Miracle-dz/UniFAR-FTUs huggingface dataset:
Project Halo Update - Progress Toward Digital Aristotle

In the winter, 2004 issue of AI Magazine, we reported Vulcan Inc.'s first step toward creating a question-answering system called "Digital Aristotle. " The goal of that first step was to assess the state of the art in applied Knowledge Representation and Reasoning (KRR) by asking AI experts to represent 70 pages from the advanced placement (AP) chemistry syllabus and to deliver knowledge-based systems capable of answering questions from that syllabus. This paper reports the next step toward realizing a Digital Aristotle: we present the design and evaluation results for a system called AURA, which enables domain experts in physics, chemistry, and biology to author a knowledge base and that then allows a different set of users to ask novel questions against that knowledge base. These results represent a substantial advance over what we reported in 2004, both in the breadth of covered subjects and in the provision of sophisticated technologies in knowledge representation and reasoning, natural language processing, and question answering to domain experts and novice users.
Carbon stock estimation on the reclaimed forest of PT Newmont Minahasa Raya in Ratatotok, Southeast Minahasa

Describing the quality of forest ecosystems using a carbon sequestration approach is important in understanding global warming. From 1997 to 2010, PT Newmont Minahasa Raya (PTNMR) has reclaimed 215 ha bare land of ex-mining area. The area now has become a fully functioning forest ecosystem. The forest absorbs CO2 from the atmosphere and accumulates it into a living biomass. The aim of this study was to estimate carbon sink in the Ratatotok forest, an ex-mining area. The study observed: 1) species composition at the various stages of plant development; 2) tree height and diameter to estimate the standing biomass; 3) carbon content of the understorey species; 4) carbon content of the ground litters; and 5) soil carbon content. Carbon content measurement employed a loss on ignition method, while total carbon was estimated using existing allometric equations. This study was conducted in the reclaimed areas from six different years, i.e. 1997, 2000, 2001, 2002, 2003 and 2007. The results showed that between 1997 and 2007, the forests captured a total of 247 t carbon per ha, equivalent to a total sequestration of 53,180 t of carbon in the entire reclaimed area (215 ha).
A RFID network design methodology for decision problem in health care

This research extends our previous work on decision makers with methodology to optimize the design of a strategy for constructing Radio Frequency Identification (RFID). RFID technology is an automatic identification system through radio frequency for transferring data. Before deploying RFID system, one of the challenging problems is RFID network planning (RNP). The RNP problem must be solved to operate the large-scale network of readers, and need to satisfy a set of requires, such as coverage rate, economic, interference. This paper extends our previous work using soft computing technique to find the optimal positions of RFID readers based on Simplified Swarm Optimization (SSO) algorithm. Meanwhile, the fuzzy-ART and K-means models are applied to efficiently and effectively search better solutions.
Efficient MPI Broadcast Communication and File Distribution for Local-Area Clusters

Large clusters of commodity workstations have become a popular system architecture for parallel computing tasks. A common need in these clusters is the distribution of files to subsets of the nodes. Without an underlying distributed file system (as is often the case), this task is usually carried out with ad hoc tools, or better, solutions which use parallel programming models. For instance, collective communication operations in MPI can be effectively exploited for cluster file distribution. Distributions of MPI such as MPICH and LAM/MPI, however, implement the MPI Bcast collective operation as serial MPI Send operations, effectively limiting the scalability of this approach. Our work explores both the optimization of MPI collective operations through use of network broadcast mechanisms and the addition of a user-level library to facilitate file distribution across a local-area cluster. Inconsistent (but repeatable) experimental results across two test configurations do not allow us to make wide claims for our methods, but our results do indicate that our mechanisms are more scalable than existing file distribution methods, including those based on the LAM/MPI implementation of MPI.
Allele-Specific Interactions between CAST AWAY and NEVERSHED Control Abscission in Arabidopsis Flowers

An advantage of analyzing abscission in genetically tractable model plants is the ability to make use of classic genetic tools such as suppression analysis. We have investigated the regulation of organ abscission by carrying out suppression analysis in Arabidopsis flowers. Plants carrying mutations in the NEVERSHED (NEV) gene, which encodes an ADP-ribosylation factor GTPase-activating protein, retain their outer floral organs after fertilization. Mutant alleles of CAST AWAY (CST), which encodes a receptor-like cytoplasmic kinase, were found to restore organ abscission in nev flowers in an allele-specific manner. To further explore the basis of the interactions between CST and NEV, we tested whether the site of a nev mutation is predictive of its ability to be suppressed. Our results suggest instead that the strength of a nev allele influences whether organ abscission can be rescued by a specific allele of CST.
A Direct Normal Irradiance Prediction Model based on VMD-WOA-ELM for Concentrating Solar Power Station

