Search

Vision-language models (VLMs) excel at tasks requiring joint understanding of visual and linguistic information. A particularly promising yet under-explored application for these models lies in answering questions based on various kinds of maps. This study investigates the efficacy of VLMs in answering questions based on choropleth maps, which are widely used for data analysis and representation. To facilitate and encourage research in this area, we introduce a novel map-based question-answering benchmark, consisting of maps from three geographical regions (United States, India, China), each containing 1000 questions. Our benchmark incorporates 43 diverse question templates, requiring nuanced understanding of relative spatial relationships, intricate map features, and complex reasoning. It also includes maps with discrete and continuous values, encompassing variations in color-mapping, category ordering, and stylistic patterns, enabling comprehensive analysis. We evaluate the performance of multiple VLMs on this benchmark, highlighting gaps in their abilities and providing insights for improving such models., Comment: 30 Pages, 46 Tables, 6 Figure

We report the results of a QCD axion dark matter search with discovery ability for Dine-Fischler-Srednicki-Zhitnitsky (DFSZ) axions using an axion haloscope. Sub-Kelvin noise temperatures are reached with an ultra low-noise Josephson parametric amplifier cooled by a dilution refrigerator. This work excludes (with a 90% confidence level) DFSZ axions with masses between 3.27 to 3.34 {\mu}eV, assuming a standard halo model with a local energy density of 0.45 GeV/cc made up 100% of axions.

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

Enterprises have a growing need to identify relevant tables in data lakes; e.g. tables that are unionable, joinable, or subsets of each other. Tabular neural models can be helpful for such data discovery tasks. In this paper, we present TabSketchFM, a neural tabular model for data discovery over data lakes. First, we propose novel pre-training: a sketch-based approach to enhance the effectiveness of data discovery in neural tabular models. Second, we finetune the pretrained model for identifying unionable, joinable, and subset table pairs and show significant improvement over previous tabular neural models. Third, we present a detailed ablation study to highlight which sketches are crucial for which tasks. Fourth, we use these finetuned models to perform table search; i.e., given a query table, find other tables in a corpus that are unionable, joinable, or that are subsets of the query. Our results demonstrate significant improvements in F1 scores for search compared to state-of-the-art techniques. Finally, we show significant transfer across datasets and tasks establishing that our model can generalize across different tasks and over different data lakes.

End-users can get functions-as-a-service from serverless platforms, which promise lower hosting costs, high availability, fault tolerance, and dynamic flexibility for hosting individual functions known as microservices. Machine learning tools are seen to be reliably useful, and the services created using these tools are in increasing demand on a large scale. The serverless platforms are uniquely suited for hosting these machine learning services to be used for large-scale applications. These platforms are well known for their cost efficiency, fault tolerance, resource scaling, robust APIs for communication, and global reach. However, machine learning services are different from the web-services in that these serverless platforms were originally designed to host web services. We aimed to understand how these serverless platforms handle machine learning workloads with our study. We examine machine learning performance on one of the serverless platforms - Google Cloud Run, which is a GPU-less infrastructure that is not designed for machine learning application deployment., Comment: 5 pages, 12 figures

This paper presents a simulation approach to enhance the power quality of a PV-micro hydro grid supplying both linear consumer load and non-linear industrial load by integrating Shunt Active Power Filter (SAPF), utilizing instantaneous PQ theory and hysteresis current control band logic. The non-linear load draws reactive power and harmonic current from the source thereby affecting the power quality. The integration of the SAPF at the point of common coupling (PCC) offers reactive power and harmonic current compensation, ensuring that the current supply to the grid remains nearly sinusoidal and proportional to the active power. By injecting equal and opposite harmonic components, the SAPF effectively reduces Total Harmonic Distortion (THD) from 7% to 2.96%, thereby enhancing the overall power quality of the PV-micro hydro grid system., Comment: 5 pages, 21 figures, submitted on IEEE powercon 2024 conference

