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The recent discovery of an axial amplitude (Higgs) mode in the long-studied charge density wave (CDW) systems GdTe$_3$ and LaTe$_3$ suggests a heretofore unidentified hidden order. A theoretical study proposed that the axial Higgs results from a hidden ferroaxial component of the CDW, which could arise from non-trivial orbital texture. Here, we report extensive experimental studies on ErTe$_3$ and HoTe$_3$ that possess a high-temperature CDW similar to other RTe$_3$ (R = rare earth), along with an additional low-temperature CDW with an orthogonal ordering vector. Combining Raman spectroscopy with large-angle convergent beam electron diffraction (LACBED), rotational anisotropy second-harmonic generation (RA-SHG), and muon-spin relaxation ($\mu$SR), we provide unambiguous evidence that the high-temperature CDW breaks translation, rotation, and all vertical and diagonal mirror symmetries, but not time-reversal or inversion. In contrast, the low-temperature CDW only additionally breaks translation symmetry. Simultaneously, Raman scattering shows the high-temperature CDW produces an axial Higgs mode while the low-temperature mode is scalar. The weak monoclinic structural distortion and clear axial response in Raman and SHG are consistent with a ferroaxial phase in RTe$_3$ driven by coupled orbital and charge orders. Thus, our study provides a new standard for uncovering unconventional orders and confirms the power of Higgs modes to reveal them., Comment: 28 pages, 5 figures

Artificial intelligence agents are increasingly capable of performing complex tasks across various domains. As these agents advance, there is a growing need to accurately measure and benchmark their capabilities, particularly in accelerating AI research and development. Current benchmarks focus on general machine learning tasks, but lack comprehensive evaluation methods for assessing AI agents' abilities in tackling research-level problems and competition-level challenges in the field of AI. We present the ML Research Benchmark (MLRB), comprising 7 competition-level tasks derived from recent machine learning conference tracks. These tasks span activities typically undertaken by AI researchers, including model training efficiency, pretraining on limited data, domain specific fine-tuning, and model compression. This paper introduces a novel benchmark and evaluates it using agent scaffolds powered by frontier models, including Claude-3 and GPT-4o. The results indicate that the Claude-3.5 Sonnet agent performs best across our benchmark, excelling in planning and developing machine learning models. However, both tested agents struggled to perform non-trivial research iterations. We observed significant performance variations across tasks, highlighting the complexity of AI development and the challenges in creating versatile agent scaffolds. While current AI agents can successfully navigate complex instructions and produce baseline results, they fall short of the capabilities required for advanced AI research. The ML Research Benchmark provides a valuable framework for assessing and comparing AI agents on tasks mirroring real-world AI research challenges.

Foundation models applied to bio-molecular space hold promise to accelerate drug discovery. Molecular representation is key to building such models. Previous works have typically focused on a single representation or view of the molecules. Here, we develop a multi-view foundation model approach, that integrates molecular views of graph, image and text. Single-view foundation models are each pre-trained on a dataset of up to 200M molecules and then aggregated into combined representations. Our multi-view model is validated on a diverse set of 18 tasks, encompassing ligand-protein binding, molecular solubility, metabolism and toxicity. We show that the multi-view models perform robustly and are able to balance the strengths and weaknesses of specific views. We then apply this model to screen compounds against a large (>100 targets) set of G Protein-Coupled receptors (GPCRs). From this library of targets, we identify 33 that are related to Alzheimer's disease. On this subset, we employ our model to identify strong binders, which are validated through structure-based modeling and identification of key binding motifs., Comment: 34 pages including supplement. 9 figures, 4 tables

Lithium-ion batteries are a key energy storage technology driving revolutions in mobile electronics, electric vehicles and renewable energy storage. Capacity retention is a vital performance measure that is frequently utilized to assess whether these batteries have approached their end-of-life. Machine learning (ML) offers a powerful tool for predicting capacity degradation based on past data, and, potentially, prior physical knowledge, but the degree to which an ML prediction can be trusted is of significant practical importance in situations where consequential decisions must be made based on battery state of health. This study explores the efficacy of fully Bayesian machine learning in forecasting battery health with the quantification of uncertainty in its predictions. Specifically, we implemented three probabilistic ML approaches and evaluated the accuracy of their predictions and uncertainty estimates: a standard Gaussian process (GP), a structured Gaussian process (sGP), and a fully Bayesian neural network (BNN). In typical applications of GP and sGP, their hyperparameters are learned from a single sample while, in contrast, BNNs are typically pre-trained on an existing dataset to learn the weight distributions before being used for inference. This difference in methodology gives the BNN an advantage in learning global trends in a dataset and makes BNNs a good choice when training data is available. However, we show that pre-training can also be leveraged for GP and sGP approaches to learn the prior distributions of the hyperparameters and that in the case of the pre-trained sGP, similar accuracy and improved uncertainty estimation compared to the BNN can be achieved. This approach offers a framework for a broad range of probabilistic machine learning scenarios where past data is available and can be used to learn priors for (hyper)parameters of probabilistic ML models.

Cu$_2$IrO$_3$ has attracted recent interest due to its proximity to the Kitaev quantum spin liquid state and the complex structural response observed at high pressures. We use x-ray spectroscopy and scattering as well as electrical transport techniques to unveil the electronic structure of Cu$_2$IrO$_3$ at ambient and high pressures. Despite featuring a $\mathrm{Ir^{4+}}$ $J_{\rm{eff}}=1/2$ state at ambient pressure, Ir $L_{3}$ edge resonant inelastic x-ray scattering reveals broadened electronic excitations that point to the importance of Ir $5d$-Cu $3d$ interaction. High pressure first drives an Ir-Ir dimer state with collapsed $\langle \mathbf{L} \cdot \mathbf{S} \rangle$ and $\langle L_z \rangle/\langle S_z \rangle$, signaling the formation of $5d$ molecular orbitals. A novel $\mathrm{Cu \to Ir}$ charge transfer is observed at the onset of phase 5 above 30 GPa at low temperatures, leading to an approximate $\mathrm{Ir^{3+}}$ and $\mathrm{Cu^{1.5+}}$ valence, with persistent insulating electrical transport seemingly driven by charge segregation of Cu 1+/2+ ions into distinct sites. Concomitant x-ray spectroscopy and scattering measurements through different thermodynamic paths demonstrate a strong electron-lattice coupling, with $J_{\rm{eff}}=1/2$ and $\mathrm{Ir^{3+}}$/$\mathrm{Cu^{1.5+}}$ electronic states occurring only in phases 1 and 5, respectively. Remarkably, the charge-transferred state can only be reached if Cu$_2$IrO$_3$ is pressurized at low temperature, suggesting that phonons play an important role in the stability of this phase. These results point to the choice of thermodynamic path across interplanar collapse transition as a key route to access novel states in intercalated iridates., Comment: 8 pages, 4 figures, see ancillary file for Supplemental Material

