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The operation of upcoming ultra-high-energy cosmic-ray, gamma-ray, and neutrino radio-detection experiments, like the Giant Radio Array for Neutrino Detection (GRAND), poses significant computational challenges involving the production of numerous simulations of particle showers and their detection, and a high data throughput. GRANDlib is an open-source software tool designed to meet these challenges. Its primary goal is to perform end-to-end simulations of the detector operation, from the interaction of ultra-high-energy particles, through -- by interfacing with external air-shower simulations -- the ensuing particle shower development and its radio emission, to its detection by antenna arrays and its processing by data-acquisition systems. Additionally, GRANDlib manages the visualization, storage, and retrieval of experimental and simulated data. We present an overview of GRANDlib to serve as the basis of future GRAND analyses., Comment: 11 pages, 9 figures, plus appendices

To cater to users' desire for an immersive browsing experience, numerous e-commerce platforms provide various recommendation scenarios, with a focus on Trigger-Induced Recommendation (TIR) tasks. However, the majority of current TIR methods heavily rely on the trigger item to understand user intent, lacking a higher-level exploration and exploitation of user intent (e.g., popular items and complementary items), which may result in an overly convergent understanding of users' short-term intent and can be detrimental to users' long-term purchasing experiences. Moreover, users' short-term intent shows uncertainty and is affected by various factors such as browsing context and historical behaviors, which poses challenges to user intent modeling. To address these challenges, we propose a novel model called Deep Uncertainty Intent Network (DUIN), comprising three essential modules: i) Explicit Intent Exploit Module extracting explicit user intent using the contrastive learning paradigm; ii) Latent Intent Explore Module exploring latent user intent by leveraging the multi-view relationships between items; iii) Intent Uncertainty Measurement Module offering a distributional estimation and capturing the uncertainty associated with user intent. Experiments on three real-world datasets demonstrate the superior performance of DUIN compared to existing baselines. Notably, DUIN has been deployed across all TIR scenarios in our e-commerce platform, with online A/B testing results conclusively validating its superiority., Comment: Accepted at CIKM 2024

Recent studies on AI security have highlighted the vulnerability of Vision-Language Pre-training (VLP) models to subtle yet intentionally designed perturbations in images and texts. Investigating multimodal systems' robustness via adversarial attacks is crucial in this field. Most multimodal attacks are sample-specific, generating a unique perturbation for each sample to construct adversarial samples. To the best of our knowledge, it is the first work through multimodal decision boundaries to explore the creation of a universal, sample-agnostic perturbation that applies to any image. Initially, we explore strategies to move sample points beyond the decision boundaries of linear classifiers, refining the algorithm to ensure successful attacks under the top $k$ accuracy metric. Based on this foundation, in visual-language tasks, we treat visual and textual modalities as reciprocal sample points and decision hyperplanes, guiding image embeddings to traverse text-constructed decision boundaries, and vice versa. This iterative process consistently refines a universal perturbation, ultimately identifying a singular direction within the input space which is exploitable to impair the retrieval performance of VLP models. The proposed algorithms support the creation of global perturbations or adversarial patches. Comprehensive experiments validate the effectiveness of our method, showcasing its data, task, and model transferability across various VLP models and datasets. Code: https://github.com/LibertazZ/MUAP, Comment: 13 pages, 8 figures, published in ACMMM2024

