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The rapid advancement of blockchain platforms has significantly accelerated the growth of decentralized applications (DApps). Similar to traditional applications, DApps integrate front-end descriptions that showcase their features to attract users, and back-end smart contracts for executing their business logic. However, inconsistencies between the features promoted in front-end descriptions and those actually implemented in the contract can confuse users and undermine DApps's trustworthiness. In this paper, we first conducted an empirical study to identify seven types of inconsistencies, each exemplified by a real-world DApp. Furthermore, we introduce HYPERION, an approach designed to automatically identify inconsistencies between front-end descriptions and back-end code implementation in DApps. This method leverages a fine-tuned large language model LLaMA2 to analyze DApp descriptions and employs dataflow-guided symbolic execution for contract bytecode analysis. Finally, HYPERION reports the inconsistency based on predefined detection patterns. The experiment on our ground truth dataset consisting of 54 DApps shows that HYPERION reaches 84.06% overall recall and 92.06% overall precision in reporting DApp inconsistencies. We also implement HYPERION to analyze 835 real-world DApps. The experimental results show that HYPERION discovers 459 real-world DApps containing at least one inconsistency., Comment: Accepted by ICSE 2025

Let $\pi\colon X\to \Delta$ be a proper smooth K\"ahler morphism from a complex manifold $X$ to the unit disc $\Delta$. Suppose the fibers $X_t=\pi^{-1}(t)$ are biholomorphic to $S$ for all $t\neq0$, where $S$ is a given projective manifold. If the canonical line bundle of $S$ is semiample, then we show that the central fiber $X_0$ is also biholomorphic to $S$. As an application, we obtain that, for smooth families over projective curves satisfying that the canonical line bundle of the generic fiber is semiample, birational isotriviality equals to isotriviality. Moreover, we also obtain a new Parshin-Arakelov type isotriviality criterion.

In recent studies on domain adaptation, significant emphasis has been placed on the advancement of learning shared knowledge from a source domain to a target domain. Recently, the large vision-language pre-trained model, i.e., CLIP has shown strong ability on zero-shot recognition, and parameter efficient tuning can further improve its performance on specific tasks. This work demonstrates that a simple domain prior boosts CLIP's zero-shot recognition in a specific domain. Besides, CLIP's adaptation relies less on source domain data due to its diverse pre-training dataset. Furthermore, we create a benchmark for zero-shot adaptation and pseudo-labeling based self-training with CLIP. Last but not least, we propose to improve the task generalization ability of CLIP from multiple unlabeled domains, which is a more practical and unique scenario. We believe our findings motivate a rethinking of domain adaptation benchmarks and the associated role of related algorithms in the era of CLIP.

Communication is essential for advancing science and technology. Quantum communication, in particular, benefits from the use of catalysts. During the communication process, these catalysts enhance performance while remaining unchanged. Although chemical catalysts that undergo deactivation typically perform worse than those that remain unaffected, quantum catalysts, referred to as embezzling catalysts, can surprisingly outperform their non-deactivating counterparts despite experiencing slight alterations. In this work, we employ embezzling quantum catalysts to enhance the transmission of both quantum and classical information. Our results reveal that using embezzling catalysts augments the efficiency of information transmission across noisy quantum channels, ensuring a non-zero catalytic channel capacity. Furthermore, we introduce catalytic superdense coding, demonstrating how embezzling catalysts can enhance the transmission of classical information. Finally, we explore methods to reduce the dimensionality of catalysts, a step toward making quantum catalysis a practical reality., Comment: 16 pages, 9 figures. Comments welcome!

Quantum teleportation is the process of transferring quantum information using classical communication and pre-shared entanglement. This process can benefit from the use of catalysts, which are ancillary entangled states that can enhance teleportation without being consumed. While chemical catalysts undergoing deactivation invariably exhibit inferior performance compared to those unaffected by deactivation, quantum catalysts, termed embezzling catalysts, that are subject to deactivation, may surprisingly outperform their non-deactivating counterparts. In this work, we present teleportation protocols with embezzling catalyst that can achieve arbitrarily high fidelity, namely the teleported state can be made arbitrarily close to the original state, with finite-dimensional embezzling catalysts. We show that some embezzling catalysts are universal, meaning that they can improve the teleportation fidelity for any pre-shared entanglement. We also explore methods to reduce the dimension of catalysts without increasing catalyst consumption, an essential step towards realizing quantum catalysis in practice., Comment: 19 pages, 11 figures. Comments welcome!

