Reviewerly/exHarmony huggingface dataset:
Intensity-Mosaic: automatic panorama mosaicking of disordered images with insufficient features

Purpose: Handling low-quality and few-feature medical images is a challenging task in automatic panorama mosaicking. Current mosaicking methods for disordered input images are based on feature point matching, whereas in this case intensity-based registration achieves better performance than feature-point registration methods. We propose a mosaicking method that enables the use of mutual information (MI) registration for mosaicking randomly ordered input images with insufficient features. Approach: Dimensionality reduction is used to map disordered input images into a low dimensional space. Based on the low dimensional representation, the image global correspondence can be recognized efficiently. For adjacent image pairs, we optimize the MI metric for registration. The panorama is then created after image blending. We demonstrate our method on relatively lower-cost handheld devices that acquire images from the retina in vivo, kidney ex vivo, and bladder phantom, all of which contain sparse features. Results: Our method is compared with three baselines: AutoStitch, "dimension reduction + SIFT," and "MI-Only." Our method compared to the first two feature-point based methods exhibits 1.25 (ex vivo microscope dataset) to two times (in vivo retina dataset) rate of mosaic completion, and MI-Only has the lowest complete rate among three datasets. When comparing the subsequent complete mosaics, our target registration errors can be 2.2 and 3.8 times reduced when using the microscopy and bladder phantom datasets. Conclusions: Using dimensional reduction increases the success rate of detecting adjacent images, which makes MI-based registration feasible and narrows the search range of MI optimization. To the best of our knowledge, this is the first mosaicking method that allows automatic stitching of disordered images with intensity-based alignment, which provides more robust and accurate results when there are insufficient features for classic mosaicking methods.
Optimal spin- and planar-quantum squeezing in superpositions of spin coherent states

We investigate the presence of spin- and planar- squeezing in generalized superpositions of atomic (or spin) coherent states (ACS). Spin-squeezing has been shown to be a useful tool in determining the presence of entanglement in multipartite systems, such as collections of two-level atoms, as well as being an indication of reduced projection noise and sub-shot-noise limited phase uncertainty in Ramsey spectroscopy, suitable for measuring phases $\phi\sim 0$. On the other hand, planar-squeezed states display reduced projection noise in two directions simultaneously and have been shown to lead to enhanced metrological precision in measuring phases without the need for explicit prior knowledge of the phase value. In this paper, we show that the generalized superposition state can be parametrized to display both spin-squeezing along all orthogonal axes and planar-squeezing along all orthogonal planes for all values of $J>1/2$. We close with an application of the maximally spin- and planar-squeezed states to quantum metrology.
Generating probabilistic safety guarantees for neural network controllers

Neural networks serve as effective controllers in a variety of complex settings due to their ability to represent expressive policies. The complex nature of neural networks, however, makes their output difficult to verify and predict, which limits their use in safety-critical applications. While simulations provide insight into the performance of neural network controllers, they are not enough to guarantee that the controller will perform safely in all scenarios. To address this problem, recent work has focused on formal methods to verify properties of neural network outputs. For neural network controllers, we can use a dynamics model to determine the output properties that must hold for the controller to operate safely. In this work, we develop a method to use the results from neural network verification tools to provide probabilistic safety guarantees on a neural network controller. We develop an adaptive verification approach to efficiently generate an overapproximation of the neural network policy. Next, we modify the traditional formulation of Markov decision process model checking to provide guarantees on the overapproximated policy given a stochastic dynamics model. Finally, we incorporate techniques in state abstraction to reduce overapproximation error during the model checking process. We show that our method is able to generate meaningful probabilistic safety guarantees for aircraft collision avoidance neural networks that are loosely inspired by Airborne Collision Avoidance System X (ACAS X), a family of collision avoidance systems that formulates the problem as a partially observable Markov decision process (POMDP).
DISCO PAL: Diachronic Spanish sonnet corpus with psychological and affective labels

