605 research outputs found

    Unsupervised Domain Adaptation on Reading Comprehension

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    Reading comprehension (RC) has been studied in a variety of datasets with the boosted performance brought by deep neural networks. However, the generalization capability of these models across different domains remains unclear. To alleviate this issue, we are going to investigate unsupervised domain adaptation on RC, wherein a model is trained on labeled source domain and to be applied to the target domain with only unlabeled samples. We first show that even with the powerful BERT contextual representation, the performance is still unsatisfactory when the model trained on one dataset is directly applied to another target dataset. To solve this, we provide a novel conditional adversarial self-training method (CASe). Specifically, our approach leverages a BERT model fine-tuned on the source dataset along with the confidence filtering to generate reliable pseudo-labeled samples in the target domain for self-training. On the other hand, it further reduces domain distribution discrepancy through conditional adversarial learning across domains. Extensive experiments show our approach achieves comparable accuracy to supervised models on multiple large-scale benchmark datasets.Comment: 8 pages, 6 figures, 5 tables, Accepted by AAAI 202

    Set-valued tableaux rule for Lascoux polynomials

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    Lascoux polynomials generalize Grassmannian stable Grothendieck polynomials and may be viewed as K-theoretic analogs of key polynomials. The latter two polynomials have combinatorial formulas involving tableaux: Lascoux and Schützenberger gave a combinatorial formula for key polynomials using right keys; Buch gave a set-valued tableau formula for Grassmannian stable Grothendieck polynomials. We establish a novel combinatorial description for Lascoux polynomials involving right keys and set-valued tableaux. Our description generalizes the tableaux formulas of key polynomials and Grassmannian stable Grothendieck polynomials. To prove our description, we construct a new abstract Kashiwara crystal structure on set-valued tableaux. This construction answers an open problem of Monical, Pechenik and Scrimshaw.Mathematics Subject Classifications: 05E05Keywords: Lascoux polynomials, set-valued tableaux, crystal operator

    China\u27s Grand Strategy and its Hegemonic Aspirations

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    The rise of China has sparked a debate on two core questions: what are China\u27s intentions, and, more specifically, does China aspire to become a global hegemon? At the heart of these questions lies the enduring topic of China\u27s grand strategy, its implementation, and its narratives. This paper addresses these questions by examining China\u27s statements regarding its national rejuvenation strategy and its use of military power. The analysis concludes that China harbors aspirations of first becoming a regional hegemon and then challenging the US-led world order. Moreover, the paper suggests that China is at a turning point in that strategic project, becoming increasingly assertive in pursuing its goals

    An Android-Based Mechanism for Energy Efficient Localization Depending on Indoor/Outdoor Context

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    Today, there is widespread use of mobile applications that take advantage of a user\u27s location. Popular usages of location information include geotagging on social media websites, driver assistance and navigation, and querying nearby locations of interest. However, the average user may not realize the high energy costs of using location services (namely the GPS) or may not make smart decisions regarding when to enable or disable location services-for example, when indoors. As a result, a mechanism that can make these decisions on the user\u27s behalf can significantly improve a smartphone\u27s battery life. In this paper, we present an energy consumption analysis of the localization methods available on modern Android smartphones and propose the addition of an indoor localization mechanism that can be triggered depending on whether a user is detected to be indoors or outdoors. Based on our energy analysis and implementation of our proposed system, we provide experimental results-monitoring battery life over time-and show that an indoor localization method triggered by indoor or outdoor context can improve smartphone battery life and, potentially, location accuracy

    A row analogue of Hecke column insertion

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    We introduce a new row insertion algorithm on decreasing tableaux and increasing tableaux, generalizing Edelman-Greene (EG) row insertion. Our row insertion algorithm is a nontrivial variation of Hecke column insertion which generalizes EG column insertion. Similar to Hecke column insertion, our row insertion is bijective and respects Hecke equivalence, and therefore recovers the expansions of Grothendieck symmetric functions into Grassmannian Grothendieck functions. In future work, we will use this row insertion to establish an expansion of products between Lascoux polynomials and certain Grothendieck polynomials, which cannot be done by Hecke column insertion

    Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos

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    Federated learning (FL), aimed at leveraging vast distributed datasets, confronts a crucial challenge: the heterogeneity of data across different silos. While previous studies have explored discrete representations to enhance model generalization across minor distributional shifts, these approaches often struggle to adapt to new data silos with significantly divergent distributions. In response, we have identified that models derived from FL exhibit markedly increased uncertainty when applied to data silos with unfamiliar distributions. Consequently, we propose an innovative yet straightforward iterative framework, termed Uncertainty-Based Extensible-Codebook Federated Learning (UEFL). This framework dynamically maps latent features to trainable discrete vectors, assesses the uncertainty, and specifically extends the discretization dictionary or codebook for silos exhibiting high uncertainty. Our approach aims to simultaneously enhance accuracy and reduce uncertainty by explicitly addressing the diversity of data distributions, all while maintaining minimal computational overhead in environments characterized by heterogeneous data silos. Through experiments conducted on five datasets, our method has demonstrated its superiority, achieving significant improvements in accuracy (by 3%--22.1%) and uncertainty reduction (by 38.83%--96.24%), thereby outperforming contemporary state-of-the-art methods. The source code is available at https://github.com/destiny301/uefl

    Studies of Phase Transitions of Chromium Coordination Compounds under High Pressure

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    In this paper, the high pressure Raman scattering spectroscopy of Cd2(HATr)4(NO3)4·H2O (Cd) was measured by diamond anvil cells (DACs) up to 10GPa. The Raman spectra of Cd at 0GPa was assigned completely. With pressure increased to 6GPa, a new Raman peak appeared and the original C-NH2 bending vibration mode and N-NH2 bending vibration mode disappeared, indicating that Cd underwent a phase transition
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