COLUMBUS, Ind. — <strong>Cummins</strong> Inc. (NYSE: CMI) announced today that Senior Vice President Tony Satterthwaite will retire, effective September 2024, after 36 years in leadership positions with the company. “Tony’s more-than three decades of visionary leadership with <strong>Cummins</strong> have produced a ...

Satterthwaite announces retirement from Cummins after 36-year career - The Republic News

Wilmington, Delaware, Oct. 31, 2023 (GLOBE NEWSWIRE) -- Global Automotive Catalyst Market is estimated to value at US$ 15.6 billion in 2023 and is expected to grow at a moderate CAGR of 3.3% over the forecast period of 2023-2030, according to the published ...

Published Market Report |Automotive Catalyst Market Analysis, Trends, Growth Opportunities, and Forecast, 2023-2030 – CAGR 3.3%, says RationalStat

Open Telematics Framework is a significant development in the trucking technology space as it eliminates the complex customization and reprogramming efforts that have historically burdened developers and incorporates the latest cybersecurity capabilities.

Cummins Collaborates on Open Telematics Framework

Valvoline Cummins eyes double-digit mkt share in 5 yrs due to innovative products. Joint venture of Valvoline and Cummins, it has completed 25 yrs of operations in India and plans to capitalize on its innovative products.

Valvoline Cummins eyes double-digit market share in 5 years

Cummins (CMI) closed the latest trading day at $216.30, indicating a -1.32% change from the previous session's end. The stock's performance was behind the S&P 500's daily loss of 0.17%. Elsewhere, the Dow saw a downswing of 0.

Cummins (CMI) Suffers a Larger Drop Than the General Market: Key Insights

In the closing of the recent trading day, Cummins (CMI) stood at $216.30, denoting a -1.32% change from the preceding trading day.

The fleet has been running a truck with this engine in Southern California. The truck is fueled exclusively with Clean Energy ultra-low carbon Renewable Natural Gas (RNG) anchored by Clean Energy’s station in Carson,

Knight Transportation field tests Cummins X15N natural gas engine

He explained that Cummins has a “process in” and a “process out ... But another item dropped off in previous years that weren’t accepted Thursday were automotive tires, she said. “There wasn’t as big of a need in the community to get rid ...

Community residents line up to recycle items with Cummins

Cummins will join Eclipse Foundation, Microsoft, and other industry partners to release an open telematics software system designed to eliminate “complex customization and repro

Cummins signs on for open telematics platform

Name of the Project Cummins Range rare earths project. Location Kimberley region of Western Australia. Project Description An enhanced scoping study has proposed a three-stage development approach for the project, with the Stage 1 direct shipping ore (DSO) operation expected to produce 300 000 t/y of phosphate over a three-year period.

