Effective Field Theory Treatment of Monopole Production by Drell-Yan and Photon Fusion for Various Spins

A resolution over the existence of magnetic charges has eluded the high energy physics community for centuries, and their search has gained momentum as recent models predict these may be observable at current colliders. They appear in field theories in two forms: the widely studied but heavily suppressed monopole with structure (soliton) and the not-so-well-covered point-like monopole. The latter was first proposed by Dirac as the source of a singular magnetic field and in effect symmetrises Maxwell's equations. Following this line of research, work by S. Baines et al. analysed these sources as matter fields that carry spins 0, $\frac{1}{2}$, or 1, in an effective field theory that is perturbative for monopoles produced at threshold where the coupling strength $g(\beta)$ is suppressed. All three cases are currently under investigation by the MoEDAL collaboration at CERN, and the theoretical expressions for kinematic distributions proposed in this work serve as guides to these searches. The cross section distributions in each case are derived from a \emph{U}(1) invariant gauge theory. It is not assumed that, like the electron, the monopole's magnetic moment is generated through spin interactions at minimal coupling, as it may be quite large. Instead, the analytical expressions in the spin $\frac{1}{2}$ and $1$ cases are kept completely general through the inclusion of a phenomenological parameter $\kappa$, related to the gyromagnetic ratio $g_R=1+\kappa$. In fact, the inclusion of this parameter gives the effective theory validity in the high energy limit if the magnetic coupling scales with the particle's velocity $\beta=\frac{v}{c}$.Comment: 11 pages, 14 figures, conference proceedin

Crowd-based cognitive perception of the physical world: Towards the internet of senses

This paper introduces a possible architecture and discusses the research directions for the realization of the Cognitive Perceptual Internet (CPI), which is enabled by the convergence of wired and wireless communications, traditional sensor networks, mobile crowd-sensing, and machine learning techniques. The CPI concept stems from the fact that mobile devices, such as smartphones and wearables, are becoming an outstanding mean for zero-effort world-sensing and digitalization thanks to their pervasive diffusion and the increasing number of embedded sensors. Data collected by such devices provide unprecedented insights into the physical world that can be inferred through cognitive processes, thus originating a digital sixth sense. In this paper, we describe how the Internet can behave like a sensing brain, thus evolving into the Internet of Senses, with network-based cognitive perception and action capabilities built upon mobile crowd-sensing mechanisms. The new concept of hyper-map is envisioned as an efficient geo-referenced repository of knowledge about the physical world. Such knowledge is acquired and augmented through heterogeneous sensors, multi-user cooperation and distributed learning mechanisms. Furthermore, we indicate the possibility to accommodate proactive sensors, in addition to common reactive sensors such as cameras, antennas, thermometers and inertial measurement units, by exploiting massive antenna arrays at millimeter-waves to enhance mobile terminals perception capabilities as well as the range of new applications. Finally, we distillate some insights about the challenges arising in the realization of the CPI, corroborated by preliminary results, and we depict a futuristic scenario where the proposed Internet of Senses becomes true

Text-image guided Diffusion Model for generating Deepfake celebrity interactions

Deepfake images are fast becoming a serious concern due to their realism. Diffusion models have recently demonstrated highly realistic visual content generation, which makes them an excellent potential tool for Deepfake generation. To curb their exploitation for Deepfakes, it is imperative to first explore the extent to which diffusion models can be used to generate realistic content that is controllable with convenient prompts. This paper devises and explores a novel method in that regard. Our technique alters the popular stable diffusion model to generate a controllable high-quality Deepfake image with text and image prompts. In addition, the original stable model lacks severely in generating quality images that contain multiple persons. The modified diffusion model is able to address this problem, it add input anchor image's latent at the beginning of inferencing rather than Gaussian random latent as input. Hence, we focus on generating forged content for celebrity interactions, which may be used to spread rumors. We also apply Dreambooth to enhance the realism of our fake images. Dreambooth trains the pairing of center words and specific features to produce more refined and personalized output images. Our results show that with the devised scheme, it is possible to create fake visual content with alarming realism, such that the content can serve as believable evidence of meetings between powerful political figures.Comment: 8 pages,8 figures, DICT

Development of a cognitive robotic system for simple surgical tasks

The introduction of robotic surgery within the operating rooms has significantly improved the quality of many surgical procedures. Recently, the research on medical robotic systems focused on increasing the level of autonomy in order to give them the possibility to carry out simple surgical actions autonomously. This paper reports on the development of technologies for introducing automation within the surgical workflow. The results have been obtained during the ongoing FP7 European funded project Intelligent Surgical Robotics (I-SUR). The main goal of the project is to demonstrate that autonomous robotic surgical systems can carry out simple surgical tasks effectively and without major intervention by surgeons. To fulfil this goal, we have developed innovative solutions (both in terms of technologies and algorithms) for the following aspects: fabrication of soft organ models starting from CT images, surgical planning and execution of movement of robot arms in contact with a deformable environment, designing a surgical interface minimizing the cognitive load of the surgeon supervising the actions, intra-operative sensing and reasoning to detect normal transitions and unexpected events. All these technologies have been integrated using a component-based software architecture to control a novel robot designed to perform the surgical actions under study. In this work we provide an overview of our system and report on preliminary results of the automatic execution of needle insertion for the cryoablation of kidney tumours