The Department of Defense (DOD) offers education and training to foreign military personnel--known as international military students--at United States (U.S.) DOD sites. After a student killed 3 people at a base in Florida in 2019, questions were raised about personnel safety at DOD sites hosting students for training. The William M. (Mac) Thornberry National Defense Authorization Act for Fiscal Year 2021 includes a provision for the Government Accountability Office (GAO) to review DOD's implementation of security vetting for international military students and accompanying family members. This report assesses the extent to which: (1) DOD has developed vetting procedures for international military students and addressed any implementation challenges; and (2) State and DOD have shared information related to students' training at DOD sites in the U.S. GAO reviewed documentation, analyzed vetting result data for October 1, 2019, through March 31, 2023, and interviewed DOD and State officials and stakeholders from six unified combatant commands and three U.S. embassies. Some stakeholders found opportunities for improvement, and GAO is making four recommendations, including for DOD to assess vetting implementation and take action to mitigate any factors affecting implementation as identified in its assessment, and for DOD and State to clarify roles and responsibilities for sharing information.

A random set $S$ is $p$-spread if, for all sets $T$, $$\mathbb{P}(S \supseteq T) \leq p^{|T|}.$$ There is a constant $C>1$ large enough that for every graph $G$ with maximum degree $D$, there is a $C/D$-spread distribution on $(D+1)$-colorings of $G$. Making use of a connection between thresholds and spread distributions due to Frankston, Kahn, Narayanan, and Park, a palette sparsification theorem of Assadi, Chen, and Khanna follows., Comment: 18 pages

We share observations and challenges from an ongoing effort to implement Explainable AI (XAI) in a domain-specific workflow for cybersecurity analysts. Specifically, we briefly describe a preliminary case study on the use of XAI for source code classification, where accurate assessment and timeliness are paramount. We find that the outputs of state-of-the-art saliency explanation techniques (e.g., SHAP or LIME) are lost in translation when interpreted by people with little AI expertise, despite these techniques being marketed for non-technical users. Moreover, we find that popular XAI techniques offer fewer insights for real-time human-AI workflows when they are post hoc and too localized in their explanations. Instead, we observe that cyber analysts need higher-level, easy-to-digest explanations that can offer as little disruption as possible to their workflows. We outline unaddressed gaps in practical and effective XAI, then touch on how emerging technologies like Large Language Models (LLMs) could mitigate these existing obstacles., Comment: ACM CHI 2024 Workshop on Human-Centered Explainable AI (HCXAI)

It is shown that the following holds for each $\varepsilon >0$. For $G$ an $n$-vertex graph of maximum degree $D$, lists $S_v$ ($v\in V(G)$), and $L_v$ chosen uniformly from the ($(1+\varepsilon)\ln n$)-subsets of $S_v$ (independent of other choices), \[ \mbox{$G$ admits a proper coloring $\sigma$ with $\sigma_v\in L_v$ $\forall v$} \] with probability tending to 1 as $D\to \infty$. When each $S_v $ is $\{1\dots D+1\}$, this is an asymptotically optimal version of the ``palette sparsification'' theorem of Assadi, Chen and Khanna that was proved in an earlier paper by the present authors., Comment: 37 pages, 0 figures. arXiv admin note: text overlap with arXiv:2306.00171

Generalizing an argument of Matiyasevich, we illustrate a method to generate infinitely many diophantine equations whose solutions can be completely described by linear recurrences. In particular, we provide an integer-coefficient polynomial $p(x, y, z)$ whose only integer roots are consecutive triples of Tribonacci numbers., Comment: This version corrects a misstated date

The Skipper-in-CMOS image sensor integrates the non-destructive readout capability of Skipper Charge Coupled Devices (Skipper-CCDs) with the high conversion gain of a pinned photodiode in a CMOS imaging process, while taking advantage of in-pixel signal processing. This allows both single photon counting as well as high frame rate readout through highly parallel processing. The first results obtained from a 15 x 15 um^2 pixel cell of a Skipper-in-CMOS sensor fabricated in Tower Semiconductor's commercial 180 nm CMOS Image Sensor process are presented. Measurements confirm the expected reduction of the readout noise with the number of samples down to deep sub-electron noise of 0.15rms e-, demonstrating the charge transfer operation from the pinned photodiode and the single photon counting operation when the sensor is exposed to light. The article also discusses new testing strategies employed for its operation and characterization., Comment: 7 pages, 10 figures

Methods for estimating heterogeneous treatment effects (HTE) from observational data have largely focused on continuous or binary outcomes, with less attention paid to survival outcomes and almost none to settings with competing risks. In this work, we develop censoring unbiased transformations (CUTs) for survival outcomes both with and without competing risks. After converting time-to-event outcomes using these CUTs, direct application of HTE learners for continuous outcomes yields consistent estimates of heterogeneous cumulative incidence effects, total effects, and separable direct effects. Our CUTs enable application of a much larger set of state of the art HTE learners for censored outcomes than had previously been available, especially in competing risks settings. We provide generic model-free learner-specific oracle inequalities bounding the finite-sample excess risk. The oracle efficiency results depend on the oracle selector and estimated nuisance functions from all steps involved in the transformation. We demonstrate the empirical performance of the proposed methods in simulation studies.

