Miracle-dz/UniFAR-FTUs huggingface dataset:
Voice quality enhancement for vocal tract rehabilitation

Vocal rehabilitation devices used by patients after Laryngectomy produce an unnatural sounding speech. Our study aims at increasing the quality of these synthetically generated voices by implementing human-like characteristics. A simplified source filter model, linear predictive coding coefficients and line spectral frequencies were used to model the vocal tract and manipulate the acoustic features of their resulting speech. Two different mapping functions were employed to convert between the features of synthetically generated voice and those of a human voice: A Gaussian mixture model and a linear regression model. The models were trained on a set of 50 human and 50 synthetic voice utterances. Both mapping functions yielded significant changes in the transformed synthetic voices and their spectra were similar to the human voices. The linear regression model mapping produced slightly better results compared to the Gaussian mixture model mapping. Listeners' tests confirmed this result, but indicated that voices re-synthesized from the transformed model coefficients, improved on the synthetic voice but still sounded unnatural. This may imply that the vocal tract model is lacking in information that produces the subjective perception of “artificial speech”. Future work will investigate an elaborate model which will include the speech production excitation and radiation signals and the transformation of their features. These models have the potential to improve the conversion of synthetically generated electrolarynx voice into human sounding one.
Supporting Development During Military Deployment and After April 2018.

CASE Ryan is a 6-year-old child new to your primary care practice after relocating from out of state with his father and younger sister. Ryan's grandmother recently expressed concerns about Ryan's social skills and behavior. He was subsequently diagnosed by a developmental and behavioral pediatrician with autism spectrum disorder, global developmental delay, and attention deficit hyperactivity disorder. At your first visit with Ryan, his father provides the following history: When Ryan was 3 years old, he was living with his mother and infant sister while his father was serving his fourth tour of duty with the marines in Afghanistan. One night, while Ryan was sleeping in bed with his mother, she died suddenly from a pulmonary embolism. Ryan's father was then called home from Afghanistan to take care of the children.Ryan's father explains that this was his first time serving as a primary caregiver for any prolonged period. He felt overwhelmed and unprepared, both cognitively and emotionally, to return from a combat zone and assume full-time, single-parent responsibilities. Ryan's father admitted knowing little about child development and had not had sustained interactions with his own children because of frequent deployments. He did not appreciate the delays and atypicalities in Ryan's development until he moved back home with his own mother and she expressed concerns. Ryan's father had his own psychological, emotional, and physical challenges from participating in active combat, including chronic pain in his shoulder from multiple gunshot wounds. Despite moving back home to be close to his family, Ryan's father admits feeling isolated and reports that "no one knows what it feels like. "How would you provide unique support to Ryan and his family? What treatment modalities are particularly important to emphasize?
MSU-AVIS dataset: Fusing Face and Voice Modalities for Biometric Recognition in Indoor Surveillance Videos

Indoor video surveillance systems often use the face modality to establish the identity of a person of interest. However, the face image may not offer sufficient discriminatory information in many scenarios due to substantial variations in pose, illumination, expression, resolution and distance between the subject and the camera. In such cases, the inclusion of an additional biometric modality can benefit the recognition process. In this regard, we consider the fusion of voice and face modalities for enhancing the recognition accuracy. The main contribution of this work is assembling a multimodal (face and voice), semi-constrained, indoor video surveillance dataset referred to as the MSU Audio-Video Indoor Surveillance (MSU-AVIS) dataset. We use a consumer-grade camera with a built-in microphone to acquire data for this purpose. We use current state-of-art deep-learning based methods to perform face and speaker recognition on the collected dataset for establishing baseline performance. We also explore multiple fusion schemes to combine face and speaker recognition to perform effective person recognition on audio-video surveillance data. Experiments convey the efficacy of the proposed multimodal fusion scheme (face and voice) over unimodal approaches in surveillance scenarios. The collected dataset is being made available for research purposes.
On the nature of the frontal zone of the Choctawhatchee Bay plume in the Gulf of Mexico

