Search

In modern online platforms, incentives are essential factors that enhance user engagement and increase platform revenue. Over recent years, uplift modeling has been introduced as a strategic approach to assign incentives to individual customers. Especially in many real-world applications, online platforms can only incentivize customers with specific budget constraints. This problem can be reformulated as the multi-choice knapsack problem. This optimization aims to select the optimal incentive for each customer to maximize the return on investment. Recent works in this field frequently tackle the budget allocation problem using a two-stage approach. However, this solution is confronted with the following challenges: (1) The causal inference methods often ignore the domain knowledge in online marketing, where the expected response curve of a customer should be monotonic and smooth as the incentive increases. (2) An optimality gap between the two stages results in inferior sub-optimal allocation performance due to the loss of the incentive recommendation information for the uplift prediction under the limited budget constraint. To address these challenges, we propose a novel End-to-End Cost-Effective Incentive Recommendation (E3IR) model under budget constraints. Specifically, our methods consist of two modules, i.e., the uplift prediction module and the differentiable allocation module. In the uplift prediction module, we construct prediction heads to capture the incremental improvement between adjacent treatments with the marketing domain constraints (i.e., monotonic and smooth). We incorporate integer linear programming (ILP) as a differentiable layer input in the allocation module. Furthermore, we conduct extensive experiments on public and real product datasets, demonstrating that our E3IR improves allocation performance compared to existing two-stage approaches., Comment: Accepted by RecSys 2024

We investigate the prospect of probing massive fields and testing gravitational theories with multi-band observations of gravitational waves emitted from coalescing compact binaries. Focusing on the dipole radiation induced by a massive field, we show that multi-band observations can probe the field with mass ranging from $10^{-16}$ eV to $10^{-15}$ eV, a parameter space that cannot be probed by the milli-Hertz band observations alone. Multi-band observations can also improve the constraints obtained with the LIGO-Virgo-KAGRA binaries by up to 3 orders of magnitude in the mass range. Moreover, we show that multi-band observations can discriminate the spin of the field, which cannot be identified with single-band observations., Comment: 8 pages,3 figures

In the field of digital content creation, generating high-quality 3D characters from single images is challenging, especially given the complexities of various body poses and the issues of self-occlusion and pose ambiguity. In this paper, we present CharacterGen, a framework developed to efficiently generate 3D characters. CharacterGen introduces a streamlined generation pipeline along with an image-conditioned multi-view diffusion model. This model effectively calibrates input poses to a canonical form while retaining key attributes of the input image, thereby addressing the challenges posed by diverse poses. A transformer-based, generalizable sparse-view reconstruction model is the other core component of our approach, facilitating the creation of detailed 3D models from multi-view images. We also adopt a texture-back-projection strategy to produce high-quality texture maps. Additionally, we have curated a dataset of anime characters, rendered in multiple poses and views, to train and evaluate our model. Our approach has been thoroughly evaluated through quantitative and qualitative experiments, showing its proficiency in generating 3D characters with high-quality shapes and textures, ready for downstream applications such as rigging and animation.

We construct a high sensitivity radon pilot detector using liquid scintillator dissolved radon for the CDEX rare-event searches program. The CDEX-10 project employs a germanium detector array immersed in a massive liquid nitrogen tank. However, radon emanated from the surface of the tank can contribute background. As a result, radon contamination in the liquid nitrogen tank must be regulated and monitored, which necessitates the use of a low-level Rn measuring device. The radon-dissolved liquid scintillator (LS) detectors utilizes cascade decay selections, yielding a unique signature. All background events occurring in the LS, with regard to the system and co-efficiency of Rn absorption, have been studied. Background activity is measured to be 59.8$\pm$18.4 $\mu$Bq per 300 mL LS. Meanwhile, it shows that radon concentration absorbed by LS will be saturated and is linearly related to radon concentration in nitrogen gas. Furthermore, the co-efficiency factor can be optimized by lowering temperature or raising pressure. Eventually the detection limit for 222^Rn in nitrogen gas is observed to be 9.6 mBq/m^3., Comment: 8 pages, 18 figures

This paper presents a Pre-Training Deep Reinforcement Learning(DRL) for avoidance navigation without map for mobile robots which map raw sensor data to control variable and navigate in an unknown environment. The efficient offline training strategy is proposed to speed up the inefficient random explorations in early stage and we also collect a universal dataset including expert experience for offline training, which is of some significance for other navigation training work. The pre-training and prioritized expert experience are proposed to reduce 80\% training time and has been verified to improve the 2 times reward of DRL. The advanced simulation gazebo with real physical modelling and dynamic equations reduce the gap between sim-to-real. We train our model a corridor environment, and evaluate the model in different environment getting the same effect. Compared to traditional method navigation, we can confirm the trained model can be directly applied into different scenarios and have the ability to no collision navigate. It was demonstrated that our DRL model have universal general capacity in different environment.

We report new Gaussian boson sampling experiments with pseudo-photon-number-resolving detection, which register up to 255 photon-click events. We consider partial photon distinguishability and develop a more complete model for the characterization of the noisy Gaussian boson sampling. In the quantum computational advantage regime, we use Bayesian tests and correlation function analysis to validate the samples against all current classical mockups. Estimating with the best classical algorithms to date, generating a single ideal sample from the same distribution on the supercomputer Frontier would take ~ 600 years using exact methods, whereas our quantum computer, Jiuzhang 3.0, takes only 1.27 us to produce a sample. Generating the hardest sample from the experiment using an exact algorithm would take Frontier ~ 3.1*10^10 years., Comment: PRL 2023 to appear

