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Climate change is negatively impacting the world's biodiversity. To build automated systems to monitor these negative biodiversity impacts, large-scale, volunteer-collected datasets like iNaturalist are built from community-identified, natural imagery. However, such volunteer-based data are opportunistic and lack a structured sampling strategy, resulting in geographic, temporal, observation quality, and socioeconomic, biases that stymie uptake of these models for downstream biodiversity monitoring tasks. Here we introduce DivShift North American West Coast (DivShift-NAWC), a curated dataset of almost 8 million iNaturalist plant images across the western coast of North America, for exploring the effects of these biases on deep learning model performance. We compare model performance across four known biases and observe that they indeed confound model performance. We suggest practical strategies for curating datasets to train deep learning models for monitoring climate change's impacts on the world's biodiversity., Comment: Accepted to NeurIPS 2024 Workshop on Tackling Climate Change with Machine Learning

Many modern software applications present numerous ethical concerns due to conflicts between users' values and companies' priorities. Intersectional communities, those with multiple marginalized identities, are disproportionately affected by these ethical issues, leading to legal, financial, and reputational issues for software companies, as well as real-world harm for intersectional users. Historically, the voices of intersectional communities have been systematically marginalized and excluded from contributing their unique perspectives to software design, perpetuating software-related ethical concerns. This work aims to fill the gap in research on intersectional users' software-related perspectives and provide software practitioners with a starting point to address their ethical concerns. We aggregated and analyzed the intersectional users' ethical concerns over time and developed a prioritization method to identify critical concerns. To achieve this, we collected posts from over 700 intersectional subreddits discussing software applications, utilized deep learning to identify ethical concerns in these posts, and employed state-of-the-art techniques to analyze their content in relation to time and priority. Our findings revealed that intersectional communities report \textit{critical} complaints related to cyberbullying, inappropriate content, and discrimination, highlighting significant flaws in modern software, particularly for intersectional users. Based on these findings, we discuss how to better address the ethical concerns of intersectional users in software development.

Investigation of students' perceptions and opinions on the use of generative artificial intelligence (GenAI) in education is a topic gaining much interest. Studies addressing this are typically conducted with large heterogeneous groups, at one moment in time. However, how students perceive and use GenAI tools can potentially depend on many factors, including their background knowledge, familiarity with the tools, and the learning goals and policies of the courses they are taking. In this study we explore how students following computing courses use GenAI for programming-related tasks across different programs and courses: Bachelor and Master, in courses in which learning programming is the learning goal, courses that require programming as a means to achieve another goal, and in courses in which programming is optional, but can be useful. We are also interested in changes over time, since GenAI capabilities are changing at a fast pace, and users are adopting GenAI increasingly. We conducted three consecutive surveys (fall `23, winter `23, and spring `24) among students of all computing programs of a large European research university. We asked questions on the use in education, ethics, and job prospects, and we included specific questions on the (dis)allowed use of GenAI tools in the courses they were taking at the time. We received 264 responses, which we quantitatively and qualitatively analyzed, to find out how students have employed GenAI tools across 59 different computing courses, and whether the opinion of an average student about these tools evolves over time. Our study contributes to the emerging discussion of how to differentiate GenAI use across different courses, and how to align its use with the learning goals of a computing course., Comment: Accepted to Koli Calling 24. Numbers in Table 1, row 1 updated

Late-stage Ca-sulfate-filled fractures are common on Mars. Notably, the Shenandoah formation in the western edge of Jezero crater preserves a variety of Ca-sulfate minerals in the fine-grained siliciclastic rocks explored by the Perseverance rover. However, the depositional environment and timing of the formation of these sulfates is unknown. To address this outstanding problem, we developed a new technique to map the crystal textures of these sulfates in situ at two stratigraphically similar locations in the Shenandoah formation, allowing us to constrain the burial depth and paleoenvironment at the time of their deposition. Our results suggest that some Ca-sulfate analyzed was formed at a burial depth greater than 80m, whereas Ca-sulfates present at another outcrop likely precipitated in a shallow-subsurface environment. These results indicate that samples collected for potential return to Earth at the two studied locations capture two different times and distinct chemical conditions in the depositional history of the Shenandoah formation providing multiple opportunities to evaluate surface and subsurface habitability.

The isotopic compositions of samples returned from Cb-type asteroid Ryugu and Ivuna-type (CI) chondrites are distinct from other carbonaceous chondrites, which has led to the suggestion that Ryugu and CI chondrites formed in a different region of the accretion disk, possibly around the orbits of Uranus and Neptune. We show that, like for Fe, Ryugu and CI chondrites also have indistinguishable Ni isotope anomalies, which differ from those of other carbonaceous chondrites. We propose that this unique Fe and Ni isotopic composition reflects different accretion efficiencies of small FeNi metal grains among the carbonaceous chondrite parent bodies. The CI chondrites incorporated these grains more efficiently, possibly because they formed at the end of the disk's lifetime, when planetesimal formation was also triggered by photoevaporation of the disk. Isotopic variations among carbonaceous chondrites may thus reflect fractionation of distinct dust components from a common reservoir, implying CI chondrites and Ryugu may have formed in the same region of the accretion disk as other carbonaceous chondrites., Comment: Published open access in Science Advances

We propose a convex controller synthesis framework for a large class of constrained linear systems, including those described by (deterministic and stochastic) partial differential equations and integral equations, commonly used in fluid dynamics, thermo-mechanical systems, quantum control, or transportation networks. Most existing control techniques rely on a (finite-dimensional) discrete description of the system, via ordinary differential equations. Here, we work instead with more general (infinite-dimensional) Hilbert spaces. This enables the discretization to be applied after the optimization (optimize-then-discretize). Using output-feedback SLS, we formulate the controller synthesis as a convex optimization problem. Structural constraints like sensor and communication delays, and locality constraints, are incorporated while preserving convexity, allowing parallel implementation and extending key SLS properties to infinite dimensions. The proposed approach and its benefits are demonstrated on a linear Boltzmann equation.

