many $n$, $ex(n,K_{2,t}^{(3)})\geq \frac{2t-1}{3} \binom{n}{2}$. These bounds together with a standard argument show that $g(t):=\lim_{n\to \infty} ex(n,K_{2,t}^{(3)})/\binom{n}{2}$ exists and that \[\frac{2t-1}{3}\leq g(t)\leq t^4.\] Addressing the question of Mubayi and Verstraëte on the growth rate of $g(t)$, we prove that as $t \to \infty$, \[g(t) = Θ(t^{1+o(1)}).\]

math.CO↗

arXiv2019

Recovery of a mixture of Gaussians by sum-of-norms clustering

Sum-of-norms clustering is a method for assigning $n$ points in $\mathbb{R}^d$ to $K$ clusters, $1\le K\le n$, using convex optimization. Recently, Panahi et al.\ proved that sum-of-norms clustering is guaranteed to recover a mixture of Gaussians under the restriction that the number of samples is not too large. The purpose of this note is to lift this restriction, i.e., show that sum-of-norms clustering with equal weights can recover a mixture of Gaussians even as the number of samples tends to infinity. Our proof relies on an interesting characterization of clusters computed by sum-of-norms clustering that was developed inside a proof of the agglomeration conjecture by Chiquet et al. Because we believe this theorem has independent interest, we restate and reprove the Chiquet et al.\ result herein.

cs.LG↗

arXiv2019

Federated Learning via Over-the-Air Computation

The stringent requirements for low-latency and privacy of the emerging high-stake applications with intelligent devices such as drones and smart vehicles make the cloud computing inapplicable in these scenarios. Instead, edge machine learning becomes increasingly attractive for performing training and inference directly at network edges without sending data to a centralized data center. This stimulates a nascent field termed as federated learning for training a machine learning model on computation, storage, energy and bandwidth limited mobile devices in a distributed manner. To preserve data privacy and address the issues of unbalanced and non-IID data points across different devices, the federated averaging algorithm has been proposed for global model aggregation by computing the weighted average of locally updated model at each selected device. However, the limited communication bandwidth becomes the main bottleneck for aggregating the locally computed updates. We thus propose a novel over-the-air computation based approach for fast global model aggregation via exploring the superposition property of a wireless multiple-access channel. This is achieved by joint device selection and beamforming design, which is modeled as a sparse and low-rank optimization problem to support efficient algorithms design. To achieve this goal, we provide a difference-of-convex-functions (DC) representation for the sparse and low-rank function to enhance sparsity and accurately detect the fixed-rank constraint in the procedure of device selection. A DC algorithm is further developed to solve the resulting DC program with global convergence guarantees. The algorithmic advantages and admirable performance of the proposed methodologies are demonstrated through extensive numerical results.

cs.LG↗

arXiv2019

Terahertz oscilloscope for recording time information of ultrashort electron beams

We propose and demonstrate a Terahertz (THz) oscilloscope for recording time information of an ultrashort electron beam. By injecting a laser-driven THz pulse with circular polarization into a dielectric tube, the electron beam is swept helically such that the time information is uniformly encoded into the angular distribution that allows one to characterize both the temporal profile and timing jitter of an electron beam. The dynamic range of the measurement in such a configuration is significantly increased compared to deflection with a linearly polarized THz pulse. With this THz oscilloscope, nearly 50-fold longitudinal compression of a relativistic electron beam to about 15 fs (rms) is directly visualized with its arrival time determined with 3 fs accuracy. This technique bridges the gap between streaking of photoelectrons with optical lasers and deflection of relativistic electron beams with radio-frequency deflectors, and should have wide applications in many ultrashort electron beam based facilities.

physics.acc-ph↗

arXiv2019

Chiral selection rules for multi-photon processes in two-dimensional honeycomb materials

We examined the chirality dependent optical selection rules in two-dimensional monolayer materials with the honeycomb lattice, and based on symmetry argument, we generalized these rules to multi-photon transitions of arbitrary orders. We also presented the phase relations between incident and outgoing photons in such processes. The results agreed nicely with our experimental observations of second and third harmonic generations. In particular, we demonstrated that the phase relation of chiral second harmonic generation can serve as a handy tool for imaging domains and domain boundaries of these monolayers. Our results can benefit future studies on chirality related optical phenomena and opto-electronic applications of such materials.

cond-mat.mes-hall↗

arXiv2019

Hypergraphs not containing a tight tree with a bounded trunk ~II: 3-trees with a trunk of size 2

