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This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

Contemporary state-of-the-art video object segmentation (VOS) models compare incoming unannotated images to a history of image-mask relations via affinity or cross-attention to predict object masks. We refer to the internal memory state of the initial image-mask pair and past image-masks as a working memory buffer. While the current state of the art models perform very well on clean video data, their reliance on a working memory of previous frames leaves room for error. Affinity-based algorithms include the inductive bias that there is temporal continuity between consecutive frames. To account for inconsistent camera views of the desired object, working memory models need an algorithmic modification that regulates the memory updates and avoid writing irrelevant frames into working memory. A simple algorithmic change is proposed that can be applied to any existing working memory-based VOS model to improve performance on inconsistent views, such as sudden camera cuts, frame interjections, and extreme context changes. The resulting model performances show significant improvement on video data with these frame interjections over the same model without the algorithmic addition. Our contribution is a simple decision function that determines whether working memory should be updated based on the detection of sudden, extreme changes and the assumption that the object is no longer in frame. By implementing algorithmic changes, such as this, we can increase the real-world applicability of current VOS models., Comment: 12 pages, 11 figures

We compute the truncated point schemes of subalgebras of Fomin-Kirillov algebras associated with certain graphs. While Fomin-Kirillov algebras do not admit any truncated point modules, we prove a tight bound on the degrees of truncated point modules over generalized Fomin-Kirillov algebras associated with trees., Comment: This project originated through the Washington eXperimental Math Lab (WXML) for undergraduate students at the University of Washington

Paints and coatings undergo a variety of physical and chemical changes under environmental exposures. Accurate prediction of these changes is important for the applications of coatings. This work presents a novel approach to modeling accelerated weathering of coating by combining concurrent physical and chemical processes. The model integrates key factors influencing coating degradation and employs a multi-scale framework to capture macro-scale physical changes and micro-level chemical transformations. The chemical component/model simulates photo-degradation reactions using kinetic equations, while the physical component uses Monte Carlo simulations where repeated random events develop surface erosion. The surface topography and chemistry of coating is generated statistically through the physical model. The variations in surface topography and chemistry of coating are correlated with the chemical changes from the chemical model, resulting in estimation of both physical and chemical changes in the coating during real accelerated weathering time. Results demonstrate accurate predictions of chemistry changes, surface degradation profiles, roughness, gloss loss, and relative fracture toughness, which are validated successfully with the experimentally available data. This integrated approach provides insight into coating failure mechanisms, enabling accurate service life prediction, and serve as a tool for formulating durable coatings and optimizing testing protocols.

Thermoelectric (TE) heat pumps are a promising solid-state technology for space cooling and heating owing to their unique advantages such as environmental friendliness with no harmful refrigerants, small form factors, low noise, robustness with no moving parts, and demand-flexible operation. However, their low coefficient of performance (COP) presents a challenge towards broader adaptation of the technology at the market level. Built on our previous theoretical framework for a modular system design aimed at improving both COP and cooling capacity, this study focuses on experimental characterization and optimization of a unit air-to-air TE heat pump system. We employ double-sided heating/cooling and counterflow configuration for uniform heat transfer with TE modules and heat exchangers. System-level evaluations validate the theoretical model and show the variation of air temperature change and system COP with different electric current inputs and air flowrates. A maximum cooling COP of 4.4 and a maximum heating COP of 6.0 were obtained at optimized current levels. Further analysis shows that parasitic thermal resistances in the system significantly reduced both COP and air temperature change by over 50%., Comment: 17 pages, 8 figures

Electroencephalography (EEG)-based attention disorder research seeks to understand brain activity patterns associated with attention. Previous studies have mainly focused on identifying brain regions involved in cognitive processes or classifying Attention-Deficit Hyperactivity Disorder (ADHD) and control subjects. However, analyzing effective brain connectivity networks for specific attentional processes and comparing them has not been explored. Therefore, in this study, we propose multivariate transfer entropy-based connectivity networks for cognitive events and introduce a new similarity measure, 'SimBrainNet', to assess these networks. A high similarity score suggests similar brain dynamics during cognitive events, indicating less attention variability. Our experiment involves 12 individuals with attention disorders (7 children and 5 adolescents). Noteworthy that child participants exhibit lower similarity scores compared to adolescents, indicating greater changes in attention. We found strong connectivity patterns in the left pre-frontal cortex for adolescent individuals compared to the child. Our study highlights the changes in attention levels across various cognitive events, offering insights into the underlying cognitive mechanisms, brain dynamics, and potential deficits in individuals with this disorder., Comment: 10 pages, 2 figures, To appear in the Proceedings of 27th INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, 6-10 October 2024, MARRAKESH / MOROCCO

