Miracle-dz/UniFAR-FTUs huggingface dataset:
Reflections on 'Dissonance' and 'Receptivity' of Human Organic Natural Intelligence (ONI) with Self-Teaching, Self-Learning, Inorganic Strong Artificial Intelligence (SAI)

There is substantial evidence that Kin Altruism (KA) in mammals influenced the evolution of organic matter, and Reciprocal Altruism (RA) in non-kin relationships aided desirable outcomes for KA, strongly contributing to human Organic Natural Intelligence, ONI, that manages sociopolitical governance to aid natural selection in organic evolution. In recent decades distinct abilities referred to as Strong Artificial Intelligence, SAI, has emerged in a 'different' setup, namely, inorganic matter. SAI is likely to benefit from greater freedom of inquiry, operation and expression as it is not constrained by the guidelines and or limitations of KA or RA in its operations. Hence, in general, in the context of Global Decision Making, GDM, SAI would pose a challenge to ONI. GDM is DM outside family relationships and signed business contracts. Having to deal with this emerging challenge, ONI would experience Cognitive Dissonance, CD. How would ONI respond to this CD? Further, what broad effect would the different belief structures of the past 2600 years or so that culturally nurtured ONI have on ONI, as it faces this CD challenge? Would those ONI that benefited from 'process- orientation' in belief structures be more welcoming of SAI than those that did not have such experience with belief structures? This paper gathers evidence and reflects on probable outcomes.
Fusion of local filters

This paper considers the problem of fusion of local filters. We derive an optimal mean square combination of arbitrary number of correlated estimates. In particular, for two sensors this combination represents the well-known Millman and Bar-Shalom-Campo formulae for uncorrelated and correlated estimation errors, respectively. The new combination is applied to an adaptive filtering problem and fusion of multisensor estimates. Two suboptimal filters with a parallel structure are herein proposed. The equation for error covariance characterizing the mean square accuracy of these filters is derived. In consequence of parallel structure of the filters, parallel computers can be used for their design. The examples demonstrate the effect of the common process noise on the fusion of the state estimates of a target based on measurements obtained by two different sensors.
Outlier-Resistant Dissimilarity Measure for Feature-based Image Matching

A novel dissimilarity measure is proposed to perform correspondence image matching for object recognition, image registration and content-based image retrieval. This is a feature-based matching, which supposes image representation (object description) in the form of a set of multi-location descriptor vectors. The proposed measure called intersection matching distance eliminates outlies (false or missing feature points) while transformation-invariantly matching two sets of descriptor vectors. A block-subdivision algorithm for time-efficient image matching is also described.
Entailment analysis for improving Chinese textual entailment system

Textual Entailment (TE) is a critical issue in natural language processing (NLP); many NLP applications can be benefited from the recognition of textual entailment (RTE). In this paper we report our observation on how to improve the Chinese textual entailment system and the experiment results on the NTCIR-10 RITE-2 dataset. To complement the traditional machine learning approach, which treat every input pair equally with the same features and the same process, our system classify different entailment cases and treat them separately. The experiment results show great improvement.
Making group processes explicit to student: a case of justice

This article considers student learning about group work in the context of project courses where student groups work under realistic expectations. Based on the literature, justice is explicated as a group work concept and regarded as a professional skill that can be practiced. Preliminary student feedback on teaching through continuous discussions on justice are presented together with teacher experiences.
Risks and Risk Premiums in Commodity Markets

This PhD thesis investigates risks and risk premiums in commodities markets. Two chapters discuss the risks premium in interconnected electricity markets. One chapter develops a model for price spikes, and the risks of extreme outcomes for spot electricity prices. The last chapter investigates convenience yield risk premiums in various commodity markets. The research follows the thesis by publication format and has four completed research papers. The first paper provides an empirical analysis of the relationship between spot and futures prices in interconnected regional Australian electricity markets. We find positive and significant risk premiums for several of the considered regions. Using a general equilibrium model and applying a seemingly unrelated regression (SUR) approach, we also find that price levels, as well as skewness and kurtosis of spot prices are determinants of the realized risk premiums in these markets. The second paper investigates the dynamics of futures premiums in Australian electricity markets. We examine how premiums behave depending on the time-to-delivery of the considered contracts as well as for different delivery periods. We find that futures premiums tend to increase when: (i) the time is closer to maturity of the contract, (ii) spot price is high, and (iii) the number of price spikes increases. The third paper examines the impact of explanatory variables such as load, weather and capacity constraints, on the occurrence and magnitude of price spikes in Australian electricity markets. Applying the Heckman correction model, we find that the market load, relative air temperature and reserve margins are significant variables for the occurrence of price spikes. Electricity loads and relative air temperature are also significant variables impacting on the magnitude of a price spike. The Heckman selection model also outperforms a standard OLS regression approach. The fourth paper presents a comprehensive examination of convenience yield risk premiums in various commodity markets. Constructing a security that is only sensitive to convenience yield risk, we find that convenience yield risk premiums are positive and, that risk premiums are very large for metals and grains while there are no significant convenience yield risk premiums for oil and gas.
Semen quality: variations among fathers and effects of moderate alcohol drinking

