Miracle-dz/UniFAR-FTUs huggingface dataset:
A probabilistic amplitude shaping scheme on amplitude and phase shift keying modulation

The probabilistic amplitude shaping(PAS) scheme based on amplitude shift keying(ASK) has attracted much attention as a signal-shaping solution. However, due to the limitation of nonlinear effects in practical applications, the robustness of the ASK modulation scheme is far worse than amplitude and phase shift keying(APSK) modulation. Therefore, we design a distributed mapping scheme based on APSK and propose the corresponding PAS scheme. To find the balance between the rate loss and the shaping gain brought by the shaping scheme, this paper utilizes Monte Carlo to simulate and successfully verify that the proposed scheme performs well at different transmission rates, with a maximum of 0.67 dB.
Behavior as a rapid indicator of reintroduction and translocation success for a cryptic mammal, the New England Cottontail (Sylvilagus transitionalis)

Interactions between animals and their environments are reflected in behavior, which is an indicator of perceived risk and habitat quality. Behavioral studies can therefore provide a rapid assessment of conservation actions. We evaluated the behavior of reintroduced and translocated New England cottontails (Sylvilagus transitionalis)– a species for which the benefits of habitat management, reintroductions, and translocations have been difficult to demonstrate via demographic studies. We first used a random forests model to create a behavioral library for the species using triaxial accelerometers deployed on captive animals that were also monitored with video. We then applied our library to compare time-activity budgets among wild catch-and-release rabbits, wild-caught translocated rabbits, and rabbits introduced into the wild from a captive population. Our library included six behaviors (feeding, grooming, vigilant, movement, resting, and investigating) with an overall classification accuracy of 96.63% and class error rates <14%. For all three groups of rabbits, resting, vigilance, and grooming were the most frequent behaviors; however, captive-bred and translocated rabbits spent significantly more time vigilant and moving than did catch-and-release rabbits. The results raise concern that time spent exploring a new environment may make reintroduced and translocated New England cottontails more vulnerable to predation than local wild rabbits and contribute to low survival rates in reintroduction programs. Our approach shows promise for developing behavioral studies as a rapid indicator of response to conservation efforts for cryptic mammals.
Optimization of ferrites structure based on integrated coil design for wireless EV charger

The miniaturization and lightweight of wireless EV chargers are of great significance. For the inductive power transfer system based on double-sided LCC compensation topology, an integrated design is proposed in which all the magnetically coupled components and compensation components are integrated into the aluminum shell of wireless chargers. For the quadrature arrangement of DD coils, including the main inductor and compensation inductor, the magnetic field distribution of the magnetic coupler is analyzed, the influence of ferrite thickness is studied, and the optimization method of ferrite shape is presented. Under the condition that the magnetic coupling effect is very little affected, the weight of ferrites in the proposed optimized scheme is reduced by 47% compared with the initial scheme. In the prototype experiment, the DC-DC efficiency reached 95.38% when the output power of the system was 6.93 kW.
Research on the early-warning model of power OPGW cable operating status based on joint judgment

Aiming at the problem that the current power OPGW optical cable early warning model adopts a single technical index, this article proposes an early-warning model of power OPGW cable operating status based on joint judgment. Firstly, a multi-element joint judgment power OPGW optical cable prediction model is constructed based on the Long Short-Term Memory (LSTM) model, and the predicted power OPGW optical cable core strain sequence provides a data basis for the early warning of the operation status of the optical cable. Then, based on the identification and classification results of the Multilayer Perceptron (MLP) model, the early warning level is divided, and the core strain data of the power OPGW optical cable obtained by the prediction module is identified and output the early warning level, so as to realize the early warning of the operation status of the power OPGW optical cable. Finally, model experimental analysis is carried out. The experimental results show that the proposed model can provide early warning for power OPGW optical cable, and further improve the risk management and control strategy of OPGW optical cable, so as to complete the operation and maintenance of power OPGW optical cable more efficiently
To go or not to go: variable density-dependent dispersal in small mammals

Population density has been widely understood to be a key influencer of dispersal behavior; however, the generality of density-dependent (DD) dispersal in vertebrates is unclear. We conducted a review of the available empirical data on small mammal DD dispersal, distinguishing between the three dispersal stages: emigration, immigration, and transience (dispersal distance). We focused on small mammals because they are a well-studied, functionally similar group of vertebrates, with a distinct ecological importance. We also examined the effect of season, body mass, study length, and study type on the strength and direction of DD dispersal. The majority of emigration and dispersal distance studies reported negative density dependence, while immigration was mostly independent of density. No correlative patterns were detected; however, interpretation of the available data was hindered by inconsistencies in experimental and analytical approach across studies. Our results suggest that the three phases of the dispersal process may be influenced differently by density and highlight the importance of distinguishing between emigration, immigration, and transience when considering the effects of density dependence. As well, our study identifies several limitations with the current available data which limit the ability to compare DD dispersal behavior across systems, and calls for future investigations that consider all three phases of dispersal in the same system.
Composite PV MPPT algorithm based on improved grey wolf optimization algorithm with perturbation observation algorithm

