Miracle-dz/UniFAR-FTUs huggingface dataset:
Motion and deformation of capsules flowing through a corner in the inertial and non-inertial regimes

We investigate the inertial and non-inertial dynamics of three-dimensional elastic capsules flowing through a square channel presenting a sharp corner. Our study analyzes the trajectory, surface area, velocity and membrane stress of the capsules in the case of a single capsule, a system of two interacting capsules and a train of ten capsules released upstream of the corner. The channel Reynolds number $Re$ ranges from 0.01 to 50 and the Capillary number $Ca$, which measures the ratio of the viscous and elastic stresses, ranges from 0.075 to 0.35. We find that in the inertial regime, the membrane stretch and stress increase dramatically as compared to the non-inertial case, and that the velocity overshoot inside the corner is also enhanced. The maximum capsule deformation is observed to depend nearly linearly on $Ca$ and $Re$. Additionally, we report a repelling mechanism between two confined capsules when their initial interspacing distance $d$ is smaller than a critical value $d_c$. The deformation of the leading capsule is found to be mitigated by the presence of the following capsule. In the case of multiple capsules flowing through the corner, we observe that the increase in the maximum surface area of the trailing capsules eventually saturates at the tail of the train. Moreover, we find that the corner tends to separate the capsules regardless of their upstream interspacing distances $d$. This study contributes to the elaboration of practical guidelines for controlling capsule breakup and predicting throughput in both inertial and non-inertial microfluidic experiments.
Few Shot Learning for Medical Imaging: A Comparative Analysis of Methodologies and Formal Mathematical Framework

Deep learning becomes an elevated context regarding disposing of many machine learning tasks and has shown a breakthrough upliftment to extract features from unstructured data. Though this flourishing context is developing in the medical image processing sector, scarcity of problem-dependent training data has become a larger issue in the way of easy application of deep learning in the medical sector. To unravel the confined data source, researchers have developed a model that can solve machine learning problems with fewer data called ``Few shot learning". Few hot learning algorithms determine to solve the data limitation problems by extracting the characteristics from a small dataset through classification and segmentation methods. In the medical sector, there is frequently a shortage of available datasets in respect of some confidential diseases. Therefore, Few shot learning gets the limelight in this data scarcity sector. In this chapter, the background and basic overview of a few shots of learning is represented. Henceforth, the classification of few-shot learning is described also. Even the paper shows a comparison of methodological approaches that are applied in medical image analysis over time. The current advancement in the implementation of few-shot learning concerning medical imaging is illustrated. The future scope of this domain in the medical imaging sector is further described.
Moving mirror-field dynamics under intrinsic decoherence

In this paper, we study the decaying dynamics in the mirror-field interaction by means of the intrinsic decoherence scheme. Factorization of the mirror-field Hamiltonian with the use of displacement operators allows us to calculate the explicit solution to Milburn’s equation for arbitrary initial conditions. We show expectation values, correlations, and Husimi functions for the solutions obtained.
Putting Natural in Natural Language Processing

Human language is firstly spoken and only secondarily written. Text, however, is a very convenient and efficient representation of language, and modern civilization has made it ubiquitous. Thus the field of NLP has overwhelmingly focused on processing written rather than spoken language. Work on spoken language, on the other hand, has been siloed off within the largely separate speech processing community which has been inordinately preoccupied with transcribing speech into text. Recent advances in deep learning have led to a fortuitous convergence in methods between speech processing and mainstream NLP. Arguably, the time is ripe for a unification of these two fields, and for starting to take spoken language seriously as the primary mode of human communication. Truly natural language processing could lead to better integration with the rest of language science and could lead to systems which are more data-efficient and more human-like, and which can communicate beyond the textual modality.
Construction of nearly uniform designs on irregular regions

ABSTRACT The uniform design is a kind of important experimental design which has great practical value in production and living. Most existing literatures on this topic focus on the construction of uniform designs on regular regions. However, because of the complexity of practical situations, the irregular design region is more common in real life. In this paper, an algorithm is proposed to the construction of nearly uniform designs on irregular regions. The basic idea is to make use of uniform designs on a larger regular region with the irregular region being a subregion. Some theoretical justifications on the proposed algorithm are provided. Both the comparisons with the existing results and a real-life example show that our proposed algorithm is effective.
Topological Fundamental Groupoid. II. An action category of the fundamental groupoid

For a path connected, locally path connected and semilocally simply connected space $X$, let $\Pi_1(X)$ denote its topologised fundamental groupoid as established in the first article of this series. Let $\mathcal{E}$ be the category of $\Pi_1(X)$-spaces in which the momentum maps are local homeomorphisms. We show that this category is isomorphic to that of covering spaces of $X$. Using this, we give different characterisations for free or proper actions of the fundamental groupoid in $\mathcal{E}$.
Anisotropic-Isotropic Transition of Cages at the Glass Transition

