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Labeling is required by the interpretive system. When a head merges with a phrase, the head provides the label. However, lexical heads and T with poor inflectional features are too weak to be labels. Although insightful, this theory leaves at least one problem that needs prompt solutions: are there other kinds of weak heads? In this paper, we address this issue by proposing that phonological features play a crucial role in the labeling algorithm and by putting forward an additional version of weak heads. That is, a head that loses phonological features in the syntax is also weak. This approach to weak heads, together with the constraint that a structure must be labeled for interpretation, can capture the distribution of empty categories in topicalization, relativization, ellipsis, and other phenomena, some of which have not received enough scholarly attention. Therefore, our syntactic-phonological approach to labeling can open up new possibilities to account for the distribution of empty categories in a principled manner. [ABSTRACT FROM AUTHOR]

In Argentina, a nation committed to prioritizing food quality, there is a pressing necessity to fortify quality control systems and establish stringent standards ensuring food safety. This study aims to scrutinize commercial samples of Origanum vulgare L. in the province of Jujuy, Argentine Republic, with the objective of assessing their authenticity and overall quality. Both macroscopic and microscopic techniques were employed to analyze composition and verify identity. Simultaneously, a thorough examination of product labels was conducted to ensure compliance with food regulations. Ethnographic techniques, including merchant interviews, were utilized to gather comprehensive insights. The findings reveal considerable variability in the composition of "oregano" samples, with instances of foreign elements such as stones and dust detected in some cases. Microscopic identification unequivocally confirmed the presence of contaminating elements. Notably, the majority of industrial products adhered to labeling standards, while a mere 10% of artisanal samples achieved similar compliance. The study also unveiled diverse culinary and medicinal applications for the examined oregano samples. These discoveries underscore the imperative for heightened oversight to guarantee the quality and purity of oregano products. The correlation between microscopic results and previous studies bolsters the reliability of this analytical approach. In conclusion, the study emphasizes the paramount importance of instituting policies and programs that advance food security. The proposed methodology emerges as an effective means to enhance quality control in the marketing of plant products in the province. [ABSTRACT FROM AUTHOR]

Purpose: The Pediatric Research Equity Act (PREA) gives the US Food and Drug Administration (FDA) authority to require pediatric studies for drug and biologics products under certain circumstances and to waive this requirement in some, or all, pediatric ages. When studies are waived for safety, PREA stipulates the safety issue must be described in labeling. This study assessed the rate of including waiver-related safety information in labeling. Methods: FDA databases were reviewed to determine the number of safety-related pediatric study waivers and issued from December 2003 through August 2020, and corresponding labeling to establish when relevant safety information was included. Descriptive comparisons were conducted across Cohort 1: December 2003–2007, Cohort 2: 2008–2011, Cohort 3: 2012–2015, and Cohort 4: 2016-August 2020. Results: One hundred sixteen safety waivers were issued [Cohort 1 (n = 1); Cohort 2 (n = 38), Cohort 3 (n = 37), and Cohort 4 (n = 40)] for 84 unique drugs or biologics. Most (106 of 116; 91%) waiver-related safety issues were described in labeling [Cohort 1 (1 of 1), Cohort 2 (33 of 38), Cohort 3 (33 of 37), and Cohort 4 (39 of 40)]. Safety waivers were most common in patients ≤ 17 years (n = 40) and least common in patients ≤ 6 months (n = 15). Products for infections (n = 32) were the most common group receiving safety waivers; 17 for non-antiviral anti-infective products including treatments for dermatologic infestations/infections, and 15 for antiviral products. Conclusion: The data confirm that FDA consistently describes waiver-related safety information in drug/biologic product labeling since the inception of PREA in December of 2003. [ABSTRACT FROM AUTHOR]

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The First Amendment to the US Constitution protects commercial speech from government interference. Commercial speech has been defined by the US Supreme Court as speech that proposes a commercial transaction, such as marketing and labeling. Companies that produce products associated with public health harms, such as alcohol, tobacco, and food, thus have a constitutional right to market these products to consumers. This article will examine the evolution of US law related to the protection of commercial speech, often at the expense of public health. It will then identify outstanding questions related to the commercial speech doctrine and the few remaining avenues available in the United States to regulate commercial speech including the use of government speech and addressing deceptive and misleading commercial speech. [ABSTRACT FROM AUTHOR]

• Graphic yellow warnings with images that reflect concrete consequences are the most effective to communicate the risks of cannabis use. • Graphic yellow warnings diminish product appeal, increases harm perception, discourages intention to try them and grab attention. • The most effective themes were mental health, smoke toxicity, aesthetic implications and driving high. Health warning label on cannabis products has been recently studied, and with the latest trends of regulation around the world, there is a need to determine the most effective ways to apply this strategy. The current study aimed to examine the effects of different health warning label designs (pictorial vs text-only, background color, warning themes) on cannabis products. An online experiment study (N =533) was carried out in Colombia with a between-subject design. Participants were randomly assigned to five package conditions: without warning, text-only white warning, text-only yellow warning, pictorial white warning, and pictorial yellow warning. Participants performed an attention task and rated each of the stimuli based on product appeal, perceived addictiveness, harm perception, and interest in trying cannabis products. Pictorial health warnings were generally the most effective. Especially, pictorial health warnings with a yellow background were found to decrease product appeal and interest in trying cannabis products, as well as increase harm perception compared to other designs. The most effective warning themes were mental health, smoke toxicity, aesthetic implications, and traffic accidents. The current study provides empirical evidence on the effectiveness of different designs of cannabis health warnings. Our results suggest that graphic yellow warnings are the most effective in communicating the risks of cannabis use. [ABSTRACT FROM AUTHOR]