A composite photovoltaic (PV) MPPT algorithm with improved gray wolf algorithm and perturbed observation (IGWO-P&O) algorithm is proposed, introducing a nonlinear convergence factor and Lévy flight strategy in the algorithm to optimize the overall search rate and global search capability, switching to the perturbed observation method when searching near the maximum power point (MPP), and improving the efficiency of maximum power point tracking (MPPT) of PV power generation by using its fast convergence property near the extreme value point.
A Study on Voxel Shape Generation and Reconstruction with VQ-VAE-2

With the recent advances in deep learning, research on reconstructing and generating 3D shapes has become increasingly popular. While there are several types of representation methods for 3D shapes, our work focuses on voxel models that are arranged in a regular grid and are easy to handle on computers. In the area of 2D image generation, generative models such as GANs have been proposed that can output higher quality generative images, but are difficult to train. The recently proposed VQ-VAE-2 is easy to learn and shows performance comparable to the latest GANs. Therefore, this work proposes a method that can generate and reconstruct sharp voxel shape using VQ-VAE-2. Experiments compare the reconstruction and generation results and demonstrate that the proposed method performs better than existing methods in both experiments and is capable to output sharper shapes.
Leveraging Sensorimotor Realities for Assistive Technology Design Bridging Smart Environments and Virtual Worlds

Sensorimotor Realities are a newly introduced concept of a computer-based physical-virtual reality with distinctive quality dimensions with respect to virtual, augmented, mixed, and extended reality environments. In this landscape of computer-based environments, Sensorimotor Realities center on the user’s sensorimotor abilities that undergo mediation in the conjunction of ambient intelligence and physical-virtual worlds. In this work, we explore the application of Sensorimotor Realities to assistive technology and accessible computing research and practice. To this end, we conduct a targeted literature review of scientific contributions published at CHI, UIST, ASSETS, and VRST—four venues where assistive technology and accessibility research for smart environments and virtual worlds has been traditionally disseminated,—and show how they can be characterized from the distinct perspective offered by the conceptual space of Sensorimotor Realities. Moreover, we present design opportunities for assistive technology enabled by the dimensions of the Sensorimotor Realities conceptual space, accompanied by examples from the scientific literature.
Multimodal Emotion Detection based on Visual and Thermal Source Fusion

The contactless emotion detection is an interesting research topic today. In this paper, we first study the physiological basis of human emotions to better understand what happens in our body when emotions arise and change. We then introduce the interconnection between the brain trunk vessels and the facial vessels. The investigation reveals that the variations of human emotions could be reflected by facial blood horizontal flow, and the detection of facial blood horizontal flow could be realized by mainly measuring the Remote photoplethysmography (rPPG) and gray scale variation on human cheeks. To validate these findings, we set up an emotional evoking experiment to capture the RGB and thermal videos of human testees, extract out horizontal facial blood flows, and finally classify these features into three different emotions (i.e., fear, happiness and sadness) by learning. The reported classification accuracy reaches 0.841, based on total 45 testees.
Dynamic Balancing of Bipedal Robot on Inclined Terrain using Polynomial Trajectories

This paper presents foot trajectory planning for bipedal robots during the swing phase. This experiment aims to design an efficient and dynamically stable foot trajectory for a 10-DOF (Degree of freedom) bipedal robot. The experiment considers the foot's quadratic, cubic, and quintic polynomial trajectories while climbing the inclined plane. Forward and Inverse kinematics is used to calculate the variation of joint angles during the swing phase. Dynamic Balance Margin DBM has been computed using Zero Moment Point ZMP principle while walking. The angle variation and the ZMP for the aforementioned polynomial trajectories are presented. The maximum DBM is achieved for quintic polynomial trajectory, leading to a considerable reduction in power consumption. Our paper presents a dynamically stable walking pattern of a bipedal robot. It also ensures repeatability, which has been validated using a stick diagram.
Optimization of CT Lung Lobe Segmentation for Edge Inference

