Miracle-dz/UniFAR-FTUs huggingface dataset:
Combating Bufferbloat in Multi-Bottleneck Networks: Equilibrium, Stability, and Algorithms

Bufferbloat is a phenomenon where router buffers are constantly being filled, resulting in high queueing delay and delay variation. Larger buffer size and more delay-sensitive applications on the Internet have made this phenomenon a pressing issue. Active queue management (AQM) algorithms, which play an important role in combating bufferbloat, have not been widely deployed due to complicated manual parameter tuning. Moreover, AQM algorithms are often designed and analyzed based on models with a single bottleneck link, rendering their performance and stability unclear in multi-bottleneck networks. In this paper, we propose a general framework to combat bufferbloat in multi-bottleneck networks. We first conduct an equilibrium analysis for a general multi-bottleneck TCP/ AQM system and develop an algorithm to compute the equilibrium point. We then decompose the system into single-bottleneck subsystems and derive sufficient conditions for the local asymptotic stability of the subsystems. Using the proposed framework, we present a case study to analyze the stability of the recently proposed Controlled Delay (CoDel) in multi-bottleneck networks and devise Self-tuning CoDel to improve the system stability and performance. Extensive simulation results show that Self-tuning CoDel effectively stabilizes queueing delay in multi-bottleneck scenarios, and thus contributes to combating bufferbloat.
Paper-based lateral flow strip assay for the detection of foodborne pathogens: principles, applications, technological challenges and opportunities

Abstract As a representative colorimetic biosnesor, paper-based LFSA have emerged as a promising and robust tool that can easily and instansly detect the presence of target biological components in food sample. Recently, LFSAs have gained a considerable attention as an alternative method for rapid diagnosis of foodborne pathogens to the conventional culture-based assays such as plate counting and PCR. One major drawback of the current LFSAs for the detection of pathogenic bacteria is the low sensitivity, limiting its practical applications in POCT. Not like many other protein-based biomarkers that are present in nM or pM range, the number of pathogenic bacteria that cause disease can be as low as few CFU/ml. Here, we review current advances in LFSAs for the detection of pathogenic bacteria in terms of chromatic agents and analyte types. Furthermore, recent approaches for signal enhancement and modifications of the LFSA architecture for multiplex detection of pathogenic bacteria are included in this review, together with the advantages and limitations of each techniques. Finally, the technological challenges and future prospect of LFSA-based POCT for the detection of pathogenic bacteria are discussed.
Old age medical patients screening positive for depression

Abstract Objective: The aim was to observe whether medical inpatients screening positive for depression using the Geriatric Depression Scale (GDS) continue to screen positive following hospital discharge. Method: Participants aged 65 or over, were recruited from consecutive admissions to a city teaching hospital. Subjects had an Abbreviated Mental Test Score (AMTS) of seven or above and a GDS-15 score of five or above. Information was collected on past psychiatric history and living arrangements. Subjects were followed-up three months later and the GDS repeated. Results: Thirty subjects were recruited and 26 (87%) followed-up. Ten (38%) no longer scored positive on the GDS, and overall the mean GDS score decreased by two points (Z = 2.235 p < 0.05). Patients with a past psychiatric history or living alone were more likely to be depressed at follow-up. No participants were referred to the psychiatric service or started on antidepressant medication during the course of the study. Conclusion: Depressive symptoms are likely to persist following hospital discharge, especially in those patients with a past psychiatric history. An understanding of the risk factors associated with persistent depressive symptoms is necessary if the patients appropriate for treatment are to be identified.
Multi-Envelope Precoding for Massive MIMO Systems

Constant envelope (CE) precoding is a recently proposed technique for massive MIMO systems that decreases the hardware complexity of base station circuitry. In this letter, we build on this idea and propose a multi-envelope precoding method, which utilizes more than one (but only a few, e.g., 2 or 3) envelope levels and assigns higher levels on the antenna group(s) requiring more power in order to increase the achievable rates. The results demonstrate substantial gains with respect to the CE approach for both cases of discrete and continuous phase shifters with almost no increase in hardware complexity and a small increase in computational complexity.
Bin-BB: Binning with Branch & Bound feature selection for improved diabetes classification

Neural Networks (NNs) are attractive for any classification problem. They are using extensively in the medical domain. As the medical data by nature is noisy and high-dimensional these NNs require more training time and provides poor generalization. These problems are avoided if pre-processing of data is done prior to training. Branch & Bound (B& B) based feature selection is one of the pre-processing technique extensively used to remove noisy and redundant features in the dataset. In this paper, we proposed a new criterion function based on likelihood ratio of class distributions to select more relevant features along with equal-depth binning of features. Pima Indians Diabetes dataset is chosen for experiments. Experimental results proved that proposed B&B equal-depth Binning (Bin-BB) helped in reducing the search space by reducing the height of B&B feature tree. The new criterion function helped for effective and efficient feature selection. As a result, performance of majority NN-based classifiers is improved on PID dataset.
Quantifying the association between progression‐free survival and overall survival in oncology trials using Kendall's τ

