Miracle-dz/UniFAR-FTUs huggingface dataset:
DIVERSITY OF SOME INDONESIA LOCAL GLUTINOUS RICE ( Oryza sativa L . Var . Glutinous ) BASED ON AGROMORPHOLOGICAL AND RAPD MARKERS

Ketan is a term for glutinous rice in Indonesia that is widely used as an ingredient in various traditional dishes or desserts. However national production of this plant is low because most of the farmers prefer to cultivate paddy. This research aimed to investigate genetic diversity of potential parents. These results will be used to create new varieties of glutinous rice. The plant materials used in the experiments included eight local upland glutinous rice cultivars from West Java, Banten, and Bengkulu province. The seeds were planted by drill seeding in the experimental plots. The observations were recorded on quantitative and qualitative characters of each cultivar. The results showed that cv. Pisitan Bulu and cv. Ketan Hideung had high values for seed weight (above 3 g/100grain), with plant height around 180 cm, long maturity (97.5 DAP for 50% flowering), and low tiller number (less than 10 tillers/clump). These weaknesses can be overcome by cv. Hitam-46 and Kelapa (plant height < 120 cm, tiller number > 20 tillers/clump and 50% flowering around 75 until 80 DAP). Almost all cultivars were resistant to blast disease. The results suggested that Pisitan Bulu, Ketan Hideung and Ketan Hitam-46 could be used as potential as parents in breeding programs. The results of this research indicated that the use of RAPD markers combined with agromorphological traits are useful for identifying genetic diversity of glutinous rice. Based on the dendogram from genetic analysis, qualitative, and quantitative characters, Ketan Kelapa and Ketan Hitam-46 were similar even though the origin was different (Ketan Hitam-46 from West Java, Ketan Kelapa from Enggano).
Testing the master constraint programme for loop quantum gravity: I. General framework

Recently, the master constraint programme for loop quantum gravity (LQG) was proposed as a classically equivalent way to impose the infinite number of Wheeler–DeWitt constraint equations in terms of a single master equation. While the proposal has some promising abstract features, it was until now barely tested in known models. In this series of five papers we fill this gap, thereby adding confidence to the proposal. We consider a wide range of models with increasingly more complicated constraint algebras, beginning with a finite-dimensional, Abelian algebra of constraint operators which are linear in the momenta and ending with an infinite-dimensional, non-Abelian algebra of constraint operators which closes with structure functions only and which are not even polynomial in the momenta. In all these models, we apply the master constraint programme successfully; however, the full flexibility of the method must be exploited in order to complete our task. This shows that the master constraint programme has a wide range of applicability but that there are many, physically interesting subtleties that must be taken care of in doing so. In particular, as we will see, that we can possibly construct a master constraint operator for a nonlinear, that is, interacting quantum field theory underlines the strength of the background-independent formulation of LQG. In this first paper, we prepare the analysis of our test models by outlining the general framework of the master constraint programme. The models themselves will be studied in the remaining four papers. As a side result, we develop the direct integral decomposition (DID) programme for solving quantum constraints as an alternative to refined algebraic quantization (RAQ).
A Deep Approach for Handwritten Musical Symbols Recognition

Preserving the world musical heritage comes down to digitalising and provision of music works to further query on the acquired data. However, to do the processing it is necessary an Optical Music Recognition (OMR) system capable of decoding the original manuscripts into a machine-readable data. Developing a precise and robust OMR system for handwritten musical scores is still an open issue. A fundamental step of improve such task is to recognise musical notes. Hence, trying to provide ways to produce a truly robust OMR sys- tem, we present in this paper a new methodology applying deep learning techniques to recognise musical notes in digitalised handwritten musical scores. The proposed methodology has been tested on a ground truth dataset of music scores reaching a minimum error rate of 3.99%, 96.46% of precision and 96.56% of recall on the HOMUS dataset.
Halo control for LHE based local adaptive tone mapping

We propose a halo control method that is suitable for smoothed local histogram equalization (LHE). Smoothed LHE based tone mapping, e.g., Apical Iridix®, is effective and widely used for local adaptive image enhancement. However, halo artifacts occur at the edge, which causes unnaturalness. There are no detailed discussions on the principles of smoothed LHE halo-artifacts. In this paper, we analyze the cause of the artifacts and propose a halo reduction/control method that uses a weighted local histogram. In addition, we separate the artifacts into light and dark and evaluate the effectivity for the human visual sense. The adequate weighting function of our method can be set automatically at each pixel by using the difference between the input and mean luminance of the local window so as to enhance the natural local edge.
On the performance of greedy algorithms for energy minimization

We revisit the well-known greedy algorithm for scheduling independent jobs on parallel processors, with the objective of energy minimization. We assess the performance of the online version, as well as the performance of the offine version, which sorts the jobs by non-increasing size before execution. We derive new approximation factors, as well as examples that show that these factors cannot be improved, thereby completely characterizing the performance of the algorithms.
How Accurately Can Your Wrist Device Recognize Daily Activities and Detect Falls?