Multi-domain learning (MDL) has emerged as a prominent research area aimed at enhancing the quality of personalized services. The key challenge in MDL lies in striking a balance between learning commonalities across domains while preserving the distinct characteristics of each domain. However, this gives rise to a challenging dilemma. On one hand, a model needs to leverage domain-specific modules, such as experts or embeddings, to preserve the uniqueness of each domain. On the other hand, due to the long-tailed distributions observed in real-world domains, some tail domains may lack sufficient samples to fully learn their corresponding modules. Unfortunately, existing approaches have not adequately addressed this dilemma. To address this issue, we propose a novel model called Crocodile, which stands for Cross-experts Covariance Loss for Disentangled Learning. Crocodile adopts a multi-embedding paradigm to facilitate model learning and employs a Covariance Loss on these embeddings to disentangle them. This disentanglement enables the model to capture diverse user interests across domains effectively. Additionally, we introduce a novel gating mechanism to further enhance the capabilities of Crocodile. Through empirical analysis, we demonstrate that our proposed method successfully resolves these two challenges and outperforms all state-of-the-art methods on publicly available datasets. We firmly believe that the analytical perspectives and design concept of disentanglement presented in our work can pave the way for future research in the field of MDL.

Kodaira fibrations have non-trivial vertical fundamental groups and their slopes are all $12$. In this paper, we show that $12$ is indeed the sharp upper bound for the slopes of fibrations with trivial vertical fundamental groups. Precisely, for each $g\geq3$ we prove the existence of fibrations of genus $g$ with trivial vertical fundamental groups whose slopes can be arbitrarily close to $12$. This gives a relative analogy of Roulleau-Urz\'ua's work on the slopes of surfaces of general type with trivial fundamental groups., Comment: Any comment is warmly welcome

Topological phononic crystals (PCs) offer a novel approach to control the behavior of acoustic or elastic waves. In this paper, we design the gradient PC structures with coupled interfaces to realize topological rainbow trapping and broadband acoustic energy harvesting. Based on the geometric symmetry of PC unit cells, we construct the coupled topological interfaces by amalgamating two PCs with distinct topological phases. Also, the gradient modulation of structural parameters along the coupled interfaces is employed for the generation of rainbow trapping. Subsequently, a polyvinylidene fluoride (PVDF) film is mounted along the coupled interfaces of the gradient PC structures, facilitating the conversion of acoustic energy into electrical energy. The numerical and experimental results show that the acoustic waves at different frequencies stop and are amplified at different positions of the coupled interfaces. Compared with the harvester in a bare structure, the topological PC energy harvester significantly enhances the output power at different excited frequencies. In the experiments, the maximum amplification ratio of the output power reaches 91. Besides, it is demonstrated that the topological rainbow trapping of acoustic waves exhibits robustness against random structural disorders. Notably, the coupled interfaces have broadband and multi-mode features, which are promising for other applications such as selective filtering and sensing enhancement.

Click-through rate (CTR) prediction is a crucial area of research in online advertising. While binary cross entropy (BCE) has been widely used as the optimization objective for treating CTR prediction as a binary classification problem, recent advancements have shown that combining BCE loss with an auxiliary ranking loss can significantly improve performance. However, the full effectiveness of this combination loss is not yet fully understood. In this paper, we uncover a new challenge associated with the BCE loss in scenarios where positive feedback is sparse: the issue of gradient vanishing for negative samples. We introduce a novel perspective on the effectiveness of the auxiliary ranking loss in CTR prediction: it generates larger gradients on negative samples, thereby mitigating the optimization difficulties when using the BCE loss only and resulting in improved classification ability. To validate our perspective, we conduct theoretical analysis and extensive empirical evaluations on public datasets. Additionally, we successfully integrate the ranking loss into Tencent's online advertising system, achieving notable lifts of 0.70% and 1.26% in Gross Merchandise Value (GMV) for two main scenarios. The code is openly accessible at: https://github.com/SkylerLinn/Understanding-the-Ranking-Loss.

