Search

Electrochemical nitrate reduction reaction(NO3RR)offers a sustainable route for ambient ammonia synthesis. While metal-nitrogen-carbon (M-N-C) single-atom catalysts have emerged as promising candidates for NO3RR, the structure-activity relations underlying their catalytic behavior remain to be elucidated. Through systematic analysis of reported experimental data and pH-field coupled microkinetic modelling on a reversible hydrogen electrode (RHE) scale, we reveal that the coordination-dependent activity originates from distinct scaling relations governed by metal-intermediate interactions. M-N-Pyrrolic catalysts demonstrate higher turnover frequencies for ammonia production, whereas M-N-Pyridinic catalysts exhibit broader activity ranges across the activity volcano plot. Meanwhile, the adsorption and protonation of nitrate, which is a step often dismissed and/or assumed to be simultaneous in many previous reports, is identified to be the rate-determining step (RDS) in NO3RR. Remarkably, our subsequent experimental validation confirms the theoretical predictions under both neutral and alkaline conditions. This study offers a comprehensive mechanistic framework for interpreting the electrocatalytic activity of M-N-C catalysts in NO3RR, showing that a classical thermodynamic limiting-potential model is not sufficiently accurate to capture the RDS and the catalytic performance trends of different materials (even on M-N-Pyrrolic and M-N-Pyridinic catalysts). These findings provide brand new insights into the reaction mechanism of NO3RR and establish fundamental design principles for electrocatalytic ammonia synthesis., Comment: 21 pages

Unconventional superconductors that spontaneously break space-group symmetries of their underlying crystal lattice are distinguished by spatial modulations of the superconducting order parameter. These states have recently captured significant attention in various strongly correlated materials, where the breaking of translational or intra-unit-cell symmetries results in the emergence of pair density waves with wavelengths extending across one or multiple unit cells. Here, we employ a spectroscopic-imaging scanning tunneling microscope to identify robust half-unit-cell modulations of the superconducting gap magnitude, coherence strength and subgap states in tunable 1T$^{\prime}$-MoTe$_2$ monolayer. The modulations are oriented perpendicular to the zigzag Mo chains and coexist with three-unit-cell primary pair density wave modulations along the Mo chains, both of which attenuate with increasing temperature and the application of external magnetic fields. Importantly, we find that the superconductivity modulations are strongly linked to unconventional electron pairing mechanisms, which significantly deviate from the Cooper pairing observed in traditional Bardeen-Cooper-Schrieffer superconductors. Our results advance the knowledge of Cooper-pair density modulations and the intricate interplay with other symmetry-breaking states in strongly correlated superconductors., Comment: 31 pages,4 figures

Holographic multiple-input and multiple-output (MIMO) communications introduce innovative antenna array configurations, such as dense arrays and volumetric arrays, which offer notable advantages over conventional planar arrays with half-wavelength element spacing. However, accurately assessing the performance of these new holographic MIMO systems necessitates careful consideration of channel matrix normalization, as it is influenced by array gain, which, in turn, depends on the array topology. Traditional normalization methods may be insufficient for assessing these advanced array topologies, potentially resulting in misleading or inaccurate evaluations. In this study, we propose electromagnetic normalization approaches for the channel matrix that accommodate arbitrary array topologies, drawing on the array gains from analytical, physical, and full-wave methods. Additionally, we introduce a normalization method for near-field MIMO channels based on a rigorous dyadic Green's function approach, which accounts for potential losses of gain at near field. Finally, we perform capacity analyses under quasi-static, ergodic, and near-field conditions, through adopting the proposed normalization techniques. Our findings indicate that channel matrix normalization should reflect the realized gains of the antenna array in target directions. Failing to accurately normalize the channel matrix can result in errors when evaluating the performance limits and benefits of unconventional holographic array topologies, potentially compromising the optimal design of holographic MIMO systems.

Spectroscopic identification of distinct nonlinear photocurrents unveils quantum geometric properties of electron wavefunctions and the momentum-space topological structures. This is especially interesting, but still puzzling, for chiral topological semimetals with possibilities of hosting giant quantized circular photogalvanic effect. Here we report a comprehensive terahertz (THz) emission spectroscopic analysis of nonlinear photoconductivity of chiral multifold CoSi at 0.26 ~ 1 eV. We find a large linear shift conductivity (17 {\mu}A/V2), and confirm a giant injection conductivity (167 {\mu}A/V2) as a consequence of strongly interfered non-quantized contributions from the vicinity of multifold nodes with opposite chiralities. The bulk injection current excited by the pump field with a complex wavevector is shown to carry both longitudinal and transverse components. Symmetry analyses further unveil weak nonlocal photon drag effect in addition to the photogalvanic effect. This work not only highlights chiral transition metal monosilicides for mid-infrared photovoltaic applications via various nonlinear optical channels, but also consolidates the THz spectroscopy for quantitative photovoltaic research.

Knowledge Distillation (KD) is a powerful approach for compressing a large model into a smaller, more efficient model, particularly beneficial for latency-sensitive applications like recommender systems. However, current KD research predominantly focuses on Computer Vision (CV) and NLP tasks, overlooking unique data characteristics and challenges inherent to recommender systems. This paper addresses these overlooked challenges, specifically: (1) mitigating data distribution shifts between teacher and student models, (2) efficiently identifying optimal teacher configurations within time and budgetary constraints, and (3) enabling computationally efficient and rapid sharing of teacher labels to support multiple students. We present a robust KD system developed and rigorously evaluated on multiple large-scale personalized video recommendation systems within Google. Our live experiment results demonstrate significant improvements in student model performance while ensuring consistent and reliable generation of high quality teacher labels from a continuous data stream of data.

