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The updated recommendations on diagnostic procedures and treatment pathways for a medical condition are documented as graphical flows in Clinical Practice Guidelines (CPGs). For effective use of the CPGs in helping medical professionals in the treatment decision process, it is necessary to fully capture the guideline knowledge, particularly the contexts and their relationships in the graph. While several existing works have utilized these guidelines to create rule bases for Clinical Decision Support Systems, limited work has been done toward directly capturing the full medical knowledge contained in CPGs. This work proposes an approach to create a contextually enriched, faithful digital representation of National Comprehensive Cancer Network (NCCN) Cancer CPGs in the form of graphs using automated extraction and node & relationship classification. We also implement semantic enrichment of the model by using Large Language Models (LLMs) for node classification, achieving an accuracy of 80.86% and 88.47% with zero-shot learning and few-shot learning, respectively. Additionally, we introduce a methodology for answering natural language questions with constraints to guideline text by leveraging LLMs to extract the relevant subgraph from the guideline knowledge base. By generating natural language answers based on subgraph paths and semantic information, we mitigate the risk of incorrect answers and hallucination associated with LLMs, ensuring factual accuracy in medical domain Question Answering.

Radiation damage significantly impacts the performance of silicon tracking detectors in Large Hadron Collider (LHC) experiments such as ATLAS and CMS, with signal reduction being the most critical effect. Adjusting sensor bias voltage and detection thresholds can help mitigate these effects, but generating simulated data that accurately mirror the performance evolution with the accumulation of luminosity, hence fluence, is crucial. The ATLAS collaboration has developed and implemented algorithms to correct simulated Monte Carlo (MC) events for radiation damage effects, achieving impressive agreement between collision data and simulated events. In preparation for the high-luminosity phase (HL-LHC), the demand for a faster ATLAS MC production algorithm becomes imperative due to escalating collision, events, tracks, and particle hit rates, imposing stringent constraints on available computing resources. This article outlines the philosophy behind the new algorithm, its implementation strategy, and the essential components involved. The results from closure tests indicate that the events simulated using the new algorithm agree with fully simulated events at the level of few \%. The first tests on computing performance show that the new algorithm is as fast as it is when no radiation damage corrections are applied., Comment: 5 pages, 2 figures, Eleventh International Workshop on Semiconductor Pixel Detectors for Particles and Imaging (18-22 November 2024), Strasbourg - France

Reinforcement Learning with Human Feedback (RLHF) and its variants have made huge strides toward the effective alignment of large language models (LLMs) to follow instructions and reflect human values. More recently, Direct Alignment Algorithms (DAAs) have emerged in which the reward modeling stage of RLHF is skipped by characterizing the reward directly as a function of the policy being learned. Examples include Direct Preference Optimization (DPO) and Simple Preference Optimization (SimPO). These methods often suffer from likelihood displacement, a phenomenon by which the probabilities of preferred responses are often reduced undesirably. In this paper, we argue that, for DAAs the reward (function) shape matters. We introduce AlphaPO, a new DAA method that leverages an $\alpha$-parameter to help change the shape of the reward function beyond the standard log reward. AlphaPO helps maintain fine-grained control over likelihood displacement and over-optimization. Compared to SimPO, one of the best performing DAAs, AlphaPO leads to about 7\% to 10\% relative improvement in alignment performance for the instruct versions of Mistral-7B and Llama3-8B. The analysis and results presented highlight the importance of the reward shape, and how one can systematically change it to affect training dynamics, as well as improve alignment performance., Comment: Preprint. Work in progress

