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We investigate the ground state phases of an extended Bose-Hubbard ladder of unit filling via the density-matrix-renormalization-group method and, in particular, the effect of rung-hoppings. In contrast to a single-chain, a commensurate supersolid emerges, and based on the Luttinger parameter, we classify them into two types. The latter leads to a reentrant gapless behavior as the onsite interaction is increased while keeping all other parameters intact. A reentrant gapped transition is also found as a function of nearest-neighbor interactions. Further, we show that the string order characterizing the Haldane phase vanishes for a finite inter-chain hopping amplitude, however small it is. Finally, we propose two experimental platforms to observe our findings, using either dipolar atoms or polar molecules and Rydberg admixed atoms., Comment: 7 pages, 9 figures

We study the sparse regression codes over flat-fading channels with multiple receive antennas. We consider a practical scenario where the channel state information is not available at the transmitter and the receiver. In this setting, we study the maximum likelihood (ML) detector for SPARC, which has a prohibitively high search complexity. We propose a novel practical decoder, named maximum likelihood matching pursuit (MLMP), which incorporates a greedy search mechanism along with the ML metric. We also introduce a parallel search mechanism for MLMP. Comparing with the existing block-orthogonal matching pursuit based decoders, we show that MLMP has significant gains in the block error rate (BLER) performance. We also show that the proposed approach has significant gains over polar codes employing pilot-aided channel estimation.

We propose a novel mechanism of defining data structures using intrinsic definitions that avoids recursion and instead utilizes monadic maps satisfying local conditions. We show that intrinsic definitions are a powerful mechanism that can capture a variety of data structures naturally. We show that they also enable a predictable verification methodology that allows engineers to write ghost code to update monadic maps and perform verification using reduction to decidable logics. We evaluate our methodology using Boogie and prove a suite of data structure manipulating programs correct., Comment: Published at PLDI 2024

Large language models (LLMs) have been shown to be proficient in correctly answering questions in the context of online discourse. However, the study of using LLMs to model human-like answers to fact-driven social media questions is still under-explored. In this work, we investigate how LLMs model the wide variety of human answers to fact-driven questions posed on several topic-specific Reddit communities, or subreddits. We collect and release a dataset of 409 fact-driven questions and 7,534 diverse, human-rated answers from 15 r/Ask{Topic} communities across 3 categories: profession, social identity, and geographic location. We find that LLMs are considerably better at modeling highly-rated human answers to such questions, as opposed to poorly-rated human answers. We present several directions for future research based on our initial findings., Comment: 4 pages, 2 figures

In this paper, we develop an LLM-powered framework for the curation and evaluation of emerging opinion mining in online health communities. We formulate emerging opinion mining as a pairwise stance detection problem between (title, comment) pairs sourced from Reddit, where post titles contain emerging health-related claims on a topic that is not predefined. The claims are either explicitly or implicitly expressed by the user. We detail (i) a method of claim identification -- the task of identifying if a post title contains a claim and (ii) an opinion mining-driven evaluation framework for stance detection using LLMs. We facilitate our exploration by releasing a novel test dataset, Long COVID-Stance, or LC-stance, which can be used to evaluate LLMs on the tasks of claim identification and stance detection in online health communities. Long Covid is an emerging post-COVID disorder with uncertain and complex treatment guidelines, thus making it a suitable use case for our task. LC-Stance contains long COVID treatment related discourse sourced from a Reddit community. Our evaluation shows that GPT-4 significantly outperforms prior works on zero-shot stance detection. We then perform thorough LLM model diagnostics, identifying the role of claim type (i.e. implicit vs explicit claims) and comment length as sources of model error.

This paper introduces the concept of Distributed Intelligent integrated Sensing and Communications (DISAC), which expands the capabilities of Integrated Sensing and Communications (ISAC) towards distributed architectures. Additionally, the DISAC framework integrates novel waveform design with new semantic and goal-oriented communication paradigms, enabling ISAC technologies to transition from traditional data fusion to the semantic composition of diverse sensed and shared information. This progress facilitates large-scale, energy-efficient support for high-precision spatial-temporal processing, optimizing ISAC resource utilization, and enabling effective multi-modal sensing performance. Addressing key challenges such as efficient data management and connect-compute resource utilization, 6G- DISAC stands to revolutionize applications in diverse sectors including transportation, healthcare, and industrial automation. Our study encapsulates the project vision, methodologies, and potential impact, marking a significant stride towards a more connected and intelligent world., Comment: Submitted for conference publication

