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Touch is a crucial sensing modality that provides rich information about object properties and interactions with the physical environment. Humans and robots both benefit from using touch to perceive and interact with the surrounding environment (Johansson and Flanagan, 2009; Li et al., 2020; Calandra et al., 2017). However, no existing systems provide rich, multi-modal digital touch-sensing capabilities through a hemispherical compliant embodiment. Here, we describe several conceptual and technological innovations to improve the digitization of touch. These advances are embodied in an artificial finger-shaped sensor with advanced sensing capabilities. Significantly, this fingertip contains high-resolution sensors (~8.3 million taxels) that respond to omnidirectional touch, capture multi-modal signals, and use on-device artificial intelligence to process the data in real time. Evaluations show that the artificial fingertip can resolve spatial features as small as 7 um, sense normal and shear forces with a resolution of 1.01 mN and 1.27 mN, respectively, perceive vibrations up to 10 kHz, sense heat, and even sense odor. Furthermore, it embeds an on-device AI neural network accelerator that acts as a peripheral nervous system on a robot and mimics the reflex arc found in humans. These results demonstrate the possibility of digitizing touch with superhuman performance. The implications are profound, and we anticipate potential applications in robotics (industrial, medical, agricultural, and consumer-level), virtual reality and telepresence, prosthetics, and e-commerce. Toward digitizing touch at scale, we open-source a modular platform to facilitate future research on the nature of touch., Comment: 28 pages

This paper presents a spatio-temporal inverse optimal control framework for understanding interactions in multi-agent systems (MAS). We employ a graph representation approach and model the dynamics of interactions between agents as state-dependent edge weights in a consensus algorithm, incorporating both spatial and temporal dynamics. Our method learns these edge weights from trajectory observations, such as provided by expert demonstrations, which allows us to capture the complexity of nonlinear, distributed interaction behaviors. We derive necessary and sufficient conditions for the optimality of these interaction weights, explaining how the network topology affects MAS coordination. The proposed method is demonstrated on a multi-agent formation control problem, where we show its effectiveness in recovering the interaction weights and coordination patterns from sample trajectory data., Comment: This work is under review at 2024 American Control Conference

Modeling human trajectories in crowded environments is challenging due to the complex nature of pedestrian behavior and interactions. This paper proposes a geometric graph neural network (GNN) architecture that integrates domain knowledge from psychological studies to model pedestrian interactions and predict future trajectories. Unlike prior studies using complete graphs, we define interaction neighborhoods using pedestrians' field of view, motion direction, and distance-based kernel functions to construct graph representations of crowds. Evaluations across multiple datasets demonstrate improved prediction accuracy through reduced average and final displacement error metrics. Our findings underscore the importance of integrating domain knowledge with data-driven approaches for effective modeling of human interactions in crowds., Comment: \c{opyright} 2024 the authors. This work has been accepted to IFAC for publication under a Creative Commons Licence CC-BY-NC-ND

In this paper, we address the vision-based autonomous landing problem in complex urban environments using deep neural networks for semantic segmentation and risk assessment. We propose employing the SegFormer, a state-of-the-art visual transformer network, for the semantic segmentation of complex, unstructured urban environments. This approach yields valuable information that can be utilized in smart autonomous landing missions, particularly in emergency landing scenarios resulting from system failures or human errors. The assessment is done in real-time flight, when images of an RGB camera at the Unmanned Aerial Vehicle (UAV) are segmented with the SegFormer into the most common classes found in urban environments. These classes are then mapped into a level of risk, considering in general, potential material damage, damaging the drone itself and endanger people. The proposed strategy is validated through several case studies, demonstrating the huge potential of semantic segmentation-based strategies to determining the safest landing areas for autonomous emergency landing, which we believe will help unleash the full potential of UAVs on civil applications within urban areas.