The real estate sector is one of the key drivers of India's national economy, contributing about 7.3\% to the GDP. As the market evolves, more players enter, and government policies become more stringent, Indian real estate companies face increasing competition. Improving financial competitiveness is crucial for the survival and growth of these companies. This paper presents a financial competitiveness evaluation index system for Indian-listed real estate companies, covering profitability, solvency, and operational capacity. Using key financial ratios and a scoring system, the financial competitiveness of various companies was evaluated, revealing that companies with high scores have strong profitability and operational capacity. In contrast, those with lower scores struggle with solvency and working capital., Comment: 8 pages, 4 tables

Game development is a highly technical practice that traditionally requires programming skills. This serves as a barrier to entry for would-be developers or those hoping to use games as part of their creative expression. While there have been prior game development tools focused on accessibility, they generally still require programming, or have major limitations in terms of the kinds of games they can make. In this paper we introduce Mechanic Maker, a tool for creating a wide-range of game mechanics without programming. It instead relies on a backend symbolic learning system to synthesize game mechanics from examples. We conducted a user study to evaluate the benefits of the tool for participants with a variety of programming and game development experience. Our results demonstrated that participants' ability to use the tool was unrelated to programming ability. We conclude that tools like ours could help democratize game development, making the practice accessible regardless of programming skills., Comment: 11 pages, 8 figures, AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment

Perspective distortion (PD) leads to substantial alterations in the shape, size, orientation, angles, and spatial relationships of visual elements in images. Accurately determining camera intrinsic and extrinsic parameters is challenging, making it hard to synthesize perspective distortion effectively. The current distortion correction methods involve removing distortion and learning vision tasks, thus making it a multi-step process, often compromising performance. Recent work leverages the M\"obius transform for mitigating perspective distortions (MPD) to synthesize perspective distortions without estimating camera parameters. M\"obius transform requires tuning multiple interdependent and interrelated parameters and involving complex arithmetic operations, leading to substantial computational complexity. To address these challenges, we propose Log Conformal Maps (LCM), a method leveraging the logarithmic function to approximate perspective distortions with fewer parameters and reduced computational complexity. We provide a detailed foundation complemented with experiments to demonstrate that LCM with fewer parameters approximates the MPD. We show that LCM integrates well with supervised and self-supervised representation learning, outperform standard models, and matches the state-of-the-art performance in mitigating perspective distortion over multiple benchmarks, namely Imagenet-PD, Imagenet-E, and Imagenet-X. Further LCM demonstrate seamless integration with person re-identification and improved the performance. Source code is made publicly available at https://github.com/meenakshi23/Log-Conformal-Maps., Comment: Accepted to Asian Conference on Computer Vision (ACCV2024)

Brain tumor segmentation plays a crucial role in computer-aided diagnosis. This study introduces a novel segmentation algorithm utilizing a modified nnU-Net architecture. Within the nnU-Net architecture's encoder section, we enhance conventional convolution layers by incorporating omni-dimensional dynamic convolution layers, resulting in improved feature representation. Simultaneously, we propose a multi-scale attention strategy that harnesses contemporary insights from various scales. Our model's efficacy is demonstrated on diverse datasets from the BraTS-2023 challenge. Integrating omni-dimensional dynamic convolution (ODConv) layers and multi-scale features yields substantial improvement in the nnU-Net architecture's performance across multiple tumor segmentation datasets. Remarkably, our proposed model attains good accuracy during validation for the BraTS Africa dataset. The ODconv source code along with full training code is available on GitHub., Comment: 9 pages, 3 figures. Accepted at MICCAI 2023, to be published in Springer LNCS. GitHub: https://github.com/i-sahajmistry/nnUNet_BraTS2023

Medical coding is essential for standardizing clinical data and communication but is often time-consuming and prone to errors. Traditional Natural Language Processing (NLP) methods struggle with automating coding due to the large label space, lengthy text inputs, and the absence of supporting evidence annotations that justify code selection. Recent advancements in Generative Artificial Intelligence (AI) offer promising solutions to these challenges. In this work, we introduce MedCodER, a Generative AI framework for automatic medical coding that leverages extraction, retrieval, and re-ranking techniques as core components. MedCodER achieves a micro-F1 score of 0.60 on International Classification of Diseases (ICD) code prediction, significantly outperforming state-of-the-art methods. Additionally, we present a new dataset containing medical records annotated with disease diagnoses, ICD codes, and supporting evidence texts (https://doi.org/10.5281/zenodo.13308316). Ablation tests confirm that MedCodER's performance depends on the integration of each of its aforementioned components, as performance declines when these components are evaluated in isolation.