We measure space- and time-correlated charge jumps on a four-qubit device, operating 107 meters below the Earth's surface in a low-radiation, cryogenic facility designed for the characterization of low-threshold particle detectors. The rock overburden of this facility reduces the cosmic ray muon flux by over 99% compared to laboratories at sea level. Combined with 4$\pi$ coverage of a movable lead shield, this facility enables quantifiable control over the flux of ionizing radiation on the qubit device. Long-time-series charge tomography measurements on these weakly charge-sensitive qubits capture discontinuous jumps in the induced charge on the qubit islands, corresponding to the interaction of ionizing radiation with the qubit substrate. The rate of these charge jumps scales with the flux of ionizing radiation on the qubit package, as characterized by a series of independent measurements on another energy-resolving detector operating simultaneously in the same cryostat with the qubits. Using lead shielding, we achieve a minimum charge jump rate of 0.19$^{+0.04}_{-0.03}$ mHz, almost an order of magnitude lower than that measured in surface tests, but a factor of roughly eight higher than expected based on reduction of ambient gammas alone. We operate four qubits for over 22 consecutive hours with zero correlated charge jumps at length scales above three millimeters., Comment: 12 pages, 6 figures, 4 tables. Minor update to the measured gamma flux ratio (Page 4 and Supplemental Section F) in the LMO detector, from 23 to 20. Typos corrected, references added. Extraneous .tex files have been removed that were causing errors with the "HTML (experimental)" arxiv feature

We have developed a calibration system based on a micro-electromechanical systems (MEMS) mirror that is capable of delivering an optical beam over a wavelength range of 180 -- 2000 nm (0.62 -- 6.89 eV) in a sub-Kelvin environment. This portable, integrated system can steer the beam over a $\sim$3 cm $\times$ 3 cm area on the surface of any sensor with a precision of $\sim$100 $\mu$m, enabling characterization of device response as a function of position. This fills a critical need in the landscape of calibration tools for sub-Kelvin devices, including those used for dark matter detection and quantum computing. These communities have a shared goal of understanding the impact of ionizing radiation on device performance, which can be pursued with our system. This paper describes the design of the first-generation calibration system and the results from successfully testing its performance at room temperature and 20 mK., Comment: 17 pages, 7 figures, submitted to SPIE

In recent years, the lack of a conclusive detection of WIMP dark matter at the 10 GeV/c$^{2}$ mass scale and above has encouraged development of low-threshold detector technology aimed at probing lighter dark matter candidates. Detectors based on Cooper-pair-breaking sensors have emerged as a promising avenue for this detection due to the low (meV-scale) energy required for breaking a Cooper pair in most superconductors. Among them, devices based on superconducting qubits are interesting candidates for sensing due to their observed sensitivity to broken Cooper pairs. We have developed an end-to-end G4CMP-based simulation framework and have used it to evaluate performance metrics of qubit-based devices operating in a gate-based "energy relaxation" readout scheme, akin to those used in recent studies of qubit sensitivity to ionizing radiation. We find that for this readout scheme, the qubit acts as a phonon sensor with an energy threshold ranging down to $\simeq$0.4 eV for near-term performance parameters., Comment: 17 pages, 10 figures

This report examines the growing interest in expanding access to postsecondary education through non-degree certificate programs and associate degree programs. This policy report seeks to better understand the implications of these trends by analyzing employment experiences and earnings outcomes of individuals whose highest level of educational attainment is below the bachelor's degree. In this paper the authors use the findings from the Programme for the International Assessment of Adults Competencies (PIAAC) survey of adult skills conducted in the United States in 2012, 2014, and 2017 to explore the earnings and employment gains to attendance and completion of postsecondary education below the bachelor's degree level. The authors explore the employment and earnings outcomes separately for three educational groups of adults: those with some postsecondary education without any certificate or degree award, those who earned a certificate below the associate's degree level, and those who earned an associate's degree. The analysis includes all individuals with postsecondary attainment below that bachelor's degree regardless of the level (less than 2-year, 2-year, or 4-year college) or sector (public or private/for-profit or nonprofit) of enrollment.

This report examines factors that are associated with the labor force participation behavior of working-age men and women in the United States with a focus on the connection between labor force participation and human capital skills and educational attainment. The analysis reveals very different patterns of labor force attachment for American men and women over the past 50 years. Findings suggest that policies targeted to bolster the human capital abilities of the working-age population to increase their labor force participation are likely to be more effective among women than among men.

