Miracle-dz/UniFAR-FTUs huggingface dataset:
Effective reaction rate on a heterogeneous surface

We examine the problem of prescribing the macroscale boundary condition to the solute convective–diffusive mass transport equation at a heterogeneous surface consisting of reactive circular disks distributed uniformly on a non-reactive surface. The reaction rate at the disks is characterized by a first-order kinetics. This problem was examined by Shah & Shaqfeh (J. Fluid Mech., vol. 782, 2015, pp. 260–299) who obtained the boundary condition in terms of an effective first-order rate constant, which they determined as a function of the Péclet number $Pe=\dot{\unicode[STIX]{x1D6FE}}a^{2}/D$ , the fraction $\unicode[STIX]{x1D719}$ of the surface area occupied by the reactive disks and the non-dimensional reaction rate constant $K=ka/D$ . Here, $a$ is the radius of the disks, $D$ is the solute diffusivity, $\dot{\unicode[STIX]{x1D6FE}}$ is the wall shear rate and $k$ is the first-order surface-reaction rate constant. Their analysis assumed that $Pe$ and $K$ are $O(1)$ while the ratio of the microscale $a$ to the macroscale $H$ is small. The macroscale transport process is convection–diffusion dominated under these conditions. We examine here the case when the non-dimensional numbers based on the macroscale $H$ are $O(1)$ . In this limit the microscale transport problem is reaction rate dominated. We find that the boundary condition can be expressed in terms of an effective rate constant only up to $O(\unicode[STIX]{x1D716})$ , where $\unicode[STIX]{x1D716}=a/H$ . Higher-order expressions for the mass flux involve both the macroscopic concentration and its surface gradient. The $O(\unicode[STIX]{x1D716})$ microscale problem is relatively easy to solve as the convective effects are unimportant and it is possible to obtain analytical expressions for the effective rate constant as a function of $\unicode[STIX]{x1D719}$ for both periodic and random arrangement of the disks without having to solve the boundary integral equation as was done by Shah and Shaqfeh. The results thus obtained are shown to be in good agreement with those obtained numerically by Shah and Shaqfeh for $Pe=0$ . In a separate study, Shah et al. (J. Fluid Mech., vol. 811, 2017, pp. 372–399) examined the inverse-geometry problem in which the disks are inert and the rest of the surface surrounding them is reactive. We show that the two problems are related when $Pe=0$ and $kH/D=O(1)$ . Finally, a related problem of determining the current density at a surface consisting of an array of microelectrodes is also examined and the analytical results obtained for the current density are found to agree well with the computed values obtained by solving the integral equation numerically by Lucas et al. (SIAM J. Appl. Maths, vol. 57(6), 1997, pp. 1615–1638) over a wide range of parameters characterizing this problem.
Automatically Evaluating Opinion Prevalence in Opinion Summarization

When faced with a large number of product reviews, it is not clear that a human can remember all of them and weight opinions representatively to write a good reference summary. We propose an automatic metric to test the prevalence of the opinions that a summary expresses, based on counting the number of reviews that are consistent with each statement in the summary, while discrediting trivial or redundant statements. To formulate this opinion prevalence metric, we consider several existing methods to score the factual consistency of a summary statement with respect to each individual source review. On a corpus of Amazon product reviews, we gather multiple human judgments of the opinion consistency, to determine which automatic metric best expresses consistency in product reviews. Using the resulting opinion prevalence metric, we show that a human authored summary has only slightly better opinion prevalence than randomly selected extracts from the source reviews, and previous extractive and abstractive unsupervised opinion summarization methods perform worse than humans. We demonstrate room for improvement with a greedy construction of extractive summaries with twice the opinion prevalence achieved by humans. Finally, we show that preprocessing source reviews by simplification can raise the opinion prevalence achieved by existing abstractive opinion summarization systems to the level of human performance.
Measured Static and Rotordynamic Characteristics of a Smooth-Stator/Grooved-Rotor Liquid Annular Seal