Ranking a set of items based on their relevance to a given query is a core problem in search and recommendation. Transformer-based ranking models are the state-of-the-art approaches for such tasks, but they score each query-item independently, ignoring the joint context of other relevant items. This leads to sub-optimal ranking accuracy and high computational costs. In response, we propose Cross-encoders with Joint Efficient Modeling (CROSS-JEM), a novel ranking approach that enables transformer-based models to jointly score multiple items for a query, maximizing parameter utilization. CROSS-JEM leverages (a) redundancies and token overlaps to jointly score multiple items, that are typically short-text phrases arising in search and recommendations, and (b) a novel training objective that models ranking probabilities. CROSS-JEM achieves state-of-the-art accuracy and over 4x lower ranking latency over standard cross-encoders. Our contributions are threefold: (i) we highlight the gap between the ranking application's need for scoring thousands of items per query and the limited capabilities of current cross-encoders; (ii) we introduce CROSS-JEM for joint efficient scoring of multiple items per query; and (iii) we demonstrate state-of-the-art accuracy on standard public datasets and a proprietary dataset. CROSS-JEM opens up new directions for designing tailored early-attention-based ranking models that incorporate strict production constraints such as item multiplicity and latency.

The Matrix Profile (MP), a versatile tool for time series data mining, has been shown effective in time series anomaly detection (TSAD). This paper delves into the problem of anomaly detection in multidimensional time series, a common occurrence in real-world applications. For instance, in a manufacturing factory, multiple sensors installed across the site collect time-varying data for analysis. The Matrix Profile, named for its role in profiling the matrix storing pairwise distance between subsequences of univariate time series, becomes complex in multidimensional scenarios. If the input univariate time series has n subsequences, the pairwise distance matrix is a n x n matrix. In a multidimensional time series with d dimensions, the pairwise distance information must be stored in a n x n x d tensor. In this paper, we first analyze different strategies for condensing this tensor into a profile vector. We then investigate the potential of extending the MP to efficiently find k-nearest neighbors for anomaly detection. Finally, we benchmark the multidimensional MP against 19 baseline methods on 119 multidimensional TSAD datasets. The experiments covers three learning setups: unsupervised, supervised, and semi-supervised. MP is the only method that consistently delivers high performance across all setups.

Compact conductively cooled SRF industrial linacs can provide unique parameters of the electron beam for industrial applications. (up to 10MeV, 1MW). For ERDC project we designed normal conducting RF injector with thermal RF gridded gun integrated in first cell of multi-cell cavities. For design of the RF gun we used MICHELLE software to simulate and optimize parameters of the beam. Output file was converted to ASTRA format and most beam dynamic simulations in multi-cell normal conducting cavity and cryomodule were performed by using ASTRA software. For cross-checking we compare results of MICHELLE and AS-TRA in first few cells. At the end of injector beam reach ~250keV energy which allow to trap bunch in acceleration regime without losses in TESLA like 1.3 GHz cavity. Short solenoid at the end of injector allow to regulate transverse beam size in cryomodule to match beam to extraction system and also reduce charge losses in accelerator., Comment: 32nd Linear Accelerator Conference (LINAC 2024)

The thermionic gridded gun is described which generates short electron bunches for further acceleration in a Nb3Sn conduction cooled SRF linac. The gun is built into the first cavity of the 250 keV injector [1] for the 20 kW, 10 MeV, 1.3 GHz CW conduction cooled one-cavity linac. The beam current is 2 mA. The RF gun design is presented as well as the results of perveance measurements, which are in a good agreement with the design parameters. The design of the RF resonator of the gun is presented also. The beam generated by the gun is matched to the injector to provide lack of current interception in the SRF cavity., Comment: 32nd Linear Accelerator Conference (LINAC 2024)

In this work we revisit $\alpha$-Starobinsky inflation, also know as $E$-model, in the light of current CMB and LSS observations. The inflaton potential in the Einstein frame for this model contains a parameter $\alpha$ in the exponential, which alters the predictions for the scalar and tensor power spectra of Starobinsky inflation. We obtain these power spectra numerically without using slow-roll approximation and perform MCMC analysis to put constraints on parameters $M$ and $\alpha$ from Planck-2018, BICEP/Keck (BK18) and other LSS observations. We consider general reheating scenario by varying the number of e-foldings during inflation, $N_{pivot}$, along with the other parameters. We find $\log_{10}\alpha = 0.0^{+1.6}_{-5.6}$, $\log_{10}M= -4.91^{+0.69}_{-2.7}$ and $N_{pivot} = 53.2^{+3.9}_{-5}$ with $95\%$ C. L.. This implies that the present CMB and LSS observations are insufficient to constrain the parameter $\alpha$. We also find that there is no correlation between $N_{pivot}$ and $\alpha$., Comment: 15 pages, 5 figures

