Journal articles: 'Label Shift'

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Chow, C. W., and H. K. Tsang. "Orthogonal label switching using polarization-shift-keying payload and amplitude-shift-keying label." IEEE Photonics Technology Letters 17, no. 11 (November 2005): 2475–77. http://dx.doi.org/10.1109/lpt.2005.857590.

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Wetterau, Lukas, Claas Abert, Dieter Suess, Manfred Albrecht, and Bernd Witzigmann. "Micromagnetic Simulations of Submicron Vortex Structures for the Detection of Superparamagnetic Labels." Sensors 20, no. 20 (October 15, 2020): 5819. http://dx.doi.org/10.3390/s20205819.

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We present a numerical investigation on the detection of superparamagnetic labels using a giant magnetoresistance (GMR) vortex structure. For this purpose, the Landau–Lifshitz–Gilbert equation was solved numerically applying an external z-field for the activation of the superparamagnetic label. Initially, the free layer’s magnetization change due to the stray field of the label is simulated. The electric response of the GMR sensor is calculated by applying a self-consistent spin-diffusion model to the precomputed magnetization configurations. It is shown that the soft-magnetic free layer reacts on the stray field of the label by shifting the magnetic vortex orthogonally to the shift direction of the label. As a consequence, the electric potential of the GMR sensor changes significantly for label shifts parallel or antiparallel to the pinning of the fixed layer. Depending on the label size and its distance to the sensor, the GMR sensor responds, changing the electric potential from 26.6 mV to 28.3 mV.

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Yusheng, Cheng, Zhao Dawei, Zhan Wenfa, and Wang Yibin. "Multi-label learning of non-equilibrium labels completion with mean shift." Neurocomputing 321 (December 2018): 92–102. http://dx.doi.org/10.1016/j.neucom.2018.09.033.

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Rezaei, Ashkan, Anqi Liu, Omid Memarrast, and Brian D. Ziebart. "Robust Fairness Under Covariate Shift." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (May 18, 2021): 9419–27. http://dx.doi.org/10.1609/aaai.v35i11.17135.

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Making predictions that are fair with regard to protected attributes (race, gender, age, etc.) has become an important requirement for classification algorithms. Existing techniques derive a fair model from sampled labeled data relying on the assumption that training and testing data are identically and independently drawn (iid) from the same distribution. In practice, distribution shift can and does occur between training and testing datasets as the characteristics of individuals interacting with the machine learning system change. We investigate fairness under covariate shift, a relaxation of the iid assumption in which the inputs or covariates change while the conditional label distribution remains the same. We seek fair decisions under these assumptions on target data with unknown labels. We propose an approach that obtains the predictor that is robust to the worst-case testing performance while satisfying target fairness requirements and matching statistical properties of the source data. We demonstrate the benefits of our approach on benchmark prediction tasks.

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Zhang, Huayi, Lei Cao, Samuel Madden, and Elke Rundensteiner. "LANCET." Proceedings of the VLDB Endowment 14, no. 11 (July 2021): 2154–66. http://dx.doi.org/10.14778/3476249.3476269.

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Cutting-edge machine learning techniques often require millions of labeled data objects to train a robust model. Because relying on humans to supply such a huge number of labels is rarely practical, automated methods for label generation are needed. Unfortunately, critical challenges in auto-labeling remain unsolved, including the following research questions: (1) which objects to ask humans to label, (2) how to automatically propagate labels to other objects, and (3) when to stop labeling. These three questions are not only each challenging in their own right, but they also correspond to tightly interdependent problems. Yet existing techniques provide at best isolated solutions to a subset of these challenges. In this work, we propose the first approach, called LANCET, that successfully addresses all three challenges in an integrated framework. LANCET is based on a theoretical foundation characterizing the properties that the labeled dataset must satisfy to train an effective prediction model, namely the Covariate-shift and the Continuity conditions. First, guided by the Covariate-shift condition, LANCET maps raw input data into a semantic feature space, where an unlabeled object is expected to share the same label with its near-by labeled neighbor. Next, guided by the Continuity condition, LANCET selects objects for labeling, aiming to ensure that unlabeled objects always have some sufficiently close labeled neighbors. These two strategies jointly maximize the accuracy of the automatically produced labels and the prediction accuracy of the machine learning models trained on these labels. Lastly, LANCET uses a distribution matching network to verify whether both the Covariate-shift and Continuity conditions hold, in which case it would be safe to terminate the labeling process. Our experiments on diverse public data sets demonstrate that LANCET consistently outperforms the state-of-the-art methods from Snuba to GOGGLES and other baselines by a large margin - up to 30 percentage points increase in accuracy.

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Chen, Hongwei, Minghua Chen, Ciyuan Qiu, and Shizhong Xie. "Orthogonal polarization shift keying label rewriting method in an all-optical label switching network." Optics Letters 32, no. 9 (April 3, 2007): 1050. http://dx.doi.org/10.1364/ol.32.001050.

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Pikramenos, George, Evaggelos Spyrou, and Stavros J. Perantonis. "Extending Partial Domain Adaptation Algorithms to the Open-Set Setting." Applied Sciences 12, no. 19 (October 6, 2022): 10052. http://dx.doi.org/10.3390/app121910052.

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Partial domain adaptation (PDA) is a framework for mitigating the covariate shift problem when target labels are contained in source labels. For this task, adversarial neural network (ANN) methods proposed in the literature have been proven to be flexible and effective. In this work, we adapt such methods to tackle the more general problem of open-set domain adaptation (OSDA), which further allows the existence of target instances with labels outside the source labels. The aim in OSDA is to mitigate the covariate shift problem and to identify target instances with labels outside the source label space. We show that the effectiveness of ANN methods utilized in the PDA setting is hindered by outlier target instances, and we propose an adaptation for effective OSDA.

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Chen, Jin, Xinxiao Wu, Yao Hu, and Jiebo Luo. "Spatial-temporal Causal Inference for Partial Image-to-video Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (May 18, 2021): 1027–35. http://dx.doi.org/10.1609/aaai.v35i2.16187.

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Image-to-video adaptation leverages off-the-shelf learned models in labeled images to help classification in unlabeled videos, thus alleviating the high computation overhead of training a video classifier from scratch. This task is very challenging since there exist two types of domain shifts between images and videos: 1) spatial domain shift caused by static appearance variance between images and video frames, and 2) temporal domain shift caused by the absence of dynamic motion in images. Moreover, for different video classes, these two domain shifts have different effects on the domain gap and should not be treated equally during adaptation. In this paper, we propose a spatial-temporal causal inference framework for image-to-video adaptation. We first construct a spatial-temporal causal graph to infer the effects of the spatial and temporal domain shifts by performing counterfactual causality. We then learn causality-guided bidirectional heterogeneous mappings between images and videos to adaptively reduce the two domain shifts. Moreover, to relax the assumption that the label spaces of the image and video domains are the same by the existing methods, we incorporate class-wise alignment into the learning of image-video mappings to perform partial image-to-video adaptation where the image label space subsumes the video label space. Extensive experiments on several video datasets have validated the effectiveness of our proposed method.