Under the framework of gravitational effective field theory, we propose a theory agnostic strategy of searching for higher-spin particles with gravitational waves from compact binary inspirals. Using this strategy, we analyze gravitational wave signals from the binary black hole merger events GW151226 and GW170608, as well as the binary neutron star merger event GW170817. We find that the existence of higher-spin particles with mass ranged from $10^{-12} {\rm eV}$ to $10^{-11} {\rm eV}$ is strongly disfavored by these events unless the particles precisely combine within a supersymmetric supermultiplet. We argue that the gravitational effective field theory also provides a framework to search for signals beyond GR from other GW observations, such as extreme-mass-ratio-inspirals., Comment: 6 pages, 2 figures

Gaussian boson sampling (GBS) is not only a feasible protocol for demonstrating quantum computational advantage, but also mathematically associated with certain graph-related and quantum chemistry problems. In particular, it is proposed that the generated samples from the GBS could be harnessed to enhance the classical stochastic algorithms in searching some graph features. Here, we use Jiuzhang, a noisy intermediate-scale quantum computer, to solve graph problems. The samples are generated from a 144-mode fully-connected photonic processor, with photon-click up to 80 in the quantum computational advantage regime. We investigate the open question of whether the GBS enhancement over the classical stochastic algorithms persists -- and how it scales -- with an increasing system size on noisy quantum devices in the computationally interesting regime. We experimentally observe the presence of GBS enhancement with large photon-click number and a robustness of the enhancement under certain noise. Our work is a step toward testing real-world problems using the existing noisy intermediate-scale quantum computers, and hopes to stimulate the development of more efficient classical and quantum-inspired algorithms.

The computational cost of searching for gravitational wave (GW) signals in low latency has always been a matter of concern. We present a self-supervised learning model applicable to the GW detection. Based on simulated massive black hole binary signals in synthetic Gaussian noise representative of space-based GW detectors Taiji and LISA sensitivity, and regarding their corresponding datasets as a GW twins in the contrastive learning method, we show that the self-supervised learning may be a highly computationally efficient method for GW signal identification., Comment: 11 pages, 9 figures,V2: some figures and corresponding fixes have been added

In this paper, a pervasive wireless channel modeling theory is first proposed, which uses a unified channel modeling method and a unified equation of channel impulse response (CIR), and can integrate important channel characteristics at different frequency bands and scenarios. Then, we apply the proposed theory to a three dimensional (3D) space-time-frequency (STF) non-stationary geometry-based stochastic model (GBSM) for the sixth generation (6G) wireless communication systems. The proposed 6G pervasive channel model (6GPCM) can characterize statistical properties of channels at all frequency bands from sub-6 GHz to visible light communication (VLC) bands and all scenarios such as unmanned aerial vehicle (UAV), maritime, (ultra-)massive multiple-input multiple-output (MIMO), reconfigurable intelligent surface (RIS), and industry Internet of things (IIoT) scenarios. By adjusting channel model parameters, the 6GPCM can be reduced to various simplified channel models for specific frequency bands and scenarios. Also, it includes standard fifth generation (5G) channel models as special cases. In addition, key statistical properties of the proposed 6GPCM are derived, simulated, and verified by various channel measurement results, which clearly demonstrates its accuracy, pervasiveness, and applicability.

Multimodal pre-training for audio-and-text has recently been proved to be effective and has significantly improved the performance of many downstream speech understanding tasks. However, these state-of-the-art pre-training audio-text models work well only when provided with large amount of parallel audio-and-text data, which brings challenges on many languages that are rich in unimodal corpora but scarce of parallel cross-modal corpus. In this paper, we investigate whether it is possible to pre-train an audio-text multimodal model with extremely low-resource parallel data and extra non-parallel unimodal data. Our pre-training framework consists of the following components: (1) Intra-modal Denoising Auto-Encoding (IDAE), which is able to reconstruct input text (audio) representations from a noisy version of itself. (2) Cross-modal Denoising Auto-Encoding (CDAE), which is pre-trained to reconstruct the input text (audio), given both a noisy version of the input text (audio) and the corresponding translated noisy audio features (text embeddings). (3) Iterative Denoising Process (IDP), which iteratively translates raw audio (text) and the corresponding text embeddings (audio features) translated from previous iteration into the new less-noisy text embeddings (audio features). We adapt a dual cross-modal Transformer as our backbone model which consists of two unimodal encoders for IDAE and two cross-modal encoders for CDAE and IDP. Our method achieves comparable performance on multiple downstream speech understanding tasks compared with the model pre-trained on fully parallel data, demonstrating the great potential of the proposed method. Our code is available at: \url{https://github.com/KarlYuKang/Low-Resource-Multimodal-Pre-training}., Comment: AAAI 2022

The success of machine learning (ML) in materials property prediction depends heavily on how the materials are represented for learning. Two dominant families of material descriptors exist, one that encodes crystal structure in the representation and the other that only uses stoichiometric information with the hope of discovering new materials. Graph neural networks (GNNs) in particular have excelled in predicting material properties within chemical accuracy. However, current GNNs are limited to only one of the above two avenues owing to the little overlap between respective material representations. Here, we introduce a new concept of formula graph which unifies stoichiometry-only and structure-based material descriptors. We further develop a self-attention integrated GNN that assimilates a formula graph and show that the proposed architecture produces material embeddings transferable between the two domains. Our model can outperform some previously proposed structure-agnostic models and their structure-based counterparts while exhibiting better sample efficiency and faster convergence. Finally, the model is applied in a challenging exemplar to predict the complex dielectric function of materials and nominate new substances that potentially exhibit epsilon-near-zero phenomena.

