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Predicting the emergence of multiple chronic conditions (MCC) is crucial for early intervention and personalized healthcare, as MCC significantly impacts patient outcomes and healthcare costs. Graph neural networks (GNNs) are effective methods for modeling complex graph data, such as those found in MCC. However, a significant challenge with GNNs is their reliance on an existing graph structure, which is not readily available for MCC. To address this challenge, we propose a novel generative framework for GNNs that constructs a representative underlying graph structure by utilizing the distribution of the data to enhance predictive analytics for MCC. Our framework employs a graph variational autoencoder (GVAE) to capture the complex relationships in patient data. This allows for a comprehensive understanding of individual health trajectories and facilitates the creation of diverse patient stochastic similarity graphs while preserving the original feature set. These variations of patient stochastic similarity graphs, generated from the GVAE decoder, are then processed by a GNN using a novel Laplacian regularization technique to refine the graph structure over time and improves the prediction accuracy of MCC. A contextual Bandit is designed to evaluate the stochastically generated graphs and identify the best-performing graph for the GNN model iteratively until model convergence. We validate the performance of the proposed contextual Bandit algorithm against $\varepsilon$-Greedy and multi-armed Bandit algorithms on a large cohort (n = 1,592) of patients with MCC. These advancements highlight the potential of the proposed approach to transform predictive healthcare analytics, enabling a more personalized and proactive approach to MCC management.

On the 29th of May 2023, the LIGO-Virgo-KAGRA Collaboration observed a compact binary coalescence event consistent with a neutron star-black hole merger, though the heavier object of mass 2.5-4.5 $M_\odot$ would fall into the purported lower mass gap. An alternative explanation for apparent observations of events in this mass range has been suggested as strongly gravitationally lensed binary neutron stars. In this scenario, magnification would lead to the source appearing closer and heavier than it really is. Here, we investigate the chances and possible consequences for the GW230529 event to be gravitationally lensed. We find this would require high magnifications and we obtain low rates for observing such an event, with a relative fraction of lensed versus unlensed observed events of $2 \times 10^{-3}$ at most. When comparing the lensed and unlensed hypotheses accounting for the latest rates and population model, we find a 1/58 chance of lensing, disfavoring this option. Moreover, when the magnification is assumed to be strong enough to bring the mass of the heavier binary component below the standard limits on neutron star masses, we find high probability for the lighter object to have a sub-solar mass, making the binary even more exotic than a mass-gap neutron star-black hole system. Even when the secondary is not sub-solar, its tidal deformability would likely be measurable, which is not the case for GW230529. Finally, we do not find evidence for extra lensing signatures such as the arrival of additional lensed images, type-II image dephasing, or microlensing. Therefore, we conclude it is unlikely for GW230529 to be a strongly gravitationally lensed binary neutron star signal., Comment: 15 pages, 11 figures

Interfacial ferroelectricity, prevalent in various parallel-stacked layered materials, allows switching of out-of-plane ferroelectric order by in-plane sliding of adjacent layers. Its resilience against doping potentially enables next-generation storage and logic devices. However, studies have been limited to indirect sensing or visualization of ferroelectricity. For transition metal dichalcogenides, there is little knowledge about the influence of ferroelectric order on their intrinsic valley and excitonic properties. Here, we report direct probing of ferroelectricity in few-layer 3R-MoS$_2$ using reflectance contrast spectroscopy. Contrary to a simple electrostatic perception, layer-hybridized excitons with out-of-plane electric dipole moment remain decoupled from ferroelectric ordering, while intralayer excitons with in-plane dipole orientation are sensitive to it. Ab initio calculations identify stacking-specific interlayer hybridization leading to this asymmetric response. Exploiting this sensitivity, we demonstrate optical readout and control of multi-state polarization with hysteretic switching in a field-effect device. Time-resolved Kerr ellipticity reveals a direct correspondence between spin-valley dynamics and stacking order.