Qutrit-based quantum circuits could help reduce the overall circuit depths, and hence the noise, when the system of interest has local dimension of three. Accessing second excited states in superconducting transmons provides a straightforward hardware realization of three-level systems, or qutrits, useful for such ternary encoding. In this work, we successfully calibrate pulse gates to a low error rate to obtain transmon qutrits. We use these qutrits to simulate one-dimensional spin-1 AKLT states (Affleck, Lieb, Kennedy, and Tasaki) which exhibit a multitude of interesting phenomena such as topologically protected ground states, string order and the existence of a robust Berry phase. We demonstrate the efficacy of qutrit-based simulation by preparing high-fidelity ground states of the AKLT Hamiltonian with open boundaries for various chain lengths. We then use ground state preparations of the perturbed AKLT Hamiltonian with periodic boundaries to calculate the Berry phase and illustrate non-trivial ground state topology. We further present scalable methods for preparing the AKLT state and computing its Berry phase, and use tensor network simulations to establish the advantage of qutrit-based implementation over qubit-based ones in the presence of noise. Our work provides a pathway toward more general spin-1 physics simulations using transmon qutrits, with applications in chemistry, magnetism, and topological phases of matter.

We use the newly developed stacky prismatic technology of Drinfeld and Bhatt-Lurie to give a uniform, group-theoretic construction of smooth stacks $\mathrm{BT}^{G,\mu}_{n}$ attached to a smooth affine group scheme $G$ over $\mathbb{Z}_p$ and $1$-bounded cocharacter $\mu$, verifying a recent conjecture of Drinfeld. This can be viewed as a refinement of results of B\"ultel-Pappas, who gave a related construction using $(G,\mu)$-displays defined via rings of Witt vectors. We show that, when $G = \mathrm{GL}_h$ and $\mu$ is a minuscule cocharacter, these stacks are isomorphic to the stack of truncated $p$-divisible groups of height $h$ and dimension $d$ (the latter depending on $\mu$). This gives a generalization of results of Ansch\"utz-Le Bras, yielding a linear algebraic classification of $p$-divisible groups over very general $p$-adic bases, and verifying another conjecture of Drinfeld. The proofs use derived algebraic geometry, combined with an animated variant of Lau's theory of higher frames and displays, and actually show representability of a wide range of stacks whose tangent complexes are $1$-bounded in a suitable sense. In particular, we also prove algebraicity for the stack of perfect $F$-gauges of Hodge-Tate weights $0,1$ and level $n$., Comment: Comments welcome!

With GW170817 being the only multimessenger gravitational wave (GW) event with an associated kilonova (KN) detected so far, there exists a pressing need for realistic estimation of the GW localization uncertainties and rates, as well as optimization of available telescope time to enable the detection of new KNe. For this purpose, we simulate GW events assuming a data-driven, GW-motivated distribution of binary parameters for the LIGO/Virgo/KAGRA (LVK) fourth and fifth observing runs (O4 and O5). For our particular case of estimating KN detection rates in the O4 and O5 runs, we map the binary neutron star (BNS) and neutron star-black hole (NSBH) properties to the optical light curves arising from r-process nucleosynthesis in the ejecta. We use the simulated population of KNe to generate follow-up observing plans, with the primary goal of optimizing detection with the Gravitational Wave Multi-Messenger Astronomy DECam Survey (GW-MMADS). We explore KN detectability as a function of mass, distance, and spin of the binaries. Out of the mergers that produce a KN in our simulations, we expect detectable KNe for DECam-like instruments at a per-year rate of: $1-16$ ($0-1$) for BNS (NSBH) in O4, and $16-165$ ($1-12$) for BNS (NSBH) in O5, using a fiducial exposure time and conditional on the uncertainty on the equation of state (EOS) and volumetric rates of the mergers. Taking into account observability constraints, our scheduler covers the location of the KN $\sim 30-38\%$ of the times for our fiducial EOS. We provide the depths needed to detect a significant fraction of our simulated mergers for the astronomical community to use in their follow-up campaigns., Comment: 18 pages, 10 figures, 10 tables