To improve the dynamic response performance of wave code communication in intelligent layered water injection of oilfield, this paper proposes an output optimal fusion control method based on MPC-PID. Firstly, depending on the well structure and the flow-pressure characteristics of the layer, the steady-state model between the differential pressure and flow of the whole well and different layer sections is established for layered water injection, and the corresponding wave code amplitude at the steady-state operating point of different layer sections is solved, the numerical calculation verifies that the increase of the nozzle opening in a single layer section will drive the pressure and flow curve of the whole well downward. Secondly, combining the dynamic response characteristics and steady-state model of the whole-well water distribution equipment, a dynamic model of layered intelligent water injection is established, and the generation process of the wave code is defined; Finally, the MPC-PID optimal fusion control algorithm structure is designed to derive the fusion control law that minimizes the cost function under fixed weights, , and the optimal weights are calculated by combining the internal model structure of controller, so the optimization performance of each algorithm in the optimal fusion control is balanced. By analyzing the control simulation results, the fast response characteristics of the fusion control method are verified. Meanwhile, the simulation comparison experiments of fast wave code communication under different methods are conducted with the actual working conditions, the results show that the fusion control method has both fast tracking control capability and strong robustness, which effectively enhances the efficiency of wave code communication and shortens the wave code operation time.

Terahertz (THz) communication is now being considered as one of possible technologies for the sixth generation (6G) wireless communication systems. In this paper, a novel three-dimensional (3D) space-time-frequency non-stationary theoretical channel model is first proposed for 6G THz wireless communication systems employing ultra-massive multiple-input multiple-output (MIMO) technologies with long traveling paths. Considering frequency-dependent diffuse scattering, which is a special property of THz channels different from millimeter wave (mmWave) channels, the relative angles and delays of rays within one cluster will evolve in the frequency domain. Then, a corresponding simulation model is proposed with discrete angles calculated using the method of equal area (MEA). The statistical properties of the proposed theoretical and simulation models are derived and compared, showing good agreements. The accuracy and flexibility of the proposed simulation model are demonstrated by comparing the simulation results of the relative angle spread and root mean square (RMS) delay spread with corresponding measurements.

The quality of vocal delivery is one of the key indicators for evaluating teacher enthusiasm, which has been widely accepted to be connected to the overall course qualities. However, existing evaluation for vocal delivery is mainly conducted with manual ratings, which faces two core challenges: subjectivity and time-consuming. In this paper, we present a novel machine learning approach that utilizes pairwise comparisons and a multimodal orthogonal fusing algorithm to generate large-scale objective evaluation results of the teacher vocal delivery in terms of fluency and passion. We collect two datasets from real-world education scenarios and the experiment results demonstrate the effectiveness of our algorithm. To encourage reproducible results, we make our code public available at \url{https://github.com/tal-ai/ML4VocalDelivery.git}., Comment: AIED'21: The 22nd International Conference on Artificial Intelligence in Education, 2021

Learning effective language representations from crowdsourced labels is crucial for many real-world machine learning tasks. A challenging aspect of this problem is that the quality of crowdsourced labels suffer high intra- and inter-observer variability. Since the high-capacity deep neural networks can easily memorize all disagreements among crowdsourced labels, directly applying existing supervised language representation learning algorithms may yield suboptimal solutions. In this paper, we propose \emph{TACMA}, a \underline{t}emporal-\underline{a}ware language representation learning heuristic for \underline{c}rowdsourced labels with \underline{m}ultiple \underline{a}nnotators. The proposed approach (1) explicitly models the intra-observer variability with attention mechanism; (2) computes and aggregates per-sample confidence scores from multiple workers to address the inter-observer disagreements. The proposed heuristic is extremely easy to implement in around 5 lines of code. The proposed heuristic is evaluated on four synthetic and four real-world data sets. The results show that our approach outperforms a wide range of state-of-the-art baselines in terms of prediction accuracy and AUC. To encourage the reproducible results, we make our code publicly available at \url{https://github.com/CrowdsourcingMining/TACMA}., Comment: The 59th Annual Meeting of the Association for Computational Linguistics Workshop on Representation Learning for NLP (ACL RepL4NLP 2021)

In this work, we study computational approaches to detect online dialogic instructions, which are widely used to help students understand learning materials, and build effective study habits. This task is rather challenging due to the widely-varying quality and pedagogical styles of dialogic instructions. To address these challenges, we utilize pre-trained language models, and propose a multi-task paradigm which enhances the ability to distinguish instances of different classes by enlarging the margin between categories via contrastive loss. Furthermore, we design a strategy to fully exploit the misclassified examples during the training stage. Extensive experiments on a real-world online educational data set demonstrate that our approach achieves superior performance compared to representative baselines. To encourage reproducible results, we make our implementation online available at \url{https://github.com/AIED2021/multitask-dialogic-instruction}., Comment: AIED'21: The 22nd International Conference on Artificial Intelligence in Education, 2021

Compositional disorder induces myriad captivating phenomena in perovskites. Target-driven discovery of perovskite solid solutions has been a great challenge due to the analytical complexity introduced by disorder. Here, we demonstrate that an unsupervised deep learning strategy can find fingerprints of disordered materials that embed perovskite formability and underlying crystal structure information by learning only from the chemical composition, manifested in (A1-xA'x)BO3 and A(B1-xB'x)O3 formulae. This phenomenon can be capitalized to predict the crystal symmetry of experimental compositions, outperforming several supervised machine learning (ML) algorithms. The educated nature of material fingerprints has led to the conception of analogical materials discovery that facilitates inverse exploration of promising perovskites based on similarity investigation with known materials. The search space of unstudied perovskites is screened from ~600,000 feasible compounds using experimental data powered ML models and automated web mining tools at a 94% success rate. This concept further provides insights on possible phase transitions and computational modelling of complex compositions. The proposed quantitative analysis of materials analogies is expected to bridge the gap between the existing materials literature and the undiscovered terrain.

