Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Performance Analysis of Low Power RF Operational Amplifier using CMOS and BiCMOS Technology

This paper presents the design and performance comparison of a two stage operational amplifier topology using CMOS and BiCMOS technology. This conventional op amp circuit was designed by using RF model of BSIM3V3 in 0.6 μm CMOS technology and 0.35 μm BiCMOS technology. Both the op amp circuits were designed and simulated, analyzed and performance parameters are compared. The performance parameters such as gain, phase margin, CMRR, PSRR, power consumption etc achieved are compared. Finally, we conclude the suitability of CMOS technology over BiCMOS technology for low power RF design.
Controlling Pollution with Fixed Inspection Capacity

In this paper I model the optimal monitoring and enforcement strategy when inspection capacity is fixed by budget or manpower constraints. I adopt a leverage enforcement structure that classifies firms into two groups with different enforcement intensities. Optimal monitoring and enforcement requires effective allocation of the fixed number of inspections to the two groups. In each period, a fixed number of firms are selected from each group for inspection, and those with the highest emissions are placed in the targeted group in which the inspection probability is higher. This transition structure induces rankorder tournaments among inspected firms. Once selected for inspection, the emissions of each firm are subject to a standard above which the firm pays a fixed penalty. I find that a regulator facing inspection capacity constraints should leverage the limited inspections by allocating more inspections to the targeted group. In addition, I show that targeting enforcement is generally superior to static enforcement. This is in accordance with findings in the literature. These results are consistent over different ranges of regulatory parameters.
Connection Management by Ants : An Application of Mobile Agents in Network Management

1 Email: oppacher@scs.carleton. ca Abstract This paper describes how biologically-inspired agents can be used to solve control and management problems in Telecommunications. These agents, inspired by the foraging behavior of ants, exhibit the desirable characteristics of simplicity of action and interaction. The collection of agents, or swarm system, deals only with local knowledge and exhibits a form of distributed control with agent communication effected through the environment. In this paper we explore the application of ant-like agents to the problem of routing in circuit switched telecommunication networks.
Fast group management in IGMP

The ability to control quickly the expansion/reduction of the multicast distribution tree is central in some recent proposals for multicast congestion control. At the lowest level, these operations are controlled by the IGMP protocol. With the current speciication of the protocol, IGMP takes a few seconds to stop distribution of a group after a request. This reduces the eeectiveness of congestion control mechanisms based on this feature. In this paper we propose a mechanism to make the LEAVE operation act instantaneously , basing on prediction techniques similar to those used in processors for optimizing jump performance. The proposed mechanisms acts on the router side only, and it is fully compatible with the existing IGMP protocol. An implementation of the mechanisms described in this paper has been included in the latest mrouted3.9 sources and is available from the author.
Using Tri-Training and Support Vector Machines for addressing the ECML-PKDD 2006 Discovery Challenge

In this paper we present and analyze the methodological approach we have used for addressing the ECML - PKDD Discovery Challenge 2006. The Challenge was concerned with the identification of individual user's spam emails based on a centrally collected training set. The task descriptions of the discovery challenge indicated that we should deviate from the classical supervised clas- sification paradigm and attempt to utilize semi-supervised and transductive ap- proaches. The format of the training data (bag-of-words providing only word IDs), did not allow either for the use of Natural Language Processing (NLP) ap- proaches, or for the use of standard spam-recognition strategies. The submitted model, which achieved 5 th place on Task A of the challenge, was derived by Tri- Training, a recent development in Semi-supervised algorithms research. Given a standard classifier, Tri-Training initially uses bagging to produce three diverse training datasets-classifiers, which are used for classifying the unlabeled data and incorporating them into the training set in a theoretically sound way. The classi- fier we have used within Tri-Training was Support Vector Machines (SVM) and more precisely the Sequential Minimal Optimization (SMO) implementation of WEKA. Moreover, we have used feature normalization and logistic regression models to produce continuous outputs. Apart from a detailed description and a discussion of the submitted model, this paper contains an extensive empirical evaluation of two popular semi-supervised classification algorithms: Transduc- tive Support Vector Machines (TSVM) and Tri-Training.
Class-Graph Inference for Adaptive Programs

Software generators can adapt components to changes in the architectures in which the components operate. The idea is to keep the architecture description separate and let the software generator mix it with specications of each component. Adaptation is done by regeneration: when the architecture changes, the components are regenerated. A software component will usually be written with a particular architecture in mind. This raises the question: how much has it committed to the particular structure of that architecture? To put it in a nutshell: How exible is a given software component? In this paper we study this question in the setting of Lieberherr’s adaptive programming. Lieberherr uses class graphs as the architecture and so-called adaptive programs as the software components. We present a polynomial-time class-graph inference algorithm for adaptive programs. The algorithm builds a representation of the set of class graphs with which a given adaptive program can work. It also decides if the set is non-empty, and if so it computes a particularly simple graph in the solution set. Several toy programs have been processed by a prototype implementation of the algorithm.
Analytic Thinking Reduces Anti-Atheist Bias in Voting Intentions