The variation of solar direct normal irradiation (DNI) affects the reliability and efficiency of concentrating solar power (CSP) generation. Taking a CSP station in Qinghai Province as the research object, a DNI prediction model based on VMD-WOA-ELM is presented. First, using Variational Mode Decomposition (VMD) to divide DNI into some intrinsic modulus function (IMF) components, then the time domain and frequency domain eigenvalues of each IMF component are extracted to form the eigenvector. Finally, the model uses Extreme Learning Machine (ELM) to recognize the kind of DNI. For solving the poor classification stability caused by ELM, Whale Optimization Algorithm (WOA) is applied to the parameter optimization of ELM to obtain the optimal classification model. By analyzing the actual prediction performance and the corresponding evaluation criteria, the accuracy and effectiveness of the established solar DNI prediction model are obtained.
Interactive Visualization Techniques for Searching Temporal Categorical Data

Temporal data has always captured people's imagination. Large databases of temporal data contain temporal patterns that can lead to the discovery of important cause-and-effect phenomena. Since discovering these patterns is a difficult task, there is a great opportunity to improve support for searching. Temporal analysis of, for example, medical records, web server logs, legal, academic, or criminal records can benefit from more effective search strategies. This dissertation describes several interactive visualization techniques designed to enhance analysts' experience in performing search, exploration, and summarization of multiple sets of temporal categorical data. These techniques are implemented in the software Lifelines2 (http://www.cs.umd.edu/hcil/lifelines2/ ). Lifelines2 is an interactive visualization system that enables analysts to dynamically change their focus in order to expose temporal ordering of event sequences and study the prevalence of such orderings. This dissertation makes four main contributions. The first three are technical contributions, and the last is a process model that generalizes user behavior. First, the Align-Rank-Filter framework is presented to help analysts perform visual search and exploration. It enables analysts to center their attention on temporal events that are the focus of their inquiry. Through a controlled experiment, alignment alone is shown to improve user performance speed by up to 60% in tasks that require understanding of temporal ordering of events. The initial successful exploration on the alignment operator led to its fuller exploitation. Further enhancements to filtering are presented to better incorporate alignment. Second, I designed and implemented the Temporal Pattern Search (TPS) algorithm for filtering to support the common, but difficult-to-specify absence of operator in a temporal pattern. TPS exploits the data structure of the visualization system, and it compares favorably to existing common approaches. Third, I present the temporal summaries technique as an overview to support grouping and comparison features in Lifelines2. They support higher-level tasks such as hypothesis generation. These features take advantage of alignment, and the entirety of the system is evaluated in several long-term case studies with domain experts working on their own problems. Fourth, from these long-term case studies, I generalize a process model that describes analyst behavior in searching and interacting with temporal categorical data. Gleaning from observations in the case studies, collaborators' interviews and commentaries, and logs of Lifelines2 usage, I recommend feature design guidelines for future visualization designers for temporal categorical data. The enthusiasm of the domain experts who used Lifelines2, the changing strategies for problem-solving, and their initial successes suggest these interactive visual ization techniques are a valuable addition to search capabilities.
Adaptive trajectory coordination for scalable multiple robot control

In this paper, we consider multiple robot display formation such that when users give a sequence of images as input, a large number of robots construct a sequence of formations to visualize the input images collectively. Users perceive a higher quality of responsiveness when robots respond to users' input in a more prompt and/or regular manner. A key problem in multiple robot display is trajectory coordination, which determines the robot's position and velocity along a given path while avoiding collisions. The response then consists of trajectory coordination computation time and robot moving time. In the multiple robot display, there is a conflict between reducing computation time and reducing robot moving time, and it is complicated to predict such a tradeoff over various environments. We propose an adaptive framework that exploits this tradeoff by prioritizing robots dynamically. Our simulation results show that our framework effectively reduces the response time in a scalable manner, and it produces balanced solutions adapting to various environments.
Scenario playing for critical success factor analysis

This paper presents a structured method, based on practical experience, to derive critical success factors using a business game called ‘scenario playing’. It aims to show why this method is superior to other well-known focusing techniques or group evaluation methods.The scenario playing business game consists of submitting to managers a sequence of reports, one after the other. Each report (called a scenario) contains randomly generated outcome values for some parameters of the business (called candidate critical success factors) which the manager deemed to be most important for the business at hand. After reviewing each report i.e. scenario, the manager has to give a value judgement about it, like ‘good’, ‘bad’ or ‘indifferent’, reflecting his perception of how the business would be doing if the report was real. A statistical method is used to evaluate these answers and deduct from them which parameters are the most important for the manager who played the given session of the game.The paper contains a short outline of the statistical evaluation (learning) algorithm used by the author and the description of a software tool that contains an implementation of this technique. Finally, the experience gained by using this tool and approach to derive critical success factors on different projects is discussed.
A New Scheduler for Real-Time Applications in Differentiated Services Networks

