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Barred galaxies exhibit boxy/peanut or X-shapes (BP/X) protruding from their disks in edge-on views. Two types of BP/X morphologies exist depending on whether the X-wings meet at the center (CX) or are off-centered (OX). Orbital studies indicate that various orbital types can generate X-shaped structures. Here, we provide a classification approach that identifies the specific orbit families responsible for generating OX and CX-shaped structures. Applying this approach to three different N-body bar models, we show that both OX and CX structures are associated with the x1 orbit family, but OX-supporting orbits possess higher angular momentum (closer to x1 orbits) than orbits in CX structures. Consequently, as the bar slows down the contribution of higher angular momentum OX-supporting orbits decreases and that of lower angular momentum orbits increases resulting in an evolution of the morphology from OX to CX. If the bar does not slow down, the shape of the BP/X structure and the fractions of OX/CX supporting orbits remain substantially unchanged. Bars that do not undergo buckling but that do slow down initially show the OX structure and are dominated by high angular momentum orbits, transitioning to a CX morphology. Bars that buckle exhibit a combination of both OX and CX supporting orbits immediately after the buckling, but become more CX dominated as their pattern speed decreases. This study demonstrates that the evolution of BP/X morphology and orbit populations strongly depends on the evolution of the bar angular momentum., Comment: 16 pages, 12 figures, Accepted for publication in ApJ

We provide the first construction of overhanging gravity water waves having the approximate form of a disk joined to a strip by a thin neck. The waves are solitary with constant vorticity, and exist when an appropriate dimensionless gravitational constant $g>0$ is sufficiently small. Our construction involves combining three explicit solutions to related problems: a disk of fluid in rigid rotation, a linear shear flow in a strip, and a rescaled version of an exceptional domain discovered by Hauswirth, H\'elein, and Pacard \cite{hauswirth-helein-pacard}. The method developed here is related to the construction of constant mean curvature surfaces through gluing., Comment: 93 pages

We consider a nonlinear control system with vector-valued measures as controls and with dynamics depending on time delayed states. First, we introduce a notion of discontinuous, bounded variation solution associated with this system and establish an equivalent representation formula for it, inspired by the approach known in delay-free impulsive control as the `graph completions' method. Then, thanks to this equivalent formulation, we prove well-posedness properties of these solutions and also derive necessary optimality conditions in the form of a Maximum Principle for an associated minimization problem.

While language model (LM)-powered chatbots and generative search engines excel at answering concrete queries, discovering information in the terrain of unknown unknowns remains challenging for users. To emulate the common educational scenario where children/students learn by listening to and participating in conversations of their parents/teachers, we create Collaborative STORM (Co-STORM). Unlike QA systems that require users to ask all the questions, Co-STORM lets users observe and occasionally steer the discourse among several LM agents. The agents ask questions on the user's behalf, allowing the user to discover unknown unknowns serendipitously. To facilitate user interaction, Co-STORM assists users in tracking the discourse by organizing the uncovered information into a dynamic mind map, ultimately generating a comprehensive report as takeaways. For automatic evaluation, we construct the WildSeek dataset by collecting real information-seeking records with user goals. Co-STORM outperforms baseline methods on both discourse trace and report quality. In a further human evaluation, 70% of participants prefer Co-STORM over a search engine, and 78% favor it over a RAG chatbot.

We explore the consequence of generalized symmetries in four-dimensional $\mathcal{N}=1$ superconformal field theories. First, we classify all possible supersymmetric gauge theories with a simple gauge group that have a nontrivial one-form symmetry and flows to a superconformal field theory. Upon identifying unbroken discrete zero-form symmetries from the ABJ anomaly, we find that many of these theories have mixed zero-form/one-form 't Hooft anomalies. Then we classify the relevant deformations of these SCFTs that preserve the anomaly. From this mixed anomaly together with the anomalies of the discrete zero-form symmetries, we find obstructions for the relevant deformations of these SCFTs to flow to a trivially gapped phase. We also study non-Lagrangian SCFTs formed by gauging copies of Argyres-Douglas theories and constrain their deformations. In particular, we explore a new duality between the diagonal gauging of two $\mathcal{D}_3(SU(N))$ theories and $SU(N)$ gauge theory with two adjoints. We also repeat our analysis for a host of non-supersymmetric gauge theories having nontrivial one-form symmetry including examples that appear to flow to Bank-Zaks type CFTs., Comment: 78 pages + references