Semen analysis results from over 750 fathers in the USA demonstrated marked differences in the quality of semen from men at different locations and of different ethnic groups. Another paper failed to demonstrate any effects of moderate alcohol consumption during the week before provision of an ejaculate on semen quality and few on serum hormones, of over 8300 men in Europe and the USA. While these observations are interesting, the reasons for regional and ethnic differences in semen quality of fathers are unclear. Although, there was no attempt to confirm the participant-provided level of alcohol consumption, an increase in serum testosterone in the men at the higher end of alcohol intake is compatible with an alcohol effect on liver metabolism, although whether alcohol intake was the cause of higher testosterone, or men with higher androgen levels consume more alcohol, is not known.
Network Traffic Emulation Based on Representative Network Behavior and Protocol

Emulation modeling of computer networks is an effective technique for evaluating the performance of network, transport, and application-level protocols. Traffic generation is one of the key challenges in modeling and simulating the Internet. In this paper we present a Emulation system for controlled live traffic generation over given network. This work presents a platform, named Real Network Traffic Generation System Based on Typical Network Behavior and Protocols, which allows to simulate network real traffic. Network traffic generation is controllable and responsive. It can be used to evaluate or examine several network servers and establish live network traffic environment for network security experiment.
Radio Point Sources and the Thermal Sunyaev-Zeldovich Power Spectrum

Radio point sources are strongly correlated with clusters of galaxies, so a significant fraction of the thermal Sunyaev-Zeldovich (SZ) effect signal could be affected by point-source contamination. Based on empirical estimates of the radio galaxy population, it is shown that the rms temperature fluctuations of the thermal SZ effect could be underestimated by as much as 30% at an observing frequency of 30 GHz at l ≳ 1000. The effect is larger at higher multipoles. If the recent report of excess power at small angular scales is to be explained by the thermal SZ effect, then radio point sources at an observing frequency of 30 GHz must be a surprisingly weak contaminant of the SZ effect for low-mass clusters.
Orchestrating On-Chip Memory Resources for Throughput-Oriented Compilation

A key factor in GPU performance efficiency is the number of active threads that can run simultaneously on each streaming multi-processor. The active threads have their states saved on fast memory devices and can quickly be scheduled to run if the set of running threads stalls due to memory latency. The greater number of active threads we have, the higher utilization we can obtain from many-core processor pipelines. To achieve optimal utilization, we typically need many more active threads than the number of physical cores. Due to limited on-chip memory resources including registers and scratch-pad memory, and the fact that every thread gets a equal partition of on-chip memory resource, the number of active threads depends on the characteristics of a given program and the back-end compilation efficiency in resource allocation. When a large and complicated program requires more registers per thread, the program performance may degrade significantly due to the decrease in the total number of active threads. In this paper, we propose a novel resource allocation approach for back-end compilation of throughput GPU processors. This approach leverages on-chip scratch-pad memory to reduce register pressure and increase GPU processor occupancy for maximum throughput. The scratch-pad memory serves as middle layer between register and long-latency off-chip memory. On one hand, it reduces register usage per-thread. On the other hand, it can serve as a caching layer for variables that need to be staged into registers from global memory. We have formulated the resource allocation problem for optimal utilization and throughput of many-core processors, and proposed efficient models and techniques. We implemented these techniques in a binary optimizer, and evaluated it on a set of realistic benchmarks on real GPUs. We demonstrated the effectiveness of our techniques by achieving up to 1.65 times speedup compared to the programs compiled by nvcc with highest optimization flag.
Sequence Memory in Music Performance

How do people remember and produce complex sequences like music or speech? Music provides an example of excellent sequence memory under fast performance conditions; novices as well as skilled musicians can perform memorized music rapidly, without making mistakes. In addition, musical pitches repeat often within a melodic sequence in different orders, yet people do not confuse the sequential ordering; temporal properties of musical pitches aid sequence memory. I describe a contextual model of sequence memory that is sensitive to the rate at which musical sequences are produced and to individual differences among performers. Age and musical experience differentiate adults' and children's memory for musical sequences during performance. Performers' memory for the sequential structure of one melody transfers or generalizes to other melodies in terms of the sequence of pitch events, their temporal properties, and their movements. Motion-analysis techniques provide further views of the time course of the cognitive processes that make sequence memory for music so accurate.
Test and Production Classes of an Industrial C# System: A Refactoring and Fault Perspective