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Smith, Lisa, Kimberly Arcand, Jeffrey Smith, Randall Smith, Jay Bookbinder, and Megan Watzke. "Examining perceptions of astronomy images across mobile platforms." Journal of Science Communication 13, no. 02 (March 25, 2014): A01. http://dx.doi.org/10.22323/2.13020201.

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Modern society has led many people to become consumers of data unlike previous generations. How this shift in the way information is communicated and received — including in areas of science — and affects perception and comprehension is still an open question. This study examined one aspect of this digital age: perceptions of astronomical images and their labels, on mobile platforms. Participants were n = 2183 respondents to an online survey, and two focus groups (n = 12 astrophysicists; n = 11 lay public). Online participants were randomly assigned to 1 of 12 images, and compared two label formats. Focus groups compared mobile devices and label formats. Results indicated that the size and quality of the images on the mobile devices affected label comprehension and engagement. The question label format was significantly preferred to the fun fact. Results are discussed in terms of effective science communication using technology.

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Murza, Kimberly A., and Barbara J. Ehren. "Considering the Language Disorder Label Debate From a School Speech-Language Pathology Lens." Perspectives of the ASHA Special Interest Groups 5, no. 1 (February 21, 2020): 47–54. http://dx.doi.org/10.1044/2019_persp-19-00077.

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Purpose The purpose of this article is to situate the recent language disorder label debate within a school's perspective. As described in two recent The ASHA Leader articles, there is international momentum to change specific language impairment to developmental language disorder . Proponents of this change cite increased public awareness and research funding as part of the rationale. However, it is unclear whether this label debate is worthwhile or even practical for the school-based speech-language pathologist (SLP). A discussion of the benefits and challenges to a shift in language disorder labels is provided. Conclusions Although there are important arguments for consistency in labeling childhood language disorder, the reality of a label change in U.S. schools is hard to imagine. School-based services are driven by eligibility through the Individuals with Disabilities Education Act, which has its own set of labels. There are myriad reasons why advocating for the developmental language disorder label may not be the best use of SLPs' time, perhaps the most important of which is that school SLPs have other urgent priorities.

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Dabia, Said, Stefan Ropke, and Tom van Woensel. "Cover Inequalities for a Vehicle Routing Problem with Time Windows and Shifts." Transportation Science 53, no. 5 (September 2019): 1354–71. http://dx.doi.org/10.1287/trsc.2018.0885.

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This paper introduces the vehicle routing problem with time windows and shifts (VRPTWS). At the depot, several shifts with nonoverlapping operating periods are available to load the planned trucks. Each shift has a limited loading capacity. We solve the VRPTWS exactly by a branch-and-cut-and-price algorithm. The master problem is a set partitioning with an additional constraint for every shift. Each constraint requires the total quantity loaded in a shift to be less than its loading capacity. For every shift, a pricing subproblem is solved by a label-setting algorithm. Shift capacity constraints define knapsack inequalities; hence we use valid inequalities inspired from knapsack inequalities to strengthen the linear programming relaxation of the master problem when solved by column generation. In particular, we use a family of tailored robust cover inequalities and a family of new nonrobust cover inequalities. Numerical results show that nonrobust cover inequalities significantly improve the algorithm.

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Litrico, Mattia, Sebastiano Battiato, Sotirios A. Tsaftaris, and Mario Valerio Giuffrida. "Semi-Supervised Domain Adaptation for Holistic Counting under Label Gap." Journal of Imaging 7, no. 10 (September 29, 2021): 198. http://dx.doi.org/10.3390/jimaging7100198.

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This paper proposes a novel approach for semi-supervised domain adaptation for holistic regression tasks, where a DNN predicts a continuous value y∈R given an input image x. The current literature generally lacks specific domain adaptation approaches for this task, as most of them mostly focus on classification. In the context of holistic regression, most of the real-world datasets not only exhibit a covariate (or domain) shift, but also a label gap—the target dataset may contain labels not included in the source dataset (and vice versa). We propose an approach tackling both covariate and label gap in a unified training framework. Specifically, a Generative Adversarial Network (GAN) is used to reduce covariate shift, and label gap is mitigated via label normalisation. To avoid overfitting, we propose a stopping criterion that simultaneously takes advantage of the Maximum Mean Discrepancy and the GAN Global Optimality condition. To restore the original label range—that was previously normalised—a handful of annotated images from the target domain are used. Our experimental results, run on 3 different datasets, demonstrate that our approach drastically outperforms the state-of-the-art across the board. Specifically, for the cell counting problem, the mean squared error (MSE) is reduced from 759 to 5.62; in the case of the pedestrian dataset, our approach lowered the MSE from 131 to 1.47. For the last experimental setup, we borrowed a task from plant biology, i.e., counting the number of leaves in a plant, and we ran two series of experiments, showing the MSE is reduced from 2.36 to 0.88 (intra-species), and from 1.48 to 0.6 (inter-species).

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Эпов, И. В. "ВЛИЯНИЕ КОНСТРУКТИВНЫХ ЭЛЕМЕНТОВ И МАТЕРИАЛОВ ОБЪЕМНОЙ УЛЬТРАВЫСОКОЧАСТОТНОЙ МЕТКИ, РАЗРАБОТАННОЙ ПО ПРИНЦИПУ ЗАМКНУТОЙ ПОЛОСКОВОЙ ЛИНИИ ПЕРЕДАЧИ, НА ЕЕ РЕЗОНАНСНУЮ ЧАСТОТУ И ДАЛЬНОСТЬ СЧИТЫВАНИЯ ПРИ КОНТАКТЕ С МЕТАЛЛАМИ." NANOINDUSTRY Russia 96, no. 3s (May 16, 2020): 764–68. http://dx.doi.org/10.22184/1993-8578.2020.13.3s.764.768.

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Данная работа посвящена исследованию RFID-систем. Была разработана математическая модель, связывающая конструктивные элементы объемной 3D-метки с ее резонансной частотой, далее бьши спроектированы на ее основе реальные объемные метки, работающие на частоте 867 МГц. Также было дано качественное объяснение сдвига резонансной частоты метки при ее контакте с металлическими поверхностями. This work is devoted to the study of RFID-systems. It presents a mathematical model linking the structural elements of the 3D volumetric label with its resonance frequency, as well as real volumetric labels operating at a frequency of 867 MHz on its basis. Besides, it gives a qualitative explanation of the shift of the resonance frequency of the label in contact with metal surfaces.

