Miracle-dz/UniFAR-FTUs huggingface dataset:
Responding to the COVID‐19 public health emergency: The usefulness of an online brief psychological intervention with Italian university students

Abstract The COVID‐19 pandemic has brought about new stressors on university students, with a negative impact on their mental health and well‐being. The purpose of this study was to examine the usefulness of a brief psychodynamically oriented intervention on general functioning by investigating changes in symptoms of depression, anxiety, hopelessness, and burnout. The sample was comprised of 67 university students (22.4% males), with a mean age of 23.27 (standard deviation (SD) = 3.27), who asked for psychological help at a psychological university service. Pre‐ and posttreatment data showed a significant improvement in general functioning and symptom reduction due to the psychological intervention. The mean change was very high for depression (d = 1.11) and high for general functioning (d = 0.70) and anxiety (d = 0.69). Our findings showed the importance of considering university students a vulnerable population that requires specific services within the university context and underlined the fact that mental‐health‐promotion policies should be extensively implemented.
Slot Filling for Voice Assistants

Voice Assistant applications are increasing in popularity and getting deployment in industrial and daily life tasks. In Voice Assistant and Dialog Systems applications, the task of Slot Filling constitutes a major design problem. The voice assistant, tasked with executing a designated job, needs to extract the necessary parameters from the spoken query input by the user. In this study, we use Turkish and English datasets to attack Slot Filling problem with Machine Learning and Deep Learning algorithms. We perform Slot Filling with Conditional Random Fields and compare its performance with recently popularized BERT-like Transformer-based pre-trained language models. We make use of Precision, Recall and F1 Score metrics in model evaluation. Experimental results show that Slot Filling performance of Transformer-based pre-trained language models exceeds the performance of conventional Conditional Random Fields. Furthermore, we observe that multilingual and cross-lingual pretrained language models outperform the models that are pretrained only on the target language. It is expected that the deployed methods and obtained results would contribute to the Dialog Systems and Voice Assistant technologies.
Millimeter-Wave High Q-factor Sixteenth Mode SIW Cavity Resonator Implemented in 0.18-µm CMOS Technology

Millimeter-wave miniaturized Substrate Integrated Waveguide (SIW) cavity resonator is proposed in 0.18-µm CMOS technology. Miniaturization of the SIW cavity resonator is achieved first by taking 1/16thparts of the standard SIW and employing folded ridge structure. Later this folded ridge sixteenth mode SIW cavity is inductively loaded by a complementary split-ring resonator, which provides internal matching within the cavity resonator. This improved matching enhances the return loss, the external Q-factor, and ultimately, the loaded Q-factor of the proposed cavity resonator. At 60 GHz resonance frequency, the fabricated prototype of the proposed cavity resonator presents a measured reflection coefficient of −33.42 dB, with the external Q- factor 112. Moreover, the active area of the fabricated cavity without measuring pads is only 0.019 mm2.
Advanced microwave equipment simulator for engineering education

Abstract A novel approach to computer simulator development of microwave measurement equipment is presented. The main feature of the software is an interface of the simulator, which is an interactive image of the real equipment allowing to manipulate the keys and other elements of the front panel by the computer mouse. For the measurement procedure, the simulator allows to use analytical formulas or upload the data from the computer models of the microwave devices and from the real measurements in *. s2p format. Implementation of the developed simulator into the educational process provides each student with an unlimited access to the measuring equipment and gives the opportunity not only to study the characteristics of microwave devices but also to gain experience with the real equipment due to interactive computer interface that is completely identical to real front panel.
Enhanced Multi-Scale Feature Cross-Fusion Network for Impedance–Optical Dual-Modal Imaging

