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2022 ◽  
Vol 11 (1) ◽  
pp. 1-50
Author(s):  
Bahar Irfan ◽  
Michael Garcia Ortiz ◽  
Natalia Lyubova ◽  
Tony Belpaeme

User identification is an essential step in creating a personalised long-term interaction with robots. This requires learning the users continuously and incrementally, possibly starting from a state without any known user. In this article, we describe a multi-modal incremental Bayesian network with online learning, which is the first method that can be applied in such scenarios. Face recognition is used as the primary biometric, and it is combined with ancillary information, such as gender, age, height, and time of interaction to improve the recognition. The Multi-modal Long-term User Recognition Dataset is generated to simulate various human-robot interaction (HRI) scenarios and evaluate our approach in comparison to face recognition, soft biometrics, and a state-of-the-art open world recognition method (Extreme Value Machine). The results show that the proposed methods significantly outperform the baselines, with an increase in the identification rate up to 47.9% in open-set and closed-set scenarios, and a significant decrease in long-term recognition performance loss. The proposed models generalise well to new users, provide stability, improve over time, and decrease the bias of face recognition. The models were applied in HRI studies for user recognition, personalised rehabilitation, and customer-oriented service, which showed that they are suitable for long-term HRI in the real world.


2022 ◽  
Vol 15 ◽  
Author(s):  
Valeriy Shafiro ◽  
Nathan Luzum ◽  
Aaron C. Moberly ◽  
Michael S. Harris

Objectives: Improved perception of environmental sounds (PES) is one of the primary benefits of cochlear implantation (CI). However, past research contains mixed findings on PES ability in contemporary CI users, which at times contrast with anecdotal clinical reports. The present review examined extant PES research to provide an evidence basis for clinical counseling, identify knowledge gaps, and suggest directions for future work in this area of CI outcome assessment.Methods: Six electronic databases were searched using medical subject headings (MeSH) and keywords broadly identified to reference CI and environmental sounds. Records published between 2000 and 2021 were screened by two independent reviewers in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement to identify studies that met the inclusion criteria. Data were subsequently extracted and evaluated according to synthesis without-meta-analysis (SWiM) guidelines.Results: Nineteen studies met the inclusion criteria. Most examined PES in post-lingually implanted adults, with one study focused on pre/perilingual adults. Environmental sound identification (ESI) in quiet using open- or closed-set response format was most commonly used in PES assessment, included in all selected studies. ESI accuracy in CI children (3 studies) and adults (16 studies), was highly variable but generally mediocre (means range: 31–87%). Only two studies evaluated ESI performance prospectively before and after CI, while most studies were cross-sectional. Overall, CI performance was consistently lower than that of normal-hearing peers. No significant differences in identification accuracy were reported between CI candidates and CI users. Environmental sound identification correlated in CI users with measures of speech perception, music and spectro-temporal processing.Conclusion: The findings of this systematic review indicate considerable limitations in the current knowledge of PES in contemporary CI users, especially in pre/perilingual late-implanted adults and children. Although no overall improvement in PES following implantation was found, large individual variability and existing methodological limitations in PES assessment may potentially obscure potential CI benefits for PES. Further research in this ecologically relevant area of assessment is needed to establish a stronger evidence basis, identify CI users with significant deficits, and improve CI users' safety and satisfaction through targeted PES rehabilitation.


Author(s):  
Zachary Birenbaum ◽  
Hieu Do ◽  
Lauren Horstmeyer ◽  
Hailey Orff ◽  
Krista Ingram ◽  
...  

Methods for long-term monitoring of coastal species such as harbor seals, are often costly, time-consuming, and highly invasive, underscoring the need for improved techniques for data collection and analysis. Here, we propose the use of automated facial recognition technology for identification of individual seals and demonstrate its utility in ecological and population studies. We created a software package, SealNet, that automates photo identification of seals, using a graphical user interface (GUI) software to identify, align and chip seal faces from photographs and a deep convolutional neural network (CNN) suitable for small datasets (e.g., 100 seals with five photos per seal). We piloted the SealNet technology with a population of harbor seals located within Casco Bay on the coast of Maine, USA. Across two-years of sampling, 2019 and 2020, at seven haul-out sites in Middle Bay, we processed 1529 images representing 408 individual seals and achieved 88% (93%) rank-1 accuracy in closed set (open set) seal identification. We identified four seals that were photographed in both years at neighboring haul-out sites, suggesting that some harbor seals exhibit site fidelity within local bays across years, and that there may be evidence of spatial connectivity among haul-out sites. Using capture-mark-recapture (CMR) calculations, we obtained a rough preliminary population estimate of 4386 seals in the Middle Bay area. SealNet software outperformed a similar face recognition method developed for primates, PrimNet, in identifying seals following training on our seal dataset. The ease and wealth of image data that can be processed using SealNet software contributes a vital tool for ecological and behavioral studies of marine mammals in the emerging field of conservation technology.