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Zhou, Fan, Xiaozhe Meng, Yuxin Feng, and Zhuo Su. "SNPD: Semi-Supervised Neural Process Dehazing Network with Asymmetry Pseudo Labels." Symmetry 14, no. 4 (April 13, 2022): 806. http://dx.doi.org/10.3390/sym14040806.

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Haze can cause a significant reduction in the contrast and brightness of images. CNN-based methods have achieved benign performance on synthetic data. However, they show weak generalization performance on real data because they are only trained on fully labeled data, ignoring the role of natural data in the network. That is, there exists distribution shift. In addition to using little real data for training image dehazing networks in the literature, few studies have designed losses to constrain the intermediate latent space and the output simultaneously. This paper presents a semi-supervised neural process dehazing network with asymmetry pseudo labels. First, we use labeled data to train a backbone network and save intermediate latent features and parameters. Then, in the latent space, the neural process maps the latent features of real data to the latent space of synthetic data to generate one pseudo label. One neural process loss is proposed here. For situations where the image may be darker after dehazing, another pseudo label is created, and one new loss is used to guide the dehazing result at the output end. We combine the two pseudo labels with designed losses to suppress the distribution shift and guide better dehazing results. Finally, the artificial and hazy natural images are tested experimentally to demonstrate the method’s effectiveness.

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Zhou, Fan, Brahim Chaib-draa, and Boyu Wang. "Multi-task Learning by Leveraging the Semantic Information." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 11088–96. http://dx.doi.org/10.1609/aaai.v35i12.17323.

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One crucial objective of multi-task learning is to align distributions across tasks so that the information between them can be transferred and shared. However, existing approaches only focused on matching the marginal feature distribution while ignoring the semantic information, which may hinder the learning performance. To address this issue, we propose to leverage the label information in multi-task learning by exploring the semantic conditional relations among tasks. We first theoretically analyze the generalization bound of multi-task learning based on the notion of Jensen-Shannon divergence, which provides new insights into the value of label information in multi-task learning. Our analysis also leads to a concrete algorithm that jointly matches the semantic distribution and controls label distribution divergence. To confirm the effectiveness of the proposed method, we first compare the algorithm with several baselines on some benchmarks and then test the algorithms under label space shift conditions. Empirical results demonstrate that the proposed method could outperform most baselines and achieve state-of-the-art performance, particularly showing the benefits under the label shift conditions.

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Bahraini, Tahereh, Peyman Azimpour, and Hadi Sadoghi Yazdi. "Modified-mean-shift-based noisy label detection for hyperspectral image classification." Computers & Geosciences 155 (October 2021): 104843. http://dx.doi.org/10.1016/j.cageo.2021.104843.

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Woods, Isaac L., Randy G. Floyd, Leah J. Singh, Haley K. Layton, Philip A. Norfolk, and Ryan L. Farmer. "What Is in a Name? A Historical Review of Intelligence Test Score Labels." Journal of Psychoeducational Assessment 37, no. 6 (July 9, 2018): 692–705. http://dx.doi.org/10.1177/0734282918786651.

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Labels for scores stemming from intelligence tests have been employed since their inception in the United States. The purpose of this study was to systematically identify and document score labels for IQs used during the past 102 years. Using pairs of reviewers, score labels from 40 tests were reviewed, and 61 unique labels were identified. Comparative analyses by score range and decade were completed. Results indicate a paradigm shift beginning in the 1980s that has slowly resulted in more common, but not universal, use of terminology that focuses on the statistical aspect of scores, rather than employing value-laden and potentially stigmatizing terms. A universal score label system would help to avoid confusion, miscommunication, and biased decision making.

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Park, Hankum, Jaeyoung Ha, Ja Young Koo, Jongmin Park, and Seung Bum Park. "Label-free target identification using in-gel fluorescence difference via thermal stability shift." Chemical Science 8, no. 2 (2017): 1127–33. http://dx.doi.org/10.1039/c6sc03238a.

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Park, Hankum, and Seung Bum Park. "Label-free target identification reveals oxidative DNA damage as the mechanism of a selective cytotoxic agent." Chemical Science 10, no. 12 (2019): 3449–58. http://dx.doi.org/10.1039/c8sc05465g.

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Li, Zhaokui, Xiangyi Tang, Wei Li, Chuanyun Wang, Cuiwei Liu, and Jinrong He. "A Two-stage Deep Domain Adaptation Method for Hyperspectral Image Classification." Remote Sensing 12, no. 7 (March 25, 2020): 1054. http://dx.doi.org/10.3390/rs12071054.

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Deep learning has attracted extensive attention in the field of hyperspectral images (HSIs) classification. However, supervised deep learning methods heavily rely on a large amount of label information. To address this problem, in this paper, we propose a two-stage deep domain adaptation method for hyperspectral image classification, which can minimize the data shift between two domains and learn a more discriminative deep embedding space with very few labeled target samples. A deep embedding space is first learned by minimizing the distance between the source domain and the target domain based on Maximum Mean Discrepancy (MMD) criterion. The Spatial–Spectral Siamese Network is then exploited to reduce the data shift and learn a more discriminative deep embedding space by minimizing the distance between samples from different domains but the same class label and maximizes the distance between samples from different domains and class labels based on pairwise loss. For the classification task, the softmax layer is replaced with a linear support vector machine, in which learning minimizes a margin-based loss instead of the cross-entropy loss. The experimental results on two sets of hyperspectral remote sensing images show that the proposed method can outperform several state-of-the-art methods.

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Gershon, Ariel, and Edward Shorter. "How amytal changed psychopharmacy: off-label uses of sodium amytal (1920–40)." History of Psychiatry 30, no. 3 (May 30, 2019): 352–58. http://dx.doi.org/10.1177/0957154x19847605.

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In the early 1930s, American neurologist and psychiatrist William Bleckwenn used sodium amytal to render catatonic patients responsive, so that he could engage in talk therapy. Bleckwenn found a new, ‘off-label’ use for this anaesthetic and anxiolytic medication in psychiatry and, in doing so, allowed for important discoveries in the diagnosis and treatment of catatonia. Pharmacological textbooks reveal a ‘label’, while the Index-Catalogue of the Library of the Surgeon-General’s Office reveals explorations ‘off label’ of barbiturates. The ‘off-label’ use of barbiturates facilitated talk therapy, heralding an important shift in psychopharmacy. Drugs previously only used as chemical restraints became a form of treatment for specific psychiatric diseases. The current strictures against off-label prescribing are overprescriptive and close off innovative new uses.

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Song, Sojin, Anh H. Nguyen, Jong Uk Lee, Misun Cha, and Sang Jun Sim. "Tracking of STAT3 signaling for anticancer drug-discovery based on localized surface plasmon resonance." Analyst 141, no. 8 (2016): 2493–501. http://dx.doi.org/10.1039/c5an02397a.

