The “Able” Hester and the “Inhuman” Cristina in The Scarlet Letter and Lives of the Saints

Published in 1850, Hawthorne’s masterpiece, The Scarlet Letter tells a story of Hester Prynne, who irretrievably breaks the law of Puritanism by giving birth to a girl through an affair with a local respectful priest named Dimmesdale and struggles to create a new life of repentance and dignity independently. Comparatively, Lives of the Saints, begins Nino Ricci’s trilogy centered on the experience of a southern Italian family in Valle del Soles before its emigration to Canada. Different from the other two novels, it focuses on the first seven years of Vittorio, living with his mother Cristina and his grandfather. The central plot of Lives of the Saints is the snakebite that Vittorio’s mother Cristina receives on her leg while she is in the stable of her father’s house, whereas later she is proved to be engaged in an adultery with a man, which is seen as a “pernicious and contagious illness”. This essay will explore the existential elements of the reaction to adultery of the two protagonists Hester and Cristina in Hawthorne’s The Scarlet Letter and Nino Ricci’s Lives of the Saints with some core concepts of existentialism to investigate that whether Cristina is the “Canadian Hester” with the great awakening

The Story of an Hour: A Text Kidnapped by Feminist Criticism

The Story of an Hour as one of Kate Chopin’s classic masterpieces, has always been regarded as a purely feminist work that authentically indicates the awakening of women’s consciousness of freedom under the oppression of patriarchal politics. Since Halliday proposed Systemic Functional Grammar and the three metafunctions of language, transitivity has been widely employed in the interpretation of literary works. The present study focuses on the vicissitude of the protagonist Mrs. Mallard’s mental status through the transitivity analysis as a useful tool and the stylistic features of the text itself as well to explore the hidden theme which has been neglected, and concludes that this classic work is kidnapped by feminist criticism. It satirizes the protagonist’s pathological emotional state of pursuing the absolute freedom in marriage

Adversarial Driving: Attacking End-to-End Autonomous Driving Systems

As the research in deep neural networks advances, deep convolutional networks become feasible for automated driving tasks. There is an emerging trend of employing end-to-end models in the automation of driving tasks. However, previous research unveils that deep neural networks are vulnerable to adversarial attacks in classification tasks. While for regression tasks such as autonomous driving, the effect of these attacks remains rarely explored. In this research, we devise two white-box targeted attacks against end-to-end autonomous driving systems. The driving model takes an image as input and outputs the steering angle. Our attacks can manipulate the behaviour of the autonomous driving system only by perturbing the input image. Both attacks can be initiated in real-time on CPUs without employing GPUs. This demo aims to raise concerns over applications of end-to-end models in safety-critical systems.Comment: 3 pages, 2 figure

Growth of Large Domain Epitaxial Graphene on the C-Face of SiC

Growth of epitaxial graphene on the C-face of SiC has been investigated. Using a confinement controlled sublimation (CCS) method, we have achieved well controlled growth and been able to observe propagation of uniform monolayer graphene. Surface patterns uncover two important aspects of the growth, i.e. carbon diffusion and stoichiometric requirement. Moreover, a new "stepdown" growth mode has been discovered. Via this mode, monolayer graphene domains can have an area of hundreds of square micrometers, while, most importantly, step bunching is avoided and the initial uniformly stepped SiC surface is preserved. The stepdown growth provides a possible route towards uniform epitaxial graphene in wafer size without compromising the initial flat surface morphology of SiC.Comment: 18 pages, 8 figure

