Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-disciplinary evolution of the genomics revolution

Pervasive cross-disciplinarity has been a key factor in the genomics revolution. Born out of the Human Genome Project (HGP), the field of genomics evolved with phenomenal speed into a dominant scientific and business force. While other efforts were intent on estimating the economic impact of the genomics revolution, we shift focus to the social and cultural capital generated by bridging together biology and computing—two of the constitutive disciplines of “genomics”. We quantify this capital by measuring the pervasiveness of bio-computing cross-disciplinarity (XD) in genomics research during and after the HGP. To provide interlocking perspectives at the career and epistemic levels, we assembled three data sets to measure XD via (i) the collaboration network between 4190 biology and computing faculty from 155 departments in the United States, (ii) cross-departmental affiliations within a comprehensive set of human genomics publications, and (iii) the application of computational concepts and methods in research published in a preeminent genomics journal. Our results show the following: First, research featuring XD collaborations has higher citation impact than other disciplinary research—an effect observed at both the career and individual article levels. Second, genomics articles featuring XD methods tend to have higher citation impact than epistemically pure articles. Third, XD researchers of computing pedigree are drawn to the biology culture. This statistical evidence acquires deeper meaning when viewed against the organizational and knowledge transfer mechanisms revealed by the data models. With cross-disciplinary initiatives set to dominate the agenda of funding agencies, our case study provides a framework for appreciating the long-term effects of these initiatives on science and its standard-bearers.
Determining the Maximum Power Transfer Points for MC-WPT Systems With Arbitrary Number of Coils

To reveal the general characteristic of the maximum power transfer points of magnetic coupling wireless power transfer (MC-WPT) systems, and to quickly and accurately determine all possible maximum power transfer points for arbitrary coil MC-WPT systems, a novel theoretical analysis method is proposed in this paper. Based on the modal theory, an accurate time-domain model of an n-coil MC-WPT system is established and the analytical current response on each coil is derived. According to the current response of the load, a detailed power spectrum distribution is presented, based on which the mathematic expressions of all possible maximum power transfer points, including the maximum output powers and corresponding operating frequencies, are deduced. The theoretical results are verified in experiment prototypes practically. The experimental results show that: 1) the output power reaches its maximum value at the modal frequencies rather than the designed natural frequency of the resonators in MC-WPT systems; 2) there are n modal frequencies of an n-coil MC-WPT system. The analysis results provide useful information to choose the operating frequency for practical MC-WPT systems with arbitrary number of coils.
BMP14 induces tenogenic differentiation of bone marrow mesenchymal stem cells in vitro

Bone marrow mesenchymal stem cells (BMSCs) are pluripotent cells, which have the capacity to differentiate into various types of mesenchymal cell phenotypes, including osteoblasts, chondroblasts, myoblasts and tendon fibroblasts (TFs). The molecular mechanism for tenogenic differentiation of BMSCs is still unknown. The present study investigated the effects of bone morphogenetic protein (BMP) 14 on BMSC differentiation in vitro. It was revealed that BMP14 significantly increased the expression of tendon markers (scleraxis and tenomodulin) at the mRNA and protein level, which led to the upregulation of sirtuin 1 (Sirt1) expression. The gain or loss of Sirt1 function may promote or inhibit tenogenic differentiation by deacetylating the peroxisome proliferator-activated receptor (PPAR)-γ. BMP14 also triggered the phosphorylation of c-Jun N-terminal kinase (JNK) and Smad1; overexpression of Sirt1 significantly increased the phosphorylation and knockdown of Sirt1 significantly decreased the phosphorylation. The inhibition of JNK and Smad significantly increased the acetylation of PPARγ and inhibited the expression of tenogenic differentiation markers. These results suggest that BMP14 may induce the tenogenic differentiation of BMSCs via the Sirt1-JNK/Smad1-PPARγ signaling pathway. The present study provided a cellular and molecular basis for the development of novel therapeutic strategies for tendon healing.
Internet use among young people with and without mental health difficulties

Objectives Research regarding adolescent internet use and mental health is sparse. However, awareness of a young person’s internet use is becoming increasingly recognised as an important element of clinical assessment and intervention, and requires the development of an evidence base. The aim of the present study was to better understand the internet use of young people experiencing mental health difficulties and to contrast it with those who currently report no concerns. Method In total, 299 young people aged 12–19 years, across a continuum of mental health difficulties, completed an online survey measuring internet use and related experiences. Young people were assigned to four groups: (a) attending inpatient services; (b) attending outpatient services; (c) a community group with mental health concerns and no clinical support; and (d) a regular community group. Results Those in the inpatient and outpatient groups visited more potentially harmful websites. Young people attending inpatient and outpatient services showed aspects of both more risky and less risky use. The community group reporting no mental health difficulties showed least risky use. The group experiencing difficulties but not receiving support showed consistently high risky use, suggesting this is a particularly vulnerable group. Conclusions Despite methodological limitations, findings suggest that those with mental health difficulties may experience more of the risks and fewer of the benefits offered by the internet. Though further research is needed to clarify these findings, clinicians should consider routine assessment of Internet use when planning interventions for young people experiencing mental health difficulties.
Dual-quaternion backstepping control for a fully-actuated rigid-body

