Miracle-dz/UniFAR-FTUs huggingface dataset:
Noninvasive and Painless Urine Glucose Detection by Using Computer-based Polarimeter

Diabetes kills millions of people worldwide each year. It challenges us as researchers to give contribution in early diagnosis to ensure a healthy life. As a matter of fact, common glucose testing devices that have been widely used so far are, at least, glucose meter and urine glucose test strip. The glucose meter ordinarily requires blood taken from patient’s finger. The glucose test strip uses patient’s urine but records unspecific urine glucose level, since the strip only provides the glucose level in some particular ranges. Instead of detecting the glucose level in blood and using the non-specific technique, a noninvasive and painless technique that can detect glucose level accurately will provide a more feasible approach for diabetes diagnosis. The noninvasive and painless urine glucose level monitoring by means of computer-based polarimeter is presented in this paper. The instrument consisted of a power source, a sample box, a light sensor, a polarizer, an analyzer, an analog to digital converter (ADC), and a computer. The concentration of urine glucose concentration was evaluated from the curve of the change in detected optical rotation angle and output potential by the computer-based polarimeter. Statistical analyses by means of Gaussian fitting and linear regression were applied to investigate the rotation angle and urine glucose concentration, respectively. From our experiment, the urine glucose level, measured by glucose test strips, of the normal patient was 100 mg/dl, and the diabetic patient was 500 mg/dl. Our polarimeter even read more precise values for the urine glucose concentrations of those normal and diabetic of the same patients, i.e. 50.61 mg/dl and 502.41 mg/dl, respectively. In other words, the results showed that our polarimeter was able to quantitatively measure the urine glucose level more accurate than urine glucose test strips. Hence, this computer-based polarimeter could be used as an alternative for early detection of urine glucose with noninvasive and painless characteristics.
Explicit and Implicit Pattern Relation Analysis for Discovering Actionable Negative Sequences

Real-life events, behaviors, and interactions produce sequential data. An important but rarely explored problem is to analyze those nonoccurring (also called negative) yet important sequences, forming negative sequence analysis (NSA). A typical NSA area is to discover negative sequential patterns (NSPs) consisting of important nonoccurring and occurring elements and patterns. The limited existing work on NSP mining relies on frequentist and downward closure property-based pattern selection, producing large and highly redundant NSPs, nonactionable for business decision-making. This work makes the first attempt for actionable NSP discovery. It builds an NSP graph representation, quantifies both explicit occurrence and implicit nonoccurrence-based element and pattern relations, and then discovers significant, diverse, and informative NSPs in the NSP graph to represent the entire NSP set for discovering actionable NSPs. A DPP-based NSP representation and actionable NSP discovery method, EINSP, introduces novel and significant contributions to NSA and sequence analysis: 1) it represents NSPs by a determinantal point process (DPP)-based graph; 2) it quantifies actionable NSPs in terms of their statistical significance, diversity, and strength of explicit/implicit element/pattern relations; and 3) it models and measures both explicit and implicit element/pattern relations in the DPP-based NSP graph to represent direct and indirect couplings between NSP items, elements, and patterns. We substantially analyze the effectiveness of EINSP in terms of various theoretical and empirical aspects, including complexity, item/pattern coverage, pattern size and diversity, implicit pattern relation strength, and data factors.
Phase Retrieval: From Computational Imaging to Machine Learning: A tutorial

Phase retrieval consists in the recovery of a complex-valued signal from intensity-only measurements. As it pervades a broad variety of applications, many researchers have striven to develop phase-retrieval algorithms. Classical approaches involve techniques as varied as generic gradient descent routines or specialized spectral methods, to name a few. However, the phase-recovery problem remains a challenge to this day. Recently, however, advances in machine learning have revitalized the study of phase retrieval in two ways: 1) significant theoretical advances have emerged from the analogy between phase retrieval and single-layer neural networks, and 2) practical breakthroughs have been obtained thanks to deep learning regularization. In this tutorial, we review phase retrieval under a unifying framework that encompasses classical and machine learning methods. We focus on three key elements: applications, an overview of recent reconstruction algorithms, and the latest theoretical results.
Sliced Gradient-Enhanced Kriging for High-Dimensional Function Approximation