We present theoretical and experimental studies of superconductivity and low temperature structural phase boundaries in lithium. We mapped the structural phase diagram of 6Li and 7Li under hydrostatic conditions between 5 top 55GPa and within the temperature range of 15 to 75K, observing the FCC-hR1-cI16 phase transitions. 6Li and 7Li show some differences at the structural boundaries, with a potential shift of the phase boundaries of 6Li to lower pressures. Density functional theory calculations and topological analysis of the electron density elucidates the superconducting properties and interatomic interactions within these phases of lithium., Comment: (24 pages, 10 figures, 2 tables, 2 parts including main manuscript and the supplementary information section)

1. Abrupt environmental changes can lead to evolutionary shifts in not only mean (optimal value), but also variance of descendants in trait evolution. There are some methods to detect shifts in optimal value but few studies consider shifts in variance. 2. We use a multi-optima and multi-variance OU process model to describe the trait evolution process with shifts in both optimal value and variance and provide analysis of how the covariance between species changes when shifts in variance occur along the path. 3. We propose a new method to detect the shifts in both variance and optimal values based on minimizing the loss function with L1 penalty. We implement our method in a new R package, ShiVa (Detection of evolutionary shifts in variance). 4. We conduct simulations to compare our method with the two methods considering only shifts in optimal values (l1ou; PhylogeneticEM). Our method shows strength in predictive ability and includes far fewer false positive shifts in optimal value compared to other methods when shifts in variance actually exist. When there are only shifts in optimal value, our method performs similarly to other methods. We applied our method to the cordylid data, ShiVa outperformed l1ou and phyloEM, exhibiting the highest log-likelihood and lowest BIC.

Machine learning models built on training data with multiple modalities can reveal new insights that are not accessible through unimodal datasets. For example, cardiac magnetic resonance images (MRIs) and electrocardiograms (ECGs) are both known to capture useful information about subjects' cardiovascular health status. A multimodal machine learning model trained from large datasets can potentially predict the onset of heart-related diseases and provide novel medical insights about the cardiovascular system. Despite the potential benefits, it is difficult for medical experts to explore multimodal representation models without visual aids and to test the predictive performance of the models on various subpopulations. To address the challenges, we developed a visual analytics system called Latent Space Explorer. Latent Space Explorer provides interactive visualizations that enable users to explore the multimodal representation of subjects, define subgroups of interest, interactively decode data with different modalities with the selected subjects, and inspect the accuracy of the embedding in downstream prediction tasks. A user study was conducted with medical experts and their feedback provided useful insights into how Latent Space Explorer can help their analysis and possible new direction for further development in the medical domain., Comment: 7 pages, 5 figures

This paper uses mathematics to analyze the challenges of geometrically noisy environments on triangulation. Given widely accepted algorithmic triangulation methods, such as O (n ln n) or a simpler O (n^3) method, we can mathematically prove that triangulation of any two dimensional polygonal region is possible, albeit impractical in some cases. Further, we consider the implications of environments in which a z-axis is present, as seen in cellular triangulation. In many of the cases where consideration of the z-axis is necessary, we recognize the absence of a fixed or known point of origin and consider methods of addressing this challenge., Comment: 4 pages, 11 figures

An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues. Most notably, results drawn from different settings and studies are not comparable, which limits reproducibility. Here, we present an artificial intelligence (AI) approach using semantic textual similarity (STS) to link symptoms and scores across previously incongruous symptom inventories. We tested the ability of four pre-trained STS models to screen thousands of symptom description pairs for related content - a challenging task typically requiring expert panels. Models were tasked to predict symptom severity across four different inventories for 6,607 participants drawn from 16 international data sources. The STS approach achieved 74.8% accuracy across five tasks, outperforming other models tested. This work suggests that incorporating contextual, semantic information can assist expert decision-making processes, yielding gains for both general and disease-specific clinical assessment.

The relation between the resolved star formation rate per unit area and the non-thermal radio continuum emission is studied in 21 Virgo cluster galaxies and the two nearby spiral galaxies, NGC6946 and M51. For the interpretation and understanding of our results we used a 3D model where star formation, 2D cosmic ray (CR) propagation, and the physics of synchrotron emission are included. Based on the linear correlation between the star formation rate per unit area and the synchrotron emission and its scatter radio-bright and radio-dim regions can be robustly defined for our sample of spiral galaxies. We identified CR diffusion or streaming as the physical causes of radio-bright regions of unperturbed symmetric spiral galaxies as NGC6946. We identified the probable causes of radio-bright regions in several galaxies as CR transport, via either gravitational tides (M51) or galactic winds (NGC4532) or ram pressure stripping (NGC4330 and NGC4522). Three galaxies are overall radio-dim: NGC4298, NGC4535, and NGC4567. Based on our model of synchrotron-emitting disks we suggest that the overall radio-dim galaxies have a significantly lower magnetic field than expected by equipartition between the magnetic and turbulent energy densities. Radio-bright regions frequently coincide with asymmetric ridges of polarized radio continuum emission, and we found a clear albeit moderate correlation between the polarized radio continuum emission and the radio/SFR ratio. When compression or shear motions of the interstellar medium (ISM) are present in the galactic disk, the radio-bright regions are linked to the commonly observed asymmetric ridges of polarized radio continuum emission and represent a useful tool for the interaction diagnostics. Based on our results, we propose a scenario for the interplay between star formation, CR electrons, and magnetic fields in spiral galaxies., Comment: 36 pages, accepted for publication in A&A

Mean decrease in impurity (MDI) is a popular feature importance measure for random forests (RFs). We show that the MDI for a feature $X_k$ in each tree in an RF is equivalent to the unnormalized $R^2$ value in a linear regression of the response on the collection of decision stumps that split on $X_k$. We use this interpretation to propose a flexible feature importance framework called MDI+. Specifically, MDI+ generalizes MDI by allowing the analyst to replace the linear regression model and $R^2$ metric with regularized generalized linear models (GLMs) and metrics better suited for the given data structure. Moreover, MDI+ incorporates additional features to mitigate known biases of decision trees against additive or smooth models. We further provide guidance on how practitioners can choose an appropriate GLM and metric based upon the Predictability, Computability, Stability framework for veridical data science. Extensive data-inspired simulations show that MDI+ significantly outperforms popular feature importance measures in identifying signal features. We also apply MDI+ to two real-world case studies on drug response prediction and breast cancer subtype classification. We show that MDI+ extracts well-established predictive genes with significantly greater stability compared to existing feature importance measures. All code and models are released in a full-fledged python package on Github.