The task of aggregating and denoising crowd-labeled data has gained increased significance with the advent of crowdsourcing platforms and massive datasets. We propose a permutation-based model for crowd labeled data that is a significant generalization of the classical Dawid-Skene model, and introduce a new error metric by which to compare different estimators. We derive global minimax rates for the permutation-based model that are sharp up to logarithmic factors, and match the minimax lower bounds derived under the simpler Dawid-Skene model. We then design two computationally-efficient estimators: the WAN estimator for the setting where the ordering of workers in terms of their abilities is approximately known, and the OBI- WAN estimator where that is not known. For each of these estimators, we provide non-asymptotic bounds on their performance. We conduct synthetic simulations and experiments on real-world crowdsourcing data, and the experimental results corroborate our theoretical findings. [ABSTRACT FROM AUTHOR]

Multi-label learning deals with training examples each represented by a single instance while associated with multiple class labels, and the task is to train a predictive model which can assign a set of proper labels for the unseen instance. Existing approaches employ the common assumption of equal labeling-importance, i.e., all associated labels are regarded to be relevant to the training instance while their relative importance in characterizing its semantics are not differentiated. Nonetheless, this common assumption does not reflect the fact that the importance degree of each relevant label is generally different, though the importance information is not directly accessible from the training examples. In this article, we show that it is beneficial to leverage the implicit relative labeling-importance (RLI) information to help induce multi-label predictive model with strong generalization performance. Specifically, RLI degrees are formalized as multinomial distribution over the label space, which can be estimated by either global label propagation procedure or local k -nearest neighbor reconstruction. Correspondingly, the multi-label predictive model is induced by fitting modeling outputs with estimated RLI degrees along with multi-label empirical loss regularization. Extensive experiments clearly validate that leveraging implicit RLI information serves as a favorable strategy to achieve effective multi-label learning. [ABSTRACT FROM AUTHOR]

Criticism of biotechnology in the food system coalesced in the 2010s around calls for a mandatory genetically modified organism (GMO) label on food products in several U.S. states. We examine the state labeling initiatives in the northeastern U.S. (CT, MA, ME, VT), with particular focus on Vermont as it was the only state to enact a GMO labeling bill in 2016. We combine interviews with stakeholders and testimony from public hearings to understand the ways in which stakeholders and the public framed the issue. We find that frames tied to individual consumer rights and economic risks were evident across all northeastern states' discourses on GMO labeling, with some states emphasizing concerns linked to public health and lack of scientific data. In Vermont, we find that frames tied to the state's agricultural identity and collective welfare resonated within the progressive, direct democratic political landscape. These collective welfare frames differ from individually focused neoliberal frames more commonly found throughout GMO labeling debates and broader food activism. [ABSTRACT FROM AUTHOR]

In this paper, for the first time, we study label propagation in heterogeneous graphs under heterophily assumption. Homophily label propagation (i.e., two connected nodes share similar labels) in homogeneous graph (with same types of vertices and relations) has been extensively studied before. Unfortunately, real-life networks (e.g., social networks) are heterogeneous, they contain different types of vertices (e.g., users, images, and texts) and relations (e.g., friendships and co-tagging) and allow for each node to propagate both the same and opposite copy of labels to its neighbors. We propose a K-partite label propagation model to handle the mystifying combination of heterogeneous nodes/relations and heterophily propagation. With this model, we develop a novel label inference algorithm framework with update rules in near-linear time complexity. Since real networks change over time, we devise an incremental approach, which supports fast updates for both new data and evidence (e.g., ground truth labels) with guaranteed efficiency. We further provide a utility function to automatically determine whether an incremental or a re-modeling approach is favored. Extensive experiments on real datasets have verified the effectiveness and efficiency of our approach, and its superiority over the state-of-the-art label propagation methods. [ABSTRACT FROM AUTHOR]

We propose a new architecture for addressing sequence labeling, termed Dual Adversarial Transfer Network (DATNet). Specifically, the proposed DATNet includes two variants, i.e., DATNet-F and DATNet-P, which are proposed to explore effective feature fusion between high and low resource. To address the noisy and imbalanced training data, we propose a novel Generalized Resource-Adversarial Discriminator (GRAD) and adopt adversarial training to boost model generalization. We investigate the effects of different components of DATNet across different domains and languages, and show that significant improvement can be obtained especially for low-resource data. Without augmenting any additional hand-crafted features, we achieve state-of-the-art performances on CoNLL, Twitter, PTB-WSJ, OntoNotes and Universal Dependencies with three popular sequence labeling tasks, i.e., Named entity recognition (NER), Part-of-Speech (POS) Tagging and Chunking. [ABSTRACT FROM AUTHOR]