Neurosymbolic (NeSy) artificial intelligence describes the combination of logic or rule-based techniques with neural networks. Compared to neural approaches, NeSy methods often possess enhanced interpretability, which is particularly promising for biomedical applications like drug discovery. However, since interpretability is broadly defined, there are no clear guidelines for assessing the biological plausibility of model interpretations. To assess interpretability in the context of drug discovery, we devise a novel prediction task, called drug mechanism-of-action (MoA) deconvolution, with an associated, tailored knowledge graph (KG), MoA-net. We then develop the MoA Retrieval System (MARS), a NeSy approach for drug discovery which leverages logical rules with learned rule weights. Using this interpretable feature alongside domain knowledge, we find that MARS and other NeSy approaches on KGs are susceptible to reasoning shortcuts, in which the prediction of true labels is driven by "degree-bias" rather than the domain-based rules. Subsequently, we demonstrate ways to identify and mitigate this. Thereafter, MARS achieves performance on par with current state-of-the-art models while producing model interpretations aligned with known MoAs., Comment: Under review. 10 pages, 5 supplementary pages. Corresponding code is here: https://github.com/laurendelong21/MARS and here: https://github.com/laurendelong21/MoA-Net

Real-time supercomputing performance analysis is a critical aspect of evaluating and optimizing computational systems in a dynamic user environment. The operation of supercomputers produce vast quantities of analytic data from multiple sources and of varying types so compiling this data in an efficient matter is critical to the process. MIT Lincoln Laboratory Supercomputing Center has been utilizing the Unity 3D game engine to create a Digital Twin of our supercomputing systems for several years to perform system monitoring. Unity offers robust visualization capabilities making it ideal for creating a sophisticated representation of the computational processes. As we scale the systems to include a diversity of resources such as accelerators and the addition of more users, we need to implement new analysis tools for the monitoring system. The workloads in research continuously change, as does the capability of Unity, and this allows us to adapt our monitoring tools to scale and incorporate features enabling efficient replay of system wide events, user isolation, and machine level granularity. Our system fully takes advantage of the modern capabilities of the Unity Engine in a way that intuitively represents the real time workload performed on a supercomputer. It allows HPC system engineers to quickly diagnose usage related errors with its responsive user interface which scales efficiently with large data sets.

Comparing bridging annotations across coreference resources is difficult, largely due to a lack of standardization across definitions and annotation schemas and narrow coverage of disparate text domains across resources. To alleviate domain coverage issues and consolidate schemas, we compare guidelines and use interpretable predictive models to examine the bridging instances annotated in the GUM, GENTLE and ARRAU corpora. Examining these cases, we find that there is a large difference in types of phenomena annotated as bridging. Beyond theoretical results, we release a harmonized, subcategorized version of the test sets of GUM, GENTLE and the ARRAU Wall Street Journal data to promote meaningful and reliable evaluation of bridging resolution across domains., Comment: The Seventh Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC 2024), EMNLP 2024 Workshop, 15 November 2024

Complex, learned motor behaviors involve the coordination of large-scale neural activity across multiple brain regions, but our understanding of the population-level dynamics within different regions tied to the same behavior remains limited. Here, we investigate the neural population dynamics underlying learned vocal production in awake-singing songbirds. We use Neuropixels probes to record the simultaneous extracellular activity of populations of neurons in two regions of the vocal motor pathway. In line with observations made in non-human primates during limb-based motor tasks, we show that the population-level activity in both the premotor nucleus HVC and the motor nucleus RA is organized on low-dimensional neural manifolds upon which coordinated neural activity is well described by temporally structured trajectories during singing behavior. Both the HVC and RA latent trajectories provide relevant information to predict vocal sequence transitions between song syllables. However, the dynamics of these latent trajectories differ between regions. Our state-space models suggest a unique and continuous-over-time correspondence between the latent space of RA and vocal output, whereas the corresponding relationship for HVC exhibits a higher degree of neural variability. We then demonstrate that comparable high-fidelity reconstruction of continuous vocal outputs can be achieved from HVC and RA neural latents and spiking activity. Unlike those that use spiking activity, however, decoding models using neural latents generalize to novel sub-populations in each region, consistent with the existence of preserved manifolds that confine vocal-motor activity in HVC and RA., Comment: 30 pages; 7 figures; 8 supplementary figures

Hundreds of exoplanets between 1-1.8 times the size of the Earth have been discovered on close in orbits. However, these planets show such a diversity in densities that some appear to be made entirely of iron, while others appear to host gaseous envelopes. To test this diversity in composition, we update the masses of 5 rocky exoplanets (HD 93963 A b, Kepler-10 b, Kepler-100 b, Kepler-407 b, and TOI-1444 b) and present the confirmation of a new planet (TOI-1011) using 187 high precision RVs from Gemini/MAROON-X and Keck/KPF. Our updated planet masses suggest compositions closer to that of the Earth than previous literature values for all planets in our sample. In particular, we report that two previously identified ``super-Mercuries'' (Kepler-100 b and HD 93963 A b) have lower masses that suggest less iron-rich compositions. We then compare the ratio of iron to rock-building species to the abundance ratios of those elements in their host stars. These updated planet compositions do not suggest a steep relationship between planet and host star compositions, contradictory to previous results, and suggest that planets and host stars have similar abundance ratios., Comment: Submitted to AJ 09/30/2024

We report tele-seismic waves detection from multiple earthquakes on an operational subsea cable from Iceland to Ireland. Using per-span laser interferometry with 100km spacing, we report clear detection of S-, P- and surface waves from multiple world-wide earthquakes, enabling seismic analysis for early warning applications., Comment: 3 pages, 2 figures. Conference submission

We showcase an application that leverages multiple agents, powered by large language models and integrated tools, to collaboratively solve complex network operation tasks across various domains. The tasks include real-time topology retrieval, network optimization using physical models, and fiber switching facilitated by a robotic arm.