Cummins Range rare earths project, Australia

Cummins

cummins

From a young age, Vishwanadham Mandala had a keen interest in technology, setting the stage for a distinguished career in the IT sector. Growing up in India, he earned a BTech and MTech in Computer Science & Engineering, fueled by a passion to harness the power of data. His dedication to academia and a relentless quest for knowledge led him to Indiana University in the United States, where he completed an MS in Data Science. He continues to push the envelope by pursuing a PhD in AI & ML. Currently, Vishwanadham is a prominent leader in the field of data engineering. Known for his leadership mantra, "Consistent work, each day, every day," he blends strategic foresight with unwavering persistence. His expertise as an AI & ML leader in critical automotive applications has advanced automotive manufacturing processes and safety standards, enhancing public safety. As a mentor and speaker, Vishwanadham is committed to developing the next generation of technology professionals, ensuring that his legacy of excellence leaves a lasting impact on the industry. Additionally, he has been a member of the Forbes Technology Council since May 2024. Path to Big Data Fascination with the transformative power of data ignited Vishwanadham’s pursuit of a career in Big Data and data engineering. His journey through critical roles at IBM, Oracle, Sierra-Cedar, and Ciena allowed him to work globally, gaining diverse experiences and honing his skills. Reflecting on his career progression, Vishwanadham notes how these experiences shaped his expertise and paved the way to his current role at Cummins, where he spearheads significant projects and integrates AI/ML technologies to drive the company's future direction. Vishwanadham's academic background has been instrumental in his professional success. With degrees from prestigious institutions in India and the United States, he notes, "My diverse educational background gave me a solid foundation in technical knowledge and critical thinking skills." This exposure to different academic environments and perspectives has enriched his understanding of global technological trends and best practices, enabling him to excel in his career and make meaningful contributions to the IT industry. "The exposure to different academic environments and perspectives enriched my understanding of global technological trends and best practices," he explains. This comprehensive background not only allowed him to grow professionally but also equipped him with the skills necessary to drive innovation and efficiency at Cummins, solidifying his role as a leader in the integration of AI and ML technologies. Game-changing Projects Across Industries Vishwanadham led a groundbreaking project that significantly impacted Ciena’s strategic direction in the data space. His innovative approach and strategic leadership were instrumental in shaping Ciena's technology roadmap, highlighting his ability to drive impactful change and foster innovation within the organization. His work showcased his expertise in data engineering and his vision for future technological advancements. Vishwanadham's experience spans various industries, including manufacturing and healthcare, where he has successfully integrated AI and ML solutions to address unique challenges. He explains that his approach involves focusing on both fundamental and superficial facets of data. By adapting to each sector's unique challenges and nuances, he tailors his solutions to best match the environment. This method of integrating AI and ML solutions has enabled Vishwanadham to continuously learn and grow with each encounter. "This method has enriched my expertise, allowing me to tackle various challenges effectively and continuously learn and grow with each encounter," he notes. By leveraging his diverse experience and innovative approach, Vishwanadham has been able to make significant contributions to the fields of manufacturing and healthcare, demonstrating his versatility and proficiency in AI and data engineering. Innovative IoT Patents for Rural Healthcare One of Vishwanadham’s most notable patents involves a groundbreaking IoT solution for medical devices, specifically designed to enhance healthcare in rural areas. This innovative patent focuses on leveraging telemetry data processing to provide real-time health insights, which can significantly improve patient outcomes in remote locations. By utilizing AI and ML, the solution aims to deliver instantaneous and accurate health data, allowing for timely medical interventions. Vishwanadham explains that this technology "enables timely medical interventions and improves access to quality healthcare for rural populations." This IoT innovation has the potential to transform rural healthcare by bridging the gap between remote patients and advanced medical care. "By leveraging AI/ML, this innovation aims to offer instantaneous and accurate health data," says Vishwanadham, highlighting the critical role of advanced technology in improving healthcare accessibility and quality in underserved areas. This patent exemplifies his commitment to using technology to create impactful solutions that address real-world challenges. Leading and Mentoring in AI Vishwanadham emphasizes that leading successful AI and ML initiatives requires a strong foundation in data science, strategic vision, and a commitment to continuous learning. He believes that a disciplined work ethic, cooperative teamwork, and innovative thinking are essential qualities for driving progress in these fields. Additionally, "Effective communication and inspiring and guiding teams are also essential for driving innovation," Vishwanadham states, highlighting the multifaceted nature of leadership in AI and ML. In addition to his leadership skills, Vishwanadham is deeply committed to mentoring and teaching in the AI and ML fields. He actively shares his extensive knowledge and experience with aspiring professionals. "Mentoring helps individuals grow and contributes to the industry's evolution by fostering a culture of continuous learning and innovation," he explains. By guiding the next generation of AI and ML experts, Vishwanadham aims to create a robust talent pool capable of tackling future challenges and driving technological advancements. This dual focus on leadership and mentorship highlights Vishwanadham's holistic approach to advancing the AI and ML fields. He understands that the future of technology depends not only on current innovations but also on preparing the next generation to continue pushing the boundaries of what is possible. AI Innovations for Safety and Supply Chains Looking to the future, Vishwanadham aspires to leverage AI and ML to significantly advance citizen safety and improve supply chains by implementing cutting-edge technologies and innovative solutions. He is particularly excited about the potential of Edge AI/ML, which he describes as "a revolutionary approach to telemetry data processing that can provide instantaneous and accurate insights." One of his current projects focuses on integrating this technology into the automotive industry to enhance vehicle safety and protect citizens. "By staying ahead of the competition through constant learning and innovation, I aim to drive transformative change," he asserts. His commitment to using advanced AI and ML technologies to solve real-world problems highlights his vision for a safer and more efficient future, where technology plays a crucial role in protecting lives and optimizing operations. In a world becoming increasingly data-centric, Vishwanadham’s expertise serves as a beacon for those navigating the complexities of modern business landscapes. His journey reminds us that mastering data is not just about understanding technology but also about leveraging it to make remarkable contributions to society. As he continues to mentor the next generation of tech professionals, his influence extends beyond projects and patents, fostering a culture of continuous learning