River plumes often feature turbulent processes in the frontal zone and interfacial region at base of the plume, which ultimately impact spreading and mixing rates with the ambient coastal ocean. The degree to which these processes govern overall plume mixing is yet to be quantified with microstructure observations. A field campaign was conducted in a river plume in the northeast Gulf of Mexico in December 2013, in order to assess mixing processes that could potentially impact transport and dispersion of surface material near coastal regions. Current velocity, density, and Turbulent Kinetic Energy Values, e, were obtained using an Acoustic Doppler Current Profiler (ADCP), a Conductivity Temperature Depth (CTD) profiler, a Vertical Microstructure Profiler (VMP), and two Acoustic Doppler Velocimeters (ADVs). The frontal region contained e values on the order of 10−5 m2 s−3, which were markedly larger than in the ambient water beneath (O 10−9 m2 s−3). An energetic wake of moderate e values (O 10−6 m2 s−3) was observed trailing the frontal edge. The interfacial region of an interior section of the plume featured opposing horizontal velocities and a e value on the order of 10−6 m2 s−3. A simplified mixing budget was used under significant assumptions to compare contributions from wind, tides, and frontal regions of the plume. The results from this order of magnitude analysis indicated that frontal processes (59%) dominated in overall mixing. This emphasizes the importance of adequate parameterization of river plume frontal processes in coastal predictive models.
Cancer patient perspectives regarding preparedness for end-of-life care: A qualitative study

ABSTRACT Purpose: The extent to which patients feel prepared for end-of-life (EOL) may be associated with important clinical outcomes. Despite growing interest in the concept of “preparedness,” however, there is insufficient information about what cancer patients actually need to feel prepared. Such information is foundational for patient-centered care, theory-building, and instrument development. Design: This qualitative study examined patient perspectives regarding preparedness for EOL care. Participants and methods: In-depth interviews were conducted with patients with advanced malignancies and limited life expectancies. Participants were drawn from a large academic cancer center and had a diverse range of malignancies. Thematic text analysis was used to analyze the data. Findings: Six overarching themes emerged. These included readiness to manage concerns about: (1) EOL planning (e.g., goals of care, location of care); (2) interactions with healthcare providers (e.g., communication, symptom control); (3) interactions with family/friends (e.g., perceived burden, support); (4) emotional well-being (e.g., existential distress, fulfillment); (5) spiritual well-being (e.g., spiritual comfort, congregational support); and (6) financial well-being (e.g., medical expenses, estate planning). Conclusions: Findings highlight areas that patients themselves regard as critical for a sense of preparedness for EOL care. Participants emphasized broader concerns than those previously construed as facets of patient preparedness, and these domains offer modifiable targets for intervention.
Area of Residence and Socioeconomic Factors Reduce Access to Biologics for Rheumatoid Arthritis Patients in Romania

Introduction The study aimed to evaluate the influence of socioeconomic factors on rheumatoid arthritis (RA) patients' access to biologics in Romania. Method Cross-sectional data were collected in January 2014 from the Romanian Registry of Rheumatic Diseases (RRRD) comprising all RA patients on biologics from 42 Romanian counties. “Territorial” access to biologics was defined by patients receiving biologics in their home county. A county was “equitable” if <25% of RA patients received biologics outside it. Results The RRRD included 4507 RA patients aged 56.7 ± 12.1 years, with a disease duration of 12.1 ± 8.3 years. Urban dwellers (67.8%) had a significantly higher prevalence of territorial biologic access than rural dwellers (83.1% compared to 74.1%; p < 0.001). Gross domestic product (GDP) in 1000 €/capita/county (odds ratio (OR) = 1.224) and number of physicians/1000 inhabitants/county (OR = 2.198) predict territorial access to biologics and also predict the number of territorially treated RA patients. Inequitable counties exhibited significantly lower socioeconomic indicators than equitable counties. Conclusion In Romania, RA patients' access to biologics varies significantly between counties. Urban dwellers and patients living in counties/regions with high living standards are more likely to receive biologics locally than those living in more deprived areas.
Subdivision schemes for smooth contact surfaces of arbitrary mesh topology in 3D