ABSTRACT Research has demonstrated that priming analytic thinking reduces adherence to religious ideas. The current studies examined whether analytic thinking primes can also increase acceptance of secular ideas and ameliorate anti-atheist prejudice. In Study 1, participants who were primed to think analytically demonstrated less anti-atheist prejudice (operationalized as willingness to vote for an atheist) and increased levels of agreement with secular ideas. Mediation analyses indicated that the ability of an analytic prime to reduce anti-atheist prejudice and facilitate agreement with secular ideas was related to increases in analytic thinking (operationalized as performance on the Cognitive Reflections Test). Study 2 extended the findings of the first study and demonstrated that, among a sample of young adults, higher levels of analytic thinking predicted decreased anti-atheist prejudice in the context of voting intentions in the 2016 U.S. presidential election. Basic and applied implications of these findings are discussed.
Superoxide-Dismutase Deficient Mutants in Common Beans (Phaseolus vulgaris L.): Genetic Control, Differential Expressions of Isozymes, and Sensitivity to Arsenic

Two common bean (Phaseolus vulgaris L.) mutants, sodPv 1 and sodPv 2, exhibiting foliar superoxide dismutase (SOD) activity of only 25% and 40% of their mother control (MC) cv. VL 63 were isolated in EMS-mutagenized (0.15%, 8 h) M2 progeny. Native-PAGE analysis revealed occurrence of Mn SOD, Fe SOD, Cu/Zn SOD I and Cu/Zn SOD II isozymes in MC, while Fe SOD, and Mn SOD were not formed in sodPv 1 and sodPv 2 leaves, respectively. In-gel activity of individual isozymes differed significantly among the parents. SOD deficiency is inherited as recessive mutations, controlled by two different nonallelic loci. Gene expressions using qRT PCR confirmed higher expressions of Cu/Zn SOD transcripts in both mutants and the absence of Fe SOD in sodPv 1 and Mn SOD in sodPv 2. In 50 μM arsenic, Cu/Zn SODs genes were further upregulated but other isoforms downregulated in the two mutants, maintaining SOD activity in its control level. In an F2 double mutants of sodPv 1 × sodPv 2, no Fe SOD, and Mn SOD expressions were detectable, while both Cu/Zn SODs are down-regulated and arsenic-induced leaf necrosis appeared. In contrast to both mutants, ROS-imaging study revealed overaccumulation of both superoxides and H2O2 in leaves of double mutant.
Continuous Reliability Monitoring Using Adaptive Critical Path Testing

As processor reliability becomes a first order design constraint, this research argues for a need to provide continuous reliability monitoring. We present an adaptive critical path monitoring architecture which provides accurate and real-time measure of the processor’s timing margin degradation. Special test patterns check a set of critical paths in the circuit-under-test. By activating the actual devices and signal paths used in normal operation of the chip, each test will capture up-to-date timing margin of these paths. The monitoring architecture dynamically adapts testing interval and complexity based on analysis of prior test results, which increases efficiency and accuracy of monitoring. Experimental results based on FPGA implementation show that the proposed monitoring unit can be easily integrated into existing designs. Monitoring overhead can be reduced to zero by scheduling tests only when a unit is idle.
N ov 2 00 2 Local Friedel sum rule on graphs

We consider graphs made of one-dimensional wires connected at vertices and on which may live a scalar potential. We are interested in a scattering situation where the graph is connected to infinite leads. We investigate relations between the scattering matrix and the continuous part of the local density of states, the injectivities, emissivities and partial local density of states. Those latter quantities can be obtained by attaching an extra lead at the point of interest and by investigating the transport in the limit of zero transmission into the additional lead. In addition to the continuous part related to the scattering states, the spectrum of graphs may present a discrete part related to states that remain uncoupled to the external leads. The theory is illustrated with the help of a few simple examples. PACS : 03.65. Nk, 73.23. -b
Neutron Scattering Study of Sr 2 Cu 3 O 4 Cl 2