The Legacy Survey of Space and Time (LSST) at Vera C. Rubin Observatory is planned to begin in the Fall of 2025. The LSST survey cadence has been designed via a community-driven process regulated by the Survey Cadence Optimization Committee (SCOC), which recommended up to 3% of the observing time to carry out Target of Opportunity (ToO) observations. Experts from the scientific community, Rubin Observatory personnel, and members of the SCOC were brought together to deliver a recommendation for the implementation of the ToO program during a workshop held in March 2024. Four main science cases were identified: gravitational wave multi-messenger astronomy, high energy neutrinos, Galactic supernovae, and small potentially hazardous asteroids possible impactors. Additional science cases were identified and briefly addressed in the documents, including lensed or poorly localized gamma-ray bursts and twilight discoveries. Trigger prioritization, automated response, and detailed strategies were discussed for each science case. This document represents the outcome of the Rubin ToO 2024 workshop, with additional contributions from members of the Rubin Science Collaborations. The implementation of the selection criteria and strategies presented in this document has been endorsed in the SCOC Phase 3 Recommendations document (PSTN-056). Although the ToO program is still to be finalized, this document serves as a baseline plan for ToO observations with the Rubin Observatory.

Following the wealth of new results enabled by multimessenger observations of the binary neutron star (BNS) merger GW170817, the next goal is increasing the number of detections of electromagnetic (EM) counterparts to gravitational wave (GW) events. Here, we study the detectability of the prompt emission and afterglows produced by the relativistic jets launched by BNS mergers that will be detected by LIGO-Virgo-KAGRA (LVK) during their fifth observing run (O5), and by next generation (XG) GW detectors (Einstein Telescope and Cosmic Explorer). We quantify the impact of various BNS merger and jet afterglow parameters on the likelihood of detection for a wide range of telescopes, focusing on the impact of the observer's viewing angle $\theta_\textrm{v}$ and the jet's core half-opening angle $\theta_\textrm{c}$. We find that during the LVK O5 run, the James Webb Space Telescope (JWST) is expected to reach the largest number of afterglow detections, up to $32\%$ of BNS mergers with $27\%$ detectable on timescales $>10$ d. Overall the detection of $20-30\%$ of events is possible across the EM spectrum with the available instruments, including the Chandra X-ray Observatory, the Nancy Grace Roman Space Telescope, the Square Kilometer Array (SKA), and the Very Large Array (VLA). In the XG era, afterglows for $20-25\%$ of events will be detectable with AXIS, Athena, Lynx, UVEX, JWST and the next generation VLA (ngVLA), reaching beyond redshift $z \gtrsim 1$. We also find that the majority of detected afterglows are expected to accompany prompt gamma-ray emission detectable by the Neil Gehrels Swift Observatory, the Space-based multi-band astronomical Variable Objects Monitor (SVOM), and the Transient High-Energy Sky and Early Universe Surveyor (THESEUS), with at least $\sim 6\%$ of afterglows being orphan., Comment: Submitted to Physical Review D, 36 pages, 13 figures

The detection of the gravitational-wave event GW230529, presumably a neutron star-black hole (NSBH) merger, by the LIGO-Virgo-KAGRA (LVK) Collaboration is an exciting discovery for multimessenger astronomy. The black hole (BH) has a high probability of falling within the ''mass gap'' between the peaks of the neutron star (NS) and the BH mass distributions. Because of the low primary mass, the binary is more likely to produce an electromagnetic counterpart than previously detected NSBH mergers. We investigate the possible kilonova (KN) emission from GW230529, and find that if it was an NSBH, there is a $\sim$ 2-41% probability (depending on the assumed equation of state) that GW230925 produced a KN with magnitude peaking at $\sim 1-2$ day post merger at $g \lesssim 23.5$, $i<23$. Hence, it could have been detected by ground-based telescopes. If it was a binary neutron star (BNS) merger, we find $\sim$ 0-12% probability that it produced a KN. Motivated by these numbers, we simulated a broader population of mgNSBH mergers that may be detected in O4, and we obtained a 9-21% chance of producing a KN, which would be detectable with $g\lesssim 25$ and $ i \lesssim 24$, typically fainter than what is expected from GW230529. Based on these findings, DECam-like instruments may be able to detect up to 80% of future mgNSBH KNe, thus up to $\sim1$ multimessenger mgNSBH per year may be discoverable at the current level of sensitivity (O4)., Comment: 13 pages, 4 figures, 2 tables