Multimodal image-text contrastive learning has shown that joint representations can be learned across modalities. Here, we show how leveraging multiple views of image data with contrastive learning can improve downstream fine-grained classification performance for species recognition, even when one view is absent. We propose ContRastive Image-remote Sensing Pre-training (CRISP)$\unicode{x2014}$a new pre-training task for ground-level and aerial image representation learning of the natural world$\unicode{x2014}$and introduce Nature Multi-View (NMV), a dataset of natural world imagery including $>3$ million ground-level and aerial image pairs for over 6,000 plant taxa across the ecologically diverse state of California. The NMV dataset and accompanying material are available at hf.co/datasets/andyvhuynh/NatureMultiView., Comment: Accepted to ECCV 2024

Despite being operational for only a short time, the Einstein Probe mission has already significantly advanced the study of rapid variability in the soft X-ray sky. We report the discovery of luminous and variable radio emission from the Einstein Probe fast X-ray transient EP240414a, the second such source with a radio counterpart. The radio emission at $3\,\rm{GHz}$ peaks at $\sim30$ days post explosion and with a spectral luminosity $\sim2\times10^{30}\,\rm{erg}\,\rm{s}^{-1}\,\rm{Hz}^{-1}$, similar to what is seen from long gamma-ray bursts, and distinct from other extra-galactic transients including supernovae and tidal disruption events, although we cannot completely rule out emission from engine driven stellar explosions e.g. the fast blue optical transients. An equipartition analysis of our radio data reveals that an outflow with at least a moderate bulk Lorentz factor ($\Gamma\gtrsim1.6$) with a minimum energy of $\sim10^{48}\,\rm{erg}$ is required to explain our observations. The apparent lack of reported gamma-ray counterpart to EP240414a could suggest that an off-axis or choked jet could be responsible for the radio emission, although a low luminosity gamma-ray burst may have gone undetected. Our observations are consistent with the hypothesis that a significant fraction of extragalactic fast X-ray transients are associated with the deaths of massive stars., Comment: 11 pages, 6 figures, submitted to ApJL

Two-dimensional (2D) semiconductors are promising for low-power complementary metal oxide semiconductor (CMOS) electronics, which require ultrathin n- and p-type transistor channels. Among 2D semiconductors, WS2 is expected to have good conduction for both electrons and holes, but p-type WS2 transistors have been difficult to realize due to the relatively deep valence band and the presence of mid-gap states with conventional metal contacts. Here, we report topological semimetal NbP as p-type electrical contacts to bilayer WS2 with up to 5.8 microamperes per micron hole current at room temperature; this is the highest to date for sub 2 nm thin WS2 and more than 50 times larger than with metals like Ni or Pd. The p-type conduction is enabled by the simultaneously high work function and low density of states of the NbP, which reduce Fermi level pinning. These contacts are sputter-deposited at room temperature, an approach compatible with CMOS fabrication, a step towards enabling ultrathin WS2 semiconductors in future nanoelectronics.

The distinguishing index gives a measure of symmetry in a graph. Given a graph $G$ with no $K_2$ component, a distinguishing edge coloring is a coloring of the edges of $G$ such that no non-trivial automorphism preserves the edge coloring. The distinguishing index, denoted $\operatorname{Dist^{\prime}}(G)$, is the smallest number of colors needed for a distinguishing edge coloring. The Mycielskian of a graph $G$, denoted $\mu(G)$, is an extension of $G$ introduced by Mycielski in 1955. In 2020, Alikhani and Soltani conjectured a relationship between $operatorname{Dist^{\prime}}(G)$ and $operatorname{Dist^{\prime}}(\mu(G))$. We prove that for all graphs $G$ with at least 3 vertices, no connected $K_2$ component, and at most one isolated vertex, $\operatorname{Dist^{\prime}}(\mu(G)) \le \operatorname{Dist^{\prime}}(G)$, exceeding their conjecture. We also prove analogous results about generalized Mycielskian graphs. Together with the work in 2022 of Boutin, Cockburn, Keough, Loeb, Perry, and Rombach this completes the conjecture of Alikhani and Soltani.

The application of Shapley values to high-dimensional, time-series-like data is computationally challenging - and sometimes impossible. For $N$ inputs the problem is $2^N$ hard. In image processing, clusters of pixels, referred to as superpixels, are used to streamline computations. This research presents an efficient solution for time-seres-like data that adapts the idea of superpixels for Shapley value computation. Motivated by a forensic DNA classification example, the method is applied to multivariate time-series-like data whose features have been classified by a convolutional neural network (CNN). In DNA processing, it is important to identify alleles from the background noise created by DNA extraction and processing. A single DNA profile has $31,200$ scan points to classify, and the classification decisions must be defensible in a court of law. This means that classification is routinely performed by human readers - a monumental and time consuming process. The application of a CNN with fast computation of meaningful Shapley values provides a potential alternative to the classification. This research demonstrates the realistic, accurate and fast computation of Shapley values for this massive task, Comment: 16 pages, 5 figures

A human interacting with a robot often forms predictions of what the robot will do next. For instance, based on the recent behavior of an autonomous car, a nearby human driver might predict that the car is going to remain in the same lane. It is important for the robot to understand the human's prediction for safe and seamless interaction: e.g., if the autonomous car knows the human thinks it is not merging -- but the autonomous car actually intends to merge -- then the car can adjust its behavior to prevent an accident. Prior works typically assume that humans make precise predictions of robot behavior. However, recent research on human-human prediction suggests the opposite: humans tend to approximate other agents by predicting their high-level behaviors. We apply this finding to develop a second-order theory of mind approach that enables robots to estimate how humans predict they will behave. To extract these high-level predictions directly from data, we embed the recent human and robot trajectories into a discrete latent space. Each element of this latent space captures a different type of behavior (e.g., merging in front of the human, remaining in the same lane) and decodes into a vector field across the state space that is consistent with the underlying behavior type. We hypothesize that our resulting high-level and course predictions of robot behavior will correspond to actual human predictions. We provide initial evidence in support of this hypothesis through a proof-of-concept user study.