In one-dimensional quantum materials, conducting electrons and the underlying lattices can undergo a spontaneous translational symmetry breaking, known as Peierls transition. For nearly a century, the Peierls transition has been understood within the paradigm of electron-electron interactions mediated by longitudinal acoustic phonons. This classical picture has recently been revised in topological semimetals, where transverse acoustic phonons can couple with conducting p-orbital electrons and give rise to an unconventional Fermi surface instability, dubbed the transverse Peierls transition (TPT). Most interestingly, the TPT induced lattice distortions can further break rotation or mirror/inversion symmetries, leading to nematic or chiral charge density waves (CDWs). Quantum materials that host the TPT, however, have not been experimentally established. Here, we report the experimental discovery of an incommensurate TPT in the tetragonal Dirac semimetal EuAl$_4$. Using inelastic x-ray scattering with meV resolution, we observe the complete softening of a transverse acoustic phonon at the CDW wavevector upon cooling, whereas the longitudinal acoustic phonon is nearly unchanged. Combining with first principles calculations, we show that the incommensurate CDW wavevector matches the calculated charge susceptibility peak and connects the nested Dirac bands with Al 3$p_{x}$ and 3$p_{y}$ orbitals. Supplemented by second harmonic generation measurements, we show that the CDW induced lattice distortions break all vertical and diagonal mirrors whereas the four-fold rotational symmetry is retained below the CDW transition. Our observations strongly suggest a chiral CDW in EuAl$_4$ and highlight the TPT as a new avenue for chiral quantum states., Comment: Supplementary materials are available upon request

Earth Observation (EO) has traditionally involved the transmission of a large volume of raw data to map the Earth surface. This results in congestion to the satellite network and delays in the availability of the results, invalidating the approach for timing-sensitive applications. Instead, the computation resources at the satellites can be used as an edge layer for compressing the data and/or doing inferences. In this paper, we investigate satellite edge computing for vessel detection with a LEO satellite constellation. First, we distribute the computation and inference load among the neighbouring satellites of the one taking the images, based on the VHRShips data set and YOLOv8. This semantic and fragmented information is then routed to a remote ground monitor through the whole constellation. The average and peak Age of Information (AoI) are reformulated to measure the freshness of the aggregated information at the receiver in this image-capture scenario. We then dimension the network (number of orbital planes and satellites per orbital plane) for a given target age and covered area that quantify the level of achievement of the task. The results show that 20 orbital planes with 20 satellites are necessary to keep the peak AoI below 60 seconds with a compression ratio > 23000, i.e., a size reduction of 99.996%, and for a approximately 100% probability of coverage., Comment: This paper was presented at the ComSoc Challenge on 'Future 6G NTN Systems' held at ICCSPA'24, where it was awarded second place

We present Syndeo: a software framework for container orchestration of Ray on Slurm. In general the idea behind Syndeo is to write code once and deploy anywhere. Specifically, Syndeo is designed to addresses the issues of portability, scalability, and security for parallel computing. The design is portable because the containerized Ray code can be re-deployed on Amazon Web Services, Microsoft Azure, Google Cloud, or Alibaba Cloud. The process is scalable because we optimize for multi-node, high-throughput computing. The process is secure because users are forced to operate with unprivileged profiles meaning administrators control the access permissions. We demonstrate Syndeo's portable, scalable, and secure design by deploying containerized parallel workflows on Slurm for which Ray does not officially support., Comment: IEEE HPEC 2024. 6 Pages

Future neuromorphic architectures will require millions of artificial synapses, making understanding the physical mechanisms behind their plasticity functionalities mandatory. In this work, we propose a simplified spin memristor, where the resistance can be controlled by magnetic field pulses, based on a Co/Pt multilayer with perpendicular magnetic anisotropy as a synapsis emulator. We demonstrate plasticity and spike time dependence plasticity (STDP) in this device and explored the underlying magnetic mechanisms using Kerr microscopy imaging and Hall magneto-transport measurements. A well-defined threshold for magnetization reversal and the continuous resistance states associated with the micromagnetic configuration are the basic properties allowing plasticity and STDP learning mechanisms in this device., Comment: 7 pages, 5 figures research paper

This article studies an inverse problem for a transmission wave equation, a system where the main coefficient has a variable jump across an internal interface given by the boundary between two subdomains. The main result obtains Lipschitz stability in recovering a zeroth-order coefficient in the equation. The proof is based on the Bukhgeim-Klibanov method and uses a new one-parameter global Carleman inequality, specifically constructed for the case of a variable main coefficient which is discontinuous across a strictly convex interface. In particular, our hypothesis allows the main coefficient to vary smoothly within each subdomain up to the interface, thereby extending the preceding literature on the subject.

