Miracle-dz/UniFAR-FTUs huggingface dataset:
High-dimensional expectile regression incorporating graphical structure among predictors

ABSTRACT With the extensive application of high-dimensional heterogeneous data in various disciplines, sparse expectile regression is increasingly favoured by people. In this paper, we make use of the graphical structure among predictors node-by-node to improve the performance of parameter estimation, model selection and prediction in sparse expectile regression. A modified alternating direction method of multipliers (ADMM) algorithm with a linearization trick is proposed to implement the proposed method numerically, and the convergence of the algorithm is proved. Simulation studies are conducted to evaluate the finite-sample performance of the proposed method. We demonstrate the practicability of the proposed approach with a dataset of the expression quantitative trait locus mapping (eQTL) experiment in Rattus norvegicus.
A Survey on Trust Models in Heterogeneous Networks

Heterogeneous networks (HetNets) merge different types of networks into an integrated network system, which has become a hot research area in recent years towards next-generation communication networks. HetNets aim to effectively exploit network resources and provide seamless connectivity for heterogeneous objects. Unlike other networks, HetNets hold such characteristics as heterogeneity, openness, distribution, multi-domain involvement, thus are susceptible to various security threats and attacks. Traditional security approaches are not sufficiently effective in defending against them. With extensive study and practice, researchers found that trust models offer effective measures to enhance the security and reliability of a network system. However, there still lacks a comprehensive survey on the recent advances of trust models in HetNets. In this paper, we fill this gap. We first retrospect the history of HetNets research and introduce important concepts related to trust. Then, we propose a set of criteria that a sound trust model should satisfy, which can also serve as a measure to evaluate the quality of a trust model, i.e., Quality of Trust (QoT). We provide taxonomies of trust models and their applications, and continue with a thorough review on trust models in HetNets. Based on the review, a list of open issues is highlighted, and corresponding future research directions are suggested to advance the research on trustworthy HetNets.
Sample-Efficient Deep Learning Techniques for Burn Severity Assessment with Limited Data Conditions

The automatic analysis of medical data and images to help diagnosis has recently become a major area in the application of deep learning. In general, deep learning techniques can be effective when a large high-quality dataset is available for model training. Thus, there is a need for sample-efficient learning techniques, particularly in the field of medical image analysis, as significant cost and effort are required to obtain a sufficient number of well-annotated high-quality training samples. In this paper, we address the problem of deep neural network training under sample deficiency by investigating several sample-efficient deep learning techniques. We concentrate on applying these techniques to skin burn image analysis and classification. We first build a large-scale, professionally annotated dataset of skin burn images, which enables the establishment of convolutional neural network (CNN) models for burn severity assessment with high accuracy. We then deliberately set data limitation conditions and adapt several sample-efficient techniques, such as transferable learning (TL), self-supervised learning (SSL), federated learning (FL), and generative adversarial network (GAN)-based data augmentation, to those conditions. Through comprehensive experimentation, we evaluate the sample-efficient deep learning techniques for burn severity assessment, and show, in particular, that SSL models learned on a small task-specific dataset can achieve comparable accuracy to a baseline model learned on a six-times larger dataset. We also demonstrate the applicability of FL and GANs to model training under different data limitation conditions that commonly occur in the area of healthcare and medicine where deep learning models are adopted.
The Prediction of the Dynamic Contact Resistance of Circuit Breaker Based on the Kernel Partial Least Squares

In order to diagnose the ablation characteristics of circuit breakers, the dynamic contact resistance measurement (DRM) has been proposed for almost 20 years. The dynamic resistance curves are easily influenced by two factors: injected current and opening velocity. The two factors are greatly affected the curves and may led a wrong diagnosis in DRM tests. Through analysis of the influence factors, an experimental model is used to simulate the opening process of the breaker in this study. The effective contact area of contact finger which is used in the experimental model under different velocities and current are measured by using the laser scanning microscope. The measurement proves that the changing of effective contact area plays determinative roles in the influencing of injected current and opening velocity. Then, the kernel partial least squares regression method is used to establish the model formula between influencing parameters and the dynamic contact resistance to realise the prediction of the dynamic contact resistance curves. Furthermore, the prediction model also can be used in other current carrying dry sliding friction conditions.
Heterogeneous large-scale group decision making with subgroup leaders: An application to the green supplier selection

