Miracle-dz/UniFAR-FTUs huggingface dataset:
Configurations of fluid membranes and vesicles

Abstract Vesicles consisting of a bilayer membrane of amphiphilic lipid molecules are remarkably flexible surfaces that show an amazing variety of shapes of different symmetry and topology. Owing to the fluidity of the membrane, shape transitions such as budding can be induced by temperature changes or the action of optical tweezers. Thermally excited shape fluctuations are both strong and slow enough to be visible by video microscopy. Depending on the physical conditions, vesicles adhere to and unbind from each other or a substrate. This article describes the systematic physical theory developed to understand the static and dynamic aspects of membrane and vesicle configurations. The preferred shapes arise from a competition between curvature energy, which derives from the bending elasticity of the membrane, geometrical constraints such as fixed surface area and fixed enclosed volume, and a signature of the bilayer aspect. These shapes of lowest energy are arranged into phase diagrams, which separate regi.
A Constrained Formulation of Maximum-Likelihood Estimation for Normal Mixture Distributions

In this context, the normal densities are sometimes referred to as component densities. The log-likelihood function L(zy), corresponding to a random sample {xl, *, xnj, is defined by L(Ty) = Ek=1 10g(Pm(xk; 'Y)) The first problem associated with maximum-likelihood estimation arises from the unboundedness of L(Qy) on r (Day, 1969). A global maximum-likelihood estimate always fails to exist. In addition, the unboundedness of L(,y) causes failures of optimization algorithms of both the EM (Redner and Walker, 1984) and quasi-Newton (Fowlkes, 1979) types. In spite of the unboundedness of L(4y), statistical theory (Kiefer, 1978) guarantees that a particular local maximizer of L('y) is strongly consistent and asymptotically efficient. Several local maximizers can exist for a given sample, and the other major maximum-likelihood difficulty is in determining when the correct one has been found. Day (1969) noted that spurious maximizers, corresponding to parameter points having some component standard deviations very small relative to others, are generated by any small number of sample points grouped sufficiently close together. The spurious maximizers, like the unboundedness of L(zy), can create difflculties when using the EM or quasi-Newton algorithms. The constrained maximum-likelihood formulation presented in the next section avoids the unconstrained problems of singularities and spurious maximizers of the type noted by Day. Section 3 contains the statement and proof of a consistency result for the corresponding constrained estimator.
Refractive properties of wire-grid metamaterials

This paper reports on a quantitative analysis of the electromagnetic properties of an arrangement of periodic metallic grids devised to provide in the 12.5–20.0 GHz frequency range a refractive index less than unity with view to enhance the functionalities of microwave horn antennas. Based on an analytical model, the ultra-refractive behavior of the metamaterial structure is demonstrated by the impedance and refractive index values retrieved from simulated scattering parameters.
VLSP 2021 - ASR Challenge: Vietnamese Automatic Speech Recognition

: Recently, Vietnamese speech recognition has been attracted by various research groups in both academics and industry. This paper presents a Vietnamese automatic speech recognition challenge for the eighth annual workshop on Vietnamese Language and Speech Processing (VLSP 2021). There are two sub-tasks in the challenge. The first task is ASR-Task1 focusing on a full pipeline development of the ASR model from scratch with both labeled and unlabeled training data provided by the organizer. The second task is ASR-Task2 focusing on spontaneous speech in different real scenarios e.g., meeting conversation, lecture speech. In the ASR-Task2, participants can use all available data sources to develop their models without any limitations. The quality of the models is evaluated by the Syllable Error Rate (SyER) metric.
Size Effect of a Piezoelectric Material as a Separator Coating Layer for Suppressing Dendritic Li Growth in Li Metal Batteries

Li metal has been intensively investigated as a next-generation rechargeable battery anode. However, its practical application as the anode material is hindered by the deposition of dendritic Li. To suppress dendritic Li growth, introducing a modified separator is considered an effective strategy since it promotes a uniform Li ion flux and strengthens thermal and mechanical stability. Herein, we present a strategy for the surface modification of separator, which involves coating the separator with a piezoelectric material (PM). The PM-coated separator shows higher thermal resistance than the pristine separator, and its modified surface properties enable the homogeneous regulation of the Li-ion flux when the separator is punctured by Li dendrite. Furthermore, PM was synthesized in different solvents via solvothermal method to explore the size effect. This strategy would be helpful to overcome the intrinsic Li metal anode problems.
Good Governance Principles in Decision-making of a State Higher Education Institution’s Governing Body

