Miracle-dz/UniFAR-FTUs huggingface dataset:
Yield of sorghum silage intercropped with pigeon pea and marandu grass in two spacings and chemical composition before and after ensiling

The aim of the present study was to evaluate yield of sorghum grown either solely (monoculture) or intercropped with Urochloa brizantha cv. Marandu and pigeon pea cv. BRS Mandarin in two spacings. The experiment was carried out in field condition and the experimental design was in a 2 x 4 factorial scheme with four replications. The treatments consisted of two sorghum spacings, combined with four forms of cultivation as follows: (T1) sorghum as a monoculture (SOR); (T2) sorghum intercropped with marandu grass (SMG); (T3) sorghum intercropped with pigeon pea (SPP); and (T4) sorghum intercropped with marandu grass and pigeon pea (SMP). The evaluated traits were: sorghum height, stem diameter of sorghum, number of sorghum leaves, and a ratio sorghum/pigeon pea /marandu grass. The data were submitted to the statistical program SISVAR, and averages compared by the Tukey test at 5% probability. The 0.45 m spacing between rows provided higher silage yield in relation to 0.90 m between sorghum rows. The highest silage yield was produced at 0.45 m spacing between rows for single sorghum and triple intercropped (T4). The triple intercropped (sorghum + pigeon + marandu grass brachiaria) did not affect silage yield with the advantage of 10% of a legume in the final composition.
IMPACT OF SUPPLY CHAIN ALIGNMENT ON CONSTRUCTION PERFORMANCE: A DEVELOPED MODEL FOR VIETNAM

There are many articles mentioning the advantages and benefits of supply chain alignment none, however, describe how to model such alignment in the supply chain. This paper offers a framework for examining and understanding the impact supply chain alignment has on performance. Based on supply chain mapping approach, a model describing alignment between processes/ flows in the supply chain network is developed. The model is then validated using a dataset of 316 enterprises operating in the Vietnam construction sector. Evidence indicates that the supply chain processes and flows were aligned. According to the results, the proposed supply chain alignment model is able to explain a 59.9% variance in operational performance, 58.9% in customer satisfaction, 34.5% in operating costs and 67.4% in business performance. To successfully align the supply chain network, companies can use the proposed model as a “road-map” to reduce high costs, to avoid the loss of control, management difficulties and/or vulnerability to opportunistic action, all of which may hinder efforts to align the supply chains.
Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation

Off-policy evaluation (OPE) in reinforcement learning allows one to evaluate novel decision policies without needing to conduct exploration, which is often costly or otherwise infeasible. We consider for the ﬁrst time the semiparametric ef-ﬁciency limits of OPE in Markov decision processes (MDPs), where actions, rewards, and states are memoryless. We show existing OPE estimators may fail to be efﬁcient in this setting. We develop a new estimator based on cross-fold estimation of q -functions and marginalized density ratios, which we term double reinforcement learning (DRL). We show that DRL is efﬁcient when both components are estimated at fourth-root rates and is also doubly robust when only one component is consistent. We investigate these properties empirically and demonstrate the performance ben-eﬁts due to harnessing memorylessness.
Physiology of Geosmin Production by Anabaena circinalis Isolated from the Murrumbidgee River, Australia

Chemical analysis and sensory assessment of river waters from the Murrumbidgee and Murray Rivers showed geosmin is an important odor compound. Experiments in culture using Anabaena circinalis isolated from the Murrumbidgee River, New South Wales, Australia showed that changes in chlorophyll per unit dry weight resulting from alterations in light intensity were paralleled by similar changes in geosmin/dry weight such that geosmin concentration was correlated with chlorophyll a over an extreme range of light conditions. A.circinalis was a prolific producer of geosmin, but a substantial proportion of the total geosmin was often retained intra-cellularly and released on sonication or treatment with copper. Therefore water treatment processes should avoid cell lysis.
Variability in relation to response to selection in chickpea

