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The costs and complexity of the American judicial system limit access to legal solutions for many Americans. Large language models (LLMs) hold great potential to improve access to justice. However, a major challenge in applying AI and LLMs in legal contexts, where consistency and reliability are crucial, is the need for System 2 reasoning. In this paper, we explore the integration of LLMs with logic programming to enhance their ability to reason, bringing their strategic capabilities closer to that of a skilled lawyer. Our objective is to translate laws and contracts into logic programs that can be applied to specific legal cases, with a focus on insurance contracts. We demonstrate that while GPT-4o fails to encode a simple health insurance contract into logical code, the recently released OpenAI o1-preview model succeeds, exemplifying how LLMs with advanced System 2 reasoning capabilities can expand access to justice.

In Laser Powder Bed Fusion (LPBF), the applied laser energy produces high thermal gradients that lead to unacceptable final part distortion. Accurate distortion prediction is essential for optimizing the 3D printing process and manufacturing a part that meets geometric accuracy requirements. This study introduces data-driven parameterized reduced-order models (ROMs) to predict distortion in LPBF across various machine process settings. We propose a ROM framework that combines Proper Orthogonal Decomposition (POD) with Gaussian Process Regression (GPR) and compare its performance against a deep-learning based parameterized graph convolutional autoencoder (GCA). The POD-GPR model demonstrates high accuracy, predicting distortions within $\pm0.001mm$, and delivers a computational speed-up of approximately 1800x., Comment: 7 pages, 4 figures, NeurIPS Machine Learning for Physical Sciences workshop

Accurate prediction over long time horizons is crucial for modeling complex physical processes such as wave propagation. Although deep neural networks show promise for real-time forecasting, they often struggle with accumulating phase and amplitude errors as predictions extend over a long period. To address this issue, we propose a novel loss decomposition strategy that breaks down the loss into separate phase and amplitude components. This technique improves the long-term prediction accuracy of neural networks in wave propagation tasks by explicitly accounting for numerical errors, improving stability, and reducing error accumulation over extended forecasts., Comment: 6 pages, 4 figures, NeurIPS Machine Learning for Physical Sciences workshop

In this paper, the inverse potentials for the resonant f states of {\alpha}-3H and {\alpha}-3He are constructed using the phase function method by utilizing an ab-initio approach. A combination of three Morse functions are joined smoothly to prepare the reference potential. While the regular Morse function captures the nuclear and Coulomb interactions at short and medium ranges, an inverse Morse function is chosen to obtain the Coulomb barrier that arises because of the long-range Coulomb interaction. This reference potential is representative of a large family of curves consisting of eight distinct model parameters and two intermediate points that define the boundaries that exist between the three regions. The phase equation is solved using the Runge-Kutta 5th order method for the input reference potential to obtain the scattering phase shifts at various center of mass energies. The model parameters are then adjusted using the genetic algorithm in an iterative fashion to minimize the mean square error between the simulated and expected phase shift values. Our approach successfully constructed the inverse potentials for the resonant f states of the {\alpha}-3H and {\alpha}-3He systems, achieving convergence with a minimized mean square error. The resonance energies and widths for the {\alpha}-3H system for the f-5/2 and f-7/2 states are determined to be [4.19 (4.14), 1.225 (0.918)] MeV and [2.20 (2.18), 0.099 (0.069)] MeV, respectively. For the f-5/2 and f-7/2 states of the {\alpha}-3He system, the resonance energies and widths are [5.03 (5.14), 1.6 (1.2)] MeV and [2.99 (2.98), 0.182(0.175)] MeV, respectively. Our ab-initio approach to solve the phase equation utilizing a combination of smoothly joined Morse functions effectively captures both short-range nuclear and long-range Coulomb interactions, providing an accurate model for nuclear scattering involving charged particles., Comment: 14 pages, 5 figures, 5 tables

This article discusses the single Depot multiple Set Orienteering Problem (sDmSOP), a recently suggested generalization of the Set Orienteering Problem (SOP). This problem aims to discover a path for each traveler over a subset of vertices, where each vertex is associated with only one cluster, and the total profit made from the clusters visited is maximized while still fitting within the available budget constraints. The profit can be collected only by visiting at least one cluster vertex. According to the SOP, each vertex cluster must have at least one of its visits counted towards the profit for that cluster. Like to the SOP, the sDmSOP restricts the number of clusters visited based on the budget for tour expenses. To address this problem, we employ the Genetic Algorithm (GA) and Variable Neighborhood Search (VNS) meta-heuristic. The optimal solution for small-sized problems is also suggested by solving the Integer Linear Programming (ILP) formulation using the General Algebraic Modeling System (GAMS) 37.1.0 with CPLEX for the sDmSOP. Promising computational results are presented that demonstrate the practicability of the proposed GA, VNS meta-heuristic, and ILP formulation by demonstrating substantial improvements to the solutions generated by VNS than GA while simultaneously needing much less time to compute than CPLEX., Comment: 14 pages, 3 figures, 2 tables