The Regeneration Cavity (RC) is a critical component of the Any Light Particle Search II (ALPS II) experiment. It increases the signal from possible axions and axion-like particles in the experiment by nearly four orders of magnitude. The total round-trip optical losses of the power circulating in the cavity must be minimized in order to maximize the resonant enhancement of the cavity, which is an important figure of merit for ALPS II. Lower optical losses also increase the cavity storage time and with the 123 meter long ALPS II RC we have demonstrated the longest storage time of a two-mirror optical cavity. We measured a storage time of $7.17 \pm 0.01$ ms, equivalent to a linewidth of 44.4 Hz and a finesse of 27,500 at a wavelength of 1064 nm., Comment: 16 pages, 8 figures, 1 table

We present the Learned Ranking Function (LRF), a system that takes short-term user-item behavior predictions as input and outputs a slate of recommendations that directly optimizes for long-term user satisfaction. Most previous work is based on optimizing the hyperparameters of a heuristic function. We propose to model the problem directly as a slate optimization problem with the objective of maximizing long-term user satisfaction. We also develop a novel constraint optimization algorithm that stabilizes objective trade-offs for multi-objective optimization. We evaluate our approach with live experiments and describe its deployment on YouTube., Comment: RecSys 24

Holographic multiple-input multiple-output (HMIMO) utilizes a compact antenna array to form a nearly continuous aperture, thereby enhancing higher capacity and more flexible configurations compared with conventional MIMO systems, making it attractive in current scientific research. Key questions naturally arise regarding the potential of HMIMO to surpass Shannon's theoretical limits and how far its capabilities can be extended. However, the traditional Shannon information theory falls short in addressing these inquiries because it only focuses on the information itself while neglecting the underlying carrier, electromagnetic (EM) waves, and environmental interactions. To fill up the gap between the theoretical analysis and the practical application for HMIMO systems, we introduce electromagnetic information theory (EIT) in this paper. This paper begins by laying the foundation for HMIMO-oriented EIT, encompassing EM wave equations and communication regions. In the context of HMIMO systems, the resultant physical limitations are presented, involving Chu's limit, Harrington's limit, Hannan's limit, and the evaluation of coupling effects. Field sampling and HMIMO-assisted oversampling are also discussed to guide the optimal HMIMO design within the EIT framework. To comprehensively depict the EM-compliant propagation process, we present the approximate and exact channel modeling approaches in near-/far-field zones. Furthermore, we discuss both traditional Shannon's information theory, employing the probabilistic method, and Kolmogorov information theory, utilizing the functional analysis, for HMIMO-oriented EIT systems.

Animated logos are a compelling and ubiquitous way individuals and brands represent themselves online. Manually authoring these logos can require significant artistic skill and effort. To help novice designers animate logos, design tools currently offer templates and animation presets. However, these solutions can be limited in their expressive range. Large language models have the potential to help novice designers create animated logos by generating animation code that is tailored to their content. In this paper, we introduce LogoMotion, an LLM-based system that takes in a layered document and generates animated logos through visually-grounded program synthesis. We introduce techniques to create an HTML representation of a canvas, identify primary and secondary elements, synthesize animation code, and visually debug animation errors. When compared with an industry standard tool, we find that LogoMotion produces animations that are more content-aware and are on par in terms of quality. We conclude with a discussion of the implications of LLM-generated animation for motion design.

In this paper, the problem of spectral-efficient communication and computation resource allocation for distributed reconfigurable intelligent surfaces (RISs) assisted probabilistic semantic communication (PSC) in industrial Internet-of-Things (IIoT) is investigated. In the considered model, multiple RISs are deployed to serve multiple users, while PSC adopts compute-then-transmit protocol to reduce the transmission data size. To support high-rate transmission, the semantic compression ratio, transmit power allocation, and distributed RISs deployment must be jointly considered. This joint communication and computation problem is formulated as an optimization problem whose goal is to maximize the sum semantic-aware transmission rate of the system under total transmit power, phase shift, RIS-user association, and semantic compression ratio constraints. To solve this problem, a many-to-many matching scheme is proposed to solve the RIS-user association subproblem, the semantic compression ratio subproblem is addressed following greedy policy, while the phase shift of RIS can be optimized using the tensor based beamforming. Numerical results verify the superiority of the proposed algorithm.

Texture plays a vital role in enhancing visual richness in both real photographs and computer-generated imagery. However, the process of editing textures often involves laborious and repetitive manual adjustments of textons, which are the recurring local patterns that characterize textures. This work introduces a fully unsupervised approach for representing textures using a compositional neural model that captures individual textons. We represent each texton as a 2D Gaussian function whose spatial support approximates its shape, and an associated feature that encodes its detailed appearance. By modeling a texture as a discrete composition of Gaussian textons, the representation offers both expressiveness and ease of editing. Textures can be edited by modifying the compositional Gaussians within the latent space, and new textures can be efficiently synthesized by feeding the modified Gaussians through a generator network in a feed-forward manner. This approach enables a wide range of applications, including transferring appearance from an image texture to another image, diversifying textures,texture interpolation, revealing/modifying texture variations, edit propagation, texture animation, and direct texton manipulation. The proposed approach contributes to advancing texture analysis, modeling, and editing techniques, and opens up new possibilities for creating visually appealing images with controllable textures., Comment: Project page: https://phtu-cs.github.io/cnt-siga24/

Magnetic impurities in superconductors are of increasing interest due to emergent Yu-Shiba-Rusinov (YSR) states and Majorana zero modes for fault-tolerant quantum computation. However, a direct relationship between the YSR multiple states and magnetic anisotropy splitting of quantum impurity spins remains poorly characterized. By using scanning tunneling microscopy, we resolve systematically individual transition-metal (Fe, Cr and Ni) impurities induced YSR multiplets as well as their Zeeman effects in K$_3$C$_{60}$ superconductor. The YSR multiplets show identical $d$ orbital-like wave functions that are symmetry-mismatched to the threefold K$_3$C$_{60}$(111) host surface, breaking point-group symmetries of the spatial distribution of YSR bound states in real space. Remarkably, we identify an unprecedented fermion-parity-preserving quantum phase transition between ground states with opposite signs of the uniaxial magnetic anisotropy that can be manipulated by an external magnetic field. These findings can be readily understood in terms of anisotropy splitting of quantum impurity spins, and thus elucidate the intricate interplay between the magnetic anisotropy and YSR multiplets., Comment: 38 pages, 4 figures in the main text

