Hadronization dynamics from the spectral representation of the gauge invariant quark propagator

Using the spectral representation of the quark propagator we study the Dirac decomposition of the gauge invariant quark propagator, whose imaginary part describes the hadronization of a quark as this interacts with the vacuum. We then demonstrate the formal gauge invariance of the so-called jet mass, that is of the coefficient of the chiral-odd part of the gauge invariant propagator, that can be expressed in any gauge as the first moment of the chiral-odd quark spectral function. This is therefore revealed to be a \textit{bona fide} QCD observable encoding aspects of the dynamical mass generation in the QCD vacuum, and is furthermore experimentally measurable in specific twist-3 longitudinal-transverse asymmetries in DIS and in semi-inclusive electron-positron collisions. In light-like axial gauges, we also obtain a new sum rule for the spectral function associated with the gauge fixing vector. We finally present a gauge-dependent formula that connects the second moment of the chiral-even coefficient of the quark spectral function to invariant mass generation and final state rescattering in the hadronization of a quark. Finding twist-4 experimental observables sensitive to this quantity is left for future work.Comment: Contribution to DIS2023: XXX International Workshop on Deep-Inelastic Scattering and Related Subjects, Michigan State University, USA, 27-31 March 202

Soliton Turbulence in Shallow Water Ocean Surface Waves

We analyze shallow water wind waves in Currituck Sound, North Carolina and experimentally confirm, for the first time, the presence of $soliton$ $turbulence$ in ocean waves. Soliton turbulence is an exotic form of nonlinear wave motion where low frequency energy may also be viewed as a $dense$ $soliton$ $gas$, described theoretically by the soliton limit of the Korteweg-deVries (KdV) equation, a $completely$ $integrable$ $soliton$ $system$: Hence the phrase "soliton turbulence" is synonymous with "integrable soliton turbulence." For periodic/quasiperiodic boundary conditions the $ergodic$ $solutions$ of KdV are exactly solvable by $finite$ $gap$ $theory$ (FGT), the basis of our data analysis. We find that large amplitude measured wave trains near the energetic peak of a storm have low frequency power spectra that behave as $\sim\omega^{-1}$. We use the linear Fourier transform to estimate this power law from the power spectrum and to filter $densely$ $packed$ $soliton$ $wave$ $trains$ from the data. We apply FGT to determine the $soliton$ $spectrum$ and find that the low frequency $\sim\omega^{-1}$ region is $soliton$ $dominated$. The solitons have $random$ $FGT$ $phases$, a $soliton$ $random$ $phase$ $approximation$, which supports our interpretation of the data as soliton turbulence. From the $probability$ $density$ $of$ $the$ $solitons$ we are able to demonstrate that the solitons are $dense$ $in$ $time$ and $highly$ $non$ $Gaussian$.Comment: 4 pages, 7 figure

The Covering-Assignment Problem for Swarm-powered Ad-hoc Clouds: A Distributed 3D Mapping Use-case

The popularity of drones is rapidly increasing across the different sectors of the economy. Aerial capabilities and relatively low costs make drones the perfect solution to improve the efficiency of those operations that are typically carried out by humans (e.g., building inspection, photo collection). The potential of drone applications can be pushed even further when they are operated in fleets and in a fully autonomous manner, acting de facto as a drone swarm. Besides automating field operations, a drone swarm can serve as an ad-hoc cloud infrastructure built on top of computing and storage resources available across the swarm members and other connected elements. Even in the absence of Internet connectivity, this cloud can serve the workloads generated by the swarm members themselves, as well as by the field agents operating within the area of interest. By considering the practical example of a swarm-powered 3D reconstruction application, we present a new optimization problem for the efficient generation and execution, on top of swarm-powered ad-hoc cloud infrastructure, of multi-node computing workloads subject to data geolocation and clustering constraints. The objective is the minimization of the overall computing times, including both networking delays caused by the inter-drone data transmission and computation delays. We prove that the problem is NP-hard and present two combinatorial formulations to model it. Computational results on the solution of the formulations show that one of them can be used to solve, within the configured time-limit, more than 50% of the considered real-world instances involving up to two hundred images and six drones

