Miracle-dz/UniFAR-FTUs huggingface dataset:
Constraining inflation with future galaxy redshift surveys

With future galaxy surveys, a huge number of Fourier modes of the distribution of the large scale structures in the Universe will become available. These modes are complementary to those of the CMB and can be used to set constraints on models of the early universe, such as inflation. Using a MCMC analysis, we compare the power of the CMB with that of the combination of CMB and galaxy survey data, to constrain the power spectrum of primordial fluctuations generated during inflation. We base our analysis on the Planck satellite and a spectroscopic redshift survey with configuration parameters close to those of the Euclid mission as examples. We first consider models of slow-roll inflation, and show that the inclusion of large scale structure data improves the constraints by nearly halving the error bars on the scalar spectral index and its running. If we attempt to reconstruct the inflationary single-field potential, a similar conclusion can be reached on the parameters characterizing the potential. We then study models with features in the power spectrum. In particular, we consider ringing features produced by a break in the potential and oscillations such as in axion monodromy. Adding large scale structures improves the constraints on features by more than a factor of two. In axion monodromy we show that there are oscillations with small amplitude and frequency in momentum space that are undetected by CMB alone but can be measured by including galaxy surveys in the analysis.
High-resolution hypernuclear spectroscopy at Jefferson Lab, Hall A

The experiment E94-107 in Hall A at Jefferson Lab started a systematic study of high resolution hypernuclear spectroscopy in the 0p-shell region of nuclei such as the hypernuclei produced in electroproduction on 9Be, 12C and 16O targets. In order to increase counting rates and provide unambiguous kaon identification two superconducting septum magnets and a ring-imaging Cherenkov detector were added to the Hall A standard equipment. The high-quality beam, the good spectrometers and the new experimental devices allowed us to obtain very good results. For the first time, measurable strength with sub-MeV energy resolution was observed for the core-excited states of Lambda 12B. A high-quality Lambda 16N hypernuclear spectrum was likewise obtained. A first measurement of the Lambda binding energy for Lambda 16N, calibrated against the elementary reaction on hydrogen, was obtained with high precision, 13.76 +/- 0.16 MeV. Similarly, the first Lambda 9Li hypernuclear spectrum shows general agreement with theory (distorted-wave impulse approximation with the SLA and BS3 electroproduction models and shell-model wave functions). Some disagreement exists with respect to the relative strength of the states making up the first multiplet. A Lambda separation energy of 8.36 MeV was obtained, in agreement with previous results. It has been shown that the electroproduction of hypernuclei can provide information complementary to that obtained with hadronic probes and the gamma-ray spectroscopy technique.
Cavity-enhanced radiative emission rate in a single-photon-emitting diode operating at 0.5 GHz

We report the observation of a Purcell enhancement in the radiative decay rate of a single quantum dot, embedded in a microcavity light-emitting-diode structure. Lateral confinement of the optical mode was achieved using an annulus of low-refractive-index aluminium oxide, formed by wet oxidation. The same layer acts as a current aperture, reducing the active area of the device without impeding the electrical properties of the p-i-n diode. This allowed single photon electroluminescence to be demonstrated at repetition rates up to 0.5 GHz.
Mining Composite Spatio-Temporal Lifestyle Patterns from Geotagged Social Data

As social networks become increasingly integrated with their users’ daily lives, and users are willing to publicly share data about their offline activities on these networks, the resultant data offers a powerful tool to non-intrusively understand city dynamics as it captures human behaviour and interactions. In this paper, we derive lifestyle patterns from the Foursquare social network data, using matrix factorization and tensor decomposition as unsupervised methods to extract latent spatio-temporal behavior patterns. The extracted patterns offer precise definition of activity levels associated with specific lifestyles and showcase that users’ behaviors are a combination of several lifestyles, in contrast to traditional circadian topology theory which classifies individuals to a specific temporal pattern. The obtained patterns can provide deeper insights into city dynamics, the people within them and how society functions.
Deep Learning for Native Advertisement Detection in Electronic News: A Comparative Study