Forty genetically, diverse chickpea genotypes were studied for genotypic and phenotypic variabilities, heritability and genetic advance in 18 quantitative traits. Highest genotypic variability was exhibited by 100 seed weight and was followed by seeds pu plant, pods per plant and seeds per pod. 100 seed weight also expressed highest heritability and expected genetic advance. Whereas seeds per plant, seeds per pod and pods per plant exhibited high expected genetic advance with considerable amount of heritability.
Abstrak— Manajemen rantai pasok dalam dunia industri merupakan hal yang sangat vital dalam menjalankan proses bisnisnya. Hampir seluruh sektor usaha di Indonesia tidak

Abstrak— Manajemen rantai pasok dalam dunia industri merupakan hal yang sangat vital dalam menjalankan proses bisnisnya. Hampir seluruh sektor usaha di Indonesia tidak bisa terlepas dengan penerapan manajemen rantai pasok, termasuk juga sektor otomotif. Pemanfaatan alur rantai pasok pada sektor otomotif sangat berpengaruh dalam proses produksi dari hulu ke hilir perusahaan. Kesuksesan peneraan manajemen rantai pasok saat ini tidak bisa terlepas dari faktor pendukung di dalamnya. Salah satu faktor yang penting adalah dukungan implementasi teknologi informasi. Penerapan teknologi informasi dalam manajemen rantai pasok bermanfaat sebagai faktor pendukung untuk mengintegrasikan rantai pasok yang berujung pada suatu kinerja rantai pasok. [1] Tujuan dari tugas akhir ini adalah untuk menganilisis keterkaitan pengaruh implementasi teknologi informasi terhadap integrasi dan kinerja rantai pasok pada industri otomotif di Jawa timur. Tugas akhir ini menggunakan teknik analisis dengan Structural Equation Model (SEM) dan generalized structured component analysis (GSCA) untuk menguji hipotesis keterkaitan antara ketiga variabel tersebut. Artikel ini menganalisis data dari kuesioner yang diberikan kepada 30 industri otomotif yang berada di propinsi Jawa timur. Dari hasil analisis yang dilakukan, didapatkan bahwa implementasi TI memiliki pengaruh yang signifikan terhadap integrasi rantai pasok, serta integrasi rantai pasok memiliki pengaruh yang signifkan pual terhadap kinerja rantai pasok. Tetapi, implementasi TI tidak memiliki pengaruh yang signifkan terhadap kinerja rantai pasok secara langsung. Kata Kunci— Implementasi Teknologi Informasi ,Integrasi Rantai Pasok, Kinerja Rantai Pasok, Industri Otomotif Jawa Timur.
Advances in Human Microbiome as an Emerging Tool in Forensics

Microbiome refers to the total microbial genes or the sum of all microscopic life forms including bacteria, fungi, archaea, protozoa and viruses, inhabiting the human body. Forensic microbiology is an evolving science based on the identification of individual microbiomes related to a crime scene. The core human microbiome from birth to death has peculiar set of organisms which can aid in identification of time and cause of death. In fact, microbial signatures are unique to individuals and considered as fingerprints. In addition, this emerging science can be useful in determining the location-based information of a person by linking the environment microbiota to the victim and to individuals’ core microbiome. Moreover, Post-Mortem Microbiome PMM including group of bacteria-thanatomicrobiome associated with the host after death are helpful tool for investigation. Furthermore, forensic microbiology can help in the interpretation of infectious diseases cases that result in sudden unexpected death. Microbial forensics can also be used to estimate the Post-Mortem Interval PMI. In addition, the gut microbiome inhabiting the digestive tract play a significant role in behavioral response to addictive drugs as well as anxiety and depression-like behavior. The microbiome and metabolites produced can provide evidence of individual’s alcohol, drug addiction or psychosis suggesting significant gut-brain interactions in drug addiction. Despite the numerous mentioned application of microbiology in forensic science, forensic examiners sometimes ignore these evidences and prefer to deal with other marks found in the crime scene. The objective of the present review was to highlight the significance of forensic microbiology field by discussing several applications of human microbiome which can be considered as an emerging tool during forensic investigation.
Online Appendix for the Paper Asset Pricing with Heterogeneous Agents and Long-Run Risk A Additional Literature