The intrinsic issue of low spatial resolution of electrical impedance tomography (EIT) is a long-standing challenge that hinders the capability of performing quantitative analysis based on EIT image. Our recent work demonstrates an impedance–optical dual-modal imaging framework and a deep learning model named multi-scale feature cross-fusion network (MSFCF-Net) to realize information fusion and high-quality EIT image reconstruction. However, this framework’s performance is limited by the accuracy of the mask image obtained from an auxiliary imaging modality. This article further proposes a two-stage deep neural network, which is the enhanced version of MSFCF-Net (named En-MSFCF-Net), to automatically improve the mask image and conduct information fusion and image reconstruction. Compared with MSFCF-Net, En-MSFCF-Net demonstrates the superior ability to correct the inaccurate mask image, leading to a more accurate conductivity estimation. Furthermore, En-MSFCF-Net also maintains the best shape preservation and conductivity prediction accuracy among given learning-based and model-based algorithms. Both the qualitative and quantitative results indicate that En-MSFCF-Net could make dual-modal imaging more robust in real-world situations.
Molecular Cloning and Functional Analysis of IrUGT86A1-like Gene in Medicinal Plant Isodon rubescens (Hemsl.) Hara

The synthesis of secondary metabolites in plants often includes glycosylation modifications. Often, the final step of constructing plant secondary metabolites is completed by glycosylation transferases, which are also involved in many cell processes. In this study, a UDP-glycosyltransferase gene (UGT) was amplified from Isodon rubescens (Hemsl.) Hara with RT-PCR and named IrUGT86A1-like (GenBank: MZ913258). Here, we found that IrUGT86A1-like gene is 1450 bp in length and encodes for 479 amino acids. Bioinformatics analysis revealed that IrUGT86A1-like is a stable and hydrophilic protein, located in the cytoplasm with a transmembrane domain. Phylogenetic analysis showed that IrUGT86A1-like protein has the closest genetic relationship with the UDP-glycosyltransferase 86A1-like protein (XP_042054241.1) of Salvia splendens. RT-qPCR analysis demonstrated that the expression of IrUGT86A1-like gene varied in different tissues; leaves had the highest expression followed by flowers, stems, and roots had the lowest expression. This expression trend is similar to the distribution of oridonin content in different tissues of I. rubescens. Additionally, IrUGT86A1-like gene was found to be positively enhanced by NaCl and MeJA treatment, and in contrast was down-regulated by ABA treatment. Finally, the prokaryotic expression vector pEASY®-Blunt E1-IrUGT86A1 was successfully used to express about 53 KD of IrUGT86A1-like protein. This research builds a foundation for further investigation on the function of this gene in the synthesis and modification of secondary metabolites.
On Robustness Properties of Support Vector Machines for Heavy-Tailed Distributions

Some results on consistency and robustness of support vector machines for both nonparametric classification and nonparametric regression were recently derived for non-negative convex loss functions L of Nemitski type, see Christmann and Steinwart (2007) and Steinwart and Christmann (2008), if a weak moment condition for the joint distribution P on X × Y is valid. However, this condition excludes heavy-tailed distributions such as the Cauchy distribution or several extreme value distributions which can occur in financial or actuarial problems. In this talk we will weaken this condition on P to only a condition on the marginal distribution PX , such that heavy-tailed conditional distributions of Y given X = x are also covered. As was already used by Huber (1967), the idea is to shift the loss function L : X × Y × R → [0,∞) by some function which is independent of the estimator. More precisely, we define a new loss function L(x, y, f(x)) := L(x, y, f(x))− L(x, y, 0). It is obvious that the “loss” L(x, y, f(x)) can be negative. Let fL,P,λ be the decision function of the original support vector machine. We define fL? ,P,λ = arg inf f∈H EPL(X, Y, f(X)) + λ‖f‖H , where H is the reproducing kernel Hilbert space of a measurable kernel k : X ×X → R and λ > 0 is some regularization parameter. We will first discuss the used “L-trick”, cite some properties of the new loss L, and show that fL? ,P,λ exists and is unique. Furthermore, this function will coincide with fL,P,λ if the latter exists. We will additionally show that fL? ,P,λ is robust in the sense of influence functions if the kernel is bounded and if the loss function L is Lipschitz continuous. This result holds true for Hampel’s influence function and also for the Bouligand influence function proposed by Christmann and Van Messem (2008).
Lung Cancer Recurrence Risk Prediction through Integrated Deep Learning Evaluation

