Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparative Experimental Study of Lossless Compression Algorithms for Enhancing Energy Efficiency in Smart Meters

An experimental comparative study of data compression algorithms is investigated for enhancing energy efficiency in low-powered smart meters. Data compression is able to reduce the RF communication time. We also propose a new lossless compression algorithm to achieve the best tradeoff between the compression ratio and computational costs. The performance of our proposed Run-Length Binary Encoding (RLBE) algorithm is compared to those obtained with other lossless compression algorithms: Huffman coding, Even-Rodeh, Exponential-Golomb, Lempel-Ziv Welch, Fibonacci coding, and the hybrid Bzip2 algorithm. The energy optimization in data transmission has been achieved under different operating conditions. The performance of each compression algorithms has been verified experimentally with real metering datasets from industrial and domestic cases.
Design and Calibration Experiment of a Novel Rigid-Flexible Hybrid Parallel Three-Dimensional Force Sensor With Deformability

To meet the requirement for multi-dimensional force sensor with flexibility in the field of human–robot interaction, a novel parallel 3-D force sensor with rigid–flexible hybrid component is proposed. The sensor can not only possess a high deformability, but also maintain constant transformation between the input and output forces in the whole deformable range. First, the static mathematical model of the sensor is established based on vector method. The isotropic performance of the sensor is then studied by using mathematical analytic approach, and the condition leading to isotropy is introduced. Furthermore, the influence of the main structural parameters on the sensor’s deformable range is analyzed. Based on that, a sensor prototype is fabricated. The calibration experiment and the moment effect experiment are performed. Experimental results prove the feasibility of the proposed sensor and the correctness of theoretical analysis. The study lays a foundation for the practical application of the novel 3-D force sensor.
Design of Event Generator and Controller for Event-triggered Control of Time Delay Systems

In this paper, novel event-triggering criterion and state feedback controller are co-designed to asymptotically stabilize input-delay linear systems. A Lyapuonv-Krasovskii functional is employed to derive triggering mechanism and sufficient condition to obtain static controller gain. It is proven that the task periods (inter-event time) generated by the proposed strategy are away from zero. Comparisons to a rival method in the literature are presented to demonstrate the superior performance of the proposed scheme.
Deep Learning for End-to-End Atrial Fibrillation Recurrence Estimation

Left atrium shape has been shown to be an independent predictor of recurrence after atrial fibrillation (AF) ablation. Shape-based representation is imperative to such an estimation process, where correspondence-based representation offers the most flexibility and ease-of-computation for population-level shape statistics. Nonetheless, population-level shape representations in the form of image segmentation and correspondence models derived from cardiac MRI require significant human resources with sufficient anatomy-specific expertise. In this paper, we propose a machine learning approach that uses deep networks to estimate AF recurrence by predicting shape descriptors directly from MRI images, with NO image pre-processing involved. We also propose a novel data augmentation scheme to effectively train a deep network in a limited training data setting. We compare this new method of estimating shape descriptors from images with the state-of-the-art correspondence-based shape modeling that requires image segmentation and correspondence optimization. Results show that the proposed method and the current state-of-the-art produce statistically similar outcomes on AF recurrence, eliminating the need for expensive pre-processing pipelines and associated human labor.
A Quasi‐Score Statistic for Homogeneity Testing against Covariate‐Varying Heterogeneity

In statistical modelling, it is often of interest to evaluate non‐negative quantities that capture heterogeneity in the population such as variances, mixing proportions and dispersion parameters. In instances of covariate‐dependent heterogeneity, the implied homogeneity hypotheses are nonstandard and existing inferential techniques are not applicable. In this paper, we develop a quasi‐score test statistic to evaluate homogeneity against heterogeneity that varies with a covariate profile through a regression model. We establish the limiting null distribution of the proposed test as a functional of mixtures of chi‐square processes. The methodology does not require the full distribution of the data to be entirely specified. Instead, a general estimating function for a finite dimensional component of the model, that is, of interest is assumed but other characteristics of the population are left completely unspecified. We apply the methodology to evaluate the excess zero proportion in zero‐inflated models for count data. Our numerical simulations show that the proposed test can greatly improve efficiency over tests of homogeneity that neglect covariate information under the alternative hypothesis. An empirical application to dental caries indices demonstrates the importance and practical utility of the methodology in detecting excess zeros in the data.
Comparison of lumbar multifidus thickness and perceived exertion during graded superman exercises with or without an abdominal drawing-in maneuver in young adults

