Miracle-dz/UniFAR-FTUs huggingface dataset:
Messenger inflation in gauge mediation and super-WIMP dark matter

We discuss phenomenological viability of a novel inflationary model in the minimal gauge mediated supersymmetry breaking scenario. In this model, cosmic inflation is realized in the flat direction along the messenger supermultiplets and a natural dark matter candidate is the gravitino from the out-of-equilibrium decay of the bino-like neutralino at late times, which is called the superWIMP scenario. The produced gravitino is warmish and can have a large free-streaming length; thus the cusp anomaly in the small scale structure formation may be mitigated. We show that the requirement of the Standard Model Higgs boson mass to be mh0 = 125.1 GeV gives a relation between the spectrum of the cosmic microwave background and the messenger mass M . We find, for the efolding number Ne = 60, the Planck 2018 constraints (TT, TE, EE+lowE+lensing+BK15+BAO, 68% confidence level) give M > 3.64×10 GeV. The gravitino dark matter mass is m3/2 < 5.8 GeV and the supersymmetry breaking scale Λ is found to be in the range (1.28−1.33)×10 GeV. Future CMB observation is expected to give tighter constraints on these parameters.
Temperature-independent hole mobility in discotic liquid crystals

Experimental measurements are presented of the hole mobilities of four conjugated discotic systems, forming columnar liquid crystals, as a function of temperature. The measurements cover the crystalline/glassy phase and mesophase of these materials. It is a remarkable fact that the mobility is almost independent of temperature in the range 30 °C–170 °C. Various explanations of a weak temperature dependence exist and these are explored. They include the small polaron of Holstein in the nonadiabatic limit and the effect of the dynamic disorder present in the system.
X-Ray Constraint on the Location of the AGN Torus in the Circinus Galaxy

The location of the obscuring “torus” in an active galactic nucleus (AGN) is still an unresolved issue. The line widths of X-ray fluorescence lines originating from the torus, particularly Fe Kα, carry key information on the radii of line-emitting regions. Utilizing XCLUMPY, an X-ray clumpy torus model, we develop a realistic model of emission line profiles from an AGN torus where we take into account line broadening due to the Keplerian motion around the black hole. Then, we apply the updated model to the best available broadband spectra (3–100 keV) of the Circinus galaxy observed with Suzaku, XMM-Newton, Nuclear Spectroscopic Telescope Array, and Chandra, including 0.62 Ms Chandra/HETG data. We confirm that the torus is Compton-thick (hydrogen column density along the equatorial plane is ), geometrically thin (torus angular width ), viewed edge-on (inclination ), and has supersolar abundance ( times solar). Simultaneously analyzing the Chandra/HETG first-, second-, and third-order spectra with consideration of the spatial extent of the Fe Kα line-emitting region, we constrain the inner radius of the torus to be times the gravitational radius, or for a black hole mass of (1.7 ± 0.3) × 106 M ⊙. This is about three times smaller than that estimated from the dust sublimation radius, suggesting that the inner side of the dusty region of the torus is composed of dust-free gas.
Time-frequency analysis assisted reconstruction of ruthenium optical constants in the sub-EUV spectral range 8 nm – 75 nm

The optical constants of ruthenium in the spectral range 8 nm – 23.75 nm with their corresponding uncertainties are derived from the reflectance of a sputtered ruthenium thin film in the Extreme Ultraviolet (EUV) spectral range measured using monochromatized synchrotron radiation. This work emphasizes the correlation between structure modelling and the reconstructed optical parameters in a detailed inverseproblem optimization strategy. Complementary X-ray Reflectivity (XRR) measurements are coupled with Markov chain Monte Carlo (MCMC) based Bayesian inferences and quasi-model-independent methods to create a model factoring the sample’s oxidation, contamination, and surface roughness. The sensitivity of the modelling scheme is tested and verified against contamination and oxidation. A notable approach mitigating the high dimensionality of the reconstruction problem is elaborated with the results of this work compared to two previously published datasets. The presented dataset is of high interest for the continuing development of Extreme Ultraviolet Lithography (EUVL) and EUV astronomy optical systems.
Multimodal Motion Prediction with Stacked Transformers