Open systems possess unique potentials in high-precision sensing, yet the majority of previous studies rely on the spectral singularities known as exceptional points. Here we theoretically propose and experimentally demonstrate universal non-Hermitian sensing in the absence of exceptional points. The scheme makes use of the intrinsic sensitivity of a non-Hermitian probe to weak external fields, which can be understood as the direct consequence of non-Hermiticity. We confirm the basic mechanism by simulating the sensor-field dynamics using photon interferometry, and, as a concrete example, demonstrate the enhanced sensing of signals encoded in the setting angle of a wave plate. While the sensitivity of the probe is ultimately limited by the measurement noise, we find the non-Hermitian sensor showing superior performance under background noises that cannot be suppressed through repetitive measurements. Our experiment opens the avenue of enhanced sensing without exceptional points, complementing existing efforts aimed at harnessing the unique features of open systems.

We present Tencent's ads recommendation system and examine the challenges and practices of learning appropriate recommendation representations. Our study begins by showcasing our approaches to preserving prior knowledge when encoding features of diverse types into embedding representations. We specifically address sequence features, numeric features, and pre-trained embedding features. Subsequently, we delve into two crucial challenges related to feature representation: the dimensional collapse of embeddings and the interest entanglement across different tasks or scenarios. We propose several practical approaches to address these challenges that result in robust and disentangled recommendation representations. We then explore several training techniques to facilitate model optimization, reduce bias, and enhance exploration. Additionally, we introduce three analysis tools that enable us to study feature correlation, dimensional collapse, and interest entanglement. This work builds upon the continuous efforts of Tencent's ads recommendation team over the past decade. It summarizes general design principles and presents a series of readily applicable solutions and analysis tools. The reported performance is based on our online advertising platform, which handles hundreds of billions of requests daily and serves millions of ads to billions of users.

Color centers in silicon carbide (SiC) have demonstrated significant promise for quantum information processing. However, the undesirable ionization process that occurs during optical manipulation frequently causes fluctuations in the charge state and performance of these defects, thereby restricting the effectiveness of spin-photon interfaces. Recent predictions indicate that divacancy defects near stacking faults possess the capability to stabilize their neutral charge states, thereby providing robustness against photoionization effects. In this work, we present a comprehensive protocol for the scalable and targeted fabrication of single divacancy arrays in 4H-SiC using a high-resolution focused helium ion beam. Through photoluminescence emission (PLE) experiments, we demonstrate long-term emission stability with minimal linewidth shift ($\sim$ 50 MHz over 3 hours) for the single c-axis divacancies within stacking faults. By measuring the ionization rate for different polytypes of divacancies, we found that the divacancies within stacking faults are more robust against resonant excitation. Additionally, angle-resolved PLE spectra reveal their two resonant-transition lines with mutually orthogonal polarizations. Notably, the PLE linewidths are approximately 7 times narrower and the spin-coherent times are 6 times longer compared to divacancies generated via carbon-ion implantation. These findings highlight the immense potential of SiC divacancies for on-chip quantum photonics and the construction of efficient spin-to-photon interfaces, indicating a significant step forward in the development of quantum technologies., Comment: 11 pages, 4 figures

We introduce ReplaceAnything3D model (RAM3D), a novel text-guided 3D scene editing method that enables the replacement of specific objects within a scene. Given multi-view images of a scene, a text prompt describing the object to replace, and a text prompt describing the new object, our Erase-and-Replace approach can effectively swap objects in the scene with newly generated content while maintaining 3D consistency across multiple viewpoints. We demonstrate the versatility of ReplaceAnything3D by applying it to various realistic 3D scenes, showcasing results of modified foreground objects that are well-integrated with the rest of the scene without affecting its overall integrity., Comment: For our project page, see https://replaceanything3d.github.io/