We study the regularity of weak solutions to nonlocal in time subdiffusion equations for a wide class of weakly singular kernels appearing in the generalised fractional derivative operator. We prove a weak Harnack inequality for nonnegative weak supersolutions and Holder continuity of weak solutions to such problems. Our results substantially extend the results from our previous work [12] by leaving the framework of distributed order fractional time derivatives and considering a general PC kernel and by also allowing for an inhomogeneity in the PDE from a Lebesgue space of mixed type., Comment: 52 pages

While $U$(1) spin liquids have been extensively studied in both quantum and classical regimes, exact classical $\mathbb{Z}_2$ spin liquids arising from models with nearest-neighbor, bilinear spin interactions are still rare. In this Letter, we explore the four-color Kitaev model as a minimal model for stabilizing classical $\mathbb{Z}_2$ spin liquids across a broad family of tricoordinated lattices. By formulating a $\mathbb{Z}_2$ lattice gauge theory, we identify this spin liquid as being described by an emergent Gauss's law with effective charge-2 condensation, and deconfined fractionalized bond-charge excitations. We complement our findings with Monte Carlo simulations, revealing a crossover from a high-temperature paramagnet to a low-temperature liquid phase characterized by residual entropy, classical $\mathbb{Z}_2$ flux order, and diffuse spin structure factors., Comment: 6 pages, 4 figures

Machine-learned interatomic potentials (MILPs) are rapidly gaining interest for molecular modeling, as they provide a balance between quantum-mechanical level descriptions of atomic interactions and reasonable computational efficiency. However, questions remain regarding the stability of simulations using these potentials, as well as the extent to which the learned potential energy function can be extrapolated safely. Past studies have reported challenges encountered when MILPs are applied to classical benchmark systems. In this work, we show that some of these challenges are related to the characteristics of the training datasets, particularly the inclusion of rigid constraints. We demonstrate that long stability in simulations with MILPs can be achieved by generating unconstrained datasets using unbiased classical simulations if the fast modes are correctly sampled. Additionally, we emphasize that in order to achieve precise energy predictions, it is important to resort to enhanced sampling techniques for dataset generation, and we demonstrate that safe extrapolation of MILPs depends on judicious choices related to the system's underlying free energy landscape and the symmetry features embedded within the machine learning models.

The implementation of realistic quantum devices requires a solid understanding of the nonlocal resources present in quantum channels, and the effects of decoherence on them. Here we quantify nonlocality of bipartite quantum channels and identify its component resisting the effects of dephasing noise. Despite its classical nature, we demonstrate that the latter plays a relevant role in performing quantum protocols, such as state transformations and quantum coding for noisy communication. In the converse direction, we show that simulating certain stochastic processes with quantum channels undergoing decoherence has a communication advantage with respect to their classical simulation.

Schenkerian Analysis (SchA) is a uniquely expressive method of music analysis, combining elements of melody, harmony, counterpoint, and form to describe the hierarchical structure supporting a work of music. However, despite its powerful analytical utility and potential to improve music understanding and generation, SchA has rarely been utilized by the computer music community. This is in large part due to the paucity of available high-quality data in a computer-readable format. With a larger corpus of Schenkerian data, it may be possible to infuse machine learning models with a deeper understanding of musical structure, thus leading to more "human" results. To encourage further research in Schenkerian analysis and its potential benefits for music informatics and generation, this paper presents three main contributions: 1) a new and growing dataset of SchAs, the largest in human- and computer-readable formats to date (>140 excerpts), 2) a novel software for visualization and collection of SchA data, and 3) a novel, flexible representation of SchA as a heterogeneous-edge graph data structure.