We develop tight bounds for the covert capacity of slotted asynchronous binary-input Discrete Memoryless Channels (DMCs) and Additive White Gaussian Noise (AWGN) channels, in which a codeword is transmitted in one of several slots with known boundaries, where the number of slots is sub-exponential in the codeword length. Our upper and lower bounds are within a multiplicative factor of $\sqrt{2}$ independent of the channel. This result partially fills a characterization gap between the covert capacity without asynchronism and the covert capacity with exponential asynchronism. Our key technical contributions consist of i) a tight upper bound for the relative entropy characterizing the effect of asynchronism on the covertness constraint in our achievability proof; ii) a careful converse analysis to characterize the maximum allowable weight or power of codewords to meet the covertness constraint. Our results suggest that, unlike the case without asynchronism, the choice of covertness metric does not change the covert capacity in the presence of asynchronism., Comment: 24 pages, 2 figures, submitted to TIT

The baryon cycle is crucial for understanding galaxy formation, as gas inflows and outflows vary throughout a galaxy's lifetime and affect its star formation rate. Despite the necessity of accretion for galaxy growth at high redshifts, direct observations of inflowing gas have proven elusive especially at $z\gtrsim2$. We present spectroscopic analysis of a galaxy at redshift $z=2.45$ which exhibits signs of inflow in several ultraviolet interstellar absorption lines, with no clear outflow signatures. The absorption lines are redshifted by $\sim$250 km sec$^{-1}$ with respect to the systemic redshift, and C IV shows a prominent inverse P-Cygni profile. Simple stellar population models suggest that this galaxy has a low metallicity ($\sim$5% solar), with a very young starburst of age $\sim$4 Myr dominating the ultraviolet luminosity. The gas inflow velocity and nebular velocity dispersion suggest an approximate halo mass of order $\sim 10^{11}M_{\odot}$, a regime in which simulations predict that bursty star formation is common at this redshift. We conclude that this system is likely in the beginning of a cycle of bursty star formation, where inflow and star formation rates are high, but where supernovae and other feedback processes have not yet launched strong outflows. In this scenario, we expect the inflow-dominated phase to be observable (e.g., with net redshifted ISM absorption) for only a short timescale after a starburst onset. This result represents a promising avenue for probing the full baryon cycle, including inflows, during the formative phases of low-mass galaxies at high redshifts., Comment: Accepted for publication in ApJL, 12 pages, 4 figures

Over the past few years alone, the lensing community has discovered thousands of strong lens candidates, and spectroscopically confirmed hundreds of them. In this time of abundance, it becomes pragmatic to focus our time and resources on the few extraordinary systems, in order to most efficiently study the universe. In this paper, we present such a system: DESI-090.9854-35.9683, a cluster-scale lens at $z_{\rm l} = 0.49$, with seven observed lensed sources around the core, and additional lensed sources further out in the cluster. From the number and the textbook configuration of the lensed images, a tight constraint on the mass potential of the lens is possible. This would allow for detailed analysis on the dark and luminous matter content within galaxy clusters, as well as a probe into dark energy and high-redshift galaxies. We present our spatially resolved kinematic measurements of this system from the Very Large Telescope Multi Unit Spectroscopic Explorer, which confirm five of these source galaxies (in ascending order, at $z_{\rm s} = 0.962, 0.962, 1.166, 1.432,$ and $1.432$). With previous Hubble Space Telescope imaging in the F140W and F200LP bands, we also present a simple two power-law profile flux-based lens model that, for a cluster lens, well models the five lensed arc families with redshifts. We determine the mass to be $M(< \theta_{\rm E}) = 4.78\times10^{13} M_{\odot}$ for the primary mass potential. From the model, we extrapolate the redshift of one of the two source galaxies not yet spectroscopically confirmed to be at $z_{\rm s}=4.52^{+1.03}_{-0.71}$., Comment: 14 pages, 6 figures

