Winner Takes Research Articles

Vertical Integration versus Open Innovation? From Winner Takes it All to Winners Make it All

This study explores the dynamics between the extremes of open innovation (OI)—from inclusive co-creation to exclusive vertical integration (VI). This analysis addresses the management issues of how Tesla and Porsche manage value capture and value distribution in the e-mobility industry through their contrasting approaches and strategic shifts between OI and VI. It develops a novel theoretical model juxtaposing the OI mode from open to closed with the value chain position from horizontal to vertical. Analyzing Tesla and Porsche’s strategic moves offers a paradigm shift for OI practitioners. Our model can enable enhanced value capture through greater openness and more inclusive value distribution through proactive inside-out strategies in core technology areas. Instead of positioning VI and OI as static polar extremes, a novel combination of VI and OI can realize untapped potential.

Winner Takes All: The Challenges and Opportunities in the Study Tour Industry in China after the Epidemic

Winner Takes All: The Challenges and Opportunities in the Study Tour Industry in China after the Epidemic

Song and dance: a memetic angle on the evolution of musicality and music via case studies of a musemeplex in Saint-Saëns and ABBA.

Applying the theory of memetics to music offers the prospect of reconciling general Darwinian principles with the style and structure of music. The nature of the units of cultural evolution in music-memes or, more specifically, musemes-can potentially shed light on the evolutionary processes and pressures attendant upon early-hominin musicality. That is, primarily conjunct, narrow-tessitura musemes (those conforming to Ratner's "singing style," and its instrumental assimilations) and primarily disjunct, wide-tessitura musemes (those conforming to Ratner's "brilliant style," and its vocal assimilations) appear to be the outcome of distinct cultural-evolutionary processes. Moreover, musemes in each category arguably acquire their fecundity (perceptual-cognitive salience, and thus transmissibility) by appealing to different music-underpinning brain and body subsystems. Given music's status as an embodied phenomenon, both singing-style and brilliant-style musemes recruit and evoke image schemata, but those in the former category draw primarily upon vocal images of line, direction and continuity; whereas those in the latter category draw primarily upon rhythmic impetus and energy. These two museme-categories may have been molded by distinct biological-evolutionary processes-the evolution of fine vocal control, and that of rhythmic synchronisation, respectively; and they might-via the process of memetic drive-have themselves acted as separate and distinct selection pressures on biological evolution, in order to optimize the environment for their replication. As a case-study of (primarily) singing-style musemes, this article argues that a passage from the love duet "Mon cœur s'ouvre à ta voix" from Camille Saint-Saëns' opera Samson et Dalila op. 47 (1877) is the cultural-evolutionary antecedent of the Introduction/Chorus/Outro material of ABBA's song "The Winner Takes It All." Discussion of their melodic and harmonic similarities supports a memetic link between elements of Saint-Saëns' duet and ABBA's song. These relationships of cultural transmission are argued to have been impelled by the fecundity of the shared musemes, which arises from the image-schematic and embodied effects of the implication-realisation structures (in Narmour's sense) that comprise them; and which is underwritten by the coevolution of musemes with vocal- and rhythmic-production mechanisms, and associated perceptual-cognitive schemata.

ADAPTIVE DOUBLE NEO-FUZZY NEURON AND ITS COMBINED LEARNING

The subject of the study in the article is the process of data classification under conditions of fuzziness and a limited volume of training sample. The goal is to enhance the double neo-fuzzy neuron within the framework of solving the data classification task with constraints on the training sample volume, processing time, as well as fuzziness and nonstationarity of input data. The tasks include improving the double neo-fuzzy neuron to enhance the system's approximation properties and developing a combined system learning method to ensure fast performance in an online mode. The approaches used are lazy learning, supervised learning, and self-learning. The following results have been obtained: the double neo-fuzzy neuron has been modified by introducing a compressive activation function at the output, creating conditions for building a neo-fuzzy network capable of adapting to non-stationary input data in an online mode and avoiding the vanishing gradient problem. Conclusion. A combined learning method for the double neo-fuzzy neuron has been proposed, involving parallel utilization of lazy learning, supervised learning, and self-learning with the "Winner Takes All" rule, followed by automatic formation of membership functions, enabling fast online classification in the presence of outliers in the input data.