Partial Label Learning (PLL) aims to learn from the data where each training example is associated with a set of candidate labels, among which only one is correct. The key to deal with such problem is to disambiguate the candidate label sets and obtain the correct assignments between instances and their candidate labels. In this paper, we interpret such assignments as instance-to-label matchings, and reformulate the task of PLL as a matching selection problem. To model such problem, we propose a novel Graph Matching based Partial Label Learning (GM-PLL) framework, where Graph Matching (GM) scheme is incorporated owing to its excellent capability of exploiting the instance and label relationship. Meanwhile, since conventional one-to-one GM algorithm does not satisfy the constraint of PLL problem that multiple instances may correspond to the same label, we extend a traditional one-to-one probabilistic matching algorithm to the many-to-one constraint, and make the proposed framework accommodate to the PLL problem. Moreover, we also propose a relaxed matching prediction model, which can improve the prediction accuracy via GM strategy. Extensive experiments on both artificial and real-world data sets demonstrate that the proposed method can achieve superior or comparable performance against the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Source coding is the canonical problem of data compression in information theory. In a locally encodable source coding, each compressed bit depends on only few bits of the input. In this paper, we show that a recently popular model of semisupervised clustering is equivalent to locally encodable source coding. In this model, the task is to perform multiclass labeling of unlabeled elements. At the beginning, we can ask in parallel a set of simple queries to an oracle who provides (possibly erroneous) binary answers to the queries. The queries cannot involve more than two (or a fixed constant number of) elements. Now the labeling of all the elements (or clustering) must be performed based on the noisy query answers. The goal is to recover all the correct labelings while minimizing the number of such queries. The equivalence to locally encodable source codes leads us to find lower bounds on the number of queries required in variety of scenarios. We provide querying schemes based on pairwise ‘same cluster’ queries - and pairwise AND queries, and show provable performance guarantees for each of the schemes. [ABSTRACT FROM AUTHOR]

Visualizing spatial data on small-screen devices such as smartphones and smartwatches poses new challenges in computational cartography. The current interfaces for map exploration require their users to zoom in and out frequently. Indeed, zooming and panning are tools suitable for choosing the map extent corresponding to an area of interest. They are not as suitable, however, for resolving the graphical clutter caused by a high feature density since zooming in to a large map scale leads to a loss of context. Therefore, in this paper, we present new external labeling methods that allow a user to navigate through dense sets of points of interest while keeping the current map extent fixed. We provide a unified model, in which labels are placed at the boundary of the map and visually associated with the corresponding features via connecting lines, which are called leaders. Since the screen space is limited, labeling all features at the same time is impractical. Therefore, at any time, we label a subset of the features. We offer interaction techniques to change the current selection of features systematically and, thus, give the user access to all features. We distinguish three methods, which allow the user either to slide the labels along the bottom side of the map or to browse the labels based on pages or stacks. We present a generic algorithmic framework that provides us with the possibility of expressing the different variants of interaction techniques as optimization problems in a unified way. We propose both exact algorithms and fast and simple heuristics that solve the optimization problems taking into account different criteria such as the ranking of the labels, the total leader length as well as the distance between leaders. In experiments on real-world data we evaluate these algorithms and discuss the three variants with respect to their strengths and weaknesses proving the flexibility of the presented algorithmic framework. [ABSTRACT FROM AUTHOR]

Extracellular vesicles (EVs) are cell-derived lipid bilayer-enclosed nanovesicles. EVs are emerging as keys for identifying molecular mechanisms by mediating intercellular communication. EVs allow the exchange of various components with neighboring and distant cells through the extracellular environment, thereby involving in various biological processes in both physiological and pathological conditions such as wound healing, immune response, and tumorigenesis. EVs are also growing rapidly as cargo carrier for their natural delivery properties. Development of bioinspired delivery nanoplatforms based on exosomes-like mimetics also showed potentials to overcome limitations of synthetic nanoparticles. EVs offer a window to multicomponent diagnosis and a tool for design therapeutics. However, for successful clinical translation of EVs, the understanding of in vivo behavior is essential. Advancements in molecular imaging enabled high-resolution in vivo tracking of EVs, providing valuable information regarding trafficking, biodistribution, cellular uptake and molecular mechanism of EVs. Recent studies have explored various methods for visualizing EVs, each imaging technique has certain strengths and limitations. Highly specific, sensitive and biocompatible labeling and tracking strategies still in demand in EV visualization. In this review, we summarized methods for labeling and tracking of EVs and discussed benefits and drawbacks for each method. Future novel imaging modalities and combined strategies will provide avenues for understanding EV behavior and accelerate their clinical translation. Unlabelled Image • Extracellular vesicles (EVs) are interesting therapeutic tools but their in vivo behavior has not been fully understood. • Commonly methods used for visualization of EVs were comprehaneively summareized and compared. • Strategies such as photoacoustic imaging and Raman spectroscopy are emerging for labeling and tracking of EVs. • Powerful molecular imaging strategies would accelerate clinical translation of EVs-based therapy. [ABSTRACT FROM AUTHOR]

This study examines consumer preferences and willingness to pay (WTP) for organic wheat products exhibiting single labels, as well as multiple labels, including organic. The additional labels considered are related to the organic label (non‐genetically modified organism [non‐GMO]) or perceived as health‐promoting (gluten‐free, low‐carb, sugar‐free). Study data were collected using a consumer survey conducted online in 2017 across 16 U.S. western states and analyzed using random parameter logit models. Findings show that organic‐labeled wheat products with additional claims were valued equally or less than the organic only version. Overall, consumer higher objective and subjective knowledge of organic standards, as well as preferences for gluten‐free products, increased their WTP for organic wheat products. Consumers unfamiliar with organic standards valued the non‐GMO label over the organic label. Additionally, consumer WTP for health‐related claims on hedonistic products was low or even negative. Hence, multiple labels on organic products generally provide no additional consumer benefit and are likely ignored. Study findings suggest that using the claim that most distinguishes the product, or is most salient, may improve product pricing. Also, non‐GMO certification rather than organic certification should be considered for some markets. Finally, consumers with preferences for gluten‐free products represent a potential market for organic wheat products. [ABSTRACT FROM AUTHOR]

A graph G with \E(G)\ = q, an injective function f : V(G) → {0, 2,4, ..., 2q} is an even vertex odd mean labeling of G that induces the values f(u)+f(v)/2 for the q pairs of adjacent vertices u,v are distinct. In this paper, we investigate an even vertex labeling for the calendula graphs. Moreover we introduce the definition of arbitrary calendula graph and prove that the arbitrary calendula graphs are also even vertex odd mean graphs. [ABSTRACT FROM AUTHOR]

