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Determining whether a state's young people are on track for a life of opportunity is a difficult task for governors and state leaders. States can be both awash in data and unable to easily access and use that data to inform policy. State longitudinal data systems that meaningfully connect workforce, higher education, K-12, and early childhood outcome data from multiple state agencies over time are foundational to strong decision-making by state leaders. State leaders are charged with determining how to advance educational and workforce opportunities, particularly for historically underserved populations, including rural students, students living in poverty, English-language learners, and students of color. Policymakers need reliable, relevant, and transparent data from PK-12 to the workforce to inform decisions and allocate resources. Researchers need access to data to assess current conditions and to identify possible solutions. Finally, the public -- including parents -- needs easily accessible, user-friendly information about readiness and opportunity for young people in the state. Well-governed and well-structured state longitudinal data systems can help. Today, most states have disparate data systems that are functionally siloed off by education level, like early childhood or K-12 or higher education. Meaningfully connected and well-governed data systems make it easier to see the relationships between metrics like early-grade reading scores, high school graduation, and wage data. Disaggregated outcome data about the youngest learners up to young adults provide invaluable insights to policymakers, researchers, and parents. This report examines how state leaders should prioritize state longitudinal data systems in legislation and policymaking.

Texas depends on a talented workforce to fuel its economy, and Texans depend on our public education system to prepare them for the jobs of today and tomorrow. But unfortunately, workforce and public education systems often operate independently, making innovation and improvement difficult. Between now and 2036, over 70% of the jobs in Texas will require a postsecondary credential. But far too few Texas students will possess those credentials. Only 22% of Texas eighth graders currently earn a postsecondary degree or credentials within six years of high school graduation. This article examines the Texas Tri-Agency Workforce Initiative. It brings together the Texas Education Agency (TEA), the Texas Higher Education Coordinating Board (THECB), and the Texas Workforce Commission (TWC). The Tri-Agency is focused on three priorities: (1) Pathways: Support efficient and flexible pathways to earning degrees, certificates, and other credentials linked to high-wage, in-demand jobs; (2) Support: Ensure students receive the support necessary to succeed at all stages of their education in their transitions to the workforce; and (3) Infrastructure: Create a robust infrastructure for interagency collaboration around common goals, data, and processes to ensure improved student outcomes and meet employers' needs.

We present the discovery of TOI 762 A b and TIC 46432937 b, two giant planets transiting M dwarf stars. Transits of both systems were first detected from observations by the NASA TESS mission, and the transiting objects are confirmed as planets through high-precision radial velocity (RV) observations carried out with VLT/ESPRESSO. TOI 762 A b is a warm sub-Saturn with a mass of 0.251 +- 0.042 M_J, a radius of 0.744 +- 0.017 R_J, and an orbital period of 3.4717 d. It transits a mid-M dwarf star with a mass of 0.442 +- 0.025 M_S and a radius of 0.4250 +- 0.0091 R_S. The star TOI 762 A has a resolved binary star companion TOI 762 B that is separated from TOI 762 A by 3.2" (~ 319 AU) and has an estimated mass of 0.227 +- 0.010 M_S. The planet TIC 46432937 b is a warm Super-Jupiter with a mass of 3.20 +- 0.11 M_J and radius of 1.188 +- 0.030 R_J. The planet's orbital period is P = 1.4404 d, and it undergoes grazing transits of its early M dwarf host star, which has a mass of 0.563 +- 0.029 M_S and a radius of 0.5299 +- 0.0091 R_S. TIC 46432937 b is one of the highest mass planets found to date transiting an M dwarf star. TIC 46432937 b is also a promising target for atmospheric observations, having the highest Transmission Spectroscopy Metric or Emission Spectroscopy Metric value of any known warm Super-Jupiter (mass greater than 3.0 M_J, equilibrium temperature below 1000 K)., Comment: 29 pages, 7 figures, 8 tables, accepted for publication in AAS Journals

The recent discoveries of Neptune-sized ultra-short period planets (USPs) challenge existing planet formation theories. It is unclear whether these residents of the Hot Neptune Desert have similar origins to smaller, rocky USPs, or if this discrete population is evidence of a different formation pathway altogether. We report the discovery of TOI-3261b, an ultra-hot Neptune with an orbital period $P$ = 0.88 days. The host star is a $V = 13.2$ magnitude, slightly super-solar metallicity ([Fe/H] $\simeq$ 0.15), inactive K1.5 main sequence star at $d = 300$ pc. Using data from the Transiting Exoplanet Survey Satellite and the Las Cumbres Observatory Global Telescope, we find that TOI-3261b has a radius of $3.82_{-0.35}^{+0.42}$ $R_{\oplus}$. Moreover, radial velocities from ESPRESSO and HARPS reveal a mass of $30.3_{-2.4}^{+2.2}$ $M_{\oplus}$, more than twice the median mass of Neptune-sized planets on longer orbits. We investigate multiple mechanisms of mass loss that can reproduce the current-day properties of TOI-3261b, simulating the evolution of the planet via tidal stripping and photoevaporation. Thermal evolution models suggest that TOI-3261b should retain an envelope potentially enriched with volatiles constituting $\sim$5% of its total mass. This is the second highest envelope mass fraction among ultra-hot Neptunes discovered to date, making TOI-3261b an ideal candidate for atmospheric follow-up observations., Comment: 20 pages, 11 figures, accepted to AJ