This paper presents a strategy to parameterize contact surfaces of arbitrary mesh topology in 3D with at least C1‐continuity for both quadrilateral and triangular meshes. In the regular mesh domain, four quadrilaterals or six triangles meet in one node, even C2‐continuity is attained. Therefore, we use subdivision surfaces, for which non‐physical pressure jumps are avoided for contact interactions. They are usually present when the contact kinematics is based on facet elements discretizing the interacting bodies. The properties of subdivision surfaces give rise to basically four different implementation strategies. Each strategy has specific features and requires more or less efforts for an implementation in a finite element program. One strategy is superior with respect to the others in the sense that it does not use nodal degrees of freedom of the finite element mesh at the contact surface. Instead, it directly uses the degrees of freedom of the smooth surface. Thereby, remarkably, it does not require an interpolation. We show how the proposed method can be used to parameterize adaptively refined meshes with hanging nodes. This is essential when dealing with finite element models whose geometry is generated by means of subdivision techniques. Three numerical 3D problems demonstrate the improved accuracy, robustness and performance of the proposed method over facet‐based contact surfaces. In particular, the third problem, adopted from biomechanics, shows the advantages when designing complex contact surfaces by means of subdivision techniques. Copyright © 2004 John Wiley & Sons, Ltd.
EEG-Based Detection of Braking Intention Under Different Car Driving Conditions

The anticipatory recognition of braking is essential to prevent traffic accidents. For instance, driving assistance systems can be useful to properly respond to emergency braking situations. Moreover, the response time to emergency braking situations can be affected and even increased by different driver's cognitive states caused by stress, fatigue, and extra workload. This work investigates the detection of emergency braking from driver's electroencephalographic (EEG) signals that precede the brake pedal actuation. Bioelectrical signals were recorded while participants were driving in a car simulator while avoiding potential collisions by performing emergency braking. In addition, participants were subjected to stress, workload, and fatigue. EEG signals were classified using support vector machines (SVM) and convolutional neural networks (CNN) in order to discriminate between braking intention and normal driving. Results showed significant recognition of emergency braking intention which was on average 71.1% for SVM and 71.8% CNN. In addition, the classification accuracy for the best participant was 80.1 and 88.1% for SVM and CNN, respectively. These results show the feasibility of incorporating recognizable driver's bioelectrical responses into advanced driver-assistance systems to carry out early detection of emergency braking situations which could be useful to reduce car accidents.
Design of Genetic Algorithm Based Power System Damping Controller

This paper presents a Genetic based optimization technique towards the optimum design of power system damping controller in a multi-machine power system. In this approach, GA incorporates an objective function, which is based on integral of squared time squared error (ISTSE) technique. The parameters of the stabilizers are tuned through simulation of non-linear model of the power system using Genetic Algorithm (GA). The dynamic performance of the system has been investigated under small disturbances as well as large perturbation. The GA based PSS is compared with the CPSS optimized using phase compensation. Simulation studies reveal that the GA based PSS enhances system dynamic performance over a wide range of operating conditions and effective in damping local and interarea modes of oscillations. Key-Words: - Dynamic stability, Power system stabilizer, Genetic algorithms, Genetic algorithm based power system stabilizer, Power system control
Culture‐independent characterization of a novel magnetotactic member affiliated to the Beta class of the Proteobacteria phylum from an acidic lagoon

