Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of the Carbon Stock in Pine Plantations in Southern Spain through ALS Data and K-Nearest Neighbor Algorithm Based Models

Accurate estimation of forest biomass to enable the mapping of forest C stocks over large areas is of considerable interest nowadays. Airborne laser scanning (ALS) systems bring a new perspective to forest inventories and subsequent biomass estimation. The objective of this research was to combine growth models used to update old inventory data to a reference year, low-density ALS data, and k-nearest neighbor (kNN) algorithm Random Forest to conduct biomass inventories aimed at estimating the C sequestration capacity in large Pinus plantations. We obtained a C stock in biomass (Wt-S) of 12.57 Mg ha−1, ranging significantly from 19.93 Mg ha−1 for P. halepensis to 49.05 Mg ha−1 for P. nigra, and a soil organic C stock of the composite soil samples (0–40 cm) ranging from 20.41 Mg ha−1 in P. sylvestris to 37.32 Mg ha−1 in P. halepensis. When generalizing these data to the whole area, we obtained an overall C-stock value of 48.01 Mg C ha−1, ranging from 23.96 Mg C ha−1 for P. halepensis to 58.09 Mg C ha−1 for P. nigra. Considering the mean value of the on-site C stock, the study area sustains 1,289,604 Mg per hectare (corresponding to 4,732,869 Mg CO2), with a net increase of 4.79 Mg ha−1 year−1. Such C cartography can help forest managers to improve forest silviculture with regard to C sequestration and, thus, climate change mitigation.
Secure Evaluation of Quantized Neural Networks

Abstract We investigate two questions in this paper: First, we ask to what extent “MPC friendly” models are already supported by major Machine Learning frameworks such as TensorFlow or PyTorch. Prior works provide protocols that only work on fixed-point integers and specialized activation functions, two aspects that are not supported by popular Machine Learning frameworks, and the need for these specialized model representations means that it is hard, and often impossible, to use e.g., TensorFlow to design, train and test models that later have to be evaluated securely. Second, we ask to what extent the functionality for evaluating Neural Networks already exists in general-purpose MPC frameworks. These frameworks have received more scrutiny, are better documented and supported on more platforms. Furthermore, they are typically flexible in terms of the threat model they support. In contrast, most secure evaluation protocols in the literature are targeted to a specific threat model and their implementations are only a “proof-of-concept”, making it very hard for their adoption in practice. We answer both of the above questions in a positive way:We observe that the quantization techniques supported by both TensorFlow, PyTorch and MXNet can provide models in a representation that can be evaluated securely; and moreover, that this evaluation can be performed by a general purpose MPC framework. We perform extensive benchmarks to understand the exact trade-offs between different corruption models, network sizes and efficiency. These experiments provide an interesting insight into cost between active and passive security, as well as honest and dishonest majority. Our work shows then that the separating line between existing ML frameworks and existing MPC protocols may be narrower than implicitly suggested by previous works.
Deriving the Reservoir Conditions for Better Water Resource Management Using Satellite-Based Earth Observations in the Lower Mekong River Basin

The Mekong River basin supported a large population and ecosystem with abundant water and nutrient supply. However, the impoundments in the river can substantially alter the flow downstream and its timing. Using limited observations, this study demonstrated an approach to derive dam characteristics, including storage and flow rate, from remote-sensing-based data. Global Reservoir and Lake Monitor (GRLM), River-Lake Hydrology (RLH), and ICESat-GLAS, which generated altimetry from Jason series and inundation areas from Landsat 8, were used to estimate the reservoir surface area and change in storage over time. The inflow simulated by the variable infiltration capacity (VIC) model from 2008 to 2016 and the reservoir storage change were used in the mass balance equation to calculate outflows for three dams in the basin. Estimated reservoir total storage closely resembled the observed data, with a Nash-Sutcliffe efficiency and coefficient of determination more than 0.90 and 0.95, respectively. An average decrease of 55% in outflows was estimated during the wet season and an increase of up to 94% in the dry season for the Lam Pao. The estimated decrease in outflows during the wet season was 70% and 60% for Sirindhorn and Ubol Ratana, respectively, along with a 36% increase in the dry season for Sirindhorn. Basin-wide demand for evapotranspiration, about 935 mm, implicitly matched with the annual water diversion from 1000 to 2300 million m3. From the storage–discharge rating curves, minimum storage was also evident in the monsoon season (June–July), and it reached the highest in November. This study demonstrated the utility of remote sensing products to assess the impacts of dams on flows in the Mekong River basin.
Is There Too Much History in Historical Yield Data

