Miracle-dz/UniFAR-FTUs huggingface dataset:
LGE-MRI for diagnosis of left atrial cardiomyopathy as identified in high-definition endocardial voltage and conduction velocity mapping

Background: Electro-anatomical voltage, conduction velocity (CV) mapping and late gadolinium enhancement magnetic resonance imaging (LGE-MRI) are different diagnostic modalities for atrial cardiomyopathy (ACM). However, discordances remain in the location and extent of detected ACM. Objectives: (1) Comparison of ACM extent and location between current modalities. (2) Development of new estimated optimised image intensity thresholds (EOIIT) for LGE-MRI identifying patients with ACM. Methods: Thirty-six ablation-naive persistent AF patients underwent LGE-MRI and high-definition electro-anatomical mapping in sinus rhythm. Significant ACM was defined as low voltage substrate (LVS) extent [≥] 5% of the left atrium (LA) surface at < 0.5mV. LGE areas were classified using the Utah, image intensity ratio (IIR > 1.20) and new EOIIT method for comparison to LVS and slow conduction areas < 0.2m/s. ROC analysis determined the LGE-extent enabling accurate diagnosis of ACM. Results: The degree and distribution of detected pathological substrate varied significantly (p < 0.001) across the mapping modalities: 3% (IQR 0-12%) of the LA displayed LVS < 0.5mV vs. 14% (3-25%) slow conduction areas < 0.2m/s vs. 16% (6-32%) LGE with Utah method vs. 17%(11-24%) using IIR > 1.20, with enhanced discrepancies on posterior LA. A linear correlation was found between the OIIT and each patients mean blood pool intensity (R2=0.89, p < 0.001). LGEMRI-based ACM diagnosis improved with the novel EOIIT (83% sensitivity, 88% specificity, AUC:0.94) in comparison to the Utah method (60% sensitivity, 75% specificity, AUC:0.76), and IIR > 1.20 (58% sensitivity, 75% specificity, AUC:0.71). Conclusion: Important discordances in distribution of pathological substrate exist between LA-LVS, CV and LGE-MRI, irrespective of the LGE-detection protocol that is used. However, the new EOIIT method improves LGE-MRI based ACM diagnosis in ablation-naive AF-patients.
Intrinsic Capacity of Rural Elderly in Thar Desert using WHO ICOPE (Integrated Care for Older Persons) Screening Tool: A Pilot Study

Introduction: ICOPE (Integrated Care for Older Persons) screening tool helps to address declines in physical and mental capacities in older people. In India majority of the older population resides in rural areas and there is a paucity of studies that demonstrates the utility of the ICOPE screening tool in India. Thus, this study was conducted to demonstrate the feasibility of using WHO ICOPE screening tool in a rural population. Methods: A cross-sectional study was conducted in the two villages of Jodhpur. WHO ICOPE screening was done for a comprehensive geriatric assessment of intrinsic capacity by determining cognitive decline, the decline in mobility, malnutrition, sensory loss, depressive symptoms, health risks, social care, and support. Additionally, demographic details and information about health conditions were gathered. Result: A total 451 participants (Male=54.5% and Feamale= 45.5%) surveyed. The mean age was 68.36 (SD=7.73) years and the majority of participants (68.3%) were illiterate. Most of the elderly have good families and lived with their children. Cognitive decline was found to be prevalent in 31.5% (n=142) of the participants, while mobility was diminished in 52.1% (n=235) participants. Eye problems were observed in 49.4% (n=223) participants, while 68.3% (n=308) were observed to be suffering from hearing loss. Limitation in mobility observed with the chair rise test and feeling of pain was found to be significantly associated with gender difference, with more limitation in females than males (p = 0.005; {chi}2 = 7.95) and more pain felt by females than males (p = 0.001; {chi}2 = 15.64). Conclusion: This tool seems suitable in identifying the intrinsic capacity of the rural elderly.
Control of Compacted Graphite Iron Production Process by Using the Thermal Analysis System

