Search

In Query-driven Travel Recommender Systems (RSs), it is crucial to understand the user intent behind challenging natural language(NL) destination queries such as the broadly worded "youth-friendly activities" or the indirect description "a high school graduation trip". Such queries are challenging due to the wide scope and subtlety of potential user intents that confound the ability of retrieval methods to infer relevant destinations from available textual descriptions such as WikiVoyage. While query reformulation (QR) has proven effective in enhancing retrieval by addressing user intent, existing QR methods tend to focus only on expanding the range of potentially matching query subtopics (breadth) or elaborating on the potential meaning of a query (depth), but not both. In this paper, we introduce Elaborative Subtopic Query Reformulation (EQR), a large language model-based QR method that combines both breadth and depth by generating potential query subtopics with information-rich elaborations. We also release TravelDest, a novel dataset for query-driven travel destination RSs. Experiments on TravelDest show that EQR achieves significant improvements in recall and precision over existing state-of-the-art QR methods., Comment: 9 pages, 7 figures,The 1st Workshop on Risks, Opportunities, and Evaluation of Generative Models in Recommender Systems (ROEGEN@RecSys 2024), October 2024, Bari, Italy

We present an adaptation of the exoplanet transit model code batman, in order to permit the generation of X-ray transits. Our underlying extended coronal model assumes an isothermal plasma that is radially symmetric. While this ignores the effect of bright, active regions, observations of transits in X-rays will require averaging across multiple epochs of data for the foreseeable future, significantly reducing the importance of more complex modelling. Our publicly available code successfully generates the predicted W-shaped transit profile in X-rays due to the optically thin nature of the emission, which concentrates the expected observational emission around the limb of the photospheric stellar disc. We provide some examples based on the best known X-ray transit target, HD 189733b, and examine the effect of varying the planet size, coronal temperature, and impact parameter on the resulting transit profile. We also derived scaling relationships for how the overall transit detectability is affected by changing these parameters. Over most of the parameter space, we find that the detectability scales linearly with the cross-sectional area of the planet in X-rays. The relationship with increasing coronal temperature is less fixed, but averages out to a power law with slope $-1/4$ except when the impact parameter is high. Indeed, varying impact parameter has little effect on detectability at all until it approaches unity., Comment: 16 pages, 8 figures, 2 tables. Accepted for publication in the Astronomical Journal. Code available at https://github.com/georgewking/batmanX-rays

In this paper, we analyze the performance of one- and two-sided amplitude shift keying (ASK) modulations in single-input single-output wireless communication aided by a reconfigurable intelligent surface (RIS). Two scenarios are considered for the channel conditions: a blocked direct channel between the transmitter and the receiver, and an unblocked one. For the receiver, a noncoherent maximum likelihood detector is proposed, which detects the transmitted data signal based on statistical knowledge of the channel. The system's performance is then evaluated by deriving the symbol error probability (SEP) for both scenarios using the proposed noncoherent receiver structures. We also present a novel optimization framework to obtain the optimal one- and two-sided ASK modulation schemes that minimize the SEP under constraints on the available average transmit power for both the blocked and unblocked direct channel scenarios. Our extensive numerical investigations showcase that the considered RIS-aided communication system achieves superior error performance with both derived SEP-optimal ASK modulation schemes as compared to respective traditional ASK modulation. It is also demonstrated that, between the two proposed modulation schemes, the two-sided one yields the best SEP. The error performance is further analyzed for different system parameters, providing a comprehensive performance investigation of RIS-assisted noncoherent wireless communication systems., Comment: 13 pages, 6 figures

Semantic communications are considered a promising beyond-Shannon/bit paradigm to reduce network traffic and increase reliability, thus making wireless networks more energy efficient, robust, and sustainable. However, the performance is limited by the efficiency of the semantic transceivers, i.e., the achievable "similarity" between the transmitted and received signals. Under strict similarity conditions, semantic transmission may not be applicable and bit communication is mandatory. In this paper, for the first time in the literature, we propose a multi-carrier Hybrid Semantic-Shannon communication system where, without loss of generality, the case of text transmission is investigated. To this end, a joint semantic-bit transmission selection and power allocation optimization problem is formulated, aiming to minimize two transmission delay metrics widely used in the literature, subject to strict similarity thresholds. Despite their non-convexity, both problems are decomposed into a convex and a mixed linear integer programming problem by using alternating optimization, both of which can be solved optimally. Furthermore, to improve the performance of the proposed hybrid schemes, a novel association of text sentences to subcarriers is proposed based on the data size of the sentences and the channel gains of the subcarriers. We show that the proposed association is optimal in terms of transmission delay. Numerical simulations verify the effectiveness of the proposed hybrid semantic-bit communication scheme and the derived sentence-to-subcarrier association, and provide useful insights into the design parameters of such systems.

Understanding when and how reionization happened is crucial for studying the early structure formation and the properties of first galaxies in the Universe. At $z>5.5$, the observed IGM optical depth shows a significant scatter, indicating an inhomogeneous reionization process. However, the nature of the inhomogeneous reionization remains debated. ASPIRE is a JWST Cycle 1 program that has spectroscopically identified $>400$ [OIII] emitters in 25 quasar fields at $z>6.5$. Combined with deep ground-based optical spectroscopy of ASPIRE quasars, ASPIRE program provides the current largest sample for IGM-galaxy connection studies during cosmic reionization. We present the first results of IGM effective optical depth measurements around [OIII] emitters using 14 ASPIRE quasar fields. We find the IGM transmission is tightly related with reionization-era galaxies to the extent that significant excess of Ly$\alpha$ transmission exists around [OIII] emitters. We measure the stacked IGM effective optical depth of IGM patches associated with [OIII] emitters and find they reach the same IGM effective optical depth at least dz~0.1 ahead of those IGM patches where no [OIII] emitters are detected, supporting earlier reionization around [OIII] emitters. Our results indicate an enhancement in IGM Ly$\alpha$ transmission around [OIII] emitters at scales beyond 25 $h^{-1}$ cMpc, consistent with the predicted topology of reionization from fluctuating UV background (UVB) models., Comment: 27 pages, 19 figures, 1 table, accepted for publication in ApJ