Although wearable accelerometers can successfully recognize activities and detect falls, their adoption in real life is low because users do not want to wear additional devices. A possible solution is an accelerometer inside a wrist device/smartwatch. However, wrist placement might perform poorly in terms of accuracy due to frequent random movements of the hand. In this paper we perform a thorough, large-scale evaluation of methods for activity recognition and fall detection on four datasets. On the first two we showed that the left wrist performs better compared to the dominant right one, and also better compared to the elbow and the chest, but worse compared to the ankle, knee and belt. On the third (Opportunity) dataset, our method outperformed the related work, indicating that our feature-preprocessing creates better input data. And finally, on a real-life unlabeled dataset the recognized activities captured the subject’s daily rhythm and activities. Our fall-detection method detected all of the fast falls and minimized the false positives, achieving 85% accuracy on the first dataset. Because the other datasets did not contain fall events, only false positives were evaluated, resulting in 9 for the second, 1 for the third and 15 for the real-life dataset (57 days data).
LLNL -‐ CONF -‐ 656112 ALE 3 D Simulation of Incompressible Flow , Heat Transfer , and Chemical Decomposition of Comp B in Slow Cookoff Experiments

An ALE3D thermal-fluids model was developed for Comp B (63% RDX, 36% TNT, 1% wax) and applied to a Scaled–Thermal-Explosion-eXperiment (STEX) with slow heating. Solubility and viscosity models were developed for a well-mixed RDXTNT slurry. Chemical kinetics parameters for a one-step Prout-Tompkins model were obtained using One-Dimensional-Time-to-Explosion (ODTX) measurements and a nonlinear regression routine. In the application of the thermal-fluids model, the strong convection of mass, momentum, and energy was reduced at high temperatures and associated low slurry viscosities to make the simulations numerically tractable. The ODTX thermal-fluids simulations show that in the fast experiments, flow reduces explosion times and self-heated liquid rises to the top of the cavity and ignites. The STEX model shows that flow leads to a small delay in the explosion time and ignition near the top of the vessel. Model comparison with STEX thermocouple measurements, suggests RDX settles in the slurry to form a solid-like material in the lower portion of the vessel and a more fluid material above.
Performance Improvement Capability: Keys to Accelerating Performance Improvement in Hospitals

Organizations differ considerably in their rate of performance improvement. Since any improvement trajectory is the fruit of a series of improvement projects, the proximate cause of this variation among organizations lies in the varied ways these projects are managed. The success of these projects depends, however, not only on the goals and efforts of the project team, but also on the context within which the projects are undertaken - and, more specifically, on the competencies on which the projects can draw. It is variation in these competencies - the organization's underlying "performance improvement capability" (PIC) - that explain the substantial and sustained differences in rates of improvement across organizations. This article describes the efforts of several hospitals to strengthen their PIC through 5 key components: 1. skills, 2. systems, 3. structure, 4, strategy, and 5. culture.
Networking Computation Industrial Manufacturing Automation Data Management in Networked Industrial Environments : State of the Art and Open Challenges Specialized Sources Multi-disciplinary Source

Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This article surveys the recent literature on data management as it applies to networked industrial environments and identifies several open research challenges for the future. As a first step, we extract important data properties (volume, variety, traffic, criticality) and identify the corresponding data enabling technologies of diverse fundamental industrial use cases, based on practical applications. Secondly, we provide a detailed outline of recent industrial architectural designs with respect to their data management philosophy (data presence, data coordination, data computation) and the extent of their distributiveness. Then, we conduct a holistic survey of the recent literature from which we derive a taxonomy of the latest advances on industrial data enabling technologies and data centric services, spanning all the way from the field level deep in the physical deployments, up to the cloud and applications level. Finally, motivated by the rich conclusions of this critical analysis, we identify interesting open challenges for future research. The concepts presented in this article thematically cover the largest part of the industrial automation pyramid layers. Our approach is multidisciplinary, as the selected publications were drawn from two fields; the communications, networking and computation field as well as the industrial, manufacturing and automation field. The article can help the readers to deeply understand how data management is currently applied in networked industrial environments, and select interesting open research opportunities to pursue.
Geologic Map of the Wildhorse Mountain 7½′ Quadrangle, Cochise County, Arizona