Empowered by the latest progress on innovative metamaterials/metasurfaces and advanced antenna technologies, holographic multiple-input multiple-output (H-MIMO) emerges as a promising technology to fulfill the extreme goals of the sixth-generation (6G) wireless networks. The antenna arrays utilized in H-MIMO comprise massive (possibly to extreme extent) numbers of antenna elements, densely spaced less than half-a-wavelength and integrated into a compact space, realizing an almost continuous aperture. Thanks to the expected low cost, size, weight, and power consumption, such apertures are expected to be largely fabricated for near-field communications. In addition, the physical features of H-MIMO enable manipulations directly on the electromagnetic (EM) wave domain and spatial multiplexing. To fully leverage this potential, near-field H-MIMO channel modeling, especially from the EM perspective, is of paramount significance. In this article, we overview near-field H-MIMO channel models elaborating on the various modeling categories and respective features, as well as their challenges and evaluation criteria. We also present EM-domain channel models that address the inherit computational and measurement complexities. Finally, the article is concluded with a set of future research directions on the topic., Comment: double column, 9 pages, 3 figures, 2 tables, accepted by IEEE Wireless Communications Magazine

In this paper, we investigate the beam training problem in the multi-user millimeter wave (mmWave) communication system, where multiple reconfigurable intelligent surfaces (RISs) are deployed to improve the coverage and the achievable rate. However, existing beam training techniques in mmWave systems suffer from the high complexity (i.e., exponential order) and low identification accuracy. To address these problems, we propose a novel hashing multi-arm beam (HMB) training scheme that reduces the training complexity to the logarithmic order with the high accuracy. Specifically, we first design a generation mechanism for HMB codebooks. Then, we propose a demultiplexing algorithm based on the soft decision to distinguish signals from different RIS reflective links. Finally, we utilize a multi-round voting mechanism to align the beams. Simulation results show that the proposed HMB training scheme enables simultaneous training for multiple RISs and multiple users, and reduces the beam training overhead to the logarithmic level. Moreover, it also shows that our proposed scheme can significantly improve the identification accuracy by at least 20% compared to existing beam training techniques.

In this paper, we investigate the millimeter-wave (mmWave) near-field beam training problem to find the correct beam direction. In order to address the high complexity and low identification accuracy of existing beam training techniques, we propose an efficient hashing multi-arm beam (HMB) training scheme for the near-field scenario. Specifically, we first design a set of sparse bases based on the polar domain sparsity of the near-field channel. Then, the random hash functions are chosen to construct the near-field multi-arm beam training codebook. Each multi-arm beam codeword is scanned in a time slot until all the predefined codewords are traversed. Finally, the soft decision and voting methods are applied to distinguish the signal from different base stations and obtain correctly aligned beams. Simulation results show that our proposed near-field HMB training method can reduce the beam training overhead to the logarithmic level, and achieve 96.4% identification accuracy of exhaustive beam training. Moreover, we also verify applicability under the far-field scenario., Comment: arXiv admin note: text overlap with arXiv:2402.04913

Millimeter wave (mmWave) has attracted considerable attention due to its wide bandwidth and high frequency. However, it is highly susceptible to blockages, resulting in significant degradation of the coverage and the sum rate. A promising approach is deploying distributed reconfigurable intelligent surfaces (RISs), which can establish extra communication links. In this paper, we investigate the impact of distributed RISs on the coverage probability and the sum rate in mmWave wireless communication systems. Specifically, we first introduce the system model, which includes the blockage, the RIS and the user distribution models, leveraging the Poisson point process. Then, we define the association criterion and derive the conditional coverage probabilities for the two cases of direct association and reflective association through RISs. Finally, we combine the two cases using Campbell's theorem and the total probability theorem to obtain the closed-form expressions for the ergodic coverage probability and the sum rate. Simulation results validate the effectiveness of the proposed analytical approach, demonstrating that the deployment of distributed RISs significantly improves the ergodic coverage probability by 45.4% and the sum rate by over 1.5 times., Comment: arXiv admin note: substantial text overlap with arXiv:2402.06154

Single-atom catalysts (SACs) with metal-nitrogen-carbon (M-N-C) structures are widely recognized as promising candidates in oxygen reduction reactions (ORR). According to the classical Sabatier principle, optimal 3d metal catalysts, such as Fe/Co-N-C, achieve superior catalytic performance due to the moderate binding strength. However, the substantial ORR activity demonstrated by weakly binding M-N-C catalysts such as NiCu-N-C challenges current understandings, emphasizing the need to explore new underlying mechanisms. In this work, we integrated a pH-field coupled microkinetic model with detailed experimental electron state analyses to verify a novel key step in the ORR reaction pathway of weak-binding SACs-the oxygen adsorption at the metal-N bridge site. This step significantly altered the adsorption scaling relations, electric field responses, and solvation effects, further impacting the key kinetic reaction barrier from HOO* to O* and pH-dependent performance. Synchrotron spectra analysis further provides evidence for the new weak-binding M-N-C model, showing an increase in electron density on the anti-bonding pi orbitals of N atoms in weak-binding M-N-C catalysts and confirming the presence of N-O bonds. These findings redefine the understanding of weak-binding M-N-C catalyst behavior, opening up new perspectives for their application in clean energy., Comment: 27 pages, 6 figures

