Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative liver surface nodularity score based on imaging for assessment of early cirrhosis in patients with chronic liver disease

Abstract Background: Early stage of cirrhosis is of great value in the diagnosis and management in patients with chronic liver disease (CLD). Recent studies have shown that quantitative liver surface nodularity (LSN) score based on imaging techniques can be used to predict the early cirrhosis stage noninvasively, with varied diagnostic accuracy and limited sample size. Hence, this study will evaluate the diagnostic accuracy of LSN in the prediction of early cirrhosis. Methods: We will conduct a comprehensive search in PubMed, Web of Science, Cochrane Library, and Chinese biomedical databases to identify eligible studies. The literature screening, data extraction, data analysis, and quality assessment will then be carried out. The summary receiver-operating-characteristic (ROC) and pooled sensitivity, specificity will be calculated to summarize the diagnostic performance of LSN using a random-effect model. A meta-regression analysis will be performed to investigate the underlying cause of the heterogeneity. Results: This study will evaluate the diagnostic accuracy of LSN score in the identification of early cirrhosis, which may further determine whether this method can be used as an alternative in the assessment of CLD patients. Conclusions: This study will help to determine the diagnostic accuracy and summarize the recent evidence on this issue. Study registration: INPLASY2020100096.
Paroxysmal kinesigenic dyskinesia associated with a novel POLG variant

Abstract Introduction: Paroxysmal kinesigenic dyskinesia (PKD) is a rare neurological disease characterized by recurrent dyskinesia or choreoathetosis triggered by sudden movements. Pathogenic variants in PRRT2 are the main cause of PKD. However, only about half of clinically diagnosed PKD patients have PRRT2 mutations, indicating that additional undiscovered causative genes could be implicated. PKD associated with POLG variant has not been reported. Patient concerns: A 14-year-old boy presented with a 2-month history of involuntary dystonic movements triggered by sudden activities. He was conscious during the attacks. Neurological examination, laboratory tests, brain magnetic resonance imaging (MRI), electroencephalogram (EEG) were all normal. Genetic analysis showed a novel variant of POLG (c.440G>T, p.Ser147Ile), which was considered to be a likely pathogenic variant in this case. Diagnoses: The patient was diagnosed with PKD. Interventions: Low dose carbamazepine was used orally for treatment. Outcomes: The patient achieved complete resolution of symptoms without any dyskinesia during the 6-month follow up. Conclusion: Our study identified the novel POLG variant (c.440G>T, p.Ser147Ile) to be a likely pathogenic variant in PKD.
A meta-analysis

Background:The purpose of this study is to compare the clinical efficacy of laparoscopic splenectomy (LS) and open splenectomy (OS) in the treatment of Idiopathic thrombocytopenic purpura. Methods: We systematically searched PubMed, Web of science, EMBASE, Clinicaltrials.gov, and Cochrane Central Register for studies (study published from July 1992–January 2020). This study analyzed the clinical effect of LS and OS on idiopathic thrombocytopenic purpur. Results:This study showed that compared with OS, the LS’s Overall response (OR: 0.60, 95% confidence interval (CI): 0.23–1.59, P= . 30), Complication (OR: 0.59, 95% CI: 0.18–1.94, P= .38), Accessory spleen(OR: 1.70, 95% CI: 0.98–2.98, P= .06), Wound infections (OR: 0.65, 95% CI: 0.26–1.59, P= .34), Pancreatic fistula (OR: 0.73, 95% CI: 0.16–3.30, P= .68), was no significant, the Operative time (weighted mean difference (WMD): 49.33, 95% CI: 36.29–62.37, P< . 00001)was longer, and the Estimated blood loss (WMD: –172.59, 95% CI: –319.96 to –25.22, P= .02), Postoperative length of stay (WMD: –4.68, 95% CI: –7.75 to –1.62, P= .003)was less. Conclusions: The therapeutic effect of LS was the same as that of OS in Overall response Complication Accessory spleen, while The operative time was longer, the Estimated blood loss was less, and the postoperative length of stay was shorter. Abbreviations: CIS = confidence interval, EBL = estimated blood loss, ITP = idiopathic thrombocytopenic purpura, LS = laparoscopic splenectomy, ORS = odd ratio, OS = open splenectomy, WMD = weighted mean difference.
Neural net classification of x-ray pistachio nut data

