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While the success of large language models (LLMs) increases demand for machine-generated text, current pay-per-token pricing schemes create a misalignment of incentives known in economics as moral hazard: Text-generating agents have strong incentive to cut costs by preferring a cheaper model over the cutting-edge one, and this can be done "behind the scenes" since the agent performs inference internally. In this work, we approach this issue from an economic perspective, by proposing a pay-for-performance, contract-based framework for incentivizing quality. We study a principal-agent game where the agent generates text using costly inference, and the contract determines the principal's payment for the text according to an automated quality evaluation. Since standard contract theory is inapplicable when internal inference costs are unknown, we introduce cost-robust contracts. As our main theoretical contribution, we characterize optimal cost-robust contracts through a direct correspondence to optimal composite hypothesis tests from statistics, generalizing a result of Saig et al. (NeurIPS'23). We evaluate our framework empirically by deriving contracts for a range of objectives and LLM evaluation benchmarks, and find that cost-robust contracts sacrifice only a marginal increase in objective value compared to their cost-aware counterparts., Comment: Comments are welcome

Obtaining accurate labels for instance segmentation is particularly challenging due to the complex nature of the task. Each image necessitates multiple annotations, encompassing not only the object's class but also its precise spatial boundaries. These requirements elevate the likelihood of errors and inconsistencies in both manual and automated annotation processes. By simulating different noise conditions, we provide a realistic scenario for assessing the robustness and generalization capabilities of instance segmentation models in different segmentation tasks, introducing COCO-N and Cityscapes-N. We also propose a benchmark for weakly annotation noise, dubbed COCO-WAN, which utilizes foundation models and weak annotations to simulate semi-automated annotation tools and their noisy labels. This study sheds light on the quality of segmentation masks produced by various models and challenges the efficacy of popular methods designed to address learning with label noise.

Computing distances and finding shortest paths in massive real-world networks is a fundamental algorithmic task in network analysis. There are two main approaches to solving this task. On one hand are traversal-based algorithms like bidirectional breadth-first search (BiBFS) with no preprocessing step and slow individual distance inquiries. On the other hand are indexing-based approaches, which maintain a large index. This allows for answering individual inquiries very fast; however, index creation is prohibitively expensive. We seek to bridge these two extremes: quickly answer distance inquiries without the need for costly preprocessing. In this work, we propose a new algorithm and data structure, WormHole, for approximate shortest path computations. WormHole leverages structural properties of social networks to build a sublinearly sized index, drawing upon the explicit core-periphery decomposition of Ben-Eliezer et al. Empirically, the preprocessing time of WormHole improves upon index-based solutions by orders of magnitude, and individual inquiries are consistently much faster than in BiBFS. The acceleration comes at the cost of a minor accuracy trade-off. Nonetheless, our empirical evidence demonstrates that WormHole accurately answers essentially all inquiries within a maximum additive error of 2. We complement these empirical results with provable theoretical guarantees, showing that WormHole requires $n^{o(1)}$ node queries per distance inquiry in random power-law networks. In contrast, any approach without a preprocessing step requires $n^{\Omega(1)}$ queries for the same task. WormHole does not require reading the whole graph. Unlike the vast majority of index-based algorithms, it returns paths, not just distances. For faster inquiry times, it can be combined effectively with other index-based solutions, by running them only on the sublinear core.

Clinical problem-solving requires processing of semantic medical knowledge such as illness scripts and numerical medical knowledge of diagnostic tests for evidence-based decision-making. As large language models (LLMs) show promising results in many aspects of language-based clinical practice, their ability to generate non-language evidence-based answers to clinical questions is inherently limited by tokenization. Therefore, we evaluated LLMs' performance on two question types: numeric (correlating findings) and semantic (differentiating entities) while examining differences within and between LLMs in medical aspects and comparing their performance to humans. To generate straightforward multi-choice questions and answers (QAs) based on evidence-based medicine (EBM), we used a comprehensive medical knowledge graph (encompassed data from more than 50,00 peer-reviewed articles) and created the "EBMQA". EBMQA contains 105,000 QAs labeled with medical and non-medical topics and classified into numerical or semantic questions. We benchmarked this dataset using more than 24,500 QAs on two state-of-the-art LLMs: Chat-GPT4 and Claude3-Opus. We evaluated the LLMs accuracy on semantic and numerical question types and according to sub-labeled topics. For validation, six medical experts were tested on 100 numerical EBMQA questions. We found that both LLMs excelled more in semantic than numerical QAs, with Claude3 surpassing GPT4 in numerical QAs. However, both LLMs showed inter and intra gaps in different medical aspects and remained inferior to humans. Thus, their medical advice should be addressed carefully.

We investigate the effective coupling between heat and fluid dynamics within a thin fluid layer in contact with a solid structure via a rough surface. Moreover, the opposing vertical surfaces of the thin layer are in relative motion. This setup is particularly relevant to grinding processes, where cooling lubricants interact with the rough surface of a rotating grinding wheel. The resulting model is non-linearly coupled through(i) temperature-dependent viscosity and (ii) convective heat transport. The underlying geometry is highly heterogeneous due to the thin, rough surface characterized by a small parameter representing both the height of the layer and the periodicity of the roughness. We analyze this non-linear system for existence, uniqueness, and energy estimates and study the limit behavior within the framework of two-scale convergence in thin domains. In this limit, we derive an effective interface model in 3D (a line in 2D) for the heat and fluid interactions inside the fluid. We implement the system numerically and validate the limit problem through direct comparison with the micromodel. Additionally, we investigate the influence of the temperature-dependent viscosity and various geometrical configurations via simulation experiments. The corresponding numerical code is freely available on GitHub.