Gradient-enhanced Kriging (GE-Kriging) is a well-established surrogate modelling technique for approximating expensive computational models. However, it tends to get impractical for high-dimensional problems due to the size of the inherent correlation matrix and the associated high-dimensional hyper-parameter tuning problem. To address these issues, a new method, called sliced GE-Kriging (SGE-Kriging), is developed in this paper for reducing both the size of the correlation matrix and the number of hyper-parameters. We first split the training sample set into multiple slices, and invoke Bayes' theorem to approximate the full likelihood function via a sliced likelihood function, in which multiple small correlation matrices are utilized to describe the correlation of the sample set rather than one large one. Then, we replace the original high-dimensional hyper-parameter tuning problem with a low-dimensional counterpart by learning the relationship between the hyper-parameters and the derivative-based global sensitivity indices. The performance of SGE-Kriging is finally validated by means of numerical experiments with several benchmarks and a high-dimensional aerodynamic modeling problem. The results show that the SGE-Kriging model features an accuracy and robustness that is comparable to the standard one but comes at much less training costs. The benefits are most evident for high-dimensional problems with tens of variables.
Reaching Consensus in the Byzantine Empire: A Comprehensive Review of BFT Consensus Algorithms

Byzantine fault-tolerant (BFT) consensus algorithms are at the core of providing safety and liveness guarantees for distributed systems that must operate in the presence of arbitrary failures. Recently, numerous new BFT algorithms have been proposed, not least due to the traction blockchain technologies have garnered in the search for consensus solutions that offer high throughput, low latency, and robust system designs. In this paper, we conduct a systematic survey of selected and distinguished BFT algorithms that have received extensive attention in academia and industry alike. We perform a qualitative comparison among all algorithms we review considering message and time complexities. Furthermore, we provide a comprehensive, step-by-step description of each surveyed algorithm by decomposing them into constituent subprotocols with intuitive figures to illustrate the message-passing pattern. We also elaborate on the strengths and weaknesses of each algorithm compared to the other state-of-the-art approaches.
Music-robust Automatic Lyrics Transcription of Polyphonic Music

Lyrics transcription of polyphonic music is challenging because singing vocals are corrupted by the background music. To improve the robustness of lyrics transcription to the background music, we propose a strategy of combining the features that emphasize the singing vocals, i.e. music-removed features that represent singing vocal extracted features, and the features that capture the singing vocals as well as the background music, i.e. music-present features. We show that these two sets of features complement each other, and their combination performs better than when they are used alone, thus improving the robustness of the acoustic model to the background music. Furthermore, language model interpolation between a general-purpose language model and an in-domain lyrics-speciﬁc language model provides further improvement in transcription results. Our experiments show that our proposed strategy outperforms the existing lyrics transcription systems for polyphonic music. Moreover, we ﬁnd that our proposed music-robust features specially improve the lyrics transcription performance in metal genre of songs, where the background music is loud and dominant.
Numerical Simulation of Aircraft Ditching of a Generic Transport Aircraft: Implementation of an Aerodynamic Model

Aircraft certification requires the analysis of water emergency landing (ditching) with respect to passenger safety. In order to support the design and certification processes, models for numerical simulation of aircraft ditching are being developed. Following the work developed in the SMAES project (SMart Aircraft in Emergency Situations, 2011-2014), which consisted in the enhancing of simulation capabilities for aircraft ditching analysis, DLR is transferring the acquired knowledge to full-scale aircraft ditching. Due to the complexity involved in fullscale modeling, a pre-processing tool is currently under development for an automatic model generation of full-scale aircraft ditching models for the explicit solver Virtual Performance Solution (VPS). The overall goal of this work is to implement and validate an aerodynamic model in the numerical simulation to allow an improvement in the state of the art of full-scale aircraft ditching. The developed model physically calculates the loads with proper aerodynamic equations and strongly improves the predictability of loads occurring during a ditching simulation. For the implementation of the aerodynamic model in the numerical simulation, the creation of a new module in the pre-processing tool is proposed. The aerodynamic calculation of lift, drag and pitch moment, based on the evolution of the angle of attack and ight velocity, is accomplished with the implementation of an external user subroutine within the numerical simulation. The superiority of the developed aerodynamic model over the state of the art is demonstrated based on comparison of results of sensitivity and parameter studies.
Predicting Performance of Heterogeneous AI Systems with Discrete-Event Simulations

In recent years, artificial intelligence (AI) technologies have found industrial applications in various fields. AI systems typically possess complex software and heterogeneous CPU/GPU hardware architecture, making it difficult to answer basic questions considering performance evaluation and software optimization. Where is the bottleneck impeding the system? How does the performance scale with the workload? How the speed-up of a specific module would contribute to the whole system? Finding the answers to these questions through experiments on the real system could require a lot of computational, human, financial, and time resources. A solution to cut these costs is to use a fast and accurate simulation model preparatory to implementing anything in the real system. In this paper, we propose a discrete-event simulation model of a high-load heterogeneous AI system in the context of video analytics. Using the proposed model, we estimate: 1) the performance scalability with the increasing number of cameras; 2) the performance impact of integrating a new module; 3) the performance gain from optimizing a single module. We show that the performance estimation accuracy of the proposed model is higher than 90%. We also demonstrate, that the considered system possesses a counter-intuitive relationship between workload and performance, which nevertheless is correctly inferred by the proposed simulation model.
DeepXPalm: Tilt and Position Rendering using Palm-worn Haptic Display and CNN-based Tactile Pattern Recognition