Forgetting presents a significant challenge during incremental training, making it particularly demanding for contemporary AI systems to assimilate new knowledge in streaming data environments. To address this issue, most approaches in Continual Learning (CL) rely on the replay of a restricted buffer of past data. However, the presence of noise in real-world scenarios, where human annotation is constrained by time limitations or where data is automatically gathered from the web, frequently renders these strategies vulnerable. In this study, we address the problem of CL under Noisy Labels (CLN) by introducing Alternate Experience Replay (AER), which takes advantage of forgetting to maintain a clear distinction between clean, complex, and noisy samples in the memory buffer. The idea is that complex or mislabeled examples, which hardly fit the previously learned data distribution, are most likely to be forgotten. To grasp the benefits of such a separation, we equip AER with Asymmetric Balanced Sampling (ABS): a new sample selection strategy that prioritizes purity on the current task while retaining relevant samples from the past. Through extensive computational comparisons, we demonstrate the effectiveness of our approach in terms of both accuracy and purity of the obtained buffer, resulting in a remarkable average gain of 4.71% points in accuracy with respect to existing loss-based purification strategies. Code is available at https://github.com/aimagelab/mammoth., Comment: 25 pages, 5 figures. Accepted at the The 35th British Machine Vision Conference 2024 (BMVC 2024), Glasgow, UK

Quantum error correction provides a path to reach practical quantum computing by combining multiple physical qubits into a logical qubit, where the logical error rate is suppressed exponentially as more qubits are added. However, this exponential suppression only occurs if the physical error rate is below a critical threshold. In this work, we present two surface code memories operating below this threshold: a distance-7 code and a distance-5 code integrated with a real-time decoder. The logical error rate of our larger quantum memory is suppressed by a factor of $\Lambda$ = 2.14 $\pm$ 0.02 when increasing the code distance by two, culminating in a 101-qubit distance-7 code with 0.143% $\pm$ 0.003% error per cycle of error correction. This logical memory is also beyond break-even, exceeding its best physical qubit's lifetime by a factor of 2.4 $\pm$ 0.3. We maintain below-threshold performance when decoding in real time, achieving an average decoder latency of 63 $\mu$s at distance-5 up to a million cycles, with a cycle time of 1.1 $\mu$s. To probe the limits of our error-correction performance, we run repetition codes up to distance-29 and find that logical performance is limited by rare correlated error events occurring approximately once every hour, or 3 $\times$ 10$^9$ cycles. Our results present device performance that, if scaled, could realize the operational requirements of large scale fault-tolerant quantum algorithms., Comment: 10 pages, 4 figures, Supplementary Information

By exciting a series of $1\mathrm{s}^{2}\, ^{1}\mathrm{S}_{0} \to 1\mathrm{s}n\mathrm{p}\, ^{1}\mathrm{P}_{1}$ transitions in helium-like nitrogen ions with linearly polarized monochromatic soft X-rays at the Elettra facility, we found a change in the angular distribution of the fluorescence sensitive to the principal quantum number $n$. In particular it is observed that the ratio of emission in directions parallel and perpendicular to the polarization of incident radiation increases with higher $n$. We find this $n$-dependence to be a manifestation of the Hanle effect, which served as a practical tool for lifetime determinations of optical transitions since its discovery in 1924. In contrast to traditional Hanle effect experiments, in which one varies the magnetic field and considers a particular excited state, we demonstrate a 'soft X-ray Hanle effect' which arises in a static magnetic field but for a series of excited states. By comparing experimental data with theoretical predictions, we were able to determine lifetimes ranging from hundreds of femtoseconds to tens of picoseconds of the $1\mathrm{s}n\mathrm{p}\, ^{1}\mathrm{P}_{1}$ levels, which find excellent agreement with atomic-structure calculations. We argue that dedicated soft X-ray measurements could yield lifetime data that is beyond current experimental reach and cannot yet be predicted with sufficient accuracy., Comment: 7 pages, 4 figures

Electrocardiogram (ECG) is a valuable tool for medical diagnosis used worldwide. Its use has contributed significantly to the prevention of cardiovascular diseases including infarctions. Although physicians need to see the printed curves for a diagnosis, nowadays there exist automated tools based on machine learning that can help diagnosis of arrhythmias and other pathologies, these tools operate on digitalized ECG data that are merely one-dimensional discrete signals (a kind of information that is much similar to digitized audio). Thus, it is interesting to have both the graphical information and the digitized data. This is possible with modern, digital equipment. Nevertheless, there still exist many analog electrocardiogram machines that plot results on paper with a printed gris measured in millimeters. This paper presents a novel image analysis method that is capable of reading a printed ECG and converting it into a sampled digital signal., Comment: 6 pages, 13 figures

It was recently noted the existence of an apparently discontinuous boundary between determinism and indeterminism in Quantum Mechanics. We propose to explore this boundary in an optical Bell's experiment by recording the distribution, of the number of strings of outcomes of a given parity interrupted by outcomes of the other parity, as a function of their length. The features of these distributions for small rotations of the angle settings near critical points may indicate whether the underlying process is in-deterministic or not. Therefore, they may show that the boundary is discontinuous, or else, that determinism decays smoothly. The conditions the experimental setup must fulfill are discussed., Comment: 5 pages, 4 figures

