Miracle-dz/UniFAR-FTUs huggingface dataset:
FaMeSumm: Investigating and Improving Faithfulness of Medical Summarization

Summaries of medical text shall be faithful by being consistent and factual with source inputs, which is an important but understudied topic for safety and efficiency in healthcare. In this paper, we investigate and improve faithfulness in summarization on a broad range of medical summarization tasks. Our investigation reveals that current summarization models often produce unfaithful outputs for medical input text. We then introduce FaMeSumm, a framework to improve faithfulness by fine-tuning pre-trained language models based on medical knowledge. FaMeSumm performs contrastive learning on designed sets of faithful and unfaithful summaries, and it incorporates medical terms and their contexts to encourage faithful generation of medical terms. We conduct comprehensive experiments on three datasets in two languages: health question and radiology report summarization datasets in English, and a patient-doctor dialogue dataset in Chinese. Results demonstrate that FaMeSumm is flexible and effective by delivering consistent improvements over mainstream language models such as BART, T5, mT5, and PEGASUS, yielding state-of-the-art performances on metrics for faithfulness and general quality. Human evaluation by doctors also shows that FaMeSumm generates more faithful outputs. Our code is available at https://github.com/psunlpgroup/FaMeSumm .
Extreme precipitation of the Waikato region, New Zealand

This study investigates the relationship between regional precipitation and large‐scale climate variations. The Waikato is an economically important region of New Zealand that is dependant on a reliable source of precipitation for agriculture and hydroelectric power generation, but is also prone to flooding. Previous long‐term precipitation studies have represented this region by a single rain gauge. This study has incorporated more data from more rain gauges and extended the analysis period to 107 years, 1900–2007.
Contour Algorithm for Connectivity

Finding connected components in a graph is a fundamental problem in graph analysis. In this work, we present a novel minimum-mapping based Contour algorithm to efficiently solve the connectivity problem. We prove that the Contour algorithm with two or higher order operators can identify all connected components of an undirected graph within $\mathcal{O}(\log d_{max})$ iterations, with each iteration involving $\mathcal{O}(m)$ work, where $d_{max}$ represents the largest diameter among all components in the given graph, and $m$ is the total number of edges in the graph. Importantly, each iteration is highly parallelizable, making use of the efficient minimum-mapping operator applied to all edges. To further enhance its practical performance, we optimize the Contour algorithm through asynchronous updates, early convergence checking, eliminating atomic operations, and choosing more efficient mapping operators. Our implementation of the Contour algorithm has been integrated into the open-source framework Arachne. Arachne extends Arkouda for large-scale interactive graph analytics, providing a Python API powered by the high-productivity parallel language Chapel. Experimental results on both real-world and synthetic graphs demonstrate the superior performance of our proposed Contour algorithm compared to state-of-the-art large-scale parallel algorithm FastSV and the fastest shared memory algorithm ConnectIt. On average, Contour achieves a speedup of 7.3x and 1.4x compared to FastSV and ConnectIt, respectively. All code for the Contour algorithm and the Arachne framework is publicly available on GitHub ( https://github.com/Bears-R-Us/arkouda-njit ), ensuring transparency and reproducibility of our work.
Combinatorics of nondeterministic walks

This paper introduces nondeterministic walks, a new variant of one-dimensional discrete walks. The main difference to classical walks is that its nondeterministic steps consist of sets of steps from a predefined set such that all possible extensions are explored in parallel. We discuss in detail the most natural nondeterministic step sets (Dyck and Motzkin step sets), and show that several nondeterministic classes of lattice paths, such as nondeterministic bridges, excursions, and meanders are algebraic. The key concept is the generalization of the ending point of a walk to its reachable points, i.e., a set of ending points. We extend our results to general step sets: We show that nondeterministic bridges and several subclasses of nondeterministic meanders are always algebraic. We conjecture the same is true for nondeterministic excursions, and we present python and Maple packages to support our conjecture. This research is motivated by the study of networks involving encapsulation and decapsulation of protocols. Our results are obtained using generating functions, analytic combinatorics, and additive combinatorics. Keywords. Random walks, analytic combinatorics, generating functions, limit laws, networking, encapsulation.
Citance-Contextualized Summarization of Scientific Papers