On 2022 March 10, a coronal mass ejection (CME) erupted from the Sun, resulting in Solar Orbiter observations at 0.45 au of both dispersive solar energetic particles arriving prior to the interplanetary CME (ICME) and locally accelerated particles near the ICME-associated shock structure as it passed the spacecraft on 2022 March 11. This shock was later detected on 2022 March 14 by the Advanced Composition Explorer (ACE), which was radially aligned with Solar Orbiter, at 1 au. Ion composition data from both spacecraft -- via the Solar Orbiter Energetic Particle Detector/ Suprathermal Ion Spectrograph (EPD/SIS) and the Ultra Low Energy Isotope Spectrometer (ULEIS) on ACE -- allows for in-depth analysis of the radial evolution of species-dependent ICME shock-associated acceleration processes for this event. We present a study of the ion spectra observed at 0.45 and 1 au during both the gradual solar energetic particle (SEP) and energetic storm particle (ESP) phases of the event. We find that the shapes of the spectra seen at each spacecraft have significant differences that were likely caused by varying shock geometry: Solar Orbiter spectra tend to lack spectral breaks, and the higher energy portions of the ACE spectra have comparable average flux to the Solar Orbiter spectra. Through an analysis of rigidity effects on the spectral breaks observed by ACE, we conclude that the 1 au observations were largely influenced by a suprathermal pool of $\mathrm{He}^{+}$ ions that were enhanced due to propagation along a stream interaction region (SIR) that was interacting with the ICME at times of observation., Comment: 15 pages, 7 figures, submitted to A&A

We present Exact Volumetric Ellipsoid Rendering (EVER), a method for real-time differentiable emission-only volume rendering. Unlike recent rasterization based approach by 3D Gaussian Splatting (3DGS), our primitive based representation allows for exact volume rendering, rather than alpha compositing 3D Gaussian billboards. As such, unlike 3DGS our formulation does not suffer from popping artifacts and view dependent density, but still achieves frame rates of $\sim\!30$ FPS at 720p on an NVIDIA RTX4090. Since our approach is built upon ray tracing it enables effects such as defocus blur and camera distortion (e.g. such as from fisheye cameras), which are difficult to achieve by rasterization. We show that our method is more accurate with fewer blending issues than 3DGS and follow-up work on view-consistent rendering, especially on the challenging large-scale scenes from the Zip-NeRF dataset where it achieves sharpest results among real-time techniques., Comment: Project page: https://half-potato.gitlab.io/posts/ever

Many applications involve reasoning about time durations before a critical event happens--also called time-to-event outcomes. When will a customer cancel a subscription, a coma patient wake up, or a convicted criminal reoffend? Time-to-event outcomes have been studied extensively within the field of survival analysis primarily by the statistical, medical, and reliability engineering communities, with textbooks already available in the 1970s and '80s. This monograph aims to provide a reasonably self-contained modern introduction to survival analysis. We focus on predicting time-to-event outcomes at the individual data point level with the help of neural networks. Our goal is to provide the reader with a working understanding of precisely what the basic time-to-event prediction problem is, how it differs from standard regression and classification, and how key "design patterns" have been used time after time to derive new time-to-event prediction models, from classical methods like the Cox proportional hazards model to modern deep learning approaches such as deep kernel Kaplan-Meier estimators and neural ordinary differential equation models. We further delve into two extensions of the basic time-to-event prediction setup: predicting which of several critical events will happen first along with the time until this earliest event happens (the competing risks setting), and predicting time-to-event outcomes given a time series that grows in length over time (the dynamic setting). We conclude with a discussion of a variety of topics such as fairness, causal reasoning, interpretability, and statistical guarantees. Our monograph comes with an accompanying code repository that implements every model and evaluation metric that we cover in detail., Comment: Code is available at https://github.com/georgehc/survival-intro

We show that strong interactions violating the conservation of lepton numbers in the neutrino sector could significantly alter the standard low entropy picture for the pre-supernova collapsing core of a massive star. A rapid neutrino-antineutrino equilibration leads to entropy generation and enhanced electron capture and, hence, a lower electron fraction than in the standard model. This would affect the downstream core evolution, the prospects for a supernova explosion, and the emergent neutrino signal. If realized by lepton-number-violating neutrino self-interactions (LNV $\nu$SI), the relevant mediator mass and coupling ranges can be probed by future accelerator-based experiments., Comment: 5+8 pages, 2+5 figures. Comments are welcome!

The recent results on the baryon acoustic oscillations measurements from the DESI collaboration have shown tantalizing hints for a time-evolving dark energy equation of state parameter $w(z)$, with a statistically significant deviation from the cosmological constant and cold dark matter (\lcdm) model. One of the simplest and theoretically well-motivated plausible candidates to explain the observed behavior of $w(z)$, is scalar-field quintessence. Here, we consider a class of models known as $\alpha$-attractor, which describe in a single framework both inflation and the late-time acceleration of the Universe. Using the recent DESI data, in conjunction with other cosmological observations, we place stringent constraints on $\alpha$-attractor models and compare them to the \lcdm\, model. We find the $\alpha$ parameter of the theory, which is physically motivated from supergravity and supersymmentry theories to have the values $3\alpha \in \{1,2,3,4,5,6,7\}$, is constrained to be $\alpha\simeq 1.89_{-0.35}^{+0.40}$. In addition, we find that the rest of the cosmological parameters of the model agree with the corresponding values of \lcdm, while a bayesian analysis finds strong support in favor of the $\alpha$-attractor model. We also highlight an interesting connection between the $\alpha$-attractor models and the stochastic gravitational wave background, where a contribution to the latter could derive from an enhancement of inflationary gravitational waves at high frequencies due to an early kination phase, thus providing an interesting alternative way to constrain the theory in the near future., Comment: 13 pages, 9 figures, comments welcome

Deep reinforcement learning (RL) policies, although optimal in terms of task rewards, may not align with the personal preferences of human users. To ensure this alignment, a naive solution would be to retrain the agent using a reward function that encodes the user's specific preferences. However, such a reward function is typically not readily available, and as such, retraining the agent from scratch can be prohibitively expensive. We propose a more practical approach - to adapt the already trained policy to user-specific needs with the help of human feedback. To this end, we infer the user's intent through trajectory-level feedback and combine it with the trained task policy via a theoretically grounded dynamic policy fusion approach. As our approach collects human feedback on the very same trajectories used to learn the task policy, it does not require any additional interactions with the environment, making it a zero-shot approach. We empirically demonstrate in a number of environments that our proposed dynamic policy fusion approach consistently achieves the intended task while simultaneously adhering to user-specific needs.

Population-Based Structural Health Monitoring (PBSHM), aims to leverage information across populations of structures in order to enhance diagnostics on those with sparse data. The discipline of transfer learning provides the mechanism for this capability. One recent paper in PBSHM proposed a geometrical view in which the structures were represented as graphs in a metric "base space" with their data captured in the "total space" of a vector bundle above the graph space. This view was more suggestive than mathematically rigorous, although it did allow certain useful arguments. One bar to more rigorous analysis was the absence of a meaningful topology on the graph space, and thus no useful notion of continuity. The current paper aims to address this problem, by moving to parametric families of structures in the base space, essentially changing points in the graph space to open balls. This allows the definition of open sets in the fibre space and thus allows continuous variation between fibres. The new ideas motivate a new geometrical mechanism for transfer learning in data are transported from one fibre to an adjacent one; i.e., from one structure to another.

