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We study how the choice of input data affects simulations of positive streamers in humid air, focusing on H2O cross sections, photoionization models, and chemistry sets. Simulations are performed in air with a mole fraction of 0%, 3% or 10% H2O using an axisymmetric fluid model. Five H2O cross section sets are considered, which lead to significant differences in the resulting electron attachment coefficient. As a result, the streamer velocity can vary by up to about 50% with 10% H2O. We compare results with three photoionization models: the Naidis model for humid air, the Aints model for humid air, and the standard Zheleznyak model for dry air. With the Naidis and in particular the Aints model, there is a significant reduction in photoionization with higher humidities. This results in higher streamer velocities and maximal electric fields, and it can also cause streamer branching in our axisymmetric simulations. Three humid air chemistry sets are considered. Differences between these sets, particularly in the formation of water clusters around positive ions, cause the streamer velocity to vary by up to about 50% with 10% H2O. A sensitivity analysis is performed to identify the most important chemical reactions in these chemistries.

One way to understand planet formation is through studying the correlations between planet occurrence rates and stellar mass. However, measuring stellar mass in the red giant regime is very difficult. In particular, the spectroscopic masses of certain evolved stars, often referred to as "retired A-stars", have been questioned in the literature. Efforts to resolve this mass controversy using spectroscopy, interferometry and asteroseismology have so far been inconclusive. A recent ensemble study found a mass-dependent mass offset, but the result was based on only 16 stars. With NASA's Transiting Exoplanet Survey Satellite (TESS), we expand the investigation of the mass discrepancy to a total of 92 low-luminosity stars, synonymous with the retired A-stars. We measure their characteristic oscillation frequency, $\mathrm{\nu}_{\mathrm{max}}$, and the large frequency separation, $\mathrm{\Delta\nu}$, from their TESS photometric time series. Using these measurements and asteroseismic scaling relations, we derive asteroseismic masses and compare them with spectroscopic masses from five surveys, to comprehensively study the alleged mass-dependent mass offset. We find a mass offset between spectroscopy and seismology that increases with stellar mass. However, we note that adopting the seismic mass scale does not have a significant effect on the planet occurrence-mass-metallicity correlation for the so-called retired A-stars. We also report seismic measurements and masses for 157 higher luminosity giants (mostly helium-core-burning) from the spectroscopic surveys., Comment: 13 pages, 7 figues

Adapting pre-trained large language models to different domains in natural language processing requires two key considerations: high computational demands and model's inability to continual adaptation. To simultaneously address both issues, this paper presents COPAL (COntinual Pruning in Adaptive Language settings), an algorithm developed for pruning large language generative models under a continual model adaptation setting. While avoiding resource-heavy finetuning or retraining, our pruning process is guided by the proposed sensitivity analysis. The sensitivity effectively measures model's ability to withstand perturbations introduced by the new dataset and finds model's weights that are relevant for all encountered datasets. As a result, COPAL allows seamless model adaptation to new domains while enhancing the resource efficiency. Our empirical evaluation on a various size of LLMs show that COPAL outperforms baseline models, demonstrating its efficacy in efficiency and adaptability., Comment: ICML2024

We study radio emissions from positive streamers in air using 3D simulations, from which the radiated electric field is computed by solving Jefimenko's equations. The simulations are performed at 0.5 bar using two photoionization methods: the Helmholtz approximation for a photon density and a Monte Carlo method using discrete photons, with the latter being the most realistic. We consider cases with single streamers, streamer branching, streamers interacting with preionization and streamer-streamer encounters. We do not observe a strong VHF radio signal during or after branching, which is confirmed by lab experiments. This indicates that the current inside a streamer discharges evolves approximately continuously during branching. On the other hand, stochastic fluctuations in streamer propagation due to Monte Carlo photoionization lead to more radio emission being emitted at frequencies of 100 MHz and above. Another process that leads to such high-frequency emission is the interaction of a streamer with a weakly preionized region, which can be present due to a previous discharge. In agreement with previous work, we observe the strongest and highest-frequency emission from streamer encounters. The amount of total energy that is radiated seems to depend primarily on the background electric field, and less on the particular streamer evolution. Finally, we present approximations for the maximal current along a streamer channel and a fit formula for a streamer's current moment.