Optimizing the interfaces in perovskite solar cells (PSCs) is essential for enhancing their performance, improving their stability, and making them commercially viable for large-scale deployment in solar energy harvesting applications. Point defects, like vacancies, have a dual role, as they can inherently provide a proper doping, but they can also reduce the collected current by trap-assisted recombination. Moreover, they can play an active role in ion migration. Using {\it ab initio} density functional theory (DFT) calculations we investigate the changes in the band alignment induced by interfacial vacancy defects in a TiO$_2$/MAPI/Cu$_2$O based PSC. Depending on the type of the vacancy (Ti, Cu, O, Pb, I) in the oxide and perovskite materials, additional doping is superimposed on the already existing background. Their effect on the performance of the PSCs becomes visible, as shown by SCAPS simulations. The most significant impact is observed for $p$ type doping of TiO$_2$ and $n$ type doping of Cu$_2$O, while the effective doping of the perovskite layer affects one of the two interfaces. We discuss these results based on modification of the band structure near the active interfaces and provide further insights concerning the optimization of electron and hole collection., Comment: 7 pages, 8 figures, 4 sections, 44 references

Modern technologies have led illicit firearms trafficking to partially merge with cybercrime, while simultaneously permitting its off-line aspects to become more sophisticated. Law enforcement officers face difficult challenges that require hi-tech solutions. This article presents a real-world system, powered by advanced Artificial Intelligence, for facilitating them in their everyday work.

Here, we report the experimental demonstration of a novel multi-mode linear bus interferometer (LBI) coupler in a six qubit superconducting quantum processor. A key feature of this coupler is an engineered multi-path interference which eliminates transverse coupling between qubits over a wide frequency range. This negligible static coupling is achieved without any flux bias tuning, and greatly reduces the impact of qubit frequency collisions. We achieve good simultaneous single qubit gate operation and low ZZ rates (below 600 Hz) across the device without staggering qubit frequencies, even in cases where qubits are as close as 10 MHz. Multi-qubit interactions are still possible through the coupler using microwave-driven resonator induced phase gates, which we utilize to demonstrate simultaneous two qubit gates with fidelities as high as 99.4%

Young (<500 Myr) planets are critical to studying how planets form and evolve. Among these young planetary systems, multi-planet configurations are particularly useful as they provide a means to control for variables within a system. Here, we report the discovery and characterization of a young planetary system, TOI-1224. We show that the planet-host resides within a young population we denote as MELANGE-5 . By employing a range of age-dating methods -- isochrone fitting, lithium abundance analysis, gyrochronology, and Gaia excess variability -- we estimate the age of MELANGE-5 to be 210$\pm$27 Myr. MELANGE-5 is situated in close proximity to previously identified younger (80 -110 Myr) associations, Crius 221 and Theia 424/Volans-Carina, motivating further work to map out the group boundaries. In addition to a planet candidate detected by the TESS pipeline and alerted as a TESS Object of Interest, TOI-1224 b, we identify a second planet, TOI-1224 c, using custom search tools optimized for young stars (Notch and LOCoR). We find the planets are 2.10$\pm$0.09$R_\oplus$ and 2.88$\pm$0.10$R_\oplus$ and orbit their host star every 4.18 and 17.95 days, respectively. With their bright ($K$=9.1 mag), small ($R_{*}$=0.44R$_{\odot}$), and cool ($T_{eff}$ =3326K) host star, these planets represent excellent candidates for atmospheric characterization with JWST., Comment: Accepted for publication in The Astronomical Journal; 33 pages, 17 figures, 9 tables

Gliomas are the most common malignant primary brain tumors in adults and one of the deadliest types of cancer. There are many challenges in treatment and monitoring due to the genetic diversity and high intrinsic heterogeneity in appearance, shape, histology, and treatment response. Treatments include surgery, radiation, and systemic therapies, with magnetic resonance imaging (MRI) playing a key role in treatment planning and post-treatment longitudinal assessment. The 2024 Brain Tumor Segmentation (BraTS) challenge on post-treatment glioma MRI will provide a community standard and benchmark for state-of-the-art automated segmentation models based on the largest expert-annotated post-treatment glioma MRI dataset. Challenge competitors will develop automated segmentation models to predict four distinct tumor sub-regions consisting of enhancing tissue (ET), surrounding non-enhancing T2/fluid-attenuated inversion recovery (FLAIR) hyperintensity (SNFH), non-enhancing tumor core (NETC), and resection cavity (RC). Models will be evaluated on separate validation and test datasets using standardized performance metrics utilized across the BraTS 2024 cluster of challenges, including lesion-wise Dice Similarity Coefficient and Hausdorff Distance. Models developed during this challenge will advance the field of automated MRI segmentation and contribute to their integration into clinical practice, ultimately enhancing patient care., Comment: 10 pages, 4 figures, 1 table