Current approaches to automatic summarization of scientific papers generate informative summaries in the form of abstracts. However, abstracts are not intended to show the relationship between a paper and the references cited in it. We propose a new contextualized summarization approach that can generate an informative summary conditioned on a given sentence containing the citation of a reference (a so-called"citance"). This summary outlines the content of the cited paper relevant to the citation location. Thus, our approach extracts and models the citances of a paper, retrieves relevant passages from cited papers, and generates abstractive summaries tailored to each citance. We evaluate our approach using $\textbf{Webis-Context-SciSumm-2023}$, a new dataset containing 540K~computer science papers and 4.6M~citances therein.
Calibrating discrete fracture-network models with a carbonate three-dimensional outcrop fracture network: Implications for naturally fractured reservoir modeling

Modeling naturally fractured reservoirs requires a detailed understanding of the three-dimensional (3D) fracture-network characteristics, whereas generally only one-dimensional (1D) data, often suffering from sampling artifacts, are available as inputs for modeling. Additional fracture properties can be derived from outcrop analogs with the scanline method, but it does not capture their full two-dimensional (2D) characteristics. We propose an improved workflow based on a 2D field-digitizing tool for mapping and analyzing fracture parameters as well as relations to bedding. From fracture data collected along 11 vertical surface outcrops in a quarry in southeast France, we quantify uncertainties in modeling fracture networks. The fracture-frequency distribution fits a Gaussian distribution that we use to evaluate the intrinsic fracture density variability within the quarry at different observation scales along well-analog scanlines. Excluding well length as a parameter, we find that 30 wells should be needed to fully (i.e., steady variance) capture the natural variability in fracture spacing. This illustrates the challenge in trying to predict fracture spacing in the subsurface from limited well data. Furthermore, for models with varying scanline orientations we find that Terzaghi-based spacing corrections fail when the required correction angle is more than 60°. We apply the 1D well analog data to calculate 3D fracture frequency using stereological relations and find that these relations only work for cases in which the orientation distribution is accurately described, as results greatly vary with small changes in the orientation distribution.
Focused helium ion and electron beam induced deposition of organometallic tips for dynamic AFM of biomolecules in liquid

We demonstrate the fabrication of sharp nanopillars of high aspect ratio onto specialized atomic force microscopy (AFM) microcantilevers and their use for high-speed AFM of DNA and nucleoproteins in liquid. The fabrication technique uses localized charged-particle-induced deposition with either a focused beam of helium ions or electrons in a helium ion microscope (HIM) or scanning electron microscope (SEM). This approach enables customized growth onto delicate substrates with nanometer-scale placement precision and in-situ imaging of the final tip structures using the HIM or SEM. Tip radii of<10 nm are obtained and the underlying microcantilever remains intact. Instead of the more commonly used organic precursors employed for bio-AFM applications, we use an organometallic precursor (tungsten hexacarbonyl) resulting in tungsten-containing tips. Transmission electron microscopy reveals a thin layer of carbon on the tips. Consequently, the interaction of the new tips with biological specimens is likely very similar to that of standard carbonaceous tips, with the added benefit of robustness. A further advantage of the organometallic tips is that compared to carbonaceous tips they better withstand UV-ozone cleaning treatments to remove residual organic contaminants between experiments. Such contaminants are unavoidable during the scanning of soft biomolecules in liquid. Our tips can also be grown onto the blunted tips of previously-used cantilevers, thus providing a means to recycle specialized cantilevers and restore their performance to the original manufacturer specifications. Finally, a focused helium ion beam milling technique to reduce the tip radii and thus further improve lateral spatial resolution in the AFM scans is demonstrated.
Gaps in Representations of Hydropower Generation in Steady-State and Dynamic Models

