Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study on Portable Load Balancer for Container Clusters

Today, most of the people in the world can not spend a day without using smartphones or PCs. They use these devices to access services provided by web applications on the Internet. These services include e-mail, social media, search engines, shopping site, etc., everything provided through the Internet. As these services become an indispensable part of the daily lives, portability of the application becomes very important. For example, those who provide these services need to be able to recover from a disaster by migrating their web applications to di erent locations. They also need to be able to expand their businesses to di erent countries, once the web service is successful in one country. It is also preferable for them to be able to migrate their services without a hassle at their convenience in order to avoid lock-ins. For the portability of web applications, providing a web application consisting of a cluster of Linux containers is a promising candidate, since Linux containers can run on any Linux system regardless of the infrastructures. A container orchestrator (also called container cluster management system) is a tool to simplify the management of a cluster of containers that are launched on multiple servers. And it is expected to provide a uniform platform for container clusters by functioning as a middleware, which will improve the portability of web applications. However, none of the existing container orchestrators meets the expectation, because none of them has a standard way to set up the route for ingress tra c from the Internet automatically. Users need to set up a route for ingress tra c manually every time they start a new web application, depending on the type of the infrastructure. The lack of this standardized automation is one of the most critical problems that prevent container orchestrators from serving as a common middleware that facilitates the portability of web applications. Without solving this problem, the migration of a web application will never be easy, and will
The physical properties of Fermi TeV BL Lac objects’ jets

We investigate the physical properties of \textit{Fermi} TeV BL Lac objects jets by modeling the quasi-simultaneous spectral energy distribution of 29 \textit{Fermi} TeV BL Lacs in the frame of a one-zone leptonic synchrotron self-Compton model. Our main results are the following: (i) There is a negative correlation between $B$ and $\delta$ in our sample, which suggests that $B$ and $\delta$ are dependent on each other mainly in Thomson regime. (ii) There are negative correlations between $\nu_{\text{sy}}$ and $r$, the $\nu_{\text{IC}}$ and $r$, which is a signature of the energy-dependence statistical acceleration or the stochastic acceleration. There is a significant correlation between $r$ and $s$, which suggests that the curvature of the electron energy distribution is attributed to the energy-dependence statistical acceleration mechanism. (iii) By assuming one proton per relativistic electron, we estimate the jet power and radiative power. A size relation $P_{\text{e}} \sim P_{\text{p}} > P_{\text{r}} \gtrsim P_{\text{B}}$ is found in our sample. The $P_{\text{e}}>P_{\text{B}}$ suggests that the jets are particle dominated, and the $P_{\text{e}}\sim P_{\text{p}}$ means that the mean energy of relativistic electrons approaches $m_{\text{p}}/m_{\text{e}}$. There are not significant correlations between $P_{\text{jet}}$ and black hole mass in high or low state with a sub-sample of 18 sources, which suggests that the jet power weakly depends on the black hole mass. (iv) There is a correlation between the changes in the flux density at 1 TeV and the changes in the $\gamma_{\text{peak}}$, which suggests the change/evolution of electron energy distribution may be mainly responsible for the flux variation.
A multiwavelength study of the star forming region IRAS 18544+0112