County crop yield data from United States Department of Agriculture National Agricultural Statistics Service (USDA-NASS) has and continues to be extensively used in the literature as well as practice. The most notable example is crop insurance; the Risk Management Agency (RMA) uses the data to set guarantees, estimate premium rates, and calculate indemnities for their area programs. Examples from the literature include investigation of rating methodologies, issues related to land use, modeling the climate-yield relationship, and supply analysis. In many of these applications, and certainly with respect to RMA and the crop insurance literature, yield data are detrended and adjusted for possible heteroskedasticity and then assumed to be independent and identically distributed. For most major crop-region combinations, county yield data exist from 1955 onwards and reflect very significant innovations in both seed and farm management technologies. Despite correcting for movements in the first two moments of the yield data generating process (DGP), these innovations have likely moved mass all around the support of the yield distribution raising doubt regarding the identically distributed assumption. This manuscript considers the rather nebulous question of how much historical yield data should be used in empirical analyses. The answer is obviously dependent on the empirical application, crop-region combination, econometric methodology, and chosen loss function. Nonetheless, we attempt to tackle this question in three ways using county-level yield data for corn, soybean, and winter wheat. First, we use distributional tests to assess if and when the adjusted yield data may result from different DGPs. Second, we consider the application to crop insurance by using an out-of-sample rating game -commonly employed in the literature -to compare rates from the full versus restricted data sets. Third, we estimate flexible time-varying DGPs and then simulate to quantify the additional error when the identically distribution assumption is imposed. Overall, the results indicate that despite accounting for time-varying movements in the first two moments, using yield data more than 30 years old can substantially increase estimation error. Given that discarding data is unappetizing, particularly so in applications with relatively small T, we investigate three methodologies that can re-incorporate the discarded data while both explicitly acknowledging the unknown DGPs are different and not requiring knowledge about the extent or form of those differences. Our results suggest gains in efficiency can be realized by using these methodologies. While our results are most applicable to the crop insurance literature, we certainly feel they suggest proceeding with caution when using historical yield data in other applications as well. Some key words: NASS yield data, changing technology, borrowing information, crop insurance
The Implications of Big Data Analytics Orientation upon Deployment

Big data analytics (BDA) is becoming a key way for leading companies to outperform their peers by utilizing big data and better understanding its business environment. However, there is evidence that many firms are facing difficulties in deploying BDA effectively into their business processes because of data silos, the rigid separation of data across divisions. In order to overcome data silos and capture the full potential of big data, it is considered important to create a corporate culture that encourages communication and sharing of data across departments. Therefore, to identify an appropriate corporate culture that supports BDA deployment in the context of big data, our research introduces BDA orientation which facilitates data flow across separate divisions and values business decisions based on insights gained from BDA. Drawing on resource-based view (RBV) and upper echelon theory, our research model examines the impact of BDA orientation on the deployment of BDA. Also, this study investigates the role of BDA orientation that mediates the effects of top management team initiative and BDA infrastructure on BDA deployment. To test our proposed model, an online survey was administered for a quantitative analysis and data from 166 Japanese upper-level managers were collected. Our findings confirm the significant impact of BDA orientation on BDA deployment, and the mediating role of BDA orientation in the suggested relationships above.
Purine Catabolism Shows a Dampened Circadian Rhythmicity in a High-fat Diet-Induced Mouse Model of Obesity