Most deep learning models of multiclass arrhythmia classification are tested on fingertip photoplethysmographic (PPG) data, which has higher signal-to-noise ratios compared to smartwatch-derived PPG, and the best reported sensitivity value for premature atrial/ventricular contraction (PAC/PVC) detection is only 75%. To improve upon PAC/PVC detection sensitivity while maintaining high AF detection, we use multi-modal data which incorporates 1D PPG, accelerometers, and heart rate data as the inputs to a computationally efficient 1D bi-directional Gated Recurrent Unit (1D-Bi-GRU) model to detect three arrhythmia classes. We used motion-artifact prone smartwatch PPG data from the NIH-funded Pulsewatch clinical trial. Our multimodal model tested on 72 subjects achieved an unprecedented 83% sensitivity for PAC/PVC detection while maintaining a high accuracy of 97.31% for AF detection. These results outperformed the best state-of-the-art model by 20.81% for PAC/PVC and 2.55% for AF detection even while our model was computationally more efficient (14 times lighter and 2.7 faster).

The integration of Semantic Communications (SemCom) and edge computing in space networks enables the optimal allocation of the scarce energy, computing, and communication resources for data-intensive applications. We use Earth Observation (EO) as a canonical functionality of satellites and review its main characteristics and challenges. We identify the potential of the space segment, represented by a low Earth orbit (LEO) satellite constellation, to serve as an edge layer for distributed intelligence. Based on that, propose a system architecture that supports semantic and goal-oriented applications for image reconstruction and object detection and localization. The simulation results show the intricate trade-offs among energy, time, and task-performance using a real dataset and State-of-the-Art (SoA) processing and communication parameters., Comment: Submitted for publication to IEEE Communications Magazine

Charge density wave (CDW) and their interplay with correlated and topological quantum states are forefront of condensed matter research. The 4$H_{b}$-TaS$_2$ is a CDW ordered quantum heterostructure that is formed by alternative stacking of Mott insulating 1T-TaS$_2$ and Ising superconducting 1H-TaS$_2$. While the $\sqrt{13}\times\sqrt{13}$ and 3$\times$3 CDWs have been respectively observed in the bulk 1T-TaS$_2$ and 2H-TaS$_2$, the CDWs and their pivotal role for unconventional superconductivity in the 4$H_{b}$-TaS$_2$ remain unsolved. In this letter, we reveal the 2-dimensional (2D) $\sqrt{13}\times\sqrt{13}$ chiral CDW in the 1T-layers and intra-unit cell coupled 2D 2$\times$2 CDW in the 1H and 1H' layers of 4$H_{b}$-TaS$_2$. Our results establish 4$H_{b}$-TaS$_2$ a novel 2.5D quantum heterostructure, where 2D quantum states emerge from 3D crystalline structure.

The finite momentum superconducting pairing states (FMPs), where Cooper pairs carry non-zero momentum, are believed to give rise to exotic physical phenomena including the pseudogap phase of cuprate high-Tc superconductors and Majorana fermions in topological superconductivity. FMPs can emerge in intertwined electronic liquids with strong spin-spin interactions or be induced by lifting the spin degeneracy under magnetic field as originally proposed by Fulde-Ferrell and Larkin-Ovchinnikov. In quantum materials with strong Ising-type spin-orbit coupling, such as the 2D transition metal dichalcogenides (TMDs), the spin degree of freedom is frozen enabling novel orbital driven FMPs via magnetoelectric effect. While evidence of orbital driven FMPs has been revealed in bilayer TMDs, its realization in 3D bulk materials remains an unresolved challenge. Here we report experimental signatures of FMP in a locally noncentrosymmetric bulk superconductor 4Hb-TaS2. Using hard X-ray diffraction and angle-resolved photoemission spectroscopy, we reveal unusual 2D chiral charge density wave (CDW) and weak interlayer hopping in 4Hb-TaS2. Below the superconducting transition temperature, the upper critical field, Hc2, linearly increases via decreasing temperature, and well exceeds the Pauli limit, thus establishing the dominant orbital pair-breaking mechanism. Remarkably, we discover a field-induced superconductivity-to-superconductivity transition that breaks continuous rotational symmetry of the s-wave uniform pairing in the Bardeen-Cooper-Schrieffer theory down to the six-fold rotation symmetry. Combining with a Ginzburg-Landau free energy analysis that incorporates magnetoelectric effect, our observations provide strong evidence of orbital driven FMP in the 3D quantum heterostructure 4Hb-TaS2.