In this paper we propose a new scheduler. Deadline fair sharing (DFS), which consists of link scheduling and dropping mechanisms to provide absolute delay and loss guarantees in DiffServ networks. The scheduling mechanism uses dynamic weight associated with each class to schedule the packets and to make a dropping decision when an absolute delay guarantee violation occurs. The dropping mechanism drops packets and detects the onset of an early violation for absolute loss guarantee. When an early violation for the absolute loss guarantee of a class is detected, the scheduling mechanism gives that class more priority for transmitting its packets to prevent that violation. The simulation results show that the proposed scheduler can provide a wide range of absolute loss and delay bounds under various traffic load conditions without controlling the traffic.
Dynamic analysis of BMP‐responsive smad activity in live zebrafish embryos

Bone morphogenetic proteins (BMPs) are critical players in development and disease, regulating such diverse processes as dorsoventral patterning, palate formation, and ossification. These ligands are classically considered to signal via BMP receptor‐specific Smad proteins 1, 5, and 8. To determine the spatiotemporal pattern of Smad1/5/8 activity and thus canonical BMP signaling in the developing zebrafish embryo, we generated a transgenic line expressing EGFP under the control of a BMP‐responsive element. EGFP is expressed in many established BMP signaling domains and is responsive to alterations in BMP type I receptor activity and smad1 and smad5 expression. This transgenic Smad1/5/8 reporter line will be useful for determining ligand and receptor requirements for specific domains of BMP activity, as well as for genetic and pharmacological screens aimed at identifying enhancers or suppressors of canonical BMP signaling. Developmental Dynamics 240:682–694, 2011. © 2011 Wiley‐Liss, Inc.
Quantitative sustainability analysis of third generation biofuels using process data from microalgae biorefinery

Microalgae biorefineries have been proposed as an important strategy for enhancing the economic profitability of bioproducts, which depends on the biotechnological potential of each species, associated with environmental conditions and technological approaches. The biorefinery concept applied to microalgae also implies valorization of residual biomass, converting it into biofuels and/or high value compounds. This study aimed to analyze the biochemical composition and the technical and economic feasibility of a biorefinery based on Phaeodactylum tricornutum cultivated in an outdoor pilot-scale bubble-columns photobioreactor, under natural conditions in Chile, for production of biofuels and high-value compounds. P. tricornutum biomass has a potential biochemical composition for using in an integrated biorefinery approach, mainly in order to optimize the environmental and economic feasibility of the process. Three different scenarios were proposed for the production of biofuels and high-value compounds. The most profitable scenarios were those that considered fucoxanthin and protein production and commercialization, due to the high price of these compounds on the market. The production of biomass as a raw material exclusively targeted for biofuel production showed itself not to be feasible under the conditions considered in the analysis. Furthermore, the assumption of economies of scale was shown to be a critical factor for the biomass price and feasibility of this approach. Technical and economic analysis is essential to identify economic bottlenecks and opportunities for addressing a microalgae product portfolio to the different market scenarios, in order to assist decision-makers and propose improvements in the cultivation and downstream processes.
Neuroimaging the genetic effects of brain-derived neurotrophic factor on human hippocampus

The genetic determinants of human brain function have long been elusive, but are now beginning to emerge. The conjunction of the sequencing of the human genome (ie, the discovery of all genes) with the development of new neuroimaging techniques has produced an astonishing leap forward in our ability to detect the effects of genes on neurobiological processes. This poster focuses on a prototypical study of the gene for brain-derived neurotrophic factor (BDNF), showing its role in human hippocampal function and memory.
Who Lives on the Wrong Side of the Environmental Tracks? Evidence from the EPA's Risk-Screening Environmental Indicators Model

This study analyzes the social and economic correlates of air pollution exposure in U.S. cities using a unique dataset created as a by-product of the EPA’s Risk-Screening Environmental Indicators model and finds evidence of disproportionate exposure to environmental hazards in communities with higher concentrations of lower-income people and people of color. We improve on previous studies of environmental inequality in three ways. First, where previous studies focus on the proximity to point sources and the total mass of pollutants released, our measure of toxic exposure reflects atmospheric dispersion and chemical toxicity. Second, we analyze the data at a fine level of geographic resolution. Third, we control for substantial regional variations in pollution, allowing us to identify exposure differences both within cities and between cities. We combine 1998 data on toxicity-adjusted exposure to air pollution with 1990 Census block group data for urbanized areas. We find that blacks tend to live both in more polluted cities in the U.S. and in more polluted neighborhoods within cities. Hispanics live in less polluted cities on average, but they live in more polluted areas within cities. We find an extremely consistent income-pollution gradient, with lower income people significantly more exposed. Our findings highlight the importance of controlling for interregional variation in pollution levels in studies of the demographic correlates of pollution.
Routing-based synthesis of digital microfluidic biochips