We present new JWST observations of the nearby, prototypical edge-on, spiral galaxy NGC 891. The northern half of the disk was observed with NIRCam in its F150W and F277W filters. Absorption is clearly visible in the mid-plane of the F150W image, along with vertical dusty plumes that closely resemble the ones seen in the optical. A $\sim 10 \times 3~{\rm kpc}^2$ area of the lower circumgalactic medium (CGM) was mapped with MIRI F770W at 12 pc scales. Thanks to the sensitivity and resolution of JWST, we detect dust emission out to $\sim 4$ kpc from the disk, in the form of filaments, arcs, and super-bubbles. Some of these filaments can be traced back to regions with recent star formation activity, suggesting that feedback-driven galactic winds play an important role in regulating baryonic cycling. The presence of dust at these altitudes raises questions about the transport mechanisms at play and suggests that small dust grains are able to survive for several tens of million years after having been ejected by galactic winds in the disk-halo interface. We lay out several scenarios that could explain this emission: dust grains may be shielded in the outer layers of cool dense clouds expelled from the galaxy disk, and/or the emission comes from the mixing layers around these cool clumps where material from the hot gas is able to cool down and mix with these cool cloudlets. This first set of data and upcoming spectroscopy will be very helpful to understand the survival of dust grains in energetic environments, and their contribution to recycling baryonic material in the mid-plane of galaxies., Comment: Accepted for publication in Astronomy & Astrophysics; 16 pages, 8 figures

Pre-trained LLMs have demonstrated substantial capabilities across a range of conventional natural language processing (NLP) tasks, such as summarization and entity recognition. In this paper, we explore the application of LLMs in the generation of high-quality protein sequences. Specifically, we adopt a suite of pre-trained LLMs, including Mistral-7B1, Llama-2-7B2, Llama-3-8B3, and gemma-7B4, to produce valid protein sequences. All of these models are publicly available.5 Unlike previous work in this field, our approach utilizes a relatively small dataset comprising 42,000 distinct human protein sequences. We retrain these models to process protein-related data, ensuring the generation of biologically feasible protein structures. Our findings demonstrate that even with limited data, the adapted models exhibit efficiency comparable to established protein-focused models such as ProGen varieties, ProtGPT2, and ProLLaMA, which were trained on millions of protein sequences. To validate and quantify the performance of our models, we conduct comparative analyses employing standard metrics such as pLDDT, RMSD, TM-score, and REU. Furthermore, we commit to making the trained versions of all four models publicly available, fostering greater transparency and collaboration in the field of computational biology., Comment: This paper has been accepted for presentation at Language and Molecules ACL 2024

A velocity field characterized by chaotic advection induces stretching and folding processes that increase the solute-solvent interface available for diffusion. Identifying chaotic flow fields with optimized mixing enhancement is relevant for applications like groundwater remediation, microfluidics, and many more. This work uses the dilution index to quantify the temporal increase in mixing of a solute within its solvent. We introduce a new approach to select a suitable grid size for each time step in the dilution index approximation, motivated by the theory of representative elementary volumes. It preserves the central feature of the dilution index, which is monotonically increasing in time and hence leads to reliable results. Our analysis highlights the importance of a suitable choice for the grid size in the dilution index approximation. We use this approach to demonstrate the mixing enhancement for two chaotic injection-extraction systems that exhibit chaotic structures: a source-sink dipole and a rotated potential mixing. By analyzing the chaotic flow fields, we identify Kolmogorov--Arnold--Moser (KAM) islands, non-mixing regions that limit the chaotic area in the domain and, thereby, the mixing enhancement. Using our new approach, we assess the choice of design parameters of the injection-extraction systems to effectively engineer chaotic mixing. We demonstrate the important role of diffusion in filling the KAM islands and reaching complete mixing in the systems., Comment: paper: 17 pages, 15 figures. supplemental material: 4 pages, 3 figures, submitted to Physical Review E

We provide an approach enabling one to employ physics-informed neural networks (PINNs) for uncertainty quantification. Our approach is applicable to systems where observations are scarce (or even lacking), these being typical situations associated with subsurface water bodies. Our novel physics-informed neural network under uncertainty (PINN-UU) integrates the space-time domain across which processes take place and uncertain parameter spaces within a unique computational domain. PINN-UU is then trained to satisfy the relevant physical principles (e.g., mass conservation) in the defined input domain. We employ a stage training approach via transfer learning to accommodate high-dimensional solution spaces. We demonstrate the effectiveness of PINN-UU in a scenario associated with reactive transport in porous media, showcasing its reliability, efficiency, and applicability to sensitivity analysis. PINN-UU emerges as a promising tool for robust uncertainty quantification, with broad applicability to groundwater systems. As such, it can be considered as a valuable alternative to traditional methods such as multi-realization Monte Carlo simulations based on direct solvers or black-box surrogate models., Comment: 25 pages, 9 figures