Medical imaging plays a critical role in patient diagnosis, with CT scans being an essential modality that provides a fine-grained, 3-dimensional insight into patients’ bodies. The large amount of information generated by CT scans can lead to long interpretation times and increased errors, particularly when compounded by radiologist fatigue. One solution is the use of Computer Aided Diagnostic (CAD) tools, which studies have shown can improve accuracy and reduce diagnosis time when used by radiologists. However, CAD tools can be computationally and memory intensive with long inference times, particularly when low-end computers or edge devices are used. This article explores multiple optimization techniques using a 3D nn-UNet lung lobe segmentation architecture in CT scans and reduces the inference time of the segmentation models by 64.9% (without performance degradation) to 95.9% (with significant performance degradation). These optimizations can reduce the overall diagnostic time for radiologists, and enable the segmentation models to run on CPU-only machines and on single board computers, which are readily available in both developed and developing countries.
Overlapping Community Discovery Algorithm Based on Seed Node Importance Selection

Mining community structure in a complex network is of great theoretical and practical significance to real life. optimization algorithm, LFM algorithm, takes a random approach in selecting seed nodes, which leads to un-stable quality of generated communities. this paper, the importance of nodes is defined as the basis for seed node selection with the help of the density peak clustering idea. The set of nodes with high node importance and their neighbors are selected as seed nodes, the seed nodes are expanded; the isolated node attribution in the network is calculated; Finally, similar communities are merged to obtain the final community structure. Experiments on real datasets and LFR benchmark network datasets can finally obtain higher quality community structure.
The comparison of gold extraction methods from the rock using thiourea and thiosulfate

Abstract Gold extraction from the rock is generally carried out using mercury. However, the high toxicity of mercury has a very dangerous impact on the environment and health. Various efforts have been made to reduce the use of mercury in gold extraction, one of which is the leaching method using thiosulphate and thiourea solution because they have low toxicity and are environmentally friendly. This study aimed to determine the results of gold extraction with thiosulphate and thiourea solution and determined the optimum concentration and time of extraction. The yield of the gold extract with thiosulphate solution was greater than that of thiourea solution. The thiosulphate solution had an optimum concentration of 0.3 M and an optimum time of 2 h. While the thiourea solution had an optimum concentration of 0.2 M and an optimum time of 3 h. The results of the Friedman test on the leaching time and concentration parameters show that leaching time has a significant effect on the Au leaching process, and the concentration parameter does not affect the Au leaching process from solid samples.
Tensor-based embedding for graph-based semi-supervised approaches

This paper presents a novel approach to multiclass classification tasks, utilizing tensor-based embeddings for graph-based semi-supervised learning. The proposed method utilizes a tensor decomposition algorithm to create embeddings that capture the essential features of the data. These are used by various graph-based semi-supervised approach to construct a graph capable to propagate the information from labeled to unlabeled nodes, classifying available data. The proposed method was tested on hyperspectral datasets. The results demonstrate the potential of such combinatory tensor-based semi-supervised approaches.
Usability for Data Sovereignty - Evaluation of Privacy Risk Quantification Interfaces

Digital medical data is becoming widely available through the ongoing digitization efforts in the medical sector. This also leads to more personal health data available for secondary usage like medical research. A common way to collect medical data in a privacy compliant way is through the informed consent of the affected person. While consent forms are typically paper-based, in the last years the concept of digital consent is becoming more and more common. Still, such consent forms can be very complex and overwhelming for the patient. For example, it can be hard to estimate the personal privacy impact when sharing data of rare medical conditions and user interfaces for data sharing need to be designed carefully. Privacy Risk Quantifications (PRQ) and Dynamic Consent (DC) are two tools to help patients to make a consent decision and choose the data for sharing. However, interfaces for those two technologies are not trivial to design. This paper develops two new interface variants with a focus on design guidelines and best practices for usability. To evaluate the new designs, a user study was conducted which shows improved usability in comparison to an existing interface. The Base variant is a prototype that was previously developed as a technical demonstrator for DC and PRQ without requirements for usability. The three interfaces are evaluated in a user study, which shows that the usability focus of the newer variants leads to a better rating by the test subjects compared to the basic prototype. It can also be seen that interfaces with more detailed explanations and a focus on visual comprehensive and compelling interfaces get a better usability score compared to the pure technical versions. Finally, it can be seen that interfaces for those technologies are ideally developed in an iterative design development cycle.
Difficulties in Diagnosing Fabry Disease in Patients with Unexplained Left Ventricular Hypertrophy (LVH): Is the Novel GLA Gene Mutation a Pathogenic Mutation or Polymorphism?