Predicting multiple plausible future trajectories of the nearby vehicles is crucial for the safety of autonomous driving. Recent motion prediction approaches attempt to achieve such multimodal motion prediction by implicitly regularizing the feature or explicitly generating multiple candidate proposals. However, it remains challenging since the latent features may concentrate on the most frequent mode of the data while the proposal-based methods depend largely on the prior knowledge to generate and select the proposals. In this work, we propose a novel transformer framework for multimodal motion prediction, termed as mmTransformer. A novel network architecture based on stacked transformers is designed to model the multimodality at feature level with a set of fixed independent proposals. A region-based training strategy is then developed to induce the multimodality of the generated proposals. Experiments on Argoverse dataset show that the proposed model achieves the state-of-the-art performance on motion prediction, substantially improving the diversity and the accuracy of the predicted trajectories. Demo video and code are available at https://decisionforce.github. io/mmTransformer.
Multi Camera Placement via Z-buffer Rendering for the Optimization of the Coverage and the Visual Hull

We can only allow human-robot-cooperation in a common work cell if the human integrity is guaranteed. A surveillance system with multiple cameras can detect collisions without contact to the human collaborator. A failure safe system needs to optimally cover the important areas of the robot work cell with safety overlap. We propose an efficient algorithm for optimally placing and orienting the cameras in a 3D CAD model of the work cell. In order to evaluate the quality of the camera constellation in each step, our method simulates the vision system using a z-buffer rendering technique for image acquisition, a voxel space for the overlap and a refined visual hull method for a conservative human reconstruction. The simulation allows to evaluate the quality with respect to the distortion of images and advanced image analysis in the presence of static and dynamic visual obstacles such as tables, racks, walls, robots and people. Our method is ideally suited for maximizing the coverage of multiple cameras or minimizing an error made by the visual hull and can be extended to probabilistic space carving.
Support Vector Regression Parameters Optimization using Golden Sine Algorithm and its application in stock market

Support vector machine modeling is a new approach in machine learning for classification showing good performance on forecasting problems of small samples and high dimensions. Later, it promoted to Support Vector Regression (SVR) for regression problems. A big challenge for achieving reliable is the choice of appropriate parameters. Here, a novel Golden sine algorithm (GSA) based SVR is proposed for proper selection of the parameters. For comparison, the performance of the proposed algorithm is compared with eleven other meta-heuristic algorithms on some historical stock prices of technological companies from Yahoo Finance website based on Mean Squared Error and Mean Absolute Percent Error. The results demonstrate that the given algorithm is efficient for tuning the parameters and is indeed competitive in terms of accuracy and computing time.
Implementation of Artificial Neural Networks for the Nepta-Uranian Interplanetary (NUIP) Mission

A celestial alignment between Neptune, Uranus, and Jupiter will occur in the early 2030s, allowing a slingshot around Jupiter to gain enough momentum to achieve planetary flyover capability around the two ice giants. The launch of the uranian probe for the departure windows of the NUIP mission is between January 2030 and January 2035, and the duration of the mission is between six and ten years, and the launch of the Nepta probe for the departure windows of the NUIP mission is between February 2031 and April 2032 and the duration of the mission is between seven and ten years. To get the most out of alignment, deep learning methods are expected to play a critical role in autonomous and intelligent spatial guidance problems. This would reduce travel time, hence mission time, and allow the spacecraft to perform well for the life of its sophisticated instruments and power systems up to fifteen years. This article proposes a design of deep neural networks, namely convolutional neural networks and recurrent neural networks, capable of predicting optimal control actions and image classification during the mission. Nepta-Uranian interplanetary mission, using only raw images taken by optimal onboard cameras. It also describes the unique requirements and constraints of the NUIP mission, which led to the design of the communications system for the Nepta-Uranian spacecraft. The proposed mission is expected to collect telemetry data on Uranus and Neptune while performing the flyovers and transmit the obtained data to Earth for further analysis. The advanced range of spectrometers and particle detectors available would allow better quantification of the ice giant's properties.
Resilient Control Under Quantization and Denial-of-Service: Codesigning a Deadbeat Controller and Transmission Protocol

