Search

We present a quantum secure imaging (QSI) scheme based on the phase encoding and weak + vacuum decoy-state BB84 protocol of quantum key distribution (QKD). It allows us to implement a computational ghost imaging (CGI) system with more simplified equipment and reconstructed algorithms by using a digital micro-mirror device (DMD) to preset the specific spatial distribution of the light intensity. What's more, the quantum bit error rate (QBER) and the secure key rate analytical functions of QKD are used to see through the intercept-resend jamming attacks and ensure the authenticity of the imaging information. In the experiment, we obtained the image of the object quickly and efficiently by measuring the signal photon counts with single-photon detector (SPD), and achieved a secure key rate of 571.0 bps and a secure QBER of 3.99%, which is well below the lower bound of QBER of 14.51$\%$. Besides, our imaging system uses a laser with invisible wavelength of 1550 nm, whose intensity is low as single-photon, that can realize weak-light imaging and is immune to the stray light or air turbulence, thus it will become a better choice for quantum security radar against intercept-resend jamming attacks., Comment: 7 pages, 5 figures

Existing keyword spotting (KWS) systems primarily rely on predefined keyword phrases. However, the ability to recognize customized keywords is crucial for tailoring interactions with intelligent devices. In this paper, we present a novel Query-by-Example (QbyE) KWS system that employs spectral-temporal graph attentive pooling and multi-task learning. This framework aims to effectively learn speaker-invariant and linguistic-informative embeddings for QbyE KWS tasks. Within this framework, we investigate three distinct network architectures for encoder modeling: LiCoNet, Conformer and ECAPA_TDNN. The experimental results on a substantial internal dataset of $629$ speakers have demonstrated the effectiveness of the proposed QbyE framework in maximizing the potential of simpler models such as LiCoNet. Particularly, LiCoNet, which is 13x more efficient, achieves comparable performance to the computationally intensive Conformer model (1.98% vs. 1.63\% FRR at 0.3 FAs/Hr).

A keyword spotting (KWS) engine that is continuously running on device is exposed to various speech signals that are usually unseen before. It is a challenging problem to build a small-footprint and high-performing KWS model with robustness under different acoustic environments. In this paper, we explore how to effectively apply adversarial examples to improve KWS robustness. We propose datasource-aware disentangled learning with adversarial examples to reduce the mismatch between the original and adversarial data as well as the mismatch across original training datasources. The KWS model architecture is based on depth-wise separable convolution and a simple attention module. Experimental results demonstrate that the proposed learning strategy improves false reject rate by $40.31%$ at $1%$ false accept rate on the internal dataset, compared to the strongest baseline without using adversarial examples. Our best-performing system achieves $98.06%$ accuracy on the Google Speech Commands V1 dataset.

We derive an equation capable of treating both the electrostatic and non-electrostatic interactions in the charged liquids. The equation derived is a complex version of the Poisson-Boltzmann equation, in which the non-electrostatic interactions are transformed to complex random fields by the field theory. Thus, the non-electrostatic interactions of the ions in the charged liquids can be simulated easily by generating random numbers according to the random fields. To solve the equation, the finite element method has been applied. The non-electrostatic interactions treated in the equation are general. In this study, we take the steric effect of ions as an example. Results show that the steric effect can be clearly caught by the equation., Comment: 13 pages, 5 figures

In this paper, we calculate the differential transverse momentum and azimuthal decorrelation cross sections, $\mathrm{d}\sigma_{t\bar{t}}/\mathrm{d}q_{\mathrm{T}}$ and $\mathrm{d}\sigma_{t\bar{t}}/\mathrm{d}\Delta\phi_{t\bar{t}}$, in top-antitop pair production at the LHC up to NNLL$+$NNLO accuracy. Due to the emergence of Coulomb singularities in both the hard sector and the corresponding anomalous dimension as the relative $t\bar{t}$ pair velocity, $\beta_{t\bar{t}}$, approaches zero, extrapolating the soft-collinear resummation that is derived in the domain where the top and antitop quarks are kinematically well-separated into the full phase space is not trivial. Focussing on two observables that are insensitive to azimuthal asymmetric divergences, $q_{\mathrm{T}}$ and $\Delta\phi_{t\bar{t}}$, we will demonstrate that a literal application of a SCET$+$HQET based resummation onto $\mathrm{d}\sigma_{t\bar{t}}/\mathrm{d}q_{\mathrm{T}}$ and $\mathrm{d}\sigma_{t\bar{t}}/\mathrm{d}\Delta\phi_{t\bar{t}}$ is only possible up to NLL accuracy. Starting at NNLL, however, such a na\"ive procedure will develop power-like divergences in $\beta_{t\bar{t}}$ in the threshold regime. To this end, two prescriptions, dubbed the D- and R-schemes, are introduced to facilitate the extrapolation of the resummation framework from the well-separated region where $\beta_{t\bar{t}}\sim\mathcal{O}(1)$ to the threshold regime $\beta_{t\bar{t}}\to0$, enabling us to compute $\mathrm{d}\sigma_{t\bar{t}}/\mathrm{d}q_{\mathrm{T}}$ and $\mathrm{d}\sigma_{t\bar{t}}/\mathrm{d}\Delta\phi_{t\bar{t}}$ at NNLL$+$NNLO accuracy throughout. Further, by comparing the results of both formulations, we can assess the theoretical uncertainty caused by the truncation of the Coulomb-enhanced terms in the perturbative series.