The objective of this paper is to describe the major principles of controlling production of compacted graphite iron, aka vermicular graphite iron, using thermal analysis (TA) of cast iron melts as a means of control. The paper goes on to show the engineering implementation of the above principles and the results of how one of a Russian foundry applied them.
Synthesis and phase transition behavior of calamitic liquid crystals containing heterocyclic core and lateral ethoxy substituent

ABSTRACT A series of new calamitic liquid crystals, 2-[3-ethoxy-(4-alkyloxybenzoyloxy)phenyl]benzothiazoles comprising a benzothiazole core, terminal alkoxy chain, ester linker and lateral ethoxy substituent were synthesized and characterized. This series comprises nine members wherein the members differ by the length of alkyloxy chain (CnH2n + 1O-, where n = 2, 4, 6, 8, 10, 12, 14, 16, 18). The thermal properties of the compounds were analyzed by differential scanning calorimetry (DSC), and the textures were identified by polarized optical microscope (POM). The member with the shortest alkoxy chain (n = 2) is a non-mesogen. The remaining members exhibited monotropic nematic phase ranging from the low member (n = 4) until the highest member (n = 18) of the series. The influence of alkoxy chain length on the mesomorphic was studied. In addition, the mesomorphic properties of the benzothiazole liquid crystals were compared with these compounds and the effect of the lateral substituents on their mesomorphism was studied.
Physical/biochemical inspired computing models for reliable nano-technology systems

Quantum-Dot Cellular Automata (QCA) and DNA self-assembly are promising nanotechnologies that are being studied as potential successors to CMOS VLSI technology. Their information processing principles are inspired by new physical (for QCA) and biochemical (DNA self-assembly) phenomena. Because they are radically different from the conventional CMOS based computation, new modeling, design, test and fault tolerance techniques are required. For QCA, modeling, design, testing and fault tolerance are studied while considering the technical background of reversible computing. A mechanical molecular QCA is proposed and applied to the analysis of logic function and energy dissipation of QCA circuits. New reversible gates are designed for QCA implementation. The test of QCA reversible gate array are discussed while test cases under different fault assumptions and array configurations are considered. A fault tolerance scheme called majority-multiplexing is investigated for QCA circuits in terms of fault tolerant capacity, signal restoration speed and implication on the reversibility of circuits. The design and error tolerance of DNA self-assembly are also studied in this dissertation. The logic design of DNA self-assembly system is investigated for using DNA self-assembly as a promising nanoscale manufacturing approach. This design problem is formulated as a combinatorial optimization problem and proven to be NP-complete. Greedy algorithms are proposed for this problem. DNA self-assembly system designed using the algorithms may generate errors during assembly and the errors are studied and modeled in this research. DNA self-assembly suffers from high error rate. A new error tolerant technique called (2k-1)×(2k-1) snake redundant block (also known as snake tile set) is proposed. The reduction of error by (2k -1)×(2k -1) snake redundant block is modeled and analyzed. Both analysis and simulation show the error tolerant technique to be effective and efficient.
Parents’ Education Anxiety and Children’s Academic Burnout: The Role of Parental Burnout and Family Function

This study aimed to explore the effect of parents’ education anxiety on children’s academic burnout, and the mediation effect of parental burnout and the moderating effect of family function. A total of 259 paired parents and children from two middle schools in central China participated in the survey. The questionnaire was conducted using the Educational Anxiety Scale, Parental Burnout Scale, Adolescent Student Burnout Inventory, and Family APGAR Index. Our results indicated that parental education anxiety had a positive predictive effect on children’s academic burnout. Moreover, parental burnout played a complete mediating role between parents’ education anxiety and children’s academic burnout. Finally, the relationship between education anxiety and parental burnout was moderated by family function, and higher family function buffered the effect of education anxiety on parental burnout. The results suggest the mechanism of parental education anxiety on children’s academic burnout, and the role of family function in alleviating parental burnout.
Providing Reliable Service for Parked-vehicle-assisted Mobile Edge Computing