Characterizing the local structural evolution is an essential step in understanding the nature of glass transition. In this work, we probe the evolution of Voronoi cell geometry in simple glass models, and find that the individual particle cages deform anisotropically in supercooled liquid and isotropically in glass. We introduce an anisotropy parameter $k$ for each Voronoi cell, which mean value exhibits a sharp change at the mode-coupling glass transition $\phi_\mathrm{c}$. Moreover, a power law of packing fraction $\phi\propto q_1^{-d}$ is discovered in the liquid regime with $d>D$, in contrast to $d=D$ in the glass regime, where $q_1$ is the first peak position of structure factor, and $D$ is the space dimension. This power law is explained by the change of $k$. The active motions in supercooled liquid are spatially correlated with long axes rather than short axes of Voronoi cells. In addition, the dynamic slowing down approaching the glass transition can be well characterized through a modified free volume model based on $k$. These findings reveal that the nonagnostic structural parameter $k$ determines glassy dynamics and is effective in identifying the structure-dynamics correlations and the glass transition.
Cluster Analysis of Hourly Wind Measurements to Reveal Synoptic Regimes Affecting Air Quality

Abstract A clustering algorithm is developed to study hourly, ground-level wind measurements obtained from a network of monitoring stations positioned throughout the San Francisco Bay Area of California. A statistical model based on principal components analysis (or empirical orthogonal functions) is used to cluster these autocorrelated and cross-correlated observations. Patterns at the synoptic time scale are isolated by using windowing and scaling operations to treat the data. Four dominant wind patterns that affect air quality are identified for the study region, and summer days from 8 yr of historical data are assigned to these modes. One cluster captures a high pressure system over the western United States, the anticyclonic winds of which block the typical marine flow through the study region. Differential heating convects a polluted air mass to a nearby valley in which severe episodes of higher-than-average ozone composition occur. A second pattern represents a seasonal, offshore ridge of high pres.
Boosting Radiology Report Generation by Infusing Comparison Prior

Recent transformer-based models have made significant strides in generating radiology reports from chest X-ray images. However, a prominent challenge remains; these models often lack prior knowledge, resulting in the generation of synthetic reports that mistakenly reference non-existent prior exams. This discrepancy can be attributed to a knowledge gap between radiologists and the generation models. While radiologists possess patient-specific prior information, the models solely receive X-ray images at a specific time point. To tackle this issue, we propose a novel approach that leverages a rule-based labeler to extract comparison prior information from radiology reports. This extracted comparison prior is then seamlessly integrated into state-of-the-art transformer-based models, enabling them to produce more realistic and comprehensive reports. Our method is evaluated on English report datasets, such as IU X-ray and MIMIC-CXR. The results demonstrate that our approach surpasses baseline models in terms of natural language generation metrics. Notably, our model generates reports that are free from false references to non-existent prior exams, setting it apart from previous models. By addressing this limitation, our approach represents a significant step towards bridging the gap between radiologists and generation models in the domain of medical report generation.
A brief introduction to satellite communications for Non-Terrestrial Networks (NTN)

At present (year 2023), approximately 2,500 satellites are currently orbiting the Earth. This number is expected to reach 50,000 satellites (that is, 20 times growth) for the next 10 years, thanks to the recent advances concerning launching satellites at low cost and with high probability of success. In this sense, it is expected that next years the world will witness a massive increase in mobile connectivity thanks to the combination of 5G deployments and satellites, building the so-called Space-Terrestrial Integrated Network (STIN), thanks to the emergence of Non-Terrestrial Networks (NTNs). This document overviews the foundations of satellite communications as a short tutorial for those interested in research and development on Space-Terrestrial Integrated Networks (STIN) and Non-Terrestrial Networks (NTN) for supporting 5G in remote areas.
PhysBench: A Benchmark Framework for Remote Physiological Sensing with New Dataset and Baseline

In recent years, due to the widespread use of internet videos, physiological remote sensing has gained more and more attention in the fields of affective computing and telemedicine. Recovering physiological signals from facial videos is a challenging task that involves a series of preprocessing, image algorithms, and post-processing to finally restore waveforms. We propose a complete and efficient end-to-end training and testing framework that provides fair comparisons for different algorithms through unified preprocessing and post-processing. In addition, we introduce a highly synchronized lossless format dataset along with a lightweight algorithm. The dataset contains over 32 hours (3.53M frames) of video from 58 subjects; by training on our collected dataset both our proposed algorithm as well as existing ones can achieve improvements.
SRTK: A Toolkit for Semantic-relevant Subgraph Retrieval