Abstract Nowadays, there are many applications of text mining over corpora from different languages. However, most of them are based on texts in prose, lacking applications that work with poetry texts. An example of an application of text mining in poetry is the usage of features derived from their individual words in order to capture the lexical, sublexical and interlexical meaning, and infer the General Affective Meaning (GAM) of the text. However, even though this proposal has been proved as useful for poetry in some languages, there is a lack of studies for both Spanish poetry and for highly-structured poetic compositions such as sonnets. This article presents a study over an annotated corpus of Spanish sonnets, in order to analyse if it is possible to build features from their individual words for predicting their GAM. The purpose of this is to model sonnets at an affective level. The article also analyses the relationship between the GAM of the sonnets and the content itself. For this, we consider the content from a psychological perspective, identifying with tags when a sonnet is related to a specific term. Then, we study how GAM changes according to each of those psychological terms. The corpus used contains 274 Spanish sonnets from authors of different centuries, from fifteenth to nineteenth. This corpus was annotated by different domain experts. The experts annotated the poems with affective and lexico-semantic features, as well as with domain concepts that belong to psychology. Thanks to this, the corpus of sonnets can be used in different applications, such as poetry recommender systems, personality text mining studies of the authors, or the usage of poetry for therapeutic purposes.
Compact linear optical scheme for Bell state generation

The capability of linear optics to generate entangled states is exploited in photonic quantum information processing, however, it is challenging to obtain entangled logical qubit states. We report, to the best of our knowledge, the most compact scheme producing the dual-rail-encoded Bell states out of four single photons. Our scheme requires a five-mode interferometer and a single photon detector, while the previously known schemes use six-mode interferometers and two photon detectors. Using computer optimization, we have found a decomposition of the five-mode interferometer with a minimum number of beam splitters and phase-shift elements. Besides compactness, our scheme also offers a success probability of $1/9$, which is higher than $2/27$ provided by the six-mode counterparts. The analysis suggests that the elevated success probability is connected to a higher order of photon interference realized by our scheme, in particular, four-photon interference is implemented in our scheme, while three-photon interference was implemented in previous counterparts.
Robust squeezed light against mode mismatch using a self imaging optical parametric oscillator

Abstract We present squeezed light that is robust against spatial mode mismatch (beam displacement, tilt, and beam-size difference), which is generated from a self-imaging optical parametric oscillator below the threshold. We investigate the quantum properties of the generated light when the oscillator is detuned from the ideal self-imaging condition for stable operation. We find that the generated light is more robust to mode mismatch than single-mode squeezed light having the same squeezing level, and it even outperforms the single-mode infinitely squeezed light as the strength of mode mismatch increases.
Hybrid Gan and Spectral Angular Distance for Cloud Removal

This paper aims to present a new algorithm to remove thin clouds and retain information in corrupted images without the use of auxiliary data. By injecting physical properties into the cycle consistent generative adversarial network (GAN), we were able to convert a cloudy multispectral image to a cloudless image. To recover information beneath clouds and shadows we create a synthetic multispectral space to obtain illumination invariant features. Multispectral vectors were transformed from Cartesian coordinates to Polar coordinates to obtain spectral angular distance (SAD) then we employed them as input to train the deep neural network (DNN). Afterward, the outputs of DNN were transformed to Cartesian coordinates to obtain shadow and cloud-free multispectral images. The proposed method, Hybrid GAN-SAD yields trustworthy reconstructed results because of exploiting transparent information from certain multispectral bands to recover uncorrupted images.
On Generating Identifiable Virtual Faces

Face anonymization with generative models have become increasingly prevalent since they sanitize private information by generating virtual face images, ensuring both privacy and image utility. Such virtual face images are usually not identifiable after the removal or protection of the original identity. In this paper, we formalize and tackle the problem of generating identifiable virtual face images. Our virtual face images are visually different from the original ones for privacy protection. In addition, they are bound with new virtual identities, which can be directly used for face recognition. We propose an Identifiable Virtual Face Generator (IVFG) to generate the virtual face images. The IVFG projects the latent vectors of the original face images into virtual ones according to a user specific key, based on which the virtual face images are generated. To make the virtual face images identifiable, we propose a multi-task learning objective as well as a triplet styled training strategy to learn the IVFG. We evaluate the performance of our virtual face images using different face recognizers on diffident face image datasets, all of which demonstrate the effectiveness of the IVFG for generate identifiable virtual face images.
Multi-class classification of mammograms with hesitancy based Hanman transform classifier on pervasive information set texture features

