Miracle-dz/UniFAR-FTUs huggingface dataset:
Reasoning about distributive laws in a concurrent refinement algebra

Distributive laws are important for algebraic reasoning in arithmetic and logic. They are equally important for algebraic reasoning about concurrent programs. In existing theories such as Concurrent Kleene Algebra, only partial correctness is handled, and many of its distributive laws are weak, in the sense that they are only refinements in one direction, rather than equalities. The focus of this paper is on strengthening our theory to support the proof of strong distributive laws that are equalities, and in doing so come up with laws that are quite general. Our concurrent refinement algebra supports total correctness by allowing both finite and infinite behaviours. It supports the rely/guarantee approach of Jones by encoding rely and guarantee conditions as rely and guarantee commands. The strong distributive laws may then be used to distribute rely and guarantee commands over sequential compositions and into (and out of) iterations. For handling data refinement of concurrent programs, strong distributive laws are essential.
GerontoVis: Data Visualization at the Confluence of Aging

Despite the explosive growth of the aging population worldwide, older adults have been largely overlooked by visualization research. This paper is a critical reflection on the underrepresentation of older adults in visualization research. We discuss why investigating visualization at the intersection of aging matters, why older adults may have been omitted from sample populations in visualization research, how aging may affect visualization use, and how this differs from traditional accessibility research. To encourage further discussion and novel scholarship in this area, we introduce GerontoVis, a term which encapsulates research and practice of data visualization design that primarily focuses on older adults. By introducing this new subfield of visualization research, we hope to shine a spotlight on this growing user population and stimulate innovation toward the development of aging-aware visualization tools. We offer a birds-eye view of the GerontoVis landscape, explore some of its unique challenges, and identify promising areas for future research.
Limitations and Solutions for Real-Time Local Inter-Domain Communication in Xen

As computer hardware becomes increasingly powerful, there is an ongoing trend towards integrating complex, legacy real-time systems using fewer hosts through virtualization. Especially in embedded systems domains such as avionics and automotive engineering, this kind of system integration can greatly reduce system weight, cost, and power requirements. When systems are integrated in this manner, network communication may become local inter-domain communication (IDC) within the same host. This paper examines the limitations of inter-domain communication in Xen, a widely used open-source virtual machine monitor (VMM) that recently has been extended to support real-time domain scheduling. We find that both the VMM scheduler and the manager domain can significantly impact real-time IDC performance under different conditions, and show that improving the VMM scheduler alone cannot deliver real-time performance for local IDC. To address those limitations, we present the RTCA, a Real-Time Communication Architecture within the manager domain in Xen, along with empirical evaluations whose results demonstrate that the latency of communication tasks can be improved dramatically from ms to μs by a combination of the RTCA and a real-time VMM scheduler.
Hawking radiation from a hot NUT-Kerr-Newman-Kasuya-Anti-de Sitter black hole

In this work, we investigate the tunneling phenomenon of a charged Dirac particle emerging from a thermal horizon of a hot NUT-Kerr-Newman-Kasuya-Anti-de Sitter (HNKNK-AdS) black hole. Considering the tunneling formalism, we report the Hawking temperature of the charged Dirac particle through the horizon and the heat capacity of the HNKNK-AdS black hole. It is found that the effective Hawking temperature for the black hole depends on the black hole's parameters, such as electric and magnetic charges, magnetic mass, the cosmological constant, and the angular momentum of the black hole. The Hawking radiation for the Kerr-Newman black hole is reduced. It is shown that there is a small correction to the Hawking thermal spectrum. The heat capacity for the HNKNK-AdS black hole is found different from the previous work, and for the Kerr-Newman black hole is also reduced.
Feeding performance of fleas on different host species: is phylogenetic distance between hosts important?