The in-plane anisotropic properties of two-dimensional (2D) group IV monochalcogenides provide an additional degree of freedom which can be used in future optoelectronic devices. Here, it is shown that the third harmonic generation (THG) signal produced by ultrathin tin (II) sulfide (SnS) is in-plane anisotropic with respect to the incident linear polarization of the laser field. We fit the experimental polarization-resolved THG (P-THG) measurements with a nonlinear optics model, which accounts for the orthorhombic crystal structure of 2D SnS. We calculate the relative magnitudes of the \{chi}^(3) tensor components by recording and simultaneously fitting both orthogonal components of the P-THG intensity. Furthermore, we introduce a THG anisotropy ratio, whose calculated values compare the total THG intensity when the excitation linear polarization is along the armchair crystallographic direction with the case when it is along the zigzag direction. Our results provide quantitative information on the anisotropic nature of the THG process in SnS, paving the way to a better understanding of anisotropic nonlinear light-matter interactions, and the development of polarization-sensitive nonlinear optical devices.

Utilizing solar energy to meet space heating and domestic hot water demand is very efficient (in terms of environmental footprint as well as cost), but in order to ensure that user demand is entirely covered throughout the year needs to be complemented with auxiliary heating systems, typically boilers and heat pumps. Naturally, the optimal control of such a system depends on an accurate prediction of solar thermal production. Experimental testing and physics-based numerical models are used to find a collector's performance curve - the mapping from solar radiation and other external conditions to heat production - but this curve changes over time once the collector is exposed to outdoor conditions. In order to deploy advanced control strategies in small domestic installations, we present an approach that uses machine learning to automatically construct and continuously adapt a model that predicts heat production. Our design is driven by the need to (a) construct and adapt models using supervision that can be extracted from low-cost instrumentation, avoiding extreme accuracy and reliability requirements; and (b) at inference time, use inputs that are typically provided in publicly available weather forecasts. Recent developments in attention-based machine learning, as well as careful adaptation of the training setup to the specifics of the task, have allowed us to design a machine learning-based solution that covers our requirements. We present positive empirical results for the predictive accuracy of our solution, and discuss the impact of these results on the end-to-end system.

Encompassing regions that were amongst the first inhabited by humans following the out-of-Africa expansion, hosting populations with the highest levels of archaic hominid introgression, and including Pacific islands that are the most isolated inhabited locations on the planet, Oceania has a rich, but understudied, human genomic landscape. Here we describe the first region-wide analysis of genome-wide data from population groups spanning Oceania and its surroundings, from island and peninsular southeast Asia to Papua New Guinea, east across the Pacific through Melanesia, Micronesia, and Polynesia, and west across the Indian Ocean to related island populations in the Andamans and Madagascar. In total we generate and analyze genome-wide data from 981 individuals from 92 different populations, 58 separate islands, and 30 countries, representing the most expansive study of Pacific genetics to date. In each sample we disentangle the Papuan and more recent Austronesian ancestries, which have admixed in various proportions across this region, using ancestry-specific analyses, and characterize the distinct patterns of settlement, migration, and archaic introgression separately in these two ancestries. We also focus on the patterns of clinically relevant genetic variation across Oceania--a landscape rippled with strong founder effects and island-specific genetic drift in allele frequencies--providing an atlas for the development of precision genetic health strategies in this understudied region of the world.

The EdResearch for Action "Overview Series" summarizes the research on key topics to provide K-12 education decision makers and advocates with an evidence base to ground discussions about how to best serve students. Authors -- leading experts from across the field of education research -- are charged with highlighting key findings from research that provide concrete, strategic insight on persistent challenges sourced from district and state leaders. This brief asks how districts and states can promote equitable success in academic early college credit courses and covers research on early college credit courses that focus on academic and general content as opposed to career and technical education (CTE).

Multi-channel electrophysiology systems for recording of neuronal activity face significant data throughput limitations, hampering real-time, data-informed experiments. These limitations impact both experimental neurobiology research and next-generation neuroprosthetics. We present a novel solution that leverages the high integration density of 22nm FDSOI CMOS technology to address these challenges. The proposed highly integrated programmable System-on-Chip comprises 68-channel 0.41 \textmu W/Ch recording frontends, spike detectors, 16-channel 0.87-4.39 \textmu W/Ch action potential and 8-channel 0.32 \textmu W/Ch local field potential codecs, as well as a MAC-assisted power-efficient processor operating at 25 MHz (5.19 \textmu W/MHz). The system supports on-chip training processes for compression, training and inference for neural spike sorting. The spike sorting achieves an average accuracy of 91.48% or 94.12% depending on the utilized features. The proposed PSoC is optimized for reduced area (9 mm2) and power. On-chip processing and compression capabilities free up the data bottlenecks in data transmission (up to 91% space saving ratio), and moreover enable a fully autonomous yet flexible processor-driven operation. Combined, these design considerations overcome data-bottlenecks by allowing on-chip feature extraction and subsequent compression., Comment: 26 pages, 14 figures, 1 table, Journal