We address the challenge of automated classification of diffuse large B-cell lymphoma (DLBCL) into its two primary subtypes: activated B-cell-like (ABC) and germinal center B-cell-like (GCB). Accurate classification between these subtypes is essential for determining the appropriate therapeutic strategy, given their distinct molecular profiles and treatment responses. Our proposed deep learning model demonstrates robust performance, achieving an average area under the curve (AUC) of (87.4 pm 5.7)\% during cross-validation. It shows a high positive predictive value (PPV), highlighting its potential for clinical application, such as triaging for molecular testing. To gain biological insights, we performed an analysis of morphological features of ABC and GCB subtypes. We segmented cell nuclei using a pre-trained deep neural network and compared the statistics of geometric and color features for ABC and GCB. We found that the distributions of these features were not very different for the two subtypes, which suggests that the visual differences between them are more subtle. These results underscore the potential of our method to assist in more precise subtype classification and can contribute to improved treatment management and outcomes for patients of DLBCL.

The advancement of digital pathology, particularly through computational analysis of whole slide images (WSI), is poised to significantly enhance diagnostic precision and efficiency. However, the large size and complexity of WSIs make it difficult to analyze and classify them using computers. This study introduces a novel method for WSI classification by automating the identification and examination of the most informative patches, thus eliminating the need to process the entire slide. Our method involves two-stages: firstly, it extracts only a few patches from the WSIs based on their pathological significance; and secondly, it employs Fisher vectors (FVs) for representing features extracted from these patches, which is known for its robustness in capturing fine-grained details. This approach not only accentuates key pathological features within the WSI representation but also significantly reduces computational overhead, thus making the process more efficient and scalable. We have rigorously evaluated the proposed method across multiple datasets to benchmark its performance against comprehensive WSI analysis and contemporary weakly-supervised learning methodologies. The empirical results indicate that our focused analysis of select patches, combined with Fisher vector representation, not only aligns with, but at times surpasses, the classification accuracy of standard practices. Moreover, this strategy notably diminishes computational load and resource expenditure, thereby establishing an efficient and precise framework for WSI analysis in the realm of digital pathology.

Whole Slide Imaging (WSI) is a cornerstone of digital pathology, offering detailed insights critical for diagnosis and research. Yet, the gigapixel size of WSIs imposes significant computational challenges, limiting their practical utility. Our novel approach addresses these challenges by leveraging various encoders for intelligent data reduction and employing a different classification model to ensure robust, permutation-invariant representations of WSIs. A key innovation of our method is the ability to distill the complex information of an entire WSI into a single vector, effectively capturing the essential features needed for accurate analysis. This approach significantly enhances the computational efficiency of WSI analysis, enabling more accurate pathological assessments without the need for extensive computational resources. This breakthrough equips us with the capability to effectively address the challenges posed by large image resolutions in whole-slide imaging, paving the way for more scalable and effective utilization of WSIs in medical diagnostics and research, marking a significant advancement in the field., Comment: arXiv admin note: text overlap with arXiv:2411.08530

Methods for image-to-video generation have achieved impressive, photo-realistic quality. However, adjusting specific elements in generated videos, such as object motion or camera movement, is often a tedious process of trial and error, e.g., involving re-generating videos with different random seeds. Recent techniques address this issue by fine-tuning a pre-trained model to follow conditioning signals, such as bounding boxes or point trajectories. Yet, this fine-tuning procedure can be computationally expensive, and it requires datasets with annotated object motion, which can be difficult to procure. In this work, we introduce SG-I2V, a framework for controllable image-to-video generation that is self-guided$\unicode{x2013}$offering zero-shot control by relying solely on the knowledge present in a pre-trained image-to-video diffusion model without the need for fine-tuning or external knowledge. Our zero-shot method outperforms unsupervised baselines while significantly narrowing down the performance gap with supervised models in terms of visual quality and motion fidelity., Comment: Project page: https://kmcode1.github.io/Projects/SG-I2V/