A tight $r$-tree $T$ is an $r$-uniform hypergraph that has an edge-ordering $e_1, e_2, \dots, e_t$ such that for each $i\geq 2$, $e_i$ has a vertex $v_i$ that does not belong to any previous edge and $e_i-v_i$ is contained in $e_j$ for some $j<i$. Kalai conjectured in 1984 that every $n$-vertex $r$-uniform hypergraph with more than $\frac{t-1}{r}\binom{n}{r-1}$ edges contains every tight $r$-tree $T$ with $t$ edges. A trunk $T'$ of a tight $r$-tree $T$ is a tight subtree $T'$ of $T$ such that vertices in $V(T)\setminus V(T')$ are leaves in $T$. Kalai's Conjecture was proved in 1987 for tight $r$-trees that have a trunk of size one. In a previous paper we proved an asymptotic version of Kalai's Conjecture for all tight $r$-trees that have a trunk of bounded size. In this paper we continue that work to establish the exact form of Kalai's Conjecture for all tight $3$-trees with at least $20$ edges that have a trunk of size two.

math.CO↗

arXiv2019

An investigation of a new metamaterial composite structures

A frequency selective device, which is composed of a kind of composite structure, is designed to control the propagation of free space electromagnetic waves in the X band of the microwave spectrum. The composite structure mainly consists of a simple frequency selective surface (FSS) structure and the designed plasma arrays. Firstly, the electromagnetic property of the FSS structure is tested, and results indicating that the FSS structure with coupling reversed phase patternings has a significant absorptivity, with a bit blue shift of the peak resonance in comparison with the single layer FSS cases. Then the designed frequency selective device is still investigated in X band frequency. Results demonstrate that this device is easy to control the propagation of microwave, and the bandgap frequencies can be shifted through varying the FSS structure or the plasma arrays, and that the properties of this structure is highly dependent on frequency.

physics.app-ph↗

arXiv2018

Doping Induced Second Harmonic Generation in Centrosymmetric Graphene from Quadrupole Response

For centrosymmetric materials such as monolayer graphene, no optical second harmonic generation (SHG) is generally expected because it is forbidden under the electric-dipole approximation. Yet we observed a strong, doping induced SHG from graphene, with its highest strength comparable to the electric-dipole allowed SHG in non-centrosymmetric 2D materials. This novel SHG has the nature of an electric-quadrupole response, arising from the effective breaking of inversion symmetry by optical dressing with an in-plane photon wave vector. More remarkably, the SHG is widely tuned by carrier doping or chemical potential, being sharply enhanced at Fermi edge resonances, but vanishing at the charge neutral point that manifests the electron-hole symmetry of massless Dirac Fermions. The striking behavior in graphene, which should also arise in graphene-like Dirac materials, expands the scope of nonlinear optics, and holds the promise of novel optoelectronic and photonic applications.

cond-mat.mes-hall↗

arXiv2018

Double Neural Counterfactual Regret Minimization

Counterfactual Regret Minimization (CRF) is a fundamental and effective technique for solving Imperfect Information Games (IIG). However, the original CRF algorithm only works for discrete state and action spaces, and the resulting strategy is maintained as a tabular representation. Such tabular representation limits the method from being directly applied to large games and continuing to improve from a poor strategy profile. In this paper, we propose a double neural representation for the imperfect information games, where one neural network represents the cumulative regret, and the other represents the average strategy. Furthermore, we adopt the counterfactual regret minimization algorithm to optimize this double neural representation. To make neural learning efficient, we also developed several novel techniques including a robust sampling method, mini-batch Monte Carlo Counterfactual Regret Minimization (MCCFR) and Monte Carlo Counterfactual Regret Minimization Plus (MCCFR+) which may be of independent interests. Experimentally, we demonstrate that the proposed double neural algorithm converges significantly better than the reinforcement learning counterpart.

cs.AI↗

arXiv2018

Cross-Technology Communications for Heterogeneous IoT Devices Through Artificial Doppler Shifts

Recent years have seen major innovations in developing energy-efficient wireless technologies such as Bluetooth Low Energy (BLE) for Internet of Things (IoT). Despite demonstrating significant benefits in providing low power transmission and massive connectivity, hardly any of these technologies have made it to directly connect to the Internet. Recent advances demonstrate the viability of direct communication among heterogeneous IoT devices with incompatible physical (PHY) layers. These techniques, however, require modifications in transmission power or time, which may affect the media access control (MAC) layer behaviors in legacy networks. In this paper, we argue that the frequency domain can serve as a free side channel with minimal interruptions to legacy networks. To this end, we propose DopplerFi, a communication framework that enables a two-way communication channel between BLE and Wi-Fi by injecting artificial Doppler shifts, which can be decoded by sensing the patterns in the Gaussian frequency shift keying (GFSK) demodulator and Channel State Information (CSI). The artificial Doppler shifts can be compensated by the inherent frequency synchronization module and thus have a negligible impact on legacy communications. Our evaluation using commercial off-the-shelf (COTS) BLE chips and 802.11-compliant testbeds have demonstrated that DopplerFi can achieve throughput up to 6.5~Kbps at the cost of merely less than 0.8% throughput loss.

cs.NI↗