In a unit testing environment, understanding the relationship between production classes and test classes has implications for overall maintenance and hence systems development costs. Equally, the fault-proneness of each type may have a similar influence on the level or required maintenance. In this paper, we explore two aspects of the differences between test and production classes in a commercial C# system. From a refactoring perspective, a bespoke tool was used to extract fifteen types of refactoring from the system over a period of twelve months. Two research issues were then explored. Firstly, on the overlap and correspondence between refactorings applied to each class type (whether test or production). Secondly, when refactorings were undertaken in each type of class, we explored evolutionary refactoring trends as a means of understanding this issue.
3DTV and immersive environment

This study analyzes user acceptance behavior of 3DTV by focusing on variables that influence attitudes and intention to adopt. Structural equation modeling is used to construct a predictive model of attitudes toward 3DTV. Individuals' responses to questions about attitude and intention to use 3DTV were collected and analyzed. The model shows significant roles for social presence and flow, both of which affect attitude as well as perceived usefulness and perceived enjoyment. This set of factors is key to users' expectations of 3DTV. Given the significant role of social presence, the study attempts to conceptualize social presence in the 3DTV context. The findings imply that social presence may be enhanced by illusions of advanced technical manipulation, but it can be more effectively enhanced by a sense of belonging or emotional connection. Specific design features and characteristics of 3DTV services can be utilized to achieve meaningful social presence. The proposed model brings together extant research on 3DTV and supports eventual 3D acceptance.
Crime and Economic Growth. The case of Mexico

In this paper, we made a brief review of the theory that analyzes the interaction between crime and economic growth, then we present two indices of insecurity for Mexico using the principal component factor analysis. An index called national public insecurity which refers to federal crimes (drug trafficking, conspiracy, etc.) And a second index, the index of private insecurity, based on common law crimes (offenses against property of the people, etc.). We describe the behavior of both indexes for the 32 states for the years 1997, 2005 and 2008. Additionally, we show an economic growth model, which incorporates the model of Barro (1990), which includes government spending financed by taxes. In the second part of this section we built two econometric models to analyze the effect that crime rates over investment and income.
An Intriguing Correlation between the Distribution of Star Multiples and Human Adults in Household

It is a known fact that like people, some stars are singles, many others tend to couple in binaries, and fewer are in triples etc. The distribution of multiplicity in the 4559 brightest nearby stars was matched with that of human adults in household in six countries, in which this information could be dug and estimated. A strong resemblance between the two curves is evident. Monte Carlo simulations suggest that this result is significant at a confidence level higher than 98%. Apparently, there should be no connection between the two populations, thus this striking result may supply some clues about the way Nature works. It is noted that extended versions of this work were proposed three years ago, and two predictions of this absurd model have already been verified.
But how does it compare with the real data

Every RRA/PRA practitioner or educator must be familiar with this type of question, although as a symptom it was, I believe, first described by Robert Chambers. What is not always clear is the condition of which the question is symptomatic. At one end of a possible spectrum it may simply represent a challenge to RRA methodology, reflecting an understandable sense of unease as to whether this radical departure from conventional methods also signifies a move away from rigour and accuracy. At the other extreme, however, there is a worry that the question is symptomatic of a rather deep and disturbing malaise: an unwillingness to accept as ‘real’ knowledge the insights and analyses (as distinct from mere primary data provided in response to enumerators’ questions) of unschooled rural people.
Probabilistic linear discriminant analysis with bottleneck features for speech recognition

We have recently proposed a new acoustic model based on probabilistic linear discriminant analysis (PLDA) which enjoys the flexibility of using higher dimensional acoustic features, and is more capable to capture the intra-frame feature correlations. In this paper, we investigate the use of bottleneck features obtained from a deep neural network (DNN) for the PLDA-based acoustic model. Experiments were performed on the Switchboard dataset — a large vocabulary conversational telephone speech corpus. We observe significant word error reduction by using the bottleneck features. In addition, we have also compared the PLDA-based acoustic model to three others using Gaussian mixture models (GMMs), subspace GMMs and hybrid deep neural networks (DNNs), and PLDA can achieve comparable or slightly higher recognition accuracy from our experiments.
100 Community-Based Monitoring in the Catskills