Heuristics for optimizing 3D mapping missions over swarm-powered ad hoc clouds

Drones have been getting more and more popular in many economy sectors. Both scientific and industrial communities aim at making the impact of drones even more disruptive by empowering collaborative autonomous behaviors -- also known as swarming behaviors -- within fleets of multiple drones. In swarming-powered 3D mapping missions, unmanned aerial vehicles typically collect the aerial pictures of the target area whereas the 3D reconstruction process is performed in a centralized manner. However, such approaches do not leverage computational and storage resources from the swarm members.We address the optimization of a swarm-powered distributed 3D mapping mission for a real-life humanitarian emergency response application through the exploitation of a swarm-powered ad hoc cloud. Producing the relevant 3D maps in a timely manner, even when the cloud connectivity is not available, is crucial to increase the chances of success of the operation. In this work, we present a mathematical programming heuristic based on decomposition and a variable neighborhood search heuristic to minimize the completion time of the 3D reconstruction process necessary in such missions. Our computational results reveal that the proposed heuristics either quickly reach optimality or improve the best known solutions for almost all tested realistic instances comprising up to 1000 images and fifteen drones

Contributions to the debate on autonomy and freedom of expression in convergence times and Open Education

[PT] O objetivo deste texto é apresentar um contributo das novas formas de comunicação, expressão, interação, colaboração e integração para as potencialidades da construção do conhecimento, por meio dos recursos educacionais abertos, na atual cultura da mThis paper aims to present a contribution of new forms of communication, expression, interaction, collaboration and integration for the potential construction of knowledge, through open educational resources, in the current culture of mobility and conver

Differential expression of exosomal microRNAs in prefrontal cortices of schizophrenia and bipolar disorder patients

Exosomes are cellular secretory vesicles containing microRNAs (miRNAs). Once secreted, exosomes are able to attach to recipient cells and release miRNAs potentially modulating the function of the recipient cell. We hypothesized that exosomal miRNA expression in brains of patients diagnosed with schizophrenia (SZ) and bipolar disorder (BD) might differ from controls, reflecting either disease-specific or common aberrations in SZ and BD patients. The sources of the analyzed samples included McLean 66 Cohort Collection (Harvard Brain Tissue Resource Center), BrainNet Europe II (BNE, a consortium of 18 brain banks across Europe) and Boston Medical Center (BMC). Exosomal miRNAs from frozen postmortem prefrontal cortices with well-preserved RNA were isolated and submitted to profiling by Luminex FLEXMAP 3D microfluidic device. Multiple statistical analyses of microarray data suggested that certain exosomal miRNAs were differentially expressed in SZ and BD subjects in comparison to controls. RT-PCR validation confirmed that two miRNAs, miR-497 in SZ samples and miR-29c in BD samples, have significantly increased expression when compared to control samples. These results warrant future studies to evaluate the potential of exosome-derived miRNAs to serve as biomarkers of SZ and BD

A tomographic approach to assessing the possibility of ring shake presence in standing chestnut trees

AbstractRing shake is a widespread phenomenon affecting a great number of species of both softwood and hardwood and is found in trees grown in temperate and tropical climates. Chestnut (Castanea sativaMill.) represents one of the most important hardwood timbers that is very often affected by ring shake. This defect seems to be the only real limit to the spread and use of chestnut wood worldwide on a scale closer to the availability of this wood. The aim of this study was to examine the potential of tomographic measurement as a non-destructive method for predicting the possibility of the presence of ring shake in standing chestnut trees. For this reason, the experiments were carried out in a chestnut coppice stand where one hundred chestnut standards were monitored using an acoustic tomographic device, and subsequently harvested by a local company and cross-sectioned corresponding to the acoustic tests. This work proposed an applied approach to predicting and determining wood quality (sound wood vs. defective wood) from tomographic data. The model, based on a non-linear approach, showed that sonic tomography can identify ring shake in a tree trunk without affecting its biological activity, overcoming the difficulties of predicting ring shake using only visual inspection

Unsupervised machine learning approaches to the qq-state Potts model

In this paper with study phase transitions of the $q$-state Potts model, through a number of unsupervised machine learning techniques, namely Principal Component Analysis (PCA), $k$-means clustering, Uniform Manifold Approximation and Projection (UMAP), and Topological Data Analysis (TDA). Even though in all cases we are able to retrieve the correct critical temperatures $T_c(q)$, for $q = 3, 4$ and $5$, results show that non-linear methods as UMAP and TDA are less dependent on finite size effects, while still being able to distinguish between first and second order phase transitions. This study may be considered as a benchmark for the use of different unsupervised machine learning algorithms in the investigation of phase transitions.Comment: Added computation of critical exponents; exposition improve