We present a near infrared (NIR) candidate star cluster catalog for the central kiloparsec of M82 based on new JWST NIRCam images. We identify star cluster candidates using the F250M filter, finding 1357 star cluster candidates with stellar masses $>10^4$ M$_\odot$. Compared to previous optical catalogs, nearly all (87%) of the candidates we identify are new. The star cluster candidates have a median intrinsic cluster radius of $\approx$1 pc and have stellar masses up to $10^6$ M$_\odot$. By comparing the color-color diagram to dust-free yggdrasil stellar population models, we estimate that the star cluster candidates have A$_{\rm V}\sim3-24$ mag, corresponding to A$_{\rm 2.5\mu m}\sim0.3-2.1$ mag. There is still appreciable dust extinction towards these clusters into the NIR. We measure the stellar masses of the star cluster candidates, assuming ages of 0 and 8 Myr. The slope of the resulting cluster mass function is $\beta=1.9\pm0.2$, in excellent agreement with studies of star clusters in other galaxies., Comment: Resubmitted to ApJL

In this work we consider three species competing with each other in the same habitat. One of the species lives in the entire habitat, competing with the other two species, while the other two inhabit two disjoint regions of the habitat. These two populations just interact on a region/interface which acts as a geographical barrier. This barrier condition causes a drastic change in species behaviour compared to the classical Lotka-Volterra competitive model, showing very rich and new different situations depending on the several parameters involved in the system., Comment: 28 pages, 8 figures

Solving very large linear systems of equations is a key computational task in science and technology. In many cases, the coefficient matrix of the linear system is rank-deficient, leading to systems that may be underdetermined, inconsistent, or both. In such cases, one generally seeks to compute the least squares solution that minimizes the residual of the problem, which can be further defined as the solution with smallest norm in cases where the coefficient matrix has a nontrivial nullspace. This work presents several new techniques for solving least squares problems involving coefficient matrices that are so large that they do not fit in main memory. The implementations include both CPU and GPU variants. All techniques rely on complete orthogonal decompositions that guarantee that both conditions of a least squares solution are met, regardless of the rank properties of the matrix. Specifically, they rely on the recently proposed "randUTV" algorithm that is particularly effective in strongly communication-constrained environments. A detailed precision and performance study reveals that the new methods, that operate on data stored on disk, are competitive with state-of-the-art methods that store all data in main memory., Comment: 26 pages, 12 figures

Due to observational challenges, the mass function of black holes (BH) at lower masses is poorly constrained in the local universe. Understanding the occupation fraction of BHs in low-mass galaxies is crucial for constraining the origins of supermassive BH seeds. Compact stellar systems (CSSs), including ultra-compact dwarf galaxies (UCDs) and compact elliptical galaxies (cEs), are potential intermediate-mass BH hosts. Despite the difficulties posed by their limited spheres of influence, stellar dynamical modeling has been effective in estimating central BH masses in CSSs. Some CSSs may harbor a BH constituting up to 20% of their host stellar mass, while others might not have a central BH. In support of our ongoing efforts to determine the BH masses in select CSSs in the Virgo cluster using JWST/NIRSpec IFU observations and orbit-superposition dynamical models, we create mock kinematic data mimicking the characteristics of observed cEs/UCDs in the Virgo cluster with different BH masses. We then construct a series of dynamical models using the orbit-superposition code FORSTAND and explore the accuracy of recovering the BH mass. We find that the mass of BHs comprising 1% or more of the total host stellar mass can be accurately determined through kinematic maps featuring higher-order velocity moments. We also assess how BH mass measurement is affected by deprojection methods, regularization factors, anisotropy parameters, orbit initial conditions, the absence of higher-order velocity moments, spatial resolution, and the signal-to-noise ratio., Comment: 18 pages, 14 figures, Accepted for publication in ApJ

Dark matter (DM) bars that shadow stellar bars have been previously shown to form in idealized simulations of isolated disk galaxies. Here, we show that DM bars commonly occur in barred disk galaxies in the TNG50 cosmological simulation suite, but do not appear in unbarred disk galaxies. Consistent with isolated simulations, DM bars are typically shorter than their stellar counterparts and are $75\%$ weaker as measured by the Fourier $A_2$ moment. DM bars dominate the shape of the inner halo potential and are easily identified in the time series of quadrupolar coefficients. We present two novel methods for measuring the bar pattern speed using these coefficients, and use them to make a measurement of the pattern speed and rotation axis orientation for one sample galaxy located in one of the high-time resolution subboxes of TNG50. The stellar and dark bars in this galaxy remain co-aligned throughout the last 8 Gyr and have identical pattern speeds throughout. Both the pattern speed and rotation axis orientation of the bars evolve considerably over the last 8 Gyr, consistent with torques on the bars due to dynamical friction and gas accretion. While the bar pattern speed generally decreases over the time course, it is seen to increase after mergers. Rather than remaining static in time, the rotation axis displays both precession and nutation possibly caused by torques outside the plane of rotation. We find that the shape of the stellar and DM mass distributions are tightly correlated with the bar pattern speed., Comment: 11 pages, 4 figures