We present $\sim300$ stellar metallicity measurements in two faint M31 dwarf galaxies, Andromeda XVI ($M_V = -7.5$) and Andromeda XXVIII ($M_V = -8.8$) derived using metallicity-sensitive Calcium H & K narrow-band Hubble Space Telescope imaging. These are the first individual stellar metallicities in And~XVI (95 stars). Our And~XXVIII sample (191 stars) is a factor of $\sim15$ increase over literature metallicities. For And~XVI, we measure $\langle \mbox{[Fe/H]}\rangle = -2.17^{+0.05}_{-0.05}$, $\sigma_{\mbox{[Fe/H]}}=0.33^{+0.07}_{-0.07}$, and $\nabla_{\mbox{[Fe/H]}} = -0.23\pm0.15$ dex $R_e^{-1}$. We find that And XVI is more metal-rich than MW UFDs of similar luminosity, which may be a result of its unusually extended star formation history. For And XXVIII, we measure $\langle \mbox{[Fe/H]}\rangle = -1.95^{+0.04}_{-0.04}$, $\sigma_{\mbox{[Fe/H]}}=0.34^{+0.07}_{-0.07}$, and $\nabla_{\mbox{[Fe/H]}} = -0.46 \pm 0.10$~dex~$R_e^{-1}$, placing it on the dwarf galaxy mass-metallicity relation. Neither galaxy has a metallicity distribution function with an abrupt metal-rich truncation, suggesting that star formation fell off gradually. The stellar metallicity gradient measurements are among the first for faint ($L \lesssim 10^6~L_{\odot}$) galaxies outside the Milky Way halo. Both galaxies' gradients are consistent with predictions from the FIRE simulations, where an age-gradient strength relationship is the observational consequence of stellar feedback that produces dark matter cores. We include a catalog for community spectroscopic follow-up, including 19 extremely metal poor ($\mbox{[Fe/H]} < -3.0$) star candidates, which make up 7% of And~XVI's MDF and 6% of And~XXVIII's., Comment: 17 pages, 5 figures, 4 tables, ApJ submitted; comments welcome!

Targeted protein degradation (TPD) is a rapidly growing field in modern drug discovery that aims to regulate the intracellular levels of proteins by harnessing the cell's innate degradation pathways to selectively target and degrade disease-related proteins. This strategy creates new opportunities for therapeutic intervention in cases where occupancy-based inhibitors have not been successful. Proteolysis-targeting chimeras (PROTACs) are at the heart of TPD strategies, which leverage the ubiquitin-proteasome system for the selective targeting and proteasomal degradation of pathogenic proteins. As the field evolves, it becomes increasingly apparent that the traditional methodologies for designing such complex molecules have limitations. This has led to the use of machine learning (ML) and generative modeling to improve and accelerate the development process. In this review, we explore the impact of ML on de novo PROTAC design $-$ an aspect of molecular design that has not been comprehensively reviewed despite its significance. We delve into the distinct characteristics of PROTAC linker design, underscoring the complexities required to create effective bifunctional molecules capable of TPD. We then examine how ML in the context of fragment-based drug design (FBDD), honed in the realm of small-molecule drug discovery, is paving the way for PROTAC linker design. Our review provides a critical evaluation of the limitations inherent in applying this method to the complex field of PROTAC development. Moreover, we review existing ML works applied to PROTAC design, highlighting pioneering efforts and, importantly, the limitations these studies face. By offering insights into the current state of PROTAC development and the integral role of ML in PROTAC design, we aim to provide valuable perspectives for researchers in their pursuit of better design strategies for this new modality.

PROTACs are a promising therapeutic modality that harnesses the cell's built-in degradation machinery to degrade specific proteins. Despite their potential, developing new PROTACs is challenging and requires significant domain expertise, time, and cost. Meanwhile, machine learning has transformed drug design and development. In this work, we present a strategy for curating open-source PROTAC data and an open-source deep learning tool for predicting the degradation activity of novel PROTAC molecules. The curated dataset incorporates important information such as $pDC_{50}$, $D_{max}$, E3 ligase type, POI amino acid sequence, and experimental cell type. Our model architecture leverages learned embeddings from pretrained machine learning models, in particular for encoding protein sequences and cell type information. We assessed the quality of the curated data and the generalization ability of our model architecture against new PROTACs and targets via three tailored studies, which we recommend other researchers to use in evaluating their degradation activity models. In each study, three models predict protein degradation in a majority vote setting, reaching a top test accuracy of 80.8% and 0.865 ROC AUC, and a test accuracy of 62.3% and 0.604 ROC AUC when generalizing to novel protein targets. Our results are not only comparable to state-of-the-art models for protein degradation prediction, but also part of an open-source implementation which is easily reproducible and less computationally complex than existing approaches., Comment: 13 pages, 10 figures

