Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Identification of Interaction Behaviors From Haptic Data During Collaborative Object Transfer

Joint object transfer is a complex task, which is less structured and less specific than what is existing in several industrial settings. When two humans are involved in such a task, they cooperate through different modalities to understand the interaction states during operation and mutually adapt to one another's actions. Mutual adaptation implies that both partners can identify how well they collaborate (i.e. infer about the interaction state) and act accordingly. These interaction states can define whether the partners work in harmony, face conflicts, or remain passive during interaction. Understanding how two humans work together during physical interactions is important when exploring the ways a robotic assistant should operate under similar settings. This study acts as a first step to implement an automatic classification mechanism during ongoing collaboration to identify the interaction state during object co-manipulation. The classification is done on a dataset consisting of data from 40 subjects, who are partnered to form 20 dyads. The dyads experiment in a physical human-human interaction (pHHI) scenario to move an object in an haptics-enabled virtual environment to reach predefined goal configurations. In this study, we propose a sliding-window approach for feature extraction and demonstrate the online classification methodology to identify interaction patterns. We evaluate our approach using 1) a support vector machine classifier (SVMc) and 2) a Gaussian Process classifier (GPc) for multi-class classification, and achieve over ${\bf 80}\%$ accuracy with both classifiers when identifying general interaction types.
Applications of Deep Learning to Audio Generation

In the recent past years, deep learning based machine learning systems have demonstrated remarkable success for a wide range of learning tasks in multiple domains such as computer vision, speech recognition and other pattern recognition based applications. The purpose of this article is to contribute a timely review and introduction of state-of-the-art deep learning techniques and their effectiveness in speech/acoustic signal processing. Thorough investigations of various deep learning architectures are provided under the categories of discriminative and generative algorithms, including the up-to-date Generative Adversarial Networks (GANs) as an integrated model. A comprehensive overview of applications in audio generation is highlighted. Based on understandings from these approaches, we discuss how deep learning methods can benefit the field of speech/acoustic signal synthesis and the potential issues that need to be addressed for prospective real-world scenarios. We hope this survey provides a valuable reference for practitioners seeking to innovate in the usage of deep learning approaches for speech/acoustic signal generation.
Role of ASHA (Accredited Social Health Activist) Worker in Screening for Low Vision at the Grass Root Level

Aim: To evaluate the usefulness of ASHA worker in detecting patient with low vision at village level. Methodology: Government of India launched National Rural Health Mission (NRHM) to address the health needs of the rural population and introduced a band of community based functionaries, Accredited Social Health Activist (ASHA). Under the Swarnim Gujarat Project (18-02-2010 to 30-03-2010), 40 selected ASHA were trained in Ranpur taluka (population-87914), Ahmedabad district, Gujarat. They detected patients with vision < 6/60. These patients were evaluated and diagnosed by ophthalmic assistants and referred to higher center, if required. Observation: 40 trained ASHA workers screened 80610 (91.69%) people out of the total population (87914) of Ranpur, which indicates good coverage. 434 patients had vision < 6/60 (prevalence of blindness-5.38 per thousand). Conclusion: ASHA is a fountainhead of community participation. Better penetration of health services armed with ASHA project can go long way in decreasing the prevalence of blindness in the nation as a hole. The major cause of low vision in India is still cataract which, luckily, is a preventable cause of blindness. However, better participation by the locals and monetary benefits to ASHA can help in better achievement of schemes objectives.
Optimal Residential Battery Scheduling using Short-term Forecasts

