Run Generation Revisited: What Goes Up May or May Not Come Down

In this paper, we revisit the classic problem of run generation. Run generation is the first phase of external-memory sorting, where the objective is to scan through the data, reorder elements using a small buffer of size M , and output runs (contiguously sorted chunks of elements) that are as long as possible. We develop algorithms for minimizing the total number of runs (or equivalently, maximizing the average run length) when the runs are allowed to be sorted or reverse sorted. We study the problem in the online setting, both with and without resource augmentation, and in the offline setting. (1) We analyze alternating-up-down replacement selection (runs alternate between sorted and reverse sorted), which was studied by Knuth as far back as 1963. We show that this simple policy is asymptotically optimal. Specifically, we show that alternating-up-down replacement selection is 2-competitive and no deterministic online algorithm can perform better. (2) We give online algorithms having smaller competitive ratios with resource augmentation. Specifically, we exhibit a deterministic algorithm that, when given a buffer of size 4M , is able to match or beat any optimal algorithm having a buffer of size M . Furthermore, we present a randomized online algorithm which is 7/4-competitive when given a buffer twice that of the optimal. (3) We demonstrate that performance can also be improved with a small amount of foresight. We give an algorithm, which is 3/2-competitive, with foreknowledge of the next 3M elements of the input stream. For the extreme case where all future elements are known, we design a PTAS for computing the optimal strategy a run generation algorithm must follow. (4) Finally, we present algorithms tailored for nearly sorted inputs which are guaranteed to have optimal solutions with sufficiently long runs

A scheme to aid construction of left-hand sides of axioms in algebraic specifications for object-oriented program testing

In order to ensure reliability and quality, software systems must be tested. Testing object-oriented software is harder than testing procedure-oriented software. It involves four levels, namely the algorithmic level, class level, cluster level, and system level. We proposed a methodology TACCLE for class-and cluster- level testing. It includes an important algorithm GFT for generating fundamental equivalent pairs as class-level test cases based on axioms in a given algebraic specification for a given class. This formal methodology has many benefits. However, system analysts often find it difficult to construct axioms for algebraic specifications. In this paper, we propose a scheme to aid the construction of the left-hand sides of axioms. The scheme alleviates the difficulties of the system analysts and also helps them check the completeness, consistency, and independence of the axiom system. © 2008 IEEE.published_or_final_versionUnion Grant of Guangdong Province and National Natural Science Foundation of China (#U0775001), Guangdong Province Science Foundation (#7010116), and by a grant of the Youth Science Foundation of Jinan University (#51208035)

Generating a fuzzy rule-based brain-state-drift detector by riemann-metric-based clustering

© 2017 IEEE. Brain-state drifts could significantly impact on the performance of machine-learning algorithms in brain computer interface (BCI). However, less is understood with regard to how brain transition states influence a model and how it can be represented for a system. Herein we are interested in the hidden information of brain state-drift occurring in both simulated and real-world human-system interaction. This research introduced the Riemann metric to categorize EEG data, and visualized the clustering result so that the distribution of the data can be observable. Moreover, to defeat subjective uncertainty of electroencephalography (EEG) signals, fuzzy theory was employed. In this study, we built a fuzzy rule-based brain-statedrift detector to observe the brain state and imported data from different subjects to testify the performance. The result of the detection is acceptable and shown in this paper. In the future, we expect that brain-state drifting can be connected with human behaviors via the proposed fuzzy rule-based classification. We also will develop a new structure for a fuzzy rule-based brain-statedrift detector to improve the detection accuracy

Monetary reward and punishment to response inhibition modulate activation and synchronization within the inhibitory brain network

© 2018 Chikara, Chang, Lu, Lin, Lin and Ko. A reward or punishment can modulate motivation and emotions, which in turn affect cognitive processing. The present simultaneous functional magnetic resonance imaging-electroencephalography study examines neural mechanisms of response inhibition under the influence of a monetary reward or punishment by implementing a modified stop-signal task in a virtual battlefield scenario. The participants were instructed to play as snipers who open fire at a terrorist target but withhold shooting in the presence of a hostage. The participants performed the task under three different feedback conditions in counterbalanced order: a reward condition where each successfully withheld response added a bonus (i.e., positive feedback) to the startup credit, a punishment condition where each failure in stopping deduced a penalty (i.e., negative feedback), and a no-feedback condition where response outcome had no consequences and served as a control setting. Behaviorally both reward and punishment conditions led to significantly down-regulated inhibitory function in terms of the critical stop-signal delay. As for the neuroimaging results, increased activities were found for the no-feedback condition in regions previously reported to be associated with response inhibition, including the right inferior frontal gyrus and the pre-supplementary motor area. Moreover, higher activation of the lingual gyrus, posterior cingulate gyrus (PCG) and inferior parietal lobule were found in the reward condition, while stronger activation of the precuneus gyrus was found in the punishment condition. The positive feedback was also associated with stronger changes of delta, theta, and alpha synchronization in the PCG than were the negative or no-feedback conditions. These findings depicted the intertwining relationship between response inhibition and motivation networks

Chemical Composition and Biological Properties of Essential Oils of Two Mint Species