We introduce MusicFlow, a cascaded text-to-music generation model based on flow matching. Based on self-supervised representations to bridge between text descriptions and music audios, we construct two flow matching networks to model the conditional distribution of semantic and acoustic features. Additionally, we leverage masked prediction as the training objective, enabling the model to generalize to other tasks such as music infilling and continuation in a zero-shot manner. Experiments on MusicCaps reveal that the music generated by MusicFlow exhibits superior quality and text coherence despite being over $2\sim5$ times smaller and requiring $5$ times fewer iterative steps. Simultaneously, the model can perform other music generation tasks and achieves competitive performance in music infilling and continuation. Our code and model will be publicly available., Comment: ICML 2024

Bid shading has become a standard practice in the digital advertising industry, in which most auctions for advertising (ad) opportunities are now of first price type. Given an ad opportunity, performing bid shading requires estimating not only the value of the opportunity but also the distribution of the highest bid from competitors (i.e. the competitive landscape). Since these two estimates tend to be very noisy in practice, first-price auction participants need a bid shading policy that is robust against relatively significant estimation errors. In this work, we provide a max-min formulation in which we maximize the surplus against an adversary that chooses a distribution both for the value and the competitive landscape, each from a Kullback-Leibler-based ambiguity set. As we demonstrate, the two ambiguity sets are essential to adjusting the shape of the bid-shading policy in a principled way so as to effectively cope with uncertainty. Our distributionally robust bid shading policy is efficient to compute and systematically outperforms its non-robust counterpart on real datasets provided by Yahoo DSP.

In this study, we investigate the performance of few-shot classification models across different domains, specifically natural images and histopathological images. We first train several few-shot classification models on natural images and evaluate their performance on histopathological images. Subsequently, we train the same models on histopathological images and compare their performance. We incorporated four histopathology datasets and one natural images dataset and assessed performance across 5-way 1-shot, 5-way 5-shot, and 5-way 10-shot scenarios using a selection of state-of-the-art classification techniques. Our experimental results reveal insights into the transferability and generalization capabilities of few-shot classification models between diverse image domains. We analyze the strengths and limitations of these models in adapting to new domains and provide recommendations for optimizing their performance in cross-domain scenarios. This research contributes to advancing our understanding of few-shot learning in the context of image classification across diverse domains.

The ZrSiS-class of layered materials offer interesting topological and magnetic characteristics suitable for spintronics applications. In this work, we have synthesized a polycrystalline NdBiTe using solid-state reaction technique and have examined the magnetic properties in 2 - 300 K temperature range using temperature and field-dependent magnetization measurements. Our magnetic and specific heat data demonstrates a long-range antiferromagnetic ordering in the material below 4.5 K. Furthermore, our isothermal magnetization data show a signature of spin-reorientation below Neel temperature. The observed nonlinearity in inverse susceptibility vs temperature data, and a hump in specific heat in 5-20 K range, indicate the existence of crystal field splitting in the material. Our transport properties measurements show the metallic behavior with positive magnetoresistance in the temperature range of 2 - 300 K. The observed rise in resistivity as function of temperature below Neel temperature infers the strongly correlated fermions, which is consistent with the observed large Sommerfeld coefficient. Consistent with experimental results, our first-principles calculations predict an antiferromagnetic semimetallic nature of NdBiTe. Further, our spin-orbit coupled simulations of electronic structure show a signature of weak topological nature of the material., Comment: 17 pages, 10 Figures

Merging has become a widespread way to cheaply combine individual models into a single model that inherits their capabilities and attains better performance. This popularity has spurred rapid development of many new merging methods, which are typically validated in disparate experimental settings and frequently differ in the assumptions made about model architecture, data availability, and computational budget. In this work, we characterize the relative merits of different merging methods by evaluating them in a shared experimental setting and precisely identifying the practical requirements of each method. Specifically, our setting focuses on using merging for compositional generalization of capabilities in image classification, image generation, and natural language processing. Additionally, we measure the computational costs of different merging methods as well as how they perform when scaling the number of models being merged. Taken together, our results clarify the state of the field of model merging and provide a comprehensive and rigorous experimental setup to test new methods.