The ADMX collaboration gathered data for its Run 1A axion dark matter search from January to June 2017, scanning with an axion haloscope over the frequency range 645-680 MHz (2.66-2.81 ueV in axion mass) at DFSZ sensitivity. The resulting axion search found no axion-like signals comprising all the dark matter in the form of a virialized galactic halo over the entire frequency range, implying lower bound exclusion limits at or below DFSZ coupling at the 90% confidence level. This paper presents expanded details of the axion search analysis of Run 1A, including review of relevant experimental systems, data-taking operations, preparation and interpretation of raw data, axion search methodology, candidate handling, and final axion limits., Comment: 27 pages, 19 figures, accepted for publication in PRD

The PHENIX experiment has performed a systematic study of identified charged-hadron ($\pi^\pm$, $K^\pm$, $p$, $\bar{p}$) production at midrapidity in $p$$+$Al, $^3$He$+$Au, Cu$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV and U$+$U collisions at $\sqrt{s_{_{NN}}}=193$ GeV. Identified charged-hadron invariant transverse-momentum ($p_T$) and transverse-mass ($m_T$) spectra are presented and interpreted in terms of radially expanding thermalized systems. The particle ratios of $K/\pi$ and $p/\pi$ have been measured in different centrality ranges of large (Cu$+$Au, U$+$U) and small ($p$$+$Al, $^3$He$+$Au) collision systems. The values of $K/\pi$ ratios measured in all considered collision systems were found to be consistent with those measured in $p$$+$$p$ collisions. However the values of $p/\pi$ ratios measured in large collision systems reach the values of $\approx0.6$, which is $\approx2$ times larger than in $p$$+$$p$ collisions. These results can be qualitatively understood in terms of the baryon enhancement expected from hadronization by recombination. Identified charged-hadron nuclear-modification factors ($R_{AB}$) are also presented. Enhancement of proton $R_{AB}$ values over meson $R_{AB}$ values was observed in central $^3$He$+$Au, Cu$+$Au, and U$+$U collisions. The proton $R_{AB}$ values measured in $p$$+$Al collision system were found to be consistent with $R_{AB}$ values of $\phi$, $\pi^\pm$, $K^\pm$, and $\pi^0$ mesons, which may indicate that the size of the system produced in $p$$+$Al collisions is too small for recombination to cause a noticeable increase in proton production., Comment: 480 authors from 78 institutions, 18 pages, 6 tables, 16 figures. v2 is version accepted for publication in Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

The Axion Dark Matter eXperiment (ADMX) has previously excluded Dine-Fischler-Srednicki-Zhitnisky (DFSZ) axions between 680-790 MHz under the assumption that the dark matter is described by the isothermal halo model. However, the precise nature of the velocity distribution of dark matter is still unknown, and alternative models have been proposed. We report the results of a non-virialized axion search over the mass range 2.81-3.31 {\mu}eV, corresponding to the frequency range 680-800 MHz. This analysis marks the most sensitive search for non-virialized axions sensitive to Doppler effects in the Milky Way Halo to date. Accounting for frequency shifts due to the detector's motion through the Galaxy, we exclude cold flow relic axions with a velocity dispersion of order 10^-7 c with 95% confidence.

Strong motivation for investing in quantum sensing arises from the need to investigate phenomena that are very weakly coupled to the matter and fields well described by the Standard Model. These can be related to the problems of dark matter, dark sectors not necessarily related to dark matter (for example sterile neutrinos), dark energy and gravity, fundamental constants, and problems with the Standard Model itself including the Strong CP problem in QCD. Resulting experimental needs typically involve the measurement of very low energy impulses or low power periodic signals that are normally buried under large backgrounds. This report documents the findings of the 2023 Quantum Sensors for High Energy Physics workshop which identified enabling quantum information science technologies that could be utilized in future particle physics experiments, targeting high energy physics science goals., Comment: 63 pages, 8 figures, Quantum Sensors for HEP workshop report, April 26-28, 2023