Microfluidic biochips are replacing the conventional biochemical analyzers, and are able to integrate on-chip all the necessary functions for biochemical analysis. The “digital” biochips are manipulating liquids as discrete droplets on a two-dimensional array of electrodes. Basic microfluidic operations, such as mixing and dilution, are performed on the array, by routing the corresponding droplets on a series of electrodes. So far, researchers have assumed that these operations are executed on virtual rectangular devices, formed by grouping several adjacent electrodes. One drawback is that all electrodes are considered occupied during the operation execution, although the droplet uses only one electrode at a time. Moreover, the operations can actually be performed by routing the droplets on any sequence of electrodes on the microfluidic array. Hence, in this paper, we eliminate the concept of virtual devices and allow the droplets to move on the chip on any route during operation execution. Thus, the synthesis problem is transformed into a routing problem. We develop an algorithm based on a Greedy Randomized Adaptive Search Procedure (GRASP) and we show that routing-based synthesis leads to significant improvements in the application completion time compared to traditional synthesis based on virtual devices. However, the disadvantage of the routing-based approach is that it may contaminate larger areas of the biochip, when synthesizing applications containing liquids which may adsorb on the surface of the microfluidic array. We have extended the GRASP-based algorithm to consider contamination avoidance during routing-based synthesis. Several real-life examples and synthetic benchmarks are used to evaluate the proposed approaches.
Thompson Sampling: An Optimal Finite Time Analysis

The question of the optimality of Thompson Sampling for solving the stochastic multi-armed bandit problem had been open since 1933. In this paper we answer it positively for the case of Bernoulli rewards by providing the rst nite-time analysis that matches the asymptotic rate given in the Lai and Robbins lower bound for the cumulative regret. The proof is accompanied by a numerical comparison with other optimal policies, experiments that have been lacking in the literature until now for the Bernoulli case.
Silicon-Based BioFETs with 3-D Nanostructure: Easy integration, precise control of nanostructure, and a low device-to-device variation

In this paper, highly sensitive Si-NW BioFETs using a novel three-dimensional nanostructure were fabricated using a top-down approach and showed excellent electrical and sensing characteristics. The newly proposed BioFETs can provide multiple advantages, including easy integration, precise control over the NW design, and a low device-to-device variation. An SHNW structure can mitigate the STC problem in fabricating three-dimensional nanostructures, and this showed better intrinsic characteristics. The effectiveness of these devices results from their higher mechanical stability, better gate controllability, and increased effective channel width. Thus, the three-dimensional Si nanostructure holds great potential as channel material in BioFETs for future biosensing applications.
An Artificial Neural Network approach for electroencephalographic signal classification towards brain-computer interface implementation

Brain-Computer Interface (BCI) can be realized by translating user's thoughts into control commands to assist paralyzed persons to communicate and control electronic devices. In this work, Electroencephalographic (EEG) signals were recorded from four subjects while they perform different mental states. We present an Artificial-Neural-Network-based approach for the purpose of classifying Electroencephalographic signals into different mental states which are equivalent to different control commands for our BCI implementation. Inputs of the Artificial Neural Network are spectral features dimensionally reduced by Principal Component Analysis. Experimental results show that the proposed method outperforms other classifiers, i.e., K-Nearest Neighbor, Naïve Bayesian, Support Vector Machine, and Linear Discriminant Analysis in our EEG dataset with highest classification results on dual and triple mental state problems of 95.36% and 76.84%, respectively.
Composition and refinement of discrete real-time systems

Reactive systems exhibit ongoing, possibly nonterminating, interaction with the environment. Real-time systems are reactive systems that must satisfy quantitative timing constraints. This paper presents a structured compositional design method for discrete real-time systems that can be used to combat the combinatorial explosion of states in the verification of large systems. A composition rule describes how the correctness of the system can be determined from the correctness of its modules, without knowledge of their internal structure. The advantage of compositional verification is clear. Each module is both simpler and smaller than the system itself. Composition requires the use of both model-checking and deductive techniques. A refinement ruleguarantees that specifications of high-level modules are preserved by their implementations. The StateTime toolset is used to automate parts of compositional designs using a combination of model-checking and simulation. The design method is illustrated using a reactor shutdown system that cannot be verified using the StateTime toolset (due to the combinatorial explosion of states) without compositional reasoning. The reactor example also illustrates the use of the refinement rule.