Classification results for agricultural products are presented using a new neural network. This neural network inherently produces higher-order decision surfaces. It achieves this with fewer hidden layer neurons than other classifiers require. This gives better generalization. It uses new techniques to select the number of hidden layer neurons and adaptive algorithms that avoid other such ad hoc parameter selection problems; it allows selection of the best classifier parameters without the need to analyze the test set results. The agriculture case study considered is the inspection and classification of pistachio nuts using x- ray imagery. Present inspection techniques cannot provide good rejection of worm damaged nuts without rejecting too many good nuts. X-ray imagery has the potential to provide 100% inspection of such agricultural products in real time. Only preliminary results are presented, but these indicate the potential to reduce major defects to 2% of the crop with 1% of good nuts rejected. Future image processing techniques that should provide better features to improve performance and allow inspection of a larger variety of nuts are noted. These techniques and variations of them have uses in a number of other agricultural product inspection problems.
Chlorpyrifos-based commercial formulation: alterations in corpuscles of Stannius of catfish

Corpuscles of Stannius (CS) and serum calcium of Heteropneustes fossilis were studied after exposure to Chlorpyrifos-Based Commercial Formulation (CBCF) for short-term (1.76 mg L−1 chlorpyrifos) and long-term (0.44 mg L−1 chlorpyrifos) application. The fish were sacrificed after 24, 48, 72 and 96 hours in short-term experiment and after 7, 14, 21 and 28 days in long-term experiment. Hypocalcaemia was noticed in CBCF-treated fish after both short- and long-term exposures. In CBCF-treated H. fossilis, AF-positive cells of CS exhibit increased granulation after 72 and 96 hours, the nuclear volume of these cells remains unchanged. The AF-negative cells of CS of CBCF-treated fish exhibit a slight increase in their nuclear volume after 96 hours. After 21 and 28 days, AF-positive cells of CS show increased granulation. Nuclear volume of these cells exhibited a decrease after 21 and 28 days. A few degenerating cells are also noticed. Nuclear volume of AF-negative cells of CS of CBCF-treated fish increase after 21 and 28 days.
Simplified Short Circuit Current Calculation for DC Distribution Grids based on a Superposition Approach using Approximated Current Courses

The use of DC distribution grids for medium and low voltage level is debated in current research. One important aspect is the short circuit current calculation, whereby for practical grid planning a simplified short circuit current calculation method is desired. Thus, this paper presents a simplified short circuit current calculation method based on a superposition approach using approximated current courses. The results demonstrate that the calculation of any desired partial short circuit current is not possible. Nevertheless, the results also highlight that a rough estimation of dimensioning relevant short circuit current characteristics for current-carrying grid equipment is possible, whereas a few restrictions are given. Therefore, we cannot guarantee a general applicability of the simplified method. Thus, further development of the promising calculation method is required. However, we recommend using transient calculations for DC distribution grids until a sufficient applicability of the simplified calculation method can be guaranteed.
Investigating the Current Collapse Mechanisms of p-GaN Gate HEMTs by Different Passivation Dielectrics

In this letter, the dynamic <italic>R</italic><sub>on</sub> degradation mechanisms of the p-GaN gate HEMTs induced by <sc>off</sc>-state stress are investigated with different passivation dielectrics AlON and SiN. The degradation mechanisms are twofold, including <italic>V</italic><sub>TH</sub> shift and surface trapping in the gate-to-drain access region, whose impacts are successfully distinguished. Surface trapping by SiN passivation is evidently proved to be the dominant factor that can almost induce a full current collapse. The <italic>V</italic><sub>TH</sub> positive shift diminishes the drain current by shrinking the overdrive <italic>V</italic><sub>GS</sub>, which however, can be compensated by a higher <italic>V</italic><sub>GS</sub> overdrive in applications. SiN passivation can effectively suppress the positive bias temperature instability effect, probably by passivating the p-GaN fast traps with hydrogen during passivation. Last, the transient measurements unveil that both the surface trapping and <italic>V</italic><sub>TH</sub> shift have a very slow recovery process.
Effect of acid Lugol solution as preservative on two representative chitineous and gelatinous zooplankton groups

