15 research outputs found
A Causal Analysis of the Plots of Intelligent Adversaries
No full textIn this paper we demonstrate a new advance in causal Bayesian graphical modelling combined with Adversarial Risk Analysis. This research aims to support strategic analyses of various defensive interventions to counter the threat arising from plots of an adversary. These plots are characterised by a sequence of preparatory phases that an adversary must necessarily pass through to achieve their hostile objective. To do this we first define a new general class of plot models. Then we demonstrate that this is a causal graphical family of models - albeit with a hybrid semantic. We show this continues to be so even in this adversarial setting. It follows that this causal graph can be used to guide a Bayesian decision analysis to counter the adversary's plot. We illustrate the causal analysis of a plot with details of a decision analysis designed to frustrate the progress of a planned terrorist attack
Temporal Delta Layer: Training Towards Brain Inspired Temporal Sparsity for Energy Efficient Deep Neural Networks
No full textIn the recent past, real-time video processing using state-of-the-art deep neural networks (DNN) has achieved human-like accuracy but at the cost of high energy consumption, making them infeasible for edge device deployment. The energy consumed by running DNNs on hardware accelerators is dominated by the number of memory read/writes and multiplyaccumulate (MAC) operations required. As a potential solution, this work explores the role of activation sparsity in efficient DNN inference. As the predominant operation in DNNs is matrix-vector multiplication of weights with activations, skipping operations and memoryfetches where (at least) one of them is zero can make inference more energy efficient. Although spatial sparsification of activations is researched extensively, introducing and exploiting temporal sparsity is much less explored in DNN literature. This work presents a new DNN layer (called temporal delta layer) whose primary objective is to induce temporal activation sparsity during training. The temporal delta layer promotes activation sparsity by performing delta operation facilitated by activation quantization and l1 norm based penalty to the cost function. During inference, the resulting model acts as a conventional quantizedDNN with high temporal activation sparsity. The new layer was incorporated as a part of the standard ResNet50 architecture to be trained and tested on the popular human action recognition dataset (UCF101). The method caused 2x improvement in activation sparsity, with 5% accuracy loss.Electrical Engineerin
Structural, electronic transport and optical properties of Cr doped PbS thin film by chemical bath deposition
No full textEffect of interphase permittivity on the electric field distribution of epoxy nanocomposites
No full textStrengthening Knowledge Management for Resilient and Sustainable Transport Systems: Insights from the KEYSTONE Project
No full textThe ongoing push for sustainable and resilient multimodal transport systems requires an innovative approach to managing knowledge within and between organisations, optimising the use of human resources, improving the social and working conditions of transport workforce, and addressing longstanding inefficiencies. The Horizon Europe KEYSTONE project tackles these challenges by developing a data, information and knowledge management framework designed to enhance operational efficiency, collaboration, and sustainability across the European logistics and transport sectors, while also empowering human resources in public control authorities and transport operators by reducing administrative burdens and increasing efficiency, consistency, and safety.The KEYSTONE strategy focuses not only on streamlining knowledge flows between diverse stakeholders, including logistics operators, enforcement authorities, and infrastructure managers, but also on enhancing human decision-making and collaboration across those networks. Its primary innovation lies in the development of secure, standardised processes for knowledge sharing that promote transparency, collaboration, optimised operations, and cross-border compliance. These processes addres
What Was So New about the New Story? Modernist Realism in the Hindi Nayī Kahānī
Get PDFThis essay examines the Hindi Nayī Kahānī, or New Story, Movement of the 1950s and 1960s, which was influential for the short stories, criticism, and literary history that its writers produced. Incorporating a view toward the larger “metaliterary” corpus in relation to which properly “literary” nayī kahānī texts were written, the essay shows how the movement inaugurated a modernist realism characterized by attention to genre, rhetoric, and style on one hand, and commitment to social reality on the other. Combining rhetorical strategies—such as shifting narrative voice, allegorical descriptions of landscape, and implicit reference to authorship and the condition of postcolonial literary production—with structural and thematic tensions between form and content, this mode developed an interchangeability between author, reader, and character, which did not previously exist in Hindi literature and which reconfigured the category of the middle class in the universally recognizable terms of alienation. Using the case of the nayī kahānī, the essay offers a new literary historical approach that moves beyond sweeping accounts of a single postcolonial mode to attend to regional realisms and modernisms
A Study on the Symptomatology and Diagnostic Methodology of Oru Thalai Vatha Bedham
Get PDFOru thalai vatha bedham clinical entity was described by Sage Yugi in his wisdom. The study conducted has come out with excellent results validating the clinical features of Oru thalai vatha bedham elucidated in an ultra short poetic segment by Yugi. The study was aimed at evolving a set of exclusive Siddha diagnostic findings for Oru thalai vatha bedham with the observation and inference of various parameters like Naadi, Neikkuri and disease acquired season, it can be concluded that all of them point to the development or vitiation of humour leading to the disease Oru thalai vatha bedham. The patient reported with the symptoms of Oru thali vatha bedham were subjected to the standard set of investigations, the results and findings of the investigations were suggestive of Oru thalai vatha bedham according to modern classification of disease. Manikadai Nool and Neikkuri findings may help in the identifying of preponderance in a person to develop Oru thalai vatha bedham hence it can be used as a screening measure to advise the preventive measures well in advance.