Analogical reasoning by comparison is considered a special case of inductive reasoning, which is fundamental to the scientific method. By reasoning analogically, learners can abstract the underlying commonalities of several entities, thereby ignoring single objects' superficial features. We tested whether different task environments designed to trigger analogical reasoning by comparison would support preschoolers' induction of the concept of material kind to predict and explain objects' floating or sinking as a central aspect of scientific reasoning. Specifically, in two experiments, we investigated whether the number of presented objects (one versus two standards), consisting of a specific material and the labeling of objects with the respective material name, would benefit preschoolers' material-based inferences. For each item set used in both experiments, we asked the children (N = 59 in Experiment 1, N = 99 in Experiment 2) to predict an object's floating or sinking by matching it to the standards and to verbally explain their selections. As expected, we found a significant effect for the number of standards in both experiments on the prediction task, suggesting that children successfully induced the relevance of material kind by comparison. However, labels did not increase the effect of the standards. In Experiment 2, we found that the children could transfer their conceptual knowledge on material kind but that transfer performance did not differ among the task environments. Our findings suggest that tasks inviting analogical reasoning by comparison with two standards are useful for promoting young children's scientific reasoning. [ABSTRACT FROM AUTHOR]

A (1,0,0)-F-Face magic mean labeling is an assignment of labels to the vertices of planar graph such that the mean weight of each face including an exterior face is constant. In this paper, the existence of (1,0,0)-F-face magic mean labeling is proven for new graphs obtained by applying graph operations like Cartesian product, subdivision, vertex duplication and edge duplication. [ABSTRACT FROM AUTHOR]

A k - L (p , q) -labeling of a graph G is a function ϕ : V (G) → { 0 , 1 , ... , k } such that | ϕ (x) − ϕ (y) | ≥ p if x and y are adjacent and | ϕ (x) − ϕ (y) | ≥ q if x and y are of distance two apart. The L (p , q) -labeling number of G , denoted by λ p , q (G) is the least k for which G has a k - L (p , q) -labeling. Let p ≥ q ≥ 1 be integers and let g be the girth of a graph G. In this paper we prove that (1) if G is a planar graph with g ≥ 5 , then λ p , q (G) ≤ (2 q − 1) Δ + 6 p + 10 q − 8. (2) Let G be a planar graph with g ≥ 6. There exists an integer Δ 0 (p , q) such that λ p , q (G) ≤ q Δ + 2 p + q − 2 if Δ ≥ Δ 0 (p , q). Our results partially generalize some known results and the second result is best possible for the case p = q = 1. [ABSTRACT FROM AUTHOR]

Objectives: In this paper, we assess compliance of cigarette packaging with policies in 9 countries that ban misleading descriptors, assess the presence of other packaging design elements that are misleading to consumers, and identify policy loopholes. Methods: Cigarette packages were systematically collected in 9 countries between 2015 and 2017 - Bangladesh, Brazil, China, India, Indonesia, Philippines, Russia, Thailand, and Vietnam. The packs were coded for banned misleading descriptors and other misleading packaging design elements. Descriptive analyses were conducted. Results: Overall, compliance with explicitly banned misleading descriptors is high across countries, with the exception of packs from Indonesia, where compliance is moderate. However, the use of other misleading packaging elements such as alternative descriptors (soft, smooth, mellow), select color descriptors (blue, gold, silver), and a slim pack shape are still widely used in packaging design across the 9 countries examined. Conclusions: Policies that include loopholes or allow for other misleading packaging design elements could weaken the impact of recommended misleading packaging and labeling regulations as laid out in the WHO Framework Convention on Tobacco Control. Stronger provisions are needed and countries should consider comprehensive plain packaging. [ABSTRACT FROM AUTHOR]

Multi-organ segmentation is a challenging task due to the label imbalance and structural differences between different organs. In this work, we propose an efficient cascaded V-Net model to improve the performance of multi-organ segmentation by establishing dense Block Level Skip Connections (BLSC) across cascaded V-Net. Our model can take full advantage of features from the first stage network and make the cascaded structure more efficient. We also combine stacked small and large kernels with an inception-like structure to help our model to learn more patterns, which produces superior results for multi-organ segmentation. In addition, some small organs are commonly occluded by large organs and have unclear boundaries with other surrounding tissues, which makes them hard to be segmented. We therefore first locate the small organs through a multi-class network and crop them randomly with the surrounding region, then segment them with a single-class network. We evaluated our model on SegTHOR 2019 challenge unseen testing set and Multi-Atlas Labeling Beyond the Cranial Vault challenge validation set. Our model has achieved an average dice score gain of 1.62 percents and 3.90 percents compared to traditional cascaded networks on these two datasets, respectively. For hard-to-segment small organs, such as the esophagus in SegTHOR 2019 challenge, our technique has achieved a gain of 5.63 percents on dice score, and four organs in Multi-Atlas Labeling Beyond the Cranial Vault challenge have achieved a gain of 5.27 percents on average dice score. [ABSTRACT FROM AUTHOR]

Video segmentation is a task of partitioning pixels that exhibit homogeneous appearance and motion into coherent spatial–temporal groups, which is still challenging for aerial applications. In this paper, a principled combination of superpixel labeling priors and Markov random field (S-MRF) is proposed for aerial video segmentation. The proposed approach has several contributions: 1) we develop a metadata-based global projection model with coordinate transformation to estimate motion information between frames; 2) the superpixel labeling priors from previous frames are incorporated into the segmentation of the current frame, leading to a highly efficient probabilistic label propagation algorithm; and 3) we perform an MRF optimization on the initial segments with propagated labeling priors to improve the temporal coherency. In addition, a new video dataset is collected and will be made publicly available to evaluate the performance of aerial video segmentation algorithms. The experimental results show that the proposed approach outperforms the state-of-the-art video segmentation methods. [ABSTRACT FROM AUTHOR]