The estimation of biomass from body lengths to carbon regressions is a common approach in plankton research. Several different chemicals for sample preservation are in use, and conversion factors to account for shrinkage effects exist, but to our knowledge the consequences of using potassium‐iodide and iodine (Lugol solution) as preservative on body sizes of different mesozooplankton groups have not been investigated. We tested the effect of 2% acidified Lugol solution on body sizes over time on two major marine mesozooplankton groups, namely larvaceans and copepods, which are representatives of gelatinous and chitineous plankton, respectively. For both, we observed a significant shrinkage effect over time (P < 0.0001). Larvaceans showed a reduction in body size by 20% within the first 2 min, which stabilized after 36 h at 22%, whereas copepods shrank by 17%. These differences were significant (P = 0.0324), with no further shrinkage observed over an additional 3 months. Failure to adequately account for shrinkage effects could result in significant biomass under‐estimation if length‐weight relationships are generated from live material.
ASSESSMENT OF SODIUM LACTATE ADDITION TO FRESH PORK SAUSAGE

Sodium lactate was added to fresh pork sausage to determine its effect on refrigerated storage of chubs and retail display of patties. An additional treatment evaluated effects of sodium lactate in fresh sausage with added textured soy concentrate. In fresh sausage stored as 454g chubs, surface discoloration was reduced (P 0.01) but resulted in more rapid surface discoloration (P<0.01). Presence of textured soy concentrate decreased (P<0.01) effectiveness of sodium lactate against microorganisms.
Performance Analysis of Arbitrary Correlated Multiantenna Receiver for Mobile Cognitive User

In limited space scenarios, the antennas in the multi-antenna cognitive radio (CR) system are closely spaced and often experience correlation among them. In this paper, the sensing performance of arbitrary correlated antennas over Nakagami-m fading channel for the mobile CR user is analysed. In particular, the analytical expression for the average detection probability for a mobile CR user employing the selection combining with triple arbitrary correlated diversity branches is derived as a special case. Furthermore, to characterize the performance of energy detector under mobility, the area under the curve of receiver operating characteristic is analysed. The derived expressions converge quickly due to the monotonically decreasing hypergeometric function of two variables. The Monte Carlo simulations substantiate the analytical expressions. Results indicate that antenna correlation deteriorates detection performance. Moreover, the high speed of CR users further decreases the detection performance, especially in the deep fading channel scenarios. This work provides a realistic sensing framework for the CR enabled vehicles.
Delay Minimization for Edge Computing with Dynamic Server Computing Capacity: A Learning Approach

The offloading decisions of K mobile users (MUs) aiming at minimizing the execution delay in a Mobile Edge Computing (MEC) scenario with non-orthogonal multiple access is considered. In this work, we assume a time-varying MEC server computing capacity which exploits additional computing resources that are freed over time, but are not known beforehand. In this setting, the optimal offloading decision depends on the different tasks of MUs, their channel fading processes and the MEC server computing capacity. We first formulate the optimization problem and identify two main challenges, namely, how to exploit the given incomplete knowledge to minimize the delay and how to handle the high dimensionality of the problem. To address these challenges, we propose a novel reinforcement learning (RL) algorithm, termed combinatorial offloading learning (COL). The name stands for its ability to handle the combinatorial nature of the solutions. Exploiting the available knowledge, we learn the offloading decision policy aiming at minimizing the delay. Furthermore, we handle the curse of dimensionality, typical of combinatorial problems, by splitting the learning task, solving K+1 smaller RL problems and using linear function approximation. Through numerical simulations, we show that COL could perform similar to a short term optimal solution with complete information and exhaustive search, and outperforms known strategies like the greedy approach.
AdaIN-Based Tunable CycleGAN for Efficient Unsupervised Low-Dose CT Denoising

