Miracle-dz/UniFAR-FTUs huggingface dataset:
Building Energy Consumption Raw Data Forecasting Using Data Cleaning and Deep Recurrent Neural Networks

With the rising focus on building energy big data analysis, there lacks a framework for raw data preprocessing to answer the question of how to handle the missing data in the raw data set. This study presents a methodology and framework for building energy consumption raw data forecasting. A case building is used to forecast the energy consumption by using deep recurrent neural networks. Four different methodologies to impute missing data in the raw data set are compared and implemented. The question of sensitivity of gap size and available data percentage on the imputation accuracy was tested. The cleaned data were then used for building energy forecasting. While the existing studies explored only the use of small recurrent networks of 2 layers and less, the question of whether a deep network of more than 2 layers would be performing better for building energy consumption forecasting should be explored. In addition, the problem of overfitting has been cited as a significant problem in using deep networks. In this study, the deep recurrent neural network is then used to explore the use of deeper networks and their regularization in the context of an energy load forecasting task. The results show a mean absolute error of 2.1 can be achieved through the 2*32 gated neural network model. In applying regularization methods to overcome model overfitting, the study found that weights regularization did indeed delay the onset of overfitting.
Niches within a niche : ecological differentiation of subterranean amphipods across Europe ’ s interstitial waters

Species that successfully colonized subterranean environments are subject to two opposing selection processes. Stringent abiotic factors select for convergent adaptations, such as loss of eyes and pigments, while interspecific competition drives between-species divergence. Subterranean species can resolve opposing selection by adaptation to physically different microhabitats. Yet, species frequently co-occur in physically homogeneous subterranean habitats, like interstitial. These co-occurrences in such a narrow ecological context can be explained either by equalizing mechanisms, in which neither of the co-occurring species has a competitive advantage, or by more complex niche models that include species’ differentiation along a trophic niche axis. We tested these hypotheses using the amphipod genus Niphargus. We analysed Europe-wide co-occurrence records of Niphargus species from interstitial habitats, split into six independent large-scale regions. Firstly, we addressed whether species’ pairwise co-occurrences are random using a probabilistic model. Secondly, we tested whether species cluster into distinct functional–morphological groups and whether ecologically or phylogenetically distinct species are more likely to co-occur. We found that 68% of species co-occurrences were not different from random expectation, indicating that most species had access to most sites within each region. The remaining 32% co-occurred either significantly more or less often than expected by chance. Cluster analysis of functional morphological characters showed that interstitial species belong to two feeding types, microand macrofeeders, likely representing two peaks of the interstitial adaptive landscape, and hinting that niche divergence, as a mechanism allowing coexistence, is favoured. Finally, we found that the number of co-occurrences increases with increasing differentiation of functional morphology, but not phylogenetic differences. We conclude that ecological differentiation may be important in shaping such interstitial communities.
Modelling productivity and resource use e ffi ciency for grassland ecosystems in the UK Aiming Qia

Estimating spatially resolved grassland productivity is essential for benchmarking the total UK productive potential to assess food, feed and fuel trade-offs in the context of whole systems analyses. Our objectives were to adapt and evaluate a well-known process-based model (PBM) and estimate productivity of improved (permanent, temporary) and semi-natural grassland systems using meta-models (MM) trained by extensive PBM scenario simulations. Observed dry matter (DM) yields in multi-site nitrogen (N) response (0, 150 and 300 kg N ha) experiments were well emulated describing the average productivity of rough grazing, permanent and temporary grassland (3.1, 7.4 and 9.8 t DM ha, respectively). Cross-validated with independent and long-term data (Park Grass Experiment), the PBM explained more variation when considering all systems combined (81%) than across all improved grasslands (61%) but little for rough grazing (26%). The PBMtrained MMs explained 48, 72 and 70% of the simulated yield variation in the grasslands of increasing management intensity, and 43 and 75% of observed variation in the combined improved and all three grassland systems, respectively. Considering the assessment of ecosystem services, like drainage and water productivity, PBM scenario simulations are essential. Compared to improved grassland rough grazing will result in 40% more groundwater recharge due to its lower simulated water use and water productivity (12 versus 25 and 43 kg ha mm for permanent and temporary grassland, respectively).
The gas turbulence in planetary nebulae : quantification and multi-D maps from long-slit , wide-spectral range echellogram