Information retrieval based knowledge base question answering (KBQA) first retrieves a subgraph to reduce search space, then reasons on the subgraph to select answer entities. Existing approaches have three issues that impede the retrieval of such subgraphs. Firstly, there is no off-the-shelf toolkit for semantic-relevant subgraph retrieval. Secondly, existing methods are knowledge-graph-dependent, resulting in outdated knowledge graphs used even in recent studies. Thirdly, previous solutions fail to incorporate the best available techniques for entity linking or path expansion. In this paper, we present SRTK, a user-friendly toolkit for semantic-relevant subgraph retrieval from large-scale knowledge graphs. SRTK is the first toolkit that streamlines the entire lifecycle of subgraph retrieval across multiple knowledge graphs. Additionally, it comes with state-of-the-art subgraph retrieval algorithms, guaranteeing an up-to-date solution set out of the box.
Distilled Mid-Fusion Transformer Networks for Multi-Modal Human Activity Recognition

Human Activity Recognition is an important task in many human-computer collaborative scenarios, whilst having various practical applications. Although uni-modal approaches have been extensively studied, they suffer from data quality and require modality-specific feature engineering, thus not being robust and effective enough for real-world deployment. By utilizing various sensors, Multi-modal Human Activity Recognition could utilize the complementary information to build models that can generalize well. While deep learning methods have shown promising results, their potential in extracting salient multi-modal spatial-temporal features and better fusing complementary information has not been fully explored. Also, reducing the complexity of the multi-modal approach for edge deployment is another problem yet to resolve. To resolve the issues, a knowledge distillation-based Multi-modal Mid-Fusion approach, DMFT, is proposed to conduct informative feature extraction and fusion to resolve the Multi-modal Human Activity Recognition task efficiently. DMFT first encodes the multi-modal input data into a unified representation. Then the DMFT teacher model applies an attentive multi-modal spatial-temporal transformer module that extracts the salient spatial-temporal features. A temporal mid-fusion module is also proposed to further fuse the temporal features. Then the knowledge distillation method is applied to transfer the learned representation from the teacher model to a simpler DMFT student model, which consists of a lite version of the multi-modal spatial-temporal transformer module, to produce the results. Evaluation of DMFT was conducted on two public multi-modal human activity recognition datasets with various state-of-the-art approaches. The experimental results demonstrate that the model achieves competitive performance in terms of effectiveness, scalability, and robustness.
Software-based Automatic Differentiation is Flawed

Various software efforts embrace the idea that object oriented programming enables a convenient implementation of the chain rule, facilitating so-called automatic differentiation via backpropagation. Such frameworks have no mechanism for simplifying the expressions (obtained via the chain rule) before evaluating them. As we illustrate below, the resulting errors tend to be unbounded.
Asymptotics of Generalized Bessel Functions and Weight Multiplicities via Large Deviations of Radial Dunkl Processes

This paper studies the asymptotic behavior of several central objects in Dunkl theory as the dimension of the underlying space grows large. Our starting point is the observation that a recent result from the random matrix theory literature implies a large deviations principle for the hydrodynamic limit of radial Dunkl processes. Using this fact, we prove a variational formula for the large-$N$ asymptotics of generalized Bessel functions, as well as a large deviations principle for the more general family of radial Heckman-Opdam processes. As an application, we prove a theorem on the asymptotic behavior of weight multiplicities of irreducible representations of compact or complex simple Lie algebras in the limit of large rank. The theorems in this paper generalize several known results describing analogous asymptotics for Dyson Brownian motion, spherical matrix integrals, and Kostka numbers.
Learning to Evaluate the Artness of AI-generated Images

Assessing the artness of AI-generated images continues to be a challenge within the realm of image generation. Most existing metrics cannot be used to perform instance-level and reference-free artness evaluation. This paper presents ArtScore, a metric designed to evaluate the degree to which an image resembles authentic artworks by artists (or conversely photographs), thereby offering a novel approach to artness assessment. We first blend pre-trained models for photo and artwork generation, resulting in a series of mixed models. Subsequently, we utilize these mixed models to generate images exhibiting varying degrees of artness with pseudo-annotations. Each photorealistic image has a corresponding artistic counterpart and a series of interpolated images that range from realistic to artistic. This dataset is then employed to train a neural network that learns to estimate quantized artness levels of arbitrary images. Extensive experiments reveal that the artness levels predicted by ArtScore align more closely with human artistic evaluation than existing evaluation metrics, such as Gram loss and ArtFID.
Sobolev regularity theory for the non-local elliptic and parabolic equations on $ C^{1,1} $ open sets