Acoustic waves (AW) have recently emerged as an energy-efficient ice removal procedure compatible with functional and industrial-relevant substrates. However, critical aspects at fundamental and experimental levels have yet to be disclosed to optimize their operational conditions. Identifying the processes and mechanisms by which different types of AWs induce de-icing are some of these issues. Herein, using model LiNbO3 systems and two types of interdigitated transducers, we analyze the de-icing and anti-icing efficiencies and mechanisms driven by Rayleigh surface acoustic waves (R-SAW) and Lamb waves with 120 and 510 um wavelengths, respectively. Through the experimental analysis of de-icing and active anti-icing processes and the finite element simulation of the AW generation, propagation, and interaction with small ice aggregates, we disclose that Lamb waves are more favorable than R-SAWs to induce de-icing and/or prevent the freezing of droplets. Prospects for applications of this study are supported by proof of concept experiments, including de-icing in an ice wind tunnel, demonstrating that Lamb waves can efficiently remove ice layers covering large LN substrates. Results indicate that the de-icing mechanism may differ for Lamb waves or R-SAWs and that the wavelength must be considered as an important parameter for controlling the efficiency.

Surrogate models combining dimensionality reduction and regression techniques are essential to reduce the need for costly high-fidelity CFD data. New approaches using $\beta$-Variational Autoencoder ($\beta$-VAE) architectures have shown promise in obtaining high-quality low-dimensional representations of high-dimensional flow data while enabling physical interpretation of their latent spaces. We propose a surrogate model based on latent space regression to predict pressure distributions on a transonic wing given the flight conditions: Mach number and angle of attack. The $\beta$-VAE model, enhanced with Principal Component Analysis (PCA), maps high-dimensional data to a low-dimensional latent space, showing a direct correlation with flight conditions. Regularization through $\beta$ requires careful tuning to improve the overall performance, while PCA pre-processing aids in constructing an effective latent space, improving autoencoder training and performance. Gaussian Process Regression is used to predict latent space variables from flight conditions, showing robust behavior independent of $\beta$, and the decoder reconstructs the high-dimensional pressure field data. This pipeline provides insight into unexplored flight conditions. Additionally, a fine-tuning process of the decoder further refines the model, reducing dependency on $\beta$ and enhancing accuracy. The structured latent space, robust regression performance, and significant improvements from fine-tuning collectively create a highly accurate and efficient surrogate model. Our methodology demonstrates the effectiveness of $\beta$-VAEs for aerodynamic surrogate modeling, offering a rapid, cost-effective, and reliable alternative for aerodynamic data prediction., Comment: 18 pages, 12 figures

Symmetry-protected topological phases have fundamentally changed our understanding of quantum matter. An archetypal example of such a quantum phase of matter is the Haldane phase, containing the spin-1 Heisenberg chain. The intrinsic quantum nature of such phases, however, often makes it challenging to study them using classical means. Here, we use trapped-ion qutrits to natively engineer spin-1 chains within the Haldane phase. Using a scalable, deterministic procedure to prepare the Affleck-Kennedy-Lieb-Tasaki (AKLT) state within the Haldane phase, we study the topological features of this system on a qudit quantum processor. Notably, we verify the long-range string order of the state, despite its short-range correlations, and observe spin fractionalization of the physical spin-1 particles into effective qubits at the chain edges, a defining feature of this system. The native realization of Haldane physics on a qudit quantum processor and the scalable preparation procedures open the door to the efficient exploration of a wide range of systems beyond spin-1/2, Comment: 12 pages, 6 figures

Quantum phase transitions offer profound insights into fundamental quantum phenomena and enhance our understanding of complex materials and systems. However, identifying quantum phase transitions in the absence of conventional order parameters poses a significant challenge. To address this, we utilize reduced fidelity susceptibility (RFS) vector field to construct phase diagrams of various quantum systems and then demonstrate its efficacy in reproducing the phase diagrams of established models with known order parameter. To this end, we propose a new method for discovering the necessary order parameters for a given quantum model and illustrate its capability by identifying a suitable order parameter for the Axial Next Nearest Neighbour Interaction (ANNNI) Model. Our analysis, which includes decomposing the observable into its eigen-projectors alongside the finite-size scaling, confirms that our method successfully can determine order parameters and thus its capable of characterizing quantum phase transitions.

