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We explore multi-log grasping using reinforcement learning and virtual visual servoing for automated forwarding in a simulated environment. Automation of forest processes is a major challenge, and many techniques regarding robot control pose different challenges due to the unstructured and harsh outdoor environment. Grasping multiple logs involves various problems of dynamics and path planning, where understanding the interaction between the grapple, logs, terrain, and obstacles requires visual information. To address these challenges, we separate image segmentation from crane control and utilise a virtual camera to provide an image stream from reconstructed 3D data. We use Cartesian control to simplify domain transfer to real-world applications. Because log piles are static, visual servoing using a 3D reconstruction of the pile and its surroundings is equivalent to using real camera data until the point of grasping. This relaxes the limits on computational resources and time for the challenge of image segmentation, and allows for data collection in situations where the log piles are not occluded. The disadvantage is the lack of information during grasping. We demonstrate that this problem is manageable and present an agent that is 95% successful in picking one or several logs from challenging piles of 2–5 logs.

A bstract The Light Dark Matter eXperiment (LDMX) is proposed to employ a thin tungsten target and a multi-GeV electron beam to carry out a missing momentum search for the production of dark matter candidate particles. We study the sensitivity for a complementary missing-energy-based search using the LDMX Electromagnetic Calorimeter as an active target with a focus on early running. In this context, we construct an event selection from a limited set of variables that projects sensitivity into previously-unexplored regions of light dark matter phase space — down to an effective dark photon interaction strength y of approximately 2 × 10 − 13 (5 × 10 − 12 ) for a 1 MeV (10 MeV) dark matter candidate mass.

A bstract The Light Dark Matter eXperiment (LDMX) is an electron-beam fixed-target experiment designed to achieve comprehensive model independent sensitivity to dark matter particles in the sub-GeV mass region. An upgrade to the LCLS-II accelerator will increase the beam energy available to LDMX from 4 to 8 GeV. Using detailed GEANT4-based simulations, we investigate the effect of the increased beam energy on the capabilities to separate signal and background, and demonstrate that the veto methodology developed for 4 GeV successfully rejects photon-induced backgrounds for at least 2 × 10 14 electrons on target at 8 GeV.

Although, for current laser pulse energies, the weakly nonlinear regime of laser wakefield acceleration is known to be the optimal for reaching the highest possible electron energies, the capabilities of upcoming large laser systems will provide the possibility of running highly nonlinear regimes of laser pulse propagation in underdense or near-critical plasmas. Using an extended particle-in-cell (PIC) model that takes into account all the relevant physics, we show that such regimes can be implemented with external guiding for a relatively long distance of propagation and allow for the stable transformation of laser energy into other types of energy, including the kinetic energy of a large number of high energy electrons and their incoherent emission of photons. This is despite the fact that the high intensity of the laser pulse triggers a number of new mechanisms of energy depletion, which we investigate systematically.

The theory for nonlinear three-wave interaction in magnetized plasmas is reconsidered using quantum hydrodynamics. The general coupling coefficients are calculated for the generalized Bohm de Broglie term. It is found that the Manley–Rowe relations are fulfilled only if the form of the particle dispersive term coincides with the standard expression. The implications of our results are discussed.

Chalcopyrite-based solar cells have reached an efficiency of 23.35%, yet further improvements have been challenging. Here we present a 23.64% certified efficiency for a (Ag,Cu)(In,Ga)Se 2 solar cell, achieved through the implementation of a series of strategies. We introduce a relatively high amount of silver ([Ag]/([Ag] + [Cu]) = 0.19) into the absorber and implement a ‘hockey stick’-like gallium profile with a high concentration of Ga close to the molybdenum back contact and a lower, constant concentration in the region closer to the CdS buffer layer. This kind of elemental profile minimizes lateral and in-depth bandgap fluctuations, reducing losses in open-circuit voltage. In addition, the resulting bandgap energy is close to the local optimum of 1.15 eV. We apply a RbF post-deposition treatment that leads to the formation of a Rb–In–Se phase, probably RbInSe 2 , passivating the absorber surface. Finally, we discuss future research directions to reach 25% efficiency. Keller et al. use high-concentration silver alloying and steep gallium grading close to the back contact to minimize bandgap fluctuations and thus voltage losses, achieving 23.6% certified efficiency in Cu(In,Ga)Se 2 solar cells.

