Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximate Optimal Power Allocation Scheme for Wireless Ad hoe Networks in Rayleigh Fading Channels

In this paper, a distributed power allocation scheme for mobile ad hoc networks in Rayleigh fast fading environments is proposed. With fluctuated SINR, outage probability is introduced as a QoS parameter. An optimization model is formulated to minimize total transmit powers with constraint conditions that outage probability of each link should be satisfied. A closedform near optimal solution to the model is presented based on the exploration of power ratio factor at the boundary of system capacity. The solution is implemented in the networks by a distributed power allocation scheme. In terms of the scheme, each link can determine its power ratio factor independently. The inequity problem of the power allocation algorithms in incremental fashion is avoided. Meanwhile the scheme is more timesaving than those schemes in iterative manner. By performance evaluation, we verify our analysis on the optimization model and validate a better result of our scheme in comparison with the counterpart. That is, a larger system capacity is admissible by our method.
The Impact of Trade Liberalization and Economic Integration on the Logistics Industry: Maritime Transport and Freight Forwarders

The ASEAN Economic Community (AEC) Blueprint targets an ASEAN single market in 2015. This is an ambitious reform agenda that seeks to ensure the free flow of services, investment, and skilled labor, along with the free flow of goods and the freer flow of capital, in the ASEAN region. For logistics services, the target is supposed to be achieved by 2013. An assessment of whether this has been achieved or not is yet to be done. Liberalization and deregulation efforts in the Philippine maritime transport industry are already heading into the direction of greater participation in ASEAN economic integration even though the AEC measures have not yet been formally sanctioned by all members. This paper examines the current status of the logistics industry in the Philippines and finds out how the opening of the economy to global markets through trade and services liberalization and the ongoing process toward economic integration through AEC 2015 impact on the structure, conduct, and performance of the logistics industry. The industry is responding to the changes in a positive way notwithstanding its characterization as a concentrated industry dominated by a few domestic firms. Firms have become more innovative in offering quality service to consumers such as better passenger accommodation, improved ticketing system, and availability of fast craft ferries. Freight forwarders, at least those surveyed for this study, equip themselves with information on how to adjust to a more liberalized and integrated environment. They are aware of the changes that will be brought about by the full implementation of the AEC measures and they also have a good idea of the challenges they will face, such as differences in commercial practices, legal systems, and contracting procedures, when they decide to locate in an ASEAN member-country. The way forward involves continuing the market-oriented reforms, especially liberalization of trade in services, while ensuring a healthy balancing of domestic industry interests with the requirements of regional economic integration.
Teen Motherhood, Labor Market Involvement and the Receipt of Public Assistance

The difficulties experienced by young women who give birth as teenagers have been well-documented and impose a significant cost to these women and to society (Maynard, 1997). For instance, teen mothers are far less likely to work compared to other women who delay child-bearing, leading to lower earnings for the family and reduced national output for society. Their lower earnings places the family at far greater risk of falling into poverty and increases expenditures on public assistance made to provide the family with a minimal living standard. These and other costs of teen childbearing have led to the proposal of several policy alternatives designed to alleviate them. In the late 1980s, three policies were implemented in experimental forms designed to allow researchers to evaluate their effectiveness along a multitude of dimensions. The three demonstrations were Ohio's Learning, Earning, and Parenting (LEAP) program, the New Chance Demonstration (NCD), and the Teenage Parent Demonstration (TPD). LEAP provided financial incentives to teen mothers in the treatment group designed to increase school enrollment/attendance that, it was hoped, would subsequently increase labor market participation and reduce welfare receipt. The NCD offered voluntary employment and training services to teenage mothers. The treatment in TPD was mandatory and included child-care and transportation assistance along with sanctions for failure to participate. This paper will synthesize the evidence from the existing academic literature regarding the effects of teen childbearing and the results of these three experiments, paying particular attention to labor market involvement and public assistance receipt in the years following birth for those women who had children as teens. The academic literature has attempted to determine whether teen motherhood itself "causes" the outcomes that follow, or whether the characteristics of those women who give birth as teens are such that they would have experienced subsequent difficulties even in the absence of the birth. Recent research suggests that perhaps little, if any, of the inferior outcomes that are associated with teen motherhood may be directly attributable to giving birth as a teen. The experimental findings, which show policies directed at improving outcomes for teen mothers are only modestly effective at best, seem to coincide with this view. The paper will first present a review of the academic literature, proceed to describe and highlight the findings of the three demonstration projects, then discuss the relationship between the two sets of results. We conclude with the implications for policy and future research. * Prepared for the conference: Synthesizing the Results of Demonstration Programs for Teen Mothers November 1997
Evolution of the Power Spectrum and Self-Similarity in the Expanding One-dimensional Universe