Optimal day-ahead scheduling of batteries relies on reliable day-ahead forecast of residential load profile, solar insolation, and time-of-use tariff. However, actual values may not be the same as the forecasted values due to various factors like weather, cloud cover, consumer behaviour, etc. This work aims at optimally scheduling the batteries installed in a residence to minimize the cost of power consumption from the grid and increasing the battery lifetime, while accounting for the variations in the forecasted load profile, solar insolation and time-of-use tariff. The proposed method uses short-term forecasts and employs a sliding window technique to generate optimal schedule on an hourly basis. The sliding window technique helps in adjusting for the variations in the forecasts used. Artificial bee colony (ABC) algorithm is used as the optimization technique during each window. Effect of allowable maximum depth-of-discharge, and sliding window size on the optimized cost are also studied. The results are compared with manual scheduling.
An Internet of Things (IoT) Management System for Improving Homecare - A Case Study

Due to the increasing of population, the number of hospital visits by patients are increasing which puts a pressure on hospitals. Nowadays, the need for taking care of patients while they are at home is essential. Internet of Things (IoT) has been widely used in different areas such as healthcare and smart homes. IoT will assist in minimizing the hospital burden of frequent patients' visits. Applying IoT in healthcare will improve the efficiency and effectiveness, bring economic benefits, and reduce human exertions. It is well known that the best health monitoring system is able to detect abnormalities and able to make diagnosis without human exertion. However, this kind of system is dealing with health conditions which are essential and sensitive that require high accuracy to be reliable. This paper presents an Electrocardiogram (ECG) monitoring framework that overcome the accuracy limitation. Signal processing and feature extraction are applied. For the diagnosis purpose a classification stage is made in two ways; threshold values and machine learning to increase the accuracy. Experiment results reveal that the proposed model is more accurate in the diagnosis of heart diseases than other researches which makes it more confident to rely on from health experts point of view.
Beyond Visual Line of Sight Piloting of UAVs Using Millimeter-Wave Cellular Networks

In this paper, we investigate the potential benefits and challenges of using millimeter wave (mm-Wave) cellular network to carry out Beyond Visual Line of Sight (BVLOS) operations for remote piloting of Unmanned Aerial Vehicles (UAVs). We evaluate the reliability and latency of the wireless link between a UAV and a serving mm-Wave Base Station (BS) in the finite blocklength regime with the ultra reliable low latency communications (URLLC) requirements of 5G. To perform a comprehensive evaluation, we integrate several realistic models including mm-Wave antenna arrays, beamforming, mm-Wave propagation, and LOS probabilities. Our results show that cooperation and coordination among BSs are critical for UAV piloting using mm-Wave technology. We analyze the performance of mm-Wave links under three scenarios representing different levels of interference mitigation. We show that there is a certain range of message size and block length where it is possible to achieve URLLC requirements with packet error probability of 10−5 and latency below 1 ms when assuming cooperation.
On the Optimal Rate of Ad Hoc ALOHA Networks

The interest in asynchronous ALOHA protocols has recently been revived by the diffusion of low-power, long-range communication technologies proposed for Machine-to-Machine scenarios. This work focuses on ad hoc sensor networks where nodes communicate in pairs generating mutual interference, and tackles the fundamental yet still open question of how to set the transmission rate so as to maximise throughput. Elaborating on a previous stochastic geometry model, we consider a SINR decoding threshold that depends on the transmission rate, and analyse the interplay between interference and noise power to find the optimal transmission rate. Results reveal important design insights, showing that: i) the optimal rate relates to other system parameters in a rather complex manner but can be expressed by a single equation and is solvable via numeric evaluation; ii) in the interference-limited region, the optimal rate depends only on the bandwidth and the path-loss exponent; iii) this optimal rate yields close-to-optimal throughput also in the noise-limited region for standard values of the path loss coefficient; iv) using the optimal rate can significantly improve network throughput compared to the use of near-optimal rates.
Investigating the Internship Experiences of Human Service Students