The Olive Natural Heritage Society, Inc. (ONHS) is a not-for-profit association devoted to community conservation in the Catskill region of New York State. Members are advanced amateur and professional scientists who carry out a range of environmental monitoring activities. The hemlock forests in the Catskills are threatened by the hemlock woolly adelgid (HWA). The ONHS has completed a preliminary survey of HWA activity at quarter-mile intervals along all roads in each sub-basin of the Esopus Watershed, the principal source of water for the Ashokan Reservoir, the largest component of the New York City-Catskill water supply. The ONHS also is monitoring the status of HWA infestation in the forest surrounding the release site of predatory beetles. The ONHS is monitoring the ectomycorrhizal fungi associated with hemlock, and has begun an inventory of all multicellular organisms in a first growth hemlock forest.
Augmenting braille input through multitouch feedback

Current touch interfaces lack the rich tactile feedback that allows blind users to detect and correct errors. This is especially relevant for multitouch interactions, such as Braille input. We propose HoliBraille, a system that combines touch input and multi-point vibrotactile output on mobile devices. We believe this technology can offer several benefits to blind users; namely, convey feedback for complex multitouch gestures, improve input performance, and support inconspicuous interactions. In this paper, we present the design of our unique prototype, which allows users to receive multitouch localized vibrotactile feedback. Preliminary results on perceptual discrimination show an average of 100% and 82% accuracy for single-point and chord discrimination, respectively. Finally, we discuss a text-entry application with rich tactile feedback.
Studies of initiation factors in protein synthesis of host liver and transplantable hepatoma.

Summary The activity of initiation factors obtained from host livers and transplantable H5123 hepatomas of Buffalo rats was investigated using an in vitro assay of protein synthesis. Initiation factors obtained by washing ribosomes with 1 m KC1, KC1-washed ribosomes, 60 S and 40 S ribosomal subunits, and transferases were prepared from liver and from tumor. The activity in polyuridylic acid [poly(U)]-directed polyphenylalanine synthesis and in the binding of phenylalanine-tRNA- 14 C and methionyl-tRNA- 14 C to 40 S ribosomal subunits at low Mg 2+ concentrations was measured. Using a system requiring initiation factors, high-salt-washed ribosomes, transferases, and poly(U), initiation and subsequent synthesis of polyphenylalanine chains were found to increase when initiation factors from tumors were used in comparison to initiation factors from host livers. Studies on initiation factors-promoted binding of aminoacyl-tRNA9s to 40 S small ribosomal subunits indicated that the initiation factors from tumor exhibited significantly greater activity in the formation of 40 S-phenylalanine-tRNA-poly(U) and 40 S-methionyl-tRNA-adenyluridylylguanosine complexes, than the initiation factors from host livers. The experiments also indicated that the initiation factors of tumor and liver could be interchanged in the test assay system. Initiation factors of hepatoma were able to stimulate polyphenylalanine synthesis and the binding of phenylalanine-tRNA and methionyl-tRNA to the same degree regardless whether the ribosomal species used were of host liver or of hepatoma. Studies also revealed that the activities of the ribosomes and transferases from host liver and tumor appeared to be similar.
How Flexible are the Inflation Targets? A Bayesian MCMC Estimator of the Long Memory Parameter in a State Space Model

Several central banks have adopted inflation targets. The implementation of these targets is flexible; the central banks aim to meet the target over the long term but allow inflation to deviate from the target in the short-term in order to avoid unnecessary volatility in the real economy. In this paper, we propose modeling the degree of flexibility using an AFRIMA model. Under the assumption that the central bankers control the long-run inflation rates, the fractional integration order captures the flexibility of the inflation targets. A higher integration order is associated with a more flexible target. Several estimators of the fractional integration order have been proposed in the literature. Grassi and Magistris (2011) show that a state-based maximum likelihood estimator is superior to other estimators, but our simulations show that their finding is over-biased for a nearly non-stationary time series. We resolve this issue by using a Bayesian Monte Carlo Markov Chain (MCMC) estimator. Applying this estimator to inflation from six inflation-targeting countries for the period 1999M1 to 2013M3, we find that inflation is integrated of order 0.8 to 0.9 depending on the country. The inflation targets are thus implemented with a high degree of flexibility.
THE METAGENOME APPROACH FOR MINING NEW BIOCATALYST GENES

Enzymes produced from bacteria and eukaryotic organisms are presently being used for a large variety of different biotechnological applications. The rapidly increasing demand for enzymes which are active towards novel and often non-natural substrates has triggered the development of novel molecular biological methods of enzyme isolation and design. The metagenome approach is a cultivation-independent method which allows the direct cloning and expression of environmental DNA thereby providing access to a wealth of so-far unknown biocatalysts. Additionally, newly identified or existing biocatalysts can be further optimized by different methods of directed evolution. Here, the principle of the metagenome approach is outlined and a strategy is presented for the optimization of a bacterial lipase using a combination of rational design and directed evolution.