We present a time-domain inspiral-merger-ringdowm (IMR) waveform model ESIGMAHM constructed within a framework we named ESIGMA for coalescing binaries of spinning black holes on moderately eccentric orbits (Huerta et al. (2018) [Phys. Rev. D 97, 024031]). We now include the effect of black hole spins on the dynamics of eccentric binaries, as well as model sub-dominant waveform harmonics emitted by them. The inspiral evolution is described by a consistent combination of latest results from post-Newtonian theory, self-force, and black hole perturbation theory. We assume that these moderately eccentric binaries radiate away most of their orbital eccentricity before merger, and seamlessly connect the eccentric inspiral with a numerical relativity based surrogate waveform model for mergers of spinning binaries on quasi-circular orbits. We validate ESIGMAHM against eccentric Numerical Relativity simulations, and also against contemporary effective-one-body and phenomenological models in the quasi-circular limit. We find that ESIGMAHM achieves match values greater than $99\%$ for quasi-circular spin-aligned binaries with mass ratios up to $8$, and above $97\%$ for non-spinning and spinning eccentric systems with small or positively aligned spins. Using IMRESIGMA, we quantify the impact of orbital eccentricity on GW signals, showing that next-generation detectors can detect eccentric sources up to $10\%$ louder than quasi-circular ones. We also show that current templated LIGO-Virgo searches will lose more than $10\%$ of optimal SNR for about $20\%$ of all eccentric sources by using only quasi-circular waveform templates. The same will result in a $25\%$ loss in detection rate for eccentric sources with mass ratios $m_1/m_2\geq 4$. Our results highlight the need for including eccentricity and higher-order modes in GW source models and searches for asymmetric eccentric BBH signals.

We report the detailed investigation of the magnetic, transport, and magnetocaloric effects of GdSbSe by magnetic susceptibility $\chi(T)$, isothermal magnetization $M(H)$, resistivity $\rho(T, H)$, and heat capacity $C_p(T)$ measurements, crystallizing in the ZrSiS-type tetragonal crystal system with space group $P4/nmm$. Temperature-dependent magnetic susceptibility measurements revealed long-range antiferromagnetic ordering with two additional magnetic anomalies below N\'eel temperature ($T_N$ $\approx$ 8.6 K), corroborated through magnetocaloric and specific heat studies. Isothermal magnetization measurements unveil hidden metamagnetic signatures through a clear deviation from linearity. In addition, the enhanced value of the Sommerfeld coefficient ($\gamma$ = 152(5) mJ/ mol K$^2$) suggests strong electronic correlations in GdSbSe. The entropy of magnetization derived from magnetic isotherms unfolds the field-induced transition from Inverse magnetocaloric Effect (IMCE) to Conventional MCE. The detailed transport properties indicate a semimetallic behavior, strongly coupled with magnetic order. Deviations from Kohler's rule and non-linear Hall resistivity anticipate the possibility of Dirac-like dispersion with non-trivial characteristics., Comment: 10 pages, 9 Figures

Gravitational waveforms capturing binary's evolution through the early-inspiral phase play a critical role in extracting orbital features that nearly disappear during the late-inspiral and subsequent merger phase due to radiation reaction forces; for instance, the effect of orbital eccentricity. Phenomenological approaches that model compact binary mergers rely heavily on combining inputs from both analytical and numerical approaches to reduce the computational cost of generating templates for data analysis purposes. In a recent work, Chattaraj et al., Phys. Rev. D 106, 124008 (2022) arXiv:2204.02377(gr-qc), we demonstrated construction of a dominant (quadrupole) mode inspiral-merger-ringdown (IMR) model for binary black holes (BBHs) on elliptical orbits. The model was constructed in time-domain and is fully analytical. The current work is an attempt to improve this model by making a few important changes in our approach. The most significant of those involves identifying initial values of orbital parameters with which the inspiral part of the model is evolved. While the ingredients remain the same as in arXiv:2204.02377(gr-qc), resulting waveforms at each stage seem to have improved as a consequence of new considerations proposed here. The updated model is validated also against an independent waveform family resulting overlaps better than $\sim 96.5\%$ within the calibrated range of binary parameters. Further, we use the prescription of the dominant mode model presented here to provide an alternate (but equivalent) model for the (dominant) quadrupole mode and extend the same to a model including the effect of selected non-quadrupole modes. Finally, while this model assumes non-spinning components, we show that this could also be used for mildly spinning systems with component spins (anti-) aligned w.r.t the orbital angular momentum., Comment: 17 pages, 12 figures. arXiv admin note: text overlap with arXiv:2204.02377