Many climate processes are characterized using large systems of nonlinear differential equations; this, along with the immense amount of data required to parameterize complex interactions, means that Earth-System Model (ESM) simulations may take weeks to run on large clusters. Uncertainty quantification may require thousands of runs, making ESM simulations impractical for preliminary assessment. Alternatives may include simplifying the processes in the model, but recent efforts have focused on using machine learning to complement these models or even act as full surrogates. \textit{We leverage machine learning, specifically fully-connected neural networks (FCNNs) and graph convolutional neural networks (GCNNs), to enable rapid simulation and uncertainty quantification in order to inform more extensive ESM simulations.} Our surrogate simulated 80 years in approximately 310 seconds on a single A100 GPU, compared to weeks for the ESM model while having mean temperature errors below $0.1^{\circ}C$ and maximum errors below $2^{\circ}C$., Comment: 10 pages, 8 figures

The MMT Adaptive optics exoPlanet characterization System (MAPS) is currently in its engineering phase, operating on-sky at the MMT Telescope on Mt. Hopkins in southern Arizona. The MAPS Adaptive Secondary Mirror's actuators are controlled by a closed loop modified PID control law and an open loop feed-forward law, which in combination allows for faster actuator response time. An essential element of achieving the secondary's performance goals involves the process of PID gain tuning. To start, we briefly discuss the design of the MAPS ASM and its actuators. We then describe the actuator positional control system and control law. Next, we discuss a few of the issues that make ASM tuning difficult. We then outline our initial attempts at tuning the actuator controllers and discuss the use of actuator positional power spectra for both tuning and determining the health and failure states of individual actuators. We conclude by presenting the results of our latest round of tuning configuration trials, which have been successful at decreasing mirror latency, increasing operational mirror modes and improving image PSF., Comment: To be published in Proceedings of SPIE, Optics and Photonics 2024. 24 pages, 16 figures, 5 tables. Lead Author, J. Johnson. Second Lead Author, A. Vaz. Project P.I., K. Morzinski. Project Second P.I.s, J. Patience and S. Sivanandam, Project Manager, M. Montoya

How do people use their faces and bodies to test the interactive abilities of a robot? Making lively, believable agents is often seen as a goal for robots and virtual agents but believability can easily break down. In this Wizard-of-Oz (WoZ) study, we observed 1169 nonverbal interactions between 20 participants and 6 types of agents. We collected the nonverbal behaviors participants used to challenge the characters physically, emotionally, and socially. The participants interacted freely with humanoid and non-humanoid forms: a robot, a human, a penguin, a pufferfish, a banana, and a toilet. We present a human behavior codebook of 188 unique nonverbal behaviors used by humans to test the virtual characters. The insights and design strategies drawn from video observations aim to help build more interaction-aware and believable robots, especially when humans push them to their limits., Comment: 8 pages, 4 figures, Accepted to International Conference on Intelligent Robots and Systems (IROS 2024), project website: https://rosielab.github.io/react-to-this/

Multi-modal large language models (MLLMs) have enabled numerous advances in understanding and reasoning in domains like vision, but we have not yet seen this broad success for time-series. Although prior works on time-series MLLMs have shown promising performance in time-series forecasting, very few works show how an LLM could be used for time-series reasoning in natural language. We propose a novel multi-modal time-series LLM approach that learns generalizable information across various domains with powerful zero-shot performance. First, we train a lightweight time-series encoder on top of an LLM to directly extract time-series information. Then, we fine-tune our model with chain-of-thought augmented time-series tasks to encourage the model to generate reasoning paths. We show that our model learns a latent representation that reflects specific time-series features (e.g. slope, frequency), as well as outperforming GPT-4o on a set of zero-shot reasoning tasks on a variety of domains., Comment: Oral Presentation at 2024 NeurIPS Workshop on Time Series in the Age of Large Models

Machine-learning (ML) models in flow cytometry have the potential to reduce error rates, increase reproducibility, and boost the efficiency of clinical labs. While numerous ML models for flow cytometry data have been proposed, few studies have described the clinical deployment of such models. Realizing the potential gains of ML models in clinical labs requires not only an accurate model, but infrastructure for automated inference, error detection, analytics and monitoring, and structured data extraction. Here, we describe an ML model for detection of Acute Myeloid Leukemia (AML), along with the infrastructure supporting clinical implementation. Our infrastructure leverages the resilience and scalability of the cloud for model inference, a Kubernetes-based workflow system that provides model reproducibility and resource management, and a system for extracting structured diagnoses from full-text reports. We also describe our model monitoring and visualization platform, an essential element for ensuring continued model accuracy. Finally, we present a post-deployment analysis of impacts on turn-around time and compare production accuracy to the original validation statistics.

HPC systems used for research run a wide variety of software and workflows. This software is often written or modified by users to meet the needs of their research projects, and rarely is built with security in mind. In this paper we explore several of the key techniques that MIT Lincoln Laboratory Supercomputing Center has deployed on its systems to manage the security implications of these workflows by providing enforced separation for processes, filesystem access, network traffic, and accelerators to make every user feel like they are running on a personal HPC.