We have investigated time evolutions of power spectra of density fluctuations for a long time after the first appearance of caustics in the expanding one-dimensional universe. It is found that when an initial power spectrum is scale-free with a power index n, self-similarity of the time evolution of the power spectrum is achieved. We find that the power spectrum can be separated roughly into three regimes according to the shape of the power spectrum: the linear regime (k < knl: regime 1), the single-caustic regime (knl < k < ksnl: regime 2), and the multicaustics regime (k > ksnl: regime 3). The power index of the power spectrum in each regime has values of n, -1, and μ, respectively, which depend on n. Even in the case of an initial power-law spectrum with a cutoff scale, there might be the possibility of self-similar evolution of the power spectrum after the appearance of the caustics. It is found, however, that self-similarity is not achieved in this case. The shape of the power spectrum on scales smaller than the cutoff scale can be separated roughly into two regimes: the virialized regime (kcut < k < kcs: regime 4), and the smallest single-caustic regime (k > kcs: regime 5). The power index of the power spectrum is ν, which may be determined by the distribution of singular points in regime 4. In regime 5, the value of the power index is -1. Moreover, we show the general property of the shape of a power spectrum with a general initial condition.
Streaming computation of combinatorial objects

We prove (mostly tight) space lower bounds for "streaming" (or "on-line") computations of four fundamental combinatorial objects: error-correcting codes, universal hash functions, extractors, and dispersers. Streaming computations for these objects are motivated algorithmically by massive data set applications and complexity-theoretically by pseudorandomness and derandomization for space-bounded probabilistic algorithms. Our results reveal a surprising separation of extractors and dispersers in terms of the space required to compute them in the streaming model. While online extractors require space linear in their output length, we construct dispersers that are computable online with exponentially less space. We also present several explicit constructions of online extractors that match the lower bound. We show that online universal and almost-universal hash functions require space linear in their output length (this bound was known previously only for "pure" universal hash functions). Finally, we show that both online encoding and online decoding of error-correcting codes require space proportional to the product of the length of the encoded message and the code's relative minimum distance. Block encoding trivially matches the lower bounds for constant rate codes.
A mutual playmate attack prevention algorithm enhancing trust levels in MANET's systems

Mobile Ad-Hoc networks are of a vital importance in today's networking arena. The working of MANET's is totally independent of any centralized control which makes them highly dynamic and easy to deploy in any environment. As MANET's are totally infrastructure less and bound less there have been always intentions done by malicious nodes to gain access over the network by violating the network guidelines. MANET's are mainly autonomous networks whose integrity relies on the trust developed among the nodes of the network. The nodes trust each other in order to forward the data packets. However the malicious agents are always in a move to gain the higher levels of trust without being identified by cooperating nodes. The malicious agents can perform individually as well as in groups. In this paper we propose an algorithm that eliminates these harmful outcomes in MANET's maintaining the level of trust among the nodes. We propose this scheme to detect and eliminate those malicious agents which work in collaboration while showing them as network friendly and trustworthy among other nodes in network. One major benefit of this scheme is that it prevents the nodes of network to develop blind mutual trust and improves the overall integrity of network.
Defense Procurement Fraud, Penalties, and Contractor Influence

Press reports of investigations of fraud, indictments, and suspensions in military procurement are associated with significantly negative average abnormal returns in the stocks of affected firms. Abnormal stock returns are significantly less negative, however, for firms ranking among the top 100 defense contractors than for unranked contractors, even after one controls for firm size, the frauds' characteristics, and the firm's recidivism. Unranked contractors are penalized heavily for procurment frauds, experiencing both a decline in market value and a subsequent loss in government‐derived revenues. Furthermore, these losses are related to the percentage of the firm's revenues that derive from government contracts. Influential contractors, in contrast, are penalized lightly, experiencing negligible changes in share value and government contract revenue.
Socioeconomic inequalities in health: a comparative analysis using the European Community Household Panel