Almost all the patients diagnosed as oru thalai vatha bedham had normal study of heamatological evidence, CT & x-ray (if needed) conforming to the correlation of disease with Primary headache syndrome. From the analysis done between Oru thalai vatha bedham cases notable variations were observed in both Siddha and Modern parameters. Interestingly, it was found that the symptoms presented by the patients in the study were those of a constant subset of symptoms of Primary headache syndrome explained in the present day classification. It correlated with all of the symptoms mentioned by Yugimuni under Oru thalai vatha bedham. Thus the author concludes by throwing lights on validation of symptomatology and exclusive Siddha diagnostic methodology for Oru thalai vatha bedham, so that a physician can arrive at proper treatment procedures by rightly diagnosing the disease
Problem-based learning in undergraduate education. A sophomore chemistry laboratory
Get PDFFor the first time in my life what we were doing in lab had meaning to the outside world other than just mixing chemicals together. -Comment turned in by a student in Chem. 291L Problem-based learning (PBL) is a pedagogical approach based on recent advances in cognitive science research on human learning (1). A PBL classroom is organized around collaborative problem-solving activities that provide a context for learning and discovery. PBL has been used in medical schools to enhance the development of clinical reasoning skills and to promote the integration of basic biomedical sciences with clinical applications. Medical education literature is replete with articles on the practice and evaluation of PBL methods, but there is very little published on the application of PBL for science education in undergraduate settings. A recent paper by Dods in this Journal describes a very interesting application of PBL in a biochemistry lecture course (2). There have been some presentations at recent ACS conferences describing the application of PBL in chemistry courses (3, 4 ). Other problembased approaches to pedagogy have been described by Wenzel and Hughes (5, 6 ). These approaches are similar to PBL in that students learn in the context of an authentic problem solving experience. This paper describes the implementation of PBL pedagogy in an undergraduate classroom setting. The author provides a brief description of PBL philosophy and PBL protocols, guidance on how to choose and design a PBL problem and integrate it into the curriculum, and a description of a laboratory course in which PBL has been successfully implemented
Bayesian Graphs of Intelligent Causation
Get PDFProbabilistic Graphical Bayesian models of causation have continued to impact
on strategic analyses designed to help evaluate the efficacy of different
interventions on systems. However, the standard causal algebras upon which
these inferences are based typically assume that the intervened population does
not react intelligently to frustrate an intervention. In an adversarial setting
this is rarely an appropriate assumption. In this paper, we extend an
established Bayesian methodology called Adversarial Risk Analysis to apply it
to settings that can legitimately be designated as causal in this graphical
sense. To embed this technology we first need to generalize the concept of a
causal graph. We then proceed to demonstrate how the predicable intelligent
reactions of adversaries to circumvent an intervention when they hear about it
can be systematically modelled within such graphical frameworks, importing
these recent developments from Bayesian game theory. The new methodologies and
supporting protocols are illustrated through applications associated with an
adversary attempting to infiltrate a friendly state
