Two-loop form factors for diphoton production in quark annihilation channel with heavy quark mass dependence

We present the computation of the two-loop form factors for diphoton production in the quark annihilation channel. These quantities are relevant for the NNLO QCD corrections to diphoton production at LHC recently presented in arXiv:2308.10885. The computation is performed retaining full dependence on the mass of the heavy quark in the loops. The master integrals are evaluated by means of differential equations which are solved exploiting the generalised power series technique.Comment: 23 pages, 5 figure

Full top-quark mass dependence in diphoton production at NNLO in QCD

In this paper we consider the diphoton production in hadronic collisions at the next-to-next-to-leading order (NNLO) in perturbative QCD, taking into account for the first time the full top quark mass dependence up to two loops (full NNLO). We show selected numerical distributions, highlighting the kinematic regions where the massive corrections are more significant. We make use of the recently computed two-loop massive amplitudes for diphoton production in the quark annihilation channel. The remaining massive contributions at NNLO are also considered, and we comment on the weight of the different types of contributions to the full and complete result.Comment: 14 pages and 5 figure

Detección de patrones y tendencias en estudiantes universitarios de carreras de ingeniería para determinar el éxito académico aplicando Machine Learning

Uno de los indicadores por el cual son medidas y evaluadas las universidades es el número o cantidad de egresados por año. Si bien uno de los tantos objetivos de las instituciones académicas es la formación de profesionales, en las universidades se evidencia que no existe una relación directa entre el número de ingresantes y el número de egresados, es decir, la cantidad de egresados no necesariamente se incrementa al aumentar la cantidad de ingresantes. Esto representa un gran problema puesto que los presupuestos con los que cuentan las universidades, sobre todo las de gestión pública, muchas veces se ven reducidos, es más, cuando un alumno logra finalizar sus estudios, el costo de la inversión para formarlo es altísimo. Claro está que obtener una mayor cantidad de egresados no solo reduciría la inversión mencionada sino que mejoraría los indicadores empleados y posicionaría mejor a cualquier universidad. Lograr identificar algún patrón o tendencia en el modo en que los alumnos cursan su respectiva carrera, o identificar las variables que influyen tanto en el éxito como en la deserción académica, sería beneficioso para que las autoridades puedan tomar las decisiones correctas. En este proyecto se tomarán las historias académicas de estudiantes de carreras de ingeniería de la Universidad Nacional de Jujuy, para identificar patrones o tendencias en el cursado que permitan determinar las variables que influyen para que un estudiante logre concluir la carrera. Se empleará Machine Learning como técnica para conducir este trabajo, encarándolo como un problema de clasificación y/o predicción.Eje: Agentes y sistemas inteligentes.Red de Universidades con Carreras en Informátic

A Drone-Based Application for Scouting Halyomorpha Halys Bugs in Orchards with Multifunctional Nets

In this work, we consider the problem of using a drone to collect information within orchards in order to scout insect pests, i.e., the stink bug Halyomorpha halys. An orchard can be modeled as an aisle-graph, which is a regular and constrained data structure formed by consecutive aisles where trees are arranged in a straight line. For monitoring the presence of bugs, a drone flies close to the trees and takes videos and/or pictures that will be analyzed offline. As the drone\u27s energy is limited, only a subset of locations in the orchard can be visited with a fully charged battery. Those places that are most likely to be infested should be selected to promptly detect the pest. We implemented the proposed approach on a DJI drone and evaluated its performance in the real-world environment

A Matheuristic for Multi-Depot Multi-Trip Vehicle Routing Problems

Starting from a real-life application, in this short paper, we propose the original Multi-Depot Multi-Trip Vehicle Routing Problem with Total Completion Times minimization (MDMT-VRP-TCT). For it, we propose a mathematical formulation as a MILP, design a matheuristic framework to quickly solve it, and experimentally test its performance. It is worth noting that this problem is original as in the literature its characteristics (i.e., multi-depot, multi-trip and total completion time) can be found separately, but never all together. Moreover, regardless of the application, our solution works in any case in which a multi-depot multi-trip vehicle routing problem must be solved