This study determined the satisfaction of the stakeholders in terms of the good governance practices of the governing body and analyzed documents of the results of the different internal and external auditing bodies or measures. The study was a descriptive-exploratory research using a validated instrument with Cronbach’s alpha . 942 to get the satisfaction level of the decision-making of the governing body. Existing documents of the office performance commitment and review, customer feedback, AACCUP, ISO 9001, COD, PRIME-HR, finance, OPCR, and stakeholder’s satisfaction were analyzed. Results show that stakeholders’ level of satisfaction in the eight principles was very much satisfied, and that the university has achieved most of its annual targets for the specific performance indicators for the seven major final outputs which include higher education program, advanced education program, research program, technical advisory extension program, support to operations, general administration and support services, and good governance conditions. On the other hand, there are specific targets for FY 2018, FY 2019, and FY 2020 that were not met. The non-attainment of targets for these years resulted in the ineligibility of the university on the incentives in 2018, and the exclusion of major offices responsible for the non-attainment of the target on the incentives given in 2019.
Interpolation, realization, and reconstruction of noisy, irregularly sampled data

Various statistical procedures related to linear prediction and optimal filtering are developed for general, irregularly sampled, data sets. The data set may be a function of time, a spatial sample, or an unordered set. In the case of time series, the underlying process may be low-frequency divergent (weakly nonstationary)
Blockchain Enabled Architecture with Selective Consensus Mechanisms for IoT Based Saffron-Agri Value Chain

The Internet of Things (IoT) is the backbone behind numerous smart and automated applications in the modern era by providing seamless connectivity and information retrieval among the physical and virtual objects. IoT networks are resource constraint platforms hence prone to security and privacy challenges. Blockchain technology comes to the forefront to improvise the security, privacy and less dependency on the third party centralized servers. There exists a rich amount of work with numerous practical applications by fusing IoT and blockchain. In blockchain technology, the consensus mechanisms are considered to be the driving force in its implementation. In this paper, we propose a simplified blockchain based internet of things (BIoT) architecture for resource constrained IoT devices with selective consensus mechanisms based on the scale of IoT networks. We have selectively highlighted some of the important consensus algorithms which are favourable for the IoT networks. We have tailored the blockchain framework in a manner that suits to the resource constrained IoT networks. To evaluate our design, we implemented a prototype leveraging the blockchain and IoT network. The preliminary results suggest that the proposed system incorporating supply chain management of Saffron agri-value chain outperforms the existing systems. Furthermore, we have carried out a detailed case study on the cultivation and marketing strategies for maintaining the originality and transparency starting from farmer-to-consumer as saffron-Agri value chain.
Computing and Information Sciences AUTOMATION OF PERFORMANCE TESTING: A REVIEW

: Quality assurance of software applications becomes crucial for achieving a competitive advantage in the market. Automating the testing process reduces the required cost and human effort. However, automating the entire testing process is still challenging for both academia and industry. It is essential to verify the robustness of the application under test (AUT) by conducting performance testing. Performance testing is a non-functional form of software testing which examines the performance features of the AUT when exposed to various workloads. The performance behavior can be measured by throughput, response time, and resource utilization of the AUT under a certain workload. Besides, performance testing finds the performance breaking points and bottlenecks during the operation of the AUT. Due to the necessity of conducting performance testing before releasing applications to the market, this paper surveys the previous related work to performance testing since 2009. Recent studies related to performance testing for testing both mobile and web-based applications are discussed. The strengths and weaknesses of these studies are discussed. Besides, a comparison between the previous studies related to performance testing is held from different perspectives.
Robust geometric computation based on the principle of independence

We present an approach to robust geometric algorithms, which we call the principle of independence. In this approach, we distinguish between independent judgments and dependent judgments, and use numerical computation only for independent judgments. The result of judgments is always consistent and hence algorithms behave stably even in the presence of large numerical errors. The basic idea of this principle is described with three examples.
Spearman's footrule under progressive censoring

Spearman's footrule, a well-known measure of rank correlation, is extended here to progressively censored rankings. Under the hypothesis of randomness ( i.e. , under random ranking), a martingale characterization is exploited in the formulation of a functional central limit theorem, and its applications in incomplete rankings are illustrated.
Dynamics of a Projectile Penetrating Sand

The experiment reported in this paper was designed to obtain data on the dynamics of a nonrotating, conical‐nosed projectile penetrating randomly‐packed sand. Position versus time measurements for the projectile in sand were obtained by means of a photographic‐electronic chronograph developed for the purpose. The striking velocity v0 of all rounds was about 700 m/sec. The negative acceleration of a 5‐in. long, 0.50‐caliber, 80‐gram projectile was found to be roughly expressible by the equation −dv/dt=αv2+βv+γ where the coefficients α, β, and γ are positive constants. This general relation includes as special cases the conventional penetration formulas of Robins‐Euler, Poncelet, and Resal. A new theory of penetration is proposed based on the equations: −dv/dt=αv2,v0>v>vc;−dv/dt=βv2+γ,vc>v>0 where the coefficients α, β, γ are positive constants and α<β. An abrupt transition in the drag force that occurs at the critical velocity vc of about 100 m/sec is believed due to transition from inelastic to quasi‐elas.
Observations of Mon R2 in the J = 2->1 line of carbon monoxide.