Hallucinations and delusions are often grouped together within the positive symptoms of psychosis. However, recent evidence suggests they may be driven by distinct computational and neural mechanisms. Examining the time course of their emergence may provide insights into the relationship between these underlying mechanisms. Participants from the second (N = 719) and third (N = 699) iterations of the North American Prodrome Longitudinal Study (NAPLS 2 and 3) were assessed for timing of CHR-P-level delusion and hallucination onset. Pre-onset symptom patterns in first-episode psychosis patients (FEP) from the Prevention and Early Intervention Program for Psychosis (PEPP-Montreal; N = 694) were also assessed. Symptom onset was determined at baseline assessment and the evolution of symptom patterns examined over 24 months. In all three samples, participants were more likely to report the onset of delusion-spectrum symptoms prior to hallucination-spectrum symptoms (odds ratios (OR): NAPLS 2 = 4.09; NAPLS 3 = 4.14; PEPP, Z = 7.01, P < 0.001) and to present with only delusions compared to only hallucinations (OR: NAPLS 2 = 5.6; NAPLS 3 = 11.11; PEPP = 42.75). Re-emergence of delusions after remission was also more common than re-emergence of hallucinations (Ps < 0.05), and hallucinations more often resolved first (Ps < 0.001). In both CHR-P samples, ratings of delusional ideation fell with the onset of hallucinations (P = 0.007). Delusions tend to emerge before hallucinations and may play a role in their development. Further work should examine the relationship between the mechanisms driving these symptoms and its utility for diagnosis and treatment.

In recent quantum algorithmic developments, a feedback-based approach has shown promise for preparing quantum many-body system ground states and solving combinatorial optimization problems. This method utilizes quantum Lyapunov control to iteratively construct quantum circuits. Here, we propose a substantial enhancement by implementing a protocol that uses ideas from quantum Lyapunov control and the counterdiabatic driving protocol, a key concept from quantum adiabaticity. Our approach introduces an additional control field inspired by counterdiabatic driving. We apply our algorithm to prepare ground states in one-dimensional quantum Ising spin chains. Comprehensive simulations demonstrate a remarkable acceleration in population transfer to low-energy states within a significantly reduced time frame compared to conventional feedback-based quantum algorithms. This acceleration translates to a reduced quantum circuit depth, a critical metric for potential quantum computer implementation. We validate our algorithm on the IBM cloud computer, highlighting its efficacy in expediting quantum computations for many-body systems and combinatorial optimization problems., Comment: 16 pages, 17 figures

In recent years, tasks of machine learning ranging from image processing & audio/video analysis to natural language understanding have been transformed by deep learning. The data content in all these scenarios are expressed via Euclidean space. However, a considerable amount of application data is structured in non-Euclidean space and is expressed as graphs, e.g. dealing with complicated interactions & object interdependencies. Modelling physical systems, learning molecular signatures, identifying protein interactions and predicting diseases involve utilising a model that can adapt from graph data. Graph neural networks (GNNs), specified as artificial-neural models, employ message transmission between graph nodes to represent graph dependencies and are primarily used in the non-Euclidean domain. Variants of GNN like Graph Recurrent Networks (GRN), Graph Auto Encoder (GAE), Graph Convolution Networks (GCN), Graph Adversarial Methods & Graph Reinforcement learning have exhibited breakthrough productivity on a wide range of tasks, especially in the field of bioinformatics, in recent years as a result of the rapid collection of biological network data. Apart from presenting all existing GNN models, mathematical analysis and comparison of the variants of all types of GNN have been highlighted in this survey. Graph neural networks are investigated for their potential real-world applications in various fields, focusing on Bioinformatics. Furthermore, resources for evaluating graph neural network models and accessing open-source code & benchmark data sets are included. Ultimately, we provide some (seven) proposals for future research in this rapidly evolving domain. GNNs have the potential to be an excellent tool for solving a wide range of biological challenges in bioinformatics research, as they are best represented as connected complex graphs., Comment: 39 pages, 13 figures, 21 tables

We present a protocol for detecting multipartite entanglement in itinerant many-body electronic systems using single particle Green's functions. To achieve this, we first establish a connection between the quantum Fisher information (QFI) and single particle Green's functions by constructing a set of witness operators built out of single electron creation and destruction operators in a doubled system. This set of witness operators is indexed by a momenta $k$. We compute the QFI for these witness operators and show that for thermal ensembles it can be expressed as an auto-convolution of the single particle spectral function. We then apply our framework to a one-dimensional fermionic system to showcase its effectiveness in detecting entanglement in itinerant electron models. We observe that the detected entanglement level is sensitive to the wave vector associated with witness operator. Our protocol will permit detecting entanglement in many-body systems using scanning tunneling microscopy and angle-resolved photoemission spectroscopy, two spectroscopies that measure the single particle Green's function. It offers the prospect of the experimental detection of entanglement through spectroscopies beyond the established route of measuring the dynamical spin response., Comment: 6 pages, 3 figures