Magnetotactic bacteria (MTB) comprise a group of motile microorganisms common in most mesothermal aquatic habitats with pH values around neutrality. However, during the last two decades, a number of MTB from extreme environments have been characterized including: cultured alkaliphilic strains belonging to the Deltaproteobacteria class of the Proteobacteria phylum; uncultured moderately thermophilic strains belonging to the Nitrospirae phylum; cultured and uncultured moderately halophilic or strongly halotolerant bacteria affiliated with the Deltaproteobacteria and Gammaproteobacteria classes and an uncultured psychrophilic species belonging to the Alphaproteobacteria class. Here, we used culture-independent techniques to characterize MTB from an acidic freshwater lagoon in Brazil (pH ∼ 4.4). MTB morphotypes found in this acidic lagoon included cocci, rods, spirilla and vibrioid cells. Magnetite (Fe3 O4 ) was the only mineral identified in magnetosomes of these MTB while magnetite magnetosome crystal morphologies within the different MTB cells included cuboctahedral (present in spirilla), elongated prismatic (present in cocci and vibrios) and bullet-shaped (present in rod-shaped cells). Intracellular pH measurements using fluorescent dyes showed that the cytoplasmic pH was close to neutral in most MTB cells and acidic in some intracellular granules. Based on 16S rRNA gene phylogenetic analyses, some of the retrieved gene sequences belonged to the genus Herbaspirillum within the Betaproteobacteria class of the Proteobacteria phylum. Fluorescent in situ hybridization using a Herbaspirillum-specific probe hybridized with vibrioid MTB in magnetically-enriched samples. Transmission electron microscopy of the Herbaspirillum-like MTB revealed the presence of many intracellular granules and a single chain of elongated prismatic magnetite magnetosomes. Diverse populations of MTB have not seemed to have been described in detail in an acid environment. In addition, this is the first report of an MTB phylogenetically affiliated with Betaproteobacteria class.
Wireless network traffic disaggregation using Bayesian nonparametric techniques

We present a machine learning spectrum awareness framework capable of characterizing and inferring the application layer protocol states of multiple interleaved wireless network traffic flows using only externally observable energy detector features. The framework is intended to inform intelligent dynamic spectrum access (DSA) strategies in a cognitive radio environment. This extends an approach we developed previously for single isolated traffic flows, which applied a Bayesian non-parametric technique to construct hidden Markov model (HMM) representations of specific protocols. The learned HMM models, with hidden states closely corresponding to actual protocol states, were used for protocol classification and state recognition given a stream of energy detector observables from an isolated traffic flow. In this work, various single protocol HMMs are combined into a factorial hidden Markov model (FHMM) representing multiple heterogeneous interleaved flows. Using the FHMM to infer the states of the interleaved flows directly from observations of the aggregate traffic, we avoid having to deinterleave the transmissions of the component flows, a particularly difficult task in cognitive radio environments with agile emitters. We demonstrate this framework on an emulated network scenario with multiple simultaneous flows carrying different application layer traffic types.
CRISPR-Mediated Reactivation of DKK3 Expression Attenuates TGF-β Signaling in Prostate Cancer

The DKK3 gene encodes a secreted protein, Dkk-3, that inhibits prostate tumor growth and metastasis. DKK3 is downregulated by promoter methylation in many types of cancer, including prostate cancer. Gene silencing studies have shown that Dkk-3 maintains normal prostate epithelial cell homeostasis by limiting TGF-β/Smad signaling. While ectopic expression of Dkk-3 leads to prostate cancer cell apoptosis, it is unclear if Dkk-3 has a physiological role in cancer cells. Here, we show that treatment of PC3 prostate cancer cells with the DNA methyltransferase (DNMT) inhibitor decitabine demethylates the DKK3 promoter, induces DKK3 expression, and inhibits TGF-β/Smad-dependent transcriptional activity. Direct induction of DKK3 expression using CRISPR-dCas9-VPR also inhibited TGF-β/Smad-dependent transcription and attenuated PC3 cell migration and proliferation. These effects were not observed in C4-2B cells, which do not respond to TGF-β. TGF-β signals can regulate gene expression directly via SMAD proteins and indirectly by increasing DNMT expression, leading to promoter methylation. Analysis of genes downregulated by promoter methylation and predicted to be regulated by TGF-β found that DKK3 induction increased expression of PTGS2, which encodes cyclooxygenase-2. Together, these observations provide support for using CRISPR-mediated induction of DKK3 as a potential therapeutic approach for prostate cancer and highlight complexities in Dkk-3 regulation of TGF-β signaling.
Hardware Transactional Memory Based on Abort Prediction and Adaptive Retry Policy for Multi-Core In-Memory Databases

