Real-world applications involve various discrete optimization problems. Designing a specialized optimizer for each of these problems is challenging, typically requiring significant domain knowledge and human efforts. Hence, developing general-purpose optimizers as an off-the-shelf tool for a wide range of problems has been a long-standing research target. This article introduces MEGO, a novel general-purpose neural optimizer trained through a fully data-driven learning-to-optimize (L2O) approach. MEGO consists of a mixture-of-experts trained on experiences from solving training problems and can be viewed as a foundation model for optimization problems with binary decision variables. When presented with a problem to solve, MEGO actively selects relevant expert models to generate high-quality solutions. MEGO can be used as a standalone sample-efficient optimizer or in conjunction with existing search methods as an initial solution generator. The generality of MEGO is validated across six problem classes, including three classic problem classes and three problem classes arising from real-world applications in compilers, network analysis, and 3D reconstruction. Trained solely on classic problem classes, MEGO performs very well on all six problem classes, significantly surpassing widely used general-purpose optimizers in both solution quality and efficiency. In some cases, MEGO even surpasses specialized state-of-the-art optimizers. Additionally, MEGO provides a similarity measure between problems, yielding a new perspective for problem classification. In the pursuit of general-purpose optimizers through L2O, MEGO represents an initial yet significant step forward.
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A novel wireless transmission scheme, as named the reconfigurable intelligent surface (RIS)-assisted received adaptive spatial modulation (RASM) scheme, is proposed in this paper. In this scheme, the adaptive spatial modulation (ASM)-based antennas selection works at the receiver by employing the characteristics of the RIS in each time slot, where the signal-to-noise ratio at specific selected antennas can be further enhanced with near few powers. Besides for the bits from constellation symbols, the extra bits can be mapped into the indices of receive antenna combinations and conveyed to the receiver through the ASM-based antenna-combination selection, thus providing higher spectral efficiency. To explicitly present the RASM scheme, the analytical performance of bit error rate of it is discussed in this paper. As a trade-off selection, the proposed scheme shows higher spectral efficiency and remains the satisfactory error performance. Simulation and analytical results demonstrate the better performance and exhibit more potential to apply in practical wireless communication.
The development of next-generation communication systems promises to enable extended reality (XR) applications, such as XR gaming with ultra-realistic content and human-grade sensory feedback. These demanding applications impose stringent performance requirements on the underlying wireless communication infrastructure. To meet the expected Quality of Experience (QoE) for XR applications, high-capacity connections are necessary, which can be achieved by using millimeter-wave (mmWave) frequency bands and employing highly directional beams. However, these narrow beams are susceptible to even minor misalignments caused by small-scale user mobility, such as changes in the orientation of the XR head-mounted device (HMD) or minor shifts in user body position. This article explores the impact of small-scale user mobility on mmWave connectivity for XR and reviews approaches to resolve the challenges arising due to small-scale mobility. To deepen our understanding of small-scale mobility during XR usage, we prepared a dataset of user mobility during XR gaming. We use this dataset to study the effects of user mobility on highly directional communication, identifying specific aspects of user mobility that significantly affect the performance of narrow-beam wireless communication systems. Our results confirm the substantial influence of small-scale mobility on beam misalignment, highlighting the need for enhanced mechanisms to effectively manage the consequences of small-scale mobility.
Travel time estimation is a crucial application in navigation apps and web mapping services. Current deterministic and probabilistic methods primarily focus on modeling individual trips, assuming independence among trips. However, in real-world scenarios, we often observe strong inter-trip correlations due to factors such as weather conditions, traffic management, and road works. In this paper, we propose to model trip-level link travel time using a Gaussian hierarchical model, which can characterize both inter-trip and intra-trip correlations. The joint distribution of travel time of multiple trips becomes a multivariate Gaussian parameterized by learnable link representations. To effectively use the sparse GPS trajectories, we also propose a data augmentation method based on trip sub-sampling, which allows for fine-grained gradient backpropagation in learning link representations. During inference, we estimate the probability distribution of the travel time of a queried trip conditional on the completed trips that are spatiotemporally adjacent. We refer to the overall framework as ProbTTE. We evaluate ProbTTE on two real-world GPS trajectory datasets, and the results demonstrate its superior performance compared to state-of-the-art deterministic and probabilistic baselines. Additionally, we find that the learned link representations align well with the physical geometry of the network, making them suitable as input for other applications.
Recently, diffusion models have increasingly demonstrated their capabilities in vision understanding. By leveraging prompt-based learning to construct sentences, these models have shown proficiency in classification and visual grounding tasks. However, existing approaches primarily showcase their ability to perform sentence-level localization, leaving the potential for leveraging contextual information for phrase-level understanding largely unexplored. In this paper, we utilize Panoptic Narrative Grounding (PNG) as a proxy task to investigate this capability further. PNG aims to segment object instances mentioned by multiple noun phrases within a given narrative text. Specifically, we introduce the DiffPNG framework, a straightforward yet effective approach that fully capitalizes on the diffusion's architecture for segmentation by decomposing the process into a sequence of localization, segmentation, and refinement steps. The framework initially identifies anchor points using cross-attention mechanisms and subsequently performs segmentation with self-attention to achieve zero-shot PNG. Moreover, we introduce a refinement module based on SAM to enhance the quality of the segmentation masks. Our extensive experiments on the PNG dataset demonstrate that DiffPNG achieves strong performance in the zero-shot PNG task setting, conclusively proving the diffusion model's capability for context-aware, phrase-level understanding. Source code is available at \url{//github.com/nini0919/DiffPNG}.