We report a neutron scattering study on the tetragonal compound ${\mathrm{Sr}}_{2}{\mathrm{Cu}}_{3}{\mathrm{O}}_{4}{\mathrm{Cl}}_{2},$ which has two-dimensional (2D) interpenetrating ${\mathrm{Cu}}_{\mathrm{I}}$ and ${\mathrm{Cu}}_{\mathrm{II}}$ subsystems, each forming a $S=1/2$ square lattice quantum Heisenberg antiferromagnet (SLQHA). The mean-field ground state is degenerate, since the intersubsystem interactions are geometrically frustrated. Magnetic neutron scattering experiments show that quantum fluctuations lift the degeneracy and cause a 2D Ising ordering of the ${\mathrm{Cu}}_{\mathrm{II}}$ subsystem. Due to quantum fluctuations a dramatic increase of the ${\mathrm{Cu}}_{\mathrm{I}}$ out-of-plane spin-wave gap is also observed. The temperature dependence and the dispersion of the spin-wave energy are quantitatively explained by spin-wave calculations which include quantum fluctuations explicitly. The values for the nearest-neighbor superexchange interactions between the ${\mathrm{Cu}}_{\mathrm{I}}$ and ${\mathrm{Cu}}_{\mathrm{II}}$ ions and between the ${\mathrm{Cu}}_{\mathrm{II}}$ ions are determined experimentally to be ${J}_{\mathrm{I}\ensuremath{-}\mathrm{I}\mathrm{I}}=\ensuremath{-}10(2)$ meV and ${J}_{\mathrm{II}}=10.5(5)$ meV, respectively. Due to its small exchange interaction ${J}_{\mathrm{II}},$ the 2D dispersion of the ${\mathrm{Cu}}_{\mathrm{II}}$ SLQHA can be measured over the whole Brillouin zone with thermal neutrons, and a dispersion at the zone boundary, predicted by theory, is confirmed. The instantaneous magnetic correlation length of the ${\mathrm{Cu}}_{\mathrm{II}}$ SLQHA is obtained up to a very high temperature, ${T/J}_{\mathrm{II}}\ensuremath{\approx}0.75. $ This result is compared with several theoretical predictions as well as recent experiments on the $S=1/2$ SLQHA.
AIZE ( Z EA MAYS L . ) : I MPACT AND M ANAGEMENT

The United States is the largest producer of maize in the world. Maize-soybean is the most prominent crop rotation in the Midwestern United States. Glyphosate-resistant volunteer maize is a problem weed not only in soybean, but also in continuous maize or sugarbeet rotations. Storm damage, harvesting problems, poor stalk quality, and insect damage, among other factors, can lead to kernel and ear losses that result in volunteer maize the following year. Volunteer maize results from the overwintering of the hybrid maize used the previous year or from failed maize stand in maize replant situation. Increased adoption of glyphosate-resistant maize resulted in increasing issues of volunteer maize. Volunteer maize also plays a role in the survival and dispersal of corn rootworm and gray leaf spot disease; therefore, it limits the benefits of maize-soybean rotation and creates challenges for insect-resistance management. Management of glyphosate-resistant volunteer maize in soybean is complicated because growers are primarily relying on glyphosate for weed control. The acetyl-coenzyme A carboxylase (ACCase) inhibiting-herbicides, also known as graminicides, are often used to control volunteer maize in soybean. Several pre-emergence herbicides exist for residual grass weed control in soybean; however, none of them provides acceptable control of volunteer * Corresponding author’s email: Amit.Jhala@unl.edu. Complimentary Contributor Copy Parminder S. Chahal, Prashant Jha, Tamra Jackson-Ziems et al. 84 maize. Glufosinate is an alternate herbicide that can be applied alone or in combination with ACCase inhibitors for control of glyphosate-resistant volunteer maize in glufosinateresistant soybean. The commercialization of multiple herbicide-resistant maize hybrids may pose new challenges for management of volunteer maize.
Reparative resynchronization in ischemic heart failure: an emerging strategy

Cardiac dyssynchrony refers to disparity in cardiac wall motion, a serious consequence of myocardial infarction associated with poor outcome. Infarct-induced scar is refractory to device-based cardiac resynchronization therapy, which relies on viable tissue. Leveraging the prospect of structural and functional regeneration, reparative resynchronization has emerged as a potentially achievable strategy. In proof-of-concept studies, stem-cell therapy eliminates contractile deficit originating from infarcted regions and secures long-term synchronization with tissue repair. Limited clinical experience suggests benefit of cell interventions in acute and chronic ischemic heart disease as adjuvant to standard of care. A regenerative resynchronization option for dyssynchronous heart failure thus merits validation.
The Effect of Future Thinking Curriculum on Future Thinking and Creativity of Junior High School Students

