Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk Parity strategy for portfolio construction: a kurtosis-based approach Risk Parity strategy per la costruzione di un portafoglio: un approccio basato sulla curtosi

Investors interested in the homogeneous distribution of responsibility for the dispersion of portfolio returns can pursue not only the equal constituents’ contribution to portfolio volatility as, alternatively, they can focus on portfolio’s extreme outcomes (either positive or negative). This leads to the proposal of a kurtosis-based risk parity strategy (KRP) that is a new risk parity strategy based on portfolio kurtosis as reference measure. In this paper, the portfolio weights, obtained with the KRP strategy. are compared with those estimated following the traditional standard deviation-based risk parity strategy. An analysis of KRP strategy performance is carried out through an out-of-sample study which takes advantage of real data from a global equity investment universe. Abstract Gli investitori, interessati a una distribuzione omogenea del rischio tra i vari asset di portafoglio, possono concentrarsi non solo sull’equa contribuzione dei costituenti alla volatilità di portafoglio poiché, in alternativa, essi si possono focalizzare sui risultati estremi di portafoglio (sia positivi che negativi) prendendo in considerazione la curtosi di portafoglio. In questo contesto, si sviluppa una strategia di parità di rischio basata sulla curtosi (strategia KRP) di portafoglio quale misura di rischio anziché sulla sua volatilità. I pesi per la costruzione del portafoglio ottenuti con la strategia KRP sono messi a confrontati con quelli ottenuti con la tradizionale strategia di parità di rischio basata sulla volatilità. Le performance della strategia KRP sono studiate con un approccio out-of-sample basato su dati reali di un universo di investimento azionario globale. Maria Debora Braga SDA Bocconi School of Management, Via Roberto Sarfatti 10, Milano, e-mail: mariadebora.braga@sdabocconi.it Consuelo Rubina Nava Università degli Studi di Torino, Lungo Dora Siena 100, Torino, e-mail: consuelorubina.nava@unito.it Maria Grazia Zoia Università Cattolica del Sacro Cuore di Milano, Largo Gemelli 1, Milano e-mail: maria.zoia@unicatt.it
A tour through the zoo of scented widgets

Software developers can choose among many popular widget toolkits to build graphical user interfaces quickly. Widgets like sliders, buttons and scrollbars are widespread and well-understood by the public. They allow users to interact with the software, to trigger operations or to set parameters. It is also recognized that widgets could be enhanced to provide a better user experience. Embedded visualizations, also known as scents, could be added to standard widgets to enhance their capabilities. In this work, we survey the literature concerning scented widgets. We propose a two-level taxonomy structured around four main dimensions: Purpose, Widget Type, Number of Scents and Scent Type, with examples of scented widgets. We also discuss our current thoughts about the scope of this survey, and call for feedback to further improve and extend the proposed taxonomy.
Plant Geometry Reconstruction From Field Data Using Neural Radiance Fields

Real-time simulations of large-scale farming operations would provide farmers with data-driven and physics-consistent decision support. These real-time farming simulations could be accomplished using predictive digital twins. Predictive digital twins of biological entities allow for a virtual simulation of real-life processes for various environmental conditions, thus paving the way for a comprehensive understanding of various biological responses. One of the first steps in constructing a predictive digital twin is the 3D reconstruction of plant geometry. While traditional approaches for the reconstruction of plant geometry exist, they require a very expensive setup using a LIDAR or destructive imaging of the plant in a controlled environment. Neural approaches for 3D scene reconstruction have alleviated the data collection burden associated with traditional 3D reconstruction methods. In this work, we demonstrate the ability to generate a 3D reconstruction (mesh) of a maize plant by leveraging a recent work in 3D computer vision, Neural Radiance Fields (NeRFs), which uses data collected from a mobile phone camera. Our approach aims to generate high-resolution geometric models for several downstream tasks, such as developing a predictive digital twin.
Association between pregnant women with rheumatoid arthritis and preeclampsia: A systematic review and meta-analysis

