Reviewerly/exHarmony huggingface dataset:
Information literacy as a site for anticipation: temporal tactics for infrastructural meaning-making and algo-rhythm awareness

Purpose The article makes an empirical and conceptual contribution to understanding the temporalities of information literacies. The paper aims to identify different ways in which anticipation of certain outcomes shapes strategies and tactics for engagement with algorithmic information intermediaries. The paper suggests that, given the dominance of predictive algorithms in society, information literacies need to be understood as sites of anticipation. Design/methodology/approach The article explores the ways in which the invisible algorithms of information intermediaries are conceptualised, made sense of and challenged by young people in their everyday lives. This is couched in a conceptual discussion of the role of anticipation in understanding expressions of information literacies in algorithmic cultures. The empirical material drawn on consists of semi-structured, pair interviews with 61 17–19 year olds, carried out in Sweden and Denmark. The analysis is carried out by means of a qualitative thematic analysis in three steps and along two sensitising concepts – agency and temporality. Findings The results are presented through three themes, anticipating personalisation, divergences and interventions. These highlight how articulating an anticipatory stance works towards connecting individual responsibilities, collective responsibilities and corporate interests and thus potentially facilitating an understanding of information as co-constituted by the socio-material conditions that enable it. This has clear implications for the framing of information literacies in relation to algorithmic systems. Originality/value The notion of algo-rhythm awareness constitutes a novel contribution to the field. By centring the role of anticipation in the emergence of information literacies, the article advances understanding of the temporalities of information.
Improved upper bounds for the hitting times of quantum walks

Continuous-time quantum walks have proven to be an extremely useful framework for the design of several quantum algorithms. Often, the running time of quantum algorithms in this framework is characterized by the quantum hitting time: the time required by the quantum walk to find a vertex of interest with a high probability. In this article, we provide improved upper bounds for the quantum hitting time that can be applied to several CTQW-based quantum algorithms. In particular, we apply our techniques to the glued-trees problem, improving their hitting time upper bound by a polynomial factor: from $O(n^5)$ to $O(n^2\log n)$. Furthermore, our methods also help to exponentially improve the dependence on precision of the continuous-time quantum walk based algorithm to find a marked node on any ergodic, reversible Markov chain by Chakraborty et al. [PRA 102, 022227 (2020)].
Deterministic transformations between unitary operations: Exponential advantage with adaptive quantum circuits and the power of indefinite causality

This work analyses the performance of quantum circuits and general processes to transform $k$ uses of an arbitrary unitary operation $U$ into another unitary operation $f(U)$. When the desired function $f$ a homomorphism, i.e., $f(UV)=f(U)f(V)$, it is known that optimal average fidelity is attainable by parallel circuits and indefinite causality does not provide any advantage. Here we show that the situation changes dramatically when considering anti-homomorphisms, i.e., $f(UV)=f(V)f(U)$. In particular, we prove that when $f$ is an anti-homomorphism, sequential circuits could exponentially outperform parallel ones and processes with indefinite causal order could outperform sequential ones. We presented explicit constructions on how to obtain such advantages for the unitary inversion task $f(U)=U^{-1}$ and the unitary transposition task $f(U)=U^T$. We also stablish a one-to-one connection between the problem of unitary estimation and parallel unitary transposition, allowing one to easily translate results from one field to the other. Finally, we apply our results to several concrete problem instances and present a method based on computer-assisted proofs to show optimality.
Multi-Constraint Latent Representation Learning for Prognosis Analysis Using Multi-Modal Data

The Cox proportional hazard model has been widely applied to cancer prognosis prediction. Nowadays, multi-modal data, such as histopathological images and gene data, have advanced this field by providing histologic phenotype and genotype information. However, how to efficiently fuse and select the complementary information of high-dimensional multi-modal data remains challenging for Cox model, as it generally does not equip with feature fusion/selection mechanism. Many previous studies typically perform feature fusion/selection in the original feature space before Cox modeling. Alternatively, learning a latent shared feature space that is tailored for Cox model and simultaneously keeps sparsity is desirable. In addition, existing Cox-based models commonly pay little attention to the actual length of the observed time that may help to boost the model’s performance. In this article, we propose a novel Cox-driven multi-constraint latent representation learning framework for prognosis analysis with multi-modal data. Specifically, for efficient feature fusion, a multi-modal latent space is learned via a bi-mapping approach under ranking and regression constraints. The ranking constraint utilizes the log-partial likelihood of Cox model to induce learning discriminative representations in a task-oriented manner. Meanwhile, the representations also benefit from regression constraint, which imposes the supervision of specific survival time on representation learning. To improve generalization and alleviate overfitting, we further introduce similarity and sparsity constraints to encourage extra consistency and sparseness. Extensive experiments on three datasets acquired from The Cancer Genome Atlas (TCGA) demonstrate that the proposed method is superior to state-of-the-art Cox-based models.
Training Dynamics for Text Summarization Models