In the evolving power system, where new renewable resources continually displace conventional generation, conventional hydropower resources can be an important asset that helps to maintain reliability and flexibility. Varying climatic patterns do affect the operational pattern of hydropower. This would potentially play a vital role in meeting and delivering energy and meeting climate policy needs. Hydropower is one of the oldest forms of renewable energy resources, however, its dependency on water availability and other constraints are not well represented in power system steady state and dynamic models. This leads to multiple gaps in operations planning especially due to high intermittent renewable generation. Operating constraints and lack of high-quality data often become a barrier to hydropower modeling which leads to inconsistencies in reliability and operational planning studies resulting in unintentional blackouts or unforeseen situations. This paper identifies some of the gaps in hydro-based generation representation in steady-state and dynamic models and provides recommendations for their mitigation.
MirrorCalib: Utilizing Human Pose Information for Mirror-based Virtual Camera Calibration

In this paper, we present the novel task of estimating the extrinsic parameters of a virtual camera relative to a real camera in exercise videos with a mirror. This task poses a significant challenge in scenarios where the views from the real and mirrored cameras have no overlap or share salient features. To address this issue, prior knowledge of a human body and 2D joint locations are utilized to estimate the camera extrinsic parameters when a person is in front of a mirror. We devise a modified eight-point algorithm to obtain an initial estimation from 2D joint locations. The 2D joint locations are then refined subject to human body constraints. Finally, a RANSAC algorithm is employed to remove outliers by comparing their epipolar distances to a predetermined threshold. MirrorCalib is evaluated on both synthetic and real datasets and achieves a rotation error of 0.62{\deg}/1.82{\deg} and a translation error of 37.33/69.51 mm on the synthetic/real dataset, which outperforms the state-of-art method.
Functional Central limit theorems for epidemic models with varying infectivity and waning immunity

We study an individual-based stochastic epidemic model in which infected individuals become susceptible again following each infection (generalized SIS model). Specifically, after each infection, the infectivity is a random function of the time elapsed since the infection, and each recovered individual loses immunity gradually (equivalently, becomes gradually susceptible) after some time according to a random susceptibility function. The epidemic dynamics is described by the average infectivity and susceptibility processes in the population together with the numbers of infected and susceptible/uninfected individuals. In \cite{forien-Zotsa2022stochastic}, a functional law of large numbers (FLLN) is proved as the population size goes to infinity, and asymptotic endemic behaviors are also studied. In this paper, we prove a functional central limit theorem (FCLT) for the stochastic fluctuations of the epidemic dynamics around the FLLN limit. The FCLT limit for the aggregate infectivity and susceptibility processes is given by a system of stochastic non-linear integral equation driven by a two-dimensional Gaussian process.
Modern Datalog on the GPU

Modern deductive database engines (e.g., LogicBlox and Souffl\'e) enable their users to write declarative queries which compute recursive deductions over extensional data, leaving their high-performance operationalization (query planning, semi-na\"ive evaluation, and parallelization) to the engine. Such engines form the backbone of modern high-throughput applications in static analysis, security auditing, social-media mining, and business analytics. State-of-the-art engines are built upon nested loop joins over explicit representations (e.g., BTrees and tries) and ubiquitously employ range indexing to accelerate iterated joins. In this work, we present GDlog: a GPU-based deductive analytics engine (implemented as a CUDA library) which achieves significant performance improvements (5--10x or more) versus prior systems. GDlog is powered by a novel range-indexed SIMD datastructure: the hash-indexed sorted array (HISA). We perform extensive evaluation on GDlog, comparing it against both CPU and GPU-based hash tables and Datalog engines, and using it to support a range of large-scale deductive queries including reachability, same generation, and context-sensitive program analysis. Our experiments show that GDlog achieves performance competitive with modern SIMD hash tables and beats prior work by an order of magnitude in runtime while offering more favorable memory footprint.
Machine Learning-Based Tea Leaf Disease Detection: A Comprehensive Review