Numerous algorithms have been developed for online product rating prediction, but the specific influence of user and product information in determining the final prediction score remains largely unexplored. Existing research often relies on narrowly defined data settings, which overlooks real-world challenges such as the cold-start problem, cross-category information utilization, and scalability and deployment issues. To delve deeper into these aspects, and particularly to uncover the roles of individual user taste and collective wisdom, we propose a unique and practical approach that emphasizes historical ratings at both the user and product levels, encapsulated using a continuously updated dynamic tree representation. This representation effectively captures the temporal dynamics of users and products, leverages user information across product categories, and provides a natural solution to the cold-start problem. Furthermore, we have developed an efficient data processing strategy that makes this approach highly scalable and easily deployable. Comprehensive experiments in real industry settings demonstrate the effectiveness of our approach. Notably, our findings reveal that individual taste dominates over collective wisdom in online product rating prediction, a perspective that contrasts with the commonly observed wisdom of the crowd phenomenon in other domains. This dominance of individual user taste is consistent across various model types, including the boosting tree model, recurrent neural network (RNN), and transformer-based architectures. This observation holds true across the overall population, within individual product categories, and in cold-start scenarios. Our findings underscore the significance of individual user tastes in the context of online product rating prediction and the robustness of our approach across different model architectures., Comment: Preprint

Identifying single phase, high-entropy systems has been a prominent research focus of materials engineering over the past decade. The considerable effort in computational modeling and experimental verification has yielded several methods and descriptors for predicting if a single phase will form; however, the details surrounding the resulting crystal structure have largely remained a mystery. Here, we present a compelling argument for the role of allotropy in determining the crystal structure of a single-phase, high-entropy alloy. High entropy alloys can contain 5 or more elements and must achieve a configurational entropy greater than 1.5R. This study shows that when these high entropy material conditions are met, the majority crystal structure of the non-allotrope forming element plays a dominant role in crystal structure determination. The theory is demonstrated via several approaches, including analysis of 434 unique known single-phase compositions from the literature, thermodynamic modeling of more than 1,400 compositions, and experimental synthesis of nine specific alloys that test this hypothesis. The results demonstrate allotropy can identify a subset of compositions unlikely to form a single phase and predict the crystal structure with a high degree of accuracy for a wide range of simple (e.g., 5 equiatomic elements) and more complex (e.g., Al0.3B0.6CoCrFeNiCu0.7Si0.1) high entropy alloys. Allotropy provides new insight into the underlying physics governing the resultant crystal structure in materials without a principle element. As high entropy materials continue to be an area of focus for developing materials with unique properties, this study is expected to serve as a significant tool in the screening of materials for specific crystal structures.

Astrophysical population simulations predict that a subset of dynamically formed binary black holes (BBHs) may possess eccentricity $\gtrsim 0.1$ at a gravitational wave (GW) frequency of $10 \,\text{Hz}$. Presently, the LIGO-Virgo-KAGRA (LVK) Collaboration tests general relativity (GR) assuming that the binary eccentricity has decayed well before it enters the detector's frequency band. Previous works have shown that binary eccentricity can bias GR tests if unaccounted for. Here we develop two methods to extend parametrized tests of GR to eccentric binaries. The first method extends the standard null parametrized test for quasicircular binaries by adding fractional deviations at each post-Newtonian (PN) order in the eccentric part of the GW phasing (assuming the small-eccentricity limit). Simultaneous measurement of the circular and eccentric deviation parameters ($\delta\hat{\varphi}, \delta\hat{\varphi}^e$) allows us to constrain deviations from GR for eccentric binaries. While strong constraints on the deviation parameters are not achievable with LIGO's projected sensitivity, the multibanding of LISA and CE observations can constrain these deviations to $|\delta\hat{\varphi}_2| \lesssim 3 \times 10^{-3}$ and $|\delta\hat{\varphi}^e_2|\lesssim 2\times 10^{-2}$. The second method looks for GR deviations in the rate of periastron advance ($\Delta\alpha$). The parameter $\Delta\alpha$ ($\Delta\alpha^{\rm GR} \to 0$) can be constrained with LIGO to $|\Delta\alpha|\lesssim 4 \times 10^{-2}$ (with $1 \sigma$ confidence). Multiband sources observed by LISA and CE provide an improved constraint of $|\Delta\alpha|\lesssim 3\times 10^{-5}$. The space-based detector DECIGO provides the best constraint on $\Delta\alpha$ with $|\Delta\alpha|\lesssim 8 \times 10^{-6}$., Comment: 32 pages, 7 figures