Pre-trained language models (e.g.BART) have shown impressive results when fine-tuned on large summarization datasets.However, little is understood about this fine-tuning process, including what knowledge is retained from pre-training time or how content selection and generation strategies are learnt across iterations.In this work, we analyze the training dynamics for generation models, focusing on summarization.Across different datasets (CNNDM, XSUM, MEDIASUM) and summary properties, such as abstractiveness and hallucination, we study what the model learns at different stages of its fine-tuning process.We find that a propensity to copy the input is learned early in the training process consistently across all datasets studied.On the other hand, factual errors, such as hallucination of unsupported facts, are learnt in the later stages, though this behavior is more varied across domains.Based on these observations, we explore complementary approaches for modifying training: first, disregarding high-loss tokens that are challenging to learn and second, disregarding low-loss tokens that are learnt very quickly in the latter stages of the training process.We show that these simple training modifications allow us to configure our model to achieve different goals, such as improving factuality or improving abstractiveness. 1
Relationship between Transformational Leadership Style and Leadership Thinking of Provincial Administration Leaders

Objectives: The transformational leadership style is accepted as suitable for leading administrative agencies to achieve outstanding results and help organizations cope with challenges. Besides, leadership thinking is also considered to have a very important role in leadership performance in administrative agencies. Therefore, the main objective of the study is to explore the relationship between transformational leadership style and leader thinking to organization's performance. Methods: The article focuses on explaining the views on transformational leadership style, healthy thinking, and the relationship between transformational leadership style and leadership thinking, and at the same time points out the current status of transformational leadership style, transformational leadership, leadership thinking as well as this relationship in practice among the leaders of provincial agencies in Vietnam. Descriptive, inductive, deductive, synthetic, and quantitative statistical methods were applied to interpret the results. Findings: Research results show that transformational leadership style, leadership thinking in the team of leaders of provincial agencies is quite average, there is a strong positive correlation between transformational leadership style. In contrast to leadership thinking, a more transformative leadership style means that it requires an innovative leadership thinking. Novelty:The results achieved when applying a transformational leadership style are quite closely related to the application of leadership thinking to solve leadership challenges. Doi: 10.28991/esj-2021-01307 Full Text: PDF
Enhancing the estimation precision of an unknown phase shift in multipartite Glauber coherent states via skew information correlations and local quantum Fisher information

Local quantum uncertainty (LQU) and local quantum Fisher information (LQFI) are both tools used to capture purely quantum correlations in multipartite quantum systems. In this paper, we study these quantifiers in the case of multipartite Glauber coherent states, which include the Greenberger–Horne–Zeilinger and Werner states. We perform a comparative study between LQFI and LQU in an isolated system. By using the Kraus operator representation, we study the behavior of these quantifiers on the dephasing channel to investigate their performances under the decoherence effect. In addition, the robustness to the decoherence effect of these two quantifiers is studied. We further examine the situation involving the multipartite Glauber coherent state to decide the sensitivity of the probe state as a resource for quantum estimation protocols.
CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training

We propose a novel open-domain questionanswering dataset based on the Common Crawl project.With a previously unseen number of around 130 million multilingual question-answer pairs (including about 60 million English data-points), we use our largescale, natural, diverse and high-quality corpus to in-domain pre-train popular language models for the task of question-answering.In our experiments, we find that our Common Crawl Question Answering dataset (CCQA) achieves promising results in zero-shot, low resource and fine-tuned settings across multiple tasks, models and benchmarks 1 .
Filter Functions for Quantum Processes under Correlated Noise