The present study aimed to investigate lumbar multifidus (LM) thickness and perceived exertion during graded superman exercises (GSE) or GSE with an abdominal drawing-in maneuver (GSE-AD) in young adults. Twelve young adult males and females, who were informed of the purpose and procedures of this study and then gave their voluntary consent to participate, were included in this study. All subjects randomly performed three GSE and GSE-AD over 3 days. Ultrasonography was used to measure the LM thickness to the left and right of the L5 region. Additionally, the issue of whole-body fatigue felt by the subject while performing the GSE or GSE-AD was measured using the Borg scale. The intrarater reliability of the LM measurement was found to be intraclass correlation coefficient (ICC[3,1])=0.97 (0.87–0.99) at resting and ICC(3,1)=0.94 (0.78–0.99) at contraction. Both the left and right LM showed significantly higher contraction during the grade 3 GSE (P< 0.05). The whole-body fatigue was significantly greater following GSE 2 and 3 than following GSE-AD 2 and 3 (P<0.05). In particular, GSE-AD had a significantly lower Borg score compared to GSE (P<0.05) and did not show any significant difference in muscle thickness (P>0.05). It is recommended that the abdominal drawing-in maneuver be applied with grade 3 GSE to enhance stability and reduce spinal fatigue.
Weakly Supervised Semantic Segmentation Using Hierarchical Multi-Image Model

In this paper, we are going to discuss the problem of weakly supervised semantic segmentation. Semantic segmentation is the problem of clustering pixels in an image which they belong to the same semantic class. This task requires a training dataset where the label of each pixel in an image has been specified. However, due to the lack of this type of ground-truth knowledge, we choose weakly supervised learning methods which use weakly labeled dataset. In a weakly labeled dataset, each image is tagged by a set of labels which indicate the presence of those semantic labels within the image, unlike the fully labeled datasets, there is no information concerning each semantic class location. We propose a Hierarchal Multi-Image Model (HMIM) where it is a two-layer graphical model. In the first layer, superpixels of all the training images are connected via the local and global appearance similarity. In the second layer, similar object candidates over all the training images are also connected. Moreover, there are some connections between the first and the second layer which consider the consistency both locally and globally. Our proposed approach incorporates the object level information as well as local region appearance in weakly supervised semantic segmentation. To evaluate our proposed approach, it is applied to the MSRC-21 dataset and we achieve comparable results.
Supporting Low-Latency Applications through Hybrid Cost-Optimised Cloudlet Placement

Low-latency applications such as augmented reality, cognitive assistance, and context-aware computation are better supported by augmenting cloud networks with cloudlet enabled edge computing solution. This overcomes the high-transmission latency arising from the edge devices in the access segment to cloud servers located in the core. In this talk, we will review our recently proposed hybrid cost-optimization framework for optimal cloudlet placement over existing passive optical access networks, subject to capacity and latency constraints. In our work, we formulate a mixed-integer non-linear program to identify ideal locations (either at the central office (CO), remote node (RN), or in the field) for cloudlet placement over three deployment areas of differing population densities. Our results point to the fact that the installation of more RN and CO-located cloudlets will yield an improved cost optimal solution than the installation of field cloudlets alone, and that the percentage of the incremental energy budget arising from the installation of cloudlets are low.
Interpreting Adversarial Robustness: A View from Decision Surface in Input Space

One popular hypothesis of neural network generalization is that the flat local minima of loss surface in parameter space leads to good generalization. However, we demonstrate that loss surface in parameter space has no obvious relationship with generalization, especially under adversarial settings. Through visualizing decision surfaces in both parameter space and input space, we instead show that the geometry property of decision surface in input space correlates well with the adversarial robustness. We then propose an adversarial robustness indicator, which can evaluate a neural network's intrinsic robustness property without testing its accuracy under adversarial attacks. Guided by it, we further propose our robust training method. Without involving adversarial training, our method could enhance network's intrinsic adversarial robustness against various adversarial attacks.
Comparative Study on Data Mining Classification Algorithms for Predicting Road Traffic Accident Severity

According to World Health Organization report the number of deaths by road traffic accident is more than 1.25 million people and every year with non-fatal accidents affecting more than 20–50 million people. Several factors are contributed on the occurrence of road traffic accident. In this study, data mining classification techniques applied to establish models (classifiers) to identify accident factors and to predict traffic accident severity using previously recorded traffic data. Using WEKA (Waikato Environment for Knowledge Analysis) data mining decision tree (J48, ID3 and CART) and Naïve Bayes classifiers are built to model the severity of injury. The classification performance of all these algorithms is compared based on their results. The experimental result shows that the accuracy of J48 classifier is higher than others.
Variations in ADIPOR1 But Not ADIPOR2 are Associated With Hypertriglyceridemia and Diabetes in an Admixed Latin American Population.