Recently, deep learning approaches using CycleGAN have been demonstrated as a powerful unsupervised learning scheme for low-dose CT denoising. Unfortunately, one of the main limitations of the CycleGAN approach is that it requires two deep neural network generators at the training phase, although only one of them is used at the inference phase. The secondary auxiliary generator is needed to enforce the cycle-consistency, but the additional memory requirements and the increase in the number of learnable parameters are major hurdles for successful CycleGAN training. Despite the use of additional generator, CycleGAN only translates between two domains, so it is not possible to investigate the intermediate level of denoising. To address this issue, here we propose a novel tunable CycleGAN architecture using a single generator. In particular, a single generator is implemented using adaptive instance normalization (AdaIN) layers so that the baseline generator converting a low-dose CT image to a routine-dose CT image can be switched to a generator converting high-dose to low-dose by simply changing the AdaIN code. Thanks to the shared baseline network, the additional memory requirement and weight increases are minimized, and the training can be done more stably even with small training data. Furthermore, by interpolating the AdaIN codes between the two domains, we can investigate various intermediate level of denoising results. Experimental results show that the proposed method outperforms the previous CycleGAN approaches while using only about half the parameters, and provide tunable denoising features that may be potentially useful in clinical environment.
Multi-Agent Coordinated Interception of Multiple Rogue Drones

Over the last few years there has been an unprecedented interest in unmanned aerial vehicles (UAVs). However, drones potentially pose great threats to security and public safety, especially when their malicious use involves critical infrastructures and public spaces. This work proposes a multiagent counter-drone system where a team of pursuer drones cooperate in order to track and jam multiple rogue drones. Specifically, a cooperative multi-agent approach is proposed in which the best joint mobility and power control actions of each agent are chosen so that the rogue drones are optimally tracked and jammed over time. Two variants of the joint optimization problem are developed and extensive simulations are conducted so as to evaluate the performance of the proposed approach.
Flexible and Stretchable Ultrasonic Transducer Array Conformed to Complex Surfaces

In this work, a flexible and stretchable ultrasonic transducer array consisting of $3\times3$ elements is proposed to achieve conformal contact with various nonplanar complex surfaces. The array is formed by integrating rigid piezoelectric ceramics with serpentine hinges in a single-layered configuration and encapsulated by compliant elastomers. The addressable electrodes arrangement allows the use of a small number of connecting wires to excite each element in the array. The developed ultrasonic transducer has a resonant frequency of 1.91 MHz and the initial acoustic characterization is demonstrated. The serpentine electrodes can be stretched up to 33% without failure and the array exhibits excellent flexibility of bending, folding, twisting, and rolling up, indicating its reliable electrical connection and stable mechanical property. Moreover, the performance of the array attached to curved surfaces, human skins, and medical catheters is also demonstrated. The results indicate a convenient and robust method to achieve the preparation of a flexible and stretchable ultrasonic transducer array, which extends conventional rigid ultrasonic transducers to future wearable applications.
Proactive Network Slice Reconfiguration by Exploiting Prediction Interval and Robust optimization

It is widely acknowledged that the agile reconfiguration of network slice according to traffic demand is of vital importance in 5G-and-beyond systems. Existing relevant works make reconfiguration decisions based either on point prediction of the uncertain demand, which lacks indications on how accurate it is, or on handcrafted uncertainty set with robust optimization, which may lead to resource over-provisioning due to the lack of prediction mechanism. To overcome these drawbacks, in this paper, we propose a predictor-optimizer framework that intelligently performs inter-slice reconfiguration with the aim of minimizing the energy consumption of serving these slices. Specifically, the predictor produces a prediction interval comprised of lower and upper bounds that bracket the future traffic demands with a prespecified probability. Then by regarding the prediction interval as the uncertainty set, we formulate the network slice reconfiguration problem as a Robust Mixed Integer Programming (RMIP). We solve this RMIP by using linearization technique and robust optimization. Numerical results demonstrate that the proposed framework outperforms traditional methods in terms of robustness and energy consumption. Meanwhile, the tradeoff between robustness and the energy consumption can be automatically adjusted according to the type of slice and traffic demands.
Crowdfunding with Cognitive Limitations