Seasonal Applicability of Refrigerant Release Technology in Room Air Conditioner

The room air conditioner is widely used in residential and office buildings for both cooling and heating. However, the operation condition of the room air conditioner changes largely due to the variations of the cooling/heating load of the room, the indoor temperature and the outdoor one, which always decreases the efficiency of the compressor and eventually the seasonal energy efficiency of the air conditioner. The refrigerant release technology relieves even eliminates the over-compression loss of scroll compressor by bypassing the refrigerant in the compression chamber to the suction pipe under small system pressure ratio conditions, By using it, the efficiency of the air conditioner can be increased. The effect of refrigerant-releasing on the annual energy performance of the room air conditioner is analyzed and the applicability is evaluated in this paper. The typical residential and office building models are built in a building load simulation software, DeST. The annual cooling and heating loads of these buildings located in different climate zones are calculated. The numerical models of the fixed speed and inverter room air conditioners with and without refrigerant-releasing function are built and verified. For the air conditioner with refrigerant-releasing, a control strategy to activate the refrigerant release is determined to keep the efficiency as well as satisfy the indoor load. Based on the calculated cooling/heating load and the models of the air conditioners, the energy consumptions of all four kinds of air conditioner during each hour can be calculated. Finally, the performance of variable and fixed speed air conditioner with and without refrigerant release in the cooling period and heating period is obtained and the contribution of refrigerant-releasing is evaluated. The results show that the variable speed air conditioner with refrigerant-releasing saves 24.2~25.5% and 18.9~25.3% energy compared to the one without refrigerant release for the office and residential buildings, respectively. For the fixed speed air conditioner, the refrigerant-releasing technology decreases 20.2~23.0% and 17.1~23.6% energy consumption for residential and office building, respectively. Variable speed air conditioner with refrigerant release saves more energy than fixed speed one. The refrigerant-releasing technology has good applicability for the room air conditioner and should be considered as an effective method to enhance the seasonal performance for it

Adversarial Detection: Attacking Object Detection in Real Time

Intelligent robots rely on object detection models to perceive the environment. Following advances in deep learning security it has been revealed that object detection models are vulnerable to adversarial attacks. However, prior research primarily focuses on attacking static images or offline videos. Therefore, it is still unclear if such attacks could jeopardize real-world robotic applications in dynamic environments. This paper bridges this gap by presenting the first real-time online attack against object detection models. We devise three attacks that fabricate bounding boxes for nonexistent objects at desired locations. The attacks achieve a success rate of about 90% within about 20 iterations. The demo video is available at: https://youtu.be/zJZ1aNlXsMU.Comment: 7 pages, 10 figure

Deep Reinforcement Learning-driven Cross-Community Energy Interaction Optimal Scheduling

In order to coordinate energy interactions among various communities and energy conversions among multi-energy subsystems within the multi-community integrated energy system under uncertain conditions, and achieve overall optimization and scheduling of the comprehensive energy system, this paper proposes a comprehensive scheduling model that utilizes a multi-agent deep reinforcement learning algorithm to learn load characteristics of different communities and make decisions based on this knowledge. In this model, the scheduling problem of the integrated energy system is transformed into a Markov decision process and solved using a data-driven deep reinforcement learning algorithm, which avoids the need for modeling complex energy coupling relationships between multi-communities and multi-energy subsystems. The simulation results show that the proposed method effectively captures the load characteristics of different communities and utilizes their complementary features to coordinate reasonable energy interactions among them. This leads to a reduction in wind curtailment rate from 16.3% to 0% and lowers the overall operating cost by 5445.6 Yuan, demonstrating significant economic and environmental benefits.Comment: in Chinese language, Accepted by Electric Power Constructio

Poor peer work does not boost student confidence

Students' low confidence, particularly in numerical topics, is thought to be a barrier to keeping them engaged with education. We studied the effects on confidence of exposure to a peer's work of varying quality (very good or bad) and neatness (messy or neat). Previous research underpinned our hypothesis that a peer's bad-quality work—which students rarely see—might boost student confidence more than very good work. We also predicted that a peer's very good work—which students are often shown—might be less discouraging if it were messy, suggesting it required effort and struggle. However, in experiments with university students and low-educated adults, these hypotheses were not supported, and all participants decreased in confidence after seeing any peer work. The failure to find support for these hypotheses can inform future research into social comparison effects on self-confidence in numerical topics. These results also have practical implications for teachers and managers who are expected to provide examples of peer work