We present first results from a dark photon dark matter search in the mass range from 44 to 52 $\mu{\rm eV}$ ($10.7 - 12.5\,{\rm GHz}$) using a room-temperature dish antenna setup called GigaBREAD. Dark photon dark matter converts to ordinary photons on a cylindrical metallic emission surface with area $0.5\,{\rm m}^2$ and is focused by a novel parabolic reflector onto a horn antenna. Signals are read out with a low-noise receiver system. A first data taking run with 24 days of data does not show evidence for dark photon dark matter in this mass range, excluding dark photon - photon mixing parameters $\chi \gtrsim 10^{-12}$ in this range at 90% confidence level. This surpasses existing constraints by about two orders of magnitude and is the most stringent bound on dark photons in this range below 49 $\mu$eV., Comment: 6 pages, 4 figures, matches published version

As with the previous reports in the series, this new report relies on the rich source of data from the Programme for the International Assessment of Adult Competencies (PIAAC), including direct assessment of literacy and numeracy skills and a host of information on education, income, and other demographic characteristics. But what is unique in this report is that it also utilizes detailed data on skill use including the frequency with which respondents reported using reading, numeracy, and writing skills in their job. This study builds on the series' previous research on the effect of skills on the earnings of American workers by examining the use of workers' skills on the job as an additional determinant of the earnings of American workers (in addition to human capital--literacy proficiencies, education, work experience--and other factors that are known to affect earnings). The paper begins with a descriptive analysis of the link between reading skill use at work and the literacy proficiencies of workers. The authors explore differences in the literacy proficiencies (mean literacy scores and levels of literacy proficiencies) of workers in each quartile of the index of reading skill use at work, and conversely, the authors present the use of reading skills at work by the level of literacy proficiencies of workers. The descriptive section also explores the connection between the occupation of workers and their use of reading skills at work. Findings from multivariate regression analyses that explore the links between earnings and the use of reading skills at work, with regression controls for literacy proficiencies, educational attainment, work experience, occupation, and demographics are presented. [For the previous report in the series, see "Skills and Labor Market Outcomes of Working-Age Americans. The Impact of Human Capital in the American Labor Market Series" (ED626553).]

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well., Comment: Accepted at IJCB 2023

Data management has traditionally relied on synthetic data generators to generate structured benchmarks, like the TPC suite, where we can control important parameters like data size and its distribution precisely. These benchmarks were central to the success and adoption of database management systems. But more and more, data management problems are of a semantic nature. An important example is finding tables that can be unioned. While any two tables with the same cardinality can be unioned, table union search is the problem of finding tables whose union is semantically coherent. Semantic problems cannot be benchmarked using synthetic data. Our current methods for creating benchmarks involve the manual curation and labeling of real data. These methods are not robust or scalable and perhaps more importantly, it is not clear how robust the created benchmarks are. We propose to use generative AI models to create structured data benchmarks for table union search. We present a novel method for using generative models to create tables with specified properties. Using this method, we create a new benchmark containing pairs of tables that are both unionable and non-unionable but related. We thoroughly evaluate recent existing table union search methods over existing benchmarks and our new benchmark. We also present and evaluate a new table search methods based on recent large language models over all benchmarks. We show that the new benchmark is more challenging for all methods than hand-curated benchmarks, specifically, the top-performing method achieves a Mean Average Precision of around 60%, over 30% less than its performance on existing manually created benchmarks. We examine why this is the case and show that the new benchmark permits more detailed analysis of methods, including a study of both false positives and false negatives that were not possible with existing benchmarks.

Within enterprises, there is a growing need to intelligently navigate data lakes, specifically focusing on data discovery. Of particular importance to enterprises is the ability to find related tables in data repositories. These tables can be unionable, joinable, or subsets of each other. There is a dearth of benchmarks for these tasks in the public domain, with related work targeting private datasets. In LakeBench, we develop multiple benchmarks for these tasks by using the tables that are drawn from a diverse set of data sources such as government data from CKAN, Socrata, and the European Central Bank. We compare the performance of 4 publicly available tabular foundational models on these tasks. None of the existing models had been trained on the data discovery tasks that we developed for this benchmark; not surprisingly, their performance shows significant room for improvement. The results suggest that the establishment of such benchmarks may be useful to the community to build tabular models usable for data discovery in data lakes.