We propose a novel, lightweight supervised dictionary learning framework for text classification based on data compression and representation. This two-phase algorithm initially employs the Lempel-Ziv-Welch (LZW) algorithm to construct a dictionary from text datasets, focusing on the conceptual significance of dictionary elements. Subsequently, dictionaries are refined considering label data, optimizing dictionary atoms to enhance discriminative power based on mutual information and class distribution. This process generates discriminative numerical representations, facilitating the training of simple classifiers such as SVMs and neural networks. We evaluate our algorithm's information-theoretic performance using information bottleneck principles and introduce the information plane area rank (IPAR) as a novel metric to quantify the information-theoretic performance. Tested on six benchmark text datasets, our algorithm competes closely with top models, especially in limited-vocabulary contexts, using significantly fewer parameters. \review{Our algorithm closely matches top-performing models, deviating by only ~2\% on limited-vocabulary datasets, using just 10\% of their parameters. However, it falls short on diverse-vocabulary datasets, likely due to the LZW algorithm's constraints with low-repetition data. This contrast highlights its efficiency and limitations across different dataset types., Comment: 12 pages, TKDE format

We report the physical properties of ThRu$_3$Si$_2$ featured with distorted Ru kagome lattice. The combined experiments of resistivity, magnetization and specific heat reveal bulk superconductivity with $T_{\rm{c}}$ = 3.8 K. The specific heat jump and calculated electron-phonon coupling indicate a moderate coupled BCS superconductor. In comparison with LaRu$_3$Si$_2$, the calculated electronic structure in ThRu$_3$Si$_2$ shows an electron-doping effect with electron filling lifted from 100 meV below flat bands to 300 meV above it. This explains the lower superconducting transition temperature and weaker electron correlations observed in ThRu$_3$Si$_2$. Our work suggests the $T_{\rm{c}}$ and electronic correlations in kagome superconductor could have intimate connection with the flat bands., Comment: 7 pages, 5 figures

Guessing random additive noise decoding (GRAND) is a recently proposed decoding paradigm particularly suitable for codes with short length and high rate. Among its variants, ordered reliability bits GRAND (ORBGRAND) exploits soft information in a simple and effective fashion to schedule its queries, thereby allowing efficient hardware implementation. Compared with maximum likelihood (ML) decoding, however, ORBGRAND still exhibits noticeable performance loss in terms of block error rate (BLER). In order to improve the performance of ORBGRAND while still retaining its amenability to hardware implementation, a new variant of ORBGRAND termed RS-ORBGRAND is proposed, whose basic idea is to reshuffle the queries of ORBGRAND so that the expected number of queries is minimized. Numerical simulations show that RS-ORBGRAND leads to noticeable gains compared with ORBGRAND and its existing variants, and is only 0.1dB away from ML decoding, for BLER as low as $10^{-6}$.

Despite the potential of diffusion models in speech enhancement, their deployment in Acoustic Echo Cancellation (AEC) has been restricted. In this paper, we propose DI-AEC, pioneering a diffusion-based stochastic regeneration approach dedicated to AEC. Further, we propose FADI-AEC, fast score-based diffusion AEC framework to save computational demands, making it favorable for edge devices. It stands out by running the score model once per frame, achieving a significant surge in processing efficiency. Apart from that, we introduce a novel noise generation technique where far-end signals are utilized, incorporating both far-end and near-end signals to refine the score model's accuracy. We test our proposed method on the ICASSP2023 Microsoft deep echo cancellation challenge evaluation dataset, where our method outperforms some of the end-to-end methods and other diffusion based echo cancellation methods.