Recommender systems with cascading architecture play an increasingly significant role in online recommendation platforms, where the approach to dealing with negative feedback is a vital issue. For instance, in short video platforms, users tend to quickly slip away from candidates that they feel aversive, and recommender systems are expected to receive these explicit negative feedbacks and make adjustments to avoid these recommendations. Considering recency effect in memories, we propose a forgetting model based on Ebbinghaus Forgetting Curve to cope with negative feedback. In addition, we introduce a Pareto optimization solver to guarantee a better trade-off between recency and model performance. In conclusion, we propose Pareto-based Multi-Objective Recommender System with forgetting curve (PMORS), which can be applied to any multi-objective recommendation and show sufficiently superiority when facing explicit negative feedback. We have conducted evaluations of PMORS and achieved favorable outcomes in short-video scenarios on both public dataset and industrial dataset. After being deployed on an online short video platform named WeChat Channels in May, 2023, PMORS has not only demonstrated promising results for both consistency and recency but also achieved an improvement of up to +1.45% GMV.

ESGReveal is an innovative method proposed for efficiently extracting and analyzing Environmental, Social, and Governance (ESG) data from corporate reports, catering to the critical need for reliable ESG information retrieval. This approach utilizes Large Language Models (LLM) enhanced with Retrieval Augmented Generation (RAG) techniques. The ESGReveal system includes an ESG metadata module for targeted queries, a preprocessing module for assembling databases, and an LLM agent for data extraction. Its efficacy was appraised using ESG reports from 166 companies across various sectors listed on the Hong Kong Stock Exchange in 2022, ensuring comprehensive industry and market capitalization representation. Utilizing ESGReveal unearthed significant insights into ESG reporting with GPT-4, demonstrating an accuracy of 76.9% in data extraction and 83.7% in disclosure analysis, which is an improvement over baseline models. This highlights the framework's capacity to refine ESG data analysis precision. Moreover, it revealed a demand for reinforced ESG disclosures, with environmental and social data disclosures standing at 69.5% and 57.2%, respectively, suggesting a pursuit for more corporate transparency. While current iterations of ESGReveal do not process pictorial information, a functionality intended for future enhancement, the study calls for continued research to further develop and compare the analytical capabilities of various LLMs. In summary, ESGReveal is a stride forward in ESG data processing, offering stakeholders a sophisticated tool to better evaluate and advance corporate sustainability efforts. Its evolution is promising in promoting transparency in corporate reporting and aligning with broader sustainable development aims.

We propose a method for dynamic scene reconstruction using deformable 3D Gaussians that is tailored for monocular video. Building upon the efficiency of Gaussian splatting, our approach extends the representation to accommodate dynamic elements via a deformable set of Gaussians residing in a canonical space, and a time-dependent deformation field defined by a multi-layer perceptron (MLP). Moreover, under the assumption that most natural scenes have large regions that remain static, we allow the MLP to focus its representational power by additionally including a static Gaussian point cloud. The concatenated dynamic and static point clouds form the input for the Gaussian Splatting rasterizer, enabling real-time rendering. The differentiable pipeline is optimized end-to-end with a self-supervised rendering loss. Our method achieves results that are comparable to state-of-the-art dynamic neural radiance field methods while allowing much faster optimization and rendering. Project website: https://lynl7130.github.io/gaufre/index.html, Comment: 10 pages, 8 figures, 4 tables