In this new policy report, the fourth in a series examining the impact of human capital in the American labor market from the Educational Testing Service (ETS) Center for Research on Human Capital Education, the authors take a unique perspective on the critical link between human capital and labor market outcomes by expanding the analysis to nonpecuniary outcomes. Using the available cognitive and background data that were collected as part of the Programme for the International Assessment of Adult Skills (PIAAC), this report explores the links between the skills of American workers and a wide variety of labor market outcomes beyond earnings, including whether a person was employed, unemployed, or not in the labor force at the time of the PIAAC survey. Across each of these categories, in addition to skill levels, key background data were examined. For the employed population, for example, employment intensity, employer-provided training, occupation, and job satisfaction data were analyzed. For those who reported they were unemployed, the duration of unemployment and previous employment experiences was examined, whereas information on previous employment experiences was examined for those who reported that they were not in the labor force at the time of the PIAAC survey. The analysis in this paper reveals that these nonpecuniary labor market outcomes are also tightly bound to respondents' literacy and numeracy skills. [For the previous report in this series, see "Skills and Earnings in the Part-Time Labor Market. The Impact of Human Capital in the American Labor Market Series" (ED626473).]

We demonstrate a novel table discovery pipeline called DIALITE that allows users to discover, integrate and analyze open data tables. DIALITE has three main stages. First, it allows users to discover tables from open data platforms using state-of-the-art table discovery techniques. Second, DIALITE integrates the discovered tables to produce an integrated table. Finally, it allows users to analyze the integration result by applying different downstreaming tasks over it. Our pipeline is flexible such that the user can easily add and compare additional discovery and integration algorithms., Comment: SIGMOD 2023

PHENIX presents a simultaneous measurement of the production of direct $\gamma$ and $\pi^0$ in $d$$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV over a $p_T$ range of 7.5 to 18 GeV/$c$ for different event samples selected by event activity, i.e. charged-particle multiplicity detected at forward rapidity. Direct-photon yields are used to empirically estimate the contribution of hard-scattering processes in the different event samples. Using this estimate, the average nuclear-modification factor $R_{d\rm Au,EXP}^{\gamma^{\rm dir}}$ is $0.925{\pm}0.023({\rm stat}){\pm}0.15^{\rm (scale)}$, consistent with unity for minimum-bias (MB) $d$$+$Au events. For event classes with moderate event activity, $R_{d\rm Au,EXP}^{\gamma^{\rm dir}}$ is consistent with the MB value within 5\% uncertainty. These results confirm that the previously observed enhancement of high-$p_T$ $\pi^0$ production found in small-system collisions with low event activity is a result of a bias in interpreting event activity within the Glauber framework. In contrast, for the top 5\% of events with the highest event activity, $R_{d\rm Au,EXP}^{\gamma^{\rm dir}}$ is suppressed by 20\% relative to the MB value with a significance of $4.5\sigma$, which may be due to final-state effects., Comment: 279 authors from 69 institutions, 8 pages, 3 figures, v1 is version submitted to Physical Review Letters. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

Reported here are transverse single-spin asymmetries ($A_{N}$) in the production of charged hadrons as a function of transverse momentum ($p_T$) and Feynman-$x$ ($x_F$) in polarized $p^{\uparrow}$+$p$, $p^{\uparrow}$+Al, and $p^{\uparrow}$+Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV. The measurements have been performed at forward and backward rapidity ($1.4<|\eta|<2.4$) over the range of $1.50$) in $p^{\uparrow}$+$p$ collisions, whereas the $p^{\uparrow}$+Al and $p^{\uparrow}$+Au results show smaller asymmetries. This finding provides new opportunities to investigate the origin of transverse single-spin asymmetries and a tool to study nuclear effects in $p$+$A$ collisions., Comment: 322 authors from 70 institutions, 13 pages, 9 figures, 13 tables, one appendix, 2015 data. v2 is version accepted for publication in Phys. Rev. D. HEPData tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

Presented are the first measurements of the transverse single-spin asymmetries ($A_N$) for neutral pions and eta mesons in $p$+Au and $p$+Al collisions at $\sqrt{s_{_{NN}}}=200$ GeV in the pseudorapidity range $|\eta|<$0.35 with the PHENIX detector at the Relativistic Heavy Ion Collider. The asymmetries are consistent with zero, similar to those for midrapidity neutral pions and eta mesons produced in $p$+$p$ collisions. These measurements show no evidence of additional effects that could potentially arise from the more complex partonic environment present in proton-nucleus collisions., Comment: 322 authors from 70 institutions, 8 pages, 2 figures, 1 table, 2015 data. v2 is version accepted for publication in Phys. Rev. D. HEPData tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