Many qubit implementations are afflicted by correlated noise not captured by standard theoretical tools that are based on Markov approximations. While independent gate operations are a key concept for quantum computing, it is actually not possible to fully describe noisy gates locally in time if noise is correlated on times longer than their duration. To address this issue, we develop a method based on the filter function formalism to perturbatively compute quantum processes in the presence of correlated classical noise. We derive a composition rule for the filter function of a sequence of gates in terms of those of the individual gates. The joint filter function allows us to efficiently compute the quantum process of the whole sequence. Moreover, we show that correlation terms arise which capture the effects of the concatenation and, thus, yield insight into the effect of noise correlations on gate sequences. Our generalization of the filter function formalism enables both qualitative and quantitative studies of algorithms and state-of-the-art tools widely used for the experimental verification of gate fidelities like randomized benchmarking, even in the presence of noise correlations.
A novel quantum algorithm for ant colony optimisation

Ant colony optimization (ACO) is a commonly used meta-heuristic to solve complex combinatorial optimization problems like traveling salesman problem (TSP), vehicle routing problem (VRP), etc. However, classical ACO algorithms provide better optimal solutions but do not reduce computation time overhead to a significant extent. Algorithmic speed-up can be achieved by using parallelism offered by quantum computing. Existing quantum algorithms to solve ACO are either quantum-inspired classical algorithms or hybrid quantum-classical algorithms. Since all these algorithms need the intervention of classical computing, leveraging the true potential of quantum computing on real quantum hardware remains a challenge. This paper's main contribution is to propose a fully quantum algorithm to solve ACO, enhancing the quantum information processing toolbox in the fault-tolerant quantum computing (FTQC) era. We have Solved the Single Source Single Destination (SSSD) shortest-path problem using our proposed adaptive quantum circuit for representing dynamic pheromone updating strategy in real IBMQ devices. Our quantum ACO technique can be further used as a quantum ORACLE to solve complex optimization problems in a fully quantum setup with significant speed up upon the availability of more qubits.
Robust Deep Reinforcement Learning for Extractive Legal Summarization

Automatic summarization of legal texts is an important and still a challenging task since legal documents are often long and complicated with unusual structures and styles. Recent advances of deep models trained end-to-end with differentiable losses can well-summarize natural text, yet when applied to legal domain, they show limited results. In this paper, we propose to use reinforcement learning to train current deep summarization models to improve their performance on the legal domain. To this end, we adopt proximal policy optimization methods and introduce novel reward functions that encourage the generation of candidate summaries satisfying both lexical and semantic criteria. We apply our method to training different summarization backbones and observe a consistent and significant performance gain across 3 public legal datasets.
Dimension reduction in recurrent networks by canonicalization

&lt;p style='text-indent:20px;'&gt;Many recurrent neural network machine learning paradigms can be formulated using state-space representations. The classical notion of canonical state-space realization is adapted in this paper to accommodate semi-infinite inputs so that it can be used as a dimension reduction tool in the recurrent networks setup. The so-called input forgetting property is identified as the key hypothesis that guarantees the existence and uniqueness (up to system isomorphisms) of canonical realizations for causal and time-invariant input/output systems with semi-infinite inputs. Additionally, the notion of optimal reduction coming from the theory of symmetric Hamiltonian systems is implemented in our setup to construct canonical realizations out of input forgetting but not necessarily canonical ones. These two procedures are studied in detail in the framework of linear fading memory input/output systems. {Finally, the notion of implicit reduction using reproducing kernel Hilbert spaces (RKHS) is introduced which allows, for systems with linear readouts, to achieve dimension reduction without the need to actually compute the reduced spaces introduced in the first part of the paper.&lt;/p&gt;
Federated personalized random forest for human activity recognition

User data usually exists in the organization or own local equipment in the form of data island. It is difficult to collect these data to train better machine learning models because of the General Data Protection Regulation (GDPR) and other laws. The emergence of federated learning enables users to jointly train machine learning models without exposing the original data. Due to the fast training speed and high accuracy of random forest, it has been applied to federated learning among several data institutions. However, for human activity recognition task scenarios, the unified model cannot provide users with personalized services. In this paper, we propose a privacy-protected federated personalized random forest framework, which considers to solve the personalized application of federated random forest in the activity recognition task. According to the characteristics of the activity recognition data, the locality sensitive hashing is used to calculate the similarity of users. Users only train with similar users instead of all users and the model is incrementally selected using the characteristics of ensemble learning, so as to train the model in a personalized way. At the same time, user privacy is protected through differential privacy during the training stage. We conduct experiments on commonly used human activity recognition datasets to analyze the effectiveness of our model.
A Deep Learning-Based Approach with Image-Driven Active Contour Loss for Medical Image Segmentation