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Yang, Liu, and Fengguang Luo. "Orthogonal modulation system with Manchester-coded payload and frequency-shift keying label." Optical Engineering 55, no. 4 (April 21, 2016): 046108. http://dx.doi.org/10.1117/1.oe.55.4.046108.

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Suturina, Elizaveta A., Daniel Häussinger, Kaspar Zimmermann, Luca Garbuio, Maxim Yulikov, Gunnar Jeschke, and Ilya Kuprov. "Model-free extraction of spin label position distributions from pseudocontact shift data." Chem. Sci. 8, no. 4 (2017): 2751–57. http://dx.doi.org/10.1039/c6sc03736d.

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Rios, Kimberly, and Dominik Mischkowski. "Shaping Responses to Torture: What You Call It Matters." Personality and Social Psychology Bulletin 45, no. 6 (October 31, 2018): 934–46. http://dx.doi.org/10.1177/0146167218802830.

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Although torture is largely ineffective for gaining information from terrorism suspects, nearly half of Americans support its use. Building upon previous work examining predictors of responses to such tactics and willingness to label them as “torture,” this research tested whether the “torture” label itself can influence attitudes. Across five experiments using two different populations, both politically liberal and conservative participants showed more negative attitudes toward “torture” than “enhanced interrogation,” even given identical descriptions of the tactics. This greater negativity in response to “torture” extended to actual behavior (signing a petition) and was driven by cognitive appraisals of severity as well as feelings of personal distress and other-directed empathic concern. Furthermore, there was a small but significant effect for such effects to be stronger among conservatives than liberals. These findings have implications for the underpinnings of attitudes toward torture, potential ways to shift such attitudes, and the psychological consequences of labels.

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Weeks, Edythe. "Highlighting the chameleon nature of power." Journal of Language and Politics 1, no. 2 (July 10, 2003): 323–54. http://dx.doi.org/10.1075/jlp.1.2.09wee.

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This essay uses a poststructural/critical race analysis, and provides a specific example of how the social practice of labeling serves to create major ideological effects, which produce and reproduce unequal race-based power relations. Certain U.S. citizens are ascribed/branded with the seemingly politically correct label, “African-American”. Many believe that the shift from “Black” to “African-American” in 1988 was the result of Blacks exercising political power and achieving a hard-won right to change their identity. Also many view the new label as the common sense preferred alternative to “Black”. This article deconstructs the term “African-American” and views it within the context of the macro and micro interactive forces of politics, economics, sociology, history and socio-cultural phenomena. Instead of the intended purpose of fostering a sense of self-esteem, the label has also served to reinforce the socially constructed binary dualisms characterizing “Blacks” as being fundamentally different from “Whites”. Moreover, the notion of Black pride, self-esteem and heritage are concepts with the power to shift culpability and blame onto the victims of a race-based system. Power appeared to have been exercised by Black/African-Americans. However, the shift to African-American was not the result of autonomous thinking. It was a “reflex without reflection” (Billig 1991:8). It “echoed” dominating ideological structures of power. The “new” label unwittingly serves to further perpetuate racist ideology inherited from a foundational institution of slavery. America can enjoy the image of having a culture of freedom, equality and egalitarianism, while maintaining justifiable race-based political, social and economic inequality gaps.

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Karabina, E. V., D. D. Sakaeva, and O. N. Lipatov. "Off-Label Drug Use in Oncology." Creative surgery and oncology 12, no. 2 (July 16, 2022): 164–71. http://dx.doi.org/10.24060/2076-3093-2022-12-2-164-171.

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The off-label use of medicines is a routine clinical practice of oncology, especially in malignant-tumour patients with no treatment alternatives left when registered-drug options have been exhausted or standard therapies reveal contraindications. The recent shift from single-gene assays to multigene panels powered by full-exome or -genome sequencing expands the capacity of precision therapy, leading to a wider agnostic off-label use of targeted drugs for detecting a particular molecular genetic disorder. Studies of the off-label drug use in oncology will clarify the feasibility and safety of such prescriptions in patients with rare forms of malignancy when registered therapies have been exhausted or standard treatment reveals contraindications. This article examines the prevalence and landscape of off-label drug use in cancer patients and elaborates on the off-label principle. The paper presents a critical reflection on the off-label use of medicines in oncology.

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Mutersbaugh, Tad. "Fighting Standards with Standards: Harmonization, Rents, and Social Accountability in Certified Agrofood Networks." Environment and Planning A: Economy and Space 37, no. 11 (November 2005): 2033–51. http://dx.doi.org/10.1068/a37369.

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In this paper I explore the remaking of globalized standards through harmonization, and its impact upon certified-organic and fair-trade agrofood networks. I focus on certification standards and discuss four shifts associated with globalized standards (an increased importance of multilateral institutions, changes to standards language, displacement of network-specific standards, and a shift away from relational standards). It is then argued, with reference to value-chain rent theory, that the shift to globalized standards has transformed rent relations in ways that benefit certain actors (that is, retailers) and imperil the earnings of others. In brief, globalized standards increase the costs of standards compliance, the full burden of which falls upon immiserated producers, to the point at which farmers see little economic advantage to certified-organic and fair-trade production. I then examine social-accountability standards that seek to ‘fight standards with standards’ by championing the consolidation of strong labor and environmental protections under a single label. The study suggests that a single-label strategy can be successful, yet must struggle to overcome a Polanyian double bind, for, in order to build broad coalitions necessary to extend the reach of protective standards, the coalitions must include corporate interests that prefer weaker, contract-based standards.

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Tasche, Dirk. "Factorizable Joint Shift in Multinomial Classification." Machine Learning and Knowledge Extraction 4, no. 3 (September 10, 2022): 779–802. http://dx.doi.org/10.3390/make4030038.

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Factorizable joint shift (FJS) was recently proposed as a type of dataset shift for which the complete characteristics can be estimated from feature data observations on the test dataset by a method called Joint Importance Aligning. For the multinomial (multiclass) classification setting, we derive a representation of factorizable joint shift in terms of the source (training) distribution, the target (test) prior class probabilities and the target marginal distribution of the features. On the basis of this result, we propose alternatives to joint importance aligning and, at the same time, point out that factorizable joint shift is not fully identifiable if no class label information on the test dataset is available and no additional assumptions are made. Other results of the paper include correction formulae for the posterior class probabilities both under general dataset shift and factorizable joint shift. In addition, we investigate the consequences of assuming factorizable joint shift for the bias caused by sample selection.

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TOLINS, JACKSON, and ELIANA COLUNGA. "How words anchor categorization: conceptual flexibility with labeled and unlabeled categories‡." Language and Cognition 7, no. 2 (July 22, 2014): 219–38. http://dx.doi.org/10.1017/langcog.2014.26.