The localization of Unmanned aerial vehicles (UAVs) in deep tunnels is extremely challenging due to their inaccessibility and hazardous environment. Conventional outdoor localization techniques (such as using GPS) and indoor localization techniques (such as those based on WiFi, Infrared (IR), Ultra-Wideband, etc.) do not work in deep tunnels. We are developing a UAV-based system for the inspection of defects in the Deep Tunnel Sewerage System (DTSS) in Singapore. To enable the UAV localization in the DTSS, we have developed a distance measurement module based on the optical flow technique. However, the standard optical flow technique does not work well in tunnels with poor lighting and a lack of features. Thus, we have developed an enhanced optical flow algorithm with prediction, to improve the distance measurement for UAVs in deep hazardous tunnels.

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

The task of image-to-multi-view generation refers to generating novel views of an instance from a single image. Recent methods achieve this by extending text-to-image latent diffusion models to multi-view version, which contains an VAE image encoder and a U-Net diffusion model. Specifically, these generation methods usually fix VAE and finetune the U-Net only. However, the significant downscaling of the latent vectors computed from the input images and independent decoding leads to notable pixel-level misalignment across multiple views. To address this, we propose a novel method for pixel-level image-to-multi-view generation. Unlike prior work, we incorporate attention layers across multi-view images in the VAE decoder of a latent video diffusion model. Specifically, we introduce a depth-truncated epipolar attention, enabling the model to focus on spatially adjacent regions while remaining memory efficient. Applying depth-truncated attn is challenging during inference as the ground-truth depth is usually difficult to obtain and pre-trained depth estimation models is hard to provide accurate depth. Thus, to enhance the generalization to inaccurate depth when ground truth depth is missing, we perturb depth inputs during training. During inference, we employ a rapid multi-view to 3D reconstruction approach, NeuS, to obtain coarse depth for the depth-truncated epipolar attention. Our model enables better pixel alignment across multi-view images. Moreover, we demonstrate the efficacy of our approach in improving downstream multi-view to 3D reconstruction tasks.

The current standard for detecting human epidermal growth factor receptor 2 (HER2) status in breast cancer patients relies on HER2 amplification, identified through fluorescence in situ hybridization (FISH) or immunohistochemistry (IHC). However, hematoxylin and eosin (H\&E) tumor stains are more widely available, and accurately predicting HER2 status using H\&E could reduce costs and expedite treatment selection. Deep Learning algorithms for H&E have shown effectiveness in predicting various cancer features and clinical outcomes, including moderate success in HER2 status prediction. In this work, we employed a customized weak supervision classification technique combined with MoCo-v2 contrastive learning to predict HER2 status. We trained our pipeline on 182 publicly available H&E Whole Slide Images (WSIs) from The Cancer Genome Atlas (TCGA), for which annotations by the pathology team at Yale School of Medicine are publicly available. Our pipeline achieved an Area Under the Curve (AUC) of 0.85 across four different test folds. Additionally, we tested our model on 44 H&E slides from the TCGA-BRCA dataset, which had an HER2 score of 2+ and included corresponding HER2 status and FISH test results. These cases are considered equivocal for IHC, requiring an expensive FISH test on their IHC slides for disambiguation. Our pipeline demonstrated an AUC of 0.81 on these challenging H&E slides. Reducing the need for FISH test can have significant implications in cancer treatment equity for underserved populations.

This paper presents a study on few-shot classification in the context of histopathology images. While few-shot learning has been studied for natural image classification, its application to histopathology is relatively unexplored. Given the scarcity of labeled data in medical imaging and the inherent challenges posed by diverse tissue types and data preparation techniques, this research evaluates the performance of state-of-the-art few-shot learning methods for various scenarios on histology data. We have considered four histopathology datasets for few-shot histopathology image classification and have evaluated 5-way 1-shot, 5-way 5-shot and 5-way 10-shot scenarios with a set of state-of-the-art classification techniques. The best methods have surpassed an accuracy of 70%, 80% and 85% in the cases of 5-way 1-shot, 5-way 5-shot and 5-way 10-shot cases, respectively. We found that for histology images popular meta-learning approaches is at par with standard fine-tuning and regularization methods. Our experiments underscore the challenges of working with images from different domains and underscore the significance of unbiased and focused evaluations in advancing computer vision techniques for specialized domains, such as histology images.