This article is concerned with the problem of stabilizing continuous-time linear time-invariant (LTI) systems subject to quantization and denial-of-service (DoS) attacks. In this context, two DoS-induced challenges emerge in the design of resilient encoding schemes, namely, the coupling between encoding strategies of different signals, and the synchronization between the encoder and decoder. These challenges are addressed by a novel proposed structure based on a deadbeat controller as well as a delicate transmission protocol for the input and output channels, and codesigned leveraging the controllability index. When both input and output channels are subject to DoS attacks and quantization, the proposed structure is shown able to decouple the encoding schemes for input, output, and estimated output signals. This property is further corroborated by designing encoding schemes as well as conditions ensuring exponential stability of the closed-loop system. On the other hand, when only the output channel is subject to network attack phenomena, the proposed structure can achieve exponential stabilization without acknowledgment (ACK) signals, in contrast to existing ACK-based results. Finally, a numerical example is given to demonstrate the practical merits of the proposed theoretical and practical approach.
Understanding Code Smell Detection via Code Review: A Study of the OpenStack Community

Code review plays an important role in software quality control. A typical review process would involve a careful check of a piece of code in an attempt to find defects and other quality issues/violations. One type of issues that may impact the quality of the software is code smells - i.e., bad programming practices that may lead to defects or maintenance issues. Yet, little is known about the extent to which code smells are identified during code reviews. To investigate the concept behind code smells identified in code reviews and what actions reviewers suggest and developers take in response to the identified smells, we conducted an empirical study of code smells in code reviews using the two most active OpenStack projects (Nova and Neutron). We manually checked 19,146 review comments obtained by keywords search and random selection, and got 1,190 smell-related reviews to study the causes of code smells and actions taken against the identified smells. Our analysis found that 1) code smells were not commonly identified in code reviews, 2) smells were usually caused by violation of coding conventions, 3) reviewers usually provided constructive feedback, including fixing (refactoring) recommendations to help developers remove smells, and 4) developers generally followed those recommendations and actioned the changes. Our results suggest that 1) developers should closely follow coding conventions in their projects to avoid introducing code smells, and 2) review-based detection of code smells is perceived to be a trustworthy approach by developers, mainly because reviews are context-sensitive (as reviewers are more aware of the context of the code given that they are part of the project’s development team).
Carbon Nanomaterials for Drug Delivery

Carbon Nanotubes (CNTs) and Graphene Have Attracted Tremendous Attention as the Most Promising Carbon Nanomaterials in the 21st Century for a Variety of Applications such as Electronics, Biomedical Engineering, Tissue Engineering, Neuroengineering, Gene Therapy and Biosensor Technology. For the Biomedical Applications, Cnts Have Been Utilized over Existing Drug Delivery Vectors due to their Ability to Cross Cell Membranes Easily and their High Aspect Ratio as Well as High Surface Area, which Provides Multiple Attachment Sites for Drug Targeting. Besides, it Has Also Been Proved that the Functionalization of CNTs May Remarkably Reduce their Cytotoxic Effects and at the Same Time Increase their Biocompatibility. So, the Functionalized CNTs Are Safer than Pristine or Purified CNTs, Thus Offering the Potential Exploitation of Nanotubes for Drug Administration. On the other Hand, More Recently Graphene and its Derivatives Have Been Enormously Investigated in the Biological Applications because of their Biocompatibility, Unique Conjugated Structure, Relatively Low Cost and Availability on both Sides of a Single Sheet for Drug Binding. In Our Study, we Have Covalently Functionalized Multiwalled Carbon Nanotubes (MWCNTs) and Graphene Oxide (GO) with Highly Hydrophilic and Biocompatible Excipients in Order to Increase their Aqueous Solubility and Biocompatibility. Various Excipients Used Were Polyvinyl Alcohol, Pluronic F38, Tween 80 and Maltodextrin. The Poorly Water-Soluble Anticancer Drugs such as, Camptothecin and Ellagic Acid, Were Loaded onto the Functionalized MWCNTs and GO via Non-Covalent Interactions. Furthermore, Drug Loading and Cytotoxic Activity of Drugs Incorporated with the Functionalized MWCNTs and GO as Nanocarriers Were Also Investigated. Drugs Loaded on both Carbon Nanocarriers Exhibited a Higher Cytotoxic Activity than Free Drug. On the other Hand, No Significant Toxicity Was Found even at Higher Concentrations when the Cells Were Incubated with the Functionalized Mwcnts and GO. Therefore, both these Functionalized Carbon Nanomaterials Are Ideal Carriers for Drug Delivery.
How do we Evaluate Self-adaptive Software Systems?: A Ten-Year Perspective of SEAMS