High-calorie diet, circadian rhythms and metabolic features are intimately linked. However, the mediator(s) between nutritional status, circadian rhythms and metabolism remain largely unknown. This article aims to clarify the key metabolic pathways bridging nutritional status and circadian rhythms based on a combination of metabolomics and molecular biological techniques. A mouse model of high-fat diet-induced obesity was established and serum samples were collected in obese and normal mice at different zeitgeber times. Gas chromatography/mass spectrometry, multivariate/univariate data analyses and metabolic pathway analysis were used to reveal changes in metabolism. Metabolites involved in the metabolism of purines, carbohydrates, fatty acids and amino acids were markedly perturbed in accordance with circadian related variations, among which purine catabolism showed a typical oscillation. What’s more, the rhythmicity of purine catabolism dampened in the high-fat diet group. The expressions of clock genes and metabolic enzymes in the liver were measured. The mRNA expression of Xanthine oxidase (Xor) was highly correlated with the rhythmicity of Clock, Rev-erbα and Bmal1, as well as the metabolites involved in purine catabolism. These data showed that a high-fat diet altered the circadian rhythm of metabolic pathways, especially purine catabolism. It had an obvious circadian oscillation and a high-fat diet dampened its circadian rhythmicity. It was suggested that circadian rhythmicity of purine catabolism is related to circadian oscillations of expression of Xor, Uox and corresponding clock genes.
Evaluation of general anaesthesia by using Propionylpromazine, Xylazine and Ketamine hydrochloride in rabbits

Safe and effective anaesthetic regimens have been described for use in rabbits, partially because of the susceptibility of this species to fatal respiratory arrest. The present study was conducted to evaluate the efficacy of anesthetics, analgesics combinations an anesthesia in twenty five local breed rabbits. Rabbits were injected intramuscularly by: Propionylpromazine 0.5mg/kg B.W. as premedictation, after 10minutes later Xylazine and Ketamine Hydrochloride at a dose of 20mg/kg, 50mg/kg respectively. The results of the physiological parameters of the control group at the period of zero time concerning rectal body temperature, respiratory rate, heart rate were 38.00±0.29 oC; 96.36 ±3.33bpm; and 147.20±6.46/minutes respectively. While in treated group at the periods 10, 20, 35, 50, 65, 80 and 95 minutes were 37.76 ±0.61; 37.34± 0.28; 37.00± 0.29; 37.00±0.35; 36.92±0.38; 35.80±0.40; 34.92± 0.53 oC; 96.36±3.33; 41.00±1.37; 45.00±2.01; 45±2.01; 40.00±1.31; 40.00±1.31; 39.20±1.01 bpm; and 147.20±6.46; 142.00±3.73; 145.00±3.26; 144.48±3.31; 130.00±4.18; 140.00±3.49; 138.68 ±2.93 beats/minutes. The results of biochemical tests: Glucose, ALP, GPT, GOT in control group (zero time) were: (137.40±1.97 mg/dl; 53.09±2.13 U/L; 51.48±4.31 U/L; 116.9±09.82 U/L) respectively. And in treated group at the periods (10, 20, 35, 50, 65, 95 minutes and 24 hours) respectively were 139.60±0.79; 207.60±5.00; 222.20±7.42; 359.20;±18.89; 341.60±15.30; 337.7±76.39 and 199.92±9.14 mg/dl; 39.74±2.74; 42.55±3.29; 39.65±4.13; 42.48±2.62; 56.56±2.16; 47.41±3.61 and 42.84±4.16 U/L; 46.17±3.92 ; 39.34±3.01; 44.69±3.05; 49.98±3.16; 51.65±4.03; 47.22±2.54 and 72.63±4.98 U/L, and 94.72±8.24; 86.22±5.59; 90.82±6.89; 76.65±4.12; 82.70±4.69; 100.6±7.39 and 126.6±7.77 U/L. The conclusion of this study investigate that the lose of righting reflex was 4.760±0.421 minutes; induction time was 8.44±1.05, the time to complete muscle relaxation was 3.920±0.321 minutes, surgical time 41.48±2.11 minutes, and recovery time was 45.76±2.43 minutes; in which the surgical period was enough for the most of surgical interference, while the recovery period was smooth and short in comparison with another anaesthetic regimen.
Accelerated Learning in the Presence of Time Varying Features with Applications to Machine Learning and Adaptive Control