This qualitative study explored the internship experiences of 15 undergraduate human service students. Students completed a paper at the end of the semester, reflecting on their overall internship experiences. Data analysis revealed four primary themes; professional, knowledge, personal, and challenges. Findings indicated that the internship was a powerful experience for students, both personally and professionally. Implications for education are provided and recommendations for future research are included. Introduction The human services internship is the capstone experience for all undergraduate human service majors at the authors’ institution. The internship requires 400 hours of work at an approved site within one semester. In addition to direct service, the students are also required to attend a weekly internship seminar. This seminar provides supervision for human services interns in a classroom setting. Activities within the seminar include group discussions, role-plays or simulations, journaling on assigned readings, peer discussions of internship experiences, selfassessment activities, process recordings, and reports on professional meetings and topics. For the majority of students, this internship is the first experience they have working with clients in a community setting. Thus, the course provides a challenging and supportive environment in which to examine internship-related issues, address problems and concerns relevant to internship, further enhance helping skills, and receive necessary information and instruction pertinent to the internship experience (Old Dominion University, 2009). The purpose of this research study was to examine human service students’ perceptions of their internship experiences. The overarching research question addressed was: How do human service students experience their internship? More specifically, we asked: To what extent did their perceptions of their internship experiences change from the beginning to the end of the semester? How did the internship experience influence the development of their professional identities? Literature Review For a number of decades, colleges and universities have considerably expanded the teaching and learning environment beyond the classroom. One example is the inception of experiential education as a recognized pedagogy. One of the major proponents of experiential education was John Dewey. In the early 20 century Dewey advocated for learning experiences where students could employ the knowledge and skills they acquired in the classroom well beyond the confines of its four-walls (Dewey, 1916/1944). In the later part of the 20 century, David A. Kolb Fall 2009 Vol6, Issue1 http://hst.coehs. uwosh.edu
Predicting neuronal dynamics with a delayed gain control model

Visual neurons respond to static images with specific dynamics: neuronal responses sum sub-additively over time, reduce in amplitude with repeated or sustained stimuli (neuronal adaptation), and are slower at low stimulus contrast. Here, we propose a simple model that predicts these seemingly disparate response patterns observed in a diverse set of measurements–intracranial electrodes in patients, fMRI, and macaque single unit spiking. The model takes a time-varying contrast time course of a stimulus as input, and produces predicted neuronal dynamics as output. Model computation consists of linear filtering, expansive exponentiation, and a divisive gain control. The gain control signal relates to but is slower than the linear signal, and this delay is critical in giving rise to predictions matched to the observed dynamics. Our model is simpler than previously proposed related models, and fitting the model to intracranial EEG data uncovers two regularities across human visual field maps: estimated linear filters (temporal receptive fields) systematically differ across and within visual field maps, and later areas exhibit more rapid and substantial gain control. The model is further generalizable to account for dynamics of contrast-dependent spike rates in macaque V1, and amplitudes of fMRI BOLD in human V1.
Vestibular, Locomotor, and Vestibulo-Autonomic Research: Fifty years of collaboration with Bernard Cohen.

My collaboration with Bernard Cohen began in 1972. Up until 2017, this collaboration has elucidated the functioning of the vestibulo-ocular reflex, the locomotor system, the functioning of the vestibulo-sympathetic reflex and how blood pressure and heart rate are controlled by the vestibular system. This paper traces a thread of modeling that I brought to the collaboration, which coupled with Bernie Cohen's expertise in vestibular, sensory-motor physiology, and clinical insights has broadened our understanding of the role of the vestibular system in a wide range of sensory motor systems. Specifically, the paper traces how the concept of a relaxation oscillator was used to model the slow and rapid phases of ocular nystagmus. A velocity signal that drives the slow compensatory eye movements was used to activate the saccadic system that reset the eyes, giving rise to relaxation oscillations that mimicked nystagmus. The model also predicted the unit activity in the PPRF that generated the quick phases of nystagmus. Studies of the slow component of ocular nystagmus introduced the idea that there was a velocity storage integrator that was a focus for visual-vestibular interaction and coded spatial orientation re gravity. Velocity storage contributes to motion sickness when there are visual-vestibular or orientation mismatches. The relaxation oscillator concept was subsequently used to model the stance and swing phases of locomotion, and how these were affected by Parkinson's disease. Finally, the relaxation oscillator was used to elucidate systolic and diastolic beats and how vaso-vagal syncope might be initiated by cerebellar-vestibular malfunction.
Are Biotransformation Studies of Therapeutic Proteins Needed? Scientific Considerations and Technical Challenges