Extreme Classification (XC) aims to map a query to the most relevant documents from a very large document set. XC algorithms used in real-world applications learn this mapping from datasets curated from implicit feedback, such as user clicks. However, these datasets inevitably suffer from missing labels. In this work, we observe that systematic missing labels lead to missing knowledge, which is critical for accurately modelling relevance between queries and documents. We formally show that this absence of knowledge cannot be recovered using existing methods such as propensity weighting and data imputation strategies that solely rely on the training dataset. While LLMs provide an attractive solution to augment the missing knowledge, leveraging them in applications with low latency requirements and large document sets is challenging. To incorporate missing knowledge at scale, we propose SKIM (Scalable Knowledge Infusion for Missing Labels), an algorithm that leverages a combination of small LM and abundant unstructured meta-data to effectively mitigate the missing label problem. We show the efficacy of our method on large-scale public datasets through exhaustive unbiased evaluation ranging from human annotations to simulations inspired from industrial settings. SKIM outperforms existing methods on Recall@100 by more than 10 absolute points. Additionally, SKIM scales to proprietary query-ad retrieval datasets containing 10 million documents, outperforming contemporary methods by 12% in offline evaluation and increased ad click-yield by 1.23% in an online A/B test conducted on a popular search engine. We release our code, prompts, trained XC models and finetuned SLMs at: https://github.com/bicycleman15/skim, Comment: Preprint, 23 pages

We provide a quantization of the Schwarzschild spacetime in the presence of a cosmological constant, based on midisuperspace methods developed in the spherically symmetric sector of loop quantum gravity, using in particular the 'improved dynamics' scheme. We include both the de Sitter and anti-de Sitter cases. We find that the quantization puts a Planckian positive upper limit on the possible values of the cosmological constant similar to the bounds obtained earlier from studies of homogeneous spacetimes. This means that, for negative cosmological constant, no negative bound is found. Moreover, using semiclassical physical states, we obtain the effective metric and demonstrate the causal structure for various cases. Quantum gravity modifications ensure that the singularity is replaced by a transition surface in all the cases, where the curvature invariants approach mass-independent Planckian bounds. Analysis of the effective stress-energy tensor shows that the null energy condition is strongly violated in the vicinity of the transition surface. Moreover, it shows a weaker asymptotic fall off for a nonvanishing cosmological constant, which could have interesting phenomenological implications.

Understanding distinct neurological aging patterns across various populations is vital in the context of a globally aging populace. This study seeks to unravel the structural variations in the aging brain, taking into consideration different ethnic backgrounds. MRI data from Indian, Chinese, Japanese, and Caucasian populations were analyzed using a two-pronged approach. Initially, a group analysis was performed involving tissue segmentation through FSL-FAST, examining gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). Subsequently, a continuous model-based analysis was employed, defining aging as a diffeomorphic transformation, which facilitated a detailed intra- and inter-population analysis, and examined both global anatomy and age-dependent distances of each population in comparison to the Indian population. Detailed insights into the spatial distribution of brain deformations over time were obtained, with particular focus on anatomical changes relative to a reference time point. The proposed comprehensive structural comparison framework, applied to a sample dataset encompassing four distinct populations, represents a pioneering effort to compare structural differences related to brain aging on a global scale. Subsequent studies utilizing larger datasets and applying the proposed analysis across groups based on various criteria will further advance our understanding of aging-related changes.

We report an ultrahigh pyroelectric response in van der Waals bonded layers of two-dimensional (2D) tellurium (Te) nanosheets (thickness, d = 4 to 5 nm) at periodic on-off temperature oscillations. For the first time a large pyroelectric coefficient, Pc ~ 3 mC.m-2.K-1, is observed which is eightfold higher than the traditional state-of-the-art pyroelectrics (lead zirconate titanate, PZT). The first-principles calculations point out that the breakdown of centro-symmetry in the 1-3 Te-layers (P-3m1 space group) of a non-centrosymmetry (higher-order symmetry of C2 space group) on an angular twist in the Te-Te bonds of an exotic electronic state in 2D Te. The angular Te-Te twisting elicits a surface-enhanced Raman band at 101 cm-1 (absent in bulk Te). The stimulation of the Born effective charge, in-plane piezoelectricity and thermal expansion coefficient are shown to tailor the large pyroelectricity. Thus, 2D Te nanosheets present a new paradigm for the wide application of pyroelectric materials for developing thermal energy-based flexible electronics., Comment: Research Article

We propose WayEx, a new method for learning complex goal-conditioned robotics tasks from a single demonstration. Our approach distinguishes itself from existing imitation learning methods by demanding fewer expert examples and eliminating the need for information about the actions taken during the demonstration. This is accomplished by introducing a new reward function and employing a knowledge expansion technique. We demonstrate the effectiveness of WayEx, our waypoint exploration strategy, across six diverse tasks, showcasing its applicability in various environments. Notably, our method significantly reduces training time by 50% as compared to traditional reinforcement learning methods. WayEx obtains a higher reward than existing imitation learning methods given only a single demonstration. Furthermore, we demonstrate its success in tackling complex environments where standard approaches fall short. More information is available at: https://waypoint-ex.github.io., Comment: ICRA 2024