The Wildhorse Mountain 7 ½′ Quadrangle includes part of the central San Pedro River and flanking valley fill, as well as areas of bedrock on both sides of the valley and at the south edge of the Quadrangle where the San Pedro River crosses bedrock at the San Pedro Narrows (Figure 1). Production of this new geologic map continues the Arizona Geological Survey mapping program of the San Pedro River valley. This mapping was done under the joint State-Federal STATEMAP program, as specified in the National Geologic Mapping Act of 1992, and was jointly funded by the Arizona Geological Survey and the U.S. Geological Survey under STATEMAP Program Contract award number 06HQAG0051. Mapping was compiled digitally using ESRI ArcGIS software. Revised and re-released with funding from the USGS National Geological and Geophysical Data Preservation Program, award no. G17AP00114. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Government.
Scaling up Multidimensional Recurrent Neural Networks for image segmentation

This work is the final result of the double degree program in association with USI (Università della Svizzera Italiana) and has been conducted in the Swiss AI lab IDSIA (Istituto Dalle Molle di Studi sull’Intelligenza Artificiale). In the machine learning community, interest in neural networks and deep learning have recently seen a continuous growth. In this context Multidimensional Recurrent Neural Networks (MDRNNs) allowed to bring the power of recurrent neural networks (RNNs) to data other than simple sequences and they have the potential to advance the state-of-the-art in many challenging problems, like image segmentation or video classification. Given this interest in MDRNNs, the main contribution of this thesis is an efficient implementation of an MDLSTM layer, which is the Long Short Term Memory (LSTM) extension of MDRNNs. Moreover a simple CPU parallelization of the algorithm is implemented and evaluated against the single core version. We also show that this parallelization achieves speedups comparable to what has been achieved on convolutional neural networks with GPU implementations. Finally the MDSLTM layer is integrated in an hybrid architecture with convolutional layers to perform semantic image segmentation, which is a difficult unsolved problem in computer vision. This architecture achieves near state of the art results on a popular image segmentation benchmark with no need for any pre or post processing.
Contextualization using hyperlinks and internal hierarchical structure of Wikipedia documents

Context surrounding hyperlinked semi-structured documents, externally in the form of citations and internally in the form of hierarchical structure, contains a wealth of useful but implicit evidence about a document's relevance. These rich sources of information should be exploited as contextual evidence. This paper proposes various methods of accumulating evidence from the context, and measures the effect of contextual evidence on retrieval effectiveness for document and focused retrieval of hyperlinked semi-structured documents. We propose a re-weighting model to contextualize (a) evidence from citations in a query-independent and query-dependent fashion (based on Markovian random walks) and (b) evidence accumulated from the internal tree structure of documents. The in-links and out-links of a node in the citation graph are used as external context, while the internal document structure provides internal, within-document context. We hypothesize that documents in a good context (having strong contextual evidence) should be good candidates to be relevant to the posed query, and vice versa. We tested several variants of contextualization and verified notable improvements in comparison with the baseline system and gold standards in the retrieval of full documents and focused elements.
Interdependent Ranking of Sources and Sinks in CCS Systems using Analytical Network Process

CO2 capture and storage (CCS) is widely regarded as a key technology for reducing greenhouse gas emissions from large industrial point sources. It entails the capture of a relatively pure CO2 from exhaust gases using different techniques, and then storing this captured gas in various geological sinks. Large-scale deployment of CCS requires the comprehensive evaluation of candidate sources and sinks present in a given geographical region. In this study, we propose an analytic network process (ANP) approach to rank simultaneously the potential CO2 sources and sinks in a CCS system. This ANP decision model allows us to incorporate the feedback dependency that exist in the preference ranking of sources and sinks due to the importance of geographic proximity as a decision criterion. A case study is then solved to demonstrate the proposed model.
The relationship between temperament, character and executive functioning

ion & concept Formation 23.92 3.46 Abstraction & concept formation –WM 24.49 3.29ion & concept formation –WM 24.49 3.29 Abstraction & concept formation RT 2125.10 565.45ion & concept formation RT 2125.10 565.45 Abstraction & concept formation WM RT 1492.40 413.93ion & concept formation WM RT 1492.40 413.93 Attention and working memory RT 425.00 95.38 Sensory-motor RT (Trial 1) 791.52 287.83 Sensory-motor RT (Trial 2) 597.84 101.74 Abstraction in executive functioning 37.94 7.63 Abstraction in executive functioning RT 1979.90 554.98ion in executive functioning RT 1979.90 554.98 Executive perseverative Errors 14.85 8.73 Verbal reasoning 14.98 5.22 Verbal reasoning RT 8266.42 2970.77 Abstraction & mental flexibility 42.49 10.19 Abstraction & mental flexibility RT 17185.31 8118.96ion & mental flexibility RT 17185.31 8118.96 Note. RT = reaction time; WM = working memory; -WM = without working memory
Dynamic resource allocation for MMOGs in cloud computing environments

