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    Patent Issued for Method and system for switching between local and remote guidance instructions for autonomous vehicles (USPTO 11886202)

    137-141页
    查看更多>>摘要：A patent by the inventors Gao, Shenglong (Sunnyvale, CA, US), Hernandez, Marcial (Pittsburgh, PA, US), Petroff, Thomas (Gibsonia, PA, US), Venator, Edward (Pittsburgh, PA, US), Zhao, Ruben (San Mateo, CA, US), filed on October 22, 2021, was published online on January 30, 2024, according to news reporting originating from Alexandria, Virginia, by NewsRx correspondents. Patent number 11886202 is assigned to Argo AI LLC (Pittsburgh, Pennsylvania, United States). The following quote was obtained by the news editors from the background information supplied by the inventors: “Autonomous vehicles (Avs) offer a range of potential benefits to society and to individuals such as mobility solutions for those who cannot drive themselves in the form of ride-sharing or autonomous taxi services, and reducing the number of road collisions that stem from errors in human judgement. Avs also provide plausible solutions to the issue of overcrowded highways as connected cars will communicate with each other and navigate an effective route based on real-time traffic information, making better use of road space by spreading demand. However, unpredictable, novel occurrences which fall outside the parameters of the Avs’ programming or training can prompt the AV to cease forward progress until the occurrence is resolved. These “edge cases” include a wide range of unexpected situations that arise in the real world. Recognizing when an “edge case” has occurred and effectively addressing it to permit continued careful forward progress is advantageous.
      原文链接:
    • NETL
    • NSTL

    Patent Issued for Detecting affective characteristics of text with gated convolutional encoder-decoder framework (USPTO 11886480)

    141-144页
    查看更多>>摘要：According to news reporting originating from Alexandria, Virginia, by NewsRx journalists, a patent by the inventors Chawla, Kushal (Kadubeesanahalli, IN), Chhaya, Niyati Himanshu (Hyderabad, IN), Khosla, Sopan (Kadubeesanahalli, IN), filed on August 29, 2022, was published online on January 30, 2024. The assignee for this patent, patent number 11886480, is Adobe Inc. (San Jose, California, United States). Reporters obtained the following quote from the background information supplied by the inventors: “Human expressions, such as written or verbal communication, typically include both a factual component and a non-factual component. The human expressions are sometimes analyzed to detect the non-factual component as a method to determine an effectiveness of delivering the factual component of the expression. Personalization of websites, targeted communications, and targeted marketing materials all rely on an accurate characterization of the non-factual component of the human expression. Analysis of the nonfactual component using machine-learning techniques is useful to filter expressions that are provided to a target. In one example, the analysis provided by a machine-learning technique provides an indication that the non-factual component of an analyzed human expression is not appropriate for a specific target group because the non-factual component lacks an affective characteristic that is particularly relevant to the target group. In such an example, the affective characteristic identified by the machine-learning technique indicates that the human expression is too informal for a communication targeted to a business acquaintance or too formal for a communication targeted to a teenager.
      原文链接:
    • NETL
    • NSTL

    No more cost in translation: Validating open-source machine translation for quantitative text analysis

    144-144页
    查看更多>>摘要：According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from osf.io: “As more and more scholars apply computational text analysis methods to multilingual corpora, machine translation has become an indispensable tool. “However, relying on commercial services for machine translation, such as Google Translate or DeepL, limits reproducibility and can be expensive. “This paper assesses the viability of a reproducible and affordable alternative: free and open-source machine translation models. We ask whether researchers who use an open-source model instead of a commercial service for machine translation would obtain substantially different measurements from their multilingual corpora. We address this question by replicating and extending an influential study by de Vries et al. (2018) on the use of machine translation in cross-lingual topic modeling, and an original study of its use in supervised text classification with Transformer-based classifiers. We find only minor differences between the measurements generated by these methods when applied to corpora translated with opensource models and commercial services, respectively. We conclude that “free” machine translation is a very valuable addition to researchers’ multilingual text analysis toolkit.
      原文链接:
    • NETL
    • NSTL