Tea leaf diseases are a major challenge to agricultural productivity, with far-reaching implications for yield and quality in the tea industry. The rise of machine learning has enabled the development of innovative approaches to combat these diseases. Early detection and diagnosis are crucial for effective crop management. For predicting tea leaf disease, several automated systems have already been developed using different image processing techniques. This paper delivers a systematic review of the literature on machine learning methodologies applied to diagnose tea leaf disease via image classification. It thoroughly evaluates the strengths and constraints of various Vision Transformer models, including Inception Convolutional Vision Transformer (ICVT), GreenViT, PlantXViT, PlantViT, MSCVT, Transfer Learning Model&Vision Transformer (TLMViT), IterationViT, IEM-ViT. Moreover, this paper also reviews models like Dense Convolutional Network (DenseNet), Residual Neural Network (ResNet)-50V2, YOLOv5, YOLOv7, Convolutional Neural Network (CNN), Deep CNN, Non-dominated Sorting Genetic Algorithm (NSGA-II), MobileNetv2, and Lesion-Aware Visual Transformer. These machine-learning models have been tested on various datasets, demonstrating their real-world applicability. This review study not only highlights current progress in the field but also provides valuable insights for future research directions in the machine learning-based detection and classification of tea leaf diseases.
Imitation Bootstrapped Reinforcement Learning

Despite the considerable potential of reinforcement learning (RL), robotic control tasks predominantly rely on imitation learning (IL) due to its better sample efficiency. However, it is costly to collect comprehensive expert demonstrations that enable IL to generalize to all possible scenarios, and any distribution shift would require recollecting data for finetuning. Therefore, RL is appealing if it can build upon IL as an efficient autonomous self-improvement procedure. We propose imitation bootstrapped reinforcement learning (IBRL), a novel framework for sample-efficient RL with demonstrations that first trains an IL policy on the provided demonstrations and then uses it to propose alternative actions for both online exploration and bootstrapping target values. Compared to prior works that oversample the demonstrations or regularize RL with an additional imitation loss, IBRL is able to utilize high quality actions from IL policies since the beginning of training, which greatly accelerates exploration and training efficiency. We evaluate IBRL on 6 simulation and 3 real-world tasks spanning various difficulty levels. IBRL significantly outperforms prior methods and the improvement is particularly more prominent in harder tasks.
Lichens of Scatarie Island Wilderness Area

In 2001, 2005 and 2007 sixty-one species of lichens were collected from Scatarie Island. Over half the species had a boreal or arctic distribution and slightly less than half had a coastal affinity. New records include five species for Cape Breton of which three species are also new records for Nova Scotia. Cyanolichen diversity is very low for Scatarie Island compared to other coastal collections from Nova Scotia. En 2001, 2005 et 2007 on a recueilli soixante et une especes de lichens dans l’ile Scatarie. Plus de la moitie de ces especes avaient une aire de repartition boreale ou arctique, et un peu moins de la moitie etaient des especes plutot cotieres. Cinq especes etaient repertoriees pour la premiere fois au Cap‑Breton, trois d’entre elles etant egalement observees pour la premiere fois en Nouvelle‑Ecosse. La diversite des cyanolichens dans l’ile Scatarie est tres faible comparee a celle d’autres collections recueillies sur les cotes de la Nouvelle‑Ecosse.
Congestion-aware Ride-pooling in Mixed Traffic for Autonomous Mobility-on-Demand Systems

