Miracle-dz/UniFAR-FTUs huggingface dataset:
Journal of Experimental

Delayed grati ﬁ cation is an important focus of research, given its potential relationship to forms of behavior, such as savings, susceptibility to addiction, and pro-social behaviors. The COVID-19 pandemic may be one of the most consequential recent examples of this phenomenon, with people ’ s willingness to delay grati ﬁ - cationaffectingtheirwillingnesstosociallydistancethemselves.COVID-19alsoprovidesanaturalisticcon-textbywhichtoevaluatetheecologicalvalidityofdelayedgrati ﬁ cation.Thisarticle outlines four large-scale online experiments (total N = 12, 906) where we ask participants to perform Money Earlier or Later (MEL) decisions (e.g., $5 today vs. $10 tomorrow) and to also report stress measures and pandemic mitigation behaviors. We found that stress increases impulsivity and that less stressed and more patient individuals socially distanced more throughout the pandemic. These results help resolve longstanding theoretical debates in the MEL literature as well as provide policymakers with scienti ﬁ c evidence that can help inform response strategies in the future.
Smart Government Toward an Innovative Concept of a “One-Stop Shop” for Interactive Online Services

—The number of available online services, their effectiveness and usage level and their level of interaction are important indicators of the “smartness” level of e-government. Increasing these indicators has positive effects not only in terms of time and effort spent by citizens and enterprises to access and use the services provided by the administration but also by triggering the optimization process of the back offices of the administration. The Municipality of Trento is launching an innovation project aimed at increasing the number, usage and interaction level of online services with the objective of moving all relevant services and processes managed by the Trento administration online. This approach is based on the innovative concept of a “one-stop shop” for interactive online services, which the Municipality wants to define and implement with the help of the Trento innovation ecosystem; going beyond the classical concept of a unique access point, Trento wants to deliver a single access point to online services that is simple , trusted , complete , connected and open to better serve the innovation needs of this core enabler of the smart city.
Interpolation schemes for geometrically exact beams: A motion approach

This paper focuses on the interpolation of the kinematic fields describing the configuration of geometrically exact beams, namely, the position and rotation fields. Two kinematic representations are investigated: the classical approach that treats the displacement and rotation fields separately and the motion approach that treats those two fields as a unit. The latter approach is found to be more consistent with the kinematic description of beams. Then, two finite element interpolation strategies are presented and contrasted. The first interpolates the displacement and rotation fields separately, whereas the second interpolates both fields as a unit, in a manner consistent with the motion approach. The performance of both strategies is evaluated in light of the fundamental requirements for the convergence of the finite element method: the ability to represent rigid‐body motions and constant strain states. It is shown that the traditional uncoupled interpolation scheme for the position field approximates that based on the motion approach and that the coupling induced by the interpolation of motion yields superior convergence rates for the representation of constant strain states. This property is known to lead to finite elements that are less prone to the locking phenomenon. Copyright © 2017 John Wiley & Sons, Ltd.
Machine learning on multisensor data from airborne remote sensing to monitor plastic litter in oceans and rivers (PlasticObs+)

This paper presents the main ideas and initial findings of the PlasticObs+ project. The long-term goal of the project is to develop an airborne based method for monitoring plastic waste on the water surface. For this project, aircrafts usually applied for oil spill detection are used. Plastic waste detection and analysis is achieved using artificial intelligence (AI) within four different AI-systems. Furthermore, results from field tests were used to determine the limits of detectability of plastic waste from different altitudes. It was shown that both color and size of the items have an influence on the detectability. In addition, the underground plays an important role. A binary classifier, based on a Convolutional Neural Network (CNN) was trained to distinguish between images containing plastic and those not polluted. The accuracy of the CNN was 93.3 % while the accuracy of the labels generated by humans was 92.6 %.
1D and 2D modelling of a chemical process using the FM-II model: Rotary drum filter