What latent features are encoded in language model (LM) representations? Recent work on training sparse autoencoders (SAEs) to disentangle interpretable features in LM representations has shown significant promise. However, evaluating the quality of these SAEs is difficult because we lack a ground-truth collection of interpretable features that we expect good SAEs to recover. We thus propose to measure progress in interpretable dictionary learning by working in the setting of LMs trained on chess and Othello transcripts. These settings carry natural collections of interpretable features -- for example, "there is a knight on F3" -- which we leverage into $\textit{supervised}$ metrics for SAE quality. To guide progress in interpretable dictionary learning, we introduce a new SAE training technique, $\textit{p-annealing}$, which improves performance on prior unsupervised metrics as well as our new metrics., Comment: Oral paper (top 5%) at the ICML 2024 Mechanistic Interpretability Workshop

Canard cascading (CC) is observed in dynamical networks with global adaptive coupling. It is a fast-slow phenomenon characterized by a recurrent sequence of fast transitions between distinct and slowly evolving quasi-stationary states. In this letter, we uncover the dynamical mechanisms behind CC, using an illustrative example of globally and adaptively coupled semiconductor lasers, where CC represents sequential switching on and off the lasers. Firstly, we show that CC is a robust and truly adaptive network effect that is scalable with network size and does not occur without adaptation. Secondly, we uncover multiple saddle slow manifolds (unstable quasi-stationary states) linked by heteroclinic orbits (fast transitions) in the phase space of the system. This allows us to identify CC with a novel heteroclinic canard orbit that organises different unstable quasi-stationary states into an intricate fast-slow limit cycle. Although individual quasi-stationary states are unstable (saddles), the CC cycle as a whole is attractive and robust to parameter changes.

Gravitational lensing has empowered telescopes to discover astronomical objects that are otherwise out of reach without being highly magnified by foreground structures. While we expect gravitational waves (GWs) from compact binary coalescences to also experience lensing, the phenomenology of highly magnified GWs has not been fully exploited. In this letter, we fill this gap and explore the observational signatures of these highly magnified GWs. We find that these signatures are robust against modeling details and can be used as smoking-gun evidence to confirm the detection of lensing of GWs without any electromagnetic observation. Additionally, diffraction becomes important in some cases, which limits the maximum possible magnification and gives waveform signatures of lensing that can only be observed by GW detectors. Even with current-generation observatories, we are already sensitive to these highly magnified GWs and can use them to probe the high-redshift Universe beyond the usual horizon., Comment: 6 pages, 3 figures

We study van der Waals heterostructures consisting of monolayer MoSe$_2$ and few-layer Cr$_2$Ge$_2$Te$_6$ fully encapsulated in hexagonal Boron Nitride using low-temperature photoluminescence and polar magneto-optic Kerr effect measurements. Photoluminescence characterization reveals a partial quenching and a change of the exciton-trion emission ratio in the heterostructure as compared to the isolated MoSe$_2$ monolayer. Under circularly polarized excitation, we find that the exciton-trion emission ratio depends on the relative orientation of excitation helicity and Cr$_2$Ge$_2$Te$_6$ magnetization, even though the photoluminescence emission itself is unpolarized. This observation hints at an ultrafast, spin-dependent interlayer charge transfer that competes with exciton and trion formation and recombination.

It is well known how to define the operator $Q$ for the total charge (i.e., positron number minus electron number) on the standard Hilbert space of the second-quantized Dirac equation. Here we ask about operators $Q_A$ representing the charge content of a region $A\subseteq \mathbb{R}^3$ in 3d physical space. There is a natural formula for $Q_A$ but, as we explain, there are difficulties about turning it into a mathematically precise definition. First, $Q_A$ can be written as a series but its convergence seems hopeless. Second, we show for some choices of $A$ that if $Q_A$ could be defined then its domain could not contain either the vacuum vector or any vector obtained from the vacuum by applying a polynomial in creation and annihilation operators. Both observations speak against the existence of $Q_A$ for generic $A$., Comment: 19 pages LaTeX