SUMMARY We asked if and how feeding performance of fleas on an auxiliary host is affected by the phylogenetic distance between this host and the principal host of a flea. We investigated the feeding of 2 flea species, Parapulex chephrenis and Xenopsylla ramesis, on a principal (Acomys cahirinus and Meriones crassus, respectively) and 8 auxiliary host species. We predicted that fleas would perform better (higher proportion of fleas would feed and take larger bloodmeals) on (a) a principal rather than an auxiliary host and (b) auxiliary hosts phylogenetically closer to a principal host. Although feeding performance of fleas differed among different hosts, we found that: (1) fleas did not always perform better on a principal host than on an auxiliary host; and (2) flea performance on an auxiliary host was not negatively correlated with phylogenetic distance of this host from the principal host. In some cases, fleas fed better on hosts that were phylogenetically distant from their principal host. We concluded that variation in flea feeding performance among host species results from interplay between (a) inherent species-specific host defence abilities, (b) inherent species-specific flea abilities to withstand host defences and (c) evolutionary tightness of association between a particular host species and a particular flea species.
Stormwater capture and antecedent moisture characteristics of permeable pavements

An approach based on individual rainfall events is introduced to mathematically describe the hydrologic responses and estimate the stormwater capture efficiencies of permeable pavement systems (PPSs). A stochastic model describing the instantaneous dynamic water balance of a PPS is established, from which the probability distribution of the antecedent moisture content of the PPS at the beginning of a rainfall event is analytically derived. Based on this probability distribution and the event‐based approach, an analytical equation that can be used for estimating the stormwater capture efficiencies of PPSs is also derived. The derived analytical equation is verified by comparing its results with those from continuous simulations for a wide range of PPSs with different sizes and underlying soils and operating under various climate conditions. It was found that the antecedent moisture contents of PPSs at the test locations are usually fairly close to zero, suggesting that PPSs at these locations are always almost empty at the start of a rainfall event. The derived analytical equation accounts for many key processes influencing the behaviour and operation of PPSs; it may serve as an easy‐to‐use tool that is essential for the planning and design of PPSs.
Adaptive time step selection for Spectral Deferred Corrections

Spectral Deferred Corrections (SDC) is an iterative method for the numerical solution of ordinary differential equations. It works by refining the numerical solution for an initial value problem by approximately solving differential equations for the error, and can be interpreted as a preconditioned fixed-point iteration for solving the fully implicit collocation problem. We adopt techniques from embedded Runge-Kutta Methods (RKM) to SDC in order to provide a mechanism for adaptive time step size selection and thus increase computational efficiency of SDC. We propose two SDC-specific estimates of the local error that are generic and require only minimal problem specific tuning. We demonstrate a gain in efficiency over standard SDC with fixed step size, compare efficiency favorably against state-of-the-art adaptive RKM and show that due to its iterative nature, adaptive SDC can cope efficiently with silent data corruption.
Novel Flexible Wearable Sensor Materials and Signal Processing for Vital Sign and Human Activity Monitoring

Advances in flexible electronic materials and smart textile, along with broad availability of smart phones, cloud and wireless systems have empowered the wearable technologies for significant impact on future of digital and personalized healthcare as well as consumer electronics. However, challenges related to lack of accuracy, reliability, high power consumption, rigid or bulky form factor and difficulty in interpretation of data have limited their wide-scale application in these potential areas. As an important solution to these challenges, we present latest advances in novel flexible electronic materials and sensors that enable comfortable and conformable body interaction and potential for invisible integration within daily apparel. Advances in novel flexible materials and sensors are described for wearable monitoring of human vital signs including, body temperature, respiratory rate and heart rate, muscle movements and activity. We then present advances in signal processing focusing on motion and noise artifact removal, data mining and aspects of sensor fusion relevant to future clinical applications of wearable technology.
Supporting concept assignment in the comprehension of Web applications