The usage information at the entrance of the dictionaries, the body text and the additional information at the end contain important information about the dictionary construction and usage. In this study, the problems of labeling and the way grammatical terms are included in general dictionaries and the labeling of grammatical terms are discussed. This article is a descriptive study, and the method of scanning, compilation, comparison and document analysis was used in our article. The differences that occur in the labeling of grammatical terms in the context of the general and special dictionary are pointed out. This study has been prepared using scanning, compilation and document analysis methods. The aim of the study is to determine whether the labeling process, which facilitates the users' job, guides them and points to the correct use of words, varies according to the attitude of each dictionary writer in lexicographic query. The most frequently used by Turkish dictionary users: TDK Turkish Dictionary (TDKTS), Example Büyük Turkish Dictionary (MBTS), Doğan Büyük Turkish Dictionary (DBTS), Ötüken Turkish Dictionary (ÖTS), Dil Derneği Turkish Dictionary (DD-TS) to identify the problems of labeling and grammatical terms in the dictionary and coding and to offer solutions. [ABSTRACT FROM AUTHOR]

Aims: Information provided on glass labels may be an effective method to reduce alcohol consumption. The aim of this study was to assess the impact of glass labels conveying unit information and a health warning in reducing ad libitum alcohol consumption. Methods: A cluster‐randomized experimental study was conducted to measure the efficacy of a labeled glass in reducing alcohol consumption in a semi naturalistic bar laboratory setting, in a sample of 81 pairs (n = 162) of UK young adult drinkers. Pairs were randomized to receive two 340‐ml glasses of beer or wine: labeled or plain (control). Alcohol consumption was assessed in an ad libitum drinking period, and urge to drink was measured at baseline and postdrinking period. Focus groups (n = 2) were conducted, and thematic analysis was used to gain an insight into the acceptability and the perceived effectiveness of the glasses. Results: Mean unit consumption was 1.62 (SD ± 0.83) units in the labeled glass condition and 1.69 (SD ± 0.82) units in the non labeled glass condition. There were no significant effects of the labeled glasses on ad libitum alcohol consumption (95% CI −0.25 to 0.37, p = 0.35), despite participants (85%) noticing the information. Qualitative analysis of focus groups indicated that although participants perceived the glasses as a useful tool for increasing awareness of units and guidelines, they were viewed as limited in their potential to change drinking behavior due to the unappealing design of the glass and a view that unit guidelines were not relevant to drinking patterns or contexts. Conclusions: Labeled glasses did not change alcohol consumption in the current study, potentially due to ineffectiveness of this type of message in a young adult population. The information on the glasses was attended to, highlighting that glasses could be a feasible tool for providing information. [ABSTRACT FROM AUTHOR]

Negative symptoms in schizophrenia show striking similarities to some depressive symptoms. Different terms are often used for these phenomenologically similar symptoms depending on the context, such as avolition (most often used in the context of schizophrenia) and lack of drive (most often used in the context of depression). To test whether clinicians assign different symptom labels to the same clinical picture based on the cued diagnosis, 98 clinical psychologists and psychiatrists were presented with two case studies that were randomly framed as characterizing an individual with either depression or schizophrenia. An interaction of the symptom label group selected by the clinicians with the framing condition confirmed our hypothesis: despite identical content, clinicians favored different clinical terms depending on the cued diagnosis (p = .025, η2partial = 0.054). This result was supported by the suspected diagnosis suggested by the clinicians; numerically, they more often confirmed than rejected the cued diagnosis. The present study is in line with earlier findings indicative of strong overlap pertaining to the phenomenology of negative symptoms in schizophrenia and depressive symptoms that suggest that clinical terminology should be streamlined. The hypothesis that core symptoms of both syndromes tap largely the same construct should be further pursued. If true, the concept of negative symptoms, currently used to describe schizophrenia alone, should be opened up for describing symptoms in other disorders. This could help to gain a deeper understanding of the transdiagnostic appearances of the negative syndrome. [ABSTRACT FROM AUTHOR]

The present study examined the effect of offense-based labels on community members' willingness to volunteer with people convicted for varying offenses and any priming effect of labeling language. Participants (N = 310) were randomly assigned to a label condition or a neutral condition and completed an anonymous online survey about their willingness to volunteer with different groups. The labeling condition utilized labels (e.g., "sex offenders," "murderers"), whereas the control condition utilized neutral descriptors (e.g., "people who have committed crimes of a sexual nature"). Overall, findings supported the hypothesis that offense-based labels were associated with less willingness to volunteer, with findings most pronounced for the "sex offender" and "child sex offender" labels. Participants in the labeling condition showed greater voluntary use of labels compared with neutral language and were more likely to use labels compared with participants in the neutral condition. Implications for influencing public opinion are discussed. [ABSTRACT FROM AUTHOR]

