A new quantity for studies of dijet azimuthal decorrelations

We introduce a new measurable quantity, $R_{\Delta \phi}$, for studies of the rapidity and transverse momentum dependence of dijet azimuthal decorrelations in hadron-hadron collisions. In pQCD, $R_{\Delta \phi}$ is computed as a ratio of three-jet and dijet cross sections in which the parton distribution functions cancel to a large extent. At the leading order, $R_{\Delta \phi}$ is proportional to $\alpha_s$, and the transverse momentum dependence of can therefore be exploited to determine $\alpha_s$. We compute the NLO pQCD theory predictions and non-perturbative corrections for $R_{\Delta \phi}$ at the LHC and the Tevatron and investigate the corresponding uncertainties. From this, we estimate the theory uncertainties for $\alpha_s$ determinations based on $R_{\Delta \phi}$ at both colliders. The potential of $R_{\Delta \phi}$ measurements for tuning Monte Carlo event generators is also demonstrated.Comment: 20 pages, 11 figures, 1 table, submitted to JHE

Study of jet transverse momentum and jet rapidity dependence of dijet azimuthal decorrelations with the DØ detector

In a collision experiment involving highly energetic particles such as hadrons, processes at high momentum transfers can provide information useful for many studies involving Quantum Chromodynamics (QCD). One way of analyzing these interactions is through angular distributions. In hadron-hadron collisions, the angular distribution between the two leading jets with the largest transverse momentum (pT) is affected by the production of additional jets. While soft radiation causes small differences in the azimuthal angular distribution of the two leading jets produced in a collision event, additional hard jets produced in the event have more pronounced influence on the distribution of the two leading jets produced in the collision. Thus, the dijet azimuthal angular distribution can serve as a variable that can be used to study the transition from soft to hard QCD processes in a collision event. This dissertation presents a triple-differential study involving the azimuthal angular distribution and the jet transverse momenta, and jet rapidities of the first two leading jets. The data used for this research are obtained from proton-antiproton (pp¯) collisions occurring at a center of mass energy of 1.96 TeV, using the DØ detector in Run II of the Tevatron Collider at the Fermi National Accelerator Laboratory (FNAL) in Illinois, USA. Comparisons are made to perturbative QCD (pQCD) predictions at next-to-leading order (NLO)

Improving Virtual Machine Implementation to Simplify Learning by using vCloud Director

VMware vCloud Director is a part of vCloud Suite, and is used to provide an advantage in facilitating remote lab environments for students by hosting a product that allows the user to run virtual machines on dedicated hardware. Educators have been encouraged to develop this tool in higher education systems across the country. For a long time, students and educators have being using the concept of VMware workstations in their lab environments. However, this typically requires students to download and install software, which can be a burden causing issues and problems resulting in loss of class productivity. The purpose of this research is to show how the use of VMware vCloud Director makes it easier for students to concentrate on learning by eliminating the need of installing virtual machine (VM) technology on the students own computer system. Using Franklin-hosted vCloud Director, students can do their assignments without downloading and installing a large amount of software, unlike the current approach. There are currently three courses at Franklin University using the vCloud Director Tool, COMP 204, Principles of Computer Networks, ISEC 325, Network Security and INFA 415, Information Analytics Architecture. Further new applications are planned to continue to improve student learning in courses where VM technology is either already in place or soon will be. *Outstanding Student Poster: 3rd Place Winnerhttps://fuse.franklin.edu/ss2014/1073/thumbnail.jp

OmniHorizon: In-the-Wild Outdoors Depth and Normal Estimation from Synthetic Omnidirectional Dataset