Stellar streams that emerge from globular clusters (GCs) are thin stellar structures spread along the orbits of progenitor clusters. Numerical modeling of these streams is essential for understanding their interaction with the host galaxy's mass distribution. Traditional methods are either computationally expensive or oversimplified, motivating us to develop a fast and accurate approach using a particle spray algorithm. By conducting a series of N-body simulations of GCs orbiting a host galaxy, we find that the position and velocity distributions of newly-escaped stream particles are consistent across various GC masses and orbital parameters. Based on these distributions, we develop a new algorithm that avoids computing the detailed internal cluster dynamics by directly drawing tracer particles from these distributions. This algorithm correctly reproduces the action space distribution of stream particles and achieves a 10% accuracy in stream morphology and velocities compared to N-body simulations., Comment: 17 pages, 10 figures, submitted to AAS journals, comments welcome! An online notebook providing instructions on this algorithm is available at https://github.com/ybillchen/particle_spray

In the present work, we focus on the space-time isogeometric discretization of a parabolic problem with a nonlocal diffusion coefficient. The use of a space-time discretization with smooth basis functions yields advantages in the approximation of the solution. The existence of the unique solution for continuous and discrete space-time variational formulations is proven. We also establish the a priori error estimate for the space-time isogeometric scheme. The non-linear system is linearized through Picard's method and a suitable preconditioner for the linearized system is provided. Finally, to confirm the theoretical findings, results of some numerical experiments are presented.

We discuss the cold dark matter plus massive neutrinos simulations of the MillenniumTNG (MTNG) project, which aim to improve understanding of how well ongoing and future large-scale galaxy surveys will measure neutrino masses. Our largest simulations, $3000\,{\rm Mpc}$ on a side, use $10240^3$ particles of mass $m_{p} = 6.66\times 10^{8}\,h^{-1}{\rm M}_\odot$ to represent cold dark matter, and $2560^3$ to represent a population of neutrinos with summed mass $M_\nu = 100\,{\rm meV}$. Smaller volume runs with $\sim 630\,{\rm Mpc}$ also include cases with $M_\nu = 0\,\textrm{and}\, 300\,{\rm meV}$. All simulations are carried out twice using the paired-and-fixed technique for cosmic variance reduction. We evolve the neutrino component using the particle-based $\delta f$ importance sampling method, which greatly reduces shot noise in the neutrino density field. In addition, we modify the GADGET-4 code to account both for the influence of relativistic and mildly relativistic components on the expansion rate and for non-Newtonian effects on the largest represented simulation scales. This allows us to quantify accurately the impact of neutrinos on basic statistical measures of nonlinear structure formation, such as the matter power spectrum and the halo mass function. We use semi-analytic models of galaxy formation to predict the galaxy population and its clustering properties as a function of summed neutrino mass, finding significant ($\sim 10\%$) impacts on the cosmic star formation rate history, the galaxy mass function, and the clustering strength. This offers the prospect of identifying combinations of summary statistics that are optimally sensitive to the neutrino mass., Comment: 19 pages, 15 figures

Polygenic risk scores (PRSs) can significantly enhance breast cancer risk prediction when combined with clinical risk factor data. While many studies have explored the value-add of PRSs, little is known about the potential impact of gene-by-gene or gene-by-environment interactions towards enhancing the risk discrimination capabilities of multi-modal models combining PRSs with clinical data. In this study, we integrated data on 318 individual genotype variants along with clinical data in a neural network to explore whether gene-by-gene (i.e., between individual variants) and/or gene-by-environment (between clinical risk factors and variants) interactions could be leveraged jointly during training to improve breast cancer risk prediction performance. We benchmarked our approach against a baseline model combining traditional univariate PRSs with clinical data in a logistic regression model and ran an interpretability analysis to identify feature interactions. While our model did not demonstrate improved performance over the baseline, we discovered 248 (<1%) statistically significant gene-by-gene and gene-by-environment interactions out of the ~53.6k possible feature pairs, the most contributory of which included rs6001930 (MKL1) and rs889312 (MAP3K1), with age and menopause being the most heavily interacting non-genetic risk factors. We also modeled the significant interactions as a network of highly connected features, suggesting that potential higher-order interactions are captured by the model. Although gene-by-environment (or gene-by-gene) interactions did not enhance breast cancer risk prediction performance in neural networks, our study provides evidence that these interactions can be leveraged by these models to inform their predictions. This study represents the first application of neural networks to screen for interactions impacting breast cancer risk using real-world data.

In this paper we consider a competitive weakly coupled elliptic system in which each species is attracted to a small region and repelled from its complement. In this setting, we establish the existence of infinitely many solutions and of a nonnegative least energy solution. We show that, as the regions of attraction shrink, least energy solutions of the system concentrate. We study this behavior and characterize their limit profile. In particular, we show that if each component of a least energy solution is attracted to a different region, then the components decouple in the limit, whereas if all the components are attracted to the same region, they remain coupled., Comment: 22 pages

Ferroelectrics under suitable electric boundary conditions can present a negative capacitance response, whereby the total voltage drop across the ferroelectric opposes the externally applied bias. When the ferroelectric is in a heterostructure, this behavior yields a voltage amplification in the other elements, an effect that could be leveraged in low-power electronic devices. Interestingly, the mentioned voltage amplification should have an accompanying elastic effect. Specifically, in the typical case that the materials in contact with the ferroelectric are non-polar dielectrics, those should present an enhanced electrostrictive response. Here we use atomistic simulations - of model PbTiO$_{3}$/SrTiO$_{3}$ ferroelectric/dielectric superlattices displaying negative capacitance - to show that this is indeed the case: we reveal the enhanced elastic response of the dielectric layer and show that it is clearly linked to the voltage amplification. We argue that this "elastic amplification" could serve as a convenient experimental fingerprint for negative capacitance. Further, we propose that it may have interest on its own, e.g. for the development of low-power electromechanical actuators.