Medical image segmentation based on deep learning technics has been more and more prevalent in recent years. The primary reasons lead to success of those methods are radical technics in improving deep learning model or global loss functions such as Cross Entropy (CE), Dice loss, … However, it is still a challenging problem because medical images are normally affected by complex noises, intensity inhomogeneity, or occlusion. To effectively solve those issues, utilizing local image information for segmentation is demonstrated as the potential way. Therefore, in this paper, we propose a new region-based loss functional based on level set method and extent to the case of multiphase segmentation. It allows our deep learning model to train end-to-end and also segment multiclass simultaneously with high accuracy instead of binary segmentation. Our proposed method is assessed on 2017 ACDC dataset and 2018 ISIC Challenge dataset. Experiments indicate that the proposed method outperforms the state-of-the-art methods in terms of Dice coefficient and Jaccard indexes. This highlights the efficiency of our approach in multiclass segmentation for medical images.
Fixation and different types of fixatives: Their role and functions: A review

Fixation is a complete and complex physiochemical process wherein cells and tissues are chemically fixed for further analyses.Fixatives are used for Solidification, Hardening, Optical differentiation, prevention of autolysis and tissue putrefaction.Various Types of fixatives (Simple, Compound, Microanatomical, Histochemical and Cytological) are used in Histology and Histopathology which includes Mercuric Chloride, Osmic Acid, formaldehyde, glutaraldehyde, osmium tetroxide, glyoxal, picric acid, Zenker's fluid and so on.Due to limited availability of the material related to the topic a need was felt to write a review article so as to make the Researchers, Students.pathologists and laboratory technicians familiar with the basic Concept of fixation and fixatives.
Emotion norms for 6000 Polish word meanings with a direct mapping to the Polish wordnet

Emotion lexicons are useful in research across various disciplines, but the availability of such resources remains limited for most languages. While existing emotion lexicons typically comprise words, it is a particular meaning of a word (rather than the word itself) that conveys emotion. To mitigate this issue, we present the Emotion Meanings dataset, a novel dataset of 6000 Polish word meanings. The word meanings are derived from the Polish wordnet (plWordNet), a large semantic network interlinking words by means of lexical and conceptual relations. The word meanings were manually rated for valence and arousal, along with a variety of basic emotion categories (anger, disgust, fear, sadness, anticipation, happiness, surprise, and trust). The annotations were found to be highly reliable, as demonstrated by the similarity between data collected in two independent samples: unsupervised (n = 21,317) and supervised (n = 561). Although we found the annotations to be relatively stable for female, male, younger, and older participants, we share both summary data and individual data to enable emotion research on different demographically specific subgroups. The word meanings are further accompanied by the relevant metadata, derived from open-source linguistic resources. Direct mapping to Princeton WordNet makes the dataset suitable for research on multiple languages. Altogether, this dataset provides a versatile resource that can be employed for emotion research in psychology, cognitive science, psycholinguistics, computational linguistics, and natural language processing.
DualFormer: Local-Global Stratified Transformer for Efficient Video Recognition

While transformers have shown great potential on video recognition with their strong capability of capturing long-range dependencies, they often suffer high computational costs induced by the self-attention to the huge number of 3D tokens. In this paper, we present a new transformer architecture termed DualFormer, which can efficiently perform space-time attention for video recognition. Concretely, DualFormer stratifies the full space-time attention into dual cascaded levels, i.e., to first learn fine-grained local interactions among nearby 3D tokens, and then to capture coarse-grained global dependencies between the query token and global pyramid contexts. Different from existing methods that apply space-time factorization or restrict attention computations within local windows for improving efficiency, our local-global stratification strategy can well capture both short- and long-range spatiotemporal dependencies, and meanwhile greatly reduces the number of keys and values in attention computation to boost efficiency. Experimental results verify the superiority of DualFormer on five video benchmarks against existing methods. In particular, DualFormer achieves 82.9%/85.2% top-1 accuracy on Kinetics-400/600 with $$\sim $$ 1000G inference FLOPs which is at least 3.2 $$\times $$ fewer than existing methods with similar performance. We have released the source code at https://github.com/sail-sg/dualformer .