View synthesis methods using implicit continuous shape representations learned from a set of images, such as the Neural Radiance Field (NeRF) method, have gained increasing attention due to their high quality imagery and scalability to high resolution. However, the heavy computation required by its volumetric approach prevents NeRF from being useful in practice; minutes are taken to render a single image of a few megapixels. Now, an image of a scene can be rendered in a level-of-detail manner, so we posit that a complicated region of the scene should be represented by a large neural network while a small neural network is capable of encoding a simple region, enabling a balance between efficiency and quality. Recursive-NeRF is our embodiment of this idea, providing an efficient and adaptive rendering and training approach for NeRF. The core of Recursive-NeRF learns uncertainties for query coordinates, representing the quality of the predicted color and volumetric intensity at each level. Only query coordinates with high uncertainties are forwarded to the next level to a bigger neural network with a more powerful representational capability. The final rendered image is a composition of results from neural networks of all levels. Our evaluation on three public datasets shows that Recursive-NeRF is more efficient than NeRF while providing state-of-the-art quality. The code will be available at https://github.com/Gword/Recursive-NeRF., Comment: 11 pages, 12 figures

A novel simultaneously transmitting and reflecting (STAR) system design relying on reconfigurable intelligent surfaces (RISs) is conceived. First, an existing prototype is reviewed and the potential benefits of STAR-RISs are discussed. Then, the key differences between conventional reflecting-only RISs and STAR-RISs are identified from the perspectives of hardware design, physics principles, and communication system design. Furthermore, the basic signal model of STAR-RISs is introduced, and three practical protocols are proposed for their operation, namely energy splitting, mode switching, and time switching. Based on the proposed protocols, a range of promising application scenarios are put forward for integrating STAR-RISs into next-generation wireless networks. By considering the downlink of a typical RIS-aided multiple-input single-output (MISO) system, numerical case studies are provided for revealing the superiority of STAR-RISs over other baselines, when employing the proposed protocols. Finally, several open research problems are discussed., Comment: 15 pages, 5 figures

Topological photonics has been introduced as a powerful platform for integrated optics, since it can deal with robust light transport, and be further extended to the quantum world. Strikingly, valley-contrasting physics in topological photonic structures contributes to valley-related edge states, their unidirectional coupling, and even valley-dependent wave-division in topological junctions. Here, we design and fabricate nanophotonic topological harpoon-shaped beam splitters (HSBSs) based on $120$-deg-bending interfaces and demonstrate the first on-chip valley-dependent quantum information process. Two-photon quantum interference, namely, HongOu-Mandel (HOM) interference with a high visibility of $0.956 \pm 0.006$, is realized with our 50/50 HSBS, which is constructed by two topologically distinct domain walls. Cascading this kind of HSBS together, we also demonstrate a simple quantum photonic circuit and generation of a path-entangled state. Our work shows that the photonic valley state can be used in quantum information processing, and it is possible to realize more complex quantum circuits with valley-dependent photonic topological insulators, which provides a novel method for on-chip quantum information processing., Comment: 6 pages, 4 figures

We investigate a novel scheme for brilliant attosecond {\gamma}-ray emission and high-yield positron production, which is accomplished with an ultra-intense laser pulse incident upon a Nano-Micro array (NMA) with substrate incorporated. This scheme is able to realize effectively electron acceleration and colliding geometry. Both the {\gamma}-ray flash and positron bunch are then generated with high conversion efficiency. At laser intensity of I_0 = 8 times 10^{23} W/cm^2, ~27% of the laser energy is transferred successfully into the {\gamma}-rays, and ~0.7% of the laser energy into the positrons. As a consequence, ultra-short (~440 as) and ultra-brilliant (~10^{24} photons s^{-1} mm^{-2} mrad^{-2} per 0.1%BW @ 15 MeV) {\gamma}-ray burst, and high-yield (1.48 times 10^{11}) and overdense (~10^{22} cm^{-3}) positron bunch are generated. We found a sub-linear scaling of laser-to-photon conversion efficiency (proportional to I_0^{0.75}) and a super-linear scaling of laser-to-positron conversion efficiency (proportional to I_0^{2.5}) with the laser intensity. Multi-dimensional particle-in-cell simulations show that particle ({\gamma} photon and positron) generation can be manipulated by laser-focusing position, and NMA's length and spacing. Optimal conditions for particle generation in NMAs are obtained, indicating that microwire array has the advantage over nanowire array in particle generation in the extreme laser fields. Furthermore, positron annihilation effect in high-energy-density (HED) environment is discussed. The scheme using NMAs would provide effective avenues toward investigating attosecond nuclear science and HED physics with the coming 10 PW laser facilities., Comment: 21 pages, 15 figures and 1 table. submitted to Physics of Plasmas

Reconfigurable intelligent surfaces (RISs) or intelligent reflecting surfaces (IRSs), are regarded as one of the most promising and revolutionizing techniques for enhancing the spectrum and/or energy efficiency of wireless systems. These devices are capable of reconfiguring the wireless propagation environment by carefully tuning the phase shifts of a large number of low-cost passive reflecting elements. In this article, we aim for answering four fundmental questions: 1) Why do we need RISs? 2) What is an RIS? 3) What are RIS's applications? 4) What are the relevant challenges and future research directions? In response, eight promising research directions are pointed out., Comment: Accepted by IEEE Communications Magazine. Keywords: Reconfigurable intelligent surface (RIS), intelligent reflecting surface (IRS). In this paper, we have answered four critical questions in RIS/IRS. More importantly, we have pointed out several promising research directions