BACKGROUND Adiponectin is a hormone secreted by adipose tissue. It regulates glycolysis and lipolysis and is involved in the pathophysiology of diabetes and related disorders. Its activity is mainly mediated by the transmembrane receptors AdipoR1 and AdipoR2, which are encoded by ADIPOR1 (1q32.1) and ADIPOR2 (12p13.33) genes, respectively. In genetic association studies, single nucleotide polymorphisms (SNPs) in or near these genes have been associated with metabolic alterations. However, these relationships are still controversial. AIM The aim of this work was to analyze possible associations between ADIPOR1/2 and diabetes and other metabolic disorders. METHODS A genetic association study was carried out in an admixed Latin American population. A sample of 200 adults was analyzed. Clinical and serum-biochemical characteristics were measured to diagnose obesity, abdominal obesity, hypertension, hyperglycemia, hypertriglyceridemia, low HDLc, insulin resistance (HOMA-IR), and diabetes. Three SNPs were genotyped in ADIPOR1 (rs10494839, rs12733285, and rs2275737) and ADIPOR2 (rs11061937, rs11612383, and rs2286383). For the association analysis, an additive model was assessed through logistic regression. An admixture adjustment was performed using a Monte-Carlo-Markov-Chain method, assuming a three-hybrid substructure (k = 3). RESULTS Two SNPs in ADIPOR1 were associated with diabetes: rs10494839 (OR = 3.88, adjusted p < 0.03) and rs12733285 (OR = 4.72, adjusted p < 0.03). Additionally, rs10494839 was associated with hypertriglyceridemia (OR = 2.16, adjusted p < 0.01). None of the SNPs in ADIPOR2 were associated with metabolic disorders. CONCLUSIONS ADIPOR1 was consistently associated with diabetes and hypertriglyceridemia. This association was maintained even after adjusting for genetic stratification. There were no significant associations involving ADIPOR2.
Differenced Prosody Features from Normal and Stressed Regions for Emotion Recognition

The physiological constraints in the human speech production mechanism restrict the emotional state of the speaker for a short duration in an emotional speech. In this work, the emotionally stressed regions are detected using the strength of the excitation (SoE) and fundamental frequency (F0) of the speech signal. These emotionally stressed regions for the different emotions are further classified using a linear kernel support vector machine (SVM) with a two-stage binary decision logic. This classifier is modelled using the differenced prosody features extracted by considering the relative difference of the prosody components between normal and emotionally stressed regions. This experimentation is carried out using Berlin emotion speech (EMO-DB) database. The proposed approach produced a better result compared to existing state-of-the-art emotion recognition systems. An accuracy of 80.83% is reported in this paper.
Users' Privacy Protection Scheme in Location Based Services

LBS (Location Based Service) has become more and more popular in recent years. LBS allows Users to explore the surrounding environment. Users need to transmit their information such as position, POI, identification to untrusted service provider who can be considered as an attacker. By using those data, the attacker may obtain users' privacy. Therefore, there is an increasing amount of research to protect the privacy of users when using the LBSs. Our research aims to improve the shortcomings of past protection methods. In the experiment, our approach is compared with other approaches by using entropy and our approach can provide better protection in three kinds of side information applications.
Modal Interactions in Microstadium Optical Cavities

The morphological dependence of resonance modes in microstadium optical cavities was investigated numerically using the finite element method by changing the length of the straight segments, which connect two half-circles. Two mechanisms resulting in deformation of the mode patterns were confirmed. One is the morphological effect of the cavity and the other is modal interactions such as mode mixing and mode exchange. When the cavity size is much larger than the resonance wavelength, the modal interactions become prominent due to an increase in the mode density, playing a significant role in the formation process of wave chaotic modes that exhibit very complicated mode patterns in microstadium optical cavities.
Bayesian Transfer Reinforcement Learning with Prior Knowledge Rules

We propose a probabilistic framework to directly insert prior knowledge in reinforcement learning (RL) algorithms by defining the behaviour policy as a Bayesian posterior distribution. Such a posterior combines task specific information with prior knowledge, thus allowing to achieve transfer learning across tasks. The resulting method is flexible and it can be easily incorporated to any standard off-policy and on-policy algorithms, such as those based on temporal differences and policy gradients. We develop a specific instance of this Bayesian transfer RL framework by expressing prior knowledge as general deterministic rules that can be useful in a large variety of tasks, such as navigation tasks. Also, we elaborate more on recent probabilistic and entropy-regularised RL by developing a novel temporal learning algorithm and show how to combine it with Bayesian transfer RL. Finally, we demonstrate our method for solving mazes and show that significant speed ups can be obtained.
Flexible Mobile Fronthaul with Coherent UDWDM-PON