We scrutinize the potential of upcoming ultra-near reactor neutrino experiments to detect radiative corrections in the elastic neutrino-electron scattering channel, focusing on the JUNO-TAO and CLOUD detectors, which employ advanced scintillator detection technologies. Previous reactor experiments have already constrained the electron neutrino charge radius, which is a neutrino property associated with a certain subset of the total radiative corrections, and have achieved limits that are only about an order of magnitude away from the Standard Model prediction. Our study demonstrates that JUNO-TAO and CLOUD could discover the neutrino charge radius in the near future, considering the established treatment of the charge radius. However, we show that it is necessary to go beyond this standard treatment. By including the complete set of one-loop level radiative corrections, we find a partial cancellation with the charge radius effect, reducing the experimental sensitivity to this quantity. Nevertheless, JUNO-TAO and CLOUD still have the potential to achieve a $5\sigma$ discovery but over longer timescales within a reasonable operational timeframe., Comment: 13 pages, 7 figures

Periorbital segmentation and distance prediction using deep learning allows for the objective quantification of disease state, treatment monitoring, and remote medicine. However, there are currently no reports of segmentation datasets for the purposes of training deep learning models with sub mm accuracy on the regions around the eyes. All images (n=2842) had the iris, sclera, lid, caruncle, and brow segmented by five trained annotators. Here, we validate this dataset through intra and intergrader reliability tests and show the utility of the data in training periorbital segmentation networks. All the annotations are publicly available for free download. Having access to segmentation datasets designed specifically for oculoplastic surgery will permit more rapid development of clinically useful segmentation networks which can be leveraged for periorbital distance prediction and disease classification. In addition to the annotations, we also provide an open-source toolkit for periorbital distance prediction from segmentation masks. The weights of all models have also been open-sourced and are publicly available for use by the community., Comment: 10 pages, 7 figures, 3 Tables

This paper is devoted to the study of $$ U_t(a,q):=\sum_{1\leq n_1

As large language models (LLMs) are applied to more use cases, creating high quality, task-specific datasets for fine-tuning becomes a bottleneck for model improvement. Using high quality human data has been the most common approach to unlock model performance, but is prohibitively expensive in many scenarios. Several alternative methods have also emerged, such as generating synthetic or hybrid data, but the effectiveness of these approaches remain unclear, especially in resource-constrained scenarios and tasks that are not easily verified. To investigate this, we group various synthetic data generation strategies into three representative categories -- Answer Augmentation, Question Rephrase and New Question -- and study the performance of student LLMs trained under various constraints, namely seed instruction set size and query budget. We demonstrate that these strategies are not equally effective across settings. Notably, the optimal data generation strategy depends strongly on the ratio between the available teacher query budget and the size of the seed instruction set. When this ratio is low, generating new answers to existing questions proves most effective, but as this ratio increases, generating new questions becomes optimal. Across all tasks, we find that choice of augmentation method and other design choices matter substantially more in low to mid data regimes than in high data regimes. We provide a practical framework for selecting the appropriate augmentation method across settings, taking into account additional factors such as the scalability of each method, the importance of verifying synthetic data, and the use of different LLMs for synthetic data generation.

In this work, we will study a numerical method that allows finding an approximation of the exact solution for a in-situ combustion model using the nonlinear mixed complementary method, which is a variation of the Newtons method for solving nonlinear systems based on an implicit finite difference scheme and a nonlinear algorithm mixed complementarity, FDA-MNCP. The method has the advantage of provide a global convergence in relation to the finite difference method and method of Newton that only has local convergence. The theory is applied to model in-situ combustion, which can be rewritten in the form of mixed complementarity also we do a comparison with the FDA-NCP method, Comment: 17 Pages

The morphologies of astronomical sources are highly complex, making it essential not only to classify the identified sources into their predefined categories but also to determine the sources that are most similar to a given query source. Image-based retrieval is essential, as it allows an astronomer with a source under study to ask a computer to sift through the large archived database of sources to find the most similar ones. This is of particular interest if the source under study does not fall into a "known" category (anomalous). Our work uses the trainable COSFIRE (Combination of Shifted Filter Responses) approach for image retrieval. COSFIRE filters are automatically configured to extract the hyperlocal geometric arrangements that uniquely describe the morphological characteristics of patterns of interest in a given image; in this case astronomical sources. This is achieved by automatically examining the shape properties of a given prototype source in an image, which ultimately determines the selectivity of a COSFIRE filter. We further utilize hashing techniques, which are efficient in terms of required computation and storage, enabling scalability in handling large data sets in the image retrieval process. We evaluated the effectiveness of our approach by conducting experiments on a benchmark data set of radio galaxies, containing 1,180 training images and 404 test images. Notably, our approach achieved a mean average precision of 91% for image retrieval, surpassing the performance of the competing DenseNet-based method. Moreover, the COSFIRE filters are significantly more computationally efficient, requiring $\sim\!14\times$ fewer operations than the DenseNet-based method., Comment: 11 pages, 7 figures

Interactions between active individuals in animal collectives lead to emergent responses that remain elusive in synthetic soft matter. Here, shape-morphing polymers are used to create bio-inspired transient solids that self-assemble with controlled mechanical properties and disassemble on demand. Dilute-suspensions of magnetic, heat-responsive liquid crystal elastomer ribbons mechanically interlock, inducing reversible aggregation. A mathematical model is developed that sheds light on the role of topological mechanisms in aggregation. Aggregation was favored for ribbons with moderate curvature at 25C above crosslinking temperature as compared to flat ribbons or higher curvature ribbons at higher temperatures. The ribbon suspensions reversibly transition between fluid- and solid-like states, exhibiting up to 6 orders-of-magnitude increase in the storage moduli of the entangled aggregates compared with the liquid dispersions. Controlled dissociation is induced by imparting kinetic energy to the individual ribbons at high magnetic field rotation speeds (> 200 RPM). Ribbon shape and the medium in which dissociation occurs were shown to govern disassembly. Imparting dynamic collective behaviors into synthetic systems may enable a range of potential applications from bio-inspired soft robotics to injectable biomaterials., Comment: 16 pages, 6 figures

