Benefits of Combining
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Before Fusion, Ask What to Keep: Contextual Calibration of Multimodal Signals
arXiv:2606.02679v1 Announce Type: new Abstract: Multimodal systems often benefit from combining information across language, sound, and visual streams, but this benefit is not guaranteed. A modality that is useful for one input may become distracting for another, and local feature responses within the same modality can disagree with evidence from other sources. This work investigates how to adjust multimodal representations before they are merged by a downstream predictor.
Dual-Exposure Imaging with Events
Announce Type: replace Abstract: By combining complementary benefits of short- and long-exposure images, Dual-Exposure Imaging (DEI) enhances image quality in low-light scenarios. However, existing DEI approaches inevitably suffer from producing artifacts due to spatial displacement from scene motion and image feature discrepancies from different exposure times. To tackle this problem, we propose a novel Event-based DEI (E-DEI) algorithm, which reconstructs high-quality images from...
Distortion-Aware Fusion of Statistical and Vision-Language Features for Blind Image Quality Assessment
Announce Type: new Abstract: Blind image quality assessment (BIQA) aims to predict perceived image quality without access to a reference image. Classical natural scene statistics (NSS) descriptors and modern vision-language model (VLM) embeddings address this problem from fundamentally different perspectives, yet whether combining them yields complementary benefits and how to weight their contributions per input image remains unexplored. We propose a distortion-aware fusion framework that...
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ZOAF: Towards Efficient Zeroth-Order Optimization for Analog/RF Circuit Design
Announce Type: new Abstract: Circuit optimization is an indispensable step in analog/RF IC design. Classical fast gradient-based optimization methods are typically infeasible due to lack of access to simulator source code and the technical barriers to implementing adjoint methods. Therefore, surrogate-based black-box optimization is widely used in practice; however, it can be costly to build and sensitive to hyperparameters, whereas population heuristics often suffer from slow convergence...
Critic-Guided Heterogeneous Multi-Agent Reasoning for Reliable Mathematical Problem Solving
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Hybrid Metaheuristic Combining the Dragonfly Algorithm and Tabu Search for the Traveling Salesman Problem
Announce Type: new Abstract: The Traveling Salesman Problem (TSP) is a classical NP-hard combinatorial optimization problem that aims to find the shortest Hamiltonian cycle visiting each city exactly once and returning to the starting point. This paper proposes a hybrid metaheuristic for the TSP by combining the Dragonfly Algorithm (DA), a swarm-intelligence-based global search method, with Tabu Search (TS), a memory-based local search technique. The proposed method follows a High-Level...
BlendServe: Optimizing Offline Inference for Auto-regressive Large Models with Resource-aware Batching
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Could cancer vaccines be next? New treatment cuts melanoma risk by nearly 50%
A new injectable therapy is showing positive results in reducing melanoma throughout a five-year period. The personalized mRNA cancer therapy, called intismeran autogene, combined with the cancer immunotherapy drug KEYTRUDA (pembrolizumab), is a collaboration between Merck and Moderna. The results from the phase 2b KEYNOTE-942 study were presented at the American Society of Clinical Oncology (ASCO) Annual Meeting in Chicago on May 27.CLICK HERE FOR MORE HEALTH STORIESAfter about a five-year...