It is shown that the following holds for each $\varepsilon>0$. For $G$ an $n$-vertex graph of maximum degree $D$ and "lists" $L_v$ ($v \in V(G)$) chosen independently and uniformly from the ($(1+\varepsilon)\ln n$)-subsets of $\{1, ..., D+1\}$, \[ G \text{ admits a proper coloring } \sigma \text{ with } \sigma_v \in L_v \forall v \] with probability tending to 1 as $D \to \infty$. This is an asymptotically optimal version of a recent "palette sparsification" theorem of Assadi, Chen, and Khanna., Comment: 29 pages

We find it is common for consumers who are not in financial distress to make credit card payments at or close to the minimum. This pattern is difficult to reconcile with economic factors but can be explained by minimum payment information presented to consumers acting as an anchor that weighs payments down. Building on Stewart (2009), we conduct a hypothetical credit card payment experiment to test an intervention to de-anchor payment choices. This intervention effectively stops consumers selecting payments at the contractual minimum. It also increases their average payments, as well as shifting the distribution of payments. By de-anchoring choices from the minimum, consumers increasingly choose the full payment amount - which potentially seems to act as a target payment for consumers. We innovate by linking the experimental responses to survey responses on financial distress and to actual credit card payment behaviours. We find that the intervention largely increases payments made by less financially-distressed consumers. We are also able to evaluate the potential external validity of our experiment and find that hypothetical responses are closely related to consumers' actual credit card payments.

In empirical studies with time-to-event outcomes, investigators often leverage observational data to conduct causal inference on the effect of exposure when randomized controlled trial data is unavailable. Model misspecification and lack of overlap are common issues in observational studies, and they often lead to inconsistent and inefficient estimators of the average treatment effect. Estimators targeting overlap weighted effects have been proposed to address the challenge of poor overlap, and methods enabling flexible machine learning for nuisance models address model misspecification. However, the approaches that allow machine learning for nuisance models have not been extended to the setting of weighted average treatment effects for time-to-event outcomes when there is poor overlap. In this work, we propose a class of one-step cross-fitted double/debiased machine learning estimators for the weighted cumulative causal effect as a function of restriction time. We prove that the proposed estimators are consistent, asymptotically linear, and reach semiparametric efficiency bounds under regularity conditions. Our simulations show that the proposed estimators using nonparametric machine learning nuisance models perform as well as established methods that require correctly-specified parametric nuisance models, illustrating that our estimators mitigate the need for oracle parametric nuisance models. We apply the proposed methods to real-world observational data from a UK primary care database to compare the effects of anti-diabetic drugs on cancer clinical outcomes.

We report LOFAR and VLA detections of extended radio emission in the spiral galaxy HCG 97b, hosted by an X-ray bright galaxy group. The extended radio emission detected at 144 MHz, 1.4 GHz and 4.86 GHz is elongated along the optical disk and has a tail that extends 27 kpc in projection towards the centre of the group at GHz frequencies or 60 kpc at 144 MHz. Chandra X-ray data show two off-nuclear ultra-luminous X-ray sources (ULXs), with the farther one being a plausible candidate for an accreting intermediate-mass black hole (IMBH). The asymmetry observed in both CO emission morphology and kinematics indicates that HCG 97b is undergoing ram-pressure stripping, with the leading side at the southeastern edge of the disk. Moreover, the VLA 4.86 GHz image reveals two bright radio blobs near one ULX, aligning with the disk and tail, respectively. The spectral indices in the disk and tail are comparable and flat ($\alpha > -1$), suggesting the presence of recent outflows potentially linked to ULX feedback. This hypothesis gains support from estimates showing that the bulk velocity of the relativistic electrons needed for transport from the disk to the tail is approximately $\sim 1300$ $\rm km~s^{-1}$. This velocity is much higher than those observed in ram-pressure stripped galaxies ($100-600$ $\rm km~s^{-1}$), implying an alternative mechanism aiding the stripping process. Therefore, we conclude that HCG 97b is subject to ram pressure, with the formation of its stripped radio tail likely influenced by the putative IMBH activities., Comment: 19 pages, 18 figures. Accepted for publication in MNRAS

In this paper, we present a grammar-based natural language framework for robot programming, specifically for pick-and-place tasks. Our approach uses a custom dictionary of action words, designed to store together words that share meaning, allowing for easy expansion of the vocabulary by adding more action words from a lexical database. We validate our Natural Language Robot Programming (NLRP) framework through simulation and real-world experimentation, using a Franka Panda robotic arm equipped with a calibrated camera-in-hand and a microphone. Participants were asked to complete a pick-and-place task using verbal commands, which were converted into text using Google's Speech-to-Text API and processed through the NLRP framework to obtain joint space trajectories for the robot. Our results indicate that our approach has a high system usability score. The framework's dictionary can be easily extended without relying on transfer learning or large data sets. In the future, we plan to compare the presented framework with different approaches of human-assisted pick-and-place tasks via a comprehensive user study., Comment: submitted to IROS 2023