Quantum ◽  
2022 ◽  
Vol 6 ◽  
pp. 617
Author(s):  
David Plankensteiner ◽  
Christoph Hotter ◽  
Helmut Ritsch

A full quantum mechanical treatment of open quantum systems via a Master equation is often limited by the size of the underlying Hilbert space. As an alternative, the dynamics can also be formulated in terms of systems of coupled differential equations for operators in the Heisenberg picture. This typically leads to an infinite hierarchy of equations for products of operators. A well-established approach to truncate this infinite set at the level of expectation values is to neglect quantum correlations of high order. This is systematically realized with a so-called cumulant expansion, which decomposes expectation values of operator products into products of a given lower order, leading to a closed set of equations. Here we present an open-source framework that fully automizes this approach: first, the equations of motion of operators up to a desired order are derived symbolically using predefined canonical commutation relations. Next, the resulting equations for the expectation values are expanded employing the cumulant expansion approach, where moments up to a chosen order specified by the user are included. Finally, a numerical solution can be directly obtained from the symbolic equations. After reviewing the theory we present the framework and showcase its usefulness in a few example problems.


2022 ◽  
Vol 32 (1) ◽  
pp. 483-498
Author(s):  
M. A. El Safty ◽  
S. A. Alblowi ◽  
Yahya Almalki ◽  
M. El Sayed
Keyword(s):  

2022 ◽  
Author(s):  
Neeran Tahir Abd Alameer ◽  
Shahad Safy Hussein
Keyword(s):  

Author(s):  
Guangyan Zhu

Let [Formula: see text] and [Formula: see text] be positive integers and let [Formula: see text] be a set of [Formula: see text] distinct positive integers. For [Formula: see text], one defines [Formula: see text]. We denote by [Formula: see text] (respectively, [Formula: see text]) the [Formula: see text] matrix having the [Formula: see text]th power of the greatest common divisor (respectively, the least common multiple) of [Formula: see text] and [Formula: see text] as its [Formula: see text]-entry. In this paper, we show that for arbitrary positive integers [Formula: see text] and [Formula: see text] with [Formula: see text], the [Formula: see text]th power matrices [Formula: see text] and [Formula: see text] are both divisible by the [Formula: see text]th power matrix [Formula: see text] if [Formula: see text] is a gcd-closed set (i.e. [Formula: see text] for all integers [Formula: see text] and [Formula: see text] with [Formula: see text]) such that [Formula: see text]. This confirms two conjectures of Shaofang Hong proposed in 2008.


2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
I. M. Taha

In the present study, we introduce and characterize the class of r -generalized fuzzy ℓ -closed sets in a fuzzy ideal topological space X , τ , ℓ in Šostak sense. Also, we show that r -generalized fuzzy closed set by Kim and Park (2002) ⟹ r -generalized fuzzy ℓ -closed set, but the converse need not be true. Moreover, if we take ℓ = ℓ 0 , the r -generalized fuzzy ℓ -closed set and r -generalized fuzzy closed set are equivalent. After that, we define fuzzy upper (lower) generalized ℓ -continuous multifunctions, and some properties of these multifunctions along with their mutual relationships are studied with the help of examples. Finally, some separation axioms of r -generalized fuzzy ℓ -closed sets are introduced and studied. Also, the notion of r -fuzzy G ∗ -connected sets is defined and studied with help of r -generalized fuzzy ℓ -closed sets.


Author(s):  
Karima Abbas ◽  
Abdelaali Boudjemaa

Abstract We study the non-equilibrium evolution of binary Bose-Einstein condensates in the presence of weak random potential with a Gaussian correlation function using the time-dependent perturbation theory. We apply this theory to construct a closed set of equations that highlight the role of the spectacular interplay between the disorder and the interspecies interactions in the time evolution of the density induced by disorder in each component. It is found that this latter increases with time favoring localization of both species. The time scale at which the theory remains valid depends on the respective system parameters. We show analytically and numerically that such a system supports a steady state that periodically changing during its time propagation. The obtained dynamical corrections indicate that disorder may transform the system into a stationary out-of-equilibrium states. Understanding this time evolution is pivotal for the realization of Floquet condensates.


Author(s):  
Wadhah Zai El Amri ◽  
Felix Reinhart ◽  
Wolfram Schenck

AbstractMany application scenarios for image recognition require learning of deep networks from small sample sizes in the order of a few hundred samples per class. Then, avoiding overfitting is critical. Common techniques to address overfitting are transfer learning, reduction of model complexity and artificial enrichment of the available data by, e.g., data augmentation. A key idea proposed in this paper is to incorporate additional samples into the training that do not belong to the classes of the target task. This can be accomplished by formulating the original classification task as an open set classification task. While the original closed set classification task is not altered at inference time, the recast as open set classification task enables the inclusion of additional data during training. Hence, the original closed set classification task is augmented with an open set task during training. We therefore call the proposed approach open set task augmentation. In order to integrate additional task-unrelated samples into the training, we employ the entropic open set loss originally proposed for open set classification tasks and also show that similar results can be obtained with a modified sum of squared errors loss function. Learning with the proposed approach benefits from the integration of additional “unknown” samples, which are often available, e.g., from open data sets, and can then be easily integrated into the learning process. We show that this open set task augmentation can improve model performance even when these additional samples are rather few or far from the domain of the target task. The proposed approach is demonstrated on two exemplary scenarios based on subsets of the ImageNet and Food-101 data sets as well as with several network architectures and two loss functions. We further shed light on the impact of the entropic open set loss on the internal representations formed by the networks. Open set task augmentation is particularly valuable when no additional data from the target classes are available—a scenario often faced in practice.


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