In this work, we study pairwise fair clustering with $\ell \ge 2$ groups, where for every cluster $C$ and every group $i \in [\ell]$, the number of points in $C$ from group $i$ must be at most $t$ times the number of points in $C$ from any other group $j \in [\ell]$, for a given integer $t$. To the best of our knowledge, only bi-criteria approximation and exponential-time algorithms follow for this problem from the prior work on fair clustering problems when $\ell > 2$. In our work, focusing on the $\ell > 2$ case, we design the first polynomial-time $(t^{\ell}\cdot \ell\cdot k)^{O(\ell)}$-approximation for this problem with $k$-median cost that does not violate the fairness constraints. We complement our algorithmic result by providing hardness of approximation results, which show that our problem even when $\ell=2$ is almost as hard as the popular uniform capacitated $k$-median, for which no polynomial-time algorithm with an approximation factor of $o(\log k)$ is known.

This paper lays a practical foundation for using abstract interpretation with an abstract domain that consists of sets of quantified first-order logic formulas. This abstract domain seems infeasible at first sight due to the complexity of the formulas involved and the enormous size of sets of formulas (abstract elements). We introduce an efficient representation of abstract elements, which eliminates redundancies based on a novel syntactic subsumption relation that under-approximates semantic entailment. We develop algorithms and data structures to efficiently compute the join of an abstract element with the abstraction of a concrete state, operating on the representation of abstract elements. To demonstrate feasibility of the domain, we use our data structures and algorithms to implement a symbolic abstraction algorithm that computes the least fixpoint of the best abstract transformer of a transition system, which corresponds to the strongest inductive invariant. We succeed at finding, for example, the least fixpoint for Paxos (which in our representation has 1,438 formulas with $\forall^*\exists^*\forall^*$ quantification) in time comparable to state-of-the-art property-directed approaches.

Restaurants are critical venues at which to investigate foodborne illness outbreaks due to shared sourcing, preparation, and distribution of foods. Formal channels to report illness after food consumption, such as 311, New York City's non-emergency municipal service platform, are underutilized. Given this, online social media platforms serve as abundant sources of user-generated content that provide critical insights into the needs of individuals and populations. We extracted restaurant reviews and metadata from Yelp to identify potential outbreaks of foodborne illness in connection with consuming food from restaurants. Because the prevalence of foodborne illnesses may increase in warmer months as higher temperatures breed more favorable conditions for bacterial growth, we aimed to identify seasonal patterns in foodborne illness reports from 311 and identify seasonal patterns of foodborne illness from Yelp reviews for New York City restaurants using a Hierarchical Sigmoid Attention Network (HSAN). We found no evidence of significant bivariate associations between any variables of interest. Given the inherent limitations of relying solely on user-generated data for public health insights, it is imperative to complement these sources with other data streams and insights from subject matter experts. Future investigations should involve conducting these analyses at more granular spatial and temporal scales to explore the presence of such differences or associations., Comment: Paper counterpart to flash talk presented at 8th Annual Conference of the UConn Center for mHealth and Social Media, Advancing Public Health and Science with Artificial Intelligence

We have investigated the physical properties of Planck Galactic Cold Clumps (PGCCs) located in the Galactic Plane, using the JCMT Plane Survey (JPS) and the SCUBA-2 Continuum Observations of Pre-protostellar Evolution (SCOPE) survey. By utilising a suite of molecular-line surveys, velocities and distances were assigned to the compact sources within the PGCCs, placing them in a Galactic context. The properties of these compact sources show no large-scale variations with Galactic environment. Investigating the star-forming content of the sample, we find that the luminosity-to-mass ratio (L/M) is an order of magnitude lower than in other Galactic studies, indicating that these objects are hosting lower levels of star formation. Finally, by comparing ATLASGAL sources that are associated or are not associated with PGCCs, we find that those associated with PGCCs are typically colder, denser, and have a lower L/M ratio, hinting that PGCCs are a distinct population of Galactic Plane sources., Comment: 18 pages, 14 figures, 7 tables. Accepted for publication in MNRAS

Functional brain networks can change rapidly as a function of stimuli or cognitive shifts. Tracking dynamic functional connectivity is particularly challenging as it requires estimating the structure of the network at each moment as well as how it is shifting through time. In this paper, we describe a general modeling framework and a set of specific models that provides substantially increased statistical power for estimating rhythmic dynamic networks, based on the assumption that for a particular experiment or task, the network state at any moment is chosen from a discrete set of possible network modes. Each model is comprised of three components: (1) a set of latent switching states that represent transitions between the expression of each network mode; (2) a set of latent oscillators, each characterized by an estimated mean oscillation frequency and an instantaneous phase and amplitude at each time point; and (3) an observation model that relates the observed activity at each electrode to a linear combination of the latent oscillators. We develop an expectation-maximization procedure to estimate the network structure for each switching state and the probability of each state being expressed at each moment. We conduct a set of simulation studies to illustrate the application of these models and quantify their statistical power, even in the face of model misspecification.

We study the problem of testing $C_k$-freeness ($k$-cycle-freeness) for fixed constant $k > 3$ in graphs with bounded arboricity (but unbounded degrees). In particular, we are interested in one-sided error algorithms, so that they must detect a copy of $C_k$ with high constant probability when the graph is $\epsilon$-far from $C_k$-free. We next state our results for constant arboricity and constant $\epsilon$ with a focus on the dependence on the number of graph vertices, $n$. The query complexity of all our algorithms grows polynomially with $1/\epsilon$. (1) As opposed to the case of $k=3$, where the complexity of testing $C_3$-freeness grows with the arboricity of the graph but not with the size of the graph (Levi, ICALP 2021) this is no longer the case already for $k=4$. We show that $\Omega(n^{1/4})$ queries are necessary for testing $C_4$-freeness, and that $\widetilde{O}(n^{1/4})$ are sufficient. The same bounds hold for $C_5$. (2) For every fixed $k \geq 6$, any one-sided error algorithm for testing $C_k$-freeness must perform $\Omega(n^{1/3})$ queries. (3) For $k=6$ we give a testing algorithm whose query complexity is $\widetilde{O}(n^{1/2})$. (4) For any fixed $k$, the query complexity of testing $C_k$-freeness is upper bounded by ${O}(n^{1-1/\lfloor k/2\rfloor})$. Our $\Omega(n^{1/4})$ lower bound for testing $C_4$-freeness in constant arboricity graphs provides a negative answer to an open problem posed by (Goldreich, 2021).