Simple Summary Few significant advances have been made over recent decades in predicting lung cancer progression risk after complete surgical removal of tumor in stage IA non-small-cell lung cancers (NSCLCs). Although several biomarkers have shown some predictive value, it is unclear whether these markers add value to traditional TNM staging. We developed an integrated deep learning evaluation (IDLE) score to combine patient’s preoperative lung CT image findings and postoperative pathologic assessment and found that this score can better predict cancer progression risk than TNM staging and tumor grade. Improved predictive value of the IDLE score was primarily due to the complementary use of tumor measurements in CT images from an entire lung as well as microscopic tissue characteristics. Our findings suggest that integrating measurements from different aspects of tumor morphology is more robust for increasing prediction accuracy than building on the measurements of similar aspects of tumor morphology. Abstract Background: Prognostic risk factors for completely resected stage IA non-small-cell lung cancers (NSCLCs) have advanced minimally over recent decades. Although several biomarkers have been found to be associated with cancer recurrence, their added value to TNM staging and tumor grade are unclear. Methods: Features of preoperative low-dose CT image and histologic findings of hematoxylin- and eosin-stained tissue sections of resected lung tumor specimens were extracted from 182 stage IA NSCLC patients in the National Lung Screening Trial. These features were combined to predict the risk of tumor recurrence or progression through integrated deep learning evaluation (IDLE). Added values of IDLE to TNM staging and tumor grade in progression risk prediction and risk stratification were evaluated. Results: The 5-year AUC of IDLE was 0.817 ± 0.037 as compared to the AUC = 0.561 ± 0.042 and 0.573 ± 0.044 from the TNM stage and tumor grade, respectively. The IDLE score was significantly associated with cancer recurrence (p < 0.0001) even after adjusting for TNM staging and tumor grade. Synergy between chest CT image markers and histological markers was the driving force of the deep learning algorithm to produce a stronger prognostic predictor. Conclusions: Integrating markers from preoperative CT images and pathologist’s readings of resected lung specimens through deep learning can improve risk stratification of stage 1A NSCLC patients over TNM staging and tumor grade alone. Our study suggests that combining markers from nonoverlapping platforms can increase the cancer risk prediction accuracy.
Endoscope-Guided Marble Foreign Body Removal Technique in an Inland Bearded Dragon (Pogona vitticeps)

Abstract A 1-yr-old intact female inland bearded dragon (Pogona vitticeps) was presented approximately 24 h after observed ingestion of a marble. Orthogonal whole-body radiographs with and without contrast revealed a spherical radiopaque foreign body within the gastric lumen and no passage of contrast material beyond the foreign body, suggestive of pyloric outflow obstruction. The complete blood count showed mild basophilia, and plasma biochemical analysis revealed a moderately elevated creatine kinase. The bearded dragon was placed under general anesthesia, and a rigid endoscope was used to visualize the foreign body at the level of the pylorus. The marble foreign body was suctioned and removed orally using a red rubber catheter and a Luer-lock syringe. The patient recovered uneventfully from the procedure and was sent home with sucralfate and omeprazole after 2 days of hospitalization. This report provides a novel, nonsurgical method for removing smooth spherical foreign bodies from the proximal gastrointestinal tract of bearded dragons. The technique may apply to various small-to-medium–sized reptiles or amphibians that present with spherical foreign bodies.
A Mini-Review Regarding the Modalities to Study Neurodevelopmental Disorders-Like Impairments in Zebrafish—Focussing on Neurobehavioural and Psychological Responses