The study of agent belief heterogeneity begins with the market selection hypothesis of Alchian (1950) and Friedman (1953). By analogy with natural selection, the market selection hypothesis states that agents with systematically wrong beliefs will eventually be driven out of the market. The influence of agent heterogeneity on market outcomes under the standard assumption of discounted expected utility is well understood, and consistent with market selection. Sandroni (2000) and Blume and Easley (2006) find strong support for this hypothesis under the assumption of time-separable preferences in an economy without growth. Yan (2008) and Cvitanić, Jouini, Malamud, and Napp (2012) analyze the survival of investors in a continuoustime framework where there are not only differences in beliefs but also potentially differences in the utility parameters of the investors. They show that it is always the investor with the lowest survival index who survives in the long run. David (2008) considers a similar model setup, in which both agents have distorted estimates of the mean growth rate of the economy, showing that—as agents with lower risk aversion undertake more aggressive trading strategies—the equity premium increases the lower the risk aversion is. Chen, Joslin, and Tran (2012) analyze how differences in beliefs about the probability of disasters affect asset prices. They show that even if there is only a small fraction of investors who are optimistic about disasters, this fraction sells insurance for the disaster states and so eliminates most of the risk premium associated with disaster risk. Bhamra and Uppal (2014) consider the case of habit utility. For recursive utility, this qualitative behavior changes fundamentally. However, there has Yan (2008) shows that the survival index increases with belief distortion, risk aversion, and the subjective time discount rate of the investor.
Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits

Policy learning using historical observational data is an important problem that has found widespread applications. However, existing literature rests on the crucial assumption that the future environment where the learned policy will be deployed is the same as the past environment that has generated the data–an assumption that is often false or too coarse an approximation. In this paper, we lift this assumption and aim to learn a distributionally robust policy with bandit observational data. We propose a novel learning algorithm that is able to learn a robust policy to adversarial perturbations and unknown covariate shifts. We ﬁrst present a policy evaluation procedure in the ambiguous environment and also give a heuristic algorithm to solve the distribution-ally robust policy learning problems efﬁciently. Additionally, we provide extensive simulations to demonstrate the robustness of our policy.
A Detailed Study of the Distributed Rough Set Based Locality Sensitive Hashing Feature Selection Technique

In the context of big data, granular computing has recently been implemented by some mathematical tools, especially Rough Set Theory (RST). As a key topic of rough set theory, feature selection has been investigated to adapt the related granular concepts of RST to deal with large amounts of data, leading to the development of the distributed RST version. However, despite of its scalability, the distributed RST version faces a key challenge tied to the partitioning of the feature search space in the distributed environment while guaranteeing data dependency. Therefore, in this manuscript, we propose a new distributed RST version based on Locality Sensitive Hashing (LSH), named LSH-dRST, for big data feature selection. LSH-dRST uses LSH to match similar features into the same bucket and maps the generated buckets into partitions to enable the splitting of the universe in a more efficient way. More precisely, in this paper, we perform a detailed analysis of the performance of LSH-dRST by comparing it to the standard distributed RST version, which is based on a random partitioning of the universe. We demonstrate that our LSH-dRST is scalable when dealing with large amounts of data. We also demonstrate that LSH-dRST ensures the partitioning of the high dimensional feature search space in a more reliable way; hence better preserving data dependency in the distributed environment and ensuring a lower computational cost.
Learning from Irregularly-Sampled Time Series: A Missing Data Perspective