Electric submersible pumps (ESPs) utilize grooved-rotor/smooth-stator (SS/GR) seals to reduce leakage and break up contaminants within the pumped fluid. Additionally, due to their decreased surface area (when compared to a smooth seal), grooved seals decrease the chance of seizure in the case of rotor-stator rubs. Despite their use in industry, the literature does not contain rotordynamic measurements for smooth-stator/circumferentially grooved-rotor liquid annular seals. This paper presents test results consisting of leakage measurements and rotordynamic coefficients for a SS/GR liquid annular sdeal. Both static and dynamic variables are investigated for various imposed preswirl ratios (PSRs), static eccentricity ratios (0–0.8), axial pressure drops (2–8 bars), and running speeds (2–8 krpm). The seals' static and dynamic features are compared to those of a smooth seal with the same length, diameter, and minimum radial clearance. Results show that the grooves reduce leakage at lower speeds (less than 5 krpm) and higher axial pressure drops, but does little at higher speeds. The grooved seal's direct stiffness is generally negative, which would be detrimental to pump rotordynamics. As expected, increasing preswirl increases the magnitude of cross-coupled stiffness and increases the whirl frequency ratio (WFR). When compared to the smooth seal, the grooved seal has smaller effective damping coefficients, indicative of poorer stability characteristics.
Is GPT a Computational Model of Emotion? Detailed Analysis

This paper investigates the emotional reasoning abilities of the GPT family of large language models via a component perspective. The paper first examines how the model reasons about autobiographical memories. Second, it systematically varies aspects of situations to impact emotion intensity and coping tendencies. Even without the use of prompt engineering, it is shown that GPT's predictions align significantly with human-provided appraisals and emotional labels. However, GPT faces difficulties predicting emotion intensity and coping responses. GPT-4 showed the highest performance in the initial study but fell short in the second, despite providing superior results after minor prompt engineering. This assessment brings up questions on how to effectively employ the strong points and address the weak areas of these models, particularly concerning response variability. These studies underscore the merits of evaluating models from a componential perspective.
Histogram Layer Time Delay Neural Networks for Passive Sonar Classification

Underwater acoustic target detection in remote marine sensing operations is challenging due to complex sound wave propagation. Despite the availability of reliable sonar systems, target recognition remains a difficult problem. Various methods address improved target recognition. However, most struggle to disentangle the high-dimensional, non-linear patterns in the observed target recordings. In this work, a novel method combines a time delay neural network and histogram layer to incorporate statistical contexts for improved feature learning and underwater acoustic target classification. The proposed method outperforms the baseline model, demonstrating the utility in incorporating statistical contexts for passive sonar target recognition. The code for this work is publicly available.
Cardinal Characteristics on Bounded Generalised Baire Spaces

We will give an overview of four families of cardinal characteristics defined on subspaces $\prod_{\alpha\in\kappa}b(\alpha)$ of the generalised Baire space ${}^\kappa\kappa$, where $\kappa$ is strongly inaccessible and $b\in{}^\kappa\kappa$. The considered families are bounded versions of the dominating, eventual difference, localisation and anti-localisation numbers, and their dual cardinals. We investigate parameters for which these cardinals are non-trivial and how the cardinals relate to each other and to other cardinals of the generalised Cicho\'n diagram. Finally we prove that different choices of parameters may lead to consistently distinct cardinals.
Piecewise-Stationary Combinatorial Semi-Bandit with Causally Related Rewards