This paper introduces the distributed and intelligent integrated sensing and communications (DISAC) concept, a transformative approach for 6G wireless networks that extends the emerging concept of integrated sensing and communications (ISAC). DISAC addresses the limitations of the existing ISAC models and, to overcome them, it introduces two novel foundational functionalities for both sensing and communications: a distributed architecture and a semantic and goal-oriented framework. The distributed architecture enables large-scale and energy-efficient tracking of connected users and objects, leveraging the fusion of heterogeneous sensors. The semantic and goal-oriented intelligent and parsimonious framework, enables the transition from classical data fusion to the composition of semantically selected information, offering new paradigms for the optimization of resource utilization and exceptional multi-modal sensing performance across various use cases. This paper details DISAC's principles, architecture, and potential applications., Comment: Under review for possible publication in an IEEE Magazine

Space missions that use low-thrust propulsion technology are becoming increasingly popular since they utilize propellant more efficiently and thus reduce mission costs. However, optimizing continuous-thrust trajectories is complex, time-consuming, and extremely sensitive to initial guesses. Hence, generating approximate trajectories that can be used as reliable initial guesses in trajectory generators is essential. This paper presents a semi-analytic approach for designing planar and three-dimensional trajectories using Hills equations. The spacecraft is assumed to be acted upon by a constant thrust acceleration magnitude. The proposed equations are employed in a Nonlinear Programming Problem (NLP) solver to obtain the thrust directions. Their applicability is tested for various design scenarios like orbit raising, orbit insertion, and rendezvous. The trajectory solutions are then validated as initial guesses in high-fidelity optimal control tools. The usefulness of this method lies in the preliminary stages of low-thrust mission design, where speed and reliability are key., Comment: 2022 AAS/AIAA Astrodynamics Specialist Conference, Charlotte, NC, August 7-11, 2022

The Finite Fourier Series (FFS) Shape-Based (SB) trajectory approximation method has been used to rapidly generate initial trajectories that satisfy the dynamics, trajectory boundary conditions, and limitation on maximum thrust acceleration. The FFS SB approach solves a nonlinear programming problem (NLP) in searching for feasible trajectories. This paper extends the development of the FFS SB approach to generate sub optimal solutions. Specifically, the objective function of the NLP problem is modified to include also a measure for the time of flight. Numerical results presented in this paper show several solutions that differ from those of the original FFS SB ones. The sub-optimal trajectories generated using a time of flight minimization are shown to be physically feasible trajectories and potential candidates for direct solvers., Comment: 2021 AAS/AIAA Astrodynamics Specialist Conference, Big Sky, Virtual, August 9-11, 2021

In this paper, we construct the Funk-Finsler structure in various models of the hyperbolic plane. In particular, in the unit disc of the Klein model, it turns out to be a Randers metric, which is a non-Berwald Douglas metric. Further, using Finsler isometries we obtain the Funk-Finsler structures in other models of the hyperbolic plane. Finally, we also investigate the geometry of this Funk-Finsler metric by explicitly computing the S-curvature, Riemann curvature, flag curvature, and Ricci curvature in the Klein unit disc.

Causal discovery is becoming a key part in medical AI research. These methods can enhance healthcare by identifying causal links between biomarkers, demographics, treatments and outcomes. They can aid medical professionals in choosing more impactful treatments and strategies. In parallel, Large Language Models (LLMs) have shown great potential in identifying patterns and generating insights from text data. In this paper we investigate applying LLMs to the problem of determining the directionality of edges in causal discovery. Specifically, we test our approach on a deidentified set of Non Small Cell Lung Cancer(NSCLC) patients that have both electronic health record and genomic panel data. Graphs are validated using Bayesian Dirichlet estimators using tabular data. Our result shows that LLMs can accurately predict the directionality of edges in causal graphs, outperforming existing state-of-the-art methods. These findings suggests that LLMs can play a significant role in advancing causal discovery and help us better understand complex systems.

Research in scientific disciplines evolves, often rapidly, over time with the emergence of novel methodologies and their associated terminologies. While methodologies themselves being conceptual in nature and rather difficult to automatically extract and characterise, in this paper, we seek to develop supervised models for automatic extraction of the names of the various constituents of a methodology, e.g., `R-CNN', `ELMo' etc. The main research challenge for this task is effectively modeling the contexts around these methodology component names in a few-shot or even a zero-shot setting. The main contributions of this paper towards effectively identifying new evolving scientific methodology names are as follows: i) we propose a factored approach to sequence modeling, which leverages a broad-level category information of methodology domains, e.g., `NLP', `RL' etc.; ii) to demonstrate the feasibility of our proposed approach of identifying methodology component names under a practical setting of fast evolving AI literature, we conduct experiments following a simulated chronological setup (newer methodologies not seen during the training process); iii) our experiments demonstrate that the factored approach outperforms state-of-the-art baselines by margins of up to 9.257\% for the methodology extraction task with the few-shot setup.

