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"Lookalike" products, including counterfeits and copycats, are increasingly common in today's global marketplace. Four lab experiments were conducted involving 653 participants, and data were analyzed through SPSS 26. Based on social representation (SR) theory and self‐construal theory, this research examines how advertising for original brands impacts purchase intent for counterfeits and copycats differently. The results suggest that ads for the original product using value‐expressive (vs. utilitarian) appeals result in lower intent to purchase copycat versions. However, ads for the original product using utilitarian (vs. value‐expressive) appeals lead to lower purchase intent for counterfeit versions. The results also show that consumer need fulfillment mediates the interactive effect between ad appeal type and product type. The current research provides useful insights for marketers or retailers grappling with ongoing competitive threats posed by lookalike products and underscores the strategic utilization of various advertising tactics to reduce the demand for copycats and counterfeits. [ABSTRACT FROM AUTHOR]

Humans often behave more prosocially when being observed in person and even in response to subtle eye cues, purportedly to manage their reputation. Previous research on this phenomenon has employed the “watching eyes paradigm,” in which adults displayed greater prosocial behavior in the presence of images of eyes versus inanimate objects. However, the robustness of the effect of eyes on prosocial behavior has recently been called into question. Therefore, the first goal of the present study was to attempt to replicate this effect. Additionally, it remains unclear whether the watching-eyes effect is driven specifically by reputation management (owing to the monitoring function of the eyes) or whether any cues indexing human presence more generally also have a similar effect. To address these questions, the current study compared prosocial behavior in the presence of eyes versus inanimate objects as well as other human features. The study was conducted as a field experiment at a children’s museum. Each week, the donation signs were changed to show eyes, noses, mouths, or chairs. Total donation amount and number of patrons per week were recorded. Participants donated more when they were exposed to eyes than to inanimate objects (chairs). We thus replicated the previously reported watching-eyes effect. Moreover, more money was donated when individuals were exposed to eyes than to more general cues of human presence (nose and mouth). The current findings suggest that eyes play a special role in promoting cooperation in humans, likely by serving as cues of monitoring and thus eliciting reputation management behavior. [ABSTRACT FROM AUTHOR]

Relationships of physical resemblance to personality similarity and social affiliation have generated considerable discussion among behavioral science researchers. A “twin-like” experimental design (involving genetically unrelated look-alikes, U-LAs) explores associations among resemblance in appearance, the Big Five personality traits, self-esteem, and social attraction within an evolutionary framework. The Personality for Professionals Inventory (PfPI), NEO/NEO-FFI-3, Rosenberg Self-Esteem Scale, and a Social Relationship Survey were variously completed by 45 U-LA pairs, identified from the “I’m Not a Look-Alike” project, Mentorn Media, and personal referrals. The mean U-LA intraclass correlations were negligible for all Big Five personality traits on the PfPI and NEO/NEO-FFI-3 (ri = −.02 and − .04, respectively). In contrast, mean ri values of.53 and.15 for monozygotic (MZA) and dizygotic (DZA) reared-apart twins, respectively, have been reported for these personality measures. The U-LA self-esteem correlation (ri = −.18) was also below the correlations reported for MZ and DZ reared-together twins (ri =.31 and.13, respectively). Finally, far fewer U-LAs expressed close social relationships (20%) than MZA (80%) and DZA (65%) twins. The present study extends earlier findings indicating that appearance is not meaningfully related to personality similarity and social relatedness. The criticism that MZ twins are alike in personality because their matched looks invite similar treatment by others is refuted. A more judicious interpretation is reactive genotype-environment correlation, namely that MZ twins’ similar personalities evoke similar reactions from others. MZ twins’ close social relations most likely derive from their perceptions of genetically based within-pair similarities that are lacking in U-LAs. [ABSTRACT FROM AUTHOR]

