Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying Opinion Influencers over Social Networks

The adaptive social learning paradigm deals with the opinion formation process by a network of communicating agents in a dynamic environment. In this study, we show that a sequence of publicly exchanged beliefs allows users to discover rich information about the underlying model. In particular, it is shown that it is possible (i) to identify the influence of each individual agent to the objective of truth learning, (ii) to discover how well-informed each agent is, and (iii) to learn the underlying network topology.
COMPUTATIONAL STUDY OF TEMPERATURE GRADIENT EFFECTS ON HOT-WIRE MEASUREMENTS

The present investigation is — to the author's knowledge — the first attempt to document, understand, and ultimately correct the effect of temperature gradients on the higher-order moments of turbulent boundary-layer fluctuations obtained through hot-wire anemometry. Results, based on velocity and temperature data generated by means of direct numerical simulations from a turbulent channel flow emphasise that the ignored effect of temperature gradients might be able to explain some of the puzzling observations in the literature. Introduction and Motivation The (insufficient) finite length of a hot-wire sensor has been known for long to attenuate the measured fluctuations. With respect to wall-bounded turbulent flows efforts date back to the early 80s. Nonetheless, it was not until Hutchins et al. (5) that the quantitative effect of spatial resolution on the spectral content of the measured signal and in turn the root- mean-square (r.m.s.) was documented, thereby enabling to distinguish between Reynolds number (Re) as well as spatial and frequency resolution effects. In particular, the debate regarding the amplitude of the inner-scaled near-wall peak of the streamwise velocity (u) r.m.s. in canonical wall-bounded turbulent flows (7) has avalanched efforts to document, understand and ultimately correct spatial resolution effects (9). Since the amplitude grows logarithmically with Re for flat-plate boundary layers (5) and channel flows (1), the actual scaling behaviour can easily be masked by other effects. Due to the inconclusiveness of recent studies with respect to turbulent pipe flows at highRe (4) a possible explanation was thought to lie in the temperature gradient that can arise across the measurement plane in internal flows (8). The present study is a first attempt to document and understand this effect and propose and assess possible (practical) solutions.
Finite-time thermodynamics: The maximal productivity of binary distillation and selection of optimal separation sequence for an ideal ternary mixture

The paper shows that an attainability region of a binary fractionating column depends on two parameters. These parameters are expressed through kinetic coe cients andmixture properties. The paper also considers a problem of nding the maximal productivity of the ternary distillation. Selection conditions for an optimal separation order and expressions for the maximal productivity of this process were obtained.
Assessment of fish biodiversity in relation to physico-chemical characteristics of Kolar River, Madhya Pradesh, India

The present study was conducted on the Kolar River, a tributary of the Narmada River. The fish diversity of the Kolar River in the Sehore district of Madhya Pradesh, Central India, is represented in this view. It was investigated from February 2021 to June 2022. The Kolar River is a vital source of water for the people who live in nearby villages, primarily for domestic use. In the community, fishing for a living and food is a common practise. The study's goal was to investigate the Kolar River's water quality and fish fauna. During the investigation, six sampling stations were chosen for the collection of data: Near reservoir, Lawakhadi, Veerpur, Near old bridge, Dana baba temple, Amdoh
Data-driven predictive maintenance method for digital welding machines

Digital welding machine (DWM) is an advanced tool for material forming. The lifespan and health status of DWMs are closely related to the safety and reliability. To address the problem of low accuracy in the lifespan prediction of DWMs, a model based on immune algorithm (IA) and long short-term memory network (LSTM) with attention mechanism is proposed. First, the degradation characteristic indicators of the lifespan of DWMs are evaluated and selected. Then, a health index is constructed using linear regression to quantitatively reflect the lifespan status of DWMs. The optimized model is used to predict the remaining lifespan, and compared with various models using 5 indicators. Finally, predictive maintenance of DWMs is carried out based on product inspection and production scheduling. the optimal solution for the objective function is obtained to calculate the best predictive maintenance method for the digital welding machine.During the lifespan prediction process, the optimized model has a 20% decrease in root mean square error and a 35.8% decrease in mean square error compared to the traditional LSTM model. The average absolute error is decreased by 14.2% and the average absolute percentage error is closer to 0, while the coefficient of determination increases by 23%. By combining with actual production line arrangements, maintenance of DWMs can be performed at the most appropriate time to minimize maintenance costs.
1 1 O ct 2 01 2 Threshold functions for distinct parts : revisiting Erdős –

We study four problems: put n distinguishable/non-distinguishable balls into k non-empty distinguishable/nondistinguishable boxes randomly. What is the threshold function k = k(n) to make almost sure that no two boxes contain the same number of balls? The non-distinguishable ball problems are very close to the Erdős–Lehner asymptotic formula for the number of partitions of the integer n into k parts with k = o(n). The problem is motivated by the statistics of an experiment, where we only can tell whether outcomes are identical or different. AMS Subject Classification (2010): 05A17, 05A18, 05A16, 05D40, 11P;
Difference Coarrays of Rational Arrays