Self-driving vehicles (SDVs) require accurate calibration of LiDARs and cameras to fuse sensor data accurately for autonomy. Traditional calibration methods typically leverage fiducials captured in a controlled and structured scene and compute correspondences to optimize over. These approaches are costly and require substantial infrastructure and operations, making it challenging to scale for vehicle fleets. In this work, we propose UniCal, a unified framework for effortlessly calibrating SDVs equipped with multiple LiDARs and cameras. Our approach is built upon a differentiable scene representation capable of rendering multi-view geometrically and photometrically consistent sensor observations. We jointly learn the sensor calibration and the underlying scene representation through differentiable volume rendering, utilizing outdoor sensor data without the need for specific calibration fiducials. This "drive-and-calibrate" approach significantly reduces costs and operational overhead compared to existing calibration systems, enabling efficient calibration for large SDV fleets at scale. To ensure geometric consistency across observations from different sensors, we introduce a novel surface alignment loss that combines feature-based registration with neural rendering. Comprehensive evaluations on multiple datasets demonstrate that UniCal outperforms or matches the accuracy of existing calibration approaches while being more efficient, demonstrating the value of UniCal for scalable calibration., Comment: ECCV 2024. Project page: https://waabi.ai/unical/

We present cosmological parameter forecasts for the Euclid 6x2pt statistics, which include the galaxy clustering and weak lensing main probes together with previously neglected cross-covariance and cross-correlation signals between imaging/photometric and spectroscopic data. The aim is understanding the impact of such terms on the Euclid performance. We produce 6x2pt cosmological forecasts, considering two different techniques: the so-called harmonic and hybrid approaches, respectively. In the first, we treat all the different Euclid probes in the same way, i.e. we consider only angular 2pt-statistics for spectroscopic and photometric clustering, as well as for weak lensing, analysing all their possible cross-covariances and cross-correlations in the spherical harmonic domain. In the second, we do not account for negligible cross-covariances between the 3D and 2D data, but consider the combination of their cross-correlation with the auto-correlation signals. We find that both cross-covariances and cross-correlation signals, have a negligible impact on the cosmological parameter constraints and, therefore, on the Euclid performance. In the case of the hybrid approach, we attribute this result to the effect of the cross-correlation between weak lensing and photometric data, which is dominant with respect to other cross-correlation signals. In the case of the 2D harmonic approach, we attribute this result to two main theoretical limitations of the 2D projected statistics implemented in this work according to the analysis of official Euclid forecasts: the high shot noise and the limited redshift range of the spectroscopic sample, together with the loss of radial information from subleading terms such as redshift-space distortions and lensing magnification. Our analysis suggests that 2D and 3D Euclid data can be safely treated as independent, with a great saving in computational resources., Comment: 32 pages, 20 figures. Comments are welcome

Denoising Diffusion Probabilistic Models (DDPM) are powerful state-of-the-art methods used to generate synthetic data from high-dimensional data distributions and are widely used for image, audio and video generation as well as many more applications in science and beyond. The manifold hypothesis states that high-dimensional data often lie on lower-dimensional manifolds within the ambient space, and is widely believed to hold in provided examples. While recent results has provided invaluable insight into how diffusion models adapt to the manifold hypothesis, they do not capture the great empirical success of these models, making this a very fruitful research direction. In this work, we study DDPMs under the manifold hypothesis and prove that they achieve rates independent of the ambient dimension in terms of learning the score. In terms of sampling, we obtain rates independent of the ambient dimension w.r.t. the Kullback-Leibler divergence, and $O(\sqrt{D})$ w.r.t. the Wasserstein distance. We do this by developing a new framework connecting diffusion models to the well-studied theory of extrema of Gaussian Processes.

Periorbital distances and features around the eyes and lids hold valuable information for disease quantification and monitoring of surgical and medical intervention. These distances are commonly measured manually, a process that is both subjective and highly time-consuming. Here, we set out to developed three deep-learning methods for segmentation and periorbital distance prediction, and also evaluate the utility of periorbital distances for disease classification. The MAE of our deep learning predicted distances was less than or very close to the error observed between trained human annotators. We compared our models to the current state-of-the-art (SOTA) method for periorbital distance prediction and found that our methods outperformed SOTA on all of our datasets on all but one periorbital measurement. We also show that robust segmentation can be achieved on diseased eyes using models trained on open-source, healthy eyes, and that periorbital distances have can be used as high-quality features in downstream classification models. Leveraging segmentation networks as intermediary steps in classification has broad implications for increasing the generalizability of classification models in ophthalmic plastic and craniofacial surgery by avoiding the out-of-distribution problem observed in traditional convolutional neural networks., Comment: 20 pages, 10 figures, 9 tables

Damage prognosis is, arguably, one of the most difficult tasks of structural health monitoring (SHM). To address common problems of damage prognosis, a population-based SHM (PBSHM) approach is adopted in the current work. In this approach the prognosis problem is considered as an information-sharing problem where data from past structures are exploited to make more accurate inferences regarding currently-degrading structures. For a given population, there may exist restrictions on the resources available to conduct monitoring; thus, the current work studies the problem of allocating such resources within a population of degrading structures with a view to maximising the damage-prognosis accuracy. The challenges of the current framework are mainly associated with the inference of outliers on the level of damage evolution, given partial data from the damage-evolution phenomenon. The current approach considers an initial population of structures for which damage evolution is extensively observed. Subsequently, a second population of structures with evolving damage is considered for which two monitoring systems are available, a low-availability and high-fidelity (low-uncertainty) one, and a widely-available and low-fidelity (high-uncertainty) one. The task of the current work is to follow an active-learning approach to identify the structures to which the high-fidelity system should be assigned in order to enhance the predictive capabilities of the machine-learning model throughout the population.

The rapid increase in unstructured data across various fields has made multi-document comprehension and summarization a critical task. Traditional approaches often fail to capture relevant context, maintain logical consistency, and extract essential information from lengthy documents. This paper explores the use of Long-context Large Language Models (LLMs) for multi-document summarization, demonstrating their exceptional capacity to grasp extensive connections, provide cohesive summaries, and adapt to various industry domains and integration with enterprise applications/systems. The paper discusses the workflow of multi-document summarization for effectively deploying long-context LLMs, supported by case studies in legal applications, enterprise functions such as HR, finance, and sourcing, as well as in the medical and news domains. These case studies show notable enhancements in both efficiency and accuracy. Technical obstacles, such as dataset diversity, model scalability, and ethical considerations like bias mitigation and factual accuracy, are carefully analyzed. Prospective research avenues are suggested to augment the functionalities and applications of long-context LLMs, establishing them as pivotal tools for transforming information processing across diverse sectors and enterprise applications.

