Adaptive Sparse Domain Selection for Weather Radar Superresolution

Accurate and high-resolution weather radar data reflecting detailed structure information of radar echo plays an important role in analysis and forecast of extreme weather. Typically, this is done using interpolation schemes, which only use several neighboring data values for computational approximation to get the estimated, resulting the loss of intense echo information. Focus on this limitation, a superresolution reconstruction algorithm of weather radar data based on adaptive sparse domain selection (ASDS) is proposed in this article. First, the ASDS algorithm gets a compact dictionary by learning the precollected data of model weather radar echo patches. Second, the most relevant subdictionaries are adaptively select for each low-resolution echo patches during the spare coding. Third, two adaptive regularization terms are introduced to further improve the reconstruction effect of the edge and intense echo information of the radar echo. Experimental results show that the ASDS algorithm substantially outperforms interpolation methods for ×2 and ×4 reconstruction in terms of both visual quality and quantitative evaluation metrics.

A Fast and Lightweight Detection Network for Multi-Scale SAR Ship Detection under Complex Backgrounds

It is very difficult to detect multi-scale synthetic aperture radar (SAR) ships, especially under complex backgrounds. Traditional constant false alarm rate methods are cumbersome in manual design and weak in migration capabilities. Based on deep learning, researchers have introduced methods that have shown good performance in order to get better detection results. However, the majority of these methods have a huge network structure and many parameters which greatly restrict the application and promotion. In this paper, a fast and lightweight detection network, namely FASC-Net, is proposed for multi-scale SAR ship detection under complex backgrounds. The proposed FASC-Net is mainly composed of ASIR-Block, Focus-Block, SPP-Block, and CAPE-Block. Specifically, without losing information, Focus-Block is placed at the forefront of FASC-Net for the first down-sampling of input SAR images at first. Then, ASIR-Block continues to down-sample the feature maps and use a small number of parameters for feature extraction. After that, the receptive field of the feature maps is increased by SPP-Block, and then CAPE-Block is used to perform feature fusion and predict targets of different scales on different feature maps. Based on this, a novel loss function is designed in the present paper in order to train the FASC-Net. The detection performance and generalization ability of FASC-Net have been demonstrated by a series of comparative experiments on the SSDD dataset, SAR-Ship-Dataset, and HRSID dataset, from which it is obvious that FASC-Net has outstanding detection performance on the three datasets and is superior to the existing excellent ship detection methods.

Pitch Accent Distribution and Focus Structure in Taifi Arabic: A Production Study

Prosodic encoding of focus in Taifi Arabic is not yet fully understood. A recent production study found significant acoustic differences between syntactically identical sentences with information focus, contrastive focus and without focus. This paper presents results from a production experiment investigating whether information and contrastive focus have prosodic effects on the pitch-accent distributions. Using question-answer paradigms, 16 native speakers of Taifi Arabic were asked to read three target sentences in different focus conditions. Results reveal that every content word is pitch-accented in utterances with and without focus. However, there are very few cases (23.12%) in which the post-focus words are deaccented. The largest percentage of deaccentuation was observed in the utterances with initial contrastive focus. The results show that focus structures in Taifi Arabic show both deaccentuation and post-focus compression. Therefore, the prosodic realization of focus in Taifi Arabic is different from their counterparts in other Arabic dialects such as Egyptian and Lebanese Arabic. These findings have an important implication for both the prosodic typology and focus typology.

Postverbal subjects in child Italian: Argument structure, discourse and the definiteness effect

While previous work on postverbal subjects in Italian has shown that young children are sensitive to the effects of argument structure and definiteness, little is known about the acquisition of postverbal subjects at the VP-periphery. In response, the present study investigated such subjects under new-information focus by monolingual Italian children (6;1 – 7;4). For this, we employed an elicitation task and a forced-choice task. The results indicated that the children use postverbal subjects at the VP-periphery felicitously, although they do not perform at ceiling. Unexpectedly, the results also suggested possible remnant difficulty with the definiteness effect. However, after comparison with adult data we argue that this is not a developmental issue and instead may suggest that our children were aware that the definiteness effect is not about definiteness per se.

The prosodic properties of narrow information focus in Central Mexican Spanish: Pitch accents, de-emphasis and phrasing

This paper investigates the different prosodic strategies used for the marking of information focus in Central Mexican Spanish. For this purpose, we carried out a study of the prosodic properties of information focus both in clause final position and in situ. Our results show important differences when compared to other varieties of Spanish. Specifically, we observe that the most frequent accent signaling information focus is a monotonal pitch accent (L* or !H*) and not L+H*. Furthermore, in many cases we observe that the pitch accent is not the only mechanism used to signal the focus: this is because we observe the presence of prosodic edges to the left of the focus, presumably functioning as an additional prosodic cue to identify it. Additionally, while we do not observe deaccenting of post-focal material, we do observe a sequence of non-rising forms (a flat pattern or “de-emphasis”) following the pitch accent that signals an in situ information focus forced by the test. With respect to phonological phrasing, our results confirm the analysis in Prieto (2006), where it is proposed that syntactic constituency is not the primary factor that regulates phrasing in Spanish.