Systems for transport and processing of granular media are challenging to analyse, operate and optimise. In the mining and mineral processing industries, these systems are chains of processes with a complex interplay among the equipment, control and processed material. The material properties have natural variations that are usually only known at certain locations. Therefore, we explored a material-oriented approach to digital twins with a particle representation of the granular media. In digital form, the material is treated as pseudo-particles, each representing a large collection of real particles of various sizes, shapes and mineral properties. Movements and changes in the state of the material are determined by the combined data from control systems, sensors, vehicle telematics and simulation models at locations where no real sensors could see. The particle-based representation enables material tracking along the chain of processes. Each digital particle can act as a carrier of observational data generated by the equipment as it interacts with the real material. This make it possible to better learn the material properties from process observations and to predict the effect on downstream processes. We tested the technique on a mining simulator and demonstrated the analysis that can be performed using data from cross-system material tracking.

The validation of a theory is commonly based on appealing to clearly distinguishable and describable features in properly reduced experimental data, while the use of ab-initio simulation for interpreting experimental data typically requires complete knowledge about initial conditions and parameters. We here apply the methodology of using machine learning for overcoming these natural limitations. We outline some basic universal ideas and show how we can use them to resolve long-standing theoretical and experimental difficulties in the problem of high-intensity laser-plasma interactions. In particular we show how an artificial neural network can \"read\" features imprinted in laser-plasma harmonic spectra that are currently analysed with spectral interferometry.

The validation of a theory is commonly based on appealing to clearly distinguishable and describable features in properly reduced experimental data, while the use of ab-initio simulation for interpreting experimental data typically requires complete knowledge about initial conditions and parameters. We here apply the methodology of using machine learning for overcoming these natural limitations. We outline some basic universal ideas and show how we can use them to resolve long-standing theoretical and experimental difficulties in the problem of high-intensity laser-plasma interactions. In particular we show how an artificial neural network can “read” features imprinted in laser-plasma harmonic spectra that are currently analysed with spectral interferometry.

Semi-supervised learning (SSL) is a learning framework that enables the use of unlabeled data with labeled data. These methods play a crucial role in reducing the burden of human labeling in training deep learning models. Many methods for SSL learn from unlabeled data through confidence-based pseudo-labeling. This technique involves assigning artificial labels to unlabeled data based on model predictions, given that these predictions exceed a confidence threshold. A drawback of this approach is that large parts of data may be ignored. This work proposes a self-supervised component for these frameworks to enable learning from all unlabeled data. The proposed self-supervision involves aligning feature predictions across weak and strong data augmentations for each unlabeled sample. We show that this approach, Double Match, leads to improved training speed and accuracy on many benchmark datasets.SSL is often studied in the closed-set scenario, where we assume that unlabeled data only contain classes present in the labeled data. More realistically, there is a risk that unlabeled data contain unseen classes, corrupted data, or outliers in other forms. This setting is referred to as open-set semi-supervised learning (OSSL). Many existing methods for OSSL use a procedure that involves selecting samples from unlabeled data that likely belong to the known classes, for inclusion in a traditional SSL objective. This work proposes an alternative approach, SeFOSS, that utilizes all unlabeled data through the inclusion of the self-supervised component proposed by Double Match. Additionally, SeFOSS uses an energy-based method for classifying data as in-distribution (ID) or out-of-distribution (OOD). Experimental evaluation shows that SeFOSS achieves strong results for both closed-set accuracy and OOD detection in many open-set scenarios. Additionally, our results indicate that traditional methods for (closed-set) SSL may perform better in the open-set scenario than what has been previously suggested by other works.Furthermore, this work proposes another method for OSSL: the Beta-model. This method proposes a novel score for ID/OOD classification and introduces the use of the expectation-maximization algorithm in OSSL, for estimating conditional distributions of scores given ID or OOD data. This method demonstrates state-of-the-art results on many benchmark problems for OSSL.