We study the piecewise stationary combinatorial semi-bandit problem with causally related rewards. In our nonstationary environment, variations in the base arms' distributions, causal relationships between rewards, or both, change the reward generation process. In such an environment, an optimal decision-maker must follow both sources of change and adapt accordingly. The problem becomes aggravated in the combinatorial semi-bandit setting, where the decision-maker only observes the outcome of the selected bundle of arms. The core of our proposed policy is the Upper Confidence Bound (UCB) algorithm. We assume the agent relies on an adaptive approach to overcome the challenge. More specifically, it employs a change-point detector based on the Generalized Likelihood Ratio (GLR) test. Besides, we introduce the notion of group restart as a new alternative restarting strategy in the decision making process in structured environments. Finally, our algorithm integrates a mechanism to trace the variations of the underlying graph structure, which captures the causal relationships between the rewards in the bandit setting. Theoretically, we establish a regret upper bound that reflects the effects of the number of structural- and distribution changes on the performance. The outcome of our numerical experiments in real-world scenarios exhibits applicability and superior performance of our proposal compared to the state-of-the-art benchmarks.
Active Robot Vision for Distant Object Change Detection: A Lightweight Training Simulator Inspired by Multi-Armed Bandits

In ground-view object change detection, the recently emerging mapless navigation has great potential to navigate a robot to objects distantly detected (e.g., books, cups, clothes) and acquire high-resolution object images, to identify their change states (no-change/appear/disappear). However, naively performing full journeys for every distant object requires huge sense/plan/action costs, proportional to the number of objects and the robot-to-object distance. To address this issue, we explore a new map-based active vision problem in this work: “Which journey should the robot select next? ” However, the feasibility of the active vision framework remains unclear; Since distant objects are only uncertainly recognized, it is unclear whether they can provide sufficient cues for action planning. This work presents an efficient simulator for feasibility testing, to accelerate the early-stage R&D cycles (e.g., prototyping, training, testing, and evaluation). The proposed simulator is designed to identify the degree of difficulty that a robot vision system (sensors/recognizers/planners/actuators) would face when applied to a given environment (workspace/objects). Notably, it requires only one real-world journey experience per distant object to function, making it suitable for an efficient R&D cycle. Another contribution of this work is to present a new lightweight planner inspired by the traditional multi-armed bandit problem. Specifically, we build a lightweight map-based planner on top of the mapless planner, which constitutes a hierarchical action planner. We verified the effectiveness of the proposed framework using a semantically non-trivial scenario “sofa as bookshelf”.
A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia

Low-lying coastal cities, exemplified by Norfolk, Virginia, face the challenge of street flooding caused by rainfall and tides, which strain transportation and sewer systems and can lead to property damage. While high-fidelity, physics-based simulations provide accurate predictions of urban pluvial flooding, their computational complexity renders them unsuitable for real-time applications. Using data from Norfolk rainfall events between 2016 and 2018, this study compares the performance of a previous surrogate model based on a random forest algorithm with two deep learning models: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). This investigation underscores the importance of using a model architecture that supports the communication of prediction uncertainty and the effective integration of relevant, multi-modal features.
Optimal connectivity for a large financial network

We investigate network formation for a set of financial institutions represented as nodes. Linkages are source of income, and at the same time they bear the risk of contagion. The optimal connectivity of the nodes results from a game, in which the risk of contagion depends on the choices of all nodes in the system. Our financial network model can be interpreted as a set of banks connected through funding relations, in which a node’s threshold to contagion is represented by its external funding capacity. A second interpretation is that of a set of insurers connected through reinsurance contracts, in which the threshold to contagion is represented by their capital. Our results show that when the threshold distribution across the nodes has higher variance, then, in equilibrium, the average connectivity is in general increasing, but the link failure probability decreases. This suggests that financial stability is best described in terms of the mechanism of network formation than in terms of simple statistics of the network topology like the average connectivity.
Non-Lorentzian atomic natural line-shape of core level multiplets: Access high energy x-ray photons in electron capture nuclear decay