Abstract Under the mainstream development trend of energy conservation, environmental protection and sustainability, green supplier selection has become a common concern of many enterprises and scholars. Meanwhile, more and more attention has been paid to the problem of large-scale group decision making (LSGDM) which requires the participation of multiple decision makers (DMs). As the factors to be considered in selecting the optimal green supplier become increasingly complex, DMs with different professional backgrounds may give different forms of assessment. Therefore, it is of great significance to study the application of heterogeneous LSGDM in green supplier selection (GSS). In this article, a new heterogeneous LSGDM method based on subgroup leaders (SLs) is proposed. First, a novel determination method of SLs is presented based on the interaction between DMs. Then, according to the heterogeneous preference information of SLs, the solution method of the collective priority vector is given, and then an approach to determine the overall consensus level is presented by considering the consensus degree within and between subgroups. Subsequently, a consensus building method is proposed to manage the non-cooperative behaviour of DMs and reduce the divergences among DMs. Finally, an example of GSS is given to demonstrate the applicability and effectiveness of the proposed method.
Design and field trial measurement results for a portable and low‐cost very‐high‐frequency/ultra‐high‐frequency channel sounder platform for Internet of things propagation research

Propagation research is vital for informing the design of reliable very high-frequency and ultra-high-frequency communications systems for the Internet of things. In this study, a cost-effective and highly portable system is proposed and then used to obtain propagation measurements in city and suburban scenarios at 71 and 869.525 MHz. The system calculates the received power, power delay profile and channel frequency response. The portable sounding receiver uses readily available parts: an RTL-SDR (covering 27 MHz-1.7 GHz) and a Raspberry Pi with a touchscreen. The Pi implements all the channel sounding signal processing algorithms in Python, in near real time. Extracted propagation data and models are presented, from example city and suburban field trials incorporating pedestrian and car use.
A nonlinear model for a three-phase 12/8 switched reluctance machine

The paper presents a methodology for the synthesis of a nonlinear model for a three-phase 12/8 switched reluctance machine (SRM) in a MATLAB/Simulink environment. The required switched reluctance motor characteristics are derived using the finite element analysis (FEA). Graphical and tabular results of the relations flux/current/position, current/torque/position, and inductance/current/position used to create the SRM model are presented. The structure of the nonlinear three-phase 12/8 SRM model is discussed and its implementation in Simulink is presented. A series of simulation and experimental results are performed to verify the accuracy of the built three-phase, 12/8 SRM (H55PWBKM-1844) nonlinear model. A very good alignment between simulation and experimental results is observed.
Effect of oestrus synchronization with different lengths of progesterone-impregnated sponges and equine chorionic gonadotropin on reproductive efficiency in Romanov ewes during the non-breeding season

This study aimed to determine the effects of different lengths of progesterone and equine chorionic gonadotropin (eCG) treatment on reproductive performance during the non-breeding season in ewes. Progesterone-impregnated sponges were inserted intravaginally for 7 d in Group 1 (G1, n = 24), 9 d in Group 2 (G2, n = 25), and 11 d in Group 3 (G3, n = 24). On the day of sponge removal, eCG (350 IU) was injected and 24 h later the ewes were exposed to rams. Pregnancy was diagnosed by transrectal ultrasonography 30 ± 3 d after mating. Oestrus symptoms of G2 and G3 started earlier than G1, but there was only a significant difference between G1 and G3 (P = 0.013). In G1, G2, and G3, the oestrus responses were 100%, 100%, and 95.8%, pregnancy rates were 79.2%, 72%, and 75%, lambing rates were 79.8%, 72%, and 75%, litter sizes were 2.5, 2.2, and 2.4, and multiple birth rates were 86.7%, 80%, and 85.7%, respectively (P > 0.05). In conclusion, the short-term progesterone-impregnated sponge and eCG treatment provided excellent oestrus induction. The effects of different days (7, 9, or 11) of progesterone treatment on reproductive indicators were similar; therefore, the stress caused by the inserted sponge can be reduced by short-term use.
Fuzzy Integral Sliding Mode Observer-Based Formation Control of Mobile Robots With Kinematic Disturbance and Unknown Leader and Follower Velocities

Fuzzy integral sliding mode observer (FISMO) based leader-follower formation control with the use of ceiling-mounted camera information is proposed for mobile robots with kinematic disturbance and no information of the velocities of leader and follower robots. Using only the posture information of both the leader and follower robots obtained from the camera sensor, the follower robot is made to follow the trajectory of a target robot that is introduced to render the leader-follower formation control more efficient. As a result, a kinematic tracking error model is proposed in terms of both the posture error between the target and follower robots and the posture error between the origin of the inertial reference frame and follower robot. Because the kinematic disturbance and the velocities of the leader and follower robots are not available, an FISMO is designed to simultaneously estimate the kinematic disturbance as well as the linear and angular velocities of the leader and follower robots. FISMO-based leader-follower output feedback formation control is then proposed based on the estimates of the kinematic disturbance and velocities and the backstepping-like feedback linearization tracking control. The experimental results comparing the proposed FISMO-based method with the robust distance-based tracking control method and integral sliding mode observer (ISMO)-based method are provided to verify the effectiveness of the proposed method.
Graph Spatio-Spectral Total Variation Model for Hyperspectral Image Denoising