Could information about future incoming packets be used to build more efficient CPU-based packet processors? Can such information be obtained accurately? This paper studies novel packet processing architectures that receive external hints about which packets are soon to arrive, thus enabling prefetching into fast cache memories of the state needed to process them, just-in-time for the packets' arrival. We explore possible approaches to (i) obtain such hints either from network devices or the end hosts in the communication and (ii) use these hints to better utilize cache memories. We show that such information (if accurate) can improve packet processing throughput by at least 50%.
Multi-resolution methods such as Adaptive Mesh Refinement (AMR) can enhance storage efficiency for HPC applications generating vast volumes of data. However, their applicability is limited and cannot be universally deployed across all applications. Furthermore, integrating lossy compression with multi-resolution techniques to further boost storage efficiency encounters significant barriers. To this end, we introduce an innovative workflow that facilitates high-quality multi-resolution data compression for both uniform and AMR simulations. Initially, to extend the usability of multi-resolution techniques, our workflow employs a compression-oriented Region of Interest (ROI) extraction method, transforming uniform data into a multi-resolution format. Subsequently, to bridge the gap between multi-resolution techniques and lossy compressors, we optimize three distinct compressors, ensuring their optimal performance on multi-resolution data. Lastly, we incorporate an advanced uncertainty visualization method into our workflow to understand the potential impacts of lossy compression. Experimental evaluation demonstrates that our workflow achieves significant compression quality improvements.
Retrieval-augmented Large Language Models (LLMs) have reshaped traditional query-answering systems, offering unparalleled user experiences. However, existing retrieval techniques often struggle to handle multi-modal query contexts. In this paper, we present an interactive Multi-modal Query Answering (MQA) system, empowered by our newly developed multi-modal retrieval framework and navigation graph index, integrated with cutting-edge LLMs. It comprises five core components: Data Preprocessing, Vector Representation, Index Construction, Query Execution, and Answer Generation, all orchestrated by a dedicated coordinator to ensure smooth data flow from input to answer generation. One notable aspect of MQA is its utilization of contrastive learning to assess the significance of different modalities, facilitating precise measurement of multi-modal information similarity. Furthermore, the system achieves efficient retrieval through our advanced navigation graph index, refined using computational pruning techniques. Another highlight of our system is its pluggable processing framework, allowing seamless integration of embedding models, graph indexes, and LLMs. This flexibility provides users diverse options for gaining insights from their multi-modal knowledge base. A preliminary video introduction of MQA is available at //youtu.be/xvUuo2ZIqWk.
We present several enhancements to the open-source ESP platform to support flexible and efficient on-chip communication for programmable accelerators in heterogeneous SoCs. These enhancements include 1) a flexible point-to-point communication mechanism between accelerators, 2) a multicast NoC that supports data forwarding to multiple accelerators simultaneously, 3) accelerator synchronization leveraging the SoC's coherence protocol, 4) an accelerator interface that offers fine-grained control over the communication mode used, and 5) an example ISA extension to support our enhancements. Our solution adds negligible area to the SoC architecture and requires minimal changes to the accelerators themselves. We have validated most of these features in complex FPGA prototypes and plan to include them in the open-source release of ESP in the coming months.
Electroencephalogram (EEG) technology, particularly high-density EEG (HD EEG) devices, is widely used in fields such as neuroscience. HD EEG devices improve the spatial resolution of EEG by placing more electrodes on the scalp, meeting the requirements of clinical diagnostic applications such as epilepsy focus localization. However, this technique faces challenges such as high acquisition costs and limited usage scenarios. In this paper, spatio-temporal adaptive diffusion models (STADMs) are proposed to pioneer the use of diffusion models for achieving spatial SR reconstruction from low-resolution (LR, 64 channels or fewer) EEG to high-resolution (HR, 256 channels) EEG. Specifically, a spatio-temporal condition module is designed to extract the spatio-temporal features of LR EEG, which then serve as conditional inputs to guide the reverse denoising process of diffusion models. Additionally, a multi-scale Transformer denoising module is constructed to leverage multi-scale convolution blocks and cross-attention-based diffusion Transformer blocks for conditional guidance to generate subject-adaptive SR EEG. Experimental results demonstrate that the proposed method effectively enhances the spatial resolution of LR EEG and quantitatively outperforms existing methods. Furthermore, STADMs demonstrate their value by applying synthetic SR EEG to classification and source localization tasks of epilepsy patients, indicating their potential to significantly improve the spatial resolution of LR EEG.
The accelerating growth of the Internet of Things (IoT) and its integration with Low-Earth Orbit (LEO) satellites demand efficient, reliable, and scalable communication protocols. Among these, the Long-Range Frequency Hopping Spread Spectrum (LR-FHSS) modulation, tailored for LEO satellite IoT communications, sparks keen interest. This work presents a joint approach to enhancing the scalability of LR-FHSS, addressing the demand for massive connectivity. We deepen into Frequency Hopping Sequence (FHS) mechanisms within LR-FHSS, spotlighting the potential of leveraging Wide-Gap sequences. Concurrently, we introduce two novel demodulator allocation strategies, namely, ``Early-Decode" and ``Early-Drop," to optimize the utilization of LoRa-specific gateway decoding resources. Our research further validates these findings with extensive simulations, offering a comprehensive look into the future potential of LR-FHSS scalability in IoT settings.
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