Time-domain astrophysics has leaped forward with the direct discovery of gravitational waves and the emergence of new generation instruments for multi-messenger studies. The capacity of the multi-messenger multi-wavelength community to effectively pursue follow-up observations is hindered by the suboptimal localization of numerous transient events and the escalating volume of alerts. Thus, we have developed an effective tool to overcome the observational and technical hurdles inherent in the emerging field of multi-messenger astrophysics. We present tilepy, a Python package for the automatic scheduling of follow-up observations of poorly localized transient events. It is ideally suited to tackle the challenge of complex follow-up in mid and small-FoV telescope campaigns, with or without human intervention. We demonstrate the capabilities of tilepy in the realm of multi-observatory, multi-wavelength campaigns, to cover the localization uncertainty region of various events ultimately aiming at pinpointing the source of the multi-messenger emission. The tilepy code is publicly available on GitHub and is sufficiently flexible to be employed either automatically or in a customized manner, tailored to collaboration and individual requirements. tilepy is also accessible via a public API and through the Astro-COLIBRI platform.

Recent work integrating Large Language Models (LLMs) has led to significant improvements in the Knowledge Base Question Answering (KBQA) task. However, we posit that existing KBQA datasets that either have simple questions, use synthetically generated logical forms, or are based on small knowledge base (KB) schemas, do not capture the true complexity of KBQA tasks. To address this, we introduce the SPINACH dataset, an expert-annotated KBQA dataset collected from forum discussions on Wikidata's "Request a Query" forum with 320 decontextualized question-SPARQL pairs. Much more complex than existing datasets, SPINACH calls for strong KBQA systems that do not rely on training data to learn the KB schema, but can dynamically explore large and often incomplete schemas and reason about them. Along with the dataset, we introduce the SPINACH agent, a new KBQA approach that mimics how a human expert would write SPARQLs for such challenging questions. Experiments on existing datasets show SPINACH's capability in KBQA, achieving a new state of the art on the QALD-7, QALD-9 Plus and QALD-10 datasets by 30.1%, 27.0%, and 10.0% in F1, respectively, and coming within 1.6% of the fine-tuned LLaMA SOTA model on WikiWebQuestions. On our new SPINACH dataset, SPINACH agent outperforms all baselines, including the best GPT-4-based KBQA agent, by 38.1% in F1.

Instruction-tuned Large Language Models (LLMs) can perform a wide range of tasks given natural language instructions to do so, but they are sensitive to how such instructions are phrased. This issue is especially concerning in healthcare, as clinicians are unlikely to be experienced prompt engineers and the potential consequences of inaccurate outputs are heightened in this domain. This raises a practical question: How robust are instruction-tuned LLMs to natural variations in the instructions provided for clinical NLP tasks? We collect prompts from medical doctors across a range of tasks and quantify the sensitivity of seven LLMs -- some general, others specialized -- to natural (i.e., non-adversarial) instruction phrasings. We find that performance varies substantially across all models, and that -- perhaps surprisingly -- domain-specific models explicitly trained on clinical data are especially brittle, compared to their general domain counterparts. Further, arbitrary phrasing differences can affect fairness, e.g., valid but distinct instructions for mortality prediction yield a range both in overall performance, and in terms of differences between demographic groups., Comment: To appear at BioNLP, ACL 2024

Indoor localization plays a vital role in the era of the IoT and robotics, with WiFi technology being a prominent choice due to its ubiquity. We present a method for creating WiFi fingerprinting datasets to enhance indoor localization systems and address the gap in WiFi fingerprinting dataset creation. We used the Simultaneous Localization And Mapping (SLAM) algorithm and employed a robotic platform to construct precise maps and localize robots in indoor environments. We developed software applications to facilitate data acquisition, fingerprinting dataset collection, and accurate ground truth map building. Subsequently, we aligned the spatial information generated via the SLAM with the WiFi scans to create a comprehensive WiFi fingerprinting dataset. The created dataset was used to train a deep neural network (DNN) for indoor localization, which can prove the usefulness of grid density. We conducted experimental validation within our office environment to demonstrate the proposed method's effectiveness, including a heatmap from the dataset showcasing the spatial distribution of WiFi signal strengths for the testing access points placed within the environment. Notably, our method offers distinct advantages over existing approaches as it eliminates the need for a predefined map of the environment, requires no preparatory steps, lessens human intervention, creates a denser fingerprinting dataset, and reduces the WiFi fingerprinting dataset creation time. Our method achieves 26% more accurate localization than the other methods and can create a six times denser fingerprinting dataset in one-third of the time compared to the traditional method. In summary, using WiFi RSSI Fingerprinting data surveyed by the SLAM-Enabled Robotic Platform, we can adapt our trained DNN model to indoor localization in any dynamic environment and enhance its scalability and applicability in real-world scenarios., Comment: Fingerprinting dataset; Robotic platform; Indoor localization; Signals strength indicator; Location-based services