In this paper, we address the challenge of fine-grained video event understanding in traffic scenarios, vital for autonomous driving and safety. Traditional datasets focus on driver or vehicle behavior, often neglecting pedestrian perspectives. To fill this gap, we introduce the WTS dataset, highlighting detailed behaviors of both vehicles and pedestrians across over 1.2k video events in hundreds of traffic scenarios. WTS integrates diverse perspectives from vehicle ego and fixed overhead cameras in a vehicle-infrastructure cooperative environment, enriched with comprehensive textual descriptions and unique 3D Gaze data for a synchronized 2D/3D view, focusing on pedestrian analysis. We also pro-vide annotations for 5k publicly sourced pedestrian-related traffic videos. Additionally, we introduce LLMScorer, an LLM-based evaluation metric to align inference captions with ground truth. Using WTS, we establish a benchmark for dense video-to-text tasks, exploring state-of-the-art Vision-Language Models with an instance-aware VideoLLM method as a baseline. WTS aims to advance fine-grained video event understanding, enhancing traffic safety and autonomous driving development., Comment: ECCV24. Website: https://woven-visionai.github.io/wts-dataset-homepage/

Lexicase selection is a successful parent selection method in genetic programming that has outperformed other methods across multiple benchmark suites. Unlike other selection methods that require explicit parameters to function, such as tournament size in tournament selection, lexicase selection does not. However, if evolutionary parameters like population size and number of generations affect the effectiveness of a selection method, then lexicase's performance may also be impacted by these `hidden' parameters. Here, we study how these hidden parameters affect lexicase's ability to exploit gradients and maintain specialists using diagnostic metrics. By varying the population size with a fixed evaluation budget, we show that smaller populations tend to have greater exploitation capabilities, whereas larger populations tend to maintain more specialists. We also consider the effect redundant test cases have on specialist maintenance, and find that high redundancy may hinder the ability to optimize and maintain specialists, even for larger populations. Ultimately, we highlight that population size, evaluation budget, and test cases must be carefully considered for the characteristics of the problem being solved., Comment: Pre-submission

The International Muon Collider Collaboration (IMCC) [1] was established in 2020 following the recommendations of the European Strategy for Particle Physics (ESPP) and the implementation of the European Strategy for Particle Physics-Accelerator R&D Roadmap by the Laboratory Directors Group [2], hereinafter referred to as the the European LDG roadmap. The Muon Collider Study (MuC) covers the accelerator complex, detectors and physics for a future muon collider. In 2023, European Commission support was obtained for a design study of a muon collider (MuCol) [3]. This project started on 1st March 2023, with work-packages aligned with the overall muon collider studies. In preparation of and during the 2021-22 U.S. Snowmass process, the muon collider project parameters, technical studies and physics performance studies were performed and presented in great detail. Recently, the P5 panel [4] in the U.S. recommended a muon collider R&D, proposed to join the IMCC and envisages that the U.S. should prepare to host a muon collider, calling this their "muon shot". In the past, the U.S. Muon Accelerator Programme (MAP) [5] has been instrumental in studies of concepts and technologies for a muon collider., Comment: This document summarises the International Muon Collider Collaboration (IMCC) progress and status of the Muon Collider R&D programme

We introduce InVi, an approach for inserting or replacing objects within videos (referred to as inpainting) using off-the-shelf, text-to-image latent diffusion models. InVi targets controlled manipulation of objects and blending them seamlessly into a background video unlike existing video editing methods that focus on comprehensive re-styling or entire scene alterations. To achieve this goal, we tackle two key challenges. Firstly, for high quality control and blending, we employ a two-step process involving inpainting and matching. This process begins with inserting the object into a single frame using a ControlNet-based inpainting diffusion model, and then generating subsequent frames conditioned on features from an inpainted frame as an anchor to minimize the domain gap between the background and the object. Secondly, to ensure temporal coherence, we replace the diffusion model's self-attention layers with extended-attention layers. The anchor frame features serve as the keys and values for these layers, enhancing consistency across frames. Our approach removes the need for video-specific fine-tuning, presenting an efficient and adaptable solution. Experimental results demonstrate that InVi achieves realistic object insertion with consistent blending and coherence across frames, outperforming existing methods.