Abstract Fabry disease (FD) is an X-linked, lysosomal glycosphingolipid storage disorder that occurs very rarely. Cardiac involvement may comprise of left ventricular hypertrophy (LVH), arrhythmias, conduction abnormalities, heart failure and valvular abnormalities. The goal of this study was to conduct gene analysis for FD in patients suffering from unexplained LVH. 120 patients over the age of 30 who were diagnosed by echocardiography with idiopathic LVH were included in the study. Patients with severe hypertension, intermediate valve disease such as moderate aortic stenosis, known FD, and a family history of autosomal dominant hypertrophic cardiomyopathy were excluded from the study. GLA gene mutations were studied by Sanger sequence analysis in all patients. Of the 120 total patients included in this study, 69 were female (58%) and 51 were male (42%). The mean age was 60.3 ± 15.7. GLA gene mutations were detected in three male patients. The detected mutations are as follows: NM_000169. 2:IVS6-10G>A (c.1000–10G>A), NM_000169. 2:c.937G>T (p.D313Y) (p.Asp313Tyr) and NM_000169. 2:c.941A>T (p.K314M) (p.Lys314Met). Early diagnosis is of vital importance in FD, which can be treated with enzyme replacement. Genetic screening in patients diagnosed with idiopathic LVH by echocardiography is important in the early diagnosis and treatment of FD. Patients over 30 years of age with idiopathic LVH should be screened for FD. Various new polymorphisms can be detected in genetic screening. Identifying new polymorphisms is important for knowing the true mutations in FD.
Modulation of premotor cortex excitability mitigates the behavioral and electrophysiological abnormalities in a Parkinson’s Disease Mouse Model

The subthalamic nucleus (STN) is important in halting ongoing behaviors, referred to as stop-signal responses. The prefrontal regions innervating the STN exhibit increased activity during the stop-signal responses, and optogenetic activation of these neurons inhibits impulsive actions in rodents. High-frequency electrical stimulation of the STN effectively treats motor symptoms of Parkinson’s disease (PD), yet its underlying circuit mechanisms are unclear. Here, we investigated the involvement of STN-projecting premotor (M2) neurons in PD mouse models and the impact of deep brain stimulation targeting the STN (DBS-STN). We found that M2 neurons exhibited enhanced burst firing and synchronous oscillations in the PD mouse model. Remarkably, high-frequency stimulation of STN-projecting M2 neurons, simulating antidromic activation, during DBS-STN relieved motor symptoms and hyperexcitability. These changes were attributed to reduced firing frequency vs. current relationship through normalized hyperpolarization-activated inward current (Ih). The M2 neurons in the PD model mouse displayed increased Ih, which was reversed by high-frequency stimulation. Additionally, the infusion of ZD7288, an HCN channel blocker, into the M2 replicated the effects of high-frequency stimulation. In conclusion, our study unveils excessive excitability and suppressive motor control through M2-STN synapses in the PD mouse model. Antidromic excitation of M2 neurons during deep brain stimulation of the STN alleviates this suppression, thereby improving motor impairment. These findings provide insights into the circuit-level dynamics underlying deep brain stimulation’s therapeutic effects in PD. Targeting M2-STN synapses may be useful for future therapeutic strategies. Significance Statement Our study provides insight into the mechanisms driving motor impairment in Parkinson’s disease (PD) and the therapeutic efficacy of deep brain stimulation (DBS). By elucidating the role of hyperpolarization-activated cyclic nucleotide-gated (HCN) channels in modulating the hyperexcitability of M2 neurons, we identify potential targets for future PD treatment strategies. Understanding the excessive excitability and suppressive motor control through M2-STN synapses in PD mouse models contributes to our knowledge of circuit-level dynamics underlying DBS effects. These findings have significant implications for improving motor symptoms and advancing the development of more precise and effective interventions for PD patients.
A cross-national exploration of shadow education use by high and low SES families

ABSTRACT Worldwide, an increasing number of students seek private supplementary tutoring, known as ‘shadow education. ’ Various studies report social class differences in the use of shadow education. High-SES families may invest in shadow education as a form of concerted cultivation, seeking to improve their children’s school achievement. In this study, we apply meta-analytic structural equation modeling to explore relationships between parental education, income, and the use of shadow education across nations and educational contexts. We find robust relationships between parental education, income and the use of shadow education. Moreover, we assess a mediating role of shadow education in the relationship between SES and achievement. Shadow education appears to fulfill a competitive function for privileged families who seek to secure advantage in educational competition. We conclude that educational research, particularly research concerned with inequality of opportunities, needs to take account of the progressively prominent position of shadow education in the educational landscape.