A tracer sample in a gravitational potential, starting from a generic initial condition, phase-mixes towards a stationary state. This evolution is accompanied by an entropy increase, and the final state is characterized by a distribution function (DF) that depends only on integrals of motion (Jeans theorem). In terms of angle-action variables, the final state is uniform in angles (high entropy) and maximally clustered in actions (low entropy). We present a method exploring this fact to constrain a gravitational potential using a sample that is stationary in it. We estimate the entropy in the action space of trial potentials and recover the true potential by minimizing this entropy. This method avoids assuming a known DF, and may be applicable to other sets of integrals. We provide expressions for the entropy of DFs depending on energy, $f(E)$, energy and angular momentum, $f(E,L)$, or three actions, $f(\vec{J})$, and investigate the bias and fluctuations in their estimates. We show that the method correctly recovers the potential parameters for spherical and axisymmetric potentials. We also present a methodology to characterize the posterior probability distribution of the parameters with an Approximate Bayesian Computation, and indicate a pathway for application to observational data. Using $N=10^4$ tracers with $20\%$-uncertainties in the 6D coordinates, we recover the flattening parameter $q$ of an axisymmetric potential with $\sigma_q/q\sim 10\%$., Comment: 17 pages + appendix + references, 13 figs. Comments are very welcome!

The Magellanic Clouds (MCs) are the Milky Way's most massive dwarf satellites. As they also represent the closest pair of galaxies in an ongoing tidal interaction, while simultaneously infalling into the Milky Way halo, they provide a unique opportunity to study in detail an ongoing three-body encounter. We present the ``YMCA (Yes, Magellanic Clouds Again) survey: probing the outer regions of the Magellanic system with VST'' based on deep optical photometry carried out with the VLT Survey Telescope (VST). YMCA targeted 110 square degrees, in the g and i filters, in the periphery of both the MCs, including a long strip in between the Large Magellanic Cloud (LMC) and the Small Magellanic Cloud (SMC). The photometry of YMCA is sufficiently deep (50\% complete down to $g \simeq 23.5-24.0$~mag) to allow for a detailed analysis of main-sequence stars in regions of the MCs remained relatively unexplored at these faint magnitudes. The resulting colour-magnitude diagrams reveal that the outskirts of the MCs are predominantly characterized by intermediate-age and old stellar populations, with limited or negligible evidence of recent star formation. The analysis of the age distribution of star clusters (SCs) within the surveyed area, both already known and newly discovered candidates, hints at a close fly-by between the LMC and SMC that occurred $\simeq 2.5-3.0$~Gyr ago, in agreement with previous results. We also report the discovery of candidate SCs with ages within the so-called ``age-gap'', questioning its real existence., Comment: 28 pages, 21 figures. Accepted for publication on A&A

Programming LLM-based knowledge and task assistants that faithfully conform to developer-provided policies is challenging. These agents must retrieve and provide consistent, accurate, and relevant information to address user's queries and needs. Yet such agents generate unfounded responses ("hallucinate"). Traditional dialogue trees can only handle a limited number of conversation flows, making them inherently brittle. To this end, we present KITA - a programmable framework for creating task-oriented conversational agents that are designed to handle complex user interactions. Unlike LLMs, KITA provides reliable grounded responses, with controllable agent policies through its expressive specification, KITA Worksheet. In contrast to dialog trees, it is resilient to diverse user queries, helpful with knowledge sources, and offers ease of programming policies through its declarative paradigm. Through a real-user study involving 62 participants, we show that KITA beats the GPT-4 with function calling baseline by 26.1, 22.5, and 52.4 points on execution accuracy, dialogue act accuracy, and goal completion rate, respectively. We also release 22 real-user conversations with KITA manually corrected to ensure accuracy., Comment: preprint

We present ~ 126 new spectroscopically identified members of the GD-1 tidal stream obtained with the 5000-fiber Dark Energy Spectroscopic Instrument (DESI). We confirm the existence of a ``cocoon'' which is broad (FWHM~2.932deg~460pc) and kinematically hot (velocity dispersion, sigma~5-8km/s) component that surrounds a narrower (FWHM~0.353deg~55pc) and colder (sigma~ 2.2-2.6km/s) thin stream component (based on a median per star velocity precision of 2.7km/s). The cocoon extends over at least a ~ 20deg segment of the stream observed by DESI. The thin and cocoon components have similar mean values of [Fe/H]: -2.54+/- 0.04dex and -2.45+/-0.06dex suggestive of a common origin. The data are consistent with the following scenarios for the origin of the cocoon. The progenitor of the GD-1 stream was an accreted globular cluster (GC) and: (a) the cocoon was produced by pre-accretion tidal stripping of the GC while it was still inside its parent dwarf galaxy; (b) the cocoon is debris from the parent dwarf galaxy; (c) an initially thin GC tidal stream was heated by impacts from dark subhalos in the Milky Way; (d) an initially thin GC stream was heated by a massive Sagittarius dwarf galaxy; or a combination of some these. In the first two cases the velocity dispersion and mean metallicity are consistent with the parent dwarf galaxy having a halo mass of ~0^9\msun. Future DESI spectroscopy and detailed modeling may enable us to distinguish between these possible origins., Comment: Submitted to ApJ, 23 pages, 13 figures 4 tables