This work treats the physical one-dimensional (1D) and two-dimensional (2D) modelling of a chemical process of filtration of slurry, which is the second step of phosphoric acid manufacture. This work focuses on the different physical laws involved in the filtration stage in order to obtain a simulator of the filter. In this work, we are interested in the 1D modelling of a rotary filter using all parameters and physical phenomena established in the filtration phase. Then, a 2D model emerged from the previous model by choosing both a temporal and a spatial variable. These two parameters are quite necessary for the construction of a 2D model based on the method of Fornasini–Marchesini (FM-II) to describe the dynamics of the system.
Thermo-Hygrometric Comfort Analysis in a Real Public Conference Room to Support a Digital-Twin Targeted to Parametric Investigations

In this paper, the efficient use of a building-plant system in terms of thermal comfort conditions for a real conference room was verified in the summer by its digital twin. A DesignBuilder model was calibrated by experimental data concerning the indoor air temperature, average radiant temperature, relative humidity and CO 2 concentration. Different situations for people's well-being were studied by varying emitter typology, control strategies, subjective parameters and internal loads. The EMS tool was used to simulate high-level control strategies. It was found that radiant ceilings in continuous operation could promote situations of undercooling, whereas a predicted plant operation is appropriate with intermittent functioning. People’s metabolism affects comfort conditions more than internal loads, determining an increase of about 3 degrees in indoor air and mean radiant temperature. Inlet temperature variations in fan coils modify comfort conditions marginally. However, these emitters interact worse with internal loads than radiant ceilings. Aside from the Fanger PMV, discomfort indices following EN 15251 were also evaluated. However, in prevision of the implementation of predictive control strategies, the degree hour approach is not recommended because it does not consider clothing resistance properly.
Estimation accuracy under covariate-adaptive randomization procedures

: In this paper we provide some general asymptotic properties of covariate-adaptive (CA) randomized designs aimed at balancing the allocations of two treatments across a set of chosen covariates. In particular, we establish the central limit theorem for a vast class of covariate-adaptive procedures characterized by i) a diﬀerent allocation function for each covariate proﬁle and ii) sequences of allocation rules instead of a pre-ﬁxed one. This result allows one to derive theoretically the asymptotic expressions of the loss of information induced by imbalance and the selection bias due to the lack of randomness, that are the fundamental properties for estimation of every CA rule, widely used in order to compare diﬀerent CA procedures. Besides providing the proofs of unsolved conjectures about some CA designs suggested in the literature, explored up to now almost exclusively through simulations, our results provide substantial insight for future suggestions and represent an accurate tool for the large sample comparisons between CA designs. A numerical study is also performed to assess the validity of the suggested approach.
The medicinal potential of plants from the Adenostemma genus

Adenostemma is a genus in the Asteraceae family consisting of more than 20 species distributed in tropical regions of Asia, Africa, Australia, America, and the Pacific Islands. They have been used in folk medicine as remedies for diseases such as fever, inflammation, edema, digestive disorders, and lung injury. This review aims to provide information about Adenostemma species, the traditional uses of these species, chemical constituents, and therapeutic potential based on scientific evidence. The literature search was conducted through Google Scholar, PubMed, ScienceDirect, and KNApSAcK using several search terms. It was revealed in our studies that Adenostemma plants have various secondary metabolites, including alkaloids, terpenoids, phenolics, flavonoids, fatty acids, saponins, and tannins. Antioxidant, anti-inflammatory, antimelanogenic, antitumor, and antiviral activities have been shown by scientific evidence of its extracts and isolated compounds. Adenostemma species were demonstrated by this investigation to offer a prospective source for the development of new drugs. However, phytochemical and pharmacological data are currently limited and require further studies.
Learnable HMD Facial De-occlusion for VR Applications in a Person-Specific Setting

