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Noninvasive brain stimulation (NIBS) encompasses transcranial stimulation techniques that can influence brain excitability. These techniques have the potential to treat conditions like depression, anxiety, and chronic pain, and to provide insights into brain function. However, a lack of standardized reporting practices limits its reproducibility and full clinical potential. This paper aims to foster interinterdisciplinarity toward adopting Computer Science Semantic reporting methods for the standardized documentation of Neuroscience NIBS studies making them explicitly Findable, Accessible, Interoperable, and Reusable (FAIR). In a large-scale systematic review of 600 repetitive transcranial magnetic stimulation (rTMS), a subarea of NIBS, dosages, we describe key properties that allow for structured descriptions and comparisons of the studies. This paper showcases the semantic publishing of NIBS in the ecosphere of knowledge-graph-based next-generation scholarly digital libraries. Specifically, the FAIR Semantic Web resource(s)-based publishing paradigm is implemented for the 600 reviewed rTMS studies in the Open Research Knowledge Graph., Comment: 8 pages, 2 figures. Accepted as a Practice Paper at The 25th International Conference on Asia-Pacific Digital Libraries (ICADL 2023) https://icadl.net/icadl2023/index.html#accepted

Thermalization phenomena, while ubiquitous in quantum systems, have traditionally been viewed as obstacles to be mitigated. In this study, we demonstrate the ability, instead, to harness thermalization to dynamically engineer and stabilize structured quantum states in a mesoscopically large ensemble of spins. Specifically, we showcase the capacity to generate, control, stabilize, and read out 'shell-like' spin texture with interacting $ {}^{ 13}\mathrm{C}$ nuclear spins in diamond, wherein spins are polarized oppositely on either side of a critical radius. The texture spans several nanometers and encompasses many hundred spins. We capitalize on the thermalization process to impose a quasi-equilibrium upon the generated texture; as a result, it is highly stable, immune to spin diffusion, and endures over multiple-minute long periods -- over a million times longer than the intrinsic interaction scale of the spins. Additionally, the texture is created and interrogated without locally controlling or probing the nuclear spins. These features are accomplished using an electron spin as a nanoscale injector of spin polarization, and employing it as a source of spatially varying dissipation, allowing for serial readout of the emergent spin texture. Long-time stabilization is achieved via prethermalization to a Floquet-induced Hamiltonian under the electronic gradient field. Our work presents a new approach to robust nanoscale spin state engineering and paves the way for new applications in quantum simulation, quantum information science, and nanoscale imaging., Comment: 8 + 32 pages

Recent advancements in the field of Natural Language Processing, particularly the development of large-scale language models that are pretrained on vast amounts of knowledge, are creating novel opportunities within the realm of Knowledge Engineering. In this paper, we investigate the usage of large language models (LLMs) in both zero-shot and in-context learning settings to tackle the problem of extracting procedures from unstructured PDF text in an incremental question-answering fashion. In particular, we leverage the current state-of-the-art GPT-4 (Generative Pre-trained Transformer 4) model, accompanied by two variations of in-context learning that involve an ontology with definitions of procedures and steps and a limited number of samples of few-shot learning. The findings highlight both the promise of this approach and the value of the in-context learning customisations. These modifications have the potential to significantly address the challenge of obtaining sufficient training data, a hurdle often encountered in deep learning-based Natural Language Processing techniques for procedure extraction., Comment: 8 pages, 4 figures, Accepted to The Twelfth International Conference on Knowledge Capture (K-Cap 2023)

We use the IllustrisTNG50 cosmological hydrodynamical simulation, complemented by a catalog of tagged globular clusters, to investigate the properties and build up of two extended luminous components: the intra-cluster light (ICL) and the intra-cluster globular clusters (ICGC). We select the 39 most massive groups and clusters in the box, spanning the range of virial masses $5 \times 10^{12} < \rm M_{200}/\rm M_{\odot} < 2 \times 10^{14}$. We find good agreement between predictions from the simulations and current observational estimates of the fraction of mass in the ICL and its radial extension. The stellar mass of the ICL is only $\sim10\%-20\%$ of the stellar mass in the central galaxy but encodes useful information on the assembly history of the group or cluster. About half the ICL in all our systems is brought in by galaxies in a narrow stellar mass range, $M_*=10^{10}-10^{11}$ $\rm M_{\odot}$. However, the contribution of low-mass galaxies ($M_*<10^{10}$ $\rm M_{\odot}$) to the build-up of the ICL varies broadly from system to system, $\sim 5\%-45\%$, a feature that might be recovered from the observable properties of the ICL at $z=0$. At fixed virial mass, systems where the accretion of dwarf galaxies plays an important role have shallower metallicity profiles, less metal content and a lower stellar mass in the ICL than systems where the main contributors are more massive galaxies. We show that intra-cluster GCs are also good tracers of this history, representing a valuable alternative when diffuse light is not detectable.