For therapeutic proteins, the currently established standard development path generally does not foresee biotransformation studies by default because it is well known that the clearance of therapeutic proteins proceeds via degradation to small peptides and individual amino acids. In contrast to small molecules, there is no general need to identify enzymes involved in biotransformation because this information is not relevant for drug–drug interaction assessment and for understanding the clearance of a therapeutic protein. Nevertheless, there are good reasons to embark on biotransformation studies, especially for complex therapeutic proteins. Typical triggers are unexpected rapid clearance, species differences in clearance not following the typical allometric relationship, a mismatch in the pharmacokinetics/pharmacodynamics (PK/PD) relationship, and the need to understand observed differences between the results of multiple bioanalytical methods (e.g., total vs. target-binding competent antibody concentrations). Early on during compound optimization, knowledge on protein biotransformation may help to design more stable drug candidates with favorable in vivo PK properties. Understanding the biotransformation of a therapeutic protein may also support designing and understanding the bioanalytical assay and ultimately the PK/PD assessment. Especially in cases where biotransformation products are pharmacologically active, quantification and assessment of their contribution to the overall pharmacological effect can be important for establishing a PK/PD relationship and extrapolation to humans. With the increasing number of complex therapeutic protein formats, the need for understanding the biotransformation of therapeutic proteins becomes more urgent. This article provides an overview on biotransformation processes, proteases involved, strategic considerations, regulatory guidelines, literature examples for in vitro and in vivo biotransformation, and technical approaches to study protein biotransformation. SIGNIFICANCE STATEMENT Understanding the biotransformation of complex therapeutic proteins can be crucial for establishing a pharmacokinetic/pharmacodynamic relationship. This article will highlight scientific, strategic, regulatory, and technological features of protein biotransformation.
Towards Prepared mums (TOP-mums) for a healthy start, a lifestyle intervention for women with overweight and a child wish: study protocol for a randomised controlled trial in the Netherlands

Introduction Periconception obesity is associated with a higher risk for adverse perinatal outcomes such as gestational diabetes mellitus, preeclampsia, large for gestational age, operative delivery and preterm birth. Lifestyle interventions during pregnancy have resulted in insufficient effects on reducing these perinatal complications. A few reasons for this disappointing effect can be suggested: (1) the time period during pregnancy for improvement of developmental circumstances is too short; (2) the periconception period in which complications originate is not included; and (3) lifestyle interventions may not have been sufficiently multidisciplinary and customised. A preconception lifestyle intervention might be more effective to reduce perinatal complications. Therefore, the aim of the Towards Prepared mums study is to evaluate the effect of a lifestyle intervention starting prior to conception on lifestyle behaviour change. Methods and analysis This protocol outlines a non-blinded, randomised controlled trial. One hundred and twelve women (18–40 years of age) with overweight or obesity (body mass index≥25.0 kg/m2) who plan to conceive within 1 year will be randomised to either the intervention or care as usual group. The intervention group will receive a multidisciplinary, customised lifestyle intervention stimulating physical activity, a healthy diet and smoking cessation, if applicable. The lifestyle intervention and monitoring will take place until 12 months postpartum. The primary outcome is difference in weight in kg from baseline to 6 weeks postpartum. Secondary outcomes are gestational weight gain, postpartum weight retention, smoking cessation, dietary and physical activity habits. Furthermore, exploratory outcomes include body composition, cardiometabolic alterations, time to pregnancy, need for assisted reproductive technologies, perinatal complications of mother and child, and lung function of the child. Vaginal and oral swabs, samples of faeces, breast milk, placenta and cord blood will be stored for evaluation of microbial flora, epigenetic markers and breast milk composition. Furthermore, a cost-effectiveness analysis will take place. Ethics and dissemination Ethical approval was obtained from the Medical Ethical Committee of Maastricht University Medical Centre+ (NL52452.068.15/METC152026). Knowledge derived from this study will be made available by publications in international peer-reviewed scientific journals and will be presented at (inter)national scientific conferences. A dissemination plan for regional and national implementation of the intervention is developed. Trial registration number ClinicalTrials.gov NCT02703753.
Identification of Potentially Functional CircRNA-miRNA-mRNA Regulatory Network in Gastric Carcinoma using Bioinformatics Analysis