This paper contributes to the literature on income-related inequalities in health across European Union Member States. The European Community Household Panel Users’ Database (ECHP-UDB) is used for this analysis. Two binary measures of health limitations are used for the full 8 waves in the ECHP-UDB. Concentration indices and mobility indices are derived for these indicators of severe limitation and any limitation, following the study by Jones and Lopez (2004). Results suggest the existence of “pro-rich” inequality in health across Member States in both the shortterm and the long-term, with health limitations concentrated among those individuals with lower incomes in the society.
Bebop on the Hockey Pitch: Cross-Disciplinary Creativity and Skills Transfer

This paper generalizes task-specific (but dissimilar) skills, from the jazz concert stage and from the hockey field, into the domain of creativity research. What is sought are clues to what skills or creativities are transferable across dissimilar domains. It is argued that certain domain-general skills are transferable across domains, but a domain-general or ‘c’ creative capacity, is not. Rather than transferring some over-arching capacity to be universally creative, this research highlights factors likely to facilitate successful cross-disciplinary creative expression and posits a correlation between the capacities for discriminant pattern-recognition, task-specific expertise, and sensory data-collection, and the transferability of creativity. Of particular significance is the capacity for informed, selective pattern-breaking based on the ‘depth’ or ‘insider’ perspective of the domain expert; such ‘expert variation and selective retention’ provides creative choices and responses that are likely to be perceived by the field as creative: valuable, novel and surprising. The author is a renowned Australian studio bassist, jazz musician, and music educator who also plays field hockey for Australia at Masters level. His recently completed Ph.D. thesis, based on a performance and composition career spanning 46 years, takes the form of an analytical autoethnography drawn from personal field notes, diaries and interviews as well as published record albums.
Application of trajectory clustering and regionalization to ocean eddies in the South China Sea

Moving characteristics of ocean eddy have become one of the research foci, based on more advanced detection and tracking method. This study applied trajectory data mining to the analysis of eddies trajectories in the South China Sea (SCS), and experimented with two methods. One of them identified moving patterns of eddies by trajectory clustering and obtained representative paths. The other one discovered flow patterns using regionalization method with contiguous constrained, which partitioned of SCS into 4 regions. In the SCS, eddies mainly propagate in three parts with various moving characteristics and present regional flow patterns. Both of these methods validate the effectiveness of the application of trajectory data mining to the ocean eddies study.
Comparative costs of home care and residential care.

PURPOSE This paper reports on Canadian research that examined the cost effectiveness of home care for seniors as a substitute for long-term institutional services. DESIGN AND METHODS Two Canadian cities were included in the research: Victoria, British Columbia, and Winnipeg, Manitoba. The research computes the costs of formal care and informal care in both settings and ensures comparable groups of clients in both settings by comparing individuals at the same level of care. RESULTS The results reveal that costs were significantly lower for community clients than for facility clients, regardless of whether costs only to the government were taken into account or whether both formal and informal costs were taken into account. When informal caregiver time is valued at either minimum wage or replacement wage, there was a substantial jump in the average annual costs for both community and facility clients relative to when informal caregiver time was valued at zero. IMPLICATIONS Nevertheless, the results reveal that home care is significantly less costly than residential care even when informal caregiver time is valued at replacement wage.
Cooperative control of centrifugal compressor's flow and pressure

In this paper a cooperative control method of centrifugal compressor's flow and pressure is proposed. Base on the analysis of the inherent characteristic of centrifugal compressor and the response time of actuators, a cooperative control strategy is obtained. This control strategy can adjust the flow's and pressure's trajectory in time which let the operating point of centrifugal compressor change smoothly. Simulation results show that the proposed method can change the operating point of centrifugal compressor smoothly in the process of adjusting flow and pressure.
Multiobjective optimization of availability and cost in repairable systems design via genetic algorithms and discrete event simulation

This paper attempts to provide a more realistic approach to the characterization of system reliability when handling redundancy allocation problems: it considers repairable series-parallel systems comprised of components subjected to corrective maintenance actions with failure-repair cycles modeled by renewal processes. A multiobjective optimization approach is applied since increasing the number of redundancies not only enlarges system reliability but also its associated costs. Then a multiobjective genetic algorithm is coupled with discrete event simulation and its solutions present the compromise between system reliability and cost. Two examples are provided. In the first one, the proposed algorithm is validated by comparison with results obtained from a system devised as to allow for analytical solutions of the objective functions. The second case analyzes a repairable system subjected to perfect repairs. Results from both examples show that the proposed method can be a valuable tool for the decision maker when choosing the system design.
The multi-fractal model of asset returns: Its estimation via GMM and its use for volatility forecasting