Using the Madelung transformation on a generalized scalar Gross-Pitaevski equation, a nonlinear continuum fluid equations are derived for a classical fluid. A unitary quantum lattice algorithm is then determined as a second order discrete representation of this Gross-Pitaevski equation and the simulations are compared to those using classical fluid dynamic techniques., Comment: 4 pages, 1 figure

The Robert Stobie Spectrograph (RSS) on the Southern African Large Telescope (SALT) offers multi-object spectroscopy over an 8' field-of-view at resolutions up to 3000. Reduction is typically conducted using RSSMOSPipeline, which performs basic data calibrations, sky subtraction, and wavelength calibration. However, flux calibration of SALT-RSS using spectrophotometric standard star observations is difficult due to variable primary mirror illumination. We describe a novel approach where stars with Sloan Digital Sky Survey spectra are included as alignment stars on RSS slitmasks and then used to perform flux calibration of the resulting data. RSS offers multiple settings that can be pieced together to cover the entire optical range, utilizing grating angle dithers to fill chip gaps. We introduce a non-linear reprojection routine that defines an exponential wavelength array spanning 3500-9500 Angstroms with gradually decreasing resolution and then reprojects several individual settings into a single 2D spectrum for each object. Our flux calibration and non-linear reprojection routines are released as part of the Calibration And Reprojection for RSS Pipeline (CARRSSPipeline; https://github.com/GeorgeTheGeorgian/CARRSSPipeline.git ), that enables the extraction of full-optical-coverage, flux-calibrated, medium-resolution one-dimensional spectra., Comment: 12 pages, 9 figures, and 4 tables. Submitted to PASP

Task offloading is of paramount importance to efficiently orchestrate vehicular wireless networks, necessitating the availability of information regarding the current network status and computational resources. However, due to the mobility of the vehicles and the limited computational resources for performing task offloading in near-real-time, such schemes may require high latency, thus, become even infeasible. To address this issue, in this paper, we present a Trajectory Prediction-based Pre-offloading Decision (TPPD) algorithm for analyzing the historical trajectories of vehicles to predict their future coordinates, thereby allowing for computational resource allocation in advance. We first utilize the Long Short-Term Memory (LSTM) network model to predict each vehicle's movement trajectory. Then, based on the task requirements and the predicted trajectories, we devise a dynamic resource allocation algorithm using a Double Deep Q-Network (DDQN) that enables the edge server to minimize task processing delay, while ensuring effective utilization of the available computational resources. Our simulation results verify the effectiveness of the proposed approach, showcasing that, as compared with traditional real-time task offloading strategies, the proposed TPPD algorithm significantly reduces task processing delay while improving resource utilization.

Transmitting an entangled state over an extended distance is crucial for the development of quantum networks. Previous demonstrations of transmitting entangled photons over long distance using satellites or fibers have use entangled photon pairs generated from bulk crystal arrangements. An alternative approach would be to generate photon pairs using silicon-on-insulator (SOI) chips. Despite numerous proof-of-concept studies, no long range distribution has been achieved using this platform because of the challenge of getting sufficient off-chip brightness. We report a SOI platform that provides an off-chip entangled photon pair brightness of between 8,000 to 460,000 pairs per second. This exceeds previous reports by three orders of magnitude in brightness. The entanglement fidelity is 99.85(6)% and 97.90(3)% respectively. Measuring one photon locally, and transmitting the other over 93 km of deployed fiber (link loss of 40 dB), achieves a count rate of 132 pairs per second with an entanglement fidelity of 93.3(3)%, after solving the additional challenges of chromatic dispersion. The source can be pumped harder to enable transmission of entangled photons over 155 km of deployed fiber (link loss of 66 dB) at a rate of 0.7 pairs per second, with an entanglement fidelity of 87.6(5)%. These results demonstrate that SOI nanophotonic chips can perform competitively with bulk crystal sources and represent an important step toward building quantum networks using integrated nanophotonic platforms.

We present a boundary data-driven magneto-hydrodynamic (MHD) simulation of the 2011-02-15 coronal mass ejection (CME) event of Active Region (AR) NOAA 11158. The simulation is driven at the lower boundary with an electric field derived from the normal magnetic field and the vertical electric current measured from the Solar Dynamics Observatory (SDO) Helioseismic Magnetic Imager (HMI) vector magnetograms. The simulation shows the build up of a pre-eruption coronal magnetic field that is close to the nonlinear force-free field (NLFFF) extrapolation, and it subsequently develops multiple eruptions. The sheared/twisted field lines of the pre-eruption magnetic field show qualitative agreement with the brightening loops in the SDO Atmospheric Imaging Assembly (AIA) hot passband images. We find that the eruption is initiated by the tether-cutting reconnection in a highly sheared field above the central polarity inversion line (PIL) and a magnetic flux rope with dipped field lines forms during the eruption. The modeled erupting magnetic field evolves to develop a complex structure containing two distinct flux ropes and produces an outgoing double-shell feature consistent with the Solar TErrestrial RElations Observatory B / Extreme UltraViolet Imager (STEREO-B/EUVI) observation of the CME. The foot points of the erupting field lines are found to correspond well with the dimming regions seen in the SDO/AIA observation of the event. These agreements suggest that the derived electric field is a promising way to drive MHD simulations to establish the realistic pre-eruption coronal field based on the observed vertical electric current and model its subsequent dynamic eruption., Comment: 17 pages, 13 figures, accepted for publication in the Astrophysical Journal

The 2023 Big ANN Challenge, held at NeurIPS 2023, focused on advancing the state-of-the-art in indexing data structures and search algorithms for practical variants of Approximate Nearest Neighbor (ANN) search that reflect the growing complexity and diversity of workloads. Unlike prior challenges that emphasized scaling up classical ANN search ~\cite{DBLP:conf/nips/SimhadriWADBBCH21}, this competition addressed filtered search, out-of-distribution data, sparse and streaming variants of ANNS. Participants developed and submitted innovative solutions that were evaluated on new standard datasets with constrained computational resources. The results showcased significant improvements in search accuracy and efficiency over industry-standard baselines, with notable contributions from both academic and industrial teams. This paper summarizes the competition tracks, datasets, evaluation metrics, and the innovative approaches of the top-performing submissions, providing insights into the current advancements and future directions in the field of approximate nearest neighbor search., Comment: Code: https://github.com/harsha-simhadri/big-ann-benchmarks/releases/tag/v0.3.0

