Optimizing Recall or Relevance? A Multi-Task Multi-Head Approach for Item-to-Item Retrieval in Recommendation

The task of item-to-item (I2I) retrieval is to identify a set of relevant and highly engaging items based on a given trigger item. It is a crucial component in modern recommendation systems, where users' previously engaged items serve as trigger items to retrieve relevant content for future engagement. However, existing I2I retrieval models in industry are primarily built on co-engagement data and optimized using the recall measure, which overly emphasizes co-engagement patterns while failing to capture semantic relevance. This often leads to overfitting short-term co-engagement trends at the expense of long-term benefits such as discovering novel interests and promoting content diversity. To address this challenge, we propose MTMH, a Multi-Task and Multi-Head I2I retrieval model that achieves both high recall and semantic relevance. Our model consists of two key components: 1) a multi-task learning loss for formally optimizing the trade-off between recall and semantic relevance, and 2) a multi-head I2I retrieval architecture for retrieving both highly co-engaged and semantically relevant items. We evaluate MTMH using proprietary data from a commercial platform serving billions of users and demonstrate that it can improve recall by up to 14.4% and semantic relevance by up to 56.6% compared with prior state-of-the-art models. We also conduct live experiments to verify that MTMH can enhance both short-term consumption metrics and long-term user-experience-related metrics. Our work provides a principled approach for jointly optimizing I2I recall and semantic relevance, which has significant implications for improving the overall performance of recommendation systems.

Highly Undersampled MRI Reconstruction via a Single Posterior Sampling of Diffusion Models

Incoherent k-space under-sampling and deep learning-based reconstruction methods have shown great success in accelerating MRI. However, the performance of most previous methods will degrade dramatically under high acceleration factors, e.g., 8$\times$ or higher. Recently, denoising diffusion models (DM) have demonstrated promising results in solving this issue; however, one major drawback of the DM methods is the long inference time due to a dramatic number of iterative reverse posterior sampling steps. In this work, a Single Step Diffusion Model-based reconstruction framework, namely SSDM-MRI, is proposed for restoring MRI images from highly undersampled k-space. The proposed method achieves one-step reconstruction by first training a conditional DM and then iteratively distilling this model. Comprehensive experiments were conducted on both publicly available fastMRI images and an in-house multi-echo GRE (QSM) subject. Overall, the results showed that SSDM-MRI outperformed other methods in terms of numerical metrics (PSNR and SSIM), qualitative error maps, image fine details, and latent susceptibility information hidden in MRI phase images. In addition, the reconstruction time for a 320*320 brain slice of SSDM-MRI is only 0.45 second, which is only comparable to that of a simple U-net, making it a highly effective solution for MRI reconstruction tasks.

Boosting Neural Language Inference via Cascaded Interactive Reasoning

Natural Language Inference (NLI) focuses on ascertaining the logical relationship (entailment, contradiction, or neutral) between a given premise and hypothesis. This task presents significant challenges due to inherent linguistic features such as diverse phrasing, semantic complexity, and contextual nuances. While Pre-trained Language Models (PLMs) built upon the Transformer architecture have yielded substantial advancements in NLI, prevailing methods predominantly utilize representations from the terminal layer. This reliance on final-layer outputs may overlook valuable information encoded in intermediate layers, potentially limiting the capacity to model intricate semantic interactions effectively. Addressing this gap, we introduce the Cascaded Interactive Reasoning Network (CIRN), a novel architecture designed for deeper semantic comprehension in NLI. CIRN implements a hierarchical feature extraction strategy across multiple network depths, operating within an interactive space where cross-sentence information is continuously integrated. This mechanism aims to mimic a process of progressive reasoning, transitioning from surface-level feature matching to uncovering more profound logical and semantic connections between the premise and hypothesis. By systematically mining latent semantic relationships at various representational levels, CIRN facilitates a more thorough understanding of the input pair. Comprehensive evaluations conducted on several standard NLI benchmark datasets reveal consistent performance gains achieved by CIRN over competitive baseline approaches, demonstrating the efficacy of leveraging multi-level interactive features for complex relational reasoning.