Irregularly-sampled time series occur in many domains including healthcare. They can be challenging to model because they do not naturally yield a fixed-dimensional representation as required by many standard machine learning models. In this paper, we consider irregular sampling from the perspective of missing data. We model observed irregularly-sampled time series data as a sequence of index-value pairs sampled from a continuous but unobserved function. We introduce an encoder-decoder framework for learning from such generic indexed sequences. We propose learning methods for this framework based on variational autoencoders and generative adversarial networks. For continuous irregularly-sampled time series, we introduce continuous convolutional layers that can efficiently interface with existing neural network architectures. Experiments show that our models are able to achieve competitive or better classification results on irregularly-sampled multivariate time series compared to recent RNN models while offering significantly faster training times.
Hierarchical Timbre-Painting and Articulation Generation

We present a fast and high-fidelity method for music generation, based on specified f0 and loudness, such that the synthesized audio mimics the timbre and articulation of a target instrument. The generation process consists of learned source-filtering networks, which reconstruct the signal at increasing resolutions. The model optimizes a multi-resolution spectral loss as the reconstruction loss, an adversarial loss to make the audio sound more realistic, and a perceptual f0 loss to align the output to the desired input pitch contour. The proposed architecture enables high-quality fitting of an instrument, given a sample that can be as short as a few minutes, and the method demonstrates state-of-the-art timbre transfer capabilities. Code and audio samples are shared at this https URL.
The potential current distribution of the coypu (Myocastor coypus) in Europe and climate change induced shifts in the near future

The coypu (Myocastor coypus) is a semi-aquatic rodent native to South America which has become invasive in Europe and other parts of the world. Although recently listed as species of European Union concern in the EU Invasive Alien Species Regulation, an analysis of the current European occurrence and of its potential current and future distribution was missing yet. We collected 24,232 coypu records (corresponding to 25,534 grid cells at 5 × 5 km) between 1980 and 2018 from a range of sources and 28 European countries and analysed them spatiotemporally, categorising them into persistence levels. Using logistic regression, we constructed consensus predictions across all persistence levels to depict the potential current distribution of the coypu in Europe and its change under four different climate scenarios for 2041–2060. From all presence grid cells, 45.5% showed at least early signs of establishment (records temporally covering a minimum of one generation length, i.e. 5 years), whereas 9.8% were considered as containing established populations (i.e. three generation lengths of continuous coverage). The mean temperature of the warmest quarter (bio10), mean diurnal temperature range (bio2) and the minimum temperature of the coldest month (bio6) were the most important of the analysed predictors. In total, 42.9% of the study area are classified as suitable under current climatic conditions, of which 72.6% are to current knowledge yet unoccupied; therefore, we show that the coypu has, by far, not yet reached all potentially suitable regions Copyright Anna Schertler et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. NeoBiota 58: 129–160 (2020) doi: 10.3897/neobiota.58. 33118 http://neobiota.pensoft. net RESEARCH ARTICLE Advancing research on alien species and biological invasions A peer-reviewed open-access journal
Student Specialization in Deep Rectified Networks With Finite Width and Input Dimension

We consider a deep ReLU / Leaky ReLU student network trained from the output of a ﬁxed teacher network of the same depth, with Stochastic Gradient Descent (SGD). The student network is over-realized : at each layer l , the number n l of student nodes is more than that ( m l ) of teacher. Under mild conditions on dataset and teacher network, we prove that when the gradient is small at every data sample, each teacher node is specialized by at least one student node at the lowest layer . For two-layer network, such specialization can be achieved by training on any dataset of polynomial size O ( K 5 / 2 d 3 (cid:15) − 1 ) . until the gradient magnitude drops to O ( (cid:15)/K 3 / 2 √ d ) . Here d is the input dimension, K = m 1 + n 1 is the total number of neurons in the lowest layer of teacher and student. Note that we require a spe-ciﬁc form of data augmentation and the sample complexity includes the additional data generated from augmentation. To our best knowledge, we are the ﬁrst to give polynomial sample complexity for student specialization of training two-layer (Leaky) ReLU networks with ﬁnite depth and width in teacher-student setting, and ﬁnite complexity for the lowest layer specialization in multi-layer case, without parametric assumption of the input (like Gaussian). Our theory suggests that teacher nodes with large fan-out weights get specialized ﬁrst when the gradient is still large, while others are specialized with small gradient, which suggests inductive bias in training. This shapes the stage of training as empirically observed in multiple previous works. Experiments on synthetic and CIFAR10 verify our ﬁndings. The code is released in https://github. com/facebookresearch/luckmatters .
Haley-Knott regression mapping of quantitative trait loci in extended pedigrees.