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abstractLabeled categories are learned faster, and are subsequently more robust than categories learned without labels. The label feedback hypothesis (Lupyan, 2012) accounts for these effects by introducing a word-driven top-down modulation of perceptual processes involved in categorization. By testing categorization flexibility with and without labels, we demonstrate the ways in which labels do and do not modulate category representations. In Experiment 1, transfer involved a change in selective attention, and results indicated that labels did not impact relearning. In Experiment 2, when transfer involved a change in the behavioral response to categories whose structures did not change, a reversal shift, learning the categories with labels speeded recovery. We take this finding as evidence that the augmentation of perceptual processes by words is on the one hand fairly weak without explicit reinforcement, but on the other allows for category representations to be more abstract, allowing greater flexibility in behavior.

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Erofeev, Alexander S., Petr V. Gorelkin, Dmitry V. Kolesov, Gleb A. Kiselev, Evgeniy V. Dubrovin, and Igor V. Yaminsky. "Label-free sensitive detection of influenza virus using PZT discs with a synthetic sialylglycopolymer receptor layer." Royal Society Open Science 6, no. 9 (September 18, 2019): 190255. http://dx.doi.org/10.1098/rsos.190255.

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We describe rapid, label-free detection of Influenza A viruses using the first radial mode of oscillations of lead zirconate titanate (PZT) piezoelectric discs with a 2 mm radius and 100 µm thickness fabricated from a piezoelectric membrane. The discs are modified with a synthetic sialylglycopolymer receptor layer, and the coated discs are inserted in a flowing virus suspension. Label-free detection of the virus is achieved by monitoring the disc radial mode resonance frequency shift. Piezo transducers with sialylglycopolymer sensor layers exhibited a long lifetime, a high sensitivity and the possibility of regeneration. We demonstrate positive, label-free detection of Influenza A viruses at concentrations below 10 5 virus particles per millilitre. We show that label-free, selective, sensitive detection of influenza viruses by home appliances is possible in principle.

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Chen, Liang, Qianjin Du, Yihang Lou, Jianzhong He, Tao Bai, and Minghua Deng. "Mutual Nearest Neighbor Contrast and Hybrid Prototype Self-Training for Universal Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6248–57. http://dx.doi.org/10.1609/aaai.v36i6.20574.

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Universal domain adaptation (UniDA) aims to transfer knowledge learned from a labeled source domain to an unlabeled target domain under domain shift and category shift. Without prior category overlap information, it is challenging to simultaneously align the common categories between two domains and separate their respective private categories. Additionally, previous studies utilize the source classifier's prediction to obtain various known labels and one generic "unknown" label of target samples. However, over-reliance on learned classifier knowledge is inevitably biased to source data, ignoring the intrinsic structure of target domain. Therefore, in this paper, we propose a novel two-stage UniDA framework called MATHS based on the principle of mutual nearest neighbor contrast and hybrid prototype discrimination. In the first stage, we design an efficient mutual nearest neighbor contrastive learning scheme to achieve feature alignment, which exploits the instance-level affinity relationship to uncover the intrinsic structure of two domains. We introduce a bimodality hypothesis for the maximum discriminative probability distribution to detect the possible target private samples, and present a data-based statistical approach to separate the common and private categories. In the second stage, to obtain more reliable label predictions, we propose an incremental pseudo-classifier for target data only, which is driven by the hybrid representative prototypes. A confidence-guided prototype contrastive loss is designed to optimize the category allocation uncertainty via a self-training mechanism. Extensive experiments on three benchmarks demonstrate that MATHS outperforms previous state-of-the-arts on most UniDA settings.

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Ji, Minbiao, Michal Arbel, Lili Zhang, Christian W. Freudiger, Steven S. Hou, Dongdong Lin, Xinju Yang, Brian J. Bacskai, and X. Sunney Xie. "Label-free imaging of amyloid plaques in Alzheimer’s disease with stimulated Raman scattering microscopy." Science Advances 4, no. 11 (November 2018): eaat7715. http://dx.doi.org/10.1126/sciadv.aat7715.

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One of the key pathological features of Alzheimer’s disease (AD) is the existence of extracellular deposition of amyloid plaques formed with misfolded amyloid-β (Aβ). The conformational change of proteins leads to enriched contents of β sheets, resulting in remarkable changes of vibrational spectra, especially the spectral shifts of the amide I mode. Here, we applied stimulated Raman scattering (SRS) microscopy to image amyloid plaques in the brain tissue of an AD mouse model. We have demonstrated the capability of SRS microscopy as a rapid, label-free imaging modality to differentiate misfolded from normal proteins based on the blue shift (~10 cm−1) of amide I SRS spectra. Furthermore, SRS imaging of Aβ plaques was verified by antibody staining of frozen thin sections and fluorescence imaging of fresh tissues. Our method may provide a new approach for studies of AD pathology, as well as other neurodegenerative diseases associated with protein misfolding.

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Štrop, Petr, František Mikeš, Marie Havranová, and Václav Žižkovský. "Human serum albumin. A study of the nature of its hydrophobic binding sites." Collection of Czechoslovak Chemical Communications 52, no. 5 (1987): 1362–74. http://dx.doi.org/10.1135/cccc19871362.

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Spectroscopic labels and hydrophobic chromatography on two different supports were used to compare the size and accessibility of the hydrophobic binding sites of human serum albumin with the accessibility of non-polar residues on the surface of other globular proteins. The binding of the labels 1-alkyl-4-(3-ethoxy-4-hydroxystyryl)pyridinium bromides (HPB) with alkyl chains of different length was investigated in the ultracentrifuge and by spectrophotometry. n-Butyl (C4-HPB) and decyl (C10-HPB) labels bind to albumin with association constants of 8 . 103 and 4 . 104, respectively, at pH 5·50, and with constant of 2·6 . 105 for the C10-HPB label at pH 9·2. Whereas C4-HPB interacts with the site of local polarity of albumin not different from the bulk solution, both the C10-HPB and C16-HPB on the other hand bind to hydrophobic sites, where the solvation of the chromophore is largely reduced as evidenced by the 46 nm shift to higher wavelengths in its spectrum. For other proteins the shift was less then 5 nm. Ten molecules of C10-HPB and four molecules of C16-HPB can be attached to one molecule of albumin. The changes in the spectrum of the bound label induced by palmitate reveal that these binding sites are essentially the same as those for fatty acids. From chromatographic experiments with labeled albumin at different pH carried out on Octyl-Sepharose and Spheron the conclusion was made that the latter support interacts preferentially with the non-polar side chains on the surface of proteins. The retention and the recovery of albumin and defatted albumin was investigated as a function of salt and alcohol concentration and compared with the same parameters of other proteins. In agreement with the proposed structure of the domains of albumin evidence was obtained that the outer surface of the albumin molecule is at neutral pH predominantly hydrophilic, and that the exceptionally large hydrophobic areas are localized solely in crevices.