The labeling of all ingredients contained in consumer products has been requested by consumers concerned regarding their safety. Consequently, regulatory agencies have set guidelines for industries on how to provide safety information regarding the ingredients in their products. However, discordant opinions were raised from stakeholders, resulting in the formation of a risk communication forum among industries, regulatory agencies, consumer groups, and academia. There are several methods that might be utilized to provide ingredient information to consumers: (1) listing all ingredients on the label of products, (2) providing major ingredients on the label of products, (3) presenting all ingredients on the websites of each manufacturer, and (4) listing major ingredients on the label of products and the remainder of ingredients (not on the label) on the websites. Each method might have its own advantages and disadvantages with respect to providing the information regarding the names of the ingredients used in consumer products to the consumers. A continuous risk communication forum might be an effective tool to facilitate an improved understanding of chemical information, toxicological science, regulatory guidelines, labeling methods, and consumers' concern. This study suggests that risk communication efforts may be helpful and a good opportunity for stakeholders to exchange opinions and reach a harmonious conclusion on labeling of consumer products ingredients. [ABSTRACT FROM AUTHOR]

We present a novel algorithm for semantic segmentation and labeling of 3D point clouds of indoor scenes, where objects in point clouds can have significant variations and complex configurations. Effective segmentation methods decomposing point clouds into semantically meaningful pieces are highly desirable for object recognition, scene understanding, scene modeling, etc. However, existing segmentation methods based on low-level geometry tend to either under-segment or over-segment point clouds. Our method takes a fundamentally different approach, where semantic segmentation is achieved along with labeling. To cope with substantial shape variation for objects in the same category, we first segment point clouds into surface patches and use unsupervised clustering to group patches in the training set into clusters, providing an intermediate representation for effectively learning patch relationships. During testing, we propose a novel patch segmentation and classification framework with multiscale processing, where the local segmentation level is automatically determined by exploiting the learned cluster based contextual information. Our method thus produces robust patch segmentation and semantic labeling results, avoiding parameter sensitivity. We further learn object-cluster relationships from the training set, and produce semantically meaningful object level segmentation. Our method outperforms state-of-the-art methods on several representative point cloud datasets, including S3DIS, SceneNN, Cornell RGB-D and ETH. [ABSTRACT FROM AUTHOR]

For the fault classification of chemical industries, the typical Fisher discriminant analysis (FDA) model requires that all the training samples should be correctly labeled. Actually, training samples tend to be polluted by mislabeled samples for some unavoidable reasons, and thus the performance of the FDA may be severely affected. However, in the chemical process monitoring community, there is little attention to fault classification with label-noise in training samples. To settle this open issue, a manifold-preserving sparse graph (MPSG)-based ensemble FDA model is initially proposed in this paper. First, under the condition of maintaining the underlying manifold structure of training samples, an MPSG is utilized to filter some mislabeled samples. Second, to improve model robustness to the left mislabeled samples, Bagging based on FDA is used to construct several subclassifiers, which are combined to form a robust classifier. Experiments on the benchmark Tennessee Eastman process and a real industrial air separation unit demonstrate the effectiveness and the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

In contrast to dominant views that the labeling algorithm (LA) detects (i) only the structurally highest copy of a moved object, or (ii) detects all copies, we propose and defend a third option: (iii) all copies are invisible to LA. The most immediate consequence of this is that objects formed by Internal Merge cannot serve as labels. We relate this proposal to a particular reinterpretation of LA theory such that LA constructs only categorial labels, barring the construction of and <φ, φ> configurations. We then propose an interface condition, Equal Embedding (EE), under which agreeing features must be equally as embedded in order for interpretation to be licensed. We argue that EE appears to fall out of minimal search requirements. We then propose a principled distinction between Agree and LA, based on their sensitivity to copies and interface relations: Both Agree and LA involve minimal search (Probe-Goal for Agree; categorial feature-detection for LA); however, copies are invisible to LA but not to Agree, and LA involves a CI relation (category-specific interpretation) whereas Agree involves an SM relation (the morpho-phonological process of feature-valuation). [ABSTRACT FROM AUTHOR]

Image datasets play a pivotal role in advancing computer vision and multimedia research. However, most of the datasets are created by extensive human effort and are extremely expensive to scale-up. To address these issues, several automatic and semi-automatic approaches have been proposed for creating datasets by refining web images. However, these approaches either include significant redundant images in the dataset or fail to provide a diverse enough set to train a robust classifier. Ideally, a representative subset should be both semantically and visually diverse so as to provide the maximum amount of information under the current budget. Most current approaches are entirely based on the analysis of visual features, which may not correlate well with image semantics, and hence, collected images may not be sufficient to give a detailed understanding of a category. In this paper, we propose a system for creating diverse image dataset collections from the web with limited manual labeling effort. It is based upon a semi-supervised sparse coding framework that employs a joint visual-semantic space to simultaneously utilize both the images and associated textual information from the web for dataset construction. In addition, the proposed system is online and is capable of collecting more discriminative images continuously as new data becomes available, which is also suitable for enriching the existing datasets. The experiments demonstrate that our system can create and enrich datasets with limited manual labeling, with better cross-dataset generalization capability and diversity compared to the state-of-the-art datasets. [ABSTRACT FROM AUTHOR]

This article addresses the combinatorial explosion problem for diagnosability analysis of discrete event systems (DESs) using bounded labeled Petri nets (LPNs). Some reduction rules are given to simplify a priori the LPN model before analyzing the diagnosability. When the conditions of these reduction rules are fulfilled, some regular unobservable transitions and some specific observable transitions are suppressed. It is proven that these rules preserve the diagnosability property of the LPN system. By using reduction rules, the memory cost for diagnosability analysis is reduced. Note to Practitioners—Fault diagnosis based on discrete-event systems has been successfully used in several fields of applications, such as transportation, telecommunication, manufacturing, and so on. At the design stage of a system, the diagnosability needs to be held, which refers to the ability to determine if the system can detect the fault after its occurrence. Therefore, the diagnosability is a critical property due to its importance in terms of safety of an industrial system. In order to allow an industrial exploitation of diagnosability analysis, this article proposes reduction rules, which make it possible to reduce a priori the size of the LPN model of an industrial system. [ABSTRACT FROM AUTHOR]

