Miracle-dz/UniFAR-FTUs huggingface dataset:
Novice Russian Research Writing: Prepositions and Prepositional Phrases

Background: The research writing of novice Russian authors tend to be markedly different from that of expert academic writers from other countries. More specifically, Russian student writing has been characterized as wordy, difficult to comprehend, syntactically complex, and excessive in terms of nominalisation. One of the main manifestations of these characteristics is the deployment of a large number of prepositions and prepositional phrases. Purpose: The purpose of this paper is to investigate the causes of this excessive use of prepositions in Russian student writing and to provide suggestions for improvement. Methods: The quantitative analysis evaluates two self-compiled corpora using the computational linguistics tool Gramulator. The first corpus consists of published research papers written by international scholars of radio engineering. The second corpus comprises first drafts of research papers written by Russian graduate and postgraduate students majoring in radio engineering. The final qualitative analysis focuses largely on the student corpus. Results: The seven most common writing features identified were as follows: excessive of-phrases, nouns/verbal nouns instead of gerunds, nouns instead of infinitives of purpose, nominalized structures instead of relative clauses, ‘strong noun + weak verb’ structures instead of ‘strong’ verbs, grammatical errors, and repetitions. Each of these features is discussed and followed by suggestions that may help both reduce the excessive number of prepositions and prepositional phrases and improve other important features of the text. Implications: The results of this study are of interest to academic writing instructors as well as the developers of teaching materials and automated evaluation tools.
Accelerating GNN Training by Adapting Large Graphs to Distributed Heterogeneous Architectures

Graph neural networks (GNNs) have been successfully applied to many important application domains on graph data. As graphs become increasingly large, existing GNN training frameworks typically use mini-batch sampling during feature aggregation to lower resource burdens, which unfortunately suffer from long memory accessing latency and inefficient data transfer of vertex features from CPU to GPU. This paper proposes 2PGraph, a system that addresses these limitations of mini-batch sampling and feature aggregation and supports fast and efficient single-GPU and distributed GNN training. First, 2PGraph presents a locality awareness GNN-training scheduling method that schedules the vertices based on the locality of the graph topology, significantly accelerating the sampling and aggregation, improving the data locality of vertex access, and limiting the range of neighborhood expansion. Second, 2PGraph proposes a GNN-layer-aware feature caching method on available GPU resources with a hit rate up to 100<inline-formula><tex-math notation="LaTeX">${\bf\%}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="bold">%</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="lu-ieq1-3305077.gif"/></alternatives></inline-formula>, which avoids redundant data transfer between CPU and GPU. Third, 2PGraph presents a self-dependence cluster-based graph partition method, achieving high sampling and cache efficiency for distributed environments. Experimental results on real-world graph datasets show that 2PGraph reduces memory access latency by up to 90<inline-formula><tex-math notation="LaTeX">${\boldsymbol{\%}}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="bold">%</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="lu-ieq2-3305077.gif"/></alternatives></inline-formula> mini-batch sampling, and data transfer time by up to 99<inline-formula><tex-math notation="LaTeX">${\boldsymbol{\%}}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="bold">%</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="lu-ieq3-3305077.gif"/></alternatives></inline-formula>. For distributed GNN training over an 8-GPU cluster, 2PGraph achieves up to 8.7<inline-formula><tex-math notation="LaTeX">$\times$</tex-math><alternatives><mml:math><mml:mo>×</mml:mo></mml:math><inline-graphic xlink:href="lu-ieq4-3305077.gif"/></alternatives></inline-formula> performance speedup over state-of-the-art approaches.
Adrenal Crisis Associated With COVID-19 Vaccination in Patients With Adrenal Insufficiency

Abstract Vaccination is generally recommended for patients with adrenal insufficiency receiving glucocorticoid replacement therapy because they are at risk of experiencing adrenal crisis during infections. Conventional vaccinations, such as those for influenza virus, have rarely been associated with adrenal crisis in patients with adrenal insufficiency; therefore, increasing the glucocorticoid dose during vaccination is not necessarily recommended. The COVID-19 mRNA vaccines exhibit a higher degree of adverse reactions, including fever and general fatigue, than those of conventional vaccines. Here, we present 3 cases of adrenal crisis associated with mRNA COVID-19 (BNT162b2) vaccination in patients with secondary adrenal insufficiency. Two patients presented with adrenal crisis after the second dose, whereas 1 presented with adrenal crisis after the first dose. Within 24 hours of vaccination, all patients presented with fatigue and appetite loss, and 2 patients were febrile. None of them increased their glucocorticoid dosage at the time of vaccination, leading to an adrenal crisis. To date, 9 cases of adrenal crisis, including ours, associated with COVID-19 vaccination have been reported. Considering the high degree of adverse reactions to COVID-19 vaccination, administration of prophylactic stress dose of glucocorticoids is strongly recommended, particularly in patients with symptomatic adverse reactions, to protect them from adrenal crisis.
Analysis of the service as a performance factor in high-level volleyball and beach volleyball