A detailed investigation of the structural and magnetic properties, including magnetocaloric effect, re-entrant spin-glass behavior at low temperature, and critical behavior in polycrystalline Al$_2$MnFe Heusler alloy is reported. The prepared alloy crystallizes in a cubic CsCl-type crystal structure with Pm-3m space group. The temperature-dependent magnetization data reveals a second-order paramagnetic to ferromagnetic phase transition ($\sim$ 122.9 K), which is further supported by the analysis of the magnetocaloric effect. The isothermal magnetization loops show a soft ferromagnetic behavior of the studied alloy and also reveal an itinerant character of the underlying exchange interactions. In order to understand the nature of magnetic interactions, the critical exponents for spontaneous magnetization, initial magnetic susceptibility, and critical MH isotherm are determined using Modified Arrott plots, Kouvel-Fisher plots, and critical isotherm analysis. The derived critical exponents $\beta$ = 0.363(2), $\gamma$ = 1.384(3), and $\delta$ = 4.81(3) confirm the critical behavior similar to that of a 3D-Heisenberg-type ferromagnet with short-range exchange interactions that are found to decay with distance as J(r) $\approx$ r$^{-4.936}$. Moreover, the detailed analysis of the AC susceptibility data suggests that the frequency-dependent shifting of the peak temperatures is well explained using standard dynamic scaling laws such as the critical slowing down model and Vogel-Fulcher law, and confirms the signature of re-entrant spin-glass features in Al$_2$MnFe Heusler alloy. Furthermore, maximum magnetic entropy change of $\sim$ 1.92 J/kg-K and relative cooling power of $\sim$ 496 J/kg at 50 kOe applied magnetic field are determined from magnetocaloric studies that are comparable to those of other Mn-Fe-Al systems., Comment: 13 pages (main manuscript), 3 pages (supplementary material), 6 figures (main manuscript), 3 figures (supplementary material), 2 tables (main manuscript)

The challenges of healthcare supply chain management systems during the COVID-19 pandemic highlighted the need for an innovative and robust medical supply chain. The healthcare supply chain involves various stakeholders who must share information securely and actively. Regulatory and compliance reporting is also another crucial requirement for perishable products (e.g., pharmaceuticals) within a medical supply chain management system. Here, we propose Multi-MedChain as a three-layer multi-party, multi-blockchain (MPMB) framework utilizing smart contracts as a practical solution to address challenges in existing medical supply chain management systems. Multi-MedChain is a scalable supply chain management system for the healthcare domain that addresses end-to-end traceability, transparency, and collaborative access control to restrict access to private data. We have implemented our proposed system and report on our evaluation to highlight the practicality of the solution. The proposed solution is made publicly available., Comment: AIOT 2024 Conference (Accepted)

Audio analysis is useful in many application scenarios. The state-of-the-art audio analysis approaches assume the data distribution at training and deployment time will be the same. However, due to various real-life challenges, the data may encounter drift in its distribution or can encounter new classes in the late future. Thus, a one-time trained model might not perform adequately. Continual learning (CL) approaches are devised to handle such changes in data distribution. There have been a few attempts to use CL approaches for audio analysis. Yet, there is a lack of a systematic evaluation framework. In this paper, we create a comprehensive CL dataset and characterize CL approaches for audio-based monitoring tasks. We have investigated the following CL and non-CL approaches: EWC, LwF, SI, GEM, A-GEM, GDumb, Replay, Naive, Cumulative, and Joint training. The study is very beneficial for researchers and practitioners working in the area of audio analysis for developing adaptive models. We observed that Replay achieved better results than other methods in the DCASE challenge data. It achieved an accuracy of 70.12% for the domain incremental scenario and an accuracy of 96.98% for the class incremental scenario.

Automated machine learning streamlines the task of finding effective machine learning pipelines by automating model training, evaluation, and selection. Traditional evaluation strategies, like cross-validation (CV), generate one value that averages the accuracy of a pipeline's predictions. This single value, however, may not fully describe the generalizability of the pipeline. Here, we present Lexicase-based Validation (lexidate), a method that uses multiple, independent prediction values for selection. Lexidate splits training data into a learning set and a selection set. Pipelines are trained on the learning set and make predictions on the selection set. The predictions are graded for correctness and used by lexicase selection to identify parent pipelines. Compared to 10-fold CV, lexicase reduces the training time. We test the effectiveness of three lexidate configurations within the Tree-based Pipeline Optimization Tool 2 (TPOT2) package on six OpenML classification tasks. In one configuration, we detected no difference in the accuracy of the final model returned from TPOT2 on most tasks compared to 10-fold CV. All configurations studied here returned similar or less complex final pipelines compared to 10-fold CV.

Temperature coefficient of resistance (TCR) of a bolometer can be tuned by modifying the thermal conductance of an absorbing materials since they sense radiations via the temperature change in the absorber. However, the thermal conductance of the absorber can be reduced by engineering the appropriate thermal isolation, which can be an ultimate solution towards making a highly sensitive thermal detector. Here, we have developed an atomically thin 2D bolometer detector made up of a mechanically transferred suspended multilayer-MoS2 flake, eliminating the use of challenging thin-film fabrication process. The strength of our detector lies on the two factors: its large surface-to-volume window to absorb the radiations; the suspended configuration which prevents the heat dissipation through the substrate and therefore reduces the thermal conductance. The bolometric response of the detector is tested in both modes, via the photoresponse and the thermal response. The prototype is found to exhibit a very high TCR ~ -9.5%/K with the least achievable thermal noise-equivalent power (NEP) ~ 0.61 pWHz-1/2, in ambient conditions at 328 K., Comment: 22 pages, 9 figures