An Area- and Energy-Efficient Spiking Neural Network With Spike-Time-Dependent Plasticity Realized With SRAM Processing-in-Memory Macro and On-Chip Unsupervised Learning.

In this article, we present a spiking neural network (SNN) based on both SRAM processing-in-memory (PIM) macro and on-chip unsupervised learning with Spike-Time-Dependent Plasticity (STDP). Co-design of algorithm and hardware for hardware-friendly SNN and efficient STDP-based learning methodology is used to improve area and energy efficiency. The proposed macro utilizes charge sharing of capacitors to perform fully parallel Reconfigurable Multi-bit PIM Multiply-Accumulate (RMPMA) operations. A thermometer-coded Programmable High-precision PIM Threshold Generator (PHPTG) is designed to achieve low differential non-linearity (DNL) and high linearity. In the macro, each column of PIM cells and a comparator act as a neuron to accumulate membrane potential and fire spikes. A simplified Winner Takes All (WTA) mechanism is used in the proposed hardware-friendly architecture. By combining the hardware-friendly STDP algorithm as well as the parallel Word Lines (WLs) and Processing Bit Lines (PBLs), we realize unsupervised learning and recognize the Modified National Institute of Standards and Technology (MNIST) dataset. The chip for the hardware implementation was fabricated with a 55 nm CMOS process. The measurement shows that the chip achieves a learning efficiency of 0.47 nJ/pixel, with a learning energy efficiency of 70.38 TOPS/W. This work paves a pathway for the on-chip learning algorithm in PIM with lower power consumption and fewer hardware resources.

Splitting Award or Winner Takes All?: Evidence from China’s National Drug Procurement Auction

Splitting Award or Winner Takes All?: Evidence from China’s National Drug Procurement Auction

Weighted heterogeneous ensemble for the classification of intrusion detection using ant colony optimization for continuous search spaces

This paper proposes a heterogeneous ensemble classifier configuration for a multiclass intrusion detection problem. The ensemble is composed of k-nearest neighbors, artificial neural networks, and naïve Bayes classifiers. The decisions of these classifiers are combined with weighted majority voting, where optimal weights are generated by ant colony optimization for continuous search spaces. As a comparison basis, we have also implemented the ensemble configuration with the unweighted majority voting or Winner Takes All strategy. To ensure the maximum variety of classifiers, we have implemented three versions of each classification algorithm by varying each classifier’s parameters making a total of nine diverse experts for the ensemble. For our empirical study, we used the full NSL-KDD dataset to classify network traffic into one of five different classes. Our results indicate that the ensemble configuration using ACOR-optimized weights is capable of resolving the conflicts between multiple classifiers and improving the overall classification accuracy of the ensemble.

The Winner Takes It All: Carbon Supersedes Hexagonal Boron Nitride with Graphene on Transition Metals at High Temperatures.

The production of high-quality hexagonal boron nitride (h-BN) is essential for the ultimate performance of 2D materials-based devices, since it is the key 2D encapsulation material. Here, a decisive guideline is reported for fabricating high-quality h-BN on transition metals. It is crucial to exclude carbon from the h-BN related process, otherwise carbon prevails over boron and nitrogen due to its larger binding energy, thereupon forming graphene on metals after high-temperature annealing. The surface reaction-assisted conversion from h-BN to graphene with high-temperature treatments is demonstrated. The pyrolysis temperature Tp is an important quality indicator for h-BN/metals. When the temperature is lower than Tp , the quality of the h-BN layer is improved upon annealing. While the annealing temperature is above Tp , in case of carbon-free conditions, the h-BN disintegrates and nitrogen desorbs from the surface more easily than boron, eventually leading to clean metal surfaces. However, once the h-BN layer is exposed to carbon, graphene forms on Pt(111) in the high-temperature regime. This not only provides an indispensable principle (avoid carbon) for fabricating high-quality h-BN materials on transition metals, but also offers a straightforward method for the surface reaction-assisted conversion from h-BN to graphene on Pt(111).

Winner Takes All! What Are Race Models, and Why and How Should Psychologists Use Them?

Interest in the processes that mediate between stimuli and responses is at the heart of most modern psychology and neuroscience. These processes cannot be directly measured but instead must be inferred from observed responses. Race models, through their ability to account for both response choices and response times, have been a key enabler of such inferences. Examples of such models appeared contemporaneously with the cognitive revolution, and since then have become increasingly prominent and elaborated, so that psychologists now have a powerful array of race models at their disposal. We showcase the state of the art for race models and describe why and how they are used.