Classification algorithms for automatically detecting sea surface oil spills from spaceborne Synthetic Aperture Radars (SARs) can usually be regarded as part of a three-step processing framework, which briefly includes image segmentation, feature extraction, and target classification. A Deep Convolutional Neural Network (DCNN), named the Oil Spill Convolutional Network (OSCNet), is proposed in this paper for SAR oil spill detection, which can do the latter two steps of the three-step processing framework. Based on VGG-16, the OSCNet is obtained by designing the architecture and adjusting hyperparameters with the data set of SAR dark patches. With the help of the big data set containing more than 20,000 SAR dark patches and data augmentation, the OSCNet can have as many as 12 weight layers. It is a relatively deep Deep Learning (DL) network for SAR oil spill detection. It is shown by the experiments based on the same data set that the classification performance of OSCNet has been significantly improved compared to that of traditional machine learning (ML). The accuracy, recall, and precision are improved from 92.50%, 81.40%, and 80.95% to 94.01%, 83.51%, and 85.70%, respectively. An important reason for this improvement is that the distinguishability of the features learned by OSCNet itself from the data set is significantly higher than that of the hand-crafted features needed by traditional ML algorithms. In addition, experiments show that data augmentation plays an important role in avoiding over-fitting and hence improves the classification performance. OSCNet has also been compared with other DL classifiers for SAR oil spill detection. Due to the huge differences in the data sets, only their similarities and differences are discussed at the principle level.

Oil slicks and lookalikes (e.g., plant oil and oil emulsion) all appear as dark areas in polarimetric Synthetic Aperture Radar (SAR) images and are highly heterogeneous, so it is very difficult to use a single feature that can allow classification of dark objects in polarimetric SAR images as oil slicks or lookalikes. We established multi-feature fusion to support the discrimination of oil slicks and lookalikes. In the paper, simple discrimination analysis is used to rationalize a preferred features subset. The features analyzed include entropy, alpha, and Single-bounce Eigenvalue Relative Difference (SERD) in the C-band polarimetric mode. We also propose a novel SAR image discrimination method for oil slicks and lookalikes based on Convolutional Neural Network (CNN). The regions of interest are selected as the training and testing samples for CNN on the three kinds of polarimetric feature images. The proposed method is applied to a training data set of 5400 samples, including 1800 crude oil, 1800 plant oil, and 1800 oil emulsion samples. In the end, the effectiveness of the method is demonstrated through the analysis of some experimental results. The classification accuracy obtained using 900 samples of test data is 91.33%. It is here observed that the proposed method not only can accurately identify the dark spots on SAR images but also verify the ability of the proposed algorithm to classify unstructured features. [ABSTRACT FROM AUTHOR]

The open-set problem is among the problems that have significantly changed the performance of face recognition algorithms in real-world scenarios. Open-set operates under the supposition that not all the probes have a pair in the gallery. Most face recognition systems in real-world scenarios focus on handling pose, expression and illumination problems on face recognition. In addition to these challenges, when the number of subjects is increased for face recognition, these problems are intensified by look-alike faces for which there are two subjects with lower intra-class variations. In such challenges, the inter-class similarity is higher than the intra-class variation for these two subjects. In fact, these look-alike faces can be created as intrinsic, situation-based and also by facial plastic surgery. This work introduces three real-world open-set face recognition methods across facial plastic surgery changes and a look-alike face by 3D face reconstruction and sparse representation. Since some real-world databases for face recognition do not have multiple images per person in the gallery, with just one image per subject in the gallery, this paper proposes a novel idea to overcome this challenge by 3D modeling from gallery images and synthesizing them for generating several images. Accordingly, a 3D model is initially reconstructed from frontal face images in a real-world gallery. Then, each 3D reconstructed face in the gallery is synthesized to several possible views and a sparse dictionary is generated based on the synthesized face image for each person. Also, a likeness dictionary is defined and its optimization problem is solved by the proposed method. Finally, the face recognition is performed for open-set face recognition using three proposed representation classifications. Promising results are achieved for face recognition across plastic surgery and look-alike faces on three databases including the plastic surgery face, look-alike face and LFW databases compared to several state-of-the-art methods. Also, several real-world and open-set scenarios are performed to evaluate the proposed method on these databases in real-world scenarios. [ABSTRACT FROM AUTHOR]