Observations of the J = 2→1 CO line have been made in the galactic source Mon R2. Observations of the 12 CO and 13 CO isotopes at 230 and 220 GHz indicate that the 13 CO is optically thick with τ 13 > 1.1. there is a close similarity between line intensities and spectral shapes in the 12 CO J = 1→0 and 2→1 transitions.
High-throughput formation and control of monodisperse liquid crystals droplets driven by an alternating current electric field in a microfluidic device

We investigate the effect of an applied ac high voltage on a confined stable nematic liquid crystal (LC) in a microfluidic device and show that this actuation leads to the formation of highly monodisperse microdroplets with an unexpected constant mean size over a large interval of the forcing frequency F and with a droplets production frequency f≃2F. We show also that despite the nonlinear feature of the droplets formation mechanism, droplets size, and size distribution are governed simply by the LC flow rate Qd and the forcing frequency F.
Young-star formation in the Magellanic H I bridge

On a obtenu une photographie Hα+N[II]. L'objet le plus remarquable est une coquille filamentaire de 160 pc de diametre. On demontre qu'une unique etoile 08 peut photoioniser cette structure. Le vent d'une etoile de ce type pourrait donner naissance a cette coquille en ∼5.10 6 ans. L'explosion d'une supernova d'energie cinetique 10 50 -10 51 ergs conduirait au meme resultat
Density-of-States Limited Contact Resistance in Graphene Field-Effect Transistors

Graphene has attracted much attention as one of promising candidates of future high-speed transistor materials because of its high carrier mobility of more than 10,000 cm2 V-1 s-1. Up to this point, we have focused on the contact properties as performance killers, as a very small density of states in graphene might suppress the current injection from metal to graphene. This paper systematically reviews the metal/graphene contact properties and discusses the present status and future requirements of the specific contact resistivity.
VLA observations of OH in galaxies. I. NGC 253 and its nuclear plume

The distribution of emission and absorption has been mapped in all four 18 cm lines of OH in the nuclear regions of NGC 253, using the VLA with a resolution of 17''. 7 x 9''. 5. A steep rotation curve is found over the inner 150 pc radius, yielding a nuclear mass of approx.5 x 10/sup 8/ M/sub sun/, the same as found from IR observations. The absorbing gas covers a range of 60--550 km s/sup -1/, wider than seen optically or as yet in H I. Clumping of the absorbing gas implies individual entities approx.20 pc in size, suggestive of giant molecular clouds in the Milky Way. The absorbing gas appears to be distributed in a toroid of mean radius 170 pc centered on the nucleus.
SYMMETRY AND INSTABILITY IN ROTATING HYDRODYNAMIC AND MAGNETOHYDRODYNAMIC FLOWS

The effect of symmetries on instabilitites in rotating hydrodynamic and magnetohydrodynamic flows is considered. Examples are described in which models of such flows or special cases have O(2) symmetry instead of the expected SO(2) symmetry. This change in symmetry has a profound effect on the nature of any instabilities and on their subsequent evolution. Both primary and secondary instabilities in systems with O(2) symmetry are discussed, and the effects of restoring SO(2) symmetry are analyzed. Implications of the results for convection in a rotating annulus, magnetohydrodynamic instabilities in accretion disks and a two‐layer baroclinic instability model are described.
Stop-less Lyot coronagraph for exoplanet characterization: design, manufacturing, and tests of the apodizer

Upcoming high-contrast imagers will all provide spectroscopic capabilities for the characterization of directly detected giant planets in wide orbits. While integral field spectroscopy (IFS) can provide both spatial and spectral information, it is usually limited in terms of field of view and resolution. The alternative is to use long slit spectroscopy coupled with coronagraphy (LSC), which can easily provide higher resolution and larger field of view. The SPHERE instrument for the VLT provides a LSC mode in its near-infrared imager and spectrograph, IRDIS. However, the fact that the occulting coronagraphic mask is merged in the focal plane with the slit reduces significantly its capacity to attenuate the diffraction, limiting the high-contrast capabilities of the instrument at close angular separations (0.3"-0.4"). To improve the diffraction suppression of the LSC in IRDIS, we recently proposed to use the stop-less Lyot coronagraph (SLLC) to build an apodized long slit coronagraph (ALSC), and we demonstrated that it improves notably the performance at small angular separation, allowing the spectral analysis of colder planets. The design of the SLLC apodizer has been optimized for an implementation in SPHERE/IRDIS, and it has recently been manufactured before being inserted into the instrument during reintegration of SPHERE in Paranal. In the current work, we present the final design of the SLLC apodizer, its specifications for the manufacturing step, and the first results obtained on SPHERE. We compare the results between the simple LSC and the new ALSC, and we draw the conclusions on the advantages and drawbacks of our design.
Understanding inversion layers and band discontinuities in hydrogenated amorphous silicon/crystalline silicon heterojunctions from the temperature dependence of the capacitance