Nowadays, a growing number of computation-intensive applications appear in our daily life. Those applications make the loads of both the core network and the mobile devices, in terms of energy and bandwidth, hugely increase. Offloading computation-intensive tasks to edge cloud is proposed to address this issue. Since edge clouds have limited computation resources compared with the remote cloud, they would get over-loaded because of the heavy computation burden. Parked-vehicle-assisted mobile edge computing becomes one of the promising solutions for this problem. However, several critical issues in parked-vehicle-assisted mobile edge computing would result in low reliable edge service. The open environment would bring about uncertainty, and the data privacy is hard to ensure. In addition, different from edge cloud, each parked vehicle only has limited parking duration and can leave unexpectedly for personal reasons. Moreover, edge cloud and vehicle adopt different execution models of computation and communication. The heterogeneous environment may result in negative effect on cooperativeness. Ignoring those issues can result in substantial performance degradation. To tackle this challenge and explore the benefits of parked-vehicle-assisted offloading, we study the task offloading and resource-allocation problem by fully considering the above issues. First, we propose a resource-management scheme to address the privacy issue. Second, we review the execution model of computation and communication in parked-vehicle-assisted computation offloading. Then, we formulate the problem into a mixed-integer nonlinear programming. The problem is hard to tackle due to its non-convex nature, which means that the time complexity of finding global optimal solution is unaffordable. Finally, we decompose the original problem into two sub-problems with lower complexity, and related algorithms are given to deal with the sub-problems. Simulation results demonstrate the effectiveness of the proposed solution.
Learn to Sketch: A fast approach for universal photo sketch

Rendering real photos to abstract sketches is an interesting application that can help us understand the key features. In this paper, we propose a universal photo sketch model via a deep convolutional neural network. Prior arts often cast this problem as an edge or contour detection. However, the edges or contours may not exactly reflect the boundaries of the contents of the photos. They also fail to reveal the occlusion that separates the objects from each other. We resolve this problem by proposing Photo2Sketch and Sketch2Photo to form a loop to bridge the gap between photos and sketches. We introduce relevant sketch references as indicators to supervise the sketch generation. Meanwhile, we also introduce an adaptive sketching process that can generate drawing with confidence, hence multiple sketches can be obtained. Experimental results show that our proposed method surpasses other state-of-the-art methods in both qualitative and quantitative measures.
Generalized Classification of DNS over HTTPS Traffic with Deep Learning

Network anomaly detection has been a challenge for both industry and academia. The alarming situation of network attacks is a worrisome problem for many Internet services. Machine learning techniques are widely investigated to detect suspicious events from network traffic flow. In this paper, we investigate the DNS over HTTPS traffic classification. The majority of related works use a variety of features from datasets. However, some of the adopted features are specific to some networking environments, and those features make the trained models not generalized to other network environments. The generalization of a machine learning model is of critical importance, since it would affect the effectiveness when the model is applied to other network environments. We design an appropriate data processing pipeline to process the CIRA-CIC-DoHBrw-2020 time series dataset, including feature selection and data imbalance handling, in order to facilitate the generalization of deep learning models. We develop truly generalized deep learning models, including the LSTM model and the BiLSTM model, to classify DoH traffic with high accuracy and low latency. While both models achieve good performance, the BiLSTM model performs better than the LSTM model does for both the accuracy and the computation time.
Real-Time Monitoring System to Evaluate Exercise Load, Hypoxic Load, and Safety in a Normobaric Hypoxic room