Any Light Particle Search II (ALPS II) is a dual optical cavity enhanced light-shining-through-a-wall (LSW) experiment at DESY in Hamburg looking for axions and axion-like particles with a target search sensitivity of $g_{a \gamma \gamma}$ down to $2 \times 10^{-11}\,\textrm{GeV}^{-1}$ for masses $m_a \leq 0.1\,\textrm{meV}$. Two 120$\,$m long strings of superconducting dipole magnets have been set up, each providing a magnetic field-length product of $560\,\textrm{T}\cdot\textrm{m}$. A resonant optical cavity with a record-worthy storage time of as high as 7$\,$ms has been constructed to encompass one magnet string. During its initial data-taking phase ALPS II will be operated with a simplified optical configuration that facilitates the characterization of the experiment. The first ALPS II science run took place in May/June 2023 and achieved an estimated search sensitivity of $g_{a \gamma \gamma}$ of around $6 \times 10^{-10}\,\textrm{GeV}^{-1}$ for $m_a \leq 0.1\,\textrm{meV}$. Data analysis and further data runs are under way. Final results on axion-photon coupling from the initial science runs of ALPS II are expected in early 2024., Comment: 6 pages, 2 figures, contribution to the proceedings of the XVIII International Conference on Topics in Astroparticle and Underground Physics (TAUP2023)

We introduce DrawTalking, an approach to building and controlling interactive worlds by sketching and speaking while telling stories. It emphasizes user control and flexibility, and gives programming-like capability without requiring code. An early open-ended study with our prototype shows that the mechanics resonate and are applicable to many creative-exploratory use cases, with the potential to inspire and inform research in future natural interfaces for creative exploration and authoring., Comment: 25 pages, 27 figures; Matching version accepted at UIST 2024

Machine learning (ML) can process large sets of data generated from complex systems, which is ideal for classification tasks as often appeared in critical phenomena. Meanwhile ML techniques have been found effective in detecting critical points, or in a broader sense phase separation, and extracting critical exponents. But there are still many unsolved issues with the ML, one of which is the meaning of hidden variables of unsupervised learning. Some say that the hidden variables and the principal component may contain basic information regarding the order parameter of the system of interest, which sounds plausible but lacks evidence. This study aims at searching for evidence supporting the conjecture that the autoencoder's (AE) single latent variable and PCA's first principal component can only serve as signals related to particle density, which happens to be the order parameter of the non-equilibrium DP model. Indeed, in some phase transition (PT) models the order parameter is the particle density, whereas in some PT models it is not. Having conducted a certain degree of random shuffling on the DP configurations, which are then fed to the neural networks as input, we find that AE's single latent variable and PCA's first principal component can indeed represent particle density. It is found that shuffling does affect the size of maximum cluster in the system, which suggests that the second principal component of the PCA is related to the maximal cluster. This has been supported by changes in the correlation length of the transition system with variations in the shuffle ratio., Comment: 18 pages, 15 figures

Characterizing the orbital angular momentum (OAM) of a vortex beam is critically important for OAM-encoded data transfer. However, in typical OAM-based applications where vortex beams transmit through diffusers, the accompanying scattering effect tends to be either deliberately prevented, or characterized and then modulated actively based on complex wavefront shaping and interferometry techniques. Here, we aim to investigate the characteristics of blurred speckles obtained after a vortex beam transmits through a ground glass diffuser. It is theoretically and experimentally demonstrated that a cross-correlation annulus can be identified by implementing the cross-correlation operation between speckle patterns corresponding to vortex beams with different OAM values. Besides, it is worth noting that, the size of the cross-correlation annulus is determined by the absolute value of the topological charge difference between the two corresponding vortex beams. Based on this mechanism, the OAM modes can be easily sorted from the incoherently measured OAM-dependent speckles as well as their cross-correlation. Furthermore, to make full use of the orthogonal feature of the OAM-dependent speckles, demultiplexing of OAM-encoded data transfer is verified using a ground glass diffuser. Both 8-bit grayscale and 24-bit RGB OAM-encoded data transfers are carried out in experiments with superior error rates. We can conclude that the OAM-dependent speckles can be not only utilized as a competitive candidate for the OAM mode sorting function in a simple way but also provide an efficient method for the demultiplexing of OAM-encoded data transfer in a practical application.

Based on a proper hypothesis on the noncommutative Fourier integral operators, we establish in this paper the strong-type $(p,p)$ (with $2\leq p\leq \infty$) estimates for the operator-valued Stein's maximal spherical means.

Musicians and fans often produce lyric videos, a form of music videos that showcase the song's lyrics, for their favorite songs. However, making such videos can be challenging and time-consuming as the lyrics need to be added in synchrony and visual harmony with the video. Informed by prior work and close examination of existing lyric videos, we propose a set of design guidelines to help creators make such videos. Our guidelines ensure the readability of the lyric text while maintaining a unified focus of attention. We instantiate these guidelines in a fully automated pipeline that converts an input music video into a lyric video. We demonstrate the robustness of our pipeline by generating lyric videos from a diverse range of input sources. A user study shows that lyric videos generated by our pipeline are effective in maintaining text readability and unifying the focus of attention.

M-N-C single-atom catalysts (SACs) have emerged as a potential substitute for the costly platinum-group catalysts in oxygen reduction reaction (ORR). However, several critical aspects of M-N-C SACs in ORR remain poorly understood, including their pH-dependent activity, selectivity for 2- or 4-electron transfer pathways, and the identification of the rate-determining steps. Herein, analyzing >100 M-N-C structures and >2000 sets of energetics, we unveil a pH-dependent evolution in ORR activity volcanos from a single-peak in alkaline media to a double-peak in acids. We found that this pH-dependent behavior in M-N-C catalysts fundamentally stems from their moderate dipole moments and polarizability for O* and HOO* adsorbates, as well as unique scaling relations among ORR adsorbates. To validate our theoretical discovery, we synthesized a series of molecular M-N-C catalysts, each characterized by well-defined atomic coordination environments. Impressively, the experiments matched our theoretical predictions on kinetic current, Tafel slope, and turnover frequency in both acidic and alkaline environments. These new insights also refine the famous Sabatier principle by emphasizing the need to avoid an "acid trap" while designing M-N-C catalysts for ORR or any other pH-dependent electrochemical applications., Comment: 24 pages, 5 Figures