Recommending links to control elections via social influence

Political parties recently learned that they must use social media campaigns along with advertising on traditional media to defeat their opponents. Before the campaign starts, it is important for a political party to establish and ensure its media presence, for example by enlarging their number of connections in the social network in order to assure a larger portion of users. Indeed, adding new connections between users increases the capabilities of a social network of spreading information, which in turn can increase the retention rate and the number of new voters. In this work, we address the problem of selecting a fixed-size set of new connections to be added to a subset of voters that, with their influence, will change the opinion of the network's users about a target candidate, maximizing its chances to win the election. We provide a constant factor approximation algorithm for this problem and we experimentally show that, with few new links and small computational time, our algorithm is able to maximize the chances to make the target candidate win the elections

Link recommendation for social influence maximization

Social link recommendation systems, like "People-you-may-know" on Facebook, "Who-to-follow" on Twitter, and "Suggested-Accounts" on Instagram assist the users of a social network in establishing new connections with other users.While these systems are becoming more and more important in the growth of social media, they tend to increase the popularity of users that are already popular. Indeed, since link recommenders aim to predict user behavior, they accelerate the creation of links that are likely to be created in the future and, consequently, reinforce social bias by suggesting few (popular) users, giving few chances to most users to create new connections and increase their popularity. In this article, we measure the popularity of a user by means of her social influence, which is her capability to influence other users' opinions, and we propose a link recommendation algorithm that evaluates the links to suggest according to their increment in social influence instead of their likelihood of being created. In detail, we give a 1 - ∈ factor approximation algorithm for the problem of maximizing the social influence of a given set of target users by suggesting a fixed number of new connections considering the Linear Threshold model asmodel for diffusion.We experimentally showthat,with fewnewlinks and small computational time, our algorithm is able to increase by far the social influence of the target users. We compare our algorithm with several baselines and show that it is the most effective one in terms of increased influence

Two-loop form factors for diphoton production in quark annihilation channel with heavy quark mass dependence

Abstract We present the computation of the two-loop form factors for diphoton production in the quark annihilation channel. These quantities are relevant for the NNLO QCD corrections to diphoton production at LHC recently presented in [1]. The computation is performed retaining full dependence on the mass of the heavy quark in the loops. The master integrals are evaluated by means of differential equations which are solved exploiting the generalised power series technique

Adding edges for maximizing weighted reachability

In this paper, we consider the problem of improving the reachability of a graph. We approach the problem from a graph augmentation perspective, in which a limited set size of edges is added to the graph to increase the overall number of reachable nodes. We call this new problem theMaximum Connectivity Improvement (MCI) problem. We first show that, for the purpose of solve solving MCI, we can focus on Directed Acyclic Graphs (DAG) only. We show that approximating the MCI problem on DAG to within any constant factor greater than 1-1/e is NP-hard even if we restrict to graphs with a single source or a single sink, and the problem remains NP-complete if we further restrict to unitary weights. Finally, this paper presents a dynamic programming algorithm for the MCI problem on trees with a single source that produces optimal solutions in polynomial time. Then, we propose two polynomial-time greedy algorithms that guarantee (1-1/e)-approximation ratio on DAGs with a single source, a single sink or two sources

Drone-Truck Cooperated Delivery Under Time Varying Dynamics

Rapid technological developments in autonomous unmanned aerial vehicles (or drones) could soon lead to their large-scale implementation in the last-mile delivery of products. However, drones have a number of problems such as limited energy budget, limited carrying capacity, etc. On the other hand, trucks have a larger carrying capacity, but they cannot reach all the places easily. Intriguingly, last-mile delivery cooperation between drones and trucks can synergistically improve delivery efficiency. In this paper, we present a drone-truck co-operated delivery framework under time-varying dynamics. Our framework minimizes the total delivery time while considering low energy consumption as the secondary objective. The empirical results support our claim and show that our algorithm can help to complete the deliveries time efficiently and saves energy