1 ps timing resolution is the entry point to signature based searches relying on secondary/tertiary vertices and particle identification. We describe a preliminary design for PSEC5, an 8-channel 40 GS/s waveform-sampling ASIC in the TSMC 65 nm process targetting 1 ps resolution at 20 mW power per channel. Each channel consists of four fast and one slow switched capacitor arrays (SCA), allowing ps time resolution combined with a long effective buffer. Each fast SCA is 1.6 ns long and has a nominal sampling rate of 40 GS/s. The slow SCA is 204.8 ns long and samples at 5 GS/s. Recording of the analog data for each channel is triggered by a fast discriminator capable of multiple triggering during the window of the slow SCA. To achieve a large dynamic range, low leakage, and high bandwidth, the SCA sampling switches are implemented as 2.5 V nMOSFETs controlled by 1.2 V shift registers. Stored analog data are digitized by an external ADC at 10 bits or better. Specifications on operational parameters include a 4 GHz analog bandwidth and a dead time of 20 microseconds, corresponding to a 50 kHz readout rate, determined by the choice of the external ADC., Comment: 16th Pisa Meeting on Advanced Detectors

Exoplanets with masses between Earth and Neptune are amongst the most commonly observed, yet their properties are poorly constrained. Their transmission spectra are often featureless, which indicate either high-altitude clouds or a high atmospheric metallicity. The archetypical warm Neptune GJ 436b is such a planet showing a flat transmission spectrum in observations with the Hubble Space Telescope (HST). Ground-based high-resolution spectroscopy (HRS) effectively probes exoplanet atmospheres at higher altitudes and can therefore be more sensitive to absorption coming from above potential cloud decks. In this paper we aim to investigate this for the exoplanet GJ 436b. We present new CRIRES+ HRS transit data of GJ 436b. Three transits were observed, but since two were during bad weather conditions, only one transit was analyzed. The radiative transfer code petitRADTRANS was used to create atmospheric models for cross-correlation and signal-injection purposes, including absorption from H$_2$O, CH$_4$, and CO. No transmission signals were detected, but atmospheric constraints can be derived. Injection of artificial transmission signals indicate that if GJ 436b would have a cloud deck at pressures P>10 mbar and a <300$\times$ solar metallicity, these CRIRES+ observations should have resulted in a detection. We estimate that the constraints presented here from one ground-based HRS transit are slightly better than those obtained with four HST transits. Combining HRS data from multiple transits is an interesting avenue for future studies of exoplanets with high-altitude clouds., Comment: Accepted for publication by A&A on July 5th, 2024

The Extremely Large Telescopes (ELTs), thanks to their large apertures and cutting-edge Multi-Conjugate Adaptive Optics (MCAO) systems, promise to deliver sharper and deeper data even than the JWST. SHARP is a concept study for a near-IR (0.95-2.45 $\mu$m) spectrograph conceived to fully exploit the collecting area and the angular resolution of the upcoming generation of ELTs. In particular, SHARP is designed for the 2nd port of MORFEO@ELT. Composed of a Multi-Object Spectrograph, NEXUS, and a multi-Integral Field Unit, VESPER, MORFEO-SHARP will deliver high angular ($\sim$30 mas) and spectral (R$\simeq$300, 2000, 6000, 17000) resolution, outperforming NIRSpec@JWST (100 mas). SHARP will enable studies of the nearby Universe and the early Universe in unprecedented detail. NEXUS is fed by a configurable slit system deploying up to 30 slits with $\sim$2.4 arcsec length and adjustable width, over a field of about 1.2"$\times$1.2" (35 mas/pix). Each slit is fed by an inversion prism able to rotate by an arbitrary angle the field that can be seen by the slit. VESPER is composed of 12 probes of 1.7"$\times$1.5" each (spaxel 31 mas) probing a field 24"$\times$70". SHARP is conceived to exploit the ELTs apertures reaching the faintest flux and the sharpest angular resolution by joining the sensitivity of NEXUS and the high spatial sampling of VESPER to MORFEO capabilities. This article provides an overview of the scientific design drivers, their solutions, and the resulting optical design of the instrument achieving the required optical performance., Comment: Proceedings of the SPIE Astronomical Telescopes and Instrumentation 2024, Volume 13096, Paper No. 130965I, 11 pp, 11 figs