Background: To examine the association between pregnant women with rheumatoid arthritis (RA) and the risk of preeclampsia. Methods: This study was registered on the International Prospective Register of Systematic Reviews (PROSPERO) under the number CRD42022361571. The primary outcome was preeclampsia. Two evaluators independently reviewed the included studies, assessed their risk of bias, and extracted the data. Unadjusted and adjusted ratios with 95% confidence intervals and 95% prediction intervals were calculated. Heterogeneity was quantified using the І2 statistic, where І2 ≥ 50% indicated the presence of significant heterogeneity. Subgroup and sensitivity analyses were performed to test the robustness of the overall findings. Results: A total of 8 studies, including 10,951,184 pregnant women, of whom 13,333 were diagnosed with RA, met the inclusion criteria. Meta-analysis revealed that pregnant women with RA were significantly more likely to develop preeclampsia than those without RA (pooled odds ratio, 1.66; 95% confidence interval, 1.52–1.80; P < .001; І2 < .001). Conclusion: RA during pregnancy is associated with higher odds of preeclampsia.
KRongBERT: Enhanced Factorization-based Morphological Approach for the Korean Pretrained Language Model

The bidirectional encoder representations 001 from transformers ( BERT ) model has achieved 002 remarkable success in various natural lan-003 guage processing tasks for languages based on 004 the Latin alphabet. However, the Korean lan-005 guage, characterized by limited data resources 006 and intricate linguistic structures, presents sub-007 stantial challenges and constraints. In this 008 paper, we introduce KRongBERT, a morpho-009 logical approach tailored to effectively un-010 derstand and analyze the unique features of 011 the Korean language. The KRongBERT ap-012 proach mitigates the out-of-vocabulary issues 013 that arise with byte-pair-encoding tokenizers 014 in Korean and provides language-speciﬁc em-015 bedding layers to support in-depth understand-016 ing. The experiments reveal that KRongBERT 017 outperforms the existing Korean text encoders. 018 We release our code at https://anonymous. 019
Mesenchymal Stem Cells and Exosomes: A Novel Therapeutic Approach for Corneal Diseases

The cornea, with its delicate structure, is vulnerable to damage from physical, chemical, and genetic factors. Corneal transplantation, including penetrating and lamellar keratoplasties, can restore the functions of the cornea in cases of severe damage. However, the process of corneal transplantation presents considerable obstacles, including a shortage of available donors, the risk of severe graft rejection, and potentially life-threatening complications. Over the past few decades, mesenchymal stem cell (MSC) therapy has become a novel alternative approach to corneal regeneration. Numerous studies have demonstrated the potential of MSCs to differentiate into different corneal cell types, such as keratocytes, epithelial cells, and endothelial cells. MSCs are considered a suitable candidate for corneal regeneration because of their promising therapeutic perspective and beneficial properties. MSCs compromise unique immunomodulation, anti-angiogenesis, and anti-inflammatory properties and secrete various growth factors, thus promoting corneal reconstruction. These effects in corneal engineering are mediated by MSCs differentiating into different lineages and paracrine action via exosomes. Early studies have proven the roles of MSC-derived exosomes in corneal regeneration by reducing inflammation, inhibiting neovascularization, and angiogenesis, and by promoting cell proliferation. This review highlights the contribution of MSCs and MSC-derived exosomes, their current usage status to overcome corneal disease, and their potential to restore different corneal layers as novel therapeutic agents. It also discusses feasible future possibilities, applications, challenges, and opportunities for future research in this field.
Recombinant Human CD19 in CHO-K1 Cells: Glycosylation Patterns as a Quality Attribute of High Yield Processes

CD19 is an essential protein in personalized CD19-targeting chimeric antigen receptor (CAR)-T cell-based cancer immunotherapies and CAR-T cell functionality evaluation. However, the recombinant expression of this “difficult to-express” (DTE) protein is challenging, and therefore, commercial access to the protein is limited. We have previously described the successful stable expression of our soluble CD19-AD2 fusion protein of the CD19 extracellular part fused with human serum albumin domain 2 (AD2) in CHO-K1 cells. The function, stability, and secretion rate of DTE proteins can be improved by culture conditions, such as reduced temperature and a shorter residence time. Moreover, glycosylation, as one of the most important post-translational modifications, represents a critical quality attribute potentially affecting CAR-T cell effector function and thus impacting therapy’s success. In this study, we increased the production rate of CD19-AD2 by 3.5-fold through applying hypothermic culture conditions. We efficiently improved the purification of our his-tagged CD19-AD2 fusion protein via a Ni-NTA-based affinity column using a stepwise increase in the imidazole concentration. The binding affinity to commercially available anti-CD19 antibodies was evaluated via Bio-Layer Interferometry (BLI). Furthermore, we revealed glycosylation patterns via Electrospray Ionization Mass Spectrometry (ESI–MS), and five highly sialylated and multi-antennary N-glycosylation sites were identified. In summary, we optimized the CD19-AD2 production and purification process and were the first to characterize five highly complex N-glycosylation sites.
On The Performance Of Sensorless Vector Controlled Induction Motor Drives With Parallel Stator Resistance And Rotor Speed Estimation