This paper considers methods for estimating the association between progression‐free and overall survival in oncology trials. Copula‐based, nonparametric, and illness‐death model–based methods are reviewed. In addition, the approach based on an underlying illness‐death model is generalized to allow general parametric models. The performance of these methods, in terms of bias and efficiency, is investigated through simulation and also illustrated using data from a clinical trial of treatments for colon cancer. The simulations suggest that the illness‐death model–based method provides good estimates of Kendall's τ across several scenarios. In some situations, copula‐based methods perform well but their performance is sensitive to the choice of copula. The Clayton copula is most appropriate in scenarios, which might realistically reflect an oncology trial, but the use of copula models in practice is questionable.
Deep Index-Compatible Hashing for Fast Image Retrieval

Deep hashing methods have achieved promising results for large-scale image retrieval recently. To accelerate the subsequent Hamming ranking process, the multi-index approach has been proposed to reduce the computations for the Hamming distance. However, the binary codes output by the previous deep hashing methods may not be optimally compatible with the multi-index approach. In this paper, we present a novel Deep Index-Compatible Hashing (DICH) method for fast image retrieval, which can learn similarity-preserving binary codes that are more compatible with the multi-index approach. With the learned binary codes, both the size of the intermediate result set produced by the multi-index approach and the number of the candidate images can be reduced, which can accelerate the Hamming ranking process. By taking advantage of the unique feature of DICH, we further propose a block-based ranking strategy to quickly rank the candidate images without calculating the Hamming distance. Extensive evaluations demonstrate that the proposed method can significantly reduce the retrieval time with almost no loss of retrieval accuracy.
LPPT Control of a Dual-Stage Grid Connected Multi-String Photovoltaic System

The objective of this paper is to implement the limited power point tracking (LPPT) control of a photovoltaic (PV) system with the multi–string configuration. Unlike the centralized or single–string configuration, the power sharing between different strings is an important concern for the multi–string configuration under the LPPT control. A suitable control scheme is developed to achieve the aforementioned power sharing. The methodology proposed is verified under both uniform irradiance and partial shading conditions.
Image Denoising Based on Online Dictionary Learning

Many state-of-the-art denoising algorithms often employ dictioanary learning methods to acquire the mapping relationship between the polluted image by noise and the original clean image. It is critical to generate the appropriate dictionary in image denoising based on dictionary learning. In order to promote the denoising efficiency, the proposed algorithm employs Online Dictionary Learning (ODL) to train the overcomplete dictionaries to make the dictionary more accurate. The dictionary updating procedure is improved with a warm start. The dictionary is updated by the last computed dictionary and the current input image patches. Hence the dictionary is more accurate to get better denoising images. In the experiments, the PSNR of ODL dictionary is 0.12dB higher than SCDL and 0.21dB higher than K-SVD in average. The proposed method can eliminate the artifacts and recover the texture details efficiently.
Impact of Measurement Noise on Millimeter Wave Beam Alignment Using Beam Subsets

This letter derives bounds on performance degradation due to measurement noise in millimeter wave beam alignment methods that train a subset of beam pairs. The analysis applies the union bound and pairwise beam selection error for a wideband channel. While the analysis is oblivious to the method to select the subset of beam pairs, inverse fingerprinting beam alignment is used as a concrete numerical example. Performance evaluation results for different bandwidths show that a wideband system suffers less from measurement noise with the same training sequence duration.
Survivability Model for Security and Dependability Analysis of a Vulnerable Critical System

This paper aims to analyze transient security and dependability of a vulnerable critical system, under vulnerability-related attack and two reactive defense strategies, from a severe vulnerability announcement until the vulnerability is fully removed from the system. By severe, we mean that the vulnerability-based malware could cause significant damage to the infected system in terms of security and dependability while infecting more and more new vulnerable computer systems. We propose a Markov chain-based survivability model for capturing the vulnerable critical system behaviors during the vulnerability elimination process. A high-level formalism based on Stochastic Reward Nets is applied to automatically generate and solve the survivability model. Survivability metrics are defined to quantify system attributes. The proposed model and metrics not only enable us to quantitatively assess the system survivability in terms of security risk and dependability, but also provide insights on the system investment decision. Numerical experiments are constructed to study the impact of key parameters on system security, dependability and profit.
Robust Estimation of Switching Transients of Non-Linear Loads and Their Comparative Studies