and innovation in the industry. From a young age, Vishwanadham Mandala had a keen interest in technology, setting the stage for a distinguished career in the IT sector. Growing up in India, he earned a BTech and MTech in Computer Science & Engineering, fueled by a passion to harness the power of data. His dedication to academia and a relentless quest for knowledge led him to Indiana University in the United States, where he completed an MS in Data Science. He continues to push the envelope by pursuing a PhD in AI & ML. Currently, Vishwanadham is a prominent leader in the field of data engineering. Known for his leadership mantra, "Consistent work, each day, every day," he blends strategic foresight with unwavering persistence. His expertise as an AI & ML leader in critical automotive applications has advanced automotive manufacturing processes and safety standards, enhancing public safety. As a mentor and speaker, Vishwanadham is committed to developing the next generation of technology professionals, ensuring that his legacy of excellence leaves a lasting impact on the industry. Additionally, he has been a member of the Forbes Technology Council since May 2024. Path to Big Data Fascination with the transformative power of data ignited Vishwanadham’s pursuit of a career in Big Data and data engineering. His journey through critical roles at IBM, Oracle, Sierra-Cedar, and Ciena allowed him to work globally, gaining diverse experiences and honing his skills. Reflecting on his career progression, Vishwanadham notes how these experiences shaped his expertise and paved the way to his current role at Cummins, where he spearheads significant projects and integrates AI/ML technologies to drive the company's future direction. Vishwanadham's academic background has been instrumental in his professional success. With degrees from prestigious institutions in India and the United States, he notes, "My diverse educational background gave me a solid foundation in technical knowledge and critical thinking skills." This exposure to different academic environments and perspectives has enriched his understanding of global technological trends and best practices, enabling him to excel in his career and make meaningful contributions to the IT industry. "The exposure to different academic environments and perspectives enriched my understanding of global technological trends and best practices," he explains. This comprehensive background not only allowed him to grow professionally but also equipped him with the skills necessary to drive innovation and efficiency at Cummins, solidifying his role as a leader in the integration of AI and ML technologies. Game-changing Projects Across Industries Vishwanadham led a groundbreaking project that significantly impacted Ciena’s strategic direction in the data space. His innovative approach and strategic leadership were instrumental in shaping Ciena's technology roadmap, highlighting his ability to drive impactful change and foster innovation within the organization. His work showcased his expertise in data engineering and his vision for future technological advancements. Vishwanadham's experience spans various industries, including manufacturing and healthcare, where he has successfully integrated AI and ML solutions to address unique challenges. He explains that his approach involves focusing on both fundamental and superficial facets of data. By adapting to each sector's unique challenges and nuances, he tailors his solutions to best match the environment. This method of integrating AI and ML solutions has enabled Vishwanadham to continuously learn and grow with each encounter. "This method has enriched my expertise, allowing me to tackle various challenges effectively and continuously learn and grow with each encounter," he notes. By leveraging his diverse experience and innovative approach, Vishwanadham has been able to make significant contributions to the fields of manufacturing and healthcare, demonstrating his versatility and proficiency in AI and data engineering. Innovative IoT Patents for Rural Healthcare One of Vishwanadham’s most notable patents involves a groundbreaking IoT solution for medical devices, specifically designed to enhance healthcare in rural areas. This innovative patent focuses on leveraging telemetry data processing to provide real-time health insights, which can significantly improve patient outcomes in remote locations. By utilizing AI and ML, the solution aims to deliver instantaneous and accurate health data, allowing for timely medical interventions. Vishwanadham explains that this technology "enables timely medical interventions and improves access to quality healthcare for rural populations." This IoT innovation has the potential to transform rural healthcare by bridging the gap between remote patients and advanced medical care. "By leveraging AI/ML, this innovation aims to offer instantaneous and accurate health data," says Vishwanadham, highlighting the critical role of advanced technology in improving healthcare accessibility and quality in underserved areas. This patent exemplifies his commitment to using technology to create impactful solutions that address real-world challenges. Leading and Mentoring in AI Vishwanadham emphasizes that leading successful AI and ML initiatives requires a strong foundation in data science, strategic vision, and a commitment to continuous learning. He believes that a disciplined work ethic, cooperative teamwork, and innovative thinking are essential qualities for driving progress in these fields. Additionally, "Effective communication and inspiring and guiding teams are also essential for driving innovation," Vishwanadham states, highlighting the multifaceted nature of leadership in AI and ML. In addition to his leadership skills, Vishwanadham is deeply committed to mentoring and teaching in the AI and ML fields. He actively shares his extensive knowledge and experience with aspiring professionals. "Mentoring helps individuals grow and contributes to the industry's evolution by fostering a culture of continuous learning and innovation," he explains. By guiding the next generation of AI and ML experts, Vishwanadham aims to create a robust talent pool capable of tackling future challenges and driving technological advancements. This dual focus on leadership and mentorship highlights Vishwanadham's holistic approach to advancing the AI and ML fields. He understands that the future of technology depends not only on current innovations but also on preparing the next generation to continue pushing the boundaries of what is possible. AI Innovations for Safety and Supply Chains Looking to the future, Vishwanadham aspires to leverage AI and ML to significantly advance citizen safety and improve supply chains by implementing cutting-edge technologies and innovative solutions. He is particularly excited about the potential of Edge AI/ML, which he describes as "a revolutionary approach to telemetry data processing that can provide instantaneous and accurate insights." One of his current projects focuses on integrating this technology into the automotive industry to enhance vehicle safety and protect citizens. "By staying ahead of the competition through constant learning and innovation, I aim to drive transformative change," he asserts. His commitment to using advanced AI and ML technologies to solve real-world problems highlights his vision for a safer and more efficient future, where technology plays a crucial role in protecting lives and optimizing operations. In a world becoming increasingly data-centric, Vishwanadham’s expertise serves as a beacon for those navigating the complexities of modern business landscapes. His journey reminds us that mastering data is not just about understanding technology but also about leveraging it to make remarkable contributions to society. As he continues to mentor the next generation of tech professionals, his influence extends beyond projects and patents, fostering a culture of continuous learning and innovation in the industry.