The spatio-spectral total variation (SSTV) model has been widely used as an effective regularization of hyperspectral images (HSIs) for various applications such as mixed noise removal. However, since SSTV computes local spatial differences uniformly, it is difficult to remove noise while preserving complex spatial structures with fine edges and textures, especially in situations of high noise intensity. To solve this problem, we propose a new TV-type regularization called graph-SSTV (GSSTV), which generates a graph explicitly reflecting the spatial structure of the target HSI from noisy HSIs and incorporates a weighted spatial difference operator designed based on this graph. Furthermore, we formulate the mixed noise removal problem as a convex optimization problem involving GSSTV and develop an efficient algorithm based on the primal-dual splitting method to solve this problem. Finally, we demonstrate the effectiveness of GSSTV compared with existing HSI regularization models through experiments on mixed noise removal. The source code will be available at https://www.mdi. c.titech. ac.jp/publications/gsstv.
Esterel on hardware

Esterel is a synchronous concurrent programming language for reactive systems (controllers, protocols, man—machine interfaces, etc.). Esterel has an efficient software implementation based on a well-defined mathematical semantics. I present a new hardware implementation of the pure synchronization subset of the language. Each program generates a specific circuit that responds to any input in one clock cycle. The circuit is shown to be semantically equivalent to the source program. The hardware translation is effectively implemented and used on the programmable active memory Perle0 developed by J. Vuillemin and his group at Digital Equipment.
Adjustable height glass spacers for bonding and aligning x-ray mirror stacks

Future sub-arcsecond resolution, large-effective area x-ray telescopes that use segmented grazing-incidence mirrors will require a bonding and alignment method that simultaneously: 1) achieves the accuracy needed for scientific observations, 2) possesses the strength needed to survive the ride to space, and 3) has high throughput to bond and align tens of thousands of components in a couple years. Current bonding and alignment processes are either over-constrained to achieve high strength with modest accuracy, or quasi-kinematic to achieve high accuracy but with lower strength. We propose an over-constrained mounting approach where spacers separating mirrors have set-and-forget adjustable height. This approach may provide the advantages of high strength and high accuracy simultaneously, while loosening initial assembly tolerances to improve process throughput. In our proposed process, glass spacers are fabricated with μm-accuracy using ultrashort pulsed laser-assisted chemical etching and Bessel beam optics. Their length is adjusted after assembly and bonding, using the same laser and optics. We show examples of our fabricated spacers assembled into stacks of mirrors and bonded using epoxy with spacer beads. In a separate experiment, we show that the length of spacers can be quickly and stably adjusted with μm-range and with nm-resolution as required for aligning x-ray mirrors. This bonding and alignment process may help solve a longstanding and critical challenge for future sub-arcsecond resolution large-effective area x-ray telescopes.
Security-Aware Orchestration of Linear Workflows on Distributed Resources

In hybrid and multi-tier distributed architectures, where data may have different security requirements and typically require processing in a pipeline fashion, resource allocation has become particularly challenging. In such environments, it is crucial to use security-aware and effective resource allocation techniques, in order to ensure the secure processing of the workload and achieve a satisfactory Quality of Service (QoS). Towards this direction, in this paper we examine the performance of security-aware resource allocation strategies for linear workflow (LW) jobs in an environment of distributed resources. Only a subset of the resources is considered secure and thus suitable for processing high risk LW jobs. Low risk LW jobs may be executed on either secure or non-secure resources. Two commonly used routing techniques are adapted in order to incorporate security awareness. Their performance is evaluated through simulation. Several scenarios are investigated, with different subset sizes of the secure resources, as well as different probabilities for a LW job to be considered high risk. The simulation results provide useful insights into how the percentage of high risk LW jobs affects the performance in each of the examined cases of secure resources.
Predictors of the Overall Survival with Azacitidine Monotherapy in Untreated Acute Myeloid Leukemia Patients Ineligible for Intensive Therapy