Using External knowledge to Enhanced PLM for Semantic Matching

Modeling semantic relevance has always been a challenging and critical task in natural language processing. In recent years, with the emergence of massive amounts of annotated data, it has become feasible to train complex models, such as neural network-based reasoning models. These models have shown excellent performance in practical applications and have achieved the current state-ofthe-art performance. However, even with such large-scale annotated data, we still need to think: Can machines learn all the knowledge necessary to perform semantic relevance detection tasks based on this data alone? If not, how can neural network-based models incorporate external knowledge into themselves, and how can relevance detection models be constructed to make full use of external knowledge? In this paper, we use external knowledge to enhance the pre-trained semantic relevance discrimination model. Experimental results on 10 public datasets show that our method achieves consistent improvements in performance compared to the baseline model.

Sensing-Then-Beamforming: Robust Transmission Design for RIS-Empowered Integrated Sensing and Covert Communication

Traditional covert communication often relies on the knowledge of the warden's channel state information, which is inherently challenging to obtain due to the non-cooperative nature and potential mobility of the warden. The integration of sensing and communication technology provides a promising solution by enabling the legitimate transmitter to sense and track the warden, thereby enhancing transmission covertness. In this paper, we develop a framework for sensing-then-beamforming in reconfigurable intelligent surface (RIS)-empowered integrated sensing and covert communication (ISCC) systems, where the transmitter (Alice) estimates and tracks the mobile aerial warden's channel using sensing echo signals while simultaneously sending covert information to multiple legitimate users (Bobs) with the assistance of RIS, under the surveillance of the warden (Willie). Considering channel estimation errors, we formulate a robust non-convex optimization problem that jointly designs the communication beamformers, the sensing signal covariance matrix at Alice, and the phase shifts at the RIS to maximize the covert sum rate of Bobs while satisfying the constraints related to covert communication, sensing, transmitter power, and the unit modulus of the RIS elements. To solve this complex problem, we develop an efficient algorithm using alternating optimization, successive convex approximation, S-procedure, sequential rank-one constraint relaxation, and semidefinite relaxation techniques. Numerical results confirm the convergence of the proposed algorithm and demonstrate its effectiveness in tracking the warden's channel while ensuring robust covert transmission. Furthermore, the results highlight the advantages of using RIS to enhance the covert transmission rate compared to baseline schemes, and also illustrate the intricate trade-off between communication and sensing in ISCC systems.

Movable Antenna Enhanced Downlink Multi-User Integrated Sensing and Communication System

This work investigates the potential of exploiting movable antennas (MAs) to enhance the performance of a multi-user downlink integrated sensing and communication (ISAC) system. Specifically, we formulate an optimization problem to maximize the transmit beampattern gain for sensing while simultaneously meeting each user's communication requirement by jointly optimizing antenna positions and beamforming design. The problem formulated is highly non-convex and involves multivariate-coupled constraints. To address these challenges, we introduce a series of auxiliary random variables and transform the original problem into an augmented Lagrangian problem. A double-loop algorithm based on a penalty dual decomposition framework is then developed to solve the problem. Numerical results validate the effectiveness of the proposed design, demonstrating its superiority over MA designs based on successive convex approximation optimization and other baseline approaches in ISAC systems. The results also highlight the advantages of MAs in achieving better sensing performance and improved beam control, especially for sparse arrays with large apertures.

Beyond Words: Advancing Long-Text Image Generation via Multimodal Autoregressive Models

Recent advancements in autoregressive and diffusion models have led to strong performance in image generation with short scene text words. However, generating coherent, long-form text in images, such as paragraphs in slides or documents, remains a major challenge for current generative models. We present the first work specifically focused on long text image generation, addressing a critical gap in existing text-to-image systems that typically handle only brief phrases or single sentences. Through comprehensive analysis of state-of-the-art autoregressive generation models, we identify the image tokenizer as a critical bottleneck in text generating quality. To address this, we introduce a novel text-focused, binary tokenizer optimized for capturing detailed scene text features. Leveraging our tokenizer, we develop \ModelName, a multimodal autoregressive model that excels in generating high-quality long-text images with unprecedented fidelity. Our model offers robust controllability, enabling customization of text properties such as font style, size, color, and alignment. Extensive experiments demonstrate that \ModelName~significantly outperforms SD3.5 Large~\cite{sd3} and GPT4o~\cite{gpt4o} with DALL-E 3~\cite{dalle3} in generating long text accurately, consistently, and flexibly. Beyond its technical achievements, \ModelName~opens up exciting opportunities for innovative applications like interleaved document and PowerPoint generation, establishing a new frontier in long-text image generating.