Rational arrays were recently proposed for direction of arrival (DOA) estimation, and some of their advantages were discussed. In this paper we discuss further advantages of rational arrays, by considering their difference coarrays. Although integer arrays such as nested arrays, and coprime arrays are well-known for their ability to identify ${{\mathcal{O}}}\left({{m^2}}\right)$ uncorrelated sources using m sensors through difference coarray domain, they can do so only when a large enough aperture is available. However, rational arrays can do so even when the aperture is constrained. We demonstrate that adding a few sensors at non-integer locations in an otherwise integer array can add a large number of fractional lags at which autocorrelation can be estimated. Appropriately designed sparse integer arrays can also be scaled to produce rational arrays that fit available aperture and have large uniform coarray segments at rational locations. Monte-Carlo simulations are provided to demonstrate the advantages, and practical issues associated with shrinking, such as increased mutual coupling, are discussed.
Semi-Supervised Medical Report Generation via Graph-Guided Hybrid Feature Consistency

Medical report generation generates the corresponding report according to the given radiology image, which has been attracting increasing research interest. However, existing methods mainly adopt supervised training which rely on large amount of medical reports that are actually unavailable owing to the labor-intensive labeling process and privacy protection protocol. In the meanwhile, the intrinsic relationships between local pathological changes in the image are often ignored, which actually are important hints to high quality report generation. To this end, we propose a Relation-Aware Mean Teacher (RAMT) framework, which follows a standard mean teacher paradigm for semi-supervised report generation. The key to the encoder of the backbone network is the Graph-guided Hybrid Feature Encoding (GHFE) module, which exploits a prior disease knowledge graph to encode the intrinsic relations between pathological changes into the graph embedding and learns a word dictionary to retrieve the semantic embedding for each potential pathological change. GHFE combines the graph embedding, semantic embedding and visual features to form hybrid features, which are sent to a Transformer-based decoder for report generation. Extensive experiments on the MIMIC-CXR and IU X-Ray datasets demonstrate the effectiveness of our proposed approach.
3-D Printed All-Dielectric GRIN Lens Antenna With an Integrated Feeder

In this paper we present the design, fabrication, and experimental verification of a new type of Graded-index (GRIN) lens antenna with an integrated feeder. The continuously varying refractive index distribution is chosen appropriately to offer the rays collimation at the lens aperture. It is practically implemented by varying the material density in a host medium, thus realizing a new type of all-dielectric high gain antenna, entirely using 3D printing. This solution can find application to high gain wireless communication and measurement systems. This GRIN lens antenna is printed in one monolithic process and does not require the feeder to be placed at a focal distance, thus complying with more strict space requirements. It accepts interchangeable feeds that can cover a wide frequency range. The directivity and gain are evaluated using near-field measurements in the Ku-band. A 40% measured aperture efficiency is achieved at 14GHz. The challenges and performance limitations that come with 3D printing, as compared to the design of idealized continuous distribution GRIN lenses are discussed.
A multidecadal oscillation in precipitation and temperature series is pronounced in low flow series from Puget Sound streams

In Pacific Northwest streams, summer low flows limit water available to competing instream (salmon) and out‐of‐stream (human) uses, creating broad interest in how and why low flows are trending. Analyses that assumed linear (monotonic) change over the last ~60 years revealed declining low flow trends in minimally disturbed streams. Here, polynomials were used to model flow trends between 1929 and 2015. A multidecadal oscillation was observed in flows, which increased initially from the 1930s until the 1950s, declined until the 1990s, and then increased again. A similar oscillation was detected in precipitation series, and opposing oscillations in surface temperature, Pacific Decadal Oscillation, and Interdecadal Pacific Oscillation series. Multidecadal oscillations with similar periods to those described here are well known in climate indices. Fitted model terms were consistent with flow trends being influenced by at least two drivers, one oscillating and the other monotonic. Anthropogenic warming is a candidate driver for the monotonic decline, and variation in (internal) climatic circulation for the oscillating trend, but others were not ruled out. The recent upturn in streamflows suggests that anthropogenic warming has not been the dominant factor driving streamflow trends, at least until 2015. Climate projections based on simulations that omit drivers of multidecadal variation are likely to underestimate the range, and rate of change, of future climatic variation.
VAN-ICP: GPU-Accelerated Approximate Nearest Neighbor Search for ICP Registration via Voxel Dilation