The main goal of this paper is to provide a novel method of parallel stator resistance and rotor speed estimation for sensorless indirect field oriented controlled induction motor drive. In this scheme, a parallel model reference adaptive system (MRAS) is designed to simultaneous estimation of rotor speed and stator resistance in order to achieve high-precise control in a wide range of motor speed. Moreover, the stability of the control scheme has been derived based on Popov's criterion. Matlab/Simulink software has been carried out in order to evaluate the effectiveness of the proposed scheme at different operating conditions. The results demonstrate the activity of the scheme at wide range speed operation with load disturbance and parameters variation. Keywords—Model Reference Adaptive System, MRAS, Induction Motor, Indirect field oriented control, Vector control. Speed Estimation, Stator Resistance Estimation.
The Characterization and Pathogenicity of a Recombinant Porcine Epidemic Diarrhea Virus Variant ECQ1

Porcine epidemic diarrhea virus (PEDV), a re-emerging enteropathogenic coronavirus, has become the predominant causative agent of lethal diarrhea in piglets, resulting in huge economic losses in many countries. Furthermore, the rapid variability of this virus has increased the emergence of novel variants with different pathogenicities. In this study, 633 fecal samples collected from diarrheic piglets in China during 2017–2019 were analyzed, and 50.08% (317/633) of these samples were PEDV-positive. The full-length spike (S) genes of 36 samples were sequenced, and a genetic evolution analysis was performed. The results showed that thirty S genes belonged to the GII-a genotype and six S genes belonged to the GII-b genotype. From the PEDV-positive samples, one strain, designated ECQ1, was successfully isolated, and its full-length genome sequence was determined. Interestingly, ECQ1 is a recombinant PEDV between the GII-a (major parent) and GII-b (minor parent) strains, with recombination occurring in the S2 domain of the S gene. The pathogenicity of ECQ1 was assessed in 5-day-old piglets and compared with that of the strain EHuB2, a representative of GII-a PEDV. Although both PEDV strains induced similar fecal viral shedding in the infected piglets, ECQ1 exhibited lower pathogenicity than did EHuB2, as evidenced by reduced mortality and less severe pathological changes in the intestines. These data suggest that PEDV strain ECQ1 is a potential live virus vaccine candidate against porcine epidemic diarrhea.
A Cold-Inducible RNA Binding Protein Gene from Acanthopagrus schlegelii: Molecular Characterization, Expression, and Association with Apoptosis to Low-Temperature Stress

In this study, the complete cDNA sequence (1552 bp) of the cold-inducible RNA binding protein gene (cirbp gene) was successfully cloned from the liver in Acanthopagrus schlegelii (initial weight: 15.0 ± 2.3 g). Results showed that Ascirbp (cirbp gene from A. schlegelii) gene has 24 phosphorylation sites, no signal peptide, and no transmembrane helix structure. AsCIRBP, with a molecular weight of 18.84 ku and an isoelectric point of 9.04 was a stable protein that encodes 182 amino acids. Subcellular localization analysis of this protein showed that it was located in the nucleus. Sequence alignment results showed that the AsCIRBP amino acid sequences of various fishes including black porgy were highly conserved, especially the RNA recognition motif (RRM). Those results of real-time quantitative PCR (qRT-PCR) demonstrated that Ascirbp gene was specifically expressed in the liver tissue of black porgy and its expression was significantly increased under cold stress or cold acclimation. The RNA interference experiment results showed that Ascirbp-dsRNA could suppress the expression of Ascirbp gene in the liver of black porgy through intraperitoneal injection. After silencing the expression of Ascirbp gene, RNAi groups were more severely damaged in the structure of the liver tissue and more prone to apoptosis under cold stress than control groups. The results of the study on the linkage between Ascirbp gene expression and mitochondrial apoptosis pathways showed that changes in the expression of the Ascirbp gene had a significant effect on the expression of key genes of apoptosis. The most striking result from silencing the expression of the Ascirbp gene was that expressions of the bcl-2 and apaf-1 gene in the liver of black porgy decreased significantly, which can block the normal apoptotic process. After the disruption of the normal apoptotic process, the expressions of p53, bax, cyto-c, caspase-9, caspase-3, diablo, and caspase-1 gene were significantly affected. These results suggest that Ascirbp gene can inhibit apoptosis and protect tissue structure in the liver tissue of black porgy at low temperatures.
Why does SGD prefer ﬂat minima?: Through the lens of dynamical systems