While galaxy rotation curves provide one of the most powerful methods for measuring dark matter profiles in the inner regions of rotation-supported galaxies, at the dwarf scale there are factors that can complicate this analysis. Given the expectation of a universal profile in dark matter-only simulations, the diversity of observed rotation curves has become an often-discussed issue in Lambda Cold Dark Matter cosmology on galactic scales. We analyze a suite of Feedback in Realistic Environments (FIRE) simulations of $10^{10}-10^{12}$ $M_\odot$ halos with standard cold dark matter, and compare the true circular velocity to rotation curve reconstructions. We find that, for galaxies with well-ordered gaseous disks, the measured rotation curve may deviate from true circular velocity by at most 10% within the radius of the disk. However, non-equilibrium behavior, non-circular motions, and non-thermal and non-kinetic stresses may cause much larger discrepancies of 50% or more. Most rotation curve reconstructions underestimate the true circular velocity, while some reconstructions transiently over-estimate it in the central few kiloparsecs due to dynamical phenomena. We further demonstrate that the features that contribute to these failures are not always visibly obvious in HI observations. If such dwarf galaxies are included in galaxy catalogs, they may give rise to the appearance of "artificial" rotation curve diversity that does not reflect the true variation in underlying dark matter profiles., Comment: 20 pages, 10 figures

The observationally-inferred size versus stellar-mass relationship (SMR) for low-mass galaxies provides an important test for galaxy formation models. However, the relationship relies on assumptions that relate observed luminosity profiles to underlying stellar mass profiles. Here we use the Feedback in Realistic Environments simulations of low-mass galaxies to explore how the predicted SMR changes depending on whether one uses star-particle counts directly or mock observations. We reproduce the SMR found in The Exploration of Local Volume Satellites survey remarkably well only when we infer stellar masses and sizes using mock observations. However, when we use star particles to directly infer stellar masses and half-mass radii, we find that our galaxies are too large and obey a SMR with too little scatter compared to observations. This discrepancy between the "true" galaxy size and mass and those derived in the mock observation approach is twofold. First, our simulated galaxies have higher and more varied MLRs at a fixed colour than those commonly-adopted, which tends to underestimate their stellar masses compared to their true, simulated values. Second, our galaxies have radially increasing MLR gradients therefore using a single MLR tends to under-predict the mass in the outer regions. Similarly, the true half-mass radius is larger than the half-light radius because the light is more concentrated than the mass. If our simulations are accurate representations of the real universe, then the relationship between galaxy size and stellar mass is even tighter for low-mass galaxies than is commonly inferred from observed relations., Comment: 11 pages, 7 figures, 1 table

We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer-based language model architectures on these tokenized time series via the cross-entropy loss. We pretrained Chronos models based on the T5 family (ranging from 20M to 710M parameters) on a large collection of publicly available datasets, complemented by a synthetic dataset that we generated via Gaussian processes to improve generalization. In a comprehensive benchmark consisting of 42 datasets, and comprising both classical local models and deep learning methods, we show that Chronos models: (a) significantly outperform other methods on datasets that were part of the training corpus; and (b) have comparable and occasionally superior zero-shot performance on new datasets, relative to methods that were trained specifically on them. Our results demonstrate that Chronos models can leverage time series data from diverse domains to improve zero-shot accuracy on unseen forecasting tasks, positioning pretrained models as a viable tool to greatly simplify forecasting pipelines., Comment: Code and model checkpoints available at https://github.com/amazon-science/chronos-forecasting

Vision-based tactile sensors have recently become popular due to their combination of low cost, very high spatial resolution, and ease of integration using widely available miniature cameras. The associated field of view and focal length, however, are difficult to package in a human-sized finger. In this paper we employ optical fiber bundles to achieve a form factor that, at 15 mm diameter, is smaller than an average human fingertip. The electronics and camera are also located remotely, further reducing package size. The sensor achieves a spatial resolution of 0.22 mm and a minimum force resolution 5 mN for normal and shear contact forces. With these attributes, the DIGIT Pinki sensor is suitable for applications such as robotic and teleoperated digital palpation. We demonstrate its utility for palpation of the prostate gland and show that it can achieve clinically relevant discrimination of prostate stiffness for phantom and ex vivo tissue., Comment: This work has been submitted to the IEEE for possible publication. The CAD design files of DIGIT Pinki are available at https://github.com/facebookresearch/digit-design