The Iterative Closest Points (ICP) algorithm and its variants have been widely applied for 3D point cloud registration which estimates the rigid transformation. As the most computationally intensive step in ICP, nearest neighbor search (NNS) takes up most of the execution time, hindering the practical applications of ICP registration. To overcome the bottleneck, we propose a novel GPU-friendly approximate nearest neighbor search (ANNS) acceleration scheme, named Voxel dilAtioN (VAN), which can efficiently convert the global search to local $\left. {\mathcal{O}(n)} \right)$. Extensive experiments demonstrate that our VAN can drastically boost the NNS processing while keeping high registration accuracy. Specifically, our GPU-based VAN-ICP achieves 2.7x, 7.6x, and 13.4x speedup on three datasets compared with the CPU-based ICP implementation of Point Cloud Library (PCL). Source codes are available at https://github.com/mfxox/VAN-ICP.
Level-Line Guided Edge Drawing for Robust Line Segment Detection

Line segment detection plays a cornerstone role in computer vision tasks. Among numerous detection methods that have been recently proposed, the ones based on edge drawing attract increasing attention owing to their excellent detection efficiency. However, the existing methods are not robust enough due to the inadequate usage of image gradients for edge drawing and line segment fitting. Based on the observation that the line segments should locate on the edge points with both consistent coordinates and level-line information, i.e., the unit vector perpendicular to the gradient orientation, this paper proposes a level-line guided edge drawing for robust line segment detection (GEDRLSD). The level-line information provides potential directions for edge tracking, which could be served as a guideline for accurate edge drawing. Additionally, the level-line information is fused in line segment fitting to improve the robustness. Numerical experiments show the superiority of the proposed GEDRLSD1 algorithm compared with state-of-the-art methods.
Multimodal Facial Action unit Detection with Physiological Signals

Most of the existing facial action unit detection models seek to improve detection accuracy by utilizing multiple visual modalities, including 3D geometry, thermal, and depth images. However, the potential usage of heterogeneous physiological modalities (e.g., heart rate and blood pressure) for AU detection is not considered in current works. Meanwhile, it’s challenging to fully utilize the hidden emotion-correlated physiological signals. In this paper, we propose deep networks to extract temporal features from periodic and non-periodic time-series signals and design an informativeness-based feature fusion module to handle the signal noise. Then, we utilize spatial-temporal visual representations to infer the physiological embeddings, allowing absent physiological data during testing. Experiments show that our multimodal framework achieves state-of-the-art performances on two widely used datasets: MMSE and BP4D.
Pattern segmentation method based on super-pixel multi-feature fusion

The paper presents an optimized segmentation algorithm for clothing patterns. It uses the depth model to improve the image quality. Super-pixel pre-segmentation enhances the ability to capture the edge information of patterns.LBP texture information, HSV color information and image pixel space information are used to optimize the parameters of Gaussian mixture model. The energy function is optimized by maxflow/mincut to realize the fast segmentation of clothing patterns. The simulation results show that the segmentation accuracy and precision of the algorithm are improved.
Development of control software for self-organizing intelligent mobile robots

Maintaining a specific geometric formation during the movement is crucial for multiagent systems of mobile robots in various applications. Proper coordination can lead to reduced system costs, increased reliability and efficiency, and system adaptability and flexibility. This research proposes a novel movement coordination method for self-governing multiagent systems of intelligent mobile robots. The proposed method uses a leader-follower technique with a virtual leader to maintain a specific geometric structure. Additionally, the epsilon greedy algorithm is utilized to avoid loops. To reduce power consumption, it is proposed to turn on only a few robots' lidars at a time. They could drive all the robots in the group, allowing them to reach the goal without colliding with obstacles. Experiments on a complex map with nine robots were conducted to test the method's effectiveness. The success rate of the swarm reaching the target position and the number of steps needed were evaluated. Testing varied angular velocities of 1 to 20 degrees and linear velocities of 0.1 to 5.5 m/s. Results show the method effectively guides the robots without collisions. This method enables a group of self-governing multiagent systems of intelligent mobile robots to maintain a desired formation while avoiding obstacles and reducing power consumption. The results of the experimental study demonstrate the method's potential to be implemented in real-world missions and traffic management systems to increase efficiency and reduce costs. The proposed method can be utilized in military missions and traffic management systems, where maintaining a specific geometric formation is crucial. The method's ability to avoid obstacles and reduce power consumption can also lead to reduced costs and increased efficiency.
Fast Multiscale 3D Reconstruction Using Single-Photon Lidar Data

Time-correlated single-photon technology is emerging as an important approach to 3D Imaging. This paper presents a reconstruction algorithm that exploits data statistics and multi-scale information to deliver clean depth and reflectivity images together with associated uncertainty maps. The statistical method has been implemented to run on graphics processing units (GPUs) that enable real-time reconstruction of moving scenes at more than 1000 depth frames per second on the 32 × 64 pixels real Quantic4x4 SPAD sensor array data. Comparisons with state-of-the-art algorithms on simulated and real data demonstrate the robust and efficient performance of the proposed method.
MTDL-NET: Morphological and Temporal Discriminative Learning for Heartbeat Classification