We show that stochastic gradient descent (SGD) escapes from sharp minima exponentially fast even before SGD reaches stationary distribution. SGD has been a de facto standard training algorithm for various machine learning tasks. However, there still exists an open question as to why SGDs ﬁnd highly generalizable parameters from non-convex target functions, such as the loss function of neural networks. An “escaping” analysis has been an appealing framework to tackle this question. Escaping analysis measures how quickly SGD escapes from sharp minima, which is likely to have low generalization abilities. Despite its importance, the framework has the limitation that it works only when SGD reaches a stationary distribution after sufﬁcient updates. In this paper, we prove that the SGD escapes from sharp minima exponentially fast even in a non-stationary setting. A key tool for the result is the Large Deviation Theory, a fundamental theory in dynamical systems. In particular, we found that a quantity called “quasi-potential” is a suitable tool to describe the SGD’s stochastic behavior throughout its training process.
Tilapia Skin Peptides Inhibit Apoptosis, Inflammation, and Oxidative Stress to Improve Dry Eye Disease In Vitro and In Vivo

Dry eye disease (DED) is a common chronic ophthalmic disorder, and there is no effective treatment to cure it. It is urgent to find new antidry eye compounds. Here, the effects and underlying mechanisms of tilapia skin peptides (TSP) on DED were investigated. In vitro, human corneal epithelial cells (HCECs) were cultured in a hypertonic medium containing TSP for 12 h. The MTT assay, calcein-AM/PI staining, reactive oxygen (ROS) analysis, and western blot were performed at the end of the experiments. In the in vivo model of DED, mice eyes were dropped with 0.3% benzalkonium chloride (BAC) and 0.1% TSP for 14 days. Mice were treated with BAC and TSP twice daily with a 10-hour time interval between treatments. After treatment, phenolic red cotton experiment, tear ferning test, and histology assay were carried out. In vitro, TSP significantly restored the cell viability of HCECs challenged by NaCl. In vivo, tear secretion function and tear fern-like crystal levels were considerably improved in the DED group after treatment with TSP. Mechanically, TSP impend the DED development by regulating Bax/Bcl-2 signal pathway, inhibiting iNOS and COX-2 protein expression, moderating ROS/Nrf2/HO-1 axis, inhibiting cell apoptosis in the corneal regions, increasing the number of conjunctival goblet cells, and arresting corneal epithelial thinning. In conclusion, TSP ameliorated DED-like disorder in BAC-induced DED mice and cell damage in NaCl-induced HCECs. TSP improved DED by suppressing apoptosis, inflammation, and oxidative stress. Consequently, this study reveals the beneficial activity of TSP in DED.
Targeted Training for Math Word Problems with Large Language Models

After recent gains achieved by large language 001 models (LLMs) on numerical reasoning tasks, 002 it has become of interest to have LLMs teach 003 small models to improve on math word prob-004 lems (MWPs). Instructing LLMs to generate 005 Chains of Thought (CoTs) to fine-tune small 006 models is an established approach. However, 007 small models are passive in this line of work, 008 and may not be able to exploit the provided 009 training data. In this paper, we propose a novel 010 targeted training strategy to match LLM’s as-011 sistance with small models’ capacities. The 012 small model will proactively request LLM’s 013 assistance when it sifts out confusing training 014 data. Then, LLM refines such data by succes-015 sively revising reasoning steps and reducing 016 question complexity before feeding the small 017 model. Experiments show that this targeted 018 training approach remarkably improves the per-019 formance of small models on a range of MWP 020 datasets by 12–25 percent, making small mod-021 els even competitive with some LLMs. 022
Interactive Viewpoint Suggestion with Deep Classification Network for Browsing 3D Object Galleries