L'objectiu final d'aquesta Tesi es identificar aspectes critics de la utilitzacio del servei en el voleibol i voleibol de platja d'alt nivell. Per aconseguir-ho quatre estudis, emmarcats dintre de l'analisi del rendiment esportiu, es van dur a terme. Els resultats de les nostres investigacions mostren que el servei es una habilitat tecnica important en les disciplines del voleibol pero no pot explicar per si mateixa el resultat d'un partit. No obstant aixo, sembla un element clau en la prevencio d'atacs contraris eficacos i el seu resultat esta clarament influenciat per les habilitats executades en salt. Una execucio reeixida del servei es una questio complexa, i te una component fisica, tecnica, estrategica i tactica. Una correcta utilitzacio d'aquesta habilitat tecnica es fonamental per desequilibrar el resultat d'un partit. Ara be, altres aspectes han estat identificats tambe com a rellevants per tenir exit en un partit de voleibol o voleibol de platja. The ultimate goal of this Dissertation was to identify critical aspects of the use of service in high-level volleyball and beach volleyball. To address this purpose four studies on different aspects of service performance, framed into the field of performance analysis, were conducted. The results of our investigation show that service is a very important skill in volleyball disciplines but it cannot explain by itself the result of a match. Nevertheless, it seems a key element in preventing an effective attack. Service behavior and its ability to impair the opponent? s game is clearly influenced by jump abilities. The successful execution of the service is a complex issue, and has a physical, technical, strategic and tactical component. Proper use of this technical skill is capital in making the result of a match uneven. However, some other aspects have been identified as relevant to success in a volleyball or beach volleyball match.
Gait Stride Length Estimation Using Embedded Machine Learning

Introduction. Spatiotemporal gait parameters, e.g., gait stride length, are measurements that are classically derived from instrumented gait analysis. Today, different solutions are available for gait assessment outside the laboratory, specifically for spatiotemporal gait parameters. Such solutions are wearable devices that comprise an inertial measurement unit (IMU) sensor and a microcontroller (MCU). However, these existing wearable devices are resource-constrained. They contain a processing unit with limited processing and memory capabilities which limit the use of machine learning to estimate spatiotemporal gait parameters directly on the device. The solution for this limitation is embedded machine learning or tiny machine learning (tinyML). This study aims to create a machine-learning model for gait stride length estimation deployable on a microcontroller. Materials and Method. Starting from a dataset consisting of 4467 gait strides from 15 healthy people, measured by IMU sensor, and using state-of-the-art machine learning frameworks and machine learning operations (MLOps) tools, a multilayer 1D convolutional float32 and int8 model for gait stride length estimation was developed. Results. The developed float32 model demonstrated a mean accuracy and precision of 0.23 ± 4.3 cm, and the int8 model demonstrated a mean accuracy and precision of 0.07 ± 4.3 cm. The memory usage for the float32 model was 284.5 kB flash and 31.9 kB RAM. The int8 model memory usage was 91.6 kB flash and 13.6 kB RAM. Both models were able to be deployed on a Cortex-M4F 64 MHz microcontroller with 1 MB flash memory and 256 kB RAM. Conclusions. This study shows that estimating gait stride length directly on a microcontroller is feasible and demonstrates the potential of embedded machine learning, or tinyML, in designing wearable sensor devices for gait analysis.
Position-Time Pattern Based Method for Analyzing Users’ Mobility

Mobile network users’ movements analysis has increasingly importance with the mobile networks evolution. Time-spatial information about current and previous locations can contribute in optimizing mobile network performance. Extracting features from this information is the most common method in analyzing users’ mobility. In this paper, a new position-time pattern based method is proposed to analyze the mobility by translating time-spatial records of each user to target-oriented image. Dataset provided from an ISP in Shanghai city for one month is used in our work. The proposed method is deducing user’ mobility behavior by visualizing serving base stations positions vs time according to unified rules for all users. Mobility pattern ambiguity resulting from neighboring base stations is processed. Moreover, K-Nearest Neighbors based method is suggested to predict base stations neighborhood. Results shows that visualizing time-spatial dataset, with proper processing of neighboring base stations issue, provide better understanding of users’ mobility data analysis.
A thermomechanical model of ice-shelf flow

An ice-shelf model which features efficient numerical techniques is developed to determine the back-force exerted by sides and pinning points, such as islands of an embayed ice shelf. The model is applied to three ideal geometries and shows that the restraint exerted by a small island, even far downstream from the grounding line, can represent about one-half of the total restraint due to the embayment. Our results are further interpreted to suggest several criteria useful for testing any ice-shelf model.
Multichannel Relay assisted NOMA-ALOHA with Reinforcement Learning based Random Access