An approach providing automatic support in the assignment of concepts to documents recovered by reverse engineering Web applications is presented. Web pages composing Web applications usually include relevant textual information from the domain of the application, while different editing formats are used for emphasising to the end users the various concepts provided by the pages. The proposed concept assignment approach exploits both the textual information contained in the Web pages, and the editing formal used to display it in order to identify automatically a set of candidate concepts describing a Web page or a set of pages. These concepts can be used by maintainers involved in the task of assigning a meaning to software artefacts recovered by reverse engineering the Web applications. Validation experiments carried out with Web applications selected from the real world showed the validity of the proposed approach. The experimental results are presented in the paper.
eRST: A Signaled Graph Theory of Discourse Relations and Organization

In this article we present Enhanced Rhetorical Structure Theory (eRST), a new theoretical framework for computational discourse analysis, based on an expansion of Rhetorical Structure Theory (RST). The framework encompasses discourse relation graphs with tree-breaking, nonprojective and concurrent relations, as well as implicit and explicit signals which give explainable rationales to our analyses. We survey shortcomings of RST and other existing frameworks, such as Segmented Discourse Representation Theory (SDRT), the Penn Discourse Treebank (PDTB) and Discourse Dependencies, and address these using constructs in the proposed theory. We provide annotation, search and visualization tools for data, and present and evaluate a freely available corpus of English annotated according to our framework, encompassing 12 spoken and written genres with over 200K tokens. Finally, we discuss automatic parsing, evaluation metrics and applications for data in our framework.
Bridging deep learning force fields and electronic structures with a physics-informed approach

This work presents a physics-informed neural network approach bridging deep-learning force field and electronic structure simulations, illustrated through twisted two-dimensional large-scale material systems. The deep potential molecular dynamics model is adopted as the backbone, and electronic structure simulation is integrated. Using Wannier functions as the basis, we categorize Wannier Hamiltonian elements based on physical principles to incorporate diverse information from a deep-learning force field model. This information-sharing mechanism streamlines the architecture of our multifunctional model, enhancing its efficiency and effectiveness. Utilizing Wannier functions as the basis lays the groundwork for predicting more physical quantities. This approach serves as a powerful tool to explore both the structural and electronic properties of large-scale systems characterized by low periodicities. By endowing an existing well-developed machine-learning force field with electronic structure simulation capabilities, the study marks a significant advancement in developing multimodal machine-learning-based computational methods that can achieve multiple functionalities traditionally exclusive to first-principles calculations.
Statistical static timing analysis using Markov chain Monte Carlo

We present a new technique for statistical static timing analysis (SSTA) based on Markov chain Monte Carlo (MCMC), that allows fast and accurate estimation of the right-hand tail of the delay distribution. A “naive” MCMC approach is inadequate for SSTA. Several modifications and enhancements, presented in this paper, enable application of MCMC to SSTA. Moreover, such an approach overcomes inherent limitations of techniques such as importance sampling and Quasi-Monte Carlo. Our results on open source designs, with an independent delay variation model, demonstrate that our technique can obtain more than an order of magnitude improvement in computation time over simple Monte Carlo, given an estimation accuracy target at a point in the tail. Our approach works by providing a large number of samples in the region of interest. Open problems include extension of algorithm applicability to a broader class of synthesis conditions, and handling of correlated delay variations. In a broader context, this work aims to show that MCMC and associated techniques can be useful in rare event analyses related to circuits, particularly for high-dimensional problems.
A simpler data structure for dynamic strings

We consider the problem of maintaining a collection of strings while efficiently supporting splits and concatenations on them, as well as comparing two substrings, and computing the longest common prefix between two suffixes. This problem can be solved in optimal time $\mathcal{O}(\log N)$ whp for the updates and $\mathcal{O}(1)$ worst-case time for the queries, where $N$ is the total collection size [Gawrychowski et al., SODA 2018]. We present here a much simpler solution based on a forest of enhanced splay trees (FeST), where both the updates and the substring comparison take $\mathcal{O}(\log n)$ amortized time, $n$ being the lengths of the strings involved, and not the total length $N$ of the strings in the collection. The longest common prefix of length $\ell$ is computed in $\mathcal{O}(\log n + \log^2\ell)$ amortized time. Our query results are correct whp. Our simpler solution enables other more general updates in $\mathcal{O}(\log n)$ amortized time, such as reversing a substring and/or mapping its symbols. We can also regard substrings as circular or as their omega extension.
Diversified and Personalized Multi-rater Medical Image Segmentation