In this paper, a new mathematical model of extended Kalman filter (EKF) and Kalman filter (KF) have been developed to track the switching transients of the power electronic based nonlinear loads in order to improve the power quality of the system. Switching transients that cause harmonic pollution, distortion in the line current and voltage profile, poor quality of power towards the consumers' side must be identified rapidly and accurately in order to maintain the healthy condition of the network. To track the load transient signatures in the real world, a laboratory experimental test bench has been developed where CPU, laser printer have been taken as nonlinear loads and their transients have been observed at the time of switching using EKF, KF and Matrix Pencil Method (MPM), an existing algorithm. A comparative study has been shown to characterize their performance efficiency. From the performance assessment point of view, EKF exhibits more reliable and faster-tracking quality rather than KF and MPM, even when the observation noise variance is much larger than the system process noise variance.
Sex Differences in Temporal but Not Spatial Attentional Capture

The accuracy of detecting or identifying a target decreases when a salient distractor is presented. This decrease is explained by the temporal or spatial diversion of attention to the distractor and thus is referred to as attentional capture. Using temporal and spatial visual search tasks, we examined whether there are sex differences in attentional capture. In Experiment 1A, a temporal visual search task measured attentional capture in the temporal domain by asking participants (97 men and 92 women) to identify a target embedded in a rapid stream of nontarget letters while ignoring a preceding peripheral distractor. In Experiment 2, a spatial visual search task measured attentional capture in the spatial domain by asking participants (146 men and 83 women) to detect a target among spatially distributed nontarget items while ignoring a distractor presented simultaneously. Our results indicate that attentional capture occurred in both tasks. In Experiment 1A, the magnitude of capture was significantly larger for women than men. In Experiment 1B, we confirmed sex differences in temporal attentional capture by recruiting a new set of participants (141 men and 85 women). In Experiment 2, the magnitude of capture was comparable between the sexes. These results suggest that women are more sensitive to bottom-up signals than men when they engage in a temporal search task and could be explained in terms of sex differences in the ability of adjusting the size of attentional window, within which attention is allocated to the most salient item.
Coreless and Conventional Axial Flux Permanent Magnet Motors for Solar Cars

Axial flux permanent magnet (AFPM) motors are suitable options for solar-powered vehicles due to their compact structure and high torque density. Furthermore, certain types of AFPM machines may be configured without stator cores, which eliminates associated losses and cogging torque and simplifies the manufacturing and assembly. This paper examines two machine designs for use in the solar-powered vehicle of the challenger class—a single rotor, single stator conventional AFPM machine, and a coreless AFPM machine with multiple stator and rotor disks. The response surface methodology (RSM) is utilized for the systematic comparison of the conventional and coreless topologies and to select the optimum designs among several hundreds of candidates. Designs with minimum losses and mass producing required torque with larger air-gap are favored. The performance of the selected designs has been studied via three-dimensional finite element analysis (FEA). The FEA parametric modeling methodology is validated by measurements on three AFPM machines of the conventional and coreless type.
A Soft Robotic Phantom to Simulate the Dynamic Respiratory Motion of Human Liver

In percutaneous interventions for hepatic tumor treatment, organ motion caused by respiration can lead to inaccurate needle placement hence less effective therapeutic and diagnostic outcome. The aim of this study was to implement soft robotics technology to develop an actuated phantom that mimics respiratory motion in liver for needle insertion procedures. The actuated liver phantom should be MR-compatible as it will be used for testing needle insertion devices and prototypes in an MRI environment while considering the respiratory motion of the liver. The soft-actuated robotic phantom can also be used for training clinicians on MRI-guided percutaneous needle interventions such as manual biopsy and ablation. Pneumatic soft actuators were design and implemented in the phantom to achieve a smooth motion similar to liver respiratory motion. In order to evaluate the performance of the phantom, three common breathing types were simulated, as well as a normal and two abnormal breathing patterns in patients. Moreover, a normal respiration was simulated with different phantom weights to be representative for different liver masses. The results revealed that the developed robotic simulator is sensitive to feeding air pressure and actuating time period, and to a less extend to the phantom mass. Based on five cycle repetitions, the simulator could reproduce different respirations with a maximal deviation of 3mm in superior, 2mm in inferior and less than 1mm in anterior-posterior directions.
Temperature monitoring of short-gate length AlGaN/GaN HEMT via an integrated sensor