Object recognition, commonly performed by a camera, is a fundamental requirement for robots to complete complex tasks. Some tasks require recognizing objects far from the robot's camera. A challenging example is Ultra-Range Gesture Recognition (URGR) in human-robot interaction where the user exhibits directive gestures at a distance of up to 25~m from the robot. However, training a model to recognize hardly visible objects located in ultra-range requires an exhaustive collection of a significant amount of labeled samples. The generation of synthetic training datasets is a recent solution to the lack of real-world data, while unable to properly replicate the realistic visual characteristics of distant objects in images. In this letter, we propose the Diffusion in Ultra-Range (DUR) framework based on a Diffusion model to generate labeled images of distant objects in various scenes. The DUR generator receives a desired distance and class (e.g., gesture) and outputs a corresponding synthetic image. We apply DUR to train a URGR model with directive gestures in which fine details of the gesturing hand are challenging to distinguish. DUR is compared to other types of generative models showcasing superiority both in fidelity and in recognition success rate when training a URGR model. More importantly, training a DUR model on a limited amount of real data and then using it to generate synthetic data for training a URGR model outperforms directly training the URGR model on real data. The synthetic-based URGR model is also demonstrated in gesture-based direction of a ground robot.

We consider the mathematical analysis and homogenization of a moving boundary problem posed for a highly heterogeneous, periodically perforated domain. More specifically, we are looking at a one-phase thermo-elasticity system with phase transformations where small inclusions, initially periodically distributed, are growing or shrinking based on a kinetic under-cooling-type law and where surface stresses are created based on the curvature of the phase interface. This growth is assumed to be uniform in each individual cell of the the perforated domain. After transforming to the initial reference configuration (utilizing the Hanzawa transformation), we use the contraction mapping principle to show the existence of a unique solution for a possibly small but $\varespilon$-independent time interval ($\varespilon$ is here the scale of heterogeneity). In the homogenization limit, we discover a macroscopic thermo-elasticity problem which is strongly non-linearly coupled (via an internal parameter called height function) to local changes in geometry. As a direct byproduct of the mathematical analysis work, we present an alternative equivalent formulation which lends itself to an effective precomputing strategy that is very much needed as the limit problem is computationally expensive., Comment: 27 pages

LLM-based code assistants are becoming increasingly popular among developers. These tools help developers improve their coding efficiency and reduce errors by providing real-time suggestions based on the developer's codebase. While beneficial, the use of these tools can inadvertently expose the developer's proprietary code to the code assistant service provider during the development process. In this work, we propose a method to mitigate the risk of code leakage when using LLM-based code assistants. CodeCloak is a novel deep reinforcement learning agent that manipulates the prompts before sending them to the code assistant service. CodeCloak aims to achieve the following two contradictory goals: (i) minimizing code leakage, while (ii) preserving relevant and useful suggestions for the developer. Our evaluation, employing StarCoder and Code Llama, LLM-based code assistants models, demonstrates CodeCloak's effectiveness on a diverse set of code repositories of varying sizes, as well as its transferability across different models. We also designed a method for reconstructing the developer's original codebase from code segments sent to the code assistant service (i.e., prompts) during the development process, to thoroughly analyze code leakage risks and evaluate the effectiveness of CodeCloak under practical development scenarios.

The effectiveness of Large Language Models (LLMs) in generating accurate responses relies heavily on the quality of input provided, particularly when employing Retrieval Augmented Generation (RAG) techniques. RAG enhances LLMs by sourcing the most relevant text chunk(s) to base queries upon. Despite the significant advancements in LLMs' response quality in recent years, users may still encounter inaccuracies or irrelevant answers; these issues often stem from suboptimal text chunk retrieval by RAG rather than the inherent capabilities of LLMs. To augment the efficacy of LLMs, it is crucial to refine the RAG process. This paper explores the existing constraints of RAG pipelines and introduces methodologies for enhancing text retrieval. It delves into strategies such as sophisticated chunking techniques, query expansion, the incorporation of metadata annotations, the application of re-ranking algorithms, and the fine-tuning of embedding algorithms. Implementing these approaches can substantially improve the retrieval quality, thereby elevating the overall performance and reliability of LLMs in processing and responding to queries.

The flow convergence method includes calculation of the proximal isovelocity surface area (PISA) and is widely used to classify mitral regurgitation (MR) with echocardiography. It constitutes a primary decision factor for determination of treatment and should therefore be a robust quantification method. However, it is known for its tendency to underestimate MR and its dependence on user expertise. The present work systematically compares different pulsatile flow profiles arising from different regurgitation orifices using transesophageal echocardiographic (TEE) probe and particle image velocimetry (PIV) as a reference in an in-vitro environment. It is found that the inter-observer variability using echocardiography is small compared to the systematic underestimation of the regurgitation volume for large orifice areas (up to 52%) where a violation of the flow convergence method assumptions occurs. From a flow perspective, a starting vortex was found as a dominant flow pattern in the regurgant jet for all orifice shapes and sizes. A series of simplified computational fluid dynamics (CFD) simulations indicate that selecting a suboptimal aliasing velocity during echocardiography measurements might be a primary source of potential underestimation in MR characterization via the PISA-based method, reaching up to 40%. In this study, it has been noted in clinical observations that physicians often select an aliasing velocity higher than necessary for optimal estimation in diagnostic procedures.