The objective of this work is automatic labelling of characters in TV video and movies, given weak supervisory information provided by an aligned transcript. We make five contributions: (i) a new strategy for obtaining stronger supervisory information from aligned transcripts; (ii) an explicit model for classifying background characters, based on their face-tracks; (iii) employing new ConvNet based face features, and (iv) a novel approach for labelling all face tracks jointly using linear programming. Each of these contributions delivers a boost in performance, and we demonstrate this on standard benchmarks using tracks provided by authors of prior work. As a fifth contribution, we also investigate the generalisation and strength of the features and classifiers by applying them “in the raw” on new video material where no supervisory information is used. In particular, to provide high quality tracks on those material, we propose efficient track classifiers to remove false positive tracks by the face tracker. Overall we achieve a dramatic improvement over the state of the art on both TV series and film datasets, and almost saturate performance on some benchmarks. [ABSTRACT FROM AUTHOR]

Honey, a complex product made by bees, represents a rich natural source of simple carbohydrates. The characteristic flavor, health benefits, and high economic value of honey depend on its origin and production methods. Therefore, labels have been developed to recognize honey quality linked to origin or production method, in order to inform consumers about these specific determinants. This review aims to deepen the knowledge of honey quality linked to the origin and to present an overview of the literature concerning the methods to determine its authenticity. Legislative aspects and the general parameters required for honey generic quality are presented, as well as honey quality control methods with emphasis on the profiling determination. [ABSTRACT FROM AUTHOR]

Using focus groups, the research analyses the mental and social processes through which consumers form perceptions and opinions about unfamiliar technologies and the derived products, taking the perception of nanotechnology and nano-products, GM and GM products as example. Our findings suggest that limited understanding of the technological principles and lack of (visible) products prevent the formation of experience-based attitudes and behavioral intentions. In this context, consumers interpret and assess cognitive interventions such as product labels or product information, as well as the trustworthiness of unfamiliar information sources, based on heuristic clues, association, mutual reassurance and previous attitudes. The established determinants of technology risk perception (e.g. knowledge, social norms, perceived risks and benefits and controllability) were the subject of constant deliberation and negotiation among participants. Consequently, the perception of risk and technology communication interventions might vary greatly across different locations and segments of the public, complicating risk communication and trust-building. [ABSTRACT FROM AUTHOR]

Activity recognition is a challenging problem with many practical applications. In addition to the visual features, recent approaches have benefited from the use of context, e.g., inter-relationships among the activities and objects. However, these approaches require data to be labeled, entirely available beforehand, and not designed to be updated continuously, which make them unsuitable for surveillance applications. In contrast, we propose a continuous-learning framework for context-aware activity recognition from unlabeled video, which has two distinct advantages over existing methods. First, it employs a novel active-learning technique that not only exploits the informativeness of the individual activities but also utilizes their contextual information during query selection; this leads to significant reduction in expensive manual annotation effort. Second, the learned models can be adapted online as more data is available. We formulate a conditional random field model that encodes the context and devise an information-theoretic approach that utilizes entropy and mutual information of the nodes to compute the set of most informative queries, which are labeled by a human. These labels are combined with graphical inference techniques for incremental updates. We provide a theoretical formulation of the active learning framework with an analytic solution. Experiments on six challenging datasets demonstrate that our framework achieves superior performance with significantly less manual labeling. [ABSTRACT FROM AUTHOR]

This paper examines very early child grammars from the minimalist perspective. It discusses the well-known erroneous strings very young children produce such as Root Infinitives in English and their Japanese counterparts, preverbal object sentences in English, and sentences without Case markers in Japanese. The main question to be addressed is whether those sentences children produce are labeled, and if so, how the labeling takes place. Assuming that - feature agreement and suffixal Case markers play crucial roles for labeling in English and Japanese respectively (Chomsky 2013; Saito 2016), I consider two possibilities. One is that children are equipped with those almost from the outset although they are not phonetically realized. This means that even the erroneous strings children produced are properly labeled. The other is that those strings are not labeled in the adult way and that children at the relevant stage are still in the process of figuring out how the {XP, YP} structure is labeled in their respective languages. I argue that the latter is a viable possibility, given the parameterization in the labeling mechanism, and receives support from the child data as well. This conclusion implies that a main part of the acquisition of syntax is for a child to discover how her/his target language labels the {XP, YP} structure. [ABSTRACT FROM AUTHOR]

The primary goal of the present paper is to argue for the hypothesis that labeling is required for linearization, which is called Labeling for Linearization (LfL). To achieve this goal, it is first argued that labels are not necessary for semantic interpretation. It is then proposed that labels are necessary for linearization at the PF-interface in that they serve as a device to encode structural asymmetries that are employed to determine precedence relations, which are asymmetric as well. It is also shown that LfL can remove several problems of the original labeling framework. Building on the idea that Spell-Out applies to the whole phase but not its subpart, it is illustrated that the LfL-based analysis can solve the problem concerning the variable ways of applying Spell-Out, which arises in the standard phase theory. Extending the LfL-based framework to Japanese, a novel analysis of particle-stranding ellipsis is also proposed. Incorporating some insights of recent approaches that particle-stranding ellipsis arises through a PF-deletion process, it is shown that the proposed analysis based on LfL offers a theoretically more suitable characterization of the PF-deletion process. In this way, the present article contributes to not only sharpening the core theoretical notions regarding structure building and linearization in terms of labeling but also deepening our understanding of the structure of Japanese. [ABSTRACT FROM AUTHOR]