Multi-fractal processes have been proposed as a new formalism for modeling the time series of returns in finance. The major attraction of these processes is their ability to generate various degrees of long memory in different powers of returns - a feature that has been found to characterize virtually all financial prices. Furthermore, elementary variants of multi-fractal models are very parsimonious formalizations as they are essentially one-parameter families of stochastic processes. The aim of this paper is to provide the characteristics of a causal multi-fractal model (replacing the earlier combinatorial approaches discussed in the literature), to estimate the parameters of this model and to use these estimates in forecasting financial volatility. We use the auto-covariances of log increments of the multi-fractal process in order to estimate its parameters consistently via GMM (Generalized Method of Moment). Simulations show that this approach leads to essentially unbiased estimates, which also have much smaller root mean squared errors than those obtained from the traditional ? scaling? approach. Our empirical estimates are used in out-of-sample forecasting of volatility for a number of important financial assets. Comparing the multi-fractal forecasts with those derived from GARCH and FIGARCH models yields results in favor of the new model: multi-fractal forecasts dominate all other forecasts in one out of four cases considered, while in the remaining cases they are head to head with one or more of their competitors.
Real-Time Analysis of Physiological Data to Support Medical Applications

This paper presents a flexible framework that performs real-time analysis of physiological data to monitor people's health conditions in any context (e.g., during daily activities, in hospital environments). Given historical physiological data, different behavioral models tailored to specific conditions (e.g., a particular disease, a specific patient) are automatically learnt. A suitable model for the currently monitored patient is exploited in the real-time stream classification phase. The framework has been designed to perform both instantaneous evaluation and stream analysis over a sliding time window. To allow ubiquitous monitoring, real-time analysis could also be executed on mobile devices. As a case study, the framework has been validated in the intensive care scenario. Experimental validation, performed on 64 patients affected by different critical illnesses, demonstrates the effectiveness and the flexibility of the proposed framework in detecting different severity levels of monitored people's clinical situations.
Microarray gene expression cancer diagnosis using Machine Learning algorithms

In this paper, we use the extreme Learning Machine (ELM) for cancer classification. We propose a two step method. In our two step feature selection method, we first use a gene importance ranking and then, finding the minimum gene subset form the top-ranked genes based on the first step. We tested our two step method in cancer datasets like Lymphoma data set and SRBCT data set. The results in the Lymphoma data set and SRBCT dataset show our two-step methods is able to achieve 100% accuracy with much fewer gene combination than other published results. The results indicate that ELM produces comparable or better classification accuracies with reduced training time and implementation complexity compared to neural networks methods like Back Propagation Networks, SANN and Support Vector Machine methods. ELM also achieves better accuracy for classification of individual categories.
Biped Locomotion : Augmenting an Intuitive Control Algorithm with Learning

Foot placement is a key determinant for the stabilization of walking speed and lateral motion of a biped. However, there is no closed form expression for the foot placement parameters in term of the walking speed or other gait parameters. A simple and intuitive control algorithm (called “Turkey Walking”) based on Virtual Model Control (VMC) was successfully applied to planar bipedal walking. However, it has deficiencies that pertain to the foot placement problem. I propose augmenting the algorithm with reinforcement learning (RL) algorithms to overcome the deficiencies. No dynamic model is required for the overall control architecture. The RL algorithms are used to learn the leg swing parameters, whereas the “Turkey Walking” (TW) algorithm generates the desired joints’ torque based on the gaits’ parameters. The control architecture is first tested on planar bipeds. After that, it is extended to a 3D biped. The research emphasizes on establishing the detailed structure of the architecture and illustrating the generality of the approach by simulation analysis.
Voting Transparency and Conflicting Interests in Central Bank Councils

This paper examines whether it is socially desirable for the individual voting records of central bank council members to be published when central bankers' preferences differ. We show that the misrepresentation of their preferences is not advantageous for central bankers although central bankers take into account the fact that they might not be re-elected. Thus, the publication of voting records is beneficial since the government can distinguish central bankers in terms of their preferences and can align the central bank council's preferences with those of the general public over time by means of its re-election decisions.