Viewpoint selection techniques have been one of the most fundamental and main topics in computer graphics for many years and closely related to some other applications, such as data visualization or camera placement. Also, according to the rapid improvements in the deep learning field, some viewpoint selection techniques based on deep learning methods have been proposed. However, these methods are typically only focused on suggesting one viewpoint of a single object. In this paper, we propose a new framework for simultaneously selecting viewpoints to compare multiple objects in 3D galleries. Our network takes rendered images of each object from various viewpoints as inputs
STUDY ON STL-BASED SLICING PROCESS FOR 3 D PRINTING

This paper presents a framework about layer contour reconstruction algorithms by STLbased slicing process for 3D printing. The experimental results by the traditional uniform slicing show the contour outline of each layer and comparison among the different slicing thickness of the cutting z-plane. We then proposed a simple but effective adaptive slicing method to work on the complicated model. Moreover, we discuss the future work to further study on addressing the accuracy of the boundaries with the adjustment of slicing thickness.
Intramyocardial Stem Cell Transplantation during Coronary Artery Bypass Surgery Safely Improves Cardiac Function: Meta-Analysis of 20 Randomized Clinical Trials

IMSC transplantation during CABG is considered one of the most promising methods to effectively deliver stem cells and has been widely studied in many trials. But the results of outcomes and safety of this modality still vary widely. We conducted this meta-analysis of randomized controlled trials (RCTs) to evaluate not only the outcome but also the safety of this promising method. A meta-analysis was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. A comprehensive literature search was undertaken using the PubMed, Scopus, and Cochrane databases. Articles were thoroughly evaluated and analyzed. Twenty publications about IMSC during CABG were included. Primary outcomes were measured using LVEF, LVESV, LVESVI, LVESD, LVEDV, LVEDVI, LVEDD, WMSI, and 6-MWT. Safety measures were depicted by total deaths, MACE, CRD, CVA, myocardial infarction, ventricular arrhythmia, and cardiac-related readmission. IMSC transplantation during CABG significantly improved LVEF (MD = 3.89%; 95% CI = 1.31% to 6.46%; p = 0.003) and WMSI (MD = 0.28; 95% CI = 0.01–0.56; p = 0.04). Most of the other outcomes showed favorable results for the IMSC group but were not statistically significant. The safety analysis also showed no significant risk difference for IMSC transplantation compared to CABG alone. IMSC during CABG can safely improve cardiac function and tend to improve cardiac volumes and dimensions. The analysis and application of influencing factors that increase patients’ responses to IMSC transplantation are important to achieve long-term improvement.
Tomato-plant Sunlit-leaf Segmentation Using Convolutional Neural Networks: Supporting Crop Water Stress Index Measurements

Many precision irrigation approaches rely on calculating the crop water stress index (CWSI) using measurements of the sunlit-leaf canopy temperature ( sunlit-leaf T c ). To this end, they typically employ machine learning and/or statistical techniques along with visible-spectrum and/or thermal imagery to identify the sunlit-leaf region and its temperature. The precise segmentation of sunlit leaves is of utmost importance; considering non-sunlit leaves while calculating CWSI, can lead to inaccurate and inefficient under-irrigation or over-irrigation. Recent work demonstrated that convolutional neural networks (CNN) can support highly precise sunlit leaf segmentation in pistachio trees. The same work introduced a complete methodology for the estimation of CWSI in pis-tachio trees and released a corresponding free-of-charge web tool: CIWA. However, the generality of the approach to other crops is not discussed. Here, we extend the CIWA methodology to tomato plants. We discuss the challenges of extending the CIWA approach to shorter plants and release the first annotated dataset for sunlit-leaf segmentation in tomato plants. We consider three CNN architectures for this task: FRRN-A, FC-DenseNet103 and ResNet101-DeepLabV3, and show that FC-DenseNet103 is the most suitable for the task. Based on these results, we introduce an extended CIWA release to enable tomato CWSI measurements.