While quiescent galaxies have comparable amounts of cool gas in their outer circumgalactic medium (CGM) compared to star-forming galaxies, they have significantly less interstellar gas. However, open questions remain on the processes causing galaxies to stop forming stars and stay quiescent . Theories suggest dynamical interactions with the hot corona prevent cool gas from reaching the galaxy, therefore predicting the inner regions of quiescent galaxy CGMs are devoid of cool gas. However, there is a lack of understanding of the inner regions of CGMs due to the lack of spatial information in quasar-sightline methods. We present integral-field spectroscopy probing 10--20~kpc (2.4--4.8 R\textsubscript{e}) around a massive quiescent galaxy using a gravitationally lensed star-forming galaxy. We detect absorption from Magnesium (MgII) implying large amounts of cool atomic gas (10\textsuperscript{8.4} -- 10\textsuperscript{9.3} M\textsubscript{$\odot$} with T$\sim$10\textsuperscript{4} Kelvin), in comparable amounts to star-forming galaxies. Lens modeling of Hubble imaging also reveals a diffuse asymmetric component of significant mass consistent with the spatial extent of the MgII absorption, and offset from the galaxy light profile. This study demonstrates the power of galaxy-scale gravitational lenses to not only probe the gas around galaxies, but to also independently probe the mass of the CGM due to it's gravitational effect., Comment: accepted nature communications physics

For decades, mainframe systems have been vital in enterprise computing, supporting essential applications across industries like banking, retail, and healthcare. To harness these legacy applications and facilitate their reuse, there is increasing interest in using Application Programming Interfaces (APIs) to expose their data and functionalities, enabling the creation of new applications. However, identifying and exposing APIs for various business use cases presents significant challenges, including understanding legacy code, separating dependent components, introducing new artifacts, and making changes without disrupting functionality or compromising key Service Level Agreements (SLAs) like Turnaround Time (TAT). We address these challenges by proposing a novel framework for creating APIs for legacy mainframe applications. Our approach involves identifying APIs by compiling artifacts such as transactions, screens, control flow blocks, inter-microservice calls, business rules, and data accesses. We use static analyses like liveness and reaching definitions to traverse the code and automatically compute API signatures, which include request/response fields. To evaluate our framework, we conducted a qualitative survey with nine mainframe developers, averaging 15 years of experience. This survey helped identify candidate APIs and estimate development time for coding these APIs on a public mainframe application, GENAPP, and two industry mainframe applications. The results showed that our framework effectively identified more candidate APIs and reduced implementation time. The API signature computation is integrated into IBM Watsonx Code Assistant for Z Refactoring Assistant. We verified the correctness of the identified APIs by executing them on an IBM Z mainframe system, demonstrating the practical viability of our approach.

The cross sections for the $^{12}\mathrm{C}(\gamma,\alpha)^{8}\mathrm{Be}\rightarrow 3\alpha$ reaction have been successfully measured using exclusive coincidence between three $\alpha$ particles, minimizing Compton background. Sequential breakup kinematics are evident, and the cross sections are presented as locally averaged histogram values. Theoretical \textsc{Fresco} CDCC-CRC calculations reproduce the experimental data, showing that the process involves electromagnetic coupling to both $^{8}\mathrm{Be}^{0^+}$ and $^{8}\mathrm{Be}^{2^+}$ states. This study confirms that the $^{12}\mathrm{C}(\gamma,\alpha)^{8}\mathrm{Be}\rightarrow 3\alpha$ reaction proceeds via a sequential mechanism, crucial for understanding its significance in radiotherapy dosimetry.

An automated knowledge modeling algorithm for Cancer Clinical Practice Guidelines (CPGs) extracts the knowledge contained in the CPG documents and transforms it into a programmatically interactable, easy-to-update structured model with minimal human intervention. The existing automated algorithms have minimal scope and cannot handle the varying complexity of the knowledge content in the CPGs for different cancer types. This work proposes an improved automated knowledge modeling algorithm to create knowledge models from the National Comprehensive Cancer Network (NCCN) CPGs in Oncology for different cancer types. The proposed algorithm has been evaluated with NCCN CPGs for four different cancer types. We also proposed an algorithm to compare the knowledge models for different versions of a guideline to discover the specific changes introduced in the treatment protocol of a new version. We created a question-answering (Q&A) framework with the guideline knowledge models as the augmented knowledge base to study our ability to query the knowledge models. We compiled a set of 32 question-answer pairs derived from two reliable data sources for the treatment of Non-Small Cell Lung Cancer (NSCLC) to evaluate the Q&A framework. The framework was evaluated against the question-answer pairs from one data source, and it can generate the answers with 54.5% accuracy from the treatment algorithm and 81.8% accuracy from the discussion part of the NCCN NSCLC guideline knowledge model.