Generating protein sequences conditioned on protein structures is an impactful technique for protein engineering. When synthesizing engineered proteins, they are commonly translated into DNA and expressed in an organism such as yeast. One difficulty in this process is that the expression rates can be low due to suboptimal codon sequences for expressing a protein in a host organism. We propose CodonMPNN, which generates a codon sequence conditioned on a protein backbone structure and an organism label. If naturally occurring DNA sequences are close to codon optimality, CodonMPNN could learn to generate codon sequences with higher expression yields than heuristic codon choices for generated amino acid sequences. Experiments show that CodonMPNN retains the performance of previous inverse folding approaches and recovers wild-type codons more frequently than baselines. Furthermore, CodonMPNN has a higher likelihood of generating high-fitness codon sequences than low-fitness codon sequences for the same protein sequence. Code is available at https://github.com/HannesStark/CodonMPNN., Comment: Appeared at the 2024 ICML AI4Science workshop

A major unsolved problem in galaxy evolution is the early appearance of massive quiescent galaxies that no longer actively form stars only $ \sim 1$ billion years after the Big Bang. Their high stellar masses and extremely compact structure indicate that they formed through rapid bursts of star formation between redshift $z \sim 6-11$. Theoretical models of galaxy evolution cannot explain their high number density, rapid growth and truncation of star formation at such early times, which likely requires extreme feedback to destroy the cold interstellar medium (the fuel for star formation). We report the discovery of a significant reservoir of hot dust in one of the most distant known examples at $z = 4.658$, GS-9209. The dust was identified using JWST's Mid-Infrared Instrument (MIRI), whose unprecedented sensitivity and high spatial resolution, for the first time, firmly show that this dust is significantly more extended than the stars by $\gtrsim 3$ times. We find that the dust has preferentially been evacuated or diluted in the galaxy center. Our analysis finds that the extended hot dust emission is consistent with recent heating by a younger and more spatially extended generation of star formation. This reveals that the earliest quiescent galaxies did not form in a single rapid burst; instead, similar to galaxy growth at later times, the center formed first with star formation continuing in an extended envelope. The growth of this galaxy is truncating from the inside out, consistent with central gas depletion from early AGN feedback., Comment: Submitted

Lattice gauge theories (LGTs) can be employed to understand a wide range of phenomena, from elementary particle scattering in high-energy physics to effective descriptions of many-body interactions in materials. Studying dynamical properties of emergent phases can be challenging as it requires solving many-body problems that are generally beyond perturbative limits. We investigate the dynamics of local excitations in a $\mathbb{Z}_2$ LGT using a two-dimensional lattice of superconducting qubits. We first construct a simple variational circuit which prepares low-energy states that have a large overlap with the ground state; then we create particles with local gates and simulate their quantum dynamics via a discretized time evolution. As the effective magnetic field is increased, our measurements show signatures of transitioning from deconfined to confined dynamics. For confined excitations, the magnetic field induces a tension in the string connecting them. Our method allows us to experimentally image string dynamics in a (2+1)D LGT from which we uncover two distinct regimes inside the confining phase: for weak confinement the string fluctuates strongly in the transverse direction, while for strong confinement transverse fluctuations are effectively frozen. In addition, we demonstrate a resonance condition at which dynamical string breaking is facilitated. Our LGT implementation on a quantum processor presents a novel set of techniques for investigating emergent particle and string dynamics.

Near-field (NF) communications is receiving renewed attention in the context of passive reconfigurable intelligent surfaces (RISs) due to their potentially extremely large dimensions. Although line-of-sight (LOS) links are expected to be dominant in NF scenarios, it is not a priori obvious whether or not the impact of non-LOS components can be neglected. Furthermore, despite being weaker than the LOS link, non-LOS links may be required to achieve multiplexing gains in multi-user multiple-input multiple-output (MIMO) scenarios. In this paper, we develop a generalized statistical NF model for RIS-assisted MIMO systems that extends the widely adopted point-scattering model to account for imperfect reflections at large surfaces like walls, ceilings, and the ground. Our simulation results confirm the accuracy of the proposed model and reveal that in various practical scenarios, the impact of non-LOS components is indeed non-negligible, and thus, needs to be carefully taken into consideration.

An active interior cloak is composed of a conformal array of radiating sources surrounding a target object. With proper configuration, this array radiates a field discontinuity which cancels out any scattering which occurs when the object is illuminated by an impinging electromagnetic wave. Unlike many other cloaking methods, an interior cloak can be completely constructed using conventional components while its active nature allows it to circumvent passivity-based constraints. This enables an interior cloak to hide an object of virtually any size, shape, and material composition as well as accommodate multiple frequencies and polarizations. Despite its potential, this approach has only been demonstrated on 2D and quasi-2D targets to date. As such, this paper presents the first 3D implementation of an interior cloak. Here, the design of a cloak array capable of hiding a conductive, circular cylinder in free-space is first presented. Cloaking performance is then validated via full-wave simulations and an experimental demonstration in which the device successfully suppresses scattering when the target is illuminated by a ridged horn antenna at 1.2 GHz.

In computer graphics (CG) education, the challenge of finding modern, versatile tools is significant, particularly when integrating both legacy and advanced technologies. Traditional frameworks, often reliant on solid, yet outdated APIs like OpenGL, limit the exploration of cutting-edge graphics techniques. To address this, we introduce pyGANDALF, a unique, lightweight, open-source CG framework built on three pillars: Entity-Component-System (ECS) architecture, Python programming, and WebGPU integration. This combination sets pyGANDALF apart by providing a streamlined ECS design with an editor layer, compatibility with WebGPU for state-of-the-art features like compute and ray tracing pipelines, and a programmer-friendly Python environment. The framework supports modern features, such as Physically Based Rendering (PBR) capabilities and integration with Universal Scene Description (USD) formats, making it suitable for both educational demonstrations and real-world applications. Evaluations by expert users confirmed that pyGANDALF effectively balances ease of use with advanced functionality, preparing students for contemporary CG development challenges., Comment: 8 pages, 6 Figures

This article presents a Visual Servoing Nonlinear Model Predictive Control (NMPC) scheme for autonomously tracking a moving target using multirotor Unmanned Aerial Vehicles (UAVs). The scheme is developed for surveillance and tracking of contour-based areas with evolving features. NMPC is used to manage input and state constraints, while additional barrier functions are incorporated in order to ensure system safety and optimal performance. The proposed control scheme is designed based on the extraction and implementation of the full dynamic model of the features describing the target and the state variables. Real-time simulations and experiments using a quadrotor UAV equipped with a camera demonstrate the effectiveness of the proposed strategy.

