Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploiting Unlabeled Data in CNNs by Self-Supervised Learning to Rank

For many applications the collection of labeled data is expensive laborious. Exploitation of unlabeled data during training is thus a long pursued objective of machine learning. Self-supervised learning addresses this by positing an auxiliary task (different, but related to the supervised task) for which data is abundantly available. In this paper, we show how ranking can be used as a proxy task for some regression problems. As another contribution, we propose an efficient backpropagation technique for Siamese networks which prevents the redundant computation introduced by the multi-branch network architecture. We apply our framework to two regression problems: Image Quality Assessment (IQA) and Crowd Counting. For both we show how to automatically generate ranked image sets from unlabeled data. Our results show that networks trained to regress to the ground truth targets for labeled data and to simultaneously learn to rank unlabeled data obtain significantly better, state-of-the-art results for both IQA and crowd counting. In addition, we show that measuring network uncertainty on the self-supervised proxy task is a good measure of informativeness of unlabeled data. This can be used to drive an algorithm for active learning and we show that this reduces labeling effort by up to 50 percent.
Adaptive Sequence Submodularity

In many machine learning applications, one needs to interactively select a sequence of items (e.g., recommending movies based on a user's feedback) or make sequential decisions in a certain order (e.g., guiding an agent through a series of states). Not only do sequences already pose a dauntingly large search space, but we must also take into account past observations, as well as the uncertainty of future outcomes. Without further structure, finding an optimal sequence is notoriously challenging, if not completely intractable. In this paper, we view the problem of adaptive and sequential decision making through the lens of submodularity and propose an adaptive greedy policy with strong theoretical guarantees. Additionally, to demonstrate the practical utility of our results, we run experiments on Amazon product recommendation and Wikipedia link prediction tasks.
Bigearthnet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding

This paper presents the BigEarthNet that is a new large-scale multi-label Sentinel-2 benchmark archive. The BigEarthNet consists of 590, 326 Sentinel-2 image patches, each of which is a section of i) 120 × 120 pixels for 10m bands; ii) 60×60 pixels for 20m bands; and iii) 20×20 pixels for 60m bands. Unlike most of the existing archives, each image patch is annotated by multiple land-cover classes (i.e., multi-labels) that are provided from the CORINE Land Cover database of the year 2018 (CLC 2018). The BigEarthNet is significantly larger than the existing archives in remote sensing (RS) and thus is much more convenient to be used as a training source in the context of deep learning. This paper first addresses the limitations of the existing archives and then describes the properties of the BigEarthNet. Experimental results obtained in the framework of RS image scene classification problems show that a shallow Convolutional Neural Network (CNN) architecture trained on the BigEarthNet provides much higher accuracy compared to a state-of-the-art CNN model pre-trained on the ImageNet (which is a very popular large-scale benchmark archive in computer vision). The BigEarthNet opens up promising directions to advance operational RS applications and research in massive Sentinel-2 image archives.
Network connectivity dynamics affect the evolution of culturally transmitted variants

The distribution of cultural variants in a population is shaped by both neutral evolutionary dynamics and by selection pressures, which include several individual cognitive biases, demographic factors and social network structures. The temporal dynamics of social network connectivity, i.e. the order in which individuals in a population interact with each other, has been largely unexplored. In this paper we investigate how, in a fully connected social network, connectivity dynamics, alone and in interaction with different cognitive biases, affect the evolution of cultural variants. Using agent-based computer simulations, we manipulate population connectivity dynamics (early, middle and late full-population connectivity); content bias, or a preference for high-quality variants; coordination bias, or whether agents tend to use self-produced variants (egocentric bias), or to switch to variants observed in others (allocentric bias); and memory size, or the number of items that agents can store in their memory. We show that connectivity dynamics affect the time-course of variant spread, with lower connectivity slowing down convergence of the population onto a single cultural variant. We also show that, compared to a neutral evolutionary model, content bias accelerates convergence and amplifies the effects of connectivity dynamics, whilst larger memory size and coordination bias, especially egocentric bias, slow down convergence. Furthermore, connectivity dynamics affect the frequency of high quality variants (adaptiveness), with late connectivity populations showing bursts of rapid change in adaptiveness followed by periods of relatively slower change, and early connectivity populations following a single-peak evolutionary dynamic. In this way, we provide for the first time a direct connection between the order of agents' interactions and punctuational evolution.
Climatology of Nor'easters and the 30 kPa Jet