Telemanipulation of deformable objects requires high precision and dexterity from the users, which can be increased by kinesthetic and tactile feedback. However, the object shape can change dynamically, causing ambiguous perception of its alignment and hence errors in the robot positioning. Therefore, recognize the tilt angle and position patterns sensed over the gripper fingertip is a classification problem that has to be solved to present a clear tactile pattern to the user. This work presents a telemanipulation system for plastic pipettes consisting of a multi-contact haptic interface LinkGlide to deliver haptic feedback at the users' palm and two tactile sensors array embedded in the 2-finger Robotiq gripper. We propose a novel approach based on Convolutional Neural Networks (CNN) to detect the tilt and position while grasping deformable objects. The CNN generates a mask based on recognized tilt and position data to render further multi-contact tactile stimuli provided to the user during the telemanipulation. The study has shown that using the CNN algorithm and the preset mask, tilt, and position recognition by users is increased from 9.67% using the direct data to 82.5%.
Vehicle Collision Risk Prediction at Intersections based on Comparison of Minimal Distance between Vehicles and Dynamic Thresholds

Accurate collision prediction algorithms are important to provide drivers reliable warning messages. This study introduces a novel approach for collision prediction at intersections. The algorithm involves the use of an index called `minimal future distance (MFD)', which is defined to be a future distance (FD) between the subject vehicle and the primary other vehicle, and a two-level dynamic threshold for performing the collision prediction task. Real-time vehicle motion information and surrounding road geometry are utilised to forecast FDs and identify MFD within an upcoming time horizon. The dynamic threshold in both emergency warning and normal warning situations consists of two parts, a vehicle heading direction-related part and a speed value-related part. Potential collisions are determined by the comparison of MFD with different dynamic thresholds in different driving scenarios. The combined use of vehicle real-time state and road geometry in the algorithm significantly increased the collision prediction accuracy. Furthermore, the use of dynamic thresholds ensured the promptness and robustness of the collision warning system. Simulation results show that the false positive rate and false negative rate of severe collisions at intersections can be robustly eliminated and those of marginal collisions can be kept low at 2.4 and 3.6%, respectively.
From Little Things Big Things Grow: A Collection with Seed Studies for Medical Systematic Review Literature Search

Medical systematic review query formulation is a highly complex task done by trained information specialists. Complexity comes from the reliance on lengthy Boolean queries, which express a detailed research question. To aid query formulation, information specialists use a set of exemplar documents, called 'seed studies', prior to query formulation. Seed studies help verify the effectiveness of a query prior to the full assessment of retrieved studies. Beyond this use of seeds, specific IR methods can exploit seed studies for guiding both automatic query formulation and new retrieval models. One major limitation of work to date is that these methods exploit 'pseudo seed studies' through retrospective use of included studies (i.e., relevance assessments). However, we show pseudo seed studies are not representative of real seed studies used by information specialists. Hence, we provide a test collection with real world seed studies used to assist with the formulation of queries. To support our collection, we provide an analysis, previously not possible, on how seed studies impact retrieval and perform several experiments using seed study based methods to compare the effectiveness of using seed studies versus pseudo seed studies. We make our test collection and the results of all of our experiments and analysis available at http://github.com/ielab/sysrev-seed-collection.
Modeling Label Correlations for Second-Order Semantic Dependency Parsing with Mean-Field Inference

Second-order semantic parsing with end-to-end mean-field inference has been shown good performance. In this work we aim to improve this method by modeling label correlations between adjacent arcs. However, direct modeling leads to memory explosion because second-order score tensors have sizes of $O(n^3L^2)$ ($n$ is the sentence length and $L$ is the number of labels), which is not affordable. To tackle this computational challenge, we leverage tensor decomposition techniques, and interestingly, we show that the large second-order score tensors have no need to be materialized during mean-field inference, thereby reducing the computational complexity from cubic to quadratic. We conduct experiments on SemEval 2015 Task 18 English datasets, showing the effectiveness of modeling label correlations. Our code is publicly available at https://github.com/sustcsonglin/mean-field-dep-parsing.
Topological multiferroic order in twisted transition metal dichalcogenide bilayers