This paper presents a modeling and optimization framework to study congestion-aware ride-pooling Autonomous Mobility-on-Demand (AMoD) systems, whereby self-driving robotaxis are providing on-demand mobility, and users headed in the same direction share the same vehicle for part of their journey. Specifically, taking a mesoscopic time-invariant perspective and on the assumption of a large number of travel requests, we first cast the joint ride-pooling assignment and routing problem as a quadratic program that does not scale with the number of demands and can be solved with off-the-shelf convex solvers. Second, we compare the proposed approach with a significantly simpler decoupled formulation, whereby only the routing is performed in a congestion-aware fashion, whilst the ride-pooling assignment part is congestion-unaware. A case study of Sioux Falls reveals that such a simplification does not significantly alter the solution and that the decisive factor is indeed the congestion-aware routing. Finally, we solve the latter problem accounting for the presence of user-centered private vehicle users in a case study of Manhattan, NYC, characterizing the performance of the car-network as a function of AMoD penetration rate and percentage of pooled rides within it. Our results show that AMoD can significantly reduce congestion and travel times, but only if at least 40% of the users are willing to be pooled together. Otherwise, for higher AMoD penetration rates and low percentage of pooled rides, the effect of the additional rebalancing empty-vehicle trips can be even more detrimental than the benefits stemming from a centralized routing, worsening congestion and leading to an up to 15% higher average travel time.
Properties of the Biot-Savart operator acting on surface currents

We investigate properties of the image and kernel of the Biot-Savart operator in the context of stellarator designs for plasma fusion. We first show that for any given coil winding surface (CWS) the image of the Biot-Savart operator is $L^2$-dense in the space of square-integrable harmonic fields defined on a plasma domain surrounded by the CWS. Then we show that harmonic fields which are harmonic in a proper neighbourhood of the underlying plasma domain can in fact be approximated in any $C^k$-norm by elements of the image of the Biot-Savart operator. In the second part of this work we establish an explicit isomorphism between the space of harmonic Neumann fields and the kernel of the Biot-Savart operator which in particular implies that the dimension of the kernel of the Biot-Savart operator coincides with the genus of the coil winding surface and hence turns out to be a homotopy invariant among regular domains in $3$-space. Lastly, we provide an iterative scheme which we show converges weakly in $W^{-\frac{1}{2},2}$-topology to elements of the kernel of the Biot-Savart operator.
Risk Analysis in the Selection of Project Managers Based on ANP and FMEA

Project managers play a crucial role in the success of projects. The selection of an appropriate project manager is a primary concern for senior managers in firms. Typically, this process involves candidate interviews and assessments of their abilities. There are various criteria for selecting a project manager, and the importance of each criterion depends on the project type, its conditions, and the risks associated with their absence in the chosen candidate. Often, senior managers in engineering companies lack awareness of the significance of these criteria and the potential risks linked to their absence. This research aims to identify these risks in selecting project managers for civil engineering projects, utilizing a combined ANP-FMEA approach. Through a comprehensive literature review, five risk categories have been identified: individual skills, power-related issues, knowledge and expertise, experience, and personality traits. Subsequently, these risks, along with their respective sub-criteria and internal relationships, were analysed using the combined ANP-FMEA technique. The results highlighted that the lack of political influence, absence of construction experience, and deficiency in project management expertise represent the most substantial risks in selecting a project manager. Moreover, upon comparison with the traditional FMEA approach, this study demonstrates the superior ability of the ANP-FMEA model in differentiating risks and pinpointing factors with elevated risk levels.
Solute mixing, dynamic uncertainty and effective dispersion in two-dimensional heterogeneous porous media

We study the mixing dynamics of a solute that is transported by advection and dispersion in a heterogeneous Darcy scale porous medium. We quantify mixing and dynamic uncertainty in terms of the mean squared solute concentration and the concentration variance. The latter measures the degree of mixing of the solute and at the same time the uncertainty around the mean concentration. Its evolution is controlled by the creation of concentration fluctuations due to solute spreading and its destruction by local dispersion. For moderate heterogeneity, this interplay can be quantified by using apparent and effective dispersion coefficients. For increasing heterogeneity we find deviations from the predicted behavior. In order to shed light on these behaviors and separate solute mixing and spreading, we decompose the solute plume into partial plumes, transport Green functions, and analyze their dynamics relative to those of the whole plume. This reveals that the variability in the dispersive scales of the Green functions in the plume and their interactions due to the strong focusing of preferential flow paths play an important role in highly heterogeneous porous media.