We investigate multichannel relay assisted non-orthogonal multiple access (NOMA) in slotted ALOHA systems, where each user randomly accesses one of different channel slots and different transmit power for uplink transmissions over two-hop links, to and from the relay. By using multi-agent reinforcement learning, we propose greedy and non-greedy random access methods so that each user can learn its best strategies of random access over multiple relay slots. Random collisions and fading over the relay slots are both considered. The behaviors of relay-aided NOMA-ALOHA strategies are evaluated with the simulation. It is shown that the greedy method outperforms the non-greedy method in terms of average success rate. For deployment of relay, the greedy method benefits in improving transmission reliability under the symmetric relay channels (between the two-hop links) compared to asymmetric channels. Thus, it is interpreted that the proposed greedy method is more promising to the NOMA-ALOHA systems under a symmetric multichannel relay.
Large-Scale Facial Expression Recognition Using Dual-Domain Affect Fusion for Noisy Labels

Building models for human facial expression recognition (FER) is made difficult by subjective, ambiguous and noisy annotations. This is especially true when assigning a single emotion class label to facial expressions for large in-the-wild FER datasets. Human facial expressions often contain a mixture of different mental states, which exacerbates the problem of single labels when used to categorize emotions. Dimensional models of affect – such as those using valence and arousal – provide significant advantages over categorical models in terms of representing human emotional states but have remained relatively under-explored. In this paper, we propose an approach for dual-domain affect fusion which investigates the relationships between discrete emotion classes and their continuous representations. In order to address the underlying uncertainty of the labels, we formulate a set of mixed labels via a dual-domain label fusion module to exploit these intrinsic relationships. Finally, we show the benefits of the proposed approach using AffectNet, Aff-Wild, and MorphSet, in the presence of natural and synthetic noise.
Transcribing Speech as Spoken and Written Dual Text Using an Autoregressive Model

This paper proposes a novel method to jointly generate spoken and written text from input speech for expanding use cases of speech-based applications. The spoken text generated us-ing speech-to-spoken text systems, i.e., speech recognition systems, has disfluencies and no punctuation marks. Thus, spoken text is often converted into written text using a spoken text-to-written text system. However, this cascading is unsuitable for overall optimization and computationally expensive. Although a speech-to-written-text system that directly outputs the written text from the speech is also developed, it cannot output the spoken text. To efficiently produce both spoken and written text from speech, our key advance is to handle a joint text of spoken and written texts in an autoregressive model. This enables us to correctly generate both spoken and written texts by considering their dependencies via a single decoding process. Our experiments demonstrate the effectiveness of the proposed method.
Intervention Request Planning with Operator Capability Model for Human-Automation Cooperative Recognition

Human-automation cooperation in the recognition phase of the autonomous driving system (cooperative recognition) has been proposed to address the challenges in the conventional cooperation method, e.g., taking over vehicle control. In cooperative recognition, the operator intervenes in the recognition of obstacles and risks that are difficult for the automated system alone. To realize cooperation and maximize the performance of the overall cooperative system, human tasks must be carefully allocated taking into account human processing capability and state in addition to driving safety, efficiency, and comfort. Since the human states are not directly observable, we formulate this problem as a Partially Observable Markov Decision Process (POMDP). Through simulator experiments, we showed that designing reward functions in the POMDP model that are biased operator decisions leads to inappropriate intervention requests, and we presented a solution. Furthermore, the intervention request scheduled by the POMDP model was able to reduce the intervention request time while maintaining driving comfort compared to the myopic policy, which requests intervention from the closest target, and also the POMDP model could adapt to the operator’s state.
Refining Stereotaxic Neurosurgery Techniques and Welfare Assessment for Long-Term Intracerebroventricular Device Implantation in Rodents

Simple Summary The development of innovative therapeutic strategies involving chronic drug delivery to specific brain regions has been crucial in preclinical neuroscience. However, these strategies involve complex surgeries due to the need for implanting a drug storage system connected to the brain through a cannula while ensuring animal welfare, which is a challenge, especially for long-term studies in rodents. In this study, we propose an optimized method with three main refinements: (i) modifying the dimensions of the implantable devices, (ii) using a combination of adhesive tissue and UV light-curing resin, and (iii) implementing a customized scoresheet to closely monitor animal welfare throughout the experiment. Overall, the proposed refinements significantly improved animal welfare, reduced complications related to surgery, increased animal survival, and ensured safe long-term implantations. Abstract Stereotaxic surgeries enable precise access to specific brain regions, being of particular interest for chronic intracerebroventricular drug delivery. However, the challenge of long-term studies at this level is to allow the implantation of drug storage devices and their correct intrathecal connection while guaranteeing animal welfare during the entire study period. In this study, we propose an optimized method for safe intrathecal device implantation, focusing on preoperative, intraoperative, and postoperative procedures, following the 3Rs principle and animal welfare regulations. Our optimized protocol introduces three main refinements. Firstly, we modify the dimensions of the implantable devices, notably diminishing the device-to-mouse weight ratio. Secondly, we use a combination of cyanoacrylate tissue adhesive and UV light-curing resin, which decreases surgery time, improves healing, and notably minimizes cannula detachment or adverse effects. Thirdly, we develop a customized welfare assessment scoresheet to accurately monitor animal well-being during long-term implantations. Taken together, these refinements positively impacted animal welfare by minimizing the negative effects on body weight, surgery-related complications, and anxiety-like behaviors. Overall, the proposed refinements have the potential to reduce animal use, enhance experimental data quality, and improve reproducibility. Additionally, these improvements can be extended to other neurosurgical techniques, thereby advancing neuroscience research, and benefiting the scientific community.
An Intelligent Framework for Cyber–Physical Satellite System and IoT-Aided Aerial Vehicle Security Threat Detection