Electronic liquid-crystal phases are observed in numerous strongly-correlated systems including high-temperature superconductors. However, identifying these exotic phases and understanding their interplay with superconductivity in topological materials remain challenging. Here we employ a cryogenic scanning tunneling microscopy to discover a smectic (stripe) charge order (CO) and a primary pair-density-wave (PDW) in topological monolayer 1T$^\prime$-MoTe$_2$. The two orders are spatially modulated unidirectionally at the same wavevector, but have a marked spatial phase difference of about 2$\pi$/5. Importantly, the primary PDW state features a two-gap superconductivity below the transition temperature of 6.0 K and induces another unique particle-hole-symmetric CO at twice the PDW wavevector. Combining these results and our density functional calculations, we reveal that the two smectic orders are primarily driven by nesting behaviors between electron and hole pockets. Our findings establish monolayer 1T$^\prime$-MoTe$_2$ as a topological paradigm for exploring electronic smecticity, which intertwines with multiple preexisting symmetry-breaking states., Comment: 16 pages, 4 figures, Supplementary materials

The identification of distinct charge transport features, deriving from nontrivial bulk band and surface states, has been a challenging subject in the field of topological systems. In topological Dirac and Weyl semimetals, nontrivial conical bands with Fermi-arc surface states give rise to negative longitudinal magnetoresistance due to chiral anomaly effect and unusual thickness dependent quantum oscillation from Weyl-orbit effect, which were demonstrated recently in experiments. In this work, we report the experimental observations of large nonlinear and nonreciprocal transport effects for both longitudinal and transverse channels in an untwinned Weyl metal of SrRuO$_3$ thin film grown on a SrTiO$_{3}$ substrate. From rigorous measurements with bias current applied along various directions with respect to the crystalline principal axes, the magnitude of nonlinear Hall signals from the transverse channel exhibits a simple sin$\alpha$ dependence at low temperatures, where $\alpha$ is the angle between bias current direction and orthorhombic [001]$_{\rm o}$, reaching a maximum when current is along orthorhombic [1-10]$_{\rm o}$. On the contrary, the magnitude of nonlinear and nonreciprocal signals in the longitudinal channel attains a maximum for bias current along [001]$_{\rm o}$, and it vanishes for bias current along [1-10]$_{\rm o}$. The observed $\alpha$-dependent nonlinear and nonreciprocal signals in longitudinal and transverse channels reveal a magnetic Weyl phase with an effective Berry curvature dipole along [1-10]$_{\rm o}$ from surface states, accompanied by 1D chiral edge modes along [001]$_{\rm o}$., Comment: 27 pages, 6 figures

Randomly-hashed item ids are used ubiquitously in recommendation models. However, the learned representations from random hashing prevents generalization across similar items, causing problems of learning unseen and long-tail items, especially when item corpus is large, power-law distributed, and evolving dynamically. In this paper, we propose using content-derived features as a replacement for random ids. We show that simply replacing ID features with content-based embeddings can cause a drop in quality due to reduced memorization capability. To strike a good balance of memorization and generalization, we propose to use Semantic IDs -- a compact discrete item representation learned from frozen content embeddings using RQ-VAE that captures the hierarchy of concepts in items -- as a replacement for random item ids. Similar to content embeddings, the compactness of Semantic IDs poses a problem of easy adaption in recommendation models. We propose novel methods for adapting Semantic IDs in industry-scale ranking models, through hashing sub-pieces of of the Semantic-ID sequences. In particular, we find that the SentencePiece model that is commonly used in LLM tokenization outperforms manually crafted pieces such as N-grams. To the end, we evaluate our approaches in a real-world ranking model for YouTube recommendations. Our experiments demonstrate that Semantic IDs can replace the direct use of video IDs by improving the generalization ability on new and long-tail item slices without sacrificing overall model quality.

The local and global features are both essential for automatic speech recognition (ASR). Many recent methods have verified that simply combining local and global features can further promote ASR performance. However, these methods pay less attention to the interaction of local and global features, and their series architectures are rigid to reflect local and global relationships. To address these issues, this paper proposes InterFormer for interactive local and global features fusion to learn a better representation for ASR. Specifically, we combine the convolution block with the transformer block in a parallel design. Besides, we propose a bidirectional feature interaction module (BFIM) and a selective fusion module (SFM) to implement the interaction and fusion of local and global features, respectively. Extensive experiments on public ASR datasets demonstrate the effectiveness of our proposed InterFormer and its superior performance over the other Transformer and Conformer models., Comment: Accepted by Interspeech 2023

In the field of cybersecurity, the ability to compute similarity scores at the function level is import. Considering that a single binary file may contain an extensive amount of functions, an effective learning framework must exhibit both high accuracy and efficiency when handling substantial volumes of data. Nonetheless, conventional methods encounter several limitations. Firstly, accurately annotating different pairs of functions with appropriate labels poses a significant challenge, thereby making it difficult to employ supervised learning methods without risk of overtraining on erroneous labels. Secondly, while SOTA models often rely on pre-trained encoders or fine-grained graph comparison techniques, these approaches suffer from drawbacks related to time and memory consumption. Thirdly, the momentum update algorithm utilized in graph-based contrastive learning models can result in information leakage. Surprisingly, none of the existing articles address this issue. This research focuses on addressing the challenges associated with large-scale BCSD. To overcome the aforementioned problems, we propose GraphMoco: a graph momentum contrast model that leverages multimodal structural information for efficient binary function representation learning on a large scale. Our approach employs a CNN-based model and departs from the usage of memory-intensive pre-trained models. We adopt an unsupervised learning strategy that effectively use the intrinsic structural information present in the binary code. Our approach eliminates the need for manual labeling of similar or dissimilar information.Importantly, GraphMoco demonstrates exceptional performance in terms of both efficiency and accuracy when operating on extensive datasets. Our experimental results indicate that our method surpasses the current SOTA approaches in terms of accuracy., Comment: 22 pages,7 figures