The star formation histories (SFHs) of galactic stellar haloes offer crucial insights into the merger history of the galaxy and the effects of those mergers on their hosts. Such measurements have revealed that while the Milky Way's most important merger was 8-10 Gyr ago, M31's largest merger was more recent, within the last few Gyr. Unfortunately, the required halo SFH measurements are extremely observationally expensive outside of the Local Group. Here we use asymptotic giant branch (AGB) stars brighter than the tip of the red giant branch (RGB) to constrain stellar halo SFHs. Both stellar population models and archival datasets show that the AGB/RGB ratio constrains the time before which 90% of the stars formed, $t_{90}$. We find AGB stars in the haloes of three highly-inclined roughly Milky Way-mass galaxies with resolved star measurements from the Hubble Space Telescope; this population is most prominent in the stellar haloes of NGC 253 and NGC 891, suggesting that their stellar haloes contain stars born at relatively late times, with inferred $t_{90}\sim 6\pm1.5$Gyr. This ratio also varies from region to region, tending towards higher values along the major axis and in tidal streams or shells. By combining our measurements with previous constraints, we find a tentative anticorrelation between halo age and stellar halo mass, a trend that exists in models of galaxy formation but has never been elucidated before, i.e, the largest stellar haloes of Milky-Way mass galaxies were assembled more recently., Comment: 20 Pages, 10 Figures

Lifted Kramers spin-degeneracy has been among the central topics of condensed-matter physics since the dawn of the band theory of solids. It underpins established practical applications as well as current frontier research, ranging from magnetic-memory technology to topological quantum matter. Traditionally, lifted Kramers spin-degeneracy has been considered to originate from two possible internal symmetry-breaking mechanisms. The first one refers to time-reversal symmetry breaking by magnetization of ferromagnets, and tends to be strong due to the non-relativistic exchange-coupling origin. The second mechanism applies to crystals with broken inversion symmetry, and tends to be comparatively weaker as it originates from the relativistic spin-orbit coupling. A recent theory work based on spin-symmetry classification has identified an unconventional magnetic phase, dubbed altermagnetic, that allows for lifting the Kramers spin degeneracy without net magnetization and inversion-symmetry breaking. Here we provide the confirmation using photoemission spectroscopy and ab initio calculations. We identify two distinct unconventional mechanisms of lifted Kramers spin degeneracy generated by the altermagnetic phase of centrosymmetric MnTe with vanishing net magnetization. Our observation of the altermagnetic lifting of the Kramers spin degeneracy can have broad consequences in magnetism. It motivates exploration and exploitation of the unconventional nature of this magnetic phase in an extended family of materials, ranging from insulators and semiconductors to metals and superconductors, that have been either identified recently or perceived for many decades as conventional antiferromagnets.

The spontaneous emergence of scale invariance, called self-organized criticality (SOC), is often attributed to a second-order absorbing-state phase transition (ASPT). Many real-world systems display SOC, yet extreme events are often overrepresented, causing significant disruption, and are called dragon kings (DK). We show analytically that the tradeoff between driving impulse and dissipation rate can create DKs in a second-order ASPT. This establishes that DKs exist in SOC systems, reveals a taxonomy of DKs, and shows that larger dissipation and smoother driving lower risk of extreme events., Comment: 6 pages, 5 figures

We propose the LLMs4OL approach, which utilizes Large Language Models (LLMs) for Ontology Learning (OL). LLMs have shown significant advancements in natural language processing, demonstrating their ability to capture complex language patterns in different knowledge domains. Our LLMs4OL paradigm investigates the following hypothesis: \textit{Can LLMs effectively apply their language pattern capturing capability to OL, which involves automatically extracting and structuring knowledge from natural language text?} To test this hypothesis, we conduct a comprehensive evaluation using the zero-shot prompting method. We evaluate nine different LLM model families for three main OL tasks: term typing, taxonomy discovery, and extraction of non-taxonomic relations. Additionally, the evaluations encompass diverse genres of ontological knowledge, including lexicosemantic knowledge in WordNet, geographical knowledge in GeoNames, and medical knowledge in UMLS., Comment: 15 pages main content, 27 pages overall, 2 Figures, accepted for publication at ISWC 2023 research track

Quantum error correction with erasure qubits promises significant advantages over standard error correction due to favorable thresholds for erasure errors. To realize this advantage in practice requires a qubit for which nearly all errors are such erasure errors, and the ability to check for erasure errors without dephasing the qubit. We demonstrate that a "dual-rail qubit" consisting of a pair of resonantly coupled transmons can form a highly coherent erasure qubit, where transmon $T_1$ errors are converted into erasure errors and residual dephasing is strongly suppressed, leading to millisecond-scale coherence within the qubit subspace. We show that single-qubit gates are limited primarily by erasure errors, with erasure probability $p_\text{erasure} = 2.19(2)\times 10^{-3}$ per gate while the residual errors are $\sim 40$ times lower. We further demonstrate mid-circuit detection of erasure errors while introducing $< 0.1\%$ dephasing error per check. Finally, we show that the suppression of transmon noise allows this dual-rail qubit to preserve high coherence over a broad tunable operating range, offering an improved capacity to avoid frequency collisions. This work establishes transmon-based dual-rail qubits as an attractive building block for hardware-efficient quantum error correction., Comment: 9+13 pages, 16 figures