The detections of Lyman-$\alpha$ ($\rm Ly\alpha$) emission in galaxies with redshifts above 5 are of utmost importance for constraining the cosmic reionization timeline, yet such detections are usually based on slit spectroscopy. Here we investigate the significant bias induced by slit placement on the estimate of $\rm Ly\alpha$ escape fraction ( $f_{\rm esc}^{\mathrm{Ly\alpha}}$), by presenting a galaxy (dubbed A2744-z6Lya) at $z=5.66$ where its deep JWST NIRSpec prism spectroscopy completely misses the strong $\rm Ly\alpha$ emission detected in the MUSE data. A2744-z6Lya exhibits a pronounced UV continuum with an extremely steep spectral slope of $\beta=-2.574_{-0.008}^{+0.008}$, and it has a stellar mass of $\mathrm{\sim10^{8.82}~M_\odot}$, a star-formation rate of $\mathrm{\sim8.35~M_\odot yr^{-1}}$ and gas-phase metallicity of $\mathrm{12+log\,(O/H)\sim7.88}$. The observed flux and rest-frame equivalent width of its Ly$\alpha$ from MUSE spectroscopy are $1.2\times \rm 10^{-16} erg~s^{-1}cm^{-2}$ and 75\r{A}, equivalent to $f_{\rm esc}^{\mathrm{Ly\alpha}}=78\pm4 \%$. However, its Ly$\alpha$ non-detection from JWST NIRSpec gives a 5-$\sigma$ upper limit of $<13 \%$, in stark contrast to that derived from MUSE. To explore the reasons for this bias, we perform spatially resolved stellar population analysis of A2744-z6Lya using the JWST NIRCam imaging data to construct 2-dimensional maps of SFR, dust extinction and neutral hydrogen column density. We find that the absence of Ly$\alpha$ in the slit regions probably stems from both the resonance scattering effect of neutral hydrogen and dust extinction. Through analyzing an extreme case in detail, this work highlights the important caveat of inferring $f_{\rm esc}^{\mathrm{Ly\alpha}}$ from slit spectroscopy, particularly when using the JWST multiplexed NIRSpec microshutter assembly., Comment: 10 pages, 4 figures, 1 table

The Yang-Lee edge singularity was originally studied from the standpoint of mathematical foundations of phase transitions, and its physical demonstration has been of active interest both theoretically and experimentally. However, the presence of an imaginary magnetic field in the Yang-Lee edge singularity has made it challenging to develop a direct observation of the anomalous scaling with negative scaling dimension associated with this critical phenomenon. We experimentally implement an imaginary magnetic field and demonstrate the Yang-Lee edge singularity through a nonunitary evolution governed by a non-Hermitian Hamiltonian in an open quantum system, where a classical system is mapped to a quantum system via the equivalent canonical partition function. In particular, we directly observe the partition function in our experiment using heralded single photons. The nonunitary quantum criticality is identified with the singularity at an exceptional point. We also demonstrate unconventional scaling laws for the finite-temperature dynamics unique to quantum systems., Comment: 11 pages, 9 figures

Sound Source Localization (SSL) involves estimating the Direction of Arrival (DOA) of sound sources. Since the DOA estimation output space is continuous, regression might be more suitable for DOA, offering higher precision. However, in practice, classification often outperforms regression, exhibiting greater robustness to interference. Conversely, classification's drawback is inherent quantization error. Within the classification paradigm, the DOA output space is discretized into intervals, each treated as a class. These classes show strong inter-class correlations, being inherently ordered, with higher similarity as intervals grow closer. Nevertheless, this has not been fully exploited. To address this, we propose an Unbiased Label Distribution (ULD) to eliminate quantization error in training targets. Furthermore, we tailor two loss functions for the soft label family: Negative Log Absolute Error (NLAE) and Mean Squared Error without activation (MSE(wo)). Finally, we introduce Weighted Adjacent Decoding (WAD) to overcome quantization error during model prediction decoding. Experimental results demonstrate our approach surpasses classification quantization limits, achieving state-of-the-art performance. Our code and supplementary materials are available at https://github.com/linfeng-feng/ULD., Comment: 12 pages

Recently, automatic speaker verification (ASV) based on deep learning is easily contaminated by adversarial attacks, which is a new type of attack that injects imperceptible perturbations to audio signals so as to make ASV produce wrong decisions. This poses a significant threat to the security and reliability of ASV systems. To address this issue, we propose a Diffusion-Based Adversarial Purification (DAP) method that enhances the robustness of ASV systems against such adversarial attacks. Our method leverages a conditional denoising diffusion probabilistic model to effectively purify the adversarial examples and mitigate the impact of perturbations. DAP first introduces controlled noise into adversarial examples, and then performs a reverse denoising process to reconstruct clean audio. Experimental results demonstrate the efficacy of the proposed DAP in enhancing the security of ASV and meanwhile minimizing the distortion of the purified audio signals., Comment: Accepted by IEEE Signal Processing Letters

Solute segregation at low angle grain boundaries (LAGB) in Mg alloys significantly affects GB energy and mobility, therefore recrystallization kinetics and corresponding texture modification. In a system featuring multiple substitutional elements at high local concentration levels, solute-solute interaction needs to be considered to interpret and predict co-segregation behavior. In this work, atomic-scale experimental and modelling techniques were applied to investigate the co-segregation behavior of Ca, Zn and Al solutes at a LAGB in a Mg alloy. Three-dimensional atom probe tomography and corresponding clustering analysis revealed a strong clustering tendency of Ca solutes at the linear dislocation arrays. Atomistic simulations indicate that the co-segregation of Ca-Ca pairs in vicinity of the dislocation core region is more energetically favorable than other solute pairs, as well as the segregation of individual solutes.