Envisioned as one of the most promising technologies, holographic multiple-input multiple-output (H-MIMO) recently attracts notable research interests for its great potential in expanding wireless possibilities and achieving fundamental wireless limits. Empowered by the nearly continuous, large and energy-efficient surfaces with powerful electromagnetic (EM) wave control capabilities, H-MIMO opens up the opportunity for signal processing in a more fundamental EM-domain, paving the way for realizing holographic imaging level communications in supporting the extremely high spectral efficiency and energy efficiency in future networks. In this article, we try to implement a generalized EM-domain near-field channel modeling and study its capacity limit of point-to-point H-MIMO systems that equips arbitrarily placed surfaces in a line-of-sight (LoS) environment. Two effective and computational-efficient channel models are established from their integral counterpart, where one is with a sophisticated formula but showcases more accurate, and another is concise with a slight precision sacrifice. Furthermore, we unveil the capacity limit using our channel model, and derive a tight upper bound based upon an elaborately built analytical framework. Our result reveals that the capacity limit grows logarithmically with the product of transmit element area, receive element area, and the combined effects of $1/{{d}_{mn}^2}$, $1/{{d}_{mn}^4}$, and $1/{{d}_{mn}^6}$ over all transmit and receive antenna elements, where $d_{mn}$ indicates the distance between each transmit and receive elements. Numerical evaluations validate the effectiveness of our channel models, and showcase the slight disparity between the upper bound and the exact capacity, which is beneficial for predicting practical system performance., Comment: double column, 17 pages, 13 figures, accepted by IEEE Journal on Selected Areas in Communications

Holographic multiple-input multiple-output (H-MIMO) is considered as one of the most promising technologies to enable future wireless communications in supporting the expected extreme requirements, such as high energy and spectral efficiency. Empowered by the powerful capability in electromagnetic (EM) wave manipulations, H-MIMO has the potential to reach the fundamental limit of the wireless environment, and opens up the possibility of signal processing in the EM-domain, which needs to be depicted carefully from an EM perspective, especially the wireless channel. To this aim, we study the line-of-sight (LOS) H-MIMO communications with arbitrary surface placements and establish an exact expression of the wireless channel in the EM-domain. To further obtain a more explicit and computationally-efficient channel models, we solve the implicit integrals of the exact channel model with moderate and reasonable assumptions. Numerical studies are executed and the results show good agreements of our established approximated channel models to the exact channel model., Comment: 6 pages

Recommender systems play an important role in many content platforms. While most recommendation research is dedicated to designing better models to improve user experience, we found that research on stabilizing the training for such models is severely under-explored. As recommendation models become larger and more sophisticated, they are more susceptible to training instability issues, i.e., loss divergence, which can make the model unusable, waste significant resources and block model developments. In this paper, we share our findings and best practices we learned for improving the training stability of a real-world multitask ranking model for YouTube recommendations. We show some properties of the model that lead to unstable training and conjecture on the causes. Furthermore, based on our observations of training dynamics near the point of training instability, we hypothesize why existing solutions would fail, and propose a new algorithm to mitigate the limitations of existing solutions. Our experiments on YouTube production dataset show the proposed algorithm can significantly improve training stability while not compromising convergence, comparing with several commonly used baseline methods., Comment: Accepted at KDD 2023; 12 pages

This paper studies the exploitation of triple polarization (TP) for multi-user (MU) holographic multiple-input multiple-output surface (HMIMOS) wireless communication systems, aiming at capacity boosting without enlarging the antenna array size. We specifically consider that both the transmitter and receiver are equipped with an HMIMOS comprising compact sub-wavelength TP patch antennas. To characterize TP MUHMIMOS systems, a TP near-field channel model is proposed using the dyadic Green's function, whose characteristics are leveraged to design a user-cluster-based precoding scheme for mitigating the cross-polarization and inter-user interference contributions. A theoretical correlation analysis for HMIMOS with infinitely small patch antennas is also presented. According to the proposed scheme, the users are assigned to one of the three polarizations, which is easy to implement, at the cost, however, of reducing the system's diversity. Our numerical results showcase that the cross-polarization channel components have a nonnegligible impact on the system performance, which is efficiently eliminated with the proposed MU precoding scheme., Comment: arXiv admin note: substantial text overlap with arXiv:2211.03479

Future wireless systems are envisioned to create an endogenously holography-capable, intelligent, and programmable radio propagation environment, that will offer unprecedented capabilities for high spectral and energy efficiency, low latency, and massive connectivity. A potential and promising technology for supporting the expected extreme requirements of the sixth-generation (6G) communication systems is the concept of the holographic multiple-input multiple-output (HMIMO), which will actualize holographic radios with reasonable power consumption and fabrication cost. The HMIMO is facilitated by ultra-thin, extremely large, and nearly continuous surfaces that incorporate reconfigurable and sub-wavelength-spaced antennas and/or metamaterials. Such surfaces comprising dense electromagnetic (EM) excited elements are capable of recording and manipulating impinging fields with utmost flexibility and precision, as well as with reduced cost and power consumption, thereby shaping arbitrary-intended EM waves with high energy efficiency. The powerful EM processing capability of HMIMO opens up the possibility of wireless communications of holographic imaging level, paving the way for signal processing techniques realized in the EM-domain, possibly in conjunction with their digital-domain counterparts. However, in spite of the significant potential, the studies on HMIMO communications are still at an initial stage, its fundamental limits remain to be unveiled, and a certain number of critical technical challenges need to be addressed. In this survey, we present a comprehensive overview of the latest advances in the HMIMO communications paradigm, with a special focus on their physical aspects, their theoretical foundations, as well as the enabling technologies for HMIMO systems. We also compare the HMIMO with existing multi-antenna technologies, especially the massive MIMO, present various..., Comment: double column, 60 pages, 26 figures, 11 tables, accepted by IEEE Communications Surveys and Tutorials