In this paper dual-quaternions are used to model a fully actuated rigid-body. A backstepping-controller that solves the trajectory tracking problem is derived and proved to provide uniform asymptotical stabilization of the error dynamics. Numerical simulations are provided where the controller is compared to existing dual-quaternion tracking controllers and it is shown to have similar performance.
PWM on the Basis of Different Configurations of Reference Signals for a Multilevel Converter on H-Bridge

Pulse-width modulation techniques (PWM) based on different configurations of reference signal are presented in this paper. Phase Shifted PWM (PS-PWM) technique for H-Bridge multilevel converters allows to evenly distribute power among the modules of the converter and also provides equal utilization of switches within a module. At the same time Carrier Disposition PWM (CD-PWM) offers a superior output voltage profile but leads to uneven power distribution. Modified CD-PWM technique allows to maintain the quality of output voltage and results in uniform utilization of switches. Hybrid PWM technique is a combination of PS-PWM and CD-PWM. Modified Hybrid PWM retains quality of output voltage and causes a uniform utilization of switches. This paper reviews the aforementioned techniques of control by the example of the H-Bridge 5-level converter. As a result of the simulation total harmonic distortion (THD) of the output voltage turned out to be lower in the case of the CD-PWM method in comparison with the PS-PWM and Hybrid PWM methods.
Exploiting fusion architectures for multispectral pedestrian detection and segmentation.

Recent research has demonstrated that the fusion of complementary information captured by multi-modal sensors (visible and infrared cameras) enables robust pedestrian detection under various surveillance situations (e.g., daytime and nighttime). In this paper, we investigate a number of fusion architectures in an attempt to identify the optimal way of incorporating multispectral information for joint semantic segmentation and pedestrian detection. We made two important findings: (1) the sum fusion strategy, which computes the sum of two feature maps at the same spatial locations, delivers the best performance of multispectral detection, while the most commonly used concatenation fusion surprisingly performs the worst; and (2) two-stream semantic segmentation without multispectral fusion is the most effective scheme to infuse semantic information as supervision for learning human-related features. Based on these studies, we present a unified multispectral fusion framework for joint training of semantic segmentation and target detection that outperforms state-of-the-art multispectral pedestrian detectors by a large margin on the KAIST benchmark dataset.
Diabetes and brain health: implications for practice

Background It is widely accepted that people with mental illness have increased risk of cardiometabolic complications such as obesity and type 2 diabetes mellitus. What is less well known is that individuals with diabetes have an increased risk of brain health complications including depression, cognitive impairment and dementia. These conditions can adversely influence disease self-management and further increase risk of other diabetes complications. Aim The aim of this paper is to highlight the increased risk of brain health complications in populations with diabetes in order to promote awareness of such complications among healthcare professionals and encourage timely intervention. Methods An overview of the prevalence and potential mechanisms linking depression and cognitive impairment with diabetes as well as implications for detection, management and brain health protection, based on a narrative review of the literature. Conclusions Early detection and effective management of depression and cognitive impairment among individuals with diabetes has the potential to minimise adverse health outcomes. In order to promote screening healthcare professionals caring for individuals with diabetes in all settings must be aware of the increased risk of brain health complications in this vulnerable population.
$\mathcal{H}_{\infty}$ static output-feedback controllers with past outputs for discrete-time uncertain linear systems

This work investigates the problem of designing robust $\mathcal{H}_{\infty}$ static output-feedback controllers for uncertain linear discrete time-invariant systems where all matrices are supposed to be affected by polytopic uncertainties. The proposed control law employs a fixed number of past measured outputs, thus enhancing the dynamics of the closed-loop system and producing controllers with memory. The method for synthesizing the memory controllers relies on a two-phase algorithm. In the first phase a parameter-dependent state-feedback gain is designed and, then, it is used as input to the second phase in which the robust memory static output-feedback controller is synthesized. Numerical examples illustrate the advantages of the proposed approach in terms of $\mathcal{H}_{\infty}$ performance when compared to other methods available in the literature.
TaCo: Comparative Visualization of Large Tabular Data