SCAN-BEST: Efficient Sub-6GHz-Aided Near-field Beam Selection with Formal Reliability Guarantees

As millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems continue to incorporate larger antenna arrays, the range of near-field propagation expands, making it more likely for users close to the transmitter to fall within the near-field regime. Traditional far-field beam training methods are no longer effective in this context. Additionally, near-field beam training presents challenges, since the training codebook must account for both angular and distance dimensions, leading to large codebook sizes. To reduce the in-band training overhead, we propose the Sub-6G Channel-Aided Near-field BEam SelecTion (SCAN-BEST) framework, which is motivated by the spatial-temporal congruence between sub-6 GHz (sub-6G) and mmWave channels. SCAN-BEST utilizes preprocessed sub-6G channel estimates as input, and employs a convolutional neural network (CNN) to predict the probability of each beam being optimal within the near-field beam training codebook. Given the prediction uncertainty arising from the variance between sub-6G and mmWave channels, we introduce a conformal risk control (CRC)-based module that generates a set of beam candidates for further limited in-band training, enabling the final beam selection to formally meet user-defined target coverage rate. Numerical results confirm the thereoretical properties of SCAN-BEST in terms of the achieved coverage rate of the beam candidates and various metrics. Moreover, SCAN-BEST enjoys good scalability and robustness to various sub-6G system configurations, including to the sizes of calibration datasets.

Ambiguity Function Analysis and Optimization of Frequency-Hopping MIMO Radar with Movable Antennas

In this paper, we propose a movable antenna (MA)-enabled frequency-hopping (FH) multiple-input multiple-output (MIMO) radar system and investigate its sensing resolution. Specifically, we derive the expression of the ambiguity function and analyze the relationship between its main lobe width and the transmit antenna positions. In particular, the optimal antenna distribution to achieve the minimum main lobe width in the angular domain is characterized. We discover that this minimum width is related to the antenna size, the antenna number, and the target angle. Meanwhile, we present lower bounds of the ambiguity function in the Doppler and delay domains, and show that the impact of the antenna size on the radar performance in these two domains is very different from that in the angular domain. Moreover, the performance enhancement brought by MAs exhibits a certain trade-off between the main lobe width and the side lobe peak levels. Therefore, we propose to balance between minimizing the side lobe levels and narrowing the main lobe of the ambiguity function by optimizing the antenna positions. To achieve this goal, we propose a low-complexity algorithm based on the Rosen's gradient projection method, and show that its performance is very close to the baseline. Simulation results are presented to validate the theoretical analysis on the properties of the ambiguity function, and demonstrate that MAs can reduce the main lobe width and suppress the side lobe levels of the ambiguity function, thereby enhancing radar performance.

TextAtlas5M: A Large-scale Dataset for Dense Text Image Generation

Text-conditioned image generation has gained significant attention in recent years and are processing increasingly longer and comprehensive text prompt. In everyday life, dense and intricate text appears in contexts like advertisements, infographics, and signage, where the integration of both text and visuals is essential for conveying complex information. However, despite these advances, the generation of images containing long-form text remains a persistent challenge, largely due to the limitations of existing datasets, which often focus on shorter and simpler text. To address this gap, we introduce TextAtlas5M, a novel dataset specifically designed to evaluate long-text rendering in text-conditioned image generation. Our dataset consists of 5 million long-text generated and collected images across diverse data types, enabling comprehensive evaluation of large-scale generative models on long-text image generation. We further curate 3000 human-improved test set TextAtlasEval across 3 data domains, establishing one of the most extensive benchmarks for text-conditioned generation. Evaluations suggest that the TextAtlasEval benchmarks present significant challenges even for the most advanced proprietary models (e.g. GPT4o with DallE-3), while their open-source counterparts show an even larger performance gap. These evidences position TextAtlas5M as a valuable dataset for training and evaluating future-generation text-conditioned image generation models.