At a time when Wisconsin faces severe workforce shortages in several key sectors and demand for skilled workers has skyrocketed, enrollment in the state's colleges and universities has generally been declining even faster than the national trend. Enrollments in Wisconsin across all public and private institutions were falling even prior to the pandemic, dropping from 384,181 in 2010 to 332,856 in 2019. This decline of 13.4% was more than twice the national drop of 6.6% over the same period and was particularly pronounced for technical colleges and other two-year institutions. The arrival of COVID-19 has compounded the problem and added urgency to longstanding questions about how the state will cultivate the workers it will need to sustain the health of its economy and citizens, particularly in fields such as nursing, teaching, and skilled trades. This study reviews state and federal financial aid in Wisconsin, laying out the main trends and focusing on the handful of grant and loan programs that account for most of the dollars spent each year. Critical questions include: (1) How do Wisconsin's current levels of financial aid and specific programs compare to past funding and programs in this state and those in other states? Has their buying power grown or decreased over time compared to the cost of tuition? Which state agencies are involved in administering these programs and is the system efficient and accessible for families? (2) How does the picture change for students at the UW System, Wisconsin Technical College System (WTCS), or private nonprofit or tribal colleges? What does financial aid mean for specific groups of students such as racial and ethnic minorities and those from low-income families? and (3) What options should policymakers entertain after considering the current state of the financial aid system in Wisconsin, those of other states, and research on the impacts of different approaches? What are the pros and cons of each potential alternative? [Additional sponsors for this report include the Higher Education Regional Alliance, the ADAMM Foundation, and the Milwaukee Regional Research Forum. For the Executive Summary, see ED620268.]

Though qualitative research has become more prevalent in practice over the last 30 years, there is still considerable uncertainty among researchers regarding how to ensure inter-rater consistency when teams are tasked with coding qualitative data. In this article, we offer an explanation of a methodology our qualitative team used to achieve systematic coding of our dataset in a way that preserved the contextual, subjective nature of the data, lent itself to the deductive and inductive creation of a layered codebook, and ensured consistent application of the codebook to varied types of data. This methodology prepared us to draw logical and substantiated conclusions during subsequent analyses; hence, the process serves as a welcome addition to the literature on consistently coding qualitative data in a manner that honors its defining characteristics. [This paper was published in "Qualitative Research" v22 n2 p194-219 2022.]

Classical law and economics is foundational to the American legal system. Centered at the University of Chicago, its assumptions, most especially that humans act both rationally and selfishly, informs the thinking of legislatures, judges, and government lawyers, and has shaped nearly every aspect of the way commercial transactions are conducted. But what if the Chicago School, as I refer to this line of thinking, is wrong? Alternative approaches such as behavioral law and economics or law and political economy contend that human decisionmaking is based on emotions or should not be regulated as a social geometry of bargains. This Article proposes a different and wholly novel reason that the Chicago School is wrong: a fundamental assumption central to many of its game theory models has been disproven. This Article shows that a 2012 breakthrough from world famous physicist Freeman Dyson shocked the world of game theory. This Article shows that Chicago School game theorists are wrong on their own terms because these 2 x 2 games such as the Prisoner's Dilemma, Chicken, and Snowdrift, ostensibly based on mutual defection and corrective justice, in fact yield to an insight of pure cooperation. These new game theory solutions can be scaled to design whole institutions and systems that honor the pure cooperation insight, holding out the possibility of cracking large scale social dilemmas like the tragedy of the commons. It demonstrates that, in such systems, pure cooperation is the best answer in the right environment and in the long run. It ends by calling for a new legal field to redesign the structures based on the outdated assumptions of the Chicago School game theorists., Comment: 8 figures; 56 pages

We report on a study of the Multiview Observatory for Solar Terrestrial Science (MOST) mission that will provide comprehensive imagery and time series data needed to understand the magnetic connection between the solar interior and the solar atmosphere/inner heliosphere. MOST will build upon the successes of SOHO and STEREO missions with new views of the Sun and enhanced instrument capabilities. This article is based on a study conducted at NASA Goddard Space Flight Center that determined the required instrument refinement, spacecraft accommodation, launch configuration, and flight dynamics for mission success. MOST is envisioned as the next generation great observatory positioned to obtain three-dimensional information of large-scale heliospheric structures such as coronal mass ejections, stream interaction regions, and the solar wind itself. The MOST mission consists of 2 pairs of spacecraft located in the vicinity of Sun-Earth Lagrange points L4 (MOST1, MOST3) and L5 (MOST2 and MOST4). The spacecraft stationed at L4 (MOST1) and L5 (MOST2) will each carry seven remote-sensing and three in-situ instrument suites, including a novel radio package known as the Faraday Effect Tracker of Coronal and Heliospheric structures (FETCH). MOST3 and MOST4 will carry only the FETCH instruments and are positioned at variable locations along the Earth orbit up to 20{\deg} ahead of L4 and 20{\deg} behind L5, respectively. FETCH will have polarized radio transmitters and receivers on all four spacecraft to measure the magnetic content of solar wind structures propagating from the Sun to Earth using the Faraday rotation technique. The MOST mission will be able to sample the magnetized plasma throughout the Sun-Earth connected space during the mission lifetime over a solar cycle., Comment: 42 pages, 19 figures, 8 tables, to appear in J. Atmospheric and Solar Terrestrial Physics

The phenomenon of high temperature superconductivity, approaching room temperature, has been realized in a number of hydrogen-dominant alloy systems under high pressure conditions1-12. A significant discovery in reaching room temperature superconductivity is the photo-induced reaction of sulfur, hydrogen, and carbon that initially forms of van der Waals solids at sub-megabar pressures. Carbonaceous sulfur hydride has been demonstrated to be tunable with respect to carbon content, leading to different superconducting final states with different structural symmetries. A modulated AC susceptibility technique adapted for a diamond anvil cell confirms a Tc of 260 kelvin at 133 GPa in carbonaceous sulfur hydride. Furthermore, direct synchrotron infrared reflectivity measurements on the same sample under the same conditions reveal a superconducting gap of ~85 meV at 100 K in close agreement to the expected value from Bardeen-Cooper-Schrieffer (BCS) theory13-18. Additionally, x-ray diffraction in tandem with AC magnetic susceptibility measurements above and below the superconducting transition temperature, and as a function of pressure at 107-133 GPa, reveal the Pnma structure of the material is responsible for the close to room-temperature superconductivity at these pressures.