Native advertising is a type of commercial hybrid content that has successfully targeted online consumers. Native ads have become popular, especially on online platforms. People often don't realize that they are reading sponsored and paid content making native advertising very effective. Despite these benefits, native advertising has provoked strong negative responses, often accompanied by criticism and avoidance of the ads. This study aims to investigate the performance of different word embedding methods (BERT, GloVe, FastText) when used in combination with different deep learning methods (BiLSTM, CNN, LSTM) in detecting native ads in electronic news. The dataset used in this study was collected from the news portal, detik.com. Among these models, the BERT-BiLSTM model achieved the highest accuracy, f1 score, recall, precision and AUC score, all with the same value of 95%, compared to the other models, showing that the BERT-BiLSTM model outperformed the other models.
Retinal Fundus Image Segmentation Based on Channel-Attention Guided Network

With the frequent use of electronic products by modern people, retinal diseases have become one of the most common maladies. Among them, Glaucoma is a serious visual disorder that contributes to irreversible visibility loss and blindness due to the destruction of the optic nerve. The clinical diagnosis of glaucoma is performed manually by experienced ophthalmologists to evaluate the diameter ratio of the optic cup (OC) to the optic disc (OD) called the cup to disc ratio (CDR). In this paper, we propose a deep learning framework named CG-Net for OC and OD segmentation simultaneously using the channel attention method. The proposed CG-Net is inspired by the U-Net with multi-scale input. In addition, a novelty channel attention block is introduced to incorporate the feature map between different layers and reduce the background noise for better attention to the target area. We extensively evaluate our framework on the ORIGA dataset and a comparison with several state-of-the-art methods also demonstrates the segmentation effectiveness of our method. The results indicate that the proposed model outperforms other methods in terms of segmentation performance and achieves a total average overlap error of 0.139 in the metrics.
Strategic Decision Making for a Resilient Supply Chain during a Pandemic

Due to the impact of the COVID-19 pandemic, many organizations experienced supply chain disruptions to various degrees. Due to the nature and magnitude of the pandemic, many commonly practiced supply chain practices became impractical, highlighting the importance of building up resilience in the supply chain network to enable them to operate effectively in uncertain conditions. In an organizational context, modern-day supply chains are prone to experience quick disruptions because of their inherent complexity. Due to the nature of the Sri Lankan economy, the scope of disruption to trade and the ripple effect of disruptions to associated industries would be different from those of developed nations. The objective of this study is to understand the concept of supply chain resilience and its applicability when facing a critical disruptive force such as a pandemic. A thorough literature review and interviews were conducted with leading FMCG companies in Sri Lanka to determine how supply chain resilience could be achieved by overcoming the most critical risks encountered during a pandemic. Based on the data collected, a conceptual framework was developed to support strategic decision-making to achieve supply chain resilience during high-impact low-frequency disruptions such as a pandemic.
Spectroscopic analysis of the phase diagram of Yang-Mills-Higgs theory

Yang-Mills-Higgs theory, being the standard-model Higgs sector for a suitable choice of gauge and custodial group, offers a rich set of physics. In particular, in some region of its parameter space it has QCD-like behavior, while in some other region it is Higgs-like. There- fore, it is possible to study a plethora of phenomena within a single theory. Here, the physics of the standard-model version is studied using lattice gauge theory at a qualitative level. To this end, the low-lying spectrum in several different channels is obtained for more than 160 different sets of bare parameters throughout the phase diagram. The theory shows quite different behaviors in the different regions, from almost Yang-Mills-like to the one of an essentially free gas of massive photons. Especially, not always is the behavior as naively expected.
Factors Influencing the Secondary Level Students’ Satisfaction in E-Learning: A Case Study of an Educational Institute in Sri Lanka