Automatic generation of loop invariants is a fundamental challenge in software verification. While this task is undecidable in general, it is decidable for certain restricted classes of programs. This work focuses on invariant generation for (branching-free) loops with a single linear update. Our primary contribution is a polynomial-space algorithm that computes the strongest algebraic invariant for simple linear loops, generating all polynomial equations that hold among program variables across all reachable states. The key to achieving our complexity bounds lies in mitigating the blowup associated with variable elimination and Gr\"obner basis computation, as seen in prior works. Our procedure runs in polynomial time when the number of program variables is fixed. We examine various applications of our results on invariant generation, focusing on invariant verification and loop synthesis. The invariant verification problem investigates whether a polynomial ideal defining an algebraic set serves as an invariant for a given linear loop. We show that this problem is coNP-complete and lies in PSPACE when the input ideal is given in dense or sparse representations, respectively. In the context of loop synthesis, we aim to construct a loop with an infinite set of reachable states that upholds a specified algebraic property as an invariant. The strong synthesis variant of this problem requires the construction of loops for which the given property is the strongest invariant. In terms of hardness, synthesising loops over integers (or rationals) is as hard as Hilbert's Tenth problem (or its analogue over the rationals). When loop constants are constrained to bit-bounded rational numbers, we demonstrate that loop synthesis and its strong variant are both decidable in PSPACE, and in NP when the number of program variables is fixed., Comment: 23 pages

Powerful foundation models, including large language models (LLMs), with Transformer architectures have ushered in a new era of Generative AI across various industries. Industry and research community have witnessed a large number of new applications, based on those foundation models. Such applications include question and answer, customer services, image and video generation, and code completions, among others. However, as the number of model parameters reaches to hundreds of billions, their deployment incurs prohibitive inference costs and high latency in real-world scenarios. As a result, the demand for cost-effective and fast inference using AI accelerators is ever more higher. To this end, our tutorial offers a comprehensive discussion on complementary inference optimization techniques using AI accelerators. Beginning with an overview of basic Transformer architectures and deep learning system frameworks, we deep dive into system optimization techniques for fast and memory-efficient attention computations and discuss how they can be implemented efficiently on AI accelerators. Next, we describe architectural elements that are key for fast transformer inference. Finally, we examine various model compression and fast decoding strategies in the same context., Comment: Tutorial published at KDD 2024. Camera-ready version

Behavioural biometric authentication systems entail an enrolment period that is burdensome for the user. In this work, we explore generating synthetic gestures from a few real user gestures with generative deep learning, with the application of training a simple (i.e. non-deep-learned) authentication model. Specifically, we show that utilising synthetic data alongside real data can reduce the number of real datapoints a user must provide to enrol into a biometric system. To validate our methods, we use the publicly available dataset of WatchAuth, a system proposed in 2022 for authenticating smartwatch payments using the physical gesture of reaching towards a payment terminal. We develop a regularised autoencoder model for generating synthetic user-specific wrist motion data representing these physical gestures, and demonstrate the diversity and fidelity of our synthetic gestures. We show that using synthetic gestures in training can improve classification ability for a real-world system. Through this technique we can reduce the number of gestures required to enrol a user into a WatchAuth-like system by more than 40% without negatively impacting its error rates.

In this paper, we further consider the symmetry-based method for seeking nonlocally related systems for partial differential equations. In particular, we show that the symmetry-based method for partial differential equations is the natural extension of Lie's reduction of order algorithm for ordinary differential equations by looking at this algorithm from a different point of view. Many examples exhibit various situations that can arise.

Mixing style transfer automates the generation of a multitrack mix for a given set of tracks by inferring production attributes from a reference song. However, existing systems for mixing style transfer are limited in that they often operate only on a fixed number of tracks, introduce artifacts, and produce mixes in an end-to-end fashion, without grounding in traditional audio effects, prohibiting interpretability and controllability. To overcome these challenges, we introduce Diff-MST, a framework comprising a differentiable mixing console, a transformer controller, and an audio production style loss function. By inputting raw tracks and a reference song, our model estimates control parameters for audio effects within a differentiable mixing console, producing high-quality mixes and enabling post-hoc adjustments. Moreover, our architecture supports an arbitrary number of input tracks without source labelling, enabling real-world applications. We evaluate our model's performance against robust baselines and showcase the effectiveness of our approach, architectural design, tailored audio production style loss, and innovative training methodology for the given task., Comment: Accepted to be published at the Proceedings of the 25th International Society for Music Information Retrieval Conference 2024

Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%. The development of new treatments is dependent upon multi-institutional collaborative clinical trials requiring reproducible and accurate centralized response assessment. We present the results of the BraTS-PEDs 2023 challenge, the first Brain Tumor Segmentation (BraTS) challenge focused on pediatric brain tumors. This challenge utilized data acquired from multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. BraTS-PEDs 2023 aimed to evaluate volumetric segmentation algorithms for pediatric brain gliomas from magnetic resonance imaging using standardized quantitative performance evaluation metrics employed across the BraTS 2023 challenges. The top-performing AI approaches for pediatric tumor analysis included ensembles of nnU-Net and Swin UNETR, Auto3DSeg, or nnU-Net with a self-supervised framework. The BraTSPEDs 2023 challenge fostered collaboration between clinicians (neuro-oncologists, neuroradiologists) and AI/imaging scientists, promoting faster data sharing and the development of automated volumetric analysis techniques. These advancements could significantly benefit clinical trials and improve the care of children with brain tumors.