Accompanied by an X-ray burst, the fast radio burst (FRB) FRB 200428 was recently confirmed as originating from the Galactic magnetar soft gamma repeater (SGR) SGR J1935+2154. Just before and after FRB 200428 was detected, the Five-hundred-meter Aperture Spherical radio Telescope (FAST) had been monitoring SGR J1935+2154 for eight hours. From UTC 2020 April 27 23:55:00 to 2020 April 28 00:50:37, FAST detected no pulsed radio emission from SGR J1935+2154, while Fermi/Gamma-ray Burst Monitor registered 34 bursts in the X/soft $\gamma$-ray band, forming a unique sample of X-ray bursts in the absence of FRBs. After a comprehensive analysis on light curves, time-integrated, and time-resolved spectral properties of these FRB-absent X-ray bursts, we compare this sample with the FRB-associated X-ray burst detected by Insight-HXMT, INTEGRAL, and Konus-Wind. The FRB-associated burst distinguishes itself from other X-ray bursts by its nonthermal spectrum and a higher spectral peak energy, but otherwise is not atypical. We also compare the cumulative energy distribution of our X-ray burst sample with that of first repeating FRB source, FRB 121102, with the calibration of FRB 200428-X-ray burst association. We find a similarity between the two, offering indirect support of the magnetar origin of cosmological FRBs. The event rate density of magnetar bursts is about $\sim 150$ times higher than the FRB event rate density at the energy of FRB 200428. This again suggests that, if all FRBs originate from magnetars, only a small fraction of X-ray bursts are associated with FRBs., Comment: 18 pages, 3 tables, 8 figures. Published in the Astrophysical Journal Letter on January 12, 2021

Quantum error correction is an essential tool for reliably performing tasks for processing quantum information on a large scale. However, integration into quantum circuits to achieve these tasks is problematic when one realizes that non-transverse operations, which are essential for universal quantum computation, lead to the spread of errors. Quantum gate teleportation has been proposed as an elegant solution for this. Here, one replaces these fragile, non-transverse inline gates with the generation of specific, highly entangled offline resource states that can be teleported into the circuit to implement the non-transverse gate. As the first important step, we create a maximally entangled state between a physical and an error-correctable logical qubit and use it as a teleportation resource. We then demonstrate the teleportation of quantum information encoded on the physical qubit into the error-corrected logical qubit with fidelities up to 0.786. Our scheme can be designed to be fully fault-tolerant so that it can be used in future large-scale quantum technologies., Comment: 6 pages, 4 figures

Based on the vision on the 6G wireless communication network, i.e., global coverage, all spectrums and all applications, we comprehensively survey 6G related wireless channel measurements, channel characteristics, and channel models for all frequency bands and all scenarios. Millimeter wave (mmWave), terahertz (THz), optical band, satellite, unmanned aerial vehicle (UAV), maritime, underwater acoustic, high-speed train (HST), vehicle-to-vehicle (V2V), massive/ ultra-massive multiple-input multiple-output (MIMO), orbital angular momentum (OAM), and industry Internet of things (IoT) communication channels were particularly investigated. The related 6G channel measurement and modeling results were also given. Finally, future research challenges on 6G channel measurements and modeling were pointed out., Comment: in Chinese

Photon-induced nuclear excitation (i.e. photo-excitation) can be used for production of nuclear isomers, which have potential applications in astrophysics, energy storing, and medical diagnosis and treatment. This paper presents a feasibility study on production of four nuclear isomers ({99m}^Tc, {103m}^Rh and {113m, 115m}^In) using high-intensity {\gamma}-ray source based on laser-electron Compton scattering (LCS), for use in the medical diagnosis and treatment. The decay properties and the medical applications of these nuclear isomers were reviewed. The cross-section curves, simulated yields and activity of product of each photo-excitation process were calculated. The cutoff energy of LCS {\gamma}-ray beam is optimized by adjusting the electron energy in order to maximize the yields as well as the activities of photo-excitation products. It is found that the achievable activity of above-mentioned isomers can exceed 10 mCi for 6-hour target irradiation at an intensity of the order of 10^{13} {\gamma}/s. Such magnitude of activity satisfies the dose requirement of medical diagnosis. Our simulation results suggest the prospect of producing medically interesting isomers with photo-excitation using the state-of-art LCS {\gamma}-ray beam facility., Comment: 15 pages, 6 figures and 1 table

Emotion states recognition using wireless signals is an emerging area of research that has an impact on neuroscientific studies of human behaviour and well-being monitoring. Currently, standoff emotion detection is mostly reliant on the analysis of facial expressions and/or eye movements acquired from optical or video cameras. Meanwhile, although they have been widely accepted for recognizing human emotions from the multimodal data, machine learning approaches have been mostly restricted to subject dependent analyses which lack of generality. In this paper, we report an experimental study which collects heartbeat and breathing signals of 15 participants from radio frequency (RF) reflections off the body followed by novel noise filtering techniques. We propose a novel deep neural network (DNN) architecture based on the fusion of raw RF data and the processed RF signal for classifying and visualising various emotion states. The proposed model achieves high classification accuracy of 71.67 % for independent subjects with 0.71, 0.72 and 0.71 precision, recall and F1-score values respectively. We have compared our results with those obtained from five different classical ML algorithms and it is established that deep learning offers a superior performance even with limited amount of raw RF and post processed time-sequence data. The deep learning model has also been validated by comparing our results with those from ECG signals. Our results indicate that using wireless signals for stand-by emotion state detection is a better alternative to other technologies with high accuracy and have much wider applications in future studies of behavioural sciences., Comment: 13 Pages, 7 Figures

Dielectric materials, with high tunability at microwave frequencies, are key components in the design of microwave communication systems. Dense Ba0.6Sr0.4TiO3 (BST) ceramics, with different grain sizes, were prepared in order to optimise the dielectric tunability via polar nano cluster effects. Dielectric permittivity and loss measurements were carried at both high and low frequencies and were supported by results from X-ray powder diffraction, scanning and transmission electron microscopies, Raman spectroscopy and piezoresponse force microscopy. The concentration of polar nano clusters, whose sizes are found to be in the range 20 to 50 nm, and the dielectric tunability increase with increasing grain size. A novel method for measurement of the microwave tunability in bulk dielectrics is presented. The highest tunability of 32% is achieved in ceramics with an average grain size of 10 um. The tunability of BST ceramics with applied DC field is demonstrated in a prototype small resonant antenna., Comment: 25pages, 6 figures

Modern communication systems will require antennas with adaptive functionality which are able to modify their performance based on the requirements of the channel. For example, mobile ad-hoc networks need directive antennas that are able to radiate in any direction across the 360{\deg} azimuth plane. Conformal antennas that can be simply operated to have multi-functional performance characteristics are therefore of interest. In this paper, we present a gradient-index lens antenna designed to radiate with a 45{\deg} beamwidth across 8 different sectors. When fed by a simple switched feeding network, the lens is able to provide 360{\deg} azimuth coverage in 45{\deg} segments. Further analysis of the radiation patterns shows how two distinct multi-beam patterns can be produced from simple feed networks and simultaneous excitation of each feeding element. The proposed lens is fabricated by multi-material 3D printing. The final lens radiates with a gain of 8.5 dBi when a single sector is excited, and with a maximum gain of 5.9 dBi in multi-beam mode. Finally, it is shown how the lens can also radiate omnidirectionally when optimised phase and amplitude weightings are applied to each port.