Control of topological edge modes is desirable for encoding quantum information resiliently against external noise. Their implementation on quantum hardware, however, remains a long-standing problem due to current limitations of circuit depth and noise, which grows with the number of time steps. By utilizing recently developed constant-depth quantum circuits in which the circuit depth is independent of time, we demonstrate successful long-time dynamics simulation of bulk and surface modes in topological insulators on noisy intermediate-scale quantum (NISQ) processors, which exhibits robust signatures of localized topological modes. We further identify a class of one-dimensional topological Hamiltonians that can be readily simulated with NISQ hardware. Our results provide a pathway towards stable long-time implementation of topological quantum spin systems on present day quantum processors.

Let $\mathfrak{g}$ be the simple Lie algebra of square matrices $(n+1)\times (n+1)$ with zero trace. There are certain relations concerning standard automorphisms that are considered ``folklore". One can find a complete proof of these in Millson and Toledano Laredo's work [Transformation Groups, Vol. 10, No. 2, 2005, pp. 217-254], but said proof is based on topological arguments which are non-trivial. The aim of this work is to verify these relations in an elementary way., Comment: 14 pages

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.

We measure the metallicities of 374 red giant branch (RGB) stars in the isolated, quenched dwarf galaxy Tucana using Hubble Space Telescope (HST) narrow-band (F395N) Calcium H & K (CaHK) imaging. Our sample is a factor of $\sim7$ larger than what is published. Our main findings are: (i) A global metallicity distribution function (MDF) with $\langle \mbox{[Fe/H]} \rangle = -1.55 \pm 0.04$ and $\sigma_{\mbox{[Fe/H]}}=0.54\pm0.03$; (ii) A metallicity gradient of $-0.54 \pm 0.07$ dex $R_e^{-1}$ ($-2.1 \pm 0.3$ dex kpc$^{-1}$) over the extent of our imaging ($\sim 2.5 R_e$), which is steeper than literature measurements. Our finding is consistent with predicted gradients from the publicly-available FIRE-2 simulations, in which bursty star formation creates stellar population gradients and dark matter cores; (iii) Tucana's bifurcated RGB has distinct metallicities: a blue RGB with $\langle \mbox{[Fe/H]} \rangle = -1.78 \pm 0.06$ and $\sigma_{\mbox{[Fe/H]}}=0.44^{+0.07}_{-0.06}$, and a red RGB with $\langle \mbox{[Fe/H]} \rangle = -1.08 \pm 0.07$ and $\sigma_{\mbox{[Fe/H]}}=0.42 \pm 0.06$; (iv) At fixed stellar mass, Tucana is more MR than MW satellites by $\sim 0.4$ dex, but its blue RGB is chemically comparable to the satellites. Tucana's MDF appears consistent with star-forming isolated dwarfs, though MDFs of the latter are not as well-populated; (v) $\sim2$% of Tucana's stars have $\mbox{[Fe/H]} < -3$ and 20% $\mbox{[Fe/H]} > -1$. We provide a catalog for community spectroscopic follow-up., Comment: Replaced with ApJ published version; 23 pages, 18 figures

We develop for the CKM and PMNS matrices a new representation with special properties. It is obtained by splitting each of these matrices into two rotations by the angle ${\sim}2\pi/3$ and a universal diagonal matrix with elements, which are cubic roots of~1. Such a representation of the CKM and PMNS matrices may indicate the $Z_{3}$ symmetry to be present in the Yukawa sector of the~SM. Identical mathematical structure of the CKM and PMNS matrices is also an extension of the quark-lepton universality. In this approach the CP violation is a natural consequence of the structure of the Yukawa couplings. The CP violating phase is not a fitted parameter and its value is governed by the parameters of two rotations. The parameters of the diagonalizing matrices of the bi-unitary transformation do not exhibit a hierarchy, which means that the origins of the hierarchy of quark masses and of the CKM matrix elements are not the same., Comment: 5 pages