Editorial Dear Colleagues, It is our pleasure to present you the 133 issue of the AGB Newsletter. Amongst the many intriguing new results announced, magnetic fields feature in several papers, be it around or inside the star. In the latter case, they may be responsible for the extra mixing that is believed to be a necessary ingredient in the models of convective stars. Or at least contribute, perhaps in combination with thermohaline mixing. With several mechanisms being proposed for the mixing, like for the mass-loss process, these might all be operating at the same time. All contributing to the final effect, one should be careful not to try to identify one ”main” responsible agent and neglect the contributions from the other mechanisms.
Impact of light traps on population density of gram pod borer, Helicoverpa armigera (Hub) (Lepidoptera: Noctuidae) and its larval parasitoid (Campoletis chlorideae Uchida) in Rod Kohi area of Dera Ismail Khan, Pakistan

One (T1) and Two (T2) light traps ha-1 were compared with control (trap (s) with no light-T3) against gram pod borer, Helicoverpa armigera (Hub.) and emergence rate of its larval parasitoid (Campolitus chlorideae) were investigated at Rod Kohi, D.I. Khan, Pakistan. T1 resulted in 7.57 mean number of H. armigera moths out of 49 total moth catch with average population of 0.306 larva per plant with 5.46% pod damage and average yield of 1980 kg ha-1. In T2, out of 45.26 total moth catch, 5.61 were H. armigera, with lowest number of larval population (0.254/plant), lesser pod damage (4.02%) and higher yield (2120 kg ha-1), while in control plots (T3), larval population density was maximum with 0.379 larvae/plant with pod damage of 10.40% and 1834 kg ha-1 yield was recorded. Mean number of 5.82 larval parasitoid were trapped in T1 while in T2 there were 5.19 adult parasitoids/trap. Maximum percent (9.047) emergence of larval parasitoids of H. armigera was recorded from larvae reared from T3 followed by T1 (7.016) while T2 reared larvae resulted in a minimum emergence of 7.0%. Highest larval mortality was recorded in T1 (9.58) followed by T2 (10.11) and T3 (10.86).
A preliminary study on the prosody of broadcast news in Hong Kong Cantonese

Broadcast news is a distinctive register. Previous studies only provided some general descriptions of the prosodic features in broadcast news but with few concrete data. Most of them were also on English news. This study investigated the prosodic features of Cantonese TV broadcast news using acoustic data. Speech using the same materials from two groups was compared: eight Hong Kong professional TV news anchors, and a control group consisting of eight university students. The results show clear differences between the two groups in terms of speech rate, pitch range and variability of syllable duration (speech rhythm). It was found that the news anchors spoke significantly faster than the control group, also with an enlarged pitch range. They also produced more variability in syllable duration. There is clearly more prosodic variation in the news register than ordinary speech. Finally, we provide some possible reasons for these features, as well as directions for future studies.
A comparative Study between Blood Smear, Whole Blood PCR and FTA Card PCR for Diagnosis of Theileria annulata and Theileria orientalis in Cattle

Bovine theileriosis, a tick borne haemoprotozoan disease, caused by, Theileria annulata and Theileria orientalis and responsible for heavy economical losses in dairy industry. Diagnosis of theileriosis is mainly based on clinical signs and conventional techniques. Polymerase chain reaction using whole blood is a modern method of diagnosis of theileriosis as it can detect infection even in low parasitemia but only concern is that its very time consuming and chances of loss of DNA is more. Hence, to overcome this, the present study reports the diagnosis of T. annulata and T. orientalis by comparing Whole blood PCR and FTA card PCR whereas a comparison between blood smear examination, whole blood PCR and FTA card PCR was done to determine the sensitivity of the techniques. The study was conducted on 50 blood samples of cattle having typical clinical signs and blood smear after staining with Giemsa’s stain were examined for presence of Theileria spp. in R.B.C. The primer sequences were used as per Kolte et al and Kakati et al. The assay employs primers specific for the gene encoding the 30-kDa major merozoite surface antigen of T. annulata and Major merozoite surface protein gene of T. orientalis. Out of total 50 blood samples, 16 were positive for Theileria sp. by blood smear examination, 19 were positive for T. annulata by Whole blood PCR and FTA card PCR whereas for T. orientalis, 26 were positive by whole blood and 27 samples by FTA card PCR. So the diagnosis of theileriosis by using FTA card PCR is found to be sensitive and less time consuming method as compared to Whole blood PCR.
Based on Uncertain Temporal Scenarios