In this paper, we present an algorithm to construct the qT distribution at NLO accuracy to arbitrary power precision, including the assembly of suitable zero-bin subtrahends, in a mathematically well-defined way for a generic choice of rapidity-divergence regularisation prescription. In its derivation, we divide the phase space into two sectors, the interior of the integration domain as well as the integration boundary, which we include here for the first time. To demonstrate the applicability and usefulness of our algorithm, we calculate the NNLP corrections for Higgs hadroproduction for the first time. We observe that our approximate NNLP-accurate qT spectra replicate the asymptotic behaviour of the full QCD calculation to a much better degree than the previously available results, both within the $q_{\mathrm{T}}\to 0$ limit and in the large-qT domain for all the involved partonic processes. While playing a minor role at larger transverse momenta, we show that the newly incorporated boundary contribution plays a vital role in the $q_{\mathrm{T}}\to 0$ limit, where any subleading power accuracy would be lost without them. In particular, our NNLP-accurate qT expansion can approximate the exact qT distribution up to $q_{\mathrm{T}}\sim30\,\text{GeV}$ at the percent level for rapidities $|Y_H|\lesssim 3$.

We propose an ensemble theory for the non-equilibrium statistics to study the thermal transport in anharmonic crystals. In the theory, lattice vibrations of the crystals are quantized by local Bosons(LBs), instead of Phonons as usually used for the thermal transport. LBs are driven by the temperature gradient and move from atom to atom in the crystals. Based on the LBs, anharmonic interactions between atoms in the crystals can be fully considered. To demonstrate our theory, we study the thermal transport in an atomic chain with a temperature drop applied on the two ends of the chain. We observe a Rabi-like oscillation in the transport of the LBs, from which we define the thermal current to get the thermal conductivity of the chain. Results show that the thermal conductivity is enhanced slowly with the increasing of the anharmonic interaction and decreases rapidly if the anharmonic interaction is increased further. In the present study, we only focus on the steady state, and the fluctuations of the thermal currents are not considered., Comment: 10 pages,11 figures

Unconventional superconductivity (USC) in a highly correlated kagome system has been theoretically proposed for years, yet the experimental realization is hard to achieve. The recently discovered vanadium-based kagome materials, which exhibit both superconductivity and charge density wave (CDW) orders, are nonmagnetic and weakly correlated, thus unlikely host USC as theories proposed. Here we report the discovery of a chromium-based kagome metal, CsCr$_3$Sb$_5$, which is contrastingly characterised by strong electron correlations, frustrated magnetism, and characteristic flat bands close to the Fermi level. Under ambient pressure, it undergoes a concurrent structural and magnetic phase transition at 55 K, accompanying with a stripe-like $4a_0$ structural modulation. At high pressure, the phase transition evolves into two transitions, probably associated with CDW and antiferromagnetic spin-density-wave orderings, respectively. These density-wave (DW)-like orders are gradually suppressed with pressure and, remarkably, a superconducting dome emerges at 3.65-8.0 GPa. The maximum of the superconducting transition temperature, $T_\mathrm{c}^{\mathrm{max}}=$ 6.4 K, appears when the DW-like orders are completely suppressed at 4.2 GPa, and the normal state exhibits a non-Fermi-liquid behaviour, reminiscent of USC and quantum criticality in iron-based superconductors. Our work offers an unprecedented platform for investigating possible USC in a correlated kagome system., Comment: 32 pages, 14 figures

Modern smart glasses leverage advanced audio sensing and machine learning technologies to offer real-time transcribing and captioning services, considerably enriching human experiences in daily communications. However, such systems frequently encounter challenges related to environmental noises, resulting in degradation to speech recognition and speaker change detection. To improve voice quality, this work investigates directional source separation using the multi-microphone array. We first explore multiple beamformers to assist source separation modeling by strengthening the directional properties of speech signals. In addition to relying on predetermined beamformers, we investigate neural beamforming in multi-channel source separation, demonstrating that automatic learning directional characteristics effectively improves separation quality. We further compare the ASR performance leveraging separated outputs to noisy inputs. Our results show that directional source separation benefits ASR for the wearer but not for the conversation partner. Lastly, we perform the joint training of the directional source separation and ASR model, achieving the best overall ASR performance., Comment: Submitted to ICASSP 2024

The quantum battery (QB) makes use of quantum effects to store and supply energy, which may outperform its classical counterpart. However, there are two challenges in this field. One is that the environment-induced decoherence causes the energy loss and aging of the QB, the other is that the decreasing of the charger-QB coupling strength with increasing their distance makes the charging of the QB become inefficient. Here, we propose a QB scheme to realize a remote charging via coupling the QB and the charger to a rectangular hollow metal waveguide. It is found that an ideal charging is realized as long as two bound states are formed in the energy spectrum of the total system consisting of the QB, the charger, and the electromagnetic environment in the waveguide. Using the constructive role of the decoherence, our QB is immune to the aging. Additionally, without resorting to the direct charger-QB interaction, our scheme works in a way of long-range and wireless-like charging. Effectively overcoming the two challenges, our result supplies an insightful guideline to the practical realization of the QB by reservoir engineering.