A crowdfunding platform enables project creators to raise funding from a crowd of contributors to develop their projects. An accurate modeling and predication of contributors’ behaviors is critical for a creator to achieve the project success. In this paper, we present a first study on a creator’s optimal crowdfunding campaign parameter choices, considering the contributors’ realistic cognitive limitations. Specffically, we consider a two-stage crowdfunding model: in Stage I a creator determines the project decisions (i.e., price and minimum number of contributors); in Stage II the contributors choose their pledging behaviors (whether to contribute and with what probabilities). We apply the cognitive hierarchy theory to model the cognitive limitations of the contributors, i.e., contributors are grouped based on their cognitive levels which reflect their capabilities of reasoning other contributors’ decisions. Although the project decisions are highly coupled between contributors’ pledging and creator’s revenue maximization, we exploit the monotonic structure of the revenue maximization problem to compute creator’s optimal solutions. We show that contributors with the same valuation exhibit the same pledging behaviors, independent of their cognitive levels (except those at the lowest cognitive levels). This explains the real-world phenomenon that projects either attract few contributors (often under 40% pledging rate) or are oversubscribed.
MOOCs Dropout Prediction Based on Hybrid Deep Neural Network

The flexibility of Massive Open Online Courses (MOOCS) have attracted more and more learners because it allows students to study at their own pace. However, this flexibility makes it easy for students to drop out of a class. Previous studies, show that the completion rate of MOOC courses was less than 10%. Therefore, a more reliable and efficient dropout prediction model is in need for the development of the MOOC platform. In this paper, our goal is to predict if a learner is going to drop out within the next 10 days, given clickstream data for the first 30 days. We propose a hybrid depth neural network to model and to predict learners' dropout behavior. In order to maintain the time series relationship of the original learning behavior records, we use one-hot encoding to transform the student's behavior record data into a two-dimensional matrix representation, and then extract the local features of the behavior matrix by a new convolutional neural network (CNN) and Squeeze-and-Excitation Networks(SE-Net), and finally extract the potential time series relationship between learning behaviors through a gated recurrent unit (GRU) network to improve the prediction performance. We conducted experiments on KDD Cup 2015 dataset and the results show that our proposed method achieves better performance than baseline methods.
Simplified Closed-Form Single-Scatter Path Loss Model of Non-Line-of-Sight Ultraviolet Communications in Noncoplanar Geometry

Considering the wide acceptability and practicability of single-scatter path loss (PL) model of non-line-of-sight (NLOS) ultraviolet communications (UVC) in noncoplanar geometry and the corresponding complexity of the triple integrals in the exact single-scatter PL model, a novel simplified PL model is proposed in this work with better analytical tractability and high accuracy. By approximately regarding the differential volume as a circular and thin plate, the triple integrals in the exact single-scatter PL model can be reduced to a single integral. With different baseline ranges between transmitter and receiver, beam divergence angles, receiver field-of-view (FOV) angles, inclination angles and azimuth angles taken into account, the results of the proposed PL model are compared with those of an exact single-scatter PL model, a Monte Carlo numerical model, and another recently reported approximate single-scatter PL model, which is called spherical crown (SC) model. Compared with the SC model, the proposed single-scatter PL model is obtained in closed form. Besides, taking the exact single-scatter PL model as the benchmark, the root-mean-square error (RMSE) of the proposed model and the SC model are calculated for comparison. It is demonstrated that the proposed model could achieve better accuracy than SC model under most conditions of our interest. Besides, it is shown that the RMSE of the proposed model is less than 0.4 dB for all considered transceiver geometry and the execution time of the proposed PL model is significantly reduced to less than 1% of the exact single-scatter PL model’s, which manifests the effectiveness of the proposed model. Finally, the experimental results are also provided to verify the correctness of the proposed model.