INTRODUCTION Mapping quantitative trait loci (QTL) in livestock species usually proceeds by using the phenotypes of a single generation of one or more large families. QTL are detected by finding a phenotype difference between those individuals receiving each of a parent’s alleles at a particular genome position. The data can be analysed using likelihood methods (Georges et ai. 1995) or least squares methods (Haley and Knott 1992).
RocNet: Recursive Octree Network for Efficient 3D Deep Representation

We introduce a deep recursive octree network for the compression of 3D voxel data. Our network compresses a voxel grid of any size down to a very small latent space in an autoencoder-like network. We show results for compressing 323, 643 and 1283 grids down to just 80 floats in the latent space. We demonstrate the effectiveness and efficiency of our proposed method on several publicly available datasets with three experiments: 3D shape classification, 3D shape reconstruction, and shape generation. Experimental results show that our algorithm maintains accuracy while consuming less memory with shorter training times compared to existing methods, especially in 3D reconstruction tasks.
Purely Bayesian counterfactuals versus Newcomb's paradox

This paper proposes a careful separation between an entity's epistemic system and their decision system. Crucially, Bayesian counterfactuals are estimated by the epistemic system; not by the decision system. Based on this remark, I prove the existence of Newcomb-like problems for which an epistemic system necessarily expects the entity to make a counterfactually bad decision. I then address (a slight generalization of) Newcomb's paradox. I solve the specific case where the player believes that the predictor applies Bayes rule with a supset of all the data available to the player. I prove that the counterfactual optimality of the 1-Box strategy depends on the player's prior on the predictor's additional data. If these additional data are not expected to reduce sufficiently the predictor's uncertainty on the player's decision, then the player's epistemic system will counterfactually prefer to 2-Box. But if the predictor's data is believed to make them quasi-omniscient, then 1-Box will be counterfactually preferred. Implications of the analysis are then discussed. More generally, I argue that, to better understand or design an entity, it is useful to clearly separate the entity's epistemic, decision, but also data collection, reward and maintenance systems, whether the entity is human, algorithmic or institutional.
Online Extrinsic Camera Calibration for Temporally Consistent IPM Using Lane Boundary Observations with a Lane Width Prior

In this paper, we propose a method for online extrinsic camera calibration, i.e., estimating pitch, yaw, roll angles and camera height from road surface in sequential driving scene images. The proposed method estimates the extrinsic camera parameters in two steps: 1) pitch and yaw angles are estimated simultaneously using a vanishing point computed from a set of lane boundary observations, and then 2) roll angle and camera height are computed by minimizing difference between lane width observations and a lane width prior. The extrinsic camera parameters are sequentially updated using extended Kalman filtering (EKF) and are finally used to generate a temporally consistent bird-eye-view (BEV) image by inverse perspective mapping (IPM). We demonstrate the superiority of the proposed method in synthetic and real-world datasets.
On Controllability of AI

Invention of artificial general intelligence is predicted to cause a shift in the trajectory of human civilization. In order to reap the benefits and avoid pitfalls of such powerful technology it is important to be able to control it. However, possibility of controlling artificial general intelligence and its more advanced version, superintelligence, has not been formally established. In this paper, we present arguments as well as supporting evidence from multiple domains indicating that advanced AI can't be fully controlled. Consequences of uncontrollability of AI are discussed with respect to future of humanity and research on AI, and AI safety and security.