A domino is a $2\times 1\times 1$ parallelepiped formed by the union of two unit cubes and a slab is a $2\times 2\times 1$ parallelepiped formed by the union of four unit cubes. We are interested in tiling regions formed by the finite union of unit cubes. We investigate domino tilings, slab tilings and mixed tilings (vertical dominoes and horizontal slabs). A flip is a local move: in a slab (resp. domino) tiling, two neighboring parallel slabs (resp. dominoes) are removed and placed back in a different position. We may interpret a three dimensional region as being made of floors. A slab or domino which crosses two floors is vertical, and horizontal otherwise. We can also define a flip in a mixed tiling, we replace four vertical dominoes in a $2\times 2\times 2$ box by two horizontal slabs and vice-versa. Inspired by the twist for domino tilings we construct a flip invariant for mixed tilings. Based on this, we then construct the triple twist for slab tilings: an invariant under flips assuming values in $\mathbb{Z}^3$. We show that if the region is a large box then the triple twist assumes a large number of possible values, at least proportional to the fourth power of the volume. We also give examples of smaller regions for which the triple twist assumes only one value and the set of tilings is connected under flips., Comment: 21 pages, 30 figures

Heterogeneous Face Recognition (HFR) systems aim to enhance the capability of face recognition in challenging cross-modal authentication scenarios. However, the significant domain gap between the source and target modalities poses a considerable challenge for cross-domain matching. Existing literature primarily focuses on developing HFR approaches for specific pairs of face modalities, necessitating the explicit training of models for each source-target combination. In this work, we introduce a novel framework designed to train a modality-agnostic HFR method capable of handling multiple modalities during inference, all without explicit knowledge of the target modality labels. We achieve this by implementing a computationally efficient automatic routing mechanism called Switch Style Modulation Blocks (SSMB) that trains various domain expert modulators which transform the feature maps adaptively reducing the domain gap. Our proposed SSMB can be trained end-to-end and seamlessly integrated into pre-trained face recognition models, transforming them into modality-agnostic HFR models. We have performed extensive evaluations on HFR benchmark datasets to demonstrate its effectiveness. The source code and protocols will be made publicly available., Comment: 8 pages

The first aim of this note is to make clear what is the equilibrium of a fifth order difference equation studied in the literature. Next the investigation of the whole asymptotic behaviour of the solutions of the equation is presented.

Dynamic Mode Decomposition (DMD) and its variants, such as extended DMD (EDMD), are broadly used to fit simple linear models to dynamical systems known from observable data. As DMD methods work well in several situations but perform poorly in others, a clarification of the assumptions under which DMD is applicable is desirable. Upon closer inspection, existing interpretations of DMD methods based on the Koopman operator are not quite satisfactory: they justify DMD under assumptions that hold only with probability zero for generic observables. Here, we give a justification for DMD as a local, leading-order reduced model for the dominant system dynamics under conditions that hold with probability one for generic observables and non-degenerate observational data. We achieve this for autonomous and for periodically forced systems of finite or infinite dimensions by constructing linearizing transformations for their dominant dynamics within attracting slow spectral submanifolds (SSMs). Our arguments also lead to a new algorithm, data-driven linearization (DDL), which is a higher-order, systematic linearization of the observable dynamics within slow SSMs. We show by examples how DDL outperforms DMD and EDMD on numerical and experimental data., Comment: To appear in Nonlinear Dynamics

An elusive goal in navigation research is to build an intelligent agent that can understand multimodal instructions including natural language and image, and perform useful navigation. To achieve this, we study a widely useful category of navigation tasks we call Multimodal Instruction Navigation with demonstration Tours (MINT), in which the environment prior is provided through a previously recorded demonstration video. Recent advances in Vision Language Models (VLMs) have shown a promising path in achieving this goal as it demonstrates capabilities in perceiving and reasoning about multimodal inputs. However, VLMs are typically trained to predict textual output and it is an open research question about how to best utilize them in navigation. To solve MINT, we present Mobility VLA, a hierarchical Vision-Language-Action (VLA) navigation policy that combines the environment understanding and common sense reasoning power of long-context VLMs and a robust low-level navigation policy based on topological graphs. The high-level policy consists of a long-context VLM that takes the demonstration tour video and the multimodal user instruction as input to find the goal frame in the tour video. Next, a low-level policy uses the goal frame and an offline constructed topological graph to generate robot actions at every timestep. We evaluated Mobility VLA in a 836m^2 real world environment and show that Mobility VLA has a high end-to-end success rates on previously unsolved multimodal instructions such as "Where should I return this?" while holding a plastic bin. A video demonstrating Mobility VLA can be found here: https://youtu.be/-Tof__Q8_5s