Features in machine learning problems are often time-varying and may be related to outputs in an algebraic or dynamical manner. The dynamic nature of these machine learning problems renders current higher order accelerated gradient descent methods unstable or weakens their convergence guarantees. Inspired by methods employed in adaptive control, this paper proposes new algorithms for the case when time-varying features are present, and demonstrates provable performance guarantees. In particular, we develop a unified variational perspective within a continuous time algorithm. This variational perspective includes higher order learning concepts and normalization, both of which stem from adaptive control, and allows stability to be established for dynamical machine learning problems where time-varying features are present. These higher order algorithms are also examined for provably correct learning in adaptive control and identification. Simulations are provided to verify the theoretical results.
Near-Optimal Arm Identification in Continuum-Armed Bandits

We consider the problem of finding a near optimal arm in continuum armed bandits. The conditions on a metric space under which this problem is solvable are discussed. We present a novel finite horizon probability analysis of the optimal arm identification problem assuming that the mean reward function is Lipschitz continuous. The empirical performance of several algorithms is analyzed and compared based on expected distance between true mean and mean of the recommended arm.
TROPHIC ANALYSES OF AN ESTUARINE MANGROVE COMMUNITY1

Fringing the southwest coast of Florida is a mangrove belt which supports large populations of birds, gamefishes, and invertebrate species of commercial importance. A study was conducted between 1967 and 1969 in the North River basin of this mangrove region to determine the energy basis for this large population of animals and to delineate the routes by which energy is transferred through the food web. This is the first of three publications summarizing the results of this study. It consists of summaries of food habits for most of the fish and aquatic invertebrate species which occur in the North River mangrove eco-system. In addition to our data, which include in excess of 10,000 analyses of stomach contents, information from other publications has been summarized where pertinent. Finally, for most species there is an estimate of relative importance in the North River system in terms of abundance.
Bit Distribution of Binary Sequence Generated by Trace Function and Legendre Symbol Over Sub Extension Field

The authors of this paper have observed the distribution of bit patterns property of a binary sequence which is generated by using a primitive polynomial, trace function, and Legendre symbol over the sub extension field. However, one of the most important measure for evaluating the randomness of a sequence which is so called the distribution of bit patterns still has not been investigated. This paper focused on the distribution of bit patterns based on some experimental results. After observing many experimental results, the authors found that the number of appearance of each bit pattern is related to the number of zeros contained in each bit pattern. Furthermore, the authors formulated an equation to explain the distribution of bit patterns in a binary sequence.
Social Interventions, Health, and Well-Being: The Long-Term and Intergenerational Effects of a School Construction Program

ABSTRACT:We analyze the long-run and intergenerational effects of a large-scale school building project (INPRES) that took place in Indonesia between 1974 and 1979. Specifically, we link the geographic rollout of INPRES to longitudinal data from the Indonesian Family Life Survey covering two generations. We find that individuals exposed to the program have better health later in life along multiple measures. We also find that the children of those exposed experience improved health and educational outcomes and that these effects are generally stronger for maternal exposure than paternal exposure. We find some evidence that household resources, neighborhood quality, and assortative mating may explain a portion of our results. Our findings highlight the importance of considering the long-run and multigenerational benefits when evaluating the costs and benefits of social interventions in a middle-income country.
Towards Software Analytics: Modeling Maintenance Activities