With the covid-19 pandemic, e-learning has shown significant growth in Sri Lanka over the last few years. As a remedy to sudden school closure during the covid-19 outbreak, educational institutes have adopted e-learning to minimize the disruption of education. Even though there are benefits, teachers complained that the satisfaction of secondary level students is declining, and it has impacted the academic performance to become low. Therefore, this research is conducted to investigate the factors influencing the secondary level students’ satisfaction in e-learning at an educational institute in Sri Lanka from students’ perspectives. This study takes 211 students from secondary-level students in an educational institute as participants. The data were gathered through online questionnaires undertaking a Quantitative approach. Overall results indicate that flexibility is the most influencing factor. Respectively, the quality of the e-learning system/platform, Interactivity, quality of the Internet, and quality of the learning material influence students’ satisfaction. As per the recommendations, the educational institute is suggested to select a suitable e-learning platform and use Learning Management System (LMS). Also, they are suggested to provide a fixed timetable for teachers. The teachers are encouraged to be more interactive and to use computer-based learning materials to deliver the content. Also, an educational institute is suggested to provide adequate teacher training in creating resource materials. The Ministry of Education is suggested to provide a free e-learning system and data package for less cost. Also, the Ministry of Education is recommended to take strategic decisions to enhance school curriculums to be interactive. E-learning system designers should be aware of the school curriculum in designing e-learning systems. And the Government is encouraged to increase the coverage and infrastructure facilities to establish a satisfying e-learning environment.
Practice And Exploration Of Auricular Therapy Teaching Based On VR Platform

Auricular therapy is a characteristic technique of acupuncture in Chinese medicine and is an important part of traditional Chinese medicine. However, the existing research results on auricular therapy, including auricular point localization and auricular stimulation response pathways, generally lack visualization, which greatly limits the promotion, teaching, and research of auricular therapy. Therefore, through technological innovation, the use of the latest 3D visualization technology such as VR can not only promote the propagation of auricular therapy, but also provide an intuitive and visible digital dynamic display for clinical training and teaching of auricular pivot adjustment to fill the gap in auricular education. The design of auricular therapy teaching based on VR platform can promote the diversification of auricular teaching in practice, and also effectively improve students' understanding of auricular knowledge points.
ON THE CONSERVATION OF CROSS HELICITY AND WAVE ACTION IN SOLAR-WIND MODELS WITH NON-WKB ALFVÉN WAVE REFLECTION

The interaction between Alfvén-wave turbulence and the background solar wind affects the cross helicity ( ∫ d 3 x v · B ? > ) in two ways. Non-WKB reflection converts outward-propagating Alfvén waves into inward-propagating Alfvén waves and vice versa, and the turbulence transfers momentum to the background flow. When both effects are accounted for, the total cross helicity is conserved. In the special case that the background density and flow speed are independent of time, the equations of cross-helicity conservation and total-energy conservation can be combined to recover a well-known equation derived by Heinemann and Olbert that has been interpreted as a non-WKB generalization of wave-action conservation. This latter equation (in contrast to cross-helicity and energy conservation) does not hold when the background varies in time.
A search for water maser emission toward obscured post-AGB star and planetary nebula candidates