Data driven materials discovery and optimization requires databases that are error free and experimentally verified. Performing material measurements are time-consuming and often restricted by the fact that material sample preparations are non-trivial, labour-intensive and expensive. Numerical modelling of materials has been studied over the years in order to address these issues and nowadays it has been developed at multi-scale and multi-physics levels. However, numerical models for nano-composites, especially for ferroelectrics are limited due to multiple unknowns including oxygen vacancy densities, grain sizes and domain boundaries existing in the system. In this work, we introduce a human-machine interactive learning framework by developing a scalable semi-empirical model to accurately predict material properties enabled by deep learning (DL). MgO-doped BST (BaxSr1-xTiO3) is selected as an example ferroelectric-dielectric composite for validation. The DL model transfer-learns the experimental features of materials from a measurement database which includes data for over 100 different ferroelectric composites collected by screening the published data and combining our own measurement data. The trained DL model is utilized in providing feedback to human researchers, who then refine computer model parameters accordingly, hence completing the interactive learning cycle. Finally, the developed DL model is applied to predict and optimise new ferroelectric-dielectric composites with the highest figure of merit (FOM) value.

Representation learning approaches require a massive amount of discriminative training data, which is unavailable in many scenarios, such as healthcare, smart city, education, etc. In practice, people refer to crowdsourcing to get annotated labels. However, due to issues like data privacy, budget limitation, shortage of domain-specific annotators, the number of crowdsourced labels is still very limited. Moreover, because of annotators' diverse expertise, crowdsourced labels are often inconsistent. Thus, directly applying existing supervised representation learning (SRL) algorithms may easily get the overfitting problem and yield suboptimal solutions. In this paper, we propose \emph{NeuCrowd}, a unified framework for SRL from crowdsourced labels. The proposed framework (1) creates a sufficient number of high-quality \emph{n}-tuplet training samples by utilizing safety-aware sampling and robust anchor generation; and (2) automatically learns a neural sampling network that adaptively learns to select effective samples for SRL networks. The proposed framework is evaluated on both one synthetic and three real-world data sets. The results show that our approach outperforms a wide range of state-of-the-art baselines in terms of prediction accuracy and AUC. To encourage reproducible results, we make our code publicly available at \url{https://github.com/tal-ai/NeuCrowd_KAIS2021}., Comment: Accepted in Knowledge and Information Systems

Metagratings have been shown to form an agile and efficient platform for extreme wavefront manipulation, going beyond the limitations of gradient metasurfaces. Previous approaches for transmissive metagratings have resorted on compound asymmetric inclusions to achieve single-channel near-perfect diffraction. However, such complex inclusions are sensitive to geometric parameters and lack the flexibility for arbitrary phase modulation, restricting applications to beam deflection. Here, we show perfect unitary diffraction in all-dielectric transmissive metagratings using rectangular inclusions by tailoring their multipole interferences. Using this principle, we experimentally demonstrate analog phase profile encoding of a hologram through displacement modulation of CMOS-compatible silicon nitride nanobars, manifesting broadband and wide-angle high diffraction efficiencies for both polarizations and across the entire visible range. Featured with extreme angle/wavelength/polarization tolerance and alleviated structural complexity for both design and fabrication, our demonstration unlocks the full potential of metagrating-based wavefront manipulation for a variety of practical applications.

Electromagnetic (EM) sensing is a wide-spread contactless examination technique in science, engineering and military. However, conventional sensing systems are mostly lack of intelligence, which not only require expensive hardware and complicated computational algorithms, but also pose important challenges for advanced in-situ sensing. To address this shortcoming, we propose the concept of intelligent sensing by designing a programmable metasurface for data-driven learnable data acquisition, and integrating it into a data-driven learnable data processing pipeline. This strategy allows to learn an optimal sensing chain in systematic sense of variational autoencoder, i.e., to jointly learn an optimal measurement strategy along with matching data post processing schemes. A three-port deep artificial neural network (ANN) is designed to characterize the measurement process, such that an optimal measurement strategy is adaptive to the subject of interest by controlling the programmable metasurface for manipulating the EM illuminations. We design and fabricate a proof-of-principle sensing system in microwave, and demonstrate experimentally its significance on the high-quality imaging and high-accuracy object recognition from a remarkably reduced number of measurements. We faithfully expect that the presented methodology will provide us with a fundamentally new perspective on the design of intelligent sensing architectures at various frequencies, and beyond.

This paper presents a complete design, analysis, and performance evaluation of a novel distributed event-triggered control and estimation strategy for DC microgrids. The primary objective of this work is to efficiently stabilize the grid voltage, and to further balance the energy level of the energy storage (ES) systems. The locally-installed distributed controllers are utilised to reduce the number of transmitted packets and battery usage of the installed sensors, based on a proposed event-triggered communication scheme. Also, to reduce the network traffic, an optimal observer is employed which utilizes a modified Kalman consensus filter (KCF) to estimate the state of the DC microgrid via the distributed sensors. Furthermore, in order to effectively provide an intelligent data exchange mechanism for the proposed event-triggered controller, the publish-subscribe communication model is employed to setup a distributed control infrastructure in industrial wireless sensor networks (WSNs). The performance of the proposed control and estimation strategy is validated via the simulations of a DC microgrid composed of renewable energy sources (RESs). The results confirm the appropriateness of the implemented strategy for the optimal utilization of the advanced industrial network architectures in the smart grids.