The Deese-Roediger-McDermott (DRM) paradigm was used to investigate consumer confusion between original and look-alike brands. The results showed that look-alike brands were falsely recognized at a higher rate than original brands and that modality (audio, visual, and audiovisual) had no effect on false recognition rates. The results suggest that the DRM paradigm provides a useful tool for analyzing consumer confusion, brand substitution, and trademark infringement. [ABSTRACT FROM AUTHOR]

The article examines look-alikes and pseudomorphism, focusing upon its examples in art. Topics examined include the similarities between seventeenth-century Dutch deathbed portraits and nineteenth century death portraits, the exhibition "'Primitivism' in 20th Century Art: Affinity of the Tribal and the Modern" at the Museum of Modern Art (MoMA) in New York City in 1984, and the essay "Split Representation in the Art of Asia and America," written by the French anthropologist and ethnologist Claude Lévi-Strauss. Various artworks are compared and contrasted as well, including "4 double grids 0°, 22°5, 45°, 67°5," by François Morellet and "Circles, grids and arcs from four corners and four sides," by Sol LeWitt.

Background CRISPR/Cas (Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR associated nucleases) is a powerful component of the prokaryotic immune system that has been adapted for targeted genetic engineering in higher organisms. A key element of CRISPR/Cas is the "guide" RNA (gRNA) that is ∼20 nucleotides (nts) in length and designed to be complementary to the intended target site. An integral requirement of the CRISPR/Cas system is that the target site be followed by a protospacer adjacent motif (PAM). Care needs to be exercised during gRNA design to avoid unintended ("off-target") interactions. Results We designed and implemented the Off-Spotter algorithm to assist with the design of optimal gRNAs. When presented with a candidate gRNA sequence and a PAM, Off-Spotter quickly and exhaustively identifies all genomic sites that satisfy the PAM constraint and are identical or nearly-identical to the provided gRNA. Off-Spotter achieves its extreme performance through purely algorithmic means and not through hardware accelerators such as graphical processing units (GPUs). Off-Spotter also allows the user to identify on-the-fly how many and which nucleotides of the gRNA comprise the "seed." Off-Spotter's output includes a histogram showing the number of potential off-targets as a function of the number of mismatches. The output also includes for each potential off-target the site's genomic location, a human genome browser hyperlink to the corresponding location, genomic annotation in the vicinity of the off-target, GC content, etc. Conclusion Off-Spotter is very fast and flexible and can help in the design of optimal gRNAs by providing several PAM choices, a run-time definition of the seed and of the allowed number of mismatches, and a flexible output interface that allows sorting of the results, optional viewing/hiding of columns, etc. A key element of Off-Spotter is that it does not have a rigid definition of the seed: instead, the user can declare both the seed's location and extent on-thefly. We expect that this flexibility in combination with Off-Spotter's speed and richly annotated output will enable experimenters to interactively and quickly explore different scenarios and gRNA possibilities. Reviewed This article was reviewed by Dr Eugene Koonin and Dr Frank Eisenhaber. [ABSTRACT FROM AUTHOR]

The article focuses on issues surrounding an individual's freedom to use a celebrity's likeness and the right of publicity that protects a celebrity's persona. Topics discussed include the freedom of speech under the First Amendment, a brief history of the right of publicity and the First Amendment, and cases that dealt with the conflict of rights such as Hanna Manufacturing Co. v. Hillerich & Bradsby Co.

The major goal of the present study is to describe a method by which synthetic aperture radar (SAR) images of oil spills can be discriminated from other phenomena of similar appearance. The optimal features of these dark formations are here identified. Because different materials have different physical properties, they form different shapes. In this case, oil films and lookalike materials have different fluid properties. In this paper, 9 shape features with a total of 95 eigenvalues were selected. Using differential evolution feature selection (DEFS), similar eigenvalues were extracted from total space of oil spills and lookalike phenomena. This process assumes that these similar eigenvalues impair classification. These similar eigenvalues are removed from the total space, and the important eigenvalues (IEs), those useful to the discrimination of the targets, are identified. At least 30 eigenvalues were found to be inappropriate for classification of our shape spaces. The proposed method was found to be capable of facilitating the selection of the top 50 IEs. This allows more accurate classification. Here, accuracy reached 94%. The results of the experiment show that this novel method performs well. It could also be made available to teams across the world very easily. [ABSTRACT FROM AUTHOR]