Immersive technologies such as Virtual Reality (VR) and Augmented Reality (AR) are among the fasted growing and fascinated technology today. As the name suggests, these technologies promise to provide users with a much better experience using immersive head-mounted displays. Medicine, culture, education, and architecture are some areas that have already taken advantage of this technology. Popular video conferencing platforms such as Microsoft Teams, Zoom, and Google Meet is working to improve user experience by allowing users to use their digital avatars. Nevertheless, they lack immersiveness and realism. What if we extend these applications to virtual reality platforms so people can feel lively talking to each other? Integrating virtual reality platforms in collaborative spaces such as virtual telepresence systems have become quite popular after globalization since it enables multiple users to share the same virtual environment, thus mimicking real-life face-to-face interactions. For a better immersive experience in virtual telepresence/communication systems, it is essential to recover the entire face, including the portion masked by the headsets (e.g., Head-Mounted Displays abbreviated as HMDs). Several methods have been proposed in the literature that deal with this problem in various forms, such as HMD removal and face inpainting. Despite some remarkable explorations, none of these methods promises to provide usable results as expected in virtual reality platforms. Addressing these challenges in the real-world deployment of AR/VR-based applications draws emerging attention. Considering the existing limitations and usability of previous solutions, we explore various research challenges and propose a practical approach to facial de-occlusion/HMD removal for virtual telepresence systems. This thesis is well-documented to motivate and introduce the audience to various research challenges in facial de-occlusion, familiarizing them with existing solutions and their inapplicability in our problem domain, followed by the idea and formulation of our proposed solution to tackle the problem. With this view, the first chapter lays the outline of this thesis. In the second chapter, we propose a method for facial de-occlusion and discuss the importance of personalized facial de-occlusion methods in enhancing the sense of realism in virtual environments. The third chapter talks about the refinement of to previously proposed network in improving the reconstruction of the eye region. Last but not least, the final chapter briefly discusses the existing face datasets in face reconstruction and inpainting, followed by an overview of the dataset we collected for this work, from acquisition to making it usable for training deep learning models. In addition, we also attempt to extend image-based facial de-occlusion to video frames using off-the-shelf approaches, briefly explained in Appendix A.
Discovery of Novel Dirac and Weyl Fermion Materials

Discovering and investigating various fermionic quasiparticles with novel topological properties in solids has become an important front in condensed matter physics and materials science. Classical examples of such quasiparticles are the celebrated massless Dirac fermions discovered in topological insulators, which have challenged our fundamental understanding of quantum phases of matter by expanding them to the symmetry-protected topological phases. Following the discovery of topological insulators, the search for materials with Dirac fermions and novel functionalities has become a central theme in the studies of solid state systems. More variants of Dirac materials including topological crystalline insulators and three-dimensional Dirac semimetals have followed. These topological materials have not only introduced novel fundamental physical concepts and properties, but have also provided new routes to developing technological applications in nanoelectronics, spintronics, and topological quantum computing platforms. In this dissertation, we present the experimental discovery of a new topological phase of matter, the Weyl semimetal state, in the monoarsenides TaAs and NbAs. Furthermore, we study various material systems with strong electron interactions, and provide compelling evidence for the existence of robust surface states in samarium hexaboride SmB6, as well as identify a Z2 topological insulator state with intriguing Dirac fermions in the low-carrier strongly-correlated cerium monopnictides CeBi and CeSb. We also investigate the honeycomb iridate Na2IrO3 and uncover linearly dispersing metallic states on its surface. Finally, we image the spin-orbit split valence band and the deposited quantum well states of the monolayer and bulk transition metal dichalcogenides MoS2 and MoSe2. The measurements presented in this dissertation constitute the first realization of emergent Weyl fermions in nature, introduce various directions for future discoveries of topological phases in strongly-correlated materials, and shed light on the spin-orbit iii
Finite-Difference Methods for Second-Order Wave Equations with Reduced Dispersion Errors