Dynamic behaviors of a physical system often originate from its spectral properties. In open systems, where the effective non-Hermitian description enables a wealth of spectral structures on the complex plane, the concomitant dynamics is significantly enriched, whereas the identification and comprehension of the underlying connections are challenging. Here we experimentally demonstrate the correspondence between the transient self acceleration of local excitations and the non-Hermitian spectral topology using lossy photonic quantum walks. Focusing first on one-dimensional quantum walks, we show that the measured short-time acceleration of the wave function is proportional to the area enclosed by the eigenspectrum. We then reveal similar correspondence in two-dimension quantum walks, where the self acceleration is proportional to the volume enclosed by the eigenspectrum in the complex parameter space. In both dimensions, the transient self acceleration crosses over to a long-time behavior dominated by a constant flow at the drift velocity. Our results unveil the universal correspondence between spectral topology and transient dynamics, and offer a sensitive probe for phenomena in non-Hermitian systems that originate from spectral geometry., Comment: 12 pages, 6 figures

The task of synthesizing novel views from a single image has useful applications in virtual reality and mobile computing, and a number of approaches to the problem have been proposed in recent years. A Multiplane Image (MPI) estimates the scene as a stack of RGBA layers, and can model complex appearance effects, anti-alias depth errors and synthesize soft edges better than methods that use textured meshes or layered depth images. And unlike neural radiance fields, an MPI can be efficiently rendered on graphics hardware. However, MPIs are highly redundant and require a large number of depth layers to achieve plausible results. Based on the observation that the depth complexity in local image regions is lower than that over the entire image, we split an MPI into many small, tiled regions, each with only a few depth planes. We call this representation a Tiled Multiplane Image (TMPI). We propose a method for generating a TMPI with adaptive depth planes for single-view 3D photography in the wild. Our synthesized results are comparable to state-of-the-art single-view MPI methods while having lower computational overhead., Comment: ICCV 2023

The product carbon footprint (PCF) is crucial for decarbonizing the supply chain, as it measures the direct and indirect greenhouse gas emissions caused by all activities during the product's life cycle. However, PCF accounting often requires expert knowledge and significant time to construct life cycle models. In this study, we test and compare the emergent ability of five large language models (LLMs) in modeling the 'cradle-to-gate' life cycles of products and generating the inventory data of inputs and outputs, revealing their limitations as a generalized PCF knowledge database. By utilizing LLMs, we propose an automatic AI-driven PCF accounting framework, called AutoPCF, which also applies deep learning algorithms to automatically match calculation parameters, and ultimately calculate the PCF. The results of estimating the carbon footprint for three case products using the AutoPCF framework demonstrate its potential in achieving automatic modeling and estimation of PCF with a large reduction in modeling time from days to minutes.

The performance of speaker verification degrades significantly in adverse acoustic environments with strong reverberation and noise. To address this issue, this paper proposes a spatial-temporal graph convolutional network (GCN) method for the multi-channel speaker verification with ad-hoc microphone arrays. It includes a feature aggregation block and a channel selection block, both of which are built on graphs. The feature aggregation block fuses speaker features among different time and channels by a spatial-temporal GCN. The graph-based channel selection block discards the noisy channels that may contribute negatively to the system. The proposed method is flexible in incorporating various kinds of graphs and prior knowledge. We compared the proposed method with six representative methods in both real-world and simulated environments. Experimental results show that the proposed method achieves a relative equal error rate (EER) reduction of $\mathbf{15.39\%}$ lower than the strongest referenced method in the simulated datasets, and $\mathbf{17.70\%}$ lower than the latter in the real datasets. Moreover, its performance is robust across different signal-to-noise ratios and reverberation time.