This paper presents the V-Lab, a VR application development framework for educational scenarios mainly involving scientific processes executed in laboratory environments such as chemistry and biology laboratories. This work is an extension of the Onlabs simulator which has been developed by the Hellenic Open University as a distance teaching enabler for similar subjects, helping to alleviate the need for access to the physical laboratory infrastructure; thus, shortening training periods of students in the laboratory and making their training during the periods of physical presence more productive and secure. The extensions of the Onlabs to deliver an enhanced and modular framework that can be extended to multiple educational scenarios is the work performed within the context of the European project XR2Learn (Leveraging the European XR industry technologies to empower immersive learning and training)., Comment: 7 pages, 3 figures, Proceedings of the 25th International Conference on Mobile Human-Computer Interaction (MobileHCI '23)

The vision of IASIS project is to turn the wave of big biomedical data heading our way into actionable knowledge for decision makers. This is achieved by integrating data from disparate sources, including genomics, electronic health records and bibliography, and applying advanced analytics methods to discover useful patterns. The goal is to turn large amounts of available data into actionable information to authorities for planning public health activities and policies. The integration and analysis of these heterogeneous sources of information will enable the best decisions to be made, allowing for diagnosis and treatment to be personalised to each individual. The project offers a common representation schema for the heterogeneous data sources. The iASiS infrastructure is able to convert clinical notes into usable data, combine them with genomic data, related bibliography, image data and more, and create a global knowledge base. This facilitates the use of intelligent methods in order to discover useful patterns across different resources. Using semantic integration of data gives the opportunity to generate information that is rich, auditable and reliable. This information can be used to provide better care, reduce errors and create more confidence in sharing data, thus providing more insights and opportunities. Data resources for two different disease categories are explored within the iASiS use cases, dementia and lung cancer., Comment: 6 pages, 2 figures, accepted at 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)

Finite modular family symmetry can emerge from top-down approaches based on heterotic string theory or Type IIB string theory. We show that, in addition to such approaches, it can also emerge from local F-Theory bottom-up constructions. As a first example of the new approach, we have analysed in detail a concrete F-Theory Fluxed $SU(5)$ Grand Unified Theory (GUT) with modular $S_4$ family symmetry. The model fits the fermion mass and mixing data very well and serves as a demonstration of the bottom-up approach to modular family symmetry in F-Theory fluxed GUTs., Comment: 39 pages, 7 figures

Reinforcement learning algorithms typically rely on the assumption that the environment dynamics and value function can be expressed in terms of a Markovian state representation. However, when state information is only partially observable, how can an agent learn such a state representation, and how can it detect when it has found one? We introduce a metric that can accomplish both objectives, without requiring access to--or knowledge of--an underlying, unobservable state space. Our metric, the $\lambda$-discrepancy, is the difference between two distinct temporal difference (TD) value estimates, each computed using TD($\lambda$) with a different value of $\lambda$. Since TD($\lambda$=0) makes an implicit Markov assumption and TD($\lambda$=1) does not, a discrepancy between these estimates is a potential indicator of a non-Markovian state representation. Indeed, we prove that the $\lambda$-discrepancy is exactly zero for all Markov decision processes and almost always non-zero for a broad class of partially observable environments. We also demonstrate empirically that, once detected, minimizing the $\lambda$-discrepancy can help with learning a memory function to mitigate the corresponding partial observability. We then train a reinforcement learning agent that simultaneously constructs two recurrent value networks with different $\lambda$ parameters and minimizes the difference between them as an auxiliary loss. The approach scales to challenging partially observable domains, where the resulting agent frequently performs significantly better (and never performs worse) than a baseline recurrent agent with only a single value network., Comment: GitHub URL: https://github.com/brownirl/lambda_discrepancy

We present a methodology for performing scans of BSM parameter spaces with reinforcement learning (RL). We identify a novel procedure using graph neural networks that is capable of exploring spaces of models without the user specifying a fixed particle content, allowing broad classes of BSM models to be explored. In theory, the technique is applicable to nearly any model space with a pre-specified gauge group. We provide a generic procedure by which a suitable graph grammar can be developed for any BSM model which features user-specified symmetry groups and a finite number of different possible particle species. As a proof of concept, we construct the graph grammar for theories with vector-like leptons that may or may not be charged under a dark U(1) group, inspired by portal matter extensions of the sub-GeV vector portal/kinetic mixing simplified dark matter models. We then use this graph grammar to create a RL environment tasked with creating models with these vector-like leptons that are consistent with a list of a variety of precision observables. The RL agent succeeds in developing models that can address the observed muon anomalous magnetic moment discrepancy while remaining consistent with flavor violation and electroweak precision observables, including both constructions that have previously been studied as well as new models which have not, to our knowledge, previously been identified. By inspecting the resulting ensembles of models that the agent produces and experimenting with different configurations for our RL environment and graph grammar, we also infer various lessons about the development of these environments that can be transferable to RL scans of more complicated model spaces, and comment on future directions for the development of this technique into a more mature tool., Comment: 57 pages, 14 figures, 15 tables

