Fusion-in-T5: Unifying Document Ranking Signals for Improved Information Retrieval

Common document ranking pipelines in search systems are cascade systems that involve multiple ranking layers to integrate different information step-by-step. In this paper, we propose a novel re-ranker Fusion-in-T5 (FiT5), which integrates text matching information, ranking features, and global document information into one single unified model via templated-based input and global attention. Experiments on passage ranking benchmarks MS MARCO and TREC DL show that FiT5, as one single model, significantly improves ranking performance over complex cascade pipelines. Analysis finds that through attention fusion, FiT5 jointly utilizes various forms of ranking information via gradually attending to related documents and ranking features, and improves the detection of subtle nuances. Our code is open-sourced at https://github.com/OpenMatch/FiT5.Comment: COLING 202

Reusable Architecture Growth for Continual Stereo Matching

The remarkable performance of recent stereo depth estimation models benefits from the successful use of convolutional neural networks to regress dense disparity. Akin to most tasks, this needs gathering training data that covers a number of heterogeneous scenes at deployment time. However, training samples are typically acquired continuously in practical applications, making the capability to learn new scenes continually even more crucial. For this purpose, we propose to perform continual stereo matching where a model is tasked to 1) continually learn new scenes, 2) overcome forgetting previously learned scenes, and 3) continuously predict disparities at inference. We achieve this goal by introducing a Reusable Architecture Growth (RAG) framework. RAG leverages task-specific neural unit search and architecture growth to learn new scenes continually in both supervised and self-supervised manners. It can maintain high reusability during growth by reusing previous units while obtaining good performance. Additionally, we present a Scene Router module to adaptively select the scene-specific architecture path at inference. Comprehensive experiments on numerous datasets show that our framework performs impressively in various weather, road, and city circumstances and surpasses the state-of-the-art methods in more challenging cross-dataset settings. Further experiments also demonstrate the adaptability of our method to unseen scenes, which can facilitate end-to-end stereo architecture learning and practical deployment.Comment: Extended version of CVPR 2022 paper "Continual Stereo Matching of Continuous Driving Scenes with Growing Architecture" - Accepted to TPAMI in 202

Enhancing Low-Resource Relation Representations through Multi-View Decoupling

Recently, prompt-tuning with pre-trained language models (PLMs) has demonstrated the significantly enhancing ability of relation extraction (RE) tasks. However, in low-resource scenarios, where the available training data is scarce, previous prompt-based methods may still perform poorly for prompt-based representation learning due to a superficial understanding of the relation. To this end, we highlight the importance of learning high-quality relation representation in low-resource scenarios for RE, and propose a novel prompt-based relation representation method, named MVRE (\underline{M}ulti-\underline{V}iew \underline{R}elation \underline{E}xtraction), to better leverage the capacity of PLMs to improve the performance of RE within the low-resource prompt-tuning paradigm. Specifically, MVRE decouples each relation into different perspectives to encompass multi-view relation representations for maximizing the likelihood during relation inference. Furthermore, we also design a Global-Local loss and a Dynamic-Initialization method for better alignment of the multi-view relation-representing virtual words, containing the semantics of relation labels during the optimization learning process and initialization. Extensive experiments on three benchmark datasets show that our method can achieve state-of-the-art in low-resource settings.Comment: Accepted to AAAI 202

Tailoring Plasmonic Bimetallic Nanocatalysts Toward Sunlight-Driven H-2 Production

Hybrid nanoparticles combining plasmonic and catalytic components have recently gained interest for their potential use in sunlight-to-chemical energy conversion. However, a deep understanding of the structure-performance that maximizes the use of the incoming energy remains elusive. Here, a suite of Au and Pd based nanostructures in core-shell and core-satellites configurations are designed and their photocatalytic activity for Hydrogen (H-2) generation under sunlight illumination is tested. Formic acid is employed as H-2 source. Core-satellite systems show a higher enhancement of the reaction upon illumination, compared to core-shell ones. Electromagnetic simulations reveal that a key difference between both configurations is the excitation of highly localized and asymmetric electric fields in the gap between both materials. In this scheme, the core Au particle acts as an antenna, efficiently capturing visible light via the excitation of localized plasmon resonances, while the surrounding Pd satellites transduce the locally-enhanced electric field into catalytic activity. These findings advance the understanding of plasmon-driven photocatalysis, and provide an important benchmark to guide the design of the next generation of plasmonic bimetallic nanostructures