The lack of diversity in physics remains a persistent worldwide problem. Despite being a quantitative discipline which relies on measurements to construct and validate hypotheses, there remains a paucity of data on both demographics and experiences of marginalized groups. In Canada, there has never been a nationwide assessment of those studying or working in physics. Here, we present findings from Canadian Physics Counts: the first national survey of equity, diversity, and inclusion (EDI) in the Canadian physics community. Our intersectional approach allowed us to gather a wealth of information on gender identity, sexual orientation, race, disability, and more. Analyses revealed key findings, including the first data on physicists who identify as non-binary or gender diverse, as well as the first data on Black and Indigenous scholars. Black physicists (1.2%) and Indigenous physicists (.3%) were found to be the most underrepresented, while White men were overrepresented across all sectors. Among respondents with a disability, 5% reported receiving full accommodations for their required needs at their place of work or study. One in four respondents from BIPOC gender diverse backgrounds identified as being disabled, and the proportion of sexually diverse students who reported having a disability was more than three times higher than the proportion of heterosexual students with a disability. The data also revealed that students represented more demographic diversity than working professionals, highlighting the importance of acting today in order to retain the diverse physicists of tomorrow. Our analysis identifies areas for intervention and offers recommendations for building a diverse and inclusive physics community in Canada that can be a global exemplar., Comment: 26 pages, 7 figures

As we learn more about the multi-scale interstellar medium (ISM) of our Galaxy, we develop a greater understanding for the complex relationships between the large-scale diffuse gas and dust in Giant Molecular Clouds (GMCs), how it moves, how it is affected by the nearby massive stars, and which portions of those GMCs eventually collapse into star forming regions. The complex interactions of those gas, dust and stellar populations form what has come to be known as the ecology of our Galaxy. Because we are deeply embedded in the plane of our Galaxy, it takes up a significant fraction of the sky, with complex dust lanes scattered throughout the optically recognisable bands of the Milky Way. These bands become bright at (sub-)millimetre wavelengths, where we can study dust thermal emission and the chemical and kinematic signatures of the gas. To properly study such large-scale environments, requires deep, large area surveys that are not possible with current facilities. Moreover, where stars form, so too do planetary systems, growing from the dust and gas in circumstellar discs, to planets and planetesimal belts. Understanding the evolution of these belts requires deep imaging capable of studying belts around young stellar objects to Kuiper belt analogues around the nearest stars. Here we present a plan for observing the Galactic Plane and circumstellar environments to quantify the physical structure, the magnetic fields, the dynamics, chemistry, star formation, and planetary system evolution of the galaxy in which we live with AtLAST; a concept for a new, 50m single-dish sub-mm telescope with a large field of view which is the only type of facility that will allow us to observe our Galaxy deeply and widely enough to make a leap forward in our understanding of our local ecology., Comment: 27 pages, submitted to Open Research Europe as part of the AtLAST collection: https://open-research-europe.ec.europa.eu/collections/atlast/about

We study auction design within the widely acclaimed model of interdependent values, introduced by Milgrom and Weber [1982]. In this model, every bidder $i$ has a private signal $s_i$ for the item for sale, and a public valuation function $v_i(s_1,\ldots,s_n)$ which maps every vector of private signals (of all bidders) into a real value. A recent line of work established the existence of approximately-optimal mechanisms within this framework, even in the more challenging scenario where each bidder's valuation function $v_i$ is also private. This body of work has primarily focused on single-item auctions with two natural classes of valuations: those exhibiting submodularity over signals (SOS) and $d$-critical valuations. In this work we advance the state of the art on interdependent values with private valuation functions, with respect to both SOS and $d$-critical valuations. For SOS valuations, we devise a new mechanism that gives an improved approximation bound of $5$ for single-item auctions. This mechanism employs a novel variant of an "eating mechanism", leveraging LP-duality to achieve feasibility with reduced welfare loss. For $d$-critical valuations, we broaden the scope of existing results beyond single-item auctions, introducing a mechanism that gives a $(d+1)$-approximation for any environment with matroid feasibility constraints on the set of agents that can be simultaneously served. Notably, this approximation bound is tight, even with respect to single-item auctions.

Thinking of flows crossing through regular porous media, we numerically explore the behavior of weak solutions to a two-scale elliptic-parabolic system that is strongly coupled by means of a suitable nonlinear dispersion term. The two-scale system of interest originates from the fast-drift periodic homogenization of a nonlinear convective-diffusion-reaction problem, where the structure of the non-linearity in the drift fits to the hydrodynamic limit of a totally asymmetric simple exclusion process for a population of particles. In this article, we focus exclusively on numerical simulations that employ two decoupled approximation schemes, viz. 'scheme 1' - a Picard-type iteration - and 'scheme 2' - a time discretization decoupling. Additionally, we describe a computational strategy which helps to drastically improve computation times. Finally, we provide several numerical experiments to illustrate what dispersion effects are introduced by a specific choice of microstructure and model ingredients., Comment: 27 pages, 10 figures, 3 tables

Cooperative transport is a striking phenomenon where multiple agents join forces to transit a payload too heavy for the individual. While social animals such as ants are routinely observed to coordinate transport at scale, reproducing the effect in artificial swarms remains challenging, as it requires synchronization in a noisy many-body system. Here we show that cooperative transport spontaneously emerges in swarms of stochastic self-propelled robots. Robots deprived of sensing and communication, are isotropically initialized around a passive circular payload, where directional motion is not expected without an external cue. And yet it moves. We find that a minute modification to the mechanical design of the individual agent dramatically changes its alignment response to an external force. We then show experimentally that by controlling the individual's friction and mass distribution, a swarm of active particles autonomously cooperates in the directional transport of larger objects. Surprisingly, transport increases with increasing payload size, and its persistence surpasses the persistence of the active particles by over an order of magnitude. A mechanical, coarse-grained description reveals that force-alignment is intrinsic and captured by a signed, charge-like parameter with units of curvature. Numerical simulations of swarms of active particles with a negative active charge corroborate the experimental findings. We analytically derive a geometrical criterion for cooperative transport which results from a bifurcation in a non-linear dynamical system. Our findings generalize existing models of active particles, offer new design rules for distributed robotic systems, and shed light on cooperation in natural swarms., Comment: Added 1 figure, 1 section, and supporting information