Mastering Data in the Modern Age with Vishwanadham Mandala

Авалоданне дадзенымі ў сучасную эпоху з Vishwanadham Mandala

বিশ্বধাম মন্ডালার সাথে আধুনিক যুগে ডেটা আয়ত্ত করা

Dominar les dades a l'Edat Moderna amb Vishwanadham Mandala

Mestring af data i den moderne tidsalder med Vishwanadham Mandala

Dominando los datos en la era moderna con Vishwanadham Mandala

שליטה בנתונים בעידן המודרני עם Vishwanadham Mandala

Padroneggiare i dati nell'era moderna con Vishwanadham Mandala

ヴィシュワナダム・マンダラによる現代のデータ活用

Vishwanadham Mandala로 현대의 데이터 마스터링

Menguasai Data di Zaman Moden dengan Vishwanadham Mandala

Opanowanie danych w erze nowoczesności z Vishwanadham Mandala

Освоение данных в современную эпоху с помощью Vishwanadham Mandala

Authors:
(1) Lukáš Korel, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic;
(2) Petr Pulc, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic;
(3) Jirí Tumpach, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic;
(4) Martin Holena, Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic. Table of Links Abstract and Introduction ANN-Based Scene Classification Methodology Experiments Conclusion and Future Research, Acknowledgments and References 5 Conclusion and Future Research In this paper was provided an insight into the possibility of using artificial neural networks for scene recognition location from a video sequence with a small set of repeated shooting locations (such as in television series) was provided. Our idea was to select more than one frame from each scene and classify the scene using that sequence of frames. We used a pretrained VGG19 network without two last layers. This results were used as an input to the trainable part our neural network architecture. We have designed six neural network models with different layer types. We have investigated different neural network layers to combine video frames, in particular average-pooling, max-pooling, product, flatten, LSTM, and bidirectional LSTM layers. The considered networks have been tested and compared on a dataset obtained from The Big Bang Theory television series. The model with max-pooling layer was not successful, its accuracy was the lowest of all models. The models with a flatten or product layer were very unstable, their standard deviation was very large. The most stable among all models was the one with an average-pooling layer. The models with unidirectional LSTM and bidirectional LSTM had similar standard deviation of the accuracy. The model with a bidirectional LSTM had the highest accuracy among all considered models. In our opinion, this is because its internal memory cells preserve information in both directions. Those results shows, that models with internal memory are able to classify with a higher accuracy than models without internal memory. Our method may have limitations due to the chosen pretrained ANN and the low dimension of some neural layer parts. In future research, it is desirable to achieve higher accuracy in scene location recognition. This task may also need modifying model parameters or using other architectures. It also may need other pretrained models or combining several pretrained models. It is also desirable that, if the ANN detects an unknown scene, it will remember it and next time it will recognize a scene from the same location properly. Acknowledgments The research reported in this paper has been supported by the Czech Science Foundation (GACR) grant 18-18080S. Computational resources were supplied by the project "e-Infrastruktura CZ" (e-INFRA LM2018140) provided within the program Projects of Large Research, Development and Innovations Infrastructures. Computational resources were provided by the ELIXIRCZ project (LM2018131), part of the international ELIXIR infrastructure. References [1] Zhong, W., Kjellström, H.: Movie scene recognition with Convolutional Neural Networks. https://www.diva-portal.org/smash/get/diva2 :859486/FULLTEXT01.pdf KTH ROYAL INSTITUTE OF TECHNOLOGY (2015) 5–36 [2] Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. https://arxiv.org/pdf/1409.1556v6.pdf Visual Geometry Group, Department of Engineering Science, University of Oxford (2015) [3] Russakovsky O., Deng J., Hao S., Krause J., Satheesh S., Ma Sean, Huang Z., Karpathy A., Khosla A., Bernstein M., Berg A. C., Fei-Fei L.: ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision 115 (2015), pp. 211–252 [4] Li-jia L., Hao S., Fei-fei L., Xing E.: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification. https://cs.stanford.edu/groups/vision/pdf/ LiSuXingFeiFeiNIPS2010.pdf NIPS (2010) [5] Felix A. Gers, Schmidhuber J., and Cummins F., Learning to forget: Continual prediction with LSTM, in Proceedings of ICANN. ENNS (1999), pp. 850-–855 [6] Benavoli A., Corani G., Mangili F.: Should We Really Use Post-Hoc Tests Based on Mean-Ranks? Journal of Machine Learning Research 17 (2016), pp. 1–10 [7] García S., Herrera F.: An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons Journal of Machine Learning Research 9 (2008), pp. 2677–2694 [8] Graves, A.: Supervised Sequence Labelling with Recurrent Neural Networks. Springer (2012) [9] Kaiming H., Xiangyu Z., Shaoqing R., Jian S.: Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 770–778 [10] Sudha V., Ganeshbabu T. R.: A Convolutional Neural Network Classifier VGG-19 Architecture for Lesion Detection and Grading in Diabetic Retinopathy Based on Deep Learning. http://www.techscience.com/cmc/v66n1/40483 Computers, Materials & Continua (2021), pp. 827–842 [11] Zhou B., Lapedriza A., Khosla A., Oliva A., Torralba A.: Places: A 10 Million Image Database for Scene Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (2018), pp. 1452–1464 This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Lukáš Korel, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic; (2) Petr Pulc, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic; (3) Jirí Tumpach, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic; (4) Martin Holena, Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic. Authors: Authors: (1) Lukáš Korel, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic; (2) Petr Pulc, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic; (3) Jirí Tumpach, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic; (4) Martin Holena, Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic. Table of Links Abstract and Introduction Abstract and Introduction ANN-Based Scene Classification ANN-Based Scene Classification Methodology Methodology Experiments Experiments Conclusion and Future Research, Acknowledgments and References Conclusion and Future Research, Acknowledgments and References 5 Conclusion and Future Research In this paper was provided an insight into the possibility of using artificial neural networks for scene recognition location from a video sequence with a small set of repeated shooting locations (such as in television series) was provided. Our idea was to select more than one frame from each scene and classify the scene using that sequence of frames. We used a pretrained VGG19 network without two last layers. This results were used as an input to the trainable part our neural network architecture. We have designed six neural network models with different layer types. We have investigated different neural network layers to combine video frames, in particular average-pooling, max-pooling, product, flatten, LSTM, and bidirectional LSTM layers. The considered networks have been tested and compared on a dataset obtained from The Big Bang Theory television series. The model with max-pooling layer was not successful, its accuracy was the lowest of all models. The models with a flatten or product layer were very unstable, their standard deviation was very large. The most stable among all models was the one with an average-pooling layer. The models with unidirectional LSTM and bidirectional LSTM had similar standard deviation of the accuracy. The model with a bidirectional LSTM had the highest accuracy among all considered models. In our opinion, this is because its internal memory cells preserve information in both directions. Those results shows, that models with internal memory are able to classify with a higher accuracy than models without internal memory. Our method may have limitations due to the chosen pretrained ANN and the low dimension of some neural layer parts. In future research, it is desirable to achieve higher accuracy in scene location recognition. This task may also need modifying model parameters or using other architectures. It also may need other pretrained models or combining several pretrained models. It is also desirable that, if the ANN detects an unknown scene, it will remember it and next time it will recognize a scene from the same location properly. Acknowledgments The research reported in this paper has been supported by the Czech Science Foundation (GACR) grant 18-18080S. Computational resources were supplied by the project "e-Infrastruktura CZ" (e-INFRA LM2018140) provided within the program Projects of Large Research, Development and Innovations Infrastructures. Computational resources were provided by the ELIXIRCZ project (LM2018131), part of the international ELIXIR infrastructure. References [1] Zhong, W., Kjellström, H.: Movie scene recognition with Convolutional Neural Networks. https://www.diva-portal.org/smash/get/diva2 :859486/FULLTEXT01.pdf KTH ROYAL INSTITUTE OF TECHNOLOGY (2015) 5–36 [2] Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. https://arxiv.org/pdf/1409.1556v6.pdf Visual Geometry Group, Department of Engineering Science, University of Oxford (2015) [3] Russakovsky O., Deng J., Hao S., Krause J., Satheesh S., Ma Sean, Huang Z., Karpathy A., Khosla A., Bernstein M., Berg A. C., Fei-Fei L.: ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision 115 (2015), pp. 211–252 [4] Li-jia L., Hao S., Fei-fei L., Xing E.: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification. https://cs.stanford.edu/groups/vision/pdf/ LiSuXingFeiFeiNIPS2010.pdf NIPS (2010) [5] Felix A. Gers, Schmidhuber J., and Cummins F., Learning to forget: Continual prediction with LSTM, in Proceedings of ICANN. ENNS (1999), pp. 850-–855 [6] Benavoli A., Corani G., Mangili F.: Should We Really Use Post-Hoc Tests Based on Mean-Ranks? Journal of Machine Learning Research 17 (2016), pp. 1–10 [7] García S., Herrera F.: An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons Journal of Machine Learning Research 9 (2008), pp. 2677–2694 [8] Graves, A.: Supervised Sequence Labelling with Recurrent Neural Networks. Springer (2012) [9] Kaiming H., Xiangyu Z., Shaoqing R., Jian S.: Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 770–778 [10] Sudha V., Ganeshbabu T. R.: A Convolutional Neural Network Classifier VGG-19 Architecture for Lesion Detection and Grading in Diabetic Retinopathy Based on Deep Learning. http://www.techscience.com/cmc/v66n1/40483 Computers, Materials & Continua (2021), pp. 827–842 [11] Zhou B., Lapedriza A., Khosla A., Oliva A., Torralba A.: Places: A 10 Million Image Database for Scene Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (2018), pp. 1452–1464 This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

Video Scene Location Recognition Using AI: Conclusion and Future Research

Enabling Acoustic Audience Feedback in Large Virtual Events: Conclusions & References