Aims. This work aims at investigating the molecular and infrared components in the massive young stellar object (MYSO) candidate IRAS 18544+0112. The purpose is to determine the nature and the origin of this infrared source. Methods. To analyze the molecular gas towards IRAS 18544+0112, we have carried out observations in a 90 �� × 90 �� region around l = 34. ◦ 69, b = −0. ◦ 65, using the Atacama Submillimeter Telescope Experiment (ASTE) in the 12 CO J = 3–2, 13 CO J = 3–2, HCO + J = 4–3 and CS J = 7–6 lines with an angular resolution of 22 �� . The infrared emission in the area has been analyzed using 2MASS and Spitzer public data. Results. From the molecular analysis, we find self-absorbed 12 CO J = 3–2 profiles, which are typical in star forming regions, but we do not find any evidence of outflow activity. Moreover, we do not detect either HCO + J = 4–3 or CS J = 7–6 in the region, which are species normally enhanced in molecular outflows and high density envelopes. The 12 CO J = 3–2 emission profile suggests the presence of expanding gas in the region. The Spitzer images reveal that the infrared source has a conspicuous extended emission bright at 8 μm with an evident shell-like morphology of ∼1. 5i n size (∼1.4 pc at the proposed distance of 3 kpc) that encircles the 24 μm emission. The non-detection of ionized gas related to IRAS 18544+0112 together with the fact that it is still embedded in a molecular clump suggest that IRAS 18544+0112 has not reached the UCHII region stage yet. Based on near infrared photometry we search for YSO candidates in the region and propose that 2MASS 18565878+0116233 is the infrared point source associated with IRAS 18544+0112. Finally, we suggest that the expansion of a larger nearby HII region, G034.8−0.7, might be related to the formation of IRAS 18544+0112.
The link between broad emission line fluctuations and non-thermal emission from the inner AGN jet

Abstract AGN reverberate when the broad emission lines respond to changes of the ionizing thermal continuum emission. Reverberation measurements have been commonly used to estimate the size of the broad-line region (BLR) and the mass of the central black hole. However, reverberation mapping studies have been mostly performed on radio-quiet sources where the contribution of the jet can be neglected. In radio-loud AGN, jets and outflows may affect substantially the relation observed between the ionizing continuum and the line emission. To investigate this relation, we have conducted a series of multi-wavelength studies of radio-loud AGN, combining optical spectral line monitoring with regular VLBI observations. Our results suggest that at least a fraction of the broad-line emitting material can be located in a sub-relativistic outflow ionized by non-thermal continuum emission generated in the jet at large distances (> 1 pc) from the central engine of AGN. This finding may have a strong impact on black hole mass estimates based on measured widths of the broad emission lines and on the gamma-ray emission mechanisms.
Does the velocity of light depend on the source movement

Data from spacecrafts tracking exhibit many anomalies that suggest the dependence of the speed of electromagnetic radiation with the motion of its source. This dependence is different from that predicted from emission theories that long ago have been demonstrated to be wrong. By relating the velocity of light and the corresponding Doppler effect with the velocity of the source at the time of detection, instead of the time of emission, it is possible to explain quantitatively and qualitatively the spacecraft anomalies. Also, a formulation of electromagnetism compatible with this conception is possible (and also compatible with the known electromagnetic phenomena). Under this theory the influence of the velocity of the source in the speed of light is somewhat subtle in many practical situations and probably went unnoticed in other phenomena.
Scheduling under energy constraints

In this paper we present a scheduling problem dealing with energy constraints (typically electrical energy). Mainly we propose an extension of specific resource constraint propagation techniques (known as "energy reasoning") to efficiently prune the search space and then to facilitate its resolution. We also present dominance rules and a branching scheme to solve the problem via tree search. Finally, computational results are provided.
Scalar runnings and a test of slow roll from CMB distortions

A future measurement of cosmic microwave \mu-distortions by an experiment with the specifications of PIXIE will provide an equivalent 3-sigma detection of the running of running of the spectral index of scalar perturbations, \beta = d\alpha/dln(k), if \mu > 7.75 E-8, covering much of the PIXIE sensitivity range. This corresponds to a resolution limit of \beta > 0.015 which is relatively large given any presumption of slow roll, a result of the current tight constraints on \alpha < 0 on CMB scales. We show that a detection of \beta at this level is in conflict with slow roll conditions if the primordial signal can be distinguished from any post-inflationary contamination.
The Development of Epitaxial Graphene For 21st Century Electronics

