Miracle-dz/UniFAR-FTUs huggingface dataset:
Mixed Far-field and Near-field Source Localization Based on Low-Rank Matrix Reconstruction

In this paper, we present a mixed far-field (FF) and near-field (NF) source localization algorithm based on low-rank matrix reconstruction (LRMR). At first, the signal covariance matrix is recovered by solving an optimization problem to obtain the signal component and suppress the noise component via LRMR. Then, according to the characteristics of the covariance matrix of a symmetric uniform linear array, the 2D parameters of range and direction-of-arrival (DOA) are separated, in which the range is eliminated by extracting the inverse diagonal elements of the reconstructed matrix. The extracted elements are reconstructed into a Toeplitz matrix, and the DOAs of the FF and NF sources are estimated. Finally, on the basis of the estimated DOA, the range parameter of the NF source is obtained. The proposed method can distinguish different types of the sources. The simulation results show that it has higher estimation accuracy than the conventional subspace-based methods in relatively low SNR, and can obtain automatic parameter pairing of the DOA and range for multiple mixed sources.
Novel biomarkers for early detection of gastric cancer

Gastric cancer (GC) remains a leading cause of cancer-related death worldwide. Less than half of GC cases are diagnosed at an advanced stage due to its lack of early symptoms. GC is a heterogeneous disease associated with a number of genetic and somatic mutations. Early detection and effective monitoring of tumor progression are essential for reducing GC disease burden and mortality. The current widespread use of semi-invasive endoscopic methods and radiologic approaches has increased the number of treatable cancers: However, these approaches are invasive, costly, and time-consuming. Thus, novel molecular noninvasive tests that detect GC alterations seem to be more sensitive and specific compared to the current methods. Recent technological advances have enabled the detection of blood-based biomarkers that could be used as diagnostic indicators and for monitoring postsurgical minimal residual disease. These biomarkers include circulating DNA, RNA, extracellular vesicles, and proteins, and their clinical applications are currently being investigated. The identification of ideal diagnostic markers for GC that have high sensitivity and specificity would improve survival rates and contribute to the advancement of precision medicine. This review provides an overview of current topics regarding the novel, recently developed diagnostic markers for GC.
Role of Lexical Boundary Information in Chunk-Level Segmentation for Speech Emotion Recognition

Chunk-level speech emotion recognition (SER) is a common modeling scheme to obtain better recognition performance than sentence-level formulations. A key open question is the role of lexical boundary information in the process of splitting a sentence into small chunks. Is there any benefit in providing precise lexical boundary information to segment the speech into chunks (e.g., word-level alignments)? This study analyzes the role of lexical boundary information by exploring alternative segmentation strategies for chunk-level SER. We compare six chunk-level segmentation strategies that either consider word-level alignments or traditional time-based segmentation methods by varying the number of chunks and the duration of the chunks. We conduct extensive experiments to evaluate these chunk-level segmentation approaches using multiples corpora, and multiple acoustic feature sets. The results show a minor contribution of the word-level timing boundaries, where centering the chunks around words does not lead to significant performance gains. Instead, the critical factor to effectively segment a sentence into data chunks is to define the number of chunks according to the number of spoken words in the sentence.
An Automated Skill Assessment Framework Based on Visual Motion Signals and a Deep Neural Network in Robot-Assisted Minimally Invasive Surgery