Water maser emission at 22 GHz is a useful probe to study the transition between the nearly spherical mass-loss in the AGB to a collimated one in the post-AGB phase. In their turn, collimated jets in the post-AGB phase could determine the shape of planetary nebulae (PNe) once photoionization starts. We intend to find new cases of post-AGB stars and PNe with water maser emission, including water fountains or water-maser-emitting PNe. We observed water maser emission in a sample of 133 objects, with a significant fraction being post-AGB and young PN candidate sources with strong obscuration. We detected this emission in 15 of them, of which seven are reported here for the first time. We identified three water fountain candidates: IRAS 17291-2147, with a total velocity spread of ~96 km/s in its water maser components and two sources (IRAS 17021-3109 and IRAS 17348-2906) that show water maser emission outside the velocity range covered by OH masers. We have also identified IRAS 17393-2727 as a possible new water-maser-emitting PN. The detection rate is higher in obscured objects (14%) than in those with optical counterparts (7%), consistent with previous results. Water maser emission seems to be common in objects that are bipolar in the near-IR (43% detection rate). The water maser spectra of water fountain candidates like IRAS 17291-2147 show significantly less maser components than others (e.g., IRAS 18113-2503). We speculate that most post-AGBs may show water maser emission with wide enough velocity spread (> 100 km/s) when observed with enough sensitivity and/or for long enough periods of time. Therefore, it may be necessary to single out a special group of "water fountains", probably defined by their high maser luminosities. We also suggest that the presence of both water and OH masers in a PN is a better tracer of its youth, rather than the presence of just one of these species.
Speech Separation Using Augmented-Discrimination Learning on Squash-Norm Embedding Vector and Node Encoder

Speech separation has been employed in important applications such as automatic speech, paralinguistics, speech recognition, hearing aids, and human-machine interactions. In recent years, deep neural networks have been widely used for speech and music separation. Some of these breakthrough successful models based on embedding vectors have been proposed, such as deep clustering. In this paper, we propose a node encoder Squash-norm deep clustering (ESDC) as an enhanced discriminative learning framework by combining node encoder, Squash-norm, and deep clustering (DC). First, a node encoder is used to create intermediate features. Node encoders are developed through a matrix factorization-based learning method for graph representations. It creates distinguishable intermediate features that play an important role in improving performance. These discriminated intermediate features are then used as input features for the separation block. The decoder block finally constructs the estimation mask through the clustering method and reconstructs the estimated signal for each source. In particular, we apply a normalization function, Squash-norm, to the input and output vectors to enhance the distinction between high-dimensional embedding vectors. This nonlinear function amplifies the differences in the input vectors, resulting in highly unique features, which are scalar products of the vectors. Similar to the input vector, Squash-norm also enhances the discrimination of the output vector, thereby enhancing the ability to construct an estimated mask by clustering the output vector. Overall, the proposed ESDC achieves 1.27–2.09 dB SDR, 1.28–2.21 dB SDRi, and 1.3–2.44 dB SI-SNRi gain compared to the DC baseline separation performance across genders on the TSP and TIMIT datasets. With the same gender, our proposed ESDC achieves 1.14–2.71 dB SDR, 0.99–2.74 dB SDRi, and 0.62–2.86 dB SI-SNRi gain compared with the DC baseline on the TIMIT dataset. In all cases, the proposed ESDC model consistently maintains STOI and PESQ higher than the DC baselines on the TSP and TIMIT datasets.
Predicting Mothers with Postpartum Depression using Machine Learning Approaches

Postpartum depression (PPD) occurs in some mothers after giving childbirth because of changes in their physical, behavioral, and emotional. This mental disorder is hard to predict and its symptoms are complex. The main objective of this research is to develop a model to predict postpartum depression risk levels using mother's family, social background, and other data-related status of the mother. Also, using Edinburgh Postpartum Depression Scale (EPDS) has classified risk levels into 4 classes mild, moderate, severe, and profound, and trained and evaluated the proposed system on a Sri Lankan mothers’ dataset based on their postnatal period within 6 months of childbirth. To build the proposed system this study has used Feed-Forward Neural Network (FFANN), Adaptive Neuro-Fuzzy Inference System with Genetic Algorithm (ANFIS - GA), Random Forest (RF), and Support Vector Machine (SVM). Because after reviewing past literature can find many models have gotten the best performance through these models. Finally, depending on the model's performance has supposed to identify which model has good performance when predicting. After model training and testing, the FFANN model (95% accuracy) and the ANFIS - GA model (testing error: 0.0600) have good performance as classification and regression types of models, respectively. Then comparing both models' performance, concluded that FFANN with multi-classification has the best performance when predicting postpartum depression risk levels. Further, it helps to identify more influential factors as well.
Dissecting the star formation history of starburst galaxies: the case of NGC 7673