We introduce a dataset comprising commercial machine translations, gathered weekly over six years across 12 translation directions. Since human A/B testing is commonly used, we assume commercial systems improve over time, which enables us to evaluate machine translation (MT) metrics based on their preference for more recent translations. Our study confirms several previous findings in MT metrics research and demonstrates the dataset's value as a testbed for metric evaluation. We release our code at https://github.com/gjwubyron/Evo

The holographic Einstein-Maxwell-dilaton model is employed to map state-of-the-art lattice QCD thermodynamics data from the temperature ($T$) axis towards the baryon-chemical potential ($\mu_B$) axis aimed at gaining a warm equation of state (EoS) of deconfined QCD matter which can be supplemented with a cool and confined part suitable for subsequent compact (neutron) star (merger) investigations. The model exhibits a critical end point (CEP) at $T_\mathrm{CEP} = \mathcal{O}(100)$ MeV and $\mu_{B \, \mathrm{CEP}} = 500 \ldots 700$ MeV with emerging first-order phase transition (FOPT) curve which extends to large values of $\mu_B$ without approaching the $\mu_B$ axis. We consider the impact and peculiarities of the related phase structure on the EoS for the employed dilaton potential and dynamical coupling parameterizations. These seem to prevent to design an overall trustable EoS without recourse to hybrid constructions.

We provide a unified framework to certify nonlocal properties of quantum channels from the correlations obtained in measurement protocols. Our approach gathers and extends fully- and semi-device independent methods for this purpose. We study the effect of different models of dephasing noise, some of which are shown to generate nonlocality and entanglement in special cases. In the extreme case of complete dephasing, the measurement protocols discussed yield particularly simple tests to certify nonlocality. These are based on the relations between bipartite quantum channels and their classical analogs: bipartite stochastic matrices defining conditional distributions.

We present SignCLIP, which re-purposes CLIP (Contrastive Language-Image Pretraining) to project spoken language text and sign language videos, two classes of natural languages of distinct modalities, into the same space. SignCLIP is an efficient method of learning useful visual representations for sign language processing from large-scale, multilingual video-text pairs, without directly optimizing for a specific task or sign language which is often of limited size. We pretrain SignCLIP on Spreadthesign, a prominent sign language dictionary consisting of ~500 thousand video clips in up to 44 sign languages, and evaluate it with various downstream datasets. SignCLIP discerns in-domain signing with notable text-to-video/video-to-text retrieval accuracy. It also performs competitively for out-of-domain downstream tasks such as isolated sign language recognition upon essential few-shot prompting or fine-tuning. We analyze the latent space formed by the spoken language text and sign language poses, which provides additional linguistic insights. Our code and models are openly available.

Due to the increasing share of renewable energy resources and the emergence of couplings of different energy carrier grids, which may support the electricity networks by providing additional flexibility, conducting research on the properties of multi-energy systems is necessary. Primarily to keep stable grid operation and provide efficient planning, the resilience of such systems against low-probability, high-impact events is central. Previous steady-state resilience studies of electricity grids also involved investigating the topological attributes from a complex network theory perspective. However, this work aims to determine the influence of complex topological attributes on the resilience of coupled energy grids. To achieve this, we set up a Monte Carlo simulation to calculate the load-shedding performance indicator for the grids when affected by high-impact events. This indicator is used to calculate resilience metrics, which express the influence of the grids on each other. The metrics are the base to search for correlations between centrality/vitality metrics and the resilience impact metric. We apply our method to a case study based on a benchmark electricity grid. Our results show that, first, our impact metric is feasible for determining the influences of the network on each other. Second, we show that increasing coupling densities can lead to lower resilience in single-carrier grids. Third, it is apparent that centrality influences the impact of the grid components' resilience.