With Vision-Language Pre-training (VLP) models demonstrating powerful multimodal interaction capabilities, the application scenarios of neural networks are no longer confined to unimodal domains but have expanded to more complex multimodal V+L downstream tasks. The security vulnerabilities of unimodal models have been extensively examined, whereas those of VLP models remain challenging. We note that in CV models, the understanding of images comes from annotated information, while VLP models are designed to learn image representations directly from raw text. Motivated by this discrepancy, we developed the Feature Guidance Attack (FGA), a novel method that uses text representations to direct the perturbation of clean images, resulting in the generation of adversarial images. FGA is orthogonal to many advanced attack strategies in the unimodal domain, facilitating the direct application of rich research findings from the unimodal to the multimodal scenario. By appropriately introducing text attack into FGA, we construct Feature Guidance with Text Attack (FGA-T). Through the interaction of attacking two modalities, FGA-T achieves superior attack effects against VLP models. Moreover, incorporating data augmentation and momentum mechanisms significantly improves the black-box transferability of FGA-T. Our method demonstrates stable and effective attack capabilities across various datasets, downstream tasks, and both black-box and white-box settings, offering a unified baseline for exploring the robustness of VLP models., Comment: 14 pages, 9 figures, published in ACMMM2024(oral)

Semantic relevance calculation is crucial for e-commerce search engines, as it ensures that the items selected closely align with customer intent. Inadequate attention to this aspect can detrimentally affect user experience and engagement. Traditional text-matching techniques are prevalent but often fail to capture the nuances of search intent accurately, so neural networks now have become a preferred solution to processing such complex text matching. Existing methods predominantly employ representation-based architectures, which strike a balance between high traffic capacity and low latency. However, they exhibit significant shortcomings in generalization and robustness when compared to interaction-based architectures. In this work, we introduce a robust interaction-based modeling paradigm to address these shortcomings. It encompasses 1) a dynamic length representation scheme for expedited inference, 2) a professional terms recognition method to identify subjects and core attributes from complex sentence structures, and 3) a contrastive adversarial training protocol to bolster the model's robustness and matching capabilities. Extensive offline evaluations demonstrate the superior robustness and effectiveness of our approach, and online A/B testing confirms its ability to improve relevance in the same exposure position, resulting in more clicks and conversions. To the best of our knowledge, this method is the first interaction-based approach for large e-commerce search relevance calculation. Notably, we have deployed it for the entire search traffic on alibaba.com, the largest B2B e-commerce platform in the world., Comment: Accepted by ECML-PKDD'24 as Outstanding Paper. 8 pages, 2 figures, 7 tables

The cosmological event horizons are observer-dependent, which might bring a paradox. As an example, in dS$_{2}$ multiverse model there are entanglement islands in crunching regions encoding the information of regions near future infinity of inflating or Minkowski bubbles, however, for two observers in different bubbles, since their island regions overlap, both observers will be able to get access to the information encoded in the overlapping region, indicating a violation of no-cloning theorem. In this paper, we present a different resolution to this paradox. Based on the Petz $\mathrm{R\acute{e}nyi}$ mutual information, we show that besides the quantum extremal surfaces there might be another boundary for the island in corresponding spacetime so that the island regions are bounded by ``division points" rather than extending to the rest of the entire spacetime. We also discuss the implications of our result., Comment: 23 pages, 8 figures

This work proposes a novel approach to bolster both the robot's risk assessment and safety measures while deepening its understanding of 3D scenes, which is achieved by leveraging Radiance Field (RF) models and 3D Gaussian Splatting. To further enhance these capabilities, we incorporate additional sampled views from the environment with the RF model. One of our key contributions is the introduction of Risk-aware Environment Masking (RaEM), which prioritizes crucial information by selecting the next-best-view that maximizes the expected information gain. This targeted approach aims to minimize uncertainties surrounding the robot's path and enhance the safety of its navigation. Our method offers a dual benefit: improved robot safety and increased efficiency in risk-aware 3D scene reconstruction and understanding. Extensive experiments in real-world scenarios demonstrate the effectiveness of our proposed approach, highlighting its potential to establish a robust and safety-focused framework for active robot exploration and 3D scene understanding.