Planets and the stars they orbit are born from the same cloud of gas and dust, and the primordial compositions of rocky exoplanets have been assumed to have iron and refractory abundance ratios consistent with their host star. To test this assumption, we modeled the interior iron-to-rock ratio of 20 super-Earth sized (1-1.8R$_{\oplus}$) exoplanets around stars with homogeneously measured stellar parameters. We computed the core mass fraction for each planet and an equivalent ``core mass fraction'' for each host star based on its Fe and Mg abundances. We then fit a linear correlation using two methods (Ordinary Least Squares and Orthogonal Distance Regression) between planetary and stellar core mass fraction, obtaining substantially different slopes between these two methods (m=1.3 $\pm$ 1.0 and m=5.6 $\pm$ 1.6, respectively). Additionally, we find that 75$\%$ of planets have a core mass fraction consistent with their host star to within 1$\sigma$, and do not identify a distinct population of high-density super-Mercuries. Overall, we conclude that current uncertainties in observational data and differences in modeling methods prevent definitive conclusions about the relationship between between planet and host star chemical compositions., Comment: Resubmitted to AJ 09/12/2024

The MIT/IEEE/Amazon GraphChallenge encourages community approaches to developing new solutions for analyzing graphs and sparse data derived from social media, sensor feeds, and scientific data to discover relationships between events as they unfold in the field. The anonymized network sensing Graph Challenge seeks to enable large, open, community-based approaches to protecting networks. Many large-scale networking problems can only be solved with community access to very broad data sets with the highest regard for privacy and strong community buy-in. Such approaches often require community-based data sharing. In the broader networking community (commercial, federal, and academia) anonymized source-to-destination traffic matrices with standard data sharing agreements have emerged as a data product that can meet many of these requirements. This challenge provides an opportunity to highlight novel approaches for optimizing the construction and analysis of anonymized traffic matrices using over 100 billion network packets derived from the largest Internet telescope in the world (CAIDA). This challenge specifies the anonymization, construction, and analysis of these traffic matrices. A GraphBLAS reference implementation is provided, but the use of GraphBLAS is not required in this Graph Challenge. As with prior Graph Challenges the goal is to provide a well-defined context for demonstrating innovation. Graph Challenge participants are free to select (with accompanying explanation) the Graph Challenge elements that are appropriate for highlighting their innovations., Comment: Accepted to IEEE HPEC 2024

In this study, we developed an Evidence-based Ensemble Neural Network, namely EVENet, for anatomical brain parcellation using diffusion MRI. The key innovation of EVENet is the design of an evidential deep learning framework to quantify predictive uncertainty at each voxel during a single inference. Using EVENet, we obtained accurate parcellation and uncertainty estimates across different datasets from healthy and clinical populations and with different imaging acquisitions. The overall network includes five parallel subnetworks, where each is dedicated to learning the FreeSurfer parcellation for a certain diffusion MRI parameter. An evidence-based ensemble methodology is then proposed to fuse the individual outputs. We perform experimental evaluations on large-scale datasets from multiple imaging sources, including high-quality diffusion MRI data from healthy adults and clinically diffusion MRI data from participants with various brain diseases (schizophrenia, bipolar disorder, attention-deficit/hyperactivity disorder, Parkinson's disease, cerebral small vessel disease, and neurosurgical patients with brain tumors). Compared to several state-of-the-art methods, our experimental results demonstrate highly improved parcellation accuracy across the multiple testing datasets despite the differences in dMRI acquisition protocols and health conditions. Furthermore, thanks to the uncertainty estimation, our EVENet approach demonstrates a good ability to detect abnormal brain regions in patients with lesions, enhancing the interpretability and reliability of the segmentation results., Comment: 15 pages, 5 figures

This paper discusses some statistical aspects of the U.K. Covid-19 pandemic response, focussing particularly on cases where we believe that a statistically questionable approach or presentation has had a substantial impact on public perception, or government policy, or both. We discuss the presentation of statistics relating to Covid risk, and the risk of the response measures, arguing that biases tended to operate in opposite directions, overplaying Covid risk and underplaying the response risks. We also discuss some issues around presentation of life loss data, excess deaths and the use of case data. The consequences of neglect of most individual variability from epidemic models, alongside the consequences of some other statistically important omissions are also covered. Finally the evidence for full stay at home lockdowns having been necessary to reverse waves of infection is examined, with new analyses provided for a number of European countries.

Large language models (LLMs) can extract information from veterinary electronic health records (EHRs), but performance differences between models, the effect of temperature settings, and the influence of text ambiguity have not been previously evaluated. This study addresses these gaps by comparing the performance of GPT-4 omni (GPT-4o) and GPT-3.5 Turbo under different conditions and investigating the relationship between human interobserver agreement and LLM errors. The LLMs and five humans were tasked with identifying six clinical signs associated with Feline chronic enteropathy in 250 EHRs from a veterinary referral hospital. At temperature 0, the performance of GPT-4o compared to the majority opinion of human respondents, achieved 96.9% sensitivity (interquartile range [IQR] 92.9-99.3%), 97.6% specificity (IQR 96.5-98.5%), 80.7% positive predictive value (IQR 70.8-84.6%), 99.5% negative predictive value (IQR 99.0-99.9%), 84.4% F1 score (IQR 77.3-90.4%), and 96.3% balanced accuracy (IQR 95.0-97.9%). The performance of GPT-4o was significantly better than that of its predecessor, GPT-3.5 Turbo, particularly with respect to sensitivity where GPT-3.5 Turbo only achieved 81.7% (IQR 78.9-84.8%). Adjusting the temperature for GPT-4o did not significantly impact classification performance. GPT-4o demonstrated greater reproducibility than human pairs regardless of temperature, with an average Cohen's kappa of 0.98 (IQR 0.98-0.99) at temperature 0 compared to 0.8 (IQR 0.78-0.81) for humans. Most GPT-4o errors occurred in instances where humans disagreed (35/43 errors, 81.4%), suggesting that these errors were more likely caused by ambiguity of the EHR than explicit model faults. Using GPT-4o to automate information extraction from veterinary EHRs is a viable alternative to manual extraction., Comment: 24 pages, 3 figures, 8 supplementary figures