With the increase of research in self-adaptive systems, there is a need to better understand the way research contributions are evaluated. Such insights will support researchers to better compare new findings when developing new knowledge for the community. However, so far there is no clear overview of how evaluations are performed in self-adaptive systems. To address this gap, we conduct a mapping study. The study focuses on experimental evaluations published in the last decade at the prime venue of research in software engineering for self-adaptive systems—the International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). Results point out that specifics of self-adaptive systems require special attention in the experimental process, including the distinction of the managing system (i.e., the target of evaluation) and the managed system, the presence of uncertainties that affect the system behavior and hence need to be taken into account in data analysis, and the potential of managed systems to be reused across experiments, beyond replications. To conclude, we offer a set of suggestions derived from our study that can be used as input to enhance future experiments in self-adaptive systems.
Assessment of Subclinical Deterioration of Right Ventricular Function by Three-Dimensional Speckle Tracking Echocardiography in Breast Cancer Patients Undergoing Anthracycline-Based Chemotherapy

Objective This study was aimed at assessing the longitudinal strain changes of RV function using three-dimensional speckle tracking echocardiography (3D STE) in breast cancer patients receiving anthracycline chemotherapy. Patients and Methods A total of 95 women with breast cancer receiving epirubicin (360 mg/m2) underwent 3D STE at baseline, the end of chemotherapy and 12 months after chemotherapy. 3D STE assessment included RV ejection fraction (EF), LV global longitudinal strain (GLS), RV GLS, and RV free wall longitudinal strain (RV FWLS). Meanwhile, serum hs‐cTnI and NT-proBNP were measured. Chemotherapy-related cardiac dysfunction (CTRCD) was defined as an absolute decrease in 3D LVEF > 10% to a value <50%, while a percent reduction of 3D LV GLS > 15% indicated subclinical CTRCD. Results Subclinical CTRCD occurred in 10 (10.5%) patients during follow-up. Compared to baseline, the 3D GLS of LV and GLS and FWLS of RV decreased significantly at 12months after chemotherapy (all p<0.01). Variations of 3D RV GLS and RV FWLS had a good discrimination for predicting subclinical CTRCD. The variation of 3D RV FWLS was the only independent predictor of subclinical CTRCD (OR, 1.37; 95% CI, 1.12–2.87; p = 0.028) in multivariate analysis. The cutoff value with −17.5% of 3D RV FWLS variation had a high predictive accuracy for subclinical CTRCD, with an AUC of 0.74, 80.5% sensitivity and 65.8% specificity. The association between 3D RV FWLS and the cumulative dose of anthracyclines was calculated by Spearman’s test (r = −0.71, p < 0.001). Conclusion Longitudinal strain analysis by 3D STE allows the identification of subclinical RV dysfunction when conventional indices of RV function are unaffected. 3D RV FWLS was superior to other parameters in early detection of the development of CTRCD in breast cancer patients receiving epirubicin therapy.
Antihyperglycemic Activity of TLC Isolates from the Leaves of Aloe megalacantha Baker in Streptozotocin-Induced Diabetic Mice