A massively multiplayer online game (MMOG) has hundreds of thousands of players who play in the game concurrently. The players consume a great deal of CPU, memory and network bandwidth resources in MMOGs. We combine MMOGs with cloud computing environments. We use virtual machine servers (VMSs) in cloud computing environments instead of traditional physical game servers. By using a multi-server architecture, we divide a game world into several zones, and each zone consists of at least a VMS to execute game processes and exchange game information among players in the zone. In addition, we design an adaptive neural fuzzy inference system (ANFIS) and also an artificial neural network (ANN) to predict the load of each zone and decide a resource allocation policy to be performed by the VMS. Experimental results show that the mean square error of the ANFIS-based load prediction is lower than that of the ANN-based load prediction. Therefore, we incorporate the ANFIS prediction method along with the five resource allocation policies to the MMOG cloud. In terms of average access time, the proposed ANFIS-based DLP+SVMS resource allocation method is 16.7% better than the ANFIS-based DLP, where DLP is an existing deep-level partitioning (DLP) method. Furthermore, the proposed method has the smallest number of VMSs used among the three methods. The evaluation results show the feasibility of applying the proposed resource allocation method to MMOG clouds.
Retrieval & Adaptation in Drj Vu, a Case-Based Reasoning System for Software Design

Several current AI techniques are based on the reuse of problem solving knowledge. Case-based reasoning (CBR) is one such technique. In CBR problem solutions are stored as cases, and to solve a new problem a suitable case is retrieved and adapted. This paper examines adaptation in the context of a case-based reasoning system for software design called Drj~t Vu. The paper describes Drj~ Vu’s twotier approach to adaptation which utilises both domain specific and domain independent adaptation knowledge. In addition we describe how D6jh Vu provides two adaptation support mechanisms (adaptive problem decomposition and adaptation-guided retrieval) to relieve the adaptation load and how these mechanisms use adaptation knowledge in different ways to achieve their goals.
4. Pairwise Alignment Using Hmms

Recall the dynamic programming that we introduced in Chapter 2 for pairwise alignment with affine gap penalties. We used a diagram for the updating among states, M corresponding to a match, and two states corresponding to inserts, which we name here X and Y as shown in Figure 4.1. The recurrence relations for updating the values of these states in the dynamic programming matrix are We will convert this algorithm into a HMM, and give a probabilistic interpretation of the gapped alignment process. 4.1 Pair HMMs We need to make two sets of changes to a diagram as shown on the left side of Figure 4.1 to turn it into an HMM. First, as shown on the right of Figure 4.1, we must give probabilities both for emissions of symbols from the states, and for transitions between states. For example, state M has emission probability distribution for emitting an aligned pair a , and state X and Y will have distributions for emitting symbol against a gap. Because state X emits symbols from sequence ab p b : a q a i x x , we write q inside the circle representing state X. We also specify transition probabilities between the states, which must satisfy the requirement that the probabilities for all the transitions leaving each state sum to one. We define two free parameters for the transition probabilities between the three main states. We denote the transition from M to an insert state (X or Y) by i x δ , and the probability of staying in an insert state by ε. However, the resulting model shown on the right side of Figure 4.1 does not generate a full model that will provide a probability distribution over all possible sequences. To do that, we need to define a Begin and an End state, as shown in Figure 4.2. These formalize the initialization and termination conditions that we needed for the dynamic programming algorithms in Chapter 2. We define the probability of a transition into the End state to be the same from each of M, X and Y; we call it τ. This will in effect determine the average length of an alignment from the model. We also set the transitions from the Begin state to be the same as from the M state. This gives us a probabilistic model that is very similar to a hidden Markov model …
The C-terminal domain of Nup93 is essential for assembly of the structural backbone of nuclear pore complexes

ETOC: Nup93 is essential for nuclear pore complex (NPC) assembly, but its interaction partners Nup188 and Nup205 are not. The C-terminus of Nup93 is necessary and sufficient for forming a minimal structural NPC backbone. The remaining part of the protein recruits the Nup62 complex to establish transport-competent NPCs and the diffusion barrier.
X-Code: MDS Array Codes with Optimal Encoding

We present a new class of MDS (maximum distance separable) array codes of size n/spl times/n (n a prime number) called X-code. The X-codes are of minimum column distance 3, namely, they can correct either one column error or two column erasures. The key novelty in X-code is that it has a simple geometrical construction which achieves encoding/update optimal complexity, i.e., a change of any single information bit affects exactly two parity bits. The key idea in our constructions is that all parity symbols are placed in rows rather than columns.
A Copula‐Based BRDF Model

In this paper, we introduce a novel approach for modeling surface reflection. We focus on using a family of probability distributions called Archimedean copulas as BRDF models. The Archimedean representation has an attractive property in that the multivariate distributions are characterized by their marginal distributions through a single univariate function only. It is shown that the proposed model meets the reciprocity property of reflection. Based on measured BRDF data, we demonstrate that the proposed approach provides a good approximation to BRDF. Empirical comparisons are made with some classically used BRDF models.