Teleoperation is increasingly recognized as a viable solution for deploying robots in hazardous environments. Controlling a robot to perform a complex or demanding task may overload operators resulting in poor performance. To design a robot controller to assist the human in executing such challenging tasks, a comprehensive understanding of the interplay between the robot's autonomous behavior and the operator's internal state is essential. In this paper, we investigate the relationships between robot autonomy and both the human user's cognitive load and trust levels, and the potential existence of three-way interactions in the robot-assisted execution of the task. Our user study (N=24) results indicate that while the autonomy level influences the teleoperator's perceived cognitive load and trust, there is no clear interaction between these factors. Instead, these elements appear to operate independently, thus highlighting the need to consider both cognitive load and trust as distinct but interrelated factors in varying the robot autonomy level in shared-control settings. This insight is crucial for the development of more effective and adaptable assistive robotic systems., Comment: 8 pages, 4 figures

Extracting meaningful information from high-dimensional data poses a formidable modeling challenge, particularly when the data is obscured by noise or represented through different modalities. This research proposes a novel non-parametric modeling approach, leveraging the Gaussian process (GP), to characterize high-dimensional data by mapping it to a latent low-dimensional manifold. This model, named the latent discriminative generative decoder (LDGD), employs both the data and associated labels in the manifold discovery process. We derive a Bayesian solution to infer the latent variables, allowing LDGD to effectively capture inherent stochasticity in the data. We demonstrate applications of LDGD on both synthetic and benchmark datasets. Not only does LDGD infer the manifold accurately, but its accuracy in predicting data points' labels surpasses state-of-the-art approaches. In the development of LDGD, we have incorporated inducing points to reduce the computational complexity of Gaussian processes for large datasets, enabling batch training for enhanced efficient processing and scalability. Additionally, we show that LDGD can robustly infer manifold and precisely predict labels for scenarios in that data size is limited, demonstrating its capability to efficiently characterize high-dimensional data with limited samples. These collective attributes highlight the importance of developing non-parametric modeling approaches to analyze high-dimensional data., Comment: 40 pages, 6 figures

Almost all current and future high-contrast imaging instruments will use a Pyramid wavefront sensor (PWFS) as a primary or secondary wavefront sensor. The main issue with the PWFS is its nonlinear response to large phase aberrations, especially under strong atmospheric turbulence. Most instruments try to increase its linearity range by using dynamic modulation, but this leads to decreased sensitivity, most prominently for low-order modes, and makes it blind to petal-piston modes. In the push toward high-contrast imaging of fainter stars and deeper contrasts, there is a strong interest in using the PWFS in its unmodulated form. Here, we present closed-loop lab results of a nonlinear reconstructor for the unmodulated PWFS of the Magellan Adaptive Optics eXtreme (MagAO-X) system based on convolutional neural networks (CNNs). We show that our nonlinear reconstructor has a dynamic range of >600 nm root-mean-square (RMS), significantly outperforming the linear reconstructor that only has a 50 nm RMS dynamic range. The reconstructor behaves well in closed loop and can obtain >80% Strehl at 875 nm under a large variety of conditions and reaches higher Strehl ratios than the linear reconstructor under all simulated conditions. The CNN reconstructor also achieves the theoretical sensitivity limit of a PWFS, showing that it does not lose its sensitivity in exchange for dynamic range. The current CNN's computational time is 690 microseconds, which enables loop speeds of >1 kHz. On-sky tests are foreseen soon and will be important for pushing future high-contrast imaging instruments toward their limits., Comment: Accepted for publication in A&A

Large Language Models (LLMs) generate responses to questions; however, their effectiveness is often hindered by sub-optimal quality of answers and occasional failures to provide accurate responses to questions. To address these challenges, a fine-tuning process is employed, involving feedback and examples to refine models. The objective is to enhance AI models through continuous feedback loops, utilizing metrics such as cosine similarity, LLM evaluation and Rouge-L scores to evaluate the models. Leveraging LLMs like GPT-3.5, GPT4ALL, and LLaMA2, and Claude, this approach is benchmarked on financial datasets, including the FinanceBench and RAG Instruct Benchmark Tester Dataset, illustrating the necessity of fine-tuning. The results showcase the capability of fine-tuned models to surpass the accuracy of zero-shot LLMs, providing superior question and answering capabilities. Notably, the combination of fine-tuning the LLM with a process known as Retrieval Augmented Generation (RAG) proves to generate responses with improved accuracy.

High-latitude ($|b|>30^{\circ}$) molecular clouds have virial parameters that exceed 1, but whether these clouds can form stars has not been studied systematically. Using JCMT SCUBA-2 archival data, we surveyed 70 fields that target high-latitude Planck galactic cold clumps (HLPCs) to find dense cores with density of $10^{5}$-$10^{6}$ cm$^{-3}$ and size of $<0.1$ pc. The sample benefits from both the representativeness of the parent sample and covering densest clumps at the high column density end ($>1\times10^{21}$ cm$^{-2}$). At an average noise rms of 15 mJy/beam, we detected Galactic dense cores in only one field, G6.04+36.77 (L183), while also identifying 12 extragalactic objects and two young stellar objects. Compared to the low-latitude clumps, dense cores are scarce in HLPCs. With synthetic observations, the densities of cores are constrained to be $n_c\lesssim10^5$ cm$^{-3}$, should they exist in HLPCs. Low-latitude clumps, Taurus clumps, and HLPCs form a sequence where a higher virial parameter corresponds to a lower dense core detection rate. If HLPCs were affected by the Local Bubble, the scarcity should favor turbulence-inhibited rather than supernova-driven star formation. Studies of the formation mechanism of the L183 molecular cloud are warranted., Comment: 9 pages for the main text. 4 figures, 1 table. Published in Astrophysical Journal Letter