We present the results of a James Webb Space Telescope (JWST) NIRCam and NIRSpec investigation into the young massive star cluster (YMC) population of NGC 3256, the most cluster-rich luminous infrared galaxy (LIRG) in the Great Observatories All Sky LIRG Survey. We detect 3061 compact YMC candidates with a $S/N \geq 3$ at F150W, F200W, and F335M. Based on yggdrasil stellar population models, we identify 116/3061 sources with F150W - F200W $> 0.47$ and F200W - F355M $> -1.37$ colors suggesting they are young (t $\leq 5$ Myr), dusty ($A_{V} = 5 - 15$), and massive ($M_{\odot} > 10^{5}$). This increases the sample of dust-enshrouded YMCs detected in this system by an order of magnitude relative to previous HST studies. With NIRSpec IFU pointings centered on the northern and southern nucleus, we extract the Pa$\alpha$ and 3.3$\mu$m PAH equivalent widths for 8 bright and isolated YMCs. Variations in both the F200W - F335M color and 3.3$\mu$m PAH emission with the Pa$\alpha$ line strength suggest a rapid dust clearing ($< 3 - 4$ Myr) for the emerging YMCs in the nuclei of NGC 3256. Finally, with both the age and dust emission accurately measured we use yggdrasil to derive the color excess (E(B - V)) for all 8 YMCs. We demonstrate that YMCs with strong 3.3$\mu$m PAH emission (F200W - F335M $> 0$) correspond to sources with E(B - V) $> 3$, which are typically missed in UV-optical studies. This underscores the importance of deep near-infrared imaging for finding and characterizing these very young and dust-embedded sources., Comment: 18 pages, 7 figures, 1 table; Accepted for publication in ApJL

The BICEP3 and BICEP Array polarimeters are small-aperture refracting telescopes located at the South Pole designed to measure primordial gravitational wave signatures in the Cosmic Microwave Background (CMB) polarization, predicted by inflation. Constraining the inflationary signal requires not only excellent sensitivity, but also careful control of instrumental systematics. Both instruments use antenna-coupled orthogonally polarized detector pairs, and the polarized sky signal is reconstructed by taking the difference in each detector pair. As a result, the differential response between detectors within a pair becomes an important systematic effect we must control. Additionally, mapping the intensity and polarization response in regions away from the main beam can inform how sidelobe levels affect CMB measurements. Extensive calibration measurements are taken in situ every austral summer for control of instrumental systematics and instrument characterisation. In this work, we detail the set of beam calibration measurements that we conduct on the BICEP receivers, from deep measurements of main beam response to polarized beam response and sidelobe mapping. We discuss the impact of these measurements for instrumental systematics studies and design choices for future CMB receivers., Comment: 13 pages, 7 figures, 1 table, Proceedings paper SPIE 2024

This paper investigates the existence and uniqueness of the mild solutions and the exact null controllability for a class of non-autonomous parabolic evolution systems with nonlocal conditions in Hilbert spaces. We present sufficient conditions for achieving exact null controllability in these systems using the theory of linear evolution systems and the Schauder fixed point theorem. Importantly, our results do not require the compactness or Lipschitz conditions for the function \( g \) in the nonlocal conditions, which are often needed in other studies. We also provide an example to demonstrate the practical application of our results., Comment: 20 pages, 0 figure

We investigate a series of galaxy properties computed using the merger trees and environmental histories from dark matter only cosmological simulations, using the predictive semi-recurrent neural network outlined in Chittenden and Tojeiro (2023), and using stochastic improvements presented in our companion paper: Behera, Tojeiro and Chittenden (2024). We apply these methods to the dark matter only runs of the IllustrisTNG simulations to understand the effects of baryon removal, and to the gigaparsec-volume pure dark matter simulation Uchuu, to understand the effects of the lower resolution or alternative metrics for halo properties. We find that the machine learning model recovers accurate summary statistics derived from the predicted star formation and stellar metallicity histories, and correspondent spectroscopy and photometry. However, the inaccuracies of the model's application to dark simulations are substantial for low mass and slowly growing haloes. For these objects, the halo mass accretion rate is exaggerated due to the lack of stellar feedback, yet the formation of the halo can be severely limited by the absence of low mass progenitors in a low resolution simulation. Furthermore, differences in the structure and environment of higher mass haloes results in an overabundance of red, quenched galaxies. These results signify progress towards a machine learning model which builds high fidelity mocks based on a physical interpretation of the galaxy-halo connection, yet they illustrate the need to account for differences in halo properties and the resolution of the simulation., Comment: 21 pages, 17 figures, submitted to MNRAS alongside companion paper: arXiv:2409.16548

We describe AthenaK: a new implementation of the Athena++ block-based adaptive mesh refinement (AMR) framework using the Kokkos programming model. Finite volume methods for Newtonian, special relativistic (SR), and general relativistic (GR) hydrodynamics and magnetohydrodynamics (MHD), and GR-radiation hydrodynamics and MHD, as well as a module for evolving Lagrangian tracer or charged test particles (e.g., cosmic rays) are implemented using the framework. In two companion papers we describe (1) a new solver for the Einstein equations based on the Z4c formalism and (2) a GRMHD solver in dynamical spacetimes also implemented using the framework, enabling new applications in numerical relativity. By adopting Kokkos, the code can be run on virtually any hardware, including CPUs, GPUs from multiple vendors, and emerging ARM processors. AthenaK shows excellent performance and weak scaling, achieving over one billion cell updates per second for hydrodynamics in three-dimensions on a single NVIDIA Grace Hopper processor and with a typical parallel efficiency of 80% on 65536 AMD GPUs on the OLCF Frontier system. Such performance portability enables AthenaK to leverage modern exascale computing systems for challenging applications in astrophysical fluid dynamics, numerical relativity, and multimessenger astrophysics., Comment: Submitted to ApJS

The self-organization of proteins into enriched compartments and the formation of complex patterns are crucial processes for life on the cellular level. Liquid-liquid phase separation is one mechanism for forming such enriched compartments. When phase-separating proteins are membrane-bound and locally disturb it, the mechanical response of the membrane mediates interactions between these proteins. How these membrane-mediated interactions influence the steady state of the protein density distribution is thus an important question to investigate in order to understand the rich diversity of protein and membrane-shape patterns present at the cellular level. This work starts with a widely used model for membrane-bound phase-separating proteins. We numerically solve our system to map out its phase space and perform a careful, systematic expansion of the model equations to characterize the phase transitions through linear stability analysis and free energy arguments. We observe that the membrane-mediated interactions, due to their long-range nature, are capable of qualitatively altering the equilibrium state of the proteins. This leads to arrested coarsening and length-scale selection instead of simple demixing and complete coarsening. In this study, we unambiguously show that long-range membrane-mediated interactions lead to pattern formation in a system that otherwise would not do so. This work provides a basis for further systematic study of membrane-bound pattern-forming systems.