Increased capacity in the access network poses capacity challenges on the transport network due to the aggregated traffic. However, there are spatial and time correlation in the user data demands that could potentially be utilized. To that end, we investigate a wireless transport network architecture that integrates beamforming and coded-caching strategies. Especially, our proposed design entails a server with multiple antennas that broadcasts content to cache nodes responsible for serving users. Traditional caching methods face the limitation of relying on the individual memory with additional overhead. Hence, we develop an efficient genetic algorithm-based scheme for beam optimization in the coded-caching system. By exploiting the advantages of beamforming and coded-caching, the architecture achieves gains in terms of multicast opportunities, interference mitigation, and reduced peak backhaul traffic. A comparative analysis of this joint design with traditional, un-coded caching schemes is also conducted to assess the benefits of the proposed approach. Additionally, we examine the impact of various buffering and decoding methods on the performance of the coded-caching scheme. Our findings suggest that proper beamforming is useful in enhancing the effectiveness of the coded-caching technique, resulting in significant reduction in peak backhaul traffic., Comment: Submitted to IEEE Future Networks World Forum, 2023

In [14], it was shown that, if M is a 3-dimensional asymptotically harmonic with minimal horospheres, then M is flat. However, there is a gap in the proof of this paper. In this paper, we provide the correct proof of the result. Thus we complete the classification of asymptotically harmonic manifolds of dimension 3: An asymptotically harmonic manifold of dimension 3 is either a flat or real hyperbolic space., Comment: 7 pages

Observations of soil moisture (SM) during excess and deficit monsoon seasons between 2000 to 2021 present a unique opportunity to understand the soil water dynamics (SWD) over core monsoon zone (CMZ) of India. This study aims to analyse SWD by investigating the SM variability, SM memory (SMM), and the coupling between the surface and subsurface SM levels. Particularly intriguing are instances of concurrent monsoonal extremes, which give rise to complex SWD patterns. Usually, it is noted that a depleted convective activity and persistence of higher temperatures during the pre-monsoon season leads to lower SM, while monsoon rains and post-monsoon showers support the prevalence of higher SM conditions. The long persistent dry spells during deficit monsoon years enhances the Bowen ratio (BR) due to the high sensible heat fluxes. On the other hand, the availability of large latent heat flux during excess monsoon and post-monsoon seasons tends to decrease the BR. This enhancement or reduction in BR is due to evapotranspiration (ET), which influences the SWD by modulating the surface subsurface SM coupling. The surface and subsurface SM coupling analysis for CMZ exhibits significant distinction in the evolution of wet and dry extremes. SM variations and persistence time scale is used as an indicator of SMM, and analysed for both surface and subsurface SM observation levels. Evidently, subsurface SM exhibits remarkably prolonged memory timescales, approximately twice that of surface SM. Furthermore, we dissect SWD linked to wet and dry extremes by analysing annual soil water balance (SWB). Our findings reveal augmented (diminished) ET during deficit (excess) years, subjected to a higher (lower) number of break events. In essence, our study underscores the significance of surface-subsurface SM observations in unravelling the intricate tapestry of SWD.

Social media sites have become a popular platform for individuals to seek and share health information. Despite the progress in natural language processing for social media mining, a gap remains in analyzing health-related texts on social discourse in the context of events. Event-driven analysis can offer insights into different facets of healthcare at an individual and collective level, including treatment options, misconceptions, knowledge gaps, etc. This paper presents a paradigm to characterize health-related information-seeking in social discourse through the lens of events. Events here are board categories defined with domain experts that capture the trajectory of the treatment/medication. To illustrate the value of this approach, we analyze Reddit posts regarding medications for Opioid Use Disorder (OUD), a critical global health concern. To the best of our knowledge, this is the first attempt to define event categories for characterizing information-seeking in OUD social discourse. Guided by domain experts, we develop TREAT-ISE, a novel multilabel treatment information-seeking event dataset to analyze online discourse on an event-based framework. This dataset contains Reddit posts on information-seeking events related to recovery from OUD, where each post is annotated based on the type of events. We also establish a strong performance benchmark (77.4% F1 score) for the task by employing several machine learning and deep learning classifiers. Finally, we thoroughly investigate the performance and errors of ChatGPT on this task, providing valuable insights into the LLM's capabilities and ongoing characterization efforts., Comment: Accepted at AAAI-2024. 9 pages, 6 tables, 2 figures