We report 850 $\mu$m continuum polarization observations toward the filamentary high-mass star-forming region NGC 2264, taken as part of the B-fields In STar forming Regions Observations (BISTRO) large program on the James Clerk Maxwell Telescope (JCMT). These data reveal a well-structured non-uniform magnetic field in the NGC 2264C and 2264D regions with a prevailing orientation around 30 deg from north to east. Field strengths estimates and a virial analysis for the major clumps indicate that NGC 2264C is globally dominated by gravity while in 2264D magnetic, gravitational, and kinetic energies are roughly balanced. We present an analysis scheme that utilizes the locally resolved magnetic field structures, together with the locally measured gravitational vector field and the extracted filamentary network. From this, we infer statistical trends showing that this network consists of two main groups of filaments oriented approximately perpendicular to one another. Additionally, gravity shows one dominating converging direction that is roughly perpendicular to one of the filament orientations, which is suggestive of mass accretion along this direction. Beyond these statistical trends, we identify two types of filaments. The type-I filament is perpendicular to the magnetic field with local gravity transitioning from parallel to perpendicular to the magnetic field from the outside to the filament ridge. The type-II filament is parallel to the magnetic field and local gravity. We interpret these two types of filaments as originating from the competition between radial collapsing, driven by filament self-gravity, and the longitudinal collapsing, driven by the region's global gravity., Comment: Accepted for publication in the Astrophysical Journal. 43 pages, 32 figures, and 4 tables (including Appendix)

We observe the magnetic field morphology towards a nearby star-forming filamentary cloud, G202.3+2.5, by the JCMT/POL-2 850 {\mu}m thermal dust polarization observation with an angular resolution of 14.4" (~0.053 pc). The average magnetic field orientation is found to be perpendicular to the filaments while showing different behaviors in the four subregions, suggesting various effects from filaments' collision in these subregions. With the kinematics obtained by N2H+ observation by IRAM, we estimate the plane-of-sky (POS) magnetic field strength by two methods, the classical Davis-Chandrasekhar-Fermi (DCF) method and the angular dispersion function (ADF) method, B_{pos,dcf} and B_{pos,adf} are ~90 {\mu}G and ~53 {\mu}G. We study the relative importance between the gravity (G), magnetic field (B) and turbulence (T) in the four subregions, find G > T > B, G >= T > B, G ~ T > B and T > G > B in the north tail, west trunk, south root and east wing, respectively. In addition, we investigate the projection effect on the DCF and ADF methods based on a similar simulation case and find the 3D magnetic field strength may be underestimated by a factor of ~3 if applying the widely-used statistical B_{pos}-to-B_{3D} factor when using DCF or ADF method, which may further underestimate/overestimate related parameters., Comment: Accepted by ApJ. 20 pages, 9 figures

Nuclear spins in solid-state platforms are promising for building rotation sensors due to their long coherence times. Among these platforms, nitrogen-vacancy centers have attracted considerable attention with ambient operating conditions. However, the current performance of NV gyroscopes remains limited by the degraded coherence when operating with large spin ensembles. Protecting the coherence of these systems requires a systematic study of the coherence decay mechanism. Here we present the use of nitrogen-15 nuclear spins of NV centers in building gyroscopes, benefiting from its simpler energy structure and vanishing nuclear quadrupole term compared with nitrogen-14 nuclear spins, though suffering from different challenges in coherence protection. We systematically reveal the coherence decay mechanism of the nuclear spin in different NV electronic spin manifolds and further develop a robust coherence protection protocol based on controlling the NV electronic spin only, achieving a 15-fold dephasing time improvement. With the developed coherence protection, we demonstrate an emulated gyroscope by measuring a designed rotation rate pattern, showing an order-of-magnitude sensitivity improvement.

Memory-based medication non-adherence is an unsolved problem that is responsible for considerable disease burden in the United States. Digital medication intake reminder solutions with minimal onboarding requirements that are usable at the point of medication acquisition may help to alleviate this problem by offering a low barrier way to help people remember to take their medications. In this paper, we propose memorAIs, a digital medication intake reminder solution that mitigates onboarding friction by leveraging optical character recognition strategies for text extraction from medication bottles and rule based expressions for text processing to create configured medication reminders as local device calendar invitations. We describe our ideation and development process, as well as limitations of the current implementation. memorAIs was the winner of the Patient Safety award at the 2023 Columbia University DivHacks Hackathon, presented by the Patient Safety Technology Challenge, sponsored by the Pittsburgh Regional Health Initiative.

We explore how the star formation rate (SFR), stellar mass, and other properties of massive dusty galaxies at cosmic noon are impacted when far-infrared (FIR)/sub-millimeter data are added to datasets containing only ultraviolet (UV) to near-infrared (NIR) data. For a sample of 92 massive (stellar mass $> 4{\times}10^{10}$ M$_{\odot}$) dusty galaxies at $z\,{\sim}\,1.5$ to 3.0 (corresponding to ${\sim}25$% of cosmic history), we fit the spectral energy distributions (SEDs) based on DECam UV-to-optical data, VICS82, NEWFIRM, and Spitzer-IRAC NIR data, and Herschel-SPIRE FIR/sub-millimeter data using the Bayesian Analysis of Galaxies for Physical Inference and Parameter Estimation (BAGPIPES) SED-fitting code. We assume a delayed tau star formation history with a log$_{10}$ prior on tau and derive the posterior distributions of stellar mass, SFR, extinction, and specific SFR. We find that adding FIR/sub-millimeter data leads to SFR estimates that can be both significantly higher or lower (typically by up to a factor of 10) than estimates based on UV-to-NIR data alone, depending on the type of galaxies involved. We find that the changes in SFR scale with changes in extinction. These results highlight the importance of including FIR/sub-millimeter data in order to accurately derive the SFRs of massive dusty galaxies at $z\,{\sim}\,2$., Comment: 6 pages, 4 figures. Published in 2023 Compendium of Undergraduate Research in Astronomy and Space Science ASP Conference Series

We investigated the effective influence of grain structures on the heat transfer between a fluid and solid domain using mathematical homogenization. The presented model consists of heat equations inside the different domains, coupled through either perfect or imperfect thermal contact. The size and the period of the grains are of order $\varepsilon$, therefore forming a thin layer. The equation parameters inside the grains also depend on $\varepsilon$. We considered two distinct scenarios: Case (a), where the grains are disconnected, and Case (b), where the grains form a connected geometry but in a way such that the fluid and solid are still in contact. In both cases, we determined the effective differential equations for the limit $\varepsilon \to 0$ via the concept of two-scale convergence for thin layers. We also presented and studied a numerical algorithm to solve the homogenized problem., Comment: Updated the article to the published version, including a small fix in the argumentation of the proof of Lemma 5