Surgical skill assessment can quantify the quality of the surgical operation via the motion state of the surgical instrument tip (SIT), which is considered one of the effective primary means by which to improve the accuracy of surgical operation. Traditional methods have displayed promising results in skill assessment. However, this success is predicated on the SIT sensors, making these approaches impractical when employing the minimally invasive surgical robot with such a tiny end size. To address the assessment issue regarding the operation quality of robot-assisted minimally invasive surgery (RAMIS), this paper proposes a new automatic framework for assessing surgical skills based on visual motion tracking and deep learning. The new method innovatively combines vision and kinematics. The kernel correlation filter (KCF) is introduced in order to obtain the key motion signals of the SIT and classify them by using the residual neural network (ResNet), realizing automated skill assessment in RAMIS. To verify its effectiveness and accuracy, the proposed method is applied to the public minimally invasive surgical robot dataset, the JIGSAWS. The results show that the method based on visual motion tracking technology and a deep neural network model can effectively and accurately assess the skill of robot-assisted surgery in near real-time. In a fairly short computational processing time of 3 to 5 s, the average accuracy of the assessment method is 92.04% and 84.80% in distinguishing two and three skill levels. This study makes an important contribution to the safe and high-quality development of RAMIS.
Geophysical survey decomposition and efficient 3D inversion of time-domain electromagnetic data

Rigorous three-dimensional (3D) forward and inverse modeling of geophysical electromagnetic (EM) data can be time-consuming and may require a large amount of memory on expensive computers. In this thesis, a novel framework, called survey decomposition, is proposed to make the 3D EM modeling more efficient. Recognizing the multi-scale nature of the EM modeling problems, the fundamental idea is to break down an EM survey, which consists of many transmitters, receivers and times/frequencies, into a number of subproblems, each of which is only concerned about data modeled by a localized source, receiver and time/frequency. The modeling is then carried out on the subproblems at different scales, instead of the original problem as a whole. Such a decomposition is able to speed up the numerical modeling, because: (1) A subproblem can have highly efficient discretizations in space and time customized to its localized source, receiver, time/frequency and the specific scale of investigation, for example, it uses a local mesh that is much smaller than the one used in the original global problem; (2) A subproblem is a self-contained EM modeling problem that does not depend on other subproblems, so it is suitable for massive parallelization; (3) Upon decomposition, no modeling is carried out on the global mesh and the amount of computation is proportional to the number of subproblems, so the scalability improves significantly. After decomposition, the large number of subproblems is further reduced by adaptive, random and dynamic subsampling of the data. The adaptive scheme matches the number of samples to the scale of investigation so that only the data necessary for the model reconstruction are selected. The framework of survey decomposition is applied to two types of time-domain
Wav2vec-Based Detection and Severity Level Classification of Dysarthria From Speech

Automatic detection and severity level classification of dysarthria directly from acoustic speech signals can be used as a tool in medical diagnosis. In this work, the pre-trained wav2vec 2.0 model is studied as a feature extractor to build detection and severity level classification systems for dysarthric speech. The experiments were carried out with the popularly used UA-speech database. In the detection experiments, the results revealed that the best performance was obtained using the embeddings from the first layer of the wav2vec model that yielded an absolute improvement of 1.23% in accuracy compared to the best performing baseline feature (spectrogram). In the studied severity level classification task, the results revealed that the embeddings from the final layer gave an absolute improvement of 10.62% in accuracy compared to the best baseline features (mel-frequency cepstral coefficients).
A GaN-HEMT Active Drain-Pumped Mixer for S-Band FMCW Radar Front-End Applications

This paper reports for the first time a drain-pumped (DP) mixer using Gallium Nitride (GaN) HEMT technology. Specifically, it describes a method aimed to predict the optimum bias conditions for active DP-mixers, leading to high conversion gain (CG) and linearity, along with the efficient use of the local oscillator drive level. A mixer prototype was designed and fabricated according to the discussed design principles; it exhibited a CG and an input third-order intercept point (IIP3) of +10dB and +11dBm, respectively, with a local oscillator power level of 20 dBm at about 3.7 GHz. In terms of gain and linearity, both figures exceed the documented limitations for the class of mixers considered in this work. To the authors’ best knowledge, this is the first DP mixer operating in the S-band. The prototype was also tested in a radar-like setup operating in the S-band frequency-modulated continuous-wave (FMCW) mode. Measurements carried out in the radar setup resulted in +39.7dB and +34.7dB of IF signal-to-noise-ratio (SNR) for the DP and the resistive mixers, respectively. For comparison purposes, a resistive mixer was designed and fabricated using the same GaN HEMT technology; a detailed comparison between the two topologies is discussed in the paper, thus further highlighting the capability of the DP-mixer for system applications.
Adolescents’ Out-of-School Physical Activity Levels and Well-Being during the COVID-19 Restrictions in Greece: A Longitudinal Study