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Lee, Choi Chuck, Dave Wanigasekera, and Charles Y. Fiakpui. "Solvolyses of some 13C- or 14C-labeled triarylvinyl bromides in aqueous acetic acid." Canadian Journal of Chemistry 64, no. 6 (June 1, 1986): 1235–39. http://dx.doi.org/10.1139/v86-205.

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The presence of different amounts of added LiClO4 in the solvolysis of triphenyl[2-14C]vinyl bromide (1-Br-2-14C) or trianisyl[2-14C]vinyl bromide (2-Br-2-14C) in 70% HOAc did not materially affect the extent of scrambling of the label arising from 1,2-aryl shifts in the triarylvinyl cation or kr, the rate constant for the 1,2-aryl shift. Solvolysis of a 2:3 mixture of (E)- and (Z)-1,2-diphenyl-2-tolylvinyl bromide ((E,Z)-3-Br) in 70% HOAc gave an 85:15 mixture of 1,2-diphenyl-2-tolylethanone (5) and 2,2-diphenyl-1-tolylethanone (6), the latter product, 6, being derived from a 1,2-phenyl shift in the 1,2-diphenyl-2-tolylvinyl cation to the more stable 2,2-diphenyl-1-tolylvinyl cation. With (E,Z)-3-Br-2-13C as substrate and analysis by gc–ms, about 26–27% scrambling of the 13C label was found in both products, 5-1,2-13C and 6-1,2-13C. Solvolyses of (E,Z)-3-Br-2-13C in 50% and 70% HOAc showed that the extent of scrambling in the major products, 5-1,2-13C, was lower in 50% HOAc than in 70% HOAc, similar to a trend previously observed for solvolyses of 1-Br-2-14C. In contrast, solvolyses of 2-Br-2-14C in 50, 70, or 90% HOAc gave essentially the same extent of scrambling. Mechanistic implications of these results are discussed.

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Yang, Jing-Jing, and Tae-Won Kang. "How Korean Retailers Expand Private Label Markets Abroad: Evidence from the Chinese Fresh Food Market." Journal of Korea Trade 26, no. 5 (August 30, 2022): 106–24. http://dx.doi.org/10.35611/jkt.2022.26.5.106.

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Purpose - The increasing share of Korean private label products (PLPs) in the domestic market helped generate lucrative revenue. In recent years, major South Korean retailers have begun to cast their sights on overseas markets and actively export their PLPs. In China, the proportion of private label fresh food (PLFF) is gradually expanding amid the development of the new retailing model. A profound understanding of the relationship between private label fresh produce and purchase intention may be the answer to helping Chinese retailer private labels expand supply chains in Korea. This study, taking Chinese retailers as an example, examines the impacts of selection factors of private label fresh food and perceived value on purchase intention. Apart from that, the relationship between the selection factors and purchase intention will be analyzed with perceived value as a mediator. Design/methodology - This work aims to empirically analyze the purchase intention of private label fresh food using statistical analysis. In this study, a hypothetical causal model consisting of 6 latent variables and 24 measured variables is developed based on the literature review. To validate the research hypotheses and the research model, SPSS23.0/AMOS23.0 is used to analyze factors such as validity and reliability, as well as structural equation modeling. Findings - The hypothetical model established in this study is of general applicability. In respect to PLFF, perceived value, while significantly influencing purchase intention in combination with four selection factors (perceived quality, perceived price, brand trust, and store image), mediates partially between the first three factors and purchase intention, which rules out the impact and mediating effect of store image on purchase intention. Originality/value - These research results, as helpful insights into the present circumstances of Chinese PLFF in the domestic market, provide useful information and guidance for Korean retailers and service providers to innovate production and service, as well as develop marketing and promotion strategies, so that they can shift private label goods with advantages from domestic demand to export, thus increasing overseas profitability. Further, this work will also contribute to relevant research.

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Jiang, Tao, Xiao Yi Lv, Fu Ru Zhong, Jia Qing Mo, Yi Xian Tu, Zhen Hong Jia, and Fu Chun Zhang. "Porous Silicon Immunosensor for Label-Free Detection of SpaA." Applied Mechanics and Materials 44-47 (December 2010): 2472–76. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.2472.

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A novel immunosensor based on porous silicon (PSi) for antigen detection was reported in this paper. The antigen (SpaA) and the specificity of the antibodies are employed as the target and the probe in our laboratory, respectively. The immunosensor structure was prepared using bioconjungation. After the antigen-antibody reaction, the red shift of the reflection spectrum of the immunosensor increases in proportion to the concentration of SpaA. The sensitivity of this immunosensor is 41nm/ µg•ml-1 and the detection limit is 2.44×102pg•ml-1, they are better compared with our previous work by using this method. Controlled experiments were also presented with non-immune antibody and the results show that this immunosensor possesses high specificity.

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Chen, Junjie. "Syntactic-Semantic Dependency Correlation in Semantic Role Labeling: A Shift in Semantic Label Distributions." Journal of Natural Language Processing 29, no. 3 (2022): 1002–9. http://dx.doi.org/10.5715/jnlp.29.1002.

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Xu, Kun, Shunming Li, Ranran Li, Jiantao Lu, Xianglian Li, and Mengjie Zeng. "Domain Adaptation Network with Double Adversarial Mechanism for Intelligent Fault Diagnosis." Applied Sciences 11, no. 17 (August 28, 2021): 7983. http://dx.doi.org/10.3390/app11177983.

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Due to the mechanical equipment working under variable speed and load for a long time, the distribution of samples is different (domain shift). The general intelligent fault diagnosis method has a good diagnostic effect only on samples with the same sample distribution, but cannot correctly predict the faults of samples with domain shift in a real situation. To settle this problem, a new intelligent fault diagnosis method, domain adaptation network with double adversarial mechanism (DAN-DAM), is proposed. The DAN-DAM model is mainly composed of a feature extractor, two label classifiers and a domain discriminator. The feature extractor and the two label classifiers form the first adversarial mechanism to achieve class-level alignment. Moreover, the discrepancy between the two classifiers is measured by Wasserstein distance. Meanwhile, the feature extractor and the domain discriminator form the second adversarial mechanism to realize domain-level alignment. In addition, maximum mean discrepancy (MMD) is used to reduce the distance between the extracted features of two domains. The DAN-DAM model is verified by multiple transfer experiments on some datasets. According to the transfer experiment results, the DAN-DAM model has a good diagnosis effect for the domain shift samples. Moreover, the diagnostic accuracy is generally higher than other mainstream diagnostic methods.