The main purpose of the study was to investigate the future thinking curriculum effects on the future thinking and creativity for junior high school students. A static-group comparison design was employed. Thirty-six ninth grade students from a local junior high school participated in the experimentation with three-unit learning activities, each unit took 45 minutes. Students’ responses in Future Thinking Checklist, Torrance Tests of Creative Thinking, and feedback questionnaire were collected and compared with t-test statistical analyses. The major results were as follows: (1) The future thinking curriculum had significant effects on enhancing creativity ability of junior high school students, including the elaboration and title ability. Eighty percent of the students expressed creative ideas beyond reality in the learning sheet. More than fifty percent of the students thought that the curriculum could inspire their creative imagination ability. They also pointed that they had confidence to use their creative imagination in the future. (2) Eighty percent of the students felt positive for the future and thought that the curriculum could improve their plot construction, past review and future prediction. Ninety percent of the students could predict the changes in the future. Sixty percent of the students thought that the curriculum could be helpful to predict the changes in the future and improve prospective thinking. (3) Eighty percent of the students could express their future life which they want to achieve. More than fifty percent of the students understood how to have a wonderful life and made it come true through the curriculum.
A Novel Threat Assessment Method for DDoS Early Warning Using Network Vulnerability Analysis

Distributed Denial of Service (DDoS) attack is one of main threats to Internet security. Due to the spatio-temporal properties of the attack, it is possible to detect the attack at its early stage. In this paper, we propose a novel method of DDoS threat assessment based on network vulnerability analysis. Both the multi-phase character in the temporal dimension and the impacts in the spatial dimension are concerned in our method. We use three metrics to assess threat, namely the ratio of progress, botnet size, and bots distribution. Experimental results show that our method is sensitive to the changes of attack states, and is easy to be implemented in an early warning system because of its simplicity.
A tractable analytical framework for evaluating opportunistic selection in time-varying channels

Time-variations in wireless channels affect opportunistic selection in a multi-node wireless system in two ways. First, the selected node can become sub-optimal by the time data transmission commences. Second, the channel changes during data transmission. We develop a comprehensive and tractable analytical framework that accurately accounts for both these effects. It differs from the extensive existing literature that primarily focuses on time-variations until the data transmission starts. We first develop a novel concept of a time-invariant effective signal-to-noise ratio (TIESNR), which tractably and accurately captures the time-variations during the data transmission phase with partial channel state information available at the receiver. Thereafter, we model the joint distribution of the signal-to-noise ratio at the time of selection and TIESNR during the data transmission and analyze the average packet error rate (PER). Extensive numerical results verify the accuracy of each step of our approach and show that ignoring the correlated time-variations during the data transmission phase can significantly underestimate the average PER.
Metadata-Based XML Serialization for Embedded C++

Mapping XML document schemas and Web service interfaces to programming languages has an important role in effective creation of quality Web service implementations. This paper presents a novel way to map XML data to the C++ programming language with emphasis on use in mobile and embedded systems. The proposed solution offers more flexibility and more compact code that is critical in embedded environments. The paper describes the concept and the architecture of the solution and compares it with existing solutions
Perceptual Learning and Aging: Improved Performance for Low-Contrast Motion Discrimination

Previous research has shown age-related differences in discriminating motion at different levels of contrast (Betts et al., 2005, 2009, 2012). A surprising result of this research is that older as compared to younger observers showed improved performance in detecting motion of large high-contrast stimuli suggesting age-related differences in center-surround antagonism. In the present study we examined whether perceptual learning methods could be used to improve motion discrimination performance for older individuals under high- and low-contrast conditions. The stimuli were centrally presented Gaussian filtered sine-wave gratings (Gabors) that were either 5° or 0.7° diameter with contrast of 0.92, 0.22, or 0.028. Older and younger participants received 3 days of training. The task was to identify if the motion direction was leftward or rightward. Duration thresholds for motion discrimination were derived using two randomly interleaved staircases and compared between pre-/post-test sessions. Both older and younger subjects showed lower duration thresholds as a result of training. The improved performance, for older subjects, due to training was observed for all size and contrast conditions, with training with small low-contrast stimuli resulting in a 23% improvement in motion discrimination performance. Older observers, as compared to younger observers, did show evidence of decreased spatial suppression across all contrast levels. These results suggest that perceptual learning techniques are effective for improving motion discrimination performance, especially for conditions that are difficult for older individuals.
Simulating biological vision with hybrid neural networks

We present an example of how vision systems can be modeled and designed by integrating a top-down computationally- based approach with a bottom-up biologi cally-motivated architecture. The specific visual processing task we address is occlusion-based object segmentation — the discrimination of objects using cues derived from object interposition. We construct a model of object segmentation using hybrid neural networks—distributed parallel systems consisting of neural units modeled at different levels af abstraction. We show that such networks are particularly useful for systems which can be modeled using the combined top-down/bottom-up approach. Our hybrid model is capable of discriminat ing objects and stratifying them in relative depth. In addition, our system can account for several classes of human perceptual phenomena, such as illusory contours. We conclude that hybrid systems serve as a powerful paradigm for understanding the information processing strategies of biological vision and for constructing artificial vision-based applications.