Ultra Dense Wavelength Division Multiplexing (UDWDM) has been proposed for future Passive Optical Networks (PONs) to support the increase of access traffic, furnishing an improved capacity while introducing point-to-point logical connections with the Wavelength-To-The-User (WTTU) concept, enabled by low cost coherent transceivers with high sensitivity. Furthermore, considering wireless networks, a centralized processing in a Cloud-Radio Access Network (C-RAN) needs reconfigurable abilities to bring benefits to mobile systems with dynamic demands of huge bandwidth between Remote Radio Heads (RRHs) at base stations and Baseband Units (BBUs) at the central office. To achieve this goal in the access network, we propose a flexible architecture design of an UDWDM-PON, which adapts the available bandwidth to different users and classes of services and can reconfigure the connections between RRHs and BBUs.
Investigation of Tear Drop Flared Tipped Antenna for Therapeutic Microwave Ablation

Tear Drop Flared Tipped structure is proposed for microwave ablation therapy. Numerical simulations using ANSOFT High Frequency Structural Simulator is introduced to optimize the antenna design. The proposed design is modelled in saline and liver tissues. With the advantage of ultra-wide bandwidth feature, the proposed antenna alleviates the high mismatch losses encountered with traditional narrow bandwidth designs recording more than −30 dB return loss over entire bandwidth with nearly spherical radiation. Results for return loss, VSWR, radiation pattern and SAR are presented.
Distance-Preserving Mapping with Euclidean Distance for 4-ary PAM

In this article we give: (a) a new construction for mapping binary sequences to permutation sequences formed from a 4-PAM constellation, and call the resulting codebooks mappings; (b) a metric for assessing the performance of mappings from our construction; (c) performance results comparing mappings from our construction against the conventional mappings in the literature. The results show that our mappings outperform the conventional mappings. Against conventional soft-decision decoded 4-PAM, our mapping showed 2.5 dB and 3.2 dB improvement over (R = 1/3, K = 4, dfree = 10) and (R = 1/3, K = 3, dfree = 6) convolutional codes, respectively.
Effects of breeding center, age and parasite burden on fecal triiodothyronine levels in forest musk deer

The objective of this study was to evaluate the effects of sex, breeding center and age on fecal triiodothyronine levels in captive forest musk deer Moschus berezovskii, and to explore the age-intensity model of gastrointestinal parasites. Furthermore, the association between fecal triiodothyronine levels and parasite egg shedding was also analyzed. We collected musk deer fecal samples from two breeding centers located in Shaanxi and Sichuan province, China. Enzyme-linked immunosorbent assays were utilized to estimate the fecal triiodothyronine concentrations and profiles, and fecal parasite eggs or oocysts were counted using the McMaster technique. Female deer from both breeding centers consistently showed higher triiodothyronine concentrations than those observed in males, which indicates that a distinct physiology pattern occurs by sex. The triiodothyronine concentration in Sichuan breeding center was significantly higher than that in Shaanxi center for both sexes, suggesting that differences in environment, diet and management practices are likely to affect the metabolism. In addition, a negative relationship between triiodothyronine concentrations and age was found (r = - 0.75, p < 0.001), and parasite egg shedding was also negatively associated with age (r = - 0.51, p < 0.001), by which we can infer that older animals evolves a more developed immune system. Finally, a positive association between parasite egg shedding and triiodothyronine levels was found, which could be explained by the additional energy metabolism resulting from parasitic infection. Results from this study might suggest metabolic and immunological adaptations in forest musk deer. These baseline data could be used to unveil metabolic status and establish parasite control strategies, which has great potential in captive population management as well as their general health evaluations.
Factors influencing pigment production by halophilic bacteria and its effect on brine evaporation rates

The disposal of reject brine, a highly concentrated waste by‐product generated by various industrial processes, represents a major economic and environmental challenge. The common practice in dealing with the large amounts of brine generated is to dispose of it in a pond and allow it to evaporate. The rate of evaporation is therefore a key factor in the effectiveness of the management of these ponds. The addition of various dyes has previously been used as a method to increase the evaporation rate. In this study, a biological approach, using pigmented halophilic bacteria (as opposed to chemical dyes), was assessed. Two bacteria, an Arthrobacter sp. and a Planococcus sp. were selected due to their ability to increase the evaporation of synthetic brine. When using industrial brine, supplementation of the brine with an iron source was required to maintain the pigment production. Under these conditions, the Planococcus sp. CP5‐4 produced a carotenoid‐like pigment, which resulted in a 20% increase in the evaporation rate of the brine. Thus, the pigment production capability of halophilic bacteria could potentially be exploited as an effective step in the management of industrial reject brines, analogous to the crystallizer ponds used to mine salt from sea water.
Sleep problems in adults with autism spectrum disorder and intellectual disability

Sleep problems (SP) are recognized as a common comorbid condition in autism spectrum disorder (ASD) and can influence core autism symptoms and mental and physical health. SPs can be lifelong and have been reported that adults on the autistic spectrum with and without intellectual disability (ID) present SPs (longer sleep latency, frequent night awakenings, and circadian rhythm sleep–wake disorders).