We investigate theoretically tunable non-Hermitian skin effect in systems with gain and loss, and find that bipolar (quadripolar) non-Hermitian skin effect characterized by topological invariants in one (two)-dimensional system. We also find the partial non-Hermitian skin effect with the coexistence of localized states and extended states. Both types of the non-Hermitian skin effect have not yet been predicted together in a single system. A feasible experimental scheme of our model is proposed to realize in electric circuits. Our investigation unveils a new type of non-Hermitian skin effect and enhance the tunability of the non-Hermitian systems by gain and loss other than the conventional non-reciprocal hopping., Comment: 7 pages, 6 figures

Existing Neural Machine Translation (NMT) models mainly handle translation in the general domain, while overlooking domains with special writing formulas, such as e-commerce and legal documents. Taking e-commerce as an example, the texts usually include amounts of domain-related words and have more grammar problems, which leads to inferior performances of current NMT methods. To address these problems, we collect two domain-related resources, including a set of term pairs (aligned Chinese-English bilingual terms) and a parallel corpus annotated for the e-commerce domain. Furthermore, we propose a two-step fine-tuning paradigm (named G2ST) with self-contrastive semantic enhancement to transfer one general NMT model to the specialized NMT model for e-commerce. The paradigm can be used for the NMT models based on Large language models (LLMs). Extensive evaluations on real e-commerce titles demonstrate the superior translation quality and robustness of our G2ST approach, as compared with state-of-the-art NMT models such as LLaMA, Qwen, GPT-3.5, and even GPT-4., Comment: 4 pages, 1 figure, WWW2024 accepted

Recommender systems have made significant strides in various industries, primarily driven by extensive efforts to enhance recommendation accuracy. However, this pursuit of accuracy has inadvertently given rise to echo chamber/filter bubble effects. Especially in industry, it could impair user's experiences and prevent user from accessing a wider range of items. One of the solutions is to take diversity into account. However, most of existing works focus on user's explicit preferences, while rarely exploring user's non-interaction preferences. These neglected non-interaction preferences are especially important for broadening user's interests in alleviating echo chamber/filter bubble effects.Therefore, in this paper, we first define diversity as two distinct definitions, i.e., user-explicit diversity (U-diversity) and user-item non-interaction diversity (N-diversity) based on user historical behaviors. Then, we propose a succinct and effective method, named as Controllable Category Diversity Framework (CCDF) to achieve both high U-diversity and N-diversity simultaneously.Specifically, CCDF consists of two stages, User-Category Matching and Constrained Item Matching. The User-Category Matching utilizes the DeepU2C model and a combined loss to capture user's preferences in categories, and then selects the top-$K$ categories with a controllable parameter $K$.These top-$K$ categories will be used as trigger information in Constrained Item Matching. Offline experimental results show that our proposed DeepU2C outperforms state-of-the-art diversity-oriented methods, especially on N-diversity task. The whole framework is validated in a real-world production environment by conducting online A/B testing., Comment: A Two-stage Controllable Category Diversity Framework for Recommendation

The recommendation has been playing a key role in many industries, e.g., e-commerce, streaming media, social media, etc. Recently, a new recommendation scenario, called Trigger-Induced Recommendation (TIR), where users are able to explicitly express their instant interests via trigger items, is emerging as an essential role in many e-commerce platforms, e.g., Alibaba.com and Amazon. Without explicitly modeling the user's instant interest, traditional recommendation methods usually obtain sub-optimal results in TIR. Even though there are a few methods considering the trigger and target items simultaneously to solve this problem, they still haven't taken into account temporal information of user behaviors, the dynamic change of user instant interest when the user scrolls down and the interactions between the trigger and target items. To tackle these problems, we propose a novel method -- Deep Evolutional Instant Interest Network (DEI2N), for click-through rate prediction in TIR scenarios. Specifically, we design a User Instant Interest Modeling Layer to predict the dynamic change of the intensity of instant interest when the user scrolls down. Temporal information is utilized in user behavior modeling. Moreover, an Interaction Layer is introduced to learn better interactions between the trigger and target items. We evaluate our method on several offline and real-world industrial datasets. Experimental results show that our proposed DEI2N outperforms state-of-the-art baselines. In addition, online A/B testing demonstrates the superiority over the existing baseline in real-world production environments., Comment: 7 pages, 6 figures, accepted by the 17th ACM International Conference on Web Search and Data Mining(WSDM'2024)