The present study aimed to identify the impact of social distancing measures on adolescents’ physical activity (PA) levels and well-being during the implementation of COVID-19 restrictive measures. There were 438 participants (207 boys and 231 girls), aged 12 to 15 years old (M = 13.5, SD = 0.55). They completed online questionnaires on well-being and PA in three waves (December 2020, February 2021, and June 2021). Correlation analyses were conducted to examine the relationship between well-being and PA variables in the three measurements. Additionally, separate three-way repeated-measures ANOVAs were conducted to capture possible differences in students’ moderate to vigorous physical activity (MVPA) levels, life satisfaction, and subjective vitality among the three measurements due to gender, age, and interaction between gender and age. A significant relation emerged between the MVPA variables and well-being. In all measurements, adolescents’ PA levels did not meet the World Health Organization (WHO) recommendations of at least 60 min per day in MVPA. Students’ MVPA levels, life satisfaction, and subjective vitality were significantly higher in the third measurement compared to the first and second ones. Moreover, significant differences emerged in life satisfaction and subjective vitality between boys and girls in the first and third measurements, respectively. The COVID-19 restrictions appeared to negatively influence adolescents’ PA and well-being. Policymakers aimed at facilitating adolescents’ well-being in a similar situation in the future should not adopt measures restricting the participation of adolescents in PA.
Well-Balanced Schemes Based on Elementary Solutions for Kinetic Models of Ionized or Ultra-Relativistic Gas

ABSTRACT Well-balanced schemes for two types of (1 + 1)-dimensional kinetic models are set up relying on scattering matrices derived from the explicit so–called elementary solutions of the corresponding stationary equations. A “matrix balancing” (or “matrix equilibration”) technique is evoked to ensure the overall mass (and positivity) preservation. The first model is a simplification of edge plasma dynamics close to a wall, like a tokamak divertor, whereas the second one is essentially a rewriting of the Anderson-Witting Bhatnagar-Gross-Krook (BGK) model of relativistic Boltzmann equation for example, photons. Numerical results on coarse grids are provided to illustrate the feasibility of the algorithms.
Effective Tax Rate: Dampak Leverage, Capital Intensity Ratio Dan Kepemilikan Institusi

The purpose of this study is to analysis impact of leverage, capital intensity ratio, and institutional on effective tax rate. As knowed that effective tax rate is tax cost effective for entity and management as agent should know what factors can have an impact on these cost. Using mining companies listed on the Indonesia Stock Exchange in 2014-2020 as a sample, this study found a negative effect of the capital intensity ratio on the effective tax rate, while leverage and institutional ownership were not found to have a significant impact. These results explain that the high value of fixed assets has an influence on the total tax cost, with depreciation costs that can be deducted in calculating taxable income according to Article 11 of the Income Tax Law
A Controllable Lifestyle Simulator for Use in Deep Reinforcement Learning Algorithms

Deep learning, especially deep reinforcement learning (DRL), has become one of the most useful tools for solving sequential decision-making problems. This is particularly relevant to recommender systems and drug administration in healthcare. This approach has the main drawback of requiring a lot of training, usually several millions of examples before it can learn an interesting policy. Therefore, in healthcare, it is crucial for training the neural network to use reliable and realistic simulations of users. These simulations can be measurements of the user’s vital signals throughout the day or the occurrence of lifestyle events such as sleeping, eating, etc. In this paper, we present a novel and highly generalizable simulation system based on state machines associated with probabilistic transitions to simulate the user’s lifestyle. Thanks to its random nature, the proposed approach can generate infinite yet realistic data from the simulation. It can also be controlled based on the simulation constraints, which is an important requirement for DRL algorithms. We demonstrate its usefulness and relevance in two simple cases of user behavior simulation using different types of input data. We also show its application for a DRL scenario involving behavior change interventions.
Signal Reconstruction for FMCW Radar Interference Mitigation Using Deep Unfolding