We develop a theory for the electrical and thermal transverse linear response functions such as the Hall, Nernst and thermal Hall effects in magnetic materials that harbor topological spin textures like skyrmions. In addition to the ordinary transverse response that arises from the Lorentz force due to the external magnetic field, there is an anomalous and a topological response. The intrinsic anomalous response derives from the momentum space Berry curvature arising from the spin-orbit coupling (SOC) in a system with a nonzero magnetization, while the topological response arises from real space Berry curvature related to the the topological charge density of the spin texture. To take into account all these effects on an equal footing, we develop a semiclassical theory that incorporates all phase-space Berry curvatures. We show within a controlled, semiclassical approach that all conductivities -- electrical, thermoelectric, and thermal Hall -- can be written as the sum of three contributions: ordinary, anomalous and topological, when the conduction electron SOC is weaker than the exchange coupling to the spin texture. All other contributions, including those arising from mixed real-momentum space Berry curvature, are negligible in the regime where our calculations are controlled. We derive various general relations that remain valid at low temperatures including the Weidemann-Franz relation between the electrical and thermal conductivities and the Mott relation between the thermoelectric and electrical conductivities. We also discuss how an in-plane Hall response arises in three-dimensional materials with sufficiently low symmetry. Finally, the Hall response is qualitatively different when the conduction electron SOC is stronger than the exchange coupling to the spin texture, where we find that the anomalous term dominates and the topological term vanishes.

The integration of Large Language Models (LLMs) into the drug discovery and development field marks a significant paradigm shift, offering novel methodologies for understanding disease mechanisms, facilitating drug discovery, and optimizing clinical trial processes. This review highlights the expanding role of LLMs in revolutionizing various stages of the drug development pipeline. We investigate how these advanced computational models can uncover target-disease linkage, interpret complex biomedical data, enhance drug molecule design, predict drug efficacy and safety profiles, and facilitate clinical trial processes. Our paper aims to provide a comprehensive overview for researchers and practitioners in computational biology, pharmacology, and AI4Science by offering insights into the potential transformative impact of LLMs on drug discovery and development.

Understanding what is normal is a key aspect of protecting a domain. Other domains invest heavily in observational science to develop models of normal behavior to better detect anomalies. Recent advances in high performance graph libraries, such as the GraphBLAS, coupled with supercomputers enables processing of the trillions of observations required. We leverage this approach to synthesize low-parameter observational models of anonymized Internet traffic with a high regard for privacy., Comment: Accepted to IEEE HPEC, 7 pages, 6 figures, 1 table, 41 references

While mirror symmetry for flag varieties and Grassmannians has been extensively studied, Schubert varieties in the Grassmannian are singular, and hence standard mirror symmetry statements are not well-defined. Nevertheless, in this article we introduce a ``superpotential'' $W^{\lambda}$ for each Grassmannian Schubert variety $X_{\lambda}$, generalizing the Marsh-Rietsch superpotential for Grassmannians, and we show that $W^{\lambda}$ governs many toric degenerations of $X_{\lambda}$. We also generalize the ``polytopal mirror theorem'' for Grassmannians from our previous work: namely, for any cluster seed $G$ for $X_{\lambda}$, we construct a corresponding Newton-Okounkov convex body $\Delta_G^{\lambda}$, and show that it coincides with the superpotential polytope $\Gamma_G^{\lambda}$, that is, it is cut out by the inequalities obtained by tropicalizing an associated Laurent expansion of $W^{\lambda}$. This gives us a toric degeneration of the Schubert variety $X_{\lambda}$ to the (singular) toric variety $Y(\mathcal{N}_{\lambda})$ of the Newton-Okounkov body. Finally, for a particular cluster seed $G=G^\lambda_{\mathrm{rec}}$ we show that the toric variety $Y(\mathcal{N}_{\lambda})$ has a small toric desingularisation, and we describe an intermediate partial desingularisation $Y(\mathcal{F}_\lambda)$ that is Gorenstein Fano. Many of our results extend to more general varieties in the Grassmannian., Comment: 70 pages, 26 figures, comments welcome!

We obtained new spectra of Kepler-34 and Kepler-35 with Keck-HIRES, nearly a decade after these systems were originally characterized with this spectrograph and other instruments, to search for RV trends from a potential third stellar-mass companion at long periods. For Kepler-34, we rule out coplanar stellar masses as low as $0.12 M_\odot$ at an orbital period of $\lesssim 52$ years. For Kepler-35, we rule out stellar masses of $0.13 M_\odot$ at orbital periods of $\lesssim 55$ years. Highly stable, extreme precision RV instruments, as well as improved methodologies in characterizing double-lined spectroscopic binaries that come with these new instruments, will provide an opportunity to push these mass limits lower in the future., Comment: Submitted to ApJ. Comments are welcome

Diffusion MRI (dMRI) is an advanced imaging technique characterizing tissue microstructure and white matter structural connectivity of the human brain. The demand for high-quality dMRI data is growing, driven by the need for better resolution and improved tissue contrast. However, acquiring high-quality dMRI data is expensive and time-consuming. In this context, deep generative modeling emerges as a promising solution to enhance image quality while minimizing acquisition costs and scanning time. In this study, we propose a novel generative approach to perform dMRI generation using deep diffusion models. It can generate high dimension (4D) and high resolution data preserving the gradients information and brain structure. We demonstrated our method through an image mapping task aimed at enhancing the quality of dMRI images from 3T to 7T. Our approach demonstrates highly enhanced performance in generating dMRI images when compared to the current state-of-the-art (SOTA) methods. This achievement underscores a substantial progression in enhancing dMRI quality, highlighting the potential of our novel generative approach to revolutionize dMRI imaging standards., Comment: 11 pages, 3 figures