Waves generated by nor'easters are responsible for a large portion of the coastal damage and erosion that occurs along the Atlantic coast of North America. Large-scale atmospheric circulations, such as the polar jet stream, are the driving forces behind their occurrence. A regional examination of variations in the 30 kPa (the 300 mb constant pressure level in the atmosphere) polar jet stream is conducted for the east coast of North America for 1942-1992. Jet stream variables of wind speed, latitudinal position, geopotential height, 30 kPa temperature, and flow pattern are obtained from 30 kPa mean monthly maps and correlated with the occurrence, intensity, and synoptic type of non-tropical Atlantic coastal storms. Over eastern North America, the polar vortex was most expanded in the early 1940's and the late 1950's and contracted, but less variable, during the remainder of the time period. Geopotential height displayed an inverse relationship with latitude with an overall decline in the 30 kPa height over the fifty-year period. Zonal (west-east) flow patterns of the polar jet were predominant, and upper tropospheric temperatures displayed an overall warming trend while jet stream wind speed remained stable but highly variable. A jet stream/coastal storm analysis revealed that trough flow patterns in the mean pressure field are associated with increased storm frequency. An examination of the storm origin and tracking frequencies displayed a statistically significant relationship between the most potentially dangerous storm types (Bahamas, Florida, and Gulf Lows) and a southerly position and decreased geopotential height of the jet stream. Knowledge of these relationships will improve our ability to understand and predict the impact of various storm-generated wave climates upon the coastal zone.
Investigating the Effect of Segmentation Methods on Neural Model based Sentiment Analysis on Informal Short Texts in Turkish

This work investigates segmentation approaches for sentiment analysis on informal short texts in Turkish. The two building blocks of the proposed work are segmentation and deep neural network model. Segmentation focuses on preprocessing of text with different methods. These methods are grouped in four: morphological, sub-word, tokenization, and hybrid approaches. We analyzed several variants for each of these four methods. The second stage focuses on evaluation of the neural model for sentiment analysis. The performance of each segmentation method is evaluated under Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model proposed in the literature for sentiment classification.
Semi-supervised Node Classification via Hierarchical Graph Convolutional Networks

Graph convolutional networks (GCNs) have been successfully applied in node classification tasks of network mining. However, most of models based on neighborhood aggregation are usually shallow and lack the "graph pooling" mechanism, which prevents the model from obtaining adequate global information. In order to increase the receptive field, we propose a novel deep Hierarchical Graph Convolutional Network (H-GCN) for semi-supervised node classification. H-GCN first repeatedly aggregates structurally similar nodes to hyper-nodes and then refines the coarsened graph to the original to restore the representation for each node. Instead of merely aggregating one- or two-hop neighborhood information, the proposed coarsening procedure enlarges the receptive field for each original node, hence more global information can be learned. Comprehensive experiments conducted on public datasets demonstrate effectiveness of the proposed method over the state-of-art methods. Notably, our model gains substantial improvements when only very few labeled samples are provided.
Local Media and Geo-situated Responses to Brexit: A Quantitative Analysis of Twitter, News and Survey Data

Societal debates and political outcomes are subject to news and social media influences, which are in turn subject to commercial and other forces. Local press are in decline, creating a ``news gap''. Research shows a contrary relationship between UK regions' economic dependence on EU membership and their voting in the 2016 UK EU membership referendum, raising questions about local awareness. We draw on a corpus of Twitter data which has been annotated for user location and Brexit vote intent, allowing us to investigate how location, topics of concern and Brexit stance are related. We compare this with a large corpus of articles from local and national news outlets, as well as survey data, finding evidence of a distinctly different focus in local reporting. National press focused more on terrorism and immigration than local press in most areas. Some Twitter users focused on immigration. Local press focused on trade, unemployment, local politics and agriculture. We find that remain voters shared interests more in keeping with local press on a per-region basis.
Incentive Effects of Fiscal Equalization: The Case of France

This empirical study shows for 22 French regions between 2002 and 2008 that fiscal equalization does not appear to distort incentives for regional governments. We find a growthenhancing impact of inter-governmental transfers on regional growth, with no major differences between donor and recipient regions. Only for the extremely poor recipient regions do we find an insignificant transfer effect. In addition, a high 'marginal tax on own tax revenues' appears to trigger income compensation efforts, yielding higher regional growth. These findings contradict previous empirical studies for federal countries that tend to find adverse incentive effects of fiscal equalization on regional governments and growth. Overall, our tentative explanation for our own contrasting results is that France’s system of transfers is relatively moderate with regard to both the volume and the 'marginal tax on regional tax revenues', and also that local governments in France have – in comparison with other industrial countries – relatively well established own revenues.
The Need of Knowledge Management Strategy for the Successfully Implementation of Reengineering Projects