Background As all we know, gastric cancer (GC) is a highly aggressive disease. Recently, circular RNA (circRNA) was found to play a vital role in regulation of GC. Some circRNAs could regulate messenger RNA (mRNA) expression by functioning as a microRNA (miRNA) sponge. Nevertheless, the circRNA-miRNA-mRNA regulatory network involved GC rarely has been explored and researched. Material/Methods All the differentially expressed circRNAs, miRNAs, and mRNA were derived from Gene Expression Omnibus (GEO) microarray data (GSE78092, GSE89143, GSE93415, and GSE54129). GC level 3 miRNA-sequencing data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Furthermore, a circRNA-miRNA-mRNA regulatory network was constructed by Cytoscape (version 3.6.1). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway revealed the functions and signaling pathways associated with these target genes. Hub genes of protein-protein interaction (PPI) network were identified by STRING database and cytoHubba. Results The regulatory network consists of 3 circRNAs, 22 miRNAs, and 128 mRNAs. Only 3 miRNAs of the network were consistent with the expression of TCGA and were associated with some clinical features. The results of the functional analysis of 128 mRNAs showed that GO analysis and KEGG pathways of inclusion criteria were 49 and 24, respectively. PPI network and Cytoscape showed that the top 10 hub genes were MYC, CTGF, TGFBR2, TGFBR1, SERPINE1, KRAS, ZEB1, THBS1, CDK6, and TNS1; 4 of which were verified by GEPIA based on TCGA. Highly expressed SERPINE1 had a poor OS (over survival) and DFS (disease-free survival), and TGFBR1 expression increased along with the increase of clinical stages. Conclusions This study looked at a circRNA-miRNA-mRNA regulatory network associated with GC and explored the potential functions of mRNA in the network, then identified a new molecular marker for prediction, prognosis, and therapeutic targets for clinical patients.
Retrocondylar Far-Lateral Approach for Resection of a Craniocervical Junction Hemangioblastoma

Abstract We present a case in which a retrocondylar far-lateral approach was utilized to resect a hemangioblastoma at the craniocervical junction. The patient was a 33-year-old man presenting with 2 months of symptoms referable to compression at the craniocervical junction (i.e., dizziness and gait instability). Though neurologically intact on exam, his imaging demonstrated a highly vascular cystic lesion dorsolateral to the medulla on the left consistent with a hemangioblastoma. Virtual reality software was critical to visualize the patient's lesion in relationship to the vertebral artery and draining vein (Fig. 1). A far-lateral retrocondylar suboccipital craniectomy with a C1 hemilamiectomy permitted resection of this lesion. The operative strategy was to address the lesion similar to an arteriovenous malformation by detaching the lesion from its arterial feeders first, and then addressing the lesion's draining vein (Fig. 2). Postoperative imaging demonstrated a gross-total resection and the patient had an uncomplicated postoperative course. This case demonstrates not only the lateral reach of a retrocondylar far-lateral approach; but also, clearly demonstrates an effective dissection strategy when approaching a hemangioblastoma. The link to the video can be found at: https://youtu.be/M0szMOdhjfE.