Fairness in AI and ML systems is increasingly linked to the proper treatment and recognition of data workers involved in training dataset development. Yet, those who collect and annotate the data, and thus have the most intimate knowledge of its development, are often excluded from critical discussions. This exclusion prevents data annotators, who are domain experts, from contributing effectively to dataset contextualization. Our investigation into the hiring and engagement practices of 52 data work requesters on platforms like Amazon Mechanical Turk reveals a gap: requesters frequently hold naive or unchallenged notions of worker identities and capabilities and rely on ad-hoc qualification tasks that fail to respect the workers' expertise. These practices not only undermine the quality of data but also the ethical standards of AI development. To rectify these issues, we advocate for policy changes to enhance how data annotation tasks are designed and managed and to ensure data workers are treated with the respect they deserve., Comment: Accepted for publication at AIES 2024

We present the discovery of a Jupiter-like planet, HD 73344 d ($m_{d}=2.55^{+0.56}_{-0.46}\ \mathrm{M_{J}}$, $a_{d}=6.70^{+0.25}_{-0.26}$ AU, $e_{d}=0.18^{+0.14}_{-0.12}$) based on 27-year radial velocity observations. HD 73344 also hosts a compact inner planetary system, including a transiting sub-Neptune HD 73344 b ($P_{b}=15.61\ \mathrm{days}$, $r_{b}=2.88^{+0.08}_{-0.07}\ \mathrm{R_{\oplus}}$) and a non-transiting Saturn-mass planet ($P_{c}=65.936\ \mathrm{days}$, $m_{c}\sin{i_c}=0.36^{+0.02}_{-0.02}\ \mathrm{M_{J}}$). By analyzing TESS light curves, we identified a stellar rotation period of $9.03\pm{1.3}$ days. Combining this with $v\sin{i_*}$ measurements from stellar spectra, we derived a stellar inclination of $63.6^{+17.4}_{-16.5}\ \rm{deg} $. Furthermore, by combining radial velocities and Hipparcos-Gaia astrometric acceleration, we characterized the three-dimensional orbit of the outer giant planet and constrained its mutual inclination relative to the innermost transiting planet to be $46 <\Delta I_{bd}< 134\ \rm{deg}\ (1\sigma)$ and $20 <\Delta I_{bd}< 160\ \rm{deg}\ (2\sigma)$, strongly disfavoring coplanar architectures. Our analytical calculations and N-body simulation reveal that the two inner planets are strongly coupled with each other and undergo nodal precession together around the orbital axis of the giant planet. During nodal precession, the orbital inclination of inner planets oscillate with time and therefore become misaligned relative to the stellar spin axis. The formation of such systems suggests a history of planet-planet scattering or misalignment between the inner and outer components of protoplanetary disks. The upcoming release of Gaia DR4 will uncover more systems similar to HD 73344 and enable the study of the flatness of exoplanet systems with a mixture of inner and outer planetary systems on a statistical level., Comment: 15 pages, 9 figures, submitted to AJ. arXiv admin note: text overlap with arXiv:2407.21377

The high incidence of irreproducible research has led to urgent appeals for transparency and equitable practices in open science. For the scientific disciplines that rely on computationally intensive analyses of large data sets, a granular understanding of the analysis methodology is an essential component of reproducibility. This paper discusses the guiding principles of a computational reproducibility framework that enables a scientist to proactively generate a complete reproducible trace as analysis unfolds, and share data, methods and executable tools as part of a scientific publication, allowing other researchers to verify results and easily re-execute the steps of the scientific investigation.

Knitting turns a one dimensional yarn into a highly ramified three-dimensional structure. As a method of additive manufacturing, it holds promise for a new class of lightweight, ultrastrong materials. Here we present a purely geometric model to predict the three-dimensional folding of knitted fabrics made only of the two traditional stitches, knit and purl., Comment: 7 pages, 3 figures, the whole megillah

Voice signals originating from the respiratory tract are utilized as valuable acoustic biomarkers for the diagnosis and assessment of respiratory diseases. Among the employed acoustic features, Mel Frequency Cepstral Coefficients (MFCC) is widely used for automatic analysis, with MFCC extraction commonly relying on default parameters. However, no comprehensive study has systematically investigated the impact of MFCC extraction parameters on respiratory disease diagnosis. In this study, we address this gap by examining the effects of key parameters, namely the number of coefficients, frame length, and hop length between frames, on respiratory condition examination. Our investigation uses four datasets: the Cambridge COVID-19 Sound database, the Coswara dataset, the Saarbrucken Voice Disorders (SVD) database, and a TACTICAS dataset. The Support Vector Machine (SVM) is employed as the classifier, given its widespread adoption and efficacy. Our findings indicate that the accuracy of MFCC decreases as hop length increases, and the optimal number of coefficients is observed to be approximately 30. The performance of MFCC varies with frame length across the datasets: for the COVID-19 datasets (Cambridge COVID-19 Sound database and Coswara dataset), performance declines with longer frame lengths, while for the SVD dataset, performance improves with increasing frame length (from 50 ms to 500 ms). Furthermore, we investigate the optimized combination of these parameters and observe substantial enhancements in accuracy. Compared to the worst combination, the SVM model achieves an accuracy of 81.1%, 80.6%, and 71.7%, with improvements of 19.6%, 16.10%, and 14.90% for the Cambridge COVID-19 Sound database, the Coswara dataset, and the SVD dataset respectively.