Graphene has been known for a long time but only recently has its potential for electronics been recognized. Its history is recalled starting from early graphene studies. A critical insight in June, 2001 brought to light that graphene could be used for electronics. This was followed by a series of proposals and measurements. The Georgia Institute of Technology (GIT) graphene electronics research project was first funded, by Intel in 2003, and later by the NSF in 2004 and the Keck foundation in 2008. The GIT group selected epitaxial graphene as the most viable route for graphene based electronics and their seminal paper on transport and structural measurements of epitaxial graphene was published in 2004. Subsequently, the field rapidly developed and multilayer graphene was discovered at GIT. This material consists of many graphene layers but it is not graphite: each layer has the electronic structure of graphene. Currently the field has developed to the point where epitaxial graphene based electronics may be realized in the not too distant future.
A Multi-Task Learning Algorithm for Non-personalized Recommendations Anonymous au thors

In this paper, we introduce a multi-task learning (MTL) algorithm for recommending non-personalized videos to watch next on industrial video sharing platforms. Personalized recommendations have been studied for decades, while researches on non-personalized solutions are very rare to be seen, which still remain a huge portion in industry. As an indispensable part in recommender system, non-personalized video recommender system also faces several real-world challenges, including maintaining high relevance between source item and target items, as well as achieving multiple competing ranking objectives. To solve these, we largely extended model-based collaborative filtering algorithm by adding related candidate generation stage, Two-tower DNN structure and a multi-task learning mechanism. Compared with typical baseline solutions, our proposed algorithm can capture both linear and non-linear relationships from user-item interactions, and live experiments demonstrate that it can significantly advance the state of the art on recommendation quality.
Prediction of Mg Alloy Corrosion Based on Machine Learning Models

Magnesium alloy is a potential biodegradable metallic material characterized by bone-like elastic modulus, which has great application prospects in medical, automotive, and aerospace industries owing to its bone-like elastic modulus, biocompatibility, and lightweight properties. However, the rapid corrosion rates of magnesium alloys seriously limit their applications. This study collected magnesium alloys’ corrosion data and developed a model to predict the corrosion potential, based on the chemical composition of magnesium alloys. We compared four machine learning algorithms: random forest (RF), multiple linear regression (MLR), support vector machine regression (SVR), and extreme gradient boosting (XGBoost). The RF algorithm offered the most accurate predictions than the other three machine learning algorithms. The input effects on corrosion potential have been investigated. Moreover, we used feature creation (transforming chemical component characteristics into atomic and physical characteristics) so that the input characteristics were not limited to specific chemical compositions. From this result, the model’s application range was widened, and machine learning was used to verify the accuracy and feasibility of predicting corrosion of magnesium alloys.
Clustering and bias measurements of SDSS voids

Using a void catalog from the SDSS survey, we present the first measurements of void clustering and the corresponding void bias. Over the range 30-200 Mpc/h the void auto-correlation is detected at 5-sigma significance for voids of radius 15-20 Mpc/h. We also measure the void-galaxy cross-correlation at higher signal-to-noise and compare the inferred void bias with the autocorrelation results. Void bias is constant with scale for voids of a given size, but its value falls from 5.6 +/- 1.0 to below zero as the void radius increases from 15 to 30 Mpc/h. The comparison of our measurements with carefully matched galaxy mock catalogs, with no free parameters related to the voids, shows that model predictions can be reliably made for void correlations. We study the dependence of void bias on tracer density and void size with a view to future applications. In combination with our previous lensing measurements of void mass profiles, these clustering measurements provide another step towards using voids as cosmological tracers.
A topological insulator and helical zero mode in silicene under an inhomogeneous electric field

Silicene is a monolayer of silicon atoms forming a two-dimensional (2D) honeycomb lattice and shares almost all the remarkable properties of graphene. The low-energy structure of silicene is described by Dirac electrons with relatively large spin–orbit interactions owing to its buckled structure. A key observation is that the band structure can be controlled by applying an electric field to a silicene sheet. In particular, the gap closes at a certain critical electric field. Examining the band structure of a silicene nanoribbon, we show that a topological phase transition occurs from a topological insulator to a band insulator with an increase of electric field. We also show that it is possible to generate helical zero modes anywhere in a silicene sheet by adjusting the electric field locally to this critical value. The region may act as a quantum wire or a quantum dot surrounded by topological and/or band insulators. We explicitly construct the wave functions for some simple geometries based on the low-energy effective Dirac theory. These results are also applicable to germanene, which is a 2D honeycomb structure of germanium.
A icE-Bs2022KotaKinabalu Impact of Flexible Working Arrangements on Millennials’ Mental Health, Well-Being and Productivity during COVID-19