The article focuses on publicity issues surrounding fantasy sports with reference to a case that addressed violation of former college athletes' right of publicity with the use of their likenesses in video games. Topics discussed include impact of fantasy sports on the sports world, history of introduction of Fantasy sports by a group of New Yorkers in 1980, and impact of rise of the internet on growth of fantasy sports. It mentions issues surrounding a a fantasy site's First Amendment rights.

The article discusses student-athlete compensation issues surrounding Johnny Manziel, a Heisman Trophy winner of the National Collegiate Athletic Association (NCAA). Topics discussed include improper benefits received by Manziel in exchange for his autograph and his suspension based on violation of NCAA rules, issues surrounding unauthorized use of athletes' likenesses under Sherman Act and NCAA's non-compensation policy to maintain amateurism in collegiate athletics.

Abstract: Numerous twin studies have demonstrated genetic influence on personality traits, yet twin methods continue to be challenged. A common misconception is that monozygotic co-twins’ personality resemblance results from similar treatment by others, due to their matched physical appearance. The present study brings unique evidence to this question by assessing the similarities in personality and self-esteem of 23 pairs of unrelated look-alike individuals. Intraclass correlations for the Big Five personality traits (ris=−.27 to .29) and the Rosenberg Self-Esteem Scale (ri =−. 03) demonstrated little within-pair resemblance. It is concluded that (1) MZ co-twins’ personality similarity mostly reflects their shared genes, and (2) reactive gene-environment correlation best explains MZ co-twins’ similar treatment by others. [Copyright &y& Elsevier]

Retailer brands are a common feature of most retail sectors today, and have made an important contribution to the financial performance of Britain's retailers. Over the past two decades, as retailers have adopted the marketing approach in their business activities, the nature and role of retailer brands in marketing strategies has changed. This paper reviews the development of retailer brands in the grocery market, from their original position as product alternatives to leading manufacturer brands, to brand alternatives in their own right. Part of this transformation has involved a revision of the packaging and presentation of these product ranges and this has led to accusations of copy-catting and theft of brand identity. The paper then explores the lookalike debate and reports on an exploratory study of retail brand recognition and the lookalike situation in those product markets which are not dominated by established manufacturer brands. [ABSTRACT FROM AUTHOR]

Classification algorithms for automatically detecting sea surface oil spills from spaceborne Synthetic Aperture Radars (SARs) can usually be regarded as part of a three-step processing framework, which briefly includes image segmentation, feature extraction, and target classification. A Deep Convolutional Neural Network (DCNN), named the Oil Spill Convolutional Network (OSCNet), is proposed in this paper for SAR oil spill detection, which can do the latter two steps of the three-step processing framework. Based on VGG-16, the OSCNet is obtained by designing the architecture and adjusting hyperparameters with the data set of SAR dark patches. With the help of the big data set containing more than 20,000 SAR dark patches and data augmentation, the OSCNet can have as many as 12 weight layers. It is a relatively deep Deep Learning (DL) network for SAR oil spill detection. It is shown by the experiments based on the same data set that the classification performance of OSCNet has been significantly improved compared to that of traditional machine learning (ML). The accuracy, recall, and precision are improved from 92.50%, 81.40%, and 80.95% to 94.01%, 83.51%, and 85.70%, respectively. An important reason for this improvement is that the distinguishability of the features learned by OSCNet itself from the data set is significantly higher than that of the hand-crafted features needed by traditional ML algorithms. In addition, experiments show that data augmentation plays an important role in avoiding over-fitting and hence improves the classification performance. OSCNet has also been compared with other DL classifiers for SAR oil spill detection. Due to the huge differences in the data sets, only their similarities and differences are discussed at the principle level. [ABSTRACT FROM AUTHOR]