We provide a framework for exploring physics beyond the Standard Model with reinforcement learning using graph representations of new physics theories. The graph structure allows for model-building without a priori specifying definite numbers of new particles. As a case study, we apply our method to a simple class of theories involving vectorlike leptons and a dark U(1) inspired by the portal matter paradigm. Using modern policy gradient methods, the agent successfully explores a model space consisting of both continuous and discrete parameters and identifies consistent theories. The minimal models found include both known and previously unstudied examples that can accommodate the muon anomalous magnetic moment and satisfy precision electroweak and flavor constraints. The method represents a step forward in enabling an automated model-building process for physics beyond the Standard Model., Comment: 10 pages, 2 figures, 2 tables

The observation of neutron stars enables the otherwise impossible study of fundamental physical processes. Timing of binary radio pulsars is particularly powerful, as it enables precise characterization of their (three-dimensional) positions and orbits. PSR J0437$-$4715 is an important millisecond pulsar for timing array experiments and is also a primary target for the Neutron Star Interior Composition ExploreR (NICER). The main aim of the NICER mission is to constrain the neutron star equation of state by inferring the compactness ($M_p/R$) of the star. Direct measurements of the mass $M_p$ from pulsar timing therefore substantially improve constraints on the radius $R$, and the equation of state. Here we use observations spanning 26 years from Murriyang, the 64-m Parkes radio telescope, to improve the timing model for this pulsar. Among the new precise measurements are the pulsar mass $M_p=1.418\pm 0.044$ M$_{\odot}$, distance $D=156.96 \pm 0.11$ pc, and orbital inclination angle $i=137.506 \pm 0.016^\circ$, which can be used to inform the X-ray pulse profile models inferred from NICER observations. We demonstrate that these results are consistent between multiple data sets from the Parkes Pulsar Timing Array (PPTA), each modelled with different noise assumptions. Using the longest available PPTA data set, we measure an apparent second derivative of the pulsar spin frequency and discuss how this can be explained either by kinematic effects due to the proper motion and radial velocity of the pulsar, or excess low-frequency noise such as a gravitational-wave background., Comment: 13 pages, 3 figures, accepted for publication in Astrophysical Journal Letters

In the present work we present an extended description of the covariant noncommutative space, which accommodates the Fuzzy Gravity model constructed previously. It is based on the historical lesson that the use of larger algebras containing all generators of the isometry of the continuous one helped in formulating a fuzzy covariant noncommutative space. Specifically a further enlargement of the isometry group leads us, in addition to the construction of the covariant noncommutative space, also to the suggestion of the group that should be gauged on such a space in order to construct a Fuzzy Gravity theory. As a result, we obtain two Fuzzy Gravity models, one in de Sitter and one in anti-de Sitter space, depending on the extension of the isometry group, and we discuss their spontaneous symmetry breaking leading to fuzzy versions of the noncommutative $SO(1,3)$ gravity. In addition we discuss for the first time how to introduce fermions in the fuzzy gravity and even more importantly how to unify the constructed noncommutative-fuzzy gravity with internal interactions based on $SO(10)$ or $SU(5)$ as grand unified theories.

We study the problem of fairly allocating indivisible goods among agents which are equipped with {\em leveled} valuation functions. Such preferences, that have been studied before in economics and fair division literature, capture a simple and intuitive economic behavior; larger bundles are always preferred to smaller ones. We provide a fine-grained analysis for various subclasses of leveled valuations focusing on two extensively studied notions of fairness, (approximate) MMS and EFX. In particular, we present a general positive result, showing the existence of $2/3$-MMS allocations under valuations that are both leveled and submodular. We also show how some of our ideas can be used beyond the class of leveled valuations; for the case of two submodular (not necessarily leveled) agents we show that there always exists a $2/3$-MMS allocation, complementing a recent impossibility result. Then, we switch to the case of subadditive and fractionally subadditive leveled agents, where we are able to show tight (lower and upper) bounds of $1/2$ on the approximation factor of MMS. Moreover, we show the existence of exact EFX allocations under general leveled valuations via a simple protocol that in addition satisfies several natural economic properties. Finally, we take a mechanism design approach and we propose protocols that are both truthful and approximately fair under leveled valuations.

AI Infrastructure plays a key role in the speed and cost-competitiveness of developing and deploying advanced AI models. The current demand for powerful AI infrastructure for model training is driven by the emergence of generative AI and foundational models, where on occasion thousands of GPUs must cooperate on a single training job for the model to be trained in a reasonable time. Delivering efficient and high-performing AI training requires an end-to-end solution that combines hardware, software and holistic telemetry to cater for multiple types of AI workloads. In this report, we describe IBM's hybrid cloud infrastructure that powers our generative AI model development. This infrastructure includes (1) Vela: an AI-optimized supercomputing capability directly integrated into the IBM Cloud, delivering scalable, dynamic, multi-tenant and geographically distributed infrastructure for large-scale model training and other AI workflow steps and (2) Blue Vela: a large-scale, purpose-built, on-premises hosting environment that is optimized to support our largest and most ambitious AI model training tasks. Vela provides IBM with the dual benefit of high performance for internal use along with the flexibility to adapt to an evolving commercial landscape. Blue Vela provides us with the benefits of rapid development of our largest and most ambitious models, as well as future-proofing against the evolving model landscape in the industry. Taken together, they provide IBM with the ability to rapidly innovate in the development of both AI models and commercial offerings., Comment: Corresponding Authors: Talia Gershon, Seetharami Seelam,Brian Belgodere, Milton Bonilla