Heartbeat classification based on Electrocardiogram (ECG) signal is crucial to the clinical diagnosis of heart diseases, which has attracted special interest both industrially and scientifically. However, previous methods on ECG mainly lay emphasis on extracting the optimal hand-crafted or deep features, while ignore to explore the potential of morphological and temporal representation to further boost the performance of heartbeat classification task. To address this challenge, in this work, we propose two main modules: (1) Masked attention embedding for extracting discriminative morphological feature; (2) Temporal feature enhanced mechanism for enhancing temporal representation of heartbeat. We combine two modules with transformer encoder architecture and obtain a simple yet effective signal classification model dubbed as MTDL-Net. Comprehensive experiments on benchmark dataset demonstrate that our method can surpass the previous methods by a clear margin quantitatively. Qualitative analysis also validate that MTDL-Net has strong feature extraction capacity and interpretability in the heartbeat classification task.
Convergence of Stochastic PDMM

In recent years, the large increase in connected devices and the data that are collected by these devices have caused a heightened interest in distributed processing. Many practical distributed networks are of heterogeneous nature, because different devices in the network can have different specifications. Because of this, it is highly desirable that algorithms operating within these networks can operate asynchronously, since in that case there is no need for clock synchronisation between the nodes, and the algorithm is not slowed down by the slowest device in the network. In this paper, we focus on the primal-dual method of multipliers (PDMM), which is a promising distributed optimisation algorithm that is suitable for distributed optimisation in heterogeneous networks. Most theoretical work that can be found in existing literature focuses on synchronous versions of PDMM. In this work, we prove the convergence of stochastic PDMM, which is a general framework that can model variations such as asynchronous PDMM and PDMM with transmission losses.
Digital financial services and human development: current landscape and research prospects

ABSTRACT This study explores and analyses the implications of digital financial services (DFS) on human development from a global perspective. Informed by a systematic literature review of studies published in the past two decades, from 2000 to 2020, this research unveils six overarching themes: contextual conditions, technological skills and financial literacy, consistent trust, shaping financial behavior, energizing economic activities, and supporting financial inclusion. Further analysis categorizes these themes into what constitutes the two intertwined dimensions of DFS: foundational conditions and effectual repercussions. We discuss how these dimensions enhance our understanding of the role information and communication technologies play in contributing to human development. We also present practical implications for different stakeholders in the financial sector.
Supervised linear dimension reduction

Supervised linear dimension reduction (SLDR) is one of the most effective methods for complexity reduction, which has been widely applied in pattern recognition, computer vision, information retrieval, and multimedia data processing. This thesis explores SLDR by enriching the theory of existing methods and by proposing new methods. In the first part of this thesis, we present theoretical analysis of Fisher’s linear discriminant analysis (LDA), one of the most representative methods for SLDR. 1) Classical asymptotic analysis of LDA is based on a fixed dimensionality, and thus does not apply in the case where the dimensionality and the training sample number are proportionally large. Besides, the classical result does not provide quantitative information on the performance of LDA. To address these limitations, we present an asymptotic generalization analysis of LDA, allowing both the dimensionality and the training sample number to be proportionally large, from which we principally obtain an asymptotic generalization bound that quantitatively describes the performance of LDA in terms of the dimensionality and the training sample number. 2) We study a new regularization method for LDA, termed the block-diagonal regularization. By partitioning variables into small groups and treating them independently, block-diagonal regularization effectively reduces the dimensionality to training sample number ratio and thus improves the generalization ability of LDA. We present a theoretical justification of the block-diagonally regularized LDA by investigating its approximation and sample errors. We show that the block-diagonally regularized LDA performs competitively compared to other types of regularized LDA, e.g., with the Tikhonov regularization and the banded regularization. In the second part of this thesis, we propose two new methods for SLDR. 1) The first method is for parametric SLDR, termed max-min distance analysis (MMDA). MMDA optimizes the projection matrix by maximizing the minimum pairwise distance of all class pairs in the dimension reduced space. Thus, it duly considers the separation of all classes and overcomes the “class separation” problem of existing parametric SLDR methods that close class pairs tend to merge in the dimension reduced space. 2) The second method is for nonparametric SLDR, which uses minimizing the asymptotic nearest neighbor classification error (MNNE) as the criterion for optimizing the projection matrix. Theoretically, we compare MNNE with other criteria, e.g., maximizing mutual information (MMI) and minimizing Bhattacharyya bound. We show that MMNE is superior to these two criteria in terms of the closeness to the Bayes optimal criterion. Empirical studies show that the proposed methods, MMDA and MNNE, achieve state-of-the-art performance for parametric and nonparametric SLDR, respectively.