With an aim to enhance the survival rate of women from breast cancer, this paper extends the Gabor, wavelet, and structure-based information set features into the pervasive information set features for more accurate breast cancer diagnosis from mammograms. The notion of a pervasive information set arises from expanding the information set to incorporate the intuitionistic fuzzy set. The effectiveness of the proposed features is evaluated on an annotated private dataset acquired from Superspeciality Cancer Hospital, New Delhi, and the public datasets comprising Digital Database for Screening Mammography, INbreast database, and the Mammographic Image Analysis Society database to validate their efficacy for multi-class categorization of mammograms employing the Hesitancy-based Hanman transform classifier. This classifier not only embodies the uncertainties in the errors between the training and test features but also typifies the deficiencies in the modelling of the membership function. The results of the Analysis of variance test confirm that the proposed features are statistically relevant and the experimental outcomes verified by expert radiologists validate the clinical significance of the proposed work. The proposed methods inhibiting the “Non-data hungry” aspect give 100% accuracy for multi-class classification on both public and private datasets which are higher than those obtained by the state of art methods available in the literature. These results will aid the radiologists in the early diagnosis of breast cancer.
Towards Social Identity in Socio-Cognitive Agents

Current architectures for social agents are designed around some specific units of social behavior that address particular challenges, such as modeling beliefs and motivations, establishing social relationships, or understanding group memberships. Although their performance might be adequate for controlled environments, deploying these agents in the wild is difficult. Moreover, the increasing demand for autonomous agents capable of living alongside humans calls for the design of more robust social agents that can cope with diverse social situations. We believe that to design such agents, their sociality and cognition should be conceived as one. This includes creating mechanisms for constructing social reality as an interpretation of the physical world with social meanings and selective deployment of cognitive resources adequate to the situation. We identify several design principles that should be considered while designing agent architectures for socio-cognitive systems. Taking these remarks into account, we propose a socio-cognitive agent model based on the concept of cognitive social frames that allow the adaptation of an agent’s cognition based on its interpretation of its surroundings, its social context. Our approach supports an agent’s reasoning about other social actors and its relationship with them. Cognitive social frames can be built around social groups, and form the basis for social group dynamics mechanisms and construct of social identity.
Comparison of Czech Transformers on Text Classification Tasks

In this paper, we present our progress in pre-training monolingual Transformers for Czech and contribute to the research community by releasing our models for public. The need for such models emerged from our effort to employ Transformers in our language-specific tasks, but we found the performance of the published multilingual models to be very limited. Since the multilingual models are usually pre-trained from 100+ languages, most of low-resourced languages (including Czech) are under-represented in these models. At the same time, there is a huge amount of monolingual training data available in web archives like Common Crawl. We have pre-trained and publicly released two monolingual Czech Transformers and compared them with relevant public models, trained (at least partially) for Czech. The paper presents the Transformers pre-training procedure as well as a comparison of pre-trained models on text classification task from various domains.
The development of LibGuides at Cape Peninsula University of Technology Libraries and the impact of the COVID-19 lockdown on their usage

LibGuides have been used by librarians at Cape Peninsula University of Technology since 2012 as a means of providing subject-based information to students, with quick links to selected resources and information supplementing information literacy training. Lecturers use the resources for preparing lectures and identifying learning materials, while researchers and postgraduate students use research support guides when writing their papers. As the worldwide pandemic caused a major lockdown, academic institutions in South Africa were closed and students sent home. However, students still needed to complete their studies, and librarians at Cape Peninsula University of Technology had to continue providing support to these students. One of the online services available was LibGuides. This article provides insight into the historical developments of LibGuides at Cape Peninsula University of Technology, the use of LibGuides during lockdown, and the experiences of a selected group of students and librarians.
Alder Lake Architecture

• Single Thread general purpose compute still critical – Low latency high IPC -ST and serial segments • Support vector and ML instructions • Drives size and power • Increase in Parallel apps (MT) • Machine Learning/AI • Concurrent usage • Focus on user experience
Email Pragmatics and Second Language Learners

This is the first edited collection focusing exclusively on how second language users interpret and engage with the processes of email writing. With chapters written by an international array of scholars, the present volume is dedicated to furthering the study of the growing field of L2 email pragmatics and addresses a range of interesting topics that have so far received comparatively scant attention. Utilising both elicited and naturally-occurring data, the research in this volume takes the reader from a consideration of learners’ pragmatic development as reflected in email writing, and their perceptions of the email medium, to relational practices in various email functions and in a variety of academic contexts. As a whole, the contributions incorporate research with learners from a range of proficiency levels, language and cultural backgrounds, and employ varied research designs in order to examine different email speech acts. The book provides valuable new insights into the dynamic and complex interplay between cultural, interlanguage, pedagogical, and medium-specific factors shaping L2 email discourse, and it is undoubtedly an important reference and resource for researchers, graduate students and experienced language teachers.
A Manifold Learning Perspective on Representation Learning: Learning Decoder and Representations without an Encoder