We study the zero exterior problem for the elliptic equation $$ \Delta^{\alpha/2}u-\lambda u=f, \quad x\in D\,; \quad u|_{D^c}=0 $$ as well as for the parabolic equation $$ u_t=\Delta^{\alpha/2}u+f, \quad t>0,\, x\in D \,; \quad u(0,\cdot)|_D=u_0, \,u|_{[0,T]\times D^c}=0. $$ Here, $\alpha\in (0,2)$, $\lambda \geq 0$ and $D$ is a $C^{1,1}$ open set. We prove uniqueness and existence of solutions in weighted Sobolev spaces, and obtain global Sobolev and H\"older estimates of solutions and their arbitrary order derivatives. We measure the Sobolev and H\"older regularities of solutions and their arbitrary derivatives using a system of weights consisting of appropriate powers of the distance to the boundary. The range of admissible powers of the distance to the boundary is sharp.
DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition

Implicit Discourse Relation Recognition (IDRR) is a sophisticated and challenging task to recognize the discourse relations between the arguments with the absence of discourse connectives. The sense labels for each discourse relation follow a hierarchical classification scheme in the annotation process (Prasad et al., 2008), forming a hierarchy structure. Most existing works do not well incorporate the hierarchy structure but focus on the syntax features and the prior knowledge of connectives in the manner of pure text classification. We argue that it is more effective to predict the paths inside the hierarchical tree (e.g.,"Comparison ->Contrast ->however") rather than flat labels (e.g., Contrast) or connectives (e.g., however). We propose a prompt-based path prediction method to utilize the interactive information and intrinsic senses among the hierarchy in IDRR. This is the first work that injects such structure information into pre-trained language models via prompt tuning, and the performance of our solution shows significant and consistent improvement against competitive baselines.
Examining the Factors of Place Sameness: A Classroom Re-creation Task in a Virtual Environment

Virtual re-creations of real-world places are attractive and becoming more popular. Attractiveness of re-created place is commonly determined by the degree of visual similarity to the original places. Even if the re-creation has poor similarity to the original, there are cases in which people recognize and allow the re-creation to have similarity as the same entity. However, the mechanisms and factors behind this contradiction have not yet been studied. This is the first study focusing on the activity and meaningofpeoplewhentheyfeelsamenesstoare-createdplace. Thispaper investigates the concept of place sameness, which is defined as the degree of similarity between an original and a virtual re-creation that represents the original place. Further, the factors of place sameness are explored using a classroom re-creation task with virtual reality (VR) technology. The participants were instructed to recreate two classrooms within virtual environmentsusingvirtualfixtures. Asresults, thedisplaydevicesinRoom B, the student desks in Room A at the memory index, display devices in RoomA,andstudentdesksandothersinRoomBattheaveragedistancehad moderate or large correlations with the place sameness index. The results suggest that the reproducibility of the number of objects related to activities in a place, and the inaccuracy of the positions of objects are factors of place sameness. Our own interpretation of the uncanny valley effect of a place was also partially observed. The main contributions of this study are the proposal of the concept of place sameness as a new perspective for virtual re-creation research and the finding of promising factors for that.
TaLU: A Hybrid Activation Function Combining Tanh and Rectified Linear Unit to Enhance Neural Networks

The application of the deep learning model in classification plays an important role in the accurate detection of the target objects. However, the accuracy is affected by the activation function in the hidden and output layer. In this paper, an activation function called TaLU, which is a combination of Tanh and Rectified Linear Units (ReLU), is used to improve the prediction. ReLU activation function is used by many deep learning researchers for its computational efficiency, ease of implementation, intuitive nature, etc. However, it suffers from a dying gradient problem. For instance, when the input is negative, its output is always zero because its gradient is zero. A number of researchers used different approaches to solve this issue. Some of the most notable are LeakyReLU, Softplus, Softsign, ELU, ThresholdedReLU, etc. This research developed TaLU, a modified activation function combining Tanh and ReLU, which mitigates the dying gradient problem of ReLU. The deep learning model with the proposed activation function was tested on MNIST and CIFAR-10, and it outperforms ReLU and some other studied activation functions in terms of accuracy(upto 6% in most cases, when used with Batch Normalization and a reasonable learning rate).
Exact augmented Lagrangians for constrained optimization problems in Hilbert spaces II: Applications

This two-part study is devoted to the analysis of the so-called exact augmented Lagrangians, introduced by Di Pillo and Grippo for finite dimensional optimization problems, in the case of optimization problems in Hilbert spaces. In the second part of our study we present applications of the general theory of exact augmented Lagrangians to several constrained variational problems and optimal control problems, including variational problems with additional constraints at the boundary, isoperimetric problems, problems with nonholonomic equality constraints (PDE constraints), and optimal control problems for linear evolution equations. We provide sufficient conditions for augmented Lagrangians for these problems to be globally/completely exact, that is, conditions under which a constrained variational problem/optimal control problem becomes equivalent to the problem of unconstrained minimization of the corresponding exact augmented Lagrangian in primal and dual variables simultaneously.