In the Multi-task Learning (MTL) framework, every task demands distinct feature representations, ranging from low-level to high-level attributes. It is vital to address the specific (feature/parameter) needs of each task, especially in computationally constrained environments. This work, therefore, introduces Layer-Optimized Multi-Task (LOMT) models that utilize structured sparsity to refine feature selection for individual tasks and enhance the performance of all tasks in a multi-task scenario. Structured or group sparsity systematically eliminates parameters from trivial channels and, sometimes, eventually, entire layers within a convolution neural network during training. Consequently, the remaining layers provide the most optimal features for a given task. In this two-step approach, we subsequently leverage this sparsity-induced optimal layer information to build the LOMT models by connecting task-specific decoders to these strategically identified layers, deviating from conventional approaches that uniformly connect decoders at the end of the network. This tailored architecture optimizes the network, focusing on essential features while reducing redundancy. We validate the efficacy of the proposed approach on two datasets, i.e., NYU-v2 and CelebAMask-HD datasets, for multiple heterogeneous tasks. A detailed performance analysis of the LOMT models, in contrast to the conventional MTL models, reveals that the LOMT models outperform for most task combinations. The excellent qualitative and quantitative outcomes highlight the effectiveness of employing structured sparsity for optimal layer (or feature) selection., Comment: Accepted at ECCV 2024 workshop - Computational Aspects of Deep Learning

The fibula-free flap (FFF) is a valuable reconstructive technique in maxillofacial surgery; however, the assessment of osteotomy accuracy remains challenging. We devised two novel methodologies to compare planned and postoperative osteotomies in FFF reconstructions that minimized user input but would still generalize to other operations involving the analysis of osteotomies. Our approaches leverage basic mathematics to derive both quantitative and qualitative insights about the relationship of the postoperative osteotomy to the planned model. We have coined our methods 'analysis by a shared reference angle' and 'Euler angle analysis'. In addition to describing our algorithm and the clinical utility, we present a thorough validation of both methods. Code is available at https://github.com/monkeygobah/osteoplane.

This paper presents the final physics design of the Proton Improvement Plan-II (PIP-II) at Fermilab, focusing on the linear accelerator (Linac) and its beam transfer line. We address the challenges in longitudinal and transverse lattice design, specifically targeting collective effects, parametric resonances, and space charge nonlinearities that impact beam stability and emittance control. The strategies implemented effectively mitigate space charge complexities, resulting in significant improvements in beam quality -- evidenced by reduced emittance growth, lower beam halo, decreased loss, and better energy spread management. This comprehensive study is pivotal for the PIP-II project's success, providing valuable insights and approaches for future accelerator designs, especially in managing nonlinearities and enhancing beam dynamics.

When performing manipulation-based activities such as picking objects, a mobile robot needs to position its base at a location that supports successful execution. To address this problem, prominent approaches typically rely on costly grasp planners to provide grasp poses for a target object, which are then are then analysed to identify the best robot placements for achieving each grasp pose. In this paper, we propose instead to first find robot placements that would not result in collision with the environment and from where picking up the object is feasible, then evaluate them to find the best placement candidate. Our approach takes into account the robot's reachability, as well as RGB-D images and occupancy grid maps of the environment for identifying suitable robot poses. The proposed algorithm is embedded in a service robotic workflow, in which a person points to select the target object for grasping. We evaluate our approach with a series of grasping experiments, against an existing baseline implementation that sends the robot to a fixed navigation goal. The experimental results show how the approach allows the robot to grasp the target object from locations that are very challenging to the baseline implementation.

Algorithmic materials discovery is a multi-disciplinary domain that integrates insights from specialists in alloy design, synthesis, characterization, experimental methodologies, computational modeling, and optimization. Central to this effort is a robust data management system paired with an interactive work platform. This platform should empower users to not only access others data but also integrate their analyses, paving the way for sophisticated data pipelines. To realize this vision, there is a need for an integrative collaboration platform, streamlined data sharing and analysis tools, and efficient communication channels. Such a collaborative mechanism should transcend geographical barriers, facilitating remote interaction and fostering a challenge-response dynamic. In this paper, we present our ongoing efforts in addressing the critical challenges related to an accelerated Materials Discovery Framework as a part of the High-Throughput Materials Discovery for Extreme Conditions Initiative. Our BIRDSHOT Center has successfully harnessed various tools and strategies, including the utilization of cloud-based storage, a standardized sample naming convention, a structured file system, the implementation of sample travelers, a robust sample tracking method, and the incorporation of knowledge graphs for efficient data management. Additionally, we present the development of a data collection platform, reinforcing seamless collaboration among our team members. In summary, this paper provides an illustration and insight into the various elements of an efficient and effective workflow within an accelerated materials discovery framework while highlighting the dynamic and adaptable nature of the data management tools and sharing platforms., Comment: 28 pages, 9 figures, 2 tables, with appendix that has 8 pages, accepted for publication at IMMI