The small-drone technology domain is the outcome of a breakthrough in technological advancement for drones. The Internet of Things (IoT) is used by drones to provide inter-location services for navigation. But, due to issues related to their architecture and design, drones are not immune to threats related to security and privacy. Establishing a secure and reliable network is essential to obtaining optimal performance from drones. While small drones offer promising avenues for growth in civil and defense industries, they are prone to attacks on safety, security, and privacy. The current architecture of small drones necessitates modifications to their data transformation and privacy mechanisms to align with domain requirements. This research paper investigates the latest trends in safety, security, and privacy related to drones, and the Internet of Drones (IoD), highlighting the importance of secure drone networks that are impervious to interceptions and intrusions. To mitigate cyber-security threats, the proposed framework incorporates intelligent machine learning models into the design and structure of IoT-aided drones, rendering adaptable and secure technology. Furthermore, in this work, a new dataset is constructed, a merged dataset comprising a drone dataset and two benchmark datasets. The proposed strategy outperforms the previous algorithms and achieves 99.89% accuracy on the drone dataset and 91.64% on the merged dataset. Overall, this intelligent framework gives a potential approach to improving the security and resilience of cyber–physical satellite systems, and IoT-aided aerial vehicle systems, addressing the rising security challenges in an interconnected world.
Design and Characterization of a Variable Gain D-Band LNA for Optimized Link Budgeting for a 6G Receiver in 22FDX

This article describes the design and characterization of a fully differential <inline-formula> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula>-band low noise amplifier (LNA) with a measured gain of 9.0–18.0 dB at 153.5 GHz over a 3-dB bandwidth of 10.8 GHz. A minimum noise figure (NF) of 7.9 dB is achieved, measured with the cold-noise source method using a source tuner for noise parameter extraction to enable high accurate NF measurements. The extracted noise parameters allow the source impedance sensitivity to be calculated, with an NF degradation of less than 1.2 dB demonstrated for a source reflection <inline-formula> <tex-math notation="LaTeX">$|\Gamma _{s}|\leq 0.3$ </tex-math></inline-formula>. Using the back gate control of the 22-nm fully depleted silicon on insulator (FDSOI) technology enables a passive gain control range of 9 dB, reducing the NF and compression point degradation while scaling the power consumption, thanks to optimal transistor biasing. The measured LNA parameters such as gain, NF, compression, and bandwidth are applied in a link budget analyzer to verify sufficient signal-to-noise and distortion ratio (SNDR) for a 100-GBit/s receiver.
Cooperative Multi-Rigid-Body Localization in Wireless Sensor Networks Using Range and Doppler Measurements

This article addresses multirigid-body localization problems in 3-D wireless sensor networks for both stationary and moving cases using range and Doppler measurements. The challenge of stationary (moving) rigid bodies localization is that not only the position (velocity) but also the rotation angles (angular velocity) need to be estimated, resulting in a nonlinear optimization problem with nonlinear constraints. Existing methods are limited to the localization of a single rigid body in a regular network and are difficult to extend to cooperative multirigid-body localization scenarios. For the stationary case, we first reformulate the cooperative localization problem as a nonconvex and smooth optimization problem with respect to two uncoupled blocks of unknown parameters and auxiliary variables. Next we propose an alternating minimization (AM)-based algorithm, which achieves localization in the absence of accurate prior information by setting the value of one block to be a minimizer of the objective with respect to the chosen block alternately. Subsequently, we propose an online updating algorithm that achieves precise localization by solving the maximum likelihood estimation problem with nonlinear constraints using the Gauss–Newton method on the orthogonal group. Both range and Doppler measurements are used to solve the localization problem in the moving case. Simulation results show that the proposed algorithms achieve better estimation accuracy and anti-noise performance than existing methods for both the stationary and moving cases, and they are suitable for both regular and irregular networks.