Not on air, but Online: The Labor Conditions of the Digital Journalist in U.S. Local Television Newsrooms

As American news preferences shift from broadcast to digital platforms, corporate-owned local television stations have hired digital teams to keep a growing array of mobile, social, web, and over-the-top platforms updated with revenue-generating and audience-friendly information. Yet, these workers are currently missing from the labor literature. Therefore, this exploratory study uses a political economy framework with a labor focus to begin to understand the day-to-day working conditions of these employees. Interviews outline workload issues including long hours of multitasking and nearly-constant connectivity even when off the clock, sped-up production expectations with a commodified information focus, and limited worker protections. The findings here aim to provide a starting point for digital journalism labor studies moving forward.

El foco, la estructura informativa y ser focalizador en el español cibaeño

In this manuscript, I examine information focus and contrastive focus in Cibaeño Dominican Spanish (CDS), in an attempt to test the predictions of Zubizarreta (1998) as well as claims of rigid word order (e.g. Cameron, 1993; Toribio, 2000). Analysis of the experimental results from 34 monolingual CDS speakers suggests that CDS does not behave according to Zubizarreta's proposal, nor does it have rigid word order. Additionally, the preference for in situ contrastive-corrective focus bears a potential for information-structure-related ambiguities. I suggest that the ser focalizador structure, which is exhaustive in CDS, is made available to resolve such cases of ambiguity. Novel ser focalizador data informs a revised syntactic analysis of the structure based on Toribio (1993).

Socio-Economic Drivers for Farming Households’ Decision to Conserve Nature around Mount Uluguru, Tanzania

Nature conservation means preservation or restoration of the environment and wildlife. The activity is just beyond someone's will; it is highly influenced by socio-economic factors. This study examined the drivers for farming households to conserve the nature around Mount Uluguru in Morogoro, Tanzania. A survey was conducted in five villages around the Arc Uluguru Mountain, and only 106 respondents were randomly selected. To supplement the information, focus group discussions were held with village leaders and environmental committee members in each village who gave their precious information for analysis. Descriptive analysis was done using frequencies, percentage, and mean for examining characteristics of the sampled population, while the binary logistic model was used to analyze the factors that drive farmers to participate in nature conservation. It was found that farming experience, access to support services and awareness in bylaws and regulations increase the chances of participating in environmental conservation amongst the household members. Also, being a male individual and having old age decreases the chances of engagement to nature conservation activities. The study recommends that, in conservation activities, gender roles should be taken into consideration, as anyone can take charge and participate in nature conservation. Furthermore, more training, materials, and equipment are needed from governmental and NGO’s to help nature conservation in Mount Uluguru.

Laras Desa: Rancangan Konsep Media Komunitas Pemberdayaan Keluarga

Family Welfare Empowerment (PKK) is a forum for women's involvement in the main development of the family empowerment. However, there are obstacles in the regeneration process. Therefore, the Magelang Regency Government made an innovative program by forming the Millennial Family Welfare Empowerment. One of the goals of the Millennial Family Welfare Empowerment is to implement innovation and accelerate family empowerment activities. This is in line with the role of community media in guarding development for community empowerment. The use of community media has not been widely used as a means of empowering families and communities., as in Tempuran District. Meanwhile, the existence of community media can basically be a channel for delivering information needed by citizens. Become a public discussion forum. As well as helping to reach an agreement to solve the problem. Finally, fostering a spirit of participation in development that focuses on community empowerment. In this study using a Systematic Literature Review (SLR) approach. Those who emphasize the collection of data and information focus on the use of media in the PKK community in Tempuran, Magelang Regency. The result of this research is the creation of “Laras Desa” Community Media, which is inspired by the 10 Main Tasks of the PKK. These 10 main tasks are broken down into main points in the “Laras Desa” content, which includes information on issues of education, health, environment, socio-culture, economy, and entertainment created and needed by partner communities.

Social Network Spam Detection Based on ALBERT and Combination of Bi-LSTM with Self-Attention

Social networks are full of spams and spammers. Although social network platforms have established a variety of strategies to prevent the spread of spam, strict information review mechanism has given birth to smarter spammers who disguise spam as text sent by ordinary users. In response to this, this paper proposes a spam detection method powered by the self-attention Bi-LSTM neural network model combined with ALBERT, a lightweight word vector model of BERT. We take advantage of ALBERT to transform social network text into word vectors and then input them to the Bi-LSTM layer. After feature extraction and combined with the information focus of the self-attention layer, the final feature vector is obtained. Finally, SoftMax classifier performs classification to obtain the result. We verify the excellence of the model with accuracy, precision, F1-score, etc. The results show that the model has better performance than others.