We present ContinuityCam, a novel approach to generate a continuous video from a single static RGB image, using an event camera. Conventional cameras struggle with high-speed motion capture due to bandwidth and dynamic range limitations. Event cameras are ideal sensors to solve this problem because they encode compressed change information at high temporal resolution. In this work, we propose a novel task called event-based continuous color video decompression, pairing single static color frames and events to reconstruct temporally continuous videos. Our approach combines continuous long-range motion modeling with a feature-plane-based synthesis neural integration model, enabling frame prediction at arbitrary times within the events. Our method does not rely on additional frames except for the initial image, increasing, thus, the robustness to sudden light changes, minimizing the prediction latency, and decreasing the bandwidth requirement. We introduce a novel single objective beamsplitter setup that acquires aligned images and events and a novel and challenging Event Extreme Decompression Dataset (E2D2) that tests the method in various lighting and motion profiles. We thoroughly evaluate our method through benchmarking reconstruction as well as various downstream tasks. Our approach significantly outperforms the event- and image- based baselines in the proposed task.

Powder metallurgy with hot isostatic pressing (PM-HIP) is an advanced manufacturing process that is envisioned to replace forging for heavy nuclear components, including the reactor pressure vessel (RPV). But PM-HIP products must at least demonstrate comparable irradiation tolerance than forgings in order to be qualified for nuclear applications. The objective of this study is to directly compare PM-HIP to forged SA508 Grade 3 Class 1 low-alloy RPV steel at two neutron irradiation conditions: ~0.5-1.0 displacements per atom (dpa) at ~270C and ~370C. PM-HIP SA508 experiences greater irradiation hardening and embrittlement (total elongation) than forged SA508. However, uniform elongation and approximate toughness are comparable across all irradiated materials, suggesting irradiated PM-HIP SA508 exhibits superior ductility at maximum load-bearing capacity. The irradiation hardening mechanism is linked to composition rather than fabrication method. Since PM-HIP SA508 has higher Mn and Ni concentration, it is more susceptible to irradiation-induced nucleation of Mn-Ni-Si-P (MNSP) nanoprecipitates and dislocation loops, which both contribute to hardening. Conversely, the forged material nucleates fewer MNSPs, causing dislocation loops to control irradiation hardening. These results show promise for the irradiation performance of PM-HIP SA508 and can motivate future nuclear code qualification of PM-HIP fabrication for RPVs.

The Giant Radio Array for Neutrino Detection (GRAND) is an envisioned observatory of ultra-high-energy particles of cosmic origin, with energies in excess of 100 PeV. GRAND uses large surface arrays of autonomous radio-detection units to look for the radio emission from extensive air showers that are triggered by the interaction of ultra-high-energy cosmic rays, gamma rays, and neutrinos in the atmosphere or underground. In particular, for ultra-high-energy neutrinos, the future final phase of GRAND aims to be sensitive enough to discover them in spite of their plausibly tiny flux. Presently, three prototype GRAND radio arrays are in operation: GRANDProto300, in China, GRAND@Auger, in Argentina, and GRAND@Nancay, in France. Their goals are to field-test the design of the radio-detection units, understand the radio background to which they are exposed, and develop tools for diagnostic, data gathering, and data analysis. This list of contributions to the 38th International Cosmic Ray Conference (ICRC 2023) presents an overview of GRAND, in its present and future incarnations, and a look at the first data collected by GRANDProto13, the first phase of GRANDProto300., Comment: Note: To access the list of contributions, please follow the "HTML" link that can be found on the arXiv page (v2: fixed name spelling of one author)