Diagnostics Based on Uncertain Temporal Scenarios V. Terziyan, V. Ryabov Department of Mathematical Information Technology, Department of Computer Science and Information Systems, University of Jyväskylä, FIN-40351, Jyväskylä Finland E-mails: {vagan,vlad}it@jyu.fi Abstract Temporal representation and reasoning has been applied so far to many areas of AI, including temporal diagnostics, where identification of temporal patterns plays an important role. Temporal scenarios of different device failures, their causes, and symptoms preceding the failure provide important additional information for making decisions concerning operation of the device. In this paper we propose an application of algebra of uncertain temporal relations to diagnostic problems. We represent uncertain temporal relations within a temporal scenario graph using the probabilities of the basic relations that can hold between two temporal primitives. Also in the paper we show how: (a) to generate temporal scenarios by integrating appropriate relational networks from already diagnosed cases; (b) to classify a new case using the measure of the distance between a network and a scenario.Temporal representation and reasoning has been applied so far to many areas of AI, including temporal diagnostics, where identification of temporal patterns plays an important role. Temporal scenarios of different device failures, their causes, and symptoms preceding the failure provide important additional information for making decisions concerning operation of the device. In this paper we propose an application of algebra of uncertain temporal relations to diagnostic problems. We represent uncertain temporal relations within a temporal scenario graph using the probabilities of the basic relations that can hold between two temporal primitives. Also in the paper we show how: (a) to generate temporal scenarios by integrating appropriate relational networks from already diagnosed cases; (b) to classify a new case using the measure of the distance between a network and a scenario.
A Storyteller's Tale: Literature Audiobooks Genre Classification Using CNN and RNN Architectures

Identifying acoustic properties that characterize reading literary genres can assist in giving a more personal and human tone to the speech of bots and automatic readings. In this paper we consider the following question: given speech segments of audiobooks, how well can we classify them according to their literary genres? In this study we consider three different literary genres: children, horror and suspense, and humorous audio books, taken from two free audio books sites: Librivox and YouTube. We ran four classification experiments: three for each pair of genres, and one for all three genres together. We repeated each experiment twice, with two different network architectures: Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Note that, throughout the reading, there are sections that are more typical to the book’s genre than others. As the samples were taken sequentially throughout the reading of the books and were short in duration, we did not expect high classification rates. Nevertheless, the accuracy of all the experiments were at least 72% for all the pair’s classifications; and at least 57% for both architectures for the three classes classifications.
. Privacy-preserving Active Learning on Sensitive Data for User Intent Classification. In Proceedings of the PAL: Privacy-Enhancing Artificial Intelligence and Language Technologies: As Part of the AAAI Spring Symposium Series

Active learning holds promise of signiﬁcantly reducing data annotation costs while maintaining reasonable model performance. However, it requires sending data to annotators for labeling. This presents a possible privacy leak when the training set includes sensitive user data. In this paper, we describe an approach for carrying out privacy preserving active learning with quantiﬁable guarantees. We evaluate our approach by showing the tradeoff between privacy, utility and annotation budget on a binary classiﬁcation task in a active learning setting.
Cognitive Factors in Thai-Naïve Mandarin Speakers' Imitation of Thai Lexical Tones

The present study investigated how cognitive factors, memory load and attention control, affected imitation of Thai tones by Mandarin speakers with no prior Thai experience. Mandarin speakers lengthened the syllable duration, enlarged the F0 excursion and moved some F0 max location earlier compared with the stimuli, even in the immediate imitation condition. Talker variability had a larger impact on imitation than memory load, whereas vowel variability did not have any effect. Perceptual assimilation patterns partially influenced imitation performance, suggesting phonological categorization in imitation and a perception-production link.
Fault Diagnosis of Oil-Immersed Power Transformers Using Kernel Based Extreme Learning Machine

Transformer fault diagnosis based on conventional neural networks (NN) and support vector machines (SVM) face some drawbacks: slow learning speed, trivial human tuned parameters and difficult parameter determination. To overcome these drawbacks, transformer fault diagnosis based on kernel based extreme learning machine (KELM) was proposed in this paper and its performance was studied. The parameters of KELM are optimized by using particle swarm optimization (PSO), and then the optimized KELM is implemented for fault classification of power transformers. To verify its effectiveness, fault diagnosis of power transformers using KELM were compared with the other two ELMs, back-propagation neural network (BPNN) and support vector machines (SVM) on dissolved gas analysis (DGA) samples. Experimental results show that the proposed method is more stable, could achieve better generalization performance, and runs at much faster learning speed.
R OLE OF MORPHOGENETIC PROTEINS-FGF-23 , SOLUBLE ALFA-K LOTHO AND GLYCOPROTEIN SCLEROSTIN IN VASCULAR CALCIFICATION IN PATIENTS WITH CHRONIC KIDNEY DISEASE