Particle may sometimes have energy outside the range of radiation detection hardware so that the signal is saturated and useful information is lost. We have therefore investigated the possibility of using an Artificial Neural Network (ANN) to restore the saturated waveforms of $\gamma$ signals. Several ANNs were tested, namely the Back Propagation (BP), Simple Recurrent (Elman), Radical Basis Function (RBF) and Generalized Radial Basis Function (GRBF) neural networks (NNs) and compared with the fitting method based on the Marrone model. The GBRFNN was found to perform best., Comment: 8 pages, 8 figures, Preprint submitted to Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment

We investigate numerically the homogenized permittivities of composites made of low index dielectric inclusions in a ferroelectric matrix under a static electric field. A refined model is used to take into account the coupling between the electrostatic problem and the electric field dependent permittivity of the ferroelectric material, leading to a local field enhancement and permittivity change in the ferroelectric. Periodic and pseudo-random structures in two dimensions are investigated and we compute the effective permittivity, losses, electrically induced anisotropy and tunability of those metamaterials. We show that the tunability of such composites might be substantially enhanced in the periodic case, whereas introducing disorder in the microstructure weaken the effect of enhanced local permittivity change. Our results may be useful to guide the synthesis of novel composite ceramics with improved characteristics for controllable microwave devices.

Silicon-on-insulator (SOI) enables for capability improvement of modern information processing systems by replacing some of their electrical counterparts. With the miniaturization of SOI platform, backscattering suppression is one of the central issues to avoid energy loss and signal distortion in telecommunications. Valley, a new degree of freedom, provides an intriguing way for topologically robust information transfer and unidirectional flow of light, in particular for subwavelength strategy that still remains challenge in topological nanophotonics. Here, we realize topological transport in a SOI valley photonic crystal (VPC) slab. In such inversion asymmetry slab, singular Berry curvature near Brillouin zone corners guarantees valley-dependent topological edge states below light cone, maintaining a balance between in-plane robustness and out-of-plane radiation. Topologically robust transport at telecommunication wavelength is observed along two sharp-bend VPC interfaces with a compact size (< 10 um), showing flat-top high transmission of around 10% bandwidth. Furthermore, topological photonic routing is achieved in a bearded-stack VPC interface, originating from broadband unidirectional excitation of the valley-chirality-locked edge state by using a microdisk as a phase vortex generator. Control of valley in SOI platform not only shows a prototype of integrated photonic devices with promising applications for delay line, routing, optical isolation and dense wavelength division multiplexing for information processing based on topological nanophotonics, but also opens a new door towards the observation of non-trivial states even in non-Hermitain systems., Comment: 20 pages, 4 figures

A measurement is reported for the response to charged particles of a liquid scintillator named EJ-335 doped with 0.5% gadolinium by weight. This liquid scintillator was used as the detection medium in a neutron detector. The measurement is based on the in-situ $\alpha$-particles from the intrinsic Uranium and Thorium contamination in the scintillator. The $\beta$-$\alpha$ and the $\alpha$-$\alpha$ cascade decays from the U/Th decay chains were used to select $\alpha$-particles. The contamination levels of U/Th were consequently measured to be $(5.54\pm0.15)\times 10^{-11}$ g/g, $(1.45\pm0.01)\times 10^{-10}$ g/g and $(1.07\pm0.01)\times 10^{-11}$ g/g for $^{232}$Th, $^{238}$U and $^{235}$U, respectively, assuming secular equilibrium. The stopping power of $\alpha$-particles in the liquid scintillator was simulated by the TRIM software. Then the Birks constant, $kB$, of the scintillator for $\alpha$-particles was determined to be $(7.28\pm0.23)$ mg/(cm$^{2}\cdot$MeV) by Birks' formulation. The response for protons is also presented assuming the $kB$ constant is the same as for $\alpha$-particles., Comment: 12 pages, 10 figures, 3 tables, prepared for submission to JINST

We calculate the branching ratios and polarization fractions of the $B \to a_1 b_1$ decays in the perturbative QCD(pQCD) approach at leading order, where $a_1$($b_1$) stands for the axial-vector $a_1(1260)[b_1(1235)]$ state. By combining the phenomenological analyses with the perturbative calculations, we find the following results: (a) the large decay rates around $10^{-5}$ to $10^{-6}$ of the $B \to a_1 b_1$ decays dominated by the longitudinal polarization(except for the $B^+ \to b_1^+ a_1^0$ mode) are predicted and basically consistent with those in the QCD factorization(QCDF) within errors, which are expected to be tested by the Large Hadron Collider and Belle-II experiments. The large $B^0 \to a_1^0 b_1^0$ branching ratio could provide hints to help explore the mechanism of the color-suppressed decays. (b) the rather different QCD behaviors between the $a_1$ and $b_1$ mesons result in the destructive(constructive) contributions in the nonfactorizable spectator diagrams with $a_1(b_1)$ emission. Therefore, an interesting pattern of the branching ratios appears for the color-suppressed $B^0 \to a_1^0 a_1^0, a_1^0 b_1^0,$ and $b_1^0 b_1^0$ modes in the pQCD approach, $Br(B^0 \to b_1^0 b_1^0) > Br(B^0 \to a_1^0 b_1^0) \gtrsim Br(B^0 \to a_1^0 a_1^0)$, which is different from $Br(B^0 \to b_1^0 b_1^0) \sim Br(B^0 \to a_1^0 b_1^0) \gtrsim Br(B^0 \to a_1^0 a_1^0)$ in the QCDF and would be verified at future experiments. (c) the large naive factorization breaking effects are observed in these $B \to a_1 b_1$ decays. Specifically, the large nonfactorizable spectator(weak annihilation) amplitudes contribute to the $B^0 \to b_1^+ a_1^-(B^+ \to a_1^+ b_1^0\; {\rm and}\; B^+ \to b_1^+ a_1^0)$ mode(s), which demand confirmations via the precise measurements., Comment: 13 pages, 1 figure, 5 tables, revtex file