Objective The prognostic factors for azacitidine in untreated acute myeloid leukemia (AML) patients ineligible for intensive therapy remain unknown. To identify prognostic factors for azacitidine monotherapy and assist clinicians in deciding whether to use azacitidine monotherapy or other therapies. Methods We retrospectively analyzed 27 patients with AML who were newly treated with azacitidine between 2013 and 2021 at our hospital. We evaluated potential predictors based on the overall survival (OS). Results A univariate analysis found that an Eastern Cooperative Oncology Group performance status (ECOG PS) ≥2 and platelet count (Plt) <27,000/μL had a significant negative influence on the OS. A multivariate analysis confirmed that both factors had significant independent adverse effects on the OS. An ECOG PS ≥2 and Plt <27,000/μL were thus assigned 1 point each, and a clinical scoring system was created. Log-rank testing showed that the 0-point group (n=12) had a median OS of 680 days [95% confidence interval (CI) 220-898 days] and a 1-year OS rate of 80.8% (95% CI 42.3-94.9%), the 1-point group (n=11) had a median OS of 90 days (95% CI 62-345 days) and a 1-year OS rate of 18.2% (95% CI 2.9-44.2%), and the 2-point group (n=4) had a median OS of 82 days [95% CI 19-not applicable (NA) days] and a 1-year OS rate of 0% (95% CI NA-NA). The p value of 0.00008 indicated that this scoring was useful. Conclusion The ECOG PS and Plt can be used to predict the OS with azacitidine monotherapy in untreated AML patients ineligible for intensive therapy.
Linking Self-Control, Hope, Positivity Ratio, Anxiety and Handwashing Habits during the Coronavirus Outbreak

The novel COVID-19 is an infectious disease caused by the coronavirus. In the early stages of the pandemic, Israel struggled to contain its local outbreak through various measures that have virtually halted daily life and placed tens of thousands of people into quarantine. This study explored the role played by self-control and hope for obtaining two desired outcomes: (1) maintaining one’s positivity ratio (having more positive than negative affect—an indicator of wellbeing) and (2) increasing one’s contagion-preventing behavior (in this study—handwashing habits). Path analysis was conducted using survey data collected from 537 Israeli adults. Our findings suggest that self-control’s association with the positivity ratio is mediated through hope and anxiety levels. Self-control positively correlates with hope levels and negatively correlates with anxiety levels. Higher hope levels correlate with a higher positivity ratio, while lower anxiety levels correlate with a higher positivity ratio. The relationship between self-control and handwashing habits was mediated by hope, anxiety, and existing handwashing habits. This study brings research a step forward in demonstrating the vital role of positive components in achieving desired psychological and behavioral outcomes during an anxiety-provoking epidemic outbreak. In addition to its theoretical innovation, the importance of this study lies in its practical value: We focus on variables that are influenced by policy, education, and communication.
Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting

The ability to predict short-term electric energy demand would provide several benefits, both at the economic and environmental level. For example, it would allow for an efficient use of resources in order to face the actual demand, reducing the costs associated to the production as well as the emission of CO 2 . To this aim, in this paper we propose a strategy based on ensemble learning in order to tackle the short-term load forecasting problem. In particular, our approach is based on a stacking ensemble learning scheme, where the predictions produced by three base learning methods are used by a top level method in order to produce final predictions. We tested the proposed scheme on a dataset reporting the energy consumption in Spain over more than nine years. The obtained experimental results show that an approach for short-term electricity consumption forecasting based on ensemble learning can help in combining predictions produced by weaker learning methods in order to obtain superior results. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that using an ensemble scheme can achieve very accurate predictions, and thus that it is a suitable approach for addressing the short-term load forecasting problem.
A Fuzzy-Logic Approach Based on Driver Decision-Making Behavior Modeling and Simulation

The present study proposes a decision-making model based on different models of driver behavior, aiming to ensure integration between road safety and crash reduction based on an examination of speed limitations under weather conditions. The present study investigated differences in road safety attitude, driver behavior, and weather conditions I-69 in Flint, Genesee County, Michigan, using the fuzzy logic approach. A questionnaire-based survey was conducted among a sample of Singaporean (n = 100) professional drivers. Safety level was assessed in relation to speed limits to determine whether the proposed speed limit contributed to a risky or safe situation. The experimental results show that the speed limits investigated on different roads/in different weather were based on the participants’ responses. The participants could increase or keep their current speed limit or reduce their speed limit a little or significantly. The study results were used to determine the speed limits needed on different roads/in different weather to reduce the number of crashes and to implement safe driving conditions based on the weather. Changing the speed limit from 80 mph to 70 mph reduced the number of crashes occurring under wet road conditions. According to the results of the fuzzy logic study algorithm, a driver’s emotions can predict outputs. For this study, the fuzzy logic algorithm evaluated drivers’ emotions according to the relation between the weather/road condition and the speed limit. The fuzzy logic would contribute to assessing a powerful feature of human control. The fuzzy logic algorithm can explain smooth relationships between the input and output. The input–output relationship estimated by fuzzy logic was used to understand differences in drivers’ feelings in varying road/weather conditions at different speed limits.