1,721,462 research outputs found
An Analysis of the Optical Variability of Active Galactic Nuclei Using Multi Band Sloan Digital Sky Survey Stripe 82 Photometric Data
No full textActive Galactic Nuclei (AGNs) are the most luminous objects in the universe and are thought to be powered by the accretion of matter onto a million to a billions solar mass black hole. AGNs emit immense amount of luminosity in almost all part of the electromagnetic spectrum and therefore it is assumed that a range of physical processes are going on in the AGN. However, the optical emission that we are primarily concerned with comes from the thermal black body emission.
Soon after its discovery, it was realised that AGNs varies a lot. However, the fundamental cause of such variability is largely unknown. In this project we have explored the AGNs optical variability as a consequence of fluctuations in accretion rates. Since AGNs light curves are stochastic and aperiodic in nature, we applied a coherent first order auto regressive perturbation to the thermal black body emission. We also calculated the ratio of amplitude of variability in two optical bands of SDSS stripe 82 data and showed that a coherent radius dependent ampltude of fluctuations which might arise out of inward moving perturbations can not only explain the simultaneous variability across all AGN bands, but can also explain the observed higher ratio of amplitude of variability in two optical bands.ProQuest Traditional Publishing Optio
Data Security in the Age of Marketing: Safeguarding Customer Information and Compliance
No full textThis chapter introduces the specifics and intricacies of data security in marketing applications, more specifically aiming towards an organization's responsibility and need to project their own and their customer's data. From identifying and understanding vulnerabilities to finding effective and robust ways to mitigate them, this chapter analyses all the variables and components when it comes to data security. With concepts such as data integrity, data confidentiality, and data availability, this chapter explains the scope and gist of data security. These concepts are also called the pillars of data security. Additionally, vulnerabilities originating from both, internal and external attacks, are mentioned and discussed in this chapter. Mitigation and prevention strategies are discussed as well in this chapter to give a clear base for making a secure and robust data security system. By using advanced technologies for protecting the data, various methods of data storage, authorization, and authentication, an organization can combat the issue of data security. Furthermore, this chapter discusses the legality and legal areas of digital security in India, the United States, and the European Union (EU), which helps in understanding the regulations and laws all over the world. Additionally, for an organization, methods of gaining their customers' trust and providing them with a transparent picture of the data security measures are explained along with appropriate examples. Finally, details about maintenance and updates are also given. To conclude, this chapter helps the reader navigate through the murky waters of the complicated world of data security in today's new and upcoming digital age
Manifestation at the Edge: Introducing the Absolute Nonexistence Superposition Hypothesis (ANSH)
No full textThe Absolute Nonexistence Superposition Hypothesis (ANSH) offers a novel interpretation of quantum superposition, proposing that unobserved quantum entities do not exist in spacetime in any classical sense. Instead of existing in multiple states simultaneously, they reside in a state of atemporal, nonlocal potential—what we term “nonexistence” relative to the framework of time and space. Upon observation or interaction, this potential manifests as a singular, localized event in spacetime. ANSH reframes superposition not as a paradox of many existences, but as the absence of existence until observation brings about manifestation. This perspective resolves key paradoxes such as Schrödinger’s Cat and the behavior of photons in the double-slit experiment, without invoking multiple worlds or branching realities. By understanding the boundary conditions of existence—such as light speed and Planck limits—ANSH suggests that quantum phenomena emerge from regions where time and space themselves cease to apply. This approach provides a unified, simplified view of quantum mechanics, grounded in the principle that existence itself is conditional and emergent upon measurement
Sublinear Time Hypergraph Sparsification via Cut and Edge Sampling Queries
No full textThe problem of sparsifying a graph or a hypergraph while approximately preserving its cut structure has been extensively studied and has many applications. In a seminal work, Benczúr and Karger (1996) showed that given any n-vertex undirected weighted graph G and a parameter ε ∈ (0,1), there is a near-linear time algorithm that outputs a weighted subgraph G' of G of size Õ(n/ε²) such that the weight of every cut in G is preserved to within a (1 ± ε)-factor in G'. The graph G' is referred to as a (1 ± ε)-approximate cut sparsifier of G. Subsequent recent work has obtained a similar result for the more general problem of hypergraph cut sparsifiers. However, all known sparsification algorithms require Ω(n + m) time where n denotes the number of vertices and m denotes the number of hyperedges in the hypergraph. Since m can be exponentially large in n, a natural question is if it is possible to create a hypergraph cut sparsifier in time polynomial in n, independent of the number of edges. We resolve this question in the affirmative, giving the first sublinear time algorithm for this problem, given appropriate query access to the hypergraph.
Specifically, we design an algorithm that constructs a (1 ± ε)-approximate cut sparsifier of a hypergraph H(V,E) in polynomial time in n, independent of the number of hyperedges, when given access to the hypergraph using the following two queries:
1) given any cut (S, ̄S), return the size |δ_E(S)| (cut value queries); and
2) given any cut (S, ̄S), return a uniformly at random edge crossing the cut (cut edge sample queries). Our algorithm outputs a sparsifier with Õ(n/ε²) edges, which is essentially optimal. We then extend our results to show that cut value and cut edge sample queries can also be used to construct hypergraph spectral sparsifiers in poly(n) time, independent of the number of hyperedges.
We complement the algorithmic results above by showing that any algorithm that has access to only one of the above two types of queries can not give a hypergraph cut sparsifier in time that is polynomial in n. Finally, we show that our algorithmic results also hold if we replace the cut edge sample queries with a pair neighbor sample query that for any pair of vertices, returns a random edge incident on them. In contrast, we show that having access only to cut value queries and queries that return a random edge incident on a given single vertex, is not sufficient
Counting and Sampling Perfect Matchings in Regular Expanding Non-Bipartite Graphs
No full textWe show that the ratio of the number of near perfect matchings to the number of perfect matchings in d-regular strong expander (non-bipartite) graphs, with 2n vertices, is a polynomial in n, thus the Jerrum and Sinclair Markov chain [Jerrum and Sinclair, 1989] mixes in polynomial time and generates an (almost) uniformly random perfect matching. Furthermore, we prove that such graphs have at least Ω(d)ⁿ many perfect matchings, thus proving the Lovasz-Plummer conjecture [L. Lovász and M.D. Plummer, 1986] for this family of graphs
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
No full text“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Learning without prejudice: Avoiding bias in webly-supervised action recognition
No full textWebly-supervised learning has recently emerged as an alternative paradigm to traditional supervised learning based on large-scale datasets with manual annotations. The key idea is that models such as CNNs can be learned from the noisy visual data available on the web. In this work we aim to exploit web data for video understanding tasks such as action recognition and detection. One of the main problems in webly-supervised learning is cleaning the noisy labeled data from the web. The state-of-the-art paradigm relies on training a first classifier on noisy data that is then used to clean the remaining dataset. Our key insight is that this procedure biases the second classifier towards samples that the first one understands. Here we train two independent CNNs, a RGB network on web images and video frames and a second network using temporal information from optical flow. We show that training the networks independently is vastly superior to selecting the frames for the flow classifier by using our RGB network. Moreover, we show benefits in enriching the training set with different data sources from heterogeneous public web databases. We demonstrate that our framework outperforms all other webly-supervised methods on two public benchmarks, UCF-101 and Thumos’14
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