“Permanent Mold Casting†Excellent Casting Method for Manufacture of Automotive Components

In this paper the study has been done why permanent mold casting method is excellent for manufacture of automotive components. In permanent mold casting process no external pressure is applied but hydrostatic pressure created by the risers is mainly responsible for casting of metal in the mold. As no external pressure is applied hence this process is also called Gravity die casting. In this process, solidification occurs much more rapidly than in sand casting, the main advantage is a permanent mold that can be used repeatedly for multiple metal castings. The mold also called a die is commonly made of steel or iron, but other metals or ceramics can be used. Permanent mold casting is typically used for high-volume production of small, simple metal parts with uniform wall thickness. Non-ferrous metals are typically used in this process, such as aluminum alloys, magnesium alloys, and copper alloys. However, irons and steels can also be cast using graphite molds. Common permanent mold parts include gears and gear housings, pipe fittings, and other automotive and aircraft components such as pistons, impellers, and wheels

Vertexica: your relational friend for graph analytics!

In this paper, we present Vertexica, a graph analytics tools on top of a relational database, which is user friendly and yet highly efficient. Instead of constraining programmers to SQL, Vertexica offers a popular vertex-centric query interface, which is more natural for analysts to express many graph queries. The programmers simply provide their vertex-compute functions and Vertexica takes care of efficiently executing them in the standard SQL engine. The advantage of using Vertexica is its ability to leverage the relational features and enable much more sophisticated graph analysis. These include expressing graph algorithms which are difficult in vertex-centric but straightforward in SQL and the ability to compose end-to-end data processing pipelines, including pre- and post- processing of graphs as well as combining multiple algorithms for deeper insights. Vertexica has a graphical user interface and we outline several demonstration scenarios including, interactive graph analysis, complex graph analysis, and continuous and time series analysis

Adversarial Attacks on Transformers-Based Malware Detectors

Signature-based malware detectors have proven to be insufficient as even a small change in malignant executable code can bypass these signature-based detectors. Many machine learning-based models have been proposed to efficiently detect a wide variety of malware. Many of these models are found to be susceptible to adversarial attacks - attacks that work by generating intentionally designed inputs that can force these models to misclassify. Our work aims to explore vulnerabilities in the current state of the art malware detectors to adversarial attacks. We train a Transformers-based malware detector, carry out adversarial attacks resulting in a misclassification rate of 23.9% and propose defenses that reduce this misclassification rate to half. An implementation of our work can be found at https://github.com/yashjakhotiya/Adversarial-Attacks-On-Transformers.Comment: Accepted to the 2022 NeurIPS ML Safety Workshop. Code available at https://github.com/yashjakhotiya/Adversarial-Attacks-On-Transformer

Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments

The spread of misinformation, propaganda, and flawed argumentation has been amplified in the Internet era. Given the volume of data and the subtlety of identifying violations of argumentation norms, supporting information analytics tasks, like content moderation, with trustworthy methods that can identify logical fallacies is essential. In this paper, we formalize prior theoretical work on logical fallacies into a comprehensive three-stage evaluation framework of detection, coarse-grained, and fine-grained classification. We adapt existing evaluation datasets for each stage of the evaluation. We employ three families of robust and explainable methods based on prototype reasoning, instance-based reasoning, and knowledge injection. The methods combine language models with background knowledge and explainable mechanisms. Moreover, we address data sparsity with strategies for data augmentation and curriculum learning. Our three-stage framework natively consolidates prior datasets and methods from existing tasks, like propaganda detection, serving as an overarching evaluation testbed. We extensively evaluate these methods on our datasets, focusing on their robustness and explainability. Our results provide insight into the strengths and weaknesses of the methods on different components and fallacy classes, indicating that fallacy identification is a challenging task that may require specialized forms of reasoning to capture various classes. We share our open-source code and data on GitHub to support further work on logical fallacy identification

The prognostic importance of scalp location in primary head and neck melanoma.