We carried out long-term monitoring of the LIGO/Virgo/KAGRA binary black hole (BBH) merger candidate S230922g in search of electromagnetic emission from the interaction of the merger remnant with an embedding active galactic nuclei (AGN) accretion disk. Using a dataset primarily composed of wide-field imaging from the Dark Energy Camera (DECam) and supplemented by additional photometric and spectroscopic resources, we searched ~ 70% of the sky area probability for transient phenomena, and discovered 6 counterpart candidates. One especially promising candidate - AT 2023aagj - exhibited temporally varying asymmetric components in spectral broad line regions, a feature potentially indicative of an off-center event such as a BBH merger. This represents the first live search and multiwavelength, photometric, and spectroscopic monitoring of a GW BBH optical counterpart candidate in the disk of an AGN., Comment: 25 pages, 9 figures. Published in PRD

In response to the growing demand for precise and affordable solutions for Image-Guided Spine Surgery (IGSS), this paper presents a comprehensive development of a Robot-Assisted and Navigation-Guided IGSS System. The endeavor involves integrating cutting-edge technologies to attain the required surgical precision and limit user radiation exposure, thereby addressing the limitations of manual surgical methods. We propose an IGSS workflow and system architecture employing a hybrid-layered approach, combining modular and integrated system architectures in distinctive layers to develop an affordable system for seamless integration, scalability, and reconfigurability. We developed and integrated the system and extensively tested it on phantoms and cadavers. The proposed system's accuracy using navigation guidance is 1.02 0.34 mm, and robot assistance is 1.11 0.49 mm on phantoms. Observing a similar performance in cadaveric validation where 84% of screw placements were grade A, 10% were grade B using navigation guidance, 90% were grade A, and 10% were grade B using robot assistance as per the Gertzbein-Robbins scale, proving its efficacy for an IGSS. The evaluated performance is adequate for an IGSS and at par with the existing systems in literature and those commercially available. The user radiation is lower than in the literature, given that the system requires only an average of 3 C-Arm images per pedicle screw placement and verification., Comment: arXiv admin note: substantial text overlap with arXiv:2406.04644

Accurate chemical compositions of star-forming regions are a critical diagnostic tool to characterize the star formation history and gas flows which regulate galaxy formation. However, the abundance discrepancy factor (ADF) between measurements from the "direct" optical electron temperature ($T_e$) method and from the recombination lines (RL) represents $\sim0.2$ dex systematic uncertainty in oxygen abundance. The degree of uncertainty for other elements is unknown. We conduct a comprehensive analysis of O$^{++}$ and N$^+$ ion abundances using optical and far-infrared spectra of a star-forming region within the nearby dwarf galaxy Haro 3, which exhibits a typical ADF. Assuming homogeneous conditions, the far-IR emission indicates an O abundance which is higher than the $T_e$ method and consistent with the RL value, as would be expected from temperature fluctuations, whereas the N abundance is too large to be explained by temperature fluctuations. Instead a component of highly obscured gas is likely required to explain the high far-IR to optical flux ratios. Accounting for this obscured component reduces both the IR-based metallicities and the inferred magnitude of temperature fluctuations, such that they cannot fully explain the ADF in Haro 3. Additionally, we find potential issues when predicting the RL fluxes from current atomic data. Our findings underscore the critical importance of resolving the cause of abundance discrepancies and understanding the biases between different metallicity methods. This work represents a promising methodology, and we identify further approaches to address the current dominant uncertainties., Comment: 18 pages, 7 figures, and 2 tables. Submitted to ApJ