Tabular data plays a vital role in many different domains. In the course of a project, changes to the structure and content of tables can result in multiple instances of a table. A challenging task when working with such derived tables is to understand what exactly has changed from one version to another. Traditional comparison tools assist users in inspecting differences between multiple table instances, however, the resulting visualizations are often hard to interpret or do not scale to large tables with thousands of rows and columns. To address these challenges, we developed TaCo, an interactive comparison tool that effectively visualizes the differences between multiple tables at various levels of granularity: (1) the aggregated differences between all table instances, (2) the differences between one table compared to all others, and (3) the detailed differences between two instances.
Hidden Anchor: A Lightweight Approach for Physical Layer Location Privacy

In hybrid wireless sensor networks, where trusted and un-trusted nodes coexist, it becomes important to allow trusted nodes to share information, especially, location information and prevent un-trusted nodes from gaining access to this information. We focus on anchor-based localization algorithms in WSNs, where a small set of specialized nodes, that is, anchor nodes, broadcast their location to the network and other nodes can use the broadcast information to estimate their own location. The main challenge is that both trusted and un-trusted nodes can measure the physical signal transmitted from anchor nodes and use it to estimate their locations. In this paper, we propose Hidden Anchor, an algorithm that provides anchor physical layer location privacy for different classes of localization algorithms. The Hidden Anchor algorithm exploits the inherently noisy wireless channel and uses identity cloning of neighboring trusted nodes to make anchors unobservable to un-trusted nodes while providing complete information to trusted nodes. Evaluation of the Hidden Anchor algorithm through analysis and simulation shows that it can hide the identity, and hence the location, of anchor nodes with very low overhead. In addition, the results show that by adding artificial noise, we can achieve significant improvement in anchor's location privacy.
BELMKN: Bayesian Extreme Learning Machines Kohonen Network

This paper proposes the Bayesian Extreme Learning Machine Kohonen Network (BELMKN) framework to solve the clustering problem. The BELMKN framework uses three levels in processing nonlinearly separable datasets to obtain efficient clustering in terms of accuracy. In the first level, the Extreme Learning Machine (ELM)-based feature learning approach captures the nonlinearity in the data distribution by mapping it onto a d-dimensional space. In the second level, ELM-based feature extracted data is used as an input for Bayesian Information Criterion (BIC) to predict the number of clusters termed as a cluster prediction. In the final level, feature-extracted data along with the cluster prediction is passed to the Kohonen Network to obtain improved clustering accuracy. The main advantage of the proposed method is to overcome the problem of having a priori identifiers or class labels for the data; it is difficult to obtain labels in most of the cases for the real world datasets. The BELMKN framework is applied to 3 synthetic datasets and 10 benchmark datasets from the UCI machine learning repository and compared with the state-of-the-art clustering methods. The experimental results show that the proposed BELMKN-based clustering outperforms other clustering algorithms for the majority of the datasets. Hence, the BELMKN framework can be used to improve the clustering accuracy of the nonlinearly separable datasets.
Regulating Access to System Sensors in Cooperating Programs

Modern operating systems such as Android, iOS, Windows Phone, and Chrome OS support a cooperating program abstraction. Instead of placing all functionality into a single program, programs cooperate to complete tasks requested by users. However, untrusted programs may exploit interactions with other programs to obtain unauthorized access to system sensors either directly or through privileged services. Researchers have proposed that programs should only be authorized to access system sensors on a user-approved input event, but these methods do not account for possible delegation done by the program receiving the user input event. Furthermore, proposed delegation methods do not enable users to control the use of their input events accurately. In this paper, we propose ENTRUST, a system that enables users to authorize sensor operations that follow their input events, even if the sensor operation is performed by a program different from the program receiving the input event. ENTRUST tracks user input as well as delegation events and restricts the execution of such events to compute unambiguous delegation paths to enable accurate and reusable authorization of sensor operations. To demonstrate this approach, we implement the ENTRUST authorization system for Android. We find, via a laboratory user study, that attacks can be prevented at a much higher rate (54-64% improvement); and via a field user study, that ENTRUST requires no more than three additional authorizations per program with respect to the first-use approach, while incurring modest performance (<1%) and memory overheads (5.5 KB per program).
Sparse Feature Grouping based on $\ell_{1/2}$ Norm Regularization