Since Intel has recently shifted Transactional Synchronization Extension (TSX) as its first mainstream Hardware Transactional Memory (HTM), HTM has greatly changed the parallel programming paradigm for transaction processing, As a result, a number of studies on HTM have been conducted actively. However, the existing studies consider only the prediction of a conflict between two transactions and provide a static HTM configuration for all workloads. To solve the problems, we propose an efficient hardware transactional memory scheme based on both abort prediction and adaptive retry policy for multi-core in-memory databases. First, the proposed scheme can predict not only conflicts between transactions running concurrently, but also the capacity and other aborts of transactions by collecting the information of previously executed transactions. Second, the proposed scheme can provide a near-optimal HTM configuration according to the characteristic of a given workload by using an adaptive retry policy based on machine learning algorithms. Finally, through our experimental performance analysis using STAMP, the proposed scheme shows about 30~40% better performance than the existing HTM-based schemes.
Effects of slow-wave activity on mood disturbance in major depressive disorder

Abstract Background Studies have demonstrated that decreases in slow-wave activity (SWA) predict decreases in depressive symptoms in those with major depressive disorder (MDD), suggesting that there may be a link between SWA and mood. The aim of the present study was to determine if the consequent change in SWA regulation following a mild homeostatic sleep challenge would predict mood disturbance. Methods Thirty-seven depressed and fifty-nine healthy adults spent three consecutive nights in the sleep laboratory. On the third night, bedtime was delayed by 3 h, as this procedure has been shown to provoke SWA. The Profile of Mood States questionnaire was administered on the morning following the baseline and sleep delay nights to measure mood disturbance. Results Results revealed that following sleep delay, a lower delta sleep ratio, indicative of inadequate dissipation of SWA from the first to the second non-rapid eye movement period, predicted increased mood disturbance in only those with MDD. Conclusions These data demonstrate that in the first half of the night, individuals with MDD who have less SWA dissipation as a consequence of impaired SWA regulation have greater mood disturbance, and may suggest that appropriate homeostatic regulation of sleep is an important factor in the disorder.
Quantitative Trait Loci Analysis of Seed Quality Characteristics in Lentil using Single Nucleotide Polymorphism Markers

Seed shape, color, and pattern of lentil (Lens culinaris Medik. subsp. culinaris) are important quality traits as they determine market class and possible end uses. A recombinant inbred line population was phenotyped for seed dimensions over multiple site–years and classified according to cotyledon and seed coat color and pattern. The objectives were to determine the heritability of seed dimensions, identify genomic regions controlling these dimensions, and map seed coat and cotyledon color genes. A genetic linkage map consisting of 563 single nucleotide polymorphisms, 10 simple sequence repeats, and four seed color loci was developed for quantitative trait loci (QTL) analysis. Loci for seed coat color and pattern mapped to linkage groups 2 (Ggc), 3 (Tgc), and 6 (Scp) while the cotyledon color locus (Yc) mapped to linkage group 1. The broad sense heritability estimates were high for seed diameter (broad‐sense heritability [H2] = 0.92) and seed plumpness (H2 = 0.94) while seed thickness (H2 = 0.60) and days to flowering (H2 = 0.45) were more moderate. There were significant seed dimension QTL on six of the seven linkage groups. The most significant QTL for diameter and plumpness was found at the cotyledon color locus (Yc). The markers identified in this study can be used to help enrich breeding populations for desired seed quality characteristics, thereby increasing efficiency in the lentil breeding program.
Long-range orientational order of a random near lattice hard sphere and hard disk process

We show that a point process of hard spheres exhibits long-range orientational order. This process is designed to be a random perturbation of a three-dimensional lattice that satisfies a specific rigidity property, examples include the FCC and HCP lattices. We also define two-dimensional near-lattice processes by local, geometry dependent hard disk conditions. Earlier results about existence of long-range orientational order carry over and we obtain the existence of infinite-volume measures on two-dimensional point configurations that turn out to follow the orientation of a fixed triangular lattice arbitrary closely.