This paper investigates the utilization of triple polarization (TP) for multi-user (MU) holographic multiple-input multi-output surface (HMIMOS) wireless communication systems, targeting capacity boosting and diversity exploitation without enlarging the antenna array sizes. We specifically consider that both the transmitter and receiver are both equipped with an HMIMOS consisting of compact sub-wavelength TP patch antennas within the near-field (NF) regime. To characterize TP MU-HMIMOS systems, a TP NF channel model is constructed using the dyadic Green's function, whose characteristics are leveraged to design two precoding schemes for mitigating the cross-polarization and inter-user interference contributions. Specifically, a user-cluster-based precoding scheme assigns different users to one of three polarizations at the expense of the system's diversity, and a two-layer precoding scheme removes interference using the Gaussian elimination method at a high computational cost. The theoretical correlation analysis for HMIMOS in the NF region is also investigated, revealing that both the spacing of transmit patch antennas and user distance impact transmit correlation factors. Our numerical results show that the users far from transmitting HMIMOS experience higher correlation than those closer within the NF regime, resulting in a lower channel capacity. Meanwhile, in terms of channel capacity, TP HMIMOS can almost achieve 1.25 times gain compared with dual-polarized HMIMOS, and 3 times compared with conventional HMIMOS. In addition, the proposed two-layer precoding scheme combined with two-layer power allocation realizes a higher spectral efficiency than other schemes without sacrificing diversity.

There has been a flurry of research in recent years on notions of fairness in ranking and recommender systems, particularly on how to evaluate if a recommender allocates exposure equally across groups of relevant items (also known as provider fairness). While this research has laid an important foundation, it gave rise to different approaches depending on whether relevant items are compared per-user/per-query or aggregated across users. Despite both being established and intuitive, we discover that these two notions can lead to opposite conclusions, a form of Simpson's Paradox. We reconcile these notions and show that the tension is due to differences in distributions of users where items are relevant, and break down the important factors of the user's recommendations. Based on this new understanding, practitioners might be interested in either notions, but might face challenges with the per-user metric due to partial observability of the relevance and user satisfaction, typical in real-world recommenders. We describe a technique based on distribution matching to estimate it in such a scenario. We demonstrate on simulated and real-world recommender data the effectiveness and usefulness of such an approach.

Function-level binary code similarity detection is a crucial aspect of cybersecurity. It enables the detection of bugs and patent infringements in released software and plays a pivotal role in preventing supply chain attacks. A practical embedding learning framework relies on the robustness of the assembly code representation and the accuracy of function-pair annotation, which is traditionally accomplished using supervised learning-based frameworks. However, annotating different function pairs with accurate labels poses considerable challenges. These supervised learning methods can be easily overtrained and suffer from representation robustness problems. To address these challenges, we propose SimCLF: A Simple Contrastive Learning Framework for Function-level Binary Embeddings. We take an unsupervised learning approach and formulate binary code similarity detection as instance discrimination. SimCLF directly operates on disassembled binary functions and could be implemented with any encoder. It does not require manually annotated information but only augmented data. Augmented data is generated using compiler optimization options and code obfuscation techniques. The experimental results demonstrate that SimCLF surpasses the state-of-the-art in accuracy and has a significant advantage in few-shot settings.

iPTF14hls is a luminous Type II supernova (SN) with a bumpy light curve that remains debated for its origin. It maintains roughly a constant effective temperature and luminosity since discovery for about 600 days, followed by a slow decay. On $\sim 1000$\ days post discovery the light curve transitions to a very steep decline. A spectrum taken during this steep decline phase shows clear signatures of shock interaction with dense circumstellar medium (CSM). Here we explore the possibility of iPTF14hls as an interaction-powered SN. The light curve of iPTF14hls can be fitted with wind-like CSMs. Analytic modeling indicates that iPTF14hls may have undertaken six episodes of mass loss during the last $\sim 200\mathrm{yr}$. Assuming that the 1954 eruption triggered the last mass-loss episode, the stellar-wind velocity is determined to be $40-70\mathrm{km}\mathrm{s}^{-1}$, depending on different models. Mass loss rates are in the range $% 0.4-3.3M_{\odot }\mathrm{yr}^{-1}$. The inferred total mass of ejecta and CSMs ($M_{\mathrm{ej}}+M_{\mathrm{CSMs}}\simeq 245M_{\odot }$) supports the idea that iPTF14hls may be a candidate for a (pulsational) pair-instability SN. Discovery and observations of more similar stellar explosions will help understand these peculiar SNe., Comment: 12 pages, 4 figures. Accepted for publication in ApJ

Electron-doped infinite-layer Sr$_{1-x}$Eu$_{x}$CuO$_{2+y}$ films over a wide doping range have been prepared epitaxially on SrTiO$_3$(001) using reactive molecular beam epitaxy. In-plane transport measurements of the single crystalline samples reveal a dome-shaped nodeless superconducting phase centered at $x$ $\sim$ 0.15, a Fermi-liquid behavior and pronounced upturn in low temperature resistivity. We show that the resistivity upturn follows square-root temperature dependence, suggesting the emergence of superconductivity via a three-dimensional percolation process. The percolative superconductivity is corroborated spectroscopically by imaging the electronic phase separation between superconducting and metallic phases with low-temperature scanning tunneling microscopy. Furthermore, we visualize interstitial and apical oxygen anions that rapidly increase in number as $x>$ 0.12, and elucidate their impacts on the superconductivity and normal-state resistivity., Comment: 5 pages, 4 figures, 9 supplemental figures

Whispering gallery mode (WGM) microcavities can efficiently store and manipulate light with strong light confinement and long photon lifetime, while coupling light into and from WGMs is intrinsically hindered by their unique feature of rotational symmetry. Here, a scattering-assisted liquid crystal (LC) micropillar WGM laser is proposed. WGM lasing at the surface of the micropillar is obviously enhanced by fluorescence scattering in the core of the micropillar. Besides, weak scattering of LC molecules also builds efficient coupling channels between the laser modes and the axial transmission modes of the micropillar-based waveguide, providing an all-in-one liquid WGM laser with functions of self-seeding and self-guiding. Furthermore, based on the hysteresis characteristics of the electrically anchored LC molecules under the interaction of thermal force, an erasable read-write liquid memory device is proposed, paving the way for the application of logic-controllable WGM lasers in optical storage and optical control.