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Moura, David P., and Hugh T. Miller. "On Legitimacy: Is Public Administration Stigmatized?" Administration & Society 51, no. 5 (October 3, 2016): 770–94. http://dx.doi.org/10.1177/0095399716670934.

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Public administration scholarship has a long-standing tradition of examining the legitimacy of public administration. One of the motives behind this literature is the necessity to defend against constant criticisms directed toward public administration research, theory, and practice. In this article, we shift the focus from defending public administration by conceptualizing and accepting it as an illegitimate and stigmatized institution—one that cannot and should not rid itself of this label. Accepting and presupposing this label of illegitimacy, independent from any particular definition of legitimacy, allows scholars and practitioners to focus on creating new knowledge and performing professional responsibilities.

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Kurniawan, Muhammad Fakih, Tiana Fitrilia, Hikmah Nur Chosida, and Nursyawal Nacing. "SAPPAN (Caesalpinia sappanL.) EXTRACT-BASED LABEL FOR DETECTION OF CHICKEN MEAT SPOILAGE." Indonesian Journal of Applied Research (IJAR) 2, no. 3 (December 8, 2021): 153–59. http://dx.doi.org/10.30997/ijar.v2i3.146.

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Brazilin in sappan is sensitive to pH, which make it possible as indicator of food quality. This research aimed to investigate performance of sappan extract as colorant for indicator label applied in chicken meat packaging. The samples were stored at room and refrigerator temperature. They were then tested for color change of label RGB (Red Green Blue), pH and TVB-N (Total Volatile Base-Nitrogen) of the meat. Data were analyzed using ANOVA at significant level of 5%. Indicator label showed the reduced RGB during storage, while pH and TVB-N increased. In case of room temperature, the color change into pink occurred in day 3 with pH 6.72 and TVB-N 1.84 mg N/100g. Meanwhile, for samples at refrigerator temperature, the label color turned into pink in day 6, with pH 6.56 and TVB-N 2.09 mg N/100g. In this regard, indicator label could detect the depletion of chicken meat quality as indicated by color shift from yellow to pink when pH and TVB-N were at critical value. This result provides significant output for development of sappan extract for indicator of meat spoilage.

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Tahmoresnezhad, Jafar, and Sattar Hashemi. "An Efficient yet Effective Random Partitioning and Feature Weighting Approach for Transfer Learning." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 02 (February 2016): 1651003. http://dx.doi.org/10.1142/s0218001416510034.

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One of the serious challenges in machine learning and pattern recognition is to transfer knowledge from related but different domains to a new unlabeled domain. Feature selection with maximum mean discrepancy (f-MMD) is a novel and effective approach to transfer knowledge from source domain (training set) into target domain (test set) where training and test sets are drawn from different distributions. However, f-MMD has serious challenges in facing datasets with large number of samples and features. Moreover, f-MMD ignores the feature-label relation in finding the reduced representation of dataset. In this paper, we exploit jointly transfer learning and class discrimination to cope with domain shift problem on which the distribution difference is considerably large. We therefore put forward a novel transfer learning and class discrimination approach, referred to as RandOm k-samplesets feature Weighting Approach (ROWA). Specifically, ROWA reduces the distribution difference across domains in an unsupervised manner where no label is available in the test set. Moreover, ROWA exploits feature-label relation to separate various classes alongside the domain transfer, and augments the relation of selected features and source domain labels. In this work, we employ disjoint/overlapping small-sized samplesets to iteratively converge to final solution. Employment of local sets along with a novel optimization problem constructs a robust and effective reduced representation for adaptation across domains. Extensive experiments on real and synthetic datasets verify that ROWA can significantly outperform state-of-the-art transfer learning approaches.

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Hsieh, Shuchen, Shu-Ling Hsieh, Chiung-wen Hsieh, Po-Chiao Lin, and Chun-Hsin Wu. "Label-Free Glucose Detection Using Cantilever Sensor Technology Based on Gravimetric Detection Principles." Journal of Analytical Methods in Chemistry 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/687265.

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Efficient maintenance of glucose homeostasis is a major challenge in diabetes therapy, where accurate and reliable glucose level detection is required. Though several methods are currently used, these suffer from impaired response and often unpredictable drift, making them unsuitable for long-term therapeutic practice. In this study, we demonstrate a method that uses a functionalized atomic force microscope (AFM) cantilever as the sensor for reliable glucose detection with sufficient sensitivity and selectivity for clinical use. We first modified the AFM tip with aminopropylsilatrane (APS) and then adsorbed glucose-specific lectin concanavalin A (Con A) onto the surface. The Con A/APS-modified probes were then used to detect glucose by monitoring shifts in the cantilever resonance frequency. To confirm the molecule-specific interaction, AFM topographical images were acquired of identically treated silicon substrates which indicated a specific attachment for glucose-Con A and not for galactose-Con A. These results demonstrate that by monitoring the frequency shift of the AFM cantilever, this sensing system can detect the interaction between Con A and glucose, one of the biomolecule recognition processes, and may assist in the detection and mass quantification of glucose for clinical applications with very high sensitivity.

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Chen, Joyce, and Lisa Cooper. "374 Therapeutic Class Labeling of EGFR Companion Diagnostics (CDxs)." Journal of Clinical and Translational Science 6, s1 (April 2022): 70. http://dx.doi.org/10.1017/cts.2022.213.

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OBJECTIVES/GOALS: Review all approved companion diagnostics (CDxs) labeling for therapeutic class language. Examine the regulatory pathways of CDx products whose labels contain therapeutic class labeling. Develop recommendations for pharmaceutical industry professionals on best practices in the co-development of CDxs and oncology therapeutic products. METHODS/STUDY POPULATION: Literature discussing companion diagnostics was reviewed from EBSCOhost, PubMed, and OVID. The Intended Use language within CDx labels on the Food and Drug Administrations “List of Cleared or Approved Companion Diagnostic Devices (In Vitro and Imaging Tools)” website were reviewed on November 1, 2021 for therapeutics class language. For CDx products with therapeutic class label language, the regulatory history was evaluated to determine the development approach taken to achieve the language. RESULTS/ANTICIPATED RESULTS: A total of 45 CDxs were identified, of which only 2 contained therapeutic class labeling, both of which were devices for the identification of epidermal growth factor receptor (EGFR) mutations for the treatment of non-small cell lung cancer (NSCLC). Three additional EGFR CDxs were approved; however, they did not contain therapeutic class labeling. The first CDx was the Cobas EGFR Mutation Test V2, which received therapeutic group label language as an update on October 27, 2020; however, prior to the therapeutic class labeling, three oncology products were named in the Intended Use: Tarceva (erlotinib), Tagrisso (osimertinib), and Iressa (gefitinib). The second CDx to incorporate therapeutic class labeling was the ONCO/Reveal Diagnostic Lung and Colon Cancer Assay upon initial approval on July 30, 2021. DISCUSSION/SIGNIFICANCE: EGFR CDxs are the first to shift towards therapeutic class labeling. Indication, molecular alterations, and mechanism of action of the approved therapeutic class products, number of products approved, as well as CDx analytical and clinical validation influence class label relevance. Discussions with the FDA are encouraged early in development.