The temperature dependence of the capacitance of very high efficiency silicon heterojunction solar cells exhibits an anomalously large increase with temperature that cannot be explained under the usual depletion approximation. Based on a full calculation of the capacitance, we show that this large increase of capacitance with temperature of p-type hydrogenated amorphous silicon (a-Si:H)/n-type crystalline silicon (c-Si) heterojunctions occurs when a strong inversion layer at the c-Si surface appears. It is further shown that due to the promotion of inversion as the temperature increases, the temperature at which strong inversion appears depends on the valence band offset and position of the Fermi level in a-Si:H. Therefore, a simple analysis of the temperature dependence of silicon heterojunction solar cells' capacitance can be used to reveal the presence of a strong inversion, to study details of the band diagram and to get insight into the heterointerface.
Bridging the Domain Gap in Satellite Pose Estimation: a Self-Training Approach based on Geometrical Constraints

Recently, unsupervised domain adaptation in satellite pose estimation has gained increasing attention, aiming at alleviating the annotation cost for training deep models. To this end, we propose a self-training framework based on the domain-agnostic geometrical constraints. Specifically, we train a neural network to predict the 2D keypoints of a satellite and then use PnP to estimate the pose. The poses of target samples are regarded as latent variables to formulate the task as a minimization problem. Furthermore, we leverage fine-grained segmentation to tackle the information loss issue caused by abstracting the satellite as sparse keypoints. Finally, we iteratively solve the minimization problem in two steps: pseudo-label generation and network training. Experimental results show that our method adapts well to the target domain. Moreover, our method won the 1st place on the sunlamp task of the second international Satellite Pose Estimation Competition.
Multi-Frequency Acoustic Steering in Rationally Pruned Disordered Networks

The frequency of a wave is a crucial parameter to understanding its propagation to a structured medium. While metamaterials can be designed to focus or steer deformations or waves toward a specific location, wave steering toward multiple targeted locations at different frequencies remains challenging. Here we show that pruning of random elastic networks allows realizing such multi-objective, multi-frequency acoustic steering. Our study opens up a viable route to the rational design of multi-frequency acoustic metamaterials.
Deep Learning of Semi-Competing Risk Data via a New Neural Expectation-Maximization Algorithm

Prognostication for lung cancer, a leading cause of mortality, remains a complex task, as it needs to quantify the associations of risk factors and health events spanning a patient’s entire life. One challenge is that an individual’s disease course involves non-terminal (e.g., disease progression) and terminal (e.g., death) events, which form semi-competing relationships. Our motivation comes from the Boston Lung Cancer Study, a large lung cancer survival cohort, which investigates how risk factors inﬂuence a patient’s disease trajectory. Following developments in the prediction of time-to-event outcomes with neural networks, deep learning has become a focal area for the development of risk prediction methods in survival analysis. However, limited work has been done to predict multi-state or semi-competing risk outcomes, where a patient may experience adverse events such as disease progression prior to death. We propose a novel neural expectation-maximization algorithm to bridge the gap between classical statistical approaches and machine learning. Our algorithm enables estimation of the non-parametric baseline hazards of each state transition, risk functions of predictors, and the degree of dependence among diﬀerent transitions, via a multi-task deep neural network with transition-speciﬁc sub-architectures. We apply our method to the Boston Lung Cancer Study and investigate the impact of clinical and genetic predictors on disease progression and mortality.
The Effects of Just-in-time Delivery on Social Engagement: A Cluster Analysis

Fooji Inc. is a social media engagement platform that has created a proprietary"Just-in-time"delivery network to provide prizes to social media marketing campaign participants in real-time. In this paper, we prove the efficacy of the"Just-in-time"delivery network through a cluster analysis that extracts and presents the underlying drivers of campaign engagement. We utilize a machine learning methodology with a principal component analysis to organize Fooji campaigns across these principal components. The arrangement of data across the principal component space allows us to expose underlying trends using a $K$-means clustering technique. The most important of these trends is the demonstration of how the"Just-in-time"delivery network improves social media engagement.