We report the first result of a direct search for a Cosmic ${\it axion}$ Background (C$a$B) - a relativistic background of axions that is not dark matter - performed with the axion haloscope, the Axion Dark Matter eXperiment (ADMX). Conventional haloscope analyses search for a signal with a narrow bandwidth, as predicted for dark matter, whereas the C$a$B will be broad. We introduce a novel analysis strategy, which searches for a C$a$B induced daily modulation in the power measured by the haloscope. Using this, we repurpose data collected to search for dark matter to set a limit on the axion photon coupling of a C$a$B originating from dark matter cascade decay via a mediator in the 800-995 MHz frequency range. We find that the present sensitivity is limited by fluctuations in the cavity readout as the instrument scans across dark matter masses. Nevertheless, we suggest that these challenges can be surmounted using superconducting qubits as single photon counters, and allow ADMX to operate as a telescope searching for axions emerging from the decay of dark matter. The daily modulation analysis technique we introduce can be deployed for various broadband RF signals, such as other forms of a C$a$B or even high-frequency gravitational waves., Comment: 9 pages, 4 figures

We investigate reentrant and dielectric loaded cavities for the purpose of extending the range of axion cavity haloscopes to lower masses, below the range where the Axion Dark Matter eXperiment (ADMX) has already searched. Reentrant and dielectric loaded cavities were simulated numerically to calculate and optimize their form factors and quality factors. A prototype reentrant cavity was built and its measured properties were compared with the simulations. We estimate the sensitivity of axion dark matter searches using reentrant and dielectric loaded cavities inserted in the existing ADMX magnet at the University of Washington and a large magnet being installed at Fermilab., Comment: 33 pages, 24 figures

Genome-wide association studies (GWAS) have successfully identified over two hundred thousand genotype-trait associations. Yet some challenges remain. First, complex traits are often associated with many single nucleotide polymorphisms (SNPs), most with small or moderate effect sizes, making them difficult to detect. Second, many complex traits share a common genetic basis due to `pleiotropy' and and though few methods consider it, leveraging pleiotropy can improve statistical power to detect genotype-trait associations with weaker effect sizes. Third, currently available statistical methods are limited in explaining the functional mechanisms through which genetic variants are associated with specific or multiple traits. We propose multi-GPA-Tree to address these challenges. The multi-GPA-Tree approach can identify risk SNPs associated with single as well as multiple traits while also identifying the combinations of functional annotations that can explain the mechanisms through which risk-associated SNPs are linked with the traits. First, we implemented simulation studies to evaluate the proposed multi-GPA-Tree method and compared its performance with an existing statistical approach.The results indicate that multi-GPA-Tree outperforms the existing statistical approach in detecting risk-associated SNPs for multiple traits. Second, we applied multi-GPA-Tree to a systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), and to a Crohn's disease (CD) and ulcertive colitis (UC) GWAS, and functional annotation data including GenoSkyline and GenoSkylinePlus. Our results demonstrate that multi-GPA-Tree can be a powerful tool that improves association mapping while facilitating understanding of the underlying genetic architecture of complex traits and potential mechanisms linking risk-associated SNPs with complex traits., Comment: 25 pages, 6 figures, 1 table

$\mathrm{\gamma}$-ray spectroscopy is a quantitative, non-destructive technique that may be utilized for the identification and quantitative isotopic estimation of radionuclides. Traditional methods of isotopic determination have various challenges that contribute to statistical and systematic uncertainties in the estimated isotopics. Furthermore, these methods typically require numerous pre-processing steps, and have only been rigorously tested in laboratory settings with limited shielding. In this work, we examine the application of a number of machine learning based regression algorithms as alternatives to conventional approaches for analyzing $\mathrm{\gamma}$-ray spectroscopy data in the Emergency Response arena. This approach not only eliminates many steps in the analysis procedure, and therefore offers potential to reduce this source of systematic uncertainty, but is also shown to offer comparable performance to conventional approaches in the Emergency Response Application.