Finite-Difference Methods for Second-Order Wave Equations with Reduced Dispersion Errors Yajun An Chair of the Supervisory Committee: Professor Kenneth P. Bube Mathematics Finite Difference (FD) schemes have been used widely in computing approximations for partial differential equations for wave propagation, as they are simple, flexible and robust. However, even for stable and accurate schemes, waves in the numerical schemes can propagate at different wave speeds than in the true medium. This phenomenon is called numerical dispersion error. Traditionally, FD schemes are designed by forcing accuracy conditions, and in spite of the advantages mentioned above, such schemes suffer from numerical dispersion errors. Traditionally, two ways have been used for the purpose of reducing dispersion error: increasing the sampling rate and using higher order accuracy. More recently, Finkelstein and Kastner (2007, 2008) propose a unified methodology for deriving new schemes that can accommodate arbitrary requirements for reduced phase or group velocity dispersion errors, defined over any region in the frequency domain. Such schemes are based on enforcing exact phase or group velocity at certain preset wavenumbers. This method has been shown to reduce dispersion errors at large wavenumbers. In this dissertation, we study the construction and behaviors of FD schemes designed to have reduced numerical dispersion error. We prove that the system of equations to select the coefficients in a centered FD scheme for second order wave equations with specified order of accuracy and exact phase velocity at preset wavenumbers can always be solved. Furthermore, from the existence of such schemes, we can show that schemes which reduce the dispersion error uniformly in an interval of the frequency domain can be constructed from a Remez algorithm. In these new schemes we propose, we can also specify wavenumbers where the exact phase or group dispersion relation can be satisfied. For an incoming signal consisting of waves of different wavenumbers, our schemes can give more accurate wave propagation speeds. Furthermore, when we apply our schemes in two dimensional media, we can obtain schemes that give small dispersion error at all propagation angles.
Dynamical thermal metamaterial response at terahertz frequencies

ABSTRACT Utilizing terahertz time domain spectroscopy, we have characterized the electromagnetic response of a planar array of complementary square metal frames fabricated upon a high resistivity Si substrate, and between the two layers existed an important ferroelectric layer strontium titanate(STO). The resonance frequency dependent magnetic and electric resonances are in excellent agreement with theory. We demonstrate, it has two distinctive reflection dips through reconfigures the metamaterial by etched a rectangular stripe, and the former metamaterial has only one resonance frequency. Cooling the metamaterial from 400 K to 200 K causes excellent blue shift. The resonance shift is owing to the variation of the dielectric constant of the low-temperature co-fired ceramic (LTCC) substrate. Because of the universality of metamaterial response over many decades of frequency, these results have implications for other regions of the electromagnetic spectrum.
Development of a circHIPK3-based ceRNA network and identification of mRNA signature in breast cancer patients harboring BRCA mutation

Background Exploring the regulatory network of competing endogenous RNAs (ceRNAs) as hallmarks for breast cancer development has great significance and could provide therapeutic targets. An mRNA signature predictive of prognosis and therapy response in BRCA carriers was developed according to circular RNA homeodomain-interacting protein kinase 3 (circHIPK3)-based ceRNA network. Method We constructed a circHIPK3-based ceRNA network based on GSE173766 dataset and identified potential mRNAs that were associated with BRCA mutation patients within this ceRNA network. A total of 11 prognostic mRNAs and a risk model were identified and developed by univariate Cox regression analysis and the LASSO regression analysis as well as stepAIC method. Genomic landscape was treated by mutect2 and fisher. Immune characteristics was analyzed by ESTIMATE, MCP-counter. TIDE analysis was conducted to predict immunotherapy. The clinical treatment outcomes of BRCA mutation patients were assessed using a nomogram. The proliferation, migration and invasion in breast cancer cell lines were examined using CCK8 assay and transwell assay. Result We found 241 mRNAs within the circHIPK3-based ceRNA network. An 11 mRNA-based signature was identified for prognostic model construction. High risk patients exhibited dismal prognosis, low response to immunotherapy, less immune cell infiltration and tumor mutation burden (TMB). High-risk patients were sensitive to six anti-tumor drugs, while low-risk patient were sensitive to 47 drugs. The risk score was the most effective on evaluating patients’ survival. The robustness and good prediction performance were validated in The Cancer Genome Atlas (TCGA) dataset and immunotherapy datasets, respectively. In addition, circHIPK3 mRNA level was upregulated, and promoted cell viability, migration and invasion in breast cancer cell lines. Conclusion The current study could improve the understanding of mRNAs in relation to BRCA mutation and pave the way to develop mRNA-based therapeutic targets for breast cancer patients with BRCA mutation.
ISLR interacts with MGAT5 to promote the malignant progression of human gastric cancer AGS cells