We report on the measurements of the fluxes and spectra of the environmental fast neutron background at the China Jinping Underground Laboratory (CJPL) with a rock overburden of about 6700 meters water equivalent, using a liquid scintillator detector doped with 0.5% gadolinium. The signature of a prompt nuclear recoil followed by a delayed high energy $\gamma$-ray cascade is used to identify neutron events. The large energy deposition of the delayed $\gamma$-rays from the $(n, \gamma)$ reaction on gadolinium, together with the excellent n-$\gamma$ discrimination capability provides a powerful background suppression which allows the measurement of a low intensity neutron flux. The neutron flux of $(1.51\pm0.03(stat.)\pm0.10(syst.))\times10^{-7}$ cm$^{-2}$s$^{-1}$ in the energy range of 1 -- 10 MeV in the Hall A of CJPL was measured based on 356 days of data. In the same energy region, measurement with the same detector placed in a one meter thick polyethylene room gives a significantly lower flux of $(4.9\pm0.9(stat.)\pm0.5(syst.))\times10^{-9}$ cm$^{-2}$s$^{-1}$ with 174 days of data. This represents a measurement of the lowest environmental fast neutron background among the underground laboratories in the world, prior to additional experiment-specific attenuation. Additionally, the fast neutron spectra both in the Hall A and the polyethylene room were reconstructed with the help of GEANT4 simulation., Comment: 11 pages, 11 figures, 3 tables, Submitted to NIM-A

We propose a general method to arbitrarily manipulate an electromagnetic wave propagating in a two-dimensional medium, without introducing any scattering. This leads to a whole class of isotropic spatially varying permittivity and permeability profiles that are invisible while shaping the field magnitude and/or phase. In addition, we propose a metamaterial structure working in the infrared that demonstrates deep sub-wavelength control of the electric field amplitude and strong reduction of the scattering. This work offers an alternative strategy to achieve invisibility with isotropic materials and paves the way for tailoring the propagation of light at the nanoscale

We develop a model for the coupling of quasi-normal modes in open photonic systems consisting of two resonators. By expressing the modes of the coupled system as a linear combination of the modes of the individual particles, we obtain a generalized eigenvalue problem involving small size dense matrices. We apply this technique to dielectric rod dimmer of rectangular cross section for Transverse Electric (TE) polarization in a two-dimensional (2D) setup. The results of our model show excellent agreement with full-wave finite element simulations. We provide a convergence analysis, and a simplified model with a few modes to study the influence of the relative position of the two resonators. This model provides interesting physical insights on the coupling scheme at stake in such systems and pave the way for systematic and efficient design and optimization of resonances in more complicated systems, for applications including sensing, antennae and spectral filtering.

The China Dark Matter Experiment (CDEX) is a low background experiment at China Jinping Underground Laboratory (CJPL) designed to directly detect dark matter with a high-purity Germanium (HPGe) detector. In the second phase CDEX-10 with a 10 kg Germanium array detector system, the liquid argon (LAr) anti-compton active shielding and cooling system is proposed. For purpose of studying the properties of LAr detector, a prototype with an active volume of 7 liters of liquid argon was built and operated. The photoelectron yields, as a critically important parameter for the prototype detector, have been measured to be 0.051-0.079 p.e./keV for 662 keV Gamma lines at different positions. The good agreement between the experimental and simulation results has provided a quite reasonable understanding and determination of the important parameters such as the Surviving Fraction of the Ar2 excimers, the absorption length for 128 nm photons in liquid argon, the reflectivity of Teflon and so on., Comment: 6 pages, 8 figures. Submitted to Chin. Phys. C

In this work, a new neutron and {\gamma}(n/{\gamma}) discrimination method based on an Elman Neural Network (ENN) is proposed to improve the discrimination performance of liquid scintillator (LS) detectors. Neutron and {\gamma} data were acquired from an EJ-335 LS detector, which was exposed in a 241Am-9Be radiation field. Neutron and {\gamma} events were discriminated using two methods of artificial neural network including the ENN and a typical Back Propagation Neural Network (BPNN) as a control. The results show that the two methods have different n/{\gamma} discrimination performances. Compared to the BPNN, the ENN provides an improved of Figure of Merit (FOM) in n/{\gamma} discrimination. The FOM increases from 0.907 {\pm} 0.034 to 0.953 {\pm} 0.037 by using the new method of the ENN. The proposed n/{\gamma} discrimination method based on ENN provides a new choice of pulse shape discrimination in neutron detection., Comment: 16 pages, 8 figures and 2 tables, version to appear in Chinese Physics C

The China Dark Matter Experiment (CDEX) is located at the China Jinping underground laboratory (CJPL) and aims to directly detect the WIMP flux with high sensitivity in the low mass region. Here we present a study of the predicted photon and electron backgrounds including the background contribution of the structure materials of the germanium detector, the passive shielding materials, and the intrinsic radioactivity of the liquid argon that serves as an anti-Compton active shielding detector. A detailed geometry is modeled and the background contribution has been simulated based on the measured radioactivities of all possible components within the GEANT4 program. Then the photon and electron background level in the energy region of interest (<10^-2 events kg-1 day-1 keV-1 (cpkkd)) is predicted based on Monte Carlo simulations. The simulated result is consistent with the design goal of CDEX-10 experiment, 0.1 cpkkd, which shows that the active and passive shield design of CDEX-10 is effective and feasible., Comment: 10 pages, 5 figures, 4 tables, submitted to Chinese Physics C