Removal of frequency-modulated continuous wave (FMCW) interference by zeroing corrupted samples causes significant distortions and peak power losses in the range-Doppler map. Existing methods aim to diminish these distortions by utilizing data from one dimension to reconstruct the corrupted samples, which do not perform well when a large number of samples are interfered and have diﬃculty recovering weak target signals.In this paper, model-based deep learning interference mitigation algorithms, called ALISTA and ALFISTA, are presented that reduce these artifacts by leveraging the full integration gain using all uncorrupted fast-time and slow-time samples. Simulations with 50% corrupted samples show that target peak power loss and velocity peak-to-sidelobe ratio (VPSR) with a 20-layer ALFISTA improves with 5.5 and 9.6 dB compared to zeroing. Furthermore, significant improvements in precision and recall are observed, even when large amounts (50-80%) of samples are missing.
Noncoherent Multiuser Grassmannian Constellations for the Mimo Multiple Access Channel

We consider the design of multiuser constellations for a multiple access channel (MAC) with K users, with M antennas each, that transmit simultaneously to a receiver equipped with N antennas through a Rayleigh block-fading channel, when no channel state information (CSI) is available to either the transmitter or the receiver. In full-diversity scenarios where the coherence time is at least T ≥ (K + 1)M, the proposed constellation design criterion is based on the asymptotic expression of the multiuser pairwise error probability (PEP) derived by Brehler and Varanasi in [1]. Although this PEP expression was previously considered intractable for optimization, in this work we derive a closed-form formula for its unconstrained gradient and perform Riemannian optimization in the Grassmannian manifold to design multiuser constellations for the MIMO MAC with state-of-the-art performance in terms of symbol error rate (SER).
A Bandit Online Convex Optimization Approach To Distributed Energy Management In Networked Systems

Modern power systems integrate renewable distributed energy resources (DERs) as an environment-friendly enhancement to meet the ever-increasing demands. However, due to the inherent unreliability of renewable energy, it is imperative to develop effective algorithms for DER management. In this work, we study the energy-sharing problem in a system consisting of several DERs. Each agent harvests and distributes renewable energy in its neighborhood to optimize the network's performance while minimizing energy waste. We model this problem as a bandit convex optimization problem with constraints, where the constraints correspond to each node's limitations for energy production. We propose a distributed decision-making policy to solve the formulated problem, that achieves ${\mathcal{O}}\left( {{T^{\frac{3}{4}}}} \right)$ regret bound and ${\mathcal{O}}\left( {{T^{\frac{3}{4}}}} \right)$ constraint violations. To reduce the constraint violations, we suggest two decision-making variations. Numerical experiments using a real-world dataset show superior performance of our proposal compared to state-of-the-art methods.
Translation, Cross-Cultural Adaptation, and Validation of the Foot Posture Index (FPI-6)—Italian Version

Since foot posture is one of the main predictors of lower limb musculoskeletal injuries, it is crucial to use appropriate tools to define the foot’s posture. The Foot Posture Index is, therefore, a reliable method to measure foot posture and is widely known and used in clinics and research. This study aimed to translate the Foot Posture Index 6 (FPI6) into Italian and to assess its psychometric properties. Translation and cross-cultural adaptation were obtained using a popular guideline. Two examinators assessed 68 subjects, and data were collected to test intra/inter-rater reliability, internal consistency and cross-cultural validity. The Italian version of FPI6 showed excellent inter- and intra-rater reliability (ICC 0.96 and 0.97), and Cronbach’s alpha coefficient was 0.9, thus showing excellent internal consistency. The FPI-6 version has proved to be reliable in terms of inter- and intra-rater reliability and can, therefore, be used in clinical practice and scientific research.
Nature of ferroelectric-paraelectric phase transition and origin of negative thermal expansion in PbTi O 3