Objective(s): Gastric cancer is a common malignant tumor with high morbidity and mortality. The present study aimed to investigate the role of the immunoglobulin superfamily containing leucine-rich repeat (ISLR) gene in gastric cancer and examine whether ISLR could interact with N-acetylglucosaminyltransferase V (MGAT5) to affect the malignant progression of gastric cancer. Materials and Methods: The expression of ISLR and MGAT5 in human normal gastric epithelial cells and human gastric cancer cells, and the transfection efficiency of ISLR interference plasmids and MGAT5 overexpression plasmids were all detected by reverse transcription-quantitative PCR (RT-qPCR) and western blot. The viability, proliferation, migration and invasion, and epithelial-mesenchymal transition (EMT) of gastric cancer cells after indicated transfection were detected by Cell counting kit-8 (CCK-8) assay, 5-ethynyl-2’-deoxyuridine (EdU) staining, wound healing assay, and transwell assay. The interaction between ISLR and MGAT5 was confirmed by co-immunoprecipitation. The expression of proteins related to migration, invasion, and EMT was detected by immunofluorescence and western blot. Results: As a result, ISLR was highly expressed in gastric cancer and was associated with poor prognosis. Interference with ISLR inhibited the viability, proliferation, migration, invasion, and EMT of gastric cancer cells. ISLR interacted with MGAT5 in gastric cancer cells. MGAT5 overexpression weakened the effects of ISLR knockdown on suppressing the viability, proliferation, migration, invasion, and EMT of gastric cancer cells. Conclusion: ISLR interacted with MGAT5 to promote the malignant progression of gastric cancer.
NEK6 Promotes the Progression of Osteosarcoma Through Activating STAT3 Signaling Pathway by Down-Regulation of miR-26a-5p

Background Osteosarcoma is a malignant tumor originating from the skeletal system. There is no effective treatment other than surgery and chemotherapy, which seriously endangers the health of children and adolescents. NEK6 is a novel discovered Serine/Threonine protein kinase that can regulate cell cycle and activate several oncogenic pathways. Methods NEK6 expression in pan-cancer including sarcoma was evaluated using analysis tools of TIMER, UALCNA and GEPIA with TCGA database, and its association with overall survival in patients with sarcoma was also analyzed. TargetScan, tarbase, microT-CDS and Starbase online software were used to predict NEK6-targeted miRNAs, including miR-26a-5p. Tumor tissues from patients with osteosarcoma were collected for NEK6 and miRNA detection using RT-qPCR. NEK6 down-regulated by siRNAs or miR-26a-5p in osteosarcoma cells was detected by RT-qPCR, Western blot and Immunofluorescence staining assays. Effects of NEK6 knockdown on proliferation, migration, invasion and apoptosis of osteosarcoma cells were detected by CCK-8, wound healing, transwell and flow cytometry, respectively. The expressions of STAT3, metastasis and apoptosis-related genes were detected by Western blot. Results High expression of NEK6 and low expression of miR-26a-5p were lowly expressed in osteosarcoma and they were negative correlation. NEK6 has been confirmed as a direct target for miR-26a-5p. In addition, NEK6 down-regulated by siRNAs or miR-26a-5p led to inhibition of cell proliferation, migration and invasion while promoting cell apoptosis. The levels of phosphorylated STAT3 and metastasis genes (MMP-2, MMP-9) were inhibited, while apoptotic gene Bax was promoted and Bcl2 was inhibited by miR-26a-5p upregulation. Conclusion NEK6 can promote osteosarcoma progression via activating STAT3 signaling pathway, which is inhibited by miR-26a-5p, suggesting that NEK6 is a potential oncogene and miR-26a-5p is a suppressor of osteosarcoma. The strategy of inhibiting of NEK6 by miR-26a-5p may be an effective approach for osteosarcoma therapy.