Unlike other data collection modes, the effect of labeling rating scales on reliability and validity, as relevant aspects of measurement quality, has seldom been addressed in online surveys. In this study, verbal and numeric rating scales were compared in split-ballot online survey experiments. In the first experiment, respondents' cognitive processes were observed by means of eye tracking, that is, determining the respondent's fixations in different areas of the screen. In the remaining experiments, data for reliability and validity analysis were collected from a German adult sample. The results show that respondents needed more fixations and more time to endorse a category when a rating scale had numeric labels. Cross-sectional reliability was lower and some hypotheses with respect to the criterion validity could not be supported when numeric rating scales were used. In conclusion, theoretical considerations and the empirical results contradict the current broad usage of numeric scales in online surveys. [ABSTRACT FROM AUTHOR]

This study examines innovators' efforts to conceptualize and communicate their novel work through categorization. Specifically, we view category formation as a controversial process of meaning making, which we theorize through the concept of "politics of meaning" and operationalize through a social semiotics approach. By analyzing the labeling controversies underlying a new culinary style publicized as "molecular gastronomy", we find that innovators' efforts at categorization unfold along four consecutive stages: experimenting with a new style, communicating the new style, contesting the dominant label, and legitimating the category meaning. Our study suggests that a new category's dominant label can substantially deviate from the innovators' intended denotations, yet nonetheless bring that category forward by triggering public negotiations around its meaning, which lead to categorical deepening and legitimation. By putting forward a "politics of meaning" view on categorizing innovation, this work advances our understanding of the connection between labeling and category formation in the context of innovation. [ABSTRACT FROM AUTHOR]

In this work, new multi-cationic aminopyrene-based labeling tags were designed and synthesized for oligosaccharide analysis by capillary electrophoresis-mass spectrometry (CE-MS). The starting compound, 8-aminopyrene-1,3,6-trisulfonic acid trisodium salt, was modified in order to form a sulfonamide derivative having three tertiary amines in the label structure. The sulfonamide derivative was further methylated to generate three permanently charged quaternary ammonium moieties on the label. The synthesized labels were characterized by NMR, IR, UV/Vis, fluorescence spectroscopy and mass spectrometry. Furthermore, the labels were applied for maltooligosaccharide standards as well as N -linked glycans labeling via reductive amination and followed by CE-MS analysis. The CE-MS analysis of maltooligosaccharides labeled by these newly synthesized labels provided the sub-micromolar limit of detection based on the extracted ion electropherogram signals. Image 1 • New multi-cationic aminopyrene-based labeling tags were designed and synthesized. • The synthesized labels were characterized by NMR, IR, UV/Vis, fluorescence spectroscopy, and mass spectrometry. • The labels were successfully applied for maltooligosaccharides and N -linked glycans labeling followed by CE-MS analysis. [ABSTRACT FROM AUTHOR]

We have developed a nuclear and fluorescence labeling strategy for nanocrystalline cellulose (CNC), an emerging biomaterial with versatile chemistry and facile preparation from renewable sources. We modified CNC through 1,1′-carbonyldiimidazole (CDI) activation with radiometal chelators desferrioxamine B and 1,4,7-triazacyclononane-1,4,7-triacetic acid (NOTA), allowing for the labeling with zirconium-89 (t ½ = 78.41 h) and copper-64 (t ½ = 12.70 h), respectively, for non-invasive positron emission tomography (PET) imaging. The far-red fluorescent dye Cy5 was added for ex vivo optical imaging, microscopy and flow cytometry. The multimodal CNC were evaluated in the syngeneic orthotopic 4T1 tumor model of human stage IV breast cancer. Modified CNC exhibited low cytotoxicity in RAW 264.7 macrophages over 96 h, and high radiolabel stability in vitro. After systemic administration, radiolabeled CNC were rapidly sequestered to the organs of the reticulo-endothelial system (RES), indicating immune recognition and no passive tumor targeting by the enhanced permeability and retention (EPR) effect. Modification with NOTA was a more favorable strategy in terms of radiolabeling yield, specific radioactivity, and both the radiolabel and dispersion stability in physiological conditions. Flow cytometry analysis of Cy5-positive immune cells from the spleen and tumor corroborated the uptake of CNC to phagocytic cells. Future studies on the in vivo behavior of CNC should be concentrated on improving the nanomaterial stability and circulation half-life under physiological conditions and optimizing further the labeling yields for the multimodality imaging strategy presented. Our studies constitute one of the first accounts of a multimodality nuclear and fluorescent probe for the evaluation of CNC biodistribution in vivo and outline the pitfalls in radiometal labeling strategies for future evaluation of targeted CNC-based drug delivery systems. Quantitative and sensitive molecular imaging methods provide information on the structure–activity relationships of the nanomaterial and guide the translation from in vitro models to clinically relevant animal models. [ABSTRACT FROM AUTHOR]

Marking fish with fluorochrome dyes has the potential to produce detectable marks in otoliths, scales, fin rays, fin spines, and other bony structures based on differential staining of chemical dyes, and it is a widely used technique for mass-marking fish that included both teleosts and elasmobranchs. This technique, which has relatively economic advantages and simple marking process, can be applied to fish of all ages, and these fluorescent marks can be retained for several months and even years. Up to now, >200 fish species have been successfully marked by this marking technique. Fluorescent labeling is a practical and available marking technique for large-scale marking hatchery-reared fish. The methodology of fluorescent labeling is reviewed, including fluorochrome labeling dyes, application method, detection, and evaluation strategies. Aspects of practical applications, limitations, and supervision of fluorescent labeling are also discussed. [ABSTRACT FROM AUTHOR]