Malaysian millennials are reported to be struggling with stress and well-being issues at the workplace during the last decade. Drawing from the Self-Determination Theory, this study examines whether flexible working arrangements (FWAs) could impact millennials' mental health, well-being and productivity at the workplace. A self-administered questionnaire was distributed to 400 millennials in the services sector during the COVID-19 period. Findings revealed that FWAs influenced millennials' mental health, well-being, and productivity as millennials value job autonomy and a friendly work ecosystem as an ideal workplace. This sees a compelling case for Malaysian businesses to adopt FWAs in a more structured way in line with Sustainable Development Goal -3.
Hash Gone Bad: Automated discovery of protocol attacks that exploit hash function weaknesses

Most cryptographic protocols use cryptographic hash functions as a building block. The security analyses of these protocols typically assume that the hash functions are perfect (such as in the random oracle model). However, in practice, most widely deployed hash functions are far from perfect – and as a result, the analysis may miss attacks that exploit the gap between the model and the actual hash function used. We develop the first methodology to systematically discover attacks on security protocols that exploit weaknesses in widely deployed hash functions. We achieve this by revisit-ing the gap between theoretical properties of hash functions and the weaknesses of real-world hash functions, from which we develop a lattice of threat models. For all of these threat models, we develop fine-grained symbolic models. Our methodology’s fine-grained models cannot be directly encoded in existing state-of-the-art analysis tools by just using their equational reasoning. We therefore develop extensions for the two leading tools, T AMARIN and P RO V ERIF . In extensive case studies using our methodology, the extended tools rediscover all attacks that were previously reported for these protocols and discover several new variants.
Recognize and Prevent the Cyberbullying Conversation on Social Networks Using Machine Learning Techniques

Social media was a common place for abusive communication in the modern day. More than 80% of online social networks have abusive or vulgar speech on their user profiles, according to a recent survey report. Cyberbullying is the act of threatening or harassing another user via the use of inappropriate, abusive, or vulgar online social media posts. The major purpose of putting these kinds of unpleasant statements on user walls is to harass teenagers, preteens, and other kids. I created the present programme to limit vulgar communication on online social media since, up until now, no application has been able to stop this cyber material from spreading in social networks online. In this suggested application, our major goal is to provide a novel representation learning approach to address the issue of recognising and preventing abusive statements in online chat. Here, we attempt to categorise the misused and legitimate text messages using well-known machine learning methods, including Porter Stemming Algorithm and Support Vector Machine (SVM). The NLT Package (Natural Language Toolkit), which divides the entire message into pieces and then assigns tokens for each and every unique word, is known as Porter Stemming. Here, we divide online bullying into five types, including "HATE, VULGAR, OFFENSIVE, SEX, and VIOLENCE," that are found in the literature.
Comparison of Emotion Recognition in Young People, Healthy Older Adults, and Patients with Mild Cognitive Impairment