Despite significant progress in applying large language models (LLMs) to the medical domain, several limitations still prevent them from practical applications. Among these are the constraints on model size and the lack of cohort-specific labeled datasets. In this work, we investigated the potential of improving a lightweight LLM, such as Llama 3.1-8B, through fine-tuning with datasets using synthetic labels. Two tasks are jointly trained by combining their respective instruction datasets. When the quality of the task-specific synthetic labels is relatively high (e.g., generated by GPT4- o), Llama 3.1-8B achieves satisfactory performance on the open-ended disease detection task, with a micro F1 score of 0.91. Conversely, when the quality of the task-relevant synthetic labels is relatively low (e.g., from the MIMIC-CXR dataset), fine-tuned Llama 3.1-8B is able to surpass its noisy teacher labels (micro F1 score of 0.67 v.s. 0.63) when calibrated against curated labels, indicating the strong inherent underlying capability of the model. These findings demonstrate the potential of fine-tuning LLMs with synthetic labels, offering a promising direction for future research on LLM specialization in the medical domain.

We present a novel methodology to improve neural network (NN) predictions of galaxy formation histories by incorporating semi-stochastic corrections to account for short-timescale variability. Our paper addresses limitations in existing models that capture broad trends in galaxy evolution, but fail to reproduce the bursty nature of star formation and chemical enrichment, resulting in inaccurate predictions of key observables such as stellar masses, optical spectra, and colour distributions. We introduce a simple technique to add stochastic components by utilizing the power spectra of galaxy formation histories. We justify our stochastic approach by studying the correlation between the phases of the halo mass assembly and star-formation histories in the IllustrisTNG simulation, and we find that they are correlated only on timescales longer than 6 Gyr, with a strong dependence on galaxy type. Building on NNs developed in Chittenden & Tojeiro (2023), trained on hydrodynamical simulations from the IllustrisTNG project, which predict time-resolved star formation and stellar metallicity histories for central and satellite galaxies based solely on the properties and evolution of their dark matter halos and environments, this approach successfully recovers realistic variability in galaxy properties at short timescales. It significantly improves the accuracy of predicted stellar masses, metallicities, spectra, and colour distributions and provides a powerful framework for generating large, realistic mock galaxy catalogs, while also enhancing our understanding of the complex interplay between galaxy evolution and dark matter halo assembly., Comment: 18 pages, 20 figures, submitted to MNRAS alongside companion paper - arXiv:2409.16079

In ultra-fast astronomical observations featuring fast transients on sub-$\mu$s time scales, the conventional Signal-to-Noise Ratio (SNR) threshold, often fixed at $5\sigma$, becomes inadequate as observational window timescales shorten, leading to unsustainably high False Alarm Rates (FAR). We provide a basic statistical framework that captures the essential noise generation processes relevant to the analysis of time series data from photon-counting detectors. In particular, we establish a protocol of defining detection limits in astronomical photon-counting experiments, such that a FAR-based criterion is preferred over the traditional SNR-based threshold scheme. We develop statistical models that account for noise sources such as dark counts, sky background, and crosstalk, and establish a probabilistic detection criterion applicable to high-speed detectors. We compare the performance of several detector technologies, including photon-counting CMOS/CCDs, SPADs, SiPMs, and PMTs, in detecting faint astronomical signals. These findings offer insights into optimizing detector choice for future ultra-fast astronomical instruments and suggest pathways for improving detection fidelity under rapid observational conditions.

Nucleon structure functions, as measured in lepton-nucleon scattering, have historically provided a critical observable in the study of partonic dynamics within the nucleon. However, at very large parton momenta it is both experimentally and theoretically challenging to extract parton distributions due to the probable onset of non-perturbative contributions and the unavailability of high precision data at critical kinematics. Extraction of the neutron structure and the d-quark distribution have been further challenging due to the necessity of applying nuclear corrections when utilizing scattering data from a deuteron target to extract free neutron structure. However, a program of experiments has been carried out recently at the energy-upgraded Jefferson Lab electron accelerator aimed at significantly reducing the nuclear correction uncertainties on the d-quark distribution function at large partonic momentum. This allows leveraging the vast body of deuterium data covering a large kinematic range to be utilized for d-quark parton distribution function extraction. We present new data from experiment E12-10-002 carried out in Jefferson Lab Hall C on the deuteron to proton cross-section ratio at large BJorken-x. These results significantly improve the precision of existing data, and provide a first look at the expected impact on quark distributions extracted from global parton distribution function fits.

We introduce a novel gene regulatory network (GRN) inference method that integrates optimal transport (OT) with a deep-learning structural inference model. Advances in next-generation sequencing enable detailed yet destructive gene expression assays at the single-cell level, resulting in the loss of cell evolutionary trajectories. Due to technological and cost constraints, single-cell experiments often feature cells sampled at irregular and sparse time points with a small sample size. Although trajectory-based structural inference models can accurately reveal the underlying interaction graph from observed data, their efficacy depends on the inputs of thousands of regularly sampled trajectories. The irregularly-sampled nature of single-cell data precludes the direct use of these powerful models for reconstructing GRNs. Optimal transport, a classical mathematical framework that minimize transportation costs between distributions, has shown promise in multi-omics data integration and cell fate prediction. Utilizing OT, our method constructs mappings between consecutively sampled cells to form cell-level trajectories, which are given as input to a structural inference model that recovers the GRN from single-cell data. Through case studies in two synthetic datasets, we demonstrate the feasibility of our proposed method and its promising performance over eight state-of-the-art GRN inference methods., Comment: for the associated code repository, see https://github.com/1250326/Integrating-OT-and-Structural-Inference-Models-for-GRN-Inference-from-Single-cell-Data

If $\mathcal{C}$ is a category of algebras closed under finite direct products, and $M_\mathcal{C}$ the commutative monoid of isomorphism classes of members of $\mathcal{C},$ with operation induced by direct product, A.Tarski defines a nonidentity element $p$ of $M_\mathcal{C}$ to be prime if, whenever it divides a product of two elements in that monoid, it divides one of them, and calls an object of $\mathcal{C}$ prime if its isomorphism class has this property. McKenzie, McNulty and Taylor ask whether the category of nonempty semigroups has any prime objects. We show in section 2 that it does not. However, for the category of monoids, and some other subcategories of semigroups, we obtain examples of prime objects in sections 3-5 In section 6, two related questions open so far as I know, are recalled. In section 7, which can be read independently of the rest of this note, we recall two related conditions that are called primeness by semigroup theorists, and obtain results and examples on the relationships among those two conditions and Tarski's, in categories of groups. Section 8 notes an interesting characterization of one of those conditions when applied to finite algebras in an arbitrary variety. Various questions are raised., Comment: 16 pages. Copy at http://math.berkeley.edu/~gbergman/papers may be updated more frequently than arXiv copy