Observed evolution of the total mass distribution with redshift is crucial to testing galaxy evolution theories. To measure the total mass distribution, strong gravitational lenses complement the resolved dynamical observations currently limited to $z \lesssim 0.5$. Here we present the lens models for a pilot sample of seven galaxy-scale lenses from the ASTRO3D Galaxy Evolution with Lenses (AGEL) survey. The AGEL lenses, modeled using HST/WFC3-F140W images with Gravitational Lens Efficient Explorer (GLEE) software, have deflector redshifts between $0.3 < z_{\rm defl} < 0.9$. Assuming a power-law density profile with slope $\gamma$, we measure the total density profile for the deflector galaxies via lens modeling. We also measure the stellar velocity dispersions ($\sigma_{\rm obs}$) for four lenses and obtain $\sigma_{\rm obs}$ from SDSS-BOSS for the remaining lenses to test our lens models by comparing observed and model-predicted velocity dispersions. For the seven AGEL lenses, we measure an average density profile slope of $-1.95 \pm 0.09$ and a $\gamma$--$z$ relation that does not evolve with redshift at $z<1$. Although our result is consistent with some observations and simulations, it differs from other studies at $z<1$ that suggest the $\gamma$--$z$ relation evolves with redshift. The apparent conflicts among observations and simulations may be due to a combination of 1) systematics in the lensing and dynamical modeling; 2) challenges in comparing observations with simulations; and 3) assuming a simple power-law for the total mass distribution. By providing more lenses at $z_{\rm defl} > 0.5$, the AGEL survey will provide stronger constraints on whether the mass profiles evolve with redshift as predicted by current theoretical models., Comment: Accepted for publication in the Astrophysical Journal (ApJ) 24 Pages, 8 Figures, 3 Tables

Radiation damage significantly impacts the performance of silicon tracking detectors in Large Hadron Collider (LHC) experiments such as ATLAS and CMS, with signal reduction being the most critical effect; adjusting sensor bias voltage and detection thresholds can help mitigate these effects, generating simulated data that accurately mirror the performance evolution with the accumulation of luminosity, hence fluence, is crucial. The ATLAS and CMS collaborations have developed and implemented algorithms to correct simulated Monte Carlo (MC) events for radiation damage effects, achieving impressive agreement between collision data and simulated events. In preparation for the high-luminosity phase (HL-LHC), the demand for a faster ATLAS MC production algorithm becomes imperative due to escalating collision, events, tracks, and particle hit rates, imposing stringent constraints on available computing resources. This article outlines the philosophy behind the new algorithm, its implementation strategy, and the essential components involved. The results from closure tests indicate that the events simulated using the new algorithm agree with fully simulated events at the level of few \%. The first tests on computing performance show that the new algorithm is as fast as it is when no radiation damage corrections are applied., Comment: 19 pages, 17 figures

Computational marketing has become increasingly important in today's digital world, facing challenges such as massive heterogeneous data, multi-channel customer journeys, and limited marketing budgets. In this paper, we propose a general framework for marketing AI systems, the Neural Optimization with Adaptive Heuristics (NOAH) framework. NOAH is the first general framework for marketing optimization that considers both to-business (2B) and to-consumer (2C) products, as well as both owned and paid channels. We describe key modules of the NOAH framework, including prediction, optimization, and adaptive heuristics, providing examples for bidding and content optimization. We then detail the successful application of NOAH to LinkedIn's email marketing system, showcasing significant wins over the legacy ranking system. Additionally, we share details and insights that are broadly useful, particularly on: (i) addressing delayed feedback with lifetime value, (ii) performing large-scale linear programming with randomization, (iii) improving retrieval with audience expansion, (iv) reducing signal dilution in targeting tests, and (v) handling zero-inflated heavy-tail metrics in statistical testing., Comment: KDD 2024