We report on the thermodynamic and transport properties of the rare-earth Zintl compound Eu$_5$Sn$_2$As$_6$, which orders as a canted antiferromagnetic magnetic semiconductor at 10.3~K. The system also displays a complex cascade of magnetic phases arising from geometric and magnetic exchange frustration, with high sensitivity to the application and direction of small magnetic fields. At low temperature, Eu$_5$Sn$_2$As$_6$ exhibits negative colossal magnetoresistance of up to a factor of $6\times10^3$. This may be a lower bound as the conductivity appears to be shunted by an unknown conduction channel, causing the resistivity to saturate. Mechanisms for the low temperature saturation of resistivity are discussed., Comment: 11 pages, 5 figures in main text, 5 appendices

Differentially private stochastic gradient descent (DP-SGD) refers to a family of optimization algorithms that provide a guaranteed level of differential privacy (DP) through DP accounting techniques. However, current accounting techniques make assumptions that diverge significantly from practical DP-SGD implementations. For example, they may assume the loss function is Lipschitz continuous and convex, sample the batches randomly with replacement, or omit the gradient clipping step. In this work, we analyze the most commonly used variant of DP-SGD, in which we sample batches cyclically with replacement, perform gradient clipping, and only release the last DP-SGD iterate. More specifically - without assuming convexity, smoothness, or Lipschitz continuity of the loss function - we establish new R\'enyi differential privacy (RDP) bounds for the last DP-SGD iterate under the mild assumption that (i) the DP-SGD stepsize is small relative to the topological constants in the loss function, and (ii) the loss function is weakly-convex. Moreover, we show that our bounds converge to previously established convex bounds when the weak-convexity parameter of the objective function approaches zero. In the case of non-Lipschitz smooth loss functions, we provide a weaker bound that scales well in terms of the number of DP-SGD iterations.

We study the existence and concentration of positive and nodal solutions to a Schr\"odinger equation in the presence of a shrinking self-focusing core of arbitrary shape. Via a suitable rescaling, the concentration gives rise to a limiting profile that solves a nonautonomous elliptic semilinear equation with a sharp sign change in the nonlinearity. We characterize the (radial or foliated Schwarz) symmetries and the (polynomial) decay of the least-energy positive and nodal limiting profiles., Comment: 19 pages

Persuasion plays a pivotal role in a wide range of applications from health intervention to the promotion of social good. Persuasive chatbots can accelerate the positive effects of persuasion in such applications. Existing methods rely on fine-tuning persuasive chatbots with task-specific training data which is costly, if not infeasible, to collect. To address this issue, we propose a method to leverage the generalizability and inherent persuasive abilities of large language models (LLMs) in creating effective and truthful persuasive chatbot for any given domain in a zero-shot manner. Unlike previous studies which used pre-defined persuasion strategies, our method first uses an LLM to generate responses, then extracts the strategies used on the fly, and replaces any unsubstantiated claims in the response with retrieved facts supporting the strategies. We applied our chatbot, PersuaBot, to three significantly different domains needing persuasion skills: donation solicitation, recommendations, and health intervention. Our experiments on simulated and human conversations show that our zero-shot approach is more persuasive than prior work, while achieving factual accuracy surpassing state-of-the-art knowledge-oriented chatbots. Our study demonstrated that when persuasive chatbots are employed responsibly for social good, it is an enabler of positive individual and social change.

Noncognitive constructs such as self-efficacy, social awareness, and academic engagement are widely acknowledged as critical components of human capital, but systematic data collection on such skills in school systems is complicated by conceptual ambiguities, measurement challenges and resource constraints. This study addresses this issue by comparing the predictive validity of two most widely used metrics on noncogntive outcomes--observable academic behaviors (e.g., absenteeism, suspensions) and student self-reported social and emotional learning (SEL) skills--for the likelihood of high school graduation and postsecondary attainment. Our findings suggest that conditional on student demographics and achievement, academic behaviors are several-fold more predictive than SEL skills for all long-run outcomes, and adding SEL skills to a model with academic behaviors improves the model's predictive power minimally. In addition, academic behaviors are particularly strong predictors for low-achieving students' long-run outcomes. Part-day absenteeism (as a result of class skipping) is the largest driver behind the strong predictive power of academic behaviors. Developing more nuanced behavioral measures in existing administrative data systems might be a fruitful strategy for schools whose intended goal centers on predicting students' educational attainment.