Abstract Background: The basic discrete emotions, namely, happiness, disgust, anger, fear, surprise, and sadness, are present across different cultures and societies. Facial emotion recognition is crucial in social interactions, but normal and pathological aging seem to affect this ability. The present research aims to identify the differences in the capacity for recognition of the six basic discrete emotions between young and older healthy controls (HOC) and mildly cognitively impaired patients (MCI). Method: The sample (N = 107) consisted of 47 young adults, 27 healthy older adults, and 33 MCI patients. Several neuropsychological scales were administered to assess the cognitive state of the participants, followed by the emotional labeling task on the Ekman 60 Faces test. Results: The MANOVA analysis was significant and revealed the presence of differences in the emotion recognition abilities of the groups. Compared to HOC, the MCI group obtained a significantly lower number of hits on fear, anger, disgust, sadness, and surprise. The happiness emotion recognition rate did not differ significantly among the three groups. Surprisingly, young people and HOC did not show significant differences. Conclusions: Our results demonstrated that MCI was associated with facial emotion recognition impairment, whereas normal aging did not seem to affect this ability.
Inhibition of USP4 attenuates epithelial-mesenchymal transition of renal tubular epithelial cells by TβRI

The process of epithelial-mesenchymal transition (EMT) is required for the progression of renal interstitial fibrosis (RIF). Ubiquitin-specific protease 4 (USP4) can facilitate development of transforming growth factor, beta 1 (TGF-β1) induced EMT in some cancer cells. However, the role of USP4 in EMT during RIF remains unknown. We aimed to explore the effect of USP4 on the EMT induced by TGF-β1 of renal tubular epithelial cells and involved mechanism in RIF. In vivo, on the 7th and 14th day after unilateral ureteral obstruction (UUO), the expression of USP4 protein in the obstructed kidneys was detected by immunohistochemistry and Western blot assay. In vitro, NRK-52E cells were stimulated with TGF-β1 10ng/ml. The protein expressions of USP4, E-cadherin and alpha smooth muscle actin (α-SMA) were detected at different time points by Western blot. After transfected with USP4 siRNA, the cells were cultured with TGF-β1 for additional 24 hours. The expressions of Ecadherin, α-SMA, and TGFβ receptor type I (TβRI) were detected by immunofluorescence. And the protein expressions of USP4, E-cadherin, α-SMA and TβRI were detected by Western blot assay. Compared with sham operation group, the expression of USP4 in UUO model group increased significantly with the prolongation of obstruction time. After NRK-52E was stimulated by TGF-β1, the expression of USP4 protein increased gradually. At 6h, 12h, and 24h, the difference between the experimental group and the control group was statistically significant. At the same time, E-cadherin decreased significantly, while α-SMA increased significantly. Compared with the TGF-β1 group, the cells in USP4 siRNA transfection group restored E-cadherin and weakened α-SMA expression. At the same time, protein expressions of USP4 and TβRI were also significantly decreased. These data imply that USP4 is a harmful molecule induced by TGF-β1, which plays an important role by upregulating the expression of TβRI and promoting EMT of renal tubular epithelial cells, thereby facilitating renal interstitial fibrosis. was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which this version posted May 9, 2021. ; https://doi.org/10.1101/2020.10. 28.358796 doi: bioRxiv preprint
Kinematical correlations of dielectrons from semileptonic decays of heavy mesons and Drell-Yan processes at BNL RHIC

We discuss kinematical correlations between charged leptons from semileptonic decays of open charm/bottom, leptons produced in the Drell-Yan mechanism as well as some other mechanisms not included so far in the literature in proton-proton scattering at BNL RHIC. The distributions of charm and bottom quarks/antiquarks are calculated in the framework of the $k_t$-factorization approach. For this calculation we use the Kwieci\'nski unintegrated parton distributions. The hadronization of heavy quarks is done by means of Peterson et al. fragmentation function. We use semileptonic decay functions found by fitting recent semileptonic data obtained by the CLEO and BABAR collaborations. The Drell-Yan processes were calculated including transverse momenta of quarks and antiquarks, also using the Kwieci\'nski parton distributions. We have also took into consideration reactions initiated by purely QED $\gamma^*\gamma^*$-fusion in elastic and inelastic pp collisions as well as recently proposed diffractive mechanism of exclusive charm-anticharm production. The contribution of the later mechanism is rather small. We get good description of the dilepton invariant mass spectrum measured recently by the PHENIX collaboration and present predictions for the dilepton pair transverse momentum distribution as well as distribution in azimuthal angle between electron and positron.