Winner Takes All? The Blockbuster Effect on Crowdfunding Platforms

Blockbuster projects on crowdfunding platforms are those that have achieved outstanding and exceptional performance. Regarding the impact of blockbuster projects on crowdfunding platforms, there are two plausible yet opposing predictions: they may exhibit both a negative effect by monopolizing backer attention and resources and a positive effect by increasing the activeness of backer side. This tension could be further complicated, considering that blockbusters are inherently heterogeneous. Drawing on the cross-side network effects literature and integrating insights from the unique features of crowdfunding, we develop a theoretical framework highlighting the multidimensional view of blockbuster effects: blockbuster projects have an overall positive effect on the performance of concurrent projects (overall effect), and such positive effect is stronger for related blockbusters (local effect) and blockbusters emerging before the focal project (temporal effect). With data from a leading crowdfunding platform, the empirical analyses largely support our proposed framework. Using off-platform data and backer data, we unveil the mechanism driving the blockbuster effects: blockbusters increase the popularity of the platform, whereby backers participating in the blockbusters tend to develop positive impressions and heightened expectations, so that they are likely to participate in other concurrent projects. Our findings have important implications for project creators and platform operators.

A Model of Pattern Separation by Single Neurons.

For efficient processing, spatiotemporal spike patterns representing similar input must be able to transform into a less similar output. A new computational model with physiologically plausible parameters shows how the neuronal process referred to as “pattern separation” can be very well achieved by single neurons if the temporal qualities of the output patterns are considered. Spike patterns generated by a varying number of neurons firing with fixed different frequencies within a gamma range are used as input. The temporal and spatial summation of dendritic input combined with theta-oscillating excitability in the output neuron by subthreshold membrane potential oscillations (SMOs) lead to high temporal separation by different delays of output spikes of similar input patterns. A Winner Takes All (WTA) mechanism with backward inhibition suffices to transform the spatial overlap of input patterns to much less temporal overlap of the output patterns. The conversion of spatial patterns input into an output with differently delayed spikes enables high separation effects. Incomplete random connectivity spreads the times up to the first spike across a spatially expanded ensemble of output neurons. With the expansion, random connectivity becomes the spatial distribution mechanism of temporal features. Additionally, a “synfire chain” circuit is proposed to reconvert temporal differences into spatial ones.

Impact of Image-Processing Routines on Mapping Glacier Surface Facies from Svalbard and the Himalayas Using Pixel-Based Methods