We present the results from the HST WFC3 and ACS data on an archetypal galaxy undergoing ram pressure stripping (RPS), ESO 137-001, in the nearby cluster Abell 3627. ESO 137-001 is known to host a prominent stripped tail detected in many bands from X-rays, Halpha to CO. The HST data reveal significant features indicative of RPS such as asymmetric dust distribution and surface brightness as well as many blue young star complexes in the tail. We study the correlation between the blue young star complexes from HST, HII regions from Halpha (MUSE) and dense molecular clouds from CO (ALMA). The correlation between the HST blue star clusters and the HII regions is very good, while their correlation with the dense CO clumps are typically not good, presumably due in part to evolutionary effects. In comparison to the Starburst99+Cloudy model, many blue regions are found to be young (< 10 Myr) and the total star formation (SF) rate in the tail is 0.3 - 0.6 M_Sun/yr for sources measured with ages less than 100 Myr, about 40% of the SF rate in the galaxy. We trace SF over at least 100 Myr and give a full picture of the recent SF history in the tail. We also demonstrate the importance of including nebular emissions and a nebular to stellar extinction correction factor when comparing the model to the broadband data. Our work on ESO 137-001 demonstrates the importance of HST data for constraining the SF history in stripped tails., Comment: 22 pages, 20 figures, 4 tables, published in MNRAS 06/2023

Minerals are solid state nuclear track detectors - nuclear recoils in a mineral leave latent damage to the crystal structure. Depending on the mineral and its temperature, the damage features are retained in the material from minutes (in low-melting point materials such as salts at a few hundred degrees C) to timescales much larger than the 4.5 Gyr-age of the Solar System (in refractory materials at room temperature). The damage features from the $O(50)$ MeV fission fragments left by spontaneous fission of $^{238}$U and other heavy unstable isotopes have long been used for fission track dating of geological samples. Laboratory studies have demonstrated the readout of defects caused by nuclear recoils with energies as small as $O(1)$ keV. This whitepaper discusses a wide range of possible applications of minerals as detectors for $E_R \gtrsim O(1)$ keV nuclear recoils: Using natural minerals, one could use the damage features accumulated over $O(10)$ Myr$-O(1)$ Gyr to measure astrophysical neutrino fluxes (from the Sun, supernovae, or cosmic rays interacting with the atmosphere) as well as search for Dark Matter. Using signals accumulated over months to few-years timescales in laboratory-manufactured minerals, one could measure reactor neutrinos or use them as Dark Matter detectors, potentially with directional sensitivity. Research groups in Europe, Asia, and America have started developing microscopy techniques to read out the $O(1) - O(100)$ nm damage features in crystals left by $O(0.1) - O(100)$ keV nuclear recoils. We report on the status and plans of these programs. The research program towards the realization of such detectors is highly interdisciplinary, combining geoscience, material science, applied and fundamental physics with techniques from quantum information and Artificial Intelligence., Comment: 115 pages, many pictures of tracks. Please see the source file for higher resolution versions of some plots. v2: matches the published version

Ram pressure stripping (RPS) is an important process to affect the evolution of cluster galaxies and their surrounding environment. We present a large MUSE mosaic for ESO 137-001 and its stripped tails, and study the detailed distributions and kinematics of the ionized gas and stars. The warm, ionized gas is detected to at least 87 kpc from the galaxy and splits into three tails. There is a clear velocity gradient roughly perpendicular to the stripping direction, which decreases along the tails and disappears beyond $\sim45$ kpc downstream. The velocity dispersion of the ionized gas increases to $\sim80$ km s$^{-1}$ at $\sim20$ kpc downstream and stays flat beyond. The stars in the galaxy disc present a regular rotation motion, while the ionized gas is already disturbed by the ram pressure. Based on the observed velocity gradient, we construct the velocity model for the residual galactic rotation in the tails and discuss the origin and implication of its fading with distance. By comparing with theoretical studies, we interpreted the increased velocity dispersion as the result of the oscillations induced by the gas flows in the galaxy wake, which may imply an enhanced degree of turbulence there. We also compare the kinematic properties of the ionized gas and molecular gas from ALMA, which shows they are co-moving and kinematically mixed through the tails. Our study demonstrates the great potential of spatially resolved spectroscopy in probing the detailed kinematic properties of the stripped gas, which can provide important information for future simulations of RPS., Comment: 18 pages, 20 figures, 2 tables, accepted for publication in MNRAS

Non-Negative Matrix Factorization (NMF) is a widely used dimension reduction method that factorizes a non-negative data matrix into two lower dimensional non-negative matrices: One is the basis or feature matrix which consists of the variables and the other is the coefficients matrix which is the projections of data points to the new basis. The features can be interpreted as sub-structures of the data. The number of sub-structures in the feature matrix is also called the rank which is the only tuning parameter in NMF. An appropriate rank will extract the key latent features while minimizing the noise from the original data. In this paper, we develop a novel rank selection method based on hypothesis testing, using a deconvolved bootstrap distribution to assess the significance level accurately despite the large amount of optimization error. In the simulation section, we compare our method with a rank selection method based on hypothesis testing using bootstrap distribution without deconvolution, and with a cross-validated imputation method1. Through simulations, we demonstrate that our method is not only accurate at estimating the true ranks for NMF especially when the features are hard to distinguish but also efficient at computation. When applied to real microbiome data (e.g. OTU data and functional metagenomic data), our method also shows the ability to extract interpretable sub-communities in the data.

Prior attainment is a major factor in both educational and career progression, but even young people who do show strong initial academic potential from less advantaged backgrounds often fall behind their better-off peers during their time in education. Indeed, the first piece in our Social Mobility: The Next Generation series found that by GCSE, disadvantaged high attainers (those eligible for free school meals in secondary school and in the top third for attainment at the end of primary school), are achieving grades that are on average more than three quarters of a grade lower per subject than students with the same ability from better off backgrounds. This report, the second in the series, looks to tell the stories behind those statistics. Using in-depth interviews with young people from the COSMO Study, it examines in greater detail the lives of young people with potential but from different socio-economic backgrounds, to better understand the ways in which their experiences differ, and where they have faced common challenges. [This report was co-published by Kantar Public. The inclusion of the Sutton Trust Opportunity Cohort in the COSMO Study has been made possible by funding provided by leading algorithmic trading firm, XTX Markets.]