In this work, we discuss our vision for neuromorphic accelerators based on integrated photonics within the framework of the Horizon Europe NEUROPULS project. Augmented integrated photonic architectures that leverage phase-change and III-V materials for optical computing will be presented. A CMOS-compatible platform will be discussed that integrates these materials to fabricate photonic neuromorphic architectures, along with a gem5-based simulation platform to model accelerator operation once it is interfaced with a RISC-V processor. This simulation platform enables accurate system-level accelerator modeling and benchmarking in terms of key metrics such as speed, energy consumption, and footprint., Comment: 4 pages, 3 figures

We introduce the general-relativistic definition of Lunar Coordinate Time (TCL) based on the IAU 2000 resolutions that provide a framework of relativistic reference systems. From this foundation, we derive a transformation equation that describes the relative rate of TCL with respect to Geocentric Coordinate Time (TCG). This equation serves as the cornerstone for constructing a relativistic TCL--TCG time conversion algorithm. Using this algorithm, we can compute both secular and periodic variations in the rate of an atomic clock placed on the Moon, relative to an identical clock on Earth. The algorithm accounts for various effects, including time dilation caused by the Moon's orbital motion around Earth, gravitational potentials of both Earth and Moon, and direct and indirect time dilation effects due to tidal perturbations caused by the Sun. Our approach provides exquisite details of the TCL--TCG transformation, accurate to a precision of 1 nanosecond. To validate our methodology for lunar coordinate time, we compare it with the formalism of local inertial frames applied to the Earth-Moon system and confirm their equivalence., Comment: 34 pages, 7 figures, 2 tables, appendix

This work presents a fully integrated tree-based combined exploration-planning algorithm: Exploration-RRT (ERRT). The algorithm is focused on providing real-time solutions for local exploration in a fully unknown and unstructured environment while directly incorporating exploratory behavior, robot-safe path planning, and robot actuation into the central problem. ERRT provides a complete sampling and tree-based solution for evaluating "where to go next" by considering a trade-off between maximizing information gain, and minimizing the distances travelled and the robot actuation along the path. The complete scheme is evaluated in extensive simulations, comparisons, as well as real-world field experiments in constrained and narrow subterranean and GPS-denied environments. The framework is fully ROS-integrated, straight-forward to use, and we open-source it at https://github.com/LTU-RAI/ExplorationRRT., Comment: 19 pages, 29 figures Transactions on Robotics

Computational problems concerning the orbit of a point under the action of a matrix group occur in numerous subfields of computer science, including complexity theory, program analysis, quantum computation, and automata theory. In many cases the focus extends beyond orbits proper to orbit closures under a suitable topology. Typically one starts from a group and several points and asks questions about the orbit closure of the points under the action of the group, e.g., whether two given orbit closures intersect. In this paper we consider a collection of what we call determination problems concerning groups and orbit closures. These problems begin with a given variety and seek to understand whether and how it arises either as an algebraic group or as an orbit closure. The how question asks whether the underlying group is $s$-generated, meaning it is topologically generated by $s$ matrices for a given number $s$. Among other applications, problems of this type have recently been studied in the context of synthesising loops subject to certain specified invariants on program variables. Our main result is a polynomial-space procedure that inputs a variety $V$ and a number $s$ and determines whether $V$ arises as an orbit closure of a point under an $s$-generated commutative matrix group. The main tools in our approach are rooted in structural properties of commutative algebraic matrix groups and lattice theory. We leave open the question of determining whether a variety is an orbit closure of a point under an algebraic matrix group (without the requirement of commutativity). In this regard, we note that a recent paper by Nosan et al. [NPSHW2021] gives an elementary procedure to compute the orbit closure of a point under finitely many matrices., Comment: 22 pages

In safety-critical robot planning or control, manually specifying safety constraints or learning them from demonstrations can be challenging. In this paper, we propose a certifiable alignment method for a robot to learn a safety constraint in its model predictive control (MPC) policy with human online directional feedback. To our knowledge, it is the first method to learn safety constraints from human feedback. The proposed method is based on an empirical observation: human directional feedback, when available, tends to guide the robot toward safer regions. The method only requires the direction of human feedback to update the learning hypothesis space. It is certifiable, providing an upper bound on the total number of human feedback in the case of successful learning of safety constraints, or declaring the misspecification of the hypothesis space, i.e., the true implicit safety constraint cannot be found within the specified hypothesis space. We evaluated the proposed method using numerical examples and user studies in two developed simulation games. Additionally, we implemented and tested the proposed method on a real-world Franka robot arm performing mobile water-pouring tasks in a user study. The simulation and experimental results demonstrate the efficacy and efficiency of our method, showing that it enables a robot to successfully learn safety constraints with a small handful (tens) of human directional corrections., Comment: 18 pages, submission to T-RO