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Beaupre, Claude, and Franziska Fischer. "The Label ‘Refugee’ and its Impacts on Border Policies." Borders in Globalization Review 1, no. 2 (August 21, 2020): 71–83. http://dx.doi.org/10.18357/bigr12202019562.

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In this paper the authors adopt a constructivist approach to explain the efforts of reborderisation following the so-called ‘Refugee Crisis’ unfolding in the European Union after a sharp influx of refugees in 2015. One of the core principles of the European Union, the freedom of movement, is heavily challenged, through the perception of security threats and economic burden that is associated with the arrival of people seeking asylum in large numbers. Through a discourse analysis centring around the label ‘refugee’, which experienced a shift in meaning, this paper aims to display the driving social force that catalysed political actions to reintroduce borders between European Union Member States as a tool to recreate the illusion of control over the influx of people. Germany and France, as pioneers of the principle of freedom of movement in Europe, serve as empirical case studies for the efforts to reinstate control through reborderisation.

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Benjdira, Bilel, Adel Ammar, Anis Koubaa, and Kais Ouni. "Data-Efficient Domain Adaptation for Semantic Segmentation of Aerial Imagery Using Generative Adversarial Networks." Applied Sciences 10, no. 3 (February 6, 2020): 1092. http://dx.doi.org/10.3390/app10031092.

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Despite the significant advances noted in semantic segmentation of aerial imagery, a considerable limitation is blocking its adoption in real cases. If we test a segmentation model on a new area that is not included in its initial training set, accuracy will decrease remarkably. This is caused by the domain shift between the new targeted domain and the source domain used to train the model. In this paper, we addressed this challenge and proposed a new algorithm that uses Generative Adversarial Networks (GAN) architecture to minimize the domain shift and increase the ability of the model to work on new targeted domains. The proposed GAN architecture contains two GAN networks. The first GAN network converts the chosen image from the target domain into a semantic label. The second GAN network converts this generated semantic label into an image that belongs to the source domain but conserves the semantic map of the target image. This resulting image will be used by the semantic segmentation model to generate a better semantic label of the first chosen image. Our algorithm is tested on the ISPRS semantic segmentation dataset and improved the global accuracy by a margin up to 24% when passing from Potsdam domain to Vaihingen domain. This margin can be increased by addition of other labeled data from the target domain. To minimize the cost of supervision in the translation process, we proposed a methodology to use these labeled data efficiently.

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Seo, Minguk, Li Lei, and Martin Egli. "Label-Free Electrophoretic Mobility Shift Assay (EMSA) for Measuring Dissociation Constants of Protein-RNA Complexes." Current Protocols in Nucleic Acid Chemistry 76, no. 1 (November 21, 2018): e70. http://dx.doi.org/10.1002/cpnc.70.

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Gómez-Gómez, Maribel, Carles Sánchez, Sergio Peransi, David Zurita, Laurent Bellieres, Sara Recuero, Manuel Rodrigo, et al. "Photonic Label-Free Biosensors for Fast and Multiplex Detection of Swine Viral Diseases." Sensors 22, no. 3 (January 18, 2022): 708. http://dx.doi.org/10.3390/s22030708.

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In this paper we present the development of photonic integrated circuit (PIC) biosensors for the label-free detection of six emerging and endemic swine viruses, namely: African Swine Fever Virus (ASFV), Classical Swine Fever Virus (CSFV), Porcine Reproductive and Respiratory Syndrome Virus (PPRSV), Porcine Parvovirus (PPV), Porcine Circovirus 2 (PCV2), and Swine Influenza Virus A (SIV). The optical biosensors are based on evanescent wave technology and, in particular, on Resonant Rings (RRs) fabricated in silicon nitride. The novel biosensors were packaged in an integrated sensing cartridge that included a microfluidic channel for buffer/sample delivery and an optical fiber array for the optical operation of the PICs. Antibodies were used as molecular recognition elements (MREs) and were selected based on western blotting and ELISA experiments to ensure the high sensitivity and specificity of the novel sensors. MREs were immobilized on RR surfaces to capture viral antigens. Antibody–antigen interactions were transduced via the RRs to a measurable resonant shift. Cell culture supernatants for all of the targeted viruses were used to validate the biosensors. Resonant shift responses were dose-dependent. The results were obtained within the framework of the SWINOSTICS project, contributing to cover the need of the novel diagnostic tools to tackle swine viral diseases.

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Zhou, Kaiyang, Yongxin Yang, Timothy Hospedales, and Tao Xiang. "Deep Domain-Adversarial Image Generation for Domain Generalisation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 13025–32. http://dx.doi.org/10.1609/aaai.v34i07.7003.

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Machine learning models typically suffer from the domain shift problem when trained on a source dataset and evaluated on a target dataset of different distribution. To overcome this problem, domain generalisation (DG) methods aim to leverage data from multiple source domains so that a trained model can generalise to unseen domains. In this paper, we propose a novel DG approach based on Deep Domain-Adversarial Image Generation (DDAIG). Specifically, DDAIG consists of three components, namely a label classifier, a domain classifier and a domain transformation network (DoTNet). The goal for DoTNet is to map the source training data to unseen domains. This is achieved by having a learning objective formulated to ensure that the generated data can be correctly classified by the label classifier while fooling the domain classifier. By augmenting the source training data with the generated unseen domain data, we can make the label classifier more robust to unknown domain changes. Extensive experiments on four DG datasets demonstrate the effectiveness of our approach.

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Stupin, D. D. "TiN Nanoporous Electrode covered by Single Cell as Bio-Electronic Sensor of Radiation Hazard." Физика и техника полупроводников 52, no. 5 (2018): 508. http://dx.doi.org/10.21883/ftp.2018.05.45852.41.

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AbstractHere we demonstrate a simple label-free bio-electronic radiation (ultra-violet) hazard detector based on non-destructive electrical impedance spectroscopy technique with a single living cell as a sensing element. Radiation results in wrong cell’s functioning and, as corollary, cell’s membrane distortion. The latter results in impedance shift for the electrode covered by the cell. The additional sensitivity of impedance shift is achieved by the simultaneous usage of the TiN porous electrodes and adaptive filtering for impedance data processing. The main advantage of the proposed sensor with respect to purely physical and/or chemical sensors is high selectivity: it a priory reacts only on the dangerous for living beings radiation.

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Journal articles on the topic 'Label Shift'

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