We find ourselves on the brink of an exciting era in observational astrophysics, driven by groundbreaking facilities like JWST, Euclid, Rubin, Roman, SKA, or ELT. Simultaneously, computational astrophysics has shown significant strides, yielding highly realistic galaxy formation simulations, thanks to both hardware and software enhancements. Bridging the gap between simulations and observations has become paramount for meaningful comparisons. We introduce py-ananke, a Python pipeline designed to generate synthetic resolved stellar surveys from cosmological simulations, adaptable to various instruments. Building upon its predecessor, ananke by Sanderson et al. 2020 (arXiv:1806.10564), which produced Gaia DR2 mock star surveys, the py-ananke package offers a user-friendly "plug & play" experience. The pipeline employs cutting-edge phase-space density estimation and initial mass function sampling to convert particle data into synthetic stars, while interpolating pre-computed stellar isochrone tracks for photometry. Additionally, it includes modules for estimating interstellar reddening, dust-induced extinctions, and for quantifying errors through dedicated modeling approaches. py-ananke promises to serve as a vital bridge between computational astrophysics and observational astronomy, facilitating preparations and making scientific predictions for the next generation of telescopes., Comment: 12 pages (6 pages of bibliography) and 1 figure. Software repository at https://github.com/athob/py-ananke

We present a badminton training system that focuses on the backhand short service. Unlike the prior motor skill training systems which focus on the trainee's posture, our system analyzes the process of moving joints with the open kinetic chain (OKC), which helps align movement and minimize muscle use for better joint control. We process the users' mocap data to visually show their last service process comparing to 4 ideal OKC characteristics that we collected from a 6-sub-elite formative study as well as recommended contact posture. We validate our system through a 12-user study that measures serving accuracy, qualitative feedback, and skeletal data with users at various skill levels and open source our skeletal analysis model for future use. While the participants' overall service accuracy was not significantly improved, our results show that our system helps participants in the short term to fine-tune their service motion closer to our ideal 4 OKC characteristics., Comment: 14 pages, The 9th Annual Conference of Taiwanese Association of Computer-Human Interaction

Hand gestures play a significant role in human interactions where non-verbal intentions, thoughts and commands are conveyed. In Human-Robot Interaction (HRI), hand gestures offer a similar and efficient medium for conveying clear and rapid directives to a robotic agent. However, state-of-the-art vision-based methods for gesture recognition have been shown to be effective only up to a user-camera distance of seven meters. Such a short distance range limits practical HRI with, for example, service robots, search and rescue robots and drones. In this work, we address the Ultra-Range Gesture Recognition (URGR) problem by aiming for a recognition distance of up to 25 meters and in the context of HRI. We propose the URGR framework, a novel deep-learning, using solely a simple RGB camera. Gesture inference is based on a single image. First, a novel super-resolution model termed High-Quality Network (HQ-Net) uses a set of self-attention and convolutional layers to enhance the low-resolution image of the user. Then, we propose a novel URGR classifier termed Graph Vision Transformer (GViT) which takes the enhanced image as input. GViT combines the benefits of a Graph Convolutional Network (GCN) and a modified Vision Transformer (ViT). Evaluation of the proposed framework over diverse test data yields a high recognition rate of 98.1%. The framework has also exhibited superior performance compared to human recognition in ultra-range distances. With the framework, we analyze and demonstrate the performance of an autonomous quadruped robot directed by human gestures in complex ultra-range indoor and outdoor environments, acquiring 96% recognition rate on average., Comment: Engineering Applications of Artificial Intelligence, In press

The Internal wave Dissipation, Energy and Mixing (IDEMIX) model presents a novel way of parameterizing internal gravity waves in the atmosphere. Using a continuous full wave spectrum in the energy balance equation and integrating over all vertical wavenumbers and frequencies results in prognostic equations for the energy density of gravity waves in multiple azimuthal compartments. It includes their non-dissipative interaction with the mean flow, allowing for an evolving and local description of momentum flux and gravity wave drag. A saturation mechanism maintains the wave field within convective stability limits, and an energetically consistent closure for critical-layer effects controls how much wave flux propagates from the troposphere into the middle atmosphere. IDEMIX can simulate zonal gravity wave drag around the mesopause, similar to a traditional gravity wave parameterization and to a state-of-the-art wave ray tracing model in an atmospheric circulation model. In addition, IDEMIX shows a reversal of the gravity wave drag around the mesopause region due to changes in the momentum flux there. When compared to empirical model data, IDEMIX captures well the summer hemisphere flow reversal, the cold summer mesospheric pole and the alternate positive and negative structures in the meridional mean flow., Comment: 21 pages, 19 figures

Ocean surface gravity waves play an important role for the air-sea momentum fluxes and the upper ocean mixing, and knowledge of the sea state leads in general circulation models to improved estimates of the ocean energy budget and allows to incorporate surface wave impacts, such as Langmuir turbulence. However, including the Stokes drift, in phase-averaged equations for the Eulerian mean motion leads to an Eulerian energy budget which is physically difficult to interpret. In this note, we show that a Lagrangian energy budget allows for a closed energy budget, in which all terms connecting the different energy compartments correspond to well known energy transfer terms. We show that the so-called Coriolis-Stokes force does not lead to an energy transfer between surface gravity waves and oceanic mean motions as previously suggested. In an energy budget for the Lagrangian mean kinetic energy, the work done by the Coriolis-Stokes force does not contribute, and should be used to estimate the kinetic energy balance in the wave affected surface mixed layer. The Lagrangian energy budget is used to discuss an energetically consistent framework, which can be used to couple a general circulation ocean model to a surface wave model., Comment: 33 pages, 7 figures, submitted to J. Phys. Oceanogr

We investigate a two-scale system featuring an upscaled parabolic dispersion-reaction equation intimately linked to a family of elliptic cell problems. The system is strongly coupled through a dispersion tensor, which depends on the solutions to the cell problems, and via the cell problems themselves, where the solution of the parabolic problem interacts nonlinearly with the drift term. This particular mathematical structure is motivated by a rigorously derived upscaled reaction-diffusion-convection model that describes the evolution of a population of interacting particles pushed by a large drift through an array of periodically placed obstacles (i.e., through a regular porous medium). We prove the existence and uniqueness of weak solutions to our system by means of an iterative scheme, where particular care is needed to ensure the uniform positivity of the dispersion tensor. Additionally, we use finite element-based approximations for the same iteration scheme to perform multiple simulation studies. Finally, we highlight how the choice of micro-geometry (building the regular porous medium) and of the nonlinear drift coupling affects the macroscopic dispersion of particles.