This paper explores a novel approach to model strategies for flattening wrinkled cloth learning from humans. A human participant study was conducted where the participants were presented with various wrinkle types and tasked with flattening the cloth using the fewest actions possible. A camera and Aruco marker were used to capture images of the cloth and finger movements, respectively. The human strategies for flattening the cloth were modeled using a supervised regression neural network, where the cloth images served as input and the human actions as output. Before training the neural network, a series of image processing techniques were applied, followed by Principal Component Analysis (PCA) to extract relevant features from each image and reduce the input dimensionality. This reduction decreased the model's complexity and computational cost. The actions predicted by the neural network closely matched the actual human actions on an independent data set, demonstrating the effectiveness of neural networks in modeling human actions for flattening wrinkled cloth., Comment: 6 Pages

In this work, we present a comparative study between continuous-wave (CW) and pulsed optical tweezers for 250 nm, 500 nm and 1-micron radius polystyrene beads at 5 different laser powers. We have used a Ti:Sapphire (MIRA 900F) laser that can be easily switched from CW to pulsed mode of operation, so there is no change in the experimental conditions in the two cases. We have measured the difference in the trap strength in both cases by fitting the power spectrum curve with Lorentzian. As it turns out, trapping with pulsed tweezers seems to be more effective for the smaller particles and as the particle size is increased both CW and pulsed tweezers appear to be equally effective at lower average laser powers but as the power is increased pulsed tweezers do a better job at stable trapping.

This paper introduces a variant of the Set Orienteering Problem (SOP), the multi-Depot multiple Set Orienteering Problem (mDmSOP). It generalizes the SOP by grouping nodes into mutually exclusive sets (clusters) with associated profits. Profit can be earned if any node within the set is visited. Multiple travelers, denoted by $t$ $(> 1)$, are employed, with each traveler linked to a specific depot. The primary objective of the problem is to maximize profit collection from the sets within a predefined budget. A novel formulation is introduced for the mDmSOP. The paper utilizes the Variable Neighborhood Search (VNS) meta-heuristic to solve the mDmSOP on small, medium, and large instances from the Generalized Traveling Salesman Problem (GTSP) benchmark. The results demonstrate the VNS's superiority in robustness and solution quality, as it requires less computational time than solving the mathematical formulation with GAMS 37.1.0 and CPLEX. Additionally, increasing the number of travelers leads to significant improvements in profits., Comment: 17 pages, 1 figure, 5 tables

We introduce MelodyFlow, an efficient text-controllable high-fidelity music generation and editing model. It operates on continuous latent representations from a low frame rate 48 kHz stereo variational auto encoder codec. Based on a diffusion transformer architecture trained on a flow-matching objective the model can edit diverse high quality stereo samples of variable duration, with simple text descriptions. We adapt the ReNoise latent inversion method to flow matching and compare it with the original implementation and naive denoising diffusion implicit model (DDIM) inversion on a variety of music editing prompts. Our results indicate that our latent inversion outperforms both ReNoise and DDIM for zero-shot test-time text-guided editing on several objective metrics. Subjective evaluations exhibit a substantial improvement over previous state of the art for music editing. Code and model weights will be publicly made available. Samples are available at https://melodyflow.github.io.

This report addresses the boundary value problem for a second-order linear singularly perturbed FIDE. Traditional methods for solving these equations often face stability issues when dealing with small perturbation parameters. We propose an exact finite difference method to solve these equations and provide a detailed stability and $\varepsilon$-uniform convergence analysis. Our approach is validated with an example, demonstrating its uniform convergence and applicability, with a convergence order of 1. The results illustrate the method's robustness in handling perturbation effects efficiently.

We report the synthesis, structural characterization, and investigation of electrical transport, magnetic and specific heat properties of bulk semiconducting layered material Ta$_2$Ni$_3$Te$_5$. Ta$_2$Ni$_3$Te$_5$ crystallizes in the centrosymmetric orthorhombic structure with space group Pnma. Temperature-dependent resistivity shows a transition from semiconducting to metallic nature below 7 K. Low-temperature magnetotransport studies show large magnetoresistance with the signature of weak anti-localization (WAL) effect. The magnetoconductivity data has been used to explore the origin of the WAL effect, extract relevant parameters, and study their variation with temperature. The presence of significant electron-phonon interaction is evident from the MR vs. B/R plot (Kohler's plot). Isothermal field-dependent magnetization studies show Berry paramagnetism as the signature for spin-orbit coupling-induced spin-momentum locking. Observation of WAL effect and spin-momentum locking phenomenon demonstrate Ta$_2$Ni$_3$Te$_5$ as a promising low dimensional material for quantum spin Hall insulator-based applications., Comment: 10 pages