We present a method to calculate the natural line width and energy dependent line shape due to fluorescence decay of core excited atoms within a full relativistic multi-reference configuration interaction theory. The atomic absorption lines show a deviation from a Lorentzian line-shape due to energy dependent matrix elements of the localized electronic state coupling to the photon field. This gives rise to spectral lines with small but visible asymmetry. One generally finds an excess of spectral weight at the high energy shoulder of the atomic absorption line. We present the example of nuclear decay of $^{55}$Fe by electron capture of an inner-shell core electron. We show that the amount of ionizing radiation in the energy window between 50 and 200 keV is around one order of magnitude larger due to the energy dependent fluorescence yield lifetime compared to the value one would obtain if one assumes a constant fluorescence decay rate. This yields a total change of energy deposited into ionizing radiation of about 1\textperthousand. Our calculations are in good agreement with previous calculations and experimental observations where data is available. Our results can be further validated by high precision measurements of electron capture nuclear decay spectra using recently developed experimental methods.
Nutritional Valuation of Biscuits from Breadfruit (Artocarpus altilis) and Wheat Flours (Triticum vulgare L.)

Aim: The elaboration of biscuits was proposed from different percentages of breadfruit flour (Artocarpus altilis) and wheat flour (Triticum vulgare L). Materials and Methods: The samples were processed in a similar way establishing adequate times, optimal temperatures, and good practices of raw material handling. The organoleptic evaluation of the product was carried out through sensory evaluation with 10 untrained tasters, to obtain the best treatment, being this substitution: 40% breadfruit flour + 60% wheat flour, from which the bromatological and microbiological characteristics of the biscuits were determined, with permissible results within the regulations. Results: With the analyzes carried out and using the nutritional calculator of the Ecuadorian agency for regulation, control, and health surveillance “for its initials in Spanish,” the labeling of the nutritional semaphore of the obtained product was determined by recording: “High” in fat content, “low” in sugar content, and “low” in salt content, which identify biscuits suitable for consumption. Conclusion: The results of the microbiological analysis quantified a mesophilic aerobic load of 298 CFU/g. The microbiological tests of molds and yeasts identified values of 18 CFU/g, being within the ranges established by the INEN standards. With respect to Escherichia coli, the results showed total absence.
Retweets Amplify the Echo Chamber Effect

The growing prominence of social media in public discourse has led to a greater scrutiny of the quality of online information and the role it plays in amplifying political polarization. However, studies of polarization on social media platforms like Twitter have been hampered by the difficulty of collecting data about the social graph, specifically follow links that shape the echo chambers users join as well as what they see in their timelines. As a proxy of the follower graph, researchers use retweets, although it is not clear how this choice affects analysis. Using a sample of the Twitter follower graph and the tweets posted by users within it, we reconstruct the retweet graph and quantify its impact on the measures of echo chambers and exposure. While we find that echo chambers exist in both graphs, they are more pronounced in the retweet graph. We compare the information users see via their follower and retweet networks to show that retweeted accounts share systematically more polarized content. This bias cannot be explained by the activity or polarization within users' own follower graph neighborhoods but by the increased attention they pay to accounts that are ideologically aligned with their own views. Our results suggest that studies relying on the retweet graphs overestimate the echo chamber effects and exposure to polarized information.
Simulation of Open Quantum Systems via Low-Depth Convex Unitary Evolutions

Simulating physical systems on quantum devices is one of the most promising applications of quantum technology. Current quantum approaches to simulating open quantum systems are still practically challenging on NISQ-era devices, because they typically require ancilla qubits and extensive controlled sequences. In this work, we propose a hybrid quantum-classical approach for simulating a class of open system dynamics called random-unitary channels. These channels naturally decompose into a series of convex unitary evolutions, which can then be efficiently sampled and run as independent circuits. The method does not require deep ancilla frameworks and thus can be implemented with lower noise costs. We implement simulations of open quantum systems up to dozens of qubits and with large channel rank.
Resolution-Aware Design of Atrous Rates for Semantic Segmentation Networks