This paper want to shown that current knowledge management approaches do not emphasise enough on knowledge sharing from reengineering project perspective. To achieve success with reengineering project, an organisation must possess and share knowledge about many different facets of this process. While many reengineering projects have resulted in improve performance, we believe that higher levels of performance improvement are possible by coupling IT capabilities with KM strategy. To explain these results it was assumed that the key to implementing with success reengineering project is having a wide knowledge management strategy. Our objective for the paper reported here was to understand the factors that motivate to share knowledge before implementing any knowledge management strategy to sustain the successfully implementation of reengineering projects.
Serum adiponectin level in hypertensive patients and its association with atherosclerotic risk factors

Abstract Aim. Hypertension is one of the major risk factors for atherosclerosis. Adiponectin is mainly synthesized by white adipose tissue; it is known to have anti-atherogenic and anti-inflammatory effects on endothelial cells and macrophages. Methods. A total of 80 individuals including 48 hypertensive and 32 normotensive individuals were included in the study. Groups were separated as obese and non-obese. Results. It was found out that the patient group had statistically higher systolic blood pressure, diastolic blood pressure, fasting plasma glucose, total cholesterol, triglyceride, low density lipoprotein, high sensitive C reactive protein and microalbuminuria values than the control group while high density lipoprotein values were significantly lower (p<0.05). When adiponectin levels of the groups were compared, the patient group had an adiponectin level of 8.66±2.75 µg/mL and the control group had an adiponectin level of 15.01±3.99 µg/mL. There was a statistically significant difference between two groups (p<0.05). There was a negative correlation between adiponectin level and atherosclerotic risk factors. Conclusion. Adiponectin level was lower in hypertensive group when compared to the control group; there was also a significant association between adiponectin and atherosclerotic risk factors. A low adiponectin level constitutes an important risk for development of atherosclerosis. Keywords: Atherosclerosis, hypertension, adiponectin Ozet Amac. Hipertansiyon aterosklerozun major risk faktorlerindendir. Adiponektin baslica beyaz adipoz dokudan sentez edilmekte ve endotelyal hucreler ve makrofajlar uzerinde antiaterojenik ve antiinflamatuar etkileri oldugu bilinmektedir. Yontem. Calismaya 48’i hipertansif 32 tanesi normotansif 80 birey alindi. Gruplar kendi arasinda obez ve nonobez olarak ayrildi. Bulgular. Hasta grubunun kontrol grubuna gore sistolik kanbasinci, diastolik kanbasinci, aclik kan sekeri, total kolesterol, trigliserit, low density lipoprotein-C, hs-CRP ve mikroalbuminuri degerleri istatistiksel olarak anlamli yuksek, high density lipoprotein-C degerleri ise anlamli dusuk bulundu (p<0,05). Gruplarin adiponektin duzeyleri karsilastirildiginda; hasta grubunda 8,66±2,75 µg/mL, kontrol grubunda 15,01±3,99 µg/mL olarak bulundu. Aradaki farklilik istatistiksel olarak anlamli idi (p<0,05). Adiponektin seviyesi ile aterosklerotik risk faktorleri arasinda negatif yonlu bir korelasyon bulundu. Sonuc. Sonuc olarak adiponektin duzeyi hipertansif grupta kontrole gore daha dusuktu ve ateroskleroz risk faktorleri ile aralarinda anlamli iliski vardi. Adiponektin duzeyinin dusuk olmasi ateroskleroz gelisimi icin onemli bir risk olusturmaktadir. Anahtar sozcukler: Ateroskleroz, hipertansiyon, adiponektin
Linewidth enhancement factor of terahertz quantum cascade lasers

The linewidth enhancement factor (LEF) of terahertz quantum cascade lasers is measured using an optical feedback self-mixing technique. As expected, a much lower LEF is found than is common for interband lasers, but instead of the predicted value of zero, the LEF depends on the laser conditions and can be as high as 0.5. The measured value tends to increase with increasing current. Cross absorption within the laser active region is suggested as a possible cause for the nonzero LEF observed.
Analysing phenotypic variation in Eucalyptus pauciflora across an elevation gradient in the Australian Alps

Research related to the phenotypic plasticity of species that face rapidly changing conditions in the near future, such as those in the Australian Alps, is extremely important for ecological conservation efforts. Eucalyptus pauciflora is a species found throughout much of the Australian Alps. In this paper, the phenotypic plasticity exhibited by the species in terms of tree height and leaves across an elevation gradient was studied to gain insight into how the species is able to survive in such a range of conditions. Height and leaf size decreased with elevation, chlorophyll content (a measure of photosynthetic potential) increased, while specific leaf area (indicative of investment in photosynthesis and growth) and leaf dry matter content (indicative of investment in structural strength) showed no significant trends across the elevation range. These results give insight into the phenotypic plasticity of E. pauciflora, and provide information on how the ecosystem may respond to climate change in the future.
Great Ratios, Balanced Growth and Stochastic Trends: Evidence for the Euro Area