Annotation ambiguity due to inherent data uncertainties such as blurred boundaries in medical scans and different observer expertise and preferences has become a major obstacle for training deep-learning based medical image segmentation models. To address it, the common practice is to gather multiple annotations from different experts, leading to the setting of multi-rater medical image segmentation. Existing works aim to either merge different annotations into the"groundtruth"that is often unattainable in numerous medical contexts, or generate diverse results, or produce personalized results corresponding to individual expert raters. Here, we bring up a more ambitious goal for multi-rater medical image segmentation, i.e., obtaining both diversified and personalized results. Specifically, we propose a two-stage framework named D-Persona (first Diversification and then Personalization). In Stage I, we exploit multiple given annotations to train a Probabilistic U-Net model, with a bound-constrained loss to improve the prediction diversity. In this way, a common latent space is constructed in Stage I, where different latent codes denote diversified expert opinions. Then, in Stage II, we design multiple attention-based projection heads to adaptively query the corresponding expert prompts from the shared latent space, and then perform the personalized medical image segmentation. We evaluated the proposed model on our in-house Nasopharyngeal Carcinoma dataset and the public lung nodule dataset (i.e., LIDC-IDRI). Extensive experiments demonstrated our D-Persona can provide diversified and personalized results at the same time, achieving new SOTA performance for multi-rater medical image segmentation. Our code will be released at https://github.com/ycwu1997/D-Persona.
Three-dimensional left atrial blood flow characteristics in patients with atrial fibrillation assessed by 4D flow CMR.

AIMS To apply 4D flow cardiac magnetic resonance (CMR) for the volumetric measurement of 3D left atrial (LA) blood flow to evaluate its potential to detect altered LA flow in patients with atrial fibrillation (AF) and to investigate associations of changes in systolic and diastolic LA flow with the current clinical risk score (CHA2DS2-VASc) used for the assessment of thromboembolic risk in AF. METHODS AND RESULTS 4D flow CMR was performed in 40 patients with a history of AF (in sinus rhythm during CMR scan, age = 61 ± 11 years), 20 age-appropriate controls (59 ± 7 years), and 10 young healthy volunteers (24 ± 2 years) to measure in vivo time-resolved 3D LA blood flow. LA velocities were characterized with respect to atrial function and timing by calculating normalized LA flow velocity histograms during ventricular systole, early diastole, mid-late diastole, and the entire cardiac cycle. Mean, median, and peak LA velocity steadily decreased when comparing young volunteers, age-appropriate controls, and AF patients by 10-44% and 8-26% for early diastole and the entire cardiac cycle, respectively (P < 0.01 for all comparisons except median velocity for young vs. older volunteers and peak velocity for older volunteers and AF patients). There were moderate but significant inverse relationships between increased CHA2DS2-VASc score and reduced mean LA velocity (early diastole: r = -0.37, P < 0.001; entire RR-interval: r = -0.33, P = 0.005), median LA velocity (r = -0.33, P = 0.003; r = -0.25, P = 0.017), and peak velocity (r = -0.36, P = 0.001; r = -0.45, P < 0.001). LA flow indices also correlated significantly with age and LA volume (R2 = 0.44-0.62, P < 0.001), but not with left ventricular ejection fraction. CONCLUSION Left atrial 4D flow CMR demonstrated significantly reduced LA blood flow velocities in patients with AF. Further study is needed to determine whether these measures can improve upon the CHA2DS2-VASc score for stroke risk prediction and enhance individual decisions on anticoagulation in patients with AF.
AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks

The increasing complexity of deep neural networks poses significant barriers to democratizing them to resource-limited edge devices. To address this challenge, split federated learning (SFL) has emerged as a promising solution by of floading the primary training workload to a server via model partitioning while enabling parallel training among edge devices. However, although system optimization substantially influences the performance of SFL under resource-constrained systems, the problem remains largely uncharted. In this paper, we provide a convergence analysis of SFL which quantifies the impact of model splitting (MS) and client-side model aggregation (MA) on the learning performance, serving as a theoretical foundation. Then, we propose AdaptSFL, a novel resource-adaptive SFL framework, to expedite SFL under resource-constrained edge computing systems. Specifically, AdaptSFL adaptively controls client-side MA and MS to balance communication-computing latency and training convergence. Extensive simulations across various datasets validate that our proposed AdaptSFL framework takes considerably less time to achieve a target accuracy than benchmarks, demonstrating the effectiveness of the proposed strategies.
KunquDB: An Attempt for Speaker Verification in the Chinese Opera Scenario

This work aims to promote Chinese opera research in both musical and speech domains, with a primary focus on overcoming the data limitations. We introduce KunquDB, a relatively large-scale, well-annotated audio-visual dataset comprising 339 speakers and 128 hours of content. Originating from the Kunqu Opera Art Canon (Kunqu yishu dadian), KunquDB is meticulously structured by dialogue lines, providing explicit annotations including character names, speaker names, gender information, vocal manner classifications, and accompanied by preliminary text transcriptions. KunquDB provides a versatile foundation for role-centric acoustic studies and advancements in speech-related research, including Automatic Speaker Verification (ASV). Beyond enriching opera research, this dataset bridges the gap between artistic expression and technological innovation. Pioneering the exploration of ASV in Chinese opera, we construct four test trials considering two distinct vocal manners in opera voices: stage speech (ST) and singing (S). Implementing domain adaptation methods effectively mitigates domain mismatches induced by these vocal manner variations while there is still room for further improvement as a benchmark.
Supporting CSCL with automatic corpus analysis technology

Process analyses are becoming more and more standard in research on computer-supported collaborative learning. This paper presents the rational as well as results of an evaluation of a tool called TagHelper, designed for streamlining the process of multi-dimensional analysis of the collaborative learning process. In comparison with a hand-coded corpus coded with a 7 dimensional coding scheme, TagHelper is able to achieve an acceptable level of agreement (Cohen's Kappa of .7 or more) along 6 out of 7 of the dimensions when we commit only to the portion of the corpus where the predictor has the highest certainty. In 5 of those cases, the percentage of the corpus where the predictor is confident enough to commit a code is at least 88% of the corpus. Consequences for theory-building with respect to automatic corpus analysis are formulated. Potential applications as a support tool for process analyses, as real-time support for facilitators of on-line discussions, and for the development of more adaptive instructional support for computer-supported collaboration are discussed.
Correlation Clustering of Organoid Images

In biological and medical research, scientists now routinely acquire microscopy images of hundreds of morphologically heterogeneous organoids and are then faced with the task of finding patterns in the image collection, i.e., subsets of organoids that appear similar and potentially represent the same morphological class. We adopt models and algorithms for correlating organoid images, i.e., for quantifying the similarity in appearance and geometry of the organoids they depict, and for clustering organoid images by consolidating conflicting correlations. For correlating organoid images, we adopt and compare two alternatives, a partial quadratic assignment problem and a twin network. For clustering organoid images, we employ the correlation clustering problem. Empirically, we learn the parameters of these models, infer a clustering of organoid images, and quantify the accuracy of the inferred clusters, with respect to a training set and a test set we contribute of state-of-the-art light microscopy images of organoids clustered manually by biologists.