Glacier surface facies are valuable indicators of changes experienced by a glacial system. The interplay of accumulation and ablation facies, followed by intermixing with dust and debris, as well as the local climate, all induce observable and mappable changes on the supraglacial terrain. In the absence or lag of continuous field monitoring, remote sensing observations become vital for maintaining a constant supply of measurable data. However, remote satellite observations suffer from atmospheric effects, resolution disparity, and use of a multitude of mapping methods. Efficient image-processing routines are, hence, necessary to prepare and test the derivable data for mapping applications. The existing literature provides an application-centric view for selection of image processing schemes. This can create confusion, as it is not clear which method of atmospheric correction would be ideal for retrieving facies spectral reflectance, nor are the effects of pansharpening examined on facies. Moreover, with a variety of supervised classifiers and target detection methods now available, it is prudent to test the impact of variations in processing schemes on the resultant thematic classifications. In this context, the current study set its experimental goals. Using very-high-resolution (VHR) WorldView-2 data, we aimed to test the effects of three common atmospheric correction methods, viz. Dark Object Subtraction (DOS), Quick Atmospheric Correction (QUAC), and Fast Line-of-Sight Atmospheric Analysis of Hypercubes (FLAASH); and two pansharpening methods, viz. Gram–Schmidt (GS) and Hyperspherical Color Sharpening (HCS), on thematic classification of facies using 12 supervised classifiers. The conventional classifiers included: Mahalanobis Distance (MHD), Maximum Likelihood (MXL), Minimum Distance to Mean (MD), Spectral Angle Mapper (SAM), and Winner Takes All (WTA). The advanced/target detection classifiers consisted of: Adaptive Coherence Estimator (ACE), Constrained Energy Minimization (CEM), Matched Filtering (MF), Mixture-Tuned Matched Filtering (MTMF), Mixture-Tuned Target-Constrained Interference-Minimized Filter (MTTCIMF), Orthogonal Space Projection (OSP), and Target-Constrained Interference-Minimized Filter (TCIMF). This experiment was performed on glaciers at two test sites, Ny-Ålesund, Svalbard, Norway; and Chandra–Bhaga basin, Himalaya, India. The overall performance suggested that the FLAASH correction delivered realistic reflectance spectra, while DOS delivered the least realistic. Spectra derived from HCS sharpened subsets seemed to match the average reflectance trends, whereas GS reduced the overall reflectance. WTA classification of the DOS subsets achieved the highest overall accuracy (0.81). MTTCIMF classification of the FLAASH subsets yielded the lowest overall accuracy of 0.01. However, FLAASH consistently provided better performance (less variable and generally accurate) than DOS and QUAC, making it the more reliable and hence recommended algorithm. While HCS-pansharpened classification achieved a lower error rate (0.71) in comparison to GS pansharpening (0.76), neither significantly improved accuracy nor efficiency. The Ny-Ålesund glacier facies were best classified using MXL (error rate = 0.49) and WTA classifiers (error rate = 0.53), whereas the Himalayan glacier facies were best classified using MD (error rate = 0.61) and WTA (error rate = 0.45). The final comparative analysis of classifiers based on the total error rate across all atmospheric corrections and pansharpening methods yielded the following reliability order: MXL > WTA > MHD > ACE > MD > CEM = MF > SAM > MTMF = TCIMF > OSP > MTTCIMF. The findings of the current study suggested that for VHR visible near-infrared (VNIR) mapping of facies, FLAASH was the best atmospheric correction, while MXL may deliver reliable thematic classification. Moreover, an extensive account of the varying exertions of each processing scheme is discussed, and could be transferable when compared against other VHR VNIR mapping methods.

The Winner Takes It All: Investor Sentiment and the Eurovision Song Contest

This paper investigates the stock market reaction to a change in investor mood following the Eurovision Song Contest—an annual international song competition and one of the most watched non-sporting events globally. Contrary to existing literature on international competitions, we find a positive swing in investor sentiment in the winning country. The elevated atmosphere is reflected in a positive abnormal return of approximately 0.35% on the first trading day after the victory. This finding is robust to various event-study methods and to various benchmarks. This positive return is reversed several days later. Further, we do not find any indication of negative sentiment in other participating countries; specifically, in countries perceived as the losers of the contest. Finally, we do not find any indication that the positive market reaction reflects economic benefits stemming from a victory. Overall, we conjecture that a competition structure is an important determinant of investor sentiment in stock markets.

Winner Takes All: A Superpixel Aided Voting Algorithm for Training Unsupervised PolSAR CNN Classifiers

Unsupervised methods play an essential role in Polarimetric SAR (PolSAR) image classification, where labeled data are difficult to obtain. However, there is still a large gap between existing unsupervised learning methods and supervised learning methods. Without the semantic constraints of labeled data, pixels within the same category are often misclassified into different categories, leaving the output to be messy. To address the previous issue, this paper proposes a fully unsupervised pipeline for training CNNs. The pipeline combines low-level superpixels and high-level CNN semantic features for high-quality pseudo label generation. It effectively eliminates the misclassified pixels by voting within the superpixel blob, while preserving the sharpness of edges. With the training process of the model, the quality of the generated labels is getting improved. Experiments on airborne (ESAR/AIRSAR) and spaceborne (RadarSat2) PolSAR images prove the effectiveness of the proposed method (measured with OA, AA, and Kappa metrics). Our method outperforms the previous unsupervised methods (H/alpha-Wishart,SM-Wishart, FDD-H, DEC, and VQC-CAE) with a large margin, and even has comparable performance to the supervised CNN model (FCN).

Winner Takes All? Product Differentiation and Two-Sided Network Effects in the Rideshare Industry

Winner Takes All? Product Differentiation and Two-Sided Network Effects in the Rideshare Industry

Winner Takes it All: Do Financial Incentives Motivate College Planning Step Completion?