Following Kydland and Prescott's (1982) seminal paper, a key question that has been debated widely remains, `Are business cycles mainly the result of permanent shocks to productivity? ' This paper attempts to answer this question in the context of the Euro area economy as a whole, given current efforts to understand the design of Euro-wide policies. To help shed light on the preceding question for the Euro area a common trends model is utilised, allowing for the study of growth and business cycles phenomena in a joint framework. This paper imposes a long-run restriction implied by a large class of Real Business Cycle (RBC) models - identifying permanent productivity shocks as innovations to a common stochastic trend in output, consumption and investment - to provide evidence on the role of balanced growth shocks for the Euro area business cycle. This approach is given further credibility by the finding of stationary great ratios, justifying the use of exogenous growth models in examining the significance of productivity shocks on real output fluctuations for the Euro area. The results are broadly supportive of standard RBC theory, with the finding that up to 60% of transitory fluctuations are caused by exogenous permanent productivity shocks. The model further finds that productivity shocks have useful explanatory power in illuminating certain macroeconomic historical episodes, in contrast to monetary and inflation shocks, which appear to have played a relatively minor role in driving output fluctuations.
Evaluating human capital obsolescence

Human resources are playing a central role in the knowledge economy that emerged in the Western world as the human capital embodied in both high-tech capital goods and the working population is a main determinant of the performance of individuals, organizations and national economies. Human resources stimulate technological change, whereas technological change stimulates the use of human resources. First, human capital is an important input factor in research & development, which is in particular emphasized by endogeneous growth theory (e.g. Romer, 1990). This is called the research effect of human capital (Corvers, 1999). Second, high-skilled workers are of crucial importance for the diffusion of new technologies in the various sectors of the economy (Bartel & Lichtenberg, 1987). This is the diffusion effect of human capital.
The presence of injunctions in clinical and non-clinical populations

Various authors within the transactional analysis community have postulated that a person’s life script is formed on the basis of received injunctions, that people with mental disorders have more destructive and numerous injunctions and that people with depressive and paranoid pathology have different sets of injunctions, with Don’t belong being more common in paranoid disorders and Don’t be important in depressive disorders. This research was conducted to check such assertions, and used Script Injunctions Scale (Gavrilov-Jerkovic et al., 2010) applied to a convenience sample of 100 adult subjects identified as non-clinical via interviews and 100 adult subjects, equally divided between paranoid and depressive, identified by psychiatrist classification based on ICD-10 criteria. The results provide partially expected validation, with statistically significant difference between the non-clinical and clinical part of the sample. The clinical group had statistically significantly higher scores on the 12 injunctions studied. Subjects with depressive characteristics had seven Injunctions which were more pronounced Don’t feel, Don’t exist, Don’t be well, Don’t be a child, Don’t, Don’t think, and Don’t be close Injunctions.
Conducted and Investigate Arsenic (As), Cobalt (Co) Copper (Cu), Manganese (Mn), Lead (Pb), and Zinc (Zn) Contamination in Agricultural' Soils and Wheat Crops Projects at Three Regions in Libya

Libya is one of the driest countries in the world [2]. The rainfall in the northern part of the country varies between 100–500 mm/year, while the southern region receives only 10 mm/year and some parts receive no precipitation at all [17]. Average evaporation rates are also high, ranging from 1.7 mm/day in the north to 6.9 mm/day in the south, much higher than the average annual rainfall. Irrigation water use has increased as part of the green revolution program to make a Libya self-sufficient in terms of food production, e.g. for wheat and barley. Agriculture in Libya developed remarkably during the last two decades, as can be seen from the large increase in the irrigated areas, which has doubled during the period 1975–2005 [11,15]. The cultivated area has also increased, from 282,250 ha in 1975 to 470,000 ha in 2000 [15]. Increased agricultural activities have resulted in higher demands for groundwater, irrigation water and applications of fertilizer and have also resulted in higher salinity (nitrate, chloride, Na, Ca, Mg, ammonia) and increases in some trace elements in soil [3]. The devel‐ opment in agriculture has occurred at great cost to the environment, and concern is now being expressed about the impact of agricultural chemicals. Whereas agricultural production increased from 1137 thousand tons in 1975 to 1779 thousand tons in 2000, the agrarian productivity per hectare decreased from 4 tons/hectare in 1975 to 3.5 tons/hectare in 1980 and 1990, and then showed a slight increase to 3.7 tons/hectare in 2000. Libya’s annual import of fertilizer is about 2 × 108 metric tons, including fertilizer nutrients N, P205, and K20.1. [5,6-7]. [6] Observed that application of fertilizers to dry land not only increases plant shoot and stem