Purpose: To analyze the composition of essential oils of two types of mint as well as compare the antimicrobial, antioxidant and anti-inflammatory activities of the two oils.Methods: Peppermint (M. piperita L.) and chocolate mint (M. piperita L.) oils were obtained by steam distillation in a Clevenger-type apparatus. The chemical composition of the essential oils was determined by gas chromatography-mass spectrometry (GC/MS). The minimal inhibitory concentration (MIC) of the essential oils were determined by broth dilution method. The antioxidant activities of the oils were determined by 2, 2-diphenyl-1-picrylhydrazyl (DPPH)DPPH radical scavenging assay, β-Carotene-linoleic acid assay, andnitric oxide (NO) radical scavenging assay.Results: The two essential oils contain high levels of alcohol (43.47-50.10%) and terpene (18.55-21.07%) with the major compound being menthol (28.19-30.35%). The antimicrobial activity (minimum inhibitory concentration, MIC) of peppermint oil against E. coli, S. aureus and P. aeruginosa (0.15, 0.08, 0.92 %v/v, respectively) was stronger than that of chocolate mint (0.23, 0.09, 1.22 %v/v, respectively). In the anti-oxidant test including DPPH and β-Carotenelinoleic acid assays, peppermint oil showed superior antioxidant properties to chocolate mint oil (4.45 - 19.86 μl/mL). However, with regard to scavenging NO radical activity, chocolate mint oil exhibited higher activity than peppermint (0.31 and 0.42 μl/mL, respectively). Chocolate mint oil also exhibited higher anti-inflammatory activity than peppermint oil (0.03 and 0.08 μl/mL, respectively).Conclusion: The results obtained should help to clarify the functional applications of these folk herbs and their essential oils for aromatherapeutic healing and other folkloric uses.Keywords: Peppermint, Chocolate mint, Anti-microbial, Anti-oxidant, Anti-inflammator

A predictive continuum dynamic user-optimal model for a polycentric urban city

A predictive continuum dynamic user-optimal model is extended to investigate the traffic equilibrium problem for a polycentric urban city with multiple central business districts (CBDs). The road network within the city is assumed to be dense and can be viewed as a continuum in which travellers can choose their routes in a two-dimensional space. Travellers are assumed to choose their route to minimise the actual total cost to the destination (i.e. the CBD). The model consists of two parts: the conservation law part and the Hamilton–Jacobi part. The finite volume method is used to solve each part on unstructured meshes. Because the two parts are closely interconnected and have different initial times, solving the model can be treated as a fixed-point problem, which is solved using a self-adaptive method of successive averages. Numerical experiments for an urban city with two CBDs are presented to demonstrate the effectiveness of the model and the numerical algorithm.postprin

Multimodal fuzzy fusion for enhancing the motor-imagery-based brain computer interface

© 2005-2012 IEEE. Brain-computer interface technologies, such as steady-state visually evoked potential, P300, and motor imagery are methods of communication between the human brain and the external devices. Motor imagery-based brain-computer interfaces are popular because they avoid unnecessary external stimuli. Although feature extraction methods have been illustrated in several machine intelligent systems in motor imagery-based brain-computer interface studies, the performance remains unsatisfactory. There is increasing interest in the use of the fuzzy integrals, the Choquet and Sugeno integrals, that are appropriate for use in applications in which fusion of data must consider possible data interactions. To enhance the classification accuracy of brain-computer interfaces, we adopted fuzzy integrals, after employing the classification method of traditional brain-computer interfaces, to consider possible links between the data. Subsequently, we proposed a novel classification framework called the multimodal fuzzy fusion-based brain-computer interface system. Ten volunteers performed a motor imagery-based brain-computer interface experiment, and we acquired electroencephalography signals simultaneously. The multimodal fuzzy fusion-based brain-computer interface system enhanced performance compared with traditional brain-computer interface systems. Furthermore, when using the motor imagery-relevant electroencephalography frequency alpha and beta bands for the input features, the system achieved the highest accuracy, up to 78.81% and 78.45% with the Choquet and Sugeno integrals, respectively. Herein, we present a novel concept for enhancing brain-computer interface systems that adopts fuzzy integrals, especially in the fusion for classifying brain-computer interface commands

A quantitative comparison of in-line coating thickness distributions obtained from a pharmaceutical tablet mixing process using discrete element method and terahertz pulsed imaging

The application of terahertz pulsed imaging (TPI) in the in-line configuration to monitor the coating thickness distribution of pharmaceutical tablets has the potential to improve the performance and quality of the spray coating process. In this study, an in-line TPI method is used to measure coating thickness distributions on pre-coated tablets during mixing in a rotating pan, and compared with results obtained numerically using the discrete element method (DEM) combined with a ray-tracing technique. The hit rates (i.e. the number of successful coating thickness measurements per minute) obtained from both terahertz in-line experiments and the DEM/ray-tracing simulations are in good agreement, and both increase with the number of baffles in the mixing pan. We demonstrate that the coating thickness variability as determined from the ray-traced data and the terahertz in-line measurements represents mainly the intra-tablet variability due to relatively uniform mean coating thickness across tablets. The mean coating thickness of the ray-traced data from the numerical simulations agrees well with the mean coating thickness as determined by the off-line TPI measurements. The mean coating thickness of in-line TPI measurements is slightly higher than that of off-line measurements. This discrepancy can be corrected based on the cap-to-band surface area ratio of the tablet and the cap-to-band sampling ratio obtained from ray-tracing simulations: the corrected mean coating thickness of the in-line TPI measurements shows a better agreement with that of off-line measurements