BACKGROUND AND OBJECTIVES: For patients with cutaneous melanoma, primary tumors located in the head and neck is associated with poor outcomes. The reason for this difference and whether it is applicable to all locations within the head and neck remains unclear. We hypothesized that scalp melanoma is uniquely distinguished from other anatomic sites and is independently responsible for the poor prognosis of head and neck melanoma. METHODS: Query and analysis of a prospectively maintained melanoma database of all patients treated for primary cutaneous melanoma from 1971 to 2010. RESULTS: Of 11 384 patients identified, 7% (n = 799) of lesions originated on the scalp. Scalp primaries were more often found in males and were associated with increased Breslow thickness and were more frequently ulcerated compared to all other anatomic sites (P = 0.0001). On multivariate analysis, scalp location was an independent predictor of worse melanoma-specific (HR 1.75; CI 1.50-2.04; P \u3c 0.0001) and overall survival (HR 1.62; CI 1.41-1.86; P \u3c 0.0001). CONCLUSIONS: This, the largest series examining scalp melanoma, confirms that scalp location is independently responsible for the negative prognosis associated with head and neck melanoma. Although the pathophysiology of this difference remains to be determined, these data argue for more rigorous surveillance of this anatomic location

Promoting novelty, rigor, and style in energy social science: towards codes of practice for appropriate methods and research design

A series of weaknesses in creativity, research design, and quality of writing continue to handicap energy social science. Many studies ask uninteresting research questions, make only marginal contributions, and lack innovative methods or application to theory. Many studies also have no explicit research design, lack rigor, or suffer from mangled structure and poor quality of writing. To help remedy these shortcomings, this Review offers suggestions for how to construct research questions; thoughtfully engage with concepts; state objectives; and appropriately select research methods. Then, the Review offers suggestions for enhancing theoretical, methodological, and empirical novelty. In terms of rigor, codes of practice are presented across seven method categories: experiments, literature reviews, data collection, data analysis, quantitative energy modeling, qualitative analysis, and case studies. We also recommend that researchers beware of hierarchies of evidence utilized in some disciplines, and that researchers place more emphasis on balance and appropriateness in research design. In terms of style, we offer tips regarding macro and microstructure and analysis, as well as coherent writing. Our hope is that this Review will inspire more interesting, robust, multi-method, comparative, interdisciplinary and impactful research that will accelerate the contribution that energy social science can make to both theory and practice

Processes of elite power and low-carbon pathways: experimentation, financialisation, and dispossession

What is a low-carbon pathway? To many, it is a way of mitigating climate change. To others, it is about addressing market failure or capturing the co-benefits attached to low-carbon systems, such as jobs or improved health. To still others, it represents building adaptive capacity and resilience in the face of climate change. However, these interpretations can fail to acknowledge how pathways of low-carbon transitions can also become intertwined with processes and structures of inequality, exclusion and injustice. Using a critical lens that draws from a variety of disciplines, this article explores three ways through which responses to climate change can entrench, exacerbate or reconfigure the power of elites. As society attempts to create a low-carbon society, including for example via coastal protection efforts, disaster recovery, or climate change mitigation and renewable energy, these efforts intersect with at least three processes of elite power: experimentation, financialisation, and dispossession. Experimentation is when elites use the world as a laboratory to test or pilot low-carbon technologies or policy models, transferring risks yet not always sharing benefits. Financialisation refers to the expansion and proliferation of finance, capital, and financial markets in the global economy and many national economies, processes of which have recently extended to renewable energy. Dispossession is when elites use decarbonisation as a process through which to appropriate land, wealth, or other assets (and in the process make society more majoritarian and/or unequal). We explore these three themes using a variety of evidence across illustrative case studies, including hard and soft coastal protection measures (Bangladesh, Netherlands), climate risk insurance (Malawi), and renewable energy auctions and associated processes of finance and investment (South Africa and Mexico)

Recomendr-entity recommendation based on ad-hoc dimensions

The growing availability and popularity of opinion rich resources on the online web resources, such as review sites and personal blogs, has made it convenient to find out about the opinions and experiences of layman people. But, simultaneously, this huge eruption of data has made it difficult to reach to a conclusion. In this thesis, I develop a novel recommendation system, Recomendr that can help users digest all the reviews about an entity and compare candidate entities based on ad-hoc dimensions specified by keywords. It expects keyword specified ad-hoc dimensions/features as input from the user and based on those features; it compares the selected range of entities using reviews provided on the related User Generated Contents (UGC) e.g. online reviews. It then rates the textual stream of data using a scoring function and returns the decision based on an aggregate opinion to the user. Evaluation of Recomendr using a data set in the laptop domain shows that it can effectively recommend the best laptop as per user-specified dimensions such as price. Recomendr is a general system that can potentially work for any entities on which online reviews or opinionated text is available