Understanding phonon and charge propagation in superconducting devices plays an important role in both performing low-threshold dark matter searches and limiting correlated errors in superconducting qubits. The Geant4 Condensed Matter Physics (G4CMP) package, originally developed for the Cryogenic Dark Matter Search (CDMS) experiment, models charge and phonon transport within silicon and germanium detectors and has been validated by experimental measurements of phonon caustics, mean charge-carrier drift velocities, and heat pulse propagation times. In this work, we present a concise framework for expanding the capabilities for phonon transport to a number of other novel substrate materials of interest to the dark matter and quantum computing communities, including sapphire (Al$_{2}$O$_{3}$), gallium arsenide (GaAs), lithium fluoride (LiF), calcium tungstate (CaWO$_{4}$), and calcium fluoride (CaF$_{2}$). We demonstrate the use of this framework in generating phonon transport properties of these materials and compare these properties with experimentally-determined values where available., Comment: 18 pages, 13 figures, 6 Tables

The ability of large language models (LLMs) to transform, interpret, and comprehend vast quantities of heterogeneous data presents a significant opportunity to enhance data-driven care delivery. However, the sensitive nature of protected health information (PHI) raises valid concerns about data privacy and trust in remote LLM platforms. In addition, the cost associated with cloud-based artificial intelligence (AI) services continues to impede widespread adoption. To address these challenges, we propose a shift in the LLM execution environment from opaque, centralized cloud providers to a decentralized and dynamic fog computing architecture. By executing open-weight LLMs in more trusted environments, such as the user's edge device or a fog layer within a local network, we aim to mitigate the privacy, trust, and financial challenges associated with cloud-based LLMs. We further present SpeziLLM, an open-source framework designed to facilitate rapid and seamless leveraging of different LLM execution layers and lowering barriers to LLM integration in digital health applications. We demonstrate SpeziLLM's broad applicability across six digital health applications, showcasing its versatility in various healthcare settings.

Using Time-Dependent Density Functional Theory (TDDFT) nonlinear nonperturbative response of the molecular system is studied for photoisomerization reaction. The 1,3-cyclohexadiene photoisomerization is probed by the high-harmonic generation (HHG) process induced by the ultrashort few femtosecond intense laser pulse. For each of the long-lived or stable isomers, we present results for HHG spectra and HHG ellipticities, as well as multi-photon ionization. Moreover, the laser-induced electronic excitations are calculated and the effect of excitations on HHG spectra is discussed. We describe the characteristic properties of the nonlinear response for each of the isomers that can be used for ultrafast detection of the isomers by tracking the specific changes in HHG response.

The Metropolitan Transit Authority (MTA) proposed building a new light rail route called the Interborough Express (IBX) to provide a direct, fast transit linkage between Queens and Brooklyn. An open-access synthetic citywide trip agenda dataset and a block-group-level mode choice model are used to assess the potential impact IBX could bring to New York City (NYC). IBX could save 28.1 minutes to potential riders across the city. For travelers either going to or departing from areas close to IBX, the average time saving is projected to be 29.7 minutes. IBX is projected to have more than 254 thousand daily ridership after its completion (69% higher than reported in the official IBX proposal). Among those riders, more than 78 thousand people (30.8%) would come from low-income households while 165 thousand people (64.7%) would start or end along the IBX corridor. The addition of IBX would attract more than 50 thousand additional daily trips to transit mode, among which more than 16 thousand would be switched from using private vehicles, reducing potential greenhouse gas (GHG) emissions by 29.28 metric tons per day. IBX can also bring significant consumer surplus benefits to the communities, which are estimated to be $1.25 USD per trip, or as high as $1.64 per trip made by a low-income traveler. While benefits are proportionately higher for lower-income users, the service does not appear to significantly reduce the proportion of travelers whose consumer surpluses fall below 10% of the population average (already quite low).

Gaussian processes (GPs) are canonical as surrogates for computer experiments because they enjoy a degree of analytic tractability. But that breaks when the response surface is constrained, say to be monotonic. Here, we provide a mono-GP construction for a single input that is highly efficient even though the calculations are non-analytic. Key ingredients include transformation of a reference process and elliptical slice sampling. We then show how mono-GP may be deployed effectively in two ways. One is additive, extending monotonicity to more inputs; the other is as a prior on injective latent warping variables in a deep Gaussian process for (non-monotonic, multi-input) non-stationary surrogate modeling. We provide illustrative and benchmarking examples throughout, showing that our methods yield improved performance over the state-of-the-art on examples from those two classes of problems.

Characterizing outer companions to hot Jupiters plays a crucial role in deciphering their origins. We present the discovery of a long-period giant planet, HD 118203 c ($m_{c}=11.79^{+0.69}_{-0.63}\ \mathrm{M_{J}}$, $a_{c}=6.28^{+0.10}_{-0.11}$ AU) exterior to a close-in eccentric hot Jupiter HD 118203 b ($P_{b}=6.135\ \mathrm{days}$, $m_{b}=2.14\pm{0.12}\ \mathrm{M_{J}}$, $r_{b}=1.14\pm{0.029}\ \mathrm{R_{J}}$, $e_{b}=0.31\pm{0.007}$) based on twenty-year radial velocities. Using Rossiter-McLaughlin (RM) observations from the Keck Planet Finder (KPF), we measured a low sky-projected spin-orbit angle $\lambda_{b}=-11^{\circ}.7^{+7.6}_{-10.0}$ for HD 118203 b and detected stellar oscillations in the host star, confirming its evolved status. Combining the RM observation with the stellar inclination measurement, we constrained the true spin-orbit angle of HD 118203 b as $\Psi_{b}<33^{\circ}.5\ (2\sigma)$, indicating the orbit normal of the hot Jupiter nearly aligned with the stellar spin axis. Furthermore, by combining radial velocities and Hipparcos-Gaia astrometric acceleration, we constrained the line-of-sight mutual inclination between the hot Jupiter and the outer planet to be $9^{\circ}.8^{+16.2}_{-9.3}$ at $2\sigma$ level. HD 118203 is one of first hot Jupiter systems where both the true spin-orbit angle of the hot Jupiter and the mutual inclination between inner and outer planets have been determined. Our results are consistent with a system-wide alignment, with low mutual inclinations between the outer giant planet, the inner hot Jupiter, and the host star. This alignment, along with the moderate eccentricity of HD 118203 c, implies that the system may have undergone coplanar high-eccentricity tidal migration. Under this framework, our dynamical analysis suggests an initial semi-major axis of 0.3 to 3.2 AU for the proto-hot Jupiter., Comment: 30 pages, 12 figures, accepted by AJ