This paper describes a new method to measure AlGaN/GaN High Electron Mobility Transistors (HEMTs) operating temperature in devices dedicated to RF application. A resistive nickel temperature sensor is integrated into HEMT active area. The technological process development permits to integrate the sensor close to the transistor hot spot providing HEMT temperature under operation. A maximal temperature of 68°C is extracted for a dissipated power of 3.5 W/ mm corresponding to a thermal resistance of 10.6 Kmm/W. This new method shows the capability to monitor component self-heating in real time and to predict its failure. Furthermore, it is shown that the sensor has no influence on DC HEMT electrical behavior and its impact on current and power cutoff frequencies is negligible.
The corticotopic organization of the human basal forebrain as revealed by regionally selective functional connectivity profiles

The cholinergic basal forebrain (CBF), comprising different groups of cortically projecting cholinergic neurons, plays a crucial role in higher cognitive processes and has been implicated in diverse neuropsychiatric disorders. A distinct corticotopic organization of CBF projections has been revealed in animal studies, but little is known about their organization in the human brain. We explored regional differences in functional connectivity (FC) profiles within the human CBF by applying a clustering approach to resting‐state functional magnetic resonance imaging (rs‐fMRI) data of healthy adult individuals (N = 85; 19–85 years). We further examined effects of age on FC of the identified CBF clusters and assessed the reproducibility of cluster‐specific FC profiles in independent data from healthy older individuals (N = 25; 65–89 years). Results showed that the human CBF is functionally organized into distinct anterior‐medial and posterior‐lateral subdivisions that largely follow anatomically defined boundaries of the medial septum/diagonal band and nucleus basalis Meynert. The anterior‐medial CBF subdivision was characterized by connectivity with the hippocampus and interconnected nodes of an extended medial cortical memory network, whereas the posterior‐lateral subdivision was specifically connected to anterior insula and dorsal anterior cingulate components of a salience/attention network. FC of both CBF subdivisions declined with increasing age, but the overall topography of subregion‐specific FC profiles was reproduced in independent rs‐fMRI data of healthy older individuals acquired in a typical clinical setting. Rs‐fMRI‐based assessments of subregion‐specific CBF function may complement established volumetric approaches for the in vivo study of CBF involvement in neuropsychiatric disorders.
Interactions Between ABCB1 Genotype and Preoperative Statin Use Impact Clinical Outcomes Among Breast Cancer Patients

Multiple clinical trials investigate statins' effects in breast cancer. The ABCB1 genotype appears to influence statin response and toxicity in the cardiovascular setting. This exploratory study aimed to investigate the interplay between preoperative statin use, ABCB1 genotype, and tumor-specific expression of the statin target 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) in breast cancer. Preoperative statin use, ABCB1 C3435T genotype, and HMGCR expression in relation to outcome were analyzed in 985 primary breast cancer patients from a population-based prospective cohort in Sweden from 2002 to 2012. Preoperative statin use (n = 80) was not associated with ABCB1 C3435T genotype (n = 576), HMGCR expression (n = 848), or clinical outcomes. ABCB1 C3435T TT-carriers had lower risk of breast cancer events than any C-carriers (adjusted hazard ratio (HRadj) 0.74; 95%CI 0.49, 1.12), but only in non-statin users (Pinteraction = 0.042). Statin users with TT genotype had higher risk of distant metastasis (HRadj 4.37; 95%CI 1.20, 15.91; Pinteraction = 0.009) and shorter overall survival than other patients (HRadj 3.77; 95%CI 1.37, 10.39; Pinteraction = 0.019). In conclusion, there were nominally significant interactions between ABCB1 genotype and preoperative statin use on clinical outcomes, while preoperative statin use was not associated with outcomes. Since this is an exploratory study of the impact of the ABCB1 genotype in relation to statin use and clinical outcomes in the breast cancer setting, the results should be interpreted with caution and warrant replication in an independent cohort, preferably in a randomized setting. Since statin use is common in breast cancer patients, it would be of interest to further elucidate the clinical impact of the ABCB1 genotype in breast cancer.
A malware detection system based on heterogeneous information network

In this era of information networks, more and more malware (malicious software) poses a serious threat to security. How to detect malware attacks in a timely and effective manner becomes particularly important. The increasingly sophisticated malware calls for new defense technologies to detect and combat novelty attack and threats. In this paper, we propose a novel malware detection method that not only depends on API calls, further analyze the relationship between them and creates higher-level semantics to avoid attackers evading detection. We construct a heterogeneous information network (HIN) through their rich relationships between software and related APIs, and then use meta-path-based methods to describe the semantic relevance to software and APIs. We use each meta-path to calculate similarities between software and aggregate different similarities with Multi-kernel Learning (MKL) to construct a malware detection system. We collected real sample data and conducted a comprehensive experiment. Through experiments we have obtained a relatively high detection rate and a relatively low false detection rate, shows the effectiveness of our proposed method.