ERIK ADOLPH vs. Uber Court Filing, retrieved on July 17, 2023, is part of . You can jump to any part in this filing . This is part 5 of 15. HackerNoon’s Legal PDF Series here A. “In construing a statute, our task is to ascertain the intent of the Legislature so as to effectuate the purpose of the enactment.” (Cummins, Inc. v. Superior Court (2005) 36 Cal.4th 478, 487.) We look first to “the words of the statute, which are the most reliable indications of the Legislature’s intent.” (Ibid.) As noted, section 2699, subdivision (c) defines “aggrieved employee,” and we have explained that “[t]he plain language of section 2699(c) has only two requirements for PAGA standing.” (Kim, supra, 9 Cal.5th at p. 83.) The plaintiff must allege that he or she is (1) “someone ‘who was employed by the alleged violator’ ” and (2) someone “ ‘against whom one or more of the alleged violations was committed.’ ” (Id. at pp. 83–84, quoting § 2699, subd. (c).) In Kim, we declined to impose additional requirements not found in the statute. (Kim, supra, 9 Cal.5th at pp. 84–91.) The plaintiff, Kim, sued his employer, alleging individual claims for damages and a PAGA claim for civil penalties. (Kim, at p. 82.) Kim settled and dismissed the individual claims for damages, proceeding only with the PAGA claim. (Kim, at p. 82.) The employer conceded that Kim had PAGA standing when he filed suit but argued that Kim’s “standing somehow ended” once his individual claims settled. (Kim, at p. 84.) According to the employer, PAGA standing is premised on an unredressed injury, and because Kim received compensation for his injury, he no longer had the status of an “aggrieved employee.” (Kim, at p. 84.) We rejected this argument, finding it inconsistent with the statutory language in several respects. (Kim, supra, 9 Cal.5th at pp. 83–86.) First, “[t]he Legislature defined PAGA standing in terms of violations . . . . Kim became an aggrieved employee, and had PAGA standing, when one or more Labor Code violations were committed against him,” and “[s]ettlement did not nullify these violations. The remedy for a Labor Code violation, through settlement or other means, is distinct from the fact of the violation itself,” and only the latter is required for PAGA standing. (Kim, at p. 84.) Second, nothing in the text of the statute requires the plaintiff to have an unredressed injury; reading such a requirement into the statute would be “at odds with the statutory definition.” (Id. at p. 85.) Third, allowing post-violation events to strip an aggrieved employee of the ability to pursue a PAGA claim “would add an expiration element to the statutory definition of standing.” (Kim, at p. 85.) Although Uber says Kim is distinguishable because the plaintiff had settled only individual claims for damages and not any claim for civil penalties under PAGA, this circumstance played no role in Kim’s reasoning. Kim made clear that only the fact of a violation is required to confer standing. The Court of Appeal in Johnson v. Maxim Healthcare Services, Inc. (2021) 66 Cal.App.5th 924 (Johnson) similarly declined to read into the statute a standing requirement not supported by its language. There, a company required its workers to sign unlawful noncompete agreements. (Id. at p. 927.) The Labor Code violations sustained by the plaintiff were time-barred, and the employer argued that the plaintiff therefore did not have PAGA standing. (Johnson, at p. 929.) Relying on Kim, the court rejected this argument and held that the plaintiff had standing to pursue her PAGA claim because she satisfied the statutory definition of an “ ‘aggrieved employee.’ ” (Johnson, at p. 930.) The fact that the plaintiff’s “individual claim may be time-barred does not nullify the alleged Labor Code violations nor strip [the plaintiff] of her standing to pursue PAGA remedies.” (Johnson, at p. 930.) As Kim and Johnson make clear, a worker becomes an “aggrieved employee” with standing to litigate claims on behalf of fellow employees upon sustaining a Labor Code violation committed by his or her employer. (See Kim, supra, 9 Cal.5th at pp. 84–85; Johnson, supra, 66 Cal.App.5th at p. 930; § 2699, subd. (c).) Standing under PAGA is not affected by enforcement of an agreement to adjudicate a plaintiff’s individual claim in another forum. Arbitrating a PAGA plaintiff’s individual claim does not nullify the fact of the violation or extinguish the plaintiff’s status as an aggrieved employee, any more than the time-barring of remedies did in Johnson or the settlement of the individual damages claims did in Kim. (See Kim, at pp. 84–85; Johnson, at p. 930.) The operative complaint alleges that Adolph experienced Labor Code violations while driving for Uber. Under Kim, Adolph’s allegations that Labor Code violations were committed against him while he was employed by Uber suffice to confer standing to bring a PAGA action. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case S274671 retrieved on September 22, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. courts.ca.gov

Comparing the Uber Lawsuit to Previous Cases: Does It Hold Up?