With the emergence of multimodal electronic health records, the evidence for an outcome may be captured across multiple modalities ranging from clinical to imaging and genomic data. Predicting outcomes effectively requires fusion frameworks capable of modeling fine-grained and multi-faceted complex interactions between modality features within and across patients. We develop an innovative fusion approach called MaxCorr MGNN that models non-linear modality correlations within and across patients through Hirschfeld-Gebelein-Renyi maximal correlation (MaxCorr) embeddings, resulting in a multi-layered graph that preserves the identities of the modalities and patients. We then design, for the first time, a generalized multi-layered graph neural network (MGNN) for task-informed reasoning in multi-layered graphs, that learns the parameters defining patient-modality graph connectivity and message passing in an end-to-end fashion. We evaluate our model an outcome prediction task on a Tuberculosis (TB) dataset consistently outperforming several state-of-the-art neural, graph-based and traditional fusion techniques., Comment: To appear in ML4MHD workshop at ICML 2023

A hybrid percolation transition (HPT) exhibits both discontinuity of the order parameter and critical behavior at the transition point. Such dynamic transitions can occur in two ways: by cluster pruning with suppression of loop formation of cut links or by cluster merging with suppression of the creation of large clusters. While the microscopic mechanism of the former is understood in detail, a similar framework is missing for the latter. By studying two distinct cluster merging models, we uncover the universal mechanism of the features of HPT-s at a microscopic level. We find that these features occur in three steps: (i) medium-sized clusters accumulate due to the suppression rule hindering the growth of large clusters, (ii) those medium size clusters eventually merge and a giant cluster increases rapidly, and (iii) the suppression effect becomes obsolete and the kinetics is governed by the Erd\H{o}s-R\'enyi type of dynamics. We show that during the second and third period, the growth of the largest component must proceed in the form of a Devil's staircase. We characterize the critical behavior by two sets of exponents associated with the order parameter and cluster size distribution, which are related to each other by a scaling relation. Extensive numerical simulations are carried out to support the theory where a specific method is applied for finite-size scaling analysis to enable handling the large fluctuations of the transition point. Our results provide a unified theoretical framework for the HPT., Comment: 15 pages, 14 figures, 4 tables

Retail sales and price projections are typically based on time series forecasting. For some product categories, the accuracy of demand forecasts achieved is low, negatively impacting inventory, transport, and replenishment planning. This paper presents our findings based on a proactive pilot exercise to explore ways to help retailers to improve forecast accuracy for such product categories. We evaluated opportunities for algorithmic interventions to improve forecast accuracy based on a sample product category, Knitwear. The Knitwear product category has a current demand forecast accuracy from non-AI models in the range of 60%. We explored how to improve the forecast accuracy using a rack approach. To generate forecasts, our decision model dynamically selects the best algorithm from an algorithm rack based on performance for a given state and context. Outcomes from our AI/ML forecasting model built using advanced feature engineering show an increase in the accuracy of demand forecast for Knitwear product category by 20%, taking the overall accuracy to 80%. Because our rack comprises algorithms that cater to a range of customer data sets, the forecasting model can be easily tailored for specific customer contexts.

Two-player games are a fruitful way to represent and reason about several important synthesis tasks. These tasks include controller synthesis (where one asks for a controller for a given plant such that the controlled plant satisfies a given temporal specification), program repair (setting values of variables to avoid exceptions), and synchronization synthesis (adding lock/unlock statements in multi-threaded programs to satisfy safety assertions). In all these applications, a solution directly corresponds to a winning strategy for one of the players in the induced game. In turn, \emph{logically-specified} games offer a powerful way to model these tasks for large or infinite-state systems. Much of the techniques proposed for solving such games typically rely on abstraction-refinement or template-based solutions. In this paper, we show how to apply classical fixpoint algorithms, that have hitherto been used in explicit, finite-state, settings, to a symbolic logical setting. We implement our techniques in a tool called GenSys-LTL and show that they are not only effective in synthesizing valid controllers for a variety of challenging benchmarks from the literature, but often compute maximal winning regions and maximally-permissive controllers. We achieve \textbf{46.38X speed-up} over the state of the art and also scale well for non-trivial LTL specifications.

Altermagnets are an emerging third elementary class of magnets. Unlike ferromagnets, their distinct crystal symmetries inhibit magnetization while, unlike antiferromagnets, they promote strong spin polarization in the band structure. The corresponding unconventional mechanism of timereversal symmetry breaking without magnetization in the electronic spectra has been regarded as a primary signature of altermagnetism, but has not been experimentally visualized to date. We directly observe strong time-reversal symmetry breaking in the band structure of altermagnetic RuO$_2$ by detecting magnetic circular dichroism in angle-resolved photoemission spectra. Our experimental results, supported by ab initio calculations, establish the microscopic electronic-structure basis for a family of novel phenomena and functionalities in fields ranging from topological matter to spintronics, that are based on the unconventional time-reversal symmetry breaking in altermagnets.