Children of undocumented immigrants exist in permanent limbo. They are fearful every day that their parents will be deported and wondering if they have any future in the country. Many of these students identify as Latinx. Instead of focusing on their education, these students struggle with this uncertainty and as a result are often absent from school or inattentive. Their teachers also struggle to motivate them and sometimes to protect them. The so-called broken immigration system hurts schools and creates victims across the spectrum of race and ethnicity in the United States, but it is especially acute for these students. This collaborative research brief updates and builds on a previous analysis of a 2017-2018 survey examining the harmful impacts of immigration enforcement on Latinx children of undocumented immigrants. It summarizes key findings from "Schools Under Siege: The Impact of Immigration Enforcement on Educational Equity," but updates the analysis with more recent data. [Additional collaborators of the research brief are the Center for the Transformation of Schools, School of Education & Information Studies, UCLA and the Latino Policy & Politics Institute, Meyer and Renee Luskin School of Public Affairs, UCLA.]

Staff scheduling is a well-known problem in operations research and finds its application at hospitals, airports, supermarkets, and many others. Its goal is to assign shifts to staff members such that a certain objective function, e.g. revenue, is maximized. Meanwhile, various constraints of the staff members and the organization need to be satisfied. Typically in staff scheduling problems, there are hard constraints on the minimum number of employees that should be available at specific points of time. Often multiple hard constraints guaranteeing the availability of specific number of employees with different roles need to be considered. Staff scheduling for demand-responsive services, such as, e.g., ride-pooling and ride-hailing services, differs in a key way from this: There are often no hard constraints on the minimum number of employees needed at fixed points in time. Rather, the number of employees working at different points in time should vary according to the demand at those points in time. Having too few employees at a point in time results in lost revenue, while having too many employees at a point in time results in not having enough employees at other points in time, since the total personnel-hours are limited. The objective is to maximize the total reward generated over a planning horizon, given a monotonic relationship between the number of shifts active at a point in time and the instantaneous reward generated at that point in time. This key difference makes it difficult to use existing staff scheduling algorithms for planning shifts in demand-responsive services. In this article, we present a novel approach for modelling and solving staff scheduling problems for demand-responsive services that optimizes for the relevant reward function.

The phenomenology of hadronic states at high energy is well described in the framework of Quantum Chromodynamics. The theory, well established by now, cannot be applied to the description of quark-antiquark states at low energy unless their degrees of freedom are treated as effective ones, a condition resulting from confinement. Own to the similarities with other quantum many-body systems we have adopted a non-perturbative scheme where quarks are treated as quasiparticles, which further interact between them, allowing meson-like states to be described as collective superposition of quasiparticle-pairs. The results of the calculations, performed in the context of the random phase approximation method, show that this scheme is a suitable one to describe meson states up to energies of the order of couple of GeV.

Standard datasets are frequently used to train and evaluate Machine Learning models. However, the assumed standardness of these datasets leads to a lack of in-depth discussion on how their labels match the derived categories for the respective use case. In other words, the standardness of the datasets seems to fog coherency and applicability, thus impeding the trust in Machine Learning models. We propose to adopt Grounded Theory and Hypotheses Testing through Visualization as methods to evaluate the match between use case, derived categories, and labels of standard datasets. To showcase the approach, we apply it to the 20 Newsgroups dataset and the MNIST dataset. For the 20 Newsgroups dataset, we demonstrate that the labels are imprecise. Therefore, we argue that neither a Machine Learning model can learn a meaningful abstraction of derived categories nor one can draw conclusions from achieving high accuracy. For the MNIST dataset, we demonstrate how the labels can be confirmed to be defined well. We conclude that a concept of standardness of a dataset implies that there is a match between use case, derived categories, and class labels, as in the case of the MNIST dataset. We argue that this is necessary to learn a meaningful abstraction and, thus, improve trust in the Machine Learning model.