We have collected archival data on NGC 7673 to constrain the star formation history that produced the young star clusters and the field stellar population in this galaxy during the last 2 Gyr. We have considered the sample of 50 star clusters detected by Hubble Space Telescope/WFPC2 in the UV, V and I bands and estimated their age, intrinsic reddening and mass via comparison of their colours with STARBURST99 models. We have found two prominent epochs of cluster formation occurred about 20 and 2 Myr ago, with somewhat minor events between 3 and 6 Myr ago. The star clusters are characterized by an intrinsic reddening E(B-V) < 0.4 mag and a mass lower than 2 x 10 6 M ⊙ . Out of the 50 star clusters, we have selected 31 located within the boundaries of the (IUE) large slit that was employed to obtain the spectrum of NGC 7673 between 1150 and 3350 A. For each cluster, we have built a synthetic spectrum corresponding to the age, mass and intrinsic reddening derived from the cluster colours, properly redshifted to NGC 7673. The spectra have then been added together in a final, clusters integrated spectrum. This and the IUE, FUSE spectra of NGC 7673 have allowed us to describe the star formation history of the unresolved stars in the field as either exponentially decaying or multiburst. In the first case, we have derived an e-folding time of 700 (900) Myr and an initial star formation rate of 16 (13) M ⊙ yr -1 when the Fitzpatrick's (Calzetti's) extinction law is used. In the case of a multiburst star formation history, we have assumed that the same bursts which produced the star clusters built up also the field population. In this way, the field population turns out to be composed by a young (<40 Myr) component three (two) times brighter than the star clusters, and a component as old as ∼850 (450) Myr, about 200 (100) times more massive than the star clusters together. These star formation histories fit equally well the observed UV spectrum of NGC 7673.
S0025315422000388jra 157..165

Humpback whales (Megaptera novaeangliae) exhibit maternally driven fidelity to feeding grounds, and yet occasionally occupy new areas. Humpback whale sightings and mortalities in the New York Bight apex (NYBA) have been increasing over the last decade, providing an opportunity to study this phenomenon in an urban habitat. Whales in this area overlap with human activities, including busy shipping traffic leading into the Port of New York and New Jersey. The site fidelity, population composition and demographics of individual whales were analysed to better inform management in this high-risk area. Whale watching and other opportunistic data collections were used to identify 101 individual humpback whales in the NYBA from spring through autumn, 2012–2018. Although mean occurrence was low (2.5 days), mean occupancy was 37.6 days, and 31.3% of whales returned from one year to the next. Individuals compared with other regional and ocean-basin-wide photo-identification catalogues (N = 52) were primarily resighted at other sites along the US East Coast, including the Gulf of Maine feeding ground. Sightings of mother-calf pairs were rare in the NYBA, suggesting that maternally directed fidelity may not be responsible for the presence of young whales in this area. Other factors including shifts in prey species distribution or changes in population structure more broadly should be investigated.
Common Object Request Broker-based Publisher-Subscriber Middleware for Internet of Things - Edge Computing

The edge computing layer in IoT reduces the flow of a massive amount of data directly to the cloud by processing some data in the local network. The middleware in the layer enables this processing of data and the communication between heterogeneous devices and services in the nearby layers. CORBA, which uses as a powerful middleware technology in developing middleware solutions in enterprise-level distributed applications, has been abandoned in the current generation. The paper presents the design, and the performance evaluation of a publisher-subscriber middleware implemented using CORBA that was studied when exploring the applicability of CORBA as an IoT edge computing middleware. The evaluation was continued in two steps to analyse several parallel connections (Load test) and handle requests in a unit time (burst test) via simulating an IoT environment in a cloud environment.