Winner Takes it All: Do Financial Incentives Motivate College Planning Step Completion?

The Winner Takes it All: Risk Factors and Bayesian Modelling of the Probability of Success in Escaping from Big Cat Predation.

Simple SummaryPredation is a complex behavioural interaction that is conditioned by biotic and abiotic factors. In their struggle for survival, the agents participating in the hunt interaction adapt their strategies seeking an opposite interest which leads to the same outcome, success in surviving. Predator/prey interaction data was obtained from on-line posted videos. The examination of records suggested that the species and age range of the predator, its status at the end of the hunt, the time elapsed between the sighting of its prey and the physical contact with it, the species of the prey and the relief of the land were determined success of escape of the prey in case of attack. The present study sheds light on the multietiological nature of predatory abilities and the strategies to fend off anti-predation strategies of the prey in big cats. The theoretical and empirical contents derived from this work will allow the design of environmental enrichment programs in captivity to be substantially improved by providing preys and enough space for them to express big cats predating strategies. The extrapolation of these results to domestic contexts may enable approaching selection strategies from two perspectives, with the aim to boost predating ability of domestic felids for pest control or to enhance defence in domestic ruminant prey from big cats.The individuals engaged in predation interactions modify their adaptation strategies to improve their efficiency to reach success in the fight for survival. This success is linked to either capturing prey (predator) or escaping (prey). Based on the graphic material available on digital platforms both of public and private access, this research aimed to evaluate the influence of those animal- and environment-dependent factors affecting the probability of successful escape of prey species in case of attack by big cats. Bayesian predictive analysis was performed to evaluate the outcomes derived from such factor combinations on the probability of successful escape. Predator species, age, status at the end of the hunting act, time lapse between first attention towards potential prey and first physical contact, prey species and the relief of the terrain, significantly conditioned (p < 0.05) escape success. Social cooperation in hunting may be more important in certain settings and for certain prey species than others. The most parsimonious model explained 36.5% of the variability in escaping success. These results can be useful to design translatable selective strategies not only seeking to boost predation abilities of domestic felids for pest control, but also, biological antipredator defence in potential domestic prey of big cats.

Proportional Representation System Whether Significance for Bangladesh: An Analysis

The purpose of this article is to ensure free, fair and credible election by adopting electoral reformation, switch from `Winner Take All” (WTA) to Proportional Representation (PR) system. In Bangladesh the election process and election result both have been questioned. Nevertheless, election is one of the key components in ensuring democracy. Particularly the free, fair and credible election is one of the basic and crucial prerequisites and elements of democratic government and governance. Bangladesh, we found an under developed political culture, lack of democratic political organizations, institutions and practices in true sense. All these indicators have been almost failed to institutionalize democracy in Bangladesh. In this backdrop, this paper attempts to explain that, a switch from the current Winner Takes All (WTA) system to Proportional Representation (PR) system can reshape the election system by keeping consistency, because of the fact that the democracy means the rule of the majority but the minority can't be uprooted as well as Proportional Representation (PR) method can help stabilize democracy in Bangladesh.

Mproved Stereo Matching Algorithm based on Census Transform and Dynamic Histogram Cost Computation

Stereo matching is a significant subject in the stereo vision algorithm. Traditional taxonomy composition consists of several issues in the stereo correspondences process such as radiometric distortion, discontinuity, and low accuracy at the low texture regions. This new taxonomy improves the local method of stereo matching algorithm based on the dynamic cost computation for disparity map measurement. This method utilised modified dynamic cost computation in the matching cost stage. A modified Census Transform with dynamic histogram is used to provide the cost volume. An adaptive bilateral filtering is applied to retain the image depth and edge information in the cost aggregation stage. A Winner Takes All (WTA) optimisation is applied in the disparity selection and a left-right check with an adaptive bilateral median filtering are employed for final refinement. Based on the dataset of standard Middlebury, the taxonomy has better accuracy and outperformed several other state-ofthe-art algorithms. Keywords—Stereo matching, disparity map, dynamic cost, census transform, local method

Free Radicals: Winner Takes All

“Winning isn’t everything; it is the only thing…” is the quote often attributed to the famous American football coach, Vince Lombardi. This column examines the near misses in public acclamation (e.g., Nobel Prize) of some otherwise famous scientists and engineers in solid state and allied pursuits.