The Warden by Anthony Trollope, is part of the HackerNoon Books Series. You can jump to any chapter in this book . Conclusion here Conclusion Our tale is now done, and it only remains to us to collect the scattered threads of our little story, and to tie them into a seemly knot. This will not be a work of labour, either to the author or to his readers; we have not to deal with many personages, or with stirring events, and were it not for the custom of the thing, we might leave it to the imagination of all concerned to conceive how affairs at Barchester arranged themselves. On the morning after the day last alluded to, Mr Harding, at an early hour, walked out of the hospital, with his daughter under his arm, and sat down quietly to breakfast at his lodgings over the chemist's shop. There was no parade about his departure; no one, not even Bunce, was there to witness it; had he walked to the apothecary's thus early to get a piece of court plaster, or a box of lozenges, he could not have done it with less appearance of an important movement. There was a tear in Eleanor's eye as she passed through the big gateway and over the bridge; but Mr Harding walked with an elastic step, and entered his new abode with a pleasant face. "Now, my dear," said he, "you have everything ready, and you can make tea here just as nicely as in the parlour at the hospital." So Eleanor took off her bonnet and made the tea. After this manner did the late Warden of Barchester Hospital accomplish his flitting, and change his residence. It was not long before the archdeacon brought his father to discuss the subject of a new warden. Of course he looked upon the nomination as his own, and he had in his eye three or four fitting candidates, seeing that Mr Cummins's plan as to the living of Puddingdale could not be brought to bear. How can I describe the astonishment which confounded him, when his father declared that he would appoint no successor to Mr Harding? "If we can get the matter set to rights, Mr Harding will return," said the bishop; "and if we cannot, it will be wrong to put any other gentleman into so cruel a position." It was in vain that the archdeacon argued and lectured, and even threatened; in vain he my-lorded his poor father in his sternest manner; in vain his "good heavens!" were ejaculated in a tone that might have moved a whole synod, let alone one weak and aged bishop. Nothing could induce his father to fill up the vacancy caused by Mr Harding's retirement. Even John Bold would have pitied the feelings with which the archdeacon returned to Plumstead: the church was falling, nay, already in ruins; its dignitaries were yielding without a struggle before the blows of its antagonists; and one of its most respected bishops, his own father,—the man considered by all the world as being in such matters under his, Dr Grantly's, control,—had positively resolved to capitulate, and own himself vanquished! And how fared the hospital under this resolve of its visitor? Badly indeed. It is now some years since Mr Harding left it, and the warden's house is still tenantless. Old Bell has died, and Billy Gazy; the one-eyed Spriggs has drunk himself to death, and three others of the twelve have been gathered into the churchyard mould. Six have gone, and the six vacancies remain unfilled! Yes, six have died, with no kind friend to solace their last moments, with no wealthy neighbour to administer comforts and ease the stings of death. Mr Harding, indeed, did not desert them; from him they had such consolation as a dying man may receive from his Christian pastor; but it was the occasional kindness of a stranger which ministered to them, and not the constant presence of a master, a neighbour, and a friend. Nor were those who remained better off than those who died. Dissensions rose among them, and contests for pre-eminence; and then they began to understand that soon one among them would be the last,—some one wretched being would be alone there in that now comfortless hospital,—the miserable relic of what had once been so good and so comfortable. The building of the hospital itself has not been allowed to go to ruins. Mr Chadwick, who still holds his stewardship, and pays the accruing rents into an account opened at a bank for the purpose, sees to that; but the whole place has become disordered and ugly. The warden's garden is a wretched wilderness, the drive and paths are covered with weeds, the flower-beds are bare, and the unshorn lawn is now a mass of long damp grass and unwholesome moss. The beauty of the place is gone; its attractions have withered. Alas! a very few years since it was the prettiest spot in Barchester, and now it is a disgrace to the city. Mr Harding did not go out to Crabtree Parva. An arrangement was made which respected the homestead of Mr Smith and his happy family, and put Mr Harding into possession of a small living within the walls of the city. It is the smallest possible parish, containing a part of the Cathedral Close and a few old houses adjoining. The church is a singular little Gothic building, perched over a gateway, through which the Close is entered, and is approached by a flight of stone steps which leads down under the archway of the gate. It is no bigger than an ordinary room,—perhaps twenty-seven feet long by eighteen wide,—but still it is a perfect church. It contains an old carved pulpit and reading-desk, a tiny altar under a window filled with dark old-coloured glass, a font, some half-dozen pews, and perhaps a dozen seats for the poor; and also a vestry. The roof is high pitched, and of black old oak, and the three large beams which support it run down to the side walls, and terminate in grotesquely carved faces,—two devils and an angel on one side, two angels and a devil on the other. Such is the church of St Cuthbert at Barchester, of which Mr Harding became rector, with a clear income of seventy-five pounds a year. Here he performs afternoon service every Sunday, and administers the Sacrament once in every three months. His audience is not large; and, had they been so, he could not have accommodated them: but enough come to fill his six pews, and on the front seat of those devoted to the poor is always to be seen our old friend Mr Bunce, decently arrayed in his bedesman's gown. Mr Harding is still precentor of Barchester; and it is very rarely the case that those who attend the Sunday morning service miss the gratification of hearing him chant the Litany, as no other man in England can do it. He is neither a discontented nor an unhappy man; he still inhabits the lodgings to which he went on leaving the hospital, but he now has them to himself. Three months after that time Eleanor became Mrs Bold, and of course removed to her husband's house. There were some difficulties to be got over on the occasion of the marriage. The archdeacon, who could not so soon overcome his grief, would not be persuaded to grace the ceremony with his presence, but he allowed his wife and children to be there. The marriage took place in the cathedral, and the bishop himself officiated. It was the last occasion on which he ever did so; and, though he still lives, it is not probable that he will ever do so again. Not long after the marriage, perhaps six months, when Eleanor's bridal-honours were fading, and persons were beginning to call her Mrs Bold without twittering, the archdeacon consented to meet John Bold at a dinner-party, and since that time they have become almost friends. The archdeacon firmly believes that his brother-in-law was, as a bachelor, an infidel, an unbeliever in the great truths of our religion; but that matrimony has opened his eyes, as it has those of others. And Bold is equally inclined to think that time has softened the asperities of the archdeacon's character. Friends though they are, they do not often revert to the feud of the hospital. Mr Harding, we say, is not an unhappy man: he keeps his lodgings, but they are of little use to him, except as being the one spot on earth which he calls his own. His time is spent chiefly at his daughter's or at the palace; he is never left alone, even should he wish to be so; and within a twelvemonth of Eleanor's marriage his determination to live at his own lodging had been so far broken through and abandoned, that he consented to have his violoncello permanently removed to his daughter's house. Every other day a message is brought to him from the bishop. "The bishop's compliments, and his lordship is not very well to-day, and he hopes Mr Harding will dine with him." This bulletin as to the old man's health is a myth; for though he is over eighty he is never ill, and will probably die some day, as a spark goes out, gradually and without a struggle. Mr Harding does dine with him very often, which means going to the palace at three and remaining till ten; and whenever he does not the bishop whines, and says that the port wine is corked, and complains that nobody attends to him, and frets himself off to bed an hour before his time. It was long before the people of Barchester forgot to call Mr Harding by his long well-known name of Warden. It had become so customary to say Mr Warden, that it was not easily dropped. "No, no," he always says when so addressed, "not warden now, only precentor." About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Anthony Trollope (1996). The Warden. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/619/pg619-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at www.gutenberg.org https://www.gutenberg.org/policy/license.html.