We experimentally and numerically study the collapse dynamics of a quantum vortex in a two-dimensional atomic superfluid following a fast interaction ramp from repulsion to attraction. We find the conditions and time scales for a superfluid vortex to radially converge into a quasi-stationary density profile, demonstrating the first spontaneous formation of a vortex soliton in an atomic Bose gas. We record an emergent universal dynamics of an azimuthal modulational instability, which amplifies initial density perturbations and leads to the eventual splitting of a vortex soliton or direct fragmentation of a superfluid into disordered, but roughly circular arrays of Townes soliton-like wavepackets. Our study sets the stage for exploring universal out-of-equilibrium dynamics of vortex quantum matter quenched to attractive interactions.

Clinical trial matching is the task of identifying trials for which patients may be potentially eligible. Typically, this task is labor-intensive and requires detailed verification of patient electronic health records (EHRs) against the stringent inclusion and exclusion criteria of clinical trials. This process is manual, time-intensive, and challenging to scale up, resulting in many patients missing out on potential therapeutic options. Recent advancements in Large Language Models (LLMs) have made automating patient-trial matching possible, as shown in multiple concurrent research studies. However, the current approaches are confined to constrained, often synthetic datasets that do not adequately mirror the complexities encountered in real-world medical data. In this study, we present the first, end-to-end large-scale empirical evaluation of clinical trial matching using real-world EHRs. Our study showcases the capability of LLMs to accurately match patients with appropriate clinical trials. We perform experiments with proprietary LLMs, including GPT-4 and GPT-3.5, as well as our custom fine-tuned model called OncoLLM and show that OncoLLM, despite its significantly smaller size, not only outperforms GPT-3.5 but also matches the performance of qualified medical doctors. All experiments were carried out on real-world EHRs that include clinical notes and available clinical trials from a single cancer center in the United States., Comment: 30 Pages, 8 Figures, Supplementary Work Attached

There is a significant need for precise and reliable forecasting of the far-field noise emanating from shipping vessels. Conventional full-order models based on the Navier-Stokes equations are unsuitable, and sophisticated model reduction methods may be ineffective for accurately predicting far-field noise in environments with seamounts and significant variations in bathymetry. Recent advances in reduced-order models, particularly those based on convolutional and recurrent neural networks, offer a faster and more accurate alternative. These models use convolutional neural networks to reduce data dimensions effectively. However, current deep-learning models face challenges in predicting wave propagation over long periods and for remote locations, often relying on auto-regressive prediction and lacking far-field bathymetry information. This research aims to improve the accuracy of deep-learning models for predicting underwater radiated noise in far-field scenarios. We propose a novel range-conditional convolutional neural network that incorporates ocean bathymetry data into the input. By integrating this architecture into a continual learning framework, we aim to generalize the model for varying bathymetry worldwide. To demonstrate the effectiveness of our approach, we analyze our model on several test cases and a benchmark scenario involving far-field prediction over Dickin's seamount in the Northeast Pacific. Our proposed architecture effectively captures transmission loss over a range-dependent, varying bathymetry profile. This architecture can be integrated into an adaptive management system for underwater radiated noise, providing real-time end-to-end mapping between near-field ship noise sources and received noise at the marine mammal's location., Comment: 14 pages, 18 figures

Retrieving information from EHR systems is essential for answering specific questions about patient journeys and improving the delivery of clinical care. Despite this fact, most EHR systems still rely on keyword-based searches. With the advent of generative large language models (LLMs), retrieving information can lead to better search and summarization capabilities. Such retrievers can also feed Retrieval-augmented generation (RAG) pipelines to answer any query. However, the task of retrieving information from EHR real-world clinical data contained within EHR systems in order to solve several downstream use cases is challenging due to the difficulty in creating query-document support pairs. We provide a blueprint for creating such datasets in an affordable manner using large language models. Our method results in a retriever that is 30-50 F-1 points better than propriety counterparts such as Ada and Mistral for oncology data elements. We further compare our model, called Onco-Retriever, against fine-tuned PubMedBERT model as well. We conduct an extensive manual evaluation on real-world EHR data along with latency analysis of the different models and provide a path forward for healthcare organizations to build domain-specific retrievers., Comment: 18 pages