Autoencoders are commonly used in representation learning. They consist of an encoder and a decoder, which provide a straightforward method to map n-dimensional data in input space to a lower m-dimensional representation space and back. The decoder itself defines an m-dimensional manifold in input space. Inspired by manifold learning, we showed that the decoder can be trained on its own by learning the representations of the training samples along with the decoder weights using gradient descent. A sum-of-squares loss then corresponds to optimizing the manifold to have the smallest Euclidean distance to the training samples, and similarly for other loss functions. We derived expressions for the number of samples needed to specify the encoder and decoder and showed that the decoder generally requires much fewer training samples to be well-specified compared to the encoder. We discuss the training of autoencoders in this perspective and relate it to previous work in the field that uses noisy training examples and other types of regularization. On the natural image data sets MNIST and CIFAR10, we demonstrated that the decoder is much better suited to learn a low-dimensional representation, especially when trained on small data sets. Using simulated gene regulatory data, we further showed that the decoder alone leads to better generalization and meaningful representations. Our approach of training the decoder alone facilitates representation learning even on small data sets and can lead to improved training of autoencoders. We hope that the simple analyses presented will also contribute to an improved conceptual understanding of representation learning.
Geometric phase in a dissipative Jaynes-Cummings model: Theoretical explanation for resonance robustness

We follow a generalized kinematic approach to compute the geometric phases acquired in both unitary and dissipative Jaynes-Cummings models, which provide a fully quantum description for a two-level system interacting with a single mode of the (cavity) electromagnetic field, in a perfect or dissipative cavity, respectively. In the dissipative model, the nonunitary effects arise from the outflow of photons through the cavity walls and the incoherent pumping of the two-level system. Our approach allows to compare the geometric phases acquired in these models, leading to an exhaustive characterization of the corrections introduced by the presence of the environment. We also provide geometric interpretations for the observed behaviors. When the resonance condition is satisfied, we show the geometric phase is robust, exhibiting a vanishing correction under a nonunitary evolution. This fact is supported with a geometrical explanation as well.
Quantum Gram-Schmidt processes and their application to efficient state readout for quantum algorithms

Many quantum algorithms that claim speedup over their classical counterparts only generates quantum states as solutions instead of their final classical description. The additional step to decode quantum states into classical vectors normally will destroy the quantum advantage in most scenarios because all existing tomographic methods require runtime that is polynomial with respect to the state dimension. In this work, we present an efficient readout protocol that yields the classical vector form of the generated state, so it will achieve the end-to-end advantage for those quantum algorithms. Our protocol suits the case in which the output state lies in the row space of the input matrix, of rank $r$, that is stored in the quantum random access memory. The quantum resources for decoding the state in ${\ensuremath{\ell}}^{2}$ norm with $\ensuremath{\epsilon}$ error require $\mathrm{poly}(r,1/\ensuremath{\epsilon})$ copies of the output state and $\mathrm{poly}(r,{\ensuremath{\kappa}}^{r},1/\ensuremath{\epsilon})$ queries to the input oracles, where $\ensuremath{\kappa}$ is the condition number of the input matrix. With our readout protocol, we completely characterize the end-to-end resources for quantum linear equation solvers and quantum singular value decomposition. One of our technical tools is an efficient quantum algorithm for performing the Gram-Schmidt orthonormal procedure, which we believe will be of independent interest.
Different types of coherence: Young-type interference versus Dicke superradiance

Dicke superradiance, i.e., the enhanced spontaneous emission of coherent radiation, is often attributed to radiation emitted by synchronized dipoles coherently oscillating in phase. At the same time, Dicke derived superradiance assuming atoms in entangled Dicke states which do not display any dipole moment. To shed light on this apparent paradox, we study the intensity distribution arising from two identical two-level atoms prepared either in an entangled Dicke state or in a separable atomic state with non-vanishing dipole moment. We find that the two configurations produce similar far field intensity patterns, however, stemming from fundamentally distinct types of coherence: while in the second case the atoms display coherence among the individual particles leading to Young-type interference as known from classical dipoles, atoms in Dicke states possess collective coherence leading to enhanced spontaneous emission. This demonstrates that the radiation generated by synchronized dipoles and Dicke superradiance are fundamentally distinct phenomena and have to be interpreted in different ways.