Videos are an accessible form of media for analyzing sports postures and providing feedback to athletes. Existing sport-specific systems embed bespoke human pose attributes and thus can be hard to scale for new attributes, especially for users without programming experiences. Some systems retain scalability by directly showing the differences between two poses, but they might not clearly visualize the key differences that viewers would like to pursue. Besides, video-based coaching systems often present feedback on the correctness of poses by augmenting videos with visual markers or reference poses. However, previewing and augmenting videos limit the analysis and visualization of human poses due to the fixed viewpoints in videos, which confine the observation of captured human movements and cause ambiguity in the augmented feedback. To address these issues, we study customizable human pose data analysis and visualization in the context of running pose attributes, such as joint angles and step distances. Based on existing literature and a formative study, we have designed and implemented a system, PoseCoach, to provide feedback on running poses for amateurs by comparing the running poses between a novice and an expert. PoseCoach adopts a customizable data analysis model to allow users' controllability in defining pose attributes of their interests through our interface. To avoid the influence of viewpoint differences and provide intuitive feedback, PoseCoach visualizes the pose differences as part-based 3D animations on a human model to imitate the demonstration of a human coach. We conduct a user study to verify our design components and conduct expert interviews to evaluate the usefulness of the system.

The multi-user Holographic Multiple-Input and Multiple-Output Surface (MU-HMIMOS) paradigm, which is capable of realizing large continuous apertures with minimal power consumption and of shaping radio wave propagation at will, has been recently considered as an energy-efficient solution for future wireless networks. The tractable channel modeling of MU-HMIMOS signal propagation is one of the most critical challenges, mainly due to the coupling effect induced by the excessively large number of closely spaced patch antennas. In this paper, we focus on this challenge for downlink communications and model the electromagnetic channel in the wavenumber domain using the Fourier plane wave representation. Based on the proposed model, we devise a Zero-Forcing (ZF) precoding scheme, capitalizing on the sampled channel variance that depends on the number and spacing of the HMIMOS patch antennas, and perform a spectral efficiency analysis. Our simulation results showcase that the more patch antennas and the larger their spacing is, the performance of the considered MU-HMIMOS system improves. In addition, it is demonstrated that our theoretical performance expressions approximate sufficiently well the simulated spectral efficiency, even for the highly correlated cases, thus verifying the effectiveness and robustness of the presented analytical framework., Comment: arXiv admin note: substantial text overlap with arXiv:2112.02803

Media streaming has been adopted for a variety of applications such as entertainment, visualization, and design. Unlike video/audio streaming where the content is usually consumed sequentially, 3D applications such as gaming require streaming 3D assets to facilitate client-side interactions such as object manipulation and viewpoint movement. Compared to audio and video streaming, 3D streaming often requires larger data sizes and yet lower latency to ensure sufficient rendering quality, resolution, and latency for perceptual comfort. Thus, streaming 3D assets can be even more challenging than streaming audios/videos, and existing solutions often suffer from long loading time or limited quality. To address this critical and timely issue, we propose a perceptually-optimized progressive 3D streaming method for spatial quality and temporal consistency in immersive interactions. Based on the human visual mechanisms in the frequency domain, our model selects and schedules the streaming dataset for optimal spatial-temporal quality. We also train a neural network for our model to accelerate this decision process for real-time client-server applications. We evaluate our method via subjective studies and objective analysis under varying network conditions (from 3G to 5G) and client devices (HMD and traditional displays), and demonstrate better visual quality and temporal consistency than alternative solutions.

Quantum oscillations in resistivity and magnetization at high magnetic fields are a macroscopic fingerprint of the energy quantization due to the cyclotron motion of quasiparticles. In a thin Weyl semimetal, a unique thickness dependent Weyl-orbit quantum oscillation was proposed to exist, originating from a nonlocal cyclotron orbit via the electron tunneling between the top and bottom Fermi-arc surface states. Here, untwinned and high crystalline Weyl metal SrRuO$_3$ thin films with different thicknesses were grown on miscut SrTiO$_3$ (001) substrates. Magneto-transport measurements were carried out in magnetic fields up to 35 T, and quantum oscillations with different frequencies were observed and compared to the calculated band structure. In particular, we discovered a frequency $F \approx$ 30 T at low temperatures and above 3 T that corresponds to a small Fermi pocket with a light effective mass. Its oscillation amplitude appears to be at maximum for film thicknesses in a range of 10 to 20 nm, and the phase of the oscillation exhibits a systematic change with the film thickness. After isolating the well separated frequencies, the constructed Landau fan diagram shows an unusual concave downward curvature in the 1/$\mu_0H_n$-$n$ curve, where $n$ is the Landau level index. Based on the rigorous analysis of the thickness and field-orientation dependence of the quantum oscillations, the oscillation with $F \approx$ 30 T is attributed to be of surface origin, which is related to the Fermi-arc surface state originating from non-overlapping Weyl nodes projected on the film's surface plane. Those findings can be understood within the framework of the Weyl-orbit quantum oscillation effect with non-adiabatic corrections., Comment: 27 pages, 6 figures