With the increasing emphasis on machine learning and artificial intelligence to drive knowledge discovery in the agricultural sciences, spatial internet of things (IoT) technologies have become increasingly important for collecting real-time, high resolution data for these models. However, managing large fleets of devices while maintaining high data quality remains an ongoing challenge as scientists iterate from prototype to mature end-to-end applications. Here, we provide a set of case studies using the framework of technology readiness levels for an open source spatial IoT system. The spatial IoT systems underwent 3 major and 14 minor system versions, had over 2,727 devices manufactured both in academic and commercial contexts, and are either in active or planned deployment across four continents. Our results show the evolution of a generalizable, open source spatial IoT system designed for agricultural scientists, and provide a model for academic researchers to overcome the challenges that exist in going from one-off prototypes to thousands of internet-connected devices., Comment: 20 pages, 5 figures, 1 table

In the realm of recommender systems, the ubiquitous adoption of deep neural networks has emerged as a dominant paradigm for modeling diverse business objectives. As user bases continue to expand, the necessity of personalization and frequent model updates have assumed paramount significance to ensure the delivery of relevant and refreshed experiences to a diverse array of members. In this work, we introduce an innovative meta-learning solution tailored to the personalization of models for individual members and other entities, coupled with the frequent updates based on the latest user interaction signals. Specifically, we leverage the Model-Agnostic Meta Learning (MAML) algorithm to adapt per-task sub-networks using recent user interaction data. Given the near infeasibility of productionizing original MAML-based models in online recommendation systems, we propose an efficient strategy to operationalize meta-learned sub-networks in production, which involves transforming them into fixed-sized vectors, termed meta embeddings, thereby enabling the seamless deployment of models with hundreds of billions of parameters for online serving. Through extensive experimentation on production data drawn from various applications at LinkedIn, we demonstrate that the proposed solution consistently outperforms the baseline models of those applications, including strong baselines such as using wide-and-deep ID based personalization approach. Our approach has enabled the deployment of a range of highly personalized AI models across diverse LinkedIn applications, leading to substantial improvements in business metrics as well as refreshed experience for our members.

Collision Detection (CD) has several applications across the domains such as robotics, visual graphics, and fluid mechanics. Finding exact collisions between the objects in the scene is quite computationally intensive. To quickly filter the object pairs that do not result in a collision, bounding boxes are built on the objects, indexed using a Bounding Volume Hierarchy(BVH), and tested for intersection before performing the expensive object-object intersection tests. In state-of-the-art CD libraries, accelerators such as GPUs are used to accelerate BVH traversal by building specialized data structures. The recent addition of ray tracing architecture to GPU hardware is designed to do the same but in the context of implementing a Ray Tracing algorithm to render a graphical scene in real-time. We present Mochi, a fast and exact collision detection engine that accelerates both the broad and narrow phases by taking advantage of the capabilities of Ray Tracing cores. We introduce multiple new reductions to perform generic CD to support three types of objects for CD: simple spherical particles, objects describable by mathematical equations, and complex objects composed of a triangle mesh. By implementing our reductions, Mochi achieves several orders of magnitude speedups on synthetic datasets and 5x-28x speedups on real-world triangle mesh datasets. We further evaluate our reductions thoroughly and provide several architectural insights on the ray tracing cores that are otherwise unknown due to their proprietorship.

Quality assessment, including inspecting the images for artifacts, is a critical step during MRI data acquisition to ensure data quality and downstream analysis or interpretation success. This study demonstrates a deep learning model to detect rigid motion in T1-weighted brain images. We leveraged a 2D CNN for three-class classification and tested it on publicly available retrospective and prospective datasets. Grad-CAM heatmaps enabled the identification of failure modes and provided an interpretation of the model's results. The model achieved average precision and recall metrics of 85% and 80% on six motion-simulated retrospective datasets. Additionally, the model's classifications on the prospective dataset showed a strong inverse correlation (-0.84) compared to average edge strength, an image quality metric indicative of motion. This model is part of the ArtifactID tool, aimed at inline automatic detection of Gibbs ringing, wrap-around, and motion artifacts. This tool automates part of the time-consuming QA process and augments expertise on-site, particularly relevant in low-resource settings where local MR knowledge is scarce., Comment: 25 pages, 9 figures, 1 table. Submitted to NMR in Biomedicine