Incubating Advances in Integrated Photonics with Emerging Sensing and Computational Capabilities

As photonic technologies continue to grow in multidimensional aspects, integrated photonics holds a unique position and continuously presents enormous possibilities to research communities. Applications span across data centers, environmental monitoring, medical diagnosis, and highly compact communication components, with further possibilities growing endlessly. Here, we provide a review of state of the art integrated photonic sensors operating in near and mid infrared wavelength regions on various material platforms. Among different materials, architectures, and technologies leading the way for on chip sensors, we discuss optical sensing principles commonly applied to biochemical and gas sensing. Our focus is particularly on passive and active optical waveguides, including dispersion engineered metamaterial based structures an essential approach for enhancing the interaction between light and analytes in chip scale sensors. We harness a diverse array of cutting edge sensing technologies, heralding a revolutionary on chip sensing paradigm. Our arsenal includes refractive index based sensing, plasmonic, and spectroscopy, forging an unparalleled foundation for innovation and precision. Furthermore, we include a brief discussion of recent trends and computational concepts incorporating Artificial Intelligence & Machine Learning (AI/ML) and deep learning approaches over the past few years to improve the qualitative and quantitative analysis of sensor measurements

Quantum decoherence of dark pulses in optical microresonators

Quantum fluctuations disrupt the cyclic motions of dissipative Kerr solitons (DKSs) in nonlinear optical microresonators and consequently cause timing jitter of the emitted pulse trains. This problem is translated to the performance of several applications that employ DKSs as compact frequency comb sources. Recently, device manufacturing and noise reduction technologies have advanced to unveil the quantum properties of DKSs. Here we investigate the quantum decoherence of DKSs existing in normal-dispersion microresonators known as dark pulses. By virtue of the very large material nonlinearity, we directly observe the quantum decoherence of dark pulses in an AlGaAs-on-insulator microresonator, and the underlying dynamical processes are resolved by injecting stochastic photons into the microresonators. Moreover, phase correlation measurements show that the uniformity of comb spacing of quantum-limited dark pulses is better than 1.2 × 10-16 and 2.5 × 10-13 when normalized to the optical carrier frequencies and repetition frequencies, respectively. Comparing DKSs generated in different material platforms explicitly confirms the advantages of dark pulses over bright solitons in terms of quantum-limited coherence. Our work establishes a critical performance assessment of DKSs, providing guidelines for coherence engineering of chip-scale optical frequency combs

Spark-based Cloud Data Analytics using Multi-Objective Optimization

International audienceData analytics in the cloud has become an integral part of enterprise businesses. Big data analytics systems, however, still lack the ability to take user performance goals and budgetary constraints for a task, collectively referred to as task objectives, and automatically configure an analytic job to achieve these objectives. This paper presents a data analytics optimizer that can automatically determine a cluster configuration with a suitable number of cores as well as other system parameters that best meet the task objectives. At a core of our work is a principled multi-objective optimization (MOO) approach that computes a Pareto optimal set of job configurations to reveal tradeoffs between different user objectives, recommends a new job configuration that best explores such tradeoffs, and employs novel optimizations to enable such recommendations within a few seconds. We present efficient incremental algorithms based on the notion of a Progressive Frontier for realizing our MOO approach and implement them into a Spark-based prototype. Detailed experiments using benchmark workloads show that our MOO techniques provide a 2-50x speedup over existing MOO methods, while offering good coverage of the Pareto frontier. When compared to Ottertune, a state-of-the-art performance tuning system, our approach recommends configurations that yield 26%-49% reduction of running time of the TPCx-BB benchmark while adapting to different application preferences on multiple objectives