Neurodevelopmental disorders (NDDs) are complex disorders which can be associated with many comorbidities and exhibit multifactorial-dependent phenotypes. An important characteristic is represented by the early onset of the symptoms, during childhood or young adulthood, with a great impact on the socio-cognitive functioning of the affected individuals. Thus, the aim of our review is to describe and to argue the necessity of early developmental stages zebrafish models, focusing on NDDs, especially autism spectrum disorders (ASD) and also on schizophrenia. The utility of the animal models in NDDs or schizophrenia research remains quite controversial. Relevant discussions can be opened regarding the specific characteristics of the animal models and the relationship with the etiologies, physiopathology, and development of these disorders. The zebrafish models behaviors displayed as early as during the pre-hatching embryo stage (locomotor activity prone to repetitive behavior), and post-hatching embryo stage, such as memory, perception, affective-like, and social behaviors can be relevant in ASD and schizophrenia research. The neurophysiological processes impaired in both ASD and schizophrenia are generally highly conserved across all vertebrates. However, the relatively late individual development and conscious social behavior exhibited later in the larval stage are some of the most important limitations of these model animal species.
Low‐intensity transcranial magnetic stimulation promotes the survival and maturation of newborn oligodendrocytes in the adult mouse brain

Neuronal activity is a potent extrinsic regulator of oligodendrocyte generation and central nervous system myelination. Clinically, repetitive transcranial magnetic stimulation (rTMS) is delivered to noninvasively modulate neuronal activity; however, the ability of rTMS to facilitate adaptive myelination has not been explored. By performing cre‐lox lineage tracing, to follow the fate of oligodendrocyte progenitor cells in the adult mouse brain, we determined that low intensity rTMS (LI‐rTMS), administered as an intermittent theta burst stimulation, but not as a continuous theta burst or 10 Hz stimulation, increased the number of newborn oligodendrocytes in the adult mouse cortex. LI‐rTMS did not alter oligodendrogenesis per se, but instead increased cell survival and enhanced myelination. These data suggest that LI‐rTMS can be used to noninvasively promote myelin addition to the brain, which has potential implications for the treatment of demyelinating diseases such as multiple sclerosis.
Tactile Feedback Enabling In-Hand Pivoting and Internal Force Control for Dual-Arm Cooperative Object Carrying

The main purpose of this letter is to demonstrate that smart exploitation of force/tactile feedback can enable successful physical cooperation of two robot manipulators to handle a common object with a high degree of dexterity. The novelty of the letter is that dexterity is provided not only by the degrees of freedom of the robot arms but also by the grasp controller of the sensorized parallel grippers, which allow the robots to manipulate the object either with a tight grasp or with a one-degree-of-freedom rolling contact. The coordinated motion of the robots depends on both the desired motion of the carried object and the control of the internal forces during transportation and in-hand manipulation. The solution exploits only kinematic models of the robots and a dynamic model of the distributed soft contact, which includes both linear force and torsional moment.
ToD4IR: A Humanised Task-Oriented Dialogue System for Industrial Robots

Despite the fact that task-oriented conversation systems have received much attention from the dialogue research community, only a handful of them have been studied in a real-world manufacturing context using industrial robots. One stumbling block is the lack of a domain-specific discourse corpus for training these systems. Another difficulty is that earlier attempts to integrate natural language interfaces (such as chatbots) into the industrial sector have primarily focused on task completion rates. When designing a dialogue system for social robots, the user experience is prioritized above industrial robots. To overcome these challenges, we provide the Industrial Robots Domain Wizard-of-Oz dataset (IRWoZ), a fully-labeled discourse dataset covering four robotics domains. It delivers simulated discussions between shop floor workers and industrial robots, with over 401 dialogues, to promote language-assisted Human-Robot Interaction (HRI) in industrial settings. Small talk concepts and human-to-human conversation strategies are provided to support human-like answer generation and give a more natural and adaptable dialogue environment to increase user experience and engagement. Finally, we propose and evaluate an end-to-end Task-oriented Dialogue for Industrial Robots (ToD4IR) using two types of pre-trained backbone models: GPT-2 and GPT-Neo, on the IRWoZ dataset. We performed a series of trials to validate ToD4IR’s performance in a real manufacturing context. Our experiments demonstrate that ToD4IR outperforms three downstream task-oriented dialogue tasks, i.e., dialogue state tracking, dialogue act generation, and response generation, on the IRWoZ dataset. Our source code of ToD4IR and the IRWoZ dataset is accessible at https://github.com/lcroy/ToD4IR for reproducible research.
Active optics upgrade using curvature WFS of VISTA for 4MOST