Our tale is now done

At any rate, come to me at once

The Warden Resigns

Dr Grantly Visits the Hospital

The Warden by Anthony Trollope, is part of the HackerNoon Books Series. You can jump to any chapter in this book . Hiram's Bedesmen here Hiram's Bedesmen The parties most interested in the movement which is about to set Barchester by the ears were not the foremost to discuss the merit of the question, as is often the case; but when the bishop, the archdeacon, the warden, the steward, and Messrs Cox and Cummins, were all busy with the matter, each in his own way, it is not to be supposed that Hiram's bedesmen themselves were altogether passive spectators. Finney, the attorney, had been among them, asking sly questions, and raising immoderate hopes, creating a party hostile to the warden, and establishing a corps in the enemy's camp, as he figuratively calls it to himself. Poor old men: whoever may be righted or wronged by this inquiry, they at any rate will assuredly be only injured: to them it can only be an unmixed evil. How can their lot be improved? all their wants are supplied; every comfort is administered; they have warm houses, good clothes, plentiful diet, and rest after a life of labour; and above all, that treasure so inestimable in declining years, a true and kind friend to listen to their sorrows, watch over their sickness, and administer comfort as regards this world, and the world to come! John Bold sometimes thinks of this, when he is talking loudly of the rights of the bedesmen, whom he has taken under his protection; but he quiets the suggestion within his breast with the high-sounding name of justice: "Fiat justitia, ruat cœlum." These old men should, by rights, have one hundred pounds a year instead of one shilling and sixpence a day, and the warden should have two hundred or three hundred pounds instead of eight hundred pounds. What is unjust must be wrong; what is wrong should be righted; and if he declined the task, who else would do it? "Each one of you is clearly entitled to one hundred pounds a year by common law": such had been the important whisper made by Finney into the ears of Abel Handy, and by him retailed to his eleven brethren. Too much must not be expected from the flesh and blood even of John Hiram's bedesmen, and the positive promise of one hundred a year to each of the twelve old men had its way with most of them. The great Bunce was not to be wiled away, and was upheld in his orthodoxy by two adherents. Abel Handy, who was the leader of the aspirants after wealth, had, alas, a stronger following. No less than five of the twelve soon believed that his views were just, making with their leader a moiety of the hospital. The other three, volatile unstable minds, vacillated between the two chieftains, now led away by the hope of gold, now anxious to propitiate the powers that still existed. It had been proposed to address a petition to the bishop as visitor, praying his lordship to see justice done to the legal recipients of John Hiram's Charity, and to send copies of this petition and of the reply it would elicit to all the leading London papers, and thereby to obtain notoriety for the subject. This it was thought would pave the way for ulterior legal proceedings. It would have been a great thing to have had the signatures and marks of all the twelve injured legatees; but this was impossible: Bunce would have cut his hand off sooner than have signed it. It was then suggested by Finney that if even eleven could be induced to sanction the document, the one obstinate recusant might have been represented as unfit to judge on such a question,—in fact, as being non compos mentis,—and the petition would have been taken as representing the feeling of the men. But this could not be done: Bunce's friends were as firm as himself, and as yet only six crosses adorned the document. It was the more provoking, as Bunce himself could write his name legibly, and one of those three doubting souls had for years boasted of like power, and possessed, indeed, a Bible, in which he was proud to show his name written by himself some thirty years ago—"Job Skulpit;" but it was thought that Job Skulpit, having forgotten his scholarship, on that account recoiled from the petition, and that the other doubters would follow as he led them. A petition signed by half the hospital would have but a poor effect. It was in Skulpit's room that the petition was now lying, waiting such additional signatures as Abel Handy, by his eloquence, could obtain for it. The six marks it bore were duly attested, thus: &c., and places were duly designated in pencil for those brethren who were now expected to join: for Skulpit alone was left a spot on which his genuine signature might be written in fair clerk-like style. Handy had brought in the document, and spread it out on the small deal table, and was now standing by it persuasive and eager. Moody had followed with an inkhorn, carefully left behind by Finney; and Spriggs bore aloft, as though it were a sword, a well-worn ink-black pen, which from time to time he endeavoured to thrust into Skulpit's unwilling hand. With the learned man were his two abettors in indecision, William Gazy and Jonathan Crumple. If ever the petition were to be forwarded, now was the time,—so said Mr Finney; and great was the anxiety on the part of those whose one hundred pounds a year, as they believed, mainly depended on the document in question. "To be kept out of all that money," as the avaricious Moody had muttered to his friend Handy, "by an old fool saying that he can write his own name like his betters!" "Well, Job," said Handy, trying to impart to his own sour, ill-omened visage a smile of approbation, in which he greatly failed; "so you're ready now, Mr Finney says; here's the place, d'ye see;"—and he put his huge brown finger down on the dirty paper;—"name or mark, it's all one. Come along, old boy; if so be we're to have the spending of this money, why the sooner the better,—that's my maxim." "To be sure," said Moody. "We a'n't none of us so young; we can't stay waiting for old Catgut no longer." It was thus these miscreants named our excellent friend. The nickname he could easily have forgiven, but the allusion to the divine source of all his melodious joy would have irritated even him. Let us hope he never knew the insult. "Only think, old Billy Gazy," said Spriggs, who rejoiced in greater youth than his brethren, but having fallen into a fire when drunk, had had one eye burnt out, one cheek burnt through, and one arm nearly burnt off, and who, therefore, in regard to personal appearance, was not the most prepossessing of men, "a hundred a year, and all to spend; only think, old Billy Gazy;" and he gave a hideous grin that showed off his misfortunes to their full extent. Old Billy Gazy was not alive to much enthusiasm. Even these golden prospects did not arouse him to do more than rub his poor old bleared eyes with the cuff of his bedesman's gown, and gently mutter: "he didn't know, not he; he didn't know." "But you'd know, Jonathan," continued Spriggs, turning to the other friend of Skulpit's, who was sitting on a stool by the table, gazing vacantly at the petition. Jonathan Crumple was a meek, mild man, who had known better days; his means had been wasted by bad children, who had made his life wretched till he had been received into the hospital, of which he had not long been a member. Since that day he had known neither sorrow nor trouble, and this attempt to fill him with new hopes was, indeed, a cruelty. "A hundred a year's a nice thing, for sartain, neighbour Spriggs," said he. "I once had nigh to that myself, but it didn't do me no good." And he gave a low sigh, as he thought of the children of his own loins who had robbed him. "And shall have again, Joe," said Handy; "and will have someone to keep it right and tight for you this time." Crumple sighed again;—he had learned the impotency of worldly wealth, and would have been satisfied, if left untempted, to have remained happy with one and sixpence a day. "Come, Skulpit," repeated