One way to increase the effectiveness of exercise is to use a normobaric hypoxic environment. However, exercise in a normobaric hypoxic environment is not always safe to non-athletes because the safety and influences are still unclear. Therefore, in this study, 24 non-athlete subjects aged 20–39 and 40–59 years were subjected to the analysis of a 15-min exercise load test on a self-propelled treadmill at three different oxygen concentrations (16%, 18%, and 20%) to determine the safe oxygen concentration of normobaric hypoxic exercise in non-athletes and to select and validate the indicators to be measured for the development of a real-time monitoring system. The measurement indicators were electrocardiogram, heart rate (HR), blood pressure, arterial oxygen saturation (SpO2), the product of the HR and systolic blood pressure and treadmill velocity. The experimental results showed that normobaric hypoxic was sufficiently effective and safe at an oxygen concentration of 16% in both age groups of 20–39 years and 40–59 years. In addition, HR and SpO2 were shown to be indices that can evaluate exercise load and hypoxic load in real-time. These results suggest that non-athletes can safely perform normobaric hypoxic exercise and that increased use by non-athletes may lead to improved health.
Multiple Deep Learning Models and Architectures with Different Numbers of States Used to Improve Retrieval Accuracy of Query-by-Example

Studies examining Spoken Term Detection (STD) and Spoken Query STD (SQ-STD) or Query by Example (QbE) using a spoken query have been conducted actively in recent years. When a spoken query is transcribed into a text using an automatic speech recognizer in SQ-STD, some misrecognition leads to retrieval accuracy deterioration. Posteriorgrams obtained using Deep Neural Network (DNN) and so on can be regarded as speaker-independent features. Although posteriorgram matching between a posteriorgram of a spoken query and posteriorgram of speech data showed high retrieval accuracy, it requires a long retrieval time and a large memory space. In earlier papers, we proposed a maximum likelihood state sequence method (MLSS) for retrieval time reduction. As described herein, we propose a method for reducing both the retrieval time and the memory space using MLSS method and multiple machine learning models with different numbers of states. The models show heterogeneous retrieval results. Their integration is probably mutually complementary and engenders retrieval accuracy improvement. Evaluation results demonstrate that the proposed method improves the retrieval accuracy, thereby reducing the retrieval time and the memory space.
One In A Hundred: Selecting the Best Predicted Sequence from Numerous Candidates for Speech Recognition

The RNN - Transducers and improved attention-based encoder-decoder models are widely applied to streaming speech recognition. Compared with these two end-to-end models, the CTC model is more efficient in training and inference. However, it cannot capture the linguistic dependencies between the output tokens. Inspired by the success of two-pass end-to-end models, we introduce a transformer decoder and the two-stage inference method into the streaming CTC model. During inference, the CTC decoder first generates many candidates in a streaming fashion. Then the transformer decoder selects the best candidate based on the corresponding acoustic encoded states. The second -stage transformer decoder can be regarded as a conditional language model. We assume that a large enough number and enough diversity of candidates generated in the first stage can compensate the CTC model for the lack of language modeling ability. All the experiments are conducted on a Chinese Mandarin dataset AISHELL-l. The results show that our proposed model can implement streaming decoding in a fast and straightforward way. Our model can achieve up to a 20% reduction in the character error rate than the baseline CTC model. In addition, our model can also perform non-streaming inference with only a little performance degradation.
Residual Dilated U-Net with Spatially Adaptive Normalization for the Restoration of Under Display Camera Images

Recent developments in display and imaging devices have prompted manufacturers to place a camera under the display screen, delivering a larger display-to-body ratio that is genuinely full-screen. However, this Under-Display Camera (UDC) imaging system suffers from severe image degradation such as noise, blur, low-light, and color-shift. This is due to the low light transmission and diffraction property of the display panels. To tackle this issue, we present an end-to-end framework based on U-Net to restore the degraded image. Since the point spread function (PSF) of the UDC degradation is known to be spatially dispersed, we utilize dilated convolutions to increase the receptive field of the model. Furthermore, we use spatially adaptive normalization to regularize feature maps to help restore the image efficiently, thereby improving the performance of our model. We show that our method can restore UDC images with fewer artifacts and produce competitive results to state-of-the-art methods.
Electrostatic tuning of the bending stiffness of simple slender multilayer composite structures