Lehman's Laws teach us that a software system will become progressively less satisfying to its users over time, unless it is continually adapted to meet new needs. Understanding software maintenance can potentially relieve many of the pains currently experienced by practitioners in the industry and assist in reducing uncertainty, improving cost-effectiveness, reliability and more. The research community classifies software maintenance into 3 main activities: Corrective: fault fixing; Perfective: system improvements; Adaptive: new feature introduction. In this work we seek to model software maintenance activities and design a commit classification method capable of yielding a high quality classification model. We performed a comparative analysis of our method and existing techniques based on 11 popular open source projects from which we had manually classified 1151 commits, over 100 commits from each of the studied projects. The model we devised was able to achieve an accuracy of 76% and Kappa of 63% (considered '"Good" in this context) for the test dataset, an improvement of over 20 percentage points, and a relative improvement of ~40% in the context of cross-project classification. We then leverage our commit classification method to demonstrate two applications: (1) a tool aimed at providing an intuitive visualization of software maintenance activities over time, and (2) an in-depth analysis of the relationship between maintenance activities and unit tests.
The Influence of Number and Timing of Pregnancies on Breast Cancer Risk for Women With BRCA1 or BRCA2 Mutations

Abstract Background Full-term pregnancy (FTP) is associated with a reduced breast cancer (BC) risk over time, but women are at increased BC risk in the immediate years following an FTP. No large prospective studies, however, have examined whether the number and timing of pregnancies are associated with BC risk for BRCA1 and BRCA2 mutation carriers. Methods Using weighted and time-varying Cox proportional hazards models, we investigated whether reproductive events are associated with BC risk for mutation carriers using a retrospective cohort (5707 BRCA1 and 3525 BRCA2 mutation carriers) and a prospective cohort (2276 BRCA1 and 1610 BRCA2 mutation carriers), separately for each cohort and the combined prospective and retrospective cohort. Results For BRCA1 mutation carriers, there was no overall association with parity compared with nulliparity (combined hazard ratio [HRc] = 0.99, 95% confidence interval [CI] = 0.83 to 1.18). Relative to being uniparous, an increased number of FTPs was associated with decreased BC risk (HRc = 0.79, 95% CI = 0.69 to 0.91; HRc = 0.70, 95% CI = 0.59 to 0.82; HRc = 0.50, 95% CI = 0.40 to 0.63, for 2, 3, and ≥4 FTPs, respectively, Ptrend < .0001) and increasing duration of breastfeeding was associated with decreased BC risk (combined cohort Ptrend = .0003). Relative to being nulliparous, uniparous BRCA1 mutation carriers were at increased BC risk in the prospective analysis (prospective hazard ration [HRp] = 1.69, 95% CI = 1.09 to 2.62). For BRCA2 mutation carriers, being parous was associated with a 30% increase in BC risk (HRc = 1.33, 95% CI = 1.05 to 1.69), and there was no apparent decrease in risk associated with multiparity except for having at least 4 FTPs vs. 1 FTP (HRc = 0.72, 95% CI = 0.54 to 0.98). Conclusions These findings suggest differential associations with parity between BRCA1 and BRCA2 mutation carriers with higher risk for uniparous BRCA1 carriers and parous BRCA2 carriers.
Genome-wide analysis of long non-coding RNA expression profile in porcine circovirus 2-infected intestinal porcine epithelial cell line by RNA sequencing

Porcine circovirus-associated disease (PCVAD), which is induced by porcine circovirus type 2 (PCV2), is responsible for severe economic losses. Recently, the role of noncoding RNAs, and in particular microRNAs, in PCV2 infection has received great attention. However, the role of long noncoding RNA (lncRNA) in PCV2 infection is unclear. Here, for the first time, we describe the expression profiles of lncRNAs in an intestinal porcine epithelial cell line (IPEC-J2) after PCV2 infection, and analyze the features of differently expressed lncRNAs and their potential target genes. After strict filtering of approximately 150 million reads, we identified 13,520 lncRNAs, including 199 lncRNAs that were differentially expressed in non-infected and PCV2-infected cells. Furthermore, trans analysis found lncRNA-regulated target genes enriched for specific Gene Ontology terms (P < 0.05), such as DNA binding, RNA binding, and transcription factor activity, which are closely associated with PCV2 infection. In addition, we analyzed the predicted target genes of differentially expressed lncRNAs, including SOD2, TNFAIP3, and ARG1, all of which are involved in infectious diseases. Our study identifies many candidate lncRNAs involved in PCV2 infection and provides new insight into the mechanisms underlying the pathogenesis of PCVAD.