5G non-public networks (NPNs) play a key role in enabling critical Industrial Internet of Things (IoT) applications in various vertical industries. Among other features, 5G NPNs enable novel operation models, where the roles and responsibilities for setting up and operating the network can be distributed among several stakeholders, i.e., among the public mobile network operators (MNOs), the industrial party who uses the 5G NPN services and 3rd parties. This results in many theoretically feasible operation models for 5G NPN, each with its own advantages and disadvantages. We investigate the resulting operation models and identify a set of nine prime models taking into account today's practical considerations. Additionally, we define a framework to qualitatively analyze the operation models and use it to evaluate and compare the identified operation models.

In this article, we find three isometric models of the Funk disc: Finsler upper half of the hyperboloid of two sheets model, the Finsler band model and the Finsler upper hemi sphere model; and we also find two new models of the Finsler-Poincar\'e disc. We explicitly describe the geodesics in each model. Moreover, we compute the Busemann function and consequently describe the horocycles in the Funk and the Hilbert disc. Finally, we prove the asymptotic harmonicity of the Funk disc. We also show that, the concept of asymptotic harmonicity of the Finsler manifolds {\it tacitly} depends on the measure, in {\it contrast} to the Riemannian case.

Gamers use a variety of different display sizes, though for PC gaming in particular, monitors in the 24 to 27 inch size range have become most popular. Particularly popular among many PC gamers, first person shooter (FPS) games represent a genre where hand-eye coordination is particularly central to the player's performance in game. In a carefully designed pair of experiments on FPS aiming, we compare player performance across a range of display sizes. First, we compare 12.5 inch, 17.3 inch and 24 inch monitors on a multi-target elimination task. Secondly, we highlight the differences between 24.5 inch and 27 inch displays with a small target experiment, specifically designed to amplify these small changes. We find a small, but statistically significant improvement from the larger monitor sizes, which is likely a combined effect between monitor size, resolution, and the player's natural viewing distance., Comment: 5 pages, 7 figures

In this research, we study the nature of $\eta$-Einstein and gradient $\eta$-Einstein soliton in the framework of almost coK\"{a}hler manifolds and $(\kappa, \mu)$-almost coK\"{a}hler manifolds. We find some expressions for scalar curvature of the almost coK\"{a}hler manifold admitting $\eta$-Einstein soliton in various cases. We also prove that if a $(\kappa, \mu)$-almost coK\"{a}hler manifold admits a gradient $\eta$-Einstein soliton, then either the manifold is coK\"{a}hler, or $N(\kappa)$-almost coKahler, or the soliton is trivial. We present an example which validates our results. Finally, we investigate the asymptotically harmonic manifolds admitting non-trivial Ricci solitons and show that they exhibit rigid behavior if for example, scalar curvature attains maximum.

We introduce Symmetric Autoencoder (SymAE), a neural-network architecture designed to automatically isolate earthquake information from far-field seismic waves. SymAE represents the measured displacement field using a code that is partitioned into two interpretable components: source and path-scattering information. We achieve this source-path representation using the scale separation principle and stochastic regularization, which traditional autoencoding methods lack. According to the scale separation principle, the variations in far-field band-limited seismic measurements resulting from finite faulting occur across two spatial scales: a slower scale associated with the source processes and a faster scale corresponding to path effects. The primary challenge in leveraging SymAE to enhance our understanding of earthquake sources lies in the lack of direct interpretability of the latent code it generates. However, despite this hurdle, we present two strategies. Firstly, we employ clustering analysis of the source codes to reveal connections between the characteristics of various earthquakes. This method is less susceptible to variations in path effects, allowing us to assess the correlation in source properties between observed earthquakes and their corresponding finite-fault models. Secondly, we introduce the generation of virtual seismograms with desired source and path information. Through this approach, we engineer seismograms to incorporate the source characteristics of a specific earthquake from different azimuths while maintaining consistent subsurface scattering effects. We propose that these virtual seismograms may facilitate a comprehensive and precise examination of directivity effects for the earthquake in question. The paper conducts an analysis of nearly forty complex earthquake events along with synthetic earthquakes.