The aim of the study was to investigate the relationship between FGF-23, Klotho, sclerostin serum level changes and diffuse arterial stiffness, calcification intensity and myocardial remodelling in patients with different stages of Chronic Kidney Disease (CKD). The control group consisted of 15 volunteers the same average age and sex. Serum FGF-23 levels (Human FGF-23 ELISA kit using monoclonal antibodies to the full FGF-23 molecule), Klotho (Human alpha-Kl ELISA using anti-Klotho antibodies) and sclerostin ( Human sclerostin ELISA kit) were applied in these patients. Blood pressure (BP) was measured to all study patients. Echocardiography was performed to patients with arterial hypertension and left ventricular mass index (LVMI) was calculated. The state of blood flow in the heart and large vessels (Doppler ultrasound Echocardiography), pulse wave velocity (Sphigmokor), calcifications presence (echocardiography, radiography of abdominal aorta by Kauppila method) and vascular wall functional ability (augmentation indices by Sphigmokor) were studied. Among 49 hypertensive patients in 27 (55.1 %) from them it was able to maintain target BP-120/80-140/80 mm Hg. , the remaining 22 (44.9%) patients took antihypertensive medications irregularly. At the start of screening they had been remained hypertensive (BP 150/90-165/100 mm Hg.). Our study demonstrated that serum levels of Klotho, FGF-23, sclerostin are early markers of cardiovascular events in CKD. It was found the clear link between increased serum FGF-23 and decreased Klotho and sclerostin as increasing CKD severity, and diffuse arterial stiffness and calcification, myocardial remodelling independent of traditional risk factors. To the final determination of the sclerostin role in CKD more exactly further researches are required. L. MILOVANOVA, D. KUDRIAVTSEVA, Y. MILOVANOV, S. MOISEEV
The Microeconomic Determinants of Educational Inequality in Nigeria

This paper analyses the determinants of educational inequality across the regions in Nigeria. Using Household data from the World Bank ‘Living Standards Measurement Survey’ of Nigeria, it examines how variations in household characteristics as well as in educational attainment affect educational inequality. The regression and decomposition analyses conducted reveal the complexity of the interaction between household characteristics and education. Educational attainment and income per capita seem to curb the increase in educational inequality. The results further indicate that urbanization and household size have negative and positive impacts on educational inequality respectively. The findings emphasize the role of household characteristics in explaining educational inequality and substantial improvements of those characteristics will improve educational distribution. Therefore, investing in programs that ensure equal access to education and support for the poor households to send their children to school will be a very helpful strategy. The findings will guide policy makers to target areas that may contribute immensely in reducing educational inequalities.
1 An Alternative Instrument for Private School Competition *

Empirical studies estimating the effect of private school competition on student outcomes commonly use the share of Catholics in the local population as an instrument for private school competition. However, it has recently been argued that since this instrument is likely to be correlated wi th unmeasured student characteristics that vary across localities, it cannot be a valid i nstrument for private school competition. I suggest using instead the local shar e of Catholics in the population in 1890 and its squared term. I show that these instru ments are very strong and are also exogenous to both student achievements and private school competition. These instruments can also be applied to estimate the tre atm nt effect of Catholic schools.
THE INFLUENCE OF LEXICAL STRESS ON FORMANT VALUES IN SPONTANEOUS HEBREW SPEECH

The purpose of the present study was to examine the influence of lexical stress on formant values (mainly F1 and F2) in spontaneous Hebrew speech. Speech samples taken from a Hebrew version of the wellknown Map Task dialogues were analysed, comparing stressed/unstressed vowels in wordfinal/non-final positions. The results showed that lexical stress has a different effect on the different vowels. Of the five vowels in the Hebrew vowel system, the vowels /a/ and /e/ were most clearly affected in a consistent manner across men and women. Similar behaviour was observed for both vowels: in word-final position the vowels were centralized similarly, regardless of stress. In non-final position, the unstressed vowels were significantly more centralized than their stressed counterparts.
Classification of Diabetic Retinopathy using Artificial Neural Network

Diabetic Retinopathy (DR) is an eye disease that can affect people with diabetes. It is the most common cause of vision loss among people with diabetes and the leading cause of vision impairment and blindness among working-age adults. However, if it is treated properly during its initial stages, the chance of blindness is reduced thereby signifying the importance of its detection. Digital color fundus images are becoming increasingly important for the diagnosis of Diabetic Retinopathy and other methods that address this problem (But, they have some drawbacks like being accurate for small set of input data). This paper proposes the use of Artificial Neural Network, thereby classifying the diabetic retinopathy with the use of large input dataset (containing textual data regarding digital color fundus images).
eview owards a global terrestrial species monitoring program

The Convention on Biological Diversity’s strategic plan lays out five goals: “(A) address the underlying causes of biodiversity loss by mainstreaming biodiversity across government and society; (B) reduce the direct pressures on biodiversity and promote sustainable use; (C) improve the status of biodiversity by safeguarding ecosystems, species and genetic diversity; (D) enhance the benefits to all from biodiver-