Layered van der Waals materials have risen as powerful platforms to artificially engineer correlated states of matter. Here we show the emergence of a multiferroic order in a twisted dichalcogenide bilayer superlattice at quarter-filling. We show that the competition between Coulomb interactions leads to the simultaneous emergence of ferrimagnetic and ferroelectric orders. We derive the magnetoelectric coupling for this system, which leads to a direct strong coupling between the charge and spin orders. We show that, due to intrinsic spin-orbit coupling effects, the electronic structure shows a non-zero Chern number, thus displaying a topological multiferroic order. We show that this topological state gives rise to interface modes at the different magnetic and ferroelectric domains of the multiferroic. We demonstrate that these topological modes can be tuned with external electric fields as well as triggered by supermoiré effects generated by a substrate. Our results put forward twisted van der Waals materials as a potential platform to explore multiferroic symmetry breaking orders and, ultimately, controllable topological excitations in magnetoelectric domains.
The FAST Ultra-Deep Survey (FUDS): Observational strategy, calibration and data reduction

Abstract The FAST Ultra-Deep Survey (FUDS) is a blind survey that aims for the direct detection of H i in galaxies at redshifts $z<0.42$ . The survey uses the multibeam receiver on the Five-hundred-metre Aperture Spherical Telescope (FAST) to map six regions, each of size $0.72\ \textrm{deg}^2$ at high sensitivity ( ${\sim}50\,\mu \textrm{Jy}$ ) and high-frequency resolution (23 kHz). The survey will enable studies of the evolution of galaxies and their H i content with an eventual sample size of ${\sim}1\,000$ . We present the science goals, observing strategy, the effects of radio frequency interference at the FAST site, our mitigation strategies and the methods for calibration, data reduction and imaging as applied to initial data. The observations and reductions for the first field, FUDS0, are completed, with around 128 H i galaxies detected in a preliminary analysis. Example spectra are given in this paper, including a comparison with data from the overlapping GAL2577 field of Arecibo Ultra-Deep Survey.
Separation of concerning things: a simpler basis for defining and programming with the C/C++ memory model (extended version)

The C/C++ memory model provides an interface and execution model for programmers of concurrent (shared-variable) code. It provides a range of mechanisms that abstract from underlying hardware memory models -- that govern how multicore architectures handle concurrent accesses to main memory -- as well as abstracting from compiler transformations. The C standard describes the memory model in terms of cross-thread relationships between events, and has been influenced by several research works that are similarly based. In this paper we provide a thread-local definition of the fundamental principles of the C memory model, which, for concise concurrent code, serves as a basis for relatively straightforward reasoning about the effects of the C ordering mechanisms. We argue that this definition is more practical from a programming perspective and is amenable to analysis by already established techniques for concurrent code. The key aspect is that the memory model definition is separate to other considerations of a rich programming language such as C, in particular, expression evaluation and optimisations, though we show how to reason about those considerations in the presence of C concurrency. A major simplification of our framework compared to the description in the C standard and related work in the literature is separating out considerations around the"lack of multicopy atomicity", a concept that is in any case irrelevant to developers of code for x86, Arm, RISC-V or SPARC architectures. We show how the framework is convenient for reasoning about well-structured code, and for formally addressing unintuitive behaviours such as"out-of-thin-air"writes.
A Multi-Transformation Evolutionary Framework for Influence Maximization in Social Networks

Influence maximization is a crucial issue for mining the deep information of social networks, which aims to select a seed set from the network to maximize the number of influenced nodes. To evaluate the influence spread of a seed set efficiently, existing studies have proposed transformations with lower computational costs to replace the expensive Monte Carlo simulation process. These alternate transformations, based on network prior knowledge, induce different search behaviors with similar characteristics to various perspectives. Specifically, it is difficult for users to determine a suitable transformation a priori. This article proposes a multi-transformation evolutionary framework for influence maximization (MTEFIM) with convergence guarantees to exploit the potential similarities and unique advantages of alternate transformations and to avoid users manually determining the most suitable one. In MTEFIM, multiple transformations are optimized simultaneously as multiple tasks. Each transformation is assigned an evolutionary solver. Three major components of MTEFIM are conducted via: 1) estimating the potential relationship across transformations based on the degree of overlap across individuals of different populations, 2) transferring individuals across populations adaptively according to the inter-transformation relationship, and 3) selecting the final output seed set containing all the transformation's knowledge. The effectiveness of MTEFIM is validated on both benchmarks and real-world social networks. The experimental results show that MTEFIM can efficiently utilize the potentially transferable knowledge across multiple transformations to achieve highly competitive performance compared to several popular IM-specific methods. The implementation of MTEFIM can be accessed through the external link in the footnote.