Ferroelectric-paraelectric (FE-PE) phase transitions have been primarily explained by the phenomenological Landau-Devonshire theory and a soft zone-center mode of vibration in the literature. In this work, we study the atomic structure and polarization evolution of PbTiO3 as a function of temperature using ab-initio molecular dynamics simulations (AIMD). In contrast to conventional MD analyses where results are averaged over time, we categorize the atomic configurations as a function of time in terms of Ti-O bond lengths in the nearest neighboring shell. We show that an appreciable amount of cubic configuration exists at temperatures about 300K below its FE-PE phase transition temperature of 763K, even though the time-averaged overall atomic configuration is tetragonal. The quantitative results depict that as the temperature increases the population of the cubic configuration increases and that of the tetragonal configuration decreases, signifying that the FE-PE phase transition is intrinsically second-order. It reveals that the thermal fluctuation of the cubic configurations in the tetragonal matrix has a significant contribution to the negative thermal expansion in the FE phase region because the cubic configuration has smaller volume and higher entropy than the tetragonal matrix. PACS number(s): 77.80. B-, 77.80. -e, 31.15. A-, 31.15. xv
In Silico Subtractive Proteomics and Molecular Docking Approaches for the Identification of Novel Inhibitors against Streptococcus pneumoniae Strain D39

Streptococcus pneumoniae is a notorious Gram-positive pathogen present asymptomatically in the nasophayrnx of humans. According to the World Health Organization (W.H.O), pneumococcus causes approximately one million deaths yearly. Antibiotic resistance in S. pneumoniae is raising considerable concern around the world. There is an immediate need to address the major issues that have arisen as a result of persistent infections caused by S. pneumoniae. In the present study, subtractive proteomics was used in which the entire proteome of the pathogen consisting of 1947 proteins is effectively decreased to a finite number of possible targets. Various kinds of bioinformatics tools and software were applied for the discovery of novel inhibitors. The CD-HIT analysis revealed 1887 non-redundant sequences from the entire proteome. These non-redundant proteins were submitted to the BLASTp against the human proteome and 1423 proteins were screened as non-homologous. Further, databases of essential genes (DEGG) and J browser identified almost 171 essential proteins. Moreover, non-homologous, essential proteins were subjected in KEGG Pathway Database which shortlisted six unique proteins. In addition, the subcellular localization of these unique proteins was checked and cytoplasmic proteins were chosen for the druggability analysis, which resulted in three proteins, namely DNA binding response regulator (SPD_1085), UDP-N-acetylmuramate—L-alanine Ligase (SPD_1349) and RNA polymerase sigma factor (SPD_0958), which can act as a promising potent drug candidate to limit the toxicity caused by S. pneumoniae. The 3D structures of these proteins were predicted by Swiss Model, utilizing the homology modeling approach. Later, molecular docking by PyRx software 0.8 version was used to screen a library of phytochemicals retrieved from PubChem and ZINC databases and already approved drugs from DrugBank database against novel druggable targets to check their binding affinity with receptor proteins. The top two molecules from each receptor protein were selected based on the binding affinity, RMSD value, and the highest conformation. Finally, the absorption, distribution, metabolism, excretion, and toxicity (ADMET) analyses were carried out by utilizing the SWISS ADME and Protox tools. This research supported the discovery of cost-effective drugs against S. pneumoniae. However, more in vivo/in vitro research should be conducted on these targets to investigate their pharmacological efficacy and their function as efficient inhibitors.