Understanding the ambient scene is imperative for several applications such as autonomous driving and navigation. While obtaining real-world image data with per-pixel labels is challenging, existing accurate synthetic image datasets primarily focus on indoor spaces with fixed lighting and scene participants, thereby severely limiting their application to outdoor scenarios. In this work we introduce OmniHorizon, a synthetic dataset with 24,335 omnidirectional views comprising of a broad range of indoor and outdoor spaces consisting of buildings, streets, and diverse vegetation. Our dataset also accounts for dynamic scene components including lighting, different times of a day settings, pedestrians, and vehicles. Furthermore, we also demonstrate a learned synthetic-to-real cross-domain inference method for in-the-wild 3D scene depth and normal estimation method using our dataset. To this end, we propose UBotNet, an architecture based on a UNet and a Bottleneck Transformer, to estimate scene-consistent normals. We show that UBotNet achieves significantly improved depth accuracy (4.6%) and normal estimation (5.75%) compared to several existing networks such as U-Net with skip-connections. Finally, we demonstrate in-the-wild depth and normal estimation on real-world images with UBotNet trained purely on our OmniHorizon dataset, showing the promise of proposed dataset and network for scene understanding.Comment: 16 pages and 18 figure

DOF-GS: Adjustable Depth-of-Field 3D Gaussian Splatting for Refocusing,Defocus Rendering and Blur Removal

3D Gaussian Splatting-based techniques have recently advanced 3D scene reconstruction and novel view synthesis, achieving high-quality real-time rendering. However, these approaches are inherently limited by the underlying pinhole camera assumption in modeling the images and hence only work for All-in-Focus (AiF) sharp image inputs. This severely affects their applicability in real-world scenarios where images often exhibit defocus blur due to the limited depth-of-field (DOF) of imaging devices. Additionally, existing 3D Gaussian Splatting (3DGS) methods also do not support rendering of DOF effects. To address these challenges, we introduce DOF-GS that allows for rendering adjustable DOF effects, removing defocus blur as well as refocusing of 3D scenes, all from multi-view images degraded by defocus blur. To this end, we re-imagine the traditional Gaussian Splatting pipeline by employing a finite aperture camera model coupled with explicit, differentiable defocus rendering guided by the Circle-of-Confusion (CoC). The proposed framework provides for dynamic adjustment of DOF effects by changing the aperture and focal distance of the underlying camera model on-demand. It also enables rendering varying DOF effects of 3D scenes post-optimization, and generating AiF images from defocused training images. Furthermore, we devise a joint optimization strategy to further enhance details in the reconstructed scenes by jointly optimizing rendered defocused and AiF images. Our experimental results indicate that DOF-GS produces high-quality sharp all-in-focus renderings conditioned on inputs compromised by defocus blur, with the training process incurring only a modest increase in GPU memory consumption. We further demonstrate the applications of the proposed method for adjustable defocus rendering and refocusing of the 3D scene from input images degraded by defocus blur

Reliability improvement and loss reduction in radial distribution system with network reconfiguration algorithms using loss sensitivity factor

Studies on load flow in electrical distribution system have always been an area of interest for research from the previous few years. Various approaches and techniques are brought into light for load flow studies within the system and simulation tools are being used to work out on varied characteristics of system. This study concentrates on these approaches and the improvements made to the already existing techniques considering time and the algorithms complexity. Also, the paper explains the network reconfiguration (NR) techniques considered in reconfiguring radial distribution network (RDN) to reduce power losses in distribution system and delivers an approach to how various network reconfiguration techniques support loss reduction and improvement of reliability in the electrical distribution network

SYNTHESIS, CHARACTERIZATION AND ANTIMICROBIAL ACTIVITY OF SOME NOVEL QUINOLINE BASED IMIDAZOLES

A simple and convenient method has been developed for the synthesis of title compounds, 1-methyl-2-phenyl-1H-imidazo[4,5-f]-quinoline and derivatives (5a-f) in reasonable yields. The commercially available 6-nitro-quinoline-5-amine (1) is used as raw material and it is reduced conveniently using SnCl2 to give the initial intermediate, quinoline-5,6-diamine (2) in good yield. Compound 2 on consecutive steps when treated on condensation followed by cyclization generated the rest of intermediates, N6-benzylidene-quinoline-5,6-diamines (3a-f) and 2-phenyl-1H-imidazo[4,5-f]-quinolines (4a-f) respectively. The chemical structures of all newly prepared compounds were elucidated using infrared, 1H NMR and mass spectral studies as well as elemental analysis. The output of this synthetic method has been provided a series of successful biologically important structures. Keywords: Quinoline, imidazole, antimicrobial activit