In recent years, feature grouping technique has shown its promising performance in many practical engineering applications. However, it's still challenging to determine the inner group structure of highly related features automatically while keeping the model sparser on the high-dimensional data. In this paper, a new sparse feature grouping algorithm is proposed based on $\ell_{1/2}$ norm regularization. Different from the existing Lasso-like methods which generally use $\ell_{1}$ norm, the proposed algorithm introduces a $\ell_{1/2}$ norm regularizer for the feature coefficients to make the learning model sparser. Moreover, the proposed algorithm adopts a grouping regularizer to encourage the feature coefficients in one group to be similar. To solve this problem, a new alternating direction optimization algorithm is proposed. Experiments are conducted on synthetic and real-life datasets, and the results show the comparative performance of the proposed method compared with several state-of-the-art methods.
Emerging Roles of Sonic Hedgehog in Adult Neurological Diseases: Neurogenesis and Beyond

Sonic hedgehog (Shh), a member of the hedgehog (Hh) family, was originally recognized as a morphogen possessing critical characters for neural development during embryogenesis. Recently, however, Shh has emerged as an important modulator in adult neural tissues through different mechanisms such as neurogenesis, anti-oxidation, anti-inflammation, and autophagy. Therefore, Shh may potentially have clinical application in neurodegenerative diseases and brain injuries. In this article, we present some examples, including ours, to show different aspects of Shh signaling and how Shh agonists or mimetics are used to alter the neuronal fates in various disease models, both in vitro and in vivo. Other potential mechanisms that are discussed include alteration of mitochondrial function and anti-aging effect; both are critical for age-related neurodegenerative diseases. A thorough understanding of the protective mechanisms elicited by Shh may provide a rationale to design innovative therapeutic regimens for various neurodegenerative diseases.
Learning Self-Informed Feature Contribution for Deep Learning-Based Acoustic Modeling

In this paper, we introduce a new feature engineering approach for deep learning-based acoustic modeling, which utilizes input feature contributions. For this purpose, we propose an auxiliary deep neural network (DNN) called a feature contribution network (FCN) whose output layer is composed of sigmoid-based contribution gates. In our framework, the FCN tries to learn element-level discriminative contributions of input features and an acoustic model network (AMN) is trained by gated features generated by element-wise multiplication between contribution gate outputs and input features. In addition, we also propose a regularization method for the FCN, which helps the FCN to activate the minimum number of the gates. The proposed methods were evaluated on the TED-LIUM release 1 corpus. We applied the proposed methods to DNN- and long short-term memory-based AMNs. Experimental results results showed that AMNs with the FCNs consistently improved recognition performance compared with AMN-only frameworks.
Forced Inter-Operator Handover for V2X Communication in Multi-Operator Environments with Regional Splitting

Regional splitting of operators is one possible approach to support multi-operator cellular V2V communication. In this approach, a geographical area is divided into nonoverlapping regions served by a unique operator. Although the regional splitting makes most of the communications among devices intra-operator, some inter-operator communications are still required in border areas. This kind of communication involves a high delay that may not be valid for the distribution of time-critical messages such as brake-after-crash messages. This paper focuses on latency reduction of inter-operator communications for the delivery of time-critical messages in multi-operator environments with regional splitting. In order to not increase the device cost, we propose a solution based on a forced inter-operator handover that is valid for devices with only one Radio Frequency (RF) unit. Analytical results demonstrate the usefulness of our proposal to reduce the latency of time-critical messages, while simulation results prove that non-time-critical messages are not affected by our proposal.
Interictal Abnormalities of Neuromagnetic Gamma Oscillations in Migraine Following Negative Emotional Stimulation

Here, we aimed to investigate brain activity in migraineurs in response to emotional stimulation. Magnetoencephalography (MEG) was used to examine 20 patients with episodic migraine (EM group), 15 patients with chronic migraine (CM group), and 35 healthy participants (control group). Neuromagnetic brain activity was elicited by emotional stimulation using photographs of facial expressions. We analyzed the latency and amplitude of M100 and M170 components and used Morlet wavelet and beamformers to analyze the spectral and spatial signatures of MEG signals in gamma band (30–100 Hz). We found that the timing and frequency of MEG activity differed across the three groups in response negative emotional stimuli. First, peak M170 amplitude was significantly lower in the CM group than in the control group. Second, compared with the control group, the average spectral power was significantly lower in the EM group and CM group at M100 and M170. Third, the average spectral powers of the M100 and M170 in the CM group were negatively correlated with either HAM-D scores or migraine attack frequency. No significant differences across groups was found for positive or neutral emotional stimuli. Furthermore, after negative emotional stimuli, the MEG source analysis demonstrated that the CM group showed a significantly higher percentage of amygdala activation than the control group for M100 and M170. Thus, during headache free phases, migraineurs have abnormal brain activity in the gamma band in response to negative emotional stimuli. Trial Registration: ChiCTR-RNC-17012599. Registered 7 September, 2017.