Background Diabetes mellitus (DM) is a group of metabolic disorders that are characterized by hyperglycemia which results from defects in insulin release or its efficient use by the human body. Although significant progress has been made to manage DM and related complications, it remains a major global health problem. To this end, the search for new antidiabetic drugs from traditionally claimed medicinal plants is important. Aloe megalacantha Baker is an endemic plant used traditionally to treat diabetes in Ethiopia. This study aimed to investigate antidiabetic activity of isolates from the leaf of A. megalacantha Baker in streptozotocin-induced diabetic mice. Methods The exudate of A. megalacantha was collected by cutting the leaves and scraping the yellow sap and then dried at room temperature. The dried exudate was subjected to repeated thin layer chromatographic (TLC) separations using a mixture of solvent system to isolate the major compounds identified on the TLC plate. A single dose of streptozotocin (50 mg/kg) was injected intraperitoneally to overnight fasted mice to induce diabetes. Antidiabetic activity of TLC isolates was assessed by in vivo methods including oral glucose tolerance test, antihyperglycemic and hypoglycemic activity tests. Results Three major isolates were obtained from the TLC analysis of the exudate of A. megalacantha. Exudate and TLC isolates were found to be non-toxic up to a dose of 2000 mg/kg. The TLC isolates (Ia and Ib) significantly (p<0.05) reduced blood glucose levels and also increased body weight change of the diabetic mice compared with control groups. Conclusion The present study demonstrated the ability of the exudate of A. megalacantha and its TLC isolates to significantly decrease blood glucose levels and increase body weights in mice, thus strengthening the claim of its traditional use in DM-related disorders.
Identification of tumor mutation burden-related hub genes and the underlying mechanism in melanoma

Background: Tumor mutation burden (TMB) has emerged as an important predictive factor for drug resistance in cancers; however, the specific mechanism underlying TMB function in melanoma remains elusive. Methods: Data on somatic mutations, RNA sequencing (RNA-seq), miRNA sequencing (miRNA-seq), and clinical characteristics for 472 melanoma patients were extracted from the TCGA cohort. RNA-seq data of melanoma cell lines were obtained from the Cancer Cell Line Encyclopedia, and sensitivity of cell lines to therapeutic agents is available in the Cancer Therapeutics Response Portal. TMB was calculated based on somatic mutation data. Differentially expressed gene analysis, weighted gene co-expression network analysis, protein-protein interaction networks, Minimal Common Oncology Data Elements, and survival analysis were leveraged to determine TMB-related hub genes. Competing endogenous RNA (ceRNA) networks were constructed to explore the molecular mechanisms underlying hub gene function. The influence of key genes on drug sensitivity was analyzed to investigate their clinical significance. Results: Elevated TMB levels were significantly correlated with improved survival outcomes. In addition, six tumor-infiltrating immune cells, including naive B cells, regulatory T cells, memory resting CD4 T cells, memory B cells, activated mast cells, and resting NK cells, were significantly overexpressed in the low-TMB group relative to the high-TMB group. Furthermore, we identified FLNC, NEXN, and TNNT3 as TMB-related hub genes, and constructed their ceRNA networks, including five miRNAs (has-miR-590-3p, has-miR-374b-5p, has-miR-3127-5p, has-miR-1913, and has-miR-1291) and 31 lncRNAs (FAM66C, MIAT, NR2F2AS1, etc.). Finally, we observed that TMB-related genes were associated with distinct therapeutic responses to AKT/mTOR pathway inhibitors. Conclusions: We identified three TMB-associated key genes, established their ceRNA networks, and investigated their influence on therapeutic responses, which could provide insights into future precision medicine.