We investigate the effect of wave-eddy interaction and dissipation of internal gravity waves propagating in a coherent meso-scale eddy simulated using a novel numerical model called the Internal Wave Energy Model based on the six-dimensional radiative transfer equation. We use an idealized mean flow structure and stratification, motivated by observations of a coherent eddy in the Canary Current System. In a spin-down simulation using the Garret-Munk model spectrum as initial conditions, we find that wave energy decreases at the eddy rim. Lateral shear leads to wave energy gain due to a developing horizontal anisotropy outisde the eddy and at the rim, while vertical shear leads to wave energy loss which is enhanced at the eddy rim. Wave energy loss by wave dissipation due to vertical shear dominates over horizontal shear. Our results show similar behaviour of the internal gravity wave in a cyclonic as well as an anticyclonic eddy. Wave dissipation by vertical wave refraction occurs predominantly at the eddy rim near the surface, where related vertical diffusivities range from $\kappa \approx \mathcal{O}(10^{-7})$ to $\mathcal{O}(10^{-5}) \, \rm m^2s^{-1}$.

Various NLP tasks require a complex hierarchical structure over nodes, where each node is a cluster of items. Examples include generating entailment graphs, hierarchical cross-document coreference resolution, annotating event and subevent relations, etc. To enable efficient annotation of such hierarchical structures, we release CHAMP, an open source tool allowing to incrementally construct both clusters and hierarchy simultaneously over any type of texts. This incremental approach significantly reduces annotation time compared to the common pairwise annotation approach and also guarantees maintaining transitivity at the cluster and hierarchy levels. Furthermore, CHAMP includes a consolidation mode, where an adjudicator can easily compare multiple cluster hierarchy annotations and resolve disagreements., Comment: EMNLP 2023

Star formation has been observed to occur at globally low yet locally varying efficiencies. As such, accurate capture of star formation in numerical simulations requires mechanisms that can replicate both its smaller-scale variations and larger-scale properties. Magnetic fields are thought to play an essential role within the turbulent interstellar medium (ISM) and affect molecular cloud collapse. However, it remains to be fully explored how a magnetised model of star formation might influence galaxy evolution. We present a new model for a sub-grid star formation recipe that depends on the magnetic field. We run isolated disk galaxy simulations to assess its impact on the regulation of star formation using the code RAMSES. Building upon existing numerical methods, our model derives the star formation efficiency from local properties of the sub-grid magnetised ISM turbulence, assuming a constant Alfv\'en speed at sub-parsec scales. Compared to its non-magnetised counterpart, our star formation model suppresses the initial starburst by a factor of two, while regulating star formation later on to a nearly constant rate of $\sim 1~M_{\odot}~\mathrm{yr}^{-1}$. Differences also arise in the local Schmidt law with a shallower power law index for the magnetised star formation model. Our results encourage further examination into the notion that magnetic fields are likely to play a non-trivial role in our understanding of star and galaxy formation., Comment: 15 pages, 11 figures; Accepted for publication in MNRAS

The internal structure of the prestellar core G208.68-19.02-N2 (G208-N2) in the Orion Molecular Cloud 3 (OMC-3) region has been studied with the Atacama Large Millimeter/submillimeter Array (ALMA). The dust continuum emission revealed a filamentary structure with a length of $\sim$5000 au and an average H$_2$ volume density of $\sim$6 $\times$ 10$^7$ cm$^{-3}$. At the tip of this filamentary structure, there is a compact object, which we call a ``nucleus", with a radius of $\sim$150--200 au and a mass of $\sim$0.1 M$_{\odot}$. The nucleus has a central density of $\sim$2 $\times$ 10$^9$ cm$^{-3}$ with a radial density profile of $r^{-1.87{\pm}0.11}$. The density scaling of the nucleus is $\sim$3.7 times higher than that of the singular isothermal sphere. This as well as the very low virial parameter of 0.39 suggest that the gravity is dominant over the pressure everywhere in the nucleus. However, there is no sign of CO outflow localized to this nucleus. The filamentary structure is traced by the N$_2$D$^+$ 3--2 emission, but not by the C$^{18}$O 2--1 emission, implying the significant CO depletion due to high density and cold temperature. Toward the nucleus, the N$_2$D$^+$ also shows the signature of depletion. This could imply either the depletion of the parent molecule, N$_2$, or the presence of the embedded very-low luminosity central source that could sublimate the CO in the very small area. The nucleus in G208-N2 is considered to be a prestellar core on the verge of first hydrostatic core (FHSC) formation or a candidate for the FHSC., Comment: 27 pages, 16 figures

Benchmarking drug efficacy is a critical step in clinical trial design and planning. The challenge is that much of the data on efficacy endpoints is stored in scientific papers in free text form, so extraction of such data is currently a largely manual task. Our objective is to automate this task as much as possible. In this study we have developed and optimised a framework to extract efficacy endpoints from text in scientific papers, using a machine learning approach. Our machine learning model predicts 25 classes associated with efficacy endpoints and leads to high F1 scores (harmonic mean of precision and recall) of 96.4% on the test set, and 93.9% and 93.7% on two case studies. These methods were evaluated against - and showed strong agreement with - subject matter experts and show significant promise in the future of automating the extraction of clinical endpoints from free text. Clinical information extraction from text data is currently a laborious manual task which scales poorly and is prone to human error. Demonstrating the ability to extract efficacy endpoints automatically shows great promise for accelerating clinical trial design moving forwards.