Vibration control and suppression in structures plays a central role in the extension of their service life and improvement of their reliability. While in many cases the solution of this problem implies the introduction of external damping devices, it is also conceivable to adaptively modify their vibratory properties, so that the occurrence of severe vibrations due to resonance phenomena can be curbed at its origin. The modification of the shear stress transfer at the interface between the core and the faces of a sandwich beam has been shown to have a remarkable effect on the bending stiffness of the structure. Such modification can be obtained by applying a normal stress between the core and the un-bonded, electrically insulated faces of the sandwich by means of a strong electrical field. An intermediate behavior between fully bonded and un-bonded layers in terms of inter-laminar shear stress can be achieved by temporary electrostatic bonding of the components. The outlined approach to the reduction of transversal vibrations in thin multi-layer beams is promising and can in principle be applied to multi-layer plates.
A Recommendation Systems Approach for Detecting Epistasis in Genomic Signals

There are a variety of methods used to understand and interpret an organism's phenotype, the physical expression of one or more genes. Epistasis, the phenomenon of one mutation affecting the resulting quantitative or qualitative phenotype, is used to assess gene variation in an attempt to find a combination of single nucleotide polymorphisms (SNPs) that contribute to a certain phenotype. Since one SNP rarely completely describes an organism's phenotype, detecting these groups, or coalitions, of mutations without relying on an exponential number of numbers is one of the main challenges in this field. To alleviate these computational bottlenecks, we propose a neighborhood-based collaborative filtering approach by viewing this data with a recommender system formulation. As such, we are able to detect statistically significant higher order SNP interaction phenotypes related to muscle mice genomic variants.
Over-Determined Semi-Blind Speech Source Separation

We propose a semi-blind speech source separation that jointly optimizes several acoustic functions, i.e., speech source separation (SS), dereverberation (DR), acoustic echo reduction (AE), and background noise reduction (BG). Instead of cascade connection of SS, AE, and DR, the proposed method performs DR, SS, and AE by a unified time-invariant filter. We assume the over-determined condition that the number of the microphones Nm. is larger than the number of near-end speech sources Ns. Joint optimization of DR, SS, AE, and BG can be performed by using the Nm - Ns dimensional subspace of the time-invariant filter for BG. Furthermore, we reveal that this subspace can be also utilized for residual acoustic echo reduction (RR) in which residual acoustic echo signal is reduced by spatial filtering. Two types of joint parameter optimization techniques for DR, SS, AE, BG, and RR are proposed based on vectorwise coordinate descent and fast multichannel nonnegative matrix factorization. Experimental results show that the proposed methods perform DR, SS, AE, and BG better than a cascade method. When the acoustic echo path between a loudspeaker and a microphone is time-varying, performance improvement of the proposed methods with RR is larger than the proposed method without RR.
The New Wavelet-like Allan Variance Based on the Atomic Function

A novel wavelet-like variance similar to the Allan variance, based on the infinitely differentiable atomic function is introduced. This variance is intended for phase and frequency instability analysis of signals arising in systems requiring high-quality time and frequency sources. Unlike the conventional Allan variance and its known generalizations and modifications, the proposed variance is appropriate for analyzing signals in presence of arbitrary power-law fluctuations due to outstanding time-domain and spectral properties of the atomic function. Theoretical analysis of model signals with known power-law spectra is performed, demonstrating the advantages of the novel mathematical tool for frequency instability investigation.
Speech Enhancement Based on Masking Approach Considering Speech Quality and Acoustic Confidence for Noisy Speech Recognition

In recent years, voice-operated applications have been widely accepted by the public, while the background noise is still a challenging issue for automatic speech recognition (ASR). This paper proposes a mask-based speech enhancement front-end approach taking into account speech quality score as well as acoustic confidence for speech enhancement to reduce the word error rate (WER) for noisy speech recognition. First, the features of speakers, phones, and noises are extracted and considered in the loss function to improve the speech quality. In addition to speech quality, this study also considers the phone confidence from the Kaldi-based ASR into the loss function for training the mask generation model to improve speech quality as well as improve noisy speech recognition performance. Compared with the baseline model, the proposed model not only improved STOI by 2.14% and PESQ by 7.22%, but also reduced 12.13% in WER.