Handy, getting impatient, "you're not going to go along with old Bunce in helping that parson to rob us all. Take the pen, man, and right yourself. Well," he added, seeing that Skulpit still doubted, "to see a man as is afraid to stand by hisself is, to my thinking, the meanest thing as is." "Sink them all for parsons, says I," growled Moody; "hungry beggars, as never thinks their bellies full till they have robbed all and everything!" "Who's to harm you, man?" argued Spriggs. "Let them look never so black at you, they can't get you put out when you're once in;—no, not old Catgut, with Calves to help him!" I am sorry to say the archdeacon himself was designated by this scurrilous allusion to his nether person. "A hundred a year to win, and nothing to lose," continued Handy. "My eyes! Well, how a man's to doubt about sich a bit of cheese as that passes me;—but some men is timorous;—some men is born with no pluck in them;—some men is cowed at the very first sight of a gentleman's coat and waistcoat." Oh, Mr Harding, if you had but taken the archdeacon's advice in that disputed case, when Joe Mutters was this ungrateful demagogue's rival candidate! "Afraid of a parson," growled Moody, with a look of ineffable scorn. "I tell ye what I'd be afraid of—I'd be afraid of not getting nothing from 'em but just what I could take by might and right;—that's the most I'd be afraid on of any parson of 'em all." "But," said Skulpit, apologetically, "Mr Harding's not so bad;—he did give us twopence a day, didn't he now?" "Twopence a day!" exclaimed Spriggs with scorn, opening awfully the red cavern of his lost eye. "Twopence a day!" muttered Moody with a curse; "sink his twopence!" "Twopence a day!" exclaimed Handy; "and I'm to go, hat in hand, and thank a chap for twopence a day, when he owes me a hundred pounds a year; no, thank ye; that may do for you, but it won't for me. Come, I say, Skulpit, are you a going to put your mark to this here paper, or are you not?" Skulpit looked round in wretched indecision to his two friends. "What d'ye think, Bill Gazy?" said he. But Bill Gazy couldn't think. He made a noise like the bleating of an old sheep, which was intended to express the agony of his doubt, and again muttered that "he didn't know." "Take hold, you old cripple," said Handy, thrusting the pen into poor Billy's hand: "there, so—ugh! you old fool, you've been and smeared it all,—there,—that'll do for you;—that's as good as the best name as ever was written": and a big blotch of ink was presumed to represent Billy Gazy's acquiescence. "Now, Jonathan," said Handy, turning to Crumple. "A hundred a year's a nice thing, for sartain," again argued Crumple. "Well, neighbour Skulpit, how's it to be?" "Oh, please yourself," said Skulpit: "please yourself, and you'll please me." The pen was thrust into Crumple's hand, and a faint, wandering, meaningless sign was made, betokening such sanction and authority as Jonathan Crumple was able to convey. "Come, Job," said Handy, softened by success, "don't let 'em have to say that old Bunce has a man like you under his thumb,—a man that always holds his head in the hospital as high as Bunce himself, though you're never axed to drink wine, and sneak, and tell lies about your betters as he does." Skulpit held the pen, and made little flourishes with it in the air, but still hesitated. "And if you'll be said by me," continued Handy, "you'll not write your name to it at all, but just put your mark like the others;"—the cloud began to clear from Skulpit's brow;—"we all know you can do it if you like, but maybe you wouldn't like to seem uppish, you know." "Well, the mark would be best," said Skulpit. "One name and the rest marks wouldn't look well, would it?" "The worst in the world," said Handy; "there—there": and stooping over the petition, the learned clerk made a huge cross on the place left for his signature. "That's the game," said Handy, triumphantly pocketing the petition; "we're all in a boat now, that is, the nine of us; and as for old Bunce, and his cronies, they may—" But as he was hobbling off to the door, with a crutch on one side and a stick on the other, he was met by Bunce himself. "Well Handy, and what may old Bunce do?" said the gray-haired, upright senior. Handy muttered something, and was departing; but he was stopped in the doorway by the huge frame of the newcomer. "You've been doing no good here, Abel Handy," said he, "'tis plain to see that; and 'tisn't much good, I'm thinking, you ever do." "I mind my own business, Master Bunce," muttered the other, "and do you do the same. It ain't nothing to you what I does;—and your spying and poking here won't do no good nor yet no harm." "I suppose then, Job," continued Bunce, not noticing his opponent, "if the truth must out, you've stuck your name to that petition of theirs at last." Skulpit looked as though he were about to sink into the ground with shame. "What is it to you what he signs?" said Handy. "I suppose if we all wants to ax for our own, we needn't ax leave of you first, Mr Bunce, big a man as you are; and as to your sneaking in here, into Job's room when he's busy, and where you're not wanted—" "I've knowed Job Skulpit, man and boy, sixty years," said Bunce, looking at the man of whom he spoke, "and that's ever since the day he was born. I knowed the mother that bore him, when she and I were little wee things, picking daisies together in the close yonder; and I've lived under the same roof with him more nor ten years; and after that I may come into his room without axing leave, and yet no sneaking neither." "So you can, Mr Bunce," said Skulpit; "so you can, any hour, day or night." "And I'm free also to tell him my mind," continued Bunce, looking at the one man and addressing the other; "and I tell him now that he's done a foolish and a wrong thing. He's turned his back upon one who is his best friend; and is playing the game of others, who care nothing for him, whether he be poor or rich, well or ill, alive or dead. A hundred a year? Are the lot of you soft enough to think that if a hundred a year be to be given, it's the likes of you that will get it?"—and he pointed to Billy Gazy, Spriggs, and Crumple. "Did any of us ever do anything worth half the money? Was it to make gentlemen of us we were brought in here, when all the world turned against us, and we couldn't longer earn our daily bread? A'n't you all as rich in your ways as he in his?"—and the orator pointed to the side on which the warden lived. "A'n't you getting all you hoped for, ay, and more than you hoped for? Wouldn't each of you have given the dearest limb of his body to secure that which now makes you so unthankful?" "We wants what John Hiram left us," said Handy. "We wants what's ourn by law; it don't matter what we expected. What's ourn by law should be ourn, and by goles we'll have it." "Law!" said Bunce, with all the scorn he knew how to command—"law! Did ye ever know a poor man yet was the better for law, or for a lawyer? Will Mr Finney ever be as good to you, Job, as that man has been? Will he see to you when you're sick, and comfort you when you're wretched? Will he—" "No, nor give you port wine, old boy, on cold winter nights! he won't do that, will he?" asked Handy; and laughing at the severity of his own wit, he and his colleagues retired, carrying with them, however, the now powerful petition. There is no help for spilt milk; and Mr Bunce could only retire to his own room, disgusted at the frailty of human nature. Job Skulpit scratched his head;—Jonathan Crumple again remarked, that, "for sartain, sure a hundred a year was very nice;"—and Billy Gazy again rubbed his eyes, and lowly muttered that "he didn't know." About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Anthony Trollope (1996). The Warden. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/619/pg619-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at [www.gutenberg.org, located](https://www.gutenberg.org, located) at https://www.gutenberg.org/policy/license.html.

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