Recent work in reinforcement learning has focused on several characteristics of learned policies that go beyond maximizing reward. These properties include fairness, explainability, generalization, and robustness. In this paper, we define interventional robustness (IR), a measure of how much variability is introduced into learned policies by incidental aspects of the training procedure, such as the order of training data or the particular exploratory actions taken by agents. A training procedure has high IR when the agents it produces take very similar actions under intervention, despite variation in these incidental aspects of the training procedure. We develop an intuitive, quantitative measure of IR and calculate it for eight algorithms in three Atari environments across dozens of interventions and states. From these experiments, we find that IR varies with the amount of training and type of algorithm and that high performance does not imply high IR, as one might expect., Comment: 17 pages, 13 figures

Disease risk models can identify high-risk patients and help clinicians provide more personalized care. However, risk models developed on one dataset may not generalize across diverse subpopulations of patients in different datasets and may have unexpected performance. It is challenging for clinical researchers to inspect risk models across different subgroups without any tools. Therefore, we developed an interactive visualization system called RMExplorer (Risk Model Explorer) to enable interactive risk model assessment. Specifically, the system allows users to define subgroups of patients by selecting clinical, demographic, or other characteristics, to explore the performance and fairness of risk models on the subgroups, and to understand the feature contributions to risk scores. To demonstrate the usefulness of the tool, we conduct a case study, where we use RMExplorer to explore three atrial fibrillation risk models by applying them to the UK Biobank dataset of 445,329 individuals. RMExplorer can help researchers to evaluate the performance and biases of risk models on subpopulations of interest in their data., Comment: IEEE VIS 2022 Short

Tracking detectors are of vital importance for collider-based high energy physics (HEP) experiments. The primary purpose of tracking detectors is the precise reconstruction of charged particle trajectories and the reconstruction of secondary vertices. The performance requirements from the community posed by the future collider experiments require an evolution of tracking systems, necessitating the development of new techniques, materials and technologies in order to fully exploit their physics potential. In this article we summarize the discussions and conclusions of the 2022 Snowmass Instrumentation Frontier subgroup on Solid State and Tracking Detectors (Snowmass IF03)., Comment: for the Snowmass Instrumentation Frontier Solid State Detector and Tracking community

Causal inference from observational data often rests on the unverifiable assumption of no unmeasured confounding. Recently, Tchetgen Tchetgen and colleagues have introduced proximal inference to leverage negative control outcomes and exposures as proxies to adjust for bias from unmeasured confounding. However, some of the key assumptions that proximal inference relies on are themselves empirically untestable. Additionally, the impact of violations of proximal inference assumptions on the bias of effect estimates is not well understood. In this paper, we derive bias formulas for proximal inference estimators under a linear structural equation model data generating process. These results are a first step toward sensitivity analysis and quantitative bias analysis of proximal inference estimators. While limited to a particular family of data generating processes, our results may offer some more general insight into the behavior of proximal inference estimators., Comment: typos and references corrected; revised DGP in section 5 for minor bias formula improvement (results and conclusions unchanged)

Valence transition could induce structural, insulator-metal, nonmagnetic-magnetic and superconducting transitions in rare-earth metals and compounds, while the underlying physics remains unclear due to the complex interaction of localized 4f electrons as well as their coupling with itinerant electrons. The valence transition in the elemental metal europium (Eu) still has remained as a matter of debate. Using resonant x-ray emission scattering and x-ray diffraction, we pressurize the states of 4f electrons in Eu and study its valence and structure transitions up to 160 GPa. We provide compelling evidence for a valence transition around 80 GPa, which coincides with a structural transition from a monoclinic (C2/c) to an orthorhombic phase (Pnma). We show that the valence transition occurs when the pressure-dependent energy gap between 4f and 5d electrons approaches the Coulomb interaction. Our discovery is critical for understanding the electrodynamics of Eu, including magnetism and high-pressure superconductivity.

The stretched diamond lattice material LiYbO2 has recently been reported to exhibit two magnetic transitions ($T_{N1} = 1.1 K$, $T_{N2} = 0.45 K$) via specific heat, magnetization, and neutron scattering measurements [Bordelon et al., Phys. Rev. B 103, 014420 (2021)]. Here we report complementary magnetic measurements down to T = 0.28 K via the local probe technique of muon spin relaxation. While we observe a rapid increase in the zero-field muon depolarization rate at $T_{N1}$, we do not observe any spontaneous muon precession for $T < T_{N1}$, which is typically associated with long-range magnetic ordering. The depolarization rate in the ordered state shows a surprising sensitivity to magnetic fields applied along the initial spin polarization direction. Using a simple one-dimensional model, we show that these results are consistent with the unusual random-phase bipartite incommensurate magnetic structure proposed by Bordelon et al. for the intermediate temperature range $T_{N2} < T < T_{N1}$. We also find evidence for temperature-independent magnetic fluctuations persisting to our lowest temperatures, but no obvious signature of the transition or spontaneous muon precession at and below TN2, respectively. This result is suggestive of quantum dynamics within a highly degenerate ground state.

1. Abrupt environmental changes can lead to evolutionary shifts in trait evolution. Identifying these shifts is an important step in understanding the evolutionary history of phenotypes. 2. We propose an ensemble variable selection method (R package ELPASO) for the evolutionary shift detection task and compare it with existing methods (R packages l1ou and PhylogeneticEM) under several scenarios. 3. The performances of methods are highly dependent on the selection criterion. When the signal sizes are small, the methods using the Bayesian information criterion (BIC) have better performances. And when the signal sizes are large enough, the methods using the phylogenetic Bayesian information criterion (pBIC) (Khabbazian et al., 2016) have better performance. Moreover, the performance is heavily impacted by measurement error and tree reconstruction error. 4. Ensemble method + pBIC tends to perform less conservatively than l1ou + pBIC, and Ensemble method + BIC is more conservatively than l1ou + BIC. PhylogeneticEM is even more conservative with small signal sizes and falls between l1ou + pBIC and Ensemble method + BIC with large signal sizes. The results can differ between the methods, but none clearly outperforms the others. By applying multiple methods to a single dataset, we can access the robustness of each detected shift, based on the agreement among methods.