The 4-meter wide-field telescope VISTA will be upgraded with the 4MOST fiber-fed spectrograph. The future field diameter will be 2.5° (before 1.65°) with fast optics of F/3.3, therefore the mechanical alignment tolerances are tight. ESO is currently refurbishing the active optics system based on curvature wavefront sensing, using four technical cameras defocused by ±1.2 mm that image four different stars. We describe the algorithm that computes the telescope misalignment using least-squares fitting, the ESO-developed package Sensitizer that generates sensitivity matrices and we discuss maximum likelihood methods.
Silicon Micromachined Metrology Components for 0.5-1.1 THz

We present the development and experimen-tal characterisation of silicon micromachined components for waveguide metrology from 0.5-1.1 THz. Silicon micromachining offers high precision and uniformity, while the mechanical properties of silicon allow for the creation of extremely thin layers. We harness these capabilities to create waveguide lines for the WM-380 (0.5-0.75 THz) and WM-250 (0.75-1.1 THz) bands in thicknesses as low as 70 µm, The development of such components enables new solutions in the field of THz metrology due to their high precision, repeatability and scaleable manu-facturing. The experimental performance of the components is first characterised using commerical vector network analyzer calibration standards. The micromachined components are then used to implement Through-Reflect-Line calibrations in both bands, which are used to error correct multiple devices. The micromachined lines offer return losses as high as 40 dB, while their expanded phase uncertainty is as low as 2°.
A Spring-Based Inductive Sensor for Soft and Flexible Robots

The development of flexible and soft robots generates new needs in terms of instrumentation, as large encountered deformations require highly stretchable strain sensors. In this regard, we contribute to the adoption of inductive sensors by providing tools to model and exploit them and showing their relevance experimentally. First, strain estimation based on voltage measurement is proposed. Compared to direct inductance evaluation, the principle is easier to implement and opens the possibility to optimize the measurement performances by tuning the circuit components and interrogation frequency. The possibility of performing a single sensor calibration independently via the elongation mode during strain sensing is outlined. A detailed characterization is then performed, which shows that the sensor produces a low hysteresis of 0.1%, a precision in the order of 0.14%, and an accuracy of 0.9%. Finally, two proofs of concept are proposed: 1) the integration with a pneumatic artificial muscle (PAM) that demonstrates the added value of the sensor for a model-free precise control of a soft system and 2) the closed-loop control of a flexible bending manipulator using the inductive sensor. The performance in the closed-loop control is demonstrated, with a sensing element that is easy to integrate mechanically, strengthening its potential to be used as a structural element as well.
Graphical Evolutionary Game Theoretic Modeling of Strategy Evolution Over Heterogeneous Networks

The rapid development of the Internet and networking technologies greatly facilitates the interactions among heterogeneous agents, while it also causes problems, such as the breakout of rumors and online bullying. Thus, it is critical to study the strategy evolution process in complex networks, that is, how heterogeneous agents interact with each other, update their opinions, and make decisions. In the literature, there have been numerous works on the modeling and analysis of decision making and opinion dynamics in social networks. However, most works assume that agents are homogeneous or only consider one single attribute in agent heterogeneity. In complex social networks, agents differ in many attributes and they constantly influence each other's decisions. How strategy evolves in complex networks with heterogeneous agents remains unknown. In this work, we consider three different attributes of agents: influence, susceptibility, and interest. We use graphical evolutionary game theory to theoretically analyze the impact of different attributes on the strategy evolution process and the evolutionary stable states (ESS). Both theoretical analysis and simulation results show that the influence attribute alone cannot change the ESS of the strategy evolution, while super agents who are both influential and stubborn have the largest impact on the ESS. Furthermore, real data validation shows that our proposed model can effectively model the information diffusion process in online social networks. This study is critical to the better understanding of agents' decision making process, and provides important guidelines on the management of social networks.