DeepLab is a widely used deep neural network for semantic segmentation, whose success is attributed to its parallel architecture called atrous spatial pyramid pooling (ASPP). ASPP uses multiple atrous convolutions with different atrous rates to extract both local and global information. However, fixed values of atrous rates are used for the ASPP module, which restricts the size of its field of view. In principle, atrous rate should be a hyperparameter to change the field of view size according to the target task or dataset. However, the manipulation of atrous rate is not governed by any guidelines. This study proposes practical guidelines for obtaining an optimal atrous rate. First, an effective receptive field for semantic segmentation is introduced to analyze the inner behavior of segmentation networks. We observed that the use of ASPP module yielded a specific pattern in the effective receptive field, which was traced to reveal the module's underlying mechanism. Accordingly, we derive practical guidelines for obtaining the optimal atrous rate, which should be controlled based on the size of input image. Compared to other values, using the optimal atrous rate consistently improved the segmentation results across multiple datasets, including the STARE, CHASE_DB1, HRF, Cityscapes, and iSAID datasets.
MORPHOLOGICAL VARIATIONS OF THE LUNG: A CADAVERIC STUDY IN MUMBAI POPULATION

Address for Correspondence: Dr.Deepak N.Khedekar, Department of Anatomy, Lokamanya Tilak Municipal Medical College & GH, Sion, Mumbai, India.400022 Tel: +91-02224063214 Mobile 9224449950 E-Mail: drdeepak2025@yahoo.co.in Introduction: The lungs are vital organs of respiration and are situated in the thoracic cavity on either side of the mediastinum. The lungs are divided by fissures into lobes which facilitate movements of lobes in relation to one another during respiration. The arrangement of lung tissue into lobes helps in uniform expansion of the whole lung in inspiration. As a medical specialist detailed knowledge of anatomical variations in lung is important. Materials and Methods: We studied the variations of fissures, lobes and hilar structures in 25 right and 25 left isolated lungs from the anatomy dissection hall at LTMMC &GH, Sion, Mumbai, India. Results: Right lung variations 8% were without any fissures. Horizontal fissure was incomplete in 20%. 1(4%) showed incomplete oblique fissure and 4 % had accessory fissures. 12 % had 3 arteries, 4 % had only one artery and remaining (84 %) had 2 arteries in the hilum. 1(4%) had one vein in the hilum,2 (8 %) had 3 veins in the hilum remaining 22 (88 %) had two veins in the hilum. Only 1 right lung shows one bronchus in the hilum, remaining showed 2 bronchi in hilum. Left lung variations:1(4 %) showed incomplete oblique fissure and 1( 4 %) showed 2 fissures and 3 lobes which is quiet interesting finding. 8 % showed 2 arteries and remaining had only one artery in the hilum. 1(4%) had one vein in the hilum and1( 4 %) had 3 veins in the hilum and remaining 92% having 2 veins in hilum. 6 (24 %) of had 2 bronchi and 1 (4 %) had only one bronchus in the hilum rest are having 1 bronchus in the hilum. Conclusion: The knowledge of variations in the lobar and hilar anatomy of the lung presented in this study is clinically important while interpreting the radiological images and performing surgical procedures.
Causal reasoning in typical computer vision tasks

Deep learning has revolutionized the field of artificial intelligence. Based on the statistical correlations uncovered by deep learning-based methods, computer vision tasks, such as autonomous driving and robotics, are growing rapidly. Despite being the basis of deep learning, such correlation strongly depends on the distribution of the original data and is susceptible to uncontrolled factors. Without the guidance of prior knowledge, statistical correlations alone cannot correctly reflect the essential causal relations and may even introduce spurious correlations. As a result, researchers are now trying to enhance deep learning-based methods with causal theory. Causal theory can model the intrinsic causal structure unaffected by data bias and effectively avoids spurious correlations. This paper aims to comprehensively review the existing causal methods in typical vision and vision-language tasks such as semantic segmentation, object detection, and image captioning. The advantages of causality and the approaches for building causal paradigms will be summarized. Future roadmaps are also proposed, including facilitating the development of causal theory and its application in other complex scenarios and systems.