The objective in the current paper is to examine the processes of how our research team negotiated meaning using an iterative design approach as we established, developed, and refined a rubric to capture comprehension processes and strategies evident in students' verbal protocols. The overarching project comprises multiple data sets, multiple scientists across (distant) institutions, and multiple teams of discourse analysts who are tasked with scoring over 20,000 verbal protocols (i.e., think aloud, self-explanation) collected in studies conducted in the last decade. Here, we describe the iterative modifications, negotiations, and realizations while coding our first subset comprising 7,559 individual verbal protocols. Drawing upon work in design research, we describe a process through which the research team has negotiated meaning around theory-driven codes and how this work has influenced our own ways of conceptualizing comprehension research, theory, and practice. [This paper was published in: "ICLS 2021 Proceedings," ISLS, 2021, pp. 139-146.]

In the last decade, several photon counting technologies have been developed opening a new window for experiments in the low photon number regime. Several ongoing and future projects in HEP benefit from these developments, which will also have a large impact outside HEP. During the next decade there is a clear technological opportunity to fully develop these sensors and produce a large impact in HEP. In this white paper we discuss the need for photon counting technologies in future projects, and present some technological opportunities to address those needs.

Collider detectors have taken advantage of the resolution and accuracy of silicon detectors for at least four decades. Future colliders will need large areas of silicon sensors for low mass trackers and sampling calorimetry. Monolithic Active Pixel Sensors (MAPS), in which Si diodes and readout circuitry are combined in the same pixels, and can be fabricated in some of standard CMOS processes, are a promising technology for high-granularity and light detectors. In this paper we review 1) the requirements on MAPS for trackers and electromagnetic calorimeters (ECal) at future colliders experiments, 2) the ongoing efforts towards dedicated MAPS for the Electron-Ion Collider (EIC) at BNL, for which the EIC Silicon Consortium was already instantiated, and 3) space-born applications for MeV $\gamma$-ray experiments with MAPS based trackers (AstroPix)., Comment: 25 pages, 18 figures, contribution to Snowmass 2021

While the mass differences between neutrino mass states are known, their absolute masses and mass hierarchy have not yet been determined. Determining the mass of neutrinos provides access to physics beyond the Standard Model and the resulting value has implications for the growth of large-scale structure in the universe over cosmic history. Because of the importance of the topic, a number of efforts are already underway to determine the mass of neutrinos including direct kinematic measurements and indirect measurements of astrophysical phenomena that constrain the sum of the mass eigenstates through models of cosmic evolution. Here, we advocate for a collaborative international effort to perform a kinematic determination of the effective electron neutrino mass using calorimetric measurements of the decay of 163Ho. This effort is justified by the success of current experiments using the technique, its high benefit-to-cost ratio, the value of approaches with different systematic errors, and the value of measuring the electron neutrino mass rather than the electron anti-neutrino mass., Comment: contribution to Snowmass 2021

Resource allocation in orthogonal frequency division multiplexing (OFDM) systems is performed through allocating blocks of subcarriers to each user. Even though OFDM is the primary waveform for 5G NR systems, research reports have noted that single carrier modulation (SCM) offers several advantages over OFDM in massive multiple input multiple output (MIMO) systems, making it a preferred candidate for some future applications such as massive machine type communications (mMTC). This paper presents a method for SCM resource allocation and the relevant information recovery algorithms at the receiver. Our emphasis is on cyclic prefixed SCM, where highly flexible and efficient frequency domain detection algorithms enable the operation of many simultaneous users in a massive MIMO uplink scenario. The proposed resource allocation method allows the number of users to exceed the number of antennas at the base station (BS). Each single carrier transmission is partitioned into $L$ interleaved streams, and each user is allocated a number of such streams. One major benefit of SCM is that each data symbol is spread over the entire bandwidth. As such, the receiver performance is dictated by the average channel gain across the transmission band rather than the channel gain at a given frequency bin or a small group of frequencies. In the proposed setup, each stream may be thought of as a resource block in SCM, analogous to resource blocks in OFDM. Hence, in the context of this paper, the terms resource blocks and streams may be used interchangeably., Comment: 6 pages, 4 figures, accepted by IEEE ICC 2022

We report the detection of CO emission in the recently discovered multiphase isolated gas cloud in the nearby galaxy cluster Abell 1367. The cloud is located about 800 kpc in projection from the center of the cluster and at a projected distance of > 80 kpc from any galaxy. It is the first and the only known isolated intra-cluster cloud detected in X-ray, H$\alpha$, and CO emission. We found a total of about $2.2\times 10^8 M_\odot$ of H$_2$ with the IRAM 30-m telescope in two regions, one associated with the peak of H$\alpha$ emission and another with the peak of X-ray emission surrounded by weak H$\alpha$ filaments. The velocity of the molecular gas is offset from the underlying H$\alpha$ emission by > 100 km s$^{-1}$ in the region where the X-ray peaks. The molecular gas may account for about 10% of the total cloud's mass, which is dominated by the hot X-ray component. The previously measured upper limit on the star formation rate in the cloud indicates that the molecular component is in a non-star-forming state, possibly due to a combination of low density of the gas and the observed level of velocity dispersion. The presence of the three gas phases associated with the cloud suggests that gas phase mixing with the surrounding intra-cluster medium is taking place. The possible origin of the orphan cloud is a late evolutionary stage of a ram pressure stripping event. In contrast, the nearby ram pressure stripped galaxy 2MASX J11443212+2006238 is in an early phase of stripping and we detected about $2.4\times 10^9 M_\odot$ of H$_2$ in its main body., Comment: Accepted for publication in A&A Letters

A convexity space is a set X with a chosen family of subsets (called convex subsets) that is closed under arbitrary intersections and directed unions. There is a lot of interest in spaces that have both a convexity space and a topological space structure. In this paper, we study the category of topological convexity spaces and extend the Stone duality between coframes and topological spaces to an adjunction between topological convexity spaces and sup-lattices. We factor this adjunction through the category of preconvexity spaces (somtimes called closure spaces)., Comment: 31 pages